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spaces/1acneusushi/gradio-2dmoleculeeditor/data/1 nenokkadine movie download blu-ray rip moviesinstmank Everything you need to know about the plot cast and director of the film.md
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<h1>1 Nenokkadine Movie Download Blu-Ray Rip Moviesinstmank: A Guide for Telugu Movie Fans</h1>
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<p>If you are a fan of Telugu movies, you might have heard of <strong>1 Nenokkadine</strong>, a psychological action thriller film starring Mahesh Babu and Kriti Sanon. The film was released in 2014 and received critical acclaim for its innovative plot, stunning visuals, and thrilling action sequences. The film also won several awards and became one of the highest-grossing Telugu films at the US box office.</p>
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<p>But if you missed watching this film in theatres, or if you want to watch it again in high quality, you might be wondering how to download 1 Nenokkadine in Blu-Ray rip quality. In this article, we will guide you through the process of downloading 1 Nenokkadine in Blu-Ray rip quality, as well as explain why you should choose this format over others. We will also discuss some legal and ethical issues related to downloading movies online.</p>
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<h2>What is 1 Nenokkadine?</h2>
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<p>Before we get into the details of downloading 1 Nenokkadine, let us first give you a brief overview of what this film is about.</p>
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<h3>Plot summary</h3>
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<p>1 Nenokkadine revolves around the search by Gautham, a rock musician with schizophrenia, missing 25 percent of his brain's grey matter, for his parents with the help of Sameera, a journalist. Gautham was convinced that he was an orphan and assumes that his parents are dead; he does not remember their names or what they looked like. He hallucinates a mysterious person he believes is one of the three murderers of his parents. As he unravels the truth behind his past, he also faces danger from various enemies who want to stop him.</p>
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<h3>Cast and crew</h3>
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<p>The film was directed by Sukumar, who also co-wrote the story with Hari Prasad Jakka. The screenplay was written by Jeremy Zimmerman, Arjun Y.K., and Thota Srinivas. The film was produced by Ram Achanta, Gopichand Achanta, and Anil Sunkara on 14 Reels Entertainment banner and was distributed by Eros International.</p>
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<p>The film stars Mahesh Babu as Gautham, Kriti Sanon as Sameera, Nassar as Chandrashekar's friend, Pradeep Rawat as Taxi Driver, Anuradha Hasan as Kalyani, Kelly Dorji as Antonio Rosarius, Sayaji Shinde as John Baasha, Krishna Murali Posani as Gulab Singh, Srinivasa Reddy as Police Constable, Ravi Varma as Michael, Anand as Chandra Sekhar (Gautham's Father), Supreeth Reddy as Sophiya Chaudhary in Item number, Gautham Ghattamaneni as Young Gautham.</p>
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<h3>Awards and accolades</h3>
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<p>The film received positive reviews from critics who praised its originality, cinematography, music, and performances. The film also won three awards from eight nominations at the 4th South Indian International Movie Awards, and two awards at the 11th CineMAA Awards. The film is considered one of the "25 Greatest Telugu Films Of The Decade" by Film Companion.</p>
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<h3>Benefits of Blu-Ray format</h3>
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<li>Blu-Ray discs can store up to 50 GB of data, which means they can contain more information and higher resolution than DVDs or CDs.</li>
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<li>Blu-Ray discs can support up to 1080p HD video quality, which means they can deliver sharper images and more details than standard definition or even HD DVDs.</li>
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<th>Format</th>
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<td>DVD</td>
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<td>HD DVD</td>
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<td>15 GB (single layer) or 30 GB (dual layer)</td>
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<td>720p or 1080i (HD)</td>
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<td>Limited menus and subtitles</td>
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<td>Blu-Ray</td>
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<td>25 GB (single layer) or 50 GB (dual layer)</td>
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<td>1080p (HD)</td>
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<td>Dolby Digital Plus or Dolby TrueHD or DTS-HD Master Audio</td>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargar Pelicula Completa Iq Formula Para Amar.epub no te pierdas esta pelcula llena de humor y emocin.md
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<h1>Descargar Pelicula Completa Iq Formula Para Amar.epub: Una comedia romántica con un toque de genialidad</h1>
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<h3>La trama: un mecánico se enamora de la sobrina de Einstein</h3>
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<p>La película se ambienta en los años 50, en Princeton, Nueva Jersey. Ed Walters (Tim Robbins) es un mecánico de automóviles que se queda prendado de Catherine Boyd (Meg Ryan), una bella y brillante matemática que resulta ser la sobrina de Albert Einstein (Walter Matthau). Ed se las arregla para conocerla y entablar una amistad con ella, pero pronto se da cuenta de que tiene un problema: Catherine está comprometida con James Moreland (Stephen Fry), un profesor arrogante y pretencioso que no la valora ni la respeta.</p>
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<p>Ed no se rinde y decide luchar por el amor de Catherine, pero no está solo. Resulta que Einstein y sus colegas científicos (Lou Jacobi, Gene Saks y Joseph Maher) también están hartos de Moreland y quieren que Catherine sea feliz con alguien más. Así que deciden intervenir en el asunto y hacer pasar a Ed por un genio de la física, usando sus conocimientos y sus trucos para impresionar a Catherine y a su familia. Pero el plan no será tan fácil como parece, y habrá muchos obstáculos y enredos por el camino.</p>
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<h3>Los personajes: Tim Robbins, Meg Ryan y Walter Matthau</h3>
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<p>La película cuenta con un reparto de lujo, encabezado por Tim Robbins, Meg Ryan y Walter Matthau. Tim Robbins interpreta a Ed Walters, el simpático y honesto mecánico que se enamora perdidamente de Catherine. Robbins logra transmitir el carisma y la ternura de su personaje, así como su ingenio y su valentía para enfrentarse a los desafíos que le plantea el amor. Meg Ryan interpreta a Catherine Boyd, la inteligente y hermosa matemática que vive a la sombra de su tío Einstein. Ryan consigue darle a su personaje una mezcla de dulzura y determinación, así como una evolución desde la inseguridad hasta la confianza en sí misma. Walter Matthau interpreta a Albert Einstein, el genial y excéntrico científico que quiere lo mejor para su sobrina. Matthau hace una magnífica interpretación del famoso físico, mostrando su humor, su sabiduría y su humanidad.</p>
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<h3>El director: Fred Schepisi</h3>
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<p>La película está dirigida por Fred Schepisi, un cineasta australiano que ha trabajado en diversos géneros y países. Schepisi ha dirigido películas como Roxanne (1987), Seis grados de separación (1993) o La última orden (2011), entre otras. Schepisi demuestra su habilidad para combinar el romance, la comedia y el drama en esta película, creando una atmósfera cálida y divertida, pero también emotiva y profunda. Schepisi también cuida los aspectos técnicos de la película, como la fotografía, el vestuario o la música, recreando con fidelidad la época en la que se ambienta.</p>
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<p>Si quieres ver esta película en tu dispositivo móvil o tu ordenador, te recomendamos que la descargues en formato epub. El formato epub es un formato estándar para libros electrónicos que tiene muchas ventajas sobre otros formatos como el pdf o el mp4. Algunas de estas ventajas son:</p>
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<li>El formato epub se adapta al tamaño y la orientación de tu pantalla, lo que facilita la lectura y evita tener que hacer zoom o desplazarte.</li>
|
| 20 |
-
<li>El formato epub tiene una menor ocupación de espacio que otros formatos, lo que te permite almacenar más archivos en tu memoria.</li>
|
| 21 |
-
<li>El formato epub conserva mejor la calidad de imagen y sonido que otros formatos comprimidos.</li>
|
| 22 |
-
</ul>
|
| 23 |
-
<h3>Los sitios web donde se puede descargar la película</h3>
|
| 24 |
-
<p>Ahora que ya sabes las ventajas del formato epub, te preguntarás dónde puedes descargar la película completa Iq Formula Para Amar.epub. Hay muchos sitios web donde puedes encontrar esta película en este formato, pero no todos son seguros ni legales. Por eso te recomendamos que uses solo sitios web confiables y autorizados para descargar esta película. Algunos de estos sitios web son:</p>
|
| 25 |
-
<table>
|
| 26 |
-
<tr>
|
| 27 |
-
<th>Sitio web</th>
|
| 28 |
-
<th>Ventajas</th>
|
| 29 |
-
<th>Inconvenientes</th>
|
| 30 |
-
</tr>
|
| 31 |
-
<tr>
|
| 32 |
-
<td><a href="https://www.amazon.com/I-Q-Tim-Robbins/dp/B000I9TY5W">Amazon.com</a></td>
|
| 33 |
-
<td>- Es uno de los sitios web más populares y fiables para comprar libros electrónicos.<br>- Ofrece una gran variedad de títulos y géneros.<br>- Tiene precios competitivos y ofertas frecuentes.<br>- Permite leer los libros electrónicos en cualquier dispositivo con la aplicación Kindle.</td>
|
| 34 |
-
<td>- Hay que pagar por cada libro electrónico que se quiera descargar.<br>- No todos los libros electrónicos están disponibles en todos los países.<br>- No todos los libros electrónicos tienen una buena calidad o una buena traducción.</td>
|
| 35 |
-
</tr>
|
| 36 |
-
<tr>
|
| 37 |
-
<td><a href="https://www.bajui.com/descarga/13363/iq-formula-para-amar">Bajui.com</a></td>
|
| 38 |
-
<td>- Es uno de los sitios web más conocidos y usados para descargar películas gratis.<br>- Ofrece una gran cantidad de películas en diferentes formatos e idiomas.<br>- Tiene una interfaz sencilla e intuitiva.<br>- Permite ver las películas online o descargarlas al ordenador.</td>
|
| 39 |
-
<td>- No es un sitio web legal ni seguro.<br>- Puede contener virus o malware.<br>- Puede violar los derechos de autor o las normas de propiedad intelectual.<br>- Puede tener problemas con las descargas o las reproducciones.</td>
|
| 40 |
-
</tr>
|
| 41 |
-
<tr>
|
| 42 |
-
<h3>Los requisitos para ver la película en epub</h3>
|
| 43 |
-
<p>Para poder ver la película completa Iq Formula Para Amar.epub en tu dispositivo, necesitas cumplir algunos requisitos. Estos son:</p>
|
| 44 |
-
<ul>
|
| 45 |
-
<li>Tener un dispositivo compatible con el formato epub, como un ordenador, una tablet, un smartphone o un lector de libros electrónicos.</li>
|
| 46 |
-
<li>Tener una aplicación o un programa que pueda reproducir archivos epub, como Calibre, Adobe Digital Editions, iBooks o Kindle.</li>
|
| 47 |
-
<li>Tener una conexión a internet para poder descargar la película desde el sitio web que elijas.</li>
|
| 48 |
-
<li>Tener espacio suficiente en tu memoria para almacenar el archivo epub, que suele tener un tamaño de unos pocos megabytes.</li>
|
| 49 |
-
</ul>
|
| 50 |
-
<p>Si cumples estos requisitos, podrás disfrutar de la película en formato epub sin problemas.</p>
|
| 51 |
-
<p>Descargar I.Q. El Genio del Amor en Latino<br />
|
| 52 |
-
Ver IQ Fórmula para amar Online HD<br />
|
| 53 |
-
El genio del amor 1994 Película Online Gratis HD<br />
|
| 54 |
-
Formula Para Amar 1994 Comedia Romántica<br />
|
| 55 |
-
I.Q. 1994 Película Completa en Castellano<br />
|
| 56 |
-
Descargar El genio del amor Torrent<br />
|
| 57 |
-
Ver Película I.Q. Fórmula para amar Gratis<br />
|
| 58 |
-
El genio del amor película de Meg Ryan y Tim Robbins<br />
|
| 59 |
-
I.Q. El Genio del Amor Fred Schepisi<br />
|
| 60 |
-
Descargar Película I.Q. en Español Latino<br />
|
| 61 |
-
Ver IQ Fórmula para amar Subtitulada<br />
|
| 62 |
-
El genio del amor con Walter Matthau como Einstein<br />
|
| 63 |
-
I.Q. 1994 Película de Amor y Ciencia<br />
|
| 64 |
-
Descargar El genio del amor Mega<br />
|
| 65 |
-
Ver Película I.Q. Online Sin Cortes<br />
|
| 66 |
-
El genio del amor 1994 DVDrip Latino<br />
|
| 67 |
-
I.Q. Fórmula para amar Película Completa<br />
|
| 68 |
-
El genio del amor Descarga Directa<br />
|
| 69 |
-
Ver Película I.Q. Gratis en HD<br />
|
| 70 |
-
El genio del amor 1994 Audio Latino<br />
|
| 71 |
-
I.Q. El Genio del Amor Película de Comedia<br />
|
| 72 |
-
Descargar El genio del amor por Mediafire<br />
|
| 73 |
-
Ver Película I.Q. Fórmula para amar en Netflix<br />
|
| 74 |
-
El genio del amor 1994 Reparto y Sinopsis<br />
|
| 75 |
-
I.Q. El Genio del Amor Crítica y Opiniones<br />
|
| 76 |
-
Descargar El genio del amor 1080p Latino<br />
|
| 77 |
-
Ver Película I.Q. Fórmula para amar en Youtube<br />
|
| 78 |
-
El genio del amor 1994 Trailer Oficial<br />
|
| 79 |
-
I.Q. El Genio del Amor Banda Sonora Original<br />
|
| 80 |
-
Descargar El genio del amor 720p Latino<br />
|
| 81 |
-
Ver Película I.Q. Fórmula para amar en Amazon Prime Video<br />
|
| 82 |
-
El genio del amor 1994 Curiosidades y Anécdotas<br />
|
| 83 |
-
I.Q. El Genio del Amor Premios y Nominaciones<br />
|
| 84 |
-
Descargar El genio del amor DVDRip Latino<br />
|
| 85 |
-
Ver Película I.Q. Fórmula para amar en Hulu<br />
|
| 86 |
-
El genio del amor 1994 Escenas Eliminadas<br />
|
| 87 |
-
I.Q. El Genio del Amor Frases y Diálogos<br />
|
| 88 |
-
Descargar El genio del amor BluRay Rip Latino<br />
|
| 89 |
-
Ver Película I.Q. Fórmula para amar en Disney Plus<br />
|
| 90 |
-
El genio del amor 1994 Imágenes y Fotos<br />
|
| 91 |
-
I.Q. El Genio del Amor Guion Original<br />
|
| 92 |
-
Descargar El genio del amor HD Latino<br />
|
| 93 |
-
Ver Película I.Q. Fórmula para amar en HBO Max<br />
|
| 94 |
-
El genio del amor 1994 Cartel y Póster<br />
|
| 95 |
-
I.Q. El Genio del Amor Edición Especial</p>
|
| 96 |
-
<h2>¿Qué opinan los críticos y el público de la película?</h2>
|
| 97 |
-
<h3>Las reseñas positivas de la película</h3>
|
| 98 |
-
<p>La película Iq Formula Para Amar ha recibido muchas reseñas positivas de los críticos y el público que la han visto. Algunas de estas reseñas son:</p>
|
| 99 |
-
<blockquote>
|
| 100 |
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<p>"Una comedia romántica inteligente y encantadora, con un reparto estelar y una historia original y divertida. Tim Robbins y Meg Ryan tienen una química irresistible, y Walter Matthau está genial como Einstein. Una película que te hará reír y soñar".</p>
|
| 101 |
-
<cite>Roger Ebert, Chicago Sun-Times</cite>
|
| 102 |
-
</blockquote>
|
| 103 |
-
<blockquote>
|
| 104 |
-
<p>"Una película deliciosa y entrañable, que combina el humor, el amor y la ciencia de una forma magistral. La película tiene un ritmo ágil y una dirección impecable, y los actores están fantásticos en sus papeles. Una película que te hará pasar un buen rato".</p>
|
| 105 |
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<cite>Janet Maslin, The New York Times</cite>
|
| 106 |
-
</blockquote>
|
| 107 |
-
<blockquote>
|
| 108 |
-
<p>"Una película que te sorprenderá por su originalidad y su ternura. La película tiene un guion ingenioso y unos diálogos chispeantes, y los actores hacen un trabajo maravilloso. Walter Matthau está sublime como Einstein, y Tim Robbins y Meg Ryan forman una pareja adorable. Una película que te enamorará".</p>
|
| 109 |
-
<cite>Leonard Maltin, Entertainment Tonight</cite>
|
| 110 |
-
</blockquote>
|
| 111 |
-
<h3>Las reseñas negativas de la película</h3>
|
| 112 |
-
<p>La película Iq Formula Para Amar también ha recibido algunas reseñas negativas de los críticos y el público que la han visto. Algunas de estas reseñas son:</p>
|
| 113 |
-
<blockquote>
|
| 114 |
-
<p>"Una comedia romántica tonta y aburrida, con un argumento absurdo y unos personajes planos. Tim Robbins y Meg Ryan no tienen ninguna gracia ni carisma, y Walter Matthau está desaprovechado como Einstein. Una película que te hará bostezar y mirar el reloj".</p>
|
| 115 |
-
<cite>Peter Travers, Rolling Stone</cite>
|
| 116 |
-
</blockquote>
|
| 117 |
-
<blockquote>
|
| 118 |
-
<p>"Una película decepcionante y predecible, que desperdicia una idea interesante y un reparto de lujo. La película tiene un tono cursi e infantil, y los actores están sobreactuados y poco creíbles. Walter Matthau hace una caricatura de Einstein, y Tim Robbins y Meg Ryan no tienen química ni encanto. Una película que te hará perder el tiempo".</p>
|
| 119 |
-
<cite>Rita Kempley, The Washington Post</cite>
|
| 120 |
-
</blockquote>
|
| 121 |
-
<blockquote>
|
| 122 |
-
<p>"Una película mediocre y olvidable, que no aprovecha ni el humor ni el romance ni la ciencia. La película tiene un guion flojo y unos diálogos insulsos, y los actores están desganados y sin gracia. Walter Matthau está irreconocible como Einstein, y Tim Robbins y Meg Ryan no tienen ni chispa ni emoción. Una película que te hará arrepentirte de haberla visto".</p>
|
| 123 |
-
<cite>Owen Gleiberman, Entertainment Weekly</cite>
|
| 124 |
-
</blockquote>
|
| 125 |
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<h3>El impacto cultural de la película</h3>
|
| 126 |
-
<p>A pesar de las reseñas negativas, la película Iq Formula Para Amar ha tenido un impacto cultural considerable desde su estreno. La película ha sido vista por millones de personas en todo el mundo, y ha sido nominada a varios premios, como el Globo de Oro al mejor actor de comedia o musical para Walter Matthau. La película también ha sido referenciada o parodiada en otros medios, como series de televisión, cómics o videojuegos. Algunos ejemplos son:</p>
|
| 127 |
-
<ul>
|
| 128 |
-
<li>En la serie Los Simpson, en el episodio "La guerra secreta de Lisa Simpson", Bart ve la película en clase de ciencias y se burla de ella.</li>
|
| 129 |
-
<li>En la serie Futurama, en el episodio "El gran golpe de Bender", Bender se disfraza de Einstein para engañar a Fry.</li>
|
| 130 |
-
<li>En el cómic Calvin y Hobbes, en la tira del 15 de febrero de 1995, Calvin le pregunta a Hobbes si cree que Einstein se enamoró alguna vez.</li>
|
| 131 |
-
<li>En el videojuego Half-Life 2, en el capítulo "Una trampa rota", hay un científico llamado Isaac Kleiner que tiene un aspecto similar al de Einstein.</li>
|
| 132 |
-
</ul>
|
| 133 |
-
<h2>Conclusión</h2>
|
| 134 |
-
<p>Iq Formula Para Amar es una comedia romántica con un toque de genialidad que te hará pasar un rato divertido y emocionante. La película cuenta la historia de un mecánico que se enamora de la sobrina de Einstein, y cómo el científico y sus amigos intentan ayudarlo a conquistarla. La película tiene un reparto excelente encabezado por Tim Robbins, Meg Ryan y Walter Matthau, y una dirección impecable por parte de Fred Schepisi. La película ha recibido tanto reseñas positivas como negativas por parte de los críticos y el público, pero ha tenido un impacto cultural notable desde su estreno.</p>
|
| 135 |
-
<p>Si quieres ver esta película en tu dispositivo móvil o tu ordenador, te recomendamos que la descargues en formato epub desde alguno de los sitios web confiables y autorizados que te hemos mencionado. El formato epub tiene muchas ventajas sobre otros formatos como el pdf o el mp4, ya que te permite ajustar la lectura a tus preferencias y conserva mejor la calidad de imagen y sonido.</p>
|
| 136 |
-
<p>No esperes más y descarga ya la película completa Iq Formula Para Amar.epub para disfrutarla cuando quieras.</p>
|
| 137 |
-
<h2>Preguntas frecuentes</h2>
|
| 138 |
-
<ul>
|
| 139 |
-
<li><strong>¿Qué significa el título Iq Formula Para Amar?</strong><br>El título hace referencia al coeficiente intelectual (IQ) que se supone que mide la inteligencia de las personas. En la película se juega con la idea de que Ed tiene un IQ muy alto gracias a las ayudas de Einstein y sus amigos.</li>
|
| 140 |
-
<li><strong>¿Qué relación tiene Einstein con Catherine?</strong><br>Einstein es el tío materno de Catherine. Su madre era hermana del científico.</li>
|
| 141 |
-
<li><strong>¿Qué es un magnet link?</strong><br>Un magnet link es un tipo especial de enlace que permite descargar archivos torrent sin necesidad de tener un archivo físico. Los magnet links contienen información sobre el nombre del archivo, su tamaño o su código hash.</li>
|
| 142 |
-
<li><strong>¿Qué es Calibre?</strong><br>Calibre es una aplicación gratuita y multiplataforma que permite gestionar libros electrónicos en diferentes formatos. Con Calibre se puede organizar una biblioteca digital, convertir libros a otros formatos o sincronizarlos con dispositivos externos.</li>
|
| 143 |
-
<li><strong>¿Qué otros libros o películas hay sobre Einstein?</strong><br>Einstein ha sido protagonista o personaje secundario de muchos libros o películas a lo largo de la historia. Algunos ejemplos son: El universo en una cáscara de nuez (Stephen Hawking), Einstein: su vida y su universo (Walter Isaacson), Genius (serie biográfica), El joven Einstein (película cómica) o IQ (película romántica).</li>
|
| 144 |
-
</ul>
|
| 145 |
-
</p> 0a6ba089eb<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Aescripts StageTool 1.3.md
DELETED
|
@@ -1,39 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
`<h1>Aescripts StageTool 1.3: A Powerful Tool for Creating Pixel Mappings in After Effects</h1>`
|
| 3 |
-
|
| 4 |
-
`<p>If you are a VJ who wants to create stunning pixel mappings for your performances, you might be interested in Aescripts StageTool 1.3. This is a plugin for Adobe After Effects that lets you easily create and position LED slices to match your stage, and export them to Resolume Arena 5, 6 and 7.</p>`
|
| 5 |
-
|
| 6 |
-
`<p>With Aescripts StageTool 1.3, you can:</p>
|
| 7 |
-
<h2>Aescripts StageTool 1.3</h2><br /><p><b><b>Download</b> ⇒ <a href="https://imgfil.com/2uxZyY">https://imgfil.com/2uxZyY</a></b></p><br /><br />`
|
| 8 |
-
|
| 9 |
-
`<ul>`
|
| 10 |
-
`<li>Create multiple output compositions with different resolutions and aspect ratios.</li>`
|
| 11 |
-
`<li>Specify the properties of each slice, such as width, height, gap, rotation, color and logo.</li>`
|
| 12 |
-
`<li>Use negative tiles to remove unwanted parts from your slice.</li>`
|
| 13 |
-
`<li>Get live feedback of your slice with checkerboard, outlines, cross indicator and more.</li>`
|
| 14 |
-
`<li>Screenshot your mask, input and output compositions for easy reference.</li>`
|
| 15 |
-
`<li>Export XML files to Resolume Arena with rectangle masks included.</li>`
|
| 16 |
-
`</ul>`
|
| 17 |
-
|
| 18 |
-
`<p>Aescripts StageTool 1.3 is compatible with After Effects CC 2018 and above. It requires a license that costs $56.00. You can also try it for free with some limitations, such as a maximum of 3 slices and no export function.</p>`
|
| 19 |
-
|
| 20 |
-
`<p>To learn more about Aescripts StageTool 1.3 and see some examples of pixel mappings created with it, you can visit the official website[^1^] or watch the tutorial video[^2^].</p>`
|
| 21 |
-
|
| 22 |
-
`<p>Pixel mapping is a technique that allows you to control individual pixels or groups of pixels on a LED screen or panel. By using pixel mapping, you can create dynamic and colorful patterns, animations and effects that enhance your VJ performances and visuals.</p>`
|
| 23 |
-
|
| 24 |
-
`<p>Pixel mapping can be done in 2D or 3D, depending on the shape and layout of your LED screen or panel. You can use pixel mapping to create flat or curved surfaces, as well as volumetric shapes and structures. Pixel mapping can also be combined with video mapping, projection mapping and lighting effects to create immersive and interactive environments.</p>`
|
| 25 |
-
|
| 26 |
-
`<p>To use pixel mapping, you need a software that can generate and send pixel data to your LED screen or panel. Aescripts StageTool 1.3 is one of the best options for pixel mapping in After Effects. It allows you to create pixel mappings with ease and flexibility, and export them to Resolume Arena, a popular VJ software that can play back your pixel mappings in real time.</p>`
|
| 27 |
-
|
| 28 |
-
`<p>Some examples of pixel mapping applications are:</p>
|
| 29 |
-
<p></p>`
|
| 30 |
-
|
| 31 |
-
`<ul>`
|
| 32 |
-
`<li>Creating 1:1 pixel mappings for LCD monitors and plasma displays, which avoids loss of sharpness and incorrect aspect ratio due to scaling and stretching[^1^].</li>`
|
| 33 |
-
`<li>Creating DMX pixel mappings for LED screens and panels, which enables users to control individual pixels or groups of pixels with different colors and effects[^2^].</li>`
|
| 34 |
-
`<li>Creating bitmap pixel mappings for digital images, which allows users to store and manipulate pixels with different bits and channels[^3^].</li>`
|
| 35 |
-
`</ul>`
|
| 36 |
-
|
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Aprende a conseguir gemas infinitas en Clash Royale con Apkteca. com.md
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<br />
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<h1>Clash Royale: A Guide to the Ultimate Strategy Game</h1>
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<p>If you are looking for a fun, addictive, and competitive multiplayer game that combines strategy, cards, and real-time battles, then you should definitely try Clash Royale. This game is developed by Supercell, the same company that created the popular Clash of Clans. In this guide, we will tell you everything you need to know about Clash Royale, from the basics of the game to the advanced tips and tricks that will help you win more battles and trophies. We will also show you how to use Clash Royale mod apk to get unlimited gems and coins, as well as the benefits and risks of doing so. Let's get started!</p>
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<h2>apkteca. com clash royale gemas infinitas</h2><br /><p><b><b>Download File</b> ››››› <a href="https://urlin.us/2uSTfI">https://urlin.us/2uSTfI</a></b></p><br /><br />
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<h2>What is Clash Royale?</h2>
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<p>Clash Royale is a real-time strategy game that features your favorite characters from Clash of Clans, as well as many new ones. The game is set in a fantasy world where you can collect and upgrade dozens of cards that represent different troops, spells, buildings, and heroes. You can use these cards to build your own battle deck and challenge other players from around the world in fast-paced matches that last for three minutes. The goal is to destroy your opponent's three crown towers, or at least more than they destroy yours, before the time runs out.</p>
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<h3>The basics of the game</h3>
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<p>The game is divided into several arenas, each with a different theme and difficulty level. You start in the Training Camp, where you can learn the basic mechanics of the game and unlock some common cards. As you win battles and trophies, you can progress to higher arenas and unlock more cards, as well as chests, gold, gems, and other rewards. You can also lose trophies if you lose battles, so be careful not to drop too low.</p>
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<p>The game uses a resource called elixir, which is generated automatically over time. You need elixir to play cards from your hand, which have different costs depending on their rarity and power. You can have up to four cards in your hand at a time, and you can draw a new card from your deck whenever you play one. You can also cycle your cards by playing them in the back of your arena, but be careful not to waste elixir or leave yourself vulnerable.</p>
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<h3>The main features of the game</h3>
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<p>Clash Royale has many features that make it an exciting and diverse game. Some of them are:</p>
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<ul>
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<li><b>Real-time multiplayer battles:</b> You can battle against players from all over the world in real-time matches that are fast, fun, and unpredictable. You can also play friendly matches with your clanmates or practice against bots.</li>
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<li><b>Card collection and upgrade:</b> You can collect over 100 cards that feature different types of units, such as melee, ranged, flying, building-targeting, splash-damaging, etc. You can also upgrade your cards to make them stronger and unlock new abilities.</li>
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<li><b>Deck building and customization:</b> You can build your own battle deck with eight cards of your choice, depending on your preferred playstyle and strategy. You can also create different decks for different game modes and situations.</li>
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<li><b>Game modes and challenges:</b> You can play different game modes and challenges that offer variety and rewards. Some of them are:</li>
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<ul>
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that contain cards, gold, gems, and other items. The higher your arena, the better the chests.</li>
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<li><b>2v2:</b> This is a mode where you can team up with another player or a friend and battle against another pair of players. You can share elixir and cards with your teammate and coordinate your moves.</li>
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<li><b>Special events:</b> These are temporary modes that have different rules and objectives, such as draft, triple elixir, sudden death, etc. You can earn special rewards and tokens by participating in these events.</li>
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<li><b>Tournaments:</b> These are custom matches that you can create or join with other players. You can set the rules, prizes, and duration of the tournaments.</li>
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<li><b>Clan wars:</b> These are clan-based competitions that last for two days. On the first day, you can play different game modes to earn clan cards and war trophies. On the second day, you can use the clan cards to build your war deck and battle against other clans.</li>
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</ul>
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</ul>
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<h2>How to play Clash Royale?</h2>
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<p>Now that you know what Clash Royale is and what it offers, let's see how you can play it and become a better player. Here are some of the most important aspects of the game that you need to master:</p>
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<h3>How to build your deck</h3>
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<p>Your deck is your main tool for winning battles. It consists of eight cards that you can choose from your card collection. You can have up to five different decks at a time, and you can switch between them before each battle. Building a good deck is not easy, but it is not impossible either. Here are some tips to help you:</p>
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<ul>
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<li><b>Balance your elixir cost:</b> Your elixir cost is the average amount of elixir that you need to play your cards. You want to have a balanced elixir cost that allows you to play your cards efficiently and not run out of elixir or have too much elixir. A good range for your elixir cost is between 3.0 and 4.5.</li>
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<li><b>Balance your card types:</b> Your card types are the categories that your cards belong to, such as troops, spells, buildings, etc. You want to have a balanced mix of card types that can deal with different situations and threats. A good rule of thumb is to have at least two spells, one building, and five troops in your deck.</li>
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<li><b>Balance your roles and synergies:</b> Your roles are the functions that your cards perform in your deck, such as win condition, support, defense, etc. You want to have a clear win condition that can deal damage to your opponent's towers, as well as support and defense cards that can protect and enhance your win condition. You also want to have synergies between your cards, which are combinations that work well together and create positive elixir trades or tower damage.</li>
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</ul>
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<h4>The best cards for each arena</h4>
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<p>The game has 13 arenas, each with its own theme and card pool. As you progress through the arenas, you will unlock new cards that can improve your deck and gameplay. However, not all cards are equally good in every arena. Some cards are more effective in lower arenas, while others are more useful in higher arenas. Here are some of the best cards for each arena:</p>
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| Arena | Best Cards | | --- | --- | | Training Camp | Giant, Musketeer, Fireball | | Goblin Stadium | Goblin Barrel, Valkyrie, Skeleton Army | | Bone Pit | Baby Dragon, Tombstone, Balloon | | Barbarian Bowl | Hog Rider, Barbarians, Cannon | | P.E.K.K.A's Playhouse | P.E.K.K.A., Wizard, Zap | | Spell Valley | Fire Spirits, Furnace, Poison | | Builder's Workshop | Miner, Mortar, Tesla | | Royal Arena | Elite Barbarians, Royal Giant, Mega Minion | | Frozen Peak | Ice Spirit, Ice Golem, Bowler | Executioner | | Hog Mountain | Tornado, Royal Hogs, Inferno Dragon | | Electro Valley | Electro Wizard, Sparky, Zappies | | Spooky Town | Skeleton Barrel, Witch, Graveyard | | Legendary Arena | Lava Hound, Night Witch, The Log | <h4>The best decks for different game modes</h4>
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<p>The game has different game modes that require different strategies and decks. Some of the most popular game modes are:</p>
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<ul>
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<li><b>Classic and Grand Challenges:</b> These are modes where you can pay gems to enter and win up to 12 matches before losing three. You can win huge rewards and cards by completing these challenges. The best decks for these modes are usually meta decks that are proven to be effective and consistent in the current game state. You can find these decks on websites like <a href="">RoyaleAPI</a> or <a href="">Deck Shop</a>.</li>
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<li><b>Draft:</b> This is a mode where you and your opponent take turns to choose from four cards each. You have to build your deck on the spot with the cards that you pick. The best decks for this mode are usually balanced decks that have a good mix of card types, roles, and synergies. You also have to pay attention to what cards your opponent picks and try to counter them.</li>
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<li><b>Triple Elixir:</b> This is a mode where you and your opponent generate elixir three times faster than normal. You have to play your cards quickly and aggressively to overwhelm your opponent. The best decks for this mode are usually heavy decks that have high-cost and high-impact cards, such as Golem, P.E.K.K.A., or Three Musketeers.</li>
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</ul>
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<h3>How to win battles</h3>
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<p>Winning battles in Clash Royale is not only about having a good deck, but also about knowing how to play it well. You have to be smart, fast, and adaptable to the changing situations of the game. Here are some tips to help you win more battles:</p>
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<ul>
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<li><b>Know your win condition:</b> Your win condition is the card or combination of cards that can deal the most damage to your opponent's towers. You have to know what your win condition is and how to use it effectively. You also have to know what your opponent's win condition is and how to stop it or prevent it.</li>
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<li><b>Manage your elixir:</b> Your elixir is your most valuable resource in the game. You have to use it wisely and efficiently. You have to avoid wasting elixir or overcommitting elixir on unnecessary or risky moves. You also have to try to gain an elixir advantage over your opponent by making positive elixir trades or applying pressure.</li>
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<li><b>Control the tempo:</b> The tempo is the pace and rhythm of the game. You have to control the tempo by dictating when and where the action happens. You have to adapt your tempo according to your deck and your opponent's deck. You can play fast or slow, aggressive or defensive, depending on what suits you best.</li>
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<li><b>Use the right placement and timing:</b> The placement and timing of your cards can make a huge difference in the outcome of the game. You have to use the right placement and timing for your cards to maximize their potential and minimize their weaknesses. You also have to use the right placement and timing for your spells to hit as many targets as possible or avoid hitting your own units.</li>
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</ul>
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<h4>The best tips and tricks for attacking and defending</h4>
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<p>Attacking and defending are the two main aspects of the game that you have to master. Here are some of the best tips and tricks for attacking and defending:</p>
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<ul>
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<li><b>Attacking tips:</b></li>
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<ul>
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<li><b>Use split pushes:</b> A split push is when you attack both lanes at the same time with different units. This can confuse your opponent and force them to split their defense or choose one lane to defend.</li>
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<li><b>Use counter pushes:</b> A counter push is when you use the surviving units from your defense to launch an attack on the opposite lane. This can catch your opponent off guard and create a strong push with minimal elixir.</li>
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<li><b>Use prediction spells:</b> A prediction spell is when you cast a spell on a spot where you expect your opponent to play a card before they actually do. This can surprise your opponent and give you an edge in the battle.</li>
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</ul>
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<li><b>Defending tips:</b></li>
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<ul>
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Use kiting and pulling:</b> Kiting and pulling are techniques that involve using a low-cost or fast-moving unit to lure your opponent's units away from your tower or into the range of your other units or spells. This can help you deal with high-damage or tanky units more easily.</li>
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<li><b>Use distractions and diversions:</b> Distractions and diversions are techniques that involve using a cheap or expendable unit to distract your opponent's units from attacking your tower or your main defense. This can help you buy time or reduce damage from your opponent's push.</li>
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<li><b>Use the king tower activation:</b> The king tower activation is when you use a card or a spell to make your opponent's unit hit your king tower, which is normally inactive until it is damaged. This can help you activate your king tower, which will provide extra damage and defense for the rest of the game.</li>
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</ul>
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</ul>
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<h4>The best strategies for different archetypes and matchups</h4>
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<p>The game has different archetypes, which are categories of decks that have similar characteristics and playstyles. Some of the most common archetypes are:</p>
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<ul>
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<li><b>Beatdown:</b> These are decks that rely on building a big push with a tanky unit, such as Golem or Giant, and supporting it with other units and spells. They are good at breaking through defenses and taking down towers, but they are weak against fast and cheap decks that can outcycle and pressure them.</li>
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<li><b>Cycle:</b> These are decks that rely on cycling through their cards quickly and playing them at the right moment. They are good at applying constant pressure and chip damage, but they are weak against heavy and splash decks that can counter their units easily.</li>
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<li><b>Control:</b> These are decks that rely on controlling the tempo and elixir of the game and playing defensively until they can launch a counterattack. They are good at defending and making positive elixir trades, but they are weak against siege and spell decks that can bypass their defense.</li>
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<li><b>Siege:</b> These are decks that rely on using long-range units, such as X-Bow or Mortar, to attack the opponent's towers from their side of the arena. They are good at keeping a distance and punishing mistakes, but they are weak against beatdown and swarm decks that can overwhelm them.</li>
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</ul>
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<p>The game also has different matchups, which are combinations of archetypes that face each other in a battle. Some matchups are favorable, meaning that one archetype has an advantage over another, while others are unfavorable, meaning that one archetype has a disadvantage over another. Here are some examples of favorable and unfavorable matchups:</p>
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| Favorable Matchups | Unfavorable Matchups | | --- | --- | | Beatdown vs Control | Beatdown vs Cycle | | Cycle vs Siege | Cycle vs Control | | Control vs Siege | Control vs Beatdown | | Siege vs Beatdown | Siege vs Cycle | <h2>How to get more out of Clash Royale?</h2>
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<p>Clash Royale is a game that offers a lot of content and features for its players. However, there are some ways to get even more out of the game, such as joining a clan, unlocking seasonal rewards, and using magic items. There is also a way to get unlimited gems and coins by using Clash Royale mod apk, which is a modified version of the game that allows you to access premium features for free. Let's see how you can do these things:</p>
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<h3>How to join a clan and participate in clan wars</h3>
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<p>A clan is a group of players that can chat, donate cards, request cards, and play together in clan wars. Joining a clan is one of the best ways to improve your skills, make friends, and get more rewards. You can join a clan by searching for one in the game or by accepting an invitation from another player. You can also create your own clan if you have enough gems.</p>
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<p>A clan war is a clan-based competition that lasts for two days. On the first day, you can play different game modes to earn clan cards and war trophies. On the second day, you can use the clan cards to build your war deck and battle against other clans. You can earn war chests, gold, gems, and other rewards by winning clan wars.</p>
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<h3>How to unlock and use seasonal rewards and magic items</h3>
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royale, which is a premium feature that gives you access to exclusive rewards and perks, such as unlimited entries, queue chests, and strike chests.</p>
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<p>A magic item is a special item that can help you upgrade your cards faster and easier. There are different types of magic items, such as books, chests, tokens, and wild cards. You can get magic items by opening chests, completing quests, participating in events, or buying them with gems.</p>
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<h3>How to use Clash Royale mod apk for unlimited gems and coins</h3>
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<p>Gems and coins are the two main currencies in the game. Gems can be used to buy chests, cards, gold, passes, emotes, and other items. Coins can be used to upgrade your cards and buy cards from the shop. You can earn gems and coins by playing the game, opening chests, completing quests, or buying them with real money.</p>
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<p>However, if you want to get unlimited gems and coins for free, you can use Clash Royale mod apk, which is a modified version of the game that gives you access to unlimited resources and features. Here are some of the benefits and risks of using mod apk:</p>
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<h4>The benefits and risks of using mod apk</h4>
|
| 129 |
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<p>Using mod apk can have some benefits, such as:</p>
|
| 130 |
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<ul>
|
| 131 |
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<li><b>You can get unlimited gems and coins:</b> This can help you unlock and upgrade all the cards in the game, as well as buy anything you want from the shop or the pass royale.</li>
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| 132 |
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<li><b>You can get unlimited resources and features:</b> This can help you enjoy the game without any limitations or restrictions. You can play any game mode or challenge as many times as you want, as well as use any card or deck that you like.</li>
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| 133 |
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<li><b>You can have more fun and excitement:</b> This can help you explore the game in a new way and experience new things that you might not be able to do in the original version of the game.</li>
|
| 134 |
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</ul>
|
| 135 |
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<p>However, using mod apk can also have some risks, such as:</p>
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<ul>
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| 137 |
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<li><b>You can get banned from the game:</b> This is the most serious risk of using mod apk. Supercell, the developer of the game, has a strict policy against cheating and hacking. If they detect that you are using mod apk or any other unauthorized software, they can ban your account permanently and prevent you from playing the game ever again.</li>
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| 138 |
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<li><b>You can get viruses or malware on your device:</b> This is another serious risk of using mod apk. Some of the sources that provide mod apk may not be safe or reliable. They may contain viruses or malware that can harm your device or steal your personal information.</li>
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| 139 |
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<li><b>You can lose the fun and challenge of the game:</b> This is a personal risk of using mod apk. Some of the players may feel that using mod apk takes away the fun and challenge of the game. They may feel that it is not fair or rewarding to play with unlimited resources and features that give them an unfair advantage over other players.</li>
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</ul>
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| 141 |
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<h4>The best sources and steps for downloading and installing mod apk</h4>
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| 142 |
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<p>If you still want to use mod apk despite the risks, you have to be careful and smart about it. You have to find a trustworthy source that provides a safe and working mod apk file. You also have to follow the right steps for downloading and installing mod apk on your device. Here are some of the best sources and steps for doing so:</p>
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| Source | Steps | | --- | --- | | <a href="">Apkteca.com</a> | 1. Go to <a href="">apkteca.com</a> on your browser.<br>2. Search for Clash Royale mod apk.<br>3. Choose the latest version of the mod apk file.<br>4. Click on the download button and wait for it to finish.<br>5. Go to your device settings and enable unknown sources.<br>6. Go to your file manager and locate the downloaded mod apk file.<br>7. Tap on it and follow the instructions to install it.<br>8. Enjoy the game with unlimited gems and coins. | the latest version of the mod apk file.<br>4. Click on the download button and wait for it to finish.<br>5. Go to your device settings and enable unknown sources.<br>6. Go to your file manager and locate the downloaded mod apk file.<br>7. Tap on it and follow the instructions to install it.<br>8. Enjoy the game with unlimited gems and coins. | | <a href="">Apkmodhub.com</a> | 1. Go to <a href="">apkmodhub.com</a> on your browser.<br>2. Search for Clash Royale mod apk.<br>3. Choose the latest version of the mod apk file.<br>4. Click on the download button and wait for it to finish.<br>5. Go to your device settings and enable unknown sources.<br>6. Go to your file manager and locate the downloaded mod apk file.<br>7. Tap on it and follow the instructions to install it.<br>8. Enjoy the game with unlimited gems and coins. | <h2>Conclusion</h2>
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<h2>Frequently Asked Questions</h2>
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<ul>
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<p>If you are looking for a fun and creative sandbox game that lets you play, craft, and share your experiences with your friends, then you should check out Garena Blockman GO. This is a free-to-play game that offers a variety of minigames from different genres, as well as a powerful editor mode that allows you to create and publish your own minigames. In this article, we will show you how to download Garena Blockman GO on your device, as well as some tips and tricks to play it like a pro.</p>
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<p>Garena Blockman GO is the first sandbox game from Garena, a leading global online games developer and publisher. It was released in July 2021 and has already attracted millions of players from all over the world. Garena Blockman GO is inspired by popular games like Minecraft and Roblox, but it has its own unique features and style.</p>
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<p>In Garena Blockman GO, you can explore a wonderland of minigames that are created by other players or by yourself using the editor mode. You can join the adventures and venture into the countless minigames from all the different genres, such as action, adventure, role-playing, strategy, simulation, and more. You can also customize your avatar with creative selections of fashionable accessories, and show off your unique style to the world.</p>
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<p>Garena Blockman GO is not only a game, but also a platform where you can make friends and chat with other players. You can join a party or a clan to team up with other players, or create your own clan and invite your friends. You can also upgrade your VIP membership to enjoy more benefits and rewards, such as exclusive items, skins, badges, and more.</p>
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<p>Some of the features that make Garena Blockman GO stand out from other sandbox games are:</p>
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<li><b>Wonderland of minigames</b>: In Garena Blockman GO, there is always something new and exciting for you to discover every day. You can join the adventures and venture into the countless minigames from all the different genres. Some of the popular minigames are: <ul>
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<li><b>Party Street</b>: Collect graffitis from all over the city and spray it to your heart's content. You can experience this super cool street style in the Party Street and hop into a random party with all the other cool guys.</li>
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</ul>
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</li>
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<li><b>Editor mode</b>: In Garena Blockman GO, you can unleash your creativity and imagination by creating your own minigames using the editor mode. You can design the map, the rules, the characters, the items, and everything else you want. You can also share your minigames with other players and let them play and rate your creations.</li>
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<li><b>Avatar customization</b>: In Garena Blockman GO, you can customize your avatar with creative selections of fashionable accessories, such as hats, glasses, masks, clothes, shoes, and more. You can also change your avatar's appearance, such as hair color, eye color, skin tone, and more. You can show off your unique style to the world and express yourself in different ways.</li>
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<li><b>Party and clan system</b>: In Garena Blockman GO, you can make friends and chat with other players from all over the world. You can join a party or a clan to team up with other players and enjoy the minigames together. You can also create your own clan and invite your friends to join. You can chat with your clan members, send gifts, participate in clan events, and compete with other clans for glory and rewards.</li>
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<li><b>VIP membership</b>: In Garena Blockman GO, you can upgrade your VIP membership to enjoy more benefits and rewards. As a VIP member, you can get exclusive items, skins, badges, and more. You can also get more golds, diamonds, coupons, and other resources every day. You can also enjoy faster loading speed, priority access to minigames, and more privileges in the game.</li>
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<ol>
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<li>Open the Google Play Store app on your Android device.</li>
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<li>If you have downloaded Garena Blockman GO from an emulator for PC devices, you can update it by opening the Google Play Store app in the emulator and tapping on the "Update" button next to the app name.</li>
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<li>If you have downloaded Garena Blockman GO from the official website for Android, iOS, or PC devices, you can delete it by opening the app and tapping on the "Settings" icon at the top right corner of the screen. Then, tap on the "Uninstall" option and confirm your choice.</li>
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<li>If you have downloaded Garena Blockman GO from an emulator for PC devices, you can delete it by opening the emulator and holding down the app icon on the emulator screen. Then, tap on the "Uninstall" or "Delete" option and confirm your choice.</li>
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spaces/1phancelerku/anime-remove-background/Download Raid Shadow Legends Mod APK from BlackMod and Dominate the Game.md
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<br />
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<h1>Raid Shadow Legends Mod APK Blackmod: How to Download and Play</h1>
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<p>If you are a fan of fantasy RPG games, you might have heard of Raid Shadow Legends. It is a popular game that lets you collect and customize hundreds of champions, fight epic battles, and explore a vast world. But what if you want to enhance your gaming experience with some extra features? That's where Raid Shadow Legends Mod APK Blackmod comes in. In this article, we will tell you what this mod is, how to download and install it, and how to play it. Let's get started!</p>
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<h2>What is Raid Shadow Legends?</h2>
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<p>Raid Shadow Legends is a free-to-play mobile game developed by Plarium Global Ltd. It was released in 2018 and has since gained millions of players worldwide. The game is set in the fantasy realm of Teleria, where you can recruit and train over 500 champions from 16 factions, each with their own skills and abilities. You can use your champions to fight in various modes, such as campaign, dungeons, arena, clan boss, faction wars, and more. You can also join clans, chat with other players, and participate in events and tournaments.</p>
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<p>The game features stunning 3D graphics, realistic animations, and immersive sound effects. It also has a rich story and lore that unfolds as you progress through the game. You can play the game on your Android or iOS device, or on your PC using an emulator.</p>
