If you are looking for a way to convert your PDF files to InDesign files, you might have come across a file named Markzware PDF2DTP-torrent.rar. But what is this file and how can you use it? In this article, we will explain what Markzware PDF2DTP is, what a torrent file is, how to download and use Markzware PDF2DTP-torrent.rar, how much it costs, and where to get it.
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A PDF file (Portable Document Format) is a file format that preserves the layout, formatting, and quality of a document across different platforms and devices. It is widely used for viewing and printing documents, but not for editing them.
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-If you are a fan of Grand Theft Auto (GTA) series, you might have heard of GTA Mumbai City Pc Game 18, a popular game that is set in the city of Mumbai, India. This game is not an official release by Rockstar Games, but a mod created by fans who wanted to experience the thrill of playing GTA in a different setting. In this article, we will review GTA Mumbai City Pc Game 18 and see what it has to offer.
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GTA Mumbai City Pc Game 18 follows the same gameplay mechanics as other GTA games. You can explore the open world of Mumbai, drive various vehicles, complete missions, fight enemies, and interact with other characters. The game also features some unique elements that reflect the culture and lifestyle of Mumbai, such as Bollywood music, local food, rickshaws, slums, and landmarks. You can also customize your character's appearance, clothes, weapons, and skills.
-GTA Mumbai City Pc Game 18 is based on GTA Vice City, which was released in 2002. Therefore, the graphics are not very impressive by today's standards. However, the game does a good job of recreating the atmosphere and scenery of Mumbai, with realistic textures, colors, and lighting. The game also runs smoothly on most PCs, as long as you have the minimum system requirements. You can also adjust the graphics settings to suit your preferences.
-GTA Mumbai City Pc Game 18 has a great soundtrack that features songs from Bollywood movies and Indian pop artists. The songs match the mood and theme of the game, and add to the immersion. The game also has voice acting for some of the main characters, but not all of them. The voice actors have Indian accents and use some Hindi words, which adds to the authenticity. The sound effects are also decent, but not very realistic.
-GTA Mumbai City Pc Game 18 has a story that revolves around a young man named Raju, who comes to Mumbai from a small village to pursue his dreams. He gets involved in the criminal underworld of Mumbai, and works for various gangs and bosses. He also meets some friends and enemies along the way, who help or hinder his progress. The story is not very original or engaging, but it provides some motivation and context for the gameplay.
-GTA Mumbai City Pc Game 18 has some pros and cons that you should consider before playing it. Here are some of them:
-| Pros | -Cons | -
|---|---|
| - A different and interesting setting for GTA fans. | -- Not an official game by Rockstar Games. | -
| - A lot of content and variety in gameplay. | -- Outdated graphics and sound quality. | -
| - A fun and catchy soundtrack. | -- A weak and cliched story. | -
| - A free download for PC users. | -- A potential risk of viruses or malware. | -
| - A creative and impressive mod by fans. | -- A possible violation of intellectual property rights. | -
GTA Mumbai City Pc Game 18 is a game that offers a new and exciting experience for GTA fans who want to explore a different city and culture. The game has a lot of content and features that make it enjoyable and entertaining. However, the game also has some drawbacks that might disappoint some players who expect high-quality graphics, sound, and story. The game is also not an official product by Rockstar Games, but a mod created by fans who might have infringed on some copyrights. Therefore, you should play this game at your own risk and discretion.
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-A5: No, GTA Mumbai City Pc Game 18 is not suitable for children under 18 years old. The game contains violence, blood, gore, profanity, drugs, alcohol, sex, nudity, gambling, crime, and other mature themes that are inappropriate for minors.
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The answer to this question depends on your personal preference and budget. However, some cues are generally considered to be the best in the game because of their stats, features, and popularity. Here are some of them:
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-The best way to play the game is to play fair and square, earn coins and cash, buy or win cues, and collect pieces of cues. This is how the game is meant to be played and enjoyed. You can also improve your skills and strategy with your cue by practicing a lot and learning from your matches and opponents. This way, you can have fun and satisfaction with the game.
-We hope this article has helped you understand how to download 8 ball pool unlock all cues and how to use them in the game. If you have any questions or comments, feel free to leave them below. And if you liked this article, please share it with your friends or fellow players. Thank you for reading!
-A: There are several ways to get free coins and cash in 8 ball pool. You can:
-A: You can upgrade your cue by using Pool Cash or Cue Pieces. Pool Cash is a premium currency that you can buy with real money or earn by playing the game. Cue Pieces are fragments of cues that you can collect by opening Victory Boxes or Legendary Boxes. To upgrade your cue, go to the Pool Shop, tap on Cues, select your cue, and tap on Upgrade.
-A: You can get Legendary Cues by opening Legendary Boxes or by collecting pieces of cues in the Pool Pass. Legendary Boxes are special boxes that contain pieces of Legendary Cues. You can buy them with Pool Cash or win them in some events or promotions. Pool Pass is a seasonal feature that allows you to earn rewards by completing challenges and leveling up. Some of the rewards are pieces of Legendary Cues.
-A: You can contact the support team of 8 ball pool by going to the Settings, tapping on Help and Support, and choosing the option that suits your issue. You can also visit the official website or social media pages of 8 ball pool and send them a message or feedback.
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-Estoy dando vueltas como Ronda -
MP4 es uno de los formatos de medios más comunes para la transmisión y descarga de vídeo desde Internet. Tiene muchas ventajas sobre otros formatos como AVI o MKV. Algunos de ellos son:
- -Si desea descargar gratis blx ckie ronda mp4 o cualquier otro video MP4 desde cualquier sitio web, puede usar uno de los siguientes métodos:
-Blxckie ronda mp4 descarga es una gran manera de disfrutar de la canción Ronda by Blxckie, uno de los artistas de hip hop más calientes de Sudáfrica en este momento. Puedes aprender más sobre los antecedentes de Blxckie, el significado de Ronda, los beneficios del formato MP4 y cómo descargar videos MP4 desde cualquier sitio web de forma gratuita en este artículo. Esperamos que le resulte útil e informativo.
-Si te gustó este artículo, por favor compártelo con tus amigos y familiares que también son fans de Blxckie y el hip hop sudafricano. También puede dejar un comentario a continuación y háganos saber lo que piensa de Blxckie ronda mp4 descarga. Gracias por leer!
-No hay una respuesta definitiva a esta pregunta, ya que diferentes sitios web pueden tener diferentes características y cualidades. Sin embargo, algunos de los factores que puede considerar al elegir un sitio web para descargar blxckie ronda mp4 son:
-Puedes probar diferentes sitios web y ver cuál funciona mejor para ti.
-Si desea convertir blxckie ronda mp4 a mp3, que es un formato de audio, puede utilizar uno de los siguientes métodos:
-Si quieres ver blxckie ronda mp4 en tu televisor, puedes usar uno de los siguientes métodos:
-La legalidad de blxckie ronda mp4 depende de varios factores, como:
-Por lo tanto, blxckie ronda mp4 puede ser legal o ilegal dependiendo de estos factores. Debes tener cuidado y discreción al descargar y ver blxckie ronda mp4.
-Si te gusta blxckie ronda mp4, también te pueden gustar otras canciones de Blxckie que puedes descargar. Aquí están algunas de sus canciones más populares que puedes encontrar en varios sitios web y plataformas:
-También puede consultar el sitio web oficial de Blxckie, el canal de YouTube, Instagram, Twitter y Facebook para obtener más actualizaciones e información sobre su música y su carrera.
64aa2da5cfSi eres un fan de los juegos de simulación, es posible que hayas oído hablar de Truck Simulator Ultimate, un juego que te permite conducir varios camiones a través de diferentes países y ciudades. El juego es desarrollado por Zuuks Games, la misma compañía que produjo Bus Simulator Ultimate, que tiene más de 300 millones de jugadores en todo el mundo. Truck Simulator Ultimate combina elementos de simulación y magnate, lo que le permite no solo conducir su camión, sino también gestionar su propio negocio, contratar empleados, ampliar su flota y participar en subastas y carreras.
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Una de las características más agradables de Truck Simulator Ultimate es que puede personalizar sus camiones con diferentes pieles, que son básicamente diferentes diseños y colores para el exterior de su camión. Las pieles pueden hacer que su camión se vea más realista, elegante o único, dependiendo de su preferencia. Puede elegir entre camiones oficiales con licencia de Mercedes-Benz u otras marcas como BMW, Ford, DAF, MAN, Volvo y más. También puedes encontrar skins inspirados en empresas famosas, países, gasolineras, o incluso películas y dibujos animados.
-En este artículo, le mostraremos cómo instalar pieles de camiones en Truck Simulator Ultimate, y cuáles son algunas de las mejores pieles de camiones para este juego. Siguiendo estos sencillos pasos, puede cambiar la apariencia de su camión y divertirse más conduciéndolo.
-Hay dos maneras de instalar pieles de camiones en Truck Simulator Ultimate: descargarlos desde la tienda de aplicaciones o la web, o copiar su URL y pegarlo en la configuración del juego. He aquí cómo hacer ambas cosas:
- -Si está utilizando un dispositivo iOS, o si desea encontrar más pieles en línea, puede visitar sitios web que ofrecen mods para Truck Simulator Ultimate. Los mods son modificaciones que añaden nuevas características o contenido al juego. Uno de los sitios web más populares para los mods es TSU Mods, que tiene más de 30 mods para diferentes camiones, coches, vehículos de policía, ambulancias, remolques y más. También puedes encontrar otros sitios web buscando "truck simulator ultimate mod" en la web.
-Una vez que haya descargado una aplicación skin o un archivo mod, debe copiar su URL (la dirección web que comienza con http:// o https://) y pegarla en la configuración del juego. Para hacer esto, siga estos pasos:
-¡Eso es todo! Ha instalado con éxito una piel de camión en Truck Simulator Ultimate. Puedes repetir estos pasos para cualquier otra piel que quieras usar.
-Ahora que sabes cómo instalar pieles de camiones en Truck Simulator Ultimate, es posible que te estés preguntando cuáles son algunas de las mejores pieles de camiones para este juego. Por supuesto, esto depende de su gusto personal y preferencia, pero aquí están algunas de nuestras recomendaciones:
-Si está buscando velocidad y estilo, es posible que desee probar la piel BMW F90 M5 2020, que es un mod que reemplaza el coche BMW original en el juego con una versión más potente y elegante. El BMW F90 M5 2020 es un sedán de alto rendimiento que tiene un diseño deportivo y un motor V8 de doble turbocompresor que puede alcanzar hasta 305 km/h. La piel también tiene características realistas, como faros, luces traseras, escapes, alerones y llantas. Puede encontrar esta piel en TSU Mods.
-Si buscas nostalgia y diversión, es posible que quieras probar la piel TOFAŞ Şahin, que es un mod que reemplaza el coche Fiat original en el juego con un coche turco clásico que fue popular en los años 1980 y 1990. El TOFAŞ Şahin es un sedán compacto que tiene un diseño simple pero encantador y una base de fans leales en Turquía. La piel también tiene características realistas, como placas de matrícula, pegatinas, parachoques y cuernos. Puede encontrar esta piel en TSU Mods.
-Truck Simulator Ultimate es un juego que ofrece mucha diversión y emoción para los amantes de la simulación. Una de las maneras de mejorar su experiencia de juego es utilizar pieles de camiones, que son diferentes diseños y colores para el exterior de su camión. Las pieles de camiones pueden hacer que su camión se vea más realista, elegante o único, dependiendo de su preferencia.
-En este artículo, te mostramos cómo instalar skins de camiones en Truck Simulator Ultimate descargándolos desde la tienda de aplicaciones o la web, o copiando su URL y pegándolo en la configuración del juego. También le dimos algunos ejemplos de las mejores pieles de camiones para Truck Simulator Ultimate, como camiones con licencia de Mercedes-Benz, BMW F90 M5 2020 y TOFAŞ Şahin.
- -Aquí están algunas de las preguntas más frecuentes sobre Truck Simulator Ultimate:
-Los requisitos mínimos del sistema para Truck Simulator Ultimate son: - Android: Android 7.0 o superior; 3 GB de RAM; 1 GB de espacio libre - iOS: iOS 11 o superior; iPhone 6S o superior; iPad Air 2 o superior; iPad Mini 4 o superior; iPod Touch (7a generación) o superior; 1 GB de espacio libre Los requisitos del sistema recomendados para Truck Simulator Ultimate son: - Android: Android 9.0 o superior; 4 GB de RAM; 2 GB de espacio libre - iOS: iOS 13 o superior; iPhone X o superior; iPad Pro (2017) o superior; iPad Air (2019) o superior; iPad Mini (2019) o superior; iPod Touch (7a generación) o superior; 2 GB de espacio libre
-Para participar en el modo multijugador y carreras en Truck Simulator Ultimate, necesitas tener una conexión a Internet y una cuenta de Zuuks. Puede crear una cuenta Z uks tocando el icono del menú en la esquina superior izquierda, luego tocando en Perfil y luego tocando en Registro. También puedes iniciar sesión con tu cuenta de Facebook o Google. Una vez que tenga una cuenta de Zuuks, puede unirse o crear salas multijugador y carreras tocando el icono del menú, luego tocando en Multijugador, y luego elegir la opción que desee. También puedes invitar a tus amigos a jugar contigo tocando el botón Invitar amigos.
-Para personalizar sus camiones con otros accesorios y modificaciones en Truck Simulator Ultimate, necesita tener suficiente dinero y reputación. Puedes ganar dinero y reputación completando entregas, participando en subastas y carreras, y cumpliendo contratos. También puede gastar dinero real para comprar monedas o diamantes, que son las monedas premium en el juego. Una vez que tenga suficiente dinero y reputación, puede personalizar sus camiones con diferentes accesorios y modificaciones tocando el icono del menú, luego tocando en Garaje, luego seleccionando su camión y luego tocando en Personalizar. Puede cambiar varios aspectos de su camión, como motor, transmisión, suspensión, frenos, neumáticos, llantas, luces, bocinas, espejos, alerones, escapes, pintura, pegatinas y más.
-Para contactar a los desarrolladores de Truck Simulator Ultimate para sugerencias y quejas, puede usar uno de los siguientes métodos: - Correo electrónico: info@zuuks.com - Facebook: https://www.facebook.com/zuuks.games - Instagram: https:/www.instagram.com/zuuksgames --Instagram: https://www.instagram.com Twitter: https://twitter.com/ZuuksGames - YouTube: https://www.youtube.com/channel/UCSZ5daJft7LuWzSyjdp_8HA Los desarrolladores siempre están abiertos a la retroalimentación y sugerencias de sus jugadores. También actualizan el juego regularmente con nuevas características y mejoras.
64aa2da5cfInstagram Reels son videos cortos, divertidos y atractivos que puedes crear y compartir en la aplicación. Son una gran manera de mostrar su creatividad, personalidad y talento. Pero a veces, es posible que se encuentre con un carrete que tiene un clip de audio increíble que desea descargar y utilizar para sus propios videos u otros fines. ¿Cómo se hace eso?
-En este artículo, te mostraremos cómo descargar audio de Instagram Reel como MP3 usando diferentes métodos y herramientas. También explicaremos cómo guardar los clips de audio de Reel para usarlos más tarde en la aplicación. Si desea descargar una canción pegadiza, un efecto de sonido divertido, o una voz en off de tendencia, tenemos todo cubierto.
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La respuesta corta es sí, pero no directamente desde la aplicación. Instagram no tiene una función integrada que te permita descargar o guardar el audio de un Reel. Sin embargo, hay algunas formas no oficiales de hacerlo usando herramientas o aplicaciones de terceros.
-Estos métodos implican copiar el enlace del Carrete y pegarlo en un sitio web o una aplicación que puede extraer el archivo de audio del video. Alternativamente, también puedes guardar el audio de un Carrete en tu cuenta de Instagram y usarlo más tarde para tus propios videos.
-Sin embargo, antes de descargar o guardar cualquier audio de carrete, asegúrese de respetar los derechos y permisos del creador original. No utilice su audio sin darles crédito o pedir su consentimiento. Además, no viole ninguna ley de derechos de autor o términos de servicio de Instagram.
-Una forma de descargar Instagram Reel audio como MP3 es utilizar un sitio web de terceros que puede convertir el enlace de vídeo en un archivo de audio. Hay muchos de estos sitios web disponibles en línea, pero le mostraremos cuatro de ellos que son gratuitos y fáciles de usar.
-Este es otro sitio web que puede ayudarle a descargar Instagram Reel audio como MP3 con facilidad. También funciona de manera similar a ReelSave.App, pero tiene algunas características adicionales que puede encontrar útiles.
-Si prefiere usar aplicaciones en lugar de sitios web o extensiones, también hay algunas opciones para usted. Aquí hay dos aplicaciones que pueden ayudarle a descargar Instagram Reel audio como MP3 en su dispositivo móvil. Ambos son gratuitos y están disponibles para usuarios de Android e iOS.
-Esta es una aplicación de edición de video popular que también puede ayudarlo a descargar Instagram Reel audio como MP3. Tiene muchas características y herramientas que puedes usar para crear videos increíbles, pero nos centraremos en cómo usarlo para descargar clips de audio de carrete.
-Esta es una aplicación sencilla y directa que puede ayudarle a descargar Instagram Reel audio como MP3. No tiene características ni herramientas adicionales, pero hace su trabajo bien y rápido.
-Si no quieres descargar Instagram Reel audio como MP3, pero quieres usarlo más tarde para tus propios videos en la aplicación, hay una manera de hacerlo. Instagram tiene una función que le permite guardar clips de audio de carrete a su cuenta y acceder a ellos en cualquier momento que desee.
-En este artículo, te hemos mostrado cómo descargar audio de Instagram Reel como MP3 usando diferentes métodos y herramientas. También hemos explicado cómo guardar los clips de audio de Reel para usarlos más adelante en la aplicación. Esperamos que este artículo le haya resultado útil e informativo. Si tiene alguna pregunta o comentario, háganoslo saber en los comentarios a continuación.
-Si quieres saber de dónde viene un audio de Instagram Reel, puedes tocar el nombre del audio en la parte inferior de la pantalla. Te llevará a una página donde podrás ver todos los vídeos que utilizan ese clip de audio. También puede ver quién creó o subió el audio original pulsando en su imagen de perfil o nombre.
-Si quieres crear tu propio audio para Instagram Reels, puedes usar cualquier aplicación de grabación de sonido o dispositivo que pueda producir un archivo MP3. También puedes usar cualquier música o efectos de sonido que tengas en tu dispositivo o en línea. Una vez que tengas tu archivo de audio listo, puedes subirlo a Instagram siguiendo estos pasos:
-Si desea editar el audio de un Instagram Reel, puede usar las herramientas integradas en la aplicación o cualquier aplicación externa que pueda editar archivos de audio. Estas son algunas de las cosas que puedes hacer con las herramientas integradas:
-Si quieres compartir un Instagram Reel con un audio específico, puedes usar la opción Compartir audio en la aplicación. Esto le permitirá enviar un mensaje directo a cualquier persona en Instagram con un enlace a su carrete y su audio. Estos son los pasos a seguir:
-Si desea silenciar el audio de un Instagram Reel, puede utilizar el botón de silencio en la aplicación. Esto le permitirá ver el video sin ningún sonido. Estos son los pasos a seguir:
-Si eres un fan de los juegos de carreras realistas, es posible que hayas oído hablar de CarX Street, un juego de simulación que ofrece gráficos impresionantes, física y personalización. CarX Street le permite explorar un gran mundo abierto con diferentes tipos de mapas, desde las concurridas calles de la ciudad hasta las carreteras de montaña en espiral y las carreteras costeras. También puede elegir entre una variedad de coches, desde los clásicos coches musculares hasta los modernos supercoches, y sintonizarlos a su gusto. Puedes competir con otros jugadores en carreras de red reales, o unirte a clubes y desafiar jefes.
-Sin embargo, tan divertido como CarX Street es, también puede ser frustrante si no tienes suficiente dinero para comprar coches nuevos o piezas, o si quieres desbloquear todos los coches y modos en el juego. Es por eso que algunos jugadores buscan un apk hack para CarX Street, que es una versión modificada del juego que le da dinero ilimitado, desbloquea todos los coches y modos, y le permite personalizar la configuración del juego. Con un hack apk, se puede disfrutar de CarX Street sin limitaciones o restricciones.
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Pero antes de descargar e instalar un hack apk para CarX Street, usted debe ser consciente de los beneficios y riesgos de usar uno. En este artículo, le mostraremos cómo encontrar, instalar y utilizar un hack apk para CarX Street, así como algunas características y consejos para el juego. Sigue leyendo para saber más.
-El primer paso para utilizar un hack apk para CarX Street es encontrar una fuente confiable para ella. Hay muchos sitios web que afirman ofrecer apks hack para varios juegos, pero no todos ellos son dignos de confianza. Algunos de ellos pueden contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. Algunos de ellos también pueden proporcionar versiones falsas o obsoletas de la apk hack que no funcionan o causar problemas con el juego.
- -Una vez que haya encontrado una fuente confiable para el apk hack para CarX Street, es necesario habilitar fuentes desconocidas en su dispositivo Android. Esto se debe a que los dispositivos Android normalmente no permiten instalar aplicaciones desde fuentes distintas de Google Play Store. Para habilitar fuentes desconocidas, vaya a Configuración > Seguridad > Fuentes desconocidas y enciéndala. También es posible que tenga que conceder algunos permisos para el hack apk al instalarlo.
-Después de habilitar fuentes desconocidas, puede descargar e instalar el apk hack para CarX Street siguiendo estos pasos:
-Se puede verificar que el hack apk está funcionando mediante la comprobación de si usted tiene dinero ilimitado y todos los coches y modos desbloqueados en el juego. También puede acceder al menú mod tocando el icono en la esquina superior izquierda de la pantalla. El menú mod te permite personalizar la configuración del juego, como velocidad, aceleración, manejo, gravedad, daños y más. También puede activar o desactivar algunas funciones, como nitro, drift, tráfico y policía.
-Ahora que ha instalado el apk hack para CarX Street, se puede utilizar para disfrutar del juego sin limitaciones o restricciones. Estas son algunas de las cosas que puede hacer con el hack apk:
-CarX Street es un juego de simulación que ofrece gráficos realistas, física y personalización. Es uno de los juegos de carreras más populares en dispositivos Android. Estas son algunas de las principales características del juego CarX Street:
-CarX Street es un juego que requiere habilidad y estrategia para dominar. Aquí hay algunos consejos y trucos para mejorar sus habilidades de carreras y rendimiento:
- -En este artículo, le mostramos cómo encontrar, instalar y utilizar un hack apk para CarX Street, así como algunas características y consejos para el juego. Esperamos que haya encontrado este artículo útil e informativo. Sin embargo, también queremos recordarle que el uso de un hack apk para CarX Street no es legal o ético, y puede causar problemas con el juego o su dispositivo. Debe usarlo bajo su propio riesgo y discreción.
-Si tienes algún comentario o preguntas sobre este artículo o el juego CarX Street, no dudes en dejar un comentario a continuación. Nos encantaría saber de ti.
-Genshin Impact es un popular juego de rol de acción que es gratuito, pero ofrece compras en el juego para objetos y personajes adicionales. El juego fue lanzado en 2020 por miHoYo, una compañía de desarrollo de videojuegos con sede en Shanghai, China. Genshin Impact ha recibido críticas positivas de críticos y jugadores por igual por sus impresionantes gráficos, un juego atractivo y una rica historia.
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Sin embargo, algunos usuarios de PC con Windows han informado que enfrentan un error de descarga al intentar instalar o actualizar el juego. El mensaje de error dice "Error de descarga de archivos del juego. Compruebe la configuración de red e inténtelo de nuevo." Este error puede impedirle disfrutar del juego y puede ser frustrante para hacer frente a.
-En este artículo, explicaremos qué causa este error y cómo puede solucionarlo usando cinco métodos simples. También responderemos algunas preguntas frecuentes sobre el juego y sus problemas de descarga.
-Descargar error genshin impacto es un error que se produce cuando se intenta descargar o actualizar los archivos de juego de Genshin impacto en su PC con Windows. El error puede detener el proceso de descarga y dañar los archivos del juego, haciéndolos inutilizables.
- -Hay varias causas posibles para este error, como:
-Algunos de los síntomas comunes de este error son:
-Afortunadamente, hay algunas formas fáciles y eficaces de corregir este error y reanudar su descarga. Aquí hay cinco métodos que puede probar:
-Lo primero que debe hacer es comprobar su conexión a Internet y asegurarse de que es estable y lo suficientemente rápido para descargar los archivos del juego. Puede utilizar una herramienta de prueba de velocidad en línea para medir su velocidad de Internet y compararla con la velocidad recomendada para descargar Genshin Impact.
-La velocidad recomendada para descargar Genshin Impact es de al menos 5 Mbps tanto para subir como para descargar. Si su velocidad es menor que eso, puede experimentar descargas lentas o interrumpidas.
-Para mejorar su velocidad de Internet, puede probar los siguientes pasos:
-Estos son los pasos para usar un servicio VPN para corregir el error de descarga:
-El último método que puede probar es descargar manualmente los archivos del juego desde una fuente de terceros y copiarlos en su carpeta de juegos. Esto puede evitar el error de descarga y ahorrarle tiempo y ancho de banda. Sin embargo, este método no es recomendado por los desarrolladores de juegos oficiales y puede plantear algunos riesgos como infección de malware, pérdida de datos o prohibición de cuentas. Por lo tanto, solo debe usar este método bajo su propio riesgo y discreción.
-Estos son los pasos para descargar manualmente los archivos del juego:
-Genshin Impact es un juego divertido e inmersivo que puedes jugar gratis en tu PC con Windows. Sin embargo, puede encontrar algunos errores de descarga que pueden impedirle instalar o actualizar el juego. Estos errores pueden ser causados por varios factores, como conexión a Internet, software antivirus, archivos de juegos dañados, configuración de DNS o problemas con el servidor.
- -Esperamos que estos métodos le ayudarán a solucionar el impacto genshin error de descarga en el PC con Windows y disfrutar de jugar el juego sin ningún problema. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación.
-Genshin puede seguir fallando en la descarga debido a varias razones tales como conexión a Internet inestable o lenta, software antivirus o firewall bloqueando la descarga, archivos de juegos dañados o incompletos, configuración incorrecta de DNS, o problemas o mantenimiento del servidor. Puede probar uno de los métodos que hemos discutido en este artículo para corregir el error de descarga y reanudar su descarga.
-El tiempo de descarga de Genshin Impact depende de la velocidad de Internet y el tamaño de los archivos del juego. Los archivos del juego son de aproximadamente 20 GB en total, pero pueden variar dependiendo del servidor y las actualizaciones. El tiempo promedio de descarga de Genshin Impact es de 1 a 2 horas, pero puede tardar más si su velocidad de Internet es lenta o si encuentra algún error de descarga.
-Para actualizar Genshin Impact en el PC, es necesario ejecutar el lanzador y haga clic en el botón Actualizar. El lanzador descargará e instalará automáticamente la última versión del juego. También puede consultar el sitio web oficial o las cuentas de redes sociales de Genshin Impact para cualquier noticia o anuncio sobre las actualizaciones.
-Para cambiar el servidor de descarga en Genshin Impact, debe ejecutar el lanzador y hacer clic en el icono de configuración en la esquina superior derecha. Luego, haga clic en la pestaña Servidor de juegos y seleccione el servidor que coincida con su región. Puede elegir entre Asia, Europa, América o TW, HK, MO. Después de seleccionar el servidor, haga clic en Guardar y reinicie el lanzador.
64aa2da5cfNo hyperlinks here
" - mocker.patch("requests.Session.get", return_value=mock_response) - - # Call the function with a URL containing no hyperlinks - result = scrape_links("https://www.example.com") - - # Assert that the function returns an empty list - assert result == [] - - # Tests that scrape_links() correctly extracts and formats hyperlinks from - # a sample HTML containing a few hyperlinks. - def test_scrape_links_with_few_hyperlinks(self, mocker): - # Mock the requests.get() function to return a response with a sample HTML containing hyperlinks - mock_response = mocker.Mock() - mock_response.status_code = 200 - mock_response.text = """ - - - - - - - - """ - mocker.patch("requests.Session.get", return_value=mock_response) - - # Call the function being tested - result = scrape_links("https://www.example.com") - - # Assert that the function returns a list of formatted hyperlinks - assert isinstance(result, list) - assert len(result) == 3 - assert result[0] == "Google (https://www.google.com)" - assert result[1] == "GitHub (https://github.com)" - assert result[2] == "CodiumAI (https://www.codium.ai)" diff --git a/spaces/DhanushPrabhuS/pothole_yolov8_nano/app.py b/spaces/DhanushPrabhuS/pothole_yolov8_nano/app.py deleted file mode 100644 index 2a8b66c577549226f509d49142377bbe6d5fdbd9..0000000000000000000000000000000000000000 --- a/spaces/DhanushPrabhuS/pothole_yolov8_nano/app.py +++ /dev/null @@ -1,104 +0,0 @@ -import gradio as gr -import cv2 -import requests -import os - -from ultralytics import YOLO - -file_urls = [ - 'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1', - 'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1', - 'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1' -] - -def download_file(url, save_name): - url = url - if not os.path.exists(save_name): - file = requests.get(url) - open(save_name, 'wb').write(file.content) - -for i, url in enumerate(file_urls): - if 'mp4' in file_urls[i]: - download_file( - file_urls[i], - f"video.mp4" - ) - else: - download_file( - file_urls[i], - f"image_{i}.jpg" - ) - -model = YOLO('best.pt') -path = [['image_0.jpg'], ['image_1.jpg']] -video_path = [['video.mp4']] - -def show_preds_image(image_path): - image = cv2.imread(image_path) - outputs = model.predict(source=image_path) - results = outputs[0].cpu().numpy() - for i, det in enumerate(results.boxes.xyxy): - cv2.rectangle( - image, - (int(det[0]), int(det[1])), - (int(det[2]), int(det[3])), - color=(0, 0, 255), - thickness=2, - lineType=cv2.LINE_AA - ) - return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - -inputs_image = [ - gr.components.Image(type="filepath", label="Input Image"), -] -outputs_image = [ - gr.components.Image(type="numpy", label="Output Image"), -] -interface_image = gr.Interface( - fn=show_preds_image, - inputs=inputs_image, - outputs=outputs_image, - title="Pothole detector app", - examples=path, - cache_examples=False, -) - -def show_preds_video(video_path): - cap = cv2.VideoCapture(video_path) - while(cap.isOpened()): - ret, frame = cap.read() - if ret: - frame_copy = frame.copy() - outputs = model.predict(source=frame) - results = outputs[0].cpu().numpy() - for i, det in enumerate(results.boxes.xyxy): - cv2.rectangle( - frame_copy, - (int(det[0]), int(det[1])), - (int(det[2]), int(det[3])), - color=(0, 0, 255), - thickness=2, - lineType=cv2.LINE_AA - ) - yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) - -inputs_video = [ - gr.components.Video(type="filepath", label="Input Video"), - -] -outputs_video = [ - gr.components.Image(type="numpy", label="Output Image"), -] -interface_video = gr.Interface( - fn=show_preds_video, - inputs=inputs_video, - outputs=outputs_video, - title="Pothole detector", - examples=video_path, - cache_examples=False, -) - -gr.TabbedInterface( - [interface_image, interface_video], - tab_names=['Image inference', 'Video inference'] -).queue().launch() \ No newline at end of file diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/pidfile.py b/spaces/Dinoking/Guccio-AI-Designer/netdissect/pidfile.py deleted file mode 100644 index 96a66814326bad444606ad829307fe225f4135e1..0000000000000000000000000000000000000000 --- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/pidfile.py +++ /dev/null @@ -1,81 +0,0 @@ -''' -Utility for simple distribution of work on multiple processes, by -making sure only one process is working on a job at once. -''' - -import os, errno, socket, atexit, time, sys - -def exit_if_job_done(directory): - if pidfile_taken(os.path.join(directory, 'lockfile.pid'), verbose=True): - sys.exit(0) - if os.path.isfile(os.path.join(directory, 'done.txt')): - with open(os.path.join(directory, 'done.txt')) as f: - msg = f.read() - print(msg) - sys.exit(0) - -def mark_job_done(directory): - with open(os.path.join(directory, 'done.txt'), 'w') as f: - f.write('Done by %d@%s %s at %s' % - (os.getpid(), socket.gethostname(), - os.getenv('STY', ''), - time.strftime('%c'))) - -def pidfile_taken(path, verbose=False): - ''' - Usage. To grab an exclusive lock for the remaining duration of the - current process (and exit if another process already has the lock), - do this: - - if pidfile_taken('job_423/lockfile.pid', verbose=True): - sys.exit(0) - - To do a batch of jobs, just run a script that does them all on - each available machine, sharing a network filesystem. When each - job grabs a lock, then this will automatically distribute the - jobs so that each one is done just once on one machine. - ''' - - # Try to create the file exclusively and write my pid into it. - try: - os.makedirs(os.path.dirname(path), exist_ok=True) - fd = os.open(path, os.O_CREAT | os.O_EXCL | os.O_RDWR) - except OSError as e: - if e.errno == errno.EEXIST: - # If we cannot because there was a race, yield the conflicter. - conflicter = 'race' - try: - with open(path, 'r') as lockfile: - conflicter = lockfile.read().strip() or 'empty' - except: - pass - if verbose: - print('%s held by %s' % (path, conflicter)) - return conflicter - else: - # Other problems get an exception. - raise - # Register to delete this file on exit. - lockfile = os.fdopen(fd, 'r+') - atexit.register(delete_pidfile, lockfile, path) - # Write my pid into the open file. - lockfile.write('%d@%s %s\n' % (os.getpid(), socket.gethostname(), - os.getenv('STY', ''))) - lockfile.flush() - os.fsync(lockfile) - # Return 'None' to say there was not a conflict. - return None - -def delete_pidfile(lockfile, path): - ''' - Runs at exit after pidfile_taken succeeds. - ''' - if lockfile is not None: - try: - lockfile.close() - except: - pass - try: - os.unlink(path) - except: - pass diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/segmodel/__init__.py b/spaces/Dinoking/Guccio-AI-Designer/netdissect/segmodel/__init__.py deleted file mode 100644 index 76b40a0a36bc2976f185dbdc344c5a7c09b65920..0000000000000000000000000000000000000000 --- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/segmodel/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .models import ModelBuilder, SegmentationModule diff --git a/spaces/DragGan/DragGan/stylegan_human/dnnlib/tflib/autosummary.py b/spaces/DragGan/DragGan/stylegan_human/dnnlib/tflib/autosummary.py deleted file mode 100644 index ede0f23dc3106112d241c70a8d4c17b2fa2af50d..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan/stylegan_human/dnnlib/tflib/autosummary.py +++ /dev/null @@ -1,193 +0,0 @@ -# Copyright (c) SenseTime Research. All rights reserved. - -# Copyright (c) 2019, NVIDIA Corporation. All rights reserved. -# -# This work is made available under the Nvidia Source Code License-NC. -# To view a copy of this license, visit -# https://nvlabs.github.io/stylegan2/license.html - -"""Helper for adding automatically tracked values to Tensorboard. - -Autosummary creates an identity op that internally keeps track of the input -values and automatically shows up in TensorBoard. The reported value -represents an average over input components. The average is accumulated -constantly over time and flushed when save_summaries() is called. - -Notes: -- The output tensor must be used as an input for something else in the - graph. Otherwise, the autosummary op will not get executed, and the average - value will not get accumulated. -- It is perfectly fine to include autosummaries with the same name in - several places throughout the graph, even if they are executed concurrently. -- It is ok to also pass in a python scalar or numpy array. In this case, it - is added to the average immediately. -""" - -from collections import OrderedDict -import numpy as np -import tensorflow as tf -from tensorboard import summary as summary_lib -from tensorboard.plugins.custom_scalar import layout_pb2 - -from . import tfutil -from .tfutil import TfExpression -from .tfutil import TfExpressionEx - -# Enable "Custom scalars" tab in TensorBoard for advanced formatting. -# Disabled by default to reduce tfevents file size. -enable_custom_scalars = False - -_dtype = tf.float64 -_vars = OrderedDict() # name => [var, ...] -_immediate = OrderedDict() # name => update_op, update_value -_finalized = False -_merge_op = None - - -def _create_var(name: str, value_expr: TfExpression) -> TfExpression: - """Internal helper for creating autosummary accumulators.""" - assert not _finalized - name_id = name.replace("/", "_") - v = tf.cast(value_expr, _dtype) - - if v.shape.is_fully_defined(): - size = np.prod(v.shape.as_list()) - size_expr = tf.constant(size, dtype=_dtype) - else: - size = None - size_expr = tf.reduce_prod(tf.cast(tf.shape(v), _dtype)) - - if size == 1: - if v.shape.ndims != 0: - v = tf.reshape(v, []) - v = [size_expr, v, tf.square(v)] - else: - v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))] - v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(v), lambda: tf.zeros(3, dtype=_dtype)) - - with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None): - var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False) # [sum(1), sum(x), sum(x**2)] - update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v)) - - if name in _vars: - _vars[name].append(var) - else: - _vars[name] = [var] - return update_op - - -def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None, condition: TfExpressionEx = True) -> TfExpressionEx: - """Create a new autosummary. - - Args: - name: Name to use in TensorBoard - value: TensorFlow expression or python value to track - passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node. - - Example use of the passthru mechanism: - - n = autosummary('l2loss', loss, passthru=n) - - This is a shorthand for the following code: - - with tf.control_dependencies([autosummary('l2loss', loss)]): - n = tf.identity(n) - """ - tfutil.assert_tf_initialized() - name_id = name.replace("/", "_") - - if tfutil.is_tf_expression(value): - with tf.name_scope("summary_" + name_id), tf.device(value.device): - condition = tf.convert_to_tensor(condition, name='condition') - update_op = tf.cond(condition, lambda: tf.group(_create_var(name, value)), tf.no_op) - with tf.control_dependencies([update_op]): - return tf.identity(value if passthru is None else passthru) - - else: # python scalar or numpy array - assert not tfutil.is_tf_expression(passthru) - assert not tfutil.is_tf_expression(condition) - if condition: - if name not in _immediate: - with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None): - update_value = tf.placeholder(_dtype) - update_op = _create_var(name, update_value) - _immediate[name] = update_op, update_value - update_op, update_value = _immediate[name] - tfutil.run(update_op, {update_value: value}) - return value if passthru is None else passthru - - -def finalize_autosummaries() -> None: - """Create the necessary ops to include autosummaries in TensorBoard report. - Note: This should be done only once per graph. - """ - global _finalized - tfutil.assert_tf_initialized() - - if _finalized: - return None - - _finalized = True - tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list]) - - # Create summary ops. - with tf.device(None), tf.control_dependencies(None): - for name, vars_list in _vars.items(): - name_id = name.replace("/", "_") - with tfutil.absolute_name_scope("Autosummary/" + name_id): - moments = tf.add_n(vars_list) - moments /= moments[0] - with tf.control_dependencies([moments]): # read before resetting - reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list] - with tf.name_scope(None), tf.control_dependencies(reset_ops): # reset before reporting - mean = moments[1] - std = tf.sqrt(moments[2] - tf.square(moments[1])) - tf.summary.scalar(name, mean) - if enable_custom_scalars: - tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std) - tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std) - - # Setup layout for custom scalars. - layout = None - if enable_custom_scalars: - cat_dict = OrderedDict() - for series_name in sorted(_vars.keys()): - p = series_name.split("/") - cat = p[0] if len(p) >= 2 else "" - chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1] - if cat not in cat_dict: - cat_dict[cat] = OrderedDict() - if chart not in cat_dict[cat]: - cat_dict[cat][chart] = [] - cat_dict[cat][chart].append(series_name) - categories = [] - for cat_name, chart_dict in cat_dict.items(): - charts = [] - for chart_name, series_names in chart_dict.items(): - series = [] - for series_name in series_names: - series.append(layout_pb2.MarginChartContent.Series( - value=series_name, - lower="xCustomScalars/" + series_name + "/margin_lo", - upper="xCustomScalars/" + series_name + "/margin_hi")) - margin = layout_pb2.MarginChartContent(series=series) - charts.append(layout_pb2.Chart(title=chart_name, margin=margin)) - categories.append(layout_pb2.Category(title=cat_name, chart=charts)) - layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories)) - return layout - -def save_summaries(file_writer, global_step=None): - """Call FileWriter.add_summary() with all summaries in the default graph, - automatically finalizing and merging them on the first call. - """ - global _merge_op - tfutil.assert_tf_initialized() - - if _merge_op is None: - layout = finalize_autosummaries() - if layout is not None: - file_writer.add_summary(layout) - with tf.device(None), tf.control_dependencies(None): - _merge_op = tf.summary.merge_all() - - file_writer.add_summary(_merge_op.eval(), global_step) diff --git a/spaces/Eddycrack864/Applio-Inference/i18n/locale_diff.py b/spaces/Eddycrack864/Applio-Inference/i18n/locale_diff.py deleted file mode 100644 index 387ddfe1b16c2f9f32b6b9682b61353837b06bd8..0000000000000000000000000000000000000000 --- a/spaces/Eddycrack864/Applio-Inference/i18n/locale_diff.py +++ /dev/null @@ -1,45 +0,0 @@ -import json -import os -from collections import OrderedDict - -# Define the standard file name -standard_file = "en_US.json" - -# Find all JSON files in the directory -dir_path = "./" -languages = [ - f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file -] - -# Load the standard file -with open(standard_file, "r", encoding="utf-8") as f: - standard_data = json.load(f, object_pairs_hook=OrderedDict) - -# Loop through each language file -for lang_file in languages: - # Load the language file - with open(lang_file, "r", encoding="utf-8") as f: - lang_data = json.load(f, object_pairs_hook=OrderedDict) - - # Find the difference between the language file and the standard file - diff = set(standard_data.keys()) - set(lang_data.keys()) - - miss = set(lang_data.keys()) - set(standard_data.keys()) - - # Add any missing keys to the language file - for key in diff: - lang_data[key] = key - - # Del any extra keys to the language file - for key in miss: - del lang_data[key] - - # Sort the keys of the language file to match the order of the standard file - lang_data = OrderedDict( - sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0])) - ) - - # Save the updated language file - with open(lang_file, "w", encoding="utf-8") as f: - json.dump(lang_data, f, ensure_ascii=False, indent=4) - f.write("\n") diff --git a/spaces/EuroPython2022/latr-vqa/README.md b/spaces/EuroPython2022/latr-vqa/README.md deleted file mode 100644 index 76299be1d93c147aff4d5163dac561e056c008b4..0000000000000000000000000000000000000000 --- a/spaces/EuroPython2022/latr-vqa/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Latr Vqa -emoji: 🌖 -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.0.24 -app_file: app.py -pinned: false -license: unknown ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/FoxMeo/fire-detector/detect.py b/spaces/FoxMeo/fire-detector/detect.py deleted file mode 100644 index 5e0c4416a4672584c43e4967d27b13e045a76843..0000000000000000000000000000000000000000 --- a/spaces/FoxMeo/fire-detector/detect.py +++ /dev/null @@ -1,196 +0,0 @@ -import argparse -import time -from pathlib import Path - -import cv2 -import torch -import torch.backends.cudnn as cudnn -from numpy import random - -from models.experimental import attempt_load -from utils.datasets import LoadStreams, LoadImages -from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ - scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path -from utils.plots import plot_one_box -from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel - - -def detect(save_img=False): - source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace - save_img = not opt.nosave and not source.endswith('.txt') # save inference images - webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( - ('rtsp://', 'rtmp://', 'http://', 'https://')) - - # Directories - save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Initialize - set_logging() - device = select_device(opt.device) - half = device.type != 'cpu' # half precision only supported on CUDA - - # Load model - model = attempt_load(weights, map_location=device) # load FP32 model - stride = int(model.stride.max()) # model stride - imgsz = check_img_size(imgsz, s=stride) # check img_size - - if trace: - model = TracedModel(model, device, opt.img_size) - - if half: - model.half() # to FP16 - - # Second-stage classifier - classify = False - if classify: - modelc = load_classifier(name='resnet101', n=2) # initialize - modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() - - # Set Dataloader - vid_path, vid_writer = None, None - if webcam: - view_img = check_imshow() - cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz, stride=stride) - else: - dataset = LoadImages(source, img_size=imgsz, stride=stride) - - # Get names and colors - names = model.module.names if hasattr(model, 'module') else model.names - colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] - - # Run inference - if device.type != 'cpu': - model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once - old_img_w = old_img_h = imgsz - old_img_b = 1 - - t0 = time.time() - for path, img, im0s, vid_cap in dataset: - img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 - img /= 255.0 # 0 - 255 to 0.0 - 1.0 - if img.ndimension() == 3: - img = img.unsqueeze(0) - - # Warmup - if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]): - old_img_b = img.shape[0] - old_img_h = img.shape[2] - old_img_w = img.shape[3] - for i in range(3): - model(img, augment=opt.augment)[0] - - # Inference - t1 = time_synchronized() - with torch.no_grad(): # Calculating gradients would cause a GPU memory leak - pred = model(img, augment=opt.augment)[0] - t2 = time_synchronized() - - # Apply NMS - pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) - t3 = time_synchronized() - - # Apply Classifier - if classify: - pred = apply_classifier(pred, modelc, img, im0s) - - # Process detections - for i, det in enumerate(pred): # detections per image - if webcam: # batch_size >= 1 - p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count - else: - p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) - - p = Path(p) # to Path - save_path = str(save_dir / p.name) # img.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt - gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh - if len(det): - # Rescale boxes from img_size to im0 size - det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() - - # Print results - for c in det[:, -1].unique(): - n = (det[:, -1] == c).sum() # detections per class - s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string - - # Write results - for *xyxy, conf, cls in reversed(det): - if save_txt: # Write to file - xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh - line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format - with open(txt_path + '.txt', 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') - - if save_img or view_img: # Add bbox to image - label = f'{names[int(cls)]} {conf:.2f}' - plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1) - - # Print time (inference + NMS) - print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') - - # Stream results - if view_img: - cv2.imshow(str(p), im0) - cv2.waitKey(1) # 1 millisecond - - # Save results (image with detections) - if save_img: - if dataset.mode == 'image': - cv2.imwrite(save_path, im0) - print(f" The image with the result is saved in: {save_path}") - else: # 'video' or 'stream' - if vid_path != save_path: # new video - vid_path = save_path - if isinstance(vid_writer, cv2.VideoWriter): - vid_writer.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 += '.mp4' - vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - vid_writer.write(im0) - - 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 '' - #print(f"Results saved to {save_dir}{s}") - - print(f'Done. ({time.time() - t0:.3f}s)') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)') - parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam - parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') - 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='display 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('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models') - parser.add_argument('--project', default='runs/detect', 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('--no-trace', action='store_true', help='don`t trace model') - opt = parser.parse_args() - print(opt) - #check_requirements(exclude=('pycocotools', 'thop')) - - with torch.no_grad(): - if opt.update: # update all models (to fix SourceChangeWarning) - for opt.weights in ['yolov7.pt']: - detect() - strip_optimizer(opt.weights) - else: - detect() diff --git a/spaces/Fr33d0m21/google-flan-t5-xxl/app.py b/spaces/Fr33d0m21/google-flan-t5-xxl/app.py deleted file mode 100644 index fced8846b9b730030ff3059c124c2857ec7fc104..0000000000000000000000000000000000000000 --- a/spaces/Fr33d0m21/google-flan-t5-xxl/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/google/flan-t5-xxl").launch() \ No newline at end of file diff --git a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/fp16_util.py b/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/fp16_util.py deleted file mode 100644 index b69341c706f17ccf9ac9b08e966d10c630c72129..0000000000000000000000000000000000000000 --- a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/fp16_util.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Helpers to inference with 16-bit precision. -""" - -import torch.nn as nn - - -def convert_module_to_f16(l): - """ - Convert primitive modules to float16. - """ - if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): - l.weight.data = l.weight.data.half() - if l.bias is not None: - l.bias.data = l.bias.data.half() - - -def convert_module_to_f32(l): - """ - Convert primitive modules to float32, undoing convert_module_to_f16(). - """ - if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): - l.weight.data = l.weight.data.float() - if l.bias is not None: - l.bias.data = l.bias.data.float() diff --git a/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/i18n.py b/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/i18n.py deleted file mode 100644 index 8e75d2bc26ff86ab1716b8d7f239ad9f5cc1e32d..0000000000000000000000000000000000000000 --- a/spaces/FridaZuley/RVC_HFKawaii/Applio-RVC-Fork/utils/i18n.py +++ /dev/null @@ -1,28 +0,0 @@ -import locale -import json -import os - - -def load_language_list(language): - with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f: - language_list = json.load(f) - return language_list - - -class I18nAuto: - def __init__(self, language=None): - if language in ["Auto", None]: - language = "es_ES" - if not os.path.exists(f"./i18n/{language}.json"): - language = "es_ES" - language = "es_ES" - self.language = language - # print("Use Language:", language) - self.language_map = load_language_list(language) - - def __call__(self, key): - return self.language_map.get(key, key) - - def print(self): - # print("Use Language:", self.language) - print("") diff --git a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/cppipc/queue.h b/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/cppipc/queue.h deleted file mode 100644 index a21f3446e06b5826af7b554c8a7d9c5d80848b62..0000000000000000000000000000000000000000 --- a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/cppipc/queue.h +++ /dev/null @@ -1,216 +0,0 @@ -#pragma once - -#include(params)...);
- });
- }
-
- template (params)...);
- });
- }
-
- template (params)...);
- }
-
- template (params)...);
- }
-
- bool pop(T& item) {
- return base_t::pop(item, [](bool) {});
- }
-
- template {html.escape(userinput)} Github Repo Pytorch | Github Repo ONNX samples from repo:
- Demo for Papercutcraft V1 Stable Diffusion model. This space was created using SD Space Creator. {{recognizedText}} Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo Download Zip https://urloso.com/2uyRiA If you have a LED fan in your computer case, you might want to customize its lighting effects and messages. But how can you do that? You need a LED fan editor software that can communicate with your fan and let you control its RGB settings. In this article, we will show you how to download and use the best LED fan editor software available. LED fan editor software is a program that allows you to create and edit animations and text messages for your LED fan. You can choose from different colors, patterns, speeds, and modes to make your fan look amazing. You can also save and load your creations and share them with other users. 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- StabilityAI OpenJourney Runwayml Stable-Diffusion AI Models API Chaining text-to-image,text-to-text Demo
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/llama_func.py b/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/llama_func.py
deleted file mode 100644
index e1c513af1bf6d1569b071eb5fc0ce441d0692f83..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/llama_func.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import os
-import logging
-
-from llama_index import download_loader
-from llama_index import (
- Document,
- LLMPredictor,
- PromptHelper,
- QuestionAnswerPrompt,
- RefinePrompt,
-)
-import colorama
-import PyPDF2
-from tqdm import tqdm
-
-from modules.presets import *
-from modules.utils import *
-from modules.config import local_embedding
-
-
-def get_index_name(file_src):
- file_paths = [x.name for x in file_src]
- file_paths.sort(key=lambda x: os.path.basename(x))
-
- md5_hash = hashlib.md5()
- for file_path in file_paths:
- with open(file_path, "rb") as f:
- while chunk := f.read(8192):
- md5_hash.update(chunk)
-
- return md5_hash.hexdigest()
-
-
-def block_split(text):
- blocks = []
- while len(text) > 0:
- blocks.append(Document(text[:1000]))
- text = text[1000:]
- return blocks
-
-
-def get_documents(file_src):
- documents = []
- logging.debug("Loading documents...")
- logging.debug(f"file_src: {file_src}")
- for file in file_src:
- filepath = file.name
- filename = os.path.basename(filepath)
- file_type = os.path.splitext(filepath)[1]
- logging.info(f"loading file: {filename}")
- try:
- if file_type == ".pdf":
- logging.debug("Loading PDF...")
- try:
- from modules.pdf_func import parse_pdf
- from modules.config import advance_docs
-
- two_column = advance_docs["pdf"].get("two_column", False)
- pdftext = parse_pdf(filepath, two_column).text
- except:
- pdftext = ""
- with open(filepath, "rb") as pdfFileObj:
- pdfReader = PyPDF2.PdfReader(pdfFileObj)
- for page in tqdm(pdfReader.pages):
- pdftext += page.extract_text()
- text_raw = pdftext
- elif file_type == ".docx":
- logging.debug("Loading Word...")
- DocxReader = download_loader("DocxReader")
- loader = DocxReader()
- text_raw = loader.load_data(file=filepath)[0].text
- elif file_type == ".epub":
- logging.debug("Loading EPUB...")
- EpubReader = download_loader("EpubReader")
- loader = EpubReader()
- text_raw = loader.load_data(file=filepath)[0].text
- elif file_type == ".xlsx":
- logging.debug("Loading Excel...")
- text_list = excel_to_string(filepath)
- for elem in text_list:
- documents.append(Document(elem))
- continue
- else:
- logging.debug("Loading text file...")
- with open(filepath, "r", encoding="utf-8") as f:
- text_raw = f.read()
- except Exception as e:
- logging.error(f"Error loading file: {filename}")
- pass
- text = add_space(text_raw)
- # text = block_split(text)
- # documents += text
- documents += [Document(text)]
- logging.debug("Documents loaded.")
- return documents
-
-
-def construct_index(
- api_key,
- file_src,
- max_input_size=4096,
- num_outputs=5,
- max_chunk_overlap=20,
- chunk_size_limit=600,
- embedding_limit=None,
- separator=" ",
-):
- from langchain.chat_models import ChatOpenAI
- from langchain.embeddings.huggingface import HuggingFaceEmbeddings
- from llama_index import GPTSimpleVectorIndex, ServiceContext, LangchainEmbedding, OpenAIEmbedding
-
- if api_key:
- os.environ["OPENAI_API_KEY"] = api_key
- else:
- # 由于一个依赖的愚蠢的设计，这里必须要有一个API KEY
- os.environ["OPENAI_API_KEY"] = "sk-xxxxxxx"
- chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
- embedding_limit = None if embedding_limit == 0 else embedding_limit
- separator = " " if separator == "" else separator
-
- prompt_helper = PromptHelper(
- max_input_size=max_input_size,
- num_output=num_outputs,
- max_chunk_overlap=max_chunk_overlap,
- embedding_limit=embedding_limit,
- chunk_size_limit=600,
- separator=separator,
- )
- index_name = get_index_name(file_src)
- if os.path.exists(f"./index/{index_name}.json"):
- logging.info("找到了缓存的索引文件，加载中……")
- return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
- else:
- try:
- documents = get_documents(file_src)
- if local_embedding:
- embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2"))
- else:
- embed_model = OpenAIEmbedding()
- logging.info("构建索引中……")
- with retrieve_proxy():
- service_context = ServiceContext.from_defaults(
- prompt_helper=prompt_helper,
- chunk_size_limit=chunk_size_limit,
- embed_model=embed_model,
- )
- index = GPTSimpleVectorIndex.from_documents(
- documents, service_context=service_context
- )
- logging.debug("索引构建完成！")
- os.makedirs("./index", exist_ok=True)
- index.save_to_disk(f"./index/{index_name}.json")
- logging.debug("索引已保存至本地!")
- return index
-
- except Exception as e:
- logging.error("索引构建失败！", e)
- print(e)
- return None
-
-
-def add_space(text):
- punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
- for cn_punc, en_punc in punctuations.items():
- text = text.replace(cn_punc, en_punc)
- return text
diff --git a/spaces/JustinLin610/ImageBind_zeroshot_demo/model_card.md b/spaces/JustinLin610/ImageBind_zeroshot_demo/model_card.md
deleted file mode 100644
index c7bb26500b6590b64ffa6350f37be80dc88612d8..0000000000000000000000000000000000000000
--- a/spaces/JustinLin610/ImageBind_zeroshot_demo/model_card.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# Model Card for ImageBind
-
-Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images.
-Input any of the six modalities and get the same sized embedding that can be used for cross-modal and multimodal tasks.
-
-# Model Details
-
-## Model Description
-
-
-Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images
-
-- **Developed by:** Meta AI
-- **Model type:** Multimodal model
-- **Language(s) (NLP):** en
-- **License:** CC BY-NC-SA 4.0
-- **Resources for more information:**
- - [GitHub Repo](https://github.com/facebookresearch/ImageBind)
-
-
-# Uses
-
-
-This model is intended only for research purposes. It provides a joint embedding space for different modalities -- image/video, text, audio, depth, IMU and thermal images.
-We hope that these joint embeddings can be used for a variety of different cross-modal research, e.g., cross-modal retrieval and combining embeddings from different modalities.
-
-## Out-of-Scope Use
-
-
-
-
-This model is *NOT* intended to be used in any real world application -- commercial or otherwise.
-It may produce harmful associations with different inputs.
-The model needs to be investigated and likely re-trained on specific data for any such application.
-The model is expected to work better on web-based visual data since it was trained on such data.
-The text encoder is likely to work only on English language text because of the underlying training datasets.
-
-# Bias, Risks, and Limitations
-
-
-Open-domain joint embedding models are prone to producing specific biases, e.g., study from [CLIP](https://github.com/openai/CLIP/blob/main/model-card.md#bias-and-fairness).
-Since our model uses such models as initialization, it will exhibit such biases too.
-Moreover, for learning joint embeddings for other modalities such as audio, thermal, depth, and IMU we leverage datasets that are relatively small. These joint embeddings are thus limited to the concepts present in the datasets. For example, the thermal datasets we used are limited to outdoor street scenes, while the depth datasets are limited to indoor scenes.
-
-
-
-# Training Details
-
-## Training Data
-
-
-
-ImageBind uses image-paired data for training -- (image, X) where X is one of text, audio, depth, IMU or thermal data.
-In particular, we initialize and freeze the image and text encoders using an OpenCLIP ViT-H encoder.
-We train audio embeddings using Audioset, depth embeddings using the SUN RGB-D dataset, IMU using the Ego4D dataset and thermal embeddings using the LLVIP dataset.
-We provide the exact training data details in the paper.
-
-
-## Training Procedure
-
-
-Please refer to the research paper and github repo for exact details on this.
-
-# Evaluation
-
-## Testing Data, Factors & Metrics
-
-We evaluate the model on a variety of different classification benchmarks for each modality.
-The evaluation details are presented in the paper.
-The models performance is measured using standard classification metrics such as accuracy and mAP.
-
-# Citation
-
-
-
-**BibTeX:**
-```
-@inproceedings{girdhar2023imagebind,
- title={ImageBind: One Embedding Space To Bind Them All},
- author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
-and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
- booktitle={CVPR},
- year={2023}
-}
-```
-
-
-# Model Card Contact
-
-Please reach out to the authors at: rgirdhar@meta.com imisra@meta.com alaaelnouby@gmail.com
-
-# How to Get Started with the Model
-
-Our github repo provides a simple example to extract embeddings from images, audio etc.
diff --git a/spaces/Kayson/InstructDiffusion/stable_diffusion/ldm/models/diffusion/ddim.py b/spaces/Kayson/InstructDiffusion/stable_diffusion/ldm/models/diffusion/ddim.py
deleted file mode 100644
index fb31215db5c3f3f703f15987d7eee6a179c9f7ec..0000000000000000000000000000000000000000
--- a/spaces/Kayson/InstructDiffusion/stable_diffusion/ldm/models/diffusion/ddim.py
+++ /dev/null
@@ -1,241 +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
-
-
-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,
- # 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}")
-
- 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,
- )
- return samples, intermediates
-
- @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,):
- 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
- 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)
- 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
-
- @torch.no_grad()
- 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):
- b, *_, device = *x.shape, x.device
-
- if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
- 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])
- 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, 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:
- 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/Korakoe/convert-sd-ckpt-cpu/app.py b/spaces/Korakoe/convert-sd-ckpt-cpu/app.py
deleted file mode 100644
index 246db2b74de4c1a16b02c81398fb189a9954a08e..0000000000000000000000000000000000000000
--- a/spaces/Korakoe/convert-sd-ckpt-cpu/app.py
+++ /dev/null
@@ -1,279 +0,0 @@
-import io
-import os
-import shutil
-import zipfile
-
-import gradio as gr
-import requests
-from huggingface_hub import create_repo, upload_folder, whoami
-
-from convert import convert_full_checkpoint
-
-MODELS_DIR = "models/"
-CKPT_FILE = MODELS_DIR + "model.ckpt"
-HF_MODEL_DIR = MODELS_DIR + "diffusers_model"
-ZIP_FILE = MODELS_DIR + "model.zip"
-
-
-def download_ckpt(url, out_path):
- with open(out_path, "wb") as out_file:
- with requests.get(url, stream=True) as r:
- r.raise_for_status()
- for chunk in r.iter_content(chunk_size=8192):
- out_file.write(chunk)
-
-
-def zip_model(model_path, zip_path):
- with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_STORED) as zip_file:
- for root, dirs, files in os.walk(model_path):
- for file in files:
- zip_file.write(
- os.path.join(root, file),
- os.path.relpath(
- os.path.join(root, file), os.path.join(model_path, "..")
- ),
- )
-
-
-def download_checkpoint_and_config(ckpt_url, config_url):
- ckpt_url = ckpt_url.strip()
- config_url = config_url.strip()
-
- if not ckpt_url.startswith("http://") and not ckpt_url.startswith("https://"):
- raise ValueError("Invalid checkpoint URL")
-
- if config_url.startswith("http://") or config_url.startswith("https://"):
- response = requests.get(config_url)
- response.raise_for_status()
- config_file = io.BytesIO(response.content)
- elif config_url != "":
- raise ValueError("Invalid config URL")
- else:
- config_file = open("original_config.yaml", "r")
-
- download_ckpt(ckpt_url, CKPT_FILE)
-
- return CKPT_FILE, config_file
-
-
-def convert_and_download(ckpt_url, config_url, scheduler_type, extract_ema):
- shutil.rmtree(MODELS_DIR, ignore_errors=True)
- os.makedirs(HF_MODEL_DIR)
-
- ckpt_path, config_file = download_checkpoint_and_config(ckpt_url, config_url)
-
- convert_full_checkpoint(
- ckpt_path,
- config_file,
- scheduler_type=scheduler_type,
- extract_ema=(extract_ema == "EMA"),
- output_path=HF_MODEL_DIR,
- )
- zip_model(HF_MODEL_DIR, ZIP_FILE)
-
- return ZIP_FILE
-
-
-def convert_and_upload(
- ckpt_url, config_url, scheduler_type, extract_ema, token, model_name
-):
- shutil.rmtree(MODELS_DIR, ignore_errors=True)
- os.makedirs(HF_MODEL_DIR)
-
- try:
- ckpt_path, config_file = download_checkpoint_and_config(ckpt_url, config_url)
-
- username = whoami(token)["name"]
- repo_name = f"{username}/{model_name}"
- repo_url = create_repo(repo_name, token=token, exist_ok=True)
- convert_full_checkpoint(
- ckpt_path,
- config_file,
- scheduler_type=scheduler_type,
- extract_ema=(extract_ema == "EMA"),
- output_path=HF_MODEL_DIR,
- )
- upload_folder(repo_id=repo_name, folder_path=HF_MODEL_DIR, token=token, commit_message=f"Upload diffusers weights")
- except Exception as e:
- return f"#### Error: {e}"
- return f"#### Success! Model uploaded to [{repo_url}]({repo_url})"
-
-
-TTILE_IMAGE = """
-
-
-"""
-
-TITLE = """
-
-
-
-"""
-
-with gr.Blocks() as interface:
- gr.HTML(TTILE_IMAGE)
- gr.HTML(TITLE)
- gr.Markdown("We will perform all of the checkpoint surgery for you, and create a clean diffusers model!")
- gr.Markdown("This converter will also remove any pickled code from third-party checkpoints.")
-
- with gr.Row():
- with gr.Column(scale=50):
- gr.Markdown("### 1. Paste a URL to your
- Convert Stable Diffusion `.ckpt` files to Hugging Face Diffusers 🔥
-
-Some other crap
-
- """
- soup = BeautifulSoup(body, "html.parser")
- links = extract_hyperlinks(soup, "http://example.com")
- self.assertEqual(
- links,
- [("Google", "https://google.com"), ("Foo", "http://example.com/foo.html")],
- )
diff --git a/spaces/Lbin123/Lbingo/src/components/chat-list.tsx b/spaces/Lbin123/Lbingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
- messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
- if (!messages.length) {
- return null
- }
-
- return (
-
- {messages.map((message, index) => (
-
- )
-}
diff --git "a/spaces/Liu-LAB/GPT-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py" "b/spaces/Liu-LAB/GPT-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py"
deleted file mode 100644
index ffbb05599ef09c9de25334ebeca2eef8022b9aaf..0000000000000000000000000000000000000000
--- "a/spaces/Liu-LAB/GPT-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py"
+++ /dev/null
@@ -1,160 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-fast_debug = False
-
-def readPdf(pdfPath):
- """
- 读取pdf文件，返回文本内容
- """
- import pdfminer
- from pdfminer.pdfparser import PDFParser
- from pdfminer.pdfdocument import PDFDocument
- from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed
- from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
- from pdfminer.pdfdevice import PDFDevice
- from pdfminer.layout import LAParams
- from pdfminer.converter import PDFPageAggregator
-
- fp = open(pdfPath, 'rb')
-
- # Create a PDF parser object associated with the file object
- parser = PDFParser(fp)
-
- # Create a PDF document object that stores the document structure.
- # Password for initialization as 2nd parameter
- document = PDFDocument(parser)
- # Check if the document allows text extraction. If not, abort.
- if not document.is_extractable:
- raise PDFTextExtractionNotAllowed
-
- # Create a PDF resource manager object that stores shared resources.
- rsrcmgr = PDFResourceManager()
-
- # Create a PDF device object.
- # device = PDFDevice(rsrcmgr)
-
- # BEGIN LAYOUT ANALYSIS.
- # Set parameters for analysis.
- laparams = LAParams(
- char_margin=10.0,
- line_margin=0.2,
- boxes_flow=0.2,
- all_texts=False,
- )
- # Create a PDF page aggregator object.
- device = PDFPageAggregator(rsrcmgr, laparams=laparams)
- # Create a PDF interpreter object.
- interpreter = PDFPageInterpreter(rsrcmgr, device)
-
- # loop over all pages in the document
- outTextList = []
- for page in PDFPage.create_pages(document):
- # read the page into a layout object
- interpreter.process_page(page)
- layout = device.get_result()
- for obj in layout._objs:
- if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
- # print(obj.get_text())
- outTextList.append(obj.get_text())
-
- return outTextList
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
- import time, glob, os
- from bs4 import BeautifulSoup
- print('begin analysis on:', file_manifest)
- for index, fp in enumerate(file_manifest):
- if ".tex" in fp:
- with open(fp, 'r', encoding='utf-8', errors='replace') as f:
- file_content = f.read()
- if ".pdf" in fp.lower():
- file_content = readPdf(fp)
- file_content = BeautifulSoup(''.join(file_content), features="lxml").body.text.encode('gbk', 'ignore').decode('gbk')
-
- prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
- i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
- i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
- chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=i_say,
- inputs_show_user=i_say_show_user,
- llm_kwargs=llm_kwargs,
- chatbot=chatbot,
- history=[],
- sys_prompt="总结文章。"
- ) # 带超时倒计时
- chatbot[-1] = (i_say_show_user, gpt_say)
- history.append(i_say_show_user); history.append(gpt_say)
- yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
- if not fast_debug: time.sleep(2)
-
- all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
- i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
- chatbot.append((i_say, "[Local Message] waiting gpt response."))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=i_say,
- inputs_show_user=i_say,
- llm_kwargs=llm_kwargs,
- chatbot=chatbot,
- history=history,
- sys_prompt="总结文章。"
- ) # 带超时倒计时
- chatbot[-1] = (i_say, gpt_say)
- history.append(i_say); history.append(gpt_say)
- yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
- res = write_results_to_file(history)
- chatbot.append(("完成了吗？", res))
- yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- history = [] # 清空历史，以免输入溢出
- import glob, os
-
- # 基本信息：功能、贡献者
- chatbot.append([
- "函数插件功能？",
- "批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。"])
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
- # 尝试导入依赖，如果缺少依赖，则给出安装建议
- try:
- import pdfminer, bs4
- except:
- report_execption(chatbot, history,
- a = f"解析项目: {txt}",
- b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
- return
- if os.path.exists(txt):
- project_folder = txt
- else:
- if txt == "": txt = '空空如也的输入栏'
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
- return
- file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
- [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
- # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
- # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
- if len(file_manifest) == 0:
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
- return
- yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
diff --git a/spaces/Lookimi/TuberTranscript/app.py b/spaces/Lookimi/TuberTranscript/app.py
deleted file mode 100644
index 0b05ba2736842fd723ca02eda326289cb6a663e9..0000000000000000000000000000000000000000
--- a/spaces/Lookimi/TuberTranscript/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-
-
-
-#importing the necessary modules
-import os
-import urllib.request
-import re
-import time
-import gradio as gr
-
-#Creating a Gradio App Menu
-#def transcript_extract():
-
- #specifying the YouTube channel URL
-channel_url = gr.inputs.Textbox(label="Channel URL")
-
-#accessing the webpage
-page = urllib.request.urlopen(channel_url)
-
-#reading the source code
-data = page.read().decode("utf-8")
-
-#creating a directory to save the transcripts
-# os.makedirs('Transcripts',exist_ok=True)
-
-#finding the transcripts
-transcript_links = re.findall(r'(\/watch\?v=[A-Za-z0-9_.-]*)', str(data))
-
-#looping through each transcript to download
-for link in transcript_links:
- video_url = 'http://www.youtube.com'+link
- #access the video page
- video_page = urllib.request.urlopen(video_url)
- #read the source code
- video_data = video_page.read().decode("utf-8")
- #find the transcript
- transcript_link = re.findall(r'(\/timedtext_editor\?[A-Za-z0-9_.-]*)', str(video_data))
- #check if there is a transcript available
- if(len(transcript_link) > 0):
- #access the transcript page
- transcript_url ='http://www.youtube.com'+ transcript_link[0]
- transcript_page = urllib.request.urlopen(transcript_url)
- transcript_data = transcript_page.read().decode("utf-8")
- #find the link to the transcript
- transcript_download_link = re.findall(r'(\/api\/timedtext\?[A-Za-z0-9_.-]*)', str(transcript_data))
- #check if the transcript is available for download
- if(len(transcript_download_link) > 0):
- #download the transcript
- # file_name = "Transcripts/" + link[9:] + ".xml"
- file_name = link[9:] + ".xml"
- download_url = 'http://www.youtube.com'+transcript_download_link[0]
- urllib.request.urlretrieve(download_url, file_name)
- print("Downloading transcript for video " + link[9:] + "...")
