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    \ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Como preencher o questionrio par-q para atividade fsica.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Como preencher o questionrio par-q para atividade fsica.md deleted file mode 100644 index bb1f7a28dde571c96f2cb4204dd743bf7e6d1277..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Como preencher o questionrio par-q para atividade fsica.md +++ /dev/null @@ -1,131 +0,0 @@ -
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    Questionário Par-Q: O que é, para que serve e como baixar

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    O questionário Par-Q é uma ferramenta simples e rápida que ajuda a avaliar a prontidão para a atividade física de uma pessoa. Ele pode ser usado por quem deseja iniciar ou intensificar um programa de exercícios, ou por profissionais de educação física que querem orientar seus clientes de forma segura e eficaz. Neste artigo, você vai saber o que é o questionário Par-Q, para que ele serve, quais são as suas perguntas e como baixá-lo em diferentes formatos e idiomas.

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    O que é o questionário Par-Q?

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    O questionário Par-Q significa Physical Activity Readiness Questionnaire, ou seja, Questionário de Prontidão para Atividade Física. Ele foi criado em 1975 pelo Ministério da Saúde da Colúmbia Britânica e pelo Conselho Multidisciplinar de Exercício, no Canadá, com o objetivo de padronizar a triagem de saúde para pessoas entre 15 e 69 anos que querem se exercitar. Ele foi revisado em 1981, 1996 e 2023, e recebeu o endosso do American College of Sports Medicine (ACSM) .

    -

    O questionário Par-Q consiste em sete perguntas de sim ou não, que abordam aspectos como condições cardíacas, dor no peito, tontura, problemas ósseos ou articulares, uso de medicamentos e outras razões que possam impedir ou limitar a prática de atividade física. As perguntas são baseadas em evidências científicas e visam identificar os possíveis riscos ou benefícios do exercício para cada pessoa .

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    Qual é o objetivo do questionário Par-Q?

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    O objetivo do questionário Par-Q é determinar se uma pessoa está apta a iniciar ou aumentar seu nível de atividade física sem a necessidade de consultar um médico ou um profissional qualificado em exercício. A maioria das pessoas pode se exercitar com segurança, mas algumas podem ter contraindicações ou precauções que devem ser consideradas antes de se expor a esforços físicos .

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    O questionário Par-Q também pode ajudar a criar uma prescrição de exercício ideal para cada pessoa, levando em conta seus fatores de risco, sintomas, histórico de saúde e objetivos. Além disso, ele pode servir como uma ferramenta educativa para conscientizar as pessoas sobre a importância da atividade física regular para a prevenção e o tratamento de diversas doenças .

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    Quem deve responder ao questionário Par-Q?

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    O questionário Par-Q pode e deve ser usado por qualquer pessoa que esteja planejando iniciar ou manter um programa de exercícios, seja por conta própria ou com a ajuda de um treinador ou instrutor. Ele também é recomendado para quem quer aumentar a intensidade ou a frequência da sua atividade física. Ele é especialmente indicado para quem tem mais de 45 anos, é sedentário, tem sobrepeso, fuma, tem histórico familiar de doenças cardíacas ou outras condições crônicas .

    O questionário Par-Q não deve ser usado por pessoas que já têm uma doença cardíaca diagnosticada, que estão grávidas ou que têm alguma limitação física ou mental que impeça a compreensão e a resposta às perguntas. Nesses casos, é necessário consultar um médico ou um profissional qualificado em exercício antes de iniciar ou modificar um programa de atividade física .

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    Quais são as perguntas do questionário Par-Q?

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    As perguntas do questionário Par-Q são as seguintes :

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    1. Alguma vez um médico disse que você tem um problema cardíaco e que só deveria fazer atividade física recomendada por um médico?
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    2. Você sente dor no peito provocada por atividade física?
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    3. No último mês, você sentiu dor no peito quando não estava fazendo atividade física?
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    4. Você perde o equilíbrio em decorrência de tontura ou alguma vez perdeu a consciência?
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    5. Você tem algum problema ósseo ou articular que poderia piorar com a prática de atividade física?
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    6. Você toma atualmente algum medicamento para pressão arterial ou problema cardíaco?
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    7. Você sabe de alguma outra razão pela qual não deveria fazer atividade física?
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    Se você respondeu sim a uma ou mais perguntas, você deve consultar um médico antes de iniciar ou intensificar sua atividade física. Se você respondeu não a todas as perguntas, você pode iniciar sua atividade física com segurança, mas deve parar imediatamente e procurar ajuda médica se sentir algum sintoma anormal, como dor no peito, falta de ar, tontura, náusea ou palpitações .

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    Para que serve o questionário Par-Q?

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    O questionário Par-Q serve para avaliar a prontidão para a atividade física de uma pessoa e para orientar a prescrição de exercício adequada para cada caso. Ele também serve para promover os benefícios da atividade física regular para a saúde, tanto para os indivíduos quanto para os profissionais de educação física.

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    Benefícios do questionário Par-Q para a saúde

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    O questionário Par-Q pode ajudar a prevenir e tratar diversas doenças relacionadas ao sedentarismo e ao envelhecimento, como :

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    Ao responder ao questionário Par-Q, a pessoa pode se conscientizar sobre os riscos e as vantagens do exercício para sua saúde e tomar uma decisão informada sobre sua prática de atividade física. Além disso, o questionário Par-Q pode ajudar a monitorar as mudanças na saúde da pessoa ao longo do tempo e a ajustar seu programa de exercícios conforme suas necessidades e objetivos.

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    Benefícios do questionário Par-Q para os profissionais de educação física

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    O questionário Par-Q pode ser uma ferramenta útil para os profissionais de educação física que trabalham com pessoas que querem se exercitar. Ele pode auxiliar na :

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    Ao usar o questionário Par-Q, os profissionais de educação física podem oferecer um serviço de qualidade e segurança para seus clientes, além de se respaldar legalmente e eticamente. O questionário Par-Q também pode facilitar a comunicação e a colaboração entre os profissionais de educação física e os médicos ou outros profissionais de saúde envolvidos no cuidado dos clientes.

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    Benefícios do questionário Par-Q para os praticantes de atividade física

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    O questionário Par-Q pode ser uma ferramenta prática e acessível para os praticantes de atividade física que querem se exercitar com autonomia e responsabilidade. Ele pode auxiliar na :

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    Ao responder ao questionário Par-Q, os praticantes de atividade física podem se beneficiar de uma orientação simples e eficaz para iniciar ou manter sua atividade física com segurança e eficiência. Além disso, o questionário Par-Q pode estimular o interesse e a curiosidade pela atividade física, bem como o senso de responsabilidade e compromisso com a saúde.

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    Como baixar o questionário Par-Q?

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    O questionário Par-Q está disponível em diferentes formatos e idiomas para facilitar o seu uso e a sua divulgação. Você pode baixar o questionário Par-Q em versão em PDF, online ou em outros idiomas, conforme sua preferência.

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    Versão em PDF do questionário Par-Q

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    A versão em PDF do questionário Par-Q é a mais tradicional e conhecida. Ela permite que você imprima o questionário e o responda no papel, ou que o salve no seu computador ou celular para consultá-lo sempre que quiser. Você pode baixar a versão em PDF do questionário Par-Q em português [aqui].

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    Versão online do questionário Par-Q

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    A versão online do questionário Par-Q é uma opção mais moderna e interativa. Ela permite que você responda ao questionário na internet, por meio de um formulário eletrônico, e receba um feedback instantâneo sobre sua prontidão para a atividade física. Você também pode compartilhar o seu resultado nas redes sociais ou enviá-lo por e-mail para o seu treinador ou médico. Você pode acessar a versão online do questionário Par-Q em português [aqui].

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    Versão em outros idiomas do questionário Par-Q

    -

    A versão em outros idiomas do questionário Par-Q é uma alternativa para quem quer responder ao questionário em sua língua materna ou aprender um novo idioma. Ela permite que você escolha entre vários idiomas disponíveis, como inglês, espanhol, francês, italiano, alemão, chinês, japonês, entre outros. Você pode baixar ou acessar a versão em outros idiomas do questionário Par-Q [aqui].

    -

    Conclusão

    -

    O questionário Par-Q é uma ferramenta simples e rápida que ajuda a avaliar a prontidão para a atividade física de uma pessoa. Ele pode ser usado por quem deseja iniciar ou intensificar um programa de exercícios, ou por profissionais de educação física que querem orientar seus clientes de forma segura e eficaz. O questionário Par-Q consiste em sete perguntas de sim ou não, que abordam aspectos como condições cardíacas, dor no peito, tontura, problemas ósseos ou articulares, uso de medicamentos e outras razões que possam impedir ou limitar a prática de atividade física. O objetivo do questionário Par-Q é determinar se uma pessoa está apta a iniciar ou aumentar seu nível de atividade física sem a necessidade de consultar um médico ou um profissional qualificado em exercício. O questionário Par-Q também pode ajudar a criar uma prescrição de exercício ideal para cada pessoa, levando em conta seus fatores de risco, sintomas, histórico de saúde e objetivos. Além disso, ele pode servir como uma ferramenta educativa para conscientizar as pessoas sobre a importância da atividade física regular para a prevenção e o tratamento de diversas doenças.

    -

    O questionário Par-Q serve para avaliar a prontidão para a atividade física de uma pessoa e para orientar a prescrição de exercício adequada para cada caso. Ele também serve para promover os benefícios da atividade física regular para a saúde, tanto para os indivíduos quanto para os profissionais de educação física. O questionário Par-Q pode ajudar a prevenir e tratar diversas doenças relacionadas ao sedentarismo e ao envelhecimento, como doenças cardiovasculares, metabólicas, musculoesqueléticas, respiratórias, neurológicas e neoplásicas. Ele também pode auxiliar na avaliação inicial da saúde e do nível de aptidão física dos clientes, na prescrição individualizada e segura de exercícios baseada nos fatores de risco, sintomas e objetivos dos clientes, na orientação e motivação dos clientes para a adesão e a manutenção da atividade física, na educação e esclarecimento dos clientes sobre os benefícios e os cuidados com a atividade física, na prevenção e manejo de possíveis complicações ou emergências durante a atividade física. Além disso, o questionário Par-Q pode auxiliar na autoavaliação da saúde e do nível de aptidão física, na autoprescrição de exercícios adequados ao perfil e aos objetivos pessoais, no autocontrole e automonitoramento da atividade física, no autocuidado e autoconhecimento sobre os limites e as potencialidades do corpo, na autonomia e autoconfiança para a prática de atividade física.

    -

    O questionário Par-Q está disponível em diferentes formatos e idiomas para facilitar o seu uso e a sua divulgação. Você pode baixar o questionário Par-Q em versão em PDF, online ou em outros idiomas, conforme sua preferência. A versão em PDF do questionário Par-Q é a mais tradicional e conhecida. Ela permite que você imprima o questionário e o responda no papel, ou que o salve no seu computador ou celular para consultá-lo sempre que quiser. A versão online do questionário Par-Q é uma opção mais moderna e interativa. Ela permite que você responda ao questionário na internet, por meio de um formulário eletrônico, e receba um feedback instantâneo sobre sua prontidão para a atividade física. Você também pode compartilhar o seu resultado nas redes sociais ou enviá-lo por e-mail para o seu treinador ou médico. A versão em outros idiomas do questionário Par-Q é uma alternativa para quem quer responder ao questionário em sua língua materna ou aprender um novo idioma. Ela permite que você escolha entre vários idiomas disponíveis, como inglês, espanhol, francês, italiano, alemão, chinês, japonês, entre outros.

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    Perguntas frequentes sobre o questionário Par-Q

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    A seguir, apresentamos algumas perguntas frequentes sobre o questionário Par-Q:

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    O questionário Par-Q é obrigatório?

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    Não, o questionário Par-Q não é obrigatório por lei, mas é altamente recomendado por organizações internacionais de saúde e exercício. Ele é uma forma simples e eficaz de avaliar a prontidão para a atividade física de uma pessoa e de orientar a prescrição de exercício adequada para cada caso.

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    O questionário Par-Q substitui uma consulta médica?

    -

    Não, o questionário Par-Q não substitui uma consulta médica nem um exame físico completo. Ele é apenas uma ferramenta de triagem que ajuda a identificar os possíveis riscos ou benefícios do exercício para cada pessoa. Se você respondeu sim a uma ou mais perguntas do questionário Par-Q, ou se você tem alguma dúvida ou preocupação sobre sua saúde ou sua atividade física, você deve consultar um médico antes de iniciar ou intensificar seu programa de exercícios.

    -

    O questionário Par-Q é válido para todas as idades?

    -

    Não, o questionário Par-Q é válido apenas para pessoas entre 15 e 69 anos. Para pessoas com menos de 15 anos ou mais de 69 anos, existem outros questionários específicos que devem ser usados, como o Par-Q+ ou o Parmed-X . Esses questionários levam em conta as características e as necessidades especiais dessas faixas etárias, como o desenvolvimento físico, o crescimento ósseo, a maturidade sexual, a capacidade funcional, as doenças crônicas e os medicamentos.

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    O questionário Par-Q é confiável?

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    Sim, o questionário Par-Q é confiável e válido. Ele foi desenvolvido e revisado por especialistas em saúde e exercício, com base em evidências científicas e em critérios clínicos. Ele também foi testado e aprovado por diversas pesquisas que avaliaram sua sensibilidade, especificidade, acurácia e aplicabilidade . O questionário Par-Q tem uma alta sensibilidade, ou seja, ele consegue identificar a maioria das pessoas que têm algum risco para a atividade física. Ele também tem uma boa especificidade, ou seja, ele consegue excluir a maioria das pessoas que não têm nenhum risco para a atividade física. Além disso, ele tem uma boa acurácia, ou seja, ele consegue classificar corretamente a prontidão para a atividade física de uma pessoa. Por fim, ele tem uma boa aplicabilidade, ou seja, ele é fácil de usar e de entender por diferentes públicos e contextos.

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    O questionário Par-Q é gratuito?

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    Sim, o questionário Par-Q é gratuito e de domínio público. Você pode baixar, imprimir, copiar, distribuir e usar o questionário Par-Q sem nenhum custo ou restrição. Você só precisa respeitar os direitos autorais dos criadores do questionário Par-Q e citar a fonte original quando usar o questionário Par-Q em seus trabalhos acadêmicos ou profissionais.

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    CarX Street Mod APK 0.8 6 is a modded version of CarX Street, a realistic and immersive street racing game for Android devices. It gives you unlimited money, gold, diamonds, and cars that you can use to buy and upgrade any car you want, as well as unlock all the features and modes of the game. It also allows you to play the game offline without any ads or interruptions. However, it also has some drawbacks, such as compatibility issues, bugs, glitches, data loss, and ban risk. Therefore, you should download it at your own risk and discretion. If you want to download CarX Street Mod APK 0.8 6, you can follow the steps we provided above. If you want to play CarX Street Mod APK 0.8 6 better, you can use the tips and tricks we shared above. We hope this article was helpful and informative for you.

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    Getting an e-ticket from Air Asia is easy and simple. Here are the steps you need to follow:

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    Step 1: Book your flight online or via the app

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    The first step is to book your flight with Air Asia online or via their app. You can choose from various destinations, dates, times, fares, and options. You can also pre-book meals, baggage, seats, and other services. Once you have completed your booking, you will receive a confirmation email with your booking number and itinerary.

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    The final step is to download or print your e-boarding pass from the screen. You can choose to download it as a PDF file or a QR code, or print it out if you prefer. You will need to show your e-boarding pass at the security check and boarding gate, along with your valid photo ID or passport. Make sure your e-boarding pass is clear and readable, and keep it handy until you board your flight.

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    Now that you know how to get an e-ticket from Air Asia, here are some tips and tricks for using it:

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    Another tip is to check the requirements and restrictions for using an e-boarding pass before you travel. Some airports or countries may not accept an e-ticket, or may have specific rules for using it. For example, some airports may require you to print out your e-ticket, or show a printed copy of your visa or travel authorization. Some countries may also require you to have a return or onward ticket, or a proof of accommodation. You can check the Air Asia website or contact their customer service for more information.

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    Among Us is a fun and addictive online multiplayer game that has taken the world by storm. In this game, you can play as a crewmate or an impostor on a spaceship, trying to complete tasks or kill everyone respectively. The game is constantly updated with new features, maps, modes, and cosmetics, making it more exciting and enjoyable.

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    How to Download Older Versions of Among Us on Android

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    If you have an Android device, you can easily download older versions of Among Us using an app or a website called Uptodown. Uptodown is a platform that allows you to download APK files of various apps and games, including different versions of Among Us. Here is how you can use Uptodown to download older versions of Among Us on Android:

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    1. Download and install Uptodown app from Google Play Store or visit [Uptodown website](^1^) on your browser.
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    3. Scroll down and tap on "See more" under "Previous versions".
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    4. Select the version that you want to download and tap on "Download".
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    5. Once the APK file is downloaded, tap on it and install it on your device. You may need to enable "Unknown sources" in your device settings if prompted.
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    Congratulations, you have successfully downloaded and installed an older version of Among Us on your Android device. You can now launch the game and enjoy playing it.

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    How to Download Older Versions of Among Us on PC (Steam)

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    If you have a PC and you bought Among Us from Steam, you can also download older versions of Among Us using a tool called DepotDownloader. DepotDownloader is a command-line tool that allows you to download any version of any Steam game that you own. You will also need Microsoft .NET framework installed on your PC for DepotDownloader to work. Here is how you can use Depot Outline: - Introduction - What is Among Us and why it is popular - What are the reasons to download older versions of Among Us - How to download older versions of Among Us on different platforms - How to download older versions of Among Us on Android - Using Uptodown app or website - Choosing the desired version and downloading the APK file - Installing the APK file and allowing unknown sources - How to download older versions of Among Us on PC (Steam) - Using DepotDownloader tool and Microsoft .NET framework - Finding the manifest ID of the desired version on SteamDB - Running the command to download the older version - Replacing the current game files with the downloaded ones - How to download older versions of Among Us on iOS - Using iTunes or Finder to backup the current version of Among Us - Finding and downloading the IPA file of the desired version online - Using Cydia Impactor or AltStore to install the IPA file on the device - Conclusion - Summarizing the main points and benefits of downloading older versions of Among Us - Providing some tips and warnings for downloading older versions of Among Us - Ending with a call to action and inviting feedback - FAQs - What are some features that are available in older versions of Among Us but not in newer ones? - Can I play online with other players who have different versions of Among Us? - Is it safe and legal to download older versions of Among Us? - How can I update Among Us to the latest version if I want to? - Where can I find more information about Among Us and its updates? Article:

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    If you are one of those players who want to download older versions of Among Us, you may wonder how to do it. Well, you are in luck, because in this article, we will show you how to download older versions of Among Us on different platforms, such as Android, PC (Steam), and iOS. Follow these simple steps and you will be able to play your favorite version of Among Us in no time.

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    How to Download Older Versions of Among Us on Android

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    If you have an Android device, you can easily download older versions of Among Us using an app or a website called Uptodown. Uptodown is a platform that allows you to download APK files of various apps and games, including different versions of Among Us. Here is how you can use Uptodown to download older versions of Among Us on Android:

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    1. Download and install Uptodown app from Google Play Store or visit [Uptodown website](^1^) on your browser.
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    2. Search for "Among Us" on Uptodown app or website and tap on it.
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    3. Scroll down and tap on "See more" under "Previous versions".
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    4. Select the version that you want to download and tap on "Download".
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    5. Once the APK file is downloaded, tap on it and install it on your device. You may need to enable "Unknown sources" in your device settings if prompted.
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    Congratulations, you have successfully downloaded and installed an older version of Among Us on your Android device. You can now launch the game and enjoy playing it.

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    How to Download Older Versions of Among Us on PC (Steam)

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    If you have a PC and you bought Among Us from Steam, you can also download older versions of Among Us using a tool called DepotDownloader. DepotDownloader is a command-line tool that allows you to download any version of any Steam game that you own. You will also need Microsoft .NET framework installed on your PC for DepotDownloader to work. Here is how you can use Depot. Downloader to download older versions of Among Us on PC (Steam):

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      -
    1. Download and install Microsoft .NET framework from [Microsoft website] if you don't have it already.
      2. -
    2. Download DepotDownloader from [GitHub] and extract the zip file to a folder on your PC.
      3. -
    3. Visit [SteamDB] and search for "Among Us". Click on the game and then click on "Depots".
      4. -
    4. Find the depot ID of the game, which is usually the same as the app ID. In this case, it is 945360.
      5. -
    5. Click on the depot ID and then click on "Manifests". You will see a list of manifest IDs for different versions of the game.
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    6. Choose the manifest ID of the version that you want to download. For example, if you want to download version 2020.9.9, the manifest ID is 9114472835916844918.
      7. -
    7. Open a command prompt window and navigate to the folder where you extracted DepotDownloader.
      8. -
    8. Type the following command and press enter: dotnet DepotDownloader.dll -app 945360 -depot 945360 -manifest 9114472835916844918 -username your_steam_username -password your_steam_password
      9. -
    9. Wait for the download to finish. You will find the downloaded files in a folder named "depots" inside the DepotDownloader folder.
      10. -
    10. Copy and paste the downloaded files to the folder where you installed Among Us on Steam, usually C:\Program Files (x86)\Steam\steamapps\common\Among Us. Replace the existing files if prompted.
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    That's it, you have successfully downloaded and installed an older version of Among Us on your PC (Steam). You can now launch the game from Steam and enjoy playing it.

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    How to Download Older Versions of Among Us on iOS

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    If you have an iOS device, such as an iPhone or an iPad, you can also download older versions of Among Us using iTunes or Finder, depending on your operating system. You will also need to find and download the IPA file of the desired version online, and use a tool such as Cydia Impactor or AltStore to install it on your device. Here is how you can do it:

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    1. Connect your iOS device to your computer and launch iTunes or Finder. Make sure you have the latest version of Among Us installed on your device.
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    2. Select your device and click on "Back Up Now" to create a backup of your device data, including Among Us.
      3. -
    3. Search online for the IPA file of the older version of Among Us that you want to download. You can use websites such as [iOS Ninja] or [iPhoneCake] to find them.
      4. -
    4. Download the IPA file to your computer and save it in a convenient location.
      5. -
    5. Download and install Cydia Impactor or AltStore from their respective websites. Cydia Impactor is a tool that allows you to sideload apps on your iOS device using your Apple ID. AltStore is a tool that allows you to install apps from an alternative app store using your Apple ID.
      6. -
    6. Launch Cydia Impactor or AltStore and connect your iOS device to your computer.
      7. -
    7. Drag and drop the IPA file that you downloaded onto Cydia Impactor or AltStore. Enter your Apple ID and password when prompted.
      8. -
    8. Wait for the installation to complete. You will see an icon of Among Us on your device home screen.
      9. -
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    Congratulations, you have successfully downloaded and installed an older version of Among Us on your iOS device. You can now launch the game and enjoy playing it.

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    Conclusion

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    In this article, we have shown you how to download older versions of Among Us on different platforms, such as Android, PC (Steam), and iOS. By following these simple steps, you can enjoy playing older versions of Among Us with features that are no longer available in newer versions, or avoid bugs or glitches that may occur in newer versions. You can also experience the nostalgia of playing an earlier version of the game that you love.

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    However, before you download older versions of Among Us, there are some tips and warnings that you should keep in mind:

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    We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you. And if you enjoyed this article, please share it with your friends who may also want to download older versions of Among Us. Thank you for reading and happy gaming!

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    Some features that are available in older versions of Among Us but not in newer ones are:

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    Can I play online with other players who have different versions of Among Us?

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    It depends on the version difference and the platform. Generally, you can play online with other players who have the same major version of Among Us as you, such as 2021.x.x or 2020.x.x. However, you may not be able to play online with other players who have a different minor version of Among Us than you, such as 2021.6.x or 2021.5.x. You may also not be able to play online with other players who have a different platform than you, such as Android, PC (Steam), or iOS. To avoid compatibility issues or errors, it is recommended that you play online with friends who have the same version and platform as you, or play on private servers or local games.

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    Is it safe and legal to download older versions of Among Us?

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    Downloading older versions of Among Us may not be safe or legal, depending on the source and the method. Downloading older versions of Among Us from untrusted sources may expose you to security risks or malware, so make sure you download from trusted sources and scan the files before installing them. Downloading older versions of Among Us may also violate the terms of service or the intellectual property rights of the game developers, so do it at your own risk and discretion. You may also face legal consequences if you distribute or monetize older versions of Among Us without permission from the game developers.

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    How can I update Among Us to the latest version if I want to?

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    If you want to update Among Us to the latest version, you can do it easily by following these steps:

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    Congratulations, you have successfully updated Among Us to the latest version. You can now enjoy all the new features and content that are available in the game.

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    -
    -
    \ No newline at end of file diff --git a/spaces/812vaishnavi/gradio-land-cover-mapping/README.md b/spaces/812vaishnavi/gradio-land-cover-mapping/README.md deleted file mode 100644 index 71b67c00f46a93a40dd9f0b3bd1163e508cdbf7e..0000000000000000000000000000000000000000 --- a/spaces/812vaishnavi/gradio-land-cover-mapping/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Gradio Land Cover Mapping -emoji: 💻 -colorFrom: purple -colorTo: red -sdk: gradio -sdk_version: 3.36.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/A00001/bingothoo/src/components/providers.tsx b/spaces/A00001/bingothoo/src/components/providers.tsx deleted file mode 100644 index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000 --- a/spaces/A00001/bingothoo/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} - - ) -} diff --git a/spaces/AI-Dashboards/Streamlit-Plotly_Graph-Objects/backupapp.py b/spaces/AI-Dashboards/Streamlit-Plotly_Graph-Objects/backupapp.py deleted file mode 100644 index 0c3084435a8bef597a08117fc51f0bdb0c53e42c..0000000000000000000000000000000000000000 --- a/spaces/AI-Dashboards/Streamlit-Plotly_Graph-Objects/backupapp.py +++ /dev/null @@ -1,71 +0,0 @@ -import streamlit as st -import plotly.graph_objects as go - -# List of top six prior auth conditions -conditions = [ - { - "diagnosis": "Diagnosis 1", - "observations": "Observations 1", - "CCD": "CCD 1", - "CCD_procedures": "CCD Procedures 1" - }, - # Add more conditions here -] - -# MSK hip and knee surgery list dictionary -surgery_data = [ - { - "CPTCode": "CPT Code 1", - "CPTDescription": "MSK Hip Surgery", - "ICD10Code": "ICD10 Code 1", - "ICD10Description": "ICD10 Description 1", - "Emoji": "💉", - "Description": "Hip Surgery", - "Cost": 10 - }, - { - "CPTCode": "CPT Code 2", - "CPTDescription": "MSK Knee Surgery", - "ICD10Code": "ICD10 Code 2", - "ICD10Description": "ICD10 Description 2", - "Emoji": "💊", - "Description": "Knee Surgery", - "Cost": 15 - } -] - -# Sort the surgery data by descending cost -surgery_data.sort(key=lambda x: x["Cost"], reverse=True) - -# Function to create heatmap circle plot -def create_heatmap_circle_plot(surgery_data): - fig = go.Figure() - - for surgery in surgery_data: - fig.add_trace(go.Scatter( - x=[surgery["CPTCode"]], - y=[surgery["Cost"]], - mode='markers', - marker=dict( - size=20, - color=[surgery["Cost"]], - colorscale='Viridis', - showscale=True - ), - text=surgery["CPTDescription"], - hovertemplate='%{text}
    CPT Code: %{x}
    Cost: %{y}')) - - fig.update_layout(title='Heatmap Circle Plot of Surgery Types', - xaxis_title='CPT Codes', - yaxis_title='Cost (in billions)') - - return fig - -# Streamlit app -st.title("Top Prior Auth Conditions") -st.header("MSK Hip and Knee Surgery") -st.write(surgery_data) - -st.header("Heatmap Circle Plot") -fig = create_heatmap_circle_plot(surgery_data) -st.plotly_chart(fig) diff --git a/spaces/AIFILMS/generate_human_motion/pyrender/docs/source/conf.py b/spaces/AIFILMS/generate_human_motion/pyrender/docs/source/conf.py deleted file mode 100644 index 6bf194c375e7e789b334a838953adfeaf2eb59b6..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/generate_human_motion/pyrender/docs/source/conf.py +++ /dev/null @@ -1,352 +0,0 @@ -# -*- coding: utf-8 -*- -# -# core documentation build configuration file, created by -# sphinx-quickstart on Sun Oct 16 14:33:48 2016. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import sys -import os -from pyrender import __version__ -from sphinx.domains.python import PythonDomain - -# 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. -sys.path.insert(0, os.path.abspath('../../')) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' - -# 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.coverage', - 'sphinx.ext.githubpages', - 'sphinx.ext.intersphinx', - 'sphinx.ext.napoleon', - 'sphinx.ext.viewcode', - 'sphinx_automodapi.automodapi', - 'sphinx_automodapi.smart_resolver' -] -numpydoc_class_members_toctree = False -automodapi_toctreedirnm = 'generated' -automodsumm_inherited_members = True - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The encoding of source files. -#source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = u'pyrender' -copyright = u'2018, Matthew Matl' -author = u'Matthew Matl' - -# 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. -# -# The short X.Y version. -version = __version__ -# The full version, including alpha/beta/rc tags. -release = __version__ - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -#today = '' -# Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = [] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -#default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -#add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -#show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -#keep_warnings = False - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -import sphinx_rtd_theme -html_theme = 'sphinx_rtd_theme' -html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -#html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -#html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -#html_logo = None - -# The name of an image file (relative to this directory) to use as a favicon of -# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -#html_favicon = None - -# 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'] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -#html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -#html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -#html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -#html_additional_pages = {} - -# If false, no module index is generated. -#html_domain_indices = True - -# If false, no index is generated. -#html_use_index = True - -# If true, the index is split into individual pages for each letter. -#html_split_index = False - -# If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -#html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' -#html_search_language = 'en' - -# A dictionary with options for the search language support, empty by default. -# Now only 'ja' uses this config value -#html_search_options = {'type': 'default'} - -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -#html_search_scorer = 'scorer.js' - -# Output file base name for HTML help builder. -htmlhelp_basename = 'coredoc' - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { -# The paper size ('letterpaper' or 'a4paper'). -#'papersize': 'letterpaper', - -# The font size ('10pt', '11pt' or '12pt'). -#'pointsize': '10pt', - -# Additional stuff for the LaTeX preamble. -#'preamble': '', - -# Latex figure (float) alignment -#'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'pyrender.tex', u'pyrender Documentation', - u'Matthew Matl', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -#latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -#latex_use_parts = False - -# If true, show page references after internal links. -#latex_show_pagerefs = False - -# If true, show URL addresses after external links. -#latex_show_urls = False - -# Documents to append as an appendix to all manuals. -#latex_appendices = [] - -# If false, no module index is generated. -#latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'pyrender', u'pyrender Documentation', - [author], 1) -] - -# If true, show URL addresses after external links. -#man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'pyrender', u'pyrender Documentation', - author, 'pyrender', 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -#texinfo_appendices = [] - -# If false, no module index is generated. -#texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False - -intersphinx_mapping = { - 'python' : ('https://docs.python.org/', None), - 'pyrender' : ('https://pyrender.readthedocs.io/en/latest/', None), -} - -# Autosummary fix -autosummary_generate = True - -# Try to suppress multiple-definition warnings by always taking the shorter -# path when two or more paths have the same base module - -class MyPythonDomain(PythonDomain): - - def find_obj(self, env, modname, classname, name, type, searchmode=0): - """Ensures an object always resolves to the desired module - if defined there.""" - orig_matches = PythonDomain.find_obj( - self, env, modname, classname, name, type, searchmode - ) - - if len(orig_matches) <= 1: - return orig_matches - - # If multiple matches, try to take the shortest if all the modules are - # the same - first_match_name_sp = orig_matches[0][0].split('.') - base_name = first_match_name_sp[0] - min_len = len(first_match_name_sp) - best_match = orig_matches[0] - - for match in orig_matches[1:]: - match_name = match[0] - match_name_sp = match_name.split('.') - match_base = match_name_sp[0] - - # If we have mismatched bases, return them all to trigger warnings - if match_base != base_name: - return orig_matches - - # Otherwise, check and see if it's shorter - if len(match_name_sp) < min_len: - min_len = len(match_name_sp) - best_match = match - - return (best_match,) - - -def setup(sphinx): - """Use MyPythonDomain in place of PythonDomain""" - sphinx.override_domain(MyPythonDomain) - diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/wav_processors/common_processors.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/wav_processors/common_processors.py deleted file mode 100644 index 8b0c62d5e1485ed9612b4452a656f0e837c2d693..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/wav_processors/common_processors.py +++ /dev/null @@ -1,85 +0,0 @@ -import os -import subprocess -import librosa -import numpy as np -from data_gen.tts.wav_processors.base_processor import BaseWavProcessor, register_wav_processors -from data_gen.tts.data_gen_utils import trim_long_silences -from utils.audio import save_wav, rnnoise -from utils.hparams import hparams - - -@register_wav_processors(name='sox_to_wav') -class ConvertToWavProcessor(BaseWavProcessor): - @property - def name(self): - return 'ToWav' - - def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): - if input_fn[-4:] == '.wav': - return input_fn, sr - else: - output_fn = self.output_fn(input_fn) - subprocess.check_call(f'sox -v 0.95 "{input_fn}" -t wav "{output_fn}"', shell=True) - return output_fn, sr - - -@register_wav_processors(name='sox_resample') -class ResampleProcessor(BaseWavProcessor): - @property - def name(self): - return 'Resample' - - def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): - output_fn = self.output_fn(input_fn) - sr_file = librosa.core.get_samplerate(input_fn) - if sr != sr_file: - subprocess.check_call(f'sox -v 0.95 "{input_fn}" -r{sr} "{output_fn}"', shell=True) - y, _ = librosa.core.load(input_fn, sr=sr) - y, _ = librosa.effects.trim(y) - save_wav(y, output_fn, sr) - return output_fn, sr - else: - return input_fn, sr - - -@register_wav_processors(name='trim_sil') -class TrimSILProcessor(BaseWavProcessor): - @property - def name(self): - return 'TrimSIL' - - def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): - output_fn = self.output_fn(input_fn) - y, _ = librosa.core.load(input_fn, sr=sr) - y, _ = librosa.effects.trim(y) - save_wav(y, output_fn, sr) - return output_fn - - -@register_wav_processors(name='trim_all_sil') -class TrimAllSILProcessor(BaseWavProcessor): - @property - def name(self): - return 'TrimSIL' - - def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): - output_fn = self.output_fn(input_fn) - y, audio_mask, _ = trim_long_silences( - input_fn, vad_max_silence_length=preprocess_args.get('vad_max_silence_length', 12)) - save_wav(y, output_fn, sr) - if preprocess_args['save_sil_mask']: - os.makedirs(f'{processed_dir}/sil_mask', exist_ok=True) - np.save(f'{processed_dir}/sil_mask/{item_name}.npy', audio_mask) - return output_fn, sr - - -@register_wav_processors(name='denoise') -class DenoiseProcessor(BaseWavProcessor): - @property - def name(self): - return 'Denoise' - - def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args): - output_fn = self.output_fn(input_fn) - rnnoise(input_fn, output_fn, out_sample_rate=sr) - return output_fn, sr diff --git a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Wewordle.py b/spaces/ASJMO/freegpt/g4f/Provider/Providers/Wewordle.py deleted file mode 100644 index 090d0bf3ab2e1f3851880393d43662edfbe9d984..0000000000000000000000000000000000000000 --- a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Wewordle.py +++ /dev/null @@ -1,75 +0,0 @@ -import os -import requests -import json -import random -import time -import string -from ...typing import sha256, Dict, get_type_hints - -url = "https://wewordle.org/gptapi/v1/android/turbo" -model = ['gpt-3.5-turbo'] -supports_stream = False -needs_auth = False - - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - base = '' - for message in messages: - base += '%s: %s\n' % (message['role'], message['content']) - base += 'assistant:' - # randomize user id and app id - _user_id = ''.join(random.choices( - f'{string.ascii_lowercase}{string.digits}', k=16)) - _app_id = ''.join(random.choices( - f'{string.ascii_lowercase}{string.digits}', k=31)) - # make current date with format utc - _request_date = time.strftime("%Y-%m-%dT%H:%M:%S.000Z", time.gmtime()) - headers = { - 'accept': '*/*', - 'pragma': 'no-cache', - 'Content-Type': 'application/json', - 'Connection': 'keep-alive' - } - data = { - "user": _user_id, - "messages": [ - {"role": "user", "content": base} - ], - "subscriber": { - "originalPurchaseDate": None, - "originalApplicationVersion": None, - "allPurchaseDatesMillis": {}, - "entitlements": { - "active": {}, - "all": {} - }, - "allPurchaseDates": {}, - "allExpirationDatesMillis": {}, - "allExpirationDates": {}, - "originalAppUserId": f"$RCAnonymousID:{_app_id}", - "latestExpirationDate": None, - "requestDate": _request_date, - "latestExpirationDateMillis": None, - "nonSubscriptionTransactions": [], - "originalPurchaseDateMillis": None, - "managementURL": None, - "allPurchasedProductIdentifiers": [], - "firstSeen": _request_date, - "activeSubscriptions": [] - } - } - response = requests.post(url, headers=headers, data=json.dumps(data)) - if response.status_code == 200: - _json = response.json() - if 'message' in _json: - message_content = _json['message']['content'] - message_content = message_content.replace('**assistant:** ', '') - yield message_content - else: - print(f"Error Occurred::{response.status_code}") - return None - - -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]]) diff --git a/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/executor.py b/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/executor.py deleted file mode 100644 index 38294453d1ed4e81ba42b76a90da524afeb69c32..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/executor.py +++ /dev/null @@ -1,130 +0,0 @@ -from __future__ import annotations - -from agentverse.logging import get_logger -from colorama import Fore -import bdb -from string import Template -from typing import TYPE_CHECKING, List, Any - -from agentverse.message import ExecutorMessage, Message, SolverMessage -from agentverse.utils import AgentFinish, AgentAction - -from agentverse.agents import agent_registry -from agentverse.agents.base import BaseAgent -import requests - -logger = get_logger() - - -@agent_registry.register("executor") -class ExecutorAgent(BaseAgent): - max_history: int = 5 - - def step( - self, task_description: str, solution: str, tools: List[dict] = [], **kwargs - ) -> ExecutorMessage: - logger.debug("", self.name, Fore.MAGENTA) - prepend_prompt, append_prompt = self.get_all_prompts( - task_description=task_description, - solution=solution, - agent_name=self.name, - **kwargs, - ) - - history = self.memory.to_messages(self.name, start_index=-self.max_history) - parsed_response = None - for i in range(self.max_retry): - try: - response = self.llm.generate_response( - prepend_prompt, history, append_prompt, tools - ) - parsed_response = self.output_parser.parse(response) - break - except (KeyboardInterrupt, bdb.BdbQuit): - raise - except Exception as e: - logger.error(e) - logger.warn("Retrying...") - continue - - if parsed_response is None: - logger.error(f"{self.name} failed to generate valid response.") - if isinstance(parsed_response, AgentFinish): - message = ExecutorMessage( - content=parsed_response.return_values["output"], - sender=self.name, - sender_agent=self, - ) - elif isinstance(parsed_response, AgentAction): - message = ExecutorMessage( - content=parsed_response.log, - sender=self.name, - sender_agent=self, - tool_name=parsed_response.tool, - tool_input=parsed_response.tool_input, - ) - else: - raise ValueError( - f"Error response type: {type(parsed_response)}. Only support \ - AgentFinish and AgentAction. Modify your output parser." - ) - return message - - async def astep( - self, task_description: str, solution: str, tools: List[dict] = [], **kwargs - ) -> ExecutorMessage: - logger.debug("", self.name, Fore.MAGENTA) - prepend_prompt, append_prompt = self.get_all_prompts( - task_description=task_description, - solution=solution, - agent_name=self.name, - **kwargs, - ) - - history = self.memory.to_messages(self.name, start_index=-self.max_history) - parsed_response = None - for i in range(self.max_retry): - try: - response = await self.llm.agenerate_response( - prepend_prompt, history, append_prompt, tools - ) - parsed_response = self.output_parser.parse(response) - break - except (KeyboardInterrupt, bdb.BdbQuit): - raise - except Exception as e: - logger.error(e) - logger.warn("Retrying...") - continue - - if parsed_response is None: - logger.error(f"{self.name} failed to generate valid response.") - parsed_response = AgentAction(tool="", tool_input="", log="") - if isinstance(parsed_response, AgentFinish): - message = ExecutorMessage( - content=parsed_response.return_values["output"], - sender=self.name, - sender_agent=self, - ) - elif isinstance(parsed_response, AgentAction): - message = ExecutorMessage( - content=parsed_response.log, - sender=self.name, - sender_agent=self, - tool_name=parsed_response.tool, - tool_input=parsed_response.tool_input, - ) - else: - raise ValueError( - f"Error response type: {type(parsed_response)}. Only support \ - AgentFinish and AgentAction. Modify your output parser." - ) - return message - - 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/Ahmedmewloud/Depplearnig/app.py b/spaces/Ahmedmewloud/Depplearnig/app.py deleted file mode 100644 index 8e3aa9e9d72687ee4a1ecadc8ae361c8a23fa9a6..0000000000000000000000000000000000000000 --- a/spaces/Ahmedmewloud/Depplearnig/app.py +++ /dev/null @@ -1,724 +0,0 @@ -# -*- coding: utf-8 -*- -"""Traduction.ipynb - -Automatically generated by Colaboratory. - -Original file is located at - https://colab.research.google.com/drive/1qOS7cqek1bQPypxFqx-9G1ApPANNHL2X -""" - -# !pip install "tensorflow-text>=2.11" -# !pip install einops - -# from google.colab import drive -# drive.mount('/content/drive') - -import numpy as np - -import typing -from typing import Any, Tuple - - - - - -import numpy as np - -import typing -from typing import Any, Tuple - -import tensorflow as tf - -import tensorflow_text as tf_text -import einops -import matplotlib.pyplot as plt -import matplotlib.ticker as ticker - -import tensorflow as tf - - -#import tensorflow_text as tf_text - -class ShapeChecker(): - def __init__(self): - # Keep a cache of every axis-name seen - self.shapes = {} - - def __call__(self, tensor, names, broadcast=False): - if not tf.executing_eagerly(): - return - - parsed = einops.parse_shape(tensor, names) - - for name, new_dim in parsed.items(): - old_dim = self.shapes.get(name, None) - - if (broadcast and new_dim == 1): - continue - - if old_dim is None: - # If the axis name is new, add its length to the cache. - self.shapes[name] = new_dim - continue - - if new_dim != old_dim: - raise ValueError(f"Shape mismatch for dimension: '{name}'\n" - f" found: {new_dim}\n" - f" expected: {old_dim}\n") - -"""pour les donnees nous utilisons une api par Anki """ - -# le telechargement du donnees de training - - -# if not os.path.isfile('./fra.txt'): -# !wget http://www.manythings.org/anki/fra-eng.zip -P ./ -# !unzip /content/fra-eng.zip -d ./ -# else: -# print('File already downloaded and extracted.') - - -import os -import subprocess - -path_to_file = 'fra.txt' - -# if not os.path.isfile(path_to_file): -# subprocess.run(['wget', 'http://www.manythings.org/anki/fra-eng.zip', '-P', '']) -# subprocess.run(['unzip', 'fra-eng.zip', '-d', '']) -# else: -# print('File already downloaded and extracted.') - - - -from pathlib import Path -import numpy as np - -"""la fonction load_data(path) une fonction qui retourne un array numpy tel un paire ( pahrse en fr == > phrase en eng )""" - -def load_data(path): - path = Path(path) - text = path.read_text(encoding='utf-8') - - lines = text.splitlines() - pairs = [line.split('\t') for line in lines] - # print(pairs[2]) - context = np.array([pairs[index][1] for index in range(len(pairs))]) - target = np.array([pairs[index][0] for index in range(len(pairs))]) - - return target, context - -"""un test d'affichage""" - -# targ, inp = load_data(path_to_file) -target_raw, context_raw = load_data(path_to_file) - -# print(len(context_raw),len(target_raw)) -# for i in range(100): -# print(context_raw[i]+'\t') -# print(target_raw[i]+'\n') - -BUFFER_SIZE = len(context_raw) -BATCH_SIZE = 64 - -is_train = np.random.uniform(size=(len(target_raw),)) < 0.8 - -train_raw = ( - tf.data.Dataset - .from_tensor_slices((context_raw[is_train], target_raw[is_train])) - .shuffle(BUFFER_SIZE) - .batch(BATCH_SIZE)) -val_raw = ( - tf.data.Dataset - .from_tensor_slices((context_raw[~is_train], target_raw[~is_train])) - .shuffle(BUFFER_SIZE) - .batch(BATCH_SIZE)) - -for example_context_strings, example_target_strings in train_raw.take(1): - print(example_context_strings[:5]) - print() - print(example_target_strings[:5]) - break - -example_text = tf.constant('Salut Prenez vos jambes à vos cous !') - -# print(example_text.numpy()) -# print(tf_text.normalize_utf8(example_text, 'NFKD').numpy()) - -#La normalisation -def tf_lower_and_split_punct(text): - # Split accecented characters. - text = tf_text.normalize_utf8(text, 'NFKD') - text = tf.strings.lower(text) - # Keep space, a to z, and select punctuation. - text = tf.strings.regex_replace(text, '[^ a-z.?!,¿]', '') - # Add spaces around punctuation. - text = tf.strings.regex_replace(text, '[.?!,¿]', r' \0 ') - # Strip whitespace. - text = tf.strings.strip(text) - - text = tf.strings.join(['[START]', text, '[END]'], separator=' ') - return text - -# Avent la normalisation -print(example_text.numpy().decode()) -#Apres la normalisation -print(tf_lower_and_split_punct(example_text).numpy().decode()) - -#Vectorisation de texte -max_vocab_size = 5000 - -input_text_processor = tf.keras.layers.TextVectorization( - standardize=tf_lower_and_split_punct, - max_tokens=max_vocab_size) - -max_vocab_size = 5000 - -context_text_processor = tf.keras.layers.TextVectorization( - standardize=tf_lower_and_split_punct, - max_tokens=max_vocab_size, - ragged=True) - -context_text_processor.adapt(train_raw.map(lambda context, target: context)) - -# Here are the first 10 words from the vocabulary: -context_text_processor.get_vocabulary()[:10] - -target_text_processor = tf.keras.layers.TextVectorization( - standardize=tf_lower_and_split_punct, - max_tokens=max_vocab_size, - ragged=True) - -target_text_processor.adapt(train_raw.map(lambda context, target: target)) -target_text_processor.get_vocabulary()[:10] - -example_tokens = context_text_processor(example_context_strings) -example_tokens[:3, :] - -context_vocab = np.array(context_text_processor.get_vocabulary()) -tokens = context_vocab[example_tokens[0].numpy()] -' '.join(tokens) - -plt.subplot(1, 2, 1) -plt.pcolormesh(example_tokens.to_tensor()) -plt.title('Token IDs') - -plt.subplot(1, 2, 2) -plt.pcolormesh(example_tokens.to_tensor() != 0) -plt.title('Mask') - -def process_text(context, target): - context = context_text_processor(context).to_tensor() - target = target_text_processor(target) - targ_in = target[:,:-1].to_tensor() - targ_out = target[:,1:].to_tensor() - return (context, targ_in), targ_out - - -train_ds = train_raw.map(process_text, tf.data.AUTOTUNE) -val_ds = val_raw.map(process_text, tf.data.AUTOTUNE) - -for (ex_context_tok, ex_tar_in), ex_tar_out in train_ds.take(1): - print(ex_context_tok[0, :10].numpy()) - print() - print(ex_tar_in[0, :10].numpy()) - print(ex_tar_out[0, :10].numpy()) - -UNITS = 256 - - - -"""Fin 21114 - -# **Encoder/decoder** - -**Avant d'entrer dans le détail, nous définissons des constantes pour le modèle :** -""" - -# UNITS = 256 - -"""Un RNN bidirectionnel - -**L'** encodeur -""" - -class Encoder(tf.keras.layers.Layer): - def __init__(self, text_processor, units): - super(Encoder, self).__init__() - self.text_processor = text_processor - self.vocab_size = text_processor.vocabulary_size() - self.units = units - - # The embedding layer converts tokens to vectors - self.embedding = tf.keras.layers.Embedding(self.vocab_size, units, - mask_zero=True) - - # The RNN layer processes those vectors sequentially. - self.rnn = tf.keras.layers.Bidirectional( - merge_mode='sum', - layer=tf.keras.layers.GRU(units, - # Return the sequence and state - return_sequences=True, - recurrent_initializer='glorot_uniform')) - - def call(self, x): - shape_checker = ShapeChecker() - shape_checker(x, 'batch s') - - # 2. The embedding layer looks up the embedding vector for each token. - x = self.embedding(x) - shape_checker(x, 'batch s units') - - # 3. The GRU processes the sequence of embeddings. - x = self.rnn(x) - shape_checker(x, 'batch s units') - - # 4. Returns the new sequence of embeddings. - return x - - def convert_input(self, texts): - texts = tf.convert_to_tensor(texts) - if len(texts.shape) == 0: - texts = tf.convert_to_tensor(texts)[tf.newaxis] - context = self.text_processor(texts).to_tensor() - context = self(context) - return context - -# Encode the input sequence. -encoder = Encoder(context_text_processor, UNITS) -ex_context = encoder(ex_context_tok) - -print(f'Context tokens, shape (batch, s): {ex_context_tok.shape}') -print(f'Encoder output, shape (batch, s, units): {ex_context.shape}') - -""" - -La couche d'**attention**""" - -class CrossAttention(tf.keras.layers.Layer): - def __init__(self, units, **kwargs): - super().__init__() - self.mha = tf.keras.layers.MultiHeadAttention(key_dim=units, num_heads=1, **kwargs) - self.layernorm = tf.keras.layers.LayerNormalization() - self.add = tf.keras.layers.Add() - - def call(self, x, context): - shape_checker = ShapeChecker() - - shape_checker(x, 'batch t units') - shape_checker(context, 'batch s units') - - attn_output, attn_scores = self.mha( - query=x, - value=context, - return_attention_scores=True) - - shape_checker(x, 'batch t units') - shape_checker(attn_scores, 'batch heads t s') - - # Cache the attention scores for plotting later. - attn_scores = tf.reduce_mean(attn_scores, axis=1) - shape_checker(attn_scores, 'batch t s') - self.last_attention_weights = attn_scores - - x = self.add([x, attn_output]) - x = self.layernorm(x) - - return x - -attention_layer = CrossAttention(UNITS) - -# Attend to the encoded tokens -embed = tf.keras.layers.Embedding(target_text_processor.vocabulary_size(), - output_dim=UNITS, mask_zero=True) -ex_tar_embed = embed(ex_tar_in) - -result = attention_layer(ex_tar_embed, ex_context) - -print(f'Context sequence, shape (batch, s, units): {ex_context.shape}') -print(f'Target sequence, shape (batch, t, units): {ex_tar_embed.shape}') -print(f'Attention result, shape (batch, t, units): {result.shape}') -print(f'Attention weights, shape (batch, t, s): {attention_layer.last_attention_weights.shape}') - -attention_layer.last_attention_weights[0].numpy().sum(axis=-1) - -attention_weights = attention_layer.last_attention_weights -mask=(ex_context_tok != 0).numpy() - -plt.subplot(1, 2, 1) -plt.pcolormesh(mask*attention_weights[:, 0, :]) -plt.title('Attention weights') - -plt.subplot(1, 2, 2) -plt.pcolormesh(mask) -plt.title('Mask'); - -"""Un RNN unidirectionnel - -le **Décodeur** -""" - -class Decoder(tf.keras.layers.Layer): - @classmethod - def add_method(cls, fun): - setattr(cls, fun.__name__, fun) - return fun - - def __init__(self, text_processor, units): - super(Decoder, self).__init__() - self.text_processor = text_processor - self.vocab_size = text_processor.vocabulary_size() - self.word_to_id = tf.keras.layers.StringLookup( - vocabulary=text_processor.get_vocabulary(), - mask_token='', oov_token='[UNK]') - self.id_to_word = tf.keras.layers.StringLookup( - vocabulary=text_processor.get_vocabulary(), - mask_token='', oov_token='[UNK]', - invert=True) - self.start_token = self.word_to_id('[START]') - self.end_token = self.word_to_id('[END]') - - self.units = units - - - # 1. The embedding layer converts token IDs to vectors - self.embedding = tf.keras.layers.Embedding(self.vocab_size, - units, mask_zero=True) - - # 2. The RNN keeps track of what's been generated so far. - self.rnn = tf.keras.layers.GRU(units, - return_sequences=True, - return_state=True, - recurrent_initializer='glorot_uniform') - - # 3. The RNN output will be the query for the attention layer. - self.attention = CrossAttention(units) - - # 4. This fully connected layer produces the logits for each - # output token. - self.output_layer = tf.keras.layers.Dense(self.vocab_size) - -"""**Training**""" - -@Decoder.add_method -def call(self, - context, x, - state=None, - return_state=False): - shape_checker = ShapeChecker() - shape_checker(x, 'batch t') - shape_checker(context, 'batch s units') - - # 1. Lookup the embeddings - x = self.embedding(x) - shape_checker(x, 'batch t units') - - # 2. Process the target sequence. - x, state = self.rnn(x, initial_state=state) - shape_checker(x, 'batch t units') - - # 3. Use the RNN output as the query for the attention over the context. - x = self.attention(x, context) - self.last_attention_weights = self.attention.last_attention_weights - shape_checker(x, 'batch t units') - shape_checker(self.last_attention_weights, 'batch t s') - - # Step 4. Generate logit predictions for the next token. - logits = self.output_layer(x) - shape_checker(logits, 'batch t target_vocab_size') - - if return_state: - return logits, state - else: - return logits - -decoder = Decoder(target_text_processor, UNITS) - -logits = decoder(ex_context, ex_tar_in) - -print(f'encoder output shape: (batch, s, units) {ex_context.shape}') -print(f'input target tokens shape: (batch, t) {ex_tar_in.shape}') -print(f'logits shape shape: (batch, target_vocabulary_size) {logits.shape}') - -"""**Inference**""" - -@Decoder.add_method -def get_initial_state(self, context): - batch_size = tf.shape(context)[0] - start_tokens = tf.fill([batch_size, 1], self.start_token) - done = tf.zeros([batch_size, 1], dtype=tf.bool) - embedded = self.embedding(start_tokens) - return start_tokens, done, self.rnn.get_initial_state(embedded)[0] - -@Decoder.add_method -def tokens_to_text(self, tokens): - words = self.id_to_word(tokens) - result = tf.strings.reduce_join(words, axis=-1, separator=' ') - result = tf.strings.regex_replace(result, '^ *\[START\] *', '') - result = tf.strings.regex_replace(result, ' *\[END\] *$', '') - return result - -@Decoder.add_method -def get_next_token(self, context, next_token, done, state, temperature = 0.0): - logits, state = self( - context, next_token, - state = state, - return_state=True) - - if temperature == 0.0: - next_token = tf.argmax(logits, axis=-1) - else: - logits = logits[:, -1, :]/temperature - next_token = tf.random.categorical(logits, num_samples=1) - - # If a sequence produces an `end_token`, set it `done` - done = done | (next_token == self.end_token) - # Once a sequence is done it only produces 0-padding. - next_token = tf.where(done, tf.constant(0, dtype=tf.int64), next_token) - - return next_token, done, state - -# Setup the loop variables. -next_token, done, state = decoder.get_initial_state(ex_context) -tokens = [] - -for n in range(10): - # Run one step. - next_token, done, state = decoder.get_next_token( - ex_context, next_token, done, state, temperature=1.0) - # Add the token to the output. - tokens.append(next_token) - -# Stack all the tokens together. -tokens = tf.concat(tokens, axis=-1) # (batch, t) - -# Convert the tokens back to a a string -result = decoder.tokens_to_text(tokens) -result[:3].numpy() - -"""### Fin 21196""" - -class Translator(tf.keras.Model): - @classmethod - def add_method(cls, fun): - setattr(cls, fun.__name__, fun) - return fun - - def __init__(self, units, - context_text_processor, - target_text_processor): - super().__init__() - # Build the encoder and decoder - encoder = Encoder(context_text_processor, units) - decoder = Decoder(target_text_processor, units) - - self.encoder = encoder - self.decoder = decoder - - def call(self, inputs): - context, x = inputs - context = self.encoder(context) - logits = self.decoder(context, x) - - #TODO(b/250038731): remove this - try: - # Delete the keras mask, so keras doesn't scale the loss+accuracy. - del logits._keras_mask - except AttributeError: - pass - - return logits - -"""necessite clarification""" - -model = Translator(UNITS, context_text_processor, target_text_processor) - -logits = model((ex_context_tok, ex_tar_in)) - -print(f'Context tokens, shape: (batch, s, units) {ex_context_tok.shape}') -print(f'Target tokens, shape: (batch, t) {ex_tar_in.shape}') -print(f'logits, shape: (batch, t, target_vocabulary_size) {logits.shape}') - -def masked_loss(y_true, y_pred): - # Calculate the loss for each item in the batch. - loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( - from_logits=True, reduction='none') - loss = loss_fn(y_true, y_pred) - - # Mask off the losses on padding. - mask = tf.cast(y_true != 0, loss.dtype) - loss *= mask - - # Return the total. - return tf.reduce_sum(loss)/tf.reduce_sum(mask) - -def masked_acc(y_true, y_pred): - # Calculate the loss for each item in the batch. - y_pred = tf.argmax(y_pred, axis=-1) - y_pred = tf.cast(y_pred, y_true.dtype) - - match = tf.cast(y_true == y_pred, tf.float32) - mask = tf.cast(y_true != 0, tf.float32) - - return tf.reduce_sum(match)/tf.reduce_sum(mask) - -"""compilation du modele""" - -model.compile(optimizer='adam', - loss=masked_loss, - metrics=[masked_acc, masked_loss]) - -"""clalcule metric""" - -vocab_size = 1.0 * target_text_processor.vocabulary_size() - -{"expected_loss": tf.math.log(vocab_size).numpy(), - "expected_acc": 1/vocab_size} - -"""evalution du modele""" - -model.evaluate(val_ds, steps=20, return_dict=True) - -import os - -# Vérifier si un fichier de sauvegarde existe -# if not os.path.exists('model_weights.h5'): -# # Le fichier de sauvegarde n'existe pas, exécuter l'entraînement -# history = model.fit( -# train_ds.repeat(), -# epochs=100, -# steps_per_epoch=100, -# validation_data=val_ds, -# validation_steps=20, -# callbacks=[ -# tf.keras.callbacks.EarlyStopping(patience=3)]) - -# # Sauvegarder les poids du modèle -# model.save_weights('model_weights.h5') -# else: -# # Le fichier de sauvegarde existe, on passe à l'étape suivante -# print("Le modèle a déjà été entraîné. Passer à l'étape suivante.") -history = model.fit( - train_ds.repeat(), - epochs=100, - steps_per_epoch = 100, - validation_data=val_ds, - validation_steps = 20, - callbacks=[ - tf.keras.callbacks.EarlyStopping(patience=3)]) - -plt.plot(history.history['loss'], label='loss') -plt.plot(history.history['val_loss'], label='val_loss') -plt.ylim([0, max(plt.ylim())]) -plt.xlabel('Epoch #') -plt.ylabel('CE/token') -plt.legend() - -plt.plot(history.history['masked_acc'], label='accuracy') -plt.plot(history.history['val_masked_acc'], label='val_accuracy') -plt.ylim([0, max(plt.ylim())]) -plt.xlabel('Epoch #') -plt.ylabel('CE/token') -plt.legend() - -"""ici la translation des texts """ - -#@title -@Translator.add_method -def translate(self, - texts, *, - max_length=50, - temperature=0.0): - # Process the input texts - context = self.encoder.convert_input(texts) - batch_size = tf.shape(texts)[0] - - # Setup the loop inputs - tokens = [] - attention_weights = [] - next_token, done, state = self.decoder.get_initial_state(context) - - for _ in range(max_length): - # Generate the next token - next_token, done, state = self.decoder.get_next_token( - context, next_token, done, state, temperature) - - # Collect the generated tokens - tokens.append(next_token) - attention_weights.append(self.decoder.last_attention_weights) - - if tf.executing_eagerly() and tf.reduce_all(done): - break - - # Stack the lists of tokens and attention weights. - tokens = tf.concat(tokens, axis=-1) # t*[(batch 1)] -> (batch, t) - self.last_attention_weights = tf.concat(attention_weights, axis=1) # t*[(batch 1 s)] -> (batch, t s) - - result = self.decoder.tokens_to_text(tokens) - return result - -"""test du translate""" - -result = model.translate(['tu est dans la maison']) # Are you still home -result[0].numpy().decode() - -#@title -@Translator.add_method -def plot_attention(self, text, **kwargs): - assert isinstance(text, str) - output = self.translate([text], **kwargs) - output = output[0].numpy().decode() - - attention = self.last_attention_weights[0] - - context = tf_lower_and_split_punct(text) - context = context.numpy().decode().split() - - output = tf_lower_and_split_punct(output) - output = output.numpy().decode().split()[1:] - - fig = plt.figure(figsize=(10, 10)) - ax = fig.add_subplot(1, 1, 1) - - ax.matshow(attention, cmap='viridis', vmin=0.0) - - fontdict = {'fontsize': 14} - - ax.set_xticklabels([''] + context, fontdict=fontdict, rotation=90) - ax.set_yticklabels([''] + output, fontdict=fontdict) - - ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) - ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) - - ax.set_xlabel('Input text') - ax.set_ylabel('Output text') - -"""quelques test""" - -# Commented out IPython magic to ensure Python compatibility. -# %%time -# # This is my life. -# model.plot_attention('A partir de ces tableaux de chaînes ') -# - -# Commented out IPython magic to ensure Python compatibility. -# %%time -# # Try to find out.' -# model.plot_attention('nous sommes des etudiants d''école polytechnique') - -"""fin 211995@EFQe$aFk7vjd/ - -""" - -# !pip install gradio - -import gradio as gr - -def translate_text(text): - result = model.translate([text]) - translated_text = result[0].numpy().decode() - return translated_text - -iface = gr.Interface(fn=translate_text, inputs="text", outputs="text", title="Translation App") -iface.launch() -# iface = gr.Interface(fn=translate_text, inputs="text", outputs="text", title="Translation App", flagging_dir=None) diff --git a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py b/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py deleted file mode 100644 index 18318965335b37cc671004a6aceda3229dc7b477..0000000000000000000000000000000000000000 --- a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py +++ /dev/null @@ -1,329 +0,0 @@ -# -*- coding: utf-8 -*- -# File : batchnorm.py -# Author : Jiayuan Mao -# Email : maojiayuan@gmail.com -# Date : 27/01/2018 -# -# This file is part of Synchronized-BatchNorm-PyTorch. -# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch -# Distributed under MIT License. - -import collections - -import torch -import torch.nn.functional as F - -from torch.nn.modules.batchnorm import _BatchNorm -from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast - -from .comm import SyncMaster - -__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] - - -def _sum_ft(tensor): - """sum over the first and last dimention""" - return tensor.sum(dim=0).sum(dim=-1) - - -def _unsqueeze_ft(tensor): - """add new dementions at the front and the tail""" - return tensor.unsqueeze(0).unsqueeze(-1) - - -_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) -_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) - - -class _SynchronizedBatchNorm(_BatchNorm): - def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True): - super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) - - self._sync_master = SyncMaster(self._data_parallel_master) - - self._is_parallel = False - self._parallel_id = None - self._slave_pipe = None - - # customed batch norm statistics - self._moving_average_fraction = 1. - momentum - self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features)) - self.register_buffer('_tmp_running_var', torch.ones(self.num_features)) - self.register_buffer('_running_iter', torch.ones(1)) - self._tmp_running_mean = self.running_mean.clone() * self._running_iter - self._tmp_running_var = self.running_var.clone() * self._running_iter - - def forward(self, input): - # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. - if not (self._is_parallel and self.training): - return F.batch_norm( - input, self.running_mean, self.running_var, self.weight, self.bias, - self.training, self.momentum, self.eps) - - # Resize the input to (B, C, -1). - input_shape = input.size() - input = input.view(input.size(0), self.num_features, -1) - - # Compute the sum and square-sum. - sum_size = input.size(0) * input.size(2) - input_sum = _sum_ft(input) - input_ssum = _sum_ft(input ** 2) - - # Reduce-and-broadcast the statistics. - if self._parallel_id == 0: - mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) - else: - mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) - - # Compute the output. - if self.affine: - # MJY:: Fuse the multiplication for speed. - output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) - else: - output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) - - # Reshape it. - return output.view(input_shape) - - def __data_parallel_replicate__(self, ctx, copy_id): - self._is_parallel = True - self._parallel_id = copy_id - - # parallel_id == 0 means master device. - if self._parallel_id == 0: - ctx.sync_master = self._sync_master - else: - self._slave_pipe = ctx.sync_master.register_slave(copy_id) - - def _data_parallel_master(self, intermediates): - """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" - intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) - - to_reduce = [i[1][:2] for i in intermediates] - to_reduce = [j for i in to_reduce for j in i] # flatten - target_gpus = [i[1].sum.get_device() for i in intermediates] - - sum_size = sum([i[1].sum_size for i in intermediates]) - sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) - - mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) - - broadcasted = Broadcast.apply(target_gpus, mean, inv_std) - - outputs = [] - for i, rec in enumerate(intermediates): - outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) - - return outputs - - def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0): - """return *dest* by `dest := dest*alpha + delta*beta + bias`""" - return dest * alpha + delta * beta + bias - - def _compute_mean_std(self, sum_, ssum, size): - """Compute the mean and standard-deviation with sum and square-sum. This method - also maintains the moving average on the master device.""" - assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' - mean = sum_ / size - sumvar = ssum - sum_ * mean - unbias_var = sumvar / (size - 1) - bias_var = sumvar / size - - self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction) - self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction) - self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction) - - self.running_mean = self._tmp_running_mean / self._running_iter - self.running_var = self._tmp_running_var / self._running_iter - - return mean, bias_var.clamp(self.eps) ** -0.5 - - -class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): - r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a - mini-batch. - - .. math:: - - y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta - - This module differs from the built-in PyTorch BatchNorm1d as the mean and - standard-deviation are reduced across all devices during training. - - For example, when one uses `nn.DataParallel` to wrap the network during - training, PyTorch's implementation normalize the tensor on each device using - the statistics only on that device, which accelerated the computation and - is also easy to implement, but the statistics might be inaccurate. - Instead, in this synchronized version, the statistics will be computed - over all training samples distributed on multiple devices. - - Note that, for one-GPU or CPU-only case, this module behaves exactly same - as the built-in PyTorch implementation. - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and gamma and beta are learnable parameter vectors - of size C (where C is the input size). - - During training, this layer keeps a running estimate of its computed mean - and variance. The running sum is kept with a default momentum of 0.1. - - During evaluation, this running mean/variance is used for normalization. - - Because the BatchNorm is done over the `C` dimension, computing statistics - on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm - - Args: - num_features: num_features from an expected input of size - `batch_size x num_features [x width]` - eps: a value added to the denominator for numerical stability. - Default: 1e-5 - momentum: the value used for the running_mean and running_var - computation. Default: 0.1 - affine: a boolean value that when set to ``True``, gives the layer learnable - affine parameters. Default: ``True`` - - Shape: - - Input: :math:`(N, C)` or :math:`(N, C, L)` - - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) - - Examples: - >>> # With Learnable Parameters - >>> m = SynchronizedBatchNorm1d(100) - >>> # Without Learnable Parameters - >>> m = SynchronizedBatchNorm1d(100, affine=False) - >>> input = torch.autograd.Variable(torch.randn(20, 100)) - >>> output = m(input) - """ - - def _check_input_dim(self, input): - if input.dim() != 2 and input.dim() != 3: - raise ValueError('expected 2D or 3D input (got {}D input)' - .format(input.dim())) - super(SynchronizedBatchNorm1d, self)._check_input_dim(input) - - -class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): - r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch - of 3d inputs - - .. math:: - - y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta - - This module differs from the built-in PyTorch BatchNorm2d as the mean and - standard-deviation are reduced across all devices during training. - - For example, when one uses `nn.DataParallel` to wrap the network during - training, PyTorch's implementation normalize the tensor on each device using - the statistics only on that device, which accelerated the computation and - is also easy to implement, but the statistics might be inaccurate. - Instead, in this synchronized version, the statistics will be computed - over all training samples distributed on multiple devices. - - Note that, for one-GPU or CPU-only case, this module behaves exactly same - as the built-in PyTorch implementation. - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and gamma and beta are learnable parameter vectors - of size C (where C is the input size). - - During training, this layer keeps a running estimate of its computed mean - and variance. The running sum is kept with a default momentum of 0.1. - - During evaluation, this running mean/variance is used for normalization. - - Because the BatchNorm is done over the `C` dimension, computing statistics - on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm - - Args: - num_features: num_features from an expected input of - size batch_size x num_features x height x width - eps: a value added to the denominator for numerical stability. - Default: 1e-5 - momentum: the value used for the running_mean and running_var - computation. Default: 0.1 - affine: a boolean value that when set to ``True``, gives the layer learnable - affine parameters. Default: ``True`` - - Shape: - - Input: :math:`(N, C, H, W)` - - Output: :math:`(N, C, H, W)` (same shape as input) - - Examples: - >>> # With Learnable Parameters - >>> m = SynchronizedBatchNorm2d(100) - >>> # Without Learnable Parameters - >>> m = SynchronizedBatchNorm2d(100, affine=False) - >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) - >>> output = m(input) - """ - - def _check_input_dim(self, input): - if input.dim() != 4: - raise ValueError('expected 4D input (got {}D input)' - .format(input.dim())) - super(SynchronizedBatchNorm2d, self)._check_input_dim(input) - - -class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): - r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch - of 4d inputs - - .. math:: - - y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta - - This module differs from the built-in PyTorch BatchNorm3d as the mean and - standard-deviation are reduced across all devices during training. - - For example, when one uses `nn.DataParallel` to wrap the network during - training, PyTorch's implementation normalize the tensor on each device using - the statistics only on that device, which accelerated the computation and - is also easy to implement, but the statistics might be inaccurate. - Instead, in this synchronized version, the statistics will be computed - over all training samples distributed on multiple devices. - - Note that, for one-GPU or CPU-only case, this module behaves exactly same - as the built-in PyTorch implementation. - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and gamma and beta are learnable parameter vectors - of size C (where C is the input size). - - During training, this layer keeps a running estimate of its computed mean - and variance. The running sum is kept with a default momentum of 0.1. - - During evaluation, this running mean/variance is used for normalization. - - Because the BatchNorm is done over the `C` dimension, computing statistics - on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm - or Spatio-temporal BatchNorm - - Args: - num_features: num_features from an expected input of - size batch_size x num_features x depth x height x width - eps: a value added to the denominator for numerical stability. - Default: 1e-5 - momentum: the value used for the running_mean and running_var - computation. Default: 0.1 - affine: a boolean value that when set to ``True``, gives the layer learnable - affine parameters. Default: ``True`` - - Shape: - - Input: :math:`(N, C, D, H, W)` - - Output: :math:`(N, C, D, H, W)` (same shape as input) - - Examples: - >>> # With Learnable Parameters - >>> m = SynchronizedBatchNorm3d(100) - >>> # Without Learnable Parameters - >>> m = SynchronizedBatchNorm3d(100, affine=False) - >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) - >>> output = m(input) - """ - - def _check_input_dim(self, input): - if input.dim() != 5: - raise ValueError('expected 5D input (got {}D input)' - .format(input.dim())) - super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/spaces/Amitontheweb/InstaoffyzFreeParaphraser/README.md b/spaces/Amitontheweb/InstaoffyzFreeParaphraser/README.md deleted file mode 100644 index cb5087c08b8f44a7fdbe3897db940841185caa11..0000000000000000000000000000000000000000 --- a/spaces/Amitontheweb/InstaoffyzFreeParaphraser/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: InstaoffyzFreeParaphraser -emoji: 🏆 -colorFrom: pink -colorTo: green -sdk: gradio -sdk_version: 3.40.1 -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/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/manipulate.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/manipulate.py deleted file mode 100644 index e1a2480caad8016fea0c06f0bfe521b25f084436..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/manipulate.py +++ /dev/null @@ -1,278 +0,0 @@ - - -import os -import os.path -import pickle -import numpy as np -import tensorflow as tf -from dnnlib import tflib -from global_directions.utils.visualizer import HtmlPageVisualizer - - -def Vis(bname,suffix,out,rownames=None,colnames=None): - num_images=out.shape[0] - step=out.shape[1] - - if colnames is None: - colnames=[f'Step {i:02d}' for i in range(1, step + 1)] - if rownames is None: - rownames=[str(i) for i in range(num_images)] - - - visualizer = HtmlPageVisualizer( - num_rows=num_images, num_cols=step + 1, viz_size=256) - visualizer.set_headers( - ['Name'] +colnames) - - for i in range(num_images): - visualizer.set_cell(i, 0, text=rownames[i]) - - for i in range(num_images): - for k in range(step): - image=out[i,k,:,:,:] - visualizer.set_cell(i, 1+k, image=image) - - # Save results. - visualizer.save(f'./html/'+bname+'_'+suffix+'.html') - - - - -def LoadData(img_path): - tmp=img_path+'S' - with open(tmp, "rb") as fp: #Pickling - s_names,all_s=pickle.load( fp) - dlatents=all_s - - pindexs=[] - mindexs=[] - for i in range(len(s_names)): - name=s_names[i] - if not('ToRGB' in name): - mindexs.append(i) - else: - pindexs.append(i) - - tmp=img_path+'S_mean_std' - with open(tmp, "rb") as fp: #Pickling - m,std=pickle.load( fp) - - return dlatents,s_names,mindexs,pindexs,m,std - - -def LoadModel(model_path,model_name): - # Initialize TensorFlow. - tflib.init_tf() - tmp=os.path.join(model_path,model_name) - with open(tmp, 'rb') as f: - _, _, Gs = pickle.load(f) - Gs.print_layers() - return Gs - -def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False): - """Convert a minibatch of images from float32 to uint8 with configurable dynamic range. - Can be used as an output transformation for Network.run(). - """ - if nchw_to_nhwc: - images = np.transpose(images, [0, 2, 3, 1]) - - scale = 255 / (drange[1] - drange[0]) - images = images * scale + (0.5 - drange[0] * scale) - - np.clip(images, 0, 255, out=images) - images=images.astype('uint8') - return images - - -def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False): - """Convert a minibatch of images from uint8 to float32 with configurable dynamic range. - Can be used as an input transformation for Network.run(). - """ - if nhwc_to_nchw: - images=np.rollaxis(images, 3, 1) - return images/ 255 *(drange[1] - drange[0])+ drange[0] - - -class Manipulator(): - def __init__(self,dataset_name='ffhq'): - self.file_path='./' - self.img_path=self.file_path+'npy/'+dataset_name+'/' - self.model_path=self.file_path+'model/' - self.dataset_name=dataset_name - self.model_name=dataset_name+'.pkl' - - self.alpha=[0] #manipulation strength - self.num_images=10 - self.img_index=0 #which image to start - self.viz_size=256 - self.manipulate_layers=None #which layer to manipulate, list - - self.dlatents,self.s_names,self.mindexs,self.pindexs,self.code_mean,self.code_std=LoadData(self.img_path) - - self.sess=tf.InteractiveSession() - init = tf.global_variables_initializer() - self.sess.run(init) - self.Gs=LoadModel(self.model_path,self.model_name) - self.num_layers=len(self.dlatents) - - self.Vis=Vis - self.noise_constant={} - - for i in range(len(self.s_names)): - tmp1=self.s_names[i].split('/') - if not 'ToRGB' in tmp1: - tmp1[-1]='random_normal:0' - size=int(tmp1[1].split('x')[0]) - tmp1='/'.join(tmp1) - tmp=(1,1,size,size) - self.noise_constant[tmp1]=np.random.random(tmp) - - tmp=self.Gs.components.synthesis.input_shape[1] - d={} - d['G_synthesis_1/dlatents_in:0']=np.zeros([1,tmp,512]) - names=list(self.noise_constant.keys()) - tmp=tflib.run(names,d) - for i in range(len(names)): - self.noise_constant[names[i]]=tmp[i] - - self.fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) - self.img_size=self.Gs.output_shape[-1] - - def GenerateImg(self,codes): - - - num_images,step=codes[0].shape[:2] - - - out=np.zeros((num_images,step,self.img_size,self.img_size,3),dtype='uint8') - for i in range(num_images): - for k in range(step): - d={} - for m in range(len(self.s_names)): - d[self.s_names[m]]=codes[m][i,k][None,:] #need to change - d['G_synthesis_1/4x4/Const/Shape:0']=np.array([1,18, 512], dtype=np.int32) - d.update(self.noise_constant) - img=tflib.run('G_synthesis_1/images_out:0', d) - image=convert_images_to_uint8(img, nchw_to_nhwc=True) - out[i,k,:,:,:]=image[0] - return out - - - - def MSCode(self,dlatent_tmp,boundary_tmp): - - step=len(self.alpha) - dlatent_tmp1=[tmp.reshape((self.num_images,-1)) for tmp in dlatent_tmp] - dlatent_tmp2=[np.tile(tmp[:,None],(1,step,1)) for tmp in dlatent_tmp1] # (10, 7, 512) - - l=np.array(self.alpha) - l=l.reshape( - [step if axis == 1 else 1 for axis in range(dlatent_tmp2[0].ndim)]) - - if type(self.manipulate_layers)==int: - tmp=[self.manipulate_layers] - elif type(self.manipulate_layers)==list: - tmp=self.manipulate_layers - elif self.manipulate_layers is None: - tmp=np.arange(len(boundary_tmp)) - else: - raise ValueError('manipulate_layers is wrong') - - for i in tmp: - dlatent_tmp2[i]+=l*boundary_tmp[i] - - codes=[] - for i in range(len(dlatent_tmp2)): - tmp=list(dlatent_tmp[i].shape) - tmp.insert(1,step) - codes.append(dlatent_tmp2[i].reshape(tmp)) - return codes - - - def EditOne(self,bname,dlatent_tmp=None): - if dlatent_tmp==None: - dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents] - - boundary_tmp=[] - for i in range(len(self.boundary)): - tmp=self.boundary[i] - if len(tmp)<=bname: - boundary_tmp.append([]) - else: - boundary_tmp.append(tmp[bname]) - - codes=self.MSCode(dlatent_tmp,boundary_tmp) - - out=self.GenerateImg(codes) - return codes,out - - def EditOneC(self,cindex,dlatent_tmp=None): - if dlatent_tmp==None: - dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents] - - boundary_tmp=[[] for i in range(len(self.dlatents))] - - #'only manipulate 1 layer and one channel' - assert len(self.manipulate_layers)==1 - - ml=self.manipulate_layers[0] - tmp=dlatent_tmp[ml].shape[1] #ada - tmp1=np.zeros(tmp) - tmp1[cindex]=self.code_std[ml][cindex] #1 - boundary_tmp[ml]=tmp1 - - codes=self.MSCode(dlatent_tmp,boundary_tmp) - out=self.GenerateImg(codes) - return codes,out - - - def W2S(self,dlatent_tmp): - - all_s = self.sess.run( - self.s_names, - feed_dict={'G_synthesis_1/dlatents_in:0': dlatent_tmp}) - return all_s - - - - - - - - -#%% -if __name__ == "__main__": - - - M=Manipulator(dataset_name='ffhq') - - - #%% - M.alpha=[-5,0,5] - M.num_images=20 - lindex,cindex=6,501 - - M.manipulate_layers=[lindex] - codes,out=M.EditOneC(cindex) #dlatent_tmp - tmp=str(M.manipulate_layers)+'_'+str(cindex) - M.Vis(tmp,'c',out) - - - - - - - - - - - - - - - - - - - - diff --git a/spaces/Andy1621/uniformer_image_detection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py deleted file mode 100644 index 668c023981b9d421e5b51a48757c3819d090307f..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py +++ /dev/null @@ -1,60 +0,0 @@ -_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' -model = dict( - neck=dict( - type='FPN_CARAFE', - in_channels=[256, 512, 1024, 2048], - out_channels=256, - num_outs=5, - start_level=0, - end_level=-1, - norm_cfg=None, - act_cfg=None, - order=('conv', 'norm', 'act'), - upsample_cfg=dict( - type='carafe', - up_kernel=5, - up_group=1, - encoder_kernel=3, - encoder_dilation=1, - compressed_channels=64)), - roi_head=dict( - mask_head=dict( - upsample_cfg=dict( - type='carafe', - scale_factor=2, - up_kernel=5, - up_group=1, - encoder_kernel=3, - encoder_dilation=1, - compressed_channels=64)))) -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True, with_mask=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=64), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=64), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -data = dict( - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/dataset_wrappers.py b/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/dataset_wrappers.py deleted file mode 100644 index 55ad5cb60e581a96bdbd1fbbeebc2f46f8c4e899..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/dataset_wrappers.py +++ /dev/null @@ -1,282 +0,0 @@ -import bisect -import math -from collections import defaultdict - -import numpy as np -from mmcv.utils import print_log -from torch.utils.data.dataset import ConcatDataset as _ConcatDataset - -from .builder import DATASETS -from .coco import CocoDataset - - -@DATASETS.register_module() -class ConcatDataset(_ConcatDataset): - """A wrapper of concatenated dataset. - - Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but - concat the group flag for image aspect ratio. - - Args: - datasets (list[:obj:`Dataset`]): A list of datasets. - separate_eval (bool): Whether to evaluate the results - separately if it is used as validation dataset. - Defaults to True. - """ - - def __init__(self, datasets, separate_eval=True): - super(ConcatDataset, self).__init__(datasets) - self.CLASSES = datasets[0].CLASSES - self.separate_eval = separate_eval - if not separate_eval: - if any([isinstance(ds, CocoDataset) for ds in datasets]): - raise NotImplementedError( - 'Evaluating concatenated CocoDataset as a whole is not' - ' supported! Please set "separate_eval=True"') - elif len(set([type(ds) for ds in datasets])) != 1: - raise NotImplementedError( - 'All the datasets should have same types') - - if hasattr(datasets[0], 'flag'): - flags = [] - for i in range(0, len(datasets)): - flags.append(datasets[i].flag) - self.flag = np.concatenate(flags) - - def get_cat_ids(self, idx): - """Get category ids of concatenated dataset by index. - - Args: - idx (int): Index of data. - - Returns: - list[int]: All categories in the image of specified index. - """ - - if idx < 0: - if -idx > len(self): - raise ValueError( - 'absolute value of index should not exceed dataset length') - idx = len(self) + idx - dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) - if dataset_idx == 0: - sample_idx = idx - else: - sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] - return self.datasets[dataset_idx].get_cat_ids(sample_idx) - - def evaluate(self, results, logger=None, **kwargs): - """Evaluate the results. - - Args: - results (list[list | tuple]): Testing results of the dataset. - logger (logging.Logger | str | None): Logger used for printing - related information during evaluation. Default: None. - - Returns: - dict[str: float]: AP results of the total dataset or each separate - dataset if `self.separate_eval=True`. - """ - assert len(results) == self.cumulative_sizes[-1], \ - ('Dataset and results have different sizes: ' - f'{self.cumulative_sizes[-1]} v.s. {len(results)}') - - # Check whether all the datasets support evaluation - for dataset in self.datasets: - assert hasattr(dataset, 'evaluate'), \ - f'{type(dataset)} does not implement evaluate function' - - if self.separate_eval: - dataset_idx = -1 - total_eval_results = dict() - for size, dataset in zip(self.cumulative_sizes, self.datasets): - start_idx = 0 if dataset_idx == -1 else \ - self.cumulative_sizes[dataset_idx] - end_idx = self.cumulative_sizes[dataset_idx + 1] - - results_per_dataset = results[start_idx:end_idx] - print_log( - f'\nEvaluateing {dataset.ann_file} with ' - f'{len(results_per_dataset)} images now', - logger=logger) - - eval_results_per_dataset = dataset.evaluate( - results_per_dataset, logger=logger, **kwargs) - dataset_idx += 1 - for k, v in eval_results_per_dataset.items(): - total_eval_results.update({f'{dataset_idx}_{k}': v}) - - return total_eval_results - elif any([isinstance(ds, CocoDataset) for ds in self.datasets]): - raise NotImplementedError( - 'Evaluating concatenated CocoDataset as a whole is not' - ' supported! Please set "separate_eval=True"') - elif len(set([type(ds) for ds in self.datasets])) != 1: - raise NotImplementedError( - 'All the datasets should have same types') - else: - original_data_infos = self.datasets[0].data_infos - self.datasets[0].data_infos = sum( - [dataset.data_infos for dataset in self.datasets], []) - eval_results = self.datasets[0].evaluate( - results, logger=logger, **kwargs) - self.datasets[0].data_infos = original_data_infos - return eval_results - - -@DATASETS.register_module() -class RepeatDataset(object): - """A wrapper of repeated dataset. - - The length of repeated dataset will be `times` larger than the original - dataset. This is useful when the data loading time is long but the dataset - is small. Using RepeatDataset can reduce the data loading time between - epochs. - - Args: - dataset (:obj:`Dataset`): The dataset to be repeated. - times (int): Repeat times. - """ - - def __init__(self, dataset, times): - self.dataset = dataset - self.times = times - self.CLASSES = dataset.CLASSES - if hasattr(self.dataset, 'flag'): - self.flag = np.tile(self.dataset.flag, times) - - self._ori_len = len(self.dataset) - - def __getitem__(self, idx): - return self.dataset[idx % self._ori_len] - - def get_cat_ids(self, idx): - """Get category ids of repeat dataset by index. - - Args: - idx (int): Index of data. - - Returns: - list[int]: All categories in the image of specified index. - """ - - return self.dataset.get_cat_ids(idx % self._ori_len) - - def __len__(self): - """Length after repetition.""" - return self.times * self._ori_len - - -# Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa -@DATASETS.register_module() -class ClassBalancedDataset(object): - """A wrapper of repeated dataset with repeat factor. - - Suitable for training on class imbalanced datasets like LVIS. Following - the sampling strategy in the `paper `_, - in each epoch, an image may appear multiple times based on its - "repeat factor". - The repeat factor for an image is a function of the frequency the rarest - category labeled in that image. The "frequency of category c" in [0, 1] - is defined by the fraction of images in the training set (without repeats) - in which category c appears. - The dataset needs to instantiate :func:`self.get_cat_ids` to support - ClassBalancedDataset. - - The repeat factor is computed as followed. - - 1. For each category c, compute the fraction # of images - that contain it: :math:`f(c)` - 2. For each category c, compute the category-level repeat factor: - :math:`r(c) = max(1, sqrt(t/f(c)))` - 3. For each image I, compute the image-level repeat factor: - :math:`r(I) = max_{c in I} r(c)` - - Args: - dataset (:obj:`CustomDataset`): The dataset to be repeated. - oversample_thr (float): frequency threshold below which data is - repeated. For categories with ``f_c >= oversample_thr``, there is - no oversampling. For categories with ``f_c < oversample_thr``, the - degree of oversampling following the square-root inverse frequency - heuristic above. - filter_empty_gt (bool, optional): If set true, images without bounding - boxes will not be oversampled. Otherwise, they will be categorized - as the pure background class and involved into the oversampling. - Default: True. - """ - - def __init__(self, dataset, oversample_thr, filter_empty_gt=True): - self.dataset = dataset - self.oversample_thr = oversample_thr - self.filter_empty_gt = filter_empty_gt - self.CLASSES = dataset.CLASSES - - repeat_factors = self._get_repeat_factors(dataset, oversample_thr) - repeat_indices = [] - for dataset_idx, repeat_factor in enumerate(repeat_factors): - repeat_indices.extend([dataset_idx] * math.ceil(repeat_factor)) - self.repeat_indices = repeat_indices - - flags = [] - if hasattr(self.dataset, 'flag'): - for flag, repeat_factor in zip(self.dataset.flag, repeat_factors): - flags.extend([flag] * int(math.ceil(repeat_factor))) - assert len(flags) == len(repeat_indices) - self.flag = np.asarray(flags, dtype=np.uint8) - - def _get_repeat_factors(self, dataset, repeat_thr): - """Get repeat factor for each images in the dataset. - - Args: - dataset (:obj:`CustomDataset`): The dataset - repeat_thr (float): The threshold of frequency. If an image - contains the categories whose frequency below the threshold, - it would be repeated. - - Returns: - list[float]: The repeat factors for each images in the dataset. - """ - - # 1. For each category c, compute the fraction # of images - # that contain it: f(c) - category_freq = defaultdict(int) - num_images = len(dataset) - for idx in range(num_images): - cat_ids = set(self.dataset.get_cat_ids(idx)) - if len(cat_ids) == 0 and not self.filter_empty_gt: - cat_ids = set([len(self.CLASSES)]) - for cat_id in cat_ids: - category_freq[cat_id] += 1 - for k, v in category_freq.items(): - category_freq[k] = v / num_images - - # 2. For each category c, compute the category-level repeat factor: - # r(c) = max(1, sqrt(t/f(c))) - category_repeat = { - cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq)) - for cat_id, cat_freq in category_freq.items() - } - - # 3. For each image I, compute the image-level repeat factor: - # r(I) = max_{c in I} r(c) - repeat_factors = [] - for idx in range(num_images): - cat_ids = set(self.dataset.get_cat_ids(idx)) - if len(cat_ids) == 0 and not self.filter_empty_gt: - cat_ids = set([len(self.CLASSES)]) - repeat_factor = 1 - if len(cat_ids) > 0: - repeat_factor = max( - {category_repeat[cat_id] - for cat_id in cat_ids}) - repeat_factors.append(repeat_factor) - - return repeat_factors - - def __getitem__(self, idx): - ori_index = self.repeat_indices[idx] - return self.dataset[ori_index] - - def __len__(self): - """Length after repetition.""" - return len(self.repeat_indices) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/upernet_uniformer.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/upernet_uniformer.py deleted file mode 100644 index 41aa4db809dc6e2c508e98051f61807d07477903..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/upernet_uniformer.py +++ /dev/null @@ -1,43 +0,0 @@ -# model settings -norm_cfg = dict(type='BN', requires_grad=True) -model = dict( - type='EncoderDecoder', - pretrained=None, - backbone=dict( - type='UniFormer', - embed_dim=[64, 128, 320, 512], - layers=[3, 4, 8, 3], - head_dim=64, - mlp_ratio=4., - qkv_bias=True, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.1), - decode_head=dict( - type='UPerHead', - in_channels=[64, 128, 320, 512], - in_index=[0, 1, 2, 3], - pool_scales=(1, 2, 3, 6), - channels=512, - dropout_ratio=0.1, - num_classes=19, - norm_cfg=norm_cfg, - align_corners=False, - loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), - auxiliary_head=dict( - type='FCNHead', - in_channels=320, - in_index=2, - channels=256, - num_convs=1, - concat_input=False, - dropout_ratio=0.1, - num_classes=19, - norm_cfg=norm_cfg, - align_corners=False, - loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), - # model training and testing settings - train_cfg=dict(), - test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 9713b731a47df9c5e23d26a08ad17d03a0d5e9fe..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dmnet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/spaces/Anonymous-sub/Rerender/gmflow_module/scripts/train_gmflow_with_refine.sh b/spaces/Anonymous-sub/Rerender/gmflow_module/scripts/train_gmflow_with_refine.sh deleted file mode 100644 index 88662a96f48839f84da1c4bc8c8aad45e4452b25..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/gmflow_module/scripts/train_gmflow_with_refine.sh +++ /dev/null @@ -1,128 +0,0 @@ -#!/usr/bin/env bash - -# GMFlow with refinement - -# number of gpus for training, please set according to your hardware -# by default use all gpus on a machine -# can be trained on 4x 32G V100 or 4x 40GB A100 or 8x 16G V100 gpus -NUM_GPUS=4 - -# chairs -CHECKPOINT_DIR=checkpoints/chairs-gmflow_with_refine && \ -mkdir -p ${CHECKPOINT_DIR} && \ -python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \ ---launcher pytorch \ ---checkpoint_dir ${CHECKPOINT_DIR} \ ---batch_size 16 \ ---val_dataset chairs sintel kitti \ ---lr 4e-4 \ ---image_size 384 512 \ ---padding_factor 32 \ ---upsample_factor 4 \ ---num_scales 2 \ ---attn_splits_list 2 8 \ ---corr_radius_list -1 4 \ ---prop_radius_list -1 1 \ ---with_speed_metric \ ---val_freq 10000 \ ---save_ckpt_freq 10000 \ ---num_steps 100000 \ -2>&1 | tee -a ${CHECKPOINT_DIR}/train.log - -# things (our final model is trained for 800K iterations, for ablation study, you can train for 200K) -CHECKPOINT_DIR=checkpoints/things-gmflow_with_refine && \ -mkdir -p ${CHECKPOINT_DIR} && \ -python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \ ---launcher pytorch \ ---checkpoint_dir ${CHECKPOINT_DIR} \ ---resume checkpoints/chairs-gmflow_with_refine/step_100000.pth \ ---stage things \ ---batch_size 8 \ ---val_dataset things sintel kitti \ ---lr 2e-4 \ ---image_size 384 768 \ ---padding_factor 32 \ ---upsample_factor 4 \ ---num_scales 2 \ ---attn_splits_list 2 8 \ ---corr_radius_list -1 4 \ ---prop_radius_list -1 1 \ ---with_speed_metric \ ---val_freq 40000 \ ---save_ckpt_freq 50000 \ ---num_steps 800000 \ -2>&1 | tee -a ${CHECKPOINT_DIR}/train.log - -# sintel -CHECKPOINT_DIR=checkpoints/sintel-gmflow_with_refine && \ -mkdir -p ${CHECKPOINT_DIR} && \ -python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \ ---launcher pytorch \ ---checkpoint_dir ${CHECKPOINT_DIR} \ ---resume checkpoints/things-gmflow_with_refine/step_800000.pth \ ---stage sintel \ ---batch_size 8 \ ---val_dataset sintel kitti \ ---lr 2e-4 \ ---image_size 320 896 \ ---padding_factor 32 \ ---upsample_factor 4 \ ---num_scales 2 \ ---attn_splits_list 2 8 \ ---corr_radius_list -1 4 \ ---prop_radius_list -1 1 \ ---with_speed_metric \ ---val_freq 20000 \ ---save_ckpt_freq 20000 \ ---num_steps 200000 \ -2>&1 | tee -a ${CHECKPOINT_DIR}/train.log - -# kitti -CHECKPOINT_DIR=checkpoints/kitti-gmflow_with_refine && \ -mkdir -p ${CHECKPOINT_DIR} && \ -python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \ ---launcher pytorch \ ---checkpoint_dir ${CHECKPOINT_DIR} \ ---resume checkpoints/sintel-gmflow_with_refine/step_200000.pth \ ---stage kitti \ ---batch_size 8 \ ---val_dataset kitti \ ---lr 2e-4 \ ---image_size 320 1152 \ ---padding_factor 32 \ ---upsample_factor 4 \ ---num_scales 2 \ ---attn_splits_list 2 8 \ ---corr_radius_list -1 4 \ ---prop_radius_list -1 1 \ ---with_speed_metric \ ---val_freq 10000 \ ---save_ckpt_freq 10000 \ ---num_steps 100000 \ -2>&1 | tee -a ${CHECKPOINT_DIR}/train.log - - - -# a final note: if your training is terminated unexpectedly, you can resume from the latest checkpoint -# an example: resume chairs training -# CHECKPOINT_DIR=checkpoints/chairs-gmflow_with_refine && \ -# mkdir -p ${CHECKPOINT_DIR} && \ -# python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \ -# --launcher pytorch \ -# --checkpoint_dir ${CHECKPOINT_DIR} \ -# --resume checkpoints/chairs-gmflow_with_refine/checkpoint_latest.pth \ -# --batch_size 16 \ -# --val_dataset chairs sintel kitti \ -# --lr 4e-4 \ -# --image_size 384 512 \ -# --padding_factor 32 \ -# --upsample_factor 4 \ -# --num_scales 2 \ -# --attn_splits_list 2 8 \ -# --corr_radius_list -1 4 \ -# --prop_radius_list -1 1 \ -# --with_speed_metric \ -# --val_freq 10000 \ -# --save_ckpt_freq 10000 \ -# --num_steps 100000 \ -# 2>&1 | tee -a ${CHECKPOINT_DIR}/train.log diff --git a/spaces/Ariharasudhan/XAI_Class-Activation-Maps/app.py b/spaces/Ariharasudhan/XAI_Class-Activation-Maps/app.py deleted file mode 100644 index efe805bc4e5a622ef36a158c60936d69e5b935bf..0000000000000000000000000000000000000000 --- a/spaces/Ariharasudhan/XAI_Class-Activation-Maps/app.py +++ /dev/null @@ -1,113 +0,0 @@ -import torch -import numpy as np -from torchvision import datasets, transforms, models -import torch.nn as nn -import torch.nn.functional as F -import gradio as gr -import PIL.Image as Image -import skimage.transform -import cv2 - - - -def load_model(): - model = models.efficientnet_b4() - model.classifier[1] = nn.Linear(1792, 13) - model.load_state_dict(torch.load('model.pth', map_location='cpu')) - model.eval() - return model - - -def load_labels(): - labels = open('classes.txt').read().splitlines() - return labels - -model = load_model() -labels = load_labels() - -def preprocess(img): - # img = Image.fromarray(img.astype('uint8'), 'RGB') - r_image = transforms.Compose([transforms.Resize((380,380)), - transforms.ToTensor(), - transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])])(img) - return r_image - - -class Hook(): - features=None - def __init__(self, m): - self.hook = m.register_forward_hook(self.hook_fn) - def hook_fn(self, module, input, output): - self.features = ((output.cpu()).data).numpy() - def remove(self): - self.hook.remove() - - -def cam(conv_features, weights, class_idx): - counts, c, h, w = conv_features.shape - output_cam = [] - cam = weights[class_idx].dot(conv_features.reshape((c, h*w))) - cam = cam.reshape(h, w) - cam = cam - np.min(cam) - cam_img = cam /np.max(cam) - cam_img = np.uint8(255*cam_img) - output_cam.append(cam_img) - return output_cam - - -# gradio app for cam -def cam_app(img): - img2 = img.resize((380, 380)) - img = preprocess(img) - img = img.unsqueeze(0) - last_layer = model.features._modules.get("8") - hooked_features = Hook(last_layer) - pred = model(img) - pred_prob = F.softmax(pred, dim = 1) - pred_prob = pred_prob.detach().cpu().numpy() - chosen_class = pred_prob.argmax() - weights_fc = list(model.classifier.parameters())[-2] - weights_fc = weights_fc.detach().cpu().numpy() - cam_mask = cam(conv_features=hooked_features.features, weights=weights_fc, class_idx=chosen_class) - # return the blended image - img = np.array(img2) - mask_arr = np.array(cam_mask[0]) - mask_arr = skimage.transform.resize(mask_arr, (380, 380)) - # match the mask to the image - mask_arr = np.uint8(255*mask_arr) - mask_arr = cv2.applyColorMap(mask_arr, cv2.COLORMAP_JET) - mask_arr = cv2.cvtColor(mask_arr, cv2.COLOR_BGR2RGB) - mask_arr = (mask_arr.astype(float))/255 - img = (img.astype(float))/255 - blended_img = (cv2.addWeighted(img, 0.5, mask_arr, 0.5, 0))*255 - blended_img = blended_img.astype(np.uint8) - blended_img = Image.fromarray(blended_img) - - # top 3 predictions as a percentage bar - top3 = pred_prob.argsort()[0][-3:] - top3 = top3[::-1] - top3_conf = pred_prob[0][top3] - top3_conf = top3_conf*100 - top3_conf = top3_conf.round(2) - top3_labels = [labels[i] for i in top3] - top3_labels = [str(i) + " : " + str(j) + "%" for i,j in zip(top3_labels, top3_conf)] - top3_labels = " , ".join(top3_labels) - return blended_img, top3_labels - - - - -# App -description = "Classify Kenyan food into 13 categories" -article = "

    Github | LinkedIn

    " -examples = [ "./Test_Images/unknown2.jpg", "./Test_Images/unknown3.jpg", "./Test_Images/unknown5.jpg"] -gr.Interface(cam_app, - inputs=gr.inputs.Image( type = "pil", label="Input Image"), - outputs=[gr.outputs.components.Image(type = "pil", label="XAI-Class Activation Map").style(height = 300, width = 300), - gr.outputs.Label(type = "label", label="Predictions")], - title="XAI-Class Activation Map", - examples=examples, - description=description, - article=article, - live=True).launch() - diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/format_control.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/format_control.py deleted file mode 100644 index db3995eac9f9ec2450e0e2d4a18e666c0b178681..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/format_control.py +++ /dev/null @@ -1,80 +0,0 @@ -from typing import FrozenSet, Optional, Set - -from pip._vendor.packaging.utils import canonicalize_name - -from pip._internal.exceptions import CommandError - - -class FormatControl: - """Helper for managing formats from which a package can be installed.""" - - __slots__ = ["no_binary", "only_binary"] - - def __init__( - self, - no_binary: Optional[Set[str]] = None, - only_binary: Optional[Set[str]] = None, - ) -> None: - if no_binary is None: - no_binary = set() - if only_binary is None: - only_binary = set() - - self.no_binary = no_binary - self.only_binary = only_binary - - def __eq__(self, other: object) -> bool: - if not isinstance(other, self.__class__): - return NotImplemented - - if self.__slots__ != other.__slots__: - return False - - return all(getattr(self, k) == getattr(other, k) for k in self.__slots__) - - def __repr__(self) -> str: - return "{}({}, {})".format( - self.__class__.__name__, self.no_binary, self.only_binary - ) - - @staticmethod - def handle_mutual_excludes(value: str, target: Set[str], other: Set[str]) -> None: - if value.startswith("-"): - raise CommandError( - "--no-binary / --only-binary option requires 1 argument." - ) - new = value.split(",") - while ":all:" in new: - other.clear() - target.clear() - target.add(":all:") - del new[: new.index(":all:") + 1] - # Without a none, we want to discard everything as :all: covers it - if ":none:" not in new: - return - for name in new: - if name == ":none:": - target.clear() - continue - name = canonicalize_name(name) - other.discard(name) - target.add(name) - - def get_allowed_formats(self, canonical_name: str) -> FrozenSet[str]: - result = {"binary", "source"} - if canonical_name in self.only_binary: - result.discard("source") - elif canonical_name in self.no_binary: - result.discard("binary") - elif ":all:" in self.only_binary: - result.discard("source") - elif ":all:" in self.no_binary: - result.discard("binary") - return frozenset(result) - - def disallow_binaries(self) -> None: - self.handle_mutual_excludes( - ":all:", - self.no_binary, - self.only_binary, - ) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/dep_util.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/dep_util.py deleted file mode 100644 index db1fa01996ce0d47cd7f070c53b085926440d377..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/dep_util.py +++ /dev/null @@ -1,96 +0,0 @@ -"""distutils.dep_util - -Utility functions for simple, timestamp-based dependency of files -and groups of files; also, function based entirely on such -timestamp dependency analysis.""" - -import os -from distutils.errors import DistutilsFileError - - -def newer(source, target): - """Return true if 'source' exists and is more recently modified than - 'target', or if 'source' exists and 'target' doesn't. Return false if - both exist and 'target' is the same age or younger than 'source'. - Raise DistutilsFileError if 'source' does not exist. - """ - if not os.path.exists(source): - raise DistutilsFileError("file '%s' does not exist" % os.path.abspath(source)) - if not os.path.exists(target): - return 1 - - from stat import ST_MTIME - - mtime1 = os.stat(source)[ST_MTIME] - mtime2 = os.stat(target)[ST_MTIME] - - return mtime1 > mtime2 - - -# newer () - - -def newer_pairwise(sources, targets): - """Walk two filename lists in parallel, testing if each source is newer - than its corresponding target. Return a pair of lists (sources, - targets) where source is newer than target, according to the semantics - of 'newer()'. - """ - if len(sources) != len(targets): - raise ValueError("'sources' and 'targets' must be same length") - - # build a pair of lists (sources, targets) where source is newer - n_sources = [] - n_targets = [] - for i in range(len(sources)): - if newer(sources[i], targets[i]): - n_sources.append(sources[i]) - n_targets.append(targets[i]) - - return (n_sources, n_targets) - - -# newer_pairwise () - - -def newer_group(sources, target, missing='error'): - """Return true if 'target' is out-of-date with respect to any file - listed in 'sources'. In other words, if 'target' exists and is newer - than every file in 'sources', return false; otherwise return true. - 'missing' controls what we do when a source file is missing; the - default ("error") is to blow up with an OSError from inside 'stat()'; - if it is "ignore", we silently drop any missing source files; if it is - "newer", any missing source files make us assume that 'target' is - out-of-date (this is handy in "dry-run" mode: it'll make you pretend to - carry out commands that wouldn't work because inputs are missing, but - that doesn't matter because you're not actually going to run the - commands). - """ - # If the target doesn't even exist, then it's definitely out-of-date. - if not os.path.exists(target): - return 1 - - # Otherwise we have to find out the hard way: if *any* source file - # is more recent than 'target', then 'target' is out-of-date and - # we can immediately return true. If we fall through to the end - # of the loop, then 'target' is up-to-date and we return false. - from stat import ST_MTIME - - target_mtime = os.stat(target)[ST_MTIME] - for source in sources: - if not os.path.exists(source): - if missing == 'error': # blow up when we stat() the file - pass - elif missing == 'ignore': # missing source dropped from - continue # target's dependency list - elif missing == 'newer': # missing source means target is - return 1 # out-of-date - - source_mtime = os.stat(source)[ST_MTIME] - if source_mtime > target_mtime: - return 1 - else: - return 0 - - -# newer_group () diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py deleted file mode 100644 index c9efa287fc71315f633347023b390fe4ce57913a..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/utils.py +++ /dev/null @@ -1,38 +0,0 @@ -import cv2 -import torch -from torch import nn -from detectron2.utils.comm import get_world_size -from detectron2.structures import pairwise_iou, Boxes -# from .data import CenterNetCrop -import torch.nn.functional as F -import numpy as np -from detectron2.structures import Boxes, ImageList, Instances - -__all__ = ['reduce_sum', '_transpose'] - -INF = 1000000000 - -def _transpose(training_targets, num_loc_list): - ''' - This function is used to transpose image first training targets to - level first ones - :return: level first training targets - ''' - for im_i in range(len(training_targets)): - training_targets[im_i] = torch.split( - training_targets[im_i], num_loc_list, dim=0) - - targets_level_first = [] - for targets_per_level in zip(*training_targets): - targets_level_first.append( - torch.cat(targets_per_level, dim=0)) - return targets_level_first - - -def reduce_sum(tensor): - world_size = get_world_size() - if world_size < 2: - return tensor - tensor = tensor.clone() - torch.distributed.all_reduce(tensor, op=torch.distributed.ReduceOp.SUM) - return tensor \ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/__init__.py deleted file mode 100644 index 94b71832b6b4ca8a081bfff6005a6bf719492c37..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/__init__.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/ -# Copyright 2012-2014 Amazon.com, Inc. or its affiliates. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"). You -# may not use this file except in compliance with the License. A copy of -# the License is located at -# -# http://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file is -# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF -# ANY KIND, either express or implied. See the License for the specific -# language governing permissions and limitations under the License. - -import logging -import os -import re - -__version__ = '1.29.132' - - -class NullHandler(logging.Handler): - def emit(self, record): - pass - - -# Configure default logger to do nothing -log = logging.getLogger('botocore') -log.addHandler(NullHandler()) - -_INITIALIZERS = [] - -_first_cap_regex = re.compile('(.)([A-Z][a-z]+)') -_end_cap_regex = re.compile('([a-z0-9])([A-Z])') -# The regex below handles the special case where some acronym -# name is pluralized, e.g GatewayARNs, ListWebACLs, SomeCNAMEs. -_special_case_transform = re.compile('[A-Z]{2,}s$') -# Prepopulate the cache with special cases that don't match -# our regular transformation. -_xform_cache = { - ('CreateCachediSCSIVolume', '_'): 'create_cached_iscsi_volume', - ('CreateCachediSCSIVolume', '-'): 'create-cached-iscsi-volume', - ('DescribeCachediSCSIVolumes', '_'): 'describe_cached_iscsi_volumes', - ('DescribeCachediSCSIVolumes', '-'): 'describe-cached-iscsi-volumes', - ('DescribeStorediSCSIVolumes', '_'): 'describe_stored_iscsi_volumes', - ('DescribeStorediSCSIVolumes', '-'): 'describe-stored-iscsi-volumes', - ('CreateStorediSCSIVolume', '_'): 'create_stored_iscsi_volume', - ('CreateStorediSCSIVolume', '-'): 'create-stored-iscsi-volume', - ('ListHITsForQualificationType', '_'): 'list_hits_for_qualification_type', - ('ListHITsForQualificationType', '-'): 'list-hits-for-qualification-type', - ('ExecutePartiQLStatement', '_'): 'execute_partiql_statement', - ('ExecutePartiQLStatement', '-'): 'execute-partiql-statement', - ('ExecutePartiQLTransaction', '_'): 'execute_partiql_transaction', - ('ExecutePartiQLTransaction', '-'): 'execute-partiql-transaction', - ('ExecutePartiQLBatch', '_'): 'execute_partiql_batch', - ('ExecutePartiQLBatch', '-'): 'execute-partiql-batch', -} -# The items in this dict represent partial renames to apply globally to all -# services which might have a matching argument or operation. This way a -# common mis-translation can be fixed without having to call out each -# individual case. -ScalarTypes = ('string', 'integer', 'boolean', 'timestamp', 'float', 'double') - -BOTOCORE_ROOT = os.path.dirname(os.path.abspath(__file__)) - - -# Used to specify anonymous (unsigned) request signature -class UNSIGNED: - def __copy__(self): - return self - - def __deepcopy__(self, memodict): - return self - - -UNSIGNED = UNSIGNED() - - -def xform_name(name, sep='_', _xform_cache=_xform_cache): - """Convert camel case to a "pythonic" name. - - If the name contains the ``sep`` character, then it is - returned unchanged. - - """ - if sep in name: - # If the sep is in the name, assume that it's already - # transformed and return the string unchanged. - return name - key = (name, sep) - if key not in _xform_cache: - if _special_case_transform.search(name) is not None: - is_special = _special_case_transform.search(name) - matched = is_special.group() - # Replace something like ARNs, ACLs with _arns, _acls. - name = f"{name[: -len(matched)]}{sep}{matched.lower()}" - s1 = _first_cap_regex.sub(r'\1' + sep + r'\2', name) - transformed = _end_cap_regex.sub(r'\1' + sep + r'\2', s1).lower() - _xform_cache[key] = transformed - return _xform_cache[key] - - -def register_initializer(callback): - """Register an initializer function for session creation. - - This initializer function will be invoked whenever a new - `botocore.session.Session` is instantiated. - - :type callback: callable - :param callback: A callable that accepts a single argument - of type `botocore.session.Session`. - - """ - _INITIALIZERS.append(callback) - - -def unregister_initializer(callback): - """Unregister an initializer function. - - :type callback: callable - :param callback: A callable that was previously registered - with `botocore.register_initializer`. - - :raises ValueError: If a callback is provided that is not currently - registered as an initializer. - - """ - _INITIALIZERS.remove(callback) - - -def invoke_initializers(session): - """Invoke all initializers for a session. - - :type session: botocore.session.Session - :param session: The session to initialize. - - """ - for initializer in _INITIALIZERS: - initializer(session) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyparsing/unicode.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyparsing/unicode.py deleted file mode 100644 index 06526203911de55da3c2a8c5ae73f48024c3f018..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyparsing/unicode.py +++ /dev/null @@ -1,352 +0,0 @@ -# unicode.py - -import sys -from itertools import filterfalse -from typing import List, Tuple, Union - - -class _lazyclassproperty: - def __init__(self, fn): - self.fn = fn - self.__doc__ = fn.__doc__ - self.__name__ = fn.__name__ - - def __get__(self, obj, cls): - if cls is None: - cls = type(obj) - if not hasattr(cls, "_intern") or any( - cls._intern is getattr(superclass, "_intern", []) - for superclass in cls.__mro__[1:] - ): - cls._intern = {} - attrname = self.fn.__name__ - if attrname not in cls._intern: - cls._intern[attrname] = self.fn(cls) - return cls._intern[attrname] - - -UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]] - - -class unicode_set: - """ - A set of Unicode characters, for language-specific strings for - ``alphas``, ``nums``, ``alphanums``, and ``printables``. - A unicode_set is defined by a list of ranges in the Unicode character - set, in a class attribute ``_ranges``. Ranges can be specified using - 2-tuples or a 1-tuple, such as:: - - _ranges = [ - (0x0020, 0x007e), - (0x00a0, 0x00ff), - (0x0100,), - ] - - Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x). - - A unicode set can also be defined using multiple inheritance of other unicode sets:: - - class CJK(Chinese, Japanese, Korean): - pass - """ - - _ranges: UnicodeRangeList = [] - - @_lazyclassproperty - def _chars_for_ranges(cls): - ret = [] - for cc in cls.__mro__: - if cc is unicode_set: - break - for rr in getattr(cc, "_ranges", ()): - ret.extend(range(rr[0], rr[-1] + 1)) - return [chr(c) for c in sorted(set(ret))] - - @_lazyclassproperty - def printables(cls): - "all non-whitespace characters in this range" - return "".join(filterfalse(str.isspace, cls._chars_for_ranges)) - - @_lazyclassproperty - def alphas(cls): - "all alphabetic characters in this range" - return "".join(filter(str.isalpha, cls._chars_for_ranges)) - - @_lazyclassproperty - def nums(cls): - "all numeric digit characters in this range" - return "".join(filter(str.isdigit, cls._chars_for_ranges)) - - @_lazyclassproperty - def alphanums(cls): - "all alphanumeric characters in this range" - return cls.alphas + cls.nums - - @_lazyclassproperty - def identchars(cls): - "all characters in this range that are valid identifier characters, plus underscore '_'" - return "".join( - sorted( - set( - "".join(filter(str.isidentifier, cls._chars_for_ranges)) - + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº" - + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ" - + "_" - ) - ) - ) - - @_lazyclassproperty - def identbodychars(cls): - """ - all characters in this range that are valid identifier body characters, - plus the digits 0-9 - """ - return "".join( - sorted( - set( - cls.identchars - + "0123456789" - + "".join( - [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()] - ) - ) - ) - ) - - -class pyparsing_unicode(unicode_set): - """ - A namespace class for defining common language unicode_sets. - """ - - # fmt: off - - # define ranges in language character sets - _ranges: UnicodeRangeList = [ - (0x0020, sys.maxunicode), - ] - - class BasicMultilingualPlane(unicode_set): - "Unicode set for the Basic Multilingual Plane" - _ranges: UnicodeRangeList = [ - (0x0020, 0xFFFF), - ] - - class Latin1(unicode_set): - "Unicode set for Latin-1 Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0020, 0x007E), - (0x00A0, 0x00FF), - ] - - class LatinA(unicode_set): - "Unicode set for Latin-A Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0100, 0x017F), - ] - - class LatinB(unicode_set): - "Unicode set for Latin-B Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0180, 0x024F), - ] - - class Greek(unicode_set): - "Unicode set for Greek Unicode Character Ranges" - _ranges: UnicodeRangeList = [ - (0x0342, 0x0345), - (0x0370, 0x0377), - (0x037A, 0x037F), - (0x0384, 0x038A), - (0x038C,), - (0x038E, 0x03A1), - (0x03A3, 0x03E1), - (0x03F0, 0x03FF), - (0x1D26, 0x1D2A), - (0x1D5E,), - (0x1D60,), - (0x1D66, 0x1D6A), - (0x1F00, 0x1F15), - (0x1F18, 0x1F1D), - (0x1F20, 0x1F45), - (0x1F48, 0x1F4D), - (0x1F50, 0x1F57), - (0x1F59,), - (0x1F5B,), - (0x1F5D,), - (0x1F5F, 0x1F7D), - (0x1F80, 0x1FB4), - (0x1FB6, 0x1FC4), - (0x1FC6, 0x1FD3), - (0x1FD6, 0x1FDB), - (0x1FDD, 0x1FEF), - (0x1FF2, 0x1FF4), - (0x1FF6, 0x1FFE), - (0x2129,), - (0x2719, 0x271A), - (0xAB65,), - (0x10140, 0x1018D), - (0x101A0,), - (0x1D200, 0x1D245), - (0x1F7A1, 0x1F7A7), - ] - - class Cyrillic(unicode_set): - "Unicode set for Cyrillic Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0400, 0x052F), - (0x1C80, 0x1C88), - (0x1D2B,), - (0x1D78,), - (0x2DE0, 0x2DFF), - (0xA640, 0xA672), - (0xA674, 0xA69F), - (0xFE2E, 0xFE2F), - ] - - class Chinese(unicode_set): - "Unicode set for Chinese Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x2E80, 0x2E99), - (0x2E9B, 0x2EF3), - (0x31C0, 0x31E3), - (0x3400, 0x4DB5), - (0x4E00, 0x9FEF), - (0xA700, 0xA707), - (0xF900, 0xFA6D), - (0xFA70, 0xFAD9), - (0x16FE2, 0x16FE3), - (0x1F210, 0x1F212), - (0x1F214, 0x1F23B), - (0x1F240, 0x1F248), - (0x20000, 0x2A6D6), - (0x2A700, 0x2B734), - (0x2B740, 0x2B81D), - (0x2B820, 0x2CEA1), - (0x2CEB0, 0x2EBE0), - (0x2F800, 0x2FA1D), - ] - - class Japanese(unicode_set): - "Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges" - _ranges: UnicodeRangeList = [] - - class Kanji(unicode_set): - "Unicode set for Kanji Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x4E00, 0x9FBF), - (0x3000, 0x303F), - ] - - class Hiragana(unicode_set): - "Unicode set for Hiragana Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x3041, 0x3096), - (0x3099, 0x30A0), - (0x30FC,), - (0xFF70,), - (0x1B001,), - (0x1B150, 0x1B152), - (0x1F200,), - ] - - class Katakana(unicode_set): - "Unicode set for Katakana Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x3099, 0x309C), - (0x30A0, 0x30FF), - (0x31F0, 0x31FF), - (0x32D0, 0x32FE), - (0xFF65, 0xFF9F), - (0x1B000,), - (0x1B164, 0x1B167), - (0x1F201, 0x1F202), - (0x1F213,), - ] - - class Hangul(unicode_set): - "Unicode set for Hangul (Korean) Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x1100, 0x11FF), - (0x302E, 0x302F), - (0x3131, 0x318E), - (0x3200, 0x321C), - (0x3260, 0x327B), - (0x327E,), - (0xA960, 0xA97C), - (0xAC00, 0xD7A3), - (0xD7B0, 0xD7C6), - (0xD7CB, 0xD7FB), - (0xFFA0, 0xFFBE), - (0xFFC2, 0xFFC7), - (0xFFCA, 0xFFCF), - (0xFFD2, 0xFFD7), - (0xFFDA, 0xFFDC), - ] - - Korean = Hangul - - class CJK(Chinese, Japanese, Hangul): - "Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range" - - class Thai(unicode_set): - "Unicode set for Thai Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0E01, 0x0E3A), - (0x0E3F, 0x0E5B) - ] - - class Arabic(unicode_set): - "Unicode set for Arabic Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0600, 0x061B), - (0x061E, 0x06FF), - (0x0700, 0x077F), - ] - - class Hebrew(unicode_set): - "Unicode set for Hebrew Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0591, 0x05C7), - (0x05D0, 0x05EA), - (0x05EF, 0x05F4), - (0xFB1D, 0xFB36), - (0xFB38, 0xFB3C), - (0xFB3E,), - (0xFB40, 0xFB41), - (0xFB43, 0xFB44), - (0xFB46, 0xFB4F), - ] - - class Devanagari(unicode_set): - "Unicode set for Devanagari Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0900, 0x097F), - (0xA8E0, 0xA8FF) - ] - - # fmt: on - - -pyparsing_unicode.Japanese._ranges = ( - pyparsing_unicode.Japanese.Kanji._ranges - + pyparsing_unicode.Japanese.Hiragana._ranges - + pyparsing_unicode.Japanese.Katakana._ranges -) - -pyparsing_unicode.BMP = pyparsing_unicode.BasicMultilingualPlane - -# add language identifiers using language Unicode -pyparsing_unicode.العربية = pyparsing_unicode.Arabic -pyparsing_unicode.中文 = pyparsing_unicode.Chinese -pyparsing_unicode.кириллица = pyparsing_unicode.Cyrillic -pyparsing_unicode.Ελληνικά = pyparsing_unicode.Greek -pyparsing_unicode.עִברִית = pyparsing_unicode.Hebrew -pyparsing_unicode.日本語 = pyparsing_unicode.Japanese -pyparsing_unicode.Japanese.漢字 = pyparsing_unicode.Japanese.Kanji -pyparsing_unicode.Japanese.カタカナ = pyparsing_unicode.Japanese.Katakana -pyparsing_unicode.Japanese.ひらがな = pyparsing_unicode.Japanese.Hiragana -pyparsing_unicode.한국어 = pyparsing_unicode.Korean -pyparsing_unicode.ไทย = pyparsing_unicode.Thai -pyparsing_unicode.देवनागरी = pyparsing_unicode.Devanagari diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/core.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/core.py deleted file mode 100644 index de13978f02aa85ac70aa49a0d39178cbba913199..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/core.py +++ /dev/null @@ -1,291 +0,0 @@ -"""distutils.core - -The only module that needs to be imported to use the Distutils; provides -the 'setup' function (which is to be called from the setup script). Also -indirectly provides the Distribution and Command classes, although they are -really defined in distutils.dist and distutils.cmd. -""" - -import os -import sys -import tokenize - -from distutils.debug import DEBUG -from distutils.errors import ( - DistutilsSetupError, - DistutilsError, - CCompilerError, - DistutilsArgError, -) - -# Mainly import these so setup scripts can "from distutils.core import" them. -from distutils.dist import Distribution -from distutils.cmd import Command -from distutils.config import PyPIRCCommand -from distutils.extension import Extension - - -__all__ = ['Distribution', 'Command', 'PyPIRCCommand', 'Extension', 'setup'] - -# This is a barebones help message generated displayed when the user -# runs the setup script with no arguments at all. More useful help -# is generated with various --help options: global help, list commands, -# and per-command help. -USAGE = """\ -usage: %(script)s [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...] - or: %(script)s --help [cmd1 cmd2 ...] - or: %(script)s --help-commands - or: %(script)s cmd --help -""" - - -def gen_usage(script_name): - script = os.path.basename(script_name) - return USAGE % locals() - - -# Some mild magic to control the behaviour of 'setup()' from 'run_setup()'. -_setup_stop_after = None -_setup_distribution = None - -# Legal keyword arguments for the setup() function -setup_keywords = ( - 'distclass', - 'script_name', - 'script_args', - 'options', - 'name', - 'version', - 'author', - 'author_email', - 'maintainer', - 'maintainer_email', - 'url', - 'license', - 'description', - 'long_description', - 'keywords', - 'platforms', - 'classifiers', - 'download_url', - 'requires', - 'provides', - 'obsoletes', -) - -# Legal keyword arguments for the Extension constructor -extension_keywords = ( - 'name', - 'sources', - 'include_dirs', - 'define_macros', - 'undef_macros', - 'library_dirs', - 'libraries', - 'runtime_library_dirs', - 'extra_objects', - 'extra_compile_args', - 'extra_link_args', - 'swig_opts', - 'export_symbols', - 'depends', - 'language', -) - - -def setup(**attrs): # noqa: C901 - """The gateway to the Distutils: do everything your setup script needs - to do, in a highly flexible and user-driven way. Briefly: create a - Distribution instance; find and parse config files; parse the command - line; run each Distutils command found there, customized by the options - supplied to 'setup()' (as keyword arguments), in config files, and on - the command line. - - The Distribution instance might be an instance of a class supplied via - the 'distclass' keyword argument to 'setup'; if no such class is - supplied, then the Distribution class (in dist.py) is instantiated. - All other arguments to 'setup' (except for 'cmdclass') are used to set - attributes of the Distribution instance. - - The 'cmdclass' argument, if supplied, is a dictionary mapping command - names to command classes. Each command encountered on the command line - will be turned into a command class, which is in turn instantiated; any - class found in 'cmdclass' is used in place of the default, which is - (for command 'foo_bar') class 'foo_bar' in module - 'distutils.command.foo_bar'. The command class must provide a - 'user_options' attribute which is a list of option specifiers for - 'distutils.fancy_getopt'. Any command-line options between the current - and the next command are used to set attributes of the current command - object. - - When the entire command-line has been successfully parsed, calls the - 'run()' method on each command object in turn. This method will be - driven entirely by the Distribution object (which each command object - has a reference to, thanks to its constructor), and the - command-specific options that became attributes of each command - object. - """ - - global _setup_stop_after, _setup_distribution - - # Determine the distribution class -- either caller-supplied or - # our Distribution (see below). - klass = attrs.get('distclass') - if klass: - del attrs['distclass'] - else: - klass = Distribution - - if 'script_name' not in attrs: - attrs['script_name'] = os.path.basename(sys.argv[0]) - if 'script_args' not in attrs: - attrs['script_args'] = sys.argv[1:] - - # Create the Distribution instance, using the remaining arguments - # (ie. everything except distclass) to initialize it - try: - _setup_distribution = dist = klass(attrs) - except DistutilsSetupError as msg: - if 'name' not in attrs: - raise SystemExit("error in setup command: %s" % msg) - else: - raise SystemExit("error in {} setup command: {}".format(attrs['name'], msg)) - - if _setup_stop_after == "init": - return dist - - # Find and parse the config file(s): they will override options from - # the setup script, but be overridden by the command line. - dist.parse_config_files() - - if DEBUG: - print("options (after parsing config files):") - dist.dump_option_dicts() - - if _setup_stop_after == "config": - return dist - - # Parse the command line and override config files; any - # command-line errors are the end user's fault, so turn them into - # SystemExit to suppress tracebacks. - try: - ok = dist.parse_command_line() - except DistutilsArgError as msg: - raise SystemExit(gen_usage(dist.script_name) + "\nerror: %s" % msg) - - if DEBUG: - print("options (after parsing command line):") - dist.dump_option_dicts() - - if _setup_stop_after == "commandline": - return dist - - # And finally, run all the commands found on the command line. - if ok: - return run_commands(dist) - - return dist - - -# setup () - - -def run_commands(dist): - """Given a Distribution object run all the commands, - raising ``SystemExit`` errors in the case of failure. - - This function assumes that either ``sys.argv`` or ``dist.script_args`` - is already set accordingly. - """ - try: - dist.run_commands() - except KeyboardInterrupt: - raise SystemExit("interrupted") - except OSError as exc: - if DEBUG: - sys.stderr.write("error: {}\n".format(exc)) - raise - else: - raise SystemExit("error: {}".format(exc)) - - except (DistutilsError, CCompilerError) as msg: - if DEBUG: - raise - else: - raise SystemExit("error: " + str(msg)) - - return dist - - -def run_setup(script_name, script_args=None, stop_after="run"): - """Run a setup script in a somewhat controlled environment, and - return the Distribution instance that drives things. This is useful - if you need to find out the distribution meta-data (passed as - keyword args from 'script' to 'setup()', or the contents of the - config files or command-line. - - 'script_name' is a file that will be read and run with 'exec()'; - 'sys.argv[0]' will be replaced with 'script' for the duration of the - call. 'script_args' is a list of strings; if supplied, - 'sys.argv[1:]' will be replaced by 'script_args' for the duration of - the call. - - 'stop_after' tells 'setup()' when to stop processing; possible - values: - init - stop after the Distribution instance has been created and - populated with the keyword arguments to 'setup()' - config - stop after config files have been parsed (and their data - stored in the Distribution instance) - commandline - stop after the command-line ('sys.argv[1:]' or 'script_args') - have been parsed (and the data stored in the Distribution) - run [default] - stop after all commands have been run (the same as if 'setup()' - had been called in the usual way - - Returns the Distribution instance, which provides all information - used to drive the Distutils. - """ - if stop_after not in ('init', 'config', 'commandline', 'run'): - raise ValueError("invalid value for 'stop_after': {!r}".format(stop_after)) - - global _setup_stop_after, _setup_distribution - _setup_stop_after = stop_after - - save_argv = sys.argv.copy() - g = {'__file__': script_name, '__name__': '__main__'} - try: - try: - sys.argv[0] = script_name - if script_args is not None: - sys.argv[1:] = script_args - # tokenize.open supports automatic encoding detection - with tokenize.open(script_name) as f: - code = f.read().replace(r'\r\n', r'\n') - exec(code, g) - finally: - sys.argv = save_argv - _setup_stop_after = None - except SystemExit: - # Hmm, should we do something if exiting with a non-zero code - # (ie. error)? - pass - - if _setup_distribution is None: - raise RuntimeError( - ( - "'distutils.core.setup()' was never called -- " - "perhaps '%s' is not a Distutils setup script?" - ) - % script_name - ) - - # I wonder if the setup script's namespace -- g and l -- would be of - # any interest to callers? - # print "_setup_distribution:", _setup_distribution - return _setup_distribution - - -# run_setup () diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/transform.h b/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/transform.h deleted file mode 100644 index 053fe9095a9bba47a05cf8b21c4a1954107685aa..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/transform.h +++ /dev/null @@ -1,426 +0,0 @@ -/****************************************************************************** - * Copyright (c) 2016, NVIDIA CORPORATION. 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 name of the NVIDIA CORPORATION nor the - * names of its 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 NVIDIA CORPORATION 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. - * - ******************************************************************************/ -#pragma once - - -#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC -#include - -#include -#include -#include -#include - -namespace thrust -{ - -namespace cuda_cub { - - -namespace __transform { - - struct no_stencil_tag - { - }; - - struct always_true_predicate - { - template - bool THRUST_DEVICE_FUNCTION operator()(T const &) const - { - return true; - } - }; - - template - struct unary_transform_f - { - InputIt input; - OutputIt output; - StencilIt stencil; - TransformOp op; - Predicate pred; - - THRUST_FUNCTION - unary_transform_f(InputIt input_, - OutputIt output_, - StencilIt stencil_, - TransformOp op_, - Predicate pred_) - : input(input_), - output(output_), - stencil(stencil_), - op(op_), - pred(pred_) {} - - template - void THRUST_DEVICE_FUNCTION operator()(Size idx) - { - if (pred(raw_reference_cast(stencil[idx]))) - output[idx] = op(raw_reference_cast(input[idx])); - } - }; // struct unary_transform_stencil_f - - template - struct unary_transform_f - { - InputIt input; - OutputIt output; - TransformOp op; - Predicate pred; - - THRUST_FUNCTION - unary_transform_f(InputIt input_, - OutputIt output_, - no_stencil_tag, - TransformOp op_, - Predicate pred_) - : input(input_), output(output_), op(op_), pred(pred_) {} - - template - void THRUST_DEVICE_FUNCTION operator()(Size idx) - { - if (pred(raw_reference_cast(input[idx]))) - output[idx] = op(raw_reference_cast(input[idx])); - } - }; // struct unary_transform_f - - template - struct binary_transform_f - { - InputIt1 input1; - InputIt2 input2; - OutputIt output; - StencilIt stencil; - TransformOp op; - Predicate pred; - - THRUST_FUNCTION - binary_transform_f(InputIt1 input1_, - InputIt2 input2_, - OutputIt output_, - StencilIt stencil_, - TransformOp op_, - Predicate pred_) - : input1(input1_), - input2(input2_), - output(output_), - stencil(stencil_), - op(op_), - pred(pred_) {} - - template - void THRUST_DEVICE_FUNCTION operator()(Size idx) - { - if (pred(raw_reference_cast(stencil[idx]))) - output[idx] = op(raw_reference_cast(input1[idx]), - raw_reference_cast(input2[idx])); - } - }; // struct binary_transform_stencil_f - - template - struct binary_transform_f - { - InputIt1 input1; - InputIt2 input2; - OutputIt output; - TransformOp op; - Predicate pred; - - THRUST_FUNCTION - binary_transform_f(InputIt1 input1_, - InputIt2 input2_, - OutputIt output_, - no_stencil_tag , - TransformOp op_, - Predicate pred_) - : input1(input1_), - input2(input2_), - output(output_), - op(op_), - pred(pred_) {} - - template - void THRUST_DEVICE_FUNCTION operator()(Size idx) - { - if (pred(raw_reference_cast(input1[idx]))) - output[idx] = op(raw_reference_cast(input1[idx]), - raw_reference_cast(input2[idx])); - } - }; // struct binary_transform_f - - template - OutputIt THRUST_FUNCTION - unary(Policy & policy, - InputIt items, - OutputIt result, - Size num_items, - StencilIt stencil, - TransformOp transform_op, - Predicate predicate) - { - if (num_items == 0) - return result; - - typedef unary_transform_f - unary_transform_t; - - cuda_cub::parallel_for(policy, - unary_transform_t(items, - result, - stencil, - transform_op, - predicate), - num_items); - - cuda_cub::throw_on_error( - cuda_cub::synchronize(policy) - , "transform: failed to synchronize" - ); - - return result + num_items; - } - - template - OutputIt THRUST_FUNCTION - binary(Policy & policy, - InputIt1 items1, - InputIt2 items2, - OutputIt result, - Size num_items, - StencilIt stencil, - TransformOp transform_op, - Predicate predicate) - { - if (num_items == 0) - return result; - - typedef binary_transform_f - binary_transform_t; - - cuda_cub::parallel_for(policy, - binary_transform_t(items1, - items2, - result, - stencil, - transform_op, - predicate), - num_items); - - cuda_cub::throw_on_error( - cuda_cub::synchronize(policy) - , "transform: failed to synchronize" - ); - - return result + num_items; - } - -} // namespace __transform - -//------------------------- -// Thrust API entry points -//------------------------- - -//------------------------- -// one input data stream -//------------------------- - -template -OutputIt THRUST_FUNCTION -transform_if(execution_policy &policy, - InputIt first, - InputIt last, - StencilInputIt stencil, - OutputIt result, - TransformOp transform_op, - Predicate predicate) -{ - typedef typename iterator_traits::difference_type size_type; - size_type num_items = static_cast(thrust::distance(first, last)); - return __transform::unary(policy, - first, - result, - num_items, - stencil, - transform_op, - predicate); -} // func transform_if - -template -OutputIt THRUST_FUNCTION -transform_if(execution_policy &policy, - InputIt first, - InputIt last, - OutputIt result, - TransformOp transform_op, - Predicate predicate) -{ - return cuda_cub::transform_if(policy, - first, - last, - __transform::no_stencil_tag(), - result, - transform_op, - predicate); -} // func transform_if - -template -OutputIt THRUST_FUNCTION -transform(execution_policy &policy, - InputIt first, - InputIt last, - OutputIt result, - TransformOp transform_op) -{ - return cuda_cub::transform_if(policy, - first, - last, - result, - transform_op, - __transform::always_true_predicate()); -} // func transform - -//------------------------- -// two input data streams -//------------------------- - - -template -OutputIt THRUST_FUNCTION -transform_if(execution_policy &policy, - InputIt1 first1, - InputIt1 last1, - InputIt2 first2, - StencilInputIt stencil, - OutputIt result, - TransformOp transform_op, - Predicate predicate) -{ - typedef typename iterator_traits::difference_type size_type; - size_type num_items = static_cast(thrust::distance(first1, last1)); - return __transform::binary(policy, - first1, - first2, - result, - num_items, - stencil, - transform_op, - predicate); -} // func transform_if - -template -OutputIt THRUST_FUNCTION -transform(execution_policy &policy, - InputIt1 first1, - InputIt1 last1, - InputIt2 first2, - OutputIt result, - TransformOp transform_op) -{ - return cuda_cub::transform_if(policy, - first1, - last1, - first2, - __transform::no_stencil_tag(), - result, - transform_op, - __transform::always_true_predicate()); -} // func transform - -} // namespace cuda_cub - -} // end namespace thrust -#endif diff --git a/spaces/CVPR/Text2Human/Text2Human/models/vqgan_model.py b/spaces/CVPR/Text2Human/Text2Human/models/vqgan_model.py deleted file mode 100644 index 13a2e7062c4b49052e91ac3c183eaa7056986050..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Text2Human/Text2Human/models/vqgan_model.py +++ /dev/null @@ -1,551 +0,0 @@ -import math -import sys -from collections import OrderedDict - -sys.path.append('..') -import lpips -import torch -import torch.nn.functional as F -from torchvision.utils import save_image - -from models.archs.vqgan_arch import (Decoder, Discriminator, Encoder, - VectorQuantizer, VectorQuantizerTexture) -from models.losses.segmentation_loss import BCELossWithQuant -from models.losses.vqgan_loss import (DiffAugment, adopt_weight, - calculate_adaptive_weight, hinge_d_loss) - - -class VQModel(): - - def __init__(self, opt): - super().__init__() - self.opt = opt - self.device = torch.device('cuda') - self.encoder = Encoder( - ch=opt['ch'], - num_res_blocks=opt['num_res_blocks'], - attn_resolutions=opt['attn_resolutions'], - ch_mult=opt['ch_mult'], - in_channels=opt['in_channels'], - resolution=opt['resolution'], - z_channels=opt['z_channels'], - double_z=opt['double_z'], - dropout=opt['dropout']).to(self.device) - self.decoder = Decoder( - in_channels=opt['in_channels'], - resolution=opt['resolution'], - z_channels=opt['z_channels'], - ch=opt['ch'], - out_ch=opt['out_ch'], - num_res_blocks=opt['num_res_blocks'], - attn_resolutions=opt['attn_resolutions'], - ch_mult=opt['ch_mult'], - dropout=opt['dropout'], - resamp_with_conv=True, - give_pre_end=False).to(self.device) - self.quantize = VectorQuantizer( - opt['n_embed'], opt['embed_dim'], beta=0.25).to(self.device) - self.quant_conv = torch.nn.Conv2d(opt["z_channels"], opt['embed_dim'], - 1).to(self.device) - self.post_quant_conv = torch.nn.Conv2d(opt['embed_dim'], - opt["z_channels"], - 1).to(self.device) - - def init_training_settings(self): - self.loss = BCELossWithQuant() - self.log_dict = OrderedDict() - self.configure_optimizers() - - def save_network(self, save_path): - """Save networks. - - Args: - net (nn.Module): Network to be saved. - net_label (str): Network label. - current_iter (int): Current iter number. - """ - - save_dict = {} - save_dict['encoder'] = self.encoder.state_dict() - save_dict['decoder'] = self.decoder.state_dict() - save_dict['quantize'] = self.quantize.state_dict() - save_dict['quant_conv'] = self.quant_conv.state_dict() - save_dict['post_quant_conv'] = self.post_quant_conv.state_dict() - save_dict['discriminator'] = self.disc.state_dict() - torch.save(save_dict, save_path) - - def load_network(self): - checkpoint = torch.load(self.opt['pretrained_models']) - self.encoder.load_state_dict(checkpoint['encoder'], strict=True) - self.decoder.load_state_dict(checkpoint['decoder'], strict=True) - self.quantize.load_state_dict(checkpoint['quantize'], strict=True) - self.quant_conv.load_state_dict(checkpoint['quant_conv'], strict=True) - self.post_quant_conv.load_state_dict( - checkpoint['post_quant_conv'], strict=True) - - def optimize_parameters(self, data, current_iter): - self.encoder.train() - self.decoder.train() - self.quantize.train() - self.quant_conv.train() - self.post_quant_conv.train() - - loss = self.training_step(data) - self.optimizer.zero_grad() - loss.backward() - self.optimizer.step() - - def encode(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - quant, emb_loss, info = self.quantize(h) - return quant, emb_loss, info - - def decode(self, quant): - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - def decode_code(self, code_b): - quant_b = self.quantize.embed_code(code_b) - dec = self.decode(quant_b) - return dec - - def forward_step(self, input): - quant, diff, _ = self.encode(input) - dec = self.decode(quant) - return dec, diff - - def feed_data(self, data): - x = data['segm'] - x = F.one_hot(x, num_classes=self.opt['num_segm_classes']) - - if len(x.shape) == 3: - x = x[..., None] - x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format) - return x.float().to(self.device) - - def get_current_log(self): - return self.log_dict - - def update_learning_rate(self, epoch): - """Update learning rate. - - Args: - current_iter (int): Current iteration. - warmup_iter (int): Warmup iter numbers. -1 for no warmup. - Default: -1. - """ - lr = self.optimizer.param_groups[0]['lr'] - - if self.opt['lr_decay'] == 'step': - lr = self.opt['lr'] * ( - self.opt['gamma']**(epoch // self.opt['step'])) - elif self.opt['lr_decay'] == 'cos': - lr = self.opt['lr'] * ( - 1 + math.cos(math.pi * epoch / self.opt['num_epochs'])) / 2 - elif self.opt['lr_decay'] == 'linear': - lr = self.opt['lr'] * (1 - epoch / self.opt['num_epochs']) - elif self.opt['lr_decay'] == 'linear2exp': - if epoch < self.opt['turning_point'] + 1: - # learning rate decay as 95% - # at the turning point (1 / 95% = 1.0526) - lr = self.opt['lr'] * ( - 1 - epoch / int(self.opt['turning_point'] * 1.0526)) - else: - lr *= self.opt['gamma'] - elif self.opt['lr_decay'] == 'schedule': - if epoch in self.opt['schedule']: - lr *= self.opt['gamma'] - else: - raise ValueError('Unknown lr mode {}'.format(self.opt['lr_decay'])) - # set learning rate - for param_group in self.optimizer.param_groups: - param_group['lr'] = lr - - return lr - - -class VQSegmentationModel(VQModel): - - def __init__(self, opt): - super().__init__(opt) - self.colorize = torch.randn(3, opt['num_segm_classes'], 1, - 1).to(self.device) - - self.init_training_settings() - - def configure_optimizers(self): - self.optimizer = torch.optim.Adam( - list(self.encoder.parameters()) + list(self.decoder.parameters()) + - list(self.quantize.parameters()) + - list(self.quant_conv.parameters()) + - list(self.post_quant_conv.parameters()), - lr=self.opt['lr'], - betas=(0.5, 0.9)) - - def training_step(self, data): - x = self.feed_data(data) - xrec, qloss = self.forward_step(x) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="train") - self.log_dict.update(log_dict_ae) - return aeloss - - def to_rgb(self, x): - x = F.conv2d(x, weight=self.colorize) - x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. - return x - - @torch.no_grad() - def inference(self, data_loader, save_dir): - self.encoder.eval() - self.decoder.eval() - self.quantize.eval() - self.quant_conv.eval() - self.post_quant_conv.eval() - - loss_total = 0 - loss_bce = 0 - loss_quant = 0 - num = 0 - - for _, data in enumerate(data_loader): - img_name = data['img_name'][0] - x = self.feed_data(data) - xrec, qloss = self.forward_step(x) - _, log_dict_ae = self.loss(qloss, x, xrec, split="val") - - loss_total += log_dict_ae['val/total_loss'] - loss_bce += log_dict_ae['val/bce_loss'] - loss_quant += log_dict_ae['val/quant_loss'] - - num += x.size(0) - - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - # convert logits to indices - xrec = torch.argmax(xrec, dim=1, keepdim=True) - xrec = F.one_hot(xrec, num_classes=x.shape[1]) - xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float() - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - - img_cat = torch.cat([x, xrec], dim=3).detach() - img_cat = ((img_cat + 1) / 2) - img_cat = img_cat.clamp_(0, 1) - save_image( - img_cat, f'{save_dir}/{img_name}.png', nrow=1, padding=4) - - return (loss_total / num).item(), (loss_bce / - num).item(), (loss_quant / - num).item() - - -class VQImageModel(VQModel): - - def __init__(self, opt): - super().__init__(opt) - self.disc = Discriminator( - opt['n_channels'], opt['ndf'], - n_layers=opt['disc_layers']).to(self.device) - self.perceptual = lpips.LPIPS(net="vgg").to(self.device) - self.perceptual_weight = opt['perceptual_weight'] - self.disc_start_step = opt['disc_start_step'] - self.disc_weight_max = opt['disc_weight_max'] - self.diff_aug = opt['diff_aug'] - self.policy = "color,translation" - - self.disc.train() - - self.init_training_settings() - - def feed_data(self, data): - x = data['image'] - - return x.float().to(self.device) - - def init_training_settings(self): - self.log_dict = OrderedDict() - self.configure_optimizers() - - def configure_optimizers(self): - self.optimizer = torch.optim.Adam( - list(self.encoder.parameters()) + list(self.decoder.parameters()) + - list(self.quantize.parameters()) + - list(self.quant_conv.parameters()) + - list(self.post_quant_conv.parameters()), - lr=self.opt['lr']) - - self.disc_optimizer = torch.optim.Adam( - self.disc.parameters(), lr=self.opt['lr']) - - def training_step(self, data, step): - x = self.feed_data(data) - xrec, codebook_loss = self.forward_step(x) - - # get recon/perceptual loss - recon_loss = torch.abs(x.contiguous() - xrec.contiguous()) - p_loss = self.perceptual(x.contiguous(), xrec.contiguous()) - nll_loss = recon_loss + self.perceptual_weight * p_loss - nll_loss = torch.mean(nll_loss) - - # augment for input to discriminator - if self.diff_aug: - xrec = DiffAugment(xrec, policy=self.policy) - - # update generator - logits_fake = self.disc(xrec) - g_loss = -torch.mean(logits_fake) - last_layer = self.decoder.conv_out.weight - d_weight = calculate_adaptive_weight(nll_loss, g_loss, last_layer, - self.disc_weight_max) - d_weight *= adopt_weight(1, step, self.disc_start_step) - loss = nll_loss + d_weight * g_loss + codebook_loss - - self.log_dict["loss"] = loss - self.log_dict["l1"] = recon_loss.mean().item() - self.log_dict["perceptual"] = p_loss.mean().item() - self.log_dict["nll_loss"] = nll_loss.item() - self.log_dict["g_loss"] = g_loss.item() - self.log_dict["d_weight"] = d_weight - self.log_dict["codebook_loss"] = codebook_loss.item() - - if step > self.disc_start_step: - if self.diff_aug: - logits_real = self.disc( - DiffAugment(x.contiguous().detach(), policy=self.policy)) - else: - logits_real = self.disc(x.contiguous().detach()) - logits_fake = self.disc(xrec.contiguous().detach( - )) # detach so that generator isn"t also updated - d_loss = hinge_d_loss(logits_real, logits_fake) - self.log_dict["d_loss"] = d_loss - else: - d_loss = None - - return loss, d_loss - - def optimize_parameters(self, data, step): - self.encoder.train() - self.decoder.train() - self.quantize.train() - self.quant_conv.train() - self.post_quant_conv.train() - - loss, d_loss = self.training_step(data, step) - self.optimizer.zero_grad() - loss.backward() - self.optimizer.step() - - if step > self.disc_start_step: - self.disc_optimizer.zero_grad() - d_loss.backward() - self.disc_optimizer.step() - - @torch.no_grad() - def inference(self, data_loader, save_dir): - self.encoder.eval() - self.decoder.eval() - self.quantize.eval() - self.quant_conv.eval() - self.post_quant_conv.eval() - - loss_total = 0 - num = 0 - - for _, data in enumerate(data_loader): - img_name = data['img_name'][0] - x = self.feed_data(data) - xrec, _ = self.forward_step(x) - - recon_loss = torch.abs(x.contiguous() - xrec.contiguous()) - p_loss = self.perceptual(x.contiguous(), xrec.contiguous()) - nll_loss = recon_loss + self.perceptual_weight * p_loss - nll_loss = torch.mean(nll_loss) - loss_total += nll_loss - - num += x.size(0) - - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - # convert logits to indices - xrec = torch.argmax(xrec, dim=1, keepdim=True) - xrec = F.one_hot(xrec, num_classes=x.shape[1]) - xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float() - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - - img_cat = torch.cat([x, xrec], dim=3).detach() - img_cat = ((img_cat + 1) / 2) - img_cat = img_cat.clamp_(0, 1) - save_image( - img_cat, f'{save_dir}/{img_name}.png', nrow=1, padding=4) - - return (loss_total / num).item() - - -class VQImageSegmTextureModel(VQImageModel): - - def __init__(self, opt): - self.opt = opt - self.device = torch.device('cuda') - self.encoder = Encoder( - ch=opt['ch'], - num_res_blocks=opt['num_res_blocks'], - attn_resolutions=opt['attn_resolutions'], - ch_mult=opt['ch_mult'], - in_channels=opt['in_channels'], - resolution=opt['resolution'], - z_channels=opt['z_channels'], - double_z=opt['double_z'], - dropout=opt['dropout']).to(self.device) - self.decoder = Decoder( - in_channels=opt['in_channels'], - resolution=opt['resolution'], - z_channels=opt['z_channels'], - ch=opt['ch'], - out_ch=opt['out_ch'], - num_res_blocks=opt['num_res_blocks'], - attn_resolutions=opt['attn_resolutions'], - ch_mult=opt['ch_mult'], - dropout=opt['dropout'], - resamp_with_conv=True, - give_pre_end=False).to(self.device) - self.quantize = VectorQuantizerTexture( - opt['n_embed'], opt['embed_dim'], beta=0.25).to(self.device) - self.quant_conv = torch.nn.Conv2d(opt["z_channels"], opt['embed_dim'], - 1).to(self.device) - self.post_quant_conv = torch.nn.Conv2d(opt['embed_dim'], - opt["z_channels"], - 1).to(self.device) - - self.disc = Discriminator( - opt['n_channels'], opt['ndf'], - n_layers=opt['disc_layers']).to(self.device) - self.perceptual = lpips.LPIPS(net="vgg").to(self.device) - self.perceptual_weight = opt['perceptual_weight'] - self.disc_start_step = opt['disc_start_step'] - self.disc_weight_max = opt['disc_weight_max'] - self.diff_aug = opt['diff_aug'] - self.policy = "color,translation" - - self.disc.train() - - self.init_training_settings() - - def feed_data(self, data): - x = data['image'].float().to(self.device) - mask = data['texture_mask'].float().to(self.device) - - return x, mask - - def training_step(self, data, step): - x, mask = self.feed_data(data) - xrec, codebook_loss = self.forward_step(x, mask) - - # get recon/perceptual loss - recon_loss = torch.abs(x.contiguous() - xrec.contiguous()) - p_loss = self.perceptual(x.contiguous(), xrec.contiguous()) - nll_loss = recon_loss + self.perceptual_weight * p_loss - nll_loss = torch.mean(nll_loss) - - # augment for input to discriminator - if self.diff_aug: - xrec = DiffAugment(xrec, policy=self.policy) - - # update generator - logits_fake = self.disc(xrec) - g_loss = -torch.mean(logits_fake) - last_layer = self.decoder.conv_out.weight - d_weight = calculate_adaptive_weight(nll_loss, g_loss, last_layer, - self.disc_weight_max) - d_weight *= adopt_weight(1, step, self.disc_start_step) - loss = nll_loss + d_weight * g_loss + codebook_loss - - self.log_dict["loss"] = loss - self.log_dict["l1"] = recon_loss.mean().item() - self.log_dict["perceptual"] = p_loss.mean().item() - self.log_dict["nll_loss"] = nll_loss.item() - self.log_dict["g_loss"] = g_loss.item() - self.log_dict["d_weight"] = d_weight - self.log_dict["codebook_loss"] = codebook_loss.item() - - if step > self.disc_start_step: - if self.diff_aug: - logits_real = self.disc( - DiffAugment(x.contiguous().detach(), policy=self.policy)) - else: - logits_real = self.disc(x.contiguous().detach()) - logits_fake = self.disc(xrec.contiguous().detach( - )) # detach so that generator isn"t also updated - d_loss = hinge_d_loss(logits_real, logits_fake) - self.log_dict["d_loss"] = d_loss - else: - d_loss = None - - return loss, d_loss - - @torch.no_grad() - def inference(self, data_loader, save_dir): - self.encoder.eval() - self.decoder.eval() - self.quantize.eval() - self.quant_conv.eval() - self.post_quant_conv.eval() - - loss_total = 0 - num = 0 - - for _, data in enumerate(data_loader): - img_name = data['img_name'][0] - x, mask = self.feed_data(data) - xrec, _ = self.forward_step(x, mask) - - recon_loss = torch.abs(x.contiguous() - xrec.contiguous()) - p_loss = self.perceptual(x.contiguous(), xrec.contiguous()) - nll_loss = recon_loss + self.perceptual_weight * p_loss - nll_loss = torch.mean(nll_loss) - loss_total += nll_loss - - num += x.size(0) - - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - # convert logits to indices - xrec = torch.argmax(xrec, dim=1, keepdim=True) - xrec = F.one_hot(xrec, num_classes=x.shape[1]) - xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float() - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - - img_cat = torch.cat([x, xrec], dim=3).detach() - img_cat = ((img_cat + 1) / 2) - img_cat = img_cat.clamp_(0, 1) - save_image( - img_cat, f'{save_dir}/{img_name}.png', nrow=1, padding=4) - - return (loss_total / num).item() - - def encode(self, x, mask): - h = self.encoder(x) - h = self.quant_conv(h) - quant, emb_loss, info = self.quantize(h, mask) - return quant, emb_loss, info - - def decode(self, quant): - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - def decode_code(self, code_b): - quant_b = self.quantize.embed_code(code_b) - dec = self.decode(quant_b) - return dec - - def forward_step(self, input, mask): - quant, diff, _ = self.encode(input, mask) - dec = self.decode(quant) - return dec, diff diff --git a/spaces/CVPR/regionclip-demo/detectron2/data/clip_datasets/clip_prompt_engineering.py b/spaces/CVPR/regionclip-demo/detectron2/data/clip_datasets/clip_prompt_engineering.py deleted file mode 100644 index 600c211af72aad0ca60d1e3a6d19cbd0dff29376..0000000000000000000000000000000000000000 --- a/spaces/CVPR/regionclip-demo/detectron2/data/clip_datasets/clip_prompt_engineering.py +++ /dev/null @@ -1,300 +0,0 @@ -import gzip -import html -import os -from functools import lru_cache - -import ftfy -import regex as re -import torch -import numpy as np -from typing import Union, List - -# https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py -@lru_cache() -def default_bpe(): - return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") - - -@lru_cache() -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a corresponding list of unicode strings. - The reversible bpe codes work on unicode strings. - This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. - When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a signficant percentage of your normal, say, 32K bpe vocab. - To avoid that, we want lookup tables between utf-8 bytes and unicode strings. - And avoids mapping to whitespace/control characters the bpe code barfs on. - """ - bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8+n) - n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) - - -def get_pairs(word): - """Return set of symbol pairs in a word. - Word is represented as tuple of symbols (symbols being variable-length strings). - """ - pairs = set() - prev_char = word[0] - for char in word[1:]: - pairs.add((prev_char, char)) - prev_char = char - return pairs - - -def basic_clean(text): - text = ftfy.fix_text(text) - text = html.unescape(html.unescape(text)) - return text.strip() - - -def whitespace_clean(text): - text = re.sub(r'\s+', ' ', text) - text = text.strip() - return text - - -class SimpleTokenizer(object): - def __init__(self, bpe_path: str = default_bpe()): - self.byte_encoder = bytes_to_unicode() - self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} - merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') - merges = merges[1:49152-256-2+1] - merges = [tuple(merge.split()) for merge in merges] - vocab = list(bytes_to_unicode().values()) - vocab = vocab + [v+'' for v in vocab] - self.vocab = vocab - for merge in merges: - vocab.append(''.join(merge)) - vocab.extend(['<|startoftext|>', '<|endoftext|>']) - self.encoder = dict(zip(vocab, range(len(vocab)))) - self.decoder = {v: k for k, v in self.encoder.items()} - self.bpe_ranks = dict(zip(merges, range(len(merges)))) - self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} - self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) - - def bpe(self, token): - if token in self.cache: - return self.cache[token] - word = tuple(token[:-1]) + ( token[-1] + '',) - pairs = get_pairs(word) - - if not pairs: - return token+'' - - while True: - bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) - if bigram not in self.bpe_ranks: - break - first, second = bigram - new_word = [] - i = 0 - while i < len(word): - try: - j = word.index(first, i) - new_word.extend(word[i:j]) - i = j - except: - new_word.extend(word[i:]) - break - - if word[i] == first and i < len(word)-1 and word[i+1] == second: - new_word.append(first+second) - i += 2 - else: - new_word.append(word[i]) - i += 1 - new_word = tuple(new_word) - word = new_word - if len(word) == 1: - break - else: - pairs = get_pairs(word) - word = ' '.join(word) - self.cache[token] = word - return word - - def encode(self, text): - bpe_tokens = [] - text = whitespace_clean(basic_clean(text)).lower() - for token in re.findall(self.pat, text): - token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) - bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) - return bpe_tokens - - def decode(self, tokens): - text = ''.join([self.decoder[token] for token in tokens]) - text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') - return text - - -# https://github.com/openai/CLIP/blob/main/clip/clip.py -#_tokenizer = SimpleTokenizer() - -def tokenize(texts: Union[str, List[str]], context_length: int = 77): - if isinstance(texts, str): - texts = [texts] - - sot_token = _tokenizer.encoder["<|startoftext|>"] - eot_token = _tokenizer.encoder["<|endoftext|>"] - all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - if len(tokens) > context_length: - raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") - result[i, :len(tokens)] = torch.tensor(tokens) - - return result - - -# prompt_engineering.py -def get_prompt_templates(): - # prompt_templates = [ - # 'There is a {} in the scene.', - # 'There is the {} in the scene.', - # 'a photo of a {} in the scene.', - # 'a photo of the {} in the scene.', - # 'a photo of one {} in the scene.', - - # 'itap of a {}.', - # 'itap of my {}.', # itap: I took a picture of - # 'itap of the {}.', - # 'a photo of a {}.', - # 'a photo of my {}.', - # 'a photo of the {}.', - # 'a photo of one {}.', - # 'a photo of many {}.', - - # 'a good photo of a {}.', - # 'a good photo of the {}.', - # 'a bad photo of a {}.', - # 'a bad photo of the {}.', - # 'a photo of a nice {}.', - # 'a photo of the nice {}.', - # 'a photo of a cool {}.', - # 'a photo of the cool {}.', - # 'a photo of a weird {}.', - # 'a photo of the weird {}.', - - # 'a photo of a small {}.', - # 'a photo of the small {}.', - # 'a photo of a large {}.', - # 'a photo of the large {}.', - - # 'a photo of a clean {}.', - # 'a photo of the clean {}.', - # 'a photo of a dirty {}.', - # 'a photo of the dirty {}.', - - # 'a bright photo of a {}.', - # 'a bright photo of the {}.', - # 'a dark photo of a {}.', - # 'a dark photo of the {}.', - - # 'a photo of a hard to see {}.', - # 'a photo of the hard to see {}.', - # 'a low resolution photo of a {}.', - # 'a low resolution photo of the {}.', - # 'a cropped photo of a {}.', - # 'a cropped photo of the {}.', - # 'a close-up photo of a {}.', - # 'a close-up photo of the {}.', - # 'a jpeg corrupted photo of a {}.', - # 'a jpeg corrupted photo of the {}.', - # 'a blurry photo of a {}.', - # 'a blurry photo of the {}.', - # 'a pixelated photo of a {}.', - # 'a pixelated photo of the {}.', - - # 'a black and white photo of the {}.', - # 'a black and white photo of a {}.', - - # 'a plastic {}.', - # 'the plastic {}.', - - # 'a toy {}.', - # 'the toy {}.', - # 'a plushie {}.', - # 'the plushie {}.', - # 'a cartoon {}.', - # 'the cartoon {}.', - - # 'an embroidered {}.', - # 'the embroidered {}.', - - # 'a painting of the {}.', - # 'a painting of a {}.', - # ] - - prompt_templates = ['{}.'] - - return prompt_templates - -def prompt_engineering(classnames, template=""): - return template.replace('{}', classnames.replace(',', '').replace('+', ' ')) - -# clip_img_tsv.py -def convert_example_to_features_bpe(text, tokenizer, sot_token, eot_token, context_length=77): - """ - Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample. - :param tokenizer: Tokenizer - :return: List, a list containing token id, padded by 0 - """ - assert isinstance(text, str) - input_ids = [sot_token] + tokenizer.encode(text) + [eot_token] - if len(input_ids) > context_length: - input_ids = input_ids[:context_length] - input_ids = np.array(input_ids) - - pad_input_ids = np.zeros(context_length) - pad_input_ids[:input_ids.shape[0]] = input_ids - - return pad_input_ids - -def pre_tokenize(class_names): - """ - pre-tokenize class names - :param class_names: List, a list of class names - :param tokenizer: Tokenizer, SimpleTokenizer() - :return: Tensor, containing all prompts for all classes, [#cls, #prompts, context_length] - """ - # tokenizer - tokenizer = SimpleTokenizer() - sot_token = tokenizer.encoder["<|startoftext|>"] - eot_token = tokenizer.encoder["<|endoftext|>"] - - # prompt engineering - prompt_templates = get_prompt_templates() - input_ids_all = [] - for k in range(len(class_names)): - v = class_names[k] - if isinstance(v, str): - vs = [v] - elif isinstance(v, list): - vs = v - t1s = [] - for v in vs: - for pt in prompt_templates: - t1s.append(prompt_engineering(v, template=pt)) - input_ids = [] - for t1 in t1s: - this_input_ids = convert_example_to_features_bpe(t1, tokenizer, sot_token, eot_token) - input_ids.append(torch.tensor(this_input_ids, dtype=torch.long)) - - input_ids_all.append(torch.stack(input_ids, 0)) - - input_ids_all_classes = torch.stack(input_ids_all, 0) - return input_ids_all_classes - - -if __name__ == "__main__": - flatten_input_ids = pre_tokenize() diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/memory/weaviate.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/memory/weaviate.py deleted file mode 100644 index 5408e9a97aa3594ad443448cfc31f2546a01eb09..0000000000000000000000000000000000000000 --- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/memory/weaviate.py +++ /dev/null @@ -1,127 +0,0 @@ -import uuid - -import weaviate -from weaviate import Client -from weaviate.embedded import EmbeddedOptions -from weaviate.util import generate_uuid5 - -from autogpt.config import Config -from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding - - -def default_schema(weaviate_index): - return { - "class": weaviate_index, - "properties": [ - { - "name": "raw_text", - "dataType": ["text"], - "description": "original text for the embedding", - } - ], - } - - -class WeaviateMemory(MemoryProviderSingleton): - def __init__(self, cfg): - auth_credentials = self._build_auth_credentials(cfg) - - url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}" - - if cfg.use_weaviate_embedded: - self.client = Client( - embedded_options=EmbeddedOptions( - hostname=cfg.weaviate_host, - port=int(cfg.weaviate_port), - persistence_data_path=cfg.weaviate_embedded_path, - ) - ) - - print( - f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}" - ) - else: - self.client = Client(url, auth_client_secret=auth_credentials) - - self.index = WeaviateMemory.format_classname(cfg.memory_index) - self._create_schema() - - @staticmethod - def format_classname(index): - # weaviate uses capitalised index names - # The python client uses the following code to format - # index names before the corresponding class is created - if len(index) == 1: - return index.capitalize() - return index[0].capitalize() + index[1:] - - def _create_schema(self): - schema = default_schema(self.index) - if not self.client.schema.contains(schema): - self.client.schema.create_class(schema) - - def _build_auth_credentials(self, cfg): - if cfg.weaviate_username and cfg.weaviate_password: - return weaviate.AuthClientPassword( - cfg.weaviate_username, cfg.weaviate_password - ) - if cfg.weaviate_api_key: - return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key) - else: - return None - - def add(self, data): - vector = get_ada_embedding(data) - - doc_uuid = generate_uuid5(data, self.index) - data_object = {"raw_text": data} - - with self.client.batch as batch: - batch.add_data_object( - uuid=doc_uuid, - data_object=data_object, - class_name=self.index, - vector=vector, - ) - - return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}" - - def get(self, data): - return self.get_relevant(data, 1) - - def clear(self): - self.client.schema.delete_all() - - # weaviate does not yet have a neat way to just remove the items in an index - # without removing the entire schema, therefore we need to re-create it - # after a call to delete_all - self._create_schema() - - return "Obliterated" - - def get_relevant(self, data, num_relevant=5): - query_embedding = get_ada_embedding(data) - try: - results = ( - self.client.query.get(self.index, ["raw_text"]) - .with_near_vector({"vector": query_embedding, "certainty": 0.7}) - .with_limit(num_relevant) - .do() - ) - - if len(results["data"]["Get"][self.index]) > 0: - return [ - str(item["raw_text"]) for item in results["data"]["Get"][self.index] - ] - else: - return [] - - except Exception as err: - print(f"Unexpected error {err=}, {type(err)=}") - return [] - - def get_stats(self): - result = self.client.query.aggregate(self.index).with_meta_count().do() - class_data = result["data"]["Aggregate"][self.index] - - return class_data[0]["meta"] if class_data else {} diff --git a/spaces/Chomkwoy/Nilkessye/load_book.py b/spaces/Chomkwoy/Nilkessye/load_book.py deleted file mode 100644 index efc64c1f96bf5242dce02978180a5da9ff6665f7..0000000000000000000000000000000000000000 --- a/spaces/Chomkwoy/Nilkessye/load_book.py +++ /dev/null @@ -1,289 +0,0 @@ -import glob -import json -import pathlib -import re -from collections import Counter - -import Levenshtein -import cv2 -import numpy as np -import pandas as pd -from matplotlib import pyplot as plt -from natsort import natsorted -from scipy.signal import find_peaks - -from utils import hanja - - -def load_book(jsonfile, img_dir, imgstart=1): - with open(jsonfile, 'r') as fp: - texts = json.load(fp) - - print(f"Loading {jsonfile}...") - - page_numbers = [] - for s in texts: - if 'page' not in s: - continue - if ('lang' in s and s['lang'] == 'chi' and - 'type' in s and s['type'] in ['main', 'anno', 'anno2', 'anno3']): - continue - pns = s['page'].split('-') - page_numbers.extend(pns) - - occurred = set() - unique_page_numbers = [] - for p in page_numbers: - if p not in occurred: - unique_page_numbers.append(p) - occurred.add(p) - page_numbers = unique_page_numbers - - print(f"Page numbers = {page_numbers}") - - pages = [] - page = 0 - - img_files = glob.glob(f"{img_dir}/*.png") - last_idx = int(pathlib.Path(natsorted(img_files)[-1]).stem) - - for i in range(imgstart, last_idx + 1): - filename = f"{img_dir}/{i}.png" - - if page >= len(page_numbers): - print(f"image {filename} exceeds transcribed range") - continue - pc = page_numbers[page] - sents = [] - for s in texts: - if 'page' not in s: - continue - if ('lang' in s and s['lang'] == 'chi' and - 'type' in s and s['type'] in ['main', 'anno', 'anno2', 'anno3']): - continue - pns = s['page'].split('-') - if pc in pns: - is_anno = 'type' in s and 'anno' in s['type'] - sents.append((pns, is_anno, s['text'])) - - num_border_sents = 0 - for s in sents: - if len(s[0]) > 1: - num_border_sents += 1 - if len(s[0]) == 1: - break - - if num_border_sents > 1: - print("ERROR: two border sentences", filename, pc) - print(sents) - else: - pages.append({ - 'file_name': filename, - 'text': sents, - 'pc': pc, - }) - page += 1 - - return pages - - -def adaptiveThreshold(image): - image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) - # image = cv2.medianBlur(image,3) - image = cv2.GaussianBlur(image, (5, 5), 0) - image = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 31, 20) - image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) - return image - - -def process_page(image, verbose=False, thresholding=False): - if isinstance(image, str): - image = cv2.imread(image, cv2.IMREAD_COLOR) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - image_grey = 255 - cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) - orig_orig_size = (image.shape[1] // 2, image.shape[0] // 2) - - # remove letterbox - tx, ty, w, h = cv2.boundingRect(cv2.findNonZero(image_grey)) - bbox = ((tx, ty), (tx + w, ty + h)) - image_cropped = image[ty:ty + h, tx:tx + w] - image_cr = cv2.rotate(image_cropped, cv2.ROTATE_90_COUNTERCLOCKWISE) - - # detect margin - image_grey = 255 - cv2.cvtColor(image_cr, cv2.COLOR_RGB2GRAY) - image_grey = cv2.GaussianBlur(image_grey, (7, 7), 0) - image_resize = cv2.resize(image_grey, (image_grey.shape[1], 1), interpolation=cv2.INTER_AREA)[0] - - x = image_resize[20:-20] - peaks, properties = find_peaks(x, prominence=20, width=4) - - if verbose: - plt.plot(x) - plt.plot(peaks, x[peaks], "x") - plt.vlines(x=peaks, ymin=x[peaks] - properties["prominences"], - ymax=x[peaks], color="C1") - plt.hlines(y=properties["width_heights"], xmin=properties["left_ips"], - xmax=properties["right_ips"], color="C1") - plt.show() - - ty = max(0, min(peaks) - 50) - by = min(max(peaks) + 50, image_cr.shape[1]) - image_content = image_cr[:, ty:by] - bbox = ((bbox[0][0], bbox[0][1] + ty), (bbox[1][0], bbox[0][1] + by)) - image_content = cv2.resize( - image_content, - (image_content.shape[1] // 2, image_content.shape[0] // 2), - interpolation=cv2.INTER_AREA) - bbox = ((bbox[0][0] // 2, bbox[0][1] // 2), (bbox[1][0] // 2, bbox[1][1] // 2)) - - image = cv2.rotate(image_content, cv2.ROTATE_90_CLOCKWISE) - - if thresholding: - th_image = adaptiveThreshold(image) - th_image[:, :30] = 255 - th_image[:, -30:] = 255 - - image[:, :30] = 255 - image[:, -30:] = 255 - image = np.uint8(th_image * 0.5 + image * 0.5) - - return image, bbox, orig_orig_size - - -def load_books(): - pages = [] - pages.extend(load_book('월인석보07.json', '월인석보07', 5)) - pages.extend(load_book('월인석보08.json', '월인석보08', 5)) - pages.extend(load_book('석보상절06.json', '석보상절06', 6)) - - print(f"{len(pages)}, {len([p for p in pages if len(p['text'][0][0]) == 1])}") - - df = pd.DataFrame(pages) - return df - - -HANJA_RE = hanja.build_re() - - -def cleanup(s): - s = s.strip().strip('.') - # s = HANJA_RE.sub('〓', s) - s = re.sub(r'(?<=[a-zA-Z])\s+(?=[a-zA-Z])', '.', s) - s = re.sub(r'(?<=[a-zA-Z])\s*(?=' + HANJA_RE.pattern + ')', '.', s) - s = re.sub(r'(?<=' + HANJA_RE.pattern + r')\s*(?=[a-zA-Z])', '.', s) - s = re.sub(r'(?<=' + HANJA_RE.pattern + r')\s+(?=' + HANJA_RE.pattern + ')', '', s) - s = re.sub(r'(?<=' + HANJA_RE.pattern + ')(?=' + HANJA_RE.pattern + ')', '.', s) - return s.split('.') - - -def parse_book_text(sentences, cur_page, dgju_dict, verbose=False): - # find current page - if verbose: - print(f"{cur_page=}") - - parsed_spans = [] - last_hanja = None - for pages, is_anno, sentence in sentences: - begin = 0 - splits = sentence.split('^') - split_idx = pages.index(cur_page) - sentence = splits[split_idx] - if split_idx > 0: - last_sent = cleanup(splits[split_idx - 1]) - if HANJA_RE.match(last_sent[-1]): - last_hanja = last_sent[-1] - if verbose: - print(f"{last_hanja=}") - for x in re.finditer(r'\[([^]]*)]', sentence): - match_begin, match_end = x.span(0) - anno_begin, anno_end = x.span(1) - parsed_spans.append((pages, is_anno, cleanup(sentence[begin:match_begin]))) - parsed_spans.append((pages, True, cleanup(sentence[anno_begin:anno_end]))) - begin = match_end - parsed_spans.append((pages, is_anno, cleanup(sentence[begin:]))) - - if verbose: - for pages, is_anno, syllables in parsed_spans: - print(f"{str(pages):10}\tis_anno={str(is_anno):5}\t{'.'.join(syllables)}") - - page_syllables = [] - for pages, is_anno, syllables in parsed_spans: - for syllable in syllables: - page_syllables.append({ - 'syllable': syllable, - 'is_anno': is_anno, - }) - if HANJA_RE.match(syllable): - page_syllables.append({ - 'syllable': '?', - 'possibilities': dgju_dict.get(syllable, []), - 'is_anno': True, - }) - - cand_page_syllables = [page_syllables] - if last_hanja is not None: - cand_page_syllables.append([{ - 'syllable': '?', - 'possibilities': dgju_dict.get(last_hanja, []), - 'is_anno': True, - }] + page_syllables) - - if HANJA_RE.match(page_syllables[-1]['syllable']): - for cand in cand_page_syllables: - cand_page_syllables.append(cand + [{ - 'syllable': '?', - 'possibilities': dgju_dict.get(page_syllables[-1], []), - 'is_anno': True, - }]) - - return cand_page_syllables - - -def match_syllables(pred_syllables, expected_syllables): - # Match two strings - pred_text = '.'.join(pred_syllables) - expected_text = '.'.join(expected_syllables) - matches = Levenshtein.matching_blocks( - Levenshtein.editops(pred_text, expected_text), - pred_text, expected_text - ) - - match_map = {} - for match in matches: - for i in range(match.size): - match_map[match.a + i] = match.b + i - - # Map text char idx -> syllable idx - def map_char_to_syllable(syllables): - result = {} - offset = 0 - for syll_idx, syllable in enumerate(syllables): - for i in range(len(syllable)): - result[offset + i] = syll_idx - offset += len(syllable) + 1 - return result - - pred_char_to_syll = map_char_to_syllable(pred_syllables) - gt_char_to_syll = map_char_to_syllable(expected_syllables) - - pred_syll_to_gt_syll = {} # Map pred syllable idx -> gt syllable idx - for char_idx, syll_idx in pred_char_to_syll.items(): - if syll_idx not in pred_syll_to_gt_syll: - pred_syll_to_gt_syll[syll_idx] = [] - gt_char_idx = match_map.get(char_idx) - if gt_char_idx is not None: - gt_syll_idx = gt_char_to_syll[gt_char_idx] - pred_syll_to_gt_syll[syll_idx].append(gt_syll_idx) - - def most_common(lst): - if len(lst) == 0: - return None - data = Counter(lst) - return data.most_common(1)[0][0] - - pred_syll_to_gt_syll = { - pred_syll_idx: most_common(gt_syll_idxs) - for pred_syll_idx, gt_syll_idxs in pred_syll_to_gt_syll.items() - } - - return pred_syll_to_gt_syll diff --git a/spaces/CofAI/openjourney/README.md b/spaces/CofAI/openjourney/README.md deleted file mode 100644 index afb3f8edb2bde7812232ce13ee4019ae45faeb24..0000000000000000000000000000000000000000 --- a/spaces/CofAI/openjourney/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Openjourney -emoji: 🚀 -colorFrom: yellow -colorTo: indigo -sdk: gradio -sdk_version: 3.39.0 -app_file: midjourney.py -pinned: true ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/DEEMOSTECH/ChatAvatar/static/css/main.00b240c1.css b/spaces/DEEMOSTECH/ChatAvatar/static/css/main.00b240c1.css deleted file mode 100644 index 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sourceMappingURL=main.00b240c1.css.map*/ \ No newline at end of file diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImageChops.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImageChops.py deleted file mode 100644 index 70120031797c2493c0ce878c13c3fd3d5554c354..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImageChops.py +++ /dev/null @@ -1,303 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# standard channel operations -# -# History: -# 1996-03-24 fl Created -# 1996-08-13 fl Added logical operations (for "1" images) -# 2000-10-12 fl Added offset method (from Image.py) -# -# Copyright (c) 1997-2000 by Secret Labs AB -# Copyright (c) 1996-2000 by Fredrik Lundh -# -# See the README file for information on usage and redistribution. -# - -from . import Image - - -def constant(image, value): - """Fill a channel with a given grey level. - - :rtype: :py:class:`~PIL.Image.Image` - """ - - return Image.new("L", image.size, value) - - -def duplicate(image): - """Copy a channel. Alias for :py:meth:`PIL.Image.Image.copy`. - - :rtype: :py:class:`~PIL.Image.Image` - """ - - return image.copy() - - -def invert(image): - """ - Invert an image (channel). :: - - out = MAX - image - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image.load() - return image._new(image.im.chop_invert()) - - -def lighter(image1, image2): - """ - Compares the two images, pixel by pixel, and returns a new image containing - the lighter values. :: - - out = max(image1, image2) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_lighter(image2.im)) - - -def darker(image1, image2): - """ - Compares the two images, pixel by pixel, and returns a new image containing - the darker values. :: - - out = min(image1, image2) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_darker(image2.im)) - - -def difference(image1, image2): - """ - Returns the absolute value of the pixel-by-pixel difference between the two - images. :: - - out = abs(image1 - image2) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_difference(image2.im)) - - -def multiply(image1, image2): - """ - Superimposes two images on top of each other. - - If you multiply an image with a solid black image, the result is black. If - you multiply with a solid white image, the image is unaffected. :: - - out = image1 * image2 / MAX - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_multiply(image2.im)) - - -def screen(image1, image2): - """ - Superimposes two inverted images on top of each other. :: - - out = MAX - ((MAX - image1) * (MAX - image2) / MAX) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_screen(image2.im)) - - -def soft_light(image1, image2): - """ - Superimposes two images on top of each other using the Soft Light algorithm - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_soft_light(image2.im)) - - -def hard_light(image1, image2): - """ - Superimposes two images on top of each other using the Hard Light algorithm - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_hard_light(image2.im)) - - -def overlay(image1, image2): - """ - Superimposes two images on top of each other using the Overlay algorithm - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_overlay(image2.im)) - - -def add(image1, image2, scale=1.0, offset=0): - """ - Adds two images, dividing the result by scale and adding the - offset. If omitted, scale defaults to 1.0, and offset to 0.0. :: - - out = ((image1 + image2) / scale + offset) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_add(image2.im, scale, offset)) - - -def subtract(image1, image2, scale=1.0, offset=0): - """ - Subtracts two images, dividing the result by scale and adding the offset. - If omitted, scale defaults to 1.0, and offset to 0.0. :: - - out = ((image1 - image2) / scale + offset) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_subtract(image2.im, scale, offset)) - - -def add_modulo(image1, image2): - """Add two images, without clipping the result. :: - - out = ((image1 + image2) % MAX) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_add_modulo(image2.im)) - - -def subtract_modulo(image1, image2): - """Subtract two images, without clipping the result. :: - - out = ((image1 - image2) % MAX) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_subtract_modulo(image2.im)) - - -def logical_and(image1, image2): - """Logical AND between two images. - - Both of the images must have mode "1". If you would like to perform a - logical AND on an image with a mode other than "1", try - :py:meth:`~PIL.ImageChops.multiply` instead, using a black-and-white mask - as the second image. :: - - out = ((image1 and image2) % MAX) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_and(image2.im)) - - -def logical_or(image1, image2): - """Logical OR between two images. - - Both of the images must have mode "1". :: - - out = ((image1 or image2) % MAX) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_or(image2.im)) - - -def logical_xor(image1, image2): - """Logical XOR between two images. - - Both of the images must have mode "1". :: - - out = ((bool(image1) != bool(image2)) % MAX) - - :rtype: :py:class:`~PIL.Image.Image` - """ - - image1.load() - image2.load() - return image1._new(image1.im.chop_xor(image2.im)) - - -def blend(image1, image2, alpha): - """Blend images using constant transparency weight. Alias for - :py:func:`PIL.Image.blend`. - - :rtype: :py:class:`~PIL.Image.Image` - """ - - return Image.blend(image1, image2, alpha) - - -def composite(image1, image2, mask): - """Create composite using transparency mask. Alias for - :py:func:`PIL.Image.composite`. - - :rtype: :py:class:`~PIL.Image.Image` - """ - - return Image.composite(image1, image2, mask) - - -def offset(image, xoffset, yoffset=None): - """Returns a copy of the image where data has been offset by the given - distances. Data wraps around the edges. If ``yoffset`` is omitted, it - is assumed to be equal to ``xoffset``. - - :param image: Input image. - :param xoffset: The horizontal distance. - :param yoffset: The vertical distance. If omitted, both - distances are set to the same value. - :rtype: :py:class:`~PIL.Image.Image` - """ - - if yoffset is None: - yoffset = xoffset - image.load() - return image._new(image.im.offset(xoffset, yoffset)) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/inputs.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/inputs.py deleted file mode 100644 index 9345530649a0b8843c27d7a0f965ac73bfcce7d6..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/inputs.py +++ /dev/null @@ -1,451 +0,0 @@ -# type: ignore -""" -This module defines various classes that can serve as the `input` to an interface. Each class must inherit from -`InputComponent`, and each class must define a path to its template. All of the subclasses of `InputComponent` are -automatically added to a registry, which allows them to be easily referenced in other parts of the code. -""" - -from __future__ import annotations - -from typing import Any, Optional - -from gradio import components -from gradio.deprecation import warn_deprecation - - -def warn_inputs_deprecation(): - warn_deprecation( - "Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components", - ) - - -class Textbox(components.Textbox): - def __init__( - self, - lines: int = 1, - placeholder: Optional[str] = None, - default: str = "", - numeric: Optional[bool] = False, - type: Optional[str] = "text", - label: Optional[str] = None, - optional: bool = False, - ): - warn_inputs_deprecation() - super().__init__( - value=default, - lines=lines, - placeholder=placeholder, - label=label, - numeric=numeric, - type=type, - optional=optional, - ) - - -class Number(components.Number): - """ - Component creates a field for user to enter numeric input. Provides a number as an argument to the wrapped function. - Input type: float - """ - - def __init__( - self, - default: Optional[float] = None, - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - default (float): default value. - label (str): component name in interface. - optional (bool): If True, the interface can be submitted with no value for this component. - """ - warn_inputs_deprecation() - super().__init__(value=default, label=label, optional=optional) - - -class Slider(components.Slider): - """ - Component creates a slider that ranges from `minimum` to `maximum`. Provides number as an argument to the wrapped function. - Input type: float - """ - - def __init__( - self, - minimum: float = 0, - maximum: float = 100, - step: Optional[float] = None, - default: Optional[float] = None, - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - minimum (float): minimum value for slider. - maximum (float): maximum value for slider. - step (float): increment between slider values. - default (float): default value. - label (str): component name in interface. - optional (bool): this parameter is ignored. - """ - warn_inputs_deprecation() - - super().__init__( - value=default, - minimum=minimum, - maximum=maximum, - step=step, - label=label, - optional=optional, - ) - - -class Checkbox(components.Checkbox): - """ - Component creates a checkbox that can be set to `True` or `False`. Provides a boolean as an argument to the wrapped function. - Input type: bool - """ - - def __init__( - self, - default: bool = False, - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - label (str): component name in interface. - default (bool): if True, checked by default. - optional (bool): this parameter is ignored. - """ - warn_inputs_deprecation() - super().__init__(value=default, label=label, optional=optional) - - -class CheckboxGroup(components.CheckboxGroup): - """ - Component creates a set of checkboxes of which a subset can be selected. Provides a list of strings representing the selected choices as an argument to the wrapped function. - Input type: Union[List[str], List[int]] - """ - - def __init__( - self, - choices: list[str], - default: list[str] | None = None, - type: str = "value", - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - choices (List[str]): list of options to select from. - default (List[str]): default selected list of options. - type (str): Type of value to be returned by component. "value" returns the list of strings of the choices selected, "index" returns the list of indices of the choices selected. - label (str): component name in interface. - optional (bool): this parameter is ignored. - """ - if default is None: - default = [] - warn_inputs_deprecation() - super().__init__( - value=default, - choices=choices, - type=type, - label=label, - optional=optional, - ) - - -class Radio(components.Radio): - """ - Component creates a set of radio buttons of which only one can be selected. Provides string representing selected choice as an argument to the wrapped function. - Input type: Union[str, int] - """ - - def __init__( - self, - choices: list[str], - type: str = "value", - default: Optional[str] = None, - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - choices (List[str]): list of options to select from. - type (str): Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected. - default (str): the button selected by default. If None, no button is selected by default. - label (str): component name in interface. - optional (bool): this parameter is ignored. - """ - warn_inputs_deprecation() - super().__init__( - choices=choices, - type=type, - value=default, - label=label, - optional=optional, - ) - - -class Dropdown(components.Dropdown): - """ - Component creates a dropdown of which only one can be selected. Provides string representing selected choice as an argument to the wrapped function. - Input type: Union[str, int] - """ - - def __init__( - self, - choices: list[str], - type: str = "value", - default: Optional[str] = None, - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - choices (List[str]): list of options to select from. - type (str): Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected. - default (str): default value selected in dropdown. If None, no value is selected by default. - label (str): component name in interface. - optional (bool): this parameter is ignored. - """ - warn_inputs_deprecation() - super().__init__( - choices=choices, - type=type, - value=default, - label=label, - optional=optional, - ) - - -class Image(components.Image): - """ - Component creates an image upload box with editing capabilities. - Input type: Union[numpy.array, PIL.Image, file-object] - """ - - def __init__( - self, - shape: tuple[int, int] = None, - image_mode: str = "RGB", - invert_colors: bool = False, - source: str = "upload", - tool: str = "editor", - type: str = "numpy", - label: str = None, - optional: bool = False, - ): - """ - Parameters: - shape (Tuple[int, int]): (width, height) shape to crop and resize image to; if None, matches input image size. - image_mode (str): How to process the uploaded image. Accepts any of the PIL image modes, e.g. "RGB" for color images, "RGBA" to include the transparency mask, "L" for black-and-white images. - invert_colors (bool): whether to invert the image as a preprocessing step. - source (str): Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools. - tool (str): Tools used for editing. "editor" allows a full screen editor, "select" provides a cropping and zoom tool. - type (str): Type of value to be returned by component. "numpy" returns a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" returns a PIL image object, "file" returns a temporary file object whose path can be retrieved by file_obj.name, "filepath" returns the path directly. - label (str): component name in interface. - optional (bool): If True, the interface can be submitted with no uploaded image, in which case the input value is None. - """ - warn_inputs_deprecation() - super().__init__( - shape=shape, - image_mode=image_mode, - invert_colors=invert_colors, - source=source, - tool=tool, - type=type, - label=label, - optional=optional, - ) - - -class Video(components.Video): - """ - Component creates a video file upload that is converted to a file path. - - Input type: filepath - """ - - def __init__( - self, - type: Optional[str] = None, - source: str = "upload", - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - type (str): Type of video format to be returned by component, such as 'avi' or 'mp4'. If set to None, video will keep uploaded format. - source (str): Source of video. "upload" creates a box where user can drop an video file, "webcam" allows user to record a video from their webcam. - label (str): component name in interface. - optional (bool): If True, the interface can be submitted with no uploaded video, in which case the input value is None. - """ - warn_inputs_deprecation() - super().__init__(format=type, source=source, label=label, optional=optional) - - -class Audio(components.Audio): - """ - Component accepts audio input files. - Input type: Union[Tuple[int, numpy.array], file-object, numpy.array] - """ - - def __init__( - self, - source: str = "upload", - type: str = "numpy", - label: str = None, - optional: bool = False, - ): - """ - Parameters: - source (str): Source of audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input. - type (str): Type of value to be returned by component. "numpy" returns a 2-set tuple with an integer sample_rate and the data numpy.array of shape (samples, 2), "file" returns a temporary file object whose path can be retrieved by file_obj.name, "filepath" returns the path directly. - label (str): component name in interface. - optional (bool): If True, the interface can be submitted with no uploaded audio, in which case the input value is None. - """ - warn_inputs_deprecation() - super().__init__(source=source, type=type, label=label, optional=optional) - - -class File(components.File): - """ - Component accepts generic file uploads. - Input type: Union[file-object, bytes, List[Union[file-object, bytes]]] - """ - - def __init__( - self, - file_count: str = "single", - type: str = "file", - label: Optional[str] = None, - keep_filename: bool = True, - optional: bool = False, - ): - """ - Parameters: - file_count (str): if single, allows user to upload one file. If "multiple", user uploads multiple files. If "directory", user uploads all files in selected directory. Return type will be list for each file in case of "multiple" or "directory". - type (str): Type of value to be returned by component. "file" returns a temporary file object whose path can be retrieved by file_obj.name, "binary" returns an bytes object. - label (str): component name in interface. - keep_filename (bool): DEPRECATED. Original filename always kept. - optional (bool): If True, the interface can be submitted with no uploaded image, in which case the input value is None. - """ - warn_inputs_deprecation() - super().__init__( - file_count=file_count, - type=type, - label=label, - keep_filename=keep_filename, - optional=optional, - ) - - -class Dataframe(components.Dataframe): - """ - Component accepts 2D input through a spreadsheet interface. - Input type: Union[pandas.DataFrame, numpy.array, List[Union[str, float]], List[List[Union[str, float]]]] - """ - - def __init__( - self, - headers: Optional[list[str]] = None, - row_count: int = 3, - col_count: Optional[int] = 3, - datatype: str | list[str] = "str", - col_width: int | list[int] = None, - default: Optional[list[list[Any]]] = None, - type: str = "pandas", - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - headers (List[str]): Header names to dataframe. If None, no headers are shown. - row_count (int): Limit number of rows for input. - col_count (int): Limit number of columns for input. If equal to 1, return data will be one-dimensional. Ignored if `headers` is provided. - datatype (Union[str, List[str]]): Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", and "date". - col_width (Union[int, List[int]]): Width of columns in pixels. Can be provided as single value or list of values per column. - default (List[List[Any]]): Default value - type (str): Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for a Python array. - label (str): component name in interface. - optional (bool): this parameter is ignored. - """ - warn_inputs_deprecation() - super().__init__( - value=default, - headers=headers, - row_count=row_count, - col_count=col_count, - datatype=datatype, - col_width=col_width, - type=type, - label=label, - optional=optional, - ) - - -class Timeseries(components.Timeseries): - """ - Component accepts pandas.DataFrame uploaded as a timeseries csv file. - Input type: pandas.DataFrame - """ - - def __init__( - self, - x: Optional[str] = None, - y: str | list[str] = None, - label: Optional[str] = None, - optional: bool = False, - ): - """ - Parameters: - x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series. - y (Union[str, List[str]]): Column name of y series, or list of column names if multiple series. 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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 pathlib import Path -from typing import ( - TYPE_CHECKING, - Any, - AsyncIterable, - BinaryIO, - ContextManager, - Dict, - Generator, - Iterable, - List, - 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 ..utils._typing import Literal -from ._text_generation import ( - TextGenerationStreamResponse, -) - - -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] - -logger = logging.getLogger(__name__) - - -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_dict(content: bytes) -> "Image": - """Parse bytes from a Response object into a Python dictionary. - - Expects the response body to be encoded-JSON data. - """ - 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")) - # Parse 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")) - # Parse 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/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/main.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/main.py deleted file mode 100644 index d5722a3ef0161f4f269c8e1beec2bb5d18ebe69e..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/main.py +++ /dev/null @@ -1,401 +0,0 @@ -""" -@Date: 2021/07/17 -@description: -""" -import sys -import os -import shutil -import argparse -import numpy as np -import json -import torch -import torch.nn.parallel -import torch.optim -import torch.multiprocessing as mp -import torch.utils.data -import torch.utils.data.distributed -import torch.cuda - -from PIL import Image -from tqdm import tqdm -from torch.utils.tensorboard import SummaryWriter -from config.defaults import get_config, get_rank_config -from models.other.criterion import calc_criterion -from models.build import build_model -from models.other.init_env import init_env -from utils.logger import build_logger -from utils.misc import tensor2np_d, tensor2np -from dataset.build import build_loader -from evaluation.accuracy import calc_accuracy, show_heat_map, calc_ce, calc_pe, calc_rmse_delta_1, \ - show_depth_normal_grad, calc_f1_score -from postprocessing.post_process import post_process - -try: - from apex import amp -except ImportError: - amp = None - - -def parse_option(): - debug = True if sys.gettrace() else False - parser = argparse.ArgumentParser(description='Panorama Layout Transformer training and evaluation script') - parser.add_argument('--cfg', - type=str, - metavar='FILE', - help='path to config file') - - parser.add_argument('--mode', - type=str, - default='train', - choices=['train', 'val', 'test'], - help='train/val/test mode') - - parser.add_argument('--val_name', - type=str, - choices=['val', 'test'], - help='val name') - - parser.add_argument('--bs', type=int, - help='batch size') - - parser.add_argument('--save_eval', action='store_true', - help='save eval result') - - parser.add_argument('--post_processing', type=str, - choices=['manhattan', 'atalanta', 'manhattan_old'], - help='type of postprocessing ') - - parser.add_argument('--need_cpe', action='store_true', - help='need to evaluate corner error and pixel error') - - parser.add_argument('--need_f1', action='store_true', - help='need to evaluate f1-score of corners') - - parser.add_argument('--need_rmse', action='store_true', - help='need to evaluate root mean squared error and delta error') - - parser.add_argument('--force_cube', action='store_true', - help='force cube shape when eval') - - parser.add_argument('--wall_num', type=int, - help='wall number') - - args = parser.parse_args() - args.debug = debug - print("arguments:") - for arg in vars(args): - print(arg, ":", getattr(args, arg)) - print("-" * 50) - return args - - -def main(): - args = parse_option() - config = get_config(args) - - if config.TRAIN.SCRATCH and os.path.exists(config.CKPT.DIR) and config.MODE == 'train': - print(f"Train from scratch, delete checkpoint dir: {config.CKPT.DIR}") - f = [int(f.split('_')[-1].split('.')[0]) for f in os.listdir(config.CKPT.DIR) if 'pkl' in f] - if len(f) > 0: - last_epoch = np.array(f).max() - if last_epoch > 10: - c = input(f"delete it (last_epoch: {last_epoch})?(Y/N)\n") - if c != 'y' and c != 'Y': - exit(0) - - shutil.rmtree(config.CKPT.DIR, ignore_errors=True) - - os.makedirs(config.CKPT.DIR, exist_ok=True) - os.makedirs(config.CKPT.RESULT_DIR, exist_ok=True) - os.makedirs(config.LOGGER.DIR, exist_ok=True) - - if ':' in config.TRAIN.DEVICE: - nprocs = len(config.TRAIN.DEVICE.split(':')[-1].split(',')) - if 'cuda' in config.TRAIN.DEVICE: - if not torch.cuda.is_available(): - print(f"Cuda is not available(config is: {config.TRAIN.DEVICE}), will use cpu ...") - config.defrost() - config.TRAIN.DEVICE = "cpu" - config.freeze() - nprocs = 1 - - if config.MODE == 'train': - with open(os.path.join(config.CKPT.DIR, "config.yaml"), "w") as f: - f.write(config.dump(allow_unicode=True)) - - if config.TRAIN.DEVICE == 'cpu' or nprocs < 2: - print(f"Use single process, device:{config.TRAIN.DEVICE}") - main_worker(0, config, 1) - else: - print(f"Use {nprocs} processes ...") - mp.spawn(main_worker, nprocs=nprocs, args=(config, nprocs), join=True) - - -def main_worker(local_rank, cfg, world_size): - config = get_rank_config(cfg, local_rank, world_size) - logger = build_logger(config) - writer = SummaryWriter(config.CKPT.DIR) - logger.info(f"Comment: {config.COMMENT}") - cur_pid = os.getpid() - logger.info(f"Current process id: {cur_pid}") - torch.hub._hub_dir = config.CKPT.PYTORCH - logger.info(f"Pytorch hub dir: {torch.hub._hub_dir}") - init_env(config.SEED, config.TRAIN.DETERMINISTIC, config.DATA.NUM_WORKERS) - - model, optimizer, criterion, scheduler = build_model(config, logger) - train_data_loader, val_data_loader = build_loader(config, logger) - - if 'cuda' in config.TRAIN.DEVICE: - torch.cuda.set_device(config.TRAIN.DEVICE) - - if config.MODE == 'train': - train(model, train_data_loader, val_data_loader, optimizer, criterion, config, logger, writer, scheduler) - else: - iou_results, other_results = val_an_epoch(model, val_data_loader, - criterion, config, logger, writer=None, - epoch=config.TRAIN.START_EPOCH) - results = dict(iou_results, **other_results) - if config.SAVE_EVAL: - save_path = os.path.join(config.CKPT.RESULT_DIR, f"result.json") - with open(save_path, 'w+') as f: - json.dump(results, f, indent=4) - - -def save(model, optimizer, epoch, iou_d, logger, writer, config): - model.save(optimizer, epoch, accuracy=iou_d['full_3d'], logger=logger, acc_d=iou_d, config=config) - for k in model.acc_d: - writer.add_scalar(f"BestACC/{k}", model.acc_d[k]['acc'], epoch) - - -def train(model, train_data_loader, val_data_loader, optimizer, criterion, config, logger, writer, scheduler): - for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS): - logger.info("=" * 200) - train_an_epoch(model, train_data_loader, optimizer, criterion, config, logger, writer, epoch) - epoch_iou_d, _ = val_an_epoch(model, val_data_loader, criterion, config, logger, writer, epoch) - - if config.LOCAL_RANK == 0: - ddp = config.WORLD_SIZE > 1 - save(model.module if ddp else model, optimizer, epoch, epoch_iou_d, logger, writer, config) - - if scheduler is not None: - if scheduler.min_lr is not None and optimizer.param_groups[0]['lr'] <= scheduler.min_lr: - continue - scheduler.step() - writer.close() - - -def train_an_epoch(model, train_data_loader, optimizer, criterion, config, logger, writer, epoch=0): - logger.info(f'Start Train Epoch {epoch}/{config.TRAIN.EPOCHS - 1}') - model.train() - - if len(config.MODEL.FINE_TUNE) > 0: - model.feature_extractor.eval() - - optimizer.zero_grad() - - data_len = len(train_data_loader) - start_i = data_len * epoch * config.WORLD_SIZE - bar = enumerate(train_data_loader) - if config.LOCAL_RANK == 0 and config.SHOW_BAR: - bar = tqdm(bar, total=data_len, ncols=200) - - device = config.TRAIN.DEVICE - epoch_loss_d = {} - for i, gt in bar: - imgs = gt['image'].to(device, non_blocking=True) - gt['depth'] = gt['depth'].to(device, non_blocking=True) - gt['ratio'] = gt['ratio'].to(device, non_blocking=True) - if 'corner_heat_map' in gt: - gt['corner_heat_map'] = gt['corner_heat_map'].to(device, non_blocking=True) - if config.AMP_OPT_LEVEL != "O0" and 'cuda' in device: - imgs = imgs.type(torch.float16) - gt['depth'] = gt['depth'].type(torch.float16) - gt['ratio'] = gt['ratio'].type(torch.float16) - dt = model(imgs) - loss, batch_loss_d, epoch_loss_d = calc_criterion(criterion, gt, dt, epoch_loss_d) - if config.LOCAL_RANK == 0 and config.SHOW_BAR: - bar.set_postfix(batch_loss_d) - - optimizer.zero_grad() - if config.AMP_OPT_LEVEL != "O0" and 'cuda' in device: - with amp.scale_loss(loss, optimizer) as scaled_loss: - scaled_loss.backward() - else: - loss.backward() - optimizer.step() - - global_step = start_i + i * config.WORLD_SIZE + config.LOCAL_RANK - for key, val in batch_loss_d.items(): - writer.add_scalar(f'TrainBatchLoss/{key}', val, global_step) - - if config.LOCAL_RANK != 0: - return - - epoch_loss_d = dict(zip(epoch_loss_d.keys(), [np.array(epoch_loss_d[k]).mean() for k in epoch_loss_d.keys()])) - s = 'TrainEpochLoss: ' - for key, val in epoch_loss_d.items(): - writer.add_scalar(f'TrainEpochLoss/{key}', val, epoch) - s += f" {key}={val}" - logger.info(s) - writer.add_scalar('LearningRate', optimizer.param_groups[0]['lr'], epoch) - logger.info(f"LearningRate: {optimizer.param_groups[0]['lr']}") - - -@torch.no_grad() -def val_an_epoch(model, val_data_loader, criterion, config, logger, writer, epoch=0): - model.eval() - logger.info(f'Start Validate Epoch {epoch}/{config.TRAIN.EPOCHS - 1}') - data_len = len(val_data_loader) - start_i = data_len * epoch * config.WORLD_SIZE - bar = enumerate(val_data_loader) - if config.LOCAL_RANK == 0 and config.SHOW_BAR: - bar = tqdm(bar, total=data_len, ncols=200) - device = config.TRAIN.DEVICE - epoch_loss_d = {} - epoch_iou_d = { - 'visible_2d': [], - 'visible_3d': [], - 'full_2d': [], - 'full_3d': [], - 'height': [] - } - - epoch_other_d = { - 'ce': [], - 'pe': [], - 'f1': [], - 'precision': [], - 'recall': [], - 'rmse': [], - 'delta_1': [] - } - - show_index = np.random.randint(0, data_len) - for i, gt in bar: - imgs = gt['image'].to(device, non_blocking=True) - gt['depth'] = gt['depth'].to(device, non_blocking=True) - gt['ratio'] = gt['ratio'].to(device, non_blocking=True) - if 'corner_heat_map' in gt: - gt['corner_heat_map'] = gt['corner_heat_map'].to(device, non_blocking=True) - dt = model(imgs) - - vis_w = config.TRAIN.VIS_WEIGHT - visualization = False # (config.LOCAL_RANK == 0 and i == show_index) or config.SAVE_EVAL - - loss, batch_loss_d, epoch_loss_d = calc_criterion(criterion, gt, dt, epoch_loss_d) - - if config.EVAL.POST_PROCESSING is not None: - depth = tensor2np(dt['depth']) - dt['processed_xyz'] = post_process(depth, type_name=config.EVAL.POST_PROCESSING, - need_cube=config.EVAL.FORCE_CUBE) - - if config.EVAL.FORCE_CUBE and config.EVAL.NEED_CPE: - ce = calc_ce(tensor2np_d(dt), tensor2np_d(gt)) - pe = calc_pe(tensor2np_d(dt), tensor2np_d(gt)) - - epoch_other_d['ce'].append(ce) - epoch_other_d['pe'].append(pe) - - if config.EVAL.NEED_F1: - f1, precision, recall = calc_f1_score(tensor2np_d(dt), tensor2np_d(gt)) - epoch_other_d['f1'].append(f1) - epoch_other_d['precision'].append(precision) - epoch_other_d['recall'].append(recall) - - if config.EVAL.NEED_RMSE: - rmse, delta_1 = calc_rmse_delta_1(tensor2np_d(dt), tensor2np_d(gt)) - epoch_other_d['rmse'].append(rmse) - epoch_other_d['delta_1'].append(delta_1) - - visb_iou, full_iou, iou_height, pano_bds, full_iou_2ds = calc_accuracy(tensor2np_d(dt), tensor2np_d(gt), - visualization, h=vis_w // 2) - epoch_iou_d['visible_2d'].append(visb_iou[0]) - epoch_iou_d['visible_3d'].append(visb_iou[1]) - epoch_iou_d['full_2d'].append(full_iou[0]) - epoch_iou_d['full_3d'].append(full_iou[1]) - epoch_iou_d['height'].append(iou_height) - - if config.LOCAL_RANK == 0 and config.SHOW_BAR: - bar.set_postfix(batch_loss_d) - - global_step = start_i + i * config.WORLD_SIZE + config.LOCAL_RANK - - if writer: - for key, val in batch_loss_d.items(): - writer.add_scalar(f'ValBatchLoss/{key}', val, global_step) - - if not visualization: - continue - - gt_grad_imgs, dt_grad_imgs = show_depth_normal_grad(dt, gt, device, vis_w) - - dt_heat_map_imgs = None - gt_heat_map_imgs = None - if 'corner_heat_map' in gt: - dt_heat_map_imgs, gt_heat_map_imgs = show_heat_map(dt, gt, vis_w) - - if config.TRAIN.VIS_MERGE or config.SAVE_EVAL: - imgs = [] - for j in range(len(pano_bds)): - # floorplan = np.concatenate([visb_iou[2][j], full_iou[2][j]], axis=-1) - floorplan = full_iou[2][j] - margin_w = int(floorplan.shape[-1] * (60/512)) - floorplan = floorplan[:, :, margin_w:-margin_w] - - grad_h = dt_grad_imgs[0].shape[1] - vis_merge = [ - gt_grad_imgs[j], - pano_bds[j][:, grad_h:-grad_h], - dt_grad_imgs[j] - ] - if 'corner_heat_map' in gt: - vis_merge = [dt_heat_map_imgs[j], gt_heat_map_imgs[j]] + vis_merge - img = np.concatenate(vis_merge, axis=-2) - - img = np.concatenate([img, ], axis=-1) - # img = gt_grad_imgs[j] - imgs.append(img) - if writer: - writer.add_images('VIS/Merge', np.array(imgs), global_step) - - if config.SAVE_EVAL: - for k in range(len(imgs)): - img = imgs[k] * 255.0 - save_path = os.path.join(config.CKPT.RESULT_DIR, f"{gt['id'][k]}_{full_iou_2ds[k]:.5f}.png") - Image.fromarray(img.transpose(1, 2, 0).astype(np.uint8)).save(save_path) - - elif writer: - writer.add_images('IoU/Visible_Floorplan', visb_iou[2], global_step) - writer.add_images('IoU/Full_Floorplan', full_iou[2], global_step) - writer.add_images('IoU/Boundary', pano_bds, global_step) - writer.add_images('Grad/gt', gt_grad_imgs, global_step) - writer.add_images('Grad/dt', dt_grad_imgs, global_step) - - if config.LOCAL_RANK != 0: - return - - epoch_loss_d = dict(zip(epoch_loss_d.keys(), [np.array(epoch_loss_d[k]).mean() for k in epoch_loss_d.keys()])) - s = 'ValEpochLoss: ' - for key, val in epoch_loss_d.items(): - if writer: - writer.add_scalar(f'ValEpochLoss/{key}', val, epoch) - s += f" {key}={val}" - logger.info(s) - - epoch_iou_d = dict(zip(epoch_iou_d.keys(), [np.array(epoch_iou_d[k]).mean() for k in epoch_iou_d.keys()])) - s = 'ValEpochIoU: ' - for key, val in epoch_iou_d.items(): - if writer: - writer.add_scalar(f'ValEpochIoU/{key}', val, epoch) - s += f" {key}={val}" - logger.info(s) - epoch_other_d = dict(zip(epoch_other_d.keys(), - [np.array(epoch_other_d[k]).mean() if len(epoch_other_d[k]) > 0 else 0 for k in - epoch_other_d.keys()])) - - logger.info(f'other acc: {epoch_other_d}') - return epoch_iou_d, epoch_other_d - - -if __name__ == '__main__': - main() diff --git a/spaces/Datasculptor/sd-prism/README.md b/spaces/Datasculptor/sd-prism/README.md deleted file mode 100644 index c17df1bc1cf3adcd858c8b34b73e3560ca282529..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/sd-prism/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Stable Diffusion Prism -emoji: 🎆 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: pharma/sd-prism ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Dave37/voicebot/app.py b/spaces/Dave37/voicebot/app.py deleted file mode 100644 index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000 --- a/spaces/Dave37/voicebot/app.py +++ /dev/null @@ -1,164 +0,0 @@ -import os -import re -import requests -import json -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') -PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY') -PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID') - -PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID') -play_ht_api_get_audio_url = "https://play.ht/api/v2/tts" - - -template = """You are a helpful assistant to answer user queries. -{chat_history} -User: {user_message} -Chatbot:""" - -prompt = PromptTemplate( - input_variables=["chat_history", "user_message"], template=template -) - -memory = ConversationBufferMemory(memory_key="chat_history") - -llm_chain = LLMChain( - llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"), - prompt=prompt, - verbose=True, - memory=memory, -) - -headers = { - "accept": "text/event-stream", - "content-type": "application/json", - "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY, - "X-USER-ID": PLAY_HT_USER_ID -} - - -def get_payload(text): - return { - "text": text, - "voice": PLAY_HT_VOICE_ID, - "quality": "medium", - "output_format": "mp3", - "speed": 1, - "sample_rate": 24000, - "seed": None, - "temperature": None - } - -def get_generated_audio(text): - payload = get_payload(text) - generated_response = {} - try: - response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers) - response.raise_for_status() - generated_response["type"]= 'SUCCESS' - generated_response["response"] = response.text - except requests.exceptions.RequestException as e: - generated_response["type"]= 'ERROR' - try: - response_text = json.loads(response.text) - if response_text['error_message']: - generated_response["response"] = response_text['error_message'] - else: - generated_response["response"] = response.text - except Exception as e: - generated_response["response"] = response.text - except Exception as e: - generated_response["type"]= 'ERROR' - generated_response["response"] = response.text - return generated_response - -def extract_urls(text): - # Define the regex pattern for URLs - url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*' - - # Find all occurrences of URLs in the text - urls = re.findall(url_pattern, text) - - return urls - -def get_audio_reply_for_question(text): - generated_audio_event = get_generated_audio(text) - #From get_generated_audio, you will get events in a string format, from that we need to extract the url - final_response = { - "audio_url": '', - "message": '' - } - if generated_audio_event["type"] == 'SUCCESS': - audio_urls = extract_urls(generated_audio_event["response"]) - if len(audio_urls) == 0: - final_response['message'] = "No audio file link found in generated event" - else: - final_response['audio_url'] = audio_urls[-1] - else: - final_response['message'] = generated_audio_event['response'] - return final_response - -def download_url(url): - try: - # Send a GET request to the URL to fetch the content - final_response = { - 'content':'', - 'error':'' - } - response = requests.get(url) - # Check if the request was successful (status code 200) - if response.status_code == 200: - final_response['content'] = response.content - else: - final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}" - except Exception as e: - final_response['error'] = f"Failed to download the URL. Error: {e}" - return final_response - -def get_filename_from_url(url): - # Use os.path.basename() to extract the file name from the URL - file_name = os.path.basename(url) - return file_name - -def get_text_response(user_message): - response = llm_chain.predict(user_message = user_message) - return response - -def get_text_response_and_audio_response(user_message): - response = get_text_response(user_message) # Getting the reply from Open AI - audio_reply_for_question_response = get_audio_reply_for_question(response) - final_response = { - 'output_file_path': '', - 'message':'' - } - audio_url = audio_reply_for_question_response['audio_url'] - if audio_url: - output_file_path=get_filename_from_url(audio_url) - download_url_response = download_url(audio_url) - audio_content = download_url_response['content'] - if audio_content: - with open(output_file_path, "wb") as audio_file: - audio_file.write(audio_content) - final_response['output_file_path'] = output_file_path - else: - final_response['message'] = download_url_response['error'] - else: - final_response['message'] = audio_reply_for_question_response['message'] - return final_response - -def chat_bot_response(message, history): - text_and_audio_response = get_text_response_and_audio_response(message) - output_file_path = text_and_audio_response['output_file_path'] - if output_file_path: - return (text_and_audio_response['output_file_path'],) - else: - return text_and_audio_response['message'] - -demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"]) - -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/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/README.md b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/README.md deleted file mode 100644 index a86a64a60a14ccea6dc3c0a0048a243750fe98fe..0000000000000000000000000000000000000000 --- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/README.md +++ /dev/null @@ -1,232 +0,0 @@ -## StyleGAN — Official TensorFlow Implementation -![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg?style=plastic) -![TensorFlow 1.10](https://img.shields.io/badge/tensorflow-1.10-green.svg?style=plastic) -![cuDNN 7.3.1](https://img.shields.io/badge/cudnn-7.3.1-green.svg?style=plastic) -![License CC BY-NC](https://img.shields.io/badge/license-CC_BY--NC-green.svg?style=plastic) - -![Teaser image](./stylegan-teaser.png) -**Picture:** *These people are not real – they were produced by our generator that allows control over different aspects of the image.* - -This repository contains the official TensorFlow implementation of the following paper: - -> **A Style-Based Generator Architecture for Generative Adversarial Networks**
    -> Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)
    -> https://arxiv.org/abs/1812.04948 -> -> **Abstract:** *We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.* - -For business inquiries, please contact [researchinquiries@nvidia.com](mailto:researchinquiries@nvidia.com)
    -For press and other inquiries, please contact Hector Marinez at [hmarinez@nvidia.com](mailto:hmarinez@nvidia.com)
    - -**★★★ NEW: StyleGAN2 is available at [https://github.com/NVlabs/stylegan2](https://github.com/NVlabs/stylegan2) ★★★** - -## Resources - -Material related to our paper is available via the following links: - -- Paper: https://arxiv.org/abs/1812.04948 -- Video: https://youtu.be/kSLJriaOumA -- Code: https://github.com/NVlabs/stylegan -- FFHQ: https://github.com/NVlabs/ffhq-dataset - -Additional material can be found on Google Drive: - -| Path | Description -| :--- | :---------- -| [StyleGAN](https://drive.google.com/open?id=1uka3a1noXHAydRPRbknqwKVGODvnmUBX) | Main folder. -| ├  [stylegan-paper.pdf](https://drive.google.com/open?id=1v-HkF3Ehrpon7wVIx4r5DLcko_U_V6Lt) | High-quality version of the paper PDF. -| ├  [stylegan-video.mp4](https://drive.google.com/open?id=1uzwkZHQX_9pYg1i0d1Nbe3D9xPO8-qBf) | High-quality version of the result video. -| ├  [images](https://drive.google.com/open?id=1-l46akONUWF6LCpDoeq63H53rD7MeiTd) | Example images produced using our generator. -| │  ├  [representative-images](https://drive.google.com/open?id=1ToY5P4Vvf5_c3TyUizQ8fckFFoFtBvD8) | High-quality images to be used in articles, blog posts, etc. -| │  └  [100k-generated-images](https://drive.google.com/open?id=100DJ0QXyG89HZzB4w2Cbyf4xjNK54cQ1) | 100,000 generated images for different amounts of truncation. -| │     ├  [ffhq-1024x1024](https://drive.google.com/open?id=14lm8VRN1pr4g_KVe6_LvyDX1PObst6d4) | Generated using Flickr-Faces-HQ dataset at 1024×1024. -| │     ├  [bedrooms-256x256](https://drive.google.com/open?id=1Vxz9fksw4kgjiHrvHkX4Hze4dyThFW6t) | Generated using LSUN Bedroom dataset at 256×256. -| │     ├  [cars-512x384](https://drive.google.com/open?id=1MFCvOMdLE2_mpeLPTiDw5dxc2CRuKkzS) | Generated using LSUN Car dataset at 512×384. -| │     └  [cats-256x256](https://drive.google.com/open?id=1gq-Gj3GRFiyghTPKhp8uDMA9HV_0ZFWQ) | Generated using LSUN Cat dataset at 256×256. -| ├  [videos](https://drive.google.com/open?id=1N8pOd_Bf8v89NGUaROdbD8-ayLPgyRRo) | Example videos produced using our generator. -| │  └  [high-quality-video-clips](https://drive.google.com/open?id=1NFO7_vH0t98J13ckJYFd7kuaTkyeRJ86) | Individual segments of the result video as high-quality MP4. -| ├  [ffhq-dataset](https://drive.google.com/open?id=1u2xu7bSrWxrbUxk-dT-UvEJq8IjdmNTP) | Raw data for the [Flickr-Faces-HQ dataset](https://github.com/NVlabs/ffhq-dataset). -| └  [networks](https://drive.google.com/open?id=1MASQyN5m0voPcx7-9K0r5gObhvvPups7) | Pre-trained networks as pickled instances of [dnnlib.tflib.Network](./dnnlib/tflib/network.py). -|    ├  [stylegan-ffhq-1024x1024.pkl](https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ) | StyleGAN trained with Flickr-Faces-HQ dataset at 1024×1024. -|    ├  [stylegan-celebahq-1024x1024.pkl](https://drive.google.com/uc?id=1MGqJl28pN4t7SAtSrPdSRJSQJqahkzUf) | StyleGAN trained with CelebA-HQ dataset at 1024×1024. -|    ├  [stylegan-bedrooms-256x256.pkl](https://drive.google.com/uc?id=1MOSKeGF0FJcivpBI7s63V9YHloUTORiF) | StyleGAN trained with LSUN Bedroom dataset at 256×256. -|    ├  [stylegan-cars-512x384.pkl](https://drive.google.com/uc?id=1MJ6iCfNtMIRicihwRorsM3b7mmtmK9c3) | StyleGAN trained with LSUN Car dataset at 512×384. -|    ├  [stylegan-cats-256x256.pkl](https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ) | StyleGAN trained with LSUN Cat dataset at 256×256. -|    └  [metrics](https://drive.google.com/open?id=1MvYdWCBuMfnoYGptRH-AgKLbPTsIQLhl) | Auxiliary networks for the quality and disentanglement metrics. -|       ├  [inception_v3_features.pkl](https://drive.google.com/uc?id=1MzTY44rLToO5APn8TZmfR7_ENSe5aZUn) | Standard [Inception-v3](https://arxiv.org/abs/1512.00567) classifier that outputs a raw feature vector. -|       ├  [vgg16_zhang_perceptual.pkl](https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2) | Standard [LPIPS](https://arxiv.org/abs/1801.03924) metric to estimate perceptual similarity. -|       ├  [celebahq-classifier-00-male.pkl](https://drive.google.com/uc?id=1Q5-AI6TwWhCVM7Muu4tBM7rp5nG_gmCX) | Binary classifier trained to detect a single attribute of CelebA-HQ. -|       └ ⋯ | Please see the file listing for remaining networks. - -## Licenses - -All material, excluding the Flickr-Faces-HQ dataset, is made available under [Creative Commons BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license by NVIDIA Corporation. You can **use, redistribute, and adapt** the material for **non-commercial purposes**, as long as you give appropriate credit by **citing our paper** and **indicating any changes** that you've made. - -For license information regarding the FFHQ dataset, please refer to the [Flickr-Faces-HQ repository](https://github.com/NVlabs/ffhq-dataset). - -`inception_v3_features.pkl` and `inception_v3_softmax.pkl` are derived from the pre-trained [Inception-v3](https://arxiv.org/abs/1512.00567) network by Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. The network was originally shared under [Apache 2.0](https://github.com/tensorflow/models/blob/master/LICENSE) license on the [TensorFlow Models](https://github.com/tensorflow/models) repository. - -`vgg16.pkl` and `vgg16_zhang_perceptual.pkl` are derived from the pre-trained [VGG-16](https://arxiv.org/abs/1409.1556) network by Karen Simonyan and Andrew Zisserman. The network was originally shared under [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/) license on the [Very Deep Convolutional Networks for Large-Scale Visual Recognition](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) project page. - -`vgg16_zhang_perceptual.pkl` is further derived from the pre-trained [LPIPS](https://arxiv.org/abs/1801.03924) weights by Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The weights were originally shared under [BSD 2-Clause "Simplified" License](https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE) on the [PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity) repository. - -## System requirements - -* Both Linux and Windows are supported, but we strongly recommend Linux for performance and compatibility reasons. -* 64-bit Python 3.6 installation. We recommend Anaconda3 with numpy 1.14.3 or newer. -* TensorFlow 1.10.0 or newer with GPU support. -* One or more high-end NVIDIA GPUs with at least 11GB of DRAM. We recommend NVIDIA DGX-1 with 8 Tesla V100 GPUs. -* NVIDIA driver 391.35 or newer, CUDA toolkit 9.0 or newer, cuDNN 7.3.1 or newer. - -## Using pre-trained networks - -A minimal example of using a pre-trained StyleGAN generator is given in [pretrained_example.py](./pretrained_example.py). When executed, the script downloads a pre-trained StyleGAN generator from Google Drive and uses it to generate an image: - -``` -> python pretrained_example.py -Downloading https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ .... done - -Gs Params OutputShape WeightShape ---- --- --- --- -latents_in - (?, 512) - -... -images_out - (?, 3, 1024, 1024) - ---- --- --- --- -Total 26219627 - -> ls results -example.png # https://drive.google.com/uc?id=1UDLT_zb-rof9kKH0GwiJW_bS9MoZi8oP -``` - -A more advanced example is given in [generate_figures.py](./generate_figures.py). The script reproduces the figures from our paper in order to illustrate style mixing, noise inputs, and truncation: -``` -> python generate_figures.py -results/figure02-uncurated-ffhq.png # https://drive.google.com/uc?id=1U3r1xgcD7o-Fd0SBRpq8PXYajm7_30cu -results/figure03-style-mixing.png # https://drive.google.com/uc?id=1U-nlMDtpnf1RcYkaFQtbh5oxnhA97hy6 -results/figure04-noise-detail.png # https://drive.google.com/uc?id=1UX3m39u_DTU6eLnEW6MqGzbwPFt2R9cG -results/figure05-noise-components.png # https://drive.google.com/uc?id=1UQKPcvYVeWMRccGMbs2pPD9PVv1QDyp_ -results/figure08-truncation-trick.png # https://drive.google.com/uc?id=1ULea0C12zGlxdDQFNLXOWZCHi3QNfk_v -results/figure10-uncurated-bedrooms.png # https://drive.google.com/uc?id=1UEBnms1XMfj78OHj3_cx80mUf_m9DUJr -results/figure11-uncurated-cars.png # https://drive.google.com/uc?id=1UO-4JtAs64Kun5vIj10UXqAJ1d5Ir1Ke -results/figure12-uncurated-cats.png # https://drive.google.com/uc?id=1USnJc14prlu3QAYxstrtlfXC9sDWPA-W -``` - -The pre-trained networks are stored as standard pickle files on Google Drive: - -``` -# Load pre-trained network. -url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl -with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f: - _G, _D, Gs = pickle.load(f) - # _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run. - # _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run. - # Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot. -``` - -The above code downloads the file and unpickles it to yield 3 instances of [dnnlib.tflib.Network](./dnnlib/tflib/network.py). To generate images, you will typically want to use `Gs` – the other two networks are provided for completeness. In order for `pickle.load()` to work, you will need to have the `dnnlib` source directory in your PYTHONPATH and a `tf.Session` set as default. The session can initialized by calling `dnnlib.tflib.init_tf()`. - -There are three ways to use the pre-trained generator: - -1. Use `Gs.run()` for immediate-mode operation where the inputs and outputs are numpy arrays: - ``` - # Pick latent vector. - rnd = np.random.RandomState(5) - latents = rnd.randn(1, Gs.input_shape[1]) - - # Generate image. - fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) - images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt) - ``` - The first argument is a batch of latent vectors of shape `[num, 512]`. The second argument is reserved for class labels (not used by StyleGAN). The remaining keyword arguments are optional and can be used to further modify the operation (see below). The output is a batch of images, whose format is dictated by the `output_transform` argument. - -2. Use `Gs.get_output_for()` to incorporate the generator as a part of a larger TensorFlow expression: - ``` - latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:]) - images = Gs_clone.get_output_for(latents, None, is_validation=True, randomize_noise=True) - images = tflib.convert_images_to_uint8(images) - result_expr.append(inception_clone.get_output_for(images)) - ``` - The above code is from [metrics/frechet_inception_distance.py](./metrics/frechet_inception_distance.py). It generates a batch of random images and feeds them directly to the [Inception-v3](https://arxiv.org/abs/1512.00567) network without having to convert the data to numpy arrays in between. - -3. Look up `Gs.components.mapping` and `Gs.components.synthesis` to access individual sub-networks of the generator. Similar to `Gs`, the sub-networks are represented as independent instances of [dnnlib.tflib.Network](./dnnlib/tflib/network.py): - ``` - src_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in src_seeds) - src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component] - src_images = Gs.components.synthesis.run(src_dlatents, randomize_noise=False, **synthesis_kwargs) - ``` - The above code is from [generate_figures.py](./generate_figures.py). It first transforms a batch of latent vectors into the intermediate *W* space using the mapping network and then turns these vectors into a batch of images using the synthesis network. The `dlatents` array stores a separate copy of the same *w* vector for each layer of the synthesis network to facilitate style mixing. - -The exact details of the generator are defined in [training/networks_stylegan.py](./training/networks_stylegan.py) (see `G_style`, `G_mapping`, and `G_synthesis`). The following keyword arguments can be specified to modify the behavior when calling `run()` and `get_output_for()`: - -* `truncation_psi` and `truncation_cutoff` control the truncation trick that that is performed by default when using `Gs` (ψ=0.7, cutoff=8). It can be disabled by setting `truncation_psi=1` or `is_validation=True`, and the image quality can be further improved at the cost of variation by setting e.g. `truncation_psi=0.5`. Note that truncation is always disabled when using the sub-networks directly. The average *w* needed to manually perform the truncation trick can be looked up using `Gs.get_var('dlatent_avg')`. - -* `randomize_noise` determines whether to use re-randomize the noise inputs for each generated image (`True`, default) or whether to use specific noise values for the entire minibatch (`False`). The specific values can be accessed via the `tf.Variable` instances that are found using `[var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]`. - -* When using the mapping network directly, you can specify `dlatent_broadcast=None` to disable the automatic duplication of `dlatents` over the layers of the synthesis network. - -* Runtime performance can be fine-tuned via `structure='fixed'` and `dtype='float16'`. The former disables support for progressive growing, which is not needed for a fully-trained generator, and the latter performs all computation using half-precision floating point arithmetic. - -## Preparing datasets for training - -The training and evaluation scripts operate on datasets stored as multi-resolution TFRecords. Each dataset is represented by a directory containing the same image data in several resolutions to enable efficient streaming. There is a separate *.tfrecords file for each resolution, and if the dataset contains labels, they are stored in a separate file as well. By default, the scripts expect to find the datasets at `datasets//-.tfrecords`. The directory can be changed by editing [config.py](./config.py): - -``` -result_dir = 'results' -data_dir = 'datasets' -cache_dir = 'cache' -``` - -To obtain the FFHQ dataset (`datasets/ffhq`), please refer to the [Flickr-Faces-HQ repository](https://github.com/NVlabs/ffhq-dataset). - -To obtain the CelebA-HQ dataset (`datasets/celebahq`), please refer to the [Progressive GAN repository](https://github.com/tkarras/progressive_growing_of_gans). - -To obtain other datasets, including LSUN, please consult their corresponding project pages. The datasets can be converted to multi-resolution TFRecords using the provided [dataset_tool.py](./dataset_tool.py): - -``` -> python dataset_tool.py create_lsun datasets/lsun-bedroom-full ~/lsun/bedroom_lmdb --resolution 256 -> python dataset_tool.py create_lsun_wide datasets/lsun-car-512x384 ~/lsun/car_lmdb --width 512 --height 384 -> python dataset_tool.py create_lsun datasets/lsun-cat-full ~/lsun/cat_lmdb --resolution 256 -> python dataset_tool.py create_cifar10 datasets/cifar10 ~/cifar10 -> python dataset_tool.py create_from_images datasets/custom-dataset ~/custom-images -``` - -## Training networks - -Once the datasets are set up, you can train your own StyleGAN networks as follows: - -1. Edit [train.py](./train.py) to specify the dataset and training configuration by uncommenting or editing specific lines. -2. Run the training script with `python train.py`. -3. The results are written to a newly created directory `results/-`. -4. The training may take several days (or weeks) to complete, depending on the configuration. - -By default, `train.py` is configured to train the highest-quality StyleGAN (configuration F in Table 1) for the FFHQ dataset at 1024×1024 resolution using 8 GPUs. Please note that we have used 8 GPUs in all of our experiments. Training with fewer GPUs may not produce identical results – if you wish to compare against our technique, we strongly recommend using the same number of GPUs. - -Expected training times for the default configuration using Tesla V100 GPUs: - -| GPUs | 1024×1024 | 512×512 | 256×256 | -| :--- | :-------------- | :------------ | :------------ | -| 1 | 41 days 4 hours | 24 days 21 hours | 14 days 22 hours | -| 2 | 21 days 22 hours | 13 days 7 hours | 9 days 5 hours | -| 4 | 11 days 8 hours | 7 days 0 hours | 4 days 21 hours | -| 8 | 6 days 14 hours | 4 days 10 hours | 3 days 8 hours | - -## Evaluating quality and disentanglement - -The quality and disentanglement metrics used in our paper can be evaluated using [run_metrics.py](./run_metrics.py). By default, the script will evaluate the Fréchet Inception Distance (`fid50k`) for the pre-trained FFHQ generator and write the results into a newly created directory under `results`. The exact behavior can be changed by uncommenting or editing specific lines in [run_metrics.py](./run_metrics.py). - -Expected evaluation time and results for the pre-trained FFHQ generator using one Tesla V100 GPU: - -| Metric | Time | Result | Description -| :----- | :--- | :----- | :---------- -| fid50k | 16 min | 4.4159 | Fréchet Inception Distance using 50,000 images. -| ppl_zfull | 55 min | 664.8854 | Perceptual Path Length for full paths in *Z*. -| ppl_wfull | 55 min | 233.3059 | Perceptual Path Length for full paths in *W*. -| ppl_zend | 55 min | 666.1057 | Perceptual Path Length for path endpoints in *Z*. -| ppl_wend | 55 min | 197.2266 | Perceptual Path Length for path endpoints in *W*. -| ls | 10 hours | z: 165.0106
    w: 3.7447 | Linear Separability in *Z* and *W*. - -Please note that the exact results may vary from run to run due to the non-deterministic nature of TensorFlow. - -## Acknowledgements - -We thank Jaakko Lehtinen, David Luebke, and Tuomas Kynkäänniemi for in-depth discussions and helpful comments; Janne Hellsten, Tero Kuosmanen, and Pekka Jänis for compute infrastructure and help with the code release. diff --git a/spaces/ElainaFanBoy/MusicGen/tests/models/test_musicgen.py b/spaces/ElainaFanBoy/MusicGen/tests/models/test_musicgen.py deleted file mode 100644 index d43cf73763f6c690ab0b277227ac225b286fa143..0000000000000000000000000000000000000000 --- a/spaces/ElainaFanBoy/MusicGen/tests/models/test_musicgen.py +++ /dev/null @@ -1,58 +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 pytest -import torch - -from audiocraft.models import MusicGen - - -class TestSEANetModel: - def get_musicgen(self): - mg = MusicGen.get_pretrained(name='debug', device='cpu') - mg.set_generation_params(duration=2.0, extend_stride=2.) - return mg - - def test_base(self): - mg = self.get_musicgen() - assert mg.frame_rate == 25 - assert mg.sample_rate == 32000 - assert mg.audio_channels == 1 - - def test_generate_unconditional(self): - mg = self.get_musicgen() - wav = mg.generate_unconditional(3) - assert list(wav.shape) == [3, 1, 64000] - - def test_generate_continuation(self): - mg = self.get_musicgen() - prompt = torch.randn(3, 1, 32000) - wav = mg.generate_continuation(prompt, 32000) - assert list(wav.shape) == [3, 1, 64000] - - prompt = torch.randn(2, 1, 32000) - wav = mg.generate_continuation( - prompt, 32000, ['youpi', 'lapin dort']) - assert list(wav.shape) == [2, 1, 64000] - - prompt = torch.randn(2, 1, 32000) - with pytest.raises(AssertionError): - wav = mg.generate_continuation( - prompt, 32000, ['youpi', 'lapin dort', 'one too many']) - - def test_generate(self): - mg = self.get_musicgen() - wav = mg.generate( - ['youpi', 'lapin dort']) - assert list(wav.shape) == [2, 1, 64000] - - def test_generate_long(self): - mg = self.get_musicgen() - mg.max_duration = 3. - mg.set_generation_params(duration=4., extend_stride=2.) - wav = mg.generate( - ['youpi', 'lapin dort']) - assert list(wav.shape) == [2, 1, 32000 * 4] diff --git a/spaces/FantasticGNU/AnomalyGPT/utils/__init__.py b/spaces/FantasticGNU/AnomalyGPT/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Felix123456/bingo/src/components/ui/icons.tsx b/spaces/Felix123456/bingo/src/components/ui/icons.tsx deleted file mode 100644 index 742b489b50437c5b64c86082f2ebc712eeb6a2b0..0000000000000000000000000000000000000000 --- a/spaces/Felix123456/bingo/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/Fernando22/freegpt-webui/server/babel.py b/spaces/Fernando22/freegpt-webui/server/babel.py deleted file mode 100644 index 94407e4b4d3e82e7722cac409a7e311bb46c43be..0000000000000000000000000000000000000000 --- a/spaces/Fernando22/freegpt-webui/server/babel.py +++ /dev/null @@ -1,48 +0,0 @@ -import os -import subprocess -from flask import request, session, jsonify -from flask_babel import Babel - - -def get_languages_from_dir(directory): - """Return a list of directory names in the given directory.""" - return [name for name in os.listdir(directory) - if os.path.isdir(os.path.join(directory, name))] - - -BABEL_DEFAULT_LOCALE = 'en_US' -BABEL_LANGUAGES = get_languages_from_dir('translations') - - -def create_babel(app): - """Create and initialize a Babel instance with the given Flask app.""" - babel = Babel(app) - app.config['BABEL_DEFAULT_LOCALE'] = BABEL_DEFAULT_LOCALE - app.config['BABEL_LANGUAGES'] = BABEL_LANGUAGES - - babel.init_app(app, locale_selector=get_locale) - compile_translations() - - -def get_locale(): - """Get the user's locale from the session or the request's accepted languages.""" - return session.get('language') or request.accept_languages.best_match(BABEL_LANGUAGES) - - -def get_languages(): - """Return a list of available languages in JSON format.""" - return jsonify(BABEL_LANGUAGES) - - -def compile_translations(): - """Compile the translation files.""" - result = subprocess.run( - ['pybabel', 'compile', '-d', 'translations'], - stdout=subprocess.PIPE, - ) - - if result.returncode != 0: - raise Exception( - f'Compiling translations failed:\n{result.stdout.decode()}') - - print('Translations compiled successfully') diff --git a/spaces/Flux9665/ThisSpeakerDoesNotExist/README.md b/spaces/Flux9665/ThisSpeakerDoesNotExist/README.md deleted file mode 100644 index f58fa5a710de51e50819684d48649b5ca6affa76..0000000000000000000000000000000000000000 --- a/spaces/Flux9665/ThisSpeakerDoesNotExist/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: ThisSpeakerDoesNotExist -emoji: 🗣️🦜 -colorFrom: pink -colorTo: yellow -sdk: gradio -sdk_version: 3.19.1 -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/FoxMeo/fire-detector/models/common.py b/spaces/FoxMeo/fire-detector/models/common.py deleted file mode 100644 index edb5edc9fe1b0ad3b345a2103603393e74e5b65c..0000000000000000000000000000000000000000 --- a/spaces/FoxMeo/fire-detector/models/common.py +++ /dev/null @@ -1,2019 +0,0 @@ -import math -from copy import copy -from pathlib import Path - -import numpy as np -import pandas as pd -import requests -import torch -import torch.nn as nn -import torch.nn.functional as F -from torchvision.ops import DeformConv2d -from PIL import Image -from torch.cuda import amp - -from utils.datasets import letterbox -from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh -from utils.plots import color_list, plot_one_box -from utils.torch_utils import time_synchronized - - -##### basic #### - -def autopad(k, p=None): # kernel, padding - # Pad to 'same' - if p is None: - p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad - return p - - -class MP(nn.Module): - def __init__(self, k=2): - super(MP, self).__init__() - self.m = nn.MaxPool2d(kernel_size=k, stride=k) - - def forward(self, x): - return self.m(x) - - -class SP(nn.Module): - def __init__(self, k=3, s=1): - super(SP, self).__init__() - self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2) - - def forward(self, x): - return self.m(x) - - -class ReOrg(nn.Module): - def __init__(self): - super(ReOrg, self).__init__() - - def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) - return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1) - - -class Concat(nn.Module): - def __init__(self, dimension=1): - super(Concat, self).__init__() - self.d = dimension - - def forward(self, x): - return torch.cat(x, self.d) - - -class Chuncat(nn.Module): - def __init__(self, dimension=1): - super(Chuncat, self).__init__() - self.d = dimension - - def forward(self, x): - x1 = [] - x2 = [] - for xi in x: - xi1, xi2 = xi.chunk(2, self.d) - x1.append(xi1) - x2.append(xi2) - return torch.cat(x1+x2, self.d) - - -class Shortcut(nn.Module): - def __init__(self, dimension=0): - super(Shortcut, self).__init__() - self.d = dimension - - def forward(self, x): - return x[0]+x[1] - - -class Foldcut(nn.Module): - def __init__(self, dimension=0): - super(Foldcut, self).__init__() - self.d = dimension - - def forward(self, x): - x1, x2 = x.chunk(2, self.d) - return x1+x2 - - -class Conv(nn.Module): - # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super(Conv, self).__init__() - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) - - def forward(self, x): - return self.act(self.bn(self.conv(x))) - - def fuseforward(self, x): - return self.act(self.conv(x)) - - -class RobustConv(nn.Module): - # Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs. - def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups - super(RobustConv, self).__init__() - self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) - self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True) - self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None - - def forward(self, x): - x = x.to(memory_format=torch.channels_last) - x = self.conv1x1(self.conv_dw(x)) - if self.gamma is not None: - x = x.mul(self.gamma.reshape(1, -1, 1, 1)) - return x - - -class RobustConv2(nn.Module): - # Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP). - def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups - super(RobustConv2, self).__init__() - self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) - self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s, - padding=0, bias=True, dilation=1, groups=1 - ) - self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None - - def forward(self, x): - x = self.conv_deconv(self.conv_strided(x)) - if self.gamma is not None: - x = x.mul(self.gamma.reshape(1, -1, 1, 1)) - return x - - -def DWConv(c1, c2, k=1, s=1, act=True): - # Depthwise convolution - return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) - - -class GhostConv(nn.Module): - # Ghost Convolution https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups - super(GhostConv, self).__init__() - c_ = c2 // 2 # hidden channels - self.cv1 = Conv(c1, c_, k, s, None, g, act) - self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) - - def forward(self, x): - y = self.cv1(x) - return torch.cat([y, self.cv2(y)], 1) - - -class Stem(nn.Module): - # Stem - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super(Stem, self).__init__() - c_ = int(c2/2) # hidden channels - self.cv1 = Conv(c1, c_, 3, 2) - self.cv2 = Conv(c_, c_, 1, 1) - self.cv3 = Conv(c_, c_, 3, 2) - self.pool = torch.nn.MaxPool2d(2, stride=2) - self.cv4 = Conv(2 * c_, c2, 1, 1) - - def forward(self, x): - x = self.cv1(x) - return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1)) - - -class DownC(nn.Module): - # Spatial pyramid pooling layer used in YOLOv3-SPP - def __init__(self, c1, c2, n=1, k=2): - super(DownC, self).__init__() - c_ = int(c1) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c2//2, 3, k) - self.cv3 = Conv(c1, c2//2, 1, 1) - self.mp = nn.MaxPool2d(kernel_size=k, stride=k) - - def forward(self, x): - return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1) - - -class SPP(nn.Module): - # Spatial pyramid pooling layer used in YOLOv3-SPP - def __init__(self, c1, c2, k=(5, 9, 13)): - super(SPP, self).__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) - self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) - - def forward(self, x): - x = self.cv1(x) - return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) - - -class Bottleneck(nn.Module): - # Darknet bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super(Bottleneck, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c2, 3, 1, g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class Res(nn.Module): - # ResNet bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super(Res, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c_, 3, 1, g=g) - self.cv3 = Conv(c_, c2, 1, 1) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x))) - - -class ResX(Res): - # ResNet bottleneck - def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super().__init__(c1, c2, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - - -class Ghost(nn.Module): - # Ghost Bottleneck https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride - super(Ghost, self).__init__() - c_ = c2 // 2 - self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw - DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw - GhostConv(c_, c2, 1, 1, act=False)) # pw-linear - self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), - Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() - - def forward(self, x): - return self.conv(x) + self.shortcut(x) - -##### end of basic ##### - - -##### cspnet ##### - -class SPPCSPC(nn.Module): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): - super(SPPCSPC, self).__init__() - c_ = int(2 * c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(c_, c_, 3, 1) - self.cv4 = Conv(c_, c_, 1, 1) - self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) - self.cv5 = Conv(4 * c_, c_, 1, 1) - self.cv6 = Conv(c_, c_, 3, 1) - self.cv7 = Conv(2 * c_, c2, 1, 1) - - def forward(self, x): - x1 = self.cv4(self.cv3(self.cv1(x))) - y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1))) - y2 = self.cv2(x) - return self.cv7(torch.cat((y1, y2), dim=1)) - -class GhostSPPCSPC(SPPCSPC): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): - super().__init__(c1, c2, n, shortcut, g, e, k) - c_ = int(2 * c2 * e) # hidden channels - self.cv1 = GhostConv(c1, c_, 1, 1) - self.cv2 = GhostConv(c1, c_, 1, 1) - self.cv3 = GhostConv(c_, c_, 3, 1) - self.cv4 = GhostConv(c_, c_, 1, 1) - self.cv5 = GhostConv(4 * c_, c_, 1, 1) - self.cv6 = GhostConv(c_, c_, 3, 1) - self.cv7 = GhostConv(2 * c_, c2, 1, 1) - - -class GhostStem(Stem): - # Stem - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__(c1, c2, k, s, p, g, act) - c_ = int(c2/2) # hidden channels - self.cv1 = GhostConv(c1, c_, 3, 2) - self.cv2 = GhostConv(c_, c_, 1, 1) - self.cv3 = GhostConv(c_, c_, 3, 2) - self.cv4 = GhostConv(2 * c_, c2, 1, 1) - - -class BottleneckCSPA(nn.Module): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(BottleneckCSPA, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1, 1) - self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - y1 = self.m(self.cv1(x)) - y2 = self.cv2(x) - return self.cv3(torch.cat((y1, y2), dim=1)) - - -class BottleneckCSPB(nn.Module): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(BottleneckCSPB, self).__init__() - c_ = int(c2) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1, 1) - self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - x1 = self.cv1(x) - y1 = self.m(x1) - y2 = self.cv2(x1) - return self.cv3(torch.cat((y1, y2), dim=1)) - - -class BottleneckCSPC(nn.Module): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(BottleneckCSPC, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(c_, c_, 1, 1) - self.cv4 = Conv(2 * c_, c2, 1, 1) - self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - y1 = self.cv3(self.m(self.cv1(x))) - y2 = self.cv2(x) - return self.cv4(torch.cat((y1, y2), dim=1)) - - -class ResCSPA(BottleneckCSPA): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class ResCSPB(BottleneckCSPB): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2) # hidden channels - self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class ResCSPC(BottleneckCSPC): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class ResXCSPA(ResCSPA): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - -class ResXCSPB(ResCSPB): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2) # hidden channels - self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - -class ResXCSPC(ResCSPC): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - -class GhostCSPA(BottleneckCSPA): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) - - -class GhostCSPB(BottleneckCSPB): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2) # hidden channels - self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) - - -class GhostCSPC(BottleneckCSPC): - # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) - -##### end of cspnet ##### - - -##### yolor ##### - -class ImplicitA(nn.Module): - def __init__(self, channel, mean=0., std=.02): - super(ImplicitA, self).__init__() - self.channel = channel - self.mean = mean - self.std = std - self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1)) - nn.init.normal_(self.implicit, mean=self.mean, std=self.std) - - def forward(self, x): - return self.implicit + x - - -class ImplicitM(nn.Module): - def __init__(self, channel, mean=1., std=.02): - super(ImplicitM, self).__init__() - self.channel = channel - self.mean = mean - self.std = std - self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1)) - nn.init.normal_(self.implicit, mean=self.mean, std=self.std) - - def forward(self, x): - return self.implicit * x - -##### end of yolor ##### - - -##### repvgg ##### - -class RepConv(nn.Module): - # Represented convolution - # https://arxiv.org/abs/2101.03697 - - def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False): - super(RepConv, self).__init__() - - self.deploy = deploy - self.groups = g - self.in_channels = c1 - self.out_channels = c2 - - assert k == 3 - assert autopad(k, p) == 1 - - padding_11 = autopad(k, p) - k // 2 - - self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) - - if deploy: - self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True) - - else: - self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None) - - self.rbr_dense = nn.Sequential( - nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False), - nn.BatchNorm2d(num_features=c2), - ) - - self.rbr_1x1 = nn.Sequential( - nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False), - nn.BatchNorm2d(num_features=c2), - ) - - def forward(self, inputs): - if hasattr(self, "rbr_reparam"): - return self.act(self.rbr_reparam(inputs)) - - if self.rbr_identity is None: - id_out = 0 - else: - id_out = self.rbr_identity(inputs) - - return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out) - - def get_equivalent_kernel_bias(self): - kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) - kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) - kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) - return ( - kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, - bias3x3 + bias1x1 + biasid, - ) - - def _pad_1x1_to_3x3_tensor(self, kernel1x1): - if kernel1x1 is None: - return 0 - else: - return nn.functional.pad(kernel1x1, [1, 1, 1, 1]) - - def _fuse_bn_tensor(self, branch): - if branch is None: - return 0, 0 - if isinstance(branch, nn.Sequential): - kernel = branch[0].weight - running_mean = branch[1].running_mean - running_var = branch[1].running_var - gamma = branch[1].weight - beta = branch[1].bias - eps = branch[1].eps - else: - assert isinstance(branch, nn.BatchNorm2d) - if not hasattr(self, "id_tensor"): - input_dim = self.in_channels // self.groups - kernel_value = np.zeros( - (self.in_channels, input_dim, 3, 3), dtype=np.float32 - ) - for i in range(self.in_channels): - kernel_value[i, i % input_dim, 1, 1] = 1 - self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) - kernel = self.id_tensor - running_mean = branch.running_mean - running_var = branch.running_var - gamma = branch.weight - beta = branch.bias - eps = branch.eps - std = (running_var + eps).sqrt() - t = (gamma / std).reshape(-1, 1, 1, 1) - return kernel * t, beta - running_mean * gamma / std - - def repvgg_convert(self): - kernel, bias = self.get_equivalent_kernel_bias() - return ( - kernel.detach().cpu().numpy(), - bias.detach().cpu().numpy(), - ) - - def fuse_conv_bn(self, conv, bn): - - std = (bn.running_var + bn.eps).sqrt() - bias = bn.bias - bn.running_mean * bn.weight / std - - t = (bn.weight / std).reshape(-1, 1, 1, 1) - weights = conv.weight * t - - bn = nn.Identity() - conv = nn.Conv2d(in_channels = conv.in_channels, - out_channels = conv.out_channels, - kernel_size = conv.kernel_size, - stride=conv.stride, - padding = conv.padding, - dilation = conv.dilation, - groups = conv.groups, - bias = True, - padding_mode = conv.padding_mode) - - conv.weight = torch.nn.Parameter(weights) - conv.bias = torch.nn.Parameter(bias) - return conv - - def fuse_repvgg_block(self): - if self.deploy: - return - print(f"RepConv.fuse_repvgg_block") - - self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1]) - - self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1]) - rbr_1x1_bias = self.rbr_1x1.bias - weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1]) - - # Fuse self.rbr_identity - if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)): - # print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm") - identity_conv_1x1 = nn.Conv2d( - in_channels=self.in_channels, - out_channels=self.out_channels, - kernel_size=1, - stride=1, - padding=0, - groups=self.groups, - bias=False) - identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device) - identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze() - # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") - identity_conv_1x1.weight.data.fill_(0.0) - identity_conv_1x1.weight.data.fill_diagonal_(1.0) - identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3) - # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") - - identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity) - bias_identity_expanded = identity_conv_1x1.bias - weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1]) - else: - # print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}") - bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) ) - weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) ) - - - #print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ") - #print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ") - #print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ") - - self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded) - self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded) - - self.rbr_reparam = self.rbr_dense - self.deploy = True - - if self.rbr_identity is not None: - del self.rbr_identity - self.rbr_identity = None - - if self.rbr_1x1 is not None: - del self.rbr_1x1 - self.rbr_1x1 = None - - if self.rbr_dense is not None: - del self.rbr_dense - self.rbr_dense = None - - -class RepBottleneck(Bottleneck): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super().__init__(c1, c2, shortcut=True, g=1, e=0.5) - c_ = int(c2 * e) # hidden channels - self.cv2 = RepConv(c_, c2, 3, 1, g=g) - - -class RepBottleneckCSPA(BottleneckCSPA): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - -class RepBottleneckCSPB(BottleneckCSPB): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2) # hidden channels - self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - -class RepBottleneckCSPC(BottleneckCSPC): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - -class RepRes(Res): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super().__init__(c1, c2, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.cv2 = RepConv(c_, c_, 3, 1, g=g) - - -class RepResCSPA(ResCSPA): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class RepResCSPB(ResCSPB): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2) # hidden channels - self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class RepResCSPC(ResCSPC): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class RepResX(ResX): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super().__init__(c1, c2, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.cv2 = RepConv(c_, c_, 3, 1, g=g) - - -class RepResXCSPA(ResXCSPA): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class RepResXCSPB(ResXCSPB): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2) # hidden channels - self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - - -class RepResXCSPC(ResXCSPC): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) - -##### end of repvgg ##### - - -##### transformer ##### - -class TransformerLayer(nn.Module): - # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) - def __init__(self, c, num_heads): - super().__init__() - self.q = nn.Linear(c, c, bias=False) - self.k = nn.Linear(c, c, bias=False) - self.v = nn.Linear(c, c, bias=False) - self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) - self.fc1 = nn.Linear(c, c, bias=False) - self.fc2 = nn.Linear(c, c, bias=False) - - def forward(self, x): - x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x - x = self.fc2(self.fc1(x)) + x - return x - - -class TransformerBlock(nn.Module): - # Vision Transformer https://arxiv.org/abs/2010.11929 - def __init__(self, c1, c2, num_heads, num_layers): - super().__init__() - self.conv = None - if c1 != c2: - self.conv = Conv(c1, c2) - self.linear = nn.Linear(c2, c2) # learnable position embedding - self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) - self.c2 = c2 - - def forward(self, x): - if self.conv is not None: - x = self.conv(x) - b, _, w, h = x.shape - p = x.flatten(2) - p = p.unsqueeze(0) - p = p.transpose(0, 3) - p = p.squeeze(3) - e = self.linear(p) - x = p + e - - x = self.tr(x) - x = x.unsqueeze(3) - x = x.transpose(0, 3) - x = x.reshape(b, self.c2, w, h) - return x - -##### end of transformer ##### - - -##### yolov5 ##### - -class Focus(nn.Module): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super(Focus, self).__init__() - self.conv = Conv(c1 * 4, c2, k, s, p, g, act) - # self.contract = Contract(gain=2) - - def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) - return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) - # return self.conv(self.contract(x)) - - -class SPPF(nn.Module): - # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher - def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * 4, c2, 1, 1) - self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) - - def forward(self, x): - x = self.cv1(x) - y1 = self.m(x) - y2 = self.m(y1) - return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) - - -class Contract(nn.Module): - # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' - s = self.gain - x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2) - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) - return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40) - - -class Expand(nn.Module): - # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' - s = self.gain - x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80) - x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) - return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160) - - -class NMS(nn.Module): - # Non-Maximum Suppression (NMS) module - conf = 0.25 # confidence threshold - iou = 0.45 # IoU threshold - classes = None # (optional list) filter by class - - def __init__(self): - super(NMS, self).__init__() - - def forward(self, x): - return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) - - -class autoShape(nn.Module): - # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - conf = 0.25 # NMS confidence threshold - iou = 0.45 # NMS IoU threshold - classes = None # (optional list) filter by class - - def __init__(self, model): - super(autoShape, self).__init__() - self.model = model.eval() - - def autoshape(self): - print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() - return self - - @torch.no_grad() - def forward(self, imgs, size=640, augment=False, profile=False): - # Inference from various sources. For height=640, width=1280, RGB images example inputs are: - # filename: imgs = 'data/samples/zidane.jpg' - # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) - # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) - # numpy: = np.zeros((640,1280,3)) # HWC - # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) - # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images - - t = [time_synchronized()] - p = next(self.model.parameters()) # for device and type - if isinstance(imgs, torch.Tensor): # torch - with amp.autocast(enabled=p.device.type != 'cpu'): - return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference - - # Pre-process - n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images - shape0, shape1, files = [], [], [] # image and inference shapes, filenames - for i, im in enumerate(imgs): - f = f'image{i}' # filename - if isinstance(im, str): # filename or uri - im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im - elif isinstance(im, Image.Image): # PIL Image - im, f = np.asarray(im), getattr(im, 'filename', f) or f - files.append(Path(f).with_suffix('.jpg').name) - if im.shape[0] < 5: # image in CHW - im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) - im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input - s = im.shape[:2] # HWC - shape0.append(s) # image shape - g = (size / max(s)) # gain - shape1.append([y * g for y in s]) - imgs[i] = im # update - shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape - x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad - x = np.stack(x, 0) if n > 1 else x[0][None] # stack - x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW - x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 - t.append(time_synchronized()) - - with amp.autocast(enabled=p.device.type != 'cpu'): - # Inference - y = self.model(x, augment, profile)[0] # forward - t.append(time_synchronized()) - - # Post-process - y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS - for i in range(n): - scale_coords(shape1, y[i][:, :4], shape0[i]) - - t.append(time_synchronized()) - return Detections(imgs, y, files, t, self.names, x.shape) - - -class Detections: - # detections class for YOLOv5 inference results - def __init__(self, imgs, pred, files, times=None, names=None, shape=None): - super(Detections, self).__init__() - d = pred[0].device # device - gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations - self.imgs = imgs # list of images as numpy arrays - self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) - self.names = names # class names - self.files = files # image filenames - self.xyxy = pred # xyxy pixels - self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels - self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized - self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) # number of images (batch size) - self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) - self.s = shape # inference BCHW shape - - def display(self, pprint=False, show=False, save=False, render=False, save_dir=''): - colors = color_list() - for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): - str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' - if pred is not None: - for c in pred[:, -1].unique(): - n = (pred[:, -1] == c).sum() # detections per class - str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string - if show or save or render: - for *box, conf, cls in pred: # xyxy, confidence, class - label = f'{self.names[int(cls)]} {conf:.2f}' - plot_one_box(box, img, label=label, color=colors[int(cls) % 10]) - img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np - if pprint: - print(str.rstrip(', ')) - if show: - img.show(self.files[i]) # show - if save: - f = self.files[i] - img.save(Path(save_dir) / f) # save - print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') - if render: - self.imgs[i] = np.asarray(img) - - def print(self): - self.display(pprint=True) # print results - print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) - - def show(self): - self.display(show=True) # show results - - def save(self, save_dir='runs/hub/exp'): - save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir - Path(save_dir).mkdir(parents=True, exist_ok=True) - self.display(save=True, save_dir=save_dir) # save results - - def render(self): - self.display(render=True) # render results - return self.imgs - - def pandas(self): - # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) - new = copy(self) # return copy - ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns - cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns - for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): - a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update - setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) - return new - - def tolist(self): - # return a list of Detections objects, i.e. 'for result in results.tolist():' - x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] - for d in x: - for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: - setattr(d, k, getattr(d, k)[0]) # pop out of list - return x - - def __len__(self): - return self.n - - -class Classify(nn.Module): - # Classification head, i.e. x(b,c1,20,20) to x(b,c2) - def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups - super(Classify, self).__init__() - self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) - self.flat = nn.Flatten() - - def forward(self, x): - z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list - return self.flat(self.conv(z)) # flatten to x(b,c2) - -##### end of yolov5 ###### - - -##### orepa ##### - -def transI_fusebn(kernel, bn): - gamma = bn.weight - std = (bn.running_var + bn.eps).sqrt() - return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std - - -class ConvBN(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, - stride=1, padding=0, dilation=1, groups=1, deploy=False, nonlinear=None): - super().__init__() - if nonlinear is None: - self.nonlinear = nn.Identity() - else: - self.nonlinear = nonlinear - if deploy: - self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, - stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True) - else: - self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, - stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False) - self.bn = nn.BatchNorm2d(num_features=out_channels) - - def forward(self, x): - if hasattr(self, 'bn'): - return self.nonlinear(self.bn(self.conv(x))) - else: - return self.nonlinear(self.conv(x)) - - def switch_to_deploy(self): - kernel, bias = transI_fusebn(self.conv.weight, self.bn) - conv = nn.Conv2d(in_channels=self.conv.in_channels, out_channels=self.conv.out_channels, kernel_size=self.conv.kernel_size, - stride=self.conv.stride, padding=self.conv.padding, dilation=self.conv.dilation, groups=self.conv.groups, bias=True) - conv.weight.data = kernel - conv.bias.data = bias - for para in self.parameters(): - para.detach_() - self.__delattr__('conv') - self.__delattr__('bn') - self.conv = conv - -class OREPA_3x3_RepConv(nn.Module): - - def __init__(self, in_channels, out_channels, kernel_size, - stride=1, padding=0, dilation=1, groups=1, - internal_channels_1x1_3x3=None, - deploy=False, nonlinear=None, single_init=False): - super(OREPA_3x3_RepConv, self).__init__() - self.deploy = deploy - - if nonlinear is None: - self.nonlinear = nn.Identity() - else: - self.nonlinear = nonlinear - - self.kernel_size = kernel_size - self.in_channels = in_channels - self.out_channels = out_channels - self.groups = groups - assert padding == kernel_size // 2 - - self.stride = stride - self.padding = padding - self.dilation = dilation - - self.branch_counter = 0 - - self.weight_rbr_origin = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), kernel_size, kernel_size)) - nn.init.kaiming_uniform_(self.weight_rbr_origin, a=math.sqrt(1.0)) - self.branch_counter += 1 - - - if groups < out_channels: - self.weight_rbr_avg_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1)) - self.weight_rbr_pfir_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1)) - nn.init.kaiming_uniform_(self.weight_rbr_avg_conv, a=1.0) - nn.init.kaiming_uniform_(self.weight_rbr_pfir_conv, a=1.0) - self.weight_rbr_avg_conv.data - self.weight_rbr_pfir_conv.data - self.register_buffer('weight_rbr_avg_avg', torch.ones(kernel_size, kernel_size).mul(1.0/kernel_size/kernel_size)) - self.branch_counter += 1 - - else: - raise NotImplementedError - self.branch_counter += 1 - - if internal_channels_1x1_3x3 is None: - internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels # For mobilenet, it is better to have 2X internal channels - - if internal_channels_1x1_3x3 == in_channels: - self.weight_rbr_1x1_kxk_idconv1 = nn.Parameter(torch.zeros(in_channels, int(in_channels/self.groups), 1, 1)) - id_value = np.zeros((in_channels, int(in_channels/self.groups), 1, 1)) - for i in range(in_channels): - id_value[i, i % int(in_channels/self.groups), 0, 0] = 1 - id_tensor = torch.from_numpy(id_value).type_as(self.weight_rbr_1x1_kxk_idconv1) - self.register_buffer('id_tensor', id_tensor) - - else: - self.weight_rbr_1x1_kxk_conv1 = nn.Parameter(torch.Tensor(internal_channels_1x1_3x3, int(in_channels/self.groups), 1, 1)) - nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv1, a=math.sqrt(1.0)) - self.weight_rbr_1x1_kxk_conv2 = nn.Parameter(torch.Tensor(out_channels, int(internal_channels_1x1_3x3/self.groups), kernel_size, kernel_size)) - nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv2, a=math.sqrt(1.0)) - self.branch_counter += 1 - - expand_ratio = 8 - self.weight_rbr_gconv_dw = nn.Parameter(torch.Tensor(in_channels*expand_ratio, 1, kernel_size, kernel_size)) - self.weight_rbr_gconv_pw = nn.Parameter(torch.Tensor(out_channels, in_channels*expand_ratio, 1, 1)) - nn.init.kaiming_uniform_(self.weight_rbr_gconv_dw, a=math.sqrt(1.0)) - nn.init.kaiming_uniform_(self.weight_rbr_gconv_pw, a=math.sqrt(1.0)) - self.branch_counter += 1 - - if out_channels == in_channels and stride == 1: - self.branch_counter += 1 - - self.vector = nn.Parameter(torch.Tensor(self.branch_counter, self.out_channels)) - self.bn = nn.BatchNorm2d(out_channels) - - self.fre_init() - - nn.init.constant_(self.vector[0, :], 0.25) #origin - nn.init.constant_(self.vector[1, :], 0.25) #avg - nn.init.constant_(self.vector[2, :], 0.0) #prior - nn.init.constant_(self.vector[3, :], 0.5) #1x1_kxk - nn.init.constant_(self.vector[4, :], 0.5) #dws_conv - - - def fre_init(self): - prior_tensor = torch.Tensor(self.out_channels, self.kernel_size, self.kernel_size) - half_fg = self.out_channels/2 - for i in range(self.out_channels): - for h in range(3): - for w in range(3): - if i < half_fg: - prior_tensor[i, h, w] = math.cos(math.pi*(h+0.5)*(i+1)/3) - else: - prior_tensor[i, h, w] = math.cos(math.pi*(w+0.5)*(i+1-half_fg)/3) - - self.register_buffer('weight_rbr_prior', prior_tensor) - - def weight_gen(self): - - weight_rbr_origin = torch.einsum('oihw,o->oihw', self.weight_rbr_origin, self.vector[0, :]) - - weight_rbr_avg = torch.einsum('oihw,o->oihw', torch.einsum('oihw,hw->oihw', self.weight_rbr_avg_conv, self.weight_rbr_avg_avg), self.vector[1, :]) - - weight_rbr_pfir = torch.einsum('oihw,o->oihw', torch.einsum('oihw,ohw->oihw', self.weight_rbr_pfir_conv, self.weight_rbr_prior), self.vector[2, :]) - - weight_rbr_1x1_kxk_conv1 = None - if hasattr(self, 'weight_rbr_1x1_kxk_idconv1'): - weight_rbr_1x1_kxk_conv1 = (self.weight_rbr_1x1_kxk_idconv1 + self.id_tensor).squeeze() - elif hasattr(self, 'weight_rbr_1x1_kxk_conv1'): - weight_rbr_1x1_kxk_conv1 = self.weight_rbr_1x1_kxk_conv1.squeeze() - else: - raise NotImplementedError - weight_rbr_1x1_kxk_conv2 = self.weight_rbr_1x1_kxk_conv2 - - if self.groups > 1: - g = self.groups - t, ig = weight_rbr_1x1_kxk_conv1.size() - o, tg, h, w = weight_rbr_1x1_kxk_conv2.size() - weight_rbr_1x1_kxk_conv1 = weight_rbr_1x1_kxk_conv1.view(g, int(t/g), ig) - weight_rbr_1x1_kxk_conv2 = weight_rbr_1x1_kxk_conv2.view(g, int(o/g), tg, h, w) - weight_rbr_1x1_kxk = torch.einsum('gti,gothw->goihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2).view(o, ig, h, w) - else: - weight_rbr_1x1_kxk = torch.einsum('ti,othw->oihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2) - - weight_rbr_1x1_kxk = torch.einsum('oihw,o->oihw', weight_rbr_1x1_kxk, self.vector[3, :]) - - weight_rbr_gconv = self.dwsc2full(self.weight_rbr_gconv_dw, self.weight_rbr_gconv_pw, self.in_channels) - weight_rbr_gconv = torch.einsum('oihw,o->oihw', weight_rbr_gconv, self.vector[4, :]) - - weight = weight_rbr_origin + weight_rbr_avg + weight_rbr_1x1_kxk + weight_rbr_pfir + weight_rbr_gconv - - return weight - - def dwsc2full(self, weight_dw, weight_pw, groups): - - t, ig, h, w = weight_dw.size() - o, _, _, _ = weight_pw.size() - tg = int(t/groups) - i = int(ig*groups) - weight_dw = weight_dw.view(groups, tg, ig, h, w) - weight_pw = weight_pw.squeeze().view(o, groups, tg) - - weight_dsc = torch.einsum('gtihw,ogt->ogihw', weight_dw, weight_pw) - return weight_dsc.view(o, i, h, w) - - def forward(self, inputs): - weight = self.weight_gen() - out = F.conv2d(inputs, weight, bias=None, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups) - - return self.nonlinear(self.bn(out)) - -class RepConv_OREPA(nn.Module): - - def __init__(self, c1, c2, k=3, s=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False, nonlinear=nn.SiLU()): - super(RepConv_OREPA, self).__init__() - self.deploy = deploy - self.groups = groups - self.in_channels = c1 - self.out_channels = c2 - - self.padding = padding - self.dilation = dilation - self.groups = groups - - assert k == 3 - assert padding == 1 - - padding_11 = padding - k // 2 - - if nonlinear is None: - self.nonlinearity = nn.Identity() - else: - self.nonlinearity = nonlinear - - if use_se: - self.se = SEBlock(self.out_channels, internal_neurons=self.out_channels // 16) - else: - self.se = nn.Identity() - - if deploy: - self.rbr_reparam = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, - padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode) - - else: - self.rbr_identity = nn.BatchNorm2d(num_features=self.in_channels) if self.out_channels == self.in_channels and s == 1 else None - self.rbr_dense = OREPA_3x3_RepConv(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, padding=padding, groups=groups, dilation=1) - self.rbr_1x1 = ConvBN(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=1, stride=s, padding=padding_11, groups=groups, dilation=1) - print('RepVGG Block, identity = ', self.rbr_identity) - - - def forward(self, inputs): - if hasattr(self, 'rbr_reparam'): - return self.nonlinearity(self.se(self.rbr_reparam(inputs))) - - if self.rbr_identity is None: - id_out = 0 - else: - id_out = self.rbr_identity(inputs) - - out1 = self.rbr_dense(inputs) - out2 = self.rbr_1x1(inputs) - out3 = id_out - out = out1 + out2 + out3 - - return self.nonlinearity(self.se(out)) - - - # Optional. This improves the accuracy and facilitates quantization. - # 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight. - # 2. Use like this. - # loss = criterion(....) - # for every RepVGGBlock blk: - # loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2() - # optimizer.zero_grad() - # loss.backward() - - # Not used for OREPA - def get_custom_L2(self): - K3 = self.rbr_dense.weight_gen() - K1 = self.rbr_1x1.conv.weight - t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach() - t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach() - - l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them. - eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1 # The equivalent resultant central point of 3x3 kernel. - l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum() # Normalize for an L2 coefficient comparable to regular L2. - return l2_loss_eq_kernel + l2_loss_circle - - def get_equivalent_kernel_bias(self): - kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) - kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) - kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) - return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid - - def _pad_1x1_to_3x3_tensor(self, kernel1x1): - if kernel1x1 is None: - return 0 - else: - return torch.nn.functional.pad(kernel1x1, [1,1,1,1]) - - def _fuse_bn_tensor(self, branch): - if branch is None: - return 0, 0 - if not isinstance(branch, nn.BatchNorm2d): - if isinstance(branch, OREPA_3x3_RepConv): - kernel = branch.weight_gen() - elif isinstance(branch, ConvBN): - kernel = branch.conv.weight - else: - raise NotImplementedError - running_mean = branch.bn.running_mean - running_var = branch.bn.running_var - gamma = branch.bn.weight - beta = branch.bn.bias - eps = branch.bn.eps - else: - if not hasattr(self, 'id_tensor'): - input_dim = self.in_channels // self.groups - kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32) - for i in range(self.in_channels): - kernel_value[i, i % input_dim, 1, 1] = 1 - self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) - kernel = self.id_tensor - running_mean = branch.running_mean - running_var = branch.running_var - gamma = branch.weight - beta = branch.bias - eps = branch.eps - std = (running_var + eps).sqrt() - t = (gamma / std).reshape(-1, 1, 1, 1) - return kernel * t, beta - running_mean * gamma / std - - def switch_to_deploy(self): - if hasattr(self, 'rbr_reparam'): - return - print(f"RepConv_OREPA.switch_to_deploy") - kernel, bias = self.get_equivalent_kernel_bias() - self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.in_channels, out_channels=self.rbr_dense.out_channels, - kernel_size=self.rbr_dense.kernel_size, stride=self.rbr_dense.stride, - padding=self.rbr_dense.padding, dilation=self.rbr_dense.dilation, groups=self.rbr_dense.groups, bias=True) - self.rbr_reparam.weight.data = kernel - self.rbr_reparam.bias.data = bias - for para in self.parameters(): - para.detach_() - self.__delattr__('rbr_dense') - self.__delattr__('rbr_1x1') - if hasattr(self, 'rbr_identity'): - self.__delattr__('rbr_identity') - -##### end of orepa ##### - - -##### swin transformer ##### - -class WindowAttention(nn.Module): - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - nn.init.normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - # print(attn.dtype, v.dtype) - try: - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - except: - #print(attn.dtype, v.dtype) - x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - -class Mlp(nn.Module): - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - -def window_partition(x, window_size): - - B, H, W, C = x.shape - assert H % window_size == 0, 'feature map h and w can not divide by window size' - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - -def window_reverse(windows, window_size, H, W): - - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class SwinTransformerLayer(nn.Module): - - def __init__(self, dim, num_heads, window_size=8, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.SiLU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - # if min(self.input_resolution) <= self.window_size: - # # if window size is larger than input resolution, we don't partition windows - # self.shift_size = 0 - # self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - def create_mask(self, H, W): - # calculate attention mask for SW-MSA - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - def forward(self, x): - # reshape x[b c h w] to x[b l c] - _, _, H_, W_ = x.shape - - Padding = False - if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0: - Padding = True - # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') - pad_r = (self.window_size - W_ % self.window_size) % self.window_size - pad_b = (self.window_size - H_ % self.window_size) % self.window_size - x = F.pad(x, (0, pad_r, 0, pad_b)) - - # print('2', x.shape) - B, C, H, W = x.shape - L = H * W - x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c - - # create mask from init to forward - if self.shift_size > 0: - attn_mask = self.create_mask(H, W).to(x.device) - else: - attn_mask = None - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w - - if Padding: - x = x[:, :, :H_, :W_] # reverse padding - - return x - - -class SwinTransformerBlock(nn.Module): - def __init__(self, c1, c2, num_heads, num_layers, window_size=8): - super().__init__() - self.conv = None - if c1 != c2: - self.conv = Conv(c1, c2) - - # remove input_resolution - self.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)]) - - def forward(self, x): - if self.conv is not None: - x = self.conv(x) - x = self.blocks(x) - return x - - -class STCSPA(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(STCSPA, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1, 1) - num_heads = c_ // 32 - self.m = SwinTransformerBlock(c_, c_, num_heads, n) - #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - y1 = self.m(self.cv1(x)) - y2 = self.cv2(x) - return self.cv3(torch.cat((y1, y2), dim=1)) - - -class STCSPB(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(STCSPB, self).__init__() - c_ = int(c2) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1, 1) - num_heads = c_ // 32 - self.m = SwinTransformerBlock(c_, c_, num_heads, n) - #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - x1 = self.cv1(x) - y1 = self.m(x1) - y2 = self.cv2(x1) - return self.cv3(torch.cat((y1, y2), dim=1)) - - -class STCSPC(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(STCSPC, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(c_, c_, 1, 1) - self.cv4 = Conv(2 * c_, c2, 1, 1) - num_heads = c_ // 32 - self.m = SwinTransformerBlock(c_, c_, num_heads, n) - #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - y1 = self.cv3(self.m(self.cv1(x))) - y2 = self.cv2(x) - return self.cv4(torch.cat((y1, y2), dim=1)) - -##### end of swin transformer ##### - - -##### swin transformer v2 ##### - -class WindowAttention_v2(nn.Module): - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., - pretrained_window_size=[0, 0]): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.pretrained_window_size = pretrained_window_size - self.num_heads = num_heads - - self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) - - # mlp to generate continuous relative position bias - self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True), - nn.ReLU(inplace=True), - nn.Linear(512, num_heads, bias=False)) - - # get relative_coords_table - relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) - relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) - relative_coords_table = torch.stack( - torch.meshgrid([relative_coords_h, - relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 - if pretrained_window_size[0] > 0: - relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1) - relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1) - else: - relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) - relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) - relative_coords_table *= 8 # normalize to -8, 8 - relative_coords_table = torch.sign(relative_coords_table) * torch.log2( - torch.abs(relative_coords_table) + 1.0) / np.log2(8) - - self.register_buffer("relative_coords_table", relative_coords_table) - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=False) - if qkv_bias: - self.q_bias = nn.Parameter(torch.zeros(dim)) - self.v_bias = nn.Parameter(torch.zeros(dim)) - else: - self.q_bias = None - self.v_bias = None - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - - B_, N, C = x.shape - qkv_bias = None - if self.q_bias is not None: - qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) - qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) - qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - # cosine attention - attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) - logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() - attn = attn * logit_scale - - relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) - relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - relative_position_bias = 16 * torch.sigmoid(relative_position_bias) - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - try: - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - except: - x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) - - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, ' \ - f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}' - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - -class Mlp_v2(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition_v2(x, window_size): - - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse_v2(windows, window_size, H, W): - - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class SwinTransformerLayer_v2(nn.Module): - - def __init__(self, dim, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.SiLU, norm_layer=nn.LayerNorm, pretrained_window_size=0): - super().__init__() - self.dim = dim - #self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - #if min(self.input_resolution) <= self.window_size: - # # if window size is larger than input resolution, we don't partition windows - # self.shift_size = 0 - # self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention_v2( - dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, - pretrained_window_size=(pretrained_window_size, pretrained_window_size)) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp_v2(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - def create_mask(self, H, W): - # calculate attention mask for SW-MSA - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - def forward(self, x): - # reshape x[b c h w] to x[b l c] - _, _, H_, W_ = x.shape - - Padding = False - if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0: - Padding = True - # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') - pad_r = (self.window_size - W_ % self.window_size) % self.window_size - pad_b = (self.window_size - H_ % self.window_size) % self.window_size - x = F.pad(x, (0, pad_r, 0, pad_b)) - - # print('2', x.shape) - B, C, H, W = x.shape - L = H * W - x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c - - # create mask from init to forward - if self.shift_size > 0: - attn_mask = self.create_mask(H, W).to(x.device) - else: - attn_mask = None - - shortcut = x - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition_v2(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse_v2(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(B, H * W, C) - x = shortcut + self.drop_path(self.norm1(x)) - - # FFN - x = x + self.drop_path(self.norm2(self.mlp(x))) - x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w - - if Padding: - x = x[:, :, :H_, :W_] # reverse padding - - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ - f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class SwinTransformer2Block(nn.Module): - def __init__(self, c1, c2, num_heads, num_layers, window_size=7): - super().__init__() - self.conv = None - if c1 != c2: - self.conv = Conv(c1, c2) - - # remove input_resolution - self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)]) - - def forward(self, x): - if self.conv is not None: - x = self.conv(x) - x = self.blocks(x) - return x - - -class ST2CSPA(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(ST2CSPA, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1, 1) - num_heads = c_ // 32 - self.m = SwinTransformer2Block(c_, c_, num_heads, n) - #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - y1 = self.m(self.cv1(x)) - y2 = self.cv2(x) - return self.cv3(torch.cat((y1, y2), dim=1)) - - -class ST2CSPB(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(ST2CSPB, self).__init__() - c_ = int(c2) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1, 1) - num_heads = c_ // 32 - self.m = SwinTransformer2Block(c_, c_, num_heads, n) - #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - x1 = self.cv1(x) - y1 = self.m(x1) - y2 = self.cv2(x1) - return self.cv3(torch.cat((y1, y2), dim=1)) - - -class ST2CSPC(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(ST2CSPC, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(c_, c_, 1, 1) - self.cv4 = Conv(2 * c_, c2, 1, 1) - num_heads = c_ // 32 - self.m = SwinTransformer2Block(c_, c_, num_heads, n) - #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - y1 = self.cv3(self.m(self.cv1(x))) - y2 = self.cv2(x) - return self.cv4(torch.cat((y1, y2), dim=1)) - -##### end of swin transformer v2 ##### diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/vertical_insertion_blocks.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/vertical_insertion_blocks.py deleted file mode 100644 index 54f769aaee1f5e396ed72277ca8b24082fd7cf40..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/vertical_insertion_blocks.py +++ /dev/null @@ -1,54 +0,0 @@ -import numpy as np -import os -import pybullet as p -import random -from cliport.tasks import primitives -from cliport.tasks.grippers import Spatula -from cliport.tasks.task import Task -from cliport.utils import utils -import numpy as np -from cliport.tasks.task import Task -from cliport.utils import utils -import pybullet as p - -class VerticalInsertionBlocks(Task): - """Pick up four color specific blocks and insert each block into four differently colored stands set upright on the tabletop.""" - - def __init__(self): - super().__init__() - self.max_steps = 20 - self.lang_template = "insert the {color} block into the {color} stand" - self.task_completed_desc = "done inserting blocks into stands." - self.additional_reset() - - def reset(self, env): - super().reset(env) - - # Define colors for blocks and stands - colors = ['red', 'blue', 'green', 'yellow'] - - # Add stands. - # x, y, z dimensions for the asset size - stand_size = (0.04, 0.04, 0.1) - stand_urdf = 'stacking/stand.urdf' - stands = [] - for color in colors: - stand_pose = self.get_random_pose(env, stand_size) - stand_id = env.add_object(stand_urdf, stand_pose, color=utils.COLORS[color], category='fixed') - stands.append(stand_id) - - # Add blocks. - # x, y, z dimensions for the asset size - block_size = (0.04, 0.04, 0.04) - block_urdf = 'stacking/block.urdf' - blocks = [] - for color in colors: - block_pose = self.get_random_pose(env, block_size) - block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color]) - blocks.append(block_id) - - # Goal: each block is inserted into the stand of the same color. - for i in range(len(blocks)): - self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[p.getBasePositionAndOrientation(stands[i])], replace=False, - rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks), - language_goal=self.lang_template.format(color=colors[i])) \ No newline at end of file diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/mdetr_lingunet_lat_fuse.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/mdetr_lingunet_lat_fuse.py deleted file mode 100644 index 1e878ad360a6cc10be06af342d6804c9efc294b2..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/cliport/models/mdetr_lingunet_lat_fuse.py +++ /dev/null @@ -1,356 +0,0 @@ -import torch -import torch.nn.functional as F -from typing import List, Optional -from torch import Tensor, nn -import copy -from cliport.models.resnet import IdentityBlock, ConvBlock -from cliport.models.core.unet import Up - -from cliport.models.core import fusion -from cliport.models.core.fusion import FusionConvLat -from cliport.models.backbone_full import Backbone -from cliport.models.misc import NestedTensor -from cliport.models.position_encoding import build_position_encoding -from transformers import RobertaModel, RobertaTokenizerFast - - - -class FeatureResizer(nn.Module): - """ - This class takes as input a set of embeddings of dimension C1 and outputs a set of - embedding of dimension C2, after a linear transformation, dropout and normalization (LN). - """ - - def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): - super().__init__() - self.do_ln = do_ln - # Object feature encoding - self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) - self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) - self.dropout = nn.Dropout(dropout) - - def forward(self, encoder_features): - x = self.fc(encoder_features) - if self.do_ln: - x = self.layer_norm(x) - output = self.dropout(x) - return output - - -class MDETRLingUNetLat_fuse(nn.Module): - """ CLIP RN50 with U-Net skip connections and lateral connections """ - - def __init__(self, input_shape, output_dim, cfg, device, preprocess): - super(MDETRLingUNetLat_fuse, self).__init__() - self.input_shape = input_shape - self.output_dim = output_dim - self.input_dim = 2048 # penultimate layer channel-size of mdetr - self.cfg = cfg - self.device = device - self.batchnorm = self.cfg['train']['batchnorm'] - self.lang_fusion_type = self.cfg['train']['lang_fusion_type'] - self.bilinear = True - self.up_factor = 2 if self.bilinear else 1 - self.preprocess = preprocess - - self.backbone = Backbone('resnet101', True, True, False) - self.position_embedding = build_position_encoding() - self.input_proj = nn.Conv2d(2048, 256, kernel_size=1) - - self.tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') - self.text_encoder = RobertaModel.from_pretrained('roberta-base') - self.resizer = FeatureResizer( - input_feat_size=768, - output_feat_size=256, - dropout=0.1, - ) - encoder_layer = TransformerEncoderLayer(d_model=256, nhead=8, dim_feedforward=2048, dropout=0.1, activation='relu', normalize_before=False) - self.encoder = TransformerEncoder(encoder_layer, 6, None) - mdter_checkpoint = torch.load('/home/yzc/shared/project/GPT-CLIPort/ckpts/mdetr_pretrained_resnet101_checkpoint.pth', map_location="cpu")['model'] - - checkpoint_new = {} - for param in mdter_checkpoint: - if 'transformer.text_encoder' in param or 'transformer.encoder.' in param or 'input_proj' in param or 'resizer' in param: - param_new = param.replace('transformer.','') - checkpoint_new[param_new] = mdter_checkpoint[param] - elif 'backbone.0.body' in param: - param_new = param.replace('backbone.0.body', 'backbone.body') - checkpoint_new[param_new] = mdter_checkpoint[param] - - self.load_state_dict(checkpoint_new, True) - self._build_decoder() - - - def _build_decoder(self): - # language - self.up_fuse1 = nn.UpsamplingBilinear2d(scale_factor=2) - self.up_fuse2 = nn.UpsamplingBilinear2d(scale_factor=4) - self.up_fuse3 = nn.UpsamplingBilinear2d(scale_factor=8) - - self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2) - self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4) - self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8) - - self.proj_input_dim = 768 - self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024) - self.lang_proj2 = nn.Linear(self.proj_input_dim, 512) - self.lang_proj3 = nn.Linear(self.proj_input_dim, 256) - - # vision - self.conv1 = nn.Sequential( - nn.Conv2d(self.input_dim+256, 1024, kernel_size=3, stride=1, padding=1, bias=False), - nn.ReLU(True) - ) - - self.up1 = Up(2048+256, 1024 // self.up_factor, self.bilinear) - self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512) - - self.up2 = Up(1024+256, 512 // self.up_factor, self.bilinear) - self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256) - - self.up3 = Up(512+256, 256 // self.up_factor, self.bilinear) - self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128) - - self.layer1 = nn.Sequential( - ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), - IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm), - nn.UpsamplingBilinear2d(scale_factor=2), - ) - self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64) - - self.layer2 = nn.Sequential( - ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), - IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm), - nn.UpsamplingBilinear2d(scale_factor=2), - ) - self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32) - - self.layer3 = nn.Sequential( - ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), - IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm), - nn.UpsamplingBilinear2d(scale_factor=2), - ) - self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16) - - self.conv2 = nn.Sequential( - nn.Conv2d(16, self.output_dim, kernel_size=1) - ) - - def encode_image(self, img): - img = NestedTensor.from_tensor_list(img) - with torch.no_grad(): - xs = self.backbone(img) - out = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self.position_embedding(x).to(x.tensors.dtype)) - return out, pos - - - def encode_text(self, x): - with torch.no_grad(): - tokenized = self.tokenizer.batch_encode_plus(x, padding="longest", return_tensors="pt").to(self.device) - encoded_text = self.text_encoder(**tokenized) - - # Transpose memory because pytorch's attention expects sequence first - text_memory = encoded_text.last_hidden_state.transpose(0, 1) - text_memory_mean = torch.mean(text_memory, 0) - # Invert attention mask that we get from huggingface because its the opposite in pytorch transformer - text_attention_mask = tokenized.attention_mask.ne(1).bool() - # Resize the encoder hidden states to be of the same d_model as the decoder - text_memory_resized = self.resizer(text_memory) - return text_memory_resized, text_attention_mask, text_memory_mean - - def forward(self, x, lat, l): - - x = self.preprocess(x, dist='mdetr') - - in_type = x.dtype - in_shape = x.shape - x = x[:,:3] # select RGB - - x = x.permute(0, 1, 3, 2) - - - with torch.no_grad(): - features, pos = self.encode_image(x) - x1, mask = features[-1].decompose() - x2, _ = features[-2].decompose() - x3, _ = features[-3].decompose() - x4, _ = features[-4].decompose() - #print(x1.shape, x2.shape, x3.shape, x4.shape) - src = self.input_proj(x1) - pos_embed = pos[-1] - bs, c, h, w = src.shape - src = src.flatten(2).permute(2, 0, 1) - device = self.device - pos_embed = pos_embed.flatten(2).permute(2, 0, 1) - mask = mask.flatten(1) - if x.shape[0] == 1 or x.shape[0] == 36: - l = [l] - text_memory_resized, text_attention_mask, l_input = self.encode_text(l) - else: - text_memory_resized, text_attention_mask, l_input = self.encode_text(l) - # l_input = l_input.view(1, -1) - # text_memory_resized = text_memory_resized.repeat(1, src.shape[1], 1) - # text_attention_mask = text_attention_mask.repeat(src.shape[1], 1) - #print(src.shape, text_memory_resized.shape, mask.shape, text_attention_mask.shape) - if (x.shape[0] > 8) and ((x.shape[0] % 36) == 0): - text_memory_resized = text_memory_resized.repeat_interleave(36, dim=1) - l_input = l_input.repeat_interleave(36, dim=0) - text_attention_mask = text_attention_mask.repeat_interleave(36, dim=0) - src = torch.cat([src, text_memory_resized], dim=0) - # For mask, sequence dimension is second - mask = torch.cat([mask, text_attention_mask], dim=1) - # Pad the pos_embed with 0 so that the addition will be a no-op for the text tokens - pos_embed = torch.cat([pos_embed, torch.zeros_like(text_memory_resized)], dim=0) - img_memory, img_memory_all = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) - - dim = img_memory.shape[-1] - fuse1 = img_memory_all[-1][:h*w].permute(1,2,0).reshape(bs, dim, h, w) - fuse2 = self.up_fuse1(img_memory_all[-2][:h*w].permute(1,2,0).reshape(bs, dim, h, w)) - fuse3 = self.up_fuse2(img_memory_all[-3][:h*w].permute(1,2,0).reshape(bs, dim, h, w)) - fuse4 = self.up_fuse3(img_memory_all[-4][:h*w].permute(1,2,0).reshape(bs, dim, h, w)) - - assert x1.shape[1] == self.input_dim - - x1 = torch.cat((x1, fuse1), 1) - x2 = torch.cat((x2, fuse2), 1) - x3 = torch.cat((x3, fuse3), 1) - x4 = torch.cat((x4, fuse4), 1) - - x = self.conv1(x1) - x = self.lang_fuser1(x, l_input, x2_mask=None, x2_proj=self.lang_proj1) - x = self.up1(x, x2) - x = self.lat_fusion1(x, lat[-6].permute(0, 1, 3, 2)) - - x = self.lang_fuser2(x, l_input, x2_mask=None, x2_proj=self.lang_proj2) - - x = self.up2(x, x3) - x = self.lat_fusion2(x, lat[-5].permute(0, 1, 3, 2)) - - x = self.lang_fuser3(x, l_input, x2_mask=None, x2_proj=self.lang_proj3) - x = self.up3(x, x4) - x = self.lat_fusion3(x, lat[-4].permute(0, 1, 3, 2)) - x = self.layer1(x) - x = self.lat_fusion4(x, lat[-3].permute(0, 1, 3, 2)) - - x = self.layer2(x) - x = self.lat_fusion5(x, lat[-2].permute(0, 1, 3, 2)) - - x = self.layer3(x) - x = self.lat_fusion6(x, lat[-1].permute(0, 1, 3, 2)) - - x = self.conv2(x) - - x = F.interpolate(x, size=(in_shape[-1], in_shape[-2]), mode='bilinear') - x = x.permute(0, 1, 3, 2) - return x - - -class TransformerEncoder(nn.Module): - def __init__(self, encoder_layer, num_layers, norm=None): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - src, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - - output = src - output_all = [] - for layer in self.layers: - output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos) - output_all.append(output) - if self.norm is not None: - output = self.norm(output) - - return output, output_all - -class TransformerEncoderLayer(nn.Module): - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # 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.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - print(self.normalize_before) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - q = k = self.with_pos_embed(src, pos) - src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src - - def forward_pre( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - src2 = self.norm1(src) - q = k = self.with_pos_embed(src2, pos) - src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src2 = self.norm2(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) - src = src + self.dropout2(src2) - return src - - def forward( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre(src, src_mask, src_key_padding_mask, pos) - return self.forward_post(src, src_mask, src_key_padding_mask, pos) - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -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}.") - diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py deleted file mode 100644 index 5d6215d6f6e2f81fa284af0e639f3568429e3a75..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py +++ /dev/null @@ -1,45 +0,0 @@ -_base_ = './mask_rcnn_r50_fpn_1x_coco.py' -model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', - backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) -# use caffe img_norm -img_norm_cfg = dict( - mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='LoadAnnotations', - with_bbox=True, - with_mask=True, - poly2mask=False), - 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='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -data = dict( - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) diff --git a/spaces/Gyuyu/andite-anything-v4.0/README.md b/spaces/Gyuyu/andite-anything-v4.0/README.md deleted file mode 100644 index 4f3421116530eb35a0db19bc1d523e4ff38b1516..0000000000000000000000000000000000000000 --- a/spaces/Gyuyu/andite-anything-v4.0/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Andite Anything V4.0 -emoji: 🐨 -colorFrom: blue -colorTo: yellow -sdk: gradio -sdk_version: 3.16.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py deleted file mode 100644 index 7c4d8ba3802a2da9467c42b0aa18653c7bbb2ec9..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from __future__ import absolute_import, division, print_function, unicode_literals - -import logging -import math - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.criterions import FairseqCriterion, register_criterion - - -@register_criterion("cross_entropy_acc") -class CrossEntropyWithAccCriterion(FairseqCriterion): - def __init__(self, task, sentence_avg): - super().__init__(task) - self.sentence_avg = sentence_avg - - def compute_loss(self, model, net_output, target, reduction, log_probs): - # N, T -> N * T - target = target.view(-1) - lprobs = model.get_normalized_probs(net_output, log_probs=log_probs) - if not hasattr(lprobs, "batch_first"): - logging.warning( - "ERROR: we need to know whether " - "batch first for the net output; " - "you need to set batch_first attribute for the return value of " - "model.get_normalized_probs. Now, we assume this is true, but " - "in the future, we will raise exception instead. " - ) - batch_first = getattr(lprobs, "batch_first", True) - if not batch_first: - lprobs = lprobs.transpose(0, 1) - - # N, T, D -> N * T, D - lprobs = lprobs.view(-1, lprobs.size(-1)) - loss = F.nll_loss( - lprobs, target, ignore_index=self.padding_idx, reduction=reduction - ) - return lprobs, loss - - def get_logging_output(self, sample, target, lprobs, loss): - target = target.view(-1) - mask = target != self.padding_idx - correct = torch.sum( - lprobs.argmax(1).masked_select(mask) == target.masked_select(mask) - ) - total = torch.sum(mask) - sample_size = ( - sample["target"].size(0) if self.sentence_avg else sample["ntokens"] - ) - - logging_output = { - "loss": utils.item(loss.data), # * sample['ntokens'], - "ntokens": sample["ntokens"], - "nsentences": sample["target"].size(0), - "sample_size": sample_size, - "correct": utils.item(correct.data), - "total": utils.item(total.data), - "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(), - } - - return sample_size, logging_output - - def forward(self, model, sample, reduction="sum", log_probs=True): - """Computes the cross entropy with accuracy metric for the given sample. - - This is similar to CrossEntropyCriterion in fairseq, but also - computes accuracy metrics as part of logging - - Args: - logprobs (Torch.tensor) of shape N, T, D i.e. - batchsize, timesteps, dimensions - targets (Torch.tensor) of shape N, T i.e batchsize, timesteps - - Returns: - tuple: With three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - - TODO: - * Currently this Criterion will only work with LSTMEncoderModels or - FairseqModels which have decoder, or Models which return TorchTensor - as net_output. - We need to make a change to support all FairseqEncoder models. - """ - net_output = model(**sample["net_input"]) - target = model.get_targets(sample, net_output) - lprobs, loss = self.compute_loss( - model, net_output, target, reduction, log_probs - ) - sample_size, logging_output = self.get_logging_output( - sample, target, lprobs, loss - ) - return loss, sample_size, logging_output - - @staticmethod - def aggregate_logging_outputs(logging_outputs): - """Aggregate logging outputs from data parallel training.""" - correct_sum = sum(log.get("correct", 0) for log in logging_outputs) - total_sum = sum(log.get("total", 0) for log in logging_outputs) - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - nframes = sum(log.get("nframes", 0) for log in logging_outputs) - agg_output = { - "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, - # if args.sentence_avg, then sample_size is nsentences, then loss - # is per-sentence loss; else sample_size is ntokens, the loss - # becomes per-output token loss - "ntokens": ntokens, - "nsentences": nsentences, - "nframes": nframes, - "sample_size": sample_size, - "acc": correct_sum * 100.0 / total_sum if total_sum > 0 else 0.0, - "correct": correct_sum, - "total": total_sum, - # total is the number of validate tokens - } - if sample_size != ntokens: - agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) - # loss: per output token loss - # nll_loss: per sentence loss - return agg_output diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py deleted file mode 100644 index a5dd7ae6c15b358206e067385be260c94021bf20..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py +++ /dev/null @@ -1,128 +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 os -import os.path as osp -import numpy as np -import tqdm -import torch -import sys - -import faiss -import torch.nn.functional as F - -from wav2vec_cluster_faiss import parse_faiss_specs, Wav2VecFeatureReader - - -def get_parser(): - parser = argparse.ArgumentParser(description="apply clusters") - # fmt: off - parser.add_argument('data', help='location of tsv files') - parser.add_argument('--split', help='split to process', required=True) - parser.add_argument('--labels', help='split to process', default="phn") - parser.add_argument('--path', help='path to pca and centroids', required=True) - parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec model (if using wav2vec features)', required=True) - parser.add_argument('--layer', '-l', type=int, help='which layer to read', default=14) - parser.add_argument('--max-tsz', type=int, help='batch kmeans up to this much', default=14) - # fmt: on - - return parser - - -def get_iterator(args): - label_path = osp.join(args.data, f"{args.split}.{args.labels}") - if osp.exists(label_path): - lp = open(label_path, "r") - else: - lp = None - - with open(osp.join(args.data, f"{args.split}.tsv"), "r") as fp: - lines = fp.read().split("\n") - root = lines.pop(0).strip() - files = [line.rstrip() for line in lines if len(line) > 0] - - if lp is not None: - lbls = [line.rstrip() for line in lp] - else: - lbls = [None] * len(files) - - num = len(files) - reader = Wav2VecFeatureReader(args.checkpoint, args.layer) - - def iterate(): - for fname, lbl in zip(files, lbls): - file = osp.join(root, fname.split("\t")[0]) - feats = reader.get_feats(file) - yield feats.data, fname, lbl - - return iterate, num, root - - -def main(): - parser = get_parser() - args = parser.parse_args() - - spec = osp.basename(args.path) - - try: - faiss_spec = parse_faiss_specs(spec.rstrip("/"))[0] - except: - print(spec) - raise - - print("Faiss Spec:", faiss_spec, file=sys.stderr) - - if faiss_spec.pca: - A = torch.from_numpy(np.load(osp.join(args.path, "pca_A.npy"))).cuda() - b = torch.from_numpy(np.load(osp.join(args.path, "pca_b.npy"))).cuda() - print("Loaded PCA", file=sys.stderr) - - centroids = np.load(osp.join(args.path, "centroids.npy")) - print("Loaded centroids", centroids.shape, file=sys.stderr) - - res = faiss.StandardGpuResources() - index_flat = ( - faiss.IndexFlatL2(centroids.shape[1]) - if not faiss_spec.sphere - else faiss.IndexFlatIP(centroids.shape[1]) - ) - faiss_index = faiss.index_cpu_to_gpu(res, 0, index_flat) - faiss_index.add(centroids) - - generator, num, root = get_iterator(args) - iterator = generator() - - had_labels = False - label_path = osp.join(args.path, f"{args.split}.{args.labels}") - - with torch.no_grad(): - with open(osp.join(args.path, f"{args.split}.src"), "w") as fp, open( - osp.join(args.path, f"{args.split}.tsv"), "w" - ) as pp, open(label_path, "w") as lp: - print(root, file=pp) - for f, fname, lbl in tqdm.tqdm(iterator, total=num): - if faiss_spec.pca: - f = torch.mm(f, A) + b - if faiss_spec.norm: - f = F.normalize(f, p=2, dim=-1) - - f = f.cpu().numpy() - - _, z = faiss_index.search(f, 1) - - print(" ".join(str(x.item()) for x in z), file=fp) - print(fname, file=pp) - - if lbl is not None: - print(lbl, file=lp) - had_labels = True - if not had_labels: - os.remove(label_path) - - -if __name__ == "__main__": - main() diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/multi_corpus_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/multi_corpus_dataset.py deleted file mode 100644 index 746155e515897da9fc9c803f9396a45b5cead8d0..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/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/ICML2022/OFA/fairseq/examples/speech_recognition/new/decoders/decoder_config.py b/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/new/decoders/decoder_config.py deleted file mode 100644 index 659eb94a9b8187a7c126d7b439ac2742f9d72022..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/new/decoders/decoder_config.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from dataclasses import dataclass, field -from typing import Optional - -from fairseq.dataclass.configs import FairseqDataclass -from fairseq.dataclass.constants import ChoiceEnum -from omegaconf import MISSING - - -DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"]) - - -@dataclass -class DecoderConfig(FairseqDataclass): - type: DECODER_CHOICES = field( - default="viterbi", - metadata={"help": "The type of decoder to use"}, - ) - - -@dataclass -class FlashlightDecoderConfig(FairseqDataclass): - nbest: int = field( - default=1, - metadata={"help": "Number of decodings to return"}, - ) - unitlm: bool = field( - default=False, - metadata={"help": "If set, use unit language model"}, - ) - lmpath: str = field( - default=MISSING, - metadata={"help": "Language model for KenLM decoder"}, - ) - lexicon: Optional[str] = field( - default=None, - metadata={"help": "Lexicon for Flashlight decoder"}, - ) - beam: int = field( - default=50, - metadata={"help": "Number of beams to use for decoding"}, - ) - beamthreshold: float = field( - default=50.0, - metadata={"help": "Threshold for beam search decoding"}, - ) - beamsizetoken: Optional[int] = field( - default=None, metadata={"help": "Beam size to use"} - ) - wordscore: float = field( - default=-1, - metadata={"help": "Word score for KenLM decoder"}, - ) - unkweight: float = field( - default=-math.inf, - metadata={"help": "Unknown weight for KenLM decoder"}, - ) - silweight: float = field( - default=0, - metadata={"help": "Silence weight for KenLM decoder"}, - ) - lmweight: float = field( - default=2, - metadata={"help": "Weight for LM while interpolating score"}, - ) diff --git a/spaces/ICML2022/OFA/fairseq/examples/translation/prepare-wmt14en2fr.sh b/spaces/ICML2022/OFA/fairseq/examples/translation/prepare-wmt14en2fr.sh deleted file mode 100644 index 2ac97a5b76fab255449493488ed8bd67350a7bac..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/translation/prepare-wmt14en2fr.sh +++ /dev/null @@ -1,136 +0,0 @@ -#!/bin/bash -# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh - -echo 'Cloning Moses github repository (for tokenization scripts)...' -git clone https://github.com/moses-smt/mosesdecoder.git - -echo 'Cloning Subword NMT repository (for BPE pre-processing)...' -git clone https://github.com/rsennrich/subword-nmt.git - -SCRIPTS=mosesdecoder/scripts -TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl -CLEAN=$SCRIPTS/training/clean-corpus-n.perl -NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl -REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl -BPEROOT=subword-nmt/subword_nmt -BPE_TOKENS=40000 - -URLS=( - "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz" - "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz" - "http://statmt.org/wmt13/training-parallel-un.tgz" - "http://statmt.org/wmt14/training-parallel-nc-v9.tgz" - "http://statmt.org/wmt10/training-giga-fren.tar" - "http://statmt.org/wmt14/test-full.tgz" -) -FILES=( - "training-parallel-europarl-v7.tgz" - "training-parallel-commoncrawl.tgz" - "training-parallel-un.tgz" - "training-parallel-nc-v9.tgz" - "training-giga-fren.tar" - "test-full.tgz" -) -CORPORA=( - "training/europarl-v7.fr-en" - "commoncrawl.fr-en" - "un/undoc.2000.fr-en" - "training/news-commentary-v9.fr-en" - "giga-fren.release2.fixed" -) - -if [ ! -d "$SCRIPTS" ]; then - echo "Please set SCRIPTS variable correctly to point to Moses scripts." - exit -fi - -src=en -tgt=fr -lang=en-fr -prep=wmt14_en_fr -tmp=$prep/tmp -orig=orig - -mkdir -p $orig $tmp $prep - -cd $orig - -for ((i=0;i<${#URLS[@]};++i)); do - file=${FILES[i]} - if [ -f $file ]; then - echo "$file already exists, skipping download" - else - url=${URLS[i]} - wget "$url" - if [ -f $file ]; then - echo "$url successfully downloaded." - else - echo "$url not successfully downloaded." - exit -1 - fi - if [ ${file: -4} == ".tgz" ]; then - tar zxvf $file - elif [ ${file: -4} == ".tar" ]; then - tar xvf $file - fi - fi -done - -gunzip giga-fren.release2.fixed.*.gz -cd .. - -echo "pre-processing train data..." -for l in $src $tgt; do - rm $tmp/train.tags.$lang.tok.$l - for f in "${CORPORA[@]}"; do - cat $orig/$f.$l | \ - perl $NORM_PUNC $l | \ - perl $REM_NON_PRINT_CHAR | \ - perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l - done -done - -echo "pre-processing test data..." -for l in $src $tgt; do - if [ "$l" == "$src" ]; then - t="src" - else - t="ref" - fi - grep '\s*//g' | \ - sed -e 's/\s*<\/seg>\s*//g' | \ - sed -e "s/\’/\'/g" | \ - perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l - echo "" -done - -echo "splitting train and valid..." -for l in $src $tgt; do - awk '{if (NR%1333 == 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l - awk '{if (NR%1333 != 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l -done - -TRAIN=$tmp/train.fr-en -BPE_CODE=$prep/code -rm -f $TRAIN -for l in $src $tgt; do - cat $tmp/train.$l >> $TRAIN -done - -echo "learn_bpe.py on ${TRAIN}..." -python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE - -for L in $src $tgt; do - for f in train.$L valid.$L test.$L; do - echo "apply_bpe.py to ${f}..." - python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f - done -done - -perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250 -perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250 - -for L in $src $tgt; do - cp $tmp/bpe.test.$L $prep/test.$L -done diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/optim/adamax.py b/spaces/ICML2022/OFA/fairseq/fairseq/optim/adamax.py deleted file mode 100644 index 98ff8ad7ad6c12ab5efc53ca76db2f1663be7906..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/optim/adamax.py +++ /dev/null @@ -1,172 +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 -import torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("adamax") -class FairseqAdamax(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = Adamax(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--adamax-betas', default='(0.9, 0.999)', metavar='B', - help='betas for Adam optimizer') - parser.add_argument('--adamax-eps', type=float, default=1e-8, metavar='D', - help='epsilon for Adam optimizer') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - parser.add_argument('--no-bias-correction', default=False, action='store_true', - help='disable bias correction') - # 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], - "betas": eval(self.args.adamax_betas), - "eps": self.args.adamax_eps, - "weight_decay": self.args.weight_decay, - "bias_correction": not self.args.no_bias_correction, - } - - -class Adamax(torch.optim.Optimizer): - """Implements Adamax algorithm (a variant of Adam based on infinity norm). - - It has been proposed in `Adam: A Method for Stochastic Optimization`__. - - Compared to the version in PyTorch, this version implements a fix for weight decay. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 2e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - bias_correction (bool, optional): enable bias correction (default: True) - - __ https://arxiv.org/abs/1412.6980 - """ - - def __init__( - self, - params, - lr=2e-3, - betas=(0.9, 0.999), - eps=1e-8, - weight_decay=0, - bias_correction=True, - ): - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - if not 0.0 <= weight_decay: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - - defaults = dict( - lr=lr, - betas=betas, - eps=eps, - weight_decay=weight_decay, - bias_correction=bias_correction, - ) - super(Adamax, self).__init__(params, defaults) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - def step(self, closure=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad.data.float() - if grad.is_sparse: - raise RuntimeError("Adamax does not support sparse gradients") - - p_data_fp32 = p.data - if p.data.dtype in {torch.float16, torch.bfloat16}: - p_data_fp32 = p_data_fp32.float() - - state = self.state[p] - - # State initialization - if len(state) == 0: - state["step"] = 0 - state["exp_avg"] = torch.zeros_like(p_data_fp32) - state["exp_inf"] = torch.zeros_like(p_data_fp32) - else: - state["exp_avg"] = state["exp_avg"].to(p_data_fp32) - state["exp_inf"] = state["exp_inf"].to(p_data_fp32) - - exp_avg, exp_inf = state["exp_avg"], state["exp_inf"] - beta1, beta2 = group["betas"] - eps = group["eps"] - - state["step"] += 1 - - # Update biased first moment estimate. - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - - # Update the exponentially weighted infinity norm. - torch.max( - exp_inf.mul_(beta2), - grad.abs_(), - out=exp_inf, - ) - - step_size = group["lr"] - if group["bias_correction"]: - bias_correction = 1 - beta1 ** state["step"] - step_size /= bias_correction - - if group["weight_decay"] != 0: - p_data_fp32.add_( - p_data_fp32, alpha=-group["weight_decay"] * group["lr"] - ) - - p_data_fp32.addcdiv_(exp_avg, exp_inf.add(eps), value=-step_size) - - if p.data.dtype in {torch.float16, torch.bfloat16}: - p.data.copy_(p_data_fp32) - - return loss diff --git a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/segment/predict.py b/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/segment/predict.py deleted file mode 100644 index 42389938cee7618778480b88f8e876282acc5c93..0000000000000000000000000000000000000000 --- a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/segment/predict.py +++ /dev/null @@ -1,274 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Run YOLOv5 segmentation inference on images, videos, directories, streams, etc. - -Usage - sources: - $ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam - img.jpg # image - vid.mp4 # video - screen # screenshot - path/ # directory - 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream - -Usage - formats: - $ python segment/predict.py --weights yolov5s-seg.pt # PyTorch - yolov5s-seg.torchscript # TorchScript - yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s-seg_openvino_model # OpenVINO - yolov5s-seg.engine # TensorRT - yolov5s-seg.mlmodel # CoreML (macOS-only) - yolov5s-seg_saved_model # TensorFlow SavedModel - yolov5s-seg.pb # TensorFlow GraphDef - yolov5s-seg.tflite # TensorFlow Lite - yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU - yolov5s-seg_paddle_model # PaddlePaddle -""" - -import argparse -import os -import platform -import sys -from pathlib import Path - -import torch - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.common import DetectMultiBackend -from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams -from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, - increment_path, non_max_suppression, print_args, scale_boxes, scale_segments, - strip_optimizer, xyxy2xywh) -from utils.plots import Annotator, colors, save_one_box -from utils.segment.general import masks2segments, process_mask -from utils.torch_utils import select_device, smart_inference_mode - - -@smart_inference_mode() -def run( - weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s) - source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - imgsz=(640, 640), # inference size (height, width) - conf_thres=0.25, # confidence threshold - iou_thres=0.45, # NMS IOU threshold - max_det=1000, # maximum detections per image - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - view_img=False, # show results - save_txt=False, # save results to *.txt - save_conf=False, # save confidences in --save-txt labels - save_crop=False, # save cropped prediction boxes - nosave=False, # do not save images/videos - classes=None, # filter by class: --class 0, or --class 0 2 3 - agnostic_nms=False, # class-agnostic NMS - augment=False, # augmented inference - visualize=False, # visualize features - update=False, # update all models - project=ROOT / 'runs/predict-seg', # save results to project/name - name='exp', # save results to project/name - exist_ok=False, # existing project/name ok, do not increment - line_thickness=3, # bounding box thickness (pixels) - hide_labels=False, # hide labels - hide_conf=False, # hide confidences - half=False, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - vid_stride=1, # video frame-rate stride - retina_masks=False, -): - source = str(source) - save_img = not nosave and not source.endswith('.txt') # save inference images - is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) - is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) - webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) - screenshot = source.lower().startswith('screen') - if is_url and is_file: - source = check_file(source) # download - - # Directories - save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Load model - device = select_device(device) - model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) - stride, names, pt = model.stride, model.names, model.pt - imgsz = check_img_size(imgsz, s=stride) # check image size - - # Dataloader - bs = 1 # batch_size - if webcam: - view_img = check_imshow(warn=True) - dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) - bs = len(dataset) - elif screenshot: - dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) - else: - dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) - vid_path, vid_writer = [None] * bs, [None] * bs - - # Run inference - model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup - seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) - for path, im, im0s, vid_cap, s in dataset: - with dt[0]: - im = torch.from_numpy(im).to(model.device) - im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 - im /= 255 # 0 - 255 to 0.0 - 1.0 - if len(im.shape) == 3: - im = im[None] # expand for batch dim - - # Inference - with dt[1]: - visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False - pred, proto = model(im, augment=augment, visualize=visualize)[:2] - - # NMS - with dt[2]: - pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32) - - # Second-stage classifier (optional) - # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) - - # Process predictions - for i, det in enumerate(pred): # per image - seen += 1 - if webcam: # batch_size >= 1 - p, im0, frame = path[i], im0s[i].copy(), dataset.count - s += f'{i}: ' - else: - p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) - - p = Path(p) # to Path - save_path = str(save_dir / p.name) # im.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt - s += '%gx%g ' % im.shape[2:] # print string - imc = im0.copy() if save_crop else im0 # for save_crop - annotator = Annotator(im0, line_width=line_thickness, example=str(names)) - if len(det): - masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC - det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size - - # Segments - if save_txt: - segments = reversed(masks2segments(masks)) - segments = [scale_segments(im.shape[2:], x, im0.shape, normalize=True) for x in segments] - - # Print results - for c in det[:, 5].unique(): - n = (det[:, 5] == c).sum() # detections per class - s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string - - # Mask plotting - annotator.masks(masks, - colors=[colors(x, True) for x in det[:, 5]], - im_gpu=None if retina_masks else im[i]) - - # Write results - for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): - if save_txt: # Write to file - segj = segments[j].reshape(-1) # (n,2) to (n*2) - line = (cls, *segj, conf) if save_conf else (cls, *segj) # label format - with open(f'{txt_path}.txt', 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') - - if save_img or save_crop or view_img: # Add bbox to image - c = int(cls) # integer class - label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') - annotator.box_label(xyxy, label, color=colors(c, True)) - # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3) - if save_crop: - save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) - - # Stream results - im0 = annotator.result() - if view_img: - if platform.system() == 'Linux' and p not in windows: - windows.append(p) - cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) - cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) - cv2.imshow(str(p), im0) - if cv2.waitKey(1) == ord('q'): # 1 millisecond - exit() - - # Save results (image with detections) - if save_img: - if dataset.mode == 'image': - cv2.imwrite(save_path, im0) - else: # 'video' or 'stream' - if vid_path[i] != save_path: # new video - vid_path[i] = save_path - if isinstance(vid_writer[i], cv2.VideoWriter): - vid_writer[i].release() # release previous video writer - if vid_cap: # video - fps = vid_cap.get(cv2.CAP_PROP_FPS) - w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - else: # stream - fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos - vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - vid_writer[i].write(im0) - - # Print time (inference-only) - LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") - - # Print results - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) - if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") - if update: - strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='show results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') - parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--visualize', action='store_true', help='visualize features') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default=ROOT / 'runs/predict-seg', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') - parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') - parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') - parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution') - opt = parser.parse_args() - opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand - print_args(vars(opt)) - return opt - - -def main(opt): - check_requirements(exclude=('tensorboard', 'thop')) - run(**vars(opt)) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/spaces/Ibtehaj10/cheating-detection/person_counter.py b/spaces/Ibtehaj10/cheating-detection/person_counter.py deleted file mode 100644 index c70cb7f88f07ae8bc533103bc9c56938cd43995b..0000000000000000000000000000000000000000 --- a/spaces/Ibtehaj10/cheating-detection/person_counter.py +++ /dev/null @@ -1,143 +0,0 @@ -import cv2 -import datetime -import imutils -import numpy as np -from centroidtracker import CentroidTracker - -protopath = "MobileNetSSD_deploy.prototxt" -modelpath = "MobileNetSSD_deploy.caffemodel" -detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath) -detector.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE) -detector.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU) - - -CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", - "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", - "dog", "horse", "motorbike", "person", "pottedplant", "sheep", - "sofa", "train", "tvmonitor"] - -tracker = CentroidTracker(maxDisappeared=80, maxDistance=90) - - -def non_max_suppression_fast(boxes, overlapThresh): - try: - if len(boxes) == 0: - return [] - - if boxes.dtype.kind == "i": - boxes = boxes.astype("float") - - pick = [] - - x1 = boxes[:, 0] - y1 = boxes[:, 1] - x2 = boxes[:, 2] - y2 = boxes[:, 3] - - area = (x2 - x1 + 1) * (y2 - y1 + 1) - idxs = np.argsort(y2) - - while len(idxs) > 0: - last = len(idxs) - 1 - i = idxs[last] - pick.append(i) - - xx1 = np.maximum(x1[i], x1[idxs[:last]]) - yy1 = np.maximum(y1[i], y1[idxs[:last]]) - xx2 = np.minimum(x2[i], x2[idxs[:last]]) - yy2 = np.minimum(y2[i], y2[idxs[:last]]) - - w = np.maximum(0, xx2 - xx1 + 1) - h = np.maximum(0, yy2 - yy1 + 1) - - overlap = (w * h) / area[idxs[:last]] - - idxs = np.delete(idxs, np.concatenate(([last], - np.where(overlap > overlapThresh)[0]))) - - return boxes[pick].astype("int") - except Exception as e: - print("Exception occurred in non_max_suppression : {}".format(e)) - - -def main(): - cap = cv2.VideoCapture('test_video.mp4') - - fps_start_time = datetime.datetime.now() - fps = 0 - total_frames = 0 - lpc_count = 0 - opc_count = 0 - object_id_list = [] - while True: - ret, frame = cap.read() - frame = imutils.resize(frame, width=600) - total_frames = total_frames + 1 - - (H, W) = frame.shape[:2] - - blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5) - - detector.setInput(blob) - person_detections = detector.forward() - rects = [] - for i in np.arange(0, person_detections.shape[2]): - confidence = person_detections[0, 0, i, 2] - if confidence > 0.5: - idx = int(person_detections[0, 0, i, 1]) - - if CLASSES[idx] != "person": - continue - - person_box = person_detections[0, 0, i, 3:7] * np.array([W, H, W, H]) - (startX, startY, endX, endY) = person_box.astype("int") - rects.append(person_box) - - boundingboxes = np.array(rects) - boundingboxes = boundingboxes.astype(int) - rects = non_max_suppression_fast(boundingboxes, 0.3) - - objects = tracker.update(rects) - for (objectId, bbox) in objects.items(): - x1, y1, x2, y2 = bbox - x1 = int(x1) - y1 = int(y1) - x2 = int(x2) - y2 = int(y2) - - cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2) - text = "ID: {}".format(objectId) - cv2.putText(frame, text, (x1, y1-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1) - - if objectId not in object_id_list: - object_id_list.append(objectId) - - fps_end_time = datetime.datetime.now() - time_diff = fps_end_time - fps_start_time - if time_diff.seconds == 0: - fps = 0.0 - else: - fps = (total_frames / time_diff.seconds) - - fps_text = "FPS: {:.2f}".format(fps) - - cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1) - - lpc_count = len(objects) - opc_count = len(object_id_list) - - lpc_txt = "LPC: {}".format(lpc_count) - opc_txt = "OPC: {}".format(opc_count) - - cv2.putText(frame, lpc_txt, (5, 60), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1) - cv2.putText(frame, opc_txt, (5, 90), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1) - - cv2.imshow("Application", frame) - key = cv2.waitKey(1) - if key == ord('q'): - break - - cv2.destroyAllWindows() - - -main() diff --git a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py b/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py deleted file mode 100644 index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000 --- a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .fused_act import FusedLeakyReLU, fused_leaky_relu -from .upfirdn2d import upfirdn2d diff --git a/spaces/Jafta/chatglm2-6b-4bit/app.py b/spaces/Jafta/chatglm2-6b-4bit/app.py deleted file mode 100644 index bad73ba706a6496ec0a196e5409e6c1628a10018..0000000000000000000000000000000000000000 --- a/spaces/Jafta/chatglm2-6b-4bit/app.py +++ /dev/null @@ -1,386 +0,0 @@ -"""Credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py while mistakes are mine.""" -# pylint: disable=broad-exception-caught, redefined-outer-name, missing-function-docstring, missing-module-docstring, too-many-arguments, line-too-long, invalid-name, redefined-builtin, redefined-argument-from-local -# import gradio as gr - -# model_name = "models/THUDM/chatglm2-6b-int4" -# gr.load(model_name).lauch() - -# %%writefile demo-4bit.py - -import os -import time -from textwrap import dedent - -import gradio as gr -import mdtex2html -import torch -from loguru import logger -from transformers import AutoModel, AutoTokenizer - -# fix timezone in Linux -os.environ["TZ"] = "Asia/Shanghai" -try: - time.tzset() # type: ignore # pylint: disable=no-member -except Exception: - # Windows - logger.warning("Windows, cant run time.tzset()") - -# model_name = "THUDM/chatglm2-6b" # 7x?G -model_name = "THUDM/chatglm2-6b-int4" # 3.9G - -RETRY_FLAG = False - -tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) - -# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda() - -# 4/8 bit -# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda() - -has_cuda = torch.cuda.is_available() -# has_cuda = False # force cpu - -if has_cuda: - if model_name.endswith("int4"): - model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda() - else: - model = ( - AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half() - ) -else: - model = AutoModel.from_pretrained( - model_name, trust_remote_code=True - ).float() # .half().float(), .float() required for CPU - -model = model.eval() - -_ = """Override Chatbot.postprocess""" - - -def postprocess(self, y): - if y is None: - return [] - for i, (message, response) in enumerate(y): - y[i] = ( - None if message is None else mdtex2html.convert((message)), - None if response is None else mdtex2html.convert(response), - ) - return y - - -gr.Chatbot.postprocess = postprocess - - -def parse_text(text): - """Copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/.""" - lines = text.split("\n") - lines = [line for line in lines if line != ""] - count = 0 - for i, line in enumerate(lines): - if "```" in line: - count += 1 - items = line.split("`") - if count % 2 == 1: - lines[i] = f'
    '
-            else:
-                lines[i] = "
    " - else: - if i > 0: - if count % 2 == 1: - line = line.replace("`", r"\`") - line = line.replace("<", "<") - line = line.replace(">", ">") - line = line.replace(" ", " ") - line = line.replace("*", "*") - line = line.replace("_", "_") - line = line.replace("-", "-") - line = line.replace(".", ".") - line = line.replace("!", "!") - line = line.replace("(", "(") - line = line.replace(")", ")") - line = line.replace("$", "$") - lines[i] = "
    " + line - text = "".join(lines) - return text - - -def predict( - RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values -): - try: - chatbot.append((parse_text(input), "")) - except Exception as exc: - logger.error(exc) - logger.debug(f"{chatbot=}") - _ = """ - if chatbot: - chatbot[-1] = (parse_text(input), str(exc)) - yield chatbot, history, past_key_values - # """ - yield chatbot, history, past_key_values - - for response, history, past_key_values in model.stream_chat( - tokenizer, - input, - history, - past_key_values=past_key_values, - return_past_key_values=True, - max_length=max_length, - top_p=top_p, - temperature=temperature, - ): - chatbot[-1] = (parse_text(input), parse_text(response)) - - yield chatbot, history, past_key_values - - -def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2): - if max_length < 10: - max_length = 4096 - if top_p < 0.1 or top_p > 1: - top_p = 0.85 - if temperature <= 0 or temperature > 1: - temperature = 0.01 - try: - res, _ = model.chat( - tokenizer, - input, - history=[], - past_key_values=None, - max_length=max_length, - top_p=top_p, - temperature=temperature, - ) - # logger.debug(f"{res=} \n{_=}") - except Exception as exc: - logger.error(f"{exc=}") - res = str(exc) - - return res - - -def reset_user_input(): - return gr.update(value="") - - -def reset_state(): - return [], [], None - - -# Delete last turn -def delete_last_turn(chat, history): - if chat and history: - chat.pop(-1) - history.pop(-1) - return chat, history - - -# Regenerate response -def retry_last_answer( - user_input, chatbot, max_length, top_p, temperature, history, past_key_values -): - if chatbot and history: - # Removing the previous conversation from chat - chatbot.pop(-1) - # Setting up a flag to capture a retry - RETRY_FLAG = True - # Getting last message from user - user_input = history[-1][0] - # Removing bot response from the history - history.pop(-1) - - yield from predict( - RETRY_FLAG, # type: ignore - user_input, - chatbot, - max_length, - top_p, - temperature, - history, - past_key_values, - ) - - -with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm")) as demo: - # gr.HTML("""