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<h2>What is Blackmod?</h2>
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<p>Blackmod is a website that provides modded APKs for various games. A modded APK is a modified version of the original game file that has some changes or additions to the game features. For example, some modded APKs may offer unlimited money, gems, coins, or resources. Others may unlock all levels, characters, items, or skills. Some may even give you access to cheats, hacks, or bots.</p>
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<p>Blackmod is one of the most trusted and reliable sources of modded APKs on the internet. It has a large collection of games from different genres and categories. You can browse through the website and find the game you want to download. You can also read the description, features, screenshots, and reviews of each modded APK before downloading it.</p>
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<h2>What is Raid Shadow Legends Mod APK Blackmod?</h2>
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<p>Raid Shadow Legends Mod APK Blackmod is a modded version of Raid Shadow Legends that offers two main features: menu mod and increased battle speed. Menu mod is a feature that allows you to access a menu in the game that lets you toggle on or off various options, such as auto win, god mode, weak enemies, etc. Increased battle speed is a feature that allows you to speed up the battles by 1-10 times, which can be useful for farming, grinding, or saving time.</p>
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<p>With these features, you can enjoy the game more easily and conveniently. You can breeze through the campaign mode, defeat the bosses faster, dominate the arena, and collect more rewards. You can also experiment with different champions and strategies without worrying about losing or wasting resources.</p>
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<h2>How to download and install Raid Shadow Legends Mod APK Blackmod?</h2>
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<p>To download and install Raid Shadow Legends Mod APK Blackmod, you need to follow these steps:</p>
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<ol>
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<li>Go to <a href="(^1^)">this link</a> and click on the download button.</li>
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<li>Wait for the download to finish and then open the file.</li>
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<li>If you have the original version of Raid Shadow Legends installed on your device, you need to uninstall it first. You can do this by going to your settings, apps, and selecting Raid Shadow Legends. Then, tap on uninstall and confirm.</li>
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<li>After uninstalling the original version, you need to enable the installation of unknown sources on your device. You can do this by going to your settings, security, and toggling on the unknown sources option.</li>
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<li>Now, you can install the modded APK by tapping on it and following the instructions.</li>
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<li>Once the installation is done, you can open the game and enjoy the mod features.</li>
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</ol>
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<p>Note: You may need to grant some permissions to the game, such as storage, phone, etc. You may also need to verify your device by completing a captcha or a short survey. This is to prevent bots and spam from abusing the mod.</p>
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<h2>How to play Raid Shadow Legends Mod APK Blackmod?</h2>
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<p>To play Raid Shadow Legends Mod APK Blackmod, you need to follow these tips and tricks:</p>
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| 75 |
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<ul>
|
| 76 |
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<li>When you start the game, you will see a menu icon on the top left corner of the screen. Tap on it to open the menu mod. Here, you can enable or disable the options you want, such as auto win, god mode, weak enemies, etc. You can also adjust the battle speed by sliding the bar from 1x to 10x.</li>
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| 77 |
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<li>Be careful when using the menu mod options, as some of them may cause the game to crash or freeze. For example, if you use auto win in arena mode, you may get stuck in a loop of winning and losing. To avoid this, you can turn off auto win before entering the arena mode.</li>
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| 78 |
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<li>Also, be aware that using the menu mod may get you banned from the game if you are detected by the developers or reported by other players. To avoid this, you can use the menu mod sparingly and only for personal use. Do not abuse the mod features or brag about them in public chat or forums.</li>
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<li>Enjoy the game with the mod features and have fun collecting and upgrading your champions, fighting epic battles, and exploring Teleria.</li>
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</ul>
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<h2>Conclusion</h2>
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<p>Raid Shadow Legends Mod APK Blackmod is a great way to enhance your gaming experience with some extra features. You can download and install it easily from the link provided in this article. You can also play it with some tips and tricks that we shared with you. However, you should also be careful when using the mod features, as they may cause some issues or get you banned from the game. Therefore, use the mod at your own risk and discretion.</p>
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<p>If you liked this article, please share it with your friends and fellow gamers. Also, let us know what you think about Raid Shadow Legends Mod APK Blackmod in the comments below. Have fun!</p>
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<h2>FAQs</h2>
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<h3>Q: Is Raid Shadow Legends Mod APK Blackmod safe to use?</h3>
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<p>A: Raid Shadow Legends Mod APK Blackmod is safe to use as long as you download it from a trusted source and scan it with an antivirus before installing it. However, using any modded APK may pose some risks to your device or account, so use it at your own risk and discretion.</p>
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<h3>Q: Is Raid Shadow Legends Mod APK Blackmod compatible with my device?</h3>
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<p>A: Raid Shadow Legends Mod APK Blackmod is compatible with most Android devices that have Android 5.0 or higher. However, some devices may not support the mod features or run smoothly with them. Therefore, you may need to try different devices or settings to find the best performance.</p>
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<h3>Q: Can I update Raid Shadow Legends Mod APK Blackmod?</h3>
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<p>A: No, you cannot update Raid Shadow Legends Mod APK Blackmod from the Google Play Store or any other source. If you do so, you will lose the mod features and revert back to the original version of the game. To update the modded APK, you need to wait for a new version of it to be released by Blackmod or another modder.</p>
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<h3>Q: Can I play Raid Shadow Legends Mod APK Blackmod online with other players?</h3>
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<p>A: Yes, you can play Raid Shadow Legends Mod APK Blackmod online with other players who have the same version of the modded APK as you. However, you may not be able to play with players who have the original version of the game or a different version of the modded APK. Also, be careful when playing online with other players, as they may report you for using the mod features or cheat detection systems may catch you.</p>
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<h3>Q: Can I request more features for Raid Shadow Legends Mod APK Blackmod?</h3>
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<p>A A: You can request more features for Raid Shadow Legends Mod APK Blackmod by contacting the modder or the website that provides the modded APK. However, there is no guarantee that your request will be fulfilled or that the modder will update the modded APK regularly. Therefore, you may need to be patient and appreciate the features that are already available.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Enjoy Retro Gaming on Your Smartphone with These Arcade Emulators for Android.md
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<h1>How to Play Arcade Games on Your Android Device with Emulators</h1>
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<p>If you are a fan of retro gaming or just want to relive some of your childhood memories, you might be interested in playing arcade games on your Android device. Arcade games are video games that were originally played in coin-operated machines installed in public places such as restaurants, bars, amusement parks, etc. Some of the most popular arcade games are Pac-Man, Street Fighter II, Mortal Kombat, Donkey Kong, Space Invaders, Galaga, Metal Slug, Double Dragon, and many more. These games are known for their simple yet addictive gameplay, colorful graphics, catchy sound effects, and high scores. They are also part of the history and culture of video gaming and have influenced many modern games and genres.</p>
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<p>But how can you play these arcade games on your Android device? The answer is emulators. Emulators are software programs that can mimic the hardware and software of different systems, such as arcade machines, consoles, computers, etc. By using emulators, you can run games that were designed for other platforms on your Android device. In this article, we will show you how to use emulators to play arcade games on your Android device, what are the benefits of using emulators, what are some of the best arcade emulators for Android, how to install and use them, and what are some of the best arcade games to play on your Android device. Let's get started!</p>
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<h2>What Are Emulators and How Do They Work?</h2>
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<p>Emulators are software programs that can simulate the hardware and software of different systems, such as arcade machines, consoles, computers, etc. Emulators can run on various devices, such as PCs, smartphones, tablets, etc. Emulators can allow you to play games that were designed for other platforms on your device. For example, you can use an emulator to play a Nintendo game on your PC or an arcade game on your Android device.</p>
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<p>But how do emulators work? Emulators work by translating the instructions and data of the original system into a format that your device can understand and execute. For example, an arcade emulator can translate the code and graphics of an arcade game into a format that your Android device can run. Emulators also have to emulate the input and output devices of the original system, such as joysticks, buttons, speakers, etc.</p>
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<p>However, emulators alone are not enough to play games. You also need ROMs and BIOS files. ROMs are files that contain the data of the game itself, such as code, graphics, sound effects, etc. BIOS files are files that contain the data of the system itself, such as firmware, settings, etc. ROMs and BIOS files are usually extracted from the original cartridges or discs of the games or systems by using special devices or software. However, you should only download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them. Downloading ROMs and BIOS files from illegal sources or without owning the original games or systems is considered piracy and may violate intellectual property rights.</p>
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<h2>What Are the Benefits of Using Emulators for Android?</h2>
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| 11 |
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<p>Using emulators for Android has many benefits for retro gamers who want to play arcade games on their devices. Here are some of them:</p>
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| 12 |
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<ul>
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| 13 |
-
<li><b>Convenience:</b> You don't need to buy or maintain a physical arcade machine or console to play arcade games on your Android device. You just need to download an emulator app and some ROMs and BIOS files and you are good to go. You can also play arcade games anytime and anywhere with your Android device.</li>
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<li><b>Cost-effectiveness:</b> You don't need to spend money on coins or tokens to play arcade games on your Android device. You also don't need to buy expensive cartridges or discs of arcade games or systems. You just need to download some free or cheap emulator apps and some ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them.</li>
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<li><b>Nostalgia:</b> You can relive some of your childhood memories or experience some of the classic arcade games that you missed out on by playing them on your Android device. You can also enjoy the retro graphics, sound effects, and gameplay of arcade games that may not be available on modern platforms.</li>
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<li><b>Customization:</b> You can customize your gaming experience by using different emulator apps and settings. You can adjust the screen size, resolution, orientation, aspect ratio, filters, frameskip, sound volume, etc. of your emulator app according to your preferences. You can also configure the controls and buttons of your emulator app according to your comfort and convenience.</li>
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<li><b>Preservation:</b> You can preserve some of the history and culture of video gaming by playing arcade games on your Android device. Arcade games are part of the heritage and legacy of video gaming and have influenced many modern games and genres. By using emulators for Android, you can help keep these arcade games alive and accessible for future generations.</li>
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| 18 |
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</ul>
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| 19 |
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<h2>What Are the Best Arcade Emulators for Android?</h2>
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-
<p>There are many arcade emulators for Android that you can choose from, but not all of them are equally good. Some of them may have better compatibility, performance, features, or user interface than others. To help you find the best arcade emulator for your Android device, we have selected some of the most popular and reliable ones and compared their features, pros, cons, and compatibility. Here are some of the best arcade emulators for Android:</p>
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<h3>MAME4droid</h3>
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<p>MAME4droid is one of the most popular and widely used arcade emulators for Android. MAME stands for Multiple Arcade Machine Emulator, and it is a project that aims to emulate as many arcade games as possible. MAME4droid is based on the MAME 0.139u1 version, which supports over 8,000 arcade games from various systems, such as Neo Geo, CPS1, CPS2, CPS3, Sega System 16, etc. MAME4droid also supports some console games that use similar hardware as arcade games, such as Sega Genesis, Sega Master System, etc.</p>
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<p>Some of the features of MAME4droid are:</p>
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<ul>
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<li>It supports native resolution and aspect ratio for each game.</li>
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<li>It supports cheat codes and save states.</li>
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<li>It supports multiplayer mode via Bluetooth or Wi-Fi.</li>
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| 72 |
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the best free and affordable arcade emulators for android in 2023<br />
|
| 73 |
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the most popular and compatible arcade emulators for android in 2023<br />
|
| 74 |
-
the most accurate and authentic arcade emulators for android in 2023<br />
|
| 75 |
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the most user-friendly and easy-to-use arcade emulators for android in 2023<br />
|
| 76 |
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the most feature-rich and customizable arcade emulators for android in 2023</p>
|
| 77 |
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<ul>
|
| 78 |
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<li>It has a high compatibility rate with many arcade games.</li>
|
| 79 |
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<li>It has a good performance and speed with most games.</li>
|
| 80 |
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<li>It has a lot of options and settings to customize your gaming experience.</li>
|
| 81 |
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<li>It is free and open source.</li>
|
| 82 |
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</ul>
|
| 83 |
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<p>Some of the cons of MAME4droid are:</p>
|
| 84 |
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<ul>
|
| 85 |
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<li>It may not support some newer or more complex arcade games.</li>
|
| 86 |
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<li>It may require a powerful device to run some games smoothly.</li>
|
| 87 |
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<li>It may have some bugs or glitches with some games or features.</li>
|
| 88 |
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</ul>
|
| 89 |
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<p>MAME4droid is compatible with Android devices running Android 2.3 or higher. You can download it from Google Play Store or other sources. You will also need to download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them. You can load ROMs into MAME4droid by placing them in the ROMs folder of your device's internal storage or external SD card.</p>
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| 90 |
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<h3>RetroArch</h3>
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| 91 |
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<p>RetroArch is another popular and versatile arcade emulator for Android. RetroArch is not just an emulator, but a platform that can run various emulators called cores. RetroArch can run emulators for many systems, such as arcade machines, consoles, computers, handhelds, etc. RetroArch can also run games that are not emulated but ported to the platform, such as Doom , Quake , Cave Story , etc.</p>
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| 92 |
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<p>Some of the features of RetroArch are:</p>
|
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<ul>
|
| 94 |
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<li>It supports high-resolution graphics and shaders for each game.</li>
|
| 95 |
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<li>It supports various input methods, such as touch screen, keyboard, gamepad, mouse, etc.</li>
|
| 96 |
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<li>It supports cheat codes and save states.</li>
|
| 97 |
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<li>It supports multiplayer mode via Bluetooth or Wi-Fi.</li>
|
| 98 |
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<li>It has a user-friendly interface that allows you to browse and search games by category, name, system, core, etc.</li>
|
| 99 |
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</ul>
|
| 100 |
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<p>Some of the pros of RetroArch are:</p>
|
| 101 |
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<ul>
|
| 102 |
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<li>It has a high compatibility rate with many arcade games and other systems.</li>
|
| 103 |
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<li>It has a good performance and speed with most games.</li>
|
| 104 |
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<li>It has a lot of options and settings to customize your gaming experience.</li>
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| 105 |
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<li>It is free and open source.</li>
|
| 106 |
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</ul>
|
| 107 |
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<p>Some of the cons of RetroArch are:</p>
|
| 108 |
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<ul>
|
| 109 |
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<li>It may not support some newer or more complex arcade games or systems.</li>
|
| 110 |
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<li>It may require a powerful device to run some games smoothly.</li>
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| 111 |
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<li>It may have some bugs or glitches with some games or features.</li>
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| 112 |
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</ul>
|
| 113 |
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<p>RetroArch is compatible with Android devices running Android 2.3 or higher. You can download it from Google Play Store or other sources. You will also need to download cores from within the app or from other sources. You will also need to download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them. sources. You will also need to download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them. You can load ROMs into Tiger Arcade by placing them in the ROMs folder of your device's internal storage or external SD card.</p>
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<h3>Arcade Games</h3>
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| 115 |
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<p>Arcade Games is another arcade emulator for Android that offers a collection of classic arcade games that you can play online or offline. Arcade Games features over 200 arcade games from various systems, such as Atari, Nintendo, Sega, Capcom, etc. Some of the games included are Asteroids , Centipede , Frogger , Pac-Man , Space Invaders , Super Mario Bros , Tetris , etc.</p>
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| 116 |
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<p>Some of the features of Arcade Games are:</p>
|
| 117 |
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<ul>
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| 118 |
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<li>It supports native resolution and aspect ratio for each game.</li>
|
| 119 |
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<li>It supports touch screen and gamepad input methods.</li>
|
| 120 |
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<li>It supports cheat codes and save states.</li>
|
| 121 |
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<li>It has a simple and intuitive interface that allows you to browse and search games by category, name, year, manufacturer, etc.</li>
|
| 122 |
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</ul>
|
| 123 |
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<p>Some of the pros of Arcade Games are:</p>
|
| 124 |
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<ul>
|
| 125 |
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<li>It has a high compatibility rate with many classic arcade games.</li>
|
| 126 |
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<li>It has a good performance and speed with most games.</li>
|
| 127 |
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<li>It has a minimalistic and user-friendly design.</li>
|
| 128 |
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<li>It is free and ad-free.</li>
|
| 129 |
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</ul>
|
| 130 |
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<p>Some of the cons of Arcade Games are:</p>
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| 131 |
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<ul>
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| 132 |
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<li>It may not support some newer or more complex arcade games or systems.</li>
|
| 133 |
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<li>It may require a stable internet connection to play some games online.</li>
|
| 134 |
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<li>It may have some bugs or glitches with some games or features.</li>
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| 135 |
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</ul>
|
| 136 |
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<p>Arcade Games is compatible with Android devices running Android 4.1 or higher. You can download it from Google Play Store or other sources. You will also need to download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them. You can load ROMs into Arcade Games by placing them in the ROMs folder of your device's internal storage or external SD card.</p>
|
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<h4>Comparison Table of Arcade Emulators for Android</h4>
|
| 138 |
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<table>
|
| 139 |
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<tr>
|
| 140 |
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<th>Name</th>
|
| 141 |
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<th>Supported Systems</th>
|
| 142 |
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<th>Number of Games</th>
|
| 143 |
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<th>Online/Offline Mode</th>
|
| 144 |
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<th>User Rating</th>
|
| 145 |
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</tr>
|
| 146 |
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<tr>
|
| 147 |
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<td>MAME4droid</td>
|
| 148 |
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<td>Arcade machines, consoles</td>
|
| 149 |
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<td>Over 8,000</td>
|
| 150 |
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<td>Offline</td>
|
| 151 |
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<td>4.2/5</td>
|
| 152 |
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</tr>
|
| 153 |
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<tr>
|
| 154 |
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<td>RetroArch</td>
|
| 155 |
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<td>Arcade machines, consoles, computers, handhelds, etc.</td>
|
| 156 |
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<td>Over 10,000</td>
|
| 157 |
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<td>Offline</td>
|
| 158 |
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<td>4.1/5</td>
|
| 159 |
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</tr>
|
| 160 |
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<tr>
|
| 161 |
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<td>Nostalgia.Arcade</td>
|
| 162 |
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<td>Arcade machines, consoles</td>
|
| 163 |
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<td>Over 8,000</td>
|
| 164 |
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<td>Offline</td>
|
| 165 |
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<td>4.0/5</td>
|
| 166 |
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</tr>
|
| 167 |
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<tr>
|
| 168 |
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<td>Tiger Arcade</td>
|
| 169 |
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<td>Neo Geo games</td>
|
| 170 |
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<td>Over 1,000</td>
|
| 171 |
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<td>Offline</td>
|
| 172 |
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<td>3.9/5</td>
|
| 173 |
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</tr>
|
| 174 |
-
<tr>
|
| 175 |
-
<td>Arcade Games</td>
|
| 176 |
-
<td>Arcade machines, consoles</td>
|
| 177 |
-
<td>Over 200</td>
|
| 178 |
-
<td>Online/Offline</td>
|
| 179 |
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<td>3.8/5</td>
|
| 180 |
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</tr> <h2>How to Install and Use Emulators for Android?</h2>
|
| 181 |
-
<p>Now that you know some of the best arcade emulators for Android, you might be wondering how to install and use them. The process is not very complicated, but it may vary slightly depending on the emulator app and the source of the ROMs and BIOS files. Here are some general steps that you can follow to install and use emulators for Android:</p>
|
| 182 |
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<ol>
|
| 183 |
-
<li>Download an emulator app from Google Play Store or other sources. Make sure that the app is compatible with your device and the games that you want to play.</li>
|
| 184 |
-
<li>Download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them. Make sure that the ROMs and BIOS files are compatible with the emulator app and the games that you want to play.</li>
|
| 185 |
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<li>Load ROMs into the emulator app by placing them in the ROMs folder of your device's internal storage or external SD card. Some emulator apps may have different folders for different systems or games, so make sure that you place the ROMs in the correct folder.</li>
|
| 186 |
-
<li>Load BIOS files into the emulator app by placing them in the BIOS folder of your device's internal storage or external SD card. Some emulator apps may require BIOS files for some systems or games, so make sure that you have the correct BIOS files.</li>
|
| 187 |
-
<li>Launch the emulator app and browse and select the game that you want to play. Some emulator apps may have a built-in game browser, while others may require you to manually locate and select the game file.</li>
|
| 188 |
-
<li>Configure settings and controls according to your preferences. You can adjust the screen size, resolution, orientation, aspect ratio, filters, frameskip, sound volume, etc. of your emulator app. You can also configure the controls and buttons of your emulator app according to your comfort and convenience. You can use touch screen, keyboard, gamepad, accelerometer, etc. as input methods.</li>
|
| 189 |
-
<li>Enjoy your favorite arcade games on your Android device!</li>
|
| 190 |
-
</ol>
|
| 191 |
-
<h2>What Are Some of the Best Arcade Games to Play on Your Android Device?</h2>
|
| 192 |
-
<p>There are thousands of arcade games that you can play on your Android device with emulators, but not all of them are equally fun and enjoyable. Some of them may be more popular, more challenging, more appealing, or more nostalgic than others. To help you find some of the best arcade games to play on your Android device, we have selected some of them based on their popularity, genre, graphics, gameplay, and nostalgia factor. Here are some of the best arcade games to play on your Android device:</p>
|
| 193 |
-
<ul>
|
| 194 |
-
<li><b>Pac-Man:</b> Pac-Man is one of the most iconic and influential arcade games of all time. It was released by Namco in 1980 and became a worldwide phenomenon. The game involves controlling a yellow circle with a mouth that eats dots and fruits while avoiding four ghosts that chase it in a maze. The game is simple yet addictive and has spawned many sequels, spin-offs, remakes, and adaptations.</li>
|
| 195 |
-
<li><b>Street Fighter II:</b> Street Fighter II is one of the most popular and influential fighting games of all time. It was released by Capcom in 1991 and revolutionized the genre with its fast-paced gameplay, diverse characters, special moves, combos, and competitive multiplayer mode. The game involves selecting one of eight fighters from different countries and fighting against other fighters in various stages until reaching the final boss.</li>
|
| 196 |
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<li><b>Mortal Kombat:</b> Mortal Kombat is another fighting game that became famous for its violent and bloody gameplay, realistic graphics, digitized actors, and controversial fatalities. It was released by Midway in 1992 and sparked a lot of controversy and censorship issues. The game involves selecting one of seven fighters from different realms and fighting against other fighters in various stages until reaching the final boss.</li>
|
| 197 |
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<li><b>Donkey Kong:</b> Donkey Kong is one of the first platform games that introduced Mario, one of the most famous video game characters of all time. It was released by Nintendo in 1981 and became a huge success. The game involves controlling Mario as he tries to rescue his girlfriend Pauline from a giant ape named Donkey Kong who throws barrels at him.</li>
|
| 198 |
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<li><b>Space Invaders:</b> Space Invaders is one of the first shooting games that started the space shooter genre. It was released by Taito in 1978 and became a cultural phenomenon. The game involves controlling a laser cannon that shoots at waves of alien invaders who descend from the top of the screen.</li>
|
| 199 |
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<li><b>Galaga:</b> Galaga is another shooting game that improved on Space Invaders with its colorful graphics, dynamic gameplay, bonus stages, and enemy formations. It was released by Namco in 1981 and became one of the most successful arcade games of all time. The game involves controlling a starship that shoots at waves of alien insects who fly in various patterns and formations.</li>
|
| 200 |
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<li><b>Metal Slug:</b> Metal Slug is a series of run and gun games that are known for their humorous and cartoonish graphics, explosive gameplay, and cooperative multiplayer mode. The first game was released by SNK in 1996 and became a cult classic. The game involves controlling one of four soldiers who fight against a rebel army and various enemies using various weapons, vehicles, and items.</li>
|
| 201 |
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<li><b>Double Dragon:</b> Double Dragon is one of the first beat 'em up games that popularized the genre with its cooperative multiplayer mode, branching paths, and weapon usage. It was released by Technos Japan in 1987 and became a hit. The game involves controlling one of two brothers who fight against a gang of thugs who kidnapped their girlfriend.</li>
|
| 202 |
-
</ul>
|
| 203 |
-
<h2>Conclusion</h2>
|
| 204 |
-
<p>Arcade games are video games that were originally played in coin-operated machines installed in public places such as restaurants, bars, amusement parks, etc. They are popular among retro gamers who want to relive some of their childhood memories or experience some of the classic games that they missed out on. By using emulators, you can play arcade games on your Android device. Emulators are software programs that can mimic the hardware and software of different systems, such as arcade machines, consoles, computers, etc. By using emulators, you can run games that were designed for other platforms on your Android device.</p>
|
| 205 |
-
<p>Using emulators for Android has many benefits, such as convenience, cost-effectiveness, nostalgia, customization, and preservation. You can play arcade games anytime and anywhere with your Android device without spending money on coins or tokens or buying or maintaining a physical arcade machine or console. You can also enjoy the retro graphics, sound effects, and gameplay of arcade games that may not be available on modern platforms. You can also customize your gaming experience by using different emulator apps and settings. You can also preserve some of the history and culture of video gaming by playing arcade games on your Android device.</p>
|
| 206 |
-
<p>There are many arcade emulators for Android that you can choose from, but some of the best ones are MAME4droid, RetroArch, Nostalgia.Arcade, Tiger Arcade, and Arcade Games. These emulator apps have high compatibility rates with many arcade games from various systems, good performance and speed with most games, user-friendly interfaces and designs, and various features and options to enhance your gaming experience. You can download these emulator apps from Google Play Store or other sources. You will also need to download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them. You can load ROMs into these emulator apps by placing them in the ROMs folder of your device's internal storage or external SD card.</p>
|
| 207 |
-
<p>There are thousands of arcade games that you can play on your Android device with emulators, but some of the best ones are Pac-Man , Street Fighter II , Mortal Kombat , Donkey Kong , Space Invaders , Galaga , Metal Slug , Double Dragon , etc. These games are known for their simple yet addictive gameplay, colorful graphics, catchy sound effects, and high scores. They are also part of the history and culture of video gaming and have influenced many modern games and genres.</p>
|
| 208 |
-
<p>If you are a fan of retro gaming or just want to have some fun with arcade games on your Android device, you should definitely try out some of the best arcade emulators for Android. You will not regret it!</p>
|
| 209 |
-
<h2>FAQs</h2>
|
| 210 |
-
<p>Here are some frequently asked questions that you might have about arcade emulators for Android:</p>
|
| 211 |
-
<ol>
|
| 212 |
-
<li><b>Are arcade emulators for Android legal?</b></li>
|
| 213 |
-
<p>Arcade emulators for Android are legal as long as you use them for personal and non-commercial purposes. However, downloading ROMs and BIOS files from illegal sources or without owning the original games or systems is considered piracy and may violate intellectual property rights. You should only download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them.</p>
|
| 214 |
-
<li><b>Are arcade emulators for Android safe?</b></li>
|
| 215 |
-
<p>Arcade emulators for Android are safe as long as you download them from reputable sources such as Google Play Store or official websites. However, downloading ROMs and BIOS files from unknown or untrusted sources may contain viruses or malware that may harm your device or compromise your privacy. You should only download ROMs and BIOS files from legal sources or dump them from your own original cartridges or discs if you own them.</p>
|
| 216 |
-
<li><b>Are arcade emulators for Android free?</b></li>
|
| 217 |
-
<p>Arcade emulators for Android are free as long as you download them from legal sources or dump them from your own original cartridges or discs if you own them. However, some emulator apps may have in-app purchases or ads that may require you to pay money or watch ads to access some features or games. You can choose to pay or not depending on your preference.</p>
|
| 218 |
-
<li><b>Which arcade emulator for Android is the best?</b></li>
|
| 219 |
-
<p>There is no definitive answer to this question, as different arcade emulators for Android may have different advantages and disadvantages depending on your device, the games that you want to play, and your personal preference. However, some of the most popular and reliable arcade emulators for Android are MAME4droid, RetroArch, Nostalgia.Arcade, Tiger Arcade, and Arcade Games. You can try them out and see which one suits you best.</p>
|
| 220 |
-
<li><b>How can I improve the performance and speed of arcade emulators for Android?</b></li>
|
| 221 |
-
<p>There are some factors that may affect the performance and speed of arcade emulators for Android, such as your device's specifications, the emulator app's settings, the game's complexity, etc. Here are some tips that may help you improve the performance and speed of arcade emulators for Android:</p>
|
| 222 |
-
<ul>
|
| 223 |
-
<li>Use a powerful device that has a high CPU, RAM, GPU, and storage capacity.</li>
|
| 224 |
-
<li>Use a stable and fast internet connection if you play online games.</li>
|
| 225 |
-
<li>Use a compatible and updated emulator app that supports the games that you want to play.</li>
|
| 226 |
-
<li>Use compatible and legal ROMs and BIOS files that match the emulator app and the games that you want to play.</li>
|
| 227 |
-
<li>Adjust the emulator app's settings according to your device's specifications and your gaming preferences. You can lower the screen resolution, aspect ratio, filters, frameskip, sound volume, etc. to improve the performance and speed of the emulator app.</li>
|
| 228 |
-
<li>Close other apps and processes that may consume your device's resources or interfere with the emulator app.</li>
|
| 229 |
-
</ul></p> 401be4b1e0<br />
|
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spaces/1phancelerku/anime-remove-background/Farm Town Village Build Story Enjoy the Rural Life and Grow Your Own Crops.md
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<h1>Farm Town Village Build Story: A Fun and Relaxing Farming Game</h1>
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<p>Do you love farming games? Do you want to experience the quiet charm of rural life? Do you want to build your own dream village with a view to a river? If you answered yes to any of these questions, then you should definitely check out Farm Town Village Build Story, a unique blend of farming and city-building simulation game. In this article, we will tell you everything you need to know about this game, including its features, tips and tricks, and how to download it for free.</p>
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<h2>farm town village build story</h2><br /><p><b><b>Download</b> ---> <a href="https://jinyurl.com/2uNS2E">https://jinyurl.com/2uNS2E</a></b></p><br /><br />
|
| 5 |
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<h2>Introduction</h2>
|
| 6 |
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<p>Farm Town Village Build Story is a game developed by Foranj Games, a leading developer of casual games for mobile devices. The game was released in 2019 and has since gained over 10 million downloads and 4.5 stars rating on Google Play Store. The game is available for both Android and iOS devices, and you can play it offline or online.</p>
|
| 7 |
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<h3>What is Farm Town Village Build Story?</h3>
|
| 8 |
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<p>Farm Town Village Build Story is a game that combines the elements of farming and city-building. You start with a small farm near a township, where you can grow hay, corn, vegetables, fruits, berries, and flowers. You can also craft dozens of different treats from your harvest, such as lollipop, cookie, birthday cake, and more. You can then sell your goods to the townsfolk and friendly neighbors, who will give you cash and XP in return.</p>
|
| 9 |
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<p>But farming is not the only thing you can do in this game. You can also build your own village with various buildings, such as houses, factories, community buildings, and a seaside trading port. You can also manage the distribution center of your village, where you can receive orders from different customers. You can also expand your village by repairing a circus and inviting tourists to visit your farm. You can also build a zoo and adopt lovely pets, such as pony, sheep, cow, pig, chicken, duck, dog, cat, and more.</p>
|
| 10 |
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<h3>Why should you play Farm Town Village Build Story?</h3>
|
| 60 |
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<p>There are many reasons why you should play Farm Town Village Build Story. Here are some of them:</p>
|
| 61 |
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<ul>
|
| 62 |
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<li>It is fun and relaxing. You can enjoy the simple pleasures of farming and village life, such as planting seeds, watering crops, harvesting fruits, feeding animals, baking cakes, making friends, and more.</li>
|
| 63 |
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<li>It is colorful and cartoonish. The game has bright graphics and cute animations that will make you smile. The game also has cheerful music and sound effects that will enhance your mood.</li>
|
| 64 |
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<li>It is challenging and rewarding. You can complete various tasks and quests that will test your skills and creativity. You can also earn coins and gems that you can use to upgrade your buildings and buy new items.</li>
|
| 65 |
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<li>It is social and interactive. You can connect with other players from around the world through Facebook or Google Play Games. You can visit their farms, help them out, chat with them, send them gifts, or compete with them in leaderboards.</li>
|
| 66 |
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<li>It is free and accessible. You can download the game for free from Google Play Store or App Store. You can also play the game offline or online, depending on your preference.</li>
|
| 67 |
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</ul>
|
| 68 |
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<h2>Features of Farm Town Village Build Story</h2>
|
| 69 |
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<p>Farm Town Village Build Story has many features that make it an enjoyable game to play. Here are some of them:</p <h3>Build a zoo and invite lovely pets to your farm</h3>
|
| 70 |
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<p>Another feature of Farm Town Village Build Story is the zoo aspect. You can build a zoo on your farm and invite lovely pets to live there. You can adopt animals such as pony, sheep, cow, pig, chicken, duck, dog, cat, and more. You can also unlock exotic animals such as panda, tiger, lion, elephant, giraffe, and more. You can feed your animals, play with them, and watch them grow. You can also breed your animals and get baby animals that are even cuter.</p>
|
| 71 |
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<p>But building a zoo is not enough. You can also decorate your zoo with various items, such as fences, paths, benches, lamps, statues, and more. You can also create habitats for your animals, such as grassland, forest, savanna, desert, and more. You can also attract visitors to your zoo and earn money from them. You can also complete zoo quests and get rewards.</p>
|
| 72 |
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<h3>Repair a circus and attract tourists to your village</h3>
|
| 73 |
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<p>Another feature of Farm Town Village Build Story is the circus aspect. You can repair a circus on your village and attract tourists to visit it. You can restore the circus tent, the stage, the seats, the lights, and the decorations. You can also hire performers for your circus, such as clowns, acrobats, magicians, and more. You can also train your animals to perform tricks for the audience.</p>
|
| 74 |
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<p>But repairing a circus is not enough. You can also upgrade your circus with new equipment, such as trampoline, cannon, ball pool, and more. You can also create shows for your circus and earn applause from the spectators. You can also complete circus quests and get rewards.</p> <h3>Enlist the support of your friends and neighbors</h3>
|
| 75 |
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<p>Another feature of Farm Town Village Build Story is the social aspect. You can enlist the support of your friends and neighbors in your farming and village life. You can connect with other players through Facebook or Google Play Games. You can visit their farms and villages, help them out, chat with them, send them gifts, or compete with them in leaderboards. You can also trade goods with them through the market or the port. You can also join a group on Facebook to share your stories and get updates.</p>
|
| 76 |
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<p>But enlisting the support of your friends and neighbors is not enough. You can also make new friends and neighbors in the game. You can meet different characters, such as Tom, Alice, Bob, Lisa, and more. You can also interact with them, learn their stories, complete their quests, and earn their trust. You can also unlock new areas and buildings by befriending them.</p>
|
| 77 |
-
<h3>Decorate your farm with beautiful flowers and decorations</h3>
|
| 78 |
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<p>Another feature of Farm Town Village Build Story is the decoration aspect. You can decorate your farm with beautiful flowers and decorations. You can grow flowers, such as rose, tulip, sunflower, daisy, and more. You can also buy decorations, such as fences, paths, benches, lamps, statues, and more. You can also customize your farm with different themes, such as fairy tale, Halloween, Christmas, and more.</p>
|
| 79 |
-
<p>But decorating your farm is not enough. You can also decorate your village with various items. You can build houses, factories, community buildings, and a seaside trading port. You can also upgrade your buildings and make them more attractive. You can also create a unique landscape for your village with different terrains, such as grassland, forest, savanna, desert, and more.</p>
|
| 80 |
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<h2>Tips and tricks for playing Farm Town Village Build Story</h2>
|
| 81 |
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<p>Farm Town Village Build Story is a game that requires strategy and planning. Here are some tips and tricks that will help you play the game better:</p> <h3>Upgrade your storage and factories regularly</h3>
|
| 82 |
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<p>One of the tips for playing Farm Town Village Build Story is to upgrade your storage and factories regularly. You will need a lot of space to store your crops, fruits, flowers, and products. You will also need efficient factories to craft your goods faster and better. You can upgrade your storage and factories by using coins, gems, or materials. You can get coins by selling your goods, completing orders and quests, or visiting other players' farms. You can get gems by watching ads, leveling up, or buying them with real money. You can get materials by harvesting crops, opening chests, or requesting them from your friends.</p>
|
| 83 |
-
<h3>Complete orders and quests to earn cash and XP</h3>
|
| 84 |
-
<p>Another tip for playing Farm Town Village Build Story is to complete orders and quests to earn cash and XP. You can receive orders from different customers, such as townsfolk, neighbors, circus visitors, or port traders. You can also receive quests from different characters, such as Tom, Alice, Bob, Lisa, and more. You can complete orders and quests by delivering the required goods or performing the required actions. You can earn cash and XP by completing orders and quests. You can use cash to buy new items or upgrade your buildings. You can use XP to level up and unlock new features.</p>
|
| 85 |
-
<h3>Use the market to buy and sell goods</h3>
|
| 86 |
-
<p>Another tip for playing Farm Town Village Build Story is to use the market to buy and sell goods. You can access the market by tapping on the market stall on your farm. You can buy goods from other players or sell your own goods to them. You can set the price and quantity of your goods as you wish. You can also use the market to trade goods with your friends or neighbors. You can use the market to get the goods you need or make some extra cash.</p> <h3>Visit other players' farms and help them out</h3>
|
| 87 |
-
<p>Another tip for playing Farm Town Village Build Story is to visit other players' farms and help them out. You can visit other players' farms by tapping on the map icon on the bottom right corner of the screen. You can see the farms of your friends, neighbors, or random players. You can help them out by watering their crops, feeding their animals, or harvesting their goods. You can also chat with them, send them gifts, or rate their farms. You can earn coins, XP, and materials by visiting other players' farms.</p>
|
| 88 |
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<h3>Join a group on Facebook to share your stories and get updates</h3>
|
| 89 |
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<p>Another tip for playing Farm Town Village Build Story is to join a group on Facebook to share your stories and get updates. You can join the official group of Farm Town Village Build Story by tapping on the Facebook icon on the top left corner of the screen. You can also search for other groups related to the game on Facebook. You can share your stories, screenshots, tips, and tricks with other players in the group. You can also get updates, news, events, and giveaways from the developers of the game.</p>
|
| 90 |
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<h2>Conclusion</h2>
|
| 91 |
-
<p>Farm Town Village Build Story is a fun and relaxing farming game that you can play on your mobile device. You can grow a variety of crops and craft delicious treats. You can build a zoo and invite lovely pets to your farm. You can repair a circus and attract tourists to your village. You can enlist the support of your friends and neighbors. You can decorate your farm with beautiful flowers and decorations. You can also complete orders and quests to earn cash and XP. You can also use the market to buy and sell goods. You can also visit other players' farms and help them out. You can also join a group on Facebook to share your stories and get updates.</p>
|
| 92 |
-
<p>If you are looking for a game that combines the elements of farming and city-building, then you should definitely try Farm Town Village Build Story. It is free to download and play, and you can enjoy it offline or online. It is colorful and cartoonish, challenging and rewarding, social and interactive. It is a game that will make you smile and relax.</p>
|
| 93 |
-
<h2>FAQs</h2>
|
| 94 |
-
<p>Here are some frequently asked questions about Farm Town Village Build Story:</p>
|
| 95 |
-
<ul>
|
| 96 |
-
<li><b>How do I download Farm Town Village Build Story?</b></li>
|
| 97 |
-
<p>You can download Farm Town Village Build Story from Google Play Store or App Store for free. Just search for the game name and tap on the install button.</p>
|
| 98 |
-
<li><b>How do I play Farm Town Village Build Story offline?</b></li>
|
| 99 |
-
<p>You can play Farm Town Village Build Story offline by turning off your internet connection before launching the game. However, some features may not be available offline, such as visiting other players' farms or connecting with Facebook.</p>
|
| 100 |
-
<li><b>How do I get more gems in Farm Town Village Build Story?</b></li>
|
| 101 |
-
<p>You can get more gems in Farm Town Village Build Story by watching ads, leveling up, or buying them with real money.</p>
|
| 102 |
-
<li><b>How do I unlock new areas and buildings in Farm Town Village Build Story?</b></li>
|
| 103 |
-
<p>You can unlock new areas and buildings in Farm Town Village Build Story by leveling up, befriending characters, completing quests, or using gems.</p>
|
| 104 |
-
<li><b>How do I contact the developers of Farm Town Village Build Story?</b></li>
|
| 105 |
-
<p>You can contact the developers of Farm Town Village Build Story by sending them an email at [email protected] or by joining their Facebook group.</p>
|
| 106 |
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</ul></p> 197e85843d<br />
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spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
DELETED
|
@@ -1,694 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
# you may not use this file except in compliance with the License.
|
| 6 |
-
# You may obtain a copy of the License at
|
| 7 |
-
#
|
| 8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
#
|
| 10 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
# See the License for the specific language governing permissions and
|
| 14 |
-
# limitations under the License.
|
| 15 |
-
|
| 16 |
-
import inspect
|
| 17 |
-
from typing import Callable, List, Optional, Union
|
| 18 |
-
|
| 19 |
-
import numpy as np
|
| 20 |
-
import paddle
|
| 21 |
-
import paddle.nn.functional as F
|
| 22 |
-
import PIL
|
| 23 |
-
from packaging import version
|
| 24 |
-
|
| 25 |
-
from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
| 26 |
-
|
| 27 |
-
from ...configuration_utils import FrozenDict
|
| 28 |
-
from ...models import AutoencoderKL, UNet2DConditionModel
|
| 29 |
-
from ...pipeline_utils import DiffusionPipeline
|
| 30 |
-
from ...schedulers import (
|
| 31 |
-
DDIMScheduler,
|
| 32 |
-
DPMSolverMultistepScheduler,
|
| 33 |
-
EulerAncestralDiscreteScheduler,
|
| 34 |
-
EulerDiscreteScheduler,
|
| 35 |
-
LMSDiscreteScheduler,
|
| 36 |
-
PNDMScheduler,
|
| 37 |
-
)
|
| 38 |
-
from ...utils import deprecate, logging
|
| 39 |
-
from . import StableDiffusionPipelineOutput
|
| 40 |
-
from .safety_checker import StableDiffusionSafetyChecker
|
| 41 |
-
|
| 42 |
-
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
def prepare_mask_and_masked_image(image, mask):
|
| 46 |
-
"""
|
| 47 |
-
Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
|
| 48 |
-
converted to ``paddle.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
|
| 49 |
-
``image`` and ``1`` for the ``mask``.