- time.sleep(3)
- else:
- print("Transcript not available for video " + link[9:])
- else:
- print("Transcript not available for video " + link[9:])
-
-#launch the gradio
-gr.Interface(fn=transcript_extract, inputs="textbox", outputs="textbox", share=True).launch()
\ No newline at end of file
diff --git a/spaces/Luelll/ChuanhuChatGPT/modules/models/base_model.py b/spaces/Luelll/ChuanhuChatGPT/modules/models/base_model.py
deleted file mode 100644
index 995bac5f72a0a1d8cc2eed8ccdfde87928ba2f41..0000000000000000000000000000000000000000
--- a/spaces/Luelll/ChuanhuChatGPT/modules/models/base_model.py
+++ /dev/null
@@ -1,593 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import traceback
-import pathlib
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-
-from ..presets import *
-from ..llama_func import *
-from ..utils import *
-from .. import shared
-from ..config import retrieve_proxy
-
-
-class ModelType(Enum):
- Unknown = -1
- OpenAI = 0
- ChatGLM = 1
- LLaMA = 2
- XMChat = 3
- StableLM = 4
- MOSS = 5
- YuanAI = 6
-
- @classmethod
- def get_type(cls, model_name: str):
- model_type = None
- model_name_lower = model_name.lower()
- if "gpt" in model_name_lower:
- model_type = ModelType.OpenAI
- elif "chatglm" in model_name_lower:
- model_type = ModelType.ChatGLM
- elif "llama" in model_name_lower or "alpaca" in model_name_lower:
- model_type = ModelType.LLaMA
- elif "xmchat" in model_name_lower:
- model_type = ModelType.XMChat
- elif "stablelm" in model_name_lower:
- model_type = ModelType.StableLM
- elif "moss" in model_name_lower:
- model_type = ModelType.MOSS
- elif "yuanai" in model_name_lower:
- model_type = ModelType.YuanAI
- else:
- model_type = ModelType.Unknown
- return model_type
-
-
-class BaseLLMModel:
- def __init__(
- self,
- model_name,
- system_prompt="",
- temperature=1.0,
- top_p=1.0,
- n_choices=1,
- stop=None,
- max_generation_token=None,
- presence_penalty=0,
- frequency_penalty=0,
- logit_bias=None,
- user="",
- ) -> None:
- self.history = []
- self.all_token_counts = []
- self.model_name = model_name
- self.model_type = ModelType.get_type(model_name)
- try:
- self.token_upper_limit = MODEL_TOKEN_LIMIT[model_name]
- except KeyError:
- self.token_upper_limit = DEFAULT_TOKEN_LIMIT
- self.interrupted = False
- self.system_prompt = system_prompt
- self.api_key = None
- self.need_api_key = False
- self.single_turn = False
-
- self.temperature = temperature
- self.top_p = top_p
- self.n_choices = n_choices
- self.stop_sequence = stop
- self.max_generation_token = None
- self.presence_penalty = presence_penalty
- self.frequency_penalty = frequency_penalty
- self.logit_bias = logit_bias
- self.user_identifier = user
-
- def get_answer_stream_iter(self):
- """stream predict, need to be implemented
- conversations are stored in self.history, with the most recent question, in OpenAI format
- should return a generator, each time give the next word (str) in the answer
- """
- logging.warning("stream predict not implemented, using at once predict instead")
- response, _ = self.get_answer_at_once()
- yield response
-
- def get_answer_at_once(self):
- """predict at once, need to be implemented
- conversations are stored in self.history, with the most recent question, in OpenAI format
- Should return:
- the answer (str)
- total token count (int)
- """
- logging.warning("at once predict not implemented, using stream predict instead")
- response_iter = self.get_answer_stream_iter()
- count = 0
- for response in response_iter:
- count += 1
- return response, sum(self.all_token_counts) + count
-
- def billing_info(self):
- """get billing infomation, inplement if needed"""
- logging.warning("billing info not implemented, using default")
- return BILLING_NOT_APPLICABLE_MSG
-
- def count_token(self, user_input):
- """get token count from input, implement if needed"""
- # logging.warning("token count not implemented, using default")
- return len(user_input)
-
- def stream_next_chatbot(self, inputs, chatbot, fake_input=None, display_append=""):
- def get_return_value():
- return chatbot, status_text
-
- status_text = i18n("开始实时传输回答……")
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
-
- user_token_count = self.count_token(inputs)
- self.all_token_counts.append(user_token_count)
- logging.debug(f"输入token计数: {user_token_count}")
-
- stream_iter = self.get_answer_stream_iter()
-
- for partial_text in stream_iter:
- chatbot[-1] = (chatbot[-1][0], partial_text + display_append)
- self.all_token_counts[-1] += 1
- status_text = self.token_message()
- yield get_return_value()
- if self.interrupted:
- self.recover()
- break
- self.history.append(construct_assistant(partial_text))
-
- def next_chatbot_at_once(self, inputs, chatbot, fake_input=None, display_append=""):
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
- if fake_input is not None:
- user_token_count = self.count_token(fake_input)
- else:
- user_token_count = self.count_token(inputs)
- self.all_token_counts.append(user_token_count)
- ai_reply, total_token_count = self.get_answer_at_once()
- self.history.append(construct_assistant(ai_reply))
- if fake_input is not None:
- self.history[-2] = construct_user(fake_input)
- chatbot[-1] = (chatbot[-1][0], ai_reply + display_append)
- if fake_input is not None:
- self.all_token_counts[-1] += count_token(construct_assistant(ai_reply))
- else:
- self.all_token_counts[-1] = total_token_count - sum(self.all_token_counts)
- status_text = self.token_message()
- return chatbot, status_text
-
- def handle_file_upload(self, files, chatbot):
- """if the model accepts multi modal input, implement this function"""
- status = gr.Markdown.update()
- if files:
- construct_index(self.api_key, file_src=files)
- status = "索引构建完成"
- return gr.Files.update(), chatbot, status
-
- def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
- fake_inputs = None
- display_append = []
- limited_context = False
- fake_inputs = real_inputs
- if files:
- from llama_index.indices.vector_store.base_query import GPTVectorStoreIndexQuery
- from llama_index.indices.query.schema import QueryBundle
- from langchain.embeddings.huggingface import HuggingFaceEmbeddings
- from langchain.chat_models import ChatOpenAI
- from llama_index import (
- GPTSimpleVectorIndex,
- ServiceContext,
- LangchainEmbedding,
- OpenAIEmbedding,
- )
- limited_context = True
- msg = "加载索引中……"
- logging.info(msg)
- # yield chatbot + [(inputs, "")], msg
- index = construct_index(self.api_key, file_src=files)
- assert index is not None, "获取索引失败"
- msg = "索引获取成功，生成回答中……"
- logging.info(msg)
- if local_embedding or self.model_type != ModelType.OpenAI:
- embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2"))
- else:
- embed_model = OpenAIEmbedding()
- # yield chatbot + [(inputs, "")], msg
- with retrieve_proxy():
- prompt_helper = PromptHelper(
- max_input_size=4096,
- num_output=5,
- max_chunk_overlap=20,
- chunk_size_limit=600,
- )
- from llama_index import ServiceContext
-
- service_context = ServiceContext.from_defaults(
- prompt_helper=prompt_helper, embed_model=embed_model
- )
- query_object = GPTVectorStoreIndexQuery(
- index.index_struct,
- service_context=service_context,
- similarity_top_k=5,
- vector_store=index._vector_store,
- docstore=index._docstore,
- response_synthesizer=None
- )
- query_bundle = QueryBundle(real_inputs)
- nodes = query_object.retrieve(query_bundle)
- reference_results = [n.node.text for n in nodes]
- reference_results = add_source_numbers(reference_results, use_source=False)
- display_append = add_details(reference_results)
- display_append = "\n\n" + "".join(display_append)
- real_inputs = (
- replace_today(PROMPT_TEMPLATE)
- .replace("{query_str}", real_inputs)
- .replace("{context_str}", "\n\n".join(reference_results))
- .replace("{reply_language}", reply_language)
- )
- elif use_websearch:
- limited_context = True
- search_results = ddg(real_inputs, max_results=5)
- reference_results = []
- for idx, result in enumerate(search_results):
- logging.debug(f"搜索结果{idx + 1}：{result}")
- domain_name = urllib3.util.parse_url(result["href"]).host
- reference_results.append([result["body"], result["href"]])
- display_append.append(
- # f"{idx+1}. [{domain_name}]({result['href']})\n"
- f"\n\n" + "".join(display_append) + "
"
- real_inputs = (
- replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
- .replace("{query}", real_inputs)
- .replace("{web_results}", "\n\n".join(reference_results))
- .replace("{reply_language}", reply_language)
- )
- else:
- display_append = ""
- return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
- def predict(
- self,
- inputs,
- chatbot,
- stream=False,
- use_websearch=False,
- files=None,
- reply_language="中文",
- should_check_token_count=True,
- ): # repetition_penalty, top_k
-
- status_text = "开始生成回答……"
- logging.info(
- "输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL
- )
- if should_check_token_count:
- yield chatbot + [(inputs, "")], status_text
- if reply_language == "跟随问题语言（不稳定）":
- reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."
-
- limited_context, fake_inputs, display_append, inputs, chatbot = self.prepare_inputs(real_inputs=inputs, use_websearch=use_websearch, files=files, reply_language=reply_language, chatbot=chatbot)
- yield chatbot + [(fake_inputs, "")], status_text
-
- if (
- self.need_api_key and
- self.api_key is None
- and not shared.state.multi_api_key
- ):
- status_text = STANDARD_ERROR_MSG + NO_APIKEY_MSG
- logging.info(status_text)
- chatbot.append((inputs, ""))
- if len(self.history) == 0:
- self.history.append(construct_user(inputs))
- self.history.append("")
- self.all_token_counts.append(0)
- else:
- self.history[-2] = construct_user(inputs)
- yield chatbot + [(inputs, "")], status_text
- return
- elif len(inputs.strip()) == 0:
- status_text = STANDARD_ERROR_MSG + NO_INPUT_MSG
- logging.info(status_text)
- yield chatbot + [(inputs, "")], status_text
- return
-
- if self.single_turn:
- self.history = []
- self.all_token_counts = []
- self.history.append(construct_user(inputs))
-
- try:
- if stream:
- logging.debug("使用流式传输")
- iter = self.stream_next_chatbot(
- inputs,
- chatbot,
- fake_input=fake_inputs,
- display_append=display_append,
- )
- for chatbot, status_text in iter:
- yield chatbot, status_text
- else:
- logging.debug("不使用流式传输")
- chatbot, status_text = self.next_chatbot_at_once(
- inputs,
- chatbot,
- fake_input=fake_inputs,
- display_append=display_append,
- )
- yield chatbot, status_text
- except Exception as e:
- traceback.print_exc()
- status_text = STANDARD_ERROR_MSG + str(e)
- yield chatbot, status_text
-
- if len(self.history) > 1 and self.history[-1]["content"] != inputs:
- logging.info(
- "回答为："
- + colorama.Fore.BLUE
- + f"{self.history[-1]['content']}"
- + colorama.Style.RESET_ALL
- )
-
- if limited_context:
- # self.history = self.history[-4:]
- # self.all_token_counts = self.all_token_counts[-2:]
- self.history = []
- self.all_token_counts = []
-
- max_token = self.token_upper_limit - TOKEN_OFFSET
-
- if sum(self.all_token_counts) > max_token and should_check_token_count:
- count = 0
- while (
- sum(self.all_token_counts)
- > self.token_upper_limit * REDUCE_TOKEN_FACTOR
- and sum(self.all_token_counts) > 0
- ):
- count += 1
- del self.all_token_counts[0]
- del self.history[:2]
- logging.info(status_text)
- status_text = f"为了防止token超限，模型忘记了早期的 {count} 轮对话"
- yield chatbot, status_text
-
- self.auto_save(chatbot)
-
- def retry(
- self,
- chatbot,
- stream=False,
- use_websearch=False,
- files=None,
- reply_language="中文",
- ):
- logging.debug("重试中……")
- if len(self.history) > 0:
- inputs = self.history[-2]["content"]
- del self.history[-2:]
- self.all_token_counts.pop()
- elif len(chatbot) > 0:
- inputs = chatbot[-1][0]
- else:
- yield chatbot, f"{STANDARD_ERROR_MSG}上下文是空的"
- return
-
- iter = self.predict(
- inputs,
- chatbot,
- stream=stream,
- use_websearch=use_websearch,
- files=files,
- reply_language=reply_language,
- )
- for x in iter:
- yield x
- logging.debug("重试完毕")
-
- # def reduce_token_size(self, chatbot):
- # logging.info("开始减少token数量……")
- # chatbot, status_text = self.next_chatbot_at_once(
- # summarize_prompt,
- # chatbot
- # )
- # max_token_count = self.token_upper_limit * REDUCE_TOKEN_FACTOR
- # num_chat = find_n(self.all_token_counts, max_token_count)
- # logging.info(f"previous_token_count: {self.all_token_counts}, keeping {num_chat} chats")
- # chatbot = chatbot[:-1]
- # self.history = self.history[-2*num_chat:] if num_chat > 0 else []
- # self.all_token_counts = self.all_token_counts[-num_chat:] if num_chat > 0 else []
- # msg = f"保留了最近{num_chat}轮对话"
- # logging.info(msg)
- # logging.info("减少token数量完毕")
- # return chatbot, msg + "，" + self.token_message(self.all_token_counts if len(self.all_token_counts) > 0 else [0])
-
- def interrupt(self):
- self.interrupted = True
-
- def recover(self):
- self.interrupted = False
-
- def set_token_upper_limit(self, new_upper_limit):
- self.token_upper_limit = new_upper_limit
- print(f"token上限设置为{new_upper_limit}")
-
- def set_temperature(self, new_temperature):
- self.temperature = new_temperature
-
- def set_top_p(self, new_top_p):
- self.top_p = new_top_p
-
- def set_n_choices(self, new_n_choices):
- self.n_choices = new_n_choices
-
- def set_stop_sequence(self, new_stop_sequence: str):
- new_stop_sequence = new_stop_sequence.split(",")
- self.stop_sequence = new_stop_sequence
-
- def set_max_tokens(self, new_max_tokens):
- self.max_generation_token = new_max_tokens
-
- def set_presence_penalty(self, new_presence_penalty):
- self.presence_penalty = new_presence_penalty
-
- def set_frequency_penalty(self, new_frequency_penalty):
- self.frequency_penalty = new_frequency_penalty
-
- def set_logit_bias(self, logit_bias):
- logit_bias = logit_bias.split()
- bias_map = {}
- encoding = tiktoken.get_encoding("cl100k_base")
- for line in logit_bias:
- word, bias_amount = line.split(":")
- if word:
- for token in encoding.encode(word):
- bias_map[token] = float(bias_amount)
- self.logit_bias = bias_map
-
- def set_user_identifier(self, new_user_identifier):
- self.user_identifier = new_user_identifier
-
- def set_system_prompt(self, new_system_prompt):
- self.system_prompt = new_system_prompt
-
- def set_key(self, new_access_key):
- self.api_key = new_access_key.strip()
- msg = i18n("API密钥更改为了") + hide_middle_chars(self.api_key)
- logging.info(msg)
- return self.api_key, msg
-
- def set_single_turn(self, new_single_turn):
- self.single_turn = new_single_turn
-
- def reset(self):
- self.history = []
- self.all_token_counts = []
- self.interrupted = False
- pathlib.Path(os.path.join(HISTORY_DIR, self.user_identifier, new_auto_history_filename(os.path.join(HISTORY_DIR, self.user_identifier)))).touch()
- return [], self.token_message([0])
-
- def delete_first_conversation(self):
- if self.history:
- del self.history[:2]
- del self.all_token_counts[0]
- return self.token_message()
-
- def delete_last_conversation(self, chatbot):
- if len(chatbot) > 0 and STANDARD_ERROR_MSG in chatbot[-1][1]:
- msg = "由于包含报错信息，只删除chatbot记录"
- chatbot.pop()
- return chatbot, self.history
- if len(self.history) > 0:
- self.history.pop()
- self.history.pop()
- if len(chatbot) > 0:
- msg = "删除了一组chatbot对话"
- chatbot.pop()
- if len(self.all_token_counts) > 0:
- msg = "删除了一组对话的token计数记录"
- self.all_token_counts.pop()
- msg = "删除了一组对话"
- return chatbot, msg
-
- def token_message(self, token_lst=None):
- if token_lst is None:
- token_lst = self.all_token_counts
- token_sum = 0
- for i in range(len(token_lst)):
- token_sum += sum(token_lst[: i + 1])
- return i18n("Token 计数: ") + f"{sum(token_lst)}" + i18n("，本次对话累计消耗了 ") + f"{token_sum} tokens"
-
- def save_chat_history(self, filename, chatbot, user_name):
- if filename == "":
- return
- if not filename.endswith(".json"):
- filename += ".json"
- return save_file(filename, self.system_prompt, self.history, chatbot, user_name)
-
- def auto_save(self, chatbot):
- history_file_path = get_history_filepath(self.user_identifier)
- save_file(history_file_path, self.system_prompt, self.history, chatbot, self.user_identifier)
-
- def export_markdown(self, filename, chatbot, user_name):
- if filename == "":
- return
- if not filename.endswith(".md"):
- filename += ".md"
- return save_file(filename, self.system_prompt, self.history, chatbot, user_name)
-
- def load_chat_history(self, filename, user_name):
- logging.debug(f"{user_name} 加载对话历史中……")
- logging.info(f"filename: {filename}")
- if type(filename) != str and filename is not None:
- filename = filename.name
- try:
- if "/" not in filename:
- history_file_path = os.path.join(HISTORY_DIR, user_name, filename)
- else:
- history_file_path = filename
- with open(history_file_path, "r") as f:
- json_s = json.load(f)
- try:
- if type(json_s["history"][0]) == str:
- logging.info("历史记录格式为旧版，正在转换……")
- new_history = []
- for index, item in enumerate(json_s["history"]):
- if index % 2 == 0:
- new_history.append(construct_user(item))
- else:
- new_history.append(construct_assistant(item))
- json_s["history"] = new_history
- logging.info(new_history)
- except:
- pass
- logging.debug(f"{user_name} 加载对话历史完毕")
- self.history = json_s["history"]
- return os.path.basename(filename), json_s["system"], json_s["chatbot"]
- except:
- # 没有对话历史或者对话历史解析失败
- logging.info(f"没有找到对话历史记录 {filename}")
- return gr.update(), self.system_prompt, gr.update()
-
- def auto_load(self):
- if self.user_identifier == "":
- self.reset()
- return self.system_prompt, gr.update()
- history_file_path = get_history_filepath(self.user_identifier)
- filename, system_prompt, chatbot = self.load_chat_history(history_file_path, self.user_identifier)
- return system_prompt, chatbot
-
-
- def like(self):
- """like the last response, implement if needed
- """
- return gr.update()
-
- def dislike(self):
- """dislike the last response, implement if needed
- """
- return gr.update()
diff --git a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/spec.py b/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/spec.py
deleted file mode 100644
index 3fa983523d7b404aed99529a94a087e921a70a86..0000000000000000000000000000000000000000
--- a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/spec.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) Meta, 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.
-"""Conveniance wrapper to perform STFT and iSTFT"""
-
-import torch as th
-
-
-def spectro(x, n_fft=512, hop_length=None, pad=0):
- *other, length = x.shape
- x = x.reshape(-1, length)
- z = th.stft(x,
- n_fft * (1 + pad),
- hop_length or n_fft // 4,
- window=th.hann_window(n_fft).to(x),
- win_length=n_fft,
- normalized=True,
- center=True,
- return_complex=True,
- pad_mode='reflect')
- _, freqs, frame = z.shape
- return z.view(*other, freqs, frame)
-
-
-def ispectro(z, hop_length=None, length=None, pad=0):
- *other, freqs, frames = z.shape
- n_fft = 2 * freqs - 2
- z = z.view(-1, freqs, frames)
- win_length = n_fft // (1 + pad)
- x = th.istft(z,
- n_fft,
- hop_length,
- window=th.hann_window(win_length).to(z.real),
- win_length=win_length,
- normalized=True,
- length=length,
- center=True)
- _, length = x.shape
- return x.view(*other, length)
diff --git a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/states.py b/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/states.py
deleted file mode 100644
index 71f229a886527291139d46e53d3cb7f947047060..0000000000000000000000000000000000000000
--- a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs3/states.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) Meta, 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.
-"""
-Utilities to save and load models.
-"""
-from contextlib import contextmanager
-
-import functools
-import hashlib
-import inspect
-import io
-from pathlib import Path
-import warnings
-
-from omegaconf import OmegaConf
-from diffq import DiffQuantizer, UniformQuantizer, restore_quantized_state
-import torch
-
-
-def get_quantizer(model, args, optimizer=None):
- """Return the quantizer given the XP quantization args."""
- quantizer = None
- if args.diffq:
- quantizer = DiffQuantizer(
- model, min_size=args.min_size, group_size=args.group_size)
- if optimizer is not None:
- quantizer.setup_optimizer(optimizer)
- elif args.qat:
- quantizer = UniformQuantizer(
- model, bits=args.qat, min_size=args.min_size)
- return quantizer
-
-
-def load_model(path_or_package, strict=False):
- """Load a model from the given serialized model, either given as a dict (already loaded)
- or a path to a file on disk."""
- if isinstance(path_or_package, dict):
- package = path_or_package
- elif isinstance(path_or_package, (str, Path)):
- with warnings.catch_warnings():
- warnings.simplefilter("ignore")
- path = path_or_package
- package = torch.load(path, 'cpu')
- else:
- raise ValueError(f"Invalid type for {path_or_package}.")
-
- klass = package["klass"]
- args = package["args"]
- kwargs = package["kwargs"]
-
- if strict:
- model = klass(*args, **kwargs)
- else:
- sig = inspect.signature(klass)
- for key in list(kwargs):
- if key not in sig.parameters:
- warnings.warn("Dropping inexistant parameter " + key)
- del kwargs[key]
- model = klass(*args, **kwargs)
-
- state = package["state"]
-
- set_state(model, state)
- return model
-
-
-def get_state(model, quantizer, half=False):
- """Get the state from a model, potentially with quantization applied.
- If `half` is True, model are stored as half precision, which shouldn't impact performance
- but half the state size."""
- if quantizer is None:
- dtype = torch.half if half else None
- state = {k: p.data.to(device='cpu', dtype=dtype) for k, p in model.state_dict().items()}
- else:
- state = quantizer.get_quantized_state()
- state['__quantized'] = True
- return state
-
-
-def set_state(model, state, quantizer=None):
- """Set the state on a given model."""
- if state.get('__quantized'):
- if quantizer is not None:
- quantizer.restore_quantized_state(model, state['quantized'])
- else:
- restore_quantized_state(model, state)
- else:
- model.load_state_dict(state)
- return state
-
-
-def save_with_checksum(content, path):
- """Save the given value on disk, along with a sha256 hash.
- Should be used with the output of either `serialize_model` or `get_state`."""
- buf = io.BytesIO()
- torch.save(content, buf)
- sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
-
- path = path.parent / (path.stem + "-" + sig + path.suffix)
- path.write_bytes(buf.getvalue())
-
-
-def serialize_model(model, training_args, quantizer=None, half=True):
- args, kwargs = model._init_args_kwargs
- klass = model.__class__
-
- state = get_state(model, quantizer, half)
- return {
- 'klass': klass,
- 'args': args,
- 'kwargs': kwargs,
- 'state': state,
- 'training_args': OmegaConf.to_container(training_args, resolve=True),
- }
-
-
-def copy_state(state):
- return {k: v.cpu().clone() for k, v in state.items()}
-
-
-@contextmanager
-def swap_state(model, state):
- """
- Context manager that swaps the state of a model, e.g:
-
- # model is in old state
- with swap_state(model, new_state):
- # model in new state
- # model back to old state
- """
- old_state = copy_state(model.state_dict())
- model.load_state_dict(state, strict=False)
- try:
- yield
- finally:
- model.load_state_dict(old_state)
-
-
-def capture_init(init):
- @functools.wraps(init)
- def __init__(self, *args, **kwargs):
- self._init_args_kwargs = (args, kwargs)
- init(self, *args, **kwargs)
-
- return __init__
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/README.md b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/README.md
deleted file mode 100644
index 1d827ae05da6978cce32f992c737b31c8c4c62a6..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LoveLive-ShojoKageki VITS
-emoji: ⚡
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py
deleted file mode 100644
index 10c0920c1a217af5bb3e1b13077568035ab3b7b5..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py
+++ /dev/null
@@ -1,123 +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]
-# ------------------------------------------------------------------------
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-DETR Transformer class.
-
-Copy-paste from torch.nn.Transformer with modifications:
- * positional encodings are passed in MHattention
- * extra LN at the end of encoder is removed
- * decoder returns a stack of activations from all decoding layers
-"""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-from .utils import (
- MLP,
- _get_activation_fn,
- _get_clones,
- gen_encoder_output_proposals,
- gen_sineembed_for_position,
- sigmoid_focal_loss,
-)
-
-
-class TextTransformer(nn.Module):
- def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
- super().__init__()
- self.num_layers = num_layers
- self.d_model = d_model
- self.nheads = nheads
- self.dim_feedforward = dim_feedforward
- self.norm = None
-
- single_encoder_layer = TransformerEncoderLayer(
- d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
- )
- self.layers = _get_clones(single_encoder_layer, num_layers)
-
- def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
- """
-
- Args:
- text_attention_mask: bs, num_token
- memory_text: bs, num_token, d_model
-
- Raises:
- RuntimeError: _description_
-
- Returns:
- output: bs, num_token, d_model
- """
-
- output = memory_text.transpose(0, 1)
-
- for layer in self.layers:
- output = layer(output, src_key_padding_mask=text_attention_mask)
-
- if self.norm is not None:
- output = self.norm(output)
-
- return output.transpose(0, 1)
-
-
-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
- self.nhead = nhead
-
- def with_pos_embed(self, tensor, pos: Optional[Tensor]):
- return tensor if pos is None else tensor + pos
-
- def forward(
- self,
- src,
- src_mask: Optional[Tensor] = None,
- src_key_padding_mask: Optional[Tensor] = None,
- pos: Optional[Tensor] = None,
- ):
- # repeat attn mask
- if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
- # bs, num_q, num_k
- src_mask = src_mask.repeat(self.nhead, 1, 1)
-
- q = k = self.with_pos_embed(src, pos)
-
- src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
-
- # 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
diff --git a/spaces/Manjushri/MusicGen/audiocraft/quantization/vq.py b/spaces/Manjushri/MusicGen/audiocraft/quantization/vq.py
deleted file mode 100644
index f67c3a0cd30d4b8993a36c587f00dc8a451d926f..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/audiocraft/quantization/vq.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
- """Residual Vector Quantizer.
-
- Args:
- dimension (int): Dimension of the codebooks.
- n_q (int): Number of residual vector quantizers used.
- q_dropout (bool): Random quantizer drop out at train time.
- bins (int): Codebook size.
- decay (float): Decay for exponential moving average over the codebooks.
- kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
- kmeans_iters (int): Number of iterations used for kmeans initialization.
- threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
- that have an exponential moving average cluster size less than the specified threshold with
- randomly selected vector from the current batch.
- orthogonal_reg_weight (float): Orthogonal regularization weights.
- orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
- orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
- for orthogonal regulariation.
- """
- def __init__(
- self,
- dimension: int = 256,
- n_q: int = 8,
- q_dropout: bool = False,
- bins: int = 1024,
- decay: float = 0.99,
- kmeans_init: bool = True,
- kmeans_iters: int = 10,
- threshold_ema_dead_code: int = 2,
- orthogonal_reg_weight: float = 0.0,
- orthogonal_reg_active_codes_only: bool = False,
- orthogonal_reg_max_codes: tp.Optional[int] = None,
- ):
- super().__init__()
- self.max_n_q = n_q
- self.n_q = n_q
- self.q_dropout = q_dropout
- self.dimension = dimension
- self.bins = bins
- self.decay = decay
- self.kmeans_init = kmeans_init
- self.kmeans_iters = kmeans_iters
- self.threshold_ema_dead_code = threshold_ema_dead_code
- self.orthogonal_reg_weight = orthogonal_reg_weight
- self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
- self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
- self.vq = ResidualVectorQuantization(
- dim=self.dimension,
- codebook_size=self.bins,
- num_quantizers=self.n_q,
- decay=self.decay,
- kmeans_init=self.kmeans_init,
- kmeans_iters=self.kmeans_iters,
- threshold_ema_dead_code=self.threshold_ema_dead_code,
- orthogonal_reg_weight=self.orthogonal_reg_weight,
- orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
- orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
- channels_last=False
- )
-
- def forward(self, x: torch.Tensor, frame_rate: int):
- n_q = self.n_q
- if self.training and self.q_dropout:
- n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
- bw_per_q = math.log2(self.bins) * frame_rate / 1000
- quantized, codes, commit_loss = self.vq(x, n_q=n_q)
- codes = codes.transpose(0, 1)
- # codes is [B, K, T], with T frames, K nb of codebooks.
- bw = torch.tensor(n_q * bw_per_q).to(x)
- return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
- def encode(self, x: torch.Tensor) -> torch.Tensor:
- """Encode a given input tensor with the specified frame rate at the given bandwidth.
- The RVQ encode method sets the appropriate number of quantizer to use
- and returns indices for each quantizer.
- """
- n_q = self.n_q
- codes = self.vq.encode(x, n_q=n_q)
- codes = codes.transpose(0, 1)
- # codes is [B, K, T], with T frames, K nb of codebooks.
- return codes
-
- def decode(self, codes: torch.Tensor) -> torch.Tensor:
- """Decode the given codes to the quantized representation.
- """
- # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
- codes = codes.transpose(0, 1)
- quantized = self.vq.decode(codes)
- return quantized
-
- @property
- def total_codebooks(self):
- return self.max_n_q
-
- @property
- def num_codebooks(self):
- return self.n_q
-
- def set_num_codebooks(self, n: int):
- assert n > 0 and n <= self.max_n_q
- self.n_q = n
diff --git a/spaces/Marshalls/testmtd/feature_extraction/extract_transform.py b/spaces/Marshalls/testmtd/feature_extraction/extract_transform.py
deleted file mode 100644
index 395d78b1f502dd3881e99b68fc229563c7a0e307..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/extract_transform.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import librosa
-import numpy as np
-from pathlib import Path
-import json
-import os.path
-import sys
-import argparse
-
-'''
-Compute transforms which r4quire to be fitted on all the data at once rather than sequential (so they dont implement the `partial_fit` function)
-'''
-
-THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.abspath(os.path.join(THIS_DIR, os.pardir))
-sys.path.append(ROOT_DIR)
-from audio_feature_utils import extract_features_hybrid, extract_features_mel, extract_features_multi_mel
-from utils import distribute_tasks
-#from scripts.feature_extraction.utils import distribute_tasks
-
-parser = argparse.ArgumentParser(description="Preprocess songs data")
-
-parser.add_argument("data_path", type=str, help="Directory contining Beat Saber level folders")
-parser.add_argument("--feature_name", metavar='', type=str, default="mel", help="mel, chroma, multi_mel")
-parser.add_argument("--transforms", metavar='', type=str, default="scaler", help="comma-separated lists of transforms to extract (scaler,pca_transform)")
-args = parser.parse_args()
-
-# makes arugments into global variables of the same name, used later in the code
-globals().update(vars(args))
-data_path = Path(data_path)
-
-## distributing tasks accross nodes ##
-from mpi4py import MPI
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-size = comm.Get_size()
-print(rank)
-assert size == 1
-candidate_files = sorted(data_path.glob('**/*'+feature_name+'.npy'), key=lambda path: path.parent.__str__())
-tasks = range(len(candidate_files))
-
-from sklearn import decomposition, preprocessing
-features = None
-for i in tasks:
- path = candidate_files[i]
- feature_file = path.__str__()
- if i == 0:
- features = np.load(feature_file)
- else:
- feature = np.load(feature_file)
- features = np.concatenate([features,feature],0)
-
-import pickle
-transforms = transforms.split(",")
-for transform in transforms:
- if transform == "2moments":
- if len(features.shape) == 3:
- features = features[:,0,:]
- C = np.dot(features.T,features)/features.shape[0]
- m = np.mean(features,0)
- pickle.dump((m,C), open(data_path.joinpath(feature_name+'_2moments.pkl'), 'wb'))
- elif transform == "2moments_ext":
- if len(features.shape) == 3:
- features = features[:,0,:]
- if features.shape[0] % 3 != 0:
- features = features[:-(features.shape[0]%3)]
- features = np.reshape(features,(-1,3*features.shape[1]))
- C = np.dot(features.T,features)/features.shape[0]
- m = np.mean(features,0)
- pickle.dump((m,C), open(data_path.joinpath(feature_name+'_2moments_ext.pkl'), 'wb'))
- elif transform == "scaler":
- scaler = preprocessing.StandardScaler().fit(features)
- pickle.dump(scaler, open(data_path.joinpath(feature_name+'_scaler.pkl'), 'wb'))
- elif transform == "pca_transform":
- feature_size = features.shape[1]
- pca = decomposition.PCA(n_components=feature_size)
- pca_transform = pca.fit(features)
- pickle.dump(pca_transform, open(data_path.joinpath(feature_name+'_pca_transform.pkl'), 'wb'))
- else:
- raise NotImplementedError("Transform type "+transform+" not implemented")
diff --git a/spaces/MatzeFix/openai-whisper-large-v2/README.md b/spaces/MatzeFix/openai-whisper-large-v2/README.md
deleted file mode 100644
index 7da6cc67247289e9769597a605996a91552d5d2a..0000000000000000000000000000000000000000
--- a/spaces/MatzeFix/openai-whisper-large-v2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Openai Whisper Large V2
-emoji: 📈
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/assign_score_withk.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/assign_score_withk.py
deleted file mode 100644
index 4906adaa2cffd1b46912fbe7d4f87ef2f9fa0012..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/assign_score_withk.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['assign_score_withk_forward', 'assign_score_withk_backward'])
-
-
-class AssignScoreWithK(Function):
- r"""Perform weighted sum to generate output features according to scores.
- Modified from `PAConv
-
-""")
-loading=("""
-Loading Time Limit Reached.
-Please choose a Simpler Prompt, or Upgrade for faster loading.
-""")
-
-def add_random_noise(prompt, noise_level=1.00):
- if noise_level == 0:
- noise_level = 0.00
- if noise_level == None:
- noise_level = 1.00
- 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 build():
- def zip_files():
- zip_name = f"{b.prompt.split(' ')[0]}_{random.randint(0, 10000)}.zip"
- with ZipFile(zip_name, "w") as zipObj:
- for file in b.imagens:
- zipObj.write(file, os.path.basename(file))
- b.imagens = []
- return zip_name
- def clear():
- return gr.update(value=0),gr.update(value=0)
- def start():
- stamp = time.time()
- return gr.update(value=stamp),gr.update(value=0)
- def end(stamp):
- ts = stamp + 360
- ti = time.time()
- if ti > ts and stamp != 0:
- return gr.update(value=1),gr.HTML.update(f"{m_out}",visible=True)
- else:
- return gr.update(value=0),None
- def im_fn(prompt,noise_level,h=None):
- try:
- if h == o:
- prompt_with_noise = add_random_noise(prompt, noise_level)
- imagem = model(prompt_with_noise)
- b.prompt = prompt
- b.imagens.append(imagem)
- return imagem
- elif h != o:
- return(None,None)
- except Exception as E:
- return None, None
- def cl_fac():
- return "",gr.HTML.update(f"{loading}")
- with gr.Blocks() as b:
- b.imagens: list = []
- with gr.Row():
- with gr.Column():
- prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt")
- noise_level = gr.Slider(minimum=0.0, maximum=10, step=0.1, label="Noise Level between images.")
- with gr.Column():
- with gr.Row():
- btn1 = gr.Button("Generate")
- btn2 = gr.Button("Clear")
- message=gr.HTML("")
- message2=gr.HTML("",visible=False)
-
- with gr.Row():
- out1 = gr.Image()
- out2 = gr.Image()
- with gr.Row():
- out3 = gr.Image()
- out4 = gr.Image()
- with gr.Row():
- out5 = gr.Image()
- out6 = gr.Image()
- with gr.Row():
- # btn3 = gr.Button("Download")
- caixa = gr.File(file_count="multiple", file_types=["text", ".json", ".csv", "image"])
-
- with gr.Row(visible=False):
- h_variavel=gr.Textbox(value="V")
- t_state=gr.Number()
- t_switch=gr.Textbox(value=0)
- auto= gr.Image()
- def clear_all():
- return "",None,None,None,None,None,None,None,None,1,gr.HTML.update("")
- fac_b = gr.Textbox(value="",visible=False)
-
- def noth():
- return gr.HTML.update("")
- #a1=btn1.click(noth,None,btn1,every=1)
- btn1.click(cl_fac,None,[fac_b,message],show_progress=False)
- b1=btn1.click(start,None,[t_state,t_switch],show_progress=True)
- sta = t_state.change(end,t_state,[t_switch,message2],every=1,show_progress=True)
- b2=btn1.click(im_fn,[prompt,noise_level,h_variavel],[out1,], show_progress=True)
- b3=out1.change(im_fn,[prompt,noise_level,h_variavel],[out2,], show_progress=True)
- b4=out2.change(im_fn,[prompt,noise_level,h_variavel],[out3,], show_progress=True)
- b5=out3.change(im_fn,[prompt,noise_level,h_variavel],[out4,], show_progress=True)
- b6=out4.change(im_fn,[prompt,noise_level,h_variavel],[out5,], show_progress=True)
- b7=out5.change(im_fn,[prompt,noise_level,h_variavel],[out6], show_progress=True)
- b8=out6.change(noth,None,[message], show_progress=False)
- b8=out6.change(zip_files,None,[caixa], show_progress=False)
- swi=t_switch.change(clear,None,[t_switch,fac_b], cancels=[sta,b2,b3,b4,b5,b6,b7],show_progress=False)
- #btn2.click(noth,None,message,cancels=[b1,sta,b2,b3,b4,b5,swi],show_progress=False)
- btn2.click(clear_all, None,[fac_b,prompt,out1,out2,out3,out4,out5,out6,t_state,t_switch,message],cancels=[b1,sta,b2,b3,b4,b5,b6,b7,b8,swi],show_progress=False)
- # btn3.click(zip_files,None,[caixa],show_progress=False)
- # caixa.change(noth,None,[message],show_progress=False)
- b.queue(concurrency_count=100).launch(show_api=False)
-build()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/models/bert_pretrainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/models/bert_pretrainer_test.py
deleted file mode 100644
index eb9ace5ccf132ec0423276b28fa1e1e473a97290..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/models/bert_pretrainer_test.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for BERT trainer network."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import
-from official.nlp.modeling import networks
-from official.nlp.modeling.models import bert_pretrainer
-
-
-# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
-# guarantees forward compatibility of this code for the V2 switchover.
-@keras_parameterized.run_all_keras_modes
-class BertPretrainerTest(keras_parameterized.TestCase):
-
- def test_bert_pretrainer(self):
- """Validate that the Keras object can be created."""
- # Build a transformer network to use within the BERT trainer.
- vocab_size = 100
- sequence_length = 512
- test_network = networks.TransformerEncoder(
- vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)
-
- # Create a BERT trainer with the created network.
- num_classes = 3
- num_token_predictions = 2
- bert_trainer_model = bert_pretrainer.BertPretrainer(
- test_network,
- num_classes=num_classes,
- num_token_predictions=num_token_predictions)
-
- # Create a set of 2-dimensional inputs (the first dimension is implicit).
- word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
- mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
- type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
- masked_lm_positions = tf.keras.Input(
- shape=(num_token_predictions,), dtype=tf.int32)
-
- # Invoke the trainer model on the inputs. This causes the layer to be built.
- outputs = bert_trainer_model(
- [word_ids, mask, type_ids, masked_lm_positions])
-
- # Validate that the outputs are of the expected shape.
- expected_lm_shape = [None, num_token_predictions, vocab_size]
- expected_classification_shape = [None, num_classes]
- self.assertAllEqual(expected_lm_shape, outputs['masked_lm'].shape.as_list())
- self.assertAllEqual(expected_classification_shape,
- outputs['classification'].shape.as_list())
-
- def test_bert_trainer_tensor_call(self):
- """Validate that the Keras object can be invoked."""
- # Build a transformer network to use within the BERT trainer. (Here, we use
- # a short sequence_length for convenience.)
- test_network = networks.TransformerEncoder(
- vocab_size=100, num_layers=2, sequence_length=2)
-
- # Create a BERT trainer with the created network.
- bert_trainer_model = bert_pretrainer.BertPretrainer(
- test_network, num_classes=2, num_token_predictions=2)
-
- # Create a set of 2-dimensional data tensors to feed into the model.
- word_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32)
- mask = tf.constant([[1, 1], [1, 0]], dtype=tf.int32)
- type_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32)
- lm_mask = tf.constant([[1, 1], [1, 0]], dtype=tf.int32)
-
- # Invoke the trainer model on the tensors. In Eager mode, this does the
- # actual calculation. (We can't validate the outputs, since the network is
- # too complex: this simply ensures we're not hitting runtime errors.)
- _ = bert_trainer_model([word_ids, mask, type_ids, lm_mask])
-
- def test_serialize_deserialize(self):
- """Validate that the BERT trainer can be serialized and deserialized."""
- # Build a transformer network to use within the BERT trainer. (Here, we use
- # a short sequence_length for convenience.)
- test_network = networks.TransformerEncoder(
- vocab_size=100, num_layers=2, sequence_length=5)
-
- # Create a BERT trainer with the created network. (Note that all the args
- # are different, so we can catch any serialization mismatches.)
- bert_trainer_model = bert_pretrainer.BertPretrainer(
- test_network, num_classes=4, num_token_predictions=3)
-
- # Create another BERT trainer via serialization and deserialization.
- config = bert_trainer_model.get_config()
- new_bert_trainer_model = bert_pretrainer.BertPretrainer.from_config(config)
-
- # Validate that the config can be forced to JSON.
- _ = new_bert_trainer_model.to_json()
-
- # If the serialization was successful, the new config should match the old.
- self.assertAllEqual(bert_trainer_model.get_config(),
- new_bert_trainer_model.get_config())
-
- def test_bert_pretrainerv2(self):
- """Validate that the Keras object can be created."""
- # Build a transformer network to use within the BERT trainer.
- vocab_size = 100
- sequence_length = 512
- test_network = networks.TransformerEncoder(
- vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)
-
- # Create a BERT trainer with the created network.
- num_token_predictions = 2
- bert_trainer_model = bert_pretrainer.BertPretrainerV2(
- encoder_network=test_network, num_masked_tokens=num_token_predictions)
-
- # Create a set of 2-dimensional inputs (the first dimension is implicit).
- word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
- mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
- type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
- lm_mask = tf.keras.Input(shape=(num_token_predictions,), dtype=tf.int32)
-
- # Invoke the trainer model on the inputs. This causes the layer to be built.
- outputs = bert_trainer_model([word_ids, mask, type_ids, lm_mask])
-
- # Validate that the outputs are of the expected shape.
- expected_lm_shape = [None, num_token_predictions, vocab_size]
- self.assertAllEqual(expected_lm_shape, outputs['lm_output'].shape.as_list())
-
- def test_v2_serialize_deserialize(self):
- """Validate that the BERT trainer can be serialized and deserialized."""
- # Build a transformer network to use within the BERT trainer. (Here, we use
- # a short sequence_length for convenience.)
- test_network = networks.TransformerEncoder(
- vocab_size=100, num_layers=2, sequence_length=5)
-
- # Create a BERT trainer with the created network. (Note that all the args
- # are different, so we can catch any serialization mismatches.)
- bert_trainer_model = bert_pretrainer.BertPretrainerV2(
- encoder_network=test_network, num_masked_tokens=2)
-
- # Create another BERT trainer via serialization and deserialization.
- config = bert_trainer_model.get_config()
- new_bert_trainer_model = bert_pretrainer.BertPretrainerV2.from_config(
- config)
-
- # Validate that the config can be forced to JSON.
- _ = new_bert_trainer_model.to_json()
-
- # If the serialization was successful, the new config should match the old.
- self.assertAllEqual(bert_trainer_model.get_config(),
- new_bert_trainer_model.get_config())
-
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/models_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/models_test.py
deleted file mode 100644
index 39676a347d65e2dc19e99a7dec4d22dfb4c60df4..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/models_test.py
+++ /dev/null
@@ -1,324 +0,0 @@
-# Copyright 2020 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.
-# ==============================================================================
-"""Tests for nlp.nhnet.models."""
-
-import os
-
-from absl import logging
-from absl.testing import parameterized
-import numpy as np
-import tensorflow as tf
-
-# pylint: disable=g-direct-tensorflow-import
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-# pylint: enable=g-direct-tensorflow-import
-from official.nlp.nhnet import configs
-from official.nlp.nhnet import models
-from official.nlp.nhnet import utils
-
-
-def all_strategy_combinations():
- return combinations.combine(
- distribution=[
- strategy_combinations.default_strategy,
- strategy_combinations.tpu_strategy,
- strategy_combinations.one_device_strategy_gpu,
- strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
- strategy_combinations.mirrored_strategy_with_two_gpus,
- ],
- mode="eager",
- )
-
-
-def distribution_forward_path(strategy,
- model,
- inputs,
- batch_size,
- mode="train"):
- dataset = tf.data.Dataset.from_tensor_slices((inputs))
- dataset = dataset.batch(batch_size)
- dataset = strategy.experimental_distribute_dataset(dataset)
-
- @tf.function
- def test_step(inputs):
- """Calculates evaluation metrics on distributed devices."""
-
- def _test_step_fn(inputs):
- """Replicated accuracy calculation."""
- return model(inputs, mode=mode, training=False)
-
- outputs = strategy.run(_test_step_fn, args=(inputs,))
- return tf.nest.map_structure(strategy.experimental_local_results, outputs)
-
- return [test_step(inputs) for inputs in dataset]
-
-
-def process_decoded_ids(predictions, end_token_id):
- """Transforms decoded tensors to lists ending with END_TOKEN_ID."""
- if isinstance(predictions, tf.Tensor):
- predictions = predictions.numpy()
- flatten_ids = predictions.reshape((-1, predictions.shape[-1]))
- results = []
- for ids in flatten_ids:
- ids = list(ids)
- if end_token_id in ids:
- ids = ids[:ids.index(end_token_id)]
- results.append(ids)
- return results
-
-
-class Bert2BertTest(tf.test.TestCase, parameterized.TestCase):
-
- def setUp(self):
- super(Bert2BertTest, self).setUp()
- self._config = utils.get_test_params()
-
- def test_model_creation(self):
- model = models.create_bert2bert_model(params=self._config)
- fake_ids = np.zeros((2, 10), dtype=np.int32)
- fake_inputs = {
- "input_ids": fake_ids,
- "input_mask": fake_ids,
- "segment_ids": fake_ids,
- "target_ids": fake_ids,
- }
- model(fake_inputs)
-
- @combinations.generate(all_strategy_combinations())
- def test_bert2bert_train_forward(self, distribution):
- seq_length = 10
- # Defines the model inside distribution strategy scope.
- with distribution.scope():
- # Forward path.
- batch_size = 2
- batches = 4
- fake_ids = np.zeros((batch_size * batches, seq_length), dtype=np.int32)
- fake_inputs = {
- "input_ids": fake_ids,
- "input_mask": fake_ids,
- "segment_ids": fake_ids,
- "target_ids": fake_ids,
- }
- model = models.create_bert2bert_model(params=self._config)
- results = distribution_forward_path(distribution, model, fake_inputs,
- batch_size)
- logging.info("Forward path results: %s", str(results))
- self.assertLen(results, batches)
-
- def test_bert2bert_decoding(self):
- seq_length = 10
- self._config.override(
- {
- "beam_size": 3,
- "len_title": seq_length,
- "alpha": 0.6,
- },
- is_strict=False)
-
- batch_size = 2
- fake_ids = np.zeros((batch_size, seq_length), dtype=np.int32)
- fake_inputs = {
- "input_ids": fake_ids,
- "input_mask": fake_ids,
- "segment_ids": fake_ids,
- }
- self._config.override({
- "padded_decode": False,
- "use_cache": False,
- },
- is_strict=False)
- model = models.create_bert2bert_model(params=self._config)
- ckpt = tf.train.Checkpoint(model=model)
-
- # Initializes variables from checkpoint to keep outputs deterministic.
- init_checkpoint = ckpt.save(os.path.join(self.get_temp_dir(), "ckpt"))
- ckpt.restore(init_checkpoint).assert_existing_objects_matched()
- top_ids, scores = model(fake_inputs, mode="predict")
-
- self._config.override({
- "padded_decode": False,
- "use_cache": True,
- },
- is_strict=False)
- model = models.create_bert2bert_model(params=self._config)
- ckpt = tf.train.Checkpoint(model=model)
- ckpt.restore(init_checkpoint).assert_existing_objects_matched()
- cached_top_ids, cached_scores = model(fake_inputs, mode="predict")
- self.assertEqual(
- process_decoded_ids(top_ids, self._config.end_token_id),
- process_decoded_ids(cached_top_ids, self._config.end_token_id))
- self.assertAllClose(scores, cached_scores)
-
- self._config.override({
- "padded_decode": True,
- "use_cache": True,
- },
- is_strict=False)
- model = models.create_bert2bert_model(params=self._config)
- ckpt = tf.train.Checkpoint(model=model)
- ckpt.restore(init_checkpoint).assert_existing_objects_matched()
- padded_top_ids, padded_scores = model(fake_inputs, mode="predict")
- self.assertEqual(
- process_decoded_ids(top_ids, self._config.end_token_id),
- process_decoded_ids(padded_top_ids, self._config.end_token_id))
- self.assertAllClose(scores, padded_scores)
-
- @combinations.generate(all_strategy_combinations())
- def test_bert2bert_eval(self, distribution):
- seq_length = 10
- padded_decode = isinstance(distribution,
- tf.distribute.experimental.TPUStrategy)
- self._config.override(
- {
- "beam_size": 3,
- "len_title": seq_length,
- "alpha": 0.6,
- "padded_decode": padded_decode,
- },
- is_strict=False)
- # Defines the model inside distribution strategy scope.
- with distribution.scope():
- # Forward path.
- batch_size = 2
- batches = 4
- fake_ids = np.zeros((batch_size * batches, seq_length), dtype=np.int32)
- fake_inputs = {
- "input_ids": fake_ids,
- "input_mask": fake_ids,
- "segment_ids": fake_ids,
- }
- model = models.create_bert2bert_model(params=self._config)
- results = distribution_forward_path(
- distribution, model, fake_inputs, batch_size, mode="predict")
- self.assertLen(results, batches)
- results = distribution_forward_path(
- distribution, model, fake_inputs, batch_size, mode="eval")
- self.assertLen(results, batches)
-
-
-class NHNetTest(tf.test.TestCase, parameterized.TestCase):
-
- def setUp(self):
- super(NHNetTest, self).setUp()
- self._nhnet_config = configs.NHNetConfig()
- self._nhnet_config.override(utils.get_test_params().as_dict())
- self._bert2bert_config = configs.BERT2BERTConfig()
- self._bert2bert_config.override(utils.get_test_params().as_dict())
-
- def _count_params(self, layer, trainable_only=True):
- """Returns the count of all model parameters, or just trainable ones."""
- if not trainable_only:
- return layer.count_params()
- else:
- return int(
- np.sum([
- tf.keras.backend.count_params(p) for p in layer.trainable_weights
- ]))
-
- def test_create_nhnet_layers(self):
- single_doc_bert, single_doc_decoder = models.get_bert2bert_layers(
- self._bert2bert_config)
- multi_doc_bert, multi_doc_decoder = models.get_nhnet_layers(
- self._nhnet_config)
- # Expects multi-doc encoder/decoder have the same number of parameters as
- # single-doc encoder/decoder.
- self.assertEqual(
- self._count_params(multi_doc_bert), self._count_params(single_doc_bert))
- self.assertEqual(
- self._count_params(multi_doc_decoder),
- self._count_params(single_doc_decoder))
-
- def test_checkpoint_restore(self):
- bert2bert_model = models.create_bert2bert_model(self._bert2bert_config)
- ckpt = tf.train.Checkpoint(model=bert2bert_model)
- init_checkpoint = ckpt.save(os.path.join(self.get_temp_dir(), "ckpt"))
- nhnet_model = models.create_nhnet_model(
- params=self._nhnet_config, init_checkpoint=init_checkpoint)
- source_weights = (
- bert2bert_model.bert_layer.trainable_weights +
- bert2bert_model.decoder_layer.trainable_weights)
- dest_weights = (
- nhnet_model.bert_layer.trainable_weights +
- nhnet_model.decoder_layer.trainable_weights)
- for source_weight, dest_weight in zip(source_weights, dest_weights):
- self.assertAllClose(source_weight.numpy(), dest_weight.numpy())
-
- @combinations.generate(all_strategy_combinations())
- def test_nhnet_train_forward(self, distribution):
- seq_length = 10
- # Defines the model inside distribution strategy scope.
- with distribution.scope():
- # Forward path.
- batch_size = 2
- num_docs = 2
- batches = 4
- fake_ids = np.zeros((batch_size * batches, num_docs, seq_length),
- dtype=np.int32)
- fake_inputs = {
- "input_ids":
- fake_ids,
- "input_mask":
- fake_ids,
- "segment_ids":
- fake_ids,
- "target_ids":
- np.zeros((batch_size * batches, seq_length * 2), dtype=np.int32),
- }
- model = models.create_nhnet_model(params=self._nhnet_config)
- results = distribution_forward_path(distribution, model, fake_inputs,
- batch_size)
- logging.info("Forward path results: %s", str(results))
- self.assertLen(results, batches)
-
- @combinations.generate(all_strategy_combinations())
- def test_nhnet_eval(self, distribution):
- seq_length = 10
- padded_decode = isinstance(distribution,
- tf.distribute.experimental.TPUStrategy)
- self._nhnet_config.override(
- {
- "beam_size": 4,
- "len_title": seq_length,
- "alpha": 0.6,
- "multi_channel_cross_attention": True,
- "padded_decode": padded_decode,
- },
- is_strict=False)
- # Defines the model inside distribution strategy scope.
- with distribution.scope():
- # Forward path.
- batch_size = 2
- num_docs = 2
- batches = 4
- fake_ids = np.zeros((batch_size * batches, num_docs, seq_length),
- dtype=np.int32)
- fake_inputs = {
- "input_ids": fake_ids,
- "input_mask": fake_ids,
- "segment_ids": fake_ids,
- "target_ids": np.zeros((batch_size * batches, 5), dtype=np.int32),
- }
- model = models.create_nhnet_model(params=self._nhnet_config)
- results = distribution_forward_path(
- distribution, model, fake_inputs, batch_size, mode="predict")
- self.assertLen(results, batches)
- results = distribution_forward_path(
- distribution, model, fake_inputs, batch_size, mode="eval")
- self.assertLen(results, batches)
-
-
-if __name__ == "__main__":
- tf.test.main()
diff --git a/spaces/NahuelCosta/DTW-CNN/README.md b/spaces/NahuelCosta/DTW-CNN/README.md
deleted file mode 100644
index c43b9728849264134c835d939a4474e5e2254fca..0000000000000000000000000000000000000000
--- a/spaces/NahuelCosta/DTW-CNN/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: DTW CNN
-emoji: 🔋
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Navneet574/algerian-forest-fire-prediction/Explore_Page.py b/spaces/Navneet574/algerian-forest-fire-prediction/Explore_Page.py
deleted file mode 100644
index d75d0e40b868ce0aed49ad1af3708153363372b3..0000000000000000000000000000000000000000
--- a/spaces/Navneet574/algerian-forest-fire-prediction/Explore_Page.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-
-
-
-def show_analysis():
- pass
\ No newline at end of file
diff --git a/spaces/Nikhil0987/hnjii/README.md b/spaces/Nikhil0987/hnjii/README.md
deleted file mode 100644
index 16ec7c50be2d707d979cdc766bb9254294d3b85d..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/hnjii/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hnjii
-emoji: 🐢
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.26.0
-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/NimaBoscarino/gradio-secrets/app.py b/spaces/NimaBoscarino/gradio-secrets/app.py
deleted file mode 100644
index 6e4f40a7c1e0dd81679cd61695638ff27b2b712c..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/gradio-secrets/app.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import gradio as gr
-import os
-
-def greet_from_secret(ignored_param):
- name = os.environ.get('NAME')
- return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet_from_secret, inputs="text", outputs="text")
-iface.launch()
diff --git a/spaces/Not-Grim-Refer/Reverse-Prompt-Engineering-Code/readme.md b/spaces/Not-Grim-Refer/Reverse-Prompt-Engineering-Code/readme.md
deleted file mode 100644
index 521b630aed398ed481a25fdd3e99c3713d814bea..0000000000000000000000000000000000000000
--- a/spaces/Not-Grim-Refer/Reverse-Prompt-Engineering-Code/readme.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Reverse-Prompt-Engineering-Code
-emoji: 🌍
-colorFrom: red
-colorTo: grey
-sdk: streamlet
-sdk_version: 1.24.0
-app_file: main.py
-pinned: true
-license: mit
-
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
diff --git a/spaces/Nyari/Super-Resolution-Anime-Diffusion/Waifu2x/Common.py b/spaces/Nyari/Super-Resolution-Anime-Diffusion/Waifu2x/Common.py
deleted file mode 100644
index 6d7f1981aad000b9e3016e87f5c34878577447a5..0000000000000000000000000000000000000000
--- a/spaces/Nyari/Super-Resolution-Anime-Diffusion/Waifu2x/Common.py
+++ /dev/null
@@ -1,255 +0,0 @@
-from contextlib import contextmanager
-from math import sqrt, log
-
-import torch
-import torch.nn as nn
-
-
-# import warnings
-# warnings.simplefilter('ignore')
-
-
-class BaseModule(nn.Module):
- def __init__(self):
- self.act_fn = None
- super(BaseModule, self).__init__()
-
- def selu_init_params(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d) and m.weight.requires_grad:
- m.weight.data.normal_(0.0, 1.0 / sqrt(m.weight.numel()))
- if m.bias is not None:
- m.bias.data.fill_(0)
- elif isinstance(m, nn.BatchNorm2d) and m.weight.requires_grad:
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- elif isinstance(m, nn.Linear) and m.weight.requires_grad:
- m.weight.data.normal_(0, 1.0 / sqrt(m.weight.numel()))
- m.bias.data.zero_()
-
- def initialize_weights_xavier_uniform(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d) and m.weight.requires_grad:
- # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
- nn.init.xavier_uniform_(m.weight)
- if m.bias is not None:
- m.bias.data.zero_()
- elif isinstance(m, nn.BatchNorm2d) and m.weight.requires_grad:
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def load_state_dict(self, state_dict, strict=True, self_state=False):
- own_state = self_state if self_state else self.state_dict()
- for name, param in state_dict.items():
- if name in own_state:
- try:
- own_state[name].copy_(param.data)
- except Exception as e:
- print("Parameter {} fails to load.".format(name))
- print("-----------------------------------------")
- print(e)
- else:
- print("Parameter {} is not in the model. ".format(name))
-
- @contextmanager
- def set_activation_inplace(self):
- if hasattr(self, "act_fn") and hasattr(self.act_fn, "inplace"):
- # save memory
- self.act_fn.inplace = True
- yield
- self.act_fn.inplace = False
- else:
- yield
-
- def total_parameters(self):
- total = sum([i.numel() for i in self.parameters()])
- trainable = sum([i.numel() for i in self.parameters() if i.requires_grad])
- print(
- "Total parameters : {}. Trainable parameters : {}".format(total, trainable)
- )
- return total
-
- def forward(self, *x):
- raise NotImplementedError
-
-
-class ResidualFixBlock(BaseModule):
- def __init__(
- self,
- in_channels,
- out_channels,
- kernel_size=3,
- padding=1,
- dilation=1,
- groups=1,
- activation=nn.SELU(),
- conv=nn.Conv2d,
- ):
- super(ResidualFixBlock, self).__init__()
- self.act_fn = activation
- self.m = nn.Sequential(
- conv(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- dilation=dilation,
- groups=groups,
- ),
- activation,
- # conv(out_channels, out_channels, kernel_size, padding=(kernel_size - 1) // 2, dilation=1, groups=groups),
- conv(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- dilation=dilation,
- groups=groups,
- ),
- )
-
- def forward(self, x):
- out = self.m(x)
- return self.act_fn(out + x)
-
-
-class ConvBlock(BaseModule):
- def __init__(
- self,
- in_channels,
- out_channels,
- kernel_size=3,
- padding=1,
- dilation=1,
- groups=1,
- activation=nn.SELU(),
- conv=nn.Conv2d,
- ):
- super(ConvBlock, self).__init__()
- self.m = nn.Sequential(
- conv(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- dilation=dilation,
- groups=groups,
- ),
- activation,
- )
-
- def forward(self, x):
- return self.m(x)
-
-
-class UpSampleBlock(BaseModule):
- def __init__(self, channels, scale, activation, atrous_rate=1, conv=nn.Conv2d):
- assert scale in [2, 4, 8], "Currently UpSampleBlock supports 2, 4, 8 scaling"
- super(UpSampleBlock, self).__init__()
- m = nn.Sequential(
- conv(
- channels,
- 4 * channels,
- kernel_size=3,
- padding=atrous_rate,
- dilation=atrous_rate,
- ),
- activation,
- nn.PixelShuffle(2),
- )
- self.m = nn.Sequential(*[m for _ in range(int(log(scale, 2)))])
-
- def forward(self, x):
- return self.m(x)
-
-
-class SpatialChannelSqueezeExcitation(BaseModule):
- # https://arxiv.org/abs/1709.01507
- # https://arxiv.org/pdf/1803.02579v1.pdf
- def __init__(self, in_channel, reduction=16, activation=nn.ReLU()):
- super(SpatialChannelSqueezeExcitation, self).__init__()
- linear_nodes = max(in_channel // reduction, 4) # avoid only 1 node case
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
- self.channel_excite = nn.Sequential(
- # check the paper for the number 16 in reduction. It is selected by experiment.
- nn.Linear(in_channel, linear_nodes),
- activation,
- nn.Linear(linear_nodes, in_channel),
- nn.Sigmoid(),
- )
- self.spatial_excite = nn.Sequential(
- nn.Conv2d(in_channel, 1, kernel_size=1, stride=1, padding=0, bias=False),
- nn.Sigmoid(),
- )
-
- def forward(self, x):
- b, c, h, w = x.size()
- #
- channel = self.avg_pool(x).view(b, c)
- # channel = F.avg_pool2d(x, kernel_size=(h,w)).view(b,c) # used for porting to other frameworks
- cSE = self.channel_excite(channel).view(b, c, 1, 1)
- x_cSE = torch.mul(x, cSE)
-
- # spatial
- sSE = self.spatial_excite(x)
- x_sSE = torch.mul(x, sSE)
- # return x_sSE
- return torch.add(x_cSE, x_sSE)
-
-
-class PartialConv(nn.Module):
- # reference:
- # Image Inpainting for Irregular Holes Using Partial Convolutions
- # http://masc.cs.gmu.edu/wiki/partialconv/show?time=2018-05-24+21%3A41%3A10
- # https://github.com/naoto0804/pytorch-inpainting-with-partial-conv/blob/master/net.py
- # https://github.com/SeitaroShinagawa/chainer-partial_convolution_image_inpainting/blob/master/common/net.py
- # partial based padding
- # https: // github.com / NVIDIA / partialconv / blob / master / models / pd_resnet.py
- def __init__(
- self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=True,
- ):
-
- super(PartialConv, self).__init__()
- self.feature_conv = nn.Conv2d(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation,
- groups,
- bias,
- )
-
- self.mask_conv = nn.Conv2d(
- 1, 1, kernel_size, stride, padding, dilation, groups, bias=False
- )
- self.window_size = self.mask_conv.kernel_size[0] * self.mask_conv.kernel_size[1]
- torch.nn.init.constant_(self.mask_conv.weight, 1.0)
-
- for param in self.mask_conv.parameters():
- param.requires_grad = False
-
- def forward(self, x):
- output = self.feature_conv(x)
- if self.feature_conv.bias is not None:
- output_bias = self.feature_conv.bias.view(1, -1, 1, 1).expand_as(output)
- else:
- output_bias = torch.zeros_like(output, device=x.device)
-
- with torch.no_grad():
- ones = torch.ones(1, 1, x.size(2), x.size(3), device=x.device)
- output_mask = self.mask_conv(ones)
- output_mask = self.window_size / output_mask
- output = (output - output_bias) * output_mask + output_bias
-
- return output
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/m2m_100/process_data/clean_histogram.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/m2m_100/process_data/clean_histogram.py
deleted file mode 100644
index e24e073dc0eb43c76e2ce717f52bb848c5b026b8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/m2m_100/process_data/clean_histogram.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--src', type=str, help='Source language')
-parser.add_argument('--tgt', type=str, help='Target language')
-parser.add_argument('--src-file', type=str, help='Input source file')
-parser.add_argument('--tgt-file', type=str, help='Input target file')
-parser.add_argument('--src-output-file', type=str, help='Output source file')
-parser.add_argument('--tgt-output-file', type=str, help='Output target file')
-parser.add_argument('--threshold', type=float, default=0.5, help='Threshold')
-parser.add_argument('--threshold-character', type=str, default=']', help='Threshold character')
-parser.add_argument('--histograms', type=str, help='Path to histograms')
-
-args = parser.parse_args()
-
-
-def read_hist(f):
- ch = []
- for line in f:
- c = line[0]
- if c == args.threshold_character:
- break
- ch.append(c)
- return ch
-
-
-with(open("{}/{}".format(args.histograms, args.src), 'r', encoding='utf8')) as f:
- ch1 = read_hist(f)
-
-with(open("{}/{}".format(args.histograms, args.tgt), 'r', encoding='utf8')) as f:
- ch2 = read_hist(f)
-
-print("Accepted characters for {}: {}".format(args.src, ch1))
-print("Accepted characters for {}: {}".format(args.tgt, ch2))
-
-with open(args.src_file, 'r', encoding='utf8') as fs1, open(args.tgt_file, 'r', encoding='utf8') as fs2, open(args.src_output_file, 'w', encoding='utf8') as fos1, open(args.tgt_output_file, 'w', encoding='utf8') as fos2:
- ls1 = fs1.readline()
- ls2 = fs2.readline()
-
- while ls1 or ls2:
- cnt1 = len([c for c in ls1.strip() if c in ch1])
- cnt2 = len([c for c in ls2.strip() if c in ch2])
-
- if cnt1 / len(ls1) > args.threshold and cnt2 / len(ls2) > args.threshold:
- fos1.write(ls1)
- fos2.write(ls2)
- else:
- print("{} {} {} \n{} {} {}".format(args.src, cnt1 / len(ls1), ls1.strip(), args.tgt, cnt2 / len(ls2), ls2.strip()))
-
- ls1 = fs1.readline()
- ls2 = fs2.readline()
-
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py
deleted file mode 100644
index 0d356e5a10241716b58a5bc04a9d204a72553ff8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py
+++ /dev/null
@@ -1,223 +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
-import torch.nn.functional as F
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss
-from fairseq import metrics, utils
-
-
-@register_criterion("guided_label_smoothed_cross_entropy_with_accuracy")
-class GuidedCrossEntAccCriterion(FairseqCriterion):
- def __init__(
- self,
- task,
- sentence_avg,
- guide_alpha,
- text_input_cost_ratio,
- label_smoothing,
- disable_text_guide_update_num=0,
- attentive_cost_regularization=0,
- ):
- """
- guide_alpha: alpha to inteplate nll and kd loss
- text_input_cost_ratio: loss ratio for text only input data
- label_smoothing: label smoothing ratio
- disable_text_guide_update_num: only use nll loss for the first N updates
- attentive_cost_regularization: ratio fo attentive cost
- """
- super().__init__(task)
- self.alpha = guide_alpha
- self.attn_beta = attentive_cost_regularization
- self.sentence_avg = sentence_avg
- self.eps = label_smoothing
- self.text_input_cost_ratio = text_input_cost_ratio
- self.disable_update_num = disable_text_guide_update_num
- assert self.alpha >= 0 and self.alpha <= 1.0
-
- @staticmethod
- def add_args(parser):
- """Add criterion-specific arguments to the parser."""
- # fmt: off
- parser.add_argument('--label-smoothing', default=0., type=float, metavar='D',
- help='epsilon for label smoothing, 0 means no label smoothing')
- # fmt: off
- parser.add_argument('--guide-alpha', default=0., type=float, metavar='D',
- help='alpha to merge kd cost from text to speech input with ce loss')
- # fmt: off
- parser.add_argument('--disable-text-guide-update-num', default=0, type=int, metavar='D',
- help='disable guided target from text for the first N updates.')