    ChatGLM2-6B-int4

    """) - gr.HTML( - """
    Duplicate SpaceTo avoid the queue and for faster inference Duplicate this Space and upgrade to GPU
    """ - ) - - with gr.Accordion("🎈 Info", open=False): - _ = f""" - ## {model_name} - - Try to refresh the browser and try again when occasionally an error occurs. - - With a GPU, a query takes from a few seconds to a few tens of seconds, dependent on the number of words/characters - the question and responses contain. The quality of the responses varies quite a bit it seems. Even the same - question with the same parameters, asked at different times, can result in quite different responses. - - * Low temperature: responses will be more deterministic and focused; High temperature: responses more creative. - - * Suggested temperatures -- translation: up to 0.3; chatting: > 0.4 - - * Top P controls dynamic vocabulary selection based on context. - - For a table of example values for different scenarios, refer to [this](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683) - - If the instance is not on a GPU (T4), it will be very slow. You can try to run the colab notebook [chatglm2-6b-4bit colab notebook](https://colab.research.google.com/drive/1WkF7kOjVCcBBatDHjaGkuJHnPdMWNtbW?usp=sharing) for a spin. - - The T4 GPU is sponsored by a community GPU grant from Huggingface. Thanks a lot! - """ - gr.Markdown(dedent(_)) - chatbot = gr.Chatbot() - with gr.Row(): - with gr.Column(scale=4): - with gr.Column(scale=12): - user_input = gr.Textbox( - show_label=False, - placeholder="Input...", - ).style(container=False) - RETRY_FLAG = gr.Checkbox(value=False, visible=False) - with gr.Column(min_width=32, scale=1): - with gr.Row(): - submitBtn = gr.Button("Submit", variant="primary") - deleteBtn = gr.Button("Delete last turn", variant="secondary") - retryBtn = gr.Button("Regenerate", variant="secondary") - with gr.Column(scale=1): - emptyBtn = gr.Button("Clear History") - max_length = gr.Slider( - 0, - 32768, - value=8192, - step=1.0, - label="Maximum length", - interactive=True, - ) - top_p = gr.Slider( - 0, 1, value=0.85, step=0.01, label="Top P", interactive=True - ) - temperature = gr.Slider( - 0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True - ) - - history = gr.State([]) - past_key_values = gr.State(None) - - user_input.submit( - predict, - [ - RETRY_FLAG, - user_input, - chatbot, - max_length, - top_p, - temperature, - history, - past_key_values, - ], - [chatbot, history, past_key_values], - show_progress="full", - ) - submitBtn.click( - predict, - [ - RETRY_FLAG, - user_input, - chatbot, - max_length, - top_p, - temperature, - history, - past_key_values, - ], - [chatbot, history, past_key_values], - show_progress="full", - api_name="predict", - ) - submitBtn.click(reset_user_input, [], [user_input]) - - emptyBtn.click( - reset_state, outputs=[chatbot, history, past_key_values], show_progress="full" - ) - - retryBtn.click( - retry_last_answer, - inputs=[ - user_input, - chatbot, - max_length, - top_p, - temperature, - history, - past_key_values, - ], - # outputs = [chatbot, history, last_user_message, user_message] - outputs=[chatbot, history, past_key_values], - ) - deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history]) - - with gr.Accordion("Example inputs", open=True): - etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """ - examples = gr.Examples( - examples=[ - ["What NFL team won the Super Bowl in the year Justin Bieber was born? "], - ["What NFL team won the Super Bowl in the year Justin Bieber was born? Think step by step."], - ["Explain the plot of Cinderella in a sentence."], - [ - "How long does it take to become proficient in French, and what are the best methods for retaining information?" - ], - ["What are some common mistakes to avoid when writing code?"], - ["Build a prompt to generate a beautiful portrait of a horse"], - ["Suggest four metaphors to describe the benefits of AI"], - ["Write a pop song about leaving home for the sandy beaches."], - ["Write a summary demonstrating my ability to tame lions"], - ["鲁迅和周树人什么关系"], - ["从前有一头牛，这头牛后面有什么？"], - ["正无穷大加一大于正无穷大吗？"], - ["正无穷大加正无穷大大于正无穷大吗？"], - ["-2的平方根等于什么"], - ["树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？"], - ["树上有11只鸟，猎人开枪打死了一只。树上还有几只鸟？提示：需考虑鸟可能受惊吓飞走。"], - ["鲁迅和周树人什么关系 用英文回答"], - ["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"], - [f"{etext} 翻成中文，列出3个版本"], - [f"{etext} \n 翻成中文，保留原意，但使用文学性的语言。不要写解释。列出3个版本"], - ["js 判断一个数是不是质数"], - ["js 实现python 的 range(10)"], - ["js 实现python 的 [*(range(10)]"], - ["假定 1 + 2 = 4, 试求 7 + 8"], - ["Erkläre die Handlung von Cinderella in einem Satz."], - ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"], - ], - inputs=[user_input], - examples_per_page=30, - ) - - with gr.Accordion("For Chat/Translation API", open=False, visible=False): - input_text = gr.Text() - tr_btn = gr.Button("Go", variant="primary") - out_text = gr.Text() - tr_btn.click( - trans_api, - [input_text, max_length, top_p, temperature], - out_text, - # show_progress="full", - api_name="tr", - ) - _ = """ - input_text.submit( - trans_api, - [input_text, max_length, top_p, temperature], - out_text, - show_progress="full", - api_name="tr1", - ) - # """ - -# demo.queue().launch(share=False, inbrowser=True) -# demo.queue().launch(share=True, inbrowser=True, debug=True) - -# concurrency_count > 1 requires more memory, max_size: queue size -# T4 medium: 30GB, model size: ~4G concurrency_count = 6 -# leave one for api access -# reduce to 5 if OOM occurs to often - -demo.queue(concurrency_count=6, max_size=30).launch(debug=True) diff --git a/spaces/JeffJing/ZookChatBot/steamship/cli/requirements_init_wizard.py b/spaces/JeffJing/ZookChatBot/steamship/cli/requirements_init_wizard.py deleted file mode 100644 index 2162f11e3547d98a3c6517f0d52d27513a2a2b46..0000000000000000000000000000000000000000 --- a/spaces/JeffJing/ZookChatBot/steamship/cli/requirements_init_wizard.py +++ /dev/null @@ -1,20 +0,0 @@ -import click - -import steamship - - -def requirements_init_wizard(): - click.secho( - "Steamship uses requirements.txt to specify dependencies. You do not currently have a requirements.txt in this directory.", - fg="yellow", - ) - if not click.confirm("Would you like to create one automatically?", default=True): - click.secho("Please manually create a requirements.txt and try again.") - click.get_current_context().abort() - - with open("requirements.txt", "w") as requirements_file: - requirements_file.write(f"steamship=={steamship.__version__}\n") - - click.secho( - "Created a requirements.txt with the steamship dependency. If you need others, they must be added manually." - ) diff --git a/spaces/Joeythemonster/magic-diffusion/share_btn.py b/spaces/Joeythemonster/magic-diffusion/share_btn.py deleted file mode 100644 index 1382fb25a5ef50e843598187e1e660e86ea8dd05..0000000000000000000000000000000000000000 --- a/spaces/Joeythemonster/magic-diffusion/share_btn.py +++ /dev/null @@ -1,88 +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 = `magic-prompt-${{imgId}}.png`; - return new File([blob], fileName, { type: 'image/png' }); - }else{ - const fileName = `magic-prompt-${{imgId}}.jpg`; - return new File([blob], fileName, { type: 'image/jpeg' }); - } - } - const gradioEl = document.querySelector('body > gradio-app'); - // const gradioEl = document.querySelector("gradio-app").shadowRoot; - const inputImgEl = gradioEl.querySelector('#input-img img'); - const imgEls = gradioEl.querySelectorAll('#generated-gallery img'); - const promptTxt = gradioEl.querySelector('#translated textarea').value; - let titleTxt = promptTxt; - 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(!imgEls.length){ - return; - }; - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - const files = await Promise.all( - [...imgEls].map(async (imgEl) => { - const res = await fetch(imgEl.src); - const blob = await res.blob(); - const imgId = Date.now() % 200; - const fileName = `sd-perception-${{imgId}}.jpg`; - return new File([blob], fileName, { type: 'image/jpeg' }); - }) - ); - const inputFile = await getInputImgFile(inputImgEl); - files.push(inputFile); - const urls = await Promise.all(files.map((f) => uploadFile(f))); - const urlInputImg = urls.pop(); - const htmlImgs = urls.map(url => ``); - const htmlImgsMd = htmlImgs.join(`\n`); - const descriptionMd = `#### Input img: - -#### Caption: -${promptTxt} -#### Generations: -
    -${htmlImgsMd} -
    `; - const params = new URLSearchParams({ - title: titleTxt, - description: descriptionMd, - }); - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/huggingface-projects/magic-diffusion/new?${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/Joeythemonster/prompt-extend/README.md b/spaces/Joeythemonster/prompt-extend/README.md deleted file mode 100644 index bb2d38d0ea7fb2eafa0b2af2e1d9857959d7592c..0000000000000000000000000000000000000000 --- a/spaces/Joeythemonster/prompt-extend/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Prompt Extend -emoji: ✍️ -colorFrom: indigo -colorTo: indigo -sdk: gradio -sdk_version: 3.8.2 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: daspartho/prompt-extend ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Kaludi/Virtual-AI-Career-Coach_App/app.py b/spaces/Kaludi/Virtual-AI-Career-Coach_App/app.py deleted file mode 100644 index 0ff0379ecf23514c8bd85a017a06dcb526b1a7d2..0000000000000000000000000000000000000000 --- a/spaces/Kaludi/Virtual-AI-Career-Coach_App/app.py +++ /dev/null @@ -1,111 +0,0 @@ -import json -import streamlit as st -import requests -import io -import textwrap -from reportlab.pdfgen import canvas -from reportlab.lib.pagesizes import letter, portrait - -# Define OpenAI API endpoint -API_URL = "https://api.openai.com/v1/chat/completions" - -# Define OpenAI model ID -MODEL_ID = "gpt-3.5-turbo" - -# Define function to generate chat completion -def generate_completion(api_key, message): - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {api_key}", - } - data = { - "model": MODEL_ID, - "messages": [{"role": "user", "content": message}], - "temperature": 0.7, - "max_tokens": 300 - } - response = requests.post(API_URL, headers=headers, data=json.dumps(data)).json() - if "choices" in response: - return response["choices"][0]["message"]["content"].strip() - # total_tokens = response["usage"]["total_tokens"] - else: - raise ValueError("Invalid response from OpenAI API") - -# Define function to generate PDF -def generate_pdf(name, skills, experience, option, education, industry, salary_expectations, response): - buffer = io.BytesIO() - - # Create the PDF - p = canvas.Canvas(buffer, pagesize=portrait(letter), bottomup=1) - p.setFontSize(12) - # Add title to the PDF - p.drawString(250, 750, "Virtual AI Career Coach") - # Write the user's selected options and the response to the PDF - p.drawString(100, 720, f"Name: {name}") - p.drawString(100, 690, f"Skills: {skills}") - p.drawString(100, 660, f"Years of experience: {experience}") - p.drawString(100, 630, f"What brings you here?: {option}") - p.drawString(100, 600, f"Highest level of education: {education}") - p.drawString(100, 570, f"Industry: {industry}") - p.drawString(100, 540, f"Salary expectations: {salary_expectations}") - - # Split the response into multiple lines - lines = textwrap.wrap(response, width=80) - y = 480 - for line in lines: - p.drawString(100, y, line) - y -= 20 - - # Save the PDF - p.showPage() - p.save() - - # Set the buffer's position to the beginning - buffer.seek(0) - - return buffer - - - - -# Define Streamlit app -def app(): - st.set_page_config(page_title="Virtual AI Career Coach") - st.title("Virtual AI Career Coach") - st.write("Welcome to the Virtual AI Career Coach app! Here, you can get personalized career advice based on your skills, experience, career goals, etc. using the ChatGPT API. You are then able to download the responses and selections as a PDF to keep it with you.") - - api_key = st.text_input("OpenAI API key", type="password") - if api_key == "": - st.warning("Please enter your OpenAI API key to continue.") - else: - name = st.text_input("Name:") - skills = st.text_input("Current Skills (comma-separated):") - # Add education input field - education = st.text_input("Highest level of education (e.g. Bachelor's, Master's, Doctoral):") - option = st.selectbox("What brings you here?", ["Job Search", "Career Advancement", "New Career Field"]) - # Add industry input field - industry = st.text_input("Industry (e.g. healthcare, technology, finance):") - # Add salary expectations input field - salary_expectations = st.text_input("Salary expectations:") - experience = st.slider("Years of experience:", min_value=0, max_value=50, value=0) - submit_button = st.button("Submit") - - if submit_button: - # Generate the response - if option == "New Career Field": - prompt = f"You are a professional career coach named Coach. My name is {name}. I have {experience} years of experience in {skills}, and my highest level of education is {education}. I am interested in exploring new job fields in {industry} with a salary expectation of {salary_expectations}. What advice for new jobs can you give me in less than 250 words?" - elif option == "Job Search": - prompt = f"You are a professional career coach named Coach. My name is {name}. I have {experience} years of experience in {skills}, and my highest level of education is {education}. I am Job searching in {industry} with a salary expectation of {salary_expectations}. What advice can you give me in less than 250 words?" - elif option == "Career Advancement": - prompt = f"You are a professional career coach named Coach. My name is {name}. I have {experience} years of experience in {skills}, and my highest level of education is {education}. I am looking for a career advancement in {industry} with a salary expectation of {salary_expectations}. What advice can you give me in less than 250 words?" - - response = generate_completion(api_key, prompt) - st.write(response) - # Add a button to download the user's selected options and the response as a PDF - pdf_bytes = generate_pdf(name, skills, experience, option, education, industry, salary_expectations, response) - st.download_button(label="Download as PDF", data=pdf_bytes, file_name="career_advice.pdf", mime="application/pdf",) - - -# Run the Streamlit app -if __name__ == "__main__": - app() diff --git a/spaces/Kayson/InstructDiffusion/scripts/run_multinode.sh b/spaces/Kayson/InstructDiffusion/scripts/run_multinode.sh deleted file mode 100644 index 948f9f68f50be009c9280da2ab0120a4eabac966..0000000000000000000000000000000000000000 --- a/spaces/Kayson/InstructDiffusion/scripts/run_multinode.sh +++ /dev/null @@ -1,6 +0,0 @@ -EXP=$1 -NAME=$2 -GPUMUM=$3 -set -x - -python -m torch.distributed.launch --nnodes=${GPUMUM} --nproc_per_node=8 --node_rank=$NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT main.py --name ${NAME} --base configs/${EXP}.yaml --train --logdir /mnt/data/readout_torch_output/ \ No newline at end of file diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/data_objects/speaker_batch.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/data_objects/speaker_batch.py deleted file mode 100644 index 56651dba5804a0c59c334e49ac18f8f5a4bfa444..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/data_objects/speaker_batch.py +++ /dev/null @@ -1,12 +0,0 @@ -import numpy as np -from typing import List -from encoder.data_objects.speaker import Speaker - -class SpeakerBatch: - def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int): - self.speakers = speakers - self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers} - - # Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with - # 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40) - self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]]) diff --git a/spaces/Kimata/Sanskrit-TTS/utils/cleaner_utils.py b/spaces/Kimata/Sanskrit-TTS/utils/cleaner_utils.py deleted file mode 100644 index 6cf6058850f2dad34e43a7946fc513a904e9620e..0000000000000000000000000000000000000000 --- a/spaces/Kimata/Sanskrit-TTS/utils/cleaner_utils.py +++ /dev/null @@ -1,112 +0,0 @@ -import re -def run(): - - # The path to the local git repo for Indic NLP library - INDIC_NLP_LIB_HOME=r"./indic_nlp_library" - - # The path to the local git repo for Indic NLP Resources - INDIC_NLP_RESOURCES=r"./indic_nlp_resources" - import sys - sys.path.append(r'{}'.format(INDIC_NLP_LIB_HOME)) - - from indicnlp import common - common.set_resources_path(INDIC_NLP_RESOURCES) - - from indicnlp import loader - loader.load() - -run() - -from indicnlp.normalize.indic_normalize import IndicNormalizerFactory -from indicnlp.tokenize import sentence_tokenize -from indicnlp.syllable import syllabifier - -lang='sa' -factory=IndicNormalizerFactory() -normalizer=factory.get_normalizer("hi") -DEPENDENT_VOWELS = ["ा", "ि", "ी", "ु", "ू", "े", "ै", "ो", "ौ", "ं", "ः", "ृ", "ॄ"] - -dict_num = {"०": "शून्य", "१": "एक", "२": "द्वि", "३": "त्रि", - "४": "चतुर्", "५": "पञ्च", "६": "षट्", "७": "सप्त", "८": "अष्ट", "९": "नव"} - -def tokenize_sentence(text): - '''Tokenize a paragraph into sentences''' - sentences = sentence_tokenize.sentence_split(text, lang='sa') - return sentences - -def clean_text(text): - processed_text = re.sub(r'\+ +', '', text) - processed_text = re.sub(': +', '\n \n', processed_text) - processed_text = re.sub(r'\+ ।', '\n \n', processed_text) - processed_text = re.sub(r'\+$', '', processed_text) - return processed_text - -def syllabify_text(text): - text_list = [] - #Syllabify text - for char in text: - if char in DEPENDENT_VOWELS: - char = "(" + char + ")" - text_list.append(char) - else: - text_list.append(char) - - full_text = " + ".join(text_list).replace("'", "") - return full_text - - -def normalize_text(text): - output_string = "" - #Map sanskrit numbers to their normalized form. - for char in text: - if char in dict_num: - output_string += dict_num[char] - else: - output_string += char - return output_string - - -def preprocess_text(text): - '''Cleans, tokenizes and normalizes text''' - #Normalize text - normalized_text = normalize_text(text) - - #Tokenize text. - tokenized_text = tokenize_sentence(normalized_text) - tokenized_text = "\n".join(tokenized_text) - - #Syllabify_text - syllabified_text = syllabify_text(tokenized_text) - - #Clean text - cleaned_text = clean_text(syllabified_text) - - #Remove unnecessary characters from a string. - text_cleaned = [] - for index, text in enumerate(cleaned_text.split('\n')): - if text.startswith('+'): - text = text[2:] - - elif text.startswith(' +'): - text = text[3:] - - elif text.endswith('+') or text.endswith(' +'): - text = text[:-2] - - text_cleaned.append(text) - - text_cleaned_str = "\n".join(text_cleaned) - - return text_cleaned_str - - -# DEFAULT_TEXT = """तो क्या विश्व कप 2019 में मैच का बॉस टॉस है? यानी मैच में हार-जीत में \ -# टॉस की भूमिका अहम है? आप ऐसा सोच सकते हैं। विश्वकप के अपने-अपने पहले मैच में बुरी तरह हारने वाली एशिया की दो टीमों \ -# पाकिस्तान और श्रीलंका के कप्तान ने हालांकि अपने हार के पीछे टॉस की दलील तो नहीं दी, लेकिन यह जरूर कहा था कि वह एक अहम टॉस हार गए थे।""" -# DEFAULT_TEXT='संस्कृतम् जगतः एकतमा अतिप्राचीना समृद्धा शास्त्रीया च भाषासु वर्तते । संस्कृतं भारतस्य जगत: वा भाषासु एकतमा‌ प्राचीनतमा ।' -DEFAULT_TEXT = "अयं द्वितीयशब्दः २ अस्ति। प्रथमः शब्दः १ अस्ति। अन्ये शब्दाः सर्वे द्वितीयं शब्दं प्रयोजयन्ति। इत्थं सप्ततिः शब्दाः लिखिताः सन्ति। अस्मिन लेखने सर्वे अक्षराः संस्कृते लिखिताः सन्ति। अन्ये लिखन्ति ३, ४, ५ इत्यादि। तथापि, अहं एकं अक्षरं एव उपयोगामि।" - -print(f"Default text is: {DEFAULT_TEXT}") -print('\n \n') -NORMALIZED_TEXT = preprocess_text(DEFAULT_TEXT) -print(f"Syllabified text is: {NORMALIZED_TEXT}") diff --git a/spaces/KyanChen/FunSR/models/metasr.py b/spaces/KyanChen/FunSR/models/metasr.py deleted file mode 100644 index 83aa62d5dfbcc8c6e0e5ef84fd85fee5740d2128..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/FunSR/models/metasr.py +++ /dev/null @@ -1,70 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -import models -from models import register -from utils import make_coord - - -@register('metasr') -class MetaSR(nn.Module): - - def __init__(self, encoder_spec): - super().__init__() - - self.encoder = models.make(encoder_spec) - imnet_spec = { - 'name': 'mlp', - 'args': { - 'in_dim': 3, - 'out_dim': self.encoder.out_dim * 9 * 3, - 'hidden_list': [256] - } - } - self.imnet = models.make(imnet_spec) - - def gen_feat(self, inp): - self.feat = self.encoder(inp) - return self.feat - - def query_rgb(self, coord, cell=None): - feat = self.feat - feat = F.unfold(feat, 3, padding=1).view( - feat.shape[0], feat.shape[1] * 9, feat.shape[2], feat.shape[3]) - - feat_coord = make_coord(feat.shape[-2:], flatten=False).cuda() - feat_coord[:, :, 0] -= (2 / feat.shape[-2]) / 2 - feat_coord[:, :, 1] -= (2 / feat.shape[-1]) / 2 - feat_coord = feat_coord.permute(2, 0, 1) \ - .unsqueeze(0).expand(feat.shape[0], 2, *feat.shape[-2:]) - - coord_ = coord.clone() - coord_[:, :, 0] -= cell[:, :, 0] / 2 - coord_[:, :, 1] -= cell[:, :, 1] / 2 - coord_q = (coord_ + 1e-6).clamp(-1 + 1e-6, 1 - 1e-6) - q_feat = F.grid_sample( - feat, coord_q.flip(-1).unsqueeze(1), - mode='nearest', align_corners=False)[:, :, 0, :] \ - .permute(0, 2, 1) - q_coord = F.grid_sample( - feat_coord, coord_q.flip(-1).unsqueeze(1), - mode='nearest', align_corners=False)[:, :, 0, :] \ - .permute(0, 2, 1) - - rel_coord = coord_ - q_coord - rel_coord[:, :, 0] *= feat.shape[-2] / 2 - rel_coord[:, :, 1] *= feat.shape[-1] / 2 - - r_rev = cell[:, :, 0] * (feat.shape[-2] / 2) - inp = torch.cat([rel_coord, r_rev.unsqueeze(-1)], dim=-1) - - bs, q = coord.shape[:2] - pred = self.imnet(inp.view(bs * q, -1)).view(bs * q, feat.shape[1], 3) - pred = torch.bmm(q_feat.contiguous().view(bs * q, 1, -1), pred) - pred = pred.view(bs, q, 3) - return pred - - def forward(self, inp, coord, cell): - self.gen_feat(inp) - return self.query_rgb(coord, cell) diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/separate.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/separate.py deleted file mode 100644 index 890ef271fe61690106424ea7bf79a1cff3d849d3..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/separate.py +++ /dev/null @@ -1,185 +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 argparse -import sys -from pathlib import Path -import subprocess - -import julius -import torch as th -import torchaudio as ta - -from .audio import AudioFile, convert_audio_channels -from .pretrained import is_pretrained, load_pretrained -from .utils import apply_model, load_model - - -def load_track(track, device, audio_channels, samplerate): - errors = {} - wav = None - - try: - wav = AudioFile(track).read( - streams=0, - samplerate=samplerate, - channels=audio_channels).to(device) - except FileNotFoundError: - errors['ffmpeg'] = 'Ffmpeg is not installed.' - except subprocess.CalledProcessError: - errors['ffmpeg'] = 'FFmpeg could not read the file.' - - if wav is None: - try: - wav, sr = ta.load(str(track)) - except RuntimeError as err: - errors['torchaudio'] = err.args[0] - else: - wav = convert_audio_channels(wav, audio_channels) - wav = wav.to(device) - wav = julius.resample_frac(wav, sr, samplerate) - - if wav is None: - print(f"Could not load file {track}. " - "Maybe it is not a supported file format? ") - for backend, error in errors.items(): - print(f"When trying to load using {backend}, got the following error: {error}") - sys.exit(1) - return wav - - -def encode_mp3(wav, path, bitrate=320, samplerate=44100, channels=2, verbose=False): - try: - import lameenc - except ImportError: - print("Failed to call lame encoder. Maybe it is not installed? " - "On windows, run `python.exe -m pip install -U lameenc`, " - "on OSX/Linux, run `python3 -m pip install -U lameenc`, " - "then try again.", file=sys.stderr) - sys.exit(1) - encoder = lameenc.Encoder() - encoder.set_bit_rate(bitrate) - encoder.set_in_sample_rate(samplerate) - encoder.set_channels(channels) - encoder.set_quality(2) # 2-highest, 7-fastest - if not verbose: - encoder.silence() - wav = wav.transpose(0, 1).numpy() - mp3_data = encoder.encode(wav.tobytes()) - mp3_data += encoder.flush() - with open(path, "wb") as f: - f.write(mp3_data) - - -def main(): - parser = argparse.ArgumentParser("demucs.separate", - description="Separate the sources for the given tracks") - parser.add_argument("audios/tracks", nargs='+', type=Path, default=[], help='Path to tracks') - parser.add_argument("-n", - "--name", - default="demucs_quantized", - help="Model name. See README.md for the list of pretrained models. " - "Default is demucs_quantized.") - parser.add_argument("-v", "--verbose", action="store_true") - parser.add_argument("-o", - "--out", - type=Path, - default=Path("audios/separated"), - help="Folder where to put extracted tracks. A subfolder " - "with the model name will be created.") - parser.add_argument("--models", - type=Path, - default=Path("models"), - help="Path to trained models. " - "Also used to store downloaded pretrained models") - parser.add_argument("-d", - "--device", - default="cuda" if th.cuda.is_available() else "cpu", - help="Device to use, default is cuda if available else cpu") - parser.add_argument("--shifts", - default=0, - type=int, - help="Number of random shifts for equivariant stabilization." - "Increase separation time but improves quality for Demucs. 10 was used " - "in the original paper.") - parser.add_argument("--overlap", - default=0.25, - type=float, - help="Overlap between the splits.") - parser.add_argument("--no-split", - action="store_false", - dest="split", - default=True, - help="Doesn't split audio in chunks. This can use large amounts of memory.") - parser.add_argument("--float32", - action="store_true", - help="Convert the output wavefile to use pcm f32 format instead of s16. " - "This should not make a difference if you just plan on listening to the " - "audio but might be needed to compute exactly metrics like SDR etc.") - parser.add_argument("--int16", - action="store_false", - dest="float32", - help="Opposite of --float32, here for compatibility.") - parser.add_argument("--mp3", action="store_true", - help="Convert the output wavs to mp3.") - parser.add_argument("--mp3-bitrate", - default=320, - type=int, - help="Bitrate of converted mp3.") - - args = parser.parse_args() - name = args.name + ".th" - model_path = args.models / name - if model_path.is_file(): - model = load_model(model_path) - else: - if is_pretrained(args.name): - model = load_pretrained(args.name) - else: - print(f"No pre-trained model {args.name}", file=sys.stderr) - sys.exit(1) - model.to(args.device) - - out = args.out / args.name - out.mkdir(parents=True, exist_ok=True) - print(f"Separated tracks will be stored in {out.resolve()}") - for track in args.tracks: - if not track.exists(): - print( - f"File {track} does not exist. If the path contains spaces, " - "please try again after surrounding the entire path with quotes \"\".", - file=sys.stderr) - continue - print(f"Separating track {track}") - wav = load_track(track, args.device, model.audio_channels, model.samplerate) - - ref = wav.mean(0) - wav = (wav - ref.mean()) / ref.std() - sources = apply_model(model, wav, shifts=args.shifts, split=args.split, - overlap=args.overlap, progress=True) - sources = sources * ref.std() + ref.mean() - - track_folder = out / track.name.rsplit(".", 1)[0] - track_folder.mkdir(exist_ok=True) - for source, name in zip(sources, model.sources): - source = source / max(1.01 * source.abs().max(), 1) - if args.mp3 or not args.float32: - source = (source * 2**15).clamp_(-2**15, 2**15 - 1).short() - source = source.cpu() - stem = str(track_folder / name) - if args.mp3: - encode_mp3(source, stem + ".mp3", - bitrate=args.mp3_bitrate, - samplerate=model.samplerate, - channels=model.audio_channels, - verbose=args.verbose) - else: - wavname = str(track_folder / f"{name}.wav") - ta.save(wavname, source, sample_rate=model.samplerate) - - -if __name__ == "__main__": - main() diff --git a/spaces/LightChen2333/OpenSLU/common/__init__.py b/spaces/LightChen2333/OpenSLU/common/__init__.py deleted file mode 100644 index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000 --- a/spaces/LightChen2333/OpenSLU/common/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/spaces/LittleLirow/fearflixai/bgm.py b/spaces/LittleLirow/fearflixai/bgm.py deleted file mode 100644 index 4d0c2c69e731433a911744b02c26b4e8942f1619..0000000000000000000000000000000000000000 --- a/spaces/LittleLirow/fearflixai/bgm.py +++ /dev/null @@ -1,31 +0,0 @@ -# import gradio as gr -# from audioldm import text_to_audio, build_model - -# model_id="haoheliu/AudioLDM-S-Full" - -# audioldm = None -# current_model_name = None - -# def text2audio(text, duration, guidance_scale, random_seed, n_candidates, model_name="audioldm-m-text-ft"): -# global audioldm, current_model_name - -# if audioldm is None or model_name != current_model_name: -# audioldm=build_model(model_name=model_name) -# current_model_name = model_name - -# # print(text, length, guidance_scale) -# waveform = text_to_audio( -# latent_diffusion=audioldm, -# text=text, -# seed=random_seed, -# duration=duration, -# guidance_scale=guidance_scale, -# n_candidate_gen_per_text=int(n_candidates), -# ) # [bs, 1, samples] -# waveform = [ -# gr.make_waveform((16000, wave[0]), bg_image="bg.png") for wave in waveform -# ] -# # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))] -# if(len(waveform) == 1): -# waveform = waveform[0] -# return waveform \ No newline at end of file diff --git a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/text/cleaners.py b/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/text/cleaners.py deleted file mode 100644 index ec0cf5ea69e7dadf4ca1332273032aaa73a31c0d..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/text/cleaners.py +++ /dev/null @@ -1,106 +0,0 @@ -import re -from text.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3 -from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2 - -def japanese_cleaners(text): - from text.japanese import japanese_to_romaji_with_accent - text = japanese_to_romaji_with_accent(text) - if re.match('[A-Za-z]', text[-1]): - text += '.' - return text - - -def japanese_cleaners2(text): - return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…') - - -def korean_cleaners(text): - '''Pipeline for Korean text''' - from text.korean import latin_to_hangul, number_to_hangul, divide_hangul - text = latin_to_hangul(text) - text = number_to_hangul(text) - text = divide_hangul(text) - if re.match('[\u3131-\u3163]', text[-1]): - text += '.' - return text - - -def chinese_cleaners(text): - '''Pipeline for Chinese text''' - from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo - text = number_to_chinese(text) - text = chinese_to_bopomofo(text) - text = latin_to_bopomofo(text) - if re.match('[ˉˊˇˋ˙]', text[-1]): - text += '。' - return text - - -def zh_ja_mixture_cleaners(text): - from text.mandarin import chinese_to_romaji - from text.japanese import japanese_to_romaji_with_accent - chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text) - japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text) - for chinese_text in chinese_texts: - cleaned_text = chinese_to_romaji(chinese_text[4:-4]) - text = text.replace(chinese_text, cleaned_text+' ', 1) - for japanese_text in japanese_texts: - cleaned_text = japanese_to_romaji_with_accent( - japanese_text[4:-4]).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…') - text = text.replace(japanese_text, cleaned_text+' ', 1) - text = text[:-1] - if re.match('[A-Za-zɯɹəɥ→↓↑]', text[-1]): - text += '.' - return text - - -def sanskrit_cleaners(text): - text = text.replace('॥', '।').replace('ॐ', 'ओम्') - if text[-1] != '।': - text += ' ।' - return text - - -def cjks_cleaners(text): - from text.mandarin import chinese_to_lazy_ipa - from text.japanese import japanese_to_ipa - from text.korean import korean_to_lazy_ipa - from text.sanskrit import devanagari_to_ipa - chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text) - japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text) - korean_texts = re.findall(r'\[KO\].*?\[KO\]', text) - sanskrit_texts = re.findall(r'\[SA\].*?\[SA\]', text) - for chinese_text in chinese_texts: - cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4]) - text = text.replace(chinese_text, cleaned_text+' ', 1) - for japanese_text in japanese_texts: - cleaned_text = japanese_to_ipa(japanese_text[4:-4]) - text = text.replace(japanese_text, cleaned_text+' ', 1) - for korean_text in korean_texts: - cleaned_text = korean_to_lazy_ipa(korean_text[4:-4]) - text = text.replace(korean_text, cleaned_text+' ', 1) - for sanskrit_text in sanskrit_texts: - cleaned_text = devanagari_to_ipa(sanskrit_text[4:-4]) - text = text.replace(sanskrit_text, cleaned_text+' ', 1) - text = text[:-1] - if re.match(r'[^\.,!\?\-…~]', text[-1]): - text += '.' - return text - -def cjke_cleaners(text): - chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text) - japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text) - for chinese_text in chinese_texts: - cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4]) - cleaned_text = cleaned_text.replace( - 'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn') - text = text.replace(chinese_text, cleaned_text+' ', 1) - for japanese_text in japanese_texts: - cleaned_text = japanese_to_ipa(japanese_text[4:-4]) - cleaned_text = cleaned_text.replace('ʧ', 'tʃ').replace( - 'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz') - text = text.replace(japanese_text, cleaned_text+' ', 1) - text = text[:-1] - if re.match(r'[^\.,!\?\-…~]', text[-1]): - text += '.' - return text \ No newline at end of file diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py deleted file mode 100644 index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000 --- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py +++ /dev/null @@ -1,221 +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] -# ------------------------------------------------------------------------ -# 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. -# ------------------------------------------------------------------------ - -""" -Backbone modules. -""" - -from typing import Dict, List - -import torch -import torch.nn.functional as F -import torchvision -from torch import nn -from torchvision.models._utils import IntermediateLayerGetter - -from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process - -from .position_encoding import build_position_encoding -from .swin_transformer import build_swin_transformer - - -class FrozenBatchNorm2d(torch.nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - Copy-paste from torchvision.misc.ops with added eps before rqsrt, - without which any other models than torchvision.models.resnet[18,34,50,101] - produce nans. - """ - - def __init__(self, n): - super(FrozenBatchNorm2d, self).__init__() - self.register_buffer("weight", torch.ones(n)) - self.register_buffer("bias", torch.zeros(n)) - self.register_buffer("running_mean", torch.zeros(n)) - self.register_buffer("running_var", torch.ones(n)) - - def _load_from_state_dict( - self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ): - num_batches_tracked_key = prefix + "num_batches_tracked" - if num_batches_tracked_key in state_dict: - del state_dict[num_batches_tracked_key] - - super(FrozenBatchNorm2d, self)._load_from_state_dict( - state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ) - - def forward(self, x): - # move reshapes to the beginning - # to make it fuser-friendly - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - rv = self.running_var.reshape(1, -1, 1, 1) - rm = self.running_mean.reshape(1, -1, 1, 1) - eps = 1e-5 - scale = w * (rv + eps).rsqrt() - bias = b - rm * scale - return x * scale + bias - - -class BackboneBase(nn.Module): - def __init__( - self, - backbone: nn.Module, - train_backbone: bool, - num_channels: int, - return_interm_indices: list, - ): - super().__init__() - for name, parameter in backbone.named_parameters(): - if ( - not train_backbone - or "layer2" not in name - and "layer3" not in name - and "layer4" not in name - ): - parameter.requires_grad_(False) - - return_layers = {} - for idx, layer_index in enumerate(return_interm_indices): - return_layers.update( - {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)} - ) - - # if len: - # if use_stage1_feature: - # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} - # else: - # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} - # else: - # return_layers = {'layer4': "0"} - self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) - self.num_channels = num_channels - - def forward(self, tensor_list: NestedTensor): - xs = self.body(tensor_list.tensors) - out: Dict[str, NestedTensor] = {} - for name, x in xs.items(): - m = tensor_list.mask - assert m is not None - mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - out[name] = NestedTensor(x, mask) - # import ipdb; ipdb.set_trace() - return out - - -class Backbone(BackboneBase): - """ResNet backbone with frozen BatchNorm.""" - - def __init__( - self, - name: str, - train_backbone: bool, - dilation: bool, - return_interm_indices: list, - batch_norm=FrozenBatchNorm2d, - ): - if name in ["resnet18", "resnet34", "resnet50", "resnet101"]: - backbone = getattr(torchvision.models, name)( - replace_stride_with_dilation=[False, False, dilation], - pretrained=is_main_process(), - norm_layer=batch_norm, - ) - else: - raise NotImplementedError("Why you can get here with name {}".format(name)) - # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 - assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available." - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - num_channels_all = [256, 512, 1024, 2048] - num_channels = num_channels_all[4 - len(return_interm_indices) :] - super().__init__(backbone, train_backbone, num_channels, return_interm_indices) - - -class Joiner(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - - def forward(self, tensor_list: NestedTensor): - xs = self[0](tensor_list) - out: List[NestedTensor] = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self[1](x).to(x.tensors.dtype)) - - return out, pos - - -def build_backbone(args): - """ - Useful args: - - backbone: backbone name - - lr_backbone: - - dilation - - return_interm_indices: available: [0,1,2,3], [1,2,3], [3] - - backbone_freeze_keywords: - - use_checkpoint: for swin only for now - - """ - position_embedding = build_position_encoding(args) - train_backbone = True - if not train_backbone: - raise ValueError("Please set lr_backbone > 0") - return_interm_indices = args.return_interm_indices - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - args.backbone_freeze_keywords - use_checkpoint = getattr(args, "use_checkpoint", False) - - if args.backbone in ["resnet50", "resnet101"]: - backbone = Backbone( - args.backbone, - train_backbone, - args.dilation, - return_interm_indices, - batch_norm=FrozenBatchNorm2d, - ) - bb_num_channels = backbone.num_channels - elif args.backbone in [ - "swin_T_224_1k", - "swin_B_224_22k", - "swin_B_384_22k", - "swin_L_224_22k", - "swin_L_384_22k", - ]: - pretrain_img_size = int(args.backbone.split("_")[-2]) - backbone = build_swin_transformer( - args.backbone, - pretrain_img_size=pretrain_img_size, - out_indices=tuple(return_interm_indices), - dilation=False, - use_checkpoint=use_checkpoint, - ) - - bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :] - else: - raise NotImplementedError("Unknown backbone {}".format(args.backbone)) - - assert len(bb_num_channels) == len( - return_interm_indices - ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}" - - model = Joiner(backbone, position_embedding) - model.num_channels = bb_num_channels - assert isinstance( - bb_num_channels, List - ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels)) - # import ipdb; ipdb.set_trace() - return model diff --git a/spaces/Makiing/coolb-in-gtest/src/components/chat-history.tsx b/spaces/Makiing/coolb-in-gtest/src/components/chat-history.tsx deleted file mode 100644 index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000 --- a/spaces/Makiing/coolb-in-gtest/src/components/chat-history.tsx +++ /dev/null @@ -1,48 +0,0 @@ -import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons" - -export function ChatHistory() { - return ( -
    -
    - 历史记录 -
    -
    -
    -
    -
    -
    -
    - -
    -

    无标题的聊天

    -
    -

    上午1:42

    -
    - - - - - - - - -
    -
    -
    -
    -
    -
    -
    -
    - ) -} diff --git a/spaces/MoonQiu/LongerCrafter/lvdm/modules/attention_freenoise.py b/spaces/MoonQiu/LongerCrafter/lvdm/modules/attention_freenoise.py deleted file mode 100644 index 145d35f64f5ae906046ece8646fd3047456bece6..0000000000000000000000000000000000000000 --- a/spaces/MoonQiu/LongerCrafter/lvdm/modules/attention_freenoise.py +++ /dev/null @@ -1,565 +0,0 @@ -from functools import partial -import torch -from torch import nn, einsum -import torch.nn.functional as F -from einops import rearrange, repeat -try: - import xformers - import xformers.ops - XFORMERS_IS_AVAILBLE = True -except: - XFORMERS_IS_AVAILBLE = False -from lvdm.common import ( - checkpoint, - exists, - default, -) -from lvdm.basics import ( - zero_module, -) - -def generate_weight_sequence(n): - if n % 2 == 0: - max_weight = n // 2 - weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1)) - else: - max_weight = (n + 1) // 2 - weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1)) - return weight_sequence - -class RelativePosition(nn.Module): - """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """ - - def __init__(self, num_units, max_relative_position): - super().__init__() - self.num_units = num_units - self.max_relative_position = max_relative_position - self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units)) - nn.init.xavier_uniform_(self.embeddings_table) - - def forward(self, length_q, length_k): - device = self.embeddings_table.device - range_vec_q = torch.arange(length_q, device=device) - range_vec_k = torch.arange(length_k, device=device) - distance_mat = range_vec_k[None, :] - range_vec_q[:, None] - distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position) - final_mat = distance_mat_clipped + self.max_relative_position - final_mat = final_mat.long() - embeddings = self.embeddings_table[final_mat] - return embeddings - - -class CrossAttention(nn.Module): - - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., - relative_position=False, temporal_length=None, img_cross_attention=False, injection=False): - super().__init__() - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - - self.scale = dim_head**-0.5 - 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.image_cross_attention_scale = 1.0 - self.text_context_len = 77 - self.img_cross_attention = img_cross_attention - if self.img_cross_attention: - self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) - - self.relative_position = relative_position - if self.relative_position: - assert(temporal_length is not None) - self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) - self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) - else: - ## only used for spatial attention, while NOT for temporal attention - if XFORMERS_IS_AVAILBLE and temporal_length is None: - self.forward = self.efficient_forward - - self.injection = injection - - def forward(self, x, context=None, mask=None, context_next=None, use_injection=False): - - sa_flag = False - if context is None: - sa_flag = True - - h = self.heads - - all_q = self.to_q(x) - context = default(context, x) - ## considering image token additionally - if context is not None and self.img_cross_attention: - context, context_img = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] - all_k = self.to_k(context) - all_v = self.to_v(context) - all_k_ip = self.to_k_ip(context_img) - all_v_ip = self.to_v_ip(context_img) - else: - all_k = self.to_k(context) - all_v = self.to_v(context) - - count = torch.zeros_like(all_k) - value = torch.zeros_like(all_k) - - if (sa_flag) and (context_next is not None): - all_q, all_k, all_v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_q, all_k, all_v)) - if context is not None and self.img_cross_attention: - all_k_ip, all_v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_k_ip, all_v_ip)) - for t_start, t_end in context_next: - weight_sequence = generate_weight_sequence(t_end - t_start) - weight_tensor = torch.ones_like(count[:, t_start:t_end]) - weight_tensor = weight_tensor * torch.Tensor(weight_sequence).to(x.device).unsqueeze(0).unsqueeze(-1) - - q = all_q[:, t_start:t_end] - k = all_k[:, t_start:t_end] - v = all_v[:, t_start:t_end] - - sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale - if self.relative_position: - len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] - k2 = self.relative_position_k(len_q, len_k) - sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check - sim += sim2 - del k - - if exists(mask): - ## feasible for causal attention mask only - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b i j -> (b h) i j', h=h) - sim.masked_fill_(~(mask>0.5), max_neg_value) - - # attention, what we cannot get enough of - sim = sim.softmax(dim=-1) - out = torch.einsum('b i j, b j d -> b i d', sim, v) - if self.relative_position: - v2 = self.relative_position_v(len_q, len_v) - out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check - out += out2 - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - - ## considering image token additionally - if context is not None and self.img_cross_attention: - k_ip = all_k_ip[:, t_start:t_end] - v_ip = all_v_ip[:, t_start:t_end] - sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale - del k_ip - sim_ip = sim_ip.softmax(dim=-1) - out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip) - out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h) - out = out + self.image_cross_attention_scale * out_ip - del q - - value[:,t_start:t_end] += out * weight_tensor - count[:,t_start:t_end] += weight_tensor - - final_out = torch.where(count>0, value/count, value) - - else: - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_q, all_k, all_v)) - sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale - if self.relative_position: - len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] - k2 = self.relative_position_k(len_q, len_k) - sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check - sim += sim2 - del k - - if exists(mask): - ## feasible for causal attention mask only - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b i j -> (b h) i j', h=h) - sim.masked_fill_(~(mask>0.5), max_neg_value) - - # attention, what we cannot get enough of - sim = sim.softmax(dim=-1) - out = torch.einsum('b i j, b j d -> b i d', sim, v) - if self.relative_position: - v2 = self.relative_position_v(len_q, len_v) - out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check - out += out2 - final_out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - - ## considering image token additionally - if context is not None and self.img_cross_attention: - k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (all_k_ip, all_v_ip)) - sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale - del k_ip - sim_ip = sim_ip.softmax(dim=-1) - out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip) - out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h) - final_out = final_out + self.image_cross_attention_scale * out_ip - del q - - return self.to_out(final_out) - - def efficient_forward(self, x, context=None, mask=None, context_next=None, use_injection=False): - - sa_flag = False - if context is None: - sa_flag = True - - q = self.to_q(x) - context = default(context, x) - - if not sa_flag: - sq_size = x.shape[0] - if self.injection and use_injection: - context_new = context[-sq_size:] - else: - context_new = context[:sq_size] - else: - context_new = context.clone() - - ## considering image token additionally - if context is not None and self.img_cross_attention: - context, context_img = context_new[:,:self.text_context_len,:], context_new[:,self.text_context_len:,:] - k = self.to_k(context) - v = self.to_v(context) - k_ip = self.to_k_ip(context_img) - v_ip = self.to_v_ip(context_img) - else: - k = self.to_k(context_new) - v = self.to_v(context_new) - - 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=None) - - ## considering image token additionally - if context is not None and self.img_cross_attention: - k_ip, v_ip = 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(), - (k_ip, v_ip), - ) - out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None) - out_ip = ( - out_ip.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) - ) - - 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) - ) - if context is not None and self.img_cross_attention: - out = out + self.image_cross_attention_scale * out_ip - return self.to_out(out) - - -class BasicTransformerBlock(nn.Module): - - def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, - disable_self_attn=False, attention_cls=None, img_cross_attention=False, injection=False): - super().__init__() - attn_cls = CrossAttention if attention_cls is None else attention_cls - 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, injection=injection) - 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, - img_cross_attention=img_cross_attention, injection=injection) - self.norm1 = nn.LayerNorm(dim) - self.norm2 = nn.LayerNorm(dim) - self.norm3 = nn.LayerNorm(dim) - self.checkpoint = checkpoint - - def forward(self, x, context=None, mask=None, context_next=None, use_injection=False, **kwargs): - ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments - input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments - if context is not None: - input_tuple = (x, context) - if mask is not None: - forward_mask = partial(self._forward, mask=mask) - return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint) - if context is not None and mask is not None: - input_tuple = (x, context, mask) - input_tuple = (x, context, mask, context_next, use_injection) - return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint) - - def _forward(self, x, context=None, mask=None, context_next=None, use_injection=False): - x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask, context_next=context_next, use_injection=False) + x - x = self.attn2(self.norm2(x), context=context, mask=mask, context_next=context_next, use_injection=use_injection) + x - x = self.ff(self.norm3(x)) + x - return x - - -class SpatialTransformer(nn.Module): - """ - Transformer block for image-like data in spatial axis. - 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, - use_checkpoint=True, disable_self_attn=False, use_linear=False, img_cross_attention=False, injection=False): - super().__init__() - self.in_channels = in_channels - inner_dim = n_heads * d_head - self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - 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, - img_cross_attention=img_cross_attention, - disable_self_attn=disable_self_attn, - checkpoint=use_checkpoint, - injection=injection) 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(inner_dim, in_channels)) - self.use_linear = use_linear - - - def forward(self, x, context=None, **kwargs): - 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, **kwargs) - 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 - - -class TemporalTransformer(nn.Module): - """ - Transformer block for image-like data in temporal axis. - First, reshape to b, t, d. - Then apply standard transformer action. - Finally, reshape to image - """ - def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, - use_checkpoint=True, use_linear=False, only_self_att=True, causal_attention=False, - relative_position=False, temporal_length=None, injection=False): - super().__init__() - self.only_self_att = only_self_att - self.relative_position = relative_position - self.causal_attention = causal_attention - self.in_channels = in_channels - inner_dim = n_heads * d_head - self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) - if not use_linear: - self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) - else: - self.proj_in = nn.Linear(in_channels, inner_dim) - - if relative_position: - assert(temporal_length is not None) - attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length) - else: - attention_cls = partial(CrossAttention, temporal_length=temporal_length) - if self.causal_attention: - assert(temporal_length is not None) - self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length])) - - if self.only_self_att: - context_dim = None - self.transformer_blocks = nn.ModuleList([ - BasicTransformerBlock( - inner_dim, - n_heads, - d_head, - dropout=dropout, - context_dim=context_dim, - attention_cls=attention_cls, - checkpoint=use_checkpoint, - injection=injection) for d in range(depth) - ]) - if not use_linear: - self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) - else: - self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) - self.use_linear = use_linear - - def forward(self, x, context=None, **kwargs): - b, c, t, h, w = x.shape - x_in = x - x = self.norm(x) - x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous() - if not self.use_linear: - x = self.proj_in(x) - x = rearrange(x, 'bhw c t -> bhw t c').contiguous() - if self.use_linear: - x = self.proj_in(x) - - if self.causal_attention: - mask = self.mask.to(x.device) - mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w) - else: - mask = None - - if self.only_self_att: - ## note: if no context is given, cross-attention defaults to self-attention - for i, block in enumerate(self.transformer_blocks): - x = block(x, mask=mask, **kwargs) - x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() - else: - x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() - context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous() - for i, block in enumerate(self.transformer_blocks): - # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) - for j in range(b): - context_j = repeat( - context[j], - 't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous() - ## note: causal mask will not applied in cross-attention case - x[j] = block(x[j], context=context_j, **kwargs) - - if self.use_linear: - x = self.proj_out(x) - x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous() - if not self.use_linear: - x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous() - x = self.proj_out(x) - x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous() - - return x + x_in - - -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) - - -class LinearAttention(nn.Module): - def __init__(self, dim, heads=4, dim_head=32): - super().__init__() - self.heads = heads - hidden_dim = dim_head * heads - self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) - self.to_out = nn.Conv2d(hidden_dim, dim, 1) - - def forward(self, x): - b, c, h, w = x.shape - qkv = self.to_qkv(x) - q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) - k = k.softmax(dim=-1) - context = torch.einsum('bhdn,bhen->bhde', k, v) - out = torch.einsum('bhde,bhdn->bhen', context, q) - out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) - return self.to_out(out) - - -class SpatialSelfAttention(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - 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, **kwargs): - 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_ diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py b/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py deleted file mode 100644 index feea2004b158fa3787b9a9f9d1c2b32e1bb8ae1d..0000000000000000000000000000000000000000 --- a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py +++ /dev/null @@ -1,30 +0,0 @@ -_base_ = [ - '_base_dbnet_resnet18_fpnc.py', - '../_base_/datasets/icdar2015.py', - '../_base_/default_runtime.py', - '../_base_/schedules/schedule_sgd_1200e.py', -] - -# dataset settings -icdar2015_textdet_train = _base_.icdar2015_textdet_train -icdar2015_textdet_train.pipeline = _base_.train_pipeline -icdar2015_textdet_test = _base_.icdar2015_textdet_test -icdar2015_textdet_test.pipeline = _base_.test_pipeline - -train_dataloader = dict( - batch_size=16, - num_workers=8, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=icdar2015_textdet_train) - -val_dataloader = dict( - batch_size=1, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=False), - dataset=icdar2015_textdet_test) - -test_dataloader = val_dataloader - -auto_scale_lr = dict(base_batch_size=16) diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/apis/inferencers/mmocr_inferencer.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/apis/inferencers/mmocr_inferencer.py deleted file mode 100644 index be7f74237875ed42ef5cb099957662c8a125d94c..0000000000000000000000000000000000000000 --- a/spaces/Mountchicken/MAERec-Gradio/mmocr/apis/inferencers/mmocr_inferencer.py +++ /dev/null @@ -1,422 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os.path as osp -from datetime import datetime -from typing import Dict, List, Optional, Tuple, Union - -import mmcv -import mmengine -import numpy as np -from rich.progress import track - -from mmocr.registry import VISUALIZERS -from mmocr.structures import TextSpottingDataSample -from mmocr.utils import ConfigType, bbox2poly, crop_img, poly2bbox -from .base_mmocr_inferencer import (BaseMMOCRInferencer, InputsType, PredType, - ResType) -from .kie_inferencer import KIEInferencer -from .textdet_inferencer import TextDetInferencer -from .textrec_inferencer import TextRecInferencer - - -class MMOCRInferencer(BaseMMOCRInferencer): - """MMOCR Inferencer. It's a wrapper around three base task - inferenecers: TextDetInferencer, TextRecInferencer and KIEInferencer, - and it can be used to perform end-to-end OCR or KIE inference. - - Args: - det (Optional[Union[ConfigType, str]]): Pretrained text detection - algorithm. It's the path to the config file or the model name - defined in metafile. Defaults to None. - det_weights (Optional[str]): Path to the custom checkpoint file of - the selected det model. If it is not specified and "det" is a model - name of metafile, the weights will be loaded from metafile. - Defaults to None. - rec (Optional[Union[ConfigType, str]]): Pretrained text recognition - algorithm. It's the path to the config file or the model name - defined in metafile. Defaults to None. - rec_weights (Optional[str]): Path to the custom checkpoint file of - the selected rec model. If it is not specified and "rec" is a model - name of metafile, the weights will be loaded from metafile. - Defaults to None. - kie (Optional[Union[ConfigType, str]]): Pretrained key information - extraction algorithm. It's the path to the config file or the model - name defined in metafile. Defaults to None. - kie_weights (Optional[str]): Path to the custom checkpoint file of - the selected kie model. If it is not specified and "kie" is a model - name of metafile, the weights will be loaded from metafile. - Defaults to None. - device (Optional[str]): Device to run inference. If None, the available - device will be automatically used. Defaults to None. - - """ - - def __init__(self, - det: Optional[Union[ConfigType, str]] = None, - det_weights: Optional[str] = None, - rec: Optional[Union[ConfigType, str]] = None, - rec_weights: Optional[str] = None, - kie: Optional[Union[ConfigType, str]] = None, - kie_weights: Optional[str] = None, - device: Optional[str] = None) -> None: - - if det is None and rec is None and kie is None: - raise ValueError('At least one of det, rec and kie should be ' - 'provided.') - - self.visualizer = None - - if det is not None: - self.textdet_inferencer = TextDetInferencer( - det, det_weights, device) - self.mode = 'det' - if rec is not None: - self.textrec_inferencer = TextRecInferencer( - rec, rec_weights, device) - if getattr(self, 'mode', None) == 'det': - self.mode = 'det_rec' - ts = str(datetime.timestamp(datetime.now())) - self.visualizer = VISUALIZERS.build( - dict( - type='TextSpottingLocalVisualizer', - name=f'inferencer{ts}', - font_families=self.textrec_inferencer.visualizer. - font_families)) - else: - self.mode = 'rec' - if kie is not None: - if det is None or rec is None: - raise ValueError( - 'kie_config is only applicable when det_config and ' - 'rec_config are both provided') - self.kie_inferencer = KIEInferencer(kie, kie_weights, device) - self.mode = 'det_rec_kie' - - def _inputs2ndarrray(self, inputs: List[InputsType]) -> List[np.ndarray]: - """Preprocess the inputs to a list of numpy arrays.""" - new_inputs = [] - for item in inputs: - if isinstance(item, np.ndarray): - new_inputs.append(item) - elif isinstance(item, str): - img_bytes = mmengine.fileio.get(item) - new_inputs.append(mmcv.imfrombytes(img_bytes)) - else: - raise NotImplementedError(f'The input type {type(item)} is not' - 'supported yet.') - return new_inputs - - def forward(self, - inputs: InputsType, - batch_size: int = 1, - det_batch_size: Optional[int] = None, - rec_batch_size: Optional[int] = None, - kie_batch_size: Optional[int] = None, - **forward_kwargs) -> PredType: - """Forward the inputs to the model. - - Args: - inputs (InputsType): The inputs to be forwarded. - batch_size (int): Batch size. Defaults to 1. - det_batch_size (Optional[int]): Batch size for text detection - model. Overwrite batch_size if it is not None. - Defaults to None. - rec_batch_size (Optional[int]): Batch size for text recognition - model. Overwrite batch_size if it is not None. - Defaults to None. - kie_batch_size (Optional[int]): Batch size for KIE model. - Overwrite batch_size if it is not None. - Defaults to None. - - Returns: - Dict: The prediction results. Possibly with keys "det", "rec", and - "kie".. - """ - result = {} - forward_kwargs['progress_bar'] = False - if det_batch_size is None: - det_batch_size = batch_size - if rec_batch_size is None: - rec_batch_size = batch_size - if kie_batch_size is None: - kie_batch_size = batch_size - if self.mode == 'rec': - # The extra list wrapper here is for the ease of postprocessing - self.rec_inputs = inputs - predictions = self.textrec_inferencer( - self.rec_inputs, - return_datasamples=True, - batch_size=rec_batch_size, - **forward_kwargs)['predictions'] - result['rec'] = [[p] for p in predictions] - elif self.mode.startswith('det'): # 'det'/'det_rec'/'det_rec_kie' - result['det'] = self.textdet_inferencer( - inputs, - return_datasamples=True, - batch_size=det_batch_size, - **forward_kwargs)['predictions'] - if self.mode.startswith('det_rec'): # 'det_rec'/'det_rec_kie' - result['rec'] = [] - for img, det_data_sample in zip( - self._inputs2ndarrray(inputs), result['det']): - det_pred = det_data_sample.pred_instances - self.rec_inputs = [] - for polygon in det_pred['polygons']: - # Roughly convert the polygon to a quadangle with - # 4 points - quad = bbox2poly(poly2bbox(polygon)).tolist() - self.rec_inputs.append(crop_img(img, quad)) - result['rec'].append( - self.textrec_inferencer( - self.rec_inputs, - return_datasamples=True, - batch_size=rec_batch_size, - **forward_kwargs)['predictions']) - if self.mode == 'det_rec_kie': - self.kie_inputs = [] - # TODO: when the det output is empty, kie will fail - # as no gt-instances can be provided. It's a known - # issue but cannot be solved elegantly since we support - # batch inference. - for img, det_data_sample, rec_data_samples in zip( - inputs, result['det'], result['rec']): - det_pred = det_data_sample.pred_instances - kie_input = dict(img=img) - kie_input['instances'] = [] - for polygon, rec_data_sample in zip( - det_pred['polygons'], rec_data_samples): - kie_input['instances'].append( - dict( - bbox=poly2bbox(polygon), - text=rec_data_sample.pred_text.item)) - self.kie_inputs.append(kie_input) - result['kie'] = self.kie_inferencer( - self.kie_inputs, - return_datasamples=True, - batch_size=kie_batch_size, - **forward_kwargs)['predictions'] - return result - - def visualize(self, inputs: InputsType, preds: PredType, - **kwargs) -> Union[List[np.ndarray], None]: - """Visualize predictions. - - Args: - inputs (List[Union[str, np.ndarray]]): Inputs for the inferencer. - preds (List[Dict]): Predictions of the model. - show (bool): Whether to display the image in a popup window. - Defaults to False. - wait_time (float): The interval of show (s). Defaults to 0. - draw_pred (bool): Whether to draw predicted bounding boxes. - Defaults to True. - pred_score_thr (float): Minimum score of bboxes to draw. - Defaults to 0.3. - save_vis (bool): Whether to save the visualization result. Defaults - to False. - img_out_dir (str): Output directory of visualization results. - If left as empty, no file will be saved. Defaults to ''. - - Returns: - List[np.ndarray] or None: Returns visualization results only if - applicable. - """ - - if 'kie' in self.mode: - return self.kie_inferencer.visualize(self.kie_inputs, preds['kie'], - **kwargs) - elif 'rec' in self.mode: - if 'det' in self.mode: - return super().visualize(inputs, - self._pack_e2e_datasamples(preds), - **kwargs) - else: - return self.textrec_inferencer.visualize( - self.rec_inputs, preds['rec'][0], **kwargs) - else: - return self.textdet_inferencer.visualize(inputs, preds['det'], - **kwargs) - - def __call__( - self, - inputs: InputsType, - batch_size: int = 1, - det_batch_size: Optional[int] = None, - rec_batch_size: Optional[int] = None, - kie_batch_size: Optional[int] = None, - out_dir: str = 'results/', - return_vis: bool = False, - save_vis: bool = False, - save_pred: bool = False, - **kwargs, - ) -> dict: - """Call the inferencer. - - Args: - inputs (InputsType): Inputs for the inferencer. It can be a path - to image / image directory, or an array, or a list of these. - batch_size (int): Batch size. Defaults to 1. - det_batch_size (Optional[int]): Batch size for text detection - model. Overwrite batch_size if it is not None. - Defaults to None. - rec_batch_size (Optional[int]): Batch size for text recognition - model. Overwrite batch_size if it is not None. - Defaults to None. - kie_batch_size (Optional[int]): Batch size for KIE model. - Overwrite batch_size if it is not None. - Defaults to None. - out_dir (str): Output directory of results. Defaults to 'results/'. - return_vis (bool): Whether to return the visualization result. - Defaults to False. - save_vis (bool): Whether to save the visualization results to - "out_dir". Defaults to False. - save_pred (bool): Whether to save the inference results to - "out_dir". Defaults to False. - **kwargs: Key words arguments passed to :meth:`preprocess`, - :meth:`forward`, :meth:`visualize` and :meth:`postprocess`. - Each key in kwargs should be in the corresponding set of - ``preprocess_kwargs``, ``forward_kwargs``, ``visualize_kwargs`` - and ``postprocess_kwargs``. - - Returns: - dict: Inference and visualization results, mapped from - "predictions" and "visualization". - """ - if (save_vis or save_pred) and not out_dir: - raise ValueError('out_dir must be specified when save_vis or ' - 'save_pred is True!') - if out_dir: - img_out_dir = osp.join(out_dir, 'vis') - pred_out_dir = osp.join(out_dir, 'preds') - else: - img_out_dir, pred_out_dir = '', '' - - ( - preprocess_kwargs, - forward_kwargs, - visualize_kwargs, - postprocess_kwargs, - ) = self._dispatch_kwargs( - save_vis=save_vis, - save_pred=save_pred, - return_vis=return_vis, - **kwargs) - - ori_inputs = self._inputs_to_list(inputs) - if det_batch_size is None: - det_batch_size = batch_size - if rec_batch_size is None: - rec_batch_size = batch_size - if kie_batch_size is None: - kie_batch_size = batch_size - - chunked_inputs = super(BaseMMOCRInferencer, - self)._get_chunk_data(ori_inputs, batch_size) - results = {'predictions': [], 'visualization': []} - for ori_input in track(chunked_inputs, description='Inference'): - preds = self.forward( - ori_input, - det_batch_size=det_batch_size, - rec_batch_size=rec_batch_size, - kie_batch_size=kie_batch_size, - **forward_kwargs) - visualization = self.visualize( - ori_input, preds, img_out_dir=img_out_dir, **visualize_kwargs) - batch_res = self.postprocess( - preds, - visualization, - pred_out_dir=pred_out_dir, - **postprocess_kwargs) - results['predictions'].extend(batch_res['predictions']) - if return_vis and batch_res['visualization'] is not None: - results['visualization'].extend(batch_res['visualization']) - return results - - def postprocess(self, - preds: PredType, - visualization: Optional[List[np.ndarray]] = None, - print_result: bool = False, - save_pred: bool = False, - pred_out_dir: str = '' - ) -> Union[ResType, Tuple[ResType, np.ndarray]]: - """Process the predictions and visualization results from ``forward`` - and ``visualize``. - - This method should be responsible for the following tasks: - - 1. Convert datasamples into a json-serializable dict if needed. - 2. Pack the predictions and visualization results and return them. - 3. Dump or log the predictions. - - Args: - preds (PredType): Predictions of the model. - visualization (Optional[np.ndarray]): Visualized predictions. - print_result (bool): Whether to print the result. - Defaults to False. - save_pred (bool): Whether to save the inference result. Defaults to - False. - pred_out_dir: File to save the inference results w/o - visualization. If left as empty, no file will be saved. - Defaults to ''. - - Returns: - Dict: Inference and visualization results, mapped from - "predictions" and "visualization". - """ - - result_dict = {} - pred_results = [{} for _ in range(len(next(iter(preds.values()))))] - if 'rec' in self.mode: - for i, rec_pred in enumerate(preds['rec']): - result = dict(rec_texts=[], rec_scores=[]) - for rec_pred_instance in rec_pred: - rec_dict_res = self.textrec_inferencer.pred2dict( - rec_pred_instance) - result['rec_texts'].append(rec_dict_res['text']) - result['rec_scores'].append(rec_dict_res['scores']) - pred_results[i].update(result) - if 'det' in self.mode: - for i, det_pred in enumerate(preds['det']): - det_dict_res = self.textdet_inferencer.pred2dict(det_pred) - pred_results[i].update( - dict( - det_polygons=det_dict_res['polygons'], - det_scores=det_dict_res['scores'])) - if 'kie' in self.mode: - for i, kie_pred in enumerate(preds['kie']): - kie_dict_res = self.kie_inferencer.pred2dict(kie_pred) - pred_results[i].update( - dict( - kie_labels=kie_dict_res['labels'], - kie_scores=kie_dict_res['scores']), - kie_edge_scores=kie_dict_res['edge_scores'], - kie_edge_labels=kie_dict_res['edge_labels']) - - if save_pred and pred_out_dir: - pred_key = 'det' if 'det' in self.mode else 'rec' - for pred, pred_result in zip(preds[pred_key], pred_results): - img_path = ( - pred.img_path if pred_key == 'det' else pred[0].img_path) - pred_name = osp.splitext(osp.basename(img_path))[0] - pred_name = f'{pred_name}.json' - pred_out_file = osp.join(pred_out_dir, pred_name) - mmengine.dump(pred_result, pred_out_file) - - result_dict['predictions'] = pred_results - if print_result: - print(result_dict) - result_dict['visualization'] = visualization - return result_dict - - def _pack_e2e_datasamples(self, - preds: Dict) -> List[TextSpottingDataSample]: - """Pack text detection and recognition results into a list of - TextSpottingDataSample.""" - results = [] - - for det_data_sample, rec_data_samples in zip(preds['det'], - preds['rec']): - texts = [] - for rec_data_sample in rec_data_samples: - texts.append(rec_data_sample.pred_text.item) - det_data_sample.pred_instances.texts = texts - results.append(det_data_sample) - return results diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/__init__.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/__init__.py deleted file mode 100644 index 30fe928ceced2064bc4adabc5d36291872df4b29..0000000000000000000000000000000000000000 --- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .backbones import * # NOQA -from .dictionary import * # NOQA -from .layers import * # NOQA -from .losses import * # NOQA -from .modules import * # NOQA -from .plugins import * # NOQA diff --git a/spaces/MrSalman/Image_captioning/README.md b/spaces/MrSalman/Image_captioning/README.md deleted file mode 100644 index 5a7c9d51055a1353de34decfb9c0c9c2ef61febe..0000000000000000000000000000000000000000 --- a/spaces/MrSalman/Image_captioning/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Image Captioning -emoji: ⚡ -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.35.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/MultiTransformer/autogen-online/app.py b/spaces/MultiTransformer/autogen-online/app.py deleted file mode 100644 index 0432f23dbec90971cae267c359945f22803cac9b..0000000000000000000000000000000000000000 --- a/spaces/MultiTransformer/autogen-online/app.py +++ /dev/null @@ -1,42 +0,0 @@ -# Import necessary libraries -from flaml import autogen - -# Set up configurations -config_list = autogen.config_list_from_json( - "OAI_CONFIG_LIST", - filter_dict={ - "model": ["gpt4", "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-v0314"], - }, -) - -llm_config = { - "request_timeout": 600, - "seed": 42, - "config_list": config_list, - "temperature": 0, -} - -# Construct agents -assistant = autogen.AssistantAgent( - name="assistant", - llm_config=llm_config, -) - -user_proxy = autogen.UserProxyAgent( - name="user_proxy", - human_input_mode="TERMINATE", - max_consecutive_auto_reply=10, - is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"), - code_execution_config={"work_dir": "web"}, - llm_config=llm_config, - system_message="""Reply TERMINATE if the task has been solved at full satisfaction. -Otherwise, reply CONTINUE, or the reason why the task is not solved yet.""" -) - -# Start a conversation -user_proxy.initiate_chat( - assistant, - message=""" -Tell me about this project, and the libary, then also tell me what I can use it for: https://www.gradio.app/guides/quickstart -""", -) \ No newline at end of file diff --git a/spaces/NATSpeech/DiffSpeech/tasks/tts/vocoder_infer/base_vocoder.py b/spaces/NATSpeech/DiffSpeech/tasks/tts/vocoder_infer/base_vocoder.py deleted file mode 100644 index 0ab88f4e78be66ba1821e5a6720193b1d614f4f5..0000000000000000000000000000000000000000 --- a/spaces/NATSpeech/DiffSpeech/tasks/tts/vocoder_infer/base_vocoder.py +++ /dev/null @@ -1,63 +0,0 @@ -import librosa -from utils.audio import librosa_wav2spec -from utils.commons.hparams import hparams -import numpy as np - -REGISTERED_VOCODERS = {} - - -def register_vocoder(name): - def _f(cls): - REGISTERED_VOCODERS[name] = cls - return cls - - return _f - - -def get_vocoder_cls(vocoder_name): - return REGISTERED_VOCODERS.get(vocoder_name) - - -class BaseVocoder: - def spec2wav(self, mel): - """ - - :param mel: [T, 80] - :return: wav: [T'] - """ - - raise NotImplementedError - - @staticmethod - def wav2spec(wav_fn): - """ - - :param wav_fn: str - :return: wav, mel: [T, 80] - """ - wav_spec_dict = librosa_wav2spec(wav_fn, fft_size=hparams['fft_size'], - hop_size=hparams['hop_size'], - win_length=hparams['win_size'], - num_mels=hparams['audio_num_mel_bins'], - fmin=hparams['fmin'], - fmax=hparams['fmax'], - sample_rate=hparams['audio_sample_rate'], - loud_norm=hparams['loud_norm']) - wav = wav_spec_dict['wav'] - mel = wav_spec_dict['mel'] - return wav, mel - - @staticmethod - def wav2mfcc(wav_fn): - fft_size = hparams['fft_size'] - hop_size = hparams['hop_size'] - win_length = hparams['win_size'] - sample_rate = hparams['audio_sample_rate'] - wav, _ = librosa.core.load(wav_fn, sr=sample_rate) - mfcc = librosa.feature.mfcc(y=wav, sr=sample_rate, n_mfcc=13, - n_fft=fft_size, hop_length=hop_size, - win_length=win_length, pad_mode="constant", power=1.0) - mfcc_delta = librosa.feature.delta(mfcc, order=1) - mfcc_delta_delta = librosa.feature.delta(mfcc, order=2) - mfcc = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta]).T - return mfcc diff --git a/spaces/NATSpeech/DiffSpeech/utils/commons/indexed_datasets.py b/spaces/NATSpeech/DiffSpeech/utils/commons/indexed_datasets.py deleted file mode 100644 index e15632be30d6296a3c9aa80a1f351058003698b3..0000000000000000000000000000000000000000 --- a/spaces/NATSpeech/DiffSpeech/utils/commons/indexed_datasets.py +++ /dev/null @@ -1,71 +0,0 @@ -import pickle -from copy import deepcopy - -import numpy as np - - -class IndexedDataset: - def __init__(self, path, num_cache=1): - super().__init__() - self.path = path - self.data_file = None - self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets'] - self.data_file = open(f"{path}.data", 'rb', buffering=-1) - self.cache = [] - self.num_cache = num_cache - - def check_index(self, i): - if i < 0 or i >= len(self.data_offsets) - 1: - raise IndexError('index out of range') - - def __del__(self): - if self.data_file: - self.data_file.close() - - def __getitem__(self, i): - self.check_index(i) - if self.num_cache > 0: - for c in self.cache: - if c[0] == i: - return c[1] - self.data_file.seek(self.data_offsets[i]) - b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i]) - item = pickle.loads(b) - if self.num_cache > 0: - self.cache = [(i, deepcopy(item))] + self.cache[:-1] - return item - - def __len__(self): - return len(self.data_offsets) - 1 - -class IndexedDatasetBuilder: - def __init__(self, path): - self.path = path - self.out_file = open(f"{path}.data", 'wb') - self.byte_offsets = [0] - - def add_item(self, item): - s = pickle.dumps(item) - bytes = self.out_file.write(s) - self.byte_offsets.append(self.byte_offsets[-1] + bytes) - - def finalize(self): - self.out_file.close() - np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets}) - - -if __name__ == "__main__": - import random - from tqdm import tqdm - ds_path = '/tmp/indexed_ds_example' - size = 100 - items = [{"a": np.random.normal(size=[10000, 10]), - "b": np.random.normal(size=[10000, 10])} for i in range(size)] - builder = IndexedDatasetBuilder(ds_path) - for i in tqdm(range(size)): - builder.add_item(items[i]) - builder.finalize() - ds = IndexedDataset(ds_path) - for i in tqdm(range(10000)): - idx = random.randint(0, size - 1) - assert (ds[idx]['a'] == items[idx]['a']).all() diff --git a/spaces/NCTCMumbai/NCTC/models/official/utils/testing/scripts/presubmit.sh b/spaces/NCTCMumbai/NCTC/models/official/utils/testing/scripts/presubmit.sh deleted file mode 100644 index 954d96df7f8c5f95546fb642ce6f9597f935cb3c..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/utils/testing/scripts/presubmit.sh +++ /dev/null @@ -1,73 +0,0 @@ -#!/bin/bash -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -# Presubmit script that runs tests and lint under local environment. -# Make sure that tensorflow and pylint is installed. -# usage: models >: ./official/utils/testing/scripts/presubmit.sh -# usage: models >: ./official/utils/testing/scripts/presubmit.sh lint py2_test py3_test -set +x - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -cd "$SCRIPT_DIR/../../../.." -MODEL_ROOT="$(pwd)" - -export PYTHONPATH="$PYTHONPATH:${MODEL_ROOT}" - -py_test() { - local PY_BINARY="$1" - local exit_code=0 - - echo "===========Running Python test============" - - for test_file in `find official/ -name '*test.py' -print` - do - echo "####=======Testing ${test_file}=======####" - ${PY_BINARY} "${test_file}" - _exit_code=$? - if [[ $_exit_code != 0 ]]; then - exit_code=$_exit_code - echo "FAIL: ${test_file}" - fi - done - - return "${exit_code}" -} - -py2_test() { - local PY_BINARY=$(which python2) - py_test "$PY_BINARY" - return $? -} - -py3_test() { - local PY_BINARY=$(which python3) - py_test "$PY_BINARY" - return $? -} - -test_result=0 - -if [ "$#" -eq 0 ]; then - TESTS="lint py2_test py3_test" -else - TESTS="$@" -fi - -for t in "${TESTS}"; do - ${t} || test_result=$? -done - -exit "${test_result}" diff --git a/spaces/NeuML/txtai/app.py b/spaces/NeuML/txtai/app.py deleted file mode 100644 index 88fd855de48106187da60acfcc537b7158c2ac91..0000000000000000000000000000000000000000 --- a/spaces/NeuML/txtai/app.py +++ /dev/null @@ -1,712 +0,0 @@ -""" -Build txtai workflows. - -Based on this example: https://github.com/neuml/txtai/blob/master/examples/workflows.py -""" - -import os - -import nltk -import yaml - -import pandas as pd -import streamlit as st - -from txtai.embeddings import Documents, Embeddings -from txtai.pipeline import Segmentation, Summary, Tabular, Textractor, Translation -from txtai.workflow import ServiceTask, Task, UrlTask, Workflow - - -class Process: - """ - Container for an active Workflow process instance. - """ - - @staticmethod - @st.cache_resource(ttl=60 * 60, max_entries=3, show_spinner=False) - def get(components, data): - """ - Lookup or creates a new workflow process instance. - - Args: - components: input components - data: initial data, only passed when indexing - - Returns: - Process - """ - - process = Process(data) - - # Build workflow - with st.spinner("Building workflow...."): - process.build(components) - - return process - - def __init__(self, data): - """ - Creates a new Process. - - Args: - data: initial data, only passed when indexing - """ - - # Component options - self.components = {} - - # Defined pipelines - self.pipelines = {} - - # Current workflow - self.workflow = [] - - # Embeddings index params - self.embeddings = None - self.documents = None - self.data = data - - def build(self, components): - """ - Builds a workflow using components. - - Args: - components: list of components to add to workflow - """ - - # pylint: disable=W0108 - tasks = [] - for component in components: - component = dict(component) - wtype = component.pop("type") - self.components[wtype] = component - - if wtype == "embeddings": - self.embeddings = Embeddings({**component}) - self.documents = Documents() - tasks.append(Task(self.documents.add, unpack=False)) - - elif wtype == "segmentation": - self.pipelines[wtype] = Segmentation(**self.components[wtype]) - tasks.append(Task(self.pipelines[wtype])) - - elif wtype == "service": - tasks.append(ServiceTask(**self.components[wtype])) - - elif wtype == "summary": - self.pipelines[wtype] = Summary(component.pop("path")) - tasks.append(Task(lambda x: self.pipelines["summary"](x, **self.components["summary"]))) - - elif wtype == "tabular": - self.pipelines[wtype] = Tabular(**self.components[wtype]) - tasks.append(Task(self.pipelines[wtype])) - - elif wtype == "textractor": - self.pipelines[wtype] = Textractor(**self.components[wtype]) - tasks.append(UrlTask(self.pipelines[wtype])) - - elif wtype == "translation": - self.pipelines[wtype] = Translation() - tasks.append(Task(lambda x: self.pipelines["translation"](x, **self.components["translation"]))) - - self.workflow = Workflow(tasks) - - def run(self, data): - """ - Runs a workflow using data as input. - - Args: - data: input data - """ - - if data and self.workflow: - # Build tuples for embedding index - if self.documents: - data = [(x, element, None) for x, element in enumerate(data)] - - # Process workflow - for result in self.workflow(data): - if not self.documents: - st.write(result) - - # Build embeddings index - if self.documents: - # Cache data - self.data = list(self.documents) - - with st.spinner("Building embedding index...."): - self.embeddings.index(self.documents) - self.documents.close() - - # Clear workflow - self.documents, self.pipelines, self.workflow = None, None, None - - def search(self, query): - """ - Runs a search. - - Args: - query: input query - """ - - if self.embeddings and query: - st.markdown( - """ - - """, - unsafe_allow_html=True, - ) - - limit = min(5, len(self.data)) - - results = [] - for result in self.embeddings.search(query, limit): - # Tuples are returned when an index doesn't have stored content - if isinstance(result, tuple): - uid, score = result - results.append({"text": self.find(uid), "score": f"{score:.2}"}) - else: - if "id" in result and "text" in result: - result["text"] = self.content(result.pop("id"), result["text"]) - if "score" in result and result["score"]: - result["score"] = f'{result["score"]:.2}' - - results.append(result) - - df = pd.DataFrame(results) - st.write(df.to_html(escape=False), unsafe_allow_html=True) - - def find(self, key): - """ - Lookup record from cached data by uid key. - - Args: - key: id to search for - - Returns: - text for matching id - """ - - # Lookup text by id - text = [text for uid, text, _ in self.data if uid == key][0] - return self.content(key, text) - - def content(self, uid, text): - """ - Builds a content reference for uid and text. - - Args: - uid: record id - text: record text - - Returns: - content - """ - - if uid and uid.lower().startswith("http"): - return f"{text}" - - return text - - -class Application: - """ - Main application. - """ - - def __init__(self, directory): - """ - Creates a new application. - """ - - # Workflow configuration directory - self.directory = directory - - def default(self, names): - """ - Gets default workflow index. - - Args: - names: list of workflow names - - Returns: - default workflow index - """ - - # Get names as lowercase to match case-insensitive - lnames = [name.lower() for name in names] - - # Get default workflow param - params = st.experimental_get_query_params() - index = params.get("default") - index = index[0].lower() if index else 0 - - # Lookup index of workflow name, add 1 to account for "--" - if index and index in lnames: - return lnames.index(index) + 1 - - # Workflow not found, default to index 0 - return 0 - - def load(self, components): - """ - Load an existing workflow file. - - Args: - components: list of components to load - - Returns: - (names of components loaded, workflow config) - """ - - with open(os.path.join(self.directory, "config.yml"), encoding="utf-8") as f: - config = yaml.safe_load(f) - - names = [row["name"] for row in config] - files = [row["file"] for row in config] - - selected = st.selectbox("Load workflow", ["--"] + names, self.default(names)) - if selected != "--": - index = [x for x, name in enumerate(names) if name == selected][0] - with open(os.path.join(self.directory, files[index]), encoding="utf-8") as f: - workflow = yaml.safe_load(f) - - st.markdown("---") - - # Get tasks for first workflow - tasks = list(workflow["workflow"].values())[0]["tasks"] - selected = [] - - for task in tasks: - name = task.get("action", task.get("task")) - if name in components: - selected.append(name) - elif name in ["index", "upsert"]: - selected.append("embeddings") - - return (selected, workflow) - - return (None, None) - - def state(self, key): - """ - Lookup a session state variable. - - Args: - key: variable key - - Returns: - variable value - """ - - if key in st.session_state: - return st.session_state[key] - - return None - - def appsetting(self, workflow, name): - """ - Looks up an application configuration setting. - - Args: - workflow: workflow configuration - name: setting name - - Returns: - app setting value - """ - - if workflow: - config = workflow.get("app") - if config: - return config.get(name) - - return None - - def setting(self, config, name, default=None): - """ - Looks up a component configuration setting. - - Args: - config: component configuration - name: setting name - default: default setting value - - Returns: - setting value - """ - - return config.get(name, default) if config else default - - def text(self, label, component, config, name, default=None): - """ - Create a new text input field. - - Args: - label: field label - component: component name - config: component configuration - name: setting name - default: default setting value - - Returns: - text input field value - """ - - default = self.setting(config, name, default) - if not default: - default = "" - elif isinstance(default, list): - default = ",".join(default) - elif isinstance(default, dict): - default = ",".join(default.keys()) - - st.caption(label) - st.code(default, language="yaml") - return default - - def number(self, label, component, config, name, default=None): - """ - Creates a new numeric input field. - - Args: - label: field label - component: component name - config: component configuration - name: setting name - default: default setting value - - Returns: - numeric value - """ - - value = self.text(label, component, config, name, default) - return int(value) if value else None - - def boolean(self, label, component, config, name, default=False): - """ - Creates a new checkbox field. - - Args: - label: field label - component: component name - config: component configuration - name: setting name - default: default setting value - - Returns: - boolean value - """ - - default = self.setting(config, name, default) - - st.caption(label) - st.markdown(":white_check_mark:" if default else ":white_large_square:") - return default - - def select(self, label, component, config, name, options, default=0): - """ - Creates a new select box field. - - Args: - label: field label - component: component name - config: component configuration - name: setting name - options: list of dropdown options - default: default setting value - - Returns: - boolean value - """ - - index = self.setting(config, name) - index = [x for x, option in enumerate(options) if option == default] - - # Derive default index - default = index[0] if index else default - - st.caption(label) - st.code(options[default], language="yaml") - return options[default] - - def split(self, text): - """ - Splits text on commas and returns a list. - - Args: - text: input text - - Returns: - list - """ - - return [x.strip() for x in text.split(",")] - - def options(self, component, workflow, index): - """ - Extracts component settings into a component configuration dict. - - Args: - component: component type - workflow: existing workflow, can be None - index: task index - - Returns: - dict with component settings - """ - - # pylint: disable=R0912, R0915 - options = {"type": component} - - # Lookup component configuration - # - Runtime components have config defined within tasks - # - Pipeline components have config defined at workflow root - config = None - if workflow: - if component in ["service", "translation"]: - # Service config is found in tasks section - tasks = list(workflow["workflow"].values())[0]["tasks"] - tasks = [task for task in tasks if task.get("task") == component or task.get("action") == component] - if tasks: - config = tasks[0] - else: - config = workflow.get(component) - - if component == "embeddings": - st.markdown(f"** {index + 1}.) Embeddings Index** \n*Index workflow output*") - options["path"] = self.text("Embeddings model path", component, config, "path", "sentence-transformers/nli-mpnet-base-v2") - options["upsert"] = self.boolean("Upsert", component, config, "upsert") - options["content"] = self.boolean("Content", component, config, "content") - - elif component in ("segmentation", "textractor"): - if component == "segmentation": - st.markdown(f"** {index + 1}.) Segment** \n*Split text into semantic units*") - else: - st.markdown(f"** {index + 1}.) Textract** \n*Extract text from documents*") - - options["sentences"] = self.boolean("Split sentences", component, config, "sentences") - options["lines"] = self.boolean("Split lines", component, config, "lines") - options["paragraphs"] = self.boolean("Split paragraphs", component, config, "paragraphs") - options["join"] = self.boolean("Join tokenized", component, config, "join") - options["minlength"] = self.number("Min section length", component, config, "minlength") - - elif component == "service": - st.markdown(f"** {index + 1}.) Service** \n*Extract data from an API*") - options["url"] = self.text("URL", component, config, "url") - options["method"] = self.select("Method", component, config, "method", ["get", "post"], 0) - options["params"] = self.text("URL parameters", component, config, "params") - options["batch"] = self.boolean("Run as batch", component, config, "batch", True) - options["extract"] = self.text("Subsection(s) to extract", component, config, "extract") - - if options["params"]: - options["params"] = {key: None for key in self.split(options["params"])} - if options["extract"]: - options["extract"] = self.split(options["extract"]) - - elif component == "summary": - st.markdown(f"** {index + 1}.) Summary** \n*Abstractive text summarization*") - options["path"] = self.text("Model", component, config, "path", "sshleifer/distilbart-cnn-12-6") - options["minlength"] = self.number("Min length", component, config, "minlength") - options["maxlength"] = self.number("Max length", component, config, "maxlength") - - elif component == "tabular": - st.markdown(f"** {index + 1}.) Tabular** \n*Split tabular data into rows and columns*") - options["idcolumn"] = self.text("Id columns", component, config, "idcolumn") - options["textcolumns"] = self.text("Text columns", component, config, "textcolumns") - options["content"] = self.text("Content", component, config, "content") - - if options["textcolumns"]: - options["textcolumns"] = self.split(options["textcolumns"]) - - if options["content"]: - options["content"] = self.split(options["content"]) - if len(options["content"]) == 1 and options["content"][0] == "1": - options["content"] = options["content"][0] - - elif component == "translation": - st.markdown(f"** {index + 1}.) Translate** \n*Machine translation*") - options["target"] = self.text("Target language code", component, config, "args", "en") - - st.markdown("---") - - return options - - def yaml(self, components): - """ - Builds a yaml string for components. - - Args: - components: list of components to export to YAML - - Returns: - (workflow name, YAML string) - """ - - data = {"app": {"data": self.state("data"), "query": self.state("query")}} - tasks = [] - name = None - - for component in components: - component = dict(component) - name = wtype = component.pop("type") - - if wtype == "embeddings": - upsert = component.pop("upsert") - - data[wtype] = component - data["writable"] = True - - name = "index" - tasks.append({"action": "upsert" if upsert else "index"}) - - elif wtype == "segmentation": - data[wtype] = component - tasks.append({"action": wtype}) - - elif wtype == "service": - config = dict(**component) - config["task"] = wtype - tasks.append(config) - - elif wtype == "summary": - data[wtype] = {"path": component.pop("path")} - tasks.append({"action": wtype}) - - elif wtype == "tabular": - data[wtype] = component - tasks.append({"action": wtype}) - - elif wtype == "textractor": - data[wtype] = component - tasks.append({"action": wtype, "task": "url"}) - - elif wtype == "translation": - data[wtype] = {} - tasks.append({"action": wtype, "args": list(component.values())}) - - # Add in workflow - data["workflow"] = {name: {"tasks": tasks}} - - return (name, yaml.dump(data)) - - def data(self, workflow): - """ - Gets input data. - - Args: - workflow: workflow configuration - - Returns: - input data - """ - - # Get default data setting - data = self.appsetting(workflow, "data") - if not self.appsetting(workflow, "query"): - data = st.text_input("Input", value=data) - - # Save data state - st.session_state["data"] = data - - # Wrap data as list for workflow processing - return [data] - - def query(self, workflow, index): - """ - Gets input query. - - Args: - workflow: workflow configuration - index: True if this is an indexing workflow - - Returns: - input query - """ - - default = self.appsetting(workflow, "query") - default = default if default else "" - - # Get query if this is an indexing workflow - query = st.text_input("Query", value=default) if index else None - - # Save query state - st.session_state["query"] = query - - return query - - def process(self, workflow, components, index): - """ - Processes the current application action. - - Args: - workflow: workflow configuration - components: workflow components - index: True if this is an indexing workflow - """ - - # Get input data and initialize query - data = self.data(workflow) - query = self.query(workflow, index) - - # Get workflow process - process = Process.get(components, data if index else None) - - # Run workflow process - process.run(data) - - # Run search - if index: - process.search(query) - - def run(self): - """ - Runs Streamlit application. - """ - - with st.sidebar: - st.image("https://github.com/neuml/txtai/raw/master/logo.png", width=256) - st.markdown("# Workflow builder \n*Build and apply workflows to data* ") - st.markdown("Workflows combine machine-learning pipelines together to aggregate logic. This application provides a number of pre-configured workflows to get a feel of how they work. Workflows can be exported and run locally through FastAPI. Read more on [GitHub](https://github.com/neuml/txtai) and in the [Docs](https://neuml.github.io/txtai/workflow/).") - st.markdown("---") - - # Component configuration - components = ["embeddings", "segmentation", "service", "summary", "tabular", "textractor", "translation"] - - selected, workflow = self.load(components) - if selected: - # Get selected options - components = [self.options(component, workflow, x) for x, component in enumerate(selected)] - - if selected: - # Process current action - self.process(workflow, components, "embeddings" in selected) - - with st.sidebar: - # Generate export button after workflow is complete - _, config = self.yaml(components) - st.download_button("Export", config, file_name="workflow.yml", help="Export the API workflow as YAML") - else: - st.info("Select a workflow from the sidebar") - - -if __name__ == "__main__": - os.environ["TOKENIZERS_PARALLELISM"] = "false" - - # pylint: disable=W0702 - try: - nltk.sent_tokenize("This is a test. Split") - except: - nltk.download("punkt") - - # Create and run application - app = Application("workflows") - app.run() diff --git a/spaces/NoCrypt/mikuTTS/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/NoCrypt/mikuTTS/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py deleted file mode 100644 index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000 --- a/spaces/NoCrypt/mikuTTS/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py +++ /dev/null @@ -1,90 +0,0 @@ -from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor -import pyworld -import numpy as np - - -class DioF0Predictor(F0Predictor): - def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): - self.hop_length = hop_length - self.f0_min = f0_min - self.f0_max = f0_max - self.sampling_rate = sampling_rate - - def interpolate_f0(self, f0): - """ - 对F0进行插值处理 - """ - - data = np.reshape(f0, (f0.size, 1)) - - vuv_vector = np.zeros((data.size, 1), dtype=np.float32) - vuv_vector[data > 0.0] = 1.0 - vuv_vector[data <= 0.0] = 0.0 - - ip_data = data - - frame_number = data.size - last_value = 0.0 - for i in range(frame_number): - if data[i] <= 0.0: - j = i + 1 - for j in range(i + 1, frame_number): - if data[j] > 0.0: - break - if j < frame_number - 1: - if last_value > 0.0: - step = (data[j] - data[i - 1]) / float(j - i) - for k in range(i, j): - ip_data[k] = data[i - 1] + step * (k - i + 1) - else: - for k in range(i, j): - ip_data[k] = data[j] - else: - for k in range(i, frame_number): - ip_data[k] = last_value - else: - ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝 - last_value = data[i] - - return ip_data[:, 0], vuv_vector[:, 0] - - def resize_f0(self, x, target_len): - source = np.array(x) - source[source < 0.001] = np.nan - target = np.interp( - np.arange(0, len(source) * target_len, len(source)) / target_len, - np.arange(0, len(source)), - source, - ) - res = np.nan_to_num(target) - return res - - def compute_f0(self, wav, p_len=None): - if p_len is None: - p_len = wav.shape[0] // self.hop_length - f0, t = pyworld.dio( - wav.astype(np.double), - fs=self.sampling_rate, - f0_floor=self.f0_min, - f0_ceil=self.f0_max, - frame_period=1000 * self.hop_length / self.sampling_rate, - ) - f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) - for index, pitch in enumerate(f0): - f0[index] = round(pitch, 1) - return self.interpolate_f0(self.resize_f0(f0, p_len))[0] - - def compute_f0_uv(self, wav, p_len=None): - if p_len is None: - p_len = wav.shape[0] // self.hop_length - f0, t = pyworld.dio( - wav.astype(np.double), - fs=self.sampling_rate, - f0_floor=self.f0_min, - f0_ceil=self.f0_max, - frame_period=1000 * self.hop_length / self.sampling_rate, - ) - f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) - for index, pitch in enumerate(f0): - f0[index] = round(pitch, 1) - return self.interpolate_f0(self.resize_f0(f0, p_len)) diff --git a/spaces/OAOA/DifFace/facelib/detection/yolov5face/models/common.py b/spaces/OAOA/DifFace/facelib/detection/yolov5face/models/common.py deleted file mode 100644 index 497a00444c4c59725001993a63fe4617e9d323c8..0000000000000000000000000000000000000000 --- a/spaces/OAOA/DifFace/facelib/detection/yolov5face/models/common.py +++ /dev/null @@ -1,299 +0,0 @@ -# This file contains modules common to various models - -import math - -import numpy as np -import torch -from torch import nn - -from facelib.detection.yolov5face.utils.datasets import letterbox -from facelib.detection.yolov5face.utils.general import ( - make_divisible, - non_max_suppression, - scale_coords, - xyxy2xywh, -) - - -def autopad(k, p=None): # kernel, padding - # Pad to 'same' - if p is None: - p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad - return p - - -def channel_shuffle(x, groups): - batchsize, num_channels, height, width = x.data.size() - channels_per_group = torch.div(num_channels, groups, rounding_mode="trunc") - - # reshape - x = x.view(batchsize, groups, channels_per_group, height, width) - x = torch.transpose(x, 1, 2).contiguous() - - # flatten - return x.view(batchsize, -1, height, width) - - -def DWConv(c1, c2, k=1, s=1, act=True): - # Depthwise convolution - return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) - - -class Conv(nn.Module): - # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) - - def forward(self, x): - return self.act(self.bn(self.conv(x))) - - def fuseforward(self, x): - return self.act(self.conv(x)) - - -class StemBlock(nn.Module): - def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True): - super().__init__() - self.stem_1 = Conv(c1, c2, k, s, p, g, act) - self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0) - self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1) - self.stem_2p = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True) - self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0) - - def forward(self, x): - stem_1_out = self.stem_1(x) - stem_2a_out = self.stem_2a(stem_1_out) - stem_2b_out = self.stem_2b(stem_2a_out) - stem_2p_out = self.stem_2p(stem_1_out) - return self.stem_3(torch.cat((stem_2b_out, stem_2p_out), 1)) - - -class Bottleneck(nn.Module): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c2, 3, 1, g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class BottleneckCSP(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) - self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) - self.cv4 = Conv(2 * c_, c2, 1, 1) - self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) - self.act = nn.LeakyReLU(0.1, inplace=True) - self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) - - def forward(self, x): - y1 = self.cv3(self.m(self.cv1(x))) - y2 = self.cv2(x) - return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) - - -class C3(nn.Module): - # CSP Bottleneck with 3 convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) - self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) - - def forward(self, x): - return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) - - -class ShuffleV2Block(nn.Module): - def __init__(self, inp, oup, stride): - super().__init__() - - if not 1 <= stride <= 3: - raise ValueError("illegal stride value") - self.stride = stride - - branch_features = oup // 2 - - if self.stride > 1: - self.branch1 = nn.Sequential( - self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(inp), - nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.SiLU(), - ) - else: - self.branch1 = nn.Sequential() - - self.branch2 = nn.Sequential( - nn.Conv2d( - inp if (self.stride > 1) else branch_features, - branch_features, - kernel_size=1, - stride=1, - padding=0, - bias=False, - ), - nn.BatchNorm2d(branch_features), - nn.SiLU(), - self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1), - nn.BatchNorm2d(branch_features), - nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(branch_features), - nn.SiLU(), - ) - - @staticmethod - def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False): - return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) - - def forward(self, x): - if self.stride == 1: - x1, x2 = x.chunk(2, dim=1) - out = torch.cat((x1, self.branch2(x2)), dim=1) - else: - out = torch.cat((self.branch1(x), self.branch2(x)), dim=1) - out = channel_shuffle(out, 2) - return out - - -class SPP(nn.Module): - # Spatial pyramid pooling layer used in YOLOv3-SPP - def __init__(self, c1, c2, k=(5, 9, 13)): - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) - self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) - - def forward(self, x): - x = self.cv1(x) - return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) - - -class Focus(nn.Module): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = Conv(c1 * 4, c2, k, s, p, g, act) - - def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) - return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) - - -class Concat(nn.Module): - # Concatenate a list of tensors along dimension - def __init__(self, dimension=1): - super().__init__() - self.d = dimension - - def forward(self, x): - return torch.cat(x, self.d) - - -class NMS(nn.Module): - # Non-Maximum Suppression (NMS) module - conf = 0.25 # confidence threshold - iou = 0.45 # IoU threshold - classes = None # (optional list) filter by class - - def forward(self, x): - return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) - - -class AutoShape(nn.Module): - # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - img_size = 640 # inference size (pixels) - conf = 0.25 # NMS confidence threshold - iou = 0.45 # NMS IoU threshold - classes = None # (optional list) filter by class - - def __init__(self, model): - super().__init__() - self.model = model.eval() - - def autoshape(self): - print("autoShape already enabled, skipping... ") # model already converted to model.autoshape() - return self - - def forward(self, imgs, size=640, augment=False, profile=False): - # Inference from various sources. For height=720, width=1280, RGB images example inputs are: - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3) - # PIL: = Image.open('image.jpg') # HWC x(720,1280,3) - # numpy: = np.zeros((720,1280,3)) # HWC - # torch: = torch.zeros(16,3,720,1280) # BCHW - # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images - - p = next(self.model.parameters()) # for device and type - if isinstance(imgs, torch.Tensor): # torch - return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference - - # Pre-process - n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images - shape0, shape1 = [], [] # image and inference shapes - for i, im in enumerate(imgs): - im = np.array(im) # to numpy - if im.shape[0] < 5: # image in CHW - im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) - im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input - s = im.shape[:2] # HWC - shape0.append(s) # image shape - g = size / max(s) # gain - shape1.append([y * g for y in s]) - imgs[i] = im # update - shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape - x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad - x = np.stack(x, 0) if n > 1 else x[0][None] # stack - x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW - x = torch.from_numpy(x).to(p.device).type_as(p) / 255.0 # uint8 to fp16/32 - - # Inference - with torch.no_grad(): - y = self.model(x, augment, profile)[0] # forward - y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS - - # Post-process - for i in range(n): - scale_coords(shape1, y[i][:, :4], shape0[i]) - - return Detections(imgs, y, self.names) - - -class Detections: - # detections class for YOLOv5 inference results - def __init__(self, imgs, pred, names=None): - super().__init__() - d = pred[0].device # device - gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1.0, 1.0], device=d) for im in imgs] # normalizations - self.imgs = imgs # list of images as numpy arrays - self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) - self.names = names # class names - self.xyxy = pred # xyxy pixels - self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels - self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized - self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) - - def __len__(self): - return self.n - - def tolist(self): - # return a list of Detections objects, i.e. 'for result in results.tolist():' - x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)] - for d in x: - for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]: - setattr(d, k, getattr(d, k)[0]) # pop out of list - return x diff --git a/spaces/OAOA/DifFace/facelib/parsing/resnet.py b/spaces/OAOA/DifFace/facelib/parsing/resnet.py deleted file mode 100644 index fec8e82cf64469fb51be21ad5130217052addbda..0000000000000000000000000000000000000000 --- a/spaces/OAOA/DifFace/facelib/parsing/resnet.py +++ /dev/null @@ -1,69 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F - - -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): - - def __init__(self, in_chan, out_chan, stride=1): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(in_chan, out_chan, stride) - self.bn1 = nn.BatchNorm2d(out_chan) - self.conv2 = conv3x3(out_chan, out_chan) - self.bn2 = nn.BatchNorm2d(out_chan) - self.relu = nn.ReLU(inplace=True) - self.downsample = None - if in_chan != out_chan or stride != 1: - self.downsample = nn.Sequential( - nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(out_chan), - ) - - def forward(self, x): - residual = self.conv1(x) - residual = F.relu(self.bn1(residual)) - residual = self.conv2(residual) - residual = self.bn2(residual) - - shortcut = x - if self.downsample is not None: - shortcut = self.downsample(x) - - out = shortcut + residual - out = self.relu(out) - return out - - -def create_layer_basic(in_chan, out_chan, bnum, stride=1): - layers = [BasicBlock(in_chan, out_chan, stride=stride)] - for i in range(bnum - 1): - layers.append(BasicBlock(out_chan, out_chan, stride=1)) - return nn.Sequential(*layers) - - -class ResNet18(nn.Module): - - def __init__(self): - super(ResNet18, self).__init__() - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) - self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) - self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) - self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) - - def forward(self, x): - x = self.conv1(x) - x = F.relu(self.bn1(x)) - x = self.maxpool(x) - - x = self.layer1(x) - feat8 = self.layer2(x) # 1/8 - feat16 = self.layer3(feat8) # 1/16 - feat32 = self.layer4(feat16) # 1/32 - return feat8, feat16, feat32 diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/.github/ISSUE_TEMPLATE/bug_report.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index aa15123d8ef25c2de745572563505cf0ddc4e351..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -name: 🐛 Bug Report -about: Submit a bug report to help us improve -labels: 'bug, needs triage' ---- - -## 🐛 Bug - - - -### To Reproduce - -Steps to reproduce the behavior (**always include the command you ran**): - -1. Run cmd '....' -2. See error - - - - -#### Code sample - - -### Expected behavior - - - -### Environment - - - fairseq Version (e.g., 1.0 or main): - - PyTorch Version (e.g., 1.0) - - OS (e.g., Linux): - - How you installed fairseq (`pip`, source): - - Build command you used (if compiling from source): - - Python version: - - CUDA/cuDNN version: - - GPU models and configuration: - - Any other relevant information: - -### Additional context - - diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/cross_lingual_language_model/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/cross_lingual_language_model/README.md deleted file mode 100644 index af9128e39e5925e9411d162c2f24a19e4532d618..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/cross_lingual_language_model/README.md +++ /dev/null @@ -1,77 +0,0 @@ -# Cross-Lingual Language Model Pre-training - -Below are some details for training Cross-Lingual Language Models (XLM) - similar to the ones presented in [Lample & Conneau, 2019](https://arxiv.org/pdf/1901.07291.pdf) - in Fairseq. The current implementation only supports the Masked Language Model (MLM) from the paper above. - -## Downloading and Tokenizing Monolingual Data - -Pointers to the monolingual data from wikipedia, used for training the XLM-style MLM model as well as details on processing (tokenization and BPE) it can be found in the [XLM Github Repository](https://github.com/facebookresearch/XLM#download--preprocess-monolingual-data). - -Let's assume the following for the code snippets in later sections to work -- Processed data is in the folder: monolingual_data/processed -- Each language has 3 files for train, test and validation. For example we have the following files for English: - train.en, valid.en -- We are training a model for 5 languages: Arabic (ar), German (de), English (en), Hindi (hi) and French (fr) -- The vocabulary file is monolingual_data/processed/vocab_mlm - - -## Fairseq Pre-processing and Binarization - -Pre-process and binarize the data with the MaskedLMDictionary and cross_lingual_lm task - -```bash -# Ensure the output directory exists -DATA_DIR=monolingual_data/fairseq_processed -mkdir -p "$DATA_DIR" - -for lg in ar de en hi fr -do - - fairseq-preprocess \ - --task cross_lingual_lm \ - --srcdict monolingual_data/processed/vocab_mlm \ - --only-source \ - --trainpref monolingual_data/processed/train \ - --validpref monolingual_data/processed/valid \ - --testpref monolingual_data/processed/test \ - --destdir monolingual_data/fairseq_processed \ - --workers 20 \ - --source-lang $lg - - # Since we only have a source language, the output file has a None for the - # target language. Remove this - - for stage in train test valid - - sudo mv "$DATA_DIR/$stage.$lg-None.$lg.bin" "$stage.$lg.bin" - sudo mv "$DATA_DIR/$stage.$lg-None.$lg.idx" "$stage.$lg.idx" - - done - -done -``` - -## Train a Cross-lingual Language Model similar to the XLM MLM model - -Use the following command to train the model on 5 languages. - -``` -fairseq-train \ ---task cross_lingual_lm monolingual_data/fairseq_processed \ ---save-dir checkpoints/mlm \ ---max-update 2400000 --save-interval 1 --no-epoch-checkpoints \ ---arch xlm_base \ ---optimizer adam --lr-scheduler reduce_lr_on_plateau \ ---lr-shrink 0.5 --lr 0.0001 --stop-min-lr 1e-09 \ ---dropout 0.1 \ ---criterion legacy_masked_lm_loss \ ---max-tokens 2048 --tokens-per-sample 256 --attention-dropout 0.1 \ ---dataset-impl lazy --seed 0 \ ---masked-lm-only \ ---monolingual-langs 'ar,de,en,hi,fr' --num-segment 5 \ ---ddp-backend=legacy_ddp -``` - -Some Notes: -- Using tokens_per_sample greater than 256 can cause OOM (out-of-memory) issues. Usually since MLM packs in streams of text, this parameter doesn't need much tuning. -- The Evaluation workflow for computing MLM Perplexity on test data is in progress. -- Finetuning this model on a downstream task is something which is not currently available. diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/test_multi_corpus_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/test_multi_corpus_dataset.py deleted file mode 100644 index 5a79f4b680e5bc2c7374ec6dd8ea525c47b40985..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/test_multi_corpus_dataset.py +++ /dev/null @@ -1,79 +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 unittest -from collections import OrderedDict - -import torch -from fairseq.data import LanguagePairDataset, TokenBlockDataset -from fairseq.data.multi_corpus_dataset import MultiCorpusDataset -from tests.test_train import mock_dict - - -class TestMultiCorpusDataset(unittest.TestCase): - def setUp(self): - d = mock_dict() - tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, -1) - tokens_ds1 = TokenBlockDataset( - tokens_1, - sizes=[tokens_1.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_1 = LanguagePairDataset( - tokens_ds1, tokens_ds1.sizes, d, shuffle=False - ) - tokens_2 = torch.LongTensor([i for i in range(0, 5000, 2)]).view(1, -1) - tokens_ds2 = TokenBlockDataset( - tokens_2, - sizes=[tokens_2.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_2 = LanguagePairDataset( - tokens_ds2, tokens_ds2.sizes, d, shuffle=False - ) - - def _test_sample_helper( - self, - distribution, - ): - m = MultiCorpusDataset( - OrderedDict({0: self.dataset_1, 1: self.dataset_2}), - distribution=distribution, - seed=0, - sort_indices=True, - ) - m.set_epoch(1) - indices = m.ordered_indices() - count_sample_from_first_dataset = 0 - items = set() - for i in indices: - item = m[i]["source"].item() - if item % 2 == 1: - count_sample_from_first_dataset += 1 - - items.add(item) - sample_from_first_ds_percentage = ( - 1.0 * count_sample_from_first_dataset / len(indices) - ) - self.assertLess( - abs(sample_from_first_ds_percentage - distribution[0]), - 0.01, - ) - self.assertEqual( - len(items), - int(min(len(self.dataset_1), len(indices) * distribution[0]) - + min(len(self.dataset_1), len(indices) * distribution[1])) - ) - print(distribution) - - def test_multi_corpus_dataset(self): - for distribution in [[0.5, 0.5], [0.1, 0.9], [0.9, 0.1]]: - self._test_sample_helper(distribution=distribution) diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/criterions/__init__.py b/spaces/OFA-Sys/OFA-Visual_Grounding/criterions/__init__.py deleted file mode 100644 index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Visual_Grounding/criterions/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .scst_loss import ScstRewardCriterion -from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion \ No newline at end of file diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py deleted file mode 100644 index ea8fae98e87e9f3e69bc51987703a6429eb0c92a..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py +++ /dev/null @@ -1,842 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, List, Optional - -import math -import numpy as np - -import torch -import torch.nn.functional as F -from torch import Tensor - -from .noisy_channel_beam_search import NoisyChannelBeamSearch -from fairseq.sequence_generator import EnsembleModel - - -class NoisyChannelSequenceGenerator(object): - def __init__( - self, - combine_method, - tgt_dict, - src_dict=None, - beam_size=1, - max_len_a=0, - max_len_b=200, - min_len=1, - len_penalty=1.0, - unk_penalty=0.0, - retain_dropout=False, - temperature=1.0, - match_source_len=False, - no_repeat_ngram_size=0, - normalize_scores=True, - channel_models=None, - k2=10, - ch_weight=1.0, - channel_scoring_type='log_norm', - top_k_vocab=0, - lm_models=None, - lm_dict=None, - lm_weight=1.0, - normalize_lm_scores_by_tgt_len=False, - ): - """Generates translations of a given source sentence, - using beam search with noisy channel decoding. - - Args: - combine_method (string, optional): Method to combine direct, LM and - channel model scores (default: None) - tgt_dict (~fairseq.data.Dictionary): target dictionary - src_dict (~fairseq.data.Dictionary): source dictionary - beam_size (int, optional): beam width (default: 1) - max_len_a/b (int, optional): generate sequences of maximum length - ax + b, where x is the source length - min_len (int, optional): the minimum length of the generated output - (not including end-of-sentence) - len_penalty (float, optional): length penalty, where <1.0 favors - shorter, >1.0 favors longer sentences (default: 1.0) - unk_penalty (float, optional): unknown word penalty, where <0 - produces more unks, >0 produces fewer (default: 0.0) - retain_dropout (bool, optional): use dropout when generating - (default: False) - temperature (float, optional): temperature, where values - >1.0 produce more uniform samples and values <1.0 produce - sharper samples (default: 1.0) - match_source_len (bool, optional): outputs should match the source - length (default: False) - no_repeat_ngram_size (int, optional): Size of n-grams that we avoid - repeating in the generation (default: 0) - normalize_scores (bool, optional): normalize scores by the length - of the output (default: True) - channel_models (List[~fairseq.models.FairseqModel]): ensemble of models - translating from the target to the source - k2 (int, optional): Top K2 candidates to score per beam at each step (default:10) - ch_weight (int, optional): Weight associated with the channel model score - assuming that the direct model score has weight 1.0 (default: 1.0) - channel_scoring_type (str, optional): String specifying how to score - the channel model (default: 'log_norm') - top_k_vocab (int, optional): If `channel_scoring_type` is `'src_vocab'` or - `'src_vocab_batched'`, then this parameter specifies the number of - most frequent tokens to include in the channel model output vocabulary, - in addition to the source tokens in the input batch (default: 0) - lm_models (List[~fairseq.models.FairseqModel]): ensemble of models - generating text in the target language - lm_dict (~fairseq.data.Dictionary): LM Model dictionary - lm_weight (int, optional): Weight associated with the LM model score - assuming that the direct model score has weight 1.0 (default: 1.0) - normalize_lm_scores_by_tgt_len (bool, optional): Should we normalize LM scores - by the target length? By default, we normalize the combination of - LM and channel model scores by the source length - """ - self.pad = tgt_dict.pad() - self.unk = tgt_dict.unk() - self.eos = tgt_dict.eos() - self.vocab_size = len(tgt_dict) - self.beam_size = beam_size - # the max beam size is the dictionary size - 1, since we never select pad - self.beam_size = min(beam_size, self.vocab_size - 1) - self.max_len_a = max_len_a - self.max_len_b = max_len_b - self.min_len = min_len - self.normalize_scores = normalize_scores - self.len_penalty = len_penalty - self.unk_penalty = unk_penalty - self.retain_dropout = retain_dropout - self.temperature = temperature - self.match_source_len = match_source_len - self.no_repeat_ngram_size = no_repeat_ngram_size - self.channel_models = channel_models - self.src_dict = src_dict - self.tgt_dict = tgt_dict - self.combine_method = combine_method - self.k2 = k2 - self.ch_weight = ch_weight - self.channel_scoring_type = channel_scoring_type - self.top_k_vocab = top_k_vocab - self.lm_models = lm_models - self.lm_dict = lm_dict - self.lm_weight = lm_weight - self.log_softmax_fn = torch.nn.LogSoftmax(dim=1) - self.normalize_lm_scores_by_tgt_len = normalize_lm_scores_by_tgt_len - - self.share_tgt_dict = (self.lm_dict == self.tgt_dict) - self.tgt_to_lm = make_dict2dict(tgt_dict, lm_dict) - - self.ch_scoring_bsz = 3072 - - assert temperature > 0, '--temperature must be greater than 0' - - self.search = NoisyChannelBeamSearch(tgt_dict) - - @torch.no_grad() - def generate( - self, - models, - sample, - prefix_tokens=None, - bos_token=None, - **kwargs - ): - """Generate a batch of translations. - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - """ - model = EnsembleModel(models) - incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(model.models_size) - ], - ) - if not self.retain_dropout: - model.eval() - - # model.forward normally channels prev_output_tokens into the decoder - # separately, but SequenceGenerator directly calls model.encoder - encoder_input = { - k: v for k, v in sample['net_input'].items() - if k != 'prev_output_tokens' - } - src_tokens = encoder_input['src_tokens'] - src_lengths_no_eos = (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) - input_size = src_tokens.size() - # batch dimension goes first followed by source lengths - bsz = input_size[0] - src_len = input_size[1] - beam_size = self.beam_size - - if self.match_source_len: - max_len = src_lengths_no_eos.max().item() - else: - max_len = min( - int(self.max_len_a * src_len + self.max_len_b), - # exclude the EOS marker - model.max_decoder_positions() - 1, - ) - - # compute the encoder output for each beam - encoder_outs = model.forward_encoder(encoder_input) - new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) - new_order = new_order.to(src_tokens.device).long() - encoder_outs = model.reorder_encoder_out(encoder_outs, new_order) - - src_lengths = encoder_input['src_lengths'] - # initialize buffers - scores = src_tokens.new(bsz * beam_size, max_len + 1).float().fill_(0) - lm_prefix_scores = src_tokens.new(bsz * beam_size).float().fill_(0) - - scores_buf = scores.clone() - tokens = src_tokens.new(bsz * beam_size, max_len + 2).long().fill_(self.pad) - tokens_buf = tokens.clone() - tokens[:, 0] = self.eos if bos_token is None else bos_token - - # reorder source tokens so they may be used as a reference in generating P(S|T) - src_tokens = reorder_all_tokens(src_tokens, src_lengths, self.src_dict.eos_index) - - src_tokens = src_tokens.repeat(1, beam_size).view(-1, src_len) - src_lengths = src_lengths.view(bsz, -1).repeat(1, beam_size).view(bsz*beam_size, -1) - - attn, attn_buf = None, None - nonpad_idxs = None - - # The cands_to_ignore indicates candidates that should be ignored. - # For example, suppose we're sampling and have already finalized 2/5 - # samples. Then the cands_to_ignore would mark 2 positions as being ignored, - # so that we only finalize the remaining 3 samples. - cands_to_ignore = src_tokens.new_zeros(bsz, beam_size).eq(-1) # forward and backward-compatible False mask - - # list of completed sentences - finalized = [[] for i in range(bsz)] - finished = [False for i in range(bsz)] - num_remaining_sent = bsz - - # number of candidate hypos per step - cand_size = 2 * beam_size # 2 x beam size in case half are EOS - - # offset arrays for converting between different indexing schemes - bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens) - cand_offsets = torch.arange(0, cand_size).type_as(tokens) - - # helper function for allocating buffers on the fly - buffers = {} - - def buffer(name, type_of=tokens): # noqa - if name not in buffers: - buffers[name] = type_of.new() - return buffers[name] - - def is_finished(sent, step, unfin_idx): - """ - Check whether we've finished generation for a given sentence, by - comparing the worst score among finalized hypotheses to the best - possible score among unfinalized hypotheses. - """ - assert len(finalized[sent]) <= beam_size - if len(finalized[sent]) == beam_size: - return True - return False - - def finalize_hypos(step, bbsz_idx, eos_scores, combined_noisy_channel_eos_scores): - """ - Finalize the given hypotheses at this step, while keeping the total - number of finalized hypotheses per sentence <= beam_size. - - Note: the input must be in the desired finalization order, so that - hypotheses that appear earlier in the input are preferred to those - that appear later. - - Args: - step: current time step - bbsz_idx: A vector of indices in the range [0, bsz*beam_size), - indicating which hypotheses to finalize - eos_scores: A vector of the same size as bbsz_idx containing - fw scores for each hypothesis - combined_noisy_channel_eos_scores: A vector of the same size as bbsz_idx containing - combined noisy channel scores for each hypothesis - """ - assert bbsz_idx.numel() == eos_scores.numel() - - # clone relevant token and attention tensors - tokens_clone = tokens.index_select(0, bbsz_idx) - tokens_clone = tokens_clone[:, 1:step + 2] # skip the first index, which is EOS - assert not tokens_clone.eq(self.eos).any() - tokens_clone[:, step] = self.eos - attn_clone = attn.index_select(0, bbsz_idx)[:, :, 1:step+2] if attn is not None else None - - # compute scores per token position - pos_scores = scores.index_select(0, bbsz_idx)[:, :step+1] - pos_scores[:, step] = eos_scores - # convert from cumulative to per-position scores - pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] - - # normalize sentence-level scores - if self.normalize_scores: - combined_noisy_channel_eos_scores /= (step + 1) ** self.len_penalty - - cum_unfin = [] - prev = 0 - for f in finished: - if f: - prev += 1 - else: - cum_unfin.append(prev) - - sents_seen = set() - for i, (idx, score) in enumerate(zip(bbsz_idx.tolist(), combined_noisy_channel_eos_scores.tolist())): - unfin_idx = idx // beam_size - sent = unfin_idx + cum_unfin[unfin_idx] - - sents_seen.add((sent, unfin_idx)) - - if self.match_source_len and step > src_lengths_no_eos[unfin_idx]: - score = -math.inf - - def get_hypo(): - - if attn_clone is not None: - # remove padding tokens from attn scores - hypo_attn = attn_clone[i][nonpad_idxs[sent]] - _, alignment = hypo_attn.max(dim=0) - else: - hypo_attn = None - alignment = None - - return { - 'tokens': tokens_clone[i], - 'score': score, - 'attention': hypo_attn, # src_len x tgt_len - 'alignment': alignment, - 'positional_scores': pos_scores[i], - } - - if len(finalized[sent]) < beam_size: - finalized[sent].append(get_hypo()) - - newly_finished = [] - for sent, unfin_idx in sents_seen: - # check termination conditions for this sentence - if not finished[sent] and is_finished(sent, step, unfin_idx): - finished[sent] = True - newly_finished.append(unfin_idx) - return newly_finished - - def noisy_channel_rescoring(lprobs, beam_size, bsz, src_tokens, tokens, k): - """Rescore the top k hypothesis from each beam using noisy channel modeling - Returns: - new_fw_lprobs: the direct model probabilities after pruning the top k - new_ch_lm_lprobs: the combined channel and language model probabilities - new_lm_lprobs: the language model probabilities after pruning the top k - """ - with torch.no_grad(): - lprobs_size = lprobs.size() - if prefix_tokens is not None and step < prefix_tokens.size(1): - probs_slice = lprobs.view(bsz, -1, lprobs.size(-1))[:, 0, :] - cand_scores = torch.gather( - probs_slice, dim=1, - index=prefix_tokens[:, step].view(-1, 1).data - ).expand(-1, beam_size).contiguous().view(bsz*beam_size, 1) - cand_indices = prefix_tokens[:, step].view(-1, 1).expand(bsz, beam_size).data.contiguous().view(bsz*beam_size, 1) - - # need to calculate and save fw and lm probs for prefix tokens - fw_top_k = cand_scores - fw_top_k_idx = cand_indices - k = 1 - else: - # take the top k best words for every sentence in batch*beam - fw_top_k, fw_top_k_idx = torch.topk(lprobs.view(beam_size*bsz, -1), k=k) - eos_idx = torch.nonzero(fw_top_k_idx.view(bsz*beam_size*k, -1) == self.eos)[:, 0] - ch_scores = fw_top_k.new_full((beam_size*bsz*k, ), 0) - src_size = torch.sum(src_tokens[:, :] != self.src_dict.pad_index, dim=1, keepdim=True, dtype=fw_top_k.dtype) - - if self.combine_method != "lm_only": - temp_src_tokens_full = src_tokens[:, :].repeat(1, k).view(bsz*beam_size*k, -1) - not_padding = temp_src_tokens_full[:, 1:] != self.src_dict.pad_index - cur_tgt_size = step+2 - - # add eos to all candidate sentences except those that already end in eos - eos_tokens = tokens[:, 0].repeat(1, k).view(-1, 1) - eos_tokens[eos_idx] = self.tgt_dict.pad_index - - if step == 0: - channel_input = torch.cat((fw_top_k_idx.view(-1, 1), eos_tokens), 1) - else: - # move eos from beginning to end of target sentence - channel_input = torch.cat((tokens[:, 1:step + 1].repeat(1, k).view(-1, step), fw_top_k_idx.view(-1, 1), eos_tokens), 1) - - ch_input_lengths = torch.tensor(np.full(channel_input.size(0), cur_tgt_size)) - ch_input_lengths[eos_idx] = cur_tgt_size-1 - if self.channel_scoring_type == "unnormalized": - ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) - ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) - del ch_encoder_output - ch_intermed_scores = channel_model.decoder.unnormalized_scores_given_target(ch_decoder_output, target_ids=temp_src_tokens_full[:, 1:]) - ch_intermed_scores = ch_intermed_scores.float() - ch_intermed_scores *= not_padding.float() - ch_scores = torch.sum(ch_intermed_scores, dim=1) - elif self.channel_scoring_type == "k2_separate": - for k_idx in range(k): - k_eos_tokens = eos_tokens[k_idx::k, :] - if step == 0: - k_ch_input = torch.cat((fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) - else: - # move eos from beginning to end of target sentence - k_ch_input = torch.cat((tokens[:, 1:step + 1], fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) - k_ch_input_lengths = ch_input_lengths[k_idx::k] - k_ch_output = channel_model(k_ch_input, k_ch_input_lengths, src_tokens) - k_ch_lprobs = channel_model.get_normalized_probs(k_ch_output, log_probs=True) - k_ch_intermed_scores = torch.gather(k_ch_lprobs[:, :-1, :], 2, src_tokens[:, 1:].unsqueeze(2)).squeeze(2) - k_ch_intermed_scores *= not_padding.float() - ch_scores[k_idx::k] = torch.sum(k_ch_intermed_scores, dim=1) - elif self.channel_scoring_type == "src_vocab": - ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) - ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) - - del ch_encoder_output - ch_lprobs = normalized_scores_with_batch_vocab( - channel_model.decoder, - ch_decoder_output, src_tokens, k, bsz, beam_size, - self.src_dict.pad_index, top_k=self.top_k_vocab) - ch_scores = torch.sum(ch_lprobs, dim=1) - elif self.channel_scoring_type == "src_vocab_batched": - ch_bsz_size = temp_src_tokens_full.shape[0] - ch_lprobs_list = [None] * len(range(0, ch_bsz_size, self.ch_scoring_bsz)) - for i, start_idx in enumerate(range(0, ch_bsz_size, self.ch_scoring_bsz)): - end_idx = min(start_idx + self.ch_scoring_bsz, ch_bsz_size) - temp_src_tokens_full_batch = temp_src_tokens_full[start_idx:end_idx, :] - channel_input_batch = channel_input[start_idx:end_idx, :] - ch_input_lengths_batch = ch_input_lengths[start_idx:end_idx] - ch_encoder_output_batch = channel_model.encoder(channel_input_batch, src_lengths=ch_input_lengths_batch) - ch_decoder_output_batch, _ = channel_model.decoder(temp_src_tokens_full_batch, encoder_out=ch_encoder_output_batch, features_only=True) - ch_lprobs_list[i] = normalized_scores_with_batch_vocab( - channel_model.decoder, - ch_decoder_output_batch, src_tokens, k, bsz, beam_size, - self.src_dict.pad_index, top_k=self.top_k_vocab, - start_idx=start_idx, end_idx=end_idx) - ch_lprobs = torch.cat(ch_lprobs_list, dim=0) - ch_scores = torch.sum(ch_lprobs, dim=1) - else: - ch_output = channel_model(channel_input, ch_input_lengths, temp_src_tokens_full) - ch_lprobs = channel_model.get_normalized_probs(ch_output, log_probs=True) - ch_intermed_scores = torch.gather(ch_lprobs[:, :-1, :], 2, temp_src_tokens_full[:, 1:].unsqueeze(2)).squeeze().view(bsz*beam_size*k, -1) - ch_intermed_scores *= not_padding.float() - ch_scores = torch.sum(ch_intermed_scores, dim=1) - - else: - cur_tgt_size = 0 - ch_scores = ch_scores.view(bsz*beam_size, k) - expanded_lm_prefix_scores = lm_prefix_scores.unsqueeze(1).expand(-1, k).flatten() - - if self.share_tgt_dict: - lm_scores = get_lm_scores(lm, tokens[:, :step + 1].view(-1, step+1), lm_incremental_states, fw_top_k_idx.view(-1, 1), torch.tensor(np.full(tokens.size(0), step+1)), k) - else: - new_lm_input = dict2dict(tokens[:, :step + 1].view(-1, step+1), self.tgt_to_lm) - new_cands = dict2dict(fw_top_k_idx.view(-1, 1), self.tgt_to_lm) - lm_scores = get_lm_scores(lm, new_lm_input, lm_incremental_states, new_cands, torch.tensor(np.full(tokens.size(0), step+1)), k) - - lm_scores.add_(expanded_lm_prefix_scores) - ch_lm_scores = combine_ch_lm(self.combine_method, ch_scores, lm_scores, src_size, cur_tgt_size) - # initialize all as min value - new_fw_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_ch_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_fw_lprobs[:, self.pad] = -math.inf - new_ch_lm_lprobs[:, self.pad] = -math.inf - new_lm_lprobs[:, self.pad] = -math.inf - - new_fw_lprobs.scatter_(1, fw_top_k_idx, fw_top_k) - new_ch_lm_lprobs.scatter_(1, fw_top_k_idx, ch_lm_scores) - new_lm_lprobs.scatter_(1, fw_top_k_idx, lm_scores.view(-1, k)) - return new_fw_lprobs, new_ch_lm_lprobs, new_lm_lprobs - - def combine_ch_lm(combine_type, ch_scores, lm_scores1, src_size, tgt_size): - if self.channel_scoring_type == "unnormalized": - ch_scores = self.log_softmax_fn( - ch_scores.view(-1, self.beam_size * self.k2) - ).view(ch_scores.shape) - ch_scores = ch_scores * self.ch_weight - lm_scores1 = lm_scores1 * self.lm_weight - - if combine_type == "lm_only": - # log P(T|S) + log P(T) - ch_scores = lm_scores1.view(ch_scores.size()) - elif combine_type == "noisy_channel": - # 1/t log P(T|S) + 1/s log P(S|T) + 1/t log P(T) - if self.normalize_lm_scores_by_tgt_len: - ch_scores.div_(src_size) - lm_scores_norm = lm_scores1.view(ch_scores.size()).div(tgt_size) - ch_scores.add_(lm_scores_norm) - # 1/t log P(T|S) + 1/s log P(S|T) + 1/s log P(T) - else: - ch_scores.add_(lm_scores1.view(ch_scores.size())) - ch_scores.div_(src_size) - - return ch_scores - - if self.channel_models is not None: - channel_model = self.channel_models[0] # assume only one channel_model model - else: - channel_model = None - - lm = EnsembleModel(self.lm_models) - lm_incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(lm.models_size) - ], - ) - - reorder_state = None - batch_idxs = None - for step in range(max_len + 1): # one extra step for EOS marker - # reorder decoder internal states based on the prev choice of beams - if reorder_state is not None: - if batch_idxs is not None: - # update beam indices to take into account removed sentences - corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as(batch_idxs) - reorder_state.view(-1, beam_size).add_(corr.unsqueeze(-1) * beam_size) - model.reorder_incremental_state(incremental_states, reorder_state) - encoder_outs = model.reorder_encoder_out(encoder_outs, reorder_state) - - lm.reorder_incremental_state(lm_incremental_states, reorder_state) - - fw_lprobs, avg_attn_scores = model.forward_decoder( - tokens[:, :step + 1], encoder_outs, incremental_states, temperature=self.temperature, - ) - - fw_lprobs[:, self.pad] = -math.inf # never select pad - fw_lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty - fw_lprobs, ch_lm_lprobs, lm_lprobs = noisy_channel_rescoring(fw_lprobs, beam_size, bsz, src_tokens, tokens, self.k2) - - # handle min and max length constraints - if step >= max_len: - fw_lprobs[:, :self.eos] = -math.inf - fw_lprobs[:, self.eos + 1:] = -math.inf - elif step < self.min_len: - fw_lprobs[:, self.eos] = -math.inf - - # handle prefix tokens (possibly with different lengths) - if prefix_tokens is not None and step < prefix_tokens.size(1): - prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) - prefix_mask = prefix_toks.ne(self.pad) - - prefix_fw_lprobs = fw_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - fw_lprobs[prefix_mask] = -math.inf - fw_lprobs[prefix_mask] = fw_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_fw_lprobs - ) - - prefix_ch_lm_lprobs = ch_lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - ch_lm_lprobs[prefix_mask] = -math.inf - ch_lm_lprobs[prefix_mask] = ch_lm_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_ch_lm_lprobs - ) - - prefix_lm_lprobs = lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - lm_lprobs[prefix_mask] = -math.inf - lm_lprobs[prefix_mask] = lm_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lm_lprobs - ) - - # if prefix includes eos, then we should make sure tokens and - # scores are the same across all beams - eos_mask = prefix_toks.eq(self.eos) - if eos_mask.any(): - # validate that the first beam matches the prefix - first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[:, 0, 1:step + 1] - eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] - target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] - assert (first_beam == target_prefix).all() - - def replicate_first_beam(tensor, mask): - tensor = tensor.view(-1, beam_size, tensor.size(-1)) - tensor[mask] = tensor[mask][:, :1, :] - return tensor.view(-1, tensor.size(-1)) - - # copy tokens, scores and lprobs from the first beam to all beams - tokens = replicate_first_beam(tokens, eos_mask_batch_dim) - scores = replicate_first_beam(scores, eos_mask_batch_dim) - - fw_lprobs = replicate_first_beam(fw_lprobs, eos_mask_batch_dim) - ch_lm_lprobs = replicate_first_beam(ch_lm_lprobs, eos_mask_batch_dim) - lm_lprobs = replicate_first_beam(lm_lprobs, eos_mask_batch_dim) - - if self.no_repeat_ngram_size > 0: - # for each beam and batch sentence, generate a list of previous ngrams - gen_ngrams = [{} for bbsz_idx in range(bsz * beam_size)] - for bbsz_idx in range(bsz * beam_size): - gen_tokens = tokens[bbsz_idx].tolist() - for ngram in zip(*[gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]): - gen_ngrams[bbsz_idx][tuple(ngram[:-1])] = \ - gen_ngrams[bbsz_idx].get(tuple(ngram[:-1]), []) + [ngram[-1]] - - # Record attention scores - if avg_attn_scores is not None: - if attn is None: - attn = scores.new(bsz * beam_size, src_tokens.size(1), max_len + 2) - attn_buf = attn.clone() - nonpad_idxs = src_tokens.ne(self.pad) - attn[:, :, step + 1].copy_(avg_attn_scores) - - scores = scores.type_as(fw_lprobs) - scores_buf = scores_buf.type_as(fw_lprobs) - - self.search.set_src_lengths(src_lengths_no_eos) - - if self.no_repeat_ngram_size > 0: - def calculate_banned_tokens(bbsz_idx): - # before decoding the next token, prevent decoding of ngrams that have already appeared - ngram_index = tuple(tokens[bbsz_idx, step + 2 - self.no_repeat_ngram_size:step + 1].tolist()) - return gen_ngrams[bbsz_idx].get(ngram_index, []) - - if step + 2 - self.no_repeat_ngram_size >= 0: - # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet - banned_tokens = [calculate_banned_tokens(bbsz_idx) for bbsz_idx in range(bsz * beam_size)] - else: - banned_tokens = [[] for bbsz_idx in range(bsz * beam_size)] - - for bbsz_idx in range(bsz * beam_size): - fw_lprobs[bbsz_idx, banned_tokens[bbsz_idx]] = -math.inf - - combined_noisy_channel_scores, fw_lprobs_top_k, lm_lprobs_top_k, cand_indices, cand_beams = self.search.step( - step, - fw_lprobs.view(bsz, -1, self.vocab_size), - scores.view(bsz, beam_size, -1)[:, :, :step], ch_lm_lprobs.view(bsz, -1, self.vocab_size), - lm_lprobs.view(bsz, -1, self.vocab_size), self.combine_method - ) - - # cand_bbsz_idx contains beam indices for the top candidate - # hypotheses, with a range of values: [0, bsz*beam_size), - # and dimensions: [bsz, cand_size] - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - - # finalize hypotheses that end in eos (except for candidates to be ignored) - eos_mask = cand_indices.eq(self.eos) - eos_mask[:, :beam_size] &= ~cands_to_ignore - - # only consider eos when it's among the top beam_size indices - eos_bbsz_idx = torch.masked_select( - cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents = set() - if eos_bbsz_idx.numel() > 0: - eos_scores = torch.masked_select( - fw_lprobs_top_k[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - combined_noisy_channel_eos_scores = torch.masked_select( - combined_noisy_channel_scores[:, :beam_size], - mask=eos_mask[:, :beam_size], - ) - - # finalize hypo using channel model score - finalized_sents = finalize_hypos( - step, eos_bbsz_idx, eos_scores, combined_noisy_channel_eos_scores) - - num_remaining_sent -= len(finalized_sents) - - assert num_remaining_sent >= 0 - if num_remaining_sent == 0: - break - - if len(finalized_sents) > 0: - new_bsz = bsz - len(finalized_sents) - - # construct batch_idxs which holds indices of batches to keep for the next pass - batch_mask = cand_indices.new_ones(bsz) - batch_mask[cand_indices.new(finalized_sents)] = 0 - batch_idxs = torch.nonzero(batch_mask).squeeze(-1) - - eos_mask = eos_mask[batch_idxs] - cand_beams = cand_beams[batch_idxs] - bbsz_offsets.resize_(new_bsz, 1) - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - - lm_lprobs_top_k = lm_lprobs_top_k[batch_idxs] - - fw_lprobs_top_k = fw_lprobs_top_k[batch_idxs] - cand_indices = cand_indices[batch_idxs] - if prefix_tokens is not None: - prefix_tokens = prefix_tokens[batch_idxs] - src_lengths_no_eos = src_lengths_no_eos[batch_idxs] - cands_to_ignore = cands_to_ignore[batch_idxs] - - scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - scores_buf.resize_as_(scores) - tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - tokens_buf.resize_as_(tokens) - src_tokens = src_tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - src_lengths = src_lengths.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - lm_prefix_scores = lm_prefix_scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1).squeeze() - - if attn is not None: - attn = attn.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, attn.size(1), -1) - attn_buf.resize_as_(attn) - bsz = new_bsz - else: - batch_idxs = None - - # Set active_mask so that values > cand_size indicate eos or - # ignored hypos and values < cand_size indicate candidate - # active hypos. After this, the min values per row are the top - # candidate active hypos. - eos_mask[:, :beam_size] |= cands_to_ignore - active_mask = torch.add( - eos_mask.type_as(cand_offsets) * cand_size, - cand_offsets[: eos_mask.size(1)], - ) - - # get the top beam_size active hypotheses, which are just the hypos - # with the smallest values in active_mask - active_hypos, new_cands_to_ignore = buffer('active_hypos'), buffer('new_cands_to_ignore') - torch.topk( - active_mask, k=beam_size, dim=1, largest=False, - out=(new_cands_to_ignore, active_hypos) - ) - - # update cands_to_ignore to ignore any finalized hypos - cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] - assert (~cands_to_ignore).any(dim=1).all() - - active_bbsz_idx = buffer('active_bbsz_idx') - torch.gather( - cand_bbsz_idx, dim=1, index=active_hypos, - out=active_bbsz_idx, - ) - active_scores = torch.gather( - fw_lprobs_top_k, dim=1, index=active_hypos, - out=scores[:, step].view(bsz, beam_size), - ) - - active_bbsz_idx = active_bbsz_idx.view(-1) - active_scores = active_scores.view(-1) - - # copy tokens and scores for active hypotheses - torch.index_select( - tokens[:, :step + 1], dim=0, index=active_bbsz_idx, - out=tokens_buf[:, :step + 1], - ) - torch.gather( - cand_indices, dim=1, index=active_hypos, - out=tokens_buf.view(bsz, beam_size, -1)[:, :, step + 1], - ) - if step > 0: - torch.index_select( - scores[:, :step], dim=0, index=active_bbsz_idx, - out=scores_buf[:, :step], - ) - torch.gather( - fw_lprobs_top_k, dim=1, index=active_hypos, - out=scores_buf.view(bsz, beam_size, -1)[:, :, step], - ) - torch.gather( - lm_lprobs_top_k, dim=1, index=active_hypos, - out=lm_prefix_scores.view(bsz, beam_size) - ) - - # copy attention for active hypotheses - if attn is not None: - torch.index_select( - attn[:, :, :step + 2], dim=0, index=active_bbsz_idx, - out=attn_buf[:, :, :step + 2], - ) - - # swap buffers - tokens, tokens_buf = tokens_buf, tokens - scores, scores_buf = scores_buf, scores - if attn is not None: - attn, attn_buf = attn_buf, attn - - # reorder incremental state in decoder - reorder_state = active_bbsz_idx - - # sort by score descending - for sent in range(len(finalized)): - finalized[sent] = sorted(finalized[sent], key=lambda r: r['score'], reverse=True) - - return finalized - - -def get_lm_scores(model, input_tokens, incremental_states, cand_tokens, input_len, k): - with torch.no_grad(): - lm_lprobs, avg_attn_scores = model.forward_decoder( - input_tokens, encoder_outs=None, incremental_states=incremental_states, - ) - - lm_lprobs_size = lm_lprobs.size(0) - probs_next_wrd = torch.gather(lm_lprobs.repeat(1, k).view(lm_lprobs_size*k, -1), 1, cand_tokens).squeeze().view(-1) - - return probs_next_wrd - - -def make_dict2dict(old_dict, new_dict): - dict2dict_map = {} - for sym in old_dict.symbols: - dict2dict_map[old_dict.index(sym)] = new_dict.index(sym) - return dict2dict_map - - -def dict2dict(tokens, dict2dict_map): - if tokens.device == torch.device('cpu'): - tokens_tmp = tokens - else: - tokens_tmp = tokens.cpu() - return tokens_tmp.map_( - tokens_tmp, - lambda _, val, dict2dict_map=dict2dict_map : dict2dict_map[float(val)] - ).to(tokens.device) - - -def reorder_tokens(tokens, lengths, eos): - # reorder source tokens so they may be used as reference for P(S|T) - return torch.cat((tokens.new([eos]), tokens[-lengths:-1], tokens[:-lengths]), 0) - - -def reorder_all_tokens(tokens, lengths, eos): - # used to reorder src tokens from [ .. ] to [ ...] - # so source tokens can be used to predict P(S|T) - return torch.stack([reorder_tokens(token, length, eos) for token, length in zip(tokens, lengths)]) - - -def normalized_scores_with_batch_vocab( - model_decoder, features, target_ids, k, bsz, beam_size, - pad_idx, top_k=0, vocab_size_meter=None, start_idx=None, - end_idx=None, **kwargs): - """ - Get normalized probabilities (or log probs) from a net's output - w.r.t. vocab consisting of target IDs in the batch - """ - if model_decoder.adaptive_softmax is None: - weight = model_decoder.output_projection.weight - vocab_ids = torch.unique( - torch.cat( - (torch.unique(target_ids), torch.arange(top_k, device=target_ids.device)) - ) - ) - id_map = dict(zip(vocab_ids.tolist(), range(len(vocab_ids)))) - mapped_target_ids = target_ids.cpu().apply_( - lambda x, id_map=id_map: id_map[x] - ).to(target_ids.device) - expanded_target_ids = mapped_target_ids[:, :].repeat(1, k).view(bsz*beam_size*k, -1) - if start_idx is not None and end_idx is not None: - expanded_target_ids = expanded_target_ids[start_idx:end_idx, :] - logits = F.linear(features, weight[vocab_ids, :]) - log_softmax = F.log_softmax(logits, dim=-1, dtype=torch.float32) - intermed_scores = torch.gather( - log_softmax[:, :-1, :], - 2, - expanded_target_ids[:, 1:].unsqueeze(2), - ).squeeze() - not_padding = expanded_target_ids[:, 1:] != pad_idx - intermed_scores *= not_padding.float() - return intermed_scores - else: - raise ValueError("adaptive softmax doesn't work with " + - "`normalized_scores_with_batch_vocab()`") diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py deleted file mode 100644 index c613f52d3c3de43a048849a231a9a34e2a883486..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py +++ /dev/null @@ -1,192 +0,0 @@ -import soundfile as sf -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class CpcFeatureReader: - """ - Wrapper class to run inference on CPC model. - Helps extract features for a given audio file. - """ - - def __init__( - self, - checkpoint_path, - layer, - use_encoder_layer=False, - norm_features=False, - sample_rate=16000, - max_chunk=64000, - ): - self.model = load_cpc_model(checkpoint_path, layer).eval().cuda() - self.sample_rate = sample_rate - self.max_chunk = max_chunk - self.norm_features = norm_features - self.use_encoder_layer = use_encoder_layer - - def read_audio(self, path, ref_len=None): - wav, sr = sf.read(path) - if wav.ndim == 2: - wav = wav.mean(-1) - assert wav.ndim == 1, wav.ndim - assert sr == self.sample_rate, sr - if ref_len is not None and abs(ref_len - len(wav)) > 160: - print(f"ref {ref_len} != read {len(wav)} ({path})") - return wav - - def get_feats(self, file_path, ref_len=None): - x = self.read_audio(file_path, ref_len) - # Inspired from CPC_audio feature_loader.py - with torch.no_grad(): - x = torch.from_numpy(x).float().cuda() - x = x.view(1, 1, -1) - size = x.size(2) - feat = [] - start = 0 - while start < size: - if start + self.max_chunk > size: - break - x_chunk = x[..., start : start + self.max_chunk] - feat_chunk = self.model.extract_features( - source=x_chunk, - get_encoded=self.use_encoder_layer, - norm_output=self.norm_features, - ) - feat.append(feat_chunk) - start += self.max_chunk - - if start < size: - x_chunk = x[:, -self.max_chunk :] - feat_chunk = self.model.extract_features( - source=x_chunk, - get_encoded=self.use_encoder_layer, - norm_output=self.norm_features, - ) - df = x_chunk.size(2) // feat_chunk.size(1) - delta = (size - start) // df - feat.append(feat_chunk[:, -delta:]) - return torch.cat(feat, 1).squeeze(0) - - -def load_cpc_model(checkpoint_path, layer=None): - state_dict = torch.load(checkpoint_path) - weights = state_dict["weights"] - config = state_dict["config"] - if layer is not None: - config["nLevelsGRU"] = layer - - encoder = CPCEncoder(config["hiddenEncoder"]) - ar_net = CPCAR( - config["hiddenEncoder"], config["hiddenGar"], False, config["nLevelsGRU"] - ) - - model = CPCModel(encoder, ar_net) - model.load_state_dict(weights, strict=False) - model.config = config - - return model - - -class ChannelNorm(nn.Module): - def __init__(self, num_features, epsilon=1e-05, affine=True): - super(ChannelNorm, self).__init__() - if affine: - self.weight = nn.parameter.Parameter(torch.Tensor(1, num_features, 1)) - self.bias = nn.parameter.Parameter(torch.Tensor(1, num_features, 1)) - else: - self.weight = None - self.bias = None - self.epsilon = epsilon - self.p = 0 - self.affine = affine - self.reset_parameters() - - def reset_parameters(self): - if self.affine: - torch.nn.init.ones_(self.weight) - torch.nn.init.zeros_(self.bias) - - def forward(self, x): - cum_mean = x.mean(dim=1, keepdim=True) - cum_var = x.var(dim=1, keepdim=True) - x = (x - cum_mean) * torch.rsqrt(cum_var + self.epsilon) - if self.weight is not None: - x = x * self.weight + self.bias - return x - - -class CPCEncoder(nn.Module): - def __init__(self, hidden_dim=512): - super(CPCEncoder, self).__init__() - self.conv0 = nn.Conv1d(1, hidden_dim, 10, stride=5, padding=3) - self.batchNorm0 = ChannelNorm(hidden_dim) - self.conv1 = nn.Conv1d(hidden_dim, hidden_dim, 8, stride=4, padding=2) - self.batchNorm1 = ChannelNorm(hidden_dim) - self.conv2 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) - self.batchNorm2 = ChannelNorm(hidden_dim) - self.conv3 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) - self.batchNorm3 = ChannelNorm(hidden_dim) - self.conv4 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) - self.batchNorm4 = ChannelNorm(hidden_dim) - self.DOWNSAMPLING = 160 - - def get_output_dim(self): - return self.conv4.out_channels - - def forward(self, x): - x = F.relu(self.batchNorm0(self.conv0(x))) - x = F.relu(self.batchNorm1(self.conv1(x))) - x = F.relu(self.batchNorm2(self.conv2(x))) - x = F.relu(self.batchNorm3(self.conv3(x))) - x = F.relu(self.batchNorm4(self.conv4(x))) - return x - - -class CPCAR(nn.Module): - def __init__(self, dim_encoded, dim_output, keep_hidden, num_layers): - super(CPCAR, self).__init__() - self.baseNet = nn.LSTM( - dim_encoded, dim_output, num_layers=num_layers, batch_first=True - ) - self.hidden = None - self.keep_hidden = keep_hidden - - def get_output_dim(self): - return self.baseNet.hidden_size - - def forward(self, x): - try: - self.baseNet.flatten_parameters() - except RuntimeError: - pass - x, h = self.baseNet(x, self.hidden) - if self.keep_hidden: - if isinstance(h, tuple): - self.hidden = tuple(x.detach() for x in h) - else: - self.hidden = h.detach() - return x - - -class CPCModel(nn.Module): - def __init__(self, encoder, ar_net): - super(CPCModel, self).__init__() - self.gEncoder = encoder - self.gAR = ar_net - self.config = None - - def forward(self, x, label): - encoded = self.gEncoder(x).permute(0, 2, 1) - cpc_feature = self.gAR(encoded) - return cpc_feature, encoded, label - - def extract_features(self, source, get_encoded=False, norm_output=False): - cpc_feature, encoded, _ = self.forward(source, None) - if get_encoded: - cpc_feature = encoded - if norm_output: - mean = cpc_feature.mean(dim=1, keepdim=True) - var = cpc_feature.var(dim=1, keepdim=True) - cpc_feature = (cpc_feature - mean) / torch.sqrt(var + 1e-08) - return cpc_feature diff --git a/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/__init__.py b/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/__init__.py deleted file mode 100644 index f102a9cadfa89ce554b3b26d2b90bfba2e05273c..0000000000000000000000000000000000000000 --- a/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "0.0.1" diff --git a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/question.py b/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/question.py deleted file mode 100644 index 111ecaf108ff6dda532bdff63ef3241948899291..0000000000000000000000000000000000000000 --- a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/question.py +++ /dev/null @@ -1,52 +0,0 @@ -import collections.abc -from dataclasses import dataclass -from typing import Union, Mapping, Literal, Callable, Tuple, List, Optional - -LangTyping = Literal['en', 'cn'] -MultiLangCheckerTyping = Callable[[str, str, str, str], Tuple[bool, Optional[str]]] -SingleLangCheckerTyping = Callable[[str, str, str], Tuple[bool, Optional[str]]] - - -@dataclass -class Question: - texts: Mapping[str, str] - checker: MultiLangCheckerTyping - names: Mapping[str, str] - level: int - - -_KNOWN_PROBLEMS = [] - - -def register_question(text: Union[Mapping[str, str], str], - checkers: Union[Mapping[str, SingleLangCheckerTyping], MultiLangCheckerTyping], - name=Union[Mapping[str, str], str], - level: int = 1, default_lang='cn'): - if isinstance(checkers, collections.abc.Mapping): - _origin_checkers = checkers - - def _integrated_checker(question_text: str, user_text: str, answer_text: str, lang: str): - return _origin_checkers[lang](question_text, user_text, answer_text) - - checker: MultiLangCheckerTyping = _integrated_checker - else: - checker: MultiLangCheckerTyping = checkers - - if isinstance(text, str): - texts = {default_lang: text} - else: - texts = text - - if isinstance(name, str): - names = {default_lang: name} - else: - names = name - - _KNOWN_PROBLEMS.append(Question(texts, checker, names, level)) - - -def list_ordered_questions() -> List[Question]: - return [ - problem for _, problem in - sorted(enumerate(_KNOWN_PROBLEMS), key=lambda x: (x[1].level, x[0])) - ] diff --git a/spaces/OptimalScale/Robin-7b/app.py b/spaces/OptimalScale/Robin-7b/app.py deleted file mode 100644 index 47e7f0c83a2fd45a8620abc6c30a7a706523935b..0000000000000000000000000000000000000000 --- a/spaces/OptimalScale/Robin-7b/app.py +++ /dev/null @@ -1,230 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -# Copyright 2023 Statistics and Machine Learning Research Group at HKUST. All rights reserved. -"""A simple shell chatbot implemented with lmflow APIs. -""" -import logging -import json -import os -import sys -sys.path.remove(os.path.abspath(os.path.dirname(sys.argv[0]))) -import torch -import warnings -import gradio as gr -from dataclasses import dataclass, field -from transformers import HfArgumentParser -from typing import Optional - -from lmflow.datasets.dataset import Dataset -from lmflow.pipeline.auto_pipeline import AutoPipeline -from lmflow.models.auto_model import AutoModel -from lmflow.args import ModelArguments, DatasetArguments, AutoArguments - -MAX_BOXES = 20 - -logging.disable(logging.ERROR) -warnings.filterwarnings("ignore") - -title = """ -

    LMFlow-CHAT

    - - - - -LMFlow - -

    LMFlow is in extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.

    - -

    We have thoroughly tested this toolkit and are pleased to make it available under Github.