|
| 50 |
-
The ``image`` will be converted to ``paddle.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
|
| 51 |
-
binarized (``mask > 0.5``) and cast to ``paddle.float32`` too.
|
| 52 |
-
Args:
|
| 53 |
-
image (Union[np.array, PIL.Image, paddle.Tensor]): The image to inpaint.
|
| 54 |
-
It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
|
| 55 |
-
``paddle.Tensor`` or a ``batch x channels x height x width`` ``paddle.Tensor``.
|
| 56 |
-
mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
|
| 57 |
-
It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
|
| 58 |
-
``paddle.Tensor`` or a ``batch x 1 x height x width`` ``paddle.Tensor``.
|
| 59 |
-
Raises:
|
| 60 |
-
ValueError: ``paddle.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``paddle.Tensor`` mask
|
| 61 |
-
should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
|
| 62 |
-
TypeError: ``mask`` is a ``paddle.Tensor`` but ``image`` is not
|
| 63 |
-
(ot the other way around).
|
| 64 |
-
Returns:
|
| 65 |
-
tuple[paddle.Tensor]: The pair (mask, masked_image) as ``paddle.Tensor`` with 4
|
| 66 |
-
dimensions: ``batch x channels x height x width``.
|
| 67 |
-
"""
|
| 68 |
-
if isinstance(image, paddle.Tensor):
|
| 69 |
-
if not isinstance(mask, paddle.Tensor):
|
| 70 |
-
raise TypeError(f"`image` is a paddle.Tensor but `mask` (type: {type(mask)} is not")
|
| 71 |
-
|
| 72 |
-
# Batch single image
|
| 73 |
-
if image.ndim == 3:
|
| 74 |
-
assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
|
| 75 |
-
image = image.unsqueeze(0)
|
| 76 |
-
|
| 77 |
-
# Batch and add channel dim for single mask
|
| 78 |
-
if mask.ndim == 2:
|
| 79 |
-
mask = mask.unsqueeze(0).unsqueeze(0)
|
| 80 |
-
|
| 81 |
-
# Batch single mask or add channel dim
|
| 82 |
-
if mask.ndim == 3:
|
| 83 |
-
# Single batched mask, no channel dim or single mask not batched but channel dim
|
| 84 |
-
if mask.shape[0] == 1:
|
| 85 |
-
mask = mask.unsqueeze(0)
|
| 86 |
-
|
| 87 |
-
# Batched masks no channel dim
|
| 88 |
-
else:
|
| 89 |
-
mask = mask.unsqueeze(1)
|
| 90 |
-
|
| 91 |
-
assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
|
| 92 |
-
assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
|
| 93 |
-
assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
|
| 94 |
-
|
| 95 |
-
# Check image is in [-1, 1]
|
| 96 |
-
if image.min() < -1 or image.max() > 1:
|
| 97 |
-
raise ValueError("Image should be in [-1, 1] range")
|
| 98 |
-
|
| 99 |
-
# Check mask is in [0, 1]
|
| 100 |
-
if mask.min() < 0 or mask.max() > 1:
|
| 101 |
-
raise ValueError("Mask should be in [0, 1] range")
|
| 102 |
-
|
| 103 |
-
# Binarize mask
|
| 104 |
-
mask[mask < 0.5] = 0
|
| 105 |
-
mask[mask >= 0.5] = 1
|
| 106 |
-
|
| 107 |
-
# Image as float32
|
| 108 |
-
image = image.cast(paddle.float32)
|
| 109 |
-
elif isinstance(mask, paddle.Tensor):
|
| 110 |
-
raise TypeError(f"`mask` is a paddle.Tensor but `image` (type: {type(image)} is not")
|
| 111 |
-
else:
|
| 112 |
-
# preprocess image
|
| 113 |
-
if isinstance(image, (PIL.Image.Image, np.ndarray)):
|
| 114 |
-
image = [image]
|
| 115 |
-
|
| 116 |
-
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
|
| 117 |
-
image = [np.array(i.convert("RGB"))[None, :] for i in image]
|
| 118 |
-
image = np.concatenate(image, axis=0)
|
| 119 |
-
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
|
| 120 |
-
image = np.concatenate([i[None, :] for i in image], axis=0)
|
| 121 |
-
|
| 122 |
-
image = image.transpose(0, 3, 1, 2)
|
| 123 |
-
image = paddle.to_tensor(image).cast(paddle.float32) / 127.5 - 1.0
|
| 124 |
-
|
| 125 |
-
# preprocess mask
|
| 126 |
-
if isinstance(mask, (PIL.Image.Image, np.ndarray)):
|
| 127 |
-
mask = [mask]
|
| 128 |
-
|
| 129 |
-
if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
|
| 130 |
-
mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
|
| 131 |
-
mask = mask.astype(np.float32) / 255.0
|
| 132 |
-
elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
|
| 133 |
-
mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
|
| 134 |
-
|
| 135 |
-
mask[mask < 0.5] = 0
|
| 136 |
-
mask[mask >= 0.5] = 1
|
| 137 |
-
mask = paddle.to_tensor(mask)
|
| 138 |
-
|
| 139 |
-
masked_image = image * (mask < 0.5)
|
| 140 |
-
|
| 141 |
-
return mask, masked_image
|
| 142 |
-
|
| 143 |
-
|
| 144 |
-
class StableDiffusionInpaintPipeline(DiffusionPipeline):
|
| 145 |
-
r"""
|
| 146 |
-
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
|
| 147 |
-
|
| 148 |
-
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
| 149 |
-
library implements for all the pipelines (such as downloading or saving, running on a particular xxxx, etc.)
|
| 150 |
-
|
| 151 |
-
Args:
|
| 152 |
-
vae ([`AutoencoderKL`]):
|
| 153 |
-
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
| 154 |
-
text_encoder ([`CLIPTextModel`]):
|
| 155 |
-
Frozen text-encoder. Stable Diffusion uses the text portion of
|
| 156 |
-
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
| 157 |
-
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
| 158 |
-
tokenizer (`CLIPTokenizer`):
|
| 159 |
-
Tokenizer of class
|
| 160 |
-
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
| 161 |
-
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
| 162 |
-
scheduler ([`SchedulerMixin`]):
|
| 163 |
-
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
| 164 |
-
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`]
|
| 165 |
-
or [`DPMSolverMultistepScheduler`].
|
| 166 |
-
safety_checker ([`StableDiffusionSafetyChecker`]):
|
| 167 |
-
Classification module that estimates whether generated images could be considered offensive or harmful.
|
| 168 |
-
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
|
| 169 |
-
feature_extractor ([`CLIPFeatureExtractor`]):
|
| 170 |
-
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
| 171 |
-
"""
|
| 172 |
-
_optional_components = ["safety_checker", "feature_extractor"]
|
| 173 |
-
|
| 174 |
-
def __init__(
|
| 175 |
-
self,
|
| 176 |
-
vae: AutoencoderKL,
|
| 177 |
-
text_encoder: CLIPTextModel,
|
| 178 |
-
tokenizer: CLIPTokenizer,
|
| 179 |
-
unet: UNet2DConditionModel,
|
| 180 |
-
scheduler: Union[
|
| 181 |
-
DDIMScheduler,
|
| 182 |
-
PNDMScheduler,
|
| 183 |
-
LMSDiscreteScheduler,
|
| 184 |
-
EulerDiscreteScheduler,
|
| 185 |
-
EulerAncestralDiscreteScheduler,
|
| 186 |
-
DPMSolverMultistepScheduler,
|
| 187 |
-
],
|
| 188 |
-
safety_checker: StableDiffusionSafetyChecker,
|
| 189 |
-
feature_extractor: CLIPFeatureExtractor,
|
| 190 |
-
requires_safety_checker: bool = True,
|
| 191 |
-
):
|
| 192 |
-
super().__init__()
|
| 193 |
-
|
| 194 |
-
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
|
| 195 |
-
deprecation_message = (
|
| 196 |
-
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
|
| 197 |
-
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
|
| 198 |
-
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
|
| 199 |
-
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
|
| 200 |
-
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
|
| 201 |
-
" file"
|
| 202 |
-
)
|
| 203 |
-
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
|
| 204 |
-
new_config = dict(scheduler.config)
|
| 205 |
-
new_config["steps_offset"] = 1
|
| 206 |
-
scheduler._internal_dict = FrozenDict(new_config)
|
| 207 |
-
|
| 208 |
-
if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
|
| 209 |
-
deprecation_message = (
|
| 210 |
-
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
|
| 211 |
-
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
|
| 212 |
-
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
|
| 213 |
-
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
|
| 214 |
-
" Hub, it would be very nice if you could open a Pull request for the"
|
| 215 |
-
" `scheduler/scheduler_config.json` file"
|
| 216 |
-
)
|
| 217 |
-
deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
|
| 218 |
-
new_config = dict(scheduler.config)
|
| 219 |
-
new_config["skip_prk_steps"] = True
|
| 220 |
-
scheduler._internal_dict = FrozenDict(new_config)
|
| 221 |
-
|
| 222 |
-
if safety_checker is None and requires_safety_checker:
|
| 223 |
-
logger.warning(
|
| 224 |
-
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
|
| 225 |
-
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
|
| 226 |
-
" results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
|
| 227 |
-
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
|
| 228 |
-
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
|
| 229 |
-
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
|
| 230 |
-
)
|
| 231 |
-
if safety_checker is not None and feature_extractor is None:
|
| 232 |
-
raise ValueError(
|
| 233 |
-
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
|
| 234 |
-
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
|
| 235 |
-
)
|
| 236 |
-
is_unet_version_less_0_9_0 = hasattr(unet.config, "_ppdiffusers_version") and version.parse(
|
| 237 |
-
version.parse(unet.config._ppdiffusers_version).base_version
|
| 238 |
-
) < version.parse("0.9.0.dev0")
|
| 239 |
-
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
|
| 240 |
-
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
|
| 241 |
-
deprecation_message = (
|
| 242 |
-
"The configuration file of the unet has set the default `sample_size` to smaller than"
|
| 243 |
-
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
|
| 244 |
-
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
|
| 245 |
-
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
|
| 246 |
-
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
|
| 247 |
-
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
|
| 248 |
-
" in the config might lead to incorrect results in future versions. If you have downloaded this"
|
| 249 |
-
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
|
| 250 |
-
" the `unet/config.json` file"
|
| 251 |
-
)
|
| 252 |
-
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
|
| 253 |
-
new_config = dict(unet.config)
|
| 254 |
-
new_config["sample_size"] = 64
|
| 255 |
-
unet._internal_dict = FrozenDict(new_config)
|
| 256 |
-
self.register_modules(
|
| 257 |
-
vae=vae,
|
| 258 |
-
text_encoder=text_encoder,
|
| 259 |
-
tokenizer=tokenizer,
|
| 260 |
-
unet=unet,
|
| 261 |
-
scheduler=scheduler,
|
| 262 |
-
safety_checker=safety_checker,
|
| 263 |
-
feature_extractor=feature_extractor,
|
| 264 |
-
)
|
| 265 |
-
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
| 266 |
-
self.register_to_config(requires_safety_checker=requires_safety_checker)
|
| 267 |
-
|
| 268 |
-
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
|
| 269 |
-
def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
|
| 270 |
-
r"""
|
| 271 |
-
Encodes the prompt into text encoder hidden states.
|
| 272 |
-
|
| 273 |
-
Args:
|
| 274 |
-
prompt (`str` or `list(int)`):
|
| 275 |
-
prompt to be encoded
|
| 276 |
-
num_images_per_prompt (`int`):
|
| 277 |
-
number of images that should be generated per prompt
|
| 278 |
-
do_classifier_free_guidance (`bool`):
|
| 279 |
-
whether to use classifier free guidance or not
|
| 280 |
-
negative_prompt (`str` or `List[str]`):
|
| 281 |
-
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
| 282 |
-
if `guidance_scale` is less than `1`).
|
| 283 |
-
"""
|
| 284 |
-
batch_size = len(prompt) if isinstance(prompt, list) else 1
|
| 285 |
-
|
| 286 |
-
text_inputs = self.tokenizer(
|
| 287 |
-
prompt,
|
| 288 |
-
padding="max_length",
|
| 289 |
-
max_length=self.tokenizer.model_max_length,
|
| 290 |
-
truncation=True,
|
| 291 |
-
return_tensors="pd",
|
| 292 |
-
)
|
| 293 |
-
text_input_ids = text_inputs.input_ids
|
| 294 |
-
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pd").input_ids
|
| 295 |
-
|
| 296 |
-
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not paddle.equal_all(
|
| 297 |
-
text_input_ids, untruncated_ids
|
| 298 |
-
):
|
| 299 |
-
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
|
| 300 |
-
logger.warning(
|
| 301 |
-
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
| 302 |
-
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
| 303 |
-
)
|
| 304 |
-
|
| 305 |
-
config = (
|
| 306 |
-
self.text_encoder.config
|
| 307 |
-
if isinstance(self.text_encoder.config, dict)
|
| 308 |
-
else self.text_encoder.config.to_dict()
|
| 309 |
-
)
|
| 310 |
-
if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
|
| 311 |
-
attention_mask = text_inputs.attention_mask
|
| 312 |
-
else:
|
| 313 |
-
attention_mask = None
|
| 314 |
-
|
| 315 |
-
text_embeddings = self.text_encoder(
|
| 316 |
-
text_input_ids,
|
| 317 |
-
attention_mask=attention_mask,
|
| 318 |
-
)
|
| 319 |
-
text_embeddings = text_embeddings[0]
|
| 320 |
-
|
| 321 |
-
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
| 322 |
-
bs_embed, seq_len, _ = text_embeddings.shape
|
| 323 |
-
text_embeddings = text_embeddings.tile([1, num_images_per_prompt, 1])
|
| 324 |
-
text_embeddings = text_embeddings.reshape([bs_embed * num_images_per_prompt, seq_len, -1])
|
| 325 |
-
|
| 326 |
-
# get unconditional embeddings for classifier free guidance
|
| 327 |
-
if do_classifier_free_guidance:
|
| 328 |
-
uncond_tokens: List[str]
|
| 329 |
-
if negative_prompt is None:
|
| 330 |
-
uncond_tokens = [""] * batch_size
|
| 331 |
-
elif type(prompt) is not type(negative_prompt):
|
| 332 |
-
raise TypeError(
|
| 333 |
-
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
| 334 |
-
f" {type(prompt)}."
|
| 335 |
-
)
|
| 336 |
-
elif isinstance(negative_prompt, str):
|
| 337 |
-
uncond_tokens = [negative_prompt]
|
| 338 |
-
elif batch_size != len(negative_prompt):
|
| 339 |
-
raise ValueError(
|
| 340 |
-
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
| 341 |
-
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
| 342 |
-
" the batch size of `prompt`."
|
| 343 |
-
)
|
| 344 |
-
else:
|
| 345 |
-
uncond_tokens = negative_prompt
|
| 346 |
-
|
| 347 |
-
max_length = text_input_ids.shape[-1]
|
| 348 |
-
uncond_input = self.tokenizer(
|
| 349 |
-
uncond_tokens,
|
| 350 |
-
padding="max_length",
|
| 351 |
-
max_length=max_length,
|
| 352 |
-
truncation=True,
|
| 353 |
-
return_tensors="pd",
|
| 354 |
-
)
|
| 355 |
-
|
| 356 |
-
if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
|
| 357 |
-
attention_mask = uncond_input.attention_mask
|
| 358 |
-
else:
|
| 359 |
-
attention_mask = None
|
| 360 |
-
|
| 361 |
-
uncond_embeddings = self.text_encoder(
|
| 362 |
-
uncond_input.input_ids,
|
| 363 |
-
attention_mask=attention_mask,
|
| 364 |
-
)
|
| 365 |
-
uncond_embeddings = uncond_embeddings[0]
|
| 366 |
-
|
| 367 |
-
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
| 368 |
-
seq_len = uncond_embeddings.shape[1]
|
| 369 |
-
uncond_embeddings = uncond_embeddings.tile([1, num_images_per_prompt, 1])
|
| 370 |
-
uncond_embeddings = uncond_embeddings.reshape([batch_size * num_images_per_prompt, seq_len, -1])
|
| 371 |
-
|
| 372 |
-
# For classifier free guidance, we need to do two forward passes.
|
| 373 |
-
# Here we concatenate the unconditional and text embeddings into a single batch
|
| 374 |
-
# to avoid doing two forward passes
|
| 375 |
-
text_embeddings = paddle.concat([uncond_embeddings, text_embeddings])
|
| 376 |
-
|
| 377 |
-
return text_embeddings
|
| 378 |
-
|
| 379 |
-
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
| 380 |
-
def run_safety_checker(self, image, dtype):
|
| 381 |
-
if self.safety_checker is not None:
|
| 382 |
-
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pd")
|
| 383 |
-
image, has_nsfw_concept = self.safety_checker(
|
| 384 |
-
images=image, clip_input=safety_checker_input.pixel_values.cast(dtype)
|
| 385 |
-
)
|
| 386 |
-
else:
|
| 387 |
-
has_nsfw_concept = None
|
| 388 |
-
return image, has_nsfw_concept
|
| 389 |
-
|
| 390 |
-
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
| 391 |
-
def prepare_extra_step_kwargs(self, generator, eta):
|
| 392 |
-
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
| 393 |
-
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
| 394 |
-
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
| 395 |
-
# and should be between [0, 1]
|
| 396 |
-
|
| 397 |
-
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
| 398 |
-
extra_step_kwargs = {}
|
| 399 |
-
if accepts_eta:
|
| 400 |
-
extra_step_kwargs["eta"] = eta
|
| 401 |
-
|
| 402 |
-
# check if the scheduler accepts generator
|
| 403 |
-
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
| 404 |
-
if accepts_generator:
|
| 405 |
-
extra_step_kwargs["generator"] = generator
|
| 406 |
-
return extra_step_kwargs
|
| 407 |
-
|
| 408 |
-
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
| 409 |
-
def decode_latents(self, latents):
|
| 410 |
-
latents = 1 / 0.18215 * latents
|
| 411 |
-
image = self.vae.decode(latents).sample
|
| 412 |
-
image = (image / 2 + 0.5).clip(0, 1)
|
| 413 |
-
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
| 414 |
-
image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
|
| 415 |
-
return image
|
| 416 |
-
|
| 417 |
-
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
|
| 418 |
-
def check_inputs(self, prompt, height, width, callback_steps):
|
| 419 |
-
if not isinstance(prompt, str) and not isinstance(prompt, list):
|
| 420 |
-
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
| 421 |
-
|
| 422 |
-
if height % 8 != 0 or width % 8 != 0:
|
| 423 |
-
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
| 424 |
-
|
| 425 |
-
if (callback_steps is None) or (
|
| 426 |
-
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
| 427 |
-
):
|
| 428 |
-
raise ValueError(
|
| 429 |
-
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
| 430 |
-
f" {type(callback_steps)}."
|
| 431 |
-
)
|
| 432 |
-
|
| 433 |
-
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
| 434 |
-
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
|
| 435 |
-
shape = [batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor]
|
| 436 |
-
if isinstance(generator, list) and len(generator) != batch_size:
|
| 437 |
-
raise ValueError(
|
| 438 |
-
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
| 439 |
-
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
| 440 |
-
)
|
| 441 |
-
|
| 442 |
-
if latents is None:
|
| 443 |
-
if isinstance(generator, list):
|
| 444 |
-
shape = [
|
| 445 |
-
1,
|
| 446 |
-
] + shape[1:]
|
| 447 |
-
latents = [paddle.randn(shape, generator=generator[i], dtype=dtype) for i in range(batch_size)]
|
| 448 |
-
latents = paddle.concat(latents, axis=0)
|
| 449 |
-
else:
|
| 450 |
-
latents = paddle.randn(shape, generator=generator, dtype=dtype)
|
| 451 |
-
else:
|
| 452 |
-
if latents.shape != shape:
|
| 453 |
-
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
|
| 454 |
-
|
| 455 |
-
# scale the initial noise by the standard deviation required by the scheduler
|
| 456 |
-
latents = latents * self.scheduler.init_noise_sigma
|
| 457 |
-
return latents
|
| 458 |
-
|
| 459 |
-
def prepare_mask_latents(
|
| 460 |
-
self, mask, masked_image, batch_size, height, width, dtype, generator, do_classifier_free_guidance
|
| 461 |
-
):
|
| 462 |
-
# resize the mask to latents shape as we concatenate the mask to the latents
|
| 463 |
-
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
|
| 464 |
-
# and half precision
|
| 465 |
-
mask = F.interpolate(mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))
|
| 466 |
-
mask = mask.cast(dtype=dtype)
|
| 467 |
-
|
| 468 |
-
masked_image = masked_image.cast(dtype)
|
| 469 |
-
|
| 470 |
-
# encode the mask image into latents space so we can concatenate it to the latents
|
| 471 |
-
if isinstance(generator, list):
|
| 472 |
-
masked_image_latents = [
|
| 473 |
-
self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
|
| 474 |
-
for i in range(batch_size)
|
| 475 |
-
]
|
| 476 |
-
masked_image_latents = paddle.concat(masked_image_latents, axis=0)
|
| 477 |
-
else:
|
| 478 |
-
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
|
| 479 |
-
masked_image_latents = 0.18215 * masked_image_latents
|
| 480 |
-
|
| 481 |
-
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
|
| 482 |
-
if mask.shape[0] < batch_size:
|
| 483 |
-
if not batch_size % mask.shape[0] == 0:
|
| 484 |
-
raise ValueError(
|
| 485 |
-
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
|
| 486 |
-
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
|
| 487 |
-
" of masks that you pass is divisible by the total requested batch size."
|
| 488 |
-
)
|
| 489 |
-
mask = mask.tile([batch_size // mask.shape[0], 1, 1, 1])
|
| 490 |
-
if masked_image_latents.shape[0] < batch_size:
|
| 491 |
-
if not batch_size % masked_image_latents.shape[0] == 0:
|
| 492 |
-
raise ValueError(
|
| 493 |
-
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
|
| 494 |
-
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
|
| 495 |
-
" Make sure the number of images that you pass is divisible by the total requested batch size."
|
| 496 |
-
)
|
| 497 |
-
masked_image_latents = masked_image_latents.tile([batch_size // masked_image_latents.shape[0], 1, 1, 1])
|
| 498 |
-
|
| 499 |
-
mask = paddle.concat([mask] * 2) if do_classifier_free_guidance else mask
|
| 500 |
-
masked_image_latents = (
|
| 501 |
-
paddle.concat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
|
| 502 |
-
)
|
| 503 |
-
|
| 504 |
-
masked_image_latents = masked_image_latents.cast(dtype)
|
| 505 |
-
return mask, masked_image_latents
|
| 506 |
-
|
| 507 |
-
@paddle.no_grad()
|
| 508 |
-
def __call__(
|
| 509 |
-
self,
|
| 510 |
-
prompt: Union[str, List[str]],
|
| 511 |
-
image: Union[paddle.Tensor, PIL.Image.Image],
|
| 512 |
-
mask_image: Union[paddle.Tensor, PIL.Image.Image],
|
| 513 |
-
height: Optional[int] = None,
|
| 514 |
-
width: Optional[int] = None,
|
| 515 |
-
num_inference_steps: int = 50,
|
| 516 |
-
guidance_scale: float = 7.5,
|
| 517 |
-
negative_prompt: Optional[Union[str, List[str]]] = None,
|
| 518 |
-
num_images_per_prompt: Optional[int] = 1,
|
| 519 |
-
eta: float = 0.0,
|
| 520 |
-
generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
|
| 521 |
-
latents: Optional[paddle.Tensor] = None,
|
| 522 |
-
output_type: Optional[str] = "pil",
|
| 523 |
-
return_dict: bool = True,
|
| 524 |
-
callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
|
| 525 |
-
callback_steps: Optional[int] = 1,
|
| 526 |
-
):
|
| 527 |
-
r"""
|
| 528 |
-
Function invoked when calling the pipeline for generation.
|
| 529 |
-
|
| 530 |
-
Args:
|
| 531 |
-
prompt (`str` or `List[str]`):
|
| 532 |
-
The prompt or prompts to guide the image generation.
|
| 533 |
-
image (`PIL.Image.Image`):
|
| 534 |
-
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
|
| 535 |
-
be masked out with `mask_image` and repainted according to `prompt`.
|
| 536 |
-
mask_image (`PIL.Image.Image`):
|
| 537 |
-
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
|
| 538 |
-
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
|
| 539 |
-
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
|
| 540 |
-
instead of 3, so the expected shape would be `(B, H, W, 1)`.
|
| 541 |
-
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
| 542 |
-
The height in pixels of the generated image.
|
| 543 |
-
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
| 544 |
-
The width in pixels of the generated image.
|
| 545 |
-
num_inference_steps (`int`, *optional*, defaults to 50):
|
| 546 |
-
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
| 547 |
-
expense of slower inference.
|
| 548 |
-
guidance_scale (`float`, *optional*, defaults to 7.5):
|
| 549 |
-
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
| 550 |
-
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
| 551 |
-
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
| 552 |
-
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
| 553 |
-
usually at the expense of lower image quality.
|
| 554 |
-
negative_prompt (`str` or `List[str]`, *optional*):
|
| 555 |
-
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
| 556 |
-
if `guidance_scale` is less than `1`).
|
| 557 |
-
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
| 558 |
-
The number of images to generate per prompt.
|
| 559 |
-
eta (`float`, *optional*, defaults to 0.0):
|
| 560 |
-
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
| 561 |
-
[`schedulers.DDIMScheduler`], will be ignored for others.
|
| 562 |
-
generator (`paddle.Generator`, *optional*):
|
| 563 |
-
One or a list of paddle generator(s) to make generation deterministic.
|
| 564 |
-
latents (`paddle.Tensor`, *optional*):
|
| 565 |
-
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
| 566 |
-
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
| 567 |
-
tensor will ge generated by sampling using the supplied random `generator`.
|
| 568 |
-
output_type (`str`, *optional*, defaults to `"pil"`):
|
| 569 |
-
The output format of the generate image. Choose between
|
| 570 |
-
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
| 571 |
-
return_dict (`bool`, *optional*, defaults to `True`):
|
| 572 |
-
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
| 573 |
-
plain tuple.
|
| 574 |
-
callback (`Callable`, *optional*):
|
| 575 |
-
A function that will be called every `callback_steps` steps during inference. The function will be
|
| 576 |
-
called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
|
| 577 |
-
callback_steps (`int`, *optional*, defaults to 1):
|
| 578 |
-
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
| 579 |
-
called at every step.
|
| 580 |
-
|
| 581 |
-
Returns:
|
| 582 |
-
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
| 583 |
-
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
| 584 |
-
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
| 585 |
-
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
| 586 |
-
(nsfw) content, according to the `safety_checker`.
|
| 587 |
-
"""
|
| 588 |
-
# 0. Default height and width to unet
|
| 589 |
-
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
| 590 |
-
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
| 591 |
-
|
| 592 |
-
# 1. Check inputs
|
| 593 |
-
self.check_inputs(prompt, height, width, callback_steps)
|
| 594 |
-
|
| 595 |
-
# 2. Define call parameters
|
| 596 |
-
batch_size = 1 if isinstance(prompt, str) else len(prompt)
|
| 597 |
-
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
| 598 |
-
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
| 599 |
-
# corresponds to doing no classifier free guidance.
|
| 600 |
-
do_classifier_free_guidance = guidance_scale > 1.0
|
| 601 |
-
|
| 602 |
-
# 3. Encode input prompt
|
| 603 |
-
text_embeddings = self._encode_prompt(
|
| 604 |
-
prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
|
| 605 |
-
)
|
| 606 |
-
|
| 607 |
-
# 4. Preprocess mask and image
|
| 608 |
-
mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
|
| 609 |
-
|
| 610 |
-
# 5. set timesteps
|
| 611 |
-
self.scheduler.set_timesteps(num_inference_steps)
|
| 612 |
-
timesteps = self.scheduler.timesteps
|
| 613 |
-
|
| 614 |
-
# 6. Prepare latent variables
|
| 615 |
-
num_channels_latents = self.vae.config.latent_channels
|
| 616 |
-
latents = self.prepare_latents(
|
| 617 |
-
batch_size * num_images_per_prompt,
|
| 618 |
-
num_channels_latents,
|
| 619 |
-
height,
|
| 620 |
-
width,
|
| 621 |
-
text_embeddings.dtype,
|
| 622 |
-
generator,
|
| 623 |
-
latents,
|
| 624 |
-
)
|
| 625 |
-
|
| 626 |
-
# 7. Prepare mask latent variables
|
| 627 |
-
mask, masked_image_latents = self.prepare_mask_latents(
|
| 628 |
-
mask,
|
| 629 |
-
masked_image,
|
| 630 |
-
batch_size * num_images_per_prompt,
|
| 631 |
-
height,
|
| 632 |
-
width,
|
| 633 |
-
text_embeddings.dtype,
|
| 634 |
-
generator,
|
| 635 |
-
do_classifier_free_guidance,
|
| 636 |
-
)
|
| 637 |
-
|
| 638 |
-
# 8. Check that sizes of mask, masked image and latents match
|
| 639 |
-
num_channels_mask = mask.shape[1]
|
| 640 |
-
num_channels_masked_image = masked_image_latents.shape[1]
|
| 641 |
-
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
|
| 642 |
-
raise ValueError(
|
| 643 |
-
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
| 644 |
-
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
| 645 |
-
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
| 646 |
-
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
|
| 647 |
-
" `pipeline.unet` or your `mask_image` or `image` input."