- parser.add_argument("--attentive-cost-regularization", default=0.0, type=float, metavar='D',
- help="use encoder attentive loss regularization with cost ratio D")
- parser.add_argument("--attentive-cost-without-normalize", action='store_true',
- help="Don't do normalization during attentive cost computation")
-
- def forward(self, model, sample, reduce=True):
- reduction = 'sum' if reduce else 'none'
- net_input = sample["net_input"]
- net_output = model(**net_input)
- attn_cost = None
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- is_dual_input = True if net_input['src_tokens'] is not None and net_input.get('src_txt_tokens') is not None else False
- target = model.get_targets(sample, net_output)
- src_token_num = 0
- if is_dual_input:
- # lprobs_spch from speech encoder and lprobs_text from text encoder
- lprobs_spch, lprobs_text = torch.chunk(lprobs, 2)
- lprobs_spch.batch_first = lprobs.batch_first
- lprobs_text.batch_first = lprobs.batch_first
-
- speech_loss, speech_nll_loss, speech_correct, speech_total = \
- self.guide_loss_and_acc(model, lprobs_spch, lprobs_text, target, reduce=(reduction == 'sum'))
- text_loss, text_nll_loss, text_correct, text_total = self.compute_loss_and_acc(model, lprobs_text, target, reduction=reduction)
- loss = (speech_loss + text_loss)
- nll_loss = (speech_nll_loss + text_nll_loss)
- correct = speech_correct + text_correct
- total = speech_total + text_total
-
- attn_cost = net_output[1].get('attn_cost')
- if attn_cost is not None:
- # attn_cost is batch_first and padding tokens have been masked already
- src_token_num = attn_cost.ne(0).sum()
- attn_cost = attn_cost.sum()
- loss = loss + attn_cost * self.attn_beta
- else:
- attn_cost = 0
- else:
- loss, nll_loss, correct, total = self.compute_loss_and_acc(model, lprobs, target, reduction=reduction)
- if sample["net_input"]['src_tokens'] is None: # text input only
- loss = loss * self.text_input_cost_ratio
- speech_loss = None
- speech_nll_loss = None
-
- sample_size, logging_output = self.get_logging_output(
- sample, loss, nll_loss, correct, total, src_token_num, speech_loss, speech_nll_loss, attn_cost, is_dual_input
- )
- return loss, sample_size, logging_output
-
- def compute_loss_and_acc(self, model, lprobs, target, reduction='sum'):
- if not lprobs.batch_first:
- lprobs = lprobs.transpose(0, 1)
- lprobs = lprobs.view(-1, lprobs.size(-1)) # -> (B x T) x C
- target = target.view(-1)
- loss, nll_loss = label_smoothed_nll_loss(
- lprobs, target, self.eps, ignore_index=self.padding_idx, reduce=(reduction == 'sum'),
- )
-
- mask = target.ne(self.padding_idx)
- correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask)))
- total = torch.sum(mask)
- return loss, nll_loss, correct, total
-
- def guide_loss_and_acc(self, model, lprobs, lprobs_teacher, target, reduce=True):
- """ lprobs_teacher is used as guide for lprobs """
- if self.alpha == 0.0 or model.num_updates < self.disable_update_num:
- return self.compute_loss_and_acc(model, lprobs, target, reduction=('sum' if reduce else 'none'))
- if not lprobs.batch_first:
- lprobs = lprobs.transpose(0, 1)
- lprobs_teacher = lprobs_teacher.transpose(0, 1)
-
- lprobs = lprobs.view(-1, lprobs.size(-1)).float() # -> (B x T) x C
- lprobs_teacher = lprobs_teacher.view(-1, lprobs_teacher.size(-1)).float() # -> (B x T) x C
- target = target.view(-1)
- loss = F.nll_loss(lprobs, target, ignore_index=self.padding_idx, reduction='sum' if reduce else 'none')
- nll_loss = loss
- probs_teacher = lprobs_teacher.exp().masked_fill_(target.unsqueeze(-1).eq(self.padding_idx), 0)
- probs_teacher = probs_teacher.detach()
- guide_loss = -(probs_teacher*lprobs).sum() if reduce else -(probs_teacher*lprobs).sum(-1, keepdim=True)
- loss = self.alpha*guide_loss + (1.0 - self.alpha)*loss
-
- mask = target.ne(self.padding_idx)
- correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask)))
- total = torch.sum(mask)
- return loss, nll_loss, correct, total
-
- def get_logging_output(
- self,
- sample,
- loss,
- nll_loss,
- correct,
- total,
- src_token_num=0,
- speech_loss=None,
- speech_nll_loss=None,
- attn_cost=None,
- is_dual_input=False,
- ):
-
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
- mul_size = 2 if is_dual_input else 1
-
- logging_output = {
- "loss": utils.item(loss.data), # * sample['ntokens'],
- "nll_loss": utils.item(nll_loss.data), # * sample['ntokens'],
- "ntokens": sample["ntokens"]*mul_size,
- "nsentences": sample["target"].size(0)*mul_size,
- "sample_size": sample_size*mul_size,
- "correct": utils.item(correct.data),
- "total": utils.item(total.data),
- "src_token_num": utils.item(src_token_num.data) if src_token_num > 0 else 0,
- "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(),
- }
-
- if speech_loss is not None:
- logging_output["speech_loss"] = utils.item(speech_loss.data)
- logging_output["speech_nll_loss"] = utils.item(speech_nll_loss.data)
- logging_output["sample_size_speech_cost"] = sample_size
- logging_output["speech_attn_loss"] = attn_cost
-
- return sample_size*mul_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)
- src_token_sum = sum(log.get("src_token_num", 0) for log in logging_outputs)
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- nll_loss_sum = sum(log.get("nll_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)
- speech_loss_sum = sum(log.get("speech_loss", 0) for log in logging_outputs)
- speech_nll_loss_sum = sum(log.get("speech_nll_loss", 0) for log in logging_outputs)
- speech_attn_loss_sum = sum(log.get("speech_attn_loss", 0) for log in logging_outputs)
- sample_size_speech = sum(log.get("sample_size_speech_cost", 0) for log in logging_outputs)
-
- agg_output = {
- "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0,
- "nll_loss": nll_loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0,
- # if args.sentence_avg, then sample_size is nsentences, and loss
- # is per-sentence loss; else sample_size is ntokens, and the loss
- # becomes per-output token loss
- "speech_loss": speech_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0,
- "speech_nll_loss": speech_nll_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0,
- "speech_attn_loss": speech_attn_loss_sum / src_token_sum / math.log(2) if src_token_sum > 0 else 0.0,
- "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,
- "src_token_num": src_token_sum,
- # total is the number of validate tokens
- }
- return agg_output
-
- @classmethod
- def reduce_metrics(cls, logging_outputs):
- """Aggregate logging outputs from data parallel training."""
- agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs)
- for k, v in agg_logging_outputs.items():
- if k in {'nsentences', 'ntokens', 'sample_size'}:
- continue
- metrics.log_scalar(k, v, round=3)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/truncated_bptt/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/truncated_bptt/README.md
deleted file mode 100644
index 86518c9d5ef09fbd4fed1512a52e9431b74f08fa..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/truncated_bptt/README.md
+++ /dev/null
@@ -1,70 +0,0 @@
-# Truncated Backpropagation Through Time (BPTT)
-
-Truncated BPTT is a useful technique for training language models on very long
-sequences. Typically a long sequences is split into chunks and a language model
-is trained over the chunks sequentially. The LM may condition on previous
-chunks, but gradients only flow through the current chunk. This technique was
-the basis for the paper: [Transformer-XL: Attentive Language Models Beyond a
-Fixed-Length Context](https://arxiv.org/abs/1901.02860), which achieved
-state-of-the-art language modeling results at the time of publication.
-
-It is slightly tricky to implement Truncated BPTT efficiently in fairseq, since
-we need to iterate over the data sequentially and disable any batch shuffling
-logic. The code provided in this example illustrates how to implement Truncated
-BPTT in fairseq by overriding ``FairseqTask::get_batch_iterator`` to iterate
-over the data sequentially. Crucially, this example supports batching and
-multi-GPU (data parallel) training.
-
-##### 0. Setup
-
-First, see the general [language modeling README](README.md) for instructions on
-preprocessing the WikiText-103 data.
-
-##### 1. Train a Transformer-XL model on WikiText-103
-
-We will train a 16-layer Transformer-XL model following the [hyperparameters
-used in the original
-paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_wt103_base.sh).
-
-The following command assumes 4 GPUs, so that the total batch size is 60
-sequences (15 x 4). Training should take ~24 hours on 4 V100 GPUs:
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \
- --user-dir examples/truncated_bptt \
- data-bin/wikitext-103/ \
- --task truncated_bptt_lm --tokens-per-sample 150 \
- --batch-size 15 --max-update 200000 \
- --arch transformer_xl --n-layer 16 --d-model 410 --n-head 10 \
- --d-head 41 --d-inner 2100 --dropout 0.1 --dropatt 0.0 --mem-len 150 \
- --optimizer adam --clip-norm 0.25 \
- --lr-scheduler cosine --warmup-updates 0 --min-lr 0.0 --lr 0.00025 \
- --log-format json --log-interval 25 \
- --fp16
-```
-
-If training on a single GPU, set `--update-freq=4` to accumulate 4x gradients
-and simulate training on 4 GPUs.
-
-##### 2. Evaluate
-
-```bash
-fairseq-eval-lm data-bin/wikitext-103/ \
- --path checkpoints/checkpoint_best.pt \
- --user-dir examples/truncated_bptt/ \
- --task truncated_bptt_lm \
- --batch-size 1 --required-batch-size-multiple 1 \
- --model-overrides '{"mem_len":640,"clamp_len":400,"same_length":True}' \
- --tokens-per-sample 64
-# ... | INFO | fairseq_cli.eval_lm | num. model params: 151123537
-# ... | INFO | fairseq_cli.eval_lm | Evaluated 245569 tokens in 83.1s (2956.82 tokens/s)
-# ... | INFO | fairseq_cli.eval_lm | Loss (base 2): 4.5668, Perplexity: 23.70
-# Compare to 24.0 test perplexity from the paper
-```
-
-*Note:* During training the model saw 150 tokens of context
-(``--tokens-per-sample=150``) and 150 extra memory tokens (``--mem-len=150``).
-During evaluation we measure perplexity on sequences of 64 tokens
-(``--tokens-per-sample=64``) and increase the memory length
-(``--model-overrides='{"mem_len":640}'``). These settings match the evaluation
-settings from [the original
-paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_wt103_base.sh).
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py
deleted file mode 100644
index 223a16f740c10b58ea45a0390814363e7b5f68b8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py
+++ /dev/null
@@ -1,233 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass, field
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import register_criterion
-from fairseq.criterions.label_smoothed_cross_entropy import (
- LabelSmoothedCrossEntropyCriterion,
- LabelSmoothedCrossEntropyCriterionConfig
-)
-
-try:
- from simuleval.metrics.latency import (
- AverageLagging,
- AverageProportion,
- DifferentiableAverageLagging
- )
- LATENCY_METRICS = {
- "average_lagging": AverageLagging,
- "average_proportion": AverageProportion,
- "differentiable_average_lagging": DifferentiableAverageLagging,
- }
-except ImportError:
- LATENCY_METRICS = None
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig(
- LabelSmoothedCrossEntropyCriterionConfig
-):
- latency_avg_weight: float = field(
- default=0.0,
- metadata={"help": "weight fot average latency loss."},
- )
- latency_var_weight: float = field(
- default=0.0,
- metadata={"help": "weight fot variance latency loss."},
- )
- latency_avg_type: str = field(
- default="differentiable_average_lagging",
- metadata={"help": "latency type for average loss"},
- )
- latency_var_type: str = field(
- default="variance_delay",
- metadata={"help": "latency typ for variance loss"},
- )
- latency_gather_method: str = field(
- default="weighted_average",
- metadata={"help": "method to gather latency loss for all heads"},
- )
- latency_update_after: int = field(
- default=0,
- metadata={"help": "Add latency loss after certain steps"},
- )
-
-@register_criterion(
- "latency_augmented_label_smoothed_cross_entropy",
- dataclass=LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig
-)
-class LatencyAugmentedLabelSmoothedCrossEntropyCriterion(
- LabelSmoothedCrossEntropyCriterion
-):
- def __init__(
- self,
- task,
- sentence_avg,
- label_smoothing,
- ignore_prefix_size,
- report_accuracy,
- latency_avg_weight,
- latency_var_weight,
- latency_avg_type,
- latency_var_type,
- latency_gather_method,
- latency_update_after,
- ):
- super().__init__(
- task, sentence_avg, label_smoothing, ignore_prefix_size, report_accuracy
- )
- assert LATENCY_METRICS is not None, "Please make sure SimulEval is installed."
-
- self.latency_avg_weight = latency_avg_weight
- self.latency_var_weight = latency_var_weight
- self.latency_avg_type = latency_avg_type
- self.latency_var_type = latency_var_type
- self.latency_gather_method = latency_gather_method
- self.latency_update_after = latency_update_after
-
- def forward(self, model, sample, reduce=True):
- net_output = model(**sample["net_input"])
- # 1. Compute cross entropy loss
- loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
-
- # 2. Compute cross latency loss
- latency_loss, expected_latency, expected_delays_var = self.compute_latency_loss(
- model, sample, net_output
- )
-
- if self.latency_update_after > 0:
- num_updates = getattr(model.decoder, "num_updates", None)
- assert num_updates is not None, (
- "model.decoder doesn't have attribute 'num_updates'"
- )
- if num_updates <= self.latency_update_after:
- latency_loss = 0
-
- loss += latency_loss
-
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
-
- logging_output = {
- "loss": loss.data,
- "nll_loss": nll_loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample["target"].size(0),
- "sample_size": sample_size,
- "latency": expected_latency,
- "delays_var": expected_delays_var,
- "latency_loss": latency_loss,
- }
-
- if self.report_accuracy:
- n_correct, total = self.compute_accuracy(model, net_output, sample)
- logging_output["n_correct"] = utils.item(n_correct.data)
- logging_output["total"] = utils.item(total.data)
- return loss, sample_size, logging_output
-
- def compute_latency_loss(self, model, sample, net_output):
- assert (
- net_output[-1].encoder_padding_mask is None
- or not net_output[-1].encoder_padding_mask[:, 0].any()
- ), (
- "Only right padding on source is supported."
- )
- # 1. Obtain the expected alignment
- alpha_list = [item["alpha"] for item in net_output[1].attn_list]
- num_layers = len(alpha_list)
- bsz, num_heads, tgt_len, src_len = alpha_list[0].size()
-
- # bsz * num_layers * num_heads, tgt_len, src_len
- alpha_all = torch.cat(alpha_list, dim=1).view(-1, tgt_len, src_len)
-
- # 2 compute expected delays
- # bsz * num_heads * num_layers, tgt_len, src_len for MMA
- steps = (
- torch.arange(1, 1 + src_len)
- .unsqueeze(0)
- .unsqueeze(1)
- .expand_as(alpha_all)
- .type_as(alpha_all)
- )
-
- expected_delays = torch.sum(steps * alpha_all, dim=-1)
-
- target_padding_mask = (
- model.get_targets(sample, net_output)
- .eq(self.padding_idx)
- .unsqueeze(1)
- .expand(bsz, num_layers * num_heads, tgt_len)
- .contiguous()
- .view(-1, tgt_len)
- )
-
- src_lengths = (
- sample["net_input"]["src_lengths"]
- .unsqueeze(1)
- .expand(bsz, num_layers * num_heads)
- .contiguous()
- .view(-1)
- )
- expected_latency = LATENCY_METRICS[self.latency_avg_type](
- expected_delays, src_lengths, None,
- target_padding_mask=target_padding_mask
- )
-
- # 2.1 average expected latency of heads
- # bsz, num_layers * num_heads
- expected_latency = expected_latency.view(bsz, -1)
- if self.latency_gather_method == "average":
- # bsz * tgt_len
- expected_latency = expected_delays.mean(dim=1)
- elif self.latency_gather_method == "weighted_average":
- weights = torch.nn.functional.softmax(expected_latency, dim=1)
- expected_latency = torch.sum(expected_latency * weights, dim=1)
- elif self.latency_gather_method == "max":
- expected_latency = expected_latency.max(dim=1)[0]
- else:
- raise NotImplementedError
-
- expected_latency = expected_latency.sum()
- avg_loss = self.latency_avg_weight * expected_latency
-
- # 2.2 variance of expected delays
- expected_delays_var = (
- expected_delays.view(bsz, -1, tgt_len).var(dim=1).mean(dim=1)
- )
- expected_delays_var = expected_delays_var.sum()
- var_loss = self.latency_avg_weight * expected_delays_var
-
- # 3. Final loss
- latency_loss = avg_loss + var_loss
-
- return latency_loss, expected_latency, expected_delays_var
-
- @classmethod
- def reduce_metrics(cls, logging_outputs) -> None:
- super().reduce_metrics(logging_outputs)
- latency = sum(
- log.get("latency", 0) for log in logging_outputs
- )
- delays_var = sum(
- log.get("delays_var", 0) for log in logging_outputs
- )
- latency_loss = sum(
- log.get("latency_loss", 0) for log in logging_outputs
- )
- nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
- metrics.log_scalar(
- "latency", latency.float() / nsentences, nsentences, round=3
- )
- metrics.log_scalar(
- "delays_var", delays_var / nsentences,
- nsentences, round=3
- )
- metrics.log_scalar(
- "latency_loss", latency_loss / nsentences,
- nsentences, round=3
- )
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py
deleted file mode 100644
index 51f58359eda387d67748f48217906ac6d16ccd08..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py
+++ /dev/null
@@ -1,147 +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 collections.abc import Collection
-from dataclasses import dataclass, field
-from typing import List
-
-from omegaconf import II
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class CosineLRScheduleConfig(FairseqDataclass):
- warmup_updates: int = field(
- default=0,
- metadata={"help": "warmup the learning rate linearly for the first N updates"},
- )
- warmup_init_lr: float = field(
- default=-1,
- metadata={
- "help": "initial learning rate during warmup phase; default is cfg.lr"
- },
- )
- lr: List[float] = field(
- default=II("optimization.lr"),
- metadata={"help": "max learning rate, must be more than cfg.min_lr"},
- )
- min_lr: float = field(default=0.0, metadata={"help": "min learning rate"})
- t_mult: float = field(
- default=1.0, metadata={"help": "factor to grow the length of each period"}
- )
- lr_period_updates: float = field(
- default=-1, metadata={"help": "initial number of updates per period"}
- )
- lr_shrink: float = field(
- default=0.1, metadata={"help": "shrink factor for annealing"}
- )
- # This is not required, but is for convenience in inferring lr_period_updates
- max_update: int = II("optimization.max_update")
-
-
-@register_lr_scheduler("cosine", dataclass=CosineLRScheduleConfig)
-class CosineLRSchedule(FairseqLRScheduler):
- """Assign LR based on a cyclical schedule that follows the cosine function.
-
- See https://arxiv.org/pdf/1608.03983.pdf for details.
-
- We also support a warmup phase where we linearly increase the learning rate
- from some initial learning rate (``--warmup-init-lr``) until the configured
- max learning rate (``--lr``).
-
- During warmup::
-
- lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates)
- lr = lrs[update_num]
-
- After warmup::
-
- lr = cfg.min_lr + 0.5*(cfg.lr - cfg.min_lr)*(1 + cos(t_curr / t_i))
-
- where ``t_curr`` is current percentage of updates within the current period
- range and ``t_i`` is the current period range, which is scaled by ``t_mul``
- after every iteration.
- """
-
- def __init__(self, cfg: CosineLRScheduleConfig, fairseq_optimizer):
- super().__init__(cfg, fairseq_optimizer)
- if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1:
- raise ValueError(
- "Cannot use a fixed learning rate schedule with cosine."
- f" Consider --lr-scheduler=fixed instead. ({cfg.lr})"
- )
-
- self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr
- assert (
- self.max_lr > cfg.min_lr
- ), f"max_lr (={cfg.lr}) must be more than min_lr (={cfg.min_lr})"
-
- warmup_end_lr = self.max_lr
- if cfg.warmup_init_lr < 0:
- cfg.warmup_init_lr = cfg.min_lr
-
- self.t_mult = cfg.t_mult
- self.period = cfg.lr_period_updates
-
- if self.period <= 0:
- assert (
- cfg.max_update > 0
- ), "Either --max_update or --lr-period-updates must be set"
- self.period = cfg.max_update - cfg.warmup_updates
-
- if cfg.warmup_updates > 0:
- # linearly warmup for the first cfg.warmup_updates
- self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates
- else:
- self.lr_step = 1
-
- self.warmup_updates = cfg.warmup_updates
- self.lr_shrink = cfg.lr_shrink
-
- # initial learning rate
- self.lr = cfg.warmup_init_lr
- self.optimizer.set_lr(self.lr)
-
- def step(self, epoch, val_loss=None):
- """Update the learning rate at the end of the given epoch."""
- super().step(epoch, val_loss)
- # we don't change the learning rate at epoch boundaries
- return self.optimizer.get_lr()
-
- def step_update(self, num_updates):
- """Update the learning rate after each update."""
- if num_updates < self.cfg.warmup_updates:
- self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step
- else:
- curr_updates = num_updates - self.cfg.warmup_updates
- if self.t_mult != 1:
- i = math.floor(
- math.log(
- 1 - curr_updates / self.period * (1 - self.t_mult), self.t_mult
- )
- )
- t_i = self.t_mult ** i * self.period
- t_curr = (
- curr_updates
- - (1 - self.t_mult ** i) / (1 - self.t_mult) * self.period
- )
- else:
- i = math.floor(curr_updates / self.period)
- t_i = self.period
- t_curr = curr_updates - (self.period * i)
-
- lr_shrink = self.lr_shrink ** i
- min_lr = self.cfg.min_lr * lr_shrink
- max_lr = self.max_lr * lr_shrink
-
- self.lr = min_lr + 0.5 * (max_lr - min_lr) * (
- 1 + math.cos(math.pi * t_curr / t_i)
- )
-
- self.optimizer.set_lr(self.lr)
- return self.lr
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/data.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/data.py
deleted file mode 100644
index 89a4ea4c9577e6131731444f149eec76978ec260..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/data.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import glob
-import os
-
-import cv2
-import PIL.Image as Image
-import numpy as np
-
-from torch.utils.data import Dataset
-import torch.nn.functional as F
-
-
-def load_image(fname, mode='RGB', return_orig=False):
- img = np.array(Image.open(fname).convert(mode))
- if img.ndim == 3:
- img = np.transpose(img, (2, 0, 1))
- out_img = img.astype('float32') / 255
- if return_orig:
- return out_img, img
- else:
- return out_img
-
-
-def ceil_modulo(x, mod):
- if x % mod == 0:
- return x
- return (x // mod + 1) * mod
-
-
-def pad_img_to_modulo(img, mod):
- channels, height, width = img.shape
- out_height = ceil_modulo(height, mod)
- out_width = ceil_modulo(width, mod)
- return np.pad(img, ((0, 0), (0, out_height - height), (0, out_width - width)), mode='symmetric')
-
-
-def pad_tensor_to_modulo(img, mod):
- batch_size, channels, height, width = img.shape
- out_height = ceil_modulo(height, mod)
- out_width = ceil_modulo(width, mod)
- return F.pad(img, pad=(0, out_width - width, 0, out_height - height), mode='reflect')
-
-
-def scale_image(img, factor, interpolation=cv2.INTER_AREA):
- if img.shape[0] == 1:
- img = img[0]
- else:
- img = np.transpose(img, (1, 2, 0))
-
- img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation)
-
- if img.ndim == 2:
- img = img[None, ...]
- else:
- img = np.transpose(img, (2, 0, 1))
- return img
-
-
-class InpaintingDataset(Dataset):
- def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):
- self.datadir = datadir
- self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, '**', '*mask*.png'), recursive=True)))
- self.img_filenames = [fname.rsplit('_mask', 1)[0] + img_suffix for fname in self.mask_filenames]
- self.pad_out_to_modulo = pad_out_to_modulo
- self.scale_factor = scale_factor
-
- def __len__(self):
- return len(self.mask_filenames)
-
- def __getitem__(self, i):
- image = load_image(self.img_filenames[i], mode='RGB')
- mask = load_image(self.mask_filenames[i], mode='L')
- result = dict(image=image, mask=mask[None, ...])
-
- if self.scale_factor is not None:
- result['image'] = scale_image(result['image'], self.scale_factor)
- result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['unpad_to_size'] = result['image'].shape[1:]
- result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
- result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
-
- return result
-
-class OurInpaintingDataset(Dataset):
- def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):
- self.datadir = datadir
- self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, 'mask', '**', '*mask*.png'), recursive=True)))
- self.img_filenames = [os.path.join(self.datadir, 'img', os.path.basename(fname.rsplit('-', 1)[0].rsplit('_', 1)[0]) + '.png') for fname in self.mask_filenames]
- self.pad_out_to_modulo = pad_out_to_modulo
- self.scale_factor = scale_factor
-
- def __len__(self):
- return len(self.mask_filenames)
-
- def __getitem__(self, i):
- result = dict(image=load_image(self.img_filenames[i], mode='RGB'),
- mask=load_image(self.mask_filenames[i], mode='L')[None, ...])
-
- if self.scale_factor is not None:
- result['image'] = scale_image(result['image'], self.scale_factor)
- result['mask'] = scale_image(result['mask'], self.scale_factor)
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
- result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
-
- return result
-
-class PrecomputedInpaintingResultsDataset(InpaintingDataset):
- def __init__(self, datadir, predictdir, inpainted_suffix='_inpainted.jpg', **kwargs):
- super().__init__(datadir, **kwargs)
- if not datadir.endswith('/'):
- datadir += '/'
- self.predictdir = predictdir
- self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)
- for fname in self.mask_filenames]
-
- def __getitem__(self, i):
- result = super().__getitem__(i)
- result['inpainted'] = load_image(self.pred_filenames[i])
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)
- return result
-
-class OurPrecomputedInpaintingResultsDataset(OurInpaintingDataset):
- def __init__(self, datadir, predictdir, inpainted_suffix="png", **kwargs):
- super().__init__(datadir, **kwargs)
- if not datadir.endswith('/'):
- datadir += '/'
- self.predictdir = predictdir
- self.pred_filenames = [os.path.join(predictdir, os.path.basename(os.path.splitext(fname)[0]) + f'_inpainted.{inpainted_suffix}')
- for fname in self.mask_filenames]
- # self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)
- # for fname in self.mask_filenames]
-
- def __getitem__(self, i):
- result = super().__getitem__(i)
- result['inpainted'] = self.file_loader(self.pred_filenames[i])
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)
- return result
-
-class InpaintingEvalOnlineDataset(Dataset):
- def __init__(self, indir, mask_generator, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None, **kwargs):
- self.indir = indir
- self.mask_generator = mask_generator
- self.img_filenames = sorted(list(glob.glob(os.path.join(self.indir, '**', f'*{img_suffix}' ), recursive=True)))
- self.pad_out_to_modulo = pad_out_to_modulo
- self.scale_factor = scale_factor
-
- def __len__(self):
- return len(self.img_filenames)
-
- def __getitem__(self, i):
- img, raw_image = load_image(self.img_filenames[i], mode='RGB', return_orig=True)
- mask = self.mask_generator(img, raw_image=raw_image)
- result = dict(image=img, mask=mask)
-
- if self.scale_factor is not None:
- result['image'] = scale_image(result['image'], self.scale_factor)
- result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
- result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
- return result
\ No newline at end of file
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/__init__.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/__init__.py
deleted file mode 100644
index 130d3011b032f91df1a9cf965625e54922f6c81b..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/utils/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .events import setup_wandb, WandbWriter
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/pspnet_unet_s5-d16.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/pspnet_unet_s5-d16.py
deleted file mode 100644
index fcff9ec4f41fad158344ecd77313dc14564f3682..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/pspnet_unet_s5-d16.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='EncoderDecoder',
- pretrained=None,
- backbone=dict(
- type='UNet',
- in_channels=3,
- base_channels=64,
- num_stages=5,
- strides=(1, 1, 1, 1, 1),
- enc_num_convs=(2, 2, 2, 2, 2),
- dec_num_convs=(2, 2, 2, 2),
- downsamples=(True, True, True, True),
- enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1),
- with_cp=False,
- conv_cfg=None,
- norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
- upsample_cfg=dict(type='InterpConv'),
- norm_eval=False),
- decode_head=dict(
- type='PSPHead',
- in_channels=64,
- in_index=4,
- channels=16,
- pool_scales=(1, 2, 3, 6),
- dropout_ratio=0.1,
- num_classes=2,
- 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=128,
- in_index=3,
- channels=64,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=2,
- 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='slide', crop_size=256, stride=170))
diff --git a/spaces/PYTHONOPTIC/FOCUSGUMMY/README.md b/spaces/PYTHONOPTIC/FOCUSGUMMY/README.md
deleted file mode 100644
index 28c3d5c75dd5564749dbd8f09a05a2ae53fb1328..0000000000000000000000000000000000000000
--- a/spaces/PYTHONOPTIC/FOCUSGUMMY/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PYTHONOPTIC
-emoji: 🐨
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/file-cache.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/file-cache.go
deleted file mode 100644
index 052b454be5ec00dd8a70328d9b808efd95d4366c..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/file-cache.go and /dev/null differ
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/batch_norm.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/batch_norm.py
deleted file mode 100644
index 03ce61030aa6f66bd50c1c82431c7b4faed0be15..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/batch_norm.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-
-import torch.distributed as dist
-import maskrcnn_benchmark.utils.comm as comm
-from torch.autograd.function import Function
-
-class FrozenBatchNorm2d(nn.Module):
- """
- BatchNorm2d where the batch statistics and the affine parameters
- are fixed
- """
-
- 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 forward(self, x):
- scale = self.weight * self.running_var.rsqrt()
- bias = self.bias - self.running_mean * scale
- scale = scale.reshape(1, -1, 1, 1)
- bias = bias.reshape(1, -1, 1, 1)
- return x * scale + bias
-
-
-class AllReduce(Function):
- @staticmethod
- def forward(ctx, input):
- input_list = [torch.zeros_like(input) for k in range(dist.get_world_size())]
- # Use allgather instead of allreduce since I don't trust in-place operations ..
- dist.all_gather(input_list, input, async_op=False)
- inputs = torch.stack(input_list, dim=0)
- return torch.sum(inputs, dim=0)
-
- @staticmethod
- def backward(ctx, grad_output):
- dist.all_reduce(grad_output, async_op=False)
- return grad_output
-
-
-class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
- """
- In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient
- when the batch size on each worker is different.
- (e.g., when scale augmentation is used, or when it is applied to mask head).
-
- This is a slower but correct alternative to `nn.SyncBatchNorm`.
-
- Note:
- There isn't a single definition of Sync BatchNorm.
-
- When ``stats_mode==""``, this module computes overall statistics by using
- statistics of each worker with equal weight. The result is true statistics
- of all samples (as if they are all on one worker) only when all workers
- have the same (N, H, W). This mode does not support inputs with zero batch size.
-
- When ``stats_mode=="N"``, this module computes overall statistics by weighting
- the statistics of each worker by their ``N``. The result is true statistics
- of all samples (as if they are all on one worker) only when all workers
- have the same (H, W). It is slower than ``stats_mode==""``.
-
- Even though the result of this module may not be the true statistics of all samples,
- it may still be reasonable because it might be preferrable to assign equal weights
- to all workers, regardless of their (H, W) dimension, instead of putting larger weight
- on larger images. From preliminary experiments, little difference is found between such
- a simplified implementation and an accurate computation of overall mean & variance.
- """
-
- def __init__(self, *args, stats_mode="", **kwargs):
- super().__init__(*args, **kwargs)
- assert stats_mode in ["", "N"]
- self._stats_mode = stats_mode
-
- def forward(self, input):
- if comm.get_world_size() == 1 or not self.training:
- return super().forward(input)
-
- B, C = input.shape[0], input.shape[1]
-
- mean = torch.mean(input, dim=[0, 2, 3])
- meansqr = torch.mean(input * input, dim=[0, 2, 3])
-
- if self._stats_mode == "":
- assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.'
- vec = torch.cat([mean, meansqr], dim=0)
- vec = AllReduce.apply(vec) * (1.0 / dist.get_world_size())
- mean, meansqr = torch.split(vec, C)
- momentum = self.momentum
- else:
- if B == 0:
- vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype)
- vec = vec + input.sum() # make sure there is gradient w.r.t input
- else:
- vec = torch.cat(
- [mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0
- )
- vec = AllReduce.apply(vec * B)
-
- total_batch = vec[-1].detach()
- momentum = total_batch.clamp(max=1) * self.momentum # no update if total_batch is 0
- total_batch = torch.max(total_batch, torch.ones_like(total_batch)) # avoid div-by-zero
- mean, meansqr, _ = torch.split(vec / total_batch, C)
-
- var = meansqr - mean * mean
- invstd = torch.rsqrt(var + self.eps)
- scale = self.weight * invstd
- bias = self.bias - mean * scale
- scale = scale.reshape(1, -1, 1, 1)
- bias = bias.reshape(1, -1, 1, 1)
-
- self.running_mean += momentum * (mean.detach() - self.running_mean)
- self.running_var += momentum * (var.detach() - self.running_var)
- return input * scale + bias
\ No newline at end of file
diff --git a/spaces/Plurigrid/LifeSim/src/components/ui/menubar.tsx b/spaces/Plurigrid/LifeSim/src/components/ui/menubar.tsx
deleted file mode 100644
index d57454816cea9b7572ad1ae6ab139d6946c4d5d5..0000000000000000000000000000000000000000
--- a/spaces/Plurigrid/LifeSim/src/components/ui/menubar.tsx
+++ /dev/null
@@ -1,236 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as MenubarPrimitive from "@radix-ui/react-menubar"
-import { Check, ChevronRight, Circle } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const MenubarMenu = MenubarPrimitive.Menu
-
-const MenubarGroup = MenubarPrimitive.Group
-
-const MenubarPortal = MenubarPrimitive.Portal
-
-const MenubarSub = MenubarPrimitive.Sub
-
-const MenubarRadioGroup = MenubarPrimitive.RadioGroup
-
-const Menubar = React.forwardRef<
- React.ElementRef
-
-## To learn more about the Agent
-
-Check out our tutorial on the Conversational Agent [here](https://haystack.deepset.ai/tutorials/24_building_chat_app)
-
-## Installation and Running
-1. Install requirements:
-`pip install -r requirements.txt`
-2. Run the streamlit app:
-`streamlit run app.py`
-3. Createa a `.env` and add your Twitter Bearer token, OpenAI Key, and SerperDev Key:
-
-`TWITTER_BEARER_TOKEN`
-
-`SERPER_KEY`
-
-`OPENAI_API_KEY`
-
-This will start up the app on `localhost:8501` where you will find a simple search bar
-
-#### The Haystack Community is on [Discord](https://discord.com/invite/VBpFzsgRVF)
diff --git a/spaces/Vegecken/sovits4dzl/preprocess_hubert_f0.py b/spaces/Vegecken/sovits4dzl/preprocess_hubert_f0.py
deleted file mode 100644
index 29a1c7ee028fefbe7905d235447d98cda34ce840..0000000000000000000000000000000000000000
--- a/spaces/Vegecken/sovits4dzl/preprocess_hubert_f0.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import math
-import multiprocessing
-import os
-import argparse
-from random import shuffle
-
-import torch
-from glob import glob
-from tqdm import tqdm
-
-import utils
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import librosa
-import numpy as np
-
-hps = utils.get_hparams_from_file("configs/config.json")
-sampling_rate = hps.data.sampling_rate
-hop_length = hps.data.hop_length
-
-
-def process_one(filename, hmodel):
- # print(filename)
- wav, sr = librosa.load(filename, sr=sampling_rate)
- soft_path = filename + ".soft.pt"
- if not os.path.exists(soft_path):
- devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
- wav16k = torch.from_numpy(wav16k).to(devive)
- c = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
- torch.save(c.cpu(), soft_path)
- f0_path = filename + ".f0.npy"
- if not os.path.exists(f0_path):
- f0 = utils.compute_f0_dio(wav, sampling_rate=sampling_rate, hop_length=hop_length)
- np.save(f0_path, f0)
-
-
-def process_batch(filenames):
- print("Loading hubert for content...")
- device = "cuda" if torch.cuda.is_available() else "cpu"
- hmodel = utils.get_hubert_model().to(device)
- print("Loaded hubert.")
- for filename in tqdm(filenames):
- process_one(filename, hmodel)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--in_dir", type=str, default="dataset/44k", help="path to input dir")
-
- args = parser.parse_args()
- filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True) # [:10]
- shuffle(filenames)
- multiprocessing.set_start_method('spawn')
-
- num_processes = 1
- chunk_size = int(math.ceil(len(filenames) / num_processes))
- chunks = [filenames[i:i + chunk_size] for i in range(0, len(filenames), chunk_size)]
- print([len(c) for c in chunks])
- processes = [multiprocessing.Process(target=process_batch, args=(chunk,)) for chunk in chunks]
- for p in processes:
- p.start()
diff --git a/spaces/Wayben/ChatGPT/modules/utils.py b/spaces/Wayben/ChatGPT/modules/utils.py
deleted file mode 100644
index a4dfab86fef9d4f1bab12d027b3589e274755153..0000000000000000000000000000000000000000
--- a/spaces/Wayben/ChatGPT/modules/utils.py
+++ /dev/null
@@ -1,424 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
-import logging
-import json
-import os
-import datetime
-import hashlib
-import csv
-import requests
-import re
-import html
-
-import gradio as gr
-from pypinyin import lazy_pinyin
-import tiktoken
-import mdtex2html
-from markdown import markdown
-from pygments import highlight
-from pygments.lexers import get_lexer_by_name
-from pygments.formatters import HtmlFormatter
-
-from modules.presets import *
-import modules.shared as shared
-
-logging.basicConfig(
- level=logging.INFO,
- format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-if TYPE_CHECKING:
- from typing import TypedDict
-
- class DataframeData(TypedDict):
- headers: List[str]
- data: List[List[str | int | bool]]
-
-
-def count_token(message):
- encoding = tiktoken.get_encoding("cl100k_base")
- input_str = f"role: {message['role']}, content: {message['content']}"
- length = len(encoding.encode(input_str))
- return length
-
-
-def markdown_to_html_with_syntax_highlight(md_str):
- def replacer(match):
- lang = match.group(1) or "text"
- code = match.group(2)
-
- try:
- lexer = get_lexer_by_name(lang, stripall=True)
- except ValueError:
- lexer = get_lexer_by_name("text", stripall=True)
-
- formatter = HtmlFormatter()
- highlighted_code = highlight(code, lexer, formatter)
-
- return f'
'
-
- code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
- md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)
-
- html_str = markdown(md_str)
- return html_str
-
-
-def normalize_markdown(md_text: str) -> str:
- lines = md_text.split("\n")
- normalized_lines = []
- inside_list = False
-
- for i, line in enumerate(lines):
- if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
- if not inside_list and i > 0 and lines[i - 1].strip() != "":
- normalized_lines.append("")
- inside_list = True
- normalized_lines.append(line)
- elif inside_list and line.strip() == "":
- if i < len(lines) - 1 and not re.match(
- r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
- ):
- normalized_lines.append(line)
- continue
- else:
- inside_list = False
- normalized_lines.append(line)
-
- return "\n".join(normalized_lines)
-
-
-def convert_mdtext(md_text):
- code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
- inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
- code_blocks = code_block_pattern.findall(md_text)
- non_code_parts = code_block_pattern.split(md_text)[::2]
-
- result = []
- for non_code, code in zip(non_code_parts, code_blocks + [""]):
- if non_code.strip():
- non_code = normalize_markdown(non_code)
- if inline_code_pattern.search(non_code):
- result.append(markdown(non_code, extensions=["tables"]))
- else:
- result.append(mdtex2html.convert(non_code, extensions=["tables"]))
- if code.strip():
- # _, code = detect_language(code) # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题
- # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题
- code = f"\n```{code}\n\n```"
- code = markdown_to_html_with_syntax_highlight(code)
- result.append(code)
- result = "".join(result)
- result += ALREADY_CONVERTED_MARK
- return result
-
-
-def convert_asis(userinput):
- return f"{highlighted_code}
-
- """
- )
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
- generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
- image_out = gr.Image(height=512)
- error_output = gr.Markdown()
-
- with gr.Column(scale=45):
- with gr.Tab("Options"):
- with gr.Group():
- neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
- auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
-
- with gr.Row():
- guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
- steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
- with gr.Row():
- width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
- height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
- seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
- with gr.Tab("Image to image"):
- with gr.Group():
- image = gr.Image(label="Image", height=256, tool="editor", type="pil")
- strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
- auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
- inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
- outputs = [image_out, error_output]
- prompt.submit(inference, inputs=inputs, outputs=outputs)
- generate.click(inference, inputs=inputs, outputs=outputs)
-
- gr.HTML("""
-
-
- Papercutcraft V1
-
- {"Add the following tokens to your prompts for the model to work properly: prefix" if prefix else ""}
-
-
-
-
- """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/akshatsanghvi/movie-recommender-system/README.md b/spaces/akshatsanghvi/movie-recommender-system/README.md
deleted file mode 100644
index 800692cbd8660a05a55572a582f7269ad826790d..0000000000000000000000000000000000000000
--- a/spaces/akshatsanghvi/movie-recommender-system/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Movie Recommender System
-emoji: 🎥
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-license: apache-2.0
----
-
-## Movie Recommender System
-A movie recommendation system, or a movie recommender system, is an ML-based approach to filter or predict the users’ film preferences using techinques like :
-- popularity-based
-- content-based
-- collabrative filtering
-
-
-In this, project I have used content-based filtering to fiter movies with similar tags (concatenated strings of story, gernre, and title column)
-
-## Links :
-
-- #### Hugging Face Space Link : [Click Me](https://huggingface.co/spaces/akshatsanghvi/movie-recommender-system)
-- #### Kaggle Dataset (Bollywood) : [Click Me](https://www.kaggle.com/datasets/pncnmnp/the-indian-movie-database)
-- #### Kaggle Dataset (Hollywood) : [Click Me](https://www.kaggle.com/datasets/neha1703/movie-genre-from-its-poster?select=MovieGenre.csv)
-
-## After you click on the HF link you'll see two buttons,
-#### "Recommend" Button : Recommends 5 similar movies from the same country as of movie.
-#### "🌍" Button : Recommends 10 similar movies from the world.
-
-## Step 1:
-
-
-## Step 2:
-
-
-## Step 3:
-
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test117/app.py b/spaces/allknowingroger/Image-Models-Test117/app.py
deleted file mode 100644
index de8671ee160b3083834732b8aa1bf0bb5b43d99e..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test117/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
- "ngttt/lora-trained-xl-colab-test",
- "thisserand/lora-trained-xl-colab",
- "EnD-Diffusers/lineart-model",
- "NEXAS/stable_diff_personl",
- "Gauri54damle/sd-multi-object-model",
- "minhalvp/SDXL-Dreambooth-HRSFC",
- "CiroN2022/skull-graphics",
- "Navu45/pokemon-model",
- "rohiladora/lora-trained-xl-colab",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
- try:
- model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
- except Exception as error:
- def the_fn(txt):
- return None
- model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
- model_idx+=1
-
-
-def send_it_idx(idx):
- def send_it_fn(prompt):
- output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
- return output
- return send_it_fn
-
-def get_prompts(prompt_text):
- return prompt_text
-
-def clear_it(val):
- if int(val) != 0:
- val = 0
- else:
- val = 0
- pass
- return val
-
-def all_task_end(cnt,t_stamp):
- to = t_stamp + 60
- et = time.time()
- if et > to and t_stamp != 0:
- d = gr.update(value=0)
- tog = gr.update(value=1)
- #print(f'to: {to} et: {et}')
- else:
- if cnt != 0:
- d = gr.update(value=et)
- else:
- d = gr.update(value=0)
- tog = gr.update(value=0)
- #print (f'passing: to: {to} et: {et}')
- pass
- return d, tog
-
-def all_task_start():
- print("\n\n\n\n\n\n\n")
- t = time.gmtime()
- t_stamp = time.time()
- current_time = time.strftime("%H:%M:%S", t)
- return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
- nn = len(models)
- return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
- with gr.Column(scale=12):
- # with gr.Row():
- # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
- with gr.Row():
- with gr.Row(scale=6):
- primary_prompt=gr.Textbox(label="Prompt", value="")
- # real_prompt=gr.Textbox(label="Real prompt")
- with gr.Row(scale=6):
- # improve_prompts_btn=gr.Button("Improve")
- with gr.Row():
- run=gr.Button("Run",variant="primary")
- clear_btn=gr.Button("Clear")
- with gr.Row():
- sd_outputs = {}
- model_idx = 1
- for model_path in models:
- with gr.Column(scale=3, min_width=320):
- with gr.Box():
- sd_outputs[model_idx] = gr.Image(label=model_path)
- pass
- model_idx += 1
- pass
- pass
-
- with gr.Row(visible=False):
- start_box=gr.Number(interactive=False)
- end_box=gr.Number(interactive=False)
- tog_box=gr.Textbox(value=0,interactive=False)
-
- start_box.change(
- all_task_end,
- [start_box, end_box],
- [start_box, tog_box],
- every=1,
- show_progress=False)
-
- primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
- run.click(all_task_start, None, [start_box, end_box, tog_box])
- runs_dict = {}
- model_idx = 1
- for model_path in models:
- runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
- model_idx += 1
- pass
- pass
-
- # improve_prompts_btn_clicked=improve_prompts_btn.click(
- # get_prompts,
- # inputs=[primary_prompt],
- # outputs=[primary_prompt],
- # cancels=list(runs_dict.values()))
- clear_btn.click(
- clear_fn,
- None,
- [primary_prompt, *list(sd_outputs.values())],
- cancels=[*list(runs_dict.values())])
- tog_box.change(
- clear_it,
- tog_box,
- tog_box,
- cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
-
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test43/app.py b/spaces/allknowingroger/Image-Models-Test43/app.py
deleted file mode 100644
index 67ff223376c7a19a4dc16178feb0c36d0e7612e8..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test43/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
- "OPERFIND/step1",
- "Yntec/ChildrenStoriesAnime",
- "Dinesh-2004/my-pet-dog",
- "rafaym/DreamBoothAvatar",
- "abwqr/text2img_vision",
- "Jinouga/makima-chainsaw-manv1",
- "Dinesh-2004/my-pet-dog",
- "abwqr/text2img_vision_2.0",
- "digiplay/SyncMix_v1.5",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
- try:
- model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
- except Exception as error:
- def the_fn(txt):
- return None
- model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
- model_idx+=1
-
-
-def send_it_idx(idx):
- def send_it_fn(prompt):
- output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
- return output
- return send_it_fn
-
-def get_prompts(prompt_text):
- return prompt_text
-
-def clear_it(val):
- if int(val) != 0:
- val = 0
- else:
- val = 0
- pass
- return val
-
-def all_task_end(cnt,t_stamp):
- to = t_stamp + 60
- et = time.time()
- if et > to and t_stamp != 0:
- d = gr.update(value=0)
- tog = gr.update(value=1)
- #print(f'to: {to} et: {et}')
- else:
- if cnt != 0:
- d = gr.update(value=et)
- else:
- d = gr.update(value=0)
- tog = gr.update(value=0)
- #print (f'passing: to: {to} et: {et}')
- pass
- return d, tog
-
-def all_task_start():
- print("\n\n\n\n\n\n\n")
- t = time.gmtime()
- t_stamp = time.time()
- current_time = time.strftime("%H:%M:%S", t)
- return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
- nn = len(models)
- return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
- with gr.Column(scale=12):
- # with gr.Row():
- # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
- with gr.Row():
- with gr.Row(scale=6):
- primary_prompt=gr.Textbox(label="Prompt", value="")
- # real_prompt=gr.Textbox(label="Real prompt")
- with gr.Row(scale=6):
- # improve_prompts_btn=gr.Button("Improve")
- with gr.Row():
- run=gr.Button("Run",variant="primary")
- clear_btn=gr.Button("Clear")
- with gr.Row():
- sd_outputs = {}
- model_idx = 1
- for model_path in models:
- with gr.Column(scale=3, min_width=320):
- with gr.Box():
- sd_outputs[model_idx] = gr.Image(label=model_path)
- pass
- model_idx += 1
- pass
- pass
-
- with gr.Row(visible=False):
- start_box=gr.Number(interactive=False)
- end_box=gr.Number(interactive=False)
- tog_box=gr.Textbox(value=0,interactive=False)
-
- start_box.change(
- all_task_end,
- [start_box, end_box],
- [start_box, tog_box],
- every=1,
- show_progress=False)
-
- primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
- run.click(all_task_start, None, [start_box, end_box, tog_box])
- runs_dict = {}
- model_idx = 1
- for model_path in models:
- runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
- model_idx += 1
- pass
- pass
-
- # improve_prompts_btn_clicked=improve_prompts_btn.click(
- # get_prompts,
- # inputs=[primary_prompt],
- # outputs=[primary_prompt],
- # cancels=list(runs_dict.values()))
- clear_btn.click(
- clear_fn,
- None,
- [primary_prompt, *list(sd_outputs.values())],
- cancels=[*list(runs_dict.values())])
- tog_box.change(
- clear_it,
- tog_box,
- tog_box,
- cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
-
\ No newline at end of file
diff --git a/spaces/alphacep/asr/README.md b/spaces/alphacep/asr/README.md
deleted file mode 100644
index d11b0de259dcb3c4ee955960b3c65e4a9aeb3ca9..0000000000000000000000000000000000000000
--- a/spaces/alphacep/asr/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Automatic Speech Recognition
-emoji: 🌍
-colorFrom: magenta
-colorTo: magenta
-sdk: gradio
-sdk_version: 3.0.26
-app_file: app.py
-pinned: true
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arpagon/whisper-demo-large-v2-es/README.md b/spaces/arpagon/whisper-demo-large-v2-es/README.md
deleted file mode 100644
index 7843cbfd85cf6bb73dfbef2f8863bb01aed27aa2..0000000000000000000000000000000000000000
--- a/spaces/arpagon/whisper-demo-large-v2-es/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Whisper Demo
-emoji: 🤫
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-tags:
-- whisper-event
-duplicated_from: whisper-event/whisper-demo
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arsalagrey/speech-recognition-vue/index.html b/spaces/arsalagrey/speech-recognition-vue/index.html
deleted file mode 100644
index d07d378647ab6ebf3b557a84a9116f9459e081c8..0000000000000000000000000000000000000000
--- a/spaces/arsalagrey/speech-recognition-vue/index.html
+++ /dev/null
@@ -1,53 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/artificialguybr/video-dubbing/TTS/Makefile b/spaces/artificialguybr/video-dubbing/TTS/Makefile
deleted file mode 100644
index 54aa6eeb186b69cbc752c7c043114b6873fc4e7d..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/Makefile
+++ /dev/null
@@ -1,81 +0,0 @@
-.DEFAULT_GOAL := help
-.PHONY: test system-deps dev-deps deps style lint install help docs
-
-help:
- @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
-
-target_dirs := tests TTS notebooks recipes
-
-test_all: ## run tests and don't stop on an error.
- nose2 --with-coverage --coverage TTS tests
- ./run_bash_tests.sh
-
-test: ## run tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests
-
-test_vocoder: ## run vocoder tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.vocoder_tests
-
-test_tts: ## run tts tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.tts_tests
-
-test_tts2: ## run tts tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.tts_tests2
-
-test_xtts:
- nose2 -F -v -B --with-coverage --coverage TTS tests.xtts_tests
-
-test_aux: ## run aux tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.aux_tests
- ./run_bash_tests.sh
-
-test_zoo: ## run zoo tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.zoo_tests
-
-inference_tests: ## run inference tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.inference_tests
-
-api_tests: ## run api tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.api_tests
-
-data_tests: ## run data tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.data_tests
-
-test_text: ## run text tests.
- nose2 -F -v -B --with-coverage --coverage TTS tests.text_tests
-
-test_failed: ## only run tests failed the last time.
- nose2 -F -v -B --with-coverage --coverage TTS tests
-
-style: ## update code style.
- black ${target_dirs}
- isort ${target_dirs}
-
-lint: ## run pylint linter.
- pylint ${target_dirs}
- black ${target_dirs} --check
- isort ${target_dirs} --check-only
-
-system-deps: ## install linux system deps
- sudo apt-get install -y libsndfile1-dev
-
-dev-deps: ## install development deps
- pip install -r requirements.dev.txt
-
-doc-deps: ## install docs dependencies
- pip install -r docs/requirements.txt
-
-build-docs: ## build the docs
- cd docs && make clean && make build
-
-hub-deps: ## install deps for torch hub use
- pip install -r requirements.hub.txt
-
-deps: ## install 🐸 requirements.
- pip install -r requirements.txt
-
-install: ## install 🐸 TTS for development.
- pip install -e .[all]
-
-docs: ## build the docs
- $(MAKE) -C docs clean && $(MAKE) -C docs html
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_vits.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_vits.py
deleted file mode 100644
index fca99556199efb79a9c65378c40faebdb2cf51b6..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_vits.py
+++ /dev/null
@@ -1,595 +0,0 @@
-import copy
-import os
-import unittest
-
-import torch
-from trainer.logging.tensorboard_logger import TensorboardLogger
-
-from tests import assertHasAttr, assertHasNotAttr, get_tests_data_path, get_tests_input_path, get_tests_output_path
-from TTS.config import load_config
-from TTS.encoder.utils.generic_utils import setup_encoder_model
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.models.vits import (
- Vits,
- VitsArgs,
- VitsAudioConfig,
- amp_to_db,
- db_to_amp,
- load_audio,
- spec_to_mel,
- wav_to_mel,
- wav_to_spec,
-)
-from TTS.tts.utils.speakers import SpeakerManager
-
-LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
-SPEAKER_ENCODER_CONFIG = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
-WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
-
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-# pylint: disable=no-self-use
-class TestVits(unittest.TestCase):
- def test_load_audio(self):
- wav, sr = load_audio(WAV_FILE)
- self.assertEqual(wav.shape, (1, 41885))
- self.assertEqual(sr, 22050)
-
- spec = wav_to_spec(wav, n_fft=1024, hop_length=512, win_length=1024, center=False)
- mel = wav_to_mel(
- wav,
- n_fft=1024,
- num_mels=80,
- sample_rate=sr,
- hop_length=512,
- win_length=1024,
- fmin=0,
- fmax=8000,
- center=False,
- )
- mel2 = spec_to_mel(spec, n_fft=1024, num_mels=80, sample_rate=sr, fmin=0, fmax=8000)
-
- self.assertEqual((mel - mel2).abs().max(), 0)
- self.assertEqual(spec.shape[0], mel.shape[0])
- self.assertEqual(spec.shape[2], mel.shape[2])
-
- spec_db = amp_to_db(spec)
- spec_amp = db_to_amp(spec_db)
-
- self.assertAlmostEqual((spec - spec_amp).abs().max(), 0, delta=1e-4)
-
- def test_dataset(self):
- """TODO:"""
- ...
-
- def test_init_multispeaker(self):
- num_speakers = 10
- args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(args)
- assertHasAttr(self, model, "emb_g")
-
- args = VitsArgs(num_speakers=0, use_speaker_embedding=True)
- model = Vits(args)
- assertHasNotAttr(self, model, "emb_g")
-
- args = VitsArgs(num_speakers=10, use_speaker_embedding=False)
- model = Vits(args)
- assertHasNotAttr(self, model, "emb_g")
-
- args = VitsArgs(d_vector_dim=101, use_d_vector_file=True)
- model = Vits(args)
- self.assertEqual(model.embedded_speaker_dim, 101)
-
- def test_init_multilingual(self):
- args = VitsArgs(language_ids_file=None, use_language_embedding=False)
- model = Vits(args)
- self.assertEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, 0)
- assertHasNotAttr(self, model, "emb_l")
-
- args = VitsArgs(language_ids_file=LANG_FILE)
- model = Vits(args)
- self.assertNotEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, 0)
- assertHasNotAttr(self, model, "emb_l")
-
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True)
- model = Vits(args)
- self.assertNotEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, args.embedded_language_dim)
- assertHasAttr(self, model, "emb_l")
-
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, embedded_language_dim=102)
- model = Vits(args)
- self.assertNotEqual(model.language_manager, None)
- self.assertEqual(model.embedded_language_dim, args.embedded_language_dim)
- assertHasAttr(self, model, "emb_l")
-
- def test_get_aux_input(self):
- aux_input = {"speaker_ids": None, "style_wav": None, "d_vectors": None, "language_ids": None}
- args = VitsArgs()
- model = Vits(args)
- aux_out = model.get_aux_input(aux_input)
-
- speaker_id = torch.randint(10, (1,))
- language_id = torch.randint(10, (1,))
- d_vector = torch.rand(1, 128)
- aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
- aux_out = model.get_aux_input(aux_input)
- self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
- self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
- self.assertEqual(aux_out["d_vectors"].shape, d_vector.unsqueeze(0).transpose(2, 1).shape)
-
- def test_voice_conversion(self):
- num_speakers = 10
- spec_len = 101
- spec_effective_len = 50
-
- args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(args)
-
- ref_inp = torch.randn(1, 513, spec_len)
- ref_inp_len = torch.randint(1, spec_effective_len, (1,))
- ref_spk_id = torch.randint(1, num_speakers, (1,)).item()
- tgt_spk_id = torch.randint(1, num_speakers, (1,)).item()
- o_hat, y_mask, (z, z_p, z_hat) = model.voice_conversion(ref_inp, ref_inp_len, ref_spk_id, tgt_spk_id)
-
- self.assertEqual(o_hat.shape, (1, 1, spec_len * 256))
- self.assertEqual(y_mask.shape, (1, 1, spec_len))
- self.assertEqual(y_mask.sum(), ref_inp_len[0])
- self.assertEqual(z.shape, (1, args.hidden_channels, spec_len))
- self.assertEqual(z_p.shape, (1, args.hidden_channels, spec_len))
- self.assertEqual(z_hat.shape, (1, args.hidden_channels, spec_len))
-
- def _create_inputs(self, config, batch_size=2):
- input_dummy = torch.randint(0, 24, (batch_size, 128)).long().to(device)
- input_lengths = torch.randint(100, 129, (batch_size,)).long().to(device)
- input_lengths[-1] = 128
- spec = torch.rand(batch_size, config.audio["fft_size"] // 2 + 1, 30).to(device)
- mel = torch.rand(batch_size, config.audio["num_mels"], 30).to(device)
- spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
- spec_lengths[-1] = spec.size(2)
- waveform = torch.rand(batch_size, 1, spec.size(2) * config.audio["hop_length"]).to(device)
- return input_dummy, input_lengths, mel, spec, spec_lengths, waveform
-
- def _check_forward_outputs(self, config, output_dict, encoder_config=None, batch_size=2):
- self.assertEqual(
- output_dict["model_outputs"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
- )
- self.assertEqual(output_dict["alignments"].shape, (batch_size, 128, 30))
- self.assertEqual(output_dict["alignments"].max(), 1)
- self.assertEqual(output_dict["alignments"].min(), 0)
- self.assertEqual(output_dict["z"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["z_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["m_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["logs_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["m_q"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(output_dict["logs_q"].shape, (batch_size, config.model_args.hidden_channels, 30))
- self.assertEqual(
- output_dict["waveform_seg"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
- )
- if encoder_config:
- self.assertEqual(output_dict["gt_spk_emb"].shape, (batch_size, encoder_config.model_params["proj_dim"]))
- self.assertEqual(output_dict["syn_spk_emb"].shape, (batch_size, encoder_config.model_params["proj_dim"]))
- else:
- self.assertEqual(output_dict["gt_spk_emb"], None)
- self.assertEqual(output_dict["syn_spk_emb"], None)
-
- def test_forward(self):
- num_speakers = 0
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- config.model_args.spec_segment_size = 10
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config)
- model = Vits(config).to(device)
- output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform)
- self._check_forward_outputs(config, output_dict)
-
- def test_multispeaker_forward(self):
- num_speakers = 10
-
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- config.model_args.spec_segment_size = 10
-
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config)
- speaker_ids = torch.randint(0, num_speakers, (8,)).long().to(device)
-
- model = Vits(config).to(device)
- output_dict = model.forward(
- input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids}
- )
- self._check_forward_outputs(config, output_dict)
-
- def test_d_vector_forward(self):
- batch_size = 2
- args = VitsArgs(
- spec_segment_size=10,
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=[os.path.join(get_tests_data_path(), "dummy_speakers.json")],
- )
- config = VitsConfig(model_args=args)
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.train()
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
- d_vectors = torch.randn(batch_size, 256).to(device)
- output_dict = model.forward(
- input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"d_vectors": d_vectors}
- )
- self._check_forward_outputs(config, output_dict)
-
- def test_multilingual_forward(self):
- num_speakers = 10
- num_langs = 3
- batch_size = 2
-
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10)
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
-
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
-
- model = Vits(config).to(device)
- output_dict = model.forward(
- input_dummy,
- input_lengths,
- spec,
- spec_lengths,
- waveform,
- aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
- )
- self._check_forward_outputs(config, output_dict)
-
- def test_secl_forward(self):
- num_speakers = 10
- num_langs = 3
- batch_size = 2
-
- speaker_encoder_config = load_config(SPEAKER_ENCODER_CONFIG)
- speaker_encoder_config.model_params["use_torch_spec"] = True
- speaker_encoder = setup_encoder_model(speaker_encoder_config).to(device)
- speaker_manager = SpeakerManager()
- speaker_manager.encoder = speaker_encoder
-
- args = VitsArgs(
- language_ids_file=LANG_FILE,
- use_language_embedding=True,
- spec_segment_size=10,
- use_speaker_encoder_as_loss=True,
- )
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
- config.audio.sample_rate = 16000
-
- input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
-
- model = Vits(config, speaker_manager=speaker_manager).to(device)
- output_dict = model.forward(
- input_dummy,
- input_lengths,
- spec,
- spec_lengths,
- waveform,
- aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
- )
- self._check_forward_outputs(config, output_dict, speaker_encoder_config)
-
- def _check_inference_outputs(self, config, outputs, input_dummy, batch_size=1):
- feat_len = outputs["z"].shape[2]
- self.assertEqual(outputs["model_outputs"].shape[:2], (batch_size, 1)) # we don't know the channel dimension
- self.assertEqual(outputs["alignments"].shape, (batch_size, input_dummy.shape[1], feat_len))
- self.assertEqual(outputs["z"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
- self.assertEqual(outputs["z_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
- self.assertEqual(outputs["m_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
- self.assertEqual(outputs["logs_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
-
- def test_inference(self):
- num_speakers = 0
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(config).to(device)
-
- batch_size = 1
- input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
- outputs = model.inference(input_dummy)
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- batch_size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
- outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- def test_multispeaker_inference(self):
- num_speakers = 10
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
- model = Vits(config).to(device)
-
- batch_size = 1
- input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- batch_size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- def test_multilingual_inference(self):
- num_speakers = 10
- num_langs = 3
- args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10)
- config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
- model = Vits(config).to(device)
-
- input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
- speaker_ids = torch.randint(0, num_speakers, (1,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (1,)).long().to(device)
- _ = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
-
- batch_size = 1
- input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
- outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- batch_size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
- speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
- lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
- outputs = model.inference(
- input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids, "language_ids": lang_ids}
- )
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
-
- def test_d_vector_inference(self):
- args = VitsArgs(
- spec_segment_size=10,
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=[os.path.join(get_tests_data_path(), "dummy_speakers.json")],
- )
- config = VitsConfig(model_args=args)
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.eval()
- # batch size = 1
- input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
- d_vectors = torch.randn(1, 256).to(device)
- outputs = model.inference(input_dummy, aux_input={"d_vectors": d_vectors})
- self._check_inference_outputs(config, outputs, input_dummy)
- # batch size = 2
- input_dummy, input_lengths, *_ = self._create_inputs(config)
- d_vectors = torch.randn(2, 256).to(device)
- outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths, "d_vectors": d_vectors})
- self._check_inference_outputs(config, outputs, input_dummy, batch_size=2)
-
- @staticmethod
- def _check_parameter_changes(model, model_ref):
- count = 0
- for item1, item2 in zip(model.named_parameters(), model_ref.named_parameters()):
- name = item1[0]
- param = item1[1]
- param_ref = item2[1]
- assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
- name, param.shape, param, param_ref
- )
- count = count + 1
-
- def _create_batch(self, config, batch_size):
- input_dummy, input_lengths, mel, spec, mel_lengths, _ = self._create_inputs(config, batch_size)
- batch = {}
- batch["tokens"] = input_dummy
- batch["token_lens"] = input_lengths
- batch["spec_lens"] = mel_lengths
- batch["mel_lens"] = mel_lengths
- batch["spec"] = spec
- batch["mel"] = mel
- batch["waveform"] = torch.rand(batch_size, 1, config.audio["sample_rate"] * 10).to(device)
- batch["d_vectors"] = None
- batch["speaker_ids"] = None
- batch["language_ids"] = None
- return batch
-
- def test_train_step(self):
- # setup the model
- with torch.autograd.set_detect_anomaly(True):
- config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
- model = Vits(config).to(device)
- model.train()
- # model to train
- optimizers = model.get_optimizer()
- criterions = model.get_criterion()
- criterions = [criterions[0].to(device), criterions[1].to(device)]
- # reference model to compare model weights
- model_ref = Vits(config).to(device)
- # # pass the state to ref model
- model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count = count + 1
- for _ in range(5):
- batch = self._create_batch(config, 2)
- for idx in [0, 1]:
- outputs, loss_dict = model.train_step(batch, criterions, idx)
- self.assertFalse(not outputs)
- self.assertFalse(not loss_dict)
- loss_dict["loss"].backward()
- optimizers[idx].step()
- optimizers[idx].zero_grad()
-
- # check parameter changes
- self._check_parameter_changes(model, model_ref)
-
- def test_train_step_upsampling(self):
- """Upsampling by the decoder upsampling layers"""
- # setup the model
- with torch.autograd.set_detect_anomaly(True):
- audio_config = VitsAudioConfig(sample_rate=22050)
- model_args = VitsArgs(
- num_chars=32,
- spec_segment_size=10,
- encoder_sample_rate=11025,
- interpolate_z=False,
- upsample_rates_decoder=[8, 8, 4, 2],
- )
- config = VitsConfig(model_args=model_args, audio=audio_config)
- model = Vits(config).to(device)
- model.train()
- # model to train
- optimizers = model.get_optimizer()
- criterions = model.get_criterion()
- criterions = [criterions[0].to(device), criterions[1].to(device)]
- # reference model to compare model weights
- model_ref = Vits(config).to(device)
- # # pass the state to ref model
- model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count = count + 1
- for _ in range(5):
- batch = self._create_batch(config, 2)
- for idx in [0, 1]:
- outputs, loss_dict = model.train_step(batch, criterions, idx)
- self.assertFalse(not outputs)
- self.assertFalse(not loss_dict)
- loss_dict["loss"].backward()
- optimizers[idx].step()
- optimizers[idx].zero_grad()
-
- # check parameter changes
- self._check_parameter_changes(model, model_ref)
-
- def test_train_step_upsampling_interpolation(self):
- """Upsampling by interpolation"""
- # setup the model
- with torch.autograd.set_detect_anomaly(True):
- audio_config = VitsAudioConfig(sample_rate=22050)
- model_args = VitsArgs(
- num_chars=32,
- spec_segment_size=10,
- encoder_sample_rate=11025,
- interpolate_z=True,
- upsample_rates_decoder=[8, 8, 2, 2],
- )
- config = VitsConfig(model_args=model_args, audio=audio_config)
- model = Vits(config).to(device)
- model.train()
- # model to train
- optimizers = model.get_optimizer()
- criterions = model.get_criterion()
- criterions = [criterions[0].to(device), criterions[1].to(device)]
- # reference model to compare model weights
- model_ref = Vits(config).to(device)
- # # pass the state to ref model
- model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
- count = 0
- for param, param_ref in zip(model.parameters(), model_ref.parameters()):
- assert (param - param_ref).sum() == 0, param
- count = count + 1
- for _ in range(5):
- batch = self._create_batch(config, 2)
- for idx in [0, 1]:
- outputs, loss_dict = model.train_step(batch, criterions, idx)
- self.assertFalse(not outputs)
- self.assertFalse(not loss_dict)
- loss_dict["loss"].backward()
- optimizers[idx].step()
- optimizers[idx].zero_grad()
-
- # check parameter changes
- self._check_parameter_changes(model, model_ref)
-
- def test_train_eval_log(self):
- batch_size = 2
- config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.run_data_dep_init = False
- model.train()
- batch = self._create_batch(config, batch_size)
- logger = TensorboardLogger(
- log_dir=os.path.join(get_tests_output_path(), "dummy_vits_logs"), model_name="vits_test_train_log"
- )
- criterion = model.get_criterion()
- criterion = [criterion[0].to(device), criterion[1].to(device)]
- outputs = [None] * 2
- outputs[0], _ = model.train_step(batch, criterion, 0)
- outputs[1], _ = model.train_step(batch, criterion, 1)
- model.train_log(batch, outputs, logger, None, 1)
-
- model.eval_log(batch, outputs, logger, None, 1)
- logger.finish()
-
- def test_test_run(self):
- config = VitsConfig(model_args=VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
- model.run_data_dep_init = False
- model.eval()
- test_figures, test_audios = model.test_run(None)
- self.assertTrue(test_figures is not None)
- self.assertTrue(test_audios is not None)
-
- def test_load_checkpoint(self):
- chkp_path = os.path.join(get_tests_output_path(), "dummy_glow_tts_checkpoint.pth")
- config = VitsConfig(VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
- chkp = {}
- chkp["model"] = model.state_dict()
- torch.save(chkp, chkp_path)
- model.load_checkpoint(config, chkp_path)
- self.assertTrue(model.training)
- model.load_checkpoint(config, chkp_path, eval=True)
- self.assertFalse(model.training)
-
- def test_get_criterion(self):
- config = VitsConfig(VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
- criterion = model.get_criterion()
- self.assertTrue(criterion is not None)
-
- def test_init_from_config(self):
- config = VitsConfig(model_args=VitsArgs(num_chars=32))
- model = Vits.init_from_config(config, verbose=False).to(device)
-
- config = VitsConfig(model_args=VitsArgs(num_chars=32, num_speakers=2))
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertTrue(not hasattr(model, "emb_g"))
-
- config = VitsConfig(model_args=VitsArgs(num_chars=32, num_speakers=2, use_speaker_embedding=True))
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertEqual(model.num_speakers, 2)
- self.assertTrue(hasattr(model, "emb_g"))
-
- config = VitsConfig(
- model_args=VitsArgs(
- num_chars=32,
- num_speakers=2,
- use_speaker_embedding=True,
- speakers_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"),
- )
- )
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertEqual(model.num_speakers, 10)
- self.assertTrue(hasattr(model, "emb_g"))
-
- config = VitsConfig(
- model_args=VitsArgs(
- num_chars=32,
- use_d_vector_file=True,
- d_vector_dim=256,
- d_vector_file=[os.path.join(get_tests_data_path(), "dummy_speakers.json")],
- )
- )
- model = Vits.init_from_config(config, verbose=False).to(device)
- self.assertTrue(model.num_speakers == 1)
- self.assertTrue(not hasattr(model, "emb_g"))
- self.assertTrue(model.embedded_speaker_dim == config.d_vector_dim)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/DiagnosticErrorListener.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/DiagnosticErrorListener.py
deleted file mode 100644
index 32ac14b63579ce7c984c2e34f2b1c80bebe328ed..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/DiagnosticErrorListener.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#
-# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
-# Use of this file is governed by the BSD 3-clause license that
-# can be found in the LICENSE.txt file in the project root.
-#
-
-
-#
-# This implementation of {@link ANTLRErrorListener} can be used to identify
-# certain potential correctness and performance problems in grammars. "Reports"
-# are made by calling {@link Parser#notifyErrorListeners} with the appropriate
-# message.