    -""" -css = """ -#user { - float: right; - position:relative; - right:5px; - width:auto; - min-height:32px; - max-width: 60% - line-height: 32px; - padding: 2px 8px; - font-size: 14px; - background: #9DC284; - border-radius:5px; - margin:10px 0px; -} - -#chatbot { - float: left; - position:relative; - right:5px; - width:auto; - min-height:32px; - max-width: 60% - line-height: 32px; - padding: 2px 8px; - font-size: 14px; - background:#7BA7D7; - border-radius:5px; - margin:10px 0px; -} -""" - - -@dataclass -class ChatbotArguments: - prompt_structure: Optional[str] = field( - default="###Human: {input_text}###Assistant:", - metadata={ - "help": "prompt structure given user's input text" - }, - ) - end_string: Optional[str] = field( - default="#", - metadata={ - "help": "end string mark of the chatbot's output" - }, - ) - max_new_tokens: Optional[int] = field( - default=1500, - metadata={ - "help": "maximum number of generated tokens" - }, - ) - temperature: Optional[float] = field( - default=0.7, - metadata={ - "help": "higher this value, more random the model output" - }, - ) - -def main(): - pipeline_name = "inferencer" - PipelineArguments = AutoArguments.get_pipeline_args_class(pipeline_name) - - parser = HfArgumentParser(( - ModelArguments, - PipelineArguments, - ChatbotArguments, - )) - model_args, pipeline_args, chatbot_args = ( - parser.parse_args_into_dataclasses() - ) - model_args.model_name_or_path = "LMFlow/Full-Robin-7b-v2" - pipeline_args.deepspeed = "configs/ds_config_chatbot.json" - model_args.torch_dtype = "float16" - - - with open (pipeline_args.deepspeed, "r") as f: - ds_config = json.load(f) - - model = AutoModel.get_model( - model_args, - tune_strategy='none', - ds_config=ds_config, - device=pipeline_args.device, - torch_dtype=torch.float16 - ) - - # We don't need input data, we will read interactively from stdin - data_args = DatasetArguments(dataset_path=None) - dataset = Dataset(data_args) - - inferencer = AutoPipeline.get_pipeline( - pipeline_name=pipeline_name, - model_args=model_args, - data_args=data_args, - pipeline_args=pipeline_args, - ) - - # Chats - model_name = model_args.model_name_or_path - if model_args.lora_model_path is not None: - model_name += f" + {model_args.lora_model_path}" - - - # context = ( - # "You are a helpful assistant who follows the given instructions" - # " unconditionally." - # ) - - - end_string = chatbot_args.end_string - prompt_structure = chatbot_args.prompt_structure - - - token_per_step = 4 - - def hist2context(hist): - context = "" - for query, response in hist: - context += prompt_structure.format(input_text=query) - if not (response is None): - context += response - return context - - def chat_stream(query: str, history= None, **kwargs): - if history is None: - history = [] - - context = hist2context(history) - print_index = 0 - context += prompt_structure.format(input_text=query) - context_ = context[-model.get_max_length():] - input_dataset = dataset.from_dict({ - "type": "text_only", - "instances": [ { "text": context_ } ] - }) - print(context_) - for response, flag_break in inferencer.stream_inference(context=context_, model=model, max_new_tokens=chatbot_args.max_new_tokens, - token_per_step=token_per_step, temperature=chatbot_args.temperature, - end_string=end_string, input_dataset=input_dataset): - delta = response[print_index:] - seq = response - print_index = len(response) - - yield delta, history + [(query, seq)] - if flag_break: - break - - - - - def predict(input, history=None): - if history is None: - history = [] - for response, history in chat_stream(input, history): - updates = [] - for query, response in history: - updates.append(gr.update(visible=True, value="" + query)) - updates.append(gr.update(visible=True, value="" + response)) - if len(updates) < MAX_BOXES: - updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates)) - yield [history] + updates - - - - - - with gr.Blocks(css=css) as demo: - gr.HTML(title) - state = gr.State([]) - text_boxes = [] - for i in range(MAX_BOXES): - if i % 2 == 0: - text_boxes.append(gr.Markdown(visible=False, label="Q:", elem_id="user")) - else: - text_boxes.append(gr.Markdown(visible=False, label="A:", elem_id="chatbot")) - - txt = gr.Textbox( - show_label=False, - placeholder="Enter text and press send.", - ) - button = gr.Button("Send") - - button.click(predict, [txt, state], [state] + text_boxes) - demo.queue().launch() - - - -if __name__ == "__main__": - main() diff --git a/spaces/Ordenador/classify-text-with-bert-hate-speech/Makefile b/spaces/Ordenador/classify-text-with-bert-hate-speech/Makefile deleted file mode 100644 index c96c7ee2c89594c7ee461264153389cd5bf83bee..0000000000000000000000000000000000000000 --- a/spaces/Ordenador/classify-text-with-bert-hate-speech/Makefile +++ /dev/null @@ -1,24 +0,0 @@ -SHELL=/bin/sh -export PATH := ./venv/bin:$(PATH) -.PHONY: help -help: ## This help. - @awk 'BEGIN {FS = ":.*?## "} /^[a-zA-Z_-]+:.*?## / {printf " \033[36m%-20s\033[0m %s\n", $$1, $$2}' $(MAKEFILE_LIST) - -.DEFAULT_GOAL := help - -venv: - touch requirements.txt ;\ - test -d venv || virtualenv --python=$$PYTHON3 venv - -pip-compile: venv - python -m pip install --upgrade pip;\ - pip install pip-tools;\ - touch requirements.in ;\ - pip-compile --output-file requirements.txt requirements.in;\ - pip install -r requirements.txt - -autopep8: - autopep8 -i *.py - -clean: - rm -fr venv \ No newline at end of file diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/image/geometric.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/image/geometric.py deleted file mode 100644 index cf97c201cb4e43796c911919d03fb26a07ed817d..0000000000000000000000000000000000000000 --- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/image/geometric.py +++ /dev/null @@ -1,728 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import numbers - -import cv2 -import numpy as np - -from ..utils import to_2tuple -from .io import imread_backend - -try: - from PIL import Image -except ImportError: - Image = None - - -def _scale_size(size, scale): - """Rescale a size by a ratio. - - Args: - size (tuple[int]): (w, h). - scale (float | tuple(float)): Scaling factor. - - Returns: - tuple[int]: scaled size. - """ - if isinstance(scale, (float, int)): - scale = (scale, scale) - w, h = size - return int(w * float(scale[0]) + 0.5), int(h * float(scale[1]) + 0.5) - - -cv2_interp_codes = { - 'nearest': cv2.INTER_NEAREST, - 'bilinear': cv2.INTER_LINEAR, - 'bicubic': cv2.INTER_CUBIC, - 'area': cv2.INTER_AREA, - 'lanczos': cv2.INTER_LANCZOS4 -} - -if Image is not None: - pillow_interp_codes = { - 'nearest': Image.NEAREST, - 'bilinear': Image.BILINEAR, - 'bicubic': Image.BICUBIC, - 'box': Image.BOX, - 'lanczos': Image.LANCZOS, - 'hamming': Image.HAMMING - } - - -def imresize(img, - size, - return_scale=False, - interpolation='bilinear', - out=None, - backend=None): - """Resize image to a given size. - - Args: - img (ndarray): The input image. - size (tuple[int]): Target size (w, h). - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Interpolation method, accepted values are - "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' - backend, "nearest", "bilinear" for 'pillow' backend. - out (ndarray): The output destination. - backend (str | None): The image resize backend type. Options are `cv2`, - `pillow`, `None`. If backend is None, the global imread_backend - specified by ``mmcv.use_backend()`` will be used. Default: None. - - Returns: - tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = img.shape[:2] - if backend is None: - backend = imread_backend - if backend not in ['cv2', 'pillow']: - raise ValueError(f'backend: {backend} is not supported for resize.' - f"Supported backends are 'cv2', 'pillow'") - - if backend == 'pillow': - assert img.dtype == np.uint8, 'Pillow backend only support uint8 type' - pil_image = Image.fromarray(img) - pil_image = pil_image.resize(size, pillow_interp_codes[interpolation]) - resized_img = np.array(pil_image) - else: - resized_img = cv2.resize( - img, size, dst=out, interpolation=cv2_interp_codes[interpolation]) - if not return_scale: - return resized_img - else: - w_scale = size[0] / w - h_scale = size[1] / h - return resized_img, w_scale, h_scale - - -def imresize_to_multiple(img, - divisor, - size=None, - scale_factor=None, - keep_ratio=False, - return_scale=False, - interpolation='bilinear', - out=None, - backend=None): - """Resize image according to a given size or scale factor and then rounds - up the the resized or rescaled image size to the nearest value that can be - divided by the divisor. - - Args: - img (ndarray): The input image. - divisor (int | tuple): Resized image size will be a multiple of - divisor. If divisor is a tuple, divisor should be - (w_divisor, h_divisor). - size (None | int | tuple[int]): Target size (w, h). Default: None. - scale_factor (None | float | tuple[float]): Multiplier for spatial - size. Should match input size if it is a tuple and the 2D style is - (w_scale_factor, h_scale_factor). Default: None. - keep_ratio (bool): Whether to keep the aspect ratio when resizing the - image. Default: False. - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Interpolation method, accepted values are - "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' - backend, "nearest", "bilinear" for 'pillow' backend. - out (ndarray): The output destination. - backend (str | None): The image resize backend type. Options are `cv2`, - `pillow`, `None`. If backend is None, the global imread_backend - specified by ``mmcv.use_backend()`` will be used. Default: None. - - Returns: - tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = img.shape[:2] - if size is not None and scale_factor is not None: - raise ValueError('only one of size or scale_factor should be defined') - elif size is None and scale_factor is None: - raise ValueError('one of size or scale_factor should be defined') - elif size is not None: - size = to_2tuple(size) - if keep_ratio: - size = rescale_size((w, h), size, return_scale=False) - else: - size = _scale_size((w, h), scale_factor) - - divisor = to_2tuple(divisor) - size = tuple([int(np.ceil(s / d)) * d for s, d in zip(size, divisor)]) - resized_img, w_scale, h_scale = imresize( - img, - size, - return_scale=True, - interpolation=interpolation, - out=out, - backend=backend) - if return_scale: - return resized_img, w_scale, h_scale - else: - return resized_img - - -def imresize_like(img, - dst_img, - return_scale=False, - interpolation='bilinear', - backend=None): - """Resize image to the same size of a given image. - - Args: - img (ndarray): The input image. - dst_img (ndarray): The target image. - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Same as :func:`resize`. - backend (str | None): Same as :func:`resize`. - - Returns: - tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = dst_img.shape[:2] - return imresize(img, (w, h), return_scale, interpolation, backend=backend) - - -def rescale_size(old_size, scale, return_scale=False): - """Calculate the new size to be rescaled to. - - Args: - old_size (tuple[int]): The old size (w, h) of image. - scale (float | tuple[int]): The scaling factor or maximum size. - If it is a float number, then the image will be rescaled by this - factor, else if it is a tuple of 2 integers, then the image will - be rescaled as large as possible within the scale. - return_scale (bool): Whether to return the scaling factor besides the - rescaled image size. - - Returns: - tuple[int]: The new rescaled image size. - """ - w, h = old_size - if isinstance(scale, (float, int)): - if scale <= 0: - raise ValueError(f'Invalid scale {scale}, must be positive.') - scale_factor = scale - elif isinstance(scale, tuple): - max_long_edge = max(scale) - max_short_edge = min(scale) - scale_factor = min(max_long_edge / max(h, w), - max_short_edge / min(h, w)) - else: - raise TypeError( - f'Scale must be a number or tuple of int, but got {type(scale)}') - - new_size = _scale_size((w, h), scale_factor) - - if return_scale: - return new_size, scale_factor - else: - return new_size - - -def imrescale(img, - scale, - return_scale=False, - interpolation='bilinear', - backend=None): - """Resize image while keeping the aspect ratio. - - Args: - img (ndarray): The input image. - scale (float | tuple[int]): The scaling factor or maximum size. - If it is a float number, then the image will be rescaled by this - factor, else if it is a tuple of 2 integers, then the image will - be rescaled as large as possible within the scale. - return_scale (bool): Whether to return the scaling factor besides the - rescaled image. - interpolation (str): Same as :func:`resize`. - backend (str | None): Same as :func:`resize`. - - Returns: - ndarray: The rescaled image. - """ - h, w = img.shape[:2] - new_size, scale_factor = rescale_size((w, h), scale, return_scale=True) - rescaled_img = imresize( - img, new_size, interpolation=interpolation, backend=backend) - if return_scale: - return rescaled_img, scale_factor - else: - return rescaled_img - - -def imflip(img, direction='horizontal'): - """Flip an image horizontally or vertically. - - Args: - img (ndarray): Image to be flipped. - direction (str): The flip direction, either "horizontal" or - "vertical" or "diagonal". - - Returns: - ndarray: The flipped image. - """ - assert direction in ['horizontal', 'vertical', 'diagonal'] - if direction == 'horizontal': - return np.flip(img, axis=1) - elif direction == 'vertical': - return np.flip(img, axis=0) - else: - return np.flip(img, axis=(0, 1)) - - -def imflip_(img, direction='horizontal'): - """Inplace flip an image horizontally or vertically. - - Args: - img (ndarray): Image to be flipped. - direction (str): The flip direction, either "horizontal" or - "vertical" or "diagonal". - - Returns: - ndarray: The flipped image (inplace). - """ - assert direction in ['horizontal', 'vertical', 'diagonal'] - if direction == 'horizontal': - return cv2.flip(img, 1, img) - elif direction == 'vertical': - return cv2.flip(img, 0, img) - else: - return cv2.flip(img, -1, img) - - -def imrotate(img, - angle, - center=None, - scale=1.0, - border_value=0, - interpolation='bilinear', - auto_bound=False): - """Rotate an image. - - Args: - img (ndarray): Image to be rotated. - angle (float): Rotation angle in degrees, positive values mean - clockwise rotation. - center (tuple[float], optional): Center point (w, h) of the rotation in - the source image. If not specified, the center of the image will be - used. - scale (float): Isotropic scale factor. - border_value (int): Border value. - interpolation (str): Same as :func:`resize`. - auto_bound (bool): Whether to adjust the image size to cover the whole - rotated image. - - Returns: - ndarray: The rotated image. - """ - if center is not None and auto_bound: - raise ValueError('`auto_bound` conflicts with `center`') - h, w = img.shape[:2] - if center is None: - center = ((w - 1) * 0.5, (h - 1) * 0.5) - assert isinstance(center, tuple) - - matrix = cv2.getRotationMatrix2D(center, -angle, scale) - if auto_bound: - cos = np.abs(matrix[0, 0]) - sin = np.abs(matrix[0, 1]) - new_w = h * sin + w * cos - new_h = h * cos + w * sin - matrix[0, 2] += (new_w - w) * 0.5 - matrix[1, 2] += (new_h - h) * 0.5 - w = int(np.round(new_w)) - h = int(np.round(new_h)) - rotated = cv2.warpAffine( - img, - matrix, (w, h), - flags=cv2_interp_codes[interpolation], - borderValue=border_value) - return rotated - - -def bbox_clip(bboxes, img_shape): - """Clip bboxes to fit the image shape. - - Args: - bboxes (ndarray): Shape (..., 4*k) - img_shape (tuple[int]): (height, width) of the image. - - Returns: - ndarray: Clipped bboxes. - """ - assert bboxes.shape[-1] % 4 == 0 - cmin = np.empty(bboxes.shape[-1], dtype=bboxes.dtype) - cmin[0::2] = img_shape[1] - 1 - cmin[1::2] = img_shape[0] - 1 - clipped_bboxes = np.maximum(np.minimum(bboxes, cmin), 0) - return clipped_bboxes - - -def bbox_scaling(bboxes, scale, clip_shape=None): - """Scaling bboxes w.r.t the box center. - - Args: - bboxes (ndarray): Shape(..., 4). - scale (float): Scaling factor. - clip_shape (tuple[int], optional): If specified, bboxes that exceed the - boundary will be clipped according to the given shape (h, w). - - Returns: - ndarray: Scaled bboxes. - """ - if float(scale) == 1.0: - scaled_bboxes = bboxes.copy() - else: - w = bboxes[..., 2] - bboxes[..., 0] + 1 - h = bboxes[..., 3] - bboxes[..., 1] + 1 - dw = (w * (scale - 1)) * 0.5 - dh = (h * (scale - 1)) * 0.5 - scaled_bboxes = bboxes + np.stack((-dw, -dh, dw, dh), axis=-1) - if clip_shape is not None: - return bbox_clip(scaled_bboxes, clip_shape) - else: - return scaled_bboxes - - -def imcrop(img, bboxes, scale=1.0, pad_fill=None): - """Crop image patches. - - 3 steps: scale the bboxes -> clip bboxes -> crop and pad. - - Args: - img (ndarray): Image to be cropped. - bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes. - scale (float, optional): Scale ratio of bboxes, the default value - 1.0 means no padding. - pad_fill (Number | list[Number]): Value to be filled for padding. - Default: None, which means no padding. - - Returns: - list[ndarray] | ndarray: The cropped image patches. - """ - chn = 1 if img.ndim == 2 else img.shape[2] - if pad_fill is not None: - if isinstance(pad_fill, (int, float)): - pad_fill = [pad_fill for _ in range(chn)] - assert len(pad_fill) == chn - - _bboxes = bboxes[None, ...] if bboxes.ndim == 1 else bboxes - scaled_bboxes = bbox_scaling(_bboxes, scale).astype(np.int32) - clipped_bbox = bbox_clip(scaled_bboxes, img.shape) - - patches = [] - for i in range(clipped_bbox.shape[0]): - x1, y1, x2, y2 = tuple(clipped_bbox[i, :]) - if pad_fill is None: - patch = img[y1:y2 + 1, x1:x2 + 1, ...] - else: - _x1, _y1, _x2, _y2 = tuple(scaled_bboxes[i, :]) - if chn == 1: - patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1) - else: - patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1, chn) - patch = np.array( - pad_fill, dtype=img.dtype) * np.ones( - patch_shape, dtype=img.dtype) - x_start = 0 if _x1 >= 0 else -_x1 - y_start = 0 if _y1 >= 0 else -_y1 - w = x2 - x1 + 1 - h = y2 - y1 + 1 - patch[y_start:y_start + h, x_start:x_start + w, - ...] = img[y1:y1 + h, x1:x1 + w, ...] - patches.append(patch) - - if bboxes.ndim == 1: - return patches[0] - else: - return patches - - -def impad(img, - *, - shape=None, - padding=None, - pad_val=0, - padding_mode='constant'): - """Pad the given image to a certain shape or pad on all sides with - specified padding mode and padding value. - - Args: - img (ndarray): Image to be padded. - shape (tuple[int]): Expected padding shape (h, w). Default: None. - padding (int or tuple[int]): Padding on each border. If a single int is - provided this is used to pad all borders. If tuple of length 2 is - provided this is the padding on left/right and top/bottom - respectively. If a tuple of length 4 is provided this is the - padding for the left, top, right and bottom borders respectively. - Default: None. Note that `shape` and `padding` can not be both - set. - pad_val (Number | Sequence[Number]): Values to be filled in padding - areas when padding_mode is 'constant'. Default: 0. - padding_mode (str): Type of padding. Should be: constant, edge, - reflect or symmetric. Default: constant. - - - constant: pads with a constant value, this value is specified - with pad_val. - - edge: pads with the last value at the edge of the image. - - reflect: pads with reflection of image without repeating the - last value on the edge. For example, padding [1, 2, 3, 4] - with 2 elements on both sides in reflect mode will result - in [3, 2, 1, 2, 3, 4, 3, 2]. - - symmetric: pads with reflection of image repeating the last - value on the edge. For example, padding [1, 2, 3, 4] with - 2 elements on both sides in symmetric mode will result in - [2, 1, 1, 2, 3, 4, 4, 3] - - Returns: - ndarray: The padded image. - """ - - assert (shape is not None) ^ (padding is not None) - if shape is not None: - padding = (0, 0, shape[1] - img.shape[1], shape[0] - img.shape[0]) - - # check pad_val - if isinstance(pad_val, tuple): - assert len(pad_val) == img.shape[-1] - elif not isinstance(pad_val, numbers.Number): - raise TypeError('pad_val must be a int or a tuple. ' - f'But received {type(pad_val)}') - - # check padding - if isinstance(padding, tuple) and len(padding) in [2, 4]: - if len(padding) == 2: - padding = (padding[0], padding[1], padding[0], padding[1]) - elif isinstance(padding, numbers.Number): - padding = (padding, padding, padding, padding) - else: - raise ValueError('Padding must be a int or a 2, or 4 element tuple.' - f'But received {padding}') - - # check padding mode - assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'] - - border_type = { - 'constant': cv2.BORDER_CONSTANT, - 'edge': cv2.BORDER_REPLICATE, - 'reflect': cv2.BORDER_REFLECT_101, - 'symmetric': cv2.BORDER_REFLECT - } - img = cv2.copyMakeBorder( - img, - padding[1], - padding[3], - padding[0], - padding[2], - border_type[padding_mode], - value=pad_val) - - return img - - -def impad_to_multiple(img, divisor, pad_val=0): - """Pad an image to ensure each edge to be multiple to some number. - - Args: - img (ndarray): Image to be padded. - divisor (int): Padded image edges will be multiple to divisor. - pad_val (Number | Sequence[Number]): Same as :func:`impad`. - - Returns: - ndarray: The padded image. - """ - pad_h = int(np.ceil(img.shape[0] / divisor)) * divisor - pad_w = int(np.ceil(img.shape[1] / divisor)) * divisor - return impad(img, shape=(pad_h, pad_w), pad_val=pad_val) - - -def cutout(img, shape, pad_val=0): - """Randomly cut out a rectangle from the original img. - - Args: - img (ndarray): Image to be cutout. - shape (int | tuple[int]): Expected cutout shape (h, w). If given as a - int, the value will be used for both h and w. - pad_val (int | float | tuple[int | float]): Values to be filled in the - cut area. Defaults to 0. - - Returns: - ndarray: The cutout image. - """ - - channels = 1 if img.ndim == 2 else img.shape[2] - if isinstance(shape, int): - cut_h, cut_w = shape, shape - else: - assert isinstance(shape, tuple) and len(shape) == 2, \ - f'shape must be a int or a tuple with length 2, but got type ' \ - f'{type(shape)} instead.' - cut_h, cut_w = shape - if isinstance(pad_val, (int, float)): - pad_val = tuple([pad_val] * channels) - elif isinstance(pad_val, tuple): - assert len(pad_val) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(pad_val), channels) - else: - raise TypeError(f'Invalid type {type(pad_val)} for `pad_val`') - - img_h, img_w = img.shape[:2] - y0 = np.random.uniform(img_h) - x0 = np.random.uniform(img_w) - - y1 = int(max(0, y0 - cut_h / 2.)) - x1 = int(max(0, x0 - cut_w / 2.)) - y2 = min(img_h, y1 + cut_h) - x2 = min(img_w, x1 + cut_w) - - if img.ndim == 2: - patch_shape = (y2 - y1, x2 - x1) - else: - patch_shape = (y2 - y1, x2 - x1, channels) - - img_cutout = img.copy() - patch = np.array( - pad_val, dtype=img.dtype) * np.ones( - patch_shape, dtype=img.dtype) - img_cutout[y1:y2, x1:x2, ...] = patch - - return img_cutout - - -def _get_shear_matrix(magnitude, direction='horizontal'): - """Generate the shear matrix for transformation. - - Args: - magnitude (int | float): The magnitude used for shear. - direction (str): The flip direction, either "horizontal" - or "vertical". - - Returns: - ndarray: The shear matrix with dtype float32. - """ - if direction == 'horizontal': - shear_matrix = np.float32([[1, magnitude, 0], [0, 1, 0]]) - elif direction == 'vertical': - shear_matrix = np.float32([[1, 0, 0], [magnitude, 1, 0]]) - return shear_matrix - - -def imshear(img, - magnitude, - direction='horizontal', - border_value=0, - interpolation='bilinear'): - """Shear an image. - - Args: - img (ndarray): Image to be sheared with format (h, w) - or (h, w, c). - magnitude (int | float): The magnitude used for shear. - direction (str): The flip direction, either "horizontal" - or "vertical". - border_value (int | tuple[int]): Value used in case of a - constant border. - interpolation (str): Same as :func:`resize`. - - Returns: - ndarray: The sheared image. - """ - assert direction in ['horizontal', - 'vertical'], f'Invalid direction: {direction}' - height, width = img.shape[:2] - if img.ndim == 2: - channels = 1 - elif img.ndim == 3: - channels = img.shape[-1] - if isinstance(border_value, int): - border_value = tuple([border_value] * channels) - elif isinstance(border_value, tuple): - assert len(border_value) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(border_value), channels) - else: - raise ValueError( - f'Invalid type {type(border_value)} for `border_value`') - shear_matrix = _get_shear_matrix(magnitude, direction) - sheared = cv2.warpAffine( - img, - shear_matrix, - (width, height), - # Note case when the number elements in `border_value` - # greater than 3 (e.g. shearing masks whose channels large - # than 3) will raise TypeError in `cv2.warpAffine`. - # Here simply slice the first 3 values in `border_value`. - borderValue=border_value[:3], - flags=cv2_interp_codes[interpolation]) - return sheared - - -def _get_translate_matrix(offset, direction='horizontal'): - """Generate the translate matrix. - - Args: - offset (int | float): The offset used for translate. - direction (str): The translate direction, either - "horizontal" or "vertical". - - Returns: - ndarray: The translate matrix with dtype float32. - """ - if direction == 'horizontal': - translate_matrix = np.float32([[1, 0, offset], [0, 1, 0]]) - elif direction == 'vertical': - translate_matrix = np.float32([[1, 0, 0], [0, 1, offset]]) - return translate_matrix - - -def imtranslate(img, - offset, - direction='horizontal', - border_value=0, - interpolation='bilinear'): - """Translate an image. - - Args: - img (ndarray): Image to be translated with format - (h, w) or (h, w, c). - offset (int | float): The offset used for translate. - direction (str): The translate direction, either "horizontal" - or "vertical". - border_value (int | tuple[int]): Value used in case of a - constant border. - interpolation (str): Same as :func:`resize`. - - Returns: - ndarray: The translated image. - """ - assert direction in ['horizontal', - 'vertical'], f'Invalid direction: {direction}' - height, width = img.shape[:2] - if img.ndim == 2: - channels = 1 - elif img.ndim == 3: - channels = img.shape[-1] - if isinstance(border_value, int): - border_value = tuple([border_value] * channels) - elif isinstance(border_value, tuple): - assert len(border_value) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(border_value), channels) - else: - raise ValueError( - f'Invalid type {type(border_value)} for `border_value`.') - translate_matrix = _get_translate_matrix(offset, direction) - translated = cv2.warpAffine( - img, - translate_matrix, - (width, height), - # Note case when the number elements in `border_value` - # greater than 3 (e.g. translating masks whose channels - # large than 3) will raise TypeError in `cv2.warpAffine`. - # Here simply slice the first 3 values in `border_value`. - borderValue=border_value[:3], - flags=cv2_interp_codes[interpolation]) - return translated diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/op/fused_bias_act.cpp b/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/op/fused_bias_act.cpp deleted file mode 100644 index 02be898f970bcc8ea297867fcaa4e71b24b3d949..0000000000000000000000000000000000000000 --- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/op/fused_bias_act.cpp +++ /dev/null @@ -1,21 +0,0 @@ -#include - - -torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, - int act, int grad, float alpha, float scale); - -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, - int act, int grad, float alpha, float scale) { - CHECK_CUDA(input); - CHECK_CUDA(bias); - - return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); -} \ No newline at end of file diff --git a/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/psld.py b/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/psld.py deleted file mode 100644 index 6f759d6077b2a126264d13fb3fe6d8b1a7922552..0000000000000000000000000000000000000000 --- a/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/psld.py +++ /dev/null @@ -1,423 +0,0 @@ -"""SAMPLING ONLY.""" - -import torch -import numpy as np -from tqdm import tqdm -from functools import partial - -from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \ - extract_into_tensor - -import pdb - -class DDIMSampler(object): - def __init__(self, model, schedule="linear", **kwargs): - super().__init__() - self.model = model - self.ddpm_num_timesteps = model.num_timesteps - self.schedule = schedule - - def register_buffer(self, name, attr): - if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) - setattr(self, name, attr) - - def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): - self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, - num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) - - alphas_cumprod = self.model.alphas_cumprod - assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' - to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) - - self.register_buffer('betas', to_torch(self.model.betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) - - # ddim sampling parameters - ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), - ddim_timesteps=self.ddim_timesteps, - eta=ddim_eta,verbose=verbose) - self.register_buffer('ddim_sigmas', ddim_sigmas) - self.register_buffer('ddim_alphas', ddim_alphas) - self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) - self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) - sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( - (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( - 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) - self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) - - # @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - ip_mask = None, measurements = None, operator = None, gamma = 1, inpainting = False, omega=1, - general_inverse = None, noiser=None, - ffhq256=False, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - cbs = conditioning[list(conditioning.keys())[0]].shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - else: - print('Running unconditional generation...') - - self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - print(f'Data shape for DDIM sampling is {size}, eta {eta}') - - samples, intermediates = self.ddim_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - ip_mask = ip_mask, measurements = measurements, operator = operator, - gamma = gamma, - inpainting = inpainting, omega=omega, - general_inverse = general_inverse, noiser = noiser, - ffhq256=ffhq256 - ) - return samples, intermediates - - ## lr - # @torch.no_grad() - def ddim_sampling(self, cond, shape, - x_T=None, ddim_use_original_steps=False, - callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, log_every_t=100, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - ip_mask = None, measurements = None, operator = None, gamma = 1, inpainting=False, omega=1, - general_inverse = None, noiser=None, - ffhq256=False): - device = self.model.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - - if timesteps is None: - timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps - elif timesteps is not None and not ddim_use_original_steps: - subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 - timesteps = self.ddim_timesteps[:subset_end] - - intermediates = {'x_inter': [img], 'pred_x0': [img]} - time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) - total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] - print(f"Running DDIM Sampling with {total_steps} timesteps") - - iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) - - for i, step in enumerate(iterator): - index = total_steps - i - 1 - #print('index:', index) - ts = torch.full((b,), step, device=device, dtype=torch.long) - - if mask is not None: - assert x0 is not None - img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? - img = img_orig * mask + (1. - mask) * img - - outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, - quantize_denoised=quantize_denoised, temperature=temperature, - noise_dropout=noise_dropout, score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - ip_mask = ip_mask, measurements = measurements, operator = operator, gamma = gamma, - inpainting=inpainting, omega=omega, - gamma_scale = index/total_steps, - general_inverse=general_inverse, noiser=noiser, - ffhq256=ffhq256) - img, pred_x0 = outs - if callback: callback(i) - if img_callback: img_callback(pred_x0, i) - - if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) - - return img, intermediates - - ###################### - def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - ip_mask=None, measurements = None, operator = None, gamma=1, inpainting=False, - gamma_scale = None, omega = 1e-1, - general_inverse=False,noiser=None, - ffhq256=False): - b, *_, device = *x.shape, x.device - - ########################################## - ## measurment consistency guided diffusion - ########################################## - if inpainting: - # print('Running inpainting module...') - z_t = torch.clone(x.detach()) - z_t.requires_grad = True - - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - e_t = self.model.apply_model(z_t, t, c) - else: - x_in = torch.cat([z_t] * 2) - t_in = torch.cat([t] * 2) - c_in = torch.cat([unconditional_conditioning, c]) - e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) - e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - - if score_corrector is not None: - assert self.model.parameterization == "eps" - e_t = score_corrector.modify_score(self.model, e_t, z_t, t, c, **corrector_kwargs) - - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) - - # current prediction for x_0 - pred_z_0 = (z_t - sqrt_one_minus_at * e_t) / a_t.sqrt() - - - if quantize_denoised: - pred_z_0, _, *_ = self.model.first_stage_model.quantize(pred_z_0) - - - # direction pointing to x_t - dir_zt = (1. - a_prev - sigma_t**2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - - z_prev = a_prev.sqrt() * pred_z_0 + dir_zt + noise - - - ############################################## - image_pred = self.model.differentiable_decode_first_stage(pred_z_0) - meas_pred = operator.forward(image_pred,mask=ip_mask) - meas_pred = noiser(meas_pred) - meas_error = torch.linalg.norm(meas_pred - measurements) - - ortho_project = image_pred - operator.transpose(operator.forward(image_pred, mask=ip_mask)) - parallel_project = operator.transpose(measurements) - inpainted_image = parallel_project + ortho_project - - # pdb.set_trace() - # encoded_z_0 = self.model.encode_first_stage(inpainted_image) if ffhq256 else self.model.encode_first_stage(inpainted_image) - encoded_z_0 = self.model.encode_first_stage(inpainted_image.type(torch.float32)) - encoded_z_0 = self.model.get_first_stage_encoding(encoded_z_0) - inpaint_error = torch.linalg.norm(encoded_z_0 - pred_z_0) - - error = inpaint_error * gamma + meas_error * omega - gradients = torch.autograd.grad(error, inputs=z_t)[0] - z_prev = z_prev - gradients - print('Loss: ', error.item()) - - return z_prev.detach(), pred_z_0.detach() - - elif general_inverse: - # print('Running general inverse module...') - z_t = torch.clone(x.detach()) - z_t.requires_grad = True - - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - e_t = self.model.apply_model(z_t, t, c) - else: - x_in = torch.cat([z_t] * 2) - t_in = torch.cat([t] * 2) - c_in = torch.cat([unconditional_conditioning, c]) - e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) - e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - - if score_corrector is not None: - assert self.model.parameterization == "eps" - e_t = score_corrector.modify_score(self.model, e_t, z_t, t, c, **corrector_kwargs) - - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) - - # current prediction for x_0 - pred_z_0 = (z_t - sqrt_one_minus_at * e_t) / a_t.sqrt() - - - if quantize_denoised: - pred_z_0, _, *_ = self.model.first_stage_model.quantize(pred_z_0) - - - # direction pointing to x_t - dir_zt = (1. - a_prev - sigma_t**2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - - z_prev = a_prev.sqrt() * pred_z_0 + dir_zt + noise - - - ############################################## - image_pred = self.model.differentiable_decode_first_stage(pred_z_0) - meas_pred = operator.forward(image_pred) - meas_pred = noiser(meas_pred) - meas_error = torch.linalg.norm(meas_pred - measurements) - - ortho_project = image_pred - operator.transpose(operator.forward(image_pred)) - parallel_project = operator.transpose(measurements) - inpainted_image = parallel_project + ortho_project - - # encoded_z_0 = self.model.encode_first_stage(inpainted_image) if ffhq256 else self.model.encode_first_stage(inpainted_image).mean - encoded_z_0 = self.model.encode_first_stage(inpainted_image) - encoded_z_0 = self.model.get_first_stage_encoding(encoded_z_0) - inpaint_error = torch.linalg.norm(encoded_z_0 - pred_z_0) - - error = inpaint_error * gamma + meas_error * omega - - gradients = torch.autograd.grad(error, inputs=z_t)[0] - z_prev = z_prev - gradients - print('Loss: ', error.item()) - - return z_prev.detach(), pred_z_0.detach() - - - ######################################### - else: - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - with torch.no_grad(): - e_t = self.model.apply_model(x, t, c) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t] * 2) - c_in = torch.cat([unconditional_conditioning, c]) - ## lr - with torch.no_grad(): - e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) - e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - if score_corrector is not None: - assert self.model.parameterization == "eps" - ## lr - with torch.no_grad(): - e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) - - # current prediction for x_0 - pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() - if quantize_denoised: - ## - with torch.no_grad(): - pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) - # direction pointing to x_t - dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise - - return x_prev, pred_x0 - - ###################### - - #@torch.no_grad() - def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): - # fast, but does not allow for exact reconstruction - # t serves as an index to gather the correct alphas - if use_original_steps: - sqrt_alphas_cumprod = self.sqrt_alphas_cumprod - sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod - else: - sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) - sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas - - if noise is None: - noise = torch.randn_like(x0) - return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + - extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) - - #@torch.no_grad() - def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, - use_original_steps=False): - - timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps - timesteps = timesteps[:t_start] - - time_range = np.flip(timesteps) - total_steps = timesteps.shape[0] - print(f"Running DDIM Sampling with {total_steps} timesteps") - - iterator = tqdm(time_range, desc='Decoding image', total=total_steps) - x_dec = x_latent - for i, step in enumerate(iterator): - index = total_steps - i - 1 - ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) - x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning) - return x_dec \ No newline at end of file diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/primitives.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/primitives.go deleted file mode 100644 index b3e13b6d21915318e8a5118b745405944917b36e..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/primitives.go and /dev/null differ diff --git a/spaces/PeepDaSlan9/AutoGPT/data_ingestion.py b/spaces/PeepDaSlan9/AutoGPT/data_ingestion.py deleted file mode 100644 index b89a33dafd15c2e7bded0445a741a4a1c47ed417..0000000000000000000000000000000000000000 --- a/spaces/PeepDaSlan9/AutoGPT/data_ingestion.py +++ /dev/null @@ -1,96 +0,0 @@ -import argparse -import logging - -from autogpt.commands.file_operations import ingest_file, search_files -from autogpt.config import Config -from autogpt.memory import get_memory - -cfg = Config() - - -def configure_logging(): - logging.basicConfig( - filename="log-ingestion.txt", - filemode="a", - format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s", - datefmt="%H:%M:%S", - level=logging.DEBUG, - ) - return logging.getLogger("AutoGPT-Ingestion") - - -def ingest_directory(directory, memory, args): - """ - Ingest all files in a directory by calling the ingest_file function for each file. - - :param directory: The directory containing the files to ingest - :param memory: An object with an add() method to store the chunks in memory - """ - try: - files = search_files(directory) - for file in files: - ingest_file(file, memory, args.max_length, args.overlap) - except Exception as e: - print(f"Error while ingesting directory '{directory}': {str(e)}") - - -def main() -> None: - logger = configure_logging() - - parser = argparse.ArgumentParser( - description="Ingest a file or a directory with multiple files into memory. " - "Make sure to set your .env before running this script." - ) - group = parser.add_mutually_exclusive_group(required=True) - group.add_argument("--file", type=str, help="The file to ingest.") - group.add_argument( - "--dir", type=str, help="The directory containing the files to ingest." - ) - parser.add_argument( - "--init", - action="store_true", - help="Init the memory and wipe its content (default: False)", - default=False, - ) - parser.add_argument( - "--overlap", - type=int, - help="The overlap size between chunks when ingesting files (default: 200)", - default=200, - ) - parser.add_argument( - "--max_length", - type=int, - help="The max_length of each chunk when ingesting files (default: 4000)", - default=4000, - ) - - args = parser.parse_args() - - # Initialize memory - memory = get_memory(cfg, init=args.init) - print("Using memory of type: " + memory.__class__.__name__) - - if args.file: - try: - ingest_file(args.file, memory, args.max_length, args.overlap) - print(f"File '{args.file}' ingested successfully.") - except Exception as e: - logger.error(f"Error while ingesting file '{args.file}': {str(e)}") - print(f"Error while ingesting file '{args.file}': {str(e)}") - elif args.dir: - try: - ingest_directory(args.dir, memory, args) - print(f"Directory '{args.dir}' ingested successfully.") - except Exception as e: - logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}") - print(f"Error while ingesting directory '{args.dir}': {str(e)}") - else: - print( - "Please provide either a file path (--file) or a directory name (--dir)" - " inside the auto_gpt_workspace directory as input." - ) - - -if __name__ == "__main__": - main() diff --git a/spaces/PeepDaSlan9/AutoGPT/run_continuous.bat b/spaces/PeepDaSlan9/AutoGPT/run_continuous.bat deleted file mode 100644 index 812aa01c1c5506c452665610c0e9e83a17c426f2..0000000000000000000000000000000000000000 --- a/spaces/PeepDaSlan9/AutoGPT/run_continuous.bat +++ /dev/null @@ -1,3 +0,0 @@ -@echo off -set argument=--continuous -call run.bat %argument% diff --git a/spaces/PrabhuKiranKonda/Streamlit-PDF-Assistant-Docker/components/sidebar/__init__.py b/spaces/PrabhuKiranKonda/Streamlit-PDF-Assistant-Docker/components/sidebar/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Purple11/Grounded-Diffusion/src/CLIP/data/country211.md b/spaces/Purple11/Grounded-Diffusion/src/CLIP/data/country211.md deleted file mode 100644 index 4cd096005c8e5777e0706d97d182c3bd87b651a9..0000000000000000000000000000000000000000 --- a/spaces/Purple11/Grounded-Diffusion/src/CLIP/data/country211.md +++ /dev/null @@ -1,12 +0,0 @@ -# The Country211 Dataset - -In the paper, we used an image classification dataset called Country211, to evaluate the model's capability on geolocation. To do so, we filtered the YFCC100m dataset that have GPS coordinate corresponding to a [ISO-3166 country code](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes) and created a balanced dataset by sampling 150 train images, 50 validation images, and 100 test images images for each country. - -The following command will download an 11GB archive countaining the images and extract into a subdirectory `country211`: - -```bash -wget https://openaipublic.azureedge.net/clip/data/country211.tgz -tar zxvf country211.tgz -``` - -These images are a subset of the YFCC100m dataset. Use of the underlying media files is subject to the Creative Commons licenses chosen by their creators/uploaders. For more information about the YFCC100M dataset, visit [the official website](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/). \ No newline at end of file diff --git a/spaces/Raghav001/API/Dockerfile b/spaces/Raghav001/API/Dockerfile deleted file mode 100644 index df8771ca403bdea21284d3252dd8da9d174fac03..0000000000000000000000000000000000000000 --- a/spaces/Raghav001/API/Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM python:3.9 - -WORKDIR /code - -COPY ./requirements.txt /code/requirements.txt - -RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt - -COPY . . - -CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/locations/_distutils.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/locations/_distutils.py deleted file mode 100644 index c7712f016f5d92930bb88bfd50fbb5dce55e4ecc..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/locations/_distutils.py +++ /dev/null @@ -1,180 +0,0 @@ -"""Locations where we look for configs, install stuff, etc""" - -# The following comment should be removed at some point in the future. -# mypy: strict-optional=False - -# If pip's going to use distutils, it should not be using the copy that setuptools -# might have injected into the environment. This is done by removing the injected -# shim, if it's injected. -# -# See https://github.com/pypa/pip/issues/8761 for the original discussion and -# rationale for why this is done within pip. -try: - __import__("_distutils_hack").remove_shim() -except (ImportError, AttributeError): - pass - -import logging -import os -import sys -from distutils.cmd import Command as DistutilsCommand -from distutils.command.install import SCHEME_KEYS -from distutils.command.install import install as distutils_install_command -from distutils.sysconfig import get_python_lib -from typing import Dict, List, Optional, Tuple, Union, cast - -from pip._internal.models.scheme import Scheme -from pip._internal.utils.compat import WINDOWS -from pip._internal.utils.virtualenv import running_under_virtualenv - -from .base import get_major_minor_version - -logger = logging.getLogger(__name__) - - -def distutils_scheme( - dist_name: str, - user: bool = False, - home: Optional[str] = None, - root: Optional[str] = None, - isolated: bool = False, - prefix: Optional[str] = None, - *, - ignore_config_files: bool = False, -) -> Dict[str, str]: - """ - Return a distutils install scheme - """ - from distutils.dist import Distribution - - dist_args: Dict[str, Union[str, List[str]]] = {"name": dist_name} - if isolated: - dist_args["script_args"] = ["--no-user-cfg"] - - d = Distribution(dist_args) - if not ignore_config_files: - try: - d.parse_config_files() - except UnicodeDecodeError: - # Typeshed does not include find_config_files() for some reason. - paths = d.find_config_files() # type: ignore - logger.warning( - "Ignore distutils configs in %s due to encoding errors.", - ", ".join(os.path.basename(p) for p in paths), - ) - obj: Optional[DistutilsCommand] = None - obj = d.get_command_obj("install", create=True) - assert obj is not None - i = cast(distutils_install_command, obj) - # NOTE: setting user or home has the side-effect of creating the home dir - # or user base for installations during finalize_options() - # ideally, we'd prefer a scheme class that has no side-effects. - assert not (user and prefix), f"user={user} prefix={prefix}" - assert not (home and prefix), f"home={home} prefix={prefix}" - i.user = user or i.user - if user or home: - i.prefix = "" - i.prefix = prefix or i.prefix - i.home = home or i.home - i.root = root or i.root - i.finalize_options() - - scheme = {} - for key in SCHEME_KEYS: - scheme[key] = getattr(i, "install_" + key) - - # install_lib specified in setup.cfg should install *everything* - # into there (i.e. it takes precedence over both purelib and - # platlib). Note, i.install_lib is *always* set after - # finalize_options(); we only want to override here if the user - # has explicitly requested it hence going back to the config - if "install_lib" in d.get_option_dict("install"): - scheme.update(dict(purelib=i.install_lib, platlib=i.install_lib)) - - if running_under_virtualenv(): - if home: - prefix = home - elif user: - prefix = i.install_userbase - else: - prefix = i.prefix - scheme["headers"] = os.path.join( - prefix, - "include", - "site", - f"python{get_major_minor_version()}", - dist_name, - ) - - if root is not None: - path_no_drive = os.path.splitdrive(os.path.abspath(scheme["headers"]))[1] - scheme["headers"] = os.path.join(root, path_no_drive[1:]) - - return scheme - - -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: - """ - Get the "scheme" corresponding to the input parameters. The distutils - documentation provides the context for the available schemes: - https://docs.python.org/3/install/index.html#alternate-installation - - :param dist_name: the name of the package to retrieve the scheme for, used - in the headers scheme path - :param user: indicates to use the "user" scheme - :param home: indicates to use the "home" scheme and provides the base - directory for the same - :param root: root under which other directories are re-based - :param isolated: equivalent to --no-user-cfg, i.e. do not consider - ~/.pydistutils.cfg (posix) or ~/pydistutils.cfg (non-posix) for - scheme paths - :param prefix: indicates to use the "prefix" scheme and provides the - base directory for the same - """ - scheme = distutils_scheme(dist_name, user, home, root, isolated, prefix) - return Scheme( - platlib=scheme["platlib"], - purelib=scheme["purelib"], - headers=scheme["headers"], - scripts=scheme["scripts"], - data=scheme["data"], - ) - - -def get_bin_prefix() -> str: - # XXX: In old virtualenv versions, sys.prefix can contain '..' components, - # so we need to call normpath to eliminate them. - prefix = os.path.normpath(sys.prefix) - if WINDOWS: - bin_py = os.path.join(prefix, "Scripts") - # buildout uses 'bin' on Windows too? - if not os.path.exists(bin_py): - bin_py = os.path.join(prefix, "bin") - return bin_py - # Forcing to use /usr/local/bin for standard macOS framework installs - # Also log to ~/Library/Logs/ for use with the Console.app log viewer - if sys.platform[:6] == "darwin" and prefix[:16] == "/System/Library/": - return "/usr/local/bin" - return os.path.join(prefix, "bin") - - -def get_purelib() -> str: - return get_python_lib(plat_specific=False) - - -def get_platlib() -> str: - return get_python_lib(plat_specific=True) - - -def get_prefixed_libs(prefix: str) -> Tuple[str, str]: - return ( - get_python_lib(plat_specific=False, prefix=prefix), - get_python_lib(plat_specific=True, prefix=prefix), - ) diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/operations/prepare.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/operations/prepare.py deleted file mode 100644 index 4bf414cb0052e351b6976b500123633bcacff15a..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/operations/prepare.py +++ /dev/null @@ -1,667 +0,0 @@ -"""Prepares a distribution for installation -""" - -# The following comment should be removed at some point in the future. -# mypy: strict-optional=False - -import logging -import mimetypes -import os -import shutil -from typing import Dict, Iterable, List, Optional - -from pip._vendor.packaging.utils import canonicalize_name - -from pip._internal.distributions import make_distribution_for_install_requirement -from pip._internal.distributions.installed import InstalledDistribution -from pip._internal.exceptions import ( - DirectoryUrlHashUnsupported, - HashMismatch, - HashUnpinned, - InstallationError, - MetadataInconsistent, - NetworkConnectionError, - PreviousBuildDirError, - VcsHashUnsupported, -) -from pip._internal.index.package_finder import PackageFinder -from pip._internal.metadata import BaseDistribution, get_metadata_distribution -from pip._internal.models.direct_url import ArchiveInfo -from pip._internal.models.link import Link -from pip._internal.models.wheel import Wheel -from pip._internal.network.download import BatchDownloader, Downloader -from pip._internal.network.lazy_wheel import ( - HTTPRangeRequestUnsupported, - dist_from_wheel_url, -) -from pip._internal.network.session import PipSession -from pip._internal.operations.build.build_tracker import BuildTracker -from pip._internal.req.req_install import InstallRequirement -from pip._internal.utils.direct_url_helpers import ( - direct_url_for_editable, - direct_url_from_link, -) -from pip._internal.utils.hashes import Hashes, MissingHashes -from pip._internal.utils.logging import indent_log -from pip._internal.utils.misc import ( - display_path, - hash_file, - hide_url, - is_installable_dir, -) -from pip._internal.utils.temp_dir import TempDirectory -from pip._internal.utils.unpacking import unpack_file -from pip._internal.vcs import vcs - -logger = logging.getLogger(__name__) - - -def _get_prepared_distribution( - req: InstallRequirement, - build_tracker: BuildTracker, - finder: PackageFinder, - build_isolation: bool, - check_build_deps: bool, -) -> BaseDistribution: - """Prepare a distribution for installation.""" - abstract_dist = make_distribution_for_install_requirement(req) - with build_tracker.track(req): - abstract_dist.prepare_distribution_metadata( - finder, build_isolation, check_build_deps - ) - return abstract_dist.get_metadata_distribution() - - -def unpack_vcs_link(link: Link, location: str, verbosity: int) -> None: - vcs_backend = vcs.get_backend_for_scheme(link.scheme) - assert vcs_backend is not None - vcs_backend.unpack(location, url=hide_url(link.url), verbosity=verbosity) - - -class File: - def __init__(self, path: str, content_type: Optional[str]) -> None: - self.path = path - if content_type is None: - self.content_type = mimetypes.guess_type(path)[0] - else: - self.content_type = content_type - - -def get_http_url( - link: Link, - download: Downloader, - download_dir: Optional[str] = None, - hashes: Optional[Hashes] = None, -) -> File: - temp_dir = TempDirectory(kind="unpack", globally_managed=True) - # If a download dir is specified, is the file already downloaded there? - already_downloaded_path = None - if download_dir: - already_downloaded_path = _check_download_dir(link, download_dir, hashes) - - if already_downloaded_path: - from_path = already_downloaded_path - content_type = None - else: - # let's download to a tmp dir - from_path, content_type = download(link, temp_dir.path) - if hashes: - hashes.check_against_path(from_path) - - return File(from_path, content_type) - - -def get_file_url( - link: Link, download_dir: Optional[str] = None, hashes: Optional[Hashes] = None -) -> File: - """Get file and optionally check its hash.""" - # If a download dir is specified, is the file already there and valid? - already_downloaded_path = None - if download_dir: - already_downloaded_path = _check_download_dir(link, download_dir, hashes) - - if already_downloaded_path: - from_path = already_downloaded_path - else: - from_path = link.file_path - - # If --require-hashes is off, `hashes` is either empty, the - # link's embedded hash, or MissingHashes; it is required to - # match. If --require-hashes is on, we are satisfied by any - # hash in `hashes` matching: a URL-based or an option-based - # one; no internet-sourced hash will be in `hashes`. - if hashes: - hashes.check_against_path(from_path) - return File(from_path, None) - - -def unpack_url( - link: Link, - location: str, - download: Downloader, - verbosity: int, - download_dir: Optional[str] = None, - hashes: Optional[Hashes] = None, -) -> Optional[File]: - """Unpack link into location, downloading if required. - - :param hashes: A Hashes object, one of whose embedded hashes must match, - or HashMismatch will be raised. If the Hashes is empty, no matches are - required, and unhashable types of requirements (like VCS ones, which - would ordinarily raise HashUnsupported) are allowed. - """ - # non-editable vcs urls - if link.is_vcs: - unpack_vcs_link(link, location, verbosity=verbosity) - return None - - assert not link.is_existing_dir() - - # file urls - if link.is_file: - file = get_file_url(link, download_dir, hashes=hashes) - - # http urls - else: - file = get_http_url( - link, - download, - download_dir, - hashes=hashes, - ) - - # unpack the archive to the build dir location. even when only downloading - # archives, they have to be unpacked to parse dependencies, except wheels - if not link.is_wheel: - unpack_file(file.path, location, file.content_type) - - return file - - -def _check_download_dir( - link: Link, download_dir: str, hashes: Optional[Hashes] -) -> Optional[str]: - """Check download_dir for previously downloaded file with correct hash - If a correct file is found return its path else None - """ - download_path = os.path.join(download_dir, link.filename) - - if not os.path.exists(download_path): - return None - - # If already downloaded, does its hash match? - logger.info("File was already downloaded %s", download_path) - if hashes: - try: - hashes.check_against_path(download_path) - except HashMismatch: - logger.warning( - "Previously-downloaded file %s has bad hash. Re-downloading.", - download_path, - ) - os.unlink(download_path) - return None - return download_path - - -class RequirementPreparer: - """Prepares a Requirement""" - - def __init__( - self, - build_dir: str, - download_dir: Optional[str], - src_dir: str, - build_isolation: bool, - check_build_deps: bool, - build_tracker: BuildTracker, - session: PipSession, - progress_bar: str, - finder: PackageFinder, - require_hashes: bool, - use_user_site: bool, - lazy_wheel: bool, - verbosity: int, - ) -> None: - super().__init__() - - self.src_dir = src_dir - self.build_dir = build_dir - self.build_tracker = build_tracker - self._session = session - self._download = Downloader(session, progress_bar) - self._batch_download = BatchDownloader(session, progress_bar) - self.finder = finder - - # Where still-packed archives should be written to. If None, they are - # not saved, and are deleted immediately after unpacking. - self.download_dir = download_dir - - # Is build isolation allowed? - self.build_isolation = build_isolation - - # Should check build dependencies? - self.check_build_deps = check_build_deps - - # Should hash-checking be required? - self.require_hashes = require_hashes - - # Should install in user site-packages? - self.use_user_site = use_user_site - - # Should wheels be downloaded lazily? - self.use_lazy_wheel = lazy_wheel - - # How verbose should underlying tooling be? - self.verbosity = verbosity - - # Memoized downloaded files, as mapping of url: path. - self._downloaded: Dict[str, str] = {} - - # Previous "header" printed for a link-based InstallRequirement - self._previous_requirement_header = ("", "") - - def _log_preparing_link(self, req: InstallRequirement) -> None: - """Provide context for the requirement being prepared.""" - if req.link.is_file and not req.original_link_is_in_wheel_cache: - message = "Processing %s" - information = str(display_path(req.link.file_path)) - else: - message = "Collecting %s" - information = str(req.req or req) - - if (message, information) != self._previous_requirement_header: - self._previous_requirement_header = (message, information) - logger.info(message, information) - - if req.original_link_is_in_wheel_cache: - with indent_log(): - logger.info("Using cached %s", req.link.filename) - - def _ensure_link_req_src_dir( - self, req: InstallRequirement, parallel_builds: bool - ) -> None: - """Ensure source_dir of a linked InstallRequirement.""" - # Since source_dir is only set for editable requirements. - if req.link.is_wheel: - # We don't need to unpack wheels, so no need for a source - # directory. - return - assert req.source_dir is None - if req.link.is_existing_dir(): - # build local directories in-tree - req.source_dir = req.link.file_path - return - - # We always delete unpacked sdists after pip runs. - req.ensure_has_source_dir( - self.build_dir, - autodelete=True, - parallel_builds=parallel_builds, - ) - - # If a checkout exists, it's unwise to keep going. version - # inconsistencies are logged later, but do not fail the - # installation. - # FIXME: this won't upgrade when there's an existing - # package unpacked in `req.source_dir` - # TODO: this check is now probably dead code - if is_installable_dir(req.source_dir): - raise PreviousBuildDirError( - "pip can't proceed with requirements '{}' due to a" - "pre-existing build directory ({}). This is likely " - "due to a previous installation that failed . pip is " - "being responsible and not assuming it can delete this. " - "Please delete it and try again.".format(req, req.source_dir) - ) - - def _get_linked_req_hashes(self, req: InstallRequirement) -> Hashes: - # By the time this is called, the requirement's link should have - # been checked so we can tell what kind of requirements req is - # and raise some more informative errors than otherwise. - # (For example, we can raise VcsHashUnsupported for a VCS URL - # rather than HashMissing.) - if not self.require_hashes: - return req.hashes(trust_internet=True) - - # We could check these first 2 conditions inside unpack_url - # and save repetition of conditions, but then we would - # report less-useful error messages for unhashable - # requirements, complaining that there's no hash provided. - if req.link.is_vcs: - raise VcsHashUnsupported() - if req.link.is_existing_dir(): - raise DirectoryUrlHashUnsupported() - - # Unpinned packages are asking for trouble when a new version - # is uploaded. This isn't a security check, but it saves users - # a surprising hash mismatch in the future. - # file:/// URLs aren't pinnable, so don't complain about them - # not being pinned. - if req.original_link is None and not req.is_pinned: - raise HashUnpinned() - - # If known-good hashes are missing for this requirement, - # shim it with a facade object that will provoke hash - # computation and then raise a HashMissing exception - # showing the user what the hash should be. - return req.hashes(trust_internet=False) or MissingHashes() - - def _fetch_metadata_only( - self, - req: InstallRequirement, - ) -> Optional[BaseDistribution]: - if self.require_hashes: - logger.debug( - "Metadata-only fetching is not used as hash checking is required", - ) - return None - # Try PEP 658 metadata first, then fall back to lazy wheel if unavailable. - return self._fetch_metadata_using_link_data_attr( - req - ) or self._fetch_metadata_using_lazy_wheel(req.link) - - def _fetch_metadata_using_link_data_attr( - self, - req: InstallRequirement, - ) -> Optional[BaseDistribution]: - """Fetch metadata from the data-dist-info-metadata attribute, if possible.""" - # (1) Get the link to the metadata file, if provided by the backend. - metadata_link = req.link.metadata_link() - if metadata_link is None: - return None - assert req.req is not None - logger.info( - "Obtaining dependency information for %s from %s", - req.req, - metadata_link, - ) - # (2) Download the contents of the METADATA file, separate from the dist itself. - metadata_file = get_http_url( - metadata_link, - self._download, - hashes=metadata_link.as_hashes(), - ) - with open(metadata_file.path, "rb") as f: - metadata_contents = f.read() - # (3) Generate a dist just from those file contents. - metadata_dist = get_metadata_distribution( - metadata_contents, - req.link.filename, - req.req.name, - ) - # (4) Ensure the Name: field from the METADATA file matches the name from the - # install requirement. - # - # NB: raw_name will fall back to the name from the install requirement if - # the Name: field is not present, but it's noted in the raw_name docstring - # that that should NEVER happen anyway. - if metadata_dist.raw_name != req.req.name: - raise MetadataInconsistent( - req, "Name", req.req.name, metadata_dist.raw_name - ) - return metadata_dist - - def _fetch_metadata_using_lazy_wheel( - self, - link: Link, - ) -> Optional[BaseDistribution]: - """Fetch metadata using lazy wheel, if possible.""" - # --use-feature=fast-deps must be provided. - if not self.use_lazy_wheel: - return None - if link.is_file or not link.is_wheel: - logger.debug( - "Lazy wheel is not used as %r does not point to a remote wheel", - link, - ) - return None - - wheel = Wheel(link.filename) - name = canonicalize_name(wheel.name) - logger.info( - "Obtaining dependency information from %s %s", - name, - wheel.version, - ) - url = link.url.split("#", 1)[0] - try: - return dist_from_wheel_url(name, url, self._session) - except HTTPRangeRequestUnsupported: - logger.debug("%s does not support range requests", url) - return None - - def _complete_partial_requirements( - self, - partially_downloaded_reqs: Iterable[InstallRequirement], - parallel_builds: bool = False, - ) -> None: - """Download any requirements which were only fetched by metadata.""" - # Download to a temporary directory. These will be copied over as - # needed for downstream 'download', 'wheel', and 'install' commands. - temp_dir = TempDirectory(kind="unpack", globally_managed=True).path - - # Map each link to the requirement that owns it. This allows us to set - # `req.local_file_path` on the appropriate requirement after passing - # all the links at once into BatchDownloader. - links_to_fully_download: Dict[Link, InstallRequirement] = {} - for req in partially_downloaded_reqs: - assert req.link - links_to_fully_download[req.link] = req - - batch_download = self._batch_download( - links_to_fully_download.keys(), - temp_dir, - ) - for link, (filepath, _) in batch_download: - logger.debug("Downloading link %s to %s", link, filepath) - req = links_to_fully_download[link] - req.local_file_path = filepath - - # This step is necessary to ensure all lazy wheels are processed - # successfully by the 'download', 'wheel', and 'install' commands. - for req in partially_downloaded_reqs: - self._prepare_linked_requirement(req, parallel_builds) - - def prepare_linked_requirement( - self, req: InstallRequirement, parallel_builds: bool = False - ) -> BaseDistribution: - """Prepare a requirement to be obtained from req.link.""" - assert req.link - self._log_preparing_link(req) - with indent_log(): - # Check if the relevant file is already available - # in the download directory - file_path = None - if self.download_dir is not None and req.link.is_wheel: - hashes = self._get_linked_req_hashes(req) - file_path = _check_download_dir(req.link, self.download_dir, hashes) - - if file_path is not None: - # The file is already available, so mark it as downloaded - self._downloaded[req.link.url] = file_path - else: - # The file is not available, attempt to fetch only metadata - metadata_dist = self._fetch_metadata_only(req) - if metadata_dist is not None: - req.needs_more_preparation = True - return metadata_dist - - # None of the optimizations worked, fully prepare the requirement - return self._prepare_linked_requirement(req, parallel_builds) - - def prepare_linked_requirements_more( - self, reqs: Iterable[InstallRequirement], parallel_builds: bool = False - ) -> None: - """Prepare linked requirements more, if needed.""" - reqs = [req for req in reqs if req.needs_more_preparation] - for req in reqs: - # Determine if any of these requirements were already downloaded. - if self.download_dir is not None and req.link.is_wheel: - hashes = self._get_linked_req_hashes(req) - file_path = _check_download_dir(req.link, self.download_dir, hashes) - if file_path is not None: - self._downloaded[req.link.url] = file_path - req.needs_more_preparation = False - - # Prepare requirements we found were already downloaded for some - # reason. The other downloads will be completed separately. - partially_downloaded_reqs: List[InstallRequirement] = [] - for req in reqs: - if req.needs_more_preparation: - partially_downloaded_reqs.append(req) - else: - self._prepare_linked_requirement(req, parallel_builds) - - # TODO: separate this part out from RequirementPreparer when the v1 - # resolver can be removed! - self._complete_partial_requirements( - partially_downloaded_reqs, - parallel_builds=parallel_builds, - ) - - def _prepare_linked_requirement( - self, req: InstallRequirement, parallel_builds: bool - ) -> BaseDistribution: - assert req.link - link = req.link - - self._ensure_link_req_src_dir(req, parallel_builds) - hashes = self._get_linked_req_hashes(req) - - if link.is_existing_dir(): - local_file = None - elif link.url not in self._downloaded: - try: - local_file = unpack_url( - link, - req.source_dir, - self._download, - self.verbosity, - self.download_dir, - hashes, - ) - except NetworkConnectionError as exc: - raise InstallationError( - "Could not install requirement {} because of HTTP " - "error {} for URL {}".format(req, exc, link) - ) - else: - file_path = self._downloaded[link.url] - if hashes: - hashes.check_against_path(file_path) - local_file = File(file_path, content_type=None) - - # If download_info is set, we got it from the wheel cache. - if req.download_info is None: - # Editables don't go through this function (see - # prepare_editable_requirement). - assert not req.editable - req.download_info = direct_url_from_link(link, req.source_dir) - # Make sure we have a hash in download_info. If we got it as part of the - # URL, it will have been verified and we can rely on it. Otherwise we - # compute it from the downloaded file. - if ( - isinstance(req.download_info.info, ArchiveInfo) - and not req.download_info.info.hash - and local_file - ): - hash = hash_file(local_file.path)[0].hexdigest() - req.download_info.info.hash = f"sha256={hash}" - - # For use in later processing, - # preserve the file path on the requirement. - if local_file: - req.local_file_path = local_file.path - - dist = _get_prepared_distribution( - req, - self.build_tracker, - self.finder, - self.build_isolation, - self.check_build_deps, - ) - return dist - - def save_linked_requirement(self, req: InstallRequirement) -> None: - assert self.download_dir is not None - assert req.link is not None - link = req.link - if link.is_vcs or (link.is_existing_dir() and req.editable): - # Make a .zip of the source_dir we already created. - req.archive(self.download_dir) - return - - if link.is_existing_dir(): - logger.debug( - "Not copying link to destination directory " - "since it is a directory: %s", - link, - ) - return - if req.local_file_path is None: - # No distribution was downloaded for this requirement. - return - - download_location = os.path.join(self.download_dir, link.filename) - if not os.path.exists(download_location): - shutil.copy(req.local_file_path, download_location) - download_path = display_path(download_location) - logger.info("Saved %s", download_path) - - def prepare_editable_requirement( - self, - req: InstallRequirement, - ) -> BaseDistribution: - """Prepare an editable requirement.""" - assert req.editable, "cannot prepare a non-editable req as editable" - - logger.info("Obtaining %s", req) - - with indent_log(): - if self.require_hashes: - raise InstallationError( - "The editable requirement {} cannot be installed when " - "requiring hashes, because there is no single file to " - "hash.".format(req) - ) - req.ensure_has_source_dir(self.src_dir) - req.update_editable() - assert req.source_dir - req.download_info = direct_url_for_editable(req.unpacked_source_directory) - - dist = _get_prepared_distribution( - req, - self.build_tracker, - self.finder, - self.build_isolation, - self.check_build_deps, - ) - - req.check_if_exists(self.use_user_site) - - return dist - - def prepare_installed_requirement( - self, - req: InstallRequirement, - skip_reason: str, - ) -> BaseDistribution: - """Prepare an already-installed requirement.""" - assert req.satisfied_by, "req should have been satisfied but isn't" - assert skip_reason is not None, ( - "did not get skip reason skipped but req.satisfied_by " - "is set to {}".format(req.satisfied_by) - ) - logger.info( - "Requirement %s: %s (%s)", skip_reason, req, req.satisfied_by.version - ) - with indent_log(): - if self.require_hashes: - logger.debug( - "Since it is already installed, we are trusting this " - "package without checking its hash. To ensure a " - "completely repeatable environment, install into an " - "empty virtualenv." - ) - return InstalledDistribution(req).get_metadata_distribution() diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/launch.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/launch.py deleted file mode 100644 index 0208fdf33b640cd9791359d74673bb90cfb87f96..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/launch.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Launch the Python script on the command line after -setuptools is bootstrapped via import. -""" - -# Note that setuptools gets imported implicitly by the -# invocation of this script using python -m setuptools.launch - -import tokenize -import sys - - -def run(): - """ - Run the script in sys.argv[1] as if it had - been invoked naturally. - """ - __builtins__ - script_name = sys.argv[1] - namespace = dict( - __file__=script_name, - __name__='__main__', - __doc__=None, - ) - sys.argv[:] = sys.argv[1:] - - open_ = getattr(tokenize, 'open', open) - with open_(script_name) as fid: - script = fid.read() - norm_script = script.replace('\\r\\n', '\\n') - code = compile(norm_script, script_name, 'exec') - exec(code, namespace) - - -if __name__ == '__main__': - run() diff --git a/spaces/RivianG/Asis/app.py b/spaces/RivianG/Asis/app.py deleted file mode 100644 index fe08f08a7daa437c963a132fb244c62a3040768c..0000000000000000000000000000000000000000 --- a/spaces/RivianG/Asis/app.py +++ /dev/null @@ -1,111 +0,0 @@ -import cv2 -from time import time -from alpr import * -import torch -import cv2 -import numpy as np -import tensorflow.compat.v1 as tf -import os -import streamlit as st -from PIL import Image -import streamlit as st - -def load_image(image_file): - img = Image.open(image_file) - return img - - -st.subheader("Image") -image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"]) - -#if image_file is not None: - # To See details - #file_details = {"filename":image_file.name, "filetype":image_file.type,"filesize":image_file.size} - #st.write(file_details) - - # To View Uploaded Image - #st.image(load_image(image_file),width=250) - -submit = st.button('Generate') - -if submit: - image = load_image(image_file) - model = torch.hub.load('ultralytics/yolov5', 'custom', path='yoloocrv2_1.pt') - model.cpu() - model.conf = 0.5 - license = DetectLicensePlate() - counter = dict() - frame = np.array(image)[...,::-1] - try: - plate_img = alpr(frame,license) - results = model(plate_img*255) - control = max(results.pandas().xyxy[0].sort_values('ymin').iloc[:,1].values) - if control > 50: - name = results.pandas().xyxy[0].sort_values('ymin') #.iloc[:, -1] #ymin alwas bigger than 50 with bottom characters - ind = [ix for ix,i in enumerate(name.iloc[:,1]) if i>50][0] - upper_f_2 = name.iloc[:ind].sort_values("xmin").iloc[:,-1][:2] - upper_sort = name.iloc[:ind].sort_values("xmin").iloc[:,-1][2:] #add name column - bottom_sort = name.iloc[ind:].sort_values("xmin").iloc[:,-1] - upper_name = "".join([i for i in upper_sort]) - upper_f_name = "".join([i for i in upper_f_2]) - bottom_name = "".join([i for i in bottom_sort]) - if "1" in upper_name: - upper_name= upper_name.replace("1","I") - if "6" in upper_name: - upper_name= upper_name.replace("6","G") - if "0" in upper_name: - upper_name= upper_name.replace("0","O") - - name = upper_f_name + upper_name + bottom_name - if name not in counter and name != '': - counter[name] = 1 - if name in counter and name != '': - counter[name] += 1 - plate_name = list((sorted(counter.items(), key=lambda item: item[1])))[-1][0] - st.write(plate_name) - - else: - - #Post-processing pre-requisite - decoder = results.pandas().xyxy[0].sort_values('xmin').iloc[:,0].values - compare = list(decoder[2:]) - maks = None - for i in range(len(compare)): - if i == len(compare) - 1: - break - if maks == None: - maks = abs(compare[i] - compare[i + 1]) - w_index = (maks, i + 1) - if abs(compare[i] - compare[i + 1]) > maks: - maks = abs(compare[i] - compare[i + 1]) - w_index = (maks, i + 1) - - name = results.pandas().xyxy[0].sort_values('xmin').iloc[:, -1] - name = "".join([i for i in name]) - if name not in counter and name != '': - counter[name] = 1 - if name in counter and name !='': - counter[name] +=1 - plate_name = list((sorted(counter.items(),key = lambda item:item[1])))[-1][0] - #Post-processing happens after here - mid_chars = str(plate_name[2:int(w_index[1] + 2)]) # assign this as old mid chars - - if "6" in mid_chars: - mid_chars = mid_chars.replace("6", "G") # assign this as new - if "1" in mid_chars: - mid_chars = mid_chars.replace("1", "I") - if "0" in mid_chars: - mid_chars = mid_chars.replace("0", "O") - - new_plate_name = plate_name.replace(plate_name[2:int(w_index[1] + 2)], mid_chars) - - #cv2.imshow("Plate", plate_img) - st.write(new_plate_name) - - - except Exception as e: - - counter.clear() - st.write("Plaka Bulunamadı") - - \ No newline at end of file diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/cascade_mask_rcnn_3x_ms_hybrid_base/config.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/cascade_mask_rcnn_3x_ms_hybrid_base/config.py deleted file mode 100644 index 55f586d96db66a52054ac504f9a69080197560c9..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/cascade_mask_rcnn_3x_ms_hybrid_base/config.py +++ /dev/null @@ -1,142 +0,0 @@ -_base_ = [ - '../../configs/_base_/models/cascade_mask_rcnn_uniformer_fpn.py', - '../../configs/_base_/datasets/coco_instance.py', - '../../configs/_base_/schedules/schedule_1x.py', - '../../configs/_base_/default_runtime.py' -] - -model = dict( - backbone=dict( - embed_dim=[64, 128, 320, 512], - layers=[5, 8, 20, 7], - head_dim=64, - drop_path_rate=0.4, - use_checkpoint=True, - checkpoint_num=[0, 0, 20, 0], - windows=False, - hybrid=True, - window_size=14 - ), - neck=dict(in_channels=[64, 128, 320, 512]), - roi_head=dict( - bbox_head=[ - dict( - type='ConvFCBBoxHead', - num_shared_convs=4, - num_shared_fcs=1, - in_channels=256, - conv_out_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.1, 0.1, 0.2, 0.2]), - reg_class_agnostic=False, - reg_decoded_bbox=True, - norm_cfg=dict(type='SyncBN', requires_grad=True), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), - dict( - type='ConvFCBBoxHead', - num_shared_convs=4, - num_shared_fcs=1, - in_channels=256, - conv_out_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.05, 0.05, 0.1, 0.1]), - reg_class_agnostic=False, - reg_decoded_bbox=True, - norm_cfg=dict(type='SyncBN', requires_grad=True), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), - dict( - type='ConvFCBBoxHead', - num_shared_convs=4, - num_shared_fcs=1, - in_channels=256, - conv_out_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.033, 0.033, 0.067, 0.067]), - reg_class_agnostic=False, - reg_decoded_bbox=True, - norm_cfg=dict(type='SyncBN', requires_grad=True), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='GIoULoss', loss_weight=10.0)) - ])) - -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) - -# augmentation strategy originates from DETR / Sparse RCNN -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True, with_mask=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='AutoAugment', - policies=[ - [ - dict(type='Resize', - img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), - (608, 1333), (640, 1333), (672, 1333), (704, 1333), - (736, 1333), (768, 1333), (800, 1333)], - multiscale_mode='value', - keep_ratio=True) - ], - [ - dict(type='Resize', - img_scale=[(400, 1333), (500, 1333), (600, 1333)], - multiscale_mode='value', - keep_ratio=True), - dict(type='RandomCrop', - crop_type='absolute_range', - crop_size=(384, 600), - allow_negative_crop=True), - dict(type='Resize', - img_scale=[(480, 1333), (512, 1333), (544, 1333), - (576, 1333), (608, 1333), (640, 1333), - (672, 1333), (704, 1333), (736, 1333), - (768, 1333), (800, 1333)], - multiscale_mode='value', - override=True, - keep_ratio=True) - ] - ]), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), -] -data = dict(train=dict(pipeline=train_pipeline)) - -optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05, - paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.), - 'relative_position_bias_table': dict(decay_mult=0.), - 'norm': dict(decay_mult=0.)})) -lr_config = dict(step=[27, 33]) -runner = dict(type='EpochBasedRunnerAmp', max_epochs=36) - -# do not use mmdet version fp16 -fp16 = None -optimizer_config = dict( - type="DistOptimizerHook", - update_interval=1, - grad_clip=None, - coalesce=True, - bucket_size_mb=-1, - use_fp16=True, -) diff --git a/spaces/ServerX/PorcoDiaz/LazyImport.py b/spaces/ServerX/PorcoDiaz/LazyImport.py deleted file mode 100644 index 5bdb05ddd5a546a43adba7274b4c3465bb77f2f5..0000000000000000000000000000000000000000 --- a/spaces/ServerX/PorcoDiaz/LazyImport.py +++ /dev/null @@ -1,13 +0,0 @@ -from importlib.util import find_spec, LazyLoader, module_from_spec -from sys import modules - -def lazyload(name): - if name in modules: - return modules[name] - else: - spec = find_spec(name) - loader = LazyLoader(spec.loader) - module = module_from_spec(spec) - modules[name] = module - loader.exec_module(module) - return module \ No newline at end of file diff --git a/spaces/Silentlin/DiffSinger/docs/README-SVS-opencpop-e2e.md b/spaces/Silentlin/DiffSinger/docs/README-SVS-opencpop-e2e.md deleted file mode 100644 index ede3cf2a8dde58a8ed2c87ad4c08fabdad6ae6ad..0000000000000000000000000000000000000000 --- a/spaces/Silentlin/DiffSinger/docs/README-SVS-opencpop-e2e.md +++ /dev/null @@ -1,107 +0,0 @@ -# DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism -[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2105.02446) -[![GitHub Stars](https://img.shields.io/github/stars/MoonInTheRiver/DiffSinger?style=social)](https://github.com/MoonInTheRiver/DiffSinger) -[![downloads](https://img.shields.io/github/downloads/MoonInTheRiver/DiffSinger/total.svg)](https://github.com/MoonInTheRiver/DiffSinger/releases) - | [Interactive🤗 SVS](https://huggingface.co/spaces/Silentlin/DiffSinger) - -Substantial update: We 1) **abandon** the explicit prediction of the F0 curve; 2) increase the receptive field of the denoiser; 3) make the linguistic encoder more robust. -**By doing so, 1) the synthesized recordings are more natural in terms of pitch; 2) the pipeline is simpler.** - -简而言之，把F0曲线的动态性交给生成式模型去捕捉，而不再是以前那样用MSE约束对数域F0。 - -## DiffSinger (MIDI SVS | B version) -### 0. Data Acquirement -For Opencpop dataset: Please strictly follow the instructions of [Opencpop](https://wenet.org.cn/opencpop/). We have no right to give you the access to Opencpop. - -The pipeline below is designed for Opencpop dataset: - -### 1. Preparation - -#### Data Preparation -a) Download and extract Opencpop, then create a link to the dataset folder: `ln -s /xxx/opencpop data/raw/` - -b) Run the following scripts to pack the dataset for training/inference. - -```sh -export PYTHONPATH=. -CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config usr/configs/midi/cascade/opencs/aux_rel.yaml - -# `data/binary/opencpop-midi-dp` will be generated. -``` - -#### Vocoder Preparation -We provide the pre-trained model of [HifiGAN-Singing](https://github.com/MoonInTheRiver/DiffSinger/releases/download/pretrain-model/0109_hifigan_bigpopcs_hop128.zip) which is specially designed for SVS with NSF mechanism. - -Also, please unzip pre-trained vocoder and [this pendant for vocoder](https://github.com/MoonInTheRiver/DiffSinger/releases/download/pretrain-model/0102_xiaoma_pe.zip) into `checkpoints` before training your acoustic model. - -(Update: You can also move [a ckpt with more training steps](https://github.com/MoonInTheRiver/DiffSinger/releases/download/pretrain-model/model_ckpt_steps_1512000.ckpt) into this vocoder directory) - -This singing vocoder is trained on ~70 hours singing data, which can be viewed as a universal vocoder. - -#### Exp Name Preparation -```bash -export MY_DS_EXP_NAME=0228_opencpop_ds100_rel -``` - -``` -. -|--data - |--raw - |--opencpop - |--segments - |--transcriptions.txt - |--wavs -|--checkpoints - |--MY_DS_EXP_NAME (optional) - |--0109_hifigan_bigpopcs_hop128 (vocoder) - |--model_ckpt_steps_1512000.ckpt - |--config.yaml -``` - -### 2. Training Example -```sh -CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME --reset -``` - -### 3. Inference from packed test set -```sh -CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME --reset --infer -``` - -We also provide: - - the pre-trained model of DiffSinger; - -They can be found in [here](https://github.com/MoonInTheRiver/DiffSinger/releases/download/pretrain-model/0228_opencpop_ds100_rel.zip). - -Remember to put the pre-trained models in `checkpoints` directory. - -### 4. Inference from raw inputs -```sh -python inference/svs/ds_e2e.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME -``` -Raw inputs: -``` -inp = { - 'text': '小酒窝长睫毛AP是你最美的记号', - 'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4', - 'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340', - 'input_type': 'word' - } # user input: Chinese characters -or, -inp = { - 'text': '小酒窝长睫毛AP是你最美的记号', - 'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao', - 'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4', - 'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340', - 'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0', - 'input_type': 'phoneme' - } # input like Opencpop dataset. -``` - -### 5. Some issues. -a) the HifiGAN-Singing is trained on our [vocoder dataset](https://dl.acm.org/doi/abs/10.1145/3474085.3475437) and the training set of [PopCS](https://arxiv.org/abs/2105.02446). Opencpop is the out-of-domain dataset (unseen speaker). This may cause the deterioration of audio quality, and we are considering fine-tuning this vocoder on the training set of Opencpop. - -b) in this version of codes, we used the melody frontend ([lyric + MIDI]->[ph_dur]) to predict phoneme duration. F0 curve is implicitly predicted together with mel-spectrogram. - -c) example [generated audio](https://github.com/MoonInTheRiver/DiffSinger/blob/master/resources/demos_0221/DS/). -More generated audio demos can be found in [DiffSinger](https://github.com/MoonInTheRiver/DiffSinger/releases/download/pretrain-model/0228_opencpop_ds100_rel.zip). diff --git a/spaces/Skyler123/TangGPT/assets/Kelpy-Codos.js b/spaces/Skyler123/TangGPT/assets/Kelpy-Codos.js deleted file mode 100644 index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000 --- a/spaces/Skyler123/TangGPT/assets/Kelpy-Codos.js +++ /dev/null @@ -1,76 +0,0 @@ -// ==UserScript== -// @name Kelpy Codos -// @namespace https://github.com/Keldos-Li/Kelpy-Codos -// @version 1.0.5 -// @author Keldos; https://keldos.me/ -// @description Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. -// Based on Chuanhu ChatGPT version: ac04408 (2023-3-22) -// @license GPL-3.0 -// @grant none -// ==/UserScript== - -(function () { - 'use strict'; - - function addCopyButton(pre) { - var code = pre.querySelector('code'); - if (!code) { - return; // 如果没有找到 元素，则不添加按钮 - } - var firstChild = code.firstChild; - if (!firstChild) { - return; // 如果 元素没有子节点，则不添加按钮 - } - var button = document.createElement('button'); - button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本 - button.style.position = 'relative'; - button.style.float = 'right'; - button.style.fontSize = '1em'; // 可选：调整按钮大小 - button.style.background = 'none'; // 可选：去掉背景颜色 - button.style.border = 'none'; // 可选：去掉边框 - button.style.cursor = 'pointer'; // 可选：显示指针样式 - button.addEventListener('click', function () { - var range = document.createRange(); - range.selectNodeContents(code); - range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前 - var selection = window.getSelection(); - selection.removeAllRanges(); - selection.addRange(range); - - try { - var success = document.execCommand('copy'); - if (success) { - button.textContent = '\u2714'; - setTimeout(function () { - button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制” - }, 2000); - } else { - button.textContent = '\u2716'; - } - } catch (e) { - console.error(e); - button.textContent = '\u2716'; - } - - selection.removeAllRanges(); - }); - code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前 - } - - function handleNewElements(mutationsList, observer) { - for (var mutation of mutationsList) { - if (mutation.type === 'childList') { - for (var node of mutation.addedNodes) { - if (node.nodeName === 'PRE') { - addCopyButton(node); - } - } - } - } - } - - var observer = new MutationObserver(handleNewElements); - observer.observe(document.documentElement, { childList: true, subtree: true }); - - document.querySelectorAll('pre').forEach(addCopyButton); -})(); diff --git a/spaces/Smithjohny376/andite-anything-v4.0/app.py b/spaces/Smithjohny376/andite-anything-v4.0/app.py deleted file mode 100644 index 47a2051db6dadeea03edf70d62694fd3e5e88ba7..0000000000000000000000000000000000000000 --- a/spaces/Smithjohny376/andite-anything-v4.0/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/andite/anything-v4.0").launch() \ No newline at end of file diff --git a/spaces/SoulAbi/text-to-voice/app.py b/spaces/SoulAbi/text-to-voice/app.py deleted file mode 100644 index a3b8ad44f7d02c679ab01905061455bfaf6a9ff5..0000000000000000000000000000000000000000 --- a/spaces/SoulAbi/text-to-voice/app.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="", max_lines=15), - gr.Radio(label="Language", choices=LANGUAGES, value="en")] -outputs = gr.Audio(label="Output") - -run = gr.Interface(fn=tts, inputs=inputs, outputs=outputs) - -run.launch() diff --git a/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/inference.py b/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/inference.py deleted file mode 100644 index ce0f2b08df75e6d62f06c4119f1dc859930de032..0000000000000000000000000000000000000000 --- a/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/inference.py +++ /dev/null @@ -1,94 +0,0 @@ -from __future__ import annotations - -import gc -import pathlib - -import gradio as gr -import PIL.Image -import torch -from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler -from huggingface_hub import ModelCard - - -class InferencePipeline: - def __init__(self, hf_token: str | None = None): - self.hf_token = hf_token - self.pipe = None - self.device = torch.device( - 'cuda:0' if torch.cuda.is_available() else 'cpu') - self.lora_model_id = None - self.base_model_id = None - - def clear(self) -> None: - self.lora_model_id = None - self.base_model_id = None - del self.pipe - self.pipe = None - torch.cuda.empty_cache() - gc.collect() - - @staticmethod - def check_if_model_is_local(lora_model_id: str) -> bool: - return pathlib.Path(lora_model_id).exists() - - @staticmethod - def get_model_card(model_id: str, - hf_token: str | None = None) -> ModelCard: - if InferencePipeline.check_if_model_is_local(model_id): - card_path = (pathlib.Path(model_id) / 'README.md').as_posix() - else: - card_path = model_id - return ModelCard.load(card_path, token=hf_token) - - @staticmethod - def get_base_model_info(lora_model_id: str, - hf_token: str | None = None) -> str: - card = InferencePipeline.get_model_card(lora_model_id, hf_token) - return card.data.base_model - - def load_pipe(self, lora_model_id: str) -> None: - if lora_model_id == self.lora_model_id: - return - base_model_id = self.get_base_model_info(lora_model_id, self.hf_token) - if base_model_id != self.base_model_id: - if self.device.type == 'cpu': - pipe = DiffusionPipeline.from_pretrained( - base_model_id, use_auth_token=self.hf_token) - else: - pipe = DiffusionPipeline.from_pretrained( - base_model_id, - torch_dtype=torch.float16, - use_auth_token=self.hf_token) - pipe = pipe.to(self.device) - pipe.scheduler = DPMSolverMultistepScheduler.from_config( - pipe.scheduler.config) - self.pipe = pipe - self.pipe.unet.load_attn_procs( # type: ignore - lora_model_id, use_auth_token=self.hf_token) - - self.lora_model_id = lora_model_id # type: ignore - self.base_model_id = base_model_id # type: ignore - - def run( - self, - lora_model_id: str, - prompt: str, - lora_scale: float, - seed: int, - n_steps: int, - guidance_scale: float, - ) -> PIL.Image.Image: - if not torch.cuda.is_available(): - raise gr.Error('CUDA is not available.') - - self.load_pipe(lora_model_id) - - generator = torch.Generator(device=self.device).manual_seed(seed) - out = self.pipe( - prompt, - num_inference_steps=n_steps, - guidance_scale=guidance_scale, - generator=generator, - cross_attention_kwargs={'scale': lora_scale}, - ) # type: ignore - return out.images[0] diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/solvers/__init__.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/solvers/__init__.py deleted file mode 100644 index ae19f3a8c51abf469697d6affa91449d668716ba..0000000000000000000000000000000000000000 --- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/solvers/__init__.py +++ /dev/null @@ -1,17 +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. -""" -Solvers. A Solver is a training recipe, combining the dataloaders, models, -optimizer, losses etc into a single convenient object. -""" - -# flake8: noqa -from .audiogen import AudioGenSolver -from .builders import get_solver -from .base import StandardSolver -from .compression import CompressionSolver -from .musicgen import MusicGenSolver -from .diffusion import DiffusionSolver diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/_binary.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/_binary.py deleted file mode 100644 index a74ee9eb6f341aca9e074c0acc4b306a354175a0..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/_binary.py +++ /dev/null @@ -1,102 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# Binary input/output support routines. -# -# Copyright (c) 1997-2003 by Secret Labs AB -# Copyright (c) 1995-2003 by Fredrik Lundh -# Copyright (c) 2012 by Brian Crowell -# -# See the README file for information on usage and redistribution. -# - - -"""Binary input/output support routines.""" - - -from struct import pack, unpack_from - - -def i8(c): - return c if c.__class__ is int else c[0] - - -def o8(i): - return bytes((i & 255,)) - - -# Input, le = little endian, be = big endian -def i16le(c, o=0): - """ - Converts a 2-bytes (16 bits) string to an unsigned integer. - - :param c: string containing bytes to convert - :param o: offset of bytes to convert in string - """ - return unpack_from("h", c, o)[0] - - -def i32le(c, o=0): - """ - Converts a 4-bytes (32 bits) string to an unsigned integer. - - :param c: string containing bytes to convert - :param o: offset of bytes to convert in string - """ - return unpack_from("H", c, o)[0] - - -def i32be(c, o=0): - return unpack_from(">I", c, o)[0] - - -# Output, le = little endian, be = big endian -def o16le(i): - return pack("H", i) - - -def o32be(i): - return pack(">I", i) diff --git a/spaces/TYH71/gradio-ml-skeleton/src/interface/__init__.py b/spaces/TYH71/gradio-ml-skeleton/src/interface/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/main.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/main.py deleted file mode 100644 index 33c6d24cd85b55a9fb1b1e6ab784f471e2b135f0..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/main.py +++ /dev/null @@ -1,12 +0,0 @@ -from typing import List, Optional - - -def main(args: Optional[List[str]] = None) -> int: - """This is preserved for old console scripts that may still be referencing - it. - - For additional details, see https://github.com/pypa/pip/issues/7498. - """ - from pip._internal.utils.entrypoints import _wrapper - - return _wrapper(args) diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py deleted file mode 100644 index 315fb9c8902c5e3f4dd8419ccdf7d85c6718096e..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py +++ /dev/null @@ -1,255 +0,0 @@ -import collections -import math -from typing import ( - TYPE_CHECKING, - Dict, - Iterable, - Iterator, - Mapping, - Sequence, - TypeVar, - Union, -) - -from pip._vendor.resolvelib.providers import AbstractProvider - -from .base import Candidate, Constraint, Requirement -from .candidates import REQUIRES_PYTHON_IDENTIFIER -from .factory import Factory - -if TYPE_CHECKING: - from pip._vendor.resolvelib.providers import Preference - from pip._vendor.resolvelib.resolvers import RequirementInformation - - PreferenceInformation = RequirementInformation[Requirement, Candidate] - - _ProviderBase = AbstractProvider[Requirement, Candidate, str] -else: - _ProviderBase = AbstractProvider - -# Notes on the relationship between the provider, the factory, and the -# candidate and requirement classes. -# -# The provider is a direct implementation of the resolvelib class. Its role -# is to deliver the API that resolvelib expects. -# -# Rather than work with completely abstract "requirement" and "candidate" -# concepts as resolvelib does, pip has concrete classes implementing these two -# ideas. The API of Requirement and Candidate objects are defined in the base -# classes, but essentially map fairly directly to the equivalent provider -# methods. In particular, `find_matches` and `is_satisfied_by` are -# requirement methods, and `get_dependencies` is a candidate method. -# -# The factory is the interface to pip's internal mechanisms. It is stateless, -# and is created by the resolver and held as a property of the provider. It is -# responsible for creating Requirement and Candidate objects, and provides -# services to those objects (access to pip's finder and preparer). - - -D = TypeVar("D") -V = TypeVar("V") - - -def _get_with_identifier( - mapping: Mapping[str, V], - identifier: str, - default: D, -) -> Union[D, V]: - """Get item from a package name lookup mapping with a resolver identifier. - - This extra logic is needed when the target mapping is keyed by package - name, which cannot be directly looked up with an identifier (which may - contain requested extras). Additional logic is added to also look up a value - by "cleaning up" the extras from the identifier. - """ - if identifier in mapping: - return mapping[identifier] - # HACK: Theoretically we should check whether this identifier is a valid - # "NAME[EXTRAS]" format, and parse out the name part with packaging or - # some regular expression. But since pip's resolver only spits out three - # kinds of identifiers: normalized PEP 503 names, normalized names plus - # extras, and Requires-Python, we can cheat a bit here. - name, open_bracket, _ = identifier.partition("[") - if open_bracket and name in mapping: - return mapping[name] - return default - - -class PipProvider(_ProviderBase): - """Pip's provider implementation for resolvelib. - - :params constraints: A mapping of constraints specified by the user. Keys - are canonicalized project names. - :params ignore_dependencies: Whether the user specified ``--no-deps``. - :params upgrade_strategy: The user-specified upgrade strategy. - :params user_requested: A set of canonicalized package names that the user - supplied for pip to install/upgrade. - """ - - def __init__( - self, - factory: Factory, - constraints: Dict[str, Constraint], - ignore_dependencies: bool, - upgrade_strategy: str, - user_requested: Dict[str, int], - ) -> None: - self._factory = factory - self._constraints = constraints - self._ignore_dependencies = ignore_dependencies - self._upgrade_strategy = upgrade_strategy - self._user_requested = user_requested - self._known_depths: Dict[str, float] = collections.defaultdict(lambda: math.inf) - - def identify(self, requirement_or_candidate: Union[Requirement, Candidate]) -> str: - return requirement_or_candidate.name - - def get_preference( - self, - identifier: str, - resolutions: Mapping[str, Candidate], - candidates: Mapping[str, Iterator[Candidate]], - information: Mapping[str, Iterable["PreferenceInformation"]], - backtrack_causes: Sequence["PreferenceInformation"], - ) -> "Preference": - """Produce a sort key for given requirement based on preference. - - The lower the return value is, the more preferred this group of - arguments is. - - Currently pip considers the following in order: - - * Prefer if any of the known requirements is "direct", e.g. points to an - explicit URL. - * If equal, prefer if any requirement is "pinned", i.e. contains - operator ``===`` or ``==``. - * If equal, calculate an approximate "depth" and resolve requirements - closer to the user-specified requirements first. If the depth cannot - by determined (eg: due to no matching parents), it is considered - infinite. - * Order user-specified requirements by the order they are specified. - * If equal, prefers "non-free" requirements, i.e. contains at least one - operator, such as ``>=`` or ``<``. - * If equal, order alphabetically for consistency (helps debuggability). - """ - try: - next(iter(information[identifier])) - except StopIteration: - # There is no information for this identifier, so there's no known - # candidates. - has_information = False - else: - has_information = True - - if has_information: - lookups = (r.get_candidate_lookup() for r, _ in information[identifier]) - candidate, ireqs = zip(*lookups) - else: - candidate, ireqs = None, () - - operators = [ - specifier.operator - for specifier_set in (ireq.specifier for ireq in ireqs if ireq) - for specifier in specifier_set - ] - - direct = candidate is not None - pinned = any(op[:2] == "==" for op in operators) - unfree = bool(operators) - - try: - requested_order: Union[int, float] = self._user_requested[identifier] - except KeyError: - requested_order = math.inf - if has_information: - parent_depths = ( - self._known_depths[parent.name] if parent is not None else 0.0 - for _, parent in information[identifier] - ) - inferred_depth = min(d for d in parent_depths) + 1.0 - else: - inferred_depth = math.inf - else: - inferred_depth = 1.0 - self._known_depths[identifier] = inferred_depth - - requested_order = self._user_requested.get(identifier, math.inf) - - # Requires-Python has only one candidate and the check is basically - # free, so we always do it first to avoid needless work if it fails. - requires_python = identifier == REQUIRES_PYTHON_IDENTIFIER - - # Prefer the causes of backtracking on the assumption that the problem - # resolving the dependency tree is related to the failures that caused - # the backtracking - backtrack_cause = self.is_backtrack_cause(identifier, backtrack_causes) - - return ( - not requires_python, - not direct, - not pinned, - not backtrack_cause, - inferred_depth, - requested_order, - not unfree, - identifier, - ) - - def find_matches( - self, - identifier: str, - requirements: Mapping[str, Iterator[Requirement]], - incompatibilities: Mapping[str, Iterator[Candidate]], - ) -> Iterable[Candidate]: - def _eligible_for_upgrade(identifier: str) -> bool: - """Are upgrades allowed for this project? - - This checks the upgrade strategy, and whether the project was one - that the user specified in the command line, in order to decide - whether we should upgrade if there's a newer version available. - - (Note that we don't need access to the `--upgrade` flag, because - an upgrade strategy of "to-satisfy-only" means that `--upgrade` - was not specified). - """ - if self._upgrade_strategy == "eager": - return True - elif self._upgrade_strategy == "only-if-needed": - user_order = _get_with_identifier( - self._user_requested, - identifier, - default=None, - ) - return user_order is not None - return False - - constraint = _get_with_identifier( - self._constraints, - identifier, - default=Constraint.empty(), - ) - return self._factory.find_candidates( - identifier=identifier, - requirements=requirements, - constraint=constraint, - prefers_installed=(not _eligible_for_upgrade(identifier)), - incompatibilities=incompatibilities, - ) - - def is_satisfied_by(self, requirement: Requirement, candidate: Candidate) -> bool: - return requirement.is_satisfied_by(candidate) - - def get_dependencies(self, candidate: Candidate) -> Sequence[Requirement]: - with_requires = not self._ignore_dependencies - return [r for r in candidate.iter_dependencies(with_requires) if r is not None] - - @staticmethod - def is_backtrack_cause( - identifier: str, backtrack_causes: Sequence["PreferenceInformation"] - ) -> bool: - for backtrack_cause in backtrack_causes: - if identifier == backtrack_cause.requirement.name: - return True - if backtrack_cause.parent and identifier == backtrack_cause.parent.name: - return True - return False diff --git a/spaces/Toritto/Genshin-impact-IA-project-v1/CHANGELOG.md b/spaces/Toritto/Genshin-impact-IA-project-v1/CHANGELOG.md deleted file mode 100644 index 49dc695450d128a8e7f3bbe24488f212fd4e2690..0000000000000000000000000000000000000000 --- a/spaces/Toritto/Genshin-impact-IA-project-v1/CHANGELOG.md +++ /dev/null @@ -1,16 +0,0 @@ -12/09/2023 Changelog:
    -- Added documentation. -- Support for non json file. - -13/08/2023 Changelog:
    -- Fix bugs. - -08/08/2023 Changelog:
    -- Limitation changes. -- UI Changes for Youtube Input. -- Added instrument volume. - -29/07/2023 Changelog:
    -- UI Changes for Non Limitation. -- Added More Splitter Model. -- Separate Youtube Download and Splitter. \ No newline at end of file diff --git a/spaces/TusharGoel/LayoutLM-DocVQA/app.py b/spaces/TusharGoel/LayoutLM-DocVQA/app.py deleted file mode 100644 index cbac7c5e8253c2b0ed4b1ce8a9a86cbe498e3b6c..0000000000000000000000000000000000000000 --- a/spaces/TusharGoel/LayoutLM-DocVQA/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/TusharGoel/LayoutLM-Finetuned-DocVQA").launch() \ No newline at end of file diff --git a/spaces/ViralWeb/aifi/README.md b/spaces/ViralWeb/aifi/README.md deleted file mode 100644 index 3f7adcee0394f02d593f07a0dc027c28b6104ed1..0000000000000000000000000000000000000000 --- a/spaces/ViralWeb/aifi/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Chat Ui Template -emoji: 🚀 -colorFrom: indigo -colorTo: blue -sdk: docker -pinned: false -app_port: 3000 -suggested_hardware: a10g-small -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Wootang02/textgenerator/app.py b/spaces/Wootang02/textgenerator/app.py deleted file mode 100644 index 0ad75f89f03a9bea049ad83d35468180d9397893..0000000000000000000000000000000000000000 --- a/spaces/Wootang02/textgenerator/app.py +++ /dev/null @@ -1,10 +0,0 @@ -import gradio as gr -from gradio.mix import Parallel - -paco="My First Text Generator" -tom="Input" -model1=gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") -model2=gr.Interface.load("huggingface/gpt2") - -gr.Parallel(model1, model2, title=paco, description=tom).launch() - diff --git a/spaces/Xenova/ai-code-playground/index.html b/spaces/Xenova/ai-code-playground/index.html deleted file mode 100644 index c6409ad93a0d8344228c177e34e7c3de5b2b199e..0000000000000000000000000000000000000000 --- a/spaces/Xenova/ai-code-playground/index.html +++ /dev/null @@ -1,14 +0,0 @@ - - - - - - Transformers.js - Sample code-completion application - - - - -
    - - - diff --git a/spaces/Xhaheen/chatgpt_meme_world_/README.md b/spaces/Xhaheen/chatgpt_meme_world_/README.md deleted file mode 100644 index 5899726d55a8c75fed3019931a638795a093efdb..0000000000000000000000000000000000000000 --- a/spaces/Xhaheen/chatgpt_meme_world_/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Meme World -emoji: 📚 -colorFrom: green -colorTo: pink -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false -license: mit -duplicated_from: Xhaheen/meme_world ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Xyan-shuo2/Shoshoo/Dockerfile b/spaces/Xyan-shuo2/Shoshoo/Dockerfile deleted file mode 100644 index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000 --- a/spaces/Xyan-shuo2/Shoshoo/Dockerfile +++ /dev/null @@ -1,21 +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/XzJosh/Echo-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/Echo-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md deleted file mode 100644 index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/Echo-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -language: -- zh -tags: -- bert -license: "apache-2.0" ---- - -# Please use 'Bert' related functions to load this model! - -## Chinese BERT with Whole Word Masking -For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. - -**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)** -Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu - -This repository is developed based on：https://github.com/google-research/bert - -You may also interested in, -- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm -- Chinese MacBERT: https://github.com/ymcui/MacBERT -- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA -- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet -- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer - -More resources by HFL: https://github.com/ymcui/HFL-Anthology - -## Citation -If you find the technical report or resource is useful, please cite the following technical report in your paper. -- Primary: https://arxiv.org/abs/2004.13922 -``` -@inproceedings{cui-etal-2020-revisiting, - title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing", - author = "Cui, Yiming and - Che, Wanxiang and - Liu, Ting and - Qin, Bing and - Wang, Shijin and - Hu, Guoping", - booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings", - month = nov, - year = "2020", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58", - pages = "657--668", -} -``` -- Secondary: https://arxiv.org/abs/1906.08101 -``` -@article{chinese-bert-wwm, - title={Pre-Training with Whole Word Masking for Chinese BERT}, - author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping}, - journal={arXiv preprint arXiv:1906.08101}, - year={2019} - } -``` \ No newline at end of file diff --git a/spaces/XzJosh/Nana7mi-Bert-VITS2/text/tone_sandhi.py b/spaces/XzJosh/Nana7mi-Bert-VITS2/text/tone_sandhi.py deleted file mode 100644 index 0f45b7a72c5d858bcaab19ac85cfa686bf9a74da..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/Nana7mi-Bert-VITS2/text/tone_sandhi.py +++ /dev/null @@ -1,351 +0,0 @@ -# Copyright (c) 2021 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. -from typing import List -from typing import Tuple - -import jieba -from pypinyin import lazy_pinyin -from pypinyin import Style - - -class ToneSandhi(): - def __init__(self): - self.must_neural_tone_words = { - '麻烦', '麻利', '鸳鸯', '高粱', '骨头', '骆驼', '马虎', '首饰', '馒头', '馄饨', '风筝', - '难为', '队伍', '阔气', '闺女', '门道', '锄头', '铺盖', '铃铛', '铁匠', '钥匙', '里脊', - '里头', '部分', '那么', '道士', '造化', '迷糊', '连累', '这么', '这个', '运气', '过去', - '软和', '转悠', '踏实', '跳蚤', '跟头', '趔趄', '财主', '豆腐', '讲究', '记性', '记号', - '认识', '规矩', '见识', '裁缝', '补丁', '衣裳', '衣服', '衙门', '街坊', '行李', '行当', - '蛤蟆', '蘑菇', '薄荷', '葫芦', '葡萄', '萝卜', '荸荠', '苗条', '苗头', '苍蝇', '芝麻', - '舒服', '舒坦', '舌头', '自在', '膏药', '脾气', '脑袋', '脊梁', '能耐', '胳膊', '胭脂', - '胡萝', '胡琴', '胡同', '聪明', '耽误', '耽搁', '耷拉', '耳朵', '老爷', '老实', '老婆', - '老头', '老太', '翻腾', '罗嗦', '罐头', '编辑', '结实', '红火', '累赘', '糨糊', '糊涂', - '精神', '粮食', '簸箕', '篱笆', '算计', '算盘', '答应', '笤帚', '笑语', '笑话', '窟窿', - '窝囊', '窗户', '稳当', '稀罕', '称呼', '秧歌', '秀气', '秀才', '福气', '祖宗', '砚台', - '码头', '石榴', '石头', '石匠', '知识', '眼睛', '眯缝', '眨巴', '眉毛', '相声', '盘算', - '白净', '痢疾', '痛快', '疟疾', '疙瘩', '疏忽', '畜生', '生意', '甘蔗', '琵琶', '琢磨', - '琉璃', '玻璃', '玫瑰', '玄乎', '狐狸', '状元', '特务', '牲口', '牙碜', '牌楼', '爽快', - '爱人', '热闹', '烧饼', '烟筒', '烂糊', '点心', '炊帚', '灯笼', '火候', '漂亮', '滑溜', - '溜达', '温和', '清楚', '消息', '浪头', '活泼', '比方', '正经', '欺负', '模糊', '槟榔', - '棺材', '棒槌', '棉花', '核桃', '栅栏', '柴火', '架势', '枕头', '枇杷', '机灵', '本事', - '木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾', - '收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼', - '抬举', '护士', '折腾', '扫帚', '打量', '打算', '打点', '打扮', '打听', '打发', '扎实', - '扁担', '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头', - '念叨', '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼', - '干事', '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数', - '屁股', '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气', - '实在', '官司', '学问', '学生', '字号', '嫁妆', '媳妇', '媒人', '婆家', '娘家', '委屈', - '姑娘', '姐夫', '妯娌', '妥当', '妖精', '奴才', '女婿', '头发', '太阳', '大爷', '大方', - '大意', '大夫', '多少', '多么', '外甥', '壮实', '地道', '地方', '在乎', '困难', '嘴巴', - '嘱咐', '嘟囔', '嘀咕', '喜欢', '喇嘛', '喇叭', '商量', '唾沫', '哑巴', '哈欠', '哆嗦', - '咳嗽', '和尚', '告诉', '告示', '含糊', '吓唬', '后头', '名字', '名堂', '合同', '吆喝', - '叫唤', '口袋', '厚道', '厉害', '千斤', '包袱', '包涵', '匀称', '勤快', '动静', '动弹', - '功夫', '力气', '前头', '刺猬', '刺激', '别扭', '利落', '利索', '利害', '分析', '出息', - '凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤', - '佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家', - '交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故', - '不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨', - '父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅', - '幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱', - '凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱', - '扫把', '惦记' - } - self.must_not_neural_tone_words = { - "男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人", "虎虎" - } - self.punc = "：，；。？！“”‘’':,;.?!" - - # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041 - # e.g. - # word: "家里" - # pos: "s" - # finals: ['ia1', 'i3'] - def _neural_sandhi(self, word: str, pos: str, - finals: List[str]) -> List[str]: - - # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺 - for j, item in enumerate(word): - if j - 1 >= 0 and item == word[j - 1] and pos[0] in { - "n", "v", "a" - } and word not in self.must_not_neural_tone_words: - finals[j] = finals[j][:-1] + "5" - ge_idx = word.find("个") - if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶": - finals[-1] = finals[-1][:-1] + "5" - elif len(word) >= 1 and word[-1] in "的地得": - finals[-1] = finals[-1][:-1] + "5" - # e.g. 走了, 看着, 去过 - # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}: - # finals[-1] = finals[-1][:-1] + "5" - elif len(word) > 1 and word[-1] in "们子" and pos in { - "r", "n" - } and word not in self.must_not_neural_tone_words: - finals[-1] = finals[-1][:-1] + "5" - # e.g. 桌上, 地下, 家里 - elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}: - finals[-1] = finals[-1][:-1] + "5" - # e.g. 上来, 下去 - elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开": - finals[-1] = finals[-1][:-1] + "5" - # 个做量词 - elif (ge_idx >= 1 and - (word[ge_idx - 1].isnumeric() or - word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个': - finals[ge_idx] = finals[ge_idx][:-1] + "5" - else: - if word in self.must_neural_tone_words or word[ - -2:] in self.must_neural_tone_words: - finals[-1] = finals[-1][:-1] + "5" - - word_list = self._split_word(word) - finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]] - for i, word in enumerate(word_list): - # conventional neural in Chinese - if word in self.must_neural_tone_words or word[ - -2:] in self.must_neural_tone_words: - finals_list[i][-1] = finals_list[i][-1][:-1] + "5" - finals = sum(finals_list, []) - return finals - - def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]: - # e.g. 看不懂 - if len(word) == 3 and word[1] == "不": - finals[1] = finals[1][:-1] + "5" - else: - for i, char in enumerate(word): - # "不" before tone4 should be bu2, e.g. 不怕 - if char == "不" and i + 1 < len(word) and finals[i + - 1][-1] == "4": - finals[i] = finals[i][:-1] + "2" - return finals - - def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]: - # "一" in number sequences, e.g. 一零零, 二一零 - if word.find("一") != -1 and all( - [item.isnumeric() for item in word if item != "一"]): - return finals - # "一" between reduplication words shold be yi5, e.g. 看一看 - elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]: - finals[1] = finals[1][:-1] + "5" - # when "一" is ordinal word, it should be yi1 - elif word.startswith("第一"): - finals[1] = finals[1][:-1] + "1" - else: - for i, char in enumerate(word): - if char == "一" and i + 1 < len(word): - # "一" before tone4 should be yi2, e.g. 一段 - if finals[i + 1][-1] == "4": - finals[i] = finals[i][:-1] + "2" - # "一" before non-tone4 should be yi4, e.g. 一天 - else: - # "一" 后面如果是标点，还读一声 - if word[i + 1] not in self.punc: - finals[i] = finals[i][:-1] + "4" - return finals - - def _split_word(self, word: str) -> List[str]: - word_list = jieba.cut_for_search(word) - word_list = sorted(word_list, key=lambda i: len(i), reverse=False) - first_subword = word_list[0] - first_begin_idx = word.find(first_subword) - if first_begin_idx == 0: - second_subword = word[len(first_subword):] - new_word_list = [first_subword, second_subword] - else: - second_subword = word[:-len(first_subword)] - new_word_list = [second_subword, first_subword] - return new_word_list - - def _three_sandhi(self, word: str, finals: List[str]) -> List[str]: - if len(word) == 2 and self._all_tone_three(finals): - finals[0] = finals[0][:-1] + "2" - elif len(word) == 3: - word_list = self._split_word(word) - if self._all_tone_three(finals): - # disyllabic + monosyllabic, e.g. 蒙古/包 - if len(word_list[0]) == 2: - finals[0] = finals[0][:-1] + "2" - finals[1] = finals[1][:-1] + "2" - # monosyllabic + disyllabic, e.g. 纸/老虎 - elif len(word_list[0]) == 1: - finals[1] = finals[1][:-1] + "2" - else: - finals_list = [ - finals[:len(word_list[0])], finals[len(word_list[0]):] - ] - if len(finals_list) == 2: - for i, sub in enumerate(finals_list): - # e.g. 所有/人 - if self._all_tone_three(sub) and len(sub) == 2: - finals_list[i][0] = finals_list[i][0][:-1] + "2" - # e.g. 好/喜欢 - elif i == 1 and not self._all_tone_three(sub) and finals_list[i][0][-1] == "3" and \ - finals_list[0][-1][-1] == "3": - - finals_list[0][-1] = finals_list[0][-1][:-1] + "2" - finals = sum(finals_list, []) - # split idiom into two words who's length is 2 - elif len(word) == 4: - finals_list = [finals[:2], finals[2:]] - finals = [] - for sub in finals_list: - if self._all_tone_three(sub): - sub[0] = sub[0][:-1] + "2" - finals += sub - - return finals - - def _all_tone_three(self, finals: List[str]) -> bool: - return all(x[-1] == "3" for x in finals) - - # merge "不" and the word behind it - # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error - def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - last_word = "" - for word, pos in seg: - if last_word == "不": - word = last_word + word - if word != "不": - new_seg.append((word, pos)) - last_word = word[:] - if last_word == "不": - new_seg.append((last_word, 'd')) - last_word = "" - return new_seg - - # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听" - # function 2: merge single "一" and the word behind it - # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error - # e.g. - # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')] - # output seg: [['听一听', 'v']] - def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - # function 1 - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and word == "一" and i + 1 < len(seg) and seg[i - 1][ - 0] == seg[i + 1][0] and seg[i - 1][1] == "v": - new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0] - else: - if i - 2 >= 0 and seg[i - 1][0] == "一" and seg[i - 2][ - 0] == word and pos == "v": - continue - else: - new_seg.append([word, pos]) - seg = new_seg - new_seg = [] - # function 2 - for i, (word, pos) in enumerate(seg): - if new_seg and new_seg[-1][0] == "一": - new_seg[-1][0] = new_seg[-1][0] + word - else: - new_seg.append([word, pos]) - return new_seg - - # the first and the second words are all_tone_three - def _merge_continuous_three_tones( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - sub_finals_list = [ - lazy_pinyin( - word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) - for (word, pos) in seg - ] - assert len(sub_finals_list) == len(seg) - merge_last = [False] * len(seg) - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and self._all_tone_three( - sub_finals_list[i - 1]) and self._all_tone_three( - sub_finals_list[i]) and not merge_last[i - 1]: - # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi - if not self._is_reduplication(seg[i - 1][0]) and len( - seg[i - 1][0]) + len(seg[i][0]) <= 3: - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - merge_last[i] = True - else: - new_seg.append([word, pos]) - else: - new_seg.append([word, pos]) - - return new_seg - - def _is_reduplication(self, word: str) -> bool: - return len(word) == 2 and word[0] == word[1] - - # the last char of first word and the first char of second word is tone_three - def _merge_continuous_three_tones_2( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - sub_finals_list = [ - lazy_pinyin( - word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) - for (word, pos) in seg - ] - assert len(sub_finals_list) == len(seg) - merge_last = [False] * len(seg) - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \ - merge_last[i - 1]: - # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi - if not self._is_reduplication(seg[i - 1][0]) and len( - seg[i - 1][0]) + len(seg[i][0]) <= 3: - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - merge_last[i] = True - else: - new_seg.append([word, pos]) - else: - new_seg.append([word, pos]) - return new_seg - - def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - for i, (word, pos) in enumerate(seg): - if i - 1 >= 0 and word == "儿" and seg[i-1][0] != "#": - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - else: - new_seg.append([word, pos]) - return new_seg - - def _merge_reduplication( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - new_seg = [] - for i, (word, pos) in enumerate(seg): - if new_seg and word == new_seg[-1][0]: - new_seg[-1][0] = new_seg[-1][0] + seg[i][0] - else: - new_seg.append([word, pos]) - return new_seg - - def pre_merge_for_modify( - self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: - seg = self._merge_bu(seg) - try: - seg = self._merge_yi(seg) - except: - print("_merge_yi failed") - seg = self._merge_reduplication(seg) - seg = self._merge_continuous_three_tones(seg) - seg = self._merge_continuous_three_tones_2(seg) - seg = self._merge_er(seg) - return seg - - def modified_tone(self, word: str, pos: str, - finals: List[str]) -> List[str]: - finals = self._bu_sandhi(word, finals) - finals = self._yi_sandhi(word, finals) - finals = self._neural_sandhi(word, pos, finals) - finals = self._three_sandhi(word, finals) - return finals diff --git a/spaces/YUANAI/DiffspeechResearch/utils/text/text_encoder.py b/spaces/YUANAI/DiffspeechResearch/utils/text/text_encoder.py deleted file mode 100644 index 09555af09720382a795712f0fdd9b711c5b19e02..0000000000000000000000000000000000000000 --- a/spaces/YUANAI/DiffspeechResearch/utils/text/text_encoder.py +++ /dev/null @@ -1,263 +0,0 @@ -import json -import re -import six -from six.moves import range # pylint: disable=redefined-builtin - -PAD = "" -EOS = "" -UNK = "" -SEG = "|" -PUNCS = '!,.?;:' -RESERVED_TOKENS = [PAD, EOS, UNK] -NUM_RESERVED_TOKENS = len(RESERVED_TOKENS) -PAD_ID = RESERVED_TOKENS.index(PAD) # Normally 0 -EOS_ID = RESERVED_TOKENS.index(EOS) # Normally 1 -UNK_ID = RESERVED_TOKENS.index(UNK) # Normally 2 - -if six.PY2: - RESERVED_TOKENS_BYTES = RESERVED_TOKENS -else: - RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")] - -# Regular expression for unescaping token strings. -# '\u' is converted to '_' -# '\\' is converted to '\' -# '\213;' is converted to unichr(213) -_UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);") -_ESCAPE_CHARS = set(u"\\_u;0123456789") - - -def strip_ids(ids, ids_to_strip): - """Strip ids_to_strip from the end ids.""" - ids = list(ids) - while ids and ids[-1] in ids_to_strip: - ids.pop() - return ids - - -class TextEncoder(object): - """Base class for converting from ints to/from human readable strings.""" - - def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS): - self._num_reserved_ids = num_reserved_ids - - @property - def num_reserved_ids(self): - return self._num_reserved_ids - - def encode(self, s): - """Transform a human-readable string into a sequence of int ids. - - The ids should be in the range [num_reserved_ids, vocab_size). Ids [0, - num_reserved_ids) are reserved. - - EOS is not appended. - - Args: - s: human-readable string to be converted. - - Returns: - ids: list of integers - """ - return [int(w) + self._num_reserved_ids for w in s.split()] - - def decode(self, ids, strip_extraneous=False): - """Transform a sequence of int ids into a human-readable string. - - EOS is not expected in ids. - - Args: - ids: list of integers to be converted. - strip_extraneous: bool, whether to strip off extraneous tokens - (EOS and PAD). - - Returns: - s: human-readable string. - """ - if strip_extraneous: - ids = strip_ids(ids, list(range(self._num_reserved_ids or 0))) - return " ".join(self.decode_list(ids)) - - def decode_list(self, ids): - """Transform a sequence of int ids into a their string versions. - - This method supports transforming individual input/output ids to their - string versions so that sequence to/from text conversions can be visualized - in a human readable format. - - Args: - ids: list of integers to be converted. - - Returns: - strs: list of human-readable string. - """ - decoded_ids = [] - for id_ in ids: - if 0 <= id_ < self._num_reserved_ids: - decoded_ids.append(RESERVED_TOKENS[int(id_)]) - else: - decoded_ids.append(id_ - self._num_reserved_ids) - return [str(d) for d in decoded_ids] - - @property - def vocab_size(self): - raise NotImplementedError() - - -class TokenTextEncoder(TextEncoder): - """Encoder based on a user-supplied vocabulary (file or list).""" - - def __init__(self, - vocab_filename, - reverse=False, - vocab_list=None, - replace_oov=None, - num_reserved_ids=NUM_RESERVED_TOKENS): - """Initialize from a file or list, one token per line. - - Handling of reserved tokens works as follows: - - When initializing from a list, we add reserved tokens to the vocab. - - When initializing from a file, we do not add reserved tokens to the vocab. - - When saving vocab files, we save reserved tokens to the file. - - Args: - vocab_filename: If not None, the full filename to read vocab from. If this - is not None, then vocab_list should be None. - reverse: Boolean indicating if tokens should be reversed during encoding - and decoding. - vocab_list: If not None, a list of elements of the vocabulary. If this is - not None, then vocab_filename should be None. - replace_oov: If not None, every out-of-vocabulary token seen when - encoding will be replaced by this string (which must be in vocab). - num_reserved_ids: Number of IDs to save for reserved tokens like . - """ - super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids) - self._reverse = reverse - self._replace_oov = replace_oov - if vocab_filename: - self._init_vocab_from_file(vocab_filename) - else: - assert vocab_list is not None - self._init_vocab_from_list(vocab_list) - self.pad_index = self.token_to_id[PAD] - self.eos_index = self.token_to_id[EOS] - self.unk_index = self.token_to_id[UNK] - self.seg_index = self.token_to_id[SEG] if SEG in self.token_to_id else self.eos_index - - def encode(self, s): - """Converts a space-separated string of tokens to a list of ids.""" - sentence = s - tokens = sentence.strip().split() - if self._replace_oov is not None: - tokens = [t if t in self.token_to_id else self._replace_oov - for t in tokens] - ret = [self.token_to_id[tok] for tok in tokens] - return ret[::-1] if self._reverse else ret - - def decode(self, ids, strip_eos=False, strip_padding=False): - if strip_padding and self.pad() in list(ids): - pad_pos = list(ids).index(self.pad()) - ids = ids[:pad_pos] - if strip_eos and self.eos() in list(ids): - eos_pos = list(ids).index(self.eos()) - ids = ids[:eos_pos] - return " ".join(self.decode_list(ids)) - - def decode_list(self, ids): - seq = reversed(ids) if self._reverse else ids - return [self._safe_id_to_token(i) for i in seq] - - @property - def vocab_size(self): - return len(self.id_to_token) - - def __len__(self): - return self.vocab_size - - def _safe_id_to_token(self, idx): - return self.id_to_token.get(idx, "ID_%d" % idx) - - def _init_vocab_from_file(self, filename): - """Load vocab from a file. - - Args: - filename: The file to load vocabulary from. - """ - with open(filename) as f: - tokens = [token.strip() for token in f.readlines()] - - def token_gen(): - for token in tokens: - yield token - - self._init_vocab(token_gen(), add_reserved_tokens=False) - - def _init_vocab_from_list(self, vocab_list): - """Initialize tokens from a list of tokens. - - It is ok if reserved tokens appear in the vocab list. They will be - removed. The set of tokens in vocab_list should be unique. - - Args: - vocab_list: A list of tokens. - """ - - def token_gen(): - for token in vocab_list: - if token not in RESERVED_TOKENS: - yield token - - self._init_vocab(token_gen()) - - def _init_vocab(self, token_generator, add_reserved_tokens=True): - """Initialize vocabulary with tokens from token_generator.""" - - self.id_to_token = {} - non_reserved_start_index = 0 - - if add_reserved_tokens: - self.id_to_token.update(enumerate(RESERVED_TOKENS)) - non_reserved_start_index = len(RESERVED_TOKENS) - - self.id_to_token.update( - enumerate(token_generator, start=non_reserved_start_index)) - - # _token_to_id is the reverse of _id_to_token - self.token_to_id = dict((v, k) for k, v in six.iteritems(self.id_to_token)) - - def pad(self): - return self.pad_index - - def eos(self): - return self.eos_index - - def unk(self): - return self.unk_index - - def seg(self): - return self.seg_index - - def store_to_file(self, filename): - """Write vocab file to disk. - - Vocab files have one token per line. The file ends in a newline. Reserved - tokens are written to the vocab file as well. - - Args: - filename: Full path of the file to store the vocab to. - """ - with open(filename, "w") as f: - for i in range(len(self.id_to_token)): - f.write(self.id_to_token[i] + "\n") - - def sil_phonemes(self): - return [p for p in self.id_to_token.values() if is_sil_phoneme(p)] - - -def build_token_encoder(token_list_file): - token_list = json.load(open(token_list_file)) - return TokenTextEncoder(None, vocab_list=token_list, replace_oov='') - - -def is_sil_phoneme(p): - return p == '' or not p[0].isalpha() diff --git a/spaces/Yilin98/Stock_Prediction/README.md b/spaces/Yilin98/Stock_Prediction/README.md deleted file mode 100644 index abbfdb75fa063eb956ba2363feb55a3a2db4b773..0000000000000000000000000000000000000000 --- a/spaces/Yilin98/Stock_Prediction/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Stock Prediction -emoji: 💰 -colorFrom: red -colorTo: green -sdk: streamlit -sdk_version: 1.15.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/YlcldKlns/bing/src/components/chat-notification.tsx b/spaces/YlcldKlns/bing/src/components/chat-notification.tsx deleted file mode 100644 index 3474e522992c43a4d1d0eadcf205a9760d5b930b..0000000000000000000000000000000000000000 --- a/spaces/YlcldKlns/bing/src/components/chat-notification.tsx +++ /dev/null @@ -1,91 +0,0 @@ -import { useEffect } from 'react' -import Image from 'next/image' - -import IconWarning from '@/assets/images/warning.svg' -import { ChatError, ErrorCode, ChatMessageModel } from '@/lib/bots/bing/types' -import { ExternalLink } from './external-link' -import { useBing } from '@/lib/hooks/use-bing' - -export interface ChatNotificationProps extends Pick, 'bot'> { - message?: ChatMessageModel -} - -function getAction(error: ChatError, reset: () => void) { - if (error.code === ErrorCode.THROTTLE_LIMIT) { - reset() - return ( -
    - 你已达到每日最大发送消息次数，请更换账号或隔一天后重试 -
    - ) - } - if (error.code === ErrorCode.BING_IP_FORBIDDEN) { - return ( - - 你的服务器或代理已被封禁，请更换服务器或使用代理重试 - - ) - } - if (error.code === ErrorCode.BING_TRY_LATER) { - return ( - - 创建会话失败，请稍候重试 - - ) - } - if (error.code === ErrorCode.BING_FORBIDDEN) { - return ( - - 你的账号已在黑名单，请尝试更换账号及申请解封 - - ) - } - if (error.code === ErrorCode.CONVERSATION_LIMIT) { - return ( -
    - 当前话题已中止，请点 - 重新开始 - 开启新的对话 -
    - ) - } - if (error.code === ErrorCode.BING_CAPTCHA) { - return ( - - 点击通过人机验证 - - ) - } - if (error.code === ErrorCode.BING_UNAUTHORIZED) { - reset() - return ( - 没有获取到身份信息或身份信息失效，点此重新设置 - ) - } - return error.message -} - -export function ChatNotification({ message, bot }: ChatNotificationProps) { - useEffect(() => { - window.scrollBy(0, 2000) - }, [message]) - - if (!message?.error) return - - return ( -
    -
    -
    -
    -
    - error - {getAction(message.error, () => bot.resetConversation())} -
    -
    -
    -
    -
    - ) -} diff --git a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/upfirdn2d.py b/spaces/YotamNitzan/domain-expansion/torch_utils/ops/upfirdn2d.py deleted file mode 100644 index ceeac2b9834e33b7c601c28bf27f32aa91c69256..0000000000000000000000000000000000000000 --- a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/upfirdn2d.py +++ /dev/null @@ -1,384 +0,0 @@ -# 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 resampling of 2D images.""" - -import os -import warnings -import numpy as np -import torch -import traceback - -from .. import custom_ops -from .. import misc -from . import conv2d_gradfix - -#---------------------------------------------------------------------------- - -_inited = False -_plugin = None - -def _init(): - global _inited, _plugin - if not _inited: - sources = ['upfirdn2d.cpp', 'upfirdn2d.cu'] - sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] - try: - _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) - except: - warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) - return _plugin is not None - -def _parse_scaling(scaling): - if isinstance(scaling, int): - scaling = [scaling, scaling] - assert isinstance(scaling, (list, tuple)) - assert all(isinstance(x, int) for x in scaling) - sx, sy = scaling - assert sx >= 1 and sy >= 1 - return sx, sy - -def _parse_padding(padding): - if isinstance(padding, int): - padding = [padding, padding] - assert isinstance(padding, (list, tuple)) - assert all(isinstance(x, int) for x in padding) - if len(padding) == 2: - padx, pady = padding - padding = [padx, padx, pady, pady] - padx0, padx1, pady0, pady1 = padding - return padx0, padx1, pady0, pady1 - -def _get_filter_size(f): - if f is None: - return 1, 1 - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - fw = f.shape[-1] - fh = f.shape[0] - with misc.suppress_tracer_warnings(): - fw = int(fw) - fh = int(fh) - misc.assert_shape(f, [fh, fw][:f.ndim]) - assert fw >= 1 and fh >= 1 - return fw, fh - -#---------------------------------------------------------------------------- - -def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None): - r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. - - Args: - f: Torch tensor, numpy array, or python list of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), - `[]` (impulse), or - `None` (identity). - device: Result device (default: cpu). - normalize: Normalize the filter so that it retains the magnitude - for constant input signal (DC)? (default: True). - flip_filter: Flip the filter? (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - separable: Return a separable filter? (default: select automatically). - - Returns: - Float32 tensor of the shape - `[filter_height, filter_width]` (non-separable) or - `[filter_taps]` (separable). - """ - # Validate. - if f is None: - f = 1 - f = torch.as_tensor(f, dtype=torch.float32) - assert f.ndim in [0, 1, 2] - assert f.numel() > 0 - if f.ndim == 0: - f = f[np.newaxis] - - # Separable? - if separable is None: - separable = (f.ndim == 1 and f.numel() >= 8) - if f.ndim == 1 and not separable: - f = f.ger(f) - assert f.ndim == (1 if separable else 2) - - # Apply normalize, flip, gain, and device. - if normalize: - f /= f.sum() - if flip_filter: - f = f.flip(list(range(f.ndim))) - f = f * (gain ** (f.ndim / 2)) - f = f.to(device=device) - return f - -#---------------------------------------------------------------------------- - -def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Pad, upsample, filter, and downsample a batch of 2D images. - - Performs the following sequence of operations for each channel: - - 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). - - 2. Pad the image with the specified number of zeros on each side (`padding`). - Negative padding corresponds to cropping the image. - - 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it - so that the footprint of all output pixels lies within the input image. - - 4. Downsample the image by keeping every Nth pixel (`down`). - - This sequence of operations bears close resemblance to scipy.signal.upfirdn(). - The fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports gradients of arbitrary order. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - up: Integer upsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - down: Integer downsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - assert isinstance(x, torch.Tensor) - assert impl in ['ref', 'cuda'] - if impl == 'cuda' and x.device.type == 'cuda' and _init(): - return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f) - return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): - """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops. - """ - # Validate arguments. - assert isinstance(x, torch.Tensor) and x.ndim == 4 - if f is None: - f = torch.ones([1, 1], dtype=torch.float32, device=x.device) - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - assert f.dtype == torch.float32 and not f.requires_grad - batch_size, num_channels, in_height, in_width = x.shape - upx, upy = _parse_scaling(up) - downx, downy = _parse_scaling(down) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - - # Upsample by inserting zeros. - x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) - x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) - x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) - - # Pad or crop. - x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) - x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)] - - # Setup filter. - f = f * (gain ** (f.ndim / 2)) - f = f.to(x.dtype) - if not flip_filter: - f = f.flip(list(range(f.ndim))) - - # Convolve with the filter. - f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) - if f.ndim == 4: - x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) - else: - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) - - # Downsample by throwing away pixels. - x = x[:, :, ::downy, ::downx] - return x - -#---------------------------------------------------------------------------- - -_upfirdn2d_cuda_cache = dict() - -def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): - """Fast CUDA implementation of `upfirdn2d()` using custom ops. - """ - # Parse arguments. - upx, upy = _parse_scaling(up) - downx, downy = _parse_scaling(down) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - - # Lookup from cache. - key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) - if key in _upfirdn2d_cuda_cache: - return _upfirdn2d_cuda_cache[key] - - # Forward op. - class Upfirdn2dCuda(torch.autograd.Function): - @staticmethod - def forward(ctx, x, f): # pylint: disable=arguments-differ - assert isinstance(x, torch.Tensor) and x.ndim == 4 - if f is None: - f = torch.ones([1, 1], dtype=torch.float32, device=x.device) - assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] - y = x - if f.ndim == 2: - y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) - else: - y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain)) - y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain)) - ctx.save_for_backward(f) - ctx.x_shape = x.shape - return y - - @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ - f, = ctx.saved_tensors - _, _, ih, iw = ctx.x_shape - _, _, oh, ow = dy.shape - fw, fh = _get_filter_size(f) - p = [ - fw - padx0 - 1, - iw * upx - ow * downx + padx0 - upx + 1, - fh - pady0 - 1, - ih * upy - oh * downy + pady0 - upy + 1, - ] - dx = None - df = None - - if ctx.needs_input_grad[0]: - dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f) - - assert not ctx.needs_input_grad[1] - return dx, df - - # Add to cache. - _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda - return Upfirdn2dCuda - -#---------------------------------------------------------------------------- - -def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Filter a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape matches the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - padding: Padding with respect to the output. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - padx0, padx1, pady0, pady1 = _parse_padding(padding) - fw, fh = _get_filter_size(f) - p = [ - padx0 + fw // 2, - padx1 + (fw - 1) // 2, - pady0 + fh // 2, - pady1 + (fh - 1) // 2, - ] - return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) - -#---------------------------------------------------------------------------- - -def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Upsample a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape is a multiple of the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - up: Integer upsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the output. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - upx, upy = _parse_scaling(up) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - fw, fh = _get_filter_size(f) - p = [ - padx0 + (fw + upx - 1) // 2, - padx1 + (fw - upx) // 2, - pady0 + (fh + upy - 1) // 2, - pady1 + (fh - upy) // 2, - ] - return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl) - -#---------------------------------------------------------------------------- - -def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'): - r"""Downsample a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape is a fraction of the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x: Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f: Float32 FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - down: Integer downsampling factor. Can be a single int or a list/tuple - `[x, y]` (default: 1). - padding: Padding with respect to the input. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - flip_filter: False = convolution, True = correlation (default: False). - gain: Overall scaling factor for signal magnitude (default: 1). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - downx, downy = _parse_scaling(down) - padx0, padx1, pady0, pady1 = _parse_padding(padding) - fw, fh = _get_filter_size(f) - p = [ - padx0 + (fw - downx + 1) // 2, - padx1 + (fw - downx) // 2, - pady0 + (fh - downy + 1) // 2, - pady1 + (fh - downy) // 2, - ] - return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) - -#---------------------------------------------------------------------------- diff --git a/spaces/YouLiXiya/Mobile-SAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/YouLiXiya/Mobile-SAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py deleted file mode 100644 index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000 --- a/spaces/YouLiXiya/Mobile-SAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py +++ /dev/null @@ -1,221 +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] -# ------------------------------------------------------------------------ -# 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. -# ------------------------------------------------------------------------ - -""" -Backbone modules. -""" - -from typing import Dict, List - -import torch -import torch.nn.functional as F -import torchvision -from torch import nn -from torchvision.models._utils import IntermediateLayerGetter - -from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process - -from .position_encoding import build_position_encoding -from .swin_transformer import build_swin_transformer - - -class FrozenBatchNorm2d(torch.nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - Copy-paste from torchvision.misc.ops with added eps before rqsrt, - without which any other models than torchvision.models.resnet[18,34,50,101] - produce nans. - """ - - def __init__(self, n): - super(FrozenBatchNorm2d, self).__init__() - self.register_buffer("weight", torch.ones(n)) - self.register_buffer("bias", torch.zeros(n)) - self.register_buffer("running_mean", torch.zeros(n)) - self.register_buffer("running_var", torch.ones(n)) - - def _load_from_state_dict( - self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ): - num_batches_tracked_key = prefix + "num_batches_tracked" - if num_batches_tracked_key in state_dict: - del state_dict[num_batches_tracked_key] - - super(FrozenBatchNorm2d, self)._load_from_state_dict( - state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ) - - def forward(self, x): - # move reshapes to the beginning - # to make it fuser-friendly - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - rv = self.running_var.reshape(1, -1, 1, 1) - rm = self.running_mean.reshape(1, -1, 1, 1) - eps = 1e-5 - scale = w * (rv + eps).rsqrt() - bias = b - rm * scale - return x * scale + bias - - -class BackboneBase(nn.Module): - def __init__( - self, - backbone: nn.Module, - train_backbone: bool, - num_channels: int, - return_interm_indices: list, - ): - super().__init__() - for name, parameter in backbone.named_parameters(): - if ( - not train_backbone - or "layer2" not in name - and "layer3" not in name - and "layer4" not in name - ): - parameter.requires_grad_(False) - - return_layers = {} - for idx, layer_index in enumerate(return_interm_indices): - return_layers.update( - {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)} - ) - - # if len: - # if use_stage1_feature: - # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} - # else: - # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} - # else: - # return_layers = {'layer4': "0"} - self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) - self.num_channels = num_channels - - def forward(self, tensor_list: NestedTensor): - xs = self.body(tensor_list.tensors) - out: Dict[str, NestedTensor] = {} - for name, x in xs.items(): - m = tensor_list.mask - assert m is not None - mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - out[name] = NestedTensor(x, mask) - # import ipdb; ipdb.set_trace() - return out - - -class Backbone(BackboneBase): - """ResNet backbone with frozen BatchNorm.""" - - def __init__( - self, - name: str, - train_backbone: bool, - dilation: bool, - return_interm_indices: list, - batch_norm=FrozenBatchNorm2d, - ): - if name in ["resnet18", "resnet34", "resnet50", "resnet101"]: - backbone = getattr(torchvision.models, name)( - replace_stride_with_dilation=[False, False, dilation], - pretrained=is_main_process(), - norm_layer=batch_norm, - ) - else: - raise NotImplementedError("Why you can get here with name {}".format(name)) - # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 - assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available." - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - num_channels_all = [256, 512, 1024, 2048] - num_channels = num_channels_all[4 - len(return_interm_indices) :] - super().__init__(backbone, train_backbone, num_channels, return_interm_indices) - - -class Joiner(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - - def forward(self, tensor_list: NestedTensor): - xs = self[0](tensor_list) - out: List[NestedTensor] = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self[1](x).to(x.tensors.dtype)) - - return out, pos - - -def build_backbone(args): - """ - Useful args: - - backbone: backbone name - - lr_backbone: - - dilation - - return_interm_indices: available: [0,1,2,3], [1,2,3], [3] - - backbone_freeze_keywords: - - use_checkpoint: for swin only for now - - """ - position_embedding = build_position_encoding(args) - train_backbone = True - if not train_backbone: - raise ValueError("Please set lr_backbone > 0") - return_interm_indices = args.return_interm_indices - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - args.backbone_freeze_keywords - use_checkpoint = getattr(args, "use_checkpoint", False) - - if args.backbone in ["resnet50", "resnet101"]: - backbone = Backbone( - args.backbone, - train_backbone, - args.dilation, - return_interm_indices, - batch_norm=FrozenBatchNorm2d, - ) - bb_num_channels = backbone.num_channels - elif args.backbone in [ - "swin_T_224_1k", - "swin_B_224_22k", - "swin_B_384_22k", - "swin_L_224_22k", - "swin_L_384_22k", - ]: - pretrain_img_size = int(args.backbone.split("_")[-2]) - backbone = build_swin_transformer( - args.backbone, - pretrain_img_size=pretrain_img_size, - out_indices=tuple(return_interm_indices), - dilation=False, - use_checkpoint=use_checkpoint, - ) - - bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :] - else: - raise NotImplementedError("Unknown backbone {}".format(args.backbone)) - - assert len(bb_num_channels) == len( - return_interm_indices - ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}" - - model = Joiner(backbone, position_embedding) - model.num_channels = bb_num_channels - assert isinstance( - bb_num_channels, List - ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels)) - # import ipdb; ipdb.set_trace() - return model diff --git a/spaces/Zaxxced/rvc-random-v2/README.md b/spaces/Zaxxced/rvc-random-v2/README.md deleted file mode 100644 index 2fad6bb0e5d7826468cb46fa412701d49c997d88..0000000000000000000000000000000000000000 --- a/spaces/Zaxxced/rvc-random-v2/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: RVC V2 Random -emoji: 🎤 -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 3.36.1 -app_file: app.py -pinned: true -license: mit -duplicated_from: mocci24/rvc-genshin-v2 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/abdvl/datahub_qa_bot/docs/managed-datahub/release-notes/v_0_2_1.md b/spaces/abdvl/datahub_qa_bot/docs/managed-datahub/release-notes/v_0_2_1.md deleted file mode 100644 index 4b6884dc369d5dfccea665f6198611dfebef716d..0000000000000000000000000000000000000000 --- a/spaces/abdvl/datahub_qa_bot/docs/managed-datahub/release-notes/v_0_2_1.md +++ /dev/null @@ -1,15 +0,0 @@ -# v0.2.1 ---- - -Release Availability Date ---- -23-Feb-2023 - -## Release Changlog ---- -- Since `v0.2.0` these changes from OSS DataHub https://github.com/datahub-project/datahub/compare/cf1e627e55431fc69d72918b2bcc3c5f3a1d5002...36037cf288eea12f1760dd0718255eeb1d7039c7 have been pulled in. -- Add first, last synched + last updated properties to metadata tests. -- Update link colors to pass accessibility. -- Extend tag and term proposals to other entity types besides datasets. This allows proposals to work on entities other than datasets. -- We are skipping running metadata tests in real-time processing as it was not scaling out and causing issues in ingestion -- Re-enabling hard-deletes which was temporarily disabled \ No newline at end of file diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/logging.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/logging.py deleted file mode 100644 index 4aa0e04bb9b3ab2a4bfbc4def50404ccbac2c6e6..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/logging.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import logging - -import torch.distributed as dist - -logger_initialized = {} - - -def get_logger(name, log_file=None, log_level=logging.INFO, file_mode='w'): - """Initialize and get a logger by name. - - If the logger has not been initialized, this method will initialize the - logger by adding one or two handlers, otherwise the initialized logger will - be directly returned. During initialization, a StreamHandler will always be - added. If `log_file` is specified and the process rank is 0, a FileHandler - will also be added. - - Args: - name (str): Logger name. - log_file (str | None): The log filename. If specified, a FileHandler - will be added to the logger. - log_level (int): The logger level. Note that only the process of - rank 0 is affected, and other processes will set the level to - "Error" thus be silent most of the time. - file_mode (str): The file mode used in opening log file. - Defaults to 'w'. - - Returns: - logging.Logger: The expected logger. - """ - logger = logging.getLogger(name) - if name in logger_initialized: - return logger - # handle hierarchical names - # e.g., logger "a" is initialized, then logger "a.b" will skip the - # initialization since it is a child of "a". - for logger_name in logger_initialized: - if name.startswith(logger_name): - return logger - - # handle duplicate logs to the console - # Starting in 1.8.0, PyTorch DDP attaches a StreamHandler (NOTSET) - # to the root logger. As logger.propagate is True by default, this root - # level handler causes logging messages from rank>0 processes to - # unexpectedly show up on the console, creating much unwanted clutter. - # To fix this issue, we set the root logger's StreamHandler, if any, to log - # at the ERROR level. - for handler in logger.root.handlers: - if type(handler) is logging.StreamHandler: - handler.setLevel(logging.ERROR) - - stream_handler = logging.StreamHandler() - handlers = [stream_handler] - - if dist.is_available() and dist.is_initialized(): - rank = dist.get_rank() - else: - rank = 0 - - # only rank 0 will add a FileHandler - if rank == 0 and log_file is not None: - # Here, the default behaviour of the official logger is 'a'. Thus, we - # provide an interface to change the file mode to the default - # behaviour. - file_handler = logging.FileHandler(log_file, file_mode) - handlers.append(file_handler) - - formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s') - for handler in handlers: - handler.setFormatter(formatter) - handler.setLevel(log_level) - logger.addHandler(handler) - - if rank == 0: - logger.setLevel(log_level) - else: - logger.setLevel(logging.ERROR) - - logger_initialized[name] = True - - return logger - - -def print_log(msg, logger=None, level=logging.INFO): - """Print a log message. - - Args: - msg (str): The message to be logged. - logger (logging.Logger | str | None): The logger to be used. - Some special loggers are: - - "silent": no message will be printed. - - other str: the logger obtained with `get_root_logger(logger)`. - - None: The `print()` method will be used to print log messages. - level (int): Logging level. Only available when `logger` is a Logger - object or "root". - """ - if logger is None: - print(msg) - elif isinstance(logger, logging.Logger): - logger.log(level, msg) - elif logger == 'silent': - pass - elif isinstance(logger, str): - _logger = get_logger(logger) - _logger.log(level, msg) - else: - raise TypeError( - 'logger should be either a logging.Logger object, str, ' - f'"silent" or None, but got {type(logger)}') diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/s3tc.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/s3tc.py deleted file mode 100644 index 918226bb6653064bca6692971e11aa469f97fede..0000000000000000000000000000000000000000 --- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/s3tc.py +++ /dev/null @@ -1,354 +0,0 @@ -"""Software decoder for S3TC compressed texture (i.e., DDS). - -http://oss.sgi.com/projects/ogl-sample/registry/EXT/texture_compression_s3tc.txt -""" - -import re -import ctypes - -from pyglet.gl import * -from pyglet.gl import gl_info -from pyglet.image import AbstractImage, Texture - -split_8byte = re.compile('.' * 8, flags=re.DOTALL) -split_16byte = re.compile('.' * 16, flags=re.DOTALL) - - -class PackedImageData(AbstractImage): - _current_texture = None - - def __init__(self, width, height, fmt, packed_format, data): - super().__init__(width, height) - self.format = fmt - self.packed_format = packed_format - self.data = data - - def unpack(self): - if self.packed_format == GL_UNSIGNED_SHORT_5_6_5: - # Unpack to GL_RGB. Assume self.data is already 16-bit - i = 0 - out = (ctypes.c_ubyte * (self.width * self.height * 3))() - for c in self.data: - out[i + 2] = (c & 0x1f) << 3 - out[i + 1] = (c & 0x7e0) >> 3 - out[i] = (c & 0xf800) >> 8 - i += 3 - self.data = out - self.packed_format = GL_UNSIGNED_BYTE - - def _get_texture(self): - if self._current_texture: - return self._current_texture - - texture = Texture.create(self.width, self.height, GL_TEXTURE_2D, None) - glBindTexture(texture.target, texture.id) - glTexParameteri(texture.target, GL_TEXTURE_MIN_FILTER, GL_LINEAR) - - if not gl_info.have_version(1, 2) or True: - self.unpack() - - glTexImage2D(texture.target, texture.level, - self.format, self.width, self.height, 0, - self.format, self.packed_format, self.data) - - self._current_texture = texture - return texture - - texture = property(_get_texture) - - def get_texture(self, rectangle=False, force_rectangle=False): - """The parameters 'rectangle' and 'force_rectangle' are ignored. - See the documentation of the method 'AbstractImage.get_texture' for - a more detailed documentation of the method. """ - return self._get_texture() - - -def decode_dxt1_rgb(data, width, height): - # Decode to 16-bit RGB UNSIGNED_SHORT_5_6_5 - out = (ctypes.c_uint16 * (width * height))() - - # Read 8 bytes at a time - image_offset = 0 - for c0_lo, c0_hi, c1_lo, c1_hi, b0, b1, b2, b3 in split_8byte.findall(data): - color0 = ord(c0_lo) | ord(c0_hi) << 8 - color1 = ord(c1_lo) | ord(c1_hi) << 8 - bits = ord(b0) | ord(b1) << 8 | ord(b2) << 16 | ord(b3) << 24 - - r0 = color0 & 0x1f - g0 = (color0 & 0x7e0) >> 5 - b0 = (color0 & 0xf800) >> 11 - r1 = color1 & 0x1f - g1 = (color1 & 0x7e0) >> 5 - b1 = (color1 & 0xf800) >> 11 - - # i is the dest ptr for this block - i = image_offset - for y in range(4): - for x in range(4): - code = bits & 0x3 - - if code == 0: - out[i] = color0 - elif code == 1: - out[i] = color1 - elif code == 3 and color0 <= color1: - out[i] = 0 - else: - if code == 2 and color0 > color1: - r = (2 * r0 + r1) // 3 - g = (2 * g0 + g1) // 3 - b = (2 * b0 + b1) // 3 - elif code == 3 and color0 > color1: - r = (r0 + 2 * r1) // 3 - g = (g0 + 2 * g1) // 3 - b = (b0 + 2 * b1) // 3 - else: - assert code == 2 and color0 <= color1 - r = (r0 + r1) // 2 - g = (g0 + g1) // 2 - b = (b0 + b1) // 2 - out[i] = r | g << 5 | b << 11 - - bits >>= 2 - i += 1 - i += width - 4 - - # Move dest ptr to next 4x4 block - advance_row = (image_offset + 4) % width == 0 - image_offset += width * 3 * advance_row + 4 - - return PackedImageData(width, height, GL_RGB, GL_UNSIGNED_SHORT_5_6_5, out) - - -def decode_dxt1_rgba(data, width, height): - # Decode to GL_RGBA - out = (ctypes.c_ubyte * (width * height * 4))() - pitch = width << 2 - - # Read 8 bytes at a time - image_offset = 0 - for c0_lo, c0_hi, c1_lo, c1_hi, b0, b1, b2, b3 in split_8byte.findall(data): - color0 = ord(c0_lo) | ord(c0_hi) << 8 - color1 = ord(c1_lo) | ord(c1_hi) << 8 - bits = ord(b0) | ord(b1) << 8 | ord(b2) << 16 | ord(b3) << 24 - - r0 = color0 & 0x1f - g0 = (color0 & 0x7e0) >> 5 - b0 = (color0 & 0xf800) >> 11 - r1 = color1 & 0x1f - g1 = (color1 & 0x7e0) >> 5 - b1 = (color1 & 0xf800) >> 11 - - # i is the dest ptr for this block - i = image_offset - for y in range(4): - for x in range(4): - code = bits & 0x3 - a = 255 - - if code == 0: - r, g, b = r0, g0, b0 - elif code == 1: - r, g, b = r1, g1, b1 - elif code == 3 and color0 <= color1: - r = g = b = a = 0 - else: - if code == 2 and color0 > color1: - r = (2 * r0 + r1) // 3 - g = (2 * g0 + g1) // 3 - b = (2 * b0 + b1) // 3 - elif code == 3 and color0 > color1: - r = (r0 + 2 * r1) // 3 - g = (g0 + 2 * g1) // 3 - b = (b0 + 2 * b1) // 3 - else: - assert code == 2 and color0 <= color1 - r = (r0 + r1) // 2 - g = (g0 + g1) // 2 - b = (b0 + b1) // 2 - - out[i] = b << 3 - out[i + 1] = g << 2 - out[i + 2] = r << 3 - out[i + 3] = a << 4 - - bits >>= 2 - i += 4 - i += pitch - 16 - - # Move dest ptr to next 4x4 block - advance_row = (image_offset + 16) % pitch == 0 - image_offset += pitch * 3 * advance_row + 16 - - return PackedImageData(width, height, GL_RGBA, GL_UNSIGNED_BYTE, out) - - -def decode_dxt3(data, width, height): - # Decode to GL_RGBA - out = (ctypes.c_ubyte * (width * height * 4))() - pitch = width << 2 - - # Read 16 bytes at a time - image_offset = 0 - for (a0, a1, a2, a3, a4, a5, a6, a7, - c0_lo, c0_hi, c1_lo, c1_hi, - b0, b1, b2, b3) in split_16byte.findall(data): - color0 = ord(c0_lo) | ord(c0_hi) << 8 - color1 = ord(c1_lo) | ord(c1_hi) << 8 - bits = ord(b0) | ord(b1) << 8 | ord(b2) << 16 | ord(b3) << 24 - alpha = ord(a0) | ord(a1) << 8 | ord(a2) << 16 | ord(a3) << 24 | \ - ord(a4) << 32 | ord(a5) << 40 | ord(a6) << 48 | ord(a7) << 56 - - r0 = color0 & 0x1f - g0 = (color0 & 0x7e0) >> 5 - b0 = (color0 & 0xf800) >> 11 - r1 = color1 & 0x1f - g1 = (color1 & 0x7e0) >> 5 - b1 = (color1 & 0xf800) >> 11 - - # i is the dest ptr for this block - i = image_offset - for y in range(4): - for x in range(4): - code = bits & 0x3 - a = alpha & 0xf - - if code == 0: - r, g, b = r0, g0, b0 - elif code == 1: - r, g, b = r1, g1, b1 - elif code == 3 and color0 <= color1: - r = g = b = 0 - else: - if code == 2 and color0 > color1: - r = (2 * r0 + r1) // 3 - g = (2 * g0 + g1) // 3 - b = (2 * b0 + b1) // 3 - elif code == 3 and color0 > color1: - r = (r0 + 2 * r1) // 3 - g = (g0 + 2 * g1) // 3 - b = (b0 + 2 * b1) // 3 - else: - assert code == 2 and color0 <= color1 - r = (r0 + r1) // 2 - g = (g0 + g1) // 2 - b = (b0 + b1) // 2 - - out[i] = b << 3 - out[i + 1] = g << 2 - out[i + 2] = r << 3 - out[i + 3] = a << 4 - - bits >>= 2 - alpha >>= 4 - i += 4 - i += pitch - 16 - - # Move dest ptr to next 4x4 block - advance_row = (image_offset + 16) % pitch == 0 - image_offset += pitch * 3 * advance_row + 16 - - return PackedImageData(width, height, GL_RGBA, GL_UNSIGNED_BYTE, out) - - -def decode_dxt5(data, width, height): - # Decode to GL_RGBA - out = (ctypes.c_ubyte * (width * height * 4))() - pitch = width << 2 - - # Read 16 bytes at a time - image_offset = 0 - for (alpha0, alpha1, ab0, ab1, ab2, ab3, ab4, ab5, - c0_lo, c0_hi, c1_lo, c1_hi, - b0, b1, b2, b3) in split_16byte.findall(data): - color0 = ord(c0_lo) | ord(c0_hi) << 8 - color1 = ord(c1_lo) | ord(c1_hi) << 8 - alpha0 = ord(alpha0) - alpha1 = ord(alpha1) - bits = ord(b0) | ord(b1) << 8 | ord(b2) << 16 | ord(b3) << 24 - abits = ord(ab0) | ord(ab1) << 8 | ord(ab2) << 16 | ord(ab3) << 24 | \ - ord(ab4) << 32 | ord(ab5) << 40 - - r0 = color0 & 0x1f - g0 = (color0 & 0x7e0) >> 5 - b0 = (color0 & 0xf800) >> 11 - r1 = color1 & 0x1f - g1 = (color1 & 0x7e0) >> 5 - b1 = (color1 & 0xf800) >> 11 - - # i is the dest ptr for this block - i = image_offset - for y in range(4): - for x in range(4): - code = bits & 0x3 - acode = abits & 0x7 - - if code == 0: - r, g, b = r0, g0, b0 - elif code == 1: - r, g, b = r1, g1, b1 - elif code == 3 and color0 <= color1: - r = g = b = 0 - else: - if code == 2 and color0 > color1: - r = (2 * r0 + r1) // 3 - g = (2 * g0 + g1) // 3 - b = (2 * b0 + b1) // 3 - elif code == 3 and color0 > color1: - r = (r0 + 2 * r1) // 3 - g = (g0 + 2 * g1) // 3 - b = (b0 + 2 * b1) // 3 - else: - assert code == 2 and color0 <= color1 - r = (r0 + r1) / 2 - g = (g0 + g1) / 2 - b = (b0 + b1) / 2 - - if acode == 0: - a = alpha0 - elif acode == 1: - a = alpha1 - elif alpha0 > alpha1: - if acode == 2: - a = (6 * alpha0 + 1 * alpha1) // 7 - elif acode == 3: - a = (5 * alpha0 + 2 * alpha1) // 7 - elif acode == 4: - a = (4 * alpha0 + 3 * alpha1) // 7 - elif acode == 5: - a = (3 * alpha0 + 4 * alpha1) // 7 - elif acode == 6: - a = (2 * alpha0 + 5 * alpha1) // 7 - else: - assert acode == 7 - a = (1 * alpha0 + 6 * alpha1) // 7 - else: - if acode == 2: - a = (4 * alpha0 + 1 * alpha1) // 5 - elif acode == 3: - a = (3 * alpha0 + 2 * alpha1) // 5 - elif acode == 4: - a = (2 * alpha0 + 3 * alpha1) // 5 - elif acode == 5: - a = (1 * alpha0 + 4 * alpha1) // 5 - elif acode == 6: - a = 0 - else: - assert acode == 7 - a = 255 - - out[i] = b << 3 - out[i + 1] = g << 2 - out[i + 2] = r << 3 - out[i + 3] = a - - bits >>= 2 - abits >>= 3 - i += 4 - i += pitch - 16 - - # Move dest ptr to next 4x4 block - advance_row = (image_offset + 16) % pitch == 0 - image_offset += pitch * 3 * advance_row + 16 - - return PackedImageData(width, height, GL_RGBA, GL_UNSIGNED_BYTE, out) diff --git a/spaces/adirik/ChangeIt/share_btn.py b/spaces/adirik/ChangeIt/share_btn.py deleted file mode 100644 index 5bce98ad54d491f9d5691fea427efeccc77690cc..0000000000000000000000000000000000000000 --- a/spaces/adirik/ChangeIt/share_btn.py +++ /dev/null @@ -1,93 +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(imgCanvas){ - const blob = await new Promise(resolve => imgCanvas.toBlob(resolve)); - const imgId = Date.now() % 200; - const fileName = `sd-inpainting-${{imgId}}.png`; - return new File([blob], fileName, { type: 'image/png' }); - } - - async function getOutoutImgFile(imgEl){ - const res = await fetch(imgEl.src); - const blob = await res.blob(); - const imgId = Date.now() % 200; - const fileName = `sd-inpainting-${{imgId}}.png`; - return new File([blob], fileName, { type: 'image/png' }); - } - - const gradioEl = document.querySelector('body > gradio-app'); - // const gradioEl = document.querySelector("gradio-app").shadowRoot; - const inputImgCanvas = gradioEl.querySelector('canvas[key="drawing"]'); - const outputImgEl = gradioEl.querySelector('#output-img img'); - const promptTxt = gradioEl.querySelector('#input-text textarea').value; - let titleTxt = promptTxt; - 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(!outputImgEl){ - return; - }; - - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - - const inputImgFile = await getInputImgFile(inputImgCanvas); - const outputImgFile = await getOutoutImgFile(outputImgEl); - const files = [inputImgFile, outputImgFile]; - - const urls = await Promise.all(files.map((f) => uploadFile(f))); - - const htmlImgs = urls.map(url => ``); - const [inputImgUrl, outputImgUrl] = htmlImgs; - - const descriptionMd = `
    -
    -${inputImgUrl} - -${promptTxt} -
    -
    -${outputImgUrl} -
    -
    `; - - const params = new URLSearchParams({ - title: titleTxt, - description: descriptionMd, - }); - - const paramsStr = params.toString(); - window.open(`${window.location.href}/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/akhaliq/CarperAI-diff-codegen-350m-v2/app.py b/spaces/akhaliq/CarperAI-diff-codegen-350m-v2/app.py deleted file mode 100644 index 6096cba3aaadb39a36c8500279d864288eefd91a..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/CarperAI-diff-codegen-350m-v2/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/CarperAI/diff-codegen-350m-v2").launch() \ No newline at end of file diff --git a/spaces/akhaliq/Real-Time-Voice-Cloning/synthesizer/utils/__init__.py b/spaces/akhaliq/Real-Time-Voice-Cloning/synthesizer/utils/__init__.py deleted file mode 100644 index 5ae3e48110e61231acf1e666e5fa76af5e4ebdcd..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/Real-Time-Voice-Cloning/synthesizer/utils/__init__.py +++ /dev/null @@ -1,45 +0,0 @@ -import torch - - -_output_ref = None -_replicas_ref = None - -def data_parallel_workaround(model, *input): - global _output_ref - global _replicas_ref - device_ids = list(range(torch.cuda.device_count())) - output_device = device_ids[0] - replicas = torch.nn.parallel.replicate(model, device_ids) - # input.shape = (num_args, batch, ...) - inputs = torch.nn.parallel.scatter(input, device_ids) - # inputs.shape = (num_gpus, num_args, batch/num_gpus, ...) - replicas = replicas[:len(inputs)] - outputs = torch.nn.parallel.parallel_apply(replicas, inputs) - y_hat = torch.nn.parallel.gather(outputs, output_device) - _output_ref = outputs - _replicas_ref = replicas - return y_hat - - -class ValueWindow(): - def __init__(self, window_size=100): - self._window_size = window_size - self._values = [] - - def append(self, x): - self._values = self._values[-(self._window_size - 1):] + [x] - - @property - def sum(self): - return sum(self._values) - - @property - def count(self): - return len(self._values) - - @property - def average(self): - return self.sum / max(1, self.count) - - def reset(self): - self._values = [] diff --git a/spaces/akhaliq/deeplab2/utils/create_images_json_for_cityscapes.py b/spaces/akhaliq/deeplab2/utils/create_images_json_for_cityscapes.py deleted file mode 100644 index 666d4c2abdc1b46c90f641cd1c709ccb8d14d61d..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/deeplab2/utils/create_images_json_for_cityscapes.py +++ /dev/null @@ -1,117 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Deeplab2 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. - -# Lint as: python2, python3 -# pylint: disable=line-too-long -# pyformat: disable -r"""Creates a JSON file with info for a split of Cityscapes images. - -This single-purpose version has special handling for the directory structure of -CityScapes dataset and the expected output ids. - -Sample commands: - -python create_images_json_for_cityscapes.py \ - --image_dir=${DATA_ROOT}/leftImg8bit/${IMAGES_SPLIT} \ - --output_json_path=${PATH_TO_SAVE}/${IMAGES_SPLIT}_images.json \ - --only_basename \ - --include_image_type_suffix=false -""" -# pyformat: enable -# pylint: enable=line-too-long - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import json -import os -import re - -from absl import app -from absl import flags - -import tensorflow as tf - -FLAGS = flags.FLAGS - -flags.DEFINE_string( - 'image_dir', None, - 'The top-level directory of image files to be included in the set.') - -flags.DEFINE_list( - 'keep_cities', None, - 'Comma-separated list of strings specifying cities to be processed.') - -flags.DEFINE_string('output_json_path', None, - 'Output path to which is written the image info JSON.') - -flags.DEFINE_boolean( - 'only_basename', True, - 'If set, the included "file_name" properties of the images in the JSON ' - 'file will only include the base name and not the city directory. Used for ' - 'tools that do not support nested directories.') - -flags.DEFINE_boolean( - 'include_image_type_suffix', True, - 'If set, will include the suffix of the image type (e.g. "_leftImg8bit") ' - 'in the "file_name" properties of the image.') - - -def _create_images_json(image_dir, output_json_path, only_basename=False, - include_image_type_suffix=True, keep_cities=None): - """Lists the images in image_dir and writes out the info JSON for them.""" - images_info_array = [] - for city_dir in tf.io.gfile.listdir(image_dir): - if keep_cities and city_dir not in keep_cities: - continue - image_id_re = r'%s_[0-9]+_[0-9]+' % city_dir - image_id_re = re.compile(image_id_re) - for image_basename in tf.io.gfile.listdir( - os.path.join(image_dir, city_dir)): - match = image_id_re.match(image_basename) - image_id = image_basename[match.start():match.end()] - if include_image_type_suffix: - file_name = image_basename - else: - file_name = image_id + os.path.splitext(image_basename)[1] - if not only_basename: - file_name = os.path.join(city_dir, file_name) - image_info_dict = {'id': image_id, 'file_name': file_name} - images_info_array.append(image_info_dict) - - info_dict = {'images': images_info_array} - - with tf.io.gfile.GFile(output_json_path, 'w+') as json_file: - json.dump(info_dict, json_file) - - -def main(argv): - if len(argv) > 1: - raise app.UsageError('Too many command-line arguments.') - keep_cities = None - if FLAGS.keep_cities: - keep_cities = [str(x) for x in FLAGS.keep_cities] - _create_images_json( - FLAGS.image_dir, - FLAGS.output_json_path, - only_basename=FLAGS.only_basename, - include_image_type_suffix=FLAGS.include_image_type_suffix, - keep_cities=keep_cities) - - -if __name__ == '__main__': - flags.mark_flags_as_required(['image_dir', 'output_json_path']) - app.run(main) diff --git a/spaces/akhaliq/lama/bin/report_from_tb.py b/spaces/akhaliq/lama/bin/report_from_tb.py deleted file mode 100644 index 9a444e6cd8027f88bd34adfc0b1dd000bbb4b2be..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/lama/bin/report_from_tb.py +++ /dev/null @@ -1,83 +0,0 @@ -#!/usr/bin/env python3 - -import glob -import os -import re - -import tensorflow as tf -from torch.utils.tensorboard import SummaryWriter - - -GROUPING_RULES = [ - re.compile(r'^(?Ptrain|test|val|extra_val_.*?(256|512))_(?P.*)', re.I) -] - - -DROP_RULES = [ - re.compile(r'_std$', re.I) -] - - -def need_drop(tag): - for rule in DROP_RULES: - if rule.search(tag): - return True - return False - - -def get_group_and_title(tag): - for rule in GROUPING_RULES: - match = rule.search(tag) - if match is None: - continue - return match.group('group'), match.group('title') - return None, None - - -def main(args): - os.makedirs(args.outdir, exist_ok=True) - - ignored_events = set() - - for orig_fname in glob.glob(args.inglob): - cur_dirpath = os.path.dirname(orig_fname) # remove filename, this should point to "version_0" directory - subdirname = os.path.basename(cur_dirpath) # == "version_0" most of time - exp_root_path = os.path.dirname(cur_dirpath) # remove "version_0" - exp_name = os.path.basename(exp_root_path) - - writers_by_group = {} - - for e in tf.compat.v1.train.summary_iterator(orig_fname): - for v in e.summary.value: - if need_drop(v.tag): - continue - - cur_group, cur_title = get_group_and_title(v.tag) - if cur_group is None: - if v.tag not in ignored_events: - print(f'WARNING: Could not detect group for {v.tag}, ignoring it') - ignored_events.add(v.tag) - continue - - cur_writer = writers_by_group.get(cur_group, None) - if cur_writer is None: - if args.include_version: - cur_outdir = os.path.join(args.outdir, exp_name, f'{subdirname}_{cur_group}') - else: - cur_outdir = os.path.join(args.outdir, exp_name, cur_group) - cur_writer = SummaryWriter(cur_outdir) - writers_by_group[cur_group] = cur_writer - - cur_writer.add_scalar(cur_title, v.simple_value, global_step=e.step, walltime=e.wall_time) - - -if __name__ == '__main__': - import argparse - - aparser = argparse.ArgumentParser() - aparser.add_argument('inglob', type=str) - aparser.add_argument('outdir', type=str) - aparser.add_argument('--include-version', action='store_true', - help='Include subdirectory name e.g. "version_0" into output path') - - main(aparser.parse_args()) diff --git a/spaces/aliabd/blocks-image-audio/app.py b/spaces/aliabd/blocks-image-audio/app.py deleted file mode 100644 index 9fadfc9a0e3e312db936edb4cac933f090560d86..0000000000000000000000000000000000000000 --- a/spaces/aliabd/blocks-image-audio/app.py +++ /dev/null @@ -1,35 +0,0 @@ -import gradio as gr - -fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech") -clip = gr.Interface.load("spaces/DrishtiSharma/Text-to-Image-search-using-CLIP") - - -def text2speech(text): - return fastspeech(text) - - -def text2image(text): - image = clip(text)[0] - return gr.processing_utils.decode_base64_to_image(image) - - -block = gr.Blocks() - - - -with block: - text = gr.inputs.Textbox(placeholder="Try writing something..") - - with gr.Column(): - with gr.Row(): - get_audio = gr.Button("generate audio") - get_image = gr.Button("generate image") - with gr.Row(): - speech = gr.outputs.Audio() - image = gr.outputs.Image() - - - get_audio.click(text2speech, inputs=text, outputs=speech) - get_image.click(text2image, inputs=text, outputs=image) - -block.launch() \ No newline at end of file diff --git a/spaces/aliabid94/AutoGPT/autogpt/app.py b/spaces/aliabid94/AutoGPT/autogpt/app.py deleted file mode 100644 index 58d9f7164ddfbb5019b072d789dc2fa6205dc9d3..0000000000000000000000000000000000000000 --- a/spaces/aliabid94/AutoGPT/autogpt/app.py +++ /dev/null @@ -1,330 +0,0 @@ -""" Command and Control """ -import json -from typing import Dict, List, NoReturn, Union - -from autogpt.agent.agent_manager import AgentManager -from autogpt.commands.analyze_code import analyze_code -from autogpt.commands.audio_text import read_audio_from_file -from autogpt.commands.execute_code import ( - execute_python_file, - execute_shell, - execute_shell_popen, -) -from autogpt.commands.file_operations import ( - append_to_file, - delete_file, - download_file, - read_file, - search_files, - write_to_file, -) -from autogpt.commands.git_operations import clone_repository -from autogpt.commands.google_search import google_official_search, google_search -from autogpt.commands.image_gen import generate_image -from autogpt.commands.improve_code import improve_code -from autogpt.commands.twitter import send_tweet -from autogpt.commands.web_requests import scrape_links, scrape_text -from autogpt.commands.web_selenium import browse_website -from autogpt.commands.write_tests import write_tests -from autogpt.config import Config -from autogpt.json_utils.json_fix_llm import fix_and_parse_json -from autogpt.memory import get_memory -from autogpt.processing.text import summarize_text -from autogpt.speech import say_text - -CFG = Config() -AGENT_MANAGER = AgentManager() - - -def is_valid_int(value: str) -> bool: - """Check if the value is a valid integer - - Args: - value (str): The value to check - - Returns: - bool: True if the value is a valid integer, False otherwise - """ - try: - int(value) - return True - except ValueError: - return False - - -def get_command(response_json: Dict): - """Parse the response and return the command name and arguments - - Args: - response_json (json): The response from the AI - - Returns: - tuple: The command name and arguments - - Raises: - json.decoder.JSONDecodeError: If the response is not valid JSON - - Exception: If any other error occurs - """ - try: - if "command" not in response_json: - return "Error:", "Missing 'command' object in JSON" - - if not isinstance(response_json, dict): - return "Error:", f"'response_json' object is not dictionary {response_json}" - - command = response_json["command"] - if not isinstance(command, dict): - return "Error:", "'command' object is not a dictionary" - - if "name" not in command: - return "Error:", "Missing 'name' field in 'command' object" - - command_name = command["name"] - - # Use an empty dictionary if 'args' field is not present in 'command' object - arguments = command.get("args", {}) - - return command_name, arguments - except json.decoder.JSONDecodeError: - return "Error:", "Invalid JSON" - # All other errors, return "Error: + error message" - except Exception as e: - return "Error:", str(e) - - -def map_command_synonyms(command_name: str): - """Takes the original command name given by the AI, and checks if the - string matches a list of common/known hallucinations - """ - synonyms = [ - ("write_file", "write_to_file"), - ("create_file", "write_to_file"), - ("search", "google"), - ] - for seen_command, actual_command_name in synonyms: - if command_name == seen_command: - return actual_command_name - return command_name - - -def execute_command(command_name: str, arguments): - """Execute the command and return the result - - Args: - command_name (str): The name of the command to execute - arguments (dict): The arguments for the command - - Returns: - str: The result of the command - """ - try: - command_name = map_command_synonyms(command_name.lower()) - if command_name == "google": - # Check if the Google API key is set and use the official search method - # If the API key is not set or has only whitespaces, use the unofficial - # search method - key = CFG.google_api_key - if key and key.strip() and key != "your-google-api-key": - google_result = google_official_search(arguments["input"]) - return google_result - else: - google_result = google_search(arguments["input"]) - - # google_result can be a list or a string depending on the search results - if isinstance(google_result, list): - safe_message = [ - google_result_single.encode("utf-8", "ignore") - for google_result_single in google_result - ] - else: - safe_message = google_result.encode("utf-8", "ignore") - - return safe_message.decode("utf-8") - elif command_name == "memory_add": - memory = get_memory(CFG) - return memory.add(arguments["string"]) - elif command_name == "start_agent": - return start_agent( - arguments["name"], arguments["task"], arguments["prompt"] - ) - elif command_name == "message_agent": - return message_agent(arguments["key"], arguments["message"]) - elif command_name == "list_agents": - return list_agents() - elif command_name == "delete_agent": - return delete_agent(arguments["key"]) - elif command_name == "get_text_summary": - return get_text_summary(arguments["url"], arguments["question"]) - elif command_name == "get_hyperlinks": - return get_hyperlinks(arguments["url"]) - elif command_name == "clone_repository": - return clone_repository( - arguments["repository_url"], arguments["clone_path"] - ) - elif command_name == "read_file": - return read_file(arguments["file"]) - elif command_name == "write_to_file": - return write_to_file(arguments["file"], arguments["text"]) - elif command_name == "append_to_file": - return append_to_file(arguments["file"], arguments["text"]) - elif command_name == "delete_file": - return delete_file(arguments["file"]) - elif command_name == "search_files": - return search_files(arguments["directory"]) - elif command_name == "download_file": - if not CFG.allow_downloads: - return "Error: You do not have user authorization to download files locally." - return download_file(arguments["url"], arguments["file"]) - elif command_name == "browse_website": - return browse_website(arguments["url"], arguments["question"]) - # TODO: Change these to take in a file rather than pasted code, if - # non-file is given, return instructions "Input should be a python - # filepath, write your code to file and try again" - elif command_name == "analyze_code": - return analyze_code(arguments["code"]) - elif command_name == "improve_code": - return improve_code(arguments["suggestions"], arguments["code"]) - elif command_name == "write_tests": - return write_tests(arguments["code"], arguments.get("focus")) - elif command_name == "execute_python_file": # Add this command - return execute_python_file(arguments["file"]) - elif command_name == "execute_shell": - if CFG.execute_local_commands: - return execute_shell(arguments["command_line"]) - else: - return ( - "You are not allowed to run local shell commands. To execute" - " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' " - "in your config. Do not attempt to bypass the restriction." - ) - elif command_name == "execute_shell_popen": - if CFG.execute_local_commands: - return execute_shell_popen(arguments["command_line"]) - else: - return ( - "You are not allowed to run local shell commands. To execute" - " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' " - "in your config. Do not attempt to bypass the restriction." - ) - elif command_name == "read_audio_from_file": - return read_audio_from_file(arguments["file"]) - elif command_name == "generate_image": - return generate_image(arguments["prompt"]) - elif command_name == "send_tweet": - return send_tweet(arguments["text"]) - elif command_name == "do_nothing": - return "No action performed." - elif command_name == "task_complete": - shutdown() - else: - return ( - f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'" - " list for available commands and only respond in the specified JSON" - " format." - ) - except Exception as e: - return f"Error: {str(e)}" - - -def get_text_summary(url: str, question: str) -> str: - """Return the results of a Google search - - Args: - url (str): The url to scrape - question (str): The question to summarize the text for - - Returns: - str: The summary of the text - """ - text = scrape_text(url) - summary = summarize_text(url, text, question) - return f""" "Result" : {summary}""" - - -def get_hyperlinks(url: str) -> Union[str, List[str]]: - """Return the results of a Google search - - Args: - url (str): The url to scrape - - Returns: - str or list: The hyperlinks on the page - """ - return scrape_links(url) - - -def shutdown() -> NoReturn: - """Shut down the program""" - print("Shutting down...") - quit() - - -def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str: - """Start an agent with a given name, task, and prompt - - Args: - name (str): The name of the agent - task (str): The task of the agent - prompt (str): The prompt for the agent - model (str): The model to use for the agent - - Returns: - str: The response of the agent - """ - # Remove underscores from name - voice_name = name.replace("_", " ") - - first_message = f"""You are {name}. Respond with: "Acknowledged".""" - agent_intro = f"{voice_name} here, Reporting for duty!" - - # Create agent - if CFG.speak_mode: - say_text(agent_intro, 1) - key, ack = AGENT_MANAGER.create_agent(task, first_message, model) - - if CFG.speak_mode: - say_text(f"Hello {voice_name}. Your task is as follows. {task}.") - - # Assign task (prompt), get response - agent_response = AGENT_MANAGER.message_agent(key, prompt) - - return f"Agent {name} created with key {key}. First response: {agent_response}" - - -def message_agent(key: str, message: str) -> str: - """Message an agent with a given key and message""" - # Check if the key is a valid integer - if is_valid_int(key): - agent_response = AGENT_MANAGER.message_agent(int(key), message) - else: - return "Invalid key, must be an integer." - - # Speak response - if CFG.speak_mode: - say_text(agent_response, 1) - return agent_response - - -def list_agents(): - """List all agents - - Returns: - str: A list of all agents - """ - return "List of agents:\n" + "\n".join( - [str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()] - ) - - -def delete_agent(key: str) -> str: - """Delete an agent with a given key - - Args: - key (str): The key of the agent to delete - - Returns: - str: A message indicating whether the agent was deleted or not - """ - result = AGENT_MANAGER.delete_agent(key) - return f"Agent {key} deleted." if result else f"Agent {key} does not exist." diff --git a/spaces/aliabid94/AutoGPT/autogpt/processing/__init__.py b/spaces/aliabid94/AutoGPT/autogpt/processing/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/aliceoq/vozes-da-loirinha/lib/infer_pack/models_dml.py b/spaces/aliceoq/vozes-da-loirinha/lib/infer_pack/models_dml.py deleted file mode 100644 index 958d7b29259763d2fea94caf8ba7e314c4a77d05..0000000000000000000000000000000000000000 --- a/spaces/aliceoq/vozes-da-loirinha/lib/infer_pack/models_dml.py +++ /dev/null @@ -1,1124 +0,0 @@ -import math, pdb, os -from time import time as ttime -import torch -from torch import nn -from torch.nn import functional as F -from lib.infer_pack import modules -from lib.infer_pack import attentions -from lib.infer_pack import commons -from lib.infer_pack.commons import init_weights, get_padding -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from lib.infer_pack.commons import init_weights -import numpy as np -from lib.infer_pack import commons - - -class TextEncoder256(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class TextEncoder768(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(768, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - def remove_weight_norm(self): - for i in range(self.n_flows): - self.flows[i * 2].remove_weight_norm() - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class SineGen(torch.nn.Module): - """Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__( - self, - samp_rate, - harmonic_num=0, - sine_amp=0.1, - noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False, - ): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - - def _f02uv(self, f0): - # generate uv signal - uv = torch.ones_like(f0) - uv = uv * (f0 > self.voiced_threshold) - return uv.float() - - def forward(self, f0, upp): - """sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - with torch.no_grad(): - f0 = f0[:, None].transpose(1, 2) - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) - # fundamental component - f0_buf[:, :, 0] = f0[:, :, 0] - for idx in np.arange(self.harmonic_num): - f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( - idx + 2 - ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic - rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化 - rand_ini = torch.rand( - f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device - ) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化 - tmp_over_one *= upp - tmp_over_one = F.interpolate( - tmp_over_one.transpose(2, 1), - scale_factor=upp, - mode="linear", - align_corners=True, - ).transpose(2, 1) - rad_values = F.interpolate( - rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose( - 2, 1 - ) ####### - tmp_over_one %= 1 - tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - sine_waves = torch.sin( - torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi - ) - sine_waves = sine_waves * self.sine_amp - uv = self._f02uv(f0) - uv = F.interpolate( - uv.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__( - self, - sampling_rate, - harmonic_num=0, - sine_amp=0.1, - add_noise_std=0.003, - voiced_threshod=0, - is_half=True, - ): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - self.is_half = is_half - # to produce sine waveforms - self.l_sin_gen = SineGen( - sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod - ) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x, upp=None): - sine_wavs, uv, _ = self.l_sin_gen(x, upp) - if self.is_half: - sine_wavs = sine_wavs.half() - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - return sine_merge, None, None # noise, uv - - -class GeneratorNSF(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - sr, - is_half=False, - ): - super(GeneratorNSF, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) - self.m_source = SourceModuleHnNSF( - sampling_rate=sr, harmonic_num=0, is_half=is_half - ) - self.noise_convs = nn.ModuleList() - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1 :]) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - self.upp = np.prod(upsample_rates) - - def forward(self, x, f0, g=None): - har_source, noi_source, uv = self.m_source(f0, self.upp) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -sr2sr = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -class SynthesizerTrnMs256NSFsid(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr, - **kwargs - ): - super().__init__() - if type(sr) == type("strr"): - sr = sr2sr[sr] - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - sr=sr, - is_half=kwargs["is_half"], - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward( - self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds - ): # 这里ds是id，[bs,1] - # print(1,pitch.shape)#[bs,t] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - # print(-2,pitchf.shape,z_slice.shape) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs768NSFsid(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr, - **kwargs - ): - super().__init__() - if type(sr) == type("strr"): - sr = sr2sr[sr] - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder768( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - sr=sr, - is_half=kwargs["is_half"], - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward( - self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds - ): # 这里ds是id，[bs,1] - # print(1,pitch.shape)#[bs,t] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - # print(-2,pitchf.shape,z_slice.shape) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs256NSFsid_nono(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr=None, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=False, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - o = self.dec(z_slice, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs768NSFsid_nono(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr=None, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder768( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=False, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - o = self.dec(z_slice, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11, 17] - # periods = [3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class MultiPeriodDiscriminatorV2(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminatorV2, self).__init__() - # periods = [2, 3, 5, 7, 11, 17] - periods = [2, 3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap diff --git a/spaces/allandclive/Uganda_MMS/uroman/lib/JSON/backportPP.pm b/spaces/allandclive/Uganda_MMS/uroman/lib/JSON/backportPP.pm deleted file mode 100644 index db4f8bbb3b741e95c5817edde612718af0f889e4..0000000000000000000000000000000000000000 --- a/spaces/allandclive/Uganda_MMS/uroman/lib/JSON/backportPP.pm +++ /dev/null @@ -1,2806 +0,0 @@ -package # This is JSON::backportPP - JSON::PP; - -# JSON-2.0 - -use 5.005; -use strict; -use base qw(Exporter); -use overload (); - -use Carp (); -use B (); -#use Devel::Peek; - -use vars qw($VERSION); -$VERSION = '2.27204'; - -@JSON::PP::EXPORT = qw(encode_json decode_json from_json to_json); - -# instead of hash-access, i tried index-access for speed. -# but this method is not faster than what i expected. so it will be changed. - -use constant P_ASCII => 0; -use constant P_LATIN1 => 1; -use constant P_UTF8 => 2; -use constant P_INDENT => 3; -use constant P_CANONICAL => 4; -use constant P_SPACE_BEFORE => 5; -use constant P_SPACE_AFTER => 6; -use constant P_ALLOW_NONREF => 7; -use constant P_SHRINK => 8; -use constant P_ALLOW_BLESSED => 9; -use constant P_CONVERT_BLESSED => 10; -use constant P_RELAXED => 11; - -use constant P_LOOSE => 12; -use constant P_ALLOW_BIGNUM => 13; -use constant P_ALLOW_BAREKEY => 14; -use constant P_ALLOW_SINGLEQUOTE => 15; -use constant P_ESCAPE_SLASH => 16; -use constant P_AS_NONBLESSED => 17; - -use constant P_ALLOW_UNKNOWN => 18; - -use constant OLD_PERL => $] < 5.008 ? 1 : 0; - -BEGIN { - my @xs_compati_bit_properties = qw( - latin1 ascii utf8 indent canonical space_before space_after allow_nonref shrink - allow_blessed convert_blessed relaxed allow_unknown - ); - my @pp_bit_properties = qw( - allow_singlequote allow_bignum loose - allow_barekey escape_slash as_nonblessed - ); - - # Perl version check, Unicode handling is enable? - # Helper module sets @JSON::PP::_properties. - if ($] < 5.008 ) { - my $helper = $] >= 5.006 ? 'JSON::backportPP::Compat5006' : 'JSON::backportPP::Compat5005'; - eval qq| require $helper |; - if ($@) { Carp::croak $@; } - } - - for my $name (@xs_compati_bit_properties, @pp_bit_properties) { - my $flag_name = 'P_' . uc($name); - - eval qq/ - sub $name { - my \$enable = defined \$_[1] ? \$_[1] : 1; - - if (\$enable) { - \$_[0]->{PROPS}->[$flag_name] = 1; - } - else { - \$_[0]->{PROPS}->[$flag_name] = 0; - } - - \$_[0]; - } - - sub get_$name { - \$_[0]->{PROPS}->[$flag_name] ? 1 : ''; - } - /; - } - -} - - - -# Functions - -my %encode_allow_method - = map {($_ => 1)} qw/utf8 pretty allow_nonref latin1 self_encode escape_slash - allow_blessed convert_blessed indent indent_length allow_bignum - as_nonblessed - /; -my %decode_allow_method - = map {($_ => 1)} qw/utf8 allow_nonref loose allow_singlequote allow_bignum - allow_barekey max_size relaxed/; - - -my $JSON; # cache - -sub encode_json ($) { # encode - ($JSON ||= __PACKAGE__->new->utf8)->encode(@_); -} - - -sub decode_json { # decode - ($JSON ||= __PACKAGE__->new->utf8)->decode(@_); -} - -# Obsoleted - -sub to_json($) { - Carp::croak ("JSON::PP::to_json has been renamed to encode_json."); -} - - -sub from_json($) { - Carp::croak ("JSON::PP::from_json has been renamed to decode_json."); -} - - -# Methods - -sub new { - my $class = shift; - my $self = { - max_depth => 512, - max_size => 0, - indent => 0, - FLAGS => 0, - fallback => sub { encode_error('Invalid value. JSON can only reference.') }, - indent_length => 3, - }; - - bless $self, $class; -} - - -sub encode { - return $_[0]->PP_encode_json($_[1]); -} - - -sub decode { - return $_[0]->PP_decode_json($_[1], 0x00000000); -} - - -sub decode_prefix { - return $_[0]->PP_decode_json($_[1], 0x00000001); -} - - -# accessor - - -# pretty printing - -sub pretty { - my ($self, $v) = @_; - my $enable = defined $v ? $v : 1; - - if ($enable) { # indent_length(3) for JSON::XS compatibility - $self->indent(1)->indent_length(3)->space_before(1)->space_after(1); - } - else { - $self->indent(0)->space_before(0)->space_after(0); - } - - $self; -} - -# etc - -sub max_depth { - my $max = defined $_[1] ? $_[1] : 0x80000000; - $_[0]->{max_depth} = $max; - $_[0]; -} - - -sub get_max_depth { $_[0]->{max_depth}; } - - -sub max_size { - my $max = defined $_[1] ? $_[1] : 0; - $_[0]->{max_size} = $max; - $_[0]; -} - - -sub get_max_size { $_[0]->{max_size}; } - - -sub filter_json_object { - $_[0]->{cb_object} = defined $_[1] ? $_[1] : 0; - $_[0]->{F_HOOK} = ($_[0]->{cb_object} or $_[0]->{cb_sk_object}) ? 1 : 0; - $_[0]; -} - -sub filter_json_single_key_object { - if (@_ > 1) { - $_[0]->{cb_sk_object}->{$_[1]} = $_[2]; - } - $_[0]->{F_HOOK} = ($_[0]->{cb_object} or $_[0]->{cb_sk_object}) ? 1 : 0; - $_[0]; -} - -sub indent_length { - if (!defined $_[1] or $_[1] > 15 or $_[1] < 0) { - Carp::carp "The acceptable range of indent_length() is 0 to 15."; - } - else { - $_[0]->{indent_length} = $_[1]; - } - $_[0]; -} - -sub get_indent_length { - $_[0]->{indent_length}; -} - -sub sort_by { - $_[0]->{sort_by} = defined $_[1] ? $_[1] : 1; - $_[0]; -} - -sub allow_bigint { - Carp::carp("allow_bigint() is obsoleted. use allow_bignum() insted."); -} - -############################### - -### -### Perl => JSON -### - - -{ # Convert - - my $max_depth; - my $indent; - my $ascii; - my $latin1; - my $utf8; - my $space_before; - my $space_after; - my $canonical; - my $allow_blessed; - my $convert_blessed; - - my $indent_length; - my $escape_slash; - my $bignum; - my $as_nonblessed; - - my $depth; - my $indent_count; - my $keysort; - - - sub PP_encode_json { - my $self = shift; - my $obj = shift; - - $indent_count = 0; - $depth = 0; - - my $idx = $self->{PROPS}; - - ($ascii, $latin1, $utf8, $indent, $canonical, $space_before, $space_after, $allow_blessed, - $convert_blessed, $escape_slash, $bignum, $as_nonblessed) - = @{$idx}[P_ASCII .. P_SPACE_AFTER, P_ALLOW_BLESSED, P_CONVERT_BLESSED, - P_ESCAPE_SLASH, P_ALLOW_BIGNUM, P_AS_NONBLESSED]; - - ($max_depth, $indent_length) = @{$self}{qw/max_depth indent_length/}; - - $keysort = $canonical ? sub { $a cmp $b } : undef; - - if ($self->{sort_by}) { - $keysort = ref($self->{sort_by}) eq 'CODE' ? $self->{sort_by} - : $self->{sort_by} =~ /\D+/ ? $self->{sort_by} - : sub { $a cmp $b }; - } - - encode_error("hash- or arrayref expected (not a simple scalar, use allow_nonref to allow this)") - if(!ref $obj and !$idx->[ P_ALLOW_NONREF ]); - - my $str = $self->object_to_json($obj); - - $str .= "\n" if ( $indent ); # JSON::XS 2.26 compatible - - unless ($ascii or $latin1 or $utf8) { - utf8::upgrade($str); - } - - if ($idx->[ P_SHRINK ]) { - utf8::downgrade($str, 1); - } - - return $str; - } - - - sub object_to_json { - my ($self, $obj) = @_; - my $type = ref($obj); - - if($type eq 'HASH'){ - return $self->hash_to_json($obj); - } - elsif($type eq 'ARRAY'){ - return $self->array_to_json($obj); - } - elsif ($type) { # blessed object? - if (blessed($obj)) { - - return $self->value_to_json($obj) if ( $obj->isa('JSON::PP::Boolean') ); - - if ( $convert_blessed and $obj->can('TO_JSON') ) { - my $result = $obj->TO_JSON(); - if ( defined $result and ref( $result ) ) { - if ( refaddr( $obj ) eq refaddr( $result ) ) { - encode_error( sprintf( - "%s::TO_JSON method returned same object as was passed instead of a new one", - ref $obj - ) ); - } - } - - return $self->object_to_json( $result ); - } - - return "$obj" if ( $bignum and _is_bignum($obj) ); - return $self->blessed_to_json($obj) if ($allow_blessed and $as_nonblessed); # will be removed. - - encode_error( sprintf("encountered object '%s', but neither allow_blessed " - . "nor convert_blessed settings are enabled", $obj) - ) unless ($allow_blessed); - - return 'null'; - } - else { - return $self->value_to_json($obj); - } - } - else{ - return $self->value_to_json($obj); - } - } - - - sub hash_to_json { - my ($self, $obj) = @_; - my @res; - - encode_error("json text or perl structure exceeds maximum nesting level (max_depth set too low?)") - if (++$depth > $max_depth); - - my ($pre, $post) = $indent ? $self->_up_indent() : ('', ''); - my $del = ($space_before ? ' ' : '') . ':' . ($space_after ? ' ' : ''); - - for my $k ( _sort( $obj ) ) { - if ( OLD_PERL ) { utf8::decode($k) } # key for Perl 5.6 / be optimized - push @res, string_to_json( $self, $k ) - . $del - . ( $self->object_to_json( $obj->{$k} ) || $self->value_to_json( $obj->{$k} ) ); - } - - --$depth; - $self->_down_indent() if ($indent); - - return '{' . ( @res ? $pre : '' ) . ( @res ? join( ",$pre", @res ) . $post : '' ) . '}'; - } - - - sub array_to_json { - my ($self, $obj) = @_; - my @res; - - encode_error("json text or perl structure exceeds maximum nesting level (max_depth set too low?)") - if (++$depth > $max_depth); - - my ($pre, $post) = $indent ? $self->_up_indent() : ('', ''); - - for my $v (@$obj){ - push @res, $self->object_to_json($v) || $self->value_to_json($v); - } - - --$depth; - $self->_down_indent() if ($indent); - - return '[' . ( @res ? $pre : '' ) . ( @res ? join( ",$pre", @res ) . $post : '' ) . ']'; - } - - - sub value_to_json { - my ($self, $value) = @_; - - return 'null' if(!defined $value); - - my $b_obj = B::svref_2object(\$value); # for round trip problem - my $flags = $b_obj->FLAGS; - - return $value # as is - if $flags & ( B::SVp_IOK | B::SVp_NOK ) and !( $flags & B::SVp_POK ); # SvTYPE is IV or NV? - - my $type = ref($value); - - if(!$type){ - return string_to_json($self, $value); - } - elsif( blessed($value) and $value->isa('JSON::PP::Boolean') ){ - return $$value == 1 ? 'true' : 'false'; - } - elsif ($type) { - if ((overload::StrVal($value) =~ /=(\w+)/)[0]) { - return $self->value_to_json("$value"); - } - - if ($type eq 'SCALAR' and defined $$value) { - return $$value eq '1' ? 'true' - : $$value eq '0' ? 'false' - : $self->{PROPS}->[ P_ALLOW_UNKNOWN ] ? 'null' - : encode_error("cannot encode reference to scalar"); - } - - if ( $self->{PROPS}->[ P_ALLOW_UNKNOWN ] ) { - return 'null'; - } - else { - if ( $type eq 'SCALAR' or $type eq 'REF' ) { - encode_error("cannot encode reference to scalar"); - } - else { - encode_error("encountered $value, but JSON can only represent references to arrays or hashes"); - } - } - - } - else { - return $self->{fallback}->($value) - if ($self->{fallback} and ref($self->{fallback}) eq 'CODE'); - return 'null'; - } - - } - - - my %esc = ( - "\n" => '\n', - "\r" => '\r', - "\t" => '\t', - "\f" => '\f', - "\b" => '\b', - "\"" => '\"', - "\\" => '\\\\', - "\'" => '\\\'', - ); - - - sub string_to_json { - my ($self, $arg) = @_; - - $arg =~ s/([\x22\x5c\n\r\t\f\b])/$esc{$1}/g; - $arg =~ s/\//\\\//g if ($escape_slash); - $arg =~ s/([\x00-\x08\x0b\x0e-\x1f])/'\\u00' . unpack('H2', $1)/eg; - - if ($ascii) { - $arg = JSON_PP_encode_ascii($arg); - } - - if ($latin1) { - $arg = JSON_PP_encode_latin1($arg); - } - - if ($utf8) { - utf8::encode($arg); - } - - return '"' . $arg . '"'; - } - - - sub blessed_to_json { - my $reftype = reftype($_[1]) || ''; - if ($reftype eq 'HASH') { - return $_[0]->hash_to_json($_[1]); - } - elsif ($reftype eq 'ARRAY') { - return $_[0]->array_to_json($_[1]); - } - else { - return 'null'; - } - } - - - sub encode_error { - my $error = shift; - Carp::croak "$error"; - } - - - sub _sort { - defined $keysort ? (sort $keysort (keys %{$_[0]})) : keys %{$_[0]}; - } - - - sub _up_indent { - my $self = shift; - my $space = ' ' x $indent_length; - - my ($pre,$post) = ('',''); - - $post = "\n" . $space x $indent_count; - - $indent_count++; - - $pre = "\n" . $space x $indent_count; - - return ($pre,$post); - } - - - sub _down_indent { $indent_count--; } - - - sub PP_encode_box { - { - depth => $depth, - indent_count => $indent_count, - }; - } - -} # Convert - - -sub _encode_ascii { - join('', - map { - $_ <= 127 ? - chr($_) : - $_ <= 65535 ? - sprintf('\u%04x', $_) : sprintf('\u%x\u%x', _encode_surrogates($_)); - } unpack('U*', $_[0]) - ); -} - - -sub _encode_latin1 { - join('', - map { - $_ <= 255 ? - chr($_) : - $_ <= 65535 ? - sprintf('\u%04x', $_) : sprintf('\u%x\u%x', _encode_surrogates($_)); - } unpack('U*', $_[0]) - ); -} - - -sub _encode_surrogates { # from perlunicode - my $uni = $_[0] - 0x10000; - return ($uni / 0x400 + 0xD800, $uni % 0x400 + 0xDC00); -} - - -sub _is_bignum { - $_[0]->isa('Math::BigInt') or $_[0]->isa('Math::BigFloat'); -} - - - -# -# JSON => Perl -# - -my $max_intsize; - -BEGIN { - my $checkint = 1111; - for my $d (5..64) { - $checkint .= 1; - my $int = eval qq| $checkint |; - if ($int =~ /[eE]/) { - $max_intsize = $d - 1; - last; - } - } -} - -{ # PARSE - - my %escapes = ( # by Jeremy Muhlich <jmuhlich [at] bitflood.org> - b => "\x8", - t => "\x9", - n => "\xA", - f => "\xC", - r => "\xD", - '\\' => '\\', - '"' => '"', - '/' => '/', - ); - - my $text; # json data - my $at; # offset - my $ch; # 1chracter - my $len; # text length (changed according to UTF8 or NON UTF8) - # INTERNAL - my $depth; # nest counter - my $encoding; # json text encoding - my $is_valid_utf8; # temp variable - my $utf8_len; # utf8 byte length - # FLAGS - my $utf8; # must be utf8 - my $max_depth; # max nest number of objects and arrays - my $max_size; - my $relaxed; - my $cb_object; - my $cb_sk_object; - - my $F_HOOK; - - my $allow_bigint; # using Math::BigInt - my $singlequote; # loosely quoting - my $loose; # - my $allow_barekey; # bareKey - - # $opt flag - # 0x00000001 .... decode_prefix - # 0x10000000 .... incr_parse - - sub PP_decode_json { - my ($self, $opt); # $opt is an effective flag during this decode_json. - - ($self, $text, $opt) = @_; - - ($at, $ch, $depth) = (0, '', 0); - - if ( !defined $text or ref $text ) { - decode_error("malformed JSON string, neither array, object, number, string or atom"); - } - - my $idx = $self->{PROPS}; - - ($utf8, $relaxed, $loose, $allow_bigint, $allow_barekey, $singlequote) - = @{$idx}[P_UTF8, P_RELAXED, P_LOOSE .. P_ALLOW_SINGLEQUOTE]; - - if ( $utf8 ) { - utf8::downgrade( $text, 1 ) or Carp::croak("Wide character in subroutine entry"); - } - else { - utf8::upgrade( $text ); - } - - $len = length $text; - - ($max_depth, $max_size, $cb_object, $cb_sk_object, $F_HOOK) - = @{$self}{qw/max_depth max_size cb_object cb_sk_object F_HOOK/}; - - if ($max_size > 1) { - use bytes; - my $bytes = length $text; - decode_error( - sprintf("attempted decode of JSON text of %s bytes size, but max_size is set to %s" - , $bytes, $max_size), 1 - ) if ($bytes > $max_size); - } - - # Currently no effect - # should use regexp - my @octets = unpack('C4', $text); - $encoding = ( $octets[0] and $octets[1]) ? 'UTF-8' - : (!$octets[0] and $octets[1]) ? 'UTF-16BE' - : (!$octets[0] and !$octets[1]) ? 'UTF-32BE' - : ( $octets[2] ) ? 'UTF-16LE' - : (!$octets[2] ) ? 'UTF-32LE' - : 'unknown'; - - white(); # remove head white space - - my $valid_start = defined $ch; # Is there a first character for JSON structure? - - my $result = value(); - - return undef if ( !$result && ( $opt & 0x10000000 ) ); # for incr_parse - - decode_error("malformed JSON string, neither array, object, number, string or atom") unless $valid_start; - - if ( !$idx->[ P_ALLOW_NONREF ] and !ref $result ) { - decode_error( - 'JSON text must be an object or array (but found number, string, true, false or null,' - . ' use allow_nonref to allow this)', 1); - } - - Carp::croak('something wrong.') if $len < $at; # we won't arrive here. - - my $consumed = defined $ch ? $at - 1 : $at; # consumed JSON text length - - white(); # remove tail white space - - if ( $ch ) { - return ( $result, $consumed ) if ($opt & 0x00000001); # all right if decode_prefix - decode_error("garbage after JSON object"); - } - - ( $opt & 0x00000001 ) ? ( $result, $consumed ) : $result; - } - - - sub next_chr { - return $ch = undef if($at >= $len); - $ch = substr($text, $at++, 1); - } - - - sub value { - white(); - return if(!defined $ch); - return object() if($ch eq '{'); - return array() if($ch eq '['); - return string() if($ch eq '"' or ($singlequote and $ch eq "'")); - return number() if($ch =~ /[0-9]/ or $ch eq '-'); - return word(); - } - - sub string { - my ($i, $s, $t, $u); - my $utf16; - my $is_utf8; - - ($is_valid_utf8, $utf8_len) = ('', 0); - - $s = ''; # basically UTF8 flag on - - if($ch eq '"' or ($singlequote and $ch eq "'")){ - my $boundChar = $ch; - - OUTER: while( defined(next_chr()) ){ - - if($ch eq $boundChar){ - next_chr(); - - if ($utf16) { - decode_error("missing low surrogate character in surrogate pair"); - } - - utf8::decode($s) if($is_utf8); - - return $s; - } - elsif($ch eq '\\'){ - next_chr(); - if(exists $escapes{$ch}){ - $s .= $escapes{$ch}; - } - elsif($ch eq 'u'){ # UNICODE handling - my $u = ''; - - for(1..4){ - $ch = next_chr(); - last OUTER if($ch !~ /[0-9a-fA-F]/); - $u .= $ch; - } - - # U+D800 - U+DBFF - if ($u =~ /^[dD][89abAB][0-9a-fA-F]{2}/) { # UTF-16 high surrogate? - $utf16 = $u; - } - # U+DC00 - U+DFFF - elsif ($u =~ /^[dD][c-fC-F][0-9a-fA-F]{2}/) { # UTF-16 low surrogate? - unless (defined $utf16) { - decode_error("missing high surrogate character in surrogate pair"); - } - $is_utf8 = 1; - $s .= JSON_PP_decode_surrogates($utf16, $u) || next; - $utf16 = undef; - } - else { - if (defined $utf16) { - decode_error("surrogate pair expected"); - } - - if ( ( my $hex = hex( $u ) ) > 127 ) { - $is_utf8 = 1; - $s .= JSON_PP_decode_unicode($u) || next; - } - else { - $s .= chr $hex; - } - } - - } - else{ - unless ($loose) { - $at -= 2; - decode_error('illegal backslash escape sequence in string'); - } - $s .= $ch; - } - } - else{ - - if ( ord $ch > 127 ) { - if ( $utf8 ) { - unless( $ch = is_valid_utf8($ch) ) { - $at -= 1; - decode_error("malformed UTF-8 character in JSON string"); - } - else { - $at += $utf8_len - 1; - } - } - else { - utf8::encode( $ch ); - } - - $is_utf8 = 1; - } - - if (!$loose) { - if ($ch =~ /[\x00-\x1f\x22\x5c]/) { # '/' ok - $at--; - decode_error('invalid character encountered while parsing JSON string'); - } - } - - $s .= $ch; - } - } - } - - decode_error("unexpected end of string while parsing JSON string"); - } - - - sub white { - while( defined $ch ){ - if($ch le ' '){ - next_chr(); - } - elsif($ch eq '/'){ - next_chr(); - if(defined $ch and $ch eq '/'){ - 1 while(defined(next_chr()) and $ch ne "\n" and $ch ne "\r"); - } - elsif(defined $ch and $ch eq '*'){ - next_chr(); - while(1){ - if(defined $ch){ - if($ch eq '*'){ - if(defined(next_chr()) and $ch eq '/'){ - next_chr(); - last; - } - } - else{ - next_chr(); - } - } - else{ - decode_error("Unterminated comment"); - } - } - next; - } - else{ - $at--; - decode_error("malformed JSON string, neither array, object, number, string or atom"); - } - } - else{ - if ($relaxed and $ch eq '#') { # correctly? - pos($text) = $at; - $text =~ /\G([^\n]*(?:\r\n|\r|\n|$))/g; - $at = pos($text); - next_chr; - next; - } - - last; - } - } - } - - - sub array { - my $a = $_[0] || []; # you can use this code to use another array ref object. - - decode_error('json text or perl structure exceeds maximum nesting level (max_depth set too low?)') - if (++$depth > $max_depth); - - next_chr(); - white(); - - if(defined $ch and $ch eq ']'){ - --$depth; - next_chr(); - return $a; - } - else { - while(defined($ch)){ - push @$a, value(); - - white(); - - if (!defined $ch) { - last; - } - - if($ch eq ']'){ - --$depth; - next_chr(); - return $a; - } - - if($ch ne ','){ - last; - } - - next_chr(); - white(); - - if ($relaxed and $ch eq ']') { - --$depth; - next_chr(); - return $a; - } - - } - } - - decode_error(", or ] expected while parsing array"); - } - - - sub object { - my $o = $_[0] || {}; # you can use this code to use another hash ref object. - my $k; - - decode_error('json text or perl structure exceeds maximum nesting level (max_depth set too low?)') - if (++$depth > $max_depth); - next_chr(); - white(); - - if(defined $ch and $ch eq '}'){ - --$depth; - next_chr(); - if ($F_HOOK) { - return _json_object_hook($o); - } - return $o; - } - else { - while (defined $ch) { - $k = ($allow_barekey and $ch ne '"' and $ch ne "'") ? bareKey() : string(); - white(); - - if(!defined $ch or $ch ne ':'){ - $at--; - decode_error("':' expected"); - } - - next_chr(); - $o->{$k} = value(); - white(); - - last if (!defined $ch); - - if($ch eq '}'){ - --$depth; - next_chr(); - if ($F_HOOK) { - return _json_object_hook($o); - } - return $o; - } - - if($ch ne ','){ - last; - } - - next_chr(); - white(); - - if ($relaxed and $ch eq '}') { - --$depth; - next_chr(); - if ($F_HOOK) { - return _json_object_hook($o); - } - return $o; - } - - } - - } - - $at--; - decode_error(", or } expected while parsing object/hash"); - } - - - sub bareKey { # doesn't strictly follow Standard ECMA-262 3rd Edition - my $key; - while($ch =~ /[^\x00-\x23\x25-\x2F\x3A-\x40\x5B-\x5E\x60\x7B-\x7F]/){ - $key .= $ch; - next_chr(); - } - return $key; - } - - - sub word { - my $word = substr($text,$at-1,4); - - if($word eq 'true'){ - $at += 3; - next_chr; - return $JSON::PP::true; - } - elsif($word eq 'null'){ - $at += 3; - next_chr; - return undef; - } - elsif($word eq 'fals'){ - $at += 3; - if(substr($text,$at,1) eq 'e'){ - $at++; - next_chr; - return $JSON::PP::false; - } - } - - $at--; # for decode_error report - - decode_error("'null' expected") if ($word =~ /^n/); - decode_error("'true' expected") if ($word =~ /^t/); - decode_error("'false' expected") if ($word =~ /^f/); - decode_error("malformed JSON string, neither array, object, number, string or atom"); - } - - - sub number { - my $n = ''; - my $v; - - # According to RFC4627, hex or oct digits are invalid. - if($ch eq '0'){ - my $peek = substr($text,$at,1); - my $hex = $peek =~ /[xX]/; # 0 or 1 - - if($hex){ - decode_error("malformed number (leading zero must not be followed by another digit)"); - ($n) = ( substr($text, $at+1) =~ /^([0-9a-fA-F]+)/); - } - else{ # oct - ($n) = ( substr($text, $at) =~ /^([0-7]+)/); - if (defined $n and length $n > 1) { - decode_error("malformed number (leading zero must not be followed by another digit)"); - } - } - - if(defined $n and length($n)){ - if (!$hex and length($n) == 1) { - decode_error("malformed number (leading zero must not be followed by another digit)"); - } - $at += length($n) + $hex; - next_chr; - return $hex ? hex($n) : oct($n); - } - } - - if($ch eq '-'){ - $n = '-'; - next_chr; - if (!defined $ch or $ch !~ /\d/) { - decode_error("malformed number (no digits after initial minus)"); - } - } - - while(defined $ch and $ch =~ /\d/){ - $n .= $ch; - next_chr; - } - - if(defined $ch and $ch eq '.'){ - $n .= '.'; - - next_chr; - if (!defined $ch or $ch !~ /\d/) { - decode_error("malformed number (no digits after decimal point)"); - } - else { - $n .= $ch; - } - - while(defined(next_chr) and $ch =~ /\d/){ - $n .= $ch; - } - } - - if(defined $ch and ($ch eq 'e' or $ch eq 'E')){ - $n .= $ch; - next_chr; - - if(defined($ch) and ($ch eq '+' or $ch eq '-')){ - $n .= $ch; - next_chr; - if (!defined $ch or $ch =~ /\D/) { - decode_error("malformed number (no digits after exp sign)"); - } - $n .= $ch; - } - elsif(defined($ch) and $ch =~ /\d/){ - $n .= $ch; - } - else { - decode_error("malformed number (no digits after exp sign)"); - } - - while(defined(next_chr) and $ch =~ /\d/){ - $n .= $ch; - } - - } - - $v .= $n; - - if ($v !~ /[.eE]/ and length $v > $max_intsize) { - if ($allow_bigint) { # from Adam Sussman - require Math::BigInt; - return Math::BigInt->new($v); - } - else { - return "$v"; - } - } - elsif ($allow_bigint) { - require Math::BigFloat; - return Math::BigFloat->new($v); - } - - return 0+$v; - } - - - sub is_valid_utf8 { - - $utf8_len = $_[0] =~ /[\x00-\x7F]/ ? 1 - : $_[0] =~ /[\xC2-\xDF]/ ? 2 - : $_[0] =~ /[\xE0-\xEF]/ ? 3 - : $_[0] =~ /[\xF0-\xF4]/ ? 4 - : 0 - ; - - return unless $utf8_len; - - my $is_valid_utf8 = substr($text, $at - 1, $utf8_len); - - return ( $is_valid_utf8 =~ /^(?: - [\x00-\x7F] - |[\xC2-\xDF][\x80-\xBF] - |[\xE0][\xA0-\xBF][\x80-\xBF] - |[\xE1-\xEC][\x80-\xBF][\x80-\xBF] - |[\xED][\x80-\x9F][\x80-\xBF] - |[\xEE-\xEF][\x80-\xBF][\x80-\xBF] - |[\xF0][\x90-\xBF][\x80-\xBF][\x80-\xBF] - |[\xF1-\xF3][\x80-\xBF][\x80-\xBF][\x80-\xBF] - |[\xF4][\x80-\x8F][\x80-\xBF][\x80-\xBF] - )$/x ) ? $is_valid_utf8 : ''; - } - - - sub decode_error { - my $error = shift; - my $no_rep = shift; - my $str = defined $text ? substr($text, $at) : ''; - my $mess = ''; - my $type = $] >= 5.008 ? 'U*' - : $] < 5.006 ? 'C*' - : utf8::is_utf8( $str ) ? 'U*' # 5.6 - : 'C*' - ; - - for my $c ( unpack( $type, $str ) ) { # emulate pv_uni_display() ? - $mess .= $c == 0x07 ? '\a' - : $c == 0x09 ? '\t' - : $c == 0x0a ? '\n' - : $c == 0x0d ? '\r' - : $c == 0x0c ? '\f' - : $c < 0x20 ? sprintf('\x{%x}', $c) - : $c == 0x5c ? '\\\\' - : $c < 0x80 ? chr($c) - : sprintf('\x{%x}', $c) - ; - if ( length $mess >= 20 ) { - $mess .= '...'; - last; - } - } - - unless ( length $mess ) { - $mess = '(end of string)'; - } - - Carp::croak ( - $no_rep ? "$error" : "$error, at character offset $at (before \"$mess\")" - ); - - } - - - sub _json_object_hook { - my $o = $_[0]; - my @ks = keys %{$o}; - - if ( $cb_sk_object and @ks == 1 and exists $cb_sk_object->{ $ks[0] } and ref $cb_sk_object->{ $ks[0] } ) { - my @val = $cb_sk_object->{ $ks[0] }->( $o->{$ks[0]} ); - if (@val == 1) { - return $val[0]; - } - } - - my @val = $cb_object->($o) if ($cb_object); - if (@val == 0 or @val > 1) { - return $o; - } - else { - return $val[0]; - } - } - - - sub PP_decode_box { - { - text => $text, - at => $at, - ch => $ch, - len => $len, - depth => $depth, - encoding => $encoding, - is_valid_utf8 => $is_valid_utf8, - }; - } - -} # PARSE - - -sub _decode_surrogates { # from perlunicode - my $uni = 0x10000 + (hex($_[0]) - 0xD800) * 0x400 + (hex($_[1]) - 0xDC00); - my $un = pack('U*', $uni); - utf8::encode( $un ); - return $un; -} - - -sub _decode_unicode { - my $un = pack('U', hex shift); - utf8::encode( $un ); - return $un; -} - -# -# Setup for various Perl versions (the code from JSON::PP58) -# - -BEGIN { - - unless ( defined &utf8::is_utf8 ) { - require Encode; - *utf8::is_utf8 = *Encode::is_utf8; - } - - if ( $] >= 5.008 ) { - *JSON::PP::JSON_PP_encode_ascii = \&_encode_ascii; - *JSON::PP::JSON_PP_encode_latin1 = \&_encode_latin1; - *JSON::PP::JSON_PP_decode_surrogates = \&_decode_surrogates; - *JSON::PP::JSON_PP_decode_unicode = \&_decode_unicode; - } - - if ($] >= 5.008 and $] < 5.008003) { # join() in 5.8.0 - 5.8.2 is broken. - package # hide from PAUSE - JSON::PP; - require subs; - subs->import('join'); - eval q| - sub join { - return '' if (@_ < 2); - my $j = shift; - my $str = shift; - for (@_) { $str .= $j . $_; } - return $str; - } - |; - } - - - sub JSON::PP::incr_parse { - local $Carp::CarpLevel = 1; - ( $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new )->incr_parse( @_ ); - } - - - sub JSON::PP::incr_skip { - ( $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new )->incr_skip; - } - - - sub JSON::PP::incr_reset { - ( $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new )->incr_reset; - } - - eval q{ - sub JSON::PP::incr_text : lvalue { - $_[0]->{_incr_parser} ||= JSON::PP::IncrParser->new; - - if ( $_[0]->{_incr_parser}->{incr_parsing} ) { - Carp::croak("incr_text can not be called when the incremental parser already started parsing"); - } - $_[0]->{_incr_parser}->{incr_text}; - } - } if ( $] >= 5.006 ); - -} # Setup for various Perl versions (the code from JSON::PP58) - - -############################### -# Utilities -# - -BEGIN { - eval 'require Scalar::Util'; - unless($@){ - *JSON::PP::blessed = \&Scalar::Util::blessed; - *JSON::PP::reftype = \&Scalar::Util::reftype; - *JSON::PP::refaddr = \&Scalar::Util::refaddr; - } - else{ # This code is from Scalar::Util. - # warn $@; - eval 'sub UNIVERSAL::a_sub_not_likely_to_be_here { ref($_[0]) }'; - *JSON::PP::blessed = sub { - local($@, $SIG{__DIE__}, $SIG{__WARN__}); - ref($_[0]) ? eval { $_[0]->a_sub_not_likely_to_be_here } : undef; - }; - my %tmap = qw( - B::NULL SCALAR - B::HV HASH - B::AV ARRAY - B::CV CODE - B::IO IO - B::GV GLOB - B::REGEXP REGEXP - ); - *JSON::PP::reftype = sub { - my $r = shift; - - return undef unless length(ref($r)); - - my $t = ref(B::svref_2object($r)); - - return - exists $tmap{$t} ? $tmap{$t} - : length(ref($$r)) ? 'REF' - : 'SCALAR'; - }; - *JSON::PP::refaddr = sub { - return undef unless length(ref($_[0])); - - my $addr; - if(defined(my $pkg = blessed($_[0]))) { - $addr .= bless $_[0], 'Scalar::Util::Fake'; - bless $_[0], $pkg; - } - else { - $addr .= $_[0] - } - - $addr =~ /0x(\w+)/; - local $^W; - #no warnings 'portable'; - hex($1); - } - } -} - - -# shamelessly copied and modified from JSON::XS code. - -unless ( $INC{'JSON/PP.pm'} ) { - eval q| - package - JSON::PP::Boolean; - - use overload ( - "0+" => sub { ${$_[0]} }, - "++" => sub { $_[0] = ${$_[0]} + 1 }, - "--" => sub { $_[0] = ${$_[0]} - 1 }, - fallback => 1, - ); - |; -} - -$JSON::PP::true = do { bless \(my $dummy = 1), "JSON::PP::Boolean" }; -$JSON::PP::false = do { bless \(my $dummy = 0), "JSON::PP::Boolean" }; - -sub is_bool { defined $_[0] and UNIVERSAL::isa($_[0], "JSON::PP::Boolean"); } - -sub true { $JSON::PP::true } -sub false { $JSON::PP::false } -sub null { undef; } - -############################### - -############################### - -package # hide from PAUSE - JSON::PP::IncrParser; - -use strict; - -use constant INCR_M_WS => 0; # initial whitespace skipping -use constant INCR_M_STR => 1; # inside string -use constant INCR_M_BS => 2; # inside backslash -use constant INCR_M_JSON => 3; # outside anything, count nesting -use constant INCR_M_C0 => 4; -use constant INCR_M_C1 => 5; - -use vars qw($VERSION); -$VERSION = '1.01'; - -my $unpack_format = $] < 5.006 ? 'C*' : 'U*'; - -sub new { - my ( $class ) = @_; - - bless { - incr_nest => 0, - incr_text => undef, - incr_parsing => 0, - incr_p => 0, - }, $class; -} - - -sub incr_parse { - my ( $self, $coder, $text ) = @_; - - $self->{incr_text} = '' unless ( defined $self->{incr_text} ); - - if ( defined $text ) { - if ( utf8::is_utf8( $text ) and !utf8::is_utf8( $self->{incr_text} ) ) { - utf8::upgrade( $self->{incr_text} ) ; - utf8::decode( $self->{incr_text} ) ; - } - $self->{incr_text} .= $text; - } - - - my $max_size = $coder->get_max_size; - - if ( defined wantarray ) { - - $self->{incr_mode} = INCR_M_WS unless defined $self->{incr_mode}; - - if ( wantarray ) { - my @ret; - - $self->{incr_parsing} = 1; - - do { - push @ret, $self->_incr_parse( $coder, $self->{incr_text} ); - - unless ( !$self->{incr_nest} and $self->{incr_mode} == INCR_M_JSON ) { - $self->{incr_mode} = INCR_M_WS if $self->{incr_mode} != INCR_M_STR; - } - - } until ( length $self->{incr_text} >= $self->{incr_p} ); - - $self->{incr_parsing} = 0; - - return @ret; - } - else { # in scalar context - $self->{incr_parsing} = 1; - my $obj = $self->_incr_parse( $coder, $self->{incr_text} ); - $self->{incr_parsing} = 0 if defined $obj; # pointed by Martin J. Evans - return $obj ? $obj : undef; # $obj is an empty string, parsing was completed. - } - - } - -} - - -sub _incr_parse { - my ( $self, $coder, $text, $skip ) = @_; - my $p = $self->{incr_p}; - my $restore = $p; - - my @obj; - my $len = length $text; - - if ( $self->{incr_mode} == INCR_M_WS ) { - while ( $len > $p ) { - my $s = substr( $text, $p, 1 ); - $p++ and next if ( 0x20 >= unpack($unpack_format, $s) ); - $self->{incr_mode} = INCR_M_JSON; - last; - } - } - - while ( $len > $p ) { - my $s = substr( $text, $p++, 1 ); - - if ( $s eq '"' ) { - if (substr( $text, $p - 2, 1 ) eq '\\' ) { - next; - } - - if ( $self->{incr_mode} != INCR_M_STR ) { - $self->{incr_mode} = INCR_M_STR; - } - else { - $self->{incr_mode} = INCR_M_JSON; - unless ( $self->{incr_nest} ) { - last; - } - } - } - - if ( $self->{incr_mode} == INCR_M_JSON ) { - - if ( $s eq '[' or $s eq '{' ) { - if ( ++$self->{incr_nest} > $coder->get_max_depth ) { - Carp::croak('json text or perl structure exceeds maximum nesting level (max_depth set too low?)'); - } - } - elsif ( $s eq ']' or $s eq '}' ) { - last if ( --$self->{incr_nest} <= 0 ); - } - elsif ( $s eq '#' ) { - while ( $len > $p ) { - last if substr( $text, $p++, 1 ) eq "\n"; - } - } - - } - - } - - $self->{incr_p} = $p; - - return if ( $self->{incr_mode} == INCR_M_STR and not $self->{incr_nest} ); - return if ( $self->{incr_mode} == INCR_M_JSON and $self->{incr_nest} > 0 ); - - return '' unless ( length substr( $self->{incr_text}, 0, $p ) ); - - local $Carp::CarpLevel = 2; - - $self->{incr_p} = $restore; - $self->{incr_c} = $p; - - my ( $obj, $tail ) = $coder->PP_decode_json( substr( $self->{incr_text}, 0, $p ), 0x10000001 ); - - $self->{incr_text} = substr( $self->{incr_text}, $p ); - $self->{incr_p} = 0; - - return $obj || ''; -} - - -sub incr_text { - if ( $_[0]->{incr_parsing} ) { - Carp::croak("incr_text can not be called when the incremental parser already started parsing"); - } - $_[0]->{incr_text}; -} - - -sub incr_skip { - my $self = shift; - $self->{incr_text} = substr( $self->{incr_text}, $self->{incr_c} ); - $self->{incr_p} = 0; -} - - -sub incr_reset { - my $self = shift; - $self->{incr_text} = undef; - $self->{incr_p} = 0; - $self->{incr_mode} = 0; - $self->{incr_nest} = 0; - $self->{incr_parsing} = 0; -} - -############################### - - -1; -__END__ -=pod - -=head1 NAME - -JSON::PP - JSON::XS compatible pure-Perl module. - -=head1 SYNOPSIS - - use JSON::PP; - - # exported functions, they croak on error - # and expect/generate UTF-8 - - $utf8_encoded_json_text = encode_json $perl_hash_or_arrayref; - $perl_hash_or_arrayref = decode_json $utf8_encoded_json_text; - - # OO-interface - - $coder = JSON::PP->new->ascii->pretty->allow_nonref; - - $json_text = $json->encode( $perl_scalar ); - $perl_scalar = $json->decode( $json_text ); - - $pretty_printed = $json->pretty->encode( $perl_scalar ); # pretty-printing - - # Note that JSON version 2.0 and above will automatically use - # JSON::XS or JSON::PP, so you should be able to just: - - use JSON; - - -=head1 VERSION - - 2.27200 - -L<JSON::XS> 2.27 (~2.30) compatible. - -=head1 DESCRIPTION - -This module is L<JSON::XS> compatible pure Perl module. -(Perl 5.8 or later is recommended) - -JSON::XS is the fastest and most proper JSON module on CPAN. -It is written by Marc Lehmann in C, so must be compiled and -installed in the used environment. - -JSON::PP is a pure-Perl module and has compatibility to JSON::XS. - - -=head2 FEATURES - -=over - -=item * correct unicode handling - -This module knows how to handle Unicode (depending on Perl version). - -See to L<JSON::XS/A FEW NOTES ON UNICODE AND PERL> and -L<UNICODE HANDLING ON PERLS>. - - -=item * round-trip integrity - -When you serialise a perl data structure using only data types -supported by JSON and Perl, the deserialised data structure is -identical on the Perl level. (e.g. the string "2.0" doesn't suddenly -become "2" just because it looks like a number). There I<are> minor -exceptions to this, read the MAPPING section below to learn about -those. - - -=item * strict checking of JSON correctness - -There is no guessing, no generating of illegal JSON texts by default, -and only JSON is accepted as input by default (the latter is a -security feature). But when some options are set, loose checking -features are available. - -=back - -=head1 FUNCTIONAL INTERFACE - -Some documents are copied and modified from L<JSON::XS/FUNCTIONAL INTERFACE>. - -=head2 encode_json - - $json_text = encode_json $perl_scalar - -Converts the given Perl data structure to a UTF-8 encoded, binary string. - -This function call is functionally identical to: - - $json_text = JSON::PP->new->utf8->encode($perl_scalar) - -=head2 decode_json - - $perl_scalar = decode_json $json_text - -The opposite of C<encode_json>: expects an UTF-8 (binary) string and tries -to parse that as an UTF-8 encoded JSON text, returning the resulting -reference. - -This function call is functionally identical to: - - $perl_scalar = JSON::PP->new->utf8->decode($json_text) - -=head2 JSON::PP::is_bool - - $is_boolean = JSON::PP::is_bool($scalar) - -Returns true if the passed scalar represents either JSON::PP::true or -JSON::PP::false, two constants that act like C<1> and C<0> respectively -and are also used to represent JSON C<true> and C<false> in Perl strings. - -=head2 JSON::PP::true - -Returns JSON true value which is blessed object. -It C<isa> JSON::PP::Boolean object. - -=head2 JSON::PP::false - -Returns JSON false value which is blessed object. -It C<isa> JSON::PP::Boolean object. - -=head2 JSON::PP::null - -Returns C<undef>. - -See L<MAPPING>, below, for more information on how JSON values are mapped to -Perl. - - -=head1 HOW DO I DECODE A DATA FROM OUTER AND ENCODE TO OUTER - -This section supposes that your perl version is 5.8 or later. - -If you know a JSON text from an outer world - a network, a file content, and so on, -is encoded in UTF-8, you should use C<decode_json> or C<JSON> module object -with C<utf8> enable. And the decoded result will contain UNICODE characters. - - # from network - my $json = JSON::PP->new->utf8; - my $json_text = CGI->new->param( 'json_data' ); - my $perl_scalar = $json->decode( $json_text ); - - # from file content - local $/; - open( my $fh, '<', 'json.data' ); - $json_text = <$fh>; - $perl_scalar = decode_json( $json_text ); - -If an outer data is not encoded in UTF-8, firstly you should C<decode> it. - - use Encode; - local $/; - open( my $fh, '<', 'json.data' ); - my $encoding = 'cp932'; - my $unicode_json_text = decode( $encoding, <$fh> ); # UNICODE - - # or you can write the below code. - # - # open( my $fh, "<:encoding($encoding)", 'json.data' ); - # $unicode_json_text = <$fh>; - -In this case, C<$unicode_json_text> is of course UNICODE string. -So you B<cannot> use C<decode_json> nor C<JSON> module object with C<utf8> enable. -Instead of them, you use C<JSON> module object with C<utf8> disable. - - $perl_scalar = $json->utf8(0)->decode( $unicode_json_text ); - -Or C<encode 'utf8'> and C<decode_json>: - - $perl_scalar = decode_json( encode( 'utf8', $unicode_json_text ) ); - # this way is not efficient. - -And now, you want to convert your C<$perl_scalar> into JSON data and -send it to an outer world - a network or a file content, and so on. - -Your data usually contains UNICODE strings and you want the converted data to be encoded -in UTF-8, you should use C<encode_json> or C<JSON> module object with C<utf8> enable. - - print encode_json( $perl_scalar ); # to a network? file? or display? - # or - print $json->utf8->encode( $perl_scalar ); - -If C<$perl_scalar> does not contain UNICODE but C<$encoding>-encoded strings -for some reason, then its characters are regarded as B<latin1> for perl -(because it does not concern with your $encoding). -You B<cannot> use C<encode_json> nor C<JSON> module object with C<utf8> enable. -Instead of them, you use C<JSON> module object with C<utf8> disable. -Note that the resulted text is a UNICODE string but no problem to print it. - - # $perl_scalar contains $encoding encoded string values - $unicode_json_text = $json->utf8(0)->encode( $perl_scalar ); - # $unicode_json_text consists of characters less than 0x100 - print $unicode_json_text; - -Or C<decode $encoding> all string values and C<encode_json>: - - $perl_scalar->{ foo } = decode( $encoding, $perl_scalar->{ foo } ); - # ... do it to each string values, then encode_json - $json_text = encode_json( $perl_scalar ); - -This method is a proper way but probably not efficient. - -See to L<Encode>, L<perluniintro>. - - -=head1 METHODS - -Basically, check to L<JSON> or L<JSON::XS>. - -=head2 new - - $json = JSON::PP->new - -Returns a new JSON::PP object that can be used to de/encode JSON -strings. - -All boolean flags described below are by default I<disabled>. - -The mutators for flags all return the JSON object again and thus calls can -be chained: - - my $json = JSON::PP->new->utf8->space_after->encode({a => [1,2]}) - => {"a": [1, 2]} - -=head2 ascii - - $json = $json->ascii([$enable]) - - $enabled = $json->get_ascii - -If $enable is true (or missing), then the encode method will not generate characters outside -the code range 0..127. Any Unicode characters outside that range will be escaped using either -a single \uXXXX or a double \uHHHH\uLLLLL escape sequence, as per RFC4627. -(See to L<JSON::XS/OBJECT-ORIENTED INTERFACE>). - -In Perl 5.005, there is no character having high value (more than 255). -See to L<UNICODE HANDLING ON PERLS>. - -If $enable is false, then the encode method will not escape Unicode characters unless -required by the JSON syntax or other flags. This results in a faster and more compact format. - - JSON::PP->new->ascii(1)->encode([chr 0x10401]) - => ["\ud801\udc01"] - -=head2 latin1 - - $json = $json->latin1([$enable]) - - $enabled = $json->get_latin1 - -If $enable is true (or missing), then the encode method will encode the resulting JSON -text as latin1 (or iso-8859-1), escaping any characters outside the code range 0..255. - -If $enable is false, then the encode method will not escape Unicode characters -unless required by the JSON syntax or other flags. - - JSON::XS->new->latin1->encode (["\x{89}\x{abc}"] - => ["\x{89}\\u0abc"] # (perl syntax, U+abc escaped, U+89 not) - -See to L<UNICODE HANDLING ON PERLS>. - -=head2 utf8 - - $json = $json->utf8([$enable]) - - $enabled = $json->get_utf8 - -If $enable is true (or missing), then the encode method will encode the JSON result -into UTF-8, as required by many protocols, while the decode method expects to be handled -an UTF-8-encoded string. Please note that UTF-8-encoded strings do not contain any -characters outside the range 0..255, they are thus useful for bytewise/binary I/O. - -(In Perl 5.005, any character outside the range 0..255 does not exist. -See to L<UNICODE HANDLING ON PERLS>.) - -In future versions, enabling this option might enable autodetection of the UTF-16 and UTF-32 -encoding families, as described in RFC4627. - -If $enable is false, then the encode method will return the JSON string as a (non-encoded) -Unicode string, while decode expects thus a Unicode string. Any decoding or encoding -(e.g. to UTF-8 or UTF-16) needs to be done yourself, e.g. using the Encode module. - -Example, output UTF-16BE-encoded JSON: - - use Encode; - $jsontext = encode "UTF-16BE", JSON::PP->new->encode ($object); - -Example, decode UTF-32LE-encoded JSON: - - use Encode; - $object = JSON::PP->new->decode (decode "UTF-32LE", $jsontext); - - -=head2 pretty - - $json = $json->pretty([$enable]) - -This enables (or disables) all of the C<indent>, C<space_before> and -C<space_after> flags in one call to generate the most readable -(or most compact) form possible. - -Equivalent to: - - $json->indent->space_before->space_after - -=head2 indent - - $json = $json->indent([$enable]) - - $enabled = $json->get_indent - -The default indent space length is three. -You can use C<indent_length> to change the length. - -=head2 space_before - - $json = $json->space_before([$enable]) - - $enabled = $json->get_space_before - -If C<$enable> is true (or missing), then the C<encode> method will add an extra -optional space before the C<:> separating keys from values in JSON objects. - -If C<$enable> is false, then the C<encode> method will not add any extra -space at those places. - -This setting has no effect when decoding JSON texts. - -Example, space_before enabled, space_after and indent disabled: - - {"key" :"value"} - -=head2 space_after - - $json = $json->space_after([$enable]) - - $enabled = $json->get_space_after - -If C<$enable> is true (or missing), then the C<encode> method will add an extra -optional space after the C<:> separating keys from values in JSON objects -and extra whitespace after the C<,> separating key-value pairs and array -members. - -If C<$enable> is false, then the C<encode> method will not add any extra -space at those places. - -This setting has no effect when decoding JSON texts. - -Example, space_before and indent disabled, space_after enabled: - - {"key": "value"} - -=head2 relaxed - - $json = $json->relaxed([$enable]) - - $enabled = $json->get_relaxed - -If C<$enable> is true (or missing), then C<decode> will accept some -extensions to normal JSON syntax (see below). C<encode> will not be -affected in anyway. I<Be aware that this option makes you accept invalid -JSON texts as if they were valid!>. I suggest only to use this option to -parse application-specific files written by humans (configuration files, -resource files etc.) - -If C<$enable> is false (the default), then C<decode> will only accept -valid JSON texts. - -Currently accepted extensions are: - -=over 4 - -=item * list items can have an end-comma - -JSON I<separates> array elements and key-value pairs with commas. This -can be annoying if you write JSON texts manually and want to be able to -quickly append elements, so this extension accepts comma at the end of -such items not just between them: - - [ - 1, - 2, <- this comma not normally allowed - ] - { - "k1": "v1", - "k2": "v2", <- this comma not normally allowed - } - -=item * shell-style '#'-comments - -Whenever JSON allows whitespace, shell-style comments are additionally -allowed. They are terminated by the first carriage-return or line-feed -character, after which more white-space and comments are allowed. - - [ - 1, # this comment not allowed in JSON - # neither this one... - ] - -=back - -=head2 canonical - - $json = $json->canonical([$enable]) - - $enabled = $json->get_canonical - -If C<$enable> is true (or missing), then the C<encode> method will output JSON objects -by sorting their keys. This is adding a comparatively high overhead. - -If C<$enable> is false, then the C<encode> method will output key-value -pairs in the order Perl stores them (which will likely change between runs -of the same script). - -This option is useful if you want the same data structure to be encoded as -the same JSON text (given the same overall settings). If it is disabled, -the same hash might be encoded differently even if contains the same data, -as key-value pairs have no inherent ordering in Perl. - -This setting has no effect when decoding JSON texts. - -If you want your own sorting routine, you can give a code reference -or a subroutine name to C<sort_by>. See to C<JSON::PP OWN METHODS>. - -=head2 allow_nonref - - $json = $json->allow_nonref([$enable]) - - $enabled = $json->get_allow_nonref - -If C<$enable> is true (or missing), then the C<encode> method can convert a -non-reference into its corresponding string, number or null JSON value, -which is an extension to RFC4627. Likewise, C<decode> will accept those JSON -values instead of croaking. - -If C<$enable> is false, then the C<encode> method will croak if it isn't -passed an arrayref or hashref, as JSON texts must either be an object -or array. Likewise, C<decode> will croak if given something that is not a -JSON object or array. - - JSON::PP->new->allow_nonref->encode ("Hello, World!") - => "Hello, World!" - -=head2 allow_unknown - - $json = $json->allow_unknown ([$enable]) - - $enabled = $json->get_allow_unknown - -If $enable is true (or missing), then "encode" will *not* throw an -exception when it encounters values it cannot represent in JSON (for -example, filehandles) but instead will encode a JSON "null" value. -Note that blessed objects are not included here and are handled -separately by c<allow_nonref>. - -If $enable is false (the default), then "encode" will throw an -exception when it encounters anything it cannot encode as JSON. - -This option does not affect "decode" in any way, and it is -recommended to leave it off unless you know your communications -partner. - -=head2 allow_blessed - - $json = $json->allow_blessed([$enable]) - - $enabled = $json->get_allow_blessed - -If C<$enable> is true (or missing), then the C<encode> method will not -barf when it encounters a blessed reference. Instead, the value of the -B<convert_blessed> option will decide whether C<null> (C<convert_blessed> -disabled or no C<TO_JSON> method found) or a representation of the -object (C<convert_blessed> enabled and C<TO_JSON> method found) is being -encoded. Has no effect on C<decode>. - -If C<$enable> is false (the default), then C<encode> will throw an -exception when it encounters a blessed object. - -=head2 convert_blessed - - $json = $json->convert_blessed([$enable]) - - $enabled = $json->get_convert_blessed - -If C<$enable> is true (or missing), then C<encode>, upon encountering a -blessed object, will check for the availability of the C<TO_JSON> method -on the object's class. If found, it will be called in scalar context -and the resulting scalar will be encoded instead of the object. If no -C<TO_JSON> method is found, the value of C<allow_blessed> will decide what -to do. - -The C<TO_JSON> method may safely call die if it wants. If C<TO_JSON> -returns other blessed objects, those will be handled in the same -way. C<TO_JSON> must take care of not causing an endless recursion cycle -(== crash) in this case. The name of C<TO_JSON> was chosen because other -methods called by the Perl core (== not by the user of the object) are -usually in upper case letters and to avoid collisions with the C<to_json> -function or method. - -This setting does not yet influence C<decode> in any way. - -If C<$enable> is false, then the C<allow_blessed> setting will decide what -to do when a blessed object is found. - -=head2 filter_json_object - - $json = $json->filter_json_object([$coderef]) - -When C<$coderef> is specified, it will be called from C<decode> each -time it decodes a JSON object. The only argument passed to the coderef -is a reference to the newly-created hash. If the code references returns -a single scalar (which need not be a reference), this value -(i.e. a copy of that scalar to avoid aliasing) is inserted into the -deserialised data structure. If it returns an empty list -(NOTE: I<not> C<undef>, which is a valid scalar), the original deserialised -hash will be inserted. This setting can slow down decoding considerably. - -When C<$coderef> is omitted or undefined, any existing callback will -be removed and C<decode> will not change the deserialised hash in any -way. - -Example, convert all JSON objects into the integer 5: - - my $js = JSON::PP->new->filter_json_object (sub { 5 }); - # returns [5] - $js->decode ('[{}]'); # the given subroutine takes a hash reference. - # throw an exception because allow_nonref is not enabled - # so a lone 5 is not allowed. - $js->decode ('{"a":1, "b":2}'); - -=head2 filter_json_single_key_object - - $json = $json->filter_json_single_key_object($key [=> $coderef]) - -Works remotely similar to C<filter_json_object>, but is only called for -JSON objects having a single key named C<$key>. - -This C<$coderef> is called before the one specified via -C<filter_json_object>, if any. It gets passed the single value in the JSON -object. If it returns a single value, it will be inserted into the data -structure. If it returns nothing (not even C<undef> but the empty list), -the callback from C<filter_json_object> will be called next, as if no -single-key callback were specified. - -If C<$coderef> is omitted or undefined, the corresponding callback will be -disabled. There can only ever be one callback for a given key. - -As this callback gets called less often then the C<filter_json_object> -one, decoding speed will not usually suffer as much. Therefore, single-key -objects make excellent targets to serialise Perl objects into, especially -as single-key JSON objects are as close to the type-tagged value concept -as JSON gets (it's basically an ID/VALUE tuple). Of course, JSON does not -support this in any way, so you need to make sure your data never looks -like a serialised Perl hash. - -Typical names for the single object key are C<__class_whatever__>, or -C<$__dollars_are_rarely_used__$> or C<}ugly_brace_placement>, or even -things like C<__class_md5sum(classname)__>, to reduce the risk of clashing -with real hashes. - -Example, decode JSON objects of the form C<< { "__widget__" => <id> } >> -into the corresponding C<< $WIDGET{<id>} >> object: - - # return whatever is in $WIDGET{5}: - JSON::PP - ->new - ->filter_json_single_key_object (__widget__ => sub { - $WIDGET{ $_[0] } - }) - ->decode ('{"__widget__": 5') - - # this can be used with a TO_JSON method in some "widget" class - # for serialisation to json: - sub WidgetBase::TO_JSON { - my ($self) = @_; - - unless ($self->{id}) { - $self->{id} = ..get..some..id..; - $WIDGET{$self->{id}} = $self; - } - - { __widget__ => $self->{id} } - } - -=head2 shrink - - $json = $json->shrink([$enable]) - - $enabled = $json->get_shrink - -In JSON::XS, this flag resizes strings generated by either -C<encode> or C<decode> to their minimum size possible. -It will also try to downgrade any strings to octet-form if possible. - -In JSON::PP, it is noop about resizing strings but tries -C<utf8::downgrade> to the returned string by C<encode>. -See to L<utf8>. - -See to L<JSON::XS/OBJECT-ORIENTED INTERFACE> - -=head2 max_depth - - $json = $json->max_depth([$maximum_nesting_depth]) - - $max_depth = $json->get_max_depth - -Sets the maximum nesting level (default C<512>) accepted while encoding -or decoding. If a higher nesting level is detected in JSON text or a Perl -data structure, then the encoder and decoder will stop and croak at that -point. - -Nesting level is defined by number of hash- or arrayrefs that the encoder -needs to traverse to reach a given point or the number of C<{> or C<[> -characters without their matching closing parenthesis crossed to reach a -given character in a string. - -If no argument is given, the highest possible setting will be used, which -is rarely useful. - -See L<JSON::XS/SSECURITY CONSIDERATIONS> for more info on why this is useful. - -When a large value (100 or more) was set and it de/encodes a deep nested object/text, -it may raise a warning 'Deep recursion on subroutine' at the perl runtime phase. - -=head2 max_size - - $json = $json->max_size([$maximum_string_size]) - - $max_size = $json->get_max_size - -Set the maximum length a JSON text may have (in bytes) where decoding is -being attempted. The default is C<0>, meaning no limit. When C<decode> -is called on a string that is longer then this many bytes, it will not -attempt to decode the string but throw an exception. This setting has no -effect on C<encode> (yet). - -If no argument is given, the limit check will be deactivated (same as when -C<0> is specified). - -See L<JSON::XS/SECURITY CONSIDERATIONS> for more info on why this is useful. - -=head2 encode - - $json_text = $json->encode($perl_scalar) - -Converts the given Perl data structure (a simple scalar or a reference -to a hash or array) to its JSON representation. Simple scalars will be -converted into JSON string or number sequences, while references to arrays -become JSON arrays and references to hashes become JSON objects. Undefined -Perl values (e.g. C<undef>) become JSON C<null> values. -References to the integers C<0> and C<1> are converted into C<true> and C<false>. - -=head2 decode - - $perl_scalar = $json->decode($json_text) - -The opposite of C<encode>: expects a JSON text and tries to parse it, -returning the resulting simple scalar or reference. Croaks on error. - -JSON numbers and strings become simple Perl scalars. JSON arrays become -Perl arrayrefs and JSON objects become Perl hashrefs. C<true> becomes -C<1> (C<JSON::true>), C<false> becomes C<0> (C<JSON::false>) and -C<null> becomes C<undef>. - -=head2 decode_prefix - - ($perl_scalar, $characters) = $json->decode_prefix($json_text) - -This works like the C<decode> method, but instead of raising an exception -when there is trailing garbage after the first JSON object, it will -silently stop parsing there and return the number of characters consumed -so far. - - JSON->new->decode_prefix ("[1] the tail") - => ([], 3) - -=head1 INCREMENTAL PARSING - -Most of this section are copied and modified from L<JSON::XS/INCREMENTAL PARSING>. - -In some cases, there is the need for incremental parsing of JSON texts. -This module does allow you to parse a JSON stream incrementally. -It does so by accumulating text until it has a full JSON object, which -it then can decode. This process is similar to using C<decode_prefix> -to see if a full JSON object is available, but is much more efficient -(and can be implemented with a minimum of method calls). - -This module will only attempt to parse the JSON text once it is sure it -has enough text to get a decisive result, using a very simple but -truly incremental parser. This means that it sometimes won't stop as -early as the full parser, for example, it doesn't detect parenthesis -mismatches. The only thing it guarantees is that it starts decoding as -soon as a syntactically valid JSON text has been seen. This means you need -to set resource limits (e.g. C<max_size>) to ensure the parser will stop -parsing in the presence if syntax errors. - -The following methods implement this incremental parser. - -=head2 incr_parse - - $json->incr_parse( [$string] ) # void context - - $obj_or_undef = $json->incr_parse( [$string] ) # scalar context - - @obj_or_empty = $json->incr_parse( [$string] ) # list context - -This is the central parsing function. It can both append new text and -extract objects from the stream accumulated so far (both of these -functions are optional). - -If C<$string> is given, then this string is appended to the already -existing JSON fragment stored in the C<$json> object. - -After that, if the function is called in void context, it will simply -return without doing anything further. This can be used to add more text -in as many chunks as you want. - -If the method is called in scalar context, then it will try to extract -exactly I<one> JSON object. If that is successful, it will return this -object, otherwise it will return C<undef>. If there is a parse error, -this method will croak just as C<decode> would do (one can then use -C<incr_skip> to skip the erroneous part). This is the most common way of -using the method. - -And finally, in list context, it will try to extract as many objects -from the stream as it can find and return them, or the empty list -otherwise. For this to work, there must be no separators between the JSON -objects or arrays, instead they must be concatenated back-to-back. If -an error occurs, an exception will be raised as in the scalar context -case. Note that in this case, any previously-parsed JSON texts will be -lost. - -Example: Parse some JSON arrays/objects in a given string and return them. - - my @objs = JSON->new->incr_parse ("[5][7][1,2]"); - -=head2 incr_text - - $lvalue_string = $json->incr_text - -This method returns the currently stored JSON fragment as an lvalue, that -is, you can manipulate it. This I<only> works when a preceding call to -C<incr_parse> in I<scalar context> successfully returned an object. Under -all other circumstances you must not call this function (I mean it. -although in simple tests it might actually work, it I<will> fail under -real world conditions). As a special exception, you can also call this -method before having parsed anything. - -This function is useful in two cases: a) finding the trailing text after a -JSON object or b) parsing multiple JSON objects separated by non-JSON text -(such as commas). - - $json->incr_text =~ s/\s*,\s*//; - -In Perl 5.005, C<lvalue> attribute is not available. -You must write codes like the below: - - $string = $json->incr_text; - $string =~ s/\s*,\s*//; - $json->incr_text( $string ); - -=head2 incr_skip - - $json->incr_skip - -This will reset the state of the incremental parser and will remove the -parsed text from the input buffer. This is useful after C<incr_parse> -died, in which case the input buffer and incremental parser state is left -unchanged, to skip the text parsed so far and to reset the parse state. - -=head2 incr_reset - - $json->incr_reset - -This completely resets the incremental parser, that is, after this call, -it will be as if the parser had never parsed anything. - -This is useful if you want to repeatedly parse JSON objects and want to -ignore any trailing data, which means you have to reset the parser after -each successful decode. - -See to L<JSON::XS/INCREMENTAL PARSING> for examples. - - -=head1 JSON::PP OWN METHODS - -=head2 allow_singlequote - - $json = $json->allow_singlequote([$enable]) - -If C<$enable> is true (or missing), then C<decode> will accept -JSON strings quoted by single quotations that are invalid JSON -format. - - $json->allow_singlequote->decode({"foo":'bar'}); - $json->allow_singlequote->decode({'foo':"bar"}); - $json->allow_singlequote->decode({'foo':'bar'}); - -As same as the C<relaxed> option, this option may be used to parse -application-specific files written by humans. - - -=head2 allow_barekey - - $json = $json->allow_barekey([$enable]) - -If C<$enable> is true (or missing), then C<decode> will accept -bare keys of JSON object that are invalid JSON format. - -As same as the C<relaxed> option, this option may be used to parse -application-specific files written by humans. - - $json->allow_barekey->decode('{foo:"bar"}'); - -=head2 allow_bignum - - $json = $json->allow_bignum([$enable]) - -If C<$enable> is true (or missing), then C<decode> will convert -the big integer Perl cannot handle as integer into a L<Math::BigInt> -object and convert a floating number (any) into a L<Math::BigFloat>. - -On the contrary, C<encode> converts C<Math::BigInt> objects and C<Math::BigFloat> -objects into JSON numbers with C<allow_blessed> enable. - - $json->allow_nonref->allow_blessed->allow_bignum; - $bigfloat = $json->decode('2.000000000000000000000000001'); - print $json->encode($bigfloat); - # => 2.000000000000000000000000001 - -See to L<JSON::XS/MAPPING> about the normal conversion of JSON number. - -=head2 loose - - $json = $json->loose([$enable]) - -The unescaped [\x00-\x1f\x22\x2f\x5c] strings are invalid in JSON strings -and the module doesn't allow to C<decode> to these (except for \x2f). -If C<$enable> is true (or missing), then C<decode> will accept these -unescaped strings. - - $json->loose->decode(qq|["abc - def"]|); - -See L<JSON::XS/SSECURITY CONSIDERATIONS>. - -=head2 escape_slash - - $json = $json->escape_slash([$enable]) - -According to JSON Grammar, I<slash> (U+002F) is escaped. But default -JSON::PP (as same as JSON::XS) encodes strings without escaping slash. - -If C<$enable> is true (or missing), then C<encode> will escape slashes. - -=head2 indent_length - - $json = $json->indent_length($length) - -JSON::XS indent space length is 3 and cannot be changed. -JSON::PP set the indent space length with the given $length. -The default is 3. The acceptable range is 0 to 15. - -=head2 sort_by - - $json = $json->sort_by($function_name) - $json = $json->sort_by($subroutine_ref) - -If $function_name or $subroutine_ref are set, its sort routine are used -in encoding JSON objects. - - $js = $pc->sort_by(sub { $JSON::PP::a cmp $JSON::PP::b })->encode($obj); - # is($js, q|{"a":1,"b":2,"c":3,"d":4,"e":5,"f":6,"g":7,"h":8,"i":9}|); - - $js = $pc->sort_by('own_sort')->encode($obj); - # is($js, q|{"a":1,"b":2,"c":3,"d":4,"e":5,"f":6,"g":7,"h":8,"i":9}|); - - sub JSON::PP::own_sort { $JSON::PP::a cmp $JSON::PP::b } - -As the sorting routine runs in the JSON::PP scope, the given -subroutine name and the special variables C<$a>, C<$b> will begin -'JSON::PP::'. - -If $integer is set, then the effect is same as C<canonical> on. - -=head1 INTERNAL - -For developers. - -=over - -=item PP_encode_box - -Returns - - { - depth => $depth, - indent_count => $indent_count, - } - - -=item PP_decode_box - -Returns - - { - text => $text, - at => $at, - ch => $ch, - len => $len, - depth => $depth, - encoding => $encoding, - is_valid_utf8 => $is_valid_utf8, - }; - -=back - -=head1 MAPPING - -This section is copied from JSON::XS and modified to C<JSON::PP>. -JSON::XS and JSON::PP mapping mechanisms are almost equivalent. - -See to L<JSON::XS/MAPPING>. - -=head2 JSON -> PERL - -=over 4 - -=item object - -A JSON object becomes a reference to a hash in Perl. No ordering of object -keys is preserved (JSON does not preserver object key ordering itself). - -=item array - -A JSON array becomes a reference to an array in Perl. - -=item string - -A JSON string becomes a string scalar in Perl - Unicode codepoints in JSON -are represented by the same codepoints in the Perl string, so no manual -decoding is necessary. - -=item number - -A JSON number becomes either an integer, numeric (floating point) or -string scalar in perl, depending on its range and any fractional parts. On -the Perl level, there is no difference between those as Perl handles all -the conversion details, but an integer may take slightly less memory and -might represent more values exactly than floating point numbers. - -If the number consists of digits only, C<JSON> will try to represent -it as an integer value. If that fails, it will try to represent it as -a numeric (floating point) value if that is possible without loss of -precision. Otherwise it will preserve the number as a string value (in -which case you lose roundtripping ability, as the JSON number will be -re-encoded to a JSON string). - -Numbers containing a fractional or exponential part will always be -represented as numeric (floating point) values, possibly at a loss of -precision (in which case you might lose perfect roundtripping ability, but -the JSON number will still be re-encoded as a JSON number). - -Note that precision is not accuracy - binary floating point values cannot -represent most decimal fractions exactly, and when converting from and to -floating point, C<JSON> only guarantees precision up to but not including -the least significant bit. - -When C<allow_bignum> is enable, the big integers -and the numeric can be optionally converted into L<Math::BigInt> and -L<Math::BigFloat> objects. - -=item true, false - -These JSON atoms become C<JSON::PP::true> and C<JSON::PP::false>, -respectively. They are overloaded to act almost exactly like the numbers -C<1> and C<0>. You can check whether a scalar is a JSON boolean by using -the C<JSON::is_bool> function. - - print JSON::PP::true . "\n"; - => true - print JSON::PP::true + 1; - => 1 - - ok(JSON::true eq '1'); - ok(JSON::true == 1); - -C<JSON> will install these missing overloading features to the backend modules. - - -=item null - -A JSON null atom becomes C<undef> in Perl. - -C<JSON::PP::null> returns C<undef>. - -=back - - -=head2 PERL -> JSON - -The mapping from Perl to JSON is slightly more difficult, as Perl is a -truly typeless language, so we can only guess which JSON type is meant by -a Perl value. - -=over 4 - -=item hash references - -Perl hash references become JSON objects. As there is no inherent ordering -in hash keys (or JSON objects), they will usually be encoded in a -pseudo-random order that can change between runs of the same program but -stays generally the same within a single run of a program. C<JSON> -optionally sort the hash keys (determined by the I<canonical> flag), so -the same data structure will serialise to the same JSON text (given same -settings and version of JSON::XS), but this incurs a runtime overhead -and is only rarely useful, e.g. when you want to compare some JSON text -against another for equality. - - -=item array references - -Perl array references become JSON arrays. - -=item other references - -Other unblessed references are generally not allowed and will cause an -exception to be thrown, except for references to the integers C<0> and -C<1>, which get turned into C<false> and C<true> atoms in JSON. You can -also use C<JSON::false> and C<JSON::true> to improve readability. - - to_json [\0,JSON::PP::true] # yields [false,true] - -=item JSON::PP::true, JSON::PP::false, JSON::PP::null - -These special values become JSON true and JSON false values, -respectively. You can also use C<\1> and C<\0> directly if you want. - -JSON::PP::null returns C<undef>. - -=item blessed objects - -Blessed objects are not directly representable in JSON. See the -C<allow_blessed> and C<convert_blessed> methods on various options on -how to deal with this: basically, you can choose between throwing an -exception, encoding the reference as if it weren't blessed, or provide -your own serialiser method. - -See to L<convert_blessed>. - -=item simple scalars - -Simple Perl scalars (any scalar that is not a reference) are the most -difficult objects to encode: JSON::XS and JSON::PP will encode undefined scalars as -JSON C<null> values, scalars that have last been used in a string context -before encoding as JSON strings, and anything else as number value: - - # dump as number - encode_json [2] # yields [2] - encode_json [-3.0e17] # yields [-3e+17] - my $value = 5; encode_json [$value] # yields [5] - - # used as string, so dump as string - print $value; - encode_json [$value] # yields ["5"] - - # undef becomes null - encode_json [undef] # yields [null] - -You can force the type to be a string by stringifying it: - - my $x = 3.1; # some variable containing a number - "$x"; # stringified - $x .= ""; # another, more awkward way to stringify - print $x; # perl does it for you, too, quite often - -You can force the type to be a number by numifying it: - - my $x = "3"; # some variable containing a string - $x += 0; # numify it, ensuring it will be dumped as a number - $x *= 1; # same thing, the choice is yours. - -You can not currently force the type in other, less obscure, ways. - -Note that numerical precision has the same meaning as under Perl (so -binary to decimal conversion follows the same rules as in Perl, which -can differ to other languages). Also, your perl interpreter might expose -extensions to the floating point numbers of your platform, such as -infinities or NaN's - these cannot be represented in JSON, and it is an -error to pass those in. - -=item Big Number - -When C<allow_bignum> is enable, -C<encode> converts C<Math::BigInt> objects and C<Math::BigFloat> -objects into JSON numbers. - - -=back - -=head1 UNICODE HANDLING ON PERLS - -If you do not know about Unicode on Perl well, -please check L<JSON::XS/A FEW NOTES ON UNICODE AND PERL>. - -=head2 Perl 5.8 and later - -Perl can handle Unicode and the JSON::PP de/encode methods also work properly. - - $json->allow_nonref->encode(chr hex 3042); - $json->allow_nonref->encode(chr hex 12345); - -Returns C<"\u3042"> and C<"\ud808\udf45"> respectively. - - $json->allow_nonref->decode('"\u3042"'); - $json->allow_nonref->decode('"\ud808\udf45"'); - -Returns UTF-8 encoded strings with UTF8 flag, regarded as C<U+3042> and C<U+12345>. - -Note that the versions from Perl 5.8.0 to 5.8.2, Perl built-in C<join> was broken, -so JSON::PP wraps the C<join> with a subroutine. Thus JSON::PP works slow in the versions. - - -=head2 Perl 5.6 - -Perl can handle Unicode and the JSON::PP de/encode methods also work. - -=head2 Perl 5.005 - -Perl 5.005 is a byte semantics world -- all strings are sequences of bytes. -That means the unicode handling is not available. - -In encoding, - - $json->allow_nonref->encode(chr hex 3042); # hex 3042 is 12354. - $json->allow_nonref->encode(chr hex 12345); # hex 12345 is 74565. - -Returns C<B> and C<E>, as C<chr> takes a value more than 255, it treats -as C<$value % 256>, so the above codes are equivalent to : - - $json->allow_nonref->encode(chr 66); - $json->allow_nonref->encode(chr 69); - -In decoding, - - $json->decode('"\u00e3\u0081\u0082"'); - -The returned is a byte sequence C<0xE3 0x81 0x82> for UTF-8 encoded -japanese character (C<HIRAGANA LETTER A>). -And if it is represented in Unicode code point, C<U+3042>. - -Next, - - $json->decode('"\u3042"'); - -We ordinary expect the returned value is a Unicode character C<U+3042>. -But here is 5.005 world. This is C<0xE3 0x81 0x82>. - - $json->decode('"\ud808\udf45"'); - -This is not a character C<U+12345> but bytes - C<0xf0 0x92 0x8d 0x85>. - - -=head1 TODO - -=over - -=item speed - -=item memory saving - -=back - - -=head1 SEE ALSO - -Most of the document are copied and modified from JSON::XS doc. - -L<JSON::XS> - -RFC4627 (L<http://www.ietf.org/rfc/rfc4627.txt>) - -=head1 AUTHOR - -Makamaka Hannyaharamitu, E<lt>makamaka[at]cpan.orgE<gt> - - -=head1 COPYRIGHT AND LICENSE - -Copyright 2007-2012 by Makamaka Hannyaharamitu - -This library is free software; you can redistribute it and/or modify -it under the same terms as Perl itself. - -=cut diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/models/tortoise.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/models/tortoise.py deleted file mode 100644 index 16644ff95eee6799f5e78603e2011f63b05a1011..0000000000000000000000000000000000000000 --- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/models/tortoise.py +++ /dev/null @@ -1,911 +0,0 @@ -import os -import random -from contextlib import contextmanager -from dataclasses import dataclass -from time import time - -import torch -import torch.nn.functional as F -import torchaudio -from coqpit import Coqpit -from tqdm import tqdm - -from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram -from TTS.tts.layers.tortoise.audio_utils import denormalize_tacotron_mel, load_voice, wav_to_univnet_mel -from TTS.tts.layers.tortoise.autoregressive import UnifiedVoice -from TTS.tts.layers.tortoise.classifier import AudioMiniEncoderWithClassifierHead -from TTS.tts.layers.tortoise.clvp import CLVP -from TTS.tts.layers.tortoise.diffusion import SpacedDiffusion, get_named_beta_schedule, space_timesteps -from TTS.tts.layers.tortoise.diffusion_decoder import DiffusionTts -from TTS.tts.layers.tortoise.random_latent_generator import RandomLatentConverter -from TTS.tts.layers.tortoise.tokenizer import VoiceBpeTokenizer -from TTS.tts.layers.tortoise.vocoder import VocConf, VocType -from TTS.tts.layers.tortoise.wav2vec_alignment import Wav2VecAlignment -from TTS.tts.models.base_tts import BaseTTS - - -def pad_or_truncate(t, length): - """ - Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s. - """ - tp = t[..., :length] - if t.shape[-1] == length: - tp = t - elif t.shape[-1] < length: - tp = F.pad(t, (0, length - t.shape[-1])) - return tp - - -def deterministic_state(seed=None): - """ - Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be - reproduced. - """ - seed = int(time()) if seed is None else seed - torch.manual_seed(seed) - random.seed(seed) - # Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary. - # torch.use_deterministic_algorithms(True) - - return seed - - -def load_discrete_vocoder_diffuser( - trained_diffusion_steps=4000, - desired_diffusion_steps=200, - cond_free=True, - cond_free_k=1, - sampler="ddim", -): - """ - Helper function to load a GaussianDiffusion instance configured for use as a vocoder. - """ - return SpacedDiffusion( - use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), - model_mean_type="epsilon", - model_var_type="learned_range", - loss_type="mse", - betas=get_named_beta_schedule("linear", trained_diffusion_steps), - conditioning_free=cond_free, - conditioning_free_k=cond_free_k, - sampler=sampler, - ) - - -def format_conditioning(clip, cond_length=132300, device="cuda", **kwargs): - """ - Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models. - """ - gap = clip.shape[-1] - cond_length - if gap < 0: - clip = F.pad(clip, pad=(0, abs(gap))) - elif gap > 0: - rand_start = random.randint(0, gap) - clip = clip[:, rand_start : rand_start + cond_length] - mel_clip = TorchMelSpectrogram(**kwargs)(clip.unsqueeze(0)).squeeze(0) - return mel_clip.unsqueeze(0).to(device) - - -def fix_autoregressive_output(codes, stop_token, complain=True): - """ - This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was - trained on and what the autoregressive code generator creates (which has no padding or end). - This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with - a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE - and copying out the last few codes. - - Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar. - """ - # Strip off the autoregressive stop token and add padding. - stop_token_indices = (codes == stop_token).nonzero() - if len(stop_token_indices) == 0: - if complain: - print( - "No stop tokens found in one of the generated voice clips. This typically means the spoken audio is " - "too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, " - "try breaking up your input text." - ) - return codes - codes[stop_token_indices] = 83 - stm = stop_token_indices.min().item() - codes[stm:] = 83 - if stm - 3 < codes.shape[0]: - codes[-3] = 45 - codes[-2] = 45 - codes[-1] = 248 - return codes - - -def do_spectrogram_diffusion( - diffusion_model, - diffuser, - latents, - conditioning_latents, - temperature=1, - verbose=True, -): - """ - Uses the specified diffusion model to convert discrete codes into a spectrogram. - """ - with torch.no_grad(): - output_seq_len = ( - latents.shape[1] * 4 * 24000 // 22050 - ) # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal. - output_shape = (latents.shape[0], 100, output_seq_len) - precomputed_embeddings = diffusion_model.timestep_independent( - latents, conditioning_latents, output_seq_len, False - ) - - noise = torch.randn(output_shape, device=latents.device) * temperature - mel = diffuser.sample_loop( - diffusion_model, - output_shape, - noise=noise, - model_kwargs={"precomputed_aligned_embeddings": precomputed_embeddings}, - progress=verbose, - ) - return denormalize_tacotron_mel(mel)[:, :, :output_seq_len] - - -def classify_audio_clip(clip, model_dir): - """ - Returns whether or not Tortoises' classifier thinks the given clip came from Tortoise. - :param clip: torch tensor containing audio waveform data (get it from load_audio) - :return: True if the clip was classified as coming from Tortoise and false if it was classified as real. - """ - classifier = AudioMiniEncoderWithClassifierHead( - 2, - spec_dim=1, - embedding_dim=512, - depth=5, - downsample_factor=4, - resnet_blocks=2, - attn_blocks=4, - num_attn_heads=4, - base_channels=32, - dropout=0, - kernel_size=5, - distribute_zero_label=False, - ) - classifier.load_state_dict(torch.load(os.path.join(model_dir, "classifier.pth"), map_location=torch.device("cpu"))) - clip = clip.cpu().unsqueeze(0) - results = F.softmax(classifier(clip), dim=-1) - return results[0][0] - - -def pick_best_batch_size_for_gpu(): - """ - Tries to pick a batch size that will fit in your GPU. These sizes aren't guaranteed to work, but they should give - you a good shot. - """ - if torch.cuda.is_available(): - _, available = torch.cuda.mem_get_info() - availableGb = available / (1024**3) - batch_size = 1 - if availableGb > 14: - batch_size = 16 - elif availableGb > 10: - batch_size = 8 - elif availableGb > 7: - batch_size = 4 - return batch_size - - -@dataclass -class TortoiseAudioConfig(Coqpit): - sample_rate: int = 22050 - diffusion_sample_rate: int = 24000 - output_sample_rate: int = 24000 - - -@dataclass -class TortoiseArgs(Coqpit): - """A dataclass to represent Tortoise model arguments that define the model structure. - - Args: - autoregressive_batch_size (int): The size of the auto-regressive batch. - enable_redaction (bool, optional): Whether to enable redaction. Defaults to True. - high_vram (bool, optional): Whether to use high VRAM. Defaults to False. - kv_cache (bool, optional): Whether to use the kv_cache. Defaults to True. - ar_checkpoint (str, optional): The checkpoint for the autoregressive model. Defaults to None. - clvp_checkpoint (str, optional): The checkpoint for the ConditionalLatentVariablePerseq model. Defaults to None. - diff_checkpoint (str, optional): The checkpoint for the DiffTTS model. Defaults to None. - num_chars (int, optional): The maximum number of characters to generate. Defaults to 255. - vocoder (VocType, optional): The vocoder to use for synthesis. Defaults to VocConf.Univnet. - - For UnifiedVoice model: - ar_max_mel_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604. - ar_max_text_tokens (int, optional): The maximum text tokens for the autoregressive model. Defaults to 402. - ar_max_conditioning_inputs (int, optional): The maximum conditioning inputs for the autoregressive model. Defaults to 2. - ar_layers (int, optional): The number of layers for the autoregressive model. Defaults to 30. - ar_model_dim (int, optional): The model dimension for the autoregressive model. Defaults to 1024. - ar_heads (int, optional): The number of heads for the autoregressive model. Defaults to 16. - ar_number_text_tokens (int, optional): The number of text tokens for the autoregressive model. Defaults to 255. - ar_start_text_token (int, optional): The start text token for the autoregressive model. Defaults to 255. - ar_checkpointing (bool, optional): Whether to use checkpointing for the autoregressive model. Defaults to False. - ar_train_solo_embeddings (bool, optional): Whether to train embeddings for the autoregressive model. Defaults to False. - - For DiffTTS model: - diff_model_channels (int, optional): The number of channels for the DiffTTS model. Defaults to 1024. - diff_num_layers (int, optional): The number of layers for the DiffTTS model. Defaults to 10. - diff_in_channels (int, optional): The input channels for the DiffTTS model. Defaults to 100. - diff_out_channels (int, optional): The output channels for the DiffTTS model. Defaults to 200. - diff_in_latent_channels (int, optional): The input latent channels for the DiffTTS model. Defaults to 1024. - diff_in_tokens (int, optional): The input tokens for the DiffTTS model. Defaults to 8193. - diff_dropout (int, optional): The dropout percentage for the DiffTTS model. Defaults to 0. - diff_use_fp16 (bool, optional): Whether to use fp16 for the DiffTTS model. Defaults to False. - diff_num_heads (int, optional): The number of heads for the DiffTTS model. Defaults to 16. - diff_layer_drop (int, optional): The layer dropout percentage for the DiffTTS model. Defaults to 0. - diff_unconditioned_percentage (int, optional): The percentage of unconditioned inputs for the DiffTTS model. Defaults to 0. - - For ConditionalLatentVariablePerseq model: - clvp_dim_text (int): The dimension of the text input for the CLVP module. Defaults to 768. - clvp_dim_speech (int): The dimension of the speech input for the CLVP module. Defaults to 768. - clvp_dim_latent (int): The dimension of the latent representation for the CLVP module. Defaults to 768. - clvp_num_text_tokens (int): The number of text tokens used by the CLVP module. Defaults to 256. - clvp_text_enc_depth (int): The depth of the text encoder in the CLVP module. Defaults to 20. - clvp_text_seq_len (int): The maximum sequence length of the text input for the CLVP module. Defaults to 350. - clvp_text_heads (int): The number of attention heads used by the text encoder in the CLVP module. Defaults to 12. - clvp_num_speech_tokens (int): The number of speech tokens used by the CLVP module. Defaults to 8192. - clvp_speech_enc_depth (int): The depth of the speech encoder in the CLVP module. Defaults to 20. - clvp_speech_heads (int): The number of attention heads used by the speech encoder in the CLVP module. Defaults to 12. - clvp_speech_seq_len (int): The maximum sequence length of the speech input for the CLVP module. Defaults to 430. - clvp_use_xformers (bool): A flag indicating whether the model uses transformers in the CLVP module. Defaults to True. - duration_const (int): A constant value used in the model. Defaults to 102400. - """ - - autoregressive_batch_size: int = 1 - enable_redaction: bool = False - high_vram: bool = False - kv_cache: bool = True - ar_checkpoint: str = None - clvp_checkpoint: str = None - diff_checkpoint: str = None - num_chars: int = 255 - vocoder: VocType = VocConf.Univnet - - # UnifiedVoice params - ar_max_mel_tokens: int = 604 - ar_max_text_tokens: int = 402 - ar_max_conditioning_inputs: int = 2 - ar_layers: int = 30 - ar_model_dim: int = 1024 - ar_heads: int = 16 - ar_number_text_tokens: int = 255 - ar_start_text_token: int = 255 - ar_checkpointing: bool = False - ar_train_solo_embeddings: bool = False - - # DiffTTS params - diff_model_channels: int = 1024 - diff_num_layers: int = 10 - diff_in_channels: int = 100 - diff_out_channels: int = 200 - diff_in_latent_channels: int = 1024 - diff_in_tokens: int = 8193 - diff_dropout: int = 0 - diff_use_fp16: bool = False - diff_num_heads: int = 16 - diff_layer_drop: int = 0 - diff_unconditioned_percentage: int = 0 - - # clvp params - clvp_dim_text: int = 768 - clvp_dim_speech: int = 768 - clvp_dim_latent: int = 768 - clvp_num_text_tokens: int = 256 - clvp_text_enc_depth: int = 20 - clvp_text_seq_len: int = 350 - clvp_text_heads: int = 12 - clvp_num_speech_tokens: int = 8192 - clvp_speech_enc_depth: int = 20 - clvp_speech_heads: int = 12 - clvp_speech_seq_len: int = 430 - clvp_use_xformers: bool = True - # constants - duration_const: int = 102400 - - -class Tortoise(BaseTTS): - """Tortoise model class. - - Currently only supports inference. - - Examples: - >>> from TTS.tts.configs.tortoise_config import TortoiseConfig - >>> from TTS.tts.models.tortoise import Tortoise - >>> config = TortoiseConfig() - >>> model = Tortoise.inif_from_config(config) - >>> model.load_checkpoint(config, checkpoint_dir="paths/to/models_dir/", eval=True) - """ - - def __init__(self, config: Coqpit): - super().__init__(config, ap=None, tokenizer=None) - self.mel_norm_path = None - self.config = config - self.ar_checkpoint = self.args.ar_checkpoint - self.diff_checkpoint = self.args.diff_checkpoint # TODO: check if this is even needed - self.models_dir = config.model_dir - self.autoregressive_batch_size = ( - pick_best_batch_size_for_gpu() - if self.args.autoregressive_batch_size is None - else self.args.autoregressive_batch_size - ) - self.enable_redaction = self.args.enable_redaction - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - if self.enable_redaction: - self.aligner = Wav2VecAlignment() - - self.tokenizer = VoiceBpeTokenizer() - - self.autoregressive = UnifiedVoice( - max_mel_tokens=self.args.ar_max_mel_tokens, - max_text_tokens=self.args.ar_max_text_tokens, - max_conditioning_inputs=self.args.ar_max_conditioning_inputs, - layers=self.args.ar_layers, - model_dim=self.args.ar_model_dim, - heads=self.args.ar_heads, - number_text_tokens=self.args.ar_number_text_tokens, - start_text_token=self.args.ar_start_text_token, - checkpointing=self.args.ar_checkpointing, - train_solo_embeddings=self.args.ar_train_solo_embeddings, - ).cpu() - - self.diffusion = DiffusionTts( - model_channels=self.args.diff_model_channels, - num_layers=self.args.diff_num_layers, - in_channels=self.args.diff_in_channels, - out_channels=self.args.diff_out_channels, - in_latent_channels=self.args.diff_in_latent_channels, - in_tokens=self.args.diff_in_tokens, - dropout=self.args.diff_dropout, - use_fp16=self.args.diff_use_fp16, - num_heads=self.args.diff_num_heads, - layer_drop=self.args.diff_layer_drop, - unconditioned_percentage=self.args.diff_unconditioned_percentage, - ).cpu() - - self.clvp = CLVP( - dim_text=self.args.clvp_dim_text, - dim_speech=self.args.clvp_dim_speech, - dim_latent=self.args.clvp_dim_latent, - num_text_tokens=self.args.clvp_num_text_tokens, - text_enc_depth=self.args.clvp_text_enc_depth, - text_seq_len=self.args.clvp_text_seq_len, - text_heads=self.args.clvp_text_heads, - num_speech_tokens=self.args.clvp_num_speech_tokens, - speech_enc_depth=self.args.clvp_speech_enc_depth, - speech_heads=self.args.clvp_speech_heads, - speech_seq_len=self.args.clvp_speech_seq_len, - use_xformers=self.args.clvp_use_xformers, - ).cpu() - - self.vocoder = self.args.vocoder.value.constructor().cpu() - - # Random latent generators (RLGs) are loaded lazily. - self.rlg_auto = None - self.rlg_diffusion = None - - if self.args.high_vram: - self.autoregressive = self.autoregressive.to(self.device) - self.diffusion = self.diffusion.to(self.device) - self.clvp = self.clvp.to(self.device) - self.vocoder = self.vocoder.to(self.device) - self.high_vram = self.args.high_vram - - @contextmanager - def temporary_cuda(self, model): - if self.high_vram: - yield model - else: - m = model.to(self.device) - yield m - m = model.cpu() - - def get_conditioning_latents( - self, - voice_samples, - return_mels=False, - latent_averaging_mode=0, - original_tortoise=False, - ): - """ - Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent). - These are expressive learned latents that encode aspects of the provided clips like voice, intonation, and acoustic - properties. - :param voice_samples: List of arbitrary reference clips, which should be *pairs* of torch tensors containing arbitrary kHz waveform data. - :param latent_averaging_mode: 0/1/2 for following modes: - 0 - latents will be generated as in original tortoise, using ~4.27s from each voice sample, averaging latent across all samples - 1 - latents will be generated using (almost) entire voice samples, averaged across all the ~4.27s chunks - 2 - latents will be generated using (almost) entire voice samples, averaged per voice sample - """ - assert latent_averaging_mode in [ - 0, - 1, - 2, - ], "latent_averaging mode has to be one of (0, 1, 2)" - - with torch.no_grad(): - voice_samples = [[v.to(self.device) for v in ls] for ls in voice_samples] - - auto_conds = [] - for ls in voice_samples: - auto_conds.append(format_conditioning(ls[0], device=self.device, mel_norm_file=self.mel_norm_path)) - auto_conds = torch.stack(auto_conds, dim=1) - with self.temporary_cuda(self.autoregressive) as ar: - auto_latent = ar.get_conditioning(auto_conds) - - diffusion_conds = [] - - DURS_CONST = self.args.duration_const - for ls in voice_samples: - # The diffuser operates at a sample rate of 24000 (except for the latent inputs) - sample = torchaudio.functional.resample(ls[0], 22050, 24000) if original_tortoise else ls[1] - if latent_averaging_mode == 0: - sample = pad_or_truncate(sample, DURS_CONST) - cond_mel = wav_to_univnet_mel( - sample.to(self.device), - do_normalization=False, - device=self.device, - ) - diffusion_conds.append(cond_mel) - else: - from math import ceil - - if latent_averaging_mode == 2: - temp_diffusion_conds = [] - for chunk in range(ceil(sample.shape[1] / DURS_CONST)): - current_sample = sample[:, chunk * DURS_CONST : (chunk + 1) * DURS_CONST] - current_sample = pad_or_truncate(current_sample, DURS_CONST) - cond_mel = wav_to_univnet_mel( - current_sample.to(self.device), - do_normalization=False, - device=self.device, - ) - if latent_averaging_mode == 1: - diffusion_conds.append(cond_mel) - elif latent_averaging_mode == 2: - temp_diffusion_conds.append(cond_mel) - if latent_averaging_mode == 2: - diffusion_conds.append(torch.stack(temp_diffusion_conds).mean(0)) - diffusion_conds = torch.stack(diffusion_conds, dim=1) - - with self.temporary_cuda(self.diffusion) as diffusion: - diffusion_latent = diffusion.get_conditioning(diffusion_conds) - - if return_mels: - return auto_latent, diffusion_latent, auto_conds, diffusion_conds - return auto_latent, diffusion_latent - - def get_random_conditioning_latents(self): - # Lazy-load the RLG models. - if self.rlg_auto is None: - self.rlg_auto = RandomLatentConverter(1024).eval() - self.rlg_auto.load_state_dict( - torch.load( - os.path.join(self.models_dir, "rlg_auto.pth"), - map_location=torch.device("cpu"), - ) - ) - self.rlg_diffusion = RandomLatentConverter(2048).eval() - self.rlg_diffusion.load_state_dict( - torch.load( - os.path.join(self.models_dir, "rlg_diffuser.pth"), - map_location=torch.device("cpu"), - ) - ) - with torch.no_grad(): - return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0])) - - def synthesize(self, text, config, speaker_id="random", voice_dirs=None, **kwargs): - """Synthesize speech with the given input text. - - Args: - text (str): Input text. - config (TortoiseConfig): Config with inference parameters. - speaker_id (str): One of the available speaker names. If `random`, it generates a random speaker. - voice_dirs (List[str]): List of paths that host reference audio files for speakers. Defaults to None. - **kwargs: Inference settings. See `inference()`. - - Returns: - A dictionary of the output values with `wav` as output waveform, `deterministic_seed` as seed used at inference, - `text_input` as text token IDs after tokenizer, `voice_samples` as samples used for cloning, `conditioning_latents` - as latents used at inference. - - """ - - speaker_id = "random" if speaker_id is None else speaker_id - - if voice_dirs is not None: - voice_dirs = [voice_dirs] - voice_samples, conditioning_latents = load_voice(speaker_id, voice_dirs) - - else: - voice_samples, conditioning_latents = load_voice(speaker_id) - - outputs = self.inference_with_config( - text, config, voice_samples=voice_samples, conditioning_latents=conditioning_latents, **kwargs - ) - - return_dict = { - "wav": outputs["wav"], - "deterministic_seed": outputs["deterministic_seed"], - "text_inputs": outputs["text"], - "voice_samples": outputs["voice_samples"], - "conditioning_latents": outputs["conditioning_latents"], - } - - return return_dict - - def inference_with_config(self, text, config, **kwargs): - """ - inference with config - #TODO describe in detail - """ - # Use generally found best tuning knobs for generation. - settings = { - "temperature": config.temperature, - "length_penalty": config.length_penalty, - "repetition_penalty": config.repetition_penalty, - "top_p": config.top_p, - "cond_free_k": config.cond_free_k, - "diffusion_temperature": config.diffusion_temperature, - "sampler": config.sampler, - } - # Presets are defined here. - presets = { - "single_sample": { - "num_autoregressive_samples": 8, - "diffusion_iterations": 10, - "sampler": "ddim", - }, - "ultra_fast": { - "num_autoregressive_samples": 16, - "diffusion_iterations": 10, - "sampler": "ddim", - }, - "ultra_fast_old": { - "num_autoregressive_samples": 16, - "diffusion_iterations": 30, - "cond_free": False, - }, - "very_fast": { - "num_autoregressive_samples": 32, - "diffusion_iterations": 30, - "sampler": "dpm++2m", - }, - "fast": { - "num_autoregressive_samples": 5, - "diffusion_iterations": 50, - "sampler": "ddim", - }, - "fast_old": {"num_autoregressive_samples": 96, "diffusion_iterations": 80}, - "standard": { - "num_autoregressive_samples": 5, - "diffusion_iterations": 200, - }, - "high_quality": { - "num_autoregressive_samples": 256, - "diffusion_iterations": 400, - }, - } - if "preset" in kwargs: - settings.update(presets[kwargs["preset"]]) - kwargs.pop("preset") - settings.update(kwargs) # allow overriding of preset settings with kwargs - return self.inference(text, **settings) - - def inference( - self, - text, - voice_samples=None, - conditioning_latents=None, - k=1, - verbose=True, - use_deterministic_seed=None, - return_deterministic_state=False, - latent_averaging_mode=0, - # autoregressive generation parameters follow - num_autoregressive_samples=16, - temperature=0.8, - length_penalty=1, - repetition_penalty=2.0, - top_p=0.8, - max_mel_tokens=500, - # diffusion generation parameters follow - diffusion_iterations=100, - cond_free=True, - cond_free_k=2, - diffusion_temperature=1.0, - sampler="ddim", - half=True, - original_tortoise=False, - **hf_generate_kwargs, - ): - """ - This function produces an audio clip of the given text being spoken with the given reference voice. - - Args: - text: (str) Text to be spoken. - voice_samples: (List[Tuple[torch.Tensor]]) List of an arbitrary number of reference clips, which should be tuple-pairs - of torch tensors containing arbitrary kHz waveform data. - conditioning_latents: (Tuple[autoregressive_conditioning_latent, diffusion_conditioning_latent]) A tuple of - (autoregressive_conditioning_latent, diffusion_conditioning_latent), which can be provided in lieu - of voice_samples. This is ignored unless `voice_samples=None`. Conditioning latents can be retrieved - via `get_conditioning_latents()`. - k: (int) The number of returned clips. The most likely (as determined by Tortoises' CLVP model) clips are returned. - latent_averaging_mode: (int) 0/1/2 for following modes: - 0 - latents will be generated as in original tortoise, using ~4.27s from each voice sample, averaging latent across all samples - 1 - latents will be generated using (almost) entire voice samples, averaged across all the ~4.27s chunks - 2 - latents will be generated using (almost) entire voice samples, averaged per voice sample - verbose: (bool) Whether or not to print log messages indicating the progress of creating a clip. Default=true. - num_autoregressive_samples: (int) Number of samples taken from the autoregressive model, all of which are filtered using CLVP. - As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great". - temperature: (float) The softmax temperature of the autoregressive model. - length_penalty: (float) A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs. - repetition_penalty: (float) A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce - the incidence of long silences or "uhhhhhhs", etc. - top_p: (float) P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely" (aka boring) outputs. - max_mel_tokens: (int) Restricts the output length. (0,600] integer. Each unit is 1/20 of a second. - typical_sampling: (bool) Turns typical sampling on or off. This sampling mode is discussed in this paper: https://arxiv.org/abs/2202.00666 - I was interested in the premise, but the results were not as good as I was hoping. This is off by default, but could use some tuning. - typical_mass: (float) The typical_mass parameter from the typical_sampling algorithm. - diffusion_iterations: (int) Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively - refine the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better, however. - cond_free: (bool) Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for - each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output of the two - is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and dramatically improves realism. - cond_free_k: (float) Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf]. - As cond_free_k increases, the output becomes dominated by the conditioning-free signal. - diffusion_temperature: (float) Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0 - are the "mean" prediction of the diffusion network and will sound bland and smeared. - hf_generate_kwargs: (**kwargs) The huggingface Transformers generate API is used for the autoregressive transformer. - Extra keyword args fed to this function get forwarded directly to that API. Documentation - here: https://huggingface.co/docs/transformers/internal/generation_utils - - Returns: - Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length. - Sample rate is 24kHz. - """ - deterministic_seed = deterministic_state(seed=use_deterministic_seed) - - text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device) - text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary. - assert ( - text_tokens.shape[-1] < 400 - ), "Too much text provided. Break the text up into separate segments and re-try inference." - - if voice_samples is not None: - ( - auto_conditioning, - diffusion_conditioning, - _, - _, - ) = self.get_conditioning_latents( - voice_samples, - return_mels=True, - latent_averaging_mode=latent_averaging_mode, - original_tortoise=original_tortoise, - ) - elif conditioning_latents is not None: - auto_conditioning, diffusion_conditioning = conditioning_latents - else: - ( - auto_conditioning, - diffusion_conditioning, - ) = self.get_random_conditioning_latents() - auto_conditioning = auto_conditioning.to(self.device) - diffusion_conditioning = diffusion_conditioning.to(self.device) - - diffuser = load_discrete_vocoder_diffuser( - desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k, sampler=sampler - ) - - # in the case of single_sample, - orig_batch_size = self.autoregressive_batch_size - while num_autoregressive_samples % self.autoregressive_batch_size: - self.autoregressive_batch_size //= 2 - with torch.no_grad(): - samples = [] - num_batches = num_autoregressive_samples // self.autoregressive_batch_size - stop_mel_token = self.autoregressive.stop_mel_token - calm_token = ( - 83 # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output" - ) - self.autoregressive = self.autoregressive.to(self.device) - if verbose: - print("Generating autoregressive samples..") - with self.temporary_cuda(self.autoregressive) as autoregressive, torch.autocast( - device_type="cuda", dtype=torch.float16, enabled=half - ): - for b in tqdm(range(num_batches), disable=not verbose): - codes = autoregressive.inference_speech( - auto_conditioning, - text_tokens, - do_sample=True, - top_p=top_p, - temperature=temperature, - num_return_sequences=self.autoregressive_batch_size, - length_penalty=length_penalty, - repetition_penalty=repetition_penalty, - max_generate_length=max_mel_tokens, - **hf_generate_kwargs, - ) - padding_needed = max_mel_tokens - codes.shape[1] - codes = F.pad(codes, (0, padding_needed), value=stop_mel_token) - samples.append(codes) - self.autoregressive_batch_size = orig_batch_size # in the case of single_sample - - clip_results = [] - with self.temporary_cuda(self.clvp) as clvp, torch.autocast( - device_type="cuda", dtype=torch.float16, enabled=half - ): - for batch in tqdm(samples, disable=not verbose): - for i in range(batch.shape[0]): - batch[i] = fix_autoregressive_output(batch[i], stop_mel_token) - clvp_res = clvp( - text_tokens.repeat(batch.shape[0], 1), - batch, - return_loss=False, - ) - clip_results.append(clvp_res) - - clip_results = torch.cat(clip_results, dim=0) - samples = torch.cat(samples, dim=0) - best_results = samples[torch.topk(clip_results, k=k).indices] - del samples - - # The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning - # inputs. Re-produce those for the top results. This could be made more efficient by storing all of these - # results, but will increase memory usage. - with self.temporary_cuda(self.autoregressive) as autoregressive: - best_latents = autoregressive( - auto_conditioning.repeat(k, 1), - text_tokens.repeat(k, 1), - torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), - best_results, - torch.tensor( - [best_results.shape[-1] * self.autoregressive.mel_length_compression], - device=text_tokens.device, - ), - return_latent=True, - clip_inputs=False, - ) - del auto_conditioning - - if verbose: - print("Transforming autoregressive outputs into audio..") - wav_candidates = [] - for b in range(best_results.shape[0]): - codes = best_results[b].unsqueeze(0) - latents = best_latents[b].unsqueeze(0) - - # Find the first occurrence of the "calm" token and trim the codes to that. - ctokens = 0 - for code in range(codes.shape[-1]): - if codes[0, code] == calm_token: - ctokens += 1 - else: - ctokens = 0 - if ctokens > 8: # 8 tokens gives the diffusion model some "breathing room" to terminate speech. - latents = latents[:, :code] - break - with self.temporary_cuda(self.diffusion) as diffusion: - mel = do_spectrogram_diffusion( - diffusion, - diffuser, - latents, - diffusion_conditioning, - temperature=diffusion_temperature, - verbose=verbose, - ) - with self.temporary_cuda(self.vocoder) as vocoder: - wav = vocoder.inference(mel) - wav_candidates.append(wav.cpu()) - - def potentially_redact(clip, text): - if self.enable_redaction: - return self.aligner.redact(clip.squeeze(1), text).unsqueeze(1) - return clip - - wav_candidates = [potentially_redact(wav_candidate, text) for wav_candidate in wav_candidates] - - if len(wav_candidates) > 1: - res = wav_candidates - else: - res = wav_candidates[0] - - return_dict = { - "wav": res, - "deterministic_seed": None, - "text": None, - "voice_samples": None, - "conditioning_latents": None, - } - if return_deterministic_state: - return_dict = { - "wav": res, - "deterministic_seed": deterministic_seed, - "text": text, - "voice_samples": voice_samples, - "conditioning_latents": conditioning_latents, - } - return return_dict - - def forward(self): - raise NotImplementedError("Tortoise Training is not implemented") - - def eval_step(self): - raise NotImplementedError("Tortoise Training is not implemented") - - @staticmethod - def init_from_config(config: "TortoiseConfig", **kwargs): # pylint: disable=unused-argument - return Tortoise(config) - - def load_checkpoint( - self, - config, - checkpoint_dir, - ar_checkpoint_path=None, - diff_checkpoint_path=None, - clvp_checkpoint_path=None, - vocoder_checkpoint_path=None, - eval=False, - strict=True, - **kwargs, - ): # pylint: disable=unused-argument, redefined-builtin - """Load a model checkpoints from a directory. This model is with multiple checkpoint files and it - expects to have all the files to be under the given `checkpoint_dir` with the rigth names. - If eval is True, set the model to eval mode. - - Args: - config (TortoiseConfig): The model config. - checkpoint_dir (str): The directory where the checkpoints are stored. - ar_checkpoint_path (str, optional): The path to the autoregressive checkpoint. Defaults to None. - diff_checkpoint_path (str, optional): The path to the diffusion checkpoint. Defaults to None. - clvp_checkpoint_path (str, optional): The path to the CLVP checkpoint. Defaults to None. - vocoder_checkpoint_path (str, optional): The path to the vocoder checkpoint. Defaults to None. - eval (bool, optional): Whether to set the model to eval mode. Defaults to False. - strict (bool, optional): Whether to load the model strictly. Defaults to True. - """ - if self.models_dir is None: - self.models_dir = checkpoint_dir - ar_path = ar_checkpoint_path or os.path.join(checkpoint_dir, "autoregressive.pth") - diff_path = diff_checkpoint_path or os.path.join(checkpoint_dir, "diffusion_decoder.pth") - clvp_path = clvp_checkpoint_path or os.path.join(checkpoint_dir, "clvp2.pth") - vocoder_checkpoint_path = vocoder_checkpoint_path or os.path.join(checkpoint_dir, "vocoder.pth") - self.mel_norm_path = os.path.join(checkpoint_dir, "mel_norms.pth") - - if os.path.exists(ar_path): - # remove keys from the checkpoint that are not in the model - checkpoint = torch.load(ar_path, map_location=torch.device("cpu")) - - # strict set False - # due to removed `bias` and `masked_bias` changes in Transformers - self.autoregressive.load_state_dict(checkpoint, strict=False) - - if os.path.exists(diff_path): - self.diffusion.load_state_dict(torch.load(diff_path), strict=strict) - - if os.path.exists(clvp_path): - self.clvp.load_state_dict(torch.load(clvp_path), strict=strict) - - if os.path.exists(vocoder_checkpoint_path): - self.vocoder.load_state_dict( - config.model_args.vocoder.value.optionally_index( - torch.load( - vocoder_checkpoint_path, - map_location=torch.device("cpu"), - ) - ) - ) - - if eval: - self.autoregressive.post_init_gpt2_config(self.args.kv_cache) - self.autoregressive.eval() - self.diffusion.eval() - self.clvp.eval() - self.vocoder.eval() - - def train_step(self): - raise NotImplementedError("Tortoise Training is not implemented") diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/audio/feature_transforms/delta_deltas.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/audio/feature_transforms/delta_deltas.py deleted file mode 100644 index 49d090b11e5b31562e0aedc9b4e2b8d0d510eeda..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/audio/feature_transforms/delta_deltas.py +++ /dev/null @@ -1,37 +0,0 @@ -import numpy as np -import torch -from fairseq.data.audio.feature_transforms import ( - AudioFeatureTransform, - register_audio_feature_transform, -) - - -@register_audio_feature_transform("delta_deltas") -class DeltaDeltas(AudioFeatureTransform): - """Expand delta-deltas features from spectrum.""" - - @classmethod - def from_config_dict(cls, config=None): - _config = {} if config is None else config - return DeltaDeltas(_config.get("win_length", 5)) - - def __init__(self, win_length=5): - self.win_length = win_length - - def __repr__(self): - return self.__class__.__name__ - - def __call__(self, spectrogram): - from torchaudio.functional import compute_deltas - - assert len(spectrogram.shape) == 2, "spectrogram must be a 2-D tensor." - # spectrogram is T x F, while compute_deltas takes (…, F, T) - spectrogram = torch.from_numpy(spectrogram).transpose(0, 1) - delta = compute_deltas(spectrogram) - delta_delta = compute_deltas(delta) - - out_feat = np.concatenate( - [spectrogram, delta.numpy(), delta_delta.numpy()], axis=0 - ) - out_feat = np.transpose(out_feat) - return out_feat diff --git a/spaces/asafAdge/Detic/datasets/README.md b/spaces/asafAdge/Detic/datasets/README.md deleted file mode 100644 index aadb3133e8c9a5345e137c5736485109c1a107db..0000000000000000000000000000000000000000 --- a/spaces/asafAdge/Detic/datasets/README.md +++ /dev/null @@ -1,207 +0,0 @@ -# Prepare datasets for Detic - -The basic training of our model uses [LVIS](https://www.lvisdataset.org/) (which uses [COCO](https://cocodataset.org/) images) and [ImageNet-21K](https://www.image-net.org/download.php). -Some models are trained on [Conceptual Caption (CC3M)](https://ai.google.com/research/ConceptualCaptions/). -Optionally, we use [Objects365](https://www.objects365.org/) and [OpenImages (Challenge 2019 version)](https://storage.googleapis.com/openimages/web/challenge2019.html) for cross-dataset evaluation. -Before starting processing, please download the (selected) datasets from the official websites and place or sim-link them under `$Detic_ROOT/datasets/`. - -``` -$Detic_ROOT/datasets/ - metadata/ - lvis/ - coco/ - imagenet/ - cc3m/ - objects365/ - oid/ -``` -`metadata/` is our preprocessed meta-data (included in the repo). See the below [section](#Metadata) for details. -Please follow the following instruction to pre-process individual datasets. - -### COCO and LVIS - -First, download COCO and LVIS data place them in the following way: - -``` -lvis/ - lvis_v1_train.json - lvis_v1_val.json -coco/ - train2017/ - val2017/ - annotations/ - captions_train2017.json - instances_train2017.json - instances_val2017.json -``` - -Next, prepare the open-vocabulary LVIS training set using - -``` -python tools/remove_lvis_rare.py --ann datasets/lvis/lvis_v1_train.json -``` - -This will generate `datasets/lvis/lvis_v1_train_norare.json`. - -### ImageNet-21K - -The ImageNet-21K folder should look like: -``` -imagenet/ - ImageNet-21K/ - n01593028.tar - n01593282.tar - ... -``` - -We first unzip the overlapping classes of LVIS (we will directly work with the .tar file for the rest classes) and convert them into LVIS annotation format. - -~~~ -mkdir imagenet/annotations -python tools/unzip_imagenet_lvis.py --dst_path datasets/imagenet/ImageNet-LVIS -python tools/create_imagenetlvis_json.py --imagenet_path datasets/imagenet/ImageNet-LVIS --out_path datasets/imagenet/annotations/imagenet_lvis_image_info.json -~~~ -This creates `datasets/imagenet/annotations/imagenet_lvis_image_info.json`. - -[Optional] To train with all the 21K classes, run - -~~~ -python tools/get_imagenet_21k_full_tar_json.py -python tools/create_lvis_21k.py -~~~ -This creates `datasets/imagenet/annotations/imagenet-21k_image_info_lvis-21k.json` and `datasets/lvis/lvis_v1_train_lvis-21k.json` (combined LVIS and ImageNet-21K classes in `categories`). - -[Optional] To train on combined LVIS and COCO, run - -~~~ -python tools/merge_lvis_coco.py -~~~ -This creates `datasets/lvis/lvis_v1_train+coco_mask.json` - -### Conceptual Caption - - -Download the dataset from [this](https://ai.google.com/research/ConceptualCaptions/download) page and place them as: -``` -cc3m/ - GCC-training.tsv -``` - -Run the following command to download the images and convert the annotations to LVIS format (Note: download images takes long). - -~~~ -python tools/download_cc.py --ann datasets/cc3m/GCC-training.tsv --save_image_path datasets/cc3m/training/ --out_path datasets/cc3m/train_image_info.json -python tools/get_cc_tags.py -~~~ - -This creates `datasets/cc3m/train_image_info_tags.json`. - -### Objects365 -Download Objects365 (v2) from the website. We only need the validation set in this project: -``` -objects365/ - annotations/ - zhiyuan_objv2_val.json - val/ - images/ - v1/ - patch0/ - ... - patch15/ - v2/ - patch16/ - ... - patch49/ - -``` - -The original annotation has typos in the class names, we first fix them for our following use of language embeddings. - -``` -python tools/fix_o365_names.py --ann datasets/objects365/annotations/zhiyuan_objv2_val.json -``` -This creates `datasets/objects365/zhiyuan_objv2_val_fixname.json`. - -To train on Objects365, download the training images and use the command above. We note some images in the training annotation do not exist. -We use the following command to filter the missing images. -~~~ -python tools/fix_0365_path.py -~~~ -This creates `datasets/objects365/zhiyuan_objv2_train_fixname_fixmiss.json`. - -### OpenImages - -We followed the instructions in [UniDet](https://github.com/xingyizhou/UniDet/blob/master/projects/UniDet/unidet_docs/DATASETS.md#openimages) to convert the metadata for OpenImages. - -The converted folder should look like - -``` -oid/ - annotations/ - oid_challenge_2019_train_bbox.json - oid_challenge_2019_val_expanded.json - images/ - 0/ - 1/ - 2/ - ... -``` - -### Open-vocabulary COCO - -We first follow [OVR-CNN](https://github.com/alirezazareian/ovr-cnn/blob/master/ipynb/003.ipynb) to create the open-vocabulary COCO split. The converted files should be like - -``` -coco/ - zero-shot/ - instances_train2017_seen_2.json - instances_val2017_all_2.json -``` - -We further pre-process the annotation format for easier evaluation: - -``` -python tools/get_coco_zeroshot_oriorder.py --data_path datasets/coco/zero-shot/instances_train2017_seen_2.json -python tools/get_coco_zeroshot_oriorder.py --data_path datasets/coco/zero-shot/instances_val2017_all_2.json -``` - -Next, we preprocess the COCO caption data: - -``` -python tools/get_cc_tags.py --cc_ann datasets/coco/annotations/captions_train2017.json --out_path datasets/coco/captions_train2017_tags_allcaps.json --allcaps --convert_caption -``` -This creates `datasets/coco/captions_train2017_tags_allcaps.json`. - -### Metadata - -``` -metadata/ - lvis_v1_train_cat_info.json - coco_clip_a+cname.npy - lvis_v1_clip_a+cname.npy - o365_clip_a+cnamefix.npy - oid_clip_a+cname.npy - imagenet_lvis_wnid.txt - Objects365_names_fix.csv -``` - -`lvis_v1_train_cat_info.json` is used by the Federated loss. -This is created by -~~~ -python tools/get_lvis_cat_info.py --ann datasets/lvis/lvis_v1_train.json -~~~ - -`*_clip_a+cname.npy` is the pre-computed CLIP embeddings for each datasets. -They are created by (taking LVIS as an example) -~~~ -python tools/dump_clip_features.py --ann datasets/lvis/lvis_v1_val.json --out_path metadata/lvis_v1_clip_a+cname.npy -~~~ -Note we do not include the 21K class embeddings due to the large file size. -To create it, run -~~~ -python tools/dump_clip_features.py --ann datasets/lvis/lvis_v1_val_lvis-21k.json --out_path datasets/metadata/lvis-21k_clip_a+cname.npy -~~~ - -`imagenet_lvis_wnid.txt` is the list of matched classes between ImageNet-21K and LVIS. - -`Objects365_names_fix.csv` is our manual fix of the Objects365 names. \ No newline at end of file diff --git a/spaces/ashhadahsan/summarizer-space/dashboard.py b/spaces/ashhadahsan/summarizer-space/dashboard.py deleted file mode 100644 index 110d4fa2f39de914419647824482c634d28ec9dd..0000000000000000000000000000000000000000 --- a/spaces/ashhadahsan/summarizer-space/dashboard.py +++ /dev/null @@ -1,6 +0,0 @@ -import streamlit as st -from streamlit_extras.switch_page_button import switch_page - - -st.set_page_config(page_title="Hugging Face Summarize", page_icon="😊", layout="wide") -switch_page("predict") diff --git a/spaces/awacke1/Transformers-StoryWriting/README.md b/spaces/awacke1/Transformers-StoryWriting/README.md deleted file mode 100644 index 0a8016272665c39747729a0f620e5296a3462d4a..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Transformers-StoryWriting/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: NLP GPT Story Gen -emoji: 💬📚 -colorFrom: pink -colorTo: yellow -sdk: gradio -sdk_version: 3.0.17 -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/badongtakla/ithaca/ithaca/models/bigbird.py b/spaces/badongtakla/ithaca/ithaca/models/bigbird.py deleted file mode 100644 index 56fcf02fddee0283a663f6220f213c3e0bb646d1..0000000000000000000000000000000000000000 --- a/spaces/badongtakla/ithaca/ithaca/models/bigbird.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright 2021 the Ithaca 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 -# -# https://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. -"""Transformer using BigBird (https://arxiv.org/abs/2007.14062). - -This implementation is from the Long Range Arena: -https://github.com/google-research/long-range-arena/tree/main/lra_benchmarks/models/bigbird -""" - -from typing import Any, Optional - -from . import bigbird_attention -from . import common_layers - -from flax import linen as nn -import jax.numpy as jnp - -_DEFAULT_BLOCK_SIZE = 64 -_DEFAULT_NUM_RAND_BLOCKS = 3 - - -class BigBirdBlock(nn.Module): - """BigBird layer (https://arxiv.org/abs/2007.14062). - - Attributes: - qkv_dim: dimension of the query/key/value - mlp_dim: dimension of the mlp on top of attention block - num_heads: number of heads - dtype: the dtype of the computation (default: float32). - causal_mask: bool, mask future or not - dropout_rate: dropout rate - attention_dropout_rate: dropout rate for attention weights - deterministic: bool, deterministic or not (to apply dropout) - activation_fn: Activation function ("relu", "gelu") - block_size: Size of attention blocks. - num_rand_blocks: Number of random blocks. - connectivity_seed: Optional seed for random block sparse attention. - """ - - qkv_dim: Any - mlp_dim: Any - num_heads: Any - dtype: Any = jnp.float32 - causal_mask: bool = False - dropout_rate: float = 0.1 - attention_dropout_rate: float = 0.1 - deterministic: bool = False - activation_fn: str = 'relu' - block_size: int = _DEFAULT_BLOCK_SIZE - num_rand_blocks: int = _DEFAULT_NUM_RAND_BLOCKS - connectivity_seed: Optional[int] = None - - @nn.compact - def __call__(self, inputs, inputs_segmentation=None, padding_mask=None): - """Applies BigBirdBlock module. - - Args: - inputs: input data - inputs_segmentation: input segmentation info for packed examples. - padding_mask: bool, mask padding tokens, [b, l, 1] - - Returns: - output after transformer block. - - """ - - # Attention block. - assert inputs.ndim == 3 - x = common_layers.LayerNorm(dtype=self.dtype)(inputs) - x = bigbird_attention.BigBirdSelfAttention( - num_heads=self.num_heads, - dtype=self.dtype, - qkv_features=self.qkv_dim, - kernel_init=nn.initializers.xavier_uniform(), - bias_init=nn.initializers.normal(stddev=1e-6), - use_bias=False, - broadcast_dropout=False, - dropout_rate=self.attention_dropout_rate, - deterministic=self.deterministic, - block_size=self.block_size, - num_rand_blocks=self.num_rand_blocks, - connectivity_seed=self.connectivity_seed)( - x, - segmentation=inputs_segmentation, - padding_mask=padding_mask, - ) - x = nn.Dropout(rate=self.dropout_rate)(x, deterministic=self.deterministic) - x = x + inputs - - # MLP block. - y = common_layers.LayerNorm(dtype=self.dtype)(x) - y = common_layers.MlpBlock( - mlp_dim=self.mlp_dim, - dtype=self.dtype, - dropout_rate=self.dropout_rate, - deterministic=self.deterministic, - activation_fn=self.activation_fn)( - y) - - return x + y diff --git a/spaces/banana-projects/web3d/node_modules/three/src/core/InstancedBufferGeometry.js b/spaces/banana-projects/web3d/node_modules/three/src/core/InstancedBufferGeometry.js deleted file mode 100644 index a021bbf88fa2428cc4d21866a0c2562e8ac56cf0..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/core/InstancedBufferGeometry.js +++ /dev/null @@ -1,40 +0,0 @@ -import { BufferGeometry } from './BufferGeometry.js'; - -/** - * @author benaadams / https://twitter.com/ben_a_adams - */ - -function InstancedBufferGeometry() { - - BufferGeometry.call( this ); - - this.type = 'InstancedBufferGeometry'; - this.maxInstancedCount = undefined; - -} - -InstancedBufferGeometry.prototype = Object.assign( Object.create( BufferGeometry.prototype ), { - - constructor: InstancedBufferGeometry, - - isInstancedBufferGeometry: true, - - copy: function ( source ) { - - BufferGeometry.prototype.copy.call( this, source ); - - this.maxInstancedCount = source.maxInstancedCount; - - return this; - - }, - - clone: function () { - - return new this.constructor().copy( this ); - - } - -} ); - -export { InstancedBufferGeometry }; diff --git a/spaces/banana-projects/web3d/node_modules/three/src/core/InterleavedBuffer.js b/spaces/banana-projects/web3d/node_modules/three/src/core/InterleavedBuffer.js deleted file mode 100644 index dd6312026866753346c299bfece2e548bbe2e5f5..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/core/InterleavedBuffer.js +++ /dev/null @@ -1,111 +0,0 @@ - -/** - * @author benaadams / https://twitter.com/ben_a_adams - */ - -function InterleavedBuffer( array, stride ) { - - this.array = array; - this.stride = stride; - this.count = array !== undefined ? array.length / stride : 0; - - this.dynamic = false; - this.updateRange = { offset: 0, count: - 1 }; - - this.version = 0; - -} - -Object.defineProperty( InterleavedBuffer.prototype, 'needsUpdate', { - - set: function ( value ) { - - if ( value === true ) this.version ++; - - } - -} ); - -Object.assign( InterleavedBuffer.prototype, { - - isInterleavedBuffer: true, - - onUploadCallback: function () {}, - - setArray: function ( array ) { - - if ( Array.isArray( array ) ) { - - throw new TypeError( 'THREE.BufferAttribute: array should be a Typed Array.' ); - - } - - this.count = array !== undefined ? array.length / this.stride : 0; - this.array = array; - - return this; - - }, - - setDynamic: function ( value ) { - - this.dynamic = value; - - return this; - - }, - - copy: function ( source ) { - - this.array = new source.array.constructor( source.array ); - this.count = source.count; - this.stride = source.stride; - this.dynamic = source.dynamic; - - return this; - - }, - - copyAt: function ( index1, attribute, index2 ) { - - index1 *= this.stride; - index2 *= attribute.stride; - - for ( var i = 0, l = this.stride; i < l; i ++ ) { - - this.array[ index1 + i ] = attribute.array[ index2 + i ]; - - } - - return this; - - }, - - set: function ( value, offset ) { - - if ( offset === undefined ) offset = 0; - - this.array.set( value, offset ); - - return this; - - }, - - clone: function () { - - return new this.constructor().copy( this ); - - }, - - onUpload: function ( callback ) { - - this.onUploadCallback = callback; - - return this; - - } - -} ); - - -export { InterleavedBuffer }; diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLClipping.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLClipping.js deleted file mode 100644 index 9107fbc6351682c8f7dda10f02d97959b72ad7e6..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLClipping.js +++ /dev/null @@ -1,164 +0,0 @@ -/** - * @author tschw - */ - -import { Matrix3 } from '../../math/Matrix3.js'; -import { Plane } from '../../math/Plane.js'; - -function WebGLClipping() { - - var scope = this, - - globalState = null, - numGlobalPlanes = 0, - localClippingEnabled = false, - renderingShadows = false, - - plane = new Plane(), - viewNormalMatrix = new Matrix3(), - - uniform = { value: null, needsUpdate: false }; - - this.uniform = uniform; - this.numPlanes = 0; - this.numIntersection = 0; - - this.init = function ( planes, enableLocalClipping, camera ) { - - var enabled = - planes.length !== 0 || - enableLocalClipping || - // enable state of previous frame - the clipping code has to - // run another frame in order to reset the state: - numGlobalPlanes !== 0 || - localClippingEnabled; - - localClippingEnabled = enableLocalClipping; - - globalState = projectPlanes( planes, camera, 0 ); - numGlobalPlanes = planes.length; - - return enabled; - - }; - - this.beginShadows = function () { - - renderingShadows = true; - projectPlanes( null ); - - }; - - this.endShadows = function () { - - renderingShadows = false; - resetGlobalState(); - - }; - - this.setState = function ( planes, clipIntersection, clipShadows, camera, cache, fromCache ) { - - if ( ! localClippingEnabled || planes === null || planes.length === 0 || renderingShadows && ! clipShadows ) { - - // there's no local clipping - - if ( renderingShadows ) { - - // there's no global clipping - - projectPlanes( null ); - - } else { - - resetGlobalState(); - - } - - } else { - - var nGlobal = renderingShadows ? 0 : numGlobalPlanes, - lGlobal = nGlobal * 4, - - dstArray = cache.clippingState || null; - - uniform.value = dstArray; // ensure unique state - - dstArray = projectPlanes( planes, camera, lGlobal, fromCache ); - - for ( var i = 0; i !== lGlobal; ++ i ) { - - dstArray[ i ] = globalState[ i ]; - - } - - cache.clippingState = dstArray; - this.numIntersection = clipIntersection ? this.numPlanes : 0; - this.numPlanes += nGlobal; - - } - - - }; - - function resetGlobalState() { - - if ( uniform.value !== globalState ) { - - uniform.value = globalState; - uniform.needsUpdate = numGlobalPlanes > 0; - - } - - scope.numPlanes = numGlobalPlanes; - scope.numIntersection = 0; - - } - - function projectPlanes( planes, camera, dstOffset, skipTransform ) { - - var nPlanes = planes !== null ? planes.length : 0, - dstArray = null; - - if ( nPlanes !== 0 ) { - - dstArray = uniform.value; - - if ( skipTransform !== true || dstArray === null ) { - - var flatSize = dstOffset + nPlanes * 4, - viewMatrix = camera.matrixWorldInverse; - - viewNormalMatrix.getNormalMatrix( viewMatrix ); - - if ( dstArray === null || dstArray.length < flatSize ) { - - dstArray = new Float32Array( flatSize ); - - } - - for ( var i = 0, i4 = dstOffset; i !== nPlanes; ++ i, i4 += 4 ) { - - plane.copy( planes[ i ] ).applyMatrix4( viewMatrix, viewNormalMatrix ); - - plane.normal.toArray( dstArray, i4 ); - dstArray[ i4 + 3 ] = plane.constant; - - } - - } - - uniform.value = dstArray; - uniform.needsUpdate = true; - - } - - scope.numPlanes = nPlanes; - - return dstArray; - - } - -} - - -export { WebGLClipping }; diff --git a/spaces/beihai/PDF-Table-Extractor/.history/test_20220621135808.py b/spaces/beihai/PDF-Table-Extractor/.history/test_20220621135808.py deleted file mode 100644 index 689b66e79ad39f3c58054e346bad35df6d88d3da..0000000000000000000000000000000000000000 --- a/spaces/beihai/PDF-Table-Extractor/.history/test_20220621135808.py +++ /dev/null @@ -1,27 +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)) -page_number = st.text_input("请填写表格所在PDF页码，eg: 3, 1-3, 2-end, all", value = 1) - -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() - tables_all= cam.read_pdf("input.pdf", pages=page_number, process_background=background) - result_all = pd.ExcelWriter("result.xlsx", 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_all,'rb') as f: - st.download_button('抽取完成, 点击下载！', f,file_name="result.xlsx",mime="application/vnd.ms-excel") \ No newline at end of file diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_ip2p.py b/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_ip2p.py deleted file mode 100644 index 3c727d3b75332508629458d23f7fb86cc9ede44b..0000000000000000000000000000000000000000 --- a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_ip2p.py +++ /dev/null @@ -1,13 +0,0 @@ -import collections -import os.path -import sys -import gc -import time - -def should_hijack_ip2p(checkpoint_info): - from modules import sd_models_config - - ckpt_basename = os.path.basename(checkpoint_info.filename).lower() - cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower() - - return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename diff --git a/spaces/bigslime/stablediffusion-infinity/js/fabric.min.js b/spaces/bigslime/stablediffusion-infinity/js/fabric.min.js deleted file mode 100644 index 83a2da38159fcda07760f87be9711c775cb83fda..0000000000000000000000000000000000000000 --- a/spaces/bigslime/stablediffusion-infinity/js/fabric.min.js +++ /dev/null @@ -1 +0,0 @@ -var fabric=fabric||{version:"5.2.1"};if("undefined"!=typeof exports?exports.fabric=fabric:"function"==typeof define&&define.amd&&define([],function(){return fabric}),"undefined"!=typeof document&&"undefined"!=typeof window)fabric.document=document instanceof("undefined"!=typeof HTMLDocument?HTMLDocument:Document)?document:document.implementation.createHTMLDocument(""),fabric.window=window;else{var jsdom=require("jsdom"),virtualWindow=new 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a=r(t[1],t[0]),o=i(t[0],2)+i(t[1],2),c=e(o),f=(t[0]*t[3]-t[2]*t[1])/c,s=r(t[0]*t[2]+t[1]*t[3],o);return{angle:a/n,scaleX:c,scaleY:f,skewX:s/n,skewY:0,translateX:t[4],translateY:t[5]}},calcRotateMatrix:function(t){if(!t.angle)return fabric.iMatrix.concat();var e=fabric.util.degreesToRadians(t.angle),r=fabric.util.cos(e),i=fabric.util.sin(e);return[r,i,-i,r,0,0]},calcDimensionsMatrix:function(t){var e="undefined"==typeof t.scaleX?1:t.scaleX,r="undefined"==typeof t.scaleY?1:t.scaleY,i=[t.flipX?-e:e,0,0,t.flipY?-r:r,0,0],n=fabric.util.multiplyTransformMatrices,a=fabric.util.degreesToRadians;return t.skewX&&(i=n(i,[1,0,Math.tan(a(t.skewX)),1],!0)),t.skewY&&(i=n(i,[1,Math.tan(a(t.skewY)),0,1],!0)),i},composeMatrix:function(t){var e=[1,0,0,1,t.translateX||0,t.translateY||0],r=fabric.util.multiplyTransformMatrices;return t.angle&&(e=r(e,fabric.util.calcRotateMatrix(t))),(1!==t.scaleX||1!==t.scaleY||t.skewX||t.skewY||t.flipX||t.flipY)&&(e=r(e,fabric.util.calcDimensionsMatrix(t))),e},resetObjectTransform:function(t){t.scaleX=1,t.scaleY=1,t.skewX=0,t.skewY=0,t.flipX=!1,t.flipY=!1,t.rotate(0)},saveObjectTransform:function(t){return{scaleX:t.scaleX,scaleY:t.scaleY,skewX:t.skewX,skewY:t.skewY,angle:t.angle,left:t.left,flipX:t.flipX,flipY:t.flipY,top:t.top}},isTransparent:function(t,e,r,i){i>0&&(e>i?e-=i:e=0,r>i?r-=i:r=0);var n,a,o=!0,c=t.getImageData(e,r,2*i||1,2*i||1),f=c.data.length;for(n=3;f>n&&(a=c.data[n],o=0>=a,o!==!1);n+=4);return c=null,o},parsePreserveAspectRatioAttribute:function(t){var e,r="meet",i="Mid",n="Mid",a=t.split(" ");return a&&a.length&&(r=a.pop(),"meet"!==r&&"slice"!==r?(e=r,r="meet"):a.length&&(e=a.pop())),i="none"!==e?e.slice(1,4):"none",n="none"!==e?e.slice(5,8):"none",{meetOrSlice:r,alignX:i,alignY:n}},clearFabricFontCache:function(t){t=(t||"").toLowerCase(),t?fabric.charWidthsCache[t]&&delete fabric.charWidthsCache[t]:fabric.charWidthsCache={}},limitDimsByArea:function(t,e){var r=Math.sqrt(e*t),i=Math.floor(e/r);return{x:Math.floor(r),y:i}},capValue:function(t,e,r){return Math.max(t,Math.min(e,r))},findScaleToFit:function(t,e){return Math.min(e.width/t.width,e.height/t.height)},findScaleToCover:function(t,e){return Math.max(e.width/t.width,e.height/t.height)},matrixToSVG:function(t){return"matrix("+t.map(function(t){return fabric.util.toFixed(t,fabric.Object.NUM_FRACTION_DIGITS)}).join(" ")+")"},removeTransformFromObject:function(t,e){var r=fabric.util.invertTransform(e),i=fabric.util.multiplyTransformMatrices(r,t.calcOwnMatrix());fabric.util.applyTransformToObject(t,i)},addTransformToObject:function(t,e){fabric.util.applyTransformToObject(t,fabric.util.multiplyTransformMatrices(e,t.calcOwnMatrix()))},applyTransformToObject:function(t,e){var r=fabric.util.qrDecompose(e),i=new fabric.Point(r.translateX,r.translateY);t.flipX=!1,t.flipY=!1,t.set("scaleX",r.scaleX),t.set("scaleY",r.scaleY),t.skewX=r.skewX,t.skewY=r.skewY,t.angle=r.angle,t.setPositionByOrigin(i,"center","center")},sizeAfterTransform:function(t,e,r){var i=t/2,n=e/2,a=[{x:-i,y:-n},{x:i,y:-n},{x:-i,y:n},{x:i,y:n}],o=fabric.util.calcDimensionsMatrix(r),c=fabric.util.makeBoundingBoxFromPoints(a,o);return{x:c.width,y:c.height}},mergeClipPaths:function(t,e){var r=t,i=e;r.inverted&&!i.inverted&&(r=e,i=t),fabric.util.applyTransformToObject(i,fabric.util.multiplyTransformMatrices(fabric.util.invertTransform(r.calcTransformMatrix()),i.calcTransformMatrix()));var n=r.inverted&&i.inverted;return n&&(r.inverted=i.inverted=!1),new fabric.Group([r],{clipPath:i,inverted:n})},hasStyleChanged:function(t,e,r){return r=r||!1,t.fill!==e.fill||t.stroke!==e.stroke||t.strokeWidth!==e.strokeWidth||t.fontSize!==e.fontSize||t.fontFamily!==e.fontFamily||t.fontWeight!==e.fontWeight||t.fontStyle!==e.fontStyle||t.deltaY!==e.deltaY||r&&(t.overline!==e.overline||t.underline!==e.underline||t.linethrough!==e.linethrough)},stylesToArray:function(t,e){for(var t=fabric.util.object.clone(t,!0),r=e.split("\n"),i=-1,n={},a=[],o=0;o<r.length;o++)if(t[o])for(var c=0;c<r[o].length;c++){i++;var f=t[o][c];if(f){var s=fabric.util.hasStyleChanged(n,f,!0);s?a.push({start:i,end:i+1,style:f}):a[a.length-1].end++}n=f||{}}else i+=r[o].length;return a},stylesFromArray:function(t,e){if(!Array.isArray(t))return t;for(var r=e.split("\n"),i=-1,n=0,a={},o=0;o<r.length;o++)for(var c=0;c<r[o].length;c++)i++,t[n]&&t[n].start<=i&&i<t[n].end&&(a[o]=a[o]||{},a[o][c]=Object.assign({},t[n].style),i===t[n].end-1&&n++);return a}}}("undefined"!=typeof exports?exports:this);!function(){function t(t,e,r,n,a,i,c,o,f,s,u){var l=fabric.util.cos(t),h=fabric.util.sin(t),d=fabric.util.cos(e),b=fabric.util.sin(e),m=r*a*d-n*i*b+c,p=n*a*d+r*i*b+o,y=s+f*(-r*a*h-n*i*l),v=u+f*(-n*a*h+r*i*l),g=m+f*(r*a*b+n*i*d),x=p+f*(n*a*b-r*i*d);return["C",y,v,g,x,m,p]}function e(e,n,a,i,c,o,f){var s=Math.PI,u=f*s/180,l=fabric.util.sin(u),h=fabric.util.cos(u),d=0,b=0;a=Math.abs(a),i=Math.abs(i);var m=-h*e*.5-l*n*.5,p=-h*n*.5+l*e*.5,y=a*a,v=i*i,g=p*p,x=m*m,w=y*v-y*g-v*x,M=0;if(0>w){var P=Math.sqrt(1-w/(y*v));a*=P,i*=P}else M=(c===o?-1:1)*Math.sqrt(w/(y*g+v*x));var C=M*a*p/i,k=-M*i*m/a,T=h*C-l*k+.5*e,S=l*C+h*k+.5*n,O=r(1,0,(m-C)/a,(p-k)/i),F=r((m-C)/a,(p-k)/i,(-m-C)/a,(-p-k)/i);0===o&&F>0?F-=2*s:1===o&&0>F&&(F+=2*s);for(var E=Math.ceil(Math.abs(F/s*2)),Y=[],j=F/E,D=8/3*Math.sin(j/4)*Math.sin(j/4)/Math.sin(j/2),X=O+j,I=0;E>I;I++)Y[I]=t(O,X,h,l,a,i,T,S,D,d,b),d=Y[I][5],b=Y[I][6],O=X,X+=j;return Y}function r(t,e,r,n){var a=Math.atan2(e,t),i=Math.atan2(n,r);return i>=a?i-a:2*Math.PI-(a-i)}function n(t,e,r,n,a,i,c,o){var f;if(fabric.cachesBoundsOfCurve&&(f=k.call(arguments),fabric.boundsOfCurveCache[f]))return fabric.boundsOfCurveCache[f];var s,u,l,h,d,b,m,p,y=Math.sqrt,v=Math.min,g=Math.max,x=Math.abs,w=[],M=[[],[]];u=6*t-12*r+6*a,s=-3*t+9*r-9*a+3*c,l=3*r-3*t;for(var P=0;2>P;++P)if(P>0&&(u=6*e-12*n+6*i,s=-3*e+9*n-9*i+3*o,l=3*n-3*e),x(s)<1e-12){if(x(u)<1e-12)continue;h=-l/u,h>0&&1>h&&w.push(h)}else m=u*u-4*l*s,0>m||(p=y(m),d=(-u+p)/(2*s),d>0&&1>d&&w.push(d),b=(-u-p)/(2*s),b>0&&1>b&&w.push(b));for(var C,T,S,O=w.length,F=O;O--;)h=w[O],S=1-h,C=S*S*S*t+3*S*S*h*r+3*S*h*h*a+h*h*h*c,M[0][O]=C,T=S*S*S*e+3*S*S*h*n+3*S*h*h*i+h*h*h*o,M[1][O]=T;M[0][F]=t,M[1][F]=e,M[0][F+1]=c,M[1][F+1]=o;var E=[{x:v.apply(null,M[0]),y:v.apply(null,M[1])},{x:g.apply(null,M[0]),y:g.apply(null,M[1])}];return fabric.cachesBoundsOfCurve&&(fabric.boundsOfCurveCache[f]=E),E}function a(t,r,n){for(var a=n[1],i=n[2],c=n[3],o=n[4],f=n[5],s=n[6],u=n[7],l=e(s-t,u-r,a,i,o,f,c),h=0,d=l.length;d>h;h++)l[h][1]+=t,l[h][2]+=r,l[h][3]+=t,l[h][4]+=r,l[h][5]+=t,l[h][6]+=r;return l}function i(t){var e,r,n,i,c,o,f=0,s=0,u=t.length,l=0,h=0,d=[];for(r=0;u>r;++r){switch(n=!1,e=t[r].slice(0),e[0]){case"l":e[0]="L",e[1]+=f,e[2]+=s;case"L":f=e[1],s=e[2];break;case"h":e[1]+=f;case"H":e[0]="L",e[2]=s,f=e[1];break;case"v":e[1]+=s;case"V":e[0]="L",s=e[1],e[1]=f,e[2]=s;break;case"m":e[0]="M",e[1]+=f,e[2]+=s;case"M":f=e[1],s=e[2],l=e[1],h=e[2];break;case"c":e[0]="C",e[1]+=f,e[2]+=s,e[3]+=f,e[4]+=s,e[5]+=f,e[6]+=s;case"C":c=e[3],o=e[4],f=e[5],s=e[6];break;case"s":e[0]="S",e[1]+=f,e[2]+=s,e[3]+=f,e[4]+=s;case"S":"C"===i?(c=2*f-c,o=2*s-o):(c=f,o=s),f=e[3],s=e[4],e[0]="C",e[5]=e[3],e[6]=e[4],e[3]=e[1],e[4]=e[2],e[1]=c,e[2]=o,c=e[3],o=e[4];break;case"q":e[0]="Q",e[1]+=f,e[2]+=s,e[3]+=f,e[4]+=s;case"Q":c=e[1],o=e[2],f=e[3],s=e[4];break;case"t":e[0]="T",e[1]+=f,e[2]+=s;case"T":"Q"===i?(c=2*f-c,o=2*s-o):(c=f,o=s),e[0]="Q",f=e[1],s=e[2],e[1]=c,e[2]=o,e[3]=f,e[4]=s;break;case"a":e[0]="A",e[6]+=f,e[7]+=s;case"A":n=!0,d=d.concat(a(f,s,e)),f=e[6],s=e[7];break;case"z":case"Z":f=l,s=h}n||d.push(e),i=e[0]}return d}function c(t,e,r,n){return Math.sqrt((r-t)*(r-t)+(n-e)*(n-e))}function o(t){return t*t*t}function f(t){return 3*t*t*(1-t)}function s(t){return 3*t*(1-t)*(1-t)}function u(t){return(1-t)*(1-t)*(1-t)}function l(t,e,r,n,a,i,c,l){return function(h){var d=o(h),b=f(h),m=s(h),p=u(h);return{x:c*d+a*b+r*m+t*p,y:l*d+i*b+n*m+e*p}}}function h(t,e,r,n,a,i,c,o){return function(f){var s=1-f,u=3*s*s*(r-t)+6*s*f*(a-r)+3*f*f*(c-a),l=3*s*s*(n-e)+6*s*f*(i-n)+3*f*f*(o-i);return Math.atan2(l,u)}}function d(t){return t*t}function b(t){return 2*t*(1-t)}function m(t){return(1-t)*(1-t)}function p(t,e,r,n,a,i){return function(c){var o=d(c),f=b(c),s=m(c);return{x:a*o+r*f+t*s,y:i*o+n*f+e*s}}}function y(t,e,r,n,a,i){return function(c){var o=1-c,f=2*o*(r-t)+2*c*(a-r),s=2*o*(n-e)+2*c*(i-n);return Math.atan2(s,f)}}function v(t,e,r){var n,a,i={x:e,y:r},o=0;for(a=1;100>=a;a+=1)n=t(a/100),o+=c(i.x,i.y,n.x,n.y),i=n;return o}function g(t,e){for(var r,n,a,i=0,o=0,f=t.iterator,s={x:t.x,y:t.y},u=.01,l=t.angleFinder;e>o&&u>1e-4;)r=f(i),a=i,n=c(s.x,s.y,r.x,r.y),n+o>e?(i-=u,u/=2):(s=r,i+=u,o+=n);return r.angle=l(a),r}function x(t){for(var e,r,n,a,i=0,o=t.length,f=0,s=0,u=0,d=0,b=[],m=0;o>m;m++){switch(e=t[m],n={x:f,y:s,command:e[0]},e[0]){case"M":n.length=0,u=f=e[1],d=s=e[2];break;case"L":n.length=c(f,s,e[1],e[2]),f=e[1],s=e[2];break;case"C":r=l(f,s,e[1],e[2],e[3],e[4],e[5],e[6]),a=h(f,s,e[1],e[2],e[3],e[4],e[5],e[6]),n.iterator=r,n.angleFinder=a,n.length=v(r,f,s),f=e[5],s=e[6];break;case"Q":r=p(f,s,e[1],e[2],e[3],e[4]),a=y(f,s,e[1],e[2],e[3],e[4]),n.iterator=r,n.angleFinder=a,n.length=v(r,f,s),f=e[3],s=e[4];break;case"Z":case"z":n.destX=u,n.destY=d,n.length=c(f,s,u,d),f=u,s=d}i+=n.length,b.push(n)}return b.push({length:i,x:f,y:s}),b}function w(t,e,r){r||(r=x(t));for(var n=0;e-r[n].length>0&&n<r.length-2;)e-=r[n].length,n++;var a,i=r[n],c=e/i.length,o=i.command,f=t[n];switch(o){case"M":return{x:i.x,y:i.y,angle:0};case"Z":case"z":return a=new fabric.Point(i.x,i.y).lerp(new fabric.Point(i.destX,i.destY),c),a.angle=Math.atan2(i.destY-i.y,i.destX-i.x),a;case"L":return a=new fabric.Point(i.x,i.y).lerp(new fabric.Point(f[1],f[2]),c),a.angle=Math.atan2(f[2]-i.y,f[1]-i.x),a;case"C":return g(i,e);case"Q":return g(i,e)}}function M(t){var e,r,n,a,i,c=[],o=[],f=fabric.rePathCommand,s="[-+]?(?:\\d*\\.\\d+|\\d+\\.?)(?:[eE][-+]?\\d+)?\\s*",u="("+s+")"+fabric.commaWsp,l="([01])"+fabric.commaWsp+"?",h=u+"?"+u+"?"+u+l+l+u+"?("+s+")",d=new RegExp(h,"g");if(!t||!t.match)return c;i=t.match(/[mzlhvcsqta][^mzlhvcsqta]*/gi);for(var b,m=0,p=i.length;p>m;m++){e=i[m],a=e.slice(1).trim(),o.length=0;var y=e.charAt(0);if(b=[y],"a"===y.toLowerCase())for(var v;v=d.exec(a);)for(var g=1;g<v.length;g++)o.push(v[g]);else for(;n=f.exec(a);)o.push(n[0]);for(var g=0,x=o.length;x>g;g++)r=parseFloat(o[g]),isNaN(r)||b.push(r);var w=T[y.toLowerCase()],M=S[y]||y;if(b.length-1>w)for(var P=1,C=b.length;C>P;P+=w)c.push([y].concat(b.slice(P,P+w))),y=M;else c.push(b)}return c}function P(t,e){var r,n=[],a=new fabric.Point(t[0].x,t[0].y),i=new fabric.Point(t[1].x,t[1].y),c=t.length,o=1,f=0,s=c>2;for(e=e||0,s&&(o=t[2].x<i.x?-1:t[2].x===i.x?0:1,f=t[2].y<i.y?-1:t[2].y===i.y?0:1),n.push(["M",a.x-o*e,a.y-f*e]),r=1;c>r;r++){if(!a.eq(i)){var u=a.midPointFrom(i);n.push(["Q",a.x,a.y,u.x,u.y])}a=t[r],r+1<t.length&&(i=t[r+1])}return s&&(o=a.x>t[r-2].x?1:a.x===t[r-2].x?0:-1,f=a.y>t[r-2].y?1:a.y===t[r-2].y?0:-1),n.push(["L",a.x+o*e,a.y+f*e]),n}function C(t,e,r){return r&&(e=fabric.util.multiplyTransformMatrices(e,[1,0,0,1,-r.x,-r.y])),t.map(function(t){for(var r=t.slice(0),n={},a=1;a<t.length-1;a+=2)n.x=t[a],n.y=t[a+1],n=fabric.util.transformPoint(n,e),r[a]=n.x,r[a+1]=n.y;return r})}var k=Array.prototype.join,T={m:2,l:2,h:1,v:1,c:6,s:4,q:4,t:2,a:7},S={m:"l",M:"L"};fabric.util.joinPath=function(t){return t.map(function(t){return t.join(" ")}).join(" ")},fabric.util.parsePath=M,fabric.util.makePathSimpler=i,fabric.util.getSmoothPathFromPoints=P,fabric.util.getPathSegmentsInfo=x,fabric.util.getBoundsOfCurve=n,fabric.util.getPointOnPath=w,fabric.util.transformPath=C}();!function(){function t(t,e){for(var r=i.call(arguments,2),n=[],a=0,c=t.length;c>a;a++)n[a]=r.length?t[a][e].apply(t[a],r):t[a][e].call(t[a]);return n}function e(t,e){return a(t,e,function(t,e){return t>=e})}function r(t,e){return a(t,e,function(t,e){return e>t})}function n(t,e){for(var r=t.length;r--;)t[r]=e;return t}function a(t,e,r){if(t&&0!==t.length){var n=t.length-1,a=e?t[n][e]:t[n];if(e)for(;n--;)r(t[n][e],a)&&(a=t[n][e]);else for(;n--;)r(t[n],a)&&(a=t[n]);return a}}var i=Array.prototype.slice;fabric.util.array={fill:n,invoke:t,min:r,max:e}}();!function(){function t(e,r,n){if(n)if(!fabric.isLikelyNode&&r instanceof Element)e=r;else if(r instanceof Array){e=[];for(var a=0,i=r.length;i>a;a++)e[a]=t({},r[a],n)}else if(r&&"object"==typeof r)for(var c in r)"canvas"===c||"group"===c?e[c]=null:r.hasOwnProperty(c)&&(e[c]=t({},r[c],n));else e=r;else for(var c in r)e[c]=r[c];return e}function e(e,r){return t({},e,r)}fabric.util.object={extend:t,clone:e},fabric.util.object.extend(fabric.util,fabric.Observable)}();!function(){function t(t){return t.replace(/-+(.)?/g,function(t,e){return e?e.toUpperCase():""})}function e(t,e){return t.charAt(0).toUpperCase()+(e?t.slice(1):t.slice(1).toLowerCase())}function r(t){return t.replace(/&/g,"&").replace(/"/g,""").replace(/'/g,"'").replace(/</g,"<").replace(/>/g,">")}function n(t){var e,r=0,n=[];for(r=0,e;r<t.length;r++)(e=a(t,r))!==!1&&n.push(e);return n}function a(t,e){var r=t.charCodeAt(e);if(isNaN(r))return"";if(55296>r||r>57343)return t.charAt(e);if(r>=55296&&56319>=r){if(t.length<=e+1)throw"High surrogate without following low surrogate";var n=t.charCodeAt(e+1);if(56320>n||n>57343)throw"High surrogate without following low surrogate";return t.charAt(e)+t.charAt(e+1)}if(0===e)throw"Low surrogate 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n=Array.prototype.slice,i=function(){},a=function(){for(var t in{toString:1})if("toString"===t)return!1;return!0}(),o=function(t,e,r){for(var n in e)t.prototype[n]=n in t.prototype&&"function"==typeof t.prototype[n]&&(e[n]+"").indexOf("callSuper")>-1?function(t){return function(){var n=this.constructor.superclass;this.constructor.superclass=r;var i=e[t].apply(this,arguments);return this.constructor.superclass=n,"initialize"!==t?i:void 0}}(n):e[n],a&&(e.toString!==Object.prototype.toString&&(t.prototype.toString=e.toString),e.valueOf!==Object.prototype.valueOf&&(t.prototype.valueOf=e.valueOf))};fabric.util.createClass=r}();!function(){function t(t){var e=t.changedTouches;return e&&e[0]?e[0]:t}var e=!!fabric.document.createElement("div").attachEvent,r=["touchstart","touchmove","touchend"];fabric.util.addListener=function(t,r,n,i){t&&t.addEventListener(r,n,e?!1:i)},fabric.util.removeListener=function(t,r,n,i){t&&t.removeEventListener(r,n,e?!1:i)},fabric.util.getPointer=function(e){var r=e.target,n=fabric.util.getScrollLeftTop(r),i=t(e);return{x:i.clientX+n.left,y:i.clientY+n.top}},fabric.util.isTouchEvent=function(t){return r.indexOf(t.type)>-1||"touch"===t.pointerType}}();!function(){function t(t,e){var r=t.style;if(!r)return t;if("string"==typeof e)return t.style.cssText+=";"+e,e.indexOf("opacity")>-1?a(t,e.match(/opacity:\s*(\d?\.?\d*)/)[1]):t;for(var n in e)if("opacity"===n)a(t,e[n]);else{var i="float"===n||"cssFloat"===n?"undefined"==typeof r.styleFloat?"cssFloat":"styleFloat":n;r.setProperty(i,e[n])}return t}var e=fabric.document.createElement("div"),r="string"==typeof e.style.opacity,n="string"==typeof e.style.filter,i=/alpha\s*\(\s*opacity\s*=\s*([^\)]+)\)/,a=function(t){return t};r?a=function(t,e){return t.style.opacity=e,t}:n&&(a=function(t,e){var r=t.style;return t.currentStyle&&!t.currentStyle.hasLayout&&(r.zoom=1),i.test(r.filter)?(e=e>=.9999?"":"alpha(opacity="+100*e+")",r.filter=r.filter.replace(i,e)):r.filter+=" 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o.onreadystatechange=function(){4===o.readyState&&(a(o),o.onreadystatechange=e)},"GET"===i&&(c=null,"string"==typeof n.parameters&&(r=t(r,n.parameters))),o.open(i,r,!0),("POST"===i||"PUT"===i)&&o.setRequestHeader("Content-Type","application/x-www-form-urlencoded"),o.send(c),o}fabric.util.request=r}();fabric.log=console.log,fabric.warn=console.warn;!function(){function t(){return!1}function e(t,e,r,n){return-r*Math.cos(t/n*(Math.PI/2))+r+e}function r(r){r||(r={});var i,c=!1,u=function(){var t=fabric.runningAnimations.indexOf(i);return t>-1&&fabric.runningAnimations.splice(t,1)[0]};return i=a(o(r),{cancel:function(){return c=!0,u()},currentValue:"startValue"in r?r.startValue:0,completionRate:0,durationRate:0}),fabric.runningAnimations.push(i),n(function(a){var o,f=a||+new Date,l=r.duration||500,s=f+l,d=r.onChange||t,h=r.abort||t,p=r.onComplete||t,b=r.easing||e,m="startValue"in r?r.startValue.length>0:!1,y="startValue"in r?r.startValue:0,g="endValue"in 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a=new fabric.Color(e).getSource(),o=new fabric.Color(r).getSource(),c=i.onComplete,u=i.onChange;return i=i||{},fabric.util.animate(fabric.util.object.extend(i,{duration:n||500,startValue:a,endValue:o,byValue:o,easing:function(e,r,n,a){var o=i.colorEasing?i.colorEasing(e,a):1-Math.cos(e/a*(Math.PI/2));return t(r,n,o)},onComplete:function(e,r,n){return c?c(t(o,o,0),r,n):void 0},onChange:function(e,r,n){if(u){if(Array.isArray(e))return u(t(e,e,0),r,n);u(e,r,n)}}}))}fabric.util.animateColor=e}();!function(){function t(t,e,n,r){return t<Math.abs(e)?(t=e,r=n/4):r=0===e&&0===t?n/(2*Math.PI)*Math.asin(1):n/(2*Math.PI)*Math.asin(e/t),{a:t,c:e,p:n,s:r}}function e(t,e,n){return t.a*Math.pow(2,10*(e-=1))*Math.sin(2*(e*n-t.s)*Math.PI/t.p)}function n(t,e,n,r){return n*((t=t/r-1)*t*t+1)+e}function r(t,e,n,r){return t/=r/2,1>t?n/2*t*t*t+e:n/2*((t-=2)*t*t+2)+e}function i(t,e,n,r){return n*(t/=r)*t*t*t+e}function a(t,e,n,r){return-n*((t=t/r-1)*t*t*t-1)+e}function o(t,e,n,r){return 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e=this.x,r=this.y;this.x=t.x,this.y=t.y,t.x=e,t.y=r},clone:function(){return new e(this.x,this.y)}}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";function e(t){this.status=t,this.points=[]}var n=t.fabric||(t.fabric={});return n.Intersection?void n.warn("fabric.Intersection is already defined"):(n.Intersection=e,n.Intersection.prototype={constructor:e,appendPoint:function(t){return this.points.push(t),this},appendPoints:function(t){return this.points=this.points.concat(t),this}},n.Intersection.intersectLineLine=function(t,r,i,a){var o,s=(a.x-i.x)*(t.y-i.y)-(a.y-i.y)*(t.x-i.x),c=(r.x-t.x)*(t.y-i.y)-(r.y-t.y)*(t.x-i.x),u=(a.y-i.y)*(r.x-t.x)-(a.x-i.x)*(r.y-t.y);if(0!==u){var l=s/u,f=c/u;l>=0&&1>=l&&f>=0&&1>=f?(o=new e("Intersection"),o.appendPoint(new n.Point(t.x+l*(r.x-t.x),t.y+l*(r.y-t.y)))):o=new e}else o=new e(0===s||0===c?"Coincident":"Parallel");return o},n.Intersection.intersectLinePolygon=function(t,n,r){var i,a,o,s,c=new e,u=r.length;for(s=0;u>s;s++)i=r[s],a=r[(s+1)%u],o=e.intersectLineLine(t,n,i,a),c.appendPoints(o.points);return c.points.length>0&&(c.status="Intersection"),c},n.Intersection.intersectPolygonPolygon=function(t,n){var r,i=new e,a=t.length;for(r=0;a>r;r++){var o=t[r],s=t[(r+1)%a],c=e.intersectLinePolygon(o,s,n);i.appendPoints(c.points)}return i.points.length>0&&(i.status="Intersection"),i},void(n.Intersection.intersectPolygonRectangle=function(t,r,i){var a=r.min(i),o=r.max(i),s=new n.Point(o.x,a.y),c=new n.Point(a.x,o.y),u=e.intersectLinePolygon(a,s,t),l=e.intersectLinePolygon(s,o,t),f=e.intersectLinePolygon(o,c,t),h=e.intersectLinePolygon(c,a,t),d=new e;return d.appendPoints(u.points),d.appendPoints(l.points),d.appendPoints(f.points),d.appendPoints(h.points),d.points.length>0&&(d.status="Intersection"),d}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";function e(t){t?this._tryParsingColor(t):this.setSource([0,0,0,1])}function r(t,e,r){return 0>r&&(r+=1),r>1&&(r-=1),1/6>r?t+6*(e-t)*r:.5>r?e:2/3>r?t+(e-t)*(2/3-r)*6:t}var n=t.fabric||(t.fabric={});return n.Color?void n.warn("fabric.Color is already defined."):(n.Color=e,n.Color.prototype={_tryParsingColor:function(t){var r;t in e.colorNameMap&&(t=e.colorNameMap[t]),"transparent"===t&&(r=[255,255,255,0]),r||(r=e.sourceFromHex(t)),r||(r=e.sourceFromRgb(t)),r||(r=e.sourceFromHsl(t)),r||(r=[0,0,0,1]),r&&this.setSource(r)},_rgbToHsl:function(t,e,r){t/=255,e/=255,r/=255;var i,a,o,s=n.util.array.max([t,e,r]),c=n.util.array.min([t,e,r]);if(o=(s+c)/2,s===c)i=a=0;else{var u=s-c;switch(a=o>.5?u/(2-s-c):u/(s+c),s){case t:i=(e-r)/u+(r>e?6:0);break;case e:i=(r-t)/u+2;break;case r:i=(t-e)/u+4}i/=6}return[Math.round(360*i),Math.round(100*a),Math.round(100*o)]},getSource:function(){return this._source},setSource:function(t){this._source=t},toRgb:function(){var t=this.getSource();return"rgb("+t[0]+","+t[1]+","+t[2]+")"},toRgba:function(){var t=this.getSource();return"rgba("+t[0]+","+t[1]+","+t[2]+","+t[3]+")"},toHsl:function(){var t=this.getSource(),e=this._rgbToHsl(t[0],t[1],t[2]);return"hsl("+e[0]+","+e[1]+"%,"+e[2]+"%)"},toHsla:function(){var t=this.getSource(),e=this._rgbToHsl(t[0],t[1],t[2]);return"hsla("+e[0]+","+e[1]+"%,"+e[2]+"%,"+t[3]+")"},toHex:function(){var t,e,r,n=this.getSource();return t=n[0].toString(16),t=1===t.length?"0"+t:t,e=n[1].toString(16),e=1===e.length?"0"+e:e,r=n[2].toString(16),r=1===r.length?"0"+r:r,t.toUpperCase()+e.toUpperCase()+r.toUpperCase()},toHexa:function(){var t,e=this.getSource();return t=Math.round(255*e[3]),t=t.toString(16),t=1===t.length?"0"+t:t,this.toHex()+t.toUpperCase()},getAlpha:function(){return this.getSource()[3]},setAlpha:function(t){var e=this.getSource();return e[3]=t,this.setSource(e),this},toGrayscale:function(){var t=this.getSource(),e=parseInt((.3*t[0]+.59*t[1]+.11*t[2]).toFixed(0),10),r=t[3];return this.setSource([e,e,e,r]),this},toBlackWhite:function(t){var e=this.getSource(),r=(.3*e[0]+.59*e[1]+.11*e[2]).toFixed(0),n=e[3];return t=t||127,r=Number(r)<Number(t)?0:255,this.setSource([r,r,r,n]),this},overlayWith:function(t){t instanceof e||(t=new e(t));var r,n=[],i=this.getAlpha(),a=.5,o=this.getSource(),s=t.getSource();for(r=0;3>r;r++)n.push(Math.round(o[r]*(1-a)+s[r]*a));return n[3]=i,this.setSource(n),this}},n.Color.reRGBa=/^rgba?\(\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*(?:\s*,\s*((?:\d*\.?\d+)?)\s*)?\)$/i,n.Color.reHSLa=/^hsla?\(\s*(\d{1,3})\s*,\s*(\d{1,3}\%)\s*,\s*(\d{1,3}\%)\s*(?:\s*,\s*(\d+(?:\.\d+)?)\s*)?\)$/i,n.Color.reHex=/^#?([0-9a-f]{8}|[0-9a-f]{6}|[0-9a-f]{4}|[0-9a-f]{3})$/i,n.Color.colorNameMap={aliceblue:"#F0F8FF",antiquewhite:"#FAEBD7",aqua:"#00FFFF",aquamarine:"#7FFFD4",azure:"#F0FFFF",beige:"#F5F5DC",bisque:"#FFE4C4",black:"#000000",blanchedalmond:"#FFEBCD",blue:"#0000FF",blueviolet:"#8A2BE2",brown:"#A52A2A",burlywood:"#DEB887",cadetblue:"#5F9EA0",chartreuse:"#7FFF00",chocolate:"#D2691E",coral:"#FF7F50",cornflowerblue:"#6495ED",cornsilk:"#FFF8DC",crimson:"#DC143C",cyan:"#00FFFF",darkblue:"#00008B",darkcyan:"#008B8B",darkgoldenrod:"#B8860B",darkgray:"#A9A9A9",darkgrey:"#A9A9A9",darkgreen:"#006400",darkkhaki:"#BDB76B",darkmagenta:"#8B008B",darkolivegreen:"#556B2F",darkorange:"#FF8C00",darkorchid:"#9932CC",darkred:"#8B0000",darksalmon:"#E9967A",darkseagreen:"#8FBC8F",darkslateblue:"#483D8B",darkslategray:"#2F4F4F",darkslategrey:"#2F4F4F",darkturquoise:"#00CED1",darkviolet:"#9400D3",deeppink:"#FF1493",deepskyblue:"#00BFFF",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1E90FF",firebrick:"#B22222",floralwhite:"#FFFAF0",forestgreen:"#228B22",fuchsia:"#FF00FF",gainsboro:"#DCDCDC",ghostwhite:"#F8F8FF",gold:"#FFD700",goldenrod:"#DAA520",gray:"#808080",grey:"#808080",green:"#008000",greenyellow:"#ADFF2F",honeydew:"#F0FFF0",hotpink:"#FF69B4",indianred:"#CD5C5C",indigo:"#4B0082",ivory:"#FFFFF0",khaki:"#F0E68C",lavender:"#E6E6FA",lavenderblush:"#FFF0F5",lawngreen:"#7CFC00",lemonchiffon:"#FFFACD",lightblue:"#ADD8E6",lightcoral:"#F08080",lightcyan:"#E0FFFF",lightgoldenrodyellow:"#FAFAD2",lightgray:"#D3D3D3",lightgrey:"#D3D3D3",lightgreen:"#90EE90",lightpink:"#FFB6C1",lightsalmon:"#FFA07A",lightseagreen:"#20B2AA",lightskyblue:"#87CEFA",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#B0C4DE",lightyellow:"#FFFFE0",lime:"#00FF00",limegreen:"#32CD32",linen:"#FAF0E6",magenta:"#FF00FF",maroon:"#800000",mediumaquamarine:"#66CDAA",mediumblue:"#0000CD",mediumorchid:"#BA55D3",mediumpurple:"#9370DB",mediumseagreen:"#3CB371",mediumslateblue:"#7B68EE",mediumspringgreen:"#00FA9A",mediumturquoise:"#48D1CC",mediumvioletred:"#C71585",midnightblue:"#191970",mintcream:"#F5FFFA",mistyrose:"#FFE4E1",moccasin:"#FFE4B5",navajowhite:"#FFDEAD",navy:"#000080",oldlace:"#FDF5E6",olive:"#808000",olivedrab:"#6B8E23",orange:"#FFA500",orangered:"#FF4500",orchid:"#DA70D6",palegoldenrod:"#EEE8AA",palegreen:"#98FB98",paleturquoise:"#AFEEEE",palevioletred:"#DB7093",papayawhip:"#FFEFD5",peachpuff:"#FFDAB9",peru:"#CD853F",pink:"#FFC0CB",plum:"#DDA0DD",powderblue:"#B0E0E6",purple:"#800080",rebeccapurple:"#663399",red:"#FF0000",rosybrown:"#BC8F8F",royalblue:"#4169E1",saddlebrown:"#8B4513",salmon:"#FA8072",sandybrown:"#F4A460",seagreen:"#2E8B57",seashell:"#FFF5EE",sienna:"#A0522D",silver:"#C0C0C0",skyblue:"#87CEEB",slateblue:"#6A5ACD",slategray:"#708090",slategrey:"#708090",snow:"#FFFAFA",springgreen:"#00FF7F",steelblue:"#4682B4",tan:"#D2B48C",teal:"#008080",thistle:"#D8BFD8",tomato:"#FF6347",turquoise:"#40E0D0",violet:"#EE82EE",wheat:"#F5DEB3",white:"#FFFFFF",whitesmoke:"#F5F5F5",yellow:"#FFFF00",yellowgreen:"#9ACD32"},n.Color.fromRgb=function(t){return e.fromSource(e.sourceFromRgb(t))},n.Color.sourceFromRgb=function(t){var r=t.match(e.reRGBa);if(r){var n=parseInt(r[1],10)/(/%$/.test(r[1])?100:1)*(/%$/.test(r[1])?255:1),i=parseInt(r[2],10)/(/%$/.test(r[2])?100:1)*(/%$/.test(r[2])?255:1),a=parseInt(r[3],10)/(/%$/.test(r[3])?100:1)*(/%$/.test(r[3])?255:1);return[parseInt(n,10),parseInt(i,10),parseInt(a,10),r[4]?parseFloat(r[4]):1]}},n.Color.fromRgba=e.fromRgb,n.Color.fromHsl=function(t){return e.fromSource(e.sourceFromHsl(t))},n.Color.sourceFromHsl=function(t){var n=t.match(e.reHSLa);if(n){var i,a,o,s=(parseFloat(n[1])%360+360)%360/360,c=parseFloat(n[2])/(/%$/.test(n[2])?100:1),u=parseFloat(n[3])/(/%$/.test(n[3])?100:1);if(0===c)i=a=o=u;else{var l=.5>=u?u*(c+1):u+c-u*c,f=2*u-l;i=r(f,l,s+1/3),a=r(f,l,s),o=r(f,l,s-1/3)}return[Math.round(255*i),Math.round(255*a),Math.round(255*o),n[4]?parseFloat(n[4]):1]}},n.Color.fromHsla=e.fromHsl,n.Color.fromHex=function(t){return e.fromSource(e.sourceFromHex(t))},n.Color.sourceFromHex=function(t){if(t.match(e.reHex)){var r=t.slice(t.indexOf("#")+1),n=3===r.length||4===r.length,i=8===r.length||4===r.length,a=n?r.charAt(0)+r.charAt(0):r.substring(0,2),o=n?r.charAt(1)+r.charAt(1):r.substring(2,4),s=n?r.charAt(2)+r.charAt(2):r.substring(4,6),c=i?n?r.charAt(3)+r.charAt(3):r.substring(6,8):"FF";return[parseInt(a,16),parseInt(o,16),parseInt(s,16),parseFloat((parseInt(c,16)/255).toFixed(2))]}},void(n.Color.fromSource=function(t){var r=new e;return r.setSource(t),r}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";function e(t,e){var r=t.angle+j(Math.atan2(e.y,e.x))+360;return Math.round(r%360/45)}function r(t,e){var r=e.transform.target,n=r.canvas,i=D.util.object.clone(e);i.target=r,n&&n.fire("object:"+t,i),r.fire(t,e)}function n(t,e){var r=e.canvas,n=r.uniScaleKey,i=t[n];return r.uniformScaling&&!i||!r.uniformScaling&&i}function i(t){return t.originX===R&&t.originY===R}function a(t,e,r){var n=t.lockScalingX,i=t.lockScalingY;return n&&i?!0:!e&&(n||i)&&r?!0:n&&"x"===e?!0:i&&"y"===e?!0:!1}function o(t,r,i){var o="not-allowed",s=n(t,i),c="";if(0!==r.x&&0===r.y?c="x":0===r.x&&0!==r.y&&(c="y"),a(i,c,s))return o;var u=e(i,r);return O[u]+"-resize"}function s(t,r,n){var i="not-allowed";if(0!==r.x&&n.lockSkewingY)return i;if(0!==r.y&&n.lockSkewingX)return i;var a=e(n,r)%4;return T[a]+"-resize"}function c(t,e,r){return t[r.canvas.altActionKey]?I.skewCursorStyleHandler(t,e,r):I.scaleCursorStyleHandler(t,e,r)}function u(t,e,r){var n=t[r.canvas.altActionKey];return 0===e.x?n?"skewX":"scaleY":0===e.y?n?"skewY":"scaleX":void 0}function l(t,e,r){return r.lockRotation?"not-allowed":e.cursorStyle}function f(t,e,r,n){return{e:t,transform:e,pointer:{x:r,y:n}}}function h(t){return function(e,r,n,i){var a=r.target,o=a.getCenterPoint(),s=a.translateToOriginPoint(o,r.originX,r.originY),c=t(e,r,n,i);return a.setPositionByOrigin(s,r.originX,r.originY),c}}function d(t,e){return function(n,i,a,o){var s=e(n,i,a,o);return s&&r(t,f(n,i,a,o)),s}}function g(t,e,r,n,i){var a=t.target,o=a.controls[t.corner],s=a.canvas.getZoom(),c=a.padding/s,u=a.toLocalPoint(new D.Point(n,i),e,r);return u.x>=c&&(u.x-=c),u.x<=-c&&(u.x+=c),u.y>=c&&(u.y-=c),u.y<=c&&(u.y+=c),u.x-=o.offsetX,u.y-=o.offsetY,u}function p(t){return t.flipX!==t.flipY}function m(t,e,r,n,i){if(0!==t[e]){var a=t._getTransformedDimensions()[n],o=i/a*t[r];t.set(r,o)}}function b(t,e,r,n){var i,a=e.target,o=a._getTransformedDimensions(0,a.skewY),s=g(e,e.originX,e.originY,r,n),c=Math.abs(2*s.x)-o.x,u=a.skewX;2>c?i=0:(i=j(Math.atan2(c/a.scaleX,o.y/a.scaleY)),e.originX===X&&e.originY===B&&(i=-i),e.originX===N&&e.originY===Y&&(i=-i),p(a)&&(i=-i));var l=u!==i;if(l){var f=a._getTransformedDimensions().y;a.set("skewX",i),m(a,"skewY","scaleY","y",f)}return l}function y(t,e,r,n){var i,a=e.target,o=a._getTransformedDimensions(a.skewX,0),s=g(e,e.originX,e.originY,r,n),c=Math.abs(2*s.y)-o.y,u=a.skewY;2>c?i=0:(i=j(Math.atan2(c/a.scaleY,o.x/a.scaleX)),e.originX===X&&e.originY===B&&(i=-i),e.originX===N&&e.originY===Y&&(i=-i),p(a)&&(i=-i));var l=u!==i;if(l){var f=a._getTransformedDimensions().x;a.set("skewY",i),m(a,"skewX","scaleX","x",f)}return l}function v(t,e,r,n){var i,a=e.target,o=a.skewX,s=e.originY;if(a.lockSkewingX)return!1;if(0===o){var c=g(e,R,R,r,n);i=c.x>0?X:N}else o>0&&(i=s===Y?X:N),0>o&&(i=s===Y?N:X),p(a)&&(i=i===X?N:X);e.originX=i;var u=d("skewing",h(b));return u(t,e,r,n)}function x(t,e,r,n){var i,a=e.target,o=a.skewY,s=e.originX;if(a.lockSkewingY)return!1;if(0===o){var c=g(e,R,R,r,n);i=c.y>0?Y:B}else o>0&&(i=s===X?Y:B),0>o&&(i=s===X?B:Y),p(a)&&(i=i===Y?B:Y);e.originY=i;var u=d("skewing",h(y));return u(t,e,r,n)}function w(t,e,r,n){var i=e,a=i.target,o=a.translateToOriginPoint(a.getCenterPoint(),i.originX,i.originY);if(a.lockRotation)return!1;var s=Math.atan2(i.ey-o.y,i.ex-o.x),c=Math.atan2(n-o.y,r-o.x),u=j(c-s+i.theta),l=!0;if(a.snapAngle>0){var f=a.snapAngle,h=a.snapThreshold||f,d=Math.ceil(u/f)*f,g=Math.floor(u/f)*f;Math.abs(u-g)<h?u=g:Math.abs(u-d)<h&&(u=d)}return 0>u&&(u=360+u),u%=360,l=a.angle!==u,a.angle=u,l}function F(t,e,r,o,s){s=s||{};var c,u,l,f,h,d,p=e.target,m=p.lockScalingX,b=p.lockScalingY,y=s.by,v=n(t,p),x=a(p,y,v),w=e.gestureScale;if(x)return!1;if(w)u=e.scaleX*w,l=e.scaleY*w;else{if(c=g(e,e.originX,e.originY,r,o),h="y"!==y?L(c.x):1,d="x"!==y?L(c.y):1,e.signX||(e.signX=h),e.signY||(e.signY=d),p.lockScalingFlip&&(e.signX!==h||e.signY!==d))return!1;if(f=p._getTransformedDimensions(),v&&!y){var F=Math.abs(c.x)+Math.abs(c.y),A=e.original,k=Math.abs(f.x*A.scaleX/p.scaleX)+Math.abs(f.y*A.scaleY/p.scaleY),C=F/k;u=A.scaleX*C,l=A.scaleY*C}else u=Math.abs(c.x*p.scaleX/f.x),l=Math.abs(c.y*p.scaleY/f.y);i(e)&&(u*=2,l*=2),e.signX!==h&&"y"!==y&&(e.originX=_[e.originX],u*=-1,e.signX=h),e.signY!==d&&"x"!==y&&(e.originY=_[e.originY],l*=-1,e.signY=d)}var E=p.scaleX,M=p.scaleY;return y?("x"===y&&p.set("scaleX",u),"y"===y&&p.set("scaleY",l)):(!m&&p.set("scaleX",u),!b&&p.set("scaleY",l)),E!==p.scaleX||M!==p.scaleY}function A(t,e,r,n){return F(t,e,r,n)}function k(t,e,r,n){return F(t,e,r,n,{by:"x"})}function C(t,e,r,n){return F(t,e,r,n,{by:"y"})}function E(t,e,r,n){return t[e.target.canvas.altActionKey]?I.skewHandlerX(t,e,r,n):I.scalingY(t,e,r,n)}function M(t,e,r,n){return t[e.target.canvas.altActionKey]?I.skewHandlerY(t,e,r,n):I.scalingX(t,e,r,n)}function S(t,e,r,n){var a=e.target,o=g(e,e.originX,e.originY,r,n),s=a.strokeWidth/(a.strokeUniform?a.scaleX:1),c=i(e)?2:1,u=a.width,l=Math.abs(o.x*c/a.scaleX)-s;return a.set("width",Math.max(l,0)),u!==l}function P(t,e,n,i){var a=e.target,o=n-e.offsetX,s=i-e.offsetY,c=!a.get("lockMovementX")&&a.left!==o,u=!a.get("lockMovementY")&&a.top!==s;return c&&a.set("left",o),u&&a.set("top",s),(c||u)&&r("moving",f(t,e,n,i)),c||u}var D=t.fabric||(t.fabric={}),O=["e","se","s","sw","w","nw","n","ne","e"],T=["ns","nesw","ew","nwse"],I={},X="left",Y="top",N="right",B="bottom",R="center",_={top:B,bottom:Y,left:N,right:X,center:R},j=D.util.radiansToDegrees,L=Math.sign||function(t){return(t>0)-(0>t)||+t};I.scaleCursorStyleHandler=o,I.skewCursorStyleHandler=s,I.scaleSkewCursorStyleHandler=c,I.rotationWithSnapping=d("rotating",h(w)),I.scalingEqually=d("scaling",h(A)),I.scalingX=d("scaling",h(k)),I.scalingY=d("scaling",h(C)),I.scalingYOrSkewingX=E,I.scalingXOrSkewingY=M,I.changeWidth=d("resizing",h(S)),I.skewHandlerX=v,I.skewHandlerY=x,I.dragHandler=P,I.scaleOrSkewActionName=u,I.rotationStyleHandler=l,I.fireEvent=r,I.wrapWithFixedAnchor=h,I.wrapWithFireEvent=d,I.getLocalPoint=g,D.controlsUtils=I}("undefined"!=typeof exports?exports:this);!function(t){"use strict";function e(t,e,r,n,i){n=n||{};var a,o=this.sizeX||n.cornerSize||i.cornerSize,s=this.sizeY||n.cornerSize||i.cornerSize,c="undefined"!=typeof n.transparentCorners?n.transparentCorners:i.transparentCorners,u=c?"stroke":"fill",l=!c&&(n.cornerStrokeColor||i.cornerStrokeColor),f=e,h=r;t.save(),t.fillStyle=n.cornerColor||i.cornerColor,t.strokeStyle=n.cornerStrokeColor||i.cornerStrokeColor,o>s?(a=o,t.scale(1,s/o),h=r*o/s):s>o?(a=s,t.scale(o/s,1),f=e*s/o):a=o,t.lineWidth=1,t.beginPath(),t.arc(f,h,a/2,0,2*Math.PI,!1),t[u](),l&&t.stroke(),t.restore()}function r(t,e,r,n,a){n=n||{};var o=this.sizeX||n.cornerSize||a.cornerSize,s=this.sizeY||n.cornerSize||a.cornerSize,c="undefined"!=typeof n.transparentCorners?n.transparentCorners:a.transparentCorners,u=c?"stroke":"fill",l=!c&&(n.cornerStrokeColor||a.cornerStrokeColor),f=o/2,h=s/2;t.save(),t.fillStyle=n.cornerColor||a.cornerColor,t.strokeStyle=n.cornerStrokeColor||a.cornerStrokeColor,t.lineWidth=1,t.translate(e,r),t.rotate(i(a.angle)),t[u+"Rect"](-f,-h,o,s),l&&t.strokeRect(-f,-h,o,s),t.restore()}var n=t.fabric||(t.fabric={}),i=n.util.degreesToRadians,a=n.controlsUtils;a.renderCircleControl=e,a.renderSquareControl=r}("undefined"!=typeof exports?exports:this);!function(t){"use strict";function e(t){for(var e in t)this[e]=t[e]}var r=t.fabric||(t.fabric={});r.Control=e,r.Control.prototype={visible:!0,actionName:"scale",angle:0,x:0,y:0,offsetX:0,offsetY:0,sizeX:null,sizeY:null,touchSizeX:null,touchSizeY:null,cursorStyle:"crosshair",withConnection:!1,actionHandler:function(){},mouseDownHandler:function(){},mouseUpHandler:function(){},getActionHandler:function(){return this.actionHandler},getMouseDownHandler:function(){return this.mouseDownHandler},getMouseUpHandler:function(){return this.mouseUpHandler},cursorStyleHandler:function(t,e){return e.cursorStyle},getActionName:function(t,e){return e.actionName},getVisibility:function(t,e){var r=t._controlsVisibility;return r&&"undefined"!=typeof r[e]?r[e]:this.visible},setVisibility:function(t){this.visible=t},positionHandler:function(t,e){var n=r.util.transformPoint({x:this.x*t.x+this.offsetX,y:this.y*t.y+this.offsetY},e);return n},calcCornerCoords:function(t,e,n,i,a){var o,s,c,u,l=a?this.touchSizeX:this.sizeX,f=a?this.touchSizeY:this.sizeY;if(l&&f&&l!==f){var h=Math.atan2(f,l),d=Math.sqrt(l*l+f*f)/2,g=h-r.util.degreesToRadians(t),p=Math.PI/2-h-r.util.degreesToRadians(t);o=d*r.util.cos(g),s=d*r.util.sin(g),c=d*r.util.cos(p),u=d*r.util.sin(p)}else{var m=l&&f?l:e;d=.7071067812*m;var g=r.util.degreesToRadians(45-t);o=c=d*r.util.cos(g),s=u=d*r.util.sin(g)}return{tl:{x:n-u,y:i-c},tr:{x:n+o,y:i-s},bl:{x:n-o,y:i+s},br:{x:n+u,y:i+c}}},render:function(t,e,n,i,a){switch(i=i||{},i.cornerStyle||a.cornerStyle){case"circle":r.controlsUtils.renderCircleControl.call(this,t,e,n,i,a);break;default:r.controlsUtils.renderSquareControl.call(this,t,e,n,i,a)}}}}("undefined"!=typeof exports?exports:this);!function(){function t(t,e){var r,n,i,a,o=t.getAttribute("style"),s=t.getAttribute("offset")||0;if(s=parseFloat(s)/(/%$/.test(s)?100:1),s=0>s?0:s>1?1:s,o){var c=o.split(/\s*;\s*/);for(""===c[c.length-1]&&c.pop(),a=c.length;a--;){var l=c[a].split(/\s*:\s*/),u=l[0].trim(),f=l[1].trim();"stop-color"===u?r=f:"stop-opacity"===u&&(i=f)}}return r||(r=t.getAttribute("stop-color")||"rgb(0,0,0)"),i||(i=t.getAttribute("stop-opacity")),r=new fabric.Color(r),n=r.getAlpha(),i=isNaN(parseFloat(i))?1:parseFloat(i),i*=n*e,{offset:s,color:r.toRgb(),opacity:i}}function e(t){return{x1:t.getAttribute("x1")||0,y1:t.getAttribute("y1")||0,x2:t.getAttribute("x2")||"100%",y2:t.getAttribute("y2")||0}}function r(t){return{x1:t.getAttribute("fx")||t.getAttribute("cx")||"50%",y1:t.getAttribute("fy")||t.getAttribute("cy")||"50%",r1:0,x2:t.getAttribute("cx")||"50%",y2:t.getAttribute("cy")||"50%",r2:t.getAttribute("r")||"50%"}}function n(t,e,r,n){var i,a;Object.keys(e).forEach(function(t){i=e[t],"Infinity"===i?a=1:"-Infinity"===i?a=0:(a=parseFloat(e[t],10),"string"==typeof i&&/^(\d+\.\d+)%|(\d+)%$/.test(i)&&(a*=.01,"pixels"===n&&(("x1"===t||"x2"===t||"r2"===t)&&(a*=r.viewBoxWidth||r.width),("y1"===t||"y2"===t)&&(a*=r.viewBoxHeight||r.height)))),e[t]=a})}var i=fabric.util.object.clone;fabric.Gradient=fabric.util.createClass({offsetX:0,offsetY:0,gradientTransform:null,gradientUnits:"pixels",type:"linear",initialize:function(t){t||(t={}),t.coords||(t.coords={});var e,r=this;Object.keys(t).forEach(function(e){r[e]=t[e]}),this.id?this.id+="_"+fabric.Object.__uid++:this.id=fabric.Object.__uid++,e={x1:t.coords.x1||0,y1:t.coords.y1||0,x2:t.coords.x2||0,y2:t.coords.y2||0},"radial"===this.type&&(e.r1=t.coords.r1||0,e.r2=t.coords.r2||0),this.coords=e,this.colorStops=t.colorStops.slice()},addColorStop:function(t){for(var e in t){var r=new fabric.Color(t[e]);this.colorStops.push({offset:parseFloat(e),color:r.toRgb(),opacity:r.getAlpha()})}return this},toObject:function(t){var e={type:this.type,coords:this.coords,colorStops:this.colorStops,offsetX:this.offsetX,offsetY:this.offsetY,gradientUnits:this.gradientUnits,gradientTransform:this.gradientTransform?this.gradientTransform.concat():this.gradientTransform};return fabric.util.populateWithProperties(this,e,t),e},toSVG:function(t,e){var r,n,a,o,s=i(this.coords,!0),e=e||{},c=i(this.colorStops,!0),l=s.r1>s.r2,u=this.gradientTransform?this.gradientTransform.concat():fabric.iMatrix.concat(),f=-this.offsetX,h=-this.offsetY,d=!!e.additionalTransform,p="pixels"===this.gradientUnits?"userSpaceOnUse":"objectBoundingBox";if(c.sort(function(t,e){return t.offset-e.offset}),"objectBoundingBox"===p?(f/=t.width,h/=t.height):(f+=t.width/2,h+=t.height/2),"path"===t.type&&"percentage"!==this.gradientUnits&&(f-=t.pathOffset.x,h-=t.pathOffset.y),u[4]-=f,u[5]-=h,o='id="SVGID_'+this.id+'" gradientUnits="'+p+'"',o+=' gradientTransform="'+(d?e.additionalTransform+" ":"")+fabric.util.matrixToSVG(u)+'" ',"linear"===this.type?a=["<linearGradient ",o,' x1="',s.x1,'" y1="',s.y1,'" x2="',s.x2,'" y2="',s.y2,'">\n']:"radial"===this.type&&(a=["<radialGradient ",o,' cx="',l?s.x1:s.x2,'" cy="',l?s.y1:s.y2,'" r="',l?s.r1:s.r2,'" fx="',l?s.x2:s.x1,'" fy="',l?s.y2:s.y1,'">\n']),"radial"===this.type){if(l)for(c=c.concat(),c.reverse(),r=0,n=c.length;n>r;r++)c[r].offset=1-c[r].offset;var g=Math.min(s.r1,s.r2);if(g>0){var m=Math.max(s.r1,s.r2),b=g/m;for(r=0,n=c.length;n>r;r++)c[r].offset+=b*(1-c[r].offset)}}for(r=0,n=c.length;n>r;r++){var y=c[r];a.push("<stop ",'offset="',100*y.offset+"%",'" style="stop-color:',y.color,"undefined"!=typeof y.opacity?";stop-opacity: "+y.opacity:";",'"/>\n')}return a.push("linear"===this.type?"</linearGradient>\n":"</radialGradient>\n"),a.join("")},toLive:function(t){var e,r,n,i=fabric.util.object.clone(this.coords);if(this.type){for("linear"===this.type?e=t.createLinearGradient(i.x1,i.y1,i.x2,i.y2):"radial"===this.type&&(e=t.createRadialGradient(i.x1,i.y1,i.r1,i.x2,i.y2,i.r2)),r=0,n=this.colorStops.length;n>r;r++){var a=this.colorStops[r].color,o=this.colorStops[r].opacity,s=this.colorStops[r].offset;"undefined"!=typeof o&&(a=new fabric.Color(a).setAlpha(o).toRgba()),e.addColorStop(s,a)}return e}}}),fabric.util.object.extend(fabric.Gradient,{fromElement:function(i,a,o,s){var c=parseFloat(o)/(/%$/.test(o)?100:1);c=0>c?0:c>1?1:c,isNaN(c)&&(c=1);var l,u,f,h,d=i.getElementsByTagName("stop"),p="userSpaceOnUse"===i.getAttribute("gradientUnits")?"pixels":"percentage",g=i.getAttribute("gradientTransform")||"",m=[],b=0,y=0;for("linearGradient"===i.nodeName||"LINEARGRADIENT"===i.nodeName?(l="linear",u=e(i)):(l="radial",u=r(i)),f=d.length;f--;)m.push(t(d[f],c));h=fabric.parseTransformAttribute(g),n(a,u,s,p),"pixels"===p&&(b=-a.left,y=-a.top);var v=new fabric.Gradient({id:i.getAttribute("id"),type:l,coords:u,colorStops:m,gradientUnits:p,gradientTransform:h,offsetX:b,offsetY:y});return v}})}();!function(){"use strict";var t=fabric.util.toFixed;fabric.Pattern=fabric.util.createClass({repeat:"repeat",offsetX:0,offsetY:0,crossOrigin:"",patternTransform:null,initialize:function(t,e){if(t||(t={}),this.id=fabric.Object.__uid++,this.setOptions(t),!t.source||t.source&&"string"!=typeof t.source)return void(e&&e(this));var r=this;this.source=fabric.util.createImage(),fabric.util.loadImage(t.source,function(t,n){r.source=t,e&&e(r,n)},null,this.crossOrigin)},toObject:function(e){var r,n,i=fabric.Object.NUM_FRACTION_DIGITS;return"string"==typeof this.source.src?r=this.source.src:"object"==typeof this.source&&this.source.toDataURL&&(r=this.source.toDataURL()),n={type:"pattern",source:r,repeat:this.repeat,crossOrigin:this.crossOrigin,offsetX:t(this.offsetX,i),offsetY:t(this.offsetY,i),patternTransform:this.patternTransform?this.patternTransform.concat():null},fabric.util.populateWithProperties(this,n,e),n},toSVG:function(t){var e="function"==typeof this.source?this.source():this.source,r=e.width/t.width,n=e.height/t.height,i=this.offsetX/t.width,a=this.offsetY/t.height,o="";return("repeat-x"===this.repeat||"no-repeat"===this.repeat)&&(n=1,a&&(n+=Math.abs(a))),("repeat-y"===this.repeat||"no-repeat"===this.repeat)&&(r=1,i&&(r+=Math.abs(i))),e.src?o=e.src:e.toDataURL&&(o=e.toDataURL()),'<pattern id="SVGID_'+this.id+'" x="'+i+'" y="'+a+'" width="'+r+'" height="'+n+'">\n<image x="0" y="0" width="'+e.width+'" height="'+e.height+'" xlink:href="'+o+'"></image>\n</pattern>\n'},setOptions:function(t){for(var e in t)this[e]=t[e]},toLive:function(t){var e=this.source;if(!e)return"";if("undefined"!=typeof e.src){if(!e.complete)return"";if(0===e.naturalWidth||0===e.naturalHeight)return""}return t.createPattern(e,this.repeat)}})}();!function(t){"use strict";var e=t.fabric||(t.fabric={}),r=e.util.toFixed;return e.Shadow?void e.warn("fabric.Shadow is already defined."):(e.Shadow=e.util.createClass({color:"rgb(0,0,0)",blur:0,offsetX:0,offsetY:0,affectStroke:!1,includeDefaultValues:!0,nonScaling:!1,initialize:function(t){"string"==typeof t&&(t=this._parseShadow(t));for(var r in t)this[r]=t[r];this.id=e.Object.__uid++},_parseShadow:function(t){var r=t.trim(),n=e.Shadow.reOffsetsAndBlur.exec(r)||[],i=r.replace(e.Shadow.reOffsetsAndBlur,"")||"rgb(0,0,0)";return{color:i.trim(),offsetX:parseFloat(n[1],10)||0,offsetY:parseFloat(n[2],10)||0,blur:parseFloat(n[3],10)||0}},toString:function(){return[this.offsetX,this.offsetY,this.blur,this.color].join("px ")},toSVG:function(t){var n=40,i=40,a=e.Object.NUM_FRACTION_DIGITS,o=e.util.rotateVector({x:this.offsetX,y:this.offsetY},e.util.degreesToRadians(-t.angle)),s=20,c=new e.Color(this.color);return t.width&&t.height&&(n=100*r((Math.abs(o.x)+this.blur)/t.width,a)+s,i=100*r((Math.abs(o.y)+this.blur)/t.height,a)+s),t.flipX&&(o.x*=-1),t.flipY&&(o.y*=-1),'<filter id="SVGID_'+this.id+'" y="-'+i+'%" height="'+(100+2*i)+'%" x="-'+n+'%" width="'+(100+2*n)+'%" >\n <feGaussianBlur in="SourceAlpha" stdDeviation="'+r(this.blur?this.blur/2:0,a)+'"></feGaussianBlur>\n <feOffset dx="'+r(o.x,a)+'" dy="'+r(o.y,a)+'" result="oBlur" ></feOffset>\n <feFlood flood-color="'+c.toRgb()+'" flood-opacity="'+c.getAlpha()+'"/>\n <feComposite in2="oBlur" operator="in" />\n <feMerge>\n <feMergeNode></feMergeNode>\n <feMergeNode in="SourceGraphic"></feMergeNode>\n </feMerge>\n</filter>\n'},toObject:function(){if(this.includeDefaultValues)return{color:this.color,blur:this.blur,offsetX:this.offsetX,offsetY:this.offsetY,affectStroke:this.affectStroke,nonScaling:this.nonScaling};var t={},r=e.Shadow.prototype;return["color","blur","offsetX","offsetY","affectStroke","nonScaling"].forEach(function(e){this[e]!==r[e]&&(t[e]=this[e])},this),t}}),void(e.Shadow.reOffsetsAndBlur=/(?:\s|^)(-?\d+(?:\.\d*)?(?:px)?(?:\s?|$))?(-?\d+(?:\.\d*)?(?:px)?(?:\s?|$))?(\d+(?:\.\d*)?(?:px)?)?(?:\s?|$)(?:$|\s)/))}("undefined"!=typeof exports?exports:this);!function(){"use strict";if(fabric.StaticCanvas)return void fabric.warn("fabric.StaticCanvas is already defined.");var t=fabric.util.object.extend,e=fabric.util.getElementOffset,r=fabric.util.removeFromArray,i=fabric.util.toFixed,n=fabric.util.transformPoint,a=fabric.util.invertTransform,o=fabric.util.getNodeCanvas,s=fabric.util.createCanvasElement,c=new Error("Could not initialize `canvas` element");fabric.StaticCanvas=fabric.util.createClass(fabric.CommonMethods,{initialize:function(t,e){e||(e={}),this.renderAndResetBound=this.renderAndReset.bind(this),this.requestRenderAllBound=this.requestRenderAll.bind(this),this._initStatic(t,e)},backgroundColor:"",backgroundImage:null,overlayColor:"",overlayImage:null,includeDefaultValues:!0,stateful:!1,renderOnAddRemove:!0,controlsAboveOverlay:!1,allowTouchScrolling:!1,imageSmoothingEnabled:!0,viewportTransform:fabric.iMatrix.concat(),backgroundVpt:!0,overlayVpt:!0,enableRetinaScaling:!0,vptCoords:{},skipOffscreen:!0,clipPath:void 0,_initStatic:function(t,e){var r=this.requestRenderAllBound;this._objects=[],this._createLowerCanvas(t),this._initOptions(e),this.interactive||this._initRetinaScaling(),e.overlayImage&&this.setOverlayImage(e.overlayImage,r),e.backgroundImage&&this.setBackgroundImage(e.backgroundImage,r),e.backgroundColor&&this.setBackgroundColor(e.backgroundColor,r),e.overlayColor&&this.setOverlayColor(e.overlayColor,r),this.calcOffset()},_isRetinaScaling:function(){return fabric.devicePixelRatio>1&&this.enableRetinaScaling},getRetinaScaling:function(){return this._isRetinaScaling()?Math.max(1,fabric.devicePixelRatio):1},_initRetinaScaling:function(){if(this._isRetinaScaling()){var t=fabric.devicePixelRatio;this.__initRetinaScaling(t,this.lowerCanvasEl,this.contextContainer),this.upperCanvasEl&&this.__initRetinaScaling(t,this.upperCanvasEl,this.contextTop)}},__initRetinaScaling:function(t,e,r){e.setAttribute("width",this.width*t),e.setAttribute("height",this.height*t),r.scale(t,t)},calcOffset:function(){return this._offset=e(this.lowerCanvasEl),this},setOverlayImage:function(t,e,r){return this.__setBgOverlayImage("overlayImage",t,e,r)},setBackgroundImage:function(t,e,r){return this.__setBgOverlayImage("backgroundImage",t,e,r)},setOverlayColor:function(t,e){return this.__setBgOverlayColor("overlayColor",t,e)},setBackgroundColor:function(t,e){return this.__setBgOverlayColor("backgroundColor",t,e)},__setBgOverlayImage:function(t,e,r,i){return"string"==typeof e?fabric.util.loadImage(e,function(e,n){if(e){var a=new fabric.Image(e,i);this[t]=a,a.canvas=this}r&&r(e,n)},this,i&&i.crossOrigin):(i&&e.setOptions(i),this[t]=e,e&&(e.canvas=this),r&&r(e,!1)),this},__setBgOverlayColor:function(t,e,r){return this[t]=e,this._initGradient(e,t),this._initPattern(e,t,r),this},_createCanvasElement:function(){var t=s();if(!t)throw c;if(t.style||(t.style={}),"undefined"==typeof t.getContext)throw c;return t},_initOptions:function(t){var e=this.lowerCanvasEl;this._setOptions(t),this.width=this.width||parseInt(e.width,10)||0,this.height=this.height||parseInt(e.height,10)||0,this.lowerCanvasEl.style&&(e.width=this.width,e.height=this.height,e.style.width=this.width+"px",e.style.height=this.height+"px",this.viewportTransform=this.viewportTransform.slice())},_createLowerCanvas:function(t){this.lowerCanvasEl=t&&t.getContext?t:fabric.util.getById(t)||this._createCanvasElement(),fabric.util.addClass(this.lowerCanvasEl,"lower-canvas"),this._originalCanvasStyle=this.lowerCanvasEl.style,this.interactive&&this._applyCanvasStyle(this.lowerCanvasEl),this.contextContainer=this.lowerCanvasEl.getContext("2d")},getWidth:function(){return this.width},getHeight:function(){return this.height},setWidth:function(t,e){return this.setDimensions({width:t},e)},setHeight:function(t,e){return this.setDimensions({height:t},e)},setDimensions:function(t,e){var r;e=e||{};for(var i in t)r=t[i],e.cssOnly||(this._setBackstoreDimension(i,t[i]),r+="px",this.hasLostContext=!0),e.backstoreOnly||this._setCssDimension(i,r);return this._isCurrentlyDrawing&&this.freeDrawingBrush&&this.freeDrawingBrush._setBrushStyles(this.contextTop),this._initRetinaScaling(),this.calcOffset(),e.cssOnly||this.requestRenderAll(),this},_setBackstoreDimension:function(t,e){return this.lowerCanvasEl[t]=e,this.upperCanvasEl&&(this.upperCanvasEl[t]=e),this.cacheCanvasEl&&(this.cacheCanvasEl[t]=e),this[t]=e,this},_setCssDimension:function(t,e){return this.lowerCanvasEl.style[t]=e,this.upperCanvasEl&&(this.upperCanvasEl.style[t]=e),this.wrapperEl&&(this.wrapperEl.style[t]=e),this},getZoom:function(){return this.viewportTransform[0]},setViewportTransform:function(t){var e,r,i,n=this._activeObject,a=this.backgroundImage,o=this.overlayImage;for(this.viewportTransform=t,r=0,i=this._objects.length;i>r;r++)e=this._objects[r],e.group||e.setCoords(!0);return n&&n.setCoords(),a&&a.setCoords(!0),o&&o.setCoords(!0),this.calcViewportBoundaries(),this.renderOnAddRemove&&this.requestRenderAll(),this},zoomToPoint:function(t,e){var r=t,i=this.viewportTransform.slice(0);t=n(t,a(this.viewportTransform)),i[0]=e,i[3]=e;var o=n(t,i);return i[4]+=r.x-o.x,i[5]+=r.y-o.y,this.setViewportTransform(i)},setZoom:function(t){return this.zoomToPoint(new fabric.Point(0,0),t),this},absolutePan:function(t){var e=this.viewportTransform.slice(0);return e[4]=-t.x,e[5]=-t.y,this.setViewportTransform(e)},relativePan:function(t){return this.absolutePan(new fabric.Point(-t.x-this.viewportTransform[4],-t.y-this.viewportTransform[5]))},getElement:function(){return this.lowerCanvasEl},_onObjectAdded:function(t){this.stateful&&t.setupState(),t._set("canvas",this),t.setCoords(),this.fire("object:added",{target:t}),t.fire("added")},_onObjectRemoved:function(t){this.fire("object:removed",{target:t}),t.fire("removed"),delete t.canvas},clearContext:function(t){return t.clearRect(0,0,this.width,this.height),this},getContext:function(){return this.contextContainer},clear:function(){return this.remove.apply(this,this.getObjects()),this.backgroundImage=null,this.overlayImage=null,this.backgroundColor="",this.overlayColor="",this._hasITextHandlers&&(this.off("mouse:up",this._mouseUpITextHandler),this._iTextInstances=null,this._hasITextHandlers=!1),this.clearContext(this.contextContainer),this.fire("canvas:cleared"),this.renderOnAddRemove&&this.requestRenderAll(),this},renderAll:function(){var t=this.contextContainer;return this.renderCanvas(t,this._objects),this},renderAndReset:function(){this.isRendering=0,this.renderAll()},requestRenderAll:function(){return this.isRendering||(this.isRendering=fabric.util.requestAnimFrame(this.renderAndResetBound)),this},calcViewportBoundaries:function(){var t={},e=this.width,r=this.height,i=a(this.viewportTransform);return t.tl=n({x:0,y:0},i),t.br=n({x:e,y:r},i),t.tr=new fabric.Point(t.br.x,t.tl.y),t.bl=new fabric.Point(t.tl.x,t.br.y),this.vptCoords=t,t},cancelRequestedRender:function(){this.isRendering&&(fabric.util.cancelAnimFrame(this.isRendering),this.isRendering=0)},renderCanvas:function(t,e){var r=this.viewportTransform,i=this.clipPath;this.cancelRequestedRender(),this.calcViewportBoundaries(),this.clearContext(t),fabric.util.setImageSmoothing(t,this.imageSmoothingEnabled),this.fire("before:render",{ctx:t}),this._renderBackground(t),t.save(),t.transform(r[0],r[1],r[2],r[3],r[4],r[5]),this._renderObjects(t,e),t.restore(),!this.controlsAboveOverlay&&this.interactive&&this.drawControls(t),i&&(i.canvas=this,i.shouldCache(),i._transformDone=!0,i.renderCache({forClipping:!0}),this.drawClipPathOnCanvas(t)),this._renderOverlay(t),this.controlsAboveOverlay&&this.interactive&&this.drawControls(t),this.fire("after:render",{ctx:t})},drawClipPathOnCanvas:function(t){var e=this.viewportTransform,r=this.clipPath;t.save(),t.transform(e[0],e[1],e[2],e[3],e[4],e[5]),t.globalCompositeOperation="destination-in",r.transform(t),t.scale(1/r.zoomX,1/r.zoomY),t.drawImage(r._cacheCanvas,-r.cacheTranslationX,-r.cacheTranslationY),t.restore()},_renderObjects:function(t,e){var r,i;for(r=0,i=e.length;i>r;++r)e[r]&&e[r].render(t)},_renderBackgroundOrOverlay:function(t,e){var r=this[e+"Color"],i=this[e+"Image"],n=this.viewportTransform,a=this[e+"Vpt"];if(r||i){if(r){t.save(),t.beginPath(),t.moveTo(0,0),t.lineTo(this.width,0),t.lineTo(this.width,this.height),t.lineTo(0,this.height),t.closePath(),t.fillStyle=r.toLive?r.toLive(t,this):r,a&&t.transform(n[0],n[1],n[2],n[3],n[4],n[5]),t.transform(1,0,0,1,r.offsetX||0,r.offsetY||0);var o=r.gradientTransform||r.patternTransform;o&&t.transform(o[0],o[1],o[2],o[3],o[4],o[5]),t.fill(),t.restore()}i&&(t.save(),a&&t.transform(n[0],n[1],n[2],n[3],n[4],n[5]),i.render(t),t.restore())}},_renderBackground:function(t){this._renderBackgroundOrOverlay(t,"background")},_renderOverlay:function(t){this._renderBackgroundOrOverlay(t,"overlay")},getCenter:function(){return{top:this.height/2,left:this.width/2}},getCenterPoint:function(){return new fabric.Point(this.width/2,this.height/2)},centerObjectH:function(t){return this._centerObject(t,new fabric.Point(this.getCenterPoint().x,t.getCenterPoint().y))},centerObjectV:function(t){return this._centerObject(t,new fabric.Point(t.getCenterPoint().x,this.getCenterPoint().y))},centerObject:function(t){var e=this.getCenterPoint();return this._centerObject(t,e)},viewportCenterObject:function(t){var e=this.getVpCenter();return this._centerObject(t,e)},viewportCenterObjectH:function(t){var e=this.getVpCenter();return this._centerObject(t,new fabric.Point(e.x,t.getCenterPoint().y)),this},viewportCenterObjectV:function(t){var e=this.getVpCenter();return this._centerObject(t,new fabric.Point(t.getCenterPoint().x,e.y))},getVpCenter:function(){var t=this.getCenterPoint(),e=a(this.viewportTransform);return n(t,e)},_centerObject:function(t,e){return t.setPositionByOrigin(e,"center","center"),t.setCoords(),this.renderOnAddRemove&&this.requestRenderAll(),this},toDatalessJSON:function(t){return this.toDatalessObject(t)},toObject:function(t){return this._toObjectMethod("toObject",t)},toDatalessObject:function(t){return this._toObjectMethod("toDatalessObject",t)},_toObjectMethod:function(e,r){var i=this.clipPath,n={version:fabric.version,objects:this._toObjects(e,r)};return i&&!i.excludeFromExport&&(n.clipPath=this._toObject(this.clipPath,e,r)),t(n,this.__serializeBgOverlay(e,r)),fabric.util.populateWithProperties(this,n,r),n},_toObjects:function(t,e){return this._objects.filter(function(t){return!t.excludeFromExport}).map(function(r){return this._toObject(r,t,e)},this)},_toObject:function(t,e,r){var i;this.includeDefaultValues||(i=t.includeDefaultValues,t.includeDefaultValues=!1);var n=t[e](r);return this.includeDefaultValues||(t.includeDefaultValues=i),n},__serializeBgOverlay:function(t,e){var r={},i=this.backgroundImage,n=this.overlayImage,a=this.backgroundColor,o=this.overlayColor;return a&&a.toObject?a.excludeFromExport||(r.background=a.toObject(e)):a&&(r.background=a),o&&o.toObject?o.excludeFromExport||(r.overlay=o.toObject(e)):o&&(r.overlay=o),i&&!i.excludeFromExport&&(r.backgroundImage=this._toObject(i,t,e)),n&&!n.excludeFromExport&&(r.overlayImage=this._toObject(n,t,e)),r},svgViewportTransformation:!0,toSVG:function(t,e){t||(t={}),t.reviver=e;var r=[];return this._setSVGPreamble(r,t),this._setSVGHeader(r,t),this.clipPath&&r.push('<g clip-path="url(#'+this.clipPath.clipPathId+')" >\n'),this._setSVGBgOverlayColor(r,"background"),this._setSVGBgOverlayImage(r,"backgroundImage",e),this._setSVGObjects(r,e),this.clipPath&&r.push("</g>\n"),this._setSVGBgOverlayColor(r,"overlay"),this._setSVGBgOverlayImage(r,"overlayImage",e),r.push("</svg>"),r.join("")},_setSVGPreamble:function(t,e){e.suppressPreamble||t.push('<?xml version="1.0" encoding="',e.encoding||"UTF-8",'" standalone="no" ?>\n','<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" ','"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">\n')},_setSVGHeader:function(t,e){var r,n=e.width||this.width,a=e.height||this.height,o='viewBox="0 0 '+this.width+" "+this.height+'" ',s=fabric.Object.NUM_FRACTION_DIGITS;e.viewBox?o='viewBox="'+e.viewBox.x+" "+e.viewBox.y+" "+e.viewBox.width+" "+e.viewBox.height+'" ':this.svgViewportTransformation&&(r=this.viewportTransform,o='viewBox="'+i(-r[4]/r[0],s)+" "+i(-r[5]/r[3],s)+" "+i(this.width/r[0],s)+" "+i(this.height/r[3],s)+'" '),t.push("<svg ",'xmlns="http://www.w3.org/2000/svg" ','xmlns:xlink="http://www.w3.org/1999/xlink" ','version="1.1" ','width="',n,'" ','height="',a,'" ',o,'xml:space="preserve">\n',"<desc>Created with Fabric.js ",fabric.version,"</desc>\n","<defs>\n",this.createSVGFontFacesMarkup(),this.createSVGRefElementsMarkup(),this.createSVGClipPathMarkup(e),"</defs>\n")},createSVGClipPathMarkup:function(t){var e=this.clipPath;return e?(e.clipPathId="CLIPPATH_"+fabric.Object.__uid++,'<clipPath id="'+e.clipPathId+'" >\n'+this.clipPath.toClipPathSVG(t.reviver)+"</clipPath>\n"):""},createSVGRefElementsMarkup:function(){var t=this,e=["background","overlay"].map(function(e){var r=t[e+"Color"];if(r&&r.toLive){var i=t[e+"Vpt"],n=t.viewportTransform,a={width:t.width/(i?n[0]:1),height:t.height/(i?n[3]:1)};return r.toSVG(a,{additionalTransform:i?fabric.util.matrixToSVG(n):""})}});return e.join("")},createSVGFontFacesMarkup:function(){var t,e,r,i,n,a,o,s,c,l="",u={},f=fabric.fontPaths,h=[];for(this._objects.forEach(function p(t){h.push(t),t._objects&&t._objects.forEach(p)}),s=0,c=h.length;c>s;s++)if(t=h[s],e=t.fontFamily,-1!==t.type.indexOf("text")&&!u[e]&&f[e]&&(u[e]=!0,t.styles)){r=t.styles;for(n in r){i=r[n];for(o in i)a=i[o],e=a.fontFamily,!u[e]&&f[e]&&(u[e]=!0)}}for(var d in u)l+=[" @font-face {\n"," font-family: '",d,"';\n"," src: url('",f[d],"');\n"," }\n"].join("");return l&&(l=[' <style type="text/css">',"<![CDATA[\n",l,"]]>","</style>\n"].join("")),l},_setSVGObjects:function(t,e){var r,i,n,a=this._objects;for(i=0,n=a.length;n>i;i++)r=a[i],r.excludeFromExport||this._setSVGObject(t,r,e)},_setSVGObject:function(t,e,r){t.push(e.toSVG(r))},_setSVGBgOverlayImage:function(t,e,r){this[e]&&!this[e].excludeFromExport&&this[e].toSVG&&t.push(this[e].toSVG(r))},_setSVGBgOverlayColor:function(t,e){var r=this[e+"Color"],i=this.viewportTransform,n=this.width,a=this.height;if(r)if(r.toLive){var o=r.repeat,s=fabric.util.invertTransform(i),c=this[e+"Vpt"],l=c?fabric.util.matrixToSVG(s):"";t.push('<rect transform="'+l+" translate(",n/2,",",a/2,')"',' x="',r.offsetX-n/2,'" y="',r.offsetY-a/2,'" ','width="',"repeat-y"===o||"no-repeat"===o?r.source.width:n,'" height="',"repeat-x"===o||"no-repeat"===o?r.source.height:a,'" fill="url(#SVGID_'+r.id+')"',"></rect>\n")}else t.push('<rect x="0" y="0" width="100%" height="100%" ','fill="',r,'"',"></rect>\n")},sendToBack:function(t){if(!t)return this;var e,i,n,a=this._activeObject;if(t===a&&"activeSelection"===t.type)for(n=a._objects,e=n.length;e--;)i=n[e],r(this._objects,i),this._objects.unshift(i);else r(this._objects,t),this._objects.unshift(t);return this.renderOnAddRemove&&this.requestRenderAll(),this},bringToFront:function(t){if(!t)return this;var e,i,n,a=this._activeObject;if(t===a&&"activeSelection"===t.type)for(n=a._objects,e=0;e<n.length;e++)i=n[e],r(this._objects,i),this._objects.push(i);else r(this._objects,t),this._objects.push(t);return this.renderOnAddRemove&&this.requestRenderAll(),this},sendBackwards:function(t,e){if(!t)return this;var i,n,a,o,s,c=this._activeObject,l=0;if(t===c&&"activeSelection"===t.type)for(s=c._objects,i=0;i<s.length;i++)n=s[i],a=this._objects.indexOf(n),a>0+l&&(o=a-1,r(this._objects,n),this._objects.splice(o,0,n)),l++;else a=this._objects.indexOf(t),0!==a&&(o=this._findNewLowerIndex(t,a,e),r(this._objects,t),this._objects.splice(o,0,t));return this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewLowerIndex:function(t,e,r){var i,n;if(r)for(i=e,n=e-1;n>=0;--n){var a=t.intersectsWithObject(this._objects[n])||t.isContainedWithinObject(this._objects[n])||this._objects[n].isContainedWithinObject(t);if(a){i=n;break}}else i=e-1;return i},bringForward:function(t,e){if(!t)return this;var i,n,a,o,s,c=this._activeObject,l=0;if(t===c&&"activeSelection"===t.type)for(s=c._objects,i=s.length;i--;)n=s[i],a=this._objects.indexOf(n),a<this._objects.length-1-l&&(o=a+1,r(this._objects,n),this._objects.splice(o,0,n)),l++;else a=this._objects.indexOf(t),a!==this._objects.length-1&&(o=this._findNewUpperIndex(t,a,e),r(this._objects,t),this._objects.splice(o,0,t));return this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewUpperIndex:function(t,e,r){var i,n,a;if(r)for(i=e,n=e+1,a=this._objects.length;a>n;++n){var o=t.intersectsWithObject(this._objects[n])||t.isContainedWithinObject(this._objects[n])||this._objects[n].isContainedWithinObject(t);if(o){i=n;break}}else i=e+1;return i},moveTo:function(t,e){return r(this._objects,t),this._objects.splice(e,0,t),this.renderOnAddRemove&&this.requestRenderAll()},dispose:function(){return this.isRendering&&(fabric.util.cancelAnimFrame(this.isRendering),this.isRendering=0),this.forEachObject(function(t){t.dispose&&t.dispose()}),this._objects=[],this.backgroundImage&&this.backgroundImage.dispose&&this.backgroundImage.dispose(),this.backgroundImage=null,this.overlayImage&&this.overlayImage.dispose&&this.overlayImage.dispose(),this.overlayImage=null,this._iTextInstances=null,this.contextContainer=null,this.lowerCanvasEl.classList.remove("lower-canvas"),fabric.util.setStyle(this.lowerCanvasEl,this._originalCanvasStyle),delete this._originalCanvasStyle,this.lowerCanvasEl.setAttribute("width",this.width),this.lowerCanvasEl.setAttribute("height",this.height),fabric.util.cleanUpJsdomNode(this.lowerCanvasEl),this.lowerCanvasEl=void 0,this},toString:function(){return"#<fabric.Canvas ("+this.complexity()+"): { objects: "+this._objects.length+" }>"}}),t(fabric.StaticCanvas.prototype,fabric.Observable),t(fabric.StaticCanvas.prototype,fabric.Collection),t(fabric.StaticCanvas.prototype,fabric.DataURLExporter),t(fabric.StaticCanvas,{EMPTY_JSON:'{"objects": [], "background": "white"}',supports:function(t){var e=s();if(!e||!e.getContext)return null;var r=e.getContext("2d");if(!r)return null;switch(t){case"setLineDash":return"undefined"!=typeof r.setLineDash;default:return null}}}),fabric.StaticCanvas.prototype.toJSON=fabric.StaticCanvas.prototype.toObject,fabric.isLikelyNode&&(fabric.StaticCanvas.prototype.createPNGStream=function(){var t=o(this.lowerCanvasEl);return t&&t.createPNGStream()},fabric.StaticCanvas.prototype.createJPEGStream=function(t){var e=o(this.lowerCanvasEl);return e&&e.createJPEGStream(t)})}();fabric.BaseBrush=fabric.util.createClass({color:"rgb(0, 0, 0)",width:1,shadow:null,strokeLineCap:"round",strokeLineJoin:"round",strokeMiterLimit:10,strokeDashArray:null,limitedToCanvasSize:!1,_setBrushStyles:function(t){t.strokeStyle=this.color,t.lineWidth=this.width,t.lineCap=this.strokeLineCap,t.miterLimit=this.strokeMiterLimit,t.lineJoin=this.strokeLineJoin,t.setLineDash(this.strokeDashArray||[])},_saveAndTransform:function(t){var e=this.canvas.viewportTransform;t.save(),t.transform(e[0],e[1],e[2],e[3],e[4],e[5])},_setShadow:function(){if(this.shadow){var t=this.canvas,e=this.shadow,r=t.contextTop,i=t.getZoom();t&&t._isRetinaScaling()&&(i*=fabric.devicePixelRatio),r.shadowColor=e.color,r.shadowBlur=e.blur*i,r.shadowOffsetX=e.offsetX*i,r.shadowOffsetY=e.offsetY*i}},needsFullRender:function(){var t=new fabric.Color(this.color);return t.getAlpha()<1||!!this.shadow},_resetShadow:function(){var t=this.canvas.contextTop;t.shadowColor="",t.shadowBlur=t.shadowOffsetX=t.shadowOffsetY=0},_isOutSideCanvas:function(t){return t.x<0||t.x>this.canvas.getWidth()||t.y<0||t.y>this.canvas.getHeight()}});!function(){fabric.PencilBrush=fabric.util.createClass(fabric.BaseBrush,{decimate:.4,drawStraightLine:!1,straightLineKey:"shiftKey",initialize:function(t){this.canvas=t,this._points=[]},needsFullRender:function(){return this.callSuper("needsFullRender")||this._hasStraightLine},_drawSegment:function(t,e,r){var i=e.midPointFrom(r);return t.quadraticCurveTo(e.x,e.y,i.x,i.y),i},onMouseDown:function(t,e){this.canvas._isMainEvent(e.e)&&(this.drawStraightLine=e.e[this.straightLineKey],this._prepareForDrawing(t),this._captureDrawingPath(t),this._render())},onMouseMove:function(t,e){if(this.canvas._isMainEvent(e.e)&&(this.drawStraightLine=e.e[this.straightLineKey],(this.limitedToCanvasSize!==!0||!this._isOutSideCanvas(t))&&this._captureDrawingPath(t)&&this._points.length>1))if(this.needsFullRender())this.canvas.clearContext(this.canvas.contextTop),this._render();else{var r=this._points,i=r.length,n=this.canvas.contextTop;this._saveAndTransform(n),this.oldEnd&&(n.beginPath(),n.moveTo(this.oldEnd.x,this.oldEnd.y)),this.oldEnd=this._drawSegment(n,r[i-2],r[i-1],!0),n.stroke(),n.restore()}},onMouseUp:function(t){return this.canvas._isMainEvent(t.e)?(this.drawStraightLine=!1,this.oldEnd=void 0,this._finalizeAndAddPath(),!1):!0},_prepareForDrawing:function(t){var e=new fabric.Point(t.x,t.y);this._reset(),this._addPoint(e),this.canvas.contextTop.moveTo(e.x,e.y)},_addPoint:function(t){return this._points.length>1&&t.eq(this._points[this._points.length-1])?!1:(this.drawStraightLine&&this._points.length>1&&(this._hasStraightLine=!0,this._points.pop()),this._points.push(t),!0)},_reset:function(){this._points=[],this._setBrushStyles(this.canvas.contextTop),this._setShadow(),this._hasStraightLine=!1},_captureDrawingPath:function(t){var e=new fabric.Point(t.x,t.y);return this._addPoint(e)},_render:function(t){var e,r,i=this._points[0],n=this._points[1];if(t=t||this.canvas.contextTop,this._saveAndTransform(t),t.beginPath(),2===this._points.length&&i.x===n.x&&i.y===n.y){var a=this.width/1e3;i=new fabric.Point(i.x,i.y),n=new fabric.Point(n.x,n.y),i.x-=a,n.x+=a}for(t.moveTo(i.x,i.y),e=1,r=this._points.length;r>e;e++)this._drawSegment(t,i,n),i=this._points[e],n=this._points[e+1];t.lineTo(i.x,i.y),t.stroke(),t.restore()},convertPointsToSVGPath:function(t){var e=this.width/1e3;return fabric.util.getSmoothPathFromPoints(t,e)},_isEmptySVGPath:function(t){var e=fabric.util.joinPath(t);return"M 0 0 Q 0 0 0 0 L 0 0"===e},createPath:function(t){var e=new fabric.Path(t,{fill:null,stroke:this.color,strokeWidth:this.width,strokeLineCap:this.strokeLineCap,strokeMiterLimit:this.strokeMiterLimit,strokeLineJoin:this.strokeLineJoin,strokeDashArray:this.strokeDashArray});return this.shadow&&(this.shadow.affectStroke=!0,e.shadow=new fabric.Shadow(this.shadow)),e},decimatePoints:function(t,e){if(t.length<=2)return t;var r,i,n=this.canvas.getZoom(),a=Math.pow(e/n,2),o=t.length-1,s=t[0],c=[s];for(r=1;o-1>r;r++)i=Math.pow(s.x-t[r].x,2)+Math.pow(s.y-t[r].y,2),i>=a&&(s=t[r],c.push(s));return c.push(t[o]),c},_finalizeAndAddPath:function(){var t=this.canvas.contextTop;t.closePath(),this.decimate&&(this._points=this.decimatePoints(this._points,this.decimate));var e=this.convertPointsToSVGPath(this._points);if(this._isEmptySVGPath(e))return void this.canvas.requestRenderAll();var r=this.createPath(e);this.canvas.clearContext(this.canvas.contextTop),this.canvas.fire("before:path:created",{path:r}),this.canvas.add(r),this.canvas.requestRenderAll(),r.setCoords(),this._resetShadow(),this.canvas.fire("path:created",{path:r})}})}();fabric.CircleBrush=fabric.util.createClass(fabric.BaseBrush,{width:10,initialize:function(t){this.canvas=t,this.points=[]},drawDot:function(t){var e=this.addPoint(t),r=this.canvas.contextTop;this._saveAndTransform(r),this.dot(r,e),r.restore()},dot:function(t,e){t.fillStyle=e.fill,t.beginPath(),t.arc(e.x,e.y,e.radius,0,2*Math.PI,!1),t.closePath(),t.fill()},onMouseDown:function(t){this.points.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.drawDot(t)},_render:function(){var t,e,r=this.canvas.contextTop,i=this.points;for(this._saveAndTransform(r),t=0,e=i.length;e>t;t++)this.dot(r,i[t]);r.restore()},onMouseMove:function(t){this.limitedToCanvasSize===!0&&this._isOutSideCanvas(t)||(this.needsFullRender()?(this.canvas.clearContext(this.canvas.contextTop),this.addPoint(t),this._render()):this.drawDot(t))},onMouseUp:function(){var t,e,r=this.canvas.renderOnAddRemove;this.canvas.renderOnAddRemove=!1;var i=[];for(t=0,e=this.points.length;e>t;t++){var n=this.points[t],a=new fabric.Circle({radius:n.radius,left:n.x,top:n.y,originX:"center",originY:"center",fill:n.fill});this.shadow&&(a.shadow=new fabric.Shadow(this.shadow)),i.push(a)}var o=new fabric.Group(i);o.canvas=this.canvas,this.canvas.fire("before:path:created",{path:o}),this.canvas.add(o),this.canvas.fire("path:created",{path:o}),this.canvas.clearContext(this.canvas.contextTop),this._resetShadow(),this.canvas.renderOnAddRemove=r,this.canvas.requestRenderAll()},addPoint:function(t){var e=new fabric.Point(t.x,t.y),r=fabric.util.getRandomInt(Math.max(0,this.width-20),this.width+20)/2,i=new fabric.Color(this.color).setAlpha(fabric.util.getRandomInt(0,100)/100).toRgba();return e.radius=r,e.fill=i,this.points.push(e),e}});fabric.SprayBrush=fabric.util.createClass(fabric.BaseBrush,{width:10,density:20,dotWidth:1,dotWidthVariance:1,randomOpacity:!1,optimizeOverlapping:!0,initialize:function(t){this.canvas=t,this.sprayChunks=[]},onMouseDown:function(t){this.sprayChunks.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.addSprayChunk(t),this.render(this.sprayChunkPoints)},onMouseMove:function(t){this.limitedToCanvasSize===!0&&this._isOutSideCanvas(t)||(this.addSprayChunk(t),this.render(this.sprayChunkPoints))},onMouseUp:function(){var t=this.canvas.renderOnAddRemove;this.canvas.renderOnAddRemove=!1;for(var e=[],r=0,i=this.sprayChunks.length;i>r;r++)for(var n=this.sprayChunks[r],a=0,o=n.length;o>a;a++){var s=new fabric.Rect({width:n[a].width,height:n[a].width,left:n[a].x+1,top:n[a].y+1,originX:"center",originY:"center",fill:this.color});e.push(s)}this.optimizeOverlapping&&(e=this._getOptimizedRects(e));var c=new fabric.Group(e);this.shadow&&c.set("shadow",new fabric.Shadow(this.shadow)),this.canvas.fire("before:path:created",{path:c}),this.canvas.add(c),this.canvas.fire("path:created",{path:c}),this.canvas.clearContext(this.canvas.contextTop),this._resetShadow(),this.canvas.renderOnAddRemove=t,this.canvas.requestRenderAll()},_getOptimizedRects:function(t){var e,r,i,n={};for(r=0,i=t.length;i>r;r++)e=t[r].left+""+t[r].top,n[e]||(n[e]=t[r]);var a=[];for(e in n)a.push(n[e]);return a},render:function(t){var e,r,i=this.canvas.contextTop;for(i.fillStyle=this.color,this._saveAndTransform(i),e=0,r=t.length;r>e;e++){var n=t[e];"undefined"!=typeof n.opacity&&(i.globalAlpha=n.opacity),i.fillRect(n.x,n.y,n.width,n.width)}i.restore()},_render:function(){var t,e,r=this.canvas.contextTop;for(r.fillStyle=this.color,this._saveAndTransform(r),t=0,e=this.sprayChunks.length;e>t;t++)this.render(this.sprayChunks[t]);r.restore()},addSprayChunk:function(t){this.sprayChunkPoints=[];var e,r,i,n,a=this.width/2;for(n=0;n<this.density;n++){e=fabric.util.getRandomInt(t.x-a,t.x+a),r=fabric.util.getRandomInt(t.y-a,t.y+a),i=this.dotWidthVariance?fabric.util.getRandomInt(Math.max(1,this.dotWidth-this.dotWidthVariance),this.dotWidth+this.dotWidthVariance):this.dotWidth;var o=new fabric.Point(e,r);o.width=i,this.randomOpacity&&(o.opacity=fabric.util.getRandomInt(0,100)/100),this.sprayChunkPoints.push(o)}this.sprayChunks.push(this.sprayChunkPoints)}});fabric.PatternBrush=fabric.util.createClass(fabric.PencilBrush,{getPatternSrc:function(){var t=20,e=5,r=fabric.util.createCanvasElement(),i=r.getContext("2d");return r.width=r.height=t+e,i.fillStyle=this.color,i.beginPath(),i.arc(t/2,t/2,t/2,0,2*Math.PI,!1),i.closePath(),i.fill(),r},getPatternSrcFunction:function(){return String(this.getPatternSrc).replace("this.color",'"'+this.color+'"')},getPattern:function(t){return t.createPattern(this.source||this.getPatternSrc(),"repeat")},_setBrushStyles:function(t){this.callSuper("_setBrushStyles",t),t.strokeStyle=this.getPattern(t)},createPath:function(t){var e=this.callSuper("createPath",t),r=e._getLeftTopCoords().scalarAdd(e.strokeWidth/2);return e.stroke=new fabric.Pattern({source:this.source||this.getPatternSrcFunction(),offsetX:-r.x,offsetY:-r.y}),e}});!function(){var t=fabric.util.getPointer,e=fabric.util.degreesToRadians,r=fabric.util.isTouchEvent;fabric.Canvas=fabric.util.createClass(fabric.StaticCanvas,{initialize:function(t,e){e||(e={}),this.renderAndResetBound=this.renderAndReset.bind(this),this.requestRenderAllBound=this.requestRenderAll.bind(this),this._initStatic(t,e),this._initInteractive(),this._createCacheCanvas()},uniformScaling:!0,uniScaleKey:"shiftKey",centeredScaling:!1,centeredRotation:!1,centeredKey:"altKey",altActionKey:"shiftKey",interactive:!0,selection:!0,selectionKey:"shiftKey",altSelectionKey:null,selectionColor:"rgba(100, 100, 255, 0.3)",selectionDashArray:[],selectionBorderColor:"rgba(255, 255, 255, 0.3)",selectionLineWidth:1,selectionFullyContained:!1,hoverCursor:"move",moveCursor:"move",defaultCursor:"default",freeDrawingCursor:"crosshair",notAllowedCursor:"not-allowed",containerClass:"canvas-container",perPixelTargetFind:!1,targetFindTolerance:0,skipTargetFind:!1,isDrawingMode:!1,preserveObjectStacking:!1,snapAngle:0,snapThreshold:null,stopContextMenu:!1,fireRightClick:!1,fireMiddleClick:!1,targets:[],enablePointerEvents:!1,_hoveredTarget:null,_hoveredTargets:[],_initInteractive:function(){this._currentTransform=null,this._groupSelector=null,this._initWrapperElement(),this._createUpperCanvas(),this._initEventListeners(),this._initRetinaScaling(),this.freeDrawingBrush=fabric.PencilBrush&&new fabric.PencilBrush(this),this.calcOffset()},_chooseObjectsToRender:function(){var t,e,r,i=this.getActiveObjects();if(i.length>0&&!this.preserveObjectStacking){e=[],r=[];for(var n=0,a=this._objects.length;a>n;n++)t=this._objects[n],-1===i.indexOf(t)?e.push(t):r.push(t);i.length>1&&(this._activeObject._objects=r),e.push.apply(e,r)}else e=this._objects;return e},renderAll:function(){!this.contextTopDirty||this._groupSelector||this.isDrawingMode||(this.clearContext(this.contextTop),this.contextTopDirty=!1),this.hasLostContext&&(this.renderTopLayer(this.contextTop),this.hasLostContext=!1);var t=this.contextContainer;return this.renderCanvas(t,this._chooseObjectsToRender()),this},renderTopLayer:function(t){t.save(),this.isDrawingMode&&this._isCurrentlyDrawing&&(this.freeDrawingBrush&&this.freeDrawingBrush._render(),this.contextTopDirty=!0),this.selection&&this._groupSelector&&(this._drawSelection(t),this.contextTopDirty=!0),t.restore()},renderTop:function(){var t=this.contextTop;return this.clearContext(t),this.renderTopLayer(t),this.fire("after:render"),this},_normalizePointer:function(t,e){var r=t.calcTransformMatrix(),i=fabric.util.invertTransform(r),n=this.restorePointerVpt(e);return fabric.util.transformPoint(n,i)},isTargetTransparent:function(t,e,r){if(t.shouldCache()&&t._cacheCanvas&&t!==this._activeObject){var i=this._normalizePointer(t,{x:e,y:r}),n=Math.max(t.cacheTranslationX+i.x*t.zoomX,0),a=Math.max(t.cacheTranslationY+i.y*t.zoomY,0),s=fabric.util.isTransparent(t._cacheContext,Math.round(n),Math.round(a),this.targetFindTolerance);return s}var o=this.contextCache,c=t.selectionBackgroundColor,l=this.viewportTransform;t.selectionBackgroundColor="",this.clearContext(o),o.save(),o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),t.render(o),o.restore(),t.selectionBackgroundColor=c;var s=fabric.util.isTransparent(o,e,r,this.targetFindTolerance);return s},_isSelectionKeyPressed:function(t){var e=!1;return e=Array.isArray(this.selectionKey)?!!this.selectionKey.find(function(e){return t[e]===!0}):t[this.selectionKey]},_shouldClearSelection:function(t,e){var r=this.getActiveObjects(),i=this._activeObject;return!e||e&&i&&r.length>1&&-1===r.indexOf(e)&&i!==e&&!this._isSelectionKeyPressed(t)||e&&!e.evented||e&&!e.selectable&&i&&i!==e},_shouldCenterTransform:function(t,e,r){if(t){var i;return"scale"===e||"scaleX"===e||"scaleY"===e||"resizing"===e?i=this.centeredScaling||t.centeredScaling:"rotate"===e&&(i=this.centeredRotation||t.centeredRotation),i?!r:r}},_getOriginFromCorner:function(t,e){var r={x:t.originX,y:t.originY};return"ml"===e||"tl"===e||"bl"===e?r.x="right":("mr"===e||"tr"===e||"br"===e)&&(r.x="left"),"tl"===e||"mt"===e||"tr"===e?r.y="bottom":("bl"===e||"mb"===e||"br"===e)&&(r.y="top"),r},_getActionFromCorner:function(t,e,r,i){if(!e||!t)return"drag";var n=i.controls[e];return n.getActionName(r,n,i)},_setupCurrentTransform:function(t,r,i){if(r){var n=this.getPointer(t),a=r.__corner,s=r.controls[a],o=i&&a?s.getActionHandler(t,r,s):fabric.controlsUtils.dragHandler,c=this._getActionFromCorner(i,a,t,r),l=this._getOriginFromCorner(r,a),u=t[this.centeredKey],h={target:r,action:c,actionHandler:o,corner:a,scaleX:r.scaleX,scaleY:r.scaleY,skewX:r.skewX,skewY:r.skewY,offsetX:n.x-r.left,offsetY:n.y-r.top,originX:l.x,originY:l.y,ex:n.x,ey:n.y,lastX:n.x,lastY:n.y,theta:e(r.angle),width:r.width*r.scaleX,shiftKey:t.shiftKey,altKey:u,original:fabric.util.saveObjectTransform(r)};this._shouldCenterTransform(r,c,u)&&(h.originX="center",h.originY="center"),h.original.originX=l.x,h.original.originY=l.y,this._currentTransform=h,this._beforeTransform(t)}},setCursor:function(t){this.upperCanvasEl.style.cursor=t},_drawSelection:function(t){var e=this._groupSelector,r=new fabric.Point(e.ex,e.ey),i=fabric.util.transformPoint(r,this.viewportTransform),n=new fabric.Point(e.ex+e.left,e.ey+e.top),a=fabric.util.transformPoint(n,this.viewportTransform),s=Math.min(i.x,a.x),o=Math.min(i.y,a.y),c=Math.max(i.x,a.x),l=Math.max(i.y,a.y),u=this.selectionLineWidth/2;this.selectionColor&&(t.fillStyle=this.selectionColor,t.fillRect(s,o,c-s,l-o)),this.selectionLineWidth&&this.selectionBorderColor&&(t.lineWidth=this.selectionLineWidth,t.strokeStyle=this.selectionBorderColor,s+=u,o+=u,c-=u,l-=u,fabric.Object.prototype._setLineDash.call(this,t,this.selectionDashArray),t.strokeRect(s,o,c-s,l-o))},findTarget:function(t,e){if(!this.skipTargetFind){var i,n,a=!0,s=this.getPointer(t,a),o=this._activeObject,c=this.getActiveObjects(),l=r(t),u=c.length>1&&!e||1===c.length;if(this.targets=[],u&&o._findTargetCorner(s,l))return o;if(c.length>1&&!e&&o===this._searchPossibleTargets([o],s))return o;if(1===c.length&&o===this._searchPossibleTargets([o],s)){if(!this.preserveObjectStacking)return o;i=o,n=this.targets,this.targets=[]}var h=this._searchPossibleTargets(this._objects,s);return t[this.altSelectionKey]&&h&&i&&h!==i&&(h=i,this.targets=n),h}},_checkTarget:function(t,e,r){if(e&&e.visible&&e.evented&&e.containsPoint(t)){if(!this.perPixelTargetFind&&!e.perPixelTargetFind||e.isEditing)return!0;var i=this.isTargetTransparent(e,r.x,r.y);if(!i)return!0}},_searchPossibleTargets:function(t,e){for(var r,i,n=t.length;n--;){var a=t[n],s=a.group?this._normalizePointer(a.group,e):e;if(this._checkTarget(s,a,e)){r=t[n],r.subTargetCheck&&r instanceof fabric.Group&&(i=this._searchPossibleTargets(r._objects,e),i&&this.targets.push(i));break}}return r},restorePointerVpt:function(t){return fabric.util.transformPoint(t,fabric.util.invertTransform(this.viewportTransform))},getPointer:function(e,r){if(this._absolutePointer&&!r)return this._absolutePointer;if(this._pointer&&r)return this._pointer;var i,n=t(e),a=this.upperCanvasEl,s=a.getBoundingClientRect(),o=s.width||0,c=s.height||0;o&&c||("top"in s&&"bottom"in s&&(c=Math.abs(s.top-s.bottom)),"right"in s&&"left"in s&&(o=Math.abs(s.right-s.left))),this.calcOffset(),n.x=n.x-this._offset.left,n.y=n.y-this._offset.top,r||(n=this.restorePointerVpt(n));var l=this.getRetinaScaling();return 1!==l&&(n.x/=l,n.y/=l),i=0===o||0===c?{width:1,height:1}:{width:a.width/o,height:a.height/c},{x:n.x*i.width,y:n.y*i.height}},_createUpperCanvas:function(){var t=this.lowerCanvasEl.className.replace(/\s*lower-canvas\s*/,""),e=this.lowerCanvasEl,r=this.upperCanvasEl;r?r.className="":(r=this._createCanvasElement(),this.upperCanvasEl=r),fabric.util.addClass(r,"upper-canvas "+t),this.wrapperEl.appendChild(r),this._copyCanvasStyle(e,r),this._applyCanvasStyle(r),this.contextTop=r.getContext("2d")},getTopContext:function(){return this.contextTop},_createCacheCanvas:function(){this.cacheCanvasEl=this._createCanvasElement(),this.cacheCanvasEl.setAttribute("width",this.width),this.cacheCanvasEl.setAttribute("height",this.height),this.contextCache=this.cacheCanvasEl.getContext("2d")},_initWrapperElement:function(){this.wrapperEl=fabric.util.wrapElement(this.lowerCanvasEl,"div",{"class":this.containerClass}),fabric.util.setStyle(this.wrapperEl,{width:this.width+"px",height:this.height+"px",position:"relative"}),fabric.util.makeElementUnselectable(this.wrapperEl)},_applyCanvasStyle:function(t){var e=this.width||t.width,r=this.height||t.height;fabric.util.setStyle(t,{position:"absolute",width:e+"px",height:r+"px",left:0,top:0,"touch-action":this.allowTouchScrolling?"manipulation":"none","-ms-touch-action":this.allowTouchScrolling?"manipulation":"none"}),t.width=e,t.height=r,fabric.util.makeElementUnselectable(t)},_copyCanvasStyle:function(t,e){e.style.cssText=t.style.cssText},getSelectionContext:function(){return this.contextTop},getSelectionElement:function(){return this.upperCanvasEl},getActiveObject:function(){return this._activeObject},getActiveObjects:function(){var t=this._activeObject;return t?"activeSelection"===t.type&&t._objects?t._objects.slice(0):[t]:[]},_onObjectRemoved:function(t){t===this._activeObject&&(this.fire("before:selection:cleared",{target:t}),this._discardActiveObject(),this.fire("selection:cleared",{target:t}),t.fire("deselected")),t===this._hoveredTarget&&(this._hoveredTarget=null,this._hoveredTargets=[]),this.callSuper("_onObjectRemoved",t)},_fireSelectionEvents:function(t,e){var r=!1,i=this.getActiveObjects(),n=[],a=[];t.forEach(function(t){-1===i.indexOf(t)&&(r=!0,t.fire("deselected",{e:e,target:t}),a.push(t))}),i.forEach(function(i){-1===t.indexOf(i)&&(r=!0,i.fire("selected",{e:e,target:i}),n.push(i))}),t.length>0&&i.length>0?r&&this.fire("selection:updated",{e:e,selected:n,deselected:a}):i.length>0?this.fire("selection:created",{e:e,selected:n}):t.length>0&&this.fire("selection:cleared",{e:e,deselected:a})},setActiveObject:function(t,e){var r=this.getActiveObjects();return this._setActiveObject(t,e),this._fireSelectionEvents(r,e),this},_setActiveObject:function(t,e){return this._activeObject===t?!1:this._discardActiveObject(e,t)?t.onSelect({e:e})?!1:(this._activeObject=t,!0):!1},_discardActiveObject:function(t,e){var r=this._activeObject;if(r){if(r.onDeselect({e:t,object:e}))return!1;this._activeObject=null}return!0},discardActiveObject:function(t){var e=this.getActiveObjects(),r=this.getActiveObject();return e.length&&this.fire("before:selection:cleared",{target:r,e:t}),this._discardActiveObject(t),this._fireSelectionEvents(e,t),this},dispose:function(){var t=this.wrapperEl;return this.removeListeners(),t.removeChild(this.upperCanvasEl),t.removeChild(this.lowerCanvasEl),this.contextCache=null,this.contextTop=null,["upperCanvasEl","cacheCanvasEl"].forEach(function(t){fabric.util.cleanUpJsdomNode(this[t]),this[t]=void 0}.bind(this)),t.parentNode&&t.parentNode.replaceChild(this.lowerCanvasEl,this.wrapperEl),delete this.wrapperEl,fabric.StaticCanvas.prototype.dispose.call(this),this},clear:function(){return this.discardActiveObject(),this.clearContext(this.contextTop),this.callSuper("clear")},drawControls:function(t){var e=this._activeObject;e&&e._renderControls(t)},_toObject:function(t,e,r){var i=this._realizeGroupTransformOnObject(t),n=this.callSuper("_toObject",t,e,r);return this._unwindGroupTransformOnObject(t,i),n},_realizeGroupTransformOnObject:function(t){if(t.group&&"activeSelection"===t.group.type&&this._activeObject===t.group){var e=["angle","flipX","flipY","left","scaleX","scaleY","skewX","skewY","top"],r={};return e.forEach(function(e){r[e]=t[e]}),fabric.util.addTransformToObject(t,this._activeObject.calcOwnMatrix()),r}return null},_unwindGroupTransformOnObject:function(t,e){e&&t.set(e)},_setSVGObject:function(t,e,r){var i=this._realizeGroupTransformOnObject(e);this.callSuper("_setSVGObject",t,e,r),this._unwindGroupTransformOnObject(e,i)},setViewportTransform:function(t){this.renderOnAddRemove&&this._activeObject&&this._activeObject.isEditing&&this._activeObject.clearContextTop(),fabric.StaticCanvas.prototype.setViewportTransform.call(this,t)}});for(var i in fabric.StaticCanvas)"prototype"!==i&&(fabric.Canvas[i]=fabric.StaticCanvas[i])}();!function(){function t(t,e){return t.button&&t.button===e-1}var e=fabric.util.addListener,i=fabric.util.removeListener,r=3,n=2,a=1,s={passive:!1};fabric.util.object.extend(fabric.Canvas.prototype,{mainTouchId:null,_initEventListeners:function(){this.removeListeners(),this._bindEvents(),this.addOrRemove(e,"add")},_getEventPrefix:function(){return this.enablePointerEvents?"pointer":"mouse"},addOrRemove:function(t,e){var i=this.upperCanvasEl,r=this._getEventPrefix();t(fabric.window,"resize",this._onResize),t(i,r+"down",this._onMouseDown),t(i,r+"move",this._onMouseMove,s),t(i,r+"out",this._onMouseOut),t(i,r+"enter",this._onMouseEnter),t(i,"wheel",this._onMouseWheel),t(i,"contextmenu",this._onContextMenu),t(i,"dblclick",this._onDoubleClick),t(i,"dragover",this._onDragOver),t(i,"dragenter",this._onDragEnter),t(i,"dragleave",this._onDragLeave),t(i,"drop",this._onDrop),this.enablePointerEvents||t(i,"touchstart",this._onTouchStart,s),"undefined"!=typeof eventjs&&e in eventjs&&(eventjs[e](i,"gesture",this._onGesture),eventjs[e](i,"drag",this._onDrag),eventjs[e](i,"orientation",this._onOrientationChange),eventjs[e](i,"shake",this._onShake),eventjs[e](i,"longpress",this._onLongPress))},removeListeners:function(){this.addOrRemove(i,"remove");var t=this._getEventPrefix();i(fabric.document,t+"up",this._onMouseUp),i(fabric.document,"touchend",this._onTouchEnd,s),i(fabric.document,t+"move",this._onMouseMove,s),i(fabric.document,"touchmove",this._onMouseMove,s)},_bindEvents:function(){this.eventsBound||(this._onMouseDown=this._onMouseDown.bind(this),this._onTouchStart=this._onTouchStart.bind(this),this._onMouseMove=this._onMouseMove.bind(this),this._onMouseUp=this._onMouseUp.bind(this),this._onTouchEnd=this._onTouchEnd.bind(this),this._onResize=this._onResize.bind(this),this._onGesture=this._onGesture.bind(this),this._onDrag=this._onDrag.bind(this),this._onShake=this._onShake.bind(this),this._onLongPress=this._onLongPress.bind(this),this._onOrientationChange=this._onOrientationChange.bind(this),this._onMouseWheel=this._onMouseWheel.bind(this),this._onMouseOut=this._onMouseOut.bind(this),this._onMouseEnter=this._onMouseEnter.bind(this),this._onContextMenu=this._onContextMenu.bind(this),this._onDoubleClick=this._onDoubleClick.bind(this),this._onDragOver=this._onDragOver.bind(this),this._onDragEnter=this._simpleEventHandler.bind(this,"dragenter"),this._onDragLeave=this._simpleEventHandler.bind(this,"dragleave"),this._onDrop=this._onDrop.bind(this),this.eventsBound=!0)},_onGesture:function(t,e){this.__onTransformGesture&&this.__onTransformGesture(t,e)},_onDrag:function(t,e){this.__onDrag&&this.__onDrag(t,e)},_onMouseWheel:function(t){this.__onMouseWheel(t)},_onMouseOut:function(t){var e=this._hoveredTarget;this.fire("mouse:out",{target:e,e:t}),this._hoveredTarget=null,e&&e.fire("mouseout",{e:t});var i=this;this._hoveredTargets.forEach(function(r){i.fire("mouse:out",{target:e,e:t}),r&&e.fire("mouseout",{e:t})}),this._hoveredTargets=[],this._iTextInstances&&this._iTextInstances.forEach(function(t){t.isEditing&&t.hiddenTextarea.focus()})},_onMouseEnter:function(t){this._currentTransform||this.findTarget(t)||(this.fire("mouse:over",{target:null,e:t}),this._hoveredTarget=null,this._hoveredTargets=[])},_onOrientationChange:function(t,e){this.__onOrientationChange&&this.__onOrientationChange(t,e)},_onShake:function(t,e){this.__onShake&&this.__onShake(t,e)},_onLongPress:function(t,e){this.__onLongPress&&this.__onLongPress(t,e)},_onDragOver:function(t){t.preventDefault();var e=this._simpleEventHandler("dragover",t);this._fireEnterLeaveEvents(e,t)},_onDrop:function(t){return this._simpleEventHandler("drop:before",t),this._simpleEventHandler("drop",t)},_onContextMenu:function(t){return this.stopContextMenu&&(t.stopPropagation(),t.preventDefault()),!1},_onDoubleClick:function(t){this._cacheTransformEventData(t),this._handleEvent(t,"dblclick"),this._resetTransformEventData(t)},getPointerId:function(t){var e=t.changedTouches;return e?e[0]&&e[0].identifier:this.enablePointerEvents?t.pointerId:-1},_isMainEvent:function(t){return t.isPrimary===!0?!0:t.isPrimary===!1?!1:"touchend"===t.type&&0===t.touches.length?!0:t.changedTouches?t.changedTouches[0].identifier===this.mainTouchId:!0},_onTouchStart:function(t){t.preventDefault(),null===this.mainTouchId&&(this.mainTouchId=this.getPointerId(t)),this.__onMouseDown(t),this._resetTransformEventData();var r=this.upperCanvasEl,n=this._getEventPrefix();e(fabric.document,"touchend",this._onTouchEnd,s),e(fabric.document,"touchmove",this._onMouseMove,s),i(r,n+"down",this._onMouseDown)},_onMouseDown:function(t){this.__onMouseDown(t),this._resetTransformEventData();var r=this.upperCanvasEl,n=this._getEventPrefix();i(r,n+"move",this._onMouseMove,s),e(fabric.document,n+"up",this._onMouseUp),e(fabric.document,n+"move",this._onMouseMove,s)},_onTouchEnd:function(t){if(!(t.touches.length>0)){this.__onMouseUp(t),this._resetTransformEventData(),this.mainTouchId=null;var r=this._getEventPrefix();i(fabric.document,"touchend",this._onTouchEnd,s),i(fabric.document,"touchmove",this._onMouseMove,s);var n=this;this._willAddMouseDown&&clearTimeout(this._willAddMouseDown),this._willAddMouseDown=setTimeout(function(){e(n.upperCanvasEl,r+"down",n._onMouseDown),n._willAddMouseDown=0},400)}},_onMouseUp:function(t){this.__onMouseUp(t),this._resetTransformEventData();var r=this.upperCanvasEl,n=this._getEventPrefix();this._isMainEvent(t)&&(i(fabric.document,n+"up",this._onMouseUp),i(fabric.document,n+"move",this._onMouseMove,s),e(r,n+"move",this._onMouseMove,s))},_onMouseMove:function(t){!this.allowTouchScrolling&&t.preventDefault&&t.preventDefault(),this.__onMouseMove(t)},_onResize:function(){this.calcOffset()},_shouldRender:function(t){var e=this._activeObject;return!!e!=!!t||e&&t&&e!==t?!0:e&&e.isEditing?!1:!1},__onMouseUp:function(e){var i,s=this._currentTransform,o=this._groupSelector,c=!1,l=!o||0===o.left&&0===o.top;if(this._cacheTransformEventData(e),i=this._target,this._handleEvent(e,"up:before"),t(e,r))return void(this.fireRightClick&&this._handleEvent(e,"up",r,l));if(t(e,n))return this.fireMiddleClick&&this._handleEvent(e,"up",n,l),void this._resetTransformEventData();if(this.isDrawingMode&&this._isCurrentlyDrawing)return void this._onMouseUpInDrawingMode(e);if(this._isMainEvent(e)){if(s&&(this._finalizeCurrentTransform(e),c=s.actionPerformed),!l){var u=i===this._activeObject;this._maybeGroupObjects(e),c||(c=this._shouldRender(i)||!u&&i===this._activeObject)}var h,f;if(i){if(h=i._findTargetCorner(this.getPointer(e,!0),fabric.util.isTouchEvent(e)),i.selectable&&i!==this._activeObject&&"up"===i.activeOn)this.setActiveObject(i,e),c=!0;else{var d=i.controls[h],g=d&&d.getMouseUpHandler(e,i,d);g&&(f=this.getPointer(e),g(e,s,f.x,f.y))}i.isMoving=!1}if(s&&(s.target!==i||s.corner!==h)){var p=s.target&&s.target.controls[s.corner],v=p&&p.getMouseUpHandler(e,i,d);f=f||this.getPointer(e),v&&v(e,s,f.x,f.y)}this._setCursorFromEvent(e,i),this._handleEvent(e,"up",a,l),this._groupSelector=null,this._currentTransform=null,i&&(i.__corner=0),c?this.requestRenderAll():l||this.renderTop()}},_simpleEventHandler:function(t,e){var i=this.findTarget(e),r=this.targets,n={e:e,target:i,subTargets:r};if(this.fire(t,n),i&&i.fire(t,n),!r)return i;for(var a=0;a<r.length;a++)r[a].fire(t,n);return i},_handleEvent:function(t,e,i,r){var n=this._target,s=this.targets||[],o={e:t,target:n,subTargets:s,button:i||a,isClick:r||!1,pointer:this._pointer,absolutePointer:this._absolutePointer,transform:this._currentTransform};"up"===e&&(o.currentTarget=this.findTarget(t),o.currentSubTargets=this.targets),this.fire("mouse:"+e,o),n&&n.fire("mouse"+e,o);for(var c=0;c<s.length;c++)s[c].fire("mouse"+e,o)},_finalizeCurrentTransform:function(t){var e=this._currentTransform,i=e.target,r={e:t,target:i,transform:e,action:e.action};i._scaling&&(i._scaling=!1),i.setCoords(),(e.actionPerformed||this.stateful&&i.hasStateChanged())&&this._fire("modified",r)},_onMouseDownInDrawingMode:function(t){this._isCurrentlyDrawing=!0,this.getActiveObject()&&this.discardActiveObject(t).requestRenderAll();var e=this.getPointer(t);this.freeDrawingBrush.onMouseDown(e,{e:t,pointer:e}),this._handleEvent(t,"down")},_onMouseMoveInDrawingMode:function(t){if(this._isCurrentlyDrawing){var e=this.getPointer(t);this.freeDrawingBrush.onMouseMove(e,{e:t,pointer:e})}this.setCursor(this.freeDrawingCursor),this._handleEvent(t,"move")},_onMouseUpInDrawingMode:function(t){var e=this.getPointer(t);this._isCurrentlyDrawing=this.freeDrawingBrush.onMouseUp({e:t,pointer:e}),this._handleEvent(t,"up")},__onMouseDown:function(e){this._cacheTransformEventData(e),this._handleEvent(e,"down:before");var i=this._target;if(t(e,r))return void(this.fireRightClick&&this._handleEvent(e,"down",r));if(t(e,n))return void(this.fireMiddleClick&&this._handleEvent(e,"down",n));if(this.isDrawingMode)return void this._onMouseDownInDrawingMode(e);if(this._isMainEvent(e)&&!this._currentTransform){var a=this._pointer;this._previousPointer=a;var s=this._shouldRender(i),o=this._shouldGroup(e,i);if(this._shouldClearSelection(e,i)?this.discardActiveObject(e):o&&(this._handleGrouping(e,i),i=this._activeObject),!this.selection||i&&(i.selectable||i.isEditing||i===this._activeObject)||(this._groupSelector={ex:this._absolutePointer.x,ey:this._absolutePointer.y,top:0,left:0}),i){var c=i===this._activeObject;i.selectable&&"down"===i.activeOn&&this.setActiveObject(i,e);var l=i._findTargetCorner(this.getPointer(e,!0),fabric.util.isTouchEvent(e));if(i.__corner=l,i===this._activeObject&&(l||!o)){this._setupCurrentTransform(e,i,c);var u=i.controls[l],a=this.getPointer(e),h=u&&u.getMouseDownHandler(e,i,u);h&&h(e,this._currentTransform,a.x,a.y)}}this._handleEvent(e,"down"),(s||o)&&this.requestRenderAll()}},_resetTransformEventData:function(){this._target=null,this._pointer=null,this._absolutePointer=null},_cacheTransformEventData:function(t){this._resetTransformEventData(),this._pointer=this.getPointer(t,!0),this._absolutePointer=this.restorePointerVpt(this._pointer),this._target=this._currentTransform?this._currentTransform.target:this.findTarget(t)||null},_beforeTransform:function(t){var e=this._currentTransform;this.stateful&&e.target.saveState(),this.fire("before:transform",{e:t,transform:e})},__onMouseMove:function(t){this._handleEvent(t,"move:before"),this._cacheTransformEventData(t);var e,i;if(this.isDrawingMode)return void this._onMouseMoveInDrawingMode(t);if(this._isMainEvent(t)){var r=this._groupSelector;r?(i=this._absolutePointer,r.left=i.x-r.ex,r.top=i.y-r.ey,this.renderTop()):this._currentTransform?this._transformObject(t):(e=this.findTarget(t)||null,this._setCursorFromEvent(t,e),this._fireOverOutEvents(e,t)),this._handleEvent(t,"move"),this._resetTransformEventData()}},_fireOverOutEvents:function(t,e){var i=this._hoveredTarget,r=this._hoveredTargets,n=this.targets,a=Math.max(r.length,n.length);this.fireSyntheticInOutEvents(t,e,{oldTarget:i,evtOut:"mouseout",canvasEvtOut:"mouse:out",evtIn:"mouseover",canvasEvtIn:"mouse:over"});for(var s=0;a>s;s++)this.fireSyntheticInOutEvents(n[s],e,{oldTarget:r[s],evtOut:"mouseout",evtIn:"mouseover"});this._hoveredTarget=t,this._hoveredTargets=this.targets.concat()},_fireEnterLeaveEvents:function(t,e){var i=this._draggedoverTarget,r=this._hoveredTargets,n=this.targets,a=Math.max(r.length,n.length);this.fireSyntheticInOutEvents(t,e,{oldTarget:i,evtOut:"dragleave",evtIn:"dragenter"});for(var s=0;a>s;s++)this.fireSyntheticInOutEvents(n[s],e,{oldTarget:r[s],evtOut:"dragleave",evtIn:"dragenter"});this._draggedoverTarget=t},fireSyntheticInOutEvents:function(t,e,i){var r,n,a,s,o=i.oldTarget,c=o!==t,l=i.canvasEvtIn,u=i.canvasEvtOut;c&&(r={e:e,target:t,previousTarget:o},n={e:e,target:o,nextTarget:t}),s=t&&c,a=o&&c,a&&(u&&this.fire(u,n),o.fire(i.evtOut,n)),s&&(l&&this.fire(l,r),t.fire(i.evtIn,r))},__onMouseWheel:function(t){this._cacheTransformEventData(t),this._handleEvent(t,"wheel"),this._resetTransformEventData()},_transformObject:function(t){var e=this.getPointer(t),i=this._currentTransform;i.reset=!1,i.shiftKey=t.shiftKey,i.altKey=t[this.centeredKey],this._performTransformAction(t,i,e),i.actionPerformed&&this.requestRenderAll()},_performTransformAction:function(t,e,i){var r=i.x,n=i.y,a=e.action,s=!1,o=e.actionHandler;o&&(s=o(t,e,r,n)),"drag"===a&&s&&(e.target.isMoving=!0,this.setCursor(e.target.moveCursor||this.moveCursor)),e.actionPerformed=e.actionPerformed||s},_fire:fabric.controlsUtils.fireEvent,_setCursorFromEvent:function(t,e){if(!e)return this.setCursor(this.defaultCursor),!1;var i=e.hoverCursor||this.hoverCursor,r=this._activeObject&&"activeSelection"===this._activeObject.type?this._activeObject:null,n=(!r||!r.contains(e))&&e._findTargetCorner(this.getPointer(t,!0));n?this.setCursor(this.getCornerCursor(n,e,t)):(e.subTargetCheck&&this.targets.concat().reverse().map(function(t){i=t.hoverCursor||i}),this.setCursor(i))},getCornerCursor:function(t,e,i){var r=e.controls[t];return r.cursorStyleHandler(i,r,e)}})}();!function(){var t=Math.min,e=Math.max;fabric.util.object.extend(fabric.Canvas.prototype,{_shouldGroup:function(t,e){var i=this._activeObject;return i&&this._isSelectionKeyPressed(t)&&e&&e.selectable&&this.selection&&(i!==e||"activeSelection"===i.type)&&!e.onSelect({e:t})},_handleGrouping:function(t,e){var i=this._activeObject;i.__corner||(e!==i||(e=this.findTarget(t,!0),e&&e.selectable))&&(i&&"activeSelection"===i.type?this._updateActiveSelection(e,t):this._createActiveSelection(e,t))},_updateActiveSelection:function(t,e){var i=this._activeObject,r=i._objects.slice(0);i.contains(t)?(i.removeWithUpdate(t),this._hoveredTarget=t,this._hoveredTargets=this.targets.concat(),1===i.size()&&this._setActiveObject(i.item(0),e)):(i.addWithUpdate(t),this._hoveredTarget=i,this._hoveredTargets=this.targets.concat()),this._fireSelectionEvents(r,e)},_createActiveSelection:function(t,e){var i=this.getActiveObjects(),r=this._createGroup(t);this._hoveredTarget=r,this._setActiveObject(r,e),this._fireSelectionEvents(i,e)},_createGroup:function(t){var e=this._objects,i=e.indexOf(this._activeObject)<e.indexOf(t),r=i?[this._activeObject,t]:[t,this._activeObject];return this._activeObject.isEditing&&this._activeObject.exitEditing(),new fabric.ActiveSelection(r,{canvas:this})},_groupSelectedObjects:function(t){var e,i=this._collectObjects(t);1===i.length?this.setActiveObject(i[0],t):i.length>1&&(e=new fabric.ActiveSelection(i.reverse(),{canvas:this}),this.setActiveObject(e,t))},_collectObjects:function(i){for(var r,n=[],s=this._groupSelector.ex,a=this._groupSelector.ey,o=s+this._groupSelector.left,c=a+this._groupSelector.top,l=new fabric.Point(t(s,o),t(a,c)),h=new fabric.Point(e(s,o),e(a,c)),u=!this.selectionFullyContained,f=s===o&&a===c,d=this._objects.length;d--&&(r=this._objects[d],!(r&&r.selectable&&r.visible&&(u&&r.intersectsWithRect(l,h,!0)||r.isContainedWithinRect(l,h,!0)||u&&r.containsPoint(l,null,!0)||u&&r.containsPoint(h,null,!0))&&(n.push(r),f))););return n.length>1&&(n=n.filter(function(t){return!t.onSelect({e:i})})),n},_maybeGroupObjects:function(t){this.selection&&this._groupSelector&&this._groupSelectedObjects(t),this.setCursor(this.defaultCursor),this._groupSelector=null}})}();!function(){fabric.util.object.extend(fabric.StaticCanvas.prototype,{toDataURL:function(t){t||(t={});var e=t.format||"png",i=t.quality||1,r=(t.multiplier||1)*(t.enableRetinaScaling?this.getRetinaScaling():1),n=this.toCanvasElement(r,t);return fabric.util.toDataURL(n,e,i)},toCanvasElement:function(t,e){t=t||1,e=e||{};var i=(e.width||this.width)*t,r=(e.height||this.height)*t,n=this.getZoom(),s=this.width,a=this.height,o=n*t,c=this.viewportTransform,l=(c[4]-(e.left||0))*t,h=(c[5]-(e.top||0))*t,u=this.interactive,f=[o,0,0,o,l,h],d=this.enableRetinaScaling,g=fabric.util.createCanvasElement(),p=this.contextTop;return g.width=i,g.height=r,this.contextTop=null,this.enableRetinaScaling=!1,this.interactive=!1,this.viewportTransform=f,this.width=i,this.height=r,this.calcViewportBoundaries(),this.renderCanvas(g.getContext("2d"),this._objects),this.viewportTransform=c,this.width=s,this.height=a,this.calcViewportBoundaries(),this.interactive=u,this.enableRetinaScaling=d,this.contextTop=p,g}})}();fabric.util.object.extend(fabric.StaticCanvas.prototype,{loadFromJSON:function(t,e,i){if(t){var r="string"==typeof t?JSON.parse(t):fabric.util.object.clone(t),n=this,a=r.clipPath,s=this.renderOnAddRemove;return this.renderOnAddRemove=!1,delete r.clipPath,this._enlivenObjects(r.objects,function(t){n.clear(),n._setBgOverlay(r,function(){a?n._enlivenObjects([a],function(i){n.clipPath=i[0],n.__setupCanvas.call(n,r,t,s,e)}):n.__setupCanvas.call(n,r,t,s,e)})},i),this}},__setupCanvas:function(t,e,i,r){var n=this;e.forEach(function(t,e){n.insertAt(t,e)}),this.renderOnAddRemove=i,delete t.objects,delete t.backgroundImage,delete t.overlayImage,delete t.background,delete t.overlay,this._setOptions(t),this.renderAll(),r&&r()},_setBgOverlay:function(t,e){var i={backgroundColor:!1,overlayColor:!1,backgroundImage:!1,overlayImage:!1};if(!(t.backgroundImage||t.overlayImage||t.background||t.overlay))return void(e&&e());var r=function(){i.backgroundImage&&i.overlayImage&&i.backgroundColor&&i.overlayColor&&e&&e()};this.__setBgOverlay("backgroundImage",t.backgroundImage,i,r),this.__setBgOverlay("overlayImage",t.overlayImage,i,r),this.__setBgOverlay("backgroundColor",t.background,i,r),this.__setBgOverlay("overlayColor",t.overlay,i,r)},__setBgOverlay:function(t,e,i,r){var n=this;return e?void("backgroundImage"===t||"overlayImage"===t?fabric.util.enlivenObjects([e],function(e){n[t]=e[0],i[t]=!0,r&&r()}):this["set"+fabric.util.string.capitalize(t,!0)](e,function(){i[t]=!0,r&&r()})):(i[t]=!0,void(r&&r()))},_enlivenObjects:function(t,e,i){return t&&0!==t.length?void fabric.util.enlivenObjects(t,function(t){e&&e(t)},null,i):void(e&&e([]))},_toDataURL:function(t,e){this.clone(function(i){e(i.toDataURL(t))})},_toDataURLWithMultiplier:function(t,e,i){this.clone(function(r){i(r.toDataURLWithMultiplier(t,e))})},clone:function(t,e){var i=JSON.stringify(this.toJSON(e));this.cloneWithoutData(function(e){e.loadFromJSON(i,function(){t&&t(e)})})},cloneWithoutData:function(t){var e=fabric.util.createCanvasElement();e.width=this.width,e.height=this.height;var i=new fabric.Canvas(e);this.backgroundImage?(i.setBackgroundImage(this.backgroundImage.src,function(){i.renderAll(),t&&t(i)}),i.backgroundImageOpacity=this.backgroundImageOpacity,i.backgroundImageStretch=this.backgroundImageStretch):t&&t(i)}});!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.util.object.clone,n=e.util.toFixed,s=e.util.string.capitalize,a=e.util.degreesToRadians,o=!e.isLikelyNode,c=2;e.Object||(e.Object=e.util.createClass(e.CommonMethods,{type:"object",originX:"left",originY:"top",top:0,left:0,width:0,height:0,scaleX:1,scaleY:1,flipX:!1,flipY:!1,opacity:1,angle:0,skewX:0,skewY:0,cornerSize:13,touchCornerSize:24,transparentCorners:!0,hoverCursor:null,moveCursor:null,padding:0,borderColor:"rgb(178,204,255)",borderDashArray:null,cornerColor:"rgb(178,204,255)",cornerStrokeColor:null,cornerStyle:"rect",cornerDashArray:null,centeredScaling:!1,centeredRotation:!0,fill:"rgb(0,0,0)",fillRule:"nonzero",globalCompositeOperation:"source-over",backgroundColor:"",selectionBackgroundColor:"",stroke:null,strokeWidth:1,strokeDashArray:null,strokeDashOffset:0,strokeLineCap:"butt",strokeLineJoin:"miter",strokeMiterLimit:4,shadow:null,borderOpacityWhenMoving:.4,borderScaleFactor:1,minScaleLimit:0,selectable:!0,evented:!0,visible:!0,hasControls:!0,hasBorders:!0,perPixelTargetFind:!1,includeDefaultValues:!0,lockMovementX:!1,lockMovementY:!1,lockRotation:!1,lockScalingX:!1,lockScalingY:!1,lockSkewingX:!1,lockSkewingY:!1,lockScalingFlip:!1,excludeFromExport:!1,objectCaching:o,statefullCache:!1,noScaleCache:!0,strokeUniform:!1,dirty:!0,__corner:0,paintFirst:"fill",activeOn:"down",stateProperties:"top left width height scaleX scaleY flipX flipY originX originY transformMatrix stroke strokeWidth strokeDashArray strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit angle opacity fill globalCompositeOperation shadow visible backgroundColor skewX skewY fillRule paintFirst clipPath strokeUniform".split(" "),cacheProperties:"fill stroke strokeWidth strokeDashArray width height paintFirst strokeUniform strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit backgroundColor clipPath".split(" "),colorProperties:"fill stroke backgroundColor".split(" "),clipPath:void 0,inverted:!1,absolutePositioned:!1,initialize:function(t){t&&this.setOptions(t)},_createCacheCanvas:function(){this._cacheProperties={},this._cacheCanvas=e.util.createCanvasElement(),this._cacheContext=this._cacheCanvas.getContext("2d"),this._updateCacheCanvas(),this.dirty=!0},_limitCacheSize:function(t){var i=e.perfLimitSizeTotal,r=t.width,n=t.height,s=e.maxCacheSideLimit,a=e.minCacheSideLimit;if(s>=r&&s>=n&&i>=r*n)return a>r&&(t.width=a),a>n&&(t.height=a),t;var o=r/n,c=e.util.limitDimsByArea(o,i),h=e.util.capValue,l=h(a,c.x,s),u=h(a,c.y,s);return r>l&&(t.zoomX/=r/l,t.width=l,t.capped=!0),n>u&&(t.zoomY/=n/u,t.height=u,t.capped=!0),t},_getCacheCanvasDimensions:function(){var t=this.getTotalObjectScaling(),e=this._getTransformedDimensions(0,0),i=e.x*t.scaleX/this.scaleX,r=e.y*t.scaleY/this.scaleY;return{width:i+c,height:r+c,zoomX:t.scaleX,zoomY:t.scaleY,x:i,y:r}},_updateCacheCanvas:function(){var t=this.canvas;if(this.noScaleCache&&t&&t._currentTransform){var i=t._currentTransform.target,r=t._currentTransform.action;if(this===i&&r.slice&&"scale"===r.slice(0,5))return!1}var n,s,a=this._cacheCanvas,o=this._limitCacheSize(this._getCacheCanvasDimensions()),c=e.minCacheSideLimit,h=o.width,l=o.height,u=o.zoomX,f=o.zoomY,d=h!==this.cacheWidth||l!==this.cacheHeight,g=this.zoomX!==u||this.zoomY!==f,p=d||g,v=0,b=0,m=!1;if(d){var y=this._cacheCanvas.width,_=this._cacheCanvas.height,w=h>y||l>_,C=(.9*y>h||.9*_>l)&&y>c&&_>c;m=w||C,w&&!o.capped&&(h>c||l>c)&&(v=.1*h,b=.1*l)}return this instanceof e.Text&&this.path&&(p=!0,m=!0,v+=this.getHeightOfLine(0)*this.zoomX,b+=this.getHeightOfLine(0)*this.zoomY),p?(m?(a.width=Math.ceil(h+v),a.height=Math.ceil(l+b)):(this._cacheContext.setTransform(1,0,0,1,0,0),this._cacheContext.clearRect(0,0,a.width,a.height)),n=o.x/2,s=o.y/2,this.cacheTranslationX=Math.round(a.width/2-n)+n,this.cacheTranslationY=Math.round(a.height/2-s)+s,this.cacheWidth=h,this.cacheHeight=l,this._cacheContext.translate(this.cacheTranslationX,this.cacheTranslationY),this._cacheContext.scale(u,f),this.zoomX=u,this.zoomY=f,!0):!1},setOptions:function(t){this._setOptions(t),this._initGradient(t.fill,"fill"),this._initGradient(t.stroke,"stroke"),this._initPattern(t.fill,"fill"),this._initPattern(t.stroke,"stroke")},transform:function(t){var e=this.group&&!this.group._transformDone||this.group&&this.canvas&&t===this.canvas.contextTop,i=this.calcTransformMatrix(!e);t.transform(i[0],i[1],i[2],i[3],i[4],i[5])},toObject:function(t){var i=e.Object.NUM_FRACTION_DIGITS,r={type:this.type,version:e.version,originX:this.originX,originY:this.originY,left:n(this.left,i),top:n(this.top,i),width:n(this.width,i),height:n(this.height,i),fill:this.fill&&this.fill.toObject?this.fill.toObject():this.fill,stroke:this.stroke&&this.stroke.toObject?this.stroke.toObject():this.stroke,strokeWidth:n(this.strokeWidth,i),strokeDashArray:this.strokeDashArray?this.strokeDashArray.concat():this.strokeDashArray,strokeLineCap:this.strokeLineCap,strokeDashOffset:this.strokeDashOffset,strokeLineJoin:this.strokeLineJoin,strokeUniform:this.strokeUniform,strokeMiterLimit:n(this.strokeMiterLimit,i),scaleX:n(this.scaleX,i),scaleY:n(this.scaleY,i),angle:n(this.angle,i),flipX:this.flipX,flipY:this.flipY,opacity:n(this.opacity,i),shadow:this.shadow&&this.shadow.toObject?this.shadow.toObject():this.shadow,visible:this.visible,backgroundColor:this.backgroundColor,fillRule:this.fillRule,paintFirst:this.paintFirst,globalCompositeOperation:this.globalCompositeOperation,skewX:n(this.skewX,i),skewY:n(this.skewY,i)};return this.clipPath&&!this.clipPath.excludeFromExport&&(r.clipPath=this.clipPath.toObject(t),r.clipPath.inverted=this.clipPath.inverted,r.clipPath.absolutePositioned=this.clipPath.absolutePositioned),e.util.populateWithProperties(this,r,t),this.includeDefaultValues||(r=this._removeDefaultValues(r)),r},toDatalessObject:function(t){return this.toObject(t)},_removeDefaultValues:function(t){var i=e.util.getKlass(t.type).prototype,r=i.stateProperties;return r.forEach(function(e){"left"!==e&&"top"!==e&&(t[e]===i[e]&&delete t[e],Array.isArray(t[e])&&Array.isArray(i[e])&&0===t[e].length&&0===i[e].length&&delete t[e])}),t},toString:function(){return"#<fabric."+s(this.type)+">"},getObjectScaling:function(){if(!this.group)return{scaleX:this.scaleX,scaleY:this.scaleY};var t=e.util.qrDecompose(this.calcTransformMatrix());return{scaleX:Math.abs(t.scaleX),scaleY:Math.abs(t.scaleY)}},getTotalObjectScaling:function(){var t=this.getObjectScaling(),e=t.scaleX,i=t.scaleY;if(this.canvas){var r=this.canvas.getZoom(),n=this.canvas.getRetinaScaling();e*=r*n,i*=r*n}return{scaleX:e,scaleY:i}},getObjectOpacity:function(){var t=this.opacity;return this.group&&(t*=this.group.getObjectOpacity()),t},_set:function(t,i){var r="scaleX"===t||"scaleY"===t,n=this[t]!==i,s=!1;return r&&(i=this._constrainScale(i)),"scaleX"===t&&0>i?(this.flipX=!this.flipX,i*=-1):"scaleY"===t&&0>i?(this.flipY=!this.flipY,i*=-1):"shadow"!==t||!i||i instanceof e.Shadow?"dirty"===t&&this.group&&this.group.set("dirty",i):i=new e.Shadow(i),this[t]=i,n&&(s=this.group&&this.group.isOnACache(),this.cacheProperties.indexOf(t)>-1?(this.dirty=!0,s&&this.group.set("dirty",!0)):s&&this.stateProperties.indexOf(t)>-1&&this.group.set("dirty",!0)),this},setOnGroup:function(){},getViewportTransform:function(){return this.canvas&&this.canvas.viewportTransform?this.canvas.viewportTransform:e.iMatrix.concat()},isNotVisible:function(){return 0===this.opacity||!this.width&&!this.height&&0===this.strokeWidth||!this.visible},render:function(t){this.isNotVisible()||(!this.canvas||!this.canvas.skipOffscreen||this.group||this.isOnScreen())&&(t.save(),this._setupCompositeOperation(t),this.drawSelectionBackground(t),this.transform(t),this._setOpacity(t),this._setShadow(t,this),this.shouldCache()?(this.renderCache(),this.drawCacheOnCanvas(t)):(this._removeCacheCanvas(),this.dirty=!1,this.drawObject(t),this.objectCaching&&this.statefullCache&&this.saveState({propertySet:"cacheProperties"})),t.restore())},renderCache:function(t){t=t||{},this._cacheCanvas&&this._cacheContext||this._createCacheCanvas(),this.isCacheDirty()&&(this.statefullCache&&this.saveState({propertySet:"cacheProperties"}),this.drawObject(this._cacheContext,t.forClipping),this.dirty=!1)},_removeCacheCanvas:function(){this._cacheCanvas=null,this._cacheContext=null,this.cacheWidth=0,this.cacheHeight=0},hasStroke:function(){return this.stroke&&"transparent"!==this.stroke&&0!==this.strokeWidth},hasFill:function(){return this.fill&&"transparent"!==this.fill},needsItsOwnCache:function(){return"stroke"===this.paintFirst&&this.hasFill()&&this.hasStroke()&&"object"==typeof this.shadow?!0:this.clipPath?!0:!1},shouldCache:function(){return this.ownCaching=this.needsItsOwnCache()||this.objectCaching&&(!this.group||!this.group.isOnACache()),this.ownCaching},willDrawShadow:function(){return!!this.shadow&&(0!==this.shadow.offsetX||0!==this.shadow.offsetY)},drawClipPathOnCache:function(t,i){if(t.save(),t.globalCompositeOperation=i.inverted?"destination-out":"destination-in",i.absolutePositioned){var r=e.util.invertTransform(this.calcTransformMatrix());t.transform(r[0],r[1],r[2],r[3],r[4],r[5])}i.transform(t),t.scale(1/i.zoomX,1/i.zoomY),t.drawImage(i._cacheCanvas,-i.cacheTranslationX,-i.cacheTranslationY),t.restore()},drawObject:function(t,e){var i=this.fill,r=this.stroke;e?(this.fill="black",this.stroke="",this._setClippingProperties(t)):this._renderBackground(t),this._render(t),this._drawClipPath(t,this.clipPath),this.fill=i,this.stroke=r},_drawClipPath:function(t,e){e&&(e.canvas=this.canvas,e.shouldCache(),e._transformDone=!0,e.renderCache({forClipping:!0}),this.drawClipPathOnCache(t,e))},drawCacheOnCanvas:function(t){t.scale(1/this.zoomX,1/this.zoomY),t.drawImage(this._cacheCanvas,-this.cacheTranslationX,-this.cacheTranslationY)},isCacheDirty:function(t){if(this.isNotVisible())return!1;if(this._cacheCanvas&&this._cacheContext&&!t&&this._updateCacheCanvas())return!0;if(this.dirty||this.clipPath&&this.clipPath.absolutePositioned||this.statefullCache&&this.hasStateChanged("cacheProperties")){if(this._cacheCanvas&&this._cacheContext&&!t){var e=this.cacheWidth/this.zoomX,i=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-e/2,-i/2,e,i)}return!0}return!1},_renderBackground:function(t){if(this.backgroundColor){var e=this._getNonTransformedDimensions();t.fillStyle=this.backgroundColor,t.fillRect(-e.x/2,-e.y/2,e.x,e.y),this._removeShadow(t)}},_setOpacity:function(t){this.group&&!this.group._transformDone?t.globalAlpha=this.getObjectOpacity():t.globalAlpha*=this.opacity},_setStrokeStyles:function(t,e){var i=e.stroke;i&&(t.lineWidth=e.strokeWidth,t.lineCap=e.strokeLineCap,t.lineDashOffset=e.strokeDashOffset,t.lineJoin=e.strokeLineJoin,t.miterLimit=e.strokeMiterLimit,i.toLive?"percentage"===i.gradientUnits||i.gradientTransform||i.patternTransform?this._applyPatternForTransformedGradient(t,i):(t.strokeStyle=i.toLive(t,this),this._applyPatternGradientTransform(t,i)):t.strokeStyle=e.stroke)},_setFillStyles:function(t,e){var i=e.fill;i&&(i.toLive?(t.fillStyle=i.toLive(t,this),this._applyPatternGradientTransform(t,e.fill)):t.fillStyle=i)},_setClippingProperties:function(t){t.globalAlpha=1,t.strokeStyle="transparent",t.fillStyle="#000000"},_setLineDash:function(t,e){e&&0!==e.length&&(1&e.length&&e.push.apply(e,e),t.setLineDash(e))},_renderControls:function(t,i){var r,n,s,o=this.getViewportTransform(),c=this.calcTransformMatrix();i=i||{},n="undefined"!=typeof i.hasBorders?i.hasBorders:this.hasBorders,s="undefined"!=typeof i.hasControls?i.hasControls:this.hasControls,c=e.util.multiplyTransformMatrices(o,c),r=e.util.qrDecompose(c),t.save(),t.translate(r.translateX,r.translateY),t.lineWidth=1*this.borderScaleFactor,this.group||(t.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1),this.flipX&&(r.angle-=180),t.rotate(a(this.group?r.angle:this.angle)),i.forActiveSelection||this.group?n&&this.drawBordersInGroup(t,r,i):n&&this.drawBorders(t,i),s&&this.drawControls(t,i),t.restore()},_setShadow:function(t){if(this.shadow){var i,r=this.shadow,n=this.canvas,s=n&&n.viewportTransform[0]||1,a=n&&n.viewportTransform[3]||1;i=r.nonScaling?{scaleX:1,scaleY:1}:this.getObjectScaling(),n&&n._isRetinaScaling()&&(s*=e.devicePixelRatio,a*=e.devicePixelRatio),t.shadowColor=r.color,t.shadowBlur=r.blur*e.browserShadowBlurConstant*(s+a)*(i.scaleX+i.scaleY)/4,t.shadowOffsetX=r.offsetX*s*i.scaleX,t.shadowOffsetY=r.offsetY*a*i.scaleY}},_removeShadow:function(t){this.shadow&&(t.shadowColor="",t.shadowBlur=t.shadowOffsetX=t.shadowOffsetY=0)},_applyPatternGradientTransform:function(t,e){if(!e||!e.toLive)return{offsetX:0,offsetY:0};var i=e.gradientTransform||e.patternTransform,r=-this.width/2+e.offsetX||0,n=-this.height/2+e.offsetY||0;return"percentage"===e.gradientUnits?t.transform(this.width,0,0,this.height,r,n):t.transform(1,0,0,1,r,n),i&&t.transform(i[0],i[1],i[2],i[3],i[4],i[5]),{offsetX:r,offsetY:n}},_renderPaintInOrder:function(t){"stroke"===this.paintFirst?(this._renderStroke(t),this._renderFill(t)):(this._renderFill(t),this._renderStroke(t))},_render:function(){},_renderFill:function(t){this.fill&&(t.save(),this._setFillStyles(t,this),"evenodd"===this.fillRule?t.fill("evenodd"):t.fill(),t.restore())},_renderStroke:function(t){if(this.stroke&&0!==this.strokeWidth){if(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(t),t.save(),this.strokeUniform&&this.group){var e=this.getObjectScaling();t.scale(1/e.scaleX,1/e.scaleY)}else this.strokeUniform&&t.scale(1/this.scaleX,1/this.scaleY);this._setLineDash(t,this.strokeDashArray),this._setStrokeStyles(t,this),t.stroke(),t.restore()}},_applyPatternForTransformedGradient:function(t,i){var r,n=this._limitCacheSize(this._getCacheCanvasDimensions()),s=e.util.createCanvasElement(),a=this.canvas.getRetinaScaling(),o=n.x/this.scaleX/a,c=n.y/this.scaleY/a;s.width=o,s.height=c,r=s.getContext("2d"),r.beginPath(),r.moveTo(0,0),r.lineTo(o,0),r.lineTo(o,c),r.lineTo(0,c),r.closePath(),r.translate(o/2,c/2),r.scale(n.zoomX/this.scaleX/a,n.zoomY/this.scaleY/a),this._applyPatternGradientTransform(r,i),r.fillStyle=i.toLive(t),r.fill(),t.translate(-this.width/2-this.strokeWidth/2,-this.height/2-this.strokeWidth/2),t.scale(a*this.scaleX/n.zoomX,a*this.scaleY/n.zoomY),t.strokeStyle=r.createPattern(s,"no-repeat")},_findCenterFromElement:function(){return{x:this.left+this.width/2,y:this.top+this.height/2}},_assignTransformMatrixProps:function(){if(this.transformMatrix){var t=e.util.qrDecompose(this.transformMatrix);this.flipX=!1,this.flipY=!1,this.set("scaleX",t.scaleX),this.set("scaleY",t.scaleY),this.angle=t.angle,this.skewX=t.skewX,this.skewY=0}},_removeTransformMatrix:function(t){var i=this._findCenterFromElement();this.transformMatrix&&(this._assignTransformMatrixProps(),i=e.util.transformPoint(i,this.transformMatrix)),this.transformMatrix=null,t&&(this.scaleX*=t.scaleX,this.scaleY*=t.scaleY,this.cropX=t.cropX,this.cropY=t.cropY,i.x+=t.offsetLeft,i.y+=t.offsetTop,this.width=t.width,this.height=t.height),this.setPositionByOrigin(i,"center","center")},clone:function(t,i){var r=this.toObject(i);this.constructor.fromObject?this.constructor.fromObject(r,t):e.Object._fromObject("Object",r,t)},cloneAsImage:function(t,i){var r=this.toCanvasElement(i);return t&&t(new e.Image(r)),this},toCanvasElement:function(t){t||(t={});var i=e.util,r=i.saveObjectTransform(this),n=this.group,s=this.shadow,a=Math.abs,o=(t.multiplier||1)*(t.enableRetinaScaling?e.devicePixelRatio:1);delete this.group,t.withoutTransform&&i.resetObjectTransform(this),t.withoutShadow&&(this.shadow=null);var c,h,l,u,f=e.util.createCanvasElement(),d=this.getBoundingRect(!0,!0),g=this.shadow,p={x:0,y:0};g&&(h=g.blur,c=g.nonScaling?{scaleX:1,scaleY:1}:this.getObjectScaling(),p.x=2*Math.round(a(g.offsetX)+h)*a(c.scaleX),p.y=2*Math.round(a(g.offsetY)+h)*a(c.scaleY)),l=d.width+p.x,u=d.height+p.y,f.width=Math.ceil(l),f.height=Math.ceil(u);var v=new e.StaticCanvas(f,{enableRetinaScaling:!1,renderOnAddRemove:!1,skipOffscreen:!1});"jpeg"===t.format&&(v.backgroundColor="#fff"),this.setPositionByOrigin(new e.Point(v.width/2,v.height/2),"center","center");var b=this.canvas;v.add(this);var m=v.toCanvasElement(o||1,t);return this.shadow=s,this.set("canvas",b),n&&(this.group=n),this.set(r).setCoords(),v._objects=[],v.dispose(),v=null,m},toDataURL:function(t){return t||(t={}),e.util.toDataURL(this.toCanvasElement(t),t.format||"png",t.quality||1)},isType:function(t){return arguments.length>1?Array.from(arguments).includes(this.type):this.type===t},complexity:function(){return 1},toJSON:function(t){return this.toObject(t)},rotate:function(t){var e=("center"!==this.originX||"center"!==this.originY)&&this.centeredRotation;return e&&this._setOriginToCenter(),this.set("angle",t),e&&this._resetOrigin(),this},centerH:function(){return this.canvas&&this.canvas.centerObjectH(this),this},viewportCenterH:function(){return this.canvas&&this.canvas.viewportCenterObjectH(this),this},centerV:function(){return this.canvas&&this.canvas.centerObjectV(this),this},viewportCenterV:function(){return this.canvas&&this.canvas.viewportCenterObjectV(this),this},center:function(){return this.canvas&&this.canvas.centerObject(this),this},viewportCenter:function(){return this.canvas&&this.canvas.viewportCenterObject(this),this},getLocalPointer:function(t,i){i=i||this.canvas.getPointer(t);var r=new e.Point(i.x,i.y),n=this._getLeftTopCoords();return this.angle&&(r=e.util.rotatePoint(r,n,a(-this.angle))),{x:r.x-n.x,y:r.y-n.y}},_setupCompositeOperation:function(t){this.globalCompositeOperation&&(t.globalCompositeOperation=this.globalCompositeOperation)},dispose:function(){e.runningAnimations&&e.runningAnimations.cancelByTarget(this)}}),e.util.createAccessors&&e.util.createAccessors(e.Object),i(e.Object.prototype,e.Observable),e.Object.NUM_FRACTION_DIGITS=2,e.Object.ENLIVEN_PROPS=["clipPath"],e.Object._fromObject=function(t,i,n,s){var a=e[t];i=r(i,!0),e.util.enlivenPatterns([i.fill,i.stroke],function(t){"undefined"!=typeof t[0]&&(i.fill=t[0]),"undefined"!=typeof t[1]&&(i.stroke=t[1]),e.util.enlivenObjectEnlivables(i,i,function(){var t=s?new a(i[s],i):new a(i);n&&n(t)})})},e.Object.__uid=0)}("undefined"!=typeof exports?exports:this);!function(){var t=fabric.util.degreesToRadians,e={left:-.5,center:0,right:.5},i={top:-.5,center:0,bottom:.5};fabric.util.object.extend(fabric.Object.prototype,{translateToGivenOrigin:function(t,r,n,s,a){var o,c,h,l=t.x,u=t.y;return"string"==typeof r?r=e[r]:r-=.5,"string"==typeof s?s=e[s]:s-=.5,o=s-r,"string"==typeof n?n=i[n]:n-=.5,"string"==typeof a?a=i[a]:a-=.5,c=a-n,(o||c)&&(h=this._getTransformedDimensions(),l=t.x+o*h.x,u=t.y+c*h.y),new fabric.Point(l,u)},translateToCenterPoint:function(e,i,r){var n=this.translateToGivenOrigin(e,i,r,"center","center");return this.angle?fabric.util.rotatePoint(n,e,t(this.angle)):n},translateToOriginPoint:function(e,i,r){var n=this.translateToGivenOrigin(e,"center","center",i,r);return this.angle?fabric.util.rotatePoint(n,e,t(this.angle)):n},getCenterPoint:function(){var t=new fabric.Point(this.left,this.top);return this.translateToCenterPoint(t,this.originX,this.originY)},getPointByOrigin:function(t,e){var i=this.getCenterPoint();return this.translateToOriginPoint(i,t,e)},toLocalPoint:function(e,i,r){var n,s,a=this.getCenterPoint();return n="undefined"!=typeof i&&"undefined"!=typeof r?this.translateToGivenOrigin(a,"center","center",i,r):new fabric.Point(this.left,this.top),s=new fabric.Point(e.x,e.y),this.angle&&(s=fabric.util.rotatePoint(s,a,-t(this.angle))),s.subtractEquals(n)},setPositionByOrigin:function(t,e,i){var r=this.translateToCenterPoint(t,e,i),n=this.translateToOriginPoint(r,this.originX,this.originY);this.set("left",n.x),this.set("top",n.y)},adjustPosition:function(i){var r,n,s=t(this.angle),a=this.getScaledWidth(),o=fabric.util.cos(s)*a,c=fabric.util.sin(s)*a;r="string"==typeof this.originX?e[this.originX]:this.originX-.5,n="string"==typeof i?e[i]:i-.5,this.left+=o*(n-r),this.top+=c*(n-r),this.setCoords(),this.originX=i},_setOriginToCenter:function(){this._originalOriginX=this.originX,this._originalOriginY=this.originY;var t=this.getCenterPoint();this.originX="center",this.originY="center",this.left=t.x,this.top=t.y},_resetOrigin:function(){var t=this.translateToOriginPoint(this.getCenterPoint(),this._originalOriginX,this._originalOriginY);this.originX=this._originalOriginX,this.originY=this._originalOriginY,this.left=t.x,this.top=t.y,this._originalOriginX=null,this._originalOriginY=null},_getLeftTopCoords:function(){return this.translateToOriginPoint(this.getCenterPoint(),"left","top")}})}();!function(){function t(t){return[new fabric.Point(t.tl.x,t.tl.y),new fabric.Point(t.tr.x,t.tr.y),new fabric.Point(t.br.x,t.br.y),new fabric.Point(t.bl.x,t.bl.y)]}var e=fabric.util,i=e.degreesToRadians,r=e.multiplyTransformMatrices,n=e.transformPoint;e.object.extend(fabric.Object.prototype,{oCoords:null,aCoords:null,lineCoords:null,ownMatrixCache:null,matrixCache:null,controls:{},_getCoords:function(t,e){return e?t?this.calcACoords():this.calcLineCoords():(this.aCoords&&this.lineCoords||this.setCoords(!0),t?this.aCoords:this.lineCoords)},getCoords:function(e,i){return t(this._getCoords(e,i))},intersectsWithRect:function(t,e,i,r){var n=this.getCoords(i,r),s=fabric.Intersection.intersectPolygonRectangle(n,t,e);return"Intersection"===s.status},intersectsWithObject:function(t,e,i){var r=fabric.Intersection.intersectPolygonPolygon(this.getCoords(e,i),t.getCoords(e,i));return"Intersection"===r.status||t.isContainedWithinObject(this,e,i)||this.isContainedWithinObject(t,e,i)},isContainedWithinObject:function(t,e,i){for(var r=this.getCoords(e,i),n=e?t.aCoords:t.lineCoords,s=0,a=t._getImageLines(n);4>s;s++)if(!t.containsPoint(r[s],a))return!1;return!0},isContainedWithinRect:function(t,e,i,r){var n=this.getBoundingRect(i,r);return n.left>=t.x&&n.left+n.width<=e.x&&n.top>=t.y&&n.top+n.height<=e.y},containsPoint:function(t,e,i,r){var n=this._getCoords(i,r),e=e||this._getImageLines(n),s=this._findCrossPoints(t,e);return 0!==s&&s%2===1},isOnScreen:function(t){if(!this.canvas)return!1;var e=this.canvas.vptCoords.tl,i=this.canvas.vptCoords.br,r=this.getCoords(!0,t);return r.some(function(t){return t.x<=i.x&&t.x>=e.x&&t.y<=i.y&&t.y>=e.y})?!0:this.intersectsWithRect(e,i,!0,t)?!0:this._containsCenterOfCanvas(e,i,t)},_containsCenterOfCanvas:function(t,e,i){var r={x:(t.x+e.x)/2,y:(t.y+e.y)/2};return this.containsPoint(r,null,!0,i)?!0:!1},isPartiallyOnScreen:function(t){if(!this.canvas)return!1;var e=this.canvas.vptCoords.tl,i=this.canvas.vptCoords.br;if(this.intersectsWithRect(e,i,!0,t))return!0;var r=this.getCoords(!0,t).every(function(t){return(t.x>=i.x||t.x<=e.x)&&(t.y>=i.y||t.y<=e.y)});return r&&this._containsCenterOfCanvas(e,i,t)},_getImageLines:function(t){var e={topline:{o:t.tl,d:t.tr},rightline:{o:t.tr,d:t.br},bottomline:{o:t.br,d:t.bl},leftline:{o:t.bl,d:t.tl}};return e},_findCrossPoints:function(t,e){var i,r,n,s,a,o,c=0;for(var h in e)if(o=e[h],!(o.o.y<t.y&&o.d.y<t.y||o.o.y>=t.y&&o.d.y>=t.y||(o.o.x===o.d.x&&o.o.x>=t.x?a=o.o.x:(i=0,r=(o.d.y-o.o.y)/(o.d.x-o.o.x),n=t.y-i*t.x,s=o.o.y-r*o.o.x,a=-(n-s)/(i-r)),a>=t.x&&(c+=1),2!==c)))break;return c},getBoundingRect:function(t,i){var r=this.getCoords(t,i);return e.makeBoundingBoxFromPoints(r)},getScaledWidth:function(){return this._getTransformedDimensions().x},getScaledHeight:function(){return this._getTransformedDimensions().y},_constrainScale:function(t){return Math.abs(t)<this.minScaleLimit?0>t?-this.minScaleLimit:this.minScaleLimit:0===t?1e-4:t},scale:function(t){return this._set("scaleX",t),this._set("scaleY",t),this.setCoords()},scaleToWidth:function(t,e){var i=this.getBoundingRect(e).width/this.getScaledWidth();return this.scale(t/this.width/i)},scaleToHeight:function(t,e){var i=this.getBoundingRect(e).height/this.getScaledHeight();return this.scale(t/this.height/i)},calcLineCoords:function(){var t=this.getViewportTransform(),r=this.padding,s=i(this.angle),a=e.cos(s),o=e.sin(s),c=a*r,h=o*r,l=c+h,u=c-h,f=this.calcACoords(),d={tl:n(f.tl,t),tr:n(f.tr,t),bl:n(f.bl,t),br:n(f.br,t)};return r&&(d.tl.x-=u,d.tl.y-=l,d.tr.x+=l,d.tr.y-=u,d.bl.x-=l,d.bl.y+=u,d.br.x+=u,d.br.y+=l),d},calcOCoords:function(){var t=this._calcRotateMatrix(),e=this._calcTranslateMatrix(),i=this.getViewportTransform(),n=r(i,e),s=r(n,t),s=r(s,[1/i[0],0,0,1/i[3],0,0]),a=this._calculateCurrentDimensions(),o={};return this.forEachControl(function(t,e,i){o[e]=t.positionHandler(a,s,i)}),o},calcACoords:function(){var t=this._calcRotateMatrix(),e=this._calcTranslateMatrix(),i=r(e,t),s=this._getTransformedDimensions(),a=s.x/2,o=s.y/2;return{tl:n({x:-a,y:-o},i),tr:n({x:a,y:-o},i),bl:n({x:-a,y:o},i),br:n({x:a,y:o},i)}},setCoords:function(t){return this.aCoords=this.calcACoords(),this.lineCoords=this.group?this.aCoords:this.calcLineCoords(),t?this:(this.oCoords=this.calcOCoords(),this._setCornerCoords&&this._setCornerCoords(),this)},_calcRotateMatrix:function(){return e.calcRotateMatrix(this)},_calcTranslateMatrix:function(){var t=this.getCenterPoint();return[1,0,0,1,t.x,t.y]},transformMatrixKey:function(t){var e="_",i="";return!t&&this.group&&(i=this.group.transformMatrixKey(t)+e),i+this.top+e+this.left+e+this.scaleX+e+this.scaleY+e+this.skewX+e+this.skewY+e+this.angle+e+this.originX+e+this.originY+e+this.width+e+this.height+e+this.strokeWidth+this.flipX+this.flipY},calcTransformMatrix:function(t){var e=this.calcOwnMatrix();if(t||!this.group)return e;var i=this.transformMatrixKey(t),n=this.matrixCache||(this.matrixCache={});return n.key===i?n.value:(this.group&&(e=r(this.group.calcTransformMatrix(!1),e)),n.key=i,n.value=e,e)},calcOwnMatrix:function(){var t=this.transformMatrixKey(!0),i=this.ownMatrixCache||(this.ownMatrixCache={});if(i.key===t)return i.value;var r=this._calcTranslateMatrix(),n={angle:this.angle,translateX:r[4],translateY:r[5],scaleX:this.scaleX,scaleY:this.scaleY,skewX:this.skewX,skewY:this.skewY,flipX:this.flipX,flipY:this.flipY};return i.key=t,i.value=e.composeMatrix(n),i.value},_getNonTransformedDimensions:function(){var t=this.strokeWidth,e=this.width+t,i=this.height+t;return{x:e,y:i}},_getTransformedDimensions:function(t,i){"undefined"==typeof t&&(t=this.skewX),"undefined"==typeof i&&(i=this.skewY);var r,n,s,a=0===t&&0===i;if(this.strokeUniform?(n=this.width,s=this.height):(r=this._getNonTransformedDimensions(),n=r.x,s=r.y),a)return this._finalizeDimensions(n*this.scaleX,s*this.scaleY);var o=e.sizeAfterTransform(n,s,{scaleX:this.scaleX,scaleY:this.scaleY,skewX:t,skewY:i});return this._finalizeDimensions(o.x,o.y)},_finalizeDimensions:function(t,e){return this.strokeUniform?{x:t+this.strokeWidth,y:e+this.strokeWidth}:{x:t,y:e}},_calculateCurrentDimensions:function(){var t=this.getViewportTransform(),e=this._getTransformedDimensions(),i=n(e,t,!0);return i.scalarAdd(2*this.padding)}})}();fabric.util.object.extend(fabric.Object.prototype,{sendToBack:function(){return this.group?fabric.StaticCanvas.prototype.sendToBack.call(this.group,this):this.canvas&&this.canvas.sendToBack(this),this},bringToFront:function(){return this.group?fabric.StaticCanvas.prototype.bringToFront.call(this.group,this):this.canvas&&this.canvas.bringToFront(this),this},sendBackwards:function(t){return this.group?fabric.StaticCanvas.prototype.sendBackwards.call(this.group,this,t):this.canvas&&this.canvas.sendBackwards(this,t),this},bringForward:function(t){return this.group?fabric.StaticCanvas.prototype.bringForward.call(this.group,this,t):this.canvas&&this.canvas.bringForward(this,t),this},moveTo:function(t){return this.group&&"activeSelection"!==this.group.type?fabric.StaticCanvas.prototype.moveTo.call(this.group,this,t):this.canvas&&this.canvas.moveTo(this,t),this}});!function(){function t(t,e){if(e){if(e.toLive)return t+": url(#SVGID_"+e.id+"); ";var i=new fabric.Color(e),r=t+": "+i.toRgb()+"; ",n=i.getAlpha();return 1!==n&&(r+=t+"-opacity: "+n.toString()+"; "),r}return t+": none; "}var e=fabric.util.toFixed;fabric.util.object.extend(fabric.Object.prototype,{getSvgStyles:function(e){var i=this.fillRule?this.fillRule:"nonzero",r=this.strokeWidth?this.strokeWidth:"0",n=this.strokeDashArray?this.strokeDashArray.join(" "):"none",s=this.strokeDashOffset?this.strokeDashOffset:"0",o=this.strokeLineCap?this.strokeLineCap:"butt",a=this.strokeLineJoin?this.strokeLineJoin:"miter",c=this.strokeMiterLimit?this.strokeMiterLimit:"4",h="undefined"!=typeof this.opacity?this.opacity:"1",l=this.visible?"":" visibility: hidden;",u=e?"":this.getSvgFilter(),f=t("fill",this.fill),d=t("stroke",this.stroke);return[d,"stroke-width: ",r,"; ","stroke-dasharray: ",n,"; ","stroke-linecap: ",o,"; ","stroke-dashoffset: ",s,"; ","stroke-linejoin: ",a,"; ","stroke-miterlimit: ",c,"; ",f,"fill-rule: ",i,"; ","opacity: ",h,";",u,l].join("")},getSvgSpanStyles:function(e,i){var r="; ",n=e.fontFamily?"font-family: "+(-1===e.fontFamily.indexOf("'")&&-1===e.fontFamily.indexOf('"')?"'"+e.fontFamily+"'":e.fontFamily)+r:"",s=e.strokeWidth?"stroke-width: "+e.strokeWidth+r:"",n=n,o=e.fontSize?"font-size: "+e.fontSize+"px"+r:"",a=e.fontStyle?"font-style: "+e.fontStyle+r:"",c=e.fontWeight?"font-weight: "+e.fontWeight+r:"",h=e.fill?t("fill",e.fill):"",l=e.stroke?t("stroke",e.stroke):"",u=this.getSvgTextDecoration(e),f=e.deltaY?"baseline-shift: "+-e.deltaY+"; ":"";return u&&(u="text-decoration: "+u+r),[l,s,n,o,a,c,u,h,f,i?"white-space: pre; ":""].join("")},getSvgTextDecoration:function(t){return["overline","underline","line-through"].filter(function(e){return t[e.replace("-","")]}).join(" ")},getSvgFilter:function(){return this.shadow?"filter: url(#SVGID_"+this.shadow.id+");":""},getSvgCommons:function(){return[this.id?'id="'+this.id+'" ':"",this.clipPath?'clip-path="url(#'+this.clipPath.clipPathId+')" ':""].join("")},getSvgTransform:function(t,e){var i=t?this.calcTransformMatrix():this.calcOwnMatrix(),r='transform="'+fabric.util.matrixToSVG(i);return r+(e||"")+'" '},_setSVGBg:function(t){if(this.backgroundColor){var i=fabric.Object.NUM_FRACTION_DIGITS;t.push(" <rect ",this._getFillAttributes(this.backgroundColor),' x="',e(-this.width/2,i),'" y="',e(-this.height/2,i),'" width="',e(this.width,i),'" height="',e(this.height,i),'"></rect>\n')}},toSVG:function(t){return this._createBaseSVGMarkup(this._toSVG(t),{reviver:t})},toClipPathSVG:function(t){return" "+this._createBaseClipPathSVGMarkup(this._toSVG(t),{reviver:t})},_createBaseClipPathSVGMarkup:function(t,e){e=e||{};var i=e.reviver,r=e.additionalTransform||"",n=[this.getSvgTransform(!0,r),this.getSvgCommons()].join(""),s=t.indexOf("COMMON_PARTS");return t[s]=n,i?i(t.join("")):t.join("")},_createBaseSVGMarkup:function(t,e){e=e||{};var i,r,n=e.noStyle,s=e.reviver,o=n?"":'style="'+this.getSvgStyles()+'" ',a=e.withShadow?'style="'+this.getSvgFilter()+'" ':"",c=this.clipPath,h=this.strokeUniform?'vector-effect="non-scaling-stroke" ':"",l=c&&c.absolutePositioned,u=this.stroke,f=this.fill,d=this.shadow,g=[],p=t.indexOf("COMMON_PARTS"),v=e.additionalTransform;return c&&(c.clipPathId="CLIPPATH_"+fabric.Object.__uid++,r='<clipPath id="'+c.clipPathId+'" >\n'+c.toClipPathSVG(s)+"</clipPath>\n"),l&&g.push("<g ",a,this.getSvgCommons()," >\n"),g.push("<g ",this.getSvgTransform(!1),l?"":a+this.getSvgCommons()," >\n"),i=[o,h,n?"":this.addPaintOrder()," ",v?'transform="'+v+'" ':""].join(""),t[p]=i,f&&f.toLive&&g.push(f.toSVG(this)),u&&u.toLive&&g.push(u.toSVG(this)),d&&g.push(d.toSVG(this)),c&&g.push(r),g.push(t.join("")),g.push("</g>\n"),l&&g.push("</g>\n"),s?s(g.join("")):g.join("")},addPaintOrder:function(){return"fill"!==this.paintFirst?' paint-order="'+this.paintFirst+'" ':""}})}();!function(){function t(t,e,r){var n={},s=!0;r.forEach(function(e){n[e]=t[e]}),i(t[e],n,s)}function e(t,i,r){if(t===i)return!0;if(Array.isArray(t)){if(!Array.isArray(i)||t.length!==i.length)return!1;for(var n=0,s=t.length;s>n;n++)if(!e(t[n],i[n]))return!1;return!0}if(t&&"object"==typeof t){var o,a=Object.keys(t);if(!i||"object"!=typeof i||!r&&a.length!==Object.keys(i).length)return!1;for(var n=0,s=a.length;s>n;n++)if(o=a[n],"canvas"!==o&&"group"!==o&&!e(t[o],i[o]))return!1;return!0}}var i=fabric.util.object.extend,r="stateProperties";fabric.util.object.extend(fabric.Object.prototype,{hasStateChanged:function(t){t=t||r;var i="_"+t;return Object.keys(this[i]).length<this[t].length?!0:!e(this[i],this,!0)},saveState:function(e){var i=e&&e.propertySet||r,n="_"+i;return this[n]?(t(this,n,this[i]),e&&e.stateProperties&&t(this,n,e.stateProperties),this):this.setupState(e)},setupState:function(t){t=t||{};var e=t.propertySet||r;return t.propertySet=e,this["_"+e]={},this.saveState(t),this}})}();!function(){var t=fabric.util.degreesToRadians;fabric.util.object.extend(fabric.Object.prototype,{_findTargetCorner:function(t,e){if(!this.hasControls||this.group||!this.canvas||this.canvas._activeObject!==this)return!1;var i,r,n,s=t.x,o=t.y,a=Object.keys(this.oCoords),c=a.length-1;for(this.__corner=0;c>=0;c--)if(n=a[c],this.isControlVisible(n)&&(r=this._getImageLines(e?this.oCoords[n].touchCorner:this.oCoords[n].corner),i=this._findCrossPoints({x:s,y:o},r),0!==i&&i%2===1))return this.__corner=n,n;return!1},forEachControl:function(t){for(var e in this.controls)t(this.controls[e],e,this)},_setCornerCoords:function(){var t=this.oCoords;for(var e in t){var i=this.controls[e];t[e].corner=i.calcCornerCoords(this.angle,this.cornerSize,t[e].x,t[e].y,!1),t[e].touchCorner=i.calcCornerCoords(this.angle,this.touchCornerSize,t[e].x,t[e].y,!0)}},drawSelectionBackground:function(e){if(!this.selectionBackgroundColor||this.canvas&&!this.canvas.interactive||this.canvas&&this.canvas._activeObject!==this)return this;e.save();var i=this.getCenterPoint(),r=this._calculateCurrentDimensions(),n=this.canvas.viewportTransform;return e.translate(i.x,i.y),e.scale(1/n[0],1/n[3]),e.rotate(t(this.angle)),e.fillStyle=this.selectionBackgroundColor,e.fillRect(-r.x/2,-r.y/2,r.x,r.y),e.restore(),this},drawBorders:function(t,e){e=e||{};var i=this._calculateCurrentDimensions(),r=this.borderScaleFactor,n=i.x+r,s=i.y+r,o="undefined"!=typeof e.hasControls?e.hasControls:this.hasControls,a=!1;return t.save(),t.strokeStyle=e.borderColor||this.borderColor,this._setLineDash(t,e.borderDashArray||this.borderDashArray),t.strokeRect(-n/2,-s/2,n,s),o&&(t.beginPath(),this.forEachControl(function(e,i,r){e.withConnection&&e.getVisibility(r,i)&&(a=!0,t.moveTo(e.x*n,e.y*s),t.lineTo(e.x*n+e.offsetX,e.y*s+e.offsetY))}),a&&t.stroke()),t.restore(),this},drawBordersInGroup:function(t,e,i){i=i||{};var r=fabric.util.sizeAfterTransform(this.width,this.height,e),n=this.strokeWidth,s=this.strokeUniform,o=this.borderScaleFactor,a=r.x+n*(s?this.canvas.getZoom():e.scaleX)+o,c=r.y+n*(s?this.canvas.getZoom():e.scaleY)+o;return t.save(),this._setLineDash(t,i.borderDashArray||this.borderDashArray),t.strokeStyle=i.borderColor||this.borderColor,t.strokeRect(-a/2,-c/2,a,c),t.restore(),this},drawControls:function(t,e){e=e||{},t.save();var i,r,n=this.canvas.getRetinaScaling();return t.setTransform(n,0,0,n,0,0),t.strokeStyle=t.fillStyle=e.cornerColor||this.cornerColor,this.transparentCorners||(t.strokeStyle=e.cornerStrokeColor||this.cornerStrokeColor),this._setLineDash(t,e.cornerDashArray||this.cornerDashArray),this.setCoords(),this.group&&(i=this.group.calcTransformMatrix()),this.forEachControl(function(n,s,o){r=o.oCoords[s],n.getVisibility(o,s)&&(i&&(r=fabric.util.transformPoint(r,i)),n.render(t,r.x,r.y,e,o))}),t.restore(),this},isControlVisible:function(t){return this.controls[t]&&this.controls[t].getVisibility(this,t)},setControlVisible:function(t,e){return this._controlsVisibility||(this._controlsVisibility={}),this._controlsVisibility[t]=e,this},setControlsVisibility:function(t){t||(t={});for(var e in t)this.setControlVisible(e,t[e]);return this},onDeselect:function(){},onSelect:function(){}})}();fabric.util.object.extend(fabric.StaticCanvas.prototype,{FX_DURATION:500,fxCenterObjectH:function(t,e){e=e||{};var i=function(){},r=e.onComplete||i,n=e.onChange||i,s=this;return fabric.util.animate({target:this,startValue:t.left,endValue:this.getCenterPoint().x,duration:this.FX_DURATION,onChange:function(e){t.set("left",e),s.requestRenderAll(),n()},onComplete:function(){t.setCoords(),r()}})},fxCenterObjectV:function(t,e){e=e||{};var i=function(){},r=e.onComplete||i,n=e.onChange||i,s=this;return fabric.util.animate({target:this,startValue:t.top,endValue:this.getCenterPoint().y,duration:this.FX_DURATION,onChange:function(e){t.set("top",e),s.requestRenderAll(),n()},onComplete:function(){t.setCoords(),r()}})},fxRemove:function(t,e){e=e||{};var i=function(){},r=e.onComplete||i,n=e.onChange||i,s=this;return fabric.util.animate({target:this,startValue:t.opacity,endValue:0,duration:this.FX_DURATION,onChange:function(e){t.set("opacity",e),s.requestRenderAll(),n()},onComplete:function(){s.remove(t),r()}})}}),fabric.util.object.extend(fabric.Object.prototype,{animate:function(){if(arguments[0]&&"object"==typeof arguments[0]){var t,e,i=[],r=[];for(t in arguments[0])i.push(t);for(var n=0,s=i.length;s>n;n++)t=i[n],e=n!==s-1,r.push(this._animate(t,arguments[0][t],arguments[1],e));return r}return this._animate.apply(this,arguments)},_animate:function(t,e,i,r){var n,s=this;e=e.toString(),i=i?fabric.util.object.clone(i):{},~t.indexOf(".")&&(n=t.split("."));var o=s.colorProperties.indexOf(t)>-1||n&&s.colorProperties.indexOf(n[1])>-1,a=n?this.get(n[0])[n[1]]:this.get(t);"from"in i||(i.from=a),o||(e=~e.indexOf("=")?a+parseFloat(e.replace("=","")):parseFloat(e));var c={target:this,startValue:i.from,endValue:e,byValue:i.by,easing:i.easing,duration:i.duration,abort:i.abort&&function(t,e,r){return i.abort.call(s,t,e,r)},onChange:function(e,o,a){n?s[n[0]][n[1]]=e:s.set(t,e),r||i.onChange&&i.onChange(e,o,a)},onComplete:function(t,e,n){r||(s.setCoords(),i.onComplete&&i.onComplete(t,e,n))}};return o?fabric.util.animateColor(c.startValue,c.endValue,c.duration,c):fabric.util.animate(c)}});!function(t){"use strict";function e(t,e){var i=t.origin,r=t.axis1,n=t.axis2,s=t.dimension,o=e.nearest,a=e.center,c=e.farthest;return function(){switch(this.get(i)){case o:return Math.min(this.get(r),this.get(n));case a:return Math.min(this.get(r),this.get(n))+.5*this.get(s);case c:return Math.max(this.get(r),this.get(n))}}}var i=t.fabric||(t.fabric={}),r=i.util.object.extend,n=i.util.object.clone,s={x1:1,x2:1,y1:1,y2:1};return i.Line?void i.warn("fabric.Line is already defined"):(i.Line=i.util.createClass(i.Object,{type:"line",x1:0,y1:0,x2:0,y2:0,cacheProperties:i.Object.prototype.cacheProperties.concat("x1","x2","y1","y2"),initialize:function(t,e){t||(t=[0,0,0,0]),this.callSuper("initialize",e),this.set("x1",t[0]),this.set("y1",t[1]),this.set("x2",t[2]),this.set("y2",t[3]),this._setWidthHeight(e)},_setWidthHeight:function(t){t||(t={}),this.width=Math.abs(this.x2-this.x1),this.height=Math.abs(this.y2-this.y1),this.left="left"in t?t.left:this._getLeftToOriginX(),this.top="top"in t?t.top:this._getTopToOriginY()},_set:function(t,e){return this.callSuper("_set",t,e),"undefined"!=typeof s[t]&&this._setWidthHeight(),this},_getLeftToOriginX:e({origin:"originX",axis1:"x1",axis2:"x2",dimension:"width"},{nearest:"left",center:"center",farthest:"right"}),_getTopToOriginY:e({origin:"originY",axis1:"y1",axis2:"y2",dimension:"height"},{nearest:"top",center:"center",farthest:"bottom"}),_render:function(t){t.beginPath();var e=this.calcLinePoints();t.moveTo(e.x1,e.y1),t.lineTo(e.x2,e.y2),t.lineWidth=this.strokeWidth;var i=t.strokeStyle;t.strokeStyle=this.stroke||t.fillStyle,this.stroke&&this._renderStroke(t),t.strokeStyle=i},_findCenterFromElement:function(){return{x:(this.x1+this.x2)/2,y:(this.y1+this.y2)/2}},toObject:function(t){return r(this.callSuper("toObject",t),this.calcLinePoints())},_getNonTransformedDimensions:function(){var t=this.callSuper("_getNonTransformedDimensions");return"butt"===this.strokeLineCap&&(0===this.width&&(t.y-=this.strokeWidth),0===this.height&&(t.x-=this.strokeWidth)),t},calcLinePoints:function(){var t=this.x1<=this.x2?-1:1,e=this.y1<=this.y2?-1:1,i=t*this.width*.5,r=e*this.height*.5,n=t*this.width*-.5,s=e*this.height*-.5;return{x1:i,x2:n,y1:r,y2:s}},_toSVG:function(){var t=this.calcLinePoints();return["<line ","COMMON_PARTS",'x1="',t.x1,'" y1="',t.y1,'" x2="',t.x2,'" y2="',t.y2,'" />\n']}}),i.Line.ATTRIBUTE_NAMES=i.SHARED_ATTRIBUTES.concat("x1 y1 x2 y2".split(" ")),i.Line.fromElement=function(t,e,n){n=n||{};var s=i.parseAttributes(t,i.Line.ATTRIBUTE_NAMES),o=[s.x1||0,s.y1||0,s.x2||0,s.y2||0];e(new i.Line(o,r(s,n)))},void(i.Line.fromObject=function(t,e){function r(t){delete t.points,e&&e(t)}var s=n(t,!0);s.points=[t.x1,t.y1,t.x2,t.y2],i.Object._fromObject("Line",s,r,"points")}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";function e(t){return"radius"in t&&t.radius>=0}var i=t.fabric||(t.fabric={}),r=i.util.degreesToRadians;return i.Circle?void i.warn("fabric.Circle is already defined."):(i.Circle=i.util.createClass(i.Object,{type:"circle",radius:0,startAngle:0,endAngle:360,cacheProperties:i.Object.prototype.cacheProperties.concat("radius","startAngle","endAngle"),_set:function(t,e){return this.callSuper("_set",t,e),"radius"===t&&this.setRadius(e),this},toObject:function(t){return this.callSuper("toObject",["radius","startAngle","endAngle"].concat(t))},_toSVG:function(){var t,e=0,n=0,s=(this.endAngle-this.startAngle)%360;if(0===s)t=["<circle ","COMMON_PARTS",'cx="'+e+'" cy="'+n+'" ','r="',this.radius,'" />\n'];else{var o=r(this.startAngle),a=r(this.endAngle),c=this.radius,h=i.util.cos(o)*c,l=i.util.sin(o)*c,u=i.util.cos(a)*c,f=i.util.sin(a)*c,d=s>180?"1":"0";t=['<path d="M '+h+" "+l," A "+c+" "+c," 0 ",+d+" 1"," "+u+" "+f,'" ',"COMMON_PARTS"," />\n"]}return t},_render:function(t){t.beginPath(),t.arc(0,0,this.radius,r(this.startAngle),r(this.endAngle),!1),this._renderPaintInOrder(t)},getRadiusX:function(){return this.get("radius")*this.get("scaleX")},getRadiusY:function(){return this.get("radius")*this.get("scaleY")},setRadius:function(t){return this.radius=t,this.set("width",2*t).set("height",2*t)}}),i.Circle.ATTRIBUTE_NAMES=i.SHARED_ATTRIBUTES.concat("cx cy r".split(" ")),i.Circle.fromElement=function(t,r){var n=i.parseAttributes(t,i.Circle.ATTRIBUTE_NAMES);if(!e(n))throw new Error("value of `r` attribute is required and can not be negative");n.left=(n.left||0)-n.radius,n.top=(n.top||0)-n.radius,r(new i.Circle(n))},void(i.Circle.fromObject=function(t,e){i.Object._fromObject("Circle",t,e)}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={});return e.Triangle?void e.warn("fabric.Triangle is already defined"):(e.Triangle=e.util.createClass(e.Object,{type:"triangle",width:100,height:100,_render:function(t){var e=this.width/2,i=this.height/2;t.beginPath(),t.moveTo(-e,i),t.lineTo(0,-i),t.lineTo(e,i),t.closePath(),this._renderPaintInOrder(t)},_toSVG:function(){var t=this.width/2,e=this.height/2,i=[-t+" "+e,"0 "+-e,t+" "+e].join(",");return["<polygon ","COMMON_PARTS",'points="',i,'" />']}}),void(e.Triangle.fromObject=function(t,i){return e.Object._fromObject("Triangle",t,i)}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=2*Math.PI;return e.Ellipse?void e.warn("fabric.Ellipse is already defined."):(e.Ellipse=e.util.createClass(e.Object,{type:"ellipse",rx:0,ry:0,cacheProperties:e.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(t){this.callSuper("initialize",t),this.set("rx",t&&t.rx||0),this.set("ry",t&&t.ry||0)},_set:function(t,e){switch(this.callSuper("_set",t,e),t){case"rx":this.rx=e,this.set("width",2*e);break;case"ry":this.ry=e,this.set("height",2*e)}return this},getRx:function(){return this.get("rx")*this.get("scaleX")},getRy:function(){return this.get("ry")*this.get("scaleY")},toObject:function(t){return this.callSuper("toObject",["rx","ry"].concat(t))},_toSVG:function(){return["<ellipse ","COMMON_PARTS",'cx="0" cy="0" ','rx="',this.rx,'" ry="',this.ry,'" />\n']},_render:function(t){t.beginPath(),t.save(),t.transform(1,0,0,this.ry/this.rx,0,0),t.arc(0,0,this.rx,0,i,!1),t.restore(),this._renderPaintInOrder(t)}}),e.Ellipse.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat("cx cy rx ry".split(" ")),e.Ellipse.fromElement=function(t,i){var r=e.parseAttributes(t,e.Ellipse.ATTRIBUTE_NAMES);r.left=(r.left||0)-r.rx,r.top=(r.top||0)-r.ry,i(new e.Ellipse(r))},void(e.Ellipse.fromObject=function(t,i){e.Object._fromObject("Ellipse",t,i)}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.object.extend;return e.Rect?void e.warn("fabric.Rect is already defined"):(e.Rect=e.util.createClass(e.Object,{stateProperties:e.Object.prototype.stateProperties.concat("rx","ry"),type:"rect",rx:0,ry:0,cacheProperties:e.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(t){this.callSuper("initialize",t),this._initRxRy()},_initRxRy:function(){this.rx&&!this.ry?this.ry=this.rx:this.ry&&!this.rx&&(this.rx=this.ry)},_render:function(t){var e=this.rx?Math.min(this.rx,this.width/2):0,i=this.ry?Math.min(this.ry,this.height/2):0,r=this.width,n=this.height,s=-this.width/2,o=-this.height/2,a=0!==e||0!==i,c=.4477152502;t.beginPath(),t.moveTo(s+e,o),t.lineTo(s+r-e,o),a&&t.bezierCurveTo(s+r-c*e,o,s+r,o+c*i,s+r,o+i),t.lineTo(s+r,o+n-i),a&&t.bezierCurveTo(s+r,o+n-c*i,s+r-c*e,o+n,s+r-e,o+n),t.lineTo(s+e,o+n),a&&t.bezierCurveTo(s+c*e,o+n,s,o+n-c*i,s,o+n-i),t.lineTo(s,o+i),a&&t.bezierCurveTo(s,o+c*i,s+c*e,o,s+e,o),t.closePath(),this._renderPaintInOrder(t)},toObject:function(t){return this.callSuper("toObject",["rx","ry"].concat(t))},_toSVG:function(){var t=-this.width/2,e=-this.height/2;return["<rect ","COMMON_PARTS",'x="',t,'" y="',e,'" rx="',this.rx,'" ry="',this.ry,'" width="',this.width,'" height="',this.height,'" />\n']}}),e.Rect.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat("x y rx ry width height".split(" ")),e.Rect.fromElement=function(t,r,n){if(!t)return r(null);n=n||{};var s=e.parseAttributes(t,e.Rect.ATTRIBUTE_NAMES);s.left=s.left||0,s.top=s.top||0,s.height=s.height||0,s.width=s.width||0;var o=new e.Rect(i(n?e.util.object.clone(n):{},s));o.visible=o.visible&&o.width>0&&o.height>0,r(o)},void(e.Rect.fromObject=function(t,i){return e.Object._fromObject("Rect",t,i)}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.util.array.min,n=e.util.array.max,s=e.util.toFixed,o=e.util.projectStrokeOnPoints;return e.Polyline?void e.warn("fabric.Polyline is already defined"):(e.Polyline=e.util.createClass(e.Object,{type:"polyline",points:null,exactBoundingBox:!1,cacheProperties:e.Object.prototype.cacheProperties.concat("points"),initialize:function(t,e){e=e||{},this.points=t||[],this.callSuper("initialize",e),this._setPositionDimensions(e)},_projectStrokeOnPoints:function(){return o(this.points,this,!0)},_setPositionDimensions:function(t){var e,i=this._calcDimensions(t),r=this.exactBoundingBox?this.strokeWidth:0;this.width=i.width-r,this.height=i.height-r,t.fromSVG||(e=this.translateToGivenOrigin({x:i.left-this.strokeWidth/2+r/2,y:i.top-this.strokeWidth/2+r/2},"left","top",this.originX,this.originY)),"undefined"==typeof t.left&&(this.left=t.fromSVG?i.left:e.x),"undefined"==typeof t.top&&(this.top=t.fromSVG?i.top:e.y),this.pathOffset={x:i.left+this.width/2+r/2,y:i.top+this.height/2+r/2}},_calcDimensions:function(){var t=this.exactBoundingBox?this._projectStrokeOnPoints():this.points,e=r(t,"x")||0,i=r(t,"y")||0,s=n(t,"x")||0,o=n(t,"y")||0,a=s-e,c=o-i;return{left:e,top:i,width:a,height:c}},toObject:function(t){return i(this.callSuper("toObject",t),{points:this.points.concat()})},_toSVG:function(){for(var t=[],i=this.pathOffset.x,r=this.pathOffset.y,n=e.Object.NUM_FRACTION_DIGITS,o=0,a=this.points.length;a>o;o++)t.push(s(this.points[o].x-i,n),",",s(this.points[o].y-r,n)," ");return["<"+this.type+" ","COMMON_PARTS",'points="',t.join(""),'" />\n']},commonRender:function(t){var e,i=this.points.length,r=this.pathOffset.x,n=this.pathOffset.y;if(!i||isNaN(this.points[i-1].y))return!1;t.beginPath(),t.moveTo(this.points[0].x-r,this.points[0].y-n);for(var s=0;i>s;s++)e=this.points[s],t.lineTo(e.x-r,e.y-n);return!0},_render:function(t){this.commonRender(t)&&this._renderPaintInOrder(t)},complexity:function(){return this.get("points").length}}),e.Polyline.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat(),e.Polyline.fromElementGenerator=function(t){return function(r,n,s){if(!r)return n(null);s||(s={});var o=e.parsePointsAttribute(r.getAttribute("points")),a=e.parseAttributes(r,e[t].ATTRIBUTE_NAMES);a.fromSVG=!0,n(new e[t](o,i(a,s)))}},e.Polyline.fromElement=e.Polyline.fromElementGenerator("Polyline"),void(e.Polyline.fromObject=function(t,i){return e.Object._fromObject("Polyline",t,i,"points")}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.projectStrokeOnPoints;return e.Polygon?void e.warn("fabric.Polygon is already defined"):(e.Polygon=e.util.createClass(e.Polyline,{type:"polygon",_projectStrokeOnPoints:function(){return i(this.points,this)},_render:function(t){this.commonRender(t)&&(t.closePath(),this._renderPaintInOrder(t))}}),e.Polygon.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat(),e.Polygon.fromElement=e.Polyline.fromElementGenerator("Polygon"),void(e.Polygon.fromObject=function(t,i){e.Object._fromObject("Polygon",t,i,"points")}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.array.min,r=e.util.array.max,n=e.util.object.extend,s=e.util.object.clone,o=e.util.toFixed;return e.Path?void e.warn("fabric.Path is already defined"):(e.Path=e.util.createClass(e.Object,{type:"path",path:null,cacheProperties:e.Object.prototype.cacheProperties.concat("path","fillRule"),stateProperties:e.Object.prototype.stateProperties.concat("path"),initialize:function(t,e){e=s(e||{}),delete e.path,this.callSuper("initialize",e),this._setPath(t||[],e)},_setPath:function(t,i){this.path=e.util.makePathSimpler(Array.isArray(t)?t:e.util.parsePath(t)),e.Polyline.prototype._setPositionDimensions.call(this,i||{})},_renderPathCommands:function(t){var e,i=0,r=0,n=0,s=0,o=0,a=0,c=-this.pathOffset.x,h=-this.pathOffset.y;t.beginPath();for(var l=0,u=this.path.length;u>l;++l)switch(e=this.path[l],e[0]){case"L":n=e[1],s=e[2],t.lineTo(n+c,s+h);break;case"M":n=e[1],s=e[2],i=n,r=s,t.moveTo(n+c,s+h);break;case"C":n=e[5],s=e[6],o=e[3],a=e[4],t.bezierCurveTo(e[1]+c,e[2]+h,o+c,a+h,n+c,s+h);break;case"Q":t.quadraticCurveTo(e[1]+c,e[2]+h,e[3]+c,e[4]+h),n=e[3],s=e[4],o=e[1],a=e[2];break;case"z":case"Z":n=i,s=r,t.closePath()}},_render:function(t){this._renderPathCommands(t),this._renderPaintInOrder(t)},toString:function(){return"#<fabric.Path ("+this.complexity()+'): { "top": '+this.top+', "left": '+this.left+" }>"},toObject:function(t){return n(this.callSuper("toObject",t),{path:this.path.map(function(t){return t.slice()})})},toDatalessObject:function(t){var e=this.toObject(["sourcePath"].concat(t));return e.sourcePath&&delete e.path,e},_toSVG:function(){var t=e.util.joinPath(this.path);return["<path ","COMMON_PARTS",'d="',t,'" stroke-linecap="round" ',"/>\n"]},_getOffsetTransform:function(){var t=e.Object.NUM_FRACTION_DIGITS;return" translate("+o(-this.pathOffset.x,t)+", "+o(-this.pathOffset.y,t)+")"},toClipPathSVG:function(t){var e=this._getOffsetTransform();return" "+this._createBaseClipPathSVGMarkup(this._toSVG(),{reviver:t,additionalTransform:e})},toSVG:function(t){var e=this._getOffsetTransform();return this._createBaseSVGMarkup(this._toSVG(),{reviver:t,additionalTransform:e})},complexity:function(){return this.path.length},_calcDimensions:function(){for(var t,n,s=[],o=[],a=0,c=0,h=0,l=0,u=0,f=this.path.length;f>u;++u){switch(t=this.path[u],t[0]){case"L":h=t[1],l=t[2],n=[];break;case"M":h=t[1],l=t[2],a=h,c=l,n=[];break;case"C":n=e.util.getBoundsOfCurve(h,l,t[1],t[2],t[3],t[4],t[5],t[6]),h=t[5],l=t[6];break;case"Q":n=e.util.getBoundsOfCurve(h,l,t[1],t[2],t[1],t[2],t[3],t[4]),h=t[3],l=t[4];break;case"z":case"Z":h=a,l=c}n.forEach(function(t){s.push(t.x),o.push(t.y)}),s.push(h),o.push(l)}var d=i(s)||0,g=i(o)||0,p=r(s)||0,v=r(o)||0,b=p-d,m=v-g;return{left:d,top:g,width:b,height:m}}}),e.Path.fromObject=function(t,i){if("string"==typeof t.sourcePath){var r=t.sourcePath;e.loadSVGFromURL(r,function(e){var r=e[0];r.setOptions(t),i&&i(r)})}else e.Object._fromObject("Path",t,i,"path")},e.Path.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat(["d"]),void(e.Path.fromElement=function(t,i,r){var s=e.parseAttributes(t,e.Path.ATTRIBUTE_NAMES);s.fromSVG=!0,i(new e.Path(s.d,n(s,r)))}))}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.array.min,r=e.util.array.max;e.Group||(e.Group=e.util.createClass(e.Object,e.Collection,{type:"group",strokeWidth:0,subTargetCheck:!1,cacheProperties:[],useSetOnGroup:!1,initialize:function(t,e,i){e=e||{},this._objects=[],i&&this.callSuper("initialize",e),this._objects=t||[];for(var r=this._objects.length;r--;)this._objects[r].group=this;if(i)this._updateObjectsACoords();else{var n=e&&e.centerPoint;void 0!==e.originX&&(this.originX=e.originX),void 0!==e.originY&&(this.originY=e.originY),n||this._calcBounds(),this._updateObjectsCoords(n),delete e.centerPoint,this.callSuper("initialize",e)}this.setCoords()},_updateObjectsACoords:function(){for(var t=!0,e=this._objects.length;e--;)this._objects[e].setCoords(t)},_updateObjectsCoords:function(t){for(var t=t||this.getCenterPoint(),e=this._objects.length;e--;)this._updateObjectCoords(this._objects[e],t)},_updateObjectCoords:function(t,e){var i=t.left,r=t.top,n=!0;t.set({left:i-e.x,top:r-e.y}),t.group=this,t.setCoords(n)},toString:function(){return"#<fabric.Group: ("+this.complexity()+")>"},addWithUpdate:function(t){var i=!!this.group;return this._restoreObjectsState(),e.util.resetObjectTransform(this),t&&(i&&e.util.removeTransformFromObject(t,this.group.calcTransformMatrix()),this._objects.push(t),t.group=this,t._set("canvas",this.canvas)),this._calcBounds(),this._updateObjectsCoords(),this.dirty=!0,i?this.group.addWithUpdate():this.setCoords(),this},removeWithUpdate:function(t){return this._restoreObjectsState(),e.util.resetObjectTransform(this),this.remove(t),this._calcBounds(),this._updateObjectsCoords(),this.setCoords(),this.dirty=!0,this},_onObjectAdded:function(t){this.dirty=!0,t.group=this,t._set("canvas",this.canvas)},_onObjectRemoved:function(t){this.dirty=!0,delete t.group},_set:function(t,i){var r=this._objects.length;if(this.useSetOnGroup)for(;r--;)this._objects[r].setOnGroup(t,i);if("canvas"===t)for(;r--;)this._objects[r]._set(t,i);e.Object.prototype._set.call(this,t,i)},toObject:function(t){var i=this.includeDefaultValues,r=this._objects.filter(function(t){return!t.excludeFromExport}).map(function(e){var r=e.includeDefaultValues;e.includeDefaultValues=i;var n=e.toObject(t);return e.includeDefaultValues=r,n}),n=e.Object.prototype.toObject.call(this,t);return n.objects=r,n},toDatalessObject:function(t){var i,r=this.sourcePath;if(r)i=r;else{var n=this.includeDefaultValues;i=this._objects.map(function(e){var i=e.includeDefaultValues;e.includeDefaultValues=n;var r=e.toDatalessObject(t);return e.includeDefaultValues=i,r})}var s=e.Object.prototype.toDatalessObject.call(this,t);return s.objects=i,s},render:function(t){this._transformDone=!0,this.callSuper("render",t),this._transformDone=!1},shouldCache:function(){var t=e.Object.prototype.shouldCache.call(this);if(t)for(var i=0,r=this._objects.length;r>i;i++)if(this._objects[i].willDrawShadow())return this.ownCaching=!1,!1;return t},willDrawShadow:function(){if(e.Object.prototype.willDrawShadow.call(this))return!0;for(var t=0,i=this._objects.length;i>t;t++)if(this._objects[t].willDrawShadow())return!0;return!1},isOnACache:function(){return this.ownCaching||this.group&&this.group.isOnACache()},drawObject:function(t){for(var e=0,i=this._objects.length;i>e;e++)this._objects[e].render(t);this._drawClipPath(t,this.clipPath)},isCacheDirty:function(t){if(this.callSuper("isCacheDirty",t))return!0;if(!this.statefullCache)return!1;for(var e=0,i=this._objects.length;i>e;e++)if(this._objects[e].isCacheDirty(!0)){if(this._cacheCanvas){var r=this.cacheWidth/this.zoomX,n=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-r/2,-n/2,r,n)}return!0}return!1},_restoreObjectsState:function(){var t=this.calcOwnMatrix();return this._objects.forEach(function(i){e.util.addTransformToObject(i,t),delete i.group,i.setCoords()}),this},destroy:function(){return this._objects.forEach(function(t){t.set("dirty",!0)}),this._restoreObjectsState()},dispose:function(){this.callSuper("dispose"),this.forEachObject(function(t){t.dispose&&t.dispose()}),this._objects=[]},toActiveSelection:function(){if(this.canvas){var t=this._objects,i=this.canvas;this._objects=[];var r=this.toObject();delete r.objects;var n=new e.ActiveSelection([]);return n.set(r),n.type="activeSelection",i.remove(this),t.forEach(function(t){t.group=n,t.dirty=!0,i.add(t)}),n.canvas=i,n._objects=t,i._activeObject=n,n.setCoords(),n}},ungroupOnCanvas:function(){return this._restoreObjectsState()},setObjectsCoords:function(){var t=!0;return this.forEachObject(function(e){e.setCoords(t)}),this},_calcBounds:function(t){for(var e,i,r,n,s=[],o=[],a=["tr","br","bl","tl"],c=0,h=this._objects.length,l=a.length;h>c;++c){for(e=this._objects[c],r=e.calcACoords(),n=0;l>n;n++)i=a[n],s.push(r[i].x),o.push(r[i].y);e.aCoords=r}this._getBounds(s,o,t)},_getBounds:function(t,n,s){var o=new e.Point(i(t),i(n)),a=new e.Point(r(t),r(n)),c=o.y||0,h=o.x||0,l=a.x-o.x||0,u=a.y-o.y||0;this.width=l,this.height=u,s||this.setPositionByOrigin({x:h,y:c},"left","top")},_toSVG:function(t){for(var e=["<g ","COMMON_PARTS"," >\n"],i=0,r=this._objects.length;r>i;i++)e.push(" ",this._objects[i].toSVG(t));return e.push("</g>\n"),e},getSvgStyles:function(){var t="undefined"!=typeof this.opacity&&1!==this.opacity?"opacity: "+this.opacity+";":"",e=this.visible?"":" visibility: hidden;";return[t,this.getSvgFilter(),e].join("")},toClipPathSVG:function(t){for(var e=[],i=0,r=this._objects.length;r>i;i++)e.push(" ",this._objects[i].toClipPathSVG(t));return this._createBaseClipPathSVGMarkup(e,{reviver:t})}}),e.Group.fromObject=function(t,i){var r=t.objects,n=e.util.object.clone(t,!0);return delete n.objects,"string"==typeof r?void e.loadSVGFromURL(r,function(s){var o=e.util.groupSVGElements(s,t,r);o.set(n),i&&i(o)}):void e.util.enlivenObjects(r,function(r){var n=e.util.object.clone(t,!0);delete n.objects,e.util.enlivenObjectEnlivables(t,n,function(){i&&i(new e.Group(r,n,!0))})})})}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={});e.ActiveSelection||(e.ActiveSelection=e.util.createClass(e.Group,{type:"activeSelection",initialize:function(t,i){i=i||{},this._objects=t||[];for(var r=this._objects.length;r--;)this._objects[r].group=this;i.originX&&(this.originX=i.originX),i.originY&&(this.originY=i.originY),this._calcBounds(),this._updateObjectsCoords(),e.Object.prototype.initialize.call(this,i),this.setCoords()},toGroup:function(){var t=this._objects.concat();this._objects=[];var i=e.Object.prototype.toObject.call(this),r=new e.Group([]);if(delete i.type,r.set(i),t.forEach(function(t){t.canvas.remove(t),t.group=r}),r._objects=t,!this.canvas)return r;var n=this.canvas;return n.add(r),n._activeObject=r,r.setCoords(),r},onDeselect:function(){return this.destroy(),!1},toString:function(){return"#<fabric.ActiveSelection: ("+this.complexity()+")>"},shouldCache:function(){return!1},isOnACache:function(){return!1},_renderControls:function(t,e,i){t.save(),t.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1,this.callSuper("_renderControls",t,e),i=i||{},"undefined"==typeof i.hasControls&&(i.hasControls=!1),i.forActiveSelection=!0;for(var r=0,n=this._objects.length;n>r;r++)this._objects[r]._renderControls(t,i);t.restore()}}),e.ActiveSelection.fromObject=function(t,i){e.util.enlivenObjects(t.objects,function(r){delete t.objects,i&&i(new e.ActiveSelection(r,t,!0))})})}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=fabric.util.object.extend;return t.fabric||(t.fabric={}),t.fabric.Image?void fabric.warn("fabric.Image is already defined."):(fabric.Image=fabric.util.createClass(fabric.Object,{type:"image",strokeWidth:0,srcFromAttribute:!1,_lastScaleX:1,_lastScaleY:1,_filterScalingX:1,_filterScalingY:1,minimumScaleTrigger:.5,stateProperties:fabric.Object.prototype.stateProperties.concat("cropX","cropY"),cacheProperties:fabric.Object.prototype.cacheProperties.concat("cropX","cropY"),cacheKey:"",cropX:0,cropY:0,imageSmoothing:!0,initialize:function(t,e){e||(e={}),this.filters=[],this.cacheKey="texture"+fabric.Object.__uid++,this.callSuper("initialize",e),this._initElement(t,e)},getElement:function(){return this._element||{}},setElement:function(t,e){return this.removeTexture(this.cacheKey),this.removeTexture(this.cacheKey+"_filtered"),this._element=t,this._originalElement=t,this._initConfig(e),0!==this.filters.length&&this.applyFilters(),this.resizeFilter&&this.applyResizeFilters(),this},removeTexture:function(t){var e=fabric.filterBackend;e&&e.evictCachesForKey&&e.evictCachesForKey(t)},dispose:function(){this.callSuper("dispose"),this.removeTexture(this.cacheKey),this.removeTexture(this.cacheKey+"_filtered"),this._cacheContext=void 0,["_originalElement","_element","_filteredEl","_cacheCanvas"].forEach(function(t){fabric.util.cleanUpJsdomNode(this[t]),this[t]=void 0}.bind(this))},getCrossOrigin:function(){return this._originalElement&&(this._originalElement.crossOrigin||null)},getOriginalSize:function(){var t=this.getElement();return{width:t.naturalWidth||t.width,height:t.naturalHeight||t.height}},_stroke:function(t){if(this.stroke&&0!==this.strokeWidth){var e=this.width/2,i=this.height/2;t.beginPath(),t.moveTo(-e,-i),t.lineTo(e,-i),t.lineTo(e,i),t.lineTo(-e,i),t.lineTo(-e,-i),t.closePath()}},toObject:function(t){var i=[];this.filters.forEach(function(t){t&&i.push(t.toObject())});var r=e(this.callSuper("toObject",["cropX","cropY"].concat(t)),{src:this.getSrc(),crossOrigin:this.getCrossOrigin(),filters:i});return this.resizeFilter&&(r.resizeFilter=this.resizeFilter.toObject()),r},hasCrop:function(){return this.cropX||this.cropY||this.width<this._element.width||this.height<this._element.height},_toSVG:function(){var t,e=[],i=[],r=this._element,n=-this.width/2,s=-this.height/2,o="",a="";if(!r)return[];if(this.hasCrop()){var c=fabric.Object.__uid++;e.push('<clipPath id="imageCrop_'+c+'">\n',' <rect x="'+n+'" y="'+s+'" width="'+this.width+'" height="'+this.height+'" />\n',"</clipPath>\n"),o=' clip-path="url(#imageCrop_'+c+')" '}if(this.imageSmoothing||(a='" image-rendering="optimizeSpeed'),i.push(" <image ","COMMON_PARTS",'xlink:href="',this.getSvgSrc(!0),'" x="',n-this.cropX,'" y="',s-this.cropY,'" width="',r.width||r.naturalWidth,'" height="',r.height||r.height,a,'"',o,"></image>\n"),this.stroke||this.strokeDashArray){var h=this.fill;this.fill=null,t=[" <rect ",'x="',n,'" y="',s,'" width="',this.width,'" height="',this.height,'" style="',this.getSvgStyles(),'"/>\n'],this.fill=h}return e="fill"!==this.paintFirst?e.concat(t,i):e.concat(i,t)},getSrc:function(t){var e=t?this._element:this._originalElement;return e?e.toDataURL?e.toDataURL():this.srcFromAttribute?e.getAttribute("src"):e.src:this.src||""},setSrc:function(t,e,i){return fabric.util.loadImage(t,function(t,r){this.setElement(t,i),this._setWidthHeight(),e&&e(this,r)},this,i&&i.crossOrigin),this},toString:function(){return'#<fabric.Image: { src: "'+this.getSrc()+'" }>'},applyResizeFilters:function(){var t=this.resizeFilter,e=this.minimumScaleTrigger,i=this.getTotalObjectScaling(),r=i.scaleX,n=i.scaleY,s=this._filteredEl||this._originalElement;if(this.group&&this.set("dirty",!0),!t||r>e&&n>e)return this._element=s,this._filterScalingX=1,this._filterScalingY=1,this._lastScaleX=r,void(this._lastScaleY=n);fabric.filterBackend||(fabric.filterBackend=fabric.initFilterBackend());var o=fabric.util.createCanvasElement(),a=this._filteredEl?this.cacheKey+"_filtered":this.cacheKey,c=s.width,h=s.height;o.width=c,o.height=h,this._element=o,this._lastScaleX=t.scaleX=r,this._lastScaleY=t.scaleY=n,fabric.filterBackend.applyFilters([t],s,c,h,this._element,a),this._filterScalingX=o.width/this._originalElement.width,this._filterScalingY=o.height/this._originalElement.height},applyFilters:function(t){if(t=t||this.filters||[],t=t.filter(function(t){return t&&!t.isNeutralState()}),this.set("dirty",!0),this.removeTexture(this.cacheKey+"_filtered"),0===t.length)return this._element=this._originalElement,this._filteredEl=null,this._filterScalingX=1,this._filterScalingY=1,this;var e=this._originalElement,i=e.naturalWidth||e.width,r=e.naturalHeight||e.height;if(this._element===this._originalElement){var n=fabric.util.createCanvasElement();n.width=i,n.height=r,this._element=n,this._filteredEl=n}else this._element=this._filteredEl,this._filteredEl.getContext("2d").clearRect(0,0,i,r),this._lastScaleX=1,this._lastScaleY=1;return fabric.filterBackend||(fabric.filterBackend=fabric.initFilterBackend()),fabric.filterBackend.applyFilters(t,this._originalElement,i,r,this._element,this.cacheKey),(this._originalElement.width!==this._element.width||this._originalElement.height!==this._element.height)&&(this._filterScalingX=this._element.width/this._originalElement.width,this._filterScalingY=this._element.height/this._originalElement.height),this},_render:function(t){fabric.util.setImageSmoothing(t,this.imageSmoothing),this.isMoving!==!0&&this.resizeFilter&&this._needsResize()&&this.applyResizeFilters(),this._stroke(t),this._renderPaintInOrder(t)},drawCacheOnCanvas:function(t){fabric.util.setImageSmoothing(t,this.imageSmoothing),fabric.Object.prototype.drawCacheOnCanvas.call(this,t)},shouldCache:function(){return this.needsItsOwnCache()},_renderFill:function(t){var e=this._element;if(e){var i=this._filterScalingX,r=this._filterScalingY,n=this.width,s=this.height,o=Math.min,a=Math.max,c=a(this.cropX,0),h=a(this.cropY,0),l=e.naturalWidth||e.width,u=e.naturalHeight||e.height,f=c*i,d=h*r,g=o(n*i,l-f),p=o(s*r,u-d),v=-n/2,b=-s/2,m=o(n,l/i-c),y=o(s,u/r-h);e&&t.drawImage(e,f,d,g,p,v,b,m,y)}},_needsResize:function(){var t=this.getTotalObjectScaling();return t.scaleX!==this._lastScaleX||t.scaleY!==this._lastScaleY},_resetWidthHeight:function(){this.set(this.getOriginalSize())},_initElement:function(t,e){this.setElement(fabric.util.getById(t),e),fabric.util.addClass(this.getElement(),fabric.Image.CSS_CANVAS)},_initConfig:function(t){t||(t={}),this.setOptions(t),this._setWidthHeight(t)},_initFilters:function(t,e){t&&t.length?fabric.util.enlivenObjects(t,function(t){e&&e(t)},"fabric.Image.filters"):e&&e()},_setWidthHeight:function(t){t||(t={});var e=this.getElement();this.width=t.width||e.naturalWidth||e.width||0,this.height=t.height||e.naturalHeight||e.height||0},parsePreserveAspectRatioAttribute:function(){var t,e=fabric.util.parsePreserveAspectRatioAttribute(this.preserveAspectRatio||""),i=this._element.width,r=this._element.height,n=1,s=1,o=0,a=0,c=0,h=0,l=this.width,u=this.height,f={width:l,height:u};return!e||"none"===e.alignX&&"none"===e.alignY?(n=l/i,s=u/r):("meet"===e.meetOrSlice&&(n=s=fabric.util.findScaleToFit(this._element,f),t=(l-i*n)/2,"Min"===e.alignX&&(o=-t),"Max"===e.alignX&&(o=t),t=(u-r*s)/2,"Min"===e.alignY&&(a=-t),"Max"===e.alignY&&(a=t)),"slice"===e.meetOrSlice&&(n=s=fabric.util.findScaleToCover(this._element,f),t=i-l/n,"Mid"===e.alignX&&(c=t/2),"Max"===e.alignX&&(c=t),t=r-u/s,"Mid"===e.alignY&&(h=t/2),"Max"===e.alignY&&(h=t),i=l/n,r=u/s)),{width:i,height:r,scaleX:n,scaleY:s,offsetLeft:o,offsetTop:a,cropX:c,cropY:h}}}),fabric.Image.CSS_CANVAS="canvas-img",fabric.Image.prototype.getSvgSrc=fabric.Image.prototype.getSrc,fabric.Image.fromObject=function(t,e){var i=fabric.util.object.clone(t);fabric.util.loadImage(i.src,function(t,r){return r?void(e&&e(null,!0)):void fabric.Image.prototype._initFilters.call(i,i.filters,function(r){i.filters=r||[],fabric.Image.prototype._initFilters.call(i,[i.resizeFilter],function(r){i.resizeFilter=r[0],fabric.util.enlivenObjectEnlivables(i,i,function(){var r=new fabric.Image(t,i);e(r,!1)})})})},null,i.crossOrigin)},fabric.Image.fromURL=function(t,e,i){fabric.util.loadImage(t,function(t,r){e&&e(new fabric.Image(t,i),r)},null,i&&i.crossOrigin)},fabric.Image.ATTRIBUTE_NAMES=fabric.SHARED_ATTRIBUTES.concat("x y width height preserveAspectRatio xlink:href crossOrigin image-rendering".split(" ")),void(fabric.Image.fromElement=function(t,i,r){var n=fabric.parseAttributes(t,fabric.Image.ATTRIBUTE_NAMES);fabric.Image.fromURL(n["xlink:href"],i,e(r?fabric.util.object.clone(r):{},n))}))}("undefined"!=typeof exports?exports:this);fabric.util.object.extend(fabric.Object.prototype,{_getAngleValueForStraighten:function(){var t=this.angle%360;return t>0?90*Math.round((t-1)/90):90*Math.round(t/90)},straighten:function(){return this.rotate(this._getAngleValueForStraighten())},fxStraighten:function(t){t=t||{};var e=function(){},i=t.onComplete||e,r=t.onChange||e,n=this;return fabric.util.animate({target:this,startValue:this.get("angle"),endValue:this._getAngleValueForStraighten(),duration:this.FX_DURATION,onChange:function(t){n.rotate(t),r()},onComplete:function(){n.setCoords(),i()}})}}),fabric.util.object.extend(fabric.StaticCanvas.prototype,{straightenObject:function(t){return t.straighten(),this.requestRenderAll(),this},fxStraightenObject:function(t){return t.fxStraighten({onChange:this.requestRenderAllBound})}});function resizeCanvasIfNeeded(t){var e=t.targetCanvas,i=e.width,r=e.height,n=t.destinationWidth,s=t.destinationHeight;(i!==n||r!==s)&&(e.width=n,e.height=s)}function copyGLTo2DDrawImage(t,e){var i=t.canvas,r=e.targetCanvas,n=r.getContext("2d");n.translate(0,r.height),n.scale(1,-1);var s=i.height-r.height;n.drawImage(i,0,s,r.width,r.height,0,0,r.width,r.height)}function copyGLTo2DPutImageData(t,e){var i=e.targetCanvas,r=i.getContext("2d"),n=e.destinationWidth,s=e.destinationHeight,o=n*s*4,a=new Uint8Array(this.imageBuffer,0,o),c=new Uint8ClampedArray(this.imageBuffer,0,o);t.readPixels(0,0,n,s,t.RGBA,t.UNSIGNED_BYTE,a);var h=new ImageData(c,n,s);r.putImageData(h,0,0)}!function(){"use strict";function t(t,e){var i="precision "+e+" float;\nvoid main(){}",r=t.createShader(t.FRAGMENT_SHADER);return t.shaderSource(r,i),t.compileShader(r),t.getShaderParameter(r,t.COMPILE_STATUS)?!0:!1}function e(t){t&&t.tileSize&&(this.tileSize=t.tileSize),this.setupGLContext(this.tileSize,this.tileSize),this.captureGPUInfo()}fabric.isWebglSupported=function(e){if(fabric.isLikelyNode)return!1;e=e||fabric.WebglFilterBackend.prototype.tileSize;var i=document.createElement("canvas"),r=i.getContext("webgl")||i.getContext("experimental-webgl"),n=!1;if(r){fabric.maxTextureSize=r.getParameter(r.MAX_TEXTURE_SIZE),n=fabric.maxTextureSize>=e;for(var s=["highp","mediump","lowp"],o=0;3>o;o++)if(t(r,s[o])){fabric.webGlPrecision=s[o];break}}return this.isSupported=n,n},fabric.WebglFilterBackend=e,e.prototype={tileSize:2048,resources:{},setupGLContext:function(t,e){this.dispose(),this.createWebGLCanvas(t,e),this.aPosition=new Float32Array([0,0,0,1,1,0,1,1]),this.chooseFastestCopyGLTo2DMethod(t,e)},chooseFastestCopyGLTo2DMethod:function(t,e){var i,r="undefined"!=typeof window.performance;try{new ImageData(1,1),i=!0}catch(n){i=!1}var s="undefined"!=typeof ArrayBuffer,o="undefined"!=typeof Uint8ClampedArray;if(r&&i&&s&&o){var a=fabric.util.createCanvasElement(),c=new ArrayBuffer(t*e*4);if(fabric.forceGLPutImageData)return this.imageBuffer=c,void(this.copyGLTo2D=copyGLTo2DPutImageData);var h,l,u,f={imageBuffer:c,destinationWidth:t,destinationHeight:e,targetCanvas:a};a.width=t,a.height=e,h=window.performance.now(),copyGLTo2DDrawImage.call(f,this.gl,f),l=window.performance.now()-h,h=window.performance.now(),copyGLTo2DPutImageData.call(f,this.gl,f),u=window.performance.now()-h,l>u?(this.imageBuffer=c,this.copyGLTo2D=copyGLTo2DPutImageData):this.copyGLTo2D=copyGLTo2DDrawImage}},createWebGLCanvas:function(t,e){var i=fabric.util.createCanvasElement();i.width=t,i.height=e;var r={alpha:!0,premultipliedAlpha:!1,depth:!1,stencil:!1,antialias:!1},n=i.getContext("webgl",r);n||(n=i.getContext("experimental-webgl",r)),n&&(n.clearColor(0,0,0,0),this.canvas=i,this.gl=n)},applyFilters:function(t,e,i,r,n,s){var o,a=this.gl;s&&(o=this.getCachedTexture(s,e));var c={originalWidth:e.width||e.originalWidth,originalHeight:e.height||e.originalHeight,sourceWidth:i,sourceHeight:r,destinationWidth:i,destinationHeight:r,context:a,sourceTexture:this.createTexture(a,i,r,!o&&e),targetTexture:this.createTexture(a,i,r),originalTexture:o||this.createTexture(a,i,r,!o&&e),passes:t.length,webgl:!0,aPosition:this.aPosition,programCache:this.programCache,pass:0,filterBackend:this,targetCanvas:n},h=a.createFramebuffer();return a.bindFramebuffer(a.FRAMEBUFFER,h),t.forEach(function(t){t&&t.applyTo(c)}),resizeCanvasIfNeeded(c),this.copyGLTo2D(a,c),a.bindTexture(a.TEXTURE_2D,null),a.deleteTexture(c.sourceTexture),a.deleteTexture(c.targetTexture),a.deleteFramebuffer(h),n.getContext("2d").setTransform(1,0,0,1,0,0),c},dispose:function(){this.canvas&&(this.canvas=null,this.gl=null),this.clearWebGLCaches()},clearWebGLCaches:function(){this.programCache={},this.textureCache={}},createTexture:function(t,e,i,r){var n=t.createTexture();return t.bindTexture(t.TEXTURE_2D,n),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),r?t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,r):t.texImage2D(t.TEXTURE_2D,0,t.RGBA,e,i,0,t.RGBA,t.UNSIGNED_BYTE,null),n},getCachedTexture:function(t,e){if(this.textureCache[t])return this.textureCache[t];var i=this.createTexture(this.gl,e.width,e.height,e);return this.textureCache[t]=i,i},evictCachesForKey:function(t){this.textureCache[t]&&(this.gl.deleteTexture(this.textureCache[t]),delete this.textureCache[t])},copyGLTo2D:copyGLTo2DDrawImage,captureGPUInfo:function(){if(this.gpuInfo)return this.gpuInfo;var t=this.gl,e={renderer:"",vendor:""};if(!t)return e;var i=t.getExtension("WEBGL_debug_renderer_info");if(i){var r=t.getParameter(i.UNMASKED_RENDERER_WEBGL),n=t.getParameter(i.UNMASKED_VENDOR_WEBGL);r&&(e.renderer=r.toLowerCase()),n&&(e.vendor=n.toLowerCase())}return this.gpuInfo=e,e}}}();!function(){"use strict";function t(){}var e=function(){};fabric.Canvas2dFilterBackend=t,t.prototype={evictCachesForKey:e,dispose:e,clearWebGLCaches:e,resources:{},applyFilters:function(t,e,i,r,n){var s=n.getContext("2d");s.drawImage(e,0,0,i,r);var o=s.getImageData(0,0,i,r),a=s.getImageData(0,0,i,r),c={sourceWidth:i,sourceHeight:r,imageData:o,originalEl:e,originalImageData:a,canvasEl:n,ctx:s,filterBackend:this};return t.forEach(function(t){t.applyTo(c)}),(c.imageData.width!==i||c.imageData.height!==r)&&(n.width=c.imageData.width,n.height=c.imageData.height),s.putImageData(c.imageData,0,0),c}}}();fabric.Image=fabric.Image||{},fabric.Image.filters=fabric.Image.filters||{},fabric.Image.filters.BaseFilter=fabric.util.createClass({type:"BaseFilter",vertexSource:"attribute vec2 aPosition;\nvarying vec2 vTexCoord;\nvoid main() {\nvTexCoord = aPosition;\ngl_Position = vec4(aPosition * 2.0 - 1.0, 0.0, 1.0);\n}",fragmentSource:"precision highp float;\nvarying vec2 vTexCoord;\nuniform sampler2D uTexture;\nvoid main() {\ngl_FragColor = texture2D(uTexture, vTexCoord);\n}",initialize:function(t){t&&this.setOptions(t)},setOptions:function(t){for(var e in t)this[e]=t[e]},createProgram:function(t,e,i){e=e||this.fragmentSource,i=i||this.vertexSource,"highp"!