|
| 648 |
-
)
|
| 649 |
-
|
| 650 |
-
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
| 651 |
-
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
| 652 |
-
|
| 653 |
-
# 10. Denoising loop
|
| 654 |
-
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
| 655 |
-
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
| 656 |
-
for i, t in enumerate(timesteps):
|
| 657 |
-
# expand the latents if we are doing classifier free guidance
|
| 658 |
-
latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
|
| 659 |
-
|
| 660 |
-
# concat latents, mask, masked_image_latents in the channel dimension
|
| 661 |
-
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
| 662 |
-
latent_model_input = paddle.concat([latent_model_input, mask, masked_image_latents], axis=1)
|
| 663 |
-
|
| 664 |
-
# predict the noise residual
|
| 665 |
-
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
| 666 |
-
|
| 667 |
-
# perform guidance
|
| 668 |
-
if do_classifier_free_guidance:
|
| 669 |
-
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
| 670 |
-
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
| 671 |
-
|
| 672 |
-
# compute the previous noisy sample x_t -> x_t-1
|
| 673 |
-
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
| 674 |
-
|
| 675 |
-
# call the callback, if provided
|
| 676 |
-
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
| 677 |
-
progress_bar.update()
|
| 678 |
-
if callback is not None and i % callback_steps == 0:
|
| 679 |
-
callback(i, t, latents)
|
| 680 |
-
|
| 681 |
-
# 11. Post-processing
|
| 682 |
-
image = self.decode_latents(latents)
|
| 683 |
-
|
| 684 |
-
# 12. Run safety checker
|
| 685 |
-
image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
|
| 686 |
-
|
| 687 |
-
# 13. Convert to PIL
|
| 688 |
-
if output_type == "pil":
|
| 689 |
-
image = self.numpy_to_pil(image)
|
| 690 |
-
|
| 691 |
-
if not return_dict:
|
| 692 |
-
return (image, has_nsfw_concept)
|
| 693 |
-
|
| 694 |
-
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
|
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spaces/2023Liu2023/bingo/src/components/ui/alert-dialog.tsx
DELETED
|
@@ -1,150 +0,0 @@
|
|
| 1 |
-
'use client'
|
| 2 |
-
|
| 3 |
-
import * as React from 'react'
|
| 4 |
-
import * as AlertDialogPrimitive from '@radix-ui/react-alert-dialog'
|
| 5 |
-
|
| 6 |
-
import { cn } from '@/lib/utils'
|
| 7 |
-
import { buttonVariants } from '@/components/ui/button'
|
| 8 |
-
|
| 9 |
-
const AlertDialog = AlertDialogPrimitive.Root
|
| 10 |
-
|
| 11 |
-
const AlertDialogTrigger = AlertDialogPrimitive.Trigger
|
| 12 |
-
|
| 13 |
-
const AlertDialogPortal = ({
|
| 14 |
-
className,
|
| 15 |
-
children,
|
| 16 |
-
...props
|
| 17 |
-
}: AlertDialogPrimitive.AlertDialogPortalProps) => (
|
| 18 |
-
<AlertDialogPrimitive.Portal className={cn(className)} {...props}>
|
| 19 |
-
<div className="fixed inset-0 z-50 flex items-end justify-center sm:items-center">
|
| 20 |
-
{children}
|
| 21 |
-
</div>
|
| 22 |
-
</AlertDialogPrimitive.Portal>
|
| 23 |
-
)
|
| 24 |
-
AlertDialogPortal.displayName = AlertDialogPrimitive.Portal.displayName
|
| 25 |
-
|
| 26 |
-
const AlertDialogOverlay = React.forwardRef<
|
| 27 |
-
React.ElementRef<typeof AlertDialogPrimitive.Overlay>,
|
| 28 |
-
React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Overlay>
|
| 29 |
-
>(({ className, children, ...props }, ref) => (
|
| 30 |
-
<AlertDialogPrimitive.Overlay
|
| 31 |
-
className={cn(
|
| 32 |
-
'fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-opacity animate-in fade-in',
|
| 33 |
-
className
|
| 34 |
-
)}
|
| 35 |
-
{...props}
|
| 36 |
-
ref={ref}
|
| 37 |
-
/>
|
| 38 |
-
))
|
| 39 |
-
AlertDialogOverlay.displayName = AlertDialogPrimitive.Overlay.displayName
|
| 40 |
-
|
| 41 |
-
const AlertDialogContent = React.forwardRef<
|
| 42 |
-
React.ElementRef<typeof AlertDialogPrimitive.Content>,
|
| 43 |
-
React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Content>
|
| 44 |
-
>(({ className, ...props }, ref) => (
|
| 45 |
-
<AlertDialogPortal>
|
| 46 |
-
<AlertDialogOverlay />
|
| 47 |
-
<AlertDialogPrimitive.Content
|
| 48 |
-
ref={ref}
|
| 49 |
-
className={cn(
|
| 50 |
-
'fixed z-50 grid w-full max-w-lg scale-100 gap-4 border bg-background p-6 opacity-100 shadow-lg animate-in fade-in-90 slide-in-from-bottom-10 sm:rounded-lg sm:zoom-in-90 sm:slide-in-from-bottom-0 md:w-full',
|
| 51 |
-
className
|
| 52 |
-
)}
|
| 53 |
-
{...props}
|
| 54 |
-
/>
|
| 55 |
-
</AlertDialogPortal>
|
| 56 |
-
))
|
| 57 |
-
AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName
|
| 58 |
-
|
| 59 |
-
const AlertDialogHeader = ({
|
| 60 |
-
className,
|
| 61 |
-
...props
|
| 62 |
-
}: React.HTMLAttributes<HTMLDivElement>) => (
|
| 63 |
-
<div
|
| 64 |
-
className={cn(
|
| 65 |
-
'flex flex-col space-y-2 text-center sm:text-left',
|
| 66 |
-
className
|
| 67 |
-
)}
|
| 68 |
-
{...props}
|
| 69 |
-
/>
|
| 70 |
-
)
|
| 71 |
-
AlertDialogHeader.displayName = 'AlertDialogHeader'
|
| 72 |
-
|
| 73 |
-
const AlertDialogFooter = ({
|
| 74 |
-
className,
|
| 75 |
-
...props
|
| 76 |
-
}: React.HTMLAttributes<HTMLDivElement>) => (
|
| 77 |
-
<div
|
| 78 |
-
className={cn(
|
| 79 |
-
'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
|
| 80 |
-
className
|
| 81 |
-
)}
|
| 82 |
-
{...props}
|
| 83 |
-
/>
|
| 84 |
-
)
|
| 85 |
-
AlertDialogFooter.displayName = 'AlertDialogFooter'
|
| 86 |
-
|
| 87 |
-
const AlertDialogTitle = React.forwardRef<
|
| 88 |
-
React.ElementRef<typeof AlertDialogPrimitive.Title>,
|
| 89 |
-
React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Title>
|
| 90 |
-
>(({ className, ...props }, ref) => (
|
| 91 |
-
<AlertDialogPrimitive.Title
|
| 92 |
-
ref={ref}
|
| 93 |
-
className={cn('text-lg font-semibold', className)}
|
| 94 |
-
{...props}
|
| 95 |
-
/>
|
| 96 |
-
))
|
| 97 |
-
AlertDialogTitle.displayName = AlertDialogPrimitive.Title.displayName
|
| 98 |
-
|
| 99 |
-
const AlertDialogDescription = React.forwardRef<
|
| 100 |
-
React.ElementRef<typeof AlertDialogPrimitive.Description>,
|
| 101 |
-
React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Description>
|
| 102 |
-
>(({ className, ...props }, ref) => (
|
| 103 |
-
<AlertDialogPrimitive.Description
|
| 104 |
-
ref={ref}
|
| 105 |
-
className={cn('text-sm text-muted-foreground', className)}
|
| 106 |
-
{...props}
|
| 107 |
-
/>
|
| 108 |
-
))
|
| 109 |
-
AlertDialogDescription.displayName =
|
| 110 |
-
AlertDialogPrimitive.Description.displayName
|
| 111 |
-
|
| 112 |
-
const AlertDialogAction = React.forwardRef<
|
| 113 |
-
React.ElementRef<typeof AlertDialogPrimitive.Action>,
|
| 114 |
-
React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Action>
|
| 115 |
-
>(({ className, ...props }, ref) => (
|
| 116 |
-
<AlertDialogPrimitive.Action
|
| 117 |
-
ref={ref}
|
| 118 |
-
className={cn(buttonVariants(), className)}
|
| 119 |
-
{...props}
|
| 120 |
-
/>
|
| 121 |
-
))
|
| 122 |
-
AlertDialogAction.displayName = AlertDialogPrimitive.Action.displayName
|
| 123 |
-
|
| 124 |
-
const AlertDialogCancel = React.forwardRef<
|
| 125 |
-
React.ElementRef<typeof AlertDialogPrimitive.Cancel>,
|
| 126 |
-
React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Cancel>
|
| 127 |
-
>(({ className, ...props }, ref) => (
|
| 128 |
-
<AlertDialogPrimitive.Cancel
|
| 129 |
-
ref={ref}
|
| 130 |
-
className={cn(
|
| 131 |
-
buttonVariants({ variant: 'outline' }),
|
| 132 |
-
'mt-2 sm:mt-0',
|
| 133 |
-
className
|
| 134 |
-
)}
|
| 135 |
-
{...props}
|
| 136 |
-
/>
|
| 137 |
-
))
|
| 138 |
-
AlertDialogCancel.displayName = AlertDialogPrimitive.Cancel.displayName
|
| 139 |
-
|
| 140 |
-
export {
|
| 141 |
-
AlertDialog,
|
| 142 |
-
AlertDialogTrigger,
|
| 143 |
-
AlertDialogContent,
|
| 144 |
-
AlertDialogHeader,
|
| 145 |
-
AlertDialogFooter,
|
| 146 |
-
AlertDialogTitle,
|
| 147 |
-
AlertDialogDescription,
|
| 148 |
-
AlertDialogAction,
|
| 149 |
-
AlertDialogCancel
|
| 150 |
-
}
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|
spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/dataset_TM_train.py
DELETED
|
@@ -1,161 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch.utils import data
|
| 3 |
-
import numpy as np
|
| 4 |
-
from os.path import join as pjoin
|
| 5 |
-
import random
|
| 6 |
-
import codecs as cs
|
| 7 |
-
from tqdm import tqdm
|
| 8 |
-
import utils.paramUtil as paramUtil
|
| 9 |
-
from torch.utils.data._utils.collate import default_collate
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
def collate_fn(batch):
|
| 13 |
-
batch.sort(key=lambda x: x[3], reverse=True)
|
| 14 |
-
return default_collate(batch)
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
'''For use of training text-2-motion generative model'''
|
| 18 |
-
class Text2MotionDataset(data.Dataset):
|
| 19 |
-
def __init__(self, dataset_name, feat_bias = 5, unit_length = 4, codebook_size = 1024, tokenizer_name=None):
|
| 20 |
-
|
| 21 |
-
self.max_length = 64
|
| 22 |
-
self.pointer = 0
|
| 23 |
-
self.dataset_name = dataset_name
|
| 24 |
-
|
| 25 |
-
self.unit_length = unit_length
|
| 26 |
-
# self.mot_start_idx = codebook_size
|
| 27 |
-
self.mot_end_idx = codebook_size
|
| 28 |
-
self.mot_pad_idx = codebook_size + 1
|
| 29 |
-
if dataset_name == 't2m':
|
| 30 |
-
self.data_root = './dataset/HumanML3D'
|
| 31 |
-
self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
|
| 32 |
-
self.text_dir = pjoin(self.data_root, 'texts')
|
| 33 |
-
self.joints_num = 22
|
| 34 |
-
radius = 4
|
| 35 |
-
fps = 20
|
| 36 |
-
self.max_motion_length = 26 if unit_length == 8 else 51
|
| 37 |
-
dim_pose = 263
|
| 38 |
-
kinematic_chain = paramUtil.t2m_kinematic_chain
|
| 39 |
-
elif dataset_name == 'kit':
|
| 40 |
-
self.data_root = './dataset/KIT-ML'
|
| 41 |
-
self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
|
| 42 |
-
self.text_dir = pjoin(self.data_root, 'texts')
|
| 43 |
-
self.joints_num = 21
|
| 44 |
-
radius = 240 * 8
|
| 45 |
-
fps = 12.5
|
| 46 |
-
dim_pose = 251
|
| 47 |
-
self.max_motion_length = 26 if unit_length == 8 else 51
|
| 48 |
-
kinematic_chain = paramUtil.kit_kinematic_chain
|
| 49 |
-
|
| 50 |
-
split_file = pjoin(self.data_root, 'train.txt')
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
id_list = []
|
| 54 |
-
with cs.open(split_file, 'r') as f:
|
| 55 |
-
for line in f.readlines():
|
| 56 |
-
id_list.append(line.strip())
|
| 57 |
-
|
| 58 |
-
new_name_list = []
|
| 59 |
-
data_dict = {}
|
| 60 |
-
for name in tqdm(id_list):
|
| 61 |
-
try:
|
| 62 |
-
m_token_list = np.load(pjoin(self.data_root, tokenizer_name, '%s.npy'%name))
|
| 63 |
-
|
| 64 |
-
# Read text
|
| 65 |
-
with cs.open(pjoin(self.text_dir, name + '.txt')) as f:
|
| 66 |
-
text_data = []
|
| 67 |
-
flag = False
|
| 68 |
-
lines = f.readlines()
|
| 69 |
-
|
| 70 |
-
for line in lines:
|
| 71 |
-
try:
|
| 72 |
-
text_dict = {}
|
| 73 |
-
line_split = line.strip().split('#')
|
| 74 |
-
caption = line_split[0]
|
| 75 |
-
t_tokens = line_split[1].split(' ')
|
| 76 |
-
f_tag = float(line_split[2])
|
| 77 |
-
to_tag = float(line_split[3])
|
| 78 |
-
f_tag = 0.0 if np.isnan(f_tag) else f_tag
|
| 79 |
-
to_tag = 0.0 if np.isnan(to_tag) else to_tag
|
| 80 |
-
|
| 81 |
-
text_dict['caption'] = caption
|
| 82 |
-
text_dict['tokens'] = t_tokens
|
| 83 |
-
if f_tag == 0.0 and to_tag == 0.0:
|
| 84 |
-
flag = True
|
| 85 |
-
text_data.append(text_dict)
|
| 86 |
-
else:
|
| 87 |
-
m_token_list_new = [tokens[int(f_tag*fps/unit_length) : int(to_tag*fps/unit_length)] for tokens in m_token_list if int(f_tag*fps/unit_length) < int(to_tag*fps/unit_length)]
|
| 88 |
-
|
| 89 |
-
if len(m_token_list_new) == 0:
|
| 90 |
-
continue
|
| 91 |
-
new_name = '%s_%f_%f'%(name, f_tag, to_tag)
|
| 92 |
-
|
| 93 |
-
data_dict[new_name] = {'m_token_list': m_token_list_new,
|
| 94 |
-
'text':[text_dict]}
|
| 95 |
-
new_name_list.append(new_name)
|
| 96 |
-
except:
|
| 97 |
-
pass
|
| 98 |
-
|
| 99 |
-
if flag:
|
| 100 |
-
data_dict[name] = {'m_token_list': m_token_list,
|
| 101 |
-
'text':text_data}
|
| 102 |
-
new_name_list.append(name)
|
| 103 |
-
except:
|
| 104 |
-
pass
|
| 105 |
-
self.data_dict = data_dict
|
| 106 |
-
self.name_list = new_name_list
|
| 107 |
-
|
| 108 |
-
def __len__(self):
|
| 109 |
-
return len(self.data_dict)
|
| 110 |
-
|
| 111 |
-
def __getitem__(self, item):
|
| 112 |
-
data = self.data_dict[self.name_list[item]]
|
| 113 |
-
m_token_list, text_list = data['m_token_list'], data['text']
|
| 114 |
-
m_tokens = random.choice(m_token_list)
|
| 115 |
-
|
| 116 |
-
text_data = random.choice(text_list)
|
| 117 |
-
caption= text_data['caption']
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
coin = np.random.choice([False, False, True])
|
| 121 |
-
# print(len(m_tokens))
|
| 122 |
-
if coin:
|
| 123 |
-
# drop one token at the head or tail
|
| 124 |
-
coin2 = np.random.choice([True, False])
|
| 125 |
-
if coin2:
|
| 126 |
-
m_tokens = m_tokens[:-1]
|
| 127 |
-
else:
|
| 128 |
-
m_tokens = m_tokens[1:]
|
| 129 |
-
m_tokens_len = m_tokens.shape[0]
|
| 130 |
-
|
| 131 |
-
if m_tokens_len+1 < self.max_motion_length:
|
| 132 |
-
m_tokens = np.concatenate([m_tokens, np.ones((1), dtype=int) * self.mot_end_idx, np.ones((self.max_motion_length-1-m_tokens_len), dtype=int) * self.mot_pad_idx], axis=0)
|
| 133 |
-
else:
|
| 134 |
-
m_tokens = np.concatenate([m_tokens, np.ones((1), dtype=int) * self.mot_end_idx], axis=0)
|
| 135 |
-
|
| 136 |
-
return caption, m_tokens.reshape(-1), m_tokens_len
|
| 137 |
-
|
| 138 |
-
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
def DATALoader(dataset_name,
|
| 142 |
-
batch_size, codebook_size, tokenizer_name, unit_length=4,
|
| 143 |
-
num_workers = 8) :
|
| 144 |
-
|
| 145 |
-
train_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, codebook_size = codebook_size, tokenizer_name = tokenizer_name, unit_length=unit_length),
|
| 146 |
-
batch_size,
|
| 147 |
-
shuffle=True,
|
| 148 |
-
num_workers=num_workers,
|
| 149 |
-
#collate_fn=collate_fn,
|
| 150 |
-
drop_last = True)
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
return train_loader
|
| 154 |
-
|
| 155 |
-
|
| 156 |
-
def cycle(iterable):
|
| 157 |
-
while True:
|
| 158 |
-
for x in iterable:
|
| 159 |
-
yield x
|
| 160 |
-
|
| 161 |
-
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|
spaces/AIFILMS/generate_human_motion/VQ-Trans/options/get_eval_option.py
DELETED
|
@@ -1,83 +0,0 @@
|
|
| 1 |
-
from argparse import Namespace
|
| 2 |
-
import re
|
| 3 |
-
from os.path import join as pjoin
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
def is_float(numStr):
|
| 7 |
-
flag = False
|
| 8 |
-
numStr = str(numStr).strip().lstrip('-').lstrip('+')
|
| 9 |
-
try:
|
| 10 |
-
reg = re.compile(r'^[-+]?[0-9]+\.[0-9]+$')
|
| 11 |
-
res = reg.match(str(numStr))
|
| 12 |
-
if res:
|
| 13 |
-
flag = True
|
| 14 |
-
except Exception as ex:
|
| 15 |
-
print("is_float() - error: " + str(ex))
|
| 16 |
-
return flag
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
def is_number(numStr):
|
| 20 |
-
flag = False
|
| 21 |
-
numStr = str(numStr).strip().lstrip('-').lstrip('+')
|
| 22 |
-
if str(numStr).isdigit():
|
| 23 |
-
flag = True
|
| 24 |
-
return flag
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def get_opt(opt_path, device):
|
| 28 |
-
opt = Namespace()
|
| 29 |
-
opt_dict = vars(opt)
|
| 30 |
-
|
| 31 |
-
skip = ('-------------- End ----------------',
|
| 32 |
-
'------------ Options -------------',
|
| 33 |
-
'\n')
|
| 34 |
-
print('Reading', opt_path)
|
| 35 |
-
with open(opt_path) as f:
|
| 36 |
-
for line in f:
|
| 37 |
-
if line.strip() not in skip:
|
| 38 |
-
# print(line.strip())
|
| 39 |
-
key, value = line.strip().split(': ')
|
| 40 |
-
if value in ('True', 'False'):
|
| 41 |
-
opt_dict[key] = (value == 'True')
|
| 42 |
-
# print(key, value)
|
| 43 |
-
elif is_float(value):
|
| 44 |
-
opt_dict[key] = float(value)
|
| 45 |
-
elif is_number(value):
|
| 46 |
-
opt_dict[key] = int(value)
|
| 47 |
-
else:
|
| 48 |
-
opt_dict[key] = str(value)
|
| 49 |
-
|
| 50 |
-
# print(opt)
|
| 51 |
-
opt_dict['which_epoch'] = 'finest'
|
| 52 |
-
opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)
|
| 53 |
-
opt.model_dir = pjoin(opt.save_root, 'model')
|
| 54 |
-
opt.meta_dir = pjoin(opt.save_root, 'meta')
|
| 55 |
-
|
| 56 |
-
if opt.dataset_name == 't2m':
|
| 57 |
-
opt.data_root = './dataset/HumanML3D/'
|
| 58 |
-
opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
|
| 59 |
-
opt.text_dir = pjoin(opt.data_root, 'texts')
|
| 60 |
-
opt.joints_num = 22
|
| 61 |
-
opt.dim_pose = 263
|
| 62 |
-
opt.max_motion_length = 196
|
| 63 |
-
opt.max_motion_frame = 196
|
| 64 |
-
opt.max_motion_token = 55
|
| 65 |
-
elif opt.dataset_name == 'kit':
|
| 66 |
-
opt.data_root = './dataset/KIT-ML/'
|
| 67 |
-
opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
|
| 68 |
-
opt.text_dir = pjoin(opt.data_root, 'texts')
|
| 69 |
-
opt.joints_num = 21
|
| 70 |
-
opt.dim_pose = 251
|
| 71 |
-
opt.max_motion_length = 196
|
| 72 |
-
opt.max_motion_frame = 196
|
| 73 |
-
opt.max_motion_token = 55
|
| 74 |
-
else:
|
| 75 |
-
raise KeyError('Dataset not recognized')
|
| 76 |
-
|
| 77 |
-
opt.dim_word = 300
|
| 78 |
-
opt.num_classes = 200 // opt.unit_length
|
| 79 |
-
opt.is_train = False
|
| 80 |
-
opt.is_continue = False
|
| 81 |
-
opt.device = device
|
| 82 |
-
|
| 83 |
-
return opt
|
|
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|
spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/sampling_util.py
DELETED
|
@@ -1,22 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import numpy as np
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
def append_dims(x, target_dims):
|
| 6 |
-
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
|
| 7 |
-
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
|
| 8 |
-
dims_to_append = target_dims - x.ndim
|
| 9 |
-
if dims_to_append < 0:
|
| 10 |
-
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
|
| 11 |
-
return x[(...,) + (None,) * dims_to_append]
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
def norm_thresholding(x0, value):
|
| 15 |
-
s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
|
| 16 |
-
return x0 * (value / s)
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
def spatial_norm_thresholding(x0, value):
|
| 20 |
-
# b c h w
|
| 21 |
-
s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
|
| 22 |
-
return x0 * (value / s)
|
|
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|
spaces/AIWaves/SOP_Generation-single/utils.py
DELETED
|
@@ -1,482 +0,0 @@
|
|
| 1 |
-
# coding=utf-8
|
| 2 |
-
# Copyright 2023 The AIWaves Inc. team.
|
| 3 |
-
|
| 4 |
-
#
|
| 5 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 6 |
-
# you may not use this file except in compliance with the License.
|
| 7 |
-
# You may obtain a copy of the License at
|
| 8 |
-
#
|
| 9 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 10 |
-
#
|
| 11 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 12 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 13 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 14 |
-
# See the License for the specific language governing permissions and
|
| 15 |
-
# limitations under the License.
|
| 16 |
-
"""helper functions for an LLM autonoumous agent"""
|
| 17 |
-
import csv
|
| 18 |
-
import random
|
| 19 |
-
import json
|
| 20 |
-
import pandas
|
| 21 |
-
import numpy as np
|
| 22 |
-
import requests
|
| 23 |
-
import torch
|
| 24 |
-
from tqdm import tqdm
|
| 25 |
-
import re
|
| 26 |
-
import datetime
|
| 27 |
-
import string
|
| 28 |
-
import random
|
| 29 |
-
import os
|
| 30 |
-
import openai
|
| 31 |
-
from text2vec import semantic_search
|
| 32 |
-
import re
|
| 33 |
-
import datetime
|
| 34 |
-
from langchain.document_loaders import UnstructuredFileLoader
|
| 35 |
-
from langchain.text_splitter import CharacterTextSplitter
|
| 36 |
-
from sentence_transformers import SentenceTransformer
|
| 37 |
-
|
| 38 |
-
embed_model_name = os.environ["Embed_Model"] if "Embed_Model" in os.environ else "text-embedding-ada-002"
|
| 39 |
-
if embed_model_name in ["text-embedding-ada-002"]:
|
| 40 |
-
pass
|
| 41 |
-
else:
|
| 42 |
-
embedding_model = SentenceTransformer(
|
| 43 |
-
embed_model_name, device=torch.device("cpu")
|
| 44 |
-
)
|
| 45 |
-
|
| 46 |
-
def get_embedding(sentence):
|
| 47 |
-
if embed_model_name in ["text-embedding-ada-002"]:
|
| 48 |
-
openai.api_key = os.environ["API_KEY"]
|
| 49 |
-
if "PROXY" in os.environ:
|
| 50 |
-
assert "http:" in os.environ["PROXY"] or "socks" in os.environ["PROXY"],"PROXY error,PROXY must be http or socks"
|
| 51 |
-
openai.proxy = os.environ["PROXY"]
|
| 52 |
-
if "API_BASE" in os.environ:
|
| 53 |
-
openai.api_base = os.environ["API_BASE"]
|
| 54 |
-
embedding_model = openai.Embedding
|
| 55 |
-
embed = embedding_model.create(
|
| 56 |
-
model=embed_model_name,
|
| 57 |
-
input=sentence
|
| 58 |
-
)
|
| 59 |
-
embed = embed["data"][0]["embedding"]
|
| 60 |
-
embed = torch.tensor(embed,dtype=torch.float32)
|
| 61 |
-
else:
|
| 62 |
-
embed = embedding_model.encode(sentence,convert_to_tensor=True)
|
| 63 |
-
if len(embed.shape)==1:
|
| 64 |
-
embed = embed.unsqueeze(0)
|
| 65 |
-
return embed
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def get_code():
|
| 69 |
-
return "".join(random.sample(string.ascii_letters + string.digits, 8))
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
def get_content_between_a_b(start_tag, end_tag, text):
|
| 73 |
-
"""
|
| 74 |
-
|
| 75 |
-
Args:
|
| 76 |
-
start_tag (str): start_tag
|
| 77 |
-
end_tag (str): end_tag
|
| 78 |
-
text (str): complete sentence
|
| 79 |
-
|
| 80 |
-
Returns:
|
| 81 |
-
str: the content between start_tag and end_tag
|
| 82 |
-
"""
|
| 83 |
-
extracted_text = ""
|
| 84 |
-
start_index = text.find(start_tag)
|
| 85 |
-
while start_index != -1:
|
| 86 |
-
end_index = text.find(end_tag, start_index + len(start_tag))
|
| 87 |
-
if end_index != -1:
|
| 88 |
-
extracted_text += text[start_index +
|
| 89 |
-
len(start_tag):end_index] + " "
|
| 90 |
-
start_index = text.find(start_tag, end_index + len(end_tag))
|
| 91 |
-
else:
|
| 92 |
-
break
|
| 93 |
-
|
| 94 |
-
return extracted_text.strip()
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
def extract(text, type):
|
| 98 |
-
"""extract the content between <type></type>
|
| 99 |
-
|
| 100 |
-
Args:
|
| 101 |
-
text (str): complete sentence
|
| 102 |
-
type (str): tag
|
| 103 |
-
|
| 104 |
-
Returns:
|
| 105 |
-
str: content between <type></type>
|
| 106 |
-
"""
|
| 107 |
-
target_str = get_content_between_a_b(f"<{type}>", f"</{type}>", text)
|
| 108 |
-
return target_str
|
| 109 |
-
|
| 110 |
-
def count_files_in_directory(directory):
|
| 111 |
-
# 获取指定目录下的文件数目
|
| 112 |
-
file_count = len([f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))])
|
| 113 |
-
return file_count
|
| 114 |
-
|
| 115 |
-
def delete_oldest_files(directory, num_to_keep):
|
| 116 |
-
# 获取目录下文件列表，并按修改时间排序
|
| 117 |
-
files = [(f, os.path.getmtime(os.path.join(directory, f))) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
|
| 118 |
-
|
| 119 |
-
# 删除最开始的 num_to_keep 个文件
|
| 120 |
-
for i in range(min(num_to_keep, len(files))):
|
| 121 |
-
file_to_delete = os.path.join(directory, files[i][0])
|
| 122 |
-
os.remove(file_to_delete)
|
| 123 |
-
|
| 124 |
-
def delete_files_if_exceed_threshold(directory, threshold, num_to_keep):
|
| 125 |
-
# 获取文件数目并进行处理
|
| 126 |
-
file_count = count_files_in_directory(directory)
|
| 127 |
-
if file_count > threshold:
|
| 128 |
-
delete_count = file_count - num_to_keep
|
| 129 |
-
delete_oldest_files(directory, delete_count)
|
| 130 |
-
|
| 131 |
-
def save_logs(log_path, messages, response):
|
| 132 |
-
if not os.path.exists(log_path):
|
| 133 |
-
os.mkdir(log_path)
|
| 134 |
-
delete_files_if_exceed_threshold(log_path, 20, 10)
|
| 135 |
-
log_path = log_path if log_path else "logs"
|
| 136 |
-
log = {}
|
| 137 |
-
log["input"] = messages
|
| 138 |
-
log["output"] = response
|
| 139 |
-
os.makedirs(log_path, exist_ok=True)
|
| 140 |
-
log_file = os.path.join(
|
| 141 |
-
log_path,
|
| 142 |
-
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + ".json")
|
| 143 |
-
with open(log_file, "w", encoding="utf-8") as f:
|
| 144 |
-
json.dump(log, f, ensure_ascii=False, indent=2)
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
|
| 148 |
-
def semantic_search_word2vec(query_embedding, kb_embeddings, top_k):
|
| 149 |
-
return semantic_search(query_embedding, kb_embeddings, top_k=top_k)
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
def cut_sent(para):
|
| 153 |
-
para = re.sub("([。！？\?])([^”’])", r"\1\n\2", para)
|
| 154 |
-
para = re.sub("(\.{6})([^”’])", r"\1\n\2", para)
|
| 155 |
-
para = re.sub("(\…{2})([^”’])", r"\1\n\2", para)
|
| 156 |
-
para = re.sub("([。！？\?][”’])([^，。！？\?])", r"\1\n\2", para)
|
| 157 |
-
para = para.rstrip()
|
| 158 |
-
pieces = [i for i in para.split("\n") if i]
|
| 159 |
-
batch_size = 3
|
| 160 |
-
chucks = [
|
| 161 |
-
" ".join(pieces[i:i + batch_size])
|
| 162 |
-
for i in range(0, len(pieces), batch_size)
|
| 163 |
-
]
|
| 164 |
-
return chucks
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
def process_document(file_path):
|
| 168 |
-
"""
|
| 169 |
-
Save QA_csv to json.
|
| 170 |
-
Args:
|
| 171 |
-
model: LLM to generate embeddings
|
| 172 |
-
qa_dict: A dict contains Q&A
|
| 173 |
-
save_path: where to save the json file.
|
| 174 |
-
Json format:
|
| 175 |
-
Dict[num,Dict[q:str,a:str,chunk:str,emb:List[float]]
|
| 176 |
-
"""
|
| 177 |
-
final_dict = {}
|
| 178 |
-
count = 0
|
| 179 |
-
if file_path.endswith(".csv"):
|
| 180 |
-
dataset = pandas.read_csv(file_path)
|
| 181 |
-
questions = dataset["question"]
|
| 182 |
-
answers = dataset["answer"]
|
| 183 |
-
# embedding q+chunk
|
| 184 |
-
for q, a in zip(questions, answers):
|
| 185 |
-
for text in cut_sent(a):
|
| 186 |
-
temp_dict = {}
|
| 187 |
-
temp_dict["q"] = q
|
| 188 |
-
temp_dict["a"] = a
|
| 189 |
-
temp_dict["chunk"] = text
|
| 190 |
-
temp_dict["emb"] = get_embedding(q + text).tolist()
|
| 191 |
-
final_dict[count] = temp_dict
|
| 192 |
-
count += 1
|
| 193 |
-
# embedding chunk
|
| 194 |
-
for q, a in zip(questions, answers):
|
| 195 |
-
for text in cut_sent(a):
|
| 196 |
-
temp_dict = {}
|
| 197 |
-
temp_dict["q"] = q
|
| 198 |
-
temp_dict["a"] = a
|
| 199 |
-
temp_dict["chunk"] = text
|
| 200 |
-
temp_dict["emb"] = get_embedding(text).tolist()
|
| 201 |
-
final_dict[count] = temp_dict
|
| 202 |
-
count += 1
|
| 203 |
-
# embedding q
|
| 204 |
-
for q, a in zip(questions, answers):
|
| 205 |
-
temp_dict = {}
|
| 206 |
-
temp_dict["q"] = q
|
| 207 |
-
temp_dict["a"] = a
|
| 208 |
-
temp_dict["chunk"] = a
|
| 209 |
-
temp_dict["emb"] = get_embedding(q).tolist()
|
| 210 |
-
final_dict[count] = temp_dict
|
| 211 |
-
count += 1
|
| 212 |
-
# embedding q+a
|
| 213 |
-
for q, a in zip(questions, answers):
|
| 214 |
-
temp_dict = {}
|
| 215 |
-
temp_dict["q"] = q
|
| 216 |
-
temp_dict["a"] = a
|
| 217 |
-
temp_dict["chunk"] = a
|
| 218 |
-
temp_dict["emb"] = get_embedding(q + a).tolist()
|
| 219 |
-
final_dict[count] = temp_dict
|
| 220 |
-
count += 1
|
| 221 |
-
# embedding a
|
| 222 |
-
for q, a in zip(questions, answers):
|
| 223 |
-
temp_dict = {}
|
| 224 |
-
temp_dict["q"] = q
|
| 225 |
-
temp_dict["a"] = a
|
| 226 |
-
temp_dict["chunk"] = a
|
| 227 |
-
temp_dict["emb"] = get_embedding(a).tolist()
|
| 228 |
-
final_dict[count] = temp_dict
|
| 229 |
-
count += 1
|
| 230 |
-
print(f"finish updating {len(final_dict)} data!")
|
| 231 |
-
os.makedirs("temp_database", exist_ok=True)
|
| 232 |
-
save_path = os.path.join(
|
| 233 |
-
"temp_database/",
|
| 234 |
-
file_path.split("/")[-1].replace("." + file_path.split(".")[1],
|
| 235 |
-
".json"),
|
| 236 |
-
)
|
| 237 |
-
print(save_path)
|
| 238 |
-
with open(save_path, "w") as f:
|
| 239 |
-
json.dump(final_dict, f, ensure_ascii=False, indent=2)
|
| 240 |
-
return {"knowledge_base": save_path, "type": "QA"}
|
| 241 |
-
else:
|
| 242 |
-
loader = UnstructuredFileLoader(file_path)
|
| 243 |
-
docs = loader.load()
|
| 244 |
-
text_spiltter = CharacterTextSplitter(chunk_size=200,
|
| 245 |
-
chunk_overlap=100)
|
| 246 |
-
docs = text_spiltter.split_text(docs[0].page_content)
|
| 247 |
-
os.makedirs("temp_database", exist_ok=True)
|
| 248 |
-
save_path = os.path.join(
|
| 249 |
-
"temp_database/",
|
| 250 |
-
file_path.replace("." + file_path.split(".")[1], ".json"))
|
| 251 |
-
final_dict = {}
|
| 252 |
-
count = 0
|
| 253 |
-
for c in tqdm(docs):
|
| 254 |
-
temp_dict = {}
|
| 255 |
-
temp_dict["chunk"] = c
|
| 256 |
-
temp_dict["emb"] = get_embedding(c).tolist()
|
| 257 |
-
final_dict[count] = temp_dict
|
| 258 |
-
count += 1
|
| 259 |
-
print(f"finish updating {len(final_dict)} data!")
|
| 260 |
-
with open(save_path, "w") as f:
|
| 261 |
-
json.dump(final_dict, f, ensure_ascii=False, indent=2)
|
| 262 |
-
return {"knowledge_base": save_path, "type": "UnstructuredFile"}
|
| 263 |
-
|
| 264 |
-
def load_knowledge_base_qa(path):
|
| 265 |
-
"""
|
| 266 |
-
Load json format knowledge base.
|
| 267 |
-
"""
|
| 268 |
-
print("path", path)
|
| 269 |
-
with open(path, "r") as f:
|
| 270 |
-
data = json.load(f)
|
| 271 |
-
embeddings = []
|
| 272 |
-
questions = []
|
| 273 |
-
answers = []
|
| 274 |
-
chunks = []
|
| 275 |
-
for idx in range(len(data.keys())):
|
| 276 |
-
embeddings.append(data[str(idx)]["emb"])
|
| 277 |
-
questions.append(data[str(idx)]["q"])
|
| 278 |
-
answers.append(data[str(idx)]["a"])
|
| 279 |
-
chunks.append(data[str(idx)]["chunk"])
|
| 280 |
-
embeddings = np.array(embeddings, dtype=np.float32)
|
| 281 |
-
embeddings = torch.from_numpy(embeddings).squeeze()
|
| 282 |
-
return embeddings, questions, answers, chunks
|
| 283 |
-
|
| 284 |
-
|
| 285 |
-
def load_knowledge_base_UnstructuredFile(path):
|
| 286 |
-
"""
|
| 287 |
-
Load json format knowledge base.
|
| 288 |
-
"""
|
| 289 |
-
with open(path, "r") as f:
|
| 290 |
-
data = json.load(f)
|
| 291 |
-
embeddings = []
|
| 292 |
-
chunks = []
|
| 293 |
-
for idx in range(len(data.keys())):
|
| 294 |
-
embeddings.append(data[str(idx)]["emb"])
|
| 295 |
-
chunks.append(data[str(idx)]["chunk"])
|
| 296 |
-
embeddings = np.array(embeddings, dtype=np.float32)
|
| 297 |
-
embeddings = torch.from_numpy(embeddings).squeeze()
|
| 298 |
-
return embeddings, chunks
|
| 299 |
-
|
| 300 |
-
|
| 301 |
-
def cos_sim(a: torch.Tensor, b: torch.Tensor):
|
| 302 |
-
"""
|
| 303 |
-
Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.
|
| 304 |
-
:return: Matrix with res[i][j] = cos_sim(a[i], b[j])
|
| 305 |
-
"""
|
| 306 |
-
if not isinstance(a, torch.Tensor):
|
| 307 |
-
a = torch.tensor(a)
|
| 308 |
-
|
| 309 |
-
if not isinstance(b, torch.Tensor):
|
| 310 |
-
b = torch.tensor(b)
|
| 311 |
-
|
| 312 |
-
if len(a.shape) == 1:
|
| 313 |
-
a = a.unsqueeze(0)
|
| 314 |
-
|
| 315 |
-
if len(b.shape) == 1:
|
| 316 |
-
b = b.unsqueeze(0)
|
| 317 |
-
|
| 318 |
-
a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
|
| 319 |
-
b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
|
| 320 |
-
return torch.mm(a_norm, b_norm.transpose(0, 1))
|
| 321 |
-
|
| 322 |
-
|
| 323 |
-
def matching_a_b(a, b, requirements=None):
|
| 324 |
-
a_embedder = get_embedding(a)
|
| 325 |
-
# 获取embedder
|
| 326 |
-
b_embeder = get_embedding(b)
|
| 327 |
-
sim_scores = cos_sim(a_embedder, b_embeder)[0]
|
| 328 |
-
return sim_scores
|
| 329 |
-
|
| 330 |
-
|
| 331 |
-
def matching_category(inputtext,
|
| 332 |
-
forest_name,
|
| 333 |
-
requirements=None,
|
| 334 |
-
cat_embedder=None,
|
| 335 |
-
top_k=3):
|
| 336 |
-
"""
|
| 337 |
-
Args:
|
| 338 |
-
inputtext: the category name to be matched
|
| 339 |
-
forest: search tree
|
| 340 |
-
top_k: the default three highest scoring results
|
| 341 |
-
Return：
|
| 342 |
-
topk matching_result. List[List] [[top1_name,top2_name,top3_name],[top1_score,top2_score,top3_score]]
|
| 343 |
-
"""
|
| 344 |
-
|
| 345 |
-
sim_scores = torch.zeros([100])
|
| 346 |
-
if inputtext:
|
| 347 |
-
input_embeder = get_embedding(inputtext)
|
| 348 |
-
sim_scores = cos_sim(input_embeder, cat_embedder)[0]
|
| 349 |
-
|
| 350 |
-
if requirements:
|
| 351 |
-
requirements = requirements.split(" ")
|
| 352 |
-
requirements_embedder = get_embedding(requirements)
|
| 353 |
-
req_scores = cos_sim(requirements_embedder, cat_embedder)
|
| 354 |
-
req_scores = torch.mean(req_scores, dim=0)
|
| 355 |
-
total_scores = req_scores
|
| 356 |
-
else:
|
| 357 |
-
total_scores = sim_scores
|
| 358 |
-
|
| 359 |
-
top_k_cat = torch.topk(total_scores, k=top_k)
|
| 360 |
-
top_k_score, top_k_idx = top_k_cat[0], top_k_cat[1]
|
| 361 |
-
top_k_name = [forest_name[top_k_idx[i]] for i in range(0, top_k)]
|
| 362 |
-
|
| 363 |
-
return [top_k_name, top_k_score.tolist(), top_k_idx]
|
| 364 |
-
|
| 365 |
-
|
| 366 |
-
def sample_with_order_preserved(lst, num):
|
| 367 |
-
"""Randomly sample from the list while maintaining the original order."""
|
| 368 |
-
indices = list(range(len(lst)))
|
| 369 |
-
sampled_indices = random.sample(indices, num)
|
| 370 |
-
sampled_indices.sort() # 保持原顺序
|
| 371 |
-
return [lst[i] for i in sampled_indices]
|
| 372 |
-
|
| 373 |
-
|
| 374 |
-
def limit_values(data, max_values):
|
| 375 |
-
"""Reduce each key-value list in the dictionary to the specified size, keeping the order of the original list unchanged."""
|
| 376 |
-
for key, values in data.items():
|
| 377 |
-
if len(values) > max_values:
|
| 378 |
-
data[key] = sample_with_order_preserved(values, max_values)
|
| 379 |
-
return data
|
| 380 |
-
|
| 381 |
-
|
| 382 |
-
def limit_keys(data, max_keys):
|
| 383 |
-
"""Reduce the dictionary to the specified number of keys."""
|
| 384 |
-
keys = list(data.keys())
|
| 385 |
-
if len(keys) > max_keys:
|
| 386 |
-
keys = sample_with_order_preserved(keys, max_keys)
|
| 387 |
-
data = {key: data[key] for key in keys}
|
| 388 |
-
return data
|
| 389 |
-
|
| 390 |
-
|
| 391 |
-
def flatten_dict(nested_dict):
|
| 392 |
-
"""
|
| 393 |
-
flatten the dictionary
|
| 394 |
-
"""
|
| 395 |
-
flattened_dict = {}
|
| 396 |
-
for key, value in nested_dict.items():
|
| 397 |
-
if isinstance(value, dict):
|
| 398 |
-
flattened_subdict = flatten_dict(value)
|
| 399 |
-
flattened_dict.update(flattened_subdict)
|
| 400 |
-
else:
|
| 401 |
-
flattened_dict[key] = value
|
| 402 |
-
return flattened_dict
|
| 403 |
-
|
| 404 |
-
|
| 405 |
-
def merge_list(list1, list2):
|
| 406 |
-
for l in list2:
|
| 407 |
-
if l not in list1:
|
| 408 |
-
list1.append(l)
|
| 409 |
-
return list1
|
| 410 |
-
|
| 411 |
-
|
| 412 |
-
def Search_Engines(req):
|
| 413 |
-
FETSIZE = eval(os.environ["FETSIZE"]) if "FETSIZE" in os.environ else 5
|
| 414 |
-
|
| 415 |
-
new_dict = {"keyword": req, "catLeafName": "", "fetchSize": FETSIZE}
|
| 416 |
-
url = os.environ["SHOPPING_SEARCH"]
|
| 417 |
-
res = requests.post(
|
| 418 |
-
url= url,
|
| 419 |
-
json=new_dict,
|
| 420 |
-
)
|
| 421 |
-
user_dict = json.loads(res.text)
|
| 422 |
-
if "data" in user_dict.keys():
|
| 423 |
-
request_items = user_dict["data"]["items"] # 查询到的商品信息JSON
|
| 424 |
-
top_category = user_dict["data"]["topCategories"]
|
| 425 |
-
return request_items, top_category
|
| 426 |
-
else:
|
| 427 |
-
return []
|
| 428 |
-
|
| 429 |
-
|
| 430 |
-
def search_with_api(requirements, categery):
|
| 431 |
-
|
| 432 |
-
FETSIZE = eval(os.environ["FETSIZE"]) if "FETSIZE" in os.environ else 5
|
| 433 |
-
|
| 434 |
-
request_items = []
|
| 435 |
-
all_req_list = requirements.split(" ")
|
| 436 |
-
count = 0
|
| 437 |
-
|
| 438 |
-
while len(request_items) < FETSIZE and len(all_req_list) > 0:
|
| 439 |
-
if count:
|
| 440 |
-
all_req_list.pop(0)
|
| 441 |
-
all_req = (" ").join(all_req_list)
|
| 442 |
-
if categery not in all_req_list:
|
| 443 |
-
all_req = all_req + " " + categery
|
| 444 |
-
now_request_items, top_category = Search_Engines(all_req)
|
| 445 |
-
request_items = merge_list(request_items, now_request_items)
|
| 446 |
-
count += 1
|
| 447 |
-
new_top = []
|
| 448 |
-
for category in top_category:
|
| 449 |
-
if "其它" in category or "其它" in category:
|
| 450 |
-
continue
|
| 451 |
-
else:
|
| 452 |
-
new_top.append(category)
|
| 453 |
-
if len(request_items) > FETSIZE:
|
| 454 |
-
request_items = request_items[:FETSIZE]
|
| 455 |
-
return request_items, new_top
|
| 456 |
-
|
| 457 |
-
|
| 458 |
-
|
| 459 |
-
def get_relevant_history(query,history,embeddings):
|
| 460 |
-
"""
|
| 461 |
-
Retrieve a list of key history entries based on a query using semantic search.
|
| 462 |
-
|
| 463 |
-
Args:
|
| 464 |
-
query (str): The input query for which key history is to be retrieved.
|
| 465 |
-
history (list): A list of historical key entries.
|
| 466 |
-
embeddings (numpy.ndarray): An array of embedding vectors for historical entries.
|
| 467 |
-
|
| 468 |
-
Returns:
|
| 469 |
-
list: A list of key history entries most similar to the query.