-#
-# Speech Recognition
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- {{statusMessage}}
-
-#
-
-from io import StringIO
-from antlr4 import Parser, DFA
-from antlr4.atn.ATNConfigSet import ATNConfigSet
-from antlr4.error.ErrorListener import ErrorListener
-
-class DiagnosticErrorListener(ErrorListener):
-
- def __init__(self, exactOnly:bool=True):
- # whether all ambiguities or only exact ambiguities are reported.
- self.exactOnly = exactOnly
-
- def reportAmbiguity(self, recognizer:Parser, dfa:DFA, startIndex:int,
- stopIndex:int, exact:bool, ambigAlts:set, configs:ATNConfigSet):
- if self.exactOnly and not exact:
- return
-
- with StringIO() as buf:
- buf.write("reportAmbiguity d=")
- buf.write(self.getDecisionDescription(recognizer, dfa))
- buf.write(": ambigAlts=")
- buf.write(str(self.getConflictingAlts(ambigAlts, configs)))
- buf.write(", input='")
- buf.write(recognizer.getTokenStream().getText(startIndex, stopIndex))
- buf.write("'")
- recognizer.notifyErrorListeners(buf.getvalue())
-
-
- def reportAttemptingFullContext(self, recognizer:Parser, dfa:DFA, startIndex:int,
- stopIndex:int, conflictingAlts:set, configs:ATNConfigSet):
- with StringIO() as buf:
- buf.write("reportAttemptingFullContext d=")
- buf.write(self.getDecisionDescription(recognizer, dfa))
- buf.write(", input='")
- buf.write(recognizer.getTokenStream().getText(startIndex, stopIndex))
- buf.write("'")
- recognizer.notifyErrorListeners(buf.getvalue())
-
- def reportContextSensitivity(self, recognizer:Parser, dfa:DFA, startIndex:int,
- stopIndex:int, prediction:int, configs:ATNConfigSet):
- with StringIO() as buf:
- buf.write("reportContextSensitivity d=")
- buf.write(self.getDecisionDescription(recognizer, dfa))
- buf.write(", input='")
- buf.write(recognizer.getTokenStream().getText(startIndex, stopIndex))
- buf.write("'")
- recognizer.notifyErrorListeners(buf.getvalue())
-
- def getDecisionDescription(self, recognizer:Parser, dfa:DFA):
- decision = dfa.decision
- ruleIndex = dfa.atnStartState.ruleIndex
-
- ruleNames = recognizer.ruleNames
- if ruleIndex < 0 or ruleIndex >= len(ruleNames):
- return str(decision)
-
- ruleName = ruleNames[ruleIndex]
- if ruleName is None or len(ruleName)==0:
- return str(decision)
-
- return str(decision) + " (" + ruleName + ")"
-
- #
- # Computes the set of conflicting or ambiguous alternatives from a
- # configuration set, if that information was not already provided by the
- # parser.
- #
- # @param reportedAlts The set of conflicting or ambiguous alternatives, as
- # reported by the parser.
- # @param configs The conflicting or ambiguous configuration set.
- # @return Returns {@code reportedAlts} if it is not {@code null}, otherwise
- # returns the set of alternatives represented in {@code configs}.
- #
- def getConflictingAlts(self, reportedAlts:set, configs:ATNConfigSet):
- if reportedAlts is not None:
- return reportedAlts
-
- result = set()
- for config in configs:
- result.add(config.alt)
-
- return result
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/sentencepiece_bpe.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/sentencepiece_bpe.py
deleted file mode 100644
index 0aa6cd7681d0c3a91a6917640972d008db8faef7..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/encoders/sentencepiece_bpe.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass, field
-from typing import Optional
-
-from fairseq import file_utils
-from fairseq.data.encoders import register_bpe
-from fairseq.dataclass import FairseqDataclass
-
-
-@dataclass
-class SentencepieceConfig(FairseqDataclass):
- sentencepiece_model: str = field(
- default="???", metadata={"help": "path to sentencepiece model"}
- )
- sentencepiece_enable_sampling: bool = field(
- default=False, metadata={"help": "enable sampling"}
- )
- sentencepiece_alpha: Optional[float] = field(
- default=None,
- metadata={
- "help": "soothing parameter for unigram sampling, "
- "and merge probability for BPE-dropout"
- },
- )
-
-
-@register_bpe("sentencepiece", dataclass=SentencepieceConfig)
-class SentencepieceBPE(object):
- def __init__(self, cfg):
- self.enable_sampling = cfg.sentencepiece_enable_sampling
- self.alpha = cfg.sentencepiece_alpha
- sentencepiece_model = file_utils.cached_path(cfg.sentencepiece_model)
- try:
- import sentencepiece as spm
-
- self.sp = spm.SentencePieceProcessor()
- self.sp.Load(sentencepiece_model)
- except ImportError:
- raise ImportError(
- "Please install sentencepiece with: pip install sentencepiece"
- )
-
- def encode(self, x: str) -> str:
- return " ".join(
- self.sp.Encode(
- x, out_type=str, enable_sampling=self.enable_sampling, alpha=self.alpha
- )
- )
-
- def decode(self, x: str) -> str:
- return x.replace(" ", "").replace("\u2581", " ").strip()
-
- def is_beginning_of_word(self, x: str) -> bool:
- if x in ["", "", "Select options
", unsafe_allow_html=True)
-with st.expander("Educational value *"):
- age_range = st.select_slider("Age range of the reader", options=AGE_RANGE)
- skill_development = st.selectbox("Skill development", options=SKILL_DEVELOPMENT)
- learning_obectives = st.selectbox(
- "Learning objectives", options=LEARNING_OBJECTIVES
- )
-with st.expander("Emotional value *"):
- theme = st.selectbox("Theme", options=THEME)
- mood = st.selectbox("Moood of story", options=MODD_OF_STORY)
- positive_messaging = st.selectbox("Skill development", options=POSITIVE_MESSAGNG)
-with st.expander("Personal *"):
- theme = st.selectbox("Gender", options=GENDER)
- fvrt_book = st.text_input("Favorite book")
-with st.expander("Book Details * "):
- chapters = st.number_input(
- "How many chapters should the book have?", min_value=3, max_value=100, value=5
- )
-
- title = st.text_input("Title of the book")
- genre = st.selectbox("Genre", options=GENRE)
- topic = st.selectbox("Topic ", options=TOPIC)
- main_name = st.text_input("Name of main character")
- type_of_main_character = st.selectbox(
- "Type of main character", TYPE_OF_MAIN_CHARACTER
- )
- antagonist_name = st.text_input("Antagonist name")
- antagonsit_type = st.selectbox("Antagonist type", options=ANTAGONIST_TYPE)
- suuporting_character_name = st.text_input("Supporting character name (if any)")
- suporting_character_type = st.selectbox(
- "Supporting character type", options=SUPPORTING_CHARACTER_TYPE
- )
- settings = st.selectbox("Setting ", options=SETTINGS)
- resolution = st.selectbox("Resolution", options=RESOLUTION)
-
-btn = st.button("Generate Book")
-if btn:
- content = []
- for x in stqdm(range(chapters), desc="Generating book"):
- if x == 0:
- prmpt = get_initial_prompts(
- genre,
- type_of_main_character,
- main_name,
- skill_development,
- learning_obectives,
- theme,
- topic,
- )
- content.append(complete_with_gpt(prmpt, 200, "gpt2", 1500, 0.7, 1.5))
- if x == 1:
- prmpt = story_setting_prompt(
- genre,
- type_of_main_character,
- main_name,
- skill_development,
- learning_obectives,
- theme,
- mood,
- antagonist_name,
- antagonsit_type,
- )
- previous = " ".join(x for x in content)
- prmpt = previous + " " + prmpt
- content.append(complete_with_gpt(prmpt, 200, "gpt2", 1500, 0.7, 1.5))
-
- if x % 3 == 0:
- prmpt = supporting_character_inclusion(
- genre,
- suuporting_character_name,
- suporting_character_type,
- positive_messaging,
- )
- previous = " ".join(x for x in content)
- prmpt = previous + " " + prmpt
- content.append(complete_with_gpt(prmpt, 200, "gpt2", 1500, 0.7, 1.5))
- if x == chapters - 1:
- prmpt = ending_scene(genre, resolution, main_name, positive_messaging)
- previous = " ".join(x for x in content)
- prmpt = previous + " " + prmpt
- content.append(complete_with_gpt(prmpt, 200, "gpt2", 1500, 0.7, 1.5))
- else:
- previous = " ".join(x for x in content)
- prmpt = previous
- content.append(complete_with_gpt(prmpt, 200, "gpt2", 1500, 0.7, 1.5))
-
- st.write(content)
- filenamee = to_pdf(convert(create_md(text=content, title=title)))
- with open(filenamee, "rb") as pdf_file:
- PDFbyte = pdf_file.read()
-
- st.download_button(
- label="Download Book",
- data=PDFbyte,
- file_name=filenamee,
- mime="application/octet-stream",
- )
diff --git a/spaces/aurora10/GPT4ALL_CHATBOT/app.py b/spaces/aurora10/GPT4ALL_CHATBOT/app.py
deleted file mode 100644
index 7361d946a8ff6c24d5f63b63d748cb4dba27b7ce..0000000000000000000000000000000000000000
--- a/spaces/aurora10/GPT4ALL_CHATBOT/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-import gradio as gr
-from gpt4all import GPT4All
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-from langchain import PromptTemplate, LLMChain
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-
-
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-model = GPT4All("ggml-gpt4all-l13b-snoozy.bin")
-
-from langchain.llms import GPT4All
-
-# Callbacks support token-wise streaming
-callbacks = [StreamingStdOutCallbackHandler()]
-
-# Verbose is required to pass to the callback manager
-llm = GPT4All(model="ggml-gpt4all-l13b-snoozy.bin", callbacks=callbacks, verbose=True)
-
-# If you want to use a custom model add the backend parameter
-# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends
-llm = GPT4All(model="ggml-gpt4all-l13b-snoozy.bin", backend="gptj", callbacks=callbacks, verbose=True)
-
-llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True, memory=memory,)
-
-def get_text_response(user_message,history):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/awacke1/AI-Standard-Operating-Procedures/README.md b/spaces/awacke1/AI-Standard-Operating-Procedures/README.md
deleted file mode 100644
index 43c70871fb49dcd3d1edfec3a3d6787173115502..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AI-Standard-Operating-Procedures/README.md
+++ /dev/null
@@ -1,239 +0,0 @@
----
-title: AI Standard Operating Procedures
-emoji: 🌡️📜🎓👀📊🚨📁
-colorFrom: yellow
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: awacke1/AI-ChatGPT-CPT-Body-Map-Cost
----
-
-## Standard Operating Procedures
-| SOP No. | Standard Operating Procedure | Description | Top Ten Keywords | Wikipedia Link | SOP Icon |
-|---------|------------------------------|-------------|-----------------|----------------|---------|
-| 1 | SOP-01: Risk Assessment | Identifying, evaluating, and prioritizing compliance risks | risk, assessment, evaluate, prioritize, compliance, identify, analysis, management, mitigation, control | https://en.wikipedia.org/wiki/Risk_assessment | 🌡️ |
-| 2 | SOP-02: Policy Development | Creating clear and concise compliance policies and procedures | policy, development, create, clear, concise, compliance, procedure, regulation, standard, guideline | https://en.wikipedia.org/wiki/Policy | 📜 |
-| 3 | SOP-03: Training | Providing regular compliance training to employees | training, compliance, regular, employee, development, program, education, workshop, seminar, course | https://en.wikipedia.org/wiki/Training | 🎓 |
-| 4 | SOP-04: Monitoring | Conducting periodic compliance audits and monitoring activities | monitoring, periodic, compliance, audit, review, assessment, evaluation, inspection, surveillance, oversight | https://en.wikipedia.org/wiki/Monitoring_and_evaluation | 👀 |
-| 5 | SOP-05: Reporting | Establishing a process for reporting and addressing compliance issues | reporting, process, establish, compliance, issue, address, record, communication, notification, investigation | https://en.wikipedia.org/wiki/Reporting | 📊 |
-| 6 | SOP-06: Incident Management | Handling compliance incidents and implementing corrective actions | incident, management, compliance, handle, implement, corrective, action, investigation, response, resolution | https://en.wikipedia.org/wiki/Incident_management | 🚨 |
-| 7 | SOP-07: Recordkeeping | Maintaining accurate and up-to-date compliance records and documentation | recordkeeping, maintain, accurate, up-to-date, compliance, documentation, archive, storage, filing, record | https://en.wikipedia.org/wiki/Record_keeping | 📁 |
-
-1. What is the purpose of SOP-01: Risk Assessment?
-- The purpose of SOP-01: Risk Assessment is to identify, evaluate, and prioritize compliance risks.
-
-2. What does the term “risk” refer to in the context of risk assessment?
-- In the context of risk assessment, the term “risk” refers to the potential for an event or situation to have a negative impact on an organization or project.
-
-3. What is the process for evaluating risks?
-- The process for evaluating risks typically involves identifying the potential risks, analyzing their likelihood and potential impact, and prioritizing them based on their severity.
-
-4. How do you prioritize risks in a risk assessment?
-- Risks can be prioritized in a risk assessment by considering their potential impact, likelihood of occurrence, and the organization’s ability to mitigate or control them.
-
-5. What is compliance risk?
-- Compliance risk refers to the risk associated with non-compliance with laws, regulations, or internal policies and procedures.
-
-6. What is the role of analysis in risk assessment?
-- Analysis plays a crucial role in risk assessment by helping to identify potential risks, evaluate their impact and likelihood, and develop strategies for mitigating or controlling them.
-
-7. What is risk management?
-- Risk management is the process of identifying, assessing, and prioritizing risks, and developing strategies to mitigate or control them.
-
-8. What is risk mitigation?
-- Risk mitigation refers to the process of minimizing or preventing the negative impact of potential risks.
-
-9. What is risk control?
-- Risk control refers to the measures taken to manage or reduce the likelihood and severity of potential risks.
-
-10. Why is risk assessment important?
-- Risk assessment is important because it helps organizations to identify and manage potential risks, leading to better decision-making, improved performance, and reduced negative impacts.
-
-
-
-1. What is the purpose of SOP-02: Policy Development?
-- The purpose of SOP-02: Policy Development is to create clear and concise compliance policies and procedures.
-
-2. What is a policy?
-- A policy is a set of guidelines or principles that are developed to guide decision-making and behavior within an organization.
-
-3. What is the process for policy development?
-- The process for policy development typically involves identifying the need for the policy, researching and gathering information, drafting the policy, obtaining feedback and approval, and implementing the policy.
-
-4. Why is it important for policies to be clear and concise?
-- It is important for policies to be clear and concise so that they can be easily understood and followed by all members of the organization. This helps to ensure that everyone is on the same page and that compliance is maintained.
-
-5. What is compliance?
-- Compliance refers to the act of following laws, regulations, or internal policies and procedures.
-
-6. What is a procedure?
-- A procedure is a set of step-by-step instructions or guidelines for how to perform a specific task or activity.
-
-7. What is a regulation?
-- A regulation is a rule or law that is put in place by a government or regulatory body to ensure compliance and standardization.
-
-8. What is a standard?
-- A standard is a set of guidelines or principles that are developed to ensure consistent and high-quality performance or behavior.
-
-9. What is a guideline?
-- A guideline is a set of recommendations or tips that are developed to assist with decision-making or performance.
-
-10. Why is policy development important?
-- Policy development is important because it helps to ensure that an organization is operating in compliance with regulations and standards, while also promoting consistency and clarity in decision-making and behavior.
-
-1. What is the purpose of SOP-03: Training?
-- The purpose of SOP-03: Training is to provide regular compliance training to employees.
-
-2. What is training?
-- Training is the process of developing skills, knowledge, or behavior through education and instruction.
-
-3. Why is regular compliance training important?
-- Regular compliance training is important to ensure that employees are aware of, and adhere to, laws, regulations, and company policies and procedures.
-
-4. What is compliance?
-- Compliance refers to the act of following laws, regulations, or internal policies and procedures.
-
-5. Who is responsible for providing compliance training?
-- It is typically the responsibility of the employer or organization to provide compliance training to their employees.
-
-6. What is employee development?
-- Employee development refers to the process of improving an employee’s skills, knowledge, and abilities through training and education programs.
-
-7. What is a training program?
-- A training program is a structured approach to employee development that is designed to improve skills, knowledge, and abilities related to a specific job or task.
-
-8. What is an education workshop?
-- An education workshop is a training session that is designed to provide participants with information and skills related to a specific topic or field.
-
-9. What is a seminar?
-- A seminar is a training event that typically involves an expert speaker or panel discussing a specific topic or issue.
-
-10. What is a training course?
-- A training course is a structured program of learning that is typically designed to improve skills or knowledge related to a specific job or task.
-
-
-1. What is the purpose of SOP-04: Monitoring?
-- The purpose of SOP-04: Monitoring is to conduct periodic compliance audits and monitoring activities.
-
-2. What is monitoring?
-- Monitoring is the process of tracking and observing an activity or process to ensure that it is operating as intended.
-
-3. What does periodic mean in the context of monitoring?
-- In the context of monitoring, periodic refers to activities that are conducted at regular intervals, rather than continuously.
-
-4. What is compliance?
-- Compliance refers to the act of following laws, regulations, or internal policies and procedures.
-
-5. What is an audit?
-- An audit is a systematic examination of an organization or process to evaluate compliance, performance, or financial status.
-
-6. What is a review?
-- A review is an evaluation of an organization or process to assess performance or compliance.
-
-7. What is an assessment?
-- An assessment is a process of evaluating the performance, compliance, or quality of an organization or process.
-
-8. What is an evaluation?
-- An evaluation is a systematic process of collecting and analyzing information to assess the effectiveness, efficiency, or relevance of an organization or process.
-
-9. What is an inspection?
-- An inspection is an examination or review of an organization or process to evaluate compliance, performance, or safety.
-
-10. What is surveillance?
-- Surveillance is the act of closely monitoring an activity or process to ensure compliance, safety, or security.
-
-1. What is the purpose of SOP-05: Reporting?
-- The purpose of SOP-05: Reporting is to establish a process for reporting and addressing compliance issues.
-
-2. What is reporting?
-- Reporting is the process of notifying others about an event or situation, typically for the purpose of documentation or action.
-
-3. What does the term “process” mean in the context of SOP-05: Reporting?
-- In the context of SOP-05: Reporting, “process” refers to the steps and procedures that are established to ensure that compliance issues are identified, reported, and addressed in a timely and effective manner.
-
-4. What is compliance?
-- Compliance refers to the act of following laws, regulations, or internal policies and procedures.
-
-5. What is a compliance issue?
-- A compliance issue is an event or situation that violates laws, regulations, or internal policies and procedures.
-
-6. What does it mean to address a compliance issue?
-- To address a compliance issue means to take appropriate steps to investigate, resolve, and prevent similar issues in the future.
-
-7. What is a record?
-- A record is a document or other form of evidence that is created or maintained for legal, administrative, or business purposes.
-
-8. What is communication?
-- Communication is the exchange of information between individuals or groups, typically through speaking, writing, or other forms of expression.
-
-9. What is notification?
-- Notification is the process of informing individuals or groups about a particular event or situation.
-
-10. What is an investigation?
-- An investigation is a process of gathering information and evidence to uncover the facts about a particular event or situation.
-
-
-1. What is the purpose of SOP-06: Incident Management?
-- The purpose of SOP-06: Incident Management is to handle compliance incidents and implement corrective actions.
-
-2. What is an incident?
-- An incident is an event or situation that is unexpected or disrupts normal operations.
-
-3. What is management?
-- Management refers to the process of planning, organizing, and controlling resources to achieve organizational goals.
-
-4. What is compliance?
-- Compliance refers to the act of following laws, regulations, or internal policies and procedures.
-
-5. What does it mean to handle an incident?
-- To handle an incident means to respond to and manage the incident in a way that minimizes its impact and prevents a recurrence.
-
-6. What does it mean to implement corrective actions?
-- To implement corrective actions means to take steps to address the root cause of an incident and prevent it from happening again.
-
-7. What is a corrective action?
-- A corrective action is a step or process that is taken to address the root cause of an incident and prevent its recurrence.
-
-8. What is an investigation?
-- An investigation is a process of gathering information and evidence to uncover the facts about a particular event or situation.
-
-9. What is a response?
-- A response is the immediate action taken in response to an incident to prevent further harm or damage.
-
-10. What is a resolution?
-- A resolution is a decision or action taken to resolve an incident or issue and to prevent its recurrence.
-
-1. What is the purpose of SOP-07: Recordkeeping?
-- The purpose of SOP-07: Recordkeeping is to maintain accurate and up-to-date compliance records and documentation.
-
-2. What is recordkeeping?
-- Recordkeeping is the process of creating, managing, and storing information for legal, administrative, or business purposes.
-
-3. What does it mean to maintain records?
-- To maintain records means to keep records accurate, complete, and up-to-date to ensure that they are reliable and useful when needed.
-
-4. What does it mean for records to be accurate and up-to-date?
-- For records to be accurate and up-to-date means that they reflect the current state of affairs and contain the correct information.
-
-5. What is compliance?
-- Compliance refers to the act of following laws, regulations, or internal policies and procedures.
-
-6. What is documentation?
-- Documentation is information that is recorded and stored for legal, administrative, or business purposes.
-
-7. What is an archive?
-- An archive is a collection of historical records or documents that are preserved for research, reference, or legal purposes.
-
-8. What is storage?
-- Storage is the physical or digital location where records or documents are kept for future reference or use.
-
-9. What is filing?
-- Filing is the process of organizing documents or records into a structured system for easy retrieval and access.
-
-10. Why is recordkeeping important?
-- Recordkeeping is important for maintaining compliance, establishing accountability, facilitating business operations, and preserving historical information/documentation.
-
-
diff --git a/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game/README.md b/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game/README.md
deleted file mode 100644
index aeabcfea652c251b5e0478d3c4071f22020e5ab5..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ⬡Hexagon⬡ 🎲Dice🎲 Fractal Math Game
-emoji: 🎲⬡⬡⬡🎲
-colorFrom: red
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Search_Streamlit/README.md b/spaces/awacke1/Search_Streamlit/README.md
deleted file mode 100644
index 81c05e9b1ae3a5683a77cb832ccbbe68c6abb61e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Search_Streamlit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 📗NLP Plot Search Memory SL🔍🎥
-emoji: 📗🔍🎥
-colorFrom: gray
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/awacke1/StreamlitHeatmapAndCluster/app.py b/spaces/awacke1/StreamlitHeatmapAndCluster/app.py
deleted file mode 100644
index 040b6f71a2b254f3826994176b1140b08ce6ef8a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/StreamlitHeatmapAndCluster/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import streamlit as st
-import nltk
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-from scipy.spatial.distance import cosine
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-import tensorflow as tf
-import tensorflow_hub as hub
-
-
-def cluster_examples(messages, embed, nc=3):
- km = KMeans(
- n_clusters=nc, init='random',
- n_init=10, max_iter=300,
- tol=1e-04, random_state=0
- )
- km = km.fit_predict(embed)
- for n in range(nc):
- idxs = [i for i in range(len(km)) if km[i] == n]
- ms = [messages[i] for i in idxs]
- st.markdown ("CLUSTER : %d"%n)
- for m in ms:
- st.markdown (m)
-
-
-def plot_heatmap(labels, heatmap, rotation=90):
- sns.set(font_scale=1.2)
- fig, ax = plt.subplots()
- g = sns.heatmap(
- heatmap,
- xticklabels=labels,
- yticklabels=labels,
- vmin=-1,
- vmax=1,
- cmap="coolwarm")
- g.set_xticklabels(labels, rotation=rotation)
- g.set_title("Textual Similarity")
- st.pyplot(fig)
-
-# Streamlit text boxes
-text = st.text_area('Enter sentences:', value="Behavior right this is a kind of Heisenberg uncertainty principle situation if I told you, then you behave differently. What would be the impressive thing is you have talked about winning a nobel prize in a system winning a nobel prize. Adjusting it and then making your own. That is when I fell in love with computers. I realized that they were a very magical device. Can go to sleep come back the next day and it is solved. You know that feels magical to me.")
-
-nc = st.slider('Select a number of clusters:', min_value=1, max_value=15, value=3)
-
-model_type = st.radio("Choose model:", ('Sentence Transformer', 'Universal Sentence Encoder'), index=0)
-
-# Model setup
-if model_type == "Sentence Transformer":
- model = SentenceTransformer('paraphrase-distilroberta-base-v1')
-elif model_type == "Universal Sentence Encoder":
- model_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
- model = hub.load(model_url)
-
-nltk.download('punkt')
-
-# Run model
-if text:
- sentences = nltk.tokenize.sent_tokenize(text)
- if model_type == "Sentence Transformer":
- embed = model.encode(sentences)
- elif model_type == "Universal Sentence Encoder":
- embed = model(sentences).numpy()
- sim = np.zeros([len(embed), len(embed)])
- for i,em in enumerate(embed):
- for j,ea in enumerate(embed):
- sim[i][j] = 1.0-cosine(em,ea)
- st.subheader("Similarity Heatmap")
- plot_heatmap(sentences, sim)
- st.subheader("Results from K-Means Clustering")
- cluster_examples(sentences, embed, nc)
\ No newline at end of file
diff --git a/spaces/awacke1/Twitter-Sentiment-Live-Realtime/app.py b/spaces/awacke1/Twitter-Sentiment-Live-Realtime/app.py
deleted file mode 100644
index 7165f7c550e19a19b4068435d84ffc2398027b40..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Twitter-Sentiment-Live-Realtime/app.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import streamlit as st
-import tweepy as tw
-import pandas as pd
-import matplotlib.pyplot as plt
-from transformers import pipeline
-import os
-
-consumer_key = 'OCgWzDW6PaBvBeVimmGBqdAg1'
-consumer_secret = 'tBKnmyg5Jfsewkpmw74gxHZbbZkGIH6Ee4rsM0lD1vFL7SrEIM'
-access_token = '1449663645412065281-LNjZoEO9lxdtxPcmLtM35BRdIKYHpk'
-access_token_secret = 'FL3SGsUWSzPVFnG7bNMnyh4vYK8W1SlABBNtdF7Xcbh7a'
-auth = tw.OAuthHandler(consumer_key, consumer_secret)
-auth.set_access_token(access_token, access_token_secret)
-api = tw.API(auth, wait_on_rate_limit=True)
-classifier = pipeline('sentiment-analysis')
-FILE_NAME = 'query_history.csv'
-HEADERS = ['Search Query', 'Number of Tweets', 'Results', 'Date']
-
-if not os.path.isfile(FILE_NAME):
- df = pd.DataFrame(columns=HEADERS)
- df.to_csv(FILE_NAME, index=False)
-
-st.set_page_config(page_title='😃 Twitter Sentiment Analysis', layout='wide')
-
-def display_history():
- df = pd.read_csv(FILE_NAME)
- st.dataframe(df.style.highlight_max(axis=0))
-
-def run():
- with st.form(key='Enter name'):
- search_words = st.text_input('Enter a word or phrase you want to know about')
- number_of_tweets = st.number_input('How many tweets do you want to see? (maximum 50)', 1, 50, 50)
- submit_button = st.form_submit_button(label='Submit')
-
- if submit_button:
- unique_tweets, tweet_list, sentiment_list = set(), [], []
- tweets = tw.Cursor(api.search_tweets, q=search_words, lang="en").items(number_of_tweets)
- for tweet in tweets:
- if tweet.text not in unique_tweets:
- unique_tweets.add(tweet.text)
- tweet_list.append(tweet.text)
- p = classifier(tweet.text)
- sentiment_list.append(p[0]['label'])
-
- df = pd.DataFrame(list(zip(tweet_list, sentiment_list)), columns=['Tweets', 'Sentiment'])
- st.write(df)
-
- summary = df.groupby('Sentiment').size().reset_index(name='Counts')
- fig, ax = plt.subplots()
- ax.pie(summary['Counts'], labels=summary['Sentiment'], autopct='%1.1f%%', startangle=90)
- ax.axis('equal')
- st.pyplot(fig)
-
- with open(FILE_NAME, mode='a', newline='') as file:
- df.to_csv(file, header=False, index=False)
-
- if st.button('Clear History'):
- os.remove(FILE_NAME)
- st.write('History has been cleared.')
-
- if st.button('Display History'):
- display_history()
-
-if __name__=='__main__':
- run()
diff --git a/spaces/azaninello/gpt2-general-english/app.py b/spaces/azaninello/gpt2-general-english/app.py
deleted file mode 100644
index 54c70d8148be45a2d3d61a4885fa28f31a48c748..0000000000000000000000000000000000000000
--- a/spaces/azaninello/gpt2-general-english/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-import transformers
-from transformers import AutoModelForCausalLM, AutoModelWithLMHead, AutoTokenizer, pipeline
-from transformers import GPT2Tokenizer, GPT2Model
-
-
-general_model = AutoModelForCausalLM.from_pretrained('gpt2')
-general_generator = pipeline("text-generation", model=general_model, tokenizer="gpt2")
-general_result = general_generator("Today is ", max_length=200)
-general_result[0]["generated_text"]
-
-
-def generator(start_your_text = ''):
- result = general_generator(start_your_text)
- return result[0]["generated_text"]
-
-iface = gr.Interface(fn=generator, inputs="text", outputs="text")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/azizalto/vanilla-ml-algorithms/page_config.py b/spaces/azizalto/vanilla-ml-algorithms/page_config.py
deleted file mode 100644
index b0c39c70dcab2fb04e67bd9c48cd54a40149b3fc..0000000000000000000000000000000000000000
--- a/spaces/azizalto/vanilla-ml-algorithms/page_config.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from datetime import date
-
-import streamlit as st
-
-
-def APP_PAGE_HEADER():
- st.set_page_config(
- page_title="ML Algorithms",
- page_icon=":camel:",
- layout="wide",
- initial_sidebar_state="collapsed",
- )
-
- hide_style = """
-
- """
- st.markdown(hide_style, unsafe_allow_html=True)
- HEADER()
-
-
-def HEADER():
- today = date.today()
- st.header("_Simple ML Algorithms explained in Math & Code_")
- st.write(str(today))
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/OutlinePass.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/OutlinePass.js
deleted file mode 100644
index 56dcba11c83b0fe0241bf42695bbc8706dae5f68..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/postprocessing/OutlinePass.js
+++ /dev/null
@@ -1,584 +0,0 @@
-/**
- * @author spidersharma / http://eduperiment.com/
- */
-
-THREE.OutlinePass = function ( resolution, scene, camera, selectedObjects ) {
-
- this.renderScene = scene;
- this.renderCamera = camera;
- this.selectedObjects = selectedObjects !== undefined ? selectedObjects : [];
- this.visibleEdgeColor = new THREE.Color( 1, 1, 1 );
- this.hiddenEdgeColor = new THREE.Color( 0.1, 0.04, 0.02 );
- this.edgeGlow = 0.0;
- this.usePatternTexture = false;
- this.edgeThickness = 1.0;
- this.edgeStrength = 3.0;
- this.downSampleRatio = 2;
- this.pulsePeriod = 0;
-
- THREE.Pass.call( this );
-
- this.resolution = ( resolution !== undefined ) ? new THREE.Vector2( resolution.x, resolution.y ) : new THREE.Vector2( 256, 256 );
-
- var pars = { minFilter: THREE.LinearFilter, magFilter: THREE.LinearFilter, format: THREE.RGBAFormat };
-
- var resx = Math.round( this.resolution.x / this.downSampleRatio );
- var resy = Math.round( this.resolution.y / this.downSampleRatio );
-
- this.maskBufferMaterial = new THREE.MeshBasicMaterial( { color: 0xffffff } );
- this.maskBufferMaterial.side = THREE.DoubleSide;
- this.renderTargetMaskBuffer = new THREE.WebGLRenderTarget( this.resolution.x, this.resolution.y, pars );
- this.renderTargetMaskBuffer.texture.name = "OutlinePass.mask";
- this.renderTargetMaskBuffer.texture.generateMipmaps = false;
-
- this.depthMaterial = new THREE.MeshDepthMaterial();
- this.depthMaterial.side = THREE.DoubleSide;
- this.depthMaterial.depthPacking = THREE.RGBADepthPacking;
- this.depthMaterial.blending = THREE.NoBlending;
-
- this.prepareMaskMaterial = this.getPrepareMaskMaterial();
- this.prepareMaskMaterial.side = THREE.DoubleSide;
- this.prepareMaskMaterial.fragmentShader = replaceDepthToViewZ( this.prepareMaskMaterial.fragmentShader, this.renderCamera );
-
- this.renderTargetDepthBuffer = new THREE.WebGLRenderTarget( this.resolution.x, this.resolution.y, pars );
- this.renderTargetDepthBuffer.texture.name = "OutlinePass.depth";
- this.renderTargetDepthBuffer.texture.generateMipmaps = false;
-
- this.renderTargetMaskDownSampleBuffer = new THREE.WebGLRenderTarget( resx, resy, pars );
- this.renderTargetMaskDownSampleBuffer.texture.name = "OutlinePass.depthDownSample";
- this.renderTargetMaskDownSampleBuffer.texture.generateMipmaps = false;
-
- this.renderTargetBlurBuffer1 = new THREE.WebGLRenderTarget( resx, resy, pars );
- this.renderTargetBlurBuffer1.texture.name = "OutlinePass.blur1";
- this.renderTargetBlurBuffer1.texture.generateMipmaps = false;
- this.renderTargetBlurBuffer2 = new THREE.WebGLRenderTarget( Math.round( resx / 2 ), Math.round( resy / 2 ), pars );
- this.renderTargetBlurBuffer2.texture.name = "OutlinePass.blur2";
- this.renderTargetBlurBuffer2.texture.generateMipmaps = false;
-
- this.edgeDetectionMaterial = this.getEdgeDetectionMaterial();
- this.renderTargetEdgeBuffer1 = new THREE.WebGLRenderTarget( resx, resy, pars );
- this.renderTargetEdgeBuffer1.texture.name = "OutlinePass.edge1";
- this.renderTargetEdgeBuffer1.texture.generateMipmaps = false;
- this.renderTargetEdgeBuffer2 = new THREE.WebGLRenderTarget( Math.round( resx / 2 ), Math.round( resy / 2 ), pars );
- this.renderTargetEdgeBuffer2.texture.name = "OutlinePass.edge2";
- this.renderTargetEdgeBuffer2.texture.generateMipmaps = false;
-
- var MAX_EDGE_THICKNESS = 4;
- var MAX_EDGE_GLOW = 4;
-
- this.separableBlurMaterial1 = this.getSeperableBlurMaterial( MAX_EDGE_THICKNESS );
- this.separableBlurMaterial1.uniforms[ "texSize" ].value = new THREE.Vector2( resx, resy );
- this.separableBlurMaterial1.uniforms[ "kernelRadius" ].value = 1;
- this.separableBlurMaterial2 = this.getSeperableBlurMaterial( MAX_EDGE_GLOW );
- this.separableBlurMaterial2.uniforms[ "texSize" ].value = new THREE.Vector2( Math.round( resx / 2 ), Math.round( resy / 2 ) );
- this.separableBlurMaterial2.uniforms[ "kernelRadius" ].value = MAX_EDGE_GLOW;
-
- // Overlay material
- this.overlayMaterial = this.getOverlayMaterial();
-
- // copy material
- if ( THREE.CopyShader === undefined )
- console.error( "THREE.OutlinePass relies on THREE.CopyShader" );
-
- var copyShader = THREE.CopyShader;
-
- this.copyUniforms = THREE.UniformsUtils.clone( copyShader.uniforms );
- this.copyUniforms[ "opacity" ].value = 1.0;
-
- this.materialCopy = new THREE.ShaderMaterial( {
- uniforms: this.copyUniforms,
- vertexShader: copyShader.vertexShader,
- fragmentShader: copyShader.fragmentShader,
- blending: THREE.NoBlending,
- depthTest: false,
- depthWrite: false,
- transparent: true
- } );
-
- this.enabled = true;
- this.needsSwap = false;
-
- this.oldClearColor = new THREE.Color();
- this.oldClearAlpha = 1;
-
- this.fsQuad = new THREE.Pass.FullScreenQuad( null );
-
- this.tempPulseColor1 = new THREE.Color();
- this.tempPulseColor2 = new THREE.Color();
- this.textureMatrix = new THREE.Matrix4();
-
- function replaceDepthToViewZ( string, camera ) {
-
- var type = camera.isPerspectiveCamera ? 'perspective' : 'orthographic';
-
- return string.replace( /DEPTH_TO_VIEW_Z/g, type + 'DepthToViewZ' );
-
- }
-
-};
-
-THREE.OutlinePass.prototype = Object.assign( Object.create( THREE.Pass.prototype ), {
-
- constructor: THREE.OutlinePass,
-
- dispose: function () {
-
- this.renderTargetMaskBuffer.dispose();
- this.renderTargetDepthBuffer.dispose();
- this.renderTargetMaskDownSampleBuffer.dispose();
- this.renderTargetBlurBuffer1.dispose();
- this.renderTargetBlurBuffer2.dispose();
- this.renderTargetEdgeBuffer1.dispose();
- this.renderTargetEdgeBuffer2.dispose();
-
- },
-
- setSize: function ( width, height ) {
-
- this.renderTargetMaskBuffer.setSize( width, height );
-
- var resx = Math.round( width / this.downSampleRatio );
- var resy = Math.round( height / this.downSampleRatio );
- this.renderTargetMaskDownSampleBuffer.setSize( resx, resy );
- this.renderTargetBlurBuffer1.setSize( resx, resy );
- this.renderTargetEdgeBuffer1.setSize( resx, resy );
- this.separableBlurMaterial1.uniforms[ "texSize" ].value = new THREE.Vector2( resx, resy );
-
- resx = Math.round( resx / 2 );
- resy = Math.round( resy / 2 );
-
- this.renderTargetBlurBuffer2.setSize( resx, resy );
- this.renderTargetEdgeBuffer2.setSize( resx, resy );
-
- this.separableBlurMaterial2.uniforms[ "texSize" ].value = new THREE.Vector2( resx, resy );
-
- },
-
- changeVisibilityOfSelectedObjects: function ( bVisible ) {
-
- function gatherSelectedMeshesCallBack( object ) {
-
- if ( object.isMesh ) {
-
- if ( bVisible ) {
-
- object.visible = object.userData.oldVisible;
- delete object.userData.oldVisible;
-
- } else {
-
- object.userData.oldVisible = object.visible;
- object.visible = bVisible;
-
- }
-
- }
-
- }
-
- for ( var i = 0; i < this.selectedObjects.length; i ++ ) {
-
- var selectedObject = this.selectedObjects[ i ];
- selectedObject.traverse( gatherSelectedMeshesCallBack );
-
- }
-
- },
-
- changeVisibilityOfNonSelectedObjects: function ( bVisible ) {
-
- var selectedMeshes = [];
-
- function gatherSelectedMeshesCallBack( object ) {
-
- if ( object.isMesh ) selectedMeshes.push( object );
-
- }
-
- for ( var i = 0; i < this.selectedObjects.length; i ++ ) {
-
- var selectedObject = this.selectedObjects[ i ];
- selectedObject.traverse( gatherSelectedMeshesCallBack );
-
- }
-
- function VisibilityChangeCallBack( object ) {
-
- if ( object.isMesh || object.isLine || object.isSprite ) {
-
- var bFound = false;
-
- for ( var i = 0; i < selectedMeshes.length; i ++ ) {
-
- var selectedObjectId = selectedMeshes[ i ].id;
-
- if ( selectedObjectId === object.id ) {
-
- bFound = true;
- break;
-
- }
-
- }
-
- if ( ! bFound ) {
-
- var visibility = object.visible;
-
- if ( ! bVisible || object.bVisible ) object.visible = bVisible;
-
- object.bVisible = visibility;
-
- }
-
- }
-
- }
-
- this.renderScene.traverse( VisibilityChangeCallBack );
-
- },
-
- updateTextureMatrix: function () {
-
- this.textureMatrix.set( 0.5, 0.0, 0.0, 0.5,
- 0.0, 0.5, 0.0, 0.5,
- 0.0, 0.0, 0.5, 0.5,
- 0.0, 0.0, 0.0, 1.0 );
- this.textureMatrix.multiply( this.renderCamera.projectionMatrix );
- this.textureMatrix.multiply( this.renderCamera.matrixWorldInverse );
-
- },
-
- render: function ( renderer, writeBuffer, readBuffer, deltaTime, maskActive ) {
-
- if ( this.selectedObjects.length > 0 ) {
-
- this.oldClearColor.copy( renderer.getClearColor() );
- this.oldClearAlpha = renderer.getClearAlpha();
- var oldAutoClear = renderer.autoClear;
-
- renderer.autoClear = false;
-
- if ( maskActive ) renderer.context.disable( renderer.context.STENCIL_TEST );
-
- renderer.setClearColor( 0xffffff, 1 );
-
- // Make selected objects invisible
- this.changeVisibilityOfSelectedObjects( false );
-
- var currentBackground = this.renderScene.background;
- this.renderScene.background = null;
-
- // 1. Draw Non Selected objects in the depth buffer
- this.renderScene.overrideMaterial = this.depthMaterial;
- renderer.setRenderTarget( this.renderTargetDepthBuffer );
- renderer.clear();
- renderer.render( this.renderScene, this.renderCamera );
-
- // Make selected objects visible
- this.changeVisibilityOfSelectedObjects( true );
-
- // Update Texture Matrix for Depth compare
- this.updateTextureMatrix();
-
- // Make non selected objects invisible, and draw only the selected objects, by comparing the depth buffer of non selected objects
- this.changeVisibilityOfNonSelectedObjects( false );
- this.renderScene.overrideMaterial = this.prepareMaskMaterial;
- this.prepareMaskMaterial.uniforms[ "cameraNearFar" ].value = new THREE.Vector2( this.renderCamera.near, this.renderCamera.far );
- this.prepareMaskMaterial.uniforms[ "depthTexture" ].value = this.renderTargetDepthBuffer.texture;
- this.prepareMaskMaterial.uniforms[ "textureMatrix" ].value = this.textureMatrix;
- renderer.setRenderTarget( this.renderTargetMaskBuffer );
- renderer.clear();
- renderer.render( this.renderScene, this.renderCamera );
- this.renderScene.overrideMaterial = null;
- this.changeVisibilityOfNonSelectedObjects( true );
-
- this.renderScene.background = currentBackground;
-
- // 2. Downsample to Half resolution
- this.fsQuad.material = this.materialCopy;
- this.copyUniforms[ "tDiffuse" ].value = this.renderTargetMaskBuffer.texture;
- renderer.setRenderTarget( this.renderTargetMaskDownSampleBuffer );
- renderer.clear();
- this.fsQuad.render( renderer );
-
- this.tempPulseColor1.copy( this.visibleEdgeColor );
- this.tempPulseColor2.copy( this.hiddenEdgeColor );
-
- if ( this.pulsePeriod > 0 ) {
-
- var scalar = ( 1 + 0.25 ) / 2 + Math.cos( performance.now() * 0.01 / this.pulsePeriod ) * ( 1.0 - 0.25 ) / 2;
- this.tempPulseColor1.multiplyScalar( scalar );
- this.tempPulseColor2.multiplyScalar( scalar );
-
- }
-
- // 3. Apply Edge Detection Pass
- this.fsQuad.material = this.edgeDetectionMaterial;
- this.edgeDetectionMaterial.uniforms[ "maskTexture" ].value = this.renderTargetMaskDownSampleBuffer.texture;
- this.edgeDetectionMaterial.uniforms[ "texSize" ].value = new THREE.Vector2( this.renderTargetMaskDownSampleBuffer.width, this.renderTargetMaskDownSampleBuffer.height );
- this.edgeDetectionMaterial.uniforms[ "visibleEdgeColor" ].value = this.tempPulseColor1;
- this.edgeDetectionMaterial.uniforms[ "hiddenEdgeColor" ].value = this.tempPulseColor2;
- renderer.setRenderTarget( this.renderTargetEdgeBuffer1 );
- renderer.clear();
- this.fsQuad.render( renderer );
-
- // 4. Apply Blur on Half res
- this.fsQuad.material = this.separableBlurMaterial1;
- this.separableBlurMaterial1.uniforms[ "colorTexture" ].value = this.renderTargetEdgeBuffer1.texture;
- this.separableBlurMaterial1.uniforms[ "direction" ].value = THREE.OutlinePass.BlurDirectionX;
- this.separableBlurMaterial1.uniforms[ "kernelRadius" ].value = this.edgeThickness;
- renderer.setRenderTarget( this.renderTargetBlurBuffer1 );
- renderer.clear();
- this.fsQuad.render( renderer );
- this.separableBlurMaterial1.uniforms[ "colorTexture" ].value = this.renderTargetBlurBuffer1.texture;
- this.separableBlurMaterial1.uniforms[ "direction" ].value = THREE.OutlinePass.BlurDirectionY;
- renderer.setRenderTarget( this.renderTargetEdgeBuffer1 );
- renderer.clear();
- this.fsQuad.render( renderer );
-
- // Apply Blur on quarter res
- this.fsQuad.material = this.separableBlurMaterial2;
- this.separableBlurMaterial2.uniforms[ "colorTexture" ].value = this.renderTargetEdgeBuffer1.texture;
- this.separableBlurMaterial2.uniforms[ "direction" ].value = THREE.OutlinePass.BlurDirectionX;
- renderer.setRenderTarget( this.renderTargetBlurBuffer2 );
- renderer.clear();
- this.fsQuad.render( renderer );
- this.separableBlurMaterial2.uniforms[ "colorTexture" ].value = this.renderTargetBlurBuffer2.texture;
- this.separableBlurMaterial2.uniforms[ "direction" ].value = THREE.OutlinePass.BlurDirectionY;
- renderer.setRenderTarget( this.renderTargetEdgeBuffer2 );
- renderer.clear();
- this.fsQuad.render( renderer );
-
- // Blend it additively over the input texture
- this.fsQuad.material = this.overlayMaterial;
- this.overlayMaterial.uniforms[ "maskTexture" ].value = this.renderTargetMaskBuffer.texture;
- this.overlayMaterial.uniforms[ "edgeTexture1" ].value = this.renderTargetEdgeBuffer1.texture;
- this.overlayMaterial.uniforms[ "edgeTexture2" ].value = this.renderTargetEdgeBuffer2.texture;
- this.overlayMaterial.uniforms[ "patternTexture" ].value = this.patternTexture;
- this.overlayMaterial.uniforms[ "edgeStrength" ].value = this.edgeStrength;
- this.overlayMaterial.uniforms[ "edgeGlow" ].value = this.edgeGlow;
- this.overlayMaterial.uniforms[ "usePatternTexture" ].value = this.usePatternTexture;
-
-
- if ( maskActive ) renderer.context.enable( renderer.context.STENCIL_TEST );
-
- renderer.setRenderTarget( readBuffer );
- this.fsQuad.render( renderer );
-
- renderer.setClearColor( this.oldClearColor, this.oldClearAlpha );
- renderer.autoClear = oldAutoClear;
-
- }
-
- if ( this.renderToScreen ) {
-
- this.fsQuad.material = this.materialCopy;
- this.copyUniforms[ "tDiffuse" ].value = readBuffer.texture;
- renderer.setRenderTarget( null );
- this.fsQuad.render( renderer );
-
- }
-
- },
-
- getPrepareMaskMaterial: function () {
-
- return new THREE.ShaderMaterial( {
-
- uniforms: {
- "depthTexture": { value: null },
- "cameraNearFar": { value: new THREE.Vector2( 0.5, 0.5 ) },
- "textureMatrix": { value: new THREE.Matrix4() }
- },
-
- vertexShader: [
- 'varying vec4 projTexCoord;',
- 'varying vec4 vPosition;',
- 'uniform mat4 textureMatrix;',
-
- 'void main() {',
-
- ' vPosition = modelViewMatrix * vec4( position, 1.0 );',
- ' vec4 worldPosition = modelMatrix * vec4( position, 1.0 );',
- ' projTexCoord = textureMatrix * worldPosition;',
- ' gl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );',
-
- '}'
- ].join( '\n' ),
-
- fragmentShader: [
- '#include motorbicyclesinhalafilmdownload
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Led Fan Editor Software Download 2021.md b/spaces/bioriAsaeru/text-to-voice/Led Fan Editor Software Download 2021.md
deleted file mode 100644
index 7bbbe5568d051a18b2caa08b392a8c83d9e35e4b..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Led Fan Editor Software Download 2021.md
+++ /dev/null
@@ -1,106 +0,0 @@
-
-LED Fan Editor Software Download: Everything You Need to Know
-
-What is LED Fan Editor Software?
-
-led fan editor software download
-
-How to Download LED Fan Editor Software?
-
-
-
-
-How to Use LED Fan Editor Software?
-
-
-
-
-Conclusion
-
-What are the Benefits of LED Fan Editor Software?
-
-
-
-
-What are the Drawbacks of LED Fan Editor Software?
-
-
-
-What are the Features of LED Fan Editor Software?
-
-
-
-
-What are the Tips for Using LED Fan Editor Software?
-
-
-
-How to Choose the Best LED Fan Editor Software?
-
-
-
-
-What are the Alternatives to LED Fan Editor Software?
-
-
-
-Conclusion
-
-
-
-
\ No newline at end of file
diff --git a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/dnnlib/__init__.py b/spaces/birsardar/stable-diffusion-mat-outpainting-primer/dnnlib/__init__.py
deleted file mode 100644
index 2f08cf36f11f9b0fd94c1b7caeadf69b98375b04..0000000000000000000000000000000000000000
--- a/spaces/birsardar/stable-diffusion-mat-outpainting-primer/dnnlib/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-from .util import EasyDict, make_cache_dir_path
diff --git a/spaces/bobrooos/test/README.md b/spaces/bobrooos/test/README.md
deleted file mode 100644
index 48bc2b9807f508beb93dfb3386b9ec696935ad96..0000000000000000000000000000000000000000
--- a/spaces/bobrooos/test/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Test
-emoji: 💻
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bradley6597/Spell-Bee-Solver/app.py b/spaces/bradley6597/Spell-Bee-Solver/app.py
deleted file mode 100644
index 6e80e8da811b073573744db34920bd03a8b581f8..0000000000000000000000000000000000000000
--- a/spaces/bradley6597/Spell-Bee-Solver/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import gradio as gr
-import pandas as pd
-import requests
-import re
-import json
-from datetime import date
-
-english_dict = pd.read_csv("dictionary.txt",
- header = None,
- sep = ' ',
- names = ['word'])
-english_dict = english_dict.reset_index(drop = True)
-english_dict = english_dict.dropna()
-
-url = 'https://spellbee.org'
-def spell_bee_solver(no_centre, centre):
- full_set = set(no_centre.lower() + centre.lower())
- spell_bee_solver = english_dict[english_dict['word'].str.contains(str(centre.lower()), regex = False)]
- final_words = list()
- for i in range(0, spell_bee_solver.shape[0]):
- words = spell_bee_solver['word'].iloc[i]
- words_set = set(words)
- if len(words_set - full_set) == 0:
- final_words.append(words)
-
- final_word_df = pd.DataFrame(final_words)
- final_word_df.columns = ['word']
- final_word_df['word_length'] = final_word_df['word'].str.len()
- final_word_df = final_word_df[final_word_df['word_length'] > 3]
- final_word_df = final_word_df.sort_values('word_length', ascending = False)
- return(final_word_df)
-
-def get_spellbee_answers(x):
- today = date.today().strftime("%Y-%m-%d")
-
- content = requests.get(url)._content
- content = re.sub(".*window.games = ", "", str(content))
- content = re.sub("(.*?)\\;.*", "\\1", content)
- content = json.loads(content)
-
- valid_words = content[today]['data']['dictionary']
- final_word_df = pd.DataFrame(valid_words, columns = ['word'])
- final_word_df['word_length'] = final_word_df['word'].str.len()
- final_word_df = final_word_df[final_word_df['word_length'] > 3]
- final_word_df = final_word_df.sort_values('word_length', ascending = False)
- return(final_word_df)
-
-with gr.Blocks() as app:
- with gr.Row():
- no_centre = gr.Textbox(label = 'Letters Outside of Centre')
- centre = gr.Textbox(label = 'Centre Letter')
- with gr.Row():
- solve_button = gr.Button(value = 'Solve')
- get_today_answers = gr.Button(value = "Get Today's answers")
- with gr.Row():
- output_df = gr.DataFrame(headers = ['word', 'word_length'])
- solve_button.click(spell_bee_solver, inputs = [no_centre, centre], outputs = [output_df])
- get_today_answers.click(get_spellbee_answers, inputs = [no_centre], outputs = [output_df])
-
-app.launch(debug = True, share = False)
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/README.md b/spaces/brjathu/HMR2.0/README.md
deleted file mode 100644
index 60bbdfb968ba4cf7734bbf1d6582b562cc604f6f..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: HMR2.0
-emoji: 🔥
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.26.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointSup/point_sup/point_utils.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointSup/point_sup/point_utils.py
deleted file mode 100644
index eed876ea9e0127c584c008bd5aab3e16e2c8c66a..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointSup/point_sup/point_utils.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import torch
-
-from detectron2.layers import cat
-
-
-def get_point_coords_from_point_annotation(instances):
- """
- Load point coords and their corresponding labels from point annotation.
-
- Args:
- instances (list[Instances]): A list of N Instances, where N is the number of images
- in the batch. These instances are in 1:1
- correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask,
- ...) associated with each instance are stored in fields.
- Returns:
- point_coords (Tensor): A tensor of shape (N, P, 2) that contains the coordinates of P
- sampled points.
- point_labels (Tensor): A tensor of shape (N, P) that contains the labels of P
- sampled points. `point_labels` takes 3 possible values:
- - 0: the point belongs to background
- - 1: the point belongs to the object
- - -1: the point is ignored during training
- """
- point_coords_list = []
- point_labels_list = []
- for instances_per_image in instances:
- if len(instances_per_image) == 0:
- continue
- point_coords = instances_per_image.gt_point_coords.to(torch.float32)
- point_labels = instances_per_image.gt_point_labels.to(torch.float32).clone()
- proposal_boxes_per_image = instances_per_image.proposal_boxes.tensor
-
- # Convert point coordinate system, ground truth points are in image coord.
- point_coords_wrt_box = get_point_coords_wrt_box(proposal_boxes_per_image, point_coords)
-
- # Ignore points that are outside predicted boxes.
- point_ignores = (
- (point_coords_wrt_box[:, :, 0] < 0)
- | (point_coords_wrt_box[:, :, 0] > 1)
- | (point_coords_wrt_box[:, :, 1] < 0)
- | (point_coords_wrt_box[:, :, 1] > 1)
- )
- point_labels[point_ignores] = -1
-
- point_coords_list.append(point_coords_wrt_box)
- point_labels_list.append(point_labels)
-
- return (
- cat(point_coords_list, dim=0),
- cat(point_labels_list, dim=0),
- )
-
-
-def get_point_coords_wrt_box(boxes_coords, point_coords):
- """
- Convert image-level absolute coordinates to box-normalized [0, 1] x [0, 1] point cooordinates.
- Args:
- boxes_coords (Tensor): A tensor of shape (R, 4) that contains bounding boxes.
- coordinates.
- point_coords (Tensor): A tensor of shape (R, P, 2) that contains
- image-normalized coordinates of P sampled points.
- Returns:
- point_coords_wrt_box (Tensor): A tensor of shape (R, P, 2) that contains
- [0, 1] x [0, 1] box-normalized coordinates of the P sampled points.
- """
- with torch.no_grad():
- point_coords_wrt_box = point_coords.clone()
- point_coords_wrt_box[:, :, 0] -= boxes_coords[:, None, 0]
- point_coords_wrt_box[:, :, 1] -= boxes_coords[:, None, 1]
- point_coords_wrt_box[:, :, 0] = point_coords_wrt_box[:, :, 0] / (
- boxes_coords[:, None, 2] - boxes_coords[:, None, 0]
- )
- point_coords_wrt_box[:, :, 1] = point_coords_wrt_box[:, :, 1] / (
- boxes_coords[:, None, 3] - boxes_coords[:, None, 1]
- )
- return point_coords_wrt_box
diff --git a/spaces/bryanmildort/stockpricepredict/README.md b/spaces/bryanmildort/stockpricepredict/README.md
deleted file mode 100644
index f53d7651101525d39c5d591296c7d84e7f41412b..0000000000000000000000000000000000000000
--- a/spaces/bryanmildort/stockpricepredict/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stockpricepredict
-emoji: 🦀
-colorFrom: purple
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-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/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/IcnsImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/IcnsImagePlugin.py
deleted file mode 100644
index 27cb89f735e2a1883b2b52ee42fd9ba34c5805fb..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/IcnsImagePlugin.py
+++ /dev/null
@@ -1,399 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# macOS icns file decoder, based on icns.py by Bob Ippolito.
-#
-# history:
-# 2004-10-09 fl Turned into a PIL plugin; removed 2.3 dependencies.
-# 2020-04-04 Allow saving on all operating systems.
-#
-# Copyright (c) 2004 by Bob Ippolito.
-# Copyright (c) 2004 by Secret Labs.
-# Copyright (c) 2004 by Fredrik Lundh.
-# Copyright (c) 2014 by Alastair Houghton.
-# Copyright (c) 2020 by Pan Jing.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-import os
-import struct
-import sys
-
-from . import Image, ImageFile, PngImagePlugin, features
-
-enable_jpeg2k = features.check_codec("jpg_2000")
-if enable_jpeg2k:
- from . import Jpeg2KImagePlugin
-
-MAGIC = b"icns"
-HEADERSIZE = 8
-
-
-def nextheader(fobj):
- return struct.unpack(">4sI", fobj.read(HEADERSIZE))
-
-
-def read_32t(fobj, start_length, size):
- # The 128x128 icon seems to have an extra header for some reason.
- (start, length) = start_length
- fobj.seek(start)
- sig = fobj.read(4)
- if sig != b"\x00\x00\x00\x00":
- msg = "Unknown signature, expecting 0x00000000"
- raise SyntaxError(msg)
- return read_32(fobj, (start + 4, length - 4), size)
-
-
-def read_32(fobj, start_length, size):
- """
- Read a 32bit RGB icon resource. Seems to be either uncompressed or
- an RLE packbits-like scheme.
- """
- (start, length) = start_length
- fobj.seek(start)
- pixel_size = (size[0] * size[2], size[1] * size[2])
- sizesq = pixel_size[0] * pixel_size[1]
- if length == sizesq * 3:
- # uncompressed ("RGBRGBGB")
- indata = fobj.read(length)
- im = Image.frombuffer("RGB", pixel_size, indata, "raw", "RGB", 0, 1)
- else:
- # decode image
- im = Image.new("RGB", pixel_size, None)
- for band_ix in range(3):
- data = []
- bytesleft = sizesq
- while bytesleft > 0:
- byte = fobj.read(1)
- if not byte:
- break
- byte = byte[0]
- if byte & 0x80:
- blocksize = byte - 125
- byte = fobj.read(1)
- for i in range(blocksize):
- data.append(byte)
- else:
- blocksize = byte + 1
- data.append(fobj.read(blocksize))
- bytesleft -= blocksize
- if bytesleft <= 0:
- break
- if bytesleft != 0:
- msg = f"Error reading channel [{repr(bytesleft)} left]"
- raise SyntaxError(msg)
- band = Image.frombuffer("L", pixel_size, b"".join(data), "raw", "L", 0, 1)
- im.im.putband(band.im, band_ix)
- return {"RGB": im}
-
-
-def read_mk(fobj, start_length, size):
- # Alpha masks seem to be uncompressed
- start = start_length[0]
- fobj.seek(start)
- pixel_size = (size[0] * size[2], size[1] * size[2])
- sizesq = pixel_size[0] * pixel_size[1]
- band = Image.frombuffer("L", pixel_size, fobj.read(sizesq), "raw", "L", 0, 1)
- return {"A": band}
-
-
-def read_png_or_jpeg2000(fobj, start_length, size):
- (start, length) = start_length
- fobj.seek(start)
- sig = fobj.read(12)
- if sig[:8] == b"\x89PNG\x0d\x0a\x1a\x0a":
- fobj.seek(start)
- im = PngImagePlugin.PngImageFile(fobj)
- Image._decompression_bomb_check(im.size)
- return {"RGBA": im}
- elif (
- sig[:4] == b"\xff\x4f\xff\x51"
- or sig[:4] == b"\x0d\x0a\x87\x0a"
- or sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a"
- ):
- if not enable_jpeg2k:
- msg = (
- "Unsupported icon subimage format (rebuild PIL "
- "with JPEG 2000 support to fix this)"
- )
- raise ValueError(msg)
- # j2k, jpc or j2c
- fobj.seek(start)
- jp2kstream = fobj.read(length)
- f = io.BytesIO(jp2kstream)
- im = Jpeg2KImagePlugin.Jpeg2KImageFile(f)
- Image._decompression_bomb_check(im.size)
- if im.mode != "RGBA":
- im = im.convert("RGBA")
- return {"RGBA": im}
- else:
- msg = "Unsupported icon subimage format"
- raise ValueError(msg)
-
-
-class IcnsFile:
- SIZES = {
- (512, 512, 2): [(b"ic10", read_png_or_jpeg2000)],
- (512, 512, 1): [(b"ic09", read_png_or_jpeg2000)],
- (256, 256, 2): [(b"ic14", read_png_or_jpeg2000)],
- (256, 256, 1): [(b"ic08", read_png_or_jpeg2000)],
- (128, 128, 2): [(b"ic13", read_png_or_jpeg2000)],
- (128, 128, 1): [
- (b"ic07", read_png_or_jpeg2000),
- (b"it32", read_32t),
- (b"t8mk", read_mk),
- ],
- (64, 64, 1): [(b"icp6", read_png_or_jpeg2000)],
- (32, 32, 2): [(b"ic12", read_png_or_jpeg2000)],
- (48, 48, 1): [(b"ih32", read_32), (b"h8mk", read_mk)],
- (32, 32, 1): [
- (b"icp5", read_png_or_jpeg2000),
- (b"il32", read_32),
- (b"l8mk", read_mk),
- ],
- (16, 16, 2): [(b"ic11", read_png_or_jpeg2000)],
- (16, 16, 1): [
- (b"icp4", read_png_or_jpeg2000),
- (b"is32", read_32),
- (b"s8mk", read_mk),
- ],
- }
-
- def __init__(self, fobj):
- """
- fobj is a file-like object as an icns resource
- """
- # signature : (start, length)
- self.dct = dct = {}
- self.fobj = fobj
- sig, filesize = nextheader(fobj)
- if not _accept(sig):
- msg = "not an icns file"
- raise SyntaxError(msg)
- i = HEADERSIZE
- while i < filesize:
- sig, blocksize = nextheader(fobj)
- if blocksize <= 0:
- msg = "invalid block header"
- raise SyntaxError(msg)
- i += HEADERSIZE
- blocksize -= HEADERSIZE
- dct[sig] = (i, blocksize)
- fobj.seek(blocksize, io.SEEK_CUR)
- i += blocksize
-
- def itersizes(self):
- sizes = []
- for size, fmts in self.SIZES.items():
- for fmt, reader in fmts:
- if fmt in self.dct:
- sizes.append(size)
- break
- return sizes
-
- def bestsize(self):
- sizes = self.itersizes()
- if not sizes:
- msg = "No 32bit icon resources found"
- raise SyntaxError(msg)
- return max(sizes)
-
- def dataforsize(self, size):
- """
- Get an icon resource as {channel: array}. Note that
- the arrays are bottom-up like windows bitmaps and will likely
- need to be flipped or transposed in some way.
- """
- dct = {}
- for code, reader in self.SIZES[size]:
- desc = self.dct.get(code)
- if desc is not None:
- dct.update(reader(self.fobj, desc, size))
- return dct
-
- def getimage(self, size=None):
- if size is None:
- size = self.bestsize()
- if len(size) == 2:
- size = (size[0], size[1], 1)
- channels = self.dataforsize(size)
-
- im = channels.get("RGBA", None)
- if im:
- return im
-
- im = channels.get("RGB").copy()
- try:
- im.putalpha(channels["A"])
- except KeyError:
- pass
- return im
-
-
-##
-# Image plugin for Mac OS icons.
-
-
-class IcnsImageFile(ImageFile.ImageFile):
- """
- PIL image support for Mac OS .icns files.
- Chooses the best resolution, but will possibly load
- a different size image if you mutate the size attribute
- before calling 'load'.
-
- The info dictionary has a key 'sizes' that is a list
- of sizes that the icns file has.
- """
-
- format = "ICNS"
- format_description = "Mac OS icns resource"
-
- def _open(self):
- self.icns = IcnsFile(self.fp)
- self.mode = "RGBA"
- self.info["sizes"] = self.icns.itersizes()
- self.best_size = self.icns.bestsize()
- self.size = (
- self.best_size[0] * self.best_size[2],
- self.best_size[1] * self.best_size[2],
- )
-
- @property
- def size(self):
- return self._size
-
- @size.setter
- def size(self, value):
- info_size = value
- if info_size not in self.info["sizes"] and len(info_size) == 2:
- info_size = (info_size[0], info_size[1], 1)
- if (
- info_size not in self.info["sizes"]
- and len(info_size) == 3
- and info_size[2] == 1
- ):
- simple_sizes = [
- (size[0] * size[2], size[1] * size[2]) for size in self.info["sizes"]
- ]
- if value in simple_sizes:
- info_size = self.info["sizes"][simple_sizes.index(value)]
- if info_size not in self.info["sizes"]:
- msg = "This is not one of the allowed sizes of this image"
- raise ValueError(msg)
- self._size = value
-
- def load(self):
- if len(self.size) == 3:
- self.best_size = self.size
- self.size = (
- self.best_size[0] * self.best_size[2],
- self.best_size[1] * self.best_size[2],
- )
-
- px = Image.Image.load(self)
- if self.im is not None and self.im.size == self.size:
- # Already loaded
- return px
- self.load_prepare()
- # This is likely NOT the best way to do it, but whatever.
- im = self.icns.getimage(self.best_size)
-
- # If this is a PNG or JPEG 2000, it won't be loaded yet
- px = im.load()
-
- self.im = im.im
- self.mode = im.mode
- self.size = im.size
-
- return px
-
-
-def _save(im, fp, filename):
- """
- Saves the image as a series of PNG files,
- that are then combined into a .icns file.
- """
- if hasattr(fp, "flush"):
- fp.flush()
-
- sizes = {
- b"ic07": 128,
- b"ic08": 256,
- b"ic09": 512,
- b"ic10": 1024,
- b"ic11": 32,
- b"ic12": 64,
- b"ic13": 256,
- b"ic14": 512,
- }
- provided_images = {im.width: im for im in im.encoderinfo.get("append_images", [])}
- size_streams = {}
- for size in set(sizes.values()):
- image = (
- provided_images[size]
- if size in provided_images
- else im.resize((size, size))
- )
-
- temp = io.BytesIO()
- image.save(temp, "png")
- size_streams[size] = temp.getvalue()
-
- entries = []
- for type, size in sizes.items():
- stream = size_streams[size]
- entries.append(
- {"type": type, "size": HEADERSIZE + len(stream), "stream": stream}
- )
-
- # Header
- fp.write(MAGIC)
- file_length = HEADERSIZE # Header
- file_length += HEADERSIZE + 8 * len(entries) # TOC
- file_length += sum(entry["size"] for entry in entries)
- fp.write(struct.pack(">i", file_length))
-
- # TOC
- fp.write(b"TOC ")
- fp.write(struct.pack(">i", HEADERSIZE + len(entries) * HEADERSIZE))
- for entry in entries:
- fp.write(entry["type"])
- fp.write(struct.pack(">i", entry["size"]))
-
- # Data
- for entry in entries:
- fp.write(entry["type"])
- fp.write(struct.pack(">i", entry["size"]))
- fp.write(entry["stream"])
-
- if hasattr(fp, "flush"):
- fp.flush()
-
-
-def _accept(prefix):
- return prefix[:4] == MAGIC
-
-
-Image.register_open(IcnsImageFile.format, IcnsImageFile, _accept)
-Image.register_extension(IcnsImageFile.format, ".icns")
-
-Image.register_save(IcnsImageFile.format, _save)
-Image.register_mime(IcnsImageFile.format, "image/icns")
-
-if __name__ == "__main__":
- if len(sys.argv) < 2:
- print("Syntax: python3 IcnsImagePlugin.py [file]")
- sys.exit()
-
- with open(sys.argv[1], "rb") as fp:
- imf = IcnsImageFile(fp)
- for size in imf.info["sizes"]:
- imf.size = size
- imf.save("out-%s-%s-%s.png" % size)
- with Image.open(sys.argv[1]) as im:
- im.save("out.png")
- if sys.platform == "windows":
- os.startfile("out.png")
diff --git a/spaces/captainChan/CaptainChan/transforms.py b/spaces/captainChan/CaptainChan/transforms.py
deleted file mode 100644
index 5a7042f3368bc832566d5c22d1e18abe5d8547f5..0000000000000000000000000000000000000000
--- a/spaces/captainChan/CaptainChan/transforms.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import math
-import numbers
-import random
-
-import cv2
-import numpy as np
-from PIL import Image
-from torchvision import transforms
-from torchvision.transforms import Compose
-
-
-def sample_asym(magnitude, size=None):
- return np.random.beta(1, 4, size) * magnitude
-
-def sample_sym(magnitude, size=None):
- return (np.random.beta(4, 4, size=size) - 0.5) * 2 * magnitude
-
-def sample_uniform(low, high, size=None):
- return np.random.uniform(low, high, size=size)
-
-def get_interpolation(type='random'):
- if type == 'random':
- choice = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA]
- interpolation = choice[random.randint(0, len(choice)-1)]
- elif type == 'nearest': interpolation = cv2.INTER_NEAREST
- elif type == 'linear': interpolation = cv2.INTER_LINEAR
- elif type == 'cubic': interpolation = cv2.INTER_CUBIC
- elif type == 'area': interpolation = cv2.INTER_AREA
- else: raise TypeError('Interpolation types only nearest, linear, cubic, area are supported!')
- return interpolation
-
-class CVRandomRotation(object):
- def __init__(self, degrees=15):
- assert isinstance(degrees, numbers.Number), "degree should be a single number."
- assert degrees >= 0, "degree must be positive."