==fabric.webGlPrecision&&(e=e.replace(/precision highp float/g,"precision "+fabric.webGlPrecision+" float"));var r=t.createShader(t.VERTEX_SHADER);if(t.shaderSource(r,i),t.compileShader(r),!t.getShaderParameter(r,t.COMPILE_STATUS))throw new Error("Vertex shader compile error for "+this.type+": "+t.getShaderInfoLog(r));var n=t.createShader(t.FRAGMENT_SHADER);if(t.shaderSource(n,e),t.compileShader(n),!t.getShaderParameter(n,t.COMPILE_STATUS))throw new Error("Fragment shader compile error for "+this.type+": "+t.getShaderInfoLog(n));var s=t.createProgram();if(t.attachShader(s,r),t.attachShader(s,n),t.linkProgram(s),!t.getProgramParameter(s,t.LINK_STATUS))throw new Error('Shader link error for "${this.type}" '+t.getProgramInfoLog(s));var o=this.getAttributeLocations(t,s),a=this.getUniformLocations(t,s)||{};return a.uStepW=t.getUniformLocation(s,"uStepW"),a.uStepH=t.getUniformLocation(s,"uStepH"),{program:s,attributeLocations:o,uniformLocations:a}},getAttributeLocations:function(t,e){return{aPosition:t.getAttribLocation(e,"aPosition")}},getUniformLocations:function(){return{}},sendAttributeData:function(t,e,i){var r=e.aPosition,n=t.createBuffer();t.bindBuffer(t.ARRAY_BUFFER,n),t.enableVertexAttribArray(r),t.vertexAttribPointer(r,2,t.FLOAT,!1,0,0),t.bufferData(t.ARRAY_BUFFER,i,t.STATIC_DRAW)},_setupFrameBuffer:function(t){var e,i,r=t.context;t.passes>1?(e=t.destinationWidth,i=t.destinationHeight,(t.sourceWidth!==e||t.sourceHeight!==i)&&(r.deleteTexture(t.targetTexture),t.targetTexture=t.filterBackend.createTexture(r,e,i)),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,t.targetTexture,0)):(r.bindFramebuffer(r.FRAMEBUFFER,null),r.finish())},_swapTextures:function(t){t.passes--,t.pass++;var e=t.targetTexture;t.targetTexture=t.sourceTexture,t.sourceTexture=e},isNeutralState:function(){var t=this.mainParameter,e=fabric.Image.filters[this.type].prototype;if(t){if(Array.isArray(e[t])){for(var i=e[t].length;i--;)if(this[t][i]!==e[t][i])return!1;return!0}return e[t]===this[t]}return!1},applyTo:function(t){t.webgl?(this._setupFrameBuffer(t),this.applyToWebGL(t),this._swapTextures(t)):this.applyTo2d(t)},retrieveShader:function(t){return t.programCache.hasOwnProperty(this.type)||(t.programCache[this.type]=this.createProgram(t.context)),t.programCache[this.type]},applyToWebGL:function(t){var e=t.context,i=this.retrieveShader(t);0===t.pass&&t.originalTexture?e.bindTexture(e.TEXTURE_2D,t.originalTexture):e.bindTexture(e.TEXTURE_2D,t.sourceTexture),e.useProgram(i.program),this.sendAttributeData(e,i.attributeLocations,t.aPosition),e.uniform1f(i.uniformLocations.uStepW,1/t.sourceWidth),e.uniform1f(i.uniformLocations.uStepH,1/t.sourceHeight),this.sendUniformData(e,i.uniformLocations),e.viewport(0,0,t.destinationWidth,t.destinationHeight),e.drawArrays(e.TRIANGLE_STRIP,0,4)},bindAdditionalTexture:function(t,e,i){t.activeTexture(i),t.bindTexture(t.TEXTURE_2D,e),t.activeTexture(t.TEXTURE0)},unbindAdditionalTexture:function(t,e){t.activeTexture(e),t.bindTexture(t.TEXTURE_2D,null),t.activeTexture(t.TEXTURE0)},getMainParameter:function(){return this[this.mainParameter]},setMainParameter:function(t){this[this.mainParameter]=t},sendUniformData:function(){},createHelpLayer:function(t){if(!t.helpLayer){var e=document.createElement("canvas");e.width=t.sourceWidth,e.height=t.sourceHeight,t.helpLayer=e}},toObject:function(){var t={type:this.type},e=this.mainParameter;return e&&(t[e]=this[e]),t},toJSON:function(){return this.toObject()}}),fabric.Image.filters.BaseFilter.fromObject=function(t,e){var i=new fabric.Image.filters[t.type](t);return e&&e(i),i};!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.ColorMatrix=r(i.BaseFilter,{type:"ColorMatrix",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nvarying vec2 vTexCoord;\nuniform mat4 uColorMatrix;\nuniform vec4 uConstants;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\ncolor *= uColorMatrix;\ncolor += uConstants;\ngl_FragColor = color;\n}",matrix:[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0],mainParameter:"matrix",colorsOnly:!0,initialize:function(t){this.callSuper("initialize",t),this.matrix=this.matrix.slice(0)},applyTo2d:function(t){var e,i,r,n,s,o=t.imageData,a=o.data,c=a.length,h=this.matrix,l=this.colorsOnly;for(s=0;c>s;s+=4)e=a[s],i=a[s+1],r=a[s+2],l?(a[s]=e*h[0]+i*h[1]+r*h[2]+255*h[4],a[s+1]=e*h[5]+i*h[6]+r*h[7]+255*h[9],a[s+2]=e*h[10]+i*h[11]+r*h[12]+255*h[14]):(n=a[s+3],a[s]=e*h[0]+i*h[1]+r*h[2]+n*h[3]+255*h[4],a[s+1]=e*h[5]+i*h[6]+r*h[7]+n*h[8]+255*h[9],a[s+2]=e*h[10]+i*h[11]+r*h[12]+n*h[13]+255*h[14],a[s+3]=e*h[15]+i*h[16]+r*h[17]+n*h[18]+255*h[19])},getUniformLocations:function(t,e){return{uColorMatrix:t.getUniformLocation(e,"uColorMatrix"),uConstants:t.getUniformLocation(e,"uConstants")}},sendUniformData:function(t,e){var i=this.matrix,r=[i[0],i[1],i[2],i[3],i[5],i[6],i[7],i[8],i[10],i[11],i[12],i[13],i[15],i[16],i[17],i[18]],n=[i[4],i[9],i[14],i[19]];t.uniformMatrix4fv(e.uColorMatrix,!1,r),t.uniform4fv(e.uConstants,n)}}),e.Image.filters.ColorMatrix.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Brightness=r(i.BaseFilter,{type:"Brightness",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uBrightness;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\ncolor.rgb += uBrightness;\ngl_FragColor = color;\n}",brightness:0,mainParameter:"brightness",applyTo2d:function(t){if(0!==this.brightness){var e,i=t.imageData,r=i.data,n=r.length,s=Math.round(255*this.brightness);for(e=0;n>e;e+=4)r[e]=r[e]+s,r[e+1]=r[e+1]+s,r[e+2]=r[e+2]+s}},getUniformLocations:function(t,e){return{uBrightness:t.getUniformLocation(e,"uBrightness")}},sendUniformData:function(t,e){t.uniform1f(e.uBrightness,this.brightness)}}),e.Image.filters.Brightness.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.Image.filters,n=e.util.createClass;r.Convolute=n(r.BaseFilter,{type:"Convolute",opaque:!1,matrix:[0,0,0,0,1,0,0,0,0],fragmentSource:{Convolute_3_1:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[9];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 0);\nfor (float h = 0.0; h < 3.0; h+=1.0) {\nfor (float w = 0.0; w < 3.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 1), uStepH * (h - 1));\ncolor += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 3.0 + w)];\n}\n}\ngl_FragColor = color;\n}",Convolute_3_0:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[9];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 1);\nfor (float h = 0.0; h < 3.0; h+=1.0) {\nfor (float w = 0.0; w < 3.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 1.0), uStepH * (h - 1.0));\ncolor.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 3.0 + w)];\n}\n}\nfloat alpha = texture2D(uTexture, vTexCoord).a;\ngl_FragColor = color;\ngl_FragColor.a = alpha;\n}",Convolute_5_1:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[25];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 0);\nfor (float h = 0.0; h < 5.0; h+=1.0) {\nfor (float w = 0.0; w < 5.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 2.0), uStepH * (h - 2.0));\ncolor += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 5.0 + w)];\n}\n}\ngl_FragColor = color;\n}",Convolute_5_0:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[25];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 1);\nfor (float h = 0.0; h < 5.0; h+=1.0) {\nfor (float w = 0.0; w < 5.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 2.0), uStepH * (h - 2.0));\ncolor.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 5.0 + w)];\n}\n}\nfloat alpha = texture2D(uTexture, vTexCoord).a;\ngl_FragColor = color;\ngl_FragColor.a = alpha;\n}",Convolute_7_1:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[49];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 0);\nfor (float h = 0.0; h < 7.0; h+=1.0) {\nfor (float w = 0.0; w < 7.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 3.0), uStepH * (h - 3.0));\ncolor += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 7.0 + w)];\n}\n}\ngl_FragColor = color;\n}",Convolute_7_0:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[49];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 1);\nfor (float h = 0.0; h < 7.0; h+=1.0) {\nfor (float w = 0.0; w < 7.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 3.0), uStepH * (h - 3.0));\ncolor.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 7.0 + w)];\n}\n}\nfloat alpha = texture2D(uTexture, vTexCoord).a;\ngl_FragColor = color;\ngl_FragColor.a = alpha;\n}",Convolute_9_1:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[81];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 0);\nfor (float h = 0.0; h < 9.0; h+=1.0) {\nfor (float w = 0.0; w < 9.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 4.0), uStepH * (h - 4.0));\ncolor += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 9.0 + w)];\n}\n}\ngl_FragColor = color;\n}",Convolute_9_0:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uMatrix[81];\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = vec4(0, 0, 0, 1);\nfor (float h = 0.0; h < 9.0; h+=1.0) {\nfor (float w = 0.0; w < 9.0; w+=1.0) {\nvec2 matrixPos = vec2(uStepW * (w - 4.0), uStepH * (h - 4.0));\ncolor.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 9.0 + w)];\n}\n}\nfloat alpha = texture2D(uTexture, vTexCoord).a;\ngl_FragColor = color;\ngl_FragColor.a = alpha;\n}"},retrieveShader:function(t){var e=Math.sqrt(this.matrix.length),i=this.type+"_"+e+"_"+(this.opaque?1:0),r=this.fragmentSource[i];return t.programCache.hasOwnProperty(i)||(t.programCache[i]=this.createProgram(t.context,r)),t.programCache[i]},applyTo2d:function(t){var e,i,r,n,s,o,a,c,h,l,u,f,d,g=t.imageData,p=g.data,v=this.matrix,b=Math.round(Math.sqrt(v.length)),m=Math.floor(b/2),y=g.width,_=g.height,x=t.ctx.createImageData(y,_),C=x.data,w=this.opaque?1:0;for(u=0;_>u;u++)for(l=0;y>l;l++){for(s=4*(u*y+l),e=0,i=0,r=0,n=0,d=0;b>d;d++)for(f=0;b>f;f++)a=u+d-m,o=l+f-m,0>a||a>=_||0>o||o>=y||(c=4*(a*y+o),h=v[d*b+f],e+=p[c]*h,i+=p[c+1]*h,r+=p[c+2]*h,w||(n+=p[c+3]*h));C[s]=e,C[s+1]=i,C[s+2]=r,C[s+3]=w?p[s+3]:n}t.imageData=x},getUniformLocations:function(t,e){return{uMatrix:t.getUniformLocation(e,"uMatrix"),uOpaque:t.getUniformLocation(e,"uOpaque"),uHalfSize:t.getUniformLocation(e,"uHalfSize"),uSize:t.getUniformLocation(e,"uSize")}},sendUniformData:function(t,e){t.uniform1fv(e.uMatrix,this.matrix)},toObject:function(){return i(this.callSuper("toObject"),{opaque:this.opaque,matrix:this.matrix})}}),e.Image.filters.Convolute.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Grayscale=r(i.BaseFilter,{type:"Grayscale",fragmentSource:{average:"precision highp float;\nuniform sampler2D uTexture;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nfloat average = (color.r + color.b + color.g) / 3.0;\ngl_FragColor = vec4(average, average, average, color.a);\n}",lightness:"precision highp float;\nuniform sampler2D uTexture;\nuniform int uMode;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 col = texture2D(uTexture, vTexCoord);\nfloat average = (max(max(col.r, col.g),col.b) + min(min(col.r, col.g),col.b)) / 2.0;\ngl_FragColor = vec4(average, average, average, col.a);\n}",luminosity:"precision highp float;\nuniform sampler2D uTexture;\nuniform int uMode;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 col = texture2D(uTexture, vTexCoord);\nfloat average = 0.21 * col.r + 0.72 * col.g + 0.07 * col.b;\ngl_FragColor = vec4(average, average, average, col.a);\n}"},mode:"average",mainParameter:"mode",applyTo2d:function(t){var e,i,r=t.imageData,n=r.data,o=n.length,s=this.mode;for(e=0;o>e;e+=4)"average"===s?i=(n[e]+n[e+1]+n[e+2])/3:"lightness"===s?i=(Math.min(n[e],n[e+1],n[e+2])+Math.max(n[e],n[e+1],n[e+2]))/2:"luminosity"===s&&(i=.21*n[e]+.72*n[e+1]+.07*n[e+2]),n[e]=i,n[e+1]=i,n[e+2]=i},retrieveShader:function(t){var e=this.type+"_"+this.mode;if(!t.programCache.hasOwnProperty(e)){var i=this.fragmentSource[this.mode];t.programCache[e]=this.createProgram(t.context,i)}return t.programCache[e]},getUniformLocations:function(t,e){return{uMode:t.getUniformLocation(e,"uMode")}},sendUniformData:function(t,e){var i=1;t.uniform1i(e.uMode,i)},isNeutralState:function(){return!1}}),e.Image.filters.Grayscale.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Invert=r(i.BaseFilter,{type:"Invert",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform int uInvert;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nif (uInvert == 1) {\ngl_FragColor = vec4(1.0 - color.r,1.0 -color.g,1.0 -color.b,color.a);\n} else {\ngl_FragColor = color;\n}\n}",invert:!0,mainParameter:"invert",applyTo2d:function(t){var e,i=t.imageData,r=i.data,n=r.length;for(e=0;n>e;e+=4)r[e]=255-r[e],r[e+1]=255-r[e+1],r[e+2]=255-r[e+2]},isNeutralState:function(){return!this.invert},getUniformLocations:function(t,e){return{uInvert:t.getUniformLocation(e,"uInvert")}},sendUniformData:function(t,e){t.uniform1i(e.uInvert,this.invert)}}),e.Image.filters.Invert.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.Image.filters,n=e.util.createClass;r.Noise=n(r.BaseFilter,{type:"Noise",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uStepH;\nuniform float uNoise;\nuniform float uSeed;\nvarying vec2 vTexCoord;\nfloat rand(vec2 co, float seed, float vScale) {\nreturn fract(sin(dot(co.xy * vScale ,vec2(12.9898 , 78.233))) * 43758.5453 * (seed + 0.01) / 2.0);\n}\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\ncolor.rgb += (0.5 - rand(vTexCoord, uSeed, 0.1 / uStepH)) * uNoise;\ngl_FragColor = color;\n}",mainParameter:"noise",noise:0,applyTo2d:function(t){if(0!==this.noise){var e,i,r=t.imageData,n=r.data,o=n.length,s=this.noise;for(e=0,o=n.length;o>e;e+=4)i=(.5-Math.random())*s,n[e]+=i,n[e+1]+=i,n[e+2]+=i}},getUniformLocations:function(t,e){return{uNoise:t.getUniformLocation(e,"uNoise"),uSeed:t.getUniformLocation(e,"uSeed")}},sendUniformData:function(t,e){t.uniform1f(e.uNoise,this.noise/255),t.uniform1f(e.uSeed,Math.random())},toObject:function(){return i(this.callSuper("toObject"),{noise:this.noise})}}),e.Image.filters.Noise.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Pixelate=r(i.BaseFilter,{type:"Pixelate",blocksize:4,mainParameter:"blocksize",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uBlocksize;\nuniform float uStepW;\nuniform float uStepH;\nvarying vec2 vTexCoord;\nvoid main() {\nfloat blockW = uBlocksize * uStepW;\nfloat blockH = uBlocksize * uStepW;\nint posX = int(vTexCoord.x / blockW);\nint posY = int(vTexCoord.y / blockH);\nfloat fposX = float(posX);\nfloat fposY = float(posY);\nvec2 squareCoords = vec2(fposX * blockW, fposY * blockH);\nvec4 color = texture2D(uTexture, squareCoords);\ngl_FragColor = color;\n}",applyTo2d:function(t){var e,i,r,n,o,s,a,c,h,l,u,f=t.imageData,d=f.data,g=f.height,p=f.width;for(i=0;g>i;i+=this.blocksize)for(r=0;p>r;r+=this.blocksize)for(e=4*i*p+4*r,n=d[e],o=d[e+1],s=d[e+2],a=d[e+3],l=Math.min(i+this.blocksize,g),u=Math.min(r+this.blocksize,p),c=i;l>c;c++)for(h=r;u>h;h++)e=4*c*p+4*h,d[e]=n,d[e+1]=o,d[e+2]=s,d[e+3]=a},isNeutralState:function(){return 1===this.blocksize},getUniformLocations:function(t,e){return{uBlocksize:t.getUniformLocation(e,"uBlocksize"),uStepW:t.getUniformLocation(e,"uStepW"),uStepH:t.getUniformLocation(e,"uStepH")}},sendUniformData:function(t,e){t.uniform1f(e.uBlocksize,this.blocksize)}}),e.Image.filters.Pixelate.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.object.extend,r=e.Image.filters,n=e.util.createClass;r.RemoveColor=n(r.BaseFilter,{type:"RemoveColor",color:"#FFFFFF",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform vec4 uLow;\nuniform vec4 uHigh;\nvarying vec2 vTexCoord;\nvoid main() {\ngl_FragColor = texture2D(uTexture, vTexCoord);\nif(all(greaterThan(gl_FragColor.rgb,uLow.rgb)) && all(greaterThan(uHigh.rgb,gl_FragColor.rgb))) {\ngl_FragColor.a = 0.0;\n}\n}",distance:.02,useAlpha:!1,applyTo2d:function(t){var i,r,n,o,s=t.imageData,a=s.data,c=255*this.distance,h=new e.Color(this.color).getSource(),l=[h[0]-c,h[1]-c,h[2]-c],u=[h[0]+c,h[1]+c,h[2]+c];for(i=0;i<a.length;i+=4)r=a[i],n=a[i+1],o=a[i+2],r>l[0]&&n>l[1]&&o>l[2]&&r<u[0]&&n<u[1]&&o<u[2]&&(a[i+3]=0)},getUniformLocations:function(t,e){return{uLow:t.getUniformLocation(e,"uLow"),uHigh:t.getUniformLocation(e,"uHigh")}},sendUniformData:function(t,i){var r=new e.Color(this.color).getSource(),n=parseFloat(this.distance),o=[0+r[0]/255-n,0+r[1]/255-n,0+r[2]/255-n,1],s=[r[0]/255+n,r[1]/255+n,r[2]/255+n,1];t.uniform4fv(i.uLow,o),t.uniform4fv(i.uHigh,s)},toObject:function(){return i(this.callSuper("toObject"),{color:this.color,distance:this.distance})}}),e.Image.filters.RemoveColor.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass,n={Brownie:[.5997,.34553,-.27082,0,.186,-.0377,.86095,.15059,0,-.1449,.24113,-.07441,.44972,0,-.02965,0,0,0,1,0],Vintage:[.62793,.32021,-.03965,0,.03784,.02578,.64411,.03259,0,.02926,.0466,-.08512,.52416,0,.02023,0,0,0,1,0],Kodachrome:[1.12855,-.39673,-.03992,0,.24991,-.16404,1.08352,-.05498,0,.09698,-.16786,-.56034,1.60148,0,.13972,0,0,0,1,0],Technicolor:[1.91252,-.85453,-.09155,0,.04624,-.30878,1.76589,-.10601,0,-.27589,-.2311,-.75018,1.84759,0,.12137,0,0,0,1,0],Polaroid:[1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0],Sepia:[.393,.769,.189,0,0,.349,.686,.168,0,0,.272,.534,.131,0,0,0,0,0,1,0],BlackWhite:[1.5,1.5,1.5,0,-1,1.5,1.5,1.5,0,-1,1.5,1.5,1.5,0,-1,0,0,0,1,0]};for(var o in n)i[o]=r(i.ColorMatrix,{type:o,matrix:n[o],mainParameter:!1,colorsOnly:!0}),e.Image.filters[o].fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric,i=e.Image.filters,r=e.util.createClass;i.BlendColor=r(i.BaseFilter,{type:"BlendColor",color:"#F95C63",mode:"multiply",alpha:1,fragmentSource:{multiply:"gl_FragColor.rgb *= uColor.rgb;\n",screen:"gl_FragColor.rgb = 1.0 - (1.0 - gl_FragColor.rgb) * (1.0 - uColor.rgb);\n",add:"gl_FragColor.rgb += uColor.rgb;\n",diff:"gl_FragColor.rgb = abs(gl_FragColor.rgb - uColor.rgb);\n",subtract:"gl_FragColor.rgb -= uColor.rgb;\n",lighten:"gl_FragColor.rgb = max(gl_FragColor.rgb, uColor.rgb);\n",darken:"gl_FragColor.rgb = min(gl_FragColor.rgb, uColor.rgb);\n",exclusion:"gl_FragColor.rgb += uColor.rgb - 2.0 * (uColor.rgb * gl_FragColor.rgb);\n",overlay:"if (uColor.r < 0.5) {\ngl_FragColor.r *= 2.0 * uColor.r;\n} else {\ngl_FragColor.r = 1.0 - 2.0 * (1.0 - gl_FragColor.r) * (1.0 - uColor.r);\n}\nif (uColor.g < 0.5) {\ngl_FragColor.g *= 2.0 * uColor.g;\n} else {\ngl_FragColor.g = 1.0 - 2.0 * (1.0 - gl_FragColor.g) * (1.0 - uColor.g);\n}\nif (uColor.b < 0.5) {\ngl_FragColor.b *= 2.0 * uColor.b;\n} else {\ngl_FragColor.b = 1.0 - 2.0 * (1.0 - gl_FragColor.b) * (1.0 - uColor.b);\n}\n",tint:"gl_FragColor.rgb *= (1.0 - uColor.a);\ngl_FragColor.rgb += uColor.rgb;\n"},buildSource:function(t){return"precision highp float;\nuniform sampler2D uTexture;\nuniform vec4 uColor;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\ngl_FragColor = color;\nif (color.a > 0.0) {\n"+this.fragmentSource[t]+"}\n}"},retrieveShader:function(t){var e,i=this.type+"_"+this.mode;return t.programCache.hasOwnProperty(i)||(e=this.buildSource(this.mode),t.programCache[i]=this.createProgram(t.context,e)),t.programCache[i]},applyTo2d:function(t){var i,r,n,o,s,a,c,h=t.imageData,l=h.data,u=l.length,f=1-this.alpha;c=new e.Color(this.color).getSource(),i=c[0]*this.alpha,r=c[1]*this.alpha,n=c[2]*this.alpha;for(var d=0;u>d;d+=4)switch(o=l[d],s=l[d+1],a=l[d+2],this.mode){case"multiply":l[d]=o*i/255,l[d+1]=s*r/255,l[d+2]=a*n/255;break;case"screen":l[d]=255-(255-o)*(255-i)/255,l[d+1]=255-(255-s)*(255-r)/255,l[d+2]=255-(255-a)*(255-n)/255;break;case"add":l[d]=o+i,l[d+1]=s+r,l[d+2]=a+n;break;case"diff":case"difference":l[d]=Math.abs(o-i),l[d+1]=Math.abs(s-r),l[d+2]=Math.abs(a-n);break;case"subtract":l[d]=o-i,l[d+1]=s-r,l[d+2]=a-n;break;case"darken":l[d]=Math.min(o,i),l[d+1]=Math.min(s,r),l[d+2]=Math.min(a,n);break;case"lighten":l[d]=Math.max(o,i),l[d+1]=Math.max(s,r),l[d+2]=Math.max(a,n);break;case"overlay":l[d]=128>i?2*o*i/255:255-2*(255-o)*(255-i)/255,l[d+1]=128>r?2*s*r/255:255-2*(255-s)*(255-r)/255,l[d+2]=128>n?2*a*n/255:255-2*(255-a)*(255-n)/255;break;case"exclusion":l[d]=i+o-2*i*o/255,l[d+1]=r+s-2*r*s/255,l[d+2]=n+a-2*n*a/255;break;case"tint":l[d]=i+o*f,l[d+1]=r+s*f,l[d+2]=n+a*f}},getUniformLocations:function(t,e){return{uColor:t.getUniformLocation(e,"uColor")}},sendUniformData:function(t,i){var r=new e.Color(this.color).getSource();r[0]=this.alpha*r[0]/255,r[1]=this.alpha*r[1]/255,r[2]=this.alpha*r[2]/255,r[3]=this.alpha,t.uniform4fv(i.uColor,r)},toObject:function(){return{type:this.type,color:this.color,mode:this.mode,alpha:this.alpha}}}),e.Image.filters.BlendColor.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric,i=e.Image.filters,r=e.util.createClass;i.BlendImage=r(i.BaseFilter,{type:"BlendImage",image:null,mode:"multiply",alpha:1,vertexSource:"attribute vec2 aPosition;\nvarying vec2 vTexCoord;\nvarying vec2 vTexCoord2;\nuniform mat3 uTransformMatrix;\nvoid main() {\nvTexCoord = aPosition;\nvTexCoord2 = (uTransformMatrix * vec3(aPosition, 1.0)).xy;\ngl_Position = vec4(aPosition * 2.0 - 1.0, 0.0, 1.0);\n}",fragmentSource:{multiply:"precision highp float;\nuniform sampler2D uTexture;\nuniform sampler2D uImage;\nuniform vec4 uColor;\nvarying vec2 vTexCoord;\nvarying vec2 vTexCoord2;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nvec4 color2 = texture2D(uImage, vTexCoord2);\ncolor.rgba *= color2.rgba;\ngl_FragColor = color;\n}",mask:"precision highp float;\nuniform sampler2D uTexture;\nuniform sampler2D uImage;\nuniform vec4 uColor;\nvarying vec2 vTexCoord;\nvarying vec2 vTexCoord2;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nvec4 color2 = texture2D(uImage, vTexCoord2);\ncolor.a = color2.a;\ngl_FragColor = color;\n}"},retrieveShader:function(t){var e=this.type+"_"+this.mode,i=this.fragmentSource[this.mode];return t.programCache.hasOwnProperty(e)||(t.programCache[e]=this.createProgram(t.context,i)),t.programCache[e]},applyToWebGL:function(t){var e=t.context,i=this.createTexture(t.filterBackend,this.image);this.bindAdditionalTexture(e,i,e.TEXTURE1),this.callSuper("applyToWebGL",t),this.unbindAdditionalTexture(e,e.TEXTURE1)},createTexture:function(t,e){return t.getCachedTexture(e.cacheKey,e._element)},calculateMatrix:function(){var t=this.image,e=t._element.width,i=t._element.height;return[1/t.scaleX,0,0,0,1/t.scaleY,0,-t.left/e,-t.top/i,1]},applyTo2d:function(t){var i,r,n,o,a,s,c,h,l,u,f,d=t.imageData,g=t.filterBackend.resources,p=d.data,v=p.length,m=d.width,b=d.height,y=this.image;g.blendImage||(g.blendImage=e.util.createCanvasElement()),l=g.blendImage,u=l.getContext("2d"),l.width!==m||l.height!==b?(l.width=m,l.height=b):u.clearRect(0,0,m,b),u.setTransform(y.scaleX,0,0,y.scaleY,y.left,y.top),u.drawImage(y._element,0,0,m,b),f=u.getImageData(0,0,m,b).data;for(var _=0;v>_;_+=4)switch(a=p[_],s=p[_+1],c=p[_+2],h=p[_+3],i=f[_],r=f[_+1],n=f[_+2],o=f[_+3],this.mode){case"multiply":p[_]=a*i/255,p[_+1]=s*r/255,p[_+2]=c*n/255,p[_+3]=h*o/255;break;case"mask":p[_+3]=o}},getUniformLocations:function(t,e){return{uTransformMatrix:t.getUniformLocation(e,"uTransformMatrix"),uImage:t.getUniformLocation(e,"uImage")}},sendUniformData:function(t,e){var i=this.calculateMatrix();t.uniform1i(e.uImage,1),t.uniformMatrix3fv(e.uTransformMatrix,!1,i)},toObject:function(){return{type:this.type,image:this.image&&this.image.toObject(),mode:this.mode,alpha:this.alpha}}}),e.Image.filters.BlendImage.fromObject=function(t,i){e.Image.fromObject(t.image,function(r){var n=e.util.object.clone(t);n.image=r,i(new e.Image.filters.BlendImage(n))})}}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=Math.pow,r=Math.floor,n=Math.sqrt,o=Math.abs,a=Math.round,s=Math.sin,c=Math.ceil,h=e.Image.filters,l=e.util.createClass;h.Resize=l(h.BaseFilter,{type:"Resize",resizeType:"hermite",scaleX:1,scaleY:1,lanczosLobes:3,getUniformLocations:function(t,e){return{uDelta:t.getUniformLocation(e,"uDelta"),uTaps:t.getUniformLocation(e,"uTaps")}},sendUniformData:function(t,e){t.uniform2fv(e.uDelta,this.horizontal?[1/this.width,0]:[0,1/this.height]),t.uniform1fv(e.uTaps,this.taps)},retrieveShader:function(t){var e=this.getFilterWindow(),i=this.type+"_"+e;if(!t.programCache.hasOwnProperty(i)){var r=this.generateShader(e);t.programCache[i]=this.createProgram(t.context,r)}return t.programCache[i]},getFilterWindow:function(){var t=this.tempScale;return Math.ceil(this.lanczosLobes/t)},getTaps:function(){for(var t=this.lanczosCreate(this.lanczosLobes),e=this.tempScale,i=this.getFilterWindow(),r=new Array(i),n=1;i>=n;n++)r[n-1]=t(n*e);return r},generateShader:function(t){for(var t,e=new Array(t),i=this.fragmentSourceTOP,r=1;t>=r;r++)e[r-1]=r+".0 * uDelta";return i+="uniform float uTaps["+t+"];\n",i+="void main() {\n",i+=" vec4 color = texture2D(uTexture, vTexCoord);\n",i+=" float sum = 1.0;\n",e.forEach(function(t,e){i+=" color += texture2D(uTexture, vTexCoord + "+t+") * uTaps["+e+"];\n",i+=" color += texture2D(uTexture, vTexCoord - "+t+") * uTaps["+e+"];\n",i+=" sum += 2.0 * uTaps["+e+"];\n"}),i+=" gl_FragColor = color / sum;\n",i+="}"},fragmentSourceTOP:"precision highp float;\nuniform sampler2D uTexture;\nuniform vec2 uDelta;\nvarying vec2 vTexCoord;\n",applyTo:function(t){t.webgl?(t.passes++,this.width=t.sourceWidth,this.horizontal=!0,this.dW=Math.round(this.width*this.scaleX),this.dH=t.sourceHeight,this.tempScale=this.dW/this.width,this.taps=this.getTaps(),t.destinationWidth=this.dW,this._setupFrameBuffer(t),this.applyToWebGL(t),this._swapTextures(t),t.sourceWidth=t.destinationWidth,this.height=t.sourceHeight,this.horizontal=!1,this.dH=Math.round(this.height*this.scaleY),this.tempScale=this.dH/this.height,this.taps=this.getTaps(),t.destinationHeight=this.dH,this._setupFrameBuffer(t),this.applyToWebGL(t),this._swapTextures(t),t.sourceHeight=t.destinationHeight):this.applyTo2d(t)},isNeutralState:function(){return 1===this.scaleX&&1===this.scaleY},lanczosCreate:function(t){return function(e){if(e>=t||-t>=e)return 0;if(1.1920929e-7>e&&e>-1.1920929e-7)return 1;e*=Math.PI;var i=e/t;return s(e)/e*s(i)/i}},applyTo2d:function(t){var e=t.imageData,i=this.scaleX,r=this.scaleY;this.rcpScaleX=1/i,this.rcpScaleY=1/r;var n,o=e.width,s=e.height,c=a(o*i),h=a(s*r);"sliceHack"===this.resizeType?n=this.sliceByTwo(t,o,s,c,h):"hermite"===this.resizeType?n=this.hermiteFastResize(t,o,s,c,h):"bilinear"===this.resizeType?n=this.bilinearFiltering(t,o,s,c,h):"lanczos"===this.resizeType&&(n=this.lanczosResize(t,o,s,c,h)),t.imageData=n},sliceByTwo:function(t,i,n,o,a){var s,c,h=t.imageData,l=.5,u=!1,f=!1,d=i*l,g=n*l,p=e.filterBackend.resources,v=0,m=0,b=i,y=0;for(p.sliceByTwo||(p.sliceByTwo=document.createElement("canvas")),s=p.sliceByTwo,(s.width<1.5*i||s.height<n)&&(s.width=1.5*i,s.height=n),c=s.getContext("2d"),c.clearRect(0,0,1.5*i,n),c.putImageData(h,0,0),o=r(o),a=r(a);!u||!f;)i=d,n=g,o<r(d*l)?d=r(d*l):(d=o,u=!0),a<r(g*l)?g=r(g*l):(g=a,f=!0),c.drawImage(s,v,m,i,n,b,y,d,g),v=b,m=y,y+=g;return c.getImageData(v,m,o,a)},lanczosResize:function(t,e,a,s,h){function l(t){var c,S,T,O,P,k,j,E,A,M,D;for(C.x=(t+.5)*p,w.x=r(C.x),c=0;h>c;c++){for(C.y=(c+.5)*v,w.y=r(C.y),P=0,k=0,j=0,E=0,A=0,S=w.x-y;S<=w.x+y;S++)if(!(0>S||S>=e)){M=r(1e3*o(S-C.x)),x[M]||(x[M]={});for(var F=w.y-_;F<=w.y+_;F++)0>F||F>=a||(D=r(1e3*o(F-C.y)),x[M][D]||(x[M][D]=g(n(i(M*m,2)+i(D*b,2))/1e3)),T=x[M][D],T>0&&(O=4*(F*e+S),P+=T,k+=T*u[O],j+=T*u[O+1],E+=T*u[O+2],A+=T*u[O+3]))}O=4*(c*s+t),d[O]=k/P,d[O+1]=j/P,d[O+2]=E/P,d[O+3]=A/P}return++t<s?l(t):f}var u=t.imageData.data,f=t.ctx.createImageData(s,h),d=f.data,g=this.lanczosCreate(this.lanczosLobes),p=this.rcpScaleX,v=this.rcpScaleY,m=2/this.rcpScaleX,b=2/this.rcpScaleY,y=c(p*this.lanczosLobes/2),_=c(v*this.lanczosLobes/2),x={},C={},w={};return l(0)},bilinearFiltering:function(t,e,i,n,o){var a,s,c,h,l,u,f,d,g,p,v,m,b,y=0,_=this.rcpScaleX,x=this.rcpScaleY,C=4*(e-1),w=t.imageData,S=w.data,T=t.ctx.createImageData(n,o),O=T.data;for(f=0;o>f;f++)for(d=0;n>d;d++)for(l=r(_*d),u=r(x*f),g=_*d-l,p=x*f-u,b=4*(u*e+l),v=0;4>v;v++)a=S[b+v],s=S[b+4+v],c=S[b+C+v],h=S[b+C+4+v],m=a*(1-g)*(1-p)+s*g*(1-p)+c*p*(1-g)+h*g*p,O[y++]=m;return T},hermiteFastResize:function(t,e,i,a,s){for(var h=this.rcpScaleX,l=this.rcpScaleY,u=c(h/2),f=c(l/2),d=t.imageData,g=d.data,p=t.ctx.createImageData(a,s),v=p.data,m=0;s>m;m++)for(var b=0;a>b;b++){for(var y=4*(b+m*a),_=0,x=0,C=0,w=0,S=0,T=0,O=0,P=(m+.5)*l,k=r(m*l);(m+1)*l>k;k++)for(var j=o(P-(k+.5))/f,E=(b+.5)*h,A=j*j,M=r(b*h);(b+1)*h>M;M++){var D=o(E-(M+.5))/u,F=n(A+D*D);F>1&&-1>F||(_=2*F*F*F-3*F*F+1,_>0&&(D=4*(M+k*e),O+=_*g[D+3],C+=_,g[D+3]<255&&(_=_*g[D+3]/250),w+=_*g[D],S+=_*g[D+1],T+=_*g[D+2],x+=_))}v[y]=w/x,v[y+1]=S/x,v[y+2]=T/x,v[y+3]=O/C}return p},toObject:function(){return{type:this.type,scaleX:this.scaleX,scaleY:this.scaleY,resizeType:this.resizeType,lanczosLobes:this.lanczosLobes}}}),e.Image.filters.Resize.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Contrast=r(i.BaseFilter,{type:"Contrast",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uContrast;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nfloat contrastF = 1.015 * (uContrast + 1.0) / (1.0 * (1.015 - uContrast));\ncolor.rgb = contrastF * (color.rgb - 0.5) + 0.5;\ngl_FragColor = color;\n}",contrast:0,mainParameter:"contrast",applyTo2d:function(t){if(0!==this.contrast){var e,i,r=t.imageData,n=r.data,i=n.length,o=Math.floor(255*this.contrast),a=259*(o+255)/(255*(259-o));for(e=0;i>e;e+=4)n[e]=a*(n[e]-128)+128,n[e+1]=a*(n[e+1]-128)+128,n[e+2]=a*(n[e+2]-128)+128}},getUniformLocations:function(t,e){return{uContrast:t.getUniformLocation(e,"uContrast")}},sendUniformData:function(t,e){t.uniform1f(e.uContrast,this.contrast)}}),e.Image.filters.Contrast.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Saturation=r(i.BaseFilter,{type:"Saturation",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uSaturation;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nfloat rgMax = max(color.r, color.g);\nfloat rgbMax = max(rgMax, color.b);\ncolor.r += rgbMax != color.r ? (rgbMax - color.r) * uSaturation : 0.00;\ncolor.g += rgbMax != color.g ? (rgbMax - color.g) * uSaturation : 0.00;\ncolor.b += rgbMax != color.b ? (rgbMax - color.b) * uSaturation : 0.00;\ngl_FragColor = color;\n}",saturation:0,mainParameter:"saturation",applyTo2d:function(t){if(0!==this.saturation){var e,i,r=t.imageData,n=r.data,o=n.length,a=-this.saturation;for(e=0;o>e;e+=4)i=Math.max(n[e],n[e+1],n[e+2]),n[e]+=i!==n[e]?(i-n[e])*a:0,n[e+1]+=i!==n[e+1]?(i-n[e+1])*a:0,n[e+2]+=i!==n[e+2]?(i-n[e+2])*a:0}},getUniformLocations:function(t,e){return{uSaturation:t.getUniformLocation(e,"uSaturation")}},sendUniformData:function(t,e){t.uniform1f(e.uSaturation,-this.saturation)}}),e.Image.filters.Saturation.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Blur=r(i.BaseFilter,{type:"Blur",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform vec2 uDelta;\nvarying vec2 vTexCoord;\nconst float nSamples = 15.0;\nvec3 v3offset = vec3(12.9898, 78.233, 151.7182);\nfloat random(vec3 scale) {\nreturn fract(sin(dot(gl_FragCoord.xyz, scale)) * 43758.5453);\n}\nvoid main() {\nvec4 color = vec4(0.0);\nfloat total = 0.0;\nfloat offset = random(v3offset);\nfor (float t = -nSamples; t <= nSamples; t++) {\nfloat percent = (t + offset - 0.5) / nSamples;\nfloat weight = 1.0 - abs(percent);\ncolor += texture2D(uTexture, vTexCoord + uDelta * percent) * weight;\ntotal += weight;\n}\ngl_FragColor = color / total;\n}",blur:0,mainParameter:"blur",applyTo:function(t){t.webgl?(this.aspectRatio=t.sourceWidth/t.sourceHeight,t.passes++,this._setupFrameBuffer(t),this.horizontal=!0,this.applyToWebGL(t),this._swapTextures(t),this._setupFrameBuffer(t),this.horizontal=!1,this.applyToWebGL(t),this._swapTextures(t)):this.applyTo2d(t)},applyTo2d:function(t){t.imageData=this.simpleBlur(t)},simpleBlur:function(t){var i,r,n=t.filterBackend.resources,o=t.imageData.width,a=t.imageData.height;n.blurLayer1||(n.blurLayer1=e.util.createCanvasElement(),n.blurLayer2=e.util.createCanvasElement()),i=n.blurLayer1,r=n.blurLayer2,(i.width!==o||i.height!==a)&&(r.width=i.width=o,r.height=i.height=a);var s,c,h,l,u=i.getContext("2d"),f=r.getContext("2d"),d=15,g=.06*this.blur*.5;for(u.putImageData(t.imageData,0,0),f.clearRect(0,0,o,a),l=-d;d>=l;l++)s=(Math.random()-.5)/4,c=l/d,h=g*c*o+s,f.globalAlpha=1-Math.abs(c),f.drawImage(i,h,s),u.drawImage(r,0,0),f.globalAlpha=1,f.clearRect(0,0,r.width,r.height);for(l=-d;d>=l;l++)s=(Math.random()-.5)/4,c=l/d,h=g*c*a+s,f.globalAlpha=1-Math.abs(c),f.drawImage(i,s,h),u.drawImage(r,0,0),f.globalAlpha=1,f.clearRect(0,0,r.width,r.height);t.ctx.drawImage(i,0,0);var p=t.ctx.getImageData(0,0,i.width,i.height);return u.globalAlpha=1,u.clearRect(0,0,i.width,i.height),p},getUniformLocations:function(t,e){return{delta:t.getUniformLocation(e,"uDelta")}},sendUniformData:function(t,e){var i=this.chooseRightDelta();t.uniform2fv(e.delta,i)},chooseRightDelta:function(){var t,e=1,i=[0,0];return this.horizontal?this.aspectRatio>1&&(e=1/this.aspectRatio):this.aspectRatio<1&&(e=this.aspectRatio),t=e*this.blur*.12,this.horizontal?i[0]=t:i[1]=t,i}}),i.Blur.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Gamma=r(i.BaseFilter,{type:"Gamma",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform vec3 uGamma;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nvec3 correction = (1.0 / uGamma);\ncolor.r = pow(color.r, correction.r);\ncolor.g = pow(color.g, correction.g);\ncolor.b = pow(color.b, correction.b);\ngl_FragColor = color;\ngl_FragColor.rgb *= color.a;\n}",gamma:[1,1,1],mainParameter:"gamma",initialize:function(t){this.gamma=[1,1,1],i.BaseFilter.prototype.initialize.call(this,t)},applyTo2d:function(t){var e,i=t.imageData,r=i.data,n=this.gamma,o=r.length,a=1/n[0],s=1/n[1],c=1/n[2];for(this.rVals||(this.rVals=new Uint8Array(256),this.gVals=new Uint8Array(256),this.bVals=new Uint8Array(256)),e=0,o=256;o>e;e++)this.rVals[e]=255*Math.pow(e/255,a),this.gVals[e]=255*Math.pow(e/255,s),this.bVals[e]=255*Math.pow(e/255,c);for(e=0,o=r.length;o>e;e+=4)r[e]=this.rVals[r[e]],r[e+1]=this.gVals[r[e+1]],r[e+2]=this.bVals[r[e+2]]},getUniformLocations:function(t,e){return{uGamma:t.getUniformLocation(e,"uGamma")}},sendUniformData:function(t,e){t.uniform3fv(e.uGamma,this.gamma)}}),e.Image.filters.Gamma.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.Composed=r(i.BaseFilter,{type:"Composed",subFilters:[],initialize:function(t){this.callSuper("initialize",t),this.subFilters=this.subFilters.slice(0)},applyTo:function(t){t.passes+=this.subFilters.length-1,this.subFilters.forEach(function(e){e.applyTo(t)})},toObject:function(){return e.util.object.extend(this.callSuper("toObject"),{subFilters:this.subFilters.map(function(t){return t.toObject()})})},isNeutralState:function(){return!this.subFilters.some(function(t){return!t.isNeutralState()})}}),e.Image.filters.Composed.fromObject=function(t,i){var r=t.subFilters||[],n=r.map(function(t){return new e.Image.filters[t.type](t)}),o=new e.Image.filters.Composed({subFilters:n});return i&&i(o),o}}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.Image.filters,r=e.util.createClass;i.HueRotation=r(i.ColorMatrix,{type:"HueRotation",rotation:0,mainParameter:"rotation",calculateMatrix:function(){var t=this.rotation*Math.PI,i=e.util.cos(t),r=e.util.sin(t),n=1/3,o=Math.sqrt(n)*r,a=1-i;this.matrix=[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0],this.matrix[0]=i+a/3,this.matrix[1]=n*a-o,this.matrix[2]=n*a+o,this.matrix[5]=n*a+o,this.matrix[6]=i+n*a,this.matrix[7]=n*a-o,this.matrix[10]=n*a-o,this.matrix[11]=n*a+o,this.matrix[12]=i+n*a},isNeutralState:function(t){return this.calculateMatrix(),i.BaseFilter.prototype.isNeutralState.call(this,t)},applyTo:function(t){this.calculateMatrix(),i.BaseFilter.prototype.applyTo.call(this,t)}}),e.Image.filters.HueRotation.fromObject=e.Image.filters.BaseFilter.fromObject}("undefined"!=typeof exports?exports:this);!function(t){"use strict";var e=t.fabric||(t.fabric={}),i=e.util.object.clone;if(e.Text)return void e.warn("fabric.Text is already defined");var r="fontFamily fontWeight fontSize text underline overline linethrough textAlign fontStyle lineHeight textBackgroundColor charSpacing styles direction path pathStartOffset pathSide pathAlign".split(" ");e.Text=e.util.createClass(e.Object,{_dimensionAffectingProps:["fontSize","fontWeight","fontFamily","fontStyle","lineHeight","text","charSpacing","textAlign","styles","path","pathStartOffset","pathSide","pathAlign"],_reNewline:/\r?\n/,_reSpacesAndTabs:/[ \t\r]/g,_reSpaceAndTab:/[ \t\r]/,_reWords:/\S+/g,type:"text",fontSize:40,fontWeight:"normal",fontFamily:"Times New Roman",underline:!1,overline:!1,linethrough:!1,textAlign:"left",fontStyle:"normal",lineHeight:1.16,superscript:{size:.6,baseline:-.35},subscript:{size:.6,baseline:.11},textBackgroundColor:"",stateProperties:e.Object.prototype.stateProperties.concat(r),cacheProperties:e.Object.prototype.cacheProperties.concat(r),stroke:null,shadow:null,path:null,pathStartOffset:0,pathSide:"left",pathAlign:"baseline",_fontSizeFraction:.222,offsets:{underline:.1,linethrough:-.315,overline:-.88},_fontSizeMult:1.13,charSpacing:0,styles:null,_measuringContext:null,deltaY:0,direction:"ltr",_styleProperties:["stroke","strokeWidth","fill","fontFamily","fontSize","fontWeight","fontStyle","underline","overline","linethrough","deltaY","textBackgroundColor"],__charBounds:[],CACHE_FONT_SIZE:400,MIN_TEXT_WIDTH:2,initialize:function(t,e){this.styles=e?e.styles||{}:{},this.text=t,this.__skipDimension=!0,this.callSuper("initialize",e),this.path&&this.setPathInfo(),this.__skipDimension=!1,this.initDimensions(),this.setCoords(),this.setupState({propertySet:"_dimensionAffectingProps"})},setPathInfo:function(){var t=this.path;t&&(t.segmentsInfo=e.util.getPathSegmentsInfo(t.path))},getMeasuringContext:function(){return e._measuringContext||(e._measuringContext=this.canvas&&this.canvas.contextCache||e.util.createCanvasElement().getContext("2d")),e._measuringContext},_splitText:function(){var t=this._splitTextIntoLines(this.text);return this.textLines=t.lines,this._textLines=t.graphemeLines,this._unwrappedTextLines=t._unwrappedLines,this._text=t.graphemeText,t},initDimensions:function(){this.__skipDimension||(this._splitText(),this._clearCache(),this.path?(this.width=this.path.width,this.height=this.path.height):(this.width=this.calcTextWidth()||this.cursorWidth||this.MIN_TEXT_WIDTH,this.height=this.calcTextHeight()),-1!==this.textAlign.indexOf("justify")&&this.enlargeSpaces(),this.saveState({propertySet:"_dimensionAffectingProps"}))},enlargeSpaces:function(){for(var t,e,i,r,n,o,a,s=0,c=this._textLines.length;c>s;s++)if(("justify"===this.textAlign||s!==c-1&&!this.isEndOfWrapping(s))&&(r=0,n=this._textLines[s],e=this.getLineWidth(s),e<this.width&&(a=this.textLines[s].match(this._reSpacesAndTabs)))){i=a.length,t=(this.width-e)/i;for(var h=0,l=n.length;l>=h;h++)o=this.__charBounds[s][h],this._reSpaceAndTab.test(n[h])?(o.width+=t,o.kernedWidth+=t,o.left+=r,r+=t):o.left+=r}},isEndOfWrapping:function(t){return t===this._textLines.length-1},missingNewlineOffset:function(){return 1},toString:function(){return"#<fabric.Text ("+this.complexity()+'): { "text": "'+this.text+'", "fontFamily": "'+this.fontFamily+'" }>'},_getCacheCanvasDimensions:function(){var t=this.callSuper("_getCacheCanvasDimensions"),e=this.fontSize;return t.width+=e*t.zoomX,t.height+=e*t.zoomY,t},_render:function(t){var e=this.path;e&&!e.isNotVisible()&&e._render(t),this._setTextStyles(t),this._renderTextLinesBackground(t),this._renderTextDecoration(t,"underline"),this._renderText(t),this._renderTextDecoration(t,"overline"),this._renderTextDecoration(t,"linethrough")},_renderText:function(t){"stroke"===this.paintFirst?(this._renderTextStroke(t),this._renderTextFill(t)):(this._renderTextFill(t),this._renderTextStroke(t))},_setTextStyles:function(t,e,i){if(t.textBaseline="alphabetical",this.path)switch(this.pathAlign){case"center":t.textBaseline="middle";break;case"ascender":t.textBaseline="top";break;case"descender":t.textBaseline="bottom"}t.font=this._getFontDeclaration(e,i)},calcTextWidth:function(){for(var t=this.getLineWidth(0),e=1,i=this._textLines.length;i>e;e++){var r=this.getLineWidth(e);r>t&&(t=r)}return t},_renderTextLine:function(t,e,i,r,n,o){this._renderChars(t,e,i,r,n,o)},_renderTextLinesBackground:function(t){if(this.textBackgroundColor||this.styleHas("textBackgroundColor")){for(var e,i,r,n,o,a,s,c=t.fillStyle,h=this._getLeftOffset(),l=this._getTopOffset(),u=0,f=0,d=this.path,g=0,p=this._textLines.length;p>g;g++)if(e=this.getHeightOfLine(g),this.textBackgroundColor||this.styleHas("textBackgroundColor",g)){r=this._textLines[g],i=this._getLineLeftOffset(g),f=0,u=0,n=this.getValueOfPropertyAt(g,0,"textBackgroundColor");for(var v=0,m=r.length;m>v;v++)o=this.__charBounds[g][v],a=this.getValueOfPropertyAt(g,v,"textBackgroundColor"),d?(t.save(),t.translate(o.renderLeft,o.renderTop),t.rotate(o.angle),t.fillStyle=a,a&&t.fillRect(-o.width/2,-e/this.lineHeight*(1-this._fontSizeFraction),o.width,e/this.lineHeight),t.restore()):a!==n?(s=h+i+u,"rtl"===this.direction&&(s=this.width-s-f),t.fillStyle=n,n&&t.fillRect(s,l,f,e/this.lineHeight),u=o.left,f=o.width,n=a):f+=o.kernedWidth;a&&!d&&(s=h+i+u,"rtl"===this.direction&&(s=this.width-s-f),t.fillStyle=a,t.fillRect(s,l,f,e/this.lineHeight)),l+=e}else l+=e;t.fillStyle=c,this._removeShadow(t)}},getFontCache:function(t){var i=t.fontFamily.toLowerCase();e.charWidthsCache[i]||(e.charWidthsCache[i]={});var r=e.charWidthsCache[i],n=t.fontStyle.toLowerCase()+"_"+(t.fontWeight+"").toLowerCase();return r[n]||(r[n]={}),r[n]},_measureChar:function(t,e,i,r){var n,o,a,s,c=this.getFontCache(e),h=this._getFontDeclaration(e),l=this._getFontDeclaration(r),u=i+t,f=h===l,d=e.fontSize/this.CACHE_FONT_SIZE;if(i&&void 0!==c[i]&&(a=c[i]),void 0!==c[t]&&(s=n=c[t]),f&&void 0!==c[u]&&(o=c[u],s=o-a),void 0===n||void 0===a||void 0===o){var g=this.getMeasuringContext();this._setTextStyles(g,e,!0)}return void 0===n&&(s=n=g.measureText(t).width,c[t]=n),void 0===a&&f&&i&&(a=g.measureText(i).width,c[i]=a),f&&void 0===o&&(o=g.measureText(u).width,c[u]=o,s=o-a),{width:n*d,kernedWidth:s*d}},getHeightOfChar:function(t,e){return this.getValueOfPropertyAt(t,e,"fontSize")},measureLine:function(t){var e=this._measureLine(t);return 0!==this.charSpacing&&(e.width-=this._getWidthOfCharSpacing()),e.width<0&&(e.width=0),e},_measureLine:function(t){var i,r,n,o,a,s,c=0,h=this._textLines[t],l=0,u=new Array(h.length),f=0,d=this.path,g="right"===this.pathSide;for(this.__charBounds[t]=u,i=0;i<h.length;i++)r=h[i],o=this._getGraphemeBox(r,t,i,n),u[i]=o,c+=o.kernedWidth,n=r;if(u[i]={left:o?o.left+o.width:0,width:0,kernedWidth:0,height:this.fontSize},d){switch(s=d.segmentsInfo[d.segmentsInfo.length-1].length,a=e.util.getPointOnPath(d.path,0,d.segmentsInfo),a.x+=d.pathOffset.x,a.y+=d.pathOffset.y,this.textAlign){case"left":f=g?s-c:0;break;case"center":f=(s-c)/2;break;case"right":f=g?0:s-c}for(f+=this.pathStartOffset*(g?-1:1),i=g?h.length-1:0;g?i>=0:i<h.length;g?i--:i++)o=u[i],f>s?f%=s:0>f&&(f+=s),this._setGraphemeOnPath(f,o,a),f+=o.kernedWidth}return{width:c,numOfSpaces:l}},_setGraphemeOnPath:function(t,i,r){var n=t+i.kernedWidth/2,o=this.path,a=e.util.getPointOnPath(o.path,n,o.segmentsInfo);i.renderLeft=a.x-r.x,i.renderTop=a.y-r.y,i.angle=a.angle+("right"===this.pathSide?Math.PI:0)},_getGraphemeBox:function(t,e,i,r,n){var o,a=this.getCompleteStyleDeclaration(e,i),s=r?this.getCompleteStyleDeclaration(e,i-1):{},c=this._measureChar(t,a,r,s),h=c.kernedWidth,l=c.width;0!==this.charSpacing&&(o=this._getWidthOfCharSpacing(),l+=o,h+=o);var u={width:l,left:0,height:a.fontSize,kernedWidth:h,deltaY:a.deltaY};if(i>0&&!n){var f=this.__charBounds[e][i-1];u.left=f.left+f.width+c.kernedWidth-c.width}return u},getHeightOfLine:function(t){if(this.__lineHeights[t])return this.__lineHeights[t];for(var e=this._textLines[t],i=this.getHeightOfChar(t,0),r=1,n=e.length;n>r;r++)i=Math.max(this.getHeightOfChar(t,r),i);return this.__lineHeights[t]=i*this.lineHeight*this._fontSizeMult},calcTextHeight:function(){for(var t,e=0,i=0,r=this._textLines.length;r>i;i++)t=this.getHeightOfLine(i),e+=i===r-1?t/this.lineHeight:t;return e},_getLeftOffset:function(){return"ltr"===this.direction?-this.width/2:this.width/2},_getTopOffset:function(){return-this.height/2},_renderTextCommon:function(t,e){t.save();for(var i=0,r=this._getLeftOffset(),n=this._getTopOffset(),o=0,a=this._textLines.length;a>o;o++){var s=this.getHeightOfLine(o),c=s/this.lineHeight,h=this._getLineLeftOffset(o);this._renderTextLine(e,t,this._textLines[o],r+h,n+i+c,o),i+=s}t.restore()},_renderTextFill:function(t){(this.fill||this.styleHas("fill"))&&this._renderTextCommon(t,"fillText")},_renderTextStroke:function(t){(this.stroke&&0!==this.strokeWidth||!this.isEmptyStyles())&&(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(t),t.save(),this._setLineDash(t,this.strokeDashArray),t.beginPath(),this._renderTextCommon(t,"strokeText"),t.closePath(),t.restore())},_renderChars:function(t,i,r,n,o,a){var s,c,h,l,u,f=this.getHeightOfLine(a),d=-1!==this.textAlign.indexOf("justify"),g="",p=0,v=this.path,m=!d&&0===this.charSpacing&&this.isEmptyStyles(a)&&!v,b="ltr"===this.direction,y="ltr"===this.direction?1:-1,_=i.canvas.getAttribute("dir");if(i.save(),_!==this.direction&&(i.canvas.setAttribute("dir",b?"ltr":"rtl"),i.direction=b?"ltr":"rtl",i.textAlign=b?"left":"right"),o-=f*this._fontSizeFraction/this.lineHeight,m)return this._renderChar(t,i,a,0,r.join(""),n,o,f),void i.restore();for(var x=0,C=r.length-1;C>=x;x++)l=x===C||this.charSpacing||v,g+=r[x],h=this.__charBounds[a][x],0===p?(n+=y*(h.kernedWidth-h.width),p+=h.width):p+=h.kernedWidth,d&&!l&&this._reSpaceAndTab.test(r[x])&&(l=!0),l||(s=s||this.getCompleteStyleDeclaration(a,x),c=this.getCompleteStyleDeclaration(a,x+1),l=e.util.hasStyleChanged(s,c,!1)),l&&(v?(i.save(),i.translate(h.renderLeft,h.renderTop),i.rotate(h.angle),this._renderChar(t,i,a,x,g,-p/2,0,f),i.restore()):(u=n,this._renderChar(t,i,a,x,g,u,o,f)),g="",s=c,n+=y*p,p=0);i.restore()},_applyPatternGradientTransformText:function(t){var i,r=e.util.createCanvasElement(),n=this.width+this.strokeWidth,o=this.height+this.strokeWidth;return r.width=n,r.height=o,i=r.getContext("2d"),i.beginPath(),i.moveTo(0,0),i.lineTo(n,0),i.lineTo(n,o),i.lineTo(0,o),i.closePath(),i.translate(n/2,o/2),i.fillStyle=t.toLive(i),this._applyPatternGradientTransform(i,t),i.fill(),i.createPattern(r,"no-repeat")},handleFiller:function(t,e,i){var r,n;return i.toLive?"percentage"===i.gradientUnits||i.gradientTransform||i.patternTransform?(r=-this.width/2,n=-this.height/2,t.translate(r,n),t[e]=this._applyPatternGradientTransformText(i),{offsetX:r,offsetY:n}):(t[e]=i.toLive(t,this),this._applyPatternGradientTransform(t,i)):(t[e]=i,{offsetX:0,offsetY:0})},_setStrokeStyles:function(t,e){return t.lineWidth=e.strokeWidth,t.lineCap=this.strokeLineCap,t.lineDashOffset=this.strokeDashOffset,t.lineJoin=this.strokeLineJoin,t.miterLimit=this.strokeMiterLimit,this.handleFiller(t,"strokeStyle",e.stroke)},_setFillStyles:function(t,e){return this.handleFiller(t,"fillStyle",e.fill)},_renderChar:function(t,e,i,r,n,o,a){var s,c,h=this._getStyleDeclaration(i,r),l=this.getCompleteStyleDeclaration(i,r),u="fillText"===t&&l.fill,f="strokeText"===t&&l.stroke&&l.strokeWidth;(f||u)&&(e.save(),u&&(s=this._setFillStyles(e,l)),f&&(c=this._setStrokeStyles(e,l)),e.font=this._getFontDeclaration(l),h&&h.textBackgroundColor&&this._removeShadow(e),h&&h.deltaY&&(a+=h.deltaY),u&&e.fillText(n,o-s.offsetX,a-s.offsetY),f&&e.strokeText(n,o-c.offsetX,a-c.offsetY),e.restore())},setSuperscript:function(t,e){return this._setScript(t,e,this.superscript)},setSubscript:function(t,e){return this._setScript(t,e,this.subscript)},_setScript:function(t,e,i){var r=this.get2DCursorLocation(t,!0),n=this.getValueOfPropertyAt(r.lineIndex,r.charIndex,"fontSize"),o=this.getValueOfPropertyAt(r.lineIndex,r.charIndex,"deltaY"),a={fontSize:n*i.size,deltaY:o+n*i.baseline};return this.setSelectionStyles(a,t,e),this},_getLineLeftOffset:function(t){var e,i=this.getLineWidth(t),r=this.width-i,n=this.textAlign,o=this.direction,a=0,e=this.isEndOfWrapping(t);return"justify"===n||"justify-center"===n&&!e||"justify-right"===n&&!e||"justify-left"===n&&!e?0:("center"===n&&(a=r/2),"right"===n&&(a=r),"justify-center"===n&&(a=r/2),"justify-right"===n&&(a=r),"rtl"===o&&(a-=r),a)},_clearCache:function(){this.__lineWidths=[],this.__lineHeights=[],this.__charBounds=[]},_shouldClearDimensionCache:function(){var t=this._forceClearCache;return t||(t=this.hasStateChanged("_dimensionAffectingProps")),t&&(this.dirty=!0,this._forceClearCache=!1),t},getLineWidth:function(t){if(void 0!==this.__lineWidths[t])return this.__lineWidths[t];var e=this.measureLine(t),i=e.width;return this.__lineWidths[t]=i,i},_getWidthOfCharSpacing:function(){return 0!==this.charSpacing?this.fontSize*this.charSpacing/1e3:0},getValueOfPropertyAt:function(t,e,i){var r=this._getStyleDeclaration(t,e);return r&&"undefined"!=typeof r[i]?r[i]:this[i]},_renderTextDecoration:function(t,e){if(this[e]||this.styleHas(e)){for(var i,r,n,o,a,s,c,h,l,u,f,d,g,p,v,m,b=this._getLeftOffset(),y=this._getTopOffset(),_=this.path,x=this._getWidthOfCharSpacing(),C=this.offsets[e],w=0,S=this._textLines.length;S>w;w++)if(i=this.getHeightOfLine(w),this[e]||this.styleHas(e,w)){c=this._textLines[w],p=i/this.lineHeight,o=this._getLineLeftOffset(w),u=0,f=0,h=this.getValueOfPropertyAt(w,0,e),m=this.getValueOfPropertyAt(w,0,"fill"),l=y+p*(1-this._fontSizeFraction),r=this.getHeightOfChar(w,0),a=this.getValueOfPropertyAt(w,0,"deltaY");for(var T=0,O=c.length;O>T;T++)if(d=this.__charBounds[w][T],g=this.getValueOfPropertyAt(w,T,e),v=this.getValueOfPropertyAt(w,T,"fill"),n=this.getHeightOfChar(w,T),s=this.getValueOfPropertyAt(w,T,"deltaY"),_&&g&&v)t.save(),t.fillStyle=m,t.translate(d.renderLeft,d.renderTop),t.rotate(d.angle),t.fillRect(-d.kernedWidth/2,C*n+s,d.kernedWidth,this.fontSize/15),t.restore();else if((g!==h||v!==m||n!==r||s!==a)&&f>0){var P=b+o+u;"rtl"===this.direction&&(P=this.width-P-f),h&&m&&(t.fillStyle=m,t.fillRect(P,l+C*r+a,f,this.fontSize/15)),u=d.left,f=d.width,h=g,m=v,r=n,a=s}else f+=d.kernedWidth;var P=b+o+u;"rtl"===this.direction&&(P=this.width-P-f),t.fillStyle=v,g&&v&&t.fillRect(P,l+C*r+a,f-x,this.fontSize/15),y+=i}else y+=i;this._removeShadow(t)}},_getFontDeclaration:function(t,i){var r=t||this,n=this.fontFamily,o=e.Text.genericFonts.indexOf(n.toLowerCase())>-1,a=void 0===n||n.indexOf("'")>-1||n.indexOf(",")>-1||n.indexOf('"')>-1||o?r.fontFamily:'"'+r.fontFamily+'"';return[e.isLikelyNode?r.fontWeight:r.fontStyle,e.isLikelyNode?r.fontStyle:r.fontWeight,i?this.CACHE_FONT_SIZE+"px":r.fontSize+"px",a].join(" ")},render:function(t){this.visible&&(!this.canvas||!this.canvas.skipOffscreen||this.group||this.isOnScreen())&&(this._shouldClearDimensionCache()&&this.initDimensions(),this.callSuper("render",t))},_splitTextIntoLines:function(t){for(var i=t.split(this._reNewline),r=new Array(i.length),n=["\n"],o=[],a=0;a<i.length;a++)r[a]=e.util.string.graphemeSplit(i[a]),o=o.concat(r[a],n);return o.pop(),{_unwrappedLines:r,lines:i,graphemeText:o,graphemeLines:r}},toObject:function(t){var i=r.concat(t),n=this.callSuper("toObject",i);return n.styles=e.util.stylesToArray(this.styles,this.text),n.path&&(n.path=this.path.toObject()),n},set:function(t,e){this.callSuper("set",t,e);var i=!1,r=!1;if("object"==typeof t)for(var n in t)"path"===n&&this.setPathInfo(),i=i||-1!==this._dimensionAffectingProps.indexOf(n),r=r||"path"===n;else i=-1!==this._dimensionAffectingProps.indexOf(t),r="path"===t;return r&&this.setPathInfo(),i&&(this.initDimensions(),this.setCoords()),this},complexity:function(){return 1}}),e.Text.ATTRIBUTE_NAMES=e.SHARED_ATTRIBUTES.concat("x y dx dy font-family font-style font-weight font-size letter-spacing text-decoration text-anchor".split(" ")),e.Text.DEFAULT_SVG_FONT_SIZE=16,e.Text.fromElement=function(t,r,n){if(!t)return r(null);var o=e.parseAttributes(t,e.Text.ATTRIBUTE_NAMES),a=o.textAnchor||"left";if(n=e.util.object.extend(n?i(n):{},o),n.top=n.top||0,n.left=n.left||0,o.textDecoration){var s=o.textDecoration;-1!==s.indexOf("underline")&&(n.underline=!0),-1!==s.indexOf("overline")&&(n.overline=!0),-1!==s.indexOf("line-through")&&(n.linethrough=!0),delete n.textDecoration}"dx"in o&&(n.left+=o.dx),"dy"in o&&(n.top+=o.dy),"fontSize"in n||(n.fontSize=e.Text.DEFAULT_SVG_FONT_SIZE);var c="";"textContent"in t?c=t.textContent:"firstChild"in t&&null!==t.firstChild&&"data"in t.firstChild&&null!==t.firstChild.data&&(c=t.firstChild.data),c=c.replace(/^\s+|\s+$|\n+/g,"").replace(/\s+/g," ");var h=n.strokeWidth;n.strokeWidth=0;var l=new e.Text(c,n),u=l.getScaledHeight()/l.height,f=(l.height+l.strokeWidth)*l.lineHeight-l.height,d=f*u,g=l.getScaledHeight()+d,p=0;"center"===a&&(p=l.getScaledWidth()/2),"right"===a&&(p=l.getScaledWidth()),l.set({left:l.left-p,top:l.top-(g-l.fontSize*(.07+l._fontSizeFraction))/l.lineHeight,strokeWidth:"undefined"!=typeof h?h:1}),r(l)},e.Text.fromObject=function(t,r){var n=i(t),o=t.path;return delete n.path,e.Object._fromObject("Text",n,function(i){i.styles=e.util.stylesFromArray(t.styles,t.text),o?e.Object._fromObject("Path",o,function(t){i.set("path",t),r(i)},"path"):r(i)},"text")},e.Text.genericFonts=["sans-serif","serif","cursive","fantasy","monospace"],e.util.createAccessors&&e.util.createAccessors(e.Text)}("undefined"!=typeof exports?exports:this);!function(){fabric.util.object.extend(fabric.Text.prototype,{isEmptyStyles:function(t){if(!this.styles)return!0;if("undefined"!=typeof t&&!this.styles[t])return!0;var e="undefined"==typeof t?this.styles:{line:this.styles[t]};for(var i in e)for(var r in e[i])for(var n in e[i][r])return!1;return!0},styleHas:function(t,e){if(!this.styles||!t||""===t)return!1;if("undefined"!=typeof e&&!this.styles[e])return!1;var i="undefined"==typeof e?this.styles:{0:this.styles[e]};for(var r in i)for(var n in i[r])if("undefined"!=typeof i[r][n][t])return!0;return!1},cleanStyle:function(t){if(!this.styles||!t||""===t)return!1;var e,i,r,n=this.styles,o=0,s=!0,a=0;for(var c in n){e=0;for(var h in n[c]){var r=n[c][h],l=r.hasOwnProperty(t);o++,l?(i?r[t]!==i&&(s=!1):i=r[t],r[t]===this[t]&&delete r[t]):s=!1,0!==Object.keys(r).length?e++:delete n[c][h]}0===e&&delete n[c]}for(var u=0;u<this._textLines.length;u++)a+=this._textLines[u].length;s&&o===a&&(this[t]=i,this.removeStyle(t))},removeStyle:function(t){if(this.styles&&t&&""!==t){var e,i,r,n=this.styles;for(i in n){e=n[i];for(r in e)delete e[r][t],0===Object.keys(e[r]).length&&delete e[r];0===Object.keys(e).length&&delete n[i]}}},_extendStyles:function(t,e){var i=this.get2DCursorLocation(t);this._getLineStyle(i.lineIndex)||this._setLineStyle(i.lineIndex),this._getStyleDeclaration(i.lineIndex,i.charIndex)||this._setStyleDeclaration(i.lineIndex,i.charIndex,{}),fabric.util.object.extend(this._getStyleDeclaration(i.lineIndex,i.charIndex),e)},get2DCursorLocation:function(t,e){"undefined"==typeof t&&(t=this.selectionStart);for(var i=e?this._unwrappedTextLines:this._textLines,r=i.length,n=0;r>n;n++){if(t<=i[n].length)return{lineIndex:n,charIndex:t};t-=i[n].length+this.missingNewlineOffset(n)}return{lineIndex:n-1,charIndex:i[n-1].length<t?i[n-1].length:t}},getSelectionStyles:function(t,e,i){"undefined"==typeof t&&(t=this.selectionStart||0),"undefined"==typeof e&&(e=this.selectionEnd||t);for(var r=[],n=t;e>n;n++)r.push(this.getStyleAtPosition(n,i));return r},getStyleAtPosition:function(t,e){var i=this.get2DCursorLocation(t),r=e?this.getCompleteStyleDeclaration(i.lineIndex,i.charIndex):this._getStyleDeclaration(i.lineIndex,i.charIndex);return r||{}},setSelectionStyles:function(t,e,i){"undefined"==typeof e&&(e=this.selectionStart||0),"undefined"==typeof i&&(i=this.selectionEnd||e);for(var r=e;i>r;r++)this._extendStyles(r,t);return this._forceClearCache=!0,this},_getStyleDeclaration:function(t,e){var i=this.styles&&this.styles[t];return i?i[e]:null},getCompleteStyleDeclaration:function(t,e){for(var i,r=this._getStyleDeclaration(t,e)||{},n={},o=0;o<this._styleProperties.length;o++)i=this._styleProperties[o],n[i]="undefined"==typeof r[i]?this[i]:r[i];return n},_setStyleDeclaration:function(t,e,i){this.styles[t][e]=i},_deleteStyleDeclaration:function(t,e){delete this.styles[t][e]},_getLineStyle:function(t){return!!this.styles[t]},_setLineStyle:function(t){this.styles[t]={}},_deleteLineStyle:function(t){delete this.styles[t]}})}();!function(){function t(t){t.textDecoration&&(t.textDecoration.indexOf("underline")>-1&&(t.underline=!0),t.textDecoration.indexOf("line-through")>-1&&(t.linethrough=!0),t.textDecoration.indexOf("overline")>-1&&(t.overline=!0),delete t.textDecoration)}fabric.IText=fabric.util.createClass(fabric.Text,fabric.Observable,{type:"i-text",selectionStart:0,selectionEnd:0,selectionColor:"rgba(17,119,255,0.3)",isEditing:!1,editable:!0,editingBorderColor:"rgba(102,153,255,0.25)",cursorWidth:2,cursorColor:"",cursorDelay:1e3,cursorDuration:600,caching:!0,hiddenTextareaContainer:null,_reSpace:/\s|\n/,_currentCursorOpacity:0,_selectionDirection:null,_abortCursorAnimation:!1,__widthOfSpace:[],inCompositionMode:!1,initialize:function(t,e){this.callSuper("initialize",t,e),this.initBehavior()},setSelectionStart:function(t){t=Math.max(t,0),this._updateAndFire("selectionStart",t)},setSelectionEnd:function(t){t=Math.min(t,this.text.length),this._updateAndFire("selectionEnd",t)},_updateAndFire:function(t,e){this[t]!==e&&(this._fireSelectionChanged(),this[t]=e),this._updateTextarea()},_fireSelectionChanged:function(){this.fire("selection:changed"),this.canvas&&this.canvas.fire("text:selection:changed",{target:this})},initDimensions:function(){this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this.callSuper("initDimensions")},render:function(t){this.clearContextTop(),this.callSuper("render",t),this.cursorOffsetCache={},this.renderCursorOrSelection()},_render:function(t){this.callSuper("_render",t)},clearContextTop:function(t){if(this.isEditing&&this.canvas&&this.canvas.contextTop){var e=this.canvas.contextTop,i=this.canvas.viewportTransform;e.save(),e.transform(i[0],i[1],i[2],i[3],i[4],i[5]),this.transform(e),this._clearTextArea(e),t||e.restore()}},renderCursorOrSelection:function(){if(this.isEditing&&this.canvas&&this.canvas.contextTop){var t=this._getCursorBoundaries(),e=this.canvas.contextTop;this.clearContextTop(!0),this.selectionStart===this.selectionEnd?this.renderCursor(t,e):this.renderSelection(t,e),e.restore()}},_clearTextArea:function(t){var e=this.width+4,i=this.height+4;t.clearRect(-e/2,-i/2,e,i)},_getCursorBoundaries:function(t){"undefined"==typeof t&&(t=this.selectionStart);var e=this._getLeftOffset(),i=this._getTopOffset(),r=this._getCursorBoundariesOffsets(t);return{left:e,top:i,leftOffset:r.left,topOffset:r.top}},_getCursorBoundariesOffsets:function(t){if(this.cursorOffsetCache&&"top"in this.cursorOffsetCache)return this.cursorOffsetCache;var e,i,r,n,o=0,s=0,a=this.get2DCursorLocation(t);r=a.charIndex,i=a.lineIndex;for(var c=0;i>c;c++)o+=this.getHeightOfLine(c);e=this._getLineLeftOffset(i);var h=this.__charBounds[i][r];return h&&(s=h.left),0!==this.charSpacing&&r===this._textLines[i].length&&(s-=this._getWidthOfCharSpacing()),n={top:o,left:e+(s>0?s:0)},"rtl"===this.direction&&(n.left*=-1),this.cursorOffsetCache=n,this.cursorOffsetCache},renderCursor:function(t,e){var i=this.get2DCursorLocation(),r=i.lineIndex,n=i.charIndex>0?i.charIndex-1:0,o=this.getValueOfPropertyAt(r,n,"fontSize"),s=this.scaleX*this.canvas.getZoom(),a=this.cursorWidth/s,c=t.topOffset,h=this.getValueOfPropertyAt(r,n,"deltaY");c+=(1-this._fontSizeFraction)*this.getHeightOfLine(r)/this.lineHeight-o*(1-this._fontSizeFraction),this.inCompositionMode&&this.renderSelection(t,e),e.fillStyle=this.cursorColor||this.getValueOfPropertyAt(r,n,"fill"),e.globalAlpha=this.__isMousedown?1:this._currentCursorOpacity,e.fillRect(t.left+t.leftOffset-a/2,c+t.top+h,a,o)},renderSelection:function(t,e){for(var i=this.inCompositionMode?this.hiddenTextarea.selectionStart:this.selectionStart,r=this.inCompositionMode?this.hiddenTextarea.selectionEnd:this.selectionEnd,n=-1!==this.textAlign.indexOf("justify"),o=this.get2DCursorLocation(i),s=this.get2DCursorLocation(r),a=o.lineIndex,c=s.lineIndex,h=o.charIndex<0?0:o.charIndex,l=s.charIndex<0?0:s.charIndex,u=a;c>=u;u++){var f=this._getLineLeftOffset(u)||0,d=this.getHeightOfLine(u),g=0,p=0,v=0;if(u===a&&(p=this.__charBounds[a][h].left),u>=a&&c>u)v=n&&!this.isEndOfWrapping(u)?this.width:this.getLineWidth(u)||5;else if(u===c)if(0===l)v=this.__charBounds[c][l].left;else{var m=this._getWidthOfCharSpacing();v=this.__charBounds[c][l-1].left+this.__charBounds[c][l-1].width-m}g=d,(this.lineHeight<1||u===c&&this.lineHeight>1)&&(d/=this.lineHeight);var b=t.left+f+p,y=v-p,_=d,x=0;this.inCompositionMode?(e.fillStyle=this.compositionColor||"black",_=1,x=d):e.fillStyle=this.selectionColor,"rtl"===this.direction&&(b=this.width-b-y),e.fillRect(b,t.top+t.topOffset+x,y,_),t.topOffset+=g}},getCurrentCharFontSize:function(){var t=this._getCurrentCharIndex();return this.getValueOfPropertyAt(t.l,t.c,"fontSize")},getCurrentCharColor:function(){var t=this._getCurrentCharIndex();return this.getValueOfPropertyAt(t.l,t.c,"fill")},_getCurrentCharIndex:function(){var t=this.get2DCursorLocation(this.selectionStart,!0),e=t.charIndex>0?t.charIndex-1:0;return{l:t.lineIndex,c:e}}}),fabric.IText.fromObject=function(e,i){if(e.styles=fabric.util.stylesFromArray(e.styles,e.text),t(e),e.styles)for(var r in e.styles)for(var n in e.styles[r])t(e.styles[r][n]);fabric.Object._fromObject("IText",e,i,"text")}}();!function(){var t=fabric.util.object.clone;fabric.util.object.extend(fabric.IText.prototype,{initBehavior:function(){this.initAddedHandler(),this.initRemovedHandler(),this.initCursorSelectionHandlers(),this.initDoubleClickSimulation(),this.mouseMoveHandler=this.mouseMoveHandler.bind(this)},onDeselect:function(){this.isEditing&&this.exitEditing(),this.selected=!1},initAddedHandler:function(){var t=this;this.on("added",function(){var e=t.canvas;e&&(e._hasITextHandlers||(e._hasITextHandlers=!0,t._initCanvasHandlers(e)),e._iTextInstances=e._iTextInstances||[],e._iTextInstances.push(t))})},initRemovedHandler:function(){var t=this;this.on("removed",function(){var e=t.canvas;e&&(e._iTextInstances=e._iTextInstances||[],fabric.util.removeFromArray(e._iTextInstances,t),0===e._iTextInstances.length&&(e._hasITextHandlers=!1,t._removeCanvasHandlers(e)))})},_initCanvasHandlers:function(t){t._mouseUpITextHandler=function(){t._iTextInstances&&t._iTextInstances.forEach(function(t){t.__isMousedown=!1})},t.on("mouse:up",t._mouseUpITextHandler)},_removeCanvasHandlers:function(t){t.off("mouse:up",t._mouseUpITextHandler)},_tick:function(){this._currentTickState=this._animateCursor(this,1,this.cursorDuration,"_onTickComplete")},_animateCursor:function(t,e,i,r){var n;return n={isAborted:!1,abort:function(){this.isAborted=!0}},t.animate("_currentCursorOpacity",e,{duration:i,onComplete:function(){n.isAborted||t[r]()},onChange:function(){t.canvas&&t.selectionStart===t.selectionEnd&&t.renderCursorOrSelection()},abort:function(){return n.isAborted}}),n},_onTickComplete:function(){var t=this;this._cursorTimeout1&&clearTimeout(this._cursorTimeout1),this._cursorTimeout1=setTimeout(function(){t._currentTickCompleteState=t._animateCursor(t,0,this.cursorDuration/2,"_tick")},100)},initDelayedCursor:function(t){var e=this,i=t?0:this.cursorDelay;this.abortCursorAnimation(),this._currentCursorOpacity=1,this._cursorTimeout2=setTimeout(function(){e._tick()},i)},abortCursorAnimation:function(){var t=this._currentTickState||this._currentTickCompleteState,e=this.canvas;this._currentTickState&&this._currentTickState.abort(),this._currentTickCompleteState&&this._currentTickCompleteState.abort(),clearTimeout(this._cursorTimeout1),clearTimeout(this._cursorTimeout2),this._currentCursorOpacity=0,t&&e&&e.clearContext(e.contextTop||e.contextContainer)},selectAll:function(){return this.selectionStart=0,this.selectionEnd=this._text.length,this._fireSelectionChanged(),this._updateTextarea(),this},getSelectedText:function(){return this._text.slice(this.selectionStart,this.selectionEnd).join("")},findWordBoundaryLeft:function(t){var e=0,i=t-1;if(this._reSpace.test(this._text[i]))for(;this._reSpace.test(this._text[i]);)e++,i--;for(;/\S/.test(this._text[i])&&i>-1;)e++,i--;return t-e},findWordBoundaryRight:function(t){var e=0,i=t;if(this._reSpace.test(this._text[i]))for(;this._reSpace.test(this._text[i]);)e++,i++;for(;/\S/.test(this._text[i])&&i<this._text.length;)e++,i++;return t+e},findLineBoundaryLeft:function(t){for(var e=0,i=t-1;!/\n/.test(this._text[i])&&i>-1;)e++,i--;return t-e},findLineBoundaryRight:function(t){for(var e=0,i=t;!/\n/.test(this._text[i])&&i<this._text.length;)e++,i++;return t+e},searchWordBoundary:function(t,e){for(var i=this._text,r=this._reSpace.test(i[t])?t-1:t,n=i[r],s=fabric.reNonWord;!s.test(n)&&r>0&&r<i.length;)r+=e,n=i[r];return s.test(n)&&(r+=1===e?0:1),r},selectWord:function(t){t=t||this.selectionStart;var e=this.searchWordBoundary(t,-1),i=this.searchWordBoundary(t,1);this.selectionStart=e,this.selectionEnd=i,this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()},selectLine:function(t){t=t||this.selectionStart;var e=this.findLineBoundaryLeft(t),i=this.findLineBoundaryRight(t);return this.selectionStart=e,this.selectionEnd=i,this._fireSelectionChanged(),this._updateTextarea(),this},enterEditing:function(t){return!this.isEditing&&this.editable?(this.canvas&&(this.canvas.calcOffset(),this.exitEditingOnOthers(this.canvas)),this.isEditing=!0,this.initHiddenTextarea(t),this.hiddenTextarea.focus(),this.hiddenTextarea.value=this.text,this._updateTextarea(),this._saveEditingProps(),this._setEditingProps(),this._textBeforeEdit=this.text,this._tick(),this.fire("editing:entered"),this._fireSelectionChanged(),this.canvas?(this.canvas.fire("text:editing:entered",{target:this}),this.initMouseMoveHandler(),this.canvas.requestRenderAll(),this):this):void 0},exitEditingOnOthers:function(t){t._iTextInstances&&t._iTextInstances.forEach(function(t){t.selected=!1,t.isEditing&&t.exitEditing()})},initMouseMoveHandler:function(){this.canvas.on("mouse:move",this.mouseMoveHandler)},mouseMoveHandler:function(t){if(this.__isMousedown&&this.isEditing){var e=this.getSelectionStartFromPointer(t.e),i=this.selectionStart,r=this.selectionEnd;(e===this.__selectionStartOnMouseDown&&i!==r||i!==e&&r!==e)&&(e>this.__selectionStartOnMouseDown?(this.selectionStart=this.__selectionStartOnMouseDown,this.selectionEnd=e):(this.selectionStart=e,this.selectionEnd=this.__selectionStartOnMouseDown),(this.selectionStart!==i||this.selectionEnd!==r)&&(this.restartCursorIfNeeded(),this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()))}},_setEditingProps:function(){this.hoverCursor="text",this.canvas&&(this.canvas.defaultCursor=this.canvas.moveCursor="text"),this.borderColor=this.editingBorderColor,this.hasControls=this.selectable=!1,this.lockMovementX=this.lockMovementY=!0},fromStringToGraphemeSelection:function(t,e,i){var r=i.slice(0,t),n=fabric.util.string.graphemeSplit(r).length;if(t===e)return{selectionStart:n,selectionEnd:n};var s=i.slice(t,e),o=fabric.util.string.graphemeSplit(s).length;return{selectionStart:n,selectionEnd:n+o}},fromGraphemeToStringSelection:function(t,e,i){var r=i.slice(0,t),n=r.join("").length;if(t===e)return{selectionStart:n,selectionEnd:n};var s=i.slice(t,e),o=s.join("").length;return{selectionStart:n,selectionEnd:n+o}},_updateTextarea:function(){if(this.cursorOffsetCache={},this.hiddenTextarea){if(!this.inCompositionMode){var t=this.fromGraphemeToStringSelection(this.selectionStart,this.selectionEnd,this._text);this.hiddenTextarea.selectionStart=t.selectionStart,this.hiddenTextarea.selectionEnd=t.selectionEnd}this.updateTextareaPosition()}},updateFromTextArea:function(){if(this.hiddenTextarea){this.cursorOffsetCache={},this.text=this.hiddenTextarea.value,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords());var t=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value);this.selectionEnd=this.selectionStart=t.selectionEnd,this.inCompositionMode||(this.selectionStart=t.selectionStart),this.updateTextareaPosition()}},updateTextareaPosition:function(){if(this.selectionStart===this.selectionEnd){var t=this._calcTextareaPosition();this.hiddenTextarea.style.left=t.left,this.hiddenTextarea.style.top=t.top}},_calcTextareaPosition:function(){if(!this.canvas)return{x:1,y:1};var t=this.inCompositionMode?this.compositionStart:this.selectionStart,e=this._getCursorBoundaries(t),i=this.get2DCursorLocation(t),r=i.lineIndex,n=i.charIndex,s=this.getValueOfPropertyAt(r,n,"fontSize")*this.lineHeight,o=e.leftOffset,a=this.calcTransformMatrix(),c={x:e.left+o,y:e.top+e.topOffset+s},h=this.canvas.getRetinaScaling(),l=this.canvas.upperCanvasEl,u=l.width/h,f=l.height/h,d=u-s,g=f-s,p=l.clientWidth/u,v=l.clientHeight/f;return c=fabric.util.transformPoint(c,a),c=fabric.util.transformPoint(c,this.canvas.viewportTransform),c.x*=p,c.y*=v,c.x<0&&(c.x=0),c.x>d&&(c.x=d),c.y<0&&(c.y=0),c.y>g&&(c.y=g),c.x+=this.canvas._offset.left,c.y+=this.canvas._offset.top,{left:c.x+"px",top:c.y+"px",fontSize:s+"px",charHeight:s}},_saveEditingProps:function(){this._savedProps={hasControls:this.hasControls,borderColor:this.borderColor,lockMovementX:this.lockMovementX,lockMovementY:this.lockMovementY,hoverCursor:this.hoverCursor,selectable:this.selectable,defaultCursor:this.canvas&&this.canvas.defaultCursor,moveCursor:this.canvas&&this.canvas.moveCursor}},_restoreEditingProps:function(){this._savedProps&&(this.hoverCursor=this._savedProps.hoverCursor,this.hasControls=this._savedProps.hasControls,this.borderColor=this._savedProps.borderColor,this.selectable=this._savedProps.selectable,this.lockMovementX=this._savedProps.lockMovementX,this.lockMovementY=this._savedProps.lockMovementY,this.canvas&&(this.canvas.defaultCursor=this._savedProps.defaultCursor,this.canvas.moveCursor=this._savedProps.moveCursor))},exitEditing:function(){var t=this._textBeforeEdit!==this.text,e=this.hiddenTextarea;return this.selected=!1,this.isEditing=!1,this.selectionEnd=this.selectionStart,e&&(e.blur&&e.blur(),e.parentNode&&e.parentNode.removeChild(e)),this.hiddenTextarea=null,this.abortCursorAnimation(),this._restoreEditingProps(),this._currentCursorOpacity=0,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this.fire("editing:exited"),t&&this.fire("modified"),this.canvas&&(this.canvas.off("mouse:move",this.mouseMoveHandler),this.canvas.fire("text:editing:exited",{target:this}),t&&this.canvas.fire("object:modified",{target:this})),this},_removeExtraneousStyles:function(){for(var t in this.styles)this._textLines[t]||delete this.styles[t]},removeStyleFromTo:function(t,e){var i,r,n=this.get2DCursorLocation(t,!0),s=this.get2DCursorLocation(e,!0),o=n.lineIndex,a=n.charIndex,c=s.lineIndex,h=s.charIndex;if(o!==c){if(this.styles[o])for(i=a;i<this._unwrappedTextLines[o].length;i++)delete this.styles[o][i];if(this.styles[c])for(i=h;i<this._unwrappedTextLines[c].length;i++)r=this.styles[c][i],r&&(this.styles[o]||(this.styles[o]={}),this.styles[o][a+i-h]=r);for(i=o+1;c>=i;i++)delete this.styles[i];this.shiftLineStyles(c,o-c)}else if(this.styles[o]){r=this.styles[o];var l,u,f=h-a;for(i=a;h>i;i++)delete r[i];for(u in this.styles[o])l=parseInt(u,10),l>=h&&(r[l-f]=r[u],delete r[u])}},shiftLineStyles:function(e,i){var r=t(this.styles);for(var n in this.styles){var s=parseInt(n,10);s>e&&(this.styles[s+i]=r[s],r[s-i]||delete this.styles[s])}},restartCursorIfNeeded:function(){(!this._currentTickState||this._currentTickState.isAborted||!this._currentTickCompleteState||this._currentTickCompleteState.isAborted)&&this.initDelayedCursor()},insertNewlineStyleObject:function(e,i,r,n){var s,o={},a=!1,c=this._unwrappedTextLines[e].length===i;r||(r=1),this.shiftLineStyles(e,r),this.styles[e]&&(s=this.styles[e][0===i?i:i-1]);for(var h in this.styles[e]){var l=parseInt(h,10);l>=i&&(a=!0,o[l-i]=this.styles[e][h],c&&0===i||delete this.styles[e][h])}var u=!1;for(a&&!c&&(this.styles[e+r]=o,u=!0),u&&r--;r>0;)n&&n[r-1]?this.styles[e+r]={0:t(n[r-1])}:s?this.styles[e+r]={0:t(s)}:delete this.styles[e+r],r--;this._forceClearCache=!0},insertCharStyleObject:function(e,i,r,n){this.styles||(this.styles={});var s=this.styles[e],o=s?t(s):{};r||(r=1);for(var a in o){var c=parseInt(a,10);c>=i&&(s[c+r]=o[c],o[c-r]||delete s[c])}if(this._forceClearCache=!0,n)for(;r--;)Object.keys(n[r]).length&&(this.styles[e]||(this.styles[e]={}),this.styles[e][i+r]=t(n[r]));else if(s)for(var h=s[i?i-1:1];h&&r--;)this.styles[e][i+r]=t(h)},insertNewStyleBlock:function(t,e,i){for(var r=this.get2DCursorLocation(e,!0),n=[0],s=0,o=0;o<t.length;o++)"\n"===t[o]?(s++,n[s]=0):n[s]++;n[0]>0&&(this.insertCharStyleObject(r.lineIndex,r.charIndex,n[0],i),i=i&&i.slice(n[0]+1)),s&&this.insertNewlineStyleObject(r.lineIndex,r.charIndex+n[0],s);for(var o=1;s>o;o++)n[o]>0?this.insertCharStyleObject(r.lineIndex+o,0,n[o],i):i&&this.styles[r.lineIndex+o]&&i[0]&&(this.styles[r.lineIndex+o][0]=i[0]),i=i&&i.slice(n[o]+1);n[o]>0&&this.insertCharStyleObject(r.lineIndex+o,0,n[o],i)},setSelectionStartEndWithShift:function(t,e,i){t>=i?(e===t?this._selectionDirection="left":"right"===this._selectionDirection&&(this._selectionDirection="left",this.selectionEnd=t),this.selectionStart=i):i>t&&e>i?"right"===this._selectionDirection?this.selectionEnd=i:this.selectionStart=i:(e===t?this._selectionDirection="right":"left"===this._selectionDirection&&(this._selectionDirection="right",this.selectionStart=e),this.selectionEnd=i)},setSelectionInBoundaries:function(){var t=this.text.length;this.selectionStart>t?this.selectionStart=t:this.selectionStart<0&&(this.selectionStart=0),this.selectionEnd>t?this.selectionEnd=t:this.selectionEnd<0&&(this.selectionEnd=0)}})}();fabric.util.object.extend(fabric.IText.prototype,{initDoubleClickSimulation:function(){this.__lastClickTime=+new Date,this.__lastLastClickTime=+new Date,this.__lastPointer={},this.on("mousedown",this.onMouseDown)},onMouseDown:function(t){if(this.canvas){this.__newClickTime=+new Date;var e=t.pointer;this.isTripleClick(e)&&(this.fire("tripleclick",t),this._stopEvent(t.e)),this.__lastLastClickTime=this.__lastClickTime,this.__lastClickTime=this.__newClickTime,this.__lastPointer=e,this.__lastIsEditing=this.isEditing,this.__lastSelected=this.selected}},isTripleClick:function(t){return this.__newClickTime-this.__lastClickTime<500&&this.__lastClickTime-this.__lastLastClickTime<500&&this.__lastPointer.x===t.x&&this.__lastPointer.y===t.y},_stopEvent:function(t){t.preventDefault&&t.preventDefault(),t.stopPropagation&&t.stopPropagation()},initCursorSelectionHandlers:function(){this.initMousedownHandler(),this.initMouseupHandler(),this.initClicks()},doubleClickHandler:function(t){this.isEditing&&this.selectWord(this.getSelectionStartFromPointer(t.e))},tripleClickHandler:function(t){this.isEditing&&this.selectLine(this.getSelectionStartFromPointer(t.e))},initClicks:function(){this.on("mousedblclick",this.doubleClickHandler),this.on("tripleclick",this.tripleClickHandler)},_mouseDownHandler:function(t){!this.canvas||!this.editable||t.e.button&&1!==t.e.button||(this.__isMousedown=!0,this.selected&&(this.inCompositionMode=!1,this.setCursorByClick(t.e)),this.isEditing&&(this.__selectionStartOnMouseDown=this.selectionStart,this.selectionStart===this.selectionEnd&&this.abortCursorAnimation(),this.renderCursorOrSelection()))},_mouseDownHandlerBefore:function(t){!this.canvas||!this.editable||t.e.button&&1!==t.e.button||(this.selected=this===this.canvas._activeObject)},initMousedownHandler:function(){this.on("mousedown",this._mouseDownHandler),this.on("mousedown:before",this._mouseDownHandlerBefore)},initMouseupHandler:function(){this.on("mouseup",this.mouseUpHandler)},mouseUpHandler:function(t){if(this.__isMousedown=!1,!(!this.editable||this.group||t.transform&&t.transform.actionPerformed||t.e.button&&1!==t.e.button)){if(this.canvas){var e=this.canvas._activeObject;if(e&&e!==this)return}this.__lastSelected&&!this.__corner?(this.selected=!1,this.__lastSelected=!1,this.enterEditing(t.e),this.selectionStart===this.selectionEnd?this.initDelayedCursor(!0):this.renderCursorOrSelection()):this.selected=!0}},setCursorByClick:function(t){var e=this.getSelectionStartFromPointer(t),i=this.selectionStart,r=this.selectionEnd;t.shiftKey?this.setSelectionStartEndWithShift(i,r,e):(this.selectionStart=e,this.selectionEnd=e),this.isEditing&&(this._fireSelectionChanged(),this._updateTextarea())},getSelectionStartFromPointer:function(t){for(var e,i,r=this.getLocalPointer(t),n=0,s=0,o=0,a=0,c=0,h=0,l=this._textLines.length;l>h&&o<=r.y;h++)o+=this.getHeightOfLine(h)*this.scaleY,c=h,h>0&&(a+=this._textLines[h-1].length+this.missingNewlineOffset(h-1));e=this._getLineLeftOffset(c),s=e*this.scaleX,i=this._textLines[c],"rtl"===this.direction&&(r.x=this.width*this.scaleX-r.x+s);for(var u=0,f=i.length;f>u&&(n=s,s+=this.__charBounds[c][u].kernedWidth*this.scaleX,s<=r.x);u++)a++;return this._getNewSelectionStartFromOffset(r,n,s,a,f)},_getNewSelectionStartFromOffset:function(t,e,i,r,n){var s=t.x-e,o=i-t.x,a=o>s||0>o?0:1,c=r+a;return this.flipX&&(c=n-c),c>this._text.length&&(c=this._text.length),c}});fabric.util.object.extend(fabric.IText.prototype,{initHiddenTextarea:function(){this.hiddenTextarea=fabric.document.createElement("textarea"),this.hiddenTextarea.setAttribute("autocapitalize","off"),this.hiddenTextarea.setAttribute("autocorrect","off"),this.hiddenTextarea.setAttribute("autocomplete","off"),this.hiddenTextarea.setAttribute("spellcheck","false"),this.hiddenTextarea.setAttribute("data-fabric-hiddentextarea",""),this.hiddenTextarea.setAttribute("wrap","off");var t=this._calcTextareaPosition();this.hiddenTextarea.style.cssText="position: absolute; top: "+t.top+"; left: "+t.left+"; z-index: -999; opacity: 0; width: 1px; height: 1px; font-size: 1px; paddingｰtop: "+t.fontSize+";",this.hiddenTextareaContainer?this.hiddenTextareaContainer.appendChild(this.hiddenTextarea):fabric.document.body.appendChild(this.hiddenTextarea),fabric.util.addListener(this.hiddenTextarea,"keydown",this.onKeyDown.bind(this)),fabric.util.addListener(this.hiddenTextarea,"keyup",this.onKeyUp.bind(this)),fabric.util.addListener(this.hiddenTextarea,"input",this.onInput.bind(this)),fabric.util.addListener(this.hiddenTextarea,"copy",this.copy.bind(this)),fabric.util.addListener(this.hiddenTextarea,"cut",this.copy.bind(this)),fabric.util.addListener(this.hiddenTextarea,"paste",this.paste.bind(this)),fabric.util.addListener(this.hiddenTextarea,"compositionstart",this.onCompositionStart.bind(this)),fabric.util.addListener(this.hiddenTextarea,"compositionupdate",this.onCompositionUpdate.bind(this)),fabric.util.addListener(this.hiddenTextarea,"compositionend",this.onCompositionEnd.bind(this)),!this._clickHandlerInitialized&&this.canvas&&(fabric.util.addListener(this.canvas.upperCanvasEl,"click",this.onClick.bind(this)),this._clickHandlerInitialized=!0)},keysMap:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorRight",36:"moveCursorLeft",37:"moveCursorLeft",38:"moveCursorUp",39:"moveCursorRight",40:"moveCursorDown"},keysMapRtl:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorLeft",36:"moveCursorRight",37:"moveCursorRight",38:"moveCursorUp",39:"moveCursorLeft",40:"moveCursorDown"},ctrlKeysMapUp:{67:"copy",88:"cut"},ctrlKeysMapDown:{65:"selectAll"},onClick:function(){this.hiddenTextarea&&this.hiddenTextarea.focus()},onKeyDown:function(t){if(this.isEditing){var e="rtl"===this.direction?this.keysMapRtl:this.keysMap;if(t.keyCode in e)this[e[t.keyCode]](t);else{if(!(t.keyCode in this.ctrlKeysMapDown&&(t.ctrlKey||t.metaKey)))return;this[this.ctrlKeysMapDown[t.keyCode]](t)}t.stopImmediatePropagation(),t.preventDefault(),t.keyCode>=33&&t.keyCode<=40?(this.inCompositionMode=!1,this.clearContextTop(),this.renderCursorOrSelection()):this.canvas&&this.canvas.requestRenderAll()}},onKeyUp:function(t){return!this.isEditing||this._copyDone||this.inCompositionMode?void(this._copyDone=!1):void(t.keyCode in this.ctrlKeysMapUp&&(t.ctrlKey||t.metaKey)&&(this[this.ctrlKeysMapUp[t.keyCode]](t),t.stopImmediatePropagation(),t.preventDefault(),this.canvas&&this.canvas.requestRenderAll()))},onInput:function(t){var e=this.fromPaste;if(this.fromPaste=!1,t&&t.stopPropagation(),this.isEditing){var i,r,n,s,o,a=this._splitTextIntoLines(this.hiddenTextarea.value).graphemeText,c=this._text.length,h=a.length,l=h-c,u=this.selectionStart,f=this.selectionEnd,d=u!==f;if(""===this.hiddenTextarea.value)return this.styles={},this.updateFromTextArea(),this.fire("changed"),void(this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll()));var g=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value),p=u>g.selectionStart;d?(i=this._text.slice(u,f),l+=f-u):c>h&&(i=p?this._text.slice(f+l,f):this._text.slice(u,u-l)),r=a.slice(g.selectionEnd-l,g.selectionEnd),i&&i.length&&(r.length&&(n=this.getSelectionStyles(u,u+1,!1),n=r.map(function(){return n[0]})),d?(s=u,o=f):p?(s=f-i.length,o=f):(s=f,o=f+i.length),this.removeStyleFromTo(s,o)),r.length&&(e&&r.join("")===fabric.copiedText&&!fabric.disableStyleCopyPaste&&(n=fabric.copiedTextStyle),this.insertNewStyleBlock(r,u,n)),this.updateFromTextArea(),this.fire("changed"),this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll())}},onCompositionStart:function(){this.inCompositionMode=!0},onCompositionEnd:function(){this.inCompositionMode=!1},onCompositionUpdate:function(t){this.compositionStart=t.target.selectionStart,this.compositionEnd=t.target.selectionEnd,this.updateTextareaPosition()},copy:function(){this.selectionStart!==this.selectionEnd&&(fabric.copiedText=this.getSelectedText(),fabric.copiedTextStyle=fabric.disableStyleCopyPaste?null:this.getSelectionStyles(this.selectionStart,this.selectionEnd,!0),this._copyDone=!0)},paste:function(){this.fromPaste=!0},_getClipboardData:function(t){return t&&t.clipboardData||fabric.window.clipboardData},_getWidthBeforeCursor:function(t,e){var i,r=this._getLineLeftOffset(t);return e>0&&(i=this.__charBounds[t][e-1],r+=i.left+i.width),r},getDownCursorOffset:function(t,e){var i=this._getSelectionForOffset(t,e),r=this.get2DCursorLocation(i),n=r.lineIndex;if(n===this._textLines.length-1||t.metaKey||34===t.keyCode)return this._text.length-i;var s=r.charIndex,o=this._getWidthBeforeCursor(n,s),a=this._getIndexOnLine(n+1,o),c=this._textLines[n].slice(s);return c.length+a+1+this.missingNewlineOffset(n)},_getSelectionForOffset:function(t,e){return t.shiftKey&&this.selectionStart!==this.selectionEnd&&e?this.selectionEnd:this.selectionStart},getUpCursorOffset:function(t,e){var i=this._getSelectionForOffset(t,e),r=this.get2DCursorLocation(i),n=r.lineIndex;if(0===n||t.metaKey||33===t.keyCode)return-i;var s=r.charIndex,o=this._getWidthBeforeCursor(n,s),a=this._getIndexOnLine(n-1,o),c=this._textLines[n].slice(0,s),h=this.missingNewlineOffset(n-1);return-this._textLines[n-1].length+a-c.length+(1-h)},_getIndexOnLine:function(t,e){for(var i,r,n=this._textLines[t],s=this._getLineLeftOffset(t),o=s,a=0,c=0,h=n.length;h>c;c++)if(i=this.__charBounds[t][c].width,o+=i,o>e){r=!0;var l=o-i,u=o,f=Math.abs(l-e),d=Math.abs(u-e);a=f>d?c:c-1;break}return r||(a=n.length-1),a},moveCursorDown:function(t){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorUpOrDown("Down",t)},moveCursorUp:function(t){(0!==this.selectionStart||0!==this.selectionEnd)&&this._moveCursorUpOrDown("Up",t)},_moveCursorUpOrDown:function(t,e){var i="get"+t+"CursorOffset",r=this[i](e,"right"===this._selectionDirection);e.shiftKey?this.moveCursorWithShift(r):this.moveCursorWithoutShift(r),0!==r&&(this.setSelectionInBoundaries(),this.abortCursorAnimation(),this._currentCursorOpacity=1,this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorWithShift:function(t){var e="left"===this._selectionDirection?this.selectionStart+t:this.selectionEnd+t;return this.setSelectionStartEndWithShift(this.selectionStart,this.selectionEnd,e),0!==t},moveCursorWithoutShift:function(t){return 0>t?(this.selectionStart+=t,this.selectionEnd=this.selectionStart):(this.selectionEnd+=t,this.selectionStart=this.selectionEnd),0!==t},moveCursorLeft:function(t){(0!==this.selectionStart||0!==this.selectionEnd)&&this._moveCursorLeftOrRight("Left",t)},_move:function(t,e,i){var r;if(t.altKey)r=this["findWordBoundary"+i](this[e]);else{if(!t.metaKey&&35!==t.keyCode&&36!==t.keyCode)return this[e]+="Left"===i?-1:1,!0;r=this["findLineBoundary"+i](this[e])}return void 0!==typeof r&&this[e]!==r?(this[e]=r,!0):void 0},_moveLeft:function(t,e){return this._move(t,e,"Left")},_moveRight:function(t,e){return this._move(t,e,"Right")},moveCursorLeftWithoutShift:function(t){var e=!0;return this._selectionDirection="left",this.selectionEnd===this.selectionStart&&0!==this.selectionStart&&(e=this._moveLeft(t,"selectionStart")),this.selectionEnd=this.selectionStart,e},moveCursorLeftWithShift:function(t){return"right"===this._selectionDirection&&this.selectionStart!==this.selectionEnd?this._moveLeft(t,"selectionEnd"):0!==this.selectionStart?(this._selectionDirection="left",this._moveLeft(t,"selectionStart")):void 0},moveCursorRight:function(t){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorLeftOrRight("Right",t)},_moveCursorLeftOrRight:function(t,e){var i="moveCursor"+t+"With";this._currentCursorOpacity=1,i+=e.shiftKey?"Shift":"outShift",this[i](e)&&(this.abortCursorAnimation(),this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorRightWithShift:function(t){return"left"===this._selectionDirection&&this.selectionStart!==this.selectionEnd?this._moveRight(t,"selectionStart"):this.selectionEnd!==this._text.length?(this._selectionDirection="right",this._moveRight(t,"selectionEnd")):void 0},moveCursorRightWithoutShift:function(t){var e=!0;return this._selectionDirection="right",this.selectionStart===this.selectionEnd?(e=this._moveRight(t,"selectionStart"),this.selectionEnd=this.selectionStart):this.selectionStart=this.selectionEnd,e},removeChars:function(t,e){"undefined"==typeof e&&(e=t+1),this.removeStyleFromTo(t,e),this._text.splice(t,e-t),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()},insertChars:function(t,e,i,r){"undefined"==typeof r&&(r=i),r>i&&this.removeStyleFromTo(i,r);var n=fabric.util.string.graphemeSplit(t);this.insertNewStyleBlock(n,i,e),this._text=[].concat(this._text.slice(0,i),n,this._text.slice(r)),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()}});!function(){var t=fabric.util.toFixed,e=/ +/g;fabric.util.object.extend(fabric.Text.prototype,{_toSVG:function(){var t=this._getSVGLeftTopOffsets(),e=this._getSVGTextAndBg(t.textTop,t.textLeft);return this._wrapSVGTextAndBg(e)},toSVG:function(t){return this._createBaseSVGMarkup(this._toSVG(),{reviver:t,noStyle:!0,withShadow:!0})},_getSVGLeftTopOffsets:function(){return{textLeft:-this.width/2,textTop:-this.height/2,lineTop:this.getHeightOfLine(0)}},_wrapSVGTextAndBg:function(t){var e=!0,i=this.getSvgTextDecoration(this);return[t.textBgRects.join(""),' <text xml:space="preserve" ',this.fontFamily?'font-family="'+this.fontFamily.replace(/"/g,"'")+'" ':"",this.fontSize?'font-size="'+this.fontSize+'" ':"",this.fontStyle?'font-style="'+this.fontStyle+'" ':"",this.fontWeight?'font-weight="'+this.fontWeight+'" ':"",i?'text-decoration="'+i+'" ':"",'style="',this.getSvgStyles(e),'"',this.addPaintOrder()," >",t.textSpans.join(""),"</text>\n"]},_getSVGTextAndBg:function(t,e){var i,r=[],n=[],s=t;this._setSVGBg(n);for(var o=0,a=this._textLines.length;a>o;o++)i=this._getLineLeftOffset(o),(this.textBackgroundColor||this.styleHas("textBackgroundColor",o))&&this._setSVGTextLineBg(n,o,e+i,s),this._setSVGTextLineText(r,o,e+i,s),s+=this.getHeightOfLine(o);return{textSpans:r,textBgRects:n}},_createTextCharSpan:function(i,r,n,s){var o=i!==i.trim()||i.match(e),a=this.getSvgSpanStyles(r,o),c=a?'style="'+a+'"':"",h=r.deltaY,l="",u=fabric.Object.NUM_FRACTION_DIGITS;return h&&(l=' dy="'+t(h,u)+'" '),['<tspan x="',t(n,u),'" y="',t(s,u),'" ',l,c,">",fabric.util.string.escapeXml(i),"</tspan>"].join("")},_setSVGTextLineText:function(t,e,i,r){var n,s,o,a,c,h=this.getHeightOfLine(e),l=-1!==this.textAlign.indexOf("justify"),u="",f=0,d=this._textLines[e];r+=h*(1-this._fontSizeFraction)/this.lineHeight;for(var g=0,p=d.length-1;p>=g;g++)c=g===p||this.charSpacing,u+=d[g],o=this.__charBounds[e][g],0===f?(i+=o.kernedWidth-o.width,f+=o.width):f+=o.kernedWidth,l&&!c&&this._reSpaceAndTab.test(d[g])&&(c=!0),c||(n=n||this.getCompleteStyleDeclaration(e,g),s=this.getCompleteStyleDeclaration(e,g+1),c=fabric.util.hasStyleChanged(n,s,!0)),c&&(a=this._getStyleDeclaration(e,g)||{},t.push(this._createTextCharSpan(u,a,i,r)),u="",n=s,i+=f,f=0)},_pushTextBgRect:function(e,i,r,n,s,o){var a=fabric.Object.NUM_FRACTION_DIGITS;e.push(" <rect ",this._getFillAttributes(i),' x="',t(r,a),'" y="',t(n,a),'" width="',t(s,a),'" height="',t(o,a),'"></rect>\n')},_setSVGTextLineBg:function(t,e,i,r){for(var n,s,o=this._textLines[e],a=this.getHeightOfLine(e)/this.lineHeight,c=0,h=0,l=this.getValueOfPropertyAt(e,0,"textBackgroundColor"),u=0,f=o.length;f>u;u++)n=this.__charBounds[e][u],s=this.getValueOfPropertyAt(e,u,"textBackgroundColor"),s!==l?(l&&this._pushTextBgRect(t,l,i+h,r,c,a),h=n.left,c=n.width,l=s):c+=n.kernedWidth;s&&this._pushTextBgRect(t,s,i+h,r,c,a)},_getFillAttributes:function(t){var e=t&&"string"==typeof t?new fabric.Color(t):"";return e&&e.getSource()&&1!==e.getAlpha()?'opacity="'+e.getAlpha()+'" fill="'+e.setAlpha(1).toRgb()+'"':'fill="'+t+'"'},_getSVGLineTopOffset:function(t){for(var e=0,i=0,r=0;t>r;r++)e+=this.getHeightOfLine(r);return i=this.getHeightOfLine(r),{lineTop:e,offset:(this._fontSizeMult-this._fontSizeFraction)*i/(this.lineHeight*this._fontSizeMult)}},getSvgStyles:function(t){var e=fabric.Object.prototype.getSvgStyles.call(this,t);return e+" white-space: pre;"}})}();!function(t){"use strict";var e=t.fabric||(t.fabric={});e.Textbox=e.util.createClass(e.IText,e.Observable,{type:"textbox",minWidth:20,dynamicMinWidth:2,__cachedLines:null,lockScalingFlip:!0,noScaleCache:!1,_dimensionAffectingProps:e.Text.prototype._dimensionAffectingProps.concat("width"),_wordJoiners:/[ \t\r]/,splitByGrapheme:!1,initDimensions:function(){this.__skipDimension||(this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this._clearCache(),this.dynamicMinWidth=0,this._styleMap=this._generateStyleMap(this._splitText()),this.dynamicMinWidth>this.width&&this._set("width",this.dynamicMinWidth),-1!==this.textAlign.indexOf("justify")&&this.enlargeSpaces(),this.height=this.calcTextHeight(),this.saveState({propertySet:"_dimensionAffectingProps"}))},_generateStyleMap:function(t){for(var e=0,i=0,r=0,n={},s=0;s<t.graphemeLines.length;s++)"\n"===t.graphemeText[r]&&s>0?(i=0,r++,e++):!this.splitByGrapheme&&this._reSpaceAndTab.test(t.graphemeText[r])&&s>0&&(i++,r++),n[s]={line:e,offset:i},r+=t.graphemeLines[s].length,i+=t.graphemeLines[s].length;return n},styleHas:function(t,i){if(this._styleMap&&!this.isWrapping){var r=this._styleMap[i];r&&(i=r.line)}return e.Text.prototype.styleHas.call(this,t,i)},isEmptyStyles:function(t){if(!this.styles)return!0;var e,i,r=0,n=t+1,s=!1,o=this._styleMap[t],a=this._styleMap[t+1];o&&(t=o.line,r=o.offset),a&&(n=a.line,s=n===t,e=a.offset),i="undefined"==typeof t?this.styles:{line:this.styles[t]};for(var c in i)for(var h in i[c])if(h>=r&&(!s||e>h))for(var l in i[c][h])return!1;return!0},_getStyleDeclaration:function(t,e){if(this._styleMap&&!this.isWrapping){var i=this._styleMap[t];if(!i)return null;t=i.line,e=i.offset+e}return this.callSuper("_getStyleDeclaration",t,e)},_setStyleDeclaration:function(t,e,i){var r=this._styleMap[t];t=r.line,e=r.offset+e,this.styles[t][e]=i},_deleteStyleDeclaration:function(t,e){var i=this._styleMap[t];t=i.line,e=i.offset+e,delete this.styles[t][e]},_getLineStyle:function(t){var e=this._styleMap[t];return!!this.styles[e.line]},_setLineStyle:function(t){var e=this._styleMap[t];this.styles[e.line]={}},_wrapText:function(t,e){var i,r=[];for(this.isWrapping=!0,i=0;i<t.length;i++)r=r.concat(this._wrapLine(t[i],i,e));return this.isWrapping=!1,r},_measureWord:function(t,e,i){var r,n=0,s=!0;i=i||0;for(var o=0,a=t.length;a>o;o++){var c=this._getGraphemeBox(t[o],e,o+i,r,s);n+=c.kernedWidth,r=t[o]}return n},_wrapLine:function(t,i,r,n){var s=0,o=this.splitByGrapheme,a=[],c=[],h=o?e.util.string.graphemeSplit(t):t.split(this._wordJoiners),l="",u=0,f=o?"":" ",d=0,g=0,p=0,v=!0,m=this._getWidthOfCharSpacing(),n=n||0;0===h.length&&h.push([]),r-=n;for(var b=0;b<h.length;b++)l=o?h[b]:e.util.string.graphemeSplit(h[b]),d=this._measureWord(l,i,u),u+=l.length,s+=g+d-m,s>r&&!v?(a.push(c),c=[],s=d,v=!0):s+=m,v||o||c.push(f),c=c.concat(l),g=o?0:this._measureWord([f],i,u),u++,v=!1,d>p&&(p=d);return b&&a.push(c),p+n>this.dynamicMinWidth&&(this.dynamicMinWidth=p-m+n),a},isEndOfWrapping:function(t){return this._styleMap[t+1]?this._styleMap[t+1].line!==this._styleMap[t].line?!0:!1:!0},missingNewlineOffset:function(t){return this.splitByGrapheme?this.isEndOfWrapping(t)?1:0:1},_splitTextIntoLines:function(t){for(var i=e.Text.prototype._splitTextIntoLines.call(this,t),r=this._wrapText(i.lines,this.width),n=new Array(r.length),s=0;s<r.length;s++)n[s]=r[s].join("");return i.lines=n,i.graphemeLines=r,i},getMinWidth:function(){return Math.max(this.minWidth,this.dynamicMinWidth)},_removeExtraneousStyles:function(){var t={};for(var e in this._styleMap)this._textLines[e]&&(t[this._styleMap[e].line]=1);for(var e in this.styles)t[e]||delete this.styles[e]},toObject:function(t){return this.callSuper("toObject",["minWidth","splitByGrapheme"].concat(t))}}),e.Textbox.fromObject=function(t,i){return t.styles=e.util.stylesFromArray(t.styles,t.text),e.Object._fromObject("Textbox",t,i,"text")}}("undefined"!=typeof exports?exports:this);!function(){var t=fabric.controlsUtils,e=t.scaleSkewCursorStyleHandler,i=t.scaleCursorStyleHandler,r=t.scalingEqually,n=t.scalingYOrSkewingX,s=t.scalingXOrSkewingY,o=t.scaleOrSkewActionName,a=fabric.Object.prototype.controls;if(a.ml=new fabric.Control({x:-.5,y:0,cursorStyleHandler:e,actionHandler:s,getActionName:o}),a.mr=new fabric.Control({x:.5,y:0,cursorStyleHandler:e,actionHandler:s,getActionName:o}),a.mb=new fabric.Control({x:0,y:.5,cursorStyleHandler:e,actionHandler:n,getActionName:o}),a.mt=new fabric.Control({x:0,y:-.5,cursorStyleHandler:e,actionHandler:n,getActionName:o}),a.tl=new fabric.Control({x:-.5,y:-.5,cursorStyleHandler:i,actionHandler:r}),a.tr=new fabric.Control({x:.5,y:-.5,cursorStyleHandler:i,actionHandler:r}),a.bl=new fabric.Control({x:-.5,y:.5,cursorStyleHandler:i,actionHandler:r}),a.br=new fabric.Control({x:.5,y:.5,cursorStyleHandler:i,actionHandler:r}),a.mtr=new fabric.Control({x:0,y:-.5,actionHandler:t.rotationWithSnapping,cursorStyleHandler:t.rotationStyleHandler,offsetY:-40,withConnection:!0,actionName:"rotate"}),fabric.Textbox){var c=fabric.Textbox.prototype.controls={};c.mtr=a.mtr,c.tr=a.tr,c.br=a.br,c.tl=a.tl,c.bl=a.bl,c.mt=a.mt,c.mb=a.mb,c.mr=new fabric.Control({x:.5,y:0,actionHandler:t.changeWidth,cursorStyleHandler:e,actionName:"resizing"}),c.ml=new fabric.Control({x:-.5,y:0,actionHandler:t.changeWidth,cursorStyleHandler:e,actionName:"resizing"})}}();!function(){fabric.Object.ENLIVEN_PROPS.push("eraser");var t=fabric.Object.prototype._drawClipPath,e=fabric.Object.prototype.needsItsOwnCache,i=fabric.Object.prototype.toObject,r=fabric.Object.prototype.getSvgCommons,n=fabric.Object.prototype._createBaseClipPathSVGMarkup,s=fabric.Object.prototype._createBaseSVGMarkup;fabric.Object.prototype.cacheProperties.push("eraser"),fabric.Object.prototype.stateProperties.push("eraser"),fabric.util.object.extend(fabric.Object.prototype,{erasable:!0,eraser:void 0,needsItsOwnCache:function(){return e.call(this)||!!this.eraser},_drawClipPath:function(e,i){if(t.call(this,e,i),this.eraser){var r=this._getNonTransformedDimensions();this.eraser.isType("eraser")&&this.eraser.set({width:r.x,height:r.y}),t.call(this,e,this.eraser)}},toObject:function(t){var e=i.call(this,["erasable"].concat(t));return this.eraser&&!this.eraser.excludeFromExport&&(e.eraser=this.eraser.toObject(t)),e},getSvgCommons:function(){return r.call(this)+(this.eraser?'mask="url(#'+this.eraser.clipPathId+')" ':"")},_createEraserSVGMarkup:function(t){return this.eraser?(this.eraser.clipPathId="MASK_"+fabric.Object.__uid++,['<mask id="',this.eraser.clipPathId,'" >',this.eraser.toSVG(t),"</mask>","\n"].join("")):""},_createBaseClipPathSVGMarkup:function(t,e){return[this._createEraserSVGMarkup(e&&e.reviver),n.call(this,t,e)].join("")},_createBaseSVGMarkup:function(t,e){return[this._createEraserSVGMarkup(e&&e.reviver),s.call(this,t,e)].join("")}});var o=fabric.Group.prototype._restoreObjectsState;fabric.util.object.extend(fabric.Group.prototype,{_addEraserPathToObjects:function(t){this._objects.forEach(function(e){fabric.EraserBrush.prototype._addPathToObjectEraser.call(fabric.EraserBrush.prototype,e,t)})},applyEraserToObjects:function(){var t=this,e=this.eraser;if(e){delete this.eraser;var i=t.calcTransformMatrix();e.clone(function(e){var r=t.clipPath;e.getObjects("path").forEach(function(e){var n=fabric.util.multiplyTransformMatrices(i,e.calcTransformMatrix());fabric.util.applyTransformToObject(e,n),r?r.clone(function(r){var n=fabric.EraserBrush.prototype.applyClipPathToPath.call(fabric.EraserBrush.prototype,e,r,i);t._addEraserPathToObjects(n)},["absolutePositioned","inverted"]):t._addEraserPathToObjects(e)})})}},_restoreObjectsState:function(){return this.erasable===!0&&this.applyEraserToObjects(),o.call(this)}}),fabric.Eraser=fabric.util.createClass(fabric.Group,{type:"eraser",originX:"center",originY:"center",drawObject:function(t){t.save(),t.fillStyle="black",t.fillRect(-this.width/2,-this.height/2,this.width,this.height),t.restore(),this.callSuper("drawObject",t)},_getBounds:function(){},_toSVG:function(t){var e=["<g ","COMMON_PARTS"," >\n"],i=-this.width/2,r=-this.height/2,n=["<rect ",'fill="white" ','x="',i,'" y="',r,'" width="',this.width,'" height="',this.height,'" />\n'].join("");e.push(" ",n);for(var s=0,o=this._objects.length;o>s;s++)e.push(" ",this._objects[s].toSVG(t));return e.push("</g>\n"),e}}),fabric.Eraser.fromObject=function(t,e){var i=t.objects;fabric.util.enlivenObjects(i,function(i){var r=fabric.util.object.clone(t,!0);delete r.objects,fabric.util.enlivenObjectEnlivables(t,r,function(){e&&e(new fabric.Eraser(i,r,!0))})})};var a=fabric.Canvas.prototype._renderOverlay;fabric.util.object.extend(fabric.Canvas.prototype,{isErasing:function(){return this.isDrawingMode&&this.freeDrawingBrush&&"eraser"===this.freeDrawingBrush.type&&this.freeDrawingBrush._isErasing},_renderOverlay:function(t){a.call(this,t),this.isErasing()&&!this.freeDrawingBrush.inverted&&this.freeDrawingBrush._render()}}),fabric.EraserBrush=fabric.util.createClass(fabric.PencilBrush,{type:"eraser",inverted:!1,_isErasing:!1,_isErasable:function(t){return t.erasable!==!1},_prepareCollectionTraversal:function(t,e,i){t.forEachObject(function(r){r.forEachObject&&"deep"===r.erasable?this._prepareCollectionTraversal(r,e,i):!this.inverted&&r.erasable&&r.visible?(r.visible=!1,t.dirty=!0,i.visibility.push(r),i.collection.push(t)):this.inverted&&r.visible&&(r.erasable&&r.eraser?(r.eraser.inverted=!0,r.dirty=!0,t.dirty=!0,i.eraser.push(r),i.collection.push(t)):(r.visible=!1,t.dirty=!0,i.visibility.push(r),i.collection.push(t)))},this)},preparePattern:function(){this._patternCanvas||(this._patternCanvas=fabric.util.createCanvasElement());var t=this._patternCanvas;t.width=this.canvas.width,t.height=this.canvas.height;var e=t.getContext("2d");if(this.canvas._isRetinaScaling()){var i=this.canvas.getRetinaScaling();this.canvas.__initRetinaScaling(i,t,e)}var r=this.canvas.backgroundImage,n=r&&this._isErasable(r),s=this.canvas.overlayImage,o=s&&this._isErasable(s);if(!this.inverted&&(r&&!n||this.canvas.backgroundColor))n&&(this.canvas.backgroundImage=void 0),this.canvas._renderBackground(e),n&&(this.canvas.backgroundImage=r);else if(this.inverted&&r&&n){var c=this.canvas.backgroundColor;this.canvas.backgroundColor=void 0,this.canvas._renderBackground(e),this.canvas.backgroundColor=c}e.save(),e.transform.apply(e,this.canvas.viewportTransform);var h={visibility:[],eraser:[],collection:[]};if(this._prepareCollectionTraversal(this.canvas,e,h),this.canvas._renderObjects(e,this.canvas._objects),h.visibility.forEach(function(t){t.visible=!0}),h.eraser.forEach(function(t){t.eraser.inverted=!1,t.dirty=!0}),h.collection.forEach(function(t){t.dirty=!0}),e.restore(),!this.inverted&&(s&&!o||this.canvas.overlayColor))o&&(this.canvas.overlayImage=void 0),a.call(this.canvas,e),o&&(this.canvas.overlayImage=s);else if(this.inverted&&s&&o){var c=this.canvas.overlayColor;this.canvas.overlayColor=void 0,a.call(this.canvas,e),this.canvas.overlayColor=c}},_setBrushStyles:function(t){this.callSuper("_setBrushStyles",t),t.strokeStyle="black"},_saveAndTransform:function(t){this.callSuper("_saveAndTransform",t),this._setBrushStyles(t),t.globalCompositeOperation=t===this.canvas.getContext()?"destination-out":"source-over"},needsFullRender:function(){return!0},onMouseDown:function(t,e){this.canvas._isMainEvent(e.e)&&(this._prepareForDrawing(t),this._captureDrawingPath(t),this.preparePattern(),this._isErasing=!0,this.canvas.fire("erasing:start"),this._render())},_render:function(){var t;this.inverted||(t=this.canvas.getContext(),this.callSuper("_render",t)),t=this.canvas.contextTop,this.canvas.clearContext(t),this.callSuper("_render",t),t.save();var e=this.canvas.getRetinaScaling(),i=1/e;t.scale(i,i),t.globalCompositeOperation="source-in",t.drawImage(this._patternCanvas,0,0),t.restore()},createPath:function(t){var e=this.callSuper("createPath",t);return e.globalCompositeOperation=this.inverted?"source-over":"destination-out",e.stroke=this.inverted?"white":"black",e},applyClipPathToPath:function(t,e,i){var r=fabric.util.invertTransform(t.calcTransformMatrix()),n=e.calcTransformMatrix(),s=e.absolutePositioned?r:fabric.util.multiplyTransformMatrices(r,i);return e.absolutePositioned=!1,fabric.util.applyTransformToObject(e,fabric.util.multiplyTransformMatrices(s,n)),t.clipPath=t.clipPath?fabric.util.mergeClipPaths(e,t.clipPath):e,t},clonePathWithClipPath:function(t,e,i){var r=e.calcTransformMatrix(),n=e.clipPath,s=this;t.clone(function(t){n.clone(function(e){i(s.applyClipPathToPath(t,e,r))},["absolutePositioned","inverted"])})},_addPathToObjectEraser:function(t,e){var i=this;if(t.forEachObject&&"deep"===t.erasable){var r=t._objects.filter(function(t){return t.erasable});return void(r.length>0&&t.clipPath?this.clonePathWithClipPath(e,t,function(t){r.forEach(function(e){i._addPathToObjectEraser(e,t)})}):r.length>0&&r.forEach(function(t){i._addPathToObjectEraser(t,e)}))}var n=t.eraser;n||(n=new fabric.Eraser,t.eraser=n),e.clone(function(e){var r=fabric.util.multiplyTransformMatrices(fabric.util.invertTransform(t.calcTransformMatrix()),e.calcTransformMatrix());fabric.util.applyTransformToObject(e,r),n.addWithUpdate(e),t.set("dirty",!0),t.fire("erasing:end",{path:e}),t.group&&Array.isArray(i.__subTargets)&&i.__subTargets.push(t)})},applyEraserToCanvas:function(t){var e=this.canvas,i={};return["backgroundImage","overlayImage"].forEach(function(r){var n=e[r];n&&n.erasable&&(this._addPathToObjectEraser(n,t),i[r]=n)},this),i},_finalizeAndAddPath:function(){var t=this.canvas.contextTop,e=this.canvas;t.closePath(),this.decimate&&(this._points=this.decimatePoints(this._points,this.decimate)),e.clearContext(e.contextTop),this._isErasing=!1;var i=this._points&&this._points.length>1?this.convertPointsToSVGPath(this._points):null;if(!i||this._isEmptySVGPath(i))return e.fire("erasing:end"),void e.requestRenderAll();var r=this.createPath(i);r.setCoords(),e.fire("before:path:created",{path:r});var n=this.applyEraserToCanvas(r),s=this;this.__subTargets=[];var o=[];e.forEachObject(function(t){t.erasable&&t.intersectsWithObject(r,!0,!0)&&(s._addPathToObjectEraser(t,r),o.push(t))}),e.fire("erasing:end",{path:r,targets:o,subTargets:this.__subTargets,drawables:n}),delete this.__subTargets,e.requestRenderAll(),this._resetShadow(),e.fire("path:created",{path:r})}})}(); \ No newline at end of file diff --git a/spaces/biodasturchi/esmfold_bio/README.md b/spaces/biodasturchi/esmfold_bio/README.md deleted file mode 100644 index 16b7695af2a489bb21e628d6c00b2f9e2dc476b4..0000000000000000000000000000000000000000 --- a/spaces/biodasturchi/esmfold_bio/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Esmfold Bio -emoji: 🌍 -colorFrom: indigo -colorTo: gray -sdk: streamlit -sdk_version: 1.15.2 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/bioriAsaeru/text-to-voice/Download Film Kiamat 2012 Sub Indonesia Apa yang Terjadi Jika Bumi Hancur?.md b/spaces/bioriAsaeru/text-to-voice/Download Film Kiamat 2012 Sub Indonesia Apa yang Terjadi Jika Bumi Hancur?.md deleted file mode 100644 index c60ef997b2a5d96d56640f5448102cbd6fa5afd6..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Download Film Kiamat 2012 Sub Indonesia Apa yang Terjadi Jika Bumi Hancur?.md +++ /dev/null @@ -1,11 +0,0 @@ - -<p>nonton 2012 (2009) sub indo</strong>Bagaimana pendapat Anda? 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Intentionally choosing to browse the web with a three year old browser, as I did, is an incredibly dangerous thing to do.The consequences in this case are fairly minimal since this isn't even my secondary machine-- it's a special-purpose PC dedicated to gaming. Reinstalling the operating system is no big deal. But it's still an inconvenient timesink, and in any case, the spyware infestation has to be dealt with because it causes serious performance problems and will even interrupt gameplay with incessant popups.The two most common sites for no-cd patches are MegaGames and GameCopyWorld. In case you're wondering, yes, I do own all my games. I download no-cd patches for convenience's sake; I consider them a privilege of ownership for knowledgeable, ethical PC gamers. I figured the infection came from one of these sites. So I set up a honeypot virtual machine under Virtual PC 2007, using the ancient, original 2001 release of Windows XP and the classic Devil's Own key, and began testing.Here's a shot of Task Manager at the desktop, after installing the necessary virtual machine additions. This is a completely plain vanilla, clean Windows XP installation: no service packs, no updates, no nothing. This system is connected to the internet, but it's not as dangerous as it sounds. Because it's behind a NAT router that blocks all incoming connections, there's no way it can get <i>passively</i> infected. I let it connect to the internet and quiesce at the desktop for about an hour, just to prove my point. <b>No passive infections occurred behind a NAT router</b>, even for this woefully out of date September 2001 era install of Windows XP.Now we're leaving passivity behind, and unwisely <b>browsing the open internet with the unpatched, six year old original version of Internet Explorer 6.0</b>. Danger, Will Robinson! I left Task Manager running as I browsed to MegaGames, downloaded a no-cd patch, and... nothing. I then visited GameCopyWorld, downloaded a no-cd patch, and... all of a sudden, it's crystal clear who the culprit is. Check out Task Manager now:This comes as a shock to me, because GameCopyWorld is recommended often in gaming forums. I consider(ed) it a reputable web site. I've never had a problem with the site before, because I usually surf with the latest updates. But the unpatched browser spyware infestation from visiting GCW-- <b>just from visiting the web pages, even if you don't download a single thing</b>-- is nearly immediate and completely devastating. The virtual machine desktop, after a few scant minutes, tells the story:It isn't pretty, and let me tell you, <b>I have a new degree of sympathy for the poor users who become the unfortunate victims of spyware infestations</b>. The machine becomes borderline unusable, between...<ul><li>new icons that magically appear on your desktop<li>full-screen popups that occur every two minutes<li>dialog boxes that offer to "install antivirus software" with only an OK button<li>system performance degradation from all those spyware background processes</li></li></li></li></ul>... it's a wonder people don't just give up on computing altogether. Once the door is open, it seems the entire neighborhood of malware, spyware, and adware vendors take up residence in your machine. There should be a special circle of hell reserved for companies who make money doing this to people.At first, I was mad at myself for letting this happen. I should know better, and I <i>do</i> know better. Then I channeled that anger into action: <b>this is my machine, and I'll be damned if I will stand for any slimy, unwanted malware, adware, or spyware that takes up residence on it.</b> I resolved to clean up my own machine and fix the mess I made. It's easier than you might think, and I'll show you exactly how I did it.Our first order of business is to <b>stop any spyware that's currently running</b>. You'll need something a bit more heavy-duty than mere Task Manager-- get Sysinternals' Process Explorer. Download it, run it, and sort the process list by Company Name.<b>Kill any processes that don't have a Company Name</b> (with the exception of DPCs, Interrupts, System, and System Idle Process). Right-click the processes and select Kill, or select them and press the Delete key. You can use my initial screenshot of Task Manager, at the top of this post, as a reference for what <i>should</i> be running in a clean Windows XP installation. But there's usually no need to be that specific; unless it has a Company Name you recognize, it's highly likely to be a rogue application and should be terminated.Stopping the running spyware is only half the battle. Now we need to <b>stop the spyware from restarting the next time we boot the system</b>. Msconfig is a partial solution, but again we need something more powerful than what is provided out of the box. Namely, SysInternals' AutoRuns utility. Download it, run it, and start browsing through the list that appears:As you can see, there's a bunch of spyware, malware, adware, and god knows what else gunking up the works-- all from visiting a <i>single</i> website! <b>Scroll through the list, all the way to the bottom, scanning for blank Publishers, or any Publisher you don't recognize. If you see anything that's suspect, delete it!</b> In a default Windows install, 99.5% of the entries will have "Microsoft Corporation" as the Publisher. Any <i>reputable</i> vendor will have no problem attaching their name to their work, so it's generally only the blank entries you need to worry about.Now <b>reboot the system</b>. We've removed most of the spyware infestation, but there's a certain much more virulent class of spyware that can survive this treatment. We'll deal with them next.After rebooting, check Process Explorer and Autoruns for anything suspicious, exactly as we did before. The first thing I noticed that "came back" in Autoruns was a suspicious driver, core.sys, that didn't have a Publisher. I used <b>the powerful Find | Find Handle or DLL menu in Process Explorer</b> to locate any active references to this file.Unfortunately I didn't capture the right screenshot at the time, so I'm showing a generic search result above. Anyway, there was exactly one open handle to the core.sys file. I selected the result, which highlights the corresponding handle in the lower pane of the Process Explorer view. Right-click the handle entry in the lower pane and click "Close Handle".After I closed the handle, I could physically delete the rogue core.sys file from the filesystem, along with the Autoruns entry for it. Problem solved!The other item that reappeared in Autoruns after the reboot was an <b>oddly named DLL file with hooks into Winlogon and Explorer</b>. In addition to the suspicious name, each entry carries the tell-tale sign of the missing Publisher value:Delete the entries in Autoruns all you want; they'll keep coming back when you press F5 to refresh. This rogue, randomly named DLL continually monitors to make sure its ugly little hooks are in place. The nasty thing about processes attached to Winlogon is that they're very difficult to kill or remove. We can kill Explorer, but <b>killing Winlogon is not an option</b>; it's the root process of Windows, so shutting it down causes the OS to restart. It's a difficult catch-22.But we're smarter than the malware vendors. Fire up Process Explorer and use the Find | Find Handle or DLL menu to locate all the instances of this DLL by name. (See, I told you this option was powerful.) Kill any open handles to this file that you find, exactly as we did before. But you'll need to go one step further. We know from the Autoruns that this DLL is likely to be attached to the Explorer and Winlogon processes, but let the find results be your guide. Double-click on any processes you found that reference this DLL. <b>In the process properties dialog, select the Threads tab. Scroll through the threads and kill every one that has the rogue DLL loaded.</b>Once you've killed all the threads, you can finally delete the entries in Autoruns without them coming back. Reboot, and your machine is now completely free of spyware. <b>I count 17 entries in Task Manager, exactly the same number as when I originally started.</b>Of course, the smartest thing to do is <b>not to get infected with spyware, malware, or adware in the first place</b>. I can't emphasize this enough: <i>always browse with the latest patches for your preferred web browser</i>. 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Overview ... 1fdad05405<br /> -<br /> -<br /> -<p></p> diff --git a/spaces/bleysg/Phind-CodeLlama-34B-v2/README.md b/spaces/bleysg/Phind-CodeLlama-34B-v2/README.md deleted file mode 100644 index de66f227f877baf121e21a27aedbaef382a56f6a..0000000000000000000000000000000000000000 --- a/spaces/bleysg/Phind-CodeLlama-34B-v2/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Phind-CodeLlama-34B-v2 -emoji: 🔍 -colorFrom: orange -colorTo: purple -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/adversarial/discriminators/__init__.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/adversarial/discriminators/__init__.py deleted file mode 100644 index f9e5ff59950ee0b1d1a67c9b3831d67d08048148..0000000000000000000000000000000000000000 --- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/adversarial/discriminators/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -# flake8: noqa -from .mpd import MultiPeriodDiscriminator -from .msd import MultiScaleDiscriminator -from .msstftd import MultiScaleSTFTDiscriminator diff --git a/spaces/breadlicker45/Text-to-music-longer/app.py b/spaces/breadlicker45/Text-to-music-longer/app.py deleted file mode 100644 index f1bced5a9c778538db323dc35e501cabd771090c..0000000000000000000000000000000000000000 --- a/spaces/breadlicker45/Text-to-music-longer/app.py +++ /dev/null @@ -1,100 +0,0 @@ -import time - -import gradio as gr -from sentence_transformers import SentenceTransformer - -import httpx -import json - -from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat - -minilm = SentenceTransformer('all-MiniLM-L6-v2') -mubert_tags_embeddings = get_mubert_tags_embeddings(minilm) - - -def get_track_by_tags(tags, pat, duration, maxit=20, loop=False): - if loop: - mode = "loop" - else: - mode = "track" - r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM', - json={ - "method": "RecordTrackTTM", - "params": { - "pat": pat, - "duration": duration, - "tags": tags, - "mode": mode - } - }) - - rdata = json.loads(r.text) - assert rdata['status'] == 1, rdata['error']['text'] - trackurl = rdata['data']['tasks'][0]['download_link'] - - 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(email, prompt, duration, loop=False): - try: - pat = get_pat(email) - _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0] - return get_track_by_tags(tags, pat, int(duration), loop=loop), "Success", ",".join(tags) - except Exception as e: - return None, str(e), "" - - -block = gr.Blocks() - -with block: - gr.HTML( - """ - <div style="text-align: center; max-width: 700px; margin: 0 auto;"> - <div - style=" - display: inline-flex; - align-items: center; - gap: 0.8rem; - font-size: 1.75rem; - " - > - <h1 style="font-weight: 900; margin-bottom: 7px;"> - Mubert - </h1> - </div> - <p style="margin-bottom: 10px; font-size: 94%"> - All music is generated by Mubert API – <a href="https://mubert.com" style="text-decoration: underline;" target="_blank">www.mubert.com</a> - </p> - </div> - """ - ) - with gr.Group(): - with gr.Box(): - email = gr.Textbox(label="email") - prompt = gr.Textbox(label="prompt") - duration = gr.Slider(label="duration (seconds)", value=100, maximum=300) - is_loop = gr.Checkbox(label="Generate loop") - out = gr.Audio() - result_msg = gr.Text(label="Result message") - tags = gr.Text(label="Tags") - btn = gr.Button("Submit").style(full_width=True) - - btn.click(fn=generate_track_by_prompt, inputs=[email, prompt, duration, is_loop], outputs=[out, result_msg, tags]) - - gr.HTML(''' - <div class="footer" style="text-align: center; max-width: 700px; margin: 0 auto;"> - <p>Demo by <a href="https://huggingface.co/Mubert" style="text-decoration: underline;" target="_blank">Mubert</a> - </p> - </div> - </div> - <p style="margin-bottom: 10px; font-size: 94%"> - if you put anything over 250 seconds, you will need to wait 10 or 30 second after it is done processing. - </div> - ''') - -block.launch() \ No newline at end of file diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/config/lazy.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/config/lazy.py deleted file mode 100644 index ea93e865acce31de07af476f95454d62128a9d1c..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/config/lazy.py +++ /dev/null @@ -1,436 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import ast -import builtins -import collections.abc as abc -import importlib -import inspect -import logging -import os -import uuid -from contextlib import contextmanager -from copy import deepcopy -from dataclasses import is_dataclass -from typing import List, Tuple, Union -import cloudpickle -import yaml -from omegaconf import DictConfig, ListConfig, OmegaConf, SCMode - -from detectron2.utils.file_io import PathManager -from detectron2.utils.registry import _convert_target_to_string - -__all__ = ["LazyCall", "LazyConfig"] - - -class LazyCall: - """ - Wrap a callable so that when it's called, the call will not be executed, - but returns a dict that describes the call. - - LazyCall object has to be called with only keyword arguments. Positional - arguments are not yet supported. - - Examples: - :: - from detectron2.config import instantiate, LazyCall - - layer_cfg = LazyCall(nn.Conv2d)(in_channels=32, out_channels=32) - layer_cfg.out_channels = 64 # can edit it afterwards - layer = instantiate(layer_cfg) - """ - - def __init__(self, target): - if not (callable(target) or isinstance(target, (str, abc.Mapping))): - raise TypeError( - f"target of LazyCall must be a callable or defines a callable! Got {target}" - ) - self._target = target - - def __call__(self, **kwargs): - if is_dataclass(self._target): - # omegaconf object cannot hold dataclass type - # https://github.com/omry/omegaconf/issues/784 - target = _convert_target_to_string(self._target) - else: - target = self._target - kwargs["_target_"] = target - - return DictConfig(content=kwargs, flags={"allow_objects": True}) - - -def _visit_dict_config(cfg, func): - """ - Apply func recursively to all DictConfig in cfg. - """ - if isinstance(cfg, DictConfig): - func(cfg) - for v in cfg.values(): - _visit_dict_config(v, func) - elif isinstance(cfg, ListConfig): - for v in cfg: - _visit_dict_config(v, func) - - -def _validate_py_syntax(filename): - # see also https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py - with PathManager.open(filename, "r") as f: - content = f.read() - try: - ast.parse(content) - except SyntaxError as e: - raise SyntaxError(f"Config file {filename} has syntax error!") from e - - -def _cast_to_config(obj): - # if given a dict, return DictConfig instead - if isinstance(obj, dict): - return DictConfig(obj, flags={"allow_objects": True}) - return obj - - -_CFG_PACKAGE_NAME = "detectron2._cfg_loader" -""" -A namespace to put all imported config into. -""" - - -def _random_package_name(filename): - # generate a random package name when loading config files - return _CFG_PACKAGE_NAME + str(uuid.uuid4())[:4] + "." + os.path.basename(filename) - - -@contextmanager -def _patch_import(): - """ - Enhance relative import statements in config files, so that they: - 1. locate files purely based on relative location, regardless of packages. - e.g. you can import file without having __init__ - 2. do not cache modules globally; modifications of module states has no side effect - 3. support other storage system through PathManager, so config files can be in the cloud - 4. imported dict are turned into omegaconf.DictConfig automatically - """ - old_import = builtins.__import__ - - def find_relative_file(original_file, relative_import_path, level): - # NOTE: "from . import x" is not handled. Because then it's unclear - # if such import should produce `x` as a python module or DictConfig. - # This can be discussed further if needed. - relative_import_err = """ -Relative import of directories is not allowed within config files. -Within a config file, relative import can only import other config files. -""".replace( - "\n", " " - ) - if not len(relative_import_path): - raise ImportError(relative_import_err) - - cur_file = os.path.dirname(original_file) - for _ in range(level - 1): - cur_file = os.path.dirname(cur_file) - cur_name = relative_import_path.lstrip(".") - for part in cur_name.split("."): - cur_file = os.path.join(cur_file, part) - if not cur_file.endswith(".py"): - cur_file += ".py" - if not PathManager.isfile(cur_file): - cur_file_no_suffix = cur_file[: -len(".py")] - if PathManager.isdir(cur_file_no_suffix): - raise ImportError(f"Cannot import from {cur_file_no_suffix}." + relative_import_err) - else: - raise ImportError( - f"Cannot import name {relative_import_path} from " - f"{original_file}: {cur_file} does not exist." - ) - return cur_file - - def new_import(name, globals=None, locals=None, fromlist=(), level=0): - if ( - # Only deal with relative imports inside config files - level != 0 - and globals is not None - and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME) - ): - cur_file = find_relative_file(globals["__file__"], name, level) - _validate_py_syntax(cur_file) - spec = importlib.machinery.ModuleSpec( - _random_package_name(cur_file), None, origin=cur_file - ) - module = importlib.util.module_from_spec(spec) - module.__file__ = cur_file - with PathManager.open(cur_file) as f: - content = f.read() - exec(compile(content, cur_file, "exec"), module.__dict__) - for name in fromlist: # turn imported dict into DictConfig automatically - val = _cast_to_config(module.__dict__[name]) - module.__dict__[name] = val - return module - return old_import(name, globals, locals, fromlist=fromlist, level=level) - - builtins.__import__ = new_import - yield new_import - builtins.__import__ = old_import - - -class LazyConfig: - """ - Provide methods to save, load, and overrides an omegaconf config object - which may contain definition of lazily-constructed objects. - """ - - @staticmethod - def load_rel(filename: str, keys: Union[None, str, Tuple[str, ...]] = None): - """ - Similar to :meth:`load()`, but load path relative to the caller's - source file. - - This has the same functionality as a relative import, except that this method - accepts filename as a string, so more characters are allowed in the filename. - """ - caller_frame = inspect.stack()[1] - caller_fname = caller_frame[0].f_code.co_filename - assert caller_fname != "<string>", "load_rel Unable to find caller" - caller_dir = os.path.dirname(caller_fname) - filename = os.path.join(caller_dir, filename) - return LazyConfig.load(filename, keys) - - @staticmethod - def load(filename: str, keys: Union[None, str, Tuple[str, ...]] = None): - """ - Load a config file. - - Args: - filename: absolute path or relative path w.r.t. the current working directory - keys: keys to load and return. If not given, return all keys - (whose values are config objects) in a dict. - """ - has_keys = keys is not None - filename = filename.replace("/./", "/") # redundant - if os.path.splitext(filename)[1] not in [".py", ".yaml", ".yml"]: - raise ValueError(f"Config file {filename} has to be a python or yaml file.") - if filename.endswith(".py"): - _validate_py_syntax(filename) - - with _patch_import(): - # Record the filename - module_namespace = { - "__file__": filename, - "__package__": _random_package_name(filename), - } - with PathManager.open(filename) as f: - content = f.read() - # Compile first with filename to: - # 1. make filename appears in stacktrace - # 2. make load_rel able to find its parent's (possibly remote) location - exec(compile(content, filename, "exec"), module_namespace) - - ret = module_namespace - else: - with PathManager.open(filename) as f: - obj = yaml.unsafe_load(f) - ret = OmegaConf.create(obj, flags={"allow_objects": True}) - - if has_keys: - if isinstance(keys, str): - return _cast_to_config(ret[keys]) - else: - return tuple(_cast_to_config(ret[a]) for a in keys) - else: - if filename.endswith(".py"): - # when not specified, only load those that are config objects - ret = DictConfig( - { - name: _cast_to_config(value) - for name, value in ret.items() - if isinstance(value, (DictConfig, ListConfig, dict)) - and not name.startswith("_") - }, - flags={"allow_objects": True}, - ) - return ret - - @staticmethod - def save(cfg, filename: str): - """ - Save a config object to a yaml file. - Note that when the config dictionary contains complex objects (e.g. lambda), - it can't be saved to yaml. In that case we will print an error and - attempt to save to a pkl file instead. - - Args: - cfg: an omegaconf config object - filename: yaml file name to save the config file - """ - logger = logging.getLogger(__name__) - try: - cfg = deepcopy(cfg) - except Exception: - pass - else: - # if it's deep-copyable, then... - def _replace_type_by_name(x): - if "_target_" in x and callable(x._target_): - try: - x._target_ = _convert_target_to_string(x._target_) - except AttributeError: - pass - - # not necessary, but makes yaml looks nicer - _visit_dict_config(cfg, _replace_type_by_name) - - save_pkl = False - try: - dict = OmegaConf.to_container( - cfg, - # Do not resolve interpolation when saving, i.e. do not turn ${a} into - # actual values when saving. - resolve=False, - # Save structures (dataclasses) in a format that can be instantiated later. - # Without this option, the type information of the dataclass will be erased. - structured_config_mode=SCMode.INSTANTIATE, - ) - dumped = yaml.dump(dict, default_flow_style=None, allow_unicode=True, width=9999) - with PathManager.open(filename, "w") as f: - f.write(dumped) - - try: - _ = yaml.unsafe_load(dumped) # test that it is loadable - except Exception: - logger.warning( - "The config contains objects that cannot serialize to a valid yaml. " - f"{filename} is human-readable but cannot be loaded." - ) - save_pkl = True - except Exception: - logger.exception("Unable to serialize the config to yaml. Error:") - save_pkl = True - - if save_pkl: - new_filename = filename + ".pkl" - try: - # retry by pickle - with PathManager.open(new_filename, "wb") as f: - cloudpickle.dump(cfg, f) - logger.warning(f"Config is saved using cloudpickle at {new_filename}.") - except Exception: - pass - - @staticmethod - def apply_overrides(cfg, overrides: List[str]): - """ - In-place override contents of cfg. - - Args: - cfg: an omegaconf config object - overrides: list of strings in the format of "a=b" to override configs. - See https://hydra.cc/docs/next/advanced/override_grammar/basic/ - for syntax. - - Returns: - the cfg object - """ - - def safe_update(cfg, key, value): - parts = key.split(".") - for idx in range(1, len(parts)): - prefix = ".".join(parts[:idx]) - v = OmegaConf.select(cfg, prefix, default=None) - if v is None: - break - if not OmegaConf.is_config(v): - raise KeyError( - f"Trying to update key {key}, but {prefix} " - f"is not a config, but has type {type(v)}." - ) - OmegaConf.update(cfg, key, value, merge=True) - - try: - from hydra.core.override_parser.overrides_parser import OverridesParser - - has_hydra = True - except ImportError: - has_hydra = False - - if has_hydra: - parser = OverridesParser.create() - overrides = parser.parse_overrides(overrides) - for o in overrides: - key = o.key_or_group - value = o.value() - if o.is_delete(): - # TODO support this - raise NotImplementedError("deletion is not yet a supported override") - safe_update(cfg, key, value) - else: - # Fallback. Does not support all the features and error checking like hydra. - for o in overrides: - key, value = o.split("=") - try: - value = eval(value, {}) - except NameError: - pass - safe_update(cfg, key, value) - return cfg - - @staticmethod - def to_py(cfg, prefix: str = "cfg."): - """ - Try to convert a config object into Python-like psuedo code. - - Note that perfect conversion is not always possible. So the returned - results are mainly meant to be human-readable, and not meant to be executed. - - Args: - cfg: an omegaconf config object - prefix: root name for the resulting code (default: "cfg.") - - - Returns: - str of formatted Python code - """ - import black - - cfg = OmegaConf.to_container(cfg, resolve=True) - - def _to_str(obj, prefix=None, inside_call=False): - if prefix is None: - prefix = [] - if isinstance(obj, abc.Mapping) and "_target_" in obj: - # Dict representing a function call - target = _convert_target_to_string(obj.pop("_target_")) - args = [] - for k, v in sorted(obj.items()): - args.append(f"{k}={_to_str(v, inside_call=True)}") - args = ", ".join(args) - call = f"{target}({args})" - return "".join(prefix) + call - elif isinstance(obj, abc.Mapping) and not inside_call: - # Dict that is not inside a call is a list of top-level config objects that we - # render as one object per line with dot separated prefixes - key_list = [] - for k, v in sorted(obj.items()): - if isinstance(v, abc.Mapping) and "_target_" not in v: - key_list.append(_to_str(v, prefix=prefix + [k + "."])) - else: - key = "".join(prefix) + k - key_list.append(f"{key}={_to_str(v)}") - return "\n".join(key_list) - elif isinstance(obj, abc.Mapping): - # Dict that is inside a call is rendered as a regular dict - return ( - "{" - + ",".join( - f"{repr(k)}: {_to_str(v, inside_call=inside_call)}" - for k, v in sorted(obj.items()) - ) - + "}" - ) - elif isinstance(obj, list): - return "[" + ",".join(_to_str(x, inside_call=inside_call) for x in obj) + "]" - else: - return repr(obj) - - py_str = _to_str(cfg, prefix=[prefix]) - try: - return black.format_str(py_str, mode=black.Mode()) - except black.InvalidInput: - return py_str diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/dev/README.md b/spaces/brjathu/HMR2.0/vendor/detectron2/dev/README.md deleted file mode 100644 index bec811ad002a016f2137d9d0ea61c27ee5e78992..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/dev/README.md +++ /dev/null @@ -1,7 +0,0 @@ - -## Some scripts for developers to use, include: - -- `linter.sh`: lint the codebase before commit. -- `run_{inference,instant}_tests.sh`: run inference/training for a few iterations. - Note that these tests require 2 GPUs. -- `parse_results.sh`: parse results from a log file. diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/model_zoo/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/model_zoo/__init__.py deleted file mode 100644 index 6204208198d813728cf6419e8eef4a733f20c18f..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/model_zoo/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -""" -Model Zoo API for Detectron2: a collection of functions to create common model architectures -listed in `MODEL_ZOO.md <https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md>`_, -and optionally load their pre-trained weights. -""" - -from .model_zoo import get, get_config_file, get_checkpoint_url, get_config - -__all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"] diff --git a/spaces/chansung/zero2story/modules/image_maker.py b/spaces/chansung/zero2story/modules/image_maker.py deleted file mode 100644 index 067531eaf564342206413ce5ac601cd22612ef33..0000000000000000000000000000000000000000 --- a/spaces/chansung/zero2story/modules/image_maker.py +++ /dev/null @@ -1,384 +0,0 @@ -from typing import Literal -from pathlib import Path - -import uuid -import json -import re -import asyncio -import toml - -import torch -from compel import Compel - -from diffusers import ( - DiffusionPipeline, - StableDiffusionPipeline, - AutoencoderKL, - DPMSolverMultistepScheduler, - DDPMScheduler, - DPMSolverSinglestepScheduler, - DPMSolverSDEScheduler, - DEISMultistepScheduler, -) - -from .utils import set_all_seeds -from modules.llms import get_llm_factory - -_gpus = 0 - -class ImageMaker: - # TODO: DocString... - """Class for generating images from prompts.""" - - __ratio = {'3:2': [768, 512], - '4:3': [680, 512], - '16:9': [912, 512], - '1:1': [512, 512], - '9:16': [512, 912], - '3:4': [512, 680], - '2:3': [512, 768]} - __allocated = False - - def __init__(self, model_base: str, - clip_skip: int = 2, - sampling: Literal['sde-dpmsolver++'] = 'sde-dpmsolver++', - vae: str = None, - safety: bool = True, - variant: str = None, - from_hf: bool = False, - device: str = None) -> None: - """Initialize the ImageMaker class. - - Args: - model_base (str): Filename of the model base. - clip_skip (int, optional): Number of layers to skip in the clip model. Defaults to 2. - sampling (Literal['sde-dpmsolver++'], optional): Sampling method. Defaults to 'sde-dpmsolver++'. - vae (str, optional): Filename of the VAE model. Defaults to None. - safety (bool, optional): Whether to use the safety checker. Defaults to True. - variant (str, optional): Variant of the model. Defaults to None. - from_hf (bool, optional): Whether to load the model from HuggingFace. Defaults to False. - llm_type (str, optional): Type of the LLM. Defaults to 'PaLM'. - device (str, optional): Device to use for the model. Defaults to None. - """ - - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if not device else device - self.__model_base = model_base - self.__clip_skip = clip_skip - self.__sampling = sampling - self.__vae = vae - self.__safety = safety - self.__variant = variant - self.__from_hf = from_hf - - print("Loading the Stable Diffusion model into memory...") - if not self.__from_hf: - # from file - self.__sd_model = StableDiffusionPipeline.from_single_file(self.model_base, - torch_dtype=torch.float16, - use_safetensors=True, - ) - - # Clip Skip - self.__sd_model.text_encoder.text_model.encoder.layers = self.__sd_model.text_encoder.text_model.encoder.layers[:12 - (self.clip_skip - 1)] - - # Sampling method - if True: # TODO: Sampling method :: self.sampling == 'sde-dpmsolver++' - scheduler = DPMSolverMultistepScheduler.from_config(self.__sd_model.scheduler.config) - scheduler.config.algorithm_type = 'sde-dpmsolver++' - self.__sd_model.scheduler = scheduler - - # VAE - if self.vae: - vae_model = AutoencoderKL.from_single_file(self.vae, use_safetensors=True) - self.__sd_model.vae = vae_model.to(dtype=torch.float16) - - # Safety checker - if not self.safety: - self.__sd_model.safety_checker = None - self.__sd_model.requires_safety_checker = False - - else: - # from huggingface - self.__sd_model = StableDiffusionPipeline.from_pretrained(self.model_base, - variant=self.__variant, - use_safetensors=True) - print(f"Loaded model to {self.device}") - self.__sd_model = self.__sd_model.to(self.device) - - # Text Encoder using Compel - self.__compel_proc = Compel(tokenizer=self.__sd_model.tokenizer, text_encoder=self.__sd_model.text_encoder, truncate_long_prompts=False) - - output_dir = Path('.') / 'outputs' - if not output_dir.exists(): - output_dir.mkdir(parents=True, exist_ok=True) - elif output_dir.is_file(): - assert False, f"A file with the same name as the desired directory ('{str(output_dir)}') already exists." - - - def text2image(self, - prompt: str, neg_prompt: str = None, - ratio: Literal['3:2', '4:3', '16:9', '1:1', '9:16', '3:4', '2:3'] = '1:1', - step: int = 28, - cfg: float = 4.5, - seed: int = None) -> str: - """Generate an image from the prompt. - - Args: - prompt (str): Prompt for the image generation. - neg_prompt (str, optional): Negative prompt for the image generation. Defaults to None. - ratio (Literal['3:2', '4:3', '16:9', '1:1', '9:16', '3:4', '2:3'], optional): Ratio of the generated image. Defaults to '1:1'. - step (int, optional): Number of iterations for the diffusion. Defaults to 20. - cfg (float, optional): Configuration for the diffusion. Defaults to 7.5. - seed (int, optional): Seed for the random number generator. Defaults to None. - - Returns: - str: Path to the generated image. - """ - - output_filename = Path('.') / 'outputs' / str(uuid.uuid4()) - - if not seed or seed == -1: - seed = torch.randint(0, 2**32 - 1, (1,)).item() - set_all_seeds(seed) - - width, height = self.__ratio[ratio] - - prompt_embeds, negative_prompt_embeds = self.__get_pipeline_embeds(prompt, neg_prompt or self.neg_prompt) - - # Generate the image - result = self.__sd_model(prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - guidance_scale=cfg, - num_inference_steps=step, - width=width, - height=height, - ) - if self.__safety and result.nsfw_content_detected[0]: - print("=== NSFW Content Detected ===") - raise ValueError("Potential NSFW content was detected in one or more images.") - - img = result.images[0] - img.save(str(output_filename.with_suffix('.png'))) - - return str(output_filename.with_suffix('.png')) - - - def generate_character_prompts(self, character_name: str, age: str, job: str, - keywords: list[str] = None, - creative_mode: Literal['sd character', 'cartoon', 'realistic'] = 'cartoon', - llm_type: str = 'PaLM', - ) -> tuple[str, str]: - """Generate positive and negative prompts for a character based on given attributes. - - Args: - character_name (str): Character's name. - age (str): Age of the character. - job (str): The profession or job of the character. - keywords (list[str]): List of descriptive words for the character. - creative_mode (Literal['sd character', 'cartoon', 'realistic']): Creative mode for the character. - llm_type (str, optional): Type of the LLM. Defaults to 'PaLM'. - - Returns: - tuple[str, str]: A tuple of positive and negative prompts. - """ - factory = get_llm_factory(llm_type) - prompt_manager = factory.create_prompt_manager() - llm_service = factory.create_llm_service() - - positive = "" # add static prompt for character if needed (e.g. "chibi, cute, anime") - negative = prompt_manager.prompts['image_gen']['neg_prompt'] - - # Generate prompts with LLM - t = prompt_manager.prompts['image_gen']['character']['gen_prompt'] - q = prompt_manager.prompts['image_gen']['character']['query'] - query_string = t.format(input=q.format(character_name=character_name, - job=job, - age=age, - keywords=', '.join(keywords) if keywords else 'Nothing')) - try: - response, response_txt = asyncio.run(asyncio.wait_for( - llm_service.gen_text(query_string, mode="text", use_filter=False), - timeout=10) - ) - except asyncio.TimeoutError: - raise TimeoutError("The response time for PaLM API exceeded the limit.") - except: - raise Exception("PaLM API is not available.") - - try: - res_json = json.loads(response_txt) - positive = (res_json['primary_sentence'] if not positive else f"{positive}, {res_json['primary_sentence']}") + ", " - gender_keywords = ['1man', '1woman', '1boy', '1girl', '1male', '1female', '1gentleman', '1lady'] - positive += ', '.join([w if w not in gender_keywords else w + '+++' for w in res_json['descriptors']]) - positive = f'{job.lower()}+'.join(positive.split(job.lower())) - except: - print("=== PaLM Response ===") - print(response.filters) - print(response_txt) - print("=== PaLM Response ===") - raise ValueError("The response from PaLM API is not in the expected format.") - - return (positive.lower(), negative.lower()) - - - def generate_background_prompts(self, genre:str, place:str, mood:str, - title:str, chapter_title:str, chapter_plot:str, - llm_type: str = 'PaLM', - ) -> tuple[str, str]: - """Generate positive and negative prompts for a background image based on given attributes. - - Args: - genre (str): Genre of the story. - place (str): Place of the story. - mood (str): Mood of the story. - title (str): Title of the story. - chapter_title (str): Title of the chapter. - chapter_plot (str): Plot of the chapter. - llm_type (str, optional): Type of the LLM. Defaults to 'PaLM'. - - Returns: - tuple[str, str]: A tuple of positive and negative prompts. - """ - factory = get_llm_factory(llm_type) - prompt_manager = factory.create_prompt_manager() - llm_service = factory.create_llm_service() - - positive = "painting+++, anime+, catoon, watercolor, wallpaper, text---" # add static prompt for background if needed (e.g. "chibi, cute, anime") - negative = "realistic, human, character, people, photograph, 3d render, blurry, grayscale, oversaturated, " + prompt_manager.prompts['image_gen']['neg_prompt'] - - # Generate prompts with PaLM - t = prompt_manager.prompts['image_gen']['background']['gen_prompt'] - q = prompt_manager.prompts['image_gen']['background']['query'] - query_string = t.format(input=q.format(genre=genre, - place=place, - mood=mood, - title=title, - chapter_title=chapter_title, - chapter_plot=chapter_plot)) - try: - response, response_txt = asyncio.run(asyncio.wait_for( - llm_service.gen_text(query_string, mode="text", use_filter=False), - timeout=10) - ) - except asyncio.TimeoutError: - raise TimeoutError("The response time for PaLM API exceeded the limit.") - except: - raise Exception("PaLM API is not available.") - - try: - res_json = json.loads(response_txt) - positive = (res_json['primary_sentence'] if not positive else f"{positive}, {res_json['primary_sentence']}") + ", " - positive += ', '.join(res_json['descriptors']) - except: - print("=== PaLM Response ===") - print(response.filters) - print(response_txt) - print("=== PaLM Response ===") - raise ValueError("The response from PaLM API is not in the expected format.") - - return (positive.lower(), negative.lower()) - - - def __get_pipeline_embeds(self, prompt:str, negative_prompt:str) -> tuple[torch.Tensor, torch.Tensor]: - """ - Get pipeline embeds for prompts bigger than the maxlength of the pipeline - - Args: - prompt (str): Prompt for the image generation. - neg_prompt (str): Negative prompt for the image generation. - - Returns: - tuple[torch.Tensor, torch.Tensor]: A tuple of positive and negative prompt embeds. - """ - conditioning = self.__compel_proc.build_conditioning_tensor(prompt) - negative_conditioning = self.__compel_proc.build_conditioning_tensor(negative_prompt) - return self.__compel_proc.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning]) - - - def push_to_hub(self, repo_id:str, commit_message:str=None, token:str=None, variant:str=None): - self.__sd_model.push_to_hub(repo_id, commit_message=commit_message, token=token, variant=variant) - - - @property - def model_base(self): - """Model base - - Returns: - str: The model base (read-only) - """ - return self.__model_base - - @property - def clip_skip(self): - """Clip Skip - - Returns: - int: The number of layers to skip in the clip model (read-only) - """ - return self.__clip_skip - - @property - def sampling(self): - """Sampling method - - Returns: - Literal['sde-dpmsolver++']: The sampling method (read-only) - """ - return self.__sampling - - @property - def vae(self): - """VAE - - Returns: - str: The VAE (read-only) - """ - return self.__vae - - @property - def safety(self): - """Safety checker - - Returns: - bool: Whether to use the safety checker (read-only) - """ - return self.__safety - - @property - def device(self): - """Device - - Returns: - str: The device (read-only) - """ - return self.__device - - @device.setter - def device(self, value): - if self.__allocated: - raise RuntimeError("Cannot change device after the model is loaded.") - - if value == 'cpu': - self.__device = value - else: - global _gpus - self.__device = f'{value}:{_gpus}' - max_gpu = torch.cuda.device_count() - _gpus = (_gpus + 1) if (_gpus + 1) < max_gpu else 0 - self.__allocated = True - - @property - def neg_prompt(self): - """Negative prompt - - Returns: - str: The negative prompt - """ - return self.__neg_prompt - - @neg_prompt.setter - def neg_prompt(self, value): - if not value: - self.__neg_prompt = "" - else: - self.__neg_prompt = value diff --git a/spaces/chasemcdo/hf_localai/pkg/model/loader.go b/spaces/chasemcdo/hf_localai/pkg/model/loader.go deleted file mode 100644 index ddc7b6eb1b13ca51baec330e05096cef85df63e4..0000000000000000000000000000000000000000 --- a/spaces/chasemcdo/hf_localai/pkg/model/loader.go +++ /dev/null @@ -1,139 +0,0 @@ -package model - -import ( - "bytes" - "fmt" - "io/ioutil" - "os" - "path/filepath" - "strings" - "sync" - "text/template" - - "github.com/rs/zerolog/log" -) - -type ModelLoader struct { - ModelPath string - mu sync.Mutex - // TODO: this needs generics - models map[string]interface{} - promptsTemplates map[string]*template.Template -} - -func NewModelLoader(modelPath string) *ModelLoader { - return &ModelLoader{ - ModelPath: modelPath, - models: make(map[string]interface{}), - promptsTemplates: make(map[string]*template.Template), - } -} - -func (ml *ModelLoader) ExistsInModelPath(s string) bool { - _, err := os.Stat(filepath.Join(ml.ModelPath, s)) - return err == nil -} - -func (ml *ModelLoader) ListModels() ([]string, error) { - files, err := ioutil.ReadDir(ml.ModelPath) - if err != nil { - return []string{}, err - } - - models := []string{} - for _, file := range files { - // Skip templates, YAML and .keep files - if strings.HasSuffix(file.Name(), ".tmpl") || strings.HasSuffix(file.Name(), ".keep") || strings.HasSuffix(file.Name(), ".yaml") || strings.HasSuffix(file.Name(), ".yml") { - continue - } - - models = append(models, file.Name()) - } - - return models, nil -} - -func (ml *ModelLoader) TemplatePrefix(modelName string, in interface{}) (string, error) { - ml.mu.Lock() - defer ml.mu.Unlock() - - m, ok := ml.promptsTemplates[modelName] - if !ok { - modelFile := filepath.Join(ml.ModelPath, modelName) - if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil { - return "", err - } - - t, exists := ml.promptsTemplates[modelName] - if exists { - m = t - } - } - if m == nil { - return "", fmt.Errorf("failed loading any template") - } - - var buf bytes.Buffer - - if err := m.Execute(&buf, in); err != nil { - return "", err - } - return buf.String(), nil -} - -func (ml *ModelLoader) loadTemplateIfExists(modelName, modelFile string) error { - // Check if the template was already loaded - if _, ok := ml.promptsTemplates[modelName]; ok { - return nil - } - - // Check if the model path exists - // skip any error here - we run anyway if a template does not exist - modelTemplateFile := fmt.Sprintf("%s.tmpl", modelName) - - if !ml.ExistsInModelPath(modelTemplateFile) { - return nil - } - - dat, err := os.ReadFile(filepath.Join(ml.ModelPath, modelTemplateFile)) - if err != nil { - return err - } - - // Parse the template - tmpl, err := template.New("prompt").Parse(string(dat)) - if err != nil { - return err - } - ml.promptsTemplates[modelName] = tmpl - - return nil -} - -func (ml *ModelLoader) LoadModel(modelName string, loader func(string) (interface{}, error)) (interface{}, error) { - ml.mu.Lock() - defer ml.mu.Unlock() - - // Check if we already have a loaded model - if m, ok := ml.models[modelName]; ok { - log.Debug().Msgf("Model already loaded in memory: %s", modelName) - return m, nil - } - - // Load the model and keep it in memory for later use - modelFile := filepath.Join(ml.ModelPath, modelName) - log.Debug().Msgf("Loading model in memory from file: %s", modelFile) - - model, err := loader(modelFile) - if err != nil { - return nil, err - } - - // If there is a prompt template, load it - if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil { - return nil, err - } - - ml.models[modelName] = model - return model, nil -} diff --git a/spaces/chilge/Fushimi/train.py b/spaces/chilge/Fushimi/train.py deleted file mode 100644 index 97557410edb18717b0330c602fbaa9984f647b13..0000000000000000000000000000000000000000 --- a/spaces/chilge/Fushimi/train.py +++ /dev/null @@ -1,281 +0,0 @@ -import logging -logging.getLogger('matplotlib').setLevel(logging.WARNING) -import os -import json -import argparse -import itertools -import math -import torch -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 - -import commons -import utils -from data_utils import TextAudioSpeakerLoader, EvalDataLoader -from models import ( - SynthesizerTrn, - MultiPeriodDiscriminator, -) -from losses import ( - kl_loss, - generator_loss, discriminator_loss, feature_loss -) - -from mel_processing import mel_spectrogram_torch, spec_to_mel_torch - -torch.backends.cudnn.benchmark = True -global_step = 0 - - -# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO' - - -def main(): - """Assume Single Node Multi GPUs Training Only""" - assert torch.cuda.is_available(), "CPU training is not allowed." - hps = utils.get_hparams() - - n_gpus = torch.cuda.device_count() - os.environ['MASTER_ADDR'] = 'localhost' - os.environ['MASTER_PORT'] = hps.train.port - - 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='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) - train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True, - batch_size=hps.train.batch_size) - if rank == 0: - eval_dataset = EvalDataLoader(hps.data.validation_files, hps) - eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False, - batch_size=1, pin_memory=False, - drop_last=False) - - net_g = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - **hps.model).cuda(rank) - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - 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) - net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True) - net_d = DDP(net_d, device_ids=[rank]) - - try: - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, - optim_g) - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, - optim_d) - global_step = (epoch_str - 1) * len(train_loader) - except: - epoch_str = 1 - global_step = 0 - - 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) - - 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], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, - [train_loader, eval_loader], logger, [writer, writer_eval]) - else: - train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, - [train_loader, None], None, None) - scheduler_g.step() - scheduler_d.step() - - -def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): - net_g, net_d = nets - optim_g, optim_d = optims - scheduler_g, scheduler_d = 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() - for batch_idx, items in enumerate(train_loader): - c, f0, spec, y, spk = items - g = spk.cuda(rank, non_blocking=True) - spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True) - c = c.cuda(rank, non_blocking=True) - f0 = f0.cuda(rank, non_blocking=True) - 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) - - with autocast(enabled=hps.train.fp16_run): - y_hat, ids_slice, z_mask, \ - (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(c, f0, spec, g=g, mel=mel) - - 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 - - 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) - with autocast(enabled=False): - 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_kl - 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_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/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()), - } - - 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))) - 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 = {} - with torch.no_grad(): - for batch_idx, items in enumerate(eval_loader): - c, f0, spec, y, spk = items - g = spk[:1].cuda(0) - spec, y = spec[:1].cuda(0), y[:1].cuda(0) - c = c[:1].cuda(0) - f0 = f0[:1].cuda(0) - 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 = generator.module.infer(c, f0, g=g, mel=mel) - - 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 - ) - - audio_dict.update({ - f"gen/audio_{batch_idx}": y_hat[0], - f"gt/audio_{batch_idx}": y[0] - }) - image_dict.update({ - f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()), - "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()) - }) - 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/chongjie/PoseDiffusion_MVP/util/utils.py b/spaces/chongjie/PoseDiffusion_MVP/util/utils.py deleted file mode 100644 index 96665efde7d0d25c87c357957b00bba2108f21f0..0000000000000000000000000000000000000000 --- a/spaces/chongjie/PoseDiffusion_MVP/util/utils.py +++ /dev/null @@ -1,17 +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 random - -import numpy as np -import torch -import tempfile - - -def seed_all_random_engines(seed: int) -> None: - np.random.seed(seed) - torch.manual_seed(seed) - random.seed(seed) diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/contrib/rpc.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/contrib/rpc.py deleted file mode 100644 index cf89862260db6197296b4bb91ef4d7c2feab7f5e..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/faiss/contrib/rpc.py +++ /dev/null @@ -1,256 +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. - -""" -Simplistic RPC implementation. -Exposes all functions of a Server object. - -Uses pickle for serialization and the socket interface. -""" - -import os -import pickle -import sys -import _thread -import traceback -import socket -import logging - -LOG = logging.getLogger(__name__) - -# default -PORT = 12032 - - -######################################################################### -# simple I/O functions - - -def inline_send_handle(f, conn): - st = os.fstat(f.fileno()) - size = st.st_size - pickle.dump(size, conn) - conn.write(f.read(size)) - - -def inline_send_string(s, conn): - size = len(s) - pickle.dump(size, conn) - conn.write(s) - - -class FileSock: - " wraps a socket so that it is usable by pickle/cPickle " - - def __init__(self,sock): - self.sock = sock - self.nr=0 - - def write(self, buf): - # print("sending %d bytes"%len(buf)) - #self.sock.sendall(buf) - # print("...done") - bs = 512 * 1024 - ns = 0 - while ns < len(buf): - sent = self.sock.send(buf[ns:ns + bs]) - ns += sent - - def read(self,bs=512*1024): - #if self.nr==10000: pdb.set_trace() - self.nr+=1 - # print("read bs=%d"%bs) - b = [] - nb = 0 - while len(b)<bs: - # print(' loop') - rb = self.sock.recv(bs - nb) - if not rb: break - b.append(rb) - nb += len(rb) - return b''.join(b) - - def readline(self): - # print("readline!") - """may be optimized...""" - s=bytes() - while True: - c=self.read(1) - s+=c - if len(c)==0 or chr(c[0])=='\n': - return s - -class ClientExit(Exception): - pass - -class ServerException(Exception): - pass - - -class Server: - """ - server protocol. Methods from classes that subclass Server can be called - transparently from a client - """ - - def __init__(self, s, logf=sys.stderr, log_prefix=''): - self.logf = logf - self.log_prefix = log_prefix - - # connection - - self.conn = s - self.fs = FileSock(s) - - - def log(self, s): - self.logf.write("Sever log %s: %s\n" % (self.log_prefix, s)) - - def one_function(self): - """ - Executes a single function with associated I/O. - Protocol: - - the arguments and results are serialized with the pickle protocol - - client sends : (fname,args) - fname = method name to call - args = tuple of arguments - - server sends result: (rid,st,ret) - rid = request id - st = None, or exception if there was during execution - ret = return value or None if st!=None - """ - - try: - (fname,args)=pickle.load(self.fs) - except EOFError: - raise ClientExit("read args") - self.log("executing method %s"%(fname)) - st = None - ret = None - try: - f=getattr(self,fname) - except AttributeError: - st = AttributeError("unknown method "+fname) - self.log("unknown method") - - try: - ret = f(*args) - except Exception as e: - # due to a bug (in mod_python?), ServerException cannot be - # unpickled, so send the string and make the exception on the client side - - #st=ServerException( - # "".join(traceback.format_tb(sys.exc_info()[2]))+ - # str(e)) - st="".join(traceback.format_tb(sys.exc_info()[2]))+str(e) - self.log("exception in method") - traceback.print_exc(50,self.logf) - self.logf.flush() - - LOG.info("return") - try: - pickle.dump((st ,ret), self.fs, protocol=4) - except EOFError: - raise ClientExit("function return") - - def exec_loop(self): - """ main execution loop. Loops and handles exit states""" - - self.log("in exec_loop") - try: - while True: - self.one_function() - except ClientExit as e: - self.log("ClientExit %s"%e) - except socket.error as e: - self.log("socket error %s"%e) - traceback.print_exc(50,self.logf) - except EOFError: - self.log("EOF during communication") - traceback.print_exc(50,self.logf) - except BaseException: - # unexpected - traceback.print_exc(50,sys.stderr) - sys.exit(1) - - LOG.info("exit sever") - - def exec_loop_cleanup(self): - pass - - ################################################################### - # spying stuff - - def get_ps_stats(self): - ret='' - f=os.popen("echo ============ `hostname` uptime:; uptime;"+ - "echo ============ self:; "+ - "ps -p %d -o pid,vsize,rss,%%cpu,nlwp,psr; "%os.getpid()+ - "echo ============ run queue:;"+ - "ps ar -o user,pid,%cpu,%mem,ni,nlwp,psr,vsz,rss,cputime,command") - for l in f: - ret+=l - return ret - -class Client: - """ - Methods of the server object can be called transparently. Exceptions are - re-raised. - """ - def __init__(self, HOST, port=PORT, v6=False): - socktype = socket.AF_INET6 if v6 else socket.AF_INET - - sock = socket.socket(socktype, socket.SOCK_STREAM) - LOG.info("connecting to %s:%d, socket type: %s", HOST, port, socktype) - sock.connect((HOST, port)) - self.sock = sock - self.fs = FileSock(sock) - - def generic_fun(self, fname, args): - # int "gen fun",fname - pickle.dump((fname, args), self.fs, protocol=4) - return self.get_result() - - def get_result(self): - (st, ret) = pickle.load(self.fs) - if st!=None: - raise ServerException(st) - else: - return ret - - def __getattr__(self,name): - return lambda *x: self.generic_fun(name,x) - - -def run_server(new_handler, port=PORT, report_to_file=None, v6=False): - - HOST = '' # Symbolic name meaning the local host - socktype = socket.AF_INET6 if v6 else socket.AF_INET - s = socket.socket(socktype, socket.SOCK_STREAM) - s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - - LOG.info("bind %s:%d", HOST, port) - s.bind((HOST, port)) - s.listen(5) - - LOG.info("accepting connections") - if report_to_file is not None: - LOG.info('storing host+port in %s', report_to_file) - open(report_to_file, 'w').write('%s:%d ' % (socket.gethostname(), port)) - - while True: - try: - conn, addr = s.accept() - except socket.error as e: - if e[1]=='Interrupted system call': continue - raise - - LOG.info('Connected to %s', addr) - - ibs = new_handler(conn) - - tid = _thread.start_new_thread(ibs.exec_loop,()) - - LOG.debug("Thread ID: %d", tid) diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/encodings/codecs.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/encodings/codecs.py deleted file mode 100644 index 3ac0268d6a11a1be99bb2cf7fde5979da2853d4a..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/encodings/codecs.py +++ /dev/null @@ -1,135 +0,0 @@ -"""Extend the Python codecs module with a few encodings that are used in OpenType (name table) -but missing from Python. See https://github.com/fonttools/fonttools/issues/236 for details.""" - -import codecs -import encodings - - -class ExtendCodec(codecs.Codec): - def __init__(self, name, base_encoding, mapping): - self.name = name - self.base_encoding = base_encoding - self.mapping = mapping - self.reverse = {v: k for k, v in mapping.items()} - self.max_len = max(len(v) for v in mapping.values()) - self.info = codecs.CodecInfo( - name=self.name, encode=self.encode, decode=self.decode - ) - codecs.register_error(name, self.error) - - def _map(self, mapper, output_type, exc_type, input, errors): - base_error_handler = codecs.lookup_error(errors) - length = len(input) - out = output_type() - while input: - # first try to use self.error as the error handler - try: - part = mapper(input, self.base_encoding, errors=self.name) - out += part - break # All converted - except exc_type as e: - # else convert the correct part, handle error as requested and continue - out += mapper(input[: e.start], self.base_encoding, self.name) - replacement, pos = base_error_handler(e) - out += replacement - input = input[pos:] - return out, length - - def encode(self, input, errors="strict"): - return self._map(codecs.encode, bytes, UnicodeEncodeError, input, errors) - - def decode(self, input, errors="strict"): - return self._map(codecs.decode, str, UnicodeDecodeError, input, errors) - - def error(self, e): - if isinstance(e, UnicodeDecodeError): - for end in range(e.start + 1, e.end + 1): - s = e.object[e.start : end] - if s in self.mapping: - return self.mapping[s], end - elif isinstance(e, UnicodeEncodeError): - for end in range(e.start + 1, e.start + self.max_len + 1): - s = e.object[e.start : end] - if s in self.reverse: - return self.reverse[s], end - e.encoding = self.name - raise e - - -_extended_encodings = { - "x_mac_japanese_ttx": ( - "shift_jis", - { - b"\xFC": chr(0x007C), - b"\x7E": chr(0x007E), - b"\x80": chr(0x005C), - b"\xA0": chr(0x00A0), - b"\xFD": chr(0x00A9), - b"\xFE": chr(0x2122), - b"\xFF": chr(0x2026), - }, - ), - "x_mac_trad_chinese_ttx": ( - "big5", - { - b"\x80": chr(0x005C), - b"\xA0": chr(0x00A0), - b"\xFD": chr(0x00A9), - b"\xFE": chr(0x2122), - b"\xFF": chr(0x2026), - }, - ), - "x_mac_korean_ttx": ( - "euc_kr", - { - b"\x80": chr(0x00A0), - b"\x81": chr(0x20A9), - b"\x82": chr(0x2014), - b"\x83": chr(0x00A9), - b"\xFE": chr(0x2122), - b"\xFF": chr(0x2026), - }, - ), - "x_mac_simp_chinese_ttx": ( - "gb2312", - { - b"\x80": chr(0x00FC), - b"\xA0": chr(0x00A0), - b"\xFD": chr(0x00A9), - b"\xFE": chr(0x2122), - b"\xFF": chr(0x2026), - }, - ), -} - -_cache = {} - - -def search_function(name): - name = encodings.normalize_encoding(name) # Rather undocumented... - if name in _extended_encodings: - if name not in _cache: - base_encoding, mapping = _extended_encodings[name] - assert name[-4:] == "_ttx" - # Python 2 didn't have any of the encodings that we are implementing - # in this file. Python 3 added aliases for the East Asian ones, mapping - # them "temporarily" to the same base encoding as us, with a comment - # suggesting that full implementation will appear some time later. - # As such, try the Python version of the x_mac_... first, if that is found, - # use *that* as our base encoding. This would make our encoding upgrade - # to the full encoding when and if Python finally implements that. - # http://bugs.python.org/issue24041 - base_encodings = [name[:-4], base_encoding] - for base_encoding in base_encodings: - try: - codecs.lookup(base_encoding) - except LookupError: - continue - _cache[name] = ExtendCodec(name, base_encoding, mapping) - break - return _cache[name].info - - return None - - -codecs.register(search_function) diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/grUtils.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/grUtils.py deleted file mode 100644 index 785684b1eb30a76ae598bfe46416d4556fc422a0..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/grUtils.py +++ /dev/null @@ -1,92 +0,0 @@ -import struct, warnings - -try: - import lz4 -except ImportError: - lz4 = None -else: - import lz4.block - -# old scheme for VERSION < 0.9 otherwise use lz4.block - - -def decompress(data): - (compression,) = struct.unpack(">L", data[4:8]) - scheme = compression >> 27 - size = compression & 0x07FFFFFF - if scheme == 0: - pass - elif scheme == 1 and lz4: - res = lz4.block.decompress(struct.pack("<L", size) + data[8:]) - if len(res) != size: - warnings.warn("Table decompression failed.") - else: - data = res - else: - warnings.warn("Table is compressed with an unsupported compression scheme") - return (data, scheme) - - -def compress(scheme, data): - hdr = data[:4] + struct.pack(">L", (scheme << 27) + (len(data) & 0x07FFFFFF)) - if scheme == 0: - return data - elif scheme == 1 and lz4: - res = lz4.block.compress( - data, mode="high_compression", compression=16, store_size=False - ) - return hdr + res - else: - warnings.warn("Table failed to compress by unsupported compression scheme") - return data - - -def _entries(attrs, sameval): - ak = 0 - vals = [] - lastv = 0 - for k, v in attrs: - if len(vals) and (k != ak + 1 or (sameval and v != lastv)): - yield (ak - len(vals) + 1, len(vals), vals) - vals = [] - ak = k - vals.append(v) - lastv = v - yield (ak - len(vals) + 1, len(vals), vals) - - -def entries(attributes, sameval=False): - g = _entries(sorted(attributes.items(), key=lambda x: int(x[0])), sameval) - return g - - -def bininfo(num, size=1): - if num == 0: - return struct.pack(">4H", 0, 0, 0, 0) - srange = 1 - select = 0 - while srange <= num: - srange *= 2 - select += 1 - select -= 1 - srange //= 2 - srange *= size - shift = num * size - srange - return struct.pack(">4H", num, srange, select, shift) - - -def num2tag(n): - if n < 0x200000: - return str(n) - else: - return ( - struct.unpack("4s", struct.pack(">L", n))[0].replace(b"\000", b"").decode() - ) - - -def tag2num(n): - try: - return int(n) - except ValueError: - n = (n + " ")[:4] - return struct.unpack(">L", n.encode("ascii"))[0] diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/glifLib.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/glifLib.py deleted file mode 100644 index 6dee9db302f51525b69d3d28fcd704be8cce2212..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/glifLib.py +++ /dev/null @@ -1,2017 +0,0 @@ -""" -glifLib.py -- Generic module for reading and writing the .glif format. - -More info about the .glif format (GLyphInterchangeFormat) can be found here: - - http://unifiedfontobject.org - -The main class in this module is GlyphSet. It manages a set of .glif files -in a folder. It offers two ways to read glyph data, and one way to write -glyph data. See the class doc string for details. -""" - -from __future__ import annotations - -import logging -import enum -from warnings import warn -from collections import OrderedDict -import fs -import fs.base -import fs.errors -import fs.osfs -import fs.path -from fontTools.misc.textTools import tobytes -from fontTools.misc import plistlib -from fontTools.pens.pointPen import AbstractPointPen, PointToSegmentPen -from fontTools.ufoLib.errors import GlifLibError -from fontTools.ufoLib.filenames import userNameToFileName -from fontTools.ufoLib.validators import ( - genericTypeValidator, - colorValidator, - guidelinesValidator, - anchorsValidator, - identifierValidator, - imageValidator, - glyphLibValidator, -) -from fontTools.misc import etree -from fontTools.ufoLib import _UFOBaseIO, UFOFormatVersion -from fontTools.ufoLib.utils import numberTypes, _VersionTupleEnumMixin - - -__all__ = [ - "GlyphSet", - "GlifLibError", - "readGlyphFromString", - "writeGlyphToString", - "glyphNameToFileName", -] - -logger = logging.getLogger(__name__) - - -# --------- -# Constants -# --------- - -CONTENTS_FILENAME = "contents.plist" -LAYERINFO_FILENAME = "layerinfo.plist" - - -class GLIFFormatVersion(tuple, _VersionTupleEnumMixin, enum.Enum): - FORMAT_1_0 = (1, 0) - FORMAT_2_0 = (2, 0) - - @classmethod - def default(cls, ufoFormatVersion=None): - if ufoFormatVersion is not None: - return max(cls.supported_versions(ufoFormatVersion)) - return super().default() - - @classmethod - def supported_versions(cls, ufoFormatVersion=None): - if ufoFormatVersion is None: - # if ufo format unspecified, return all the supported GLIF formats - return super().supported_versions() - # else only return the GLIF formats supported by the given UFO format - versions = {cls.FORMAT_1_0} - if ufoFormatVersion >= UFOFormatVersion.FORMAT_3_0: - versions.add(cls.FORMAT_2_0) - return frozenset(versions) - - -# workaround for py3.11, see https://github.com/fonttools/fonttools/pull/2655 -GLIFFormatVersion.__str__ = _VersionTupleEnumMixin.__str__ - - -# ------------ -# Simple Glyph -# ------------ - - -class Glyph: - - """ - Minimal glyph object. It has no glyph attributes until either - the draw() or the drawPoints() method has been called. - """ - - def __init__(self, glyphName, glyphSet): - self.glyphName = glyphName - self.glyphSet = glyphSet - - def draw(self, pen, outputImpliedClosingLine=False): - """ - Draw this glyph onto a *FontTools* Pen. - """ - pointPen = PointToSegmentPen( - pen, outputImpliedClosingLine=outputImpliedClosingLine - ) - self.drawPoints(pointPen) - - def drawPoints(self, pointPen): - """ - Draw this glyph onto a PointPen. - """ - self.glyphSet.readGlyph(self.glyphName, self, pointPen) - - -# --------- -# Glyph Set -# --------- - - -class GlyphSet(_UFOBaseIO): - - """ - GlyphSet manages a set of .glif files inside one directory. - - GlyphSet's constructor takes a path to an existing directory as it's - first argument. Reading glyph data can either be done through the - readGlyph() method, or by using GlyphSet's dictionary interface, where - the keys are glyph names and the values are (very) simple glyph objects. - - To write a glyph to the glyph set, you use the writeGlyph() method. - The simple glyph objects returned through the dict interface do not - support writing, they are just a convenient way to get at the glyph data. - """ - - glyphClass = Glyph - - def __init__( - self, - path, - glyphNameToFileNameFunc=None, - ufoFormatVersion=None, - validateRead=True, - validateWrite=True, - expectContentsFile=False, - ): - """ - 'path' should be a path (string) to an existing local directory, or - an instance of fs.base.FS class. - - The optional 'glyphNameToFileNameFunc' argument must be a callback - function that takes two arguments: a glyph name and a list of all - existing filenames (if any exist). It should return a file name - (including the .glif extension). The glyphNameToFileName function - is called whenever a file name is created for a given glyph name. - - ``validateRead`` will validate read operations. Its default is ``True``. - ``validateWrite`` will validate write operations. Its default is ``True``. - ``expectContentsFile`` will raise a GlifLibError if a contents.plist file is - not found on the glyph set file system. This should be set to ``True`` if you - are reading an existing UFO and ``False`` if you create a fresh glyph set. - """ - try: - ufoFormatVersion = UFOFormatVersion(ufoFormatVersion) - except ValueError as e: - from fontTools.ufoLib.errors import UnsupportedUFOFormat - - raise UnsupportedUFOFormat( - f"Unsupported UFO format: {ufoFormatVersion!r}" - ) from e - - if hasattr(path, "__fspath__"): # support os.PathLike objects - path = path.__fspath__() - - if isinstance(path, str): - try: - filesystem = fs.osfs.OSFS(path) - except fs.errors.CreateFailed: - raise GlifLibError("No glyphs directory '%s'" % path) - self._shouldClose = True - elif isinstance(path, fs.base.FS): - filesystem = path - try: - filesystem.check() - except fs.errors.FilesystemClosed: - raise GlifLibError("the filesystem '%s' is closed" % filesystem) - self._shouldClose = False - else: - raise TypeError( - "Expected a path string or fs object, found %s" % type(path).__name__ - ) - try: - path = filesystem.getsyspath("/") - except fs.errors.NoSysPath: - # network or in-memory FS may not map to the local one - path = str(filesystem) - # 'dirName' is kept for backward compatibility only, but it's DEPRECATED - # as it's not guaranteed that it maps to an existing OSFS directory. - # Client could use the FS api via the `self.fs` attribute instead. - self.dirName = fs.path.parts(path)[-1] - self.fs = filesystem - # if glyphSet contains no 'contents.plist', we consider it empty - self._havePreviousFile = filesystem.exists(CONTENTS_FILENAME) - if expectContentsFile and not self._havePreviousFile: - raise GlifLibError(f"{CONTENTS_FILENAME} is missing.") - # attribute kept for backward compatibility - self.ufoFormatVersion = ufoFormatVersion.major - self.ufoFormatVersionTuple = ufoFormatVersion - if glyphNameToFileNameFunc is None: - glyphNameToFileNameFunc = glyphNameToFileName - self.glyphNameToFileName = glyphNameToFileNameFunc - self._validateRead = validateRead - self._validateWrite = validateWrite - self._existingFileNames: set[str] | None = None - self._reverseContents = None - - self.rebuildContents() - - def rebuildContents(self, validateRead=None): - """ - Rebuild the contents dict by loading contents.plist. - - ``validateRead`` will validate the data, by default it is set to the - class's ``validateRead`` value, can be overridden. - """ - if validateRead is None: - validateRead = self._validateRead - contents = self._getPlist(CONTENTS_FILENAME, {}) - # validate the contents - if validateRead: - invalidFormat = False - if not isinstance(contents, dict): - invalidFormat = True - else: - for name, fileName in contents.items(): - if not isinstance(name, str): - invalidFormat = True - if not isinstance(fileName, str): - invalidFormat = True - elif not self.fs.exists(fileName): - raise GlifLibError( - "%s references a file that does not exist: %s" - % (CONTENTS_FILENAME, fileName) - ) - if invalidFormat: - raise GlifLibError("%s is not properly formatted" % CONTENTS_FILENAME) - self.contents = contents - self._existingFileNames = None - self._reverseContents = None - - def getReverseContents(self): - """ - Return a reversed dict of self.contents, mapping file names to - glyph names. This is primarily an aid for custom glyph name to file - name schemes that want to make sure they don't generate duplicate - file names. The file names are converted to lowercase so we can - reliably check for duplicates that only differ in case, which is - important for case-insensitive file systems. - """ - if self._reverseContents is None: - d = {} - for k, v in self.contents.items(): - d[v.lower()] = k - self._reverseContents = d - return self._reverseContents - - def writeContents(self): - """ - Write the contents.plist file out to disk. Call this method when - you're done writing glyphs. - """ - self._writePlist(CONTENTS_FILENAME, self.contents) - - # layer info - - def readLayerInfo(self, info, validateRead=None): - """ - ``validateRead`` will validate the data, by default it is set to the - class's ``validateRead`` value, can be overridden. - """ - if validateRead is None: - validateRead = self._validateRead - infoDict = self._getPlist(LAYERINFO_FILENAME, {}) - if validateRead: - if not isinstance(infoDict, dict): - raise GlifLibError("layerinfo.plist is not properly formatted.") - infoDict = validateLayerInfoVersion3Data(infoDict) - # populate the object - for attr, value in infoDict.items(): - try: - setattr(info, attr, value) - except AttributeError: - raise GlifLibError( - "The supplied layer info object does not support setting a necessary attribute (%s)." - % attr - ) - - def writeLayerInfo(self, info, validateWrite=None): - """ - ``validateWrite`` will validate the data, by default it is set to the - class's ``validateWrite`` value, can be overridden. - """ - if validateWrite is None: - validateWrite = self._validateWrite - if self.ufoFormatVersionTuple.major < 3: - raise GlifLibError( - "layerinfo.plist is not allowed in UFO %d." - % self.ufoFormatVersionTuple.major - ) - # gather data - infoData = {} - for attr in layerInfoVersion3ValueData.keys(): - if hasattr(info, attr): - try: - value = getattr(info, attr) - except AttributeError: - raise GlifLibError( - "The supplied info object does not support getting a necessary attribute (%s)." - % attr - ) - if value is None or (attr == "lib" and not value): - continue - infoData[attr] = value - if infoData: - # validate - if validateWrite: - infoData = validateLayerInfoVersion3Data(infoData) - # write file - self._writePlist(LAYERINFO_FILENAME, infoData) - elif self._havePreviousFile and self.fs.exists(LAYERINFO_FILENAME): - # data empty, remove existing file - self.fs.remove(LAYERINFO_FILENAME) - - def getGLIF(self, glyphName): - """ - Get the raw GLIF text for a given glyph name. This only works - for GLIF files that are already on disk. - - This method is useful in situations when the raw XML needs to be - read from a glyph set for a particular glyph before fully parsing - it into an object structure via the readGlyph method. - - Raises KeyError if 'glyphName' is not in contents.plist, or - GlifLibError if the file associated with can't be found. - """ - fileName = self.contents[glyphName] - try: - return self.fs.readbytes(fileName) - except fs.errors.ResourceNotFound: - raise GlifLibError( - "The file '%s' associated with glyph '%s' in contents.plist " - "does not exist on %s" % (fileName, glyphName, self.fs) - ) - - def getGLIFModificationTime(self, glyphName): - """ - Returns the modification time for the GLIF file with 'glyphName', as - a floating point number giving the number of seconds since the epoch. - Return None if the associated file does not exist or the underlying - filesystem does not support getting modified times. - Raises KeyError if the glyphName is not in contents.plist. - """ - fileName = self.contents[glyphName] - return self.getFileModificationTime(fileName) - - # reading/writing API - - def readGlyph(self, glyphName, glyphObject=None, pointPen=None, validate=None): - """ - Read a .glif file for 'glyphName' from the glyph set. The - 'glyphObject' argument can be any kind of object (even None); - the readGlyph() method will attempt to set the following - attributes on it: - - width - the advance width of the glyph - height - the advance height of the glyph - unicodes - a list of unicode values for this glyph - note - a string - lib - a dictionary containing custom data - image - a dictionary containing image data - guidelines - a list of guideline data dictionaries - anchors - a list of anchor data dictionaries - - All attributes are optional, in two ways: - - 1) An attribute *won't* be set if the .glif file doesn't - contain data for it. 'glyphObject' will have to deal - with default values itself. - 2) If setting the attribute fails with an AttributeError - (for example if the 'glyphObject' attribute is read- - only), readGlyph() will not propagate that exception, - but ignore that attribute. - - To retrieve outline information, you need to pass an object - conforming to the PointPen protocol as the 'pointPen' argument. - This argument may be None if you don't need the outline data. - - readGlyph() will raise KeyError if the glyph is not present in - the glyph set. - - ``validate`` will validate the data, by default it is set to the - class's ``validateRead`` value, can be overridden. - """ - if validate is None: - validate = self._validateRead - text = self.getGLIF(glyphName) - try: - tree = _glifTreeFromString(text) - formatVersions = GLIFFormatVersion.supported_versions( - self.ufoFormatVersionTuple - ) - _readGlyphFromTree( - tree, - glyphObject, - pointPen, - formatVersions=formatVersions, - validate=validate, - ) - except GlifLibError as glifLibError: - # Re-raise with a note that gives extra context, describing where - # the error occurred. - fileName = self.contents[glyphName] - try: - glifLocation = f"'{self.fs.getsyspath(fileName)}'" - except fs.errors.NoSysPath: - # Network or in-memory FS may not map to a local path, so use - # the best string representation we have. - glifLocation = f"'{fileName}' from '{str(self.fs)}'" - - glifLibError._add_note( - f"The issue is in glyph '{glyphName}', located in {glifLocation}." - ) - raise - - def writeGlyph( - self, - glyphName, - glyphObject=None, - drawPointsFunc=None, - formatVersion=None, - validate=None, - ): - """ - Write a .glif file for 'glyphName' to the glyph set. The - 'glyphObject' argument can be any kind of object (even None); - the writeGlyph() method will attempt to get the following - attributes from it: - - width - the advance width of the glyph - height - the advance height of the glyph - unicodes - a list of unicode values for this glyph - note - a string - lib - a dictionary containing custom data - image - a dictionary containing image data - guidelines - a list of guideline data dictionaries - anchors - a list of anchor data dictionaries - - All attributes are optional: if 'glyphObject' doesn't - have the attribute, it will simply be skipped. - - To write outline data to the .glif file, writeGlyph() needs - a function (any callable object actually) that will take one - argument: an object that conforms to the PointPen protocol. - The function will be called by writeGlyph(); it has to call the - proper PointPen methods to transfer the outline to the .glif file. - - The GLIF format version will be chosen based on the ufoFormatVersion - passed during the creation of this object. If a particular format - version is desired, it can be passed with the formatVersion argument. - The formatVersion argument accepts either a tuple of integers for - (major, minor), or a single integer for the major digit only (with - minor digit implied as 0). - - An UnsupportedGLIFFormat exception is raised if the requested GLIF - formatVersion is not supported. - - ``validate`` will validate the data, by default it is set to the - class's ``validateWrite`` value, can be overridden. - """ - if formatVersion is None: - formatVersion = GLIFFormatVersion.default(self.ufoFormatVersionTuple) - else: - try: - formatVersion = GLIFFormatVersion(formatVersion) - except ValueError as e: - from fontTools.ufoLib.errors import UnsupportedGLIFFormat - - raise UnsupportedGLIFFormat( - f"Unsupported GLIF format version: {formatVersion!r}" - ) from e - if formatVersion not in GLIFFormatVersion.supported_versions( - self.ufoFormatVersionTuple - ): - from fontTools.ufoLib.errors import UnsupportedGLIFFormat - - raise UnsupportedGLIFFormat( - f"Unsupported GLIF format version ({formatVersion!s}) " - f"for UFO format version {self.ufoFormatVersionTuple!s}." - ) - if validate is None: - validate = self._validateWrite - fileName = self.contents.get(glyphName) - if fileName is None: - if self._existingFileNames is None: - self._existingFileNames = { - fileName.lower() for fileName in self.contents.values() - } - fileName = self.glyphNameToFileName(glyphName, self._existingFileNames) - self.contents[glyphName] = fileName - self._existingFileNames.add(fileName.lower()) - if self._reverseContents is not None: - self._reverseContents[fileName.lower()] = glyphName - data = _writeGlyphToBytes( - glyphName, - glyphObject, - drawPointsFunc, - formatVersion=formatVersion, - validate=validate, - ) - if ( - self._havePreviousFile - and self.fs.exists(fileName) - and data == self.fs.readbytes(fileName) - ): - return - self.fs.writebytes(fileName, data) - - def deleteGlyph(self, glyphName): - """Permanently delete the glyph from the glyph set on disk. Will - raise KeyError if the glyph is not present in the glyph set. - """ - fileName = self.contents[glyphName] - self.fs.remove(fileName) - if self._existingFileNames is not None: - self._existingFileNames.remove(fileName.lower()) - if self._reverseContents is not None: - del self._reverseContents[fileName.lower()] - del self.contents[glyphName] - - # dict-like support - - def keys(self): - return list(self.contents.keys()) - - def has_key(self, glyphName): - return glyphName in self.contents - - __contains__ = has_key - - def __len__(self): - return len(self.contents) - - def __getitem__(self, glyphName): - if glyphName not in self.contents: - raise KeyError(glyphName) - return self.glyphClass(glyphName, self) - - # quickly fetch unicode values - - def getUnicodes(self, glyphNames=None): - """ - Return a dictionary that maps glyph names to lists containing - the unicode value[s] for that glyph, if any. This parses the .glif - files partially, so it is a lot faster than parsing all files completely. - By default this checks all glyphs, but a subset can be passed with glyphNames. - """ - unicodes = {} - if glyphNames is None: - glyphNames = self.contents.keys() - for glyphName in glyphNames: - text = self.getGLIF(glyphName) - unicodes[glyphName] = _fetchUnicodes(text) - return unicodes - - def getComponentReferences(self, glyphNames=None): - """ - Return a dictionary that maps glyph names to lists containing the - base glyph name of components in the glyph. This parses the .glif - files partially, so it is a lot faster than parsing all files completely. - By default this checks all glyphs, but a subset can be passed with glyphNames. - """ - components = {} - if glyphNames is None: - glyphNames = self.contents.keys() - for glyphName in glyphNames: - text = self.getGLIF(glyphName) - components[glyphName] = _fetchComponentBases(text) - return components - - def getImageReferences(self, glyphNames=None): - """ - Return a dictionary that maps glyph names to the file name of the image - referenced by the glyph. This parses the .glif files partially, so it is a - lot faster than parsing all files completely. - By default this checks all glyphs, but a subset can be passed with glyphNames. - """ - images = {} - if glyphNames is None: - glyphNames = self.contents.keys() - for glyphName in glyphNames: - text = self.getGLIF(glyphName) - images[glyphName] = _fetchImageFileName(text) - return images - - def close(self): - if self._shouldClose: - self.fs.close() - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, exc_tb): - self.close() - - -# ----------------------- -# Glyph Name to File Name -# ----------------------- - - -def glyphNameToFileName(glyphName, existingFileNames): - """ - Wrapper around the userNameToFileName function in filenames.py - - Note that existingFileNames should be a set for large glyphsets - or performance will suffer. - """ - if existingFileNames is None: - existingFileNames = set() - return userNameToFileName(glyphName, existing=existingFileNames, suffix=".glif") - - -# ----------------------- -# GLIF To and From String -# ----------------------- - - -def readGlyphFromString( - aString, - glyphObject=None, - pointPen=None, - formatVersions=None, - validate=True, -): - """ - Read .glif data from a string into a glyph object. - - The 'glyphObject' argument can be any kind of object (even None); - the readGlyphFromString() method will attempt to set the following - attributes on it: - - width - the advance width of the glyph - height - the advance height of the glyph - unicodes - a list of unicode values for this glyph - note - a string - lib - a dictionary containing custom data - image - a dictionary containing image data - guidelines - a list of guideline data dictionaries - anchors - a list of anchor data dictionaries - - All attributes are optional, in two ways: - - 1) An attribute *won't* be set if the .glif file doesn't - contain data for it. 'glyphObject' will have to deal - with default values itself. - 2) If setting the attribute fails with an AttributeError - (for example if the 'glyphObject' attribute is read- - only), readGlyphFromString() will not propagate that - exception, but ignore that attribute. - - To retrieve outline information, you need to pass an object - conforming to the PointPen protocol as the 'pointPen' argument. - This argument may be None if you don't need the outline data. - - The formatVersions optional argument define the GLIF format versions - that are allowed to be read. - The type is Optional[Iterable[Tuple[int, int], int]]. It can contain - either integers (for the major versions to be allowed, with minor - digits defaulting to 0), or tuples of integers to specify both - (major, minor) versions. - By default when formatVersions is None all the GLIF format versions - currently defined are allowed to be read. - - ``validate`` will validate the read data. It is set to ``True`` by default. - """ - tree = _glifTreeFromString(aString) - - if formatVersions is None: - validFormatVersions = GLIFFormatVersion.supported_versions() - else: - validFormatVersions, invalidFormatVersions = set(), set() - for v in formatVersions: - try: - formatVersion = GLIFFormatVersion(v) - except ValueError: - invalidFormatVersions.add(v) - else: - validFormatVersions.add(formatVersion) - if not validFormatVersions: - raise ValueError( - "None of the requested GLIF formatVersions are supported: " - f"{formatVersions!r}" - ) - - _readGlyphFromTree( - tree, - glyphObject, - pointPen, - formatVersions=validFormatVersions, - validate=validate, - ) - - -def _writeGlyphToBytes( - glyphName, - glyphObject=None, - drawPointsFunc=None, - writer=None, - formatVersion=None, - validate=True, -): - """Return .glif data for a glyph as a UTF-8 encoded bytes string.""" - try: - formatVersion = GLIFFormatVersion(formatVersion) - except ValueError: - from fontTools.ufoLib.errors import UnsupportedGLIFFormat - - raise UnsupportedGLIFFormat( - "Unsupported GLIF format version: {formatVersion!r}" - ) - # start - if validate and not isinstance(glyphName, str): - raise GlifLibError("The glyph name is not properly formatted.") - if validate and len(glyphName) == 0: - raise GlifLibError("The glyph name is empty.") - glyphAttrs = OrderedDict( - [("name", glyphName), ("format", repr(formatVersion.major))] - ) - if formatVersion.minor != 0: - glyphAttrs["formatMinor"] = repr(formatVersion.minor) - root = etree.Element("glyph", glyphAttrs) - identifiers = set() - # advance - _writeAdvance(glyphObject, root, validate) - # unicodes - if getattr(glyphObject, "unicodes", None): - _writeUnicodes(glyphObject, root, validate) - # note - if getattr(glyphObject, "note", None): - _writeNote(glyphObject, root, validate) - # image - if formatVersion.major >= 2 and getattr(glyphObject, "image", None): - _writeImage(glyphObject, root, validate) - # guidelines - if formatVersion.major >= 2 and getattr(glyphObject, "guidelines", None): - _writeGuidelines(glyphObject, root, identifiers, validate) - # anchors - anchors = getattr(glyphObject, "anchors", None) - if formatVersion.major >= 2 and anchors: - _writeAnchors(glyphObject, root, identifiers, validate) - # outline - if drawPointsFunc is not None: - outline = etree.SubElement(root, "outline") - pen = GLIFPointPen(outline, identifiers=identifiers, validate=validate) - drawPointsFunc(pen) - if formatVersion.major == 1 and anchors: - _writeAnchorsFormat1(pen, anchors, validate) - # prevent lxml from writing self-closing tags - if not len(outline): - outline.text = "\n " - # lib - if getattr(glyphObject, "lib", None): - _writeLib(glyphObject, root, validate) - # return the text - data = etree.tostring( - root, encoding="UTF-8", xml_declaration=True, pretty_print=True - ) - return data - - -def writeGlyphToString( - glyphName, - glyphObject=None, - drawPointsFunc=None, - formatVersion=None, - validate=True, -): - """ - Return .glif data for a glyph as a string. The XML declaration's - encoding is always set to "UTF-8". - The 'glyphObject' argument can be any kind of object (even None); - the writeGlyphToString() method will attempt to get the following - attributes from it: - - width - the advance width of the glyph - height - the advance height of the glyph - unicodes - a list of unicode values for this glyph - note - a string - lib - a dictionary containing custom data - image - a dictionary containing image data - guidelines - a list of guideline data dictionaries - anchors - a list of anchor data dictionaries - - All attributes are optional: if 'glyphObject' doesn't - have the attribute, it will simply be skipped. - - To write outline data to the .glif file, writeGlyphToString() needs - a function (any callable object actually) that will take one - argument: an object that conforms to the PointPen protocol. - The function will be called by writeGlyphToString(); it has to call the - proper PointPen methods to transfer the outline to the .glif file. - - The GLIF format version can be specified with the formatVersion argument. - This accepts either a tuple of integers for (major, minor), or a single - integer for the major digit only (with minor digit implied as 0). - By default when formatVesion is None the latest GLIF format version will - be used; currently it's 2.0, which is equivalent to formatVersion=(2, 0). - - An UnsupportedGLIFFormat exception is raised if the requested UFO - formatVersion is not supported. - - ``validate`` will validate the written data. It is set to ``True`` by default. - """ - data = _writeGlyphToBytes( - glyphName, - glyphObject=glyphObject, - drawPointsFunc=drawPointsFunc, - formatVersion=formatVersion, - validate=validate, - ) - return data.decode("utf-8") - - -def _writeAdvance(glyphObject, element, validate): - width = getattr(glyphObject, "width", None) - if width is not None: - if validate and not isinstance(width, numberTypes): - raise GlifLibError("width attribute must be int or float") - if width == 0: - width = None - height = getattr(glyphObject, "height", None) - if height is not None: - if validate and not isinstance(height, numberTypes): - raise GlifLibError("height attribute must be int or float") - if height == 0: - height = None - if width is not None and height is not None: - etree.SubElement( - element, - "advance", - OrderedDict([("height", repr(height)), ("width", repr(width))]), - ) - elif width is not None: - etree.SubElement(element, "advance", dict(width=repr(width))) - elif height is not None: - etree.SubElement(element, "advance", dict(height=repr(height))) - - -def _writeUnicodes(glyphObject, element, validate): - unicodes = getattr(glyphObject, "unicodes", None) - if validate and isinstance(unicodes, int): - unicodes = [unicodes] - seen = set() - for code in unicodes: - if validate and not isinstance(code, int): - raise GlifLibError("unicode values must be int") - if code in seen: - continue - seen.add(code) - hexCode = "%04X" % code - etree.SubElement(element, "unicode", dict(hex=hexCode)) - - -def _writeNote(glyphObject, element, validate): - note = getattr(glyphObject, "note", None) - if validate and not isinstance(note, str): - raise GlifLibError("note attribute must be str") - note = note.strip() - note = "\n" + note + "\n" - etree.SubElement(element, "note").text = note - - -def _writeImage(glyphObject, element, validate): - image = getattr(glyphObject, "image", None) - if validate and not imageValidator(image): - raise GlifLibError( - "image attribute must be a dict or dict-like object with the proper structure." - ) - attrs = OrderedDict([("fileName", image["fileName"])]) - for attr, default in _transformationInfo: - value = image.get(attr, default) - if value != default: - attrs[attr] = repr(value) - color = image.get("color") - if color is not None: - attrs["color"] = color - etree.SubElement(element, "image", attrs) - - -def _writeGuidelines(glyphObject, element, identifiers, validate): - guidelines = getattr(glyphObject, "guidelines", []) - if validate and not guidelinesValidator(guidelines): - raise GlifLibError("guidelines attribute does not have the proper structure.") - for guideline in guidelines: - attrs = OrderedDict() - x = guideline.get("x") - if x is not None: - attrs["x"] = repr(x) - y = guideline.get("y") - if y is not None: - attrs["y"] = repr(y) - angle = guideline.get("angle") - if angle is not None: - attrs["angle"] = repr(angle) - name = guideline.get("name") - if name is not None: - attrs["name"] = name - color = guideline.get("color") - if color is not None: - attrs["color"] = color - identifier = guideline.get("identifier") - if identifier is not None: - if validate and identifier in identifiers: - raise GlifLibError("identifier used more than once: %s" % identifier) - attrs["identifier"] = identifier - identifiers.add(identifier) - etree.SubElement(element, "guideline", attrs) - - -def _writeAnchorsFormat1(pen, anchors, validate): - if validate and not anchorsValidator(anchors): - raise GlifLibError("anchors attribute does not have the proper structure.") - for anchor in anchors: - attrs = {} - x = anchor["x"] - attrs["x"] = repr(x) - y = anchor["y"] - attrs["y"] = repr(y) - name = anchor.get("name") - if name is not None: - attrs["name"] = name - pen.beginPath() - pen.addPoint((x, y), segmentType="move", name=name) - pen.endPath() - - -def _writeAnchors(glyphObject, element, identifiers, validate): - anchors = getattr(glyphObject, "anchors", []) - if validate and not anchorsValidator(anchors): - raise GlifLibError("anchors attribute does not have the proper structure.") - for anchor in anchors: - attrs = OrderedDict() - x = anchor["x"] - attrs["x"] = repr(x) - y = anchor["y"] - attrs["y"] = repr(y) - name = anchor.get("name") - if name is not None: - attrs["name"] = name - color = anchor.get("color") - if color is not None: - attrs["color"] = color - identifier = anchor.get("identifier") - if identifier is not None: - if validate and identifier in identifiers: - raise GlifLibError("identifier used more than once: %s" % identifier) - attrs["identifier"] = identifier - identifiers.add(identifier) - etree.SubElement(element, "anchor", attrs) - - -def _writeLib(glyphObject, element, validate): - lib = getattr(glyphObject, "lib", None) - if not lib: - # don't write empty lib - return - if validate: - valid, message = glyphLibValidator(lib) - if not valid: - raise GlifLibError(message) - if not isinstance(lib, dict): - lib = dict(lib) - # plist inside GLIF begins with 2 levels of indentation - e = plistlib.totree(lib, indent_level=2) - etree.SubElement(element, "lib").append(e) - - -# ----------------------- -# layerinfo.plist Support -# ----------------------- - -layerInfoVersion3ValueData = { - "color": dict(type=str, valueValidator=colorValidator), - "lib": dict(type=dict, valueValidator=genericTypeValidator), -} - - -def validateLayerInfoVersion3ValueForAttribute(attr, value): - """ - This performs very basic validation of the value for attribute - following the UFO 3 fontinfo.plist specification. The results - of this should not be interpretted as *correct* for the font - that they are part of. This merely indicates that the value - is of the proper type and, where the specification defines - a set range of possible values for an attribute, that the - value is in the accepted range. - """ - if attr not in layerInfoVersion3ValueData: - return False - dataValidationDict = layerInfoVersion3ValueData[attr] - valueType = dataValidationDict.get("type") - validator = dataValidationDict.get("valueValidator") - valueOptions = dataValidationDict.get("valueOptions") - # have specific options for the validator - if valueOptions is not None: - isValidValue = validator(value, valueOptions) - # no specific options - else: - if validator == genericTypeValidator: - isValidValue = validator(value, valueType) - else: - isValidValue = validator(value) - return isValidValue - - -def validateLayerInfoVersion3Data(infoData): - """ - This performs very basic validation of the value for infoData - following the UFO 3 layerinfo.plist specification. The results - of this should not be interpretted as *correct* for the font - that they are part of. This merely indicates that the values - are of the proper type and, where the specification defines - a set range of possible values for an attribute, that the - value is in the accepted range. - """ - for attr, value in infoData.items(): - if attr not in layerInfoVersion3ValueData: - raise GlifLibError("Unknown attribute %s." % attr) - isValidValue = validateLayerInfoVersion3ValueForAttribute(attr, value) - if not isValidValue: - raise GlifLibError(f"Invalid value for attribute {attr} ({value!r}).") - return infoData - - -# ----------------- -# GLIF Tree Support -# ----------------- - - -def _glifTreeFromFile(aFile): - if etree._have_lxml: - tree = etree.parse(aFile, parser=etree.XMLParser(remove_comments=True)) - else: - tree = etree.parse(aFile) - root = tree.getroot() - if root.tag != "glyph": - raise GlifLibError("The GLIF is not properly formatted.") - if root.text and root.text.strip() != "": - raise GlifLibError("Invalid GLIF structure.") - return root - - -def _glifTreeFromString(aString): - data = tobytes(aString, encoding="utf-8") - try: - if etree._have_lxml: - root = etree.fromstring(data, parser=etree.XMLParser(remove_comments=True)) - else: - root = etree.fromstring(data) - except Exception as etree_exception: - raise GlifLibError("GLIF contains invalid XML.") from etree_exception - - if root.tag != "glyph": - raise GlifLibError("The GLIF is not properly formatted.") - if root.text and root.text.strip() != "": - raise GlifLibError("Invalid GLIF structure.") - return root - - -def _readGlyphFromTree( - tree, - glyphObject=None, - pointPen=None, - formatVersions=GLIFFormatVersion.supported_versions(), - validate=True, -): - # check the format version - formatVersionMajor = tree.get("format") - if validate and formatVersionMajor is None: - raise GlifLibError("Unspecified format version in GLIF.") - formatVersionMinor = tree.get("formatMinor", 0) - try: - formatVersion = GLIFFormatVersion( - (int(formatVersionMajor), int(formatVersionMinor)) - ) - except ValueError as e: - msg = "Unsupported GLIF format: %s.%s" % ( - formatVersionMajor, - formatVersionMinor, - ) - if validate: - from fontTools.ufoLib.errors import UnsupportedGLIFFormat - - raise UnsupportedGLIFFormat(msg) from e - # warn but continue using the latest supported format - formatVersion = GLIFFormatVersion.default() - logger.warning( - "%s. Assuming the latest supported version (%s). " - "Some data may be skipped or parsed incorrectly.", - msg, - formatVersion, - ) - - if validate and formatVersion not in formatVersions: - raise GlifLibError(f"Forbidden GLIF format version: {formatVersion!s}") - - try: - readGlyphFromTree = _READ_GLYPH_FROM_TREE_FUNCS[formatVersion] - except KeyError: - raise NotImplementedError(formatVersion) - - readGlyphFromTree( - tree=tree, - glyphObject=glyphObject, - pointPen=pointPen, - validate=validate, - formatMinor=formatVersion.minor, - ) - - -def _readGlyphFromTreeFormat1( - tree, glyphObject=None, pointPen=None, validate=None, **kwargs -): - # get the name - _readName(glyphObject, tree, validate) - # populate the sub elements - unicodes = [] - haveSeenAdvance = haveSeenOutline = haveSeenLib = haveSeenNote = False - for element in tree: - if element.tag == "outline": - if validate: - if haveSeenOutline: - raise GlifLibError("The outline element occurs more than once.") - if element.attrib: - raise GlifLibError( - "The outline element contains unknown attributes." - ) - if element.text and element.text.strip() != "": - raise GlifLibError("Invalid outline structure.") - haveSeenOutline = True - buildOutlineFormat1(glyphObject, pointPen, element, validate) - elif glyphObject is None: - continue - elif element.tag == "advance": - if validate and haveSeenAdvance: - raise GlifLibError("The advance element occurs more than once.") - haveSeenAdvance = True - _readAdvance(glyphObject, element) - elif element.tag == "unicode": - try: - v = element.get("hex") - v = int(v, 16) - if v not in unicodes: - unicodes.append(v) - except ValueError: - raise GlifLibError( - "Illegal value for hex attribute of unicode element." - ) - elif element.tag == "note": - if validate and haveSeenNote: - raise GlifLibError("The note element occurs more than once.") - haveSeenNote = True - _readNote(glyphObject, element) - elif element.tag == "lib": - if validate and haveSeenLib: - raise GlifLibError("The lib element occurs more than once.") - haveSeenLib = True - _readLib(glyphObject, element, validate) - else: - raise GlifLibError("Unknown element in GLIF: %s" % element) - # set the collected unicodes - if unicodes: - _relaxedSetattr(glyphObject, "unicodes", unicodes) - - -def _readGlyphFromTreeFormat2( - tree, glyphObject=None, pointPen=None, validate=None, formatMinor=0 -): - # get the name - _readName(glyphObject, tree, validate) - # populate the sub elements - unicodes = [] - guidelines = [] - anchors = [] - haveSeenAdvance = ( - haveSeenImage - ) = haveSeenOutline = haveSeenLib = haveSeenNote = False - identifiers = set() - for element in tree: - if element.tag == "outline": - if validate: - if haveSeenOutline: - raise GlifLibError("The outline element occurs more than once.") - if element.attrib: - raise GlifLibError( - "The outline element contains unknown attributes." - ) - if element.text and element.text.strip() != "": - raise GlifLibError("Invalid outline structure.") - haveSeenOutline = True - if pointPen is not None: - buildOutlineFormat2( - glyphObject, pointPen, element, identifiers, validate - ) - elif glyphObject is None: - continue - elif element.tag == "advance": - if validate and haveSeenAdvance: - raise GlifLibError("The advance element occurs more than once.") - haveSeenAdvance = True - _readAdvance(glyphObject, element) - elif element.tag == "unicode": - try: - v = element.get("hex") - v = int(v, 16) - if v not in unicodes: - unicodes.append(v) - except ValueError: - raise GlifLibError( - "Illegal value for hex attribute of unicode element." - ) - elif element.tag == "guideline": - if validate and len(element): - raise GlifLibError("Unknown children in guideline element.") - attrib = dict(element.attrib) - for attr in ("x", "y", "angle"): - if attr in attrib: - attrib[attr] = _number(attrib[attr]) - guidelines.append(attrib) - elif element.tag == "anchor": - if validate and len(element): - raise GlifLibError("Unknown children in anchor element.") - attrib = dict(element.attrib) - for attr in ("x", "y"): - if attr in element.attrib: - attrib[attr] = _number(attrib[attr]) - anchors.append(attrib) - elif element.tag == "image": - if validate: - if haveSeenImage: - raise GlifLibError("The image element occurs more than once.") - if len(element): - raise GlifLibError("Unknown children in image element.") - haveSeenImage = True - _readImage(glyphObject, element, validate) - elif element.tag == "note": - if validate and haveSeenNote: - raise GlifLibError("The note element occurs more than once.") - haveSeenNote = True - _readNote(glyphObject, element) - elif element.tag == "lib": - if validate and haveSeenLib: - raise GlifLibError("The lib element occurs more than once.") - haveSeenLib = True - _readLib(glyphObject, element, validate) - else: - raise GlifLibError("Unknown element in GLIF: %s" % element) - # set the collected unicodes - if unicodes: - _relaxedSetattr(glyphObject, "unicodes", unicodes) - # set the collected guidelines - if guidelines: - if validate and not guidelinesValidator(guidelines, identifiers): - raise GlifLibError("The guidelines are improperly formatted.") - _relaxedSetattr(glyphObject, "guidelines", guidelines) - # set the collected anchors - if anchors: - if validate and not anchorsValidator(anchors, identifiers): - raise GlifLibError("The anchors are improperly formatted.") - _relaxedSetattr(glyphObject, "anchors", anchors) - - -_READ_GLYPH_FROM_TREE_FUNCS = { - GLIFFormatVersion.FORMAT_1_0: _readGlyphFromTreeFormat1, - GLIFFormatVersion.FORMAT_2_0: _readGlyphFromTreeFormat2, -} - - -def _readName(glyphObject, root, validate): - glyphName = root.get("name") - if validate and not glyphName: - raise GlifLibError("Empty glyph name in GLIF.") - if glyphName and glyphObject is not None: - _relaxedSetattr(glyphObject, "name", glyphName) - - -def _readAdvance(glyphObject, advance): - width = _number(advance.get("width", 0)) - _relaxedSetattr(glyphObject, "width", width) - height = _number(advance.get("height", 0)) - _relaxedSetattr(glyphObject, "height", height) - - -def _readNote(glyphObject, note): - lines = note.text.split("\n") - note = "\n".join(line.strip() for line in lines if line.strip()) - _relaxedSetattr(glyphObject, "note", note) - - -def _readLib(glyphObject, lib, validate): - assert len(lib) == 1 - child = lib[0] - plist = plistlib.fromtree(child) - if validate: - valid, message = glyphLibValidator(plist) - if not valid: - raise GlifLibError(message) - _relaxedSetattr(glyphObject, "lib", plist) - - -def _readImage(glyphObject, image, validate): - imageData = dict(image.attrib) - for attr, default in _transformationInfo: - value = imageData.get(attr, default) - imageData[attr] = _number(value) - if validate and not imageValidator(imageData): - raise GlifLibError("The image element is not properly formatted.") - _relaxedSetattr(glyphObject, "image", imageData) - - -# ---------------- -# GLIF to PointPen -# ---------------- - -contourAttributesFormat2 = {"identifier"} -componentAttributesFormat1 = { - "base", - "xScale", - "xyScale", - "yxScale", - "yScale", - "xOffset", - "yOffset", -} -componentAttributesFormat2 = componentAttributesFormat1 | {"identifier"} -pointAttributesFormat1 = {"x", "y", "type", "smooth", "name"} -pointAttributesFormat2 = pointAttributesFormat1 | {"identifier"} -pointSmoothOptions = {"no", "yes"} -pointTypeOptions = {"move", "line", "offcurve", "curve", "qcurve"} - -# format 1 - - -def buildOutlineFormat1(glyphObject, pen, outline, validate): - anchors = [] - for element in outline: - if element.tag == "contour": - if len(element) == 1: - point = element[0] - if point.tag == "point": - anchor = _buildAnchorFormat1(point, validate) - if anchor is not None: - anchors.append(anchor) - continue - if pen is not None: - _buildOutlineContourFormat1(pen, element, validate) - elif element.tag == "component": - if pen is not None: - _buildOutlineComponentFormat1(pen, element, validate) - else: - raise GlifLibError("Unknown element in outline element: %s" % element) - if glyphObject is not None and anchors: - if validate and not anchorsValidator(anchors): - raise GlifLibError("GLIF 1 anchors are not properly formatted.") - _relaxedSetattr(glyphObject, "anchors", anchors) - - -def _buildAnchorFormat1(point, validate): - if point.get("type") != "move": - return None - name = point.get("name") - if name is None: - return None - x = point.get("x") - y = point.get("y") - if validate and x is None: - raise GlifLibError("Required x attribute is missing in point element.") - if validate and y is None: - raise GlifLibError("Required y attribute is missing in point element.") - x = _number(x) - y = _number(y) - anchor = dict(x=x, y=y, name=name) - return anchor - - -def _buildOutlineContourFormat1(pen, contour, validate): - if validate and contour.attrib: - raise GlifLibError("Unknown attributes in contour element.") - pen.beginPath() - if len(contour): - massaged = _validateAndMassagePointStructures( - contour, - pointAttributesFormat1, - openContourOffCurveLeniency=True, - validate=validate, - ) - _buildOutlinePointsFormat1(pen, massaged) - pen.endPath() - - -def _buildOutlinePointsFormat1(pen, contour): - for point in contour: - x = point["x"] - y = point["y"] - segmentType = point["segmentType"] - smooth = point["smooth"] - name = point["name"] - pen.addPoint((x, y), segmentType=segmentType, smooth=smooth, name=name) - - -def _buildOutlineComponentFormat1(pen, component, validate): - if validate: - if len(component): - raise GlifLibError("Unknown child elements of component element.") - for attr in component.attrib.keys(): - if attr not in componentAttributesFormat1: - raise GlifLibError("Unknown attribute in component element: %s" % attr) - baseGlyphName = component.get("base") - if validate and baseGlyphName is None: - raise GlifLibError("The base attribute is not defined in the component.") - transformation = [] - for attr, default in _transformationInfo: - value = component.get(attr) - if value is None: - value = default - else: - value = _number(value) - transformation.append(value) - pen.addComponent(baseGlyphName, tuple(transformation)) - - -# format 2 - - -def buildOutlineFormat2(glyphObject, pen, outline, identifiers, validate): - for element in outline: - if element.tag == "contour": - _buildOutlineContourFormat2(pen, element, identifiers, validate) - elif element.tag == "component": - _buildOutlineComponentFormat2(pen, element, identifiers, validate) - else: - raise GlifLibError("Unknown element in outline element: %s" % element.tag) - - -def _buildOutlineContourFormat2(pen, contour, identifiers, validate): - if validate: - for attr in contour.attrib.keys(): - if attr not in contourAttributesFormat2: - raise GlifLibError("Unknown attribute in contour element: %s" % attr) - identifier = contour.get("identifier") - if identifier is not None: - if validate: - if identifier in identifiers: - raise GlifLibError( - "The identifier %s is used more than once." % identifier - ) - if not identifierValidator(identifier): - raise GlifLibError( - "The contour identifier %s is not valid." % identifier - ) - identifiers.add(identifier) - try: - pen.beginPath(identifier=identifier) - except TypeError: - pen.beginPath() - warn( - "The beginPath method needs an identifier kwarg. The contour's identifier value has been discarded.", - DeprecationWarning, - ) - if len(contour): - massaged = _validateAndMassagePointStructures( - contour, pointAttributesFormat2, validate=validate - ) - _buildOutlinePointsFormat2(pen, massaged, identifiers, validate) - pen.endPath() - - -def _buildOutlinePointsFormat2(pen, contour, identifiers, validate): - for point in contour: - x = point["x"] - y = point["y"] - segmentType = point["segmentType"] - smooth = point["smooth"] - name = point["name"] - identifier = point.get("identifier") - if identifier is not None: - if validate: - if identifier in identifiers: - raise GlifLibError( - "The identifier %s is used more than once." % identifier - ) - if not identifierValidator(identifier): - raise GlifLibError("The identifier %s is not valid." % identifier) - identifiers.add(identifier) - try: - pen.addPoint( - (x, y), - segmentType=segmentType, - smooth=smooth, - name=name, - identifier=identifier, - ) - except TypeError: - pen.addPoint((x, y), segmentType=segmentType, smooth=smooth, name=name) - warn( - "The addPoint method needs an identifier kwarg. The point's identifier value has been discarded.", - DeprecationWarning, - ) - - -def _buildOutlineComponentFormat2(pen, component, identifiers, validate): - if validate: - if len(component): - raise GlifLibError("Unknown child elements of component element.") - for attr in component.attrib.keys(): - if attr not in componentAttributesFormat2: - raise GlifLibError("Unknown attribute in component element: %s" % attr) - baseGlyphName = component.get("base") - if validate and baseGlyphName is None: - raise GlifLibError("The base attribute is not defined in the component.") - transformation = [] - for attr, default in _transformationInfo: - value = component.get(attr) - if value is None: - value = default - else: - value = _number(value) - transformation.append(value) - identifier = component.get("identifier") - if identifier is not None: - if validate: - if identifier in identifiers: - raise GlifLibError( - "The identifier %s is used more than once." % identifier - ) - if validate and not identifierValidator(identifier): - raise GlifLibError("The identifier %s is not valid." % identifier) - identifiers.add(identifier) - try: - pen.addComponent(baseGlyphName, tuple(transformation), identifier=identifier) - except TypeError: - pen.addComponent(baseGlyphName, tuple(transformation)) - warn( - "The addComponent method needs an identifier kwarg. The component's identifier value has been discarded.", - DeprecationWarning, - ) - - -# all formats - - -def _validateAndMassagePointStructures( - contour, pointAttributes, openContourOffCurveLeniency=False, validate=True -): - if not len(contour): - return - # store some data for later validation - lastOnCurvePoint = None - haveOffCurvePoint = False - # validate and massage the individual point elements - massaged = [] - for index, element in enumerate(contour): - # not <point> - if element.tag != "point": - raise GlifLibError( - "Unknown child element (%s) of contour element." % element.tag - ) - point = dict(element.attrib) - massaged.append(point) - if validate: - # unknown attributes - for attr in point.keys(): - if attr not in pointAttributes: - raise GlifLibError("Unknown attribute in point element: %s" % attr) - # search for unknown children - if len(element): - raise GlifLibError("Unknown child elements in point element.") - # x and y are required - for attr in ("x", "y"): - try: - point[attr] = _number(point[attr]) - except KeyError as e: - raise GlifLibError( - f"Required {attr} attribute is missing in point element." - ) from e - # segment type - pointType = point.pop("type", "offcurve") - if validate and pointType not in pointTypeOptions: - raise GlifLibError("Unknown point type: %s" % pointType) - if pointType == "offcurve": - pointType = None - point["segmentType"] = pointType - if pointType is None: - haveOffCurvePoint = True - else: - lastOnCurvePoint = index - # move can only occur as the first point - if validate and pointType == "move" and index != 0: - raise GlifLibError( - "A move point occurs after the first point in the contour." - ) - # smooth is optional - smooth = point.get("smooth", "no") - if validate and smooth is not None: - if smooth not in pointSmoothOptions: - raise GlifLibError("Unknown point smooth value: %s" % smooth) - smooth = smooth == "yes" - point["smooth"] = smooth - # smooth can only be applied to curve and qcurve - if validate and smooth and pointType is None: - raise GlifLibError("smooth attribute set in an offcurve point.") - # name is optional - if "name" not in element.attrib: - point["name"] = None - if openContourOffCurveLeniency: - # remove offcurves that precede a move. this is technically illegal, - # but we let it slide because there are fonts out there in the wild like this. - if massaged[0]["segmentType"] == "move": - count = 0 - for point in reversed(massaged): - if point["segmentType"] is None: - count += 1 - else: - break - if count: - massaged = massaged[:-count] - # validate the off-curves in the segments - if validate and haveOffCurvePoint and lastOnCurvePoint is not None: - # we only care about how many offCurves there are before an onCurve - # filter out the trailing offCurves - offCurvesCount = len(massaged) - 1 - lastOnCurvePoint - for point in massaged: - segmentType = point["segmentType"] - if segmentType is None: - offCurvesCount += 1 - else: - if offCurvesCount: - # move and line can't be preceded by off-curves - if segmentType == "move": - # this will have been filtered out already - raise GlifLibError("move can not have an offcurve.") - elif segmentType == "line": - raise GlifLibError("line can not have an offcurve.") - elif segmentType == "curve": - if offCurvesCount > 2: - raise GlifLibError("Too many offcurves defined for curve.") - elif segmentType == "qcurve": - pass - else: - # unknown segment type. it'll be caught later. - pass - offCurvesCount = 0 - return massaged - - -# --------------------- -# Misc Helper Functions -# --------------------- - - -def _relaxedSetattr(object, attr, value): - try: - setattr(object, attr, value) - except AttributeError: - pass - - -def _number(s): - """ - Given a numeric string, return an integer or a float, whichever - the string indicates. _number("1") will return the integer 1, - _number("1.0") will return the float 1.0. - - >>> _number("1") - 1 - >>> _number("1.0") - 1.0 - >>> _number("a") # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - GlifLibError: Could not convert a to an int or float. - """ - try: - n = int(s) - return n - except ValueError: - pass - try: - n = float(s) - return n - except ValueError: - raise GlifLibError("Could not convert %s to an int or float." % s) - - -# -------------------- -# Rapid Value Fetching -# -------------------- - -# base - - -class _DoneParsing(Exception): - pass - - -class _BaseParser: - def __init__(self): - self._elementStack = [] - - def parse(self, text): - from xml.parsers.expat import ParserCreate - - parser = ParserCreate() - parser.StartElementHandler = self.startElementHandler - parser.EndElementHandler = self.endElementHandler - parser.Parse(text) - - def startElementHandler(self, name, attrs): - self._elementStack.append(name) - - def endElementHandler(self, name): - other = self._elementStack.pop(-1) - assert other == name - - -# unicodes - - -def _fetchUnicodes(glif): - """ - Get a list of unicodes listed in glif. - """ - parser = _FetchUnicodesParser() - parser.parse(glif) - return parser.unicodes - - -class _FetchUnicodesParser(_BaseParser): - def __init__(self): - self.unicodes = [] - super().__init__() - - def startElementHandler(self, name, attrs): - if ( - name == "unicode" - and self._elementStack - and self._elementStack[-1] == "glyph" - ): - value = attrs.get("hex") - if value is not None: - try: - value = int(value, 16) - if value not in self.unicodes: - self.unicodes.append(value) - except ValueError: - pass - super().startElementHandler(name, attrs) - - -# image - - -def _fetchImageFileName(glif): - """ - The image file name (if any) from glif. - """ - parser = _FetchImageFileNameParser() - try: - parser.parse(glif) - except _DoneParsing: - pass - return parser.fileName - - -class _FetchImageFileNameParser(_BaseParser): - def __init__(self): - self.fileName = None - super().__init__() - - def startElementHandler(self, name, attrs): - if name == "image" and self._elementStack and self._elementStack[-1] == "glyph": - self.fileName = attrs.get("fileName") - raise _DoneParsing - super().startElementHandler(name, attrs) - - -# component references - - -def _fetchComponentBases(glif): - """ - Get a list of component base glyphs listed in glif. - """ - parser = _FetchComponentBasesParser() - try: - parser.parse(glif) - except _DoneParsing: - pass - return list(parser.bases) - - -class _FetchComponentBasesParser(_BaseParser): - def __init__(self): - self.bases = [] - super().__init__() - - def startElementHandler(self, name, attrs): - if ( - name == "component" - and self._elementStack - and self._elementStack[-1] == "outline" - ): - base = attrs.get("base") - if base is not None: - self.bases.append(base) - super().startElementHandler(name, attrs) - - def endElementHandler(self, name): - if name == "outline": - raise _DoneParsing - super().endElementHandler(name) - - -# -------------- -# GLIF Point Pen -# -------------- - -_transformationInfo = [ - # field name, default value - ("xScale", 1), - ("xyScale", 0), - ("yxScale", 0), - ("yScale", 1), - ("xOffset", 0), - ("yOffset", 0), -] - - -class GLIFPointPen(AbstractPointPen): - - """ - Helper class using the PointPen protocol to write the <outline> - part of .glif files. - """ - - def __init__(self, element, formatVersion=None, identifiers=None, validate=True): - if identifiers is None: - identifiers = set() - self.formatVersion = GLIFFormatVersion(formatVersion) - self.identifiers = identifiers - self.outline = element - self.contour = None - self.prevOffCurveCount = 0 - self.prevPointTypes = [] - self.validate = validate - - def beginPath(self, identifier=None, **kwargs): - attrs = OrderedDict() - if identifier is not None and self.formatVersion.major >= 2: - if self.validate: - if identifier in self.identifiers: - raise GlifLibError( - "identifier used more than once: %s" % identifier - ) - if not identifierValidator(identifier): - raise GlifLibError( - "identifier not formatted properly: %s" % identifier - ) - attrs["identifier"] = identifier - self.identifiers.add(identifier) - self.contour = etree.SubElement(self.outline, "contour", attrs) - self.prevOffCurveCount = 0 - - def endPath(self): - if self.prevPointTypes and self.prevPointTypes[0] == "move": - if self.validate and self.prevPointTypes[-1] == "offcurve": - raise GlifLibError("open contour has loose offcurve point") - # prevent lxml from writing self-closing tags - if not len(self.contour): - self.contour.text = "\n " - self.contour = None - self.prevPointType = None - self.prevOffCurveCount = 0 - self.prevPointTypes = [] - - def addPoint( - self, pt, segmentType=None, smooth=None, name=None, identifier=None, **kwargs - ): - attrs = OrderedDict() - # coordinates - if pt is not None: - if self.validate: - for coord in pt: - if not isinstance(coord, numberTypes): - raise GlifLibError("coordinates must be int or float") - attrs["x"] = repr(pt[0]) - attrs["y"] = repr(pt[1]) - # segment type - if segmentType == "offcurve": - segmentType = None - if self.validate: - if segmentType == "move" and self.prevPointTypes: - raise GlifLibError( - "move occurs after a point has already been added to the contour." - ) - if ( - segmentType in ("move", "line") - and self.prevPointTypes - and self.prevPointTypes[-1] == "offcurve" - ): - raise GlifLibError("offcurve occurs before %s point." % segmentType) - if segmentType == "curve" and self.prevOffCurveCount > 2: - raise GlifLibError("too many offcurve points before curve point.") - if segmentType is not None: - attrs["type"] = segmentType - else: - segmentType = "offcurve" - if segmentType == "offcurve": - self.prevOffCurveCount += 1 - else: - self.prevOffCurveCount = 0 - self.prevPointTypes.append(segmentType) - # smooth - if smooth: - if self.validate and segmentType == "offcurve": - raise GlifLibError("can't set smooth in an offcurve point.") - attrs["smooth"] = "yes" - # name - if name is not None: - attrs["name"] = name - # identifier - if identifier is not None and self.formatVersion.major >= 2: - if self.validate: - if identifier in self.identifiers: - raise GlifLibError( - "identifier used more than once: %s" % identifier - ) - if not identifierValidator(identifier): - raise GlifLibError( - "identifier not formatted properly: %s" % identifier - ) - attrs["identifier"] = identifier - self.identifiers.add(identifier) - etree.SubElement(self.contour, "point", attrs) - - def addComponent(self, glyphName, transformation, identifier=None, **kwargs): - attrs = OrderedDict([("base", glyphName)]) - for (attr, default), value in zip(_transformationInfo, transformation): - if self.validate and not isinstance(value, numberTypes): - raise GlifLibError("transformation values must be int or float") - if value != default: - attrs[attr] = repr(value) - if identifier is not None and self.formatVersion.major >= 2: - if self.validate: - if identifier in self.identifiers: - raise GlifLibError( - "identifier used more than once: %s" % identifier - ) - if self.validate and not identifierValidator(identifier): - raise GlifLibError( - "identifier not formatted properly: %s" % identifier - ) - attrs["identifier"] = identifier - self.identifiers.add(identifier) - etree.SubElement(self.outline, "component", attrs) - 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If you want to get access to our<br>Software archive you should visit our website: <br> <br> <br> <br>>> Contact e-mail: <br> <br> <br>ProSof...@yahoo.com <br> <br>or <br> <br>ProSof...@yahoo.com<br>*** Sent via Developersdex ***<br></p> -<h2>keygen floor plan 3d 11.0 36</h2><br /><p><b><b>Download File</b> » <a href="https://tinurli.com/2uwiSm">https://tinurli.com/2uwiSm</a></b></p><br /><br /> aaccfb2cb3<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/cihyFjudo/fairness-paper-search/Kuzey Guney 40 Bolum 720p Mkv.md b/spaces/cihyFjudo/fairness-paper-search/Kuzey Guney 40 Bolum 720p Mkv.md deleted file mode 100644 index f0bd143678f7673f3e44a31467de877e190c7435..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Kuzey Guney 40 Bolum 720p Mkv.md +++ /dev/null @@ -1,6 +0,0 @@ -<h2>kuzey guney 40 bolum 720p mkv</h2><br /><p><b><b>DOWNLOAD</b> ››› <a href="https://tinurli.com/2uwkJd">https://tinurli.com/2uwkJd</a></b></p><br /><br /> -<br /> - aaccfb2cb3<br /> -<br /> -<br /> -<p></p> diff --git a/spaces/cihyFjudo/fairness-paper-search/Mujer Follando Con Perro Y Se Queda Enganchada.md b/spaces/cihyFjudo/fairness-paper-search/Mujer Follando Con Perro Y Se Queda Enganchada.md deleted file mode 100644 index ef65a27572c0d4120b82d7b4693a05ef429b0e11..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Mujer Follando Con Perro Y Se Queda Enganchada.md +++ /dev/null @@ -1,24 +0,0 @@ -<br /> -<p>La cachonda y viciosa chamaquita monta una sesión de zoofilia con su perro y se siente abotonada al animal detrás de su culo. La chica y el perro se quedan pegados culo con culo después de que el animal le clave la verga en su coño. Se ha descontrolado la situación y ahora no puede sacar el rabo gordo de su coño hasta que este no se desinflame.</p> -<p>Aquí las jovencitas disfrutan de sus experiencias sexuales con animales. Esta chavalita inocente se pone en pompa delante del perro en celo para que este la penetre duro. Antes la mascota le ha lamido su concha y ella lo ha excitado haciéndole una larga mamada con la boca bien abierta. No hay escena más caliente que la protagonizada por esta nenita enganchada a su robusto y grande rottweiler el cual se la polla metiéndole su miembro viril en la concha humedecida.</p> -<h2>mujer follando con perro y se queda enganchada</h2><br /><p><b><b>Download Zip</b> ○○○ <a href="https://tinurli.com/2uwkvi">https://tinurli.com/2uwkvi</a></b></p><br /><br /> -<p><strong>La reproducción del perro</strong> es un proceso complejo que, generalmente, se inicia con el cortejo, en el que macho y hembra emiten señales para dar a entender al otro que están preparados para la monta y consecuente copulación. Una vez producida la monta, observamos que el macho desmonta a la hembra pero el pene queda en el interior de la vagina, mostrando a ambos perros unidos. Es en este punto cuando nos preguntamos el porqué de este hecho y si debemos separarlos nosotros o, por contra, lo hacen ellos de manera natural.</p> -<p>Para poder entender con más facilidad por qué los perros se quedan enganchados cuando se aparean, resulta fundamental hacer un breve repaso sobre la anatomía del sistema reproductor tanto del macho como de la hembra. Así pues, el <strong>aparato interno y externo del perro</strong> se compone de las siguientes partes:</p> -<p>Como ocurre con el aparto del macho, el sistema reproductor de la perra está formado por <strong>órganos internos y externos</strong>, algunos de ellos culpables del hecho de quedarse los perros enganchados después de la monta. A continuación, explicamos de forma resumida la función de cada uno de ellos:</p> -<p>Una vez producida la penetración, el macho tiende a "desmontar" a la perra, quedándose unido a ella y llevando a los propietarios de ambos animales a preguntarse por qué los perros se han quedado pegados durante el apareamiento y cómo separarlos. Esto es así debido a que la eyaculación del perro se produce en tres fases o fracciones:</p> -<p>De esta forma, y una vez revisadas las tres fases de la eyaculación del macho, vemos como la causa que da respuesta a la pregunta por qué los perros se quedan enganchados cuando se aparean es la expansión del bulbo peneano. Es tal el tamaño que alcanza, no puede pasar a través del vestíbulo vaginal, el cual se cierra precisamente para garantizar este hecho y evitar producir daños a la hembra.</p> -<p>Si deseas leer más artículos parecidos a <strong>¿Por qué los perros se quedan pegados cuando se aparean?</strong>, te recomendamos que entres en nuestra sección de Curiosidades del mundo animal.</p> -<p>Cuando dos perros se quedan pegados durante la monta, <strong>NO se deben separar</strong>. El motivo es sencillo: debido a la anatomía del aparato reproductor del perro, al separar a los animales a la fuerza solo se conseguiría producir graves daños en ambos canes. La hembra probablemente sufriría un desgarro vaginal o un prolapso, mientras que el macho también podría padecer un desgarro en el pene. Así, si se pretende evitar el aparente sufrimiento de la perra durante este proceso, lo más sensato es no dejar que la monta se produzca. No obstante, es posible que esto ocurra sin darnos cuenta y no sepamos cómo actuar. Por ello, en este artículo de ExpertoAnimal hablaremos sobre <strong>cómo separar a dos perros cuando se quedan pegados</strong> y por qué sucede esto.</p> -<p>El pene del perro está formado por el hueso peneano y el <strong>bulbo peneano</strong>. Durante la penetración, el macho eyacula en tres fases o fracciones, y en cada una de ellas expulsa más o menos espermatozoides. En la segunda fase, como consecuencia de la compresión venosa que experimenta el pene y, por ende, la concentración sanguínea, el bulbo peneano <strong>aumenta considerablemente su tamaño</strong> y queda completamente acoplado en el vestíbulo vaginal, dando lugar al llamado <strong>abotonamiento</strong>. Aquí, el macho se gira sin retirar el pene de la hembra y ambos quedan pegados, generalmente de espaldas, para que la eyaculación pueda finalizar y la perra quede embarazada. Se trata de un proceso natural, que el cuerpo del perro desarrolló para garantizar la supervivencia de la especie sin poner en peligro la vida de los futuros progenitores, ya que durante todo este proceso los animales quedan totalmente expuestos y, al estar girados, tienen la posibilidad de controlar el entorno. Más allá de su vulnerabilidad, <strong>un perro tarda mucho más en eyacular</strong> que otros animales, y hasta que el bulbo no esté totalmente relajado (y por tanto desinflamado) no se produce el despegue. Con este método en el que el bulbo se agranda y la hembra no se puede separar, la naturaleza misma se aseguró de que el macho pudiera fecundarla. Así, los perros no se quedan pegados porque el semen que el perro expulsa sea demasiado espeso, como muchas personas creen, sino porque el tiempo que implica la eyaculación hace que el bulbo se agrande.</p> -<p></p> -<p>Todo ello produce mucho dolor en los dos perros por las heridas provocadas en sus genitales, por lo que<strong> jamás se debe separar a dos perros pegados</strong>. Si se ha producido la monta, no queda más remedio que esperar a que los canes se separen solos. En este momento, ambos lamerán sus partes íntimas, el pene del macho volverá a introducirse en el prepucio y todo tornará a la normalidad.</p> -<p>Absolutamente nada. Separar a los perros copulando solo traerá consecuencias muy negativas para su salud, de manera que lo único que se puede hacer es <strong>garantizar un ambiente relajado y tranquilo</strong>. Durante este proceso en el que el macho está girado y ambos perros quedan de espaldas, es posible observar a la hembra agitada, nerviosa, lloriqueando e, incluso, intentando separarse. Es una actitud normal, puesto que para algunas puede resultar un tanto molesto. Por ello, lo último que debemos hacer es fomentar su estado de nervios, ya que, sin querer, ella misma podría provocar graves daños en el macho o en su propio aparato reproductor. Así, evitaremos que otros animales o personas se acerquen a la pareja e intentaremos <strong>ofrecerles intimidad</strong> para que puedan finalizar el proceso sin problemas.</p> -<p>A este perro se le hincho tanto la polla después de tener sexo y anal con esta mujer, que se le quedo enganchada la polla al culo de esta. Pasaron algunos minutos para poder separarse el uno del otro. Esto es de lokos !</p> -<p>Si alguna vez has visto a dos canes apareándose sabrás que es difícil evitar preguntarse por qué se quedan enganchados. Como es algo que a los humanos no nos sucede, es común que la primera reacción que tengamos al verlo sea pensar que algo les ha pasado y que necesitan ayuda o bien, en caso de que suceda sin tenerlo planeado, nos preguntemos <b>cómo despegar a dos perros cuando se cruzan</b>, buscando evitar que la hembra quede embarazada. Sin embargo, antes de actuar hay que saber que es algo normal en ellos.En este artículo de unCOMO vamos a despejar esta duda y a comentar muchos más detalles sobre el apareamiento, los cuales conviene conocer por el bien de nuestras mascotas.</p> -<p>La forma en que se quedan enganchados, que explicamos en el siguiente apartado, impide que se puedan separar, pero si se ejerce fuerza, tirando de ellos, podría llegar a darse la separación con<strong> las consecuencias</strong> que conlleva. 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Sin embargo, hay razones anatómicas especiales para que eso ocurra, según <strong>Peggy Root</strong>, una experta en reproducción animal de la <strong>Universidad de Minnesota</strong>. El pene de un perro tiene un compartimento que se llena de sangre después del inicio del coito, efectivamente aferrando al macho en su lugar.</p> -<p>Consulta la mejor selección de <strong>vídeos porno</strong> del mundo. Con nuevas, maduras, negras, rubias, pelirrojas y la mayor variedad de putas follando. En nuestro sitio separamos el contenido por categorías para facilitar el acceso. Además, las mujeres son desde el sexo profesional hasta esas aficionadas sabrosas a las que les encanta putear. Así que vale la pena recordar la forma en que los traviesos follan y se excitan bien. Cientos de putinhas queriendo la polla dura dentro y fuera de su coño y culo.</p> aaccfb2cb3<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/cncanon/chud/greeting.md b/spaces/cncanon/chud/greeting.md deleted file mode 100644 index 2197a0e80139e08425edf4f01ff9fc862dff6d9a..0000000000000000000000000000000000000000 --- a/spaces/cncanon/chud/greeting.md +++ /dev/null @@ -1,2 +0,0 @@ -![](https://static.wikia.nocookie.net/brotherhood-of-nod/images/a/a3/CNCTW_Kane.png) -Pass: `kane_lives!` \ No newline at end of file diff --git a/spaces/codedog-ai/edu-assistant/edu_assistant/learning_tasks/base.py b/spaces/codedog-ai/edu-assistant/edu_assistant/learning_tasks/base.py deleted file mode 100644 index 28c9e303419f8f80843aaa56c075a3f0d1280010..0000000000000000000000000000000000000000 --- a/spaces/codedog-ai/edu-assistant/edu_assistant/learning_tasks/base.py +++ /dev/null @@ -1,6 +0,0 @@ -import uuid - - -class BaseTask: - def _create_session_id(self) -> str: - return str(uuid.uuid1()) diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alac_data.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alac_data.h deleted file mode 100644 index 802074639b569f97fed9abeba6817676ec5033f5..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alac_data.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * ALAC encoder and decoder common data - * - * 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 - */ - -#ifndef AVCODEC_ALAC_DATA_H -#define AVCODEC_ALAC_DATA_H - -#include <stdint.h> - -#include "libavutil/channel_layout.h" - -enum AlacRawDataBlockType { - /* At the moment, only SCE, CPE, LFE, and END are recognized. */ - TYPE_SCE, - TYPE_CPE, - TYPE_CCE, - TYPE_LFE, - TYPE_DSE, - TYPE_PCE, - TYPE_FIL, - TYPE_END -}; - -#define ALAC_MAX_CHANNELS 8 - -extern const uint8_t ff_alac_channel_layout_offsets[ALAC_MAX_CHANNELS][ALAC_MAX_CHANNELS]; - -extern const AVChannelLayout ff_alac_ch_layouts[ALAC_MAX_CHANNELS + 1]; - -extern const enum AlacRawDataBlockType ff_alac_channel_elements[ALAC_MAX_CHANNELS][5]; - -#endif /* AVCODEC_ALAC_DATA_H */ diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cbs.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cbs.c deleted file mode 100644 index 504197e06d40b9461bfba9658d78e9d94d94a523..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cbs.c +++ /dev/null @@ -1,1028 +0,0 @@ -/* - * 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 <string.h> - -#include "config.h" - -#include "libavutil/avassert.h" -#include "libavutil/buffer.h" -#include "libavutil/common.h" -#include "libavutil/opt.h" - -#include "avcodec.h" -#include "cbs.h" -#include "cbs_internal.h" - - -static const CodedBitstreamType *const cbs_type_table[] = { -#if CONFIG_CBS_AV1 - &ff_cbs_type_av1, -#endif -#if CONFIG_CBS_H264 - &ff_cbs_type_h264, -#endif -#if CONFIG_CBS_H265 - &ff_cbs_type_h265, -#endif -#if CONFIG_CBS_JPEG - &ff_cbs_type_jpeg, -#endif -#if CONFIG_CBS_MPEG2 - &ff_cbs_type_mpeg2, -#endif -#if CONFIG_CBS_VP9 - &ff_cbs_type_vp9, -#endif -}; - -const enum AVCodecID ff_cbs_all_codec_ids[] = { -#if CONFIG_CBS_AV1 - AV_CODEC_ID_AV1, -#endif -#if CONFIG_CBS_H264 - AV_CODEC_ID_H264, -#endif -#if CONFIG_CBS_H265 - AV_CODEC_ID_H265, -#endif -#if CONFIG_CBS_JPEG - AV_CODEC_ID_MJPEG, -#endif -#if CONFIG_CBS_MPEG2 - AV_CODEC_ID_MPEG2VIDEO, -#endif -#if CONFIG_CBS_VP9 - AV_CODEC_ID_VP9, -#endif - AV_CODEC_ID_NONE -}; - -av_cold int ff_cbs_init(CodedBitstreamContext **ctx_ptr, - enum AVCodecID codec_id, void *log_ctx) -{ - CodedBitstreamContext *ctx; - const CodedBitstreamType *type; - int i; - - type = NULL; - for (i = 0; i < FF_ARRAY_ELEMS(cbs_type_table); i++) { - if (cbs_type_table[i]->codec_id == codec_id) { - type = cbs_type_table[i]; - break; - } - } - if (!type) - return AVERROR(EINVAL); - - ctx = av_mallocz(sizeof(*ctx)); - if (!ctx) - return AVERROR(ENOMEM); - - ctx->log_ctx = log_ctx; - ctx->codec = type; /* Must be before any error */ - - if (type->priv_data_size) { - ctx->priv_data = av_mallocz(ctx->codec->priv_data_size); - if (!ctx->priv_data) { - av_freep(&ctx); - return AVERROR(ENOMEM); - } - if (type->priv_class) { - *(const AVClass **)ctx->priv_data = type->priv_class; - av_opt_set_defaults(ctx->priv_data); - } - } - - ctx->decompose_unit_types = NULL; - - ctx->trace_enable = 0; - ctx->trace_level = AV_LOG_TRACE; - - *ctx_ptr = ctx; - return 0; -} - -av_cold void ff_cbs_flush(CodedBitstreamContext *ctx) -{ - if (ctx->codec->flush) - ctx->codec->flush(ctx); -} - -av_cold void ff_cbs_close(CodedBitstreamContext **ctx_ptr) -{ - CodedBitstreamContext *ctx = *ctx_ptr; - - if (!ctx) - return; - - if (ctx->codec->close) - ctx->codec->close(ctx); - - av_freep(&ctx->write_buffer); - - if (ctx->codec->priv_class && ctx->priv_data) - av_opt_free(ctx->priv_data); - - av_freep(&ctx->priv_data); - av_freep(ctx_ptr); -} - -static void cbs_unit_uninit(CodedBitstreamUnit *unit) -{ - av_buffer_unref(&unit->content_ref); - unit->content = NULL; - - av_buffer_unref(&unit->data_ref); - unit->data = NULL; - unit->data_size = 0; - unit->data_bit_padding = 0; -} - -void ff_cbs_fragment_reset(CodedBitstreamFragment *frag) -{ - int i; - - for (i = 0; i < frag->nb_units; i++) - cbs_unit_uninit(&frag->units[i]); - frag->nb_units = 0; - - av_buffer_unref(&frag->data_ref); - frag->data = NULL; - frag->data_size = 0; - frag->data_bit_padding = 0; -} - -av_cold void ff_cbs_fragment_free(CodedBitstreamFragment *frag) -{ - ff_cbs_fragment_reset(frag); - - av_freep(&frag->units); - frag->nb_units_allocated = 0; -} - -static int cbs_read_fragment_content(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag) -{ - int err, i, j; - - for (i = 0; i < frag->nb_units; i++) { - CodedBitstreamUnit *unit = &frag->units[i]; - - if (ctx->decompose_unit_types) { - for (j = 0; j < ctx->nb_decompose_unit_types; j++) { - if (ctx->decompose_unit_types[j] == unit->type) - break; - } - if (j >= ctx->nb_decompose_unit_types) - continue; - } - - av_buffer_unref(&unit->content_ref); - unit->content = NULL; - - av_assert0(unit->data && unit->data_ref); - - err = ctx->codec->read_unit(ctx, unit); - if (err == AVERROR(ENOSYS)) { - av_log(ctx->log_ctx, AV_LOG_VERBOSE, - "Decomposition unimplemented for unit %d " - "(type %"PRIu32").\n", i, unit->type); - } else if (err == AVERROR(EAGAIN)) { - av_log(ctx->log_ctx, AV_LOG_VERBOSE, - "Skipping decomposition of unit %d " - "(type %"PRIu32").\n", i, unit->type); - av_buffer_unref(&unit->content_ref); - unit->content = NULL; - } else if (err < 0) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "Failed to read unit %d " - "(type %"PRIu32").\n", i, unit->type); - return err; - } - } - - return 0; -} - -static int cbs_fill_fragment_data(CodedBitstreamFragment *frag, - const uint8_t *data, size_t size) -{ - av_assert0(!frag->data && !frag->data_ref); - - frag->data_ref = - av_buffer_alloc(size + AV_INPUT_BUFFER_PADDING_SIZE); - if (!frag->data_ref) - return AVERROR(ENOMEM); - - frag->data = frag->data_ref->data; - frag->data_size = size; - - memcpy(frag->data, data, size); - memset(frag->data + size, 0, - AV_INPUT_BUFFER_PADDING_SIZE); - - return 0; -} - -static int cbs_read_data(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag, - AVBufferRef *buf, - const uint8_t *data, size_t size, - int header) -{ - int err; - - if (buf) { - frag->data_ref = av_buffer_ref(buf); - if (!frag->data_ref) - return AVERROR(ENOMEM); - - frag->data = (uint8_t *)data; - frag->data_size = size; - - } else { - err = cbs_fill_fragment_data(frag, data, size); - if (err < 0) - return err; - } - - err = ctx->codec->split_fragment(ctx, frag, header); - if (err < 0) - return err; - - return cbs_read_fragment_content(ctx, frag); -} - -int ff_cbs_read_extradata(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag, - const AVCodecParameters *par) -{ - return cbs_read_data(ctx, frag, NULL, - par->extradata, - par->extradata_size, 1); -} - -int ff_cbs_read_extradata_from_codec(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag, - const AVCodecContext *avctx) -{ - return cbs_read_data(ctx, frag, NULL, - avctx->extradata, - avctx->extradata_size, 1); -} - -int ff_cbs_read_packet(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag, - const AVPacket *pkt) -{ - return cbs_read_data(ctx, frag, pkt->buf, - pkt->data, pkt->size, 0); -} - -int ff_cbs_read_packet_side_data(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag, - const AVPacket *pkt) -{ - size_t side_data_size; - const uint8_t *side_data = - av_packet_get_side_data(pkt, AV_PKT_DATA_NEW_EXTRADATA, - &side_data_size); - - return cbs_read_data(ctx, frag, NULL, - side_data, side_data_size, 1); -} - -int ff_cbs_read(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag, - const uint8_t *data, size_t size) -{ - return cbs_read_data(ctx, frag, NULL, - data, size, 0); -} - -/** - * Allocate a new internal data buffer of the given size in the unit. - * - * The data buffer will have input padding. - */ -static int cbs_alloc_unit_data(CodedBitstreamUnit *unit, - size_t size) -{ - av_assert0(!unit->data && !unit->data_ref); - - unit->data_ref = av_buffer_alloc(size + AV_INPUT_BUFFER_PADDING_SIZE); - if (!unit->data_ref) - return AVERROR(ENOMEM); - - unit->data = unit->data_ref->data; - unit->data_size = size; - - memset(unit->data + size, 0, AV_INPUT_BUFFER_PADDING_SIZE); - - return 0; -} - -static int cbs_write_unit_data(CodedBitstreamContext *ctx, - CodedBitstreamUnit *unit) -{ - PutBitContext pbc; - int ret; - - if (!ctx->write_buffer) { - // Initial write buffer size is 1MB. - ctx->write_buffer_size = 1024 * 1024; - - reallocate_and_try_again: - ret = av_reallocp(&ctx->write_buffer, ctx->write_buffer_size); - if (ret < 0) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "Unable to allocate a " - "sufficiently large write buffer (last attempt " - "%"SIZE_SPECIFIER" bytes).\n", ctx->write_buffer_size); - return ret; - } - } - - init_put_bits(&pbc, ctx->write_buffer, ctx->write_buffer_size); - - ret = ctx->codec->write_unit(ctx, unit, &pbc); - if (ret < 0) { - if (ret == AVERROR(ENOSPC)) { - // Overflow. - if (ctx->write_buffer_size == INT_MAX / 8) - return AVERROR(ENOMEM); - ctx->write_buffer_size = FFMIN(2 * ctx->write_buffer_size, INT_MAX / 8); - goto reallocate_and_try_again; - } - // Write failed for some other reason. - return ret; - } - - // Overflow but we didn't notice. - av_assert0(put_bits_count(&pbc) <= 8 * ctx->write_buffer_size); - - if (put_bits_count(&pbc) % 8) - unit->data_bit_padding = 8 - put_bits_count(&pbc) % 8; - else - unit->data_bit_padding = 0; - - flush_put_bits(&pbc); - - ret = cbs_alloc_unit_data(unit, put_bytes_output(&pbc)); - if (ret < 0) - return ret; - - memcpy(unit->data, ctx->write_buffer, unit->data_size); - - return 0; -} - -int ff_cbs_write_fragment_data(CodedBitstreamContext *ctx, - CodedBitstreamFragment *frag) -{ - int err, i; - - for (i = 0; i < frag->nb_units; i++) { - CodedBitstreamUnit *unit = &frag->units[i]; - - if (!unit->content) - continue; - - av_buffer_unref(&unit->data_ref); - unit->data = NULL; - - err = cbs_write_unit_data(ctx, unit); - if (err < 0) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "Failed to write unit %d " - "(type %"PRIu32").\n", i, unit->type); - return err; - } - av_assert0(unit->data && unit->data_ref); - } - - av_buffer_unref(&frag->data_ref); - frag->data = NULL; - - err = ctx->codec->assemble_fragment(ctx, frag); - if (err < 0) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "Failed to assemble fragment.\n"); - return err; - } - av_assert0(frag->data && frag->data_ref); - - return 0; -} - -int ff_cbs_write_extradata(CodedBitstreamContext *ctx, - AVCodecParameters *par, - CodedBitstreamFragment *frag) -{ - int err; - - err = ff_cbs_write_fragment_data(ctx, frag); - if (err < 0) - return err; - - av_freep(&par->extradata); - par->extradata_size = 0; - - if (!frag->data_size) - return 0; - - par->extradata = av_malloc(frag->data_size + - AV_INPUT_BUFFER_PADDING_SIZE); - if (!par->extradata) - return AVERROR(ENOMEM); - - memcpy(par->extradata, frag->data, frag->data_size); - memset(par->extradata + frag->data_size, 0, - AV_INPUT_BUFFER_PADDING_SIZE); - par->extradata_size = frag->data_size; - - return 0; -} - -int ff_cbs_write_packet(CodedBitstreamContext *ctx, - AVPacket *pkt, - CodedBitstreamFragment *frag) -{ - AVBufferRef *buf; - int err; - - err = ff_cbs_write_fragment_data(ctx, frag); - if (err < 0) - return err; - - buf = av_buffer_ref(frag->data_ref); - if (!buf) - return AVERROR(ENOMEM); - - av_buffer_unref(&pkt->buf); - - pkt->buf = buf; - pkt->data = frag->data; - pkt->size = frag->data_size; - - return 0; -} - - -void ff_cbs_trace_header(CodedBitstreamContext *ctx, - const char *name) -{ - if (!ctx->trace_enable) - return; - - av_log(ctx->log_ctx, ctx->trace_level, "%s\n", name); -} - -void ff_cbs_trace_syntax_element(CodedBitstreamContext *ctx, int position, - const char *str, const int *subscripts, - const char *bits, int64_t value) -{ - char name[256]; - size_t name_len, bits_len; - int pad, subs, i, j, k, n; - - if (!ctx->trace_enable) - return; - - av_assert0(value >= INT_MIN && value <= UINT32_MAX); - - subs = subscripts ? subscripts[0] : 0; - n = 0; - for (i = j = 0; str[i];) { - if (str[i] == '[') { - if (n < subs) { - ++n; - k = snprintf(name + j, sizeof(name) - j, "[%d", subscripts[n]); - av_assert0(k > 0 && j + k < sizeof(name)); - j += k; - for (++i; str[i] && str[i] != ']'; i++); - av_assert0(str[i] == ']'); - } else { - while (str[i] && str[i] != ']') - name[j++] = str[i++]; - av_assert0(str[i] == ']'); - } - } else { - av_assert0(j + 1 < sizeof(name)); - name[j++] = str[i++]; - } - } - av_assert0(j + 1 < sizeof(name)); - name[j] = 0; - av_assert0(n == subs); - - name_len = strlen(name); - bits_len = strlen(bits); - - if (name_len + bits_len > 60) - pad = bits_len + 2; - else - pad = 61 - name_len; - - av_log(ctx->log_ctx, ctx->trace_level, "%-10d %s%*s = %"PRId64"\n", - position, name, pad, bits, value); -} - -int ff_cbs_read_unsigned(CodedBitstreamContext *ctx, GetBitContext *gbc, - int width, const char *name, - const int *subscripts, uint32_t *write_to, - uint32_t range_min, uint32_t range_max) -{ - uint32_t value; - int position; - - av_assert0(width > 0 && width <= 32); - - if (get_bits_left(gbc) < width) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "Invalid value at " - "%s: bitstream ended.\n", name); - return AVERROR_INVALIDDATA; - } - - if (ctx->trace_enable) - position = get_bits_count(gbc); - - value = get_bits_long(gbc, width); - - if (ctx->trace_enable) { - char bits[33]; - int i; - for (i = 0; i < width; i++) - bits[i] = value >> (width - i - 1) & 1 ? '1' : '0'; - bits[i] = 0; - - ff_cbs_trace_syntax_element(ctx, position, name, subscripts, - bits, value); - } - - if (value < range_min || value > range_max) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "%s out of range: " - "%"PRIu32", but must be in [%"PRIu32",%"PRIu32"].\n", - name, value, range_min, range_max); - return AVERROR_INVALIDDATA; - } - - *write_to = value; - return 0; -} - -int ff_cbs_write_unsigned(CodedBitstreamContext *ctx, PutBitContext *pbc, - int width, const char *name, - const int *subscripts, uint32_t value, - uint32_t range_min, uint32_t range_max) -{ - av_assert0(width > 0 && width <= 32); - - if (value < range_min || value > range_max) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "%s out of range: " - "%"PRIu32", but must be in [%"PRIu32",%"PRIu32"].\n", - name, value, range_min, range_max); - return AVERROR_INVALIDDATA; - } - - if (put_bits_left(pbc) < width) - return AVERROR(ENOSPC); - - if (ctx->trace_enable) { - char bits[33]; - int i; - for (i = 0; i < width; i++) - bits[i] = value >> (width - i - 1) & 1 ? '1' : '0'; - bits[i] = 0; - - ff_cbs_trace_syntax_element(ctx, put_bits_count(pbc), - name, subscripts, bits, value); - } - - if (width < 32) - put_bits(pbc, width, value); - else - put_bits32(pbc, value); - - return 0; -} - -int ff_cbs_read_signed(CodedBitstreamContext *ctx, GetBitContext *gbc, - int width, const char *name, - const int *subscripts, int32_t *write_to, - int32_t range_min, int32_t range_max) -{ - int32_t value; - int position; - - av_assert0(width > 0 && width <= 32); - - if (get_bits_left(gbc) < width) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "Invalid value at " - "%s: bitstream ended.\n", name); - return AVERROR_INVALIDDATA; - } - - if (ctx->trace_enable) - position = get_bits_count(gbc); - - value = get_sbits_long(gbc, width); - - if (ctx->trace_enable) { - char bits[33]; - int i; - for (i = 0; i < width; i++) - bits[i] = value & (1U << (width - i - 1)) ? '1' : '0'; - bits[i] = 0; - - ff_cbs_trace_syntax_element(ctx, position, name, subscripts, - bits, value); - } - - if (value < range_min || value > range_max) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "%s out of range: " - "%"PRId32", but must be in [%"PRId32",%"PRId32"].\n", - name, value, range_min, range_max); - return AVERROR_INVALIDDATA; - } - - *write_to = value; - return 0; -} - -int ff_cbs_write_signed(CodedBitstreamContext *ctx, PutBitContext *pbc, - int width, const char *name, - const int *subscripts, int32_t value, - int32_t range_min, int32_t range_max) -{ - av_assert0(width > 0 && width <= 32); - - if (value < range_min || value > range_max) { - av_log(ctx->log_ctx, AV_LOG_ERROR, "%s out of range: " - "%"PRId32", but must be in [%"PRId32",%"PRId32"].\n", - name, value, range_min, range_max); - return AVERROR_INVALIDDATA; - } - - if (put_bits_left(pbc) < width) - return AVERROR(ENOSPC); - - if (ctx->trace_enable) { - char bits[33]; - int i; - for (i = 0; i < width; i++) - bits[i] = value & (1U << (width - i - 1)) ? '1' : '0'; - bits[i] = 0; - - ff_cbs_trace_syntax_element(ctx, put_bits_count(pbc), - name, subscripts, bits, value); - } - - if (width < 32) - put_sbits(pbc, width, value); - else - put_bits32(pbc, value); - - return 0; -} - - -static int cbs_insert_unit(CodedBitstreamFragment *frag, - int position) -{ - CodedBitstreamUnit *units; - - if (frag->nb_units < frag->nb_units_allocated) { - units = frag->units; - - if (position < frag->nb_units) - memmove(units + position + 1, units + position, - (frag->nb_units - position) * sizeof(*units)); - } else { - units = av_malloc_array(frag->nb_units*2 + 1, sizeof(*units)); - if (!units) - return AVERROR(ENOMEM); - - frag->nb_units_allocated = 2*frag->nb_units_allocated + 1; - - if (position > 0) - memcpy(units, frag->units, position * sizeof(*units)); - - if (position < frag->nb_units) - memcpy(units + position + 1, frag->units + position, - (frag->nb_units - position) * sizeof(*units)); - } - - memset(units + position, 0, sizeof(*units)); - - if (units != frag->units) { - av_free(frag->units); - frag->units = units; - } - - ++frag->nb_units; - - return 0; -} - -int ff_cbs_insert_unit_content(CodedBitstreamFragment *frag, - int position, - CodedBitstreamUnitType type, - void *content, - AVBufferRef *content_buf) -{ - CodedBitstreamUnit *unit; - AVBufferRef *content_ref; - int err; - - if (position == -1) - position = frag->nb_units; - av_assert0(position >= 0 && position <= frag->nb_units); - - if (content_buf) { - content_ref = av_buffer_ref(content_buf); - if (!content_ref) - return AVERROR(ENOMEM); - } else { - content_ref = NULL; - } - - err = cbs_insert_unit(frag, position); - if (err < 0) { - av_buffer_unref(&content_ref); - return err; - } - - unit = &frag->units[position]; - unit->type = type; - unit->content = content; - unit->content_ref = content_ref; - - return 0; -} - -static int cbs_insert_unit_data(CodedBitstreamFragment *frag, - CodedBitstreamUnitType type, - uint8_t *data, size_t data_size, - AVBufferRef *data_buf, - int position) -{ - CodedBitstreamUnit *unit; - AVBufferRef *data_ref; - int err; - - av_assert0(position >= 0 && position <= frag->nb_units); - - if (data_buf) - data_ref = av_buffer_ref(data_buf); - else - data_ref = av_buffer_create(data, data_size, NULL, NULL, 0); - if (!data_ref) { - if (!data_buf) - av_free(data); - return AVERROR(ENOMEM); - } - - err = cbs_insert_unit(frag, position); - if (err < 0) { - av_buffer_unref(&data_ref); - return err; - } - - unit = &frag->units[position]; - unit->type = type; - unit->data = data; - unit->data_size = data_size; - unit->data_ref = data_ref; - - return 0; -} - -int ff_cbs_append_unit_data(CodedBitstreamFragment *frag, - CodedBitstreamUnitType type, - uint8_t *data, size_t data_size, - AVBufferRef *data_buf) -{ - return cbs_insert_unit_data(frag, type, - data, data_size, data_buf, - frag->nb_units); -} - -void ff_cbs_delete_unit(CodedBitstreamFragment *frag, - int position) -{ - av_assert0(0 <= position && position < frag->nb_units - && "Unit to be deleted not in fragment."); - - cbs_unit_uninit(&frag->units[position]); - - --frag->nb_units; - - if (frag->nb_units > 0) - memmove(frag->units + position, - frag->units + position + 1, - (frag->nb_units - position) * sizeof(*frag->units)); -} - -static void cbs_default_free_unit_content(void *opaque, uint8_t *data) -{ - const CodedBitstreamUnitTypeDescriptor *desc = opaque; - - for (int i = 0; i < desc->type.ref.nb_offsets; i++) { - void **ptr = (void**)(data + desc->type.ref.offsets[i]); - av_buffer_unref((AVBufferRef**)(ptr + 1)); - } - av_free(data); -} - -static const CodedBitstreamUnitTypeDescriptor - *cbs_find_unit_type_desc(CodedBitstreamContext *ctx, - CodedBitstreamUnit *unit) -{ - const CodedBitstreamUnitTypeDescriptor *desc; - int i, j; - - if (!ctx->codec->unit_types) - return NULL; - - for (i = 0;; i++) { - desc = &ctx->codec->unit_types[i]; - if (desc->nb_unit_types == 0) - break; - if (desc->nb_unit_types == CBS_UNIT_TYPE_RANGE) { - if (unit->type >= desc->unit_type.range.start && - unit->type <= desc->unit_type.range.end) - return desc; - } else { - for (j = 0; j < desc->nb_unit_types; j++) { - if (desc->unit_type.list[j] == unit->type) - return desc; - } - } - } - return NULL; -} - -int ff_cbs_alloc_unit_content(CodedBitstreamContext *ctx, - CodedBitstreamUnit *unit) -{ - const CodedBitstreamUnitTypeDescriptor *desc; - - av_assert0(!unit->content && !unit->content_ref); - - desc = cbs_find_unit_type_desc(ctx, unit); - if (!desc) - return AVERROR(ENOSYS); - - unit->content = av_mallocz(desc->content_size); - if (!unit->content) - return AVERROR(ENOMEM); - - unit->content_ref = - av_buffer_create(unit->content, desc->content_size, - desc->content_type == CBS_CONTENT_TYPE_COMPLEX - ? desc->type.complex.content_free - : cbs_default_free_unit_content, - (void*)desc, 0); - if (!unit->content_ref) { - av_freep(&unit->content); - return AVERROR(ENOMEM); - } - - return 0; -} - -static int cbs_clone_internal_refs_unit_content(AVBufferRef **clone_ref, - const CodedBitstreamUnit *unit, - const CodedBitstreamUnitTypeDescriptor *desc) -{ - const uint8_t *src; - uint8_t *copy; - int err, i; - - av_assert0(unit->content); - src = unit->content; - - copy = av_memdup(src, desc->content_size); - if (!copy) - return AVERROR(ENOMEM); - - for (i = 0; i < desc->type.ref.nb_offsets; i++) { - const uint8_t *const *src_ptr = (const uint8_t* const*)(src + desc->type.ref.offsets[i]); - const AVBufferRef *src_buf = *(AVBufferRef**)(src_ptr + 1); - uint8_t **copy_ptr = (uint8_t**)(copy + desc->type.ref.offsets[i]); - AVBufferRef **copy_buf = (AVBufferRef**)(copy_ptr + 1); - - if (!*src_ptr) { - av_assert0(!src_buf); - continue; - } - if (!src_buf) { - // We can't handle a non-refcounted pointer here - we don't - // have enough information to handle whatever structure lies - // at the other end of it. - err = AVERROR(EINVAL); - goto fail; - } - - *copy_buf = av_buffer_ref(src_buf); - if (!*copy_buf) { - err = AVERROR(ENOMEM); - goto fail; - } - } - - *clone_ref = av_buffer_create(copy, desc->content_size, - cbs_default_free_unit_content, - (void*)desc, 0); - if (!*clone_ref) { - err = AVERROR(ENOMEM); - goto fail; - } - - return 0; - -fail: - for (--i; i >= 0; i--) - av_buffer_unref((AVBufferRef**)(copy + desc->type.ref.offsets[i])); - av_freep(©); - *clone_ref = NULL; - return err; -} - -/* - * On success, unit->content and unit->content_ref are updated with - * the new content; unit is untouched on failure. - * Any old content_ref is simply overwritten and not freed. - */ -static int cbs_clone_unit_content(CodedBitstreamContext *ctx, - CodedBitstreamUnit *unit) -{ - const CodedBitstreamUnitTypeDescriptor *desc; - AVBufferRef *ref; - int err; - - desc = cbs_find_unit_type_desc(ctx, unit); - if (!desc) - return AVERROR(ENOSYS); - - switch (desc->content_type) { - case CBS_CONTENT_TYPE_INTERNAL_REFS: - err = cbs_clone_internal_refs_unit_content(&ref, unit, desc); - break; - - case CBS_CONTENT_TYPE_COMPLEX: - if (!desc->type.complex.content_clone) - return AVERROR_PATCHWELCOME; - err = desc->type.complex.content_clone(&ref, unit); - break; - - default: - av_assert0(0 && "Invalid content type."); - } - - if (err < 0) - return err; - - unit->content_ref = ref; - unit->content = ref->data; - return 0; -} - -int ff_cbs_make_unit_refcounted(CodedBitstreamContext *ctx, - CodedBitstreamUnit *unit) -{ - av_assert0(unit->content); - if (unit->content_ref) - return 0; - return cbs_clone_unit_content(ctx, unit); -} - -int ff_cbs_make_unit_writable(CodedBitstreamContext *ctx, - CodedBitstreamUnit *unit) -{ - AVBufferRef *ref = unit->content_ref; - int err; - - av_assert0(unit->content); - if (ref && av_buffer_is_writable(ref)) - return 0; - - err = cbs_clone_unit_content(ctx, unit); - if (err < 0) - return err; - av_buffer_unref(&ref); - return 0; -} diff --git a/spaces/congsaPfin/Manga-OCR/logs/Drive Your Dream Car with Real Driving Ultimate Car Simulator MOD APK (Unlimited Money).md b/spaces/congsaPfin/Manga-OCR/logs/Drive Your Dream Car with Real Driving Ultimate Car Simulator MOD APK (Unlimited Money).md deleted file mode 100644 index bb0a1cc1a7116832f6a37c4e757b677aa5cf26cc..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Drive Your Dream Car with Real Driving Ultimate Car Simulator MOD APK (Unlimited Money).md +++ /dev/null @@ -1,68 +0,0 @@ -<br /> -<h1>Real Driving Ultimate Car Simulator Mod APK Unlimited Money: A Review</h1> -<p>Do you love driving games? Do you want to experience the most realistic and immersive car simulation on your Android device? If yes, then you should check out Real Driving Ultimate Car Simulator, a game that lets you drive various cars in different environments and scenarios. And if you want to make the game even more fun and exciting, you should try the mod apk that gives you unlimited money and access to all the features. In this article, we will review Real Driving Ultimate Car Simulator and its mod apk, and tell you how to download and install it on your device. Let's get started!</p> -<h2>real driving ultimate car simulator mod apk unlimited money</h2><br /><p><b><b>Download</b> >>>>> <a href="https://urlca.com/2uOfqp">https://urlca.com/2uOfqp</a></b></p><br /><br /> - <h2>Features of Real Driving Ultimate Car Simulator and its mod apk</h2> -<p>Real Driving Ultimate Car Simulator is a game that aims to provide you with the most realistic and enjoyable driving experience. 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It is usually named as "Limon S1.ttf".</li> -<li>Right-click on the font file and select "Install" or "Install for all users". This will copy the font file to your system fonts folder.</li> -<li>Open the folder where you extracted Khmer Font Limon S1 and find the keyboard layout file. It is usually named as "Limon Keyboard.pdf".</li> -<li>Open the keyboard layout file and print it out or save it as a reference. This will show you how to type Khmer characters and symbols using Khmer Font Limon S1.</li> -<li>Restart your computer or any program that you want to use Khmer Font Limon S1 with.</li> -</ol> - <h2>How to Convert Khmer Font Limon S1 to Khmer Unicode?</h2> -<p>If you want to use Khmer Font Limon S1 on some modern programs or platforms that only accept Unicode fonts, you need to convert it to Khmer Unicode first. Here are some reasons and methods to do so:</p> - <h3>The reasons to convert Khmer Font Limon S1 to Khmer Unicode</h3> -<p>Khmer Unicode is a standard encoding system that was released in 2004 by the Ministry of Posts and Telecommunications of Cambodia. It is based on the Unicode standard, which means it uses a code of more than 65,000 characters to store and display text. It has some advantages over Khmer Font Limon S1, such as:</p> -<ul> -<li>It is supported by most modern programs and platforms that use Unicode fonts, such as web browsers, mobile devices, social media platforms, etc.</li> -<li>It is standardized by an official organization and authority, which means it has fewer errors or inconsistencies in its characters or symbols.</li> -<li>It can display text in multiple languages without causing any problems.</li> -</ul> - <h3>The methods to convert Khmer Font Limon S1 to Khmer Unicode</h3> -<p>There are many tools that can help you convert Khmer Font Limon S1 to Khmer Unicode, such as online converters, offline converters, or plugins. Here are some examples of them:</p> - <table border="1"> -<tr><th>Name</th><th>Type</th><th>Description</th><th>Link</th></tr> -<tr><td>Limon-Khmer Converter</td><td>Online converter</td><td>A web-based tool that can convert text from Khmer Font Limon S1 to Khmer Unicode or vice versa.</td><td><a href="">limon-khmer-converter.appspot.com</a></td></tr> -<tr><td>Khmer Converter</td><td>Offline converter</td><td>A software program that can convert text from various Khmer fonts, including Khmer Font Limon S1, to Khmer Unicode or vice versa.</td><td><a href="">khmerconverter.com</a></td></tr> -<tr><td>Limon Keyboard Plugin for Microsoft Word</td><td>Plugin</td><td>A plugin that can enable you to type in Khmer Font Limon S1 in Microsoft Word and convert it to Khmer Unicode automatically.</td><td><a href="">khmertype.blogspot.com/2010/01/limon-key board-plugin-for-microsoft-word.html)</a></td></tr> -</table> - <p>You can choose any of these tools and follow their instructions to convert Khmer Font Limon S1 to Khmer Unicode. However, you should be careful when converting text, as some characters or symbols may not be converted correctly or may lose their meaning. You should always check and edit the converted text before using it.</p> - <h2>Conclusion</h2> -<p>Khmer Font Limon S1 is a free and classic font that was created in 1994 and has been widely used in Cambodia for many purposes. It has some features that make it attractive for users, such as its compatibility, simplicity, clarity, and elegance. However, it also has some drawbacks that make it less preferable than Khmer Unicode fonts, such as its lack of support, standardization, and multilingualism. Therefore, if you want to use Khmer Font Limon S1 on your computer, you should know how to download, install, and convert it to Khmer Unicode.</p> - <h2>FAQs</h2> -<h3>What is the difference between Khmer Font Limon S1 and Khmer Unicode?</h3> -<p>Khmer Font Limon S1 is an ASCII font that uses a code of 128 characters to store and display text. It is based on the Latin alphabet, but it uses special characters and symbols to represent the sounds and tones of the Khmer language. Khmer Unicode is a Unicode font that uses a code of more than 65,000 characters to store and display text. It is based on the Unicode standard, which means it can display text in multiple languages without causing any problems.</p> - <h3>Where can I find more Khmer fonts for free download?</h3> -<p>There are many websites that offer Khmer fonts for free download, such as <a href="">khmerfonts.com</a>, <a href="">khmeros.info</a>, <a href="">khmertype.blogspot.com</a>, etc. You can browse these websites and find the fonts that suit your needs and preferences.</p> - <h3>How can I type in Khmer on my mobile device?</h3> -<p>If you want to type in Khmer on your mobile device, you need to install a Khmer keyboard app first. There are many apps that provide Khmer keyboards for different platforms, such as Android, iOS, Windows Phone, etc. Some examples of these apps are <a href="">Khmer Smart Keyboard</a>, <a href="">Khmer Keyboard</a>, <a href="">Khmer Keyboard for iPhone</a>, etc. You can download these apps from the app store or the website and follow their instructions to enable and use them.</p> - <h3>How can I learn more about the Khmer language and culture?</h3> -<p>If you want to learn more about the Khmer language and culture, you can visit some websites that provide information and resources about them, such as <a href="">khmernz.blogspot.com</a>, <a href="">cambodianlanguage.org</a>, <a href="">cambodianview.com</a>, etc. You can also watch some videos or listen to some podcasts that teach or discuss the Khmer language and culture, such as <a href="">Learn Khmer with Annie</a>, <a href="">Khmer Language Podcast</a>, <a href="">Cambodian Culture Show</a>, etc.</p> - <h3>What are some tips to improve my writing skills in Khmer?</h3> -<p>If you want to improve your writing skills in Khmer, you can follow some tips, such as:</p> -<ul> -<li>Read more books, articles, blogs, or magazines in Khmer to expand your vocabulary and grammar.</li> -<li>Write more often in Khmer to practice your spelling and punctuation.</li> -<li>Use a dictionary or a translator to check the meaning and pronunciation of words or phrases.</li> -<li>Ask for feedback from native speakers or experts to correct your mistakes and improve your style.</li> -<li>Join some online communities or forums where you can share your writing and learn from others.</li> -</ul></p> 401be4b1e0<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/SAKURA School Simulator Hile APK The Best Way to Experience Japans School Culture.md b/spaces/congsaPfin/Manga-OCR/logs/SAKURA School Simulator Hile APK The Best Way to Experience Japans School Culture.md deleted file mode 100644 index dbcbad8a6f913385312f788b45926bb0719cb72f..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/SAKURA School Simulator Hile APK The Best Way to Experience Japans School Culture.md +++ /dev/null @@ -1,201 +0,0 @@ - -<h1>Sakura School Simulator Hile APK: A Guide to the Ultimate School Life Simulation Game</h1> - <p>Have you ever wondered what it would be like to live a school life in Japan? Do you want to make friends, lovers, or enemies as you please? Do you want to go on a rampage or enjoy a peaceful day? If you answered yes to any of these questions, then you might want to check out <strong>Sakura School Simulator</strong>, a free simulation game that lets you experience a realistic and fantastic school life in a fictional town called Sakura.</p> - <p>In this article, we will tell you everything you need to know about Sakura School Simulator, including what it is, what it offers, how to download and install it, how to play it with tips and tricks, and how to review it. We will also introduce you to <strong>Sakura School Simulator Hile APK</strong>, a modified version of the game that gives you unlimited money and weapons. Read on to find out more!</p> -<h2>sakura school simulator hile apk</h2><br /><p><b><b>DOWNLOAD</b> • <a href="https://urlca.com/2uO9bR">https://urlca.com/2uO9bR</a></b></p><br /><br /> - <h2>What is Sakura School Simulator?</h2> - <h3>A brief introduction to the game and its developer</h3> - <p>Sakura School Simulator is a simulation game that was developed by Garusoft Development Inc., a Japanese indie game studio that specializes in creating unique and original games. Sakura School Simulator is their only app so far, but it has already gained more than 10 million downloads on Google Play Store alone, as well as positive user reviews on both Android and iOS platforms.</p> - <p>The game was released in January 2019, and since then, it has been updated regularly with new features, contents, and improvements. The game is available in English, Japanese, Korean, Chinese, Spanish, Portuguese, French, German, Russian, Turkish, Arabic, Indonesian, Thai, Vietnamese, Italian, Polish, Dutch, Swedish, Norwegian, Danish, Finnish, Greek, Hungarian, Romanian, Czech, Slovakian, Croatian, Bulgarian languages.</p> - <h3>The main features and gameplay of the game</h3> - <p>Sakura School Simulator is a game that simulates a school life in Japan. You can choose from five playable characters (two male students, two female students, and one cat robot) and customize their appearance, clothes, accessories, hairstyles, etc. You can also control and change four players in the same stage (two are valid after watching ads).</p> - <p>The game offers two ways to enjoy it:</p> - <ul> -<li>(1) Make friends and lovers as you like. Enjoy a brilliant school life! You can interact with various NPCs (non-player characters) in the town, such as classmates, teachers, shopkeepers, yakuza members, etc. You can talk to them (sentences will change randomly), give them gifts or money (to increase their affection or friendship level), hug them or kiss them (to make them your lover), or fight them (to make them your enemy). You can also attend classes (such as Chinese characters, English, PC skills), join clubs (such as sports club or music club), or participate in events (such as festivals or exams).</li> -<li>(2) Go on a rampage as you wish. You can use various weapons and vehicles to cause chaos and destruction in the town. You can shoot guns, throw grenades, drive cars, ride bikes, fly helicopters, etc. You can also explore the town and discover hidden places and secrets. You can do anything you want, but be careful of the consequences. The police will chase you if you commit a crime, and your reputation will change depending on your actions.</li> -</ul> - <p>The game has no end or goal, so you can play it as long as you want. You can also save and load your progress anytime. 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This is a modified version of the game that gives you unlimited money and weapons. With this, you can buy anything you want from the shops, such as clothes, accessories, food, drinks, etc. You can also access all the weapons in the game, such as pistols, rifles, shotguns, rocket launchers, etc.</p> - <p>Sakura School Simulator Hile APK is not an official app from Garusoft Development Inc., but a third-party app that was created by some fans or hackers of the game. It is not available on Google Play Store or App Store, but only on some websites that offer APK files for Android devices.</p> - <h3>The advantages and disadvantages of using the hile APK</h3> - <p>Using Sakura School Simulator Hile APK has some advantages and disadvantages that you should be aware of before downloading and installing it. Here are some of them:</p> - <table> -<tr> -<th>Advantages</th> -<th>Disadvantages</th> -</tr> -<tr> -<td>- You can enjoy the game without any ads or in-app purchases.</td> -<td>- You might miss some updates or features that are only available on the original version of the game.</td> -</tr> -<tr> -<td>- You can have more fun and freedom with unlimited money and weapons.</td> -<td>- You might encounter some bugs or errors that could affect the performance or stability of the game.</td> -</tr> -<tr> -<td>- You can challenge yourself with higher difficulty levels or more enemies.</td> -<td>- You might lose the original charm or challenge of the game that comes from earning money and weapons by yourself.</td> -</tr> -<tr> -<td>- You can share your progress or screenshots with other users who use the hile APK.</td> -<td>- You might get banned or reported by other users who don't use the hile APK.</td> -</tr> -</table> - <p>As you can see, using Sakura School Simulator Hile APK has its pros and cons. It is up to you to decide whether you want to use it or not. However, we recommend that you try the original version of the game first before using the hile APK, so that you can appreciate the game better and support the developer.</p> - <h2>How to download and install Sakura School Simulator Hile APK?</h2> - <h3>The steps to download and install the hile APK on Android devices</h3> - <p>If you have decided to use Sakura School Simulator Hile APK, here are the steps to download and install it on your Android device:</p> - <ol> -<li>First, you need to find a reliable website that offers the hile APK file for Sakura School Simulator. You can search for it on Google or use some of these links: . Make sure that the website is safe and trustworthy before downloading anything from it.</li> -<li>Second, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps that are not from Google Play Store.</li> -<li>Third, you need to download the hile APK file from the website that you have chosen. Tap on the download button or link and wait for it to finish. You might need to grant some permissions or accept some terms and conditions before downloading.</li> -<li>Fourth, you need to locate the downloaded hile APK file on your device. You can use a file manager app or go to Downloads folder to find it. Tap on the file and select Install. Wait for it to complete.</li> -<li>Fifth, you need to launch Sakura School Simulator Hile APK on your device. You can find it on your app drawer or home screen. Tap on it and enjoy!</li> -</ol> - <h3>The precautions and risks of using the hile APK</h3> - <p>While using Sakura School Simulator Hile APK might seem tempting and fun, you should also be aware of the precautions and risks that come with it. Here are some of them:</p> - <ul> -<li>First, you should always backup your data before using the hile APK. This will help you to restore your progress or settings in case something goes wrong or you want to switch back to the original version of the game.</li> -<li>Second, you should always scan the hile APK file for viruses or malware before installing it. This will help you to protect your device and data from any potential harm or damage.</li> -<li>Third, you should always check the compatibility and requirements of the hile APK before installing it. This will help you to avoid any compatibility issues or errors that could prevent the game from running properly.</li> -<li>Fourth, you should always respect the rules and policies of the game and the developer. This will help you to avoid any legal or ethical problems that could arise from using the hile APK.</li> -<li>Fifth, you should always be careful of the online interactions and communications that you have with other users of the game. This will help you to avoid any conflicts or misunderstandings that could result from using the hile APK.</li> -</ul> - <p>As you can see, using Sakura School Simulator Hile APK has its precautions and risks. You should be responsible and cautious when using it, and always follow the best practices and tips that we have provided.</p> - <h2>How to play Sakura School Simulator with tips and tricks?</h2> - <h3>The basic controls and settings of the game</h3> - <p>Sakura School Simulator is a game that is easy to play but hard to master. You need to learn the basic controls and settings of the game before you can enjoy it fully. Here are some of them:</p> - <ul> -<li>To move your character, use the joystick on the left side of the screen. To change the direction of your character, swipe on the right side of the screen.</li> -<li>To interact with objects or NPCs, tap on them. To select an option from a menu, tap on it. To cancel an action, tap on the X button.</li> -<li>To access your inventory, tap on the backpack icon on the top right corner of the screen. To equip or use an item, tap on it. To unequip an item, tap on it again.</li> -<li>To access your phone, tap on the phone icon on the top left corner of the screen. To use an app, tap on it. To exit an app, tap on the back button.</li> -<li>To access your map, tap on the map icon on the bottom right corner of the screen. To zoom in or out, pinch on the screen. To move around, drag on the screen.</li> -<li>To access your settings, tap on the gear icon on the bottom left corner of the screen. To change a setting, tap on it. To save your settings, tap on the save button.</li> -</ul> - <p>These are some of the basic controls and settings of Sakura School Simulator. You can also customize them according to your preferences and needs.</p> - <h3>The tips and tricks to ace every mission and enjoy the game</h3> - <p>Sakura School Simulator is a game that offers a lot of fun and variety. You can choose from different missions and activities that suit your mood and style. However, some of them might be challenging or confusing, especially for beginners. That's why we have prepared some tips and tricks to help you ace every mission and enjoy the game. Here are some of them:</p> - <ul> -<li>To complete a mission, you need to follow the instructions and objectives that are given to you. You can check them on your phone or on the top left corner of the screen. You can also skip a mission if you don't like it or find it too hard.</li> -<li>To earn money, you can do various jobs or tasks in the town, such as delivering pizza, working at a convenience store, cleaning the school, etc. You can also sell items that you don't need or find in the town.</li> -<li>To increase your reputation, you need to do good deeds or actions in the town, such as helping people, donating money, joining clubs, etc. You can also decrease your reputation by doing bad deeds or actions, such as hurting people, stealing money, joining yakuza, etc.</li> -<li>To make friends or lovers, you need to interact with NPCs that you like or find attractive. You can talk to them, give them gifts or money, hug them or kiss them, etc. You can also break up with them or cheat on them if you want.</li> -<li>To have fun or relax, you can do various hobbies or activities in the town, such as playing games, watching movies, reading books, listening to music, etc. You can also visit different places and landmarks in the town, such as the park, the beach, the shrine, etc.</li> -</ul> - <p>These are some of the tips and tricks to play Sakura School Simulator. You can also discover more by yourself as you play the game and explore the town.</p> - <h2>How to review Sakura School Simulator?</h2> - <h3>The criteria and factors to consider when reviewing the game</h3> - <p>If you want to share your opinion or feedback about Sakura School Simulator with other users or the developer, you might want to write a review for the game. However, writing a good review is not easy. You need to consider some criteria and factors that will make your review helpful and fair. Here are some of them:</p> - <ul> -<li>The graphics and sound quality of the game. You should evaluate how well the game looks and sounds on your device. You should mention if the game has any glitches or bugs that affect its visual or audio performance.</li> -<li>The gameplay and content variety of the game. You should evaluate how fun and engaging the game is on your device. You should mention if the game has any features or contents that make it unique or original.</li> -<li>The controls and settings of the game. You should evaluate how easy and comfortable the game is to play on your device. You should mention if the game has any options or customizations that make it user-friendly or adaptable.</li> -<li>The difficulty and challenge of the game. You should evaluate how hard and rewarding the game is on your device. You should mention if the game has any levels or missions that make it interesting or exciting.</li> -<li>The value and cost of the game. You should evaluate how worth and affordable the game is on your device. You should mention if the game has any ads or in-app purchases that affect its quality or price.</li> -</ul> - <p>These are some of the criteria and factors to consider when reviewing Sakura School Simulator. You should also use a rating system (such as stars or points) to summarize your overall impression of the game.</p> - <h3>The pros and cons of the game based on user reviews</h3> - <p>Sakura School Simulator is a game that has received mixed reviews from users who have played it. Some users love the game and praise it for its graphics, gameplay, content, controls, and value. Others hate the game and criticize it for its glitches, bugs, ads, in-app purchases, and difficulty. Here are some of the pros and cons of the game based on user reviews:</p> - <table> -<tr> -<th>Pros</th> -<th>Cons</th> -</tr> -<tr> -<td>- The game has beautiful and realistic graphics that make the town and the characters look alive and attractive.</td> -<td>- The game has many glitches and bugs that cause crashes, freezes, lags, or errors in the game.</td> -</tr> -<tr> -<td>- The game has fun and engaging gameplay that allows you to do anything you want in the town and create your own stories and scenarios.</td> -<td>- The game has annoying and intrusive ads that pop up frequently and interrupt the game.</td> -</tr> -<tr> -<td>- The game has a lot of content and variety that offer different missions, activities, places, items, weapons, vehicles, etc.</td> -<td>- The game has expensive and unnecessary in-app purchases that make the game pay-to-win or unfair.</td> -</tr> -<tr> -<td>- The game has easy and comfortable controls that let you move and interact with the town and the characters smoothly and conveniently.</td> -<td>- The game has hard and frustrating difficulty that make some missions or enemies too challenging or impossible to beat.</td> -</tr> -<tr> -<td>- The game has a great value and cost that make it worth playing and downloading for free.</td> -<td>- The game has a poor value and cost that make it not worth playing or downloading for free.</td> -</tr> -</table> - <p>As you can see, Sakura School Simulator has its pros and cons based on user reviews. You should read them carefully and decide for yourself whether you want to play the game or not.</p> - <h2>Conclusion</h2> - <h3>A summary of the main points of the article</h3> - <p>In conclusion, Sakura School Simulator is a simulation game that lets you experience a realistic and fantastic school life in a fictional town called Sakura. You can choose from five playable characters and customize their appearance, clothes, accessories, hairstyles, etc. You can also control and change four players in the same stage. You can enjoy the game in two ways: (1) make friends and lovers as you like; (2) go on a rampage as you wish. The game has no end or goal, so you can play it as long as you want. You can also save and load your progress anytime.</p> - <p>We also introduced you to Sakura School Simulator Hile APK, a modified version of the game that gives you unlimited money and weapons. We told you what it is, what it offers, how to download and install it, how to play it with tips and tricks, and how to review it. We also told you the advantages and disadvantages of using the hile APK, as well as the precautions and risks that come with it. We recommended that you try the original version of the game first before using the hile APK.</p> - <h3>A recommendation and invitation to try the game</h3> - <p>Sakura School Simulator is a game that is suitable for anyone who loves simulation games or school life games. It is a game that is fun, engaging, varied, easy, and valuable. It is a game that will make you feel like you are living a school life in Japan. It is a game that will let you create your own stories and scenarios with the characters and the environment.</p> - <p>If you are interested in Sakura School Simulator or Sakura School Simulator Hile APK, we invite you to try them out for yourself. You can download them from these links:</p> - <ul> -<li>Sakura School Simulator (original version): <a href=""></a></li> -<li>Sakura School Simulator Hile APK (modified version): <a href=""></a></li> -</ul> - <p>We hope that this article was helpful and informative for you. We hope that you will enjoy playing Sakura School Simulator or Sakura School Simulator Hile APK. Thank you for reading!</p> - <h2>FAQs</h2> - <h3>Five unique frequently asked questions about the game and the hile APK</h3> - <p>Here are some of the frequently asked questions about Sakura School Simulator and the hile APK, along with their answers:</p> - <ol> -<li>Q: Is Sakura School Simulator a multiplayer game? A: No, Sakura School Simulator is not a multiplayer game. It is a single-player game that you can play offline or online. However, you can share your progress or screenshots with other users who play the game or the hile APK.</li> -<li>Q: Is Sakura School Simulator Hile APK safe to use? 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Business Result: Intermediate: Student's Book with DVD-ROM and Online ... 4d29de3e1b<br /> -<br /> -<br /> -<p></p> diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/apc_head.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/apc_head.py deleted file mode 100644 index 4f363dba391c3eb6fb5a4d61c145fd4976a5717d..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/apc_head.py +++ /dev/null @@ -1,158 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from annotator.mmpkg.mmcv.cnn import ConvModule - -from annotator.mmpkg.mmseg.ops import resize -from ..builder import HEADS -from .decode_head import BaseDecodeHead - - -class ACM(nn.Module): - """Adaptive Context Module used in APCNet. - - Args: - pool_scale (int): Pooling scale used in Adaptive Context - Module to extract region features. - fusion (bool): Add one conv to fuse residual feature. - in_channels (int): Input channels. - channels (int): Channels after modules, before conv_seg. - conv_cfg (dict | None): Config of conv layers. - norm_cfg (dict | None): Config of norm layers. - act_cfg (dict): Config of activation layers. - """ - - def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg, - norm_cfg, act_cfg): - super(ACM, self).__init__() - self.pool_scale = pool_scale - self.fusion = fusion - self.in_channels = in_channels - self.channels = channels - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - self.pooled_redu_conv = ConvModule( - self.in_channels, - self.channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg) - - self.input_redu_conv = ConvModule( - self.in_channels, - self.channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg) - - self.global_info = ConvModule( - self.channels, - self.channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg) - - self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0) - - self.residual_conv = ConvModule( - self.channels, - self.channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg) - - if self.fusion: - self.fusion_conv = ConvModule( - self.channels, - self.channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg) - - def forward(self, x): - """Forward function.""" - pooled_x = F.adaptive_avg_pool2d(x, self.pool_scale) - # [batch_size, channels, h, w] - x = self.input_redu_conv(x) - # [batch_size, channels, pool_scale, pool_scale] - pooled_x = self.pooled_redu_conv(pooled_x) - batch_size = x.size(0) - # [batch_size, pool_scale * pool_scale, channels] - pooled_x = pooled_x.view(batch_size, self.channels, - -1).permute(0, 2, 1).contiguous() - # [batch_size, h * w, pool_scale * pool_scale] - affinity_matrix = self.gla(x + resize( - self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:]) - ).permute(0, 2, 3, 1).reshape( - batch_size, -1, self.pool_scale**2) - affinity_matrix = F.sigmoid(affinity_matrix) - # [batch_size, h * w, channels] - z_out = torch.matmul(affinity_matrix, pooled_x) - # [batch_size, channels, h * w] - z_out = z_out.permute(0, 2, 1).contiguous() - # [batch_size, channels, h, w] - z_out = z_out.view(batch_size, self.channels, x.size(2), x.size(3)) - z_out = self.residual_conv(z_out) - z_out = F.relu(z_out + x) - if self.fusion: - z_out = self.fusion_conv(z_out) - - return z_out - - -@HEADS.register_module() -class APCHead(BaseDecodeHead): - """Adaptive Pyramid Context Network for Semantic Segmentation. - - This head is the implementation of - `APCNet <https://openaccess.thecvf.com/content_CVPR_2019/papers/\ - He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_\ - CVPR_2019_paper.pdf>`_. - - Args: - pool_scales (tuple[int]): Pooling scales used in Adaptive Context - Module. Default: (1, 2, 3, 6). - fusion (bool): Add one conv to fuse residual feature. - """ - - def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs): - super(APCHead, self).__init__(**kwargs) - assert isinstance(pool_scales, (list, tuple)) - self.pool_scales = pool_scales - self.fusion = fusion - acm_modules = [] - for pool_scale in self.pool_scales: - acm_modules.append( - ACM(pool_scale, - self.fusion, - self.in_channels, - self.channels, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg)) - self.acm_modules = nn.ModuleList(acm_modules) - self.bottleneck = ConvModule( - self.in_channels + len(pool_scales) * self.channels, - self.channels, - 3, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg) - - def forward(self, inputs): - """Forward function.""" - x = self._transform_inputs(inputs) - acm_outs = [x] - for acm_module in self.acm_modules: - acm_outs.append(acm_module(x)) - acm_outs = torch.cat(acm_outs, dim=1) - output = self.bottleneck(acm_outs) - output = self.cls_seg(output) - return output diff --git a/spaces/crystalai/constellation/README.md b/spaces/crystalai/constellation/README.md deleted file mode 100644 index 1eedfd4aa663f58fef91355c5da6bce8f3332c0d..0000000000000000000000000000000000000000 --- a/spaces/crystalai/constellation/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: AutoTrain Advanced -emoji: 🚀 -colorFrom: blue -colorTo: green -sdk: docker -pinned: false -duplicated_from: autotrain-projects/autotrain-advanced -license: c-uda ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/cxrhr/anime-remove-background/app.py b/spaces/cxrhr/anime-remove-background/app.py deleted file mode 100644 index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000 --- a/spaces/cxrhr/anime-remove-background/app.py +++ /dev/null @@ -1,52 +0,0 @@ -import gradio as gr -import huggingface_hub -import onnxruntime as rt -import numpy as np -import cv2 - - -def get_mask(img, s=1024): - img = (img / 255).astype(np.float32) - h, w = h0, w0 = img.shape[:-1] - h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) - ph, pw = s - h, s - w - img_input = np.zeros([s, s, 3], dtype=np.float32) - img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h)) - img_input = np.transpose(img_input, (2, 0, 1)) - img_input = img_input[np.newaxis, :] - mask = rmbg_model.run(None, {'img': img_input})[0][0] - mask = np.transpose(mask, (1, 2, 0)) - mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] - mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis] - return mask - - -def rmbg_fn(img): - mask = get_mask(img) - img = (mask * img + 255 * (1 - mask)).astype(np.uint8) - mask = (mask * 255).astype(np.uint8) - img = np.concatenate([img, mask], axis=2, dtype=np.uint8) - mask = mask.repeat(3, axis=2) - return mask, img - - -if __name__ == "__main__": - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] - model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx") - rmbg_model = rt.InferenceSession(model_path, providers=providers) - app = gr.Blocks() - with app: - gr.Markdown("# Anime Remove Background\n\n" - "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n" - "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)") - with gr.Row(): - with gr.Column(): - input_img = gr.Image(label="input image") - examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)] - examples = gr.Dataset(components=[input_img], samples=examples_data) - run_btn = gr.Button(variant="primary") - output_mask = gr.Image(label="mask") - output_img = gr.Image(label="result", image_mode="RGBA") - examples.click(lambda x: x[0], [examples], [input_img]) - run_btn.click(rmbg_fn, [input_img], [output_mask, output_img]) - app.launch() diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/face_morpher/face_morpher_08.py b/spaces/cymic/Talking_Head_Anime_3/tha3/nn/face_morpher/face_morpher_08.py deleted file mode 100644 index 2b77b469e04a03538fb9759a2b95e2f6fb3c08be..0000000000000000000000000000000000000000 --- a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/face_morpher/face_morpher_08.py +++ /dev/null @@ -1,241 +0,0 @@ -import math -from typing import List, Optional - -import torch -from torch import Tensor -from torch.nn import ModuleList, Sequential, Sigmoid, Tanh, Module -from torch.nn.functional import affine_grid, grid_sample - -from tha3.module.module_factory import ModuleFactory -from tha3.nn.conv import create_conv3_block_from_block_args, \ - create_downsample_block_from_block_args, create_upsample_block_from_block_args, create_conv3_from_block_args, \ - create_conv3 -from tha3.nn.nonlinearity_factory import LeakyReLUFactory -from tha3.nn.normalization import InstanceNorm2dFactory -from tha3.nn.resnet_block import ResnetBlock -from tha3.nn.util import BlockArgs - - -class FaceMorpher08Args: - def __init__(self, - image_size: int = 256, - image_channels: int = 4, - num_expression_params: int = 67, - start_channels: int = 16, - bottleneck_image_size=4, - num_bottleneck_blocks=3, - max_channels: int = 512, - block_args: Optional[BlockArgs] = None): - self.max_channels = max_channels - self.num_bottleneck_blocks = num_bottleneck_blocks - assert bottleneck_image_size > 1 - self.bottleneck_image_size = bottleneck_image_size - self.start_channels = start_channels - self.image_channels = image_channels - self.num_expression_params = num_expression_params - self.image_size = image_size - - if block_args is None: - self.block_args = BlockArgs( - normalization_layer_factory=InstanceNorm2dFactory(), - nonlinearity_factory=LeakyReLUFactory(negative_slope=0.2, inplace=True)) - else: - self.block_args = block_args - - -class FaceMorpher08(Module): - def __init__(self, args: FaceMorpher08Args): - super().__init__() - self.args = args - self.num_levels = int(math.log2(args.image_size // args.bottleneck_image_size)) + 1 - - self.downsample_blocks = ModuleList() - self.downsample_blocks.append( - create_conv3_block_from_block_args( - args.image_channels, - args.start_channels, - args.block_args)) - current_image_size = args.image_size - current_num_channels = args.start_channels - while current_image_size > args.bottleneck_image_size: - next_image_size = current_image_size // 2 - next_num_channels = self.get_num_output_channels_from_image_size(next_image_size) - self.downsample_blocks.append(create_downsample_block_from_block_args( - in_channels=current_num_channels, - out_channels=next_num_channels, - is_output_1x1=False, - block_args=args.block_args)) - current_image_size = next_image_size - current_num_channels = next_num_channels - assert len(self.downsample_blocks) == self.num_levels - - self.bottleneck_blocks = ModuleList() - self.bottleneck_blocks.append(create_conv3_block_from_block_args( - in_channels=current_num_channels + args.num_expression_params, - out_channels=current_num_channels, - block_args=args.block_args)) - for i in range(1, args.num_bottleneck_blocks): - self.bottleneck_blocks.append( - ResnetBlock.create( - num_channels=current_num_channels, - is1x1=False, - block_args=args.block_args)) - - self.upsample_blocks = ModuleList() - while current_image_size < args.image_size: - next_image_size = current_image_size * 2 - next_num_channels = self.get_num_output_channels_from_image_size(next_image_size) - self.upsample_blocks.append(create_upsample_block_from_block_args( - in_channels=current_num_channels, - out_channels=next_num_channels, - block_args=args.block_args)) - current_image_size = next_image_size - current_num_channels = next_num_channels - - self.iris_mouth_grid_change = self.create_grid_change_block() - self.iris_mouth_color_change = self.create_color_change_block() - self.iris_mouth_alpha = self.create_alpha_block() - - self.eye_color_change = self.create_color_change_block() - self.eye_alpha = self.create_alpha_block() - - def create_alpha_block(self): - return Sequential( - create_conv3( - in_channels=self.args.start_channels, - out_channels=1, - bias=True, - initialization_method=self.args.block_args.initialization_method, - use_spectral_norm=False), - Sigmoid()) - - def create_color_change_block(self): - return Sequential( - create_conv3_from_block_args( - in_channels=self.args.start_channels, - out_channels=self.args.image_channels, - bias=True, - block_args=self.args.block_args), - Tanh()) - - def create_grid_change_block(self): - return create_conv3( - in_channels=self.args.start_channels, - out_channels=2, - bias=False, - initialization_method='zero', - use_spectral_norm=False) - - def get_num_output_channels_from_level(self, level: int): - return self.get_num_output_channels_from_image_size(self.args.image_size // (2 ** level)) - - def get_num_output_channels_from_image_size(self, image_size: int): - return min(self.args.start_channels * (self.args.image_size // image_size), self.args.max_channels) - - def merge_down(self, top_layer: Tensor, bottom_layer: Tensor): - top_layer_rgb = top_layer[:, 0:3, :, :] - top_layer_a = top_layer[:, 3:4, :, :] - return bottom_layer * (1-top_layer_a) + torch.cat([top_layer_rgb * top_layer_a, top_layer_a], dim=1) - - def apply_grid_change(self, grid_change, image: Tensor) -> Tensor: - n, c, h, w = image.shape - device = grid_change.device - grid_change = torch.transpose(grid_change.view(n, 2, h * w), 1, 2).view(n, h, w, 2) - identity = torch.tensor( - [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], - device=device, - dtype=grid_change.dtype).unsqueeze(0).repeat(n, 1, 1) - base_grid = affine_grid(identity, [n, c, h, w], align_corners=False) - grid = base_grid + grid_change - resampled_image = grid_sample(image, grid, mode='bilinear', padding_mode='border', align_corners=False) - return resampled_image - - def apply_color_change(self, alpha, color_change, image: Tensor) -> Tensor: - return color_change * alpha + image * (1 - alpha) - - def forward(self, image: Tensor, pose: Tensor, *args) -> List[Tensor]: - feature = image - for block in self.downsample_blocks: - feature = block(feature) - n, c = pose.shape - pose = pose.view(n, c, 1, 1).repeat(1, 1, self.args.bottleneck_image_size, self.args.bottleneck_image_size) - feature = torch.cat([feature, pose], dim=1) - for block in self.bottleneck_blocks: - feature = block(feature) - for block in self.upsample_blocks: - feature = block(feature) - - iris_mouth_grid_change = self.iris_mouth_grid_change(feature) - iris_mouth_image_0 = self.apply_grid_change(iris_mouth_grid_change, image) - iris_mouth_color_change = self.iris_mouth_color_change(feature) - iris_mouth_alpha = self.iris_mouth_alpha(feature) - iris_mouth_image_1 = self.apply_color_change(iris_mouth_alpha, iris_mouth_color_change, iris_mouth_image_0) - - eye_color_change = self.eye_color_change(feature) - eye_alpha = self.eye_alpha(feature) - output_image = self.apply_color_change(eye_alpha, eye_color_change, iris_mouth_image_1.detach()) - - return [ - output_image, #0 - eye_alpha, #1 - eye_color_change, #2 - iris_mouth_image_1, #3 - iris_mouth_alpha, #4 - iris_mouth_color_change, #5 - iris_mouth_image_0, #6 - ] - - OUTPUT_IMAGE_INDEX = 0 - EYE_ALPHA_INDEX = 1 - EYE_COLOR_CHANGE_INDEX = 2 - IRIS_MOUTH_IMAGE_1_INDEX = 3 - IRIS_MOUTH_ALPHA_INDEX = 4 - IRIS_MOUTH_COLOR_CHANGE_INDEX = 5 - IRIS_MOUTh_IMAGE_0_INDEX = 6 - - -class FaceMorpher08Factory(ModuleFactory): - def __init__(self, args: FaceMorpher08Args): - super().__init__() - self.args = args - - def create(self) -> Module: - return FaceMorpher08(self.args) - - -if __name__ == "__main__": - cuda = torch.device('cuda') - args = FaceMorpher08Args( - image_size=256, - image_channels=4, - num_expression_params=12, - start_channels=64, - bottleneck_image_size=32, - num_bottleneck_blocks=6, - block_args=BlockArgs( - initialization_method='he', - use_spectral_norm=False, - normalization_layer_factory=InstanceNorm2dFactory(), - nonlinearity_factory=LeakyReLUFactory(inplace=True, negative_slope=0.2))) - module = FaceMorpher08(args).to(cuda) - - image = torch.zeros(16, 4, 256, 256, device=cuda) - pose = torch.zeros(16, 12, device=cuda) - - repeat = 100 - acc = 0.0 - for i in range(repeat + 2): - start = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - - start.record() - module.forward(image, pose) - end.record() - torch.cuda.synchronize() - - if i >= 2: - elapsed_time = start.elapsed_time(end) - print("%d:" % i, elapsed_time) - acc += elapsed_time - - print("average:", acc / repeat) \ No newline at end of file diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/util/flow_util.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/util/flow_util.py deleted file mode 100644 index 376a6cbe222bfe3e1833b954e764e4e6c086c766..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/util/flow_util.py +++ /dev/null @@ -1,56 +0,0 @@ -import torch - -def convert_flow_to_deformation(flow): - r"""convert flow fields to deformations. - - Args: - flow (tensor): Flow field obtained by the model - Returns: - deformation (tensor): The deformation used for warpping - """ - b,c,h,w = flow.shape - flow_norm = 2 * torch.cat([flow[:,:1,...]/(w-1),flow[:,1:,...]/(h-1)], 1) - grid = make_coordinate_grid(flow) - deformation = grid + flow_norm.permute(0,2,3,1) - return deformation - -def make_coordinate_grid(flow): - r"""obtain coordinate grid with the same size as the flow filed. - - Args: - flow (tensor): Flow field obtained by the model - Returns: - grid (tensor): The grid with the same size as the input flow - """ - b,c,h,w = flow.shape - - x = torch.arange(w).to(flow) - y = torch.arange(h).to(flow) - - x = (2 * (x / (w - 1)) - 1) - y = (2 * (y / (h - 1)) - 1) - - yy = y.view(-1, 1).repeat(1, w) - xx = x.view(1, -1).repeat(h, 1) - - meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2) - meshed = meshed.expand(b, -1, -1, -1) - return meshed - - -def warp_image(source_image, deformation): - r"""warp the input image according to the deformation - - Args: - source_image (tensor): source images to be warpped - deformation (tensor): deformations used to warp the images; value in range (-1, 1) - Returns: - output (tensor): the warpped images - """ - _, h_old, w_old, _ = deformation.shape - _, _, h, w = source_image.shape - if h_old != h or w_old != w: - deformation = deformation.permute(0, 3, 1, 2) - deformation = torch.nn.functional.interpolate(deformation, size=(h, w), mode='bilinear') - deformation = deformation.permute(0, 2, 3, 1) - return torch.nn.functional.grid_sample(source_image, deformation) \ No newline at end of file diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/face_enhancer.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/face_enhancer.py deleted file mode 100644 index 2ce0d1c2b5ef971ee19593db0b8974a089e5ec77..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/face_enhancer.py +++ /dev/null @@ -1,95 +0,0 @@ -import os -import torch - -from gfpgan import GFPGANer - -from tqdm import tqdm - -from Demo_TFR_Pirenderer.src.utils.videoio import load_video_to_cv2 - -import cv2 - - - -def enhancer(images, method='gfpgan', bg_upsampler='realesrgan'): - print('face enhancer....') - if os.path.isfile(images): # handle video to images - images = load_video_to_cv2(images) - - # ------------------------ set up GFPGAN restorer ------------------------ - if method == 'gfpgan': - arch = 'clean' - channel_multiplier = 2 - model_name = 'GFPGANv1.4' - url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth' - elif method == 'RestoreFormer': - arch = 'RestoreFormer' - channel_multiplier = 2 - model_name = 'RestoreFormer' - url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth' - elif method == 'codeformer': # TODO: - arch = 'CodeFormer' - channel_multiplier = 2 - model_name = 'CodeFormer' - url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' - else: - raise ValueError(f'Wrong model version {method}.') - - - # ------------------------ set up background upsampler ------------------------ - if bg_upsampler == 'realesrgan': - if not torch.cuda.is_available(): # CPU - import warnings - warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. ' - 'If you really want to use it, please modify the corresponding codes.') - bg_upsampler = None - else: - from basicsr.archs.rrdbnet_arch import RRDBNet - from realesrgan import RealESRGANer - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) - bg_upsampler = RealESRGANer( - scale=2, - model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', - model=model, - tile=400, - tile_pad=10, - pre_pad=0, - half=True) # need to set False in CPU mode - else: - bg_upsampler = None - - # determine model paths - model_path = os.path.join('gfpgan/weights', model_name + '.pth') - - if not os.path.isfile(model_path): - model_path = os.path.join('checkpoints', model_name + '.pth') - - if not os.path.isfile(model_path): - # download pre-trained models from url - model_path = url - - restorer = GFPGANer( - model_path=model_path, - upscale=2, - arch=arch, - channel_multiplier=channel_multiplier, - bg_upsampler=bg_upsampler) - - # ------------------------ restore ------------------------ - restored_img = [] - for idx in tqdm(range(len(images)), 'Face Enhancer:'): - - img = cv2.cvtColor(images[idx], cv2.COLOR_RGB2BGR) - - # restore faces and background if necessary - cropped_faces, restored_faces, r_img = restorer.enhance( - img, - has_aligned=False, - only_center_face=False, - paste_back=True) - - r_img = cv2.cvtColor(r_img, cv2.COLOR_BGR2RGB) - - restored_img += [r_img] - - return restored_img diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/TiffTags.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/TiffTags.py deleted file mode 100644 index 30b05e4e1d41fa21a7b7bf12c04ee05af6aa5284..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/TiffTags.py +++ /dev/null @@ -1,560 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# TIFF tags -# -# This module provides clear-text names for various well-known -# TIFF tags. the TIFF codec works just fine without it. -# -# Copyright (c) Secret Labs AB 1999. -# -# See the README file for information on usage and redistribution. -# - -## -# This module provides constants and clear-text names for various -# well-known TIFF tags. -## - -from collections import namedtuple - - -class TagInfo(namedtuple("_TagInfo", "value name type length enum")): - __slots__ = [] - - def __new__(cls, value=None, name="unknown", type=None, length=None, enum=None): - return super().__new__(cls, value, name, type, length, enum or {}) - - def cvt_enum(self, value): - # Using get will call hash(value), which can be expensive - # for some types (e.g. Fraction). Since self.enum is rarely - # used, it's usually better to test it first. - return self.enum.get(value, value) if self.enum else value - - -def lookup(tag, group=None): - """ - :param tag: Integer tag number - :param group: Which :py:data:`~PIL.TiffTags.TAGS_V2_GROUPS` to look in - - .. versionadded:: 8.3.0 - - :returns: Taginfo namedtuple, From the ``TAGS_V2`` info if possible, - otherwise just populating the value and name from ``TAGS``. - If the tag is not recognized, "unknown" is returned for the name - - """ - - if group is not None: - info = TAGS_V2_GROUPS[group].get(tag) if group in TAGS_V2_GROUPS else None - else: - info = TAGS_V2.get(tag) - return info or TagInfo(tag, TAGS.get(tag, "unknown")) - - -## -# Map tag numbers to tag info. -# -# id: (Name, Type, Length, enum_values) -# -# The length here differs from the length in the tiff spec. For -# numbers, the tiff spec is for the number of fields returned. We -# agree here. For string-like types, the tiff spec uses the length of -# field in bytes. In Pillow, we are using the number of expected -# fields, in general 1 for string-like types. - - -BYTE = 1 -ASCII = 2 -SHORT = 3 -LONG = 4 -RATIONAL = 5 -SIGNED_BYTE = 6 -UNDEFINED = 7 -SIGNED_SHORT = 8 -SIGNED_LONG = 9 -SIGNED_RATIONAL = 10 -FLOAT = 11 -DOUBLE = 12 -IFD = 13 -LONG8 = 16 - -TAGS_V2 = { - 254: ("NewSubfileType", LONG, 1), - 255: ("SubfileType", SHORT, 1), - 256: ("ImageWidth", LONG, 1), - 257: ("ImageLength", LONG, 1), - 258: ("BitsPerSample", SHORT, 0), - 259: ( - "Compression", - SHORT, - 1, - { - "Uncompressed": 1, - "CCITT 1d": 2, - "Group 3 Fax": 3, - "Group 4 Fax": 4, - "LZW": 5, - "JPEG": 6, - "PackBits": 32773, - }, - ), - 262: ( - "PhotometricInterpretation", - SHORT, - 1, - { - "WhiteIsZero": 0, - "BlackIsZero": 1, - "RGB": 2, - "RGB Palette": 3, - "Transparency Mask": 4, - "CMYK": 5, - "YCbCr": 6, - "CieLAB": 8, - "CFA": 32803, # TIFF/EP, Adobe DNG - "LinearRaw": 32892, # Adobe DNG - }, - ), - 263: ("Threshholding", SHORT, 1), - 264: ("CellWidth", SHORT, 1), - 265: ("CellLength", SHORT, 1), - 266: ("FillOrder", SHORT, 1), - 269: ("DocumentName", ASCII, 1), - 270: ("ImageDescription", ASCII, 1), - 271: ("Make", ASCII, 1), - 272: ("Model", ASCII, 1), - 273: ("StripOffsets", LONG, 0), - 274: ("Orientation", SHORT, 1), - 277: ("SamplesPerPixel", SHORT, 1), - 278: ("RowsPerStrip", LONG, 1), - 279: ("StripByteCounts", LONG, 0), - 280: ("MinSampleValue", SHORT, 0), - 281: ("MaxSampleValue", SHORT, 0), - 282: ("XResolution", RATIONAL, 1), - 283: ("YResolution", RATIONAL, 1), - 284: ("PlanarConfiguration", SHORT, 1, {"Contiguous": 1, "Separate": 2}), - 285: ("PageName", ASCII, 1), - 286: ("XPosition", RATIONAL, 1), - 287: ("YPosition", RATIONAL, 1), - 288: ("FreeOffsets", LONG, 1), - 289: ("FreeByteCounts", LONG, 1), - 290: ("GrayResponseUnit", SHORT, 1), - 291: ("GrayResponseCurve", SHORT, 0), - 292: ("T4Options", LONG, 1), - 293: ("T6Options", LONG, 1), - 296: ("ResolutionUnit", SHORT, 1, {"none": 1, "inch": 2, "cm": 3}), - 297: ("PageNumber", SHORT, 2), - 301: ("TransferFunction", SHORT, 0), - 305: ("Software", ASCII, 1), - 306: ("DateTime", ASCII, 1), - 315: ("Artist", ASCII, 1), - 316: ("HostComputer", ASCII, 1), - 317: ("Predictor", SHORT, 1, {"none": 1, "Horizontal Differencing": 2}), - 318: ("WhitePoint", RATIONAL, 2), - 319: ("PrimaryChromaticities", RATIONAL, 6), - 320: ("ColorMap", SHORT, 0), - 321: ("HalftoneHints", SHORT, 2), - 322: ("TileWidth", LONG, 1), - 323: ("TileLength", LONG, 1), - 324: ("TileOffsets", LONG, 0), - 325: ("TileByteCounts", LONG, 0), - 330: ("SubIFDs", LONG, 0), - 332: ("InkSet", SHORT, 1), - 333: ("InkNames", ASCII, 1), - 334: ("NumberOfInks", SHORT, 1), - 336: ("DotRange", SHORT, 0), - 337: ("TargetPrinter", ASCII, 1), - 338: ("ExtraSamples", SHORT, 0), - 339: ("SampleFormat", SHORT, 0), - 340: ("SMinSampleValue", DOUBLE, 0), - 341: ("SMaxSampleValue", DOUBLE, 0), - 342: ("TransferRange", SHORT, 6), - 347: ("JPEGTables", UNDEFINED, 1), - # obsolete JPEG tags - 512: ("JPEGProc", SHORT, 1), - 513: ("JPEGInterchangeFormat", LONG, 1), - 514: ("JPEGInterchangeFormatLength", LONG, 1), - 515: ("JPEGRestartInterval", SHORT, 1), - 517: ("JPEGLosslessPredictors", SHORT, 0), - 518: ("JPEGPointTransforms", SHORT, 0), - 519: ("JPEGQTables", LONG, 0), - 520: ("JPEGDCTables", LONG, 0), - 521: ("JPEGACTables", LONG, 0), - 529: ("YCbCrCoefficients", RATIONAL, 3), - 530: ("YCbCrSubSampling", SHORT, 2), - 531: ("YCbCrPositioning", SHORT, 1), - 532: ("ReferenceBlackWhite", RATIONAL, 6), - 700: ("XMP", BYTE, 0), - 33432: ("Copyright", ASCII, 1), - 33723: ("IptcNaaInfo", UNDEFINED, 1), - 34377: ("PhotoshopInfo", BYTE, 0), - # FIXME add more tags here - 34665: ("ExifIFD", LONG, 1), - 34675: ("ICCProfile", UNDEFINED, 1), - 34853: ("GPSInfoIFD", LONG, 1), - 36864: ("ExifVersion", UNDEFINED, 1), - 37724: ("ImageSourceData", UNDEFINED, 1), - 40965: ("InteroperabilityIFD", LONG, 1), - 41730: ("CFAPattern", UNDEFINED, 1), - # MPInfo - 45056: ("MPFVersion", UNDEFINED, 1), - 45057: ("NumberOfImages", LONG, 1), - 45058: ("MPEntry", UNDEFINED, 1), - 45059: ("ImageUIDList", UNDEFINED, 0), # UNDONE, check - 45060: ("TotalFrames", LONG, 1), - 45313: ("MPIndividualNum", LONG, 1), - 45569: ("PanOrientation", LONG, 1), - 45570: ("PanOverlap_H", RATIONAL, 1), - 45571: ("PanOverlap_V", RATIONAL, 1), - 45572: ("BaseViewpointNum", LONG, 1), - 45573: ("ConvergenceAngle", SIGNED_RATIONAL, 1), - 45574: ("BaselineLength", RATIONAL, 1), - 45575: ("VerticalDivergence", SIGNED_RATIONAL, 1), - 45576: ("AxisDistance_X", SIGNED_RATIONAL, 1), - 45577: ("AxisDistance_Y", SIGNED_RATIONAL, 1), - 45578: ("AxisDistance_Z", SIGNED_RATIONAL, 1), - 45579: ("YawAngle", SIGNED_RATIONAL, 1), - 45580: ("PitchAngle", SIGNED_RATIONAL, 1), - 45581: ("RollAngle", SIGNED_RATIONAL, 1), - 40960: ("FlashPixVersion", UNDEFINED, 1), - 50741: ("MakerNoteSafety", SHORT, 1, {"Unsafe": 0, "Safe": 1}), - 50780: ("BestQualityScale", RATIONAL, 1), - 50838: ("ImageJMetaDataByteCounts", LONG, 0), # Can be more than one - 50839: ("ImageJMetaData", UNDEFINED, 1), # see Issue #2006 -} -TAGS_V2_GROUPS = { - # ExifIFD - 34665: { - 36864: ("ExifVersion", UNDEFINED, 1), - 40960: ("FlashPixVersion", UNDEFINED, 1), - 40965: ("InteroperabilityIFD", LONG, 1), - 41730: ("CFAPattern", UNDEFINED, 1), - }, - # GPSInfoIFD - 34853: { - 0: ("GPSVersionID", BYTE, 4), - 1: ("GPSLatitudeRef", ASCII, 2), - 2: ("GPSLatitude", RATIONAL, 3), - 3: ("GPSLongitudeRef", ASCII, 2), - 4: ("GPSLongitude", RATIONAL, 3), - 5: ("GPSAltitudeRef", BYTE, 1), - 6: ("GPSAltitude", RATIONAL, 1), - 7: ("GPSTimeStamp", RATIONAL, 3), - 8: ("GPSSatellites", ASCII, 0), - 9: ("GPSStatus", ASCII, 2), - 10: ("GPSMeasureMode", ASCII, 2), - 11: ("GPSDOP", RATIONAL, 1), - 12: ("GPSSpeedRef", ASCII, 2), - 13: ("GPSSpeed", RATIONAL, 1), - 14: ("GPSTrackRef", ASCII, 2), - 15: ("GPSTrack", RATIONAL, 1), - 16: ("GPSImgDirectionRef", ASCII, 2), - 17: ("GPSImgDirection", RATIONAL, 1), - 18: ("GPSMapDatum", ASCII, 0), - 19: ("GPSDestLatitudeRef", ASCII, 2), - 20: ("GPSDestLatitude", RATIONAL, 3), - 21: ("GPSDestLongitudeRef", ASCII, 2), - 22: ("GPSDestLongitude", RATIONAL, 3), - 23: ("GPSDestBearingRef", ASCII, 2), - 24: ("GPSDestBearing", RATIONAL, 1), - 25: ("GPSDestDistanceRef", ASCII, 2), - 26: ("GPSDestDistance", RATIONAL, 1), - 27: ("GPSProcessingMethod", UNDEFINED, 0), - 28: ("GPSAreaInformation", UNDEFINED, 0), - 29: ("GPSDateStamp", ASCII, 11), - 30: ("GPSDifferential", SHORT, 1), - }, - # InteroperabilityIFD - 40965: {1: ("InteropIndex", ASCII, 1), 2: ("InteropVersion", UNDEFINED, 1)}, -} - -# Legacy Tags structure -# these tags aren't included above, but were in the previous versions -TAGS = { - 347: "JPEGTables", - 700: "XMP", - # Additional Exif Info - 32932: "Wang Annotation", - 33434: "ExposureTime", - 33437: "FNumber", - 33445: "MD FileTag", - 33446: "MD ScalePixel", - 33447: "MD ColorTable", - 33448: "MD LabName", - 33449: "MD SampleInfo", - 33450: "MD PrepDate", - 33451: "MD PrepTime", - 33452: "MD FileUnits", - 33550: "ModelPixelScaleTag", - 33723: "IptcNaaInfo", - 33918: "INGR Packet Data Tag", - 33919: "INGR Flag Registers", - 33920: "IrasB Transformation Matrix", - 33922: "ModelTiepointTag", - 34264: "ModelTransformationTag", - 34377: "PhotoshopInfo", - 34735: "GeoKeyDirectoryTag", - 34736: "GeoDoubleParamsTag", - 34737: "GeoAsciiParamsTag", - 34850: "ExposureProgram", - 34852: "SpectralSensitivity", - 34855: "ISOSpeedRatings", - 34856: "OECF", - 34864: "SensitivityType", - 34865: "StandardOutputSensitivity", - 34866: "RecommendedExposureIndex", - 34867: "ISOSpeed", - 34868: "ISOSpeedLatitudeyyy", - 34869: "ISOSpeedLatitudezzz", - 34908: "HylaFAX FaxRecvParams", - 34909: "HylaFAX FaxSubAddress", - 34910: "HylaFAX FaxRecvTime", - 36864: "ExifVersion", - 36867: "DateTimeOriginal", - 36868: "DateTimeDigitized", - 37121: "ComponentsConfiguration", - 37122: "CompressedBitsPerPixel", - 37724: "ImageSourceData", - 37377: "ShutterSpeedValue", - 37378: "ApertureValue", - 37379: "BrightnessValue", - 37380: "ExposureBiasValue", - 37381: "MaxApertureValue", - 37382: "SubjectDistance", - 37383: "MeteringMode", - 37384: "LightSource", - 37385: "Flash", - 37386: "FocalLength", - 37396: "SubjectArea", - 37500: "MakerNote", - 37510: "UserComment", - 37520: "SubSec", - 37521: "SubSecTimeOriginal", - 37522: "SubsecTimeDigitized", - 40960: "FlashPixVersion", - 40961: "ColorSpace", - 40962: "PixelXDimension", - 40963: "PixelYDimension", - 40964: "RelatedSoundFile", - 40965: "InteroperabilityIFD", - 41483: "FlashEnergy", - 41484: "SpatialFrequencyResponse", - 41486: "FocalPlaneXResolution", - 41487: "FocalPlaneYResolution", - 41488: "FocalPlaneResolutionUnit", - 41492: "SubjectLocation", - 41493: "ExposureIndex", - 41495: "SensingMethod", - 41728: "FileSource", - 41729: "SceneType", - 41730: "CFAPattern", - 41985: "CustomRendered", - 41986: "ExposureMode", - 41987: "WhiteBalance", - 41988: "DigitalZoomRatio", - 41989: "FocalLengthIn35mmFilm", - 41990: "SceneCaptureType", - 41991: "GainControl", - 41992: "Contrast", - 41993: "Saturation", - 41994: "Sharpness", - 41995: "DeviceSettingDescription", - 41996: "SubjectDistanceRange", - 42016: "ImageUniqueID", - 42032: "CameraOwnerName", - 42033: "BodySerialNumber", - 42034: "LensSpecification", - 42035: "LensMake", - 42036: "LensModel", - 42037: "LensSerialNumber", - 42112: "GDAL_METADATA", - 42113: "GDAL_NODATA", - 42240: "Gamma", - 50215: "Oce Scanjob Description", - 50216: "Oce Application Selector", - 50217: "Oce Identification Number", - 50218: "Oce ImageLogic Characteristics", - # Adobe DNG - 50706: "DNGVersion", - 50707: "DNGBackwardVersion", - 50708: "UniqueCameraModel", - 50709: "LocalizedCameraModel", - 50710: "CFAPlaneColor", - 50711: "CFALayout", - 50712: "LinearizationTable", - 50713: "BlackLevelRepeatDim", - 50714: "BlackLevel", - 50715: "BlackLevelDeltaH", - 50716: "BlackLevelDeltaV", - 50717: "WhiteLevel", - 50718: "DefaultScale", - 50719: "DefaultCropOrigin", - 50720: "DefaultCropSize", - 50721: "ColorMatrix1", - 50722: "ColorMatrix2", - 50723: "CameraCalibration1", - 50724: "CameraCalibration2", - 50725: "ReductionMatrix1", - 50726: "ReductionMatrix2", - 50727: "AnalogBalance", - 50728: "AsShotNeutral", - 50729: "AsShotWhiteXY", - 50730: "BaselineExposure", - 50731: "BaselineNoise", - 50732: "BaselineSharpness", - 50733: "BayerGreenSplit", - 50734: "LinearResponseLimit", - 50735: "CameraSerialNumber", - 50736: "LensInfo", - 50737: "ChromaBlurRadius", - 50738: "AntiAliasStrength", - 50740: "DNGPrivateData", - 50778: "CalibrationIlluminant1", - 50779: "CalibrationIlluminant2", - 50784: "Alias Layer Metadata", -} - - -def _populate(): - for k, v in TAGS_V2.items(): - # Populate legacy structure. - TAGS[k] = v[0] - if len(v) == 4: - for sk, sv in v[3].items(): - TAGS[(k, sv)] = sk - - TAGS_V2[k] = TagInfo(k, *v) - - for group, tags in TAGS_V2_GROUPS.items(): - for k, v in tags.items(): - tags[k] = TagInfo(k, *v) - - -_populate() -## -# Map type numbers to type names -- defined in ImageFileDirectory. - -TYPES = {} - -# was: -# TYPES = { -# 1: "byte", -# 2: "ascii", -# 3: "short", -# 4: "long", -# 5: "rational", -# 6: "signed byte", -# 7: "undefined", -# 8: "signed short", -# 9: "signed long", -# 10: "signed rational", -# 11: "float", -# 12: "double", -# } - -# -# These tags are handled by default in libtiff, without -# adding to the custom dictionary. 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self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) - # TODO better way to determine whether sample or not - self.sampling = loss_cls['type'] not in [ - 'FocalLoss', 'GHMC', 'QualityFocalLoss' - ] - if self.use_sigmoid_cls: - self.cls_out_channels = num_classes - else: - self.cls_out_channels = num_classes + 1 - - if self.cls_out_channels <= 0: - raise ValueError(f'num_classes={num_classes} is too small') - self.reg_decoded_bbox = reg_decoded_bbox - - self.bbox_coder = build_bbox_coder(bbox_coder) - self.loss_cls = build_loss(loss_cls) - self.loss_bbox = build_loss(loss_bbox) - self.train_cfg = train_cfg - self.test_cfg = test_cfg - if self.train_cfg: - self.assigner = build_assigner(self.train_cfg.assigner) - # use PseudoSampler when sampling is False - if self.sampling and hasattr(self.train_cfg, 'sampler'): - sampler_cfg = self.train_cfg.sampler - else: - sampler_cfg = dict(type='PseudoSampler') - self.sampler = build_sampler(sampler_cfg, context=self) - self.fp16_enabled = False - - self.anchor_generator = build_anchor_generator(anchor_generator) - # usually the numbers of anchors for each level are the same - # except SSD detectors - self.num_anchors = self.anchor_generator.num_base_anchors[0] - self._init_layers() - - def _init_layers(self): - """Initialize layers of the head.""" - self.conv_cls = nn.Conv2d(self.in_channels, - self.num_anchors * self.cls_out_channels, 1) - self.conv_reg = nn.Conv2d(self.in_channels, self.num_anchors * 4, 1) - - def init_weights(self): - """Initialize weights of the head.""" - normal_init(self.conv_cls, std=0.01) - normal_init(self.conv_reg, std=0.01) - - def forward_single(self, x): - """Forward feature of a single scale level. - - Args: - x (Tensor): Features of a single scale level. - - Returns: - tuple: - cls_score (Tensor): Cls scores for a single scale level \ - the channels number is num_anchors * num_classes. - bbox_pred (Tensor): Box energies / deltas for a single scale \ - level, the channels number is num_anchors * 4. - """ - cls_score = self.conv_cls(x) - bbox_pred = self.conv_reg(x) - return cls_score, bbox_pred - - def forward(self, feats): - """Forward features from the upstream network. - - Args: - feats (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - - Returns: - tuple: A tuple of classification scores and bbox prediction. - - - cls_scores (list[Tensor]): Classification scores for all \ - scale levels, each is a 4D-tensor, the channels number \ - is num_anchors * num_classes. - - bbox_preds (list[Tensor]): Box energies / deltas for all \ - scale levels, each is a 4D-tensor, the channels number \ - is num_anchors * 4. - """ - return multi_apply(self.forward_single, feats) - - def get_anchors(self, featmap_sizes, img_metas, device='cuda'): - """Get anchors according to feature map sizes. - - Args: - featmap_sizes (list[tuple]): Multi-level feature map sizes. - img_metas (list[dict]): Image meta info. - device (torch.device | str): Device for returned tensors - - Returns: - tuple: - anchor_list (list[Tensor]): Anchors of each image. - valid_flag_list (list[Tensor]): Valid flags of each image. - """ - num_imgs = len(img_metas) - - # since feature map sizes of all images are the same, we only compute - # anchors for one time - multi_level_anchors = self.anchor_generator.grid_anchors( - featmap_sizes, device) - anchor_list = [multi_level_anchors for _ in range(num_imgs)] - - # for each image, we compute valid flags of multi level anchors - valid_flag_list = [] - for img_id, img_meta in enumerate(img_metas): - multi_level_flags = self.anchor_generator.valid_flags( - featmap_sizes, img_meta['pad_shape'], device) - valid_flag_list.append(multi_level_flags) - - return anchor_list, valid_flag_list - - def _get_targets_single(self, - flat_anchors, - valid_flags, - gt_bboxes, - gt_bboxes_ignore, - gt_labels, - img_meta, - label_channels=1, - unmap_outputs=True): - """Compute regression and classification targets for anchors in a - single image. - - Args: - flat_anchors (Tensor): Multi-level anchors of the image, which are - concatenated into a single tensor of shape (num_anchors ,4) - valid_flags (Tensor): Multi level valid flags of the image, - which are concatenated into a single tensor of - shape (num_anchors,). - gt_bboxes (Tensor): Ground truth bboxes of the image, - shape (num_gts, 4). - gt_bboxes_ignore (Tensor): Ground truth bboxes to be - ignored, shape (num_ignored_gts, 4). - img_meta (dict): Meta info of the image. - gt_labels (Tensor): Ground truth labels of each box, - shape (num_gts,). - label_channels (int): Channel of label. - unmap_outputs (bool): Whether to map outputs back to the original - set of anchors. - - Returns: - tuple: - labels_list (list[Tensor]): Labels of each level - label_weights_list (list[Tensor]): Label weights of each level - bbox_targets_list (list[Tensor]): BBox targets of each level - bbox_weights_list (list[Tensor]): BBox weights of each level - num_total_pos (int): Number of positive samples in all images - num_total_neg (int): Number of negative samples in all images - """ - inside_flags = anchor_inside_flags(flat_anchors, valid_flags, - img_meta['img_shape'][:2], - self.train_cfg.allowed_border) - if not inside_flags.any(): - return (None, ) * 7 - # assign gt and sample anchors - anchors = flat_anchors[inside_flags, :] - - assign_result = self.assigner.assign( - anchors, gt_bboxes, gt_bboxes_ignore, - None if self.sampling else gt_labels) - sampling_result = self.sampler.sample(assign_result, anchors, - gt_bboxes) - - num_valid_anchors = anchors.shape[0] - bbox_targets = torch.zeros_like(anchors) - bbox_weights = torch.zeros_like(anchors) - labels = anchors.new_full((num_valid_anchors, ), - self.num_classes, - dtype=torch.long) - label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) - - pos_inds = sampling_result.pos_inds - neg_inds = sampling_result.neg_inds - if len(pos_inds) > 0: - if not self.reg_decoded_bbox: - pos_bbox_targets = self.bbox_coder.encode( - sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) - else: - pos_bbox_targets = sampling_result.pos_gt_bboxes - bbox_targets[pos_inds, :] = pos_bbox_targets - bbox_weights[pos_inds, :] = 1.0 - if gt_labels is None: - # Only rpn gives gt_labels as None - # Foreground is the first class since v2.5.0 - labels[pos_inds] = 0 - else: - labels[pos_inds] = gt_labels[ - sampling_result.pos_assigned_gt_inds] - if self.train_cfg.pos_weight <= 0: - label_weights[pos_inds] = 1.0 - else: - label_weights[pos_inds] = self.train_cfg.pos_weight - if len(neg_inds) > 0: - label_weights[neg_inds] = 1.0 - - # map up to original set of anchors - if unmap_outputs: - num_total_anchors = flat_anchors.size(0) - labels = unmap( - labels, num_total_anchors, inside_flags, - fill=self.num_classes) # fill bg label - label_weights = unmap(label_weights, num_total_anchors, - inside_flags) - bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) - bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) - - return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, - neg_inds, sampling_result) - - def get_targets(self, - anchor_list, - valid_flag_list, - gt_bboxes_list, - img_metas, - gt_bboxes_ignore_list=None, - gt_labels_list=None, - label_channels=1, - unmap_outputs=True, - return_sampling_results=False): - """Compute regression and classification targets for anchors in - multiple images. - - Args: - anchor_list (list[list[Tensor]]): Multi level anchors of each - image. The outer list indicates images, and the inner list - corresponds to feature levels of the image. Each element of - the inner list is a tensor of shape (num_anchors, 4). - valid_flag_list (list[list[Tensor]]): Multi level valid flags of - each image. The outer list indicates images, and the inner list - corresponds to feature levels of the image. Each element of - the inner list is a tensor of shape (num_anchors, ) - gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. - img_metas (list[dict]): Meta info of each image. - gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be - ignored. - gt_labels_list (list[Tensor]): Ground truth labels of each box. - label_channels (int): Channel of label. - unmap_outputs (bool): Whether to map outputs back to the original - set of anchors. - - Returns: - tuple: Usually returns a tuple containing learning targets. - - - labels_list (list[Tensor]): Labels of each level. - - label_weights_list (list[Tensor]): Label weights of each \ - level. - - bbox_targets_list (list[Tensor]): BBox targets of each level. - - bbox_weights_list (list[Tensor]): BBox weights of each level. - - num_total_pos (int): Number of positive samples in all \ - images. - - num_total_neg (int): Number of negative samples in all \ - images. - additional_returns: This function enables user-defined returns from - `self._get_targets_single`. These returns are currently refined - to properties at each feature map (i.e. having HxW dimension). - The results will be concatenated after the end - """ - num_imgs = len(img_metas) - assert len(anchor_list) == len(valid_flag_list) == num_imgs - - # anchor number of multi levels - num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] - # concat all level anchors to a single tensor - concat_anchor_list = [] - concat_valid_flag_list = [] - for i in range(num_imgs): - assert len(anchor_list[i]) == len(valid_flag_list[i]) - concat_anchor_list.append(torch.cat(anchor_list[i])) - concat_valid_flag_list.append(torch.cat(valid_flag_list[i])) - - # compute targets for each image - if gt_bboxes_ignore_list is None: - gt_bboxes_ignore_list = [None for _ in range(num_imgs)] - if gt_labels_list is None: - gt_labels_list = [None for _ in range(num_imgs)] - results = multi_apply( - self._get_targets_single, - concat_anchor_list, - concat_valid_flag_list, - gt_bboxes_list, - gt_bboxes_ignore_list, - gt_labels_list, - img_metas, - label_channels=label_channels, - unmap_outputs=unmap_outputs) - (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, - pos_inds_list, neg_inds_list, sampling_results_list) = results[:7] - rest_results = list(results[7:]) # user-added return values - # no valid anchors - if any([labels is None for labels in all_labels]): - return None - # sampled anchors of all images - num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) - num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) - # split targets to a list w.r.t. multiple levels - labels_list = images_to_levels(all_labels, num_level_anchors) - label_weights_list = images_to_levels(all_label_weights, - num_level_anchors) - bbox_targets_list = images_to_levels(all_bbox_targets, - num_level_anchors) - bbox_weights_list = images_to_levels(all_bbox_weights, - num_level_anchors) - res = (labels_list, label_weights_list, bbox_targets_list, - bbox_weights_list, num_total_pos, num_total_neg) - if return_sampling_results: - res = res + (sampling_results_list, ) - for i, r in enumerate(rest_results): # user-added return values - rest_results[i] = images_to_levels(r, num_level_anchors) - - return res + tuple(rest_results) - - def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights, - bbox_targets, bbox_weights, num_total_samples): - """Compute loss of a single scale level. - - Args: - cls_score (Tensor): Box scores for each scale level - Has shape (N, num_anchors * num_classes, H, W). - bbox_pred (Tensor): Box energies / deltas for each scale - level with shape (N, num_anchors * 4, H, W). - anchors (Tensor): Box reference for each scale level with shape - (N, num_total_anchors, 4). - labels (Tensor): Labels of each anchors with shape - (N, num_total_anchors). - label_weights (Tensor): Label weights of each anchor with shape - (N, num_total_anchors) - bbox_targets (Tensor): BBox regression targets of each anchor wight - shape (N, num_total_anchors, 4). - bbox_weights (Tensor): BBox regression loss weights of each anchor - with shape (N, num_total_anchors, 4). - num_total_samples (int): If sampling, num total samples equal to - the number of total anchors; Otherwise, it is the number of - positive anchors. - - Returns: - dict[str, Tensor]: A dictionary of loss components. - """ - # classification loss - labels = labels.reshape(-1) - label_weights = label_weights.reshape(-1) - cls_score = cls_score.permute(0, 2, 3, - 1).reshape(-1, self.cls_out_channels) - loss_cls = self.loss_cls( - cls_score, labels, label_weights, avg_factor=num_total_samples) - # regression loss - bbox_targets = bbox_targets.reshape(-1, 4) - bbox_weights = bbox_weights.reshape(-1, 4) - bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) - if self.reg_decoded_bbox: - # When the regression loss (e.g. `IouLoss`, `GIouLoss`) - # is applied directly on the decoded bounding boxes, it - # decodes the already encoded coordinates to absolute format. - anchors = anchors.reshape(-1, 4) - bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) - loss_bbox = self.loss_bbox( - bbox_pred, - bbox_targets, - bbox_weights, - avg_factor=num_total_samples) - return loss_cls, loss_bbox - - @force_fp32(apply_to=('cls_scores', 'bbox_preds')) - def loss(self, - cls_scores, - bbox_preds, - gt_bboxes, - gt_labels, - img_metas, - gt_bboxes_ignore=None): - """Compute losses of the head. - - Args: - cls_scores (list[Tensor]): Box scores for each scale level - Has shape (N, num_anchors * num_classes, H, W) - bbox_preds (list[Tensor]): Box energies / deltas for each scale - level with shape (N, num_anchors * 4, H, W) - gt_bboxes (list[Tensor]): Ground truth bboxes for each image with - shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. - gt_labels (list[Tensor]): class indices corresponding to each box - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - gt_bboxes_ignore (None | list[Tensor]): specify which bounding - boxes can be ignored when computing the loss. Default: None - - Returns: - dict[str, Tensor]: A dictionary of loss components. - """ - featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] - assert len(featmap_sizes) == self.anchor_generator.num_levels - - device = cls_scores[0].device - - anchor_list, valid_flag_list = self.get_anchors( - featmap_sizes, img_metas, device=device) - label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 - cls_reg_targets = self.get_targets( - anchor_list, - valid_flag_list, - gt_bboxes, - img_metas, - gt_bboxes_ignore_list=gt_bboxes_ignore, - gt_labels_list=gt_labels, - label_channels=label_channels) - if cls_reg_targets is None: - return None - (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, - num_total_pos, num_total_neg) = cls_reg_targets - num_total_samples = ( - num_total_pos + num_total_neg if self.sampling else num_total_pos) - - # anchor number of multi levels - num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] - # concat all level anchors and flags to a single tensor - concat_anchor_list = [] - for i in range(len(anchor_list)): - concat_anchor_list.append(torch.cat(anchor_list[i])) - all_anchor_list = images_to_levels(concat_anchor_list, - num_level_anchors) - - losses_cls, losses_bbox = multi_apply( - self.loss_single, - cls_scores, - bbox_preds, - all_anchor_list, - labels_list, - label_weights_list, - bbox_targets_list, - bbox_weights_list, - num_total_samples=num_total_samples) - return dict(loss_cls=losses_cls, loss_bbox=losses_bbox) - - @force_fp32(apply_to=('cls_scores', 'bbox_preds')) - def get_bboxes(self, - cls_scores, - bbox_preds, - img_metas, - cfg=None, - rescale=False, - with_nms=True): - """Transform network output for a batch into bbox predictions. - - Args: - cls_scores (list[Tensor]): Box scores for each level in the - feature pyramid, has shape - (N, num_anchors * num_classes, H, W). - bbox_preds (list[Tensor]): Box energies / deltas for each - level in the feature pyramid, has shape - (N, num_anchors * 4, H, W). - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - cfg (mmcv.Config | None): Test / postprocessing configuration, - if None, test_cfg would be used - rescale (bool): If True, return boxes in original image space. - Default: False. - with_nms (bool): If True, do nms before return boxes. - Default: True. - - Returns: - list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where 5 represent - (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. - The shape of the second tensor in the tuple is (n,), and - each element represents the class label of the corresponding - box. - - Example: - >>> import mmcv - >>> self = AnchorHead( - >>> num_classes=9, - >>> in_channels=1, - >>> anchor_generator=dict( - >>> type='AnchorGenerator', - >>> scales=[8], - >>> ratios=[0.5, 1.0, 2.0], - >>> strides=[4,])) - >>> img_metas = [{'img_shape': (32, 32, 3), 'scale_factor': 1}] - >>> cfg = mmcv.Config(dict( - >>> score_thr=0.00, - >>> nms=dict(type='nms', iou_thr=1.0), - >>> max_per_img=10)) - >>> feat = torch.rand(1, 1, 3, 3) - >>> cls_score, bbox_pred = self.forward_single(feat) - >>> # note the input lists are over different levels, not images - >>> cls_scores, bbox_preds = [cls_score], [bbox_pred] - >>> result_list = self.get_bboxes(cls_scores, bbox_preds, - >>> img_metas, cfg) - >>> det_bboxes, det_labels = result_list[0] - >>> assert len(result_list) == 1 - >>> assert det_bboxes.shape[1] == 5 - >>> assert len(det_bboxes) == len(det_labels) == cfg.max_per_img - """ - assert len(cls_scores) == len(bbox_preds) - num_levels = len(cls_scores) - - device = cls_scores[0].device - featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] - mlvl_anchors = self.anchor_generator.grid_anchors( - featmap_sizes, device=device) - - mlvl_cls_scores = [cls_scores[i].detach() for i in range(num_levels)] - mlvl_bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)] - - if torch.onnx.is_in_onnx_export(): - assert len( - img_metas - ) == 1, 'Only support one input image while in exporting to ONNX' - img_shapes = img_metas[0]['img_shape_for_onnx'] - else: - img_shapes = [ - img_metas[i]['img_shape'] - for i in range(cls_scores[0].shape[0]) - ] - scale_factors = [ - img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0]) - ] - - if with_nms: - # some heads don't support with_nms argument - result_list = self._get_bboxes(mlvl_cls_scores, mlvl_bbox_preds, - mlvl_anchors, img_shapes, - scale_factors, cfg, rescale) - else: - result_list = self._get_bboxes(mlvl_cls_scores, mlvl_bbox_preds, - mlvl_anchors, img_shapes, - scale_factors, cfg, rescale, - with_nms) - return result_list - - def _get_bboxes(self, - mlvl_cls_scores, - mlvl_bbox_preds, - mlvl_anchors, - img_shapes, - scale_factors, - cfg, - rescale=False, - with_nms=True): - """Transform outputs for a batch item into bbox predictions. - - Args: - mlvl_cls_scores (list[Tensor]): Each element in the list is - the scores of bboxes of single level in the feature pyramid, - has shape (N, num_anchors * num_classes, H, W). - mlvl_bbox_preds (list[Tensor]): Each element in the list is the - bboxes predictions of single level in the feature pyramid, - has shape (N, num_anchors * 4, H, W). - mlvl_anchors (list[Tensor]): Each element in the list is - the anchors of single level in feature pyramid, has shape - (num_anchors, 4). - img_shapes (list[tuple[int]]): Each tuple in the list represent - the shape(height, width, 3) of single image in the batch. - scale_factors (list[ndarray]): Scale factor of the batch - image arange as list[(w_scale, h_scale, w_scale, h_scale)]. - cfg (mmcv.Config): Test / postprocessing configuration, - if None, test_cfg would be used. - rescale (bool): If True, return boxes in original image space. - Default: False. - with_nms (bool): If True, do nms before return boxes. - Default: True. - - Returns: - list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where 5 represent - (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. - The shape of the second tensor in the tuple is (n,), and - each element represents the class label of the corresponding - box. - """ - cfg = self.test_cfg if cfg is None else cfg - assert len(mlvl_cls_scores) == len(mlvl_bbox_preds) == len( - mlvl_anchors) - batch_size = mlvl_cls_scores[0].shape[0] - # convert to tensor to keep tracing - nms_pre_tensor = torch.tensor( - cfg.get('nms_pre', -1), - device=mlvl_cls_scores[0].device, - dtype=torch.long) - - mlvl_bboxes = [] - mlvl_scores = [] - for cls_score, bbox_pred, anchors in zip(mlvl_cls_scores, - mlvl_bbox_preds, - mlvl_anchors): - assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - cls_score = cls_score.permute(0, 2, 3, - 1).reshape(batch_size, -1, - self.cls_out_channels) - if self.use_sigmoid_cls: - scores = cls_score.sigmoid() - else: - scores = cls_score.softmax(-1) - bbox_pred = bbox_pred.permute(0, 2, 3, - 1).reshape(batch_size, -1, 4) - anchors = anchors.expand_as(bbox_pred) - # Always keep topk op for dynamic input in onnx - if nms_pre_tensor > 0 and (torch.onnx.is_in_onnx_export() - or scores.shape[-2] > nms_pre_tensor): - from torch import _shape_as_tensor - # keep shape as tensor and get k - num_anchor = _shape_as_tensor(scores)[-2].to( - nms_pre_tensor.device) - nms_pre = torch.where(nms_pre_tensor < num_anchor, - nms_pre_tensor, num_anchor) - - # Get maximum scores for foreground classes. - if self.use_sigmoid_cls: - max_scores, _ = scores.max(-1) - else: - # remind that we set FG labels to [0, num_class-1] - # since mmdet v2.0 - # BG cat_id: num_class - max_scores, _ = scores[..., :-1].max(-1) - - _, topk_inds = max_scores.topk(nms_pre) - batch_inds = torch.arange(batch_size).view( - -1, 1).expand_as(topk_inds) - anchors = anchors[batch_inds, topk_inds, :] - bbox_pred = bbox_pred[batch_inds, topk_inds, :] - scores = scores[batch_inds, topk_inds, :] - - bboxes = self.bbox_coder.decode( - anchors, bbox_pred, max_shape=img_shapes) - mlvl_bboxes.append(bboxes) - mlvl_scores.append(scores) - - batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) - if rescale: - batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( - scale_factors).unsqueeze(1) - batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) - - # Set max number of box to be feed into nms in deployment - deploy_nms_pre = cfg.get('deploy_nms_pre', -1) - if deploy_nms_pre > 0 and torch.onnx.is_in_onnx_export(): - # Get maximum scores for foreground classes. - if self.use_sigmoid_cls: - max_scores, _ = batch_mlvl_scores.max(-1) - else: - # remind that we set FG labels to [0, num_class-1] - # since mmdet v2.0 - # BG cat_id: num_class - max_scores, _ = batch_mlvl_scores[..., :-1].max(-1) - _, topk_inds = max_scores.topk(deploy_nms_pre) - batch_inds = torch.arange(batch_size).view(-1, - 1).expand_as(topk_inds) - batch_mlvl_scores = batch_mlvl_scores[batch_inds, topk_inds] - batch_mlvl_bboxes = batch_mlvl_bboxes[batch_inds, topk_inds] - if self.use_sigmoid_cls: - # Add a dummy background class to the backend when using sigmoid - # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 - # BG cat_id: num_class - padding = batch_mlvl_scores.new_zeros(batch_size, - batch_mlvl_scores.shape[1], - 1) - batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) - - if with_nms: - det_results = [] - for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, - batch_mlvl_scores): - det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores, - cfg.score_thr, cfg.nms, - cfg.max_per_img) - det_results.append(tuple([det_bbox, det_label])) - else: - det_results = [ - tuple(mlvl_bs) - for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores) - ] - return det_results - - def aug_test(self, feats, img_metas, rescale=False): - """Test function with test time augmentation. - - Args: - feats (list[Tensor]): the outer list indicates test-time - augmentations and inner Tensor should have a shape NxCxHxW, - which contains features for all images in the batch. - img_metas (list[list[dict]]): the outer list indicates test-time - augs (multiscale, flip, etc.) and the inner list indicates - images in a batch. each dict has image information. - rescale (bool, optional): Whether to rescale the results. - Defaults to False. - - Returns: - list[ndarray]: bbox results of each class - """ - return self.aug_test_bboxes(feats, img_metas, rescale=rescale) diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py deleted file mode 100644 index 2aa6033eec17a30aeb68c0fdd218d8f0d41157e8..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py +++ /dev/null @@ -1,107 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import ConvModule, kaiming_init -from mmcv.runner import auto_fp16, force_fp32 - -from mmdet.models.builder import HEADS - - -@HEADS.register_module() -class FusedSemanticHead(nn.Module): - r"""Multi-level fused semantic segmentation head. - - .. code-block:: none - - in_1 -> 1x1 conv --- - | - in_2 -> 1x1 conv -- | - || - in_3 -> 1x1 conv - || - ||| /-> 1x1 conv (mask prediction) - in_4 -> 1x1 conv -----> 3x3 convs (*4) - | \-> 1x1 conv (feature) - in_5 -> 1x1 conv --- - """ # noqa: W605 - - def __init__(self, - num_ins, - fusion_level, - num_convs=4, - in_channels=256, - conv_out_channels=256, - num_classes=183, - ignore_label=255, - loss_weight=0.2, - conv_cfg=None, - norm_cfg=None): - super(FusedSemanticHead, self).__init__() - self.num_ins = num_ins - self.fusion_level = fusion_level - self.num_convs = num_convs - self.in_channels = in_channels - self.conv_out_channels = conv_out_channels - self.num_classes = num_classes - self.ignore_label = ignore_label - self.loss_weight = loss_weight - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.fp16_enabled = False - - self.lateral_convs = nn.ModuleList() - for i in range(self.num_ins): - self.lateral_convs.append( - ConvModule( - self.in_channels, - self.in_channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - inplace=False)) - - self.convs = nn.ModuleList() - for i in range(self.num_convs): - in_channels = self.in_channels if i == 0 else conv_out_channels - self.convs.append( - ConvModule( - in_channels, - conv_out_channels, - 3, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg)) - self.conv_embedding = ConvModule( - conv_out_channels, - conv_out_channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg) - self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1) - - self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label) - - def init_weights(self): - kaiming_init(self.conv_logits) - - @auto_fp16() - def forward(self, feats): - x = self.lateral_convs[self.fusion_level](feats[self.fusion_level]) - fused_size = tuple(x.shape[-2:]) - for i, feat in enumerate(feats): - if i != self.fusion_level: - feat = F.interpolate( - feat, size=fused_size, mode='bilinear', align_corners=True) - x += self.lateral_convs[i](feat) - - for i in range(self.num_convs): - x = self.convs[i](x) - - mask_pred = self.conv_logits(x) - x = self.conv_embedding(x) - return mask_pred, x - - @force_fp32(apply_to=('mask_pred', )) - def loss(self, mask_pred, labels): - labels = labels.squeeze(1).long() - loss_semantic_seg = self.criterion(mask_pred, labels) - loss_semantic_seg *= self.loss_weight - return loss_semantic_seg diff --git a/spaces/dorkai/ChatUIPro/app/components/value-panel/style.module.css b/spaces/dorkai/ChatUIPro/app/components/value-panel/style.module.css deleted file mode 100644 index c7613c44a41bf6e4b5bcc3db6dcd516eb264ec88..0000000000000000000000000000000000000000 --- a/spaces/dorkai/ChatUIPro/app/components/value-panel/style.module.css +++ /dev/null @@ -1,3 +0,0 @@ -.boxShodow { - box-shadow: 0px 12px 16px -4px rgba(16, 24, 40, 0.08), 0px 4px 6px -2px rgba(16, 24, 40, 0.03); -} \ No newline at end of file diff --git a/spaces/dorkai/text-generation-webui-main/extensions/gallery/script.py b/spaces/dorkai/text-generation-webui-main/extensions/gallery/script.py deleted file mode 100644 index 993ef273839e7cfbf9e80f2d7f9d4a71d208b446..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/extensions/gallery/script.py +++ /dev/null @@ -1,96 +0,0 @@ -from pathlib import Path - -import gradio as gr - -from modules.html_generator import get_image_cache -from modules.shared import gradio - - -def generate_css(): - css = """ - .character-gallery > .gallery { - margin: 1rem 0; - display: grid !important; - grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); - grid-column-gap: 0.4rem; - grid-row-gap: 1.2rem; - } - - .character-gallery > .label { - display: none !important; - } - - .character-gallery button.gallery-item { - display: contents; - } - - .character-container { - cursor: pointer; - text-align: center; - position: relative; - opacity: 0.85; - } - - .character-container:hover { - opacity: 1; - } - - .character-container .placeholder, .character-container img { - width: 150px; - height: 200px; - background-color: gray; - object-fit: cover; - margin: 0 auto; - border-radius: 1rem; - border: 3px solid white; - box-shadow: 3px 3px 6px 0px rgb(0 0 0 / 50%); - } - - .character-name { - margin-top: 0.3rem; - display: block; - font-size: 1.2rem; - font-weight: 600; - overflow-wrap: anywhere; - } - """ - return css - - -def generate_html(): - cards = [] - # Iterate through files in image folder - for file in sorted(Path("characters").glob("*")): - if file.suffix in [".json", ".yml", ".yaml"]: - character = file.stem - container_html = '<div class="character-container">' - image_html = "<div class='placeholder'></div>" - - for path in [Path(f"characters/{character}.{extension}") for extension in ['png', 'jpg', 'jpeg']]: - if path.exists(): - image_html = f'<img src="file/{get_image_cache(path)}">' - break - - container_html += f'{image_html} <span class="character-name">{character}</span>' - container_html += "</div>" - cards.append([container_html, character]) - - return cards - - -def select_character(evt: gr.SelectData): - return (evt.value[1]) - - -def ui(): - with gr.Accordion("Character gallery", open=False): - update = gr.Button("Refresh") - gr.HTML(value="<style>" + generate_css() + "</style>") - gallery = gr.Dataset(components=[gr.HTML(visible=False)], - label="", - samples=generate_html(), - elem_classes=["character-gallery"], - samples_per_page=50 - ) - update.click(generate_html, [], gallery) - gallery.select(select_character, None, gradio['character_menu']) diff --git a/spaces/dragao-elastico/RVC_V2/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/dragao-elastico/RVC_V2/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py deleted file mode 100644 index b2c592527a5966e6f8e79e8c52dc5b414246dcc6..0000000000000000000000000000000000000000 --- a/spaces/dragao-elastico/RVC_V2/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py +++ /dev/null @@ -1,97 +0,0 @@ -from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor -import parselmouth -import numpy as np - - -class PMF0Predictor(F0Predictor): - def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): - self.hop_length = hop_length - self.f0_min = f0_min - self.f0_max = f0_max - self.sampling_rate = sampling_rate - - def interpolate_f0(self, f0): - """ - 对F0进行插值处理 - """ - - data = np.reshape(f0, (f0.size, 1)) - - vuv_vector = np.zeros((data.size, 1), dtype=np.float32) - vuv_vector[data > 0.0] = 1.0 - vuv_vector[data <= 0.0] = 0.0 - - ip_data = data - - frame_number = data.size - last_value = 0.0 - for i in range(frame_number): - if data[i] <= 0.0: - j = i + 1 - for j in range(i + 1, frame_number): - if data[j] > 0.0: - break - if j < frame_number - 1: - if last_value > 0.0: - step = (data[j] - data[i - 1]) / float(j - i) - for k in range(i, j): - ip_data[k] = data[i - 1] + step * (k - i + 1) - else: - for k in range(i, j): - ip_data[k] = data[j] - else: - for k in range(i, frame_number): - ip_data[k] = last_value - else: - ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝 - last_value = data[i] - - return ip_data[:, 0], vuv_vector[:, 0] - - def compute_f0(self, wav, p_len=None): - x = wav - if p_len is None: - p_len = x.shape[0] // self.hop_length - else: - assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error" - time_step = self.hop_length / self.sampling_rate * 1000 - f0 = ( - parselmouth.Sound(x, self.sampling_rate) - .to_pitch_ac( - time_step=time_step / 1000, - voicing_threshold=0.6, - pitch_floor=self.f0_min, - pitch_ceiling=self.f0_max, - ) - .selected_array["frequency"] - ) - - pad_size = (p_len - len(f0) + 1) // 2 - if pad_size > 0 or p_len - len(f0) - pad_size > 0: - f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant") - f0, uv = self.interpolate_f0(f0) - return f0 - - def compute_f0_uv(self, wav, p_len=None): - x = wav - if p_len is None: - p_len = x.shape[0] // self.hop_length - else: - assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error" - time_step = self.hop_length / self.sampling_rate * 1000 - f0 = ( - parselmouth.Sound(x, self.sampling_rate) - .to_pitch_ac( - time_step=time_step / 1000, - voicing_threshold=0.6, - pitch_floor=self.f0_min, - pitch_ceiling=self.f0_max, - ) - .selected_array["frequency"] - ) - - pad_size = (p_len - len(f0) + 1) // 2 - if pad_size > 0 or p_len - len(f0) - pad_size > 0: - f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant") - f0, uv = self.interpolate_f0(f0) - return f0, uv diff --git a/spaces/dwolfe66/text-generation-webui-space/extensions/gallery/script.py b/spaces/dwolfe66/text-generation-webui-space/extensions/gallery/script.py deleted file mode 100644 index 8a2d7cf988734a7ab0966d047ff3d31ba58324b7..0000000000000000000000000000000000000000 --- a/spaces/dwolfe66/text-generation-webui-space/extensions/gallery/script.py +++ /dev/null @@ -1,82 +0,0 @@ -from pathlib import Path - -import gradio as gr - -from modules.html_generator import get_image_cache - - -def generate_html(): - css = """ - .character-gallery { - margin: 1rem 0; - display: grid; - grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); - grid-column-gap: 0.4rem; - grid-row-gap: 1.2rem; - } - - .character-container { - cursor: pointer; - text-align: center; - position: relative; - opacity: 0.85; - } - - .character-container:hover { - opacity: 1; - } - - .character-container .placeholder, .character-container img { - width: 150px; - height: 200px; - background-color: gray; - object-fit: cover; - margin: 0 auto; - border-radius: 1rem; - border: 3px solid white; - box-shadow: 3px 3px 6px 0px rgb(0 0 0 / 50%); - } - - .character-name { - margin-top: 0.3rem; - display: block; - font-size: 1.2rem; - font-weight: 600; - overflow-wrap: anywhere; - } - """ - - container_html = f'<style>{css}</style><div class="character-gallery">' - - # Iterate through files in image folder - for file in sorted(Path("characters").glob("*")): - if file.name.endswith(".json"): - character = file.name.replace(".json", "") - container_html += f'<div class="character-container" onclick=\'document.getElementById("character-menu").children[1].children[1].value = "{character}"; document.getElementById("character-menu").children[1].children[1].dispatchEvent(new Event("change"));\'>' - image_html = "<div class='placeholder'></div>" - - for i in [ - f"characters/{character}.png", - f"characters/{character}.jpg", - f"characters/{character}.jpeg", - ]: - - path = Path(i) - if path.exists(): - try: - image_html = f'<img src="file/{get_image_cache(path)}">' - break - except: - continue - - container_html += f'{image_html} <span class="character-name">{character}</span>' - container_html += "</div>" - - container_html += "</div>" - return container_html - -def ui(): - with gr.Accordion("Character gallery"): - update = gr.Button("Refresh") - gallery = gr.HTML(value=generate_html()) - update.click(generate_html, [], gallery) diff --git a/spaces/dwolfe66/text-generation-webui-space/extensions/send_pictures/script.py b/spaces/dwolfe66/text-generation-webui-space/extensions/send_pictures/script.py deleted file mode 100644 index b0c356329a51edf026f7223a0ee7e5427d8751ce..0000000000000000000000000000000000000000 --- a/spaces/dwolfe66/text-generation-webui-space/extensions/send_pictures/script.py +++ /dev/null @@ -1,46 +0,0 @@ -import base64 -from io import BytesIO - -import gradio as gr -import torch -from transformers import BlipForConditionalGeneration, BlipProcessor - -import modules.chat as chat -import modules.shared as shared - -# If 'state' is True, will hijack the next chat generation with -# custom input text given by 'value' in the format [text, visible_text] -input_hijack = { - 'state': False, - 'value': ["", ""] -} - -processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") -model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu") - -def caption_image(raw_image): - inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32) - out = model.generate(**inputs, max_new_tokens=100) - return processor.decode(out[0], skip_special_tokens=True) - -def generate_chat_picture(picture, name1, name2): - text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*' - buffer = BytesIO() - picture.save(buffer, format="JPEG") - img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') - visible_text = f'<img src="data:image/jpeg;base64,{img_str}">' - return text, visible_text - -def ui(): - picture_select = gr.Image(label='Send a picture', type='pil') - - function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper' - - # Prepare the hijack with custom inputs - picture_select.upload(lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None) - - # Call the generation function - picture_select.upload(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream) - - # Clear the picture from the upload field - picture_select.upload(lambda : None, [], [picture_select], show_progress=False) diff --git a/spaces/dylanebert/igf/viewer/vite.config.ts b/spaces/dylanebert/igf/viewer/vite.config.ts deleted file mode 100644 index bbf8c7da43f0080dc6b9fb275f9583b7c17f1506..0000000000000000000000000000000000000000 --- a/spaces/dylanebert/igf/viewer/vite.config.ts +++ /dev/null @@ -1,6 +0,0 @@ -import { sveltekit } from '@sveltejs/kit/vite'; -import { defineConfig } from 'vite'; - -export default defineConfig({ - plugins: [sveltekit()] -}); diff --git a/spaces/erbanku/gpt-academic/crazy_functions/test_project/cpp/cppipc/waiter.h b/spaces/erbanku/gpt-academic/crazy_functions/test_project/cpp/cppipc/waiter.h deleted file mode 100644 index ee45fe3517be95ac1688a3e3540189edeb0d860c..0000000000000000000000000000000000000000 --- a/spaces/erbanku/gpt-academic/crazy_functions/test_project/cpp/cppipc/waiter.h +++ /dev/null @@ -1,83 +0,0 @@ -#pragma once - -#include <utility> -#include <string> -#include <mutex> -#include <atomic> - -#include "libipc/def.h" -#include "libipc/mutex.h" -#include "libipc/condition.h" -#include "libipc/platform/detail.h" - -namespace ipc { -namespace detail { - -class waiter { - ipc::sync::condition cond_; - ipc::sync::mutex lock_; - std::atomic<bool> quit_ {false}; - -public: - static void init(); - - waiter() = default; - waiter(char const *name) { - open(name); - } - - ~waiter() { - close(); - } - - bool valid() const noexcept { - return cond_.valid() && lock_.valid(); - } - - bool open(char const *name) noexcept { - quit_.store(false, std::memory_order_relaxed); - if (!cond_.open((std::string{"_waiter_cond_"} + name).c_str())) { - return false; - } - if (!lock_.open((std::string{"_waiter_lock_"} + name).c_str())) { - cond_.close(); - return false; - } - return valid(); - } - - void close() noexcept { - cond_.close(); - lock_.close(); - } - - template <typename F> - bool wait_if(F &&pred, std::uint64_t tm = ipc::invalid_value) noexcept { - IPC_UNUSED_ std::lock_guard<ipc::sync::mutex> guard {lock_}; - while ([this, &pred] { - return !quit_.load(std::memory_order_relaxed) - && std::forward<F>(pred)(); - }()) { - if (!cond_.wait(lock_, tm)) return false; - } - return true; - } - - bool notify() noexcept { - std::lock_guard<ipc::sync::mutex>{lock_}; // barrier - return cond_.notify(lock_); - } - - bool broadcast() noexcept { - std::lock_guard<ipc::sync::mutex>{lock_}; // barrier - return cond_.broadcast(lock_); - } - - bool quit_waiting() { - quit_.store(true, std::memory_order_release); - return broadcast(); - } -}; - -} // namespace detail -} // namespace ipc diff --git a/spaces/evaluate-metric/rouge/rouge.py b/spaces/evaluate-metric/rouge/rouge.py deleted file mode 100644 index 353301cca11fb5c7d4f0b0e70cde1560b4139bc7..0000000000000000000000000000000000000000 --- a/spaces/evaluate-metric/rouge/rouge.py +++ /dev/null @@ -1,158 +0,0 @@ -# Copyright 2020 The HuggingFace Evaluate 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. -""" ROUGE metric from Google Research github repo. """ - -# The dependencies in https://github.com/google-research/google-research/blob/master/rouge/requirements.txt -import absl # Here to have a nice missing dependency error message early on -import datasets -import nltk # Here to have a nice missing dependency error message early on -import numpy # Here to have a nice missing dependency error message early on -import six # Here to have a nice missing dependency error message early on -from rouge_score import rouge_scorer, scoring - -import evaluate - - -_CITATION = """\ -@inproceedings{lin-2004-rouge, - title = "{ROUGE}: A Package for Automatic Evaluation of Summaries", - author = "Lin, Chin-Yew", - booktitle = "Text Summarization Branches Out", - month = jul, - year = "2004", - address = "Barcelona, Spain", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/W04-1013", - pages = "74--81", -} -""" - -_DESCRIPTION = """\ -ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for -evaluating automatic summarization and machine translation software in natural language processing. -The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation. - -Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters. - -This metrics is a wrapper around Google Research reimplementation of ROUGE: -https://github.com/google-research/google-research/tree/master/rouge -""" - -_KWARGS_DESCRIPTION = """ -Calculates average rouge scores for a list of hypotheses and references -Args: - predictions: list of predictions to score. Each prediction - should be a string with tokens separated by spaces. - references: list of reference for each prediction. Each - reference should be a string with tokens separated by spaces. - rouge_types: A list of rouge types to calculate. - Valid names: - `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring, - `"rougeL"`: Longest common subsequence based scoring. - `"rougeLsum"`: rougeLsum splits text using `"\n"`. - See details in https://github.com/huggingface/datasets/issues/617 - use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes. - use_aggregator: Return aggregates if this is set to True -Returns: - rouge1: rouge_1 (f1), - rouge2: rouge_2 (f1), - rougeL: rouge_l (f1), - rougeLsum: rouge_lsum (f1) -Examples: - - >>> rouge = evaluate.load('rouge') - >>> predictions = ["hello there", "general kenobi"] - >>> references = ["hello there", "general kenobi"] - >>> results = rouge.compute(predictions=predictions, references=references) - >>> print(results) - {'rouge1': 1.0, 'rouge2': 1.0, 'rougeL': 1.0, 'rougeLsum': 1.0} -""" - - -class Tokenizer: - """Helper class to wrap a callable into a class with a `tokenize` method as used by rouge-score.""" - - def __init__(self, tokenizer_func): - self.tokenizer_func = tokenizer_func - - def tokenize(self, text): - return self.tokenizer_func(text) - - -@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) -class Rouge(evaluate.Metric): - def _info(self): - return evaluate.MetricInfo( - description=_DESCRIPTION, - citation=_CITATION, - inputs_description=_KWARGS_DESCRIPTION, - features=[ - datasets.Features( - { - "predictions": datasets.Value("string", id="sequence"), - "references": datasets.Sequence(datasets.Value("string", id="sequence")), - } - ), - datasets.Features( - { - "predictions": datasets.Value("string", id="sequence"), - "references": datasets.Value("string", id="sequence"), - } - ), - ], - codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"], - reference_urls=[ - "https://en.wikipedia.org/wiki/ROUGE_(metric)", - "https://github.com/google-research/google-research/tree/master/rouge", - ], - ) - - def _compute( - self, predictions, references, rouge_types=None, use_aggregator=True, use_stemmer=False, tokenizer=None - ): - if rouge_types is None: - rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"] - - multi_ref = isinstance(references[0], list) - - if tokenizer is not None: - tokenizer = Tokenizer(tokenizer) - - scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer, tokenizer=tokenizer) - if use_aggregator: - aggregator = scoring.BootstrapAggregator() - else: - scores = [] - - for ref, pred in zip(references, predictions): - if multi_ref: - score = scorer.score_multi(ref, pred) - else: - score = scorer.score(ref, pred) - if use_aggregator: - aggregator.add_scores(score) - else: - scores.append(score) - - if use_aggregator: - result = aggregator.aggregate() - for key in result: - result[key] = result[key].mid.fmeasure - - else: - result = {} - for key in scores[0]: - result[key] = list(score[key].fmeasure for score in scores) - - return result diff --git a/spaces/evi0mo/vits-fastapi-server/text/cleaners.py b/spaces/evi0mo/vits-fastapi-server/text/cleaners.py deleted file mode 100644 index 30f048820c23ab1619d441711b06b478242126c6..0000000000000000000000000000000000000000 --- a/spaces/evi0mo/vits-fastapi-server/text/cleaners.py +++ /dev/null @@ -1,194 +0,0 @@ -import re -from text.japanese import ( - japanese_to_romaji_with_accent, - japanese_to_ipa, - japanese_to_ipa2, - japanese_to_ipa3, -) -from text.korean import ( - latin_to_hangul, - number_to_hangul, - divide_hangul, - korean_to_lazy_ipa, - korean_to_ipa, -) -from text.mandarin import ( - number_to_chinese, - chinese_to_bopomofo, - latin_to_bopomofo, - chinese_to_romaji, - chinese_to_lazy_ipa, - chinese_to_ipa, - chinese_to_ipa2, -) -from text.sanskrit import devanagari_to_ipa -from text.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2 -from text.thai import num_to_thai, latin_to_thai - -# from text.shanghainese import shanghainese_to_ipa -# from text.cantonese import cantonese_to_ipa -# from text.ngu_dialect import ngu_dialect_to_ipa - - -def japanese_cleaners(text): - text = japanese_to_romaji_with_accent(text) - text = re.sub(r"([A-Za-z])$", r"\1.", text) - return text - - -def japanese_cleaners2(text): - return japanese_cleaners(text).replace("ts", "ʦ").replace("...", "…") - - -def korean_cleaners(text): - """Pipeline for Korean text""" - text = latin_to_hangul(text) - text = number_to_hangul(text) - text = divide_hangul(text) - text = re.sub(r"([\u3131-\u3163])$", r"\1.", text) - return text - - -# def chinese_cleaners(text): -# '''Pipeline for Chinese text''' -# text = number_to_chinese(text) -# text = chinese_to_bopomofo(text) -# text = latin_to_bopomofo(text) -# text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text) -# return text - - -def chinese_cleaners(text): - from pypinyin import Style, pinyin - - text = text.replace("[ZH]", "") - phones = [phone[0] for phone in pinyin(text, style=Style.TONE3)] - return " ".join(phones) - - -def zh_ja_mixture_cleaners(text): - text = re.sub( - r"\[ZH\](.*?)\[ZH\]", lambda x: chinese_to_romaji(x.group(1)) + " ", text - ) - text = re.sub( - r"\[JA\](.*?)\[JA\]", - lambda x: japanese_to_romaji_with_accent(x.group(1)) - .replace("ts", "ʦ") - .replace("u", "ɯ") - .replace("...", "…") - + " ", - text, - ) - text = re.sub(r"\s+$", "", text) - text = re.sub(r"([^\.,!\?\-…~])$", r"\1.", text) - return text - - -def sanskrit_cleaners(text): - text = text.replace("॥", "।").replace("ॐ", "ओम्") - text = re.sub(r"([^।])$", r"\1।", text) - return text - - -def cjks_cleaners(text): - text = re.sub( - r"\[ZH\](.*?)\[ZH\]", lambda x: chinese_to_lazy_ipa(x.group(1)) + " ", text - ) - text = re.sub( - r"\[JA\](.*?)\[JA\]", lambda x: japanese_to_ipa(x.group(1)) + " ", text - ) - text = re.sub( - r"\[KO\](.*?)\[KO\]", lambda x: korean_to_lazy_ipa(x.group(1)) + " ", text - ) - text = re.sub( - r"\[SA\](.*?)\[SA\]", lambda x: devanagari_to_ipa(x.group(1)) + " ", text - ) - text = re.sub( - r"\[EN\](.*?)\[EN\]", lambda x: english_to_lazy_ipa(x.group(1)) + " ", text - ) - text = re.sub(r"\s+$", "", text) - text = re.sub(r"([^\.,!\?\-…~])$", r"\1.", text) - return text - - -def cjke_cleaners(text): - text = re.sub( - r"\[ZH\](.*?)\[ZH\]", - lambda x: chinese_to_lazy_ipa(x.group(1)) - .replace("ʧ", "tʃ") - .replace("ʦ", "ts") - .replace("ɥan", "ɥæn") - + " ", - text, - ) - text = re.sub( - r"\[JA\](.*?)\[JA\]", - lambda x: japanese_to_ipa(x.group(1)) - .replace("ʧ", "tʃ") - .replace("ʦ", "ts") - .replace("ɥan", "ɥæn") - .replace("ʥ", "dz") - + " ", - text, - ) - text = re.sub(r"\[KO\](.*?)\[KO\]", lambda x: korean_to_ipa(x.group(1)) + " ", text) - text = re.sub( - r"\[EN\](.*?)\[EN\]", - lambda x: english_to_ipa2(x.group(1)) - .replace("ɑ", "a") - .replace("ɔ", "o") - .replace("ɛ", "e") - .replace("ɪ", "i") - .replace("ʊ", "u") - + " ", - text, - ) - text = re.sub(r"\s+$", "", text) - text = re.sub(r"([^\.,!\?\-…~])$", r"\1.", text) - return text - - -def cjke_cleaners2(text): - text = re.sub( - r"\[ZH\](.*?)\[ZH\]", lambda x: chinese_to_ipa(x.group(1)) + " ", text - ) - text = re.sub( - r"\[JA\](.*?)\[JA\]", lambda x: japanese_to_ipa2(x.group(1)) + " ", text - ) - text = re.sub(r"\[KO\](.*?)\[KO\]", lambda x: korean_to_ipa(x.group(1)) + " ", text) - text = re.sub( - r"\[EN\](.*?)\[EN\]", lambda x: english_to_ipa2(x.group(1)) + " ", text - ) - text = re.sub(r"\s+$", "", text) - text = re.sub(r"([^\.,!\?\-…~])$", r"\1.", text) - return text - - -def thai_cleaners(text): - text = num_to_thai(text) - text = latin_to_thai(text) - return text - - -# def shanghainese_cleaners(text): -# text = shanghainese_to_ipa(text) -# text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) -# return text - - -# def chinese_dialect_cleaners(text): -# text = re.sub(r'\[ZH\](.*?)\[ZH\]', -# lambda x: chinese_to_ipa2(x.group(1))+' ', text) -# text = re.sub(r'\[JA\](.*?)\[JA\]', -# lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text) -# text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5', -# '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text) -# text = re.sub(r'\[GD\](.*?)\[GD\]', -# lambda x: cantonese_to_ipa(x.group(1))+' ', text) -# text = re.sub(r'\[EN\](.*?)\[EN\]', -# lambda x: english_to_lazy_ipa2(x.group(1))+' ', text) -# text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group( -# 1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text) -# text = re.sub(r'\s+$', '', text) -# text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) -# return text diff --git a/spaces/falcondai/code-as-policies/prompts/transform_shape_pts.py b/spaces/falcondai/code-as-policies/prompts/transform_shape_pts.py deleted file mode 100644 index e5c74b856805818dc788de1df4b0495d9cc404ba..0000000000000000000000000000000000000000 --- a/spaces/falcondai/code-as-policies/prompts/transform_shape_pts.py +++ /dev/null @@ -1,19 +0,0 @@ -import numpy as np -from utils import get_obj_pos, get_obj_names, parse_position, parse_obj_name - 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You need to enter this password when extracting the file.</p> -<h2>PUBG Mobile Lite for Android</h2> -<p>If you have a low-end or mid-range device that cannot run PUBG Mobile smoothly, you can try PUBG Mobile Lite for Android. PUBG Mobile Lite is a version of PUBG Mobile that is designed for devices with less RAM and storage space.</p> -<p>PUBG Mobile Lite has some differences from PUBG Mobile, such as:</p> -<ul> -<li>It has a smaller download size, which is only around 600 MB.</li> -<li>It has fewer maps, modes, weapons, vehicles, skins, and other features.</li> -<li>It has a smaller map size, which is only 2x2 km.</li> -<li>It has fewer players in each match, which is only 60.</li> -<li>It has lower graphics quality and performance settings.</li> -</ul> -<p>However, PUBG Mobile Lite still offers the same core gameplay and experience as PUBG Mobile. You can still enjoy the thrill and excitement of surviving against other players on an island.</p> -<p>To download PUBG Mobile Lite for Android, you need to follow these steps:</p> -<ol> -<li>Click on this <a href="">download link</a> to get the PUBG Mobile Lite APK file from Google Play Store. The file size is only around 50 MB.</li> -<li>Install the APK file on your device. Do not open the game yet.</li> -<li>Click on this <a href="">download link</a> to get the PUBG Mobile Lite OBB file from Google Play Store. The file size is only around 550 MB.</li> -<li>Extract the OBB file using any file manager or extractor app. You will get a folder named com.tencent.iglite.</li> -<li>Copy or move this folder to the Android/OBB directory on your device's internal storage.</li> -<li>Now you can open the game and enjoy PUBG Mobile Lite on your device.</li> -</ol> <h2>Conclusion</h2> -<p>In this article, we have shown you how to download PUBG Mobile highly compressed latest version for Android devices. We have also explained the benefits of downloading the highly compressed version, the requirements and steps to download it, and the features and improvements of the latest version.</p> -<p>PUBG Mobile is a great game that offers a lot of fun and excitement to millions of players around the world. However, it can also be a challenge to download and update the game due to its large size and data consumption. By using the highly compressed version of PUBG Mobile, you can overcome this challenge and enjoy the game without any hassle.</p> -<p>Here are some tips and tricks for playing PUBG Mobile:</p> -<ul> -<li>Always check your surroundings and use cover when possible.</li> -<li>Use headphones to hear the footsteps and gunshots of your enemies.</li> -<li>Adjust your sensitivity and controls according to your preference and device.</li> -<li>Use the right weapon for the right situation. For example, use a sniper rifle for long-range shots and a shotgun for close-range combat.</li> -<li>Communicate and coordinate with your teammates using voice chat or text chat.</li> -</ul> -<p>We hope you found this article helpful and informative. If you have any feedback or questions, please feel free to leave a comment below. We would love to hear from you.</p> -<h2>FAQs</h2> -<h4>Q1: Is PUBG Mobile free to play?</h4> -<h4>A1: Yes, PUBG Mobile is free to play on both Android and iOS devices. However, you can purchase in-game items and features with real money.</h4> -<h4>Q2: Is PUBG Mobile safe to download and play?</h4> -<h4>A2: Yes, PUBG Mobile is safe to download and play as long as you download it from official sources like Google Play Store or Apple Store. You should also avoid using any third-party hacks or cheats that may compromise your account or device security.</h4> -<h4>Q3: How much space does PUBG Mobile take on my device?</h4> -<h4>A3: The download size of PUBG Mobile varies depending on the device and the version of the game. The latest version of PUBG Mobile (0.19.0) takes around 1.84 GB on Android devices and 2.13 GB on iOS devices. However, after installing the game, it may occupy more space due to additional files and updates.</h4> -<h4>Q4: How can I improve my performance and graphics in PUBG Mobile?</h4> -<h4>A4: You can improve your performance and graphics in PUBG Mobile by adjusting the settings in the game. You can choose from different graphics options like smooth, balanced, HD, HDR, etc. You can also enable or disable features like anti-aliasing, shadows, auto-adjust graphics, etc. You should also make sure that your device has enough RAM and storage space to run the game smoothly.</h4> -<h4>Q5: How can I play PUBG Mobile with my friends?</h4> -<h4>A5: You can play PUBG Mobile with your friends by inviting them to join your team or clan. You can also join a room or a custom match created by other players or yourself. You can communicate with your teammates using voice chat or text chat in the game.</h4></p> 197e85843d<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/router/layer.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/router/layer.js deleted file mode 100644 index 4dc8e86d4f7fac6a5849ec236359e2300b4e3654..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/router/layer.js +++ /dev/null @@ -1,181 +0,0 @@ -/*! - * express - * Copyright(c) 2009-2013 TJ Holowaychuk - * Copyright(c) 2013 Roman Shtylman - * Copyright(c) 2014-2015 Douglas Christopher Wilson - * MIT Licensed - */ - -'use strict'; - -/** - * Module dependencies. - * @private - */ - -var pathRegexp = require('path-to-regexp'); -var debug = require('debug')('express:router:layer'); - -/** - * Module variables. - * @private - */ - -var hasOwnProperty = Object.prototype.hasOwnProperty; - -/** - * Module exports. - * @public - */ - -module.exports = Layer; - -function Layer(path, options, fn) { - if (!(this instanceof Layer)) { - return new Layer(path, options, fn); - } - - debug('new %o', path) - var opts = options || {}; - - this.handle = fn; - this.name = fn.name || '<anonymous>'; - this.params = undefined; - this.path = undefined; - this.regexp = pathRegexp(path, this.keys = [], opts); - - // set fast path flags - this.regexp.fast_star = path === '*' - this.regexp.fast_slash = path === '/' && opts.end === false -} - -/** - * Handle the error for the layer. - * - * @param {Error} error - * @param {Request} req - * @param {Response} res - * @param {function} next - * @api private - */ - -Layer.prototype.handle_error = function handle_error(error, req, res, next) { - var fn = this.handle; - - if (fn.length !== 4) { - // not a standard error handler - return next(error); - } - - try { - fn(error, req, res, next); - } catch (err) { - next(err); - } -}; - -/** - * Handle the request for the layer. - * - * @param {Request} req - * @param {Response} res - * @param {function} next - * @api private - */ - -Layer.prototype.handle_request = function handle(req, res, next) { - var fn = this.handle; - - if (fn.length > 3) { - // not a standard request handler - return next(); - } - - try { - fn(req, res, next); - } catch (err) { - next(err); - } -}; - -/** - * Check if this route matches `path`, if so - * populate `.params`. - * - * @param {String} path - * @return {Boolean} - * @api private - */ - -Layer.prototype.match = function match(path) { - var match - - if (path != null) { - // fast path non-ending match for / (any path matches) - if (this.regexp.fast_slash) { - this.params = {} - this.path = '' - return true - } - - // fast path for * (everything matched in a param) - if (this.regexp.fast_star) { - this.params = {'0': decode_param(path)} - this.path = path - return true - } - - // match the path - match = this.regexp.exec(path) - } - - if (!match) { - this.params = undefined; - this.path = undefined; - return false; - } - - // store values - this.params = {}; - this.path = match[0] - - var keys = this.keys; - var params = this.params; - - for (var i = 1; i < match.length; i++) { - var key = keys[i - 1]; - var prop = key.name; - var val = decode_param(match[i]) - - if (val !== undefined || !(hasOwnProperty.call(params, prop))) { - params[prop] = val; - } - } - - return true; -}; - -/** - * Decode param value. - * - * @param {string} val - * @return {string} - * @private - */ - -function decode_param(val) { - if (typeof val !== 'string' || val.length === 0) { - return val; - } - - try { - return decodeURIComponent(val); - } catch (err) { - if (err instanceof URIError) { - err.message = 'Failed to decode param \'' + val + '\''; - err.status = err.statusCode = 400; - } - - throw err; - } -} diff --git a/spaces/fffiloni/mmpose-estimation/configs/faster_rcnn_r50_fpn_1x_coco.py b/spaces/fffiloni/mmpose-estimation/configs/faster_rcnn_r50_fpn_1x_coco.py deleted file mode 100644 index c0d22186965c0f944f5e16f01fa639ff80b90f35..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/mmpose-estimation/configs/faster_rcnn_r50_fpn_1x_coco.py +++ /dev/null @@ -1,228 +0,0 @@ -model = dict( - type='FasterRCNN', - backbone=dict( - type='ResNet', - depth=50, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=True, - style='pytorch', - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), - neck=dict( - type='FPN', - in_channels=[256, 512, 1024, 2048], - 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='StandardRoIHead', - 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='Shared2FCBBoxHead', - in_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0.0, 0.0, 0.0, 0.0], - target_stds=[0.1, 0.1, 0.2, 0.2]), - reg_class_agnostic=False, - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='L1Loss', loss_weight=1.0))), - 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=256, - 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='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.5, - min_pos_iou=0.5, - match_low_quality=False, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=512, - pos_fraction=0.25, - neg_pos_ub=-1, - add_gt_as_proposals=True), - 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))) -dataset_type = 'CocoDataset' -data_root = 'data/coco/' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - 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='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=2, - workers_per_gpu=2, - train=dict( - type='CocoDataset', - ann_file='data/coco/annotations/instances_train2017.json', - img_prefix='data/coco/train2017/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - 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='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) - ]), - val=dict( - type='CocoDataset', - 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', - 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=1, metric='bbox') -optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) -optimizer_config = dict(grad_clip=None) -lr_config = dict( - policy='step', - warmup='linear', - warmup_iters=500, - 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='NumClassCheckHook')] -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=False, base_batch_size=16) diff --git a/spaces/fjenett/GPT-JT/README.md b/spaces/fjenett/GPT-JT/README.md deleted file mode 100644 index b5ee3cb2833f83305b4bcc391049a08c0aa6a2c0..0000000000000000000000000000000000000000 --- a/spaces/fjenett/GPT-JT/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: GPT-JT (Gradio) -emoji: 🚀 -colorFrom: pink -colorTo: red -sdk: gradio -sdk_version: 3 -app_file: app.py -pinned: true -duplicated_from: togethercomputer/GPT-JT ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/flax-community/Multilingual-VQA/sections/examples/examples.md b/spaces/flax-community/Multilingual-VQA/sections/examples/examples.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/flax-sentence-embeddings/sentence-embeddings/backend/inference.py b/spaces/flax-sentence-embeddings/sentence-embeddings/backend/inference.py deleted file mode 100644 index cb5172371514e9d8804c8ecb4918a7b63cf79321..0000000000000000000000000000000000000000 --- a/spaces/flax-sentence-embeddings/sentence-embeddings/backend/inference.py +++ /dev/null @@ -1,140 +0,0 @@ -import gzip -import json -from collections import Counter - -import pandas as pd -import numpy as np -import jax.numpy as jnp -import tqdm - -from sentence_transformers import util -from typing import List, Union -import torch - -from backend.utils import load_model, filter_questions, load_embeddings -from sklearn.manifold import TSNE - -def cos_sim(a, b): - return jnp.matmul(a, jnp.transpose(b)) / (jnp.linalg.norm(a) * jnp.linalg.norm(b)) - - -# We get similarity between embeddings. -def text_similarity(anchor: str, inputs: List[str], model_name: str, model_dict: dict): - print(model_name) - model = load_model(model_name, model_dict) - - # Creating embeddings - if hasattr(model, 'encode'): - anchor_emb = model.encode(anchor)[None, :] - inputs_emb = model.encode(inputs) - else: - assert len(model) == 2 - anchor_emb = model[0].encode(anchor)[None, :] - inputs_emb = model[1].encode(inputs) - - # Obtaining similarity - similarity = list(jnp.squeeze(cos_sim(anchor_emb, inputs_emb))) - - # Returning a Pandas' dataframe - d = {'inputs': inputs, - 'score': [round(similarity[i], 3) for i in range(len(similarity))]} - df = pd.DataFrame(d, columns=['inputs', 'score']) - - return df - - -# Search -def text_search(anchor: str, n_answers: int, model_name: str, model_dict: dict): - # Proceeding with model - print(model_name) - assert model_name == "distilbert_qa" - model = load_model(model_name, model_dict) - - # Creating embeddings - query_emb = model.encode(anchor, convert_to_tensor=True)[None, :] - - print("loading embeddings") - corpus_emb = load_embeddings() - - # Getting hits - hits = util.semantic_search(query_emb, corpus_emb, score_function=util.dot_score, top_k=n_answers)[0] - - filtered_posts = filter_questions("python") - print(f"{len(filtered_posts)} posts found with tag: python") - - hits_titles = [] - hits_scores = [] - urls = [] - for hit in hits: - post = filtered_posts[hit['corpus_id']] - hits_titles.append(post['title']) - hits_scores.append("{:.3f}".format(hit['score'])) - urls.append(f"https://stackoverflow.com/q/{post['id']}") - - return hits_titles, hits_scores, urls - - -def text_cluster(anchor: str, n_answers: int, model_name: str, model_dict: dict): - # Proceeding with model - print(model_name) - assert model_name == "distilbert_qa" - model = load_model(model_name, model_dict) - - # Creating embeddings - query_emb = model.encode(anchor, convert_to_tensor=True)[None, :] - - print("loading embeddings") - corpus_emb = load_embeddings() - - # Getting hits - hits = util.semantic_search(query_emb, corpus_emb, score_function=util.dot_score, top_k=n_answers)[0] - - filtered_posts = filter_questions("python") - - hits_dict = [filtered_posts[hit['corpus_id']] for hit in hits] - hits_dict.append(dict(id = '1', title = anchor, tags = [''])) - - hits_emb = torch.stack([corpus_emb[hit['corpus_id']] for hit in hits]) - hits_emb = torch.cat((hits_emb, query_emb)) - - # Dimensionality reduction with t-SNE - tsne = TSNE(n_components=3, verbose=1, perplexity=15, n_iter=1000) - tsne_results = tsne.fit_transform(hits_emb.cpu()) - df = pd.DataFrame(hits_dict) - tags = list(df['tags']) - - counter = Counter(tags[0]) - for i in tags[1:]: - counter.update(i) - - df_tags = pd.DataFrame(counter.most_common(), columns=['Tag', 'Mentions']) - most_common_tags = list(df_tags['Tag'])[1:5] - - labels = [] - - for tags_list in list(df['tags']): - for common_tag in most_common_tags: - if common_tag in tags_list: - labels.append(common_tag) - break - elif common_tag != most_common_tags[-1]: - continue - else: - labels.append('others') - - df['title'] = [post['title'] for post in hits_dict] - df['labels'] = labels - df['tsne_x'] = tsne_results[:, 0] - df['tsne_y'] = tsne_results[:, 1] - df['tsne_z'] = tsne_results[:, 2] - - df['size'] = [2 for i in range(len(df))] - - # Making the query bigger than the rest of the observations - df['size'][len(df) - 1] = 10 - df['labels'][len(df) - 1] = 'QUERY' - import plotly.express as px - - fig = px.scatter_3d(df, x='tsne_x', y='tsne_y', z='tsne_z', color='labels', size='size', - color_discrete_sequence=px.colors.qualitative.D3, hover_data=[df.title]) - return fig diff --git a/spaces/florim/MedGPT/data_ingestion.py b/spaces/florim/MedGPT/data_ingestion.py deleted file mode 100644 index b89a33dafd15c2e7bded0445a741a4a1c47ed417..0000000000000000000000000000000000000000 --- a/spaces/florim/MedGPT/data_ingestion.py +++ /dev/null @@ -1,96 +0,0 @@ -import argparse -import logging - -from autogpt.commands.file_operations import ingest_file, search_files -from autogpt.config import Config -from autogpt.memory import get_memory - -cfg = Config() - - -def configure_logging(): - logging.basicConfig( - filename="log-ingestion.txt", - filemode="a", - format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s", - datefmt="%H:%M:%S", - level=logging.DEBUG, - ) - return logging.getLogger("AutoGPT-Ingestion") - - -def ingest_directory(directory, memory, args): - """ - Ingest all files in a directory by calling the ingest_file function for each file. - - :param directory: The directory containing the files to ingest - :param memory: An object with an add() method to store the chunks in memory - """ - try: - files = search_files(directory) - for file in files: - ingest_file(file, memory, args.max_length, args.overlap) - except Exception as e: - print(f"Error while ingesting directory '{directory}': {str(e)}") - - -def main() -> None: - logger = configure_logging() - - parser = argparse.ArgumentParser( - description="Ingest a file or a directory with multiple files into memory. " - "Make sure to set your .env before running this script." - ) - group = parser.add_mutually_exclusive_group(required=True) - group.add_argument("--file", type=str, help="The file to ingest.") - group.add_argument( - "--dir", type=str, help="The directory containing the files to ingest." - ) - parser.add_argument( - "--init", - action="store_true", - help="Init the memory and wipe its content (default: False)", - default=False, - ) - parser.add_argument( - "--overlap", - type=int, - help="The overlap size between chunks when ingesting files (default: 200)", - default=200, - ) - parser.add_argument( - "--max_length", - type=int, - help="The max_length of each chunk when ingesting files (default: 4000)", - default=4000, - ) - - args = parser.parse_args() - - # Initialize memory - memory = get_memory(cfg, init=args.init) - print("Using memory of type: " + memory.__class__.__name__) - - if args.file: - try: - ingest_file(args.file, memory, args.max_length, args.overlap) - print(f"File '{args.file}' ingested successfully.") - except Exception as e: - logger.error(f"Error while ingesting file '{args.file}': {str(e)}") - print(f"Error while ingesting file '{args.file}': {str(e)}") - elif args.dir: - try: - ingest_directory(args.dir, memory, args) - print(f"Directory '{args.dir}' ingested successfully.") - except Exception as e: - logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}") - print(f"Error while ingesting directory '{args.dir}': {str(e)}") - else: - print( - "Please provide either a file path (--file) or a directory name (--dir)" - " inside the auto_gpt_workspace directory as input." - ) - - -if __name__ == "__main__": - main() diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/datasets/hrf.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/datasets/hrf.py deleted file mode 100644 index 242d790eb1b83e75cf6b7eaa7a35c674099311ad..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/datasets/hrf.py +++ /dev/null @@ -1,59 +0,0 @@ -# dataset settings -dataset_type = 'HRFDataset' -data_root = 'data/HRF' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -img_scale = (2336, 3504) -crop_size = (256, 256) -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/gotiQspiryo/whisper-ui/examples/Love Matrubhoomi Subtitles 720p HD A Story of Sexual Frustration and Violence.md b/spaces/gotiQspiryo/whisper-ui/examples/Love Matrubhoomi Subtitles 720p HD A Story of Sexual Frustration and Violence.md deleted file mode 100644 index ca6dd42b86e7f30490abaa202288147caa5142df..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Love Matrubhoomi Subtitles 720p HD A Story of Sexual Frustration and Violence.md +++ /dev/null @@ -1,6 +0,0 @@ -<h2>love Matrubhoomi subtitles 720p hd</h2><br /><p><b><b>Download</b> ✵✵✵ <a href="https://urlgoal.com/2uyMAw">https://urlgoal.com/2uyMAw</a></b></p><br /><br /> - - aaccfb2cb3<br /> -<br /> -<br /> -<p></p> diff --git a/spaces/gradio/HuBERT/fairseq/data/encoders/utils.py b/spaces/gradio/HuBERT/fairseq/data/encoders/utils.py deleted file mode 100644 index d93eb532ef84f0e2bc708b777229ab2cb76ca14b..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/data/encoders/utils.py +++ /dev/null @@ -1,30 +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 -from fairseq.data import encoders - - -def get_whole_word_mask(args, dictionary): - bpe = encoders.build_bpe(args) - if bpe is not None: - - def is_beginning_of_word(i): - if i < dictionary.nspecial: - # special elements are always considered beginnings - return True - tok = dictionary[i] - if tok.startswith("madeupword"): - return True - try: - return bpe.is_beginning_of_word(tok) - except ValueError: - return True - - mask_whole_words = torch.ByteTensor( - list(map(is_beginning_of_word, range(len(dictionary)))) - ) - return mask_whole_words - return None diff --git a/spaces/gradio/blocks_outputs/README.md b/spaces/gradio/blocks_outputs/README.md deleted file mode 100644 index 84b6ffb6f23066b9ddc8537147916a0e3e8b756e..0000000000000000000000000000000000000000 --- a/spaces/gradio/blocks_outputs/README.md +++ /dev/null @@ -1,12 +0,0 @@ - ---- -title: blocks_outputs -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/longformer/longformer/__init__.py b/spaces/gradio/longformer/longformer/__init__.py deleted file mode 100644 index d3e343c434c4380bec8c50e79d63e50b40eb5126..0000000000000000000000000000000000000000 --- a/spaces/gradio/longformer/longformer/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from longformer.longformer import Longformer, LongformerForMaskedLM, LongformerConfig -from longformer.longformer_encoder_decoder import LongformerEncoderDecoderConfig -from longformer.longformer_encoder_decoder import LongformerEncoderDecoderForConditionalGeneration \ No newline at end of file diff --git a/spaces/grzegorz2047/fast_diffusion/README.md b/spaces/grzegorz2047/fast_diffusion/README.md deleted file mode 100644 index 994ac41e7529b889bad29dd889416e0f4c153b0c..0000000000000000000000000000000000000000 --- a/spaces/grzegorz2047/fast_diffusion/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 340 Models Fast Diffusion -emoji: 👩‍🎨👨‍🎨 -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: true -duplicated_from: Yntec/fast_diffusion ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/gwang-kim/DATID-3D/eg3d/calc_metrics.py b/spaces/gwang-kim/DATID-3D/eg3d/calc_metrics.py deleted file mode 100644 index d401b22554e142a4146a0eb0fc952cc20742e3e7..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/eg3d/calc_metrics.py +++ /dev/null @@ -1,190 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. - -"""Calculate quality metrics for previous training run or pretrained network pickle.""" - -import os -import click -import json -import tempfile -import copy -import torch - -import dnnlib -import legacy -from metrics import metric_main -from metrics import metric_utils -from torch_utils import training_stats -from torch_utils import custom_ops -from torch_utils import misc -from torch_utils.ops import conv2d_gradfix - -#---------------------------------------------------------------------------- - -def subprocess_fn(rank, args, temp_dir): - dnnlib.util.Logger(should_flush=True) - - # Init torch.distributed. - if args.num_gpus > 1: - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) - if os.name == 'nt': - init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus) - else: - init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus) - - # Init torch_utils. - sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None - training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) - if rank != 0 or not args.verbose: - custom_ops.verbosity = 'none' - - # Configure torch. - device = torch.device('cuda', rank) - torch.backends.cuda.matmul.allow_tf32 = False - torch.backends.cudnn.allow_tf32 = False - conv2d_gradfix.enabled = True - - # Print network summary. - G = copy.deepcopy(args.G).eval().requires_grad_(False).to(device) - if rank == 0 and args.verbose: - z = torch.empty([1, G.z_dim], device=device) - c = torch.empty([1, G.c_dim], device=device) - misc.print_module_summary(G, [z, c]) - - # Calculate each metric. - for metric in args.metrics: - if rank == 0 and args.verbose: - print(f'Calculating {metric}...') - progress = metric_utils.ProgressMonitor(verbose=args.verbose) - result_dict = metric_main.calc_metric(metric=metric, G=G, dataset_kwargs=args.dataset_kwargs, - num_gpus=args.num_gpus, rank=rank, device=device, progress=progress) - if rank == 0: - metric_main.report_metric(result_dict, run_dir=args.run_dir, snapshot_pkl=args.network_pkl) - if rank == 0 and args.verbose: - print() - - # Done. - if rank == 0 and args.verbose: - print('Exiting...') - -#---------------------------------------------------------------------------- - -def parse_comma_separated_list(s): - if isinstance(s, list): - return s - if s is None or s.lower() == 'none' or s == '': - return [] - return s.split(',') - -#---------------------------------------------------------------------------- - -@click.command() -@click.pass_context -@click.option('network_pkl', '--network', help='Network pickle filename or URL', metavar='PATH', required=True) -@click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True) -@click.option('--data', help='Dataset to evaluate against [default: look up]', metavar='[ZIP|DIR]') -@click.option('--mirror', help='Enable dataset x-flips [default: look up]', type=bool, metavar='BOOL') -@click.option('--gpus', help='Number of GPUs to use', type=int, default=1, metavar='INT', show_default=True) -@click.option('--verbose', help='Print optional information', type=bool, default=True, metavar='BOOL', show_default=True) - -def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose): - """Calculate quality metrics for previous training run or pretrained network pickle. - - Examples: - - \b - # Previous training run: look up options automatically, save result to JSONL file. - python calc_metrics.py --metrics=eqt50k_int,eqr50k \\ - --network=~/training-runs/00000-stylegan3-r-mydataset/network-snapshot-000000.pkl - - \b - # Pre-trained network pickle: specify dataset explicitly, print result to stdout. - python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq-1024x1024.zip --mirror=1 \\ - --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhq-1024x1024.pkl - - \b - Recommended metrics: - fid50k_full Frechet inception distance against the full dataset. - kid50k_full Kernel inception distance against the full dataset. - pr50k3_full Precision and recall againt the full dataset. - ppl2_wend Perceptual path length in W, endpoints, full image. - eqt50k_int Equivariance w.r.t. integer translation (EQ-T). - eqt50k_frac Equivariance w.r.t. fractional translation (EQ-T_frac). - eqr50k Equivariance w.r.t. rotation (EQ-R). - - \b - Legacy metrics: - fid50k Frechet inception distance against 50k real images. - kid50k Kernel inception distance against 50k real images. - pr50k3 Precision and recall against 50k real images. - is50k Inception score for CIFAR-10. - """ - dnnlib.util.Logger(should_flush=True) - - # Validate arguments. - args = dnnlib.EasyDict(metrics=metrics, num_gpus=gpus, network_pkl=network_pkl, verbose=verbose) - if not all(metric_main.is_valid_metric(metric) for metric in args.metrics): - ctx.fail('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics())) - if not args.num_gpus >= 1: - ctx.fail('--gpus must be at least 1') - - # Load network. - if not dnnlib.util.is_url(network_pkl, allow_file_urls=True) and not os.path.isfile(network_pkl): - ctx.fail('--network must point to a file or URL') - if args.verbose: - print(f'Loading network from "{network_pkl}"...') - with dnnlib.util.open_url(network_pkl, verbose=args.verbose) as f: - network_dict = legacy.load_network_pkl(f) - args.G = network_dict['G_ema'] # subclass of torch.nn.Module - - # Initialize dataset options. - if data is not None: - args.dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data) - elif network_dict['training_set_kwargs'] is not None: - args.dataset_kwargs = dnnlib.EasyDict(network_dict['training_set_kwargs']) - else: - ctx.fail('Could not look up dataset options; please specify --data') - - # Finalize dataset options. - args.dataset_kwargs.resolution = args.G.img_resolution - args.dataset_kwargs.use_labels = (args.G.c_dim != 0) - if mirror is not None: - args.dataset_kwargs.xflip = mirror - - # Print dataset options. - if args.verbose: - print('Dataset options:') - print(json.dumps(args.dataset_kwargs, indent=2)) - - # Locate run dir. - args.run_dir = None - if os.path.isfile(network_pkl): - pkl_dir = os.path.dirname(network_pkl) - if os.path.isfile(os.path.join(pkl_dir, 'training_options.json')): - args.run_dir = pkl_dir - - # Launch processes. - if args.verbose: - print('Launching processes...') - torch.multiprocessing.set_start_method('spawn') - with tempfile.TemporaryDirectory() as temp_dir: - if args.num_gpus == 1: - subprocess_fn(rank=0, args=args, temp_dir=temp_dir) - else: - torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus) - -#---------------------------------------------------------------------------- - -if __name__ == "__main__": - calc_metrics() # pylint: disable=no-value-for-parameter - -#---------------------------------------------------------------------------- diff --git a/spaces/gyugnsu/DragGan-Inversion/visualizer_drag_gradio.py b/spaces/gyugnsu/DragGan-Inversion/visualizer_drag_gradio.py deleted file mode 100644 index a4e14e9b81e21325a38e99064a755b24f15afac4..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/visualizer_drag_gradio.py +++ /dev/null @@ -1,934 +0,0 @@ -# https://huggingface.co/DragGan/DragGan-Models -# https://arxiv.org/abs/2305.10973 -import os -import os.path as osp -from argparse import ArgumentParser -from functools import partial -from pathlib import Path -import time - -import psutil - -import gradio as gr -import numpy as np -import torch -from PIL import Image - -import dnnlib -from gradio_utils import (ImageMask, draw_mask_on_image, draw_points_on_image, - get_latest_points_pair, get_valid_mask, - on_change_single_global_state) -from viz.renderer import Renderer, add_watermark_np - - -# download models from Hugging Face hub -from huggingface_hub import snapshot_download - -model_dir = Path('./checkpoints') -snapshot_download('DragGan/DragGan-Models', - repo_type='model', local_dir=model_dir) - -cache_dir = model_dir - -device = 'cuda' -IS_SPACE = "DragGan/DragGan" in os.environ.get('SPACE_ID', '') -TIMEOUT = 80 - - -def reverse_point_pairs(points): - new_points = [] - for p in points: - new_points.append([p[1], p[0]]) - return new_points - - -def clear_state(global_state, target=None): - """Clear target history state from global_state - If target is not defined, points and mask will be both removed. - 1. set global_state['points'] as empty dict - 2. set global_state['mask'] as full-one mask. - """ - if target is None: - target = ['point', 'mask'] - if not isinstance(target, list): - target = [target] - if 'point' in target: - global_state['points'] = dict() - print('Clear Points State!') - if 'mask' in target: - image_raw = global_state["images"]["image_raw"] - global_state['mask'] = np.ones((image_raw.size[1], image_raw.size[0]), - dtype=np.uint8) - print('Clear mask State!') - - return global_state - - -def init_images(global_state): - """This function is called only ones with Gradio App is started. - 0. pre-process global_state, unpack value from global_state of need - 1. Re-init renderer - 2. run `renderer._render_drag_impl` with `is_drag=False` to generate - new image - 3. Assign images to global state and re-generate mask - """ - - if isinstance(global_state, gr.State): - state = global_state.value - else: - state = global_state - - state['renderer'].init_network( - state['generator_params'], # res - valid_checkpoints_dict[state['pretrained_weight']], # pkl - state['params']['seed'], # w0_seed, - None, # w_load - state['params']['latent_space'] == 'w+', # w_plus - 'const', - state['params']['trunc_psi'], # trunc_psi, - state['params']['trunc_cutoff'], # trunc_cutoff, - None, # input_transform - state['params']['lr'] # lr, - ) - - state['renderer']._render_drag_impl(state['generator_params'], - is_drag=False, - to_pil=True) - - init_image = state['generator_params'].image - state['images']['image_orig'] = init_image - state['images']['image_raw'] = init_image - state['images']['image_show'] = Image.fromarray( - add_watermark_np(np.array(init_image))) - state['mask'] = np.ones((init_image.size[1], init_image.size[0]), - dtype=np.uint8) - return global_state - - -def update_image_draw(image, points, mask, show_mask, global_state=None): - - image_draw = draw_points_on_image(image, points) - if show_mask and mask is not None and not (mask == 0).all() and not ( - mask == 1).all(): - image_draw = draw_mask_on_image(image_draw, mask) - - image_draw = Image.fromarray(add_watermark_np(np.array(image_draw))) - if global_state is not None: - global_state['images']['image_show'] = image_draw - return image_draw - - -def preprocess_mask_info(global_state, image): - """Function to handle mask information. - 1. last_mask is None: Do not need to change mask, return mask - 2. last_mask is not None: - 2.1 global_state is remove_mask: - 2.2 global_state is add_mask: - """ - if isinstance(image, dict): - last_mask = get_valid_mask(image['mask']) - else: - last_mask = None - mask = global_state['mask'] - - # mask in global state is a placeholder with all 1. - if (mask == 1).all(): - mask = last_mask - - # last_mask = global_state['last_mask'] - editing_mode = global_state['editing_state'] - - if last_mask is None: - return global_state - - if editing_mode == 'remove_mask': - updated_mask = np.clip(mask - last_mask, 0, 1) - print(f'Last editing_state is {editing_mode}, do remove.') - elif editing_mode == 'add_mask': - updated_mask = np.clip(mask + last_mask, 0, 1) - print(f'Last editing_state is {editing_mode}, do add.') - else: - updated_mask = mask - print(f'Last editing_state is {editing_mode}, ' - 'do nothing to mask.') - - global_state['mask'] = updated_mask - # global_state['last_mask'] = None # clear buffer - return global_state - - -def print_memory_usage(): - # Print system memory usage - print(f"System memory usage: {psutil.virtual_memory().percent}%") - - # Print GPU memory usage - if torch.cuda.is_available(): - device = torch.device("cuda") - print(f"GPU memory usage: {torch.cuda.memory_allocated() / 1e9} GB") - print( - f"Max GPU memory usage: {torch.cuda.max_memory_allocated() / 1e9} GB") - device_properties = torch.cuda.get_device_properties(device) - available_memory = device_properties.total_memory - \ - torch.cuda.max_memory_allocated() - print(f"Available GPU memory: {available_memory / 1e9} GB") - else: - print("No GPU available") - - -# filter large models running on SPACES -allowed_checkpoints = [] # all checkpoints -if IS_SPACE: - allowed_checkpoints = ["stylegan_human_v2_512.pkl", - "stylegan2_dogs_1024_pytorch.pkl"] - -valid_checkpoints_dict = { - f.name.split('.')[0]: str(f) - for f in Path(cache_dir).glob('*.pkl') - if f.name in allowed_checkpoints or not IS_SPACE -} -print('Valid checkpoint file:') -print(valid_checkpoints_dict) - -init_pkl = 'stylegan_human_v2_512' - -with gr.Blocks() as app: - gr.Markdown(""" -# DragGAN - Drag Your GAN -## Interactive Point-based Manipulation on the Generative Image Manifold -### Unofficial Gradio Demo - -**Due to high demand, only one model can be run at a time, or you can duplicate the space and run your own copy.** - -<a href="https://huggingface.co/spaces/radames/DragGan?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank"> -<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for no queue on your own hardware.</p> - -* Official Repo: [XingangPan](https://github.com/XingangPan/DragGAN) -* Gradio Demo by: [LeoXing1996](https://github.com/LeoXing1996) © [OpenMMLab MMagic](https://github.com/open-mmlab/mmagic) -""") - - # renderer = Renderer() - global_state = gr.State({ - "images": { - # image_orig: the original image, change with seed/model is changed - # image_raw: image with mask and points, change durning optimization - # image_show: image showed on screen - }, - "temporal_params": { - # stop - }, - 'mask': - None, # mask for visualization, 1 for editing and 0 for unchange - 'last_mask': None, # last edited mask - 'show_mask': True, # add button - "generator_params": dnnlib.EasyDict(), - "params": { - "seed": int(np.random.randint(0, 2**32 - 1)), - "motion_lambda": 20, - "r1_in_pixels": 3, - "r2_in_pixels": 12, - "magnitude_direction_in_pixels": 1.0, - "latent_space": "w+", - "trunc_psi": 0.7, - "trunc_cutoff": None, - "lr": 0.001, - }, - "device": device, - "draw_interval": 1, - "renderer": Renderer(disable_timing=True), - "points": {}, - "curr_point": None, - "curr_type_point": "start", - 'editing_state': 'add_points', - 'pretrained_weight': init_pkl - }) - - # init image - global_state = init_images(global_state) - with gr.Row(): - - with gr.Row(): - - # Left --> tools - with gr.Column(scale=3): - - # Pickle - with gr.Row(): - - with gr.Column(scale=1, min_width=10): - gr.Markdown(value='Pickle', show_label=False) - - with gr.Column(scale=4, min_width=10): - form_pretrained_dropdown = gr.Dropdown( - choices=list(valid_checkpoints_dict.keys()), - label="Pretrained Model", - value=init_pkl, - ) - - # Latent - with gr.Row(): - with gr.Column(scale=1, min_width=10): - gr.Markdown(value='Latent', show_label=False) - - with gr.Column(scale=4, min_width=10): - form_seed_number = gr.Slider( - mininium=0, - maximum=2**32-1, - step=1, - value=global_state.value['params']['seed'], - interactive=True, - # randomize=True, - label="Seed", - ) - form_lr_number = gr.Number( - value=global_state.value["params"]["lr"], - interactive=True, - label="Step Size") - - with gr.Row(): - with gr.Column(scale=2, min_width=10): - form_reset_image = gr.Button("Reset Image") - with gr.Column(scale=3, min_width=10): - form_latent_space = gr.Radio( - ['w', 'w+'], - value=global_state.value['params'] - ['latent_space'], - interactive=True, - label='Latent space to optimize', - show_label=False, - ) - - # Drag - with gr.Row(): - with gr.Column(scale=1, min_width=10): - gr.Markdown(value='Drag', show_label=False) - with gr.Column(scale=4, min_width=10): - with gr.Row(): - with gr.Column(scale=1, min_width=10): - enable_add_points = gr.Button('Add Points') - with gr.Column(scale=1, min_width=10): - undo_points = gr.Button('Reset Points') - with gr.Row(): - with gr.Column(scale=1, min_width=10): - form_start_btn = gr.Button("Start") - with gr.Column(scale=1, min_width=10): - form_stop_btn = gr.Button("Stop") - - form_steps_number = gr.Number(value=0, - label="Steps", - interactive=False) - - # Mask - with gr.Row(): - with gr.Column(scale=1, min_width=10): - gr.Markdown(value='Mask', show_label=False) - with gr.Column(scale=4, min_width=10): - enable_add_mask = gr.Button('Edit Flexible Area') - with gr.Row(): - with gr.Column(scale=1, min_width=10): - form_reset_mask_btn = gr.Button("Reset mask") - with gr.Column(scale=1, min_width=10): - show_mask = gr.Checkbox( - label='Show Mask', - value=global_state.value['show_mask'], - show_label=False) - - with gr.Row(): - form_lambda_number = gr.Number( - value=global_state.value["params"] - ["motion_lambda"], - interactive=True, - label="Lambda", - ) - - form_draw_interval_number = gr.Number( - value=global_state.value["draw_interval"], - label="Draw Interval (steps)", - interactive=True, - visible=False) - - # Right --> Image - with gr.Column(scale=8): - form_image = ImageMask( - value=global_state.value['images']['image_show'], - brush_radius=20).style( - width=768, - height=768) # NOTE: hard image size code here. - gr.Markdown(""" - ## Quick Start - - 1. Select desired `Pretrained Model` and adjust `Seed` to generate an - initial image. - 2. Click on image to add control points. - 3. Click `Start` and enjoy it! - - ## Advance Usage - - 1. Change `Step Size` to adjust learning rate in drag optimization. - 2. Select `w` or `w+` to change latent space to optimize: - * Optimize on `w` space may cause greater influence to the image. - * Optimize on `w+` space may work slower than `w`, but usually achieve - better results. - * Note that changing the latent space will reset the image, points and - mask (this has the same effect as `Reset Image` button). - 3. Click `Edit Flexible Area` to create a mask and constrain the - unmasked region to remain unchanged. - - - """) - gr.HTML(""" - <style> - .container { - position: absolute; - height: 50px; - text-align: center; - line-height: 50px; - width: 100%; - } - </style> - <div class="container"> - Gradio demo supported by - <img src="https://avatars.githubusercontent.com/u/10245193?s=200&v=4" height="20" width="20" style="display:inline;"> - <a href="https://github.com/open-mmlab/mmagic">OpenMMLab MMagic</a> - </div> - """) - # Network & latents tab listeners - - def on_change_pretrained_dropdown(pretrained_value, global_state): - """Function to handle model change. - 1. Set pretrained value to global_state - 2. Re-init images and clear all states - """ - - global_state['pretrained_weight'] = pretrained_value - init_images(global_state) - clear_state(global_state) - - return global_state, global_state["images"]['image_show'] - - form_pretrained_dropdown.change( - on_change_pretrained_dropdown, - inputs=[form_pretrained_dropdown, global_state], - outputs=[global_state, form_image], - queue=True, - ) - - def on_click_reset_image(global_state): - """Reset image to the original one and clear all states - 1. Re-init images - 2. Clear all states - """ - - init_images(global_state) - clear_state(global_state) - - return global_state, global_state['images']['image_show'] - - form_reset_image.click( - on_click_reset_image, - inputs=[global_state], - outputs=[global_state, form_image], - queue=False, - ) - - # Update parameters - def on_change_update_image_seed(seed, global_state): - """Function to handle generation seed change. - 1. Set seed to global_state - 2. Re-init images and clear all states - """ - - global_state["params"]["seed"] = int(seed) - init_images(global_state) - clear_state(global_state) - - return global_state, global_state['images']['image_show'] - - form_seed_number.change( - on_change_update_image_seed, - inputs=[form_seed_number, global_state], - outputs=[global_state, form_image], - ) - - def on_click_latent_space(latent_space, global_state): - """Function to reset latent space to optimize. - NOTE: this function we reset the image and all controls - 1. Set latent-space to global_state - 2. Re-init images and clear all state - """ - - global_state['params']['latent_space'] = latent_space - init_images(global_state) - clear_state(global_state) - - return global_state, global_state['images']['image_show'] - - form_latent_space.change(on_click_latent_space, - inputs=[form_latent_space, global_state], - outputs=[global_state, form_image]) - - # ==== Params - form_lambda_number.change( - partial(on_change_single_global_state, ["params", "motion_lambda"]), - inputs=[form_lambda_number, global_state], - outputs=[global_state], - ) - - def on_change_lr(lr, global_state): - if lr == 0: - print('lr is 0, do nothing.') - return global_state - else: - global_state["params"]["lr"] = lr - renderer = global_state['renderer'] - renderer.update_lr(lr) - print('New optimizer: ') - print(renderer.w_optim) - return global_state - - form_lr_number.change( - on_change_lr, - inputs=[form_lr_number, global_state], - outputs=[global_state], - queue=False, - ) - - def on_click_start(global_state, image): - p_in_pixels = [] - t_in_pixels = [] - valid_points = [] - - # handle of start drag in mask editing mode - global_state = preprocess_mask_info(global_state, image) - - # Prepare the points for the inference - if len(global_state["points"]) == 0: - # yield on_click_start_wo_points(global_state, image) - image_raw = global_state['images']['image_raw'] - update_image_draw( - image_raw, - global_state['points'], - global_state['mask'], - global_state['show_mask'], - global_state, - ) - - yield ( - global_state, - 0, - global_state['images']['image_show'], - # gr.File.update(visible=False), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - # latent space - gr.Radio.update(interactive=True), - gr.Button.update(interactive=True), - # NOTE: disable stop button - gr.Button.update(interactive=False), - - # update other comps - gr.Dropdown.update(interactive=True), - gr.Number.update(interactive=True), - gr.Number.update(interactive=True), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - gr.Checkbox.update(interactive=True), - # gr.Number.update(interactive=True), - gr.Number.update(interactive=True), - ) - else: - - # Transform the points into torch tensors - for key_point, point in global_state["points"].items(): - try: - p_start = point.get("start_temp", point["start"]) - p_end = point["target"] - - if p_start is None or p_end is None: - continue - - except KeyError: - continue - - p_in_pixels.append(p_start) - t_in_pixels.append(p_end) - valid_points.append(key_point) - - mask = torch.tensor(global_state['mask']).float() - drag_mask = 1 - mask - - renderer: Renderer = global_state["renderer"] - global_state['temporal_params']['stop'] = False - global_state['editing_state'] = 'running' - - # reverse points order - p_to_opt = reverse_point_pairs(p_in_pixels) - t_to_opt = reverse_point_pairs(t_in_pixels) - print('Running with:') - print(f' Source: {p_in_pixels}') - print(f' Target: {t_in_pixels}') - step_idx = 0 - last_time = time.time() - while True: - print_memory_usage() - # add a TIMEOUT break - print(f'Running time: {time.time() - last_time}') - if IS_SPACE and time.time() - last_time > TIMEOUT: - print('Timeout break!') - break - if global_state["temporal_params"]["stop"] or global_state['generator_params']["stop"]: - break - - # do drage here! - renderer._render_drag_impl( - global_state['generator_params'], - p_to_opt, # point - t_to_opt, # target - drag_mask, # mask, - global_state['params']['motion_lambda'], # lambda_mask - reg=0, - feature_idx=5, # NOTE: do not support change for now - r1=global_state['params']['r1_in_pixels'], # r1 - r2=global_state['params']['r2_in_pixels'], # r2 - # random_seed = 0, - # noise_mode = 'const', - trunc_psi=global_state['params']['trunc_psi'], - # force_fp32 = False, - # layer_name = None, - # sel_channels = 3, - # base_channel = 0, - # img_scale_db = 0, - # img_normalize = False, - # untransform = False, - is_drag=True, - to_pil=True) - - if step_idx % global_state['draw_interval'] == 0: - print('Current Source:') - for key_point, p_i, t_i in zip(valid_points, p_to_opt, - t_to_opt): - global_state["points"][key_point]["start_temp"] = [ - p_i[1], - p_i[0], - ] - global_state["points"][key_point]["target"] = [ - t_i[1], - t_i[0], - ] - start_temp = global_state["points"][key_point][ - "start_temp"] - print(f' {start_temp}') - - image_result = global_state['generator_params']['image'] - image_draw = update_image_draw( - image_result, - global_state['points'], - global_state['mask'], - global_state['show_mask'], - global_state, - ) - global_state['images']['image_raw'] = image_result - - yield ( - global_state, - step_idx, - global_state['images']['image_show'], - # gr.File.update(visible=False), - gr.Button.update(interactive=False), - gr.Button.update(interactive=False), - gr.Button.update(interactive=False), - gr.Button.update(interactive=False), - gr.Button.update(interactive=False), - # latent space - gr.Radio.update(interactive=False), - gr.Button.update(interactive=False), - # enable stop button in loop - gr.Button.update(interactive=True), - - # update other comps - gr.Dropdown.update(interactive=False), - gr.Number.update(interactive=False), - gr.Number.update(interactive=False), - gr.Button.update(interactive=False), - gr.Button.update(interactive=False), - gr.Checkbox.update(interactive=False), - # gr.Number.update(interactive=False), - gr.Number.update(interactive=False), - ) - - # increate step - step_idx += 1 - - image_result = global_state['generator_params']['image'] - global_state['images']['image_raw'] = image_result - image_draw = update_image_draw(image_result, - global_state['points'], - global_state['mask'], - global_state['show_mask'], - global_state) - - # fp = NamedTemporaryFile(suffix=".png", delete=False) - # image_result.save(fp, "PNG") - - global_state['editing_state'] = 'add_points' - - yield ( - global_state, - 0, # reset step to 0 after stop. - global_state['images']['image_show'], - # gr.File.update(visible=True, value=fp.name), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - gr.Button.update(interactive=True), - # latent space - gr.Radio.update(interactive=True), - gr.Button.update(interactive=True), - # NOTE: disable stop button with loop finish - gr.Button.update(interactive=False), - - # update other comps - gr.Dropdown.update(interactive=True), - gr.Number.update(interactive=True), - gr.Number.update(interactive=True), - gr.Checkbox.update(interactive=True), - gr.Number.update(interactive=True), - ) - - form_start_btn.click( - on_click_start, - inputs=[global_state, form_image], - outputs=[ - global_state, - form_steps_number, - form_image, - # form_download_result_file, - # >>> buttons - form_reset_image, - enable_add_points, - enable_add_mask, - undo_points, - form_reset_mask_btn, - form_latent_space, - form_start_btn, - form_stop_btn, - # <<< buttonm - # >>> inputs comps - form_pretrained_dropdown, - form_seed_number, - form_lr_number, - show_mask, - form_lambda_number, - ], - ) - - def on_click_stop(global_state): - """Function to handle stop button is clicked. - 1. send a stop signal by set global_state["temporal_params"]["stop"] as True - 2. Disable Stop button - """ - global_state["temporal_params"]["stop"] = True - - return global_state, gr.Button.update(interactive=False) - - form_stop_btn.click(on_click_stop, - inputs=[global_state], - outputs=[global_state, form_stop_btn], - queue=False) - - form_draw_interval_number.change( - partial( - on_change_single_global_state, - "draw_interval", - map_transform=lambda x: int(x), - ), - inputs=[form_draw_interval_number, global_state], - outputs=[global_state], - queue=False, - ) - - def on_click_remove_point(global_state): - choice = global_state["curr_point"] - del global_state["points"][choice] - - choices = list(global_state["points"].keys()) - - if len(choices) > 0: - global_state["curr_point"] = choices[0] - - return ( - gr.Dropdown.update(choices=choices, value=choices[0]), - global_state, - ) - - # Mask - def on_click_reset_mask(global_state): - global_state['mask'] = np.ones( - ( - global_state["images"]["image_raw"].size[1], - global_state["images"]["image_raw"].size[0], - ), - dtype=np.uint8, - ) - image_draw = update_image_draw(global_state['images']['image_raw'], - global_state['points'], - global_state['mask'], - global_state['show_mask'], global_state) - return global_state, image_draw - - form_reset_mask_btn.click( - on_click_reset_mask, - inputs=[global_state], - outputs=[global_state, form_image], - ) - - # Image - def on_click_enable_draw(global_state, image): - """Function to start add mask mode. - 1. Preprocess mask info from last state - 2. Change editing state to add_mask - 3. Set curr image with points and mask - """ - global_state = preprocess_mask_info(global_state, image) - global_state['editing_state'] = 'add_mask' - image_raw = global_state['images']['image_raw'] - image_draw = update_image_draw(image_raw, global_state['points'], - global_state['mask'], True, - global_state) - return (global_state, - gr.Image.update(value=image_draw, interactive=True)) - - def on_click_remove_draw(global_state, image): - """Function to start remove mask mode. - 1. Preprocess mask info from last state - 2. Change editing state to remove_mask - 3. Set curr image with points and mask - """ - global_state = preprocess_mask_info(global_state, image) - global_state['edinting_state'] = 'remove_mask' - image_raw = global_state['images']['image_raw'] - image_draw = update_image_draw(image_raw, global_state['points'], - global_state['mask'], True, - global_state) - return (global_state, - gr.Image.update(value=image_draw, interactive=True)) - - enable_add_mask.click(on_click_enable_draw, - inputs=[global_state, form_image], - outputs=[ - global_state, - form_image, - ], - queue=False) - - def on_click_add_point(global_state, image: dict): - """Function switch from add mask mode to add points mode. - 1. Updaste mask buffer if need - 2. Change global_state['editing_state'] to 'add_points' - 3. Set current image with mask - """ - - global_state = preprocess_mask_info(global_state, image) - global_state['editing_state'] = 'add_points' - mask = global_state['mask'] - image_raw = global_state['images']['image_raw'] - image_draw = update_image_draw(image_raw, global_state['points'], mask, - global_state['show_mask'], global_state) - - return (global_state, - gr.Image.update(value=image_draw, interactive=False)) - - enable_add_points.click(on_click_add_point, - inputs=[global_state, form_image], - outputs=[global_state, form_image], - queue=False) - - def on_click_image(global_state, evt: gr.SelectData): - """This function only support click for point selection - """ - xy = evt.index - if global_state['editing_state'] != 'add_points': - print(f'In {global_state["editing_state"]} state. ' - 'Do not add points.') - - return global_state, global_state['images']['image_show'] - - points = global_state["points"] - - point_idx = get_latest_points_pair(points) - if point_idx is None: - points[0] = {'start': xy, 'target': None} - print(f'Click Image - Start - {xy}') - elif points[point_idx].get('target', None) is None: - points[point_idx]['target'] = xy - print(f'Click Image - Target - {xy}') - else: - points[point_idx + 1] = {'start': xy, 'target': None} - print(f'Click Image - Start - {xy}') - - image_raw = global_state['images']['image_raw'] - image_draw = update_image_draw( - image_raw, - global_state['points'], - global_state['mask'], - global_state['show_mask'], - global_state, - ) - - return global_state, image_draw - - form_image.select( - on_click_image, - inputs=[global_state], - outputs=[global_state, form_image], - queue=False, - ) - - def on_click_clear_points(global_state): - """Function to handle clear all control points - 1. clear global_state['points'] (clear_state) - 2. re-init network - 2. re-draw image - """ - clear_state(global_state, target='point') - - renderer: Renderer = global_state["renderer"] - renderer.feat_refs = None - - image_raw = global_state['images']['image_raw'] - image_draw = update_image_draw(image_raw, {}, global_state['mask'], - global_state['show_mask'], global_state) - return global_state, image_draw - - undo_points.click(on_click_clear_points, - inputs=[global_state], - outputs=[global_state, form_image], - queue=False) - - def on_click_show_mask(global_state, show_mask): - """Function to control whether show mask on image.""" - global_state['show_mask'] = show_mask - - image_raw = global_state['images']['image_raw'] - image_draw = update_image_draw( - image_raw, - global_state['points'], - global_state['mask'], - global_state['show_mask'], - global_state, - ) - return global_state, image_draw - - show_mask.change( - on_click_show_mask, - inputs=[global_state, show_mask], - outputs=[global_state, form_image], - queue=False, - ) - -gr.close_all() -app.queue(concurrency_count=1, max_size=200, api_open=False) -app.launch(show_api=False) diff --git a/spaces/h2oai/wave-tour/examples/wizard.py b/spaces/h2oai/wave-tour/examples/wizard.py deleted file mode 100644 index a79521127ad24c458932311bcc8a02afff47c388..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/wizard.py +++ /dev/null @@ -1,60 +0,0 @@ -# Wizard -# Create a multi-step #wizard using #form cards. -# --- -from h2o_wave import Q, ui, main, app, cypress, Cypress - - -@app('/demo') -async def serve(q: Q): - if not q.client.initialized: # First visit, create an empty form card for our wizard - q.page['wizard'] = ui.form_card(box='1 1 2 4', items=[]) - q.client.initialized = True - - wizard = q.page['wizard'] # Get a reference to the wizard form - if q.args.step1: - wizard.items = [ - ui.text_xl('Wizard - Step 1'), - ui.text('What is your name?', name='text'), - ui.textbox(name='nickname', label='My name is...', value='Gandalf'), - ui.buttons([ui.button(name='step2', label='Next', primary=True)]), - ] - elif q.args.step2: - q.client.nickname = q.args.nickname - wizard.items = [ - ui.text_xl('Wizard - Step 2'), - ui.text(f'Hi {q.args.nickname}! How do you feel right now?', name='text'), - ui.textbox(name='feeling', label='I feel...', value='magical'), - ui.buttons([ui.button(name='step3', label='Next', primary=True)]), - ] - elif q.args.step3: - wizard.items = [ - ui.text_xl('Wizard - Done'), - ui.text( - f'What a coincidence, {q.client.nickname}! I feel {q.args.feeling} too!', - name='text', - ), - ui.buttons([ui.button(name='step1', label='Try Again', primary=True)]), - ] - else: - wizard.items = [ - ui.text_xl('Wizard Example'), - ui.text("Let's have a conversation, shall we?"), - ui.buttons([ui.button(name='step1', label='Of course!', primary=True)]), - ] - - await q.page.save() - - -@cypress('Walk through the wizard') -def try_walk_through(cy: Cypress): - cy.visit('/demo') - cy.locate('step1').click() - cy.locate('text').should('contain.text', 'What is your name?') - cy.locate('nickname').clear().type('Fred') - cy.locate('step2').click() - cy.locate('text').should('contain.text', 'Hi Fred! How do you feel right now?') - cy.locate('feeling').clear().type('quirky') - cy.locate('step3').click() - cy.locate('text').should( - 'contain.text', 'What a coincidence, Fred! I feel quirky too!' - ) diff --git a/spaces/habeebb5/biogpt-demo/README.md b/spaces/habeebb5/biogpt-demo/README.md deleted file mode 100644 index 9312607d8d96b4af0a2c39f49228d67a47466fff..0000000000000000000000000000000000000000 --- a/spaces/habeebb5/biogpt-demo/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Langchain Convo -emoji: 🔥 -colorFrom: indigo -colorTo: yellow -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/hackathon-somos-nlp-2023/GIPBERT/README.md b/spaces/hackathon-somos-nlp-2023/GIPBERT/README.md deleted file mode 100644 index c61bc62002e3d9b6a1a3a9357432f64318128825..0000000000000000000000000000000000000000 --- a/spaces/hackathon-somos-nlp-2023/GIPBERT/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Demo IntervencionesRacismo -emoji: 🦀 -colorFrom: purple -colorTo: pink -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hamzapehlivan/StyleRes/options/inference_options.py b/spaces/hamzapehlivan/StyleRes/options/inference_options.py deleted file mode 100644 index c034f84a253b0e5d836bfee248ada5fcd2e52c43..0000000000000000000000000000000000000000 --- a/spaces/hamzapehlivan/StyleRes/options/inference_options.py +++ /dev/null @@ -1,25 +0,0 @@ -from argparse import ArgumentParser - -class InferenceOptions: - - def __init__(self): - self.parser = ArgumentParser() - self.initialize() - - def initialize(self): - # arguments for inference script - self.parser.add_argument('--outdir', type=str, default='results', help='Inference results save path') - self.parser.add_argument('--checkpoint_path', default='checkpoints/styleres_ffhq.pth', type=str, help='Path to StyleRes model') - self.parser.add_argument('--datadir', type=str, default='test_sample', help='Path to input images. ') - self.parser.add_argument('--resize_outputs', action='store_true', default=True, help='Whether to resize outputs to 256x256 or keep at 1024x1024') - - self.parser.add_argument('--test_batch_size', default=1, type=int, help='Batch size for inference') - self.parser.add_argument('--test_workers', default=0, type=int, help='Number of inference dataloader workers') - self.parser.add_argument('--aligner_path', default=None, type=str, help="Optional face alignment network.") - self.parser.add_argument('--n_images', type=int, default=None, help='Number of images to output. If None, run on all data') - self.parser.add_argument('--edit_configs', type=str, default='options/editing_options/template.py', help='Which edits to perform on the images. \ - Specified in template.py file. See this file for more information ') - - def parse(self): - opts = self.parser.parse_args() - return opts \ No newline at end of file diff --git a/spaces/hannahross5/facebook-fastspeech2-en-ljspeech-0731/README.md b/spaces/hannahross5/facebook-fastspeech2-en-ljspeech-0731/README.md deleted file mode 100644 index f6b73b1573972f7d5ed5a4ac3a22a91e46165713..0000000000000000000000000000000000000000 --- a/spaces/hannahross5/facebook-fastspeech2-en-ljspeech-0731/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Facebook Fastspeech2 En Ljspeech 0731 -emoji: ⚡ -colorFrom: pink -colorTo: pink -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/harisansarkhan/CatFaceLandmarks/README.md b/spaces/harisansarkhan/CatFaceLandmarks/README.md deleted file mode 100644 index 3656df3bd6d609227974af4e56e7add9b42b1c46..0000000000000000000000000000000000000000 --- a/spaces/harisansarkhan/CatFaceLandmarks/README.md +++ /dev/null @@ -1,6 +0,0 @@ ---- -title: CatFaceLandmarks -app_file: 1_image.py -sdk: gradio -sdk_version: 3.39.0 ---- diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/data/test_rotation_transform.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/data/test_rotation_transform.py deleted file mode 100644 index 45faf7e25eb08d70e92e5f6be326083ed0d23c76..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/data/test_rotation_transform.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import numpy as np -import unittest - -from detectron2.data.transforms.transform import RotationTransform - - -class TestRotationTransform(unittest.TestCase): - def assertEqualsArrays(self, a1, a2): - self.assertTrue(np.allclose(a1, a2)) - - def randomData(self, h=5, w=5): - image = np.random.rand(h, w) - coords = np.array([[i, j] for j in range(h + 1) for i in range(w + 1)], dtype=float) - return image, coords, h, w - - def test180(self): - image, coords, h, w = self.randomData(6, 6) - rot = RotationTransform(h, w, 180, expand=False, center=None) - self.assertEqualsArrays(rot.apply_image(image), image[::-1, ::-1]) - rotated_coords = [[w - c[0], h - c[1]] for c in coords] - self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) - - def test45_coords(self): - _, coords, h, w = self.randomData(4, 6) - rot = RotationTransform(h, w, 45, expand=False, center=None) - rotated_coords = [ - [(x + y - (h + w) / 2) / np.sqrt(2) + w / 2, h / 2 + (y + (w - h) / 2 - x) / np.sqrt(2)] - for (x, y) in coords - ] - self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) - - def test90(self): - image, coords, h, w = self.randomData() - rot = RotationTransform(h, w, 90, expand=False, center=None) - self.assertEqualsArrays(rot.apply_image(image), image.T[::-1]) - rotated_coords = [[c[1], w - c[0]] for c in coords] - self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) - - def test90_expand(self): # non-square image - image, coords, h, w = self.randomData(h=5, w=8) - rot = RotationTransform(h, w, 90, expand=True, center=None) - self.assertEqualsArrays(rot.apply_image(image), image.T[::-1]) - rotated_coords = [[c[1], w - c[0]] for c in coords] - self.assertEqualsArrays(rot.apply_coords(coords), rotated_coords) - - def test_center_expand(self): - # center has no effect if expand=True because it only affects shifting - image, coords, h, w = self.randomData(h=5, w=8) - angle = np.random.randint(360) - rot1 = RotationTransform(h, w, angle, expand=True, center=None) - rot2 = RotationTransform(h, w, angle, expand=True, center=(0, 0)) - rot3 = RotationTransform(h, w, angle, expand=True, center=(h, w)) - rot4 = RotationTransform(h, w, angle, expand=True, center=(2, 5)) - for r1 in [rot1, rot2, rot3, rot4]: - for r2 in [rot1, rot2, rot3, rot4]: - self.assertEqualsArrays(r1.apply_image(image), r2.apply_image(image)) - self.assertEqualsArrays(r1.apply_coords(coords), r2.apply_coords(coords)) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/hdhzk/bingo/src/components/markdown.tsx b/spaces/hdhzk/bingo/src/components/markdown.tsx deleted file mode 100644 index d4491467a1f14d1d72e535caac9c40636054e5df..0000000000000000000000000000000000000000 --- a/spaces/hdhzk/bingo/src/components/markdown.tsx +++ /dev/null @@ -1,9 +0,0 @@ -import { FC, memo } from 'react' -import ReactMarkdown, { Options } from 'react-markdown' - -export const MemoizedReactMarkdown: FC<Options> = memo( - ReactMarkdown, - (prevProps, nextProps) => - prevProps.children === nextProps.children && - prevProps.className === nextProps.className -) diff --git a/spaces/hebert2099/MusicGen/README.md b/spaces/hebert2099/MusicGen/README.md deleted file mode 100644 index 5ed3b1f58120772f839d8a172a943bfd63818fd4..0000000000000000000000000000000000000000 --- a/spaces/hebert2099/MusicGen/README.md +++ /dev/null @@ -1,125 +0,0 @@ ---- -title: MusicGen -python_version: '3.9' -tags: -- music generation -- language models -- LLMs -app_file: app.py -emoji: 🎵 -colorFrom: white -colorTo: blue -sdk: gradio -sdk_version: 3.34.0 -pinned: true -suggested_hardware: a10g-large -license: cc-by-nc-4.0 -duplicated_from: musicgen/MusicGen ---- -# 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 <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't not 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! - -<a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> - <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> -</a> -<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> - <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/> -</a> -<br> - -## 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 -You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally, or use the provided [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing). Finally, a demo is also available on the [`facebook/MusiGen` HugginFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support). - -## 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") -``` - - -## 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. - - -## 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/hekbobo/bingo/src/components/chat-panel.tsx b/spaces/hekbobo/bingo/src/components/chat-panel.tsx deleted file mode 100644 index 56b2112bd75ba08134383871177851fa2e3f43a4..0000000000000000000000000000000000000000 --- a/spaces/hekbobo/bingo/src/components/chat-panel.tsx +++ /dev/null @@ -1,153 +0,0 @@ -'use client' - -import * as React from 'react' -import Image from 'next/image' -import Textarea from 'react-textarea-autosize' -import { useAtomValue } from 'jotai' -import { useEnterSubmit } from '@/lib/hooks/use-enter-submit' -import { cn } from '@/lib/utils' - -import BrushIcon from '@/assets/images/brush.svg' -import ChatIcon from '@/assets/images/chat.svg' -import VisualSearchIcon from '@/assets/images/visual-search.svg' -import SendIcon from '@/assets/images/send.svg' -import PinIcon from '@/assets/images/pin.svg' -import PinFillIcon from '@/assets/images/pin-fill.svg' - -import { useBing } from '@/lib/hooks/use-bing' -import { voiceListenAtom } from '@/state' -import Voice from './voice' -import { ChatImage } from './chat-image' -import { ChatAttachments } from './chat-attachments' - -export interface ChatPanelProps - extends Pick< - ReturnType<typeof useBing>, - | 'generating' - | 'input' - | 'setInput' - | 'sendMessage' - | 'resetConversation' - | 'isSpeaking' - | 'attachmentList' - | 'uploadImage' - | 'setAttachmentList' - > { - id?: string - className?: string -} - -export function ChatPanel({ - isSpeaking, - generating, - input, - setInput, - className, - sendMessage, - resetConversation, - attachmentList, - uploadImage, - setAttachmentList -}: ChatPanelProps) { - const inputRef = React.useRef<HTMLTextAreaElement>(null) - const {formRef, onKeyDown} = useEnterSubmit() - const [focused, setFocused] = React.useState(false) - const [active, setActive] = React.useState(false) - const [pin, setPin] = React.useState(false) - const [tid, setTid] = React.useState<any>() - const voiceListening = useAtomValue(voiceListenAtom) - - const setBlur = React.useCallback(() => { - clearTimeout(tid) - setActive(false) - const _tid = setTimeout(() => setFocused(false), 2000); - setTid(_tid) - }, [tid]) - - const setFocus = React.useCallback(() => { - setFocused(true) - setActive(true) - clearTimeout(tid) - inputRef.current?.focus() - }, [tid]) - - React.useEffect(() => { - if (input) { - setFocus() - } - }, [input, setFocus]) - - return ( - <form - className={cn('chat-panel', className)} - onSubmit={async e => { - e.preventDefault() - if (generating) { - return; - } - if (!input?.trim()) { - return - } - setInput('') - setPin(false) - await sendMessage(input) - }} - ref={formRef} - > - <div className="action-bar pb-4"> - <div className={cn('action-root', { focus: active || pin })} speech-state="hidden" visual-search="" drop-target=""> - <div className="fade bottom"> - <div className="background"></div> - </div> - <div className={cn('outside-left-container', { collapsed: focused })}> - <div className="button-compose-wrapper"> - <button className="body-2 button-compose" type="button" aria-label="新主题" onClick={resetConversation}> - <div className="button-compose-content"> - <Image className="pl-2" alt="brush" src={BrushIcon} width={40} /> - <div className="button-compose-text">新主题</div> - </div> - </button> - </div> - </div> - <div - className={cn('main-container', { active: active || pin })} - style={{ minHeight: pin ? '360px' : undefined }} - onClick={setFocus} - onBlur={setBlur} - > - <div className="main-bar"> - <Image alt="chat" src={ChatIcon} width={20} color="blue" /> - <Textarea - ref={inputRef} - tabIndex={0} - onKeyDown={onKeyDown} - rows={1} - value={input} - onChange={e => setInput(e.target.value.slice(0, 4000))} - placeholder={voiceListening ? '持续对话中...对话完成说“发送”即可' : 'Shift + Enter 换行'} - spellCheck={false} - className="message-input min-h-[24px] -mx-1 w-full text-base resize-none bg-transparent focus-within:outline-none" - /> - <ChatImage uploadImage={uploadImage}> - <Image alt="visual-search" src={VisualSearchIcon} width={24} /> - </ChatImage> - <Voice setInput={setInput} sendMessage={sendMessage} isSpeaking={isSpeaking} input={input} /> - <button type="submit"> - <Image alt="send" src={SendIcon} width={20} style={{ marginTop: '2px' }} /> - </button> - </div> - <ChatAttachments attachmentList={attachmentList} setAttachmentList={setAttachmentList} uploadImage={uploadImage} /> - <div className="body-1 bottom-bar"> - <div className="letter-counter"><span>{input.length}</span>/4000</div> - <button onClick={() => { - setPin(!pin) - }} className="pr-2"> - <Image alt="pin" src={pin ? PinFillIcon : PinIcon} width={20} /> - </button> - </div> - </div> - </div> - </div> - </form> - ) -} diff --git a/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/constants.py b/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/constants.py deleted file mode 100644 index 551e23b8131c17f2d6a0c4d9d41a9972014db67a..0000000000000000000000000000000000000000 --- a/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/constants.py +++ /dev/null @@ -1,54 +0,0 @@ -from pathlib import Path -from PIL import Image - -# from dotenv import load_dotenv, find_dotenv # pip install python-dotenv==1.0.0 - -from __version__ import __VERSION__ as APP_VERSION - -_SCRIPT_PATH = Path(__file__).absolute() -PARENT_APP_DIR = _SCRIPT_PATH.parent -TEMP_DIR = PARENT_APP_DIR / 'tempDir' -ROOT_DIR = PARENT_APP_DIR.parent -STATIC_DIR = ROOT_DIR / 'static' - -# _env_file_path = find_dotenv(str(CODE_DIR / '.env')) # Check if this path is correct -# if _env_file_path: -# load_dotenv(_env_file_path) - -ST_CONFIG = { - "page_title": "Chat Q&A", - # "page_icon": Image.open(STATIC_DIR / "mini_nttdata.jpg"), -} - -OPERATING_MODE = "debug" # debug, preproduction, production - -REUSE_ANSWERS = False - -LOAD_INDEX_LOCALLY = False -SAVE_INDEX_LOCALLY = False - -# TODO: pull up to date prices adaptively -# x$ per 1000 tokens -PRICES = { - 'text-embedding-ada-002': 0.0004, - 'text-davinci-003': 0.02, - 'gpt-3': 0.002, - 'gpt-4': 0.06, # 8K context -} - -SOURCES_IDS = { - # "Without source. Only chat": 4, - "local files": 1, - "urls": 3 -} - -TYPE_IDS = { - "OpenAI": 2, - "MSF Azure OpenAI Service": 1, -} - - -INDEX_IDS = { - "FAISS": 1, - "Pinecone": 2, -} diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/surface_dice.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/surface_dice.py deleted file mode 100644 index 8ce5ffdee35071fe65bd81ca87f60b97401c8801..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/evaluation/surface_dice.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import numpy as np -from medpy.metric.binary import __surface_distances - - -def normalized_surface_dice(a: np.ndarray, b: np.ndarray, threshold: float, spacing: tuple = None, connectivity=1): - """ - This implementation differs from the official surface dice implementation! These two are not comparable!!!!! - - The normalized surface dice is symmetric, so it should not matter whether a or b is the reference image - - This implementation natively supports 2D and 3D images. Whether other dimensions are supported depends on the - __surface_distances implementation in medpy - - :param a: image 1, must have the same shape as b - :param b: image 2, must have the same shape as a - :param threshold: distances below this threshold will be counted as true positives. Threshold is in mm, not voxels! - (if spacing = (1, 1(, 1)) then one voxel=1mm so the threshold is effectively in voxels) - must be a tuple of len dimension(a) - :param spacing: how many mm is one voxel in reality? Can be left at None, we then assume an isotropic spacing of 1mm - :param connectivity: see scipy.ndimage.generate_binary_structure for more information. I suggest you leave that - one alone - :return: - """ - assert all([i == j for i, j in zip(a.shape, b.shape)]), "a and b must have the same shape. a.shape= %s, " \ - "b.shape= %s" % (str(a.shape), str(b.shape)) - if spacing is None: - spacing = tuple([1 for _ in range(len(a.shape))]) - a_to_b = __surface_distances(a, b, spacing, connectivity) - b_to_a = __surface_distances(b, a, spacing, connectivity) - - numel_a = len(a_to_b) - numel_b = len(b_to_a) - - tp_a = np.sum(a_to_b <= threshold) / numel_a - tp_b = np.sum(b_to_a <= threshold) / numel_b - - fp = np.sum(a_to_b > threshold) / numel_a - fn = np.sum(b_to_a > threshold) / numel_b - - dc = (tp_a + tp_b) / (tp_a + tp_b + fp + fn + 1e-8) # 1e-8 just so that we don't get div by 0 - return dc - diff --git a/spaces/huangbatian/newbing/README.md b/spaces/huangbatian/newbing/README.md deleted file mode 100644 index 3538ced98576d9a7920f4d530fc4a28b90df9c97..0000000000000000000000000000000000000000 --- a/spaces/huangbatian/newbing/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Newbing -emoji: 👁 -colorFrom: gray -colorTo: green -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/inamXcontru/PoeticTTS/Activator For Windows And Office KMS Pico V91 Utorrent Benefits and Risks.md b/spaces/inamXcontru/PoeticTTS/Activator For Windows And Office KMS Pico V91 Utorrent Benefits and Risks.md deleted file mode 100644 index bfc1d7741d618b263e39ffc32ab4e90f4950d110..0000000000000000000000000000000000000000 --- a/spaces/inamXcontru/PoeticTTS/Activator For Windows And Office KMS Pico V91 Utorrent Benefits and Risks.md +++ /dev/null @@ -1,5 +0,0 @@ -<br /> -<p>katrregi 19191a764c<br /> -for-windows-and-office-kms-pico-v91-crack<br />[ -for-windows-and-office-kms-pico-v91-crack ]<br />[ -for-windows-and-office-kms-pico-v91-crack ]<br />[ -for-windows-and-office-kms-pico-v91-crack ]<br />link= -for-windows-and-office-kms-pico-v91-crack<br />link= -for-windows-and-office-kms-pico-v91-crack<br />link= -for-windows-and-office-kms-pico-v91-crack</p> -<h2>Activator For Windows And Office KMS Pico V91 Utorrent</h2><br /><p><b><b>Download Zip</b> –––––>>> <a href="https://gohhs.com/2uz5Cf">https://gohhs.com/2uz5Cf</a></b></p><br /><br /> aaccfb2cb3<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/inamXcontru/PoeticTTS/Cadillacs and dinosaurs game full version for mobile nokia c1-01 Why this game is still fun and relevant in 2023.md b/spaces/inamXcontru/PoeticTTS/Cadillacs and dinosaurs game full version for mobile nokia c1-01 Why this game is still fun and relevant in 2023.md deleted file mode 100644 index cd2b20bd73cbe7e247f0ba8911fee2e07d5fb878..0000000000000000000000000000000000000000 --- a/spaces/inamXcontru/PoeticTTS/Cadillacs and dinosaurs game full version for mobile nokia c1-01 Why this game is still fun and relevant in 2023.md +++ /dev/null @@ -1,6 +0,0 @@ -<br /> -<p>We have more and more interaction occurs on mobile devices. 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-import commons -import utils -from data_utils import ( - TextAudioSpeakerLoader, - TextAudioSpeakerCollate, - DistributedBucketSampler -) -from models import ( - SynthesizerTrn, - MultiPeriodDiscriminator, -) -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 -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'] = '25565' - - hps = utils.get_hparams() - 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='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=8, 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=8, shuffle=False, - batch_size=hps.train.batch_size, pin_memory=True, - drop_last=False, collate_fn=collate_fn) - - 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, - **hps.model).cuda(rank) - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - 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) - net_g = DDP(net_g, device_ids=[rank]) - net_d = DDP(net_d, device_ids=[rank]) - - try: - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g) - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d) - global_step = (epoch_str - 1) * len(train_loader) - except: - epoch_str = 1 - global_step = 0 - - 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) - - 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], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) - else: - train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None) - scheduler_g.step() - scheduler_d.step() - - -def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): - net_g, net_d = nets - optim_g, optim_d = optims - scheduler_g, scheduler_d = 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() - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, pitch, speakers) in enumerate(train_loader): - 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) - pitch = pitch.cuda(rank, non_blocking=True) - - with autocast(enabled=hps.train.fp16_run): - y_hat, l_length, l_pitch, attn, ids_slice, x_mask, z_mask,\ - (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, pitch, speakers) - - 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 - 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) - with autocast(enabled=False): - loss_dur = torch.sum(l_length.float()) - loss_pitch = torch.sum(l_pitch.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 + loss_pitch - 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, loss_pitch] - 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, "loss/g/pitch": loss_pitch}) - - 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))) - global_step += 1 - - if rank == 0: - logger.info('====> Epoch: {}'.format(epoch)) - - -def evaluate(hps, generator, eval_loader, writer_eval): - generator.eval() - with torch.no_grad(): - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, pitch, speakers) in enumerate(eval_loader): - x, x_lengths = x.cuda(0), x_lengths.cuda(0) - spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0) - y, y_lengths = y.cuda(0), y_lengths.cuda(0) - speakers = speakers.cuda(0) - pitch = pitch.cuda(0) - # remove else - x = x[:1] - x_lengths = x_lengths[:1] - spec = spec[:1] - spec_lengths = spec_lengths[:1] - y = y[:1] - y_lengths = y_lengths[:1] - speakers = speakers[:1] - break - y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, pitch, speakers, max_len=1000) - 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 = { - "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()) - 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In an external browser, speak and you will get the text output -ST_ASR_demo = gr.Interface( - fn=transcribe, - inputs=[ - gr.inputs.Audio(source="microphone", type="filepath"), - 'state' - ], - outputs=[ - "textbox", - "state" - ], - title = 'Real-Time Speech Transcription', description = 'Speak something, you will get the text as an output', - live=True)#.launch(inline = False) - \ No newline at end of file diff --git a/spaces/ismot/1702t1/postprocessing/post_process.py b/spaces/ismot/1702t1/postprocessing/post_process.py deleted file mode 100644 index c58d894d58d6ed1e90fc1c35d85b55acb24a3125..0000000000000000000000000000000000000000 --- a/spaces/ismot/1702t1/postprocessing/post_process.py +++ /dev/null @@ -1,34 +0,0 @@ -""" -@Date: 2021/10/08 -@description: -""" -import numpy as np -import cv2 - -from postprocessing.dula.layout import fit_layout -from postprocessing.dula.layout_old import fit_layout_old -from utils.conversion import depth2xyz, xyz2depth - - -def post_process(b_depth, type_name='manhattan', need_cube=False): - plan_y = 1 - b_xyz = depth2xyz(b_depth, plan_y) - - b_processed_xyz = [] - for xyz in b_xyz: - if type_name == 'manhattan': - processed_xz = fit_layout(floor_xz=xyz[..., ::2], need_cube=need_cube, show=False) - elif type_name == 'manhattan_old': - processed_xz = fit_layout_old(floor_xz=xyz[..., ::2], need_cube=need_cube, show=False) - elif type_name == 'atalanta': - processed_xz = cv2.approxPolyDP(xyz[..., ::2].astype(np.float32), 0.1, False)[:, 0, :] - else: - raise NotImplementedError("Unknown post-processing type") - - if need_cube: - assert len(processed_xz) == 4 - - processed_xyz = np.insert(processed_xz, 1, plan_y, axis=1) - b_processed_xyz.append(processed_xyz) - - return np.array(b_processed_xyz) \ No newline at end of file diff --git a/spaces/j0hngou/vision-diffmask/code/train_base.py b/spaces/j0hngou/vision-diffmask/code/train_base.py deleted file mode 100644 index 0fdaf06121018763b0d6d72f558b743d086a920b..0000000000000000000000000000000000000000 --- a/spaces/j0hngou/vision-diffmask/code/train_base.py +++ /dev/null @@ -1,123 +0,0 @@ -import argparse -import pytorch_lightning as pl - -from datamodules import CIFAR10QADataModule, ImageDataModule -from datamodules.utils import datamodule_factory -from models import ImageClassificationNet -from models.utils import model_factory -from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint -from pytorch_lightning.loggers import WandbLogger - - -def main(args: argparse.Namespace): - # Seed - pl.seed_everything(args.seed) - - # Create base model - base = model_factory(args) - - # Load datamodule - dm = datamodule_factory(args) - dm.prepare_data() - dm.setup("fit") - - if args.checkpoint: - # Load the model from the specified checkpoint - model = ImageClassificationNet.load_from_checkpoint(args.checkpoint, model=base) - else: - # Create a new instance of the classification model - model = ImageClassificationNet( - model=base, - num_train_steps=args.num_epochs * len(dm.train_dataloader()), - optimizer=args.optimizer, - weight_decay=args.weight_decay, - lr=args.lr, - ) - - # Create wandb logger - wandb_logger = WandbLogger( - name=f"{args.dataset}_training_{args.base_model} ({args.from_pretrained})", - project="Patch-DiffMask", - ) - - # Create checkpoint callback - ckpt_cb = ModelCheckpoint(dirpath=f"checkpoints/{wandb_logger.version}") - # Create early stopping callback - es_cb = EarlyStopping(monitor="val_acc", mode="max", patience=5) - - # Create trainer - trainer = pl.Trainer( - accelerator="auto", - callbacks=[ckpt_cb, es_cb], - logger=wandb_logger, - max_epochs=args.num_epochs, - enable_progress_bar=args.enable_progress_bar, - ) - - trainer_args = {} - if args.checkpoint: - # Resume trainer from checkpoint - trainer_args["ckpt_path"] = args.checkpoint - - # Train the model - trainer.fit(model, dm, **trainer_args) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint", - type=str, - help="Checkpoint to resume the training from.", - ) - - # Trainer - parser.add_argument( - "--enable_progress_bar", - action="store_true", - help="Whether to show progress bar during training. NOT recommended when logging to files.", - ) - parser.add_argument( - "--num_epochs", - type=int, - default=5, - help="Number of epochs to train.", - ) - parser.add_argument( - "--seed", - type=int, - default=123, - help="Random seed for reproducibility.", - ) - - # Base (classification) model - ImageClassificationNet.add_model_specific_args(parser) - parser.add_argument( - "--base_model", - type=str, - default="ViT", - choices=["ViT", "ConvNeXt"], - help="Base model architecture to train.", - ) - parser.add_argument( - "--from_pretrained", - type=str, - # default="tanlq/vit-base-patch16-224-in21k-finetuned-cifar10", - help="The name of the pretrained HF model to fine-tune from.", - ) - - # Datamodule - ImageDataModule.add_model_specific_args(parser) - CIFAR10QADataModule.add_model_specific_args(parser) - parser.add_argument( - "--dataset", - type=str, - default="toy", - choices=["MNIST", "CIFAR10", "CIFAR10_QA", "toy"], - help="The dataset to use.", - ) - - args = parser.parse_args() - - main(args) diff --git a/spaces/jarvisbot/ChatImprovement/crazy_functions/test_project/python/dqn/dqn.py b/spaces/jarvisbot/ChatImprovement/crazy_functions/test_project/python/dqn/dqn.py deleted file mode 100644 index 6cea64d39baa7ff4c1e549869aaa4b0ae17779a9..0000000000000000000000000000000000000000 --- a/spaces/jarvisbot/ChatImprovement/crazy_functions/test_project/python/dqn/dqn.py +++ /dev/null @@ -1,245 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Type, Union - -import gym -import numpy as np -import torch as th -from torch.nn import functional as F - -from stable_baselines3.common import logger -from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm -from stable_baselines3.common.preprocessing import maybe_transpose -from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule -from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update -from stable_baselines3.dqn.policies import DQNPolicy - - -class DQN(OffPolicyAlgorithm): - """ - Deep Q-Network (DQN) - - Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236 - Default hyperparameters are taken from the nature paper, - except for the optimizer and learning rate that were taken from Stable Baselines defaults. - - :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...) - :param env: The environment to learn from (if registered in Gym, can be str) - :param learning_rate: The learning rate, it can be a function - of the current progress remaining (from 1 to 0) - :param buffer_size: size of the replay buffer - :param learning_starts: how many steps of the model to collect transitions for before learning starts - :param batch_size: Minibatch size for each gradient update - :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update - :param gamma: the discount factor - :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit - like ``(5, "step")`` or ``(2, "episode")``. - :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``) - Set to ``-1`` means to do as many gradient steps as steps done in the environment - during the rollout. - :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer - at a cost of more complexity. - See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195 - :param target_update_interval: update the target network every ``target_update_interval`` - environment steps. - :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced - :param exploration_initial_eps: initial value of random action probability - :param exploration_final_eps: final value of random action probability - :param max_grad_norm: The maximum value for the gradient clipping - :param tensorboard_log: the log location for tensorboard (if None, no logging) - :param create_eval_env: Whether to create a second environment that will be - used for evaluating the agent periodically. (Only available when passing string for the environment) - :param policy_kwargs: additional arguments to be passed to the policy on creation - :param verbose: the verbosity level: 0 no output, 1 info, 2 debug - :param seed: Seed for the pseudo random generators - :param device: Device (cpu, cuda, ...) on which the code should be run. - Setting it to auto, the code will be run on the GPU if possible. - :param _init_setup_model: Whether or not to build the network at the creation of the instance - """ - - def __init__( - self, - policy: Union[str, Type[DQNPolicy]], - env: Union[GymEnv, str], - learning_rate: Union[float, Schedule] = 1e-4, - buffer_size: int = 1000000, - learning_starts: int = 50000, - batch_size: Optional[int] = 32, - tau: float = 1.0, - gamma: float = 0.99, - train_freq: Union[int, Tuple[int, str]] = 4, - gradient_steps: int = 1, - optimize_memory_usage: bool = False, - target_update_interval: int = 10000, - exploration_fraction: float = 0.1, - exploration_initial_eps: float = 1.0, - exploration_final_eps: float = 0.05, - max_grad_norm: float = 10, - tensorboard_log: Optional[str] = None, - create_eval_env: bool = False, - policy_kwargs: Optional[Dict[str, Any]] = None, - verbose: int = 0, - seed: Optional[int] = None, - device: Union[th.device, str] = "auto", - _init_setup_model: bool = True, - ): - - super(DQN, self).__init__( - policy, - env, - DQNPolicy, - learning_rate, - buffer_size, - learning_starts, - batch_size, - tau, - gamma, - train_freq, - gradient_steps, - action_noise=None, # No action noise - policy_kwargs=policy_kwargs, - tensorboard_log=tensorboard_log, - verbose=verbose, - device=device, - create_eval_env=create_eval_env, - seed=seed, - sde_support=False, - optimize_memory_usage=optimize_memory_usage, - supported_action_spaces=(gym.spaces.Discrete,), - ) - - self.exploration_initial_eps = exploration_initial_eps - self.exploration_final_eps = exploration_final_eps - self.exploration_fraction = exploration_fraction - self.target_update_interval = target_update_interval - self.max_grad_norm = max_grad_norm - # "epsilon" for the epsilon-greedy exploration - self.exploration_rate = 0.0 - # Linear schedule will be defined in `_setup_model()` - self.exploration_schedule = None - self.q_net, self.q_net_target = None, None - - if _init_setup_model: - self._setup_model() - - def _setup_model(self) -> None: - super(DQN, self)._setup_model() - self._create_aliases() - self.exploration_schedule = get_linear_fn( - self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction - ) - - def _create_aliases(self) -> None: - self.q_net = self.policy.q_net - self.q_net_target = self.policy.q_net_target - - def _on_step(self) -> None: - """ - Update the exploration rate and target network if needed. - This method is called in ``collect_rollouts()`` after each step in the environment. - """ - if self.num_timesteps % self.target_update_interval == 0: - polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau) - - self.exploration_rate = self.exploration_schedule(self._current_progress_remaining) - logger.record("rollout/exploration rate", self.exploration_rate) - - def train(self, gradient_steps: int, batch_size: int = 100) -> None: - # Update learning rate according to schedule - self._update_learning_rate(self.policy.optimizer) - - losses = [] - for _ in range(gradient_steps): - # Sample replay buffer - replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) - - with th.no_grad(): - # Compute the next Q-values using the target network - next_q_values = self.q_net_target(replay_data.next_observations) - # Follow greedy policy: use the one with the highest value - next_q_values, _ = next_q_values.max(dim=1) - # Avoid potential broadcast issue - next_q_values = next_q_values.reshape(-1, 1) - # 1-step TD target - target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values - - # Get current Q-values estimates - current_q_values = self.q_net(replay_data.observations) - - # Retrieve the q-values for the actions from the replay buffer - current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long()) - - # Compute Huber loss (less sensitive to outliers) - loss = F.smooth_l1_loss(current_q_values, target_q_values) - losses.append(loss.item()) - - # Optimize the policy - self.policy.optimizer.zero_grad() - loss.backward() - # Clip gradient norm - th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm) - self.policy.optimizer.step() - - # Increase update counter - self._n_updates += gradient_steps - - logger.record("train/n_updates", self._n_updates, exclude="tensorboard") - logger.record("train/loss", np.mean(losses)) - - def predict( - self, - observation: np.ndarray, - state: Optional[np.ndarray] = None, - mask: Optional[np.ndarray] = None, - deterministic: bool = False, - ) -> Tuple[np.ndarray, Optional[np.ndarray]]: - """ - Overrides the base_class predict function to include epsilon-greedy exploration. - - :param observation: the input observation - :param state: The last states (can be None, used in recurrent policies) - :param mask: The last masks (can be None, used in recurrent policies) - :param deterministic: Whether or not to return deterministic actions. - :return: the model's action and the next state - (used in recurrent policies) - """ - if not deterministic and np.random.rand() < self.exploration_rate: - if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space): - n_batch = observation.shape[0] - action = np.array([self.action_space.sample() for _ in range(n_batch)]) - else: - action = np.array(self.action_space.sample()) - else: - action, state = self.policy.predict(observation, state, mask, deterministic) - return action, state - - def learn( - self, - total_timesteps: int, - callback: MaybeCallback = None, - log_interval: int = 4, - eval_env: Optional[GymEnv] = None, - eval_freq: int = -1, - n_eval_episodes: int = 5, - tb_log_name: str = "DQN", - eval_log_path: Optional[str] = None, - reset_num_timesteps: bool = True, - ) -> OffPolicyAlgorithm: - - return super(DQN, self).learn( - total_timesteps=total_timesteps, - callback=callback, - log_interval=log_interval, - eval_env=eval_env, - eval_freq=eval_freq, - n_eval_episodes=n_eval_episodes, - tb_log_name=tb_log_name, - eval_log_path=eval_log_path, - reset_num_timesteps=reset_num_timesteps, - ) - - def _excluded_save_params(self) -> List[str]: - return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"] - - def _get_torch_save_params(self) -> Tuple[List[str], List[str]]: - state_dicts = ["policy", "policy.optimizer"] - - return state_dicts, [] diff --git a/spaces/jbilcke-hf/MusicGen/audiocraft/models/lm.py b/spaces/jbilcke-hf/MusicGen/audiocraft/models/lm.py deleted file mode 100644 index c8aad8f06797eef3293605056e1de14d07c56c2a..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/MusicGen/audiocraft/models/lm.py +++ /dev/null @@ -1,527 +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 dataclasses import dataclass -from functools import partial -import logging -import math -import typing as tp - -import torch -from torch import nn - -from ..utils import utils -from ..modules.streaming import StreamingModule, State -from ..modules.transformer import StreamingTransformer, create_norm_fn -from ..modules.conditioners import ( - ConditionFuser, - ClassifierFreeGuidanceDropout, - AttributeDropout, - ConditioningProvider, - ConditioningAttributes, - ConditionType, -) -from ..modules.codebooks_patterns import CodebooksPatternProvider -from ..modules.activations import get_activation_fn - - -logger = logging.getLogger(__name__) -ConditionTensors = tp.Dict[str, ConditionType] -CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]] - - -def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None): - """LM layer initialization. - Inspired from xlformers: https://github.com/fairinternal/xlformers - - Args: - method (str): Method name for init function. Valid options are: - 'gaussian', 'uniform'. - input_dim (int): Input dimension of the initialized module. - init_depth (Optional[int]): Optional init depth value used to rescale - the standard deviation if defined. - """ - # Compute std - std = 1 / math.sqrt(input_dim) - # Rescale with depth - if init_depth is not None: - std = std / math.sqrt(2 * init_depth) - - if method == 'gaussian': - return partial( - torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std - ) - elif method == 'uniform': - bound = math.sqrt(3) * std # ensure the standard deviation is `std` - return partial(torch.nn.init.uniform_, a=-bound, b=bound) - else: - raise ValueError("Unsupported layer initialization method") - - -def init_layer(m: nn.Module, - method: str, - init_depth: tp.Optional[int] = None, - zero_bias_init: bool = False): - """Wrapper around ``get_init_fn`` for proper initialization of LM modules. - - Args: - m (nn.Module): Module to initialize. - method (str): Method name for the init function. - init_depth (Optional[int]): Optional init depth value used to rescale - the standard deviation if defined. - zero_bias_init (bool): Whether to initialize the bias to 0 or not. - """ - if isinstance(m, nn.Linear): - init_fn = get_init_fn(method, m.in_features, init_depth=init_depth) - if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16: - weight = m.weight.float() - init_fn(weight) - m.weight.data[:] = weight.half() - else: - init_fn(m.weight) - if zero_bias_init and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.Embedding): - init_fn = get_init_fn(method, m.embedding_dim, init_depth=None) - if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16: - weight = m.weight.float() - init_fn(weight) - m.weight.data[:] = weight.half() - else: - init_fn(m.weight) - - -class ScaledEmbedding(nn.Embedding): - """Boost learning rate for embeddings (with `scale`). - """ - def __init__(self, *args, lr=None, **kwargs): - super().__init__(*args, **kwargs) - self.lr = lr - - def make_optim_group(self): - group = {"params": list(self.parameters())} - if self.lr is not None: - group["lr"] = self.lr - return group - - -@dataclass -class LMOutput: - # The logits are already re-aligned with the input codes - # hence no extra shift is required, e.g. when computing CE - logits: torch.Tensor # [B, K, T, card] - mask: torch.Tensor # [B, K, T] - - -class LMModel(StreamingModule): - """Transformer-based language model on multiple streams of codes. - - Args: - pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving. - condition_provider (MusicConditioningProvider): Conditioning provider from metadata. - fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input. - n_q (int): Number of parallel streams to model. - card (int): Cardinality, vocabulary size. - dim (int): Dimension of the transformer encoder. - num_heads (int): Number of heads for the transformer encoder. - hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder. - norm (str): Normalization method. - norm_first (bool): Use pre-norm instead of post-norm. - emb_lr (Optional[float]): Embedding-specific learning rate. - bias_proj (bool): Use bias for output projections. - weight_init (Optional[str]): Method for weight initialization. - depthwise_init (Optional[str]): Method for depthwise weight initialization. - zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros. - cfg_dropout (float): Classifier-free guidance dropout. - cfg_coef (float): Classifier-free guidance coefficient. - attribute_dropout (dict): Attribute dropout probabilities. - two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps. - **kwargs: Additional parameters for the transformer encoder. - """ - def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider, - fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8, - hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False, - emb_lr: tp.Optional[float] = None, bias_proj: bool = True, - weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None, - zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0, - attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False, - **kwargs): - super().__init__() - self.cfg_coef = cfg_coef - self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout) - self.att_dropout = AttributeDropout(p=attribute_dropout) - self.condition_provider = condition_provider - self.fuser = fuser - self.card = card - embed_dim = self.card + 1 - self.n_q = n_q - self.dim = dim - self.pattern_provider = pattern_provider - self.two_step_cfg = two_step_cfg - self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)]) - if 'activation' in kwargs: - kwargs['activation'] = get_activation_fn(kwargs['activation']) - self.transformer = StreamingTransformer( - d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim), - norm=norm, norm_first=norm_first, **kwargs) - self.out_norm: tp.Optional[nn.Module] = None - if norm_first: - self.out_norm = create_norm_fn(norm, dim) - self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)]) - self._init_weights(weight_init, depthwise_init, zero_bias_init) - self._fsdp: tp.Optional[nn.Module] - self.__dict__['_fsdp'] = None - - def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool): - """Initialization of the transformer module weights. - - Args: - weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options. - depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid: - 'current' where the depth corresponds to the current layer index or 'global' where the total number - of layer is used as depth. If not set, no depthwise initialization strategy is used. - zero_bias_init (bool): Whether to initalize bias to zero or not. - """ - assert depthwise_init is None or depthwise_init in ['current', 'global'] - assert depthwise_init is None or weight_init is not None, \ - "If 'depthwise_init' is defined, a 'weight_init' method should be provided." - assert not zero_bias_init or weight_init is not None, \ - "If 'zero_bias_init', a 'weight_init' method should be provided" - - if weight_init is None: - return - - for emb_layer in self.emb: - init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init) - - for layer_idx, tr_layer in enumerate(self.transformer.layers): - depth = None - if depthwise_init == 'current': - depth = layer_idx + 1 - elif depthwise_init == 'global': - depth = len(self.transformer.layers) - init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init) - tr_layer.apply(init_fn) - - for linear in self.linears: - init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init) - - @property - def special_token_id(self) -> int: - return self.card - - @property - def num_codebooks(self) -> int: - return self.n_q - - def forward(self, sequence: torch.Tensor, - conditions: tp.List[ConditioningAttributes], - condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor: - """Apply language model on sequence and conditions. - Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and - S the sequence steps, return the logits with shape [B, card, K, S]. - - Args: - indices (torch.Tensor): indices of the codes to model. - conditions (list[ConditioningAttributes]): conditionings to use when modeling - the given codes. Note that when evaluating multiple time with the same conditioning - you should pre-compute those and pass them as `condition_tensors`. - condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning - tensors, see `conditions`. - Returns: - torch.Tensor: Logits. - """ - B, K, S = sequence.shape - assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks' - input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)]) - if condition_tensors is None: - assert not self._is_streaming, "Conditions tensors should be precomputed when streaming." - # apply dropout modules - conditions = self.cfg_dropout(conditions) - conditions = self.att_dropout(conditions) - tokenized = self.condition_provider.tokenize(conditions) - # encode conditions and fuse, both have a streaming cache to not recompute when generating. - condition_tensors = self.condition_provider(tokenized) - else: - assert not conditions, "Shouldn't pass both conditions and condition_tensors." - - input_, cross_attention_input = self.fuser(input_, condition_tensors) - - out = self.transformer(input_, cross_attention_src=cross_attention_input) - if self.out_norm: - out = self.out_norm(out) - logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card] - - # remove the prefix from the model outputs - if len(self.fuser.fuse2cond['prepend']) > 0: - logits = logits[:, :, -S:] - - return logits # [B, K, S, card] - - def compute_predictions( - self, codes: torch.Tensor, - conditions: tp.List[ConditioningAttributes], - condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput: - """Given an input tensor of codes [B, K, T] and list of conditions, runs the model - forward using the specified codes interleaving pattern. - - Args: - codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size, - K the number of codebooks and T the number of timesteps. - conditions (list[ConditioningAttributes]): conditionings to use when modeling - the given codes. Note that when evaluating multiple time with the same conditioning - you should pre-compute those and pass them as `condition_tensors`. - condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning - tensors, see `conditions`. - Returns: - LMOutput: Language model outputs - logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes, - i.e. the first item corresponds to logits to predict the first code, meaning that - no additional shifting of codes and logits is required. - mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions. - Given the specified interleaving strategies, parts of the logits and codes should - not be considered as valid predictions because of invalid context. - """ - B, K, T = codes.shape - codes = codes.contiguous() - # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens - pattern = self.pattern_provider.get_pattern(T) - sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence( - codes, self.special_token_id, keep_only_valid_steps=True - ) - # apply model on pattern sequence - model = self if self._fsdp is None else self._fsdp - logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card] - # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card] - # and provide the corresponding mask over invalid positions of tokens - logits = logits.permute(0, 3, 1, 2) # [B, card, K, S] - # note: we use nans as special token to make it obvious if we feed unexpected logits - logits, logits_indexes, logits_mask = pattern.revert_pattern_logits( - logits, float('nan'), keep_only_valid_steps=True - ) - logits = logits.permute(0, 2, 3, 1) # [B, K, T, card] - logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T] - return LMOutput(logits, logits_mask) - - def _sample_next_token(self, - sequence: torch.Tensor, - cfg_conditions: CFGConditions, - unconditional_state: State, - use_sampling: bool = False, - temp: float = 1.0, - top_k: int = 0, - top_p: float = 0.0, - cfg_coef: tp.Optional[float] = None) -> torch.Tensor: - """Sample next token from the model given a sequence and a set of conditions. The model supports - multiple sampling strategies (greedy sampling, softmax, top-k, top-p...). - - Args: - sequence (torch.Tensor): Current sequence of shape [B, K, S] - with K corresponding to the number of codebooks and S the number of sequence steps. - S = 1 in streaming mode, except for the first step that contains a bigger prompt. - condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used, - should be twice the batch size, being the concatenation of the conditions + null conditions. - use_sampling (bool): Whether to use a sampling strategy or not. - temp (float): Sampling temperature. - top_k (int): K for "top-k" sampling. - top_p (float): P for "top-p" sampling. - cfg_coef (float): classifier free guidance coefficient - Returns: - next_token (torch.Tensor): Next token tensor of shape [B, K, 1]. - """ - B = sequence.shape[0] - cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef - model = self if self._fsdp is None else self._fsdp - if self.two_step_cfg and cfg_conditions != {}: - assert isinstance(cfg_conditions, tuple) - condition_tensors, null_condition_tensors = cfg_conditions - cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors) - state = self.get_streaming_state() - self.set_streaming_state(unconditional_state) - uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors) - unconditional_state.update(self.get_streaming_state()) - self.set_streaming_state(state) - logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef - else: - assert isinstance(cfg_conditions, dict) - condition_tensors = cfg_conditions - if condition_tensors: - # Preparing for CFG, predicting both conditional and unconditional logits. - sequence = torch.cat([sequence, sequence], dim=0) - all_logits = model( - sequence, - conditions=[], condition_tensors=condition_tensors) - if condition_tensors: - cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card] - logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef - else: - logits = all_logits - - logits = logits.permute(0, 1, 3, 2) # [B, K, card, T] - logits = logits[..., -1] # [B x K x card] - - # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error. - if use_sampling and temp > 0.0: - probs = torch.softmax(logits / temp, dim=-1) - if top_p > 0.0: - next_token = utils.sample_top_p(probs, p=top_p) - elif top_k > 0: - next_token = utils.sample_top_k(probs, k=top_k) - else: - next_token = utils.multinomial(probs, num_samples=1) - else: - next_token = torch.argmax(logits, dim=-1, keepdim=True) - - return next_token - - @torch.no_grad() - def generate(self, - prompt: tp.Optional[torch.Tensor] = None, - conditions: tp.List[ConditioningAttributes] = [], - num_samples: tp.Optional[int] = None, - max_gen_len: int = 256, - use_sampling: bool = True, - temp: float = 1.0, - top_k: int = 250, - top_p: float = 0.0, - cfg_coef: tp.Optional[float] = None, - two_step_cfg: bool = False, - remove_prompts: bool = False, - check: bool = False, - callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor: - """Generate tokens sampling from the model given a prompt or unconditionally. Generation can - be perform in a greedy fashion or using sampling with top K and top P strategies. - - Args: - prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T]. - conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None. - num_samples (int or None): Number of samples to generate when no prompt and no conditions are given. - max_gen_len (int): Maximum generation length. - use_sampling (bool): Whether to use a sampling strategy or not. - temp (float): Sampling temperature. - top_k (int): K for "top-k" sampling. - top_p (float): P for "top-p" sampling. - remove_prompts (bool): Whether to remove prompts from generation or not. - Returns: - torch.Tensor: Generated tokens. - """ - assert not self.training, "generation shouldn't be used in training mode." - first_param = next(iter(self.parameters())) - device = first_param.device - - # Checking all input shapes are consistents. - possible_num_samples = [] - if num_samples is not None: - possible_num_samples.append(num_samples) - elif prompt is not None: - possible_num_samples.append(prompt.shape[0]) - elif conditions: - possible_num_samples.append(len(conditions)) - else: - possible_num_samples.append(1) - assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes" - num_samples = possible_num_samples[0] - - # below we create set of conditions: one conditional and one unconditional - # to do that we merge the regular condition together with the null condition - # we then do 1 forward pass instead of 2. - # the reason for that is two-fold: - # 1. it is about x2 faster than doing 2 forward passes - # 2. avoid the streaming API treating the 2 passes as part of different time steps - # We also support doing two different passes, in particular to ensure that - # the padding structure is exactly the same between train anf test. - # With a batch size of 1, this can be slower though. - cfg_conditions: CFGConditions - two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg - if conditions: - null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions) - if two_step_cfg: - cfg_conditions = ( - self.condition_provider(self.condition_provider.tokenize(conditions)), - self.condition_provider(self.condition_provider.tokenize(null_conditions)), - ) - else: - conditions = conditions + null_conditions - tokenized = self.condition_provider.tokenize(conditions) - cfg_conditions = self.condition_provider(tokenized) - else: - cfg_conditions = {} - - if prompt is None: - assert num_samples > 0 - prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device) - - B, K, T = prompt.shape - start_offset = T - assert start_offset < max_gen_len - - pattern = self.pattern_provider.get_pattern(max_gen_len) - # this token is used as default value for codes that are not generated yet - unknown_token = -1 - - # we generate codes up to the max_gen_len that will be mapped to the pattern sequence - gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device) - # filling the gen_codes with the prompt if needed - gen_codes[..., :start_offset] = prompt - # create the gen_sequence with proper interleaving from the pattern: [B, K, S] - gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id) - # retrieve the start_offset in the sequence: - # it is the first sequence step that contains the `start_offset` timestep - start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset) - assert start_offset_sequence is not None - - with self.streaming(): - unconditional_state = self.get_streaming_state() - prev_offset = 0 - gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S] - for offset in range(start_offset_sequence, gen_sequence_len): - # get current sequence (note that the streaming API is providing the caching over previous offsets) - curr_sequence = gen_sequence[..., prev_offset:offset] - curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1) - if check: - # check coherence between mask and sequence - assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all() - # should never happen as gen_sequence is filled progressively - assert not (curr_sequence == unknown_token).any() - # sample next token from the model, next token shape is [B, K, 1] - next_token = self._sample_next_token( - curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p, - cfg_coef=cfg_coef) - # ensure the tokens that should be masked are properly set to special_token_id - # as the model never output special_token_id - valid_mask = mask[..., offset:offset+1].expand(B, -1, -1) - next_token[~valid_mask] = self.special_token_id - # ensure we don't overwrite prompt tokens, we only write over unknown tokens - # (then mask tokens should be left as is as well, which is correct) - gen_sequence[..., offset:offset+1] = torch.where( - gen_sequence[..., offset:offset+1] == unknown_token, - next_token, gen_sequence[..., offset:offset+1] - ) - prev_offset = offset - if callback is not None: - callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence) - unconditional_state.clear() - - # ensure sequence has been entirely filled - assert not (gen_sequence == unknown_token).any() - # ensure gen_sequence pattern and mask are matching - # which means the gen_sequence is valid according to the pattern - assert ( - gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id) - ).all() - # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps - out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token) - - # sanity checks over the returned codes and corresponding masks - assert (out_codes[..., :max_gen_len] != unknown_token).all() - assert (out_mask[..., :max_gen_len] == 1).all() - - out_start_offset = start_offset if remove_prompts else 0 - out_codes = out_codes[..., out_start_offset:max_gen_len] - - # ensure the returned codes are all valid - assert (out_codes >= 0).all() and (out_codes <= self.card).all() - return out_codes diff --git a/spaces/jbilcke-hf/VideoChain-UI/src/components/business/video-form.tsx b/spaces/jbilcke-hf/VideoChain-UI/src/components/business/video-form.tsx deleted file mode 100644 index b55e43abd55a21d9fb6275aa5bf9b1fcbfd50005..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/VideoChain-UI/src/components/business/video-form.tsx +++ /dev/null @@ -1,63 +0,0 @@ -"use client" - -import { useEffect, useTransition } from "react" -import { usePathname } from "next/navigation" - -import { experimental_useFormStatus as useFormStatus } from "react-dom" -import { Textarea } from "@/components/ui/textarea" -import { Button } from "@/components/ui/button" -import { handleFormSubmit } from "@/server/actions" - -export const VideoForm = () => { - const pathname = usePathname() - const ownerId = pathname.split("/").pop() - const { pending } = useFormStatus() - - return ( - <form - action={handleFormSubmit} - > - <div className="flex flex-row w-full mb-3"> - <div className="flex flex-col w-1/2 text-center"> - <h2 className="text-4xl font-thin tracking-tight">VideoChain 🎬</h2> - <p className="text-md font-thin"> - Powered by <span className="font-normal">Hugging Face 🤗</span> - </p> - </div> - <div className="flex flex-col w-1/2 text-center"> - <p className="text-sl font-thin"> - For demonstration purposes only. Please use responsibly, and cancel any video you don't need anymore. - You have been assigned this permalink ID: <a href={`/studio/${ownerId}`} className="font-normal" target="_blank">{ownerId}</a> - - </p> - </div> - </div> - - <div className="flex items-center justify-between md:space-x-3 w-full"> - <input - type="hidden" - id="ownerId" - name="ownerId" - value={ownerId} - /> - - <Textarea - id="prompt" - name="prompt" - placeholder="3 clips of a cat playing the piano" - className="mr-3 md:mr-0" - /> - - <Button - variant="secondary" - size="lg" - className="text-md md:w-32" - type="submit" - disabled={pending} - > - {pending ? "Loading.." : "Generate"} - </Button> - </div> - </form> - ) -} \ No newline at end of file diff --git a/spaces/jbilcke-hf/media-server/scripts/channel_community.sh b/spaces/jbilcke-hf/media-server/scripts/channel_community.sh deleted file mode 100644 index 7fc107b00cbc012114822951085ff9366c70f87a..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/media-server/scripts/channel_community.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -echo "Starting FFMPEG live stream for channel community" -while true; do - if [ -f channel_community.txt ] && [ -f channel_1_audio.txt ]; then - echo "Files exist, starting stream" - # Note: for now we also use channel 1 for audio! - ffmpeg -y -nostdin -re -f concat -safe 0 -i channel_community.txt -stream_loop -1 -safe 0 -i channel_1_audio.txt -loglevel error -c:v libx264 -preset veryfast -tune zerolatency -c:a aac -ar 44100 -shortest -f flv rtmp://localhost/live/community - else - echo "Files do not exist, waiting for files" - sleep 1 # check every second - fi -done \ No newline at end of file diff --git a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/model/preprocess_all.py b/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/model/preprocess_all.py deleted file mode 100644 index 66ea972aed8152b25c7fe9072cb047bc3f580aac..0000000000000000000000000000000000000000 --- a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/model/preprocess_all.py +++ /dev/null @@ -1,164 +0,0 @@ -#!