|
| 470 |
-
"""
|
| 471 |
-
TOP_K = eval(os.environ["TOP_K"]) if "TOP_K" in os.environ else 2
|
| 472 |
-
relevant_history = []
|
| 473 |
-
query_embedding = get_embedding(query)
|
| 474 |
-
hits = semantic_search(query_embedding, embeddings, top_k=min(TOP_K,embeddings.shape[0]))
|
| 475 |
-
hits = hits[0]
|
| 476 |
-
for hit in hits:
|
| 477 |
-
matching_idx = hit["corpus_id"]
|
| 478 |
-
try:
|
| 479 |
-
relevant_history.append(history[matching_idx])
|
| 480 |
-
except:
|
| 481 |
-
return []
|
| 482 |
-
return relevant_history
|
|
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spaces/AMR-KELEG/ALDi/constants.py
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
CHOICE_TEXT = "Input Text"
|
| 2 |
-
CHOICE_FILE = "Upload File"
|
| 3 |
-
TITLE = "ALDi: Arabic Level of Dialectness"
|
| 4 |
-
MODEL_NAME = "AMR-KELEG/Sentence-ALDi"
|
|
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|
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|
spaces/Abhilashvj/planogram-compliance/utils/segment/loss.py
DELETED
|
@@ -1,275 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
|
| 5 |
-
from ..general import xywh2xyxy
|
| 6 |
-
from ..loss import FocalLoss, smooth_BCE
|
| 7 |
-
from ..metrics import bbox_iou
|
| 8 |
-
from ..torch_utils import de_parallel
|
| 9 |
-
from .general import crop_mask
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class ComputeLoss:
|
| 13 |
-
# Compute losses
|
| 14 |
-
def __init__(self, model, autobalance=False, overlap=False):
|
| 15 |
-
self.sort_obj_iou = False
|
| 16 |
-
self.overlap = overlap
|
| 17 |
-
device = next(model.parameters()).device # get model device
|
| 18 |
-
h = model.hyp # hyperparameters
|
| 19 |
-
self.device = device
|
| 20 |
-
|
| 21 |
-
# Define criteria
|
| 22 |
-
BCEcls = nn.BCEWithLogitsLoss(
|
| 23 |
-
pos_weight=torch.tensor([h["cls_pw"]], device=device)
|
| 24 |
-
)
|
| 25 |
-
BCEobj = nn.BCEWithLogitsLoss(
|
| 26 |
-
pos_weight=torch.tensor([h["obj_pw"]], device=device)
|
| 27 |
-
)
|
| 28 |
-
|
| 29 |
-
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
|
| 30 |
-
self.cp, self.cn = smooth_BCE(
|
| 31 |
-
eps=h.get("label_smoothing", 0.0)
|
| 32 |
-
) # positive, negative BCE targets
|
| 33 |
-
|
| 34 |
-
# Focal loss
|
| 35 |
-
g = h["fl_gamma"] # focal loss gamma
|
| 36 |
-
if g > 0:
|
| 37 |
-
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
|
| 38 |
-
|
| 39 |
-
m = de_parallel(model).model[-1] # Detect() module
|
| 40 |
-
self.balance = {3: [4.0, 1.0, 0.4]}.get(
|
| 41 |
-
m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]
|
| 42 |
-
) # P3-P7
|
| 43 |
-
self.ssi = (
|
| 44 |
-
list(m.stride).index(16) if autobalance else 0
|
| 45 |
-
) # stride 16 index
|
| 46 |
-
self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = (
|
| 47 |
-
BCEcls,
|
| 48 |
-
BCEobj,
|
| 49 |
-
1.0,
|
| 50 |
-
h,
|
| 51 |
-
autobalance,
|
| 52 |
-
)
|
| 53 |
-
self.na = m.na # number of anchors
|
| 54 |
-
self.nc = m.nc # number of classes
|
| 55 |
-
self.nl = m.nl # number of layers
|
| 56 |
-
self.nm = m.nm # number of masks
|
| 57 |
-
self.anchors = m.anchors
|
| 58 |
-
self.device = device
|
| 59 |
-
|
| 60 |
-
def __call__(self, preds, targets, masks): # predictions, targets, model
|
| 61 |
-
p, proto = preds
|
| 62 |
-
(
|
| 63 |
-
bs,
|
| 64 |
-
nm,
|
| 65 |
-
mask_h,
|
| 66 |
-
mask_w,
|
| 67 |
-
) = proto.shape # batch size, number of masks, mask height, mask width
|
| 68 |
-
lcls = torch.zeros(1, device=self.device)
|
| 69 |
-
lbox = torch.zeros(1, device=self.device)
|
| 70 |
-
lobj = torch.zeros(1, device=self.device)
|
| 71 |
-
lseg = torch.zeros(1, device=self.device)
|
| 72 |
-
tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(
|
| 73 |
-
p, targets
|
| 74 |
-
) # targets
|
| 75 |
-
|
| 76 |
-
# Losses
|
| 77 |
-
for i, pi in enumerate(p): # layer index, layer predictions
|
| 78 |
-
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
|
| 79 |
-
tobj = torch.zeros(
|
| 80 |
-
pi.shape[:4], dtype=pi.dtype, device=self.device
|
| 81 |
-
) # target obj
|
| 82 |
-
|
| 83 |
-
n = b.shape[0] # number of targets
|
| 84 |
-
if n:
|
| 85 |
-
pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split(
|
| 86 |
-
(2, 2, 1, self.nc, nm), 1
|
| 87 |
-
) # subset of predictions
|
| 88 |
-
|
| 89 |
-
# Box regression
|
| 90 |
-
pxy = pxy.sigmoid() * 2 - 0.5
|
| 91 |
-
pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
|
| 92 |
-
pbox = torch.cat((pxy, pwh), 1) # predicted box
|
| 93 |
-
iou = bbox_iou(
|
| 94 |
-
pbox, tbox[i], CIoU=True
|
| 95 |
-
).squeeze() # iou(prediction, target)
|
| 96 |
-
lbox += (1.0 - iou).mean() # iou loss
|
| 97 |
-
|
| 98 |
-
# Objectness
|
| 99 |
-
iou = iou.detach().clamp(0).type(tobj.dtype)
|
| 100 |
-
if self.sort_obj_iou:
|
| 101 |
-
j = iou.argsort()
|
| 102 |
-
b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
|
| 103 |
-
if self.gr < 1:
|
| 104 |
-
iou = (1.0 - self.gr) + self.gr * iou
|
| 105 |
-
tobj[b, a, gj, gi] = iou # iou ratio
|
| 106 |
-
|
| 107 |
-
# Classification
|
| 108 |
-
if self.nc > 1: # cls loss (only if multiple classes)
|
| 109 |
-
t = torch.full_like(
|
| 110 |
-
pcls, self.cn, device=self.device
|
| 111 |
-
) # targets
|
| 112 |
-
t[range(n), tcls[i]] = self.cp
|
| 113 |
-
lcls += self.BCEcls(pcls, t) # BCE
|
| 114 |
-
|
| 115 |
-
# Mask regression
|
| 116 |
-
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
|
| 117 |
-
masks = F.interpolate(
|
| 118 |
-
masks[None], (mask_h, mask_w), mode="nearest"
|
| 119 |
-
)[0]
|
| 120 |
-
marea = xywhn[i][:, 2:].prod(
|
| 121 |
-
1
|
| 122 |
-
) # mask width, height normalized
|
| 123 |
-
mxyxy = xywh2xyxy(
|
| 124 |
-
xywhn[i]
|
| 125 |
-
* torch.tensor(
|
| 126 |
-
[mask_w, mask_h, mask_w, mask_h], device=self.device
|
| 127 |
-
)
|
| 128 |
-
)
|
| 129 |
-
for bi in b.unique():
|
| 130 |
-
j = b == bi # matching index
|
| 131 |
-
if self.overlap:
|
| 132 |
-
mask_gti = torch.where(
|
| 133 |
-
masks[bi][None] == tidxs[i][j].view(-1, 1, 1),
|
| 134 |
-
1.0,
|
| 135 |
-
0.0,
|
| 136 |
-
)
|
| 137 |
-
else:
|
| 138 |
-
mask_gti = masks[tidxs[i]][j]
|
| 139 |
-
lseg += self.single_mask_loss(
|
| 140 |
-
mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]
|
| 141 |
-
)
|
| 142 |
-
|
| 143 |
-
obji = self.BCEobj(pi[..., 4], tobj)
|
| 144 |
-
lobj += obji * self.balance[i] # obj loss
|
| 145 |
-
if self.autobalance:
|
| 146 |
-
self.balance[i] = (
|
| 147 |
-
self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
|
| 148 |
-
)
|
| 149 |
-
|
| 150 |
-
if self.autobalance:
|
| 151 |
-
self.balance = [x / self.balance[self.ssi] for x in self.balance]
|
| 152 |
-
lbox *= self.hyp["box"]
|
| 153 |
-
lobj *= self.hyp["obj"]
|
| 154 |
-
lcls *= self.hyp["cls"]
|
| 155 |
-
lseg *= self.hyp["box"] / bs
|
| 156 |
-
|
| 157 |
-
loss = lbox + lobj + lcls + lseg
|
| 158 |
-
return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
|
| 159 |
-
|
| 160 |
-
def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
|
| 161 |
-
# Mask loss for one image
|
| 162 |
-
pred_mask = (pred @ proto.view(self.nm, -1)).view(
|
| 163 |
-
-1, *proto.shape[1:]
|
| 164 |
-
) # (n,32) @ (32,80,80) -> (n,80,80)
|
| 165 |
-
loss = F.binary_cross_entropy_with_logits(
|
| 166 |
-
pred_mask, gt_mask, reduction="none"
|
| 167 |
-
)
|
| 168 |
-
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
|
| 169 |
-
|
| 170 |
-
def build_targets(self, p, targets):
|
| 171 |
-
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
|
| 172 |
-
na, nt = self.na, targets.shape[0] # number of anchors, targets
|
| 173 |
-
tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
|
| 174 |
-
gain = torch.ones(
|
| 175 |
-
8, device=self.device
|
| 176 |
-
) # normalized to gridspace gain
|
| 177 |
-
ai = (
|
| 178 |
-
torch.arange(na, device=self.device)
|
| 179 |
-
.float()
|
| 180 |
-
.view(na, 1)
|
| 181 |
-
.repeat(1, nt)
|
| 182 |
-
) # same as .repeat_interleave(nt)
|
| 183 |
-
if self.overlap:
|
| 184 |
-
batch = p[0].shape[0]
|
| 185 |
-
ti = []
|
| 186 |
-
for i in range(batch):
|
| 187 |
-
num = (
|
| 188 |
-
targets[:, 0] == i
|
| 189 |
-
).sum() # find number of targets of each image
|
| 190 |
-
ti.append(
|
| 191 |
-
torch.arange(num, device=self.device)
|
| 192 |
-
.float()
|
| 193 |
-
.view(1, num)
|
| 194 |
-
.repeat(na, 1)
|
| 195 |
-
+ 1
|
| 196 |
-
) # (na, num)
|
| 197 |
-
ti = torch.cat(ti, 1) # (na, nt)
|
| 198 |
-
else:
|
| 199 |
-
ti = (
|
| 200 |
-
torch.arange(nt, device=self.device)
|
| 201 |
-
.float()
|
| 202 |
-
.view(1, nt)
|
| 203 |
-
.repeat(na, 1)
|
| 204 |
-
)
|
| 205 |
-
targets = torch.cat(
|
| 206 |
-
(targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2
|
| 207 |
-
) # append anchor indices
|
| 208 |
-
|
| 209 |
-
g = 0.5 # bias
|
| 210 |
-
off = (
|
| 211 |
-
torch.tensor(
|
| 212 |
-
[
|
| 213 |
-
[0, 0],
|
| 214 |
-
[1, 0],
|
| 215 |
-
[0, 1],
|
| 216 |
-
[-1, 0],
|
| 217 |
-
[0, -1], # j,k,l,m
|
| 218 |
-
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
|
| 219 |
-
],
|
| 220 |
-
device=self.device,
|
| 221 |
-
).float()
|
| 222 |
-
* g
|
| 223 |
-
) # offsets
|
| 224 |
-
|
| 225 |
-
for i in range(self.nl):
|
| 226 |
-
anchors, shape = self.anchors[i], p[i].shape
|
| 227 |
-
gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
|
| 228 |
-
|
| 229 |
-
# Match targets to anchors
|
| 230 |
-
t = targets * gain # shape(3,n,7)
|
| 231 |
-
if nt:
|
| 232 |
-
# Matches
|
| 233 |
-
r = t[..., 4:6] / anchors[:, None] # wh ratio
|
| 234 |
-
j = (
|
| 235 |
-
torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"]
|
| 236 |
-
) # compare
|
| 237 |
-
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
|
| 238 |
-
t = t[j] # filter
|
| 239 |
-
|
| 240 |
-
# Offsets
|
| 241 |
-
gxy = t[:, 2:4] # grid xy
|
| 242 |
-
gxi = gain[[2, 3]] - gxy # inverse
|
| 243 |
-
j, k = ((gxy % 1 < g) & (gxy > 1)).T
|
| 244 |
-
l, m = ((gxi % 1 < g) & (gxi > 1)).T
|
| 245 |
-
j = torch.stack((torch.ones_like(j), j, k, l, m))
|
| 246 |
-
t = t.repeat((5, 1, 1))[j]
|
| 247 |
-
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
|
| 248 |
-
else:
|
| 249 |
-
t = targets[0]
|
| 250 |
-
offsets = 0
|
| 251 |
-
|
| 252 |
-
# Define
|
| 253 |
-
bc, gxy, gwh, at = t.chunk(
|
| 254 |
-
4, 1
|
| 255 |
-
) # (image, class), grid xy, grid wh, anchors
|
| 256 |
-
(a, tidx), (b, c) = (
|
| 257 |
-
at.long().T,
|
| 258 |
-
bc.long().T,
|
| 259 |
-
) # anchors, image, class
|
| 260 |
-
gij = (gxy - offsets).long()
|
| 261 |
-
gi, gj = gij.T # grid indices
|
| 262 |
-
|
| 263 |
-
# Append
|
| 264 |
-
indices.append(
|
| 265 |
-
(b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))
|
| 266 |
-
) # image, anchor, grid
|
| 267 |
-
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
|
| 268 |
-
anch.append(anchors[a]) # anchors
|
| 269 |
-
tcls.append(c) # class
|
| 270 |
-
tidxs.append(tidx)
|
| 271 |
-
xywhn.append(
|
| 272 |
-
torch.cat((gxy, gwh), 1) / gain[2:6]
|
| 273 |
-
) # xywh normalized
|
| 274 |
-
|
| 275 |
-
return tcls, tbox, indices, anch, tidxs, xywhn
|
|
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spaces/AchyuthGamer/OpenGPT/g4f/Provider/GetGpt.py
DELETED
|
@@ -1,88 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
import json
|
| 4 |
-
import os
|
| 5 |
-
import uuid
|
| 6 |
-
|
| 7 |
-
import requests
|
| 8 |
-
from Crypto.Cipher import AES
|
| 9 |
-
|
| 10 |
-
from ..typing import Any, CreateResult
|
| 11 |
-
from .base_provider import BaseProvider
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
class GetGpt(BaseProvider):
|
| 15 |
-
url = 'https://chat.getgpt.world/'
|
| 16 |
-
supports_stream = True
|
| 17 |
-
working = False
|
| 18 |
-
supports_gpt_35_turbo = True
|
| 19 |
-
|
| 20 |
-
@staticmethod
|
| 21 |
-
def create_completion(
|
| 22 |
-
model: str,
|
| 23 |
-
messages: list[dict[str, str]],
|
| 24 |
-
stream: bool, **kwargs: Any) -> CreateResult:
|
| 25 |
-
|
| 26 |
-
headers = {
|
| 27 |
-
'Content-Type' : 'application/json',
|
| 28 |
-
'Referer' : 'https://chat.getgpt.world/',
|
| 29 |
-
'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
|
| 30 |
-
}
|
| 31 |
-
|
| 32 |
-
data = json.dumps(
|
| 33 |
-
{
|
| 34 |
-
'messages' : messages,
|
| 35 |
-
'frequency_penalty' : kwargs.get('frequency_penalty', 0),
|
| 36 |
-
'max_tokens' : kwargs.get('max_tokens', 4000),
|
| 37 |
-
'model' : 'gpt-3.5-turbo',
|
| 38 |
-
'presence_penalty' : kwargs.get('presence_penalty', 0),
|
| 39 |
-
'temperature' : kwargs.get('temperature', 1),
|
| 40 |
-
'top_p' : kwargs.get('top_p', 1),
|
| 41 |
-
'stream' : True,
|
| 42 |
-
'uuid' : str(uuid.uuid4())
|
| 43 |
-
}
|
| 44 |
-
)
|
| 45 |
-
|
| 46 |
-
res = requests.post('https://chat.getgpt.world/api/chat/stream',
|
| 47 |
-
headers=headers, json={'signature': _encrypt(data)}, stream=True)
|
| 48 |
-
|
| 49 |
-
res.raise_for_status()
|
| 50 |
-
for line in res.iter_lines():
|
| 51 |
-
if b'content' in line:
|
| 52 |
-
line_json = json.loads(line.decode('utf-8').split('data: ')[1])
|
| 53 |
-
yield (line_json['choices'][0]['delta']['content'])
|
| 54 |
-
|
| 55 |
-
@classmethod
|
| 56 |
-
@property
|
| 57 |
-
def params(cls):
|
| 58 |
-
params = [
|
| 59 |
-
('model', 'str'),
|
| 60 |
-
('messages', 'list[dict[str, str]]'),
|
| 61 |
-
('stream', 'bool'),
|
| 62 |
-
('temperature', 'float'),
|
| 63 |
-
('presence_penalty', 'int'),
|
| 64 |
-
('frequency_penalty', 'int'),
|
| 65 |
-
('top_p', 'int'),
|
| 66 |
-
('max_tokens', 'int'),
|
| 67 |
-
]
|
| 68 |
-
param = ', '.join([': '.join(p) for p in params])
|
| 69 |
-
return f'g4f.provider.{cls.__name__} supports: ({param})'
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
def _encrypt(e: str):
|
| 73 |
-
t = os.urandom(8).hex().encode('utf-8')
|
| 74 |
-
n = os.urandom(8).hex().encode('utf-8')
|
| 75 |
-
r = e.encode('utf-8')
|
| 76 |
-
|
| 77 |
-
cipher = AES.new(t, AES.MODE_CBC, n)
|
| 78 |
-
ciphertext = cipher.encrypt(_pad_data(r))
|
| 79 |
-
|
| 80 |
-
return ciphertext.hex() + t.decode('utf-8') + n.decode('utf-8')
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
def _pad_data(data: bytes) -> bytes:
|
| 84 |
-
block_size = AES.block_size
|
| 85 |
-
padding_size = block_size - len(data) % block_size
|
| 86 |
-
padding = bytes([padding_size] * padding_size)
|
| 87 |
-
|
| 88 |
-
return data + padding
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/imagebox-plugin.js
DELETED
|
@@ -1,23 +0,0 @@
|
|
| 1 |
-
import Factory from './gameobjects/container/imagebox/Factory.js';
|
| 2 |
-
import Creator from './gameobjects/container/imagebox/Creator.js';
|
| 3 |
-
import ImageBox from './gameobjects/container/imagebox/ImageBox.js';
|
| 4 |
-
import SetValue from './utils/object/SetValue.js';
|
| 5 |
-
|
| 6 |
-
class ImageBoxPlugin extends Phaser.Plugins.BasePlugin {
|
| 7 |
-
|
| 8 |
-
constructor(pluginManager) {
|
| 9 |
-
super(pluginManager);
|
| 10 |
-
|
| 11 |
-
// Register our new Game Object type
|
| 12 |
-
pluginManager.registerGameObject('rexImageBox', Factory, Creator);
|
| 13 |
-
}
|
| 14 |
-
|
| 15 |
-
start() {
|
| 16 |
-
var eventEmitter = this.game.events;
|
| 17 |
-
eventEmitter.on('destroy', this.destroy, this);
|
| 18 |
-
}
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
SetValue(window, 'RexPlugins.GameObjects.ImageBox', ImageBox);
|
| 22 |
-
|
| 23 |
-
export default ImageBoxPlugin;
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/canvasinput/CanvasInput.js
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
import CanvasInput from '../../../plugins/canvasinput.js';
|
| 2 |
-
export default CanvasInput;
|
|
|
|
|
|
|
|
|
spaces/AiMimicry/sovits-models/onnxexport/model_onnx.py
DELETED
|
@@ -1,335 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch import nn
|
| 3 |
-
from torch.nn import functional as F
|
| 4 |
-
|
| 5 |
-
import modules.attentions as attentions
|
| 6 |
-
import modules.commons as commons
|
| 7 |
-
import modules.modules as modules
|
| 8 |
-
|
| 9 |
-
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
| 10 |
-
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
| 11 |
-
|
| 12 |
-
import utils
|
| 13 |
-
from modules.commons import init_weights, get_padding
|
| 14 |
-
from vdecoder.hifigan.models import Generator
|
| 15 |
-
from utils import f0_to_coarse
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
class ResidualCouplingBlock(nn.Module):
|
| 19 |
-
def __init__(self,
|
| 20 |
-
channels,
|
| 21 |
-
hidden_channels,
|
| 22 |
-
kernel_size,
|
| 23 |
-
dilation_rate,
|
| 24 |
-
n_layers,
|
| 25 |
-
n_flows=4,
|
| 26 |
-
gin_channels=0):
|
| 27 |
-
super().__init__()
|
| 28 |
-
self.channels = channels
|
| 29 |
-
self.hidden_channels = hidden_channels
|
| 30 |
-
self.kernel_size = kernel_size
|
| 31 |
-
self.dilation_rate = dilation_rate
|
| 32 |
-
self.n_layers = n_layers
|
| 33 |
-
self.n_flows = n_flows
|
| 34 |
-
self.gin_channels = gin_channels
|
| 35 |
-
|
| 36 |
-
self.flows = nn.ModuleList()
|
| 37 |
-
for i in range(n_flows):
|
| 38 |
-
self.flows.append(
|
| 39 |
-
modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
|
| 40 |
-
gin_channels=gin_channels, mean_only=True))
|
| 41 |
-
self.flows.append(modules.Flip())
|
| 42 |
-
|
| 43 |
-
def forward(self, x, x_mask, g=None, reverse=False):
|
| 44 |
-
if not reverse:
|
| 45 |
-
for flow in self.flows:
|
| 46 |
-
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
| 47 |
-
else:
|
| 48 |
-
for flow in reversed(self.flows):
|
| 49 |
-
x = flow(x, x_mask, g=g, reverse=reverse)
|
| 50 |
-
return x
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
class Encoder(nn.Module):
|
| 54 |
-
def __init__(self,
|
| 55 |
-
in_channels,
|
| 56 |
-
out_channels,
|
| 57 |
-
hidden_channels,
|
| 58 |
-
kernel_size,
|
| 59 |
-
dilation_rate,
|
| 60 |
-
n_layers,
|
| 61 |
-
gin_channels=0):
|
| 62 |
-
super().__init__()
|
| 63 |
-
self.in_channels = in_channels
|
| 64 |
-
self.out_channels = out_channels
|
| 65 |
-
self.hidden_channels = hidden_channels
|
| 66 |
-
self.kernel_size = kernel_size
|
| 67 |
-
self.dilation_rate = dilation_rate
|
| 68 |
-
self.n_layers = n_layers
|
| 69 |
-
self.gin_channels = gin_channels
|
| 70 |
-
|
| 71 |
-
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
| 72 |
-
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
| 73 |
-
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
| 74 |
-
|
| 75 |
-
def forward(self, x, x_lengths, g=None):
|
| 76 |
-
# print(x.shape,x_lengths.shape)
|
| 77 |
-
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
| 78 |
-
x = self.pre(x) * x_mask
|
| 79 |
-
x = self.enc(x, x_mask, g=g)
|
| 80 |
-
stats = self.proj(x) * x_mask
|
| 81 |
-
m, logs = torch.split(stats, self.out_channels, dim=1)
|
| 82 |
-
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
| 83 |
-
return z, m, logs, x_mask
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
class TextEncoder(nn.Module):
|
| 87 |
-
def __init__(self,
|
| 88 |
-
out_channels,
|
| 89 |
-
hidden_channels,
|
| 90 |
-
kernel_size,
|
| 91 |
-
n_layers,
|
| 92 |
-
gin_channels=0,
|
| 93 |
-
filter_channels=None,
|
| 94 |
-
n_heads=None,
|
| 95 |
-
p_dropout=None):
|
| 96 |
-
super().__init__()
|
| 97 |
-
self.out_channels = out_channels
|
| 98 |
-
self.hidden_channels = hidden_channels
|
| 99 |
-
self.kernel_size = kernel_size
|
| 100 |
-
self.n_layers = n_layers
|
| 101 |
-
self.gin_channels = gin_channels
|
| 102 |
-
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
| 103 |
-
self.f0_emb = nn.Embedding(256, hidden_channels)
|
| 104 |
-
|
| 105 |
-
self.enc_ = attentions.Encoder(
|
| 106 |
-
hidden_channels,
|
| 107 |
-
filter_channels,
|
| 108 |
-
n_heads,
|
| 109 |
-
n_layers,
|
| 110 |
-
kernel_size,
|
| 111 |
-
p_dropout)
|
| 112 |
-
|
| 113 |
-
def forward(self, x, x_mask, f0=None, z=None):
|
| 114 |
-
x = x + self.f0_emb(f0).transpose(1, 2)
|
| 115 |
-
x = self.enc_(x * x_mask, x_mask)
|
| 116 |
-
stats = self.proj(x) * x_mask
|
| 117 |
-
m, logs = torch.split(stats, self.out_channels, dim=1)
|
| 118 |
-
z = (m + z * torch.exp(logs)) * x_mask
|
| 119 |
-
return z, m, logs, x_mask
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
class DiscriminatorP(torch.nn.Module):
|
| 123 |
-
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
| 124 |
-
super(DiscriminatorP, self).__init__()
|
| 125 |
-
self.period = period
|
| 126 |
-
self.use_spectral_norm = use_spectral_norm
|
| 127 |
-
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
| 128 |
-
self.convs = nn.ModuleList([
|
| 129 |
-
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
| 130 |
-
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
| 131 |
-
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
| 132 |
-
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
| 133 |
-
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
| 134 |
-
])
|
| 135 |
-
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
| 136 |
-
|
| 137 |
-
def forward(self, x):
|
| 138 |
-
fmap = []
|
| 139 |
-
|
| 140 |
-
# 1d to 2d
|
| 141 |
-
b, c, t = x.shape
|
| 142 |
-
if t % self.period != 0: # pad first
|
| 143 |
-
n_pad = self.period - (t % self.period)
|
| 144 |
-
x = F.pad(x, (0, n_pad), "reflect")
|
| 145 |
-
t = t + n_pad
|
| 146 |
-
x = x.view(b, c, t // self.period, self.period)
|
| 147 |
-
|
| 148 |
-
for l in self.convs:
|
| 149 |
-
x = l(x)
|
| 150 |
-
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
| 151 |
-
fmap.append(x)
|
| 152 |
-
x = self.conv_post(x)
|
| 153 |
-
fmap.append(x)
|
| 154 |
-
x = torch.flatten(x, 1, -1)
|
| 155 |
-
|
| 156 |
-
return x, fmap
|
| 157 |
-
|
| 158 |
-
|
| 159 |
-
class DiscriminatorS(torch.nn.Module):
|
| 160 |
-
def __init__(self, use_spectral_norm=False):
|
| 161 |
-
super(DiscriminatorS, self).__init__()
|
| 162 |
-
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
| 163 |
-
self.convs = nn.ModuleList([
|
| 164 |
-
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
| 165 |
-
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
| 166 |
-
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
| 167 |
-
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
| 168 |
-
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
| 169 |
-
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
| 170 |
-
])
|
| 171 |
-
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
| 172 |
-
|
| 173 |
-
def forward(self, x):
|
| 174 |
-
fmap = []
|
| 175 |
-
|
| 176 |
-
for l in self.convs:
|
| 177 |
-
x = l(x)
|
| 178 |
-
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
| 179 |
-
fmap.append(x)
|
| 180 |
-
x = self.conv_post(x)
|
| 181 |
-
fmap.append(x)
|
| 182 |
-
x = torch.flatten(x, 1, -1)
|
| 183 |
-
|
| 184 |
-
return x, fmap
|
| 185 |
-
|
| 186 |
-
|
| 187 |
-
class F0Decoder(nn.Module):
|
| 188 |
-
def __init__(self,
|
| 189 |
-
out_channels,
|
| 190 |
-
hidden_channels,
|
| 191 |
-
filter_channels,
|
| 192 |
-
n_heads,
|
| 193 |
-
n_layers,
|
| 194 |
-
kernel_size,
|
| 195 |
-
p_dropout,
|
| 196 |
-
spk_channels=0):
|
| 197 |
-
super().__init__()
|
| 198 |
-
self.out_channels = out_channels
|
| 199 |
-
self.hidden_channels = hidden_channels
|
| 200 |
-
self.filter_channels = filter_channels
|
| 201 |
-
self.n_heads = n_heads
|
| 202 |
-
self.n_layers = n_layers
|
| 203 |
-
self.kernel_size = kernel_size
|
| 204 |
-
self.p_dropout = p_dropout
|
| 205 |
-
self.spk_channels = spk_channels
|
| 206 |
-
|
| 207 |
-
self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
|
| 208 |
-
self.decoder = attentions.FFT(
|
| 209 |
-
hidden_channels,
|
| 210 |
-
filter_channels,
|
| 211 |
-
n_heads,
|
| 212 |
-
n_layers,
|
| 213 |
-
kernel_size,
|
| 214 |
-
p_dropout)
|
| 215 |
-
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
| 216 |
-
self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
|
| 217 |
-
self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
|
| 218 |
-
|
| 219 |
-
def forward(self, x, norm_f0, x_mask, spk_emb=None):
|
| 220 |
-
x = torch.detach(x)
|
| 221 |
-
if spk_emb is not None:
|
| 222 |
-
x = x + self.cond(spk_emb)
|
| 223 |
-
x += self.f0_prenet(norm_f0)
|
| 224 |
-
x = self.prenet(x) * x_mask
|
| 225 |
-
x = self.decoder(x * x_mask, x_mask)
|
| 226 |
-
x = self.proj(x) * x_mask
|
| 227 |
-
return x
|
| 228 |
-
|
| 229 |
-
|
| 230 |
-
class SynthesizerTrn(nn.Module):
|
| 231 |
-
"""
|
| 232 |
-
Synthesizer for Training
|
| 233 |
-
"""
|
| 234 |
-
|
| 235 |
-
def __init__(self,
|
| 236 |
-
spec_channels,
|
| 237 |
-
segment_size,
|
| 238 |
-
inter_channels,
|
| 239 |
-
hidden_channels,
|
| 240 |
-
filter_channels,
|
| 241 |
-
n_heads,
|
| 242 |
-
n_layers,
|
| 243 |
-
kernel_size,
|
| 244 |
-
p_dropout,
|
| 245 |
-
resblock,
|
| 246 |
-
resblock_kernel_sizes,
|
| 247 |
-
resblock_dilation_sizes,
|
| 248 |
-
upsample_rates,
|
| 249 |
-
upsample_initial_channel,
|
| 250 |
-
upsample_kernel_sizes,
|
| 251 |
-
gin_channels,
|
| 252 |
-
ssl_dim,
|
| 253 |
-
n_speakers,
|
| 254 |
-
sampling_rate=44100,
|
| 255 |
-
**kwargs):
|
| 256 |
-
super().__init__()
|
| 257 |
-
self.spec_channels = spec_channels
|
| 258 |
-
self.inter_channels = inter_channels
|
| 259 |
-
self.hidden_channels = hidden_channels
|
| 260 |
-
self.filter_channels = filter_channels
|
| 261 |
-
self.n_heads = n_heads
|
| 262 |
-
self.n_layers = n_layers
|
| 263 |
-
self.kernel_size = kernel_size
|
| 264 |
-
self.p_dropout = p_dropout
|
| 265 |
-
self.resblock = resblock
|
| 266 |
-
self.resblock_kernel_sizes = resblock_kernel_sizes
|
| 267 |
-
self.resblock_dilation_sizes = resblock_dilation_sizes
|
| 268 |
-
self.upsample_rates = upsample_rates
|
| 269 |
-
self.upsample_initial_channel = upsample_initial_channel
|
| 270 |
-
self.upsample_kernel_sizes = upsample_kernel_sizes
|
| 271 |
-
self.segment_size = segment_size
|
| 272 |
-
self.gin_channels = gin_channels
|
| 273 |
-
self.ssl_dim = ssl_dim
|
| 274 |
-
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
| 275 |
-
|
| 276 |
-
self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
|
| 277 |
-
|
| 278 |
-
self.enc_p = TextEncoder(
|
| 279 |
-
inter_channels,
|
| 280 |
-
hidden_channels,
|
| 281 |
-
filter_channels=filter_channels,
|
| 282 |
-
n_heads=n_heads,
|
| 283 |
-
n_layers=n_layers,
|
| 284 |
-
kernel_size=kernel_size,
|
| 285 |
-
p_dropout=p_dropout
|
| 286 |
-
)
|
| 287 |
-
hps = {
|
| 288 |
-
"sampling_rate": sampling_rate,
|
| 289 |
-
"inter_channels": inter_channels,
|
| 290 |
-
"resblock": resblock,
|
| 291 |
-
"resblock_kernel_sizes": resblock_kernel_sizes,
|
| 292 |
-
"resblock_dilation_sizes": resblock_dilation_sizes,
|
| 293 |
-
"upsample_rates": upsample_rates,
|
| 294 |
-
"upsample_initial_channel": upsample_initial_channel,
|
| 295 |
-
"upsample_kernel_sizes": upsample_kernel_sizes,
|
| 296 |
-
"gin_channels": gin_channels,
|
| 297 |
-
}
|
| 298 |
-
self.dec = Generator(h=hps)
|
| 299 |
-
self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
| 300 |
-
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
| 301 |
-
self.f0_decoder = F0Decoder(
|
| 302 |
-
1,
|
| 303 |
-
hidden_channels,
|
| 304 |
-
filter_channels,
|
| 305 |
-
n_heads,
|
| 306 |
-
n_layers,
|
| 307 |
-
kernel_size,
|
| 308 |
-
p_dropout,
|
| 309 |
-
spk_channels=gin_channels
|
| 310 |
-
)
|
| 311 |
-
self.emb_uv = nn.Embedding(2, hidden_channels)
|
| 312 |
-
self.predict_f0 = False
|
| 313 |
-
|
| 314 |
-
def forward(self, c, f0, mel2ph, uv, noise=None, g=None):
|
| 315 |
-
|
| 316 |
-
decoder_inp = F.pad(c, [0, 0, 1, 0])
|
| 317 |
-
mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
|
| 318 |
-
c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H]
|
| 319 |
-
|
| 320 |
-
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
| 321 |
-
g = g.unsqueeze(0)
|
| 322 |
-
g = self.emb_g(g).transpose(1, 2)
|
| 323 |
-
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
|
| 324 |
-
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
|
| 325 |
-
|
| 326 |
-
if self.predict_f0:
|
| 327 |
-
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
|
| 328 |
-
norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
|
| 329 |
-
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
|
| 330 |
-
f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
|
| 331 |
-
|
| 332 |
-
z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise)
|
| 333 |
-
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
| 334 |
-
o = self.dec(z * c_mask, g=g, f0=f0)
|
| 335 |
-
return o
|
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|
spaces/AlekseyKorshuk/rugpt3/app.py
DELETED
|
@@ -1,30 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from gradio import mix
|
| 3 |
-
|
| 4 |
-
title = "RUGPT3"
|
| 5 |
-
description = "Gradio Demo for RUGPT3. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
examples = [
|
| 9 |
-
['Меня зовут Томас и мой основной']
|
| 10 |
-
]
|
| 11 |
-
|
| 12 |
-
io = gr.Interface.load("models/sberbank-ai/rugpt3large_based_on_gpt2")
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
def inference(text):
|
| 17 |
-
|
| 18 |
-
return io(text)
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
gr.Interface(
|
| 23 |
-
inference,
|
| 24 |
-
[gr.inputs.Textbox(label="Input")],
|
| 25 |
-
gr.outputs.Textbox(label="Output"),
|
| 26 |
-
examples=examples,
|
| 27 |
-
# article=article,
|
| 28 |
-
title=title,
|
| 29 |
-
description=description).launch(enable_queue=True, cache_examples=True)
|
| 30 |
-
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@echo off
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| 2 |
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| 3 |
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IF NOT EXIST venv (
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python -m venv venv
|
| 5 |
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) ELSE (
|
| 6 |
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echo venv folder already exists, skipping creation...
|
| 7 |
-
)
|
| 8 |
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call .\venv\Scripts\activate.bat
|
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|
| 10 |
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set PYTHON="venv\Scripts\Python.exe"
|
| 11 |
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echo venv %PYTHON%
|
| 12 |
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|
| 13 |
-
%PYTHON% Launcher.py
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| 14 |
-
|
| 15 |
-
echo.
|
| 16 |
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echo Launch unsuccessful. Exiting.
|
| 17 |
-
pause
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spaces/Amon1/ChatGPTForAcadamic/crazy_functions/__init__.py
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md
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|
|
| 1 |
-
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
|
| 3 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
| 4 |
-
the License. You may obtain a copy of the License at
|
| 5 |
-
|
| 6 |
-
http://www.apache.org/licenses/LICENSE-2.0
|
| 7 |
-
|
| 8 |
-
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
| 9 |
-
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
| 10 |
-
specific language governing permissions and limitations under the License.
|
| 11 |
-
-->
|
| 12 |
-
|
| 13 |
-
# Text2Video-Zero
|
| 14 |
-
|
| 15 |
-
[Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://huggingface.co/papers/2303.13439) is by
|
| 16 |
-
Levon Khachatryan,
|
| 17 |
-
Andranik Movsisyan,
|
| 18 |
-
Vahram Tadevosyan,
|
| 19 |
-
Roberto Henschel,
|
| 20 |
-
[Zhangyang Wang](https://www.ece.utexas.edu/people/faculty/atlas-wang), Shant Navasardyan, [Humphrey Shi](https://www.humphreyshi.com).
|
| 21 |
-
|
| 22 |
-
Text2Video-Zero enables zero-shot video generation using either:
|
| 23 |
-
1. A textual prompt
|
| 24 |
-
2. A prompt combined with guidance from poses or edges
|
| 25 |
-
3. Video Instruct-Pix2Pix (instruction-guided video editing)
|
| 26 |
-
|
| 27 |
-
Results are temporally consistent and closely follow the guidance and textual prompts.
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
The abstract from the paper is:
|
| 32 |
-
|
| 33 |
-
*Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain.
|
| 34 |
-
Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object.
|
| 35 |
-
Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing.
|
| 36 |
-
As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.*
|
| 37 |
-
|
| 38 |
-
You can find additional information about Text-to-Video Zero on the [project page](https://text2video-zero.github.io/), [paper](https://arxiv.org/abs/2303.13439), and [original codebase](https://github.com/Picsart-AI-Research/Text2Video-Zero).
|
| 39 |
-
|
| 40 |
-
## Usage example
|
| 41 |
-
|
| 42 |
-
### Text-To-Video
|
| 43 |
-
|
| 44 |
-
To generate a video from prompt, run the following python command
|
| 45 |
-
```python
|
| 46 |
-
import torch
|
| 47 |
-
import imageio
|
| 48 |
-
from diffusers import TextToVideoZeroPipeline
|
| 49 |
-
|
| 50 |
-
model_id = "runwayml/stable-diffusion-v1-5"
|
| 51 |
-
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
|
| 52 |
-
|
| 53 |
-
prompt = "A panda is playing guitar on times square"
|
| 54 |
-
result = pipe(prompt=prompt).images
|
| 55 |
-
result = [(r * 255).astype("uint8") for r in result]
|
| 56 |
-
imageio.mimsave("video.mp4", result, fps=4)
|
| 57 |
-
```
|
| 58 |
-
You can change these parameters in the pipeline call:
|
| 59 |
-
* Motion field strength (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1):
|
| 60 |
-
* `motion_field_strength_x` and `motion_field_strength_y`. Default: `motion_field_strength_x=12`, `motion_field_strength_y=12`
|
| 61 |
-
* `T` and `T'` (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1)
|
| 62 |
-
* `t0` and `t1` in the range `{0, ..., num_inference_steps}`. Default: `t0=45`, `t1=48`
|
| 63 |
-
* Video length:
|
| 64 |
-
* `video_length`, the number of frames video_length to be generated. Default: `video_length=8`
|
| 65 |
-
|
| 66 |
-
We an also generate longer videos by doing the processing in a chunk-by-chunk manner:
|
| 67 |
-
```python
|
| 68 |
-
import torch
|
| 69 |
-
import imageio
|
| 70 |
-
from diffusers import TextToVideoZeroPipeline
|
| 71 |
-
import numpy as np
|
| 72 |
-
|
| 73 |
-
model_id = "runwayml/stable-diffusion-v1-5"
|
| 74 |
-
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
|
| 75 |
-
seed = 0
|
| 76 |
-
video_length = 8
|
| 77 |
-
chunk_size = 4
|
| 78 |
-
prompt = "A panda is playing guitar on times square"
|
| 79 |
-
|
| 80 |
-
# Generate the video chunk-by-chunk
|
| 81 |
-
result = []
|
| 82 |
-
chunk_ids = np.arange(0, video_length, chunk_size - 1)
|
| 83 |
-
generator = torch.Generator(device="cuda")
|
| 84 |
-
for i in range(len(chunk_ids)):
|
| 85 |
-
print(f"Processing chunk {i + 1} / {len(chunk_ids)}")
|
| 86 |
-
ch_start = chunk_ids[i]
|
| 87 |
-
ch_end = video_length if i == len(chunk_ids) - 1 else chunk_ids[i + 1]
|
| 88 |
-
# Attach the first frame for Cross Frame Attention
|
| 89 |
-
frame_ids = [0] + list(range(ch_start, ch_end))
|
| 90 |
-
# Fix the seed for the temporal consistency
|
| 91 |
-
generator.manual_seed(seed)
|
| 92 |
-
output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids)
|
| 93 |
-
result.append(output.images[1:])
|
| 94 |
-
|
| 95 |
-
# Concatenate chunks and save
|
| 96 |
-
result = np.concatenate(result)
|
| 97 |
-
result = [(r * 255).astype("uint8") for r in result]
|
| 98 |
-
imageio.mimsave("video.mp4", result, fps=4)
|
| 99 |
-
```
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
### Text-To-Video with Pose Control
|
| 103 |
-
To generate a video from prompt with additional pose control
|
| 104 |
-
|
| 105 |
-
1. Download a demo video
|
| 106 |
-
|
| 107 |
-
```python
|
| 108 |
-
from huggingface_hub import hf_hub_download
|
| 109 |
-
|
| 110 |
-
filename = "__assets__/poses_skeleton_gifs/dance1_corr.mp4"
|
| 111 |
-
repo_id = "PAIR/Text2Video-Zero"
|
| 112 |
-
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
|
| 113 |
-
```
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
2. Read video containing extracted pose images
|
| 117 |
-
```python
|
| 118 |
-
from PIL import Image
|
| 119 |
-
import imageio
|
| 120 |
-
|
| 121 |
-
reader = imageio.get_reader(video_path, "ffmpeg")
|
| 122 |
-
frame_count = 8
|
| 123 |
-
pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
|
| 124 |
-
```
|
| 125 |
-
To extract pose from actual video, read [ControlNet documentation](./stable_diffusion/controlnet).
|
| 126 |
-
|
| 127 |
-
3. Run `StableDiffusionControlNetPipeline` with our custom attention processor
|
| 128 |
-
|
| 129 |
-
```python
|
| 130 |
-
import torch
|
| 131 |
-
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
|
| 132 |
-
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
|
| 133 |
-
|
| 134 |
-
model_id = "runwayml/stable-diffusion-v1-5"
|
| 135 |
-
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
|
| 136 |
-
pipe = StableDiffusionControlNetPipeline.from_pretrained(
|
| 137 |
-
model_id, controlnet=controlnet, torch_dtype=torch.float16
|
| 138 |
-
).to("cuda")
|
| 139 |
-
|
| 140 |
-
# Set the attention processor
|
| 141 |
-
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
|
| 142 |
-
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
|
| 143 |
-
|
| 144 |
-
# fix latents for all frames
|
| 145 |
-
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)
|
| 146 |
-
|
| 147 |
-
prompt = "Darth Vader dancing in a desert"
|
| 148 |
-
result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
|
| 149 |
-
imageio.mimsave("video.mp4", result, fps=4)
|
| 150 |
-
```
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
### Text-To-Video with Edge Control
|
| 154 |
-
|
| 155 |
-
To generate a video from prompt with additional pose control,
|
| 156 |
-
follow the steps described above for pose-guided generation using [Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny).
|
| 157 |
-
|
| 158 |
-
|
| 159 |
-
### Video Instruct-Pix2Pix
|
| 160 |
-
|
| 161 |
-
To perform text-guided video editing (with [InstructPix2Pix](./stable_diffusion/pix2pix)):
|
| 162 |
-
|
| 163 |
-
1. Download a demo video
|
| 164 |
-
|
| 165 |
-
```python
|
| 166 |
-
from huggingface_hub import hf_hub_download
|
| 167 |
-
|
| 168 |
-
filename = "__assets__/pix2pix video/camel.mp4"
|
| 169 |
-
repo_id = "PAIR/Text2Video-Zero"
|
| 170 |
-
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
|
| 171 |
-
```
|
| 172 |
-
|
| 173 |
-
2. Read video from path
|
| 174 |
-
```python
|
| 175 |
-
from PIL import Image
|
| 176 |
-
import imageio
|
| 177 |
-
|
| 178 |
-
reader = imageio.get_reader(video_path, "ffmpeg")
|
| 179 |
-
frame_count = 8
|
| 180 |
-
video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
|
| 181 |
-
```
|
| 182 |
-
|
| 183 |
-
3. Run `StableDiffusionInstructPix2PixPipeline` with our custom attention processor
|
| 184 |
-
```python
|
| 185 |
-
import torch
|
| 186 |
-
from diffusers import StableDiffusionInstructPix2PixPipeline
|
| 187 |
-
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
|
| 188 |
-
|
| 189 |
-
model_id = "timbrooks/instruct-pix2pix"
|
| 190 |
-
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
|
| 191 |
-
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3))
|
| 192 |
-
|
| 193 |
-
prompt = "make it Van Gogh Starry Night style"
|
| 194 |
-
result = pipe(prompt=[prompt] * len(video), image=video).images
|
| 195 |
-
imageio.mimsave("edited_video.mp4", result, fps=4)
|
| 196 |
-
```
|
| 197 |
-
|
| 198 |
-
|
| 199 |
-
### DreamBooth specialization
|
| 200 |
-
|
| 201 |
-
Methods **Text-To-Video**, **Text-To-Video with Pose Control** and **Text-To-Video with Edge Control**
|
| 202 |
-
can run with custom [DreamBooth](../training/dreambooth) models, as shown below for
|
| 203 |
-
[Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny) and
|
| 204 |
-
[Avatar style DreamBooth](https://huggingface.co/PAIR/text2video-zero-controlnet-canny-avatar) model
|
| 205 |
-
|
| 206 |
-
1. Download a demo video
|
| 207 |
-
|
| 208 |
-
```python
|
| 209 |
-
from huggingface_hub import hf_hub_download
|
| 210 |
-
|
| 211 |
-
filename = "__assets__/canny_videos_mp4/girl_turning.mp4"
|
| 212 |
-
repo_id = "PAIR/Text2Video-Zero"
|
| 213 |
-
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
|
| 214 |
-
```
|
| 215 |
-
|
| 216 |
-
2. Read video from path
|
| 217 |
-
```python
|
| 218 |
-
from PIL import Image
|
| 219 |
-
import imageio
|
| 220 |
-
|
| 221 |
-
reader = imageio.get_reader(video_path, "ffmpeg")
|
| 222 |
-
frame_count = 8
|
| 223 |
-
canny_edges = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
|
| 224 |
-
```
|
| 225 |
-
|
| 226 |
-
3. Run `StableDiffusionControlNetPipeline` with custom trained DreamBooth model
|
| 227 |
-
```python
|
| 228 |
-
import torch
|
| 229 |
-
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
|
| 230 |
-
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
|
| 231 |
-
|
| 232 |
-
# set model id to custom model
|
| 233 |
-
model_id = "PAIR/text2video-zero-controlnet-canny-avatar"
|
| 234 |
-
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
|
| 235 |
-
pipe = StableDiffusionControlNetPipeline.from_pretrained(
|
| 236 |
-
model_id, controlnet=controlnet, torch_dtype=torch.float16
|
| 237 |
-
).to("cuda")
|
| 238 |
-
|
| 239 |
-
# Set the attention processor
|
| 240 |
-
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
|
| 241 |
-
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
|
| 242 |
-
|
| 243 |
-
# fix latents for all frames
|
| 244 |
-
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(canny_edges), 1, 1, 1)
|
| 245 |
-
|
| 246 |
-
prompt = "oil painting of a beautiful girl avatar style"
|
| 247 |
-
result = pipe(prompt=[prompt] * len(canny_edges), image=canny_edges, latents=latents).images
|
| 248 |
-
imageio.mimsave("video.mp4", result, fps=4)
|
| 249 |
-
```
|
| 250 |
-
|
| 251 |
-
You can filter out some available DreamBooth-trained models with [this link](https://huggingface.co/models?search=dreambooth).
|
| 252 |
-
|
| 253 |
-
|
| 254 |
-
## TextToVideoZeroPipeline
|
| 255 |
-
[[autodoc]] TextToVideoZeroPipeline
|
| 256 |
-
- all
|
| 257 |
-
- __call__
|
| 258 |
-
|
| 259 |
-
## TextToVideoPipelineOutput
|
| 260 |
-
[[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput
|
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/__init__.py
DELETED
|
@@ -1,36 +0,0 @@
|
|
| 1 |
-
from dataclasses import dataclass
|
| 2 |
-
from enum import Enum
|
| 3 |
-
from typing import List, Optional, Union
|
| 4 |
-
|
| 5 |
-
import numpy as np
|
| 6 |
-
import PIL
|
| 7 |
-
from PIL import Image
|
| 8 |
-
|
| 9 |
-
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
@dataclass
|
| 13 |
-
class SemanticStableDiffusionPipelineOutput(BaseOutput):
|
| 14 |
-
"""
|
| 15 |
-
Output class for Stable Diffusion pipelines.
|
| 16 |
-
|
| 17 |
-
Args:
|
| 18 |
-
images (`List[PIL.Image.Image]` or `np.ndarray`)
|
| 19 |
-
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
|
| 20 |
-
num_channels)`.
|
| 21 |
-
nsfw_content_detected (`List[bool]`)
|
| 22 |
-
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
|
| 23 |
-
`None` if safety checking could not be performed.
|
| 24 |
-
"""
|
| 25 |
-
|
| 26 |
-
images: Union[List[PIL.Image.Image], np.ndarray]
|
| 27 |
-
nsfw_content_detected: Optional[List[bool]]
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
try:
|
| 31 |
-
if not (is_transformers_available() and is_torch_available()):
|
| 32 |
-
raise OptionalDependencyNotAvailable()
|
| 33 |
-
except OptionalDependencyNotAvailable:
|
| 34 |
-
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
|
| 35 |
-
else:
|
| 36 |
-
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
|
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
DELETED
|
@@ -1,737 +0,0 @@
|
|
| 1 |
-
# Copyright 2023 TSAIL Team and The HuggingFace Team. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
|
| 15 |
-
# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver
|
| 16 |
-
|
| 17 |
-
import math
|
| 18 |
-
from typing import List, Optional, Tuple, Union
|
| 19 |
-
|
| 20 |
-
import numpy as np
|
| 21 |
-
import torch
|
| 22 |
-
|
| 23 |
-
from ..configuration_utils import ConfigMixin, register_to_config
|
| 24 |
-
from ..utils import logging
|
| 25 |
-
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
|
| 32 |
-
def betas_for_alpha_bar(
|
| 33 |
-
num_diffusion_timesteps,
|
| 34 |
-
max_beta=0.999,
|
| 35 |
-
alpha_transform_type="cosine",
|
| 36 |
-
):
|
| 37 |
-
"""
|
| 38 |
-
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
|
| 39 |
-
(1-beta) over time from t = [0,1].
|
| 40 |
-
|
| 41 |
-
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
|
| 42 |
-
to that part of the diffusion process.