- self.degrees = degrees
-
- @staticmethod
- def get_params(degrees):
- return sample_sym(degrees)
-
- def __call__(self, img):
- angle = self.get_params(self.degrees)
- src_h, src_w = img.shape[:2]
- M = cv2.getRotationMatrix2D(center=(src_w/2, src_h/2), angle=angle, scale=1.0)
- abs_cos, abs_sin = abs(M[0,0]), abs(M[0,1])
- dst_w = int(src_h * abs_sin + src_w * abs_cos)
- dst_h = int(src_h * abs_cos + src_w * abs_sin)
- M[0, 2] += (dst_w - src_w)/2
- M[1, 2] += (dst_h - src_h)/2
-
- flags = get_interpolation()
- return cv2.warpAffine(img, M, (dst_w, dst_h), flags=flags, borderMode=cv2.BORDER_REPLICATE)
-
-class CVRandomAffine(object):
- def __init__(self, degrees, translate=None, scale=None, shear=None):
- assert isinstance(degrees, numbers.Number), "degree should be a single number."
- assert degrees >= 0, "degree must be positive."
- self.degrees = degrees
-
- if translate is not None:
- assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
- "translate should be a list or tuple and it must be of length 2."
- for t in translate:
- if not (0.0 <= t <= 1.0):
- raise ValueError("translation values should be between 0 and 1")
- self.translate = translate
-
- if scale is not None:
- assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
- "scale should be a list or tuple and it must be of length 2."
- for s in scale:
- if s <= 0:
- raise ValueError("scale values should be positive")
- self.scale = scale
-
- if shear is not None:
- if isinstance(shear, numbers.Number):
- if shear < 0:
- raise ValueError("If shear is a single number, it must be positive.")
- self.shear = [shear]
- else:
- assert isinstance(shear, (tuple, list)) and (len(shear) == 2), \
- "shear should be a list or tuple and it must be of length 2."
- self.shear = shear
- else:
- self.shear = shear
-
- def _get_inverse_affine_matrix(self, center, angle, translate, scale, shear):
- # https://github.com/pytorch/vision/blob/v0.4.0/torchvision/transforms/functional.py#L717
- from numpy import sin, cos, tan
-
- if isinstance(shear, numbers.Number):
- shear = [shear, 0]
-
- if not isinstance(shear, (tuple, list)) and len(shear) == 2:
- raise ValueError(
- "Shear should be a single value or a tuple/list containing " +
- "two values. Got {}".format(shear))
-
- rot = math.radians(angle)
- sx, sy = [math.radians(s) for s in shear]
-
- cx, cy = center
- tx, ty = translate
-
- # RSS without scaling
- a = cos(rot - sy) / cos(sy)
- b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
- c = sin(rot - sy) / cos(sy)
- d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)
-
- # Inverted rotation matrix with scale and shear
- # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
- M = [d, -b, 0,
- -c, a, 0]
- M = [x / scale for x in M]
-
- # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
- M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
- M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)
-
- # Apply center translation: C * RSS^-1 * C^-1 * T^-1
- M[2] += cx
- M[5] += cy
- return M
-
- @staticmethod
- def get_params(degrees, translate, scale_ranges, shears, height):
- angle = sample_sym(degrees)
- if translate is not None:
- max_dx = translate[0] * height
- max_dy = translate[1] * height
- translations = (np.round(sample_sym(max_dx)), np.round(sample_sym(max_dy)))
- else:
- translations = (0, 0)
-
- if scale_ranges is not None:
- scale = sample_uniform(scale_ranges[0], scale_ranges[1])
- else:
- scale = 1.0
-
- if shears is not None:
- if len(shears) == 1:
- shear = [sample_sym(shears[0]), 0.]
- elif len(shears) == 2:
- shear = [sample_sym(shears[0]), sample_sym(shears[1])]
- else:
- shear = 0.0
-
- return angle, translations, scale, shear
-
-
- def __call__(self, img):
- src_h, src_w = img.shape[:2]
- angle, translate, scale, shear = self.get_params(
- self.degrees, self.translate, self.scale, self.shear, src_h)
-
- M = self._get_inverse_affine_matrix((src_w/2, src_h/2), angle, (0, 0), scale, shear)
- M = np.array(M).reshape(2,3)
-
- startpoints = [(0, 0), (src_w - 1, 0), (src_w - 1, src_h - 1), (0, src_h - 1)]
- project = lambda x, y, a, b, c: int(a*x + b*y + c)
- endpoints = [(project(x, y, *M[0]), project(x, y, *M[1])) for x, y in startpoints]
-
- rect = cv2.minAreaRect(np.array(endpoints))
- bbox = cv2.boxPoints(rect).astype(dtype=np.int)
- max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
- min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
-
- dst_w = int(max_x - min_x)
- dst_h = int(max_y - min_y)
- M[0, 2] += (dst_w - src_w) / 2
- M[1, 2] += (dst_h - src_h) / 2
-
- # add translate
- dst_w += int(abs(translate[0]))
- dst_h += int(abs(translate[1]))
- if translate[0] < 0: M[0, 2] += abs(translate[0])
- if translate[1] < 0: M[1, 2] += abs(translate[1])
-
- flags = get_interpolation()
- return cv2.warpAffine(img, M, (dst_w , dst_h), flags=flags, borderMode=cv2.BORDER_REPLICATE)
-
-class CVRandomPerspective(object):
- def __init__(self, distortion=0.5):
- self.distortion = distortion
-
- def get_params(self, width, height, distortion):
- offset_h = sample_asym(distortion * height / 2, size=4).astype(dtype=np.int)
- offset_w = sample_asym(distortion * width / 2, size=4).astype(dtype=np.int)
- topleft = ( offset_w[0], offset_h[0])
- topright = (width - 1 - offset_w[1], offset_h[1])
- botright = (width - 1 - offset_w[2], height - 1 - offset_h[2])
- botleft = ( offset_w[3], height - 1 - offset_h[3])
-
- startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1), (0, height - 1)]
- endpoints = [topleft, topright, botright, botleft]
- return np.array(startpoints, dtype=np.float32), np.array(endpoints, dtype=np.float32)
-
- def __call__(self, img):
- height, width = img.shape[:2]
- startpoints, endpoints = self.get_params(width, height, self.distortion)
- M = cv2.getPerspectiveTransform(startpoints, endpoints)
-
- # TODO: more robust way to crop image
- rect = cv2.minAreaRect(endpoints)
- bbox = cv2.boxPoints(rect).astype(dtype=np.int)
- max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
- min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
- min_x, min_y = max(min_x, 0), max(min_y, 0)
-
- flags = get_interpolation()
- img = cv2.warpPerspective(img, M, (max_x, max_y), flags=flags, borderMode=cv2.BORDER_REPLICATE)
- img = img[min_y:, min_x:]
- return img
-
-class CVRescale(object):
-
- def __init__(self, factor=4, base_size=(128, 512)):
- """ Define image scales using gaussian pyramid and rescale image to target scale.
-
- Args:
- factor: the decayed factor from base size, factor=4 keeps target scale by default.
- base_size: base size the build the bottom layer of pyramid
- """
- if isinstance(factor, numbers.Number):
- self.factor = round(sample_uniform(0, factor))
- elif isinstance(factor, (tuple, list)) and len(factor) == 2:
- self.factor = round(sample_uniform(factor[0], factor[1]))
- else:
- raise Exception('factor must be number or list with length 2')
- # assert factor is valid
- self.base_h, self.base_w = base_size[:2]
-
- def __call__(self, img):
- if self.factor == 0: return img
- src_h, src_w = img.shape[:2]
- cur_w, cur_h = self.base_w, self.base_h
- scale_img = cv2.resize(img, (cur_w, cur_h), interpolation=get_interpolation())
- for _ in range(self.factor):
- scale_img = cv2.pyrDown(scale_img)
- scale_img = cv2.resize(scale_img, (src_w, src_h), interpolation=get_interpolation())
- return scale_img
-
-class CVGaussianNoise(object):
- def __init__(self, mean=0, var=20):
- self.mean = mean
- if isinstance(var, numbers.Number):
- self.var = max(int(sample_asym(var)), 1)
- elif isinstance(var, (tuple, list)) and len(var) == 2:
- self.var = int(sample_uniform(var[0], var[1]))
- else:
- raise Exception('degree must be number or list with length 2')
-
- def __call__(self, img):
- noise = np.random.normal(self.mean, self.var**0.5, img.shape)
- img = np.clip(img + noise, 0, 255).astype(np.uint8)
- return img
-
-class CVMotionBlur(object):
- def __init__(self, degrees=12, angle=90):
- if isinstance(degrees, numbers.Number):
- self.degree = max(int(sample_asym(degrees)), 1)
- elif isinstance(degrees, (tuple, list)) and len(degrees) == 2:
- self.degree = int(sample_uniform(degrees[0], degrees[1]))
- else:
- raise Exception('degree must be number or list with length 2')
- self.angle = sample_uniform(-angle, angle)
-
- def __call__(self, img):
- M = cv2.getRotationMatrix2D((self.degree // 2, self.degree // 2), self.angle, 1)
- motion_blur_kernel = np.zeros((self.degree, self.degree))
- motion_blur_kernel[self.degree // 2, :] = 1
- motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (self.degree, self.degree))
- motion_blur_kernel = motion_blur_kernel / self.degree
- img = cv2.filter2D(img, -1, motion_blur_kernel)
- img = np.clip(img, 0, 255).astype(np.uint8)
- return img
-
-class CVGeometry(object):
- def __init__(self, degrees=15, translate=(0.3, 0.3), scale=(0.5, 2.),
- shear=(45, 15), distortion=0.5, p=0.5):
- self.p = p
- type_p = random.random()
- if type_p < 0.33:
- self.transforms = CVRandomRotation(degrees=degrees)
- elif type_p < 0.66:
- self.transforms = CVRandomAffine(degrees=degrees, translate=translate, scale=scale, shear=shear)
- else:
- self.transforms = CVRandomPerspective(distortion=distortion)
-
- def __call__(self, img):
- if random.random() < self.p:
- img = np.array(img)
- return Image.fromarray(self.transforms(img))
- else: return img
-
-class CVDeterioration(object):
- def __init__(self, var, degrees, factor, p=0.5):
- self.p = p
- transforms = []
- if var is not None:
- transforms.append(CVGaussianNoise(var=var))
- if degrees is not None:
- transforms.append(CVMotionBlur(degrees=degrees))
- if factor is not None:
- transforms.append(CVRescale(factor=factor))
-
- random.shuffle(transforms)
- transforms = Compose(transforms)
- self.transforms = transforms
-
- def __call__(self, img):
- if random.random() < self.p:
- img = np.array(img)
- return Image.fromarray(self.transforms(img))
- else: return img
-
-
-class CVColorJitter(object):
- def __init__(self, brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1, p=0.5):
- self.p = p
- self.transforms = transforms.ColorJitter(brightness=brightness, contrast=contrast,
- saturation=saturation, hue=hue)
-
- def __call__(self, img):
- if random.random() < self.p: return self.transforms(img)
- else: return img
diff --git a/spaces/captchaboy/sendmespecs/README.md b/spaces/captchaboy/sendmespecs/README.md
deleted file mode 100644
index c3c307c8ac36ded9a6b1b67b332cd588e1afa997..0000000000000000000000000000000000000000
--- a/spaces/captchaboy/sendmespecs/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sendmespecs
-emoji: 🌖
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/config/test_yacs_config.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/config/test_yacs_config.py
deleted file mode 100644
index 01dd6955f78e2700ffc10ed723ab1c95df0e5a18..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/config/test_yacs_config.py
+++ /dev/null
@@ -1,270 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-
-import os
-import tempfile
-import unittest
-import torch
-from omegaconf import OmegaConf
-
-from detectron2 import model_zoo
-from detectron2.config import configurable, downgrade_config, get_cfg, upgrade_config
-from detectron2.layers import ShapeSpec
-from detectron2.modeling import build_model
-
-_V0_CFG = """
-MODEL:
- RPN_HEAD:
- NAME: "TEST"
-VERSION: 0
-"""
-
-_V1_CFG = """
-MODEL:
- WEIGHT: "/path/to/weight"
-"""
-
-
-class TestConfigVersioning(unittest.TestCase):
- def test_upgrade_downgrade_consistency(self):
- cfg = get_cfg()
- # check that custom is preserved
- cfg.USER_CUSTOM = 1
-
- down = downgrade_config(cfg, to_version=0)
- up = upgrade_config(down)
- self.assertTrue(up == cfg)
-
- def _merge_cfg_str(self, cfg, merge_str):
- f = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False)
- try:
- f.write(merge_str)
- f.close()
- cfg.merge_from_file(f.name)
- finally:
- os.remove(f.name)
- return cfg
-
- def test_auto_upgrade(self):
- cfg = get_cfg()
- latest_ver = cfg.VERSION
- cfg.USER_CUSTOM = 1
-
- self._merge_cfg_str(cfg, _V0_CFG)
-
- self.assertEqual(cfg.MODEL.RPN.HEAD_NAME, "TEST")
- self.assertEqual(cfg.VERSION, latest_ver)
-
- def test_guess_v1(self):
- cfg = get_cfg()
- latest_ver = cfg.VERSION
- self._merge_cfg_str(cfg, _V1_CFG)
- self.assertEqual(cfg.VERSION, latest_ver)
-
-
-class _TestClassA(torch.nn.Module):
- @configurable
- def __init__(self, arg1, arg2, arg3=3):
- super().__init__()
- self.arg1 = arg1
- self.arg2 = arg2
- self.arg3 = arg3
- assert arg1 == 1
- assert arg2 == 2
- assert arg3 == 3
-
- @classmethod
- def from_config(cls, cfg):
- args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
- return args
-
-
-class _TestClassB(_TestClassA):
- @configurable
- def __init__(self, input_shape, arg1, arg2, arg3=3):
- """
- Doc of _TestClassB
- """
- assert input_shape == "shape"
- super().__init__(arg1, arg2, arg3)
-
- @classmethod
- def from_config(cls, cfg, input_shape): # test extra positional arg in from_config
- args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
- args["input_shape"] = input_shape
- return args
-
-
-class _LegacySubClass(_TestClassB):
- # an old subclass written in cfg style
- def __init__(self, cfg, input_shape, arg4=4):
- super().__init__(cfg, input_shape)
- assert self.arg1 == 1
- assert self.arg2 == 2
- assert self.arg3 == 3
-
-
-class _NewSubClassNewInit(_TestClassB):
- # test new subclass with a new __init__
- @configurable
- def __init__(self, input_shape, arg4=4, **kwargs):
- super().__init__(input_shape, **kwargs)
- assert self.arg1 == 1
- assert self.arg2 == 2
- assert self.arg3 == 3
-
-
-class _LegacySubClassNotCfg(_TestClassB):
- # an old subclass written in cfg style, but argument is not called "cfg"
- def __init__(self, config, input_shape):
- super().__init__(config, input_shape)
- assert self.arg1 == 1
- assert self.arg2 == 2
- assert self.arg3 == 3
-
-
-class _TestClassC(_TestClassB):
- @classmethod
- def from_config(cls, cfg, input_shape, **kwargs): # test extra kwarg overwrite
- args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
- args["input_shape"] = input_shape
- args.update(kwargs)
- return args
-
-
-class _TestClassD(_TestClassA):
- @configurable
- def __init__(self, input_shape: ShapeSpec, arg1: int, arg2, arg3=3):
- assert input_shape == "shape"
- super().__init__(arg1, arg2, arg3)
-
- # _TestClassA.from_config does not have input_shape args.
- # Test whether input_shape will be forwarded to __init__
-
-
-@configurable(from_config=lambda cfg, arg2: {"arg1": cfg.ARG1, "arg2": arg2, "arg3": cfg.ARG3})
-def _test_func(arg1, arg2=2, arg3=3, arg4=4):
- return arg1, arg2, arg3, arg4
-
-
-class TestConfigurable(unittest.TestCase):
- def testInitWithArgs(self):
- _ = _TestClassA(arg1=1, arg2=2, arg3=3)
- _ = _TestClassB("shape", arg1=1, arg2=2)
- _ = _TestClassC("shape", arg1=1, arg2=2)
- _ = _TestClassD("shape", arg1=1, arg2=2, arg3=3)
-
- def testPatchedAttr(self):
- self.assertTrue("Doc" in _TestClassB.__init__.__doc__)
- self.assertEqual(_TestClassD.__init__.__annotations__["arg1"], int)
-
- def testInitWithCfg(self):
- cfg = get_cfg()
- cfg.ARG1 = 1
- cfg.ARG2 = 2
- cfg.ARG3 = 3
- _ = _TestClassA(cfg)
- _ = _TestClassB(cfg, input_shape="shape")
- _ = _TestClassC(cfg, input_shape="shape")
- _ = _TestClassD(cfg, input_shape="shape")
- _ = _LegacySubClass(cfg, input_shape="shape")
- _ = _NewSubClassNewInit(cfg, input_shape="shape")
- _ = _LegacySubClassNotCfg(cfg, input_shape="shape")
- with self.assertRaises(TypeError):
- # disallow forwarding positional args to __init__ since it's prone to errors
- _ = _TestClassD(cfg, "shape")
-
- # call with kwargs instead
- _ = _TestClassA(cfg=cfg)
- _ = _TestClassB(cfg=cfg, input_shape="shape")
- _ = _TestClassC(cfg=cfg, input_shape="shape")
- _ = _TestClassD(cfg=cfg, input_shape="shape")
- _ = _LegacySubClass(cfg=cfg, input_shape="shape")
- _ = _NewSubClassNewInit(cfg=cfg, input_shape="shape")
- _ = _LegacySubClassNotCfg(config=cfg, input_shape="shape")
-
- def testInitWithCfgOverwrite(self):
- cfg = get_cfg()
- cfg.ARG1 = 1
- cfg.ARG2 = 999 # wrong config
- with self.assertRaises(AssertionError):
- _ = _TestClassA(cfg, arg3=3)
-
- # overwrite arg2 with correct config later:
- _ = _TestClassA(cfg, arg2=2, arg3=3)
- _ = _TestClassB(cfg, input_shape="shape", arg2=2, arg3=3)
- _ = _TestClassC(cfg, input_shape="shape", arg2=2, arg3=3)
- _ = _TestClassD(cfg, input_shape="shape", arg2=2, arg3=3)
-
- # call with kwargs cfg=cfg instead
- _ = _TestClassA(cfg=cfg, arg2=2, arg3=3)
- _ = _TestClassB(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
- _ = _TestClassC(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
- _ = _TestClassD(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
-
- def testInitWithCfgWrongArgs(self):
- cfg = get_cfg()
- cfg.ARG1 = 1
- cfg.ARG2 = 2
- with self.assertRaises(TypeError):
- _ = _TestClassB(cfg, "shape", not_exist=1)
- with self.assertRaises(TypeError):
- _ = _TestClassC(cfg, "shape", not_exist=1)
- with self.assertRaises(TypeError):
- _ = _TestClassD(cfg, "shape", not_exist=1)
-
- def testBadClass(self):
- class _BadClass1:
- @configurable
- def __init__(self, a=1, b=2):
- pass
-
- class _BadClass2:
- @configurable
- def __init__(self, a=1, b=2):
- pass
-
- def from_config(self, cfg): # noqa
- pass
-
- class _BadClass3:
- @configurable
- def __init__(self, a=1, b=2):
- pass
-
- # bad name: must be cfg
- @classmethod
- def from_config(cls, config): # noqa
- pass
-
- with self.assertRaises(AttributeError):
- _ = _BadClass1(a=1)
-
- with self.assertRaises(TypeError):
- _ = _BadClass2(a=1)
-
- with self.assertRaises(TypeError):
- _ = _BadClass3(get_cfg())
-
- def testFuncWithCfg(self):
- cfg = get_cfg()
- cfg.ARG1 = 10
- cfg.ARG3 = 30
-
- self.assertEqual(_test_func(1), (1, 2, 3, 4))
- with self.assertRaises(TypeError):
- _test_func(cfg)
- self.assertEqual(_test_func(cfg, arg2=2), (10, 2, 30, 4))
- self.assertEqual(_test_func(cfg, arg1=100, arg2=20), (100, 20, 30, 4))
- self.assertEqual(_test_func(cfg, arg1=100, arg2=20, arg4=40), (100, 20, 30, 40))
-
- self.assertTrue(callable(_test_func.from_config))
-
- def testOmegaConf(self):
- cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
- cfg = OmegaConf.create(cfg.dump())
- if not torch.cuda.is_available():
- cfg.MODEL.DEVICE = "cpu"
- # test that a model can be built with omegaconf config as well
- build_model(cfg)
diff --git a/spaces/chansung/LLM-As-Chatbot/models/kullm.py b/spaces/chansung/LLM-As-Chatbot/models/kullm.py
deleted file mode 100644
index 5152b2d7627b6ae4e5fd2bfd680af23d48e18c8c..0000000000000000000000000000000000000000
--- a/spaces/chansung/LLM-As-Chatbot/models/kullm.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import torch
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from optimum.bettertransformer import BetterTransformer
-
-def load_model(
- base,
- finetuned,
- mode_cpu,
- mode_mps,
- mode_full_gpu,
- mode_8bit,
- mode_4bit,
- force_download_ckpt
-):
- tokenizer = AutoTokenizer.from_pretrained(base)
-
- if mode_cpu:
- print("cpu mode")
- model = AutoModelForCausalLM.from_pretrained(
- base,
- device_map={"": "cpu"},
- use_safetensors=False
- )
-
- elif mode_mps:
- print("mps mode")
- model = AutoModelForCausalLM.from_pretrained(
- base,
- device_map={"": "mps"},
- torch_dtype=torch.float16,
- use_safetensors=False
- )
-
- else:
- print("gpu mode")
- print(f"8bit = {mode_8bit}, 4bit = {mode_4bit}")
- model = AutoModelForCausalLM.from_pretrained(
- base,
- load_in_8bit=mode_8bit,
- load_in_4bit=mode_4bit,
- torch_dtype=torch.float16,
- device_map="auto",
- use_safetensors=False
- )
-
- if not mode_8bit and not mode_4bit:
- model.half()
-
- # model = BetterTransformer.transform(model)
- return model, tokenizer
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/exps/default/yolov3.py b/spaces/chendl/compositional_test/multimodal/YOLOX/exps/default/yolov3.py
deleted file mode 100644
index c747f8ae9f42549a1dbd7f03d8ee80e235d6467a..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/exps/default/yolov3.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-import torch.nn as nn
-
-from yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
- def __init__(self):
- super(Exp, self).__init__()
- self.depth = 1.0
- self.width = 1.0
- self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
-
- def get_model(self, sublinear=False):
- def init_yolo(M):
- for m in M.modules():
- if isinstance(m, nn.BatchNorm2d):
- m.eps = 1e-3
- m.momentum = 0.03
- if "model" not in self.__dict__:
- from yolox.models import YOLOX, YOLOFPN, YOLOXHead
- backbone = YOLOFPN()
- head = YOLOXHead(self.num_classes, self.width, in_channels=[128, 256, 512], act="lrelu")
- self.model = YOLOX(backbone, head)
- self.model.apply(init_yolo)
- self.model.head.initialize_biases(1e-2)
-
- return self.model
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/README.md b/spaces/chendl/compositional_test/transformers/examples/legacy/README.md
deleted file mode 100644
index eaf64f624637778d9b07fe3e034c30ca0acb70e9..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-
-# Legacy examples
-
-This folder contains examples which are not actively maintained (mostly contributed by the community).
-
-Using these examples together with a recent version of the library usually requires to make small (sometimes big) adaptations to get the scripts working.
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/movement-pruning/emmental/__init__.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/movement-pruning/emmental/__init__.py
deleted file mode 100644
index 6646667ea883781c3bd6b9cff0267b68ee1478e4..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/movement-pruning/emmental/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from .configuration_bert_masked import MaskedBertConfig
-from .modeling_bert_masked import (
- MaskedBertForMultipleChoice,
- MaskedBertForQuestionAnswering,
- MaskedBertForSequenceClassification,
- MaskedBertForTokenClassification,
- MaskedBertModel,
-)
-from .modules import *
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/dictTools.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/dictTools.py
deleted file mode 100644
index 259613b27048c458980986167d429847d270691f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/dictTools.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Misc dict tools."""
-
-
-__all__ = ["hashdict"]
-
-# https://stackoverflow.com/questions/1151658/python-hashable-dicts
-class hashdict(dict):
- """
- hashable dict implementation, suitable for use as a key into
- other dicts.
-
- >>> h1 = hashdict({"apples": 1, "bananas":2})
- >>> h2 = hashdict({"bananas": 3, "mangoes": 5})
- >>> h1+h2
- hashdict(apples=1, bananas=3, mangoes=5)
- >>> d1 = {}
- >>> d1[h1] = "salad"
- >>> d1[h1]
- 'salad'
- >>> d1[h2]
- Traceback (most recent call last):
- ...
- KeyError: hashdict(bananas=3, mangoes=5)
-
- based on answers from
- http://stackoverflow.com/questions/1151658/python-hashable-dicts
-
- """
-
- def __key(self):
- return tuple(sorted(self.items()))
-
- def __repr__(self):
- return "{0}({1})".format(
- self.__class__.__name__,
- ", ".join("{0}={1}".format(str(i[0]), repr(i[1])) for i in self.__key()),
- )
-
- def __hash__(self):
- return hash(self.__key())
-
- def __setitem__(self, key, value):
- raise TypeError(
- "{0} does not support item assignment".format(self.__class__.__name__)
- )
-
- def __delitem__(self, key):
- raise TypeError(
- "{0} does not support item assignment".format(self.__class__.__name__)
- )
-
- def clear(self):
- raise TypeError(
- "{0} does not support item assignment".format(self.__class__.__name__)
- )
-
- def pop(self, *args, **kwargs):
- raise TypeError(
- "{0} does not support item assignment".format(self.__class__.__name__)
- )
-
- def popitem(self, *args, **kwargs):
- raise TypeError(
- "{0} does not support item assignment".format(self.__class__.__name__)
- )
-
- def setdefault(self, *args, **kwargs):
- raise TypeError(
- "{0} does not support item assignment".format(self.__class__.__name__)
- )
-
- def update(self, *args, **kwargs):
- raise TypeError(
- "{0} does not support item assignment".format(self.__class__.__name__)
- )
-
- # update is not ok because it mutates the object
- # __add__ is ok because it creates a new object
- # while the new object is under construction, it's ok to mutate it
- def __add__(self, right):
- result = hashdict(self)
- dict.update(result, right)
- return result
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/t2CharStringPen.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/t2CharStringPen.py
deleted file mode 100644
index 41ab0f92f2b683ac2dc87ca1b16f54047d0fef81..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/pens/t2CharStringPen.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) 2009 Type Supply LLC
-# Author: Tal Leming
-
-from fontTools.misc.roundTools import otRound, roundFunc
-from fontTools.misc.psCharStrings import T2CharString
-from fontTools.pens.basePen import BasePen
-from fontTools.cffLib.specializer import specializeCommands, commandsToProgram
-
-
-class T2CharStringPen(BasePen):
- """Pen to draw Type 2 CharStrings.
-
- The 'roundTolerance' argument controls the rounding of point coordinates.
- It is defined as the maximum absolute difference between the original
- float and the rounded integer value.
- The default tolerance of 0.5 means that all floats are rounded to integer;
- a value of 0 disables rounding; values in between will only round floats
- which are close to their integral part within the tolerated range.
- """
-
- def __init__(self, width, glyphSet, roundTolerance=0.5, CFF2=False):
- super(T2CharStringPen, self).__init__(glyphSet)
- self.round = roundFunc(roundTolerance)
- self._CFF2 = CFF2
- self._width = width
- self._commands = []
- self._p0 = (0, 0)
-
- def _p(self, pt):
- p0 = self._p0
- pt = self._p0 = (self.round(pt[0]), self.round(pt[1]))
- return [pt[0] - p0[0], pt[1] - p0[1]]
-
- def _moveTo(self, pt):
- self._commands.append(("rmoveto", self._p(pt)))
-
- def _lineTo(self, pt):
- self._commands.append(("rlineto", self._p(pt)))
-
- def _curveToOne(self, pt1, pt2, pt3):
- _p = self._p
- self._commands.append(("rrcurveto", _p(pt1) + _p(pt2) + _p(pt3)))
-
- def _closePath(self):
- pass
-
- def _endPath(self):
- pass
-
- def getCharString(self, private=None, globalSubrs=None, optimize=True):
- commands = self._commands
- if optimize:
- maxstack = 48 if not self._CFF2 else 513
- commands = specializeCommands(
- commands, generalizeFirst=False, maxstack=maxstack
- )
- program = commandsToProgram(commands)
- if self._width is not None:
- assert (
- not self._CFF2
- ), "CFF2 does not allow encoding glyph width in CharString."
- program.insert(0, otRound(self._width))
- if not self._CFF2:
- program.append("endchar")
- charString = T2CharString(
- program=program, private=private, globalSubrs=globalSubrs
- )
- return charString
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/varLib/builder.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/varLib/builder.py
deleted file mode 100644
index 94cc5bf063b1dc67ff58bdb7f2bd3d642bee4ce4..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/varLib/builder.py
+++ /dev/null
@@ -1,157 +0,0 @@
-from fontTools import ttLib
-from fontTools.ttLib.tables import otTables as ot
-
-# VariationStore
-
-
-def buildVarRegionAxis(axisSupport):
- self = ot.VarRegionAxis()
- self.StartCoord, self.PeakCoord, self.EndCoord = [float(v) for v in axisSupport]
- return self
-
-
-def buildVarRegion(support, axisTags):
- assert all(tag in axisTags for tag in support.keys()), (
- "Unknown axis tag found.",
- support,
- axisTags,
- )
- self = ot.VarRegion()
- self.VarRegionAxis = []
- for tag in axisTags:
- self.VarRegionAxis.append(buildVarRegionAxis(support.get(tag, (0, 0, 0))))
- return self
-
-
-def buildVarRegionList(supports, axisTags):
- self = ot.VarRegionList()
- self.RegionAxisCount = len(axisTags)
- self.Region = []
- for support in supports:
- self.Region.append(buildVarRegion(support, axisTags))
- self.RegionCount = len(self.Region)
- return self
-
-
-def _reorderItem(lst, mapping):
- return [lst[i] for i in mapping]
-
-
-def VarData_calculateNumShorts(self, optimize=False):
- count = self.VarRegionCount
- items = self.Item
- bit_lengths = [0] * count
- for item in items:
- # The "+ (i < -1)" magic is to handle two's-compliment.
- # That is, we want to get back 7 for -128, whereas
- # bit_length() returns 8. Similarly for -65536.
- # The reason "i < -1" is used instead of "i < 0" is that
- # the latter would make it return 0 for "-1" instead of 1.
- bl = [(i + (i < -1)).bit_length() for i in item]
- bit_lengths = [max(*pair) for pair in zip(bl, bit_lengths)]
- # The addition of 8, instead of seven, is to account for the sign bit.
- # This "((b + 8) >> 3) if b else 0" when combined with the above
- # "(i + (i < -1)).bit_length()" is a faster way to compute byte-lengths
- # conforming to:
- #
- # byte_length = (0 if i == 0 else
- # 1 if -128 <= i < 128 else
- # 2 if -65536 <= i < 65536 else
- # ...)
- byte_lengths = [((b + 8) >> 3) if b else 0 for b in bit_lengths]
-
- # https://github.com/fonttools/fonttools/issues/2279
- longWords = any(b > 2 for b in byte_lengths)
-
- if optimize:
- # Reorder columns such that wider columns come before narrower columns
- mapping = []
- mapping.extend(i for i, b in enumerate(byte_lengths) if b > 2)
- mapping.extend(i for i, b in enumerate(byte_lengths) if b == 2)
- mapping.extend(i for i, b in enumerate(byte_lengths) if b == 1)
-
- byte_lengths = _reorderItem(byte_lengths, mapping)
- self.VarRegionIndex = _reorderItem(self.VarRegionIndex, mapping)
- self.VarRegionCount = len(self.VarRegionIndex)
- for i in range(len(items)):
- items[i] = _reorderItem(items[i], mapping)
-
- if longWords:
- self.NumShorts = (
- max((i for i, b in enumerate(byte_lengths) if b > 2), default=-1) + 1
- )
- self.NumShorts |= 0x8000
- else:
- self.NumShorts = (
- max((i for i, b in enumerate(byte_lengths) if b > 1), default=-1) + 1
- )
-
- self.VarRegionCount = len(self.VarRegionIndex)
- return self
-
-
-ot.VarData.calculateNumShorts = VarData_calculateNumShorts
-
-
-def VarData_CalculateNumShorts(self, optimize=True):
- """Deprecated name for VarData_calculateNumShorts() which
- defaults to optimize=True. Use varData.calculateNumShorts()
- or varData.optimize()."""
- return VarData_calculateNumShorts(self, optimize=optimize)
-
-
-def VarData_optimize(self):
- return VarData_calculateNumShorts(self, optimize=True)
-
-
-ot.VarData.optimize = VarData_optimize
-
-
-def buildVarData(varRegionIndices, items, optimize=True):
- self = ot.VarData()
- self.VarRegionIndex = list(varRegionIndices)
- regionCount = self.VarRegionCount = len(self.VarRegionIndex)
- records = self.Item = []
- if items:
- for item in items:
- assert len(item) == regionCount
- records.append(list(item))
- self.ItemCount = len(self.Item)
- self.calculateNumShorts(optimize=optimize)
- return self
-
-
-def buildVarStore(varRegionList, varDataList):
- self = ot.VarStore()
- self.Format = 1
- self.VarRegionList = varRegionList
- self.VarData = list(varDataList)
- self.VarDataCount = len(self.VarData)
- return self
-
-
-# Variation helpers
-
-
-def buildVarIdxMap(varIdxes, glyphOrder):
- self = ot.VarIdxMap()
- self.mapping = {g: v for g, v in zip(glyphOrder, varIdxes)}
- return self
-
-
-def buildDeltaSetIndexMap(varIdxes):
- mapping = list(varIdxes)
- if all(i == v for i, v in enumerate(mapping)):
- return None
- self = ot.DeltaSetIndexMap()
- self.mapping = mapping
- self.Format = 1 if len(mapping) > 0xFFFF else 0
- return self
-
-
-def buildVarDevTable(varIdx):
- self = ot.Device()
- self.DeltaFormat = 0x8000
- self.StartSize = varIdx >> 16
- self.EndSize = varIdx & 0xFFFF
- return self
diff --git a/spaces/cihyFjudo/fairness-paper-search/AutoData 3.40 crack and full version download - Pastebin.com[3].md b/spaces/cihyFjudo/fairness-paper-search/AutoData 3.40 crack and full version download - Pastebin.com[3].md
deleted file mode 100644
index 42663822e47657ce7d4ad49cae3a730c06f5ebd1..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/AutoData 3.40 crack and full version download - Pastebin.com[3].md
+++ /dev/null
@@ -1,6 +0,0 @@
-Autodata 3.40 ita download gratis 1184
-
- aaccfb2cb3
-
-
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Explore Hindi Superhit Movies Online Free A Treasure Trove of Quality Entertainment.md b/spaces/cihyFjudo/fairness-paper-search/Explore Hindi Superhit Movies Online Free A Treasure Trove of Quality Entertainment.md
deleted file mode 100644
index 17545ab126ca251e061d64d83c429d2a4991628c..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Explore Hindi Superhit Movies Online Free A Treasure Trove of Quality Entertainment.md
+++ /dev/null
@@ -1,32 +0,0 @@
-
-hindi superhit movies online free
-
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Mano Solo-La Marmaille Nue Full Album Zip VERIFIED.md b/spaces/cihyFjudo/fairness-paper-search/Mano Solo-La Marmaille Nue Full Album Zip VERIFIED.md
deleted file mode 100644
index 127db860fe9f0310256a0b2aec4992d43c382536..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Mano Solo-La Marmaille Nue Full Album Zip VERIFIED.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Mano Solo-La Marmaille Nue full album zip
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Structural Geology by Haakon Fossen pdf Download Free PDF of the 2nd Edition with E-learning Modules and Exercises.md b/spaces/cihyFjudo/fairness-paper-search/Structural Geology by Haakon Fossen pdf Download Free PDF of the 2nd Edition with E-learning Modules and Exercises.md
deleted file mode 100644
index c629d9dbf3aaaf0862fcfc6da794d0152d8fb337..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Structural Geology by Haakon Fossen pdf Download Free PDF of the 2nd Edition with E-learning Modules and Exercises.md
+++ /dev/null
@@ -1,6 +0,0 @@
-StructuralGeologyByHaakonFossenPdf
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/BmpImagePlugin.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/BmpImagePlugin.py
deleted file mode 100644
index 5bda0a5b05d8b6a6a0ccaa91da3475e34c9b1cf3..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/BmpImagePlugin.py
+++ /dev/null
@@ -1,471 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# BMP file handler
-#
-# Windows (and OS/2) native bitmap storage format.
-#
-# history:
-# 1995-09-01 fl Created
-# 1996-04-30 fl Added save
-# 1997-08-27 fl Fixed save of 1-bit images
-# 1998-03-06 fl Load P images as L where possible
-# 1998-07-03 fl Load P images as 1 where possible
-# 1998-12-29 fl Handle small palettes
-# 2002-12-30 fl Fixed load of 1-bit palette images
-# 2003-04-21 fl Fixed load of 1-bit monochrome images
-# 2003-04-23 fl Added limited support for BI_BITFIELDS compression
-#
-# Copyright (c) 1997-2003 by Secret Labs AB
-# Copyright (c) 1995-2003 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import os
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i16le as i16
-from ._binary import i32le as i32
-from ._binary import o8
-from ._binary import o16le as o16
-from ._binary import o32le as o32
-
-#
-# --------------------------------------------------------------------
-# Read BMP file
-
-BIT2MODE = {
- # bits => mode, rawmode
- 1: ("P", "P;1"),
- 4: ("P", "P;4"),
- 8: ("P", "P"),
- 16: ("RGB", "BGR;15"),
- 24: ("RGB", "BGR"),
- 32: ("RGB", "BGRX"),
-}
-
-
-def _accept(prefix):
- return prefix[:2] == b"BM"
-
-
-def _dib_accept(prefix):
- return i32(prefix) in [12, 40, 64, 108, 124]
-
-
-# =============================================================================
-# Image plugin for the Windows BMP format.
-# =============================================================================
-class BmpImageFile(ImageFile.ImageFile):
- """Image plugin for the Windows Bitmap format (BMP)"""
-
- # ------------------------------------------------------------- Description
- format_description = "Windows Bitmap"
- format = "BMP"
-
- # -------------------------------------------------- BMP Compression values
- COMPRESSIONS = {"RAW": 0, "RLE8": 1, "RLE4": 2, "BITFIELDS": 3, "JPEG": 4, "PNG": 5}
- for k, v in COMPRESSIONS.items():
- vars()[k] = v
-
- def _bitmap(self, header=0, offset=0):
- """Read relevant info about the BMP"""
- read, seek = self.fp.read, self.fp.seek
- if header:
- seek(header)
- # read bmp header size @offset 14 (this is part of the header size)
- file_info = {"header_size": i32(read(4)), "direction": -1}
-
- # -------------------- If requested, read header at a specific position
- # read the rest of the bmp header, without its size
- header_data = ImageFile._safe_read(self.fp, file_info["header_size"] - 4)
-
- # -------------------------------------------------- IBM OS/2 Bitmap v1
- # ----- This format has different offsets because of width/height types
- if file_info["header_size"] == 12:
- file_info["width"] = i16(header_data, 0)
- file_info["height"] = i16(header_data, 2)
- file_info["planes"] = i16(header_data, 4)
- file_info["bits"] = i16(header_data, 6)
- file_info["compression"] = self.RAW
- file_info["palette_padding"] = 3
-
- # --------------------------------------------- Windows Bitmap v2 to v5
- # v3, OS/2 v2, v4, v5
- elif file_info["header_size"] in (40, 64, 108, 124):
- file_info["y_flip"] = header_data[7] == 0xFF
- file_info["direction"] = 1 if file_info["y_flip"] else -1
- file_info["width"] = i32(header_data, 0)
- file_info["height"] = (
- i32(header_data, 4)
- if not file_info["y_flip"]
- else 2**32 - i32(header_data, 4)
- )
- file_info["planes"] = i16(header_data, 8)
- file_info["bits"] = i16(header_data, 10)
- file_info["compression"] = i32(header_data, 12)
- # byte size of pixel data
- file_info["data_size"] = i32(header_data, 16)
- file_info["pixels_per_meter"] = (
- i32(header_data, 20),
- i32(header_data, 24),
- )
- file_info["colors"] = i32(header_data, 28)
- file_info["palette_padding"] = 4
- self.info["dpi"] = tuple(x / 39.3701 for x in file_info["pixels_per_meter"])
- if file_info["compression"] == self.BITFIELDS:
- if len(header_data) >= 52:
- for idx, mask in enumerate(
- ["r_mask", "g_mask", "b_mask", "a_mask"]
- ):
- file_info[mask] = i32(header_data, 36 + idx * 4)
- else:
- # 40 byte headers only have the three components in the
- # bitfields masks, ref:
- # https://msdn.microsoft.com/en-us/library/windows/desktop/dd183376(v=vs.85).aspx
- # See also
- # https://github.com/python-pillow/Pillow/issues/1293
- # There is a 4th component in the RGBQuad, in the alpha
- # location, but it is listed as a reserved component,
- # and it is not generally an alpha channel
- file_info["a_mask"] = 0x0
- for mask in ["r_mask", "g_mask", "b_mask"]:
- file_info[mask] = i32(read(4))
- file_info["rgb_mask"] = (
- file_info["r_mask"],
- file_info["g_mask"],
- file_info["b_mask"],
- )
- file_info["rgba_mask"] = (
- file_info["r_mask"],
- file_info["g_mask"],
- file_info["b_mask"],
- file_info["a_mask"],
- )
- else:
- msg = f"Unsupported BMP header type ({file_info['header_size']})"
- raise OSError(msg)
-
- # ------------------ Special case : header is reported 40, which
- # ---------------------- is shorter than real size for bpp >= 16
- self._size = file_info["width"], file_info["height"]
-
- # ------- If color count was not found in the header, compute from bits
- file_info["colors"] = (
- file_info["colors"]
- if file_info.get("colors", 0)
- else (1 << file_info["bits"])
- )
- if offset == 14 + file_info["header_size"] and file_info["bits"] <= 8:
- offset += 4 * file_info["colors"]
-
- # ---------------------- Check bit depth for unusual unsupported values
- self.mode, raw_mode = BIT2MODE.get(file_info["bits"], (None, None))
- if self.mode is None:
- msg = f"Unsupported BMP pixel depth ({file_info['bits']})"
- raise OSError(msg)
-
- # ---------------- Process BMP with Bitfields compression (not palette)
- decoder_name = "raw"
- if file_info["compression"] == self.BITFIELDS:
- SUPPORTED = {
- 32: [
- (0xFF0000, 0xFF00, 0xFF, 0x0),
- (0xFF000000, 0xFF0000, 0xFF00, 0x0),
- (0xFF000000, 0xFF0000, 0xFF00, 0xFF),
- (0xFF, 0xFF00, 0xFF0000, 0xFF000000),
- (0xFF0000, 0xFF00, 0xFF, 0xFF000000),
- (0x0, 0x0, 0x0, 0x0),
- ],
- 24: [(0xFF0000, 0xFF00, 0xFF)],
- 16: [(0xF800, 0x7E0, 0x1F), (0x7C00, 0x3E0, 0x1F)],
- }
- MASK_MODES = {
- (32, (0xFF0000, 0xFF00, 0xFF, 0x0)): "BGRX",
- (32, (0xFF000000, 0xFF0000, 0xFF00, 0x0)): "XBGR",
- (32, (0xFF000000, 0xFF0000, 0xFF00, 0xFF)): "ABGR",
- (32, (0xFF, 0xFF00, 0xFF0000, 0xFF000000)): "RGBA",
- (32, (0xFF0000, 0xFF00, 0xFF, 0xFF000000)): "BGRA",
- (32, (0x0, 0x0, 0x0, 0x0)): "BGRA",
- (24, (0xFF0000, 0xFF00, 0xFF)): "BGR",
- (16, (0xF800, 0x7E0, 0x1F)): "BGR;16",
- (16, (0x7C00, 0x3E0, 0x1F)): "BGR;15",
- }
- if file_info["bits"] in SUPPORTED:
- if (
- file_info["bits"] == 32
- and file_info["rgba_mask"] in SUPPORTED[file_info["bits"]]
- ):
- raw_mode = MASK_MODES[(file_info["bits"], file_info["rgba_mask"])]
- self.mode = "RGBA" if "A" in raw_mode else self.mode
- elif (
- file_info["bits"] in (24, 16)
- and file_info["rgb_mask"] in SUPPORTED[file_info["bits"]]
- ):
- raw_mode = MASK_MODES[(file_info["bits"], file_info["rgb_mask"])]
- else:
- msg = "Unsupported BMP bitfields layout"
- raise OSError(msg)
- else:
- msg = "Unsupported BMP bitfields layout"
- raise OSError(msg)
- elif file_info["compression"] == self.RAW:
- if file_info["bits"] == 32 and header == 22: # 32-bit .cur offset
- raw_mode, self.mode = "BGRA", "RGBA"
- elif file_info["compression"] in (self.RLE8, self.RLE4):
- decoder_name = "bmp_rle"
- else:
- msg = f"Unsupported BMP compression ({file_info['compression']})"
- raise OSError(msg)
-
- # --------------- Once the header is processed, process the palette/LUT
- if self.mode == "P": # Paletted for 1, 4 and 8 bit images
- # ---------------------------------------------------- 1-bit images
- if not (0 < file_info["colors"] <= 65536):
- msg = f"Unsupported BMP Palette size ({file_info['colors']})"
- raise OSError(msg)
- else:
- padding = file_info["palette_padding"]
- palette = read(padding * file_info["colors"])
- greyscale = True
- indices = (
- (0, 255)
- if file_info["colors"] == 2
- else list(range(file_info["colors"]))
- )
-
- # ----------------- Check if greyscale and ignore palette if so
- for ind, val in enumerate(indices):
- rgb = palette[ind * padding : ind * padding + 3]
- if rgb != o8(val) * 3:
- greyscale = False
-
- # ------- If all colors are grey, white or black, ditch palette
- if greyscale:
- self.mode = "1" if file_info["colors"] == 2 else "L"
- raw_mode = self.mode
- else:
- self.mode = "P"
- self.palette = ImagePalette.raw(
- "BGRX" if padding == 4 else "BGR", palette
- )
-
- # ---------------------------- Finally set the tile data for the plugin
- self.info["compression"] = file_info["compression"]
- args = [raw_mode]
- if decoder_name == "bmp_rle":
- args.append(file_info["compression"] == self.RLE4)
- else:
- args.append(((file_info["width"] * file_info["bits"] + 31) >> 3) & (~3))
- args.append(file_info["direction"])
- self.tile = [
- (
- decoder_name,
- (0, 0, file_info["width"], file_info["height"]),
- offset or self.fp.tell(),
- tuple(args),
- )
- ]
-
- def _open(self):
- """Open file, check magic number and read header"""
- # read 14 bytes: magic number, filesize, reserved, header final offset
- head_data = self.fp.read(14)
- # choke if the file does not have the required magic bytes
- if not _accept(head_data):
- msg = "Not a BMP file"
- raise SyntaxError(msg)
- # read the start position of the BMP image data (u32)
- offset = i32(head_data, 10)
- # load bitmap information (offset=raster info)
- self._bitmap(offset=offset)
-
-
-class BmpRleDecoder(ImageFile.PyDecoder):
- _pulls_fd = True
-
- def decode(self, buffer):
- rle4 = self.args[1]
- data = bytearray()
- x = 0
- while len(data) < self.state.xsize * self.state.ysize:
- pixels = self.fd.read(1)
- byte = self.fd.read(1)
- if not pixels or not byte:
- break
- num_pixels = pixels[0]
- if num_pixels:
- # encoded mode
- if x + num_pixels > self.state.xsize:
- # Too much data for row
- num_pixels = max(0, self.state.xsize - x)
- if rle4:
- first_pixel = o8(byte[0] >> 4)
- second_pixel = o8(byte[0] & 0x0F)
- for index in range(num_pixels):
- if index % 2 == 0:
- data += first_pixel
- else:
- data += second_pixel
- else:
- data += byte * num_pixels
- x += num_pixels
- else:
- if byte[0] == 0:
- # end of line
- while len(data) % self.state.xsize != 0:
- data += b"\x00"
- x = 0
- elif byte[0] == 1:
- # end of bitmap
- break
- elif byte[0] == 2:
- # delta
- bytes_read = self.fd.read(2)
- if len(bytes_read) < 2:
- break
- right, up = self.fd.read(2)
- data += b"\x00" * (right + up * self.state.xsize)
- x = len(data) % self.state.xsize
- else:
- # absolute mode
- if rle4:
- # 2 pixels per byte
- byte_count = byte[0] // 2
- bytes_read = self.fd.read(byte_count)
- for byte_read in bytes_read:
- data += o8(byte_read >> 4)
- data += o8(byte_read & 0x0F)
- else:
- byte_count = byte[0]
- bytes_read = self.fd.read(byte_count)
- data += bytes_read
- if len(bytes_read) < byte_count:
- break
- x += byte[0]
-
- # align to 16-bit word boundary
- if self.fd.tell() % 2 != 0:
- self.fd.seek(1, os.SEEK_CUR)
- rawmode = "L" if self.mode == "L" else "P"
- self.set_as_raw(bytes(data), (rawmode, 0, self.args[-1]))
- return -1, 0
-
-
-# =============================================================================
-# Image plugin for the DIB format (BMP alias)
-# =============================================================================
-class DibImageFile(BmpImageFile):
- format = "DIB"
- format_description = "Windows Bitmap"
-
- def _open(self):
- self._bitmap()
-
-
-#
-# --------------------------------------------------------------------
-# Write BMP file
-
-
-SAVE = {
- "1": ("1", 1, 2),
- "L": ("L", 8, 256),
- "P": ("P", 8, 256),
- "RGB": ("BGR", 24, 0),
- "RGBA": ("BGRA", 32, 0),
-}
-
-
-def _dib_save(im, fp, filename):
- _save(im, fp, filename, False)
-
-
-def _save(im, fp, filename, bitmap_header=True):
- try:
- rawmode, bits, colors = SAVE[im.mode]
- except KeyError as e:
- msg = f"cannot write mode {im.mode} as BMP"
- raise OSError(msg) from e
-
- info = im.encoderinfo
-
- dpi = info.get("dpi", (96, 96))
-
- # 1 meter == 39.3701 inches
- ppm = tuple(map(lambda x: int(x * 39.3701 + 0.5), dpi))
-
- stride = ((im.size[0] * bits + 7) // 8 + 3) & (~3)
- header = 40 # or 64 for OS/2 version 2
- image = stride * im.size[1]
-
- if im.mode == "1":
- palette = b"".join(o8(i) * 4 for i in (0, 255))
- elif im.mode == "L":
- palette = b"".join(o8(i) * 4 for i in range(256))
- elif im.mode == "P":
- palette = im.im.getpalette("RGB", "BGRX")
- colors = len(palette) // 4
- else:
- palette = None
-
- # bitmap header
- if bitmap_header:
- offset = 14 + header + colors * 4
- file_size = offset + image
- if file_size > 2**32 - 1:
- msg = "File size is too large for the BMP format"
- raise ValueError(msg)
- fp.write(
- b"BM" # file type (magic)
- + o32(file_size) # file size
- + o32(0) # reserved
- + o32(offset) # image data offset
- )
-
- # bitmap info header
- fp.write(
- o32(header) # info header size
- + o32(im.size[0]) # width
- + o32(im.size[1]) # height
- + o16(1) # planes
- + o16(bits) # depth
- + o32(0) # compression (0=uncompressed)
- + o32(image) # size of bitmap
- + o32(ppm[0]) # resolution
- + o32(ppm[1]) # resolution
- + o32(colors) # colors used
- + o32(colors) # colors important
- )
-
- fp.write(b"\0" * (header - 40)) # padding (for OS/2 format)
-
- if palette:
- fp.write(palette)
-
- ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, stride, -1))])
-
-
-#
-# --------------------------------------------------------------------
-# Registry
-
-
-Image.register_open(BmpImageFile.format, BmpImageFile, _accept)
-Image.register_save(BmpImageFile.format, _save)
-
-Image.register_extension(BmpImageFile.format, ".bmp")
-
-Image.register_mime(BmpImageFile.format, "image/bmp")
-
-Image.register_decoder("bmp_rle", BmpRleDecoder)
-
-Image.register_open(DibImageFile.format, DibImageFile, _dib_accept)
-Image.register_save(DibImageFile.format, _dib_save)
-
-Image.register_extension(DibImageFile.format, ".dib")
-
-Image.register_mime(DibImageFile.format, "image/bmp")
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/verifier.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/verifier.py
deleted file mode 100644
index a500c7814adf8ce52e911e0679d0b98335ae6597..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/verifier.py
+++ /dev/null
@@ -1,307 +0,0 @@
-#
-# DEPRECATED: implementation for ffi.verify()
-#
-import sys, os, binascii, shutil, io
-from . import __version_verifier_modules__
-from . import ffiplatform
-from .error import VerificationError
-
-if sys.version_info >= (3, 3):
- import importlib.machinery
- def _extension_suffixes():
- return importlib.machinery.EXTENSION_SUFFIXES[:]
-else:
- import imp
- def _extension_suffixes():
- return [suffix for suffix, _, type in imp.get_suffixes()
- if type == imp.C_EXTENSION]
-
-
-if sys.version_info >= (3,):
- NativeIO = io.StringIO
-else:
- class NativeIO(io.BytesIO):
- def write(self, s):
- if isinstance(s, unicode):
- s = s.encode('ascii')
- super(NativeIO, self).write(s)
-
-
-class Verifier(object):
-
- def __init__(self, ffi, preamble, tmpdir=None, modulename=None,
- ext_package=None, tag='', force_generic_engine=False,
- source_extension='.c', flags=None, relative_to=None, **kwds):
- if ffi._parser._uses_new_feature:
- raise VerificationError(
- "feature not supported with ffi.verify(), but only "
- "with ffi.set_source(): %s" % (ffi._parser._uses_new_feature,))
- self.ffi = ffi
- self.preamble = preamble
- if not modulename:
- flattened_kwds = ffiplatform.flatten(kwds)
- vengine_class = _locate_engine_class(ffi, force_generic_engine)
- self._vengine = vengine_class(self)
- self._vengine.patch_extension_kwds(kwds)
- self.flags = flags
- self.kwds = self.make_relative_to(kwds, relative_to)
- #
- if modulename:
- if tag:
- raise TypeError("can't specify both 'modulename' and 'tag'")
- else:
- key = '\x00'.join(['%d.%d' % sys.version_info[:2],
- __version_verifier_modules__,
- preamble, flattened_kwds] +
- ffi._cdefsources)
- if sys.version_info >= (3,):
- key = key.encode('utf-8')
- k1 = hex(binascii.crc32(key[0::2]) & 0xffffffff)
- k1 = k1.lstrip('0x').rstrip('L')
- k2 = hex(binascii.crc32(key[1::2]) & 0xffffffff)
- k2 = k2.lstrip('0').rstrip('L')
- modulename = '_cffi_%s_%s%s%s' % (tag, self._vengine._class_key,
- k1, k2)
- suffix = _get_so_suffixes()[0]
- self.tmpdir = tmpdir or _caller_dir_pycache()
- self.sourcefilename = os.path.join(self.tmpdir, modulename + source_extension)
- self.modulefilename = os.path.join(self.tmpdir, modulename + suffix)
- self.ext_package = ext_package
- self._has_source = False
- self._has_module = False
-
- def write_source(self, file=None):
- """Write the C source code. It is produced in 'self.sourcefilename',
- which can be tweaked beforehand."""
- with self.ffi._lock:
- if self._has_source and file is None:
- raise VerificationError(
- "source code already written")
- self._write_source(file)
-
- def compile_module(self):
- """Write the C source code (if not done already) and compile it.
- This produces a dynamic link library in 'self.modulefilename'."""
- with self.ffi._lock:
- if self._has_module:
- raise VerificationError("module already compiled")
- if not self._has_source:
- self._write_source()
- self._compile_module()
-
- def load_library(self):
- """Get a C module from this Verifier instance.
- Returns an instance of a FFILibrary class that behaves like the
- objects returned by ffi.dlopen(), but that delegates all
- operations to the C module. If necessary, the C code is written
- and compiled first.
- """
- with self.ffi._lock:
- if not self._has_module:
- self._locate_module()
- if not self._has_module:
- if not self._has_source:
- self._write_source()
- self._compile_module()
- return self._load_library()
-
- def get_module_name(self):
- basename = os.path.basename(self.modulefilename)
- # kill both the .so extension and the other .'s, as introduced
- # by Python 3: 'basename.cpython-33m.so'
- basename = basename.split('.', 1)[0]
- # and the _d added in Python 2 debug builds --- but try to be
- # conservative and not kill a legitimate _d
- if basename.endswith('_d') and hasattr(sys, 'gettotalrefcount'):
- basename = basename[:-2]
- return basename
-
- def get_extension(self):
- ffiplatform._hack_at_distutils() # backward compatibility hack
- if not self._has_source:
- with self.ffi._lock:
- if not self._has_source:
- self._write_source()
- sourcename = ffiplatform.maybe_relative_path(self.sourcefilename)
- modname = self.get_module_name()
- return ffiplatform.get_extension(sourcename, modname, **self.kwds)
-
- def generates_python_module(self):
- return self._vengine._gen_python_module
-
- def make_relative_to(self, kwds, relative_to):
- if relative_to and os.path.dirname(relative_to):
- dirname = os.path.dirname(relative_to)
- kwds = kwds.copy()
- for key in ffiplatform.LIST_OF_FILE_NAMES:
- if key in kwds:
- lst = kwds[key]
- if not isinstance(lst, (list, tuple)):
- raise TypeError("keyword '%s' should be a list or tuple"
- % (key,))
- lst = [os.path.join(dirname, fn) for fn in lst]
- kwds[key] = lst
- return kwds
-
- # ----------
-
- def _locate_module(self):
- if not os.path.isfile(self.modulefilename):
- if self.ext_package:
- try:
- pkg = __import__(self.ext_package, None, None, ['__doc__'])
- except ImportError:
- return # cannot import the package itself, give up
- # (e.g. it might be called differently before installation)
- path = pkg.__path__
- else:
- path = None
- filename = self._vengine.find_module(self.get_module_name(), path,
- _get_so_suffixes())
- if filename is None:
- return
- self.modulefilename = filename
- self._vengine.collect_types()
- self._has_module = True
-
- def _write_source_to(self, file):
- self._vengine._f = file
- try:
- self._vengine.write_source_to_f()
- finally:
- del self._vengine._f
-
- def _write_source(self, file=None):
- if file is not None:
- self._write_source_to(file)
- else:
- # Write our source file to an in memory file.
- f = NativeIO()
- self._write_source_to(f)
- source_data = f.getvalue()
-
- # Determine if this matches the current file
- if os.path.exists(self.sourcefilename):
- with open(self.sourcefilename, "r") as fp:
- needs_written = not (fp.read() == source_data)
- else:
- needs_written = True
-
- # Actually write the file out if it doesn't match
- if needs_written:
- _ensure_dir(self.sourcefilename)
- with open(self.sourcefilename, "w") as fp:
- fp.write(source_data)
-
- # Set this flag
- self._has_source = True
-
- def _compile_module(self):
- # compile this C source
- tmpdir = os.path.dirname(self.sourcefilename)
- outputfilename = ffiplatform.compile(tmpdir, self.get_extension())
- try:
- same = ffiplatform.samefile(outputfilename, self.modulefilename)
- except OSError:
- same = False
- if not same:
- _ensure_dir(self.modulefilename)
- shutil.move(outputfilename, self.modulefilename)
- self._has_module = True
-
- def _load_library(self):
- assert self._has_module
- if self.flags is not None:
- return self._vengine.load_library(self.flags)
- else:
- return self._vengine.load_library()
-
-# ____________________________________________________________
-
-_FORCE_GENERIC_ENGINE = False # for tests
-
-def _locate_engine_class(ffi, force_generic_engine):
- if _FORCE_GENERIC_ENGINE:
- force_generic_engine = True
- if not force_generic_engine:
- if '__pypy__' in sys.builtin_module_names:
- force_generic_engine = True
- else:
- try:
- import _cffi_backend
- except ImportError:
- _cffi_backend = '?'
- if ffi._backend is not _cffi_backend:
- force_generic_engine = True
- if force_generic_engine:
- from . import vengine_gen
- return vengine_gen.VGenericEngine
- else:
- from . import vengine_cpy
- return vengine_cpy.VCPythonEngine
-
-# ____________________________________________________________
-
-_TMPDIR = None
-
-def _caller_dir_pycache():
- if _TMPDIR:
- return _TMPDIR
- result = os.environ.get('CFFI_TMPDIR')
- if result:
- return result
- filename = sys._getframe(2).f_code.co_filename
- return os.path.abspath(os.path.join(os.path.dirname(filename),
- '__pycache__'))
-
-def set_tmpdir(dirname):
- """Set the temporary directory to use instead of __pycache__."""
- global _TMPDIR
- _TMPDIR = dirname
-
-def cleanup_tmpdir(tmpdir=None, keep_so=False):
- """Clean up the temporary directory by removing all files in it
- called `_cffi_*.{c,so}` as well as the `build` subdirectory."""
- tmpdir = tmpdir or _caller_dir_pycache()
- try:
- filelist = os.listdir(tmpdir)
- except OSError:
- return
- if keep_so:
- suffix = '.c' # only remove .c files
- else:
- suffix = _get_so_suffixes()[0].lower()
- for fn in filelist:
- if fn.lower().startswith('_cffi_') and (
- fn.lower().endswith(suffix) or fn.lower().endswith('.c')):
- try:
- os.unlink(os.path.join(tmpdir, fn))
- except OSError:
- pass
- clean_dir = [os.path.join(tmpdir, 'build')]
- for dir in clean_dir:
- try:
- for fn in os.listdir(dir):
- fn = os.path.join(dir, fn)
- if os.path.isdir(fn):
- clean_dir.append(fn)
- else:
- os.unlink(fn)
- except OSError:
- pass
-
-def _get_so_suffixes():
- suffixes = _extension_suffixes()
- if not suffixes:
- # bah, no C_EXTENSION available. Occurs on pypy without cpyext
- if sys.platform == 'win32':
- suffixes = [".pyd"]
- else:
- suffixes = [".so"]
-
- return suffixes
-
-def _ensure_dir(filename):
- dirname = os.path.dirname(filename)
- if dirname and not os.path.isdir(dirname):
- os.makedirs(dirname)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/zoneinfo/rebuild.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/zoneinfo/rebuild.py
deleted file mode 100644
index 684c6586f091350c347f2b6150935f5214ffec27..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/zoneinfo/rebuild.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import logging
-import os
-import tempfile
-import shutil
-import json
-from subprocess import check_call, check_output
-from tarfile import TarFile
-
-from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
-
-
-def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
- """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
-
- filename is the timezone tarball from ``ftp.iana.org/tz``.
-
- """
- tmpdir = tempfile.mkdtemp()
- zonedir = os.path.join(tmpdir, "zoneinfo")
- moduledir = os.path.dirname(__file__)
- try:
- with TarFile.open(filename) as tf:
- for name in zonegroups:
- tf.extract(name, tmpdir)
- filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
-
- _run_zic(zonedir, filepaths)
-
- # write metadata file
- with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
- json.dump(metadata, f, indent=4, sort_keys=True)
- target = os.path.join(moduledir, ZONEFILENAME)
- with TarFile.open(target, "w:%s" % format) as tf:
- for entry in os.listdir(zonedir):
- entrypath = os.path.join(zonedir, entry)
- tf.add(entrypath, entry)
- finally:
- shutil.rmtree(tmpdir)
-
-
-def _run_zic(zonedir, filepaths):
- """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
-
- Recent versions of ``zic`` default to ``-b slim``, while older versions
- don't even have the ``-b`` option (but default to "fat" binaries). The
- current version of dateutil does not support Version 2+ TZif files, which
- causes problems when used in conjunction with "slim" binaries, so this
- function is used to ensure that we always get a "fat" binary.
- """
-
- try:
- help_text = check_output(["zic", "--help"])
- except OSError as e:
- _print_on_nosuchfile(e)
- raise
-
- if b"-b " in help_text:
- bloat_args = ["-b", "fat"]
- else:
- bloat_args = []
-
- check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
-
-
-def _print_on_nosuchfile(e):
- """Print helpful troubleshooting message
-
- e is an exception raised by subprocess.check_call()
-
- """
- if e.errno == 2:
- logging.error(
- "Could not find zic. Perhaps you need to install "
- "libc-bin or some other package that provides it, "
- "or it's not in your PATH?")
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/frwu.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/frwu.c
deleted file mode 100644
index cf183f84107d6140de54e608a9677afb4d82e7af..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/frwu.c
+++ /dev/null
@@ -1,128 +0,0 @@
-/*
- * Forward Uncompressed
- *
- * Copyright (c) 2009 Reimar Döffinger
-
-
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|---|---|---|
| Money | -Limited | -Unlimited | -
| Scenarios | -Locked until completed | -Unlocked from the start | -
| Countries | -Locked until completed | -Unlocked from the start | -
| Bugs/Glitches | -Few or none | -Possible or frequent | -
| MALWARE/VIRUSES | -None or safe | -Possible or risky | -
| Legal/Ethical Issues | -None or acceptable | -Possible or questionable | -
| Source: Created by the author based on web search results. | ||
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| Source: Created by the author based on web search results. | ||
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401be4b1e0If you are a fan of chess games, you might remember Chess Titans, a 3D chess game that was included in Windows 7. It was a popular and fun game that allowed you to play against the computer or another human player, with different difficulty levels and realistic graphics. However, if you have upgraded to Windows 10, you might have noticed that Chess Titans is no longer available. So, how can you get Chess Titans on Windows 10? Is it possible to download and install it on your PC? In this article, we will answer these questions and more. We will tell you what Chess Titans is, why Microsoft removed it from Windows 10, how to download and install it on your PC, and how to play it. Let's get started!
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Chess Titans is a chess game with 3D graphics developed by Oberon Games and included in Windows 7. It is a fully animated, photorealistic interactive game with ten difficulty levels. It can be played by two participants, or one player against the computer.
-Chess Titans was first released in 2006 as part of the Windows Vista Ultimate Extras package, which was a collection of additional features and games for Windows Vista users. It was later included in all editions of Windows 7, except for the Starter and Home Basic editions. Chess Titans was one of the most popular games in Windows 7, along with other classics like Solitaire, Minesweeper, and Mahjong. However, when Microsoft released Windows 8 in 2012, they decided to remove Chess Titans and other games from the operating system. They also did not include them in Windows 10, which was launched in 2015.
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The gameplay of Chess Titans follows the standard rules of chess, with some optional features that you can enable or disable. For example, you can turn on or off hints, legal moves, move animations, undo moves, timers, and more. You can also save your game progress and resume it later.
-If you are wondering why Microsoft decided to remove Chess Titans and other games from Windows 10, there are several reasons behind their decision.
-One of the main reasons why Microsoft removed Chess Titans and other games from Windows 10 was to make the operating system more lightweight and efficient. By removing unnecessary features and programs, they aimed to improve the performance and security of Windows 10. They also wanted to encourage users to download new games and apps from the Microsoft Store, which is their online platform for digital content. They also wanted to update the games and apps to make them more compatible with modern devices and features, such as touchscreens, cloud services, and social media integration.
-Although Microsoft removed Chess Titans and other games from Windows 10, they did not leave the users without any options. They offered some alternatives that users can download from the Microsoft Store for free or for a small fee. Some of these alternatives are:
-These are some of the alternatives to Chess Titans that Microsoft offers to Windows 10 users. However, if you are still looking for Chess Titans specifically, there is a way to download and install it on your PC.
-If you want to play Chess Titans on Windows 10, you will need to get it from a third-party source. This means that you will have to download it from a website that is not affiliated with Microsoft or the official Microsoft Store. However, before you do that, you should be aware of the precautions and risks of downloading Chess Titans from a third-party source.
-Here are the steps that you need to follow to download and install Chess Titans on Windows 10:
-While downloading Chess Titans from a third-party source might seem like an easy solution, it is not without its drawbacks. Here are some of the precautions and risks that you should consider before downloading Chess Titans from a third-party source:
-These are some of the precautions and risks that you should consider before downloading Chess Titans from a third-party source. If you are not comfortable with them, you might want to look for another chess game that is compatible with Windows 10 and available on the Microsoft Store.
-If you have successfully downloaded and installed Chess Titans on Windows 10, you might be wondering how to play it. Here are some of the options and settings that you can use to customize your game experience.
-When you launch Chess Titans, you will see a menu with four options: Play, Options, Help, and Exit. Here is what each option does:
-These are some of the options and settings that you can use to play Chess Titans on Windows 10. You can also access them by clicking on the icons at the top right corner of the game window.
-If you want to improve your chess skills with Chess Titans, here are some tips and tricks that you can follow:
-These are some of the tips and tricks that you can use to improve your chess skills with Chess Titans. Of course, there is no substitute for experience and practice, so keep playing and have fun!
-In this article, we have covered everything you need to know about Chess Titans download for Windows 10. We have explained what Chess Titans is, why Microsoft removed it from Windows 10, how to download and install it on your PC, how to play it, and how to improve your chess skills with it. We hope that this article has been helpful and informative for you.
-Here is a summary of the main points that we have discussed in this article:
-Now that you know how to get Chess Titans on Windows 10, why not give it a try and see how much you enjoy it? You can download it from the link that we provided above, or look for other sources online. Just remember to be careful and scan the file before installing it. You can also check out the other games and apps that Microsoft offers on the Microsoft Store, or look for other chess games that are compatible with Windows 10. Whatever you choose, we hope that you have fun and improve your chess skills with Chess Titans!
-Here are some of the frequently asked questions that you might have about Chess Titans download for Windows 10:
-Yes, Chess Titans is free to download and play. However, you will need to get it from a third-party source, as it is not available on the Microsoft Store or the official Microsoft website.
-Chess Titans is safe to play, as long as you download it from a trustworthy and secure website. You should always scan the downloaded file with an antivirus program before running it. You should also backup your data and system before installing Chess Titans on your PC.
-Chess Titans was designed for Windows 7 and might not work properly on Windows 10. You might experience compatibility or performance issues such as crashes, glitches, errors, or slow loading times. Therefore, you should always backup your data and system before installing Chess Titans on your PC.
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MilkChoco is a game that combines the elements of a shooter and a MOBA (multiplayer online battle arena). You can choose from various heroes with different abilities, such as Assault, Sniper, Medic, Bomber, Ghost, Recon, Shield, Ice Bang, Air, Iron, Death, Escort, Carog, Claw, and Star. Each hero has its own ranking, weapons, and skills that you can upgrade and customize.