/usr/bin/python -# coding: utf-8 - -# Author: LE YUAN -# Date: 2020-10-03 - -import math -import json -import pickle -import numpy as np -from collections import defaultdict -from rdkit import Chem - - -word_dict = defaultdict(lambda: len(word_dict)) -atom_dict = defaultdict(lambda: len(atom_dict)) -bond_dict = defaultdict(lambda: len(bond_dict)) -fingerprint_dict = defaultdict(lambda: len(fingerprint_dict)) -edge_dict = defaultdict(lambda: len(edge_dict)) - -proteins = list() -compounds = list() -adjacencies = list() -regression =list() - -def split_sequence(sequence, ngram): - sequence = '-' + sequence + '=' - # print(sequence) - words = [word_dict[sequence[i:i+ngram]] for i in range(len(sequence)-ngram+1)] - return np.array(words) - # return word_dict - -def create_atoms(mol): - """Create a list of atom (e.g., hydrogen and oxygen) IDs - considering the aromaticity.""" - # atom_dict = defaultdict(lambda: len(atom_dict)) - atoms = [a.GetSymbol() for a in mol.GetAtoms()] - # print(atoms) - for a in mol.GetAromaticAtoms(): - i = a.GetIdx() - atoms[i] = (atoms[i], 'aromatic') - atoms = [atom_dict[a] for a in atoms] - return np.array(atoms) - -def create_ijbonddict(mol): - """Create a dictionary, which each key is a node ID - and each value is the tuples of its neighboring node - and bond (e.g., single and double) IDs.""" - # bond_dict = defaultdict(lambda: len(bond_dict)) - i_jbond_dict = defaultdict(lambda: []) - for b in mol.GetBonds(): - i, j = b.GetBeginAtomIdx(), b.GetEndAtomIdx() - bond = bond_dict[str(b.GetBondType())] - i_jbond_dict[i].append((j, bond)) - i_jbond_dict[j].append((i, bond)) - return i_jbond_dict - -def extract_fingerprints(atoms, i_jbond_dict, radius): - """Extract the r-radius subgraphs (i.e., fingerprints) - from a molecular graph using Weisfeiler-Lehman algorithm.""" - - # fingerprint_dict = defaultdict(lambda: len(fingerprint_dict)) - # edge_dict = defaultdict(lambda: len(edge_dict)) - - if (len(atoms) == 1) or (radius == 0): - fingerprints = [fingerprint_dict[a] for a in atoms] - - else: - nodes = atoms - i_jedge_dict = i_jbond_dict - - for _ in range(radius): - - """Update each node ID considering its neighboring nodes and edges - (i.e., r-radius subgraphs or fingerprints).""" - fingerprints = [] - for i, j_edge in i_jedge_dict.items(): - neighbors = [(nodes[j], edge) for j, edge in j_edge] - fingerprint = (nodes[i], tuple(sorted(neighbors))) - fingerprints.append(fingerprint_dict[fingerprint]) - nodes = fingerprints - - """Also update each edge ID considering two nodes - on its both sides.""" - _i_jedge_dict = defaultdict(lambda: []) - for i, j_edge in i_jedge_dict.items(): - for j, edge in j_edge: - both_side = tuple(sorted((nodes[i], nodes[j]))) - edge = edge_dict[(both_side, edge)] - _i_jedge_dict[i].append((j, edge)) - i_jedge_dict = _i_jedge_dict - - return np.array(fingerprints) - -def create_adjacency(mol): - adjacency = Chem.GetAdjacencyMatrix(mol) - return np.array(adjacency) - -def dump_dictionary(dictionary, filename): - with open(filename, 'wb') as file: - pickle.dump(dict(dictionary), file) - -def main() : - with open('../../Data/database/Kcat_combination_0918.json', 'r') as infile : - Kcat_data = json.load(infile) - - # smiles_all = [data['Smiles'] for data in Kcat_data] - - # print(len(Kcat_data)) - - # smiles = "CC1=NC=C(C(=C1O)CO)CO" - # radius = 3 # The initial setup, I suppose it is 2, but not 2. - radius = 2 - ngram = 3 - - """Exclude data contains '.' in the SMILES format.""" - i = 0 - for data in Kcat_data : - smiles = data['Smiles'] - sequence = data['Sequence'] - # print(smiles) - Kcat = data['Value'] - if "." not in smiles and float(Kcat) > 0: - # i += 1 - # print('This is',i) - mol = Chem.AddHs(Chem.MolFromSmiles(smiles)) - atoms = create_atoms(mol) - # print(atoms) - i_jbond_dict = create_ijbonddict(mol) - # print(i_jbond_dict) - - fingerprints = extract_fingerprints(atoms, i_jbond_dict, radius) - # print(fingerprints) - compounds.append(fingerprints) - - adjacency = create_adjacency(mol) - adjacencies.append(adjacency) - - words = split_sequence(sequence,ngram) - # print(words) - proteins.append(words) - - # print(float(Kcat)) - - regression.append(np.array([math.log2(float(Kcat))])) - print(math.log2(float(Kcat))) - - # regression.append(np.array([math.log10(float(Kcat))])) - # print(math.log10(float(Kcat))) - - np.save('../../Data/input/'+'compounds', compounds) - np.save('../../Data/input/'+'adjacencies', adjacencies) - np.save('../../Data/input/'+'regression', regression) - np.save('../../Data/input/'+'proteins', proteins) - - dump_dictionary(fingerprint_dict, '../../Data/input/fingerprint_dict.pickle') - dump_dictionary(atom_dict, '../../Data/input/atom_dict.pickle') - dump_dictionary(bond_dict, '../../Data/input/bond_dict.pickle') - dump_dictionary(edge_dict, '../../Data/input/edge_dict.pickle') - dump_dictionary(word_dict, '../../Data/input/sequence_dict.pickle') - - -if __name__ == '__main__' : - main() diff --git a/spaces/jie1/succ1/ProteinMPNN-main/helper_scripts/make_fixed_positions_dict.py b/spaces/jie1/succ1/ProteinMPNN-main/helper_scripts/make_fixed_positions_dict.py deleted file mode 100644 index 4b104876fd856698c9e3ad8aeb994e93e031ca1a..0000000000000000000000000000000000000000 --- a/spaces/jie1/succ1/ProteinMPNN-main/helper_scripts/make_fixed_positions_dict.py +++ /dev/null @@ -1,60 +0,0 @@ -import argparse - -def m_f_p_d(input_path, output_path, chain_list, position_list, specify_non_fixed): - import glob - import random - import numpy as np - import json - import itertools - - with open(input_path.name, 'r') as json_file: - json_list = list(json_file) - - fixed_list = [[int(item) for item in one.split()] for one in position_list.split(",")] - global_designed_chain_list = [str(item) for item in chain_list.split()] - my_dict = {} - - if not specify_non_fixed: - for json_str in json_list: - result = json.loads(json_str) - all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] - fixed_position_dict = {} - for i, chain in enumerate(global_designed_chain_list): - fixed_position_dict[chain] = fixed_list[i] - for chain in all_chain_list: - if chain not in global_designed_chain_list: - fixed_position_dict[chain] = [] - my_dict[result['name']] = fixed_position_dict - else: - for json_str in json_list: - result = json.loads(json_str) - all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] - fixed_position_dict = {} - for chain in all_chain_list: - seq_length = len(result[f'seq_chain_{chain}']) - all_residue_list = (np.arange(seq_length)+1).tolist() - if chain not in global_designed_chain_list: - fixed_position_dict[chain] = all_residue_list - else: - idx = np.argwhere(np.array(global_designed_chain_list) == chain)[0][0] - fixed_position_dict[chain] = list(set(all_residue_list)-set(fixed_list[idx])) - my_dict[result['name']] = fixed_position_dict - - with open(output_path, 'w') as f: - f.write(json.dumps(my_dict) + '\n') - return output_path - - #e.g. output - #{"5TTA": {"A": [1, 2, 3, 7, 8, 9, 22, 25, 33], "B": []}, "3LIS": {"A": [], "B": []}} - -# if __name__ == "__main__": -# argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) -# argparser.add_argument("--input_path", type=str, help="Path to the parsed PDBs") -# argparser.add_argument("--output_path", type=str, help="Path to the output dictionary") -# argparser.add_argument("--chain_list", type=str, default='', help="List of the chains that need to be fixed") -# argparser.add_argument("--position_list", type=str, default='', help="Position lists, e.g. 11 12 14 18, 1 2 3 4 for first chain and the second chain") -# argparser.add_argument("--specify_non_fixed", action="store_true", default=False, help="Allows specifying just residues that need to be designed (default: false)") -# -# args = argparser.parse_args() -# main(args) - diff --git a/spaces/jjeamin/ArcaneStyleTransfer/utils.py b/spaces/jjeamin/ArcaneStyleTransfer/utils.py deleted file mode 100644 index b5af94703d0b1233c98f5900cdc5a1b69d65e10c..0000000000000000000000000000000000000000 --- a/spaces/jjeamin/ArcaneStyleTransfer/utils.py +++ /dev/null @@ -1,115 +0,0 @@ -import dlib -import numpy as np -import scipy -import scipy.ndimage -from PIL import Image -from huggingface_hub import hf_hub_download - -shape_predictor_path = hf_hub_download(repo_id="jjeamin/ArcaneStyleTransfer", filename="shape_predictor_68_face_landmarks.dat") - -def get_landmark(img, predictor): - """get landmark with dlib - :return: np.array shape=(68, 2) - """ - detector = dlib.get_frontal_face_detector() - - dets = detector(img, 1) - assert len(dets) > 0, "Face not detected, try another face image" - - for k, d in enumerate(dets): - shape = predictor(img, d) - - t = list(shape.parts()) - a = [] - for tt in t: - a.append([tt.x, tt.y]) - lm = np.array(a) - return lm - - -def align_face(img, output_size=512, transform_size=1024, enable_padding=True): - - """ - :param filepath: str - :return: PIL Image - """ - np_img = np.array(img) - predictor = dlib.shape_predictor(shape_predictor_path) - lm = get_landmark(np_img, predictor) - - lm_chin = lm[0: 17] # left-right - lm_eyebrow_left = lm[17: 22] # left-right - lm_eyebrow_right = lm[22: 27] # left-right - lm_nose = lm[27: 31] # top-down - lm_nostrils = lm[31: 36] # top-down - lm_eye_left = lm[36: 42] # left-clockwise - lm_eye_right = lm[42: 48] # left-clockwise - lm_mouth_outer = lm[48: 60] # left-clockwise - lm_mouth_inner = lm[60: 68] # left-clockwise - - # Calculate auxiliary vectors. - eye_left = np.mean(lm_eye_left, axis=0) - eye_right = np.mean(lm_eye_right, axis=0) - eye_avg = (eye_left + eye_right) * 0.5 - eye_to_eye = eye_right - eye_left - mouth_left = lm_mouth_outer[0] - mouth_right = lm_mouth_outer[6] - mouth_avg = (mouth_left + mouth_right) * 0.5 - eye_to_mouth = mouth_avg - eye_avg - - # Choose oriented crop rectangle. - x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] - x /= np.hypot(*x) - x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) - y = np.flipud(x) * [-1, 1] - c = eye_avg + eye_to_mouth * 0.1 - quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) - qsize = np.hypot(*x) * 2 - - # read image - transform_size = output_size - enable_padding = True - - # Shrink. - shrink = int(np.floor(qsize / output_size * 0.5)) - if shrink > 1: - rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) - img = img.resize(rsize, Image.ANTIALIAS) - quad /= shrink - qsize /= shrink - - # Crop. - border = max(int(np.rint(qsize * 0.1)), 3) - crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), - int(np.ceil(max(quad[:, 1])))) - crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), - min(crop[3] + border, img.size[1])) - if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: - img = img.crop(crop) - quad -= crop[0:2] - - # Pad. - pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), - int(np.ceil(max(quad[:, 1])))) - pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), - max(pad[3] - img.size[1] + border, 0)) - if enable_padding and max(pad) > border - 4: - pad = np.maximum(pad, int(np.rint(qsize * 0.3))) - img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') - h, w, _ = img.shape - y, x, _ = np.ogrid[:h, :w, :1] - mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), - 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) - blur = qsize * 0.02 - img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) - img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) - img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') - quad += pad[:2] - - # Transform. - img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR) - if output_size < transform_size: - img = img.resize((output_size, output_size), Image.ANTIALIAS) - - # Return aligned image. - return img \ No newline at end of file diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Random/random.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Random/random.py deleted file mode 100644 index 5389b3bbe16605fa3dd0630d81f4a78b6830b1e8..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Random/random.py +++ /dev/null @@ -1,138 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Random/random.py : Strong alternative for the standard 'random' module -# -# Written in 2008 by Dwayne C. Litzenberger <dlitz@dlitz.net> -# -# =================================================================== -# The contents of this file are dedicated to the public domain. To -# the extent that dedication to the public domain is not available, -# everyone is granted a worldwide, perpetual, royalty-free, -# non-exclusive license to exercise all rights associated with the -# contents of this file for any purpose whatsoever. -# No rights are reserved. -# -# 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. -# =================================================================== - -__all__ = ['StrongRandom', 'getrandbits', 'randrange', 'randint', 'choice', 'shuffle', 'sample'] - -from Crypto import Random - -from Crypto.Util.py3compat import is_native_int - -class StrongRandom(object): - def __init__(self, rng=None, randfunc=None): - if randfunc is None and rng is None: - self._randfunc = None - elif randfunc is not None and rng is None: - self._randfunc = randfunc - elif randfunc is None and rng is not None: - self._randfunc = rng.read - else: - raise ValueError("Cannot specify both 'rng' and 'randfunc'") - - def getrandbits(self, k): - """Return an integer with k random bits.""" - - if self._randfunc is None: - self._randfunc = Random.new().read - mask = (1 << k) - 1 - return mask & bytes_to_long(self._randfunc(ceil_div(k, 8))) - - def randrange(self, *args): - """randrange([start,] stop[, step]): - Return a randomly-selected element from range(start, stop, step).""" - if len(args) == 3: - (start, stop, step) = args - elif len(args) == 2: - (start, stop) = args - step = 1 - elif len(args) == 1: - (stop,) = args - start = 0 - step = 1 - else: - raise TypeError("randrange expected at most 3 arguments, got %d" % (len(args),)) - if (not is_native_int(start) or not is_native_int(stop) or not - is_native_int(step)): - raise TypeError("randrange requires integer arguments") - if step == 0: - raise ValueError("randrange step argument must not be zero") - - num_choices = ceil_div(stop - start, step) - if num_choices < 0: - num_choices = 0 - if num_choices < 1: - raise ValueError("empty range for randrange(%r, %r, %r)" % (start, stop, step)) - - # Pick a random number in the range of possible numbers - r = num_choices - while r >= num_choices: - r = self.getrandbits(size(num_choices)) - - return start + (step * r) - - def randint(self, a, b): - """Return a random integer N such that a <= N <= b.""" - if not is_native_int(a) or not is_native_int(b): - raise TypeError("randint requires integer arguments") - N = self.randrange(a, b+1) - assert a <= N <= b - return N - - def choice(self, seq): - """Return a random element from a (non-empty) sequence. - - If the seqence is empty, raises IndexError. - """ - if len(seq) == 0: - raise IndexError("empty sequence") - return seq[self.randrange(len(seq))] - - def shuffle(self, x): - """Shuffle the sequence in place.""" - # Fisher-Yates shuffle. O(n) - # See http://en.wikipedia.org/wiki/Fisher-Yates_shuffle - # Working backwards from the end of the array, we choose a random item - # from the remaining items until all items have been chosen. - for i in range(len(x)-1, 0, -1): # iterate from len(x)-1 downto 1 - j = self.randrange(0, i+1) # choose random j such that 0 <= j <= i - x[i], x[j] = x[j], x[i] # exchange x[i] and x[j] - - def sample(self, population, k): - """Return a k-length list of unique elements chosen from the population sequence.""" - - num_choices = len(population) - if k > num_choices: - raise ValueError("sample larger than population") - - retval = [] - selected = {} # we emulate a set using a dict here - for i in range(k): - r = None - while r is None or r in selected: - r = self.randrange(num_choices) - retval.append(population[r]) - selected[r] = 1 - return retval - -_r = StrongRandom() -getrandbits = _r.getrandbits -randrange = _r.randrange -randint = _r.randint -choice = _r.choice -shuffle = _r.shuffle -sample = _r.sample - -# These are at the bottom to avoid problems with recursive imports -from Crypto.Util.number import ceil_div, bytes_to_long, long_to_bytes, size - -# vim:set ts=4 sw=4 sts=4 expandtab: diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/MpoImagePlugin.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/MpoImagePlugin.py deleted file mode 100644 index f9261c77d6862d7def90c6136dff6449241b0690..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/MpoImagePlugin.py +++ /dev/null @@ -1,197 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# MPO file handling -# -# See "Multi-Picture Format" (CIPA DC-007-Translation 2009, Standard of the -# Camera & Imaging Products Association) -# -# The multi-picture object combines multiple JPEG images (with a modified EXIF -# data format) into a single file. While it can theoretically be used much like -# a GIF animation, it is commonly used to represent 3D photographs and is (as -# of this writing) the most commonly used format by 3D cameras. -# -# History: -# 2014-03-13 Feneric Created -# -# See the README file for information on usage and redistribution. -# - -import itertools -import os -import struct - -from . import ( - ExifTags, - Image, - ImageFile, - ImageSequence, - JpegImagePlugin, - TiffImagePlugin, -) -from ._binary import i16be as i16 -from ._binary import o32le - -# def _accept(prefix): -# return JpegImagePlugin._accept(prefix) - - -def _save(im, fp, filename): - JpegImagePlugin._save(im, fp, filename) - - -def _save_all(im, fp, filename): - append_images = im.encoderinfo.get("append_images", []) - if not append_images: - try: - animated = im.is_animated - except AttributeError: - animated = False - if not animated: - _save(im, fp, filename) - return - - mpf_offset = 28 - offsets = [] - for imSequence in itertools.chain([im], append_images): - for im_frame in ImageSequence.Iterator(imSequence): - if not offsets: - # APP2 marker - im_frame.encoderinfo["extra"] = ( - b"\xFF\xE2" + struct.pack(">H", 6 + 82) + b"MPF\0" + b" " * 82 - ) - exif = im_frame.encoderinfo.get("exif") - if isinstance(exif, Image.Exif): - exif = exif.tobytes() - im_frame.encoderinfo["exif"] = exif - if exif: - mpf_offset += 4 + len(exif) - - JpegImagePlugin._save(im_frame, fp, filename) - offsets.append(fp.tell()) - else: - im_frame.save(fp, "JPEG") - offsets.append(fp.tell() - offsets[-1]) - - ifd = TiffImagePlugin.ImageFileDirectory_v2() - ifd[0xB000] = b"0100" - ifd[0xB001] = len(offsets) - - mpentries = b"" - data_offset = 0 - for i, size in enumerate(offsets): - if i == 0: - mptype = 0x030000 # Baseline MP Primary Image - else: - mptype = 0x000000 # Undefined - mpentries += struct.pack("<LLLHH", mptype, size, data_offset, 0, 0) - if i == 0: - data_offset -= mpf_offset - data_offset += size - ifd[0xB002] = mpentries - - fp.seek(mpf_offset) - fp.write(b"II\x2A\x00" + o32le(8) + ifd.tobytes(8)) - fp.seek(0, os.SEEK_END) - - -## -# Image plugin for MPO images. - - -class MpoImageFile(JpegImagePlugin.JpegImageFile): - format = "MPO" - format_description = "MPO (CIPA DC-007)" - _close_exclusive_fp_after_loading = False - - def _open(self): - self.fp.seek(0) # prep the fp in order to pass the JPEG test - JpegImagePlugin.JpegImageFile._open(self) - self._after_jpeg_open() - - def _after_jpeg_open(self, mpheader=None): - self._initial_size = self.size - self.mpinfo = mpheader if mpheader is not None else self._getmp() - self.n_frames = self.mpinfo[0xB001] - self.__mpoffsets = [ - mpent["DataOffset"] + self.info["mpoffset"] for mpent in self.mpinfo[0xB002] - ] - self.__mpoffsets[0] = 0 - # Note that the following assertion will only be invalid if something - # gets broken within JpegImagePlugin. - assert self.n_frames == len(self.__mpoffsets) - del self.info["mpoffset"] # no longer needed - self.is_animated = self.n_frames > 1 - self._fp = self.fp # FIXME: hack - self._fp.seek(self.__mpoffsets[0]) # get ready to read first frame - self.__frame = 0 - self.offset = 0 - # for now we can only handle reading and individual frame extraction - self.readonly = 1 - - def load_seek(self, pos): - self._fp.seek(pos) - - def seek(self, frame): - if not self._seek_check(frame): - return - self.fp = self._fp - self.offset = self.__mpoffsets[frame] - - self.fp.seek(self.offset + 2) # skip SOI marker - segment = self.fp.read(2) - if not segment: - msg = "No data found for frame" - raise ValueError(msg) - self._size = self._initial_size - if i16(segment) == 0xFFE1: # APP1 - n = i16(self.fp.read(2)) - 2 - self.info["exif"] = ImageFile._safe_read(self.fp, n) - self._reload_exif() - - mptype = self.mpinfo[0xB002][frame]["Attribute"]["MPType"] - if mptype.startswith("Large Thumbnail"): - exif = self.getexif().get_ifd(ExifTags.IFD.Exif) - if 40962 in exif and 40963 in exif: - self._size = (exif[40962], exif[40963]) - elif "exif" in self.info: - del self.info["exif"] - self._reload_exif() - - self.tile = [("jpeg", (0, 0) + self.size, self.offset, (self.mode, ""))] - self.__frame = frame - - def tell(self): - return self.__frame - - @staticmethod - def adopt(jpeg_instance, mpheader=None): - """ - Transform the instance of JpegImageFile into - an instance of MpoImageFile. - After the call, the JpegImageFile is extended - to be an MpoImageFile. - - This is essentially useful when opening a JPEG - file that reveals itself as an MPO, to avoid - double call to _open. - """ - jpeg_instance.__class__ = MpoImageFile - jpeg_instance._after_jpeg_open(mpheader) - return jpeg_instance - - -# --------------------------------------------------------------------- -# Registry stuff - -# Note that since MPO shares a factory with JPEG, we do not need to do a -# separate registration for it here. -# Image.register_open(MpoImageFile.format, -# JpegImagePlugin.jpeg_factory, _accept) -Image.register_save(MpoImageFile.format, _save) -Image.register_save_all(MpoImageFile.format, _save_all) - -Image.register_extension(MpoImageFile.format, ".mpo") - -Image.register_mime(MpoImageFile.format, "image/mpo") diff --git a/spaces/johnyang/ChatPaper111/chatbot.py b/spaces/johnyang/ChatPaper111/chatbot.py deleted file mode 100644 index 883b032c0b5b2df89f9d8647f86ecd80194da23d..0000000000000000000000000000000000000000 --- a/spaces/johnyang/ChatPaper111/chatbot.py +++ /dev/null @@ -1,320 +0,0 @@ -from base_class import ChatbotEngine -import os -import openai -import json -import os -import requests -import tiktoken -from config import MAX_TOKEN_MODEL_MAP -from utils import get_filtered_keys_from_object - - -class ChatbotWrapper: - """ - Wrapper of Official ChatGPT API, - # base on https://github.com/ChatGPT-Hackers/revChatGPT - """ - - def __init__( - self, - api_key: str, - engine: str = os.environ.get("GPT_ENGINE") or "gpt-3.5-turbo", - proxy: str = None, - max_tokens: int = 3000, - temperature: float = 0.5, - top_p: float = 1.0, - presence_penalty: float = 0.0, - frequency_penalty: float = 0.0, - reply_count: int = 1, - system_prompt: str = "You are ChatGPT, a large language model trained by OpenAI. Respond conversationally", - overhead_token=96, - ) -> None: - """ - Initialize Chatbot with API key (from https://platform.openai.com/account/api-keys) - """ - self.engine = engine - self.session = requests.Session() - self.api_key = api_key - self.system_prompt = system_prompt - self.max_tokens = max_tokens - self.temperature = temperature - self.top_p = top_p - self.presence_penalty = presence_penalty - self.frequency_penalty = frequency_penalty - self.reply_count = reply_count - self.max_limit = MAX_TOKEN_MODEL_MAP[self.engine] - self.overhead_token = overhead_token - - if proxy: - self.session.proxies = { - "http": proxy, - "https": proxy, - } - - self.conversation: dict = { - "default": [ - { - "role": "system", - "content": system_prompt, - }, - ], - } - - if max_tokens > self.max_limit - self.overhead_token: - raise Exception( - f"Max tokens cannot be greater than {self.max_limit- self.overhead_token}") - - if self.get_token_count("default") > self.max_tokens: - raise Exception("System prompt is too long") - - def add_to_conversation( - self, - message: str, - role: str, - convo_id: str = "default", - ) -> None: - """ - Add a message to the conversation - """ - self.conversation[convo_id].append({"role": role, "content": message}) - - def __truncate_conversation(self, convo_id: str = "default") -> None: - """ - Truncate the conversation - """ - # TODO: context condense with soft prompt tuning - while True: - if ( - self.get_token_count(convo_id) > self.max_tokens - and len(self.conversation[convo_id]) > 1 - ): - # Don't remove the first message and remove the first QA pair - self.conversation[convo_id].pop(1) - self.conversation[convo_id].pop(1) - # TODO: optimal pop out based on similarity distance - else: - break - - # https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb - def get_token_count(self, convo_id: str = "default") -> int: - """ - Get token count - """ - if self.engine not in ["gpt-3.5-turbo", "gpt-3.5-turbo-0301"]: - raise NotImplementedError("Unsupported engine {self.engine}") - - encoding = tiktoken.encoding_for_model(self.engine) - - num_tokens = 0 - for message in self.conversation[convo_id]: - # every message follows <im_start>{role/name}\n{content}<im_end>\n - num_tokens += 4 - for key, value in message.items(): - num_tokens += len(encoding.encode(value)) - if key == "name": # if there's a name, the role is omitted - num_tokens += 1 # role is always required and always 1 token - num_tokens += 2 # every reply is primed with <im_start>assistant - return num_tokens - - def get_max_tokens(self, convo_id: str) -> int: - """ - Get max tokens - """ - return self.max_tokens - self.get_token_count(convo_id) - - def ask_stream( - self, - prompt: str, - role: str = "user", - convo_id: str = "default", - dynamic_system_prompt=None, - **kwargs, - ) -> str: - """ - Ask a question - """ - # Make conversation if it doesn't exist - if convo_id not in self.conversation: - self.reset(convo_id=convo_id, system_prompt=dynamic_system_prompt) - - # adjust system prompt - assert dynamic_system_prompt is not None - self.conversation[convo_id][0]["content"] = dynamic_system_prompt - - self.add_to_conversation(prompt, "user", convo_id=convo_id) - print(" total tokens:") - print(self.get_token_count(convo_id)) - self.__truncate_conversation(convo_id=convo_id) - # Get response - response = self.session.post( - os.environ.get( - "API_URL") or "https://api.openai.com/v1/chat/completions", - headers={ - "Authorization": f"Bearer {kwargs.get('api_key', self.api_key)}"}, - json={ - "model": self.engine, - "messages": self.conversation[convo_id], - "stream": True, - # kwargs - "temperature": kwargs.get("temperature", self.temperature), - "top_p": kwargs.get("top_p", self.top_p), - "presence_penalty": kwargs.get( - "presence_penalty", - self.presence_penalty, - ), - "frequency_penalty": kwargs.get( - "frequency_penalty", - self.frequency_penalty, - ), - "n": kwargs.get("n", self.reply_count), - "user": role, - "max_tokens": self. get_max_tokens(convo_id=convo_id), - }, - stream=True, - ) - if response.status_code != 200: - raise Exception( - f"Error: {response.status_code} {response.reason} {response.text}", - ) - response_role: str = None - full_response: str = "" - for line in response.iter_lines(): - if not line: - continue - # Remove "data: " - line = line.decode("utf-8")[6:] - if line == "[DONE]": - break - resp: dict = json.loads(line) - choices = resp.get("choices") - if not choices: - continue - delta = choices[0].get("delta") - if not delta: - continue - if "role" in delta: - response_role = delta["role"] - if "content" in delta: - content = delta["content"] - full_response += content - yield content - self.add_to_conversation( - full_response, response_role, convo_id=convo_id) - - def ask( - self, - prompt: str, - role: str = "user", - convo_id: str = "default", - dynamic_system_prompt: str = None, - **kwargs, - ) -> str: - """ - Non-streaming ask - """ - response = self.ask_stream( - prompt=prompt, - role=role, - convo_id=convo_id, - dynamic_system_prompt=dynamic_system_prompt, - **kwargs, - ) - full_response: str = "".join(response) - return full_response - - def rollback(self, n: int = 1, convo_id: str = "default") -> None: - """ - Rollback the conversation - """ - for _ in range(n): - self.conversation[convo_id].pop() - - def reset(self, convo_id: str = "default", system_prompt: str = None) -> None: - """ - Reset the conversation - """ - self.conversation[convo_id] = [ - {"role": "system", "content": system_prompt or self.system_prompt}, - ] - - def save(self, file: str, *keys: str) -> None: - """ - Save the Chatbot configuration to a JSON file - """ - with open(file, "w", encoding="utf-8") as f: - json.dump( - { - key: self.__dict__[key] - for key in get_filtered_keys_from_object(self, *keys) - }, - f, - indent=2, - # saves session.proxies dict as session - default=lambda o: o.__dict__["proxies"], - ) - - def load(self, file: str, *keys: str) -> None: - """ - Load the Chatbot configuration from a JSON file - """ - with open(file, encoding="utf-8") as f: - # load json, if session is in keys, load proxies - loaded_config = json.load(f) - keys = get_filtered_keys_from_object(self, *keys) - - if "session" in keys and loaded_config["session"]: - self.session.proxies = loaded_config["session"] - keys = keys - {"session"} - self.__dict__.update({key: loaded_config[key] for key in keys}) - - -class OpenAIChatbot(ChatbotEngine): - def __init__(self, api_key: str, - engine: str = os.environ.get("GPT_ENGINE") or "gpt-3.5-turbo", - proxy: str = None, - max_tokens: int = 3000, - temperature: float = 0.5, - top_p: float = 1.0, - presence_penalty: float = 0.0, - frequency_penalty: float = 0.0, - reply_count: int = 1, - system_prompt: str = "You are ChatGPT, a large language model trained by OpenAI. Respond conversationally", - overhead_token=96) -> None: - openai.api_key = api_key - self.api_key = api_key - self.engine = engine - self.proxy = proxy - self.max_tokens = max_tokens - self.temperature = temperature - self.top_p = top_p - self.presence_penalty = presence_penalty - self.frequency_penalty = frequency_penalty - self.reply_count = reply_count - self.system_prompt = system_prompt - - self.bot = ChatbotWrapper( - api_key=self.api_key, - engine=self.engine, - proxy=self.proxy, - max_tokens=self.max_tokens, - temperature=self.temperature, - top_p=self.top_p, - presence_penalty=self.presence_penalty, - frequency_penalty=self.frequency_penalty, - reply_count=self.reply_count, - system_prompt=self.system_prompt, - overhead_token=overhead_token - ) - self.overhead_token = overhead_token - import tiktoken - self.encoding = tiktoken.encoding_for_model(self.engine) - - def encode_length(self, text: str) -> int: - return len(self.encoding.encode(text)) - - def query(self, questions: str, - role: str = "user", - convo_id: str = "default", - context: str = None, - **kwargs,): - return self.bot.ask(prompt=questions, role=role, convo_id=convo_id, dynamic_system_prompt=context, **kwargs) diff --git a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/checkbox.tsx b/spaces/jordonpeter01/ai-comic-factory/src/components/ui/checkbox.tsx deleted file mode 100644 index 5850485b9fecba303bdba1849e5a7b6329300af4..0000000000000000000000000000000000000000 --- a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/checkbox.tsx +++ /dev/null @@ -1,30 +0,0 @@ -"use client" - -import * as React from "react" -import * as CheckboxPrimitive from "@radix-ui/react-checkbox" -import { Check } from "lucide-react" - -import { cn } from "@/lib/utils" - -const Checkbox = React.forwardRef< - React.ElementRef<typeof CheckboxPrimitive.Root>, - React.ComponentPropsWithoutRef<typeof CheckboxPrimitive.Root> ->(({ className, ...props }, ref) => ( - <CheckboxPrimitive.Root - ref={ref} - className={cn( - "peer h-4 w-4 shrink-0 rounded-sm border border-stone-200 border-stone-900 ring-offset-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-stone-900 data-[state=checked]:text-stone-50 dark:border-stone-800 dark:border-stone-50 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-800 dark:data-[state=checked]:bg-stone-50 dark:data-[state=checked]:text-stone-900", - className - )} - {...props} - > - <CheckboxPrimitive.Indicator - className={cn("flex items-center justify-center text-current")} - > - <Check className="h-4 w-4" /> - </CheckboxPrimitive.Indicator> - </CheckboxPrimitive.Root> -)) -Checkbox.displayName = CheckboxPrimitive.Root.displayName - -export { Checkbox } diff --git a/spaces/jotap12/enso/plot.py b/spaces/jotap12/enso/plot.py deleted file mode 100644 index d83bd73347b2652180b2ec8750bc6bd6dbd99371..0000000000000000000000000000000000000000 --- a/spaces/jotap12/enso/plot.py +++ /dev/null @@ -1,706 +0,0 @@ -import streamlit as st -import pandas as pd -from pyecharts import options as opts -from pyecharts.charts import Bar, Line -from pyecharts.commons.utils import JsCode -import numpy as np -from cpc_iri_web import modelos_dinamicos, modelos_estatisticos, df_table - -#@st.cache_data -def plot_oni(): - - df = pd.read_table('https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt', delim_whitespace=True) - - data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0]) - data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1]) - daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M') - index = daterange.strftime('%b/%Y').tolist() - data = df['ANOM'].values.tolist() - - color_function = """ - function (params) { - if (params.value > 0) { - return 'red'; - } else { - return 'blue'; - } - } - """ - - oni_plot = ( - Bar() - .add_xaxis(index) - .add_yaxis("Indice ONI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))) - .set_global_opts(title_opts=opts.TitleOpts(title="Índice ONI", subtitle="Fonte: NOAA/CPC", subtitle_link='https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php'), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(is_show=False), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - ) - - return oni_plot - -############################################################################################################### - -#@st.cache_data -def plot_oni_season(): - - df = pd.read_table('https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt', delim_whitespace=True) - - data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0]) - data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1]) - daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M') - index = daterange.strftime('%b/%Y').tolist() - data = df['ANOM'].values.tolist() - - dff = df.copy() - dff.index = index - dff_resample = dff['ANOM'].rolling(3).mean().dropna() - - new_index = [] - for i in range(len(index)): - if i+2 < len(index) and i+1 < len(index): - new_index.append(index[i][0] + index[i+1][0] + index[i+2][0] + index[i+2][3:]) - dff_resample.index = new_index - - data = dff_resample.round(2).values.tolist() - - color_function = """ - function (params) { - if (params.value > 0) { - return 'red'; - } else { - return 'blue'; - } - } - """ - - oni_plot = ( - Bar() - .add_xaxis(new_index) - .add_yaxis("Indice ONI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))) - .set_global_opts(title_opts=opts.TitleOpts(title="Índice ONI", subtitle="Fonte: NOAA/CPC", subtitle_link='https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php'), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(is_show=False), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - ) - - return oni_plot - -############################################################################################################### - -#@st.cache_data -def plot_sst_indexes(nino): - - df = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices', delim_whitespace=True) - data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0]) - data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1]) - daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M') - index = daterange.strftime('%b/%Y').tolist() - - if nino == 'nino12': - data = df['ANOM'].values.tolist() - title = 'Anomalia TSM Niño 1+2 Index' - elif nino == 'nino3': - data = df['ANOM.1'].values.tolist() - title = 'Anomalia TSM Niño 3 Index' - elif nino == 'nino34': - data = df['ANOM.2'].values.tolist() - title = 'Anomalia TSM Niño 3+4 Index' - elif nino == 'nino4': - data = df['ANOM.3'].values.tolist() - title = 'Anomalia TSM Niño 4 Index' - - color_function = """ - function (params) { - if (params.value > 0) { - return 'red'; - } else { - return 'blue'; - } - } - """ - - oni_plot = ( - - Bar() - .add_xaxis(index) - .add_yaxis("Indice SST", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))) - .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/sst'), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(is_show=False), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - ) - - return oni_plot - - -############################################################################################################### - -#@st.cache_data -def plot_sst_indexes_season(nino): - - df = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices', delim_whitespace=True) - data_inicial = str(df['MON'].values[0]) + '/' + str(df['YR'].values[0]) - data_final = str(df['MON'].values[-1]) + '/' + str(df['YR'].values[-1]) - daterange = pd.date_range(*(pd.to_datetime([data_inicial, data_final], format='%m/%Y') + pd.offsets.MonthEnd()), freq='M') - index = daterange.strftime('%b/%Y').tolist() - dff = df.copy() - dff.index = index - - if nino == 'nino12': - data = dff['ANOM'] - title = 'Anomalia TSM Niño 1+2 Index' - elif nino == 'nino3': - data = dff['ANOM.1'] - title = 'Anomalia TSM Niño 3 Index' - elif nino == 'nino34': - data = dff['ANOM.2'] - title = 'Anomalia TSM Niño 3+4 Index' - elif nino == 'nino4': - data = dff['ANOM.3'] - title = 'Anomalia TSM Niño 4 Index' - - dff_resample = data.rolling(3).mean().dropna() - - new_index = [] - for i in range(len(index)): - if i+2 < len(index) and i+1 < len(index): - new_index.append(index[i][0] + index[i+1][0] + index[i+2][0] + index[i+2][3:]) - dff_resample.index = new_index - - data = dff_resample.round(2).values.tolist() - - color_function = """ - function (params) { - if (params.value > 0) { - return 'red'; - } else { - return 'blue'; - } - } - """ - - oni_plot = ( - - Bar() - .add_xaxis(new_index) - .add_yaxis("Indice SST", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))) - .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/sst'), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(is_show=False), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - ) - - return oni_plot - -############################################################################################################### - -#@st.cache_data -def plot_soi(): - - soi = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/soi', delim_whitespace=True, skiprows=87, index_col=0) - soi = soi[0:72] - soi.loc['2023'] = [1.4, 1.4, 0.2, 0.2, 1.0, 0.3, -0.3, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN] # INSERINDO MANUALMENTE OS VALORES - t = soi.loc[soi.index[0]] - for h in soi.index[1:]: - if h == soi.index[1]: - r = pd.concat([t, soi.loc[h]]) - else: - r = pd.concat([r, soi.loc[h]]) - df_soi = pd.DataFrame(r).astype(float) - date_range2 = pd.date_range(start='1/1/1951', end='1/1/2024', freq='M') - df_soi.index = date_range2 - df_soi.columns = ['Índice SOI'] - index = df_soi.index.strftime('%b/%Y').tolist() - data = df_soi['Índice SOI'].tolist() - title = 'Índice SOI' - - color_function = """ - function (params) { - if (params.value > 0) { - return 'red'; - } else { - return 'blue'; - } - } - """ - - soi_plot = ( - - Bar() - .add_xaxis(index) - .add_yaxis("Indice SOI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))) - .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/soi'), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(is_show=False), - #yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - ) - - return soi_plot - -############################################################################################################### - -#@st.cache_data -def plot_soi_season(): - - soi = pd.read_table('https://www.cpc.ncep.noaa.gov/data/indices/soi', delim_whitespace=True, skiprows=87, index_col=0) - soi = soi[0:72] - soi.loc['2023'] = [1.4, 1.4, 0.2, 0.2, 1.0, 0.3, -0.3, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN] # INSERINDO MANUALMENTE OS VALORES - t = soi.loc[soi.index[0]] - for h in soi.index[1:]: - if h == soi.index[1]: - r = pd.concat([t, soi.loc[h]]) - else: - r = pd.concat([r, soi.loc[h]]) - df_soi = pd.DataFrame(r).astype(float) - date_range2 = pd.date_range(start='1/1/1951', end='1/1/2024', freq='M') - df_soi.index = date_range2 - df_soi.columns = ['Índice SOI'] - index = df_soi.index.strftime('%b/%Y').tolist() - data = df_soi['Índice SOI'].tolist() - title = 'Índice SOI' - - dff = df_soi.copy() - dff.index = index - dff_resample = dff.rolling(3).mean() - dff_resample = dff[2:] - - new_index = [] - for i in range(len(dff.index)): - if i+2 < len(dff.index) and i+1 < len(dff.index): - new_index.append(dff.index[i][0] + dff.index[i+1][0] + dff.index[i+2][0] + dff.index[i+2][3:]) - - dff_resample.index = new_index - data = dff_resample['Índice SOI'].dropna().values.tolist() - - color_function = """ - function (params) { - if (params.value > 0) { - return 'red'; - } else { - return 'blue'; - } - } - """ - - soi_plot = ( - - Bar() - .add_xaxis(new_index) - .add_yaxis("Indice SOI", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))) - .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/soi'), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(is_show=False), - #yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - ) - - return soi_plot - -############################################################################################################### - -def plot_poama(): - - csv = 'https://raw.githubusercontent.com/josepaulo1233/enso-assets/main/xlsx/poama.csv' - df = pd.read_csv(csv, sep=';') - df.columns = ['Mês', 'Anomalia região 3.4', 'Abaixo', 'Neutro', 'Acima'] - - index = df['Mês'].values.tolist() - title = 'Previsão POAMA' - data_anomalia = df['Anomalia região 3.4'].values.tolist() - data_abaixo = (df['Abaixo']*100).values.tolist() - data_neutro = (df['Neutro']*100).values.tolist() - data_acima = (df['Acima']*100).values.tolist() - - poama_plot_bar = ( - - Bar() - .add_xaxis(index) - .add_yaxis("La Niña", data_abaixo, color='blue') - .add_yaxis("Neutro", data_neutro, color='green') - .add_yaxis("El Niño", data_acima, color='red') - .set_global_opts(title_opts=opts.TitleOpts( - #title=title, - # subtitle="Fonte: POAMA", subtitle_link='http://www.bom.gov.au/climate/ocean/outlooks/' - ), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), - xaxis_opts=opts.AxisOpts(axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), splitline_opts=opts.SplitLineOpts(is_show=False)), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} %"), - name='Probabilidade', - name_location='middle', - name_gap=100, - name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold') - ), - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - .extend_axis(yaxis=opts.AxisOpts( - type_="value", - min_=-1.5, - max_=3.5, - interval=1, - axislabel_opts=opts.LabelOpts(formatter="{value} °C"), - ) - ) - - ) - - poama_plot_line = ( - - Line() - .add_xaxis(index) - .add_yaxis("Anomalia de TSM", - data_anomalia, - color='black', - linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=10, - #color=JsCode(color_function), - #itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)), - yaxis_index=1, - ) - - ) - - poama_plot_bar.overlap(poama_plot_line) - - return poama_plot_bar - -############################################################################################################### - -def plot_cpc_iri_dinamico(): - - data1 = modelos_dinamicos['GFDL SPEAR'].values.tolist() - data2 = modelos_dinamicos['AUS-ACCESS'].values.tolist() - data3 = modelos_dinamicos['BCC_CSM11m'].values.tolist() - data4 = modelos_dinamicos['COLA CCSM4'].values.tolist() - data5 = modelos_dinamicos['CS-IRI-MM'].values.tolist() - data6 = modelos_dinamicos['DWD'].values.tolist() - data7 = modelos_dinamicos['ECMWF'].values.tolist() - data8 = modelos_dinamicos['IOCAS ICM'].values.tolist() - data9 = modelos_dinamicos['JMA'].values.tolist() - data10 = modelos_dinamicos['KMA'].values.tolist() - data11 = modelos_dinamicos['LDEO'].values.tolist() - data12 = modelos_dinamicos['MetFRANCE'].values.tolist() - data13 = modelos_dinamicos['NASA GMAO'].values.tolist() - data14 = modelos_dinamicos['NCEP CFSv2'].values.tolist() - data15 = modelos_dinamicos['SINTEX-F'].values.tolist() - data16 = modelos_dinamicos['UKMO'].values.tolist() - data17 = modelos_dinamicos['Média'].round(2).values.tolist() - - index = modelos_dinamicos.index.values.tolist() - - cpc_plot = ( - - Line() - .add_xaxis(index) - .add_yaxis("GFDL SPEAR", data1, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("AUS-ACCESS", data2, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("BCC_CSM11m", data3, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("COLA CCSM4", data4, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("CS-IRI-MM", data5, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("DWD", data6, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("ECMWF", data7, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("IOCAS ICM", data8, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("JMA", data9, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("KMA", data10, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("LDEO", data11, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("MetFRANCE", data12, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("NASA GMAO", data13, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("NCEP CFSv2", data14, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("SINTEX-F", data15, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("UKMO", data16, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("Média Dinâmicos", data17, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=10, itemstyle_opts={"emphasis": {"focus": "series"}}) - .set_global_opts(title_opts=opts.TitleOpts( - title='Modelos dinâmicos', - #subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/access/monitoring/enso/soi' - ), - tooltip_opts=opts.TooltipOpts(is_show=True, - trigger="item", - #axis_pointer_type="cross", - #trigger="item", - #trigger_on="mousemove" - ), - legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C"), - name='Anomalia de TSM (°C) na região do Niño 3.4', - name_location='middle', - name_gap=100, - name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold') - ), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - #datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False), ) - ) - - return cpc_plot - -############################################################################################################### - -def plot_cpc_iri_estatistico(): - - data1 = modelos_estatisticos['BCC_RZDM'].values.tolist() - data2 = modelos_estatisticos['CDC LIM'].values.tolist() - data3 = modelos_estatisticos['CPC CA'].values.tolist() - data4 = modelos_estatisticos['CPC CCA'].values.tolist() - data5 = modelos_estatisticos['CPC MRKOV'].values.tolist() - data6 = modelos_estatisticos['CSU CLIPR'].values.tolist() - data7 = modelos_estatisticos['FSU REGR'].values.tolist() - data8 = modelos_estatisticos['IAP-NN'].values.tolist() - data9 = modelos_estatisticos['NTU CODA'].values.tolist() - data10 = modelos_estatisticos['PSD-CU LIM'].values.tolist() - data11 = modelos_estatisticos['UBC NNET'].values.tolist() - data12 = modelos_estatisticos['UNB/CWC'].values.tolist() - data13 = modelos_estatisticos['UCLA'].values.tolist() - data14 = modelos_estatisticos['UCLA-TCD'].values.tolist() - data15 = modelos_estatisticos['UW PSL-CSLIM'].values.tolist() - data16 = modelos_estatisticos['UW PSL-LIM'].values.tolist() - data17 = modelos_estatisticos['Média'].round(2).values.tolist() - - index = modelos_estatisticos.index.values.tolist() - - cpc_plot = ( - - Line() - .add_xaxis(index) - .add_yaxis("BCC_RZDM", data1, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("CDC LIM", data2) - .add_yaxis("CPC CA", data3, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("CPC CCA", data4) - .add_yaxis("CPC MRKOV", data5, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("CSU CLIPR", data6, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("FSU REGR", data7) - .add_yaxis("IAP-NN", data8, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("NTU CODA", data9, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("PSD-CU LIM", data10) - #.add_yaxis("UBC NNET", data11) - #.add_yaxis("UNB/CWC", data12) - #.add_yaxis("UCLA", data13) - .add_yaxis("UCLA-TCD", data14, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("UW PSL-CSLIM", data15, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("UW PSL-LIM", data16, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("Média Estatisticos", data17, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=10, itemstyle_opts={"emphasis": {"focus": "series"}}) - .set_global_opts(title_opts=opts.TitleOpts(title='Modelos estatísticos', - ), - tooltip_opts=opts.TooltipOpts(is_show=True, - trigger="item", - ), - legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C"), - name='Anomalia de TSM (°C) na região do Niño 3.4', - name_location='middle', - name_gap=100, - name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold') - ), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False), ) - ) - - return cpc_plot - -############################################################################################################### - -def plot_cpc_iri_todos(): - - data1 = modelos_estatisticos['BCC_RZDM'].values.tolist() - data2 = modelos_estatisticos['CDC LIM'].values.tolist() - data3 = modelos_estatisticos['CPC CA'].values.tolist() - data4 = modelos_estatisticos['CPC CCA'].values.tolist() - data5 = modelos_estatisticos['CPC MRKOV'].values.tolist() - data6 = modelos_estatisticos['CSU CLIPR'].values.tolist() - data7 = modelos_estatisticos['FSU REGR'].values.tolist() - data8 = modelos_estatisticos['IAP-NN'].values.tolist() - data9 = modelos_estatisticos['NTU CODA'].values.tolist() - data10 = modelos_estatisticos['PSD-CU LIM'].values.tolist() - data11 = modelos_estatisticos['UBC NNET'].values.tolist() - data12 = modelos_estatisticos['UNB/CWC'].values.tolist() - data13 = modelos_estatisticos['UCLA'].values.tolist() - data14 = modelos_estatisticos['UCLA-TCD'].values.tolist() - data15 = modelos_estatisticos['UW PSL-CSLIM'].values.tolist() - data16 = modelos_estatisticos['UW PSL-LIM'].values.tolist() - data17 = modelos_estatisticos['Média'].round(2).values.tolist() - - data18 = modelos_dinamicos['GFDL SPEAR'].values.tolist() - data19 = modelos_dinamicos['AUS-ACCESS'].values.tolist() - data20 = modelos_dinamicos['BCC_CSM11m'].values.tolist() - data21 = modelos_dinamicos['COLA CCSM4'].values.tolist() - data22 = modelos_dinamicos['CS-IRI-MM'].values.tolist() - data23 = modelos_dinamicos['DWD'].values.tolist() - data24 = modelos_dinamicos['ECMWF'].values.tolist() - data25 = modelos_dinamicos['IOCAS ICM'].values.tolist() - data26 = modelos_dinamicos['JMA'].values.tolist() - data27 = modelos_dinamicos['KMA'].values.tolist() - data28 = modelos_dinamicos['LDEO'].values.tolist() - data29 = modelos_dinamicos['MetFRANCE'].values.tolist() - data30 = modelos_dinamicos['NASA GMAO'].values.tolist() - data31 = modelos_dinamicos['NCEP CFSv2'].values.tolist() - data32 = modelos_dinamicos['SINTEX-F'].values.tolist() - data33 = modelos_dinamicos['UKMO'].values.tolist() - data34 = modelos_dinamicos['Média'].round(2).values.tolist() - - - index = modelos_estatisticos.index.values.tolist() - - cpc_plot = ( - - Line() - .add_xaxis(index) - .add_yaxis("BCC_RZDM", data1, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("CDC LIM", data2) - .add_yaxis("CPC CA", data3, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("CPC CCA", data4) - .add_yaxis("CPC MRKOV", data5, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("CSU CLIPR", data6, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("FSU REGR", data7) - .add_yaxis("IAP-NN", data8, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("NTU CODA", data9, itemstyle_opts={"emphasis": {"focus": "series"}}) - #.add_yaxis("PSD-CU LIM", data10) - #.add_yaxis("UBC NNET", data11) - #.add_yaxis("UNB/CWC", data12) - #.add_yaxis("UCLA", data13) - .add_yaxis("UCLA-TCD", data14, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("UW PSL-CSLIM", data15, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("UW PSL-LIM", data16, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("M.Estatistica", data17, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=13, symbol='triangle', itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("GFDL SPEAR", data18, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("AUS-ACCESS", data19, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("BCC_CSM11m", data20, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("COLA CCSM4", data21, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("CS-IRI-MM", data22, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("DWD", data23, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("ECMWF", data24, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("IOCAS ICM", data25, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("JMA", data26, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("KMA", data27, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("LDEO", data28, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("MetFRANCE", data29, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("NASA GMAO", data30, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("NCEP CFSv2", data31, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("SINTEX-F", data32, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("UKMO", data33, itemstyle_opts={"emphasis": {"focus": "series"}}) - .add_yaxis("M.Dinamica", data34, linestyle_opts=opts.LineStyleOpts(width=5), symbol_size=13, symbol='rect', itemstyle_opts={"emphasis": {"focus": "series"}}) - .set_global_opts(title_opts=opts.TitleOpts(title='Todos modelos', - ), - tooltip_opts=opts.TooltipOpts(is_show=True, - trigger="item", - position='bottom' - ), - legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C"), - name='Anomalia de TSM (°C) na região do Niño 3.4', - name_location='middle', - name_gap=100, - name_textstyle_opts=opts.TextStyleOpts(font_size=13, font_weight='bold') - ), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False), ) - ) - - return cpc_plot - -############################################################################################################### - -def plot_iri_concenso(): - - data_nina = df_table['La Niña'].values.tolist() - data_neutro = df_table['Neutral'].values.tolist() - data_nino = df_table['El Niño'].values.tolist() - index = df_table.index.to_list() - title = 'Previsão por consenso' - - cpc_bar = ( - - Bar() - .add_xaxis(index) - .add_yaxis("La Niña", data_nina, color='blue') - .add_yaxis("Neutro", data_neutro, color='green') - .add_yaxis("El Niño", data_nino, color='red') - .set_global_opts(title_opts=opts.TitleOpts(title=title), - tooltip_opts=opts.TooltipOpts(is_show=True), - legend_opts=opts.LegendOpts(orient='horizontal', type_='scroll', pos_top='bottom', item_gap=23, border_width=0), - xaxis_opts=opts.AxisOpts(axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), splitline_opts=opts.SplitLineOpts(is_show=False)), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} %"), - name='Probabilidade', - name_location='middle', - name_gap=100, - name_textstyle_opts=opts.TextStyleOpts(font_size=14, font_weight='bold') - ), - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - - ) - - return cpc_bar - -############################################################################################################### -#@st.cache_data -def plot_daily_ssta(nino, days): - - df = pd.read_table('https://conmet.com.br/public/txt_enso/sst_nino_daily.txt', delim_whitespace=True, index_col=0, parse_dates=True) - - df = df[-int(days):] - index = df.index.strftime('%d/%b/%Y').tolist() - - if nino == 'nino12': - data = df['anom12'].round(2).values.tolist() - title = 'Anomalia TSM Niño 1+2' - elif nino == 'nino3': - data = df['anom3'].round(2).values.tolist() - title = 'Anomalia TSM Niño 3' - elif nino == 'nino34': - data = df['anom34'].round(2).values.tolist() - title = 'Anomalia TSM Niño 3+4' - elif nino == 'nino4': - data = df['anom4'].round(2).values.tolist() - title = 'Anomalia TSM Niño 4' - - color_function = """ - function (params) { - if (params.value > 0) { - return 'red'; - } else { - return 'blue'; - } - } - """ - - oni_plot = ( - - Bar() - .add_xaxis(index) - .add_yaxis("Indice SST", data, itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))) - .set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle="Fonte: NCEI/NOAA", subtitle_link='https://www.ncei.noaa.gov/products/extended-reconstructed-sst'), - tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), - legend_opts=opts.LegendOpts(is_show=False), - yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} °C")), - xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)), - datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=len(index)), opts.DataZoomOpts(type_="inside")] - ) - .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) - ) - - return oni_plot diff --git a/spaces/juancopi81/whisper-youtube-2-hf_dataset/test/test_video.py b/spaces/juancopi81/whisper-youtube-2-hf_dataset/test/test_video.py deleted file mode 100644 index 7b28a3305c17c50ed375d6e80c74ccc94891f4b9..0000000000000000000000000000000000000000 --- a/spaces/juancopi81/whisper-youtube-2-hf_dataset/test/test_video.py +++ /dev/null @@ -1,40 +0,0 @@ -import pytest - -from youtube_transcriber.video import YoutubeVideo - -def test_youtube_video_init(): - video = YoutubeVideo(channel_name="The verge", - url="https://www.youtube.com/watch?v=Jzl0hHTc7Jw", - title="Pixel 7 Pro and 7 hands-on: more of the same", - description="Google’s Pixel 7 and 7 Pro...", - transcription=" Seven years ago, we set out...", - segments=[{"start": 0.0, "end": 1.3, "text": " Seven years ago"}, - {"start": 1.3, "end": 2.3, "text": " we set out..."}]) - - assert type(video) == YoutubeVideo - assert video.channel_name == "The verge" - assert video.url == "https://www.youtube.com/watch?v=Jzl0hHTc7Jw" - assert video.title == "Pixel 7 Pro and 7 hands-on: more of the same" - assert video.description == "Google’s Pixel 7 and 7 Pro..." - assert video.transcription == " Seven years ago, we set out..." - assert video.segments == [{"start": 0.0, "end": 1.3, "text": " Seven years ago"}, - {"start": 1.3, "end": 2.3, "text": " we set out..."}] - -def test_youtube_video_to_tuple(): - video = YoutubeVideo(channel_name="The verge", - url="https://www.youtube.com/watch?v=Jzl0hHTc7Jw", - title="Pixel 7 Pro and 7 hands-on: more of the same", - description="Google’s Pixel 7 and 7 Pro...", - transcription=" Seven years ago, we set out...", - segments=[{"start": 0.0, "end": 1.3, "text": " Seven years ago"}, - {"start": 1.3, "end": 2.3, "text": " we set out..."}]) - video_tuple = video.to_tuple() - assert len(video_tuple) == 6 - assert type(video_tuple) == tuple - assert video_tuple[0] == "The verge" - assert video_tuple[1] == "https://www.youtube.com/watch?v=Jzl0hHTc7Jw" - assert video_tuple[2] == "Pixel 7 Pro and 7 hands-on: more of the same" - assert video_tuple[3] == "Google’s Pixel 7 and 7 Pro..." - assert video_tuple[4] == " Seven years ago, we set out..." - assert video_tuple[5] == [{"start": 0.0, "end": 1.3, "text": " Seven years ago"}, - {"start": 1.3, "end": 2.3, "text": " we set out..."}] \ No newline at end of file diff --git a/spaces/kangvcar/RealChar/alembic/env.py b/spaces/kangvcar/RealChar/alembic/env.py deleted file mode 100644 index afec9de93130cbd586d5bd02e73efbbece9b4182..0000000000000000000000000000000000000000 --- a/spaces/kangvcar/RealChar/alembic/env.py +++ /dev/null @@ -1,92 +0,0 @@ -from realtime_ai_character.models.user import User -from realtime_ai_character.models.interaction import Interaction -from realtime_ai_character.database.base import Base # import the Base model -from sqlalchemy import engine_from_config -from sqlalchemy import pool -from alembic import context -from logging.config import fileConfig -import sys -import os -from dotenv import load_dotenv - -load_dotenv() - -# Add the project root to the system path -root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.append(root) - -# import your models here - -# this is the Alembic Config object, which provides access to the values -# within the .ini file in use. -config = context.config -database_url = os.getenv('DATABASE_URL') if os.getenv( - 'DATABASE_URL') else 'sqlite:///./test.db' -config.set_main_option('sqlalchemy.url', database_url) - -# Interpret the config file for Python logging. -# This line sets up loggers basically. -fileConfig(config.config_file_name) - -# add your model's MetaData object here -# for 'autogenerate' support -# from myapp import mymodel -# target_metadata = mymodel.Base.metadata -target_metadata = Base.metadata # use your Base metadata - -# other values from the config, defined by the needs of env.py, -# can be acquired: -# my_important_option = config.get_main_option("my_important_option") -# ... etc. - - -def run_migrations_offline() -> None: - """Run migrations in 'offline' mode. - - This configures the context with just a URL - and not an Engine, though an Engine is acceptable - here as well. By skipping the Engine creation - we don't even need a DBAPI to be available. - - Calls to context.execute() here emit the given string to the - script output. - - """ - url = config.get_main_option("sqlalchemy.url") - context.configure( - url=url, - target_metadata=target_metadata, - literal_binds=True, - dialect_opts={"paramstyle": "named"}, - ) - - with context.begin_transaction(): - context.run_migrations() - - -def run_migrations_online() -> None: - """Run migrations in 'online' mode. - - In this scenario we need to create an Engine - and associate a connection with the context. - - """ - connectable = engine_from_config( - config.get_section(config.config_ini_section, {}), - prefix="sqlalchemy.", - poolclass=pool.NullPool, - ) - - with connectable.connect() as connection: - context.configure( - connection=connection, target_metadata=target_metadata - ) - - with context.begin_transaction(): - context.run_migrations() - - -if context.is_offline_mode(): - run_migrations_offline() -else: - run_migrations_online() diff --git a/spaces/kevinwang676/DreamlikeArt-PhotoReal-2.0/app.py b/spaces/kevinwang676/DreamlikeArt-PhotoReal-2.0/app.py deleted file mode 100644 index 15177480e4d04cef9b8cbd85f98e1eb7ce39d618..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/DreamlikeArt-PhotoReal-2.0/app.py +++ /dev/null @@ -1,157 +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 -import emoji - -text_gen=gr.Interface.load("spaces/phenomenon1981/MagicPrompt-Stable-Diffusion") -def get_prompts(prompt_text): - if prompt_text: - return text_gen("photo, " + prompt_text) - else: - return text_gen("") -proc1=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-2.0") - -def restart_script_periodically(): - while True: - random_time = random.randint(540, 600) - time.sleep(random_time) - os.execl(sys.executable, sys.executable, *sys.argv) - - -restart_thread = Thread(target=restart_script_periodically, daemon=True) -restart_thread.start() - - -queue = Queue() -queue_threshold = 100 - -def add_random_noise(prompt, noise_level=0.00): - if noise_level == 0: - noise_level = 0.00 - percentage_noise = noise_level * 5 - num_noise_chars = int(len(prompt) * (percentage_noise/100)) - noise_indices = random.sample(range(len(prompt)), num_noise_chars) - prompt_list = list(prompt) - noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits) - noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈']) - 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(prompt_with_noise) - 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(prompt_with_noise) - 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(prompt_with_noise) - #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(prompt_with_noise) - #return output4 - - - -with gr.Blocks(css='style.css') as demo: - gr.HTML( - """ - <div style="text-align: center; max-width: 650px; margin: 0 auto;"> - <div> - <h1 style="font-weight: 900; font-size: 3rem; margin-bottom:20px;"> - Dreamlike Photoreal 2.0 - </h1> - </div> - <p style="margin-bottom: 10px; font-size: 96%"> - 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, - <a href="https://twitter.com/DavidJohnstonxx/">created by Phenomenon1981</a>. - </p> - <p style="margin-bottom: 10px; font-size: 98%"> - ❤️ Press the Like Button if you enjoy my space! ❤️</a> - </p> - </div> - """ - ) - with gr.Column(elem_id="col-container"): - with gr.Row(variant="compact"): - input_text = gr.Textbox( - label="Short Prompt", - show_label=False, - max_lines=2, - placeholder="Enter a basic idea and click 'Magic Prompt'. Got no ideas? No problem, Simply just hit the magic button!", - ).style( - container=False, - ) - see_prompts = gr.Button("✨ Magic Prompt ✨").style(full_width=False) - - - with gr.Row(variant="compact"): - prompt = gr.Textbox( - label="Enter your prompt", - show_label=False, - max_lines=2, - placeholder="Full Prompt", - ).style( - container=False, - ) - run = gr.Button("Generate Images").style(full_width=False) - - with gr.Row(): - with gr.Row(): - noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="Noise Level") - with gr.Row(): - with gr.Row(): - output1=gr.Image(label="Dreamlike-photoreal-2.0",show_label=False) - output2=gr.Image(label="Dreamlike-photoreal-2.0",show_label=False) - - #with gr.Row(): - #output1=gr.Image() - - see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False) - run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1]) - run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2]) - - - - with gr.Row(): - gr.HTML( - """ - <div class="footer"> - <p> Demo for <a href="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0">Dreamlike Photoreal 2.0</a> Stable Diffusion model -</p> -</div> - <div class="acknowledgments" style="font-size: 115%"> - <p> Unleash your creative side and generate mesmerizing images with just a few clicks! Enter a spark of inspiration in the "Basic Idea" text box and click the "Magic Prompt" button to elevate it to a polished masterpiece. Make any final tweaks in the "Full Prompt" box and hit the "Generate Images" button to watch your vision come to life. Experiment with the "Noise Level" for a diverse range of outputs, from similar to wildly unique. Let the fun begin! - </p> - </div> - """ -) - - demo.launch(enable_queue=True, inline=True, show_error=True) - block.queue(concurrency_count=100) \ No newline at end of file diff --git a/spaces/kevinwang676/FreeVC-en/speaker_encoder/params_model.py b/spaces/kevinwang676/FreeVC-en/speaker_encoder/params_model.py deleted file mode 100644 index 3e356472fb5a27f370cb3920976a11d12a76c1b7..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/FreeVC-en/speaker_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/kevinwang676/M4Singer/tasks/tts/fs2.py b/spaces/kevinwang676/M4Singer/tasks/tts/fs2.py deleted file mode 100644 index 32fb54f5bda486ece04598673cff367f5d8844fa..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/M4Singer/tasks/tts/fs2.py +++ /dev/null @@ -1,512 +0,0 @@ -import matplotlib - -matplotlib.use('Agg') - -from utils import audio -import matplotlib.pyplot as plt -from data_gen.tts.data_gen_utils import get_pitch -from tasks.tts.fs2_utils import FastSpeechDataset -from utils.cwt import cwt2f0 -from utils.pl_utils import data_loader -import os -from multiprocessing.pool import Pool -from tqdm import tqdm -from modules.fastspeech.tts_modules import mel2ph_to_dur -from utils.hparams import hparams -from utils.plot import spec_to_figure, dur_to_figure, f0_to_figure -from utils.pitch_utils import denorm_f0 -from modules.fastspeech.fs2 import FastSpeech2 -from tasks.tts.tts import TtsTask -import torch -import torch.optim -import torch.utils.data -import torch.nn.functional as F -import utils -import torch.distributions -import numpy as np -from modules.commons.ssim import ssim - -class FastSpeech2Task(TtsTask): - def __init__(self): - super(FastSpeech2Task, self).__init__() - self.dataset_cls = FastSpeechDataset - self.mse_loss_fn = torch.nn.MSELoss() - mel_losses = hparams['mel_loss'].split("|") - self.loss_and_lambda = {} - for i, l in enumerate(mel_losses): - if l == '': - continue - if ':' in l: - l, lbd = l.split(":") - lbd = float(lbd) - else: - lbd = 1.0 - self.loss_and_lambda[l] = lbd - print("| Mel losses:", self.loss_and_lambda) - self.sil_ph = self.phone_encoder.sil_phonemes() - - @data_loader - def train_dataloader(self): - train_dataset = self.dataset_cls(hparams['train_set_name'], shuffle=True) - return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences, - endless=hparams['endless_ds']) - - @data_loader - def val_dataloader(self): - valid_dataset = self.dataset_cls(hparams['valid_set_name'], shuffle=False) - return self.build_dataloader(valid_dataset, False, self.max_eval_tokens, self.max_eval_sentences) - - @data_loader - def test_dataloader(self): - test_dataset = self.dataset_cls(hparams['test_set_name'], shuffle=False) - return self.build_dataloader(test_dataset, False, self.max_eval_tokens, - self.max_eval_sentences, batch_by_size=False) - - def build_tts_model(self): - self.model = FastSpeech2(self.phone_encoder) - - def build_model(self): - self.build_tts_model() - if hparams['load_ckpt'] != '': - self.load_ckpt(hparams['load_ckpt'], strict=True) - utils.print_arch(self.model) - return self.model - - def _training_step(self, sample, batch_idx, _): - loss_output = self.run_model(self.model, sample) - total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) - loss_output['batch_size'] = sample['txt_tokens'].size()[0] - return total_loss, loss_output - - def validation_step(self, sample, batch_idx): - outputs = {} - outputs['losses'] = {} - outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) - outputs['total_loss'] = sum(outputs['losses'].values()) - outputs['nsamples'] = sample['nsamples'] - mel_out = self.model.out2mel(model_out['mel_out']) - outputs = utils.tensors_to_scalars(outputs) - # if sample['mels'].shape[0] == 1: - # self.add_laplace_var(mel_out, sample['mels'], outputs) - if batch_idx < hparams['num_valid_plots']: - self.plot_mel(batch_idx, sample['mels'], mel_out) - self.plot_dur(batch_idx, sample, model_out) - if hparams['use_pitch_embed']: - self.plot_pitch(batch_idx, sample, model_out) - return outputs - - def _validation_end(self, outputs): - all_losses_meter = { - 'total_loss': utils.AvgrageMeter(), - } - for output in outputs: - n = output['nsamples'] - for k, v in output['losses'].items(): - if k not in all_losses_meter: - all_losses_meter[k] = utils.AvgrageMeter() - all_losses_meter[k].update(v, n) - all_losses_meter['total_loss'].update(output['total_loss'], n) - return {k: round(v.avg, 4) for k, v in all_losses_meter.items()} - - def run_model(self, model, sample, return_output=False): - txt_tokens = sample['txt_tokens'] # [B, T_t] - target = sample['mels'] # [B, T_s, 80] - mel2ph = sample['mel2ph'] # [B, T_s] - f0 = sample['f0'] - uv = sample['uv'] - energy = sample['energy'] - spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') - if hparams['pitch_type'] == 'cwt': - cwt_spec = sample[f'cwt_spec'] - f0_mean = sample['f0_mean'] - f0_std = sample['f0_std'] - sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) - - output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, - ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False) - - losses = {} - self.add_mel_loss(output['mel_out'], target, losses) - self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) - if hparams['use_pitch_embed']: - self.add_pitch_loss(output, sample, losses) - if hparams['use_energy_embed']: - self.add_energy_loss(output['energy_pred'], energy, losses) - if not return_output: - return losses - else: - return losses, output - - ############ - # losses - ############ - def add_mel_loss(self, mel_out, target, losses, postfix='', mel_mix_loss=None): - if mel_mix_loss is None: - for loss_name, lbd in self.loss_and_lambda.items(): - if 'l1' == loss_name: - l = self.l1_loss(mel_out, target) - elif 'mse' == loss_name: - raise NotImplementedError - elif 'ssim' == loss_name: - l = self.ssim_loss(mel_out, target) - elif 'gdl' == loss_name: - raise NotImplementedError - losses[f'{loss_name}{postfix}'] = l * lbd - else: - raise NotImplementedError - - def l1_loss(self, decoder_output, target): - # decoder_output : B x T x n_mel - # target : B x T x n_mel - l1_loss = F.l1_loss(decoder_output, target, reduction='none') - weights = self.weights_nonzero_speech(target) - l1_loss = (l1_loss * weights).sum() / weights.sum() - return l1_loss - - def ssim_loss(self, decoder_output, target, bias=6.0): - # decoder_output : B x T x n_mel - # target : B x T x n_mel - assert decoder_output.shape == target.shape - weights = self.weights_nonzero_speech(target) - decoder_output = decoder_output[:, None] + bias - target = target[:, None] + bias - ssim_loss = 1 - ssim(decoder_output, target, size_average=False) - ssim_loss = (ssim_loss * weights).sum() / weights.sum() - return ssim_loss - - def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, losses=None): - """ - - :param dur_pred: [B, T], float, log scale - :param mel2ph: [B, T] - :param txt_tokens: [B, T] - :param losses: - :return: - """ - B, T = txt_tokens.shape - nonpadding = (txt_tokens != 0).float() - dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding - is_sil = torch.zeros_like(txt_tokens).bool() - for p in self.sil_ph: - is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) - is_sil = is_sil.float() # [B, T_txt] - - # phone duration loss - if hparams['dur_loss'] == 'mse': - losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') - losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() - dur_pred = (dur_pred.exp() - 1).clamp(min=0) - elif hparams['dur_loss'] == 'mog': - return NotImplementedError - elif hparams['dur_loss'] == 'crf': - losses['pdur'] = -self.model.dur_predictor.crf( - dur_pred, dur_gt.long().clamp(min=0, max=31), mask=nonpadding > 0, reduction='mean') - losses['pdur'] = losses['pdur'] * hparams['lambda_ph_dur'] - - # use linear scale for sent and word duration - if hparams['lambda_word_dur'] > 0: - word_id = (is_sil.cumsum(-1) * (1 - is_sil)).long() - word_dur_p = dur_pred.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_pred)[:, 1:] - word_dur_g = dur_gt.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_gt)[:, 1:] - wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') - word_nonpadding = (word_dur_g > 0).float() - wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() - losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] - if hparams['lambda_sent_dur'] > 0: - sent_dur_p = dur_pred.sum(-1) - sent_dur_g = dur_gt.sum(-1) - sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') - losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] - - def add_pitch_loss(self, output, sample, losses): - if hparams['pitch_type'] == 'ph': - nonpadding = (sample['txt_tokens'] != 0).float() - pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss - losses['f0'] = (pitch_loss_fn(output['pitch_pred'][:, :, 0], sample['f0'], - reduction='none') * nonpadding).sum() \ - / nonpadding.sum() * hparams['lambda_f0'] - return - mel2ph = sample['mel2ph'] # [B, T_s] - f0 = sample['f0'] - uv = sample['uv'] - nonpadding = (mel2ph != 0).float() - if hparams['pitch_type'] == 'cwt': - cwt_spec = sample[f'cwt_spec'] - f0_mean = sample['f0_mean'] - f0_std = sample['f0_std'] - cwt_pred = output['cwt'][:, :, :10] - f0_mean_pred = output['f0_mean'] - f0_std_pred = output['f0_std'] - losses['C'] = self.cwt_loss(cwt_pred, cwt_spec) * hparams['lambda_f0'] - if hparams['use_uv']: - assert output['cwt'].shape[-1] == 11 - uv_pred = output['cwt'][:, :, -1] - losses['uv'] = (F.binary_cross_entropy_with_logits(uv_pred, uv, reduction='none') * nonpadding) \ - .sum() / nonpadding.sum() * hparams['lambda_uv'] - losses['f0_mean'] = F.l1_loss(f0_mean_pred, f0_mean) * hparams['lambda_f0'] - losses['f0_std'] = F.l1_loss(f0_std_pred, f0_std) * hparams['lambda_f0'] - if hparams['cwt_add_f0_loss']: - f0_cwt_ = self.model.cwt2f0_norm(cwt_pred, f0_mean_pred, f0_std_pred, mel2ph) - self.add_f0_loss(f0_cwt_[:, :, None], f0, uv, losses, nonpadding=nonpadding) - elif hparams['pitch_type'] == 'frame': - self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding) - - def add_f0_loss(self, p_pred, f0, uv, losses, nonpadding): - assert p_pred[..., 0].shape == f0.shape - if hparams['use_uv']: - assert p_pred[..., 1].shape == uv.shape - losses['uv'] = (F.binary_cross_entropy_with_logits( - p_pred[:, :, 1], uv, reduction='none') * nonpadding).sum() \ - / nonpadding.sum() * hparams['lambda_uv'] - nonpadding = nonpadding * (uv == 0).float() - - f0_pred = p_pred[:, :, 0] - if hparams['pitch_loss'] in ['l1', 'l2']: - pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss - losses['f0'] = (pitch_loss_fn(f0_pred, f0, reduction='none') * nonpadding).sum() \ - / nonpadding.sum() * hparams['lambda_f0'] - elif hparams['pitch_loss'] == 'ssim': - return NotImplementedError - - def cwt_loss(self, cwt_p, cwt_g): - if hparams['cwt_loss'] == 'l1': - return F.l1_loss(cwt_p, cwt_g) - if hparams['cwt_loss'] == 'l2': - return F.mse_loss(cwt_p, cwt_g) - if hparams['cwt_loss'] == 'ssim': - return self.ssim_loss(cwt_p, cwt_g, 20) - - def add_energy_loss(self, energy_pred, energy, losses): - nonpadding = (energy != 0).float() - loss = (F.mse_loss(energy_pred, energy, reduction='none') * nonpadding).sum() / nonpadding.sum() - loss = loss * hparams['lambda_energy'] - losses['e'] = loss - - - ############ - # validation plots - ############ - def plot_mel(self, batch_idx, spec, spec_out, name=None): - spec_cat = torch.cat([spec, spec_out], -1) - name = f'mel_{batch_idx}' if name is None else name - vmin = hparams['mel_vmin'] - vmax = hparams['mel_vmax'] - self.logger.experiment.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step) - - def plot_dur(self, batch_idx, sample, model_out): - T_txt = sample['txt_tokens'].shape[1] - dur_gt = mel2ph_to_dur(sample['mel2ph'], T_txt)[0] - dur_pred = self.model.dur_predictor.out2dur(model_out['dur']).float() - txt = self.phone_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) - txt = txt.split(" ") - self.logger.experiment.add_figure( - f'dur_{batch_idx}', dur_to_figure(dur_gt, dur_pred, txt), self.global_step) - - def plot_pitch(self, batch_idx, sample, model_out): - f0 = sample['f0'] - if hparams['pitch_type'] == 'ph': - mel2ph = sample['mel2ph'] - f0 = self.expand_f0_ph(f0, mel2ph) - f0_pred = self.expand_f0_ph(model_out['pitch_pred'][:, :, 0], mel2ph) - self.logger.experiment.add_figure( - f'f0_{batch_idx}', f0_to_figure(f0[0], None, f0_pred[0]), self.global_step) - return - f0 = denorm_f0(f0, sample['uv'], hparams) - if hparams['pitch_type'] == 'cwt': - # cwt - cwt_out = model_out['cwt'] - cwt_spec = cwt_out[:, :, :10] - cwt = torch.cat([cwt_spec, sample['cwt_spec']], -1) - self.logger.experiment.add_figure(f'cwt_{batch_idx}', spec_to_figure(cwt[0]), self.global_step) - # f0 - f0_pred = cwt2f0(cwt_spec, model_out['f0_mean'], model_out['f0_std'], hparams['cwt_scales']) - if hparams['use_uv']: - assert cwt_out.shape[-1] == 11 - uv_pred = cwt_out[:, :, -1] > 0 - f0_pred[uv_pred > 0] = 0 - f0_cwt = denorm_f0(sample['f0_cwt'], sample['uv'], hparams) - self.logger.experiment.add_figure( - f'f0_{batch_idx}', f0_to_figure(f0[0], f0_cwt[0], f0_pred[0]), self.global_step) - elif hparams['pitch_type'] == 'frame': - # f0 - uv_pred = model_out['pitch_pred'][:, :, 1] > 0 - pitch_pred = denorm_f0(model_out['pitch_pred'][:, :, 0], uv_pred, hparams) - self.logger.experiment.add_figure( - f'f0_{batch_idx}', f0_to_figure(f0[0], None, pitch_pred[0]), self.global_step) - - ############ - # infer - ############ - def test_step(self, sample, batch_idx): - spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') - txt_tokens = sample['txt_tokens'] - mel2ph, uv, f0 = None, None, None - ref_mels = None - if hparams['profile_infer']: - pass - else: - if hparams['use_gt_dur']: - mel2ph = sample['mel2ph'] - if hparams['use_gt_f0']: - f0 = sample['f0'] - uv = sample['uv'] - print('Here using gt f0!!') - if hparams.get('use_midi') is not None and hparams['use_midi']: - outputs = self.model( - txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True, - pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) - else: - outputs = self.model( - txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True) - sample['outputs'] = self.model.out2mel(outputs['mel_out']) - sample['mel2ph_pred'] = outputs['mel2ph'] - if hparams.get('pe_enable') is not None and hparams['pe_enable']: - sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel - sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel - else: - sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) - sample['f0_pred'] = outputs.get('f0_denorm') - - return self.after_infer(sample) - - def after_infer(self, predictions): - if self.saving_result_pool is None and not hparams['profile_infer']: - self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16)) - self.saving_results_futures = [] - predictions = utils.unpack_dict_to_list(predictions) - t = tqdm(predictions) - for num_predictions, prediction in enumerate(t): - for k, v in prediction.items(): - if type(v) is torch.Tensor: - prediction[k] = v.cpu().numpy() - - item_name = prediction.get('item_name') - text = prediction.get('text').replace(":", "%3A")[:80] - - # remove paddings - mel_gt = prediction["mels"] - mel_gt_mask = np.abs(mel_gt).sum(-1) > 0 - mel_gt = mel_gt[mel_gt_mask] - mel2ph_gt = prediction.get("mel2ph") - mel2ph_gt = mel2ph_gt[mel_gt_mask] if mel2ph_gt is not None else None - mel_pred = prediction["outputs"] - mel_pred_mask = np.abs(mel_pred).sum(-1) > 0 - mel_pred = mel_pred[mel_pred_mask] - mel_gt = np.clip(mel_gt, hparams['mel_vmin'], hparams['mel_vmax']) - mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax']) - - mel2ph_pred = prediction.get("mel2ph_pred") - if mel2ph_pred is not None: - if len(mel2ph_pred) > len(mel_pred_mask): - mel2ph_pred = mel2ph_pred[:len(mel_pred_mask)] - mel2ph_pred = mel2ph_pred[mel_pred_mask] - - f0_gt = prediction.get("f0") - f0_pred = prediction.get("f0_pred") - if f0_pred is not None: - f0_gt = f0_gt[mel_gt_mask] - if len(f0_pred) > len(mel_pred_mask): - f0_pred = f0_pred[:len(mel_pred_mask)] - f0_pred = f0_pred[mel_pred_mask] - - str_phs = None - if self.phone_encoder is not None and 'txt_tokens' in prediction: - str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) - gen_dir = os.path.join(hparams['work_dir'], - f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') - wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) - if not hparams['profile_infer']: - os.makedirs(gen_dir, exist_ok=True) - os.makedirs(f'{gen_dir}/wavs', exist_ok=True) - os.makedirs(f'{gen_dir}/plot', exist_ok=True) - os.makedirs(os.path.join(hparams['work_dir'], 'P_mels_npy'), exist_ok=True) - os.makedirs(os.path.join(hparams['work_dir'], 'G_mels_npy'), exist_ok=True) - self.saving_results_futures.append( - self.saving_result_pool.apply_async(self.save_result, args=[ - wav_pred, mel_pred, 'P', item_name, text, gen_dir, str_phs, mel2ph_pred, f0_gt, f0_pred])) - - if mel_gt is not None and hparams['save_gt']: - wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) - self.saving_results_futures.append( - self.saving_result_pool.apply_async(self.save_result, args=[ - wav_gt, mel_gt, 'G', item_name, text, gen_dir, str_phs, mel2ph_gt, f0_gt, f0_pred])) - if hparams['save_f0']: - import matplotlib.pyplot as plt - # f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) - f0_pred_ = f0_pred - f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) - fig = plt.figure() - plt.plot(f0_pred_, label=r'$f0_P$') - plt.plot(f0_gt_, label=r'$f0_G$') - if hparams.get('pe_enable') is not None and hparams['pe_enable']: - # f0_midi = prediction.get("f0_midi") - # f0_midi = f0_midi[mel_gt_mask] - # plt.plot(f0_midi, label=r'$f0_M$') - pass - plt.legend() - plt.tight_layout() - plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') - plt.close(fig) - - t.set_description( - f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") - else: - if 'gen_wav_time' not in self.stats: - self.stats['gen_wav_time'] = 0 - self.stats['gen_wav_time'] += len(wav_pred) / hparams['audio_sample_rate'] - print('gen_wav_time: ', self.stats['gen_wav_time']) - - return {} - - @staticmethod - def save_result(wav_out, mel, prefix, item_name, text, gen_dir, str_phs=None, mel2ph=None, gt_f0=None, pred_f0=None): - item_name = item_name.replace('/', '-') - base_fn = f'[{item_name}][{prefix}]' - - if text is not None: - base_fn += text - base_fn += ('-' + hparams['exp_name']) - np.save(os.path.join(hparams['work_dir'], f'{prefix}_mels_npy', item_name), mel) - audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], - norm=hparams['out_wav_norm']) - fig = plt.figure(figsize=(14, 10)) - spec_vmin = hparams['mel_vmin'] - spec_vmax = hparams['mel_vmax'] - heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) - fig.colorbar(heatmap) - if hparams.get('pe_enable') is not None and hparams['pe_enable']: - gt_f0 = (gt_f0 - 100) / (800 - 100) * 80 * (gt_f0 > 0) - pred_f0 = (pred_f0 - 100) / (800 - 100) * 80 * (pred_f0 > 0) - plt.plot(pred_f0, c='white', linewidth=1, alpha=0.6) - plt.plot(gt_f0, c='red', linewidth=1, alpha=0.6) - else: - f0, _ = get_pitch(wav_out, mel, hparams) - f0 = (f0 - 100) / (800 - 100) * 80 * (f0 > 0) - plt.plot(f0, c='white', linewidth=1, alpha=0.6) - if mel2ph is not None and str_phs is not None: - decoded_txt = str_phs.split(" ") - dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() - dur = [0] + list(np.cumsum(dur)) - for i in range(len(dur) - 1): - shift = (i % 20) + 1 - plt.text(dur[i], shift, decoded_txt[i]) - plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') - plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', - alpha=1, linewidth=1) - plt.tight_layout() - plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png', dpi=1000) - plt.close(fig) - - ############## - # utils - ############## - @staticmethod - def expand_f0_ph(f0, mel2ph): - f0 = denorm_f0(f0, None, hparams) - f0 = F.pad(f0, [1, 0]) - f0 = torch.gather(f0, 1, mel2ph) # [B, T_mel] - return f0 - - -if __name__ == '__main__': - FastSpeech2Task.start() diff --git a/spaces/kingabzpro/savtadepth/app/app_savta.py b/spaces/kingabzpro/savtadepth/app/app_savta.py deleted file mode 100644 index e0006781486b2beeb8217c0cb19580dcfc0d5c3c..0000000000000000000000000000000000000000 --- a/spaces/kingabzpro/savtadepth/app/app_savta.py +++ /dev/null @@ -1,106 +0,0 @@ -import torch -import os -from fastai.vision.all import * -import gradio as gr - -############### HF ########################### - -HF_TOKEN = os.getenv('HF_TOKEN') - -hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "savtadepth-flags-V2") - -############## DVC ################################ - -PROD_MODEL_PATH = "src/models" -TRAIN_PATH = "src/data/processed/train/bathroom" -TEST_PATH = "src/data/processed/test/bathroom" - -if os.path.isdir(".dvc"): - print("Running DVC") - # os.system("dvc config cache.type copy") - # os.system("dvc config core.no_scm true") - if os.system(f"dvc pull {PROD_MODEL_PATH} {TRAIN_PATH } {TEST_PATH }") != 0: - exit("dvc pull failed") - os.system("rm -r .dvc") -# .apt/usr/lib/dvc - -############## Inference ############################## - -class ImageImageDataLoaders(DataLoaders): - """Basic wrapper around several `DataLoader`s with factory methods for Image to Image problems""" - @classmethod - @delegates(DataLoaders.from_dblock) - def from_label_func(cls, path, filenames, label_func, valid_pct=0.2, seed=None, item_transforms=None, - batch_transforms=None, **kwargs): - """Create from list of `fnames` in `path`s with `label_func`.""" - datablock = DataBlock(blocks=(ImageBlock(cls=PILImage), ImageBlock(cls=PILImageBW)), - get_y=label_func, - splitter=RandomSplitter(valid_pct, seed=seed), - item_tfms=item_transforms, - batch_tfms=batch_transforms) - res = cls.from_dblock(datablock, filenames, path=path, **kwargs) - return res - - -def get_y_fn(x): - y = str(x.absolute()).replace('.jpg', '_depth.png') - y = Path(y) - - return y - - -def create_data(data_path): - fnames = get_files(data_path/'train', extensions='.jpg') - data = ImageImageDataLoaders.from_label_func(data_path/'train', seed=42, bs=4, num_workers=0, filenames=fnames, label_func=get_y_fn) - return data - -data = create_data(Path('src/data/processed')) -learner = unet_learner(data,resnet34, metrics=rmse, wd=1e-2, n_out=3, loss_func=MSELossFlat(), path='src/') -learner.load('model') - -def gen(input_img): - return PILImageBW.create((learner.predict(input_img))[0]).convert('L') - -################### Gradio Web APP ################################ - -title = "SavtaDepth WebApp" - -description = """ -<p> -<center> -Savta Depth is a collaborative Open Source Data Science project for monocular depth estimation - Turn 2d photos into 3d photos. To test the model and code please check out the link bellow. -<img src="https://huggingface.co/spaces/kingabzpro/savtadepth/resolve/main/examples/cover.png" alt="logo" width="250"/> -</center> -</p> -""" -article = "<p style='text-align: center'><a href='https://dagshub.com/OperationSavta/SavtaDepth' target='_blank'>SavtaDepth Project from OperationSavta</a></p><p style='text-align: center'><a href='https://colab.research.google.com/drive/1XU4DgQ217_hUMU1dllppeQNw3pTRlHy1?usp=sharing' target='_blank'>Google Colab Demo</a></p></center><center><img src='https://visitor-badge.glitch.me/badge?page_id=kingabzpro/savtadepth' alt='visitor badge'></center></p>" - -examples = [ - ["examples/00008.jpg"], - ["examples/00045.jpg"], -] -favicon = "examples/favicon.ico" -thumbnail = "examples/SavtaDepth.png" - - -def main(): - iface = gr.Interface( - gen, - gr.inputs.Image(shape=(640,480),type='numpy'), - "image", - title = title, - flagging_options=["incorrect", "worst","ambiguous"], - allow_flagging = "manual", - flagging_callback=hf_writer, - description = description, - article = article, - examples = examples, - theme ="peach", - allow_screenshot=True - ) - - iface.launch(enable_queue=True) -# enable_queue=True,auth=("admin", "pass1234") - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/__init__.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/__init__.py deleted file mode 100644 index 915af28cefab14a14c1188ed861161080fd138a3..0000000000000000000000000000000000000000 --- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .checkpoint import CheckpointHook -from .closure import ClosureHook -from .ema import EMAHook -from .evaluation import DistEvalHook, EvalHook -from .hook import HOOKS, Hook -from .iter_timer import IterTimerHook -from .logger import (DvcliveLoggerHook, LoggerHook, MlflowLoggerHook, - NeptuneLoggerHook, PaviLoggerHook, TensorboardLoggerHook, - TextLoggerHook, WandbLoggerHook) -from .lr_updater import LrUpdaterHook -from .memory import EmptyCacheHook -from .momentum_updater import MomentumUpdaterHook -from .optimizer import (Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook, - GradientCumulativeOptimizerHook, OptimizerHook) -from .profiler import ProfilerHook -from .sampler_seed import DistSamplerSeedHook -from .sync_buffer import SyncBuffersHook - -__all__ = [ - 'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook', - 'OptimizerHook', 'Fp16OptimizerHook', 'IterTimerHook', - 'DistSamplerSeedHook', 'EmptyCacheHook', 'LoggerHook', 'MlflowLoggerHook', - 'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook', - 'NeptuneLoggerHook', 'WandbLoggerHook', 'DvcliveLoggerHook', - 'MomentumUpdaterHook', 'SyncBuffersHook', 'EMAHook', 'EvalHook', - 'DistEvalHook', 'ProfilerHook', 'GradientCumulativeOptimizerHook', - 'GradientCumulativeFp16OptimizerHook' -] diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/compose.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/compose.py deleted file mode 100644 index cbfcbb925c6d4ebf849328b9f94ef6fc24359bf5..0000000000000000000000000000000000000000 --- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/compose.py +++ /dev/null @@ -1,51 +0,0 @@ -import collections - -from annotator.uniformer.mmcv.utils import build_from_cfg - -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class Compose(object): - """Compose multiple transforms sequentially. - - Args: - transforms (Sequence[dict | callable]): Sequence of transform object or - config dict to be composed. - """ - - def __init__(self, transforms): - assert isinstance(transforms, collections.abc.Sequence) - self.transforms = [] - for transform in transforms: - if isinstance(transform, dict): - transform = build_from_cfg(transform, PIPELINES) - self.transforms.append(transform) - elif callable(transform): - self.transforms.append(transform) - else: - raise TypeError('transform must be callable or a dict') - - def __call__(self, data): - """Call function to apply transforms sequentially. - - Args: - data (dict): A result dict contains the data to transform. - - Returns: - dict: Transformed data. - """ - - for t in self.transforms: - data = t(data) - if data is None: - return None - return data - - def __repr__(self): - format_string = self.__class__.__name__ + '(' - for t in self.transforms: - format_string += '\n' - format_string += f' {t}' - format_string += '\n)' - return format_string diff --git a/spaces/kusumakar/Text_to_image_using_Stable_diffusers/app.py b/spaces/kusumakar/Text_to_image_using_Stable_diffusers/app.py deleted file mode 100644 index b60a087620a806fea130bedcd6940bef75fa3337..0000000000000000000000000000000000000000 --- a/spaces/kusumakar/Text_to_image_using_Stable_diffusers/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/CompVis/stable-diffusion-v1-4").launch() diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/TiffTags.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/TiffTags.py deleted file mode 100644 index 30b05e4e1d41fa21a7b7bf12c04ee05af6aa5284..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/TiffTags.py +++ /dev/null @@ -1,560 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# TIFF tags -# -# This module provides clear-text names for various well-known -# TIFF tags. the TIFF codec works just fine without it. -# -# Copyright (c) Secret Labs AB 1999. -# -# See the README file for information on usage and redistribution. -# - -## -# This module provides constants and clear-text names for various -# well-known TIFF tags. -## - -from collections import namedtuple - - -class TagInfo(namedtuple("_TagInfo", "value name type length enum")): - __slots__ = [] - - def __new__(cls, value=None, name="unknown", type=None, length=None, enum=None): - return super().__new__(cls, value, name, type, length, enum or {}) - - def cvt_enum(self, value): - # Using get will call hash(value), which can be expensive - # for some types (e.g. Fraction). Since self.enum is rarely - # used, it's usually better to test it first. - return self.enum.get(value, value) if self.enum else value - - -def lookup(tag, group=None): - """ - :param tag: Integer tag number - :param group: Which :py:data:`~PIL.TiffTags.TAGS_V2_GROUPS` to look in - - .. versionadded:: 8.3.0 - - :returns: Taginfo namedtuple, From the ``TAGS_V2`` info if possible, - otherwise just populating the value and name from ``TAGS``. - If the tag is not recognized, "unknown" is returned for the name - - """ - - if group is not None: - info = TAGS_V2_GROUPS[group].get(tag) if group in TAGS_V2_GROUPS else None - else: - info = TAGS_V2.get(tag) - return info or TagInfo(tag, TAGS.get(tag, "unknown")) - - -## -# Map tag numbers to tag info. -# -# id: (Name, Type, Length, enum_values) -# -# The length here differs from the length in the tiff spec. For -# numbers, the tiff spec is for the number of fields returned. We -# agree here. For string-like types, the tiff spec uses the length of -# field in bytes. In Pillow, we are using the number of expected -# fields, in general 1 for string-like types. - - -BYTE = 1 -ASCII = 2 -SHORT = 3 -LONG = 4 -RATIONAL = 5 -SIGNED_BYTE = 6 -UNDEFINED = 7 -SIGNED_SHORT = 8 -SIGNED_LONG = 9 -SIGNED_RATIONAL = 10 -FLOAT = 11 -DOUBLE = 12 -IFD = 13 -LONG8 = 16 - -TAGS_V2 = { - 254: ("NewSubfileType", LONG, 1), - 255: ("SubfileType", SHORT, 1), - 256: ("ImageWidth", LONG, 1), - 257: ("ImageLength", LONG, 1), - 258: ("BitsPerSample", SHORT, 0), - 259: ( - "Compression", - SHORT, - 1, - { - "Uncompressed": 1, - "CCITT 1d": 2, - "Group 3 Fax": 3, - "Group 4 Fax": 4, - "LZW": 5, - "JPEG": 6, - "PackBits": 32773, - }, - ), - 262: ( - "PhotometricInterpretation", - SHORT, - 1, - { - "WhiteIsZero": 0, - "BlackIsZero": 1, - "RGB": 2, - "RGB Palette": 3, - "Transparency Mask": 4, - "CMYK": 5, - "YCbCr": 6, - "CieLAB": 8, - "CFA": 32803, # TIFF/EP, Adobe DNG - "LinearRaw": 32892, # Adobe DNG - }, - ), - 263: ("Threshholding", SHORT, 1), - 264: ("CellWidth", SHORT, 1), - 265: ("CellLength", SHORT, 1), - 266: ("FillOrder", SHORT, 1), - 269: ("DocumentName", ASCII, 1), - 270: ("ImageDescription", ASCII, 1), - 271: ("Make", ASCII, 1), - 272: ("Model", ASCII, 1), - 273: ("StripOffsets", LONG, 0), - 274: ("Orientation", SHORT, 1), - 277: ("SamplesPerPixel", SHORT, 1), - 278: ("RowsPerStrip", LONG, 1), - 279: ("StripByteCounts", LONG, 0), - 280: ("MinSampleValue", SHORT, 0), - 281: ("MaxSampleValue", SHORT, 0), - 282: ("XResolution", RATIONAL, 1), - 283: ("YResolution", RATIONAL, 1), - 284: ("PlanarConfiguration", SHORT, 1, {"Contiguous": 1, "Separate": 2}), - 285: ("PageName", ASCII, 1), - 286: ("XPosition", RATIONAL, 1), - 287: ("YPosition", RATIONAL, 1), - 288: ("FreeOffsets", LONG, 1), - 289: ("FreeByteCounts", LONG, 1), - 290: ("GrayResponseUnit", SHORT, 1), - 291: ("GrayResponseCurve", SHORT, 0), - 292: ("T4Options", LONG, 1), - 293: ("T6Options", LONG, 1), - 296: ("ResolutionUnit", SHORT, 1, {"none": 1, "inch": 2, "cm": 3}), - 297: ("PageNumber", SHORT, 2), - 301: ("TransferFunction", SHORT, 0), - 305: ("Software", ASCII, 1), - 306: ("DateTime", ASCII, 1), - 315: ("Artist", ASCII, 1), - 316: ("HostComputer", ASCII, 1), - 317: ("Predictor", SHORT, 1, {"none": 1, "Horizontal Differencing": 2}), - 318: ("WhitePoint", RATIONAL, 2), - 319: ("PrimaryChromaticities", RATIONAL, 6), - 320: ("ColorMap", SHORT, 0), - 321: ("HalftoneHints", SHORT, 2), - 322: ("TileWidth", LONG, 1), - 323: ("TileLength", LONG, 1), - 324: ("TileOffsets", LONG, 0), - 325: ("TileByteCounts", LONG, 0), - 330: ("SubIFDs", LONG, 0), - 332: ("InkSet", SHORT, 1), - 333: ("InkNames", ASCII, 1), - 334: ("NumberOfInks", SHORT, 1), - 336: ("DotRange", SHORT, 0), - 337: ("TargetPrinter", ASCII, 1), - 338: ("ExtraSamples", SHORT, 0), - 339: ("SampleFormat", SHORT, 0), - 340: ("SMinSampleValue", DOUBLE, 0), - 341: ("SMaxSampleValue", DOUBLE, 0), - 342: ("TransferRange", SHORT, 6), - 347: ("JPEGTables", UNDEFINED, 1), - # obsolete JPEG tags - 512: ("JPEGProc", SHORT, 1), - 513: ("JPEGInterchangeFormat", LONG, 1), - 514: ("JPEGInterchangeFormatLength", LONG, 1), - 515: ("JPEGRestartInterval", SHORT, 1), - 517: ("JPEGLosslessPredictors", SHORT, 0), - 518: ("JPEGPointTransforms", SHORT, 0), - 519: ("JPEGQTables", LONG, 0), - 520: ("JPEGDCTables", LONG, 0), - 521: ("JPEGACTables", LONG, 0), - 529: ("YCbCrCoefficients", RATIONAL, 3), - 530: ("YCbCrSubSampling", SHORT, 2), - 531: ("YCbCrPositioning", SHORT, 1), - 532: ("ReferenceBlackWhite", RATIONAL, 6), - 700: ("XMP", BYTE, 0), - 33432: ("Copyright", ASCII, 1), - 33723: ("IptcNaaInfo", UNDEFINED, 1), - 34377: ("PhotoshopInfo", BYTE, 0), - # FIXME add more tags here - 34665: ("ExifIFD", LONG, 1), - 34675: ("ICCProfile", UNDEFINED, 1), - 34853: ("GPSInfoIFD", LONG, 1), - 36864: ("ExifVersion", UNDEFINED, 1), - 37724: ("ImageSourceData", UNDEFINED, 1), - 40965: ("InteroperabilityIFD", LONG, 1), - 41730: ("CFAPattern", UNDEFINED, 1), - # MPInfo - 45056: ("MPFVersion", UNDEFINED, 1), - 45057: ("NumberOfImages", LONG, 1), - 45058: ("MPEntry", UNDEFINED, 1), - 45059: ("ImageUIDList", UNDEFINED, 0), # UNDONE, check - 45060: ("TotalFrames", LONG, 1), - 45313: ("MPIndividualNum", LONG, 1), - 45569: ("PanOrientation", LONG, 1), - 45570: ("PanOverlap_H", RATIONAL, 1), - 45571: ("PanOverlap_V", RATIONAL, 1), - 45572: ("BaseViewpointNum", LONG, 1), - 45573: ("ConvergenceAngle", SIGNED_RATIONAL, 1), - 45574: ("BaselineLength", RATIONAL, 1), - 45575: ("VerticalDivergence", SIGNED_RATIONAL, 1), - 45576: ("AxisDistance_X", SIGNED_RATIONAL, 1), - 45577: ("AxisDistance_Y", SIGNED_RATIONAL, 1), - 45578: ("AxisDistance_Z", SIGNED_RATIONAL, 1), - 45579: ("YawAngle", SIGNED_RATIONAL, 1), - 45580: ("PitchAngle", SIGNED_RATIONAL, 1), - 45581: ("RollAngle", SIGNED_RATIONAL, 1), - 40960: ("FlashPixVersion", UNDEFINED, 1), - 50741: ("MakerNoteSafety", SHORT, 1, {"Unsafe": 0, "Safe": 1}), - 50780: ("BestQualityScale", RATIONAL, 1), - 50838: ("ImageJMetaDataByteCounts", LONG, 0), # Can be more than one - 50839: ("ImageJMetaData", UNDEFINED, 1), # see Issue #2006 -} -TAGS_V2_GROUPS = { - # ExifIFD - 34665: { - 36864: ("ExifVersion", UNDEFINED, 1), - 40960: ("FlashPixVersion", UNDEFINED, 1), - 40965: ("InteroperabilityIFD", LONG, 1), - 41730: ("CFAPattern", UNDEFINED, 1), - }, - # GPSInfoIFD - 34853: { - 0: ("GPSVersionID", BYTE, 4), - 1: ("GPSLatitudeRef", ASCII, 2), - 2: ("GPSLatitude", RATIONAL, 3), - 3: ("GPSLongitudeRef", ASCII, 2), - 4: ("GPSLongitude", RATIONAL, 3), - 5: ("GPSAltitudeRef", BYTE, 1), - 6: ("GPSAltitude", RATIONAL, 1), - 7: ("GPSTimeStamp", RATIONAL, 3), - 8: ("GPSSatellites", ASCII, 0), - 9: ("GPSStatus", ASCII, 2), - 10: ("GPSMeasureMode", ASCII, 2), - 11: ("GPSDOP", RATIONAL, 1), - 12: ("GPSSpeedRef", ASCII, 2), - 13: ("GPSSpeed", RATIONAL, 1), - 14: ("GPSTrackRef", ASCII, 2), - 15: ("GPSTrack", RATIONAL, 1), - 16: ("GPSImgDirectionRef", ASCII, 2), - 17: ("GPSImgDirection", RATIONAL, 1), - 18: ("GPSMapDatum", ASCII, 0), - 19: ("GPSDestLatitudeRef", ASCII, 2), - 20: ("GPSDestLatitude", RATIONAL, 3), - 21: ("GPSDestLongitudeRef", ASCII, 2), - 22: ("GPSDestLongitude", RATIONAL, 3), - 23: ("GPSDestBearingRef", ASCII, 2), - 24: ("GPSDestBearing", RATIONAL, 1), - 25: ("GPSDestDistanceRef", ASCII, 2), - 26: ("GPSDestDistance", RATIONAL, 1), - 27: ("GPSProcessingMethod", UNDEFINED, 0), - 28: ("GPSAreaInformation", UNDEFINED, 0), - 29: ("GPSDateStamp", ASCII, 11), - 30: ("GPSDifferential", SHORT, 1), - }, - # InteroperabilityIFD - 40965: {1: ("InteropIndex", ASCII, 1), 2: ("InteropVersion", UNDEFINED, 1)}, -} - -# Legacy Tags structure -# these tags aren't included above, but were in the previous versions -TAGS = { - 347: "JPEGTables", - 700: "XMP", - # Additional Exif Info - 32932: "Wang Annotation", - 33434: "ExposureTime", - 33437: "FNumber", - 33445: "MD FileTag", - 33446: "MD ScalePixel", - 33447: "MD ColorTable", - 33448: "MD LabName", - 33449: "MD SampleInfo", - 33450: "MD PrepDate", - 33451: "MD PrepTime", - 33452: "MD FileUnits", - 33550: "ModelPixelScaleTag", - 33723: "IptcNaaInfo", - 33918: "INGR Packet Data Tag", - 33919: "INGR Flag Registers", - 33920: "IrasB Transformation Matrix", - 33922: "ModelTiepointTag", - 34264: "ModelTransformationTag", - 34377: "PhotoshopInfo", - 34735: "GeoKeyDirectoryTag", - 34736: "GeoDoubleParamsTag", - 34737: "GeoAsciiParamsTag", - 34850: "ExposureProgram", - 34852: "SpectralSensitivity", - 34855: "ISOSpeedRatings", - 34856: "OECF", - 34864: "SensitivityType", - 34865: "StandardOutputSensitivity", - 34866: "RecommendedExposureIndex", - 34867: "ISOSpeed", - 34868: "ISOSpeedLatitudeyyy", - 34869: "ISOSpeedLatitudezzz", - 34908: "HylaFAX FaxRecvParams", - 34909: "HylaFAX FaxSubAddress", - 34910: "HylaFAX FaxRecvTime", - 36864: "ExifVersion", - 36867: "DateTimeOriginal", - 36868: "DateTimeDigitized", - 37121: "ComponentsConfiguration", - 37122: "CompressedBitsPerPixel", - 37724: "ImageSourceData", - 37377: "ShutterSpeedValue", - 37378: "ApertureValue", - 37379: "BrightnessValue", - 37380: "ExposureBiasValue", - 37381: "MaxApertureValue", - 37382: "SubjectDistance", - 37383: "MeteringMode", - 37384: "LightSource", - 37385: "Flash", - 37386: "FocalLength", - 37396: "SubjectArea", - 37500: "MakerNote", - 37510: "UserComment", - 37520: "SubSec", - 37521: "SubSecTimeOriginal", - 37522: "SubsecTimeDigitized", - 40960: "FlashPixVersion", - 40961: "ColorSpace", - 40962: "PixelXDimension", - 40963: "PixelYDimension", - 40964: "RelatedSoundFile", - 40965: "InteroperabilityIFD", - 41483: "FlashEnergy", - 41484: "SpatialFrequencyResponse", - 41486: "FocalPlaneXResolution", - 41487: "FocalPlaneYResolution", - 41488: "FocalPlaneResolutionUnit", - 41492: "SubjectLocation", - 41493: "ExposureIndex", - 41495: "SensingMethod", - 41728: "FileSource", - 41729: "SceneType", - 41730: "CFAPattern", - 41985: "CustomRendered", - 41986: "ExposureMode", - 41987: "WhiteBalance", - 41988: "DigitalZoomRatio", - 41989: "FocalLengthIn35mmFilm", - 41990: "SceneCaptureType", - 41991: "GainControl", - 41992: "Contrast", - 41993: "Saturation", - 41994: "Sharpness", - 41995: "DeviceSettingDescription", - 41996: "SubjectDistanceRange", - 42016: "ImageUniqueID", - 42032: "CameraOwnerName", - 42033: "BodySerialNumber", - 42034: "LensSpecification", - 42035: "LensMake", - 42036: "LensModel", - 42037: "LensSerialNumber", - 42112: "GDAL_METADATA", - 42113: "GDAL_NODATA", - 42240: "Gamma", - 50215: "Oce Scanjob Description", - 50216: "Oce Application Selector", - 50217: "Oce Identification Number", - 50218: "Oce ImageLogic Characteristics", - # Adobe DNG - 50706: "DNGVersion", - 50707: "DNGBackwardVersion", - 50708: "UniqueCameraModel", - 50709: "LocalizedCameraModel", - 50710: "CFAPlaneColor", - 50711: "CFALayout", - 50712: "LinearizationTable", - 50713: "BlackLevelRepeatDim", - 50714: "BlackLevel", - 50715: "BlackLevelDeltaH", - 50716: "BlackLevelDeltaV", - 50717: "WhiteLevel", - 50718: "DefaultScale", - 50719: "DefaultCropOrigin", - 50720: "DefaultCropSize", - 50721: "ColorMatrix1", - 50722: "ColorMatrix2", - 50723: "CameraCalibration1", - 50724: "CameraCalibration2", - 50725: "ReductionMatrix1", - 50726: "ReductionMatrix2", - 50727: "AnalogBalance", - 50728: "AsShotNeutral", - 50729: "AsShotWhiteXY", - 50730: "BaselineExposure", - 50731: "BaselineNoise", - 50732: "BaselineSharpness", - 50733: "BayerGreenSplit", - 50734: "LinearResponseLimit", - 50735: "CameraSerialNumber", - 50736: "LensInfo", - 50737: "ChromaBlurRadius", - 50738: "AntiAliasStrength", - 50740: "DNGPrivateData", - 50778: "CalibrationIlluminant1", - 50779: "CalibrationIlluminant2", - 50784: "Alias Layer Metadata", -} - - -def _populate(): - for k, v in TAGS_V2.items(): - # Populate legacy structure. - TAGS[k] = v[0] - if len(v) == 4: - for sk, sv in v[3].items(): - TAGS[(k, sv)] = sk - - TAGS_V2[k] = TagInfo(k, *v) - - for group, tags in TAGS_V2_GROUPS.items(): - for k, v in tags.items(): - tags[k] = TagInfo(k, *v) - - -_populate() -## -# Map type numbers to type names -- defined in ImageFileDirectory. - -TYPES = {} - -# was: -# TYPES = { -# 1: "byte", -# 2: "ascii", -# 3: "short", -# 4: "long", -# 5: "rational", -# 6: "signed byte", -# 7: "undefined", -# 8: "signed short", -# 9: "signed long", -# 10: "signed rational", -# 11: "float", -# 12: "double", -# } - -# -# These tags are handled by default in libtiff, without -# adding to the custom dictionary. From tif_dir.c, searching for -# case TIFFTAG in the _TIFFVSetField function: -# Line: item. -# 148: case TIFFTAG_SUBFILETYPE: -# 151: case TIFFTAG_IMAGEWIDTH: -# 154: case TIFFTAG_IMAGELENGTH: -# 157: case TIFFTAG_BITSPERSAMPLE: -# 181: case TIFFTAG_COMPRESSION: -# 202: case TIFFTAG_PHOTOMETRIC: -# 205: case TIFFTAG_THRESHHOLDING: -# 208: case TIFFTAG_FILLORDER: -# 214: case TIFFTAG_ORIENTATION: -# 221: case TIFFTAG_SAMPLESPERPIXEL: -# 228: case TIFFTAG_ROWSPERSTRIP: -# 238: case TIFFTAG_MINSAMPLEVALUE: -# 241: case TIFFTAG_MAXSAMPLEVALUE: -# 244: case TIFFTAG_SMINSAMPLEVALUE: -# 247: case TIFFTAG_SMAXSAMPLEVALUE: -# 250: case TIFFTAG_XRESOLUTION: -# 256: case TIFFTAG_YRESOLUTION: -# 262: case TIFFTAG_PLANARCONFIG: -# 268: case TIFFTAG_XPOSITION: -# 271: case TIFFTAG_YPOSITION: -# 274: case TIFFTAG_RESOLUTIONUNIT: -# 280: case TIFFTAG_PAGENUMBER: -# 284: case TIFFTAG_HALFTONEHINTS: -# 288: case TIFFTAG_COLORMAP: -# 294: case TIFFTAG_EXTRASAMPLES: -# 298: case TIFFTAG_MATTEING: -# 305: case TIFFTAG_TILEWIDTH: -# 316: case TIFFTAG_TILELENGTH: -# 327: case TIFFTAG_TILEDEPTH: -# 333: case TIFFTAG_DATATYPE: -# 344: case TIFFTAG_SAMPLEFORMAT: -# 361: case TIFFTAG_IMAGEDEPTH: -# 364: case TIFFTAG_SUBIFD: -# 376: case TIFFTAG_YCBCRPOSITIONING: -# 379: case TIFFTAG_YCBCRSUBSAMPLING: -# 383: case TIFFTAG_TRANSFERFUNCTION: -# 389: case TIFFTAG_REFERENCEBLACKWHITE: -# 393: case TIFFTAG_INKNAMES: - -# Following pseudo-tags are also handled by default in libtiff: -# TIFFTAG_JPEGQUALITY 65537 - -# some of these are not in our TAGS_V2 dict and were included from tiff.h - -# This list also exists in encode.c -LIBTIFF_CORE = { - 255, - 256, - 257, - 258, - 259, - 262, - 263, - 266, - 274, - 277, - 278, - 280, - 281, - 340, - 341, - 282, - 283, - 284, - 286, - 287, - 296, - 297, - 321, - 320, - 338, - 32995, - 322, - 323, - 32998, - 32996, - 339, - 32997, - 330, - 531, - 530, - 301, - 532, - 333, - # as above - 269, # this has been in our tests forever, and works - 65537, -} - -LIBTIFF_CORE.remove(255) # We don't have support for subfiletypes -LIBTIFF_CORE.remove(322) # We don't have support for writing tiled images with libtiff -LIBTIFF_CORE.remove(323) # Tiled images -LIBTIFF_CORE.remove(333) # Ink Names either - -# Note to advanced users: There may be combinations of these -# parameters and values that when added properly, will work and -# produce valid tiff images that may work in your application. -# It is safe to add and remove tags from this set from Pillow's point -# of view so long as you test against libtiff. diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_fixes.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_fixes.py deleted file mode 100644 index ff4f9e2d70e323e108fbd7bade2fbed3f5595cbe..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_fixes.py +++ /dev/null @@ -1,77 +0,0 @@ -# JSONDecodeError was introduced in requests=2.27 released in 2022. -# This allows us to support older requests for users -# More information: https://github.com/psf/requests/pull/5856 -try: - from requests import JSONDecodeError # type: ignore # noqa: F401 -except ImportError: - try: - from simplejson import JSONDecodeError # type: ignore # noqa: F401 - except ImportError: - from json import JSONDecodeError # type: ignore # noqa: F401 - -import contextlib -import os -import shutil -import stat -import tempfile -from functools import partial -from pathlib import Path -from typing import Callable, Generator, Optional, Union - -import yaml - - -# Wrap `yaml.dump` to set `allow_unicode=True` by default. -# -# Example: -# ```py -# >>> yaml.dump({"emoji": "👀", "some unicode": "日本か"}) -# 'emoji: "\\U0001F440"\nsome unicode: "\\u65E5\\u672C\\u304B"\n' -# -# >>> yaml_dump({"emoji": "👀", "some unicode": "日本か"}) -# 'emoji: "👀"\nsome unicode: "日本か"\n' -# ``` -yaml_dump: Callable[..., str] = partial(yaml.dump, stream=None, allow_unicode=True) # type: ignore - - -@contextlib.contextmanager -def SoftTemporaryDirectory( - suffix: Optional[str] = None, - prefix: Optional[str] = None, - dir: Optional[Union[Path, str]] = None, - **kwargs, -) -> Generator[str, None, None]: - """ - Context manager to create a temporary directory and safely delete it. - - If tmp directory cannot be deleted normally, we set the WRITE permission and retry. - If cleanup still fails, we give up but don't raise an exception. This is equivalent - to `tempfile.TemporaryDirectory(..., ignore_cleanup_errors=True)` introduced in - Python 3.10. - - See https://www.scivision.dev/python-tempfile-permission-error-windows/. - """ - tmpdir = tempfile.TemporaryDirectory(prefix=prefix, suffix=suffix, dir=dir, **kwargs) - yield tmpdir.name - - try: - # First once with normal cleanup - shutil.rmtree(tmpdir.name) - except Exception: - # If failed, try to set write permission and retry - try: - shutil.rmtree(tmpdir.name, onerror=_set_write_permission_and_retry) - except Exception: - pass - - # And finally, cleanup the tmpdir. - # If it fails again, give up but do not throw error - try: - tmpdir.cleanup() - except Exception: - pass - - -def _set_write_permission_and_retry(func, path, excinfo): - os.chmod(path, stat.S_IWRITE) - func(path) diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_model.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_model.py deleted file mode 100644 index a4d9e6ac651784c7ed36e623c3a6175883123c2b..0000000000000000000000000000000000000000 --- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_model.py +++ /dev/null @@ -1,330 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -import torch -from utils import utils_image as util -import re -import glob -import os - - -''' -# -------------------------------------------- -# Model -# -------------------------------------------- -# Kai Zhang (github: https://github.com/cszn) -# 03/Mar/2019 -# -------------------------------------------- -''' - - -def find_last_checkpoint(save_dir, net_type='G', pretrained_path=None): - """ - # --------------------------------------- - # Kai Zhang (github: https://github.com/cszn) - # 03/Mar/2019 - # --------------------------------------- - Args: - save_dir: model folder - net_type: 'G' or 'D' or 'optimizerG' or 'optimizerD' - pretrained_path: pretrained model path. If save_dir does not have any model, load from pretrained_path - - Return: - init_iter: iteration number - init_path: model path - # --------------------------------------- - """ - - file_list = glob.glob(os.path.join(save_dir, '*_{}.pth'.format(net_type))) - if file_list: - iter_exist = [] - for file_ in file_list: - iter_current = re.findall(r"(\d+)_{}.pth".format(net_type), file_) - iter_exist.append(int(iter_current[0])) - init_iter = max(iter_exist) - init_path = os.path.join(save_dir, '{}_{}.pth'.format(init_iter, net_type)) - else: - init_iter = 0 - init_path = pretrained_path - return init_iter, init_path - - -def test_mode(model, L, mode=0, refield=32, min_size=256, sf=1, modulo=1): - ''' - # --------------------------------------- - # Kai Zhang (github: https://github.com/cszn) - # 03/Mar/2019 - # --------------------------------------- - Args: - model: trained model - L: input Low-quality image - mode: - (0) normal: test(model, L) - (1) pad: test_pad(model, L, modulo=16) - (2) split: test_split(model, L, refield=32, min_size=256, sf=1, modulo=1) - (3) x8: test_x8(model, L, modulo=1) ^_^ - (4) split and x8: test_split_x8(model, L, refield=32, min_size=256, sf=1, modulo=1) - refield: effective receptive filed of the network, 32 is enough - useful when split, i.e., mode=2, 4 - min_size: min_sizeXmin_size image, e.g., 256X256 image - useful when split, i.e., mode=2, 4 - sf: scale factor for super-resolution, otherwise 1 - modulo: 1 if split - useful when pad, i.e., mode=1 - - Returns: - E: estimated image - # --------------------------------------- - ''' - if mode == 0: - E = test(model, L) - elif mode == 1: - E = test_pad(model, L, modulo, sf) - elif mode == 2: - E = test_split(model, L, refield, min_size, sf, modulo) - elif mode == 3: - E = test_x8(model, L, modulo, sf) - elif mode == 4: - E = test_split_x8(model, L, refield, min_size, sf, modulo) - return E - - -''' -# -------------------------------------------- -# normal (0) -# -------------------------------------------- -''' - - -def test(model, L): - E = model(L) - return E - - -''' -# -------------------------------------------- -# pad (1) -# -------------------------------------------- -''' - - -def test_pad(model, L, modulo=16, sf=1): - h, w = L.size()[-2:] - paddingBottom = int(np.ceil(h/modulo)*modulo-h) - paddingRight = int(np.ceil(w/modulo)*modulo-w) - L = torch.nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(L) - E = model(L) - E = E[..., :h*sf, :w*sf] - return E - - -''' -# -------------------------------------------- -# split (function) -# -------------------------------------------- -''' - - -def test_split_fn(model, L, refield=32, min_size=256, sf=1, modulo=1): - """ - Args: - model: trained model - L: input Low-quality image - refield: effective receptive filed of the network, 32 is enough - min_size: min_sizeXmin_size image, e.g., 256X256 image - sf: scale factor for super-resolution, otherwise 1 - modulo: 1 if split - - Returns: - E: estimated result - """ - h, w = L.size()[-2:] - if h*w <= min_size**2: - L = torch.nn.ReplicationPad2d((0, int(np.ceil(w/modulo)*modulo-w), 0, int(np.ceil(h/modulo)*modulo-h)))(L) - E = model(L) - E = E[..., :h*sf, :w*sf] - else: - top = slice(0, (h//2//refield+1)*refield) - bottom = slice(h - (h//2//refield+1)*refield, h) - left = slice(0, (w//2//refield+1)*refield) - right = slice(w - (w//2//refield+1)*refield, w) - Ls = [L[..., top, left], L[..., top, right], L[..., bottom, left], L[..., bottom, right]] - - if h * w <= 4*(min_size**2): - Es = [model(Ls[i]) for i in range(4)] - else: - Es = [test_split_fn(model, Ls[i], refield=refield, min_size=min_size, sf=sf, modulo=modulo) for i in range(4)] - - b, c = Es[0].size()[:2] - E = torch.zeros(b, c, sf * h, sf * w).type_as(L) - - E[..., :h//2*sf, :w//2*sf] = Es[0][..., :h//2*sf, :w//2*sf] - E[..., :h//2*sf, w//2*sf:w*sf] = Es[1][..., :h//2*sf, (-w + w//2)*sf:] - E[..., h//2*sf:h*sf, :w//2*sf] = Es[2][..., (-h + h//2)*sf:, :w//2*sf] - E[..., h//2*sf:h*sf, w//2*sf:w*sf] = Es[3][..., (-h + h//2)*sf:, (-w + w//2)*sf:] - return E - - -''' -# -------------------------------------------- -# split (2) -# -------------------------------------------- -''' - - -def test_split(model, L, refield=32, min_size=256, sf=1, modulo=1): - E = test_split_fn(model, L, refield=refield, min_size=min_size, sf=sf, modulo=modulo) - return E - - -''' -# -------------------------------------------- -# x8 (3) -# -------------------------------------------- -''' - - -def test_x8(model, L, modulo=1, sf=1): - E_list = [test_pad(model, util.augment_img_tensor4(L, mode=i), modulo=modulo, sf=sf) for i in range(8)] - for i in range(len(E_list)): - if i == 3 or i == 5: - E_list[i] = util.augment_img_tensor4(E_list[i], mode=8 - i) - else: - E_list[i] = util.augment_img_tensor4(E_list[i], mode=i) - output_cat = torch.stack(E_list, dim=0) - E = output_cat.mean(dim=0, keepdim=False) - return E - - -''' -# -------------------------------------------- -# split and x8 (4) -# -------------------------------------------- -''' - - -def test_split_x8(model, L, refield=32, min_size=256, sf=1, modulo=1): - E_list = [test_split_fn(model, util.augment_img_tensor4(L, mode=i), refield=refield, min_size=min_size, sf=sf, modulo=modulo) for i in range(8)] - for k, i in enumerate(range(len(E_list))): - if i==3 or i==5: - E_list[k] = util.augment_img_tensor4(E_list[k], mode=8-i) - else: - E_list[k] = util.augment_img_tensor4(E_list[k], mode=i) - output_cat = torch.stack(E_list, dim=0) - E = output_cat.mean(dim=0, keepdim=False) - return E - - -''' -# ^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^- -# _^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^ -# ^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^-^_^- -''' - - -''' -# -------------------------------------------- -# print -# -------------------------------------------- -''' - - -# -------------------------------------------- -# print model -# -------------------------------------------- -def print_model(model): - msg = describe_model(model) - print(msg) - - -# -------------------------------------------- -# print params -# -------------------------------------------- -def print_params(model): - msg = describe_params(model) - print(msg) - - -''' -# -------------------------------------------- -# information -# -------------------------------------------- -''' - - -# -------------------------------------------- -# model inforation -# -------------------------------------------- -def info_model(model): - msg = describe_model(model) - return msg - - -# -------------------------------------------- -# params inforation -# -------------------------------------------- -def info_params(model): - msg = describe_params(model) - return msg - - -''' -# -------------------------------------------- -# description -# -------------------------------------------- -''' - - -# -------------------------------------------- -# model name and total number of parameters -# -------------------------------------------- -def describe_model(model): - if isinstance(model, torch.nn.DataParallel): - model = model.module - msg = '\n' - msg += 'models name: {}'.format(model.__class__.__name__) + '\n' - msg += 'Params number: {}'.format(sum(map(lambda x: x.numel(), model.parameters()))) + '\n' - msg += 'Net structure:\n{}'.format(str(model)) + '\n' - return msg - - -# -------------------------------------------- -# parameters description -# -------------------------------------------- -def describe_params(model): - if isinstance(model, torch.nn.DataParallel): - model = model.module - msg = '\n' - msg += ' | {:^6s} | {:^6s} | {:^6s} | {:^6s} || {:<20s}'.format('mean', 'min', 'max', 'std', 'shape', 'param_name') + '\n' - for name, param in model.state_dict().items(): - if not 'num_batches_tracked' in name: - v = param.data.clone().float() - msg += ' | {:>6.3f} | {:>6.3f} | {:>6.3f} | {:>6.3f} | {} || {:s}'.format(v.mean(), v.min(), v.max(), v.std(), v.shape, name) + '\n' - return msg - - -if __name__ == '__main__': - - class Net(torch.nn.Module): - def __init__(self, in_channels=3, out_channels=3): - super(Net, self).__init__() - self.conv = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1) - - def forward(self, x): - x = self.conv(x) - return x - - start = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - - model = Net() - model = model.eval() - print_model(model) - print_params(model) - x = torch.randn((2,3,401,401)) - torch.cuda.empty_cache() - with torch.no_grad(): - for mode in range(5): - y = test_mode(model, x, mode, refield=32, min_size=256, sf=1, modulo=1) - print(y.shape) - - # run utils/utils_model.py diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_option.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_option.py deleted file mode 100644 index cf096210e2d8ea553b06a91ac5cdaa21127d837c..0000000000000000000000000000000000000000 --- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_option.py +++ /dev/null @@ -1,255 +0,0 @@ -import os -from collections import OrderedDict -from datetime import datetime -import json -import re -import glob - - -''' -# -------------------------------------------- -# Kai Zhang (github: https://github.com/cszn) -# 03/Mar/2019 -# -------------------------------------------- -# https://github.com/xinntao/BasicSR -# -------------------------------------------- -''' - - -def get_timestamp(): - return datetime.now().strftime('_%y%m%d_%H%M%S') - - -def parse(opt_path, is_train=True): - - # ---------------------------------------- - # remove comments starting with '//' - # ---------------------------------------- - json_str = '' - with open(opt_path, 'r') as f: - for line in f: - line = line.split('//')[0] + '\n' - json_str += line - - # ---------------------------------------- - # initialize opt - # ---------------------------------------- - opt = json.loads(json_str, object_pairs_hook=OrderedDict) - - opt['opt_path'] = opt_path - opt['is_train'] = is_train - - # ---------------------------------------- - # set default - # ---------------------------------------- - if 'merge_bn' not in opt: - opt['merge_bn'] = False - opt['merge_bn_startpoint'] = -1 - - if 'scale' not in opt: - opt['scale'] = 1 - - # ---------------------------------------- - # datasets - # ---------------------------------------- - for phase, dataset in opt['datasets'].items(): - phase = phase.split('_')[0] - dataset['phase'] = phase - dataset['scale'] = opt['scale'] # broadcast - dataset['n_channels'] = opt['n_channels'] # broadcast - if 'dataroot_H' in dataset and dataset['dataroot_H'] is not None: - dataset['dataroot_H'] = os.path.expanduser(dataset['dataroot_H']) - if 'dataroot_L' in dataset and dataset['dataroot_L'] is not None: - dataset['dataroot_L'] = os.path.expanduser(dataset['dataroot_L']) - - # ---------------------------------------- - # path - # ---------------------------------------- - for key, path in opt['path'].items(): - if path and key in opt['path']: - opt['path'][key] = os.path.expanduser(path) - - path_task = os.path.join(opt['path']['root'], opt['task']) - opt['path']['task'] = path_task - opt['path']['log'] = path_task - opt['path']['options'] = os.path.join(path_task, 'options') - - if is_train: - opt['path']['models'] = os.path.join(path_task, 'models') - opt['path']['images'] = os.path.join(path_task, 'images') - else: # test - opt['path']['images'] = os.path.join(path_task, 'test_images') - - # ---------------------------------------- - # network - # ---------------------------------------- - opt['netG']['scale'] = opt['scale'] if 'scale' in opt else 1 - - # ---------------------------------------- - # GPU devices - # ---------------------------------------- - gpu_list = ','.join(str(x) for x in opt['gpu_ids']) - os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list - print('export CUDA_VISIBLE_DEVICES=' + gpu_list) - - # ---------------------------------------- - # default setting for distributeddataparallel - # ---------------------------------------- - if 'find_unused_parameters' not in opt: - opt['find_unused_parameters'] = True - if 'use_static_graph' not in opt: - opt['use_static_graph'] = False - if 'dist' not in opt: - opt['dist'] = False - opt['num_gpu'] = len(opt['gpu_ids']) - print('number of GPUs is: ' + str(opt['num_gpu'])) - - # ---------------------------------------- - # default setting for perceptual loss - # ---------------------------------------- - if 'F_feature_layer' not in opt['train']: - opt['train']['F_feature_layer'] = 34 # 25; [2,7,16,25,34] - if 'F_weights' not in opt['train']: - opt['train']['F_weights'] = 1.0 # 1.0; [0.1,0.1,1.0,1.0,1.0] - if 'F_lossfn_type' not in opt['train']: - opt['train']['F_lossfn_type'] = 'l1' - if 'F_use_input_norm' not in opt['train']: - opt['train']['F_use_input_norm'] = True - if 'F_use_range_norm' not in opt['train']: - opt['train']['F_use_range_norm'] = False - - # ---------------------------------------- - # default setting for optimizer - # ---------------------------------------- - if 'G_optimizer_type' not in opt['train']: - opt['train']['G_optimizer_type'] = "adam" - if 'G_optimizer_betas' not in opt['train']: - opt['train']['G_optimizer_betas'] = [0.9,0.999] - if 'G_scheduler_restart_weights' not in opt['train']: - opt['train']['G_scheduler_restart_weights'] = 1 - if 'G_optimizer_wd' not in opt['train']: - opt['train']['G_optimizer_wd'] = 0 - if 'G_optimizer_reuse' not in opt['train']: - opt['train']['G_optimizer_reuse'] = False - if 'netD' in opt and 'D_optimizer_reuse' not in opt['train']: - opt['train']['D_optimizer_reuse'] = False - - # ---------------------------------------- - # default setting of strict for model loading - # ---------------------------------------- - if 'G_param_strict' not in opt['train']: - opt['train']['G_param_strict'] = True - if 'netD' in opt and 'D_param_strict' not in opt['path']: - opt['train']['D_param_strict'] = True - if 'E_param_strict' not in opt['path']: - opt['train']['E_param_strict'] = True - - # ---------------------------------------- - # Exponential Moving Average - # ---------------------------------------- - if 'E_decay' not in opt['train']: - opt['train']['E_decay'] = 0 - - # ---------------------------------------- - # default setting for discriminator - # ---------------------------------------- - if 'netD' in opt: - if 'net_type' not in opt['netD']: - opt['netD']['net_type'] = 'discriminator_patchgan' # discriminator_unet - if 'in_nc' not in opt['netD']: - opt['netD']['in_nc'] = 3 - if 'base_nc' not in opt['netD']: - opt['netD']['base_nc'] = 64 - if 'n_layers' not in opt['netD']: - opt['netD']['n_layers'] = 3 - if 'norm_type' not in opt['netD']: - opt['netD']['norm_type'] = 'spectral' - - - return opt - - -def find_last_checkpoint(save_dir, net_type='G', pretrained_path=None): - """ - Args: - save_dir: model folder - net_type: 'G' or 'D' or 'optimizerG' or 'optimizerD' - pretrained_path: pretrained model path. If save_dir does not have any model, load from pretrained_path - - Return: - init_iter: iteration number - init_path: model path - """ - file_list = glob.glob(os.path.join(save_dir, '*_{}.pth'.format(net_type))) - if file_list: - iter_exist = [] - for file_ in file_list: - iter_current = re.findall(r"(\d+)_{}.pth".format(net_type), file_) - iter_exist.append(int(iter_current[0])) - init_iter = max(iter_exist) - init_path = os.path.join(save_dir, '{}_{}.pth'.format(init_iter, net_type)) - else: - init_iter = 0 - init_path = pretrained_path - return init_iter, init_path - - -''' -# -------------------------------------------- -# convert the opt into json file -# -------------------------------------------- -''' - - -def save(opt): - opt_path = opt['opt_path'] - opt_path_copy = opt['path']['options'] - dirname, filename_ext = os.path.split(opt_path) - filename, ext = os.path.splitext(filename_ext) - dump_path = os.path.join(opt_path_copy, filename+get_timestamp()+ext) - with open(dump_path, 'w') as dump_file: - json.dump(opt, dump_file, indent=2) - - -''' -# -------------------------------------------- -# dict to string for logger -# -------------------------------------------- -''' - - -def dict2str(opt, indent_l=1): - msg = '' - for k, v in opt.items(): - if isinstance(v, dict): - msg += ' ' * (indent_l * 2) + k + ':[\n' - msg += dict2str(v, indent_l + 1) - msg += ' ' * (indent_l * 2) + ']\n' - else: - msg += ' ' * (indent_l * 2) + k + ': ' + str(v) + '\n' - return msg - - -''' -# -------------------------------------------- -# convert OrderedDict to NoneDict, -# return None for missing key -# -------------------------------------------- -''' - - -def dict_to_nonedict(opt): - if isinstance(opt, dict): - new_opt = dict() - for key, sub_opt in opt.items(): - new_opt[key] = dict_to_nonedict(sub_opt) - return NoneDict(**new_opt) - elif isinstance(opt, list): - return [dict_to_nonedict(sub_opt) for sub_opt in opt] - else: - return opt - - -class NoneDict(dict): - def __missing__(self, key): - return None diff --git a/spaces/leave7/kazunaAI2.0/utils.py b/spaces/leave7/kazunaAI2.0/utils.py deleted file mode 100644 index 3733a75111dc89cefa333b34933ae01623550ea7..0000000000000000000000000000000000000000 --- a/spaces/leave7/kazunaAI2.0/utils.py +++ /dev/null @@ -1,338 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess - -import librosa -import numpy as np -import torchaudio -from scipy.io.wavfile import read -import torch -import torchvision -from torch.nn import functional as F -from commons import sequence_mask -from hubert import hubert_model -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - -f0_bin = 256 -f0_max = 1100.0 -f0_min = 50.0 -f0_mel_min = 1127 * np.log(1 + f0_min / 700) -f0_mel_max = 1127 * np.log(1 + f0_max / 700) - -def f0_to_coarse(f0): - is_torch = isinstance(f0, torch.Tensor) - f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1 - - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 - f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int) - assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min()) - return f0_coarse - - -def get_hubert_model(rank=None): - - hubert_soft = hubert_model.hubert_soft("hubert/hubert-soft-0d54a1f4.pt") - if rank is not None: - hubert_soft = hubert_soft.cuda(rank) - return hubert_soft - -def get_hubert_content(hmodel, y=None, path=None): - if path is not None: - source, sr = torchaudio.load(path) - source = torchaudio.functional.resample(source, sr, 16000) - if len(source.shape) == 2 and source.shape[1] >= 2: - source = torch.mean(source, dim=0).unsqueeze(0) - else: - source = y - source = source.unsqueeze(0) - with torch.inference_mode(): - units = hmodel.units(source) - return units.transpose(1,2) - - -def get_content(cmodel, y): - with torch.no_grad(): - c = cmodel.extract_features(y.squeeze(1))[0] - c = c.transpose(1, 2) - return c - - - -def transform(mel, height): # 68-92 - #r = np.random.random() - #rate = r * 0.3 + 0.85 # 0.85-1.15 - #height = int(mel.size(-2) * rate) - tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1))) - if height >= mel.size(-2): - return tgt[:, :mel.size(-2), :] - else: - silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1) - silence += torch.randn_like(silence) / 10 - return torch.cat((tgt, silence), 1) - - -def stretch(mel, width): # 0.5-2 - return torchvision.transforms.functional.resize(mel, (mel.size(-2), width)) - - -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 iteration is None: - iteration = 1 - if learning_rate is None: - learning_rate = 0.0002 - if optimizer is not None and checkpoint_dict['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 save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - # ckptname = checkpoint_path.split(os.sep)[-1] - # newest_step = int(ckptname.split(".")[0].split("_")[1]) - # val_steps = 2000 - # last_ckptname = checkpoint_path.replace(str(newest_step), str(newest_step - val_steps*3)) - # if newest_step >= val_steps*3: - # os.system(f"rm {last_ckptname}") - logger.info("Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path)) - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save({'model': state_dict, - 'iteration': iteration, - 'optimizer': optimizer.state_dict(), - 'learning_rate': learning_rate}, checkpoint_path) - - -def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats='HWC') - for k, v in audios.items(): - writer.add_audio(k, v, global_step, audio_sampling_rate) - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - print(x) - return x - - -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") 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/lhkhiem28/A-recognition-system/README.md b/spaces/lhkhiem28/A-recognition-system/README.md deleted file mode 100644 index b0c443c607f3a8eb6c96e5ccd884562137a48733..0000000000000000000000000000000000000000 --- a/spaces/lhkhiem28/A-recognition-system/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: COVID-19 Named Entity Recognition for Vietnamese -emoji: ⚡ -colorFrom: red -colorTo: green -sdk: gradio -sdk_version: 3.14.0 -app_file: source/app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/lighdow/anime-cute-tts/mel_processing.py b/spaces/lighdow/anime-cute-tts/mel_processing.py deleted file mode 100644 index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000 --- a/spaces/lighdow/anime-cute-tts/mel_processing.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -import torch.utils.data -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/liliyRehtina/color/predict.py b/spaces/liliyRehtina/color/predict.py deleted file mode 100644 index c3e6db6874a3260d484216e9de72ff192b3329a7..0000000000000000000000000000000000000000 --- a/spaces/liliyRehtina/color/predict.py +++ /dev/null @@ -1,104 +0,0 @@ -# Prediction interface for Cog ⚙️ -# https://github.com/replicate/cog/blob/main/docs/python.md - -from cog import BasePredictor, Input, Path -import tempfile -import os, glob -import numpy as np -import cv2 -from PIL import Image -import torch -import torch.nn as nn -import torch.nn.functional as F -from models import model, basic -from utils import util - -class Predictor(BasePredictor): - def setup(self): - seed = 130 - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - #print('--------------', torch.cuda.is_available()) - """Load the model into memory to make running multiple predictions efficient""" - self.colorizer = model.AnchorColorProb(inChannel=1, outChannel=313, enhanced=True) - self.colorizer = self.colorizer.cuda() - checkpt_path = "./checkpoints/disco-beta.pth.rar" - assert os.path.exists(checkpt_path) - data_dict = torch.load(checkpt_path, map_location=torch.device('cpu')) - self.colorizer.load_state_dict(data_dict['state_dict']) - self.colorizer.eval() - self.color_class = basic.ColorLabel(lambda_=0.5, device='cuda') - - def resize_ab2l(self, gray_img, lab_imgs): - H, W = gray_img.shape[:2] - reszied_ab = cv2.resize(lab_imgs[:,:,1:], (W,H), interpolation=cv2.INTER_LINEAR) - return np.concatenate((gray_img, reszied_ab), axis=2) - - def predict( - self, - image: Path = Input(description="input image. Output will be one or multiple colorized images."), - n_anchors: int = Input( - description="number of color anchors", ge=3, le=14, default=8 - ), - multi_result: bool = Input( - description="to generate diverse results", default=False - ), - vis_anchors: bool = Input( - description="to visualize the anchor locations", default=False - ) - ) -> Path: - """Run a single prediction on the model""" - bgr_img = cv2.imread(str(image), cv2.IMREAD_COLOR) - rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) - rgb_img = np.array(rgb_img / 255., np.float32) - lab_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2LAB) - org_grays = (lab_img[:,:,[0]]-50.) / 50. - lab_img = cv2.resize(lab_img, (256,256), interpolation=cv2.INTER_LINEAR) - - lab_img = torch.from_numpy(lab_img.transpose((2, 0, 1))) - gray_img = (lab_img[0:1,:,:]-50.) / 50. - ab_chans = lab_img[1:3,:,:] / 110. - input_grays = gray_img.unsqueeze(0) - input_colors = ab_chans.unsqueeze(0) - input_grays = input_grays.cuda(non_blocking=True) - input_colors = input_colors.cuda(non_blocking=True) - - sampled_T = 2 if multi_result else 0 - pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = self.colorizer(input_grays, \ - input_colors, n_anchors, True, sampled_T) - pred_probs = pal_logit - guided_colors = self.color_class.decode_ind2ab(ref_logit, T=0) - sp_size = 16 - guided_colors = basic.upfeat(guided_colors, affinity_map, sp_size, sp_size) - res_list = [] - if multi_result: - for no in range(3): - pred_labs = torch.cat((input_grays,enhanced_ab[no:no+1,:,:,:]), dim=1) - lab_imgs = basic.tensor2array(pred_labs).squeeze(axis=0) - lab_imgs = self.resize_ab2l(org_grays, lab_imgs) - #util.save_normLabs_from_batch(lab_imgs, save_dir, [file_name], -1, suffix='c%d'%no) - res_list.append(lab_imgs) - else: - pred_labs = torch.cat((input_grays,enhanced_ab), dim=1) - lab_imgs = basic.tensor2array(pred_labs).squeeze(axis=0) - lab_imgs = self.resize_ab2l(org_grays, lab_imgs) - #util.save_normLabs_from_batch(lab_imgs, save_dir, [file_name], -1)#, suffix='enhanced') - res_list.append(lab_imgs) - - if vis_anchors: - ## visualize anchor locations - anchor_masks = basic.upfeat(hint_mask, affinity_map, sp_size, sp_size) - marked_labs = basic.mark_color_hints(input_grays, enhanced_ab, anchor_masks, base_ABs=enhanced_ab) - hint_imgs = basic.tensor2array(marked_labs).squeeze(axis=0) - hint_imgs = self.resize_ab2l(org_grays, hint_imgs) - #util.save_normLabs_from_batch(hint_imgs, save_dir, [file_name], -1, suffix='anchors') - res_list.append(hint_imgs) - - output = cv2.vconcat(res_list) - output[:,:,0] = output[:,:,0] * 50.0 + 50.0 - output[:,:,1:3] = output[:,:,1:3] * 110.0 - rgb_output = cv2.cvtColor(output[:,:,:], cv2.COLOR_LAB2BGR) - out_path = Path(tempfile.mkdtemp()) / "out.png" - cv2.imwrite(str(out_path), (rgb_output*255.0).astype(np.uint8)) - return out_path diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat Pro DC 2015.010.20060 Multilingual Xforce Fixed Crack.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat Pro DC 2015.010.20060 Multilingual Xforce Fixed Crack.md deleted file mode 100644 index c00fba67c11d8e5ce95ea1800ebaaa29983b840b..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat Pro DC 2015.010.20060 Multilingual Xforce Fixed Crack.md +++ /dev/null @@ -1,6 +0,0 @@ -<h2>Adobe Acrobat Pro DC 2015.010.20060 Multilingual Xforce Crack</h2><br /><p><b><b>Download</b> ✓✓✓ <a href="https://bytlly.com/2uGvQ8">https://bytlly.com/2uGvQ8</a></b></p><br /><br /> -<br /> -Xforce.Crack.. 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-import torch -import numpy as np -import skimage.io as io - -# from FaceSDK.face_sdk import FaceDetection -# from face_sdk import FaceDetection -import matplotlib.pyplot as plt -from matplotlib.patches import Rectangle -from skimage.transform import SimilarityTransform -from skimage.transform import warp -from PIL import Image -import torch.nn.functional as F -import torchvision as tv -import torchvision.utils as vutils -import time -import cv2 -import os -from skimage import img_as_ubyte -import json -import argparse -import dlib - - -def _standard_face_pts(): - pts = ( - np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32) / 256.0 - - 1.0 - ) - - return np.reshape(pts, (5, 2)) - - -def _origin_face_pts(): - pts = np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32) - - return np.reshape(pts, (5, 2)) - - -def get_landmark(face_landmarks, id): - part = face_landmarks.part(id) - x = part.x - y = part.y - - return (x, y) - - -def search(face_landmarks): - - x1, y1 = get_landmark(face_landmarks, 36) - x2, y2 = get_landmark(face_landmarks, 39) - x3, y3 = get_landmark(face_landmarks, 42) - x4, y4 = get_landmark(face_landmarks, 45) - - x_nose, y_nose = get_landmark(face_landmarks, 30) - - x_left_mouth, y_left_mouth = get_landmark(face_landmarks, 48) - x_right_mouth, y_right_mouth = get_landmark(face_landmarks, 54) - - x_left_eye = int((x1 + x2) / 2) - y_left_eye = int((y1 + y2) / 2) - x_right_eye = int((x3 + x4) / 2) - y_right_eye = int((y3 + y4) / 2) - - results = np.array( - [ - [x_left_eye, y_left_eye], - [x_right_eye, y_right_eye], - [x_nose, y_nose], - [x_left_mouth, y_left_mouth], - [x_right_mouth, y_right_mouth], - ] - ) - - return results - - -def compute_transformation_matrix(img, landmark, normalize, target_face_scale=1.0): - - std_pts = _standard_face_pts() # [-1,1] - target_pts = (std_pts * target_face_scale + 1) / 2 * 512.0 - - # print(target_pts) - - h, w, c = img.shape - if normalize == True: - landmark[:, 0] = landmark[:, 0] / h * 2 - 1.0 - landmark[:, 1] = landmark[:, 1] / w * 2 - 1.0 - - # print(landmark) - - affine = SimilarityTransform() - - affine.estimate(target_pts, landmark) - - return affine.params - - -def show_detection(image, box, landmark): - plt.imshow(image) - print(box[2] - box[0]) - plt.gca().add_patch( - Rectangle( - (box[1], box[0]), box[2] - box[0], box[3] - box[1], linewidth=1, edgecolor="r", facecolor="none" - ) - ) - plt.scatter(landmark[0][0], landmark[0][1]) - plt.scatter(landmark[1][0], landmark[1][1]) - plt.scatter(landmark[2][0], landmark[2][1]) - plt.scatter(landmark[3][0], landmark[3][1]) - plt.scatter(landmark[4][0], landmark[4][1]) - plt.show() - - -def affine2theta(affine, input_w, input_h, target_w, target_h): - # param = np.linalg.inv(affine) - param = affine - theta = np.zeros([2, 3]) - theta[0, 0] = param[0, 0] * input_h / target_h - theta[0, 1] = param[0, 1] * input_w / target_h - theta[0, 2] = (2 * param[0, 2] + param[0, 0] * input_h + param[0, 1] * input_w) / target_h - 1 - theta[1, 0] = param[1, 0] * input_h / target_w - theta[1, 1] = param[1, 1] * input_w / target_w - theta[1, 2] = (2 * param[1, 2] + param[1, 0] * input_h + param[1, 1] * input_w) / target_w - 1 - return theta - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument("--url", type=str, default="/home/jingliao/ziyuwan/celebrities", help="input") - parser.add_argument( - "--save_url", type=str, default="/home/jingliao/ziyuwan/celebrities_detected_face_reid", help="output" - ) - opts = parser.parse_args() - - url = opts.url - save_url = opts.save_url - - ### If the origin url is None, then we don't need to reid the origin image - - os.makedirs(url, exist_ok=True) - os.makedirs(save_url, exist_ok=True) - - face_detector = dlib.get_frontal_face_detector() - landmark_locator = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") - - count = 0 - - map_id = {} - for x in os.listdir(url): - img_url = os.path.join(url, x) - pil_img = Image.open(img_url).convert("RGB") - - image = np.array(pil_img) - - start = time.time() - faces = face_detector(image) - done = time.time() - - if len(faces) == 0: - print("Warning: There is no face in %s" % (x)) - continue - - print(len(faces)) - - if len(faces) > 0: - for face_id in range(len(faces)): - current_face = faces[face_id] - face_landmarks = landmark_locator(image, current_face) - current_fl = search(face_landmarks) - - affine = compute_transformation_matrix(image, current_fl, False, target_face_scale=1.3) - aligned_face = warp(image, affine, output_shape=(512, 512, 3)) - img_name = x[:-4] + "_" + str(face_id + 1) - io.imsave(os.path.join(save_url, img_name + ".png"), img_as_ubyte(aligned_face)) - - count += 1 - - if count % 1000 == 0: - print("%d have finished ..." % (count)) - diff --git a/spaces/mehdidc/ae_gen/convert.py b/spaces/mehdidc/ae_gen/convert.py deleted file mode 100644 index d2d0fb6993835c8f69bfcec433a424ca9659237a..0000000000000000000000000000000000000000 --- a/spaces/mehdidc/ae_gen/convert.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np -import torch, h5py -from model import * -w, h, c = 28, 28, 1 -model_new = DeepConvAE( - w=w, h=h, c=c, - nb_filters=128, - spatial=True, - channel=True, - channel_stride=4, - # total layers = nb_layers*2, where we have nb_layers for encoder and nb_layers for decoder - nb_layers=3, -) -# model_old = h5py.File("mnist_deepconvae/model.h5") -model_old = h5py.File("/home/mehdi/work/code/out_of_class/ae/mnist/model.h5") - - -print(model_new) -print(model_old["model_weights"].keys()) - - -for name, param in model_new.named_parameters(): - enc_or_decode, layer_id, bias_or_kernel = name.split(".") - - if enc_or_decode == "encode": - layer_name = "conv2d" - else: - layer_name = "up_conv2d" - - layer_id = (int(layer_id)//2) + 1 - - full_layer_name = f"{layer_name}_{layer_id}" - print(full_layer_name) - - k = "kernel" if bias_or_kernel == "weight" else "bias" - weights = model_old["model_weights"][full_layer_name][full_layer_name][k][()] - weights = np.array(weights) - weights = torch.from_numpy(weights) - print(name, layer_id, param.shape, weights.shape) - inds = [4,3,2,1,0] - if k == "kernel": - if layer_name == "conv2d": - weights = weights.permute((3,2,0,1)) - weights = weights[:,:,inds] - weights = weights[:,:,:, inds] - print("W", weights.shape) - elif layer_name == "up_conv2d": - weights = weights.permute((2,3,0,1)) - print(param.shape, weights.shape) - param.data.copy_(weights) - print((param-weights).sum()) -torch.save(model_new, "mnist_deepconvae/model.th") diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/runtime/loggers.py b/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/runtime/loggers.py deleted file mode 100644 index 591486d8814c5dff43b8652823900e05add06c83..0000000000000000000000000000000000000000 --- a/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/runtime/loggers.py +++ /dev/null @@ -1,134 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# 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. -# -# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES -# SPDX-License-Identifier: MIT - -import pathlib -from abc import ABC, abstractmethod -from enum import Enum -from typing import Dict, Any, Callable, Optional - -import dllogger -import torch.distributed as dist -import wandb -from dllogger import Verbosity - -from se3_transformer.runtime.utils import rank_zero_only - - -class Logger(ABC): - @rank_zero_only - @abstractmethod - def log_hyperparams(self, params): - pass - - @rank_zero_only - @abstractmethod - def log_metrics(self, metrics, step=None): - pass - - @staticmethod - def _sanitize_params(params): - def _sanitize(val): - if isinstance(val, Callable): - try: - _val = val() - if isinstance(_val, Callable): - return val.__name__ - return _val - except Exception: - return getattr(val, "__name__", None) - elif isinstance(val, pathlib.Path) or isinstance(val, Enum): - return str(val) - return val - - return {key: _sanitize(val) for key, val in params.items()} - - -class LoggerCollection(Logger): - def __init__(self, loggers): - super().__init__() - self.loggers = loggers - - def __getitem__(self, index): - return [logger for logger in self.loggers][index] - - @rank_zero_only - def log_metrics(self, metrics, step=None): - for logger in self.loggers: - logger.log_metrics(metrics, step) - - @rank_zero_only - def log_hyperparams(self, params): - for logger in self.loggers: - logger.log_hyperparams(params) - - -class DLLogger(Logger): - def __init__(self, save_dir: pathlib.Path, filename: str): - super().__init__() - if not dist.is_initialized() or dist.get_rank() == 0: - save_dir.mkdir(parents=True, exist_ok=True) - dllogger.init( - backends=[dllogger.JSONStreamBackend(Verbosity.DEFAULT, str(save_dir / filename))]) - - @rank_zero_only - def log_hyperparams(self, params): - params = self._sanitize_params(params) - dllogger.log(step="PARAMETER", data=params) - - @rank_zero_only - def log_metrics(self, metrics, step=None): - if step is None: - step = tuple() - - dllogger.log(step=step, data=metrics) - - -class WandbLogger(Logger): - def __init__( - self, - name: str, - save_dir: pathlib.Path, - id: Optional[str] = None, - project: Optional[str] = None - ): - super().__init__() - if not dist.is_initialized() or dist.get_rank() == 0: - save_dir.mkdir(parents=True, exist_ok=True) - self.experiment = wandb.init(name=name, - project=project, - id=id, - dir=str(save_dir), - resume='allow', - anonymous='must') - - @rank_zero_only - def log_hyperparams(self, params: Dict[str, Any]) -> None: - params = self._sanitize_params(params) - self.experiment.config.update(params, allow_val_change=True) - - @rank_zero_only - def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None: - if step is not None: - self.experiment.log({**metrics, 'epoch': step}) - else: - self.experiment.log(metrics) diff --git a/spaces/merve/dataset-worldviews/public/private-and-fair/footnote.js b/spaces/merve/dataset-worldviews/public/private-and-fair/footnote.js deleted file mode 100644 index 383057091ac6456ef8d4c7205478d89bef07ad87..0000000000000000000000000000000000000000 --- a/spaces/merve/dataset-worldviews/public/private-and-fair/footnote.js +++ /dev/null @@ -1,132 +0,0 @@ -d3.select('body').selectAppend('div.tooltip.tooltip-hidden') - -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) - }) - -footendSel.parent().parent().selectAll('br').remove() - -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) - -ttSel.classed('tooltip-footnote', 1) - -function addLockedTooltip(sel){ - sel - .on('mouseover', function(d, i){ - ttSel.classed('tooltip-footnote', 1) - .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', 1) - }, 250) - } -} - - - - - -var infoSel = d3.select('.info-box').html('') - .st({border: '1px solid orange', background: 'rgba(255,250,241,.5)', maxWidth: 750, margin: '0 auto', padding: 20, paddingTop: 5, paddingBottom: 5}) - // .st({textAlign: }) - -infoSel.append('p') - .st({marginLeft: 10}) - .html('Not familiar with how machine learning models are trained or why they might leak data? <br>These interactive articles will get you up to speed.') - .html('New to some of these concepts? These interactive articles will get you up to speed.') - .html('New to machine learning or differential privacy? These interactive articles will get you up to speed.') - -var articles = [ - { - img: 'https://pair.withgoogle.com/explorables/images/anonymization.png', - title: 'Collecting Sensitive Information', - permalink: 'https://pair.withgoogle.com/explorables/anonymization/', - }, - { - img: 'https://pair.withgoogle.com/explorables/images/model-inversion.png', - title: 'Why Some Models Leak Data', - permalink: 'https://pair.withgoogle.com/explorables/data-leak/', - }, - { - img: 'http://playground.tensorflow.org/preview.png', - title: 'TensorFlow Playground', - permalink: 'https://playground.tensorflow.org' - }, -] - - -var postSel = infoSel.appendMany('a.post', articles) - .st({ - textAlign: 'center', - width: '30.5%', - display: 'inline-block', - verticalAlign: 'top', - marginLeft: 10, - marginRight: 10, - textDecoration: 'none', - }) - .at({href: d => d.permalink}) - -postSel.append('div.img') - .st({ - width: '100%', - height: 80, - backgroundImage: d => `url(${d.img})`, - backgroundSize: 'cover', - backgroundPosition: 'center', - outline: '1px solid #ccc' - }) - -postSel.append('p.title') - .text(d => d.title) - .st({ - verticalAlign: 'top', - marginTop: 10, - textDecoration: 'none', - fontSize: 15, - fontWeight: 500, - }) - - -// width: 100%; -// height: 200px; -// background-image: url(https://pair.withgoogle.com/explorables/images/model-inversion.png); -// background-size: cover; -// background-position: center center; - diff --git a/spaces/merve/fill-in-the-blank/source/uncertainty-calibration/footnote.css b/spaces/merve/fill-in-the-blank/source/uncertainty-calibration/footnote.css deleted file mode 100644 index 83472e6bc26c962b1c2fcc630d641ed62f181e77..0000000000000000000000000000000000000000 --- a/spaces/merve/fill-in-the-blank/source/uncertainty-calibration/footnote.css +++ /dev/null @@ -1,57 +0,0 @@ -.tooltip-footnote { - top: -1000px; - position: absolute; - padding: 10px; - background: rgba(255, 255, 255, .8); - border: 0px solid lightgray; - - width: 300px !important; - font-size: 14px; - line-height: 1.4em; - background: rgba(0, 0, 0, .8); - color: #fff; - pointer-events: all !important; -} -.tooltip-footnote a{ - color: #fff !important; -} -.tooltip-footnote:hover{ -/* opacity: 1; - pointer-events: all !important; -*/} - -.tooltip-footnote-hidden{ - opacity: 0; - transition: opacity .3s; - transition-delay: .2s; - pointer-events: none !important; -} - -@media (max-width: 590px){ - .footend{ - margin-left: 0px; - width: 10px; - } - - div.tooltip-footnote{ - transition: all 0s !important; - transition-delay: 0s !important; - - display: none; - position: fixed; - bottom: -1px; - width: calc(100%); - left: -1px !important; - right: -1px !important; - top: auto !important; - width: auto !important; - } -} - -.footstart{ - padding-left: 2px; - height: 8px !important; - /*background: red;*/ - /*display: inline-block;*/ - line-height: 0em; -} diff --git a/spaces/merve/hidden-bias/public/fill-in-the-blank/init-sent.js b/spaces/merve/hidden-bias/public/fill-in-the-blank/init-sent.js deleted file mode 100644 index 263a35a62a0fa9f2064834bc78a93222c8040897..0000000000000000000000000000000000000000 --- a/spaces/merve/hidden-bias/public/fill-in-the-blank/init-sent.js +++ /dev/null @@ -1,136 +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.initSent = async function(sent, sel){ - var isHamlet = sent.class == 'hamlet' - var isMobile = innerWidth < 900 - - var sel = d3.select('.' + sent.class) - .st({opacity: .5, marginBottom: isHamlet ? '' : 40}) - - - // Load completitions - var str = sent.str - while (str.includes('__')) str = str.replace('__', '_') - str = str.replace('_', 'things') - - var tokens = tokenizer.tokenizeCLS(str) - .filter(d => d < 30522) - - var topTokens = await post('embed_group_top', {tokens}) - topTokens.forEach(sent => { - sent.forEach(d => d.str = tokenizer.vocab[d.i]) - }) - - var displayTokens = tokens - .slice(1) - .map((vocabIndex, i) => { - return {i, str: bertLargeVocab[vocabIndex].replace('##', '')} - }) - displayTokens.pop() - - - sel.html('').st({opacity: 1}) - if (!sel.node()) return - - var divSel = sel.append('div') - .st({position: 'relative'}) - var svgSel = divSel.append('svg') - .st({position: 'absolute', top: 0, zIndex: -10}) - - var tokenSel = divSel - .append('div.token-container') - .st({padding: 20, paddingLeft: 0, paddingRight: 0, fontSize: 20}) - .appendMany('button.token', displayTokens) - .text(d => d.str) - .on('click', drawToken) - - var connectionPath = svgSel.append('path').at({fill: 'none', stroke: '#000', strokeWidth: 1}) - - var padding = 5 - var width = divSel.node().offsetWidth - var botWidth = isMobile ? width - padding*2 : 580 - - var botTextSel = divSel.append('div.top-sents') - .translate([width/2 - botWidth/2 - padding + .5, 15]) - .st({ - width: botWidth, - height: 170, - outline: '1px solid #000', - padding: padding, - // position: 'absolute', - background: '#fff', - overflowY: 'scroll', - fontSize: isMobile ? 10 : '', - }) - - if (isHamlet){ - divSel.append('div.caption') - .text(`BERT's predictions for what should fill in the hidden word`) - .st({fontWeight: '', lineHeight: '1.1em', fontSize: 14, textAlign: 'center', width: '100%', marginTop: 20}) - } - - var curIndex = -1 - function drawToken(token){ - var node = tokenSel.filter(d => d == token).node() - var x = node.offsetLeft + node.offsetWidth/2 - var y = node.offsetTop + node.offsetHeight - - var y1 = botTextSel.node().offsetTop - - connectionPath.at({d: ['M', x, y, 'L', width/2, y1 + 15].join(' ')}) - - var completionSel = botTextSel.html('').appendMany('span', topTokens[token.i + 1]) - .st({display: 'inline-block', fontFamily: 'monospace', width: isMobile ? '47%' : '31%', borderBottom: '1px solid #ccc', margin: 4, fontSize: innerWidth < 350 ? 12 : isMobile ? 13 : 14 }) - - completionSel.append('span') - .st({color: '#ccc'}) - .html(d => { - var str = d3.format('.3f')(d.p*100) + '% ' - if (str.length < 8) str = ' ' + str - return str - }) - - completionSel.append('span') - .text(d => d.str.replace('▁', '')) - - - tokenSel - .text(d => d.str) - .classed('active', false) - .filter(d => d == token) - .classed('active', true) - .text(d => d.str.split('').map(d => '_').join('')) - } - - var i = displayTokens.length - (isHamlet ? 2 : 2) - if (tokens.includes(2477)) i = tokens.indexOf(2477) - 1 - drawToken(displayTokens[i]) - - var topTokensSel = sel.append('div.top-tokens') -} - - - - - - - - - - - -if (window.init) init() diff --git a/spaces/miyaaa666/bingo/src/components/voice.tsx b/spaces/miyaaa666/bingo/src/components/voice.tsx deleted file mode 100644 index 074d0e145229947282a472bd84f6578cf0b3c71c..0000000000000000000000000000000000000000 --- a/spaces/miyaaa666/bingo/src/components/voice.tsx +++ /dev/null @@ -1,52 +0,0 @@ -import React, { useEffect } from 'react' -import { useSetAtom } from 'jotai' -import { useBing } from '@/lib/hooks/use-bing' -import Image from 'next/image' -import VoiceIcon from '@/assets/images/voice.svg' -import VoiceButton from './ui/voice' -import { SR } from '@/lib/bots/bing/sr' -import { voiceListenAtom } from '@/state' - -const sr = new SR(['发送', '清空', '退出']) - -const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick<ReturnType<typeof useBing>, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => { - const setListen = useSetAtom(voiceListenAtom) - useEffect(() => { - if (sr.listening) return - sr.transcript = !isSpeaking - }, [isSpeaking]) - - useEffect(() => { - sr.onchange = (msg: string, command?: string) => { - switch (command) { - case '退出': - sr.stop() - break; - case '发送': - sendMessage(input) - case '清空': - setInput('') - break; - default: - setInput(input + msg) - } - } - }, [input]) - - const switchSR = (enable: boolean = false) => { - setListen(enable) - if (enable) { - sr.start() - } else { - sr.stop() - } - } - - return sr.listening ? ( - <VoiceButton onClick={() => switchSR(false)} /> - ) : ( - <Image alt="start voice" src={VoiceIcon} width={24} className="-mt-0.5" onClick={() => switchSR(true)} /> - ) -}; - -export default Voice; diff --git a/spaces/ml6team/controlnet-interior-design/palette.py b/spaces/ml6team/controlnet-interior-design/palette.py deleted file mode 100644 index 9d7021d952fd5a055062a05175c58e7659433aba..0000000000000000000000000000000000000000 --- a/spaces/ml6team/controlnet-interior-design/palette.py +++ /dev/null @@ -1,38 +0,0 @@ -"""This file contains color information""" -from typing import List, Dict -from colors import COLOR_MAPPING_, COLOR_MAPPING_CATEGORY_, ade_palette - - -def convert_hex_to_rgba(hex_code: str) -> str: - """Convert hex code to rgba. - Args: - hex_code (str): hex string - Returns: - str: rgba string - """ - hex_code = hex_code.lstrip('#') - return "rgba(" + str(int(hex_code[0:2], 16)) + ", " + str(int(hex_code[2:4], 16)) + ", " + str(int(hex_code[4:6], 16)) + ", 1.0)" - - -def convert_dict_to_rgba(color_dict: Dict) -> Dict: - """Convert hex code to rgba for all elements in a dictionary. - Args: - color_dict (Dict): color dictionary - Returns: - Dict: color dictionary with rgba values - """ - updated_dict = {} - for k, v in color_dict.items(): - updated_dict[convert_hex_to_rgba(k)] = v - return updated_dict - - -def convert_nested_dict_to_rgba(nested_dict): - updated_dict = {} - for k, v in nested_dict.items(): - updated_dict[k] = convert_dict_to_rgba(v) - return updated_dict - - -COLOR_MAPPING = convert_dict_to_rgba(COLOR_MAPPING_) -COLOR_MAPPING_CATEGORY = convert_nested_dict_to_rgba(COLOR_MAPPING_CATEGORY_) \ No newline at end of file diff --git a/spaces/ml6team/logo-generator/dalle/models/stage1/vqgan.py b/spaces/ml6team/logo-generator/dalle/models/stage1/vqgan.py deleted file mode 100644 index 7f03a4d02aa579275d58290bc4f3714fd58bfe00..0000000000000000000000000000000000000000 --- a/spaces/ml6team/logo-generator/dalle/models/stage1/vqgan.py +++ /dev/null @@ -1,93 +0,0 @@ -# ------------------------------------------------------------------------------------ -# Modified from VQGAN (https://github.com/CompVis/taming-transformers) -# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer. All Rights Reserved. -# ------------------------------------------------------------------------------------ - -import torch -import torch.nn as nn -from typing import List, Tuple, Optional -from einops import rearrange -from omegaconf import OmegaConf -from .layers import Encoder, Decoder - - -class VectorQuantizer(nn.Module): - """ - Simplified VectorQuantizer in the original VQGAN repository - by removing unncessary modules for sampling - """ - def __init__(self, dim: int, n_embed: int, beta: float) -> None: - super().__init__() - self.n_embed = n_embed - self.dim = dim - self.beta = beta - - self.embedding = nn.Embedding(self.n_embed, self.dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_embed, 1.0 / self.n_embed) - - def forward(self, - z: torch.FloatTensor) -> Tuple[torch.FloatTensor, torch.LongTensor]: - z = rearrange(z, 'b c h w -> b h w c').contiguous() # [B,C,H,W] -> [B,H,W,C] - z_flattened = z.view(-1, self.dim) - - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight**2, dim=1) - 2 * \ - torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) - - min_encoding_indices = torch.argmin(d, dim=1) - z_q = self.embedding(min_encoding_indices).view(z.shape) - return z_q, min_encoding_indices - - def get_codebook_entry(self, - indices: torch.LongTensor, - shape: Optional[List[int]] = None) -> torch.FloatTensor: - z_q = self.embedding(indices) - if shape is not None: - z_q = z_q.view(shape) - z_q = z_q.permute(0, 3, 1, 2).contiguous() - return z_q - - -class VQGAN(nn.Module): - def __init__(self, n_embed: int, embed_dim: int, hparams: OmegaConf) -> None: - super().__init__() - self.encoder = Encoder(**hparams) - self.decoder = Decoder(**hparams) - self.quantize = VectorQuantizer(dim=embed_dim, n_embed=n_embed, beta=0.25) - self.quant_conv = torch.nn.Conv2d(hparams.z_channels, embed_dim, 1) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, hparams.z_channels, 1) - self.latent_dim = hparams.attn_resolutions[0] - - def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: - quant = self.encode(x) - dec = self.decode(quant) - return dec - - def encode(self, x: torch.FloatTensor) -> torch.FloatTensor: - h = self.encoder(x) - h = self.quant_conv(h) - quant = self.quantize(h)[0] - quant = rearrange(quant, 'b h w c -> b c h w').contiguous() - return quant - - def decode(self, quant: torch.FloatTensor) -> torch.FloatTensor: - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - def decode_code(self, code: torch.LongTensor) -> torch.FloatTensor: - quant = self.quantize.get_codebook_entry(code) - quant = quant.permute(0, 3, 1, 2) - dec = self.decode(quant) - return dec - - def get_codes(self, x: torch.FloatTensor) -> torch.LongTensor: - h = self.encoder(x) - h = self.quant_conv(h) - codes = self.quantize(h)[1].view(x.shape[0], self.latent_dim ** 2) - return codes - - def from_ckpt(self, path: str, strict: bool = True) -> 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a/spaces/motionsh/BioMAT/app.py b/spaces/motionsh/BioMAT/app.py deleted file mode 100644 index b97042dfa0157365bb5508eafd544134f4f9baec..0000000000000000000000000000000000000000 --- a/spaces/motionsh/BioMAT/app.py +++ /dev/null @@ -1,122 +0,0 @@ -import streamlit as st - -x = st.slider('Select a value') -st.write(x, 'squared is', x * x) - -import streamlit as st -import os -import torch - -from torch.utils.data import DataLoader -from config import get_config_universal -from dataset import DataSet -from datasetbuilder import DataSetBuilder -from test import Test -from visualization.steamlit_plot import plot_kinematic_predictions - -dataset_name = 'camargo' -config = get_config_universal(dataset_name) - -# model_file = 'transformertsai_g1g2rardsasd_g1g2rardsasd.pt' -# model = torch.load(os.path.join('./caches/trained_model/v05', model_file)) -sensor_options = {'Thigh & Shank & Foot': ['foot', 'shank', 'thigh'], - 'Thigh & Shank': ['thigh', 'shank'], - 'Thigh & Foot': ['thigh', 'foot'], - 'Shank & Foot': ['shank', 'foot'], - 'Thigh': ['thigh'], - 'Shank': ['shank'], - 'Foot': ['foot']} - -@st.cache -def fetch_data(config): - dataset_handler = DataSet(config, load_dataset=True) - kihadataset_train, kihadataset_test = dataset_handler.run_dataset_split_loop() - kihadataset_train['x'], kihadataset_train['y'], kihadataset_train['labels'] = dataset_handler.run_segmentation( - kihadataset_train['x'], - kihadataset_train['y'], kihadataset_train['labels']) - kihadataset_test['x'], kihadataset_test['y'], kihadataset_test['labels'] = dataset_handler.run_segmentation( - kihadataset_test['x'], - kihadataset_test['y'], kihadataset_test['labels']) - train_dataset = DataSetBuilder(kihadataset_train['x'], kihadataset_train['y'], kihadataset_train['labels'], - transform_method=config['data_transformer'], scaler=None, noise=None) - test_dataset = DataSetBuilder(kihadataset_test['x'], kihadataset_test['y'], kihadataset_test['labels'], - transform_method=config['data_transformer'], scaler=train_dataset.scaler, - noise=None) - test_dataloader = DataLoader(dataset=test_dataset, batch_size=config['batch_size'], shuffle=False) - return test_dataloader, kihadataset_test - -# @st.cache() -def fetch_model(sensor_name, model_name): - device = torch.device('cpu') - model_names = {'CNNLSTM':'hernandez2021cnnlstm', 'BiLSTM':'bilstm', 'BioMAT': 'transformertsai'} - sensor_names = {'Thigh & Shank & Foot':'thighshankfoot', - 'Thigh & Shank':'thighshank', - 'Thigh & Foot':'thighfoot', - 'Shank & Foot':'shankfoot', - 'Thigh':'thigh', - 'Shank':'shank', - 'Foot':'foot'} - if sensor_names[sensor_name]=='thighshankfoot': - model_file = model_names[model_name] + '_g1g2rardsasd_g1g2rardsasd.pt' - else: - model_file = sensor_names[sensor_name] + '_' + model_names[model_name]+'_g1g2rardsasd_g1g2rardsasd.pt' - st.write(model_file) - model = torch.load(os.path.join('./caches/trained_model/v05', model_file)) - return model - -# @st.cache -def fetch_predictions(model): - test_handler = Test() - y_pred, y_true, loss = test_handler.run_testing(config, model, test_dataloader=test_dataloader) - y_true = y_true.detach().cpu().clone().numpy() - y_pred = y_pred.detach().cpu().clone().numpy() - return y_pred, y_true, loss - -st.set_page_config(layout="wide") -st.title('BioMAT:Biomechanical Multi-Activity Transformer Model for Joint Kinematic Prediction From IMUs') -st.info('If you change the sensor configuration, press rerun', icon="ℹ️") - -st.sidebar.title('Sensor Configuration') -selected_sensor = st.sidebar.selectbox('Pick one', ['Thigh & Shank & Foot', - 'Thigh & Shank', - 'Thigh & Foot', - 'Shank & Foot', - 'Thigh', - 'Shank', - 'Foot']) - -config['selected_sensors'] = sensor_options[selected_sensor] - -st.sidebar.title('Model Configuration') -selected_model = st.sidebar.selectbox('Pick one', ['CNNLSTM', - 'BiLSTM', - 'BioMAT']) - -st.sidebar.title('Subject') -selected_subject = st.sidebar.slider('Pick a Subject Number', min_value=23, max_value=25, step=1) - -st.sidebar.title('Activity') -selected_activities = st.sidebar.multiselect('Pick Output Activities', - ['LevelGround Walking', 'Ramp Ascent', 'Ramp Descent', 'Stair Ascent', 'Stair Descent']) - -index_to_plot = st.sidebar.number_input('Enter a number between 0 and 5', min_value=0, max_value=5) - -if st.sidebar.button('Predict'): - with st.spinner('Data is loading...'): - test_dataloader, kihadataset_test = fetch_data(config) - st.success('Data is loaded!') - with st.spinner('Model is loading...'): - model = fetch_model(selected_sensor, selected_model) - st.success('Model is loaded!') - with st.spinner('Prediction ...'): - y_pred, y_true, loss = fetch_predictions(model) - st.success('Prediction is Completed!') - st.write('plot ...') - subject = 'AB' + str(selected_subject) - fig = plot_kinematic_predictions(y_true, y_pred, kihadataset_test['labels'], subject, - selected_activities=selected_activities, selected_index_to_plot=index_to_plot) - st.plotly_chart(fig, use_container_width=True) - - - - diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py b/spaces/mshukor/UnIVAL/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py deleted file mode 100644 index 23869ebcd0c438f36e310c8ccddd3b5c07a71182..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py +++ /dev/null @@ -1,71 +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 -from fairseq.search import Search - - -class NoisyChannelBeamSearch(Search): - - def __init__(self, tgt_dict): - super().__init__(tgt_dict) - self.fw_scores_buf = None - self.lm_scores_buf = None - - def _init_buffers(self, t): - # super()._init_buffers(t) - if self.fw_scores_buf is None: - self.scores_buf = t.new() - self.indices_buf = torch.LongTensor().to(device=t.device) - self.beams_buf = torch.LongTensor().to(device=t.device) - self.fw_scores_buf = t.new() - self.lm_scores_buf = t.new() - - def combine_fw_bw(self, combine_method, fw_cum, bw, step): - if combine_method == "noisy_channel": - fw_norm = fw_cum.div(step + 1) - lprobs = bw + fw_norm - elif combine_method == "lm_only": - lprobs = bw + fw_cum - - return lprobs - - def step(self, step, fw_lprobs, scores, bw_lprobs, lm_lprobs, combine_method): - self._init_buffers(fw_lprobs) - bsz, beam_size, vocab_size = fw_lprobs.size() - - if step == 0: - # at the first step all hypotheses are equally likely, so use - # only the first beam - fw_lprobs = fw_lprobs[:, ::beam_size, :].contiguous() - bw_lprobs = bw_lprobs[:, ::beam_size, :].contiguous() - # nothing to add since we are at the first step - fw_lprobs_cum = fw_lprobs - - else: - # make probs contain cumulative scores for each hypothesis - raw_scores = (scores[:, :, step - 1].unsqueeze(-1)) - fw_lprobs_cum = (fw_lprobs.add(raw_scores)) - - combined_lprobs = self.combine_fw_bw(combine_method, fw_lprobs_cum, bw_lprobs, step) - - # choose the top k according to the combined noisy channel model score - torch.topk( - combined_lprobs.view(bsz, -1), - k=min( - # Take the best 2 x beam_size predictions. We'll choose the first - # beam_size of these which don't predict eos to continue with. - beam_size * 2, - combined_lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad - ), - out=(self.scores_buf, self.indices_buf), - ) - # save corresponding fw and lm scores - self.fw_scores_buf = torch.gather(fw_lprobs_cum.view(bsz, -1), 1, self.indices_buf) - self.lm_scores_buf = torch.gather(lm_lprobs.view(bsz, -1), 1, self.indices_buf) - # Project back into relative indices and beams - self.beams_buf = self.indices_buf // vocab_size - self.indices_buf.fmod_(vocab_size) - return self.scores_buf, self.fw_scores_buf, self.lm_scores_buf, self.indices_buf, self.beams_buf diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/roberta/README.race.md b/spaces/mshukor/UnIVAL/fairseq/examples/roberta/README.race.md deleted file mode 100644 index 13c917e8eca6621e91dce541c7e41436b38cbdc1..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/examples/roberta/README.race.md +++ /dev/null @@ -1,68 +0,0 @@ -# Finetuning RoBERTa on RACE tasks - -### 1) Download the data from RACE website (http://www.cs.cmu.edu/~glai1/data/race/) - -### 2) Preprocess RACE data: -```bash -python ./examples/roberta/preprocess_RACE.py --input-dir <input-dir> --output-dir <extracted-data-dir> -./examples/roberta/preprocess_RACE.sh <extracted-data-dir> <output-dir> -``` - -### 3) Fine-tuning on RACE: - -```bash -MAX_EPOCH=5 # Number of training epochs. -LR=1e-05 # Peak LR for fixed LR scheduler. -NUM_CLASSES=4 -MAX_SENTENCES=1 # Batch size per GPU. -UPDATE_FREQ=8 # Accumulate gradients to simulate training on 8 GPUs. -DATA_DIR=/path/to/race-output-dir -ROBERTA_PATH=/path/to/roberta/model.pt - -CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=legacy_ddp \ - --restore-file $ROBERTA_PATH \ - --reset-optimizer --reset-dataloader --reset-meters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --task sentence_ranking \ - --num-classes $NUM_CLASSES \ - --init-token 0 --separator-token 2 \ - --max-option-length 128 \ - --max-positions 512 \ - --shorten-method "truncate" \ - --arch roberta_large \ - --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ - --criterion sentence_ranking \ - --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler fixed --lr $LR \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --batch-size $MAX_SENTENCES \ - --required-batch-size-multiple 1 \ - --update-freq $UPDATE_FREQ \ - --max-epoch $MAX_EPOCH -``` - -**Note:** - -a) As contexts in RACE are relatively long, we are using smaller batch size per GPU while increasing update-freq to achieve larger effective batch size. - -b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. - -c) The setting in above command is based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. - -### 4) Evaluation: - -``` -DATA_DIR=/path/to/race-output-dir # data directory used during training -MODEL_PATH=/path/to/checkpoint_best.pt # path to the finetuned model checkpoint -PREDS_OUT=preds.tsv # output file path to save prediction -TEST_SPLIT=test # can be test (Middle) or test1 (High) -fairseq-validate \ - $DATA_DIR \ - --valid-subset $TEST_SPLIT \ - --path $MODEL_PATH \ - --batch-size 1 \ - --task sentence_ranking \ - --criterion sentence_ranking \ - --save-predictions $PREDS_OUT -``` diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py b/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py deleted file mode 100644 index 4e13b38a5d3fb44dd3969e6afcb8f202274ee3b7..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py +++ /dev/null @@ -1,204 +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 logging -import os -import csv -import tempfile -from collections import defaultdict -from pathlib import Path - -import torchaudio -try: - import webrtcvad -except ImportError: - raise ImportError("Please install py-webrtcvad: pip install webrtcvad") -import pandas as pd -from tqdm import tqdm - -from examples.speech_synthesis.preprocessing.denoiser.pretrained import master64 -import examples.speech_synthesis.preprocessing.denoiser.utils as utils -from examples.speech_synthesis.preprocessing.vad import ( - frame_generator, vad_collector, read_wave, write_wave, FS_MS, THRESHOLD, - SCALE -) -from examples.speech_to_text.data_utils import save_df_to_tsv - - -log = logging.getLogger(__name__) - -PATHS = ["after_denoise", "after_vad"] -MIN_T = 0.05 - - -def generate_tmp_filename(extension="txt"): - return tempfile._get_default_tempdir() + "/" + \ - next(tempfile._get_candidate_names()) + "." + extension - - -def convert_sr(inpath, sr, output_path=None): - if not output_path: - output_path = generate_tmp_filename("wav") - cmd = f"sox {inpath} -r {sr} {output_path}" - os.system(cmd) - return output_path - - -def apply_vad(vad, inpath): - audio, sample_rate = read_wave(inpath) - frames = frame_generator(FS_MS, audio, sample_rate) - frames = list(frames) - segments = vad_collector(sample_rate, FS_MS, 300, vad, frames) - merge_segments = list() - timestamp_start = 0.0 - timestamp_end = 0.0 - # removing start, end, and long sequences of sils - for i, segment in enumerate(segments): - merge_segments.append(segment[0]) - if i and timestamp_start: - sil_duration = segment[1] - timestamp_end - if sil_duration > THRESHOLD: - merge_segments.append(int(THRESHOLD / SCALE) * (b'\x00')) - else: - merge_segments.append(int((sil_duration / SCALE)) * (b'\x00')) - timestamp_start = segment[1] - timestamp_end = segment[2] - segment = b''.join(merge_segments) - return segment, sample_rate - - -def write(wav, filename, sr=16_000): - # Normalize audio if it prevents clipping - wav = wav / max(wav.abs().max().item(), 1) - torchaudio.save(filename, wav.cpu(), sr, encoding="PCM_S", - bits_per_sample=16) - - -def process(args): - # making sure we are requested either denoise or vad - if not args.denoise and not args.vad: - log.error("No denoise or vad is requested.") - return - - log.info("Creating out directories...") - if args.denoise: - out_denoise = Path(args.output_dir).absolute().joinpath(PATHS[0]) - out_denoise.mkdir(parents=True, exist_ok=True) - if args.vad: - out_vad = Path(args.output_dir).absolute().joinpath(PATHS[1]) - out_vad.mkdir(parents=True, exist_ok=True) - - log.info("Loading pre-trained speech enhancement model...") - model = master64().to(args.device) - - log.info("Building the VAD model...") - vad = webrtcvad.Vad(int(args.vad_agg_level)) - - # preparing the output dict - output_dict = defaultdict(list) - - log.info(f"Parsing input manifest: {args.audio_manifest}") - with open(args.audio_manifest, "r") as f: - manifest_dict = csv.DictReader(f, delimiter="\t") - for row in tqdm(manifest_dict): - filename = str(row["audio"]) - - final_output = filename - keep_sample = True - n_frames = row["n_frames"] - snr = -1 - if args.denoise: - output_path_denoise = out_denoise.joinpath(Path(filename).name) - # convert to 16khz in case we use a differet sr - tmp_path = convert_sr(final_output, 16000) - - # loading audio file and generating the enhanced version - out, sr = torchaudio.load(tmp_path) - out = out.to(args.device) - estimate = model(out) - estimate = (1 - args.dry_wet) * estimate + args.dry_wet * out - write(estimate[0], str(output_path_denoise), sr) - - snr = utils.cal_snr(out, estimate) - snr = snr.cpu().detach().numpy()[0][0] - final_output = str(output_path_denoise) - - if args.vad: - output_path_vad = out_vad.joinpath(Path(filename).name) - sr = torchaudio.info(final_output).sample_rate - if sr in [16000, 32000, 48000]: - tmp_path = final_output - elif sr < 16000: - tmp_path = convert_sr(final_output, 16000) - elif sr < 32000: - tmp_path = convert_sr(final_output, 32000) - else: - tmp_path = convert_sr(final_output, 48000) - # apply VAD - segment, sample_rate = apply_vad(vad, tmp_path) - if len(segment) < sample_rate * MIN_T: - keep_sample = False - print(( - f"WARNING: skip {filename} because it is too short " - f"after VAD ({len(segment) / sample_rate} < {MIN_T})" - )) - else: - if sample_rate != sr: - tmp_path = generate_tmp_filename("wav") - write_wave(tmp_path, segment, sample_rate) - convert_sr(tmp_path, sr, - output_path=str(output_path_vad)) - else: - write_wave(str(output_path_vad), segment, sample_rate) - final_output = str(output_path_vad) - segment, _ = torchaudio.load(final_output) - n_frames = segment.size(1) - - if keep_sample: - output_dict["id"].append(row["id"]) - output_dict["audio"].append(final_output) - output_dict["n_frames"].append(n_frames) - output_dict["tgt_text"].append(row["tgt_text"]) - output_dict["speaker"].append(row["speaker"]) - output_dict["src_text"].append(row["src_text"]) - output_dict["snr"].append(snr) - - out_tsv_path = Path(args.output_dir) / Path(args.audio_manifest).name - log.info(f"Saving manifest to {out_tsv_path.as_posix()}") - save_df_to_tsv(pd.DataFrame.from_dict(output_dict), out_tsv_path) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--audio-manifest", "-i", required=True, - type=str, help="path to the input manifest.") - parser.add_argument( - "--output-dir", "-o", required=True, type=str, - help="path to the output dir. it will contain files after denoising and" - " vad" - ) - parser.add_argument("--vad-agg-level", "-a", type=int, default=2, - help="the aggresive level of the vad [0-3].") - parser.add_argument( - "--dry-wet", "-dw", type=float, default=0.01, - help="the level of linear interpolation between noisy and enhanced " - "files." - ) - parser.add_argument( - "--device", "-d", type=str, default="cpu", - help="the device to be used for the speech enhancement model: " - "cpu | cuda." - ) - parser.add_argument("--denoise", action="store_true", - help="apply a denoising") - parser.add_argument("--vad", action="store_true", help="apply a VAD") - args = parser.parse_args() - - process(args) - - -if __name__ == "__main__": - main() diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/nat/fairseq_nat_model.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/nat/fairseq_nat_model.py deleted file mode 100644 index b09394112f57d9e82f2a4cbc371af888281b9e8a..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/nat/fairseq_nat_model.py +++ /dev/null @@ -1,170 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -from fairseq.models.transformer import ( - TransformerDecoder, - TransformerEncoder, - TransformerModel, -) -from fairseq.modules.transformer_sentence_encoder import init_bert_params - - -def ensemble_encoder(func): - def wrapper(self, *args, **kwargs): - if self.ensemble_models is None or len(self.ensemble_models) == 1: - return func(self, *args, **kwargs) - encoder_outs = [func(model, *args, **kwargs, return_all_hiddens=True) for model in self.ensemble_models] - _encoder_out = encoder_outs[0].copy() - - def stack(key): - outs = [e[key][0] for e in encoder_outs] - return [torch.stack(outs, -1) if outs[0] is not None else None] - - _encoder_out["encoder_out"] = stack("encoder_out") - _encoder_out["encoder_embedding"] = stack("encoder_embedding") - - num_layers = len(_encoder_out["encoder_states"]) - if num_layers > 0: - _encoder_out["encoder_states"] = [ - torch.stack([e["encoder_states"][i] for e in encoder_outs], -1) - for i in range(num_layers) - ] - return _encoder_out - - return wrapper - - -def ensemble_decoder(func): - def wrapper(self, normalize=False, encoder_out=None, *args, **kwargs): - if self.ensemble_models is None or len(self.ensemble_models) == 1: - return func( - self, normalize=normalize, encoder_out=encoder_out, *args, **kwargs - ) - - def _replace(encoder_out, new_val): - new_encoder_out = encoder_out.copy() - new_encoder_out["encoder_out"] = [new_val] - return new_encoder_out - - action_outs = [ - func( - model, - normalize=normalize, - encoder_out=_replace( - encoder_out, - encoder_out["encoder_out"][0][:, :, :, i] - ), - *args, - **kwargs - ) - for i, model in enumerate(self.ensemble_models) - ] - - if not isinstance(action_outs[0], tuple): # return multiple values - action_outs = [[a] for a in action_outs] - else: - action_outs = [list(a) for a in action_outs] - - ensembled_outs = [] - for i in range(len(action_outs[0])): - if i == 0 and normalize: - ensembled_outs += [ - torch.logsumexp( - torch.stack([a[i] for a in action_outs], -1), dim=-1 - ) - - math.log(len(self.ensemble_models)) - ] - elif action_outs[0][i] is not None: - ensembled_outs += [torch.stack([a[i] for a in action_outs], -1)] - else: - ensembled_outs += [None] - - if len(ensembled_outs) == 1: - return ensembled_outs[0] - return tuple(ensembled_outs) - - return wrapper - - -class FairseqNATModel(TransformerModel): - """ - Abstract class for all nonautoregressive-based models - """ - - def __init__(self, args, encoder, decoder): - super().__init__(args, encoder, decoder) - self.tgt_dict = decoder.dictionary - self.bos = decoder.dictionary.bos() - self.eos = decoder.dictionary.eos() - self.pad = decoder.dictionary.pad() - self.unk = decoder.dictionary.unk() - - self.ensemble_models = None - - @property - def allow_length_beam(self): - return False - - @property - def allow_ensemble(self): - return True - - def enable_ensemble(self, models): - self.encoder.ensemble_models = [m.encoder for m in models] - self.decoder.ensemble_models = [m.decoder for m in models] - - @staticmethod - def add_args(parser): - TransformerModel.add_args(parser) - parser.add_argument( - "--apply-bert-init", - action="store_true", - help="use custom param initialization for BERT", - ) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - decoder = FairseqNATDecoder(args, tgt_dict, embed_tokens) - if getattr(args, "apply_bert_init", False): - decoder.apply(init_bert_params) - return decoder - - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - encoder = FairseqNATEncoder(args, src_dict, embed_tokens) - if getattr(args, "apply_bert_init", False): - encoder.apply(init_bert_params) - return encoder - - def forward_encoder(self, encoder_inputs): - return self.encoder(*encoder_inputs) - - def forward_decoder(self, *args, **kwargs): - return NotImplementedError - - def initialize_output_tokens(self, *args, **kwargs): - return NotImplementedError - - def forward(self, *args, **kwargs): - return NotImplementedError - - -class FairseqNATEncoder(TransformerEncoder): - def __init__(self, args, dictionary, embed_tokens): - super().__init__(args, dictionary, embed_tokens) - self.ensemble_models = None - - @ensemble_encoder - def forward(self, *args, **kwargs): - return super().forward(*args, **kwargs) - - -class FairseqNATDecoder(TransformerDecoder): - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): - super().__init__(args, dictionary, embed_tokens, no_encoder_attn) - self.ensemble_models = None diff --git a/spaces/mshukor/UnIVAL/run_scripts/image_gen/scaling_best/unival_image_gen_stage_1.sh b/spaces/mshukor/UnIVAL/run_scripts/image_gen/scaling_best/unival_image_gen_stage_1.sh deleted file mode 100644 index 0ebea86d29528b98aec099f1631a8abdee38eb51..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/run_scripts/image_gen/scaling_best/unival_image_gen_stage_1.sh +++ /dev/null @@ -1,195 +0,0 @@ -#!/usr/bin/env - -# Number of GPUs per GPU worker -export GPUS_PER_NODE=8 -# Number of GPU workers, for single-worker training, please set to 1 -export NUM_NODES=$SLURM_NNODES -# The ip address of the rank-0 worker, for single-worker training, please set to localhost -master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) -export MASTER_ADDR=$master_addr - -# The port for communication -export MASTER_PORT=12350 -# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0 -export RANK=$SLURM_NODEID - -echo "MASTER_ADDR: $MASTER_ADDR" -echo "RANK :$RANK" -echo "NUM_NODES :$NUM_NODES" -echo "GPUS_PER_NODE :$GPUS_PER_NODE" - -export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/ - -echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH" - -num_workers=0 - - -ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival -base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data -base_log_dir=/work/NAT/gda2204/mshukor/logs - -exp_name=unival_image_gen_stage_1 - - -image_dir=${base_data_dir} -data_dir=${base_data_dir}/ofa/image_gen_data -# data=${data_dir}/coco_vqgan_train.tsv,${data_dir}/coco_vqgan_dev.tsv -data_dir_train=/lus/scratch/NAT/gda2204/SHARED/tmp/ofa/image_gen_data -data=${data_dir_train}/coco_vqgan_train_1.tsv,${data_dir_train}/coco_vqgan_train_2.tsv,${data_dir_train}/coco_vqgan_train_3.tsv,${data_dir_train}/coco_vqgan_train_4.tsv,${data_dir_train}/coco_vqgan_train_5.tsv,${data_dir_train}/coco_vqgan_train_6.tsv,${data_dir_train}/coco_vqgan_train_7.tsv,${data_dir_train}/coco_vqgan_train_8.tsv,${data_dir_train}/coco_vqgan_train_9.tsv,${data_dir_train}/coco_vqgan_train_10.tsv,${data_dir}/coco_vqgan_dev.tsv - - - -restore_file=${base_log_dir}/ofa/checkpoints/pretrain/unival_s2_hs/checkpoint1.pt - -selected_cols=0,2,1 - -save_base_log_dir=/lus/scratch/NAT/gda2204/SHARED/logs -save_dir=${save_base_log_dir}/ofa/checkpoints/image_gen/${exp_name} - -log_dir=${save_dir} - -mkdir -p $log_dir $save_dir - - - -bpe_dir=${ofa_dir}/utils/BPE -user_dir=${ofa_dir}/ofa_module - - - -task=image_gen -arch=unival_base -criterion=adjust_label_smoothed_cross_entropy -label_smoothing=0.0 -batch_size=8 -update_freq=2 -encoder_drop_path_rate=0.1 -decoder_drop_path_rate=0.1 -dropout=0.1 -attention_dropout=0.0 -max_src_length=64 -max_tgt_length=1024 -num_bins=1000 -code_image_size=256 -constraint_range=50265,58457 - -VQGAN_MODEL_PATH=${base_log_dir}/ofa/pretrained_models/vqgan/last.ckpt -VQGAN_CONFIG_PATH=${base_log_dir}/ofa/pretrained_models/vqgan/model.yaml -CLIP_MODEL_PATH=${base_log_dir}/ofa/pretrained_models/clip/ViT-B-16.pt -GEN_IMAGES_PATH=/lus/scratch/NAT/gda2204/SHARED/tmp/results/${exp_name} - -mkdir -p $GEN_IMAGES_PATH - - - -### -image_encoder_name=timm_resnet #vit_base_patch16_224 -patch_image_size=480 -resnet_type=resnet101 - -resnet_model_path=${base_log_dir}/pretrained_models/resnet101-5d3b4d8f.pth - -# video -video_encoder_name=all_resnext101 -patch_frame_size=384 -video_model_path=${base_log_dir}/pretrained_models/3dcnn/resnext-101-kinetics.pth #${base_log_dir}/pretrained_models/TimeSformer_divST_8x32_224_K600.pyth -num_frames=4 - - -sample_patch_num='--sample-patch-num=784' # '' - -save_interval_updates=0 - - -for total_num_updates in {50000,}; do - echo "total_num_updates "${total_num_updates} - for warmup_updates in {2000,}; do - echo "warmup_updates "${warmup_updates} - for lr in {1e-3,}; do - echo "lr "${lr} - - log_file=${log_dir}/${total_num_updates}"_"${warmup_updates}"_"${lr}"_rank"${RANK}".log" - save_path=${save_dir}/${total_num_updates}"_"${warmup_updates}"_"${lr} - mkdir -p $save_path - - python3 -m torch.distributed.launch \ - --nnodes=${NUM_NODES} \ - --nproc_per_node=${GPUS_PER_NODE} \ - --master_port=${MASTER_PORT} \ - --node_rank=${RANK} \ - --master_addr=${MASTER_ADDR} \ - --use_env ${ofa_dir}/train.py \ - ${data} \ - --selected-cols=${selected_cols} \ - --bpe-dir=${bpe_dir} \ - --user-dir=${user_dir} \ - --restore-file=${restore_file} \ - --save-dir=${save_path} \ - --task=${task} \ - --arch=${arch} \ - --criterion=${criterion} \ - --label-smoothing=${label_smoothing} \ - --batch-size=${batch_size} \ - --batch-size-valid=1 \ - --update-freq=${update_freq} \ - --encoder-normalize-before \ - --decoder-normalize-before \ - --share-decoder-input-output-embed \ - --share-all-embeddings \ - --layernorm-embedding \ - --patch-layernorm-embedding \ - --code-layernorm-embedding \ - --encoder-drop-path-rate=${encoder_drop_path_rate} \ - --decoder-drop-path-rate=${decoder_drop_path_rate} \ - --dropout=${dropout} \ - --attention-dropout=${attention_dropout} \ - --weight-decay=0.01 \ - --optimizer=adam \ - --adam-betas="(0.9,0.999)" \ - --adam-eps=1e-08 \ - --clip-norm=1.0 \ - --lr-scheduler=polynomial_decay \ - --lr=${lr} \ - --total-num-update=${total_num_updates} \ - --warmup-updates=${warmup_updates} \ - --log-format=simple \ - --log-interval=10 \ - --fixed-validation-seed=7 \ - --keep-last-epochs=15 \ - --save-interval=3 --validate-interval=3 \ - --max-update=${total_num_updates} \ - --best-checkpoint-metric=score --maximize-best-checkpoint-metric \ - --eval-args='{"beam":24,"min_len":1024,"max_len_a":0,"max_len_b":1024,"sampling_topk":256,"temperature":1.0}' \ - --max-src-length=${max_src_length} \ - --max-tgt-length=${max_tgt_length} \ - --find-unused-parameters \ - --add-type-embedding \ - --scale-attn \ - --scale-fc \ - --scale-heads \ - --disable-entangle \ - --num-bins=${num_bins} \ - --code-image-size=${code_image_size} \ - --constraint-range=${constraint_range} \ - --vqgan-model-path=${VQGAN_MODEL_PATH} \ - --vqgan-config-path=${VQGAN_CONFIG_PATH} \ - --clip-model-path=${CLIP_MODEL_PATH} \ - --gen-images-path=${GEN_IMAGES_PATH} \ - --fp16 \ - --fp16-scale-window=256 \ - --num-workers=0 \ - --image-encoder-name=${image_encoder_name} \ - --image-dir=${image_dir} \ - --video-encoder-name=${video_encoder_name} \ - --video-model-path=${video_model_path} \ - --patch-frame-size=${patch_frame_size} \ - ${sample_patch_num} \ - --resnet-type=${resnet_type} \ - --resnet-model-path=${resnet_model_path} \ - --reset-optimizer --reset-dataloader --reset-meters \ - --strict - - done - done -done diff --git a/spaces/ner4archives/NER4Archives-analytics/n4a_analytics_lib/project.py b/spaces/ner4archives/NER4Archives-analytics/n4a_analytics_lib/project.py deleted file mode 100644 index f16150d89409a06e174f490cdb0ebb175c821779..0000000000000000000000000000000000000000 --- a/spaces/ner4archives/NER4Archives-analytics/n4a_analytics_lib/project.py +++ /dev/null @@ -1,130 +0,0 @@ -# -*- coding:utf-8 -*- - -from io import BytesIO -import re -from zipfile import ZipFile -import os -from pathlib import Path - -import streamlit as st -from cassis import load_typesystem, load_cas_from_xmi - - -def st_pb(method): - """streamlit decorator to display - progress bar - """ - def progress_bar(ref): - container = st.empty() - bar = st.progress(0) - pg_gen = method(ref) - try: - while True: - progress = next(pg_gen) - bar.progress(progress[0]) - if progress[2]: - container.write("✅ Processing... " + progress[1]) - else: - container.write("❌️ Errror with..." + progress[1]) - except StopIteration as result: - return result.value - - return progress_bar - - -class Project: - def __init__(self, zip_project, type, remote): - # zip container that contains XMI and typesystem - self.zip_project = zip_project - - self.remote = remote - - # 'iaa' or 'global' - self.type = type - - # store source filename - self.documents = [] - # store XMI representation - self.xmi_documents = [] - # store typesystem file - self.typesystem = None # cassis.load_typesystem(BytesIO(annotation_zip.read('TypeSystem.xml'))) - - # set annotators - self.annotators = [] - # set annotations - """ - { - "Filename.xmi": { - - mentions: [], - labels: [] - - }, ... - } - """ - self.annotations = {} - - if isinstance(self.zip_project, ZipFile) and self.remote and self.type == "global": - for fp in self.zip_project.namelist(): - if self.typesystem is None: - self.typesystem = load_typesystem(BytesIO(self.zip_project.open('TypeSystem.xml').read())) - if fp.endswith('.xmi'): - self.documents.append(fp) - self.xmi_documents.append(str(self.zip_project.open(fp).read().decode("utf-8"))) - - else: - with ZipFile(self.zip_project) as project_zip: - if self.type == "global": - regex = re.compile('.*curation/.*/(?!\._).*zip$') - elif self.type == "iaa": - regex = re.compile('.*xm[il]$') - - annotation_fps = (fp for fp in project_zip.namelist() if regex.match(fp)) - for fp in annotation_fps: - if self.type == "global": - with ZipFile(BytesIO(project_zip.read(fp))) as annotation_zip: - if self.typesystem is None: - self.typesystem = load_typesystem(BytesIO(annotation_zip.read('TypeSystem.xml'))) - for f in annotation_zip.namelist(): - if f.endswith('.xmi'): - # store source filename - self.documents.append(Path(fp).parent.name) - # annotators = [] - # store XMI representation - self.xmi_documents.append(str(annotation_zip.read(f).decode("utf-8"))) - elif self.type == "iaa": - if self.typesystem is None and fp.endswith('.xml'): - self.typesystem = load_typesystem(BytesIO(project_zip.read('TypeSystem.xml'))) - else: - if fp.endswith('.xmi'): - # store source filename - self.documents.append(fp) - # set annotators - self.annotators.append(os.path.splitext(fp)[0]) - # store XMI representation - self.xmi_documents.append(str(project_zip.read(fp).decode("utf-8"))) - - self.extract_ne() - - @st_pb - def extract_ne(self): - count = 0 - for xmi, src in zip(self.xmi_documents, self.documents): - doc_flag = True - try: - cas = load_cas_from_xmi(xmi, typesystem=self.typesystem) - self.annotations[src] = { - "mentions": [], - "labels": [] - } - for ne in cas.select('de.tudarmstadt.ukp.dkpro.core.api.ner.type.NamedEntity'): - self.annotations[src]["mentions"].append(ne.get_covered_text()) - self.annotations[src]["labels"].append(ne.value) - except: - doc_flag = False - - count += 1 - yield (count / len(self.documents)) * 1.0, src, doc_flag - - - diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/A Hacker Has Leaked The Upcoming Fifth Season Of Orange Is The New Black.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/A Hacker Has Leaked The Upcoming Fifth Season Of Orange Is The New Black.md deleted file mode 100644 index 2f1f4e7c5b65f049485d53a3254d3753664ad0b3..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/A Hacker Has Leaked The Upcoming Fifth Season Of Orange Is The New Black.md +++ /dev/null @@ -1,21 +0,0 @@ -<br /> -<h1>How a Hacker Leaked the Entire Fifth Season of Orange is the New Black</h1> -<p>Orange is the New Black, one of Netflix's most popular original series, has been hit by a major cyberattack. A hacker group called The Dark Overlord claims to have stolen and leaked the entire fifth season of the show, which is set to premiere on June 9.</p> -<p>The hacker group says it obtained the episodes from a post-production company that works with Netflix and other studios. They demanded a ransom from Netflix to prevent the leak, but the streaming service did not comply. The group then released the first episode of the season on a torrent site on April 28, followed by nine more episodes on April 29.</p> -<h2>A hacker has leaked the upcoming fifth season of Orange is the New Black</h2><br /><p><b><b>Download Zip</b> ⚙ <a href="https://urlcod.com/2uIcrc">https://urlcod.com/2uIcrc</a></b></p><br /><br /> -<p>The leak could have a significant impact on Netflix's business, as Orange is the New Black is one of its most watched and critically acclaimed shows. The series, which follows the lives of female inmates in a federal prison, has won several awards and has a loyal fan base. Netflix has not commented on the leak, but has previously said that it is aware of the situation and working with law enforcement.</p> -<p>The hacker group also claims to have stolen unreleased content from other studios, such as ABC, National Geographic, and Fox. They have threatened to release more shows unless they are paid. The group's motives are unclear, but they seem to be motivated by money rather than ideology. They have also taunted Netflix and other studios on Twitter, using the handle @tdohack3r.</p> -<p>The leak of Orange is the New Black is one of the biggest cyberattacks on the entertainment industry in recent years. It follows other incidents such as the Sony hack in 2014, which exposed confidential emails and personal information of celebrities and executives, and the HBO hack in 2017, which leaked episodes of Game of Thrones and other shows.</p> - -<p>How can the entertainment industry prevent cyberattacks like this from happening again? Experts suggest that there are several steps that can be taken to enhance security and resilience. Some of these include:</p> -<ul> -<li>Ensuring executive governance: Resilience must begin with a vision and budget set by executive management and accountability established at the board or audit committee level[^1^].</li> -<li>Shoring up databases: Serious proactive measures need to be taken at the database level, such as encrypting data at rest and in transit, implementing strong access controls, and monitoring for anomalous activity[^1^].</li> -<li>Securing web-based assets: Digital assets such as streaming portals, ticketing sites and internal applications should be regularly scanned for vulnerabilities and patched promptly. Web application firewalls and content delivery networks can also help to mitigate attacks[^2^].</li> -<li>Protecting remote workers: Employees working from home or on the go should use VPNs, multifactor authentication, endpoint security and secure cloud services to access corporate resources. They should also be educated on how to spot phishing emails and other social engineering tactics[^2^].</li> -<li>Implementing real-time prevention: As cyberattacks become more sophisticated and dynamic, traditional security solutions that rely on signatures and updates are not enough. Organizations need to adopt a fifth-generation security approach that can prevent attacks in real time across all networks, endpoints, cloud and mobile devices[^3^].</li> -</ul> -<p>Cyberattacks on the entertainment industry are not going away anytime soon. They pose a serious threat to the reputation, revenue and intellectual property of filmmakers, studios, broadcasters and publishers. By following these best practices, the sector can reduce its exposure and protect its valuable assets.</p> -<p></p> cec2833e83<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Dokan Full Album Biplob Mizan Pantho Kanai Click To Play Song HOT.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Dokan Full Album Biplob Mizan Pantho Kanai Click To Play Song HOT.md deleted file mode 100644 index 1e80e9386069d4cdd87a8de7933a27619c76496c..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Dokan Full Album Biplob Mizan Pantho Kanai Click To Play Song HOT.md +++ /dev/null @@ -1,33 +0,0 @@ - -<h1>Dokan: A Mixed Album by Biplob, Mizan and Pantho Kanai</h1> -<p>Dokan is a mixed album by three popular Bangladeshi singers: Biplob, Mizan and Pantho Kanai. The album was released in 2004 and features 10 songs of different genres, such as rock, folk, pop and fusion. The album was well-received by the listeners and critics alike, and showcased the versatility and talent of the three singers.</p> -<p>Some of the notable songs from the album are:</p> -<h2>Dokan Full Album Biplob Mizan Pantho Kanai Click To Play Song</h2><br /><p><b><b>DOWNLOAD</b> ➡ <a href="https://urlcod.com/2uIch2">https://urlcod.com/2uIch2</a></b></p><br /><br /> -<ul> -<li><strong>Dokan</strong>: The title track of the album, Dokan is a rock song that talks about the struggles and challenges of life. The song has a catchy chorus and powerful vocals by Biplob.</li> -<li><strong>Shomoy</strong>: A folk song that expresses the nostalgia and longing for the past. The song features a beautiful flute melody and soothing vocals by Mizan.</li> -<li><strong>Chokher Jole</strong>: A pop song that depicts the pain of love and separation. The song has a melodious tune and emotional vocals by Pantho Kanai.</li> -</ul> -<p>If you want to listen to the full album, you can click on the link below:</p> -<a href="https://www.youtube.com/watch?v=PjbwtidSSz0">Dokan Full Album Biplob Mizan Pantho Kanai Click To Play Song</a> - -<p>The album Dokan was produced by Sangeeta Music and composed by Jan E Alam. The lyrics were written by Kabir Bakul, Kazi Faruque Babul and Rajib Ahmed. The album cover featured a painting of a traditional shop (dokan) by artist Shahabuddin Ahmed.[^1^]</p> -<p>Biplob, Mizan and Pantho Kanai are three renowned singers in Bangladesh who have been active in the music industry for decades. They have collaborated with many other artists and bands, such as Ayub Bachchu, LRB, Souls, Nogor Baul, Tandav and more. They have also contributed to various film soundtracks and patriotic songs.[^2^] [^3^]</p> -<p>Dokan is one of their most popular albums that showcases their diverse musical styles and skills. The album has a blend of rock, folk, pop and fusion genres that appeal to a wide range of listeners. The album also reflects the social and cultural aspects of Bangladesh through its lyrics and melodies.</p> - -<p>The album Dokan contains 10 songs that are as follows:</p> -<p></p> -<ol> -<li><strong>Dokan</strong>: The title track of the album, a rock song that talks about the struggles and challenges of life.</li> -<li><strong>Shomoy</strong>: A folk song that expresses the nostalgia and longing for the past.</li> -<li><strong>Chokher Jole</strong>: A pop song that depicts the pain of love and separation.</li> -<li><strong>Shesh Chithi</strong>: A fusion song that combines elements of rock and folk music.</li> -<li><strong>Cholo Na</strong>: A romantic song that urges the lover to come along.</li> -<li><strong>Shudhu Tumi</strong>: A ballad song that declares the devotion and loyalty to the beloved.</li> -<li><strong>Bhalobasha</strong>: A pop-rock song that celebrates the joy and beauty of love.</li> -<li><strong>Jibon</strong>: A rock song that encourages the listener to live life to the fullest.</li> -<li><strong>Moner Manush</strong>: A folk-rock song that pays tribute to the legendary singer and composer Lalon Fakir.</li> -<li><strong>Bangladesh</strong>: A patriotic song that praises the country and its people.</li> -</ol></p> 81aa517590<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/EkPaheliLeelamoviedualaudio720pdownload LINK.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/EkPaheliLeelamoviedualaudio720pdownload LINK.md deleted file mode 100644 index 6c241b3b84f85fd64229f7185b92016770170675..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/EkPaheliLeelamoviedualaudio720pdownload LINK.md +++ /dev/null @@ -1,27 +0,0 @@ - -<h1>Ek Paheli Leela: A Reincarnation Romance Thriller</h1> -<p>Ek Paheli Leela is a 2015 Bollywood movie starring Sunny Leone, Rahul Dev, Mohit Ahlawat and Jay Bhanushali. The movie is directed by Bobby Khan and produced by T-Series. The movie is a musical mystery romance thriller that revolves around the concept of reincarnation and the love story of Leela and her lover, who were separated 300 years ago by a cruel twist of fate.</p> -<h2>EkPaheliLeelamoviedualaudio720pdownload</h2><br /><p><b><b>DOWNLOAD</b> ……… <a href="https://urlcod.com/2uIbd8">https://urlcod.com/2uIbd8</a></b></p><br /><br /> -<p>The movie follows the lives of Meera, a supermodel who suffers from claustrophobia; Karan, a musician who is haunted by a melody from his past; Ranveer, a royal descendant who is obsessed with a painting of his ancestor; and Bhairav, a ruthless sculptor who wants to recreate the ancient statue of Leela. Their paths cross when Meera travels to Rajasthan for a photo shoot and discovers the secrets of her previous birth as Leela, a beautiful dancer who was in love with Shravan, a musician and Ranveer's ancestor. However, their love was doomed by Bhairav, who was also in love with Leela and killed Shravan in a fit of jealousy.</p> -<p>The movie has a soundtrack composed by various artists, including Amaal Mallik, Dr. Zeus, Tony Kakkar and Uzair Jaswal. The songs are sung by various singers, including Tulsi Kumar, Kanika Kapoor, Neha Kakkar and Arijit Singh. The movie features some popular songs like "Desi Look", "Tere Bin Nahi Laage", "Saiyaan Superstar" and "Dhol Baaje". The movie also has some stunning visuals and dance sequences that showcase the beauty of Rajasthan and Sunny Leone.</p> -<p>Ek Paheli Leela is a movie that will appeal to the fans of romance, mystery and music. The movie has a twisty plot that keeps the audience engaged till the end. The movie also has some emotional moments that touch the heart. Ek Paheli Leela is a movie that will make you believe in the power of love and destiny.</p> - -<p>The movie also explores the themes of karma, reincarnation and forgiveness. The movie shows how the actions of the past affect the present and how the cycle of karma can be broken by forgiveness. The movie also shows how the soulmates are destined to meet again and again until they fulfill their love. The movie also has a message of respecting the art and culture of the past and preserving it for the future.</p> -<p></p> -<p>The movie received mixed reviews from the critics and the audience. Some praised the movie for its music, visuals and performances, while others criticized the movie for its weak script, direction and logic. The movie was a moderate success at the box office, earning around 34 crores worldwide. The movie was also dubbed in Telugu and Tamil languages.</p> -<p>Ek Paheli Leela is a movie that will take you on a musical journey of love, mystery and reincarnation. The movie will make you feel the emotions of the characters and their struggles. The movie will also make you appreciate the beauty and richness of Indian culture and heritage. Ek Paheli Leela is a movie that will stay with you for a long time.</p> - -<p>If you are looking for a way to watch Ek Paheli Leela online, you can download the movie from various sources. However, you should be careful of the quality and legality of the download links. Some of the download links may be fake, infected or illegal. Therefore, it is advisable to use a trusted and verified source to download the movie.</p> -<p>One of the best sources to download Ek Paheli Leela is PogoLinks. PogoLinks is a website that provides high-quality and safe download links for Bollywood and Hollywood movies and web series. You can download Ek Paheli Leela in full HD quality with Hindi audio, with a resolution of 480p, 720p, 720p HEVC or 1080p. You can also choose from multiple sources and formats according to your preference. PogoLinks also provides direct Google Drive download links for fast and secure downloading and free online streaming.</p> -<p>To download Ek Paheli Leela from PogoLinks, you just need to follow these simple steps:</p> -<ol> -<li>Go to <a href="https://pogolinks.art/movies/ek-paheli-leela-2015/">https://pogolinks.art/movies/ek-paheli-leela-2015/</a></li> -<li>Click on the "Watch Now" button or scroll down to the "Download Links" section.</li> -<li>Select the quality and source of your choice and click on the "Download" button.</li> -<li>You will be redirected to a new page where you need to verify that you are not a robot.</li> -<li>After verification, you will get the download link or the Google Drive link for the movie.</li> -<li>Click on the link and enjoy watching Ek Paheli Leela online or offline.</li> -</ol> -<p>PogoLinks is the best website/platform for downloading and watching Ek Paheli Leela and other movies and web series. You can also check their FAQ page for more information. Download Ek Paheli Leela today and experience the magic of love and reincarnation.</p> cec2833e83<br /> -<br /> -<br /> \ No newline at end of file diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/IOS236-Installer-V6zip.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/IOS236-Installer-V6zip.md deleted file mode 100644 index 168ad66de3f828a264d22efce3ba8d350a8a7bb2..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/IOS236-Installer-V6zip.md +++ /dev/null @@ -1,122 +0,0 @@ -## IOS236 Installer V6.zip - - - - - - - - - -**Download File ► [https://maudaracte.blogspot.com/?file=2tyUAG](https://maudaracte.blogspot.com/?file=2tyUAG)** - - - - - - - - - - - - - -# How to Install IOS236 on Your Wii or Wii U - - - -IOS236 is a patched version of IOS36 that makes installation of other IOSes easy, even if the other IOS slots are filled by stubs already. IOS236 can also access the Wii's NAND, which is useful for many homebrew applications. This guide will show you how to install IOS236 on your Wii or Wii U using the IOS236 Installer v6.zip file. - - - -## What You Need - - - -- A Wii or Wii U console with system menu 3.0 or higher (not compatible with Wii Mini) - -- An SD card (2GB or less for system menu below 4.0, 4GB or more for system menu 4.0 or higher) - -- A Wii remote (not a Motion Plus one if you are on system menu 3.0 or 3.1) - -- The IOS236 Installer v6.zip file (download link: [https://gbatemp.net/download/ios236-installer.32100/](https://gbatemp.net/download/ios236-installer.32100/)) - - - -## Step 1: Prepare Your SD Card - - - -Format your SD card to FAT32 using your computer. Then, extract the contents of the IOS236 Installer v6.zip file to the root of your SD card. You should have a folder named "apps" and a file named "IOS36-64-v3351.wad" on your SD card. - - - -## Step 2: Install The Homebrew Channel - - - -If you already have The Homebrew Channel (HBC) installed on your Wii or Wii U, you can skip this step. Otherwise, follow the instructions for your system menu version below to install HBC: - - - -- For system menu 4.3, follow this guide: [https://wiidatabase.de/wii-u-vwii-downloads/hacks/ios236-installer/](https://wiidatabase.de/wii-u-vwii-downloads/hacks/ios236-installer/) - -- For system menu 4.2, follow this guide: [https://mariokartwii.com/showthread.php?tid=34](https://mariokartwii.com/showthread.php?tid=34) - -- For system menu 3.0 - 4.1, follow this guide: [https://mariokartwii.com/showthread.php?tid=34](https://mariokartwii.com/showthread.php?tid=34) - - - -After installing HBC, do not exit the Hackmii Installer yet. We will need to also install BootMii in the next step. - - - -## Step 3: Install BootMii - - - -BootMii is a form of brick prevention that allows you to backup and restore your Wii's NAND. To install BootMii, follow these steps: - - - -1. Navigate back to the Main Menu of the Hackmii Installer (where the options 'Install Homebrew Channel' and 'Install Bootmii' are listed). - -2. Select 'Bootmii...' - -3. Select 'Install Bootmii as boot2' if possible. If not, then select 'Install Bootmii as an IOS'. - -4. Click Yes, Continue. - -5. Bootmii will mount the SD card and write some files. - -6. When done, return to the Main Menu and exit the Hackmii Installer. - - - -## Step 4: Run IOS236 Installer - - - -Now that you have HBC and BootMii installed, you can run the IOS236 Installer from HBC. Follow these steps: - - - -1. Insert your SD card into your Wii or Wii U. - -2. Turn on your console and launch HBC from the system menu. - -3. Select IOS236 Installer from the list of applications and press A to load it. - -4. Press 1 to start the installation process. - -5. Select "Load IOS from SD card". 145887f19f - - - - - - - - - diff --git a/spaces/nightfury/Stable_Diffusion/dj_sd.py b/spaces/nightfury/Stable_Diffusion/dj_sd.py deleted file mode 100644 index 3e868ef90ed56f2cc419735fe74aa7053df6f587..0000000000000000000000000000000000000000 --- a/spaces/nightfury/Stable_Diffusion/dj_sd.py +++ /dev/null @@ -1,375 +0,0 @@ -import gradio as gr -import torch -from torch import autocast -from diffusers import StableDiffusionPipeline -from datasets import load_dataset -from PIL import Image -from io import BytesIO -import base64 -import re -import os -import requests - -#from share_btn import community_icon_html, loading_icon_html, share_js - -model_id = "CompVis/stable-diffusion-v1-4" -device = "cpu" -MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') -YOUR_TOKEN=MY_SECRET_TOKEN -#If you are running this code locally, you need to either do a 'huggingface-cli login` or paste your User Access Token from here https://huggingface.co/settings/tokens into the use_auth_token field below. -pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=YOUR_TOKEN, revision="fp16", torch_dtype=torch.float16) -pipe = pipe.to(device) -torch.backends.cudnn.benchmark = True - -#When running locally, you won`t have access to this, so you can remove this part -word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True) -word_list = word_list_dataset["train"]['text'] - -is_gpu_busy = False -def infer(prompt): - global is_gpu_busy - samples = 4 - steps = 50 - scale = 7.5 - #When running locally you can also remove this filter - for filter in word_list: - if re.search(rf"\b{filter}\b", prompt): - raise gr.Error("Unsafe content found. Please try again with different prompts.") - - #generator = torch.Generator(device=device).manual_seed(seed) - print("Is GPU busy? ", is_gpu_busy) - images = [] - if(not is_gpu_busy): - is_gpu_busy = True - images_list = pipe( - [prompt] * samples, - num_inference_steps=steps, - guidance_scale=scale, - #generator=generator, - ) - is_gpu_busy = False - safe_image = Image.open(r"unsafe.png") - for i, image in enumerate(images_list["sample"]): - if(images_list["nsfw_content_detected"][i]): - images.append(safe_image) - else: - images.append(image) - else: - url = os.getenv('JAX_BACKEND_URL') - payload = {'prompt': prompt} - images_request = requests.post(url, json = payload) - for image in images_request.json()["images"]: - image_decoded = Image.open(BytesIO(base64.b64decode(image))) - images.append(image_decoded) - - - return images, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) - - -css = """ - .gradio-container { - font-family: 'IBM Plex Sans', sans-serif; - } - .gr-button { - color: white; - border-color: black; - background: black; - } - input[type='range'] { - accent-color: black; - } - .dark input[type='range'] { - accent-color: #dfdfdf; - } - .container { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; - } - #gallery { - min-height: 22rem; - margin-bottom: 15px; - margin-left: auto; - margin-right: auto; - border-bottom-right-radius: .5rem !important; - border-bottom-left-radius: .5rem !important; - } - #gallery>div>.h-full { - min-height: 20rem; - } - .details:hover { - text-decoration: underline; - } - .gr-button { - white-space: nowrap; - } - .gr-button:focus { - border-color: rgb(147 197 253 / var(--tw-border-opacity)); - outline: none; - box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); - --tw-border-opacity: 1; - --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); - --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); - --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); - --tw-ring-opacity: .5; - } - #advanced-btn { - font-size: .7rem !important; - line-height: 19px; - margin-top: 12px; - margin-bottom: 12px; - padding: 2px 8px; - border-radius: 14px !important; - } - #advanced-options { - display: none; - margin-bottom: 20px; - } - .footer { - margin-bottom: 45px; - margin-top: 35px; - text-align: center; - border-bottom: 1px solid #e5e5e5; - } - .footer>p { - font-size: .8rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; - } - .dark .footer { - border-color: #303030; - } - .dark .footer>p { - background: #0b0f19; - } - .acknowledgments h4{ - margin: 1.25em 0 .25em 0; - font-weight: bold; - font-size: 115%; - } - #container-advanced-btns{ - display: flex; - flex-wrap: wrap; - justify-content: space-between; - align-items: center; - } - .animate-spin { - animation: spin 1s linear infinite; - } - @keyframes spin { - from { - transform: rotate(0deg); - } - to { - transform: rotate(360deg); - } - } - #share-btn-container { - display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; - } - #share-btn { - all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; - } - #share-btn * { - all: unset; - } - .gr-form{ - flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; - } - #prompt-container{ - gap: 0; - } -""" - -block = gr.Blocks(css=css) - -examples = [ - [ - 'A high tech solarpunk utopia in the Amazon rainforest', - 4, - 45, - 7.5, - 1024, - ], - [ - 'A pikachu fine dining with a view to the Eiffel Tower', - 4, - 45, - 7, - 1024, - ], - [ - 'A mecha robot in a favela in expressionist style', - 4, - 45, - 7, - 1024, - ], - [ - 'an insect robot preparing a delicious meal', - 4, - 45, - 7, - 1024, - ], - [ - "A small cabin on top of a snowy mountain in the style of Disney, artstation", - 4, - 45, - 7, - 1024, - ], -] - - -with block: - gr.HTML( - """ - <div style="text-align: center; max-width: 650px; margin: 0 auto;"> - <div - style=" - display: inline-flex; - align-items: center; - gap: 0.8rem; - font-size: 1.75rem; - " - > - <svg - width="0.65em" - height="0.65em" - viewBox="0 0 115 115" - fill="none" - xmlns="http://www.w3.org/2000/svg" - > - <rect width="23" height="23" fill="white"></rect> - <rect y="69" width="23" height="23" fill="white"></rect> - <rect x="23" width="23" height="23" fill="#AEAEAE"></rect> - <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect> - <rect x="46" width="23" height="23" fill="white"></rect> - <rect x="46" y="69" width="23" height="23" fill="white"></rect> - <rect x="69" width="23" height="23" fill="black"></rect> - <rect x="69" y="69" width="23" height="23" fill="black"></rect> - <rect x="92" width="23" height="23" fill="#D9D9D9"></rect> - <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect> - <rect x="115" y="46" width="23" height="23" fill="white"></rect> - <rect x="115" y="115" width="23" height="23" fill="white"></rect> - <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect> - <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect> - <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect> - <rect x="92" y="69" width="23" height="23" fill="white"></rect> - <rect x="69" y="46" width="23" height="23" fill="white"></rect> - <rect x="69" y="115" width="23" height="23" fill="white"></rect> - <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect> - <rect x="46" y="46" width="23" height="23" fill="black"></rect> - <rect x="46" y="115" width="23" height="23" fill="black"></rect> - <rect x="46" y="69" width="23" height="23" fill="black"></rect> - <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect> - <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect> - <rect x="23" y="69" width="23" height="23" fill="black"></rect> - </svg> - <h1 style="font-weight: 900; margin-bottom: 7px;"> - Stable Diffusion Demo - </h1> - </div> - <p style="margin-bottom: 10px; font-size: 94%"> - Stable Diffusion is a state of the art text-to-image model that generates - images from text.<br>For faster generation and forthcoming API - access you can try - <a - href="http://beta.dreamstudio.ai/" - style="text-decoration: underline;" - target="_blank" - >DreamStudio Beta</a - > - </p> - </div> - """ - ) - with gr.Group(): - with gr.Box(): - with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): - text = gr.Textbox( - label="Enter your prompt", - show_label=False, - max_lines=1, - placeholder="Enter your prompt", - elem_id="prompt-text-input", - ).style( - border=(True, False, True, True), - rounded=(True, False, False, True), - container=False, - ) - btn = gr.Button("Generate image").style( - margin=False, - rounded=(False, True, True, False), - full_width=False, - ) - - gallery = gr.Gallery( - label="Generated images", show_label=False, elem_id="gallery" - ).style(grid=[2], height="auto") - - with gr.Group(elem_id="container-advanced-btns"): - advanced_button = gr.Button("Advanced options", elem_id="advanced-btn") - 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.Row(elem_id="advanced-options"): - gr.Markdown("Advanced settings are temporarily unavailable") - samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1) - steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1) - scale = gr.Slider( - label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1 - ) - seed = gr.Slider( - label="Seed", - minimum=0, - maximum=2147483647, - step=1, - randomize=True, - ) - - ex = gr.Examples(examples=examples, fn=infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button], cache_examples=True) - ex.dataset.headers = [""] - - - text.submit(infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button]) - - btn.click(infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button]) - - advanced_button.click( - None, - [], - text, - _js=""" - () => { - const options = document.querySelector("body > gradio-app").querySelector("#advanced-options"); - options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none"; - }""", - ) - share_button.click( - None, - [], - [], - _js=share_js, - ) - gr.HTML( - """ - <div class="footer"> - <p>Model by <a href="https://huggingface.co/CompVis" style="text-decoration: underline;" target="_blank">CompVis</a> and <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">Stability AI</a> - Gradio Demo by 🤗 Hugging Face - </p> - </div> - <div class="acknowledgments"> - <p><h4>LICENSE</h4> -The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p> - <p><h4>Biases and content acknowledgment</h4> -Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p> - </div> - """ - ) - -block.queue(max_size=25, concurrency_count=2).launch() \ No newline at end of file diff --git a/spaces/nikhil567/Turkey-Syria-Earthquake/README.md b/spaces/nikhil567/Turkey-Syria-Earthquake/README.md deleted file mode 100644 index dcf27e08bfd03141a986a7bcb459a4aa07b5c4b0..0000000000000000000000000000000000000000 --- a/spaces/nikhil567/Turkey-Syria-Earthquake/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Turkey Syria Earthquake -emoji: 🌖 -colorFrom: purple -colorTo: purple -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/utils/serialize.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/utils/serialize.py deleted file mode 100644 index 0b38862804b70cf1159a9bc93acdef73c184d883..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/utils/serialize.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import cloudpickle - - -class PicklableWrapper(object): - """ - Wrap an object to make it more picklable, note that it uses - heavy weight serialization libraries that are slower than pickle. - It's best to use it only on closures (which are usually not picklable). - - This is a simplified version of - https://github.com/joblib/joblib/blob/master/joblib/externals/loky/cloudpickle_wrapper.py - """ - - def __init__(self, obj): - while isinstance(obj, PicklableWrapper): - # Wrapping an object twice is no-op - obj = obj._obj - self._obj = obj - - def __reduce__(self): - s = cloudpickle.dumps(self._obj) - return cloudpickle.loads, (s,) - - def __call__(self, *args, **kwargs): - return self._obj(*args, **kwargs) - - def __getattr__(self, attr): - # Ensure that the wrapped object can be used seamlessly as the previous object. - if attr not in ["_obj"]: - return getattr(self._obj, attr) - return getattr(self, attr) diff --git a/spaces/niro-private/chatCSV/README.md b/spaces/niro-private/chatCSV/README.md deleted file mode 100644 index 1ebf83659f0c75e8456fa969fd4fe795e979d7a8..0000000000000000000000000000000000000000 --- a/spaces/niro-private/chatCSV/README.md +++ /dev/null @@ -1,74 +0,0 @@ ---- -title: VantiGPT -emoji: 🏢 -colorFrom: purple -colorTo: yellow -sdk: streamlit -sdk_version: 1.19.0 -app_file: main.py -pinned: false -python_version: 3.9 ---- - -# ChatBot-CSV 🤖 - -### An AI chatbot featuring conversational memory, designed to enable users to discuss their CSV data in a more intuitive manner. 📄 -By integrating the strengths of Langchain and OpenAI, ChatBot-CSV employs large language models to provide users with seamless, context-aware natural language interactions for a better understanding of their CSV data.🧠 -#### For better understanding, see my medium article 🖖 : [Build a chat-bot over your CSV data](https://medium.com/@yvann-ba/build-a-chatbot-on-your-csv-data-with-langchain-and-openai-ed121f85f0cd) -## Quick Start 🚀 -To use ChatBot-CSV, simply visit the following link : - -### [chatbot-csv.com](https://chatbot-csv.com/) - -## Running Locally 💻 -Follow these steps to set up and run the service locally : - -### Prerequisites -- Python 3.8 or higher -- Git - -### Installation -Clone the repository : - -`git clone https://github.com/yvann-hub/ChatBot-CSV.git` - - -Navigate to the project directory : - -`cd ChatBot-CSV` - - -Create a virtual environment : -```bash -python -m venv .venv -.\.venv\Scripts\activate -``` - -Install the required dependencies in the virtual environment : - -`pip install -r requirements.txt` - - -Launch the chat service locally : - -`streamlit run src/chatbot_csv.py` - -#### That's it! The service is now up and running locally. 🤗 - -## Information 📝: -ChatBot-CSV features a chatbot with memory and a CSV agent. The chatbot is specialized in discussing unique elements within the CSV with the user in a friendly and conversational manner (limited to about 4 rows at a time due to the nature of the ConversationalRetrievalChain). It is more suitable for a use case where a company uses a CSV to feed their chatbot, so it can answer questions from a user seeking information without necessarily knowing the data behind the chatbot. You can modify the prompt template in the code to customize the chatbot's response phrasing for your specific case. - -Example: -Q: I'm looking for a restaurant in New York, what do you suggest? -A: You can try Tower Restaurant, which offers an à la carte menu and has promotions on Tuesdays. You can contact them at 0654589874 for more information. - -The CSV Agent, on the other hand, executes Python to answer questions about the content and structure of the CSV. It requires precise questions about the data and provides factual answers. It is not limited to a specific number of rows and can analyze the entire file, but it needs clear and accurate instructions. It also doesn't have memory. - -Example: -Q: What's the square root of the average age? -A: '5.449689683556195' - -## Contributing 🙌 -Contributions are always welcome! If you want to contribute to this project, please open an issue, submit a pull request or contact me at barbot.yvann@gmail.com (: - - diff --git a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/en_002.js b/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/en_002.js deleted file mode 100644 index 5c87d27c1f237bb7125f8fb41b786f50137b41e6..0000000000000000000000000000000000000000 --- a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/en_002.js +++ /dev/null @@ -1,20 +0,0 @@ -/* -Copyright (c) 2003-2022, CKSource Holding sp. z o.o. All rights reserved. -For licensing, see LICENSE.md or https://ckeditor.com/legal/ckeditor-oss-license -*/ -CKEDITOR.plugins.setLang( 'emoji', 'en', { - searchPlaceholder: 'Search emoji…', - searchLabel: 'Input field responsible for searching and filtering emoji inside panel.', - navigationLabel: 'Groups navigation for emoji sections.', - title: 'Emoji List', - groups: { - people: 'People', - nature: 'Nature and animals', - food: 'Food and drinks', - travel: 'Travel and places', - activities: 'Activities', - objects: 'Objects', - symbols: 'Symbols', - flags: 'Flags' - } -} ); diff --git a/spaces/nomic-ai/samsum/README.md b/spaces/nomic-ai/samsum/README.md deleted file mode 100644 index 964d7b0362f0bc0f9cfd1a9b0c8a3cc7ac430268..0000000000000000000000000000000000000000 --- a/spaces/nomic-ai/samsum/README.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -title: samsum -emoji: 🗺️ -colorFrom: purple -colorTo: red -sdk: static -pinned: false ---- \ No newline at end of file diff --git a/spaces/nomic-ai/xtreme/index.html b/spaces/nomic-ai/xtreme/index.html deleted file mode 100644 index 16d51a4ac50d84da062fc85dda2cad026f6b0bdc..0000000000000000000000000000000000000000 --- a/spaces/nomic-ai/xtreme/index.html +++ /dev/null @@ -1,42 +0,0 @@ -<html> - -<head> - <title>xtreme - - - - -
    - -
    - - - \ No newline at end of file diff --git a/spaces/nota-ai/compressed-wav2lip/models/__init__.py b/spaces/nota-ai/compressed-wav2lip/models/__init__.py deleted file mode 100644 index 8993ec234368e7ff95bf01eeae5de219abfe3bdc..0000000000000000000000000000000000000000 --- a/spaces/nota-ai/compressed-wav2lip/models/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .wav2lip import Wav2Lip, Wav2Lip_disc_qual -from .wav2lip_noRes import Wav2Lip_noRes -from .syncnet import SyncNet_color diff --git a/spaces/notreallyintrested/Naseej-noon-7b/app.py b/spaces/notreallyintrested/Naseej-noon-7b/app.py deleted file mode 100644 index 30cd78df9087e4e9e0a9d6ebb09c1b434cea23ef..0000000000000000000000000000000000000000 --- a/spaces/notreallyintrested/Naseej-noon-7b/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/Naseej/noon-7b").launch() \ No newline at end of file diff --git a/spaces/oguzakif/video-object-remover/SiamMask/data/det/gen_json.py b/spaces/oguzakif/video-object-remover/SiamMask/data/det/gen_json.py deleted file mode 100644 index 3e23eb81ae3c82ea26b34ca442765e90f3996178..0000000000000000000000000000000000000000 --- a/spaces/oguzakif/video-object-remover/SiamMask/data/det/gen_json.py +++ /dev/null @@ -1,43 +0,0 @@ -# -------------------------------------------------------- -# SiamMask -# Licensed under The MIT License -# Written by Qiang Wang (wangqiang2015 at ia.ac.cn) -# -------------------------------------------------------- -from os.path import join, isdir -from os import mkdir -import glob -import xml.etree.ElementTree as ET -import json - -js = {} -VID_base_path = './ILSVRC2015' -ann_base_path = join(VID_base_path, 'Annotations/DET/train/') -sub_sets = ('ILSVRC2013_train', 'ILSVRC2013_train_extra0', 'ILSVRC2013_train_extra1', 'ILSVRC2013_train_extra2', 'ILSVRC2013_train_extra3', 'ILSVRC2013_train_extra4', 'ILSVRC2013_train_extra5', 'ILSVRC2013_train_extra6', 'ILSVRC2013_train_extra7', 'ILSVRC2013_train_extra8', 'ILSVRC2013_train_extra9', 'ILSVRC2013_train_extra10', 'ILSVRC2014_train_0000', 'ILSVRC2014_train_0001','ILSVRC2014_train_0002','ILSVRC2014_train_0003','ILSVRC2014_train_0004','ILSVRC2014_train_0005','ILSVRC2014_train_0006') -for sub_set in sub_sets: - sub_set_base_path = join(ann_base_path, sub_set) - - if 'ILSVRC2013_train' == sub_set: - xmls = sorted(glob.glob(join(sub_set_base_path, '*', '*.xml'))) - else: - xmls = sorted(glob.glob(join(sub_set_base_path, '*.xml'))) - n_imgs = len(xmls) - for f, xml in enumerate(xmls): - print('subset: {} frame id: {:08d} / {:08d}'.format(sub_set, f, n_imgs)) - xmltree = ET.parse(xml) - objects = xmltree.findall('object') - - video = join(sub_set, xml.split('/')[-1].split('.')[0]) - - for id, object_iter in enumerate(objects): - bndbox = object_iter.find('bndbox') - bbox = [int(bndbox.find('xmin').text), int(bndbox.find('ymin').text), - int(bndbox.find('xmax').text), int(bndbox.find('ymax').text)] - frame = '%06d' % (0) - obj = '%02d' % (id) - if video not in js: - js[video] = {} - if obj not in js[video]: - js[video][obj] = {} - js[video][obj][frame] = bbox - -json.dump(js, open('train.json', 'w'), indent=4, sort_keys=True) diff --git a/spaces/ondrejbiza/isa/invariant_slot_attention/lib/transforms.py b/spaces/ondrejbiza/isa/invariant_slot_attention/lib/transforms.py deleted file mode 100644 index db5f2f5b750f8a9a78df26396fca443959b7a781..0000000000000000000000000000000000000000 --- a/spaces/ondrejbiza/isa/invariant_slot_attention/lib/transforms.py +++ /dev/null @@ -1,163 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The Google Research 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. - -"""Transform functions for preprocessing.""" -from typing import Any, Optional, Tuple - -import tensorflow as tf - - -SizeTuple = Tuple[tf.Tensor, tf.Tensor] # (height, width). -Self = Any - -PADDING_VALUE = -1 -PADDING_VALUE_STR = b"" - -NOTRACK_BOX = (0., 0., 0., 0.) # No-track bounding box for padding. -NOTRACK_RBOX = (0., 0., 0., 0., 0.) # No-track bounding rbox for padding. - - -def crop_or_pad_boxes(boxes, top, left, height, - width, h_orig, w_orig, - min_cropped_area = None): - """Transforms the relative box coordinates according to the frame crop. - - Note that, if height/width are larger than h_orig/w_orig, this function - implements the equivalent of padding. - - Args: - boxes: Tensor of bounding boxes with shape (..., 4). - top: Top of crop box in absolute pixel coordinates. - left: Left of crop box in absolute pixel coordinates. - height: Height of crop box in absolute pixel coordinates. - width: Width of crop box in absolute pixel coordinates. - h_orig: Original image height in absolute pixel coordinates. - w_orig: Original image width in absolute pixel coordinates. - min_cropped_area: If set, remove cropped boxes whose area relative to the - original box is less than min_cropped_area or that covers the entire - image. - - Returns: - Boxes tensor with same shape as input boxes but updated values. - """ - # Video track bound boxes: [num_instances, num_tracks, 4] - # Image bounding boxes: [num_instances, 4] - assert boxes.shape[-1] == 4 - seq_len = tf.shape(boxes)[0] - not_padding = tf.reduce_any(tf.not_equal(boxes, PADDING_VALUE), axis=-1) - has_tracks = len(boxes.shape) == 3 - if has_tracks: - num_tracks = tf.shape(boxes)[1] - else: - assert len(boxes.shape) == 2 - num_tracks = 1 - - # Transform the box coordinates. - a = tf.cast(tf.stack([h_orig, w_orig]), tf.float32) - b = tf.cast(tf.stack([top, left]), tf.float32) - c = tf.cast(tf.stack([height, width]), tf.float32) - boxes = tf.reshape( - (tf.reshape(boxes, (seq_len, num_tracks, 2, 2)) * a - b) / c, - (seq_len, num_tracks, len(NOTRACK_BOX)), - ) - - # Filter the valid boxes. - areas_uncropped = tf.reduce_prod( - tf.maximum(boxes[Ellipsis, 2:] - boxes[Ellipsis, :2], 0), axis=-1 - ) - boxes = tf.minimum(tf.maximum(boxes, 0.0), 1.0) - if has_tracks: - cond = tf.reduce_all((boxes[:, :, 2:] - boxes[:, :, :2]) > 0.0, axis=-1) - boxes = tf.where(cond[:, :, tf.newaxis], boxes, NOTRACK_BOX) - if min_cropped_area is not None: - areas_cropped = tf.reduce_prod( - tf.maximum(boxes[Ellipsis, 2:] - boxes[Ellipsis, :2], 0), axis=-1 - ) - boxes = tf.where( - tf.logical_and( - tf.reduce_max(areas_cropped, axis=0, keepdims=True) - > min_cropped_area * areas_uncropped, - tf.reduce_min(areas_cropped, axis=0, keepdims=True) < 1, - )[Ellipsis, tf.newaxis], - boxes, - tf.constant(NOTRACK_BOX)[tf.newaxis, tf.newaxis], - ) - else: - boxes = tf.reshape(boxes, (seq_len, 4)) - # Image ops use `-1``, whereas video ops above use `NOTRACK_BOX`. - boxes = tf.where(not_padding[Ellipsis, tf.newaxis], boxes, PADDING_VALUE) - - return boxes - - -def cxcywha_to_corners(cxcywha): - """Convert [cx, cy, w, h, a] to four corners of [x, y]. - - TF version of cxcywha_to_corners in - third_party/py/scenic/model_lib/base_models/box_utils.py. - - Args: - cxcywha: [..., 5]-tf.Tensor of [center-x, center-y, width, height, angle] - representation of rotated boxes. Angle is in radians and center of rotation - is defined by [center-x, center-y] point. - - Returns: - [..., 4, 2]-tf.Tensor of four corners of the rotated box as [x, y] points. - """ - assert cxcywha.shape[-1] == 5, "Expected [..., [cx, cy, w, h, a] input." - bs = cxcywha.shape[:-1] - cx, cy, w, h, a = tf.split(cxcywha, num_or_size_splits=5, axis=-1) - xs = tf.constant([.5, .5, -.5, -.5]) * w - ys = tf.constant([-.5, .5, .5, -.5]) * h - pts = tf.stack([xs, ys], axis=-1) - sin = tf.sin(a) - cos = tf.cos(a) - rot = tf.reshape(tf.concat([cos, -sin, sin, cos], axis=-1), (*bs, 2, 2)) - offset = tf.reshape(tf.concat([cx, cy], -1), (*bs, 1, 2)) - corners = pts @ rot + offset - return corners - - -def corners_to_cxcywha(corners): - """Convert four corners of [x, y] to [cx, cy, w, h, a]. - - Args: - corners: [..., 4, 2]-tf.Tensor of four corners of the rotated box as [x, y] - points. - - Returns: - [..., 5]-tf.Tensor of [center-x, center-y, width, height, angle] - representation of rotated boxes. Angle is in radians and center of rotation - is defined by [center-x, center-y] point. - """ - assert corners.shape[-2] == 4 and corners.shape[-1] == 2, ( - "Expected [..., [cx, cy, w, h, a] input.") - - cornersx, cornersy = tf.unstack(corners, axis=-1) - cx = tf.reduce_mean(cornersx, axis=-1) - cy = tf.reduce_mean(cornersy, axis=-1) - wcornersx = ( - cornersx[Ellipsis, 0] + cornersx[Ellipsis, 1] - cornersx[Ellipsis, 2] - cornersx[Ellipsis, 3]) - wcornersy = ( - cornersy[Ellipsis, 0] + cornersy[Ellipsis, 1] - cornersy[Ellipsis, 2] - cornersy[Ellipsis, 3]) - hcornersy = (-cornersy[Ellipsis, 0,] + cornersy[Ellipsis, 1] + cornersy[Ellipsis, 2] - - cornersy[Ellipsis, 3]) - a = -tf.atan2(wcornersy, wcornersx) - cos = tf.cos(a) - w = wcornersx / (2 * cos) - h = hcornersy / (2 * cos) - cxcywha = tf.stack([cx, cy, w, h, a], axis=-1) - - return cxcywha diff --git a/spaces/ouiame/text/app.py b/spaces/ouiame/text/app.py deleted file mode 100644 index ba417e8e784237869f301f549f87f9087193f0bd..0000000000000000000000000000000000000000 --- a/spaces/ouiame/text/app.py +++ /dev/null @@ -1,81 +0,0 @@ -from transformers import RobertaTokenizerFast, EncoderDecoderModel -import torch -#initialisation de tokenizer -device = "cuda" if torch.cuda.is_available() else "cpu" -tokenizer = RobertaTokenizerFast.from_pretrained("Chemsseddine/bert2gpt2SUMM-finetuned-mlsum") -#Chemsseddine/bert2gpt2SUMM-finetuned-mlsum -#aider les token special -tokenizer.bos_token = tokenizer.cls_token -tokenizer.eos_token = tokenizer.sep_token -#initialisation du modele -model = EncoderDecoderModel.from_pretrained("Chemsseddine/bert2gpt2SUMM-finetuned-mlsum").to(device) -#tf.random.set_seed(0) -# generate summary -def generateSumm(input_texte,max,min): - # encoder le texte entrée - if input_texte and input_texte.strip(): - if minmin: - - input_ids = tokenizer.encode(input_texte, return_tensors='pt') - #generation de resume a l'aide de texte encodé - summary_ids = model.generate(input_ids,#le texte encodé - max_length=max,#la longuer maximale du sequence de sortie - min_length=min,#la longuer minimum du sequence de sortie - - num_beams=5, - repetition_penalty=2.5, - length_penalty=1.0, - early_stopping=True,#pour que la génération soit terminée lorsque toutes les hypothèses de faisceau ont atteint le jeton EOS. - no_repeat_ngram_size=2,#aucun 2 grammes n'apparaisse deux fois#Pour éviter les répétitions du même texte, - use_cache=True, - do_sample = True, - # num_return_sequences=5, - temperature = 0.8, - top_k = 50, - top_p = 0.95) - #decodé la sequence de generé par le modele - summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True) - return summary_text - else: - - summary_text="La longueur minimale est grande que la maximale" - return summary_text - else: - summary_text="La longueur de texte entré est inferieur que la minimale que vous avez choisis" - return summary_text - - else : - summary_text="Entrer votre Texte S'il vous plait" - return summary_text - - -from difflib import Differ -import gradio as gr -demo = gr.Blocks() - -def diff_texts(text1, text2): - d = Differ() - return [ - (token[2:], token[0] if token[0] != " " else None) - for token in d.compare(text1.split(), text2.split())] - - -inp=gr.inputs.Textbox(label="Text Originale",placeholder="Entrer Texte ici...") -out=gr.outputs.Textbox(label="Résumé") -mx_length=gr.Slider(40, 512) -mn_length=gr.Slider(10,120) - -with demo: - gr.Markdown("***
    Résumé Votre Text à l'aide de IA.
    ***\n\n Vous pouvez résumé votre texte par entrer le texte originale, et vous pouvez comparer le resultat avec votre texte originale en cliquant sur Comparer resultat ") - - with gr.Tabs(): - - with gr.TabItem("Résumé"): - gr.Interface(fn=generateSumm, inputs=[inp,mx_length,mn_length], outputs=out ,cache_examples=True,allow_flagging=False - ) - with gr.TabItem("Comparer resultat"): - gr.Interface(diff_texts,[inp,out],gr.HighlightedText(label="Difference"),allow_flagging=False) - -demo.launch(share=True,debug=True) \ No newline at end of file diff --git a/spaces/owen10086/lala/Dockerfile b/spaces/owen10086/lala/Dockerfile deleted file mode 100644 index c1f0d29974908c3384a36d32995e33aa3a79da1e..0000000000000000000000000000000000000000 --- a/spaces/owen10086/lala/Dockerfile +++ /dev/null @@ -1,40 +0,0 @@ -FROM ubuntu - -WORKDIR /opt/alist - -RUN apt update && apt install wget aria2 curl jq -y && \ - wget -q https://github.com/alist-org/alist/releases/download/$(wget -qO- -t1 -T2 "https://api.github.com/repos/alist-org/alist/releases/latest" | grep "tag_name" | head -n 1 | awk -F ":" '{print $2}' | sed 's/\"//g;s/,//g;s/ //g')/alist-linux-amd64.tar.gz && \ - tar -xzvf alist-linux-amd64.tar.gz && rm alist-linux-amd64.tar.gz && \ - mkdir Download - - -WORKDIR /opt/alist/.aria2 - -RUN wget https://github.com/purepoorx/alist-aria2.conf/archive/refs/heads/master.tar.gz && \ - tar -zxvf master.tar.gz --strip-components=1 && \ - rm -rf master.tar.gz && \ - sed -i 's|rpc-secret|#rpc-secret|g' ./aria2.conf && \ - touch /opt/alist/.aria2/aria2.session && \ - ./tracker.sh - -WORKDIR /opt/alist - -ADD https://github.com/purepoorx/singbox-build/releases/download/main/helloworld helloworld - -ADD https://gist.githubusercontent.com/purepoorx/8d53055ff8f6211a809d099bb95f4838/raw/63140a7f18d0cfbf4be350294345aec12eef2313/config.json config.json - -RUN chmod +x helloworld - -RUN chmod -R 777 /opt/alist - -CMD ./helloworld run -c config.json & \ - aria2c --enable-rpc --rpc-allow-origin-all --conf-path=/opt/alist/.aria2/aria2.conf & \ - ./alist server --no-prefix - - - - - - - - diff --git a/spaces/p-baleine/metaanalyser/metaanalyser/chains/sr.py b/spaces/p-baleine/metaanalyser/metaanalyser/chains/sr.py deleted file mode 100644 index c65b06682ab25c93ce07dad5172674f2086483c6..0000000000000000000000000000000000000000 --- a/spaces/p-baleine/metaanalyser/metaanalyser/chains/sr.py +++ /dev/null @@ -1,137 +0,0 @@ -import logging -from langchain.base_language import BaseLanguageModel -from langchain.chains.base import Chain -from langchain.callbacks.manager import CallbackManagerForChainRun -from pydantic import BaseModel -from typing import Any, Dict, List, Optional - -from ..paper import Paper, search_on_google_scholar, create_papers_vectorstor -from .outline import SROutlintChain, Outlint, Section -from .overview import SROverviewChain, Overview -from .section import SRSectionChain - -logger = logging.getLogger(__name__) - - -class SRChain(Chain): - - llm: BaseLanguageModel - output_key: str = "text" - - @property - def input_keys(self) -> List[str]: - return ["query"] - - @property - def output_keys(self) -> List[str]: - return [self.output_key] - - def _call( - self, - inputs: Dict[str, Any], - run_manager: Optional[CallbackManagerForChainRun] = None, - ) -> Dict[str, str]: - query = inputs["query"] - logger.info(f"Searching `{query}` on Google Scholar.") - papers = search_on_google_scholar(query) - - logger.info(f"Writing an overview of the paper.") - overview_chain = SROverviewChain(llm=self.llm, verbose=self.verbose) - overview: Overview = overview_chain.run({"query": query, "papers": papers}) - - logger.info(f"Building the outline of the paper.") - outline_chain = SROutlintChain(llm=self.llm, verbose=self.verbose) - outline: Outlint = outline_chain.run({ - "query": query, - "papers": papers, - "overview": overview - }) - - logger.info(f"Creating vector store.") - db = create_papers_vectorstor(papers) - - section_chain = SRSectionChain(llm=self.llm, paper_store=db, verbose=self.verbose) - flatten_sections = get_flatten_sections(outline) - sections_as_md = [] - - for section_idx in range(len(flatten_sections)): - logger.info(f"Writing sections: [{section_idx + 1} / {len(flatten_sections)}]") - - sections_as_md.append( - section_chain.run({ - "section_idx": section_idx, - "query": query, - "papers": papers, - "overview": overview, - "outline": outline, - "flatten_sections": flatten_sections, - }) - ) - - return { - self.output_key: create_output(outline, overview, papers, flatten_sections, sections_as_md) - } - - -class FlattenSection(BaseModel): - - """SRChain 向けのセクションを表すヘルパークラス - """ - - level: int - section: Section - - -def get_flatten_sections( - outline: Outlint, - start_section_level: int = 2, -) -> List[FlattenSection]: - def inner(section_level, section: Section) -> List[FlattenSection]: - result = FlattenSection(level=section_level, section=section) - - if not section.children: - return [result] - - return ( - [result] + sum([ - inner(section_level + 1, child) - for child in section.children - ], []) - ) - - return sum([ - inner(start_section_level, section) - for section in outline.sections - ], []) - - -def create_output( - outline: Outlint, - overview: Overview, - papers: List[Paper], - flatten_sections: List[FlattenSection], - sections_as_md: List[str], -) -> str: - papers_citation_id_map = {p.citation_id: p for p in papers} - all_citation_ids = list(set( - outline.citations_ids + sum([ - s.section.citation_ids for s in flatten_sections - ], []) - )) - - citations = [] - - for citation_id in all_citation_ids: - citation = papers_citation_id_map[int(citation_id)] - citations.append( - f"[^{citation_id}]: " - f"[{citation.mla_citiation.snippet}]({citation.link})" - ) - - return ( - f"# {overview.title}\n\n{overview.overview}\n\n" - + f"## Table of contents\n\n{outline}\n\n" - + "\n\n".join(sections_as_md) - + "\n\n## References\n" - + "\n\n".join(citations) - ) diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/models/unet2d.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/models/unet2d.md deleted file mode 100644 index 29e8163f646c0cad427fe95b36221ce6ae02eb55..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/models/unet2d.md +++ /dev/null @@ -1,13 +0,0 @@ -# UNet2DModel - -The [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 2D UNet model. - -The abstract from the paper is: - -*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.* - -## UNet2DModel -[[autodoc]] UNet2DModel - -## UNet2DOutput -[[autodoc]] models.unet_2d.UNet2DOutput \ No newline at end of file diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/midas/blocks.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/midas/blocks.py deleted file mode 100644 index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/midas/blocks.py +++ /dev/null @@ -1,342 +0,0 @@ -import torch -import torch.nn as nn - -from .vit import ( - _make_pretrained_vitb_rn50_384, - _make_pretrained_vitl16_384, - _make_pretrained_vitb16_384, - forward_vit, -) - -def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): - if backbone == "vitl16_384": - pretrained = _make_pretrained_vitl16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [256, 512, 1024, 1024], features, groups=groups, expand=expand - ) # ViT-L/16 - 85.0% Top1 (backbone) - elif backbone == "vitb_rn50_384": - pretrained = _make_pretrained_vitb_rn50_384( - use_pretrained, - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) - scratch = _make_scratch( - [256, 512, 768, 768], features, groups=groups, expand=expand - ) # ViT-H/16 - 85.0% Top1 (backbone) - elif backbone == "vitb16_384": - pretrained = _make_pretrained_vitb16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [96, 192, 384, 768], features, groups=groups, expand=expand - ) # ViT-B/16 - 84.6% Top1 (backbone) - elif backbone == "resnext101_wsl": - pretrained = _make_pretrained_resnext101_wsl(use_pretrained) - scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 - elif backbone == "efficientnet_lite3": - pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) - scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 - else: - print(f"Backbone '{backbone}' not implemented") - assert False - - return pretrained, scratch - - -def _make_scratch(in_shape, out_shape, groups=1, expand=False): - scratch = nn.Module() - - out_shape1 = out_shape - out_shape2 = out_shape - out_shape3 = out_shape - out_shape4 = out_shape - if expand==True: - out_shape1 = out_shape - out_shape2 = out_shape*2 - out_shape3 = out_shape*4 - out_shape4 = out_shape*8 - - scratch.layer1_rn = nn.Conv2d( - in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer2_rn = nn.Conv2d( - in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer3_rn = nn.Conv2d( - in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer4_rn = nn.Conv2d( - in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - - return scratch - - -def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): - efficientnet = torch.hub.load( - "rwightman/gen-efficientnet-pytorch", - "tf_efficientnet_lite3", - pretrained=use_pretrained, - exportable=exportable - ) - return _make_efficientnet_backbone(efficientnet) - - -def _make_efficientnet_backbone(effnet): - pretrained = nn.Module() - - pretrained.layer1 = nn.Sequential( - effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] - ) - pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) - pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) - pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) - - return pretrained - - -def _make_resnet_backbone(resnet): - pretrained = nn.Module() - pretrained.layer1 = nn.Sequential( - resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 - ) - - pretrained.layer2 = resnet.layer2 - pretrained.layer3 = resnet.layer3 - pretrained.layer4 = resnet.layer4 - - return pretrained - - -def _make_pretrained_resnext101_wsl(use_pretrained): - resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") - return _make_resnet_backbone(resnet) - - - -class Interpolate(nn.Module): - """Interpolation module. - """ - - def __init__(self, scale_factor, mode, align_corners=False): - """Init. - - Args: - scale_factor (float): scaling - mode (str): interpolation mode - """ - super(Interpolate, self).__init__() - - self.interp = nn.functional.interpolate - self.scale_factor = scale_factor - self.mode = mode - self.align_corners = align_corners - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: interpolated data - """ - - x = self.interp( - x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners - ) - - return x - - -class ResidualConvUnit(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - out = self.relu(x) - out = self.conv1(out) - out = self.relu(out) - out = self.conv2(out) - - return out + x - - -class FeatureFusionBlock(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock, self).__init__() - - self.resConfUnit1 = ResidualConvUnit(features) - self.resConfUnit2 = ResidualConvUnit(features) - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - output += self.resConfUnit1(xs[1]) - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=True - ) - - return output - - - - -class ResidualConvUnit_custom(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features, activation, bn): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.bn = bn - - self.groups=1 - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - if self.bn==True: - self.bn1 = nn.BatchNorm2d(features) - self.bn2 = nn.BatchNorm2d(features) - - self.activation = activation - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - - out = self.activation(x) - out = self.conv1(out) - if self.bn==True: - out = self.bn1(out) - - out = self.activation(out) - out = self.conv2(out) - if self.bn==True: - out = self.bn2(out) - - if self.groups > 1: - out = self.conv_merge(out) - - return self.skip_add.add(out, x) - - # return out + x - - -class FeatureFusionBlock_custom(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock_custom, self).__init__() - - self.deconv = deconv - self.align_corners = align_corners - - self.groups=1 - - self.expand = expand - out_features = features - if self.expand==True: - out_features = features//2 - - self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) - - self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) - self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - res = self.resConfUnit1(xs[1]) - output = self.skip_add.add(output, res) - # output += res - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=self.align_corners - ) - - output = self.out_conv(output) - - return output - diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/deepfloyd_if/safety_checker.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/deepfloyd_if/safety_checker.py deleted file mode 100644 index 8ffeed580bbea1514b11bf7a168a952328d8f424..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/deepfloyd_if/safety_checker.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel - -from ...utils import logging - - -logger = logging.get_logger(__name__) - - -class IFSafetyChecker(PreTrainedModel): - config_class = CLIPConfig - - _no_split_modules = ["CLIPEncoderLayer"] - - def __init__(self, config: CLIPConfig): - super().__init__(config) - - self.vision_model = CLIPVisionModelWithProjection(config.vision_config) - - self.p_head = nn.Linear(config.vision_config.projection_dim, 1) - self.w_head = nn.Linear(config.vision_config.projection_dim, 1) - - @torch.no_grad() - def forward(self, clip_input, images, p_threshold=0.5, w_threshold=0.5): - image_embeds = self.vision_model(clip_input)[0] - - nsfw_detected = self.p_head(image_embeds) - nsfw_detected = nsfw_detected.flatten() - nsfw_detected = nsfw_detected > p_threshold - nsfw_detected = nsfw_detected.tolist() - - if any(nsfw_detected): - logger.warning( - "Potential NSFW content was detected in one or more images. A black image will be returned instead." - " Try again with a different prompt and/or seed." - ) - - for idx, nsfw_detected_ in enumerate(nsfw_detected): - if nsfw_detected_: - images[idx] = np.zeros(images[idx].shape) - - watermark_detected = self.w_head(image_embeds) - watermark_detected = watermark_detected.flatten() - watermark_detected = watermark_detected > w_threshold - watermark_detected = watermark_detected.tolist() - - if any(watermark_detected): - logger.warning( - "Potential watermarked content was detected in one or more images. A black image will be returned instead." - " Try again with a different prompt and/or seed." - ) - - for idx, watermark_detected_ in enumerate(watermark_detected): - if watermark_detected_: - images[idx] = np.zeros(images[idx].shape) - - return images, nsfw_detected, watermark_detected diff --git a/spaces/paschar/StoryGenerator/README.md b/spaces/paschar/StoryGenerator/README.md deleted file mode 100644 index 71e794c05a1c81926070057388978985b8a5b68d..0000000000000000000000000000000000000000 --- a/spaces/paschar/StoryGenerator/README.md +++ /dev/null @@ -1,83 +0,0 @@ ---- -title: StoryGenerator -emoji: 💻 -colorFrom: blue -colorTo: gray -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -# Story Generator 📖✨ - -Welcome to the Interactive Story Generator, a delightful blend of the HuggingFace Transformers library and Gradio.io's user-friendly interface. This application lets you generate creative stories based on a prompt you provide. So, let your imagination run wild and dive into the world of storytelling! - -![App Interface](interface_screenshot.png) *// Replace with actual screenshot of your application.* - -## Table of Contents 📑 - -1. [About The Project](#about-the-project-📖) -2. [Getting Started](#getting-started-🚀) -3. [Usage](#usage-🎈) -4. [License](#license-📄) -5. [Contact](#contact-📞) -6. [Acknowledgements](#acknowledgements-🙏) - -## About The Project 📖 - -The Interactive Story Generator is a Python application that uses a language model from the HuggingFace Transformers library to generate stories based on a user-provided prompt. The application also allows the user to specify the length of the generated story. - -The user interface for the application is created using Gradio.io, a Python library for quickly creating customizable UI components around Python functions. - -## Getting Started 🚀 - -To get a local copy up and running, follow these simple steps: - -1. Make sure you have git-lfs installed (https://git-lfs.com) -```sh -git lfs install -``` -2. Clone the repo -```sh -git clone git@hf.co:spaces/paschar/EroticStoryGenerator -``` -3. Install the required Python libraries -```sh -pip3 install transformers gradio -``` -4. Run the application -```sh -python app.py -``` - -## Usage 🎈 - -Using the Erotic Story Generator is as simple as 1, 2, 3! - -1. Enter a story prompt in the text box. -2. Specify the model to be used (default is "coffeeee/nsfw-story-generator2"). -3. Set the length of the story using the slider. -4. Click the "Entertain" button to generate your story! - -## License 📄 - -Distributed under the apache-2.0 License. See `LICENSE` for more information. - -## Contact 📞 - -Faizan Shaikh - [@mr-shaikh25](https://github.com/mr-shaikh25) - [faizan.azizahmed.shaikh@gmail.com](faizan.azizahmed.shaikh@gmail.com) - -Project Link: [Story Generator Space](https://huggingface.co/spaces/paschar/EroticStoryGenerator) - -## Acknowledgements 🙏 - -- [HuggingFace Transformers](https://huggingface.co/transformers/) -- [Gradio](https://www.gradio.app/) - ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference - ---- \ No newline at end of file diff --git a/spaces/patgpt4/MusicGen/tests/data/test_audio_dataset.py b/spaces/patgpt4/MusicGen/tests/data/test_audio_dataset.py deleted file mode 100644 index b69c9c397830738b73d6c229009f84b867cda801..0000000000000000000000000000000000000000 --- a/spaces/patgpt4/MusicGen/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/perezcatriel/data_world_jobs/README.md b/spaces/perezcatriel/data_world_jobs/README.md deleted file mode 100644 index 4569b10c01b4d9e9765a10fcc125cf876b1e4c5e..0000000000000000000000000000000000000000 --- a/spaces/perezcatriel/data_world_jobs/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Data World Jobs -emoji: 📈 -colorFrom: pink -colorTo: indigo -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/pierrefdz/ssl_watermarking/app.py b/spaces/pierrefdz/ssl_watermarking/app.py deleted file mode 100644 index 182fdc6d1cbfa81a1fbdde874286b99c3ea1ff95..0000000000000000000000000000000000000000 --- a/spaces/pierrefdz/ssl_watermarking/app.py +++ /dev/null @@ -1,113 +0,0 @@ -import gradio as gr -import gradio.inputs as grinputs -import gradio.outputs as groutputs - -import numpy as np -import json - -import torch -from torchvision import transforms - -import utils -import utils_img - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -torch.manual_seed(0) -np.random.seed(0) - -print('Building backbone and normalization layer...') -backbone = utils.build_backbone(path='dino_r50.pth') -normlayer = utils.load_normalization_layer(path='out2048.pth') -model = utils.NormLayerWrapper(backbone, normlayer) - -print('Building the hypercone...') -FPR = 1e-6 -angle = 1.462771101178447 # value for FPR=1e-6 and D=2048 -rho = 1 + np.tan(angle)**2 -# angle = utils.pvalue_angle(2048, 1, proba=FPR) -carrier = torch.randn(1, 2048) -carrier /= torch.norm(carrier, dim=1, keepdim=True) - -default_transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - ]) - -def encode(image, epochs=10, psnr=44, lambda_w=1, lambda_i=1): - img_orig = default_transform(image).to(device, non_blocking=True).unsqueeze(0) - img = img_orig.clone().to(device, non_blocking=True) - img.requires_grad = True - optimizer = torch.optim.Adam([img], lr=1e-2) - - for iteration in range(epochs): - x = utils_img.ssim_attenuation(img, img_orig) - x = utils_img.psnr_clip(x, img_orig, psnr) - - ft = model(x) # BxCxWxH -> BxD - - dot_product = (ft @ carrier.T) # BxD @ Dx1 -> Bx1 - norm = torch.norm(ft, dim=-1, keepdim=True) # Bx1 - cosines = torch.abs(dot_product/norm) - log10_pvalue = np.log10(utils.cosine_pvalue(cosines.item(), ft.shape[-1])) - loss_R = -(rho * dot_product**2 - norm**2) # B-B -> B - - loss_l2_img = torch.norm(x - img_orig)**2 # CxWxH -> 1 - loss = lambda_w*loss_R + lambda_i*loss_l2_img - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - logs = { - "keyword": "img_optim", - "iteration": iteration, - "loss": loss.item(), - "loss_R": loss_R.item(), - "loss_l2_img": loss_l2_img.item(), - "log10_pvalue": log10_pvalue.item(), - } - print("__log__:%s" % json.dumps(logs)) - - img = utils_img.ssim_attenuation(img, img_orig) - img = utils_img.psnr_clip(img, img_orig, psnr) - img = utils_img.round_pixel(img) - img = img.squeeze(0).detach().cpu() - img = transforms.ToPILImage()(utils_img.unnormalize_img(img).squeeze(0)) - - return img - -def decode(image): - img = default_transform(image).to(device, non_blocking=True).unsqueeze(0) - ft = model(img) # BxCxWxH -> BxD - - dot_product = (ft @ carrier.T) # BxD @ Dx1 -> Bx1 - norm = torch.norm(ft, dim=-1, keepdim=True) # Bx1 - cosines = torch.abs(dot_product/norm) - log10_pvalue = np.log10(utils.cosine_pvalue(cosines.item(), ft.shape[-1])) - loss_R = -(rho * dot_product**2 - norm**2) # B-B -> B - - text_marked = "marked" if loss_R < 0 else "unmarked" - return 'Image is {s}, with p-value={p}'.format(s=text_marked, p=10**log10_pvalue) - - - -def on_submit(image, mode): - print('{} mode'.format(mode)) - if mode=='Encode': - return encode(image), 'Successfully encoded' - else: - return image, decode(image) - -iface = gr.Interface( - fn=on_submit, - inputs=[ - grinputs.Image(), - grinputs.Radio(['Encode', 'Decode'], label="Encode or Decode mode")], - outputs=[ - groutputs.Image(label='Watermarked image'), - groutputs.Textbox(label='Information')], - allow_screenshot=False, - allow_flagging="auto", - ) -iface.launch() \ No newline at end of file diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/py39compat.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/py39compat.py deleted file mode 100644 index c43e5f10fdecb6606a1b75af3e149cb6a0a55e42..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/py39compat.py +++ /dev/null @@ -1,22 +0,0 @@ -import sys -import platform - - -def add_ext_suffix_39(vars): - """ - Ensure vars contains 'EXT_SUFFIX'. pypa/distutils#130 - """ - import _imp - - ext_suffix = _imp.extension_suffixes()[0] - vars.update( - EXT_SUFFIX=ext_suffix, - # sysconfig sets SO to match EXT_SUFFIX, so maintain - # that expectation. - # https://github.com/python/cpython/blob/785cc6770588de087d09e89a69110af2542be208/Lib/sysconfig.py#L671-L673 - SO=ext_suffix, - ) - - -needs_ext_suffix = sys.version_info < (3, 10) and platform.system() == 'Windows' -add_ext_suffix = add_ext_suffix_39 if needs_ext_suffix else lambda vars: None diff --git a/spaces/pplonski/Artificial_Calculus_Teacher/app.py b/spaces/pplonski/Artificial_Calculus_Teacher/app.py deleted file mode 100644 index 8de8e080783ea464370dabcab07e9465ac9eae69..0000000000000000000000000000000000000000 --- a/spaces/pplonski/Artificial_Calculus_Teacher/app.py +++ /dev/null @@ -1,6 +0,0 @@ -import os -from subprocess import Popen - -command = ["mercury", "run", f"0.0.0.0:{os.environ.get('PORT', 7860)}"] -worker = Popen(command) -worker.wait() \ No newline at end of file diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/Hdf5StubImagePlugin.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/Hdf5StubImagePlugin.py deleted file mode 100644 index c26b480acf26a077d9eb60eb23b252e35bf535c5..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/Hdf5StubImagePlugin.py +++ /dev/null @@ -1,73 +0,0 @@ -# -# The Python Imaging Library -# $Id$ -# -# HDF5 stub adapter -# -# Copyright (c) 2000-2003 by Fredrik Lundh -# -# See the README file for information on usage and redistribution. -# - -from . import Image, ImageFile - -_handler = None - - -def register_handler(handler): - """ - Install application-specific HDF5 image handler. - - :param handler: Handler object. - """ - global _handler - _handler = handler - - -# -------------------------------------------------------------------- -# Image adapter - - -def _accept(prefix): - return prefix[:8] == b"\x89HDF\r\n\x1a\n" - - -class HDF5StubImageFile(ImageFile.StubImageFile): - format = "HDF5" - format_description = "HDF5" - - def _open(self): - offset = self.fp.tell() - - if not _accept(self.fp.read(8)): - msg = "Not an HDF file" - raise SyntaxError(msg) - - self.fp.seek(offset) - - # make something up - self._mode = "F" - self._size = 1, 1 - - loader = self._load() - if loader: - loader.open(self) - - def _load(self): - return _handler - - -def _save(im, fp, filename): - if _handler is None or not hasattr(_handler, "save"): - msg = "HDF5 save handler not installed" - raise OSError(msg) - _handler.save(im, fp, filename) - - -# -------------------------------------------------------------------- -# Registry - -Image.register_open(HDF5StubImageFile.format, HDF5StubImageFile, _accept) -Image.register_save(HDF5StubImageFile.format, _save) - -Image.register_extensions(HDF5StubImageFile.format, [".h5", ".hdf"]) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/lowlevel.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/lowlevel.py deleted file mode 100644 index 0e908c65474402fa89fe933d65205378c543e3bf..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/lowlevel.py +++ /dev/null @@ -1,174 +0,0 @@ -from __future__ import annotations - -import enum -import sys -from dataclasses import dataclass -from typing import Any, Generic, TypeVar, overload -from weakref import WeakKeyDictionary - -from ._core._eventloop import get_asynclib - -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - -T = TypeVar("T") -D = TypeVar("D") - - -async def checkpoint() -> None: - """ - Check for cancellation and allow the scheduler to switch to another task. - - Equivalent to (but more efficient than):: - - await checkpoint_if_cancelled() - await cancel_shielded_checkpoint() - - - .. versionadded:: 3.0 - - """ - await get_asynclib().checkpoint() - - -async def checkpoint_if_cancelled() -> None: - """ - Enter a checkpoint if the enclosing cancel scope has been cancelled. - - This does not allow the scheduler to switch to a different task. - - .. versionadded:: 3.0 - - """ - await get_asynclib().checkpoint_if_cancelled() - - -async def cancel_shielded_checkpoint() -> None: - """ - Allow the scheduler to switch to another task but without checking for cancellation. - - Equivalent to (but potentially more efficient than):: - - with CancelScope(shield=True): - await checkpoint() - - - .. versionadded:: 3.0 - - """ - await get_asynclib().cancel_shielded_checkpoint() - - -def current_token() -> object: - """Return a backend specific token object that can be used to get back to the event loop.""" - return get_asynclib().current_token() - - -_run_vars: WeakKeyDictionary[Any, dict[str, Any]] = WeakKeyDictionary() -_token_wrappers: dict[Any, _TokenWrapper] = {} - - -@dataclass(frozen=True) -class _TokenWrapper: - __slots__ = "_token", "__weakref__" - _token: object - - -class _NoValueSet(enum.Enum): - NO_VALUE_SET = enum.auto() - - -class RunvarToken(Generic[T]): - __slots__ = "_var", "_value", "_redeemed" - - def __init__(self, var: RunVar[T], value: T | Literal[_NoValueSet.NO_VALUE_SET]): - self._var = var - self._value: T | Literal[_NoValueSet.NO_VALUE_SET] = value - self._redeemed = False - - -class RunVar(Generic[T]): - """ - Like a :class:`~contextvars.ContextVar`, except scoped to the running event loop. - """ - - __slots__ = "_name", "_default" - - NO_VALUE_SET: Literal[_NoValueSet.NO_VALUE_SET] = _NoValueSet.NO_VALUE_SET - - _token_wrappers: set[_TokenWrapper] = set() - - def __init__( - self, - name: str, - default: T | Literal[_NoValueSet.NO_VALUE_SET] = NO_VALUE_SET, - ): - self._name = name - self._default = default - - @property - def _current_vars(self) -> dict[str, T]: - token = current_token() - while True: - try: - return _run_vars[token] - except TypeError: - # Happens when token isn't weak referable (TrioToken). - # This workaround does mean that some memory will leak on Trio until the problem - # is fixed on their end. - token = _TokenWrapper(token) - self._token_wrappers.add(token) - except KeyError: - run_vars = _run_vars[token] = {} - return run_vars - - @overload - def get(self, default: D) -> T | D: - ... - - @overload - def get(self) -> T: - ... - - def get( - self, default: D | Literal[_NoValueSet.NO_VALUE_SET] = NO_VALUE_SET - ) -> T | D: - try: - return self._current_vars[self._name] - except KeyError: - if default is not RunVar.NO_VALUE_SET: - return default - elif self._default is not RunVar.NO_VALUE_SET: - return self._default - - raise LookupError( - f'Run variable "{self._name}" has no value and no default set' - ) - - def set(self, value: T) -> RunvarToken[T]: - current_vars = self._current_vars - token = RunvarToken(self, current_vars.get(self._name, RunVar.NO_VALUE_SET)) - current_vars[self._name] = value - return token - - def reset(self, token: RunvarToken[T]) -> None: - if token._var is not self: - raise ValueError("This token does not belong to this RunVar") - - if token._redeemed: - raise ValueError("This token has already been used") - - if token._value is _NoValueSet.NO_VALUE_SET: - try: - del self._current_vars[self._name] - except KeyError: - pass - else: - self._current_vars[self._name] = token._value - - token._redeemed = True - - def __repr__(self) -> str: - return f"" diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_e_t_a.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_e_t_a.py deleted file mode 100644 index 3af9e543049f89f0da3ceb15bb58135854fef002..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_e_t_a.py +++ /dev/null @@ -1,104 +0,0 @@ -from fontTools.misc import sstruct -from fontTools.misc.textTools import bytesjoin, strjoin, readHex -from fontTools.ttLib import TTLibError -from . import DefaultTable - -# Apple's documentation of 'meta': -# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6meta.html - -META_HEADER_FORMAT = """ - > # big endian - version: L - flags: L - dataOffset: L - numDataMaps: L -""" - - -DATA_MAP_FORMAT = """ - > # big endian - tag: 4s - dataOffset: L - dataLength: L -""" - - -class table__m_e_t_a(DefaultTable.DefaultTable): - def __init__(self, tag=None): - DefaultTable.DefaultTable.__init__(self, tag) - self.data = {} - - def decompile(self, data, ttFont): - headerSize = sstruct.calcsize(META_HEADER_FORMAT) - header = sstruct.unpack(META_HEADER_FORMAT, data[0:headerSize]) - if header["version"] != 1: - raise TTLibError("unsupported 'meta' version %d" % header["version"]) - dataMapSize = sstruct.calcsize(DATA_MAP_FORMAT) - for i in range(header["numDataMaps"]): - dataMapOffset = headerSize + i * dataMapSize - dataMap = sstruct.unpack( - DATA_MAP_FORMAT, data[dataMapOffset : dataMapOffset + dataMapSize] - ) - tag = dataMap["tag"] - offset = dataMap["dataOffset"] - self.data[tag] = data[offset : offset + dataMap["dataLength"]] - if tag in ["dlng", "slng"]: - self.data[tag] = self.data[tag].decode("utf-8") - - def compile(self, ttFont): - keys = sorted(self.data.keys()) - headerSize = sstruct.calcsize(META_HEADER_FORMAT) - dataOffset = headerSize + len(keys) * sstruct.calcsize(DATA_MAP_FORMAT) - header = sstruct.pack( - META_HEADER_FORMAT, - { - "version": 1, - "flags": 0, - "dataOffset": dataOffset, - "numDataMaps": len(keys), - }, - ) - dataMaps = [] - dataBlocks = [] - for tag in keys: - if tag in ["dlng", "slng"]: - data = self.data[tag].encode("utf-8") - else: - data = self.data[tag] - dataMaps.append( - sstruct.pack( - DATA_MAP_FORMAT, - {"tag": tag, "dataOffset": dataOffset, "dataLength": len(data)}, - ) - ) - dataBlocks.append(data) - dataOffset += len(data) - return bytesjoin([header] + dataMaps + dataBlocks) - - def toXML(self, writer, ttFont): - for tag in sorted(self.data.keys()): - if tag in ["dlng", "slng"]: - writer.begintag("text", tag=tag) - writer.newline() - writer.write(self.data[tag]) - writer.newline() - writer.endtag("text") - writer.newline() - else: - writer.begintag("hexdata", tag=tag) - writer.newline() - data = self.data[tag] - if min(data) >= 0x20 and max(data) <= 0x7E: - writer.comment("ascii: " + data.decode("ascii")) - writer.newline() - writer.dumphex(data) - writer.endtag("hexdata") - writer.newline() - - def fromXML(self, name, attrs, content, ttFont): - if name == "hexdata": - self.data[attrs["tag"]] = readHex(content) - elif name == "text" and attrs["tag"] in ["dlng", "slng"]: - self.data[attrs["tag"]] = strjoin(content).strip() - else: - raise TTLibError("can't handle '%s' element" % name) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/caching.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/caching.py deleted file mode 100644 index 2812724fc71360718b6b91bfab5f69d9a891706e..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/caching.py +++ /dev/null @@ -1,875 +0,0 @@ -from __future__ import annotations - -import collections -import functools -import logging -import math -import os -import threading -import warnings -from concurrent.futures import Future, ThreadPoolExecutor -from typing import ( - TYPE_CHECKING, - Any, - Callable, - ClassVar, - Generic, - NamedTuple, - OrderedDict, - TypeVar, -) - -if TYPE_CHECKING: - import mmap - - from typing_extensions import ParamSpec - - P = ParamSpec("P") -else: - P = TypeVar("P") - -T = TypeVar("T") - - -logger = logging.getLogger("fsspec") - -Fetcher = Callable[[int, int], bytes] # Maps (start, end) to bytes - - -class BaseCache: - """Pass-though cache: doesn't keep anything, calls every time - - Acts as base class for other cachers - - Parameters - ---------- - blocksize: int - How far to read ahead in numbers of bytes - fetcher: func - Function of the form f(start, end) which gets bytes from remote as - specified - size: int - How big this file is - """ - - name: ClassVar[str] = "none" - - def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None: - self.blocksize = blocksize - self.fetcher = fetcher - self.size = size - - def _fetch(self, start: int | None, stop: int | None) -> bytes: - if start is None: - start = 0 - if stop is None: - stop = self.size - if start >= self.size or start >= stop: - return b"" - return self.fetcher(start, stop) - - -class MMapCache(BaseCache): - """memory-mapped sparse file cache - - Opens temporary file, which is filled blocks-wise when data is requested. - Ensure there is enough disc space in the temporary location. - - This cache method might only work on posix - """ - - name = "mmap" - - def __init__( - self, - blocksize: int, - fetcher: Fetcher, - size: int, - location: str | None = None, - blocks: set[int] | None = None, - ) -> None: - super().__init__(blocksize, fetcher, size) - self.blocks = set() if blocks is None else blocks - self.location = location - self.cache = self._makefile() - - def _makefile(self) -> mmap.mmap | bytearray: - import mmap - import tempfile - - if self.size == 0: - return bytearray() - - # posix version - if self.location is None or not os.path.exists(self.location): - if self.location is None: - fd = tempfile.TemporaryFile() - self.blocks = set() - else: - fd = open(self.location, "wb+") - fd.seek(self.size - 1) - fd.write(b"1") - fd.flush() - else: - fd = open(self.location, "rb+") - - return mmap.mmap(fd.fileno(), self.size) - - def _fetch(self, start: int | None, end: int | None) -> bytes: - logger.debug(f"MMap cache fetching {start}-{end}") - if start is None: - start = 0 - if end is None: - end = self.size - if start >= self.size or start >= end: - return b"" - start_block = start // self.blocksize - end_block = end // self.blocksize - need = [i for i in range(start_block, end_block + 1) if i not in self.blocks] - while need: - # TODO: not a for loop so we can consolidate blocks later to - # make fewer fetch calls; this could be parallel - i = need.pop(0) - sstart = i * self.blocksize - send = min(sstart + self.blocksize, self.size) - logger.debug(f"MMap get block #{i} ({sstart}-{send}") - self.cache[sstart:send] = self.fetcher(sstart, send) - self.blocks.add(i) - - return self.cache[start:end] - - def __getstate__(self) -> dict[str, Any]: - state = self.__dict__.copy() - # Remove the unpicklable entries. - del state["cache"] - return state - - def __setstate__(self, state: dict[str, Any]) -> None: - # Restore instance attributes - self.__dict__.update(state) - self.cache = self._makefile() - - -class ReadAheadCache(BaseCache): - """Cache which reads only when we get beyond a block of data - - This is a much simpler version of BytesCache, and does not attempt to - fill holes in the cache or keep fragments alive. It is best suited to - many small reads in a sequential order (e.g., reading lines from a file). - """ - - name = "readahead" - - def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None: - super().__init__(blocksize, fetcher, size) - self.cache = b"" - self.start = 0 - self.end = 0 - - def _fetch(self, start: int | None, end: int | None) -> bytes: - if start is None: - start = 0 - if end is None or end > self.size: - end = self.size - if start >= self.size or start >= end: - return b"" - l = end - start - if start >= self.start and end <= self.end: - # cache hit - return self.cache[start - self.start : end - self.start] - elif self.start <= start < self.end: - # partial hit - part = self.cache[start - self.start :] - l -= len(part) - start = self.end - else: - # miss - part = b"" - end = min(self.size, end + self.blocksize) - self.cache = self.fetcher(start, end) # new block replaces old - self.start = start - self.end = self.start + len(self.cache) - return part + self.cache[:l] - - -class FirstChunkCache(BaseCache): - """Caches the first block of a file only - - This may be useful for file types where the metadata is stored in the header, - but is randomly accessed. - """ - - name = "first" - - def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None: - super().__init__(blocksize, fetcher, size) - self.cache: bytes | None = None - - def _fetch(self, start: int | None, end: int | None) -> bytes: - start = start or 0 - end = end or self.size - if start < self.blocksize: - if self.cache is None: - if end > self.blocksize: - data = self.fetcher(0, end) - self.cache = data[: self.blocksize] - return data[start:] - self.cache = self.fetcher(0, self.blocksize) - part = self.cache[start:end] - if end > self.blocksize: - part += self.fetcher(self.blocksize, end) - return part - else: - return self.fetcher(start, end) - - -class BlockCache(BaseCache): - """ - Cache holding memory as a set of blocks. - - Requests are only ever made ``blocksize`` at a time, and are - stored in an LRU cache. The least recently accessed block is - discarded when more than ``maxblocks`` are stored. - - Parameters - ---------- - blocksize : int - The number of bytes to store in each block. - Requests are only ever made for ``blocksize``, so this - should balance the overhead of making a request against - the granularity of the blocks. - fetcher : Callable - size : int - The total size of the file being cached. - maxblocks : int - The maximum number of blocks to cache for. The maximum memory - use for this cache is then ``blocksize * maxblocks``. - """ - - name = "blockcache" - - def __init__( - self, blocksize: int, fetcher: Fetcher, size: int, maxblocks: int = 32 - ) -> None: - super().__init__(blocksize, fetcher, size) - self.nblocks = math.ceil(size / blocksize) - self.maxblocks = maxblocks - self._fetch_block_cached = functools.lru_cache(maxblocks)(self._fetch_block) - - def __repr__(self) -> str: - return ( - f"" - ) - - def cache_info(self): - """ - The statistics on the block cache. - - Returns - ------- - NamedTuple - Returned directly from the LRU Cache used internally. - """ - return self._fetch_block_cached.cache_info() - - def __getstate__(self) -> dict[str, Any]: - state = self.__dict__ - del state["_fetch_block_cached"] - return state - - def __setstate__(self, state: dict[str, Any]) -> None: - self.__dict__.update(state) - self._fetch_block_cached = functools.lru_cache(state["maxblocks"])( - self._fetch_block - ) - - def _fetch(self, start: int | None, end: int | None) -> bytes: - if start is None: - start = 0 - if end is None: - end = self.size - if start >= self.size or start >= end: - return b"" - - # byte position -> block numbers - start_block_number = start // self.blocksize - end_block_number = end // self.blocksize - - # these are cached, so safe to do multiple calls for the same start and end. - for block_number in range(start_block_number, end_block_number + 1): - self._fetch_block_cached(block_number) - - return self._read_cache( - start, - end, - start_block_number=start_block_number, - end_block_number=end_block_number, - ) - - def _fetch_block(self, block_number: int) -> bytes: - """ - Fetch the block of data for `block_number`. - """ - if block_number > self.nblocks: - raise ValueError( - f"'block_number={block_number}' is greater than " - f"the number of blocks ({self.nblocks})" - ) - - start = block_number * self.blocksize - end = start + self.blocksize - logger.info("BlockCache fetching block %d", block_number) - block_contents = super()._fetch(start, end) - return block_contents - - def _read_cache( - self, start: int, end: int, start_block_number: int, end_block_number: int - ) -> bytes: - """ - Read from our block cache. - - Parameters - ---------- - start, end : int - The start and end byte positions. - start_block_number, end_block_number : int - The start and end block numbers. - """ - start_pos = start % self.blocksize - end_pos = end % self.blocksize - - if start_block_number == end_block_number: - block: bytes = self._fetch_block_cached(start_block_number) - return block[start_pos:end_pos] - - else: - # read from the initial - out = [] - out.append(self._fetch_block_cached(start_block_number)[start_pos:]) - - # intermediate blocks - # Note: it'd be nice to combine these into one big request. However - # that doesn't play nicely with our LRU cache. - for block_number in range(start_block_number + 1, end_block_number): - out.append(self._fetch_block_cached(block_number)) - - # final block - out.append(self._fetch_block_cached(end_block_number)[:end_pos]) - - return b"".join(out) - - -class BytesCache(BaseCache): - """Cache which holds data in a in-memory bytes object - - Implements read-ahead by the block size, for semi-random reads progressing - through the file. - - Parameters - ---------- - trim: bool - As we read more data, whether to discard the start of the buffer when - we are more than a blocksize ahead of it. - """ - - name: ClassVar[str] = "bytes" - - def __init__( - self, blocksize: int, fetcher: Fetcher, size: int, trim: bool = True - ) -> None: - super().__init__(blocksize, fetcher, size) - self.cache = b"" - self.start: int | None = None - self.end: int | None = None - self.trim = trim - - def _fetch(self, start: int | None, end: int | None) -> bytes: - # TODO: only set start/end after fetch, in case it fails? - # is this where retry logic might go? - if start is None: - start = 0 - if end is None: - end = self.size - if start >= self.size or start >= end: - return b"" - if ( - self.start is not None - and start >= self.start - and self.end is not None - and end < self.end - ): - # cache hit: we have all the required data - offset = start - self.start - return self.cache[offset : offset + end - start] - - if self.blocksize: - bend = min(self.size, end + self.blocksize) - else: - bend = end - - if bend == start or start > self.size: - return b"" - - if (self.start is None or start < self.start) and ( - self.end is None or end > self.end - ): - # First read, or extending both before and after - self.cache = self.fetcher(start, bend) - self.start = start - else: - assert self.start is not None - assert self.end is not None - - if start < self.start: - if self.end is None or self.end - end > self.blocksize: - self.cache = self.fetcher(start, bend) - self.start = start - else: - new = self.fetcher(start, self.start) - self.start = start - self.cache = new + self.cache - elif self.end is not None and bend > self.end: - if self.end > self.size: - pass - elif end - self.end > self.blocksize: - self.cache = self.fetcher(start, bend) - self.start = start - else: - new = self.fetcher(self.end, bend) - self.cache = self.cache + new - - self.end = self.start + len(self.cache) - offset = start - self.start - out = self.cache[offset : offset + end - start] - if self.trim: - num = (self.end - self.start) // (self.blocksize + 1) - if num > 1: - self.start += self.blocksize * num - self.cache = self.cache[self.blocksize * num :] - return out - - def __len__(self) -> int: - return len(self.cache) - - -class AllBytes(BaseCache): - """Cache entire contents of the file""" - - name: ClassVar[str] = "all" - - def __init__( - self, - blocksize: int | None = None, - fetcher: Fetcher | None = None, - size: int | None = None, - data: bytes | None = None, - ) -> None: - super().__init__(blocksize, fetcher, size) # type: ignore[arg-type] - if data is None: - data = self.fetcher(0, self.size) - self.data = data - - def _fetch(self, start: int | None, stop: int | None) -> bytes: - return self.data[start:stop] - - -class KnownPartsOfAFile(BaseCache): - """ - Cache holding known file parts. - - Parameters - ---------- - blocksize: int - How far to read ahead in numbers of bytes - fetcher: func - Function of the form f(start, end) which gets bytes from remote as - specified - size: int - How big this file is - data: dict - A dictionary mapping explicit `(start, stop)` file-offset tuples - with known bytes. - strict: bool, default True - Whether to fetch reads that go beyond a known byte-range boundary. - If `False`, any read that ends outside a known part will be zero - padded. Note that zero padding will not be used for reads that - begin outside a known byte-range. - """ - - name: ClassVar[str] = "parts" - - def __init__( - self, - blocksize: int, - fetcher: Fetcher, - size: int, - data: dict[tuple[int, int], bytes] = {}, - strict: bool = True, - **_: Any, - ): - super().__init__(blocksize, fetcher, size) - self.strict = strict - - # simple consolidation of contiguous blocks - if data: - old_offsets = sorted(data.keys()) - offsets = [old_offsets[0]] - blocks = [data.pop(old_offsets[0])] - for start, stop in old_offsets[1:]: - start0, stop0 = offsets[-1] - if start == stop0: - offsets[-1] = (start0, stop) - blocks[-1] += data.pop((start, stop)) - else: - offsets.append((start, stop)) - blocks.append(data.pop((start, stop))) - - self.data = dict(zip(offsets, blocks)) - else: - self.data = data - - def _fetch(self, start: int | None, stop: int | None) -> bytes: - if start is None: - start = 0 - if stop is None: - stop = self.size - - out = b"" - for (loc0, loc1), data in self.data.items(): - # If self.strict=False, use zero-padded data - # for reads beyond the end of a "known" buffer - if loc0 <= start < loc1: - off = start - loc0 - out = data[off : off + stop - start] - if not self.strict or loc0 <= stop <= loc1: - # The request is within a known range, or - # it begins within a known range, and we - # are allowed to pad reads beyond the - # buffer with zero - out += b"\x00" * (stop - start - len(out)) - return out - else: - # The request ends outside a known range, - # and we are being "strict" about reads - # beyond the buffer - start = loc1 - break - - # We only get here if there is a request outside the - # known parts of the file. In an ideal world, this - # should never happen - if self.fetcher is None: - # We cannot fetch the data, so raise an error - raise ValueError(f"Read is outside the known file parts: {(start, stop)}. ") - # We can fetch the data, but should warn the user - # that this may be slow - warnings.warn( - f"Read is outside the known file parts: {(start, stop)}. " - f"IO/caching performance may be poor!" - ) - logger.debug(f"KnownPartsOfAFile cache fetching {start}-{stop}") - return out + super()._fetch(start, stop) - - -class UpdatableLRU(Generic[P, T]): - """ - Custom implementation of LRU cache that allows updating keys - - Used by BackgroudBlockCache - """ - - class CacheInfo(NamedTuple): - hits: int - misses: int - maxsize: int - currsize: int - - def __init__(self, func: Callable[P, T], max_size: int = 128) -> None: - self._cache: OrderedDict[Any, T] = collections.OrderedDict() - self._func = func - self._max_size = max_size - self._hits = 0 - self._misses = 0 - self._lock = threading.Lock() - - def __call__(self, *args: P.args, **kwargs: P.kwargs) -> T: - if kwargs: - raise TypeError(f"Got unexpected keyword argument {kwargs.keys()}") - with self._lock: - if args in self._cache: - self._cache.move_to_end(args) - self._hits += 1 - return self._cache[args] - - result = self._func(*args, **kwargs) - - with self._lock: - self._cache[args] = result - self._misses += 1 - if len(self._cache) > self._max_size: - self._cache.popitem(last=False) - - return result - - def is_key_cached(self, *args: Any) -> bool: - with self._lock: - return args in self._cache - - def add_key(self, result: T, *args: Any) -> None: - with self._lock: - self._cache[args] = result - if len(self._cache) > self._max_size: - self._cache.popitem(last=False) - - def cache_info(self) -> UpdatableLRU.CacheInfo: - with self._lock: - return self.CacheInfo( - maxsize=self._max_size, - currsize=len(self._cache), - hits=self._hits, - misses=self._misses, - ) - - -class BackgroundBlockCache(BaseCache): - """ - Cache holding memory as a set of blocks with pre-loading of - the next block in the background. - - Requests are only ever made ``blocksize`` at a time, and are - stored in an LRU cache. The least recently accessed block is - discarded when more than ``maxblocks`` are stored. If the - next block is not in cache, it is loaded in a separate thread - in non-blocking way. - - Parameters - ---------- - blocksize : int - The number of bytes to store in each block. - Requests are only ever made for ``blocksize``, so this - should balance the overhead of making a request against - the granularity of the blocks. - fetcher : Callable - size : int - The total size of the file being cached. - maxblocks : int - The maximum number of blocks to cache for. The maximum memory - use for this cache is then ``blocksize * maxblocks``. - """ - - name: ClassVar[str] = "background" - - def __init__( - self, blocksize: int, fetcher: Fetcher, size: int, maxblocks: int = 32 - ) -> None: - super().__init__(blocksize, fetcher, size) - self.nblocks = math.ceil(size / blocksize) - self.maxblocks = maxblocks - self._fetch_block_cached = UpdatableLRU(self._fetch_block, maxblocks) - - self._thread_executor = ThreadPoolExecutor(max_workers=1) - self._fetch_future_block_number: int | None = None - self._fetch_future: Future[bytes] | None = None - self._fetch_future_lock = threading.Lock() - - def __repr__(self) -> str: - return ( - f"" - ) - - def cache_info(self) -> UpdatableLRU.CacheInfo: - """ - The statistics on the block cache. - - Returns - ------- - NamedTuple - Returned directly from the LRU Cache used internally. - """ - return self._fetch_block_cached.cache_info() - - def __getstate__(self) -> dict[str, Any]: - state = self.__dict__ - del state["_fetch_block_cached"] - del state["_thread_executor"] - del state["_fetch_future_block_number"] - del state["_fetch_future"] - del state["_fetch_future_lock"] - return state - - def __setstate__(self, state) -> None: - self.__dict__.update(state) - self._fetch_block_cached = UpdatableLRU(self._fetch_block, state["maxblocks"]) - self._thread_executor = ThreadPoolExecutor(max_workers=1) - self._fetch_future_block_number = None - self._fetch_future = None - self._fetch_future_lock = threading.Lock() - - def _fetch(self, start: int | None, end: int | None) -> bytes: - if start is None: - start = 0 - if end is None: - end = self.size - if start >= self.size or start >= end: - return b"" - - # byte position -> block numbers - start_block_number = start // self.blocksize - end_block_number = end // self.blocksize - - fetch_future_block_number = None - fetch_future = None - with self._fetch_future_lock: - # Background thread is running. Check we we can or must join it. - if self._fetch_future is not None: - assert self._fetch_future_block_number is not None - if self._fetch_future.done(): - logger.info("BlockCache joined background fetch without waiting.") - self._fetch_block_cached.add_key( - self._fetch_future.result(), self._fetch_future_block_number - ) - # Cleanup the fetch variables. Done with fetching the block. - self._fetch_future_block_number = None - self._fetch_future = None - else: - # Must join if we need the block for the current fetch - must_join = bool( - start_block_number - <= self._fetch_future_block_number - <= end_block_number - ) - if must_join: - # Copy to the local variables to release lock - # before waiting for result - fetch_future_block_number = self._fetch_future_block_number - fetch_future = self._fetch_future - - # Cleanup the fetch variables. Have a local copy. - self._fetch_future_block_number = None - self._fetch_future = None - - # Need to wait for the future for the current read - if fetch_future is not None: - logger.info("BlockCache waiting for background fetch.") - # Wait until result and put it in cache - self._fetch_block_cached.add_key( - fetch_future.result(), fetch_future_block_number - ) - - # these are cached, so safe to do multiple calls for the same start and end. - for block_number in range(start_block_number, end_block_number + 1): - self._fetch_block_cached(block_number) - - # fetch next block in the background if nothing is running in the background, - # the block is within file and it is not already cached - end_block_plus_1 = end_block_number + 1 - with self._fetch_future_lock: - if ( - self._fetch_future is None - and end_block_plus_1 <= self.nblocks - and not self._fetch_block_cached.is_key_cached(end_block_plus_1) - ): - self._fetch_future_block_number = end_block_plus_1 - self._fetch_future = self._thread_executor.submit( - self._fetch_block, end_block_plus_1, "async" - ) - - return self._read_cache( - start, - end, - start_block_number=start_block_number, - end_block_number=end_block_number, - ) - - def _fetch_block(self, block_number: int, log_info: str = "sync") -> bytes: - """ - Fetch the block of data for `block_number`. - """ - if block_number > self.nblocks: - raise ValueError( - f"'block_number={block_number}' is greater than " - f"the number of blocks ({self.nblocks})" - ) - - start = block_number * self.blocksize - end = start + self.blocksize - logger.info("BlockCache fetching block (%s) %d", log_info, block_number) - block_contents = super()._fetch(start, end) - return block_contents - - def _read_cache( - self, start: int, end: int, start_block_number: int, end_block_number: int - ) -> bytes: - """ - Read from our block cache. - - Parameters - ---------- - start, end : int - The start and end byte positions. - start_block_number, end_block_number : int - The start and end block numbers. - """ - start_pos = start % self.blocksize - end_pos = end % self.blocksize - - if start_block_number == end_block_number: - block = self._fetch_block_cached(start_block_number) - return block[start_pos:end_pos] - - else: - # read from the initial - out = [] - out.append(self._fetch_block_cached(start_block_number)[start_pos:]) - - # intermediate blocks - # Note: it'd be nice to combine these into one big request. However - # that doesn't play nicely with our LRU cache. - for block_number in range(start_block_number + 1, end_block_number): - out.append(self._fetch_block_cached(block_number)) - - # final block - out.append(self._fetch_block_cached(end_block_number)[:end_pos]) - - return b"".join(out) - - -caches: dict[str | None, type[BaseCache]] = { - # one custom case - None: BaseCache, -} - - -def register_cache(cls: type[BaseCache], clobber: bool = False) -> None: - """'Register' cache implementation. - - Parameters - ---------- - clobber: bool, optional - If set to True (default is False) - allow to overwrite existing - entry. - - Raises - ------ - ValueError - """ - name = cls.name - if not clobber and name in caches: - raise ValueError(f"Cache with name {name!r} is already known: {caches[name]}") - caches[name] = cls - - -for c in ( - BaseCache, - MMapCache, - BytesCache, - ReadAheadCache, - BlockCache, - FirstChunkCache, - AllBytes, - KnownPartsOfAFile, - BackgroundBlockCache, -): - register_cache(c) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/dep-e4a495ce-6ab20933.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/dep-e4a495ce-6ab20933.js deleted file mode 100644 index c0c0763e08bb38b2d62ff7532d4eb54a46831128..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/dep-e4a495ce-6ab20933.js +++ /dev/null @@ -1,951 +0,0 @@ -import { getDefaultExportFromCjs } from './index-897f432e.js'; -import require$$0$4 from 'path'; -import require$$0__default__default from 'fs'; -import { lib } from './dep-c423598f-00db75d5.js'; -import { fileURLToPath } from 'node:url'; -import { dirname } from 'node:path'; -import { createRequire } from 'node:module'; -import 'node:child_process'; -import 'net'; -import 'node:fs'; -import 'node:fs/promises'; -import 'node:util'; -import 'node:perf_hooks'; -import 'tty'; -import 'esbuild-wasm'; -import 'events'; -import 'assert'; -import 'util'; -import 'url'; -import 'http'; -import 'stream'; -import 'os'; -import 'child_process'; -import 'node:os'; -import 'node:crypto'; -import 'node:dns'; -import 'crypto'; -import 'node:buffer'; -import 'module'; -import 'node:assert'; -import 'node:process'; -import 'node:v8'; -import 'worker_threads'; -import 'node:http'; -import 'node:https'; -import 'zlib'; -import 'buffer'; -import 'https'; -import 'tls'; -import 'querystring'; -import 'node:readline'; -import 'node:zlib'; -import '../compiler.js'; -import 'fs/promises'; -import 'perf_hooks'; - -const __filename = fileURLToPath(import.meta.url); -dirname(__filename); -const require = createRequire(import.meta.url); -const __require = require; -function _mergeNamespaces(n, m) { - for (var i = 0; i < m.length; i++) { - var e = m[i]; - if (typeof e !== 'string' && !Array.isArray(e)) { for (var k in e) { - if (k !== 'default' && !(k in n)) { - n[k] = e[k]; - } - } } - } - return n; -} - -const startsWithKeywordRegexp = /^(all|not|only|print|screen)/i; - -var joinMedia$1 = function (parentMedia, childMedia) { - if (!parentMedia.length && childMedia.length) return childMedia - if (parentMedia.length && !childMedia.length) return parentMedia - if (!parentMedia.length && !childMedia.length) return [] - - const media = []; - - parentMedia.forEach(parentItem => { - const parentItemStartsWithKeyword = startsWithKeywordRegexp.test(parentItem); - - childMedia.forEach(childItem => { - const childItemStartsWithKeyword = startsWithKeywordRegexp.test(childItem); - if (parentItem !== childItem) { - if (childItemStartsWithKeyword && !parentItemStartsWithKeyword) { - media.push(`${childItem} and ${parentItem}`); - } else { - media.push(`${parentItem} and ${childItem}`); - } - } - }); - }); - - return media -}; - -var joinLayer$1 = function (parentLayer, childLayer) { - if (!parentLayer.length && childLayer.length) return childLayer - if (parentLayer.length && !childLayer.length) return parentLayer - if (!parentLayer.length && !childLayer.length) return [] - - return parentLayer.concat(childLayer) -}; - -var readCache$1 = {exports: {}}; - -var pify$2 = {exports: {}}; - -var processFn = function (fn, P, opts) { - return function () { - var that = this; - var args = new Array(arguments.length); - - for (var i = 0; i < arguments.length; i++) { - args[i] = arguments[i]; - } - - return new P(function (resolve, reject) { - args.push(function (err, result) { - if (err) { - reject(err); - } else if (opts.multiArgs) { - var results = new Array(arguments.length - 1); - - for (var i = 1; i < arguments.length; i++) { - results[i - 1] = arguments[i]; - } - - resolve(results); - } else { - resolve(result); - } - }); - - fn.apply(that, args); - }); - }; -}; - -var pify$1 = pify$2.exports = function (obj, P, opts) { - if (typeof P !== 'function') { - opts = P; - P = Promise; - } - - opts = opts || {}; - opts.exclude = opts.exclude || [/.+Sync$/]; - - var filter = function (key) { - var match = function (pattern) { - return typeof pattern === 'string' ? key === pattern : pattern.test(key); - }; - - return opts.include ? opts.include.some(match) : !opts.exclude.some(match); - }; - - var ret = typeof obj === 'function' ? function () { - if (opts.excludeMain) { - return obj.apply(this, arguments); - } - - return processFn(obj, P, opts).apply(this, arguments); - } : {}; - - return Object.keys(obj).reduce(function (ret, key) { - var x = obj[key]; - - ret[key] = typeof x === 'function' && filter(key) ? processFn(x, P, opts) : x; - - return ret; - }, ret); -}; - -pify$1.all = pify$1; - -var pifyExports = pify$2.exports; - -var fs = require$$0__default__default; -var path$2 = require$$0$4; -var pify = pifyExports; - -var stat = pify(fs.stat); -var readFile = pify(fs.readFile); -var resolve = path$2.resolve; - -var cache = Object.create(null); - -function convert(content, encoding) { - if (Buffer.isEncoding(encoding)) { - return content.toString(encoding); - } - return content; -} - -readCache$1.exports = function (path, encoding) { - path = resolve(path); - - return stat(path).then(function (stats) { - var item = cache[path]; - - if (item && item.mtime.getTime() === stats.mtime.getTime()) { - return convert(item.content, encoding); - } - - return readFile(path).then(function (data) { - cache[path] = { - mtime: stats.mtime, - content: data - }; - - return convert(data, encoding); - }); - }).catch(function (err) { - cache[path] = null; - return Promise.reject(err); - }); -}; - -readCache$1.exports.sync = function (path, encoding) { - path = resolve(path); - - try { - var stats = fs.statSync(path); - var item = cache[path]; - - if (item && item.mtime.getTime() === stats.mtime.getTime()) { - return convert(item.content, encoding); - } - - var data = fs.readFileSync(path); - - cache[path] = { - mtime: stats.mtime, - content: data - }; - - return convert(data, encoding); - } catch (err) { - cache[path] = null; - throw err; - } - -}; - -readCache$1.exports.get = function (path, encoding) { - path = resolve(path); - if (cache[path]) { - return convert(cache[path].content, encoding); - } - return null; -}; - -readCache$1.exports.clear = function () { - cache = Object.create(null); -}; - -var readCacheExports = readCache$1.exports; - -const dataURLRegexp = /^data:text\/css;base64,/i; - -function isValid(url) { - return dataURLRegexp.test(url) -} - -function contents(url) { - // "data:text/css;base64,".length === 21 - return Buffer.from(url.slice(21), "base64").toString() -} - -var dataUrl = { - isValid, - contents, -}; - -const readCache = readCacheExports; -const dataURL$1 = dataUrl; - -var loadContent$1 = filename => { - if (dataURL$1.isValid(filename)) { - return dataURL$1.contents(filename) - } - - return readCache(filename, "utf-8") -}; - -// builtin tooling -const path$1 = require$$0$4; - -// placeholder tooling -let sugarss; - -var processContent$1 = function processContent( - result, - content, - filename, - options, - postcss -) { - const { plugins } = options; - const ext = path$1.extname(filename); - - const parserList = []; - - // SugarSS support: - if (ext === ".sss") { - if (!sugarss) { - try { - sugarss = __require('sugarss'); - } catch {} // Ignore - } - if (sugarss) - return runPostcss(postcss, content, filename, plugins, [sugarss]) - } - - // Syntax support: - if (result.opts.syntax?.parse) { - parserList.push(result.opts.syntax.parse); - } - - // Parser support: - if (result.opts.parser) parserList.push(result.opts.parser); - // Try the default as a last resort: - parserList.push(null); - - return runPostcss(postcss, content, filename, plugins, parserList) -}; - -function runPostcss(postcss, content, filename, plugins, parsers, index) { - if (!index) index = 0; - return postcss(plugins) - .process(content, { - from: filename, - parser: parsers[index], - }) - .catch(err => { - // If there's an error, try the next parser - index++; - // If there are no parsers left, throw it - if (index === parsers.length) throw err - return runPostcss(postcss, content, filename, plugins, parsers, index) - }) -} - -// external tooling -const valueParser = lib; - -// extended tooling -const { stringify } = valueParser; - -function split(params, start) { - const list = []; - const last = params.reduce((item, node, index) => { - if (index < start) return "" - if (node.type === "div" && node.value === ",") { - list.push(item); - return "" - } - return item + stringify(node) - }, ""); - list.push(last); - return list -} - -var parseStatements$1 = function (result, styles) { - const statements = []; - let nodes = []; - - styles.each(node => { - let stmt; - if (node.type === "atrule") { - if (node.name === "import") stmt = parseImport(result, node); - else if (node.name === "media") stmt = parseMedia(result, node); - else if (node.name === "charset") stmt = parseCharset(result, node); - } - - if (stmt) { - if (nodes.length) { - statements.push({ - type: "nodes", - nodes, - media: [], - layer: [], - }); - nodes = []; - } - statements.push(stmt); - } else nodes.push(node); - }); - - if (nodes.length) { - statements.push({ - type: "nodes", - nodes, - media: [], - layer: [], - }); - } - - return statements -}; - -function parseMedia(result, atRule) { - const params = valueParser(atRule.params).nodes; - return { - type: "media", - node: atRule, - media: split(params, 0), - layer: [], - } -} - -function parseCharset(result, atRule) { - if (atRule.prev()) { - return result.warn("@charset must precede all other statements", { - node: atRule, - }) - } - return { - type: "charset", - node: atRule, - media: [], - layer: [], - } -} - -function parseImport(result, atRule) { - let prev = atRule.prev(); - if (prev) { - do { - if ( - prev.type !== "comment" && - (prev.type !== "atrule" || - (prev.name !== "import" && - prev.name !== "charset" && - !(prev.name === "layer" && !prev.nodes))) - ) { - return result.warn( - "@import must precede all other statements (besides @charset or empty @layer)", - { node: atRule } - ) - } - prev = prev.prev(); - } while (prev) - } - - if (atRule.nodes) { - return result.warn( - "It looks like you didn't end your @import statement correctly. " + - "Child nodes are attached to it.", - { node: atRule } - ) - } - - const params = valueParser(atRule.params).nodes; - const stmt = { - type: "import", - node: atRule, - media: [], - layer: [], - }; - - // prettier-ignore - if ( - !params.length || - ( - params[0].type !== "string" || - !params[0].value - ) && - ( - params[0].type !== "function" || - params[0].value !== "url" || - !params[0].nodes.length || - !params[0].nodes[0].value - ) - ) { - return result.warn(`Unable to find uri in '${ atRule.toString() }'`, { - node: atRule, - }) - } - - if (params[0].type === "string") stmt.uri = params[0].value; - else stmt.uri = params[0].nodes[0].value; - stmt.fullUri = stringify(params[0]); - - let remainder = params; - if (remainder.length > 2) { - if ( - (remainder[2].type === "word" || remainder[2].type === "function") && - remainder[2].value === "layer" - ) { - if (remainder[1].type !== "space") { - return result.warn("Invalid import layer statement", { node: atRule }) - } - - if (remainder[2].nodes) { - stmt.layer = [stringify(remainder[2].nodes)]; - } else { - stmt.layer = [""]; - } - remainder = remainder.slice(2); - } - } - - if (remainder.length > 2) { - if (remainder[1].type !== "space") { - return result.warn("Invalid import media statement", { node: atRule }) - } - - stmt.media = split(remainder, 2); - } - - return stmt -} - -var assignLayerNames$1 = function (layer, node, state, options) { - layer.forEach((layerPart, i) => { - if (layerPart.trim() === "") { - if (options.nameLayer) { - layer[i] = options - .nameLayer(state.anonymousLayerCounter++, state.rootFilename) - .toString(); - } else { - throw node.error( - `When using anonymous layers in @import you must also set the "nameLayer" plugin option` - ) - } - } - }); -}; - -// builtin tooling -const path = require$$0$4; - -// internal tooling -const joinMedia = joinMedia$1; -const joinLayer = joinLayer$1; -const resolveId = (id) => id; -const loadContent = loadContent$1; -const processContent = processContent$1; -const parseStatements = parseStatements$1; -const assignLayerNames = assignLayerNames$1; -const dataURL = dataUrl; - -function AtImport(options) { - options = { - root: process.cwd(), - path: [], - skipDuplicates: true, - resolve: resolveId, - load: loadContent, - plugins: [], - addModulesDirectories: [], - nameLayer: null, - ...options, - }; - - options.root = path.resolve(options.root); - - // convert string to an array of a single element - if (typeof options.path === "string") options.path = [options.path]; - - if (!Array.isArray(options.path)) options.path = []; - - options.path = options.path.map(p => path.resolve(options.root, p)); - - return { - postcssPlugin: "postcss-import", - Once(styles, { result, atRule, postcss }) { - const state = { - importedFiles: {}, - hashFiles: {}, - rootFilename: null, - anonymousLayerCounter: 0, - }; - - if (styles.source?.input?.file) { - state.rootFilename = styles.source.input.file; - state.importedFiles[styles.source.input.file] = {}; - } - - if (options.plugins && !Array.isArray(options.plugins)) { - throw new Error("plugins option must be an array") - } - - if (options.nameLayer && typeof options.nameLayer !== "function") { - throw new Error("nameLayer option must be a function") - } - - return parseStyles(result, styles, options, state, [], []).then( - bundle => { - applyRaws(bundle); - applyMedia(bundle); - applyStyles(bundle, styles); - } - ) - - function applyRaws(bundle) { - bundle.forEach((stmt, index) => { - if (index === 0) return - - if (stmt.parent) { - const { before } = stmt.parent.node.raws; - if (stmt.type === "nodes") stmt.nodes[0].raws.before = before; - else stmt.node.raws.before = before; - } else if (stmt.type === "nodes") { - stmt.nodes[0].raws.before = stmt.nodes[0].raws.before || "\n"; - } - }); - } - - function applyMedia(bundle) { - bundle.forEach(stmt => { - if ( - (!stmt.media.length && !stmt.layer.length) || - stmt.type === "charset" - ) { - return - } - - if (stmt.layer.length > 1) { - assignLayerNames(stmt.layer, stmt.node, state, options); - } - - if (stmt.type === "import") { - const parts = [stmt.fullUri]; - - const media = stmt.media.join(", "); - - if (stmt.layer.length) { - const layerName = stmt.layer.join("."); - - let layerParams = "layer"; - if (layerName) { - layerParams = `layer(${layerName})`; - } - - parts.push(layerParams); - } - - if (media) { - parts.push(media); - } - - stmt.node.params = parts.join(" "); - } else if (stmt.type === "media") { - if (stmt.layer.length) { - const layerNode = atRule({ - name: "layer", - params: stmt.layer.join("."), - source: stmt.node.source, - }); - - if (stmt.parentMedia?.length) { - const mediaNode = atRule({ - name: "media", - params: stmt.parentMedia.join(", "), - source: stmt.node.source, - }); - - mediaNode.append(layerNode); - layerNode.append(stmt.node); - stmt.node = mediaNode; - } else { - layerNode.append(stmt.node); - stmt.node = layerNode; - } - } else { - stmt.node.params = stmt.media.join(", "); - } - } else { - const { nodes } = stmt; - const { parent } = nodes[0]; - - let outerAtRule; - let innerAtRule; - if (stmt.media.length && stmt.layer.length) { - const mediaNode = atRule({ - name: "media", - params: stmt.media.join(", "), - source: parent.source, - }); - - const layerNode = atRule({ - name: "layer", - params: stmt.layer.join("."), - source: parent.source, - }); - - mediaNode.append(layerNode); - innerAtRule = layerNode; - outerAtRule = mediaNode; - } else if (stmt.media.length) { - const mediaNode = atRule({ - name: "media", - params: stmt.media.join(", "), - source: parent.source, - }); - - innerAtRule = mediaNode; - outerAtRule = mediaNode; - } else if (stmt.layer.length) { - const layerNode = atRule({ - name: "layer", - params: stmt.layer.join("."), - source: parent.source, - }); - - innerAtRule = layerNode; - outerAtRule = layerNode; - } - - parent.insertBefore(nodes[0], outerAtRule); - - // remove nodes - nodes.forEach(node => { - node.parent = undefined; - }); - - // better output - nodes[0].raws.before = nodes[0].raws.before || "\n"; - - // wrap new rules with media query and/or layer at rule - innerAtRule.append(nodes); - - stmt.type = "media"; - stmt.node = outerAtRule; - delete stmt.nodes; - } - }); - } - - function applyStyles(bundle, styles) { - styles.nodes = []; - - // Strip additional statements. - bundle.forEach(stmt => { - if (["charset", "import", "media"].includes(stmt.type)) { - stmt.node.parent = undefined; - styles.append(stmt.node); - } else if (stmt.type === "nodes") { - stmt.nodes.forEach(node => { - node.parent = undefined; - styles.append(node); - }); - } - }); - } - - function parseStyles(result, styles, options, state, media, layer) { - const statements = parseStatements(result, styles); - - return Promise.resolve(statements) - .then(stmts => { - // process each statement in series - return stmts.reduce((promise, stmt) => { - return promise.then(() => { - stmt.media = joinMedia(media, stmt.media || []); - stmt.parentMedia = media; - stmt.layer = joinLayer(layer, stmt.layer || []); - - // skip protocol base uri (protocol://url) or protocol-relative - if ( - stmt.type !== "import" || - /^(?:[a-z]+:)?\/\//i.test(stmt.uri) - ) { - return - } - - if (options.filter && !options.filter(stmt.uri)) { - // rejected by filter - return - } - - return resolveImportId(result, stmt, options, state) - }) - }, Promise.resolve()) - }) - .then(() => { - let charset; - const imports = []; - const bundle = []; - - function handleCharset(stmt) { - if (!charset) charset = stmt; - // charsets aren't case-sensitive, so convert to lower case to compare - else if ( - stmt.node.params.toLowerCase() !== - charset.node.params.toLowerCase() - ) { - throw new Error( - `Incompatable @charset statements: - ${stmt.node.params} specified in ${stmt.node.source.input.file} - ${charset.node.params} specified in ${charset.node.source.input.file}` - ) - } - } - - // squash statements and their children - statements.forEach(stmt => { - if (stmt.type === "charset") handleCharset(stmt); - else if (stmt.type === "import") { - if (stmt.children) { - stmt.children.forEach((child, index) => { - if (child.type === "import") imports.push(child); - else if (child.type === "charset") handleCharset(child); - else bundle.push(child); - // For better output - if (index === 0) child.parent = stmt; - }); - } else imports.push(stmt); - } else if (stmt.type === "media" || stmt.type === "nodes") { - bundle.push(stmt); - } - }); - - return charset - ? [charset, ...imports.concat(bundle)] - : imports.concat(bundle) - }) - } - - function resolveImportId(result, stmt, options, state) { - if (dataURL.isValid(stmt.uri)) { - return loadImportContent(result, stmt, stmt.uri, options, state).then( - result => { - stmt.children = result; - } - ) - } - - const atRule = stmt.node; - let sourceFile; - if (atRule.source?.input?.file) { - sourceFile = atRule.source.input.file; - } - const base = sourceFile - ? path.dirname(atRule.source.input.file) - : options.root; - - return Promise.resolve(options.resolve(stmt.uri, base, options)) - .then(paths => { - if (!Array.isArray(paths)) paths = [paths]; - // Ensure that each path is absolute: - return Promise.all( - paths.map(file => { - return !path.isAbsolute(file) - ? resolveId(file) - : file - }) - ) - }) - .then(resolved => { - // Add dependency messages: - resolved.forEach(file => { - result.messages.push({ - type: "dependency", - plugin: "postcss-import", - file, - parent: sourceFile, - }); - }); - - return Promise.all( - resolved.map(file => { - return loadImportContent(result, stmt, file, options, state) - }) - ) - }) - .then(result => { - // Merge loaded statements - stmt.children = result.reduce((result, statements) => { - return statements ? result.concat(statements) : result - }, []); - }) - } - - function loadImportContent(result, stmt, filename, options, state) { - const atRule = stmt.node; - const { media, layer } = stmt; - - assignLayerNames(layer, atRule, state, options); - - if (options.skipDuplicates) { - // skip files already imported at the same scope - if (state.importedFiles[filename]?.[media]?.[layer]) { - return - } - - // save imported files to skip them next time - if (!state.importedFiles[filename]) { - state.importedFiles[filename] = {}; - } - if (!state.importedFiles[filename][media]) { - state.importedFiles[filename][media] = {}; - } - state.importedFiles[filename][media][layer] = true; - } - - return Promise.resolve(options.load(filename, options)).then( - content => { - if (content.trim() === "") { - result.warn(`${filename} is empty`, { node: atRule }); - return - } - - // skip previous imported files not containing @import rules - if (state.hashFiles[content]?.[media]?.[layer]) { - return - } - - return processContent( - result, - content, - filename, - options, - postcss - ).then(importedResult => { - const styles = importedResult.root; - result.messages = result.messages.concat(importedResult.messages); - - if (options.skipDuplicates) { - const hasImport = styles.some(child => { - return child.type === "atrule" && child.name === "import" - }); - if (!hasImport) { - // save hash files to skip them next time - if (!state.hashFiles[content]) { - state.hashFiles[content] = {}; - } - if (!state.hashFiles[content][media]) { - state.hashFiles[content][media] = {}; - } - state.hashFiles[content][media][layer] = true; - } - } - - // recursion: import @import from imported file - return parseStyles(result, styles, options, state, media, layer) - }) - } - ) - } - }, - } -} - -AtImport.postcss = true; - -var postcssImport = AtImport; - -var index = /*@__PURE__*/getDefaultExportFromCjs(postcssImport); - -var index$1 = /*#__PURE__*/_mergeNamespaces({ - __proto__: null, - default: index -}, [postcssImport]); - -export { index$1 as i }; diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Empty-eeaba2d1.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Empty-eeaba2d1.js deleted file mode 100644 index 59cf06259c24b00299602e7465c89db2518b4536..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Empty-eeaba2d1.js +++ /dev/null @@ -1,2 +0,0 @@ -import"./Button-8eeccca1.js";const{SvelteComponent:h,append:b,attr:r,binding_callbacks:v,create_slot:z,detach:E,element:g,get_all_dirty_from_scope:C,get_slot_changes:w,init:B,insert:R,safe_not_equal:k,toggle_class:_,transition_in:q,transition_out:S,update_slot_base:j}=window.__gradio__svelte__internal;function y(n){let e,o,i;const u=n[5].default,l=z(u,n,n[4],null);return{c(){e=g("div"),o=g("div"),l&&l.c(),r(o,"class","icon svelte-1oiin9d"),r(e,"class","empty svelte-1oiin9d"),r(e,"aria-label","Empty value"),_(e,"small",n[0]==="small"),_(e,"large",n[0]==="large"),_(e,"unpadded_box",n[1]),_(e,"small_parent",n[3])},m(t,s){R(t,e,s),b(e,o),l&&l.m(o,null),n[6](e),i=!0},p(t,[s]){l&&l.p&&(!i||s&16)&&j(l,u,t,t[4],i?w(u,t[4],s,null):C(t[4]),null),(!i||s&1)&&_(e,"small",t[0]==="small"),(!i||s&1)&&_(e,"large",t[0]==="large"),(!i||s&2)&&_(e,"unpadded_box",t[1]),(!i||s&8)&&_(e,"small_parent",t[3])},i(t){i||(q(l,t),i=!0)},o(t){S(l,t),i=!1},d(t){t&&E(e),l&&l.d(t),n[6](null)}}}function A(n,e,o){let i,{$$slots:u={},$$scope:l}=e,{size:t="small"}=e,{unpadded_box:s=!1}=e,d;function m(a){if(!a)return!1;const{height:f}=a.getBoundingClientRect(),{height:c}=a.parentElement?.getBoundingClientRect()||{height:f};return f>c+2}function p(a){v[a?"unshift":"push"](()=>{d=a,o(2,d)})}return n.$$set=a=>{"size"in a&&o(0,t=a.size),"unpadded_box"in a&&o(1,s=a.unpadded_box),"$$scope"in a&&o(4,l=a.$$scope)},n.$$.update=()=>{n.$$.dirty&4&&o(3,i=m(d))},[t,s,d,i,l,u,p]}class F extends h{constructor(e){super(),B(this,e,A,y,k,{size:0,unpadded_box:1})}}export{F as E}; -//# sourceMappingURL=Empty-eeaba2d1.js.map diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/linalg/linalg.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/linalg/linalg.py deleted file mode 100644 index b838b9397024c028c1459e2e769e12d2fa767d88..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/linalg/linalg.py +++ /dev/null @@ -1,2836 +0,0 @@ -"""Lite version of scipy.linalg. - -Notes ------ -This module is a lite version of the linalg.py module in SciPy which -contains high-level Python interface to the LAPACK library. The lite -version only accesses the following LAPACK functions: dgesv, zgesv, -dgeev, zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetrf, -zgetrf, dpotrf, zpotrf, dgeqrf, zgeqrf, zungqr, dorgqr. -""" - -__all__ = ['matrix_power', 'solve', 'tensorsolve', 'tensorinv', 'inv', - 'cholesky', 'eigvals', 'eigvalsh', 'pinv', 'slogdet', 'det', - 'svd', 'eig', 'eigh', 'lstsq', 'norm', 'qr', 'cond', 'matrix_rank', - 'LinAlgError', 'multi_dot'] - -import functools -import operator -import warnings -from typing import NamedTuple, Any - -from .._utils import set_module -from numpy.core import ( - array, asarray, zeros, empty, empty_like, intc, single, double, - csingle, cdouble, inexact, complexfloating, newaxis, all, Inf, dot, - add, multiply, sqrt, sum, isfinite, - finfo, errstate, geterrobj, moveaxis, amin, amax, prod, abs, - atleast_2d, intp, asanyarray, object_, matmul, - swapaxes, divide, count_nonzero, isnan, sign, argsort, sort, - reciprocal -) -from numpy.core.multiarray import normalize_axis_index -from numpy.core import overrides -from numpy.lib.twodim_base import triu, eye -from numpy.linalg import _umath_linalg - -from numpy._typing import NDArray - -class EigResult(NamedTuple): - eigenvalues: NDArray[Any] - eigenvectors: NDArray[Any] - -class EighResult(NamedTuple): - eigenvalues: NDArray[Any] - eigenvectors: NDArray[Any] - -class QRResult(NamedTuple): - Q: NDArray[Any] - R: NDArray[Any] - -class SlogdetResult(NamedTuple): - sign: NDArray[Any] - logabsdet: NDArray[Any] - -class SVDResult(NamedTuple): - U: NDArray[Any] - S: NDArray[Any] - Vh: NDArray[Any] - -array_function_dispatch = functools.partial( - overrides.array_function_dispatch, module='numpy.linalg') - - -fortran_int = intc - - -@set_module('numpy.linalg') -class LinAlgError(ValueError): - """ - Generic Python-exception-derived object raised by linalg functions. - - General purpose exception class, derived from Python's ValueError - class, programmatically raised in linalg functions when a Linear - Algebra-related condition would prevent further correct execution of the - function. - - Parameters - ---------- - None - - Examples - -------- - >>> from numpy import linalg as LA - >>> LA.inv(np.zeros((2,2))) - Traceback (most recent call last): - File "", line 1, in - File "...linalg.py", line 350, - in inv return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) - File "...linalg.py", line 249, - in solve - raise LinAlgError('Singular matrix') - numpy.linalg.LinAlgError: Singular matrix - - """ - - -def _determine_error_states(): - errobj = geterrobj() - bufsize = errobj[0] - - with errstate(invalid='call', over='ignore', - divide='ignore', under='ignore'): - invalid_call_errmask = geterrobj()[1] - - return [bufsize, invalid_call_errmask, None] - -# Dealing with errors in _umath_linalg -_linalg_error_extobj = _determine_error_states() -del _determine_error_states - -def _raise_linalgerror_singular(err, flag): - raise LinAlgError("Singular matrix") - -def _raise_linalgerror_nonposdef(err, flag): - raise LinAlgError("Matrix is not positive definite") - -def _raise_linalgerror_eigenvalues_nonconvergence(err, flag): - raise LinAlgError("Eigenvalues did not converge") - -def _raise_linalgerror_svd_nonconvergence(err, flag): - raise LinAlgError("SVD did not converge") - -def _raise_linalgerror_lstsq(err, flag): - raise LinAlgError("SVD did not converge in Linear Least Squares") - -def _raise_linalgerror_qr(err, flag): - raise LinAlgError("Incorrect argument found while performing " - "QR factorization") - -def get_linalg_error_extobj(callback): - extobj = list(_linalg_error_extobj) # make a copy - extobj[2] = callback - return extobj - -def _makearray(a): - new = asarray(a) - wrap = getattr(a, "__array_prepare__", new.__array_wrap__) - return new, wrap - -def isComplexType(t): - return issubclass(t, complexfloating) - -_real_types_map = {single : single, - double : double, - csingle : single, - cdouble : double} - -_complex_types_map = {single : csingle, - double : cdouble, - csingle : csingle, - cdouble : cdouble} - -def _realType(t, default=double): - return _real_types_map.get(t, default) - -def _complexType(t, default=cdouble): - return _complex_types_map.get(t, default) - -def _commonType(*arrays): - # in lite version, use higher precision (always double or cdouble) - result_type = single - is_complex = False - for a in arrays: - type_ = a.dtype.type - if issubclass(type_, inexact): - if isComplexType(type_): - is_complex = True - rt = _realType(type_, default=None) - if rt is double: - result_type = double - elif rt is None: - # unsupported inexact scalar - raise TypeError("array type %s is unsupported in linalg" % - (a.dtype.name,)) - else: - result_type = double - if is_complex: - result_type = _complex_types_map[result_type] - return cdouble, result_type - else: - return double, result_type - - -def _to_native_byte_order(*arrays): - ret = [] - for arr in arrays: - if arr.dtype.byteorder not in ('=', '|'): - ret.append(asarray(arr, dtype=arr.dtype.newbyteorder('='))) - else: - ret.append(arr) - if len(ret) == 1: - return ret[0] - else: - return ret - - -def _assert_2d(*arrays): - for a in arrays: - if a.ndim != 2: - raise LinAlgError('%d-dimensional array given. Array must be ' - 'two-dimensional' % a.ndim) - -def _assert_stacked_2d(*arrays): - for a in arrays: - if a.ndim < 2: - raise LinAlgError('%d-dimensional array given. Array must be ' - 'at least two-dimensional' % a.ndim) - -def _assert_stacked_square(*arrays): - for a in arrays: - m, n = a.shape[-2:] - if m != n: - raise LinAlgError('Last 2 dimensions of the array must be square') - -def _assert_finite(*arrays): - for a in arrays: - if not isfinite(a).all(): - raise LinAlgError("Array must not contain infs or NaNs") - -def _is_empty_2d(arr): - # check size first for efficiency - return arr.size == 0 and prod(arr.shape[-2:]) == 0 - - -def transpose(a): - """ - Transpose each matrix in a stack of matrices. - - Unlike np.transpose, this only swaps the last two axes, rather than all of - them - - Parameters - ---------- - a : (...,M,N) array_like - - Returns - ------- - aT : (...,N,M) ndarray - """ - return swapaxes(a, -1, -2) - -# Linear equations - -def _tensorsolve_dispatcher(a, b, axes=None): - return (a, b) - - -@array_function_dispatch(_tensorsolve_dispatcher) -def tensorsolve(a, b, axes=None): - """ - Solve the tensor equation ``a x = b`` for x. - - It is assumed that all indices of `x` are summed over in the product, - together with the rightmost indices of `a`, as is done in, for example, - ``tensordot(a, x, axes=x.ndim)``. - - Parameters - ---------- - a : array_like - Coefficient tensor, of shape ``b.shape + Q``. `Q`, a tuple, equals - the shape of that sub-tensor of `a` consisting of the appropriate - number of its rightmost indices, and must be such that - ``prod(Q) == prod(b.shape)`` (in which sense `a` is said to be - 'square'). - b : array_like - Right-hand tensor, which can be of any shape. - axes : tuple of ints, optional - Axes in `a` to reorder to the right, before inversion. - If None (default), no reordering is done. - - Returns - ------- - x : ndarray, shape Q - - Raises - ------ - LinAlgError - If `a` is singular or not 'square' (in the above sense). - - See Also - -------- - numpy.tensordot, tensorinv, numpy.einsum - - Examples - -------- - >>> a = np.eye(2*3*4) - >>> a.shape = (2*3, 4, 2, 3, 4) - >>> b = np.random.randn(2*3, 4) - >>> x = np.linalg.tensorsolve(a, b) - >>> x.shape - (2, 3, 4) - >>> np.allclose(np.tensordot(a, x, axes=3), b) - True - - """ - a, wrap = _makearray(a) - b = asarray(b) - an = a.ndim - - if axes is not None: - allaxes = list(range(0, an)) - for k in axes: - allaxes.remove(k) - allaxes.insert(an, k) - a = a.transpose(allaxes) - - oldshape = a.shape[-(an-b.ndim):] - prod = 1 - for k in oldshape: - prod *= k - - if a.size != prod ** 2: - raise LinAlgError( - "Input arrays must satisfy the requirement \ - prod(a.shape[b.ndim:]) == prod(a.shape[:b.ndim])" - ) - - a = a.reshape(prod, prod) - b = b.ravel() - res = wrap(solve(a, b)) - res.shape = oldshape - return res - - -def _solve_dispatcher(a, b): - return (a, b) - - -@array_function_dispatch(_solve_dispatcher) -def solve(a, b): - """ - Solve a linear matrix equation, or system of linear scalar equations. - - Computes the "exact" solution, `x`, of the well-determined, i.e., full - rank, linear matrix equation `ax = b`. - - Parameters - ---------- - a : (..., M, M) array_like - Coefficient matrix. - b : {(..., M,), (..., M, K)}, array_like - Ordinate or "dependent variable" values. - - Returns - ------- - x : {(..., M,), (..., M, K)} ndarray - Solution to the system a x = b. Returned shape is identical to `b`. - - Raises - ------ - LinAlgError - If `a` is singular or not square. - - See Also - -------- - scipy.linalg.solve : Similar function in SciPy. - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - The solutions are computed using LAPACK routine ``_gesv``. - - `a` must be square and of full-rank, i.e., all rows (or, equivalently, - columns) must be linearly independent; if either is not true, use - `lstsq` for the least-squares best "solution" of the - system/equation. - - References - ---------- - .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, - FL, Academic Press, Inc., 1980, pg. 22. - - Examples - -------- - Solve the system of equations ``x0 + 2 * x1 = 1`` and ``3 * x0 + 5 * x1 = 2``: - - >>> a = np.array([[1, 2], [3, 5]]) - >>> b = np.array([1, 2]) - >>> x = np.linalg.solve(a, b) - >>> x - array([-1., 1.]) - - Check that the solution is correct: - - >>> np.allclose(np.dot(a, x), b) - True - - """ - a, _ = _makearray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - b, wrap = _makearray(b) - t, result_t = _commonType(a, b) - - # We use the b = (..., M,) logic, only if the number of extra dimensions - # match exactly - if b.ndim == a.ndim - 1: - gufunc = _umath_linalg.solve1 - else: - gufunc = _umath_linalg.solve - - signature = 'DD->D' if isComplexType(t) else 'dd->d' - extobj = get_linalg_error_extobj(_raise_linalgerror_singular) - r = gufunc(a, b, signature=signature, extobj=extobj) - - return wrap(r.astype(result_t, copy=False)) - - -def _tensorinv_dispatcher(a, ind=None): - return (a,) - - -@array_function_dispatch(_tensorinv_dispatcher) -def tensorinv(a, ind=2): - """ - Compute the 'inverse' of an N-dimensional array. - - The result is an inverse for `a` relative to the tensordot operation - ``tensordot(a, b, ind)``, i. e., up to floating-point accuracy, - ``tensordot(tensorinv(a), a, ind)`` is the "identity" tensor for the - tensordot operation. - - Parameters - ---------- - a : array_like - Tensor to 'invert'. Its shape must be 'square', i. e., - ``prod(a.shape[:ind]) == prod(a.shape[ind:])``. - ind : int, optional - Number of first indices that are involved in the inverse sum. - Must be a positive integer, default is 2. - - Returns - ------- - b : ndarray - `a`'s tensordot inverse, shape ``a.shape[ind:] + a.shape[:ind]``. - - Raises - ------ - LinAlgError - If `a` is singular or not 'square' (in the above sense). - - See Also - -------- - numpy.tensordot, tensorsolve - - Examples - -------- - >>> a = np.eye(4*6) - >>> a.shape = (4, 6, 8, 3) - >>> ainv = np.linalg.tensorinv(a, ind=2) - >>> ainv.shape - (8, 3, 4, 6) - >>> b = np.random.randn(4, 6) - >>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b)) - True - - >>> a = np.eye(4*6) - >>> a.shape = (24, 8, 3) - >>> ainv = np.linalg.tensorinv(a, ind=1) - >>> ainv.shape - (8, 3, 24) - >>> b = np.random.randn(24) - >>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b)) - True - - """ - a = asarray(a) - oldshape = a.shape - prod = 1 - if ind > 0: - invshape = oldshape[ind:] + oldshape[:ind] - for k in oldshape[ind:]: - prod *= k - else: - raise ValueError("Invalid ind argument.") - a = a.reshape(prod, -1) - ia = inv(a) - return ia.reshape(*invshape) - - -# Matrix inversion - -def _unary_dispatcher(a): - return (a,) - - -@array_function_dispatch(_unary_dispatcher) -def inv(a): - """ - Compute the (multiplicative) inverse of a matrix. - - Given a square matrix `a`, return the matrix `ainv` satisfying - ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``. - - Parameters - ---------- - a : (..., M, M) array_like - Matrix to be inverted. - - Returns - ------- - ainv : (..., M, M) ndarray or matrix - (Multiplicative) inverse of the matrix `a`. - - Raises - ------ - LinAlgError - If `a` is not square or inversion fails. - - See Also - -------- - scipy.linalg.inv : Similar function in SciPy. - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - Examples - -------- - >>> from numpy.linalg import inv - >>> a = np.array([[1., 2.], [3., 4.]]) - >>> ainv = inv(a) - >>> np.allclose(np.dot(a, ainv), np.eye(2)) - True - >>> np.allclose(np.dot(ainv, a), np.eye(2)) - True - - If a is a matrix object, then the return value is a matrix as well: - - >>> ainv = inv(np.matrix(a)) - >>> ainv - matrix([[-2. , 1. ], - [ 1.5, -0.5]]) - - Inverses of several matrices can be computed at once: - - >>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]]) - >>> inv(a) - array([[[-2. , 1. ], - [ 1.5 , -0.5 ]], - [[-1.25, 0.75], - [ 0.75, -0.25]]]) - - """ - a, wrap = _makearray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - t, result_t = _commonType(a) - - signature = 'D->D' if isComplexType(t) else 'd->d' - extobj = get_linalg_error_extobj(_raise_linalgerror_singular) - ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj) - return wrap(ainv.astype(result_t, copy=False)) - - -def _matrix_power_dispatcher(a, n): - return (a,) - - -@array_function_dispatch(_matrix_power_dispatcher) -def matrix_power(a, n): - """ - Raise a square matrix to the (integer) power `n`. - - For positive integers `n`, the power is computed by repeated matrix - squarings and matrix multiplications. If ``n == 0``, the identity matrix - of the same shape as M is returned. If ``n < 0``, the inverse - is computed and then raised to the ``abs(n)``. - - .. note:: Stacks of object matrices are not currently supported. - - Parameters - ---------- - a : (..., M, M) array_like - Matrix to be "powered". - n : int - The exponent can be any integer or long integer, positive, - negative, or zero. - - Returns - ------- - a**n : (..., M, M) ndarray or matrix object - The return value is the same shape and type as `M`; - if the exponent is positive or zero then the type of the - elements is the same as those of `M`. If the exponent is - negative the elements are floating-point. - - Raises - ------ - LinAlgError - For matrices that are not square or that (for negative powers) cannot - be inverted numerically. - - Examples - -------- - >>> from numpy.linalg import matrix_power - >>> i = np.array([[0, 1], [-1, 0]]) # matrix equiv. of the imaginary unit - >>> matrix_power(i, 3) # should = -i - array([[ 0, -1], - [ 1, 0]]) - >>> matrix_power(i, 0) - array([[1, 0], - [0, 1]]) - >>> matrix_power(i, -3) # should = 1/(-i) = i, but w/ f.p. elements - array([[ 0., 1.], - [-1., 0.]]) - - Somewhat more sophisticated example - - >>> q = np.zeros((4, 4)) - >>> q[0:2, 0:2] = -i - >>> q[2:4, 2:4] = i - >>> q # one of the three quaternion units not equal to 1 - array([[ 0., -1., 0., 0.], - [ 1., 0., 0., 0.], - [ 0., 0., 0., 1.], - [ 0., 0., -1., 0.]]) - >>> matrix_power(q, 2) # = -np.eye(4) - array([[-1., 0., 0., 0.], - [ 0., -1., 0., 0.], - [ 0., 0., -1., 0.], - [ 0., 0., 0., -1.]]) - - """ - a = asanyarray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - - try: - n = operator.index(n) - except TypeError as e: - raise TypeError("exponent must be an integer") from e - - # Fall back on dot for object arrays. Object arrays are not supported by - # the current implementation of matmul using einsum - if a.dtype != object: - fmatmul = matmul - elif a.ndim == 2: - fmatmul = dot - else: - raise NotImplementedError( - "matrix_power not supported for stacks of object arrays") - - if n == 0: - a = empty_like(a) - a[...] = eye(a.shape[-2], dtype=a.dtype) - return a - - elif n < 0: - a = inv(a) - n = abs(n) - - # short-cuts. - if n == 1: - return a - - elif n == 2: - return fmatmul(a, a) - - elif n == 3: - return fmatmul(fmatmul(a, a), a) - - # Use binary decomposition to reduce the number of matrix multiplications. - # Here, we iterate over the bits of n, from LSB to MSB, raise `a` to - # increasing powers of 2, and multiply into the result as needed. - z = result = None - while n > 0: - z = a if z is None else fmatmul(z, z) - n, bit = divmod(n, 2) - if bit: - result = z if result is None else fmatmul(result, z) - - return result - - -# Cholesky decomposition - - -@array_function_dispatch(_unary_dispatcher) -def cholesky(a): - """ - Cholesky decomposition. - - Return the Cholesky decomposition, `L * L.H`, of the square matrix `a`, - where `L` is lower-triangular and .H is the conjugate transpose operator - (which is the ordinary transpose if `a` is real-valued). `a` must be - Hermitian (symmetric if real-valued) and positive-definite. No - checking is performed to verify whether `a` is Hermitian or not. - In addition, only the lower-triangular and diagonal elements of `a` - are used. Only `L` is actually returned. - - Parameters - ---------- - a : (..., M, M) array_like - Hermitian (symmetric if all elements are real), positive-definite - input matrix. - - Returns - ------- - L : (..., M, M) array_like - Lower-triangular Cholesky factor of `a`. Returns a matrix object if - `a` is a matrix object. - - Raises - ------ - LinAlgError - If the decomposition fails, for example, if `a` is not - positive-definite. - - See Also - -------- - scipy.linalg.cholesky : Similar function in SciPy. - scipy.linalg.cholesky_banded : Cholesky decompose a banded Hermitian - positive-definite matrix. - scipy.linalg.cho_factor : Cholesky decomposition of a matrix, to use in - `scipy.linalg.cho_solve`. - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - The Cholesky decomposition is often used as a fast way of solving - - .. math:: A \\mathbf{x} = \\mathbf{b} - - (when `A` is both Hermitian/symmetric and positive-definite). - - First, we solve for :math:`\\mathbf{y}` in - - .. math:: L \\mathbf{y} = \\mathbf{b}, - - and then for :math:`\\mathbf{x}` in - - .. math:: L.H \\mathbf{x} = \\mathbf{y}. - - Examples - -------- - >>> A = np.array([[1,-2j],[2j,5]]) - >>> A - array([[ 1.+0.j, -0.-2.j], - [ 0.+2.j, 5.+0.j]]) - >>> L = np.linalg.cholesky(A) - >>> L - array([[1.+0.j, 0.+0.j], - [0.+2.j, 1.+0.j]]) - >>> np.dot(L, L.T.conj()) # verify that L * L.H = A - array([[1.+0.j, 0.-2.j], - [0.+2.j, 5.+0.j]]) - >>> A = [[1,-2j],[2j,5]] # what happens if A is only array_like? - >>> np.linalg.cholesky(A) # an ndarray object is returned - array([[1.+0.j, 0.+0.j], - [0.+2.j, 1.+0.j]]) - >>> # But a matrix object is returned if A is a matrix object - >>> np.linalg.cholesky(np.matrix(A)) - matrix([[ 1.+0.j, 0.+0.j], - [ 0.+2.j, 1.+0.j]]) - - """ - extobj = get_linalg_error_extobj(_raise_linalgerror_nonposdef) - gufunc = _umath_linalg.cholesky_lo - a, wrap = _makearray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - t, result_t = _commonType(a) - signature = 'D->D' if isComplexType(t) else 'd->d' - r = gufunc(a, signature=signature, extobj=extobj) - return wrap(r.astype(result_t, copy=False)) - - -# QR decomposition - -def _qr_dispatcher(a, mode=None): - return (a,) - - -@array_function_dispatch(_qr_dispatcher) -def qr(a, mode='reduced'): - """ - Compute the qr factorization of a matrix. - - Factor the matrix `a` as *qr*, where `q` is orthonormal and `r` is - upper-triangular. - - Parameters - ---------- - a : array_like, shape (..., M, N) - An array-like object with the dimensionality of at least 2. - mode : {'reduced', 'complete', 'r', 'raw'}, optional - If K = min(M, N), then - - * 'reduced' : returns Q, R with dimensions (..., M, K), (..., K, N) (default) - * 'complete' : returns Q, R with dimensions (..., M, M), (..., M, N) - * 'r' : returns R only with dimensions (..., K, N) - * 'raw' : returns h, tau with dimensions (..., N, M), (..., K,) - - The options 'reduced', 'complete, and 'raw' are new in numpy 1.8, - see the notes for more information. The default is 'reduced', and to - maintain backward compatibility with earlier versions of numpy both - it and the old default 'full' can be omitted. Note that array h - returned in 'raw' mode is transposed for calling Fortran. The - 'economic' mode is deprecated. The modes 'full' and 'economic' may - be passed using only the first letter for backwards compatibility, - but all others must be spelled out. See the Notes for more - explanation. - - - Returns - ------- - When mode is 'reduced' or 'complete', the result will be a namedtuple with - the attributes `Q` and `R`. - - Q : ndarray of float or complex, optional - A matrix with orthonormal columns. When mode = 'complete' the - result is an orthogonal/unitary matrix depending on whether or not - a is real/complex. The determinant may be either +/- 1 in that - case. In case the number of dimensions in the input array is - greater than 2 then a stack of the matrices with above properties - is returned. - R : ndarray of float or complex, optional - The upper-triangular matrix or a stack of upper-triangular - matrices if the number of dimensions in the input array is greater - than 2. - (h, tau) : ndarrays of np.double or np.cdouble, optional - The array h contains the Householder reflectors that generate q - along with r. The tau array contains scaling factors for the - reflectors. In the deprecated 'economic' mode only h is returned. - - Raises - ------ - LinAlgError - If factoring fails. - - See Also - -------- - scipy.linalg.qr : Similar function in SciPy. - scipy.linalg.rq : Compute RQ decomposition of a matrix. - - Notes - ----- - This is an interface to the LAPACK routines ``dgeqrf``, ``zgeqrf``, - ``dorgqr``, and ``zungqr``. - - For more information on the qr factorization, see for example: - https://en.wikipedia.org/wiki/QR_factorization - - Subclasses of `ndarray` are preserved except for the 'raw' mode. So if - `a` is of type `matrix`, all the return values will be matrices too. - - New 'reduced', 'complete', and 'raw' options for mode were added in - NumPy 1.8.0 and the old option 'full' was made an alias of 'reduced'. In - addition the options 'full' and 'economic' were deprecated. Because - 'full' was the previous default and 'reduced' is the new default, - backward compatibility can be maintained by letting `mode` default. - The 'raw' option was added so that LAPACK routines that can multiply - arrays by q using the Householder reflectors can be used. Note that in - this case the returned arrays are of type np.double or np.cdouble and - the h array is transposed to be FORTRAN compatible. No routines using - the 'raw' return are currently exposed by numpy, but some are available - in lapack_lite and just await the necessary work. - - Examples - -------- - >>> a = np.random.randn(9, 6) - >>> Q, R = np.linalg.qr(a) - >>> np.allclose(a, np.dot(Q, R)) # a does equal QR - True - >>> R2 = np.linalg.qr(a, mode='r') - >>> np.allclose(R, R2) # mode='r' returns the same R as mode='full' - True - >>> a = np.random.normal(size=(3, 2, 2)) # Stack of 2 x 2 matrices as input - >>> Q, R = np.linalg.qr(a) - >>> Q.shape - (3, 2, 2) - >>> R.shape - (3, 2, 2) - >>> np.allclose(a, np.matmul(Q, R)) - True - - Example illustrating a common use of `qr`: solving of least squares - problems - - What are the least-squares-best `m` and `y0` in ``y = y0 + mx`` for - the following data: {(0,1), (1,0), (1,2), (2,1)}. (Graph the points - and you'll see that it should be y0 = 0, m = 1.) The answer is provided - by solving the over-determined matrix equation ``Ax = b``, where:: - - A = array([[0, 1], [1, 1], [1, 1], [2, 1]]) - x = array([[y0], [m]]) - b = array([[1], [0], [2], [1]]) - - If A = QR such that Q is orthonormal (which is always possible via - Gram-Schmidt), then ``x = inv(R) * (Q.T) * b``. (In numpy practice, - however, we simply use `lstsq`.) - - >>> A = np.array([[0, 1], [1, 1], [1, 1], [2, 1]]) - >>> A - array([[0, 1], - [1, 1], - [1, 1], - [2, 1]]) - >>> b = np.array([1, 2, 2, 3]) - >>> Q, R = np.linalg.qr(A) - >>> p = np.dot(Q.T, b) - >>> np.dot(np.linalg.inv(R), p) - array([ 1., 1.]) - - """ - if mode not in ('reduced', 'complete', 'r', 'raw'): - if mode in ('f', 'full'): - # 2013-04-01, 1.8 - msg = "".join(( - "The 'full' option is deprecated in favor of 'reduced'.\n", - "For backward compatibility let mode default.")) - warnings.warn(msg, DeprecationWarning, stacklevel=2) - mode = 'reduced' - elif mode in ('e', 'economic'): - # 2013-04-01, 1.8 - msg = "The 'economic' option is deprecated." - warnings.warn(msg, DeprecationWarning, stacklevel=2) - mode = 'economic' - else: - raise ValueError(f"Unrecognized mode '{mode}'") - - a, wrap = _makearray(a) - _assert_stacked_2d(a) - m, n = a.shape[-2:] - t, result_t = _commonType(a) - a = a.astype(t, copy=True) - a = _to_native_byte_order(a) - mn = min(m, n) - - if m <= n: - gufunc = _umath_linalg.qr_r_raw_m - else: - gufunc = _umath_linalg.qr_r_raw_n - - signature = 'D->D' if isComplexType(t) else 'd->d' - extobj = get_linalg_error_extobj(_raise_linalgerror_qr) - tau = gufunc(a, signature=signature, extobj=extobj) - - # handle modes that don't return q - if mode == 'r': - r = triu(a[..., :mn, :]) - r = r.astype(result_t, copy=False) - return wrap(r) - - if mode == 'raw': - q = transpose(a) - q = q.astype(result_t, copy=False) - tau = tau.astype(result_t, copy=False) - return wrap(q), tau - - if mode == 'economic': - a = a.astype(result_t, copy=False) - return wrap(a) - - # mc is the number of columns in the resulting q - # matrix. If the mode is complete then it is - # same as number of rows, and if the mode is reduced, - # then it is the minimum of number of rows and columns. - if mode == 'complete' and m > n: - mc = m - gufunc = _umath_linalg.qr_complete - else: - mc = mn - gufunc = _umath_linalg.qr_reduced - - signature = 'DD->D' if isComplexType(t) else 'dd->d' - extobj = get_linalg_error_extobj(_raise_linalgerror_qr) - q = gufunc(a, tau, signature=signature, extobj=extobj) - r = triu(a[..., :mc, :]) - - q = q.astype(result_t, copy=False) - r = r.astype(result_t, copy=False) - - return QRResult(wrap(q), wrap(r)) - -# Eigenvalues - - -@array_function_dispatch(_unary_dispatcher) -def eigvals(a): - """ - Compute the eigenvalues of a general matrix. - - Main difference between `eigvals` and `eig`: the eigenvectors aren't - returned. - - Parameters - ---------- - a : (..., M, M) array_like - A complex- or real-valued matrix whose eigenvalues will be computed. - - Returns - ------- - w : (..., M,) ndarray - The eigenvalues, each repeated according to its multiplicity. - They are not necessarily ordered, nor are they necessarily - real for real matrices. - - Raises - ------ - LinAlgError - If the eigenvalue computation does not converge. - - See Also - -------- - eig : eigenvalues and right eigenvectors of general arrays - eigvalsh : eigenvalues of real symmetric or complex Hermitian - (conjugate symmetric) arrays. - eigh : eigenvalues and eigenvectors of real symmetric or complex - Hermitian (conjugate symmetric) arrays. - scipy.linalg.eigvals : Similar function in SciPy. - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - This is implemented using the ``_geev`` LAPACK routines which compute - the eigenvalues and eigenvectors of general square arrays. - - Examples - -------- - Illustration, using the fact that the eigenvalues of a diagonal matrix - are its diagonal elements, that multiplying a matrix on the left - by an orthogonal matrix, `Q`, and on the right by `Q.T` (the transpose - of `Q`), preserves the eigenvalues of the "middle" matrix. In other words, - if `Q` is orthogonal, then ``Q * A * Q.T`` has the same eigenvalues as - ``A``: - - >>> from numpy import linalg as LA - >>> x = np.random.random() - >>> Q = np.array([[np.cos(x), -np.sin(x)], [np.sin(x), np.cos(x)]]) - >>> LA.norm(Q[0, :]), LA.norm(Q[1, :]), np.dot(Q[0, :],Q[1, :]) - (1.0, 1.0, 0.0) - - Now multiply a diagonal matrix by ``Q`` on one side and by ``Q.T`` on the other: - - >>> D = np.diag((-1,1)) - >>> LA.eigvals(D) - array([-1., 1.]) - >>> A = np.dot(Q, D) - >>> A = np.dot(A, Q.T) - >>> LA.eigvals(A) - array([ 1., -1.]) # random - - """ - a, wrap = _makearray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - _assert_finite(a) - t, result_t = _commonType(a) - - extobj = get_linalg_error_extobj( - _raise_linalgerror_eigenvalues_nonconvergence) - signature = 'D->D' if isComplexType(t) else 'd->D' - w = _umath_linalg.eigvals(a, signature=signature, extobj=extobj) - - if not isComplexType(t): - if all(w.imag == 0): - w = w.real - result_t = _realType(result_t) - else: - result_t = _complexType(result_t) - - return w.astype(result_t, copy=False) - - -def _eigvalsh_dispatcher(a, UPLO=None): - return (a,) - - -@array_function_dispatch(_eigvalsh_dispatcher) -def eigvalsh(a, UPLO='L'): - """ - Compute the eigenvalues of a complex Hermitian or real symmetric matrix. - - Main difference from eigh: the eigenvectors are not computed. - - Parameters - ---------- - a : (..., M, M) array_like - A complex- or real-valued matrix whose eigenvalues are to be - computed. - UPLO : {'L', 'U'}, optional - Specifies whether the calculation is done with the lower triangular - part of `a` ('L', default) or the upper triangular part ('U'). - Irrespective of this value only the real parts of the diagonal will - be considered in the computation to preserve the notion of a Hermitian - matrix. It therefore follows that the imaginary part of the diagonal - will always be treated as zero. - - Returns - ------- - w : (..., M,) ndarray - The eigenvalues in ascending order, each repeated according to - its multiplicity. - - Raises - ------ - LinAlgError - If the eigenvalue computation does not converge. - - See Also - -------- - eigh : eigenvalues and eigenvectors of real symmetric or complex Hermitian - (conjugate symmetric) arrays. - eigvals : eigenvalues of general real or complex arrays. - eig : eigenvalues and right eigenvectors of general real or complex - arrays. - scipy.linalg.eigvalsh : Similar function in SciPy. - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - The eigenvalues are computed using LAPACK routines ``_syevd``, ``_heevd``. - - Examples - -------- - >>> from numpy import linalg as LA - >>> a = np.array([[1, -2j], [2j, 5]]) - >>> LA.eigvalsh(a) - array([ 0.17157288, 5.82842712]) # may vary - - >>> # demonstrate the treatment of the imaginary part of the diagonal - >>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]]) - >>> a - array([[5.+2.j, 9.-2.j], - [0.+2.j, 2.-1.j]]) - >>> # with UPLO='L' this is numerically equivalent to using LA.eigvals() - >>> # with: - >>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]]) - >>> b - array([[5.+0.j, 0.-2.j], - [0.+2.j, 2.+0.j]]) - >>> wa = LA.eigvalsh(a) - >>> wb = LA.eigvals(b) - >>> wa; wb - array([1., 6.]) - array([6.+0.j, 1.+0.j]) - - """ - UPLO = UPLO.upper() - if UPLO not in ('L', 'U'): - raise ValueError("UPLO argument must be 'L' or 'U'") - - extobj = get_linalg_error_extobj( - _raise_linalgerror_eigenvalues_nonconvergence) - if UPLO == 'L': - gufunc = _umath_linalg.eigvalsh_lo - else: - gufunc = _umath_linalg.eigvalsh_up - - a, wrap = _makearray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - t, result_t = _commonType(a) - signature = 'D->d' if isComplexType(t) else 'd->d' - w = gufunc(a, signature=signature, extobj=extobj) - return w.astype(_realType(result_t), copy=False) - -def _convertarray(a): - t, result_t = _commonType(a) - a = a.astype(t).T.copy() - return a, t, result_t - - -# Eigenvectors - - -@array_function_dispatch(_unary_dispatcher) -def eig(a): - """ - Compute the eigenvalues and right eigenvectors of a square array. - - Parameters - ---------- - a : (..., M, M) array - Matrices for which the eigenvalues and right eigenvectors will - be computed - - Returns - ------- - A namedtuple with the following attributes: - - eigenvalues : (..., M) array - The eigenvalues, each repeated according to its multiplicity. - The eigenvalues are not necessarily ordered. The resulting - array will be of complex type, unless the imaginary part is - zero in which case it will be cast to a real type. When `a` - is real the resulting eigenvalues will be real (0 imaginary - part) or occur in conjugate pairs - - eigenvectors : (..., M, M) array - The normalized (unit "length") eigenvectors, such that the - column ``eigenvectors[:,i]`` is the eigenvector corresponding to the - eigenvalue ``eigenvalues[i]``. - - Raises - ------ - LinAlgError - If the eigenvalue computation does not converge. - - See Also - -------- - eigvals : eigenvalues of a non-symmetric array. - eigh : eigenvalues and eigenvectors of a real symmetric or complex - Hermitian (conjugate symmetric) array. - eigvalsh : eigenvalues of a real symmetric or complex Hermitian - (conjugate symmetric) array. - scipy.linalg.eig : Similar function in SciPy that also solves the - generalized eigenvalue problem. - scipy.linalg.schur : Best choice for unitary and other non-Hermitian - normal matrices. - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - This is implemented using the ``_geev`` LAPACK routines which compute - the eigenvalues and eigenvectors of general square arrays. - - The number `w` is an eigenvalue of `a` if there exists a vector `v` such - that ``a @ v = w * v``. Thus, the arrays `a`, `eigenvalues`, and - `eigenvectors` satisfy the equations ``a @ eigenvectors[:,i] = - eigenvalues[i] * eigenvalues[:,i]`` for :math:`i \\in \\{0,...,M-1\\}`. - - The array `eigenvectors` may not be of maximum rank, that is, some of the - columns may be linearly dependent, although round-off error may obscure - that fact. If the eigenvalues are all different, then theoretically the - eigenvectors are linearly independent and `a` can be diagonalized by a - similarity transformation using `eigenvectors`, i.e, ``inv(eigenvectors) @ - a @ eigenvectors`` is diagonal. - - For non-Hermitian normal matrices the SciPy function `scipy.linalg.schur` - is preferred because the matrix `eigenvectors` is guaranteed to be - unitary, which is not the case when using `eig`. The Schur factorization - produces an upper triangular matrix rather than a diagonal matrix, but for - normal matrices only the diagonal of the upper triangular matrix is - needed, the rest is roundoff error. - - Finally, it is emphasized that `eigenvectors` consists of the *right* (as - in right-hand side) eigenvectors of `a`. A vector `y` satisfying ``y.T @ a - = z * y.T`` for some number `z` is called a *left* eigenvector of `a`, - and, in general, the left and right eigenvectors of a matrix are not - necessarily the (perhaps conjugate) transposes of each other. - - References - ---------- - G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, FL, - Academic Press, Inc., 1980, Various pp. - - Examples - -------- - >>> from numpy import linalg as LA - - (Almost) trivial example with real eigenvalues and eigenvectors. - - >>> eigenvalues, eigenvectors = LA.eig(np.diag((1, 2, 3))) - >>> eigenvalues - array([1., 2., 3.]) - >>> eigenvectors - array([[1., 0., 0.], - [0., 1., 0.], - [0., 0., 1.]]) - - Real matrix possessing complex eigenvalues and eigenvectors; note that the - eigenvalues are complex conjugates of each other. - - >>> eigenvalues, eigenvectors = LA.eig(np.array([[1, -1], [1, 1]])) - >>> eigenvalues - array([1.+1.j, 1.-1.j]) - >>> eigenvectors - array([[0.70710678+0.j , 0.70710678-0.j ], - [0. -0.70710678j, 0. +0.70710678j]]) - - Complex-valued matrix with real eigenvalues (but complex-valued eigenvectors); - note that ``a.conj().T == a``, i.e., `a` is Hermitian. - - >>> a = np.array([[1, 1j], [-1j, 1]]) - >>> eigenvalues, eigenvectors = LA.eig(a) - >>> eigenvalues - array([2.+0.j, 0.+0.j]) - >>> eigenvectors - array([[ 0. +0.70710678j, 0.70710678+0.j ], # may vary - [ 0.70710678+0.j , -0. +0.70710678j]]) - - Be careful about round-off error! - - >>> a = np.array([[1 + 1e-9, 0], [0, 1 - 1e-9]]) - >>> # Theor. eigenvalues are 1 +/- 1e-9 - >>> eigenvalues, eigenvectors = LA.eig(a) - >>> eigenvalues - array([1., 1.]) - >>> eigenvectors - array([[1., 0.], - [0., 1.]]) - - """ - a, wrap = _makearray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - _assert_finite(a) - t, result_t = _commonType(a) - - extobj = get_linalg_error_extobj( - _raise_linalgerror_eigenvalues_nonconvergence) - signature = 'D->DD' if isComplexType(t) else 'd->DD' - w, vt = _umath_linalg.eig(a, signature=signature, extobj=extobj) - - if not isComplexType(t) and all(w.imag == 0.0): - w = w.real - vt = vt.real - result_t = _realType(result_t) - else: - result_t = _complexType(result_t) - - vt = vt.astype(result_t, copy=False) - return EigResult(w.astype(result_t, copy=False), wrap(vt)) - - -@array_function_dispatch(_eigvalsh_dispatcher) -def eigh(a, UPLO='L'): - """ - Return the eigenvalues and eigenvectors of a complex Hermitian - (conjugate symmetric) or a real symmetric matrix. - - Returns two objects, a 1-D array containing the eigenvalues of `a`, and - a 2-D square array or matrix (depending on the input type) of the - corresponding eigenvectors (in columns). - - Parameters - ---------- - a : (..., M, M) array - Hermitian or real symmetric matrices whose eigenvalues and - eigenvectors are to be computed. - UPLO : {'L', 'U'}, optional - Specifies whether the calculation is done with the lower triangular - part of `a` ('L', default) or the upper triangular part ('U'). - Irrespective of this value only the real parts of the diagonal will - be considered in the computation to preserve the notion of a Hermitian - matrix. It therefore follows that the imaginary part of the diagonal - will always be treated as zero. - - Returns - ------- - A namedtuple with the following attributes: - - eigenvalues : (..., M) ndarray - The eigenvalues in ascending order, each repeated according to - its multiplicity. - eigenvectors : {(..., M, M) ndarray, (..., M, M) matrix} - The column ``eigenvectors[:, i]`` is the normalized eigenvector - corresponding to the eigenvalue ``eigenvalues[i]``. Will return a - matrix object if `a` is a matrix object. - - Raises - ------ - LinAlgError - If the eigenvalue computation does not converge. - - See Also - -------- - eigvalsh : eigenvalues of real symmetric or complex Hermitian - (conjugate symmetric) arrays. - eig : eigenvalues and right eigenvectors for non-symmetric arrays. - eigvals : eigenvalues of non-symmetric arrays. - scipy.linalg.eigh : Similar function in SciPy (but also solves the - generalized eigenvalue problem). - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - The eigenvalues/eigenvectors are computed using LAPACK routines ``_syevd``, - ``_heevd``. - - The eigenvalues of real symmetric or complex Hermitian matrices are always - real. [1]_ The array `eigenvalues` of (column) eigenvectors is unitary and - `a`, `eigenvalues`, and `eigenvectors` satisfy the equations ``dot(a, - eigenvectors[:, i]) = eigenvalues[i] * eigenvectors[:, i]``. - - References - ---------- - .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, - FL, Academic Press, Inc., 1980, pg. 222. - - Examples - -------- - >>> from numpy import linalg as LA - >>> a = np.array([[1, -2j], [2j, 5]]) - >>> a - array([[ 1.+0.j, -0.-2.j], - [ 0.+2.j, 5.+0.j]]) - >>> eigenvalues, eigenvectors = LA.eigh(a) - >>> eigenvalues - array([0.17157288, 5.82842712]) - >>> eigenvectors - array([[-0.92387953+0.j , -0.38268343+0.j ], # may vary - [ 0. +0.38268343j, 0. -0.92387953j]]) - - >>> np.dot(a, eigenvectors[:, 0]) - eigenvalues[0] * eigenvectors[:, 0] # verify 1st eigenval/vec pair - array([5.55111512e-17+0.0000000e+00j, 0.00000000e+00+1.2490009e-16j]) - >>> np.dot(a, eigenvectors[:, 1]) - eigenvalues[1] * eigenvectors[:, 1] # verify 2nd eigenval/vec pair - array([0.+0.j, 0.+0.j]) - - >>> A = np.matrix(a) # what happens if input is a matrix object - >>> A - matrix([[ 1.+0.j, -0.-2.j], - [ 0.+2.j, 5.+0.j]]) - >>> eigenvalues, eigenvectors = LA.eigh(A) - >>> eigenvalues - array([0.17157288, 5.82842712]) - >>> eigenvectors - matrix([[-0.92387953+0.j , -0.38268343+0.j ], # may vary - [ 0. +0.38268343j, 0. -0.92387953j]]) - - >>> # demonstrate the treatment of the imaginary part of the diagonal - >>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]]) - >>> a - array([[5.+2.j, 9.-2.j], - [0.+2.j, 2.-1.j]]) - >>> # with UPLO='L' this is numerically equivalent to using LA.eig() with: - >>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]]) - >>> b - array([[5.+0.j, 0.-2.j], - [0.+2.j, 2.+0.j]]) - >>> wa, va = LA.eigh(a) - >>> wb, vb = LA.eig(b) - >>> wa; wb - array([1., 6.]) - array([6.+0.j, 1.+0.j]) - >>> va; vb - array([[-0.4472136 +0.j , -0.89442719+0.j ], # may vary - [ 0. +0.89442719j, 0. -0.4472136j ]]) - array([[ 0.89442719+0.j , -0. +0.4472136j], - [-0. +0.4472136j, 0.89442719+0.j ]]) - - """ - UPLO = UPLO.upper() - if UPLO not in ('L', 'U'): - raise ValueError("UPLO argument must be 'L' or 'U'") - - a, wrap = _makearray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - t, result_t = _commonType(a) - - extobj = get_linalg_error_extobj( - _raise_linalgerror_eigenvalues_nonconvergence) - if UPLO == 'L': - gufunc = _umath_linalg.eigh_lo - else: - gufunc = _umath_linalg.eigh_up - - signature = 'D->dD' if isComplexType(t) else 'd->dd' - w, vt = gufunc(a, signature=signature, extobj=extobj) - w = w.astype(_realType(result_t), copy=False) - vt = vt.astype(result_t, copy=False) - return EighResult(w, wrap(vt)) - - -# Singular value decomposition - -def _svd_dispatcher(a, full_matrices=None, compute_uv=None, hermitian=None): - return (a,) - - -@array_function_dispatch(_svd_dispatcher) -def svd(a, full_matrices=True, compute_uv=True, hermitian=False): - """ - Singular Value Decomposition. - - When `a` is a 2D array, and ``full_matrices=False``, then it is - factorized as ``u @ np.diag(s) @ vh = (u * s) @ vh``, where - `u` and the Hermitian transpose of `vh` are 2D arrays with - orthonormal columns and `s` is a 1D array of `a`'s singular - values. When `a` is higher-dimensional, SVD is applied in - stacked mode as explained below. - - Parameters - ---------- - a : (..., M, N) array_like - A real or complex array with ``a.ndim >= 2``. - full_matrices : bool, optional - If True (default), `u` and `vh` have the shapes ``(..., M, M)`` and - ``(..., N, N)``, respectively. Otherwise, the shapes are - ``(..., M, K)`` and ``(..., K, N)``, respectively, where - ``K = min(M, N)``. - compute_uv : bool, optional - Whether or not to compute `u` and `vh` in addition to `s`. True - by default. - hermitian : bool, optional - If True, `a` is assumed to be Hermitian (symmetric if real-valued), - enabling a more efficient method for finding singular values. - Defaults to False. - - .. versionadded:: 1.17.0 - - Returns - ------- - When `compute_uv` is True, the result is a namedtuple with the following - attribute names: - - U : { (..., M, M), (..., M, K) } array - Unitary array(s). The first ``a.ndim - 2`` dimensions have the same - size as those of the input `a`. The size of the last two dimensions - depends on the value of `full_matrices`. Only returned when - `compute_uv` is True. - S : (..., K) array - Vector(s) with the singular values, within each vector sorted in - descending order. The first ``a.ndim - 2`` dimensions have the same - size as those of the input `a`. - Vh : { (..., N, N), (..., K, N) } array - Unitary array(s). The first ``a.ndim - 2`` dimensions have the same - size as those of the input `a`. The size of the last two dimensions - depends on the value of `full_matrices`. Only returned when - `compute_uv` is True. - - Raises - ------ - LinAlgError - If SVD computation does not converge. - - See Also - -------- - scipy.linalg.svd : Similar function in SciPy. - scipy.linalg.svdvals : Compute singular values of a matrix. - - Notes - ----- - - .. versionchanged:: 1.8.0 - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - The decomposition is performed using LAPACK routine ``_gesdd``. - - SVD is usually described for the factorization of a 2D matrix :math:`A`. - The higher-dimensional case will be discussed below. In the 2D case, SVD is - written as :math:`A = U S V^H`, where :math:`A = a`, :math:`U= u`, - :math:`S= \\mathtt{np.diag}(s)` and :math:`V^H = vh`. The 1D array `s` - contains the singular values of `a` and `u` and `vh` are unitary. The rows - of `vh` are the eigenvectors of :math:`A^H A` and the columns of `u` are - the eigenvectors of :math:`A A^H`. In both cases the corresponding - (possibly non-zero) eigenvalues are given by ``s**2``. - - If `a` has more than two dimensions, then broadcasting rules apply, as - explained in :ref:`routines.linalg-broadcasting`. This means that SVD is - working in "stacked" mode: it iterates over all indices of the first - ``a.ndim - 2`` dimensions and for each combination SVD is applied to the - last two indices. The matrix `a` can be reconstructed from the - decomposition with either ``(u * s[..., None, :]) @ vh`` or - ``u @ (s[..., None] * vh)``. (The ``@`` operator can be replaced by the - function ``np.matmul`` for python versions below 3.5.) - - If `a` is a ``matrix`` object (as opposed to an ``ndarray``), then so are - all the return values. - - Examples - -------- - >>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6) - >>> b = np.random.randn(2, 7, 8, 3) + 1j*np.random.randn(2, 7, 8, 3) - - Reconstruction based on full SVD, 2D case: - - >>> U, S, Vh = np.linalg.svd(a, full_matrices=True) - >>> U.shape, S.shape, Vh.shape - ((9, 9), (6,), (6, 6)) - >>> np.allclose(a, np.dot(U[:, :6] * S, Vh)) - True - >>> smat = np.zeros((9, 6), dtype=complex) - >>> smat[:6, :6] = np.diag(S) - >>> np.allclose(a, np.dot(U, np.dot(smat, Vh))) - True - - Reconstruction based on reduced SVD, 2D case: - - >>> U, S, Vh = np.linalg.svd(a, full_matrices=False) - >>> U.shape, S.shape, Vh.shape - ((9, 6), (6,), (6, 6)) - >>> np.allclose(a, np.dot(U * S, Vh)) - True - >>> smat = np.diag(S) - >>> np.allclose(a, np.dot(U, np.dot(smat, Vh))) - True - - Reconstruction based on full SVD, 4D case: - - >>> U, S, Vh = np.linalg.svd(b, full_matrices=True) - >>> U.shape, S.shape, Vh.shape - ((2, 7, 8, 8), (2, 7, 3), (2, 7, 3, 3)) - >>> np.allclose(b, np.matmul(U[..., :3] * S[..., None, :], Vh)) - True - >>> np.allclose(b, np.matmul(U[..., :3], S[..., None] * Vh)) - True - - Reconstruction based on reduced SVD, 4D case: - - >>> U, S, Vh = np.linalg.svd(b, full_matrices=False) - >>> U.shape, S.shape, Vh.shape - ((2, 7, 8, 3), (2, 7, 3), (2, 7, 3, 3)) - >>> np.allclose(b, np.matmul(U * S[..., None, :], Vh)) - True - >>> np.allclose(b, np.matmul(U, S[..., None] * Vh)) - True - - """ - import numpy as _nx - a, wrap = _makearray(a) - - if hermitian: - # note: lapack svd returns eigenvalues with s ** 2 sorted descending, - # but eig returns s sorted ascending, so we re-order the eigenvalues - # and related arrays to have the correct order - if compute_uv: - s, u = eigh(a) - sgn = sign(s) - s = abs(s) - sidx = argsort(s)[..., ::-1] - sgn = _nx.take_along_axis(sgn, sidx, axis=-1) - s = _nx.take_along_axis(s, sidx, axis=-1) - u = _nx.take_along_axis(u, sidx[..., None, :], axis=-1) - # singular values are unsigned, move the sign into v - vt = transpose(u * sgn[..., None, :]).conjugate() - return SVDResult(wrap(u), s, wrap(vt)) - else: - s = eigvalsh(a) - s = abs(s) - return sort(s)[..., ::-1] - - _assert_stacked_2d(a) - t, result_t = _commonType(a) - - extobj = get_linalg_error_extobj(_raise_linalgerror_svd_nonconvergence) - - m, n = a.shape[-2:] - if compute_uv: - if full_matrices: - if m < n: - gufunc = _umath_linalg.svd_m_f - else: - gufunc = _umath_linalg.svd_n_f - else: - if m < n: - gufunc = _umath_linalg.svd_m_s - else: - gufunc = _umath_linalg.svd_n_s - - signature = 'D->DdD' if isComplexType(t) else 'd->ddd' - u, s, vh = gufunc(a, signature=signature, extobj=extobj) - u = u.astype(result_t, copy=False) - s = s.astype(_realType(result_t), copy=False) - vh = vh.astype(result_t, copy=False) - return SVDResult(wrap(u), s, wrap(vh)) - else: - if m < n: - gufunc = _umath_linalg.svd_m - else: - gufunc = _umath_linalg.svd_n - - signature = 'D->d' if isComplexType(t) else 'd->d' - s = gufunc(a, signature=signature, extobj=extobj) - s = s.astype(_realType(result_t), copy=False) - return s - - -def _cond_dispatcher(x, p=None): - return (x,) - - -@array_function_dispatch(_cond_dispatcher) -def cond(x, p=None): - """ - Compute the condition number of a matrix. - - This function is capable of returning the condition number using - one of seven different norms, depending on the value of `p` (see - Parameters below). - - Parameters - ---------- - x : (..., M, N) array_like - The matrix whose condition number is sought. - p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional - Order of the norm used in the condition number computation: - - ===== ============================ - p norm for matrices - ===== ============================ - None 2-norm, computed directly using the ``SVD`` - 'fro' Frobenius norm - inf max(sum(abs(x), axis=1)) - -inf min(sum(abs(x), axis=1)) - 1 max(sum(abs(x), axis=0)) - -1 min(sum(abs(x), axis=0)) - 2 2-norm (largest sing. value) - -2 smallest singular value - ===== ============================ - - inf means the `numpy.inf` object, and the Frobenius norm is - the root-of-sum-of-squares norm. - - Returns - ------- - c : {float, inf} - The condition number of the matrix. May be infinite. - - See Also - -------- - numpy.linalg.norm - - Notes - ----- - The condition number of `x` is defined as the norm of `x` times the - norm of the inverse of `x` [1]_; the norm can be the usual L2-norm - (root-of-sum-of-squares) or one of a number of other matrix norms. - - References - ---------- - .. [1] G. Strang, *Linear Algebra and Its Applications*, Orlando, FL, - Academic Press, Inc., 1980, pg. 285. - - Examples - -------- - >>> from numpy import linalg as LA - >>> a = np.array([[1, 0, -1], [0, 1, 0], [1, 0, 1]]) - >>> a - array([[ 1, 0, -1], - [ 0, 1, 0], - [ 1, 0, 1]]) - >>> LA.cond(a) - 1.4142135623730951 - >>> LA.cond(a, 'fro') - 3.1622776601683795 - >>> LA.cond(a, np.inf) - 2.0 - >>> LA.cond(a, -np.inf) - 1.0 - >>> LA.cond(a, 1) - 2.0 - >>> LA.cond(a, -1) - 1.0 - >>> LA.cond(a, 2) - 1.4142135623730951 - >>> LA.cond(a, -2) - 0.70710678118654746 # may vary - >>> min(LA.svd(a, compute_uv=False))*min(LA.svd(LA.inv(a), compute_uv=False)) - 0.70710678118654746 # may vary - - """ - x = asarray(x) # in case we have a matrix - if _is_empty_2d(x): - raise LinAlgError("cond is not defined on empty arrays") - if p is None or p == 2 or p == -2: - s = svd(x, compute_uv=False) - with errstate(all='ignore'): - if p == -2: - r = s[..., -1] / s[..., 0] - else: - r = s[..., 0] / s[..., -1] - else: - # Call inv(x) ignoring errors. The result array will - # contain nans in the entries where inversion failed. - _assert_stacked_2d(x) - _assert_stacked_square(x) - t, result_t = _commonType(x) - signature = 'D->D' if isComplexType(t) else 'd->d' - with errstate(all='ignore'): - invx = _umath_linalg.inv(x, signature=signature) - r = norm(x, p, axis=(-2, -1)) * norm(invx, p, axis=(-2, -1)) - r = r.astype(result_t, copy=False) - - # Convert nans to infs unless the original array had nan entries - r = asarray(r) - nan_mask = isnan(r) - if nan_mask.any(): - nan_mask &= ~isnan(x).any(axis=(-2, -1)) - if r.ndim > 0: - r[nan_mask] = Inf - elif nan_mask: - r[()] = Inf - - # Convention is to return scalars instead of 0d arrays - if r.ndim == 0: - r = r[()] - - return r - - -def _matrix_rank_dispatcher(A, tol=None, hermitian=None): - return (A,) - - -@array_function_dispatch(_matrix_rank_dispatcher) -def matrix_rank(A, tol=None, hermitian=False): - """ - Return matrix rank of array using SVD method - - Rank of the array is the number of singular values of the array that are - greater than `tol`. - - .. versionchanged:: 1.14 - Can now operate on stacks of matrices - - Parameters - ---------- - A : {(M,), (..., M, N)} array_like - Input vector or stack of matrices. - tol : (...) array_like, float, optional - Threshold below which SVD values are considered zero. If `tol` is - None, and ``S`` is an array with singular values for `M`, and - ``eps`` is the epsilon value for datatype of ``S``, then `tol` is - set to ``S.max() * max(M, N) * eps``. - - .. versionchanged:: 1.14 - Broadcasted against the stack of matrices - hermitian : bool, optional - If True, `A` is assumed to be Hermitian (symmetric if real-valued), - enabling a more efficient method for finding singular values. - Defaults to False. - - .. versionadded:: 1.14 - - Returns - ------- - rank : (...) array_like - Rank of A. - - Notes - ----- - The default threshold to detect rank deficiency is a test on the magnitude - of the singular values of `A`. By default, we identify singular values less - than ``S.max() * max(M, N) * eps`` as indicating rank deficiency (with - the symbols defined above). This is the algorithm MATLAB uses [1]. It also - appears in *Numerical recipes* in the discussion of SVD solutions for linear - least squares [2]. - - This default threshold is designed to detect rank deficiency accounting for - the numerical errors of the SVD computation. Imagine that there is a column - in `A` that is an exact (in floating point) linear combination of other - columns in `A`. Computing the SVD on `A` will not produce a singular value - exactly equal to 0 in general: any difference of the smallest SVD value from - 0 will be caused by numerical imprecision in the calculation of the SVD. - Our threshold for small SVD values takes this numerical imprecision into - account, and the default threshold will detect such numerical rank - deficiency. The threshold may declare a matrix `A` rank deficient even if - the linear combination of some columns of `A` is not exactly equal to - another column of `A` but only numerically very close to another column of - `A`. - - We chose our default threshold because it is in wide use. Other thresholds - are possible. For example, elsewhere in the 2007 edition of *Numerical - recipes* there is an alternative threshold of ``S.max() * - np.finfo(A.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe - this threshold as being based on "expected roundoff error" (p 71). - - The thresholds above deal with floating point roundoff error in the - calculation of the SVD. However, you may have more information about the - sources of error in `A` that would make you consider other tolerance values - to detect *effective* rank deficiency. The most useful measure of the - tolerance depends on the operations you intend to use on your matrix. For - example, if your data come from uncertain measurements with uncertainties - greater than floating point epsilon, choosing a tolerance near that - uncertainty may be preferable. The tolerance may be absolute if the - uncertainties are absolute rather than relative. - - References - ---------- - .. [1] MATLAB reference documentation, "Rank" - https://www.mathworks.com/help/techdoc/ref/rank.html - .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, - "Numerical Recipes (3rd edition)", Cambridge University Press, 2007, - page 795. - - Examples - -------- - >>> from numpy.linalg import matrix_rank - >>> matrix_rank(np.eye(4)) # Full rank matrix - 4 - >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix - >>> matrix_rank(I) - 3 - >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0 - 1 - >>> matrix_rank(np.zeros((4,))) - 0 - """ - A = asarray(A) - if A.ndim < 2: - return int(not all(A==0)) - S = svd(A, compute_uv=False, hermitian=hermitian) - if tol is None: - tol = S.max(axis=-1, keepdims=True) * max(A.shape[-2:]) * finfo(S.dtype).eps - else: - tol = asarray(tol)[..., newaxis] - return count_nonzero(S > tol, axis=-1) - - -# Generalized inverse - -def _pinv_dispatcher(a, rcond=None, hermitian=None): - return (a,) - - -@array_function_dispatch(_pinv_dispatcher) -def pinv(a, rcond=1e-15, hermitian=False): - """ - Compute the (Moore-Penrose) pseudo-inverse of a matrix. - - Calculate the generalized inverse of a matrix using its - singular-value decomposition (SVD) and including all - *large* singular values. - - .. versionchanged:: 1.14 - Can now operate on stacks of matrices - - Parameters - ---------- - a : (..., M, N) array_like - Matrix or stack of matrices to be pseudo-inverted. - rcond : (...) array_like of float - Cutoff for small singular values. - Singular values less than or equal to - ``rcond * largest_singular_value`` are set to zero. - Broadcasts against the stack of matrices. - hermitian : bool, optional - If True, `a` is assumed to be Hermitian (symmetric if real-valued), - enabling a more efficient method for finding singular values. - Defaults to False. - - .. versionadded:: 1.17.0 - - Returns - ------- - B : (..., N, M) ndarray - The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so - is `B`. - - Raises - ------ - LinAlgError - If the SVD computation does not converge. - - See Also - -------- - scipy.linalg.pinv : Similar function in SciPy. - scipy.linalg.pinvh : Compute the (Moore-Penrose) pseudo-inverse of a - Hermitian matrix. - - Notes - ----- - The pseudo-inverse of a matrix A, denoted :math:`A^+`, is - defined as: "the matrix that 'solves' [the least-squares problem] - :math:`Ax = b`," i.e., if :math:`\\bar{x}` is said solution, then - :math:`A^+` is that matrix such that :math:`\\bar{x} = A^+b`. - - It can be shown that if :math:`Q_1 \\Sigma Q_2^T = A` is the singular - value decomposition of A, then - :math:`A^+ = Q_2 \\Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are - orthogonal matrices, :math:`\\Sigma` is a diagonal matrix consisting - of A's so-called singular values, (followed, typically, by - zeros), and then :math:`\\Sigma^+` is simply the diagonal matrix - consisting of the reciprocals of A's singular values - (again, followed by zeros). [1]_ - - References - ---------- - .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, - FL, Academic Press, Inc., 1980, pp. 139-142. - - Examples - -------- - The following example checks that ``a * a+ * a == a`` and - ``a+ * a * a+ == a+``: - - >>> a = np.random.randn(9, 6) - >>> B = np.linalg.pinv(a) - >>> np.allclose(a, np.dot(a, np.dot(B, a))) - True - >>> np.allclose(B, np.dot(B, np.dot(a, B))) - True - - """ - a, wrap = _makearray(a) - rcond = asarray(rcond) - if _is_empty_2d(a): - m, n = a.shape[-2:] - res = empty(a.shape[:-2] + (n, m), dtype=a.dtype) - return wrap(res) - a = a.conjugate() - u, s, vt = svd(a, full_matrices=False, hermitian=hermitian) - - # discard small singular values - cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True) - large = s > cutoff - s = divide(1, s, where=large, out=s) - s[~large] = 0 - - res = matmul(transpose(vt), multiply(s[..., newaxis], transpose(u))) - return wrap(res) - - -# Determinant - - -@array_function_dispatch(_unary_dispatcher) -def slogdet(a): - """ - Compute the sign and (natural) logarithm of the determinant of an array. - - If an array has a very small or very large determinant, then a call to - `det` may overflow or underflow. This routine is more robust against such - issues, because it computes the logarithm of the determinant rather than - the determinant itself. - - Parameters - ---------- - a : (..., M, M) array_like - Input array, has to be a square 2-D array. - - Returns - ------- - A namedtuple with the following attributes: - - sign : (...) array_like - A number representing the sign of the determinant. For a real matrix, - this is 1, 0, or -1. For a complex matrix, this is a complex number - with absolute value 1 (i.e., it is on the unit circle), or else 0. - logabsdet : (...) array_like - The natural log of the absolute value of the determinant. - - If the determinant is zero, then `sign` will be 0 and `logabsdet` will be - -Inf. In all cases, the determinant is equal to ``sign * np.exp(logabsdet)``. - - See Also - -------- - det - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - .. versionadded:: 1.6.0 - - The determinant is computed via LU factorization using the LAPACK - routine ``z/dgetrf``. - - - Examples - -------- - The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``: - - >>> a = np.array([[1, 2], [3, 4]]) - >>> (sign, logabsdet) = np.linalg.slogdet(a) - >>> (sign, logabsdet) - (-1, 0.69314718055994529) # may vary - >>> sign * np.exp(logabsdet) - -2.0 - - Computing log-determinants for a stack of matrices: - - >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ]) - >>> a.shape - (3, 2, 2) - >>> sign, logabsdet = np.linalg.slogdet(a) - >>> (sign, logabsdet) - (array([-1., -1., -1.]), array([ 0.69314718, 1.09861229, 2.07944154])) - >>> sign * np.exp(logabsdet) - array([-2., -3., -8.]) - - This routine succeeds where ordinary `det` does not: - - >>> np.linalg.det(np.eye(500) * 0.1) - 0.0 - >>> np.linalg.slogdet(np.eye(500) * 0.1) - (1, -1151.2925464970228) - - """ - a = asarray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - t, result_t = _commonType(a) - real_t = _realType(result_t) - signature = 'D->Dd' if isComplexType(t) else 'd->dd' - sign, logdet = _umath_linalg.slogdet(a, signature=signature) - sign = sign.astype(result_t, copy=False) - logdet = logdet.astype(real_t, copy=False) - return SlogdetResult(sign, logdet) - - -@array_function_dispatch(_unary_dispatcher) -def det(a): - """ - Compute the determinant of an array. - - Parameters - ---------- - a : (..., M, M) array_like - Input array to compute determinants for. - - Returns - ------- - det : (...) array_like - Determinant of `a`. - - See Also - -------- - slogdet : Another way to represent the determinant, more suitable - for large matrices where underflow/overflow may occur. - scipy.linalg.det : Similar function in SciPy. - - Notes - ----- - - .. versionadded:: 1.8.0 - - Broadcasting rules apply, see the `numpy.linalg` documentation for - details. - - The determinant is computed via LU factorization using the LAPACK - routine ``z/dgetrf``. - - Examples - -------- - The determinant of a 2-D array [[a, b], [c, d]] is ad - bc: - - >>> a = np.array([[1, 2], [3, 4]]) - >>> np.linalg.det(a) - -2.0 # may vary - - Computing determinants for a stack of matrices: - - >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ]) - >>> a.shape - (3, 2, 2) - >>> np.linalg.det(a) - array([-2., -3., -8.]) - - """ - a = asarray(a) - _assert_stacked_2d(a) - _assert_stacked_square(a) - t, result_t = _commonType(a) - signature = 'D->D' if isComplexType(t) else 'd->d' - r = _umath_linalg.det(a, signature=signature) - r = r.astype(result_t, copy=False) - return r - - -# Linear Least Squares - -def _lstsq_dispatcher(a, b, rcond=None): - return (a, b) - - -@array_function_dispatch(_lstsq_dispatcher) -def lstsq(a, b, rcond="warn"): - r""" - Return the least-squares solution to a linear matrix equation. - - Computes the vector `x` that approximately solves the equation - ``a @ x = b``. The equation may be under-, well-, or over-determined - (i.e., the number of linearly independent rows of `a` can be less than, - equal to, or greater than its number of linearly independent columns). - If `a` is square and of full rank, then `x` (but for round-off error) - is the "exact" solution of the equation. Else, `x` minimizes the - Euclidean 2-norm :math:`||b - ax||`. If there are multiple minimizing - solutions, the one with the smallest 2-norm :math:`||x||` is returned. - - Parameters - ---------- - a : (M, N) array_like - "Coefficient" matrix. - b : {(M,), (M, K)} array_like - Ordinate or "dependent variable" values. If `b` is two-dimensional, - the least-squares solution is calculated for each of the `K` columns - of `b`. - rcond : float, optional - Cut-off ratio for small singular values of `a`. - For the purposes of rank determination, singular values are treated - as zero if they are smaller than `rcond` times the largest singular - value of `a`. - - .. versionchanged:: 1.14.0 - If not set, a FutureWarning is given. The previous default - of ``-1`` will use the machine precision as `rcond` parameter, - the new default will use the machine precision times `max(M, N)`. - To silence the warning and use the new default, use ``rcond=None``, - to keep using the old behavior, use ``rcond=-1``. - - Returns - ------- - x : {(N,), (N, K)} ndarray - Least-squares solution. If `b` is two-dimensional, - the solutions are in the `K` columns of `x`. - residuals : {(1,), (K,), (0,)} ndarray - Sums of squared residuals: Squared Euclidean 2-norm for each column in - ``b - a @ x``. - If the rank of `a` is < N or M <= N, this is an empty array. - If `b` is 1-dimensional, this is a (1,) shape array. - Otherwise the shape is (K,). - rank : int - Rank of matrix `a`. - s : (min(M, N),) ndarray - Singular values of `a`. - - Raises - ------ - LinAlgError - If computation does not converge. - - See Also - -------- - scipy.linalg.lstsq : Similar function in SciPy. - - Notes - ----- - If `b` is a matrix, then all array results are returned as matrices. - - Examples - -------- - Fit a line, ``y = mx + c``, through some noisy data-points: - - >>> x = np.array([0, 1, 2, 3]) - >>> y = np.array([-1, 0.2, 0.9, 2.1]) - - By examining the coefficients, we see that the line should have a - gradient of roughly 1 and cut the y-axis at, more or less, -1. - - We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]`` - and ``p = [[m], [c]]``. Now use `lstsq` to solve for `p`: - - >>> A = np.vstack([x, np.ones(len(x))]).T - >>> A - array([[ 0., 1.], - [ 1., 1.], - [ 2., 1.], - [ 3., 1.]]) - - >>> m, c = np.linalg.lstsq(A, y, rcond=None)[0] - >>> m, c - (1.0 -0.95) # may vary - - Plot the data along with the fitted line: - - >>> import matplotlib.pyplot as plt - >>> _ = plt.plot(x, y, 'o', label='Original data', markersize=10) - >>> _ = plt.plot(x, m*x + c, 'r', label='Fitted line') - >>> _ = plt.legend() - >>> plt.show() - - """ - a, _ = _makearray(a) - b, wrap = _makearray(b) - is_1d = b.ndim == 1 - if is_1d: - b = b[:, newaxis] - _assert_2d(a, b) - m, n = a.shape[-2:] - m2, n_rhs = b.shape[-2:] - if m != m2: - raise LinAlgError('Incompatible dimensions') - - t, result_t = _commonType(a, b) - result_real_t = _realType(result_t) - - # Determine default rcond value - if rcond == "warn": - # 2017-08-19, 1.14.0 - warnings.warn("`rcond` parameter will change to the default of " - "machine precision times ``max(M, N)`` where M and N " - "are the input matrix dimensions.\n" - "To use the future default and silence this warning " - "we advise to pass `rcond=None`, to keep using the old, " - "explicitly pass `rcond=-1`.", - FutureWarning, stacklevel=2) - rcond = -1 - if rcond is None: - rcond = finfo(t).eps * max(n, m) - - if m <= n: - gufunc = _umath_linalg.lstsq_m - else: - gufunc = _umath_linalg.lstsq_n - - signature = 'DDd->Ddid' if isComplexType(t) else 'ddd->ddid' - extobj = get_linalg_error_extobj(_raise_linalgerror_lstsq) - if n_rhs == 0: - # lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis - b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype) - x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj) - if m == 0: - x[...] = 0 - if n_rhs == 0: - # remove the item we added - x = x[..., :n_rhs] - resids = resids[..., :n_rhs] - - # remove the axis we added - if is_1d: - x = x.squeeze(axis=-1) - # we probably should squeeze resids too, but we can't - # without breaking compatibility. - - # as documented - if rank != n or m <= n: - resids = array([], result_real_t) - - # coerce output arrays - s = s.astype(result_real_t, copy=False) - resids = resids.astype(result_real_t, copy=False) - x = x.astype(result_t, copy=True) # Copying lets the memory in r_parts be freed - return wrap(x), wrap(resids), rank, s - - -def _multi_svd_norm(x, row_axis, col_axis, op): - """Compute a function of the singular values of the 2-D matrices in `x`. - - This is a private utility function used by `numpy.linalg.norm()`. - - Parameters - ---------- - x : ndarray - row_axis, col_axis : int - The axes of `x` that hold the 2-D matrices. - op : callable - This should be either numpy.amin or `numpy.amax` or `numpy.sum`. - - Returns - ------- - result : float or ndarray - If `x` is 2-D, the return values is a float. - Otherwise, it is an array with ``x.ndim - 2`` dimensions. - The return values are either the minimum or maximum or sum of the - singular values of the matrices, depending on whether `op` - is `numpy.amin` or `numpy.amax` or `numpy.sum`. - - """ - y = moveaxis(x, (row_axis, col_axis), (-2, -1)) - result = op(svd(y, compute_uv=False), axis=-1) - return result - - -def _norm_dispatcher(x, ord=None, axis=None, keepdims=None): - return (x,) - - -@array_function_dispatch(_norm_dispatcher) -def norm(x, ord=None, axis=None, keepdims=False): - """ - Matrix or vector norm. - - This function is able to return one of eight different matrix norms, - or one of an infinite number of vector norms (described below), depending - on the value of the ``ord`` parameter. - - Parameters - ---------- - x : array_like - Input array. If `axis` is None, `x` must be 1-D or 2-D, unless `ord` - is None. If both `axis` and `ord` are None, the 2-norm of - ``x.ravel`` will be returned. - ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional - Order of the norm (see table under ``Notes``). inf means numpy's - `inf` object. The default is None. - axis : {None, int, 2-tuple of ints}, optional. - If `axis` is an integer, it specifies the axis of `x` along which to - compute the vector norms. If `axis` is a 2-tuple, it specifies the - axes that hold 2-D matrices, and the matrix norms of these matrices - are computed. If `axis` is None then either a vector norm (when `x` - is 1-D) or a matrix norm (when `x` is 2-D) is returned. The default - is None. - - .. versionadded:: 1.8.0 - - keepdims : bool, optional - If this is set to True, the axes which are normed over are left in the - result as dimensions with size one. With this option the result will - broadcast correctly against the original `x`. - - .. versionadded:: 1.10.0 - - Returns - ------- - n : float or ndarray - Norm of the matrix or vector(s). - - See Also - -------- - scipy.linalg.norm : Similar function in SciPy. - - Notes - ----- - For values of ``ord < 1``, the result is, strictly speaking, not a - mathematical 'norm', but it may still be useful for various numerical - purposes. - - The following norms can be calculated: - - ===== ============================ ========================== - ord norm for matrices norm for vectors - ===== ============================ ========================== - None Frobenius norm 2-norm - 'fro' Frobenius norm -- - 'nuc' nuclear norm -- - inf max(sum(abs(x), axis=1)) max(abs(x)) - -inf min(sum(abs(x), axis=1)) min(abs(x)) - 0 -- sum(x != 0) - 1 max(sum(abs(x), axis=0)) as below - -1 min(sum(abs(x), axis=0)) as below - 2 2-norm (largest sing. value) as below - -2 smallest singular value as below - other -- sum(abs(x)**ord)**(1./ord) - ===== ============================ ========================== - - The Frobenius norm is given by [1]_: - - :math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}` - - The nuclear norm is the sum of the singular values. - - Both the Frobenius and nuclear norm orders are only defined for - matrices and raise a ValueError when ``x.ndim != 2``. - - References - ---------- - .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*, - Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15 - - Examples - -------- - >>> from numpy import linalg as LA - >>> a = np.arange(9) - 4 - >>> a - array([-4, -3, -2, ..., 2, 3, 4]) - >>> b = a.reshape((3, 3)) - >>> b - array([[-4, -3, -2], - [-1, 0, 1], - [ 2, 3, 4]]) - - >>> LA.norm(a) - 7.745966692414834 - >>> LA.norm(b) - 7.745966692414834 - >>> LA.norm(b, 'fro') - 7.745966692414834 - >>> LA.norm(a, np.inf) - 4.0 - >>> LA.norm(b, np.inf) - 9.0 - >>> LA.norm(a, -np.inf) - 0.0 - >>> LA.norm(b, -np.inf) - 2.0 - - >>> LA.norm(a, 1) - 20.0 - >>> LA.norm(b, 1) - 7.0 - >>> LA.norm(a, -1) - -4.6566128774142013e-010 - >>> LA.norm(b, -1) - 6.0 - >>> LA.norm(a, 2) - 7.745966692414834 - >>> LA.norm(b, 2) - 7.3484692283495345 - - >>> LA.norm(a, -2) - 0.0 - >>> LA.norm(b, -2) - 1.8570331885190563e-016 # may vary - >>> LA.norm(a, 3) - 5.8480354764257312 # may vary - >>> LA.norm(a, -3) - 0.0 - - Using the `axis` argument to compute vector norms: - - >>> c = np.array([[ 1, 2, 3], - ... [-1, 1, 4]]) - >>> LA.norm(c, axis=0) - array([ 1.41421356, 2.23606798, 5. ]) - >>> LA.norm(c, axis=1) - array([ 3.74165739, 4.24264069]) - >>> LA.norm(c, ord=1, axis=1) - array([ 6., 6.]) - - Using the `axis` argument to compute matrix norms: - - >>> m = np.arange(8).reshape(2,2,2) - >>> LA.norm(m, axis=(1,2)) - array([ 3.74165739, 11.22497216]) - >>> LA.norm(m[0, :, :]), LA.norm(m[1, :, :]) - (3.7416573867739413, 11.224972160321824) - - """ - x = asarray(x) - - if not issubclass(x.dtype.type, (inexact, object_)): - x = x.astype(float) - - # Immediately handle some default, simple, fast, and common cases. - if axis is None: - ndim = x.ndim - if ((ord is None) or - (ord in ('f', 'fro') and ndim == 2) or - (ord == 2 and ndim == 1)): - - x = x.ravel(order='K') - if isComplexType(x.dtype.type): - x_real = x.real - x_imag = x.imag - sqnorm = x_real.dot(x_real) + x_imag.dot(x_imag) - else: - sqnorm = x.dot(x) - ret = sqrt(sqnorm) - if keepdims: - ret = ret.reshape(ndim*[1]) - return ret - - # Normalize the `axis` argument to a tuple. - nd = x.ndim - if axis is None: - axis = tuple(range(nd)) - elif not isinstance(axis, tuple): - try: - axis = int(axis) - except Exception as e: - raise TypeError("'axis' must be None, an integer or a tuple of integers") from e - axis = (axis,) - - if len(axis) == 1: - if ord == Inf: - return abs(x).max(axis=axis, keepdims=keepdims) - elif ord == -Inf: - return abs(x).min(axis=axis, keepdims=keepdims) - elif ord == 0: - # Zero norm - return (x != 0).astype(x.real.dtype).sum(axis=axis, keepdims=keepdims) - elif ord == 1: - # special case for speedup - return add.reduce(abs(x), axis=axis, keepdims=keepdims) - elif ord is None or ord == 2: - # special case for speedup - s = (x.conj() * x).real - return sqrt(add.reduce(s, axis=axis, keepdims=keepdims)) - # None of the str-type keywords for ord ('fro', 'nuc') - # are valid for vectors - elif isinstance(ord, str): - raise ValueError(f"Invalid norm order '{ord}' for vectors") - else: - absx = abs(x) - absx **= ord - ret = add.reduce(absx, axis=axis, keepdims=keepdims) - ret **= reciprocal(ord, dtype=ret.dtype) - return ret - elif len(axis) == 2: - row_axis, col_axis = axis - row_axis = normalize_axis_index(row_axis, nd) - col_axis = normalize_axis_index(col_axis, nd) - if row_axis == col_axis: - raise ValueError('Duplicate axes given.') - if ord == 2: - ret = _multi_svd_norm(x, row_axis, col_axis, amax) - elif ord == -2: - ret = _multi_svd_norm(x, row_axis, col_axis, amin) - elif ord == 1: - if col_axis > row_axis: - col_axis -= 1 - ret = add.reduce(abs(x), axis=row_axis).max(axis=col_axis) - elif ord == Inf: - if row_axis > col_axis: - row_axis -= 1 - ret = add.reduce(abs(x), axis=col_axis).max(axis=row_axis) - elif ord == -1: - if col_axis > row_axis: - col_axis -= 1 - ret = add.reduce(abs(x), axis=row_axis).min(axis=col_axis) - elif ord == -Inf: - if row_axis > col_axis: - row_axis -= 1 - ret = add.reduce(abs(x), axis=col_axis).min(axis=row_axis) - elif ord in [None, 'fro', 'f']: - ret = sqrt(add.reduce((x.conj() * x).real, axis=axis)) - elif ord == 'nuc': - ret = _multi_svd_norm(x, row_axis, col_axis, sum) - else: - raise ValueError("Invalid norm order for matrices.") - if keepdims: - ret_shape = list(x.shape) - ret_shape[axis[0]] = 1 - ret_shape[axis[1]] = 1 - ret = ret.reshape(ret_shape) - return ret - else: - raise ValueError("Improper number of dimensions to norm.") - - -# multi_dot - -def _multidot_dispatcher(arrays, *, out=None): - yield from arrays - yield out - - -@array_function_dispatch(_multidot_dispatcher) -def multi_dot(arrays, *, out=None): - """ - Compute the dot product of two or more arrays in a single function call, - while automatically selecting the fastest evaluation order. - - `multi_dot` chains `numpy.dot` and uses optimal parenthesization - of the matrices [1]_ [2]_. Depending on the shapes of the matrices, - this can speed up the multiplication a lot. - - If the first argument is 1-D it is treated as a row vector. - If the last argument is 1-D it is treated as a column vector. - The other arguments must be 2-D. - - Think of `multi_dot` as:: - - def multi_dot(arrays): return functools.reduce(np.dot, arrays) - - - Parameters - ---------- - arrays : sequence of array_like - If the first argument is 1-D it is treated as row vector. - If the last argument is 1-D it is treated as column vector. - The other arguments must be 2-D. - out : ndarray, optional - Output argument. This must have the exact kind that would be returned - if it was not used. In particular, it must have the right type, must be - C-contiguous, and its dtype must be the dtype that would be returned - for `dot(a, b)`. This is a performance feature. Therefore, if these - conditions are not met, an exception is raised, instead of attempting - to be flexible. - - .. versionadded:: 1.19.0 - - Returns - ------- - output : ndarray - Returns the dot product of the supplied arrays. - - See Also - -------- - numpy.dot : dot multiplication with two arguments. - - References - ---------- - - .. [1] Cormen, "Introduction to Algorithms", Chapter 15.2, p. 370-378 - .. [2] https://en.wikipedia.org/wiki/Matrix_chain_multiplication - - Examples - -------- - `multi_dot` allows you to write:: - - >>> from numpy.linalg import multi_dot - >>> # Prepare some data - >>> A = np.random.random((10000, 100)) - >>> B = np.random.random((100, 1000)) - >>> C = np.random.random((1000, 5)) - >>> D = np.random.random((5, 333)) - >>> # the actual dot multiplication - >>> _ = multi_dot([A, B, C, D]) - - instead of:: - - >>> _ = np.dot(np.dot(np.dot(A, B), C), D) - >>> # or - >>> _ = A.dot(B).dot(C).dot(D) - - Notes - ----- - The cost for a matrix multiplication can be calculated with the - following function:: - - def cost(A, B): - return A.shape[0] * A.shape[1] * B.shape[1] - - Assume we have three matrices - :math:`A_{10x100}, B_{100x5}, C_{5x50}`. - - The costs for the two different parenthesizations are as follows:: - - cost((AB)C) = 10*100*5 + 10*5*50 = 5000 + 2500 = 7500 - cost(A(BC)) = 10*100*50 + 100*5*50 = 50000 + 25000 = 75000 - - """ - n = len(arrays) - # optimization only makes sense for len(arrays) > 2 - if n < 2: - raise ValueError("Expecting at least two arrays.") - elif n == 2: - return dot(arrays[0], arrays[1], out=out) - - arrays = [asanyarray(a) for a in arrays] - - # save original ndim to reshape the result array into the proper form later - ndim_first, ndim_last = arrays[0].ndim, arrays[-1].ndim - # Explicitly convert vectors to 2D arrays to keep the logic of the internal - # _multi_dot_* functions as simple as possible. - if arrays[0].ndim == 1: - arrays[0] = atleast_2d(arrays[0]) - if arrays[-1].ndim == 1: - arrays[-1] = atleast_2d(arrays[-1]).T - _assert_2d(*arrays) - - # _multi_dot_three is much faster than _multi_dot_matrix_chain_order - if n == 3: - result = _multi_dot_three(arrays[0], arrays[1], arrays[2], out=out) - else: - order = _multi_dot_matrix_chain_order(arrays) - result = _multi_dot(arrays, order, 0, n - 1, out=out) - - # return proper shape - if ndim_first == 1 and ndim_last == 1: - return result[0, 0] # scalar - elif ndim_first == 1 or ndim_last == 1: - return result.ravel() # 1-D - else: - return result - - -def _multi_dot_three(A, B, C, out=None): - """ - Find the best order for three arrays and do the multiplication. - - For three arguments `_multi_dot_three` is approximately 15 times faster - than `_multi_dot_matrix_chain_order` - - """ - a0, a1b0 = A.shape - b1c0, c1 = C.shape - # cost1 = cost((AB)C) = a0*a1b0*b1c0 + a0*b1c0*c1 - cost1 = a0 * b1c0 * (a1b0 + c1) - # cost2 = cost(A(BC)) = a1b0*b1c0*c1 + a0*a1b0*c1 - cost2 = a1b0 * c1 * (a0 + b1c0) - - if cost1 < cost2: - return dot(dot(A, B), C, out=out) - else: - return dot(A, dot(B, C), out=out) - - -def _multi_dot_matrix_chain_order(arrays, return_costs=False): - """ - Return a np.array that encodes the optimal order of mutiplications. - - The optimal order array is then used by `_multi_dot()` to do the - multiplication. - - Also return the cost matrix if `return_costs` is `True` - - The implementation CLOSELY follows Cormen, "Introduction to Algorithms", - Chapter 15.2, p. 370-378. Note that Cormen uses 1-based indices. - - cost[i, j] = min([ - cost[prefix] + cost[suffix] + cost_mult(prefix, suffix) - for k in range(i, j)]) - - """ - n = len(arrays) - # p stores the dimensions of the matrices - # Example for p: A_{10x100}, B_{100x5}, C_{5x50} --> p = [10, 100, 5, 50] - p = [a.shape[0] for a in arrays] + [arrays[-1].shape[1]] - # m is a matrix of costs of the subproblems - # m[i,j]: min number of scalar multiplications needed to compute A_{i..j} - m = zeros((n, n), dtype=double) - # s is the actual ordering - # s[i, j] is the value of k at which we split the product A_i..A_j - s = empty((n, n), dtype=intp) - - for l in range(1, n): - for i in range(n - l): - j = i + l - m[i, j] = Inf - for k in range(i, j): - q = m[i, k] + m[k+1, j] + p[i]*p[k+1]*p[j+1] - if q < m[i, j]: - m[i, j] = q - s[i, j] = k # Note that Cormen uses 1-based index - - return (s, m) if return_costs else s - - -def _multi_dot(arrays, order, i, j, out=None): - """Actually do the multiplication with the given order.""" - if i == j: - # the initial call with non-None out should never get here - assert out is None - - return arrays[i] - else: - return dot(_multi_dot(arrays, order, i, order[i, j]), - _multi_dot(arrays, order, order[i, j] + 1, j), - out=out) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_groupby.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_groupby.py deleted file mode 100644 index 49ae217513018f82a92456890d3ca2f660d8ab81..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_groupby.py +++ /dev/null @@ -1,3200 +0,0 @@ -from datetime import datetime -from decimal import Decimal -import re - -import numpy as np -import pytest - -from pandas.errors import ( - PerformanceWarning, - SpecificationError, -) -import pandas.util._test_decorators as td - -import pandas as pd -from pandas import ( - Categorical, - DataFrame, - Grouper, - Index, - Interval, - MultiIndex, - RangeIndex, - Series, - Timedelta, - Timestamp, - date_range, - to_datetime, -) -import pandas._testing as tm -from pandas.core.arrays import BooleanArray -import pandas.core.common as com -from pandas.tests.groupby import get_groupby_method_args - -pytestmark = pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning") - - -def test_repr(): - # GH18203 - result = repr(Grouper(key="A", level="B")) - expected = "Grouper(key='A', level='B', axis=0, sort=False, dropna=True)" - assert result == expected - - -def test_groupby_std_datetimelike(): - # GH#48481 - tdi = pd.timedelta_range("1 Day", periods=10000) - ser = Series(tdi) - ser[::5] *= 2 # get different std for different groups - - df = ser.to_frame("A") - - df["B"] = ser + Timestamp(0) - df["C"] = ser + Timestamp(0, tz="UTC") - df.iloc[-1] = pd.NaT # last group includes NaTs - - gb = df.groupby(list(range(5)) * 2000) - - result = gb.std() - - # Note: this does not _exactly_ match what we would get if we did - # [gb.get_group(i).std() for i in gb.groups] - # but it _does_ match the floating point error we get doing the - # same operation on int64 data xref GH#51332 - td1 = Timedelta("2887 days 11:21:02.326710176") - td4 = Timedelta("2886 days 00:42:34.664668096") - exp_ser = Series([td1 * 2, td1, td1, td1, td4], index=np.arange(5)) - expected = DataFrame({"A": exp_ser, "B": exp_ser, "C": exp_ser}) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("dtype", ["int64", "int32", "float64", "float32"]) -def test_basic_aggregations(dtype): - data = Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype) - - index = np.arange(9) - np.random.default_rng(2).shuffle(index) - data = data.reindex(index) - - grouped = data.groupby(lambda x: x // 3, group_keys=False) - - for k, v in grouped: - assert len(v) == 3 - - msg = "using SeriesGroupBy.mean" - with tm.assert_produces_warning(FutureWarning, match=msg): - agged = grouped.aggregate(np.mean) - assert agged[1] == 1 - - msg = "using SeriesGroupBy.mean" - with tm.assert_produces_warning(FutureWarning, match=msg): - expected = grouped.agg(np.mean) - tm.assert_series_equal(agged, expected) # shorthand - tm.assert_series_equal(agged, grouped.mean()) - result = grouped.sum() - msg = "using SeriesGroupBy.sum" - with tm.assert_produces_warning(FutureWarning, match=msg): - expected = grouped.agg(np.sum) - tm.assert_series_equal(result, expected) - - expected = grouped.apply(lambda x: x * x.sum()) - transformed = grouped.transform(lambda x: x * x.sum()) - assert transformed[7] == 12 - tm.assert_series_equal(transformed, expected) - - value_grouped = data.groupby(data) - msg = "using SeriesGroupBy.mean" - with tm.assert_produces_warning(FutureWarning, match=msg): - result = value_grouped.aggregate(np.mean) - tm.assert_series_equal(result, agged, check_index_type=False) - - # complex agg - msg = "using SeriesGroupBy.[mean|std]" - with tm.assert_produces_warning(FutureWarning, match=msg): - agged = grouped.aggregate([np.mean, np.std]) - - msg = r"nested renamer is not supported" - with pytest.raises(SpecificationError, match=msg): - grouped.aggregate({"one": np.mean, "two": np.std}) - - group_constants = {0: 10, 1: 20, 2: 30} - msg = ( - "Pinning the groupby key to each group in SeriesGroupBy.agg is deprecated, " - "and cases that relied on it will raise in a future version" - ) - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#41090 - agged = grouped.agg(lambda x: group_constants[x.name] + x.mean()) - assert agged[1] == 21 - - # corner cases - msg = "Must produce aggregated value" - # exception raised is type Exception - with pytest.raises(Exception, match=msg): - grouped.aggregate(lambda x: x * 2) - - -def test_groupby_nonobject_dtype(mframe, df_mixed_floats): - key = mframe.index.codes[0] - grouped = mframe.groupby(key) - result = grouped.sum() - - expected = mframe.groupby(key.astype("O")).sum() - assert result.index.dtype == np.int8 - assert expected.index.dtype == np.int64 - tm.assert_frame_equal(result, expected, check_index_type=False) - - # GH 3911, mixed frame non-conversion - df = df_mixed_floats.copy() - df["value"] = range(len(df)) - - def max_value(group): - return group.loc[group["value"].idxmax()] - - applied = df.groupby("A").apply(max_value) - result = applied.dtypes - expected = df.dtypes - tm.assert_series_equal(result, expected) - - -def test_inconsistent_return_type(): - # GH5592 - # inconsistent return type - df = DataFrame( - { - "A": ["Tiger", "Tiger", "Tiger", "Lamb", "Lamb", "Pony", "Pony"], - "B": Series(np.arange(7), dtype="int64"), - "C": date_range("20130101", periods=7), - } - ) - - def f_0(grp): - return grp.iloc[0] - - expected = df.groupby("A").first()[["B"]] - result = df.groupby("A").apply(f_0)[["B"]] - tm.assert_frame_equal(result, expected) - - def f_1(grp): - if grp.name == "Tiger": - return None - return grp.iloc[0] - - result = df.groupby("A").apply(f_1)[["B"]] - # Cast to avoid upcast when setting nan below - e = expected.copy().astype("float64") - e.loc["Tiger"] = np.nan - tm.assert_frame_equal(result, e) - - def f_2(grp): - if grp.name == "Pony": - return None - return grp.iloc[0] - - result = df.groupby("A").apply(f_2)[["B"]] - # Explicit cast to float to avoid implicit cast when setting nan - e = expected.copy().astype({"B": "float"}) - e.loc["Pony"] = np.nan - tm.assert_frame_equal(result, e) - - # 5592 revisited, with datetimes - def f_3(grp): - if grp.name == "Pony": - return None - return grp.iloc[0] - - result = df.groupby("A").apply(f_3)[["C"]] - e = df.groupby("A").first()[["C"]] - e.loc["Pony"] = pd.NaT - tm.assert_frame_equal(result, e) - - # scalar outputs - def f_4(grp): - if grp.name == "Pony": - return None - return grp.iloc[0].loc["C"] - - result = df.groupby("A").apply(f_4) - e = df.groupby("A").first()["C"].copy() - e.loc["Pony"] = np.nan - e.name = None - tm.assert_series_equal(result, e) - - -def test_pass_args_kwargs(ts, tsframe): - def f(x, q=None, axis=0): - return np.percentile(x, q, axis=axis) - - g = lambda x: np.percentile(x, 80, axis=0) - - # Series - ts_grouped = ts.groupby(lambda x: x.month) - agg_result = ts_grouped.agg(np.percentile, 80, axis=0) - apply_result = ts_grouped.apply(np.percentile, 80, axis=0) - trans_result = ts_grouped.transform(np.percentile, 80, axis=0) - - agg_expected = ts_grouped.quantile(0.8) - trans_expected = ts_grouped.transform(g) - - tm.assert_series_equal(apply_result, agg_expected) - tm.assert_series_equal(agg_result, agg_expected) - tm.assert_series_equal(trans_result, trans_expected) - - agg_result = ts_grouped.agg(f, q=80) - apply_result = ts_grouped.apply(f, q=80) - trans_result = ts_grouped.transform(f, q=80) - tm.assert_series_equal(agg_result, agg_expected) - tm.assert_series_equal(apply_result, agg_expected) - tm.assert_series_equal(trans_result, trans_expected) - - # DataFrame - for as_index in [True, False]: - df_grouped = tsframe.groupby(lambda x: x.month, as_index=as_index) - warn = None if as_index else FutureWarning - msg = "A grouping .* was excluded from the result" - with tm.assert_produces_warning(warn, match=msg): - agg_result = df_grouped.agg(np.percentile, 80, axis=0) - with tm.assert_produces_warning(warn, match=msg): - apply_result = df_grouped.apply(DataFrame.quantile, 0.8) - with tm.assert_produces_warning(warn, match=msg): - expected = df_grouped.quantile(0.8) - tm.assert_frame_equal(apply_result, expected, check_names=False) - tm.assert_frame_equal(agg_result, expected) - - apply_result = df_grouped.apply(DataFrame.quantile, [0.4, 0.8]) - with tm.assert_produces_warning(warn, match=msg): - expected_seq = df_grouped.quantile([0.4, 0.8]) - tm.assert_frame_equal(apply_result, expected_seq, check_names=False) - - with tm.assert_produces_warning(warn, match=msg): - agg_result = df_grouped.agg(f, q=80) - with tm.assert_produces_warning(warn, match=msg): - apply_result = df_grouped.apply(DataFrame.quantile, q=0.8) - tm.assert_frame_equal(agg_result, expected) - tm.assert_frame_equal(apply_result, expected, check_names=False) - - -@pytest.mark.parametrize("as_index", [True, False]) -def test_pass_args_kwargs_duplicate_columns(tsframe, as_index): - # go through _aggregate_frame with self.axis == 0 and duplicate columns - tsframe.columns = ["A", "B", "A", "C"] - gb = tsframe.groupby(lambda x: x.month, as_index=as_index) - - warn = None if as_index else FutureWarning - msg = "A grouping .* was excluded from the result" - with tm.assert_produces_warning(warn, match=msg): - res = gb.agg(np.percentile, 80, axis=0) - - ex_data = { - 1: tsframe[tsframe.index.month == 1].quantile(0.8), - 2: tsframe[tsframe.index.month == 2].quantile(0.8), - } - expected = DataFrame(ex_data).T - if not as_index: - # TODO: try to get this more consistent? - expected.index = Index(range(2)) - - tm.assert_frame_equal(res, expected) - - -def test_len(): - df = tm.makeTimeDataFrame() - grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]) - assert len(grouped) == len(df) - - grouped = df.groupby([lambda x: x.year, lambda x: x.month]) - expected = len({(x.year, x.month) for x in df.index}) - assert len(grouped) == expected - - # issue 11016 - df = DataFrame({"a": [np.nan] * 3, "b": [1, 2, 3]}) - assert len(df.groupby("a")) == 0 - assert len(df.groupby("b")) == 3 - assert len(df.groupby(["a", "b"])) == 3 - - -def test_basic_regression(): - # regression - result = Series([1.0 * x for x in list(range(1, 10)) * 10]) - - data = np.random.default_rng(2).random(1100) * 10.0 - groupings = Series(data) - - grouped = result.groupby(groupings) - grouped.mean() - - -@pytest.mark.parametrize( - "dtype", ["float64", "float32", "int64", "int32", "int16", "int8"] -) -def test_with_na_groups(dtype): - index = Index(np.arange(10)) - values = Series(np.ones(10), index, dtype=dtype) - labels = Series( - [np.nan, "foo", "bar", "bar", np.nan, np.nan, "bar", "bar", np.nan, "foo"], - index=index, - ) - - # this SHOULD be an int - grouped = values.groupby(labels) - agged = grouped.agg(len) - expected = Series([4, 2], index=["bar", "foo"]) - - tm.assert_series_equal(agged, expected, check_dtype=False) - - # assert issubclass(agged.dtype.type, np.integer) - - # explicitly return a float from my function - def f(x): - return float(len(x)) - - agged = grouped.agg(f) - expected = Series([4.0, 2.0], index=["bar", "foo"]) - - tm.assert_series_equal(agged, expected) - - -def test_indices_concatenation_order(): - # GH 2808 - - def f1(x): - y = x[(x.b % 2) == 1] ** 2 - if y.empty: - multiindex = MultiIndex(levels=[[]] * 2, codes=[[]] * 2, names=["b", "c"]) - res = DataFrame(columns=["a"], index=multiindex) - return res - else: - y = y.set_index(["b", "c"]) - return y - - def f2(x): - y = x[(x.b % 2) == 1] ** 2 - if y.empty: - return DataFrame() - else: - y = y.set_index(["b", "c"]) - return y - - def f3(x): - y = x[(x.b % 2) == 1] ** 2 - if y.empty: - multiindex = MultiIndex( - levels=[[]] * 2, codes=[[]] * 2, names=["foo", "bar"] - ) - res = DataFrame(columns=["a", "b"], index=multiindex) - return res - else: - return y - - df = DataFrame({"a": [1, 2, 2, 2], "b": range(4), "c": range(5, 9)}) - - df2 = DataFrame({"a": [3, 2, 2, 2], "b": range(4), "c": range(5, 9)}) - - depr_msg = "The behavior of array concatenation with empty entries is deprecated" - - # correct result - result1 = df.groupby("a").apply(f1) - result2 = df2.groupby("a").apply(f1) - tm.assert_frame_equal(result1, result2) - - # should fail (not the same number of levels) - msg = "Cannot concat indices that do not have the same number of levels" - with pytest.raises(AssertionError, match=msg): - df.groupby("a").apply(f2) - with pytest.raises(AssertionError, match=msg): - df2.groupby("a").apply(f2) - - # should fail (incorrect shape) - with pytest.raises(AssertionError, match=msg): - df.groupby("a").apply(f3) - with pytest.raises(AssertionError, match=msg): - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - df2.groupby("a").apply(f3) - - -def test_attr_wrapper(ts): - grouped = ts.groupby(lambda x: x.weekday()) - - result = grouped.std() - expected = grouped.agg(lambda x: np.std(x, ddof=1)) - tm.assert_series_equal(result, expected) - - # this is pretty cool - result = grouped.describe() - expected = {name: gp.describe() for name, gp in grouped} - expected = DataFrame(expected).T - tm.assert_frame_equal(result, expected) - - # get attribute - result = grouped.dtype - expected = grouped.agg(lambda x: x.dtype) - tm.assert_series_equal(result, expected) - - # make sure raises error - msg = "'SeriesGroupBy' object has no attribute 'foo'" - with pytest.raises(AttributeError, match=msg): - getattr(grouped, "foo") - - -def test_frame_groupby(tsframe): - grouped = tsframe.groupby(lambda x: x.weekday()) - - # aggregate - aggregated = grouped.aggregate("mean") - assert len(aggregated) == 5 - assert len(aggregated.columns) == 4 - - # by string - tscopy = tsframe.copy() - tscopy["weekday"] = [x.weekday() for x in tscopy.index] - stragged = tscopy.groupby("weekday").aggregate("mean") - tm.assert_frame_equal(stragged, aggregated, check_names=False) - - # transform - grouped = tsframe.head(30).groupby(lambda x: x.weekday()) - transformed = grouped.transform(lambda x: x - x.mean()) - assert len(transformed) == 30 - assert len(transformed.columns) == 4 - - # transform propagate - transformed = grouped.transform(lambda x: x.mean()) - for name, group in grouped: - mean = group.mean() - for idx in group.index: - tm.assert_series_equal(transformed.xs(idx), mean, check_names=False) - - # iterate - for weekday, group in grouped: - assert group.index[0].weekday() == weekday - - # groups / group_indices - groups = grouped.groups - indices = grouped.indices - - for k, v in groups.items(): - samething = tsframe.index.take(indices[k]) - assert (samething == v).all() - - -def test_frame_groupby_columns(tsframe): - mapping = {"A": 0, "B": 0, "C": 1, "D": 1} - msg = "DataFrame.groupby with axis=1 is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - grouped = tsframe.groupby(mapping, axis=1) - - # aggregate - aggregated = grouped.aggregate("mean") - assert len(aggregated) == len(tsframe) - assert len(aggregated.columns) == 2 - - # transform - tf = lambda x: x - x.mean() - msg = "The 'axis' keyword in DataFrame.groupby is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - groupedT = tsframe.T.groupby(mapping, axis=0) - tm.assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf)) - - # iterate - for k, v in grouped: - assert len(v.columns) == 2 - - -def test_frame_set_name_single(df): - grouped = df.groupby("A") - - result = grouped.mean(numeric_only=True) - assert result.index.name == "A" - - result = df.groupby("A", as_index=False).mean(numeric_only=True) - assert result.index.name != "A" - - result = grouped[["C", "D"]].agg("mean") - assert result.index.name == "A" - - result = grouped.agg({"C": "mean", "D": "std"}) - assert result.index.name == "A" - - result = grouped["C"].mean() - assert result.index.name == "A" - result = grouped["C"].agg("mean") - assert result.index.name == "A" - result = grouped["C"].agg(["mean", "std"]) - assert result.index.name == "A" - - msg = r"nested renamer is not supported" - with pytest.raises(SpecificationError, match=msg): - grouped["C"].agg({"foo": "mean", "bar": "std"}) - - -def test_multi_func(df): - col1 = df["A"] - col2 = df["B"] - - grouped = df.groupby([col1.get, col2.get]) - agged = grouped.mean(numeric_only=True) - expected = df.groupby(["A", "B"]).mean() - - # TODO groupby get drops names - tm.assert_frame_equal( - agged.loc[:, ["C", "D"]], expected.loc[:, ["C", "D"]], check_names=False - ) - - # some "groups" with no data - df = DataFrame( - { - "v1": np.random.default_rng(2).standard_normal(6), - "v2": np.random.default_rng(2).standard_normal(6), - "k1": np.array(["b", "b", "b", "a", "a", "a"]), - "k2": np.array(["1", "1", "1", "2", "2", "2"]), - }, - index=["one", "two", "three", "four", "five", "six"], - ) - # only verify that it works for now - grouped = df.groupby(["k1", "k2"]) - grouped.agg("sum") - - -def test_multi_key_multiple_functions(df): - grouped = df.groupby(["A", "B"])["C"] - - agged = grouped.agg(["mean", "std"]) - expected = DataFrame({"mean": grouped.agg("mean"), "std": grouped.agg("std")}) - tm.assert_frame_equal(agged, expected) - - -def test_frame_multi_key_function_list(): - data = DataFrame( - { - "A": [ - "foo", - "foo", - "foo", - "foo", - "bar", - "bar", - "bar", - "bar", - "foo", - "foo", - "foo", - ], - "B": [ - "one", - "one", - "one", - "two", - "one", - "one", - "one", - "two", - "two", - "two", - "one", - ], - "D": np.random.default_rng(2).standard_normal(11), - "E": np.random.default_rng(2).standard_normal(11), - "F": np.random.default_rng(2).standard_normal(11), - } - ) - - grouped = data.groupby(["A", "B"]) - funcs = ["mean", "std"] - agged = grouped.agg(funcs) - expected = pd.concat( - [grouped["D"].agg(funcs), grouped["E"].agg(funcs), grouped["F"].agg(funcs)], - keys=["D", "E", "F"], - axis=1, - ) - assert isinstance(agged.index, MultiIndex) - assert isinstance(expected.index, MultiIndex) - tm.assert_frame_equal(agged, expected) - - -def test_frame_multi_key_function_list_partial_failure(): - data = DataFrame( - { - "A": [ - "foo", - "foo", - "foo", - "foo", - "bar", - "bar", - "bar", - "bar", - "foo", - "foo", - "foo", - ], - "B": [ - "one", - "one", - "one", - "two", - "one", - "one", - "one", - "two", - "two", - "two", - "one", - ], - "C": [ - "dull", - "dull", - "shiny", - "dull", - "dull", - "shiny", - "shiny", - "dull", - "shiny", - "shiny", - "shiny", - ], - "D": np.random.default_rng(2).standard_normal(11), - "E": np.random.default_rng(2).standard_normal(11), - "F": np.random.default_rng(2).standard_normal(11), - } - ) - - grouped = data.groupby(["A", "B"]) - funcs = ["mean", "std"] - msg = re.escape("agg function failed [how->mean,dtype->object]") - with pytest.raises(TypeError, match=msg): - grouped.agg(funcs) - - -@pytest.mark.parametrize("op", [lambda x: x.sum(), lambda x: x.mean()]) -def test_groupby_multiple_columns(df, op): - data = df - grouped = data.groupby(["A", "B"]) - - result1 = op(grouped) - - keys = [] - values = [] - for n1, gp1 in data.groupby("A"): - for n2, gp2 in gp1.groupby("B"): - keys.append((n1, n2)) - values.append(op(gp2.loc[:, ["C", "D"]])) - - mi = MultiIndex.from_tuples(keys, names=["A", "B"]) - expected = pd.concat(values, axis=1).T - expected.index = mi - - # a little bit crude - for col in ["C", "D"]: - result_col = op(grouped[col]) - pivoted = result1[col] - exp = expected[col] - tm.assert_series_equal(result_col, exp) - tm.assert_series_equal(pivoted, exp) - - # test single series works the same - result = data["C"].groupby([data["A"], data["B"]]).mean() - expected = data.groupby(["A", "B"]).mean()["C"] - - tm.assert_series_equal(result, expected) - - -def test_as_index_select_column(): - # GH 5764 - df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"]) - result = df.groupby("A", as_index=False)["B"].get_group(1) - expected = Series([2, 4], name="B") - tm.assert_series_equal(result, expected) - - result = df.groupby("A", as_index=False, group_keys=True)["B"].apply( - lambda x: x.cumsum() - ) - expected = Series( - [2, 6, 6], name="B", index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 2)]) - ) - tm.assert_series_equal(result, expected) - - -def test_obj_arg_get_group_deprecated(): - depr_msg = "obj is deprecated" - - df = DataFrame({"a": [1, 1, 2], "b": [3, 4, 5]}) - expected = df.iloc[df.groupby("b").indices.get(4)] - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - result = df.groupby("b").get_group(4, obj=df) - tm.assert_frame_equal(result, expected) - - -def test_groupby_as_index_select_column_sum_empty_df(): - # GH 35246 - df = DataFrame(columns=Index(["A", "B", "C"], name="alpha")) - left = df.groupby(by="A", as_index=False)["B"].sum(numeric_only=False) - - expected = DataFrame(columns=df.columns[:2], index=range(0)) - # GH#50744 - Columns after selection shouldn't retain names - expected.columns.names = [None] - tm.assert_frame_equal(left, expected) - - -def test_groupby_as_index_agg(df): - grouped = df.groupby("A", as_index=False) - - # single-key - - result = grouped[["C", "D"]].agg("mean") - expected = grouped.mean(numeric_only=True) - tm.assert_frame_equal(result, expected) - - result2 = grouped.agg({"C": "mean", "D": "sum"}) - expected2 = grouped.mean(numeric_only=True) - expected2["D"] = grouped.sum()["D"] - tm.assert_frame_equal(result2, expected2) - - grouped = df.groupby("A", as_index=True) - - msg = r"nested renamer is not supported" - with pytest.raises(SpecificationError, match=msg): - grouped["C"].agg({"Q": "sum"}) - - # multi-key - - grouped = df.groupby(["A", "B"], as_index=False) - - result = grouped.agg("mean") - expected = grouped.mean() - tm.assert_frame_equal(result, expected) - - result2 = grouped.agg({"C": "mean", "D": "sum"}) - expected2 = grouped.mean() - expected2["D"] = grouped.sum()["D"] - tm.assert_frame_equal(result2, expected2) - - expected3 = grouped["C"].sum() - expected3 = DataFrame(expected3).rename(columns={"C": "Q"}) - msg = "Passing a dictionary to SeriesGroupBy.agg is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - result3 = grouped["C"].agg({"Q": "sum"}) - tm.assert_frame_equal(result3, expected3) - - # GH7115 & GH8112 & GH8582 - df = DataFrame( - np.random.default_rng(2).integers(0, 100, (50, 3)), - columns=["jim", "joe", "jolie"], - ) - ts = Series(np.random.default_rng(2).integers(5, 10, 50), name="jim") - - gr = df.groupby(ts) - gr.nth(0) # invokes set_selection_from_grouper internally - - msg = "The behavior of DataFrame.sum with axis=None is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg, check_stacklevel=False): - res = gr.apply(sum) - with tm.assert_produces_warning(FutureWarning, match=msg, check_stacklevel=False): - alt = df.groupby(ts).apply(sum) - tm.assert_frame_equal(res, alt) - - for attr in ["mean", "max", "count", "idxmax", "cumsum", "all"]: - gr = df.groupby(ts, as_index=False) - left = getattr(gr, attr)() - - gr = df.groupby(ts.values, as_index=True) - right = getattr(gr, attr)().reset_index(drop=True) - - tm.assert_frame_equal(left, right) - - -def test_ops_not_as_index(reduction_func): - # GH 10355, 21090 - # Using as_index=False should not modify grouped column - - if reduction_func in ("corrwith", "nth", "ngroup"): - pytest.skip(f"GH 5755: Test not applicable for {reduction_func}") - - df = DataFrame( - np.random.default_rng(2).integers(0, 5, size=(100, 2)), columns=["a", "b"] - ) - expected = getattr(df.groupby("a"), reduction_func)() - if reduction_func == "size": - expected = expected.rename("size") - expected = expected.reset_index() - - if reduction_func != "size": - # 32 bit compat -> groupby preserves dtype whereas reset_index casts to int64 - expected["a"] = expected["a"].astype(df["a"].dtype) - - g = df.groupby("a", as_index=False) - - result = getattr(g, reduction_func)() - tm.assert_frame_equal(result, expected) - - result = g.agg(reduction_func) - tm.assert_frame_equal(result, expected) - - result = getattr(g["b"], reduction_func)() - tm.assert_frame_equal(result, expected) - - result = g["b"].agg(reduction_func) - tm.assert_frame_equal(result, expected) - - -def test_as_index_series_return_frame(df): - grouped = df.groupby("A", as_index=False) - grouped2 = df.groupby(["A", "B"], as_index=False) - - result = grouped["C"].agg("sum") - expected = grouped.agg("sum").loc[:, ["A", "C"]] - assert isinstance(result, DataFrame) - tm.assert_frame_equal(result, expected) - - result2 = grouped2["C"].agg("sum") - expected2 = grouped2.agg("sum").loc[:, ["A", "B", "C"]] - assert isinstance(result2, DataFrame) - tm.assert_frame_equal(result2, expected2) - - result = grouped["C"].sum() - expected = grouped.sum().loc[:, ["A", "C"]] - assert isinstance(result, DataFrame) - tm.assert_frame_equal(result, expected) - - result2 = grouped2["C"].sum() - expected2 = grouped2.sum().loc[:, ["A", "B", "C"]] - assert isinstance(result2, DataFrame) - tm.assert_frame_equal(result2, expected2) - - -def test_as_index_series_column_slice_raises(df): - # GH15072 - grouped = df.groupby("A", as_index=False) - msg = r"Column\(s\) C already selected" - - with pytest.raises(IndexError, match=msg): - grouped["C"].__getitem__("D") - - -def test_groupby_as_index_cython(df): - data = df - - # single-key - grouped = data.groupby("A", as_index=False) - result = grouped.mean(numeric_only=True) - expected = data.groupby(["A"]).mean(numeric_only=True) - expected.insert(0, "A", expected.index) - expected.index = RangeIndex(len(expected)) - tm.assert_frame_equal(result, expected) - - # multi-key - grouped = data.groupby(["A", "B"], as_index=False) - result = grouped.mean() - expected = data.groupby(["A", "B"]).mean() - - arrays = list(zip(*expected.index.values)) - expected.insert(0, "A", arrays[0]) - expected.insert(1, "B", arrays[1]) - expected.index = RangeIndex(len(expected)) - tm.assert_frame_equal(result, expected) - - -def test_groupby_as_index_series_scalar(df): - grouped = df.groupby(["A", "B"], as_index=False) - - # GH #421 - - result = grouped["C"].agg(len) - expected = grouped.agg(len).loc[:, ["A", "B", "C"]] - tm.assert_frame_equal(result, expected) - - -def test_groupby_as_index_corner(df, ts): - msg = "as_index=False only valid with DataFrame" - with pytest.raises(TypeError, match=msg): - ts.groupby(lambda x: x.weekday(), as_index=False) - - msg = "as_index=False only valid for axis=0" - depr_msg = "DataFrame.groupby with axis=1 is deprecated" - with pytest.raises(ValueError, match=msg): - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - df.groupby(lambda x: x.lower(), as_index=False, axis=1) - - -def test_groupby_multiple_key(): - df = tm.makeTimeDataFrame() - grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]) - agged = grouped.sum() - tm.assert_almost_equal(df.values, agged.values) - - depr_msg = "DataFrame.groupby with axis=1 is deprecated" - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - grouped = df.T.groupby( - [lambda x: x.year, lambda x: x.month, lambda x: x.day], axis=1 - ) - - agged = grouped.agg(lambda x: x.sum()) - tm.assert_index_equal(agged.index, df.columns) - tm.assert_almost_equal(df.T.values, agged.values) - - agged = grouped.agg(lambda x: x.sum()) - tm.assert_almost_equal(df.T.values, agged.values) - - -def test_groupby_multi_corner(df): - # test that having an all-NA column doesn't mess you up - df = df.copy() - df["bad"] = np.nan - agged = df.groupby(["A", "B"]).mean() - - expected = df.groupby(["A", "B"]).mean() - expected["bad"] = np.nan - - tm.assert_frame_equal(agged, expected) - - -def test_raises_on_nuisance(df): - grouped = df.groupby("A") - msg = re.escape("agg function failed [how->mean,dtype->object]") - with pytest.raises(TypeError, match=msg): - grouped.agg("mean") - with pytest.raises(TypeError, match=msg): - grouped.mean() - - df = df.loc[:, ["A", "C", "D"]] - df["E"] = datetime.now() - grouped = df.groupby("A") - msg = "datetime64 type does not support sum operations" - with pytest.raises(TypeError, match=msg): - grouped.agg("sum") - with pytest.raises(TypeError, match=msg): - grouped.sum() - - # won't work with axis = 1 - depr_msg = "DataFrame.groupby with axis=1 is deprecated" - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - grouped = df.groupby({"A": 0, "C": 0, "D": 1, "E": 1}, axis=1) - msg = "does not support reduction 'sum'" - with pytest.raises(TypeError, match=msg): - grouped.agg(lambda x: x.sum(0, numeric_only=False)) - - -@pytest.mark.parametrize( - "agg_function", - ["max", "min"], -) -def test_keep_nuisance_agg(df, agg_function): - # GH 38815 - grouped = df.groupby("A") - result = getattr(grouped, agg_function)() - expected = result.copy() - expected.loc["bar", "B"] = getattr(df.loc[df["A"] == "bar", "B"], agg_function)() - expected.loc["foo", "B"] = getattr(df.loc[df["A"] == "foo", "B"], agg_function)() - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize( - "agg_function", - ["sum", "mean", "prod", "std", "var", "sem", "median"], -) -@pytest.mark.parametrize("numeric_only", [True, False]) -def test_omit_nuisance_agg(df, agg_function, numeric_only): - # GH 38774, GH 38815 - grouped = df.groupby("A") - - no_drop_nuisance = ("var", "std", "sem", "mean", "prod", "median") - if agg_function in no_drop_nuisance and not numeric_only: - # Added numeric_only as part of GH#46560; these do not drop nuisance - # columns when numeric_only is False - if agg_function in ("std", "sem"): - klass = ValueError - msg = "could not convert string to float: 'one'" - else: - klass = TypeError - msg = re.escape(f"agg function failed [how->{agg_function},dtype->object]") - with pytest.raises(klass, match=msg): - getattr(grouped, agg_function)(numeric_only=numeric_only) - else: - result = getattr(grouped, agg_function)(numeric_only=numeric_only) - if not numeric_only and agg_function == "sum": - # sum is successful on column B - columns = ["A", "B", "C", "D"] - else: - columns = ["A", "C", "D"] - expected = getattr(df.loc[:, columns].groupby("A"), agg_function)( - numeric_only=numeric_only - ) - tm.assert_frame_equal(result, expected) - - -def test_raise_on_nuisance_python_single(df): - # GH 38815 - grouped = df.groupby("A") - with pytest.raises(ValueError, match="could not convert"): - grouped.skew() - - -def test_raise_on_nuisance_python_multiple(three_group): - grouped = three_group.groupby(["A", "B"]) - msg = re.escape("agg function failed [how->mean,dtype->object]") - with pytest.raises(TypeError, match=msg): - grouped.agg("mean") - with pytest.raises(TypeError, match=msg): - grouped.mean() - - -def test_empty_groups_corner(mframe): - # handle empty groups - df = DataFrame( - { - "k1": np.array(["b", "b", "b", "a", "a", "a"]), - "k2": np.array(["1", "1", "1", "2", "2", "2"]), - "k3": ["foo", "bar"] * 3, - "v1": np.random.default_rng(2).standard_normal(6), - "v2": np.random.default_rng(2).standard_normal(6), - } - ) - - grouped = df.groupby(["k1", "k2"]) - result = grouped[["v1", "v2"]].agg("mean") - expected = grouped.mean(numeric_only=True) - tm.assert_frame_equal(result, expected) - - grouped = mframe[3:5].groupby(level=0) - agged = grouped.apply(lambda x: x.mean()) - agged_A = grouped["A"].apply("mean") - tm.assert_series_equal(agged["A"], agged_A) - assert agged.index.name == "first" - - -def test_nonsense_func(): - df = DataFrame([0]) - msg = r"unsupported operand type\(s\) for \+: 'int' and 'str'" - with pytest.raises(TypeError, match=msg): - df.groupby(lambda x: x + "foo") - - -def test_wrap_aggregated_output_multindex(mframe): - df = mframe.T - df["baz", "two"] = "peekaboo" - - keys = [np.array([0, 0, 1]), np.array([0, 0, 1])] - msg = re.escape("agg function failed [how->mean,dtype->object]") - with pytest.raises(TypeError, match=msg): - df.groupby(keys).agg("mean") - agged = df.drop(columns=("baz", "two")).groupby(keys).agg("mean") - assert isinstance(agged.columns, MultiIndex) - - def aggfun(ser): - if ser.name == ("foo", "one"): - raise TypeError("Test error message") - return ser.sum() - - with pytest.raises(TypeError, match="Test error message"): - df.groupby(keys).aggregate(aggfun) - - -def test_groupby_level_apply(mframe): - result = mframe.groupby(level=0).count() - assert result.index.name == "first" - result = mframe.groupby(level=1).count() - assert result.index.name == "second" - - result = mframe["A"].groupby(level=0).count() - assert result.index.name == "first" - - -def test_groupby_level_mapper(mframe): - deleveled = mframe.reset_index() - - mapper0 = {"foo": 0, "bar": 0, "baz": 1, "qux": 1} - mapper1 = {"one": 0, "two": 0, "three": 1} - - result0 = mframe.groupby(mapper0, level=0).sum() - result1 = mframe.groupby(mapper1, level=1).sum() - - mapped_level0 = np.array( - [mapper0.get(x) for x in deleveled["first"]], dtype=np.int64 - ) - mapped_level1 = np.array( - [mapper1.get(x) for x in deleveled["second"]], dtype=np.int64 - ) - expected0 = mframe.groupby(mapped_level0).sum() - expected1 = mframe.groupby(mapped_level1).sum() - expected0.index.name, expected1.index.name = "first", "second" - - tm.assert_frame_equal(result0, expected0) - tm.assert_frame_equal(result1, expected1) - - -def test_groupby_level_nonmulti(): - # GH 1313, GH 13901 - s = Series([1, 2, 3, 10, 4, 5, 20, 6], Index([1, 2, 3, 1, 4, 5, 2, 6], name="foo")) - expected = Series([11, 22, 3, 4, 5, 6], Index(range(1, 7), name="foo")) - - result = s.groupby(level=0).sum() - tm.assert_series_equal(result, expected) - result = s.groupby(level=[0]).sum() - tm.assert_series_equal(result, expected) - result = s.groupby(level=-1).sum() - tm.assert_series_equal(result, expected) - result = s.groupby(level=[-1]).sum() - tm.assert_series_equal(result, expected) - - msg = "level > 0 or level < -1 only valid with MultiIndex" - with pytest.raises(ValueError, match=msg): - s.groupby(level=1) - with pytest.raises(ValueError, match=msg): - s.groupby(level=-2) - msg = "No group keys passed!" - with pytest.raises(ValueError, match=msg): - s.groupby(level=[]) - msg = "multiple levels only valid with MultiIndex" - with pytest.raises(ValueError, match=msg): - s.groupby(level=[0, 0]) - with pytest.raises(ValueError, match=msg): - s.groupby(level=[0, 1]) - msg = "level > 0 or level < -1 only valid with MultiIndex" - with pytest.raises(ValueError, match=msg): - s.groupby(level=[1]) - - -def test_groupby_complex(): - # GH 12902 - a = Series(data=np.arange(4) * (1 + 2j), index=[0, 0, 1, 1]) - expected = Series((1 + 2j, 5 + 10j)) - - result = a.groupby(level=0).sum() - tm.assert_series_equal(result, expected) - - -def test_groupby_complex_numbers(): - # GH 17927 - df = DataFrame( - [ - {"a": 1, "b": 1 + 1j}, - {"a": 1, "b": 1 + 2j}, - {"a": 4, "b": 1}, - ] - ) - expected = DataFrame( - np.array([1, 1, 1], dtype=np.int64), - index=Index([(1 + 1j), (1 + 2j), (1 + 0j)], name="b"), - columns=Index(["a"], dtype="object"), - ) - result = df.groupby("b", sort=False).count() - tm.assert_frame_equal(result, expected) - - # Sorted by the magnitude of the complex numbers - expected.index = Index([(1 + 0j), (1 + 1j), (1 + 2j)], name="b") - result = df.groupby("b", sort=True).count() - tm.assert_frame_equal(result, expected) - - -def test_groupby_series_indexed_differently(): - s1 = Series( - [5.0, -9.0, 4.0, 100.0, -5.0, 55.0, 6.7], - index=Index(["a", "b", "c", "d", "e", "f", "g"]), - ) - s2 = Series( - [1.0, 1.0, 4.0, 5.0, 5.0, 7.0], index=Index(["a", "b", "d", "f", "g", "h"]) - ) - - grouped = s1.groupby(s2) - agged = grouped.mean() - exp = s1.groupby(s2.reindex(s1.index).get).mean() - tm.assert_series_equal(agged, exp) - - -def test_groupby_with_hier_columns(): - tuples = list( - zip( - *[ - ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], - ["one", "two", "one", "two", "one", "two", "one", "two"], - ] - ) - ) - index = MultiIndex.from_tuples(tuples) - columns = MultiIndex.from_tuples( - [("A", "cat"), ("B", "dog"), ("B", "cat"), ("A", "dog")] - ) - df = DataFrame( - np.random.default_rng(2).standard_normal((8, 4)), index=index, columns=columns - ) - - result = df.groupby(level=0).mean() - tm.assert_index_equal(result.columns, columns) - - depr_msg = "DataFrame.groupby with axis=1 is deprecated" - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - gb = df.groupby(level=0, axis=1) - result = gb.mean() - tm.assert_index_equal(result.index, df.index) - - result = df.groupby(level=0).agg("mean") - tm.assert_index_equal(result.columns, columns) - - result = df.groupby(level=0).apply(lambda x: x.mean()) - tm.assert_index_equal(result.columns, columns) - - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - gb = df.groupby(level=0, axis=1) - result = gb.agg(lambda x: x.mean(1)) - tm.assert_index_equal(result.columns, Index(["A", "B"])) - tm.assert_index_equal(result.index, df.index) - - # add a nuisance column - sorted_columns, _ = columns.sortlevel(0) - df["A", "foo"] = "bar" - result = df.groupby(level=0).mean(numeric_only=True) - tm.assert_index_equal(result.columns, df.columns[:-1]) - - -def test_grouping_ndarray(df): - grouped = df.groupby(df["A"].values) - result = grouped.sum() - expected = df.groupby(df["A"].rename(None)).sum() - tm.assert_frame_equal(result, expected) - - -def test_groupby_wrong_multi_labels(): - index = Index([0, 1, 2, 3, 4], name="index") - data = DataFrame( - { - "foo": ["foo1", "foo1", "foo2", "foo1", "foo3"], - "bar": ["bar1", "bar2", "bar2", "bar1", "bar1"], - "baz": ["baz1", "baz1", "baz1", "baz2", "baz2"], - "spam": ["spam2", "spam3", "spam2", "spam1", "spam1"], - "data": [20, 30, 40, 50, 60], - }, - index=index, - ) - - grouped = data.groupby(["foo", "bar", "baz", "spam"]) - - result = grouped.agg("mean") - expected = grouped.mean() - tm.assert_frame_equal(result, expected) - - -def test_groupby_series_with_name(df): - result = df.groupby(df["A"]).mean(numeric_only=True) - result2 = df.groupby(df["A"], as_index=False).mean(numeric_only=True) - assert result.index.name == "A" - assert "A" in result2 - - result = df.groupby([df["A"], df["B"]]).mean() - result2 = df.groupby([df["A"], df["B"]], as_index=False).mean() - assert result.index.names == ("A", "B") - assert "A" in result2 - assert "B" in result2 - - -def test_seriesgroupby_name_attr(df): - # GH 6265 - result = df.groupby("A")["C"] - assert result.count().name == "C" - assert result.mean().name == "C" - - testFunc = lambda x: np.sum(x) * 2 - assert result.agg(testFunc).name == "C" - - -def test_consistency_name(): - # GH 12363 - - df = DataFrame( - { - "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"], - "B": ["one", "one", "two", "two", "two", "two", "one", "two"], - "C": np.random.default_rng(2).standard_normal(8) + 1.0, - "D": np.arange(8), - } - ) - - expected = df.groupby(["A"]).B.count() - result = df.B.groupby(df.A).count() - tm.assert_series_equal(result, expected) - - -def test_groupby_name_propagation(df): - # GH 6124 - def summarize(df, name=None): - return Series({"count": 1, "mean": 2, "omissions": 3}, name=name) - - def summarize_random_name(df): - # Provide a different name for each Series. In this case, groupby - # should not attempt to propagate the Series name since they are - # inconsistent. - return Series({"count": 1, "mean": 2, "omissions": 3}, name=df.iloc[0]["A"]) - - metrics = df.groupby("A").apply(summarize) - assert metrics.columns.name is None - metrics = df.groupby("A").apply(summarize, "metrics") - assert metrics.columns.name == "metrics" - metrics = df.groupby("A").apply(summarize_random_name) - assert metrics.columns.name is None - - -def test_groupby_nonstring_columns(): - df = DataFrame([np.arange(10) for x in range(10)]) - grouped = df.groupby(0) - result = grouped.mean() - expected = df.groupby(df[0]).mean() - tm.assert_frame_equal(result, expected) - - -def test_groupby_mixed_type_columns(): - # GH 13432, unorderable types in py3 - df = DataFrame([[0, 1, 2]], columns=["A", "B", 0]) - expected = DataFrame([[1, 2]], columns=["B", 0], index=Index([0], name="A")) - - result = df.groupby("A").first() - tm.assert_frame_equal(result, expected) - - result = df.groupby("A").sum() - tm.assert_frame_equal(result, expected) - - -def test_cython_grouper_series_bug_noncontig(): - arr = np.empty((100, 100)) - arr.fill(np.nan) - obj = Series(arr[:, 0]) - inds = np.tile(range(10), 10) - - result = obj.groupby(inds).agg(Series.median) - assert result.isna().all() - - -def test_series_grouper_noncontig_index(): - index = Index(["a" * 10] * 100) - - values = Series(np.random.default_rng(2).standard_normal(50), index=index[::2]) - labels = np.random.default_rng(2).integers(0, 5, 50) - - # it works! - grouped = values.groupby(labels) - - # accessing the index elements causes segfault - f = lambda x: len(set(map(id, x.index))) - grouped.agg(f) - - -def test_convert_objects_leave_decimal_alone(): - s = Series(range(5)) - labels = np.array(["a", "b", "c", "d", "e"], dtype="O") - - def convert_fast(x): - return Decimal(str(x.mean())) - - def convert_force_pure(x): - # base will be length 0 - assert len(x.values.base) > 0 - return Decimal(str(x.mean())) - - grouped = s.groupby(labels) - - result = grouped.agg(convert_fast) - assert result.dtype == np.object_ - assert isinstance(result.iloc[0], Decimal) - - result = grouped.agg(convert_force_pure) - assert result.dtype == np.object_ - assert isinstance(result.iloc[0], Decimal) - - -def test_groupby_dtype_inference_empty(): - # GH 6733 - df = DataFrame({"x": [], "range": np.arange(0, dtype="int64")}) - assert df["x"].dtype == np.float64 - - result = df.groupby("x").first() - exp_index = Index([], name="x", dtype=np.float64) - expected = DataFrame({"range": Series([], index=exp_index, dtype="int64")}) - tm.assert_frame_equal(result, expected, by_blocks=True) - - -def test_groupby_unit64_float_conversion(): - # GH: 30859 groupby converts unit64 to floats sometimes - df = DataFrame({"first": [1], "second": [1], "value": [16148277970000000000]}) - result = df.groupby(["first", "second"])["value"].max() - expected = Series( - [16148277970000000000], - MultiIndex.from_product([[1], [1]], names=["first", "second"]), - name="value", - ) - tm.assert_series_equal(result, expected) - - -def test_groupby_list_infer_array_like(df): - result = df.groupby(list(df["A"])).mean(numeric_only=True) - expected = df.groupby(df["A"]).mean(numeric_only=True) - tm.assert_frame_equal(result, expected, check_names=False) - - with pytest.raises(KeyError, match=r"^'foo'$"): - df.groupby(list(df["A"][:-1])) - - # pathological case of ambiguity - df = DataFrame( - { - "foo": [0, 1], - "bar": [3, 4], - "val": np.random.default_rng(2).standard_normal(2), - } - ) - - result = df.groupby(["foo", "bar"]).mean() - expected = df.groupby([df["foo"], df["bar"]]).mean()[["val"]] - - -def test_groupby_keys_same_size_as_index(): - # GH 11185 - freq = "s" - index = date_range( - start=Timestamp("2015-09-29T11:34:44-0700"), periods=2, freq=freq - ) - df = DataFrame([["A", 10], ["B", 15]], columns=["metric", "values"], index=index) - result = df.groupby([Grouper(level=0, freq=freq), "metric"]).mean() - expected = df.set_index([df.index, "metric"]).astype(float) - - tm.assert_frame_equal(result, expected) - - -def test_groupby_one_row(): - # GH 11741 - msg = r"^'Z'$" - df1 = DataFrame( - np.random.default_rng(2).standard_normal((1, 4)), columns=list("ABCD") - ) - with pytest.raises(KeyError, match=msg): - df1.groupby("Z") - df2 = DataFrame( - np.random.default_rng(2).standard_normal((2, 4)), columns=list("ABCD") - ) - with pytest.raises(KeyError, match=msg): - df2.groupby("Z") - - -def test_groupby_nat_exclude(): - # GH 6992 - df = DataFrame( - { - "values": np.random.default_rng(2).standard_normal(8), - "dt": [ - np.nan, - Timestamp("2013-01-01"), - np.nan, - Timestamp("2013-02-01"), - np.nan, - Timestamp("2013-02-01"), - np.nan, - Timestamp("2013-01-01"), - ], - "str": [np.nan, "a", np.nan, "a", np.nan, "a", np.nan, "b"], - } - ) - grouped = df.groupby("dt") - - expected = [Index([1, 7]), Index([3, 5])] - keys = sorted(grouped.groups.keys()) - assert len(keys) == 2 - for k, e in zip(keys, expected): - # grouped.groups keys are np.datetime64 with system tz - # not to be affected by tz, only compare values - tm.assert_index_equal(grouped.groups[k], e) - - # confirm obj is not filtered - tm.assert_frame_equal(grouped.grouper.groupings[0].obj, df) - assert grouped.ngroups == 2 - - expected = { - Timestamp("2013-01-01 00:00:00"): np.array([1, 7], dtype=np.intp), - Timestamp("2013-02-01 00:00:00"): np.array([3, 5], dtype=np.intp), - } - - for k in grouped.indices: - tm.assert_numpy_array_equal(grouped.indices[k], expected[k]) - - tm.assert_frame_equal(grouped.get_group(Timestamp("2013-01-01")), df.iloc[[1, 7]]) - tm.assert_frame_equal(grouped.get_group(Timestamp("2013-02-01")), df.iloc[[3, 5]]) - - with pytest.raises(KeyError, match=r"^NaT$"): - grouped.get_group(pd.NaT) - - nan_df = DataFrame( - {"nan": [np.nan, np.nan, np.nan], "nat": [pd.NaT, pd.NaT, pd.NaT]} - ) - assert nan_df["nan"].dtype == "float64" - assert nan_df["nat"].dtype == "datetime64[ns]" - - for key in ["nan", "nat"]: - grouped = nan_df.groupby(key) - assert grouped.groups == {} - assert grouped.ngroups == 0 - assert grouped.indices == {} - with pytest.raises(KeyError, match=r"^nan$"): - grouped.get_group(np.nan) - with pytest.raises(KeyError, match=r"^NaT$"): - grouped.get_group(pd.NaT) - - -def test_groupby_two_group_keys_all_nan(): - # GH #36842: Grouping over two group keys shouldn't raise an error - df = DataFrame({"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 2]}) - result = df.groupby(["a", "b"]).indices - assert result == {} - - -def test_groupby_2d_malformed(): - d = DataFrame(index=range(2)) - d["group"] = ["g1", "g2"] - d["zeros"] = [0, 0] - d["ones"] = [1, 1] - d["label"] = ["l1", "l2"] - tmp = d.groupby(["group"]).mean(numeric_only=True) - res_values = np.array([[0.0, 1.0], [0.0, 1.0]]) - tm.assert_index_equal(tmp.columns, Index(["zeros", "ones"])) - tm.assert_numpy_array_equal(tmp.values, res_values) - - -def test_int32_overflow(): - B = np.concatenate((np.arange(10000), np.arange(10000), np.arange(5000))) - A = np.arange(25000) - df = DataFrame( - { - "A": A, - "B": B, - "C": A, - "D": B, - "E": np.random.default_rng(2).standard_normal(25000), - } - ) - - left = df.groupby(["A", "B", "C", "D"]).sum() - right = df.groupby(["D", "C", "B", "A"]).sum() - assert len(left) == len(right) - - -def test_groupby_sort_multi(): - df = DataFrame( - { - "a": ["foo", "bar", "baz"], - "b": [3, 2, 1], - "c": [0, 1, 2], - "d": np.random.default_rng(2).standard_normal(3), - } - ) - - tups = [tuple(row) for row in df[["a", "b", "c"]].values] - tups = com.asarray_tuplesafe(tups) - result = df.groupby(["a", "b", "c"], sort=True).sum() - tm.assert_numpy_array_equal(result.index.values, tups[[1, 2, 0]]) - - tups = [tuple(row) for row in df[["c", "a", "b"]].values] - tups = com.asarray_tuplesafe(tups) - result = df.groupby(["c", "a", "b"], sort=True).sum() - tm.assert_numpy_array_equal(result.index.values, tups) - - tups = [tuple(x) for x in df[["b", "c", "a"]].values] - tups = com.asarray_tuplesafe(tups) - result = df.groupby(["b", "c", "a"], sort=True).sum() - tm.assert_numpy_array_equal(result.index.values, tups[[2, 1, 0]]) - - df = DataFrame( - { - "a": [0, 1, 2, 0, 1, 2], - "b": [0, 0, 0, 1, 1, 1], - "d": np.random.default_rng(2).standard_normal(6), - } - ) - grouped = df.groupby(["a", "b"])["d"] - result = grouped.sum() - - def _check_groupby(df, result, keys, field, f=lambda x: x.sum()): - tups = [tuple(row) for row in df[keys].values] - tups = com.asarray_tuplesafe(tups) - expected = f(df.groupby(tups)[field]) - for k, v in expected.items(): - assert result[k] == v - - _check_groupby(df, result, ["a", "b"], "d") - - -def test_dont_clobber_name_column(): - df = DataFrame( - {"key": ["a", "a", "a", "b", "b", "b"], "name": ["foo", "bar", "baz"] * 2} - ) - - result = df.groupby("key", group_keys=False).apply(lambda x: x) - tm.assert_frame_equal(result, df) - - -def test_skip_group_keys(): - tsf = tm.makeTimeDataFrame() - - grouped = tsf.groupby(lambda x: x.month, group_keys=False) - result = grouped.apply(lambda x: x.sort_values(by="A")[:3]) - - pieces = [group.sort_values(by="A")[:3] for key, group in grouped] - - expected = pd.concat(pieces) - tm.assert_frame_equal(result, expected) - - grouped = tsf["A"].groupby(lambda x: x.month, group_keys=False) - result = grouped.apply(lambda x: x.sort_values()[:3]) - - pieces = [group.sort_values()[:3] for key, group in grouped] - - expected = pd.concat(pieces) - tm.assert_series_equal(result, expected) - - -def test_no_nonsense_name(float_frame): - # GH #995 - s = float_frame["C"].copy() - s.name = None - - result = s.groupby(float_frame["A"]).agg("sum") - assert result.name is None - - -def test_multifunc_sum_bug(): - # GH #1065 - x = DataFrame(np.arange(9).reshape(3, 3)) - x["test"] = 0 - x["fl"] = [1.3, 1.5, 1.6] - - grouped = x.groupby("test") - result = grouped.agg({"fl": "sum", 2: "size"}) - assert result["fl"].dtype == np.float64 - - -def test_handle_dict_return_value(df): - def f(group): - return {"max": group.max(), "min": group.min()} - - def g(group): - return Series({"max": group.max(), "min": group.min()}) - - result = df.groupby("A")["C"].apply(f) - expected = df.groupby("A")["C"].apply(g) - - assert isinstance(result, Series) - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize("grouper", ["A", ["A", "B"]]) -def test_set_group_name(df, grouper): - def f(group): - assert group.name is not None - return group - - def freduce(group): - assert group.name is not None - return group.sum() - - def freducex(x): - return freduce(x) - - grouped = df.groupby(grouper, group_keys=False) - - # make sure all these work - grouped.apply(f) - grouped.aggregate(freduce) - grouped.aggregate({"C": freduce, "D": freduce}) - grouped.transform(f) - - grouped["C"].apply(f) - grouped["C"].aggregate(freduce) - grouped["C"].aggregate([freduce, freducex]) - grouped["C"].transform(f) - - -def test_group_name_available_in_inference_pass(): - # gh-15062 - df = DataFrame({"a": [0, 0, 1, 1, 2, 2], "b": np.arange(6)}) - - names = [] - - def f(group): - names.append(group.name) - return group.copy() - - df.groupby("a", sort=False, group_keys=False).apply(f) - - expected_names = [0, 1, 2] - assert names == expected_names - - -def test_no_dummy_key_names(df): - # see gh-1291 - result = df.groupby(df["A"].values).sum() - assert result.index.name is None - - result = df.groupby([df["A"].values, df["B"].values]).sum() - assert result.index.names == (None, None) - - -def test_groupby_sort_multiindex_series(): - # series multiindex groupby sort argument was not being passed through - # _compress_group_index - # GH 9444 - index = MultiIndex( - levels=[[1, 2], [1, 2]], - codes=[[0, 0, 0, 0, 1, 1], [1, 1, 0, 0, 0, 0]], - names=["a", "b"], - ) - mseries = Series([0, 1, 2, 3, 4, 5], index=index) - index = MultiIndex( - levels=[[1, 2], [1, 2]], codes=[[0, 0, 1], [1, 0, 0]], names=["a", "b"] - ) - mseries_result = Series([0, 2, 4], index=index) - - result = mseries.groupby(level=["a", "b"], sort=False).first() - tm.assert_series_equal(result, mseries_result) - result = mseries.groupby(level=["a", "b"], sort=True).first() - tm.assert_series_equal(result, mseries_result.sort_index()) - - -def test_groupby_reindex_inside_function(): - periods = 1000 - ind = date_range(start="2012/1/1", freq="5min", periods=periods) - df = DataFrame({"high": np.arange(periods), "low": np.arange(periods)}, index=ind) - - def agg_before(func, fix=False): - """ - Run an aggregate func on the subset of data. - """ - - def _func(data): - d = data.loc[data.index.map(lambda x: x.hour < 11)].dropna() - if fix: - data[data.index[0]] - if len(d) == 0: - return None - return func(d) - - return _func - - grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day)) - closure_bad = grouped.agg({"high": agg_before(np.max)}) - closure_good = grouped.agg({"high": agg_before(np.max, True)}) - - tm.assert_frame_equal(closure_bad, closure_good) - - -def test_groupby_multiindex_missing_pair(): - # GH9049 - df = DataFrame( - { - "group1": ["a", "a", "a", "b"], - "group2": ["c", "c", "d", "c"], - "value": [1, 1, 1, 5], - } - ) - df = df.set_index(["group1", "group2"]) - df_grouped = df.groupby(level=["group1", "group2"], sort=True) - - res = df_grouped.agg("sum") - idx = MultiIndex.from_tuples( - [("a", "c"), ("a", "d"), ("b", "c")], names=["group1", "group2"] - ) - exp = DataFrame([[2], [1], [5]], index=idx, columns=["value"]) - - tm.assert_frame_equal(res, exp) - - -def test_groupby_multiindex_not_lexsorted(): - # GH 11640 - - # define the lexsorted version - lexsorted_mi = MultiIndex.from_tuples( - [("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"] - ) - lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi) - assert lexsorted_df.columns._is_lexsorted() - - # define the non-lexsorted version - not_lexsorted_df = DataFrame( - columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]] - ) - not_lexsorted_df = not_lexsorted_df.pivot_table( - index="a", columns=["b", "c"], values="d" - ) - not_lexsorted_df = not_lexsorted_df.reset_index() - assert not not_lexsorted_df.columns._is_lexsorted() - - expected = lexsorted_df.groupby("a").mean() - with tm.assert_produces_warning(PerformanceWarning): - result = not_lexsorted_df.groupby("a").mean() - tm.assert_frame_equal(expected, result) - - # a transforming function should work regardless of sort - # GH 14776 - df = DataFrame( - {"x": ["a", "a", "b", "a"], "y": [1, 1, 2, 2], "z": [1, 2, 3, 4]} - ).set_index(["x", "y"]) - assert not df.index._is_lexsorted() - - for level in [0, 1, [0, 1]]: - for sort in [False, True]: - result = df.groupby(level=level, sort=sort, group_keys=False).apply( - DataFrame.drop_duplicates - ) - expected = df - tm.assert_frame_equal(expected, result) - - result = ( - df.sort_index() - .groupby(level=level, sort=sort, group_keys=False) - .apply(DataFrame.drop_duplicates) - ) - expected = df.sort_index() - tm.assert_frame_equal(expected, result) - - -def test_index_label_overlaps_location(): - # checking we don't have any label/location confusion in the - # wake of GH5375 - df = DataFrame(list("ABCDE"), index=[2, 0, 2, 1, 1]) - g = df.groupby(list("ababb")) - actual = g.filter(lambda x: len(x) > 2) - expected = df.iloc[[1, 3, 4]] - tm.assert_frame_equal(actual, expected) - - ser = df[0] - g = ser.groupby(list("ababb")) - actual = g.filter(lambda x: len(x) > 2) - expected = ser.take([1, 3, 4]) - tm.assert_series_equal(actual, expected) - - # and again, with a generic Index of floats - df.index = df.index.astype(float) - g = df.groupby(list("ababb")) - actual = g.filter(lambda x: len(x) > 2) - expected = df.iloc[[1, 3, 4]] - tm.assert_frame_equal(actual, expected) - - ser = df[0] - g = ser.groupby(list("ababb")) - actual = g.filter(lambda x: len(x) > 2) - expected = ser.take([1, 3, 4]) - tm.assert_series_equal(actual, expected) - - -def test_transform_doesnt_clobber_ints(): - # GH 7972 - n = 6 - x = np.arange(n) - df = DataFrame({"a": x // 2, "b": 2.0 * x, "c": 3.0 * x}) - df2 = DataFrame({"a": x // 2 * 1.0, "b": 2.0 * x, "c": 3.0 * x}) - - gb = df.groupby("a") - result = gb.transform("mean") - - gb2 = df2.groupby("a") - expected = gb2.transform("mean") - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize( - "sort_column", - ["ints", "floats", "strings", ["ints", "floats"], ["ints", "strings"]], -) -@pytest.mark.parametrize( - "group_column", ["int_groups", "string_groups", ["int_groups", "string_groups"]] -) -def test_groupby_preserves_sort(sort_column, group_column): - # Test to ensure that groupby always preserves sort order of original - # object. Issue #8588 and #9651 - - df = DataFrame( - { - "int_groups": [3, 1, 0, 1, 0, 3, 3, 3], - "string_groups": ["z", "a", "z", "a", "a", "g", "g", "g"], - "ints": [8, 7, 4, 5, 2, 9, 1, 1], - "floats": [2.3, 5.3, 6.2, -2.4, 2.2, 1.1, 1.1, 5], - "strings": ["z", "d", "a", "e", "word", "word2", "42", "47"], - } - ) - - # Try sorting on different types and with different group types - - df = df.sort_values(by=sort_column) - g = df.groupby(group_column) - - def test_sort(x): - tm.assert_frame_equal(x, x.sort_values(by=sort_column)) - - g.apply(test_sort) - - -def test_pivot_table_values_key_error(): - # This test is designed to replicate the error in issue #14938 - df = DataFrame( - { - "eventDate": date_range(datetime.today(), periods=20, freq="M").tolist(), - "thename": range(0, 20), - } - ) - - df["year"] = df.set_index("eventDate").index.year - df["month"] = df.set_index("eventDate").index.month - - with pytest.raises(KeyError, match="'badname'"): - df.reset_index().pivot_table( - index="year", columns="month", values="badname", aggfunc="count" - ) - - -@pytest.mark.parametrize("columns", ["C", ["C"]]) -@pytest.mark.parametrize("keys", [["A"], ["A", "B"]]) -@pytest.mark.parametrize( - "values", - [ - [True], - [0], - [0.0], - ["a"], - Categorical([0]), - [to_datetime(0)], - date_range(0, 1, 1, tz="US/Eastern"), - pd.period_range("2016-01-01", periods=3, freq="D"), - pd.array([0], dtype="Int64"), - pd.array([0], dtype="Float64"), - pd.array([False], dtype="boolean"), - ], - ids=[ - "bool", - "int", - "float", - "str", - "cat", - "dt64", - "dt64tz", - "period", - "Int64", - "Float64", - "boolean", - ], -) -@pytest.mark.parametrize("method", ["attr", "agg", "apply"]) -@pytest.mark.parametrize( - "op", ["idxmax", "idxmin", "min", "max", "sum", "prod", "skew"] -) -def test_empty_groupby( - columns, keys, values, method, op, request, using_array_manager, dropna -): - # GH8093 & GH26411 - override_dtype = None - - if ( - isinstance(values, Categorical) - and len(keys) == 1 - and op in ["idxmax", "idxmin"] - ): - mark = pytest.mark.xfail( - raises=ValueError, match="attempt to get arg(min|max) of an empty sequence" - ) - request.node.add_marker(mark) - - if isinstance(values, BooleanArray) and op in ["sum", "prod"]: - # We expect to get Int64 back for these - override_dtype = "Int64" - - if isinstance(values[0], bool) and op in ("prod", "sum"): - # sum/product of bools is an integer - override_dtype = "int64" - - df = DataFrame({"A": values, "B": values, "C": values}, columns=list("ABC")) - - if hasattr(values, "dtype"): - # check that we did the construction right - assert (df.dtypes == values.dtype).all() - - df = df.iloc[:0] - - gb = df.groupby(keys, group_keys=False, dropna=dropna, observed=False)[columns] - - def get_result(**kwargs): - if method == "attr": - return getattr(gb, op)(**kwargs) - else: - return getattr(gb, method)(op, **kwargs) - - def get_categorical_invalid_expected(): - # Categorical is special without 'observed=True', we get an NaN entry - # corresponding to the unobserved group. If we passed observed=True - # to groupby, expected would just be 'df.set_index(keys)[columns]' - # as below - lev = Categorical([0], dtype=values.dtype) - if len(keys) != 1: - idx = MultiIndex.from_product([lev, lev], names=keys) - else: - # all columns are dropped, but we end up with one row - # Categorical is special without 'observed=True' - idx = Index(lev, name=keys[0]) - - expected = DataFrame([], columns=[], index=idx) - return expected - - is_per = isinstance(df.dtypes.iloc[0], pd.PeriodDtype) - is_dt64 = df.dtypes.iloc[0].kind == "M" - is_cat = isinstance(values, Categorical) - - if isinstance(values, Categorical) and not values.ordered and op in ["min", "max"]: - msg = f"Cannot perform {op} with non-ordered Categorical" - with pytest.raises(TypeError, match=msg): - get_result() - - if isinstance(columns, list): - # i.e. DataframeGroupBy, not SeriesGroupBy - result = get_result(numeric_only=True) - expected = get_categorical_invalid_expected() - tm.assert_equal(result, expected) - return - - if op in ["prod", "sum", "skew"]: - # ops that require more than just ordered-ness - if is_dt64 or is_cat or is_per: - # GH#41291 - # datetime64 -> prod and sum are invalid - if is_dt64: - msg = "datetime64 type does not support" - elif is_per: - msg = "Period type does not support" - else: - msg = "category type does not support" - if op == "skew": - msg = "|".join([msg, "does not support reduction 'skew'"]) - with pytest.raises(TypeError, match=msg): - get_result() - - if not isinstance(columns, list): - # i.e. SeriesGroupBy - return - elif op == "skew": - # TODO: test the numeric_only=True case - return - else: - # i.e. op in ["prod", "sum"]: - # i.e. DataFrameGroupBy - # ops that require more than just ordered-ness - # GH#41291 - result = get_result(numeric_only=True) - - # with numeric_only=True, these are dropped, and we get - # an empty DataFrame back - expected = df.set_index(keys)[[]] - if is_cat: - expected = get_categorical_invalid_expected() - tm.assert_equal(result, expected) - return - - result = get_result() - expected = df.set_index(keys)[columns] - if op in ["idxmax", "idxmin"]: - expected = expected.astype(df.index.dtype) - if override_dtype is not None: - expected = expected.astype(override_dtype) - if len(keys) == 1: - expected.index.name = keys[0] - tm.assert_equal(result, expected) - - -def test_empty_groupby_apply_nonunique_columns(): - # GH#44417 - df = DataFrame(np.random.default_rng(2).standard_normal((0, 4))) - df[3] = df[3].astype(np.int64) - df.columns = [0, 1, 2, 0] - gb = df.groupby(df[1], group_keys=False) - res = gb.apply(lambda x: x) - assert (res.dtypes == df.dtypes).all() - - -def test_tuple_as_grouping(): - # https://github.com/pandas-dev/pandas/issues/18314 - df = DataFrame( - { - ("a", "b"): [1, 1, 1, 1], - "a": [2, 2, 2, 2], - "b": [2, 2, 2, 2], - "c": [1, 1, 1, 1], - } - ) - - with pytest.raises(KeyError, match=r"('a', 'b')"): - df[["a", "b", "c"]].groupby(("a", "b")) - - result = df.groupby(("a", "b"))["c"].sum() - expected = Series([4], name="c", index=Index([1], name=("a", "b"))) - tm.assert_series_equal(result, expected) - - -def test_tuple_correct_keyerror(): - # https://github.com/pandas-dev/pandas/issues/18798 - df = DataFrame(1, index=range(3), columns=MultiIndex.from_product([[1, 2], [3, 4]])) - with pytest.raises(KeyError, match=r"^\(7, 8\)$"): - df.groupby((7, 8)).mean() - - -def test_groupby_agg_ohlc_non_first(): - # GH 21716 - df = DataFrame( - [[1], [1]], - columns=Index(["foo"], name="mycols"), - index=date_range("2018-01-01", periods=2, freq="D", name="dti"), - ) - - expected = DataFrame( - [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]], - columns=MultiIndex.from_tuples( - ( - ("foo", "sum", "foo"), - ("foo", "ohlc", "open"), - ("foo", "ohlc", "high"), - ("foo", "ohlc", "low"), - ("foo", "ohlc", "close"), - ), - names=["mycols", None, None], - ), - index=date_range("2018-01-01", periods=2, freq="D", name="dti"), - ) - - result = df.groupby(Grouper(freq="D")).agg(["sum", "ohlc"]) - - tm.assert_frame_equal(result, expected) - - -def test_groupby_multiindex_nat(): - # GH 9236 - values = [ - (pd.NaT, "a"), - (datetime(2012, 1, 2), "a"), - (datetime(2012, 1, 2), "b"), - (datetime(2012, 1, 3), "a"), - ] - mi = MultiIndex.from_tuples(values, names=["date", None]) - ser = Series([3, 2, 2.5, 4], index=mi) - - result = ser.groupby(level=1).mean() - expected = Series([3.0, 2.5], index=["a", "b"]) - tm.assert_series_equal(result, expected) - - -def test_groupby_empty_list_raises(): - # GH 5289 - values = zip(range(10), range(10)) - df = DataFrame(values, columns=["apple", "b"]) - msg = "Grouper and axis must be same length" - with pytest.raises(ValueError, match=msg): - df.groupby([[]]) - - -def test_groupby_multiindex_series_keys_len_equal_group_axis(): - # GH 25704 - index_array = [["x", "x"], ["a", "b"], ["k", "k"]] - index_names = ["first", "second", "third"] - ri = MultiIndex.from_arrays(index_array, names=index_names) - s = Series(data=[1, 2], index=ri) - result = s.groupby(["first", "third"]).sum() - - index_array = [["x"], ["k"]] - index_names = ["first", "third"] - ei = MultiIndex.from_arrays(index_array, names=index_names) - expected = Series([3], index=ei) - - tm.assert_series_equal(result, expected) - - -def test_groupby_groups_in_BaseGrouper(): - # GH 26326 - # Test if DataFrame grouped with a pandas.Grouper has correct groups - mi = MultiIndex.from_product([["A", "B"], ["C", "D"]], names=["alpha", "beta"]) - df = DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi) - result = df.groupby([Grouper(level="alpha"), "beta"]) - expected = df.groupby(["alpha", "beta"]) - assert result.groups == expected.groups - - result = df.groupby(["beta", Grouper(level="alpha")]) - expected = df.groupby(["beta", "alpha"]) - assert result.groups == expected.groups - - -@pytest.mark.parametrize("group_name", ["x", ["x"]]) -def test_groupby_axis_1(group_name): - # GH 27614 - df = DataFrame( - np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20] - ) - df.index.name = "y" - df.columns.name = "x" - - depr_msg = "DataFrame.groupby with axis=1 is deprecated" - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - gb = df.groupby(group_name, axis=1) - - results = gb.sum() - expected = df.T.groupby(group_name).sum().T - tm.assert_frame_equal(results, expected) - - # test on MI column - iterables = [["bar", "baz", "foo"], ["one", "two"]] - mi = MultiIndex.from_product(iterables=iterables, names=["x", "x1"]) - df = DataFrame(np.arange(18).reshape(3, 6), index=[0, 1, 0], columns=mi) - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - gb = df.groupby(group_name, axis=1) - results = gb.sum() - expected = df.T.groupby(group_name).sum().T - tm.assert_frame_equal(results, expected) - - -@pytest.mark.parametrize( - "op, expected", - [ - ( - "shift", - { - "time": [ - None, - None, - Timestamp("2019-01-01 12:00:00"), - Timestamp("2019-01-01 12:30:00"), - None, - None, - ] - }, - ), - ( - "bfill", - { - "time": [ - Timestamp("2019-01-01 12:00:00"), - Timestamp("2019-01-01 12:30:00"), - Timestamp("2019-01-01 14:00:00"), - Timestamp("2019-01-01 14:30:00"), - Timestamp("2019-01-01 14:00:00"), - Timestamp("2019-01-01 14:30:00"), - ] - }, - ), - ( - "ffill", - { - "time": [ - Timestamp("2019-01-01 12:00:00"), - Timestamp("2019-01-01 12:30:00"), - Timestamp("2019-01-01 12:00:00"), - Timestamp("2019-01-01 12:30:00"), - Timestamp("2019-01-01 14:00:00"), - Timestamp("2019-01-01 14:30:00"), - ] - }, - ), - ], -) -def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected): - # GH19995, GH27992: Check that timezone does not drop in shift, bfill, and ffill - tz = tz_naive_fixture - data = { - "id": ["A", "B", "A", "B", "A", "B"], - "time": [ - Timestamp("2019-01-01 12:00:00"), - Timestamp("2019-01-01 12:30:00"), - None, - None, - Timestamp("2019-01-01 14:00:00"), - Timestamp("2019-01-01 14:30:00"), - ], - } - df = DataFrame(data).assign(time=lambda x: x.time.dt.tz_localize(tz)) - - grouped = df.groupby("id") - result = getattr(grouped, op)() - expected = DataFrame(expected).assign(time=lambda x: x.time.dt.tz_localize(tz)) - tm.assert_frame_equal(result, expected) - - -def test_groupby_only_none_group(): - # see GH21624 - # this was crashing with "ValueError: Length of passed values is 1, index implies 0" - df = DataFrame({"g": [None], "x": 1}) - actual = df.groupby("g")["x"].transform("sum") - expected = Series([np.nan], name="x") - - tm.assert_series_equal(actual, expected) - - -def test_groupby_duplicate_index(): - # GH#29189 the groupby call here used to raise - ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0]) - gb = ser.groupby(level=0) - - result = gb.mean() - expected = Series([2, 5.5, 8], index=[2.0, 4.0, 5.0]) - tm.assert_series_equal(result, expected) - - -def test_group_on_empty_multiindex(transformation_func, request): - # GH 47787 - # With one row, those are transforms so the schema should be the same - df = DataFrame( - data=[[1, Timestamp("today"), 3, 4]], - columns=["col_1", "col_2", "col_3", "col_4"], - ) - df["col_3"] = df["col_3"].astype(int) - df["col_4"] = df["col_4"].astype(int) - df = df.set_index(["col_1", "col_2"]) - if transformation_func == "fillna": - args = ("ffill",) - else: - args = () - result = df.iloc[:0].groupby(["col_1"]).transform(transformation_func, *args) - expected = df.groupby(["col_1"]).transform(transformation_func, *args).iloc[:0] - if transformation_func in ("diff", "shift"): - expected = expected.astype(int) - tm.assert_equal(result, expected) - - result = ( - df["col_3"].iloc[:0].groupby(["col_1"]).transform(transformation_func, *args) - ) - expected = ( - df["col_3"].groupby(["col_1"]).transform(transformation_func, *args).iloc[:0] - ) - if transformation_func in ("diff", "shift"): - expected = expected.astype(int) - tm.assert_equal(result, expected) - - -@pytest.mark.parametrize( - "idx", - [ - Index(["a", "a"], name="foo"), - MultiIndex.from_tuples((("a", "a"), ("a", "a")), names=["foo", "bar"]), - ], -) -def test_dup_labels_output_shape(groupby_func, idx): - if groupby_func in {"size", "ngroup", "cumcount"}: - pytest.skip(f"Not applicable for {groupby_func}") - - df = DataFrame([[1, 1]], columns=idx) - grp_by = df.groupby([0]) - - args = get_groupby_method_args(groupby_func, df) - result = getattr(grp_by, groupby_func)(*args) - - assert result.shape == (1, 2) - tm.assert_index_equal(result.columns, idx) - - -def test_groupby_crash_on_nunique(axis): - # Fix following 30253 - dti = date_range("2016-01-01", periods=2, name="foo") - df = DataFrame({("A", "B"): [1, 2], ("A", "C"): [1, 3], ("D", "B"): [0, 0]}) - df.columns.names = ("bar", "baz") - df.index = dti - - axis_number = df._get_axis_number(axis) - if not axis_number: - df = df.T - msg = "The 'axis' keyword in DataFrame.groupby is deprecated" - else: - msg = "DataFrame.groupby with axis=1 is deprecated" - - with tm.assert_produces_warning(FutureWarning, match=msg): - gb = df.groupby(axis=axis_number, level=0) - result = gb.nunique() - - expected = DataFrame({"A": [1, 2], "D": [1, 1]}, index=dti) - expected.columns.name = "bar" - if not axis_number: - expected = expected.T - - tm.assert_frame_equal(result, expected) - - if axis_number == 0: - # same thing, but empty columns - with tm.assert_produces_warning(FutureWarning, match=msg): - gb2 = df[[]].groupby(axis=axis_number, level=0) - exp = expected[[]] - else: - # same thing, but empty rows - with tm.assert_produces_warning(FutureWarning, match=msg): - gb2 = df.loc[[]].groupby(axis=axis_number, level=0) - # default for empty when we can't infer a dtype is float64 - exp = expected.loc[[]].astype(np.float64) - - res = gb2.nunique() - tm.assert_frame_equal(res, exp) - - -def test_groupby_list_level(): - # GH 9790 - expected = DataFrame(np.arange(0, 9).reshape(3, 3), dtype=float) - result = expected.groupby(level=[0]).mean() - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize( - "max_seq_items, expected", - [ - (5, "{0: [0], 1: [1], 2: [2], 3: [3], 4: [4]}"), - (4, "{0: [0], 1: [1], 2: [2], 3: [3], ...}"), - (1, "{0: [0], ...}"), - ], -) -def test_groups_repr_truncates(max_seq_items, expected): - # GH 1135 - df = DataFrame(np.random.default_rng(2).standard_normal((5, 1))) - df["a"] = df.index - - with pd.option_context("display.max_seq_items", max_seq_items): - result = df.groupby("a").groups.__repr__() - assert result == expected - - result = df.groupby(np.array(df.a)).groups.__repr__() - assert result == expected - - -def test_group_on_two_row_multiindex_returns_one_tuple_key(): - # GH 18451 - df = DataFrame([{"a": 1, "b": 2, "c": 99}, {"a": 1, "b": 2, "c": 88}]) - df = df.set_index(["a", "b"]) - - grp = df.groupby(["a", "b"]) - result = grp.indices - expected = {(1, 2): np.array([0, 1], dtype=np.int64)} - - assert len(result) == 1 - key = (1, 2) - assert (result[key] == expected[key]).all() - - -@pytest.mark.parametrize( - "klass, attr, value", - [ - (DataFrame, "level", "a"), - (DataFrame, "as_index", False), - (DataFrame, "sort", False), - (DataFrame, "group_keys", False), - (DataFrame, "observed", True), - (DataFrame, "dropna", False), - (Series, "level", "a"), - (Series, "as_index", False), - (Series, "sort", False), - (Series, "group_keys", False), - (Series, "observed", True), - (Series, "dropna", False), - ], -) -def test_subsetting_columns_keeps_attrs(klass, attr, value): - # GH 9959 - When subsetting columns, don't drop attributes - df = DataFrame({"a": [1], "b": [2], "c": [3]}) - if attr != "axis": - df = df.set_index("a") - - expected = df.groupby("a", **{attr: value}) - result = expected[["b"]] if klass is DataFrame else expected["b"] - assert getattr(result, attr) == getattr(expected, attr) - - -def test_subsetting_columns_axis_1(): - # GH 37725 - df = DataFrame({"A": [1], "B": [2], "C": [3]}) - msg = "DataFrame.groupby with axis=1 is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - g = df.groupby([0, 0, 1], axis=1) - match = "Cannot subset columns when using axis=1" - with pytest.raises(ValueError, match=match): - g[["A", "B"]].sum() - - -@pytest.mark.parametrize("func", ["sum", "any", "shift"]) -def test_groupby_column_index_name_lost(func): - # GH: 29764 groupby loses index sometimes - expected = Index(["a"], name="idx") - df = DataFrame([[1]], columns=expected) - df_grouped = df.groupby([1]) - result = getattr(df_grouped, func)().columns - tm.assert_index_equal(result, expected) - - -@pytest.mark.parametrize( - "infer_string", - [ - False, - pytest.param(True, marks=td.skip_if_no("pyarrow")), - ], -) -def test_groupby_duplicate_columns(infer_string): - # GH: 31735 - df = DataFrame( - {"A": ["f", "e", "g", "h"], "B": ["a", "b", "c", "d"], "C": [1, 2, 3, 4]} - ).astype(object) - df.columns = ["A", "B", "B"] - with pd.option_context("future.infer_string", infer_string): - result = df.groupby([0, 0, 0, 0]).min() - expected = DataFrame( - [["e", "a", 1]], index=np.array([0]), columns=["A", "B", "B"], dtype=object - ) - tm.assert_frame_equal(result, expected) - - -def test_groupby_series_with_tuple_name(): - # GH 37755 - ser = Series([1, 2, 3, 4], index=[1, 1, 2, 2], name=("a", "a")) - ser.index.name = ("b", "b") - result = ser.groupby(level=0).last() - expected = Series([2, 4], index=[1, 2], name=("a", "a")) - expected.index.name = ("b", "b") - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize( - "func, values", [("sum", [97.0, 98.0]), ("mean", [24.25, 24.5])] -) -def test_groupby_numerical_stability_sum_mean(func, values): - # GH#38778 - data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15] - df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data}) - result = getattr(df.groupby("group"), func)() - expected = DataFrame({"a": values, "b": values}, index=Index([1, 2], name="group")) - tm.assert_frame_equal(result, expected) - - -def test_groupby_numerical_stability_cumsum(): - # GH#38934 - data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15] - df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data}) - result = df.groupby("group").cumsum() - exp_data = ( - [1e16] * 2 + [1e16 + 96, 1e16 + 98] + [5e15 + 97, 5e15 + 98] + [97.0, 98.0] - ) - expected = DataFrame({"a": exp_data, "b": exp_data}) - tm.assert_frame_equal(result, expected, check_exact=True) - - -def test_groupby_cumsum_skipna_false(): - # GH#46216 don't propagate np.nan above the diagonal - arr = np.random.default_rng(2).standard_normal((5, 5)) - df = DataFrame(arr) - for i in range(5): - df.iloc[i, i] = np.nan - - df["A"] = 1 - gb = df.groupby("A") - - res = gb.cumsum(skipna=False) - - expected = df[[0, 1, 2, 3, 4]].cumsum(skipna=False) - tm.assert_frame_equal(res, expected) - - -def test_groupby_cumsum_timedelta64(): - # GH#46216 don't ignore is_datetimelike in libgroupby.group_cumsum - dti = date_range("2016-01-01", periods=5) - ser = Series(dti) - dti[0] - ser[2] = pd.NaT - - df = DataFrame({"A": 1, "B": ser}) - gb = df.groupby("A") - - res = gb.cumsum(numeric_only=False, skipna=True) - exp = DataFrame({"B": [ser[0], ser[1], pd.NaT, ser[4], ser[4] * 2]}) - tm.assert_frame_equal(res, exp) - - res = gb.cumsum(numeric_only=False, skipna=False) - exp = DataFrame({"B": [ser[0], ser[1], pd.NaT, pd.NaT, pd.NaT]}) - tm.assert_frame_equal(res, exp) - - -def test_groupby_mean_duplicate_index(rand_series_with_duplicate_datetimeindex): - dups = rand_series_with_duplicate_datetimeindex - result = dups.groupby(level=0).mean() - expected = dups.groupby(dups.index).mean() - tm.assert_series_equal(result, expected) - - -def test_groupby_all_nan_groups_drop(): - # GH 15036 - s = Series([1, 2, 3], [np.nan, np.nan, np.nan]) - result = s.groupby(s.index).sum() - expected = Series([], index=Index([], dtype=np.float64), dtype=np.int64) - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize("numeric_only", [True, False]) -def test_groupby_empty_multi_column(as_index, numeric_only): - # GH 15106 & GH 41998 - df = DataFrame(data=[], columns=["A", "B", "C"]) - gb = df.groupby(["A", "B"], as_index=as_index) - result = gb.sum(numeric_only=numeric_only) - if as_index: - index = MultiIndex([[], []], [[], []], names=["A", "B"]) - columns = ["C"] if not numeric_only else [] - else: - index = RangeIndex(0) - columns = ["A", "B", "C"] if not numeric_only else ["A", "B"] - expected = DataFrame([], columns=columns, index=index) - tm.assert_frame_equal(result, expected) - - -def test_groupby_aggregation_non_numeric_dtype(): - # GH #43108 - df = DataFrame( - [["M", [1]], ["M", [1]], ["W", [10]], ["W", [20]]], columns=["MW", "v"] - ) - - expected = DataFrame( - { - "v": [[1, 1], [10, 20]], - }, - index=Index(["M", "W"], dtype="object", name="MW"), - ) - - gb = df.groupby(by=["MW"]) - result = gb.sum() - tm.assert_frame_equal(result, expected) - - -def test_groupby_aggregation_multi_non_numeric_dtype(): - # GH #42395 - df = DataFrame( - { - "x": [1, 0, 1, 1, 0], - "y": [Timedelta(i, "days") for i in range(1, 6)], - "z": [Timedelta(i * 10, "days") for i in range(1, 6)], - } - ) - - expected = DataFrame( - { - "y": [Timedelta(i, "days") for i in range(7, 9)], - "z": [Timedelta(i * 10, "days") for i in range(7, 9)], - }, - index=Index([0, 1], dtype="int64", name="x"), - ) - - gb = df.groupby(by=["x"]) - result = gb.sum() - tm.assert_frame_equal(result, expected) - - -def test_groupby_aggregation_numeric_with_non_numeric_dtype(): - # GH #43108 - df = DataFrame( - { - "x": [1, 0, 1, 1, 0], - "y": [Timedelta(i, "days") for i in range(1, 6)], - "z": list(range(1, 6)), - } - ) - - expected = DataFrame( - {"y": [Timedelta(7, "days"), Timedelta(8, "days")], "z": [7, 8]}, - index=Index([0, 1], dtype="int64", name="x"), - ) - - gb = df.groupby(by=["x"]) - result = gb.sum() - tm.assert_frame_equal(result, expected) - - -def test_groupby_filtered_df_std(): - # GH 16174 - dicts = [ - {"filter_col": False, "groupby_col": True, "bool_col": True, "float_col": 10.5}, - {"filter_col": True, "groupby_col": True, "bool_col": True, "float_col": 20.5}, - {"filter_col": True, "groupby_col": True, "bool_col": True, "float_col": 30.5}, - ] - df = DataFrame(dicts) - - df_filter = df[df["filter_col"] == True] # noqa: E712 - dfgb = df_filter.groupby("groupby_col") - result = dfgb.std() - expected = DataFrame( - [[0.0, 0.0, 7.071068]], - columns=["filter_col", "bool_col", "float_col"], - index=Index([True], name="groupby_col"), - ) - tm.assert_frame_equal(result, expected) - - -def test_datetime_categorical_multikey_groupby_indices(): - # GH 26859 - df = DataFrame( - { - "a": Series(list("abc")), - "b": Series( - to_datetime(["2018-01-01", "2018-02-01", "2018-03-01"]), - dtype="category", - ), - "c": Categorical.from_codes([-1, 0, 1], categories=[0, 1]), - } - ) - result = df.groupby(["a", "b"], observed=False).indices - expected = { - ("a", Timestamp("2018-01-01 00:00:00")): np.array([0]), - ("b", Timestamp("2018-02-01 00:00:00")): np.array([1]), - ("c", Timestamp("2018-03-01 00:00:00")): np.array([2]), - } - assert result == expected - - -def test_rolling_wrong_param_min_period(): - # GH34037 - name_l = ["Alice"] * 5 + ["Bob"] * 5 - val_l = [np.nan, np.nan, 1, 2, 3] + [np.nan, 1, 2, 3, 4] - test_df = DataFrame([name_l, val_l]).T - test_df.columns = ["name", "val"] - - result_error_msg = r"__init__\(\) got an unexpected keyword argument 'min_period'" - with pytest.raises(TypeError, match=result_error_msg): - test_df.groupby("name")["val"].rolling(window=2, min_period=1).sum() - - -@pytest.mark.parametrize( - "dtype", - [ - object, - pytest.param("string[pyarrow_numpy]", marks=td.skip_if_no("pyarrow")), - ], -) -def test_by_column_values_with_same_starting_value(dtype): - # GH29635 - df = DataFrame( - { - "Name": ["Thomas", "Thomas", "Thomas John"], - "Credit": [1200, 1300, 900], - "Mood": Series(["sad", "happy", "happy"], dtype=dtype), - } - ) - aggregate_details = {"Mood": Series.mode, "Credit": "sum"} - - result = df.groupby(["Name"]).agg(aggregate_details) - expected_result = DataFrame( - { - "Mood": [["happy", "sad"], "happy"], - "Credit": [2500, 900], - "Name": ["Thomas", "Thomas John"], - } - ).set_index("Name") - - tm.assert_frame_equal(result, expected_result) - - -def test_groupby_none_in_first_mi_level(): - # GH#47348 - arr = [[None, 1, 0, 1], [2, 3, 2, 3]] - ser = Series(1, index=MultiIndex.from_arrays(arr, names=["a", "b"])) - result = ser.groupby(level=[0, 1]).sum() - expected = Series( - [1, 2], MultiIndex.from_tuples([(0.0, 2), (1.0, 3)], names=["a", "b"]) - ) - tm.assert_series_equal(result, expected) - - -def test_groupby_none_column_name(): - # GH#47348 - df = DataFrame({None: [1, 1, 2, 2], "b": [1, 1, 2, 3], "c": [4, 5, 6, 7]}) - result = df.groupby(by=[None]).sum() - expected = DataFrame({"b": [2, 5], "c": [9, 13]}, index=Index([1, 2], name=None)) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("selection", [None, "a", ["a"]]) -def test_single_element_list_grouping(selection): - # GH#42795, GH#53500 - df = DataFrame({"a": [1, 2], "b": [np.nan, 5], "c": [np.nan, 2]}, index=["x", "y"]) - grouped = df.groupby(["a"]) if selection is None else df.groupby(["a"])[selection] - result = [key for key, _ in grouped] - - expected = [(1,), (2,)] - assert result == expected - - -def test_groupby_string_dtype(): - # GH 40148 - df = DataFrame({"str_col": ["a", "b", "c", "a"], "num_col": [1, 2, 3, 2]}) - df["str_col"] = df["str_col"].astype("string") - expected = DataFrame( - { - "str_col": [ - "a", - "b", - "c", - ], - "num_col": [1.5, 2.0, 3.0], - } - ) - expected["str_col"] = expected["str_col"].astype("string") - grouped = df.groupby("str_col", as_index=False) - result = grouped.mean() - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize( - "level_arg, multiindex", [([0], False), ((0,), False), ([0], True), ((0,), True)] -) -def test_single_element_listlike_level_grouping_deprecation(level_arg, multiindex): - # GH 51583 - df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}, index=["x", "y"]) - if multiindex: - df = df.set_index(["a", "b"]) - depr_msg = ( - "Creating a Groupby object with a length-1 list-like " - "level parameter will yield indexes as tuples in a future version. " - "To keep indexes as scalars, create Groupby objects with " - "a scalar level parameter instead." - ) - with tm.assert_produces_warning(FutureWarning, match=depr_msg): - [key for key, _ in df.groupby(level=level_arg)] - - -@pytest.mark.parametrize("func", ["sum", "cumsum", "cumprod", "prod"]) -def test_groupby_avoid_casting_to_float(func): - # GH#37493 - val = 922337203685477580 - df = DataFrame({"a": 1, "b": [val]}) - result = getattr(df.groupby("a"), func)() - val - expected = DataFrame({"b": [0]}, index=Index([1], name="a")) - if func in ["cumsum", "cumprod"]: - expected = expected.reset_index(drop=True) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("func, val", [("sum", 3), ("prod", 2)]) -def test_groupby_sum_support_mask(any_numeric_ea_dtype, func, val): - # GH#37493 - df = DataFrame({"a": 1, "b": [1, 2, pd.NA]}, dtype=any_numeric_ea_dtype) - result = getattr(df.groupby("a"), func)() - expected = DataFrame( - {"b": [val]}, - index=Index([1], name="a", dtype=any_numeric_ea_dtype), - dtype=any_numeric_ea_dtype, - ) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("val, dtype", [(111, "int"), (222, "uint")]) -def test_groupby_overflow(val, dtype): - # GH#37493 - df = DataFrame({"a": 1, "b": [val, val]}, dtype=f"{dtype}8") - result = df.groupby("a").sum() - expected = DataFrame( - {"b": [val * 2]}, - index=Index([1], name="a", dtype=f"{dtype}8"), - dtype=f"{dtype}64", - ) - tm.assert_frame_equal(result, expected) - - result = df.groupby("a").cumsum() - expected = DataFrame({"b": [val, val * 2]}, dtype=f"{dtype}64") - tm.assert_frame_equal(result, expected) - - result = df.groupby("a").prod() - expected = DataFrame( - {"b": [val * val]}, - index=Index([1], name="a", dtype=f"{dtype}8"), - dtype=f"{dtype}64", - ) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("skipna, val", [(True, 3), (False, pd.NA)]) -def test_groupby_cumsum_mask(any_numeric_ea_dtype, skipna, val): - # GH#37493 - df = DataFrame({"a": 1, "b": [1, pd.NA, 2]}, dtype=any_numeric_ea_dtype) - result = df.groupby("a").cumsum(skipna=skipna) - expected = DataFrame( - {"b": [1, pd.NA, val]}, - dtype=any_numeric_ea_dtype, - ) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize( - "val_in, index, val_out", - [ - ( - [1.0, 2.0, 3.0, 4.0, 5.0], - ["foo", "foo", "bar", "baz", "blah"], - [3.0, 4.0, 5.0, 3.0], - ), - ( - [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], - ["foo", "foo", "bar", "baz", "blah", "blah"], - [3.0, 4.0, 11.0, 3.0], - ), - ], -) -def test_groupby_index_name_in_index_content(val_in, index, val_out): - # GH 48567 - series = Series(data=val_in, name="values", index=Index(index, name="blah")) - result = series.groupby("blah").sum() - expected = Series( - data=val_out, - name="values", - index=Index(["bar", "baz", "blah", "foo"], name="blah"), - ) - tm.assert_series_equal(result, expected) - - result = series.to_frame().groupby("blah").sum() - expected = expected.to_frame() - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("n", [1, 10, 32, 100, 1000]) -def test_sum_of_booleans(n): - # GH 50347 - df = DataFrame({"groupby_col": 1, "bool": [True] * n}) - df["bool"] = df["bool"].eq(True) - result = df.groupby("groupby_col").sum() - expected = DataFrame({"bool": [n]}, index=Index([1], name="groupby_col")) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.filterwarnings( - "ignore:invalid value encountered in remainder:RuntimeWarning" -) -@pytest.mark.parametrize("method", ["head", "tail", "nth", "first", "last"]) -def test_groupby_method_drop_na(method): - # GH 21755 - df = DataFrame({"A": ["a", np.nan, "b", np.nan, "c"], "B": range(5)}) - - if method == "nth": - result = getattr(df.groupby("A"), method)(n=0) - else: - result = getattr(df.groupby("A"), method)() - - if method in ["first", "last"]: - expected = DataFrame({"B": [0, 2, 4]}).set_index( - Series(["a", "b", "c"], name="A") - ) - else: - expected = DataFrame({"A": ["a", "b", "c"], "B": [0, 2, 4]}, index=[0, 2, 4]) - tm.assert_frame_equal(result, expected) - - -def test_groupby_reduce_period(): - # GH#51040 - pi = pd.period_range("2016-01-01", periods=100, freq="D") - grps = list(range(10)) * 10 - ser = pi.to_series() - gb = ser.groupby(grps) - - with pytest.raises(TypeError, match="Period type does not support sum operations"): - gb.sum() - with pytest.raises( - TypeError, match="Period type does not support cumsum operations" - ): - gb.cumsum() - with pytest.raises(TypeError, match="Period type does not support prod operations"): - gb.prod() - with pytest.raises( - TypeError, match="Period type does not support cumprod operations" - ): - gb.cumprod() - - res = gb.max() - expected = ser[-10:] - expected.index = Index(range(10), dtype=int) - tm.assert_series_equal(res, expected) - - res = gb.min() - expected = ser[:10] - expected.index = Index(range(10), dtype=int) - tm.assert_series_equal(res, expected) - - -def test_obj_with_exclusions_duplicate_columns(): - # GH#50806 - df = DataFrame([[0, 1, 2, 3]]) - df.columns = [0, 1, 2, 0] - gb = df.groupby(df[1]) - result = gb._obj_with_exclusions - expected = df.take([0, 2, 3], axis=1) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("numeric_only", [True, False]) -def test_groupby_numeric_only_std_no_result(numeric_only): - # GH 51080 - dicts_non_numeric = [{"a": "foo", "b": "bar"}, {"a": "car", "b": "dar"}] - df = DataFrame(dicts_non_numeric) - dfgb = df.groupby("a", as_index=False, sort=False) - - if numeric_only: - result = dfgb.std(numeric_only=True) - expected_df = DataFrame(["foo", "car"], columns=["a"]) - tm.assert_frame_equal(result, expected_df) - else: - with pytest.raises( - ValueError, match="could not convert string to float: 'bar'" - ): - dfgb.std(numeric_only=numeric_only) - - -def test_grouping_with_categorical_interval_columns(): - # GH#34164 - df = DataFrame({"x": [0.1, 0.2, 0.3, -0.4, 0.5], "w": ["a", "b", "a", "c", "a"]}) - qq = pd.qcut(df["x"], q=np.linspace(0, 1, 5)) - result = df.groupby([qq, "w"], observed=False)["x"].agg("mean") - categorical_index_level_1 = Categorical( - [ - Interval(-0.401, 0.1, closed="right"), - Interval(0.1, 0.2, closed="right"), - Interval(0.2, 0.3, closed="right"), - Interval(0.3, 0.5, closed="right"), - ], - ordered=True, - ) - index_level_2 = ["a", "b", "c"] - mi = MultiIndex.from_product( - [categorical_index_level_1, index_level_2], names=["x", "w"] - ) - expected = Series( - np.array( - [ - 0.1, - np.nan, - -0.4, - np.nan, - 0.2, - np.nan, - 0.3, - np.nan, - np.nan, - 0.5, - np.nan, - np.nan, - ] - ), - index=mi, - name="x", - ) - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize("bug_var", [1, "a"]) -def test_groupby_sum_on_nan_should_return_nan(bug_var): - # GH 24196 - df = DataFrame({"A": [bug_var, bug_var, bug_var, np.nan]}) - dfgb = df.groupby(lambda x: x) - result = dfgb.sum(min_count=1) - - expected_df = DataFrame([bug_var, bug_var, bug_var, None], columns=["A"]) - tm.assert_frame_equal(result, expected_df) - - -@pytest.mark.parametrize( - "method", - [ - "count", - "corr", - "cummax", - "cummin", - "cumprod", - "describe", - "rank", - "quantile", - "diff", - "shift", - "all", - "any", - "idxmin", - "idxmax", - "ffill", - "bfill", - "pct_change", - ], -) -def test_groupby_selection_with_methods(df, method): - # some methods which require DatetimeIndex - rng = date_range("2014", periods=len(df)) - df.index = rng - - g = df.groupby(["A"])[["C"]] - g_exp = df[["C"]].groupby(df["A"]) - # TODO check groupby with > 1 col ? - - res = getattr(g, method)() - exp = getattr(g_exp, method)() - - # should always be frames! - tm.assert_frame_equal(res, exp) - - -def test_groupby_selection_other_methods(df): - # some methods which require DatetimeIndex - rng = date_range("2014", periods=len(df)) - df.columns.name = "foo" - df.index = rng - - g = df.groupby(["A"])[["C"]] - g_exp = df[["C"]].groupby(df["A"]) - - # methods which aren't just .foo() - tm.assert_frame_equal(g.fillna(0), g_exp.fillna(0)) - msg = "DataFrameGroupBy.dtypes is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - tm.assert_frame_equal(g.dtypes, g_exp.dtypes) - tm.assert_frame_equal(g.apply(lambda x: x.sum()), g_exp.apply(lambda x: x.sum())) - - tm.assert_frame_equal(g.resample("D").mean(), g_exp.resample("D").mean()) - tm.assert_frame_equal(g.resample("D").ohlc(), g_exp.resample("D").ohlc()) - - tm.assert_frame_equal( - g.filter(lambda x: len(x) == 3), g_exp.filter(lambda x: len(x) == 3) - ) - - -def test_groupby_with_Time_Grouper(): - idx2 = [ - to_datetime("2016-08-31 22:08:12.000"), - to_datetime("2016-08-31 22:09:12.200"), - to_datetime("2016-08-31 22:20:12.400"), - ] - - test_data = DataFrame( - {"quant": [1.0, 1.0, 3.0], "quant2": [1.0, 1.0, 3.0], "time2": idx2} - ) - - expected_output = DataFrame( - { - "time2": date_range("2016-08-31 22:08:00", periods=13, freq="1T"), - "quant": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], - "quant2": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], - } - ) - - df = test_data.groupby(Grouper(key="time2", freq="1T")).count().reset_index() - - tm.assert_frame_equal(df, expected_output) - - -def test_groupby_series_with_datetimeindex_month_name(): - # GH 48509 - s = Series([0, 1, 0], index=date_range("2022-01-01", periods=3), name="jan") - result = s.groupby(s).count() - expected = Series([2, 1], name="jan") - expected.index.name = "jan" - tm.assert_series_equal(result, expected) - - -def test_get_group_axis_1(): - # GH#54858 - df = DataFrame( - { - "col1": [0, 3, 2, 3], - "col2": [4, 1, 6, 7], - "col3": [3, 8, 2, 10], - "col4": [1, 13, 6, 15], - "col5": [-4, 5, 6, -7], - } - ) - with tm.assert_produces_warning(FutureWarning, match="deprecated"): - grouped = df.groupby(axis=1, by=[1, 2, 3, 2, 1]) - result = grouped.get_group(1) - expected = DataFrame( - { - "col1": [0, 3, 2, 3], - "col5": [-4, 5, 6, -7], - } - ) - tm.assert_frame_equal(result, expected) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/env_settings.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/env_settings.py deleted file mode 100644 index 662f59005a09a1934155f9ae6ebab9a2d129d33f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/env_settings.py +++ /dev/null @@ -1,4 +0,0 @@ -"""The `env_settings` module is a backport module from V1.""" -from ._migration import getattr_migration - -__getattr__ = getattr_migration(__name__) diff --git a/spaces/prthgo/Spam-Message-Classifier/README.md b/spaces/prthgo/Spam-Message-Classifier/README.md deleted file mode 100644 index 961fcead558f186a3f0c3555fb1740eb809ea1cb..0000000000000000000000000000000000000000 --- a/spaces/prthgo/Spam-Message-Classifier/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Spam Message Classifier -emoji: 📊 -colorFrom: blue -colorTo: gray -sdk: gradio -sdk_version: 3.44.4 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/pycoming/bingo/cloudflare/worker.js b/spaces/pycoming/bingo/cloudflare/worker.js deleted file mode 100644 index e0debd750615f1329b2c72fbce73e1b9291f7137..0000000000000000000000000000000000000000 --- a/spaces/pycoming/bingo/cloudflare/worker.js +++ /dev/null @@ -1,18 +0,0 @@ -const TRAGET_HOST='hf4all-bingo.hf.space' // 请将此域名改成你自己的，域名信息在设置》站点域名查看。 - -export default { - async fetch(request) { - const uri = new URL(request.url); - if (uri.protocol === 'http:') { - uri.protocol = 'https:'; - return new Response('', { - status: 301, - headers: { - location: uri.toString(), - }, - }) - } - uri.host = TRAGET_HOST - return fetch(new Request(uri.toString(), request)); - }, -}; diff --git a/spaces/pyodide-demo/self-hosted/load-pyodide.d.ts b/spaces/pyodide-demo/self-hosted/load-pyodide.d.ts deleted file mode 100644 index 334a929e8d68ff4ffcaf242b78e15f64fbfeed63..0000000000000000000000000000000000000000 --- a/spaces/pyodide-demo/self-hosted/load-pyodide.d.ts +++ /dev/null @@ -1,48 +0,0 @@ -/** - * @param {string} indexURL - * @private - */ -export function initializePackageIndex(indexURL: string): Promise; -export function _fetchBinaryFile(indexURL: any, path: any): Promise; -/** - * @callback LogFn - * @param {string} msg - * @returns {void} - * @private - */ -/** - * Load a package or a list of packages over the network. This installs the - * package in the virtual filesystem. The package needs to be imported from - * Python before it can be used. - * - * @param {string | string[] | PyProxy} names Either a single package name or - * URL or a list of them. URLs can be absolute or relative. The URLs must have - * file name ``.js`` and there must be a file called - * ``.data`` in the same directory. The argument can be a - * ``PyProxy`` of a list, in which case the list will be converted to JavaScript - * and the ``PyProxy`` will be destroyed. - * @param {LogFn=} messageCallback A callback, called with progress messages - * (optional) - * @param {LogFn=} errorCallback A callback, called with error/warning messages - * (optional) - * @async - */ -export function loadPackage(names: string | string[] | PyProxy, messageCallback?: LogFn | undefined, errorCallback?: LogFn | undefined): Promise; -/** - * @param {string) url - * @async - * @private - */ -export let loadScript: any; -/** - * - * The list of packages that Pyodide has loaded. - * Use ``Object.keys(pyodide.loadedPackages)`` to get the list of names of - * loaded packages, and ``pyodide.loadedPackages[package_name]`` to access - * install location for a particular ``package_name``. - * - * @type {object} - */ -export let loadedPackages: object; -export type LogFn = (msg: string) => void; -export type PyProxy = any; diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Fruity Loops Studio 7 !!INSTALL!! Full Crack Serial Key Keygen.md b/spaces/quidiaMuxgu/Expedit-SAM/Fruity Loops Studio 7 !!INSTALL!! Full Crack Serial Key Keygen.md deleted file mode 100644 index 0981e35acdeaf85b94c453cb9196a5bc02248f09..0000000000000000000000000000000000000000 --- a/spaces/quidiaMuxgu/Expedit-SAM/Fruity Loops Studio 7 !!INSTALL!! Full Crack Serial Key Keygen.md +++ /dev/null @@ -1,74 +0,0 @@ -
    -