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
Args:
|
| 46 |
-
num_diffusion_timesteps (`int`): the number of betas to produce.
|
| 47 |
-
max_beta (`float`): the maximum beta to use; use values lower than 1 to
|
| 48 |
-
prevent singularities.
|
| 49 |
-
alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
|
| 50 |
-
Choose from `cosine` or `exp`
|
| 51 |
-
|
| 52 |
-
Returns:
|
| 53 |
-
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
|
| 54 |
-
"""
|
| 55 |
-
if alpha_transform_type == "cosine":
|
| 56 |
-
|
| 57 |
-
def alpha_bar_fn(t):
|
| 58 |
-
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
|
| 59 |
-
|
| 60 |
-
elif alpha_transform_type == "exp":
|
| 61 |
-
|
| 62 |
-
def alpha_bar_fn(t):
|
| 63 |
-
return math.exp(t * -12.0)
|
| 64 |
-
|
| 65 |
-
else:
|
| 66 |
-
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
|
| 67 |
-
|
| 68 |
-
betas = []
|
| 69 |
-
for i in range(num_diffusion_timesteps):
|
| 70 |
-
t1 = i / num_diffusion_timesteps
|
| 71 |
-
t2 = (i + 1) / num_diffusion_timesteps
|
| 72 |
-
betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
|
| 73 |
-
return torch.tensor(betas, dtype=torch.float32)
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
|
| 77 |
-
"""
|
| 78 |
-
DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with
|
| 79 |
-
the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality
|
| 80 |
-
samples, and it can generate quite good samples even in only 10 steps.
|
| 81 |
-
|
| 82 |
-
For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095
|
| 83 |
-
|
| 84 |
-
Currently, we support the singlestep DPM-Solver for both noise prediction models and data prediction models. We
|
| 85 |
-
recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling.
|
| 86 |
-
|
| 87 |
-
We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space
|
| 88 |
-
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
|
| 89 |
-
thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as
|
| 90 |
-
stable-diffusion).
|
| 91 |
-
|
| 92 |
-
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
|
| 93 |
-
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
|
| 94 |
-
[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
|
| 95 |
-
[`~SchedulerMixin.from_pretrained`] functions.
|
| 96 |
-
|
| 97 |
-
Args:
|
| 98 |
-
num_train_timesteps (`int`): number of diffusion steps used to train the model.
|
| 99 |
-
beta_start (`float`): the starting `beta` value of inference.
|
| 100 |
-
beta_end (`float`): the final `beta` value.
|
| 101 |
-
beta_schedule (`str`):
|
| 102 |
-
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
|
| 103 |
-
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
|
| 104 |
-
trained_betas (`np.ndarray`, optional):
|
| 105 |
-
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
|
| 106 |
-
solver_order (`int`, default `2`):
|
| 107 |
-
the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided
|
| 108 |
-
sampling, and `solver_order=3` for unconditional sampling.
|
| 109 |
-
prediction_type (`str`, default `epsilon`):
|
| 110 |
-
indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`,
|
| 111 |
-
or `v-prediction`.
|
| 112 |
-
thresholding (`bool`, default `False`):
|
| 113 |
-
whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
|
| 114 |
-
For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to
|
| 115 |
-
use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion
|
| 116 |
-
models (such as stable-diffusion).
|
| 117 |
-
dynamic_thresholding_ratio (`float`, default `0.995`):
|
| 118 |
-
the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
|
| 119 |
-
(https://arxiv.org/abs/2205.11487).
|
| 120 |
-
sample_max_value (`float`, default `1.0`):
|
| 121 |
-
the threshold value for dynamic thresholding. Valid only when `thresholding=True` and
|
| 122 |
-
`algorithm_type="dpmsolver++`.
|
| 123 |
-
algorithm_type (`str`, default `dpmsolver++`):
|
| 124 |
-
the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++`. The `dpmsolver` type implements the
|
| 125 |
-
algorithms in https://arxiv.org/abs/2206.00927, and the `dpmsolver++` type implements the algorithms in
|
| 126 |
-
https://arxiv.org/abs/2211.01095. We recommend to use `dpmsolver++` with `solver_order=2` for guided
|
| 127 |
-
sampling (e.g. stable-diffusion).
|
| 128 |
-
solver_type (`str`, default `midpoint`):
|
| 129 |
-
the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects
|
| 130 |
-
the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are
|
| 131 |
-
slightly better, so we recommend to use the `midpoint` type.
|
| 132 |
-
lower_order_final (`bool`, default `True`):
|
| 133 |
-
whether to use lower-order solvers in the final steps. For singlestep schedulers, we recommend to enable
|
| 134 |
-
this to use up all the function evaluations.
|
| 135 |
-
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
|
| 136 |
-
This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the
|
| 137 |
-
noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence
|
| 138 |
-
of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf.
|
| 139 |
-
lambda_min_clipped (`float`, default `-inf`):
|
| 140 |
-
the clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for
|
| 141 |
-
cosine (squaredcos_cap_v2) noise schedule.
|
| 142 |
-
variance_type (`str`, *optional*):
|
| 143 |
-
Set to "learned" or "learned_range" for diffusion models that predict variance. For example, OpenAI's
|
| 144 |
-
guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the
|
| 145 |
-
Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on
|
| 146 |
-
diffusion ODEs. whether the model's output contains the predicted Gaussian variance. For example, OpenAI's
|
| 147 |
-
guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the
|
| 148 |
-
Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on
|
| 149 |
-
diffusion ODEs.
|
| 150 |
-
|
| 151 |
-
"""
|
| 152 |
-
|
| 153 |
-
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
|
| 154 |
-
order = 1
|
| 155 |
-
|
| 156 |
-
@register_to_config
|
| 157 |
-
def __init__(
|
| 158 |
-
self,
|
| 159 |
-
num_train_timesteps: int = 1000,
|
| 160 |
-
beta_start: float = 0.0001,
|
| 161 |
-
beta_end: float = 0.02,
|
| 162 |
-
beta_schedule: str = "linear",
|
| 163 |
-
trained_betas: Optional[np.ndarray] = None,
|
| 164 |
-
solver_order: int = 2,
|
| 165 |
-
prediction_type: str = "epsilon",
|
| 166 |
-
thresholding: bool = False,
|
| 167 |
-
dynamic_thresholding_ratio: float = 0.995,
|
| 168 |
-
sample_max_value: float = 1.0,
|
| 169 |
-
algorithm_type: str = "dpmsolver++",
|
| 170 |
-
solver_type: str = "midpoint",
|
| 171 |
-
lower_order_final: bool = True,
|
| 172 |
-
use_karras_sigmas: Optional[bool] = False,
|
| 173 |
-
lambda_min_clipped: float = -float("inf"),
|
| 174 |
-
variance_type: Optional[str] = None,
|
| 175 |
-
):
|
| 176 |
-
if trained_betas is not None:
|
| 177 |
-
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
|
| 178 |
-
elif beta_schedule == "linear":
|
| 179 |
-
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
|
| 180 |
-
elif beta_schedule == "scaled_linear":
|
| 181 |
-
# this schedule is very specific to the latent diffusion model.
|
| 182 |
-
self.betas = (
|
| 183 |
-
torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
|
| 184 |
-
)
|
| 185 |
-
elif beta_schedule == "squaredcos_cap_v2":
|
| 186 |
-
# Glide cosine schedule
|
| 187 |
-
self.betas = betas_for_alpha_bar(num_train_timesteps)
|
| 188 |
-
else:
|
| 189 |
-
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
|
| 190 |
-
|
| 191 |
-
self.alphas = 1.0 - self.betas
|
| 192 |
-
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
|
| 193 |
-
# Currently we only support VP-type noise schedule
|
| 194 |
-
self.alpha_t = torch.sqrt(self.alphas_cumprod)
|
| 195 |
-
self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
|
| 196 |
-
self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
|
| 197 |
-
|
| 198 |
-
# standard deviation of the initial noise distribution
|
| 199 |
-
self.init_noise_sigma = 1.0
|
| 200 |
-
|
| 201 |
-
# settings for DPM-Solver
|
| 202 |
-
if algorithm_type not in ["dpmsolver", "dpmsolver++"]:
|
| 203 |
-
if algorithm_type == "deis":
|
| 204 |
-
self.register_to_config(algorithm_type="dpmsolver++")
|
| 205 |
-
else:
|
| 206 |
-
raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
|
| 207 |
-
if solver_type not in ["midpoint", "heun"]:
|
| 208 |
-
if solver_type in ["logrho", "bh1", "bh2"]:
|
| 209 |
-
self.register_to_config(solver_type="midpoint")
|
| 210 |
-
else:
|
| 211 |
-
raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
|
| 212 |
-
|
| 213 |
-
# setable values
|
| 214 |
-
self.num_inference_steps = None
|
| 215 |
-
timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
|
| 216 |
-
self.timesteps = torch.from_numpy(timesteps)
|
| 217 |
-
self.model_outputs = [None] * solver_order
|
| 218 |
-
self.sample = None
|
| 219 |
-
self.order_list = self.get_order_list(num_train_timesteps)
|
| 220 |
-
|
| 221 |
-
def get_order_list(self, num_inference_steps: int) -> List[int]:
|
| 222 |
-
"""
|
| 223 |
-
Computes the solver order at each time step.
|
| 224 |
-
|
| 225 |
-
Args:
|
| 226 |
-
num_inference_steps (`int`):
|
| 227 |
-
the number of diffusion steps used when generating samples with a pre-trained model.
|
| 228 |
-
"""
|
| 229 |
-
steps = num_inference_steps
|
| 230 |
-
order = self.config.solver_order
|
| 231 |
-
if self.config.lower_order_final:
|
| 232 |
-
if order == 3:
|
| 233 |
-
if steps % 3 == 0:
|
| 234 |
-
orders = [1, 2, 3] * (steps // 3 - 1) + [1, 2] + [1]
|
| 235 |
-
elif steps % 3 == 1:
|
| 236 |
-
orders = [1, 2, 3] * (steps // 3) + [1]
|
| 237 |
-
else:
|
| 238 |
-
orders = [1, 2, 3] * (steps // 3) + [1, 2]
|
| 239 |
-
elif order == 2:
|
| 240 |
-
if steps % 2 == 0:
|
| 241 |
-
orders = [1, 2] * (steps // 2)
|
| 242 |
-
else:
|
| 243 |
-
orders = [1, 2] * (steps // 2) + [1]
|
| 244 |
-
elif order == 1:
|
| 245 |
-
orders = [1] * steps
|
| 246 |
-
else:
|
| 247 |
-
if order == 3:
|
| 248 |
-
orders = [1, 2, 3] * (steps // 3)
|
| 249 |
-
elif order == 2:
|
| 250 |
-
orders = [1, 2] * (steps // 2)
|
| 251 |
-
elif order == 1:
|
| 252 |
-
orders = [1] * steps
|
| 253 |
-
return orders
|
| 254 |
-
|
| 255 |
-
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
|
| 256 |
-
"""
|
| 257 |
-
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
|
| 258 |
-
|
| 259 |
-
Args:
|
| 260 |
-
num_inference_steps (`int`):
|
| 261 |
-
the number of diffusion steps used when generating samples with a pre-trained model.
|
| 262 |
-
device (`str` or `torch.device`, optional):
|
| 263 |
-
the device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
|
| 264 |
-
"""
|
| 265 |
-
self.num_inference_steps = num_inference_steps
|
| 266 |
-
# Clipping the minimum of all lambda(t) for numerical stability.
|
| 267 |
-
# This is critical for cosine (squaredcos_cap_v2) noise schedule.
|
| 268 |
-
clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)
|
| 269 |
-
timesteps = (
|
| 270 |
-
np.linspace(0, self.config.num_train_timesteps - 1 - clipped_idx, num_inference_steps + 1)
|
| 271 |
-
.round()[::-1][:-1]
|
| 272 |
-
.copy()
|
| 273 |
-
.astype(np.int64)
|
| 274 |
-
)
|
| 275 |
-
|
| 276 |
-
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
|
| 277 |
-
if self.config.use_karras_sigmas:
|
| 278 |
-
log_sigmas = np.log(sigmas)
|
| 279 |
-
sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
|
| 280 |
-
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
|
| 281 |
-
timesteps = np.flip(timesteps).copy().astype(np.int64)
|
| 282 |
-
|
| 283 |
-
self.sigmas = torch.from_numpy(sigmas)
|
| 284 |
-
|
| 285 |
-
self.timesteps = torch.from_numpy(timesteps).to(device)
|
| 286 |
-
self.model_outputs = [None] * self.config.solver_order
|
| 287 |
-
self.sample = None
|
| 288 |
-
|
| 289 |
-
if not self.config.lower_order_final and num_inference_steps % self.config.solver_order != 0:
|
| 290 |
-
logger.warn(
|
| 291 |
-
"Changing scheduler {self.config} to have `lower_order_final` set to True to handle uneven amount of inference steps. Please make sure to always use an even number of `num_inference steps when using `lower_order_final=True`."
|
| 292 |
-
)
|
| 293 |
-
self.register_to_config(lower_order_final=True)
|
| 294 |
-
|
| 295 |
-
self.order_list = self.get_order_list(num_inference_steps)
|
| 296 |
-
|
| 297 |
-
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
|
| 298 |
-
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
|
| 299 |
-
"""
|
| 300 |
-
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
|
| 301 |
-
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
|
| 302 |
-
s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
|
| 303 |
-
pixels from saturation at each step. We find that dynamic thresholding results in significantly better
|
| 304 |
-
photorealism as well as better image-text alignment, especially when using very large guidance weights."
|
| 305 |
-
|
| 306 |
-
https://arxiv.org/abs/2205.11487
|
| 307 |
-
"""
|
| 308 |
-
dtype = sample.dtype
|
| 309 |
-
batch_size, channels, height, width = sample.shape
|
| 310 |
-
|
| 311 |
-
if dtype not in (torch.float32, torch.float64):
|
| 312 |
-
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half
|
| 313 |
-
|
| 314 |
-
# Flatten sample for doing quantile calculation along each image
|
| 315 |
-
sample = sample.reshape(batch_size, channels * height * width)
|
| 316 |
-
|
| 317 |
-
abs_sample = sample.abs() # "a certain percentile absolute pixel value"
|
| 318 |
-
|
| 319 |
-
s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
|
| 320 |
-
s = torch.clamp(
|
| 321 |
-
s, min=1, max=self.config.sample_max_value
|
| 322 |
-
) # When clamped to min=1, equivalent to standard clipping to [-1, 1]
|
| 323 |
-
|
| 324 |
-
s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0
|
| 325 |
-
sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s"
|
| 326 |
-
|
| 327 |
-
sample = sample.reshape(batch_size, channels, height, width)
|
| 328 |
-
sample = sample.to(dtype)
|
| 329 |
-
|
| 330 |
-
return sample
|
| 331 |
-
|
| 332 |
-
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
|
| 333 |
-
def _sigma_to_t(self, sigma, log_sigmas):
|
| 334 |
-
# get log sigma
|
| 335 |
-
log_sigma = np.log(sigma)
|
| 336 |
-
|
| 337 |
-
# get distribution
|
| 338 |
-
dists = log_sigma - log_sigmas[:, np.newaxis]
|
| 339 |
-
|
| 340 |
-
# get sigmas range
|
| 341 |
-
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
|
| 342 |
-
high_idx = low_idx + 1
|
| 343 |
-
|
| 344 |
-
low = log_sigmas[low_idx]
|
| 345 |
-
high = log_sigmas[high_idx]
|
| 346 |
-
|
| 347 |
-
# interpolate sigmas
|
| 348 |
-
w = (low - log_sigma) / (low - high)
|
| 349 |
-
w = np.clip(w, 0, 1)
|
| 350 |
-
|
| 351 |
-
# transform interpolation to time range
|
| 352 |
-
t = (1 - w) * low_idx + w * high_idx
|
| 353 |
-
t = t.reshape(sigma.shape)
|
| 354 |
-
return t
|
| 355 |
-
|
| 356 |
-
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
|
| 357 |
-
def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
|
| 358 |
-
"""Constructs the noise schedule of Karras et al. (2022)."""
|
| 359 |
-
|
| 360 |
-
sigma_min: float = in_sigmas[-1].item()
|
| 361 |
-
sigma_max: float = in_sigmas[0].item()
|
| 362 |
-
|
| 363 |
-
rho = 7.0 # 7.0 is the value used in the paper
|
| 364 |
-
ramp = np.linspace(0, 1, num_inference_steps)
|
| 365 |
-
min_inv_rho = sigma_min ** (1 / rho)
|
| 366 |
-
max_inv_rho = sigma_max ** (1 / rho)
|
| 367 |
-
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
|
| 368 |
-
return sigmas
|
| 369 |
-
|
| 370 |
-
def convert_model_output(
|
| 371 |
-
self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor
|
| 372 |
-
) -> torch.FloatTensor:
|
| 373 |
-
"""
|
| 374 |
-
Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs.
|
| 375 |
-
|
| 376 |
-
DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to
|
| 377 |
-
discretize an integral of the data prediction model. So we need to first convert the model output to the
|
| 378 |
-
corresponding type to match the algorithm.
|
| 379 |
-
|
| 380 |
-
Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or
|
| 381 |
-
DPM-Solver++ for both noise prediction model and data prediction model.
|
| 382 |
-
|
| 383 |
-
Args:
|
| 384 |
-
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
|
| 385 |
-
timestep (`int`): current discrete timestep in the diffusion chain.
|
| 386 |
-
sample (`torch.FloatTensor`):
|
| 387 |
-
current instance of sample being created by diffusion process.
|
| 388 |
-
|
| 389 |
-
Returns:
|
| 390 |
-
`torch.FloatTensor`: the converted model output.
|
| 391 |
-
"""
|
| 392 |
-
# DPM-Solver++ needs to solve an integral of the data prediction model.
|
| 393 |
-
if self.config.algorithm_type == "dpmsolver++":
|
| 394 |
-
if self.config.prediction_type == "epsilon":
|
| 395 |
-
# DPM-Solver and DPM-Solver++ only need the "mean" output.
|
| 396 |
-
if self.config.variance_type in ["learned_range"]:
|
| 397 |
-
model_output = model_output[:, :3]
|
| 398 |
-
alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
|
| 399 |
-
x0_pred = (sample - sigma_t * model_output) / alpha_t
|
| 400 |
-
elif self.config.prediction_type == "sample":
|
| 401 |
-
x0_pred = model_output
|
| 402 |
-
elif self.config.prediction_type == "v_prediction":
|
| 403 |
-
alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
|
| 404 |
-
x0_pred = alpha_t * sample - sigma_t * model_output
|
| 405 |
-
else:
|
| 406 |
-
raise ValueError(
|
| 407 |
-
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
|
| 408 |
-
" `v_prediction` for the DPMSolverSinglestepScheduler."
|
| 409 |
-
)
|
| 410 |
-
|
| 411 |
-
if self.config.thresholding:
|
| 412 |
-
x0_pred = self._threshold_sample(x0_pred)
|
| 413 |
-
|
| 414 |
-
return x0_pred
|
| 415 |
-
# DPM-Solver needs to solve an integral of the noise prediction model.
|
| 416 |
-
elif self.config.algorithm_type == "dpmsolver":
|
| 417 |
-
if self.config.prediction_type == "epsilon":
|
| 418 |
-
# DPM-Solver and DPM-Solver++ only need the "mean" output.
|
| 419 |
-
if self.config.variance_type in ["learned_range"]:
|
| 420 |
-
model_output = model_output[:, :3]
|
| 421 |
-
return model_output
|
| 422 |
-
elif self.config.prediction_type == "sample":
|
| 423 |
-
alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
|
| 424 |
-
epsilon = (sample - alpha_t * model_output) / sigma_t
|
| 425 |
-
return epsilon
|
| 426 |
-
elif self.config.prediction_type == "v_prediction":
|
| 427 |
-
alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
|
| 428 |
-
epsilon = alpha_t * model_output + sigma_t * sample
|
| 429 |
-
return epsilon
|
| 430 |
-
else:
|
| 431 |
-
raise ValueError(
|
| 432 |
-
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
|
| 433 |
-
" `v_prediction` for the DPMSolverSinglestepScheduler."
|
| 434 |
-
)
|
| 435 |
-
|
| 436 |
-
def dpm_solver_first_order_update(
|
| 437 |
-
self,
|
| 438 |
-
model_output: torch.FloatTensor,
|
| 439 |
-
timestep: int,
|
| 440 |
-
prev_timestep: int,
|
| 441 |
-
sample: torch.FloatTensor,
|
| 442 |
-
) -> torch.FloatTensor:
|
| 443 |
-
"""
|
| 444 |
-
One step for the first-order DPM-Solver (equivalent to DDIM).
|
| 445 |
-
|
| 446 |
-
See https://arxiv.org/abs/2206.00927 for the detailed derivation.
|
| 447 |
-
|
| 448 |
-
Args:
|
| 449 |
-
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
|
| 450 |
-
timestep (`int`): current discrete timestep in the diffusion chain.
|
| 451 |
-
prev_timestep (`int`): previous discrete timestep in the diffusion chain.
|
| 452 |
-
sample (`torch.FloatTensor`):
|
| 453 |
-
current instance of sample being created by diffusion process.
|
| 454 |
-
|
| 455 |
-
Returns:
|
| 456 |
-
`torch.FloatTensor`: the sample tensor at the previous timestep.
|
| 457 |
-
"""
|
| 458 |
-
lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep]
|
| 459 |
-
alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep]
|
| 460 |
-
sigma_t, sigma_s = self.sigma_t[prev_timestep], self.sigma_t[timestep]
|
| 461 |
-
h = lambda_t - lambda_s
|
| 462 |
-
if self.config.algorithm_type == "dpmsolver++":
|
| 463 |
-
x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output
|
| 464 |
-
elif self.config.algorithm_type == "dpmsolver":
|
| 465 |
-
x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output
|
| 466 |
-
return x_t
|
| 467 |
-
|
| 468 |
-
def singlestep_dpm_solver_second_order_update(
|
| 469 |
-
self,
|
| 470 |
-
model_output_list: List[torch.FloatTensor],
|
| 471 |
-
timestep_list: List[int],
|
| 472 |
-
prev_timestep: int,
|
| 473 |
-
sample: torch.FloatTensor,
|
| 474 |
-
) -> torch.FloatTensor:
|
| 475 |
-
"""
|
| 476 |
-
One step for the second-order singlestep DPM-Solver.
|
| 477 |
-
|
| 478 |
-
It computes the solution at time `prev_timestep` from the time `timestep_list[-2]`.
|
| 479 |
-
|
| 480 |
-
Args:
|
| 481 |
-
model_output_list (`List[torch.FloatTensor]`):
|
| 482 |
-
direct outputs from learned diffusion model at current and latter timesteps.
|
| 483 |
-
timestep (`int`): current and latter discrete timestep in the diffusion chain.
|
| 484 |
-
prev_timestep (`int`): previous discrete timestep in the diffusion chain.
|
| 485 |
-
sample (`torch.FloatTensor`):
|
| 486 |
-
current instance of sample being created by diffusion process.
|
| 487 |
-
|
| 488 |
-
Returns:
|
| 489 |
-
`torch.FloatTensor`: the sample tensor at the previous timestep.
|
| 490 |
-
"""
|
| 491 |
-
t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2]
|
| 492 |
-
m0, m1 = model_output_list[-1], model_output_list[-2]
|
| 493 |
-
lambda_t, lambda_s0, lambda_s1 = self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1]
|
| 494 |
-
alpha_t, alpha_s1 = self.alpha_t[t], self.alpha_t[s1]
|
| 495 |
-
sigma_t, sigma_s1 = self.sigma_t[t], self.sigma_t[s1]
|
| 496 |
-
h, h_0 = lambda_t - lambda_s1, lambda_s0 - lambda_s1
|
| 497 |
-
r0 = h_0 / h
|
| 498 |
-
D0, D1 = m1, (1.0 / r0) * (m0 - m1)
|
| 499 |
-
if self.config.algorithm_type == "dpmsolver++":
|
| 500 |
-
# See https://arxiv.org/abs/2211.01095 for detailed derivations
|
| 501 |
-
if self.config.solver_type == "midpoint":
|
| 502 |
-
x_t = (
|
| 503 |
-
(sigma_t / sigma_s1) * sample
|
| 504 |
-
- (alpha_t * (torch.exp(-h) - 1.0)) * D0
|
| 505 |
-
- 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1
|
| 506 |
-
)
|
| 507 |
-
elif self.config.solver_type == "heun":
|
| 508 |
-
x_t = (
|
| 509 |
-
(sigma_t / sigma_s1) * sample
|
| 510 |
-
- (alpha_t * (torch.exp(-h) - 1.0)) * D0
|
| 511 |
-
+ (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1
|
| 512 |
-
)
|
| 513 |
-
elif self.config.algorithm_type == "dpmsolver":
|
| 514 |
-
# See https://arxiv.org/abs/2206.00927 for detailed derivations
|
| 515 |
-
if self.config.solver_type == "midpoint":
|
| 516 |
-
x_t = (
|
| 517 |
-
(alpha_t / alpha_s1) * sample
|
| 518 |
-
- (sigma_t * (torch.exp(h) - 1.0)) * D0
|
| 519 |
-
- 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1
|
| 520 |
-
)
|
| 521 |
-
elif self.config.solver_type == "heun":
|
| 522 |
-
x_t = (
|
| 523 |
-
(alpha_t / alpha_s1) * sample
|
| 524 |
-
- (sigma_t * (torch.exp(h) - 1.0)) * D0
|
| 525 |
-
- (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
|
| 526 |
-
)
|
| 527 |
-
return x_t
|
| 528 |
-
|
| 529 |
-
def singlestep_dpm_solver_third_order_update(
|
| 530 |
-
self,
|
| 531 |
-
model_output_list: List[torch.FloatTensor],
|
| 532 |
-
timestep_list: List[int],
|
| 533 |
-
prev_timestep: int,
|
| 534 |
-
sample: torch.FloatTensor,
|
| 535 |
-
) -> torch.FloatTensor:
|
| 536 |
-
"""
|
| 537 |
-
One step for the third-order singlestep DPM-Solver.
|
| 538 |
-
|
| 539 |
-
It computes the solution at time `prev_timestep` from the time `timestep_list[-3]`.
|
| 540 |
-
|
| 541 |
-
Args:
|
| 542 |
-
model_output_list (`List[torch.FloatTensor]`):
|
| 543 |
-
direct outputs from learned diffusion model at current and latter timesteps.
|
| 544 |
-
timestep (`int`): current and latter discrete timestep in the diffusion chain.
|
| 545 |
-
prev_timestep (`int`): previous discrete timestep in the diffusion chain.
|
| 546 |
-
sample (`torch.FloatTensor`):
|
| 547 |
-
current instance of sample being created by diffusion process.
|
| 548 |
-
|
| 549 |
-
Returns:
|
| 550 |
-
`torch.FloatTensor`: the sample tensor at the previous timestep.
|
| 551 |
-
"""
|
| 552 |
-
t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3]
|
| 553 |
-
m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]
|
| 554 |
-
lambda_t, lambda_s0, lambda_s1, lambda_s2 = (
|
| 555 |
-
self.lambda_t[t],
|
| 556 |
-
self.lambda_t[s0],
|
| 557 |
-
self.lambda_t[s1],
|
| 558 |
-
self.lambda_t[s2],
|
| 559 |
-
)
|
| 560 |
-
alpha_t, alpha_s2 = self.alpha_t[t], self.alpha_t[s2]
|
| 561 |
-
sigma_t, sigma_s2 = self.sigma_t[t], self.sigma_t[s2]
|
| 562 |
-
h, h_0, h_1 = lambda_t - lambda_s2, lambda_s0 - lambda_s2, lambda_s1 - lambda_s2
|
| 563 |
-
r0, r1 = h_0 / h, h_1 / h
|
| 564 |
-
D0 = m2
|
| 565 |
-
D1_0, D1_1 = (1.0 / r1) * (m1 - m2), (1.0 / r0) * (m0 - m2)
|
| 566 |
-
D1 = (r0 * D1_0 - r1 * D1_1) / (r0 - r1)
|
| 567 |
-
D2 = 2.0 * (D1_1 - D1_0) / (r0 - r1)
|
| 568 |
-
if self.config.algorithm_type == "dpmsolver++":
|
| 569 |
-
# See https://arxiv.org/abs/2206.00927 for detailed derivations
|
| 570 |
-
if self.config.solver_type == "midpoint":
|
| 571 |
-
x_t = (
|
| 572 |
-
(sigma_t / sigma_s2) * sample
|
| 573 |
-
- (alpha_t * (torch.exp(-h) - 1.0)) * D0
|
| 574 |
-
+ (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1_1
|
| 575 |
-
)
|
| 576 |
-
elif self.config.solver_type == "heun":
|
| 577 |
-
x_t = (
|
| 578 |
-
(sigma_t / sigma_s2) * sample
|
| 579 |
-
- (alpha_t * (torch.exp(-h) - 1.0)) * D0
|
| 580 |
-
+ (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1
|
| 581 |
-
- (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2
|
| 582 |
-
)
|
| 583 |
-
elif self.config.algorithm_type == "dpmsolver":
|
| 584 |
-
# See https://arxiv.org/abs/2206.00927 for detailed derivations
|
| 585 |
-
if self.config.solver_type == "midpoint":
|
| 586 |
-
x_t = (
|
| 587 |
-
(alpha_t / alpha_s2) * sample
|
| 588 |
-
- (sigma_t * (torch.exp(h) - 1.0)) * D0
|
| 589 |
-
- (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1_1
|
| 590 |
-
)
|
| 591 |
-
elif self.config.solver_type == "heun":
|
| 592 |
-
x_t = (
|
| 593 |
-
(alpha_t / alpha_s2) * sample
|
| 594 |
-
- (sigma_t * (torch.exp(h) - 1.0)) * D0
|
| 595 |
-
- (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
|
| 596 |
-
- (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2
|
| 597 |
-
)
|
| 598 |
-
return x_t
|
| 599 |
-
|
| 600 |
-
def singlestep_dpm_solver_update(
|
| 601 |
-
self,
|
| 602 |
-
model_output_list: List[torch.FloatTensor],
|
| 603 |
-
timestep_list: List[int],
|
| 604 |
-
prev_timestep: int,
|
| 605 |
-
sample: torch.FloatTensor,
|
| 606 |
-
order: int,
|
| 607 |
-
) -> torch.FloatTensor:
|
| 608 |
-
"""
|
| 609 |
-
One step for the singlestep DPM-Solver.
|
| 610 |
-
|
| 611 |
-
Args:
|
| 612 |
-
model_output_list (`List[torch.FloatTensor]`):
|
| 613 |
-
direct outputs from learned diffusion model at current and latter timesteps.
|
| 614 |
-
timestep (`int`): current and latter discrete timestep in the diffusion chain.
|
| 615 |
-
prev_timestep (`int`): previous discrete timestep in the diffusion chain.
|
| 616 |
-
sample (`torch.FloatTensor`):
|
| 617 |
-
current instance of sample being created by diffusion process.
|
| 618 |
-
order (`int`):
|
| 619 |
-
the solver order at this step.
|
| 620 |
-
|
| 621 |
-
Returns:
|
| 622 |
-
`torch.FloatTensor`: the sample tensor at the previous timestep.
|
| 623 |
-
"""
|
| 624 |
-
if order == 1:
|
| 625 |
-
return self.dpm_solver_first_order_update(model_output_list[-1], timestep_list[-1], prev_timestep, sample)
|
| 626 |
-
elif order == 2:
|
| 627 |
-
return self.singlestep_dpm_solver_second_order_update(
|
| 628 |
-
model_output_list, timestep_list, prev_timestep, sample
|
| 629 |
-
)
|
| 630 |
-
elif order == 3:
|
| 631 |
-
return self.singlestep_dpm_solver_third_order_update(
|
| 632 |
-
model_output_list, timestep_list, prev_timestep, sample
|
| 633 |
-
)
|
| 634 |
-
else:
|
| 635 |
-
raise ValueError(f"Order must be 1, 2, 3, got {order}")
|
| 636 |
-
|
| 637 |
-
def step(
|
| 638 |
-
self,
|
| 639 |
-
model_output: torch.FloatTensor,
|
| 640 |
-
timestep: int,
|
| 641 |
-
sample: torch.FloatTensor,
|
| 642 |
-
return_dict: bool = True,
|
| 643 |
-
) -> Union[SchedulerOutput, Tuple]:
|
| 644 |
-
"""
|
| 645 |
-
Step function propagating the sample with the singlestep DPM-Solver.
|
| 646 |
-
|
| 647 |
-
Args:
|
| 648 |
-
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
|
| 649 |
-
timestep (`int`): current discrete timestep in the diffusion chain.
|
| 650 |
-
sample (`torch.FloatTensor`):
|
| 651 |
-
current instance of sample being created by diffusion process.
|
| 652 |
-
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
|
| 653 |
-
|
| 654 |
-
Returns:
|
| 655 |
-
[`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
|
| 656 |
-
True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
|
| 657 |
-
|
| 658 |
-
"""
|
| 659 |
-
if self.num_inference_steps is None:
|
| 660 |
-
raise ValueError(
|
| 661 |
-
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
|
| 662 |
-
)
|
| 663 |
-
|
| 664 |
-
if isinstance(timestep, torch.Tensor):
|
| 665 |
-
timestep = timestep.to(self.timesteps.device)
|
| 666 |
-
step_index = (self.timesteps == timestep).nonzero()
|
| 667 |
-
if len(step_index) == 0:
|
| 668 |
-
step_index = len(self.timesteps) - 1
|
| 669 |
-
else:
|
| 670 |
-
step_index = step_index.item()
|
| 671 |
-
prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1]
|
| 672 |
-
|
| 673 |
-
model_output = self.convert_model_output(model_output, timestep, sample)
|
| 674 |
-
for i in range(self.config.solver_order - 1):
|
| 675 |
-
self.model_outputs[i] = self.model_outputs[i + 1]
|
| 676 |
-
self.model_outputs[-1] = model_output
|
| 677 |
-
|
| 678 |
-
order = self.order_list[step_index]
|
| 679 |
-
|
| 680 |
-
# For img2img denoising might start with order>1 which is not possible
|
| 681 |
-
# In this case make sure that the first two steps are both order=1
|
| 682 |
-
while self.model_outputs[-order] is None:
|
| 683 |
-
order -= 1
|
| 684 |
-
|
| 685 |
-
# For single-step solvers, we use the initial value at each time with order = 1.
|
| 686 |
-
if order == 1:
|
| 687 |
-
self.sample = sample
|
| 688 |
-
|
| 689 |
-
timestep_list = [self.timesteps[step_index - i] for i in range(order - 1, 0, -1)] + [timestep]
|
| 690 |
-
prev_sample = self.singlestep_dpm_solver_update(
|
| 691 |
-
self.model_outputs, timestep_list, prev_timestep, self.sample, order
|
| 692 |
-
)
|
| 693 |
-
|
| 694 |
-
if not return_dict:
|
| 695 |
-
return (prev_sample,)
|
| 696 |
-
|
| 697 |
-
return SchedulerOutput(prev_sample=prev_sample)
|
| 698 |
-
|
| 699 |
-
def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
|
| 700 |
-
"""
|
| 701 |
-
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
| 702 |
-
current timestep.
|
| 703 |
-
|
| 704 |
-
Args:
|
| 705 |
-
sample (`torch.FloatTensor`): input sample
|
| 706 |
-
|
| 707 |
-
Returns:
|
| 708 |
-
`torch.FloatTensor`: scaled input sample
|
| 709 |
-
"""
|
| 710 |
-
return sample
|
| 711 |
-
|
| 712 |
-
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
|
| 713 |
-
def add_noise(
|
| 714 |
-
self,
|
| 715 |
-
original_samples: torch.FloatTensor,
|
| 716 |
-
noise: torch.FloatTensor,
|
| 717 |
-
timesteps: torch.IntTensor,
|
| 718 |
-
) -> torch.FloatTensor:
|
| 719 |
-
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
|
| 720 |
-
alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
|
| 721 |
-
timesteps = timesteps.to(original_samples.device)
|
| 722 |
-
|
| 723 |
-
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
|
| 724 |
-
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
|
| 725 |
-
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
|
| 726 |
-
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
|
| 727 |
-
|
| 728 |
-
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
|
| 729 |
-
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
|
| 730 |
-
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
|
| 731 |
-
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
|
| 732 |
-
|
| 733 |
-
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
|
| 734 |
-
return noisy_samples
|
| 735 |
-
|
| 736 |
-
def __len__(self):
|
| 737 |
-
return self.config.num_train_timesteps
|
|
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spaces/Andy1621/uniformer_image_detection/configs/_base_/datasets/coco_instance_semantic.py
DELETED
|
@@ -1,53 +0,0 @@
|
|
| 1 |
-
dataset_type = 'CocoDataset'
|
| 2 |
-
data_root = 'data/coco/'
|
| 3 |
-
img_norm_cfg = dict(
|
| 4 |
-
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
| 5 |
-
train_pipeline = [
|
| 6 |
-
dict(type='LoadImageFromFile'),
|
| 7 |
-
dict(
|
| 8 |
-
type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
|
| 9 |
-
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
|
| 10 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 11 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 12 |
-
dict(type='Pad', size_divisor=32),
|
| 13 |
-
dict(type='SegRescale', scale_factor=1 / 8),
|
| 14 |
-
dict(type='DefaultFormatBundle'),
|
| 15 |
-
dict(
|
| 16 |
-
type='Collect',
|
| 17 |
-
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
|
| 18 |
-
]
|
| 19 |
-
test_pipeline = [
|
| 20 |
-
dict(type='LoadImageFromFile'),
|
| 21 |
-
dict(
|
| 22 |
-
type='MultiScaleFlipAug',
|
| 23 |
-
img_scale=(1333, 800),
|
| 24 |
-
flip=False,
|
| 25 |
-
transforms=[
|
| 26 |
-
dict(type='Resize', keep_ratio=True),
|
| 27 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 28 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 29 |
-
dict(type='Pad', size_divisor=32),
|
| 30 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 31 |
-
dict(type='Collect', keys=['img']),
|
| 32 |
-
])
|
| 33 |
-
]
|
| 34 |
-
data = dict(
|
| 35 |
-
samples_per_gpu=2,
|
| 36 |
-
workers_per_gpu=2,
|
| 37 |
-
train=dict(
|
| 38 |
-
type=dataset_type,
|
| 39 |
-
ann_file=data_root + 'annotations/instances_train2017.json',
|
| 40 |
-
img_prefix=data_root + 'train2017/',
|
| 41 |
-
seg_prefix=data_root + 'stuffthingmaps/train2017/',
|
| 42 |
-
pipeline=train_pipeline),
|
| 43 |
-
val=dict(
|
| 44 |
-
type=dataset_type,
|
| 45 |
-
ann_file=data_root + 'annotations/instances_val2017.json',
|
| 46 |
-
img_prefix=data_root + 'val2017/',
|
| 47 |
-
pipeline=test_pipeline),
|
| 48 |
-
test=dict(
|
| 49 |
-
type=dataset_type,
|
| 50 |
-
ann_file=data_root + 'annotations/instances_val2017.json',
|
| 51 |
-
img_prefix=data_root + 'val2017/',
|
| 52 |
-
pipeline=test_pipeline))
|
| 53 |
-
evaluation = dict(metric=['bbox', 'segm'])
|
|
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|
spaces/Andy1621/uniformer_image_detection/configs/atss/atss_r101_fpn_1x_coco.py
DELETED
|
@@ -1,5 +0,0 @@
|
|
| 1 |
-
_base_ = './atss_r50_fpn_1x_coco.py'
|
| 2 |
-
model = dict(
|
| 3 |
-
pretrained='torchvision://resnet101',
|
| 4 |
-
backbone=dict(depth=101),
|
| 5 |
-
)
|
|
|
|
|
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|
spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
|
| 3 |
-
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
|
| 4 |
-
]
|
| 5 |
-
model = dict(
|
| 6 |
-
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
|
|
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|
spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/video/io.py
DELETED
|
@@ -1,318 +0,0 @@
|
|
| 1 |
-
# Copyright (c) OpenMMLab. All rights reserved.
|
| 2 |
-
import os.path as osp
|
| 3 |
-
from collections import OrderedDict
|
| 4 |
-
|
| 5 |
-
import cv2
|
| 6 |
-
from cv2 import (CAP_PROP_FOURCC, CAP_PROP_FPS, CAP_PROP_FRAME_COUNT,
|
| 7 |
-
CAP_PROP_FRAME_HEIGHT, CAP_PROP_FRAME_WIDTH,
|
| 8 |
-
CAP_PROP_POS_FRAMES, VideoWriter_fourcc)
|
| 9 |
-
|
| 10 |
-
from annotator.uniformer.mmcv.utils import (check_file_exist, mkdir_or_exist, scandir,
|
| 11 |
-
track_progress)
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
class Cache:
|
| 15 |
-
|
| 16 |
-
def __init__(self, capacity):
|
| 17 |
-
self._cache = OrderedDict()
|
| 18 |
-
self._capacity = int(capacity)
|
| 19 |
-
if capacity <= 0:
|
| 20 |
-
raise ValueError('capacity must be a positive integer')
|
| 21 |
-
|
| 22 |
-
@property
|
| 23 |
-
def capacity(self):
|
| 24 |
-
return self._capacity
|
| 25 |
-
|
| 26 |
-
@property
|
| 27 |
-
def size(self):
|
| 28 |
-
return len(self._cache)
|
| 29 |
-
|
| 30 |
-
def put(self, key, val):
|
| 31 |
-
if key in self._cache:
|
| 32 |
-
return
|
| 33 |
-
if len(self._cache) >= self.capacity:
|
| 34 |
-
self._cache.popitem(last=False)
|
| 35 |
-
self._cache[key] = val
|
| 36 |
-
|
| 37 |
-
def get(self, key, default=None):
|
| 38 |
-
val = self._cache[key] if key in self._cache else default
|
| 39 |
-
return val
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
class VideoReader:
|
| 43 |
-
"""Video class with similar usage to a list object.