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MilkChoco has many features that make it a fun and competitive game to play. Here are some of them:
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In this article, we will explore everything you need to know about Mortal Kombat (1995), from its plot and characters to its production and reception. We will also tell you how you can watch this movie online for free in Hindi if you are interested in experiencing this classic action movie in a different language
Mortal Kombat (1995) is based on the first two games of the Mortal Kombat series, which are set in a fictional universe where different realms are in conflict with each other. The main premise of the movie is that the evil sorcerer Shang Tsung, who serves the Emperor of Outworld, has organized a tournament called Mortal Kombat, where he invites the best fighters from Earthrealm to compete against his warriors. If Shang Tsung's team wins ten consecutive tournaments, Outworld will be able to invade and conquer Earthrealm. However, if Earthrealm's champions can defeat Shang Tsung and his minions, they will prevent this fate and save their world.
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Mortal Kombat (1995) was directed by Paul W.S. Anderson, who later became known for his work on the Resident Evil and Monster Hunter franchises. The screenplay was written by Kevin Droney, who also wrote for TV shows such as The Highlander and Witchblade. The movie was produced by Lawrence Kasanoff, who also created the Mortal Kombat: Defenders of the Realm animated series and the Mortal Kombat: Annihilation sequel.
-The movie had a budget of $18 million and was filmed in various locations, such as Los Angeles, Thailand, and England. The movie featured a diverse and talented cast of actors and actresses, such as Christopher Lambert as Raiden, Robin Shou as Liu Kang, Linden Ashby as Johnny Cage, Bridgette Wilson as Sonya Blade, Cary-Hiroyuki Tagawa as Shang Tsung, Talisa Soto as Kitana, Trevor Goddard as Kano, Chris Casamassa as Scorpion, François Petit as Sub-Zero, Keith Cooke as Reptile, and Tom Woodruff Jr. as Goro. The movie also employed several stuntmen, choreographers, composers, and other crew members who contributed to the movie's action, music, and visual effects.
-The movie was released on August 18, 1995 in the United States and on September 15, 1995 in India. The movie was a commercial success, grossing over $122 million worldwide against its budget. The movie was also well-received by critics and audiences alike, earning a 44% rating on Rotten Tomatoes and a 5.8/10 score on IMDb. The movie won several awards and nominations, such as the BMI Film Music Award for George S. Clinton's score, the Saturn Award for Best Make-up for Goro's animatronic suit, and the MTV Movie Award for Best Fight for Johnny Cage vs. Scorpion.
Mortal Kombat (1995) is not only a great movie in its own right, but also a landmark in the history of video game adaptations and action movies. The movie has spawned several sequels and spin-offs, such as Mortal Kombat: Annihilation (1997), Mortal Kombat: Conquest (1998-1999), Mortal Kombat: The Journey Begins (1995), Mortal Kombat: Defenders of the Realm (1996), Mortal Kombat: Legacy (2011-2013), and Mortal Kombat Legends: Scorpion's Revenge (2020). The movie has also inspired many video games and merchandise, such as Mortal Kombat Trilogy (1996), Mortal Kombat 4 (1997), Mortal Kombat Mythologies: Sub-Zero (1997), Mortal Kombat Gold (1999), Mortal Kombat: Deadly Alliance (2002), Mortal Kombat: Deception (2004), Mortal Kombat: Shaolin Monks (2005), Mortal Kombat: Armageddon (2006), Mortal Kombat vs. DC Universe (2008), Mortal Kombat (2011), Mortal Kombat X (2015), Mortal Kombat 11 (2019), and many others. The movie has also been referenced and homaged in many other media, such as The Simpsons, Family Guy, Robot Chicken, South Park, Wreck-It Ralph, Ready Player One, Deadpool 2, and many others.
-Mortal Kombat (1995) has also had a significant impact on popular culture and fandom. The movie has introduced millions of people to the world of Mortal Kombat and its characters, as well as to the genre of martial arts and fantasy movies. The movie has also created a loyal fan base that has followed the franchise through its ups and downs, and has celebrated its achievements and milestones. The movie has also influenced many other filmmakers and creators who have drawn inspiration from its style, tone, and themes. The movie has also become a part of the collective memory of many fans who grew up watching it or discovered it later in life.
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-On the other hand, if you choose the illegal option, you will be risking your safety, privacy, and legality by using websites and apps that offer pirated copies of the movie for free download or streaming in Hindi, such as Filmyzilla. Filmyzilla is one of the most notorious websites that provides illegal downloads and streams of movies and TV shows in various languages, including Hindi. However, using Filmyzilla or similar websites is not only illegal, but also dangerous. Here are some of the risks and consequences of using illegal sources to watch or download movies:
-Therefore, we strongly advise you to avoid using illegal sources to watch or download movies, such as Filmyzilla, and instead use the legal options that we have listed above. Not only will you be supporting the creators and owners of the movies and TV shows that you enjoy, but you will also be protecting yourself from harm and trouble.
-Mortal Kombat (1995) is a classic action movie that is based on a popular video game of the same name. It tells the story of three Earthrealm fighters who participate in a tournament to save their world from the evil forces of Outworld. The movie features an exciting plot, memorable characters, spectacular fight scenes, stunning visual effects, catchy music, and iconic catchphrases. The movie is also a landmark in the history of video game adaptations and action movies, as it has spawned several sequels and spin-offs, inspired many video games and merchandise, influenced many other filmmakers and creators, and impacted popular culture and fandom.
-If you are interested in watching Mortal Kombat (1995) online for free in Hindi, you have two options: legal or illegal. However, we strongly recommend that you choose the legal option, as it is safer, more ethical, and more respectful to the creators and owners of the movie. You can watch or stream the movie legally in Hindi with subtitles or dubbing on platforms and websites such as Amazon Prime Video, YouTube, Google Play Movies & TV, or iTunes. You should avoid using illegal sources to watch or download the movie, such as Filmyzilla, as they pose many risks and consequences for you and your device.
-In conclusion, Mortal Kombat (1995) is a great movie that you should watch if you are a fan of martial arts, fantasy, or video games. It is one of the best video game movies ever made, as well as one of the most faithful adaptations of a video game to film. It is also a cult classic that has a loyal fan base and a lasting legacy. You can watch it online for free in Hindi if you want to experience it in a different language
Now that you have read this article, you might have some questions about Mortal Kombat (1995) or the topic of watching movies online for free in Hindi. Here are some of the frequently asked questions (FAQs) that we have answered for you:
-Mortal Kombat (1995) is rated PG-13 in the United States and 15 in India, which means that it contains some violence, blood, gore, and mild language that may not be appropriate for younger viewers. The movie is based on a video game that is known for its graphic and brutal fatalities, which are toned down but still present in the movie. Therefore, we advise you to use your discretion and parental guidance when watching this movie with children.
-The original version of Mortal Kombat (1995) is in English, while the Hindi version is either dubbed or subtitled in Hindi. The Hindi version may also have some minor changes or edits in the dialogue, scenes, or music to suit the preferences and sensibilities of the Hindi-speaking audience. However, the overall plot, characters, and themes of the movie remain the same in both versions.
-The best way to watch Mortal Kombat (1995) online for free in Hindi without any ads or interruptions is to use a legal platform or website that offers a free trial or a subscription service. For example, you can watch or stream the movie on Amazon Prime Video with a 30-day free trial or a Prime membership, which also gives you access to many other movies and TV shows. You can also watch or stream the movie on YouTube, Google Play Movies & TV, or iTunes with a small fee, which also allows you to watch it offline. These platforms and websites provide high-quality downloads and streams of the movie with no ads or interruptions.
-If you enjoyed Mortal Kombat (1995), you might also like some of these other movies that are similar to it in terms of genre, style, or theme:
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-We hope that this article has helped you learn more about Mortal Kombat ( 1995) and how to watch it online for free in Hindi. We hope that you have enjoyed reading this article as much as we have enjoyed writing it. We also hope that you will watch this movie and appreciate its quality and legacy. Thank you for your time and attention.
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Soul Knight is not only a solo adventure game. You can also team up with your friends or other players around the world for an online co-op adventure or an offline multiplayer LAN game. You can join up to three other players and work together to clear the dungeons and defeat the bosses. You can also chat with your teammates and share items with them.
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3cee63e6c2DOWNLOAD ✸✸✸ https://tiurll.com/2uCiLO
-
-## Coming soon
-
-- User setting to share conversations with model authors (done ✅)
-- LLM watermarking
diff --git a/spaces/kukuhtw/AutoGPT/autogpt/configurator.py b/spaces/kukuhtw/AutoGPT/autogpt/configurator.py
deleted file mode 100644
index 1dc3be124f638b8859eb459bcb2d46696f62e2b7..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/autogpt/configurator.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""Configurator module."""
-import click
-from colorama import Back, Fore, Style
-
-from autogpt import utils
-from autogpt.config import Config
-from autogpt.logs import logger
-from autogpt.memory import get_supported_memory_backends
-
-CFG = Config()
-
-
-def create_config(
- continuous: bool,
- continuous_limit: int,
- ai_settings_file: str,
- skip_reprompt: bool,
- speak: bool,
- debug: bool,
- gpt3only: bool,
- gpt4only: bool,
- memory_type: str,
- browser_name: str,
- allow_downloads: bool,
- skip_news: bool,
-) -> None:
- """Updates the config object with the given arguments.
-
- Args:
- continuous (bool): Whether to run in continuous mode
- continuous_limit (int): The number of times to run in continuous mode
- ai_settings_file (str): The path to the ai_settings.yaml file
- skip_reprompt (bool): Whether to skip the re-prompting messages at the beginning of the script
- speak (bool): Whether to enable speak mode
- debug (bool): Whether to enable debug mode
- gpt3only (bool): Whether to enable GPT3.5 only mode
- gpt4only (bool): Whether to enable GPT4 only mode
- memory_type (str): The type of memory backend to use
- browser_name (str): The name of the browser to use when using selenium to scrape the web
- allow_downloads (bool): Whether to allow Auto-GPT to download files natively
- skips_news (bool): Whether to suppress the output of latest news on startup
- """
- CFG.set_debug_mode(False)
- CFG.set_continuous_mode(False)
- CFG.set_speak_mode(False)
-
- if debug:
- logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
- CFG.set_debug_mode(True)
-
- if continuous:
- logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
- logger.typewriter_log(
- "WARNING: ",
- Fore.RED,
- "Continuous mode is not recommended. It is potentially dangerous and may"
- " cause your AI to run forever or carry out actions you would not usually"
- " authorise. Use at your own risk.",
- )
- CFG.set_continuous_mode(True)
-
- if continuous_limit:
- logger.typewriter_log(
- "Continuous Limit: ", Fore.GREEN, f"{continuous_limit}"
- )
- CFG.set_continuous_limit(continuous_limit)
-
- # Check if continuous limit is used without continuous mode
- if continuous_limit and not continuous:
- raise click.UsageError("--continuous-limit can only be used with --continuous")
-
- if speak:
- logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
- CFG.set_speak_mode(True)
-
- if gpt3only:
- logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
- CFG.set_smart_llm_model(CFG.fast_llm_model)
-
- if gpt4only:
- logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
- CFG.set_fast_llm_model(CFG.smart_llm_model)
-
- if memory_type:
- supported_memory = get_supported_memory_backends()
- chosen = memory_type
- if chosen not in supported_memory:
- logger.typewriter_log(
- "ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
- Fore.RED,
- f"{supported_memory}",
- )
- logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
- else:
- CFG.memory_backend = chosen
-
- if skip_reprompt:
- logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
- CFG.skip_reprompt = True
-
- if ai_settings_file:
- file = ai_settings_file
-
- # Validate file
- (validated, message) = utils.validate_yaml_file(file)
- if not validated:
- logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
- logger.double_check()
- exit(1)
-
- logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
- CFG.ai_settings_file = file
- CFG.skip_reprompt = True
-
- if allow_downloads:
- logger.typewriter_log("Native Downloading:", Fore.GREEN, "ENABLED")
- logger.typewriter_log(
- "WARNING: ",
- Fore.YELLOW,
- f"{Back.LIGHTYELLOW_EX}Auto-GPT will now be able to download and save files to your machine.{Back.RESET} "
- + "It is recommended that you monitor any files it downloads carefully.",
- )
- logger.typewriter_log(
- "WARNING: ",
- Fore.YELLOW,
- f"{Back.RED + Style.BRIGHT}ALWAYS REMEMBER TO NEVER OPEN FILES YOU AREN'T SURE OF!{Style.RESET_ALL}",
- )
- CFG.allow_downloads = True
-
- if skip_news:
- CFG.skip_news = True
-
- if browser_name:
- CFG.selenium_web_browser = browser_name
diff --git a/spaces/kukuhtw/AutoGPT/ui/utils.py b/spaces/kukuhtw/AutoGPT/ui/utils.py
deleted file mode 100644
index 71703e2009afac0582300f5d99a91ddec4119e04..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/ui/utils.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import os
-import re
-
-def format_directory(directory):
- output = []
- def helper(directory, level, output):
- files = os.listdir(directory)
- for i, item in enumerate(files):
- is_folder = os.path.isdir(os.path.join(directory, item))
- joiner = "├── " if i < len(files) - 1 else "└── "
- item_html = item + "/" if is_folder else f"{item}"
- output.append("│ " * level + joiner + item_html)
- if is_folder:
- helper(os.path.join(directory, item), level + 1, output)
- output.append(os.path.basename(directory) + "/")
- helper(directory, 1, output)
- return "\n".join(output)
-
-DOWNLOAD_OUTPUTS_JS = """
-() => {
- const a = document.createElement('a');
- a.href = 'file=outputs.zip';
- a.download = 'outputs.zip';
- document.body.appendChild(a);
- a.click();
- document.body.removeChild(a);
-}"""
-
-def remove_color(text):
- ansi_escape = re.compile(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])')
- return ansi_escape.sub('', text)
\ No newline at end of file
diff --git a/spaces/kxqt/Expedit-SAM/scripts/amg.py b/spaces/kxqt/Expedit-SAM/scripts/amg.py
deleted file mode 100644
index ab2662037e7a6160bfd766bc414233133e357ae4..0000000000000000000000000000000000000000
--- a/spaces/kxqt/Expedit-SAM/scripts/amg.py
+++ /dev/null
@@ -1,335 +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 cv2 # type: ignore
-
-from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
-
-import argparse
-import json
-import os
-from typing import Any, Dict, List
-
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-
-parser = argparse.ArgumentParser(
- description=(
- "Runs automatic mask generation on an input image or directory of images, "
- "and outputs masks as either PNGs or COCO-style RLEs. Requires open-cv, "
- "as well as pycocotools if saving in RLE format."
- )
-)
-
-parser.add_argument(
- "--input",
- type=str,
- required=True,
- help="Path to either a single input image or folder of images.",
-)
-
-parser.add_argument(
- "--output",
- type=str,
- required=True,
- help=(
- "Path to the directory where masks will be output. Output will be either a folder "
- "of PNGs per image or a single json with COCO-style masks."
- ),
-)
-
-parser.add_argument(
- "--model-type",
- type=str,
- default="default",
- help="The type of model to load, in ['default', 'vit_l', 'vit_b']",
-)
-
-parser.add_argument(
- "--checkpoint",
- type=str,
- required=True,
- help="The path to the SAM checkpoint to use for mask generation.",
-)
-
-parser.add_argument("--device", type=str, default="cuda", help="The device to run generation on.")
-
-parser.add_argument(
- "--convert-to-rle",
- action="store_true",
- help=(
- "Save masks as COCO RLEs in a single json instead of as a folder of PNGs. "
- "Requires pycocotools."
- ),
-)
-
-amg_settings = parser.add_argument_group("AMG Settings")
-
-amg_settings.add_argument(
- "--points-per-side",
- type=int,
- default=None,
- help="Generate masks by sampling a grid over the image with this many points to a side.",
-)
-
-amg_settings.add_argument(
- "--points-per-batch",
- type=int,
- default=None,
- help="How many input points to process simultaneously in one batch.",
-)
-
-amg_settings.add_argument(
- "--pred-iou-thresh",
- type=float,
- default=None,
- help="Exclude masks with a predicted score from the model that is lower than this threshold.",
-)
-
-amg_settings.add_argument(
- "--stability-score-thresh",
- type=float,
- default=None,
- help="Exclude masks with a stability score lower than this threshold.",
-)
-
-amg_settings.add_argument(
- "--stability-score-offset",
- type=float,
- default=None,
- help="Larger values perturb the mask more when measuring stability score.",
-)
-
-amg_settings.add_argument(
- "--box-nms-thresh",
- type=float,
- default=None,
- help="The overlap threshold for excluding a duplicate mask.",
-)
-
-amg_settings.add_argument(
- "--crop-n-layers",
- type=int,
- default=None,
- help=(
- "If >0, mask generation is run on smaller crops of the image to generate more masks. "
- "The value sets how many different scales to crop at."
- ),
-)
-
-amg_settings.add_argument(
- "--crop-nms-thresh",
- type=float,
- default=None,
- help="The overlap threshold for excluding duplicate masks across different crops.",
-)
-
-amg_settings.add_argument(
- "--crop-overlap-ratio",
- type=int,
- default=None,
- help="Larger numbers mean image crops will overlap more.",
-)
-
-amg_settings.add_argument(
- "--crop-n-points-downscale-factor",
- type=int,
- default=None,
- help="The number of points-per-side in each layer of crop is reduced by this factor.",
-)
-
-amg_settings.add_argument(
- "--min-mask-region-area",
- type=int,
- default=None,
- help=(
- "Disconnected mask regions or holes with area smaller than this value "
- "in pixels are removed by postprocessing."
- ),
-)
-
-# add hourglass settings
-amg_settings.add_argument(
- "--use_hourglass",
- action="store_true",
- help="Use hourglass method to expedite mask generation.",
-)
-
-amg_settings.add_argument(
- "--hourglass_clustering_location",
- type=int,
- default=6,
- help="location of clustering, ranging from [0, num of layers of transformer)"
-)
-
-amg_settings.add_argument(
- "--hourglass_num_cluster",
- type=int,
- default=100,
- help="num of clusters, no more than total number of features"
-)
-
-amg_settings.add_argument(
- "--hourglass_cluster_iters",
- type=int,
- default=5,
- help="num of iterations in clustering"
-)
-
-amg_settings.add_argument(
- "--hourglass_temperture",
- type=float,
- default=5e-3,
- help="temperture in clustering and reconstruction"
-)
-
-amg_settings.add_argument(
- "--hourglass_cluster_window_size",
- type=int,
- default=5,
- help="window size in clustering"
-)
-
-amg_settings.add_argument(
- "--hourglass_reconstruction_k",
- type=int,
- default=20,
- help="k in token reconstruction layer of hourglass vit"
-)
-
-def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:
- header = "id,area,bbox_x0,bbox_y0,bbox_w,bbox_h,point_input_x,point_input_y,predicted_iou,stability_score,crop_box_x0,crop_box_y0,crop_box_w,crop_box_h" # noqa
- metadata = [header]
- for i, mask_data in enumerate(masks):
- mask = mask_data["segmentation"]
- filename = f"{i}.png"
- cv2.imwrite(os.path.join(path, filename), mask * 255)
- mask_metadata = [
- str(i),
- str(mask_data["area"]),
- *[str(x) for x in mask_data["bbox"]],
- *[str(x) for x in mask_data["point_coords"][0]],
- str(mask_data["predicted_iou"]),
- str(mask_data["stability_score"]),
- *[str(x) for x in mask_data["crop_box"]],
- ]
- row = ",".join(mask_metadata)
- metadata.append(row)
- metadata_path = os.path.join(path, "metadata.csv")
- with open(metadata_path, "w") as f:
- f.write("\n".join(metadata))
-
- return
-
-
-def get_amg_kwargs(args):
- amg_kwargs = {
- "points_per_side": args.points_per_side,
- "points_per_batch": args.points_per_batch,
- "pred_iou_thresh": args.pred_iou_thresh,
- "stability_score_thresh": args.stability_score_thresh,
- "stability_score_offset": args.stability_score_offset,
- "box_nms_thresh": args.box_nms_thresh,
- "crop_n_layers": args.crop_n_layers,
- "crop_nms_thresh": args.crop_nms_thresh,
- "crop_overlap_ratio": args.crop_overlap_ratio,
- "crop_n_points_downscale_factor": args.crop_n_points_downscale_factor,
- "min_mask_region_area": args.min_mask_region_area,
- }
- amg_kwargs = {k: v for k, v in amg_kwargs.items() if v is not None}
- return amg_kwargs
-
-
-def get_hourglass_kwargs(args):
- hourglass_kwargs = {
- "use_hourglass": args.use_hourglass,
- "hourglass_clustering_location": args.hourglass_clustering_location,
- "hourglass_num_cluster": args.hourglass_num_cluster,
- "hourglass_cluster_iters": args.hourglass_cluster_iters,
- "hourglass_temperture": args.hourglass_temperture,
- "hourglass_cluster_window_size": args.hourglass_cluster_window_size,
- "hourglass_reconstruction_k": args.hourglass_reconstruction_k,
- }
- hourglass_kwargs = {k: v for k, v in hourglass_kwargs.items() if v is not None}
- return hourglass_kwargs
-
-
-def show_anns(anns):
- if len(anns) == 0:
- return
- sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
- ax = plt.gca()
- ax.set_autoscale_on(False)
- for ann in sorted_anns:
- m = ann['segmentation']
- img = np.ones((m.shape[0], m.shape[1], 3))
- color_mask = np.random.random((1, 3)).tolist()[0]
- for i in range(3):
- img[:,:,i] = color_mask[i]
- ax.imshow(np.dstack((img, m*0.35)))
-
-
-def main(args: argparse.Namespace) -> None:
- print("Loading model...")
- hourglass_kwargs = get_hourglass_kwargs(args)
- sam = sam_model_registry[args.model_type](checkpoint=args.checkpoint, **hourglass_kwargs)
- _ = sam.to(device=args.device)
- output_mode = "coco_rle" if args.convert_to_rle else "binary_mask"
- amg_kwargs = get_amg_kwargs(args)
- generator = SamAutomaticMaskGenerator(sam, output_mode=output_mode, **amg_kwargs)
-
- if not os.path.isdir(args.input):
- targets = [args.input]
- else:
- targets = [
- f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f))
- ]
- targets = [os.path.join(args.input, f) for f in targets]
-
- os.makedirs(args.output, exist_ok=True)
-
- plt.figure(figsize=(20,20))
-
- total_time = 0
- warmup = 0
- for i, t in enumerate(targets):
- print(f"Processing '{t}'...")
- image = cv2.imread(t)
- if image is None:
- print(f"Could not load '{t}' as an image, skipping...")
- continue
- image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
- start = time.perf_counter()
- masks = generator.generate(image)
- eta = time.perf_counter() - start
- if i > warmup:
- total_time += eta
-
- base = os.path.basename(t)
- base = os.path.splitext(base)[0]
- save_base = os.path.join(args.output, base)
- if output_mode == "binary_mask":
- os.makedirs(save_base, exist_ok=True)
- write_masks_to_folder(masks, save_base)
- else:
- save_file = save_base + ".json"
- with open(save_file, "w") as f:
- json.dump(masks, f)
-
- plt.clf()
- plt.imshow(image)
- show_anns(masks)
- plt.axis('off')
- plt.savefig(os.path.join(save_base, base + '.png'), bbox_inches='tight', pad_inches=0)
- print("Done!")
- print(f"Average time per image: {total_time / (len(targets) - warmup)} seconds")
-
-
-if __name__ == "__main__":
- args = parser.parse_args()
- main(args)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/http_parser.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/http_parser.py
deleted file mode 100644
index 5a66ce4b9eec19777800ddc3c0f5e66b2270f9d3..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/http_parser.py
+++ /dev/null
@@ -1,969 +0,0 @@
-import abc
-import asyncio
-import collections
-import re
-import string
-import zlib
-from contextlib import suppress
-from enum import IntEnum
-from typing import (
- Any,
- Generic,
- List,
- NamedTuple,
- Optional,
- Pattern,
- Set,
- Tuple,
- Type,
- TypeVar,
- Union,
- cast,
-)
-
-from multidict import CIMultiDict, CIMultiDictProxy, istr
-from yarl import URL
-
-from . import hdrs
-from .base_protocol import BaseProtocol
-from .helpers import NO_EXTENSIONS, BaseTimerContext
-from .http_exceptions import (
- BadHttpMessage,
- BadStatusLine,
- ContentEncodingError,
- ContentLengthError,
- InvalidHeader,
- LineTooLong,
- TransferEncodingError,
-)
-from .http_writer import HttpVersion, HttpVersion10
-from .log import internal_logger
-from .streams import EMPTY_PAYLOAD, StreamReader
-from .typedefs import Final, RawHeaders
-
-try:
- import brotli
-
- HAS_BROTLI = True
-except ImportError: # pragma: no cover
- HAS_BROTLI = False
-
-
-__all__ = (
- "HeadersParser",
- "HttpParser",
- "HttpRequestParser",
- "HttpResponseParser",
- "RawRequestMessage",
- "RawResponseMessage",
-)
-
-ASCIISET: Final[Set[str]] = set(string.printable)
-
-# See https://tools.ietf.org/html/rfc7230#section-3.1.1
-# and https://tools.ietf.org/html/rfc7230#appendix-B
-#
-# method = token
-# tchar = "!" / "#" / "$" / "%" / "&" / "'" / "*" / "+" / "-" / "." /
-# "^" / "_" / "`" / "|" / "~" / DIGIT / ALPHA
-# token = 1*tchar
-METHRE: Final[Pattern[str]] = re.compile(r"[!#$%&'*+\-.^_`|~0-9A-Za-z]+")
-VERSRE: Final[Pattern[str]] = re.compile(r"HTTP/(\d+).(\d+)")
-HDRRE: Final[Pattern[bytes]] = re.compile(rb"[\x00-\x1F\x7F()<>@,;:\[\]={} \t\\\\\"]")
-
-
-class RawRequestMessage(NamedTuple):
- method: str
- path: str
- version: HttpVersion
- headers: "CIMultiDictProxy[str]"
- raw_headers: RawHeaders
- should_close: bool
- compression: Optional[str]
- upgrade: bool
- chunked: bool
- url: URL
-
-
-RawResponseMessage = collections.namedtuple(
- "RawResponseMessage",
- [
- "version",
- "code",
- "reason",
- "headers",
- "raw_headers",
- "should_close",
- "compression",
- "upgrade",
- "chunked",
- ],
-)
-
-
-_MsgT = TypeVar("_MsgT", RawRequestMessage, RawResponseMessage)
-
-
-class ParseState(IntEnum):
-
- PARSE_NONE = 0
- PARSE_LENGTH = 1
- PARSE_CHUNKED = 2
- PARSE_UNTIL_EOF = 3
-
-
-class ChunkState(IntEnum):
- PARSE_CHUNKED_SIZE = 0
- PARSE_CHUNKED_CHUNK = 1
- PARSE_CHUNKED_CHUNK_EOF = 2
- PARSE_MAYBE_TRAILERS = 3
- PARSE_TRAILERS = 4
-
-
-class HeadersParser:
- def __init__(
- self,
- max_line_size: int = 8190,
- max_headers: int = 32768,
- max_field_size: int = 8190,
- ) -> None:
- self.max_line_size = max_line_size
- self.max_headers = max_headers
- self.max_field_size = max_field_size
-
- def parse_headers(
- self, lines: List[bytes]
- ) -> Tuple["CIMultiDictProxy[str]", RawHeaders]:
- headers: CIMultiDict[str] = CIMultiDict()
- raw_headers = []
-
- lines_idx = 1
- line = lines[1]
- line_count = len(lines)
-
- while line:
- # Parse initial header name : value pair.
- try:
- bname, bvalue = line.split(b":", 1)
- except ValueError:
- raise InvalidHeader(line) from None
-
- bname = bname.strip(b" \t")
- bvalue = bvalue.lstrip()
- if HDRRE.search(bname):
- raise InvalidHeader(bname)
- if len(bname) > self.max_field_size:
- raise LineTooLong(
- "request header name {}".format(
- bname.decode("utf8", "xmlcharrefreplace")
- ),
- str(self.max_field_size),
- str(len(bname)),
- )
-
- header_length = len(bvalue)
-
- # next line
- lines_idx += 1
- line = lines[lines_idx]
-
- # consume continuation lines
- continuation = line and line[0] in (32, 9) # (' ', '\t')
-
- if continuation:
- bvalue_lst = [bvalue]
- while continuation:
- header_length += len(line)
- if header_length > self.max_field_size:
- raise LineTooLong(
- "request header field {}".format(
- bname.decode("utf8", "xmlcharrefreplace")
- ),
- str(self.max_field_size),
- str(header_length),
- )
- bvalue_lst.append(line)
-
- # next line
- lines_idx += 1
- if lines_idx < line_count:
- line = lines[lines_idx]
- if line:
- continuation = line[0] in (32, 9) # (' ', '\t')
- else:
- line = b""
- break
- bvalue = b"".join(bvalue_lst)
- else:
- if header_length > self.max_field_size:
- raise LineTooLong(
- "request header field {}".format(
- bname.decode("utf8", "xmlcharrefreplace")
- ),
- str(self.max_field_size),
- str(header_length),
- )
-
- bvalue = bvalue.strip()
- name = bname.decode("utf-8", "surrogateescape")
- value = bvalue.decode("utf-8", "surrogateescape")
-
- headers.add(name, value)
- raw_headers.append((bname, bvalue))
-
- return (CIMultiDictProxy(headers), tuple(raw_headers))
-
-
-class HttpParser(abc.ABC, Generic[_MsgT]):
- def __init__(
- self,
- protocol: Optional[BaseProtocol] = None,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- limit: int = 2**16,
- max_line_size: int = 8190,
- max_headers: int = 32768,
- max_field_size: int = 8190,
- timer: Optional[BaseTimerContext] = None,
- code: Optional[int] = None,
- method: Optional[str] = None,
- readall: bool = False,
- payload_exception: Optional[Type[BaseException]] = None,
- response_with_body: bool = True,
- read_until_eof: bool = False,
- auto_decompress: bool = True,
- ) -> None:
- self.protocol = protocol
- self.loop = loop
- self.max_line_size = max_line_size
- self.max_headers = max_headers
- self.max_field_size = max_field_size
- self.timer = timer
- self.code = code
- self.method = method
- self.readall = readall
- self.payload_exception = payload_exception
- self.response_with_body = response_with_body
- self.read_until_eof = read_until_eof
-
- self._lines: List[bytes] = []
- self._tail = b""
- self._upgraded = False
- self._payload = None
- self._payload_parser: Optional[HttpPayloadParser] = None
- self._auto_decompress = auto_decompress
- self._limit = limit
- self._headers_parser = HeadersParser(max_line_size, max_headers, max_field_size)
-
- @abc.abstractmethod
- def parse_message(self, lines: List[bytes]) -> _MsgT:
- pass
-
- def feed_eof(self) -> Optional[_MsgT]:
- if self._payload_parser is not None:
- self._payload_parser.feed_eof()
- self._payload_parser = None
- else:
- # try to extract partial message
- if self._tail:
- self._lines.append(self._tail)
-
- if self._lines:
- if self._lines[-1] != "\r\n":
- self._lines.append(b"")
- with suppress(Exception):
- return self.parse_message(self._lines)
- return None
-
- def feed_data(
- self,
- data: bytes,
- SEP: bytes = b"\r\n",
- EMPTY: bytes = b"",
- CONTENT_LENGTH: istr = hdrs.CONTENT_LENGTH,
- METH_CONNECT: str = hdrs.METH_CONNECT,
- SEC_WEBSOCKET_KEY1: istr = hdrs.SEC_WEBSOCKET_KEY1,
- ) -> Tuple[List[Tuple[_MsgT, StreamReader]], bool, bytes]:
-
- messages = []
-
- if self._tail:
- data, self._tail = self._tail + data, b""
-
- data_len = len(data)
- start_pos = 0
- loop = self.loop
-
- while start_pos < data_len:
-
- # read HTTP message (request/response line + headers), \r\n\r\n
- # and split by lines
- if self._payload_parser is None and not self._upgraded:
- pos = data.find(SEP, start_pos)
- # consume \r\n
- if pos == start_pos and not self._lines:
- start_pos = pos + 2
- continue
-
- if pos >= start_pos:
- # line found
- self._lines.append(data[start_pos:pos])
- start_pos = pos + 2
-
- # \r\n\r\n found
- if self._lines[-1] == EMPTY:
- try:
- msg: _MsgT = self.parse_message(self._lines)
- finally:
- self._lines.clear()
-
- def get_content_length() -> Optional[int]:
- # payload length
- length_hdr = msg.headers.get(CONTENT_LENGTH)
- if length_hdr is None:
- return None
-
- try:
- length = int(length_hdr)
- except ValueError:
- raise InvalidHeader(CONTENT_LENGTH)
-
- if length < 0:
- raise InvalidHeader(CONTENT_LENGTH)
-
- return length
-
- length = get_content_length()
- # do not support old websocket spec
- if SEC_WEBSOCKET_KEY1 in msg.headers:
- raise InvalidHeader(SEC_WEBSOCKET_KEY1)
-
- self._upgraded = msg.upgrade
-
- method = getattr(msg, "method", self.method)
-
- assert self.protocol is not None
- # calculate payload
- if (
- (length is not None and length > 0)
- or msg.chunked
- and not msg.upgrade
- ):
- payload = StreamReader(
- self.protocol,
- timer=self.timer,
- loop=loop,
- limit=self._limit,
- )
- payload_parser = HttpPayloadParser(
- payload,
- length=length,
- chunked=msg.chunked,
- method=method,
- compression=msg.compression,
- code=self.code,
- readall=self.readall,
- response_with_body=self.response_with_body,
- auto_decompress=self._auto_decompress,
- )
- if not payload_parser.done:
- self._payload_parser = payload_parser
- elif method == METH_CONNECT:
- assert isinstance(msg, RawRequestMessage)
- payload = StreamReader(
- self.protocol,
- timer=self.timer,
- loop=loop,
- limit=self._limit,
- )
- self._upgraded = True
- self._payload_parser = HttpPayloadParser(
- payload,
- method=msg.method,
- compression=msg.compression,
- readall=True,
- auto_decompress=self._auto_decompress,
- )
- else:
- if (
- getattr(msg, "code", 100) >= 199
- and length is None
- and self.read_until_eof
- ):
- payload = StreamReader(
- self.protocol,
- timer=self.timer,
- loop=loop,
- limit=self._limit,
- )
- payload_parser = HttpPayloadParser(
- payload,
- length=length,
- chunked=msg.chunked,
- method=method,
- compression=msg.compression,
- code=self.code,
- readall=True,
- response_with_body=self.response_with_body,
- auto_decompress=self._auto_decompress,
- )
- if not payload_parser.done:
- self._payload_parser = payload_parser
- else:
- payload = EMPTY_PAYLOAD
-
- messages.append((msg, payload))
- else:
- self._tail = data[start_pos:]
- data = EMPTY
- break
-
- # no parser, just store
- elif self._payload_parser is None and self._upgraded:
- assert not self._lines
- break
-
- # feed payload
- elif data and start_pos < data_len:
- assert not self._lines
- assert self._payload_parser is not None
- try:
- eof, data = self._payload_parser.feed_data(data[start_pos:])
- except BaseException as exc:
- if self.payload_exception is not None:
- self._payload_parser.payload.set_exception(
- self.payload_exception(str(exc))
- )
- else:
- self._payload_parser.payload.set_exception(exc)
-
- eof = True
- data = b""
-
- if eof:
- start_pos = 0
- data_len = len(data)
- self._payload_parser = None
- continue
- else:
- break
-
- if data and start_pos < data_len:
- data = data[start_pos:]
- else:
- data = EMPTY
-
- return messages, self._upgraded, data
-
- def parse_headers(
- self, lines: List[bytes]
- ) -> Tuple[
- "CIMultiDictProxy[str]", RawHeaders, Optional[bool], Optional[str], bool, bool
- ]:
- """Parses RFC 5322 headers from a stream.
-
- Line continuations are supported. Returns list of header name
- and value pairs. Header name is in upper case.
- """
- headers, raw_headers = self._headers_parser.parse_headers(lines)
- close_conn = None
- encoding = None
- upgrade = False
- chunked = False
-
- # keep-alive
- conn = headers.get(hdrs.CONNECTION)
- if conn:
- v = conn.lower()
- if v == "close":
- close_conn = True
- elif v == "keep-alive":
- close_conn = False
- elif v == "upgrade":
- upgrade = True
-
- # encoding
- enc = headers.get(hdrs.CONTENT_ENCODING)
- if enc:
- enc = enc.lower()
- if enc in ("gzip", "deflate", "br"):
- encoding = enc
-
- # chunking
- te = headers.get(hdrs.TRANSFER_ENCODING)
- if te is not None:
- if "chunked" == te.lower():
- chunked = True
- else:
- raise BadHttpMessage("Request has invalid `Transfer-Encoding`")
-
- if hdrs.CONTENT_LENGTH in headers:
- raise BadHttpMessage(
- "Content-Length can't be present with Transfer-Encoding",
- )
-
- return (headers, raw_headers, close_conn, encoding, upgrade, chunked)
-
- def set_upgraded(self, val: bool) -> None:
- """Set connection upgraded (to websocket) mode.
-
- :param bool val: new state.
- """
- self._upgraded = val
-
-
-class HttpRequestParser(HttpParser[RawRequestMessage]):
- """Read request status line.
-
- Exception .http_exceptions.BadStatusLine
- could be raised in case of any errors in status line.
- Returns RawRequestMessage.
- """
-
- def parse_message(self, lines: List[bytes]) -> RawRequestMessage:
- # request line
- line = lines[0].decode("utf-8", "surrogateescape")
- try:
- method, path, version = line.split(None, 2)
- except ValueError:
- raise BadStatusLine(line) from None
-
- if len(path) > self.max_line_size:
- raise LineTooLong(
- "Status line is too long", str(self.max_line_size), str(len(path))
- )
-
- # method
- if not METHRE.match(method):
- raise BadStatusLine(method)
-
- # version
- try:
- if version.startswith("HTTP/"):
- n1, n2 = version[5:].split(".", 1)
- version_o = HttpVersion(int(n1), int(n2))
- else:
- raise BadStatusLine(version)
- except Exception:
- raise BadStatusLine(version)
-
- if method == "CONNECT":
- # authority-form,
- # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.3
- url = URL.build(authority=path, encoded=True)
- elif path.startswith("/"):
- # origin-form,
- # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.1
- path_part, _hash_separator, url_fragment = path.partition("#")
- path_part, _question_mark_separator, qs_part = path_part.partition("?")
-
- # NOTE: `yarl.URL.build()` is used to mimic what the Cython-based
- # NOTE: parser does, otherwise it results into the same
- # NOTE: HTTP Request-Line input producing different
- # NOTE: `yarl.URL()` objects
- url = URL.build(
- path=path_part,
- query_string=qs_part,
- fragment=url_fragment,
- encoded=True,
- )
- else:
- # absolute-form for proxy maybe,
- # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.2
- url = URL(path, encoded=True)
-
- # read headers
- (
- headers,
- raw_headers,
- close,
- compression,
- upgrade,
- chunked,
- ) = self.parse_headers(lines)
-
- if close is None: # then the headers weren't set in the request
- if version_o <= HttpVersion10: # HTTP 1.0 must asks to not close
- close = True
- else: # HTTP 1.1 must ask to close.
- close = False
-
- return RawRequestMessage(
- method,
- path,
- version_o,
- headers,
- raw_headers,
- close,
- compression,
- upgrade,
- chunked,
- url,
- )
-
-
-class HttpResponseParser(HttpParser[RawResponseMessage]):
- """Read response status line and headers.
-
- BadStatusLine could be raised in case of any errors in status line.
- Returns RawResponseMessage.
- """
-
- def parse_message(self, lines: List[bytes]) -> RawResponseMessage:
- line = lines[0].decode("utf-8", "surrogateescape")
- try:
- version, status = line.split(None, 1)
- except ValueError:
- raise BadStatusLine(line) from None
-
- try:
- status, reason = status.split(None, 1)
- except ValueError:
- reason = ""
-
- if len(reason) > self.max_line_size:
- raise LineTooLong(
- "Status line is too long", str(self.max_line_size), str(len(reason))
- )
-
- # version
- match = VERSRE.match(version)
- if match is None:
- raise BadStatusLine(line)
- version_o = HttpVersion(int(match.group(1)), int(match.group(2)))
-
- # The status code is a three-digit number
- try:
- status_i = int(status)
- except ValueError:
- raise BadStatusLine(line) from None
-
- if status_i > 999:
- raise BadStatusLine(line)
-
- # read headers
- (
- headers,
- raw_headers,
- close,
- compression,
- upgrade,
- chunked,
- ) = self.parse_headers(lines)
-
- if close is None:
- close = version_o <= HttpVersion10
-
- return RawResponseMessage(
- version_o,
- status_i,
- reason.strip(),
- headers,
- raw_headers,
- close,
- compression,
- upgrade,
- chunked,
- )
-
-
-class HttpPayloadParser:
- def __init__(
- self,
- payload: StreamReader,
- length: Optional[int] = None,
- chunked: bool = False,
- compression: Optional[str] = None,
- code: Optional[int] = None,
- method: Optional[str] = None,
- readall: bool = False,
- response_with_body: bool = True,
- auto_decompress: bool = True,
- ) -> None:
- self._length = 0
- self._type = ParseState.PARSE_NONE
- self._chunk = ChunkState.PARSE_CHUNKED_SIZE
- self._chunk_size = 0
- self._chunk_tail = b""
- self._auto_decompress = auto_decompress
- self.done = False
-
- # payload decompression wrapper
- if response_with_body and compression and self._auto_decompress:
- real_payload: Union[StreamReader, DeflateBuffer] = DeflateBuffer(
- payload, compression
- )
- else:
- real_payload = payload
-
- # payload parser
- if not response_with_body:
- # don't parse payload if it's not expected to be received
- self._type = ParseState.PARSE_NONE
- real_payload.feed_eof()
- self.done = True
-
- elif chunked:
- self._type = ParseState.PARSE_CHUNKED
- elif length is not None:
- self._type = ParseState.PARSE_LENGTH
- self._length = length
- if self._length == 0:
- real_payload.feed_eof()
- self.done = True
- else:
- if readall and code != 204:
- self._type = ParseState.PARSE_UNTIL_EOF
- elif method in ("PUT", "POST"):
- internal_logger.warning( # pragma: no cover
- "Content-Length or Transfer-Encoding header is required"
- )
- self._type = ParseState.PARSE_NONE
- real_payload.feed_eof()
- self.done = True
-
- self.payload = real_payload
-
- def feed_eof(self) -> None:
- if self._type == ParseState.PARSE_UNTIL_EOF:
- self.payload.feed_eof()
- elif self._type == ParseState.PARSE_LENGTH:
- raise ContentLengthError(
- "Not enough data for satisfy content length header."
- )
- elif self._type == ParseState.PARSE_CHUNKED:
- raise TransferEncodingError(
- "Not enough data for satisfy transfer length header."
- )
-
- def feed_data(
- self, chunk: bytes, SEP: bytes = b"\r\n", CHUNK_EXT: bytes = b";"
- ) -> Tuple[bool, bytes]:
- # Read specified amount of bytes
- if self._type == ParseState.PARSE_LENGTH:
- required = self._length
- chunk_len = len(chunk)
-
- if required >= chunk_len:
- self._length = required - chunk_len
- self.payload.feed_data(chunk, chunk_len)
- if self._length == 0:
- self.payload.feed_eof()
- return True, b""
- else:
- self._length = 0
- self.payload.feed_data(chunk[:required], required)
- self.payload.feed_eof()
- return True, chunk[required:]
-
- # Chunked transfer encoding parser
- elif self._type == ParseState.PARSE_CHUNKED:
- if self._chunk_tail:
- chunk = self._chunk_tail + chunk
- self._chunk_tail = b""
-
- while chunk:
-
- # read next chunk size
- if self._chunk == ChunkState.PARSE_CHUNKED_SIZE:
- pos = chunk.find(SEP)
- if pos >= 0:
- i = chunk.find(CHUNK_EXT, 0, pos)
- if i >= 0:
- size_b = chunk[:i] # strip chunk-extensions
- else:
- size_b = chunk[:pos]
-
- try:
- size = int(bytes(size_b), 16)
- except ValueError:
- exc = TransferEncodingError(
- chunk[:pos].decode("ascii", "surrogateescape")
- )
- self.payload.set_exception(exc)
- raise exc from None
-
- chunk = chunk[pos + 2 :]
- if size == 0: # eof marker
- self._chunk = ChunkState.PARSE_MAYBE_TRAILERS
- else:
- self._chunk = ChunkState.PARSE_CHUNKED_CHUNK
- self._chunk_size = size
- self.payload.begin_http_chunk_receiving()
- else:
- self._chunk_tail = chunk
- return False, b""
-
- # read chunk and feed buffer
- if self._chunk == ChunkState.PARSE_CHUNKED_CHUNK:
- required = self._chunk_size
- chunk_len = len(chunk)
-
- if required > chunk_len:
- self._chunk_size = required - chunk_len
- self.payload.feed_data(chunk, chunk_len)
- return False, b""
- else:
- self._chunk_size = 0
- self.payload.feed_data(chunk[:required], required)
- chunk = chunk[required:]
- self._chunk = ChunkState.PARSE_CHUNKED_CHUNK_EOF
- self.payload.end_http_chunk_receiving()
-
- # toss the CRLF at the end of the chunk
- if self._chunk == ChunkState.PARSE_CHUNKED_CHUNK_EOF:
- if chunk[:2] == SEP:
- chunk = chunk[2:]
- self._chunk = ChunkState.PARSE_CHUNKED_SIZE
- else:
- self._chunk_tail = chunk
- return False, b""
-
- # if stream does not contain trailer, after 0\r\n
- # we should get another \r\n otherwise
- # trailers needs to be skiped until \r\n\r\n
- if self._chunk == ChunkState.PARSE_MAYBE_TRAILERS:
- head = chunk[:2]
- if head == SEP:
- # end of stream
- self.payload.feed_eof()
- return True, chunk[2:]
- # Both CR and LF, or only LF may not be received yet. It is
- # expected that CRLF or LF will be shown at the very first
- # byte next time, otherwise trailers should come. The last
- # CRLF which marks the end of response might not be
- # contained in the same TCP segment which delivered the
- # size indicator.
- if not head:
- return False, b""
- if head == SEP[:1]:
- self._chunk_tail = head
- return False, b""
- self._chunk = ChunkState.PARSE_TRAILERS
-
- # read and discard trailer up to the CRLF terminator
- if self._chunk == ChunkState.PARSE_TRAILERS:
- pos = chunk.find(SEP)
- if pos >= 0:
- chunk = chunk[pos + 2 :]
- self._chunk = ChunkState.PARSE_MAYBE_TRAILERS
- else:
- self._chunk_tail = chunk
- return False, b""
-
- # Read all bytes until eof
- elif self._type == ParseState.PARSE_UNTIL_EOF:
- self.payload.feed_data(chunk, len(chunk))
-
- return False, b""
-
-
-class DeflateBuffer:
- """DeflateStream decompress stream and feed data into specified stream."""
-
- decompressor: Any
-
- def __init__(self, out: StreamReader, encoding: Optional[str]) -> None:
- self.out = out
- self.size = 0
- self.encoding = encoding
- self._started_decoding = False
-
- if encoding == "br":
- if not HAS_BROTLI: # pragma: no cover
- raise ContentEncodingError(
- "Can not decode content-encoding: brotli (br). "
- "Please install `Brotli`"
- )
-
- class BrotliDecoder:
- # Supports both 'brotlipy' and 'Brotli' packages
- # since they share an import name. The top branches
- # are for 'brotlipy' and bottom branches for 'Brotli'
- def __init__(self) -> None:
- self._obj = brotli.Decompressor()
-
- def decompress(self, data: bytes) -> bytes:
- if hasattr(self._obj, "decompress"):
- return cast(bytes, self._obj.decompress(data))
- return cast(bytes, self._obj.process(data))
-
- def flush(self) -> bytes:
- if hasattr(self._obj, "flush"):
- return cast(bytes, self._obj.flush())
- return b""
-
- self.decompressor = BrotliDecoder()
- else:
- zlib_mode = 16 + zlib.MAX_WBITS if encoding == "gzip" else zlib.MAX_WBITS
- self.decompressor = zlib.decompressobj(wbits=zlib_mode)
-
- def set_exception(self, exc: BaseException) -> None:
- self.out.set_exception(exc)
-
- def feed_data(self, chunk: bytes, size: int) -> None:
- if not size:
- return
-
- self.size += size
-
- # RFC1950
- # bits 0..3 = CM = 0b1000 = 8 = "deflate"
- # bits 4..7 = CINFO = 1..7 = windows size.
- if (
- not self._started_decoding
- and self.encoding == "deflate"
- and chunk[0] & 0xF != 8
- ):
- # Change the decoder to decompress incorrectly compressed data
- # Actually we should issue a warning about non-RFC-compliant data.
- self.decompressor = zlib.decompressobj(wbits=-zlib.MAX_WBITS)
-
- try:
- chunk = self.decompressor.decompress(chunk)
- except Exception:
- raise ContentEncodingError(
- "Can not decode content-encoding: %s" % self.encoding
- )
-
- self._started_decoding = True
-
- if chunk:
- self.out.feed_data(chunk, len(chunk))
-
- def feed_eof(self) -> None:
- chunk = self.decompressor.flush()
-
- if chunk or self.size > 0:
- self.out.feed_data(chunk, len(chunk))
- if self.encoding == "deflate" and not self.decompressor.eof:
- raise ContentEncodingError("deflate")
-
- self.out.feed_eof()
-
- def begin_http_chunk_receiving(self) -> None:
- self.out.begin_http_chunk_receiving()
-
- def end_http_chunk_receiving(self) -> None:
- self.out.end_http_chunk_receiving()
-
-
-HttpRequestParserPy = HttpRequestParser
-HttpResponseParserPy = HttpResponseParser
-RawRequestMessagePy = RawRequestMessage
-RawResponseMessagePy = RawResponseMessage
-
-try:
- if not NO_EXTENSIONS:
- from ._http_parser import ( # type: ignore[import,no-redef]
- HttpRequestParser,
- HttpResponseParser,
- RawRequestMessage,
- RawResponseMessage,
- )
-
- HttpRequestParserC = HttpRequestParser
- HttpResponseParserC = HttpResponseParser
- RawRequestMessageC = RawRequestMessage
- RawResponseMessageC = RawResponseMessage
-except ImportError: # pragma: no cover
- pass
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/ADCD 1.13 Torrent.md b/spaces/lincquiQcaudo/Top-20-Diffusion/ADCD 1.13 Torrent.md
deleted file mode 100644
index 76f217bc573834fd1ddb50ca8311a98714fba2ba..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/ADCD 1.13 Torrent.md
+++ /dev/null
@@ -1,6 +0,0 @@
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If you have some Flash SWF files that you want to convert to video formats, such as AVI, MPEG, WMV, MOV, MP4, etc., you may need a powerful and easy-to-use tool to help you. iWisoft Flash SWF to Video Converter 3.4 is one of the best options for SWF to video conversion. It can convert Macromedia Flash SWF to video/audio/picture files of most popular formats with excellent quality and fast speed.
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In this article, we will show you how to use iWisoft Flash SWF to Video Converter 3.4 to convert Flash SWF to video in a few simple steps.
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d5da3c52bfBlender 2.80 is a major update to the popular open source 3D software that brings a redesigned user interface, a new real-time render engine, improved tools and gizmos, and much more. Whether you are into animation, modeling, VFX, games, or any other aspect of 3D creation, Blender 2.80 has something for you.
-One of the most noticeable changes in Blender 2.80 is the new user interface that puts the focus on the artwork that you create. A new dark theme and modern icon set were introduced, along with a new toolbar and quick favorites menu that provide rapid access to often-used tools. Keyboard, mouse and tablet interaction got a refresh with left click select as the new default[^1^].
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Blender 2.80 also introduces templates and workspaces that let you quickly get started with tasks like sculpting, texture painting or motion tracking. They can be customized to create your own efficient working environment[^1^].
-Thanks to the new modern 3D viewport, you will be able to display a scene optimized for the task you are performing. A new Workbench render engine was designed for getting work done in the viewport, supporting tasks like scene layout, modeling and sculpting. The engine also features overlays, providing fine control over which utilities are visible on top of the render[^1^].
-Overlays also work on top of Eevee and Cycles render previews, so you can edit and paint the scene with full shading. Eevee is a new physically based real-time renderer that works both as a renderer for final frames, and as the engine driving Blenderâs realtime viewport for creating assets. It has advanced features such as volumetrics, screen-space reflections and refractions, subsurface scattering, soft and contact shadows, depth of field, camera motion blur and bloom[^1^] [^2^].
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-Blender 2.80 is free to use, share, change and sell your work. It is made by hundreds of contributors from around the world who are passionate about 3D creation. You can download Blender 2.80 from the official website[^2^] or from one of the many mirrors available online.
- -If you want to learn more about Blender 2.80 and its features, you can check out the online manual, watch tutorials on YouTube or other platforms, join online communities like Blender Artists or Blender Stack Exchange, or enroll in courses offered by Blender Cloud or other providers.
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7196e7f11aSi tienes una laptop Sony Vaio PCG-61A11U y quieres instalar el driver de red para conectarte a internet, hay varias opciones que puedes seguir. En este artÃculo te explicamos cómo hacerlo paso a paso.
-El driver de red es el software que permite que tu laptop se comunique con el adaptador de red inalámbrica o el cable ethernet. Sin este driver, no podrás acceder a internet ni a otras redes locales. Por eso, es importante tenerlo actualizado y compatible con tu sistema operativo.
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Para descargar el driver de red para Sony Vaio PCG-61A11U, puedes usar uno de estos métodos:
-Esperamos que este artÃculo te haya sido útil para descargar el driver de red para Sony Vaio PCG-61A11U. Recuerda que si tienes algún problema o duda, puedes contactar con el soporte técnico de Sony o consultar los foros de ayuda en lÃnea.
- -Además del driver de red, es posible que necesites actualizar otros drivers para mejorar el rendimiento y la seguridad de tu laptop Sony Vaio PCG-61A11U. Algunos de los drivers más importantes son los de la tarjeta gráfica, el sonido, el teclado, el touchpad, la cámara web y el lector de tarjetas. Estos drivers te permiten aprovechar al máximo las funciones y caracterÃsticas de tu equipo.
-Para actualizar estos drivers, puedes seguir el mismo método que usaste para el driver de red. Es decir, puedes visitar la página oficial de Sony, usar un programa como Driver Easy o Driver Booster o ver un video tutorial en YouTube. Solo tienes que buscar el driver que corresponda a tu modelo y sistema operativo y descargarlo e instalarlo.
-Te recomendamos que actualices los drivers de tu laptop Sony Vaio PCG-61A11U con regularidad, al menos una vez al mes. Asà podrás evitar problemas de compatibilidad, errores, fallas o pérdida de datos. También podrás disfrutar de una mejor experiencia de usuario y una mayor velocidad y estabilidad en tu conexión a internet.
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Fbus by maestro 42 is a software developed by Maestro Team, a group of programmers who specialize in Nokia phone solutions. Fbus by maestro 42 can unlock, flash, reset or backup your Nokia DCT4 phones using a simple fbus cable that connects your phone to your PC. Fbus by maestro 42 supports most of the Nokia DCT4 models, such as 3100, 3200, 3510i, 6100, 6600, 7210, 7250, 8310 and more. Fbus by maestro 42 can also remove the security code of your Nokia phones in case you forgot it or someone changed it without your knowledge.
- -If you want to download fbus by maestro 42, you have several options to choose from. Here are some of the sources where you can download fbus by maestro 42:
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- - -Fbus by maestro 42 is a simple and effective software that can help you unlock, flash, reset or backup your Nokia DCT4 phones using a simple fbus cable. You can download fbus by maestro 42 from various sources and use it easily on your PC.
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If you are looking for a way to enhance your night flying experience in Microsoft Flight Simulator X, you might want to try Fsx Shockwave 3D Lights Redux. This add-on by A2A Simulations adds over 40 new lighting effects to your aircraft, including strobes, beacons, navigation, and runway lights. These lights cast realistic light out into 3D space, creating stunning visuals and a more immersive flying experience.
-In this article, we will show you how to download, install, and enjoy Fsx Shockwave 3D Lights Redux on your FSX. We will also provide some tips and tricks to get the most out of this add-on.
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The first step is to download Fsx Shockwave 3D Lights Redux from a reliable source. You can purchase it from simMarket[^1^], where you can also find other products by A2A Simulations. The price is â¬11.99 (about $13.50) and you will get a Mega-Pack that supports both FS2004 and FSX. If you have previously purchased Shockwave 3D Lights from simMarket, you can get an upgrade price of â¬4.99 (about $5.60).
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- -Once you have downloaded Fsx Shockwave 3D Lights Redux, you will need to install it on your FSX. The installation process is easy and low-risk, as the installer backs up all files into a single, organized backup directory. You can also uninstall the add-on at any time if you wish.
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d5da3c52bfBahubali - The Beginning is a 2015 Indian epic action film directed by S.S. Rajamouli. It is the first installment of a two-part series that tells the story of Amarendra Bahubali, a legendary prince who must reclaim his rightful place as the king of Mahishmati from his evil cousin Bhallaladeva. The film is one of the highest-grossing Indian films of all time and has received critical acclaim for its visual effects, cinematography, music, and performances.
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If you are a fan of epic movies with stunning visuals, thrilling action, and captivating drama, then you should definitely watch Bahubali - The Beginning dubbed in Hindi full movie in MP4. In this article, we will tell you why you should watch this movie in Hindi, how to download it from a reliable source, what to expect from it, and how to enjoy it. So, let's get started!
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If you are a fan of romantic movies, you might have heard of Endless Love, a 2014 American romantic drama film directed by Shana Feste. The film is a remake of the 1981 film of the same name, which was based on a novel by Scott Spencer. The film stars Gabriella Wilde and Alex Pettyfer as two young lovers who face opposition from their parents and society.
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The film ends with David narrating that he does not know what will happen next, but he knows that he will always love Jade.
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-Alex Pettyfer is known for his roles in films like I Am Number Four, Beastly, Magic Mike, and The Butler. He delivers a charming and charismatic performance as David, who is a kind-hearted, loyal, brave, and romantic young man. He shows his range as an actor by expressing his anger, frustration, pain, joy, and love with conviction.
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-In conclusion, Endless Love is a romantic drama film that tells the story of two young lovers who face opposition from their parents and society. The film has beautiful cinematography, soundtrack, and performances, but also suffers from some clichés, melodrama, and lack of originality. The film explores various themes such as love, family, class, freedom, destiny, and choices. It is a movie that will touch your heart and make you believe in the power of love. If you want to download Endless Love movie in Hindi, read on to find out how.
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Here are some of the frequently asked questions about Endless Love movie:
-The actors of Endless Love movie are Gabriella Wilde as Jade Butterfield, Alex Pettyfer as David Elliot, Bruce Greenwood as Hugh Butterfield, Joely Richardson as Anne Butterfield, Rhys Wakefield as Keith Butterfield, Dayo Okeniyi as Mace Green, Patrick Johnson as Miles, Emma Rigby as Jenny, Robert Patrick as Harry Elliot, Anna Enger as Sabine, Fabianne Therese as Checka, and Sharon Conley as Dr. Edie Watanabe.
-Yes, Endless Love movie is based on a book of the same name by Scott Spencer, published in 1979. The book is a romantic novel that tells the story of David Axelrod and Jade Butterfield, two teenagers who fall in love and become obsessed with each other. The book was a bestseller and was praised for its literary style and psychological depth. The book was also adapted into a film in 1981, starring Brooke Shields and Martin Hewitt.
-No, Endless Love movie is not available on Netflix at the moment. However, you can watch it on other streaming platforms like Amazon Prime Video, Hulu, HBO Max, Peacock, or Vudu.
-Endless Love movie is 104 minutes long.
-No, there is no sequel to Endless Love movie. However, there is a sequel to the book by Scott Spencer called Waking the Dead, published in 1986. The book follows David Axelrod as he becomes a politician and encounters a woman who resembles his dead lover Jade.
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Depending on the source you downloaded from, you may have different options to choose the difficulty level of the game. Generally, you can choose between one suit (easy), two suits (medium), or four suits (hard). The more suits you have, the harder it is to complete the game. You can change the difficulty level anytime before starting a new game.
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-Sor Juana Inés de la Cruz fue una de las más destacadas poetisas del Siglo de Oro español y una de las primeras defensoras de los derechos de las mujeres. Su obra abarca diversos géneros como la lÃrica, el teatro, la prosa y la epÃstola. Entre sus poemas más famosos se encuentra el soneto "En perseguirme mundo ¿qué interesas?", en el que expresa su rechazo a las crÃticas y persecuciones que sufrÃa por dedicarse al estudio y al cultivo de su inteligencia.
-Este soneto es un ejemplo del conceptismo, una corriente literaria que se caracteriza por el uso de juegos verbales ingeniosos, paradojas, antÃtesis y retruécanos. Estas figuras literarias buscan sorprender al lector y transmitir ideas profundas y complejas con brevedad y claridad. Veamos algunos ejemplos de estas figuras en el poema de Sor Juana:
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Estas figuras literarias le permiten a Sor Juana expresar su rebeldÃa ante un mundo que la juzga y la condena por ser mujer y por ser sabia. Con su poesÃa, ella demuestra su talento, su erudición y su independencia, y se convierte en una voz precursora del feminismo y de la libertad intelectual.
Here is a possible continuation of the article: - -Además de las figuras literarias, otro aspecto importante del soneto de Sor Juana Inés de la Cruz es el contexto histórico y biográfico en el que fue escrito. Sor Juana Inés de la Cruz nació en 1648 en la hacienda de San Miguel Nepantla, en el actual Estado de México. Fue hija natural de una criolla y un español, y desde muy niña mostró una gran inteligencia y curiosidad por el saber. Aprendió a leer y escribir a los tres años, y a los ocho escribió su primera loa. En 1659 se trasladó con su familia a la Ciudad de México, donde fue dama de honor de la virreina Leonor Carreto, esposa del virrey Antonio Sebastián de Toledo. En la corte virreinal deslumbró por su erudición y su poesÃa, y fue apadrinada por los marqueses de Mancera.
-En 1667 ingresó en un convento de las carmelitas descalzas, pero lo abandonó al poco tiempo por problemas de salud. Dos años después entró en otro convento, el de las jerónimas de San Jerónimo, donde permaneció hasta su muerte en 1695. Allà convirtió su celda en un verdadero centro cultural, donde reunió una amplia biblioteca, realizó experimentos cientÃficos, compuso música y escribió obras de diversos géneros. También recibió la visita de poetas e intelectuales, como Carlos de Sigüenza y Góngora, y mantuvo una estrecha amistad con la nueva virreina, Luisa Manrique de Lara, condesa de Paredes.
-Sin embargo, su vida no estuvo exenta de dificultades y conflictos. Su afán de conocimiento y su libertad de expresión chocaron con los prejuicios y las presiones de una sociedad patriarcal y religiosa que no toleraba que una mujer se dedicara al estudio y a la escritura. Sor Juana Inés tuvo que enfrentarse a las crÃticas y censuras de algunos eclesiásticos que la acusaban de soberbia e inmodestia. Entre ellos se destacó el obispo de Puebla, Manuel Fernández de Santa Cruz, quien publicó sin su permiso una carta suya en la que criticaba un sermón del jesuita portugués Antonio Vieira. El obispo le añadió una carta propia bajo el seudónimo de Sor Filotea de la Cruz, en la que le aconsejaba que abandonara sus estudios teológicos y se dedicara a la vida monástica.
-Sor Juana Inés respondió con una carta admirable, conocida como Respuesta a Sor Filotea de la Cruz, en la que defendió su derecho al saber y al pensamiento crÃtico. En ella expuso sus razones para entrar en el convento, su pasión por el aprendizaje desde la infancia, su admiración por las mujeres sabias de la historia y su rechazo al matrimonio. También argumentó que el estudio era un medio para acercarse a Dios y que no habÃa ninguna ley divina ni humana que prohibiera a las mujeres cultivar su inteligencia. La carta es un documento excepcional que revela la personalidad y el talento de Sor Juana Inés, asà como su valentÃa para enfrentarse al poder establecido.
-Después de esta carta, Sor Juana Inés sufrió una fuerte represión por parte del clero. Tuvo que vender su biblioteca y sus instrumentos cientÃficos, y renunciar a sus actividades intelectuales. Se dedicó entonces a las labores propias del convento, como cuidar a los enfermos durante una epidemia de cólera que azotó la ciudad en 1695. Fue asà como contrajo la enfermedad y murió el 17 de abril de ese año.
- -Su obra quedó silenciada durante mucho tiempo, hasta que fue redescubierta en el siglo XIX por algunos
7196e7f11aRunning on CPU 🥶 This demo does not work on CPU.
' - -MAX_SEED = np.iinfo(np.int32).max -CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv( - 'CACHE_EXAMPLES') == '1' -MAX_IMAGE_SIZE = int(os.getenv('MAX_IMAGE_SIZE', '1024')) -USE_TORCH_COMPILE = os.getenv('USE_TORCH_COMPILE') == '1' -ENABLE_CPU_OFFLOAD = os.getenv('ENABLE_CPU_OFFLOAD') == '1' - -device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') -if torch.cuda.is_available(): - pipe = DiffusionPipeline.from_pretrained( - 'stabilityai/stable-diffusion-xl-base-1.0', - torch_dtype=torch.float16, - use_safetensors=True, - variant='fp16') - refiner = DiffusionPipeline.from_pretrained( - 'stabilityai/stable-diffusion-xl-refiner-1.0', - torch_dtype=torch.float16, - use_safetensors=True, - variant='fp16') - - if ENABLE_CPU_OFFLOAD: - pipe.enable_model_cpu_offload() - refiner.enable_model_cpu_offload() - else: - pipe.to(device) - refiner.to(device) - - if USE_TORCH_COMPILE: - pipe.unet = torch.compile(pipe.unet, - mode='reduce-overhead', - fullgraph=True) -else: - pipe = None - refiner = None - - -def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: - if randomize_seed: - seed = random.randint(0, MAX_SEED) - return seed - - -def generate(prompt: str, - negative_prompt: str = '', - prompt_2: str = '', - negative_prompt_2: str = '', - use_negative_prompt: bool = False, - use_prompt_2: bool = False, - use_negative_prompt_2: bool = False, - seed: int = 0, - width: int = 1024, - height: int = 1024, - guidance_scale_base: float = 5.0, - guidance_scale_refiner: float = 5.0, - num_inference_steps_base: int = 50, - num_inference_steps_refiner: int = 50, - apply_refiner: bool = False) -> PIL.Image.Image: - generator = torch.Generator().manual_seed(seed) - - if not use_negative_prompt: - negative_prompt = None # type: ignore - if not use_prompt_2: - prompt_2 = None # type: ignore - if not use_negative_prompt_2: - negative_prompt_2 = None # type: ignore - - if not apply_refiner: - return pipe(prompt=prompt, - negative_prompt=negative_prompt, - prompt_2=prompt_2, - negative_prompt_2=negative_prompt_2, - width=width, - height=height, - guidance_scale=guidance_scale_base, - num_inference_steps=num_inference_steps_base, - generator=generator, - output_type='pil').images[0] - else: - latents = pipe(prompt=prompt, - negative_prompt=negative_prompt, - prompt_2=prompt_2, - negative_prompt_2=negative_prompt_2, - width=width, - height=height, - guidance_scale=guidance_scale_base, - num_inference_steps=num_inference_steps_base, - generator=generator, - output_type='latent').images - image = refiner(prompt=prompt, - negative_prompt=negative_prompt, - prompt_2=prompt_2, - negative_prompt_2=negative_prompt_2, - guidance_scale=guidance_scale_refiner, - num_inference_steps=num_inference_steps_refiner, - image=latents, - generator=generator).images[0] - return image - - -examples = [ - 'Astronaut in a jungle, cold color palette, muted colors, detailed, 8k', - 'An astronaut riding a green horse', -] - -with gr.Blocks(css='style.css') as demo: - gr.Markdown(DESCRIPTION) - gr.DuplicateButton(value='Duplicate Space for private use', - elem_id='duplicate-button', - visible=os.getenv('SHOW_DUPLICATE_BUTTON') == '1') - with gr.Box(): - with gr.Row(): - prompt = gr.Text( - label='Prompt', - show_label=False, - max_lines=1, - placeholder='Enter your prompt', - container=False, - ) - run_button = gr.Button('Run', scale=0) - result = gr.Image(label='Result', show_label=False) - with gr.Accordion('Advanced options', open=False): - with gr.Row(): - use_negative_prompt = gr.Checkbox(label='Use negative prompt', - value=False) - use_prompt_2 = gr.Checkbox(label='Use prompt 2', value=False) - use_negative_prompt_2 = gr.Checkbox( - label='Use negative prompt 2', value=False) - negative_prompt = gr.Text( - label='Negative prompt', - max_lines=1, - placeholder='Enter a negative prompt', - visible=False, - ) - prompt_2 = gr.Text( - label='Prompt 2', - max_lines=1, - placeholder='Enter your prompt', - visible=False, - ) - negative_prompt_2 = gr.Text( - label='Negative prompt 2', - max_lines=1, - placeholder='Enter a negative prompt', - visible=False, - ) - - seed = gr.Slider(label='Seed', - minimum=0, - maximum=MAX_SEED, - step=1, - value=0) - randomize_seed = gr.Checkbox(label='Randomize seed', value=True) - with gr.Row(): - width = gr.Slider( - label='Width', - minimum=256, - maximum=MAX_IMAGE_SIZE, - step=32, - value=1024, - ) - height = gr.Slider( - label='Height', - minimum=256, - maximum=MAX_IMAGE_SIZE, - step=32, - value=1024, - ) - apply_refiner = gr.Checkbox(label='Apply refiner', value=False) - with gr.Row(): - guidance_scale_base = gr.Slider( - label='Guidance scale for base', - minimum=1, - maximum=20, - step=0.1, - value=5.0) - num_inference_steps_base = gr.Slider( - label='Number of inference steps for base', - minimum=10, - maximum=100, - step=1, - value=50) - with gr.Row(visible=False) as refiner_params: - guidance_scale_refiner = gr.Slider( - label='Guidance scale for refiner', - minimum=1, - maximum=20, - step=0.1, - value=5.0) - num_inference_steps_refiner = gr.Slider( - label='Number of inference steps for refiner', - minimum=10, - maximum=100, - step=1, - value=50) - - gr.Examples(examples=examples, - inputs=prompt, - outputs=result, - fn=generate, - cache_examples=CACHE_EXAMPLES) - - use_negative_prompt.change( - fn=lambda x: gr.update(visible=x), - inputs=use_negative_prompt, - outputs=negative_prompt, - queue=False, - api_name=False, - ) - use_prompt_2.change( - fn=lambda x: gr.update(visible=x), - inputs=use_prompt_2, - outputs=prompt_2, - queue=False, - api_name=False, - ) - use_negative_prompt_2.change( - fn=lambda x: gr.update(visible=x), - inputs=use_negative_prompt_2, - outputs=negative_prompt_2, - queue=False, - api_name=False, - ) - apply_refiner.change( - fn=lambda x: gr.update(visible=x), - inputs=apply_refiner, - outputs=refiner_params, - queue=False, - api_name=False, - ) - - inputs = [ - prompt, - negative_prompt, - prompt_2, - negative_prompt_2, - use_negative_prompt, - use_prompt_2, - use_negative_prompt_2, - seed, - width, - height, - guidance_scale_base, - guidance_scale_refiner, - num_inference_steps_base, - num_inference_steps_refiner, - apply_refiner, - ] - prompt.submit( - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - queue=False, - api_name=False, - ).then( - fn=generate, - inputs=inputs, - outputs=result, - api_name='run', - ) - negative_prompt.submit( - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - queue=False, - api_name=False, - ).then( - fn=generate, - inputs=inputs, - outputs=result, - api_name=False, - ) - run_button.click( - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - queue=False, - api_name=False, - ).then( - fn=generate, - inputs=inputs, - outputs=result, - api_name=False, - ) -demo.queue(max_size=20).launch() diff --git a/spaces/subhajitmaji/MusicGen/audiocraft/modules/codebooks_patterns.py b/spaces/subhajitmaji/MusicGen/audiocraft/modules/codebooks_patterns.py deleted file mode 100644 index c5b35cbea8cff84aa56116dbdd860fc72a913a13..0000000000000000000000000000000000000000 --- a/spaces/subhajitmaji/MusicGen/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/sunshineatnoon/TextureScraping/swapae/data/unaligned_lmdb_dataset.py b/spaces/sunshineatnoon/TextureScraping/swapae/data/unaligned_lmdb_dataset.py deleted file mode 100644 index 0fd688bbfcbbc304d8a741cbc1afbaac08465f60..0000000000000000000000000000000000000000 --- a/spaces/sunshineatnoon/TextureScraping/swapae/data/unaligned_lmdb_dataset.py +++ /dev/null @@ -1,31 +0,0 @@ -import random -import os.path -from swapae.data.base_dataset import BaseDataset -from swapae.data.lmdb_dataset import LMDBDataset -import swapae.util - - -class UnalignedLMDBDataset(BaseDataset): - def __init__(self, opt): - super().__init__(opt) - self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA' - self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB' - - self.dataset_A = LMDBDataset(util.copyconf(opt, dataroot=self.dir_A)) - self.dataset_B = LMDBDataset(util.copyconf(opt, dataroot=self.dir_B)) - self.B_indices = list(range(len(self.dataset_B))) - - - def __len__(self): - return max(len(self.dataset_A), len(self.dataset_B)) - - def __getitem__(self, index): - if index == 0 and self.opt.isTrain: - random.shuffle(self.B_indices) - - result = self.dataset_A.__getitem__(index % len(self.dataset_A)) - B_index = self.B_indices[index % len(self.dataset_B)] - B_result = self.dataset_B.__getitem__(B_index) - result["real_B"] = B_result["real_A"] - result["path_B"] = B_result["path_A"] - return result diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Applied Acoustics Chromaphone V1.0.6 WIN.OSX Incl. 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-The game was well received by critics and players alike, who praised its graphics, sound, gameplay, and innovation. It sold over six million copies worldwide and became one of the best-selling PlayStation games of all time. It also won several awards, such as the Best Platform Game at the Interactive Achievement Awards and the Best New Character at the GameSpot Awards. The game spawned two direct sequels on the PlayStation: Crash Bandicoot 2: Cortex Strikes Back in 1997 and Crash Bandicoot: Warped in 1998. These games improved upon the original game by adding new features, such as more moves, levels, items, and characters. The game also inspired many spin-offs in different genres, such as racing, party, and action-adventure games.