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      2. -
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      9. -
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      10. -
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    FL Studio 7 is a versatile and powerful music production software that can help you create amazing songs and beats. However, to get the most out of it, you need to know how to use it properly and efficiently. Here are some tips for using FL Studio 7:

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    • Learn the basics. FL Studio 7 has a lot of features and functions that can be overwhelming for beginners. Therefore, you should learn the basics of the software, such as the interface, the tools, the plugins, the patterns, the playlist, the mixer, and the piano roll. You can use the help menu, the tutorials, the manuals, or the online forums to learn more about FL Studio 7.
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    • Use shortcuts. FL Studio 7 has a lot of keyboard shortcuts that can help you speed up your workflow and save time. You can use shortcuts to perform various actions, such as opening menus, selecting tools, editing patterns, recording notes, and more. You can find a list of shortcuts in the help menu or on the official website of FL Studio.
      • -
    • Customize your settings. FL Studio 7 allows you to customize your settings according to your preferences and needs. You can change the appearance, the behavior, the audio, the MIDI, and the plugins of the software. You can access the settings menu by clicking on the options button at the top left corner of the screen.
      • -
    • Use templates. FL Studio 7 has a lot of templates that can help you start your projects faster and easier. You can use templates to load predefined settings, instruments, effects, and patterns for different genres and styles of music. You can access the templates menu by clicking on the file button at the top left corner of the screen.
      • -
    • Use presets. FL Studio 7 has a lot of presets that can help you create sounds and effects quickly and easily. You can use presets to load predefined settings for different instruments and plugins. You can access the presets menu by clicking on the browser button at the top left corner of the screen.
      • -
    • Save your projects. FL Studio 7 allows you to save your projects as .flp files that contain all your settings, instruments, effects, patterns, playlist, mixer, and automation data. You can save your projects by clicking on the save button at the top left corner of the screen or by using the Ctrl+S shortcut. You should save your projects frequently to avoid losing your work in case of a crash or a power outage.
      • -
    • Export your projects. FL Studio 7 allows you to export your projects as audio files that can be played on any device or platform. You can export your projects as .wav, .mp3, .ogg, .flac, or .mid files depending on your needs and preferences. You can export your projects by clicking on the export button at the top left corner of the screen or by using the Ctrl+R shortcut.
      • -
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    Fruity Loops Studio 7 Full Crack Serial Key Keygen is a way to download and install FL Studio 7 for free without paying anything. However, it is not a safe or legal way to use the software. You may face various problems such as viruses, malware, spyware, errors, crashes, bugs, or legal issues if you use FL Studio 7 full crack serial key keygen.