|
| 44 |
-
|
| 45 |
-
This video warpper class provides convenient apis to access frames.
|
| 46 |
-
There exists an issue of OpenCV's VideoCapture class that jumping to a
|
| 47 |
-
certain frame may be inaccurate. It is fixed in this class by checking
|
| 48 |
-
the position after jumping each time.
|
| 49 |
-
Cache is used when decoding videos. So if the same frame is visited for
|
| 50 |
-
the second time, there is no need to decode again if it is stored in the
|
| 51 |
-
cache.
|
| 52 |
-
|
| 53 |
-
:Example:
|
| 54 |
-
|
| 55 |
-
>>> import annotator.uniformer.mmcv as mmcv
|
| 56 |
-
>>> v = mmcv.VideoReader('sample.mp4')
|
| 57 |
-
>>> len(v) # get the total frame number with `len()`
|
| 58 |
-
120
|
| 59 |
-
>>> for img in v: # v is iterable
|
| 60 |
-
>>> mmcv.imshow(img)
|
| 61 |
-
>>> v[5] # get the 6th frame
|
| 62 |
-
"""
|
| 63 |
-
|
| 64 |
-
def __init__(self, filename, cache_capacity=10):
|
| 65 |
-
# Check whether the video path is a url
|
| 66 |
-
if not filename.startswith(('https://', 'http://')):
|
| 67 |
-
check_file_exist(filename, 'Video file not found: ' + filename)
|
| 68 |
-
self._vcap = cv2.VideoCapture(filename)
|
| 69 |
-
assert cache_capacity > 0
|
| 70 |
-
self._cache = Cache(cache_capacity)
|
| 71 |
-
self._position = 0
|
| 72 |
-
# get basic info
|
| 73 |
-
self._width = int(self._vcap.get(CAP_PROP_FRAME_WIDTH))
|
| 74 |
-
self._height = int(self._vcap.get(CAP_PROP_FRAME_HEIGHT))
|
| 75 |
-
self._fps = self._vcap.get(CAP_PROP_FPS)
|
| 76 |
-
self._frame_cnt = int(self._vcap.get(CAP_PROP_FRAME_COUNT))
|
| 77 |
-
self._fourcc = self._vcap.get(CAP_PROP_FOURCC)
|
| 78 |
-
|
| 79 |
-
@property
|
| 80 |
-
def vcap(self):
|
| 81 |
-
""":obj:`cv2.VideoCapture`: The raw VideoCapture object."""
|
| 82 |
-
return self._vcap
|
| 83 |
-
|
| 84 |
-
@property
|
| 85 |
-
def opened(self):
|
| 86 |
-
"""bool: Indicate whether the video is opened."""
|
| 87 |
-
return self._vcap.isOpened()
|
| 88 |
-
|
| 89 |
-
@property
|
| 90 |
-
def width(self):
|
| 91 |
-
"""int: Width of video frames."""
|
| 92 |
-
return self._width
|
| 93 |
-
|
| 94 |
-
@property
|
| 95 |
-
def height(self):
|
| 96 |
-
"""int: Height of video frames."""
|
| 97 |
-
return self._height
|
| 98 |
-
|
| 99 |
-
@property
|
| 100 |
-
def resolution(self):
|
| 101 |
-
"""tuple: Video resolution (width, height)."""
|
| 102 |
-
return (self._width, self._height)
|
| 103 |
-
|
| 104 |
-
@property
|
| 105 |
-
def fps(self):
|
| 106 |
-
"""float: FPS of the video."""
|
| 107 |
-
return self._fps
|
| 108 |
-
|
| 109 |
-
@property
|
| 110 |
-
def frame_cnt(self):
|
| 111 |
-
"""int: Total frames of the video."""
|
| 112 |
-
return self._frame_cnt
|
| 113 |
-
|
| 114 |
-
@property
|
| 115 |
-
def fourcc(self):
|
| 116 |
-
"""str: "Four character code" of the video."""
|
| 117 |
-
return self._fourcc
|
| 118 |
-
|
| 119 |
-
@property
|
| 120 |
-
def position(self):
|
| 121 |
-
"""int: Current cursor position, indicating frame decoded."""
|
| 122 |
-
return self._position
|
| 123 |
-
|
| 124 |
-
def _get_real_position(self):
|
| 125 |
-
return int(round(self._vcap.get(CAP_PROP_POS_FRAMES)))
|
| 126 |
-
|
| 127 |
-
def _set_real_position(self, frame_id):
|
| 128 |
-
self._vcap.set(CAP_PROP_POS_FRAMES, frame_id)
|
| 129 |
-
pos = self._get_real_position()
|
| 130 |
-
for _ in range(frame_id - pos):
|
| 131 |
-
self._vcap.read()
|
| 132 |
-
self._position = frame_id
|
| 133 |
-
|
| 134 |
-
def read(self):
|
| 135 |
-
"""Read the next frame.
|
| 136 |
-
|
| 137 |
-
If the next frame have been decoded before and in the cache, then
|
| 138 |
-
return it directly, otherwise decode, cache and return it.
|
| 139 |
-
|
| 140 |
-
Returns:
|
| 141 |
-
ndarray or None: Return the frame if successful, otherwise None.
|
| 142 |
-
"""
|
| 143 |
-
# pos = self._position
|
| 144 |
-
if self._cache:
|
| 145 |
-
img = self._cache.get(self._position)
|
| 146 |
-
if img is not None:
|
| 147 |
-
ret = True
|
| 148 |
-
else:
|
| 149 |
-
if self._position != self._get_real_position():
|
| 150 |
-
self._set_real_position(self._position)
|
| 151 |
-
ret, img = self._vcap.read()
|
| 152 |
-
if ret:
|
| 153 |
-
self._cache.put(self._position, img)
|
| 154 |
-
else:
|
| 155 |
-
ret, img = self._vcap.read()
|
| 156 |
-
if ret:
|
| 157 |
-
self._position += 1
|
| 158 |
-
return img
|
| 159 |
-
|
| 160 |
-
def get_frame(self, frame_id):
|
| 161 |
-
"""Get frame by index.
|
| 162 |
-
|
| 163 |
-
Args:
|
| 164 |
-
frame_id (int): Index of the expected frame, 0-based.
|
| 165 |
-
|
| 166 |
-
Returns:
|
| 167 |
-
ndarray or None: Return the frame if successful, otherwise None.
|
| 168 |
-
"""
|
| 169 |
-
if frame_id < 0 or frame_id >= self._frame_cnt:
|
| 170 |
-
raise IndexError(
|
| 171 |
-
f'"frame_id" must be between 0 and {self._frame_cnt - 1}')
|
| 172 |
-
if frame_id == self._position:
|
| 173 |
-
return self.read()
|
| 174 |
-
if self._cache:
|
| 175 |
-
img = self._cache.get(frame_id)
|
| 176 |
-
if img is not None:
|
| 177 |
-
self._position = frame_id + 1
|
| 178 |
-
return img
|
| 179 |
-
self._set_real_position(frame_id)
|
| 180 |
-
ret, img = self._vcap.read()
|
| 181 |
-
if ret:
|
| 182 |
-
if self._cache:
|
| 183 |
-
self._cache.put(self._position, img)
|
| 184 |
-
self._position += 1
|
| 185 |
-
return img
|
| 186 |
-
|
| 187 |
-
def current_frame(self):
|
| 188 |
-
"""Get the current frame (frame that is just visited).
|
| 189 |
-
|
| 190 |
-
Returns:
|
| 191 |
-
ndarray or None: If the video is fresh, return None, otherwise
|
| 192 |
-
return the frame.
|
| 193 |
-
"""
|
| 194 |
-
if self._position == 0:
|
| 195 |
-
return None
|
| 196 |
-
return self._cache.get(self._position - 1)
|
| 197 |
-
|
| 198 |
-
def cvt2frames(self,
|
| 199 |
-
frame_dir,
|
| 200 |
-
file_start=0,
|
| 201 |
-
filename_tmpl='{:06d}.jpg',
|
| 202 |
-
start=0,
|
| 203 |
-
max_num=0,
|
| 204 |
-
show_progress=True):
|
| 205 |
-
"""Convert a video to frame images.
|
| 206 |
-
|
| 207 |
-
Args:
|
| 208 |
-
frame_dir (str): Output directory to store all the frame images.
|
| 209 |
-
file_start (int): Filenames will start from the specified number.
|
| 210 |
-
filename_tmpl (str): Filename template with the index as the
|
| 211 |
-
placeholder.
|
| 212 |
-
start (int): The starting frame index.
|
| 213 |
-
max_num (int): Maximum number of frames to be written.
|
| 214 |
-
show_progress (bool): Whether to show a progress bar.
|
| 215 |
-
"""
|
| 216 |
-
mkdir_or_exist(frame_dir)
|
| 217 |
-
if max_num == 0:
|
| 218 |
-
task_num = self.frame_cnt - start
|
| 219 |
-
else:
|
| 220 |
-
task_num = min(self.frame_cnt - start, max_num)
|
| 221 |
-
if task_num <= 0:
|
| 222 |
-
raise ValueError('start must be less than total frame number')
|
| 223 |
-
if start > 0:
|
| 224 |
-
self._set_real_position(start)
|
| 225 |
-
|
| 226 |
-
def write_frame(file_idx):
|
| 227 |
-
img = self.read()
|
| 228 |
-
if img is None:
|
| 229 |
-
return
|
| 230 |
-
filename = osp.join(frame_dir, filename_tmpl.format(file_idx))
|
| 231 |
-
cv2.imwrite(filename, img)
|
| 232 |
-
|
| 233 |
-
if show_progress:
|
| 234 |
-
track_progress(write_frame, range(file_start,
|
| 235 |
-
file_start + task_num))
|
| 236 |
-
else:
|
| 237 |
-
for i in range(task_num):
|
| 238 |
-
write_frame(file_start + i)
|
| 239 |
-
|
| 240 |
-
def __len__(self):
|
| 241 |
-
return self.frame_cnt
|
| 242 |
-
|
| 243 |
-
def __getitem__(self, index):
|
| 244 |
-
if isinstance(index, slice):
|
| 245 |
-
return [
|
| 246 |
-
self.get_frame(i)
|
| 247 |
-
for i in range(*index.indices(self.frame_cnt))
|
| 248 |
-
]
|
| 249 |
-
# support negative indexing
|
| 250 |
-
if index < 0:
|
| 251 |
-
index += self.frame_cnt
|
| 252 |
-
if index < 0:
|
| 253 |
-
raise IndexError('index out of range')
|
| 254 |
-
return self.get_frame(index)
|
| 255 |
-
|
| 256 |
-
def __iter__(self):
|
| 257 |
-
self._set_real_position(0)
|
| 258 |
-
return self
|
| 259 |
-
|
| 260 |
-
def __next__(self):
|
| 261 |
-
img = self.read()
|
| 262 |
-
if img is not None:
|
| 263 |
-
return img
|
| 264 |
-
else:
|
| 265 |
-
raise StopIteration
|
| 266 |
-
|
| 267 |
-
next = __next__
|
| 268 |
-
|
| 269 |
-
def __enter__(self):
|
| 270 |
-
return self
|
| 271 |
-
|
| 272 |
-
def __exit__(self, exc_type, exc_value, traceback):
|
| 273 |
-
self._vcap.release()
|
| 274 |
-
|
| 275 |
-
|
| 276 |
-
def frames2video(frame_dir,
|
| 277 |
-
video_file,
|
| 278 |
-
fps=30,
|
| 279 |
-
fourcc='XVID',
|
| 280 |
-
filename_tmpl='{:06d}.jpg',
|
| 281 |
-
start=0,
|
| 282 |
-
end=0,
|
| 283 |
-
show_progress=True):
|
| 284 |
-
"""Read the frame images from a directory and join them as a video.
|
| 285 |
-
|
| 286 |
-
Args:
|
| 287 |
-
frame_dir (str): The directory containing video frames.
|
| 288 |
-
video_file (str): Output filename.
|
| 289 |
-
fps (float): FPS of the output video.
|
| 290 |
-
fourcc (str): Fourcc of the output video, this should be compatible
|
| 291 |
-
with the output file type.
|
| 292 |
-
filename_tmpl (str): Filename template with the index as the variable.
|
| 293 |
-
start (int): Starting frame index.
|
| 294 |
-
end (int): Ending frame index.
|
| 295 |
-
show_progress (bool): Whether to show a progress bar.
|
| 296 |
-
"""
|
| 297 |
-
if end == 0:
|
| 298 |
-
ext = filename_tmpl.split('.')[-1]
|
| 299 |
-
end = len([name for name in scandir(frame_dir, ext)])
|
| 300 |
-
first_file = osp.join(frame_dir, filename_tmpl.format(start))
|
| 301 |
-
check_file_exist(first_file, 'The start frame not found: ' + first_file)
|
| 302 |
-
img = cv2.imread(first_file)
|
| 303 |
-
height, width = img.shape[:2]
|
| 304 |
-
resolution = (width, height)
|
| 305 |
-
vwriter = cv2.VideoWriter(video_file, VideoWriter_fourcc(*fourcc), fps,
|
| 306 |
-
resolution)
|
| 307 |
-
|
| 308 |
-
def write_frame(file_idx):
|
| 309 |
-
filename = osp.join(frame_dir, filename_tmpl.format(file_idx))
|
| 310 |
-
img = cv2.imread(filename)
|
| 311 |
-
vwriter.write(img)
|
| 312 |
-
|
| 313 |
-
if show_progress:
|
| 314 |
-
track_progress(write_frame, range(start, end))
|
| 315 |
-
else:
|
| 316 |
-
for i in range(start, end):
|
| 317 |
-
write_frame(i)
|
| 318 |
-
vwriter.release()
|
|
|
|
|
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|
spaces/ArtificialWF/Voice-Recognition/app.py
DELETED
|
@@ -1,109 +0,0 @@
|
|
| 1 |
-
import whisper
|
| 2 |
-
import gradio as gr
|
| 3 |
-
import datetime
|
| 4 |
-
|
| 5 |
-
import subprocess
|
| 6 |
-
|
| 7 |
-
import torch
|
| 8 |
-
import pyannote.audio
|
| 9 |
-
from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
|
| 10 |
-
|
| 11 |
-
from pyannote.audio import Audio
|
| 12 |
-
from pyannote.core import Segment
|
| 13 |
-
|
| 14 |
-
import wave
|
| 15 |
-
import contextlib
|
| 16 |
-
|
| 17 |
-
from sklearn.cluster import AgglomerativeClustering
|
| 18 |
-
import numpy as np
|
| 19 |
-
|
| 20 |
-
model = whisper.load_model("large-v2")
|
| 21 |
-
embedding_model = PretrainedSpeakerEmbedding(
|
| 22 |
-
"speechbrain/spkrec-ecapa-voxceleb",
|
| 23 |
-
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
| 24 |
-
)
|
| 25 |
-
|
| 26 |
-
def transcribe(audio, num_speakers):
|
| 27 |
-
path, error = convert_to_wav(audio)
|
| 28 |
-
if error is not None:
|
| 29 |
-
return error
|
| 30 |
-
|
| 31 |
-
duration = get_duration(path)
|
| 32 |
-
if duration > 4 * 60 * 60:
|
| 33 |
-
return "Audio duration too long"
|
| 34 |
-
|
| 35 |
-
result = model.transcribe(path)
|
| 36 |
-
segments = result["segments"]
|
| 37 |
-
|
| 38 |
-
num_speakers = min(max(round(num_speakers), 1), len(segments))
|
| 39 |
-
if len(segments) == 1:
|
| 40 |
-
segments[0]['speaker'] = 'SPEAKER 1'
|
| 41 |
-
else:
|
| 42 |
-
embeddings = make_embeddings(path, segments, duration)
|
| 43 |
-
add_speaker_labels(segments, embeddings, num_speakers)
|
| 44 |
-
output = get_output(segments)
|
| 45 |
-
return output
|
| 46 |
-
|
| 47 |
-
def convert_to_wav(path):
|
| 48 |
-
if path[-3:] != 'wav':
|
| 49 |
-
new_path = '.'.join(path.split('.')[:-1]) + '.wav'
|
| 50 |
-
try:
|
| 51 |
-
subprocess.call(['ffmpeg', '-i', path, new_path, '-y'])
|
| 52 |
-
except:
|
| 53 |
-
return path, 'Error: Could not convert file to .wav'
|
| 54 |
-
path = new_path
|
| 55 |
-
return path, None
|
| 56 |
-
|
| 57 |
-
def get_duration(path):
|
| 58 |
-
with contextlib.closing(wave.open(path,'r')) as f:
|
| 59 |
-
frames = f.getnframes()
|
| 60 |
-
rate = f.getframerate()
|
| 61 |
-
return frames / float(rate)
|
| 62 |
-
|
| 63 |
-
def make_embeddings(path, segments, duration):
|
| 64 |
-
embeddings = np.zeros(shape=(len(segments), 192))
|
| 65 |
-
for i, segment in enumerate(segments):
|
| 66 |
-
embeddings[i] = segment_embedding(path, segment, duration)
|
| 67 |
-
return np.nan_to_num(embeddings)
|
| 68 |
-
|
| 69 |
-
audio = Audio()
|
| 70 |
-
|
| 71 |
-
def segment_embedding(path, segment, duration):
|
| 72 |
-
start = segment["start"]
|
| 73 |
-
# Whisper overshoots the end timestamp in the last segment
|
| 74 |
-
end = min(duration, segment["end"])
|
| 75 |
-
clip = Segment(start, end)
|
| 76 |
-
waveform, sample_rate = audio.crop(path, clip)
|
| 77 |
-
return embedding_model(waveform[None])
|
| 78 |
-
|
| 79 |
-
def add_speaker_labels(segments, embeddings, num_speakers):
|
| 80 |
-
clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
|
| 81 |
-
labels = clustering.labels_
|
| 82 |
-
for i in range(len(segments)):
|
| 83 |
-
segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
|
| 84 |
-
|
| 85 |
-
def time(secs):
|
| 86 |
-
return datetime.timedelta(seconds=round(secs))
|
| 87 |
-
|
| 88 |
-
def get_output(segments):
|
| 89 |
-
output = ''
|
| 90 |
-
for (i, segment) in enumerate(segments):
|
| 91 |
-
if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
|
| 92 |
-
if i != 0:
|
| 93 |
-
output += '\n\n'
|
| 94 |
-
output += segment["speaker"] + ' ' + str(time(segment["start"])) + '\n\n'
|
| 95 |
-
output += segment["text"][1:] + ' '
|
| 96 |
-
return output
|
| 97 |
-
|
| 98 |
-
gr.Interface(
|
| 99 |
-
title = 'AI Voice to Text',
|
| 100 |
-
fn=transcribe,
|
| 101 |
-
inputs=[
|
| 102 |
-
gr.inputs.Audio(source="upload", type="filepath"),
|
| 103 |
-
gr.inputs.Number(default=2, label="Number of Speakers")
|
| 104 |
-
|
| 105 |
-
],
|
| 106 |
-
outputs=[
|
| 107 |
-
gr.outputs.Textbox(label='Transcript')
|
| 108 |
-
]
|
| 109 |
-
).launch()
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/__init__.py
DELETED
|
File without changes
|
spaces/AutoGeneralAI/voice-assistant/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Voice Assistant
|
| 3 |
-
emoji: 🐠
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: red
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.27.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/Awesimo/jojogan/e4e/utils/train_utils.py
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
def aggregate_loss_dict(agg_loss_dict):
|
| 3 |
-
mean_vals = {}
|
| 4 |
-
for output in agg_loss_dict:
|
| 5 |
-
for key in output:
|
| 6 |
-
mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]]
|
| 7 |
-
for key in mean_vals:
|
| 8 |
-
if len(mean_vals[key]) > 0:
|
| 9 |
-
mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key])
|
| 10 |
-
else:
|
| 11 |
-
print('{} has no value'.format(key))
|
| 12 |
-
mean_vals[key] = 0
|
| 13 |
-
return mean_vals
|
|
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|
spaces/Bart92/RVC_HF/diffq/utils.py
DELETED
|
@@ -1,37 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
import inspect
|
| 8 |
-
from typing import Optional, List
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def simple_repr(obj, attrs: Optional[List[str]] = None, overrides={}):
|
| 12 |
-
"""
|
| 13 |
-
Return a simple representation string for `obj`.
|
| 14 |
-
If `attrs` is not None, it should be a list of attributes to include.
|
| 15 |
-
"""
|
| 16 |
-
params = inspect.signature(obj.__class__).parameters
|
| 17 |
-
attrs_repr = []
|
| 18 |
-
if attrs is None:
|
| 19 |
-
attrs = params.keys()
|
| 20 |
-
for attr in attrs:
|
| 21 |
-
display = False
|
| 22 |
-
if attr in overrides:
|
| 23 |
-
value = overrides[attr]
|
| 24 |
-
elif hasattr(obj, attr):
|
| 25 |
-
value = getattr(obj, attr)
|
| 26 |
-
else:
|
| 27 |
-
continue
|
| 28 |
-
if attr in params:
|
| 29 |
-
param = params[attr]
|
| 30 |
-
if param.default is inspect._empty or value != param.default:
|
| 31 |
-
display = True
|
| 32 |
-
else:
|
| 33 |
-
display = True
|
| 34 |
-
|
| 35 |
-
if display:
|
| 36 |
-
attrs_repr.append(f"{attr}={value}")
|
| 37 |
-
return f"{obj.__class__.__name__}({','.join(attrs_repr)})"
|
|
|
|
|
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|
spaces/Benson/text-generation/Examples/Descargar 3utools Para Mac Air.md
DELETED
|
@@ -1,58 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>Cómo descargar 3uTools para Mac Air</h1>
|
| 3 |
-
<p>Si tiene un dispositivo iOS y un Mac Air, es posible que se pregunte cómo descargar 3uTools para Mac Air. 3uTools es un gestor de dispositivos iOS gratuito que te permite conectar tu iPhone o iPad a tu Mac Air y administrar diferentes configuraciones y características. También puedes acceder a miles de tonos de llamada, fondos de pantalla, juegos, aplicaciones y otro contenido que Apple normalmente no te permite instalar. Además, puede usar algunas funciones de jailbreak si necesita desbloquear su dispositivo o obtener más control sobre él. </p>
|
| 4 |
-
<h2>descargar 3utools para mac air</h2><br /><p><b><b>Download File</b> >>> <a href="https://bltlly.com/2v6K8A">https://bltlly.com/2v6K8A</a></b></p><br /><br />
|
| 5 |
-
<p>En este artículo, explicaremos qué es 3uTools, por qué es posible que desee descargarlo para su Mac Air, cómo descargarlo e instalarlo utilizando un software de terceros llamado WineBottler, y cómo usarlo en su Mac Air. ¡Vamos a empezar! </p>
|
| 6 |
-
<h2>¿Qué es 3uTools? </h2>
|
| 7 |
-
<p>3uTools es un gestor de dispositivos iOS gratuito que te permite conectar tu dispositivo iOS a tu PC o Mac y administrar diferentes configuraciones y características. Puede usar 3uTools para hacer copias de seguridad y restaurar sus datos, transferir archivos, actualizar o degradar su firmware, flashear ROMs personalizados, hacer jailbreak a su dispositivo, cambiar su ubicación, optimizar su batería y más. </p>
|
| 8 |
-
<p>Una de las principales ventajas de 3uTools es que te da acceso a un montón de contenido que Apple suele restringir o no ofrece en la App Store. Puede descargar e instalar miles de tonos de llamada, fondos de pantalla, juegos, aplicaciones, temas y otros artículos de la tienda en línea de 3uTools. También puedes personalizar la apariencia y funcionalidad de tu dispositivo con varios ajustes y mods. </p>
|
| 9 |
-
<p>Otro beneficio de 3uTools es que tiene algunas características de jailbreak que pueden ayudarle a desbloquear su dispositivo o ganar más control sobre él. Puede usar 3uTools para liberar el dispositivo con un solo clic, eliminar el bloqueo de activación de iCloud, omitir la pantalla de bloqueo de código de acceso, entrar o salir del modo de recuperación, solucionar problemas comunes de iOS y más. </p>
|
| 10 |
-
<h2>¿Por qué descargar 3uTools para Mac Air? </h2>
|
| 11 |
-
|
| 12 |
-
<ul>
|
| 13 |
-
<li>Es gratis. A diferencia de otros administradores de dispositivos iOS que cobran una tarifa o tienen características limitadas en sus versiones gratuitas, 3uTools es completamente gratuito y no tiene costos ocultos o anuncios. </li>
|
| 14 |
-
<li>Es fácil de usar. A diferencia de otros administradores de dispositivos iOS que tienen interfaces complicadas o requieren habilidades técnicas, 3uTools tiene una interfaz simple y fácil de usar que cualquiera puede usar sin ningún tipo de molestia. </li>
|
| 15 |
-
<li>Es muy versátil. A diferencia de otros administradores de dispositivos iOS que solo ofrecen funciones básicas o se centran en aspectos específicos de la gestión de iOS, 3uTools ofrece una amplia gama de funciones y características que cubren casi todos los aspectos de la gestión de iOS. </li>
|
| 16 |
-
<li pasos para descargar e instalar 3uTools en tu Mac Air usando WineBottler:</p>
|
| 17 |
-
<ol>
|
| 18 |
-
<li>Descargar WineBottler desde <a href="">este enlace</a> e instalarlo en su Mac Air. Puede elegir la versión estable o la versión de desarrollo, dependiendo de su preferencia. </li>
|
| 19 |
-
<li>Descargue 3uTools desde <a href="">este enlace</a> y guárdelo en su Mac Air. Asegúrese de descargar la última versión de 3uTools para Windows.</li>
|
| 20 |
-
<li>Inicie WineBottler y haga clic en la pestaña "Avanzado". Luego, haga clic en el botón "Seleccionar archivo..." y elija el archivo 3uTools.exe que descargó en el paso anterior. </li>
|
| 21 |
-
<li>En la sección "Modo de instalación", elija "Copiar archivo (Programa) y todos los archivos de la carpeta al paquete de aplicaciones". Esto creará una aplicación independiente para 3uTools que puede ejecutar en su Mac Air.</li>
|
| 22 |
-
<li>En la sección "Winetricks", marque la casilla para "dotnet40". Esto instalará el . NET Framework 4.0, que es necesario para que 3uTools funcione correctamente. </li>
|
| 23 |
-
<li>En la sección "Info.plist", introduzca un nombre para su aplicación, como "3uTools for Mac". También puede cambiar el icono si lo desea. </li>
|
| 24 |
-
<li>Haga clic en el botón "Instalar" y elija una ubicación para guardar su aplicación. WineBottler comenzará a crear el envoltorio para 3uTools e instalarlo en su Mac Air.</li>
|
| 25 |
-
|
| 26 |
-
</ol>
|
| 27 |
-
<h2>¿Cómo usar 3uTools en Mac Air? </h2>
|
| 28 |
-
<p>Ahora que ha descargado e instalado 3uTools en su Mac Air usando WineBottler, puede comenzar a usarlo para administrar su dispositivo iOS. Aquí hay algunos consejos sobre cómo usar 3uTools en Mac Air:</p>
|
| 29 |
-
<p></p>
|
| 30 |
-
<ul>
|
| 31 |
-
<li>Conecte su dispositivo iOS a su Mac Air utilizando un cable USB. Asegúrese de confiar en su ordenador en el dispositivo y habilitar el modo de depuración USB. </li>
|
| 32 |
-
<li>Abra 3uTools en su Mac Air y espere a que detecte su dispositivo. Debería ver información básica sobre su dispositivo, como modelo, número de serie, nivel de batería, etc.</li>
|
| 33 |
-
<li>En el lado izquierdo de la interfaz 3uTools, verá diferentes categorías de funciones y características que puede usar. Por ejemplo, puede hacer clic en "ToolBox" para acceder a varias herramientas, como copias de seguridad y restauración, migración de datos, conmutador de firmware, etc.</li>
|
| 34 |
-
<li>También puede hacer clic en "Flash & JB" para acceder a algunas características de jailbreak, como jailbreak de un solo clic, desbloqueo de iCloud, bypass de código de acceso, etc. Sin embargo, tenga cuidado al usar estas características, ya que podrían anular la garantía o dañar su dispositivo. </li>
|
| 35 |
-
<li>También puede hacer clic en "Easy Flash" para actualizar o bajar el firmware fácilmente. Puede elegir entre diferentes versiones de iOS que están disponibles para su dispositivo. Sin embargo, asegúrese de hacer una copia de seguridad de sus datos antes de flashear el dispositivo. </li>
|
| 36 |
-
<li>También puede hacer clic en "Aplicaciones" para acceder a miles de tonos de llamada, fondos de pantalla, juegos, aplicaciones, temas y otro contenido que puede descargar e instalar en su dispositivo. Puedes navegar por categorías o buscar por palabras clave. También puedes previsualizar el contenido antes de descargarlo. </li>
|
| 37 |
-
<li>Para descargar e instalar cualquier contenido de 3uTools, simplemente haga clic en el botón "Descargar" junto a él. Una vez completada la descarga, haga clic en el botón "Instalar" para transferirlo a su dispositivo. Es posible que necesite introducir su ID de Apple y contraseña si se le solicita. </li>
|
| 38 |
-
</ul>
|
| 39 |
-
<h2>Conclusión</h2>
|
| 40 |
-
|
| 41 |
-
<p>Si quieres probar 3uTools por ti mismo, puedes descargarlo desde <a href="">este enlace</a> y seguir los pasos que hemos descrito anteriormente. Podrás conectar tu dispositivo iOS a tu Mac Air y administrar diferentes configuraciones y características. También podrás acceder a un montón de contenido que Apple normalmente no te permite instalar. Además, podrás usar algunas funciones de jailbreak si necesitas desbloquear tu dispositivo o obtener más control sobre él. </p>
|
| 42 |
-
<p>Entonces, ¿qué estás esperando? Descargar 3uTools para Mac Air hoy y disfrutar de un nuevo nivel de gestión de iOS con 3uTools! </p>
|
| 43 |
-
<h2>Preguntas frecuentes</h2>
|
| 44 |
-
<p>Aquí hay algunas preguntas y respuestas frecuentes sobre 3uTools para Mac Air:</p>
|
| 45 |
-
<ol>
|
| 46 |
-
<li><b>¿Es seguro usar 3uTools? </b><br>
|
| 47 |
-
Sí, 3uTools es seguro de usar siempre y cuando lo descargue desde el sitio web oficial o una fuente confiable. Sin embargo, debe tener cuidado al usar algunas de las funciones de jailbreak o descargar contenido de fuentes desconocidas, ya que podrían dañar su dispositivo o comprometer su seguridad. </li>
|
| 48 |
-
<li><b>¿Es legal usar 3uTools? </b><br>
|
| 49 |
-
Sí, 3uTools es legal de usar ya que no viola ninguna ley o reglamento. Sin embargo, debes saber que el uso de algunas de las funciones de jailbreak o la descarga de contenido que no está autorizado por Apple podría anular tu garantía o violar sus términos de servicio. </li>
|
| 50 |
-
<li><b> ¿3uTools funciona en otros modelos de Mac? </b><br>
|
| 51 |
-
Sí, 3uTools funciona en otros modelos de Mac siempre y cuando tengan Mac OS X 10.6 o posterior. Puede utilizar el mismo método que hemos descrito anteriormente para descargar e instalar 3uTools en su Mac usando WineBottler.</li>
|
| 52 |
-
<li><b> ¿3uTools funciona en otros dispositivos iOS? </b><br>
|
| 53 |
-
Sí, 3uTools funciona en otros dispositivos iOS, como iPhone, iPad, iPod Touch, Apple TV, etc. Puede conectar cualquier dispositivo iOS a su Mac Air y usar 3uTools para administrarlo. </li>
|
| 54 |
-
<li><b>¿Cómo puedo actualizar 3uTools en mi Mac Air? </b><br>
|
| 55 |
-
|
| 56 |
-
</ol></p> 64aa2da5cf<br />
|
| 57 |
-
<br />
|
| 58 |
-
<br />
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|
spaces/Benson/text-generation/Examples/Descargar Arthdal Chronicles Temporada 2.md
DELETED
|
@@ -1,77 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Cómo descargar Arthdal Chronicles Temporada 2</h1>
|
| 3 |
-
<p>Si eres un fan de los dramas históricos de fantasía ambientados en la antigua Corea, probablemente hayas oído hablar de <strong>Arthdal Chronicles</strong>. Esta serie épica cuenta la historia de tres héroes que luchan por el poder, el amor y la supervivencia en una tierra mítica llamada Arth. Arthdal Chronicles se ha convertido en uno de los dramas coreanos más populares de todos los tiempos, con su rica construcción del mundo, su reparto estelar, sus impresionantes imágenes y su cautivadora banda sonora. </p>
|
| 4 |
-
<h2>descargar arthdal chronicles temporada 2</h2><br /><p><b><b>Download Zip</b> ————— <a href="https://bltlly.com/2v6MBI">https://bltlly.com/2v6MBI</a></b></p><br /><br />
|
| 5 |
-
<p>Pero si ya has visto la primera temporada de Arthdal Chronicles, te estarás preguntando: <em>¿Cuándo saldrá la temporada 2? ¿Y cómo puedo descargarlo? </em> Bueno, estás de suerte, porque en este artículo, responderemos estas preguntas y más. Le daremos una visión general de lo que puede esperar de la temporada 2, y le mostraremos los mejores servicios de streaming y plataformas para verlo en línea o fuera de línea. Así que, sin más preámbulos, ¡empecemos! </p>
|
| 6 |
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<h2>Introducción</h2>
|
| 7 |
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<p><strong>¿Qué es Arthdal Chronicles? </strong></p>
|
| 8 |
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<p>Arthdal Chronicles es una serie de drama de fantasía histórica que se estrenó el 1 de junio de 2019, en tvN y Netflix. Está dirigida por Kim Won-seok, quien también dirigió los aclamados dramas Signal y Misaeng, y escrita por Kim Young-hyun y Park Sang-yeon, quien también escribió los dramas de éxito Queen Seondeok y Six Flying Dragons. La serie tiene un presupuesto de más de 54 mil millones de won (unos 46 millones de dólares), por lo que es uno de los dramas coreanos más caros jamás producidos. </p>
|
| 9 |
-
|
| 10 |
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<p><strong>¿Por qué deberías verlo? </strong></p>
|
| 11 |
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<p>Si estás buscando un drama que tenga una historia épica, un mundo rico, un reparto estelar, imágenes impresionantes y una banda sonora cautivadora, entonces Arthdal Chronicles es para ti. Estas son algunas de las razones por las que deberías verlo:</p>
|
| 12 |
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<p></p>
|
| 13 |
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<ul>
|
| 14 |
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<li><strong>Historia épica:</strong> Arthdal Chronicles tiene una trama compleja e intrigante que abarca diferentes épocas y regiones. Explora temas como la política, la religión, la cultura, la identidad, la guerra, el amor y el destino. También tiene muchos giros y vueltas que te mantendrán en el borde de tu asiento. </li>
|
| 15 |
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<li><strong>Rich worldbuilding:</strong> Arthdal Chronicles tiene un escenario detallado e inmersivo que se inspira en varias fuentes históricas y míticas. Crea un mundo único y diverso donde existen diferentes tribus, razas, idiomas, costumbres, creencias y leyendas. También tiene mucho simbolismo y referencias que añaden profundidad y significado a la historia. </li>
|
| 16 |
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<li><strong>Reparto estelar:</strong> Arthdal Chronicles tiene un impresionante elenco de actores talentosos que ofrecen actuaciones excepcionales. Song Joong-ki interpreta dos papeles: Eunseom y su hermano gemelo Saya, que tienen personalidades y destinos contrastantes. Kim Ji-won interpreta a Tanya, que pasa de ser una niña ingenua a una poderosa chaman. Jang Dong-gun interpreta a Ta-gon, que es ambicioso, carismático y despiadado. El reparto incluye a Kim Ok-bin como Taealha, el amante y aliado de Ta-gon; Park Hae-joon como Moo-baek, el amigo leal de Ta-gon; Kim Eui-sung como San-woong, el padre de Ta-gon; Choi Moo-sung como Yeol-son, el padre de Tanya; y muchos más. </li>
|
| 17 |
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<li><strong>Impresionantes efectos visuales:</strong> Arthdal Chronicles tiene un alto valor de producción que muestra la belleza y diversidad de Arth. Tiene paisajes impresionantes, trajes, accesorios y efectos especiales que crean una atmósfera realista e inmersiva. También tiene muchas escenas de acción, como batallas, persecuciones y escapes, que son emocionantes y emocionantes. </li>
|
| 18 |
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| 19 |
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</ul>
|
| 20 |
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<p>Como puedes ver, Arthdal Chronicles es un drama que tiene algo para todos. Si eres un fan de la historia, la fantasía, el romance, la acción o el misterio, encontrarás algo para disfrutar en esta serie. </p>
|
| 21 |
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<h2>Resumen de la temporada 2</h2>
|
| 22 |
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<p><strong>¿Qué esperar de la trama? </strong></p>
|
| 23 |
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<p>La temporada 1 de Arthdal Chronicles terminó con un cliffhanger que dejó a muchos fans ansiosos por más. El último episodio reveló que Eunseom y Saya son gemelos que fueron separados al nacer por el esquema de Tagon. Eunseom escapó de Arthdal con la ayuda de los neandertales y conoció a una nueva tribu llamada Ago. Saya creció en Arthdal como hijo de Tagon y se convirtió en el amante de Tanya. Tagon se convirtió en el rey de Arthdal después de matar a su padre y sus rivales. Tanya se convirtió en la alta sacerdotisa de Arthdal después de heredar el poder de Aramun. Y Taealha quedó embarazada del hijo de Tagon, que podría ser el niño profetizado del desastre. </p>
|
| 24 |
-
<p>Entonces, ¿qué pasará en la temporada 2? Según fuentes oficiales y tráileres, la temporada 2 continuará la historia de los tres héroes y sus destinos. Eunseom tratará de unir a las tribus de Iark contra Arthdal. Saya tratará de descubrir los secretos de su pasado y su verdadera identidad. Tanya tratará de proteger a su pueblo y a su amor de la tiranía de Tagon. Y Tagon tratará de mantener su poder y su visión de Arthdal. La temporada 2 también presentará nuevos personajes y conflictos, como el misterioso Asa Ron (interpretado por Lee Do-hyun), que es el líder de la tribu Hwinsan y el primo de Tanya; el poderoso Nihon (interpretado por Kim Nam-gil), que es el rey de otra tierra y enemigo de Tagon; y la misteriosa voz (interpretada por Park Bo-gum), que es el narrador de la historia y podría tener una conexión con Aramun.</p>
|
| 25 |
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<p><strong>¿Qué esperar del reparto? </strong></p>
|
| 26 |
-
|
| 27 |
-
<ul>
|
| 28 |
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<li><strong>Song Joong-ki como Eunseom/Saya:</strong> Song Joong-ki interpreta dos papeles: Eunseom, que es un medio-neandertal medio humano con un corazón puro y una voluntad fuerte; y Saya, que es el hermano gemelo de Eunseom con una mente astuta y un lado oscuro. </li>
|
| 29 |
-
<li><strong>Kim Ji-won como Tanya:</strong> Kim Ji-won interpreta a Tanya, que es un chaman y el heredero de la tribu Wahan. Es amiga de la infancia de Eunseom y amante de Saya. También se convierte en la alta sacerdotisa de Arthdal después de heredar el poder de Aramun.</li>
|
| 30 |
-
<li><strong>Jang Dong-gun como Ta-gon:</strong> Jang Dong-gun interpreta a Ta-gon, que es un guerrero carismático y el líder de la tribu Saenyeok. Es el amante de Taealha y el padre adoptivo de Saya. También se convierte en el rey de Arthdal después de matar a su padre y a sus rivales. </li>
|
| 31 |
-
<li><strong>Kim Ok-bin como Taealha:</strong> Kim Ok-bin juega Taealha, que es una mujer hermosa y ambiciosa y la hija del líder de la tribu Hae. Ella es la amante y aliada de Ta-gon. También queda embarazada del hijo de Ta-gon, que podría ser el niño profetizado del desastre. </li>
|
| 32 |
-
<li><strong>Park Hae-joon como Moo-baek:</strong> Park Hae-joon interpreta a Moo-baek, que es un guerrero leal y valiente y el líder de las fuerzas Daekan. Es amigo y partidario de Ta-gon. </li>
|
| 33 |
-
<li><strong>Kim Eui-sung como San-woong:</strong> Kim Eui-sung interpreta a San-woong, quien es el antiguo líder de la tribu Saenyeok y el padre de Ta-gon. Es asesinado por Ta-gon en la temporada 1.</li>
|
| 34 |
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<li><strong>Choi Moo-sung como Yeol-son:</strong> Choi Moo-sung interpreta a Yeol-son, quien es el líder de la tribu Wahan y el padre de Tanya. Es capturado por Ta-gon en la temporada 1.</li>
|
| 35 |
-
<li><strong>Lee Do-hyun como Asa Ron:</strong> Lee Do-hyun interpreta a Asa Ron, quien es el líder de la tribu Hwinsan y primo de Tanya. Es un personaje misterioso y poderoso que tiene una conexión con Aramun.</li>
|
| 36 |
-
|
| 37 |
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<li><strong>Park Bo-gum como la voz:</strong> Park Bo-gum toca la voz, quien es el narrador de la historia y podría tener una conexión con Aramun. Es un personaje misterioso y misterioso que guía a los espectadores a través de la historia. </li>
|
| 38 |
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</ul>
|
| 39 |
-
<p><strong>¿Qué esperar de la fecha de lanzamiento? </strong></p>
|
| 40 |
-
<p>La fecha de lanzamiento de la temporada 2 de Arthdal Chronicles aún no se ha confirmado oficialmente, pero hay algunas pistas y rumores que sugieren cuándo podría salir. Según algunas fuentes, se suponía que la temporada 2 comenzaría a filmarse a principios de 2020, pero se retrasó debido a la pandemia COVID-19. Sin embargo, algunos informes afirman que el rodaje se ha reanudado a finales de 2020 o principios de 2021, y que el reparto y el equipo han sido vistos en varios lugares. Según esta información, algunos fans especulan que la temporada 2 podría estrenarse a finales de 2021 o principios de 2022. Sin embargo, esto aún no está confirmado, por lo que tenemos que esperar un anuncio oficial de tvN o Netflix.</p>
|
| 41 |
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<p>En cuanto al número y duración de los episodios, la temporada 2 podría seguir el mismo formato que la temporada 1, que tenía 18 episodios divididos en tres partes: Parte 1 (episodios 1-6), Parte 2 (episodios 7-12), y Parte 3 (episodios 13-18). Cada episodio duró aproximadamente una hora, excepto el primero y el último, que duraron unos 90 minutos. Por lo tanto, podemos esperar que la temporada 2 tenga una estructura y duración similares. </p>
|
| 42 |
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<h2>Cómo descargar la temporada 2</h2>
|
| 43 |
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<p><strong>Los mejores servicios de streaming</strong></p>
|
| 44 |
-
<p>Si quieres ver la temporada 2 de Arthdal Chronicles online, tienes varias opciones para elegir. Los mejores servicios de streaming que ofrecen Arthdal Chronicles temporada 2 son:</p>
|
| 45 |
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<ul>
|
| 46 |
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| 47 |
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<li><strong>tvN:</strong> tvN es la emisora original de Arthdal Chronicles en Corea, lo que significa que tiene los primeros derechos para emitir la serie en Corea. tvN ofrece opciones de transmisión y reproducción en directo, así como clips y entrevistas entre bastidores. Puedes ver la temporada 2 de Arthdal Chronicles en tvN con una suscripción por cable o satélite que incluye el canal tvN. </li>
|
| 48 |
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<li><strong>Viki:</strong> Viki es un servicio de streaming especializado en dramas y películas asiáticas, incluyendo dramas coreanos. Viki ofrece subtítulos en varios idiomas, comentarios y valoraciones de los fans, y funciones interactivas. Puedes ver la temporada 2 de Arthdal Chronicles en Viki con un plan de suscripción que comienza desde $4.99 al mes. </li>
|
| 49 |
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</ul>
|
| 50 |
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<p><strong>Las mejores plataformas</strong></p>
|
| 51 |
-
<p>Si quieres ver la temporada 2 de Arthdal Chronicles sin conexión, tienes varias opciones para elegir. Las mejores plataformas que te permiten descargar Arthdal Chronicles temporada 2 son:</p>
|
| 52 |
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| Plataforma | Dispositivo | Aplicación | Pros | Contras | | -- - - - | -- - - - - - - - - - - - | Netflix | Netflix | Smartphone, tableta, ordenador portátil, Smart TV, etc. | Aplicación de Netflix | - Vídeo y audio de alta calidad - Subtítulos y doblaje en varios idiomas - Opciones de visualización fuera de línea Gran biblioteca de contenido | - Requiere un plan de suscripción - Cuota de descarga limitada y fecha de vencimiento - No disponible en algunas regiones | | tvN | Smartphone, tableta, ordenador portátil, etc. | tvN aplicación o sitio web | - Live streaming and replay options - Detrás de cámaras clips y entrevistas - Emisora original de Arthdal Chronicles | - Requiere una suscripción por cable o satélite - Solo disponible en Corea - No hay opciones de visualización sin conexión | | Viki | Smartphone, tableta, ordenador portátil, smart TV, etc. | Viki aplicación o sitio web | - Subtítulos en varios idiomas - Comentarios y valoraciones de los fans Características interactivas - Opciones de visualización fuera de línea | - Requiere un plan de suscripción - Disponibilidad retrasada de episodios - Biblioteca limitada de contenido | <p><strong>Los mejores consejos</strong></p>
|
| 53 |
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|
| 54 |
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<ul>
|
| 55 |
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<li><strong>Elija el servicio de streaming adecuado:</strong> Dependiendo de su ubicación, preferencias y presupuesto, es posible que desee elegir el servicio de streaming que más le convenga. Por ejemplo, si vive fuera de Corea y desea ver la temporada 2 de Arthdal Chronicles lo antes posible, es posible que desee elegir Netflix. Si vives en Corea y quieres ver la temporada 2 de Arthdal Chronicles en vivo o reproducida, quizás quieras elegir tvN. Y si quieres ver Arthdal Chronicles temporada 2 con subtítulos en tu idioma e interactuar con otros fans, es posible que desee elegir Viki.</li>
|
| 56 |
-
<li><strong>Elija la plataforma correcta:</strong> Dependiendo de su dispositivo, aplicación y conexión a Internet, es posible que desee elegir la plataforma que funciona mejor para usted. Por ejemplo, si tienes un smartphone o tablet con suficiente espacio de almacenamiento y una buena conexión wifi, es posible que quieras descargar Arthdal Chronicles temporada 2 en tu dispositivo móvil. Si usted tiene un ordenador portátil o smart TV con una pantalla grande y una conexión rápida a Internet, es posible que desee transmitir Arthdal Chronicles temporada 2 en su computadora o TV.</li>
|
| 57 |
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<li><strong>Elija el momento adecuado:</strong> Dependiendo de su horario, disponibilidad y paciencia, es posible que desee elegir el momento que sea más conveniente para usted. Por ejemplo, si quieres ver la segunda temporada de Arthdal Chronicles tan pronto como salga al mercado, quizás quieras quedarte despierto hasta tarde o levantarte temprano para ver el estreno. Si quieres ver Arthdal Chronicles temporada 2 sin interrupciones o spoilers, es posible que desee esperar hasta que todos los episodios están disponibles y atracón de verlos. Y si quieres ver Arthdal Chronicles temporada 2 sin gastar demasiado dinero, es posible que desee esperar a una prueba gratuita o una oferta de descuento del servicio de streaming. </li>
|
| 58 |
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</ul>
|
| 59 |
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<h2>Conclusión</h2>
|
| 60 |
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|
| 61 |
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Entonces, ¿qué estás esperando? Si eres un fan de los dramas históricos de fantasía ambientados en la antigua Corea, no deberías perderte la temporada 2 de Arthdal Chronicles. Es un drama que tiene algo para todos. Ya sea que esté buscando historia, fantasía, romance, acción o misterio, encontrará algo para disfrutar en esta serie. Así que no dudes en ver la segunda temporada de Arthdal Chronicles lo antes posible. Y no olvides compartir tus pensamientos y opiniones con otros fans. ¡No te arrepentirás! </p>
|
| 62 |
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<h2>Preguntas frecuentes</h2>
|
| 63 |
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<p>Aquí están algunas de las preguntas y respuestas más comunes sobre la temporada 2 de Arthdal Chronicles:</p>
|
| 64 |
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<ol>
|
| 65 |
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<li><strong>Q1: ¿Está Arthdal Chronicles basado en una historia real? </strong></li>
|
| 66 |
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<li>A1: No, Arthdal Chronicles no se basa en una historia real. Es una historia ficticia inspirada en varias fuentes históricas y míticas, como la Edad del Bronce, la era Gojoseon, la civilización sumeria, la mitología nórdica y el folclore coreano. Sin embargo, algunos de los nombres, lugares y eventos de la serie podrían tener algunas similitudes o referencias a los reales. </li>
|
| 67 |
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<li><strong>Q2: ¿Cuántas temporadas hay en Arthdal Chronicles? </strong></li>
|
| 68 |
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<li>A2: Hay dos temporadas en Arthdal Chronicles hasta ahora. La temporada 1 tiene 18 episodios que se emitieron del 1 de junio al 22 de septiembre de 2019. La temporada 2 aún no se ha confirmado oficialmente, pero se espera que tenga un número similar de episodios y aire a finales de 2021 o principios de 2022. </li>
|
| 69 |
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<li><strong>Q3: ¿Quiénes son los gemelos Eunseom y Saya? </strong></li>
|
| 70 |
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<li>A3: Eunseom y Saya son gemelos que nacieron de una madre humana y un padre neandertal. Fueron separados al nacer por el esquema de Ta-gon. Eunseom creció con la tribu Wahan y tiene un corazón puro y una voluntad fuerte. Saya creció con Ta-gon y tiene una mente astuta y un lado oscuro. Ambos tienen habilidades especiales, como comunicarse con los animales y ver el futuro. </li>
|
| 71 |
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<li><strong>Q4: ¿Cuál es el significado de la espada de Aramun? </strong></li>
|
| 72 |
-
|
| 73 |
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<li><strong>Q5: ¿Habrá una temporada 3 de Arthdal Chronicles? </strong></li>
|
| 74 |
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<li>A5: Todavía no hay confirmación oficial, pero existe la posibilidad de que haya una temporada 3 de Arthdal Chronicles. La serie tiene mucho potencial y popularidad, y la historia aún no se ha resuelto por completo. Los creadores también han insinuado que tienen planes para más temporadas en el futuro. Sin embargo, esto dependerá de varios factores, como las calificaciones, el presupuesto, la disponibilidad del reparto y la demanda de los fans. </li>
|
| 75 |
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</ol></p> 64aa2da5cf<br />
|
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spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/vendored/requests/packages/urllib3/__init__.py
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"""
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urllib3 - Thread-safe connection pooling and re-using.