-If you want to play Crash Bandicoot 1 on PC today, you have three main options: playing the official remastered version of the game in the Crash Bandicoot N. Sane Trilogy by Activision in 2017; playing the unofficial bootleg compilation of the first three Crash Bandicoot games by vasyaXYI in 1999; or playing the emulation of the original PlayStation version of the game using software such as ePSXe or PCSX. Let's take a look at each option in more detail.
-, which is a collection of the first three Crash Bandicoot games remade from the ground up by Vicarious Visions and published by Activision in 2017 for various platforms, including PC. The remastered version features updated graphics, sound, and controls, while retaining the original gameplay and level design. The remastered version also adds some new features, such as time trials, online leaderboards, and achievements. The remastered version is available for purchase on Steam for $39.99 USD.
-The unofficial bootleg compilation of Crash Bandicoot 1 is part of the Crash Bandicoot Collection 1, 2, 3 by vasyaXYI, which is a bootleg compilation of the first three Crash Bandicoot games for PlayStation. This compilation is possible (without barely any ripping, to boot) because of how small the games are. All games included are US versions, unmodified. The bootleg compilation was released in 1999 and can be downloaded for free from the Internet Archive. The bootleg compilation can be played on PC using a PlayStation emulator or a disc drive.
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-Now that we have seen the options for playing Crash Bandicoot 1 on PC, let's compare them and see what are the pros and cons of each option. Here is a table that summarizes the main advantages and disadvantages of each option:
- | Option | Pros | Cons | | --- | --- | --- | | The official remastered version | - Improved graphics, sound, and controls | - New features, such as time trials, online leaderboards, and achievements | - Official and legal | - Easy to install and play | - Higher price ($39.99 USD) | - Higher system requirements | - Possible bugs and glitches | - May lose some of the original charm and nostalgia | | The unofficial bootleg compilation | - Low cost (free) | - Easy to install and play | - Nostalgia factor | - Includes all three games in one disc | - Poor quality | - Illegal and unethical | - Compatibility problems with modern systems | - May not work with some emulators or disc drives | | The emulation of the original PlayStation version | - Authentic and faithful to the original game | - Customizable and flexible (can adjust settings, use cheats, save states, etc.) | - Low cost (free or cheap) | - Can play other PlayStation games as well | - Technical difficulties (need to configure emulator and game file) | - Ethical concerns (need to own the original game or obtain it legally) | - Performance issues (may lag, crash, or have graphical errors) |So, which option is the best way to play Crash Bandicoot 1 on PC? Well, that depends on what you are looking for and what you are willing to compromise. There is no definitive answer to this question, as different players may have different preferences and opinions. However, we can try to give some general guidelines and recommendations based on some common criteria, such as fun, convenience, reliability, and value.
-, you might want to go for the official remastered version. This option offers the best graphics, sound, and controls, as well as some new features that add more challenge and replay value to the game. You will also get to experience the game in a fresh and modern way, while still keeping the original gameplay and level design. The only downside is that you will have to pay a relatively high price for the game and meet the system requirements to run it smoothly. You may also encounter some bugs and glitches along the way, or feel that the game has lost some of its original charm and nostalgia.
-If you are looking for the most convenient and reliable way to play Crash Bandicoot 1 on PC, you might want to go for the unofficial bootleg compilation. This option offers the easiest and fastest way to install and play the game, as you only need to download one file and run it on your PC using an emulator or a disc drive. You will also get to enjoy the nostalgia factor of playing the game as it was back in 1999, as well as having all three games in one disc. The only downside is that you will have to deal with the poor quality of the game, such as the low resolution, pixelated graphics, distorted sound, and clunky controls. You will also have to face the legal and ethical issues of playing a bootleg game that infringes on the copyrights of the original developers and publishers. You may also have compatibility problems with modern systems or some emulators or disc drives.
-, you might want to go for the emulation of the original PlayStation version. This option offers the most faithful and accurate way to play the game as it was in 1996, without any changes or modifications. You will also get to customize and adjust the game to your liking, such as changing the settings, using cheats, saving states, etc. You will also be able to play other PlayStation games as well, if you have the emulator and the game files. The only downside is that you will have to deal with the technical difficulties of setting up and configuring the emulator and the game file, which may require some knowledge and skills. You will also have to deal with the ethical concerns of owning or obtaining the original game legally, which may not be easy or possible for some players. You may also have performance issues, such as lag, crash, or graphical errors, depending on your system and emulator.
-As you can see, each option has its own pros and cons, and there is no clear winner or loser. The best way to play Crash Bandicoot 1 on PC depends on your personal preference and situation. You may want to try out different options and see which one suits you best. Or you may want to stick with one option and enjoy it as much as you can. The choice is yours.
-In conclusion, Crash Bandicoot 1 is a classic 3D platformer that was released in 1996 for the PlayStation. It is a fun and challenging game that features a lovable character, a colorful world, and a catchy soundtrack. If you want to play this game on PC today, you have three main options: playing the official remastered version in the Crash Bandicoot N. Sane Trilogy by Activision in 2017; playing the unofficial bootleg compilation of the first three Crash Bandicoot games by vasyaXYI in 1999; or playing the emulation of the original PlayStation version using software such as ePSXe or PCSX. Each option has its own advantages and disadvantages, and the best way to play depends on your needs and preferences. We hope that this article has helped you understand more about Crash Bandicoot 1 and how to play it on PC.
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-, and items. It also has a charming and humorous story that features a memorable cast of characters, a vibrant and colorful world, and a catchy and upbeat soundtrack. It is a fun and challenging game that appeals to both casual and hardcore gamers.
-A: Yes, Crash Bandicoot 1 is hard, especially for modern standards. The game has a high difficulty curve that requires precise timing, reflexes, and skills. The game also has a limited number of lives and checkpoints, which means that you have to restart the level or the game if you run out of them. The game also has some frustrating and unfair moments, such as hidden traps, cheap deaths, and tricky jumps. The game is not impossible to beat, but it will test your patience and perseverance.
-A: Yes, Crash Bandicoot 1 is worth playing, especially if you are a fan of platformers or retro games. The game is a classic that has influenced many other games in the genre and the industry. The game is also a part of the Crash Bandicoot series, which is one of the most popular and beloved franchises in gaming history. The game is also a nostalgic trip for many players who grew up with it or played it in their childhood. The game is also a fun and enjoyable experience that will make you laugh, smile, and rage.
-A: It depends on your skill level, play style, and goals. According to HowLongToBeat.com, the average time to beat Crash Bandicoot 1 is about 6 hours for the main story, 9 hours for the main story plus extras, and 15 hours for the completionist run. However, these times may vary depending on how fast or slow you play, how many times you die or retry, how much you explore or collect, and how much you aim for 100% completion or achievements.
-A: As we mentioned before, you have three main options for playing Crash Bandicoot 1 on PC: playing the official remastered version in the Crash Bandicoot N. Sane Trilogy by Activision in 2017; playing the unofficial bootleg compilation of the first three Crash Bandicoot games by vasyaXYI in 1999; or playing the emulation of the original PlayStation version using software such as ePSXe or PCSX. If you want to buy or download any of these options, here are some links that may help you:
- - The official remastered version: https://store.steampowered.com/app/731490/Crash_Bandicoot_N_Sane_Trilogy/ - The unofficial bootleg compilation: https://archive.org/details/crash-bandicoot-collection - The emulation of the original PlayStation version: https://www.emuparadise.me/Sony_Playstation_ISOs/Crash_Bandicoot_[U]/36829Please note that we do not endorse or support any illegal or unethical activities related to downloading or playing video games. Please use these links at your own risk and discretion.
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Before we get into the details of how to install and use the driver for PW-DN4210D, let's first understand what this device is and why you need a driver for it.
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-A driver is a software program that allows your computer to communicate with a hardware device. Without a driver, your computer will not be able to recognize or use the device properly. Therefore, you need a driver for PW-DN4210D if you want to use it on your computer.
-to download the driver for PW-DN4210D is from the official website of Proware, which is https://oemdrivers.com/network-proware-pw-dn4210d. There you can find the latest and compatible driver for your device and your operating system. Alternatively, you can also download the driver from other sources, such as https://www.minihere.com/pw-dn4210d-ar9271-150mbps-usb-wifi-adapter-driver-download.html, which provides the driver for Windows 7/8/10 and Linux.
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To install the driver for PW-DN4210D, you need to open Device Manager on your computer. You can do this by pressing Windows key + X and selecting Device Manager from the menu. Alternatively, you can also search for Device Manager in the Start menu or Cortana. Once you open Device Manager, you will see a list of devices connected to your computer. Expand the Network adapters category and look for a device named Wireless Network Adapter or something similar with a yellow exclamation mark next to it. This means that the device is not recognized by your computer and needs a driver.
-To install the driver for PW-DN4210D, you need to right-click on the device and select Update driver software from the context menu. You will see a window that asks you how do you want to search for driver software. Choose Browse my computer for driver software option.
-To install the driver for PW-DN4210D, you need to browse to the folder where you extracted the driver file in step 2. Click on Browse button and navigate to the folder named PW-DN4210D_Win7_8_10. Select this folder and click OK. Then click Next button to start installing the driver.
-the driver for PW-DN4210D, you will see a message that says "Windows has successfully updated your driver software". You can click Close to exit the window. You may need to restart your computer for the changes to take effect.
-After installing the driver for PW-DN4210D, you can use the device to connect to a wireless network and access the internet. Here are some tips on how to use the device on Windows.
-To connect to a wireless network with PW-DN4210D, use these steps:
-You can now browse the web, stream videos, play games and do other online activities with your PW-DN4210D USB WiFi adapter.
-To configure the wireless settings with PW-DN4210D, use these steps:
-You can also access more advanced settings by clicking on Change adapter options under Advanced network settings. This will open Network Connections where you can right-click on your wireless adapter and select Properties. You can then configure various protocols, services and features for your wireless connection.
-WPS stands for Wi-Fi Protected Setup, which is a feature that allows you to connect to a secure wireless network without entering a password. To enable WPS function with PW-DN4210D, use these steps:
-You can now enjoy a secure and fast wireless connection with your PW-DN4210D USB WiFi adapter.
-PW-DN4210D is a USB WiFi adapter that can provide you with high-speed wireless internet connection. It supports IEEE 802.11n standard, has a detachable 4dBi antenna and a WPS button. To use it on Windows, you need to download and install the driver from the official website or other sources. Then you can connect to a wireless network, configure the wireless settings and enable WPS function with ease. We hope this article has helped you learn how to download, install and use the driver for PW-DN4210D on Windows operating system.
-Here are some frequently asked questions about PW-DN4210D USB WiFi adapter and its driver.
-Yes, PW-DN4210D is compatible with Windows 11. You can use the same driver as for Windows 10 or download it from https://oemdrivers.com/network-proware-pw-dn4210d.
-To uninstall the driver for PW-DN4210D, use these steps:
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-You can also manually download the latest driver file from the website and follow the steps 2 to 7 in the previous section to install it.
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- - \ No newline at end of file diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/more_itertools/recipes.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/more_itertools/recipes.py deleted file mode 100644 index 521abd7c2ca633f90a5ba13a8060c5c3d0c32205..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/more_itertools/recipes.py +++ /dev/null @@ -1,620 +0,0 @@ -"""Imported from the recipes section of the itertools documentation. - -All functions taken from the recipes section of the itertools library docs -[1]_. -Some backward-compatible usability improvements have been made. - -.. [1] http://docs.python.org/library/itertools.html#recipes - -""" -import warnings -from collections import deque -from itertools import ( - chain, - combinations, - count, - cycle, - groupby, - islice, - repeat, - starmap, - tee, - zip_longest, -) -import operator -from random import randrange, sample, choice - -__all__ = [ - 'all_equal', - 'consume', - 'convolve', - 'dotproduct', - 'first_true', - 'flatten', - 'grouper', - 'iter_except', - 'ncycles', - 'nth', - 'nth_combination', - 'padnone', - 'pad_none', - 'pairwise', - 'partition', - 'powerset', - 'prepend', - 'quantify', - 'random_combination_with_replacement', - 'random_combination', - 'random_permutation', - 'random_product', - 'repeatfunc', - 'roundrobin', - 'tabulate', - 'tail', - 'take', - 'unique_everseen', - 'unique_justseen', -] - - -def take(n, iterable): - """Return first *n* items of the iterable as a list. - - >>> take(3, range(10)) - [0, 1, 2] - - If there are fewer than *n* items in the iterable, all of them are - returned. - - >>> take(10, range(3)) - [0, 1, 2] - - """ - return list(islice(iterable, n)) - - -def tabulate(function, start=0): - """Return an iterator over the results of ``func(start)``, - ``func(start + 1)``, ``func(start + 2)``... - - *func* should be a function that accepts one integer argument. - - If *start* is not specified it defaults to 0. It will be incremented each - time the iterator is advanced. - - >>> square = lambda x: x ** 2 - >>> iterator = tabulate(square, -3) - >>> take(4, iterator) - [9, 4, 1, 0] - - """ - return map(function, count(start)) - - -def tail(n, iterable): - """Return an iterator over the last *n* items of *iterable*. - - >>> t = tail(3, 'ABCDEFG') - >>> list(t) - ['E', 'F', 'G'] - - """ - return iter(deque(iterable, maxlen=n)) - - -def consume(iterator, n=None): - """Advance *iterable* by *n* steps. If *n* is ``None``, consume it - entirely. - - Efficiently exhausts an iterator without returning values. Defaults to - consuming the whole iterator, but an optional second argument may be - provided to limit consumption. - - >>> i = (x for x in range(10)) - >>> next(i) - 0 - >>> consume(i, 3) - >>> next(i) - 4 - >>> consume(i) - >>> next(i) - Traceback (most recent call last): - File " Airport Simulator 2015 Crack Serial KeyDOWNLOAD ○ https://urlcod.com/2uyX7M - - aaccfb2cb3 - - - diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/dist.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/dist.py deleted file mode 100644 index 6de029f5c96ea237d8b9e4fc5f8e1d605f506d35..0000000000000000000000000000000000000000 --- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/dist.py +++ /dev/null @@ -1,67 +0,0 @@ -# Ultralytics YOLO 🚀, AGPL-3.0 license - -import os -import re -import shutil -import socket -import sys -import tempfile -from pathlib import Path - -from . import USER_CONFIG_DIR -from .torch_utils import TORCH_1_9 - - -def find_free_network_port() -> int: - """Finds a free port on localhost. - - It is useful in single-node training when we don't want to connect to a real main node but have to set the - `MASTER_PORT` environment variable. - """ - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind(('127.0.0.1', 0)) - return s.getsockname()[1] # port - - -def generate_ddp_file(trainer): - """Generates a DDP file and returns its file name.""" - module, name = f'{trainer.__class__.__module__}.{trainer.__class__.__name__}'.rsplit('.', 1) - - content = f'''overrides = {vars(trainer.args)} \nif __name__ == "__main__": - from {module} import {name} - from ultralytics.yolo.utils import DEFAULT_CFG_DICT - - cfg = DEFAULT_CFG_DICT.copy() - cfg.update(save_dir='') # handle the extra key 'save_dir' - trainer = {name}(cfg=cfg, overrides=overrides) - trainer.train()''' - (USER_CONFIG_DIR / 'DDP').mkdir(exist_ok=True) - with tempfile.NamedTemporaryFile(prefix='_temp_', - suffix=f'{id(trainer)}.py', - mode='w+', - encoding='utf-8', - dir=USER_CONFIG_DIR / 'DDP', - delete=False) as file: - file.write(content) - return file.name - - -def generate_ddp_command(world_size, trainer): - """Generates and returns command for distributed training.""" - import __main__ # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218 - if not trainer.resume: - shutil.rmtree(trainer.save_dir) # remove the save_dir - file = str(Path(sys.argv[0]).resolve()) - safe_pattern = re.compile(r'^[a-zA-Z0-9_. /\\-]{1,128}$') # allowed characters and maximum of 100 characters - if not (safe_pattern.match(file) and Path(file).exists() and file.endswith('.py')): # using CLI - file = generate_ddp_file(trainer) - dist_cmd = 'torch.distributed.run' if TORCH_1_9 else 'torch.distributed.launch' - port = find_free_network_port() - cmd = [sys.executable, '-m', dist_cmd, '--nproc_per_node', f'{world_size}', '--master_port', f'{port}', file] - return cmd, file - - -def ddp_cleanup(trainer, file): - """Delete temp file if created.""" - if f'{id(trainer)}.py' in file: # if temp_file suffix in file - os.remove(file) diff --git a/spaces/vishnu0001/text2mesh/shap_e/models/transmitter/channels_encoder.py b/spaces/vishnu0001/text2mesh/shap_e/models/transmitter/channels_encoder.py deleted file mode 100644 index c39cd3980100354b973cbaa7e3b5e26e5729b288..0000000000000000000000000000000000000000 --- a/spaces/vishnu0001/text2mesh/shap_e/models/transmitter/channels_encoder.py +++ /dev/null @@ -1,959 +0,0 @@ -from abc import ABC, abstractmethod -from dataclasses import dataclass -from functools import partial -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union - -import numpy as np -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from PIL import Image -from torch import torch - -from shap_e.models.generation.perceiver import SimplePerceiver -from shap_e.models.generation.transformer import Transformer -from shap_e.models.nn.camera import DifferentiableProjectiveCamera -from shap_e.models.nn.encoding import ( - MultiviewPointCloudEmbedding, - MultiviewPoseEmbedding, - PosEmbLinear, -) -from shap_e.models.nn.ops import PointSetEmbedding -from shap_e.rendering.point_cloud import PointCloud -from shap_e.rendering.view_data import ProjectiveCamera -from shap_e.util.collections import AttrDict - -from .base import ChannelsEncoder - - -class TransformerChannelsEncoder(ChannelsEncoder, ABC): - """ - Encode point clouds using a transformer model with an extra output - token used to extract a latent vector. - """ - - def __init__( - self, - *, - device: torch.device, - dtype: torch.dtype, - param_shapes: Dict[str, Tuple[int]], - params_proj: Dict[str, Any], - d_latent: int = 512, - latent_bottleneck: Optional[Dict[str, Any]] = None, - latent_warp: Optional[Dict[str, Any]] = None, - n_ctx: int = 1024, - width: int = 512, - layers: int = 12, - heads: int = 8, - init_scale: float = 0.25, - latent_scale: float = 1.0, - ): - super().__init__( - device=device, - param_shapes=param_shapes, - params_proj=params_proj, - d_latent=d_latent, - latent_bottleneck=latent_bottleneck, - latent_warp=latent_warp, - ) - self.width = width - self.device = device - self.dtype = dtype - - self.n_ctx = n_ctx - - self.backbone = Transformer( - device=device, - dtype=dtype, - n_ctx=n_ctx + self.latent_ctx, - width=width, - layers=layers, - heads=heads, - init_scale=init_scale, - ) - self.ln_pre = nn.LayerNorm(width, device=device, dtype=dtype) - self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype) - self.register_parameter( - "output_tokens", - nn.Parameter(torch.randn(self.latent_ctx, width, device=device, dtype=dtype)), - ) - self.output_proj = nn.Linear(width, d_latent, device=device, dtype=dtype) - self.latent_scale = latent_scale - - @abstractmethod - def encode_input(self, batch: AttrDict, options: Optional[AttrDict] = None) -> torch.Tensor: - pass - - def encode_to_channels( - self, batch: AttrDict, options: Optional[AttrDict] = None - ) -> torch.Tensor: - h = self.encode_input(batch, options=options) - h = torch.cat([h, self.output_tokens[None].repeat(len(h), 1, 1)], dim=1) - h = self.ln_pre(h) - h = self.backbone(h) - h = h[:, -self.latent_ctx :] - h = self.ln_post(h) - h = self.output_proj(h) - return h - - -class PerceiverChannelsEncoder(ChannelsEncoder, ABC): - """ - Encode point clouds using a perceiver model with an extra output - token used to extract a latent vector. - """ - - def __init__( - self, - *, - device: torch.device, - dtype: torch.dtype, - param_shapes: Dict[str, Tuple[int]], - params_proj: Dict[str, Any], - min_unrolls: int, - max_unrolls: int, - d_latent: int = 512, - latent_bottleneck: Optional[Dict[str, Any]] = None, - latent_warp: Optional[Dict[str, Any]] = None, - width: int = 512, - layers: int = 12, - xattn_layers: int = 1, - heads: int = 8, - init_scale: float = 0.25, - # Training hparams - inner_batch_size: Union[int, List[int]] = 1, - data_ctx: int = 1, - ): - super().__init__( - device=device, - param_shapes=param_shapes, - params_proj=params_proj, - d_latent=d_latent, - latent_bottleneck=latent_bottleneck, - latent_warp=latent_warp, - ) - self.width = width - self.device = device - self.dtype = dtype - - if isinstance(inner_batch_size, int): - inner_batch_size = [inner_batch_size] - self.inner_batch_size = inner_batch_size - self.data_ctx = data_ctx - self.min_unrolls = min_unrolls - self.max_unrolls = max_unrolls - - encoder_fn = lambda inner_batch_size: SimplePerceiver( - device=device, - dtype=dtype, - n_ctx=self.data_ctx + self.latent_ctx, - n_data=inner_batch_size, - width=width, - layers=xattn_layers, - heads=heads, - init_scale=init_scale, - ) - self.encoder = ( - encoder_fn(self.inner_batch_size[0]) - if len(self.inner_batch_size) == 1 - else nn.ModuleList([encoder_fn(inner_bsz) for inner_bsz in self.inner_batch_size]) - ) - self.processor = Transformer( - device=device, - dtype=dtype, - n_ctx=self.data_ctx + self.latent_ctx, - layers=layers - xattn_layers, - width=width, - heads=heads, - init_scale=init_scale, - ) - self.ln_pre = nn.LayerNorm(width, device=device, dtype=dtype) - self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype) - self.register_parameter( - "output_tokens", - nn.Parameter(torch.randn(self.latent_ctx, width, device=device, dtype=dtype)), - ) - self.output_proj = nn.Linear(width, d_latent, device=device, dtype=dtype) - - @abstractmethod - def get_h_and_iterator( - self, batch: AttrDict, options: Optional[AttrDict] = None - ) -> Tuple[torch.Tensor, Iterable[Union[torch.Tensor, Tuple]]]: - """ - :return: a tuple of ( - the initial output tokens of size [batch_size, data_ctx + latent_ctx, width], - an iterator over the given data - ) - """ - - def encode_to_channels( - self, batch: AttrDict, options: Optional[AttrDict] = None - ) -> torch.Tensor: - h, it = self.get_h_and_iterator(batch, options=options) - n_unrolls = self.get_n_unrolls() - - for _ in range(n_unrolls): - data = next(it) - if isinstance(data, tuple): - for data_i, encoder_i in zip(data, self.encoder): - h = encoder_i(h, data_i) - else: - h = self.encoder(h, data) - h = self.processor(h) - - h = self.output_proj(self.ln_post(h[:, -self.latent_ctx :])) - return h - - def get_n_unrolls(self): - if self.training: - n_unrolls = torch.randint( - self.min_unrolls, self.max_unrolls + 1, size=(), device=self.device - ) - dist.broadcast(n_unrolls, 0) - n_unrolls = n_unrolls.item() - else: - n_unrolls = self.max_unrolls - return n_unrolls - - -@dataclass -class DatasetIterator: - - embs: torch.Tensor # [batch_size, dataset_size, *shape] - batch_size: int - - def __iter__(self): - self._reset() - return self - - def __next__(self): - _outer_batch_size, dataset_size, *_shape = self.embs.shape - - while True: - start = self.idx - self.idx += self.batch_size - end = self.idx - if end <= dataset_size: - break - self._reset() - - return self.embs[:, start:end] - - def _reset(self): - self._shuffle() - self.idx = 0 # pylint: disable=attribute-defined-outside-init - - def _shuffle(self): - outer_batch_size, dataset_size, *shape = self.embs.shape - idx = torch.stack( - [ - torch.randperm(dataset_size, device=self.embs.device) - for _ in range(outer_batch_size) - ], - dim=0, - ) - idx = idx.view(outer_batch_size, dataset_size, *([1] * len(shape))) - idx = torch.broadcast_to(idx, self.embs.shape) - self.embs = torch.gather(self.embs, 1, idx) - - -class PointCloudTransformerChannelsEncoder(TransformerChannelsEncoder): - """ - Encode point clouds using a transformer model with an extra output - token used to extract a latent vector. - """ - - def __init__( - self, - *, - input_channels: int = 6, - **kwargs, - ): - super().__init__(**kwargs) - self.input_channels = input_channels - self.input_proj = nn.Linear( - input_channels, self.width, device=self.device, dtype=self.dtype - ) - - def encode_input(self, batch: AttrDict, options: Optional[AttrDict] = None) -> torch.Tensor: - _ = options - points = batch.points - h = self.input_proj(points.permute(0, 2, 1)) # NCL -> NLC - return h - - -class PointCloudPerceiverChannelsEncoder(PerceiverChannelsEncoder): - """ - Encode point clouds using a transformer model with an extra output - token used to extract a latent vector. - """ - - def __init__( - self, - *, - cross_attention_dataset: str = "pcl", - fps_method: str = "fps", - # point cloud hyperparameters - input_channels: int = 6, - pos_emb: Optional[str] = None, - # multiview hyperparameters - image_size: int = 256, - patch_size: int = 32, - pose_dropout: float = 0.0, - use_depth: bool = False, - max_depth: float = 5.0, - # point conv hyperparameters - pointconv_radius: float = 0.5, - pointconv_samples: int = 32, - pointconv_hidden: Optional[List[int]] = None, - pointconv_patch_size: int = 1, - pointconv_stride: int = 1, - pointconv_padding_mode: str = "zeros", - use_pointconv: bool = False, - # other hyperparameters - **kwargs, - ): - super().__init__(**kwargs) - assert cross_attention_dataset in ( - "pcl", - "multiview", - "dense_pose_multiview", - "multiview_pcl", - "pcl_and_multiview_pcl", - "incorrect_multiview_pcl", - "pcl_and_incorrect_multiview_pcl", - ) - assert fps_method in ("fps", "first") - self.cross_attention_dataset = cross_attention_dataset - self.fps_method = fps_method - self.input_channels = input_channels - self.input_proj = PosEmbLinear( - pos_emb, - input_channels, - self.width, - device=self.device, - dtype=self.dtype, - ) - self.use_pointconv = use_pointconv - if use_pointconv: - if pointconv_hidden is None: - pointconv_hidden = [self.width] - self.point_conv = PointSetEmbedding( - n_point=self.data_ctx, - radius=pointconv_radius, - n_sample=pointconv_samples, - d_input=self.input_proj.weight.shape[0], - d_hidden=pointconv_hidden, - patch_size=pointconv_patch_size, - stride=pointconv_stride, - padding_mode=pointconv_padding_mode, - fps_method=fps_method, - device=self.device, - dtype=self.dtype, - ) - if self.cross_attention_dataset == "multiview": - self.image_size = image_size - self.patch_size = patch_size - self.pose_dropout = pose_dropout - self.use_depth = use_depth - self.max_depth = max_depth - pos_ctx = (image_size // patch_size) ** 2 - self.register_parameter( - "pos_emb", - nn.Parameter( - torch.randn( - pos_ctx * self.inner_batch_size, - self.width, - device=self.device, - dtype=self.dtype, - ) - ), - ) - self.patch_emb = nn.Conv2d( - in_channels=3 if not use_depth else 4, - out_channels=self.width, - kernel_size=patch_size, - stride=patch_size, - device=self.device, - dtype=self.dtype, - ) - self.camera_emb = nn.Sequential( - nn.Linear( - 3 * 4 + 1, self.width, device=self.device, dtype=self.dtype - ), # input size is for origin+x+y+z+fov - nn.GELU(), - nn.Linear(self.width, 2 * self.width, device=self.device, dtype=self.dtype), - ) - elif self.cross_attention_dataset == "dense_pose_multiview": - # The number of output features is halved, because a patch_size of - # 32 ends up with a large patch_emb weight. - self.view_pose_width = self.width // 2 - self.image_size = image_size - self.patch_size = patch_size - self.use_depth = use_depth - self.max_depth = max_depth - self.mv_pose_embed = MultiviewPoseEmbedding( - posemb_version="nerf", - n_channels=4 if self.use_depth else 3, - out_features=self.view_pose_width, - device=self.device, - dtype=self.dtype, - ) - pos_ctx = (image_size // patch_size) ** 2 - # Positional embedding is unnecessary because pose information is baked into each pixel - self.patch_emb = nn.Conv2d( - in_channels=self.view_pose_width, - out_channels=self.width, - kernel_size=patch_size, - stride=patch_size, - device=self.device, - dtype=self.dtype, - ) - - elif ( - self.cross_attention_dataset == "multiview_pcl" - or self.cross_attention_dataset == "incorrect_multiview_pcl" - ): - self.view_pose_width = self.width // 2 - self.image_size = image_size - self.patch_size = patch_size - self.max_depth = max_depth - assert use_depth - self.mv_pcl_embed = MultiviewPointCloudEmbedding( - posemb_version="nerf", - n_channels=3, - out_features=self.view_pose_width, - device=self.device, - dtype=self.dtype, - ) - self.patch_emb = nn.Conv2d( - in_channels=self.view_pose_width, - out_channels=self.width, - kernel_size=patch_size, - stride=patch_size, - device=self.device, - dtype=self.dtype, - ) - - elif ( - self.cross_attention_dataset == "pcl_and_multiview_pcl" - or self.cross_attention_dataset == "pcl_and_incorrect_multiview_pcl" - ): - self.view_pose_width = self.width // 2 - self.image_size = image_size - self.patch_size = patch_size - self.max_depth = max_depth - assert use_depth - self.mv_pcl_embed = MultiviewPointCloudEmbedding( - posemb_version="nerf", - n_channels=3, - out_features=self.view_pose_width, - device=self.device, - dtype=self.dtype, - ) - self.patch_emb = nn.Conv2d( - in_channels=self.view_pose_width, - out_channels=self.width, - kernel_size=patch_size, - stride=patch_size, - device=self.device, - dtype=self.dtype, - ) - - def get_h_and_iterator( - self, batch: AttrDict, options: Optional[AttrDict] = None - ) -> Tuple[torch.Tensor, Iterable]: - """ - :return: a tuple of ( - the initial output tokens of size [batch_size, data_ctx + latent_ctx, width], - an iterator over the given data - ) - """ - options = AttrDict() if options is None else options - - # Build the initial query embeddings - points = batch.points.permute(0, 2, 1) # NCL -> NLC - if self.use_pointconv: - points = self.input_proj(points).permute(0, 2, 1) # NLC -> NCL - xyz = batch.points[:, :3] - data_tokens = self.point_conv(xyz, points).permute(0, 2, 1) # NCL -> NLC - else: - fps_samples = self.sample_pcl_fps(points) - data_tokens = self.input_proj(fps_samples) - batch_size = points.shape[0] - latent_tokens = self.output_tokens.unsqueeze(0).repeat(batch_size, 1, 1) - h = self.ln_pre(torch.cat([data_tokens, latent_tokens], dim=1)) - assert h.shape == (batch_size, self.data_ctx + self.latent_ctx, self.width) - - # Build the dataset embedding iterator - dataset_fn = { - "pcl": self.get_pcl_dataset, - "multiview": self.get_multiview_dataset, - "dense_pose_multiview": self.get_dense_pose_multiview_dataset, - "pcl_and_multiview_pcl": self.get_pcl_and_multiview_pcl_dataset, - "multiview_pcl": self.get_multiview_pcl_dataset, - }[self.cross_attention_dataset] - it = dataset_fn(batch, options=options) - - return h, it - - def sample_pcl_fps(self, points: torch.Tensor) -> torch.Tensor: - return sample_pcl_fps(points, data_ctx=self.data_ctx, method=self.fps_method) - - def get_pcl_dataset( - self, - batch: AttrDict, - options: Optional[AttrDict[str, Any]] = None, - inner_batch_size: Optional[int] = None, - ) -> Iterable: - _ = options - if inner_batch_size is None: - inner_batch_size = self.inner_batch_size[0] - points = batch.points.permute(0, 2, 1) # NCL -> NLC - dataset_emb = self.input_proj(points) - assert dataset_emb.shape[1] >= inner_batch_size - return iter(DatasetIterator(dataset_emb, batch_size=inner_batch_size)) - - def get_multiview_dataset( - self, - batch: AttrDict, - options: Optional[AttrDict] = None, - inner_batch_size: Optional[int] = None, - ) -> Iterable: - _ = options - - if inner_batch_size is None: - inner_batch_size = self.inner_batch_size[0] - - dataset_emb = self.encode_views(batch) - batch_size, num_views, n_patches, width = dataset_emb.shape - - assert num_views >= inner_batch_size - - it = iter(DatasetIterator(dataset_emb, batch_size=inner_batch_size)) - - def gen(): - while True: - examples = next(it) - assert examples.shape == (batch_size, self.inner_batch_size, n_patches, self.width) - views = examples.reshape(batch_size, -1, width) + self.pos_emb - yield views - - return gen() - - def get_dense_pose_multiview_dataset( - self, - batch: AttrDict, - options: Optional[AttrDict] = None, - inner_batch_size: Optional[int] = None, - ) -> Iterable: - _ = options - - if inner_batch_size is None: - inner_batch_size = self.inner_batch_size[0] - - dataset_emb = self.encode_dense_pose_views(batch) - batch_size, num_views, n_patches, width = dataset_emb.shape - - assert num_views >= inner_batch_size - - it = iter(DatasetIterator(dataset_emb, batch_size=inner_batch_size)) - - def gen(): - while True: - examples = next(it) - assert examples.shape == (batch_size, inner_batch_size, n_patches, self.width) - views = examples.reshape(batch_size, -1, width) - yield views - - return gen() - - def get_pcl_and_multiview_pcl_dataset( - self, - batch: AttrDict, - options: Optional[AttrDict] = None, - use_distance: bool = True, - ) -> Iterable: - _ = options - - pcl_it = self.get_pcl_dataset( - batch, options=options, inner_batch_size=self.inner_batch_size[0] - ) - multiview_pcl_emb = self.encode_multiview_pcl(batch, use_distance=use_distance) - batch_size, num_views, n_patches, width = multiview_pcl_emb.shape - - assert num_views >= self.inner_batch_size[1] - - multiview_pcl_it = iter( - DatasetIterator(multiview_pcl_emb, batch_size=self.inner_batch_size[1]) - ) - - def gen(): - while True: - pcl = next(pcl_it) - multiview_pcl = next(multiview_pcl_it) - assert multiview_pcl.shape == ( - batch_size, - self.inner_batch_size[1], - n_patches, - self.width, - ) - yield pcl, multiview_pcl.reshape(batch_size, -1, width) - - return gen() - - def get_multiview_pcl_dataset( - self, - batch: AttrDict, - options: Optional[AttrDict] = None, - inner_batch_size: Optional[int] = None, - use_distance: bool = True, - ) -> Iterable: - _ = options - - if inner_batch_size is None: - inner_batch_size = self.inner_batch_size[0] - - multiview_pcl_emb = self.encode_multiview_pcl(batch, use_distance=use_distance) - batch_size, num_views, n_patches, width = multiview_pcl_emb.shape - - assert num_views >= inner_batch_size - - multiview_pcl_it = iter(DatasetIterator(multiview_pcl_emb, batch_size=inner_batch_size)) - - def gen(): - while True: - multiview_pcl = next(multiview_pcl_it) - assert multiview_pcl.shape == ( - batch_size, - inner_batch_size, - n_patches, - self.width, - ) - yield multiview_pcl.reshape(batch_size, -1, width) - - return gen() - - def encode_views(self, batch: AttrDict) -> torch.Tensor: - """ - :return: [batch_size, num_views, n_patches, width] - """ - all_views = self.views_to_tensor(batch.views).to(self.device) - if self.use_depth: - all_views = torch.cat([all_views, self.depths_to_tensor(batch.depths)], dim=2) - all_cameras = self.cameras_to_tensor(batch.cameras).to(self.device) - - batch_size, num_views, _, _, _ = all_views.shape - - views_proj = self.patch_emb( - all_views.reshape([batch_size * num_views, *all_views.shape[2:]]) - ) - views_proj = ( - views_proj.reshape([batch_size, num_views, self.width, -1]) - .permute(0, 1, 3, 2) - .contiguous() - ) # [batch_size x num_views x n_patches x width] - - # [batch_size, num_views, 1, 2 * width] - camera_proj = self.camera_emb(all_cameras).reshape( - [batch_size, num_views, 1, self.width * 2] - ) - pose_dropout = self.pose_dropout if self.training else 0.0 - mask = torch.rand(batch_size, 1, 1, 1, device=views_proj.device) >= pose_dropout - camera_proj = torch.where(mask, camera_proj, torch.zeros_like(camera_proj)) - scale, shift = camera_proj.chunk(2, dim=3) - views_proj = views_proj * (scale + 1.0) + shift - return views_proj - - def encode_dense_pose_views(self, batch: AttrDict) -> torch.Tensor: - """ - :return: [batch_size, num_views, n_patches, width] - """ - all_views = self.views_to_tensor(batch.views).to(self.device) - if self.use_depth: - depths = self.depths_to_tensor(batch.depths) - all_views = torch.cat([all_views, depths], dim=2) - - dense_poses, _ = self.dense_pose_cameras_to_tensor(batch.cameras) - dense_poses = dense_poses.permute(0, 1, 4, 5, 2, 3) - position, direction = dense_poses[:, :, 0], dense_poses[:, :, 1] - all_view_poses = self.mv_pose_embed(all_views, position, direction) - - batch_size, num_views, _, _, _ = all_view_poses.shape - - views_proj = self.patch_emb( - all_view_poses.reshape([batch_size * num_views, *all_view_poses.shape[2:]]) - ) - views_proj = ( - views_proj.reshape([batch_size, num_views, self.width, -1]) - .permute(0, 1, 3, 2) - .contiguous() - ) # [batch_size x num_views x n_patches x width] - - return views_proj - - def encode_multiview_pcl(self, batch: AttrDict, use_distance: bool = True) -> torch.Tensor: - """ - :return: [batch_size, num_views, n_patches, width] - """ - all_views = self.views_to_tensor(batch.views).to(self.device) - depths = self.raw_depths_to_tensor(batch.depths) - all_view_alphas = self.view_alphas_to_tensor(batch.view_alphas).to(self.device) - mask = all_view_alphas >= 0.999 - - dense_poses, camera_z = self.dense_pose_cameras_to_tensor(batch.cameras) - dense_poses = dense_poses.permute(0, 1, 4, 5, 2, 3) - - origin, direction = dense_poses[:, :, 0], dense_poses[:, :, 1] - if use_distance: - ray_depth_factor = torch.sum(direction * camera_z[..., None, None], dim=2, keepdim=True) - depths = depths / ray_depth_factor - position = origin + depths * direction - all_view_poses = self.mv_pcl_embed(all_views, origin, position, mask) - - batch_size, num_views, _, _, _ = all_view_poses.shape - - views_proj = self.patch_emb( - all_view_poses.reshape([batch_size * num_views, *all_view_poses.shape[2:]]) - ) - views_proj = ( - views_proj.reshape([batch_size, num_views, self.width, -1]) - .permute(0, 1, 3, 2) - .contiguous() - ) # [batch_size x num_views x n_patches x width] - - return views_proj - - def views_to_tensor(self, views: Union[torch.Tensor, List[List[Image.Image]]]) -> torch.Tensor: - """ - Returns a [batch x num_views x 3 x size x size] tensor in the range [-1, 1]. - """ - if isinstance(views, torch.Tensor): - return views - - tensor_batch = [] - num_views = len(views[0]) - for inner_list in views: - assert len(inner_list) == num_views - inner_batch = [] - for img in inner_list: - img = img.resize((self.image_size,) * 2).convert("RGB") - inner_batch.append( - torch.from_numpy(np.array(img)).to(device=self.device, dtype=torch.float32) - / 127.5 - - 1 - ) - tensor_batch.append(torch.stack(inner_batch, dim=0)) - return torch.stack(tensor_batch, dim=0).permute(0, 1, 4, 2, 3) - - def depths_to_tensor( - self, depths: Union[torch.Tensor, List[List[Image.Image]]] - ) -> torch.Tensor: - """ - Returns a [batch x num_views x 1 x size x size] tensor in the range [-1, 1]. - """ - if isinstance(depths, torch.Tensor): - return depths - - tensor_batch = [] - num_views = len(depths[0]) - for inner_list in depths: - assert len(inner_list) == num_views - inner_batch = [] - for arr in inner_list: - tensor = torch.from_numpy(arr).clamp(max=self.max_depth) / self.max_depth - tensor = tensor * 2 - 1 - tensor = F.interpolate( - tensor[None, None], - (self.image_size,) * 2, - mode="nearest", - ) - inner_batch.append(tensor.to(device=self.device, dtype=torch.float32)) - tensor_batch.append(torch.cat(inner_batch, dim=0)) - return torch.stack(tensor_batch, dim=0) - - def view_alphas_to_tensor( - self, view_alphas: Union[torch.Tensor, List[List[Image.Image]]] - ) -> torch.Tensor: - """ - Returns a [batch x num_views x 1 x size x size] tensor in the range [0, 1]. - """ - if isinstance(view_alphas, torch.Tensor): - return view_alphas - - tensor_batch = [] - num_views = len(view_alphas[0]) - for inner_list in view_alphas: - assert len(inner_list) == num_views - inner_batch = [] - for img in inner_list: - tensor = ( - torch.from_numpy(np.array(img)).to(device=self.device, dtype=torch.float32) - / 255.0 - ) - tensor = F.interpolate( - tensor[None, None], - (self.image_size,) * 2, - mode="nearest", - ) - inner_batch.append(tensor) - tensor_batch.append(torch.cat(inner_batch, dim=0)) - return torch.stack(tensor_batch, dim=0) - - def raw_depths_to_tensor( - self, depths: Union[torch.Tensor, List[List[Image.Image]]] - ) -> torch.Tensor: - """ - Returns a [batch x num_views x 1 x size x size] tensor - """ - if isinstance(depths, torch.Tensor): - return depths - - tensor_batch = [] - num_views = len(depths[0]) - for inner_list in depths: - assert len(inner_list) == num_views - inner_batch = [] - for arr in inner_list: - tensor = torch.from_numpy(arr).clamp(max=self.max_depth) - tensor = F.interpolate( - tensor[None, None], - (self.image_size,) * 2, - mode="nearest", - ) - inner_batch.append(tensor.to(device=self.device, dtype=torch.float32)) - tensor_batch.append(torch.cat(inner_batch, dim=0)) - return torch.stack(tensor_batch, dim=0) - - def cameras_to_tensor( - self, cameras: Union[torch.Tensor, List[List[ProjectiveCamera]]] - ) -> torch.Tensor: - """ - Returns a [batch x num_views x 3*4+1] tensor of camera information. - """ - if isinstance(cameras, torch.Tensor): - return cameras - outer_batch = [] - for inner_list in cameras: - inner_batch = [] - for camera in inner_list: - inner_batch.append( - np.array( - [ - *camera.x, - *camera.y, - *camera.z, - *camera.origin, - camera.x_fov, - ] - ) - ) - outer_batch.append(np.stack(inner_batch, axis=0)) - return torch.from_numpy(np.stack(outer_batch, axis=0)).float() - - def dense_pose_cameras_to_tensor( - self, cameras: Union[torch.Tensor, List[List[ProjectiveCamera]]] - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Returns a tuple of (rays, z_directions) where - - rays: [batch, num_views, height, width, 2, 3] tensor of camera information. - - z_directions: [batch, num_views, 3] tensor of camera z directions. - """ - if isinstance(cameras, torch.Tensor): - raise NotImplementedError - - for inner_list in cameras: - assert len(inner_list) == len(cameras[0]) - - camera = cameras[0][0] - flat_camera = DifferentiableProjectiveCamera( - origin=torch.from_numpy( - np.stack( - [cam.origin for inner_list in cameras for cam in inner_list], - axis=0, - ) - ).to(self.device), - x=torch.from_numpy( - np.stack( - [cam.x for inner_list in cameras for cam in inner_list], - axis=0, - ) - ).to(self.device), - y=torch.from_numpy( - np.stack( - [cam.y for inner_list in cameras for cam in inner_list], - axis=0, - ) - ).to(self.device), - z=torch.from_numpy( - np.stack( - [cam.z for inner_list in cameras for cam in inner_list], - axis=0, - ) - ).to(self.device), - width=camera.width, - height=camera.height, - x_fov=camera.x_fov, - y_fov=camera.y_fov, - ) - batch_size = len(cameras) * len(cameras[0]) - coords = ( - flat_camera.image_coords() - .to(flat_camera.origin.device) - .unsqueeze(0) - .repeat(batch_size, 1, 1) - ) - rays = flat_camera.camera_rays(coords) - return ( - rays.view(len(cameras), len(cameras[0]), camera.height, camera.width, 2, 3).to( - self.device - ), - flat_camera.z.view(len(cameras), len(cameras[0]), 3).to(self.device), - ) - - -def sample_pcl_fps(points: torch.Tensor, data_ctx: int, method: str = "fps") -> torch.Tensor: - """ - Run farthest-point sampling on a batch of point clouds. - - :param points: batch of shape [N x num_points]. - :param data_ctx: subsample count. - :param method: either 'fps' or 'first'. Using 'first' assumes that the - points are already sorted according to FPS sampling. - :return: batch of shape [N x min(num_points, data_ctx)]. - """ - n_points = points.shape[1] - if n_points == data_ctx: - return points - if method == "first": - return points[:, :data_ctx] - elif method == "fps": - batch = points.cpu().split(1, dim=0) - fps = [sample_fps(x, n_samples=data_ctx) for x in batch] - return torch.cat(fps, dim=0).to(points.device) - else: - raise ValueError(f"unsupported farthest-point sampling method: {method}") - - -def sample_fps(example: torch.Tensor, n_samples: int) -> torch.Tensor: - """ - :param example: [1, n_points, 3 + n_channels] - :return: [1, n_samples, 3 + n_channels] - """ - points = example.cpu().squeeze(0).numpy() - coords, raw_channels = points[:, :3], points[:, 3:] - n_points, n_channels = raw_channels.shape - assert n_samples <= n_points - channels = {str(idx): raw_channels[:, idx] for idx in range(n_channels)} - max_points = min(32768, n_points) - fps_pcl = ( - PointCloud(coords=coords, channels=channels) - .random_sample(max_points) - .farthest_point_sample(n_samples) - ) - fps_channels = np.stack([fps_pcl.channels[str(idx)] for idx in range(n_channels)], axis=1) - fps = np.concatenate([fps_pcl.coords, fps_channels], axis=1) - fps = torch.from_numpy(fps).unsqueeze(0) - assert fps.shape == (1, n_samples, 3 + n_channels) - return fps diff --git a/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/detect_symbols.py b/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/detect_symbols.py deleted file mode 100644 index 16626d7b1b57f4a5940d5cd242119856bfda2e31..0000000000000000000000000000000000000000 --- a/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/detect_symbols.py +++ /dev/null @@ -1,153 +0,0 @@ -import argparse -import json -import os -import platform -import shutil -import time -from pathlib import Path - -import cv2 -import torch -import torch.backends.cudnn as cudnn -from numpy import random - -from models.experimental import attempt_load -from utils.datasets import LoadStreams, LoadImages -from utils.general import ( - check_img_size, non_max_suppression, apply_classifier, scale_coords, - xyxy2xywh, plot_one_box, strip_optimizer, set_logging) -from utils.torch_utils import select_device, load_classifier, time_synchronized - - -def detect(save_img=False): - results = [] - out, source, weights, view_img, save_txt, imgsz = \ - opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size - webcam = source.isnumeric() or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') - - # Initialize - set_logging() - device = select_device(opt.device) - half = device.type != 'cpu' # half precision only supported on CUDA - - # Load model - model = attempt_load(weights, map_location=device) # load FP32 model - imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size - if half: - model.half() # to FP16 - - # Second-stage classifier - classify = False - if classify: - modelc = load_classifier(name='resnet101', n=2) # initialize - modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights - modelc.to(device).eval() - - # Set Dataloader - vid_path, vid_writer = None, None - if webcam: - view_img = True - cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz) - else: - save_img = True - dataset = LoadImages(source, img_size=imgsz) - - # Get names and colors - names = model.module.names if hasattr(model, 'module') else model.names - colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] - - # Run inference - t0 = time.time() - img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once - for path, img, im0s, vid_cap in dataset: - img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 - img /= 255.0 # 0 - 255 to 0.0 - 1.0 - if img.ndimension() == 3: - img = img.unsqueeze(0) - - # Inference - t1 = time_synchronized() - pred = model(img, augment=opt.augment)[0] - - # Apply NMS - pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) - t2 = time_synchronized() - - # Apply Classifier - if classify: - pred = apply_classifier(pred, modelc, img, im0s) - - # Process detections - for i, det in enumerate(pred): # detections per image - if webcam: # batch_size >= 1 - p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() - else: - p, s, im0 = path, '', im0s - - save_path = str(Path(out) / Path(p).name) - txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '') - s += '%gx%g ' % img.shape[2:] # print string - gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh - if det is not None and len(det): - # Rescale boxes from img_size to im0 size - det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() - - # Print results - for c in det[:, -1].unique(): - n = (det[:, -1] == c).sum() # detections per class - s += '%g %ss, ' % (n, names[int(c)]) # add to string - - # Write results - #for *xyxy, conf, cls in reversed(det): - # if save_txt: # Write to file - # xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh - # with open(txt_path + '.txt', 'a') as f: - # f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format - - - for *xyxy, conf, cls in reversed(det): - xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist() # ADDED BY FRANCOIS - results.append({ - "measurementType": names[int(cls)], - "noResponse": False, - "boundingBox": { - "x": int(xywh[0] - xywh[2]/2), - "y": int(xywh[1] - xywh[3]/2), - "width": int(xywh[2]), - "height": int(xywh[3]) - }, - "confidence": float(conf) - }) - - print("\n$$$") - print(json.dumps(results)) - print("$$$\n") - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') - parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam - parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder - parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') - 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='display results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models') - opt = parser.parse_args() - print(opt) - - with torch.no_grad(): - if opt.update: # update all models (to fix SourceChangeWarning) - for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: - detect() - strip_optimizer(opt.weights) - else: - detect() diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/tools/search_engine_serpapi.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/tools/search_engine_serpapi.py deleted file mode 100644 index 750184198c17873ca20c84ac3a40b0365b7f1f29..0000000000000000000000000000000000000000 --- a/spaces/wffcyrus/MetaGPT-v1/metagpt/tools/search_engine_serpapi.py +++ /dev/null @@ -1,115 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/23 18:27 -@Author : alexanderwu -@File : search_engine_serpapi.py -""" -from typing import Any, Dict, Optional, Tuple - -import aiohttp -from pydantic import BaseModel, Field, validator - -from metagpt.config import CONFIG - - -class SerpAPIWrapper(BaseModel): - search_engine: Any #: :meta private: - params: dict = Field( - default={ - "engine": "google", - "google_domain": "google.com", - "gl": "us", - "hl": "en", - } - ) - serpapi_api_key: Optional[str] = None - aiosession: Optional[aiohttp.ClientSession] = None - - class Config: - arbitrary_types_allowed = True - - @validator("serpapi_api_key", always=True) - @classmethod - def check_serpapi_api_key(cls, val: str): - val = val or CONFIG.serpapi_api_key - if not val: - raise ValueError( - "To use, make sure you provide the serpapi_api_key when constructing an object. Alternatively, " - "ensure that the environment variable SERPAPI_API_KEY is set with your API key. You can obtain " - "an API key from https://serpapi.com/." - ) - return val - - async def run(self, query, max_results: int = 8, as_string: bool = True, **kwargs: Any) -> str: - """Run query through SerpAPI and parse result async.""" - return self._process_response(await self.results(query, max_results), as_string=as_string) - - async def results(self, query: str, max_results: int) -> dict: - """Use aiohttp to run query through SerpAPI and return the results async.""" - - def construct_url_and_params() -> Tuple[str, Dict[str, str]]: - params = self.get_params(query) - params["source"] = "python" - params["num"] = max_results - params["output"] = "json" - url = "https://serpapi.com/search" - return url, params - - url, params = construct_url_and_params() - if not self.aiosession: - async with aiohttp.ClientSession() as session: - async with session.get(url, params=params) as response: - res = await response.json() - else: - async with self.aiosession.get(url, params=params) as response: - res = await response.json() - - return res - - def get_params(self, query: str) -> Dict[str, str]: - """Get parameters for SerpAPI.""" - _params = { - "api_key": self.serpapi_api_key, - "q": query, - } - params = {**self.params, **_params} - return params - - @staticmethod - def _process_response(res: dict, as_string: bool) -> str: - """Process response from SerpAPI.""" - # logger.debug(res) - focus = ["title", "snippet", "link"] - get_focused = lambda x: {i: j for i, j in x.items() if i in focus} - - if "error" in res.keys(): - raise ValueError(f"Got error from SerpAPI: {res['error']}") - if "answer_box" in res.keys() and "answer" in res["answer_box"].keys(): - toret = res["answer_box"]["answer"] - elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys(): - toret = res["answer_box"]["snippet"] - elif "answer_box" in res.keys() and "snippet_highlighted_words" in res["answer_box"].keys(): - toret = res["answer_box"]["snippet_highlighted_words"][0] - elif "sports_results" in res.keys() and "game_spotlight" in res["sports_results"].keys(): - toret = res["sports_results"]["game_spotlight"] - elif "knowledge_graph" in res.keys() and "description" in res["knowledge_graph"].keys(): - toret = res["knowledge_graph"]["description"] - elif "snippet" in res["organic_results"][0].keys(): - toret = res["organic_results"][0]["snippet"] - else: - toret = "No good search result found" - - toret_l = [] - if "answer_box" in res.keys() and "snippet" in res["answer_box"].keys(): - toret_l += [get_focused(res["answer_box"])] - if res.get("organic_results"): - toret_l += [get_focused(i) for i in res.get("organic_results")] - - return str(toret) + "\n" + str(toret_l) if as_string else toret_l - - -if __name__ == "__main__": - import fire - - fire.Fire(SerpAPIWrapper().run) diff --git a/spaces/wouaf/WOUAF-Text-to-Image/attribution.py b/spaces/wouaf/WOUAF-Text-to-Image/attribution.py deleted file mode 100644 index 6975880cd40239d1b4c1fa96a4ece988c4f53228..0000000000000000000000000000000000000000 --- a/spaces/wouaf/WOUAF-Text-to-Image/attribution.py +++ /dev/null @@ -1,190 +0,0 @@ -import torch -import numpy as np -from torch_utils.ops import bias_act -from torch_utils import misc - - - -def normalize_2nd_moment(x, dim=1, eps=1e-8): - return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() - - -class FullyConnectedLayer_normal(torch.nn.Module): - def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - bias = True, # Apply additive bias before the activation function? - bias_init = 0, # Initial value for the additive bias. - ): - super().__init__() - self.fc = torch.nn.Linear(in_features, out_features, bias=bias) - if bias: - with torch.no_grad(): - self.fc.bias.fill_(bias_init) - - def forward(self, x): - output = self.fc(x) - return output - - -class MappingNetwork_normal(torch.nn.Module): - def __init__(self, - in_features, # Number of input features. - int_dim, - num_layers = 8, # Number of mapping layers. - mapping_normalization = False #2nd normalization - ): - super().__init__() - layers = [torch.nn.Linear(in_features, int_dim), torch.nn.LeakyReLU(0.2)] - for i in range(1, num_layers): - layers.append(torch.nn.Linear(int_dim, int_dim)) - layers.append(torch.nn.LeakyReLU(0.2)) - - self.net = torch.nn.Sequential(*layers) - self.normalization = mapping_normalization - - def forward(self, x): - if self.normalization: - x = normalize_2nd_moment(x) - output = self.net(x) - return output - - -class DecodingNetwork(torch.nn.Module): - def __init__(self, - in_features, # Number of input features. - out_dim, - num_layers = 8, # Number of mapping layers. - ): - super().__init__() - layers = [] - for i in range(num_layers-1): - layers.append(torch.nn.Linear(in_features, in_features)) - layers.append(torch.nn.ReLU()) - - layers.append(torch.nn.Linear(in_features, out_dim)) - - self.net = torch.nn.Sequential(*layers) - - def forward(self, x): - x = torch.nn.functional.normalize(x, dim=1) - output = self.net(x) - return output - - -class FullyConnectedLayer(torch.nn.Module): - def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 1, # Learning rate multiplier. - bias_init = 0, # Initial value for the additive bias. - ): - super().__init__() - self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) - self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None - self.weight_gain = lr_multiplier / np.sqrt(in_features) - self.bias_gain = lr_multiplier - - def forward(self, x): - w = self.weight.to(x.dtype) * self.weight_gain - b = self.bias - if b is not None: - b = b.to(x.dtype) - if self.bias_gain != 1: - b = b * self.bias_gain - - if self.activation == 'linear' and b is not None: - x = torch.addmm(b.unsqueeze(0), x, w.t()) - else: - x = x.matmul(w.t()) - x = bias_act.bias_act(x, b, act=self.activation) - return x - - -class MappingNetwork(torch.nn.Module): - def __init__(self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output, None = do not broadcast. - num_layers = 8, # Number of mapping layers. - embed_features = None, # Label embedding dimensionality, None = same as w_dim. - layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.995, # Decay for tracking the moving average of W during training, None = do not track. - normalization = None # Normalization input using normalize_2nd_moment - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.num_ws = num_ws - self.num_layers = num_layers - self.w_avg_beta = w_avg_beta - self.normalization = normalization - - if embed_features is None: - embed_features = w_dim - if c_dim == 0: - embed_features = 0 - if layer_features is None: - layer_features = w_dim - features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] - - if c_dim > 0: - self.embed = FullyConnectedLayer(c_dim, embed_features) - for idx in range(num_layers): - in_features = features_list[idx] - out_features = features_list[idx + 1] - layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) - setattr(self, f'fc{idx}', layer) - - if num_ws is not None and w_avg_beta is not None: - self.register_buffer('w_avg', torch.zeros([w_dim])) - - def forward(self, z, c=None, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False): - # Embed, normalize, and concat inputs. - x = None - with torch.autograd.profiler.record_function('input'): - if self.z_dim > 0: - misc.assert_shape(z, [None, self.z_dim]) - if self.normalization: - x = normalize_2nd_moment(z.to(torch.float32)) - else: - x = z - x = z.to(torch.float32) - if self.c_dim > 0: - raise ValueError("This implementation does not need class index") - misc.assert_shape(c, [None, self.c_dim]) - y = normalize_2nd_moment(self.embed(c.to(torch.float32))) - y = self.embed(c.to(torch.float32)) - x = torch.cat([x, y], dim=1) if x is not None else y - - # Main layers. - for idx in range(self.num_layers): - layer = getattr(self, f'fc{idx}') - x = layer(x) - - # Update moving average of W. - if self.w_avg_beta is not None and self.training and not skip_w_avg_update: - with torch.autograd.profiler.record_function('update_w_avg'): - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) - - # Broadcast. - if self.num_ws is not None: - with torch.autograd.profiler.record_function('broadcast'): - x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) - - # Apply truncation. - if truncation_psi != 1: - with torch.autograd.profiler.record_function('truncate'): - assert self.w_avg_beta is not None - if self.num_ws is None or truncation_cutoff is None: - x = self.w_avg.lerp(x, truncation_psi) - else: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) - return x diff --git a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/language/vlpencoder.py b/spaces/xdecoder/Instruct-X-Decoder/xdecoder/language/vlpencoder.py deleted file mode 100644 index ce6fd4709255e8869749d7401babb373b187d697..0000000000000000000000000000000000000000 --- a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/language/vlpencoder.py +++ /dev/null @@ -1,168 +0,0 @@ - -import torch -from torch import nn -from torch.nn import functional as F - -from timm.models.layers import trunc_normal_ - -from .registry import register_model -from ..utils import configurable -from .LangEncoder import build_tokenizer, build_lang_encoder -from utils.misc import prompt_engineering, get_prompt_templates - - -class LanguageEncoder(nn.Module): - - @configurable - def __init__( - self, - tokenizer, - tokenizer_type, - lang_encoder, - lang_projection, - max_token_num, - ): - super().__init__() - self.tokenizer = tokenizer - self.tokenizer_type = tokenizer_type - self.lang_encoder = lang_encoder - self.lang_proj = lang_projection - self.max_token_num = max_token_num - self.logit_scale = nn.Parameter(torch.ones([])) - - @classmethod - def from_config(cls, cfg): - tokenizer = build_tokenizer(cfg['MODEL']['TEXT']) - tokenizer_type = cfg['MODEL']['TEXT']['TOKENIZER'] - lang_encoder = build_lang_encoder(cfg['MODEL']['TEXT'], tokenizer, cfg['VERBOSE']) - max_token_num = cfg['MODEL']['TEXT']['CONTEXT_LENGTH'] - - dim_lang = cfg['MODEL']['TEXT']['WIDTH'] - dim_projection = cfg['MODEL']['DIM_PROJ'] - lang_projection = nn.Parameter(torch.empty(dim_lang, dim_projection)) - trunc_normal_(lang_projection, std=.02) - - return { - "tokenizer": tokenizer, - "tokenizer_type": tokenizer_type, - "lang_encoder": lang_encoder, - "lang_projection": lang_projection, - "max_token_num": max_token_num, - } - - def get_text_embeddings(self, class_names, name='default', is_eval=False, add_bgd=False, prompt=True, norm=True): - if not is_eval: - if prompt: - # randomly sample one template - arbitary_concepts = [ - prompt_engineering(class_names[label].replace('-other','').replace('-merged','').replace('-stuff',''), topk=10000, suffix='.') \ - for label in range(len(class_names)) - ] - if add_bgd: - arbitary_concepts.append("A background in coco.") - else: - arbitary_concepts = class_names - - input_ids = [] - attention_masks = [] - for txt in arbitary_concepts: - tokens = self.tokenizer( - txt, padding='max_length', truncation=True, max_length=self.max_token_num, return_tensors='pt' - ) - tokens['input_ids'].squeeze_() - tokens['attention_mask'].squeeze_() - - input_ids.append(tokens['input_ids']) - attention_masks.append(tokens['attention_mask']) - - arbitary_tokens = torch.stack(input_ids) - arbitary_attention_masks = torch.stack(attention_masks) - - text_emb = self.forward_language((arbitary_tokens.cuda(), arbitary_attention_masks.cuda()), norm=norm) - setattr(self, '{}_text_embeddings'.format(name), text_emb) - else: - with torch.no_grad(): - def extract_mean_emb(txts): - tokens = self.tokenizer( - txts, padding='max_length', truncation=True, max_length=self.max_token_num, return_tensors='pt' - ) - clss_embedding = self.forward_language((tokens['input_ids'].cuda(), tokens['attention_mask'].cuda()), norm=norm) - clss_embedding = clss_embedding.mean(dim=0) - clss_embedding /= clss_embedding.norm() - return clss_embedding - - templates = get_prompt_templates() - clss_embeddings = [] - if prompt: - for clss in class_names: - txts = [template.format(clss.replace('-other','').replace('-merged','').replace('-stuff','')) for template in templates] - clss_embeddings.append(extract_mean_emb(txts)) - else: - clss_embeddings.append(extract_mean_emb(class_names)) - - if add_bgd: - txts = ["A background in coco."] - clss_embeddings.append(extract_mean_emb(txts)) - - text_emb = torch.stack(clss_embeddings, dim=0) - setattr(self, '{}_text_embeddings'.format(name), text_emb) - - def get_text_token_embeddings(self, txts, name='default', token=False, norm=False): - if not token: - tokens = self.tokenizer( - txts, padding='max_length', truncation=True, max_length=self.max_token_num, return_tensors='pt' - ) - tokens = {key: value.cuda() for key, value in tokens.items()} - else: - tokens = txts - token_emb, class_emb = self.forward_language_token((tokens['input_ids'], tokens['attention_mask']), norm=norm) - ret = {"tokens": tokens, - "token_emb": token_emb, - "class_emb": class_emb,} - setattr(self, '{}_token_embeddings'.format(name), ret) - return ret - - def forward_language(self, texts, norm=True): - x = self.lang_encoder(*texts) - x = x['last_hidden_state'] - - if self.tokenizer_type == 'clip': - x = x[torch.arange(x.size(0)), texts[0].argmax(dim=-1)] - else: - x = x[:, 0] - - x = x @ self.lang_proj - if norm: - x = x / (x.norm(dim=-1, keepdim=True) + 1e-7) - return x - - def forward_language_token(self, texts, norm=False): - x = self.lang_encoder(*texts) - token_x = x['last_hidden_state'] - - if self.tokenizer_type == 'clip': - class_x = token_x[torch.arange(token_x.size(0)), texts[0].argmax(dim=-1)] - else: - class_x = token_x[:, 0] - - class_x = class_x @ self.lang_proj - token_x = token_x @ self.lang_proj - - if norm: - class_x = class_x / (class_x.norm(dim=-1, keepdim=True) + 1e-7) - token_x = token_x / (token_x.norm(dim=-1, keepdim=True) + 1e-7) - - return token_x, class_x - - def compute_similarity(self, v_emb, name='default', fake=False): - if fake: - return None - v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7) - t_emb = getattr(self, '{}_text_embeddings'.format(name)) - output = self.logit_scale.exp() * v_emb @ t_emb.unsqueeze(0).transpose(1, 2) - return output - - -@register_model -def get_language_model(cfg, **kwargs): - return LanguageEncoder(cfg) \ No newline at end of file diff --git a/spaces/xswu/HPSv2/evaluate.py b/spaces/xswu/HPSv2/evaluate.py deleted file mode 100644 index f17e81bfa16ad20a6a9bf4b421fac7ddcde7b29c..0000000000000000000000000000000000000000 --- a/spaces/xswu/HPSv2/evaluate.py +++ /dev/null @@ -1,220 +0,0 @@ -from cProfile import label -import os -import json -import numpy as np -from tqdm import tqdm -from argparse import ArgumentParser -from PIL import Image - -import torch -from torch.utils.data import Dataset, DataLoader - -from src.open_clip import create_model_and_transforms, get_tokenizer -from src.training.train import calc_ImageReward, inversion_score -from src.training.data import ImageRewardDataset, collate_rank, RankingDataset - - -parser = ArgumentParser() -parser.add_argument('--data-type', type=str, choices=['benchmark', 'test', 'ImageReward', 'drawbench']) -parser.add_argument('--data-path', type=str, help='path to dataset') -parser.add_argument('--image-path', type=str, help='path to image files') -parser.add_argument('--checkpoint', type=str, help='path to checkpoint') -parser.add_argument('--batch-size', type=int, default=20) -args = parser.parse_args() - -batch_size = args.batch_size -args.model = "ViT-H-14" -args.precision = 'amp' -print(args.model) -device = 'cuda' if torch.cuda.is_available() else 'cpu' -model, preprocess_train, preprocess_val = create_model_and_transforms( - args.model, - 'laion2B-s32B-b79K', - precision=args.precision, - device=device, - jit=False, - force_quick_gelu=False, - force_custom_text=False, - force_patch_dropout=False, - force_image_size=None, - pretrained_image=False, - image_mean=None, - image_std=None, - light_augmentation=True, - aug_cfg={}, - output_dict=True, - with_score_predictor=False, - with_region_predictor=False -) - -checkpoint = torch.load(args.checkpoint) -model.load_state_dict(checkpoint['state_dict']) -tokenizer = get_tokenizer(args.model) -model.eval() - -class BenchmarkDataset(Dataset): - def __init__(self, meta_file, image_folder,transforms, tokenizer): - self.transforms = transforms - self.image_folder = image_folder - self.tokenizer = tokenizer - self.open_image = Image.open - with open(meta_file, 'r') as f: - self.annotations = json.load(f) - - def __len__(self): - return len(self.annotations) - - def __getitem__(self, idx): - try: - img_path = os.path.join(self.image_folder, f'{idx:05d}.jpg') - images = self.transforms(self.open_image(os.path.join(img_path))) - caption = self.tokenizer(self.annotations[idx]) - return images, caption - except: - print('file not exist') - return self.__getitem__((idx + 1) % len(self)) - -def evaluate_IR(data_path, image_folder, model): - meta_file = data_path + '/ImageReward_test.json' - dataset = ImageRewardDataset(meta_file, image_folder, preprocess_val, tokenizer) - dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=4, collate_fn=collate_rank) - - score = 0 - total = len(dataset) - with torch.no_grad(): - for batch in tqdm(dataloader): - images, num_images, labels, texts = batch - images = images.to(device=device, non_blocking=True) - texts = texts.to(device=device, non_blocking=True) - num_images = num_images.to(device=device, non_blocking=True) - labels = labels.to(device=device, non_blocking=True) - - with torch.cuda.amp.autocast(): - outputs = model(images, texts) - image_features, text_features, logit_scale = outputs["image_features"], outputs["text_features"], outputs["logit_scale"] - logits_per_image = logit_scale * image_features @ text_features.T - paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] - - predicted = [torch.argsort(-k) for k in paired_logits_list] - hps_ranking = [[predicted[i].tolist().index(j) for j in range(n)] for i,n in enumerate(num_images)] - labels = [label for label in labels.split(num_images.tolist())] - score +=sum([calc_ImageReward(paired_logits_list[i].tolist(), labels[i]) for i in range(len(hps_ranking))]) - print('ImageReward:', score/total) - -def evaluate_rank(data_path, image_folder, model): - meta_file = data_path + '/test.json' - dataset = RankingDataset(meta_file, image_folder, preprocess_val, tokenizer) - dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=4, collate_fn=collate_rank) - - score = 0 - total = len(dataset) - all_rankings = [] - with torch.no_grad(): - for batch in tqdm(dataloader): - images, num_images, labels, texts = batch - images = images.to(device=device, non_blocking=True) - texts = texts.to(device=device, non_blocking=True) - num_images = num_images.to(device=device, non_blocking=True) - labels = labels.to(device=device, non_blocking=True) - - with torch.cuda.amp.autocast(): - outputs = model(images, texts) - image_features, text_features, logit_scale = outputs["image_features"], outputs["text_features"], outputs["logit_scale"] - logits_per_image = logit_scale * image_features @ text_features.T - paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] - - predicted = [torch.argsort(-k) for k in paired_logits_list] - hps_ranking = [[predicted[i].tolist().index(j) for j in range(n)] for i,n in enumerate(num_images)] - labels = [label for label in labels.split(num_images.tolist())] - all_rankings.extend(hps_ranking) - score += sum([inversion_score(hps_ranking[i], labels[i]) for i in range(len(hps_ranking))]) - print('ranking_acc:', score/total) - with open('logs/hps_rank.json', 'w') as f: - json.dump(all_rankings, f) - -def collate_eval(batch): - images = torch.stack([sample[0] for sample in batch]) - captions = torch.cat([sample[1] for sample in batch]) - return images, captions - - -def evaluate_benchmark(data_path, root_dir, model): - meta_dir = data_path - model_list = os.listdir(root_dir) - style_list = os.listdir(os.path.join(root_dir, model_list[0])) - - score = {} - for model_id in model_list: - score[model_id]={} - for style in style_list: - # score[model_id][style] = [0] * 10 - score[model_id][style] = [] - image_folder = os.path.join(root_dir, model_id, style) - meta_file = os.path.join(meta_dir, f'{style}.json') - dataset = BenchmarkDataset(meta_file, image_folder, preprocess_val, tokenizer) - dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_eval) - - with torch.no_grad(): - for i, batch in enumerate(dataloader): - images, texts = batch - images = images.to(device=device, non_blocking=True) - texts = texts.to(device=device, non_blocking=True) - - with torch.cuda.amp.autocast(): - outputs = model(images, texts) - image_features, text_features = outputs["image_features"], outputs["text_features"] - logits_per_image = image_features @ text_features.T - # score[model_id][style][i] = torch.sum(torch.diagonal(logits_per_image)).cpu().item() / 80 - score[model_id][style].extend(torch.diagonal(logits_per_image).cpu().tolist()) - print('-----------benchmark score ---------------- ') - for model_id, data in score.items(): - for style , res in data.items(): - avg_score = [np.mean(res[i:i+80]) for i in range(0, 800, 80)] - print(model_id, '\t', style, '\t', np.mean(avg_score), '\t', np.std(avg_score)) - - -def evaluate_benchmark_DB(data_path, root_dir, model): - meta_file = data_path + '/drawbench.json' - model_list = os.listdir(root_dir) - - - score = {} - for model_id in model_list: - image_folder = os.path.join(root_dir, model_id) - dataset = BenchmarkDataset(meta_file, image_folder, preprocess_val, tokenizer) - dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=4, collate_fn=collate_eval) - score[model_id] = 0 - with torch.no_grad(): - for batch in tqdm(dataloader): - images, texts = batch - images = images.to(device=device, non_blocking=True) - texts = texts.to(device=device, non_blocking=True) - - with torch.cuda.amp.autocast(): - outputs = model(images, texts) - image_features, text_features = outputs["image_features"], outputs["text_features"] - logits_per_image = image_features @ text_features.T - diag = torch.diagonal(logits_per_image) - score[model_id] += torch.sum(diag).cpu().item() - score[model_id] = score[model_id] / len(dataset) - # with open('logs/benchmark_score_DB.json', 'w') as f: - # json.dump(score, f) - print('-----------drawbench score ---------------- ') - for model, data in score.items(): - print(model, '\t', '\t', np.mean(data)) - - -if args.data_type == 'ImageReward': - evaluate_IR(args.data_path, args.image_path, model) -elif args.data_type == 'test': - evaluate_rank(args.data_path, args.image_path, model) -elif args.data_type == 'benchmark': - evaluate_benchmark(args.data_path, args.image_path, model) -elif args.data_type == 'drawbench': - evaluate_benchmark_DB(args.data_path, args.image_path, model) -else: - raise NotImplementedError - - - - diff --git a/spaces/yangban/catordog/app.py b/spaces/yangban/catordog/app.py deleted file mode 100644 index 21323c8a25b045e0700cd1dfc4a6a79d4dbeef87..0000000000000000000000000000000000000000 --- a/spaces/yangban/catordog/app.py +++ /dev/null @@ -1,21 +0,0 @@ - - -from fastai.vision.all import * -import gradio as gr - -def is_cat(x): return x[0].isupper() - -learn = load_learner('model.pkl') - -categories = ('Dog', 'Cat') - -def classify_image(img): - pred,idx,probs = learn.predict(img) - return dict(zip(categories, map(float, probs))) - -image = gr.inputs.Image(shape=(192, 192)) -label = gr.outputs.Label() -examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg'] - -intf = gr.Interface(fn=classify_image, inputs=image, outputs=label,examples=examples) -intf.launch(inline=False) \ No newline at end of file diff --git a/spaces/yerfor/SyntaSpeech/utils/metrics/diagonal_metrics.py b/spaces/yerfor/SyntaSpeech/utils/metrics/diagonal_metrics.py deleted file mode 100644 index ba9807c1a594b38632c4731391e2d4fa3289037b..0000000000000000000000000000000000000000 --- a/spaces/yerfor/SyntaSpeech/utils/metrics/diagonal_metrics.py +++ /dev/null @@ -1,74 +0,0 @@ -import torch - - -def get_focus_rate(attn, src_padding_mask=None, tgt_padding_mask=None): - ''' - attn: bs x L_t x L_s - ''' - if src_padding_mask is not None: - attn = attn * (1 - src_padding_mask.float())[:, None, :] - - if tgt_padding_mask is not None: - attn = attn * (1 - tgt_padding_mask.float())[:, :, None] - - focus_rate = attn.max(-1).values.sum(-1) - focus_rate = focus_rate / attn.sum(-1).sum(-1) - return focus_rate - - -def get_phone_coverage_rate(attn, src_padding_mask=None, src_seg_mask=None, tgt_padding_mask=None): - ''' - attn: bs x L_t x L_s - ''' - src_mask = attn.new(attn.size(0), attn.size(-1)).bool().fill_(False) - if src_padding_mask is not None: - src_mask |= src_padding_mask - if src_seg_mask is not None: - src_mask |= src_seg_mask - - attn = attn * (1 - src_mask.float())[:, None, :] - if tgt_padding_mask is not None: - attn = attn * (1 - tgt_padding_mask.float())[:, :, None] - - phone_coverage_rate = attn.max(1).values.sum(-1) - # phone_coverage_rate = phone_coverage_rate / attn.sum(-1).sum(-1) - phone_coverage_rate = phone_coverage_rate / (1 - src_mask.float()).sum(-1) - return phone_coverage_rate - - -def get_diagonal_focus_rate(attn, attn_ks, target_len, src_padding_mask=None, tgt_padding_mask=None, - band_mask_factor=5, band_width=50): - ''' - attn: bx x L_t x L_s - attn_ks: shape: tensor with shape [batch_size], input_lens/output_lens - - diagonal: y=k*x (k=attn_ks, x:output, y:input) - 1 0 0 - 0 1 0 - 0 0 1 - y>=k*(x-width) and y<=k*(x+width):1 - else:0 - ''' - # width = min(target_len/band_mask_factor, 50) - width1 = target_len / band_mask_factor - width2 = target_len.new(target_len.size()).fill_(band_width) - width = torch.where(width1 < width2, width1, width2).float() - base = torch.ones(attn.size()).to(attn.device) - zero = torch.zeros(attn.size()).to(attn.device) - x = torch.arange(0, attn.size(1)).to(attn.device)[None, :, None].float() * base - y = torch.arange(0, attn.size(2)).to(attn.device)[None, None, :].float() * base - cond = (y - attn_ks[:, None, None] * x) - cond1 = cond + attn_ks[:, None, None] * width[:, None, None] - cond2 = cond - attn_ks[:, None, None] * width[:, None, None] - mask1 = torch.where(cond1 < 0, zero, base) - mask2 = torch.where(cond2 > 0, zero, base) - mask = mask1 * mask2 - - if src_padding_mask is not None: - attn = attn * (1 - src_padding_mask.float())[:, None, :] - if tgt_padding_mask is not None: - attn = attn * (1 - tgt_padding_mask.float())[:, :, None] - - diagonal_attn = attn * mask - diagonal_focus_rate = diagonal_attn.sum(-1).sum(-1) / attn.sum(-1).sum(-1) - return diagonal_focus_rate, mask diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/version.py b/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/version.py deleted file mode 100644 index b794fd409a5e3b3b65ad76a43d6a01a318877640..