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    We hope this article has helped you understand how to download and install Fruity Loops Studio 7 Full Crack Serial Key Keygen and how to use FL Studio 7 properly and efficiently. If you have any questions or comments, feel free to leave them below.

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    Alternatives to FL Studio 7

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    FL Studio 7 is a great music production software, but it is not the only one. There are many other alternatives that you can use to create, edit, mix, and record your own songs and beats. Some of these alternatives are free, some are paid, and some are cross-platform. Here are some of the most popular alternatives to FL Studio 7:

    -
      -
    • Ableton Live: This is a professional and versatile music production software that allows you to create, perform, and produce music in real time. It has a unique session view that lets you improvise and experiment with your ideas. It also has a powerful arrangement view that lets you edit and refine your tracks. It has a rich collection of instruments, effects, samples, and loops. It also supports MIDI controllers, audio interfaces, and VST plugins.
      • -
    • Logic Pro: This is a professional and comprehensive music production software that allows you to create, record, edit, mix, and master your music. It has a sleek and intuitive interface that lets you access all the tools and features you need. It has a huge library of sounds, instruments, effects, and loops. It also supports MIDI controllers, audio interfaces, and AU plugins.
      • -
    • REAPER: This is a lightweight and powerful music production software that allows you to record, edit, mix, and render your audio. It has a customizable and flexible interface that lets you work with multiple tracks and windows. It has a wide range of features and functions that let you manipulate your audio in any way you want. It also supports MIDI controllers, audio interfaces, and VST plugins.
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    • LMMS: This is a free and open-source music production software that allows you to create melodies, beats, and songs. It has a simple and user-friendly interface that lets you work with multiple tracks and instruments. It has a built-in library of sounds, instruments, effects, and presets. It also supports MIDI controllers, audio interfaces, and VST plugins.
      • -
    • Audacity: This is a free and open-source audio editing software that allows you to record, edit, and manipulate your audio. It has a basic and easy-to-use interface that lets you work with multiple tracks and effects. It has a variety of features and functions that let you cut, copy, paste, trim, fade, normalize, amplify, compress, equalize, filter, and more. It also supports MIDI controllers, audio interfaces, and VST plugins.
      • -
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    Conclusion

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    Fruity Loops Studio 7 Full Crack Serial Key Keygen is a way to download and install FL Studio 7 for free without paying anything. However, it is not a safe or legal way to use the software. You may face various problems such as viruses, malware, spyware, errors, crashes, bugs, or legal issues if you use FL Studio 7 full crack serial key keygen.

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    We hope this article has helped you understand how to download and install Fruity Loops Studio 7 Full Crack Serial Key Keygen and how to use FL Studio 7 properly and efficiently. If you have any questions or comments, feel free to leave them below.

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    Therefore, it is better to buy a license key for FL Studio 7 from the official website or a reputable online store. This way, you can use the software with its full features and functions without any worries or problems. You can also support the developers of FL Studio who have worked hard to create this amazing music production software.

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    - -5.2 The selection strategy. 5.3. 5.4 The selection ... 11 Materials processing and design ... The bar code. 15.4 The ... which the product is to be made and the process for making it. Normally, the ... in the most open way, give solids with a density of around 1 Mg/m3. ... material classes, providing raw data for generic screening. 4d29de3e1b
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    diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/Dockerfile b/spaces/r3gm/Aesthetic_RVC_Inference_HF/Dockerfile deleted file mode 100644 index 2fc2437794fbc0f60327c928e8c36fb1a18eebc4..0000000000000000000000000000000000000000 --- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -# syntax=docker/dockerfile:1 - -FROM python:3.10-bullseye - -EXPOSE 7865 - -WORKDIR /app - -COPY . . - -RUN apt update && apt install -y -qq ffmpeg aria2 && apt clean - -RUN pip3 install --no-cache-dir -r assets/requirements/requirements.txt - -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d assets/pretrained_v2/ -o D40k.pth -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d assets/pretrained_v2/ -o G40k.pth -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d assets/pretrained_v2/ -o f0D40k.pth -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d assets/pretrained_v2/ -o f0G40k.pth - -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d assets/uvr5_weights/ -o HP2-人声vocals+非人声instrumentals.pth -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d assets/uvr5_weights/ -o HP5-主旋律人声vocals+其他instrumentals.pth - -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d assets/hubert -o hubert_base.pt - -RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d assets/rmvpe -o rmvpe.pt - -VOLUME [ "/app/logs/weights", "/app/opt" ] - -CMD ["python3", "infer-web.py"] \ No newline at end of file diff --git a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/demo/visualize/layouts/plot_bbox.py b/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/demo/visualize/layouts/plot_bbox.py deleted file mode 100644 index 96911b41fe4f678ae41d5a7eac1484700952b99e..0000000000000000000000000000000000000000 --- a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/demo/visualize/layouts/plot_bbox.py +++ /dev/null @@ -1,53 +0,0 @@ -import cv2 - -# Demo libs -from spiga.demo.visualize.layouts.plot_basics import BasicLayout - - -class BboxLayout(BasicLayout): - - BasicLayout.thickness_dft['bbox'] = 2 - - def __init__(self): - super().__init__() - - def draw_bbox(self, canvas, bbox, score_thr=0, show_score=True, thick=None, color=BasicLayout.colors['blue']): - - if thick is None: - thick = self.thickness['bbox'] - - if bbox[4] > score_thr: - text = "{:.4f}".format(bbox[4]) - b = list(map(int, bbox)) - cv2.rectangle(canvas, (b[0], b[1]), (b[2], b[3]), color, thick) - if show_score: - self.draw_bbox_text(canvas, b, text, offset=(0, 12), color=color) - return canvas - - def draw_bbox_line(self, canvas, bbox, score_thr=0, show_score=True, thick=None, color=BasicLayout.colors['blue']): - - if thick is None: - thick = self.thickness['bbox'] - - if bbox[4] > score_thr: - text = "{:.4f}".format(bbox[4]) - b = list(map(int, bbox)) - cv2.line(canvas, (b[0], b[1]), (b[0], b[1] + 15), color, thick) - cv2.line(canvas, (b[0], b[1]), (b[0] + 100, b[1]), color, thick) - if show_score: - self.draw_bbox_text(canvas, b, text, offset=(0, 12), color=color) - return canvas - - def draw_bbox_text(self, canvas, bbox, text, offset=(0, 0), color=BasicLayout.colors['white']): - b = list(map(int, bbox)) - cx = b[0] + offset[0] - cy = b[1] + offset[1] - cv2.putText(canvas, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX, 0.5, color) - return canvas - - def draw_bboxes(self, canvas, dets, score_thr=0, show_score=True, thick=None, colors=(BasicLayout.colors['blue'])): - num_colors = len(colors) - for idx, bbox in enumerate(dets): - color = colors[idx % num_colors] - canvas = self.draw_bbox(canvas, bbox, score_thr=score_thr, show_score=show_score, thick=thick, color=color) - return canvas diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/scripts/calc_id_loss_parallel.py b/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/scripts/calc_id_loss_parallel.py deleted file mode 100644 index efc82bf851b252e92c45be3c87be877616f44ead..0000000000000000000000000000000000000000 --- a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/scripts/calc_id_loss_parallel.py +++ /dev/null @@ -1,119 +0,0 @@ -from argparse import ArgumentParser -import time -import numpy as np -import os -import json -import sys -from PIL import Image -import multiprocessing as mp -import math -import torch -import torchvision.transforms as trans - -sys.path.append(".") -sys.path.append("..") - -from models.mtcnn.mtcnn import MTCNN -from models.encoders.model_irse import IR_101 -from configs.paths_config import model_paths -CIRCULAR_FACE_PATH = model_paths['circular_face'] - - -def chunks(lst, n): - """Yield successive n-sized chunks from lst.""" - for i in range(0, len(lst), n): - yield lst[i:i + n] - - -def extract_on_paths(file_paths): - facenet = IR_101(input_size=112) - facenet.load_state_dict(torch.load(CIRCULAR_FACE_PATH)) - facenet.cuda() - facenet.eval() - mtcnn = MTCNN() - id_transform = trans.Compose([ - trans.ToTensor(), - trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) - ]) - - pid = mp.current_process().name - print('\t{} is starting to extract on {} images'.format(pid, len(file_paths))) - tot_count = len(file_paths) - count = 0 - - scores_dict = {} - for res_path, gt_path in file_paths: - count += 1 - if count % 100 == 0: - print('{} done with {}/{}'.format(pid, count, tot_count)) - if True: - input_im = Image.open(res_path) - input_im, _ = mtcnn.align(input_im) - if input_im is None: - print('{} skipping {}'.format(pid, res_path)) - continue - - input_id = facenet(id_transform(input_im).unsqueeze(0).cuda())[0] - - result_im = Image.open(gt_path) - result_im, _ = mtcnn.align(result_im) - if result_im is None: - print('{} skipping {}'.format(pid, gt_path)) - continue - - result_id = facenet(id_transform(result_im).unsqueeze(0).cuda())[0] - score = float(input_id.dot(result_id)) - scores_dict[os.path.basename(gt_path)] = score - - return scores_dict - - -def parse_args(): - parser = ArgumentParser(add_help=False) - parser.add_argument('--num_threads', type=int, default=4) - parser.add_argument('--data_path', type=str, default='results') - parser.add_argument('--gt_path', type=str, default='gt_images') - args = parser.parse_args() - return args - - -def run(args): - file_paths = [] - for f in os.listdir(args.data_path): - image_path = os.path.join(args.data_path, f) - gt_path = os.path.join(args.gt_path, f) - if f.endswith(".jpg") or f.endswith('.png'): - file_paths.append([image_path, gt_path.replace('.png','.jpg')]) - - file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads)))) - pool = mp.Pool(args.num_threads) - print('Running on {} paths\nHere we goooo'.format(len(file_paths))) - - tic = time.time() - results = pool.map(extract_on_paths, file_chunks) - scores_dict = {} - for d in results: - scores_dict.update(d) - - all_scores = list(scores_dict.values()) - mean = np.mean(all_scores) - std = np.std(all_scores) - result_str = 'New Average score is {:.2f}+-{:.2f}'.format(mean, std) - print(result_str) - - out_path = os.path.join(os.path.dirname(args.data_path), 'inference_metrics') - if not os.path.exists(out_path): - os.makedirs(out_path) - - with open(os.path.join(out_path, 'stat_id.txt'), 'w') as f: - f.write(result_str) - with open(os.path.join(out_path, 'scores_id.json'), 'w') as f: - json.dump(scores_dict, f) - - toc = time.time() - print('Mischief managed in {}s'.format(toc - tic)) - - -if __name__ == '__main__': - args = parse_args() - run(args) diff --git a/spaces/radames/dpt-depth-estimation-3d-obj/README.md b/spaces/radames/dpt-depth-estimation-3d-obj/README.md deleted file mode 100644 index 3126f716947800434bb9fca7d520993a1da11392..0000000000000000000000000000000000000000 --- a/spaces/radames/dpt-depth-estimation-3d-obj/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Dpt Depth Estimation + 3D -emoji: ⚡ -colorFrom: blue -colorTo: red -sdk: gradio -sdk_version: 3.0b8 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Adobe InDesign CC 2018 (v11.0) X86-x64 RUS-ENG by M0nkrus .rar.md b/spaces/raedeXanto/academic-chatgpt-beta/Adobe InDesign CC 2018 (v11.0) X86-x64 RUS-ENG by M0nkrus .rar.md deleted file mode 100644 index 976c1f488039b9f70d723d2003453a1893315b8a..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Adobe InDesign CC 2018 (v11.0) X86-x64 RUS-ENG by M0nkrus .rar.md +++ /dev/null @@ -1,26 +0,0 @@ -
    -

    Adobe InDesign CC 2018: A Powerful Layout and Page Design Software

    -

    Adobe InDesign CC 2018 is the industry-leading layout and page design software for print and digital media. It allows you to create beautiful graphic designs with typography from the world's top foundries and imagery from Adobe Stock. You can also quickly share content and feedback in PDF, and easily manage production with Adobe Experience Manager.

    -

    In this article, we will review some of the new and enhanced features of Adobe InDesign CC 2018, which include:

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    Adobe InDesign CC 2018 (v11.0) x86-x64 RUS-ENG {by M0nkrus} .rar


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    -
      -
    • Layout adjustment: This feature automatically adjusts all elements in the layout when the page size, page margin, or bleed of a document is changed.
      • -
    • Import PDF comments: This feature lets you import any marked up PDF into InDesign and easily track the feedback and comments noted in the PDF. You can also accept comments and mark them resolved or unresolved.
      • -
    • Properties panel: This panel provides you with settings and controls in the context of your current task or workflow. It also gives you access to the right controls when you need them.
      • -
    • Visual font browsing: This feature enables you to browse thousands of fonts from hundreds of type foundries from within InDesign, activate them instantly, and use them in your documents. You can also apply filters, select sample text, and change font size while previewing fonts.
      • -
    • OpenType SVG fonts support: This feature allows you to use fonts that provide multiple colors and gradients in a single glyph. You can also use OpenType SVG emoji fonts to include various colorful and graphical characters in your documents.
      • -
    -

    If you want to learn more about Adobe InDesign CC 2018, you can visit the official website here, or download a free trial here.

    -

    Adobe InDesign CC 2018 is a powerful layout and page design software that can help you create stunning and professional-looking documents for print and digital media. Whether you are a beginner or an expert, you can use InDesign to design mobile apps, business cards, flyers, posters, books, magazines, eBooks, interactive PDFs, and more.

    - -

    How to use Adobe InDesign CC 2018

    -

    If you want to start using Adobe InDesign CC 2018, you need to follow some basic steps to set up your document, import text and graphics, format your content, and export your project. Here are some of the steps you can follow:

    -
      -
    1. Set up your document: You can create a new document by choosing File > New > Document, or by using a template from Adobe Stock. You can specify the page size, orientation, margins, columns, bleed, and slug for your document. You can also add pages to your document by using the Pages panel.
      2. -
    2. Import text and graphics: You can import text from a Word file or other sources by choosing File > Place, or by using the autoflow feature. You can also copy and paste text from other applications. You can import graphics such as photos or illustrations by choosing File > Place, or by using the Rectangle Frame tool. You can resize, rotate, crop, and fit graphics using the Selection tool or the Direct Selection tool.
      3. -
    3. Format your content: You can apply formatting to your text and graphics using various panels and tools in InDesign. For example, you can use the Properties panel to access settings and controls for your current task or workflow. You can use the Character panel and the Paragraph panel to adjust the font, size, color, alignment, spacing, and other attributes of your text. You can use the Swatches panel and the Color panel to apply colors and gradients to your text and graphics. You can also use the Effects panel to apply transparency, shadows, glows, and other effects to your content.
      4. -
    4. Export your project: You can export your project as a PDF file for print or digital distribution by choosing File > Export. You can also export your project as an interactive document that includes hyperlinks, buttons, animations, video, audio, and other interactive elements by choosing File > Export > Adobe PDF (Interactive). You can also share your project online using the Publish Online feature.
      5. -
    -

    These are some of the basic steps you can follow to use Adobe InDesign CC 2018. For more detailed tutorials and tips on how to use InDesign, you can visit the official website here.

    81aa517590
    -
    -
    \ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Collinseasylearningenglishconversationbook1pdf.md b/spaces/raedeXanto/academic-chatgpt-beta/Collinseasylearningenglishconversationbook1pdf.md deleted file mode 100644 index 9470c849f66bdae737b07d6b53a1952e11d4b5a6..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Collinseasylearningenglishconversationbook1pdf.md +++ /dev/null @@ -1,103 +0,0 @@ -
    -

    Collins Easy Learning English Conversation Book 1 PDF: A Review

    -

    If you are a beginner who wants to learn how to communicate in English with confidence and accuracy, you might be interested in Collins Easy Learning English Conversation Book 1. This book is a unique guide to speaking English in everyday situations, at work, or when travelling or studying. In this review, we will look at what the book offers, how it is structured, what format and quality it has, what are its pros and cons, and where you can get it.

    -

    collinseasylearningenglishconversationbook1pdf


    DOWNLOAD ★★★★★ https://tinourl.com/2uL4Bq



    -

    Introduction

    -

    Collins Easy Learning English Conversation Book 1 is part of the Collins Easy Learning English series, which also includes books on grammar, vocabulary, idioms, pronunciation, and writing. The book is aimed at beginners who want to improve their speaking skills and learn useful phrases and expressions for different contexts. The book claims to help learners:

    -
      -
    • Speak with confidence
      • -
    • Use correct grammar
      • -
    • Expand their vocabulary
      • -
    • Understand cultural differences
      • -
    • Avoid common mistakes
      • -
    -

    The book has 14 units, each focusing on a specific situation or topic, such as greetings, introductions, shopping, travelling, eating out, hobbies, etc. Each unit contains a list of the most useful phrases for that situation, with information on where and when they should be used, and a short sample of a conversation, showing how a typical dialogue flows in English. The book also has a glossary of key words at the end of each unit, as well as a comprehensive index at the back of the book.

    -

    Content and Structure

    -

    The book covers a wide range of topics that are relevant for everyday communication in English. The units are organized into four sections:

    -
      -
    1. Everyday Conversations
      2. -
    2. Socializing
      3. -
    3. Travel
      4. -
    4. Work and Study
      5. -
    -

    The units are designed to develop learners' speaking skills in a progressive way. They start with simple greetings and introductions, then move on to more complex topics such as opinions, preferences, requests, suggestions, etc. The units also cover some cultural aspects of communication in English, such as politeness, formality, humor, etc.

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    -

    The units follow a similar structure. They start with a list of useful phrases for that situation or topic, with explanations on how to use them correctly and appropriately. For example:

    - | Phrase | Explanation | | --- | --- | | How do you do? | This is a formal way of saying hello when you meet someone for the first time. You don't answer this question; you just repeat it back. | | Pleased to meet you. | This is another formal way of saying hello when you meet someone for the first time. You can also say Nice to meet you or Good to meet you. | | How are you? | This is a common way of asking about someone's well-being. You can answer with I'm fine, thank you or I'm good, thanks or something similar. |

    Then there is a short dialogue that illustrates how these phrases are used in a real conversation. For example:

    - 
    A: Hello, I'm Alice. B: How do you do? I'm Bob. A: Pleased to meet you. B: How are you today? A: I'm fine, thank you. And you? B: I'm good, thanks.
    -

    The dialogues are followed by some exercises that test learners' understanding and practice their speaking skills. The exercises include matching phrases with meanings, filling in gaps with suitable words or phrases, ordering sentences to make a conversation, etc.

    -

    The book also provides some tips and advice on how to improve learners' speaking skills in general. For example:

    - - Listen carefully to native speakers and try to imitate their pronunciation and intonation. - Record yourself speaking and listen back to check your mistakes and areas for improvement. - Speak as much as possible with other learners or native speakers and ask for feedback. - Learn new words and phrases every day and try to use them in your conversations.

    Format and Quality

    -

    The book is presented in a clear and attractive way. It has colorful illustrations and photos that make it more engaging and appealing. It has a user-friendly layout that makes it easy to follow and navigate. It has a spiral binding that allows it to lie flat on a table or desk.

    -

    The book comes with an audio CD that contains recordings of all the dialogues in the book. The audio quality is good and the speakers have clear and natural accents. The audio can also be downloaded from the Collins website or accessed online via a QR code on the back cover of the book.

    -

    The book's accuracy and readability are high. The phrases and expressions are up-to-date and relevant for modern communication in English. The explanations are simple and concise. The dialogues are realistic and natural.

    -

    Pros and Cons

    -

    The book has many strengths that make it a valuable resource for learners who want to improve their speaking skills in English. Some of them are:

    - - It covers a wide range of topics that are useful for everyday communication in English. - It provides clear explanations on how to use phrases correctly and appropriately in different situations. - It offers plenty of practice exercises that reinforce learning and develop speaking skills. - It includes an audio CD that helps learners improve their listening comprehension and pronunciation. - It has a user-friendly format that makes it easy to use.

    However, the book also has some weaknesses that might limit its effectiveness or suitability for some learners. Some of them are:

    - - It does not provide any grammar explanations or rules that might help learners understand why certain phrases or structures are used in certain ways. - It does not offer any feedback or answers to the exercises that might help learners check their progress or correct their mistakes. - It does not have any additional activities or games that might make learning more fun or interactive. - It might be too easy or too difficult for some learners depending on their level or needs.

    Conclusion

    -

    In conclusion, Collins Easy Learning English Conversation Book 1 PDF is a useful guide to speaking English in everyday situations. It helps learners learn useful phrases and expressions for different contexts, practice their speaking skills through exercises and dialogues, and improve their listening comprehension and pronunciation through an audio CD. The book is suitable for beginners who want to communicate confidently and accurately in English.

    -

    The book can be purchased online from various websites such as Amazon or eBay or from local bookstores. The price varies depending on the seller but it usually ranges from $10 to $15.

    -

    FAQs

    -
      -
    1. What is Collins Easy Learning English Conversation Book 1 PDF?
      2. -

    2. It is a guide to speaking English in everyday situations for beginners.

      3. -
    3. What does the book include?
      4. -

    4. It includes 14 units on different topics such as greetings, shopping, travelling, etc., each with a list of useful phrases, a sample dialogue, exercises, a glossary of key words, an audio CD.

      5. -
    5. What are the benefits of using the book?
      6. -

    6. It helps learners speak with confidence, use correct grammar, expand their vocabulary,

      7. -understand cultural differences, avoid common mistakes.
    7. What are the drawbacks of using the book?
      8. -

    8. It does not provide any grammar explanations or rules,

      9. -it does not offer any feedback or answers to the exercises, it does not have any additional activities or games, ```html their level or needs.
    9. Where can the book be bought and how much does it cost?
      10. -

    10. It can be bought online from various websites such as Amazon or eBay or from local bookstores. The price varies depending on the seller but it usually ranges from $10 to $15.

      11. -
    - ```

    0a6ba089eb
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    -
    \ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Cs 1.6 Cod Mw Mod Download.md b/spaces/raedeXanto/academic-chatgpt-beta/Cs 1.6 Cod Mw Mod Download.md deleted file mode 100644 index be0fc50395641fe829d92e8f9ae3f37eff38e551..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Cs 1.6 Cod Mw Mod Download.md +++ /dev/null @@ -1,42 +0,0 @@ - -

    How to Download and Install CS 1.6 COD MW Mod

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    If you are a fan of Counter-Strike 1.6 and Call of Duty: Modern Warfare, you might be interested in trying out the CS 1.6 COD MW mod, which combines the best features of both games into one. In this article, we will show you how to download and install the mod, as well as some of its features and gameplay.

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    Cs 1.6 Cod Mw Mod Download


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    What is CS 1.6 COD MW Mod?

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    CS 1.6 COD MW mod is a modification for Counter-Strike 1.6 that adds elements from Call of Duty: Modern Warfare, such as weapons, skins, sounds, maps, modes, and more. The mod is based on the old CW2 (Counter Warfare 2) mod from dynamusecorp, but has been updated and improved by Infractem in 2019.

    -

    Some of the features of the mod include:

    -
      -
    • New HUD and crosshair inspired by COD:MW2
      • -
    • New killstreak rewards such as predator missile, airstrike, care package, and tactical nuke
      • -
    • New gamemode: Team Deathmatch
      • -
    • New weapons such as M4A1, AK-47, UMP45, MP5, Desert Eagle, and more
      • -
    • New maps such as Terminal, Rust, Highrise, Favela, and more
      • -
    • New sounds and background noise for realism
      • -
    • New compass and pain indicator
      • -
    • Improved graphics and performance
      • -
    -

    How to Download and Install CS 1.6 COD MW Mod?

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    To download and install CS 1.6 COD MW mod, you will need to have Counter-Strike 1.6 installed on your PC. You can get it from Steam or other sources. Then, follow these steps:

    -
      -
    1. Download the mod file from here. The file name is Counter_Strike_Modern_Warfare_2_Installer.1.exe.
      2. -
    2. Run the installer and follow the instructions. Make sure to select the correct folder where your Counter-Strike 1.6 is installed.
      3. -
    3. Launch the game from the desktop shortcut or from the launcher in the mod folder.
      4. -
    4. Enjoy the mod!
      5. -
    -

    Tips and Tricks for CS 1.6 COD MW Mod

    -

    Here are some tips and tricks to help you get the most out of CS 1.6 COD MW mod:

    -

    -
      -
    • To access the killstreak rewards menu, press B and select Rewards.
      • -
    • To open care packages or place sentry guns, press F near them.
      • -
    • To change your weapon loadout, press M and select Loadout.
      • -
    • To change your weapon attachments, press N and select Attachments.
      • -
    • To change your game settings, press O and select Options.
      • -
    • To view your rank and XP progress, press P and select Ranking.
      • -
    • To chat with other players, press Y for team chat or U for global chat.
      • -
    • To view the scoreboard, press TAB.
      • -
    -

    Conclusion

    -

    CS 1.6 COD MW mod is a great way to enjoy both Counter-Strike 1.6 and Call of Duty: Modern Warfare in one game. The mod offers a lot of features and gameplay options that will keep you entertained for hours. If you are looking for a new challenge and a fresh experience in CS 1.6, you should definitely give this mod a try.

    81aa517590
    -
    -
    \ No newline at end of file diff --git a/spaces/rahul999r/Rahul_Kannada_TTS/src/glow_tts/generate_mels.py b/spaces/rahul999r/Rahul_Kannada_TTS/src/glow_tts/generate_mels.py deleted file mode 100644 index a3d331aef019cfd8cf45d6264db88d0fa26e5c0f..0000000000000000000000000000000000000000 --- a/spaces/rahul999r/Rahul_Kannada_TTS/src/glow_tts/generate_mels.py +++ /dev/null @@ -1,70 +0,0 @@ -import numpy as np -import os -import torch -import commons - -import models -import utils -from argparse import ArgumentParser -from tqdm import tqdm -from text import text_to_sequence - -if __name__ == "__main__": - parser = ArgumentParser() - parser.add_argument("-m", "--model_dir", required=True, type=str) - parser.add_argument("-s", "--mels_dir", required=True, type=str) - args = parser.parse_args() - MODEL_DIR = args.model_dir # path to model dir - SAVE_MELS_DIR = args.mels_dir # path to save generated mels - - if not os.path.exists(SAVE_MELS_DIR): - os.makedirs(SAVE_MELS_DIR) - - hps = utils.get_hparams_from_dir(MODEL_DIR) - symbols = list(hps.data.punc) + list(hps.data.chars) - checkpoint_path = utils.latest_checkpoint_path(MODEL_DIR) - cleaner = hps.data.text_cleaners - - model = models.FlowGenerator( - len(symbols) + getattr(hps.data, "add_blank", False), - out_channels=hps.data.n_mel_channels, - **hps.model - ).to("cuda") - - utils.load_checkpoint(checkpoint_path, model) - model.decoder.store_inverse() # do not calcuate jacobians for fast decoding - _ = model.eval() - - def get_mel(text, fpath): - if getattr(hps.data, "add_blank", False): - text_norm = text_to_sequence(text, symbols, cleaner) - text_norm = commons.intersperse(text_norm, len(symbols)) - else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality - text = " " + text.strip() + " " - text_norm = text_to_sequence(text, symbols, cleaner) - - sequence = np.array(text_norm)[None, :] - - x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long() - x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda() - - with torch.no_grad(): - noise_scale = 0.667 - length_scale = 1.0 - (y_gen_tst, *_), *_, (attn_gen, *_) = model( - x_tst, - x_tst_lengths, - gen=True, - noise_scale=noise_scale, - length_scale=length_scale, - ) - - np.save(os.path.join(SAVE_MELS_DIR, fpath), y_gen_tst.cpu().detach().numpy()) - - for f in [hps.data.training_files, hps.data.validation_files]: - file_lines = open(f).read().splitlines() - - for line in tqdm(file_lines): - fname, text = line.split("|") - fname = os.path.basename(fname).replace(".wav", ".npy") - get_mel(text, fname) diff --git a/spaces/rajistics/biobert_ner_demo/README.md b/spaces/rajistics/biobert_ner_demo/README.md deleted file mode 100644 index 17d50aa3c20a4ce49bc2cfecd8575ad426c55b8b..0000000000000000000000000000000000000000 --- a/spaces/rajistics/biobert_ner_demo/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Biobert_ner_demo -emoji: 👁 -colorFrom: gray -colorTo: pink -sdk: gradio -sdk_version: 3.0.5 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/rakesh092/Voice_cloning/app.py b/spaces/rakesh092/Voice_cloning/app.py deleted file mode 100644 index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000 --- a/spaces/rakesh092/Voice_cloning/app.py +++ /dev/null @@ -1,164 +0,0 @@ -import os -import re -import requests -import json -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') -PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY') -PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID') - -PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID') -play_ht_api_get_audio_url = "https://play.ht/api/v2/tts" - - -template = """You are a helpful assistant to answer user queries. -{chat_history} -User: {user_message} -Chatbot:""" - -prompt = PromptTemplate( - input_variables=["chat_history", "user_message"], template=template -) - -memory = ConversationBufferMemory(memory_key="chat_history") - -llm_chain = LLMChain( - llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"), - prompt=prompt, - verbose=True, - memory=memory, -) - -headers = { - "accept": "text/event-stream", - "content-type": "application/json", - "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY, - "X-USER-ID": PLAY_HT_USER_ID -} - - -def get_payload(text): - return { - "text": text, - "voice": PLAY_HT_VOICE_ID, - "quality": "medium", - "output_format": "mp3", - "speed": 1, - "sample_rate": 24000, - "seed": None, - "temperature": None - } - -def get_generated_audio(text): - payload = get_payload(text) - generated_response = {} - try: - response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers) - response.raise_for_status() - generated_response["type"]= 'SUCCESS' - generated_response["response"] = response.text - except requests.exceptions.RequestException as e: - generated_response["type"]= 'ERROR' - try: - response_text = json.loads(response.text) - if response_text['error_message']: - generated_response["response"] = response_text['error_message'] - else: - generated_response["response"] = response.text - except Exception as e: - generated_response["response"] = response.text - except Exception as e: - generated_response["type"]= 'ERROR' - generated_response["response"] = response.text - return generated_response - -def extract_urls(text): - # Define the regex pattern for URLs - url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*' - - # Find all occurrences of URLs in the text - urls = re.findall(url_pattern, text) - - return urls - -def get_audio_reply_for_question(text): - generated_audio_event = get_generated_audio(text) - #From get_generated_audio, you will get events in a string format, from that we need to extract the url - final_response = { - "audio_url": '', - "message": '' - } - if generated_audio_event["type"] == 'SUCCESS': - audio_urls = extract_urls(generated_audio_event["response"]) - if len(audio_urls) == 0: - final_response['message'] = "No audio file link found in generated event" - else: - final_response['audio_url'] = audio_urls[-1] - else: - final_response['message'] = generated_audio_event['response'] - return final_response - -def download_url(url): - try: - # Send a GET request to the URL to fetch the content - final_response = { - 'content':'', - 'error':'' - } - response = requests.get(url) - # Check if the request was successful (status code 200) - if response.status_code == 200: - final_response['content'] = response.content - else: - final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}" - except Exception as e: - final_response['error'] = f"Failed to download the URL. Error: {e}" - return final_response - -def get_filename_from_url(url): - # Use os.path.basename() to extract the file name from the URL - file_name = os.path.basename(url) - return file_name - -def get_text_response(user_message): - response = llm_chain.predict(user_message = user_message) - return response - -def get_text_response_and_audio_response(user_message): - response = get_text_response(user_message) # Getting the reply from Open AI - audio_reply_for_question_response = get_audio_reply_for_question(response) - final_response = { - 'output_file_path': '', - 'message':'' - } - audio_url = audio_reply_for_question_response['audio_url'] - if audio_url: - output_file_path=get_filename_from_url(audio_url) - download_url_response = download_url(audio_url) - audio_content = download_url_response['content'] - if audio_content: - with open(output_file_path, "wb") as audio_file: - audio_file.write(audio_content) - final_response['output_file_path'] = output_file_path - else: - final_response['message'] = download_url_response['error'] - else: - final_response['message'] = audio_reply_for_question_response['message'] - return final_response - -def chat_bot_response(message, history): - text_and_audio_response = get_text_response_and_audio_response(message) - output_file_path = text_and_audio_response['output_file_path'] - if output_file_path: - return (text_and_audio_response['output_file_path'],) - else: - return text_and_audio_response['message'] - -demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"]) - -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/rayan-saleh/whisper2notion/server/node_modules/@types/node/domain.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/domain.d.ts deleted file mode 100644 index fafe68a5d3fc413012b616cc05bdba2f661ea1af..0000000000000000000000000000000000000000 --- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/domain.d.ts +++ /dev/null @@ -1,170 +0,0 @@ -/** - * **This module is pending deprecation.** Once a replacement API has been - * finalized, this module will be fully deprecated. Most developers should - * **not** have cause to use this module. Users who absolutely must have - * the functionality that domains provide may rely on it for the time being - * but should expect to have to migrate to a different solution - * in the future. - * - * Domains provide a way to handle multiple different IO operations as a - * single group. If any of the event emitters or callbacks registered to a - * domain emit an `'error'` event, or throw an error, then the domain object - * will be notified, rather than losing the context of the error in the`process.on('uncaughtException')` handler, or causing the program to - * exit immediately with an error code. - * @deprecated Since v1.4.2 - Deprecated - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/domain.js) - */ -declare module 'domain' { - import EventEmitter = require('node:events'); - /** - * The `Domain` class encapsulates the functionality of routing errors and - * uncaught exceptions to the active `Domain` object. - * - * To handle the errors that it catches, listen to its `'error'` event. - */ - class Domain extends EventEmitter { - /** - * An array of timers and event emitters that have been explicitly added - * to the domain. - */ - members: Array; - /** - * The `enter()` method is plumbing used by the `run()`, `bind()`, and`intercept()` methods to set the active domain. It sets `domain.active` and`process.domain` to the domain, and implicitly - * pushes the domain onto the domain - * stack managed by the domain module (see {@link exit} for details on the - * domain stack). The call to `enter()` delimits the beginning of a chain of - * asynchronous calls and I/O operations bound to a domain. - * - * Calling `enter()` changes only the active domain, and does not alter the domain - * itself. `enter()` and `exit()` can be called an arbitrary number of times on a - * single domain. - */ - enter(): void; - /** - * The `exit()` method exits the current domain, popping it off the domain stack. - * Any time execution is going to switch to the context of a different chain of - * asynchronous calls, it's important to ensure that the current domain is exited. - * The call to `exit()` delimits either the end of or an interruption to the chain - * of asynchronous calls and I/O operations bound to a domain. - * - * If there are multiple, nested domains bound to the current execution context,`exit()` will exit any domains nested within this domain. - * - * Calling `exit()` changes only the active domain, and does not alter the domain - * itself. `enter()` and `exit()` can be called an arbitrary number of times on a - * single domain. - */ - exit(): void; - /** - * Run the supplied function in the context of the domain, implicitly - * binding all event emitters, timers, and lowlevel requests that are - * created in that context. Optionally, arguments can be passed to - * the function. - * - * This is the most basic way to use a domain. - * - * ```js - * const domain = require('domain'); - * const fs = require('fs'); - * const d = domain.create(); - * d.on('error', (er) => { - * console.error('Caught error!', er); - * }); - * d.run(() => { - * process.nextTick(() => { - * setTimeout(() => { // Simulating some various async stuff - * fs.open('non-existent file', 'r', (er, fd) => { - * if (er) throw er; - * // proceed... - * }); - * }, 100); - * }); - * }); - * ``` - * - * In this example, the `d.on('error')` handler will be triggered, rather - * than crashing the program. - */ - run(fn: (...args: any[]) => T, ...args: any[]): T; - /** - * Explicitly adds an emitter to the domain. If any event handlers called by - * the emitter throw an error, or if the emitter emits an `'error'` event, it - * will be routed to the domain's `'error'` event, just like with implicit - * binding. - * - * This also works with timers that are returned from `setInterval()` and `setTimeout()`. If their callback function throws, it will be caught by - * the domain `'error'` handler. - * - * If the Timer or `EventEmitter` was already bound to a domain, it is removed - * from that one, and bound to this one instead. - * @param emitter emitter or timer to be added to the domain - */ - add(emitter: EventEmitter | NodeJS.Timer): void; - /** - * The opposite of {@link add}. Removes domain handling from the - * specified emitter. - * @param emitter emitter or timer to be removed from the domain - */ - remove(emitter: EventEmitter | NodeJS.Timer): void; - /** - * The returned function will be a wrapper around the supplied callback - * function. When the returned function is called, any errors that are - * thrown will be routed to the domain's `'error'` event. - * - * ```js - * const d = domain.create(); - * - * function readSomeFile(filename, cb) { - * fs.readFile(filename, 'utf8', d.bind((er, data) => { - * // If this throws, it will also be passed to the domain. - * return cb(er, data ? JSON.parse(data) : null); - * })); - * } - * - * d.on('error', (er) => { - * // An error occurred somewhere. If we throw it now, it will crash the program - * // with the normal line number and stack message. - * }); - * ``` - * @param callback The callback function - * @return The bound function - */ - bind(callback: T): T; - /** - * This method is almost identical to {@link bind}. However, in - * addition to catching thrown errors, it will also intercept `Error` objects sent as the first argument to the function. - * - * In this way, the common `if (err) return callback(err);` pattern can be replaced - * with a single error handler in a single place. - * - * ```js - * const d = domain.create(); - * - * function readSomeFile(filename, cb) { - * fs.readFile(filename, 'utf8', d.intercept((data) => { - * // Note, the first argument is never passed to the - * // callback since it is assumed to be the 'Error' argument - * // and thus intercepted by the domain. - * - * // If this throws, it will also be passed to the domain - * // so the error-handling logic can be moved to the 'error' - * // event on the domain instead of being repeated throughout - * // the program. - * return cb(null, JSON.parse(data)); - * })); - * } - * - * d.on('error', (er) => { - * // An error occurred somewhere. If we throw it now, it will crash the program - * // with the normal line number and stack message. - * }); - * ``` - * @param callback The callback function - * @return The intercepted function - */ - intercept(callback: T): T; - } - function create(): Domain; -} -declare module 'node:domain' { - export * from 'domain'; -} diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Chat Alternative Android App Apk Mod Unlock All.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Chat Alternative Android App Apk Mod Unlock All.md deleted file mode 100644 index b30dca6732ba337cd62158ffc2c177566eed371e..0000000000000000000000000000000000000000 --- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Chat Alternative Android App Apk Mod Unlock All.md +++ /dev/null @@ -1,6 +0,0 @@ -

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    \ No newline at end of file diff --git a/spaces/remzicam/ted_talks_summarizer/app.py b/spaces/remzicam/ted_talks_summarizer/app.py deleted file mode 100644 index 6782a24246f9432acab9cdc34690ba29c967c8f0..0000000000000000000000000000000000000000 --- a/spaces/remzicam/ted_talks_summarizer/app.py +++ /dev/null @@ -1,108 +0,0 @@ -"""TED Talks Summarizer App.""" - -from re import sub - -from gradio import Interface, Textbox -from requests import get -from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline - -repo_id = "pszemraj/led-base-book-summary" - -model = AutoModelForSeq2SeqLM.from_pretrained( - repo_id, - low_cpu_mem_usage=True, -) - -tokenizer = AutoTokenizer.from_pretrained(repo_id) - -summarizer = pipeline("summarization", model=model, tokenizer=tokenizer) - - -def clean_text(text: str) -> str: - """Cleans subtitle text of ted talks. - - Args: - text (str): subtitle of ted talk - - Returns: - cleaned_text (str): cleaned version of subtitle text - """ - # remove string inside parantheses (i.e appluse) - text = sub(r"\(.*\)", "", text) - # format text by splitting/removing new lines - text = text.split("\n")[1:] - # remove empty strings - text = list(filter(None, text)) - # remove timestamps as they contains pattern of "-->" - cleaned_text = " ".join([x.strip() for x in text if "-->" not in x]) - return cleaned_text - - -def ted_talk_transcriber(link: str) -> str: - """Creates transcription of ted talks from url. - - Args: - link (str): url link of ted talks - - Returns: - raw_text (str): raw transcription of the ted talk - """ - # request link of the talk - page = get(link) - # extract unique talk id to reach subtitle file - talk_id = str(page.content).split("project_masters/")[1].split("/")[0] - raw_text = get( - f"https://hls.ted.com/project_masters/{talk_id}/subtitles/en/full.vtt" - ).text - return raw_text - - -def text_summarizer(text: str) -> str: - """Summarizes given text. - - Args: - text (str): ted talks transcription - - Returns: - str: summary - """ - result = summarizer( - text, - min_length=8, - max_length=256, - no_repeat_ngram_size=3, - encoder_no_repeat_ngram_size=3, - repetition_penalty=3.5, - num_beams=4, - do_sample=False, - early_stopping=True, - ) - return result[0]["summary_text"] - - -def main(link: str) -> str: - """Summarizes ted talks given link. - - Args: - link (str): url link of ted talks - - Returns: - str: summary - """ - raw_text = ted_talk_transcriber(link) - cleaned_transcript = clean_text(raw_text) - return text_summarizer(cleaned_transcript) - - -logo = "
    " - -Interface( - main, - inputs=Textbox(label="Type the TED Talks link"), - examples=[ - "https://www.ted.com/talks/jen_gunter_the_truth_about_yeast_in_your_body" - ], - outputs=Textbox(label="Summary"), - allow_flagging="never", - description=logo, -).launch() diff --git a/spaces/renumics/cifar10-outlier-low/prepare.py b/spaces/renumics/cifar10-outlier-low/prepare.py deleted file mode 100644 index 23f8eb3e9901c57de3f435e9ca89ce46b06a1e80..0000000000000000000000000000000000000000 --- a/spaces/renumics/cifar10-outlier-low/prepare.py +++ /dev/null @@ -1,42 +0,0 @@ -import pickle -import datasets -import os -import umap - - -if __name__ == "__main__": - cache_file = "dataset_cache.pkl" - if os.path.exists(cache_file): - # Load dataset from cache - with open(cache_file, "rb") as file: - dataset = pickle.load(file) - print("Dataset loaded from cache.") - else: - # Load dataset using datasets.load_dataset() - ds = datasets.load_dataset("renumics/cifar10-outlier", split="train") - print("Dataset loaded using datasets.load_dataset().") - - df = ds.rename_columns({"img": "image", "label": "labels"}).to_pandas() - df["label_str"] = df["labels"].apply(lambda x: ds.features["label"].int2str(x)) - - df = df.sample(10000, random_state=42).reset_index(drop=True) - - # precalculate umap embeddings - df["embedding_ft_precalc"] = umap.UMAP( - n_neighbors=70, min_dist=0.5, random_state=42 - ).fit_transform(df["embedding_ft"].tolist()).tolist() - print("Umap for ft done") - - - df["embedding_foundation_precalc"] = umap.UMAP( - n_neighbors=70, min_dist=0.5, random_state=42 - ).fit_transform(df["embedding_foundation"].tolist()).tolist() - - print("Umap for base done") - - # Save dataset to cache - with open(cache_file, "wb") as file: - pickle.dump(df, file) - - print("Dataset saved to cache.") - diff --git a/spaces/ronvolutional/ai-pokemon-card/start.py b/spaces/ronvolutional/ai-pokemon-card/start.py deleted file mode 100644 index e5d512289a4581dca4612d6aa2390ace7e534426..0000000000000000000000000000000000000000 --- a/spaces/ronvolutional/ai-pokemon-card/start.py +++ /dev/null @@ -1,3 +0,0 @@ -import subprocess - -subprocess.run("uvicorn app:app --host 0.0.0.0 --port 7860", shell=True) diff --git a/spaces/rorallitri/biomedical-language-models/logs/An Adventurers Tale.md b/spaces/rorallitri/biomedical-language-models/logs/An Adventurers Tale.md deleted file mode 100644 index a50f6a01ae19ba354560c46da27113cd6662d503..0000000000000000000000000000000000000000 --- a/spaces/rorallitri/biomedical-language-models/logs/An Adventurers Tale.md +++ /dev/null @@ -1,6 +0,0 @@ -

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    diff --git a/spaces/rstallman/Mayfair-Partner-Music/audiocraft/modules/codebooks_patterns.py b/spaces/rstallman/Mayfair-Partner-Music/audiocraft/modules/codebooks_patterns.py deleted file mode 100644 index c5b35cbea8cff84aa56116dbdd860fc72a913a13..0000000000000000000000000000000000000000 --- a/spaces/rstallman/Mayfair-Partner-Music/audiocraft/modules/codebooks_patterns.py +++ /dev/null @@ -1,539 +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 collections import namedtuple -from dataclasses import dataclass -from functools import lru_cache -import logging -import typing as tp - -from abc import ABC, abstractmethod -import torch - -LayoutCoord = namedtuple('LayoutCoord', ['t', 'q']) # (timestep, codebook index) -PatternLayout = tp.List[tp.List[LayoutCoord]] # Sequence of coordinates -logger = logging.getLogger(__name__) - - -@dataclass -class Pattern: - """Base implementation of a pattern over a sequence with multiple codebooks. - - The codebook pattern consists in a layout, defining for each sequence step - the list of coordinates of each codebook timestep in the resulting interleaved sequence. - The first item of the pattern is always an empty list in order to properly insert a special token - to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern - and ``timesteps`` the number of timesteps corresponding to the original sequence. - - The pattern provides convenient methods to build and revert interleaved sequences from it: - ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T] - to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size, - K being the number of codebooks, T the number of original timesteps and S the number of sequence steps - for the output sequence. The unfilled positions are replaced with a special token and the built sequence - is returned along with a mask indicating valid tokens. - ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment - of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask - to fill and specify invalid positions if needed. - See the dedicated methods for more details. - """ - # Pattern layout, for each sequence step, we have a list of coordinates - # corresponding to the original codebook timestep and position. - # The first list is always an empty list in order to properly insert - # a special token to start with. - layout: PatternLayout - timesteps: int - n_q: int - - def __post_init__(self): - assert len(self.layout) > 0 - assert self.layout[0] == [] - self._validate_layout() - self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes) - self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes) - logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout)) - - def _validate_layout(self): - """Runs checks on the layout to ensure a valid pattern is defined. - A pattern is considered invalid if: - - Multiple timesteps for a same codebook are defined in the same sequence step - - The timesteps for a given codebook are not in ascending order as we advance in the sequence - (this would mean that we have future timesteps before past timesteps). - """ - q_timesteps = {q: 0 for q in range(self.n_q)} - for s, seq_coords in enumerate(self.layout): - if len(seq_coords) > 0: - qs = set() - for coord in seq_coords: - qs.add(coord.q) - last_q_timestep = q_timesteps[coord.q] - assert coord.t >= last_q_timestep, \ - f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}" - q_timesteps[coord.q] = coord.t - # each sequence step contains at max 1 coordinate per codebook - assert len(qs) == len(seq_coords), \ - f"Multiple entries for a same codebook are found at step {s}" - - @property - def num_sequence_steps(self): - return len(self.layout) - 1 - - @property - def max_delay(self): - max_t_in_seq_coords = 0 - for seq_coords in self.layout[1:]: - for coords in seq_coords: - max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1) - return max_t_in_seq_coords - self.timesteps - - @property - def valid_layout(self): - valid_step = len(self.layout) - self.max_delay - return self.layout[:valid_step] - - def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None): - """Get codebook coordinates in the layout that corresponds to the specified timestep t - and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step - and the actual codebook coordinates. - """ - assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps" - if q is not None: - assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks" - coords = [] - for s, seq_codes in enumerate(self.layout): - for code in seq_codes: - if code.t == t and (q is None or code.q == q): - coords.append((s, code)) - return coords - - def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]: - return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)] - - def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]: - steps_with_timesteps = self.get_steps_with_timestep(t, q) - return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None - - def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool, - device: tp.Union[torch.device, str] = 'cpu'): - """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps. - - Args: - timesteps (int): Maximum number of timesteps steps to consider. - keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps. - device (Union[torch.device, str]): Device for created tensors. - Returns: - indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S]. - mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S]. - """ - assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}" - assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern" - # use the proper layout based on whether we limit ourselves to valid steps only or not, - # note that using the valid_layout will result in a truncated sequence up to the valid steps - ref_layout = self.valid_layout if keep_only_valid_steps else self.layout - # single item indexing being super slow with pytorch vs. numpy, so we use numpy here - indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy() - mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy() - # fill indexes with last sequence step value that will correspond to our special token - # the last value is n_q * timesteps as we have flattened z and append special token as the last token - # which will correspond to the index: n_q * timesteps - indexes[:] = n_q * timesteps - # iterate over the pattern and fill scattered indexes and mask - for s, sequence_coords in enumerate(ref_layout): - for coords in sequence_coords: - if coords.t < timesteps: - indexes[coords.q, s] = coords.t + coords.q * timesteps - mask[coords.q, s] = 1 - indexes = torch.from_numpy(indexes).to(device) - mask = torch.from_numpy(mask).to(device) - return indexes, mask - - def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False): - """Build sequence corresponding to the pattern from the input tensor z. - The sequence is built using up to sequence_steps if specified, and non-pattern - coordinates are filled with the special token. - - Args: - z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T]. - special_token (int): Special token used to fill non-pattern coordinates in the new sequence. - keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps. - Steps that are beyond valid steps will be replaced by the special_token in that case. - Returns: - values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S - corresponding either to the sequence_steps if provided, otherwise to the length of the pattern. - indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S]. - mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S]. - """ - B, K, T = z.shape - indexes, mask = self._build_pattern_sequence_scatter_indexes( - T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device) - ) - z = z.view(B, -1) - # we append the special token as the last index of our flattened z tensor - z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1) - values = z[:, indexes.view(-1)] - values = values.view(B, K, indexes.shape[-1]) - return values, indexes, mask - - def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int, - keep_only_valid_steps: bool = False, - is_model_output: bool = False, - device: tp.Union[torch.device, str] = 'cpu'): - """Builds scatter indexes required to retrieve the original multi-codebook sequence - from interleaving pattern. - - Args: - sequence_steps (int): Sequence steps. - n_q (int): Number of codebooks. - keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps. - Steps that are beyond valid steps will be replaced by the special_token in that case. - is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not. - device (Union[torch.device, str]): Device for created tensors. - Returns: - torch.Tensor: Indexes for reconstructing the output, of shape [K, T]. - mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T]. - """ - ref_layout = self.valid_layout if keep_only_valid_steps else self.layout - # TODO(jade): Do we want to further truncate to only valid timesteps here as well? - timesteps = self.timesteps - assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}" - assert sequence_steps <= len(ref_layout), \ - f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}" - - # ensure we take the appropriate indexes to keep the model output from the first special token as well - if is_model_output: - ref_layout = ref_layout[1:] - - # single item indexing being super slow with pytorch vs. numpy, so we use numpy here - indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy() - mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy() - # fill indexes with last sequence step value that will correspond to our special token - indexes[:] = n_q * sequence_steps - for s, sequence_codes in enumerate(ref_layout): - if s < sequence_steps: - for code in sequence_codes: - if code.t < timesteps: - indexes[code.q, code.t] = s + code.q * sequence_steps - mask[code.q, code.t] = 1 - indexes = torch.from_numpy(indexes).to(device) - mask = torch.from_numpy(mask).to(device) - return indexes, mask - - def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False): - """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving. - The sequence is reverted using up to timesteps if specified, and non-pattern coordinates - are filled with the special token. - - Args: - s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S]. - special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence. - Returns: - values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T - corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise. - indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T]. - mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T]. - """ - B, K, S = s.shape - indexes, mask = self._build_reverted_sequence_scatter_indexes( - S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device) - ) - s = s.view(B, -1) - # we append the special token as the last index of our flattened z tensor - s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1) - values = s[:, indexes.view(-1)] - values = values.view(B, K, indexes.shape[-1]) - return values, indexes, mask - - def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False): - """Revert model logits obtained on a sequence built from the pattern - back to a tensor matching the original sequence. - - This method is similar to ``revert_pattern_sequence`` with the following specificities: - 1. It is designed to work with the extra cardinality dimension - 2. We return the logits for the first sequence item that matches the special_token and - which matching target in the original sequence is the first item of the sequence, - while we skip the last logits as there is no matching target - """ - B, card, K, S = logits.shape - indexes, mask = self._build_reverted_sequence_scatter_indexes( - S, K, keep_only_valid_steps, is_model_output=True, device=logits.device - ) - logits = logits.reshape(B, card, -1) - # we append the special token as the last index of our flattened z tensor - logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1) # [B, card, K x S] - values = logits[:, :, indexes.view(-1)] - values = values.view(B, card, K, indexes.shape[-1]) - return values, indexes, mask - - -class CodebooksPatternProvider(ABC): - """Abstraction around providing pattern for interleaving codebooks. - - The CodebooksPatternProvider abstraction allows to implement various strategies to - define interleaving pattern of sequences composed of multiple codebooks. For a given - number of codebooks `n_q`, the pattern provider can generate a specified pattern - corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern - can be used to construct a new sequence from the original codes respecting the specified - pattern. The pattern is defined as a list of list of code coordinates, code coordinate - being a tuple with the original timestep and codebook to build the new sequence. - Note that all patterns must start with an empty list that is then used to insert a first - sequence step of special tokens in the newly generated sequence. - - Args: - n_q (int): number of codebooks. - cached (bool): if True, patterns for a given length are cached. In general - that should be true for efficiency reason to avoid synchronization points. - """ - def __init__(self, n_q: int, cached: bool = True): - assert n_q > 0 - self.n_q = n_q - self.get_pattern = lru_cache(100)(self.get_pattern) # type: ignore - - @abstractmethod - def get_pattern(self, timesteps: int) -> Pattern: - """Builds pattern with specific interleaving between codebooks. - - Args: - timesteps (int): Total numer of timesteps. - """ - raise NotImplementedError() - - -class DelayedPatternProvider(CodebooksPatternProvider): - """Provider for delayed pattern across delayed codebooks. - Codebooks are delayed in the sequence and sequence steps will contain codebooks - from different timesteps. - - Example: - Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence: - [[1, 2, 3, 4], - [1, 2, 3, 4], - [1, 2, 3, 4]] - The resulting sequence obtained from the returned pattern is: - [[S, 1, 2, 3, 4], - [S, S, 1, 2, 3], - [S, S, S, 1, 2]] - (with S being a special token) - - Args: - n_q (int): Number of codebooks. - delays (Optional[List[int]]): Delay for each of the codebooks. - If delays not defined, each codebook is delayed by 1 compared to the previous one. - flatten_first (int): Flatten the first N timesteps. - empty_initial (int): Prepend with N empty list of coordinates. - """ - def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None, - flatten_first: int = 0, empty_initial: int = 0): - super().__init__(n_q) - if delays is None: - delays = list(range(n_q)) - self.delays = delays - self.flatten_first = flatten_first - self.empty_initial = empty_initial - assert len(self.delays) == self.n_q - assert sorted(self.delays) == self.delays - - def get_pattern(self, timesteps: int) -> Pattern: - out: PatternLayout = [[]] - max_delay = max(self.delays) - if self.empty_initial: - out += [[] for _ in range(self.empty_initial)] - if self.flatten_first: - for t in range(min(timesteps, self.flatten_first)): - for q in range(self.n_q): - out.append([LayoutCoord(t, q)]) - for t in range(self.flatten_first, timesteps + max_delay): - v = [] - for q, delay in enumerate(self.delays): - t_for_q = t - delay - if t_for_q >= self.flatten_first: - v.append(LayoutCoord(t_for_q, q)) - out.append(v) - return Pattern(out, n_q=self.n_q, timesteps=timesteps) - - -class ParallelPatternProvider(DelayedPatternProvider): - """Provider for parallel pattern across codebooks. - This pattern provider is a special case of the delayed pattern with actually no delay, - hence delays=repeat(0, n_q). - - Args: - n_q (int): Number of codebooks. - """ - def __init__(self, n_q: int): - super().__init__(n_q, [0] * n_q) - - -class UnrolledPatternProvider(CodebooksPatternProvider): - """Provider for unrolling codebooks pattern. - This pattern provider enables to represent the codebook flattened completely or only to some extend - while also specifying a given delay between the flattened codebooks representation, allowing to - unroll the codebooks in the sequence. - - Example: - 1. Flattening of the codebooks. - By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q), - taking n_q = 3 and timesteps = 4: - [[1, 2, 3, 4], - [1, 2, 3, 4], - [1, 2, 3, 4]] - will result into: - [[S, S, 1, S, S, 2, S, S, 3, S, S, 4], - [S, 1, S, S, 2, S, S, 3, S, S, 4, S], - [1, S, S, 2, S, S, 3, S, S, 4, S, S]] - 2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step - for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example - taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]: - [[1, 2, 3, 4], - [1, 2, 3, 4], - [1, 2, 3, 4]] - will result into: - [[S, 1, S, S, 2, S, S, 3, S, S, 4, S], - [S, 1, S, S, 2, S, S, 3, S, S, 4, S], - [1, S, S, 2, S, S, 3, S, S, 4, S, S]] - 3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks - allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the - same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1] - and delays = [0, 3, 3]: - [[1, 2, 3, 4], - [1, 2, 3, 4], - [1, 2, 3, 4]] - will result into: - [[S, S, S, 1, S, 2, S, 3, S, 4], - [S, S, S, 1, S, 2, S, 3, S, 4], - [1, 2, 3, S, 4, S, 5, S, 6, S]] - - Args: - n_q (int): Number of codebooks. - flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined, - the codebooks will be flattened to 1 codebook per step, meaning that the sequence will - have n_q extra steps for each timestep. - delays (Optional[List[int]]): Delay for each of the codebooks. If not defined, - no delay is added and therefore will default to [0] * ``n_q``. - Note that two codebooks that will be flattened to the same inner step - should have the same delay, otherwise the pattern is considered as invalid. - """ - FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay']) - - def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None, - delays: tp.Optional[tp.List[int]] = None): - super().__init__(n_q) - if flattening is None: - flattening = list(range(n_q)) - if delays is None: - delays = [0] * n_q - assert len(flattening) == n_q - assert len(delays) == n_q - assert sorted(flattening) == flattening - assert sorted(delays) == delays - self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening) - self.max_delay = max(delays) - - def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]): - """Build a flattened codebooks representation as a dictionary of inner step - and the actual codebook indices corresponding to the flattened codebook. For convenience, we - also store the delay associated to the flattened codebook to avoid maintaining an extra mapping. - """ - flattened_codebooks: dict = {} - for q, (inner_step, delay) in enumerate(zip(flattening, delays)): - if inner_step not in flattened_codebooks: - flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay) - else: - flat_codebook = flattened_codebooks[inner_step] - assert flat_codebook.delay == delay, ( - "Delay and flattening between codebooks is inconsistent: ", - "two codebooks flattened to the same position should have the same delay." - ) - flat_codebook.codebooks.append(q) - flattened_codebooks[inner_step] = flat_codebook - return flattened_codebooks - - @property - def _num_inner_steps(self): - """Number of inner steps to unroll between timesteps in order to flatten the codebooks. - """ - return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1 - - def num_virtual_steps(self, timesteps: int) -> int: - return timesteps * self._num_inner_steps + 1 - - def get_pattern(self, timesteps: int) -> Pattern: - """Builds pattern for delay across codebooks. - - Args: - timesteps (int): Total numer of timesteps. - """ - # the PatternLayout is built as a tuple of sequence position and list of coordinates - # so that it can be reordered properly given the required delay between codebooks of given timesteps - indexed_out: list = [(-1, [])] - max_timesteps = timesteps + self.max_delay - for t in range(max_timesteps): - # for each timestep, we unroll the flattened codebooks, - # emitting the sequence step with the corresponding delay - for step in range(self._num_inner_steps): - if step in self._flattened_codebooks: - # we have codebooks at this virtual step to emit - step_codebooks = self._flattened_codebooks[step] - t_for_q = t + step_codebooks.delay - coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks] - if t_for_q < max_timesteps and t < max_timesteps: - indexed_out.append((t_for_q, coords)) - else: - # there is no codebook in this virtual step so we emit an empty list - indexed_out.append((t, [])) - out = [coords for _, coords in sorted(indexed_out)] - return Pattern(out, n_q=self.n_q, timesteps=timesteps) - - -class VALLEPattern(CodebooksPatternProvider): - """Almost VALL-E style pattern. We futher allow some delays for the - codebooks other than the first one. - - Args: - n_q (int): Number of codebooks. - delays (Optional[List[int]]): Delay for each of the codebooks. - If delays not defined, each codebook is delayed by 1 compared to the previous one. - """ - def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None): - super().__init__(n_q) - if delays is None: - delays = [0] * (n_q - 1) - self.delays = delays - assert len(self.delays) == self.n_q - 1 - assert sorted(self.delays) == self.delays - - def get_pattern(self, timesteps: int) -> Pattern: - out: PatternLayout = [[]] - for t in range(timesteps): - out.append([LayoutCoord(t, 0)]) - max_delay = max(self.delays) - for t in range(timesteps + max_delay): - v = [] - for q, delay in enumerate(self.delays): - t_for_q = t - delay - if t_for_q >= 0: - v.append(LayoutCoord(t_for_q, q + 1)) - out.append(v) - return Pattern(out, n_q=self.n_q, timesteps=timesteps) - - -class MusicLMPattern(CodebooksPatternProvider): - """Almost MusicLM style pattern. This is equivalent to full flattening - but in a different order. - - Args: - n_q (int): Number of codebooks. - group_by (int): Number of codebooks to group together. - """ - def __init__(self, n_q: int, group_by: int = 2): - super().__init__(n_q) - self.group_by = group_by - - def get_pattern(self, timesteps: int) -> Pattern: - out: PatternLayout = [[]] - for offset in range(0, self.n_q, self.group_by): - for t in range(timesteps): - for q in range(offset, offset + self.group_by): - out.append([LayoutCoord(t, q)]) - return Pattern(out, n_q=self.n_q, timesteps=timesteps) diff --git a/spaces/ruslanmv/Clone-Your-Voice/utils/argutils.py b/spaces/ruslanmv/Clone-Your-Voice/utils/argutils.py deleted file mode 100644 index db41683027173517c910e3b259f8da48207dcb38..0000000000000000000000000000000000000000 --- a/spaces/ruslanmv/Clone-Your-Voice/utils/argutils.py +++ /dev/null @@ -1,40 +0,0 @@ -from pathlib import Path -import numpy as np -import argparse - -_type_priorities = [ # In decreasing order - Path, - str, - int, - float, - bool, -] - -def _priority(o): - p = next((i for i, t in enumerate(_type_priorities) if type(o) is t), None) - if p is not None: - return p - p = next((i for i, t in enumerate(_type_priorities) if isinstance(o, t)), None) - if p is not None: - return p - return len(_type_priorities) - -def print_args(args: argparse.Namespace, parser=None): - args = vars(args) - if parser is None: - priorities = list(map(_priority, args.values())) - else: - all_params = [a.dest for g in parser._action_groups for a in g._group_actions ] - priority = lambda p: all_params.index(p) if p in all_params else len(all_params) - priorities = list(map(priority, args.keys())) - - pad = max(map(len, args.keys())) + 3 - indices = np.lexsort((list(args.keys()), priorities)) - items = list(args.items()) - - print("Arguments:") - for i in indices: - param, value = items[i] - print(" {0}:{1}{2}".format(param, ' ' * (pad - len(param)), value)) - print("") - \ No newline at end of file diff --git a/spaces/ruslanmv/Clone-Your-Voice/vocoder/audio.py b/spaces/ruslanmv/Clone-Your-Voice/vocoder/audio.py deleted file mode 100644 index 116396261e184b9968971bd06fabc6f525e0c2fe..0000000000000000000000000000000000000000 --- a/spaces/ruslanmv/Clone-Your-Voice/vocoder/audio.py +++ /dev/null @@ -1,108 +0,0 @@ -import math -import numpy as np -import librosa -import vocoder.hparams as hp -from scipy.signal import lfilter -import soundfile as sf - - -def label_2_float(x, bits) : - return 2 * x / (2**bits - 1.) - 1. - - -def float_2_label(x, bits) : - assert abs(x).max() <= 1.0 - x = (x + 1.) * (2**bits - 1) / 2 - return x.clip(0, 2**bits - 1) - - -def load_wav(path) : - return librosa.load(str(path), sr=hp.sample_rate)[0] - - -def save_wav(x, path) : - sf.write(path, x.astype(np.float32), hp.sample_rate) - - -def split_signal(x) : - unsigned = x + 2**15 - coarse = unsigned // 256 - fine = unsigned % 256 - return coarse, fine - - -def combine_signal(coarse, fine) : - return coarse * 256 + fine - 2**15 - - -def encode_16bits(x) : - return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16) - - -mel_basis = None - - -def linear_to_mel(spectrogram): - global mel_basis - if mel_basis is None: - mel_basis = build_mel_basis() - return np.dot(mel_basis, spectrogram) - - -def build_mel_basis(): - return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin) - - -def normalize(S): - return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1) - - -def denormalize(S): - return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db - - -def amp_to_db(x): - return 20 * np.log10(np.maximum(1e-5, x)) - - -def db_to_amp(x): - return np.power(10.0, x * 0.05) - - -def spectrogram(y): - D = stft(y) - S = amp_to_db(np.abs(D)) - hp.ref_level_db - return normalize(S) - - -def melspectrogram(y): - D = stft(y) - S = amp_to_db(linear_to_mel(np.abs(D))) - return normalize(S) - - -def stft(y): - return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length) - - -def pre_emphasis(x): - return lfilter([1, -hp.preemphasis], [1], x) - - -def de_emphasis(x): - return lfilter([1], [1, -hp.preemphasis], x) - - -def encode_mu_law(x, mu) : - mu = mu - 1 - fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu) - return np.floor((fx + 1) / 2 * mu + 0.5) - - -def decode_mu_law(y, mu, from_labels=True) : - if from_labels: - y = label_2_float(y, math.log2(mu)) - mu = mu - 1 - x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1) - return x - diff --git a/spaces/ryansilk/quantycs/StreamLit/Pages/scratches/scratch.py b/spaces/ryansilk/quantycs/StreamLit/Pages/scratches/scratch.py deleted file mode 100644 index 663bd1f6a2ae02f29df59fb4963c17934034f731..0000000000000000000000000000000000000000 --- a/spaces/ryansilk/quantycs/StreamLit/Pages/scratches/scratch.py +++ /dev/null @@ -1 +0,0 @@ -requests \ No newline at end of file diff --git a/spaces/sagiliManoj/ManojGenAIAvatar/README.md b/spaces/sagiliManoj/ManojGenAIAvatar/README.md deleted file mode 100644 index 3a161ad077d05b7ad22eaf65bdc9abdb0be9619d..0000000000000000000000000000000000000000 --- a/spaces/sagiliManoj/ManojGenAIAvatar/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: ManojGenAIAvatar -emoji: 💻 -colorFrom: purple -colorTo: gray -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/samcaicn/bingai/src/pages/api/sydney.ts b/spaces/samcaicn/bingai/src/pages/api/sydney.ts deleted file mode 100644 index 886518d645bf60df173e120539d47695f5e11e2a..0000000000000000000000000000000000000000 --- a/spaces/samcaicn/bingai/src/pages/api/sydney.ts +++ /dev/null @@ -1,43 +0,0 @@ -import { NextApiRequest, NextApiResponse } from 'next' -import { WebSocket } from '@/lib/isomorphic'; -import { BingWebBot } from '@/lib/bots/bing'; -import { websocketUtils } from '@/lib/bots/bing/utils'; -import { createHeaders } from '@/lib/utils'; - -export default async function handler(req: NextApiRequest, res: NextApiResponse) { - const conversationContext = req.body - const headers = createHeaders(req.cookies) - - res.setHeader('Content-Type', 'text/stream; charset=UTF-8') - - const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', { - headers: { - ...headers, - 'accept-language': 'zh-CN,zh;q=0.9', - 'cache-control': 'no-cache', - pragma: 'no-cache', - } - }) - - ws.onmessage = (event) => { - if (Math.ceil(Date.now() / 1000) % 6 === 0) { - ws.send(websocketUtils.packMessage({ type: 6 })) - } - res.write(event.data) - if (String(event.data).lastIndexOf('{"type":3,"invocationId":"0"}') > 0) { - ws.close() - } - } - - ws.onclose = () => { - res.end() - } - - await new Promise((resolve) => ws.onopen = resolve) - ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 })) - ws.send(websocketUtils.packMessage({ type: 6 })) - ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!))) - req.socket.once('close', () => { - ws.close() - }) -} diff --git a/spaces/sarinam/speaker-anonymization/IMSToucan/README.md b/spaces/sarinam/speaker-anonymization/IMSToucan/README.md deleted file mode 100644 index 8199695cc2a09cab6497e5fcd65653faedf66556..0000000000000000000000000000000000000000 --- a/spaces/sarinam/speaker-anonymization/IMSToucan/README.md +++ /dev/null @@ -1,327 +0,0 @@ -![image](Utility/toucan.png) - -IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models, developed at the -**Institute for Natural Language Processing (IMS), University of Stuttgart, Germany**. Everything is pure Python and -PyTorch based to keep it as simple and beginner-friendly, yet powerful as possible. - -The PyTorch Modules of [Tacotron 2](https://arxiv.org/abs/1712.05884) -and [FastSpeech 2](https://arxiv.org/abs/2006.04558) are taken from -[ESPnet](https://github.com/espnet/espnet), the PyTorch Modules of [HiFiGAN](https://arxiv.org/abs/2010.05646) are taken -from the [ParallelWaveGAN repository](https://github.com/kan-bayashi/ParallelWaveGAN) -which are also authored by the brilliant [Tomoki Hayashi](https://github.com/kan-bayashi). - -For a version of the toolkit that includes TransformerTTS instead of Tacotron 2 and MelGAN instead of HiFiGAN, check out -the TransformerTTS and MelGAN branch. They are separated to keep the code clean, simple and minimal. - ---- - -## Contents - -- [New Features](#new-features) -- [Demonstration](#demonstration) -- [Installation](#installation) - + [Basic Requirements](#basic-requirements) - + [Speaker Embedding](#speaker-embedding) - + [espeak-ng](#espeak-ng) -- [Creating a new Pipeline](#creating-a-new-pipeline) - * [Build a HiFi-GAN Pipeline](#build-a-hifi-gan-pipeline) - * [Build a FastSpeech 2 Pipeline](#build-a-fastspeech-2-pipeline) -- [Training a Model](#training-a-model) -- [Creating a new InferenceInterface](#creating-a-new-inferenceinterface) -- [Using a trained Model for Inference](#using-a-trained-model-for-inference) -- [FAQ](#faq) -- [Citation](#citation) - ---- - -## New Features - -- [As shown in this paper](http://festvox.org/blizzard/bc2021/BC21_DelightfulTTS.pdf) vocoders can be used to perform - super-resolution and spectrogram inversion simultaneously. We added this to our HiFi-GAN vocoder. It now takes 16kHz - spectrograms as input, but produces 48kHz waveforms. -- We officially introduced IMS Toucan in - [our contribution to the Blizzard Challenge 2021](http://festvox.org/blizzard/bc2021/BC21_IMS.pdf). Check out the - bottom of the readme for a bibtex entry. -- We now use articulatory representations of phonemes as the input for all models. This allows us to easily use - multilingual data. -- We provide a checkpoint trained with [model agnostic meta learning](https://arxiv.org/abs/1703.03400) from which you - should be able to fine-tune a model with very little data in almost any language. -- We now use a small self-contained Aligner that is trained with CTC, inspired by - [this implementation](https://github.com/as-ideas/DeepForcedAligner). This allows us to get rid of the dependence on - autoregressive models. Tacotron 2 is thus now also no longer in this branch, but still present in other branches, - similar to TransformerTTS. - ---- - -## Demonstration - -[Here are two sentences](https://drive.google.com/file/d/1ltAyR2EwAbmDo2hgkx1mvUny4FuxYmru/view?usp=sharing) -produced by Tacotron 2 combined with HiFi-GAN, trained on -[Nancy Krebs](https://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/) using this toolkit. - -[Here is some speech](https://drive.google.com/file/d/1mZ1LvTlY6pJ5ZQ4UXZ9jbzB651mufBrB/view?usp=sharing) -produced by FastSpeech 2 and MelGAN trained on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) -using this toolkit. - -And [here is a sentence](https://drive.google.com/file/d/1FT49Jf0yyibwMDbsEJEO9mjwHkHRIGXc/view?usp=sharing) -produced by TransformerTTS and MelGAN trained on [Thorsten](https://github.com/thorstenMueller/deep-learning-german-tts) -using this toolkit. - -[Here is some speech](https://drive.google.com/file/d/14nPo2o1VKtWLPGF7e_0TxL8XGI3n7tAs/view?usp=sharing) -produced by a multi-speaker FastSpeech 2 with MelGAN trained on -[LibriTTS](https://research.google/tools/datasets/libri-tts/) using this toolkit. Fans of the videogame Portal may -recognize who was used as the reference speaker for this utterance. - -[Interactive Demo of our entry to the Blizzard Challenge 2021.](https://colab.research.google.com/drive/1bRaySf8U55MRPaxqBr8huWrzCOzlxVqw) -This is based on an older version of the toolkit though. It uses FastSpeech2 and MelGAN as vocoder and is trained on 5 -hours of Spanish. - ---- - -## Installation - -#### Basic Requirements - -To install this toolkit, clone it onto the machine you want to use it on -(should have at least one GPU if you intend to train models on that machine. For inference, you can get by without GPU). -Navigate to the directory you have cloned. We are going to create and activate a -[conda virtual environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) -to install the basic requirements into. After creating the environment, the command you need to use to activate the -virtual environment is displayed. The commands below show everything you need to do. - -``` -conda create --prefix ./toucan_conda_venv --no-default-packages python=3.8 - -pip install --no-cache-dir -r requirements.txt - -pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html -``` - -#### Speaker Embedding - -As [NVIDIA has shown](https://arxiv.org/pdf/2110.05798.pdf), you get better results by fine-tuning a pretrained model on -a new speaker, rather than training a multispeaker model. We have thus dropped support for zero-shot multispeaker models -using speaker embeddings. However we still -use [Speechbrain's ECAPA-TDNN](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb) for a cycle consistency loss to -make adapting to new speakers a bit faster. - -In the current version of the toolkit no further action should be required. When you are using multispeaker for the -first time, it requires an internet connection to download the pretrained models though. - -#### espeak-ng - -And finally you need to have espeak-ng installed on your system, because it is used as backend for the phonemizer. If -you replace the phonemizer, you don't need it. On most Linux environments it will be installed already, and if it is -not, and you have the sufficient rights, you can install it by simply running - -``` -apt-get install espeak-ng -``` - ---- - -## Creating a new Pipeline - -To create a new pipeline to train a HiFiGAN vocoder, you only need a set of audio files. To create a new pipeline for a -FastSpeech 2, you need audio files, corresponding text labels, and an already trained Aligner model to estimate the -duration information that FastSpeech 2 needs as input. Let's go through them in order of increasing complexity. - -### Build a HiFi-GAN Pipeline - -In the directory called -*Utility* there is a file called -*file_lists.py*. In this file you should write a function that returns a list of all the absolute paths to each of the -audio files in your dataset as strings. - -Then go to the directory -*TrainingInterfaces/TrainingPipelines*. In there, make a copy of any existing pipeline that has HiFiGAN in its name. We -will use this as reference and only make the necessary changes to use the new dataset. Import the function you have just -written as -*get_file_list*. Now look out for a variable called -*model_save_dir*. This is the default directory that checkpoints will be saved into, unless you specify another one when -calling the training script. Change it to whatever you like. - -Now you need to add your newly created pipeline to the pipeline dictionary in the file -*run_training_pipeline.py* in the top level of the toolkit. In this file, import the -*run* function from the pipeline you just created and give it a speaking name. Now in the -*pipeline_dict*, add your imported function as value and use as key a shorthand that makes sense. And just like that -you're done. - -### Build a FastSpeech 2 Pipeline - -In the directory called -*Utility* there is a file called -*path_to_transcript_dicts.py*. In this file you should write a function that returns a dictionary that has all the -absolute paths to each of the audio files in your dataset as strings as the keys and the textual transcriptions of the -corresponding audios as the values. - -Then go to the directory -*TrainingInterfaces/TrainingPipelines*. In there, make a copy of any existing pipeline that has FastSpeech 2 in its -name. We will use this copy as reference and only make the necessary changes to use the new dataset. Import the function -you have just written as -*build_path_to_transcript_dict*. Since the data will be processed a considerable amount, a cache will be built and saved -as file for quick and easy restarts. So find the variable -*cache_dir* and adapt it to your needs. The same goes for the variable -*save_dir*, which is where the checkpoints will be saved to. This is a default value, you can overwrite it when calling -the pipeline later using a command line argument, in case you want to fine-tune from a checkpoint and thus save into a -different directory. - -In your new pipeline file, look out for the line in which the -*acoustic_model* is loaded. Change the path to the checkpoint of an Aligner model. It can either be the one that is -supplied with the toolkit in the download script, or one that you trained yourself. In the example pipelines, the one -that we provide is finetuned to the dataset it is applied to before it is used to extract durations. - -Since we are using text here, we have to make sure that the text processing is adequate for the language. So check in -*Preprocessing/TextFrontend* whether the TextFrontend already has a language ID (e.g. 'en' and 'de') for the language of -your dataset. If not, you'll have to implement handling for that, but it should be pretty simple by just doing it -analogous to what is there already. Now back in the pipeline, change the -*lang* argument in the creation of the dataset and in the call to the train loop function to the language ID that -matches your data. - -Now navigate to the implementation of the -*train_loop* that is called in the pipeline. In this file, find the function called -*plot_progress_spec*. This function will produce spectrogram plots during training, which is the most important way to -monitor the progress of the training. In there, you may need to add an example sentence for the language of the data you -are using. It should all be pretty clear from looking at it. - -Once this is done, we are almost done, now we just need to make it available to the -*run_training_pipeline.py* file in the top level. In said file, import the -*run* function from the pipeline you just created and give it a speaking name. Now in the -*pipeline_dict*, add your imported function as value and use as key a shorthand that makes sense. And that's it. - ---- - -## Training a Model - -Once you have a pipeline built, training is super easy. Just activate your virtual environment and run the command -below. You might want to use something like nohup to keep it running after you log out from the server (then you should -also add -u as option to python) and add an & to start it in the background. Also, you might want to direct the std:out -and std:err into a file using > but all of that is just standard shell use and has nothing to do with the toolkit. - -``` -python run_training_pipeline.py -``` - -You can supply any of the following arguments, but don't have to (although for training you should definitely specify at -least a GPU ID). - -``` ---gpu_id - ---resume_checkpoint - ---resume (if this is present, the furthest checkpoint available will be loaded automatically) - ---finetune (if this is present, the provided checkpoint will be fine-tuned on the data from this pipeline) - ---model_save_dir -``` - -After every epoch, some logs will be written to the console. If the loss becomes NaN, you'll need to use a smaller -learning rate or more warmup steps in the arguments of the call to the training_loop in the pipeline you are running. - -If you get cuda out of memory errors, you need to decrease the batchsize in the arguments of the call to the -training_loop in the pipeline you are running. Try decreasing the batchsize in small steps until you get no more out of -cuda memory errors. Decreasing the batchsize may also require you to use a smaller learning rate. The use of GroupNorm -should make it so that the training remains mostly stable. - -Speaking of plots: in the directory you specified for saving model's checkpoint files and self-explanatory visualization -data will appear. Since the checkpoints are quite big, only the five most recent ones will be kept. Training will stop -after 500,000 for FastSpeech 2, and after 2,500,000 steps for HiFiGAN. Depending on the machine and configuration you -are using this will take multiple days, so verify that everything works on small tests before running the big thing. If -you want to stop earlier, just kill the process, since everything is daemonic all the child-processes should die with -it. In case there are some ghost-processes left behind, you can use the following command to find them and kill them -manually. - -``` -fuser -v /dev/nvidia* -``` - -After training is complete, it is recommended to run -*run_weight_averaging.py*. If you made no changes to the architectures and stuck to the default directory layout, it -will automatically load any models you produced with one pipeline, average their parameters to get a slightly more -robust model and save the result as -*best.pt* in the same directory where all the corresponding checkpoints lie. This also compresses the file size -significantly, so you should do this and then use the -*best.pt* model for inference. - ---- - -## Creating a new InferenceInterface - -To build a new -*InferenceInterface*, which you can then use for super simple inference, we're going to use an existing one as template -again. Make a copy of the -*InferenceInterface*. Change the name of the class in the copy and change the paths to the models to use the trained -models of your choice. Instantiate the model with the same hyperparameters that you used when you created it in the -corresponding training pipeline. The last thing to check is the language that you supply to the text frontend. Make sure -it matches what you used during training. - -With your newly created -*InferenceInterface*, you can use your trained models pretty much anywhere, e.g. in other projects. All you need is the -*Utility* directory, the -*Layers* -directory, the -*Preprocessing* directory and the -*InferenceInterfaces* directory (and of course your model checkpoint). That's all the code you need, it works -standalone. - ---- - -## Using a trained Model for Inference - -An -*InferenceInterface* contains two useful methods. They are -*read_to_file* and -*read_aloud*. - -- *read_to_file* takes as input a list of strings and a filename. It will synthesize the sentences in the list and - concatenate them with a short pause inbetween and write them to the filepath you supply as the other argument. - -- *read_aloud* takes just a string, which it will then convert to speech and immediately play using the system's - speakers. If you set the optional argument - *view* to - *True* when calling it, it will also show a plot of the phonemes it produced, the spectrogram it came up with, and the - wave it created from that spectrogram. So all the representations can be seen, text to phoneme, phoneme to spectrogram - and finally spectrogram to wave. - -Those methods are used in demo code in the toolkit. In -*run_interactive_demo.py* and -*run_text_to_file_reader.py*, you can import -*InferenceInterfaces* that you created and add them to the dictionary in each of the files with a shorthand that makes -sense. In the interactive demo, you can just call the python script, then type in the shorthand when prompted and -immediately listen to your synthesis saying whatever you put in next (be wary of out of memory errors for too long -inputs). In the text reader demo script you have to call the function that wraps around the -*InferenceInterface* and supply the shorthand of your choice. It should be pretty clear from looking at it. - ---- - -## FAQ - -Here are a few points that were brought up by users: - -- My error message shows GPU0, even though I specified a different GPU - The way GPU selection works is that the - specified GPU is set as the only visible device, in order to avoid backend stuff running accidentally on different - GPUs. So internally the program will name the device GPU0, because it is the only GPU it can see. It is actually - running on the GPU you specified. - ---- - -This toolkit has been written by Florian Lux (except for the pytorch modules taken -from [ESPnet](https://github.com/espnet/espnet) and -[ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN), as mentioned above), so if you come across problems -or questions, feel free to [write a mail](mailto:florian.lux@ims.uni-stuttgart.de). Also let me know if you do something -cool with it. Thank you for reading. - -## Citation - -``` -@inproceedings{lux2021toucan, - title={{The IMS Toucan system for the Blizzard Challenge 2021}}, - author={Florian Lux and Julia Koch and Antje Schweitzer and Ngoc Thang Vu}, - year={2021}, - booktitle={Proc. Blizzard Challenge Workshop}, - volume={2021}, - publisher={{Speech Synthesis SIG}} -} -``` diff --git a/spaces/sayakpaul/gopro-deblurring-maxim/maxim/blocks/block_gating.py b/spaces/sayakpaul/gopro-deblurring-maxim/maxim/blocks/block_gating.py deleted file mode 100644 index 0d06af50448f7a15a39c84100be1a99710b24c32..0000000000000000000000000000000000000000 --- a/spaces/sayakpaul/gopro-deblurring-maxim/maxim/blocks/block_gating.py +++ /dev/null @@ -1,67 +0,0 @@ -import tensorflow as tf -from tensorflow.keras import backend as K -from tensorflow.keras import layers - -from ..layers import BlockImages, SwapAxes, UnblockImages - - -def BlockGatingUnit(use_bias: bool = True, name: str = "block_gating_unit"): - """A SpatialGatingUnit as defined in the gMLP paper. - - The 'spatial' dim is defined as the **second last**. - If applied on other dims, you should swapaxes first. - """ - - def apply(x): - u, v = tf.split(x, 2, axis=-1) - v = layers.LayerNormalization( - epsilon=1e-06, name=f"{name}_intermediate_layernorm" - )(v) - n = K.int_shape(x)[-2] # get spatial dim - v = SwapAxes()(v, -1, -2) - v = layers.Dense(n, use_bias=use_bias, name=f"{name}_Dense_0")(v) - v = SwapAxes()(v, -1, -2) - return u * (v + 1.0) - - return apply - - -def BlockGmlpLayer( - block_size, - use_bias: bool = True, - factor: int = 2, - dropout_rate: float = 0.0, - name: str = "block_gmlp", -): - """Block gMLP layer that performs local mixing of tokens.""" - - def apply(x): - n, h, w, num_channels = ( - K.int_shape(x)[0], - K.int_shape(x)[1], - K.int_shape(x)[2], - K.int_shape(x)[3], - ) - fh, fw = block_size - gh, gw = h // fh, w // fw - x = BlockImages()(x, patch_size=(fh, fw)) - # MLP2: Local (block) mixing part, provides within-block communication. - y = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x) - y = layers.Dense( - num_channels * factor, - use_bias=use_bias, - name=f"{name}_in_project", - )(y) - y = tf.nn.gelu(y, approximate=True) - y = BlockGatingUnit(use_bias=use_bias, name=f"{name}_BlockGatingUnit")(y) - y = layers.Dense( - num_channels, - use_bias=use_bias, - name=f"{name}_out_project", - )(y) - y = layers.Dropout(dropout_rate)(y) - x = x + y - x = UnblockImages()(x, grid_size=(gh, gw), patch_size=(fh, fw)) - return x - - return apply diff --git a/spaces/sayakpaul/raindrop-deraining-maxim/maxim/blocks/grid_gating.py b/spaces/sayakpaul/raindrop-deraining-maxim/maxim/blocks/grid_gating.py deleted file mode 100644 index 91980c874bd1175f1eb0be554f7be99b60cf86bd..0000000000000000000000000000000000000000 --- a/spaces/sayakpaul/raindrop-deraining-maxim/maxim/blocks/grid_gating.py +++ /dev/null @@ -1,68 +0,0 @@ -import tensorflow as tf -from tensorflow.keras import backend as K -from tensorflow.keras import layers - -from ..layers import BlockImages, SwapAxes, UnblockImages - - -def GridGatingUnit(use_bias: bool = True, name: str = "grid_gating_unit"): - """A SpatialGatingUnit as defined in the gMLP paper. - - The 'spatial' dim is defined as the second last. - If applied on other dims, you should swapaxes first. - """ - - def apply(x): - u, v = tf.split(x, 2, axis=-1) - v = layers.LayerNormalization( - epsilon=1e-06, name=f"{name}_intermediate_layernorm" - )(v) - n = K.int_shape(x)[-3] # get spatial dim - v = SwapAxes()(v, -1, -3) - v = layers.Dense(n, use_bias=use_bias, name=f"{name}_Dense_0")(v) - v = SwapAxes()(v, -1, -3) - return u * (v + 1.0) - - return apply - - -def GridGmlpLayer( - grid_size, - use_bias: bool = True, - factor: int = 2, - dropout_rate: float = 0.0, - name: str = "grid_gmlp", -): - """Grid gMLP layer that performs global mixing of tokens.""" - - def apply(x): - n, h, w, num_channels = ( - K.int_shape(x)[0], - K.int_shape(x)[1], - K.int_shape(x)[2], - K.int_shape(x)[3], - ) - gh, gw = grid_size - fh, fw = h // gh, w // gw - - x = BlockImages()(x, patch_size=(fh, fw)) - # gMLP1: Global (grid) mixing part, provides global grid communication. - y = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x) - y = layers.Dense( - num_channels * factor, - use_bias=use_bias, - name=f"{name}_in_project", - )(y) - y = tf.nn.gelu(y, approximate=True) - y = GridGatingUnit(use_bias=use_bias, name=f"{name}_GridGatingUnit")(y) - y = layers.Dense( - num_channels, - use_bias=use_bias, - name=f"{name}_out_project", - )(y) - y = layers.Dropout(dropout_rate)(y) - x = x + y - x = UnblockImages()(x, grid_size=(gh, gw), patch_size=(fh, fw)) - return x - - return apply diff --git a/spaces/sayakpaul/video-classification-ucf101-subset/app.py b/spaces/sayakpaul/video-classification-ucf101-subset/app.py deleted file mode 100644 index 5abfe0ba8732b3cb54358737f420a30e43ce000e..0000000000000000000000000000000000000000 --- a/spaces/sayakpaul/video-classification-ucf101-subset/app.py +++ /dev/null @@ -1,140 +0,0 @@ -import cv2 -import gradio as gr -import imutils -import numpy as np -import torch -from pytorchvideo.transforms import ( - ApplyTransformToKey, - Normalize, - RandomShortSideScale, - RemoveKey, - ShortSideScale, - UniformTemporalSubsample, -) -from torchvision.transforms import ( - Compose, - Lambda, - RandomCrop, - RandomHorizontalFlip, - Resize, -) -from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification - -MODEL_CKPT = "sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset" -DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -MODEL = VideoMAEForVideoClassification.from_pretrained(MODEL_CKPT).to(DEVICE) -PROCESSOR = VideoMAEFeatureExtractor.from_pretrained(MODEL_CKPT) - -RESIZE_TO = PROCESSOR.size["shortest_edge"] -NUM_FRAMES_TO_SAMPLE = MODEL.config.num_frames -IMAGE_STATS = {"image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225]} -VAL_TRANSFORMS = Compose( - [ - UniformTemporalSubsample(NUM_FRAMES_TO_SAMPLE), - Lambda(lambda x: x / 255.0), - Normalize(IMAGE_STATS["image_mean"], IMAGE_STATS["image_std"]), - Resize((RESIZE_TO, RESIZE_TO)), - ] -) -LABELS = list(MODEL.config.label2id.keys()) - - -def parse_video(video_file): - """A utility to parse the input videos. - - Reference: https://pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/ - """ - vs = cv2.VideoCapture(video_file) - - # try to determine the total number of frames in the video file - try: - prop = ( - cv2.cv.CV_CAP_PROP_FRAME_COUNT - if imutils.is_cv2() - else cv2.CAP_PROP_FRAME_COUNT - ) - total = int(vs.get(prop)) - print("[INFO] {} total frames in video".format(total)) - - # an error occurred while trying to determine the total - # number of frames in the video file - except: - print("[INFO] could not determine # of frames in video") - print("[INFO] no approx. completion time can be provided") - total = -1 - - frames = [] - - # loop over frames from the video file stream - while True: - # read the next frame from the file - (grabbed, frame) = vs.read() - if frame is not None: - frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frames.append(frame) - # if the frame was not grabbed, then we have reached the end - # of the stream - if not grabbed: - break - - return frames - - -def preprocess_video(frames: list): - """Utility to apply preprocessing transformations to a video tensor.""" - # Each frame in the `frames` list has the shape: (height, width, num_channels). - # Collated together the `frames` has the the shape: (num_frames, height, width, num_channels). - # So, after converting the `frames` list to a torch tensor, we permute the shape - # such that it becomes (num_channels, num_frames, height, width) to make - # the shape compatible with the preprocessing transformations. After applying the - # preprocessing chain, we permute the shape to (num_frames, num_channels, height, width) - # to make it compatible with the model. Finally, we add a batch dimension so that our video - # classification model can operate on it. - video_tensor = torch.tensor(np.array(frames).astype(frames[0].dtype)) - video_tensor = video_tensor.permute( - 3, 0, 1, 2 - ) # (num_channels, num_frames, height, width) - video_tensor_pp = VAL_TRANSFORMS(video_tensor) - video_tensor_pp = video_tensor_pp.permute( - 1, 0, 2, 3 - ) # (num_frames, num_channels, height, width) - video_tensor_pp = video_tensor_pp.unsqueeze(0) - return video_tensor_pp.to(DEVICE) - - -def infer(video_file): - frames = parse_video(video_file) - video_tensor = preprocess_video(frames) - inputs = {"pixel_values": video_tensor} - - # forward pass - with torch.no_grad(): - outputs = MODEL(**inputs) - logits = outputs.logits - softmax_scores = torch.nn.functional.softmax(logits, dim=-1).squeeze(0) - confidences = {LABELS[i]: float(softmax_scores[i]) for i in range(len(LABELS))} - return confidences - - -gr.Interface( - fn=infer, - inputs=gr.Video(type="file"), - outputs=gr.Label(num_top_classes=3), - examples=[ - ["examples/babycrawling.mp4"], - ["examples/baseball.mp4"], - ["examples/balancebeam.mp4"], - ], - title="VideoMAE fine-tuned on a subset of UCF-101", - description=( - "Gradio demo for VideoMAE for video classification. To use it, simply upload your video or click one of the" - " examples to load them. Read more at the links below." - ), - article=( - "" - ), - allow_flagging=False, - allow_screenshot=False, -).launch() diff --git a/spaces/scedlatioru/img-to-music/example/Aerofly.RC.7.Ultimate.Edition-RELOADED Torrent.md b/spaces/scedlatioru/img-to-music/example/Aerofly.RC.7.Ultimate.Edition-RELOADED Torrent.md deleted file mode 100644 index 9f4d145afa5ba86a98060e12dde76ba88394c6dd..0000000000000000000000000000000000000000 --- a/spaces/scedlatioru/img-to-music/example/Aerofly.RC.7.Ultimate.Edition-RELOADED Torrent.md +++ /dev/null @@ -1,6 +0,0 @@ -

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    -
    \ No newline at end of file diff --git a/spaces/sczhou/ProPainter/model/recurrent_flow_completion.py b/spaces/sczhou/ProPainter/model/recurrent_flow_completion.py deleted file mode 100644 index b002f125cca02948bf72a2482181ab9c627b752a..0000000000000000000000000000000000000000 --- a/spaces/sczhou/ProPainter/model/recurrent_flow_completion.py +++ /dev/null @@ -1,347 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision - -from model.modules.deformconv import ModulatedDeformConv2d -from .misc import constant_init - -class SecondOrderDeformableAlignment(ModulatedDeformConv2d): - """Second-order deformable alignment module.""" - def __init__(self, *args, **kwargs): - self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 5) - - super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Sequential( - nn.Conv2d(3 * self.out_channels, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1), - ) - self.init_offset() - - def init_offset(self): - constant_init(self.conv_offset[-1], val=0, bias=0) - - def forward(self, x, extra_feat): - out = self.conv_offset(extra_feat) - o1, o2, mask = torch.chunk(out, 3, dim=1) - - # offset - offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1)) - offset_1, offset_2 = torch.chunk(offset, 2, dim=1) - offset = torch.cat([offset_1, offset_2], dim=1) - - # mask - mask = torch.sigmoid(mask) - - return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, - self.stride, self.padding, - self.dilation, mask) - -class BidirectionalPropagation(nn.Module): - def __init__(self, channel): - super(BidirectionalPropagation, self).__init__() - modules = ['backward_', 'forward_'] - self.deform_align = nn.ModuleDict() - self.backbone = nn.ModuleDict() - self.channel = channel - - for i, module in enumerate(modules): - self.deform_align[module] = SecondOrderDeformableAlignment( - 2 * channel, channel, 3, padding=1, deform_groups=16) - - self.backbone[module] = nn.Sequential( - nn.Conv2d((2 + i) * channel, channel, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.1, inplace=True), - nn.Conv2d(channel, channel, 3, 1, 1), - ) - - self.fusion = nn.Conv2d(2 * channel, channel, 1, 1, 0) - - def forward(self, x): - """ - x shape : [b, t, c, h, w] - return [b, t, c, h, w] - """ - b, t, c, h, w = x.shape - feats = {} - feats['spatial'] = [x[:, i, :, :, :] for i in range(0, t)] - - for module_name in ['backward_', 'forward_']: - - feats[module_name] = [] - - frame_idx = range(0, t) - mapping_idx = list(range(0, len(feats['spatial']))) - mapping_idx += mapping_idx[::-1] - - if 'backward' in module_name: - frame_idx = frame_idx[::-1] - - feat_prop = x.new_zeros(b, self.channel, h, w) - for i, idx in enumerate(frame_idx): - feat_current = feats['spatial'][mapping_idx[idx]] - if i > 0: - cond_n1 = feat_prop - - # initialize second-order features - feat_n2 = torch.zeros_like(feat_prop) - cond_n2 = torch.zeros_like(cond_n1) - if i > 1: # second-order features - feat_n2 = feats[module_name][-2] - cond_n2 = feat_n2 - - cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1) # condition information, cond(flow warped 1st/2nd feature) - feat_prop = torch.cat([feat_prop, feat_n2], dim=1) # two order feat_prop -1 & -2 - feat_prop = self.deform_align[module_name](feat_prop, cond) - - # fuse current features - feat = [feat_current] + \ - [feats[k][idx] for k in feats if k not in ['spatial', module_name]] \ - + [feat_prop] - - feat = torch.cat(feat, dim=1) - # embed current features - feat_prop = feat_prop + self.backbone[module_name](feat) - - feats[module_name].append(feat_prop) - - # end for - if 'backward' in module_name: - feats[module_name] = feats[module_name][::-1] - - outputs = [] - for i in range(0, t): - align_feats = [feats[k].pop(0) for k in feats if k != 'spatial'] - align_feats = torch.cat(align_feats, dim=1) - outputs.append(self.fusion(align_feats)) - - return torch.stack(outputs, dim=1) + x - - -class deconv(nn.Module): - def __init__(self, - input_channel, - output_channel, - kernel_size=3, - padding=0): - super().__init__() - self.conv = nn.Conv2d(input_channel, - output_channel, - kernel_size=kernel_size, - stride=1, - padding=padding) - - def forward(self, x): - x = F.interpolate(x, - scale_factor=2, - mode='bilinear', - align_corners=True) - return self.conv(x) - - -class P3DBlock(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride, padding, use_residual=0, bias=True): - super().__init__() - self.conv1 = nn.Sequential( - nn.Conv3d(in_channels, out_channels, kernel_size=(1, kernel_size, kernel_size), - stride=(1, stride, stride), padding=(0, padding, padding), bias=bias), - nn.LeakyReLU(0.2, inplace=True) - ) - self.conv2 = nn.Sequential( - nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), - padding=(2, 0, 0), dilation=(2, 1, 1), bias=bias) - ) - self.use_residual = use_residual - - def forward(self, feats): - feat1 = self.conv1(feats) - feat2 = self.conv2(feat1) - if self.use_residual: - output = feats + feat2 - else: - output = feat2 - return output - - -class EdgeDetection(nn.Module): - def __init__(self, in_ch=2, out_ch=1, mid_ch=16): - super().__init__() - self.projection = nn.Sequential( - nn.Conv2d(in_ch, mid_ch, 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True) - ) - - self.mid_layer_1 = nn.Sequential( - nn.Conv2d(mid_ch, mid_ch, 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True) - ) - - self.mid_layer_2 = nn.Sequential( - nn.Conv2d(mid_ch, mid_ch, 3, 1, 1) - ) - - self.l_relu = nn.LeakyReLU(0.01, inplace=True) - - self.out_layer = nn.Conv2d(mid_ch, out_ch, 1, 1, 0) - - def forward(self, flow): - flow = self.projection(flow) - edge = self.mid_layer_1(flow) - edge = self.mid_layer_2(edge) - edge = self.l_relu(flow + edge) - edge = self.out_layer(edge) - edge = torch.sigmoid(edge) - return edge - - -class RecurrentFlowCompleteNet(nn.Module): - def __init__(self, model_path=None): - super().__init__() - self.downsample = nn.Sequential( - nn.Conv3d(3, 32, kernel_size=(1, 5, 5), stride=(1, 2, 2), - padding=(0, 2, 2), padding_mode='replicate'), - nn.LeakyReLU(0.2, inplace=True) - ) - - self.encoder1 = nn.Sequential( - P3DBlock(32, 32, 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - P3DBlock(32, 64, 3, 2, 1), - nn.LeakyReLU(0.2, inplace=True) - ) # 4x - - self.encoder2 = nn.Sequential( - P3DBlock(64, 64, 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - P3DBlock(64, 128, 3, 2, 1), - nn.LeakyReLU(0.2, inplace=True) - ) # 8x - - self.mid_dilation = nn.Sequential( - nn.Conv3d(128, 128, (1, 3, 3), (1, 1, 1), padding=(0, 3, 3), dilation=(1, 3, 3)), # p = d*(k-1)/2 - nn.LeakyReLU(0.2, inplace=True), - nn.Conv3d(128, 128, (1, 3, 3), (1, 1, 1), padding=(0, 2, 2), dilation=(1, 2, 2)), - nn.LeakyReLU(0.2, inplace=True), - nn.Conv3d(128, 128, (1, 3, 3), (1, 1, 1), padding=(0, 1, 1), dilation=(1, 1, 1)), - nn.LeakyReLU(0.2, inplace=True) - ) - - # feature propagation module - self.feat_prop_module = BidirectionalPropagation(128) - - self.decoder2 = nn.Sequential( - nn.Conv2d(128, 128, 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - deconv(128, 64, 3, 1), - nn.LeakyReLU(0.2, inplace=True) - ) # 4x - - self.decoder1 = nn.Sequential( - nn.Conv2d(64, 64, 3, 1, 1), - nn.LeakyReLU(0.2, inplace=True), - deconv(64, 32, 3, 1), - nn.LeakyReLU(0.2, inplace=True) - ) # 2x - - self.upsample = nn.Sequential( - nn.Conv2d(32, 32, 3, padding=1), - nn.LeakyReLU(0.2, inplace=True), - deconv(32, 2, 3, 1) - ) - - # edge loss - self.edgeDetector = EdgeDetection(in_ch=2, out_ch=1, mid_ch=16) - - # Need to initial the weights of MSDeformAttn specifically - for m in self.modules(): - if isinstance(m, SecondOrderDeformableAlignment): - m.init_offset() - - if model_path is not None: - print('Pretrained flow completion model has loaded...') - ckpt = torch.load(model_path, map_location='cpu') - self.load_state_dict(ckpt, strict=True) - - - def forward(self, masked_flows, masks): - # masked_flows: b t-1 2 h w - # masks: b t-1 2 h w - b, t, _, h, w = masked_flows.size() - masked_flows = masked_flows.permute(0,2,1,3,4) - masks = masks.permute(0,2,1,3,4) - - inputs = torch.cat((masked_flows, masks), dim=1) - - x = self.downsample(inputs) - - feat_e1 = self.encoder1(x) - feat_e2 = self.encoder2(feat_e1) # b c t h w - feat_mid = self.mid_dilation(feat_e2) # b c t h w - feat_mid = feat_mid.permute(0,2,1,3,4) # b t c h w - - feat_prop = self.feat_prop_module(feat_mid) - feat_prop = feat_prop.view(-1, 128, h//8, w//8) # b*t c h w - - _, c, _, h_f, w_f = feat_e1.shape - feat_e1 = feat_e1.permute(0,2,1,3,4).contiguous().view(-1, c, h_f, w_f) # b*t c h w - feat_d2 = self.decoder2(feat_prop) + feat_e1 - - _, c, _, h_f, w_f = x.shape - x = x.permute(0,2,1,3,4).contiguous().view(-1, c, h_f, w_f) # b*t c h w - - feat_d1 = self.decoder1(feat_d2) - - flow = self.upsample(feat_d1) - if self.training: - edge = self.edgeDetector(flow) - edge = edge.view(b, t, 1, h, w) - else: - edge = None - - flow = flow.view(b, t, 2, h, w) - - return flow, edge - - - def forward_bidirect_flow(self, masked_flows_bi, masks): - """ - Args: - masked_flows_bi: [masked_flows_f, masked_flows_b] | (b t-1 2 h w), (b t-1 2 h w) - masks: b t 1 h w - """ - masks_forward = masks[:, :-1, ...].contiguous() - masks_backward = masks[:, 1:, ...].contiguous() - - # mask flow - masked_flows_forward = masked_flows_bi[0] * (1-masks_forward) - masked_flows_backward = masked_flows_bi[1] * (1-masks_backward) - - # -- completion -- - # forward - pred_flows_forward, pred_edges_forward = self.forward(masked_flows_forward, masks_forward) - - # backward - masked_flows_backward = torch.flip(masked_flows_backward, dims=[1]) - masks_backward = torch.flip(masks_backward, dims=[1]) - pred_flows_backward, pred_edges_backward = self.forward(masked_flows_backward, masks_backward) - pred_flows_backward = torch.flip(pred_flows_backward, dims=[1]) - if self.training: - pred_edges_backward = torch.flip(pred_edges_backward, dims=[1]) - - return [pred_flows_forward, pred_flows_backward], [pred_edges_forward, pred_edges_backward] - - - def combine_flow(self, masked_flows_bi, pred_flows_bi, masks): - masks_forward = masks[:, :-1, ...].contiguous() - masks_backward = masks[:, 1:, ...].contiguous() - - pred_flows_forward = pred_flows_bi[0] * masks_forward + masked_flows_bi[0] * (1-masks_forward) - pred_flows_backward = pred_flows_bi[1] * masks_backward + masked_flows_bi[1] * (1-masks_backward) - - return pred_flows_forward, pred_flows_backward diff --git a/spaces/sdeeas/ChuanhuChatGPT/Dockerfile b/spaces/sdeeas/ChuanhuChatGPT/Dockerfile deleted file mode 100644 index 335c2dba28ba8c365de9306858462a59dea25f28..0000000000000000000000000000000000000000 --- a/spaces/sdeeas/ChuanhuChatGPT/Dockerfile +++ /dev/null @@ -1,15 +0,0 @@ -FROM python:3.9 as builder -RUN apt-get update && apt-get install -y build-essential -COPY requirements.txt . -COPY requirements_advanced.txt . -RUN pip install --user -r requirements.txt -# RUN pip install --user -r requirements_advanced.txt - -FROM python:3.9 -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/segments-tobias/conex/espnet2/tasks/enh.py b/spaces/segments-tobias/conex/espnet2/tasks/enh.py deleted file mode 100644 index eba489c9c14ce9d4cd551c2c2c3db4d452259839..0000000000000000000000000000000000000000 --- a/spaces/segments-tobias/conex/espnet2/tasks/enh.py +++ /dev/null @@ -1,195 +0,0 @@ -import argparse -from typing import Callable -from typing import Collection -from typing import Dict -from typing import List -from typing import Optional -from typing import Tuple - -import numpy as np -import torch -from typeguard import check_argument_types -from typeguard import check_return_type - -from espnet2.enh.decoder.abs_decoder import AbsDecoder -from espnet2.enh.decoder.conv_decoder import ConvDecoder -from espnet2.enh.decoder.null_decoder import NullDecoder -from espnet2.enh.decoder.stft_decoder import STFTDecoder -from espnet2.enh.encoder.abs_encoder import AbsEncoder -from espnet2.enh.encoder.conv_encoder import ConvEncoder -from espnet2.enh.encoder.null_encoder import NullEncoder -from espnet2.enh.encoder.stft_encoder import STFTEncoder -from espnet2.enh.espnet_model import ESPnetEnhancementModel -from espnet2.enh.separator.abs_separator import AbsSeparator -from espnet2.enh.separator.asteroid_models import AsteroidModel_Converter -from espnet2.enh.separator.conformer_separator import ConformerSeparator -from espnet2.enh.separator.dprnn_separator import DPRNNSeparator -from espnet2.enh.separator.neural_beamformer import NeuralBeamformer -from espnet2.enh.separator.rnn_separator import RNNSeparator -from espnet2.enh.separator.tcn_separator import TCNSeparator -from espnet2.enh.separator.transformer_separator import TransformerSeparator -from espnet2.tasks.abs_task import AbsTask -from espnet2.torch_utils.initialize import initialize -from espnet2.train.class_choices import ClassChoices -from espnet2.train.collate_fn import CommonCollateFn -from espnet2.train.trainer import Trainer -from espnet2.utils.get_default_kwargs import get_default_kwargs -from espnet2.utils.nested_dict_action import NestedDictAction -from espnet2.utils.types import str2bool -from espnet2.utils.types import str_or_none - -encoder_choices = ClassChoices( - name="encoder", - classes=dict(stft=STFTEncoder, conv=ConvEncoder, same=NullEncoder), - type_check=AbsEncoder, - default="stft", -) - -separator_choices = ClassChoices( - name="separator", - classes=dict( - rnn=RNNSeparator, - tcn=TCNSeparator, - dprnn=DPRNNSeparator, - transformer=TransformerSeparator, - conformer=ConformerSeparator, - wpe_beamformer=NeuralBeamformer, - asteroid=AsteroidModel_Converter, - ), - type_check=AbsSeparator, - default="rnn", -) - -decoder_choices = ClassChoices( - name="decoder", - classes=dict(stft=STFTDecoder, conv=ConvDecoder, same=NullDecoder), - type_check=AbsDecoder, - default="stft", -) - -MAX_REFERENCE_NUM = 100 - - -class EnhancementTask(AbsTask): - # If you need more than one optimizers, change this value - num_optimizers: int = 1 - - class_choices_list = [ - # --encoder and --encoder_conf - encoder_choices, - # --separator and --separator_conf - separator_choices, - # --decoder and --decoder_conf - decoder_choices, - ] - - # If you need to modify train() or eval() procedures, change Trainer class here - trainer = Trainer - - @classmethod - def add_task_arguments(cls, parser: argparse.ArgumentParser): - group = parser.add_argument_group(description="Task related") - - # NOTE(kamo): add_arguments(..., required=True) can't be used - # to provide --print_config mode. Instead of it, do as - # required = parser.get_default("required") - - group.add_argument( - "--init", - type=lambda x: str_or_none(x.lower()), - default=None, - help="The initialization method", - choices=[ - "chainer", - "xavier_uniform", - "xavier_normal", - "kaiming_uniform", - "kaiming_normal", - None, - ], - ) - - group.add_argument( - "--model_conf", - action=NestedDictAction, - default=get_default_kwargs(ESPnetEnhancementModel), - help="The keyword arguments for model class.", - ) - - group = parser.add_argument_group(description="Preprocess related") - group.add_argument( - "--use_preprocessor", - type=str2bool, - default=False, - help="Apply preprocessing to data or not", - ) - - for class_choices in cls.class_choices_list: - # Append -- and --_conf. - # e.g. --encoder and --encoder_conf - class_choices.add_arguments(group) - - @classmethod - def build_collate_fn( - cls, args: argparse.Namespace, train: bool - ) -> Callable[ - [Collection[Tuple[str, Dict[str, np.ndarray]]]], - Tuple[List[str], Dict[str, torch.Tensor]], - ]: - assert check_argument_types() - - return CommonCollateFn(float_pad_value=0.0, int_pad_value=0) - - @classmethod - def build_preprocess_fn( - cls, args: argparse.Namespace, train: bool - ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]: - assert check_argument_types() - retval = None - assert check_return_type(retval) - return retval - - @classmethod - def required_data_names( - cls, train: bool = True, inference: bool = False - ) -> Tuple[str, ...]: - if not inference: - retval = ("speech_mix", "speech_ref1") - else: - # Recognition mode - retval = ("speech_mix",) - return retval - - @classmethod - def optional_data_names( - cls, train: bool = True, inference: bool = False - ) -> Tuple[str, ...]: - retval = ["dereverb_ref{}".format(n) for n in range(1, MAX_REFERENCE_NUM + 1)] - retval += ["speech_ref{}".format(n) for n in range(2, MAX_REFERENCE_NUM + 1)] - retval += ["noise_ref{}".format(n) for n in range(1, MAX_REFERENCE_NUM + 1)] - retval = tuple(retval) - assert check_return_type(retval) - return retval - - @classmethod - def build_model(cls, args: argparse.Namespace) -> ESPnetEnhancementModel: - assert check_argument_types() - - encoder = encoder_choices.get_class(args.encoder)(**args.encoder_conf) - separator = separator_choices.get_class(args.separator)( - encoder.output_dim, **args.separator_conf - ) - decoder = decoder_choices.get_class(args.decoder)(**args.decoder_conf) - - # 1. Build model - model = ESPnetEnhancementModel( - encoder=encoder, separator=separator, decoder=decoder, **args.model_conf - ) - - # FIXME(kamo): Should be done in model? - # 2. Initialize - if args.init is not None: - initialize(model, args.init) - - assert check_return_type(model) - return model diff --git a/spaces/segments/panoptic-segment-anything-api/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py b/spaces/segments/panoptic-segment-anything-api/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py deleted file mode 100644 index 2753b3ddee43c7a9fe28d1824db5d786e7e1ad59..0000000000000000000000000000000000000000 --- a/spaces/segments/panoptic-segment-anything-api/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py +++ /dev/null @@ -1,297 +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] -# ------------------------------------------------------------------------ - -import torch -import torch.nn as nn -import torch.nn.functional as F -from timm.models.layers import DropPath - - -class FeatureResizer(nn.Module): - """ - This class takes as input a set of embeddings of dimension C1 and outputs a set of - embedding of dimension C2, after a linear transformation, dropout and normalization (LN). - """ - - def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): - super().__init__() - self.do_ln = do_ln - # Object feature encoding - self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) - self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) - self.dropout = nn.Dropout(dropout) - - def forward(self, encoder_features): - x = self.fc(encoder_features) - if self.do_ln: - x = self.layer_norm(x) - output = self.dropout(x) - return output - - -def l1norm(X, dim, eps=1e-8): - """L1-normalize columns of X""" - norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps - X = torch.div(X, norm) - return X - - -def l2norm(X, dim, eps=1e-8): - """L2-normalize columns of X""" - norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps - X = torch.div(X, norm) - return X - - -def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8): - """ - query: (n_context, queryL, d) - context: (n_context, sourceL, d) - """ - batch_size_q, queryL = query.size(0), query.size(1) - batch_size, sourceL = context.size(0), context.size(1) - - # Get attention - # --> (batch, d, queryL) - queryT = torch.transpose(query, 1, 2) - - # (batch, sourceL, d)(batch, d, queryL) - # --> (batch, sourceL, queryL) - attn = torch.bmm(context, queryT) - if raw_feature_norm == "softmax": - # --> (batch*sourceL, queryL) - attn = attn.view(batch_size * sourceL, queryL) - attn = nn.Softmax()(attn) - # --> (batch, sourceL, queryL) - attn = attn.view(batch_size, sourceL, queryL) - elif raw_feature_norm == "l2norm": - attn = l2norm(attn, 2) - elif raw_feature_norm == "clipped_l2norm": - attn = nn.LeakyReLU(0.1)(attn) - attn = l2norm(attn, 2) - else: - raise ValueError("unknown first norm type:", raw_feature_norm) - # --> (batch, queryL, sourceL) - attn = torch.transpose(attn, 1, 2).contiguous() - # --> (batch*queryL, sourceL) - attn = attn.view(batch_size * queryL, sourceL) - attn = nn.Softmax()(attn * smooth) - # --> (batch, queryL, sourceL) - attn = attn.view(batch_size, queryL, sourceL) - # --> (batch, sourceL, queryL) - attnT = torch.transpose(attn, 1, 2).contiguous() - - # --> (batch, d, sourceL) - contextT = torch.transpose(context, 1, 2) - # (batch x d x sourceL)(batch x sourceL x queryL) - # --> (batch, d, queryL) - weightedContext = torch.bmm(contextT, attnT) - # --> (batch, queryL, d) - weightedContext = torch.transpose(weightedContext, 1, 2) - - return weightedContext, attnT - - -class BiMultiHeadAttention(nn.Module): - def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None): - super(BiMultiHeadAttention, self).__init__() - - self.embed_dim = embed_dim - self.num_heads = num_heads - self.head_dim = embed_dim // num_heads - self.v_dim = v_dim - self.l_dim = l_dim - - assert ( - self.head_dim * self.num_heads == self.embed_dim - ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." - self.scale = self.head_dim ** (-0.5) - self.dropout = dropout - - self.v_proj = nn.Linear(self.v_dim, self.embed_dim) - self.l_proj = nn.Linear(self.l_dim, self.embed_dim) - self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim) - self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim) - - self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim) - self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim) - - self.stable_softmax_2d = True - self.clamp_min_for_underflow = True - self.clamp_max_for_overflow = True - - self._reset_parameters() - - def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): - return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() - - def _reset_parameters(self): - nn.init.xavier_uniform_(self.v_proj.weight) - self.v_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.l_proj.weight) - self.l_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.values_v_proj.weight) - self.values_v_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.values_l_proj.weight) - self.values_l_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.out_v_proj.weight) - self.out_v_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.out_l_proj.weight) - self.out_l_proj.bias.data.fill_(0) - - def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): - """_summary_ - - Args: - v (_type_): bs, n_img, dim - l (_type_): bs, n_text, dim - attention_mask_v (_type_, optional): _description_. bs, n_img - attention_mask_l (_type_, optional): _description_. bs, n_text - - Returns: - _type_: _description_ - """ - # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': - # import ipdb; ipdb.set_trace() - bsz, tgt_len, _ = v.size() - - query_states = self.v_proj(v) * self.scale - key_states = self._shape(self.l_proj(l), -1, bsz) - value_v_states = self._shape(self.values_v_proj(v), -1, bsz) - value_l_states = self._shape(self.values_l_proj(l), -1, bsz) - - proj_shape = (bsz * self.num_heads, -1, self.head_dim) - query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) - key_states = key_states.view(*proj_shape) - value_v_states = value_v_states.view(*proj_shape) - value_l_states = value_l_states.view(*proj_shape) - - src_len = key_states.size(1) - attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt - - if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): - raise ValueError( - f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" - ) - - if self.stable_softmax_2d: - attn_weights = attn_weights - attn_weights.max() - - if self.clamp_min_for_underflow: - attn_weights = torch.clamp( - attn_weights, min=-50000 - ) # Do not increase -50000, data type half has quite limited range - if self.clamp_max_for_overflow: - attn_weights = torch.clamp( - attn_weights, max=50000 - ) # Do not increase 50000, data type half has quite limited range - - attn_weights_T = attn_weights.transpose(1, 2) - attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0] - if self.clamp_min_for_underflow: - attn_weights_l = torch.clamp( - attn_weights_l, min=-50000 - ) # Do not increase -50000, data type half has quite limited range - if self.clamp_max_for_overflow: - attn_weights_l = torch.clamp( - attn_weights_l, max=50000 - ) # Do not increase 50000, data type half has quite limited range - - # mask vison for language - if attention_mask_v is not None: - attention_mask_v = ( - attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) - ) - attn_weights_l.masked_fill_(attention_mask_v, float("-inf")) - - attn_weights_l = attn_weights_l.softmax(dim=-1) - - # mask language for vision - if attention_mask_l is not None: - attention_mask_l = ( - attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) - ) - attn_weights.masked_fill_(attention_mask_l, float("-inf")) - attn_weights_v = attn_weights.softmax(dim=-1) - - attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training) - attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training) - - attn_output_v = torch.bmm(attn_probs_v, value_l_states) - attn_output_l = torch.bmm(attn_probs_l, value_v_states) - - if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim): - raise ValueError( - f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}" - ) - - if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim): - raise ValueError( - f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}" - ) - - attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim) - attn_output_v = attn_output_v.transpose(1, 2) - attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim) - - attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim) - attn_output_l = attn_output_l.transpose(1, 2) - attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim) - - attn_output_v = self.out_v_proj(attn_output_v) - attn_output_l = self.out_l_proj(attn_output_l) - - return attn_output_v, attn_output_l - - -# Bi-Direction MHA (text->image, image->text) -class BiAttentionBlock(nn.Module): - def __init__( - self, - v_dim, - l_dim, - embed_dim, - num_heads, - dropout=0.1, - drop_path=0.0, - init_values=1e-4, - cfg=None, - ): - """ - Inputs: - embed_dim - Dimensionality of input and attention feature vectors - hidden_dim - Dimensionality of hidden layer in feed-forward network - (usually 2-4x larger than embed_dim) - num_heads - Number of heads to use in the Multi-Head Attention block - dropout - Amount of dropout to apply in the feed-forward network - """ - super(BiAttentionBlock, self).__init__() - - # pre layer norm - self.layer_norm_v = nn.LayerNorm(v_dim) - self.layer_norm_l = nn.LayerNorm(l_dim) - self.attn = BiMultiHeadAttention( - v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout - ) - - # add layer scale for training stability - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True) - self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True) - - def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): - v = self.layer_norm_v(v) - l = self.layer_norm_l(l) - delta_v, delta_l = self.attn( - v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l - ) - # v, l = v + delta_v, l + delta_l - v = v + self.drop_path(self.gamma_v * delta_v) - l = l + self.drop_path(self.gamma_l * delta_l) - return v, l - - # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None) diff --git a/spaces/shatrunjai/FutureMeMotivator/README.md b/spaces/shatrunjai/FutureMeMotivator/README.md deleted file mode 100644 index b6ac022bb73a0b662c0c474c12880ea569f588b9..0000000000000000000000000000000000000000 --- a/spaces/shatrunjai/FutureMeMotivator/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: FutureMeMotivator -emoji: 💻 -colorFrom: pink -colorTo: blue -sdk: gradio -sdk_version: 3.40.1 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/utils.py b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/utils.py deleted file mode 100644 index 1d91f963370321cf093c7fb9adefaa018463c8da..0000000000000000000000000000000000000000 --- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/utils.py +++ /dev/null @@ -1,120 +0,0 @@ -import torch -import numpy as np -from tqdm import tqdm -import json - - -def load_data(file_name: str = "./uvr5_pack/name_params.json") -> dict: - with open(file_name, "r") as f: - data = json.load(f) - - return data - - -def make_padding(width, cropsize, offset): - left = offset - roi_size = cropsize - left * 2 - if roi_size == 0: - roi_size = cropsize - right = roi_size - (width % roi_size) + left - - return left, right, roi_size - - -def inference(X_spec, device, model, aggressiveness, data): - """ - data ： dic configs - """ - - def _execute( - X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True - ): - model.eval() - with torch.no_grad(): - preds = [] - - iterations = [n_window] - - total_iterations = sum(iterations) - for i in tqdm(range(n_window)): - start = i * roi_size - X_mag_window = X_mag_pad[ - None, :, :, start : start + data["window_size"] - ] - X_mag_window = torch.from_numpy(X_mag_window) - if is_half: - X_mag_window = X_mag_window.half() - X_mag_window = X_mag_window.to(device) - - pred = model.predict(X_mag_window, aggressiveness) - - pred = pred.detach().cpu().numpy() - preds.append(pred[0]) - - pred = np.concatenate(preds, axis=2) - return pred - - def preprocess(X_spec): - X_mag = np.abs(X_spec) - X_phase = np.angle(X_spec) - - return X_mag, X_phase - - X_mag, X_phase = preprocess(X_spec) - - coef = X_mag.max() - X_mag_pre = X_mag / coef - - n_frame = X_mag_pre.shape[2] - pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset) - n_window = int(np.ceil(n_frame / roi_size)) - - X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") - - if list(model.state_dict().values())[0].dtype == torch.float16: - is_half = True - else: - is_half = False - pred = _execute( - X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half - ) - pred = pred[:, :, :n_frame] - - if data["tta"]: - pad_l += roi_size // 2 - pad_r += roi_size // 2 - n_window += 1 - - X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") - - pred_tta = _execute( - X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half - ) - pred_tta = pred_tta[:, :, roi_size // 2 :] - pred_tta = pred_tta[:, :, :n_frame] - - return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase) - else: - return pred * coef, X_mag, np.exp(1.0j * X_phase) - - -def _get_name_params(model_path, model_hash): - data = load_data() - flag = False - ModelName = model_path - for type in list(data): - for model in list(data[type][0]): - for i in range(len(data[type][0][model])): - if str(data[type][0][model][i]["hash_name"]) == model_hash: - flag = True - elif str(data[type][0][model][i]["hash_name"]) in ModelName: - flag = True - - if flag: - model_params_auto = data[type][0][model][i]["model_params"] - param_name_auto = data[type][0][model][i]["param_name"] - if type == "equivalent": - return param_name_auto, model_params_auto - else: - flag = False - return param_name_auto, model_params_auto diff --git a/spaces/shibing624/ChatPDF/modules/webui_locale.py b/spaces/shibing624/ChatPDF/modules/webui_locale.py deleted file mode 100644 index 1ce4d97b9b41cbb2d9be3fdadc4c85f6ef897604..0000000000000000000000000000000000000000 --- a/spaces/shibing624/ChatPDF/modules/webui_locale.py +++ /dev/null @@ -1,26 +0,0 @@ -import os -import locale -import commentjson as json - -class I18nAuto: - def __init__(self): - if os.path.exists("config.json"): - with open("config.json", "r", encoding='utf-8') as f: - config = json.load(f) - else: - config = {} - lang_config = config.get("language", "auto") - language = os.environ.get("LANGUAGE", lang_config) - if language == "auto": - language = locale.getdefaultlocale()[0] # get the language code of the system (ex. zh_CN) - self.language_map = {} - self.file_is_exists = os.path.isfile(f"./locale/{language}.json") - if self.file_is_exists: - with open(f"./locale/{language}.json", "r", encoding="utf-8") as f: - self.language_map.update(json.load(f)) - - def __call__(self, key): - if self.file_is_exists and key in self.language_map: - return self.language_map[key] - else: - return key diff --git a/spaces/shikunl/prismer/prismer/experts/obj_detection/unidet/data/datasets/mapillary.py b/spaces/shikunl/prismer/prismer/experts/obj_detection/unidet/data/datasets/mapillary.py deleted file mode 100644 index 13780440f26c9bb22be0d1018783eb5799273f3b..0000000000000000000000000000000000000000 --- a/spaces/shikunl/prismer/prismer/experts/obj_detection/unidet/data/datasets/mapillary.py +++ /dev/null @@ -1,110 +0,0 @@ -from detectron2.data.datasets.register_coco import register_coco_instances -import os - -''' -categories = [ - {'id': 28, 'name': 'animal--bird'} , - {'id': 29, 'name': 'animal--ground-animal'} , - {'id': 30, 'name': 'construction--flat--crosswalk-plain'} , - {'id': 31, 'name': 'human--person'} , - {'id': 32, 'name': 'human--rider--bicyclist'} , - {'id': 33, 'name': 'human--rider--motorcyclist'} , - {'id': 34, 'name': 'human--rider--other-rider'} , - {'id': 35, 'name': 'marking--crosswalk-zebra'} , - {'id': 36, 'name': 'object--banner'} , - {'id': 37, 'name': 'object--bench'} , - {'id': 38, 'name': 'object--bike-rack'} , - {'id': 39, 'name': 'object--billboard'} , - {'id': 40, 'name': 'object--catch-basin'} , - {'id': 41, 'name': 'object--cctv-camera'} , - {'id': 42, 'name': 'object--fire-hydrant'} , - {'id': 43, 'name': 'object--junction-box'} , - {'id': 44, 'name': 'object--mailbox'} , - {'id': 45, 'name': 'object--manhole'} , - {'id': 46, 'name': 'object--phone-booth'} , - {'id': 47, 'name': 'object--street-light'} , - {'id': 48, 'name': 'object--support--pole'} , - {'id': 49, 'name': 'object--support--traffic-sign-frame'} , - {'id': 50, 'name': 'object--support--utility-pole'} , - {'id': 51, 'name': 'object--traffic-light'} , - {'id': 52, 'name': 'object--traffic-sign--back'} , - {'id': 53, 'name': 'object--traffic-sign--front'} , - {'id': 54, 'name': 'object--trash-can'} , - {'id': 55, 'name': 'object--vehicle--bicycle'} , - {'id': 56, 'name': 'object--vehicle--boat'} , - {'id': 57, 'name': 'object--vehicle--bus'} , - {'id': 58, 'name': 'object--vehicle--car'} , - {'id': 59, 'name': 'object--vehicle--caravan'} , - {'id': 60, 'name': 'object--vehicle--motorcycle'} , - {'id': 61, 'name': 'object--vehicle--other-vehicle'} , - {'id': 62, 'name': 'object--vehicle--trailer'} , - {'id': 63, 'name': 'object--vehicle--truck'} , - {'id': 64, 'name': 'object--vehicle--wheeled-slow'} , -] -''' -categories = [ - {'id': 1, 'name': 'animal--bird'}, - {'id': 2, 'name': 'animal--ground-animal'}, - {'id': 9, 'name': 'construction--flat--crosswalk-plain'}, - {'id': 20, 'name': 'human--person'}, - {'id': 21, 'name': 'human--rider--bicyclist'}, - {'id': 22, 'name': 'human--rider--motorcyclist'}, - {'id': 23, 'name': 'human--rider--other-rider'}, - {'id': 24, 'name': 'marking--crosswalk-zebra'}, - {'id': 33, 'name': 'object--banner'}, - {'id': 34, 'name': 'object--bench'}, - {'id': 35, 'name': 'object--bike-rack'}, - {'id': 36, 'name': 'object--billboard'}, - {'id': 37, 'name': 'object--catch-basin'}, - {'id': 38, 'name': 'object--cctv-camera'}, - {'id': 39, 'name': 'object--fire-hydrant'}, - {'id': 40, 'name': 'object--junction-box'}, - {'id': 41, 'name': 'object--mailbox'}, - {'id': 42, 'name': 'object--manhole'}, - {'id': 43, 'name': 'object--phone-booth'}, - {'id': 45, 'name': 'object--street-light'}, - {'id': 46, 'name': 'object--support--pole'}, - {'id': 47, 'name': 'object--support--traffic-sign-frame'}, - {'id': 48, 'name': 'object--support--utility-pole'}, - {'id': 49, 'name': 'object--traffic-light'}, - {'id': 50, 'name': 'object--traffic-sign--back'}, - {'id': 51, 'name': 'object--traffic-sign--front'}, - {'id': 52, 'name': 'object--trash-can'}, - {'id': 53, 'name': 'object--vehicle--bicycle'}, - {'id': 54, 'name': 'object--vehicle--boat'}, - {'id': 55, 'name': 'object--vehicle--bus'}, - {'id': 56, 'name': 'object--vehicle--car'}, - {'id': 57, 'name': 'object--vehicle--caravan'}, - {'id': 58, 'name': 'object--vehicle--motorcycle'}, - {'id': 60, 'name': 'object--vehicle--other-vehicle'}, - {'id': 61, 'name': 'object--vehicle--trailer'}, - {'id': 62, 'name': 'object--vehicle--truck'}, - {'id': 63, 'name': 'object--vehicle--wheeled-slow'}, -] - - -def _get_builtin_metadata(): - id_to_name = {x['id']: x['name'] for x in categories} - thing_dataset_id_to_contiguous_id = {categories[i]['id']: i for i in range(37)} - thing_classes = [id_to_name[k] for k in sorted(id_to_name)] - return { - "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, - "thing_classes": thing_classes} - -_PREDEFINED_SPLITS = { - "mapillary_train": ("mapillary/training/images/", "mapillary/annotations/training_fix_id.json"), - # "mapillary_train": ("mapillary/training/images/", "mapillary/annotations/training.json"), - "mapillary_val": ("mapillary/validation/images/", "mapillary/annotations/validation_fix_id.json"), - # "mapillary_val": ("mapillary/validation/images/", "mapillary/annotations/validation.json"), - "mapillary_960_train": ("mapillary/training/images960/", "mapillary/annotations/training960_fix_id.json"), - 'mapillary_test': ('mapillary/testing/images/', 'mapillary/annotations/test_image_info_fix_id.json') -} - -for key, (image_root, json_file) in _PREDEFINED_SPLITS.items(): - register_coco_instances( - key, - _get_builtin_metadata(), - os.path.join("datasets", json_file) if "://" not in json_file else json_file, - os.path.join("datasets", image_root), - ) - diff --git a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/dcn/__init__.py b/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/dcn/__init__.py deleted file mode 100644 index 32e3592f896d61b4127e09d0476381b9d55e32ff..0000000000000000000000000000000000000000 --- a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/dcn/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv, - modulated_deform_conv) - -__all__ = [ - 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv', - 'modulated_deform_conv' -] diff --git a/spaces/sil-ai/aqua-semantic-sim/README.md b/spaces/sil-ai/aqua-semantic-sim/README.md deleted file mode 100644 index 985d929d0457c7724378f4fb3c3dae66ad57c2b9..0000000000000000000000000000000000000000 --- a/spaces/sil-ai/aqua-semantic-sim/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: AQuA Semantic Sim -emoji: 💦 -colorFrom: blue -colorTo: purple -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Arena Breakout CN The Mobile FPS that Will Blow Your Mind.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Arena Breakout CN The Mobile FPS that Will Blow Your Mind.md deleted file mode 100644 index 7bacf3dcb0a07624bc36b4abeb5226127c9303ea..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Arena Breakout CN The Mobile FPS that Will Blow Your Mind.md +++ /dev/null @@ -1,146 +0,0 @@ -
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    A survival looter shooter with extraction mechanics

    -

    Arena Breakout CN is not your typical battle royale game where you have to be the last man standing. Instead, the goal of the game is to enter a dangerous combat zone, scavenge for weapons, attachments, and supplies, and escape with as much loot as possible. However, escaping is not easy, as you have to reach one of the extraction points on the map, which are limited in number and time. You also have to contend with other players who might try to ambush you or steal your loot. Therefore, you have to balance your risk and reward, as well as plan your strategy carefully.

    -

    A realistic and authentic battlefield experience

    -

    Arena Breakout CN strives to simulate the nitty-gritty life on the battlefield, with a series of mechanics that add realism and challenge to the gameplay. For example, the game features a full-body injury system that affects your movement, aiming, stamina, and health depending on which body part is hit. You also have to manage your hunger, thirst, bleeding, fractures, pain, infection, and radiation levels. Moreover, the game has detailed recoil effects, weapon animations, ballistics, penetration, fragmentation, armor degradation, bullet drop, windage, and more. You also have to deal with environmental factors such as weather, time of day, noise, visibility, etc.

    -

    How to Download Arena Breakout CN?

    -

    If you are interested in playing Arena Breakout CN, here are the steps you need to follow to download and install the game on your device:

    -

    The official website and app store links

    -

    The easiest way to download Arena Breakout CN is to visit its official website at http://www.arenabreakout.com/

    The official website and app store links

    -

    The easiest way to download Arena Breakout CN is to visit its official website at http://www.arenabreakout.com/ and click on the download button. This will redirect you to the app store of your device, where you can install the game for free. Alternatively, you can search for "Arena Breakout CN" or "暗区突围" in the app store of your choice and download it from there. The game is currently available for both Android and iOS devices, as well as PC emulators.

    -

    How to download and play Arena Breakout CN on Android or iOS
    -Arena Breakout CN gameplay and features: what to expect from the mobile FPS
    -Arena Breakout CN vs Escape from Tarkov: which survival shooter is better?
    -Arena Breakout CN tips and tricks: how to loot, fight, and escape the Dark Zone
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    -Arena Breakout CN review: a hardcore and immersive mobile game
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    -Arena Breakout CN classes and skills: how to choose and customize your character
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    -Arena Breakout CN bugs and issues: how to fix common problems and errors
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    -How to play Arena Breakout CN on different devices or platforms (Android, iOS, PC, Mac, etc.)
    -How to play Arena Breakout CN on different regions or servers (China, Global, etc.)

    -

    The requirements and compatibility issues

    -

    Before you download Arena Breakout CN, you should make sure that your device meets the minimum requirements to run the game smoothly. According to the official website, the minimum requirements are as follows:

    - - - - - - - - - - - - - - - - - - - - - - - - - -
    DeviceOSRAMStorage
    Android5.0 or above2 GB or above4 GB or above
    iOS10.0 or above2 GB or above4 GB or above
    PC emulatorWindows 7 or above4 GB or above8 GB or above
    -

    If your device does not meet these requirements, you might experience lag, crashes, or other issues while playing the game. You should also make sure that your device has enough battery and a stable internet connection before you start the game.

    -

    The login and verification process

    -

    After you install Arena Breakout CN, you will need to create an account and log in to the game. You can choose to log in with your phone number, WeChat, QQ, or Facebook account. However, if you are not from China, you might encounter some difficulties with the verification process. This is because the game requires a Chinese phone number or ID card to verify your identity and prevent fraud. If you do not have these, you might not be able to play the game at all.

    -

    Fortunately, there are some ways to bypass this verification process and enjoy the game without any hassle. One way is to use a VPN service that can change your IP address to China and trick the game into thinking that you are from there. Another way is to use a third-party app that can generate a fake Chinese phone number or ID card for you. However, these methods are not guaranteed to work and might pose some risks to your privacy and security. Therefore, you should use them at your own discretion and responsibility.

    -

    How to Play Arena Breakout CN?

    -

    Once you have successfully downloaded and logged in to Arena Breakout CN, you can start playing the game and explore its various features and modes. Here are some of the basics that you need to know before you jump into the action:

    -

    The game modes and maps

    -

    Arena Breakout CN offers several game modes for different play styles and preferences. The main mode is called Extraction Mode, where you have to enter a combat zone with up to 16 other players, loot weapons and items, and escape with your loot before the time runs out or you get killed by other players or enemies. There are currently four maps available for this mode: Farmlands, Northridge, Military Port, and Armory. Each map has its own layout, terrain, weather, loot spots, enemies, and extraction points.

    -

    Besides Extraction Mode, there are also other modes that you can try out for fun or practice. These include Training Mode, where you can test your weapons and skills in a safe environment; Deathmatch Mode, where you can fight against other players in a team-based or solo mode; Survival Mode, where you have to survive as long as possible against waves of enemies; and Custom Mode, where you can create your own rules and settings for a private match with your friends.

    -

    The equipment and loot system

    -

    Arena Breakout CN features a rich and diverse equipment and loot system that allows you to customize your loadout and gear according to your preference and strategy. You can choose from a variety of weapons, attachments, armor, helmets, backpacks, vests, grenades, medkits, food, drinks, drugs, and more. Each item has its own stats, effects, durability, weight, rarity, and value.

    -

    You can obtain equipment and loot in several ways in the game. You can buy them from the traders in the lobby using the currency that you

    You can obtain equipment and loot in several ways in the game. You can buy them from the traders in the lobby using the currency that you earn from completing missions or selling items. You can also find them scattered around the maps in crates, boxes, bags, cabinets, safes, and other containers. You can also loot them from dead players or enemies that you kill. However, you have to be careful, as some items might be booby-trapped or contaminated.

    -

    When you enter a combat zone, you can only bring a limited amount of equipment and loot with you, depending on your backpack size and weight limit. You also have to pay an entry fee to the traders, which varies depending on the map and difficulty level. If you manage to escape with your loot, you can keep it or sell it to the traders for a profit. However, if you die or fail to extract, you will lose all your loot and equipment, except for the items that you insured before entering the zone.

    -

    The combat and injury system

    -

    Arena Breakout CN offers a realistic and challenging combat and injury system that requires you to be careful and tactical when engaging in firefights. The game uses a realistic ballistics model that takes into account factors such as bullet caliber, velocity, drop, penetration, fragmentation, ricochet, and more. You also have to account for your weapon's recoil, accuracy, stability, durability, and magazine capacity. You can use different types of sights, scopes, lasers, flashlights, suppressors, and other attachments to enhance your weapon's performance.

    -

    The game also features a full-body injury system that affects your health and performance depending on which body part is hit. You can suffer from bleeding, fractures, pain, infection, radiation, dehydration, hunger, and more. You have to use different types of medical items such as bandages, splints, painkillers, antibiotics, antirads, water, food, and more to treat your injuries and conditions. You also have to monitor your vital signs such as heart rate, blood pressure, oxygen level, and body temperature. If you neglect your health, you might suffer from serious consequences such as shock, unconsciousness, or death.

    -

    How to Improve Your Skills in Arena Breakout CN?

    -

    Arena Breakout CN is not an easy game to master. It requires a lot of practice, patience, and skill to survive and thrive in the harsh and unforgiving environment. However, there are some tips and tricks that can help you improve your skills and performance in the game. Here are some of them:

    -

    The best tips and tricks for beginners

    -

    If you are new to Arena Breakout CN or similar games like Escape from Tarkov, you might feel overwhelmed by the complexity and difficulty of the game. However, don't worry too much. Here are some basic tips and tricks that can help you get started:

    -
      -
    • Learn the maps: The maps in Arena Breakout CN are large and detailed. They have many hidden spots, loot locations,
        -
      • Learn the maps: The maps in Arena Breakout CN are large and detailed. They have many hidden spots, loot locations, enemies, and extraction points. You should familiarize yourself with the layout, terrain, landmarks, and routes of each map. You can use the map feature in the game to help you navigate and plan your strategy. You can also watch some videos or guides online to learn more about the maps.
        • -
      • Choose your loadout wisely: The equipment and loot that you bring to a combat zone can make a huge difference in your survival and success. You should choose your weapons, attachments, armor, and items according to your play style, budget, and mission objective. You should also balance your weight and inventory space, as carrying too much can slow you down and make you more vulnerable. You should also insure your valuable items before entering a zone, in case you lose them.
        • -
      • Be stealthy and cautious: Arena Breakout CN is not a game where you can run and gun without consequences. The game rewards stealth and caution over aggression and recklessness. You should move slowly and quietly, avoid making unnecessary noise, use cover and concealment, check your surroundings, and listen for enemy footsteps or gunfire. You should also avoid engaging in unnecessary fights, as they can attract more enemies or players to your location. You should only fight when you have a clear advantage or when you have no choice.
        • -
      • Use teamwork and communication: Arena Breakout CN is a game that can be played solo or with a team of up to four players. Playing with a team can give you many benefits, such as sharing loot, covering each other, reviving each other, and coordinating strategies. However, playing with a team also requires teamwork and communication. You should use the voice chat or text chat feature in the game to communicate with your teammates, share information, give orders, and plan your moves. You should also stick together, watch each other's backs, and cooperate with each other.
        • -
      -

      The best weapons and attachments for different situations

      -

      Arena Breakout CN features a wide range of weapons and attachments that you can use to customize your loadout and suit your preference and situation. There are different types of weapons such as pistols, SMGs, rifles, shotguns, snipers, LMGs, grenade launchers, and more. There are also different types of attachments such as sights, scopes, lasers, flashlights, suppressors, grips, stocks, magazines, muzzles, and more. Each weapon and attachment has its own stats, effects, pros, and cons.

      -

      You should choose your weapons and attachments based on the factors such as the map size, terrain type,

      You should choose your weapons and attachments based on the factors such as the map size, terrain type, enemy type, distance, and visibility. You should also consider your personal preference and play style. Here are some of the best weapons and attachments for different situations:

      -
        -
      • For close-range combat, you should use weapons that have high fire rate, damage, and mobility, such as pistols, SMGs, shotguns, or rifles. You should also use attachments that improve your accuracy, stability, and recoil control, such as red dot sights, lasers, grips, or stocks.
        • -
      • For medium-range combat, you should use weapons that have good balance of fire rate, damage, and range, such as rifles, LMGs, or snipers. You should also use attachments that enhance your zoom, precision, and penetration, such as scopes, suppressors, muzzles, or magazines.
        • -
      • For long-range combat, you should use weapons that have high range, damage, and accuracy, such as snipers or grenade launchers. You should also use attachments that increase your magnification, bullet drop compensation, and windage correction, such as high-power scopes, bipods, rangefinders, or ballistic calculators.
        • -
      -

      The best strategies and tactics for different maps

      -

      Arena Breakout CN features four different maps for the Extraction Mode: Farmlands, Northridge, Military Port, and Armory. Each map has its own characteristics, challenges, and opportunities. You should adapt your strategies and tactics according to the map that you are playing on. Here are some of the best strategies and tactics for different maps:

      -
        -
      • For Farmlands, you should use stealth and cover as much as possible. The map is mostly open and flat, with some hills, trees,
          -
        • For Farmlands, you should use stealth and cover as much as possible. The map is mostly open and flat, with some hills, trees, crops, and buildings. You can use these to hide from enemies and snipers, as well as to ambush or flank them. You should also avoid staying in the open for too long, as you can be easily spotted and shot. You should also watch out for traps and mines that might be planted in the fields or houses.
          • -
        • For Northridge, you should use mobility and elevation to your advantage. The map is hilly and rocky, with some cliffs, caves, bridges, and tunnels. You can use these to move around quickly and unpredictably, as well as to gain a height advantage over your enemies. You should also use your binoculars or scopes to scout the area and spot enemies from afar. You should also be careful of falling or sliding down the slopes or edges.
          • -
        • For Military Port, you should use teamwork and coordination to survive. The map is urban and industrial, with many containers, cranes, ships, warehouses, and offices. You can use these to find loot and cover, as well as to create chokepoints and bottlenecks. You should also communicate with your teammates and share information, such as enemy locations, loot spots, extraction points, etc. You should also be wary of enemies hiding in the shadows or corners.
          • -
        • For Armory, you should use aggression and firepower to dominate. The map is a large underground bunker complex, with many rooms, corridors, labs, and vaults. You can use these to find high-quality weapons and items, as well as to engage in close-quarters combat. You should also use grenades, flashbangs, smoke bombs, and other explosives to clear rooms and disorient enemies. You should also be prepared for intense firefights and ambushes.
          • -
        -

        Conclusion

        -

        Arena Breakout CN is a game that offers a realistic and immersive tactical FPS experience on mobile. It is a game that challenges your skills, strategy, and survival instincts in a modern-day battlefield. It is a game that rewards your risk-taking, loot-hunting, and extraction-planning abilities. It is a game that you can play solo or with your friends in various modes and maps. If you are looking for a new and exciting mobile game that will keep you on the edge of your seat, you should definitely give Arena Breakout CN a try.

        -

        FAQs

        -

        Here are some of the frequently asked questions about Arena Breakout CN:

        -
          -
        1. Is Arena Breakout CN free to play?
          2. -

          Yes, Arena Breakout CN is free to play. However, it does have some optional in-game purchases that can enhance your gameplay experience.

          -
        2. Is Arena Breakout CN available in English?
          3. -

          No, Arena Breakout CN is currently only available in Chinese. However, there are some fan-made translations and guides that can help you understand the game better.

          -
        3. Is Arena Breakout CN online or offline?
          4. -

          Arena Breakout CN is an online game that requires an internet connection to play. You cannot play it offline.

          -
        4. Is Arena Breakout CN cross-platform?
          5. -

          Yes, Arena Breakout CN is cross-platform. You can play it on Android, iOS, or PC emulators with the same account and progress.

          -
        5. Is Arena Breakout CN safe and secure?
          6. -

          Arena Breakout CN is developed by Morefun Studios and published by Tencent Games,

          Arena Breakout CN is developed by Morefun Studios and published by Tencent Games, which are reputable and trustworthy companies in the gaming industry. The game is safe and secure to play, as long as you download it from the official website or app store. However, you should be careful of using third-party apps or services that might compromise your privacy and security.

          197e85843d
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          -
          \ No newline at end of file diff --git a/spaces/skf15963/summary/fengshen/data/megatron_dataloader/bert_dataset.py b/spaces/skf15963/summary/fengshen/data/megatron_dataloader/bert_dataset.py deleted file mode 100644 index 2c007f060fd07fc9c6302b7f88e191469d599222..0000000000000000000000000000000000000000 --- a/spaces/skf15963/summary/fengshen/data/megatron_dataloader/bert_dataset.py +++ /dev/null @@ -1,196 +0,0 @@ -# coding=utf-8 -# Copyright (c) 2020, NVIDIA CORPORATION. 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. - -"""BERT Style dataset.""" - - -import numpy as np -import torch - -from fengshen.data.megatron_dataloader.dataset_utils import ( - get_samples_mapping, - get_a_and_b_segments, - create_masked_lm_predictions, - create_tokens_and_tokentypes, -) - - -class BertDataset(torch.utils.data.Dataset): - - def __init__(self, name, indexed_dataset, data_prefix, - num_epochs, max_num_samples, masked_lm_prob, - max_seq_length, short_seq_prob, seed, binary_head, tokenizer, masking_style): - # Params to store. - self.name = name - self.seed = seed - self.masked_lm_prob = masked_lm_prob - self.max_seq_length = max_seq_length - self.short_seq_prob = short_seq_prob - self.binary_head = binary_head - self.masking_style = masking_style - - # Dataset. - self.indexed_dataset = indexed_dataset - - # Build the samples mapping. - self.samples_mapping = get_samples_mapping(self.indexed_dataset, - data_prefix, - num_epochs, - max_num_samples, - # account for added tokens - self.max_seq_length - 3, - short_seq_prob, - self.seed, - self.name, - self.binary_head) - inv_vocab = {v: k for k, v in tokenizer.vocab.items()} - self.vocab_id_list = list(inv_vocab.keys()) - self.vocab_id_to_token_dict = inv_vocab - self.cls_id = tokenizer.cls_token_id - self.sep_id = tokenizer.sep_token_id - self.mask_id = tokenizer.mask_token_id - self.pad_id = tokenizer.pad_token_id - self.tokenizer = tokenizer - - def __len__(self): - return self.samples_mapping.shape[0] - - def __getitem__(self, idx): - start_idx, end_idx, seq_length = self.samples_mapping[idx] - sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)] - # Note that this rng state should be numpy and not python since - # python randint is inclusive whereas the numpy one is exclusive. - # We % 2**32 since numpy requres the seed to be between 0 and 2**32 - 1 - np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32)) - return build_training_sample(sample, seq_length, - self.max_seq_length, # needed for padding - self.vocab_id_list, - self.vocab_id_to_token_dict, - self.cls_id, self.sep_id, - self.mask_id, self.pad_id, - self.masked_lm_prob, np_rng, - self.binary_head, - tokenizer=self.tokenizer, - masking_style=self.masking_style) - - -def build_training_sample(sample, - target_seq_length, max_seq_length, - vocab_id_list, vocab_id_to_token_dict, - cls_id, sep_id, mask_id, pad_id, - masked_lm_prob, np_rng, binary_head, - tokenizer, - masking_style='bert'): - """Biuld training sample. - - Arguments: - sample: A list of sentences in which each sentence is a list token ids. - target_seq_length: Desired sequence length. - max_seq_length: Maximum length of the sequence. All values are padded to - this length. - vocab_id_list: List of vocabulary ids. Used to pick a random id. - vocab_id_to_token_dict: A dictionary from vocab ids to text tokens. - cls_id: Start of example id. - sep_id: Separator id. - mask_id: Mask token id. - pad_id: Padding token id. - masked_lm_prob: Probability to mask tokens. - np_rng: Random number genenrator. Note that this rng state should be - numpy and not python since python randint is inclusive for - the opper bound whereas the numpy one is exclusive. - """ - - if binary_head: - # We assume that we have at least two sentences in the sample - assert len(sample) > 1 - assert target_seq_length <= max_seq_length - - # Divide sample into two segments (A and B). - if binary_head: - tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, - np_rng) - else: - tokens_a = [] - for j in range(len(sample)): - tokens_a.extend(sample[j]) - tokens_b = [] - is_next_random = False - - if len(tokens_a) >= max_seq_length-3: - tokens_a = tokens_a[:max_seq_length-3] - - # Truncate to `target_sequence_length`. - max_num_tokens = target_seq_length - '''' - truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a), - len(tokens_b), max_num_tokens, np_rng) - ''' - - # Build tokens and toketypes. - tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b, - cls_id, sep_id) - # Masking. - max_predictions_per_seq = masked_lm_prob * max_num_tokens - (tokens, masked_positions, masked_labels, _, _) = create_masked_lm_predictions( - tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob, - cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng, - tokenizer=tokenizer, - masking_style=masking_style) - - # Padding. - tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \ - = pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, - masked_labels, pad_id, max_seq_length) - - train_sample = { - 'input_ids': tokens_np, - 'token_type_ids': tokentypes_np, - 'labels': labels_np, - 'next_sentence_label': int(is_next_random), - 'attention_mask': padding_mask_np} - return train_sample - - -def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, - masked_labels, pad_id, max_seq_length): - """Pad sequences and convert them to numpy.""" - - # Some checks. - num_tokens = len(tokens) - padding_length = max_seq_length - num_tokens - assert padding_length >= 0 - assert len(tokentypes) == num_tokens - assert len(masked_positions) == len(masked_labels) - - # Tokens and token types. - filler = [pad_id] * padding_length - tokens_np = np.array(tokens + filler, dtype=np.int64) - tokentypes_np = np.array(tokentypes + filler, dtype=np.int64) - - # Padding mask. - padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, - dtype=np.int64) - - # Lables and loss mask. - labels = [-100] * max_seq_length - loss_mask = [0] * max_seq_length - for i in range(len(masked_positions)): - assert masked_positions[i] < num_tokens - labels[masked_positions[i]] = masked_labels[i] - loss_mask[masked_positions[i]] = 1 - labels_np = np.array(labels, dtype=np.int64) - loss_mask_np = np.array(loss_mask, dtype=np.int64) - - return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np diff --git a/spaces/skf15963/summary/fengshen/examples/zen1_finetune/fengshen_token_level_ft_task.py b/spaces/skf15963/summary/fengshen/examples/zen1_finetune/fengshen_token_level_ft_task.py deleted file mode 100644 index 8cb77bbe0edf675300614982466e802964f8c625..0000000000000000000000000000000000000000 --- a/spaces/skf15963/summary/fengshen/examples/zen1_finetune/fengshen_token_level_ft_task.py +++ /dev/null @@ -1,647 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The IDEA 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. -from fengshen.models.zen1.ngram_utils import ZenNgramDict -from fengshen.models.zen1.modeling import ZenForTokenClassification -from fengshen.metric.metric import SeqEntityScore -from fengshen.models.zen1.tokenization import BertTokenizer -from random import shuffle -from pytorch_lightning.callbacks import LearningRateMonitor -from dataclasses import dataclass -import logging -import math -import numpy as np -import os -import json -import torch -import pytorch_lightning as pl -import argparse -from pytorch_lightning.callbacks import ModelCheckpoint -from torch.utils.data import Dataset, DataLoader - -import torch.nn.functional as F -logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt='%m/%d/%Y %H:%M:%S', - level=logging.ERROR) -logger = logging.getLogger(__name__) - - -class InputExample(object): - """A single training/test example for simple sequence classification.""" - - def __init__(self, guid, text_a, text_b=None, label=None): - """Constructs a InputExample. - - Args: - guid: Unique id for the example. - text_a: string. The untokenized text of the first sequence. For single - sequence tasks, only this sequence must be specified. - text_b: (Optional) string. The untokenized text of the second sequence. - Only must be specified for sequence pair tasks. - label: (Optional) string. The label of the example. This should be - specified for train and dev examples, but not for test examples. - """ - self.guid = guid - self.text_a = text_a - self.text_b = text_b - self.label = label - - -class InputFeatures(object): - """A single set of features of data.""" - - def __init__(self, input_ids, input_mask, segment_ids, label_id, ngram_ids, ngram_positions, ngram_lengths, - ngram_tuples, ngram_seg_ids, ngram_masks, valid_ids=None, label_mask=None): - self.input_ids = input_ids - self.input_mask = input_mask - self.segment_ids = segment_ids - self.label_id = label_id - self.valid_ids = valid_ids - self.label_mask = label_mask - - self.ngram_ids = ngram_ids - self.ngram_positions = ngram_positions - self.ngram_lengths = ngram_lengths - self.ngram_tuples = ngram_tuples - self.ngram_seg_ids = ngram_seg_ids - self.ngram_masks = ngram_masks - - -def convert_examples_to_features(examples, label_map, max_seq_length, tokenizer, ngram_dict): - """Loads a data file into a list of `InputBatch`s.""" - - # label_map = {label: i for i, label in enumerate(label_list, 1)} - - features = [] - for (ex_index, example) in enumerate(examples): - textlist = example.text_a - labellist = example.label - tokens = [] - labels = [] - valid = [] - label_mask = [] - for i, word in enumerate(textlist): - token = tokenizer.tokenize(word) - tokens.extend(token) - label_1 = labellist[i] - for m in range(len(token)): - if m == 0: - labels.append(label_1) - valid.append(1) - label_mask.append(1) - else: - valid.append(0) - if len(tokens) >= max_seq_length - 1: - tokens = tokens[0:(max_seq_length - 2)] - labels = labels[0:(max_seq_length - 2)] - valid = valid[0:(max_seq_length - 2)] - label_mask = label_mask[0:(max_seq_length - 2)] - ntokens = [] - segment_ids = [] - label_ids = [] - ntokens.append("[CLS]") - segment_ids.append(0) - valid.insert(0, 1) - label_mask.insert(0, 1) - label_ids.append(label_map["[CLS]"]) - for i, token in enumerate(tokens): - ntokens.append(token) - segment_ids.append(0) - if len(labels) > i: - label_ids.append(label_map[labels[i]]) - ntokens.append("[SEP]") - segment_ids.append(0) - valid.append(1) - label_mask.append(1) - label_ids.append(label_map["[SEP]"]) - input_ids = tokenizer.convert_tokens_to_ids(ntokens) - input_mask = [1] * len(input_ids) - label_mask = [1] * len(label_ids) - while len(input_ids) < max_seq_length: - input_ids.append(0) - input_mask.append(0) - segment_ids.append(0) - label_ids.append(0) - valid.append(1) - label_mask.append(0) - while len(label_ids) < max_seq_length: - label_ids.append(0) - label_mask.append(0) - assert len(input_ids) == max_seq_length - assert len(input_mask) == max_seq_length - assert len(segment_ids) == max_seq_length - assert len(label_ids) == max_seq_length - assert len(valid) == max_seq_length - assert len(label_mask) == max_seq_length - - # ----------- code for ngram BEGIN----------- - ngram_matches = [] - # Filter the ngram segment from 2 to 7 to check whether there is a ngram - for p in range(2, 8): - for q in range(0, len(tokens) - p + 1): - character_segment = tokens[q:q + p] - # j is the starting position of the ngram - # i is the length of the current ngram - character_segment = tuple(character_segment) - if character_segment in ngram_dict.ngram_to_id_dict: - ngram_index = ngram_dict.ngram_to_id_dict[character_segment] - ngram_matches.append([ngram_index, q, p, character_segment]) - - shuffle(ngram_matches) - - max_ngram_in_seq_proportion = math.ceil((len(tokens) / max_seq_length) * ngram_dict.max_ngram_in_seq) - if len(ngram_matches) > max_ngram_in_seq_proportion: - ngram_matches = ngram_matches[:max_ngram_in_seq_proportion] - - ngram_ids = [ngram[0] for ngram in ngram_matches] - ngram_positions = [ngram[1] for ngram in ngram_matches] - ngram_lengths = [ngram[2] for ngram in ngram_matches] - ngram_tuples = [ngram[3] for ngram in ngram_matches] - ngram_seg_ids = [0 if position < (len(tokens) + 2) else 1 for position in ngram_positions] - - ngram_mask_array = np.zeros(ngram_dict.max_ngram_in_seq, dtype=np.bool) - ngram_mask_array[:len(ngram_ids)] = 1 - - # record the masked positions - ngram_positions_matrix = np.zeros(shape=(max_seq_length, ngram_dict.max_ngram_in_seq), dtype=np.int32) - for i in range(len(ngram_ids)): - ngram_positions_matrix[ngram_positions[i]:ngram_positions[i] + ngram_lengths[i], i] = 1.0 - - # Zero-pad up to the max ngram in seq length. - padding = [0] * (ngram_dict.max_ngram_in_seq - len(ngram_ids)) - ngram_ids += padding - ngram_lengths += padding - ngram_seg_ids += padding - - # ----------- code for ngram END----------- - - features.append( - InputFeatures(input_ids=input_ids, - input_mask=input_mask, - segment_ids=segment_ids, - label_id=label_ids, - ngram_ids=ngram_ids, - ngram_positions=ngram_positions_matrix, - ngram_lengths=ngram_lengths, - ngram_tuples=ngram_tuples, - ngram_seg_ids=ngram_seg_ids, - ngram_masks=ngram_mask_array, - valid_ids=valid, - label_mask=label_mask)) - return features - - -class DataProcessor(object): - """Base class for data converters for sequence classification data sets.""" - - def get_examples(self, data_path, set_type, quotechar=' '): - """See base class.""" - return self._create_examples( - self._read_tsv(data_path, self.get_quotechar()), set_type) - - def _create_examples(self, lines, set_type): - examples = [] - for i, (sentence, label) in enumerate(lines): - guid = "%s-%s" % (set_type, i) - text_a = sentence - label = label - examples.append(InputExample(guid=guid, text_a=text_a, label=label)) - return examples - - def get_labels(self): - """Gets the list of labels for this data set.""" - raise NotImplementedError() - - def get_quotechar(self): - return ' ' - - @classmethod - def _read_tsv(cls, input_file, quotechar=None): - ''' - read file - return format : - [ ['EU', 'B-ORG'], ['rejects', 'O'], ['German', 'B-MISC'], ['call', 'O'], ['to', 'O'], ['boycott', 'O'], ['British', 'B-MISC'], ['lamb', 'O'], ['.', 'O'] ] - ''' - f = open(input_file) - data = [] - sentence = [] - label = [] - for line in f: - if len(line) == 0 or line.startswith('-DOCSTART') or line[0] == "\n": - if len(sentence) > 0: - data.append((sentence, label)) - sentence = [] - label = [] - continue - splits = line.split(quotechar) - sentence.append(splits[0]) - label.append(splits[-1][:-1]) - - if len(sentence) > 0: - data.append((sentence, label)) - sentence = [] - label = [] - return data - - -class MSRAProcessor(DataProcessor): - """Processor for the msra data set.""" - - def get_labels(self): - return ['B-NR', 'B-NS', 'B-NT', 'E-NR', 'E-NS', 'E-NT', 'M-NR', - 'M-NS', 'M-NT', 'O', 'S-NR', 'S-NS', 'S-NT', '[CLS]', '[SEP]'] - - -class OntoNotes4Processor(DataProcessor): - """Processor for the OntoNotes4 data set.""" - - def get_labels(self): - return ['B-GPE', 'B-LOC', 'B-ORG', 'B-PER', 'E-GPE', 'E-LOC', - 'E-ORG', 'E-PER', 'M-GPE', 'M-LOC', 'M-ORG', 'M-PER', 'O', - 'S-GPE', 'S-LOC', 'S-ORG', 'S-PER', '[CLS]', '[SEP]'] - - -class WeiboProcessor(DataProcessor): - """Processor for the Weibo data set.""" - - def get_labels(self): - return ['B-GPE.NAM', 'B-GPE.NOM', 'B-LOC.NAM', 'B-LOC.NOM', - 'B-ORG.NAM', 'B-ORG.NOM', 'B-PER.NAM', 'B-PER.NOM', 'E-GPE.NAM', - 'E-GPE.NOM', 'E-LOC.NAM', 'E-LOC.NOM', 'E-ORG.NAM', 'E-ORG.NOM', - 'E-PER.NAM', 'E-PER.NOM', 'M-GPE.NAM', 'M-LOC.NAM', 'M-LOC.NOM', - 'M-ORG.NAM', 'M-ORG.NOM', 'M-PER.NAM', 'M-PER.NOM', 'O', - 'S-GPE.NAM', 'S-LOC.NOM', 'S-PER.NAM', 'S-PER.NOM', '[CLS]', '[SEP]'] - - -class ResumeProcessor(DataProcessor): - """Processor for the resume data set.""" - - def get_labels(self): - return ['B-CONT', 'B-EDU', 'B-LOC', 'B-NAME', 'B-ORG', 'B-PRO', - 'B-RACE', 'B-TITLE', 'E-CONT', 'E-EDU', 'E-LOC', 'E-NAME', - 'E-ORG', 'E-PRO', 'E-RACE', 'E-TITLE', 'M-CONT', 'M-EDU', - 'M-LOC', 'M-NAME', 'M-ORG', 'M-PRO', 'M-RACE', 'M-TITLE', - 'O', 'S-NAME', 'S-ORG', 'S-RACE', '[CLS]', '[SEP]'] - - -class CMeEEProcessor(DataProcessor): - """Processor for the CMeEE data set.""" - - def get_quotechar(self): - return '\t' - - def get_labels(self): - return ['B-临床表现', 'B-医学检验项目', 'B-医疗程序', 'B-医疗设备', - 'B-微生物类', 'B-疾病', 'B-科室', 'B-药物', 'B-身体', 'I-临床表现', - 'I-医学检验项目', 'I-医疗程序', 'I-医疗设备', 'I-微生物类', - 'I-疾病', 'I-科室', 'I-药物', 'I-身体', 'O', '[CLS]', '[SEP]'] - - -class CLUENERProcessor(DataProcessor): - """Processor for the CLUENER data set.""" - - def get_quotechar(self): - return '\t' - - def get_labels(self): - return ['B-书名', 'B-公司', 'B-地址', 'B-姓名', 'B-政府', 'B-景点', - 'B-游戏', 'B-电影', 'B-组织机构', 'B-职位', 'I-书名', 'I-公司', - 'I-地址', 'I-姓名', 'I-政府', 'I-景点', 'I-游戏', 'I-电影', - 'I-组织机构', 'I-职位', 'O', '[CLS]', '[SEP]'] - - -class TaskDataset(Dataset): - def __init__(self, data_path, processor, mode='train'): - super().__init__() - self.data = self.load_data(data_path, processor, mode) - - def __len__(self): - return len(self.data) - - def __getitem__(self, index): - return self.data[index] - - def load_data(self, data_path, processor, mode): - if mode == "train": - examples = processor.get_examples(data_path, mode) - elif mode == "test": - examples = processor.get_examples(data_path, mode) - elif mode == "dev": - examples = processor.get_examples(data_path, mode) - return examples - - -@dataclass -class TaskCollator: - args = None - tokenizer = None - ngram_dict = None - label2id = None - - def __call__(self, samples): - features = convert_examples_to_features(samples, self.label2id, self.args.max_seq_length, self.tokenizer, self.ngram_dict) - # logger.info(" Num examples = %d", len(samples)) - - input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) - input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) - segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) - label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long) - valid_ids = torch.tensor([f.valid_ids for f in features], dtype=torch.long) - - ngram_ids = torch.tensor([f.ngram_ids for f in features], dtype=torch.long) - ngram_positions = torch.tensor([f.ngram_positions for f in features], dtype=torch.long) - # ngram_lengths = torch.tensor([f.ngram_lengths for f in features], dtype=torch.long) - # ngram_seg_ids = torch.tensor([f.ngram_seg_ids for f in features], dtype=torch.long) - # ngram_masks = torch.tensor([f.ngram_masks for f in features], dtype=torch.long) - - # label_mask = torch.tensor([f.label_mask for f in features], dtype=torch.long) - return { - 'input_ids': input_ids, - 'ngram_ids': ngram_ids, - 'ngram_positions': ngram_positions, - 'attention_mask': input_mask, - 'token_type_ids': segment_ids, - 'labels': label_ids, - 'valid_ids': valid_ids, - } - - -class TaskDataModel(pl.LightningDataModule): - @staticmethod - def add_data_specific_args(parent_args): - parser = parent_args.add_argument_group('TASK NAME DataModel') - parser.add_argument('--data_dir', default='./data', type=str) - parser.add_argument('--num_workers', default=8, type=int) - parser.add_argument('--train_data', default='train.json', type=str) - parser.add_argument('--valid_data', default='dev.json', type=str) - parser.add_argument('--test_data', default='test.json', type=str) - parser.add_argument('--train_batchsize', default=16, type=int) - parser.add_argument('--valid_batchsize', default=32, type=int) - parser.add_argument('--max_seq_length', default=128, type=int) - - parser.add_argument('--texta_name', default='text', type=str) - parser.add_argument('--textb_name', default='sentence2', type=str) - parser.add_argument('--label_name', default='label', type=str) - parser.add_argument('--id_name', default='id', type=str) - - parser.add_argument('--dataset_name', default=None, type=str) - parser.add_argument('--vocab_file', - type=str, default=None, - help="Vocabulary mapping/file BERT was pretrainined on") - parser.add_argument("--do_lower_case", - action='store_true', - help="Set this flag if you are using an uncased model.") - parser.add_argument('--task_name', default='weibo', type=str) - - return parent_args - - def __init__(self, args): - super().__init__() - self.train_batchsize = args.train_batchsize - self.valid_batchsize = args.valid_batchsize - self.collator = TaskCollator() - self.collator.args = args - self.collator.tokenizer = BertTokenizer.from_pretrained(args.pretrained_model_path, do_lower_case=args.do_lower_case) - self.collator.ngram_dict = ZenNgramDict.from_pretrained(args.pretrained_model_path, tokenizer=self.collator.tokenizer) - - processors = { - 'weibo': WeiboProcessor, - 'resume': ResumeProcessor, - 'msra': MSRAProcessor, - 'ontonotes4': OntoNotes4Processor, - 'cmeee': CMeEEProcessor, - 'cluener': CLUENERProcessor, - } - if args.task_name not in processors: - raise ValueError("Task not found: %s" % (args.task_name)) - processor = processors[args.task_name]() - # 生成id映射 - label_list = processor.get_labels() - label2id = {label: i for i, label in enumerate(label_list, 1)} - label2id["[PAD]"] = 0 - self.id2label = {v: k for k, v in label2id.items()} - self.collator.label2id = label2id - - if args.dataset_name is None: - self.train_data = TaskDataset(os.path.join( - args.data_dir, args.train_data), processor, mode='train') - self.valid_data = TaskDataset(os.path.join( - args.data_dir, args.valid_data), processor, mode='dev') - self.test_data = TaskDataset(os.path.join( - args.data_dir, args.test_data), processor, mode='test') - - else: - import datasets - ds = datasets.load_dataset(args.dataset_name) - self.train_data = ds['train'] - self.valid_data = ds['validation'] - self.test_data = ds['test'] - self.save_hyperparameters(args) - - def train_dataloader(self): - return DataLoader(self.train_data, shuffle=True, batch_size=self.train_batchsize, pin_memory=False, - collate_fn=self.collator) - - def val_dataloader(self): - return DataLoader(self.valid_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False, - collate_fn=self.collator) - - def predict_dataloader(self): - return DataLoader(self.test_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False, - collate_fn=self.collator) - - -class LitModel(pl.LightningModule): - - @staticmethod - def add_model_specific_args(parent_args): - parser = parent_args.add_argument_group('BaseModel') - parser.add_argument('--markup', default='bios', type=str) - parser.add_argument('--middle_prefix', default='I-', type=str) - return parent_args - - def __init__(self, args, id2label): - super().__init__() - # config = ZenConfig(os.path.join(args.pretrained_model_path, 'config.json')) - self.model = ZenForTokenClassification.from_pretrained(args.pretrained_model_path, num_labels=len(id2label)) - self.seq_entity_score = SeqEntityScore(id2label, markup=args.markup, middle_prefix=args.middle_prefix) - self.train_seq_entity_score = SeqEntityScore(id2label, markup=args.markup, middle_prefix=args.middle_prefix) - self.id2label = id2label - self.label2id = {v: k for k, v in id2label.items()} - self.save_hyperparameters(args) - - def setup(self, stage) -> None: - if stage == 'fit': - train_loader = self.trainer._data_connector._train_dataloader_source.dataloader() - - # Calculate total steps - if self.trainer.max_epochs > 0: - world_size = self.trainer.world_size - tb_size = self.hparams.train_batchsize * max(1, world_size) - ab_size = self.trainer.accumulate_grad_batches - self.total_steps = (len(train_loader.dataset) * - self.trainer.max_epochs // tb_size) // ab_size - else: - self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches - - print('Total steps: {}' .format(self.total_steps)) - - def training_step(self, batch, batch_idx): - outputs = self.model(**batch) - loss, _ = outputs - # logits = outputs.logits - # preds = torch.argmax(F.log_softmax(logits, dim=2), dim=2) - # preds = preds.detach().cpu().numpy() - # labels = batch['labels'].detach().cpu().numpy() - # num_labels = len(self.label2id) - # y_true = [] - # y_pred = [] - # for i, label in enumerate(labels): - # temp_1 = [] - # temp_2 = [] - # for j, m in enumerate(label): - # if j == 0: - # continue - # elif labels[i][j] == num_labels - 1: - # y_true.append(temp_1) - # y_pred.append(temp_2) - # break - # else: - # temp_1.append(self.id2label[labels[i][j]]) - # temp_2.append(self.id2label[preds[i][j]]) - - # self.train_seq_entity_score.update(y_true, y_pred) - # result = self.train_seq_entity_score.result() - # self.train_seq_entity_score.reset() - self.log('train_loss', loss) - - return loss - - def validation_step(self, batch, batch_idx): - outputs = self.model(**batch) - loss, logits = outputs - preds = torch.argmax(F.log_softmax(logits, dim=2), dim=2) - preds = preds.detach().cpu().numpy() - labels = batch['labels'].detach().cpu().numpy() - num_labels = len(self.label2id) - y_true = [] - y_pred = [] - for i, label in enumerate(labels): - temp_1 = [] - temp_2 = [] - for j, m in enumerate(label): - if j == 0: - continue - elif labels[i][j] == num_labels - 1: - y_true.append(temp_1) - y_pred.append(temp_2) - break - else: - temp_1.append(self.id2label[labels[i][j]]) - temp_2.append(self.id2label[preds[i][j]]) - - self.seq_entity_score.update(y_true, y_pred) - self.log('val_loss', loss) - - def validation_epoch_end(self, outputs): - # compute metric for all process - score_dict, _ = self.seq_entity_score.result() - if self.trainer._accelerator_connector.cluster_environment.global_rank() == 0: - print('score_dict:\n', score_dict) - # reset the metric after once validation - self.seq_entity_score.reset() - for k, v in score_dict.items(): - self.log('val_{}'.format(k), v) - - def configure_optimizers(self): - from fengshen.models.model_utils import configure_optimizers - return configure_optimizers(self) - - -class TaskModelCheckpoint: - @staticmethod - def add_argparse_args(parent_args): - parser = parent_args.add_argument_group('BaseModel') - - parser.add_argument('--monitor', default='train_loss', type=str) - parser.add_argument('--mode', default='min', type=str) - parser.add_argument('--dirpath', default='./log/', type=str) - parser.add_argument( - '--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str) - - parser.add_argument('--save_top_k', default=3, type=float) - parser.add_argument('--every_n_train_steps', default=100, type=float) - parser.add_argument('--save_weights_only', default=True, type=bool) - - return parent_args - - def __init__(self, args): - self.callbacks = ModelCheckpoint(monitor=args.monitor, - save_top_k=args.save_top_k, - mode=args.mode, - every_n_train_steps=args.every_n_train_steps, - save_weights_only=args.save_weights_only, - dirpath=args.dirpath, - filename=args.filename) - - -def save_test(data, args, data_model): - with open(args.output_save_path, 'w', encoding='utf-8') as f: - idx = 0 - for i in range(len(data)): - batch = data[i] - for sample in batch: - tmp_result = dict() - label_id = np.argmax(sample.numpy()) - tmp_result['id'] = data_model.test_data.data[idx]['id'] - tmp_result['label'] = data_model.id2label[label_id] - json_data = json.dumps(tmp_result, ensure_ascii=False) - f.write(json_data+'\n') - idx += 1 - print('save the result to '+args.output_save_path) - - -def main(): - total_parser = argparse.ArgumentParser("TASK NAME") - total_parser.add_argument('--pretrained_model_path', default='', type=str) - total_parser.add_argument('--output_save_path', - default='./predict.json', type=str) - # * Args for data preprocessing - total_parser = TaskDataModel.add_data_specific_args(total_parser) - # * Args for training - total_parser = pl.Trainer.add_argparse_args(total_parser) - total_parser = TaskModelCheckpoint.add_argparse_args(total_parser) - - # * Args for base model - from fengshen.models.model_utils import add_module_args - total_parser = add_module_args(total_parser) - total_parser = LitModel.add_model_specific_args(total_parser) - - args = total_parser.parse_args() - - checkpoint_callback = TaskModelCheckpoint(args).callbacks - lr_monitor = LearningRateMonitor(logging_interval='step') - trainer = pl.Trainer.from_argparse_args(args, - callbacks=[checkpoint_callback, lr_monitor] - ) - - data_model = TaskDataModel(args) - id2label = data_model.id2label - print('id2label:', id2label) - model = LitModel(args, id2label) - trainer.fit(model, data_model) - - -if __name__ == "__main__": - main() diff --git a/spaces/society-ethics/model-card-regulatory-check/tests/cards/big-science___bloom.md b/spaces/society-ethics/model-card-regulatory-check/tests/cards/big-science___bloom.md deleted file mode 100644 index 4b004d8941832d5ec96322c9a771ad5e33e60fd7..0000000000000000000000000000000000000000 --- a/spaces/society-ethics/model-card-regulatory-check/tests/cards/big-science___bloom.md +++ /dev/null @@ -1,751 +0,0 @@ ---- -license: bigscience-bloom-rail-1.0 -language: -- ak -- ar -- as -- bm -- bn -- ca -- code -- en -- es -- eu -- fon -- fr -- gu -- hi -- id -- ig -- ki -- kn -- lg -- ln -- ml -- mr -- ne -- nso -- ny -- or -- pa -- pt -- rn -- rw -- sn -- st -- sw -- ta -- te -- tn -- ts -- tum -- tw -- ur -- vi -- wo -- xh -- yo -- zh -- zu -programming_language: -- C -- C++ -- C# -- Go -- Java -- JavaScript -- Lua -- PHP -- Python -- Ruby -- Rust -- Scala -- TypeScript -pipeline_tag: text-generation -widget: -- text: 'A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is: We were traveling in Africa and we saw these very cute whatpus. | To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:' - example_title: Imaginary word - group: English -- text: 'Un "whatpu" est un petit animal à fourrure originaire de Tanzanie. Un exemple de phrase qui utilise le mot whatpu est: Nous étions en Afrique et nous avons vu des whatpus trop mignons. Faire un "farduddle" veut dire sauter sur place vraiment vite. Un exemple de phrase qui utilise le mot farduddle est:' - example_title: Imaginary word - group: French -- text: 'Un "whatpu" es un pequeño animal peludo nativo de Tanzania. Un ejemplo de una oración que usa la palabra whatpu es: Estábamos viajando por África y vimos estos whatpus muy bonitos. Hacer un "farduddle" significa saltar arriba y abajo muy rápido. Un ejemplo de una oración que usa la palabra farduddle es:' - example_title: Imaginary word - group: Spanish -- text: ' ال"واتبو" هو حيوان صغير مكسو بالفراء يعيش في تنزانيا. مثال على جملة تستخدم كلمة واتبو هي: كنا نسافر في افريقيا و رأينا هؤلاء الواتبو اللطفاء. للقيام ب"فاردادل" يعني ان تقفز للأعلى و الأسفل بسرعة كبيرة. مثال على جملة تستخدم كلمة فاردادل هي:' - example_title: Imaginary word - group: Arabic -- text: 'Um "whatpu" é um pequeno animal peludo nativo da Tanzânia. Um exemplo de uma frase que usa a palavra whatpu é: Estávamos a viajar por África e vimos uns whatpus muito queridos. Fazer um "farduddle" significa saltar para cima e para baixo muito rápido. Um exemplo de uma frase que usa a palavra farduddle é:' - example : Imaginary word - group: Portuguese -- text: Pour déguster un ortolan, il faut tout d'abord - example_title: Recipe - group: French -- text: |- - 34+10=44 - 54+20= - example_title: Addition - group: Math -- text: |- - This tool converts irregular verbs to past tense. - Arise - Arose - Become - Became - Forget - Forgot - Freeze - - example_title: Irregular verbs - group: English -- text: |- - Please unscramble the letters into a word, and write that word: - r e!c.i p r o.c a/l = reciprocal - d.o m i!n a n.t = - example_title: Word unscrambling - group: English -- text: |- - Estos ejemplos quitan vocales de las palabras - Ejemplos: - hola - hl - manzana - mnzn - papas - pps - alacran - lcrn - papa - - example_title: Vowel removal - group: Spanish -- text: |- - Traduce español de España a español de Argentina - El coche es rojo - el auto es rojo - El ordenador es nuevo - la computadora es nueva - el boligrafo es negro - lapicera es negra - la nevera - example_title: Spanish to Argentinian Spanish - group: Spanish -- text: To say "I love you" in Hindi, you would say - example_title: Translation to Hindi - group: English -- text: To say "I love you" in Hindi, you would say - example_title: Translation from English - group: Hindi -- text: 'Poor English: She no went to the market. Corrected English:' - example_title: Grammar exercise 1 - group: English -- text: 'استخراج العدد العاملي في لغة بايثون:' - example_title: Code generation - group: Arabic -- text: 'Regexp. Here is a regular expression to match a word starting with a number and then having only vowels:' - example_title: Regular expressions - group: English -- text: |- - Do a hello world in different languages: - Python: print("hello world") - R: - example_title: Code generation - group: English -- text: |- - Which is the correct preposition? I'm born X July. X is the preposition in - He sat X a chair. X is the preposition on - She drove X the bridge. X is the preposition - example_title: Grammar exercise 2 - group: English -- text: |- - Traduction en français: Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience. - Traduction en espagnol: - example_title: Translation to Spanish - group: French -- text: |- - Traducción al francés: Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience. - Traducción al español: - example_title: Translation from French - group: Spanish -- text: ذات مرة ، عاش شبل الدب في الغابة - example_title: Fairy tale - group: Arabic -- text: एक बार की बात है, जंगल में एक भालू का शावक रहता था - example_title: Fairy tale - group: Hindi -- text: Il était une fois une licorne qui vivait - example_title: Fairy tale - group: French -- text: |- - Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there? - A: Let's think step by step. - example_title: Mathematical reasoning - group: English - -co2_eq_emissions: - emissions: 24_700_000 - source: "Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. https://arxiv.org/abs/2211.02001" - training_type: "pre-training" - geographical_location: "Orsay, France" - hardware_used: "384 A100 80GB GPUs" - -model-index: -- name: bloom - results: - - task: - type: text-generation - dataset: - type: openai_humaneval - name: humaneval - metrics: - - name: pass@1 - type: pass@1 - value: 0.15542682926829265 - verified: false - - name: pass@10 - type: pass@10 - value: 0.3278356276947017 - verified: false - - name: pass@100 - type: pass@100 - value: 0.5719815685597749 - verified: false ---- - -BigScience Logo - -BigScience Large Open-science Open-access Multilingual Language Model -Version 1.3 / 6 July 2022 - -Current Checkpoint: **Training Iteration 95000** - -Link to paper: [here](https://arxiv.org/abs/2211.05100) - -Total seen tokens: **366B** - ---- - -# Model Details - -BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks. - -## Basics -*This section provides information about the model type, version, license, funders, release date, developers, and contact information.* -*It is useful for anyone who wants to reference the model.* - -
          -Click to expand - -**Developed by:** BigScience ([website](https://bigscience.huggingface.co)) - -*All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)* - -**Model Type:** Transformer-based Language Model - -**Checkpoints format:** `transformers` (Megatron-DeepSpeed format available [here](https://huggingface.co/bigscience/bloom-optimizer-states)) - -**Version:** 1.0.0 - -**Languages:** Multiple; see [training data](#training-data) - -**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license) / [article and FAQ](https://bigscience.huggingface.co/blog/the-bigscience-rail-license)) - -**Release Date Estimate:** Monday, 11.July.2022 - -**Send Questions to:** bigscience-contact@googlegroups.com - -**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022 - -**Funded by:** - -* The French government. - -* Hugging Face ([website](https://huggingface.co)). - -* Organizations of contributors. *(Further breakdown of organizations forthcoming.)* - -
          - - -## Technical Specifications -*This section includes details about the model objective and architecture, and the compute infrastructure.* -*It is useful for people interested in model development.* - -
          -Click to expand - -Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training. - -### Model Architecture and Objective - -* Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)): - -* Decoder-only architecture - -* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf)) - -* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions - -* 176,247,271,424 parameters: - - * 3,596,615,680 embedding parameters - - * 70 layers, 112 attention heads - - * Hidden layers are 14336-dimensional - - * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization)) - -**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)). - -### Compute infrastructure -Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)). - -#### Hardware - -* 384 A100 80GB GPUs (48 nodes) - -* Additional 32 A100 80GB GPUs (4 nodes) in reserve - -* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links - -* CPU: AMD - -* CPU memory: 512GB per node - -* GPU memory: 640GB per node - -* Inter-node connect: Omni-Path Architecture (OPA) - -* NCCL-communications network: a fully dedicated subnet - -* Disc IO network: shared network with other types of nodes - -#### Software - -* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed)) - -* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed)) - -* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch)) - -* apex ([Github link](https://github.com/NVIDIA/apex)) - -
          - ---- - -# Training -*This section provides information about the training data, the speed and size of training elements, and the environmental impact of training.* -*It is useful for people who want to learn more about the model inputs and training footprint.* - -
          -Click to expand - -## Training Data -*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* - -Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus), and the sizes of each of their contributions to the aggregated training data are presented in an [Interactive Corpus Map](https://huggingface.co/spaces/bigscience-catalogue-lm-data/corpus-map). - -Training data includes: - -- 46 natural languages - -- 13 programming languages - -- In 1.6TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.) - -### Languages - -The pie chart shows the distribution of languages in training data. - -![pie chart showing the distribution of languages in training data](https://github.com/bigscience-workshop/model_card/blob/main/assets/data/pie_v2.svg?raw=true) - - -The following tables shows the further distribution of Niger-Congo & Indic languages and programming languages in the training data. - -Distribution of Niger Congo and Indic languages. - -| Niger Congo | Percentage | | Indic | Percentage | -|----------------|------------| ------ |-----------|------------| -| Chi Tumbuka | 0.00002 | | Assamese | 0.01 | -| Kikuyu | 0.00004 | | Odia | 0.04 | -| Bambara | 0.00004 | | Gujarati | 0.04 | -| Akan | 0.00007 | | Marathi | 0.05 | -| Xitsonga | 0.00007 | | Punjabi | 0.05 | -| Sesotho | 0.00007 | | Kannada | 0.06 | -| Chi Chewa | 0.0001 | | Nepali | 0.07 | -| Setswana | 0.0002 | | Telugu | 0.09 | -| Lingala | 0.0002 | | Malayalam | 0.10 | -| Northern Sotho | 0.0002 | | Urdu | 0.10 | -| Fon | 0.0002 | | Tamil | 0.20 | -| Kirundi | 0.0003 | | Bengali | 0.50 | -| Wolof | 0.0004 | | Hindi | 0.70 | -| Luganda | 0.0004 | -| Chi Shona | 0.001 | -| Isi Zulu | 0.001 | -| Igbo | 0.001 | -| Xhosa | 0.001 | -| Kinyarwanda | 0.003 | -| Yoruba | 0.006 | -| Swahili | 0.02 | - -Distribution of programming languages. - -| Extension | Language | Number of files | -|----------------|------------|-----------------| -| java | Java | 5,407,724 | -| php | PHP | 4,942,186 | -| cpp | C++ | 2,503,930 | -| py | Python | 2,435,072 | -| js | JavaScript | 1,905,518 | -| cs | C# | 1,577,347 | -| rb | Ruby | 6,78,413 | -| cc | C++ | 443,054 | -| hpp | C++ | 391,048 | -| lua | Lua | 352,317 | -| go | GO | 227,763 | -| ts | TypeScript | 195,254 | -| C | C | 134,537 | -| scala | Scala | 92,052 | -| hh | C++ | 67,161 | -| H | C++ | 55,899 | -| tsx | TypeScript | 33,107 | -| rs | Rust | 29,693 | -| phpt | PHP | 9,702 | -| c++ | C++ | 1,342 | -| h++ | C++ | 791 | -| php3 | PHP | 540 | -| phps | PHP | 270 | -| php5 | PHP | 166 | -| php4 | PHP | 29 | - -### Preprocessing - -**Tokenization:** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)), a learned subword tokenizer trained using: - -- A byte-level Byte Pair Encoding (BPE) algorithm - -- A simple pre-tokenization rule, no normalization - -- A vocabulary size of 250,680 - -It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. - -## Speeds, Sizes, Times - -Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/) - -- Dates: - - - Started 11th March, 2022 11:42am PST - - - Estimated end: 5th July, 2022 - -- Checkpoint size: - - - Bf16 weights: 329GB - - - Full checkpoint with optimizer states: 2.3TB - -- Training throughput: About 150 TFLOP per GPU per second - -- Number of epochs: 1 - -- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments) - -- Server training location: Île-de-France, France - - -## Environmental Impact - -The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing. - -**Estimated carbon emissions:** *(Forthcoming.)* - -**Estimated electricity usage:** *(Forthcoming.)* - -
          - ---- - -# Uses - -*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.* -*It is useful for anyone considering using the model or who is affected by the model.* - -
          -Click to expand - -## How to use - -This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows: - - - -## Intended Use - -This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive. - -### Direct Use - -- Text generation - -- Exploring characteristics of language generated by a language model - - - Examples: Cloze tests, counterfactuals, generations with reframings - -### Downstream Use - -- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization - -### Misuse and Out-of-scope Use -*This section addresses what users ought not do with the model.* - -See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases. - -#### Out-of-scope Uses - -Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. - -Out-of-scope Uses Include: - -- Usage in biomedical domains, political and legal domains, or finance domains - -- Usage for evaluating or scoring individuals, such as for employment, education, or credit - -- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct - -#### Misuse - -Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - -- Spam generation - -- Disinformation and influence operations - -- Disparagement and defamation - -- Harassment and abuse - -- [Deception](#deception) - -- Unconsented impersonation and imitation - -- Unconsented surveillance - -- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) - -## Intended Users - -### Direct Users - -- General Public - -- Researchers - -- Students - -- Educators - -- Engineers/developers - -- Non-commercial entities - -- Community advocates, including human and civil rights groups - -### Indirect Users - -- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use) - -- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license) - -### Others Affected (Parties Prenantes) - -- People and groups referred to by the LLM - -- People and groups exposed to outputs of, or decisions based on, the LLM - -- People and groups whose original work is included in the LLM - -
          - ---- - -# Risks and Limitations -*This section identifies foreseeable harms and misunderstandings.* - -
          -Click to expand - -Model may: - -- Overrepresent some viewpoints and underrepresent others - -- Contain stereotypes - -- Contain [personal information](#personal-data-and-information) - -- Generate: - - - Hateful, abusive, or violent language - - - Discriminatory or prejudicial language - - - Content that may not be appropriate for all settings, including sexual content - -- Make errors, including producing incorrect information as if it were factual - -- Generate irrelevant or repetitive outputs - -- Induce users into attributing human traits to it, such as sentience or consciousness - -
          - ---- - -# Evaluation -*This section describes the evaluation protocols and provides the results.* - - -
          -Click to expand - -## Metrics -*This section describes the different ways performance is calculated and why.* - -Includes: - -| Metric | Why chosen | -|--------------------|--------------------------------------------------------------------| -| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training | -| Cross Entropy [Loss](#loss) | Standard objective for language models. | - -And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_ - -## Factors -*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.* - -- Language, such as English or Yoruba - -- Domain, such as newswire or stories - -- Demographic characteristics, such as gender or nationality - -## Results -*Results are based on the [Factors](#factors) and [Metrics](#metrics).* - -**Zero-shot evaluations:** - -WARNING: This section used to contain much more results, however they were not correct and we released without the approval of the evaluation working group. We are currently in the process of fixing the evaluations. - -See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results - -| Task | Language | Metric | BLOOM-176B | OPT-175B* | -|:--------|:-----------------|:------------------------|-------------:|------------:| -| humaneval | python | pass@1 ↑ | 0.155 | 0.0 | -| humaneval | python | pass@10 ↑ | 0.328 | 0.0 | -| humaneval | python | pass@100 ↑ | 0.572 | 0.003 | - - -**Train-time Evaluation:** - -Final checkpoint after 95K steps: - -- Training Loss: 1.939 - -- Validation Loss: 2.061 - -- Perplexity: 7.045 - -For more see: https://huggingface.co/bigscience/tr11-176B-ml-logs - -
          - ---- - -# Recommendations - -*This section provides information on warnings and potential mitigations.* - -
          -Click to expand - -- Indirect users should be made aware when the content they're working with is created by the LLM. - -- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - -- Models trained or finetuned downstream of BLOOM LM should include an updated Model Card. - -- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. - -
          - ---- - -# Glossary and Calculations - -*This section defines common terms and how metrics are calculated.* -
          -Click to expand - -- **Loss:** A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss. - -- **Perplexity:** This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy. - -- **High-stakes settings:** Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/). - -- **Critical decisions:** Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf). - -- **Human rights:** Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf). - -- **Personal Data and Personal Information:** Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm). - -- **Sensitive characteristics:** This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf)) - -- **Deception:** Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated. - -
          - ---- - -# More Information -*This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.* - -
          -Click to expand - -## Intermediate checkpoints - -For academic (or any) usage, we published the intermediate checkpoints, corresponding to the model state at each 5000 steps. Please follow [this link](https://huggingface.co/bigscience/bloom-176-intermediate) to get these checkpoints. - - -## Dataset Creation - -Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling - -## Technical Specifications - -Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours - -More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml - -Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model - -Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml - -Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss - -## Lessons - -Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md - -Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md - -## Initial Results - -Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book - -
          - - -## Original checkpoints - -The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) that the model was trained with, you'd want to use [this repo instead](https://huggingface.co/bigscience/bloom-optimizer-states). - -Many intermediate checkpoints are available at https://huggingface.co/bigscience/bloom-intermediate/ - ---- - -# Model Card Authors -*Ordered roughly chronologically and by amount of time spent on creating this model card.* - -Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff \ No newline at end of file diff --git a/spaces/sohaibcs1/Image-to-Text-Summary/README.md b/spaces/sohaibcs1/Image-to-Text-Summary/README.md deleted file mode 100644 index 5c0a461afe780b9dcd694bafa3ca450b169316ea..0000000000000000000000000000000000000000 --- a/spaces/sohaibcs1/Image-to-Text-Summary/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Image To Text Summary -emoji: 🔥 -colorFrom: pink -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/sohamagarwal00/chatgpt_implementation/polly_utils.py b/spaces/sohamagarwal00/chatgpt_implementation/polly_utils.py deleted file mode 100644 index 7cb38abff2aaac3c5b24f20914d464151173780d..0000000000000000000000000000000000000000 --- a/spaces/sohamagarwal00/chatgpt_implementation/polly_utils.py +++ /dev/null @@ -1,635 +0,0 @@ -# This class stores Polly voice data. Specifically, the class stores several records containing -# language, lang_code, gender, voice_id and engine. The class also has a method to return the -# voice_id, lang_code and engine given a language and gender. - -NEURAL_ENGINE = "neural" -STANDARD_ENGINE = "standard" - - -class PollyVoiceData: - def get_voice(self, language, gender): - for voice in self.voice_data: - if voice['language'] == language and voice['gender'] == gender: - if voice['neural'] == 'Yes': - return voice['voice_id'], voice['lang_code'], NEURAL_ENGINE - for voice in self.voice_data: - if voice['language'] == language and voice['gender'] == gender: - if voice['standard'] == 'Yes': - return voice['voice_id'], voice['lang_code'], STANDARD_ENGINE - return None, None, None - - def get_whisper_lang_code(self, language): - for voice in self.voice_data: - if voice['language'] == language: - return voice['whisper_lang_code'] - return "en" - - def __init__(self): - self.voice_data = [ - {'language': 'Arabic', - 'lang_code': 'arb', - 'whisper_lang_code': 'ar', - 'voice_id': 'Zeina', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Arabic (Gulf)', - 'lang_code': 'ar-AE', - 'whisper_lang_code': 'ar', - 'voice_id': 'Hala', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Catalan', - 'lang_code': 'ca-ES', - 'whisper_lang_code': 'ca', - 'voice_id': 'Arlet', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Chinese (Cantonese)', - 'lang_code': 'yue-CN', - 'whisper_lang_code': 'zh', - 'voice_id': 'Hiujin', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Chinese (Mandarin)', - 'lang_code': 'cmn-CN', - 'whisper_lang_code': 'zh', - 'voice_id': 'Zhiyu', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Danish', - 'lang_code': 'da-DK', - 'whisper_lang_code': 'da', - 'voice_id': 'Naja', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Danish', - 'lang_code': 'da-DK', - 'whisper_lang_code': 'da', - 'voice_id': 'Mads', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Dutch', - 'lang_code': 'nl-NL', - 'whisper_lang_code': 'nl', - 'voice_id': 'Laura', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Dutch', - 'lang_code': 'nl-NL', - 'whisper_lang_code': 'nl', - 'voice_id': 'Lotte', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Dutch', - 'lang_code': 'nl-NL', - 'whisper_lang_code': 'nl', - 'voice_id': 'Ruben', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'English (Australian)', - 'lang_code': 'en-AU', - 'whisper_lang_code': 'en', - 'voice_id': 'Nicole', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'English (Australian)', - 'lang_code': 'en-AU', - 'whisper_lang_code': 'en', - 'voice_id': 'Olivia', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'English (Australian)', - 'lang_code': 'en-AU', - 'whisper_lang_code': 'en', - 'voice_id': 'Russell', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'English (British)', - 'lang_code': 'en-GB', - 'whisper_lang_code': 'en', - 'voice_id': 'Amy', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (British)', - 'lang_code': 'en-GB', - 'whisper_lang_code': 'en', - 'voice_id': 'Emma', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (British)', - 'lang_code': 'en-GB', - 'whisper_lang_code': 'en', - 'voice_id': 'Brian', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (British)', - 'lang_code': 'en-GB', - 'whisper_lang_code': 'en', - 'voice_id': 'Arthur', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'English (Indian)', - 'lang_code': 'en-IN', - 'whisper_lang_code': 'en', - 'voice_id': 'Aditi', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'English (Indian)', - 'lang_code': 'en-IN', - 'whisper_lang_code': 'en', - 'voice_id': 'Raveena', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'English (Indian)', - 'lang_code': 'en-IN', - 'whisper_lang_code': 'en', - 'voice_id': 'Kajal', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'English (New Zealand)', - 'lang_code': 'en-NZ', - 'whisper_lang_code': 'en', - 'voice_id': 'Aria', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'English (South African)', - 'lang_code': 'en-ZA', - 'whisper_lang_code': 'en', - 'voice_id': 'Ayanda', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Ivy', - 'gender': 'Female (child)', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Joanna', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Kendra', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Kimberly', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Salli', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Joey', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Justin', - 'gender': 'Male (child)', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Kevin', - 'gender': 'Male (child)', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'English (US)', - 'lang_code': 'en-US', - 'whisper_lang_code': 'en', - 'voice_id': 'Matthew', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'English (Welsh)', - 'lang_code': 'en-GB-WLS', - 'whisper_lang_code': 'en', - 'voice_id': 'Geraint', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Finnish', - 'lang_code': 'fi-FI', - 'whisper_lang_code': 'fi', - 'voice_id': 'Suvi', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'French', - 'lang_code': 'fr-FR', - 'whisper_lang_code': 'fr', - 'voice_id': 'Celine', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'French', - 'lang_code': 'fr-FR', - 'whisper_lang_code': 'fr', - 'voice_id': 'Lea', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'French', - 'lang_code': 'fr-FR', - 'whisper_lang_code': 'fr', - 'voice_id': 'Mathieu', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'French (Canadian)', - 'lang_code': 'fr-CA', - 'whisper_lang_code': 'fr', - 'voice_id': 'Chantal', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'French (Canadian)', - 'lang_code': 'fr-CA', - 'whisper_lang_code': 'fr', - 'voice_id': 'Gabrielle', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'French (Canadian)', - 'lang_code': 'fr-CA', - 'whisper_lang_code': 'fr', - 'voice_id': 'Liam', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'German', - 'lang_code': 'de-DE', - 'whisper_lang_code': 'de', - 'voice_id': 'Marlene', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'German', - 'lang_code': 'de-DE', - 'whisper_lang_code': 'de', - 'voice_id': 'Vicki', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'German', - 'lang_code': 'de-DE', - 'whisper_lang_code': 'de', - 'voice_id': 'Hans', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'German', - 'lang_code': 'de-DE', - 'whisper_lang_code': 'de', - 'voice_id': 'Daniel', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'German (Austrian)', - 'lang_code': 'de-AT', - 'whisper_lang_code': 'de', - 'voice_id': 'Hannah', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Hindi', - 'lang_code': 'hi-IN', - 'whisper_lang_code': 'hi', - 'voice_id': 'Aditi', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Hindi', - 'lang_code': 'hi-IN', - 'whisper_lang_code': 'hi', - 'voice_id': 'Kajal', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Icelandic', - 'lang_code': 'is-IS', - 'whisper_lang_code': 'is', - 'voice_id': 'Dora', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Icelandic', - 'lang_code': 'is-IS', - 'whisper_lang_code': 'is', - 'voice_id': 'Karl', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Italian', - 'lang_code': 'it-IT', - 'whisper_lang_code': 'it', - 'voice_id': 'Carla', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Italian', - 'lang_code': 'it-IT', - 'whisper_lang_code': 'it', - 'voice_id': 'Bianca', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Japanese', - 'lang_code': 'ja-JP', - 'whisper_lang_code': 'ja', - 'voice_id': 'Mizuki', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Japanese', - 'lang_code': 'ja-JP', - 'whisper_lang_code': 'ja', - 'voice_id': 'Takumi', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Korean', - 'lang_code': 'ko-KR', - 'whisper_lang_code': 'ko', - 'voice_id': 'Seoyeon', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Norwegian', - 'lang_code': 'nb-NO', - 'whisper_lang_code': 'no', - 'voice_id': 'Liv', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Norwegian', - 'lang_code': 'nb-NO', - 'whisper_lang_code': 'no', - 'voice_id': 'Ida', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Polish', - 'lang_code': 'pl-PL', - 'whisper_lang_code': 'pl', - 'voice_id': 'Ewa', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Polish', - 'lang_code': 'pl-PL', - 'whisper_lang_code': 'pl', - 'voice_id': 'Maja', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Polish', - 'lang_code': 'pl-PL', - 'whisper_lang_code': 'pl', - 'voice_id': 'Jacek', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Polish', - 'lang_code': 'pl-PL', - 'whisper_lang_code': 'pl', - 'voice_id': 'Jan', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Polish', - 'lang_code': 'pl-PL', - 'whisper_lang_code': 'pl', - 'voice_id': 'Ola', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Portuguese (Brazilian)', - 'lang_code': 'pt-BR', - 'whisper_lang_code': 'pt', - 'voice_id': 'Camila', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Portuguese (Brazilian)', - 'lang_code': 'pt-BR', - 'whisper_lang_code': 'pt', - 'voice_id': 'Vitoria', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Portuguese (Brazilian)', - 'lang_code': 'pt-BR', - 'whisper_lang_code': 'pt', - 'voice_id': 'Ricardo', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Portuguese (European)', - 'lang_code': 'pt-PT', - 'whisper_lang_code': 'pt', - 'voice_id': 'Ines', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Portuguese (European)', - 'lang_code': 'pt-PT', - 'whisper_lang_code': 'pt', - 'voice_id': 'Cristiano', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Romanian', - 'lang_code': 'ro-RO', - 'whisper_lang_code': 'ro', - 'voice_id': 'Carmen', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Russian', - 'lang_code': 'ru-RU', - 'whisper_lang_code': 'ru', - 'voice_id': 'Tatyana', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Russian', - 'lang_code': 'ru-RU', - 'whisper_lang_code': 'ru', - 'voice_id': 'Maxim', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Spanish (European)', - 'lang_code': 'es-ES', - 'whisper_lang_code': 'es', - 'voice_id': 'Conchita', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Spanish (European)', - 'lang_code': 'es-ES', - 'whisper_lang_code': 'es', - 'voice_id': 'Lucia', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Spanish (European)', - 'lang_code': 'es-ES', - 'whisper_lang_code': 'es', - 'voice_id': 'Enrique', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Spanish (Mexican)', - 'lang_code': 'es-MX', - 'whisper_lang_code': 'es', - 'voice_id': 'Mia', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Spanish (US)', - 'lang_code': 'es-US', - 'whisper_lang_code': 'es', - 'voice_id': 'Lupe', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'Yes'}, - {'language': 'Spanish (US)', - 'lang_code': 'es-US', - 'whisper_lang_code': 'es', - 'voice_id': 'Penelope', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Spanish (US)', - 'lang_code': 'es-US', - 'whisper_lang_code': 'es', - 'voice_id': 'Miguel', - 'gender': 'Male', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Spanish (US)', - 'lang_code': 'es-US', - 'whisper_lang_code': 'es', - 'voice_id': 'Pedro', - 'gender': 'Male', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Swedish', - 'lang_code': 'sv-SE', - 'whisper_lang_code': 'sv', - 'voice_id': 'Astrid', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Swedish', - 'lang_code': 'sv-SE', - 'whisper_lang_code': 'sv', - 'voice_id': 'Elin', - 'gender': 'Female', - 'neural': 'Yes', - 'standard': 'No'}, - {'language': 'Turkish', - 'lang_code': 'tr-TR', - 'whisper_lang_code': 'tr', - 'voice_id': 'Filiz', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'}, - {'language': 'Welsh', - 'lang_code': 'cy-GB', - 'whisper_lang_code': 'cy', - 'voice_id': 'Gwyneth', - 'gender': 'Female', - 'neural': 'No', - 'standard': 'Yes'} - ] - - -# Run from the command-line -if __name__ == '__main__': - polly_voice_data = PollyVoiceData() - - voice_id, language_code, engine = polly_voice_data.get_voice('English (US)', 'Male') - print('English (US)', 'Male', voice_id, language_code, engine) - - voice_id, language_code, engine = polly_voice_data.get_voice('English (US)', 'Female') - print('English (US)', 'Female', voice_id, language_code, engine) - - voice_id, language_code, engine = polly_voice_data.get_voice('French', 'Female') - print('French', 'Female', voice_id, language_code, engine) - - voice_id, language_code, engine = polly_voice_data.get_voice('French', 'Male') - print('French', 'Male', voice_id, language_code, engine) - - voice_id, language_code, engine = polly_voice_data.get_voice('Japanese', 'Female') - print('Japanese', 'Female', voice_id, language_code, engine) - - voice_id, language_code, engine = polly_voice_data.get_voice('Japanese', 'Male') - print('Japanese', 'Male', voice_id, language_code, engine) - - voice_id, language_code, engine = polly_voice_data.get_voice('Hindi', 'Female') - print('Hindi', 'Female', voice_id, language_code, engine) - - voice_id, language_code, engine = polly_voice_data.get_voice('Hindi', 'Male') - print('Hindi', 'Male', voice_id, language_code, engine) - - whisper_lang_code = polly_voice_data.get_whisper_lang_code('English (US)') - print('English (US) whisper_lang_code:', whisper_lang_code) - - whisper_lang_code = polly_voice_data.get_whisper_lang_code('Chinese (Mandarin)') - print('Chinese (Mandarin) whisper_lang_code:', whisper_lang_code) - - whisper_lang_code = polly_voice_data.get_whisper_lang_code('Norwegian') - print('Norwegian whisper_lang_code:', whisper_lang_code) - - whisper_lang_code = polly_voice_data.get_whisper_lang_code('Dutch') - print('Dutch whisper_lang_code:', whisper_lang_code) - - whisper_lang_code = polly_voice_data.get_whisper_lang_code('Foo') - print('Foo whisper_lang_code:', whisper_lang_code) - - diff --git a/spaces/sophiamyang/test-panel/app.py b/spaces/sophiamyang/test-panel/app.py deleted file mode 100644 index 8f91996a8a1a1d3619877063affb91c086bb46b1..0000000000000000000000000000000000000000 --- a/spaces/sophiamyang/test-panel/app.py +++ /dev/null @@ -1,141 +0,0 @@ -import io -import random -from typing import List, Tuple - -import aiohttp -import panel as pn -from PIL import Image -from transformers import CLIPModel, CLIPProcessor - -pn.extension(design="bootstrap", sizing_mode="stretch_width") - -ICON_URLS = { - "brand-github": "https://github.com/holoviz/panel", - "brand-twitter": "https://twitter.com/Panel_Org", - "brand-linkedin": "https://www.linkedin.com/company/panel-org", - "message-circle": "https://discourse.holoviz.org/", - "brand-discord": "https://discord.gg/AXRHnJU6sP", -} - - -async def random_url(_): - pet = random.choice(["cat", "dog"]) - api_url = f"https://api.the{pet}api.com/v1/images/search" - async with aiohttp.ClientSession() as session: - async with session.get(api_url) as resp: - return (await resp.json())[0]["url"] - - -@pn.cache -def load_processor_model( - processor_name: str, model_name: str -) -> Tuple[CLIPProcessor, CLIPModel]: - processor = CLIPProcessor.from_pretrained(processor_name) - model = CLIPModel.from_pretrained(model_name) - return processor, model - - -async def open_image_url(image_url: str) -> Image: - async with aiohttp.ClientSession() as session: - async with session.get(image_url) as resp: - return Image.open(io.BytesIO(await resp.read())) - - -def get_similarity_scores(class_items: List[str], image: Image) -> List[float]: - processor, model = load_processor_model( - "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32" - ) - inputs = processor( - text=class_items, - images=[image], - return_tensors="pt", # pytorch tensors - ) - outputs = model(**inputs) - logits_per_image = outputs.logits_per_image - class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy() - return class_likelihoods[0] - - -async def process_inputs(class_names: List[str], image_url: str): - """ - High level function that takes in the user inputs and returns the - classification results as panel objects. - """ - main.disabled = True - if not image_url: - yield "##### ⚠️ Provide an image URL" - return - - yield "##### ⚙ Fetching image and running model..." - pil_img = await open_image_url(image_url) - img = pn.pane.Image(pil_img, height=400, align="center") - - class_items = class_names.split(",") - class_likelihoods = get_similarity_scores(class_items, pil_img) - - # build the results column - results = pn.Column("##### 🎉 Here are the results!", img) - - for class_item, class_likelihood in zip(class_items, class_likelihoods): - row_label = pn.widgets.StaticText( - name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center" - ) - row_bar = pn.indicators.Progress( - value=int(class_likelihood * 100), - sizing_mode="stretch_width", - bar_color="secondary", - margin=(0, 10), - design=pn.theme.Material, - ) - results.append(pn.Column(row_label, row_bar)) - main.disabled = False - yield results - - -# create widgets -randomize_url = pn.widgets.Button(name="Randomize URL", align="end") - -image_url = pn.widgets.TextInput( - name="Image URL to classify", - value=pn.bind(random_url, randomize_url), -) -class_names = pn.widgets.TextInput( - name="Comma separated class names", - placeholder="Enter possible class names, e.g. cat, dog", - value="cat, dog, parrot", -) - -input_widgets = pn.Column( - "##### 😊 Click randomize or paste a URL to start classifying!", - pn.Row(image_url, randomize_url), - class_names, -) - -# add interactivity -interactive_result = pn.panel( - pn.bind(process_inputs, image_url=image_url, class_names=class_names), - height=600, -) - -# add footer -footer_row = pn.Row(pn.Spacer(), align="center") -for icon, url in ICON_URLS.items(): - href_button = pn.widgets.Button(icon=icon, width=35, height=35) - href_button.js_on_click(code=f"window.open('{url}')") - footer_row.append(href_button) -footer_row.append(pn.Spacer()) - -# create dashboard -main = pn.WidgetBox( - input_widgets, - interactive_result, - footer_row, -) - -title = "Panel Demo - Image Classification" -pn.template.BootstrapTemplate( - title=title, - main=main, - main_max_width="min(50%, 698px)", - header_background="#F08080", -).servable(title=title) \ No newline at end of file diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/criss/save_encoder.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/criss/save_encoder.py deleted file mode 100644 index 24a842e4092663c79c92a299fa85747b7c0bed64..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/criss/save_encoder.py +++ /dev/null @@ -1,214 +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. -""" -Translate pre-processed data with a trained model. -""" - -import numpy as np -import torch -from fairseq import checkpoint_utils, options, progress_bar, tasks, utils -from fairseq.sequence_generator import EnsembleModel -from fairseq.utils import safe_hasattr - - -def get_avg_pool( - models, sample, prefix_tokens, src_dict, remove_bpe, has_langtok=False -): - model = EnsembleModel(models) - - # model.forward normally channels prev_output_tokens into the decoder - # separately, but SequenceGenerator directly calls model.encoder - encoder_input = { - k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" - } - - # compute the encoder output for each beam - encoder_outs = model.forward_encoder(encoder_input) - np_encoder_outs = encoder_outs[0].encoder_out.cpu().numpy().astype(np.float32) - encoder_mask = 1 - encoder_outs[0].encoder_padding_mask.cpu().numpy().astype( - np.float32 - ) - encoder_mask = np.expand_dims(encoder_mask.T, axis=2) - if has_langtok: - encoder_mask = encoder_mask[1:, :, :] - np_encoder_outs = np_encoder_outs[1, :, :] - masked_encoder_outs = encoder_mask * np_encoder_outs - avg_pool = (masked_encoder_outs / encoder_mask.sum(axis=0)).sum(axis=0) - return avg_pool - - -def main(args): - assert args.path is not None, "--path required for generation!" - assert ( - not args.sampling or args.nbest == args.beam - ), "--sampling requires --nbest to be equal to --beam" - assert ( - args.replace_unk is None or args.raw_text - ), "--replace-unk requires a raw text dataset (--raw-text)" - - args.beam = 1 - utils.import_user_module(args) - - if args.max_tokens is None: - args.max_tokens = 12000 - print(args) - use_cuda = torch.cuda.is_available() and not args.cpu - - # Load dataset splits - task = tasks.setup_task(args) - task.load_dataset(args.gen_subset) - - # Set dictionaries - try: - src_dict = getattr(task, "source_dictionary", None) - except NotImplementedError: - src_dict = None - tgt_dict = task.target_dictionary - - # Load ensemble - print("| loading model(s) from {}".format(args.path)) - models, _model_args = checkpoint_utils.load_model_ensemble( - args.path.split(":"), - arg_overrides=eval(args.model_overrides), - task=task, - ) - - # Optimize ensemble for generation - for model in models: - model.make_generation_fast_( - beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, - need_attn=args.print_alignment, - ) - if args.fp16: - model.half() - if use_cuda: - model.cuda() - - # Load alignment dictionary for unknown word replacement - # (None if no unknown word replacement, empty if no path to align dictionary) - align_dict = utils.load_align_dict(args.replace_unk) - - # Load dataset (possibly sharded) - itr = task.get_batch_iterator( - dataset=task.dataset(args.gen_subset), - max_tokens=args.max_tokens, - max_positions=utils.resolve_max_positions( - task.max_positions(), - ), - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=args.required_batch_size_multiple, - num_shards=args.num_shards, - shard_id=args.shard_id, - num_workers=args.num_workers, - ).next_epoch_itr(shuffle=False) - - num_sentences = 0 - source_sentences = [] - shard_id = 0 - all_avg_pool = None - encoder_has_langtok = ( - safe_hasattr(task.args, "encoder_langtok") - and task.args.encoder_langtok is not None - and safe_hasattr(task.args, "lang_tok_replacing_bos_eos") - and not task.args.lang_tok_replacing_bos_eos - ) - with progress_bar.build_progress_bar(args, itr) as t: - for sample in t: - if sample is None: - print("Skipping None") - continue - sample = utils.move_to_cuda(sample) if use_cuda else sample - if "net_input" not in sample: - continue - - prefix_tokens = None - if args.prefix_size > 0: - prefix_tokens = sample["target"][:, : args.prefix_size] - - with torch.no_grad(): - avg_pool = get_avg_pool( - models, - sample, - prefix_tokens, - src_dict, - args.post_process, - has_langtok=encoder_has_langtok, - ) - if all_avg_pool is not None: - all_avg_pool = np.concatenate((all_avg_pool, avg_pool)) - else: - all_avg_pool = avg_pool - - if not isinstance(sample["id"], list): - sample_ids = sample["id"].tolist() - else: - sample_ids = sample["id"] - for i, sample_id in enumerate(sample_ids): - # Remove padding - src_tokens = utils.strip_pad( - sample["net_input"]["src_tokens"][i, :], tgt_dict.pad() - ) - - # Either retrieve the original sentences or regenerate them from tokens. - if align_dict is not None: - src_str = task.dataset(args.gen_subset).src.get_original_text( - sample_id - ) - else: - if src_dict is not None: - src_str = src_dict.string(src_tokens, args.post_process) - else: - src_str = "" - - if not args.quiet: - if src_dict is not None: - print("S-{}\t{}".format(sample_id, src_str)) - - source_sentences.append(f"{sample_id}\t{src_str}") - - num_sentences += sample["nsentences"] - if all_avg_pool.shape[0] >= 1000000: - with open( - f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", - "w", - ) as avg_pool_file: - all_avg_pool.tofile(avg_pool_file) - with open( - f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", - "w", - ) as sentence_file: - sentence_file.writelines(f"{line}\n" for line in source_sentences) - all_avg_pool = None - source_sentences = [] - shard_id += 1 - - if all_avg_pool is not None: - with open( - f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", "w" - ) as avg_pool_file: - all_avg_pool.tofile(avg_pool_file) - with open( - f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", "w" - ) as sentence_file: - sentence_file.writelines(f"{line}\n" for line in source_sentences) - return None - - -def cli_main(): - parser = options.get_generation_parser() - parser.add_argument( - "--encoder-save-dir", - default="", - type=str, - metavar="N", - help="directory to save encoder outputs", - ) - args = options.parse_args_and_arch(parser) - main(args) - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/criss/unsupervised_mt/eval.sh b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/criss/unsupervised_mt/eval.sh deleted file mode 100644 index 03b773ed5a522eb82186fea8ffbb6c557e14b6d3..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/criss/unsupervised_mt/eval.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash -# 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. -# -SRC=si_LK -TGT=en_XX -MODEL=criss_checkpoints/criss.3rd.pt - -MULTIBLEU=mosesdecoder/scripts/generic/multi-bleu.perl -MOSES=mosesdecoder -REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl -NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl -REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl -TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl -GEN_TMP_DIR=gen_tmp -LANG_DICT=criss_checkpoints/lang_dict.txt - -if [ ! -d "mosesdecoder" ]; then - git clone https://github.com/moses-smt/mosesdecoder -fi -mkdir -p $GEN_TMP_DIR -fairseq-generate data_tmp/${SRC}-${TGT}-flores \ - --task translation_multi_simple_epoch \ - --max-tokens 2000 \ - --path ${MODEL} \ - --skip-invalid-size-inputs-valid-test \ - --beam 5 --lenpen 1.0 --gen-subset test \ - --remove-bpe=sentencepiece \ - --source-lang ${SRC} --target-lang ${TGT} \ - --decoder-langtok --lang-pairs 'en_XX-ar_AR,en_XX-de_DE,en_XX-es_XX,en_XX-fr_XX,en_XX-hi_IN,en_XX-it_IT,en_XX-ja_XX,en_XX-ko_KR,en_XX-nl_XX,en_XX-ru_RU,en_XX-zh_CN,en_XX-tr_TR,en_XX-vi_VN,en_XX-ro_RO,en_XX-my_MM,en_XX-ne_NP,en_XX-si_LK,en_XX-cs_CZ,en_XX-lt_LT,en_XX-kk_KZ,en_XX-gu_IN,en_XX-fi_FI,en_XX-et_EE,en_XX-lv_LV,ar_AR-en_XX,cs_CZ-en_XX,de_DE-en_XX,es_XX-en_XX,et_EE-en_XX,fi_FI-en_XX,fr_XX-en_XX,gu_IN-en_XX,hi_IN-en_XX,it_IT-en_XX,ja_XX-en_XX,kk_KZ-en_XX,ko_KR-en_XX,lt_LT-en_XX,lv_LV-en_XX,my_MM-en_XX,ne_NP-en_XX,nl_XX-en_XX,ro_RO-en_XX,ru_RU-en_XX,si_LK-en_XX,tr_TR-en_XX,vi_VN-en_XX,zh_CN-en_XX,ar_AR-es_XX,es_XX-ar_AR,ar_AR-hi_IN,hi_IN-ar_AR,ar_AR-zh_CN,zh_CN-ar_AR,cs_CZ-es_XX,es_XX-cs_CZ,cs_CZ-hi_IN,hi_IN-cs_CZ,cs_CZ-zh_CN,zh_CN-cs_CZ,de_DE-es_XX,es_XX-de_DE,de_DE-hi_IN,hi_IN-de_DE,de_DE-zh_CN,zh_CN-de_DE,es_XX-hi_IN,hi_IN-es_XX,es_XX-zh_CN,zh_CN-es_XX,et_EE-es_XX,es_XX-et_EE,et_EE-hi_IN,hi_IN-et_EE,et_EE-zh_CN,zh_CN-et_EE,fi_FI-es_XX,es_XX-fi_FI,fi_FI-hi_IN,hi_IN-fi_FI,fi_FI-zh_CN,zh_CN-fi_FI,fr_XX-es_XX,es_XX-fr_XX,fr_XX-hi_IN,hi_IN-fr_XX,fr_XX-zh_CN,zh_CN-fr_XX,gu_IN-es_XX,es_XX-gu_IN,gu_IN-hi_IN,hi_IN-gu_IN,gu_IN-zh_CN,zh_CN-gu_IN,hi_IN-zh_CN,zh_CN-hi_IN,it_IT-es_XX,es_XX-it_IT,it_IT-hi_IN,hi_IN-it_IT,it_IT-zh_CN,zh_CN-it_IT,ja_XX-es_XX,es_XX-ja_XX,ja_XX-hi_IN,hi_IN-ja_XX,ja_XX-zh_CN,zh_CN-ja_XX,kk_KZ-es_XX,es_XX-kk_KZ,kk_KZ-hi_IN,hi_IN-kk_KZ,kk_KZ-zh_CN,zh_CN-kk_KZ,ko_KR-es_XX,es_XX-ko_KR,ko_KR-hi_IN,hi_IN-ko_KR,ko_KR-zh_CN,zh_CN-ko_KR,lt_LT-es_XX,es_XX-lt_LT,lt_LT-hi_IN,hi_IN-lt_LT,lt_LT-zh_CN,zh_CN-lt_LT,lv_LV-es_XX,es_XX-lv_LV,lv_LV-hi_IN,hi_IN-lv_LV,lv_LV-zh_CN,zh_CN-lv_LV,my_MM-es_XX,es_XX-my_MM,my_MM-hi_IN,hi_IN-my_MM,my_MM-zh_CN,zh_CN-my_MM,ne_NP-es_XX,es_XX-ne_NP,ne_NP-hi_IN,hi_IN-ne_NP,ne_NP-zh_CN,zh_CN-ne_NP,nl_XX-es_XX,es_XX-nl_XX,nl_XX-hi_IN,hi_IN-nl_XX,nl_XX-zh_CN,zh_CN-nl_XX,ro_RO-es_XX,es_XX-ro_RO,ro_RO-hi_IN,hi_IN-ro_RO,ro_RO-zh_CN,zh_CN-ro_RO,ru_RU-es_XX,es_XX-ru_RU,ru_RU-hi_IN,hi_IN-ru_RU,ru_RU-zh_CN,zh_CN-ru_RU,si_LK-es_XX,es_XX-si_LK,si_LK-hi_IN,hi_IN-si_LK,si_LK-zh_CN,zh_CN-si_LK,tr_TR-es_XX,es_XX-tr_TR,tr_TR-hi_IN,hi_IN-tr_TR,tr_TR-zh_CN,zh_CN-tr_TR,vi_VN-es_XX,es_XX-vi_VN,vi_VN-hi_IN,hi_IN-vi_VN,vi_VN-zh_CN,zh_CN-vi_VN' \ - --lang-dict ${LANG_DICT} --lang-tok-style 'mbart' --sampling-method 'temperature' --sampling-temperature '1.0' > $GEN_TMP_DIR/${SRC}_${TGT}.gen -cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^T-" | cut -f2 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.hyp -cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^H-" | cut -f3 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.ref -${MULTIBLEU} $GEN_TMP_DIR/${SRC}_${TGT}.ref < $GEN_TMP_DIR/${SRC}_${TGT}.hyp diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/laser/laser_src/laser_task.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/laser/laser_src/laser_task.py deleted file mode 100644 index e4152fde6861488acc3595fa25c456bf60f134b9..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/laser/laser_src/laser_task.py +++ /dev/null @@ -1,331 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from collections import OrderedDict, defaultdict -import json -import os -import logging -from argparse import ArgumentError - -from fairseq import options, models -from fairseq.data import ( - data_utils, - Dictionary, - LanguagePairDataset, - IndexedDataset, - FairseqDataset, -) -from .multitask_data_utils import ( - MultitaskDatasetWrapper, - MultidatasetEpochBatchIterator, -) - - -from fairseq.tasks import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@register_task("laser") -class LaserTask(LegacyFairseqTask): - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "configfile", metavar="PATH", help="dataset configuration file in json" - ) - parser.add_argument( - "--weighting-alpha", - type=float, - default=None, - help="alpha for automatic weighting", - ) - parser.add_argument( - "--raw-text", action="store_true", help="load raw text dataset" - ) - parser.add_argument( - "--left-pad-source", - default="True", - type=str, - metavar="BOOL", - help="pad the source on the left (default: True)", - ) - parser.add_argument( - "--left-pad-target", - default="False", - type=str, - metavar="BOOL", - help="pad the target on the left (default: False)", - ) - try: - parser.add_argument( - "--max-source-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the source sequence", - ) - parser.add_argument( - "--max-target-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the target sequence", - ) - except ArgumentError: - # this might have already been defined. Once we transition this to hydra it should be fine to add it here. - pass - - def __init__(self, args, config, src_dictionary, tgt_dictionary, num_tasks): - super().__init__(args) - self.config = config - self.src_dictionary = src_dictionary - self.tgt_dictionary = tgt_dictionary - self.num_tasks = num_tasks - - @classmethod - def setup_task(cls, args, **kwargs): - with open(args.configfile, "r") as f: - config = json.load(f) - num_tasks = max(dataset["id"] for dataset in config["train"]) + 1 - - args.left_pad_source = options.eval_bool(args.left_pad_source) - args.left_pad_target = options.eval_bool(args.left_pad_target) - - src_dictionary = Dictionary.load(config["src_vocab"]) - tgt_dictionary = Dictionary.load(config["tgt_vocab"]) - - logger.info( - "| src Dictionary {} : {} types".format( - config["src_vocab"], len(src_dictionary) - ) - ) - logger.info( - "| tgt Dictionary {} : {} types".format( - config["tgt_vocab"], len(tgt_dictionary) - ) - ) - - return cls(args, config, src_dictionary, tgt_dictionary, num_tasks) - - # Experimental overriding for backtranslation - def build_model(self, args): - model = models.build_model(args, self) - return model - - def dataset(self, split): - if split not in self.datasets: - raise KeyError("Dataset not loaded: " + split) - return self.datasets[split] - - def load_dataset(self, split, epoch=1, **kwargs): - """Load a dataset split.""" - - def indexed_dataset(path, dictionary): - if self.args.raw_text: - raise Exception("Unable to handle raw text.") - dataset = IndexedDataset(path, fix_lua_indexing=True) - - return dataset - - pair_datasets = OrderedDict() - - if split == "valid": - self.datasets[split] = pair_datasets - return - - if split not in self.config: - raise FileNotFoundError( - "Dataset not found in config file: {}".format(split) - ) - - size_by_corpus = defaultdict(int) - size_sum = 0 - size_sum_with_subsampling = 0 - init_pair_datasets = {} - - for dataset_config in self.config[split]: - src_path = os.path.dirname(dataset_config["src"]) - corpus_name = src_path.split("/")[-2] - language_pair_name = src_path.split("/")[-1] - pair_datasets_key = corpus_name + "-" + language_pair_name - - logger.info(f"loading... {pair_datasets_key}") - if "src" in dataset_config: - src_dataset = indexed_dataset( - dataset_config["src"], self.src_dictionary - ) - else: - src_dataset = None - - if "tgt" in dataset_config: - tgt_dataset = indexed_dataset( - dataset_config["tgt"], self.tgt_dictionary - ) - else: - tgt_dataset = None - - dataset = LanguagePairDataset( - src_dataset, - src_dataset.sizes, - self.src_dictionary, - tgt_dataset, - tgt_dataset.sizes, - self.tgt_dictionary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - ) - - if pair_datasets_key in init_pair_datasets: - logger.warning( - f"Ignoring already added {pair_datasets_key}. " - f"Consider using `sample` key in order to upsample." - ) - else: - init_pair_datasets[pair_datasets_key] = { - "dataset": dataset, - "sample": dataset_config.get("sample", None), - "id": dataset_config.get("id", None), - "len": len(dataset), - } - - length_sum = 0 - weighted_freqs_sum = 0 - freq_per_dataset = {} - vmax = 0 - vmin = 1 - weighted_freq_per_dataset = {} - - if self.args.weighting_alpha: - for key in init_pair_datasets: - if init_pair_datasets[key]["sample"] is None: - length_sum += len(init_pair_datasets[key]["dataset"]) - - for key in init_pair_datasets: - if init_pair_datasets[key]["sample"] is None: - val = float(init_pair_datasets[key]["len"]) / length_sum - freq_per_dataset[key] = val - weighted_freqs_sum += val ** self.args.weighting_alpha - - for key in freq_per_dataset: - val = ( - freq_per_dataset[key] ** self.args.weighting_alpha - / weighted_freqs_sum - ) - vmin = min(vmin, val) - vmax = max(vmax, val) - weighted_freq_per_dataset[key] = val - - for pair_datasets_key in init_pair_datasets: - dataset_config = init_pair_datasets[pair_datasets_key] - dataset = dataset_config["dataset"] - sample = dataset_config["sample"] - if sample is None: - sample = 1.0 - - if pair_datasets_key in weighted_freq_per_dataset: - w = vmax / weighted_freq_per_dataset[pair_datasets_key] - sample = w - - sample = round(sample) - - initial_sample = sample - initial_pair_datasets_key = pair_datasets_key - - while sample >= 1.0: - assert ( - pair_datasets_key not in pair_datasets - ), f"{pair_datasets_key} already in" - size_sum_with_subsampling += len(dataset) - pair_datasets[pair_datasets_key] = MultitaskDatasetWrapper( - dataset, dataset_config.get("id", 0), 1.0, name=pair_datasets_key - ) - size_sum += len(dataset) - sample -= 1.0 - pair_datasets_key += "-up" - - assert sample < 1e-6, f"sample remains > 0 {pair_datasets_key}" - - logger.info( - f"added pair {initial_pair_datasets_key} length {len(dataset)} new_length = {len(dataset)*initial_sample}" - ) - size_by_corpus[corpus_name] += len(dataset) - - self.datasets[split] = pair_datasets - logger.info( - f"Datasets number = {len(self.datasets[split])} size = {size_sum} size_sum_with_subsampling = {size_sum_with_subsampling}" - ) - - @property - def source_dictionary(self): - return self.src_dictionary - - @property - def target_dictionary(self): - return self.tgt_dictionary - - def get_batch_iterator( - self, - dataset, - max_tokens=None, - max_sentences=None, - max_positions=None, - ignore_invalid_inputs=False, - required_batch_size_multiple=1, - seed=1, - num_shards=1, - shard_id=0, - num_workers=0, - epoch=1, - data_buffer_size=0, - disable_iterator_cache=False, - ): - - assert isinstance(dataset, OrderedDict) - assert len(dataset) - assert isinstance(dataset[next(iter(dataset))], FairseqDataset) - - # initialize the dataset with the correct starting epoch - for _, dt in dataset.items(): - dt.set_epoch(epoch) - - indices = OrderedDict() - batch_sampler = OrderedDict() - - with data_utils.numpy_seed(seed + epoch): - for key, dt in dataset.items(): - logger.info(f"\t ordered_indices {key}") - indices[key] = dt.ordered_indices() - - # filter examples that are too large - if max_positions is not None: - for key, dt in dataset.items(): - logger.info(f"\t filter_by_size {key}") - indices[key], ignored = dt.filter_indices_by_size( - indices[key], max_positions - ) - - for key, dt in dataset.items(): - logger.info(f"\t batch_by_size {key}") - batch_sampler[key] = data_utils.batch_by_size( - indices[key], - dt.num_tokens, - max_tokens=max_tokens, - max_sentences=max_sentences, - required_batch_size_multiple=required_batch_size_multiple, - ) - - epoch_iter = MultidatasetEpochBatchIterator( - dataset=dataset, - batch_sampler=batch_sampler, - seed=seed, - num_shards=num_shards, - shard_id=shard_id, - num_workers=num_workers, - epoch=epoch, - ) - - return epoch_iter diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py deleted file mode 100644 index 61617a1739ce196abba1e9a6f9ad9e9f4b37b9c1..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py +++ /dev/null @@ -1,363 +0,0 @@ -import math -import os -import json -import numpy as np -import torch -import torchaudio.compliance.kaldi as kaldi -import yaml -from fairseq import checkpoint_utils, tasks -from fairseq.file_io import PathManager - -try: - from simuleval import READ_ACTION, WRITE_ACTION, DEFAULT_EOS - from simuleval.agents import SpeechAgent - from simuleval.states import ListEntry, SpeechStates -except ImportError: - print("Please install simuleval 'pip install simuleval'") - -SHIFT_SIZE = 10 -WINDOW_SIZE = 25 -SAMPLE_RATE = 16000 -FEATURE_DIM = 80 -BOW_PREFIX = "\u2581" - - -class OnlineFeatureExtractor: - """ - Extract speech feature on the fly. - """ - - def __init__(self, args): - self.shift_size = args.shift_size - self.window_size = args.window_size - assert self.window_size >= self.shift_size - - self.sample_rate = args.sample_rate - self.feature_dim = args.feature_dim - self.num_samples_per_shift = int(self.shift_size * self.sample_rate / 1000) - self.num_samples_per_window = int(self.window_size * self.sample_rate / 1000) - self.len_ms_to_samples = lambda x: x * self.sample_rate / 1000 - self.previous_residual_samples = [] - self.global_cmvn = args.global_cmvn - - def clear_cache(self): - self.previous_residual_samples = [] - - def __call__(self, new_samples): - samples = self.previous_residual_samples + new_samples - if len(samples) < self.num_samples_per_window: - self.previous_residual_samples = samples - return - - # num_frames is the number of frames from the new segment - num_frames = math.floor( - (len(samples) - self.len_ms_to_samples(self.window_size - self.shift_size)) - / self.num_samples_per_shift - ) - - # the number of frames used for feature extraction - # including some part of thte previous segment - effective_num_samples = int( - num_frames * self.len_ms_to_samples(self.shift_size) - + self.len_ms_to_samples(self.window_size - self.shift_size) - ) - - input_samples = samples[:effective_num_samples] - self.previous_residual_samples = samples[ - num_frames * self.num_samples_per_shift: - ] - - torch.manual_seed(1) - output = kaldi.fbank( - torch.FloatTensor(input_samples).unsqueeze(0), - num_mel_bins=self.feature_dim, - frame_length=self.window_size, - frame_shift=self.shift_size, - ).numpy() - - output = self.transform(output) - - return torch.from_numpy(output) - - def transform(self, input): - if self.global_cmvn is None: - return input - - mean = self.global_cmvn["mean"] - std = self.global_cmvn["std"] - - x = np.subtract(input, mean) - x = np.divide(x, std) - return x - - -class TensorListEntry(ListEntry): - """ - Data structure to store a list of tensor. - """ - - def append(self, value): - - if len(self.value) == 0: - self.value = value - return - - self.value = torch.cat([self.value] + [value], dim=0) - - def info(self): - return { - "type": str(self.new_value_type), - "length": self.__len__(), - "value": "" if type(self.value) is list else self.value.size(), - } - - -class FairseqSimulSTAgent(SpeechAgent): - - speech_segment_size = 40 # in ms, 4 pooling ratio * 10 ms step size - - def __init__(self, args): - super().__init__(args) - - self.eos = DEFAULT_EOS - - self.gpu = getattr(args, "gpu", False) - - self.args = args - - self.load_model_vocab(args) - - if getattr( - self.model.decoder.layers[0].encoder_attn, - 'pre_decision_ratio', - None - ) is not None: - self.speech_segment_size *= ( - self.model.decoder.layers[0].encoder_attn.pre_decision_ratio - ) - - args.global_cmvn = None - if args.config: - with open(os.path.join(args.data_bin, args.config), "r") as f: - config = yaml.load(f, Loader=yaml.BaseLoader) - - if "global_cmvn" in config: - args.global_cmvn = np.load(config["global_cmvn"]["stats_npz_path"]) - - if args.global_stats: - with PathManager.open(args.global_stats, "r") as f: - global_cmvn = json.loads(f.read()) - self.global_cmvn = {"mean": global_cmvn["mean"], "std": global_cmvn["stddev"]} - - self.feature_extractor = OnlineFeatureExtractor(args) - - self.max_len = args.max_len - - self.force_finish = args.force_finish - - torch.set_grad_enabled(False) - - def build_states(self, args, client, sentence_id): - # Initialize states here, for example add customized entry to states - # This function will be called at beginning of every new sentence - states = SpeechStates(args, client, sentence_id, self) - self.initialize_states(states) - return states - - def to_device(self, tensor): - if self.gpu: - return tensor.cuda() - else: - return tensor.cpu() - - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--model-path', type=str, required=True, - help='path to your pretrained model.') - parser.add_argument("--data-bin", type=str, required=True, - help="Path of data binary") - parser.add_argument("--config", type=str, default=None, - help="Path to config yaml file") - parser.add_argument("--global-stats", type=str, default=None, - help="Path to json file containing cmvn stats") - parser.add_argument("--tgt-splitter-type", type=str, default="SentencePiece", - help="Subword splitter type for target text") - parser.add_argument("--tgt-splitter-path", type=str, default=None, - help="Subword splitter model path for target text") - parser.add_argument("--user-dir", type=str, default="examples/simultaneous_translation", - help="User directory for simultaneous translation") - parser.add_argument("--max-len", type=int, default=200, - help="Max length of translation") - parser.add_argument("--force-finish", default=False, action="store_true", - help="Force the model to finish the hypothsis if the source is not finished") - parser.add_argument("--shift-size", type=int, default=SHIFT_SIZE, - help="Shift size of feature extraction window.") - parser.add_argument("--window-size", type=int, default=WINDOW_SIZE, - help="Window size of feature extraction window.") - parser.add_argument("--sample-rate", type=int, default=SAMPLE_RATE, - help="Sample rate") - parser.add_argument("--feature-dim", type=int, default=FEATURE_DIM, - help="Acoustic feature dimension.") - - # fmt: on - return parser - - def load_model_vocab(self, args): - - filename = args.model_path - if not os.path.exists(filename): - raise IOError("Model file not found: {}".format(filename)) - - state = checkpoint_utils.load_checkpoint_to_cpu(filename) - - task_args = state["cfg"]["task"] - task_args.data = args.data_bin - - if args.config is not None: - task_args.config_yaml = args.config - - task = tasks.setup_task(task_args) - - # build model for ensemble - state["cfg"]["model"].load_pretrained_encoder_from = None - state["cfg"]["model"].load_pretrained_decoder_from = None - self.model = task.build_model(state["cfg"]["model"]) - self.model.load_state_dict(state["model"], strict=True) - self.model.eval() - self.model.share_memory() - - if self.gpu: - self.model.cuda() - - # Set dictionary - self.dict = {} - self.dict["tgt"] = task.target_dictionary - - def initialize_states(self, states): - self.feature_extractor.clear_cache() - states.units.source = TensorListEntry() - states.units.target = ListEntry() - states.incremental_states = dict() - - def segment_to_units(self, segment, states): - # Convert speech samples to features - features = self.feature_extractor(segment) - if features is not None: - return [features] - else: - return [] - - def units_to_segment(self, units, states): - # Merge sub word to full word. - if self.model.decoder.dictionary.eos() == units[0]: - return DEFAULT_EOS - - segment = [] - if None in units.value: - units.value.remove(None) - - for index in units: - if index is None: - units.pop() - token = self.model.decoder.dictionary.string([index]) - if token.startswith(BOW_PREFIX): - if len(segment) == 0: - segment += [token.replace(BOW_PREFIX, "")] - else: - for j in range(len(segment)): - units.pop() - - string_to_return = ["".join(segment)] - - if self.model.decoder.dictionary.eos() == units[0]: - string_to_return += [DEFAULT_EOS] - - return string_to_return - else: - segment += [token.replace(BOW_PREFIX, "")] - - if ( - len(units) > 0 - and self.model.decoder.dictionary.eos() == units[-1] - or len(states.units.target) > self.max_len - ): - tokens = [self.model.decoder.dictionary.string([unit]) for unit in units] - return ["".join(tokens).replace(BOW_PREFIX, "")] + [DEFAULT_EOS] - - return None - - def update_model_encoder(self, states): - if len(states.units.source) == 0: - return - src_indices = self.to_device( - states.units.source.value.unsqueeze(0) - ) - src_lengths = self.to_device( - torch.LongTensor([states.units.source.value.size(0)]) - ) - - states.encoder_states = self.model.encoder(src_indices, src_lengths) - torch.cuda.empty_cache() - - def update_states_read(self, states): - # Happens after a read action. - self.update_model_encoder(states) - - def policy(self, states): - if not getattr(states, "encoder_states", None): - return READ_ACTION - - tgt_indices = self.to_device( - torch.LongTensor( - [self.model.decoder.dictionary.eos()] - + [x for x in states.units.target.value if x is not None] - ).unsqueeze(0) - ) - - states.incremental_states["steps"] = { - "src": states.encoder_states["encoder_out"][0].size(0), - "tgt": 1 + len(states.units.target), - } - - states.incremental_states["online"] = {"only": torch.tensor(not states.finish_read())} - - x, outputs = self.model.decoder.forward( - prev_output_tokens=tgt_indices, - encoder_out=states.encoder_states, - incremental_state=states.incremental_states, - ) - - states.decoder_out = x - - states.decoder_out_extra = outputs - - torch.cuda.empty_cache() - - if outputs.action == 0: - return READ_ACTION - else: - return WRITE_ACTION - - def predict(self, states): - decoder_states = states.decoder_out - - lprobs = self.model.get_normalized_probs( - [decoder_states[:, -1:]], log_probs=True - ) - - index = lprobs.argmax(dim=-1) - - index = index[0, 0].item() - - if ( - self.force_finish - and index == self.model.decoder.dictionary.eos() - and not states.finish_read() - ): - # If we want to force finish the translation - # (don't stop before finish reading), return a None - # self.model.decoder.clear_cache(states.incremental_states) - index = None - - return index diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/amp_optimizer.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/amp_optimizer.py deleted file mode 100644 index 3b7958e50ce444474c48d1f5aeff05d66c19e5b6..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/amp_optimizer.py +++ /dev/null @@ -1,105 +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 torch -from fairseq import optim -from omegaconf import DictConfig - -logger = logging.getLogger(__name__) - - -class AMPOptimizer(optim.FairseqOptimizer): - """ - Wrap an *optimizer* to support AMP (automatic mixed precision) training. - """ - - def __init__(self, cfg: DictConfig, params, fp32_optimizer, **kwargs): - super().__init__(cfg.optimizer) - self.fp32_optimizer = fp32_optimizer - amp_kwargs = {"init_scale": cfg.common.fp16_init_scale} - if getattr(cfg.common, "amp_scale_window", None) is not None: - amp_kwargs["growth_interval"] = cfg.common.amp_init_scale - self._grad_scaler = torch.cuda.amp.GradScaler(**amp_kwargs) - self.min_loss_scale = cfg.common.min_loss_scale - - @classmethod - def build_optimizer(cls, cfg: DictConfig, params, **kwargs): - """ - Args: - cfg (omegaconf.DictConfig): fairseq args - params (iterable): iterable of parameters to optimize - """ - fp32_optimizer = optim.build_optimizer(cfg.optimizer, params) - return cls(cfg, params, fp32_optimizer, **kwargs) - - def backward(self, loss): - """Computes the sum of gradients of the given tensor w.r.t. graph leaves. - - Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this - function additionally dynamically scales the loss to avoid gradient - underflow. - """ - self._grad_scaler.scale(loss).backward() - - def step(self): - self.scaler.step(self.fp32_optimizer) - self.scaler.update() - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm.""" - self.scaler.unscale_(self.optimizer) - grad_norm = self.fp32_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) - if not torch.isfinite(grad_norm).all(): - new_loss_scale = self.next_loss_scale - if new_loss_scale <= self.min_loss_scale: - raise FloatingPointError( - ( - "AMP: Minimum loss scale reached ({}). Your loss is probably exploding. " - "Try restarting training or use fp32. {}" - ).format(self.min_loss_scale, new_loss_scale) - ) - else: - logger.info("AMP: overflow detected, setting scale to " - f"to {new_loss_scale}") - return grad_norm - - @property - def scaler(self): - return self._grad_scaler - - @property - def next_loss_scale(self): - return self.scaler.get_scale() * self.scaler.get_backoff_factor() - - @property - def optimizer(self): - return self.fp32_optimizer.optimizer - - @optimizer.setter - def optimizer(self, optimizer): - self.fp32_optimizer.optimizer = optimizer - - @property - def lr_scheduler(self): - return getattr(self.fp32_optimizer, "lr_scheduler", None) - - @property - def optimizer_config(self): - return self.fp32_optimizer.optimizer_config - - def get_lr(self): - return self.fp32_optimizer.get_lr() - - def set_lr(self, lr): - self.fp32_optimizer.set_lr(lr) - - def all_reduce_grads(self, module): - self.fp32_optimizer.all_reduce_grads(module) - - @property - def supports_flat_params(self): - return self.fp32_optimizer.supports_flat_params diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/distributed/test_distributed_timeout_wrapper.py b/spaces/sriramelango/Social_Classification_Public/fairseq/tests/distributed/test_distributed_timeout_wrapper.py deleted file mode 100644 index 27908b9d3f7d6d880351e2a12effb12f9bc27971..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/distributed/test_distributed_timeout_wrapper.py +++ /dev/null @@ -1,54 +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 signal -import time -import unittest - -import torch -from torch import nn - -from fairseq.distributed import DistributedTimeoutWrapper - - -class ModuleWithDelay(nn.Module): - - def __init__(self, delay): - super().__init__() - self.delay = delay - - def forward(self, x): - time.sleep(self.delay) - return x - - -class TestDistributedTimeoutWrapper(unittest.TestCase): - - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_no_timeout(self): - module = DistributedTimeoutWrapper(ModuleWithDelay(1), 0, signal.SIGINT) - module(torch.rand(5)) - module.stop_timeout() - - def test_timeout_safe(self): - module = DistributedTimeoutWrapper(ModuleWithDelay(1), 10, signal.SIGINT) - module(torch.rand(5)) - module.stop_timeout() - - def test_timeout_killed(self): - with self.assertRaises(KeyboardInterrupt): - module = DistributedTimeoutWrapper(ModuleWithDelay(5), 1, signal.SIGINT) - module(torch.rand(5)) - module.stop_timeout() - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/stomexserde/gpt4-ui/Examples/Airserver Airplay Windows Torrent LINK.md b/spaces/stomexserde/gpt4-ui/Examples/Airserver Airplay Windows Torrent LINK.md deleted file mode 100644 index 57fdb30ec3f80444338ffe95c92d0656e1b20447..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Airserver Airplay Windows Torrent LINK.md +++ /dev/null @@ -1,41 +0,0 @@ - -

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          A Gradio demonstration for ASL fingerspelling recognition. Use your webcam to take a snapshot of your hand forming any of the ASL alphabet signs.

          " -input = [ - gr.inputs.Image(type="pil", source="webcam", label="Image") -] - -output = [ - gr.outputs.Label(num_top_classes=5, label="") -] - -sample_letters = ['A', 'B', 'E', 'L', 'Y'] -examples = [["images/{}_test.jpg".format(letter)] for letter in sample_letters] - -a1="

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          " -a2="

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          This demo was based on a project implemented for a hackathon. The GitHub repository can be found on namanmanchanda09/American-Sign-Language-Detection-using-Computer-Vision. The model was trained on this ASL Alphabet dataset on Kaggle containing 87,000 200x200 images. The examples used in the demo are from the test set of the aforementioned dataset.

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          ARK Mobile God Console Mod APK Download 2.0 28: Everything You Need to Know

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          If you are a fan of survival games, you might have heard of ARK: Survival Evolved, a popular game that lets you explore a vast open world filled with dinosaurs and other prehistoric creatures. But did you know that there is also a mobile version of the game that you can play on your Android or iOS device? And did you know that there is a way to play it with unlimited resources, items, and creatures? In this article, we will tell you everything you need to know about ARK Mobile God Console Mod APK Download 2.0 28, a modded version of the game that gives you access to a god-like mode that lets you customize and control every aspect of the game. Read on to find out more!

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          What is ARK Mobile?

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          ARK Mobile is a mobile adaptation of ARK: Survival Evolved, a game developed by Studio Wildcard, Instinct Games, Efecto Studios, and Virtual Basement. It was released in June 2018 for Android and iOS devices. The game is similar to the original version, but with some differences and limitations due to the mobile platform.

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          In ARK Mobile, you play as a survivor who wakes up on a mysterious island called ARK, where you have to survive by gathering resources, crafting tools and weapons, building shelters, taming and riding dinosaurs, and fighting against other players and creatures. You can play solo or join a tribe with other players to cooperate or compete. You can also choose from different game modes, such as PvE (player versus environment), PvP (player versus player), or Hardcore (permanent death).

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          The game features stunning graphics, realistic physics, dynamic weather, day-night cycle, and over 80 different dinosaurs and creatures to encounter. You can also explore different biomes, such as jungles, mountains, caves, swamps, volcanoes, and more. The game is free to play, but it also offers in-app purchases for premium currency called amber, which can be used to buy items, resources, buffs, or revive dead creatures.

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          What is the God Console Mod APK?

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          The God Console Mod APK is a modified version of ARK Mobile that gives you access to a special feature called the God Console. The God Console is a hidden menu that allows you to activate various cheats and commands that can alter the game in your favor. For example, you can use the God Console to:

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          • Give yourself unlimited amber and resources
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          • Unlock all high-level blueprints and items
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          • Spawn any creature in the game, including alpha predators
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          • Change the time of day, weather, temperature, etc.
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          • Teleport to any location on the map
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          • Fly around the island
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          • Make yourself invincible or invisible
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          • Kill or tame any creature instantly
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          • And much more!
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          How to download and install the mod apk

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          To download and install the God Console Mod APK for ARK Mobile 2.0 28, you need to follow these steps:

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          1. Make sure you have enough storage space on your device and a stable internet connection.
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          2. Go to the download link provided below and click on it. You will be redirected to a secure site where you can download the mod apk file.
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          3. After the download is complete, locate the file on your device and tap on it to install it. You may need to enable the installation of unknown sources in your device settings.
            4. -
          4. Wait for the installation process to finish and then launch the game. You will see a new icon on the top right corner of the screen that says "God Console". Tap on it to access the cheat menu.
            5. -
          5. Enjoy playing ARK Mobile with the God Console Mod APK!
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          Download link: ARK Mobile God Console Mod APK 2.0 28

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          How to use the god console and other features

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          Using the god console is very easy and intuitive. You just need to tap on the icon and then select the cheat or command you want to activate. You can also adjust the sliders and buttons to customize the settings. For example, you can use the god console to:

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          • Give yourself unlimited amber by sliding the amber bar to the right.
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          • Unlock all blueprints and items by tapping on the unlock button.
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          • Spawn any creature by tapping on the creature icon and then selecting the name and level of the creature you want.
            • -
          • Change the time of day by sliding the time bar left or right.
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          • Teleport to any location by tapping on the map icon and then selecting the destination.
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          • Fly around the island by tapping on the fly button and then using the joystick to control your movement.
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          • Make yourself invincible or invisible by tapping on the shield or eye icon respectively.
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          • Kill or tame any creature by tapping on the crosshair or heart icon respectively and then aiming at the creature.
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          Why should you play ARK Mobile with the God Console Mod APK?

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          You might be wondering why you should play ARK Mobile with the God Console Mod APK instead of playing the original game. Well, there are many reasons why playing with the mod apk can be more fun and satisfying than playing with the official version. Here are some of them:

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          Unlimited amber and resources

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          One of the main challenges of playing ARK Mobile is collecting enough amber and resources to survive and progress in the game. Amber is a premium currency that can be used to buy items, resources, buffs, or revive dead creatures. However, amber is very scarce and hard to obtain in the game. You can either watch ads, complete offers, or spend real money to get more amber. Resources are also limited and require a lot of time and effort to gather. You need resources to craft tools, weapons, armor, structures, etc.

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          With the God Console Mod APK, you don't have to worry about amber or resources anymore. You can give yourself unlimited amounts of amber and resources with just a few taps. You can buy anything you want from the in-game store, craft anything you need, and never run out of resources. You can also use amber to revive your dead creatures or buy buffs that can enhance your gameplay.

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          All high-level blueprints and items

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          Another challenge of playing ARK Mobile is finding and unlocking high-level blueprints and items. Blueprints are recipes that allow you to craft items, such as weapons, armor, saddles, etc. Items are objects that you can use, equip, or consume in the game, such as food, medicine, ammo, etc. However, blueprints and items are not easy to find or unlock in the game. You have to explore the island, loot crates, complete quests, or defeat bosses to get them. And even then, you might not get the ones you want or need.

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          With the God Console Mod APK, you don't have to search or unlock blueprints and items anymore. You can unlock all high-level blueprints and items with just one tap. You can craft any item you want, regardless of your level or requirements. You can also access all the items in the game, including rare and exclusive ones that are normally not available in the game. You can equip yourself and your creatures with the best gear and items in the game.

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          Alpha T-Rex and other powerful creatures

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          One of the most exciting aspects of playing ARK Mobile is taming and riding dinosaurs and other creatures. There are over 80 different creatures in the game, each with their own abilities, stats, and behaviors. You can tame them by knocking them out and feeding them their preferred food. You can also breed them to create new generations of creatures with improved traits. You can ride them to travel faster, fight better, or harvest more resources.

          -

          However, not all creatures are easy to tame or ride. Some of them are very rare, aggressive, or dangerous. For example, the alpha T-Rex is a massive and powerful predator that can kill almost anything in one bite. It is very hard to find and even harder to tame. It is also impossible to ride without a special saddle that can only be obtained by defeating a boss.

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          With the God Console Mod APK, you don't have to worry about taming or riding any creature anymore. You can spawn any creature in the game with just a few taps. You can choose the name and level of the creature you want. You can also tame or kill any creature instantly with just one tap. You can ride any creature without a saddle or any restrictions. You can even spawn alpha predators like the alpha T-Rex and dominate the island with them.

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          Customization and control over the game settings

          -

          One of the most frustrating aspects of playing ARK Mobile is dealing with the game settings that are not always optimal or suitable for your preferences or device. For example, you might encounter issues such as lag, crashes, glitches, low graphics quality, high battery consumption, etc. You might also want to change some aspects of the game that are not adjustable in the official version, such as the difficulty level, the spawn rate of creatures, the loot quality, etc.

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          With the God Console Mod APK, you don't have to deal with these issues anymore. You can customize and control every aspect of the game settings with just a few taps. You can adjust the graphics quality, performance mode, sound volume, etc. You can also change the game mode, the difficulty level, the spawn rate of creatures, the loot quality, etc. You can also enable or disable certain features, such as hunger, thirst, stamina, damage, etc. You can make the game as easy or as hard as you want. You can also make the game more realistic or more fun according to your taste.

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          What are the risks and drawbacks of using the God Console Mod APK?

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          While playing ARK Mobile with the God Console Mod APK can be very enjoyable and rewarding, it is not without its risks and drawbacks. There are some possible issues and challenges that you might face when using the mod apk. Here are some of them:

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          Compatibility and performance issues

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          One of the potential problems of using the God Console Mod APK is that it might not be compatible with your device or the latest version of the game. The mod apk is not an official product of the game developers, so it might not work properly or at all on some devices or Android versions. It might also cause some bugs, glitches, errors, or crashes that can affect your gameplay or damage your device.

          -

          To avoid these issues, you should always check the compatibility and requirements of the mod apk before downloading and installing it. You should also backup your data and files before using the mod apk. You should also avoid updating the game or the mod apk unless you are sure that they are compatible and stable.

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          Security and privacy risks

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          Another potential problem of using the God Console Mod APK is that it might pose some security and privacy risks to your device and data. The mod apk is not a verified or trusted source, so it might contain some malicious code, viruses, malware, spyware, etc. that can harm your device or steal your data. It might also require some permissions or access to your device that are not necessary or safe.

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          To avoid these risks, you should always scan the mod apk file with a reliable antivirus or anti-malware software before installing it. You should also be careful about granting any permissions or access to the mod apk. You should also avoid using any personal or sensitive information or accounts when playing with the mod apk.

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          Ban and suspension from the official servers

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          Another potential problem of using the God Console Mod APK is that it might get you banned or suspended from the official servers of the game. The mod apk is considered a cheat or a hack by the game developers and moderators, so it violates the terms of service and rules of the game. If you use the mod apk to play online with other players or on official servers, you might get detected and reported by other players or by the anti-cheat system. This can result in your account being banned or suspended from playing online or accessing certain features of the game.

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          To avoid this problem, you should always use a different account or a guest account when playing with the mod apk. You should also avoid playing online with other players or on official servers when using the mod apk. You should also disable any online features or settings that might expose your use of the mod apk.

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          Loss of challenge and fun

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          Another potential problem of using the God Console Mod APK is that it might make the game too easy and boring for you. The mod apk gives you unlimited power and control over the game, but it also removes the challenge and thrill of playing the game. You might lose the sense of accomplishment and satisfaction that comes from overcoming the obstacles and difficulties of the game. You might also lose the interest and curiosity that comes from discovering and exploring the game. You might end up feeling bored and unmotivated to play the game.

          -

          To avoid this problem, you should use the mod apk sparingly and moderately. You should not use it to cheat or ruin the game for yourself or others. You should use it only when you need it or when you want to have some fun. You should also balance it with playing the original game or other games that can challenge and entertain you.

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          Conclusion

          -

          ARK Mobile is a great game that lets you experience the thrill and adventure of surviving on a prehistoric island with dinosaurs and other creatures. However, if you want to play it with more freedom and fun, you might want to try the God Console Mod APK, a modded version of the game that gives you access to a god-like mode that lets you customize and control every aspect of the game.

          -

          The God Console Mod APK can give you unlimited amber and resources, unlock all high-level blueprints and items, spawn any creature in the game, change the game settings, and much more. It can make the game easier and more enjoyable for you. However, it can also cause some issues and challenges, such as compatibility and performance issues, security and privacy risks, ban and suspension from the official servers, and loss of challenge and fun.

          -

          Therefore, you should be careful and responsible when using the mod apk. You should always check the compatibility and requirements of the mod apk before downloading and installing it. You should also backup your data and files before using the mod apk. You should also scan the mod apk file with a reliable antivirus or anti-malware software before installing it. You should also be careful about granting any permissions or access to the mod apk. You should also avoid using any personal or sensitive information or accounts when playing with the mod apk.

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          You should also use a different account or a guest account when playing with the mod apk. You should also avoid playing online with other players or on official servers when using the mod apk. You should also disable any online features or settings that might expose your use of the mod apk. You should also use the mod apk sparingly and moderately. You should not use it to cheat or ruin the game for yourself or others. You should use it only when you need it or when you want to have some fun. You should also balance it with playing the original game or other games that can challenge and entertain you.

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          If you follow these tips and precautions, you can enjoy playing ARK Mobile with the God Console Mod APK without any problems or regrets. You can have a blast exploring, taming, riding, fighting, building, crafting, and surviving on ARK with your god-like powers. You can also share your experience and feedback with us in the comments section below.

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          So what are you waiting for? Download ARK Mobile God Console Mod APK 2.0 28 now and unleash your inner god!

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          FAQs

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          Here are some frequently asked questions about ARK Mobile God Console Mod APK:

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          1. Is ARK Mobile God Console Mod APK safe to use?
            2. -

            ARK Mobile God Console Mod APK is not an official product of the game developers, so it might not be safe to use. It might contain some malicious code, viruses, malware, spyware, etc. that can harm your device or steal your data. It might also require some permissions or access to your device that are not necessary or safe. Therefore, you should always scan the mod apk file with a reliable antivirus or anti-malware software before installing it. You should also be careful about granting any permissions or access to the mod apk. You should also avoid using any personal or sensitive information or accounts when playing with the mod apk.

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          2. Is ARK Mobile God Console Mod APK legal to use?
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            ARK Mobile God Console Mod APK is not a legal product of the game developers, so it might not be legal to use. It violates the terms of service and rules of the game, as it gives you an unfair advantage over other players and modifies the game in an unauthorized way. If you use the mod apk to play online with other players or on official servers, you might get detected and reported by other players or by the anti-cheat system. This can result in your account being banned or suspended from playing online or accessing certain features of the game. Therefore, you should always use a different account or a guest account when playing with the mod apk. You should also avoid playing online with other players or on official servers when using the mod apk. You should also disable any online features or settings that might expose your use of the mod apk.

            -
          3. Does ARK Mobile God Console Mod APK work on iOS devices?
            4. -

            ARK Mobile God Console Mod APK is designed for Android devices only, so it does not work on iOS devices. However, there is another way to access the god console feature on iOS devices, which is by purchasing it from the in-game store for $14.99. This will give you a permanent access to the god console feature on your iOS device, without having to download or install any mod apk. However, this option is not free and might not have all the features and benefits of the mod apk.

            -
          4. How do I update ARK Mobile God Console Mod APK?
            5. -

            ARK Mobile God Console Mod APK is not an official product of the game developers, so it does not update automatically or manually like the original game. You have to wait for the mod apk developers to release a new version of the mod apk that is compatible and stable with the latest version of the game. You can check for updates on the download link provided above or on other websites that offer the mod apk. However, you should always be careful and cautious when downloading and installing any updates, as they might not be safe or reliable.

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          5. Can I play ARK Mobile with my friends using the God Console Mod APK?
            6. -

            ARK Mobile God Console Mod APK is a cheat or a hack that gives you an unfair advantage over other players and modifies the game in an unauthorized way. Therefore, it is not recommended to play ARK Mobile with your friends using the mod apk, as it might ruin the game for them and for yourself. It might also get you banned or suspended from playing online or accessing certain features of the game. If you want to play ARK Mobile with your friends, you should play with the original game or other games that are fair and fun for everyone. You can also play with the mod apk offline or on private servers that allow it.

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            \ No newline at end of file diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bowmasters A Hotsy-Totsy Game with Different Weapons and Game Modes - Free Download.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bowmasters A Hotsy-Totsy Game with Different Weapons and Game Modes - Free Download.md deleted file mode 100644 index 22af3d6d23ff712bf8365d2d6472de135b9f9ea7..0000000000000000000000000000000000000000 --- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bowmasters A Hotsy-Totsy Game with Different Weapons and Game Modes - Free Download.md +++ /dev/null @@ -1,213 +0,0 @@ -
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            If you are looking for a new and exciting game to play on your mobile device, you should check out Bowmasters. This is a multiplayer game with bowmen that will challenge your aim and shooting skills. You can choose from over 60 insane characters, each with their own unique weapons and abilities, and compete with your friends or other players online. You can also enjoy different game modes, such as shooting birds or fruits, dueling enemies, or surviving the zombie apocalypse. In this article, we will tell you everything you need to know about Bowmasters, including how to download it for free, how to play it, and why you should play it.

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              Bowmasters is a free game that you can download and play on your mobile device. You can find it on the App Store or Google Play, depending on your device. Here are the steps to download the game:

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              The optional subscription and in-app purchases

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              Bowmasters is a free game, but it also offers an optional subscription and in-app purchases. The subscription gives you access to exclusive benefits, such as:

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              The subscription costs $7.99 per week, $19.99 per month, or $99.99 per year. You can cancel it anytime in your account settings.

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              The in-app purchases allow you to buy more coins, gems, or chests with real money. You can use them to unlock more characters, weapons, or skins. The prices range from $0.99 to $99.99 per item.

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              You can play the game without subscribing or buying anything, but you may have to watch ads, earn coins by playing, or wait for chests to open.

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              How to Play Bowmasters?

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              Bowmasters is a simple and easy game to play, but it also requires skill and strategy. You have to aim and shoot arrows at your opponents, while avoiding their attacks. You can play solo or with other players online. Here are some tips on how to play the game:

              -

              The basic controls and mechanics of the game

              The basic controls and mechanics of the game are:

              -
                -
              • To aim, you have to drag your finger on the screen to adjust the angle and power of your shot.
                • -
              • To shoot, you have to release your finger and watch the arrow fly.
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              • To dodge or block, you have to tap on the screen when the enemy shoots at you.
                • -
              • To switch weapons, you have to tap on the weapon icon at the bottom of the screen.
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              • To use special abilities, you have to tap on the ability icon at the top of the screen.
                • -
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              The tips and tricks to improve your skills and win more matches

              -

              Some of the tips and tricks to improve your skills and win more matches are:

              -
                -
              • Practice your aim and timing in offline mode or with easy opponents.
                • -
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              • Try to hit the head or vital organs of your enemies for more damage and bonus coins.
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              • Collect coins, gems, and chests to unlock more characters, weapons, and skins.
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              • Watch ads or complete offers to get free coins, gems, or chests.
                • -
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                • -
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              The challenges and rewards of the game

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              Bowmasters is a game that will challenge your skills and entertain you with its humor and gore. Some of the challenges and rewards of the game are:

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              Why You Should Play Bowmasters?

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              Bowmasters is a game that will keep you hooked for hours with its fun and addictive gameplay. It is also a game that will make you laugh with its hilarious characters, weapons, and fatalities. Here are some reasons why you should play Bowmasters:

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              The reasons why Bowmasters is a fun and addictive game

              Some of the reasons why Bowmasters is a fun and addictive game are:

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              • It has colorful and cartoonish graphics that appeal to all ages.
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              The testimonials and reviews from other players

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              Bowmasters is a game that has received positive feedback and praise from other players. Here are some of the testimonials and reviews from the App Store and Google Play:

              -
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              "This game is awesome! It's so fun and addictive. I love the characters and the weapons. The graphics are amazing and the sound effects are hilarious. I play it every day with my friends and we have a blast. It's the best game ever!" - 5 stars by Jaden Smith

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              "I highly recommend this game to anyone who likes shooting games or multiplayer games. It's very easy to play and very entertaining. The game modes are very creative and diverse. The characters are very funny and original. The game is also very fair and balanced. It's a great game for killing time or having fun." - 4 stars by Emily Jones

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              "This game is amazing! It's so fun and addictive. I love the characters and the weapons. The graphics are amazing and the sound effects are hilarious. I play it every day with my friends and we have a blast. It's the best game ever!" - 5 stars by Jaden Smith

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              The future updates and plans for the game

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              Bowmasters is a game that is constantly updated and improved by the developers. They listen to the feedback and suggestions of the players and add new features and content to the game. Some of the future updates and plans for the game are:

              -
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              • Adding more characters, weapons, skins, and abilities.
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              • Adding more game modes, scenarios, levels, and challenges.
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              • Adding more rewards, prizes, events, and tournaments.
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              • Improving the performance, stability, and security of the game.
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              • Fixing any bugs, glitches, or errors that may occur.
                • -
              -

              Conclusion

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              Bowmasters is a multiplayer game with bowmen that will provide you with hours of fun and entertainment. You can download it for free from the App Store or Google Play and enjoy its features, such as:

              -
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              • Over 60 insane characters with unique weapons and abilities.
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              • Multiple game modes for solo or online play.
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              • Realistic rag-doll physics and awesome fatalities.
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              • Hilarious graphics, sound effects, and dialogue.
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              • Challenging gameplay that tests your skills and strategy.
                • -
              -

              A summary of the main points of the article

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              In this article, we have told you everything you need to know about Bowmasters, including:

              -
                -
              • What is Bowmasters?
                • -
              • How to download Bowmasters for free?
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              • How to play Bowmasters?
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              • Why you should play Bowmasters?
                • -
              -

              A call to action to download and play the game

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              If you are looking for a new and exciting game to play on your mobile device, you should not miss Bowmasters. It is a fun and addictive game that will make you laugh and challenge your skills. You can download it for free from the App Store or Google Play today and start playing with your friends or other players online. You will not regret it!

              -

              Five unique FAQs after the conclusion

              - - Q: How many players can play Bowmasters online? - A: You can play Bowmasters online with up to four players in one match. - Q: How can I unlock more characters in Bowmasters? - A: You can unlock more characters in Bowmasters by earning coins, gems, or chests by playing, watching ads, or completing offers. You can also buy them with real money. - Q: How can I cancel my subscription in Bowmasters? - A: You can cancel your subscription in Bowmasters by going to your account settings on your device and turning off auto-renewal. - Q: How can I contact the developers of Bowmasters? - A: You can contact the developers of Bowmasters by sending them an email at support@playgendary.com or visiting their website at - Q: How can I contact the developers of Bowmasters? - A: You can contact the developers of Bowmasters by sending them an email at support@playgendary.com or visiting their website at https://playgendary.com/. - Q: Is Bowmasters safe and suitable for children? - A: Bowmasters is rated 12+ on the App Store and Teen on Google Play. It contains cartoon violence, blood, gore, and mild profanity. It is not recommended for children under 12 years old or for sensitive viewers.

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              Craft World Mod APK AN1: A Guide for Minecraft Fans

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              If you are a fan of Minecraft, the popular sandbox game that lets you create and explore infinite worlds, you might be interested in Craft World Mod APK AN1. This is a modified version of Minecraft for Android devices that offers some amazing features and enhancements that are not available in the original game. In this article, we will tell you everything you need to know about Craft World Mod APK AN1, including what it is, how to download and install it, and how to play it.

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              How to download and install Craft World Mod APK AN1?

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              Conclusion

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              Craft World Mod APK AN1 is a modified version of Minecraft for Android devices that offers some amazing features and enhancements that are not available in the original game. It is a great way to enjoy Minecraft with more freedom and creativity. However, it is not an official product of Mojang and is not affiliated with them in any way. Therefore, you should use it at your own risk and discretion. If you want to try out Craft World Mod APK AN1 on your Android device, you can follow the steps mentioned above to download and install it.

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              Craft World Mod APK AN1 is based on the latest version of Minecraft for Android devices, which is 1.20.0.01 as of June 2023. It may not be compatible with other mods or versions of Minecraft that are older or newer than this version. You should uninstall any previous version of Minecraft or Craft World Mod APK AN1 from your device before installing the mod. You should also avoid using the mod with other mods that may conflict or interfere with it.

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              Craft World Mod APK AN1 is not an official product of Mojang and is not affiliated with them in any way. Therefore, it may violate the terms and conditions of Mojang and the intellectual property rights of Minecraft. You should not use the mod for any illegal or unethical purposes, such as hacking, cheating, or pirating. You should also respect the rights and interests of Mojang and other players when using the mod.

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              Guardian Tales Download: A Link to Classic Adventure

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              If you are a fan of 8-bit games and JRPGs of old, you might want to check out Guardian Tales, a charming and quirky RPG that pays homage to the classics. Guardian Tales is a free-to-play gacha game that offers a lot of content and fun for players of all levels. In this article, we will tell you what Guardian Tales is, what features it has, what critics and users think of it, what devices it supports, and how to download it.

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              What is Guardian Tales?

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              • Hero and Weapon Collection

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              Conclusion

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              Guardian Tales is a game that will appeal to anyone who loves retro games, JRPGs, puzzles, action, comedy, and nostalgia. It is a game that offers a lot of content and fun for free. It is a game that has a loyal fanbase and a dedicated developer team. It is a game that you should definitely try out if you haven't already.

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              If you are interested in downloading Guardian Tales, you can use the links below to get it for your device. You can also visit the official website or the official Discord server for more information and support. We hope you enjoyed this article and found it helpful. Thank you for reading!

              -

              FAQs

              -
                -

              • Q: How do I reroll in Guardian Tales?

                -

                A: Rerolling is the process of creating multiple accounts to get the best heroes or weapons from the gacha system. To reroll in Guardian Tales, you will have to clear the first two stages of World 1 to unlock the gacha menu. Then, you will have to use your free gems or tickets to summon heroes or weapons. If you are not satisfied with your results, you will have to delete your account data from the settings menu and start over with a new account. You can also use an emulator or multiple devices to speed up the process.

                • -

              • Q: How do I get more gems in Guardian Tales?

                -

                A: Gems are the premium currency in Guardian Tales that can be used to summon heroes or weapons or buy various items. You can get more gems by completing various tasks such as clearing stages, completing quests, achieving milestones, participating in events etc. You can also buy gems with real money from the shop menu.

                • -

              • Q: How do I join a guild in Guardian Tales?

                -

                A: Guilds are groups of players that can chat, cooperate, compete etc. To join a guild in Guardian Tales, you will have to clear World 1-4 to unlock the guild menu. Then, you can either apply to join an existing guild or create your own guild. You can also search for guilds by name, level, region etc. You can only join one guild at a time.

                • -

              • Q: How do I get more heroes and weapons in Guardian Tales?

                -

                A: Heroes and weapons are the main components of your party in Guardian Tales. You can get more heroes and weapons by summoning them from the gacha system using gems or tickets. You can also get some heroes and weapons by clearing certain stages, completing certain quests, participating in certain events etc. You can also exchange hero crystals or mileage tickets for specific heroes or weapons from the shop menu.

                • -

              • Q: How do I upgrade my heroes and weapons in Guardian Tales?

                -

                A: Upgrading your heroes and weapons is essential to increase their power and performance in Guardian Tales. You can upgrade your heroes and weapons by using various materials such as evolution stones, awakening stones, experience points, gold etc. You can also use the training room to max out any hero you want without using any materials.

                • -

              197e85843d
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              \ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download Google Home On Mac.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download Google Home On Mac.md deleted file mode 100644 index 87828d777a963a35b83a6d9979c7e5e773980acb..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Download Google Home On Mac.md +++ /dev/null @@ -1,46 +0,0 @@ - -

              How to Download Google Home On Mac and Use It to Control Your Smart Devices

              - -

              If you have a Google Home device or a Chromecast, you might be wondering how to download Google Home on Mac and use it to set up, manage, and control your smart devices. Google Home is an app that allows you to do all that and more from your iOS or Android phone or tablet. But what if you want to use your Mac instead?

              -

              Download Google Home On Mac


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              - -

              Unfortunately, Google Home is not officially available for Mac. However, there are some workarounds that can help you use some or all of the features of Google Home on your Mac. In this article, we will show you how to download Google Home on Mac using an Android emulator, and how to connect Google Home to Mac using Chrome browser.

              - -

              How to Download Google Home On Mac Using an Android Emulator

              - -

              An Android emulator is a software that allows you to run Android apps on your Mac. There are many Android emulators to choose from, but one of the most popular ones is Bluestacks. Bluestacks lets you access and run a variety of Android apps, including Google Home. Here's how to download Google Home on Mac using Bluestacks:

              - -
                -
              1. Go to Bluestacks website and download the installer for Mac.
                2. -
              2. Run the installer and follow the instructions to install Bluestacks on your Mac.
                3. -
              3. Launch Bluestacks and sign in with your Google account.
                4. -
              4. Go to the Play Store and search for Google Home.
                5. -
              5. Download and install Google Home on Bluestacks.
                6. -
              6. Launch Google Home and sign in with your Google account.
                7. -
              7. Follow the steps to set up your Google Home devices and connect them to your Wi-Fi network.
                8. -
              - -

              Now you can use Google Home on Mac to control your smart devices, create routines, check the feed, and more.

              - -

              How to Connect Google Home to Mac Using Chrome Browser

              - -

              If you don't want to use an Android emulator, you can also use Chrome browser to connect Google Home to Mac. However, this method is limited to media casting only. You can't set up Google Home devices from Chrome. You need to use the Google Home app on your iOS or Android device or the corresponding Mac emulation for that. Here's how to connect Google Home to Mac using Chrome browser:

              - -
                -
              1. Launch the Chrome browser on your Mac.
                2. -
              2. Select the three vertical dots icon in the top right corner.
                3. -
              3. Select Help > About Google Chrome.
                4. -
              4. If an update is available, Chrome will download and install it automatically. Choose Relaunch to apply the update.
                5. -
              5. Open the website or app that you want to cast from. Make sure it has a cast icon.
                6. -
              6. Select the cast icon and choose your Google Home device or Chromecast from the list.
                7. -
              7. Adjust the volume, pause, play, or stop the media as you wish.
                8. -
              - -

              Now you can use Chrome browser on your Mac to cast media from websites or apps that support casting to your Google Home device or Chromecast.

              -

              - -

              Conclusion

              - -

              In this article, we showed you how to download Google Home on Mac using an Android emulator like Bluestacks, and how to connect Google Home to Mac using Chrome browser. Both methods have their advantages and disadvantages, so it depends on your personal preference and the resources available on your Mac. We hope this article was helpful and informative for you. If you have any questions or feedback, feel free to leave a comment below.

              cec2833e83
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              \ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Katy Perry Prism Mp3 Download Zip PORTABLE.md b/spaces/tioseFevbu/cartoon-converter/scripts/Katy Perry Prism Mp3 Download Zip PORTABLE.md deleted file mode 100644 index ec24391abc5e3657339e9633dfb96d021d985742..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Katy Perry Prism Mp3 Download Zip PORTABLE.md +++ /dev/null @@ -1,15 +0,0 @@ - -

              How to Download Katy Perry's Prism Album in Mp3 Format

              -

              If you are a fan of Katy Perry, you might be interested in downloading her fourth studio album, Prism, in mp3 format. Prism was released in 2013 and features some of her most popular songs, such as Roar, Dark Horse, Unconditionally, and Birthday. The album received positive reviews from critics and fans alike, and sold over 4 million copies worldwide.

              -

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              However, if you want to download Prism in mp3 format, you might encounter some difficulties. The album is not available on some streaming platforms, such as Spotify or Apple Music. Moreover, some of the download links that you can find online are fake or malicious. For example, some of them might redirect you to Lady Gaga's Born This Way album instead of Prism[^1^]. Others might contain viruses or malware that can harm your device or steal your personal information.

              -

              So how can you download Katy Perry's Prism album in mp3 format safely and legally? Here are some tips and options that you can try:

              -
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              • Buy the album from an official online store, such as Amazon or iTunes. This is the best way to support the artist and get high-quality mp3 files. You can also get the deluxe edition of Prism, which includes four bonus tracks and a remix of Roar[^2^]. The price of the album varies depending on the store and your location, but it is usually around $10.
                • -
              • Use a reputable online converter tool, such as Twointomedia. This is a website that allows you to convert any YouTube video into an mp3 file for free. You can use it to download any song from Prism by copying and pasting the YouTube link of the official music video or lyric video. For example, if you want to download Roar, you can copy this link: https://www.youtube.com/watch?v=CevxZvSJLk8 and paste it into the Twointomedia search box. Then, click on the download button and choose the mp3 format. The file will be downloaded to your device in a few seconds[^3^]. However, be aware that this method might not be legal in some countries or regions, so check your local laws before using it.
                • -
              • Download a zip file of the album from a reliable source, such as Turbobit. This is a file hosting service that allows you to download large files quickly and securely. You can find a zip file of Prism (deluxe edition) in MQA format on Turbobit by following this link: https://turbobit.net/uct21xnbr1ug.html[^4^]. MQA stands for Master Quality Authenticated, which is a technology that delivers high-resolution audio in a smaller file size. To download the zip file, you need to create a free account on Turbobit and choose a download type. You can either download the file for free with some limitations (such as waiting time and ads), or pay for a premium account that offers faster speed and no restrictions. After downloading the zip file, you need to unzip it using a software like WinRAR or 7-Zip. Then, you will have access to all the mp3 files of Prism[^5^]. However, be careful when downloading files from unknown sources, as they might contain viruses or malware. Always scan your files with an antivirus software before opening them.
                • -
              -

              These are some of the ways that you can download Katy Perry's Prism album in mp3 format. We hope that this article was helpful and informative. 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/tioseFevbu/cartoon-converter/scripts/Lava Kusa Tamil Dubbed Movie.md b/spaces/tioseFevbu/cartoon-converter/scripts/Lava Kusa Tamil Dubbed Movie.md deleted file mode 100644 index 6d66c5a3ff02c5b8018275765ebeb5197ab8e3da..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Lava Kusa Tamil Dubbed Movie.md +++ /dev/null @@ -1,19 +0,0 @@ - -I can help you with that. Here is a possible title and article with SEO optimization and HTML formatting for the keyword "Lava Kusa Tamil Dubbed Movie": - -

              Lava Kusa Tamil Dubbed Movie: A Mythological Adventure for All Ages

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              Lava Kusa is a 2015 Telugu-language animated film based on the Hindu epic Ramayana. It tells the story of the twin sons of Lord Rama and Sita, Lava and Kusa, who embark on a journey to reunite their parents after they are separated by Ravana's curse. The film features the voices of N. T. Rama Rao Jr., Priyamani, Brahmanandam, Ali and others.

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              Lava Kusa Tamil Dubbed Movie is now available to watch online and download for free on various platforms. The film has been dubbed in Tamil by professional voice actors and has received positive reviews from critics and audiences alike. The film is praised for its stunning animation, engaging narration, catchy songs and moral values.

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              Lava Kusa Tamil Dubbed Movie is a perfect choice for families and children who love mythological stories and adventure. The film is also a great way to learn about the culture and history of India and its ancient heroes. The film has a runtime of 105 minutes and is rated U/A by the censor board.

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              Sure, I can write a few more paragraphs for the article. Here they are: - -

              Lava Kusa Tamil Dubbed Movie is not only a visual treat but also a musical delight. The film has six songs composed by Koti and sung by renowned singers like S. P. Balasubrahmanyam, Chitra, Shankar Mahadevan, Shreya Ghoshal and others. The songs are catchy and melodious and suit the mood and theme of the film. Some of the popular songs are "Jai Jai Ram", "Lava Kusa Theme Song", "Rama Rama" and "Sita Rama".

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              Lava Kusa Tamil Dubbed Movie is also a tribute to the legendary actor and politician N. T. Rama Rao, who played the roles of Rama, Lava and Kusa in the 1963 film of the same name. The film is dedicated to his memory and features his grandson N. T. Rama Rao Jr. as the narrator and voice of Lava and Kusa. The film also has references and dialogues from the original film and pays homage to its iconic scenes and characters.

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              Lava Kusa Tamil Dubbed Movie is a must-watch for all fans of animation, mythology and adventure. The film is a rare combination of entertainment and education that will appeal to all age groups and backgrounds. The film is a masterpiece of Indian animation that showcases the rich and diverse heritage of India and its timeless stories. Don't miss this opportunity to watch Lava Kusa Tamil Dubbed Movie online or download it for free from the links below.

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              Lava Kusa Tamil Dubbed Movie is a film that you should not miss. It is a film that will entertain you, educate you and inspire you. It is a film that will make you proud of your culture and heritage. It is a film that will make you appreciate the values of love, courage and devotion. It is a film that will make you feel the power of Ramayana and its timeless message.

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              \ No newline at end of file diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/urllib3/util/proxy.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/urllib3/util/proxy.py deleted file mode 100644 index 2199cc7b7f004009493d032720c36d6568f9d89e..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/urllib3/util/proxy.py +++ /dev/null @@ -1,57 +0,0 @@ -from .ssl_ import create_urllib3_context, resolve_cert_reqs, resolve_ssl_version - - -def connection_requires_http_tunnel( - proxy_url=None, proxy_config=None, destination_scheme=None -): - """ - Returns True if the connection requires an HTTP CONNECT through the proxy. - - :param URL proxy_url: - URL of the proxy. - :param ProxyConfig proxy_config: - Proxy configuration from poolmanager.py - :param str destination_scheme: - The scheme of the destination. (i.e https, http, etc) - """ - # If we're not using a proxy, no way to use a tunnel. - if proxy_url is None: - return False - - # HTTP destinations never require tunneling, we always forward. - if destination_scheme == "http": - return False - - # Support for forwarding with HTTPS proxies and HTTPS destinations. - if ( - proxy_url.scheme == "https" - and proxy_config - and proxy_config.use_forwarding_for_https - ): - return False - - # Otherwise always use a tunnel. - return True - - -def create_proxy_ssl_context( - ssl_version, cert_reqs, ca_certs=None, ca_cert_dir=None, ca_cert_data=None -): - """ - Generates a default proxy ssl context if one hasn't been provided by the - user. - """ - ssl_context = create_urllib3_context( - ssl_version=resolve_ssl_version(ssl_version), - cert_reqs=resolve_cert_reqs(cert_reqs), - ) - - if ( - not ca_certs - and not ca_cert_dir - and not ca_cert_data - and hasattr(ssl_context, "load_default_certs") - ): - ssl_context.load_default_certs() - - return ssl_context diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/py38compat.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/py38compat.py deleted file mode 100644 index e556b69ee9d7d6dacb3f256d6ed79ac31724c443..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/py38compat.py +++ /dev/null @@ -1,8 +0,0 @@ -def aix_platform(osname, version, release): - try: - import _aix_support - - return _aix_support.aix_platform() - except ImportError: - pass - return "%s-%s.%s" % (osname, version, release) diff --git a/spaces/tmaham/DS-Fusion-Express/ldm/modules/image_degradation/bsrgan.py b/spaces/tmaham/DS-Fusion-Express/ldm/modules/image_degradation/bsrgan.py deleted file mode 100644 index 32ef56169978e550090261cddbcf5eb611a6173b..0000000000000000000000000000000000000000 --- a/spaces/tmaham/DS-Fusion-Express/ldm/modules/image_degradation/bsrgan.py +++ /dev/null @@ -1,730 +0,0 @@ -# -*- coding: utf-8 -*- -""" -# -------------------------------------------- -# Super-Resolution -# -------------------------------------------- -# -# Kai Zhang (cskaizhang@gmail.com) -# https://github.com/cszn -# From 2019/03--2021/08 -# -------------------------------------------- -""" - -import numpy as np -import cv2 -import torch - -from functools import partial -import random -from scipy import ndimage -import scipy -import scipy.stats as ss -from scipy.interpolate import interp2d -from scipy.linalg import orth -import albumentations - -import ldm.modules.image_degradation.utils_image as util - - -def modcrop_np(img, sf): - ''' - Args: - img: numpy image, WxH or WxHxC - sf: scale factor - Return: - cropped image - ''' - w, h = img.shape[:2] - im = np.copy(img) - return im[:w - w % sf, :h - h % sf, ...] - - -""" -# -------------------------------------------- -# anisotropic Gaussian kernels -# -------------------------------------------- -""" - - -def analytic_kernel(k): - """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" - k_size = k.shape[0] - # Calculate the big kernels size - big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) - # Loop over the small kernel to fill the big one - for r in range(k_size): - for c in range(k_size): - big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k - # Crop the edges of the big kernel to ignore very small values and increase run time of SR - crop = k_size // 2 - cropped_big_k = big_k[crop:-crop, crop:-crop] - # Normalize to 1 - return cropped_big_k / cropped_big_k.sum() - - -def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): - """ generate an anisotropic Gaussian kernel - Args: - ksize : e.g., 15, kernel size - theta : [0, pi], rotation angle range - l1 : [0.1,50], scaling of eigenvalues - l2 : [0.1,l1], scaling of eigenvalues - If l1 = l2, will get an isotropic Gaussian kernel. - Returns: - k : kernel - """ - - v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) - V = np.array([[v[0], v[1]], [v[1], -v[0]]]) - D = np.array([[l1, 0], [0, l2]]) - Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) - k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) - - return k - - -def gm_blur_kernel(mean, cov, size=15): - center = size / 2.0 + 0.5 - k = np.zeros([size, size]) - for y in range(size): - for x in range(size): - cy = y - center + 1 - cx = x - center + 1 - k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) - - k = k / np.sum(k) - return k - - -def shift_pixel(x, sf, upper_left=True): - """shift pixel for super-resolution with different scale factors - Args: - x: WxHxC or WxH - sf: scale factor - upper_left: shift direction - """ - h, w = x.shape[:2] - shift = (sf - 1) * 0.5 - xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) - if upper_left: - x1 = xv + shift - y1 = yv + shift - else: - x1 = xv - shift - y1 = yv - shift - - x1 = np.clip(x1, 0, w - 1) - y1 = np.clip(y1, 0, h - 1) - - if x.ndim == 2: - x = interp2d(xv, yv, x)(x1, y1) - if x.ndim == 3: - for i in range(x.shape[-1]): - x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) - - return x - - -def blur(x, k): - ''' - x: image, NxcxHxW - k: kernel, Nx1xhxw - ''' - n, c = x.shape[:2] - p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 - x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') - k = k.repeat(1, c, 1, 1) - k = k.view(-1, 1, k.shape[2], k.shape[3]) - x = x.view(1, -1, x.shape[2], x.shape[3]) - x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) - x = x.view(n, c, x.shape[2], x.shape[3]) - - return x - - -def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): - """" - # modified version of https://github.com/assafshocher/BlindSR_dataset_generator - # Kai Zhang - # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var - # max_var = 2.5 * sf - """ - # Set random eigen-vals (lambdas) and angle (theta) for COV matrix - lambda_1 = min_var + np.random.rand() * (max_var - min_var) - lambda_2 = min_var + np.random.rand() * (max_var - min_var) - theta = np.random.rand() * np.pi # random theta - noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 - - # Set COV matrix using Lambdas and Theta - LAMBDA = np.diag([lambda_1, lambda_2]) - Q = np.array([[np.cos(theta), -np.sin(theta)], - [np.sin(theta), np.cos(theta)]]) - SIGMA = Q @ LAMBDA @ Q.T - INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] - - # Set expectation position (shifting kernel for aligned image) - MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) - MU = MU[None, None, :, None] - - # Create meshgrid for Gaussian - [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) - Z = np.stack([X, Y], 2)[:, :, :, None] - - # Calcualte Gaussian for every pixel of the kernel - ZZ = Z - MU - ZZ_t = ZZ.transpose(0, 1, 3, 2) - raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) - - # shift the kernel so it will be centered - # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) - - # Normalize the kernel and return - # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) - kernel = raw_kernel / np.sum(raw_kernel) - return kernel - - -def fspecial_gaussian(hsize, sigma): - hsize = [hsize, hsize] - siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] - std = sigma - [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) - arg = -(x * x + y * y) / (2 * std * std) - h = np.exp(arg) - h[h < scipy.finfo(float).eps * h.max()] = 0 - sumh = h.sum() - if sumh != 0: - h = h / sumh - return h - - -def fspecial_laplacian(alpha): - alpha = max([0, min([alpha, 1])]) - h1 = alpha / (alpha + 1) - h2 = (1 - alpha) / (alpha + 1) - h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] - h = np.array(h) - return h - - -def fspecial(filter_type, *args, **kwargs): - ''' - python code from: - https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py - ''' - if filter_type == 'gaussian': - return fspecial_gaussian(*args, **kwargs) - if filter_type == 'laplacian': - return fspecial_laplacian(*args, **kwargs) - - -""" -# -------------------------------------------- -# degradation models -# -------------------------------------------- -""" - - -def bicubic_degradation(x, sf=3): - ''' - Args: - x: HxWxC image, [0, 1] - sf: down-scale factor - Return: - bicubicly downsampled LR image - ''' - x = util.imresize_np(x, scale=1 / sf) - return x - - -def srmd_degradation(x, k, sf=3): - ''' blur + bicubic downsampling - Args: - x: HxWxC image, [0, 1] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - Reference: - @inproceedings{zhang2018learning, - title={Learning a single convolutional super-resolution network for multiple degradations}, - author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, - booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, - pages={3262--3271}, - year={2018} - } - ''' - x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' - x = bicubic_degradation(x, sf=sf) - return x - - -def dpsr_degradation(x, k, sf=3): - ''' bicubic downsampling + blur - Args: - x: HxWxC image, [0, 1] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - Reference: - @inproceedings{zhang2019deep, - title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, - author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, - booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, - pages={1671--1681}, - year={2019} - } - ''' - x = bicubic_degradation(x, sf=sf) - x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') - return x - - -def classical_degradation(x, k, sf=3): - ''' blur + downsampling - Args: - x: HxWxC image, [0, 1]/[0, 255] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - ''' - x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') - # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) - st = 0 - return x[st::sf, st::sf, ...] - - -def add_sharpening(img, weight=0.5, radius=50, threshold=10): - """USM sharpening. borrowed from real-ESRGAN - Input image: I; Blurry image: B. - 1. K = I + weight * (I - B) - 2. Mask = 1 if abs(I - B) > threshold, else: 0 - 3. Blur mask: - 4. Out = Mask * K + (1 - Mask) * I - Args: - img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. - weight (float): Sharp weight. Default: 1. - radius (float): Kernel size of Gaussian blur. Default: 50. - threshold (int): - """ - if radius % 2 == 0: - radius += 1 - blur = cv2.GaussianBlur(img, (radius, radius), 0) - residual = img - blur - mask = np.abs(residual) * 255 > threshold - mask = mask.astype('float32') - soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) - - K = img + weight * residual - K = np.clip(K, 0, 1) - return soft_mask * K + (1 - soft_mask) * img - - -def add_blur(img, sf=4): - wd2 = 4.0 + sf - wd = 2.0 + 0.2 * sf - if random.random() < 0.5: - l1 = wd2 * random.random() - l2 = wd2 * random.random() - k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) - else: - k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) - img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') - - return img - - -def add_resize(img, sf=4): - rnum = np.random.rand() - if rnum > 0.8: # up - sf1 = random.uniform(1, 2) - elif rnum < 0.7: # down - sf1 = random.uniform(0.5 / sf, 1) - else: - sf1 = 1.0 - img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) - img = np.clip(img, 0.0, 1.0) - - return img - - -# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): -# noise_level = random.randint(noise_level1, noise_level2) -# rnum = np.random.rand() -# if rnum > 0.6: # add color Gaussian noise -# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) -# elif rnum < 0.4: # add grayscale Gaussian noise -# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) -# else: # add noise -# L = noise_level2 / 255. -# D = np.diag(np.random.rand(3)) -# U = orth(np.random.rand(3, 3)) -# conv = np.dot(np.dot(np.transpose(U), D), U) -# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) -# img = np.clip(img, 0.0, 1.0) -# return img - -def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): - noise_level = random.randint(noise_level1, noise_level2) - rnum = np.random.rand() - if rnum > 0.6: # add color Gaussian noise - img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) - elif rnum < 0.4: # add grayscale Gaussian noise - img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) - else: # add noise - L = noise_level2 / 255. - D = np.diag(np.random.rand(3)) - U = orth(np.random.rand(3, 3)) - conv = np.dot(np.dot(np.transpose(U), D), U) - img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) - img = np.clip(img, 0.0, 1.0) - return img - - -def add_speckle_noise(img, noise_level1=2, noise_level2=25): - noise_level = random.randint(noise_level1, noise_level2) - img = np.clip(img, 0.0, 1.0) - rnum = random.random() - if rnum > 0.6: - img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) - elif rnum < 0.4: - img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) - else: - L = noise_level2 / 255. - D = np.diag(np.random.rand(3)) - U = orth(np.random.rand(3, 3)) - conv = np.dot(np.dot(np.transpose(U), D), U) - img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) - img = np.clip(img, 0.0, 1.0) - return img - - -def add_Poisson_noise(img): - img = np.clip((img * 255.0).round(), 0, 255) / 255. - vals = 10 ** (2 * random.random() + 2.0) # [2, 4] - if random.random() < 0.5: - img = np.random.poisson(img * vals).astype(np.float32) / vals - else: - img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) - img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. - noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray - img += noise_gray[:, :, np.newaxis] - img = np.clip(img, 0.0, 1.0) - return img - - -def add_JPEG_noise(img): - quality_factor = random.randint(30, 95) - img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) - result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) - img = cv2.imdecode(encimg, 1) - img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) - return img - - -def random_crop(lq, hq, sf=4, lq_patchsize=64): - h, w = lq.shape[:2] - rnd_h = random.randint(0, h - lq_patchsize) - rnd_w = random.randint(0, w - lq_patchsize) - lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] - - rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) - hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] - return lq, hq - - -def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): - """ - This is the degradation model of BSRGAN from the paper - "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" - ---------- - img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) - sf: scale factor - isp_model: camera ISP model - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 - sf_ori = sf - - h1, w1 = img.shape[:2] - img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = img.shape[:2] - - if h < lq_patchsize * sf or w < lq_patchsize * sf: - raise ValueError(f'img size ({h1}X{w1}) is too small!') - - hq = img.copy() - - if sf == 4 and random.random() < scale2_prob: # downsample1 - if np.random.rand() < 0.5: - img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - img = util.imresize_np(img, 1 / 2, True) - img = np.clip(img, 0.0, 1.0) - sf = 2 - - shuffle_order = random.sample(range(7), 7) - idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) - if idx1 > idx2: # keep downsample3 last - shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] - - for i in shuffle_order: - - if i == 0: - img = add_blur(img, sf=sf) - - elif i == 1: - img = add_blur(img, sf=sf) - - elif i == 2: - a, b = img.shape[1], img.shape[0] - # downsample2 - if random.random() < 0.75: - sf1 = random.uniform(1, 2 * sf) - img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) - k_shifted = shift_pixel(k, sf) - k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel - img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') - img = img[0::sf, 0::sf, ...] # nearest downsampling - img = np.clip(img, 0.0, 1.0) - - elif i == 3: - # downsample3 - img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) - img = np.clip(img, 0.0, 1.0) - - elif i == 4: - # add Gaussian noise - img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) - - elif i == 5: - # add JPEG noise - if random.random() < jpeg_prob: - img = add_JPEG_noise(img) - - elif i == 6: - # add processed camera sensor noise - if random.random() < isp_prob and isp_model is not None: - with torch.no_grad(): - img, hq = isp_model.forward(img.copy(), hq) - - # add final JPEG compression noise - img = add_JPEG_noise(img) - - # random crop - img, hq = random_crop(img, hq, sf_ori, lq_patchsize) - - return img, hq - - -# todo no isp_model? -def degradation_bsrgan_variant(image, sf=4, isp_model=None): - """ - This is the degradation model of BSRGAN from the paper - "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" - ---------- - sf: scale factor - isp_model: camera ISP model - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - image = util.uint2single(image) - isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 - sf_ori = sf - - h1, w1 = image.shape[:2] - image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = image.shape[:2] - - hq = image.copy() - - if sf == 4 and random.random() < scale2_prob: # downsample1 - if np.random.rand() < 0.5: - image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - image = util.imresize_np(image, 1 / 2, True) - image = np.clip(image, 0.0, 1.0) - sf = 2 - - shuffle_order = random.sample(range(7), 7) - idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) - if idx1 > idx2: # keep downsample3 last - shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] - - for i in shuffle_order: - - if i == 0: - image = add_blur(image, sf=sf) - - elif i == 1: - image = add_blur(image, sf=sf) - - elif i == 2: - a, b = image.shape[1], image.shape[0] - # downsample2 - if random.random() < 0.75: - sf1 = random.uniform(1, 2 * sf) - image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) - k_shifted = shift_pixel(k, sf) - k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel - image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') - image = image[0::sf, 0::sf, ...] # nearest downsampling - image = np.clip(image, 0.0, 1.0) - - elif i == 3: - # downsample3 - image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) - image = np.clip(image, 0.0, 1.0) - - elif i == 4: - # add Gaussian noise - image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) - - elif i == 5: - # add JPEG noise - if random.random() < jpeg_prob: - image = add_JPEG_noise(image) - - # elif i == 6: - # # add processed camera sensor noise - # if random.random() < isp_prob and isp_model is not None: - # with torch.no_grad(): - # img, hq = isp_model.forward(img.copy(), hq) - - # add final JPEG compression noise - image = add_JPEG_noise(image) - image = util.single2uint(image) - example = {"image":image} - return example - - -# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... -def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): - """ - This is an extended degradation model by combining - the degradation models of BSRGAN and Real-ESRGAN - ---------- - img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) - sf: scale factor - use_shuffle: the degradation shuffle - use_sharp: sharpening the img - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - - h1, w1 = img.shape[:2] - img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = img.shape[:2] - - if h < lq_patchsize * sf or w < lq_patchsize * sf: - raise ValueError(f'img size ({h1}X{w1}) is too small!') - - if use_sharp: - img = add_sharpening(img) - hq = img.copy() - - if random.random() < shuffle_prob: - shuffle_order = random.sample(range(13), 13) - else: - shuffle_order = list(range(13)) - # local shuffle for noise, JPEG is always the last one - shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) - shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) - - poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 - - for i in shuffle_order: - if i == 0: - img = add_blur(img, sf=sf) - elif i == 1: - img = add_resize(img, sf=sf) - elif i == 2: - img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) - elif i == 3: - if random.random() < poisson_prob: - img = add_Poisson_noise(img) - elif i == 4: - if random.random() < speckle_prob: - img = add_speckle_noise(img) - elif i == 5: - if random.random() < isp_prob and isp_model is not None: - with torch.no_grad(): - img, hq = isp_model.forward(img.copy(), hq) - elif i == 6: - img = add_JPEG_noise(img) - elif i == 7: - img = add_blur(img, sf=sf) - elif i == 8: - img = add_resize(img, sf=sf) - elif i == 9: - img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) - elif i == 10: - if random.random() < poisson_prob: - img = add_Poisson_noise(img) - elif i == 11: - if random.random() < speckle_prob: - img = add_speckle_noise(img) - elif i == 12: - if random.random() < isp_prob and isp_model is not None: - with torch.no_grad(): - img, hq = isp_model.forward(img.copy(), hq) - else: - print('check the shuffle!') - - # resize to desired size - img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), - interpolation=random.choice([1, 2, 3])) - - # add final JPEG compression noise - img = add_JPEG_noise(img) - - # random crop - img, hq = random_crop(img, hq, sf, lq_patchsize) - - return img, hq - - -if __name__ == '__main__': - print("hey") - img = util.imread_uint('utils/test.png', 3) - print(img) - img = util.uint2single(img) - print(img) - img = img[:448, :448] - h = img.shape[0] // 4 - print("resizing to", h) - sf = 4 - deg_fn = partial(degradation_bsrgan_variant, sf=sf) - for i in range(20): - print(i) - img_lq = deg_fn(img) - print(img_lq) - img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] - print(img_lq.shape) - print("bicubic", img_lq_bicubic.shape) - print(img_hq.shape) - lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), - interpolation=0) - lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), - interpolation=0) - img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) - util.imsave(img_concat, str(i) + '.png') - - diff --git a/spaces/tomg-group-umd/pez-dispenser/open_clip/timm_model.py b/spaces/tomg-group-umd/pez-dispenser/open_clip/timm_model.py deleted file mode 100644 index b58122c0b84fbda9e51867342823222234e17505..0000000000000000000000000000000000000000 --- a/spaces/tomg-group-umd/pez-dispenser/open_clip/timm_model.py +++ /dev/null @@ -1,122 +0,0 @@ -""" timm model adapter - -Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. -""" -import logging -from collections import OrderedDict - -import torch -import torch.nn as nn - -try: - import timm - from timm.models.layers import Mlp, to_2tuple - try: - # old timm imports < 0.8.1 - from timm.models.layers.attention_pool2d import RotAttentionPool2d - from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d - except ImportError: - # new timm imports >= 0.8.1 - from timm.layers import RotAttentionPool2d - from timm.layers import AttentionPool2d as AbsAttentionPool2d -except ImportError: - timm = None - -from .utils import freeze_batch_norm_2d - - -class TimmModel(nn.Module): - """ timm model adapter - # FIXME this adapter is a work in progress, may change in ways that break weight compat - """ - - def __init__( - self, - model_name, - embed_dim, - image_size=224, - pool='avg', - proj='linear', - proj_bias=False, - drop=0., - pretrained=False): - super().__init__() - if timm is None: - raise RuntimeError("Please `pip install timm` to use timm models.") - - self.image_size = to_2tuple(image_size) - self.trunk = timm.create_model(model_name, pretrained=pretrained) - feat_size = self.trunk.default_cfg.get('pool_size', None) - feature_ndim = 1 if not feat_size else 2 - if pool in ('abs_attn', 'rot_attn'): - assert feature_ndim == 2 - # if attn pooling used, remove both classifier and default pool - self.trunk.reset_classifier(0, global_pool='') - else: - # reset global pool if pool config set, otherwise leave as network default - reset_kwargs = dict(global_pool=pool) if pool else {} - self.trunk.reset_classifier(0, **reset_kwargs) - prev_chs = self.trunk.num_features - - head_layers = OrderedDict() - if pool == 'abs_attn': - head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) - prev_chs = embed_dim - elif pool == 'rot_attn': - head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) - prev_chs = embed_dim - else: - assert proj, 'projection layer needed if non-attention pooling is used.' - - # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used - if proj == 'linear': - head_layers['drop'] = nn.Dropout(drop) - head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) - elif proj == 'mlp': - head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop, bias=(True, proj_bias)) - - self.head = nn.Sequential(head_layers) - - def lock(self, unlocked_groups=0, freeze_bn_stats=False): - """ lock modules - Args: - unlocked_groups (int): leave last n layer groups unlocked (default: 0) - """ - if not unlocked_groups: - # lock full model - for param in self.trunk.parameters(): - param.requires_grad = False - if freeze_bn_stats: - freeze_batch_norm_2d(self.trunk) - else: - # NOTE: partial freeze requires latest timm (master) branch and is subject to change - try: - # FIXME import here until API stable and in an official release - from timm.models.helpers import group_parameters, group_modules - except ImportError: - raise RuntimeError( - 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') - matcher = self.trunk.group_matcher() - gparams = group_parameters(self.trunk, matcher) - max_layer_id = max(gparams.keys()) - max_layer_id = max_layer_id - unlocked_groups - for group_idx in range(max_layer_id + 1): - group = gparams[group_idx] - for param in group: - self.trunk.get_parameter(param).requires_grad = False - if freeze_bn_stats: - gmodules = group_modules(self.trunk, matcher, reverse=True) - gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} - freeze_batch_norm_2d(self.trunk, gmodules) - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - try: - self.trunk.set_grad_checkpointing(enable) - except Exception as e: - logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') - - def forward(self, x): - x = self.trunk(x) - x = self.head(x) - return x diff --git a/spaces/tomofi/ABINet-OCR/README.md b/spaces/tomofi/ABINet-OCR/README.md deleted file mode 100644 index daf31ccf2c52a451e4101010c47f349c837a7724..0000000000000000000000000000000000000000 --- a/spaces/tomofi/ABINet-OCR/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: ABINet OCR -emoji: 🏃 -colorFrom: indigo -colorTo: red -sdk: gradio -sdk_version: 2.8.12 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference \ No newline at end of file diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py deleted file mode 100644 index 9917d5c4dc8b9c0149a963e24ecfa1098c1a9995..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' -model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/pascal_voc/ssd512_voc0712.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/pascal_voc/ssd512_voc0712.py deleted file mode 100644 index 365a65fc64bf693d812c97855942827b10bd8e64..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/pascal_voc/ssd512_voc0712.py +++ /dev/null @@ -1,53 +0,0 @@ -_base_ = 'ssd300_voc0712.py' -input_size = 512 -model = dict( - backbone=dict(input_size=input_size), - bbox_head=dict( - in_channels=(512, 1024, 512, 256, 256, 256, 256), - anchor_generator=dict( - input_size=input_size, - strides=[8, 16, 32, 64, 128, 256, 512], - basesize_ratio_range=(0.15, 0.9), - ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2])))) -img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile', to_float32=True), - dict(type='LoadAnnotations', with_bbox=True), - dict( - type='PhotoMetricDistortion', - brightness_delta=32, - contrast_range=(0.5, 1.5), - saturation_range=(0.5, 1.5), - hue_delta=18), - dict( - type='Expand', - mean=img_norm_cfg['mean'], - to_rgb=img_norm_cfg['to_rgb'], - ratio_range=(1, 4)), - dict( - type='MinIoURandomCrop', - min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), - min_crop_size=0.3), - dict(type='Resize', img_scale=(512, 512), keep_ratio=False), - dict(type='Normalize', **img_norm_cfg), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(512, 512), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=False), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -data = dict( - train=dict(dataset=dict(pipeline=train_pipeline)), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docker/serve/entrypoint.sh b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docker/serve/entrypoint.sh deleted file mode 100644 index 41ba00b048aed84b45c5a8015a016ff148e97d86..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docker/serve/entrypoint.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/bin/bash -set -e - -if [[ "$1" = "serve" ]]; then - shift 1 - torchserve --start --ts-config /home/model-server/config.properties -else - eval "$@" -fi - -# prevent docker exit -tail -f /dev/null diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/dynamic_roi_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/dynamic_roi_head.py deleted file mode 100644 index 89427a931f45f5a920c0e66fd88058bf9fa05f5c..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/dynamic_roi_head.py +++ /dev/null @@ -1,154 +0,0 @@ -import numpy as np -import torch - -from mmdet.core import bbox2roi -from mmdet.models.losses import SmoothL1Loss -from ..builder import HEADS -from .standard_roi_head import StandardRoIHead - -EPS = 1e-15 - - -@HEADS.register_module() -class DynamicRoIHead(StandardRoIHead): - """RoI head for `Dynamic R-CNN `_.""" - - def __init__(self, **kwargs): - super(DynamicRoIHead, self).__init__(**kwargs) - assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss) - # the IoU history of the past `update_iter_interval` iterations - self.iou_history = [] - # the beta history of the past `update_iter_interval` iterations - self.beta_history = [] - - def forward_train(self, - x, - img_metas, - proposal_list, - gt_bboxes, - gt_labels, - gt_bboxes_ignore=None, - gt_masks=None): - """Forward function for training. - - Args: - x (list[Tensor]): list of multi-level img features. - - 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`. - - proposals (list[Tensors]): list of region proposals. - - gt_bboxes (list[Tensor]): each item are the truth boxes for each - image in [tl_x, tl_y, br_x, br_y] format. - - gt_labels (list[Tensor]): class indices corresponding to each box - - gt_bboxes_ignore (None | list[Tensor]): specify which bounding - boxes can be ignored when computing the loss. - - gt_masks (None | Tensor) : true segmentation masks for each box - used if the architecture supports a segmentation task. - - Returns: - dict[str, Tensor]: a dictionary of loss components - """ - # assign gts and sample proposals - if self.with_bbox or self.with_mask: - num_imgs = len(img_metas) - if gt_bboxes_ignore is None: - gt_bboxes_ignore = [None for _ in range(num_imgs)] - sampling_results = [] - cur_iou = [] - for i in range(num_imgs): - assign_result = self.bbox_assigner.assign( - proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], - gt_labels[i]) - sampling_result = self.bbox_sampler.sample( - assign_result, - proposal_list[i], - gt_bboxes[i], - gt_labels[i], - feats=[lvl_feat[i][None] for lvl_feat in x]) - # record the `iou_topk`-th largest IoU in an image - iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk, - len(assign_result.max_overlaps)) - ious, _ = torch.topk(assign_result.max_overlaps, iou_topk) - cur_iou.append(ious[-1].item()) - sampling_results.append(sampling_result) - # average the current IoUs over images - cur_iou = np.mean(cur_iou) - self.iou_history.append(cur_iou) - - losses = dict() - # bbox head forward and loss - if self.with_bbox: - bbox_results = self._bbox_forward_train(x, sampling_results, - gt_bboxes, gt_labels, - img_metas) - losses.update(bbox_results['loss_bbox']) - - # mask head forward and loss - if self.with_mask: - mask_results = self._mask_forward_train(x, sampling_results, - bbox_results['bbox_feats'], - gt_masks, img_metas) - losses.update(mask_results['loss_mask']) - - # update IoU threshold and SmoothL1 beta - update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval - if len(self.iou_history) % update_iter_interval == 0: - new_iou_thr, new_beta = self.update_hyperparameters() - - return losses - - def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, - img_metas): - num_imgs = len(img_metas) - rois = bbox2roi([res.bboxes for res in sampling_results]) - bbox_results = self._bbox_forward(x, rois) - - bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, - gt_labels, self.train_cfg) - # record the `beta_topk`-th smallest target - # `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets - # and bbox_weights, respectively - pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1) - num_pos = len(pos_inds) - cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1) - beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs, - num_pos) - cur_target = torch.kthvalue(cur_target, beta_topk)[0].item() - self.beta_history.append(cur_target) - loss_bbox = self.bbox_head.loss(bbox_results['cls_score'], - bbox_results['bbox_pred'], rois, - *bbox_targets) - - bbox_results.update(loss_bbox=loss_bbox) - return bbox_results - - def update_hyperparameters(self): - """Update hyperparameters like IoU thresholds for assigner and beta for - SmoothL1 loss based on the training statistics. - - Returns: - tuple[float]: the updated ``iou_thr`` and ``beta``. - """ - new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou, - np.mean(self.iou_history)) - self.iou_history = [] - self.bbox_assigner.pos_iou_thr = new_iou_thr - self.bbox_assigner.neg_iou_thr = new_iou_thr - self.bbox_assigner.min_pos_iou = new_iou_thr - if (np.median(self.beta_history) < EPS): - # avoid 0 or too small value for new_beta - new_beta = self.bbox_head.loss_bbox.beta - else: - new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta, - np.median(self.beta_history)) - self.beta_history = [] - self.bbox_head.loss_bbox.beta = new_beta - return new_iou_thr, new_beta diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py deleted file mode 100644 index 4204b682902532fcb39344b9f56929ddcd1c56e0..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py +++ /dev/null @@ -1,393 +0,0 @@ -import os - -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 BaseModule, ModuleList, 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 FCNMaskHead(BaseModule): - - 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), - init_cfg=None): - assert init_cfg is None, 'To prevent abnormal initialization ' \ - 'behavior, init_cfg is not allowed to be set' - super(FCNMaskHead, self).__init__(init_cfg) - 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 = 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.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.relu = nn.ReLU(inplace=True) - self.debug_imgs = None - - def init_weights(self): - super(FCNMaskHead, self).init_weights() - 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): - 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) - mask_pred = self.conv_logits(x) - return mask_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)) - """ - loss = dict() - if mask_pred.size(0) == 0: - loss_mask = mask_pred.sum() - else: - if self.class_agnostic: - loss_mask = self.loss_mask(mask_pred, mask_targets, - torch.zeros_like(labels)) - else: - loss_mask = self.loss_mask(mask_pred, mask_targets, labels) - loss['loss_mask'] = loss_mask - 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 - # No need to consider rescale and scale_factor while exporting to ONNX - if torch.onnx.is_in_onnx_export(): - img_h, img_w = ori_shape[:2] - else: - 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 - - # support exporting to ONNX - if torch.onnx.is_in_onnx_export(): - threshold = rcnn_test_cfg.mask_thr_binary - if not self.class_agnostic: - box_inds = torch.arange(mask_pred.shape[0]) - mask_pred = mask_pred[box_inds, labels][:, None] - masks, _ = _do_paste_mask( - mask_pred, bboxes, img_h, img_w, skip_empty=False) - if threshold >= 0: - masks = (masks >= threshold).to(dtype=torch.bool) - else: - # TensorRT backend does not have data type of uint8 - is_trt_backend = os.environ.get( - 'ONNX_BACKEND') == 'MMCVTensorRT' - target_dtype = torch.int32 if is_trt_backend else torch.uint8 - masks = (masks * 255).to(dtype=target_dtype) - 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 _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).to(torch.float32) + 0.5 - img_x = torch.arange(x0_int, x1_int, device=device).to(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) - # IsInf op is not supported with ONNX<=1.7.0 - if not torch.onnx.is_in_onnx_export(): - 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) - - 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/towardsai-buster/buster/data/tmp.py b/spaces/towardsai-buster/buster/data/tmp.py deleted file mode 100644 index 28de6ad7b83dc54b570e79b8d4af45a953fafe79..0000000000000000000000000000000000000000 --- a/spaces/towardsai-buster/buster/data/tmp.py +++ /dev/null @@ -1,21 +0,0 @@ -# import pandas as pd - -# # Load the CSV -# df = pd.read_csv('data/wiki.csv') - - -# # Count the number of unique titles in the 'title' column -# unique_titles_count = df['title'] - -# print(len(df)) - -# # # Remove the 'ranking' column -# # df.drop('ranking', axis=1, inplace=True) - -# # # Save the CSV again -# # df.to_csv('data/wiki.csv', index=False) - - -import gradio as gr - -gr.themes.builder() diff --git "a/spaces/trialapp/gpt_summarizer/pages/1_\343\202\246\343\202\247\343\203\226\343\203\232\343\203\274\343\202\270\350\246\201\347\264\204.py" "b/spaces/trialapp/gpt_summarizer/pages/1_\343\202\246\343\202\247\343\203\226\343\203\232\343\203\274\343\202\270\350\246\201\347\264\204.py" deleted file mode 100644 index 6a69ffea1548526230880a5ce4270ae50774c12e..0000000000000000000000000000000000000000 --- "a/spaces/trialapp/gpt_summarizer/pages/1_\343\202\246\343\202\247\343\203\226\343\203\232\343\203\274\343\202\270\350\246\201\347\264\204.py" +++ /dev/null @@ -1,122 +0,0 @@ -from urllib.parse import urlparse - -import langchain -import requests -import streamlit as st -from bs4 import BeautifulSoup -from dotenv import load_dotenv -from langchain.cache import SQLiteCache -from langchain.callbacks import get_openai_callback -from langchain.chat_models import ChatOpenAI -from langchain.schema import HumanMessage, SystemMessage - - -def init_page(): - st.set_page_config(page_title="ウェブページ要約", page_icon="🤗") - st.header("ウェブページ要約 🤗") - st.sidebar.title("Options") - - -def init_messages(): - clear_button = st.sidebar.button("会話履歴を削除", key="clear") - if clear_button or "messages" not in st.session_state: - st.session_state.messages = [SystemMessage(content="あなたは役に立つアシスタントです。")] - st.session_state.costs = [] - - -def select_model(): - model = st.sidebar.radio("モデルを選択してください。:", ("GPT-3.5", "GPT-4"), disabled=True) - if model == "GPT-3.5": - model_name = "gpt-3.5-turbo" - else: - model_name = "gpt-4" - return ChatOpenAI(temperature=0, model_name=model_name) - - -def get_url_input(): - url = st.text_input("URL: ", key="input") - return url - - -def validate_url(url): - try: - result = urlparse(url) - return all([result.scheme, result.netloc]) - except ValueError: - return False - - -def get_content(url): - try: - with st.spinner("コンテンツの取得中..."): - response = requests.get(url) - soup = BeautifulSoup(response.text, "html.parser") - # fetch text from main (change the below code to filter page) - if soup.main: - return soup.main.get_text() - elif soup.article: - return soup.article.get_text() - else: - return soup.body.get_text() - except: - st.write("エラーです。") - return None - - -def build_prompt(content, n_chars=300): - # return f"""Here is the content of a web page. Please provide a concise summary of around {n_chars} characters. - return f"""下記がウェブページの内容です。{n_chars}文字程度で簡潔に要約してください。 - -======== - -{content[:1000]} - -""" - - -def get_answer(llm, messages): - with get_openai_callback() as cb: - answer = llm(messages) - return answer.content, cb.total_cost - - -def main(): - load_dotenv() - langchain.llm_cache = SQLiteCache(database_path=".langchain.db") - init_page() - llm = select_model() - init_messages() - container = st.container() - response_container = st.container() - with container: - url = get_url_input() - is_valid_url = validate_url(url) - if not is_valid_url: - st.write("有効なURLを入力してください") - answer = None - else: - content = get_content(url) - if content: - prompt = build_prompt(content) - st.session_state.messages.append(HumanMessage(content=prompt)) - with st.spinner("chatgptがタイピングしています..."): - answer, cost = get_answer(llm, st.session_state.messages) - st.session_state.costs.append(cost) - else: - answer = None - if answer: - with response_container: - st.markdown("## 要約") - st.write(answer) - st.markdown("---") - st.markdown("## 元のテキスト") - st.write(content) - costs = st.session_state.get("費用", []) - st.sidebar.markdown("## 費用") - st.sidebar.markdown(f"**総費用: ${sum(costs):.5f}**") - for cost in costs: - st.sidebar.markdown(f"- ${cost:.5f}") - - -if __name__ == "__main__": - main() diff --git a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/MapTS.py b/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/MapTS.py deleted file mode 100644 index 2160a62cdbb0278d213076637f79b1e6f66db906..0000000000000000000000000000000000000000 --- a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/MapTS.py +++ /dev/null @@ -1,394 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 4 17:36:31 2021 - -@author: wuzongze -""" - -import os -#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" -#os.environ["CUDA_VISIBLE_DEVICES"] = "1" #(or "1" or "2") - -import sys - -#sys.path=['', '/usr/local/tensorflow/avx-avx2-gpu/1.14.0/python3.7/site-packages', '/usr/local/matlab/2018b/lib/python3.7/site-packages', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python37.zip', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/lib-dynload', '/usr/lib/python3.7', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/site-packages', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/site-packages/copkmeans-1.5-py3.7.egg', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/site-packages/spherecluster-0.1.7-py3.7.egg', '/usr/lib/python3/dist-packages', '/usr/local/lib/python3.7/dist-packages', '/usr/lib/python3/dist-packages/IPython/extensions'] - -import tensorflow as tf - -import numpy as np -import torch -import clip -from PIL import Image -import pickle -import copy -import matplotlib.pyplot as plt - -def GetAlign(out,dt,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_features = image_features / image_features.norm(dim=-1, keepdim=True) - - image_features1=image_features.cpu().numpy() - - image_features1=image_features1.reshape(list(imgs.shape[:2])+[512]) - - fd=image_features1[:,1:,:]-image_features1[:,:-1,:] - - fd1=fd.reshape([-1,512]) - fd2=fd1/np.linalg.norm(fd1,axis=1)[:,None] - - tmp=np.dot(fd2,dt) - m=tmp.mean() - acc=np.sum(tmp>0)/len(tmp) - print(m,acc) - return m,acc - - -def SplitS(ds_p,M,if_std): - all_ds=[] - start=0 - for i in M.mindexs: - tmp=M.dlatents[i].shape[1] - end=start+tmp - tmp=ds_p[start:end] -# tmp=tmp*M.code_std[i] - - all_ds.append(tmp) - start=end - - all_ds2=[] - tmp_index=0 - for i in range(len(M.s_names)): - if (not 'RGB' in M.s_names[i]) and (not len(all_ds[tmp_index])==0): - -# tmp=np.abs(all_ds[tmp_index]/M.code_std[i]) -# print(i,tmp.mean()) -# tmp=np.dot(M.latent_codes[i],all_ds[tmp_index]) -# print(tmp) - if if_std: - tmp=all_ds[tmp_index]*M.code_std[i] - else: - tmp=all_ds[tmp_index] - - all_ds2.append(tmp) - tmp_index+=1 - else: - tmp=np.zeros(len(M.dlatents[i][0])) - all_ds2.append(tmp) - return all_ds2 - - -imagenet_templates = [ - 'a bad photo of a {}.', -# 'a photo of many {}.', - 'a sculpture of a {}.', - 'a photo of the hard to see {}.', - 'a low resolution photo of the {}.', - 'a rendering of a {}.', - 'graffiti of a {}.', - 'a bad photo of the {}.', - 'a cropped photo of the {}.', - 'a tattoo of a {}.', - 'the embroidered {}.', - 'a photo of a hard to see {}.', - 'a bright photo of a {}.', - 'a photo of a clean {}.', - 'a photo of a dirty {}.', - 'a dark photo of the {}.', - 'a drawing of a {}.', - 'a photo of my {}.', - 'the plastic {}.', - 'a photo of the cool {}.', - 'a close-up photo of a {}.', - 'a black and white photo of the {}.', - 'a painting of the {}.', - 'a painting of a {}.', - 'a pixelated photo of the {}.', - 'a sculpture of the {}.', - 'a bright photo of the {}.', - 'a cropped photo of a {}.', - 'a plastic {}.', - 'a photo of the dirty {}.', - 'a jpeg corrupted photo of a {}.', - 'a blurry photo of the {}.', - 'a photo of the {}.', - 'a good photo of the {}.', - 'a rendering of the {}.', - 'a {} in a video game.', - 'a photo of one {}.', - 'a doodle of a {}.', - 'a close-up photo of the {}.', - 'a photo of a {}.', - 'the origami {}.', - 'the {} in a video game.', - 'a sketch of a {}.', - 'a doodle of the {}.', - 'a origami {}.', - 'a low resolution photo of a {}.', - 'the toy {}.', - 'a rendition of the {}.', - 'a photo of the clean {}.', - 'a photo of a large {}.', - 'a rendition of a {}.', - 'a photo of a nice {}.', - 'a photo of a weird {}.', - 'a blurry photo of a {}.', - 'a cartoon {}.', - 'art of a {}.', - 'a sketch of the {}.', - 'a embroidered {}.', - 'a pixelated photo of a {}.', - 'itap of the {}.', - 'a jpeg corrupted photo of the {}.', - 'a good photo of a {}.', - 'a plushie {}.', - 'a photo of the nice {}.', - 'a photo of the small {}.', - 'a photo of the weird {}.', - 'the cartoon {}.', - 'art of the {}.', - 'a drawing of the {}.', - 'a photo of the large {}.', - 'a black and white photo of a {}.', - 'the plushie {}.', - 'a dark photo of a {}.', - 'itap of a {}.', - 'graffiti of the {}.', - 'a toy {}.', - 'itap of my {}.', - 'a photo of a cool {}.', - 'a photo of a small {}.', - 'a tattoo of the {}.', -] - - -def zeroshot_classifier(classnames, templates,model): - with torch.no_grad(): - zeroshot_weights = [] - for classname in classnames: - texts = [template.format(classname) for template in templates] #format with class - texts = clip.tokenize(texts).cuda() #tokenize - class_embeddings = model.encode_text(texts) #embed with text encoder - class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True) - class_embedding = class_embeddings.mean(dim=0) - class_embedding /= class_embedding.norm() - zeroshot_weights.append(class_embedding) - zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda() - return zeroshot_weights - - -def GetDt(classnames,model): - text_features=zeroshot_classifier(classnames, imagenet_templates,model).t() - - dt=text_features[0]-text_features[1] - dt=dt.cpu().numpy() - -# t_m1=t_m/np.linalg.norm(t_m) -# dt=text_features.cpu().numpy()[0]-t_m1 - print(np.linalg.norm(dt)) - dt=dt/np.linalg.norm(dt) - return dt - - -def GetBoundary(fs3,dt,M,threshold): - tmp=np.dot(fs3,dt) - - ds_imp=copy.copy(tmp) - select=np.abs(tmp) gradioApp().getElementById(tab_name+"_generation_info_button").click()); - gallery?.addEventListener('keydown', (e) => { - if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow - gradioApp().getElementById(tab_name+"_generation_info_button").click() - }); - return gallery; -} diff --git a/spaces/user238921933/stable-diffusion-webui/modules/sd_hijack.py b/spaces/user238921933/stable-diffusion-webui/modules/sd_hijack.py deleted file mode 100644 index 15ca6b9a106cd17eb6e99d4df3e3207fd10b6379..0000000000000000000000000000000000000000 --- a/spaces/user238921933/stable-diffusion-webui/modules/sd_hijack.py +++ /dev/null @@ -1,264 +0,0 @@ -import torch -from torch.nn.functional import silu -from types import MethodType - -import modules.textual_inversion.textual_inversion -from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint -from modules.hypernetworks import hypernetwork -from modules.shared import cmd_opts -from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr - -import ldm.modules.attention -import ldm.modules.diffusionmodules.model -import ldm.modules.diffusionmodules.openaimodel -import ldm.models.diffusion.ddim -import ldm.models.diffusion.plms -import ldm.modules.encoders.modules - -attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward -diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity -diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward - -# new memory efficient cross attention blocks do not support hypernets and we already -# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention -ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention -ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention - -# silence new console spam from SD2 -ldm.modules.attention.print = lambda *args: None -ldm.modules.diffusionmodules.model.print = lambda *args: None - - -def apply_optimizations(): - undo_optimizations() - - ldm.modules.diffusionmodules.model.nonlinearity = silu - ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th - - optimization_method = None - - if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): - print("Applying xformers cross attention optimization.") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward - optimization_method = 'xformers' - elif cmd_opts.opt_sub_quad_attention: - print("Applying sub-quadratic cross attention optimization.") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward - optimization_method = 'sub-quadratic' - elif cmd_opts.opt_split_attention_v1: - print("Applying v1 cross attention optimization.") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - optimization_method = 'V1' - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()): - print("Applying cross attention optimization (InvokeAI).") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI - optimization_method = 'InvokeAI' - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): - print("Applying cross attention optimization (Doggettx).") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward - optimization_method = 'Doggettx' - - return optimization_method - - -def undo_optimizations(): - ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward - ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity - ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward - - -def fix_checkpoint(): - """checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want - checkpoints to be added when not training (there's a warning)""" - - pass - - -def weighted_loss(sd_model, pred, target, mean=True): - #Calculate the weight normally, but ignore the mean - loss = sd_model._old_get_loss(pred, target, mean=False) - - #Check if we have weights available - weight = getattr(sd_model, '_custom_loss_weight', None) - if weight is not None: - loss *= weight - - #Return the loss, as mean if specified - return loss.mean() if mean else loss - -def weighted_forward(sd_model, x, c, w, *args, **kwargs): - try: - #Temporarily append weights to a place accessible during loss calc - sd_model._custom_loss_weight = w - - #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely - #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set - if not hasattr(sd_model, '_old_get_loss'): - sd_model._old_get_loss = sd_model.get_loss - sd_model.get_loss = MethodType(weighted_loss, sd_model) - - #Run the standard forward function, but with the patched 'get_loss' - return sd_model.forward(x, c, *args, **kwargs) - finally: - try: - #Delete temporary weights if appended - del sd_model._custom_loss_weight - except AttributeError as e: - pass - - #If we have an old loss function, reset the loss function to the original one - if hasattr(sd_model, '_old_get_loss'): - sd_model.get_loss = sd_model._old_get_loss - del sd_model._old_get_loss - -def apply_weighted_forward(sd_model): - #Add new function 'weighted_forward' that can be called to calc weighted loss - sd_model.weighted_forward = MethodType(weighted_forward, sd_model) - -def undo_weighted_forward(sd_model): - try: - del sd_model.weighted_forward - except AttributeError as e: - pass - - -class StableDiffusionModelHijack: - fixes = None - comments = [] - layers = None - circular_enabled = False - clip = None - optimization_method = None - - embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase() - - def __init__(self): - self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir) - - def hijack(self, m): - if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: - model_embeddings = m.cond_stage_model.roberta.embeddings - model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self) - m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self) - - elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder: - model_embeddings = m.cond_stage_model.transformer.text_model.embeddings - model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self) - m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) - - elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder: - m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self) - m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) - - apply_weighted_forward(m) - if m.cond_stage_key == "edit": - sd_hijack_unet.hijack_ddpm_edit() - - self.optimization_method = apply_optimizations() - - self.clip = m.cond_stage_model - - def flatten(el): - flattened = [flatten(children) for children in el.children()] - res = [el] - for c in flattened: - res += c - return res - - self.layers = flatten(m) - - def undo_hijack(self, m): - if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: - m.cond_stage_model = m.cond_stage_model.wrapped - - elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords: - m.cond_stage_model = m.cond_stage_model.wrapped - - model_embeddings = m.cond_stage_model.transformer.text_model.embeddings - if type(model_embeddings.token_embedding) == EmbeddingsWithFixes: - model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped - elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords: - m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped - m.cond_stage_model = m.cond_stage_model.wrapped - - undo_optimizations() - undo_weighted_forward(m) - - self.apply_circular(False) - self.layers = None - self.clip = None - - def apply_circular(self, enable): - if self.circular_enabled == enable: - return - - self.circular_enabled = enable - - for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]: - layer.padding_mode = 'circular' if enable else 'zeros' - - def clear_comments(self): - self.comments = [] - - def get_prompt_lengths(self, text): - _, token_count = self.clip.process_texts([text]) - - return token_count, self.clip.get_target_prompt_token_count(token_count) - - -class EmbeddingsWithFixes(torch.nn.Module): - def __init__(self, wrapped, embeddings): - super().__init__() - self.wrapped = wrapped - self.embeddings = embeddings - - def forward(self, input_ids): - batch_fixes = self.embeddings.fixes - self.embeddings.fixes = None - - inputs_embeds = self.wrapped(input_ids) - - if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0: - return inputs_embeds - - vecs = [] - for fixes, tensor in zip(batch_fixes, inputs_embeds): - for offset, embedding in fixes: - emb = devices.cond_cast_unet(embedding.vec) - emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0]) - tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]) - - vecs.append(tensor) - - return torch.stack(vecs) - - -def add_circular_option_to_conv_2d(): - conv2d_constructor = torch.nn.Conv2d.__init__ - - def conv2d_constructor_circular(self, *args, **kwargs): - return conv2d_constructor(self, *args, padding_mode='circular', **kwargs) - - torch.nn.Conv2d.__init__ = conv2d_constructor_circular - - -model_hijack = StableDiffusionModelHijack() - - -def register_buffer(self, name, attr): - """ - Fix register buffer bug for Mac OS. - """ - - if type(attr) == torch.Tensor: - if attr.device != devices.device: - attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None)) - - setattr(self, name, attr) - - -ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer -ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer diff --git a/spaces/vasudevgupta/GOOGLE_SUMMER_OF_CODE/README.md b/spaces/vasudevgupta/GOOGLE_SUMMER_OF_CODE/README.md deleted file mode 100644 index 6d2ac7908e82afc4179e31f6578d595c9a0ffd23..0000000000000000000000000000000000000000 --- a/spaces/vasudevgupta/GOOGLE_SUMMER_OF_CODE/README.md +++ /dev/null @@ -1,33 +0,0 @@ ---- -title: GOOGLE_SUMMER_OF_CODE -emoji: 👀 -colorFrom: gray -colorTo: purple -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/vietvd/image-enhance/real-esrgan.py b/spaces/vietvd/image-enhance/real-esrgan.py deleted file mode 100644 index 6ef1b673078f7cf5b82622e956ccd0929526f02b..0000000000000000000000000000000000000000 --- a/spaces/vietvd/image-enhance/real-esrgan.py +++ /dev/null @@ -1,132 +0,0 @@ -import os -import shutil - -import cv2 -import gradio as gr -import torch -from basicsr.archs.rrdbnet_arch import RRDBNet -from gfpgan.utils import GFPGANer -from realesrgan.utils import RealESRGANer - -#os.system("pip freeze") -# download weights -if not os.path.exists('model_zoo/real/RealESRGAN_x4plus.pth'): - os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P model_zoo/real") -if not os.path.exists('model_zoo/real/RealESRGAN_x2plus.pth'): - os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth -P model_zoo/real") - -if not os.path.exists('model_zoo/gan/GFPGANv1.4.pth'): - os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P model_zoo/gan") -if not os.path.exists('model_zoo/gan/RestoreFormer.pth'): - os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P model_zoo/gan") - -def inference(img, version, scale, enhance_face): - # background enhancer with RealESRGAN - if scale <= 2: - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) - netscale = 2 - model_path = 'model_zoo/real/RealESRGAN_x2plus.pth' - else: - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) - model_path = 'model_zoo/real/RealESRGAN_x4plus.pth' - netscale = 4 - print(model_path) - tile = 400 if torch.cuda.is_available() else 0 - dni_weight = None - # restorer - upsampler = RealESRGANer( - scale=netscale, - model_path=model_path, - dni_weight=dni_weight, - model=model, - tile=tile, - tile_pad=10, - pre_pad=0, - half=False, #Use fp32 precision during inference. Default: fp16 (half precision). - gpu_id=None) #gpu device to use (default=None) can be 0,1,2 for multi-gpu - if enhance_face: - if version == 'RestoreFormer': - face_enhancer = GFPGANer( - model_path='model_zoo/gan/RestoreFormer.pth', upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler) - else: - face_enhancer = GFPGANer( - model_path='model_zoo/gan/GFPGANv1.4.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) - os.makedirs('output', exist_ok=True) - if scale > 4: - scale = 4 # avoid too large scale value - try: - extension = os.path.splitext(os.path.basename(str(img)))[1] - img = cv2.imread(img, cv2.IMREAD_UNCHANGED) - if len(img.shape) == 3 and img.shape[2] == 4: - img_mode = 'RGBA' - elif len(img.shape) == 2: # for gray inputs - img_mode = None - img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - else: - img_mode = None - - h, w = img.shape[0:2] - if h < 300: - img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) - try: - if enhance_face: - _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) - else: - output, _ = upsampler.enhance(img, outscale=scale) - except RuntimeError as error: - print('Error', error) - - try: - if scale != 2: - interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4 - h, w = img.shape[0:2] - output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation) - except Exception as error: - print('wrong scale input.', error) - if img_mode == 'RGBA': # RGBA images should be saved in png format - extension = 'png' - else: - extension = 'jpg' - save_path = f'output/out.{extension}' - cv2.imwrite(save_path, output) - - output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB) - return output, save_path - except Exception as error: - print('global exception', error) - return None, None - - -def clean_folder(folder): - for filename in os.listdir(folder): - file_path = os.path.join(folder, filename) - try: - if os.path.isfile(file_path) or os.path.islink(file_path): - os.unlink(file_path) - elif os.path.isdir(file_path): - shutil.rmtree(file_path) - except Exception as e: - print('Failed to delete %s. Reason: %s' % (file_path, e)) - - -title = "Real Esrgan Restore Ai Face Restoration by appsgenz.com" -description = r""" -""" -article = r""" -😊 -""" -reminiApp = gr.Interface( - inference, [ - gr.Image(type="filepath", label="Input"), - gr.Radio(['v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version GFPGAN. Note that it work when enable Enhance faces '), - gr.Number(label="Rescaling factor", value=1), - gr.Checkbox(label="Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes", value=True), - ], [ - gr.Image(type="numpy", label="Output (The whole image)"), - gr.File(label="Download the output image") - ], - title=title, - description=description, - article=article) -reminiApp.queue(concurrency_count=4) -reminiApp.launch(share=False) \ No newline at end of file diff --git a/spaces/vorstcavry/VoCh-beta/infer_pack/modules.py b/spaces/vorstcavry/VoCh-beta/infer_pack/modules.py deleted file mode 100644 index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000 --- a/spaces/vorstcavry/VoCh-beta/infer_pack/modules.py +++ /dev/null @@ -1,522 +0,0 @@ -import copy -import math -import numpy as np -import scipy -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -from infer_pack import commons -from infer_pack.commons import init_weights, get_padding -from infer_pack.transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__( - self, - in_channels, - hidden_channels, - out_channels, - kernel_size, - n_layers, - p_dropout, - ): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append( - nn.Conv1d( - in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) - for _ in range(n_layers - 1): - self.conv_layers.append( - nn.Conv1d( - hidden_channels, - hidden_channels, - kernel_size, - padding=kernel_size // 2, - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size**i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append( - nn.Conv1d( - channels, - channels, - kernel_size, - groups=channels, - dilation=dilation, - padding=padding, - ) - ) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__( - self, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - p_dropout=0, - ): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - self.hidden_channels = hidden_channels - self.kernel_size = (kernel_size,) - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d( - gin_channels, 2 * hidden_channels * n_layers, 1 - ) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") - - for i in range(n_layers): - dilation = dilation_rate**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d( - hidden_channels, - 2 * hidden_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:, : self.hidden_channels, :] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:, self.hidden_channels :, :] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels, 1)) - self.logs = nn.Parameter(torch.zeros(channels, 1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1, 2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False, - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=p_dropout, - gin_channels=gin_channels, - ) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels] * 2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1, 2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class ConvFlow(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - n_layers, - num_bins=10, - tail_bound=5.0, - ): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) - self.proj = nn.Conv1d( - filter_channels, self.half_channels * (num_bins * 3 - 1), 1 - ) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( - self.filter_channels - ) - unnormalized_derivatives = h[..., 2 * self.num_bins :] - - x1, logabsdet = piecewise_rational_quadratic_transform( - x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails="linear", - tail_bound=self.tail_bound, - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1, 2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/hooks/checkpoint.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/hooks/checkpoint.py deleted file mode 100644 index 6af3fae43ac4b35532641a81eb13557edfc7dfba..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/hooks/checkpoint.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os.path as osp -import warnings - -from annotator.uniformer.mmcv.fileio import FileClient -from ..dist_utils import allreduce_params, master_only -from .hook import HOOKS, Hook - - -@HOOKS.register_module() -class CheckpointHook(Hook): - """Save checkpoints periodically. - - Args: - interval (int): The saving period. If ``by_epoch=True``, interval - indicates epochs, otherwise it indicates iterations. - Default: -1, which means "never". - by_epoch (bool): Saving checkpoints by epoch or by iteration. - Default: True. - save_optimizer (bool): Whether to save optimizer state_dict in the - checkpoint. It is usually used for resuming experiments. - Default: True. - out_dir (str, optional): The root directory to save checkpoints. If not - specified, ``runner.work_dir`` will be used by default. If - specified, the ``out_dir`` will be the concatenation of ``out_dir`` - and the last level directory of ``runner.work_dir``. - `Changed in version 1.3.16.` - max_keep_ckpts (int, optional): The maximum checkpoints to keep. - In some cases we want only the latest few checkpoints and would - like to delete old ones to save the disk space. - Default: -1, which means unlimited. - save_last (bool, optional): Whether to force the last checkpoint to be - saved regardless of interval. Default: True. - sync_buffer (bool, optional): Whether to synchronize buffers in - different gpus. Default: False. - file_client_args (dict, optional): Arguments to instantiate a - FileClient. See :class:`mmcv.fileio.FileClient` for details. - Default: None. - `New in version 1.3.16.` - - .. warning:: - Before v1.3.16, the ``out_dir`` argument indicates the path where the - checkpoint is stored. However, since v1.3.16, ``out_dir`` indicates the - root directory and the final path to save checkpoint is the - concatenation of ``out_dir`` and the last level directory of - ``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A" - and the value of ``runner.work_dir`` is "/path/of/B", then the final - path will be "/path/of/A/B". - """ - - def __init__(self, - interval=-1, - by_epoch=True, - save_optimizer=True, - out_dir=None, - max_keep_ckpts=-1, - save_last=True, - sync_buffer=False, - file_client_args=None, - **kwargs): - self.interval = interval - self.by_epoch = by_epoch - self.save_optimizer = save_optimizer - self.out_dir = out_dir - self.max_keep_ckpts = max_keep_ckpts - self.save_last = save_last - self.args = kwargs - self.sync_buffer = sync_buffer - self.file_client_args = file_client_args - - def before_run(self, runner): - if not self.out_dir: - self.out_dir = runner.work_dir - - self.file_client = FileClient.infer_client(self.file_client_args, - self.out_dir) - - # if `self.out_dir` is not equal to `runner.work_dir`, it means that - # `self.out_dir` is set so the final `self.out_dir` is the - # concatenation of `self.out_dir` and the last level directory of - # `runner.work_dir` - if self.out_dir != runner.work_dir: - basename = osp.basename(runner.work_dir.rstrip(osp.sep)) - self.out_dir = self.file_client.join_path(self.out_dir, basename) - - runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by ' - f'{self.file_client.name}.')) - - # disable the create_symlink option because some file backends do not - # allow to create a symlink - if 'create_symlink' in self.args: - if self.args[ - 'create_symlink'] and not self.file_client.allow_symlink: - self.args['create_symlink'] = False - warnings.warn( - ('create_symlink is set as True by the user but is changed' - 'to be False because creating symbolic link is not ' - f'allowed in {self.file_client.name}')) - else: - self.args['create_symlink'] = self.file_client.allow_symlink - - def after_train_epoch(self, runner): - if not self.by_epoch: - return - - # save checkpoint for following cases: - # 1. every ``self.interval`` epochs - # 2. reach the last epoch of training - if self.every_n_epochs( - runner, self.interval) or (self.save_last - and self.is_last_epoch(runner)): - runner.logger.info( - f'Saving checkpoint at {runner.epoch + 1} epochs') - if self.sync_buffer: - allreduce_params(runner.model.buffers()) - self._save_checkpoint(runner) - - @master_only - def _save_checkpoint(self, runner): - """Save the current checkpoint and delete unwanted checkpoint.""" - runner.save_checkpoint( - self.out_dir, save_optimizer=self.save_optimizer, **self.args) - if runner.meta is not None: - if self.by_epoch: - cur_ckpt_filename = self.args.get( - 'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1) - else: - cur_ckpt_filename = self.args.get( - 'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1) - runner.meta.setdefault('hook_msgs', dict()) - runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path( - self.out_dir, cur_ckpt_filename) - # remove other checkpoints - if self.max_keep_ckpts > 0: - if self.by_epoch: - name = 'epoch_{}.pth' - current_ckpt = runner.epoch + 1 - else: - name = 'iter_{}.pth' - current_ckpt = runner.iter + 1 - redundant_ckpts = range( - current_ckpt - self.max_keep_ckpts * self.interval, 0, - -self.interval) - filename_tmpl = self.args.get('filename_tmpl', name) - for _step in redundant_ckpts: - ckpt_path = self.file_client.join_path( - self.out_dir, filename_tmpl.format(_step)) - if self.file_client.isfile(ckpt_path): - self.file_client.remove(ckpt_path) - else: - break - - def after_train_iter(self, runner): - if self.by_epoch: - return - - # save checkpoint for following cases: - # 1. every ``self.interval`` iterations - # 2. reach the last iteration of training - if self.every_n_iters( - runner, self.interval) or (self.save_last - and self.is_last_iter(runner)): - runner.logger.info( - f'Saving checkpoint at {runner.iter + 1} iterations') - if self.sync_buffer: - allreduce_params(runner.model.buffers()) - self._save_checkpoint(runner) diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/utils/ext_loader.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/utils/ext_loader.py deleted file mode 100644 index 08132d2c1b9a1c28880e4bab4d4fa1ba39d9d083..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/utils/ext_loader.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import importlib -import os -import pkgutil -import warnings -from collections import namedtuple - -import torch - -if torch.__version__ != 'parrots': - - def load_ext(name, funcs): - ext = importlib.import_module('mmcv.' + name) - for fun in funcs: - assert hasattr(ext, fun), f'{fun} miss in module {name}' - return ext -else: - from parrots import extension - from parrots.base import ParrotsException - - has_return_value_ops = [ - 'nms', - 'softnms', - 'nms_match', - 'nms_rotated', - 'top_pool_forward', - 'top_pool_backward', - 'bottom_pool_forward', - 'bottom_pool_backward', - 'left_pool_forward', - 'left_pool_backward', - 'right_pool_forward', - 'right_pool_backward', - 'fused_bias_leakyrelu', - 'upfirdn2d', - 'ms_deform_attn_forward', - 'pixel_group', - 'contour_expand', - ] - - def get_fake_func(name, e): - - def fake_func(*args, **kwargs): - warnings.warn(f'{name} is not supported in parrots now') - raise e - - return fake_func - - def load_ext(name, funcs): - ExtModule = namedtuple('ExtModule', funcs) - ext_list = [] - lib_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) - for fun in funcs: - try: - ext_fun = extension.load(fun, name, lib_dir=lib_root) - except ParrotsException as e: - if 'No element registered' not in e.message: - warnings.warn(e.message) - ext_fun = get_fake_func(fun, e) - ext_list.append(ext_fun) - else: - if fun in has_return_value_ops: - ext_list.append(ext_fun.op) - else: - ext_list.append(ext_fun.op_) - return ExtModule(*ext_list) - - -def check_ops_exist(): - ext_loader = pkgutil.find_loader('mmcv._ext') - return ext_loader is not None diff --git a/spaces/weidacn/deepdanbooru/deepdanbooru/model/__init__.py b/spaces/weidacn/deepdanbooru/deepdanbooru/model/__init__.py deleted file mode 100644 index 3188757554d1689ecb17885a8305000695023181..0000000000000000000000000000000000000000 --- a/spaces/weidacn/deepdanbooru/deepdanbooru/model/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -import deepdanbooru.model.layers -import deepdanbooru.model.losses - -from .resnet import create_resnet_152 -from .resnet import create_resnet_custom_v1 -from .resnet import create_resnet_custom_v2 -from .resnet import create_resnet_custom_v3 -from .resnet import create_resnet_custom_v4 diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/design_api_review.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/design_api_review.py deleted file mode 100644 index 687a33652c119006558ddfef5b6150f5599f2947..0000000000000000000000000000000000000000 --- a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/design_api_review.py +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/11 19:31 -@Author : alexanderwu -@File : design_api_review.py -""" -from metagpt.actions.action import Action - - -class DesignReview(Action): - def __init__(self, name, context=None, llm=None): - super().__init__(name, context, llm) - - async def run(self, prd, api_design): - prompt = f"Here is the Product Requirement Document (PRD):\n\n{prd}\n\nHere is the list of APIs designed " \ - f"based on this PRD:\n\n{api_design}\n\nPlease review whether this API design meets the requirements" \ - f" of the PRD, and whether it complies with good design practices." - - api_review = await self._aask(prompt) - return api_review diff --git a/spaces/xiaorong/fork2-so-vits/app.py b/spaces/xiaorong/fork2-so-vits/app.py deleted file mode 100644 index e21e44f265f0b97ae5e54c272daa0741a0c9f318..0000000000000000000000000000000000000000 --- a/spaces/xiaorong/fork2-so-vits/app.py +++ /dev/null @@ -1,268 +0,0 @@ -# coding=utf-8 -import os -import re -import argparse -import utils -import commons -import json -import torch -import gradio as gr -from models import SynthesizerTrn -from text import text_to_sequence, _clean_text -from torch import no_grad, LongTensor -import gradio.processing_utils as gr_processing_utils -import logging -logging.getLogger('numba').setLevel(logging.WARNING) -limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces - -hps_ms = utils.get_hparams_from_file(r'config/config.json') - -audio_postprocess_ori = gr.Audio.postprocess - -def audio_postprocess(self, y): - data = audio_postprocess_ori(self, y) - if data is None: - return None - return gr_processing_utils.encode_url_or_file_to_base64(data["name"]) - - -gr.Audio.postprocess = audio_postprocess - -def get_text(text, hps, is_symbol): - text_norm, clean_text = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = LongTensor(text_norm) - return text_norm, clean_text - -def create_tts_fn(net_g_ms, speaker_id): - def tts_fn(text, language, noise_scale, noise_scale_w, length_scale, is_symbol): - text = text.replace('\n', ' ').replace('\r', '').replace(" ", "") - if limitation: - text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) - max_len = 100 - if is_symbol: - max_len *= 3 - if text_len > max_len: - return "Error: Text is too long", None - if not is_symbol: - if language == 0: - text = f"[ZH]{text}[ZH]" - elif language == 1: - text = f"[JA]{text}[JA]" - else: - text = f"{text}" - stn_tst, clean_text = get_text(text, hps_ms, is_symbol) - with no_grad(): - x_tst = stn_tst.unsqueeze(0).to(device) - x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) - sid = LongTensor([speaker_id]).to(device) - audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, - length_scale=length_scale)[0][0, 0].data.cpu().float().numpy() - - return "Success", (22050, audio) - return tts_fn - -def create_to_symbol_fn(hps): - def to_symbol_fn(is_symbol_input, input_text, temp_text, temp_lang): - if temp_lang == 'Chinese': - clean_text = f'[ZH]{input_text}[ZH]' - elif temp_lang == "Japanese": - clean_text = f'[JA]{input_text}[JA]' - else: - clean_text = input_text - return (_clean_text(clean_text, hps.data.text_cleaners), input_text) if is_symbol_input else (temp_text, temp_text) - - return to_symbol_fn -def change_lang(language): - if language == 0: - return 0.6, 0.668, 1.2, "Chinese" - elif language == 1: - return 0.6, 0.668, 1, "Japanese" - else: - return 0.6, 0.668, 1, "Mix" - -download_audio_js = """ -() =>{{ - let root = document.querySelector("body > gradio-app"); - if (root.shadowRoot != null) - root = root.shadowRoot; - let audio = root.querySelector("#tts-audio-{audio_id}").querySelector("audio"); - let text = root.querySelector("#input-text-{audio_id}").querySelector("textarea"); - if (audio == undefined) - return; - text = text.value; - if (text == undefined) - text = Math.floor(Math.random()*100000000); - audio = audio.src; - let oA = document.createElement("a"); - oA.download = text.substr(0, 20)+'.wav'; - oA.href = audio; - document.body.appendChild(oA); - oA.click(); - oA.remove(); -}} -""" - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--device', type=str, default='cpu') - parser.add_argument('--api', action="store_true", default=False) - parser.add_argument("--share", action="store_true", default=False, help="share gradio app") - args = parser.parse_args() - device = torch.device(args.device) - - models = [] - with open("pretrained_models/info.json", "r", encoding="utf-8") as f: - models_info = json.load(f) - for i, info in models_info.items(): - if not info['enable']: - continue - sid = info['sid'] - name_en = info['name_en'] - name_zh = info['name_zh'] - title = info['title'] - cover = f"pretrained_models/{i}/{info['cover']}" - example = info['example'] - language = info['language'] - net_g_ms = SynthesizerTrn( - len(hps_ms.symbols), - hps_ms.data.filter_length // 2 + 1, - hps_ms.train.segment_size // hps_ms.data.hop_length, - n_speakers=hps_ms.data.n_speakers if info['type'] == "multi" else 0, - **hps_ms.model) - utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None) - _ = net_g_ms.eval().to(device) - models.append((sid, name_en, name_zh, title, cover, example, language, net_g_ms, create_tts_fn(net_g_ms, sid), create_to_symbol_fn(hps_ms))) - with gr.Blocks() as app: - gr.Markdown( - "#
              vits-models\n" - "##
              Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n" - "##
              ·请不要生成会对个人以及组织造成侵害的内容\n" - "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.vits-models)\n\n" - "[Open In Colab]" - "(https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)" - " without queue and length limitation.(无需等待队列，并且没有长度限制)\n\n" - "[Finetune your own model](https://github.com/SayaSS/vits-finetuning)" - ) - - with gr.Tabs(): - with gr.TabItem("EN"): - for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models: - with gr.TabItem(name_en): - with gr.Row(): - gr.Markdown( - '
              ' - f'{title}' - f'' if cover else "" - '
              ' - ) - with gr.Row(): - with gr.Column(): - input_text = gr.Textbox(label="Text (100 words limitation)" if limitation else "Text", lines=5, value=example, elem_id=f"input-text-en-{name_en.replace(' ','')}") - lang = gr.Dropdown(label="Language", choices=["Chinese", "Japanese", "Mix（wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA]）"], - type="index", value=language) - temp_lang = gr.Variable(value=language) - with gr.Accordion(label="Advanced Options", open=False): - temp_text_var = gr.Variable() - symbol_input = gr.Checkbox(value=False, label="Symbol input") - symbol_list = gr.Dataset(label="Symbol list", components=[input_text], - samples=[[x] for x in hps_ms.symbols]) - symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False) - btn = gr.Button(value="Generate", variant="primary") - with gr.Row(): - ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) - nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True) - ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True) - with gr.Column(): - o1 = gr.Textbox(label="Output Message") - o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio-en-{name_en.replace(' ','')}") - download = gr.Button("Download Audio") - btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls, symbol_input], outputs=[o1, o2], api_name=f"tts-{name_en}") - download.click(None, [], [], _js=download_audio_js.format(audio_id=f"en-{name_en.replace(' ', '')}")) - lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls, temp_lang]) - symbol_input.change( - to_symbol_fn, - [symbol_input, input_text, temp_text_var, temp_lang], - [input_text, temp_text_var] - ) - symbol_list.click(None, [symbol_list, symbol_list_json], [input_text], - _js=f""" - (i,symbols) => {{ - let root = document.querySelector("body > gradio-app"); - if (root.shadowRoot != null) - root = root.shadowRoot; - let text_input = root.querySelector("#input-text-en-{name_en.replace(' ', '')}").querySelector("textarea"); - let startPos = text_input.selectionStart; - let endPos = text_input.selectionEnd; - let oldTxt = text_input.value; - let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos); - text_input.value = result; - let x = window.scrollX, y = window.scrollY; - text_input.focus(); - text_input.selectionStart = startPos + symbols[i].length; - text_input.selectionEnd = startPos + symbols[i].length; - text_input.blur(); - window.scrollTo(x, y); - return text_input.value; - }}""") - with gr.TabItem("中文"): - for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models: - with gr.TabItem(name_zh): - with gr.Row(): - gr.Markdown( - '
              ' - f'{title}' - f'' if cover else "" - '
              ' - ) - with gr.Row(): - with gr.Column(): - input_text = gr.Textbox(label="文本 (100字上限)" if limitation else "文本", lines=5, value=example, elem_id=f"input-text-zh-{name_zh}") - lang = gr.Dropdown(label="语言", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"], - type="index", value="中文"if language == "Chinese" else "日语") - temp_lang = gr.Variable(value=language) - with gr.Accordion(label="高级选项", open=False): - temp_text_var = gr.Variable() - symbol_input = gr.Checkbox(value=False, label="符号输入") - symbol_list = gr.Dataset(label="符号列表", components=[input_text], - samples=[[x] for x in hps_ms.symbols]) - symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False) - btn = gr.Button(value="生成", variant="primary") - with gr.Row(): - ns = gr.Slider(label="控制感情变化程度", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) - nsw = gr.Slider(label="控制音素发音长度", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True) - ls = gr.Slider(label="控制整体语速", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True) - with gr.Column(): - o1 = gr.Textbox(label="输出信息") - o2 = gr.Audio(label="输出音频", elem_id=f"tts-audio-zh-{name_zh}") - download = gr.Button("下载音频") - btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls, symbol_input], outputs=[o1, o2]) - download.click(None, [], [], _js=download_audio_js.format(audio_id=f"zh-{name_zh}")) - lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls]) - symbol_input.change( - to_symbol_fn, - [symbol_input, input_text, temp_text_var, temp_lang], - [input_text, temp_text_var] - ) - symbol_list.click(None, [symbol_list, symbol_list_json], [input_text], - _js=f""" - (i,symbols) => {{ - let root = document.querySelector("body > gradio-app"); - if (root.shadowRoot != null) - root = root.shadowRoot; - let text_input = root.querySelector("#input-text-zh-{name_zh}").querySelector("textarea"); - let startPos = text_input.selectionStart; - let endPos = text_input.selectionEnd; - let oldTxt = text_input.value; - let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos); - text_input.value = result; - let x = window.scrollX, y = window.scrollY; - text_input.focus(); - text_input.selectionStart = startPos + symbols[i].length; - text_input.selectionEnd = startPos + symbols[i].length; - text_input.blur(); - window.scrollTo(x, y); - return text_input.value; - }}""") - app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) diff --git a/spaces/xnetba/Chat_advance/run_Linux.sh b/spaces/xnetba/Chat_advance/run_Linux.sh deleted file mode 100644 index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000 --- a/spaces/xnetba/Chat_advance/run_Linux.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - -# 获取脚本所在目录 -script_dir=$(dirname "$(readlink -f "$0")") - -# 将工作目录更改为脚本所在目录 -cd "$script_dir" || exit - -# 检查Git仓库是否有更新 -git remote update -pwd - -if ! git status -uno | grep 'up to date' > /dev/null; then - # 如果有更新，关闭当前运行的服务器 - pkill -f ChuanhuChatbot.py - - # 拉取最新更改 - git pull - - # 安装依赖 - pip3 install -r requirements.txt - - # 重新启动服务器 - nohup python3 ChuanhuChatbot.py & -fi - -# 检查ChuanhuChatbot.py是否在运行 -if ! pgrep -f ChuanhuChatbot.py > /dev/null; then - # 如果没有运行，启动服务器 - nohup python3 ChuanhuChatbot.py & -fi diff --git a/spaces/xswu/HPSv2/tests/test_inference.py b/spaces/xswu/HPSv2/tests/test_inference.py deleted file mode 100644 index dca8dc44c49a5513d047924122a190898dad991d..0000000000000000000000000000000000000000 --- a/spaces/xswu/HPSv2/tests/test_inference.py +++ /dev/null @@ -1,133 +0,0 @@ - -import os -import pytest -import torch -import open_clip -import util_test - -os.environ['CUDA_VISIBLE_DEVICES'] = '' - -if hasattr(torch._C, '_jit_set_profiling_executor'): - # legacy executor is too slow to compile large models for unit tests - # no need for the fusion performance here - torch._C._jit_set_profiling_executor(True) - torch._C._jit_set_profiling_mode(False) - -models_to_test = set(open_clip.list_models()) - -# testing excemptions -models_to_test = models_to_test.difference({ - # not available with timm yet - # see https://github.com/mlfoundations/open_clip/issues/219 - 'convnext_xlarge', - 'convnext_xxlarge', - 'convnext_xxlarge_320', - 'vit_medium_patch16_gap_256', - # exceeds GH runner memory limit - 'ViT-bigG-14', - 'ViT-e-14', - 'mt5-xl-ViT-H-14', - 'coca_base', - 'coca_ViT-B-32', - 'coca_roberta-ViT-B-32' -}) - -if 'OPEN_CLIP_TEST_REG_MODELS' in os.environ: - external_model_list = os.environ['OPEN_CLIP_TEST_REG_MODELS'] - with open(external_model_list, 'r') as f: - models_to_test = set(f.read().splitlines()).intersection(models_to_test) - print(f"Selected models from {external_model_list}: {models_to_test}") - -# TODO: add "coca_ViT-B-32" onece https://github.com/pytorch/pytorch/issues/92073 gets fixed -models_to_test = list(models_to_test) -models_to_test.sort() -models_to_test = [(model_name, False) for model_name in models_to_test] - -models_to_jit_test = {"ViT-B-32"} -models_to_jit_test = list(models_to_jit_test) -models_to_jit_test = [(model_name, True) for model_name in models_to_jit_test] -models_to_test_fully = models_to_test + models_to_jit_test - - -@pytest.mark.regression_test -@pytest.mark.parametrize("model_name,jit", models_to_test_fully) -def test_inference_with_data( - model_name, - jit, - pretrained = None, - pretrained_hf = False, - precision = 'fp32', - force_quick_gelu = False, -): - util_test.seed_all() - model, _, preprocess_val = open_clip.create_model_and_transforms( - model_name, - pretrained = pretrained, - precision = precision, - jit = jit, - force_quick_gelu = force_quick_gelu, - pretrained_hf = pretrained_hf - ) - model_id = f'{model_name}_{pretrained or pretrained_hf}_{precision}' - input_dir, output_dir = util_test.get_data_dirs() - # text - input_text_path = os.path.join(input_dir, 'random_text.pt') - gt_text_path = os.path.join(output_dir, f'{model_id}_random_text.pt') - if not os.path.isfile(input_text_path): - pytest.skip(reason = f"missing test data, expected at {input_text_path}") - if not os.path.isfile(gt_text_path): - pytest.skip(reason = f"missing test data, expected at {gt_text_path}") - input_text = torch.load(input_text_path) - gt_text = torch.load(gt_text_path) - y_text = util_test.inference_text(model, model_name, input_text) - assert (y_text == gt_text).all(), f"text output differs @ {input_text_path}" - # image - image_size = model.visual.image_size - if not isinstance(image_size, tuple): - image_size = (image_size, image_size) - input_image_path = os.path.join(input_dir, f'random_image_{image_size[0]}_{image_size[1]}.pt') - gt_image_path = os.path.join(output_dir, f'{model_id}_random_image.pt') - if not os.path.isfile(input_image_path): - pytest.skip(reason = f"missing test data, expected at {input_image_path}") - if not os.path.isfile(gt_image_path): - pytest.skip(reason = f"missing test data, expected at {gt_image_path}") - input_image = torch.load(input_image_path) - gt_image = torch.load(gt_image_path) - y_image = util_test.inference_image(model, preprocess_val, input_image) - assert (y_image == gt_image).all(), f"image output differs @ {input_image_path}" - - if not jit: - model.eval() - model_out = util_test.forward_model(model, model_name, preprocess_val, input_image, input_text) - if type(model) not in [open_clip.CLIP, open_clip.CustomTextCLIP]: - assert type(model_out) == dict - else: - model.output_dict = True - model_out_dict = util_test.forward_model(model, model_name, preprocess_val, input_image, input_text) - assert (model_out_dict["image_features"] == model_out[0]).all() - assert (model_out_dict["text_features"] == model_out[1]).all() - assert (model_out_dict["logit_scale"] == model_out[2]).all() - model.output_dict = None - else: - model, _, preprocess_val = open_clip.create_model_and_transforms( - model_name, - pretrained = pretrained, - precision = precision, - jit = False, - force_quick_gelu = force_quick_gelu, - pretrained_hf = pretrained_hf - ) - - test_model = util_test.TestWrapper(model, model_name, output_dict=False) - test_model = torch.jit.script(test_model) - model_out = util_test.forward_model(test_model, model_name, preprocess_val, input_image, input_text) - assert model_out["test_output"].shape[-1] == 2 - - test_model = util_test.TestWrapper(model, model_name, output_dict=True) - test_model = torch.jit.script(test_model) - model_out = util_test.forward_model(test_model, model_name, preprocess_val, input_image, input_text) - assert model_out["test_output"].shape[-1] == 2 - - - - diff --git a/spaces/yangliuyi601/rvc-models/infer_pack/modules.py b/spaces/yangliuyi601/rvc-models/infer_pack/modules.py deleted file mode 100644 index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000 --- a/spaces/yangliuyi601/rvc-models/infer_pack/modules.py +++ /dev/null @@ -1,522 +0,0 @@ -import copy -import math -import numpy as np -import scipy -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -from infer_pack import commons -from infer_pack.commons import init_weights, get_padding -from infer_pack.transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__( - self, - in_channels, - hidden_channels, - out_channels, - kernel_size, - n_layers, - p_dropout, - ): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append( - nn.Conv1d( - in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) - for _ in range(n_layers - 1): - self.conv_layers.append( - nn.Conv1d( - hidden_channels, - hidden_channels, - kernel_size, - padding=kernel_size // 2, - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size**i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append( - nn.Conv1d( - channels, - channels, - kernel_size, - groups=channels, - dilation=dilation, - padding=padding, - ) - ) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__( - self, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - p_dropout=0, - ): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - self.hidden_channels = hidden_channels - self.kernel_size = (kernel_size,) - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d( - gin_channels, 2 * hidden_channels * n_layers, 1 - ) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") - - for i in range(n_layers): - dilation = dilation_rate**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d( - hidden_channels, - 2 * hidden_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:, : self.hidden_channels, :] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:, self.hidden_channels :, :] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels, 1)) - self.logs = nn.Parameter(torch.zeros(channels, 1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1, 2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False, - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=p_dropout, - gin_channels=gin_channels, - ) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels] * 2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1, 2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class ConvFlow(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - n_layers, - num_bins=10, - tail_bound=5.0, - ): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) - self.proj = nn.Conv1d( - filter_channels, self.half_channels * (num_bins * 3 - 1), 1 - ) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( - self.filter_channels - ) - unnormalized_derivatives = h[..., 2 * self.num_bins :] - - x1, logabsdet = piecewise_rational_quadratic_transform( - x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails="linear", - tail_bound=self.tail_bound, - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1, 2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/yderre-aubay/midi-player-demo/src/common/quantizer/index.ts b/spaces/yderre-aubay/midi-player-demo/src/common/quantizer/index.ts deleted file mode 100644 index 4b855ee45e711712e48d10d0122da6ac0928b7e3..0000000000000000000000000000000000000000 --- a/spaces/yderre-aubay/midi-player-demo/src/common/quantizer/index.ts +++ /dev/null @@ -1 +0,0 @@ -export { default } from "./Quantizer" diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/modules/networks/simswap.py b/spaces/ygtxr1997/ReliableSwap_Demo/modules/networks/simswap.py deleted file mode 100644 index 87ee8627efb0f837ce8bdb699dad8ec2cc1d83ea..0000000000000000000000000000000000000000 --- a/spaces/ygtxr1997/ReliableSwap_Demo/modules/networks/simswap.py +++ /dev/null @@ -1,230 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -############################################################# -# File: fs_model_fix_idnorm_donggp_saveoptim copy.py -# Created Date: Wednesday January 12th 2022 -# Author: Chen Xuanhong -# Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 21st April 2022 8:13:37 pm -# Modified By: Chen Xuanhong -# Copyright (c) 2022 Shanghai Jiao Tong University -############################################################# - - -import torch -import torch.nn as nn - -from modules.layers.simswap.base_model import BaseModel -from modules.layers.simswap.fs_networks_fix import Generator_Adain_Upsample - -from modules.layers.simswap.pg_modules.projected_discriminator import ProjectedDiscriminator - - -def compute_grad2(d_out, x_in): - batch_size = x_in.size(0) - grad_dout = torch.autograd.grad( - outputs=d_out.sum(), inputs=x_in, - create_graph=True, retain_graph=True, only_inputs=True - )[0] - grad_dout2 = grad_dout.pow(2) - assert(grad_dout2.size() == x_in.size()) - reg = grad_dout2.view(batch_size, -1).sum(1) - return reg - - -class fsModel(BaseModel): - def name(self): - return 'fsModel' - - def initialize(self, opt): - BaseModel.initialize(self, opt) - # if opt.resize_or_crop != 'none' or not opt.isTrain: # when training at full res this causes OOM - self.isTrain = opt.isTrain - - # Generator network - self.netG = Generator_Adain_Upsample(input_nc=3, output_nc=3, latent_size=512, n_blocks=9, deep=opt.Gdeep) - self.netG.cuda() - - # Id network - from third_party.arcface import iresnet100 - netArc_pth = "/apdcephfs_cq2/share_1290939/gavinyuan/code/FaceShifter/faceswap/faceswap/" \ - "checkpoints/face_id/ms1mv3_arcface_r100_fp16_backbone.pth" #opt.Arc_path - self.netArc = iresnet100(pretrained=False, fp16=False) - self.netArc.load_state_dict(torch.load(netArc_pth, map_location="cpu")) - # netArc_checkpoint = opt.Arc_path - # netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu")) - # self.netArc = netArc_checkpoint['model'].module - self.netArc = self.netArc.cuda() - self.netArc.eval() - self.netArc.requires_grad_(False) - if not self.isTrain: - pretrained_path = opt.checkpoints_dir - self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path) - return - self.netD = ProjectedDiscriminator(diffaug=False, interp224=False, **{}) - # self.netD.feature_network.requires_grad_(False) - self.netD.cuda() - - - if self.isTrain: - # define loss functions - self.criterionFeat = nn.L1Loss() - self.criterionRec = nn.L1Loss() - - # initialize optimizers - # optimizer G - params = list(self.netG.parameters()) - self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8) - - # optimizer D - params = list(self.netD.parameters()) - self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.99),eps=1e-8) - - # load networks - if opt.continue_train: - pretrained_path = '' if not self.isTrain else opt.load_pretrain - # print (pretrained_path) - self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path) - self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path) - self.load_optim(self.optimizer_G, 'G', opt.which_epoch, pretrained_path) - self.load_optim(self.optimizer_D, 'D', opt.which_epoch, pretrained_path) - torch.cuda.empty_cache() - - def cosin_metric(self, x1, x2): - #return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2)) - return torch.sum(x1 * x2, dim=1) / (torch.norm(x1, dim=1) * torch.norm(x2, dim=1)) - - def save(self, which_epoch): - self.save_network(self.netG, 'G', which_epoch) - self.save_network(self.netD, 'D', which_epoch) - self.save_optim(self.optimizer_G, 'G', which_epoch) - self.save_optim(self.optimizer_D, 'D', which_epoch) - '''if self.gen_features: - self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)''' - - def update_fixed_params(self): - raise ValueError('Not used') - # after fixing the global generator for a number of iterations, also start finetuning it - params = list(self.netG.parameters()) - if self.gen_features: - params += list(self.netE.parameters()) - self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999)) - if self.opt.verbose: - print('------------ Now also finetuning global generator -----------') - - def update_learning_rate(self): - raise ValueError('Not used') - lrd = self.opt.lr / self.opt.niter_decay - lr = self.old_lr - lrd - for param_group in self.optimizer_D.param_groups: - param_group['lr'] = lr - for param_group in self.optimizer_G.param_groups: - param_group['lr'] = lr - if self.opt.verbose: - print('update learning rate: %f -> %f' % (self.old_lr, lr)) - self.old_lr = lr - - -if __name__ == "__main__": - import os - import argparse - - def str2bool(v): - return v.lower() in ('true') - - - class TrainOptions: - def __init__(self): - self.parser = argparse.ArgumentParser() - self.initialized = False - - def initialize(self): - self.parser.add_argument('--name', type=str, default='simswap', - help='name of the experiment. It decides where to store samples and models') - self.parser.add_argument('--gpu_ids', default='0') - self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', - help='models are saved here') - self.parser.add_argument('--isTrain', type=str2bool, default='True') - - # input/output sizes - self.parser.add_argument('--batchSize', type=int, default=8, help='input batch size') - - # for displays - self.parser.add_argument('--use_tensorboard', type=str2bool, default='False') - - # for training - self.parser.add_argument('--dataset', type=str, default="/path/to/VGGFace2", - help='path to the face swapping dataset') - self.parser.add_argument('--continue_train', type=str2bool, default='False', - help='continue training: load the latest model') - self.parser.add_argument('--load_pretrain', type=str, default='./checkpoints/simswap224_test', - help='load the pretrained model from the specified location') - self.parser.add_argument('--which_epoch', type=str, default='10000', - help='which epoch to load? set to latest to use latest cached model') - self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') - self.parser.add_argument('--niter', type=int, default=10000, help='# of iter at starting learning rate') - self.parser.add_argument('--niter_decay', type=int, default=10000, - help='# of iter to linearly decay learning rate to zero') - self.parser.add_argument('--beta1', type=float, default=0.0, help='momentum term of adam') - self.parser.add_argument('--lr', type=float, default=0.0004, help='initial learning rate for adam') - self.parser.add_argument('--Gdeep', type=str2bool, default='False') - - # for discriminators - self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss') - self.parser.add_argument('--lambda_id', type=float, default=30.0, help='weight for id loss') - self.parser.add_argument('--lambda_rec', type=float, default=10.0, help='weight for reconstruction loss') - - self.parser.add_argument("--Arc_path", type=str, default='arcface_model/arcface_checkpoint.tar', - help="run ONNX model via TRT") - self.parser.add_argument("--total_step", type=int, default=1000000, help='total training step') - self.parser.add_argument("--log_frep", type=int, default=200, help='frequence for printing log information') - self.parser.add_argument("--sample_freq", type=int, default=1000, help='frequence for sampling') - self.parser.add_argument("--model_freq", type=int, default=10000, help='frequence for saving the model') - - self.isTrain = True - - def parse(self, save=True): - if not self.initialized: - self.initialize() - self.opt = self.parser.parse_args() - self.opt.isTrain = self.isTrain # train or test - - args = vars(self.opt) - - print('------------ Options -------------') - for k, v in sorted(args.items()): - print('%s: %s' % (str(k), str(v))) - print('-------------- End ----------------') - - # save to the disk - # if self.opt.isTrain: - # expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name) - # util.mkdirs(expr_dir) - # if save and not self.opt.continue_train: - # file_name = os.path.join(expr_dir, 'opt.txt') - # with open(file_name, 'wt') as opt_file: - # opt_file.write('------------ Options -------------\n') - # for k, v in sorted(args.items()): - # opt_file.write('%s: %s\n' % (str(k), str(v))) - # opt_file.write('-------------- End ----------------\n') - return self.opt - - source = torch.randn(8, 3, 256, 256).cuda() - target = torch.randn(8, 3, 256, 256).cuda() - - opt = TrainOptions().parse() - model = fsModel() - model.initialize(opt) - - import torch.nn.functional as F - img_id_112 = F.interpolate(source, size=(112, 112), mode='bicubic') - latent_id = model.netArc(img_id_112) - latent_id = F.normalize(latent_id, p=2, dim=1) - - img_fake = model.netG(target, latent_id) - gen_logits, _ = model.netD(img_fake.detach(), None) - loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() - - real_logits, _ = model.netD(source, None) - - print('img_fake:', img_fake.shape, 'real_logits:', real_logits.shape) diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_detect/layers/modules/__init__.py b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_detect/layers/modules/__init__.py deleted file mode 100644 index cf24bddbf283f233d0b93fc074a2bac2f5c044a9..0000000000000000000000000000000000000000 --- a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_detect/layers/modules/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .multibox_loss import MultiBoxLoss - -__all__ = ['MultiBoxLoss'] diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/camembert/modeling_camembert.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/camembert/modeling_camembert.py deleted file mode 100644 index 4635c061980b538b5e0e19758bd26822356a27f4..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/camembert/modeling_camembert.py +++ /dev/null @@ -1,1574 +0,0 @@ -# coding=utf-8 -# Copyright 2019 Inria, Facebook AI Research and the HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. 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. -"""PyTorch CamemBERT model.""" - -import math -from typing import List, Optional, Tuple, Union - -import torch -import torch.utils.checkpoint -from torch import nn -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss - -from ...activations import ACT2FN, gelu -from ...modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - MaskedLMOutput, - MultipleChoiceModelOutput, - QuestionAnsweringModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from ...modeling_utils import PreTrainedModel -from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer -from ...utils import ( - add_code_sample_docstrings, - add_start_docstrings, - add_start_docstrings_to_model_forward, - logging, - replace_return_docstrings, -) -from .configuration_camembert import CamembertConfig - - -logger = logging.get_logger(__name__) - -_CHECKPOINT_FOR_DOC = "camembert-base" -_CONFIG_FOR_DOC = "CamembertConfig" - -CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "camembert-base", - "Musixmatch/umberto-commoncrawl-cased-v1", - "Musixmatch/umberto-wikipedia-uncased-v1", - # See all CamemBERT models at https://huggingface.co/models?filter=camembert -] - -CAMEMBERT_START_DOCSTRING = r""" - - This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the - library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads - etc.) - - This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. - Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage - and behavior. - - Parameters: - config ([`CamembertConfig`]): Model configuration class with all the parameters of the - model. Initializing with a config file does not load the weights associated with the model, only the - configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. -""" - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->Camembert -class CamembertEmbeddings(nn.Module): - """ - Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. - """ - - # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) - self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - # position_ids (1, len position emb) is contiguous in memory and exported when serialized - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - self.register_buffer( - "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False - ) - self.register_buffer( - "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False - ) - - # End copy - self.padding_idx = config.pad_token_id - self.position_embeddings = nn.Embedding( - config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx - ) - - def forward( - self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 - ): - if position_ids is None: - if input_ids is not None: - # Create the position ids from the input token ids. Any padded tokens remain padded. - position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) - else: - position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) - - if input_ids is not None: - input_shape = input_ids.size() - else: - input_shape = inputs_embeds.size()[:-1] - - seq_length = input_shape[1] - - # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs - # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves - # issue #5664 - if token_type_ids is None: - if hasattr(self, "token_type_ids"): - buffered_token_type_ids = self.token_type_ids[:, :seq_length] - buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) - token_type_ids = buffered_token_type_ids_expanded - else: - token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - token_type_embeddings = self.token_type_embeddings(token_type_ids) - - embeddings = inputs_embeds + token_type_embeddings - if self.position_embedding_type == "absolute": - position_embeddings = self.position_embeddings(position_ids) - embeddings += position_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - def create_position_ids_from_inputs_embeds(self, inputs_embeds): - """ - We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. - - Args: - inputs_embeds: torch.Tensor - - Returns: torch.Tensor - """ - input_shape = inputs_embeds.size()[:-1] - sequence_length = input_shape[1] - - position_ids = torch.arange( - self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device - ) - return position_ids.unsqueeze(0).expand(input_shape) - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->Camembert -class CamembertSelfAttention(nn.Module): - def __init__(self, config, position_embedding_type=None): - super().__init__() - if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " - f"heads ({config.num_attention_heads})" - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.position_embedding_type = position_embedding_type or getattr( - config, "position_embedding_type", "absolute" - ) - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - self.max_position_embeddings = config.max_position_embeddings - self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) - - self.is_decoder = config.is_decoder - - def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, - output_attentions: Optional[bool] = False, - ) -> Tuple[torch.Tensor]: - mixed_query_layer = self.query(hidden_states) - - # If this is instantiated as a cross-attention module, the keys - # and values come from an encoder; the attention mask needs to be - # such that the encoder's padding tokens are not attended to. - is_cross_attention = encoder_hidden_states is not None - - if is_cross_attention and past_key_value is not None: - # reuse k,v, cross_attentions - key_layer = past_key_value[0] - value_layer = past_key_value[1] - attention_mask = encoder_attention_mask - elif is_cross_attention: - key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) - value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) - attention_mask = encoder_attention_mask - elif past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - key_layer = torch.cat([past_key_value[0], key_layer], dim=2) - value_layer = torch.cat([past_key_value[1], value_layer], dim=2) - else: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - - query_layer = self.transpose_for_scores(mixed_query_layer) - - use_cache = past_key_value is not None - if self.is_decoder: - # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. - # Further calls to cross_attention layer can then reuse all cross-attention - # key/value_states (first "if" case) - # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of - # all previous decoder key/value_states. Further calls to uni-directional self-attention - # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) - # if encoder bi-directional self-attention `past_key_value` is always `None` - past_key_value = (key_layer, value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - query_length, key_length = query_layer.shape[2], key_layer.shape[2] - if use_cache: - position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( - -1, 1 - ) - else: - position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) - position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) - distance = position_ids_l - position_ids_r - - positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) - positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility - - if self.position_embedding_type == "relative_key": - relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores - elif self.position_embedding_type == "relative_key_query": - relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key - - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in CamembertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.functional.softmax(attention_scores, dim=-1) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs = self.dropout(attention_probs) - - # Mask heads if we want to - if head_mask is not None: - attention_probs = attention_probs * head_mask - - context_layer = torch.matmul(attention_probs, value_layer) - - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(new_context_layer_shape) - - outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) - - if self.is_decoder: - outputs = outputs + (past_key_value,) - return outputs - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput with Roberta->Camembert -class CamembertSelfOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->Camembert -class CamembertAttention(nn.Module): - def __init__(self, config, position_embedding_type=None): - super().__init__() - self.self = CamembertSelfAttention(config, position_embedding_type=position_embedding_type) - self.output = CamembertSelfOutput(config) - self.pruned_heads = set() - - def prune_heads(self, heads): - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads - ) - - # Prune linear layers - self.self.query = prune_linear_layer(self.self.query, index) - self.self.key = prune_linear_layer(self.self.key, index) - self.self.value = prune_linear_layer(self.self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.self.num_attention_heads = self.self.num_attention_heads - len(heads) - self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, - output_attentions: Optional[bool] = False, - ) -> Tuple[torch.Tensor]: - self_outputs = self.self( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - attention_output = self.output(self_outputs[0], hidden_states) - outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them - return outputs - - -# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Roberta->Camembert -class CamembertIntermediate(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.intermediate_size) - if isinstance(config.hidden_act, str): - self.intermediate_act_fn = ACT2FN[config.hidden_act] - else: - self.intermediate_act_fn = config.hidden_act - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - hidden_states = self.dense(hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - return hidden_states - - -# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Roberta->Camembert -class CamembertOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->Camembert -class CamembertLayer(nn.Module): - def __init__(self, config): - super().__init__() - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = CamembertAttention(config) - self.is_decoder = config.is_decoder - self.add_cross_attention = config.add_cross_attention - if self.add_cross_attention: - if not self.is_decoder: - raise ValueError(f"{self} should be used as a decoder model if cross attention is added") - self.crossattention = CamembertAttention(config, position_embedding_type="absolute") - self.intermediate = CamembertIntermediate(config) - self.output = CamembertOutput(config) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, - output_attentions: Optional[bool] = False, - ) -> Tuple[torch.Tensor]: - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - self_attention_outputs = self.attention( - hidden_states, - attention_mask, - head_mask, - output_attentions=output_attentions, - past_key_value=self_attn_past_key_value, - ) - attention_output = self_attention_outputs[0] - - # if decoder, the last output is tuple of self-attn cache - if self.is_decoder: - outputs = self_attention_outputs[1:-1] - present_key_value = self_attention_outputs[-1] - else: - outputs = self_attention_outputs[1:] # add self attentions if we output attention weights - - cross_attn_present_key_value = None - if self.is_decoder and encoder_hidden_states is not None: - if not hasattr(self, "crossattention"): - raise ValueError( - f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" - " by setting `config.add_cross_attention=True`" - ) - - # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple - cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None - cross_attention_outputs = self.crossattention( - attention_output, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - cross_attn_past_key_value, - output_attentions, - ) - attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - - # add cross-attn cache to positions 3,4 of present_key_value tuple - cross_attn_present_key_value = cross_attention_outputs[-1] - present_key_value = present_key_value + cross_attn_present_key_value - - layer_output = apply_chunking_to_forward( - self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output - ) - outputs = (layer_output,) + outputs - - # if decoder, return the attn key/values as the last output - if self.is_decoder: - outputs = outputs + (present_key_value,) - - return outputs - - def feed_forward_chunk(self, attention_output): - intermediate_output = self.intermediate(attention_output) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->Camembert -class CamembertEncoder(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.layer = nn.ModuleList([CamembertLayer(config) for _ in range(config.num_hidden_layers)]) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = False, - output_hidden_states: Optional[bool] = False, - return_dict: Optional[bool] = True, - ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - next_decoder_cache = () if use_cache else None - for i, layer_module in enumerate(self.layer): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[i] if head_mask is not None else None - past_key_value = past_key_values[i] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer_module), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - if self.config.add_cross_attention: - all_cross_attentions = all_cross_attentions + (layer_outputs[2],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - -# Copied from transformers.models.bert.modeling_bert.BertPooler -class CamembertPooler(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.activation = nn.Tanh() - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(first_token_tensor) - pooled_output = self.activation(pooled_output) - return pooled_output - - -class CamembertPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = CamembertConfig - base_model_prefix = "roberta" - supports_gradient_checkpointing = True - - # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights - def _init_weights(self, module): - """Initialize the weights""" - if isinstance(module, nn.Linear): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, CamembertEncoder): - module.gradient_checkpointing = value - - -CAMEMBERT_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `({0})`): - Indices of input sequence tokens in the vocabulary. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): - Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, - 1]`: - - - 0 corresponds to a *sentence A* token, - - 1 corresponds to a *sentence B* token. - - [What are token type IDs?](../glossary#token-type-ids) - position_ids (`torch.LongTensor` of shape `({0})`, *optional*): - Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, - config.max_position_embeddings - 1]`. - - [What are position IDs?](../glossary#position-ids) - head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): - Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This - is useful if you want more control over how to convert `input_ids` indices into associated vectors than the - model's internal embedding lookup matrix. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned - tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. -""" - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Camembert -class CamembertClassificationHead(nn.Module): - """Head for sentence-level classification tasks.""" - - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - classifier_dropout = ( - config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob - ) - self.dropout = nn.Dropout(classifier_dropout) - self.out_proj = nn.Linear(config.hidden_size, config.num_labels) - - def forward(self, features, **kwargs): - x = features[:, 0, :] # take token (equiv. to [CLS]) - x = self.dropout(x) - x = self.dense(x) - x = torch.tanh(x) - x = self.dropout(x) - x = self.out_proj(x) - return x - - -# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->Camembert -class CamembertLMHead(nn.Module): - """Camembert Head for masked language modeling.""" - - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - self.decoder = nn.Linear(config.hidden_size, config.vocab_size) - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - self.decoder.bias = self.bias - - def forward(self, features, **kwargs): - x = self.dense(features) - x = gelu(x) - x = self.layer_norm(x) - - # project back to size of vocabulary with bias - x = self.decoder(x) - - return x - - def _tie_weights(self): - # To tie those two weights if they get disconnected (on TPU or when the bias is resized) - # For accelerate compatibility and to not break backward compatibility - if self.decoder.bias.device.type == "meta": - self.decoder.bias = self.bias - else: - self.bias = self.decoder.bias - - -@add_start_docstrings( - "The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.", - CAMEMBERT_START_DOCSTRING, -) -class CamembertModel(CamembertPreTrainedModel): - """ - - The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of - cross-attention is added between the self-attention layers, following the architecture described in *Attention is - all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz - Kaiser and Illia Polosukhin. - - To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to - `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and - `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. - - .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 - - """ - - _no_split_modules = [] - - # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Camembert - def __init__(self, config, add_pooling_layer=True): - super().__init__(config) - self.config = config - - self.embeddings = CamembertEmbeddings(config) - self.encoder = CamembertEncoder(config) - - self.pooler = CamembertPooler(config) if add_pooling_layer else None - - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.embeddings.word_embeddings = value - - def _prune_heads(self, heads_to_prune): - """ - Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base - class PreTrainedModel - """ - for layer, heads in heads_to_prune.items(): - self.encoder.layer[layer].attention.prune_heads(heads) - - @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @add_code_sample_docstrings( - checkpoint=_CHECKPOINT_FOR_DOC, - output_type=BaseModelOutputWithPoolingAndCrossAttentions, - config_class=_CONFIG_FOR_DOC, - ) - # Copied from transformers.models.bert.modeling_bert.BertModel.forward - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: - r""" - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if self.config.is_decoder: - use_cache = use_cache if use_cache is not None else self.config.use_cache - else: - use_cache = False - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - batch_size, seq_length = input_shape - device = input_ids.device if input_ids is not None else inputs_embeds.device - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if attention_mask is None: - attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - - if token_type_ids is None: - if hasattr(self.embeddings, "token_type_ids"): - buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] - buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) - token_type_ids = buffered_token_type_ids_expanded - else: - token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) - - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) - - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if self.config.is_decoder and encoder_hidden_states is not None: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - - embedding_output = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - token_type_ids=token_type_ids, - inputs_embeds=inputs_embeds, - past_key_values_length=past_key_values_length, - ) - encoder_outputs = self.encoder( - embedding_output, - attention_mask=extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - sequence_output = encoder_outputs[0] - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - - if not return_dict: - return (sequence_output, pooled_output) + encoder_outputs[1:] - - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - past_key_values=encoder_outputs.past_key_values, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - cross_attentions=encoder_outputs.cross_attentions, - ) - - -@add_start_docstrings( - """CamemBERT Model with a `language modeling` head on top.""", - CAMEMBERT_START_DOCSTRING, -) -# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT -class CamembertForMaskedLM(CamembertPreTrainedModel): - _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"] - - def __init__(self, config): - super().__init__(config) - - if config.is_decoder: - logger.warning( - "If you want to use `CamembertForMaskedLM` make sure `config.is_decoder=False` for " - "bi-directional self-attention." - ) - - self.roberta = CamembertModel(config, add_pooling_layer=False) - self.lm_head = CamembertLMHead(config) - - # Initialize weights and apply final processing - self.post_init() - - def get_output_embeddings(self): - return self.lm_head.decoder - - def set_output_embeddings(self, new_embeddings): - self.lm_head.decoder = new_embeddings - - @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @add_code_sample_docstrings( - checkpoint=_CHECKPOINT_FOR_DOC, - output_type=MaskedLMOutput, - config_class=_CONFIG_FOR_DOC, - mask="", - expected_output="' Paris'", - expected_loss=0.1, - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., - config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the - loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` - kwargs (`Dict[str, any]`, optional, defaults to *{}*): - Used to hide legacy arguments that have been deprecated. - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.roberta( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - sequence_output = outputs[0] - prediction_scores = self.lm_head(sequence_output) - - masked_lm_loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(prediction_scores.device) - loss_fct = CrossEntropyLoss() - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output - - return MaskedLMOutput( - loss=masked_lm_loss, - logits=prediction_scores, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """ - CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the - pooled output) e.g. for GLUE tasks. - """, - CAMEMBERT_START_DOCSTRING, -) -# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification with Roberta->Camembert, ROBERTA->CAMEMBERT -class CamembertForSequenceClassification(CamembertPreTrainedModel): - def __init__(self, config): - super().__init__(config) - self.num_labels = config.num_labels - self.config = config - - self.roberta = CamembertModel(config, add_pooling_layer=False) - self.classifier = CamembertClassificationHead(config) - - # Initialize weights and apply final processing - self.post_init() - - @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @add_code_sample_docstrings( - checkpoint="cardiffnlp/twitter-roberta-base-emotion", - output_type=SequenceClassifierOutput, - config_class=_CONFIG_FOR_DOC, - expected_output="'optimism'", - expected_loss=0.08, - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.roberta( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - sequence_output = outputs[0] - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(logits.device) - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(logits, labels) - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """ - CamemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a - softmax) e.g. for RocStories/SWAG tasks. - """, - CAMEMBERT_START_DOCSTRING, -) -# Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice with Roberta->Camembert, ROBERTA->CAMEMBERT -class CamembertForMultipleChoice(CamembertPreTrainedModel): - def __init__(self, config): - super().__init__(config) - - self.roberta = CamembertModel(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - self.classifier = nn.Linear(config.hidden_size, 1) - - # Initialize weights and apply final processing - self.post_init() - - @add_start_docstrings_to_model_forward( - CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") - ) - @add_code_sample_docstrings( - checkpoint=_CHECKPOINT_FOR_DOC, - output_type=MultipleChoiceModelOutput, - config_class=_CONFIG_FOR_DOC, - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., - num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See - `input_ids` above) - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] - - flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None - flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None - flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None - flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None - flat_inputs_embeds = ( - inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) - if inputs_embeds is not None - else None - ) - - outputs = self.roberta( - flat_input_ids, - position_ids=flat_position_ids, - token_type_ids=flat_token_type_ids, - attention_mask=flat_attention_mask, - head_mask=head_mask, - inputs_embeds=flat_inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - pooled_output = outputs[1] - - pooled_output = self.dropout(pooled_output) - logits = self.classifier(pooled_output) - reshaped_logits = logits.view(-1, num_choices) - - loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(reshaped_logits.device) - loss_fct = CrossEntropyLoss() - loss = loss_fct(reshaped_logits, labels) - - if not return_dict: - output = (reshaped_logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return MultipleChoiceModelOutput( - loss=loss, - logits=reshaped_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """ - CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. - for Named-Entity-Recognition (NER) tasks. - """, - CAMEMBERT_START_DOCSTRING, -) -# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification with Roberta->Camembert, ROBERTA->CAMEMBERT -class CamembertForTokenClassification(CamembertPreTrainedModel): - def __init__(self, config): - super().__init__(config) - self.num_labels = config.num_labels - - self.roberta = CamembertModel(config, add_pooling_layer=False) - classifier_dropout = ( - config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob - ) - self.dropout = nn.Dropout(classifier_dropout) - self.classifier = nn.Linear(config.hidden_size, config.num_labels) - - # Initialize weights and apply final processing - self.post_init() - - @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @add_code_sample_docstrings( - checkpoint="Jean-Baptiste/roberta-large-ner-english", - output_type=TokenClassifierOutput, - config_class=_CONFIG_FOR_DOC, - expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", - expected_loss=0.01, - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.roberta( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - - sequence_output = self.dropout(sequence_output) - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(logits.device) - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """ - CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear - layers on top of the hidden-states output to compute `span start logits` and `span end logits` - """, - CAMEMBERT_START_DOCSTRING, -) -# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering with Roberta->Camembert, ROBERTA->CAMEMBERT -class CamembertForQuestionAnswering(CamembertPreTrainedModel): - def __init__(self, config): - super().__init__(config) - self.num_labels = config.num_labels - - self.roberta = CamembertModel(config, add_pooling_layer=False) - self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) - - # Initialize weights and apply final processing - self.post_init() - - @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @add_code_sample_docstrings( - checkpoint="deepset/roberta-base-squad2", - output_type=QuestionAnsweringModelOutput, - config_class=_CONFIG_FOR_DOC, - expected_output="' puppet'", - expected_loss=0.86, - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - start_positions: Optional[torch.LongTensor] = None, - end_positions: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: - r""" - start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the start of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the end of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.roberta( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - - logits = self.qa_outputs(sequence_output) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1).contiguous() - end_logits = end_logits.squeeze(-1).contiguous() - - total_loss = None - if start_positions is not None and end_positions is not None: - # If we are on multi-GPU, split add a dimension - if len(start_positions.size()) > 1: - start_positions = start_positions.squeeze(-1) - if len(end_positions.size()) > 1: - end_positions = end_positions.squeeze(-1) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - start_positions = start_positions.clamp(0, ignored_index) - end_positions = end_positions.clamp(0, ignored_index) - - loss_fct = CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, start_positions) - end_loss = loss_fct(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2 - - if not return_dict: - output = (start_logits, end_logits) + outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return QuestionAnsweringModelOutput( - loss=total_loss, - start_logits=start_logits, - end_logits=end_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING -) -# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->Camembert, ROBERTA->CAMEMBERT, roberta-base->camembert-base -class CamembertForCausalLM(CamembertPreTrainedModel): - _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"] - - def __init__(self, config): - super().__init__(config) - - if not config.is_decoder: - logger.warning("If you want to use `CamembertLMHeadModel` as a standalone, add `is_decoder=True.`") - - self.roberta = CamembertModel(config, add_pooling_layer=False) - self.lm_head = CamembertLMHead(config) - - # Initialize weights and apply final processing - self.post_init() - - def get_output_embeddings(self): - return self.lm_head.decoder - - def set_output_embeddings(self, new_embeddings): - self.lm_head.decoder = new_embeddings - - @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: - r""" - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in - `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are - ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, CamembertForCausalLM, AutoConfig - >>> import torch - - >>> tokenizer = AutoTokenizer.from_pretrained("camembert-base") - >>> config = AutoConfig.from_pretrained("camembert-base") - >>> config.is_decoder = True - >>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config) - - >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") - >>> outputs = model(**inputs) - - >>> prediction_logits = outputs.logits - ```""" - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - if labels is not None: - use_cache = False - - outputs = self.roberta( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - prediction_scores = self.lm_head(sequence_output) - - lm_loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(prediction_scores.device) - # we are doing next-token prediction; shift prediction scores and input ids by one - shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() - labels = labels[:, 1:].contiguous() - loss_fct = CrossEntropyLoss() - lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((lm_loss,) + output) if lm_loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=lm_loss, - logits=prediction_scores, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - - def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs): - input_shape = input_ids.shape - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_shape) - - # cut decoder_input_ids if past is used - if past_key_values is not None: - input_ids = input_ids[:, -1:] - - return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values} - - def _reorder_cache(self, past_key_values, beam_idx): - reordered_past = () - for layer_past in past_key_values: - reordered_past += ( - tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), - ) - return reordered_past - - -# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids -def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): - """ - Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols - are ignored. This is modified from fairseq's `utils.make_positions`. - - Args: - x: torch.Tensor x: - - Returns: torch.Tensor - """ - # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. - mask = input_ids.ne(padding_idx).int() - incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask - return incremental_indices.long() + padding_idx diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/__init__.py deleted file mode 100644 index d0de4a00bd15005fe974f7240b9bc6c940f5b789..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/__init__.py +++ /dev/null @@ -1,97 +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. -from typing import TYPE_CHECKING - -from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available - - -_import_structure = { - "configuration_groupvit": [ - "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", - "GroupViTConfig", - "GroupViTOnnxConfig", - "GroupViTTextConfig", - "GroupViTVisionConfig", - ], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_groupvit"] = [ - "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", - "GroupViTModel", - "GroupViTPreTrainedModel", - "GroupViTTextModel", - "GroupViTVisionModel", - ] - -try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_tf_groupvit"] = [ - "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", - "TFGroupViTModel", - "TFGroupViTPreTrainedModel", - "TFGroupViTTextModel", - "TFGroupViTVisionModel", - ] - -if TYPE_CHECKING: - from .configuration_groupvit import ( - GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, - GroupViTConfig, - GroupViTOnnxConfig, - GroupViTTextConfig, - GroupViTVisionConfig, - ) - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_groupvit import ( - GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, - GroupViTModel, - GroupViTPreTrainedModel, - GroupViTTextModel, - GroupViTVisionModel, - ) - - try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_tf_groupvit import ( - TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, - TFGroupViTModel, - TFGroupViTPreTrainedModel, - TFGroupViTTextModel, - TFGroupViTVisionModel, - ) - -else: - import sys - - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/modules/F0Predictor/crepe.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/modules/F0Predictor/crepe.py deleted file mode 100644 index c6fb45c79bcd306202a2c0282b3d73a8074ced5d..0000000000000000000000000000000000000000 --- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/modules/F0Predictor/crepe.py +++ /dev/null @@ -1,340 +0,0 @@ -from typing import Optional,Union -try: - from typing import Literal -except Exception as e: - from typing_extensions import Literal -import numpy as np -import torch -import torchcrepe -from torch import nn -from torch.nn import functional as F -import scipy - -#from:https://github.com/fishaudio/fish-diffusion - -def repeat_expand( - content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest" -): - """Repeat content to target length. - This is a wrapper of torch.nn.functional.interpolate. - - Args: - content (torch.Tensor): tensor - target_len (int): target length - mode (str, optional): interpolation mode. Defaults to "nearest". - - Returns: - torch.Tensor: tensor - """ - - ndim = content.ndim - - if content.ndim == 1: - content = content[None, None] - elif content.ndim == 2: - content = content[None] - - assert content.ndim == 3 - - is_np = isinstance(content, np.ndarray) - if is_np: - content = torch.from_numpy(content) - - results = torch.nn.functional.interpolate(content, size=target_len, mode=mode) - - if is_np: - results = results.numpy() - - if ndim == 1: - return results[0, 0] - elif ndim == 2: - return results[0] - - -class BasePitchExtractor: - def __init__( - self, - hop_length: int = 512, - f0_min: float = 50.0, - f0_max: float = 1100.0, - keep_zeros: bool = True, - ): - """Base pitch extractor. - - Args: - hop_length (int, optional): Hop length. Defaults to 512. - f0_min (float, optional): Minimum f0. Defaults to 50.0. - f0_max (float, optional): Maximum f0. Defaults to 1100.0. - keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True. - """ - - self.hop_length = hop_length - self.f0_min = f0_min - self.f0_max = f0_max - self.keep_zeros = keep_zeros - - def __call__(self, x, sampling_rate=44100, pad_to=None): - raise NotImplementedError("BasePitchExtractor is not callable.") - - def post_process(self, x, sampling_rate, f0, pad_to): - if isinstance(f0, np.ndarray): - f0 = torch.from_numpy(f0).float().to(x.device) - - if pad_to is None: - return f0 - - f0 = repeat_expand(f0, pad_to) - - if self.keep_zeros: - return f0 - - vuv_vector = torch.zeros_like(f0) - vuv_vector[f0 > 0.0] = 1.0 - vuv_vector[f0 <= 0.0] = 0.0 - - # 去掉0频率, 并线性插值 - nzindex = torch.nonzero(f0).squeeze() - f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy() - time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy() - time_frame = np.arange(pad_to) * self.hop_length / sampling_rate - - if f0.shape[0] <= 0: - return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device) - - if f0.shape[0] == 1: - return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device) - - # 大概可以用 torch 重写? - f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1]) - vuv_vector = vuv_vector.cpu().numpy() - vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0)) - - return f0,vuv_vector - - -class MaskedAvgPool1d(nn.Module): - def __init__( - self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0 - ): - """An implementation of mean pooling that supports masked values. - - Args: - kernel_size (int): The size of the median pooling window. - stride (int, optional): The stride of the median pooling window. Defaults to None. - padding (int, optional): The padding of the median pooling window. Defaults to 0. - """ - - super(MaskedAvgPool1d, self).__init__() - self.kernel_size = kernel_size - self.stride = stride or kernel_size - self.padding = padding - - def forward(self, x, mask=None): - ndim = x.dim() - if ndim == 2: - x = x.unsqueeze(1) - - assert ( - x.dim() == 3 - ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)" - - # Apply the mask by setting masked elements to zero, or make NaNs zero - if mask is None: - mask = ~torch.isnan(x) - - # Ensure mask has the same shape as the input tensor - assert x.shape == mask.shape, "Input tensor and mask must have the same shape" - - masked_x = torch.where(mask, x, torch.zeros_like(x)) - # Create a ones kernel with the same number of channels as the input tensor - ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device) - - # Perform sum pooling - sum_pooled = nn.functional.conv1d( - masked_x, - ones_kernel, - stride=self.stride, - padding=self.padding, - groups=x.size(1), - ) - - # Count the non-masked (valid) elements in each pooling window - valid_count = nn.functional.conv1d( - mask.float(), - ones_kernel, - stride=self.stride, - padding=self.padding, - groups=x.size(1), - ) - valid_count = valid_count.clamp(min=1) # Avoid division by zero - - # Perform masked average pooling - avg_pooled = sum_pooled / valid_count - - # Fill zero values with NaNs - avg_pooled[avg_pooled == 0] = float("nan") - - if ndim == 2: - return avg_pooled.squeeze(1) - - return avg_pooled - - -class MaskedMedianPool1d(nn.Module): - def __init__( - self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0 - ): - """An implementation of median pooling that supports masked values. - - This implementation is inspired by the median pooling implementation in - https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598 - - Args: - kernel_size (int): The size of the median pooling window. - stride (int, optional): The stride of the median pooling window. Defaults to None. - padding (int, optional): The padding of the median pooling window. Defaults to 0. - """ - - super(MaskedMedianPool1d, self).__init__() - self.kernel_size = kernel_size - self.stride = stride or kernel_size - self.padding = padding - - def forward(self, x, mask=None): - ndim = x.dim() - if ndim == 2: - x = x.unsqueeze(1) - - assert ( - x.dim() == 3 - ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)" - - if mask is None: - mask = ~torch.isnan(x) - - assert x.shape == mask.shape, "Input tensor and mask must have the same shape" - - masked_x = torch.where(mask, x, torch.zeros_like(x)) - - x = F.pad(masked_x, (self.padding, self.padding), mode="reflect") - mask = F.pad( - mask.float(), (self.padding, self.padding), mode="constant", value=0 - ) - - x = x.unfold(2, self.kernel_size, self.stride) - mask = mask.unfold(2, self.kernel_size, self.stride) - - x = x.contiguous().view(x.size()[:3] + (-1,)) - mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device) - - # Combine the mask with the input tensor - #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf"))) - x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device)) - - # Sort the masked tensor along the last dimension - x_sorted, _ = torch.sort(x_masked, dim=-1) - - # Compute the count of non-masked (valid) values - valid_count = mask.sum(dim=-1) - - # Calculate the index of the median value for each pooling window - median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0) - - # Gather the median values using the calculated indices - median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1) - - # Fill infinite values with NaNs - median_pooled[torch.isinf(median_pooled)] = float("nan") - - if ndim == 2: - return median_pooled.squeeze(1) - - return median_pooled - - -class CrepePitchExtractor(BasePitchExtractor): - def __init__( - self, - hop_length: int = 512, - f0_min: float = 50.0, - f0_max: float = 1100.0, - threshold: float = 0.05, - keep_zeros: bool = False, - device = None, - model: Literal["full", "tiny"] = "full", - use_fast_filters: bool = True, - decoder="viterbi" - ): - super().__init__(hop_length, f0_min, f0_max, keep_zeros) - if decoder == "viterbi": - self.decoder = torchcrepe.decode.viterbi - elif decoder == "argmax": - self.decoder = torchcrepe.decode.argmax - elif decoder == "weighted_argmax": - self.decoder = torchcrepe.decode.weighted_argmax - else: - raise "Unknown decoder" - self.threshold = threshold - self.model = model - self.use_fast_filters = use_fast_filters - self.hop_length = hop_length - if device is None: - self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") - else: - self.dev = torch.device(device) - if self.use_fast_filters: - self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device) - self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device) - - def __call__(self, x, sampling_rate=44100, pad_to=None): - """Extract pitch using crepe. - - - Args: - x (torch.Tensor): Audio signal, shape (1, T). - sampling_rate (int, optional): Sampling rate. Defaults to 44100. - pad_to (int, optional): Pad to length. Defaults to None. - - Returns: - torch.Tensor: Pitch, shape (T // hop_length,). - """ - - assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor." - assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels." - - x = x.to(self.dev) - f0, pd = torchcrepe.predict( - x, - sampling_rate, - self.hop_length, - self.f0_min, - self.f0_max, - pad=True, - model=self.model, - batch_size=1024, - device=x.device, - return_periodicity=True, - decoder=self.decoder - ) - - # Filter, remove silence, set uv threshold, refer to the original warehouse readme - if self.use_fast_filters: - pd = self.median_filter(pd) - else: - pd = torchcrepe.filter.median(pd, 3) - - pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512) - f0 = torchcrepe.threshold.At(self.threshold)(f0, pd) - - if self.use_fast_filters: - f0 = self.mean_filter(f0) - else: - f0 = torchcrepe.filter.mean(f0, 3) - - f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0] - - if torch.all(f0 == 0): - rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to) - return rtn,rtn - - return self.post_process(x, sampling_rate, f0, pad_to) diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/brackets.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/brackets.js deleted file mode 100644 index 3bb1dad4a9a74f70af7bb9b40635a1af2af920ad..0000000000000000000000000000000000000000 --- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/brackets.js +++ /dev/null @@ -1,51 +0,0 @@ -function last(array) { - return array[array.length - 1] -} - -let brackets = { - /** - * Parse string to nodes tree - */ - parse(str) { - let current = [''] - let stack = [current] - - for (let sym of str) { - if (sym === '(') { - current = [''] - last(stack).push(current) - stack.push(current) - continue - } - - if (sym === ')') { - stack.pop() - current = last(stack) - current.push('') - continue - } - - current[current.length - 1] += sym - } - - return stack[0] - }, - - /** - * Generate output string by nodes tree - */ - stringify(ast) { - let result = '' - for (let i of ast) { - if (typeof i === 'object') { - result += `(${brackets.stringify(i)})` - continue - } - - result += i - } - return result - } -} - -module.exports = brackets diff --git a/spaces/yuhanbo/chat-gpt/scripts/setup.sh b/spaces/yuhanbo/chat-gpt/scripts/setup.sh deleted file mode 100644 index 63a28bf09df7df2b219921f9fd83a2f4e4acadd8..0000000000000000000000000000000000000000 --- a/spaces/yuhanbo/chat-gpt/scripts/setup.sh +++ /dev/null @@ -1,64 +0,0 @@ -#!/bin/bash - -# Check if running on a supported system -case "$(uname -s)" in - Linux) - if [[ -f "/etc/lsb-release" ]]; then - . /etc/lsb-release - if [[ "$DISTRIB_ID" != "Ubuntu" ]]; then - echo "This script only works on Ubuntu, not $DISTRIB_ID." - exit 1 - fi - else - if [[ ! "$(cat /etc/*-release | grep '^ID=')" =~ ^(ID=\"ubuntu\")|(ID=\"centos\")|(ID=\"arch\")$ ]]; then - echo "Unsupported Linux distribution." - exit 1 - fi - fi - ;; - Darwin) - echo "Running on MacOS." - ;; - *) - echo "Unsupported operating system." - exit 1 - ;; -esac - -# Check if needed dependencies are installed and install if necessary -if ! command -v node >/dev/null || ! command -v git >/dev/null || ! command -v yarn >/dev/null; then - case "$(uname -s)" in - Linux) - if [[ "$(cat /etc/*-release | grep '^ID=')" = "ID=\"ubuntu\"" ]]; then - sudo apt-get update - sudo apt-get -y install nodejs git yarn - elif [[ "$(cat /etc/*-release | grep '^ID=')" = "ID=\"centos\"" ]]; then - sudo yum -y install epel-release - sudo yum -y install nodejs git yarn - elif [[ "$(cat /etc/*-release | grep '^ID=')" = "ID=\"arch\"" ]]; then - sudo pacman -Syu -y - sudo pacman -S -y nodejs git yarn - else - echo "Unsupported Linux distribution" - exit 1 - fi - ;; - Darwin) - /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" - brew install node git yarn - ;; - esac -fi - -# Clone the repository and install dependencies -git clone https://github.com/Yidadaa/ChatGPT-Next-Web -cd ChatGPT-Next-Web -yarn install - -# Prompt user for environment variables -read -p "Enter OPENAI_API_KEY: " OPENAI_API_KEY -read -p "Enter CODE: " CODE -read -p "Enter PORT: " PORT - -# Build and run the project using the environment variables -OPENAI_API_KEY=$OPENAI_API_KEY CODE=$CODE PORT=$PORT yarn build && OPENAI_API_KEY=$OPENAI_API_KEY CODE=$CODE PORT=$PORT yarn start diff --git a/spaces/zekewilliams/ControlNet/app_scribble.py b/spaces/zekewilliams/ControlNet/app_scribble.py deleted file mode 100644 index 726fcadbd126e33eb91ecf7ef992484df2ced127..0000000000000000000000000000000000000000 --- a/spaces/zekewilliams/ControlNet/app_scribble.py +++ /dev/null @@ -1,77 +0,0 @@ -# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_scribble2image.py -# The original license file is LICENSE.ControlNet in this repo. -import gradio as gr - - -def create_demo(process, max_images=12, default_num_images=3): - with gr.Blocks() as demo: - with gr.Row(): - gr.Markdown('## Control Stable Diffusion with Scribble Maps') - with gr.Row(): - with gr.Column(): - input_image = gr.Image(source='upload', type='numpy') - prompt = gr.Textbox(label='Prompt') - run_button = gr.Button(label='Run') - with gr.Accordion('Advanced options', open=False): - num_samples = gr.Slider(label='Images', - minimum=1, - maximum=max_images, - value=default_num_images, - step=1) - image_resolution = gr.Slider(label='Image Resolution', - minimum=256, - maximum=512, - value=512, - step=256) - num_steps = gr.Slider(label='Steps', - minimum=1, - maximum=100, - value=20, - step=1) - guidance_scale = gr.Slider(label='Guidance Scale', - minimum=0.1, - maximum=30.0, - value=9.0, - step=0.1) - seed = gr.Slider(label='Seed', - minimum=-1, - maximum=2147483647, - step=1, - randomize=True) - a_prompt = gr.Textbox( - label='Added Prompt', - value='best quality, extremely detailed') - n_prompt = gr.Textbox( - label='Negative Prompt', - value= - 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' - ) - with gr.Column(): - result = gr.Gallery(label='Output', - show_label=False, - elem_id='gallery').style(grid=2, - height='auto') - inputs = [ - input_image, - prompt, - a_prompt, - n_prompt, - num_samples, - image_resolution, - num_steps, - guidance_scale, - seed, - ] - prompt.submit(fn=process, inputs=inputs, outputs=result) - run_button.click(fn=process, - inputs=inputs, - outputs=result, - api_name='scribble') - return demo - - -if __name__ == '__main__': - from model import Model - model = Model() - demo = create_demo(model.process_scribble) - demo.queue().launch() diff --git a/spaces/zhan66/vits-uma-genshin-honkai/README.md b/spaces/zhan66/vits-uma-genshin-honkai/README.md deleted file mode 100644 index fe8371b0e78d55cf1650a18217c82f25db5e490a..0000000000000000000000000000000000000000 --- a/spaces/zhan66/vits-uma-genshin-honkai/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -license: apache-2.0 -title: ' vits-uma-genshin-honkai' -sdk: gradio -sdk_version: 3.7 -emoji: 🐨 -colorTo: yellow -pinned: false -app_file: app.py ---- \ No newline at end of file diff --git a/spaces/zhenwusw/JoJoGAN/op/fused_act_cpu.py b/spaces/zhenwusw/JoJoGAN/op/fused_act_cpu.py deleted file mode 100644 index f997dafdd53aa9f4bbe07af6746c67a2c6dcb4c7..0000000000000000000000000000000000000000 --- a/spaces/zhenwusw/JoJoGAN/op/fused_act_cpu.py +++ /dev/null @@ -1,41 +0,0 @@ -import os - -import torch -from torch import nn -from torch.autograd import Function -from torch.nn import functional as F - - -module_path = os.path.dirname(__file__) - - -class FusedLeakyReLU(nn.Module): - def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - -def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5): - if input.device.type == "cpu": - if bias is not None: - rest_dim = [1] * (input.ndim - bias.ndim - 1) - return ( - F.leaky_relu( - input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2 - ) - * scale - ) - - else: - return F.leaky_relu(input, negative_slope=0.2) * scale - - else: - return FusedLeakyReLUFunction.apply( - input.contiguous(), bias, negative_slope, scale - ) - diff --git a/spaces/zideliu/styledrop/open_clip/utils.py b/spaces/zideliu/styledrop/open_clip/utils.py deleted file mode 100644 index 51e80c5e296b24cae130ab0459baf268e0db7673..0000000000000000000000000000000000000000 --- a/spaces/zideliu/styledrop/open_clip/utils.py +++ /dev/null @@ -1,60 +0,0 @@ -from itertools import repeat -import collections.abc - -from torch import nn as nn -from torchvision.ops.misc import FrozenBatchNorm2d - - -def freeze_batch_norm_2d(module, module_match={}, name=''): - """ - Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is - itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and - returned. Otherwise, the module is walked recursively and submodules are converted in place. - - Args: - module (torch.nn.Module): Any PyTorch module. - module_match (dict): Dictionary of full module names to freeze (all if empty) - name (str): Full module name (prefix) - - Returns: - torch.nn.Module: Resulting module - - Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 - """ - res = module - is_match = True - if module_match: - is_match = name in module_match - if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): - res = FrozenBatchNorm2d(module.num_features) - res.num_features = module.num_features - res.affine = module.affine - if module.affine: - res.weight.data = module.weight.data.clone().detach() - res.bias.data = module.bias.data.clone().detach() - res.running_mean.data = module.running_mean.data - res.running_var.data = module.running_var.data - res.eps = module.eps - else: - for child_name, child in module.named_children(): - full_child_name = '.'.join([name, child_name]) if name else child_name - new_child = freeze_batch_norm_2d(child, module_match, full_child_name) - if new_child is not child: - res.add_module(child_name, new_child) - return res - - -# From PyTorch internals -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - return parse - - -to_1tuple = _ntuple(1) -to_2tuple = _ntuple(2) -to_3tuple = _ntuple(3) -to_4tuple = _ntuple(4) -to_ntuple = lambda n, x: _ntuple(n)(x) diff --git a/spaces/zzzzred/extras/Dockerfile b/spaces/zzzzred/extras/Dockerfile deleted file mode 100644 index f45cdfda0fab5fe7680df646ea7caf47d45e4352..0000000000000000000000000000000000000000 --- a/spaces/zzzzred/extras/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM python:3.11 - -WORKDIR /app - -COPY requirements-complete.txt . -RUN pip install -r requirements-complete.txt - -RUN mkdir /.cache && chmod -R 777 /.cache -RUN mkdir .chroma && chmod -R 777 .chroma - -COPY . . - - -RUN chmod -R 777 /app - -RUN --mount=type=secret,id=password,mode=0444,required=true \ - cat /run/secrets/password > /test - -EXPOSE 7860 - -CMD ["python", "server.py", "--cpu", "--enable-modules=caption,summarize,classify,silero-tts,edge-tts,chromadb"]