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = '0.1.0' diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnextv2/configuration_convnextv2.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnextv2/configuration_convnextv2.py deleted file mode 100644 index 14dfcf85124e7f8b150b0e418718ee2a5eeccbfb..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnextv2/configuration_convnextv2.py +++ /dev/null @@ -1,115 +0,0 @@ -# coding=utf-8 -# Copyright 2023 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" ConvNeXTV2 model configuration""" - - -from ...configuration_utils import PretrainedConfig -from ...utils import logging -from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices - - -logger = logging.get_logger(__name__) - -CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP = { - "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json", -} - - -class ConvNextV2Config(BackboneConfigMixin, PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`ConvNextV2Model`]. It is used to instantiate an - ConvNeXTV2 model according to the specified arguments, defining the model architecture. Instantiating a - configuration with the defaults will yield a similar configuration to that of the ConvNeXTV2 - [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) architecture. - - Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the - documentation from [`PretrainedConfig`] for more information. - - Args: - num_channels (`int`, *optional*, defaults to 3): - The number of input channels. - patch_size (`int`, optional, defaults to 4): - Patch size to use in the patch embedding layer. - num_stages (`int`, optional, defaults to 4): - The number of stages in the model. - hidden_sizes (`List[int]`, *optional*, defaults to `[96, 192, 384, 768]`): - Dimensionality (hidden size) at each stage. - depths (`List[int]`, *optional*, defaults to `[3, 3, 9, 3]`): - Depth (number of blocks) for each stage. - hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): - The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`, - `"selu"` and `"gelu_new"` are supported. - initializer_range (`float`, *optional*, defaults to 0.02): - The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - layer_norm_eps (`float`, *optional*, defaults to 1e-12): - The epsilon used by the layer normalization layers. - drop_path_rate (`float`, *optional*, defaults to 0.0): - The drop rate for stochastic depth. - out_features (`List[str]`, *optional*): - If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. - (depending on how many stages the model has). If unset and `out_indices` is set, will default to the - corresponding stages. If unset and `out_indices` is unset, will default to the last stage. - out_indices (`List[int]`, *optional*): - If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how - many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. - If unset and `out_features` is unset, will default to the last stage. - - Example: - ```python - >>> from transformers import ConvNeXTV2Config, ConvNextV2Model - - >>> # Initializing a ConvNeXTV2 convnextv2-tiny-1k-224 style configuration - >>> configuration = ConvNeXTV2Config() - - >>> # Initializing a model (with random weights) from the convnextv2-tiny-1k-224 style configuration - >>> model = ConvNextV2Model(configuration) - - >>> # Accessing the model configuration - >>> configuration = model.config - ```""" - model_type = "convnextv2" - - def __init__( - self, - num_channels=3, - patch_size=4, - num_stages=4, - hidden_sizes=None, - depths=None, - hidden_act="gelu", - initializer_range=0.02, - layer_norm_eps=1e-12, - drop_path_rate=0.0, - image_size=224, - out_features=None, - out_indices=None, - **kwargs, - ): - super().__init__(**kwargs) - - self.num_channels = num_channels - self.patch_size = patch_size - self.num_stages = num_stages - self.hidden_sizes = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes - self.depths = [3, 3, 9, 3] if depths is None else depths - self.hidden_act = hidden_act - self.initializer_range = initializer_range - self.layer_norm_eps = layer_norm_eps - self.drop_path_rate = drop_path_rate - self.image_size = image_size - self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)] - self._out_features, self._out_indices = get_aligned_output_features_output_indices( - out_features=out_features, out_indices=out_indices, stage_names=self.stage_names - ) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gptsan_japanese/configuration_gptsan_japanese.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gptsan_japanese/configuration_gptsan_japanese.py deleted file mode 100644 index d20b79daacfd1713aa1efc2f192ae600ec3789f2..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gptsan_japanese/configuration_gptsan_japanese.py +++ /dev/null @@ -1,158 +0,0 @@ -# coding=utf-8 -# Copyright 2023, HuggingFace Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" GPTSAN-japanese model configuration""" -from ...configuration_utils import PretrainedConfig -from ...utils import logging - - -logger = logging.get_logger(__name__) - -GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP = { - "tanreinama/GPTSAN-2.8B-spout_is_uniform": ( - "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json" - ), -} - - -class GPTSanJapaneseConfig(PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`GPTSanJapaneseModel`]. It is used to instantiate - a GPTSANJapanese model according to the specified arguments, defining the model architecture. Instantiating a - configuration with the defaults will yield a similar configuration to that of the GPTSANJapanese - [Tanrei/GPTSAN-japanese](https://huggingface.co/Tanrei/GPTSAN-japanese) architecture. - - Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the - documentation from [`PretrainedConfig`] for more information. - - Arguments: - vocab_size (`int`, *optional*, defaults to 36000): - Vocabulary size of the GPTSANJapanese model. Defines the number of different tokens that can be represented - by the `inputs_ids` passed when calling [`GPTSanJapaneseModel`]. - max_position_embeddings (`int`, *optional*, defaults to 1280): - The maximum sequence length that this model might ever be used with. Defaults set this to 1280. - d_model (`int`, *optional*, defaults to 1024): - Size of the encoder layers and the pooler layer. - d_ff (`int`, *optional*, defaults to 8192): - Size of the intermediate feed forward layer in each `SwitchTransformersBlock`. - d_ext (`int`, *optional*, defaults to 4096): - Size of the intermediate feed forward layer in each Extra-layers. - d_spout (`int`, *optional*, defaults to 128): - Size of the `spout` vector. - num_switch_layers (`int`, *optional*, defaults to 10): - Number of layers in the Switch Transformer layer. - num_ext_layers (`int`, *optional*, defaults to 0): - Number of layers in the Extra-layers. - num_heads (`int`, *optional*, defaults to 16): - Number of attention heads for each attention layer in the Transformer encoder. - num_experts (`int`, *optional*, defaults to 16): - Number of experts for each SwitchTransformer layer. - expert_capacity (`int`, *optional*, defaults to 128): - Number of tokens that can be stored in each expert. If set to 1, the model will behave like a regular - Transformer. - dropout_rate (`float`, *optional*, defaults to 0.0): - The ratio for all dropout layers. - layer_norm_eps (`float`, *optional*, defaults to 1e-5): - The epsilon used by the layer normalization layers. - router_bias (`bool`, *optional*, defaults to `False`): - Whether to add a bias to the router. - router_jitter_noise (`float`, *optional*, defaults to 0.0): - Amount of noise to add to the router. Set it to 0.0 during prediction or set small value (usually 1e-2) - during training. - router_dtype (`str`, *optional*, default to `"float32"`): - The `dtype` used for the routers. It is preferable to keep the `dtype` to `"float32"` as specified in the - *selective precision* discussion in [the paper](https://arxiv.org/abs/2101.03961). - router_ignore_padding_tokens (`bool`, *optional*, defaults to `False`): - Whether to ignore padding tokens when routing. - output_hidden_states (`bool`, *optional*, default to `False`): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - output_attentions (`bool`, *optional*, defaults to `False`): - Whether or not to return the attentions tensors of all attention layers. - initializer_factor (`float`, *optional*, defaults to 0.002): - A factor for initializing all weight matrices. - output_router_logits (`bool`, *optional*, default to `False`): - Whether or not to return the router logits of all experts. - use_cache (`bool`, *optional*, defaults to `True`): - Whether or not the model should return the last key/values attentions (not used by all models) - """ - model_type = "gptsan-japanese" - keys_to_ignore_at_inference = [ - "past_key_values", - ] - attribute_map = { - "hidden_size": "d_model", - "num_attention_heads": "num_heads", - "num_hidden_layers": "num_layers", - } - - def __init__( - self, - vocab_size=36000, - max_position_embeddings=1280, - d_model=1024, - d_ff=8192, - d_ext=4096, - d_spout=128, - num_switch_layers=10, - num_ext_layers=0, - num_heads=16, - num_experts=16, - expert_capacity=128, - dropout_rate=0.0, - layer_norm_epsilon=1e-5, - router_bias=False, - router_jitter_noise=0.0, - router_dtype="float32", - router_ignore_padding_tokens=False, - output_hidden_states=False, - output_attentions=False, - initializer_factor=0.002, - output_router_logits=False, - use_cache=True, - separator_token_id=35998, - pad_token_id=35995, - eos_token_id=35999, - **kwargs, - ): - self.vocab_size = vocab_size - self.max_position_embeddings = max_position_embeddings - self.d_model = d_model - self.d_ff = d_ff - self.d_ext = d_ext - self.d_spout = d_spout - self.num_switch_layers = num_switch_layers - self.num_ext_layers = num_ext_layers - self.num_layers = num_switch_layers + num_ext_layers - self.num_heads = num_heads - self.num_experts = num_experts - self.expert_capacity = expert_capacity - self.dropout_rate = dropout_rate - self.layer_norm_epsilon = layer_norm_epsilon - self.router_bias = router_bias - self.router_jitter_noise = router_jitter_noise - self.router_dtype = router_dtype - self.router_ignore_padding_tokens = router_ignore_padding_tokens - self.output_hidden_states = output_hidden_states - self.output_attentions = output_attentions - self.initializer_factor = initializer_factor - self.output_router_logits = output_router_logits - self.use_cache = use_cache - - super().__init__( - separator_token_id=separator_token_id, - pad_token_id=pad_token_id, - eos_token_id=eos_token_id, - **kwargs, - ) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/maskformer/modeling_maskformer.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/maskformer/modeling_maskformer.py deleted file mode 100644 index 87b91ed64b62d32cdc7feaa8f7232e559ecd06d5..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/maskformer/modeling_maskformer.py +++ /dev/null @@ -1,1971 +0,0 @@ -# coding=utf-8 -# Copyright 2022 Meta Platforms, Inc.s and The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" PyTorch MaskFormer model.""" - -import math -from dataclasses import dataclass -from numbers import Number -from typing import Dict, List, Optional, Tuple - -import numpy as np -import torch -from torch import Tensor, nn - -from ... import AutoBackbone -from ...activations import ACT2FN -from ...modeling_outputs import BaseModelOutputWithCrossAttentions -from ...modeling_utils import PreTrainedModel -from ...utils import ( - ModelOutput, - add_start_docstrings, - add_start_docstrings_to_model_forward, - is_scipy_available, - logging, - replace_return_docstrings, - requires_backends, -) -from ..detr import DetrConfig -from .configuration_maskformer import MaskFormerConfig -from .configuration_maskformer_swin import MaskFormerSwinConfig - - -if is_scipy_available(): - from scipy.optimize import linear_sum_assignment - -logger = logging.get_logger(__name__) - - -_CONFIG_FOR_DOC = "MaskFormerConfig" -_CHECKPOINT_FOR_DOC = "facebook/maskformer-swin-base-ade" - -MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "facebook/maskformer-swin-base-ade", - # See all MaskFormer models at https://huggingface.co/models?filter=maskformer -] - - -@dataclass -# Copied from transformers.models.detr.modeling_detr.DetrDecoderOutput -class DetrDecoderOutput(BaseModelOutputWithCrossAttentions): - """ - Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, - namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them - gone through a layernorm. This is useful when training the model with auxiliary decoding losses. - - Args: - last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): - Sequence of hidden-states at the output of the last layer of the model. - hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of - shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer - plus the initial embedding outputs. - attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): - Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, - sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in - the self-attention heads. - cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): - Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, - sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, - used to compute the weighted average in the cross-attention heads. - intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): - Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a - layernorm. - """ - - intermediate_hidden_states: Optional[torch.FloatTensor] = None - - -@dataclass -class MaskFormerPixelLevelModuleOutput(ModelOutput): - """ - MaskFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the - `encoder` and `decoder`. By default, the `encoder` is a MaskFormerSwin Transformer and the `decoder` is a Feature - Pyramid Network (FPN). - - The `encoder_last_hidden_state` are referred on the paper as **images features**, while `decoder_last_hidden_state` - as **pixel embeddings** - - Args: - encoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`): - Last hidden states (final feature map) of the last stage of the encoder. - encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at - the output of each stage. - decoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`): - Last hidden states (final feature map) of the last stage of the decoder. - decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at - the output of each stage. - """ - - encoder_last_hidden_state: Optional[torch.FloatTensor] = None - decoder_last_hidden_state: Optional[torch.FloatTensor] = None - encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - - -@dataclass -class MaskFormerPixelDecoderOutput(ModelOutput): - """ - MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state - and (optionally) the hidden states. - - Args: - last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Last hidden states (final feature map) of the last stage of the model. - hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of - shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer - plus the initial embedding outputs. - attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): - Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, - sequence_length)`. Attentions weights from Detr's decoder after the attention softmax, used to compute the - weighted average in the self-attention heads. - """ - - last_hidden_state: torch.FloatTensor = None - hidden_states: Optional[Tuple[torch.FloatTensor]] = None - attentions: Optional[Tuple[torch.FloatTensor]] = None - - -@dataclass -class MaskFormerModelOutput(ModelOutput): - """ - Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits. - - Args: - encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Last hidden states (final feature map) of the last stage of the encoder model (backbone). - pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN). - transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): - Last hidden states (final feature map) of the last stage of the transformer decoder model. - encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder - model at the output of each stage. - pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel - decoder model at the output of each stage. - transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the - transformer decoder at the output of each stage. - hidden_states `tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and - `decoder_hidden_states` - attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): - Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, - sequence_length)`. Attentions weights from Detr's decoder after the attention softmax, used to compute the - weighted average in the self-attention heads. - """ - - encoder_last_hidden_state: Optional[torch.FloatTensor] = None - pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None - transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None - encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - pixel_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - transformer_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - hidden_states: Optional[Tuple[torch.FloatTensor]] = None - attentions: Optional[Tuple[torch.FloatTensor]] = None - - -@dataclass -class MaskFormerForInstanceSegmentationOutput(ModelOutput): - """ - Class for outputs of [`MaskFormerForInstanceSegmentation`]. - - This output can be directly passed to [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or or - [`~MaskFormerImageProcessor.post_process_instance_segmentation`] or - [`~MaskFormerImageProcessor.post_process_panoptic_segmentation`] depending on the task. Please, see - [`~MaskFormerImageProcessor] for details regarding usage. - - Args: - loss (`torch.Tensor`, *optional*): - The computed loss, returned when labels are present. - class_queries_logits (`torch.FloatTensor`): - A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each - query. Note the `+ 1` is needed because we incorporate the null class. - masks_queries_logits (`torch.FloatTensor`): - A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each - query. - encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Last hidden states (final feature map) of the last stage of the encoder model (backbone). - pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN). - transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): - Last hidden states (final feature map) of the last stage of the transformer decoder model. - encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder - model at the output of each stage. - pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel - decoder model at the output of each stage. - transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of - shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the transformer decoder at the output - of each stage. - hidden_states `tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and - `decoder_hidden_states`. - attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): - Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, - sequence_length)`. Attentions weights from Detr's decoder after the attention softmax, used to compute the - weighted average in the self-attention heads. - """ - - loss: Optional[torch.FloatTensor] = None - class_queries_logits: torch.FloatTensor = None - masks_queries_logits: torch.FloatTensor = None - auxiliary_logits: torch.FloatTensor = None - encoder_last_hidden_state: Optional[torch.FloatTensor] = None - pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None - transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None - encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - pixel_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - transformer_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None - hidden_states: Optional[Tuple[torch.FloatTensor]] = None - attentions: Optional[Tuple[torch.FloatTensor]] = None - - -def upsample_like(pixel_values: Tensor, like: Tensor, mode: str = "bilinear") -> Tensor: - """ - An utility function that upsamples `pixel_values` to match the dimension of `like`. - - Args: - pixel_values (`torch.Tensor`): - The tensor we wish to upsample. - like (`torch.Tensor`): - The tensor we wish to use as size target. - mode (str, *optional*, defaults to `"bilinear"`): - The interpolation mode. - - Returns: - `torch.Tensor`: The upsampled tensor - """ - _, _, height, width = like.shape - upsampled = nn.functional.interpolate(pixel_values, size=(height, width), mode=mode, align_corners=False) - return upsampled - - -# refactored from original implementation -def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor: - r""" - Compute the DICE loss, similar to generalized IOU for masks as follows: - - $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$ - - In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow - - $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$ - - Args: - inputs (`torch.Tensor`): - A tensor representing a mask. - labels (`torch.Tensor`): - A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs - (0 for the negative class and 1 for the positive class). - num_masks (`int`): - The number of masks present in the current batch, used for normalization. - - Returns: - `torch.Tensor`: The computed loss. - """ - probs = inputs.sigmoid().flatten(1) - numerator = 2 * (probs * labels).sum(-1) - denominator = probs.sum(-1) + labels.sum(-1) - loss = 1 - (numerator + 1) / (denominator + 1) - loss = loss.sum() / num_masks - return loss - - -# refactored from original implementation -def sigmoid_focal_loss( - inputs: Tensor, labels: Tensor, num_masks: int, alpha: float = 0.25, gamma: float = 2 -) -> Tensor: - r""" - Focal loss proposed in [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) originally used in - RetinaNet. The loss is computed as follows: - - $$ \mathcal{L}_{\text{focal loss} = -(1 - p_t)^{\gamma}\log{(p_t)} $$ - - where \\(CE(p_t) = -\log{(p_t)}}\\), CE is the standard Cross Entropy Loss - - Please refer to equation (1,2,3) of the paper for a better understanding. - - Args: - inputs (`torch.Tensor`): - A float tensor of arbitrary shape. - labels (`torch.Tensor`): - A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs - (0 for the negative class and 1 for the positive class). - num_masks (`int`): - The number of masks present in the current batch, used for normalization. - alpha (float, *optional*, defaults to 0.25): - Weighting factor in range (0,1) to balance positive vs negative examples. - gamma (float, *optional*, defaults to 2.0): - Exponent of the modulating factor \\(1 - p_t\\) to balance easy vs hard examples. - - Returns: - `torch.Tensor`: The computed loss. - """ - criterion = nn.BCEWithLogitsLoss(reduction="none") - probs = inputs.sigmoid() - cross_entropy_loss = criterion(inputs, labels) - p_t = probs * labels + (1 - probs) * (1 - labels) - loss = cross_entropy_loss * ((1 - p_t) ** gamma) - - if alpha >= 0: - alpha_t = alpha * labels + (1 - alpha) * (1 - labels) - loss = alpha_t * loss - - loss = loss.mean(1).sum() / num_masks - return loss - - -# refactored from original implementation -def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor: - """ - A pair wise version of the dice loss, see `dice_loss` for usage. - - Args: - inputs (`torch.Tensor`): - A tensor representing a mask - labels (`torch.Tensor`): - A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs - (0 for the negative class and 1 for the positive class). - - Returns: - `torch.Tensor`: The computed loss between each pairs. - """ - inputs = inputs.sigmoid().flatten(1) - numerator = 2 * torch.matmul(inputs, labels.T) - # using broadcasting to get a [num_queries, NUM_CLASSES] matrix - denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :] - loss = 1 - (numerator + 1) / (denominator + 1) - return loss - - -# refactored from original implementation -def pair_wise_sigmoid_focal_loss(inputs: Tensor, labels: Tensor, alpha: float = 0.25, gamma: float = 2.0) -> Tensor: - r""" - A pair wise version of the focal loss, see `sigmoid_focal_loss` for usage. - - Args: - inputs (`torch.Tensor`): - A tensor representing a mask. - labels (`torch.Tensor`): - A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs - (0 for the negative class and 1 for the positive class). - alpha (float, *optional*, defaults to 0.25): - Weighting factor in range (0,1) to balance positive vs negative examples. - gamma (float, *optional*, defaults to 2.0): - Exponent of the modulating factor \\(1 - p_t\\) to balance easy vs hard examples. - - Returns: - `torch.Tensor`: The computed loss between each pairs. - """ - if alpha < 0: - raise ValueError("alpha must be positive") - - height_and_width = inputs.shape[1] - - criterion = nn.BCEWithLogitsLoss(reduction="none") - prob = inputs.sigmoid() - cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs)) - focal_pos = ((1 - prob) ** gamma) * cross_entropy_loss_pos - focal_pos *= alpha - - cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs)) - - focal_neg = (prob**gamma) * cross_entropy_loss_neg - focal_neg *= 1 - alpha - - loss = torch.matmul(focal_pos, labels.T) + torch.matmul(focal_neg, (1 - labels).T) - - return loss / height_and_width - - -# Copied from transformers.models.detr.modeling_detr.DetrAttention -class DetrAttention(nn.Module): - """ - Multi-headed attention from 'Attention Is All You Need' paper. - - Here, we add position embeddings to the queries and keys (as explained in the DETR paper). - """ - - def __init__( - self, - embed_dim: int, - num_heads: int, - dropout: float = 0.0, - bias: bool = True, - ): - super().__init__() - self.embed_dim = embed_dim - self.num_heads = num_heads - self.dropout = dropout - self.head_dim = embed_dim // num_heads - if self.head_dim * num_heads != self.embed_dim: - raise ValueError( - f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" - f" {num_heads})." - ) - self.scaling = self.head_dim**-0.5 - - self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - - def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): - return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() - - def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs): - position_embeddings = kwargs.pop("position_embeddings", None) - - if kwargs: - raise ValueError(f"Unexpected arguments {kwargs.keys()}") - - if position_embeddings is not None and object_queries is not None: - raise ValueError( - "Cannot specify both position_embeddings and object_queries. Please use just object_queries" - ) - - if position_embeddings is not None: - logger.warning_once( - "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" - ) - object_queries = position_embeddings - - return tensor if object_queries is None else tensor + object_queries - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - object_queries: Optional[torch.Tensor] = None, - key_value_states: Optional[torch.Tensor] = None, - spatial_position_embeddings: Optional[torch.Tensor] = None, - output_attentions: bool = False, - **kwargs, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - """Input shape: Batch x Time x Channel""" - - position_embeddings = kwargs.pop("position_ebmeddings", None) - key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None) - - if kwargs: - raise ValueError(f"Unexpected arguments {kwargs.keys()}") - - if position_embeddings is not None and object_queries is not None: - raise ValueError( - "Cannot specify both position_embeddings and object_queries. Please use just object_queries" - ) - - if key_value_position_embeddings is not None and spatial_position_embeddings is not None: - raise ValueError( - "Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings" - ) - - if position_embeddings is not None: - logger.warning_once( - "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" - ) - object_queries = position_embeddings - - if key_value_position_embeddings is not None: - logger.warning_once( - "key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead" - ) - spatial_position_embeddings = key_value_position_embeddings - - # if key_value_states are provided this layer is used as a cross-attention layer - # for the decoder - is_cross_attention = key_value_states is not None - batch_size, target_len, embed_dim = hidden_states.size() - - # add position embeddings to the hidden states before projecting to queries and keys - if object_queries is not None: - hidden_states_original = hidden_states - hidden_states = self.with_pos_embed(hidden_states, object_queries) - - # add key-value position embeddings to the key value states - if spatial_position_embeddings is not None: - key_value_states_original = key_value_states - key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings) - - # get query proj - query_states = self.q_proj(hidden_states) * self.scaling - # get key, value proj - if is_cross_attention: - # cross_attentions - key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) - value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) - else: - # self_attention - key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) - value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) - - proj_shape = (batch_size * self.num_heads, -1, self.head_dim) - query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) - key_states = key_states.view(*proj_shape) - value_states = value_states.view(*proj_shape) - - source_len = key_states.size(1) - - attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) - - if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): - raise ValueError( - f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" - f" {attn_weights.size()}" - ) - - if attention_mask is not None: - if attention_mask.size() != (batch_size, 1, target_len, source_len): - raise ValueError( - f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" - f" {attention_mask.size()}" - ) - attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask - attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) - - attn_weights = nn.functional.softmax(attn_weights, dim=-1) - - if output_attentions: - # this operation is a bit awkward, but it's required to - # make sure that attn_weights keeps its gradient. - # In order to do so, attn_weights have to reshaped - # twice and have to be reused in the following - attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) - attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) - else: - attn_weights_reshaped = None - - attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) - - attn_output = torch.bmm(attn_probs, value_states) - - if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): - raise ValueError( - f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" - f" {attn_output.size()}" - ) - - attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) - attn_output = attn_output.transpose(1, 2) - attn_output = attn_output.reshape(batch_size, target_len, embed_dim) - - attn_output = self.out_proj(attn_output) - - return attn_output, attn_weights_reshaped - - -# Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer -class DetrDecoderLayer(nn.Module): - def __init__(self, config: DetrConfig): - super().__init__() - self.embed_dim = config.d_model - - self.self_attn = DetrAttention( - embed_dim=self.embed_dim, - num_heads=config.decoder_attention_heads, - dropout=config.attention_dropout, - ) - self.dropout = config.dropout - self.activation_fn = ACT2FN[config.activation_function] - self.activation_dropout = config.activation_dropout - - self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) - self.encoder_attn = DetrAttention( - self.embed_dim, - config.decoder_attention_heads, - dropout=config.attention_dropout, - ) - self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) - self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) - self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) - self.final_layer_norm = nn.LayerNorm(self.embed_dim) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - object_queries: Optional[torch.Tensor] = None, - query_position_embeddings: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = False, - **kwargs, - ): - """ - Args: - hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` - attention_mask (`torch.FloatTensor`): attention mask of size - `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative - values. - object_queries (`torch.FloatTensor`, *optional*): - object_queries that are added to the hidden states - in the cross-attention layer. - query_position_embeddings (`torch.FloatTensor`, *optional*): - position embeddings that are added to the queries and keys - in the self-attention layer. - encoder_hidden_states (`torch.FloatTensor`): - cross attention input to the layer of shape `(batch, seq_len, embed_dim)` - encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size - `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative - values. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - """ - position_embeddings = kwargs.pop("position_embeddings", None) - - if kwargs: - raise ValueError(f"Unexpected arguments {kwargs.keys()}") - - if position_embeddings is not None and object_queries is not None: - raise ValueError( - "Cannot specify both position_embeddings and object_queries. Please use just object_queries" - ) - - if position_embeddings is not None: - logger.warning_once( - "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" - ) - object_queries = position_embeddings - - residual = hidden_states - - # Self Attention - hidden_states, self_attn_weights = self.self_attn( - hidden_states=hidden_states, - object_queries=query_position_embeddings, - attention_mask=attention_mask, - output_attentions=output_attentions, - ) - - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - hidden_states = self.self_attn_layer_norm(hidden_states) - - # Cross-Attention Block - cross_attn_weights = None - if encoder_hidden_states is not None: - residual = hidden_states - - hidden_states, cross_attn_weights = self.encoder_attn( - hidden_states=hidden_states, - object_queries=query_position_embeddings, - key_value_states=encoder_hidden_states, - attention_mask=encoder_attention_mask, - spatial_position_embeddings=object_queries, - output_attentions=output_attentions, - ) - - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - hidden_states = self.encoder_attn_layer_norm(hidden_states) - - # Fully Connected - residual = hidden_states - hidden_states = self.activation_fn(self.fc1(hidden_states)) - hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) - hidden_states = self.fc2(hidden_states) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - hidden_states = self.final_layer_norm(hidden_states) - - outputs = (hidden_states,) - - if output_attentions: - outputs += (self_attn_weights, cross_attn_weights) - - return outputs - - -# Copied from transformers.models.detr.modeling_detr._expand_mask -def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, target_len: Optional[int] = None): - """ - Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`. - """ - batch_size, source_len = mask.size() - target_len = target_len if target_len is not None else source_len - - expanded_mask = mask[:, None, None, :].expand(batch_size, 1, target_len, source_len).to(dtype) - - inverted_mask = 1.0 - expanded_mask - - return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min) - - -class DetrDecoder(nn.Module): - """ - Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`]. - - The decoder updates the query embeddings through multiple self-attention and cross-attention layers. - - Some small tweaks for DETR: - - - object_queries and query_position_embeddings are added to the forward pass. - - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. - - Args: - config: DetrConfig - """ - - def __init__(self, config: DetrConfig): - super().__init__() - self.config = config - self.dropout = config.dropout - self.layerdrop = config.decoder_layerdrop - - self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)]) - # in DETR, the decoder uses layernorm after the last decoder layer output - self.layernorm = nn.LayerNorm(config.d_model) - - self.gradient_checkpointing = False - - def forward( - self, - inputs_embeds=None, - attention_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - object_queries=None, - query_position_embeddings=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - **kwargs, - ): - r""" - Args: - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): - The query embeddings that are passed into the decoder. - - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: - - - 1 for queries that are **not masked**, - - 0 for queries that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention - of the decoder. - encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): - Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected - in `[0, 1]`: - - - 1 for pixels that are real (i.e. **not masked**), - - 0 for pixels that are padding (i.e. **masked**). - - object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Position embeddings that are added to the queries and keys in each cross-attention layer. - query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): - , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer. - 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. - """ - position_embeddings = kwargs.pop("position_embeddings", None) - if kwargs: - raise ValueError(f"Unexpected arguments {kwargs.keys()}") - - if position_embeddings is not None and object_queries is not None: - raise ValueError( - "Cannot specify both position_embeddings and object_queries. Please use just object_queries" - ) - - if position_embeddings is not None: - logger.warning_once( - "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" - ) - object_queries = position_embeddings - - 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 inputs_embeds is not None: - hidden_states = inputs_embeds - input_shape = inputs_embeds.size()[:-1] - - combined_attention_mask = None - - if attention_mask is not None and combined_attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - combined_attention_mask = combined_attention_mask + _expand_mask( - attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] - ) - - # expand encoder attention mask - if encoder_hidden_states is not None and encoder_attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) - - # optional intermediate hidden states - intermediate = () if self.config.auxiliary_loss else None - - # decoder layers - all_hidden_states = () if output_hidden_states else None - all_self_attns = () if output_attentions else None - all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None - - for idx, decoder_layer in enumerate(self.layers): - # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) - if output_hidden_states: - all_hidden_states += (hidden_states,) - if self.training: - dropout_probability = torch.rand([]) - if dropout_probability < self.layerdrop: - continue - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(decoder_layer), - hidden_states, - combined_attention_mask, - encoder_hidden_states, - encoder_attention_mask, - None, - ) - else: - layer_outputs = decoder_layer( - hidden_states, - attention_mask=combined_attention_mask, - object_queries=object_queries, - query_position_embeddings=query_position_embeddings, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - ) - - hidden_states = layer_outputs[0] - - if self.config.auxiliary_loss: - hidden_states = self.layernorm(hidden_states) - intermediate += (hidden_states,) - - if output_attentions: - all_self_attns += (layer_outputs[1],) - - if encoder_hidden_states is not None: - all_cross_attentions += (layer_outputs[2],) - - # finally, apply layernorm - hidden_states = self.layernorm(hidden_states) - - # add hidden states from the last decoder layer - if output_hidden_states: - all_hidden_states += (hidden_states,) - - # stack intermediate decoder activations - if self.config.auxiliary_loss: - intermediate = torch.stack(intermediate) - - if not return_dict: - return tuple( - v - for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate] - if v is not None - ) - return DetrDecoderOutput( - last_hidden_state=hidden_states, - hidden_states=all_hidden_states, - attentions=all_self_attns, - cross_attentions=all_cross_attentions, - intermediate_hidden_states=intermediate, - ) - - -# refactored from original implementation -class MaskFormerHungarianMatcher(nn.Module): - """This class computes an assignment between the labels and the predictions of the network. - - For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more - predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are - un-matched (and thus treated as non-objects). - """ - - def __init__(self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0): - """Creates the matcher - - Params: - cost_class (float, *optional*, defaults to 1.0): - This is the relative weight of the classification error in the matching cost. - cost_mask (float, *optional*, defaults to 1.0): - This is the relative weight of the focal loss of the binary mask in the matching cost. - cost_dice (float, *optional*, defaults to 1.0): - This is the relative weight of the dice loss of the binary mask in the matching cost - """ - super().__init__() - if cost_class == 0 and cost_mask == 0 and cost_dice == 0: - raise ValueError("All costs cant be 0") - self.cost_class = cost_class - self.cost_mask = cost_mask - self.cost_dice = cost_dice - - @torch.no_grad() - def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]: - """Performs the matching - - Params: - masks_queries_logits (`torch.Tensor`): - A tensor` of dim `batch_size, num_queries, num_labels` with the - classification logits. - class_queries_logits (`torch.Tensor`): - A tensor` of dim `batch_size, num_queries, height, width` with the - predicted masks. - - class_labels (`torch.Tensor`): - A tensor` of dim `num_target_boxes` (where num_target_boxes is the number - of ground-truth objects in the target) containing the class labels. - mask_labels (`torch.Tensor`): - A tensor` of dim `num_target_boxes, height, width` containing the target - masks. - - Returns: - `List[Tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where: - - index_i is the indices of the selected predictions (in order) - - index_j is the indices of the corresponding selected labels (in order) - For each batch element, it holds: - len(index_i) = len(index_j) = min(num_queries, num_target_boxes). - """ - indices: List[Tuple[np.array]] = [] - - preds_masks = masks_queries_logits - preds_probs = class_queries_logits - # iterate through batch size - for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels): - # downsample the target mask, save memory - target_mask = nn.functional.interpolate(target_mask[:, None], size=pred_mask.shape[-2:], mode="nearest") - pred_probs = pred_probs.softmax(-1) - # Compute the classification cost. Contrary to the loss, we don't use the NLL, - # but approximate it in 1 - proba[target class]. - # The 1 is a constant that doesn't change the matching, it can be ommitted. - cost_class = -pred_probs[:, labels] - # flatten spatial dimension "q h w -> q (h w)" - pred_mask_flat = pred_mask.flatten(1) # [num_queries, height*width] - # same for target_mask "c h w -> c (h w)" - target_mask_flat = target_mask[:, 0].flatten(1) # [num_total_labels, height*width] - # compute the focal loss between each mask pairs -> shape (num_queries, num_labels) - cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat) - # Compute the dice loss betwen each mask pairs -> shape (num_queries, num_labels) - cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat) - # final cost matrix - cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice - # do the assigmented using the hungarian algorithm in scipy - assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.cpu()) - indices.append(assigned_indices) - - # It could be stacked in one tensor - matched_indices = [ - (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices - ] - return matched_indices - - def __repr__(self): - head = "Matcher " + self.__class__.__name__ - body = [ - f"cost_class: {self.cost_class}", - f"cost_mask: {self.cost_mask}", - f"cost_dice: {self.cost_dice}", - ] - _repr_indent = 4 - lines = [head] + [" " * _repr_indent + line for line in body] - return "\n".join(lines) - - -# copied and adapted from original implementation -class MaskFormerLoss(nn.Module): - def __init__( - self, - num_labels: int, - matcher: MaskFormerHungarianMatcher, - weight_dict: Dict[str, float], - eos_coef: float, - ): - """ - The MaskFormer Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we compute - hungarian assignment between ground truth masks and the outputs of the model 2) we supervise each pair of - matched ground-truth / prediction (supervise class and mask) - - Args: - num_labels (`int`): - The number of classes. - matcher (`MaskFormerHungarianMatcher`): - A torch module that computes the assigments between the predictions and labels. - weight_dict (`Dict[str, float]`): - A dictionary of weights to be applied to the different losses. - eos_coef (`float`): - Weight to apply to the null class. - """ - - super().__init__() - requires_backends(self, ["scipy"]) - self.num_labels = num_labels - self.matcher = matcher - self.weight_dict = weight_dict - self.eos_coef = eos_coef - empty_weight = torch.ones(self.num_labels + 1) - empty_weight[-1] = self.eos_coef - self.register_buffer("empty_weight", empty_weight) - - def _max_by_axis(self, the_list: List[List[int]]) -> List[int]: - maxes = the_list[0] - for sublist in the_list[1:]: - for index, item in enumerate(sublist): - maxes[index] = max(maxes[index], item) - return maxes - - def _pad_images_to_max_in_batch(self, tensors: List[Tensor]) -> Tuple[Tensor, Tensor]: - # get the maximum size in the batch - max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors]) - batch_size = len(tensors) - # compute finel size - batch_shape = [batch_size] + max_size - b, _, h, w = batch_shape - # get metadata - dtype = tensors[0].dtype - device = tensors[0].device - padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device) - padding_masks = torch.ones((b, h, w), dtype=torch.bool, device=device) - # pad the tensors to the size of the biggest one - for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks): - padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor) - padding_mask[: tensor.shape[1], : tensor.shape[2]] = False - - return padded_tensors, padding_masks - - def loss_labels( - self, class_queries_logits: Tensor, class_labels: List[Tensor], indices: Tuple[np.array] - ) -> Dict[str, Tensor]: - """Compute the losses related to the labels using cross entropy. - - Args: - class_queries_logits (`torch.Tensor`): - A tensor of shape `batch_size, num_queries, num_labels` - class_labels (`List[torch.Tensor]`): - List of class labels of shape `(labels)`. - indices (`Tuple[np.array])`: - The indices computed by the Hungarian matcher. - - Returns: - `Dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key: - - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels. - """ - - pred_logits = class_queries_logits - batch_size, num_queries, _ = pred_logits.shape - criterion = nn.CrossEntropyLoss(weight=self.empty_weight) - idx = self._get_predictions_permutation_indices(indices) - # shape = (batch_size, num_queries) - target_classes_o = torch.cat([target[j] for target, (_, j) in zip(class_labels, indices)]) - # shape = (batch_size, num_queries) - target_classes = torch.full( - (batch_size, num_queries), fill_value=self.num_labels, dtype=torch.int64, device=pred_logits.device - ) - target_classes[idx] = target_classes_o - # target_classes is a (batch_size, num_labels, num_queries), we need to permute pred_logits "b q c -> b c q" - pred_logits_transposed = pred_logits.transpose(1, 2) - loss_ce = criterion(pred_logits_transposed, target_classes) - losses = {"loss_cross_entropy": loss_ce} - return losses - - def loss_masks( - self, masks_queries_logits: Tensor, mask_labels: List[Tensor], indices: Tuple[np.array], num_masks: int - ) -> Dict[str, Tensor]: - """Compute the losses related to the masks using focal and dice loss. - - Args: - masks_queries_logits (`torch.Tensor`): - A tensor of shape `batch_size, num_queries, height, width` - mask_labels (`torch.Tensor`): - List of mask labels of shape `(labels, height, width)`. - indices (`Tuple[np.array])`: - The indices computed by the Hungarian matcher. - num_masks (`int)`: - The number of masks, used for normalization. - - Returns: - `Dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys: - - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks. - - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth - masks. - """ - src_idx = self._get_predictions_permutation_indices(indices) - tgt_idx = self._get_targets_permutation_indices(indices) - # shape (batch_size * num_queries, height, width) - pred_masks = masks_queries_logits[src_idx] - # shape (batch_size, num_queries, height, width) - # pad all and stack the targets to the num_labels dimension - target_masks, _ = self._pad_images_to_max_in_batch(mask_labels) - target_masks = target_masks[tgt_idx] - # upsample predictions to the target size, we have to add one dim to use interpolate - pred_masks = nn.functional.interpolate( - pred_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False - ) - pred_masks = pred_masks[:, 0].flatten(1) - - target_masks = target_masks.flatten(1) - losses = { - "loss_mask": sigmoid_focal_loss(pred_masks, target_masks, num_masks), - "loss_dice": dice_loss(pred_masks, target_masks, num_masks), - } - return losses - - def _get_predictions_permutation_indices(self, indices): - # permute predictions following indices - batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) - predictions_indices = torch.cat([src for (src, _) in indices]) - return batch_indices, predictions_indices - - def _get_targets_permutation_indices(self, indices): - # permute labels following indices - batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) - target_indices = torch.cat([tgt for (_, tgt) in indices]) - return batch_indices, target_indices - - def forward( - self, - masks_queries_logits: Tensor, - class_queries_logits: Tensor, - mask_labels: List[Tensor], - class_labels: List[Tensor], - auxiliary_predictions: Optional[Dict[str, Tensor]] = None, - ) -> Dict[str, Tensor]: - """ - This performs the loss computation. - - Args: - masks_queries_logits (`torch.Tensor`): - A tensor of shape `batch_size, num_queries, height, width` - class_queries_logits (`torch.Tensor`): - A tensor of shape `batch_size, num_queries, num_labels` - mask_labels (`torch.Tensor`): - List of mask labels of shape `(labels, height, width)`. - class_labels (`List[torch.Tensor]`): - List of class labels of shape `(labels)`. - auxiliary_predictions (`Dict[str, torch.Tensor]`, *optional*): - if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], then it contains the logits from the - inner layers of the Detr's Decoder. - - Returns: - `Dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys: - - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels. - - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks. - - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth - masks. - if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], the dictionary contains addional losses - for each auxiliary predictions. - """ - - # retrieve the matching between the outputs of the last layer and the labels - indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels) - # compute the average number of target masks for normalization purposes - num_masks: Number = self.get_num_masks(class_labels, device=class_labels[0].device) - # get all the losses - losses: Dict[str, Tensor] = { - **self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks), - **self.loss_labels(class_queries_logits, class_labels, indices), - } - # in case of auxiliary losses, we repeat this process with the output of each intermediate layer. - if auxiliary_predictions is not None: - for idx, aux_outputs in enumerate(auxiliary_predictions): - masks_queries_logits = aux_outputs["masks_queries_logits"] - class_queries_logits = aux_outputs["class_queries_logits"] - loss_dict = self.forward(masks_queries_logits, class_queries_logits, mask_labels, class_labels) - loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()} - losses.update(loss_dict) - - return losses - - def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor: - """ - Computes the average number of target masks across the batch, for normalization purposes. - """ - num_masks = sum([len(classes) for classes in class_labels]) - num_masks_pt = torch.as_tensor([num_masks], dtype=torch.float, device=device) - return num_masks_pt - - -class MaskFormerFPNConvLayer(nn.Module): - def __init__(self, in_features: int, out_features: int, kernel_size: int = 3, padding: int = 1): - """ - A basic module that executes conv - norm - in sequence used in MaskFormer. - - Args: - in_features (`int`): - The number of input features (channels). - out_features (`int`): - The number of outputs features (channels). - """ - super().__init__() - self.layers = [ - nn.Conv2d(in_features, out_features, kernel_size=kernel_size, padding=padding, bias=False), - nn.GroupNorm(32, out_features), - nn.ReLU(inplace=True), - ] - for i, layer in enumerate(self.layers): - # Provide backwards compatibility from when the class inherited from nn.Sequential - # In nn.Sequential subclasses, the name given to the layer is its index in the sequence. - # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g. - # self.my_layer_name = Layer() - # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register - # explicitly - self.add_module(str(i), layer) - - def forward(self, input: Tensor) -> Tensor: - hidden_state = input - for layer in self.layers: - hidden_state = layer(hidden_state) - return hidden_state - - -class MaskFormerFPNLayer(nn.Module): - def __init__(self, in_features: int, lateral_features: int): - """ - A Feature Pyramid Network Layer (FPN) layer. It creates a feature map by aggregating features from the previous - and backbone layer. Due to the spatial mismatch, the tensor coming from the previous layer is upsampled. - - Args: - in_features (`int`): - The number of input features (channels). - lateral_features (`int`): - The number of lateral features (channels). - """ - super().__init__() - self.proj = nn.Sequential( - nn.Conv2d(lateral_features, in_features, kernel_size=1, padding=0, bias=False), - nn.GroupNorm(32, in_features), - ) - - self.block = MaskFormerFPNConvLayer(in_features, in_features) - - def forward(self, down: Tensor, left: Tensor) -> Tensor: - left = self.proj(left) - down = nn.functional.interpolate(down, size=left.shape[-2:], mode="nearest") - down += left - down = self.block(down) - return down - - -class MaskFormerFPNModel(nn.Module): - def __init__(self, in_features: int, lateral_widths: List[int], feature_size: int = 256): - """ - Feature Pyramid Network, given an input tensor and a set of feature map of different feature/spatial size, it - creates a list of feature maps with the same feature size. - - Args: - in_features (`int`): - The number of input features (channels). - lateral_widths (`List[int]`): - A list with the features (channels) size of each lateral connection. - feature_size (int, *optional*, defaults to 256): - The features (channels) of the resulting feature maps. - """ - super().__init__() - self.stem = MaskFormerFPNConvLayer(in_features, feature_size) - self.layers = nn.Sequential( - *[MaskFormerFPNLayer(feature_size, lateral_width) for lateral_width in lateral_widths[::-1]] - ) - - def forward(self, features: List[Tensor]) -> List[Tensor]: - fpn_features = [] - last_feature = features[-1] - other_features = features[:-1] - output = self.stem(last_feature) - for layer, left in zip(self.layers, other_features[::-1]): - output = layer(output, left) - fpn_features.append(output) - return fpn_features - - -class MaskFormerPixelDecoder(nn.Module): - def __init__(self, *args, feature_size: int = 256, mask_feature_size: int = 256, **kwargs): - r""" - Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic - Segmentation](https://arxiv.org/abs/2107.06278). It first runs the backbone's features into a Feature Pyramid - Network creating a list of feature maps. Then, it projects the last one to the correct `mask_size`. - - Args: - feature_size (`int`, *optional*, defaults to 256): - The feature size (channel dimension) of the FPN feature maps. - mask_feature_size (`int`, *optional*, defaults to 256): - The features (channels) of the target masks size \\(C_{\epsilon}\\) in the paper. - """ - super().__init__() - - self.fpn = MaskFormerFPNModel(*args, feature_size=feature_size, **kwargs) - self.mask_projection = nn.Conv2d(feature_size, mask_feature_size, kernel_size=3, padding=1) - - def forward( - self, features: List[Tensor], output_hidden_states: bool = False, return_dict: bool = True - ) -> MaskFormerPixelDecoderOutput: - fpn_features = self.fpn(features) - # we use the last feature map - last_feature_projected = self.mask_projection(fpn_features[-1]) - - if not return_dict: - return (last_feature_projected, tuple(fpn_features)) if output_hidden_states else (last_feature_projected,) - - return MaskFormerPixelDecoderOutput( - last_hidden_state=last_feature_projected, hidden_states=tuple(fpn_features) if output_hidden_states else () - ) - - -# copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding -class MaskFormerSinePositionEmbedding(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one used by the Attention is all you - need paper, generalized to work on images. - """ - - def __init__( - self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: Optional[float] = None - ): - super().__init__() - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - self.scale = 2 * math.pi if scale is None else scale - - def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: - if mask is None: - mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) - not_mask = (~mask).to(x.dtype) - y_embed = not_mask.cumsum(1) - x_embed = not_mask.cumsum(2) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=x.dtype, device=x.device) - dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class PredictionBlock(nn.Module): - def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None: - super().__init__() - self.layers = [nn.Linear(in_dim, out_dim), activation] - # Maintain submodule indexing as if part of a Sequential block - for i, layer in enumerate(self.layers): - self.add_module(str(i), layer) - - def forward(self, input: Tensor) -> Tensor: - hidden_state = input - for layer in self.layers: - hidden_state = layer(hidden_state) - return hidden_state - - -class MaskformerMLPPredictionHead(nn.Module): - def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3): - """ - A classic Multi Layer Perceptron (MLP). - - Args: - input_dim (`int`): - The input dimensions. - hidden_dim (`int`): - The hidden dimensions. - output_dim (`int`): - The output dimensions. - num_layers (int, *optional*, defaults to 3): - The number of layers. - """ - super().__init__() - in_dims = [input_dim] + [hidden_dim] * (num_layers - 1) - out_dims = [hidden_dim] * (num_layers - 1) + [output_dim] - - self.layers = [] - for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)): - activation = nn.ReLU() if i < num_layers - 1 else nn.Identity() - layer = PredictionBlock(in_dim, out_dim, activation=activation) - self.layers.append(layer) - # Provide backwards compatibility from when the class inherited from nn.Sequential - # In nn.Sequential subclasses, the name given to the layer is its index in the sequence. - # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g. - # self.my_layer_name = Layer() - # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register - # explicitly - self.add_module(str(i), layer) - - def forward(self, input: Tensor) -> Tensor: - hidden_state = input - for layer in self.layers: - hidden_state = layer(hidden_state) - return hidden_state - - -class MaskFormerPixelLevelModule(nn.Module): - def __init__(self, config: MaskFormerConfig): - """ - Pixel Level Module proposed in [Per-Pixel Classification is Not All You Need for Semantic - Segmentation](https://arxiv.org/abs/2107.06278). It runs the input image through a backbone and a pixel - decoder, generating an image feature map and pixel embeddings. - - Args: - config ([`MaskFormerConfig`]): - The configuration used to instantiate this model. - """ - super().__init__() - - # TODD: add method to load pretrained weights of backbone - backbone_config = config.backbone_config - if backbone_config.model_type == "swin": - # for backwards compatibility - backbone_config = MaskFormerSwinConfig.from_dict(backbone_config.to_dict()) - backbone_config.out_features = ["stage1", "stage2", "stage3", "stage4"] - self.encoder = AutoBackbone.from_config(backbone_config) - - feature_channels = self.encoder.channels - self.decoder = MaskFormerPixelDecoder( - in_features=feature_channels[-1], - feature_size=config.fpn_feature_size, - mask_feature_size=config.mask_feature_size, - lateral_widths=feature_channels[:-1], - ) - - def forward( - self, pixel_values: Tensor, output_hidden_states: bool = False, return_dict: bool = True - ) -> MaskFormerPixelLevelModuleOutput: - features = self.encoder(pixel_values).feature_maps - decoder_output = self.decoder(features, output_hidden_states, return_dict=return_dict) - - if not return_dict: - last_hidden_state = decoder_output[0] - outputs = (features[-1], last_hidden_state) - if output_hidden_states: - hidden_states = decoder_output[1] - outputs = outputs + (tuple(features),) + (hidden_states,) - return outputs - - return MaskFormerPixelLevelModuleOutput( - # the last feature is actually the output from the last layer - encoder_last_hidden_state=features[-1], - decoder_last_hidden_state=decoder_output.last_hidden_state, - encoder_hidden_states=tuple(features) if output_hidden_states else (), - decoder_hidden_states=decoder_output.hidden_states if output_hidden_states else (), - ) - - -class MaskFormerTransformerModule(nn.Module): - """ - The MaskFormer's transformer module. - """ - - def __init__(self, in_features: int, config: MaskFormerConfig): - super().__init__() - hidden_size = config.decoder_config.hidden_size - should_project = in_features != hidden_size - self.position_embedder = MaskFormerSinePositionEmbedding(num_pos_feats=hidden_size // 2, normalize=True) - self.queries_embedder = nn.Embedding(config.decoder_config.num_queries, hidden_size) - self.input_projection = nn.Conv2d(in_features, hidden_size, kernel_size=1) if should_project else None - self.decoder = DetrDecoder(config=config.decoder_config) - - def forward( - self, - image_features: Tensor, - output_hidden_states: bool = False, - output_attentions: bool = False, - return_dict: Optional[bool] = None, - ) -> DetrDecoderOutput: - if self.input_projection is not None: - image_features = self.input_projection(image_features) - object_queries = self.position_embedder(image_features) - # repeat the queries "q c -> b q c" - batch_size = image_features.shape[0] - queries_embeddings = self.queries_embedder.weight.unsqueeze(0).repeat(batch_size, 1, 1) - inputs_embeds = torch.zeros_like(queries_embeddings, requires_grad=True) - - batch_size, num_channels, height, width = image_features.shape - # rearrange both image_features and object_queries "b c h w -> b (h w) c" - image_features = image_features.view(batch_size, num_channels, height * width).permute(0, 2, 1) - object_queries = object_queries.view(batch_size, num_channels, height * width).permute(0, 2, 1) - - decoder_output: DetrDecoderOutput = self.decoder( - inputs_embeds=inputs_embeds, - attention_mask=None, - encoder_hidden_states=image_features, - encoder_attention_mask=None, - object_queries=object_queries, - query_position_embeddings=queries_embeddings, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - return decoder_output - - -MASKFORMER_START_DOCSTRING = r""" - This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use - it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and - behavior. - - Parameters: - config ([`MaskFormerConfig`]): 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. -""" - -MASKFORMER_INPUTS_DOCSTRING = r""" - Args: - pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See - [`MaskFormerImageProcessor.__call__`] for details. - pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): - Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - - - 1 for pixels that are real (i.e. **not masked**), - - 0 for pixels that are padding (i.e. **masked**). - - [What are attention masks?](../glossary#attention-mask) - 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. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of Detr's decoder attention layers. - return_dict (`bool`, *optional*): - Whether or not to return a [`~MaskFormerModelOutput`] instead of a plain tuple. -""" - - -class MaskFormerPreTrainedModel(PreTrainedModel): - config_class = MaskFormerConfig - base_model_prefix = "model" - main_input_name = "pixel_values" - - def _init_weights(self, module: nn.Module): - xavier_std = self.config.init_xavier_std - std = self.config.init_std - if isinstance(module, MaskFormerTransformerModule): - if module.input_projection is not None: - nn.init.xavier_uniform_(module.input_projection.weight, gain=xavier_std) - nn.init.constant_(module.input_projection.bias, 0) - # FPN - elif isinstance(module, MaskFormerFPNModel): - nn.init.xavier_uniform_(module.stem.get_submodule("0").weight, gain=xavier_std) - - elif isinstance(module, MaskFormerFPNLayer): - nn.init.xavier_uniform_(module.proj[0].weight, gain=xavier_std) - - elif isinstance(module, MaskFormerFPNConvLayer): - nn.init.xavier_uniform_(module.get_submodule("0").weight, gain=xavier_std) - # The MLP head - elif isinstance(module, MaskformerMLPPredictionHead): - # I was not able to find the correct initializer in the original implementation - # we'll use xavier - for submodule in module.modules(): - if isinstance(submodule, nn.Linear): - nn.init.xavier_uniform_(submodule.weight, gain=xavier_std) - nn.init.constant_(submodule.bias, 0) - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - # copied from DETR - if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): - # 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=std) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=std) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, MaskFormerPixelLevelModule): - module.encoder.gradient_checkpointing = value - if isinstance(module, DetrDecoder): - module.gradient_checkpointing = value - - -@add_start_docstrings( - "The bare MaskFormer Model outputting raw hidden-states without any specific head on top.", - MASKFORMER_START_DOCSTRING, -) -class MaskFormerModel(MaskFormerPreTrainedModel): - def __init__(self, config: MaskFormerConfig): - super().__init__(config) - self.pixel_level_module = MaskFormerPixelLevelModule(config) - self.transformer_module = MaskFormerTransformerModule( - in_features=self.pixel_level_module.encoder.channels[-1], config=config - ) - - self.post_init() - - @add_start_docstrings_to_model_forward(MASKFORMER_INPUTS_DOCSTRING) - @replace_return_docstrings(output_type=MaskFormerModelOutput, config_class=_CONFIG_FOR_DOC) - def forward( - self, - pixel_values: Tensor, - pixel_mask: Optional[Tensor] = None, - output_hidden_states: Optional[bool] = None, - output_attentions: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> MaskFormerModelOutput: - r""" - Returns: - - Examples: - - ```python - >>> from transformers import AutoImageProcessor, MaskFormerModel - >>> from PIL import Image - >>> import requests - - >>> # load MaskFormer fine-tuned on ADE20k semantic segmentation - >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-ade") - >>> model = MaskFormerModel.from_pretrained("facebook/maskformer-swin-base-ade") - - >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" - >>> image = Image.open(requests.get(url, stream=True).raw) - - >>> inputs = image_processor(image, return_tensors="pt") - - >>> # forward pass - >>> outputs = model(**inputs) - - >>> # the decoder of MaskFormer outputs hidden states of shape (batch_size, num_queries, hidden_size) - >>> transformer_decoder_last_hidden_state = outputs.transformer_decoder_last_hidden_state - >>> list(transformer_decoder_last_hidden_state.shape) - [1, 100, 256] - ```""" - - if pixel_values is None: - raise ValueError("You have to specify pixel_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 - - batch_size, _, height, width = pixel_values.shape - - if pixel_mask is None: - pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device) - - pixel_level_module_output = self.pixel_level_module( - pixel_values, output_hidden_states, return_dict=return_dict - ) - image_features = pixel_level_module_output[0] - pixel_embeddings = pixel_level_module_output[1] - - transformer_module_output = self.transformer_module(image_features, output_hidden_states, output_attentions) - queries = transformer_module_output.last_hidden_state - - encoder_hidden_states = None - pixel_decoder_hidden_states = None - transformer_decoder_hidden_states = None - hidden_states = None - - if output_hidden_states: - encoder_hidden_states = pixel_level_module_output[2] - pixel_decoder_hidden_states = pixel_level_module_output[3] - transformer_decoder_hidden_states = transformer_module_output[1] - hidden_states = encoder_hidden_states + pixel_decoder_hidden_states + transformer_decoder_hidden_states - - output = MaskFormerModelOutput( - encoder_last_hidden_state=image_features, - pixel_decoder_last_hidden_state=pixel_embeddings, - transformer_decoder_last_hidden_state=queries, - encoder_hidden_states=encoder_hidden_states, - pixel_decoder_hidden_states=pixel_decoder_hidden_states, - transformer_decoder_hidden_states=transformer_decoder_hidden_states, - hidden_states=hidden_states, - attentions=transformer_module_output.attentions, - ) - - if not return_dict: - output = tuple(v for v in output.values()) - - return output - - -class MaskFormerForInstanceSegmentation(MaskFormerPreTrainedModel): - def __init__(self, config: MaskFormerConfig): - super().__init__(config) - self.model = MaskFormerModel(config) - hidden_size = config.decoder_config.hidden_size - # + 1 because we add the "null" class - self.class_predictor = nn.Linear(hidden_size, config.num_labels + 1) - self.mask_embedder = MaskformerMLPPredictionHead(hidden_size, hidden_size, config.mask_feature_size) - - self.matcher = MaskFormerHungarianMatcher( - cost_class=1.0, cost_dice=config.dice_weight, cost_mask=config.mask_weight - ) - - self.weight_dict: Dict[str, float] = { - "loss_cross_entropy": config.cross_entropy_weight, - "loss_mask": config.mask_weight, - "loss_dice": config.dice_weight, - } - - self.criterion = MaskFormerLoss( - config.num_labels, - matcher=self.matcher, - weight_dict=self.weight_dict, - eos_coef=config.no_object_weight, - ) - - self.post_init() - - def get_loss_dict( - self, - masks_queries_logits: Tensor, - class_queries_logits: Tensor, - mask_labels: Tensor, - class_labels: Tensor, - auxiliary_logits: Dict[str, Tensor], - ) -> Dict[str, Tensor]: - loss_dict: Dict[str, Tensor] = self.criterion( - masks_queries_logits, class_queries_logits, mask_labels, class_labels, auxiliary_logits - ) - # weight each loss by `self.weight_dict[
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