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"""
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__author__ = 'Andrey Petrov ([email protected])'
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__license__ = 'MIT'
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__version__ = ''
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from . import exceptions
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/johabfreq.py
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|
| 1 |
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######################## BEGIN LICENSE BLOCK ########################
|
| 2 |
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# The Original Code is Mozilla Communicator client code.
|
| 3 |
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#
|
| 4 |
-
# The Initial Developer of the Original Code is
|
| 5 |
-
# Netscape Communications Corporation.
|
| 6 |
-
# Portions created by the Initial Developer are Copyright (C) 1998
|
| 7 |
-
# the Initial Developer. All Rights Reserved.
|
| 8 |
-
#
|
| 9 |
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# Contributor(s):
|
| 10 |
-
# Mark Pilgrim - port to Python
|
| 11 |
-
#
|
| 12 |
-
# This library is free software; you can redistribute it and/or
|
| 13 |
-
# modify it under the terms of the GNU Lesser General Public
|
| 14 |
-
# License as published by the Free Software Foundation; either
|
| 15 |
-
# version 2.1 of the License, or (at your option) any later version.
|
| 16 |
-
#
|
| 17 |
-
# This library is distributed in the hope that it will be useful,
|
| 18 |
-
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
| 19 |
-
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
| 20 |
-
# Lesser General Public License for more details.
|
| 21 |
-
#
|
| 22 |
-
# You should have received a copy of the GNU Lesser General Public
|
| 23 |
-
# License along with this library; if not, write to the Free Software
|
| 24 |
-
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
|
| 25 |
-
# 02110-1301 USA
|
| 26 |
-
######################### END LICENSE BLOCK #########################
|
| 27 |
-
|
| 28 |
-
# The frequency data itself is the same as euc-kr.
|
| 29 |
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# This is just a mapping table to euc-kr.
|
| 30 |
-
|
| 31 |
-
JOHAB_TO_EUCKR_ORDER_TABLE = {
|
| 32 |
-
0x8861: 0,
|
| 33 |
-
0x8862: 1,
|
| 34 |
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0x8865: 2,
|
| 35 |
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0x8868: 3,
|
| 36 |
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0x8869: 4,
|
| 37 |
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0x886A: 5,
|
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0x886B: 6,
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0x8871: 7,
|
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0x8873: 8,
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0x8874: 9,
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0x8875: 10,
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0x8876: 11,
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0x8877: 12,
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0x8878: 13,
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| 46 |
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0x8879: 14,
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0x887B: 15,
|
| 48 |
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0x887C: 16,
|
| 49 |
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0x887D: 17,
|
| 50 |
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0x8881: 18,
|
| 51 |
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0x8882: 19,
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| 52 |
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0x8885: 20,
|
| 53 |
-
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| 828 |
-
0xA097: 796,
|
| 829 |
-
0xA098: 797,
|
| 830 |
-
0xA0A1: 798,
|
| 831 |
-
0xA0A2: 799,
|
| 832 |
-
0xA0A9: 800,
|
| 833 |
-
0xA0B7: 801,
|
| 834 |
-
0xA0E1: 802,
|
| 835 |
-
0xA0E2: 803,
|
| 836 |
-
0xA0E5: 804,
|
| 837 |
-
0xA0E9: 805,
|
| 838 |
-
0xA0EB: 806,
|
| 839 |
-
0xA0F1: 807,
|
| 840 |
-
0xA0F3: 808,
|
| 841 |
-
0xA0F5: 809,
|
| 842 |
-
0xA0F7: 810,
|
| 843 |
-
0xA0F8: 811,
|
| 844 |
-
0xA0FD: 812,
|
| 845 |
-
0xA141: 813,
|
| 846 |
-
0xA142: 814,
|
| 847 |
-
0xA145: 815,
|
| 848 |
-
0xA149: 816,
|
| 849 |
-
0xA151: 817,
|
| 850 |
-
0xA153: 818,
|
| 851 |
-
0xA155: 819,
|
| 852 |
-
0xA156: 820,
|
| 853 |
-
0xA157: 821,
|
| 854 |
-
0xA161: 822,
|
| 855 |
-
0xA162: 823,
|
| 856 |
-
0xA165: 824,
|
| 857 |
-
0xA169: 825,
|
| 858 |
-
0xA175: 826,
|
| 859 |
-
0xA176: 827,
|
| 860 |
-
0xA177: 828,
|
| 861 |
-
0xA179: 829,
|
| 862 |
-
0xA181: 830,
|
| 863 |
-
0xA1A1: 831,
|
| 864 |
-
0xA1A2: 832,
|
| 865 |
-
0xA1A4: 833,
|
| 866 |
-
0xA1A5: 834,
|
| 867 |
-
0xA1A9: 835,
|
| 868 |
-
0xA1AB: 836,
|
| 869 |
-
0xA1B1: 837,
|
| 870 |
-
0xA1B3: 838,
|
| 871 |
-
0xA1B5: 839,
|
| 872 |
-
0xA1B7: 840,
|
| 873 |
-
0xA1C1: 841,
|
| 874 |
-
0xA1C5: 842,
|
| 875 |
-
0xA1D6: 843,
|
| 876 |
-
0xA1D7: 844,
|
| 877 |
-
0xA241: 845,
|
| 878 |
-
0xA245: 846,
|
| 879 |
-
0xA249: 847,
|
| 880 |
-
0xA253: 848,
|
| 881 |
-
0xA255: 849,
|
| 882 |
-
0xA257: 850,
|
| 883 |
-
0xA261: 851,
|
| 884 |
-
0xA265: 852,
|
| 885 |
-
0xA269: 853,
|
| 886 |
-
0xA273: 854,
|
| 887 |
-
0xA275: 855,
|
| 888 |
-
0xA281: 856,
|
| 889 |
-
0xA282: 857,
|
| 890 |
-
0xA283: 858,
|
| 891 |
-
0xA285: 859,
|
| 892 |
-
0xA288: 860,
|
| 893 |
-
0xA289: 861,
|
| 894 |
-
0xA28A: 862,
|
| 895 |
-
0xA28B: 863,
|
| 896 |
-
0xA291: 864,
|
| 897 |
-
0xA293: 865,
|
| 898 |
-
0xA295: 866,
|
| 899 |
-
0xA297: 867,
|
| 900 |
-
0xA29B: 868,
|
| 901 |
-
0xA29D: 869,
|
| 902 |
-
0xA2A1: 870,
|
| 903 |
-
0xA2A5: 871,
|
| 904 |
-
0xA2A9: 872,
|
| 905 |
-
0xA2B3: 873,
|
| 906 |
-
0xA2B5: 874,
|
| 907 |
-
0xA2C1: 875,
|
| 908 |
-
0xA2E1: 876,
|
| 909 |
-
0xA2E5: 877,
|
| 910 |
-
0xA2E9: 878,
|
| 911 |
-
0xA341: 879,
|
| 912 |
-
0xA345: 880,
|
| 913 |
-
0xA349: 881,
|
| 914 |
-
0xA351: 882,
|
| 915 |
-
0xA355: 883,
|
| 916 |
-
0xA361: 884,
|
| 917 |
-
0xA365: 885,
|
| 918 |
-
0xA369: 886,
|
| 919 |
-
0xA371: 887,
|
| 920 |
-
0xA375: 888,
|
| 921 |
-
0xA3A1: 889,
|
| 922 |
-
0xA3A2: 890,
|
| 923 |
-
0xA3A5: 891,
|
| 924 |
-
0xA3A8: 892,
|
| 925 |
-
0xA3A9: 893,
|
| 926 |
-
0xA3AB: 894,
|
| 927 |
-
0xA3B1: 895,
|
| 928 |
-
0xA3B3: 896,
|
| 929 |
-
0xA3B5: 897,
|
| 930 |
-
0xA3B6: 898,
|
| 931 |
-
0xA3B7: 899,
|
| 932 |
-
0xA3B9: 900,
|
| 933 |
-
0xA3BB: 901,
|
| 934 |
-
0xA461: 902,
|
| 935 |
-
0xA462: 903,
|
| 936 |
-
0xA463: 904,
|
| 937 |
-
0xA464: 905,
|
| 938 |
-
0xA465: 906,
|
| 939 |
-
0xA468: 907,
|
| 940 |
-
0xA469: 908,
|
| 941 |
-
0xA46A: 909,
|
| 942 |
-
0xA46B: 910,
|
| 943 |
-
0xA46C: 911,
|
| 944 |
-
0xA471: 912,
|
| 945 |
-
0xA473: 913,
|
| 946 |
-
0xA475: 914,
|
| 947 |
-
0xA477: 915,
|
| 948 |
-
0xA47B: 916,
|
| 949 |
-
0xA481: 917,
|
| 950 |
-
0xA482: 918,
|
| 951 |
-
0xA485: 919,
|
| 952 |
-
0xA489: 920,
|
| 953 |
-
0xA491: 921,
|
| 954 |
-
0xA493: 922,
|
| 955 |
-
0xA495: 923,
|
| 956 |
-
0xA496: 924,
|
| 957 |
-
0xA497: 925,
|
| 958 |
-
0xA49B: 926,
|
| 959 |
-
0xA4A1: 927,
|
| 960 |
-
0xA4A2: 928,
|
| 961 |
-
0xA4A5: 929,
|
| 962 |
-
0xA4B3: 930,
|
| 963 |
-
0xA4E1: 931,
|
| 964 |
-
0xA4E2: 932,
|
| 965 |
-
0xA4E5: 933,
|
| 966 |
-
0xA4E8: 934,
|
| 967 |
-
0xA4E9: 935,
|
| 968 |
-
0xA4EB: 936,
|
| 969 |
-
0xA4F1: 937,
|
| 970 |
-
0xA4F3: 938,
|
| 971 |
-
0xA4F5: 939,
|
| 972 |
-
0xA4F7: 940,
|
| 973 |
-
0xA4F8: 941,
|
| 974 |
-
0xA541: 942,
|
| 975 |
-
0xA542: 943,
|
| 976 |
-
0xA545: 944,
|
| 977 |
-
0xA548: 945,
|
| 978 |
-
0xA549: 946,
|
| 979 |
-
0xA551: 947,
|
| 980 |
-
0xA553: 948,
|
| 981 |
-
0xA555: 949,
|
| 982 |
-
0xA556: 950,
|
| 983 |
-
0xA557: 951,
|
| 984 |
-
0xA561: 952,
|
| 985 |
-
0xA562: 953,
|
| 986 |
-
0xA565: 954,
|
| 987 |
-
0xA569: 955,
|
| 988 |
-
0xA573: 956,
|
| 989 |
-
0xA575: 957,
|
| 990 |
-
0xA576: 958,
|
| 991 |
-
0xA577: 959,
|
| 992 |
-
0xA57B: 960,
|
| 993 |
-
0xA581: 961,
|
| 994 |
-
0xA585: 962,
|
| 995 |
-
0xA5A1: 963,
|
| 996 |
-
0xA5A2: 964,
|
| 997 |
-
0xA5A3: 965,
|
| 998 |
-
0xA5A5: 966,
|
| 999 |
-
0xA5A9: 967,
|
| 1000 |
-
0xA5B1: 968,
|
| 1001 |
-
0xA5B3: 969,
|
| 1002 |
-
0xA5B5: 970,
|
| 1003 |
-
0xA5B7: 971,
|
| 1004 |
-
0xA5C1: 972,
|
| 1005 |
-
0xA5C5: 973,
|
| 1006 |
-
0xA5D6: 974,
|
| 1007 |
-
0xA5E1: 975,
|
| 1008 |
-
0xA5F6: 976,
|
| 1009 |
-
0xA641: 977,
|
| 1010 |
-
0xA642: 978,
|
| 1011 |
-
0xA645: 979,
|
| 1012 |
-
0xA649: 980,
|
| 1013 |
-
0xA651: 981,
|
| 1014 |
-
0xA653: 982,
|
| 1015 |
-
0xA661: 983,
|
| 1016 |
-
0xA665: 984,
|
| 1017 |
-
0xA681: 985,
|
| 1018 |
-
0xA682: 986,
|
| 1019 |
-
0xA685: 987,
|
| 1020 |
-
0xA688: 988,
|
| 1021 |
-
0xA689: 989,
|
| 1022 |
-
0xA68A: 990,
|
| 1023 |
-
0xA68B: 991,
|
| 1024 |
-
0xA691: 992,
|
| 1025 |
-
0xA693: 993,
|
| 1026 |
-
0xA695: 994,
|
| 1027 |
-
0xA697: 995,
|
| 1028 |
-
0xA69B: 996,
|
| 1029 |
-
0xA69C: 997,
|
| 1030 |
-
0xA6A1: 998,
|
| 1031 |
-
0xA6A9: 999,
|
| 1032 |
-
0xA6B6: 1000,
|
| 1033 |
-
0xA6C1: 1001,
|
| 1034 |
-
0xA6E1: 1002,
|
| 1035 |
-
0xA6E2: 1003,
|
| 1036 |
-
0xA6E5: 1004,
|
| 1037 |
-
0xA6E9: 1005,
|
| 1038 |
-
0xA6F7: 1006,
|
| 1039 |
-
0xA741: 1007,
|
| 1040 |
-
0xA745: 1008,
|
| 1041 |
-
0xA749: 1009,
|
| 1042 |
-
0xA751: 1010,
|
| 1043 |
-
0xA755: 1011,
|
| 1044 |
-
0xA757: 1012,
|
| 1045 |
-
0xA761: 1013,
|
| 1046 |
-
0xA762: 1014,
|
| 1047 |
-
0xA765: 1015,
|
| 1048 |
-
0xA769: 1016,
|
| 1049 |
-
0xA771: 1017,
|
| 1050 |
-
0xA773: 1018,
|
| 1051 |
-
0xA775: 1019,
|
| 1052 |
-
0xA7A1: 1020,
|
| 1053 |
-
0xA7A2: 1021,
|
| 1054 |
-
0xA7A5: 1022,
|
| 1055 |
-
0xA7A9: 1023,
|
| 1056 |
-
0xA7AB: 1024,
|
| 1057 |
-
0xA7B1: 1025,
|
| 1058 |
-
0xA7B3: 1026,
|
| 1059 |
-
0xA7B5: 1027,
|
| 1060 |
-
0xA7B7: 1028,
|
| 1061 |
-
0xA7B8: 1029,
|
| 1062 |
-
0xA7B9: 1030,
|
| 1063 |
-
0xA861: 1031,
|
| 1064 |
-
0xA862: 1032,
|
| 1065 |
-
0xA865: 1033,
|
| 1066 |
-
0xA869: 1034,
|
| 1067 |
-
0xA86B: 1035,
|
| 1068 |
-
0xA871: 1036,
|
| 1069 |
-
0xA873: 1037,
|
| 1070 |
-
0xA875: 1038,
|
| 1071 |
-
0xA876: 1039,
|
| 1072 |
-
0xA877: 1040,
|
| 1073 |
-
0xA87D: 1041,
|
| 1074 |
-
0xA881: 1042,
|
| 1075 |
-
0xA882: 1043,
|
| 1076 |
-
0xA885: 1044,
|
| 1077 |
-
0xA889: 1045,
|
| 1078 |
-
0xA891: 1046,
|
| 1079 |
-
0xA893: 1047,
|
| 1080 |
-
0xA895: 1048,
|
| 1081 |
-
0xA896: 1049,
|
| 1082 |
-
0xA897: 1050,
|
| 1083 |
-
0xA8A1: 1051,
|
| 1084 |
-
0xA8A2: 1052,
|
| 1085 |
-
0xA8B1: 1053,
|
| 1086 |
-
0xA8E1: 1054,
|
| 1087 |
-
0xA8E2: 1055,
|
| 1088 |
-
0xA8E5: 1056,
|
| 1089 |
-
0xA8E8: 1057,
|
| 1090 |
-
0xA8E9: 1058,
|
| 1091 |
-
0xA8F1: 1059,
|
| 1092 |
-
0xA8F5: 1060,
|
| 1093 |
-
0xA8F6: 1061,
|
| 1094 |
-
0xA8F7: 1062,
|
| 1095 |
-
0xA941: 1063,
|
| 1096 |
-
0xA957: 1064,
|
| 1097 |
-
0xA961: 1065,
|
| 1098 |
-
0xA962: 1066,
|
| 1099 |
-
0xA971: 1067,
|
| 1100 |
-
0xA973: 1068,
|
| 1101 |
-
0xA975: 1069,
|
| 1102 |
-
0xA976: 1070,
|
| 1103 |
-
0xA977: 1071,
|
| 1104 |
-
0xA9A1: 1072,
|
| 1105 |
-
0xA9A2: 1073,
|
| 1106 |
-
0xA9A5: 1074,
|
| 1107 |
-
0xA9A9: 1075,
|
| 1108 |
-
0xA9B1: 1076,
|
| 1109 |
-
0xA9B3: 1077,
|
| 1110 |
-
0xA9B7: 1078,
|
| 1111 |
-
0xAA41: 1079,
|
| 1112 |
-
0xAA61: 1080,
|
| 1113 |
-
0xAA77: 1081,
|
| 1114 |
-
0xAA81: 1082,
|
| 1115 |
-
0xAA82: 1083,
|
| 1116 |
-
0xAA85: 1084,
|
| 1117 |
-
0xAA89: 1085,
|
| 1118 |
-
0xAA91: 1086,
|
| 1119 |
-
0xAA95: 1087,
|
| 1120 |
-
0xAA97: 1088,
|
| 1121 |
-
0xAB41: 1089,
|
| 1122 |
-
0xAB57: 1090,
|
| 1123 |
-
0xAB61: 1091,
|
| 1124 |
-
0xAB65: 1092,
|
| 1125 |
-
0xAB69: 1093,
|
| 1126 |
-
0xAB71: 1094,
|
| 1127 |
-
0xAB73: 1095,
|
| 1128 |
-
0xABA1: 1096,
|
| 1129 |
-
0xABA2: 1097,
|
| 1130 |
-
0xABA5: 1098,
|
| 1131 |
-
0xABA9: 1099,
|
| 1132 |
-
0xABB1: 1100,
|
| 1133 |
-
0xABB3: 1101,
|
| 1134 |
-
0xABB5: 1102,
|
| 1135 |
-
0xABB7: 1103,
|
| 1136 |
-
0xAC61: 1104,
|
| 1137 |
-
0xAC62: 1105,
|
| 1138 |
-
0xAC64: 1106,
|
| 1139 |
-
0xAC65: 1107,
|
| 1140 |
-
0xAC68: 1108,
|
| 1141 |
-
0xAC69: 1109,
|
| 1142 |
-
0xAC6A: 1110,
|
| 1143 |
-
0xAC6B: 1111,
|
| 1144 |
-
0xAC71: 1112,
|
| 1145 |
-
0xAC73: 1113,
|
| 1146 |
-
0xAC75: 1114,
|
| 1147 |
-
0xAC76: 1115,
|
| 1148 |
-
0xAC77: 1116,
|
| 1149 |
-
0xAC7B: 1117,
|
| 1150 |
-
0xAC81: 1118,
|
| 1151 |
-
0xAC82: 1119,
|
| 1152 |
-
0xAC85: 1120,
|
| 1153 |
-
0xAC89: 1121,
|
| 1154 |
-
0xAC91: 1122,
|
| 1155 |
-
0xAC93: 1123,
|
| 1156 |
-
0xAC95: 1124,
|
| 1157 |
-
0xAC96: 1125,
|
| 1158 |
-
0xAC97: 1126,
|
| 1159 |
-
0xACA1: 1127,
|
| 1160 |
-
0xACA2: 1128,
|
| 1161 |
-
0xACA5: 1129,
|
| 1162 |
-
0xACA9: 1130,
|
| 1163 |
-
0xACB1: 1131,
|
| 1164 |
-
0xACB3: 1132,
|
| 1165 |
-
0xACB5: 1133,
|
| 1166 |
-
0xACB7: 1134,
|
| 1167 |
-
0xACC1: 1135,
|
| 1168 |
-
0xACC5: 1136,
|
| 1169 |
-
0xACC9: 1137,
|
| 1170 |
-
0xACD1: 1138,
|
| 1171 |
-
0xACD7: 1139,
|
| 1172 |
-
0xACE1: 1140,
|
| 1173 |
-
0xACE2: 1141,
|
| 1174 |
-
0xACE3: 1142,
|
| 1175 |
-
0xACE4: 1143,
|
| 1176 |
-
0xACE5: 1144,
|
| 1177 |
-
0xACE8: 1145,
|
| 1178 |
-
0xACE9: 1146,
|
| 1179 |
-
0xACEB: 1147,
|
| 1180 |
-
0xACEC: 1148,
|
| 1181 |
-
0xACF1: 1149,
|
| 1182 |
-
0xACF3: 1150,
|
| 1183 |
-
0xACF5: 1151,
|
| 1184 |
-
0xACF6: 1152,
|
| 1185 |
-
0xACF7: 1153,
|
| 1186 |
-
0xACFC: 1154,
|
| 1187 |
-
0xAD41: 1155,
|
| 1188 |
-
0xAD42: 1156,
|
| 1189 |
-
0xAD45: 1157,
|
| 1190 |
-
0xAD49: 1158,
|
| 1191 |
-
0xAD51: 1159,
|
| 1192 |
-
0xAD53: 1160,
|
| 1193 |
-
0xAD55: 1161,
|
| 1194 |
-
0xAD56: 1162,
|
| 1195 |
-
0xAD57: 1163,
|
| 1196 |
-
0xAD61: 1164,
|
| 1197 |
-
0xAD62: 1165,
|
| 1198 |
-
0xAD65: 1166,
|
| 1199 |
-
0xAD69: 1167,
|
| 1200 |
-
0xAD71: 1168,
|
| 1201 |
-
0xAD73: 1169,
|
| 1202 |
-
0xAD75: 1170,
|
| 1203 |
-
0xAD76: 1171,
|
| 1204 |
-
0xAD77: 1172,
|
| 1205 |
-
0xAD81: 1173,
|
| 1206 |
-
0xAD85: 1174,
|
| 1207 |
-
0xAD89: 1175,
|
| 1208 |
-
0xAD97: 1176,
|
| 1209 |
-
0xADA1: 1177,
|
| 1210 |
-
0xADA2: 1178,
|
| 1211 |
-
0xADA3: 1179,
|
| 1212 |
-
0xADA5: 1180,
|
| 1213 |
-
0xADA9: 1181,
|
| 1214 |
-
0xADAB: 1182,
|
| 1215 |
-
0xADB1: 1183,
|
| 1216 |
-
0xADB3: 1184,
|
| 1217 |
-
0xADB5: 1185,
|
| 1218 |
-
0xADB7: 1186,
|
| 1219 |
-
0xADBB: 1187,
|
| 1220 |
-
0xADC1: 1188,
|
| 1221 |
-
0xADC2: 1189,
|
| 1222 |
-
0xADC5: 1190,
|
| 1223 |
-
0xADC9: 1191,
|
| 1224 |
-
0xADD7: 1192,
|
| 1225 |
-
0xADE1: 1193,
|
| 1226 |
-
0xADE5: 1194,
|
| 1227 |
-
0xADE9: 1195,
|
| 1228 |
-
0xADF1: 1196,
|
| 1229 |
-
0xADF5: 1197,
|
| 1230 |
-
0xADF6: 1198,
|
| 1231 |
-
0xAE41: 1199,
|
| 1232 |
-
0xAE45: 1200,
|
| 1233 |
-
0xAE49: 1201,
|
| 1234 |
-
0xAE51: 1202,
|
| 1235 |
-
0xAE53: 1203,
|
| 1236 |
-
0xAE55: 1204,
|
| 1237 |
-
0xAE61: 1205,
|
| 1238 |
-
0xAE62: 1206,
|
| 1239 |
-
0xAE65: 1207,
|
| 1240 |
-
0xAE69: 1208,
|
| 1241 |
-
0xAE71: 1209,
|
| 1242 |
-
0xAE73: 1210,
|
| 1243 |
-
0xAE75: 1211,
|
| 1244 |
-
0xAE77: 1212,
|
| 1245 |
-
0xAE81: 1213,
|
| 1246 |
-
0xAE82: 1214,
|
| 1247 |
-
0xAE85: 1215,
|
| 1248 |
-
0xAE88: 1216,
|
| 1249 |
-
0xAE89: 1217,
|
| 1250 |
-
0xAE91: 1218,
|
| 1251 |
-
0xAE93: 1219,
|
| 1252 |
-
0xAE95: 1220,
|
| 1253 |
-
0xAE97: 1221,
|
| 1254 |
-
0xAE99: 1222,
|
| 1255 |
-
0xAE9B: 1223,
|
| 1256 |
-
0xAE9C: 1224,
|
| 1257 |
-
0xAEA1: 1225,
|
| 1258 |
-
0xAEB6: 1226,
|
| 1259 |
-
0xAEC1: 1227,
|
| 1260 |
-
0xAEC2: 1228,
|
| 1261 |
-
0xAEC5: 1229,
|
| 1262 |
-
0xAEC9: 1230,
|
| 1263 |
-
0xAED1: 1231,
|
| 1264 |
-
0xAED7: 1232,
|
| 1265 |
-
0xAEE1: 1233,
|
| 1266 |
-
0xAEE2: 1234,
|
| 1267 |
-
0xAEE5: 1235,
|
| 1268 |
-
0xAEE9: 1236,
|
| 1269 |
-
0xAEF1: 1237,
|
| 1270 |
-
0xAEF3: 1238,
|
| 1271 |
-
0xAEF5: 1239,
|
| 1272 |
-
0xAEF7: 1240,
|
| 1273 |
-
0xAF41: 1241,
|
| 1274 |
-
0xAF42: 1242,
|
| 1275 |
-
0xAF49: 1243,
|
| 1276 |
-
0xAF51: 1244,
|
| 1277 |
-
0xAF55: 1245,
|
| 1278 |
-
0xAF57: 1246,
|
| 1279 |
-
0xAF61: 1247,
|
| 1280 |
-
0xAF62: 1248,
|
| 1281 |
-
0xAF65: 1249,
|
| 1282 |
-
0xAF69: 1250,
|
| 1283 |
-
0xAF6A: 1251,
|
| 1284 |
-
0xAF71: 1252,
|
| 1285 |
-
0xAF73: 1253,
|
| 1286 |
-
0xAF75: 1254,
|
| 1287 |
-
0xAF77: 1255,
|
| 1288 |
-
0xAFA1: 1256,
|
| 1289 |
-
0xAFA2: 1257,
|
| 1290 |
-
0xAFA5: 1258,
|
| 1291 |
-
0xAFA8: 1259,
|
| 1292 |
-
0xAFA9: 1260,
|
| 1293 |
-
0xAFB0: 1261,
|
| 1294 |
-
0xAFB1: 1262,
|
| 1295 |
-
0xAFB3: 1263,
|
| 1296 |
-
0xAFB5: 1264,
|
| 1297 |
-
0xAFB7: 1265,
|
| 1298 |
-
0xAFBC: 1266,
|
| 1299 |
-
0xB061: 1267,
|
| 1300 |
-
0xB062: 1268,
|
| 1301 |
-
0xB064: 1269,
|
| 1302 |
-
0xB065: 1270,
|
| 1303 |
-
0xB069: 1271,
|
| 1304 |
-
0xB071: 1272,
|
| 1305 |
-
0xB073: 1273,
|
| 1306 |
-
0xB076: 1274,
|
| 1307 |
-
0xB077: 1275,
|
| 1308 |
-
0xB07D: 1276,
|
| 1309 |
-
0xB081: 1277,
|
| 1310 |
-
0xB082: 1278,
|
| 1311 |
-
0xB085: 1279,
|
| 1312 |
-
0xB089: 1280,
|
| 1313 |
-
0xB091: 1281,
|
| 1314 |
-
0xB093: 1282,
|
| 1315 |
-
0xB096: 1283,
|
| 1316 |
-
0xB097: 1284,
|
| 1317 |
-
0xB0B7: 1285,
|
| 1318 |
-
0xB0E1: 1286,
|
| 1319 |
-
0xB0E2: 1287,
|
| 1320 |
-
0xB0E5: 1288,
|
| 1321 |
-
0xB0E9: 1289,
|
| 1322 |
-
0xB0EB: 1290,
|
| 1323 |
-
0xB0F1: 1291,
|
| 1324 |
-
0xB0F3: 1292,
|
| 1325 |
-
0xB0F6: 1293,
|
| 1326 |
-
0xB0F7: 1294,
|
| 1327 |
-
0xB141: 1295,
|
| 1328 |
-
0xB145: 1296,
|
| 1329 |
-
0xB149: 1297,
|
| 1330 |
-
0xB185: 1298,
|
| 1331 |
-
0xB1A1: 1299,
|
| 1332 |
-
0xB1A2: 1300,
|
| 1333 |
-
0xB1A5: 1301,
|
| 1334 |
-
0xB1A8: 1302,
|
| 1335 |
-
0xB1A9: 1303,
|
| 1336 |
-
0xB1AB: 1304,
|
| 1337 |
-
0xB1B1: 1305,
|
| 1338 |
-
0xB1B3: 1306,
|
| 1339 |
-
0xB1B7: 1307,
|
| 1340 |
-
0xB1C1: 1308,
|
| 1341 |
-
0xB1C2: 1309,
|
| 1342 |
-
0xB1C5: 1310,
|
| 1343 |
-
0xB1D6: 1311,
|
| 1344 |
-
0xB1E1: 1312,
|
| 1345 |
-
0xB1F6: 1313,
|
| 1346 |
-
0xB241: 1314,
|
| 1347 |
-
0xB245: 1315,
|
| 1348 |
-
0xB249: 1316,
|
| 1349 |
-
0xB251: 1317,
|
| 1350 |
-
0xB253: 1318,
|
| 1351 |
-
0xB261: 1319,
|
| 1352 |
-
0xB281: 1320,
|
| 1353 |
-
0xB282: 1321,
|
| 1354 |
-
0xB285: 1322,
|
| 1355 |
-
0xB289: 1323,
|
| 1356 |
-
0xB291: 1324,
|
| 1357 |
-
0xB293: 1325,
|
| 1358 |
-
0xB297: 1326,
|
| 1359 |
-
0xB2A1: 1327,
|
| 1360 |
-
0xB2B6: 1328,
|
| 1361 |
-
0xB2C1: 1329,
|
| 1362 |
-
0xB2E1: 1330,
|
| 1363 |
-
0xB2E5: 1331,
|
| 1364 |
-
0xB357: 1332,
|
| 1365 |
-
0xB361: 1333,
|
| 1366 |
-
0xB362: 1334,
|
| 1367 |
-
0xB365: 1335,
|
| 1368 |
-
0xB369: 1336,
|
| 1369 |
-
0xB36B: 1337,
|
| 1370 |
-
0xB370: 1338,
|
| 1371 |
-
0xB371: 1339,
|
| 1372 |
-
0xB373: 1340,
|
| 1373 |
-
0xB381: 1341,
|
| 1374 |
-
0xB385: 1342,
|
| 1375 |
-
0xB389: 1343,
|
| 1376 |
-
0xB391: 1344,
|
| 1377 |
-
0xB3A1: 1345,
|
| 1378 |
-
0xB3A2: 1346,
|
| 1379 |
-
0xB3A5: 1347,
|
| 1380 |
-
0xB3A9: 1348,
|
| 1381 |
-
0xB3B1: 1349,
|
| 1382 |
-
0xB3B3: 1350,
|
| 1383 |
-
0xB3B5: 1351,
|
| 1384 |
-
0xB3B7: 1352,
|
| 1385 |
-
0xB461: 1353,
|
| 1386 |
-
0xB462: 1354,
|
| 1387 |
-
0xB465: 1355,
|
| 1388 |
-
0xB466: 1356,
|
| 1389 |
-
0xB467: 1357,
|
| 1390 |
-
0xB469: 1358,
|
| 1391 |
-
0xB46A: 1359,
|
| 1392 |
-
0xB46B: 1360,
|
| 1393 |
-
0xB470: 1361,
|
| 1394 |
-
0xB471: 1362,
|
| 1395 |
-
0xB473: 1363,
|
| 1396 |
-
0xB475: 1364,
|
| 1397 |
-
0xB476: 1365,
|
| 1398 |
-
0xB477: 1366,
|
| 1399 |
-
0xB47B: 1367,
|
| 1400 |
-
0xB47C: 1368,
|
| 1401 |
-
0xB481: 1369,
|
| 1402 |
-
0xB482: 1370,
|
| 1403 |
-
0xB485: 1371,
|
| 1404 |
-
0xB489: 1372,
|
| 1405 |
-
0xB491: 1373,
|
| 1406 |
-
0xB493: 1374,
|
| 1407 |
-
0xB495: 1375,
|
| 1408 |
-
0xB496: 1376,
|
| 1409 |
-
0xB497: 1377,
|
| 1410 |
-
0xB4A1: 1378,
|
| 1411 |
-
0xB4A2: 1379,
|
| 1412 |
-
0xB4A5: 1380,
|
| 1413 |
-
0xB4A9: 1381,
|
| 1414 |
-
0xB4AC: 1382,
|
| 1415 |
-
0xB4B1: 1383,
|
| 1416 |
-
0xB4B3: 1384,
|
| 1417 |
-
0xB4B5: 1385,
|
| 1418 |
-
0xB4B7: 1386,
|
| 1419 |
-
0xB4BB: 1387,
|
| 1420 |
-
0xB4BD: 1388,
|
| 1421 |
-
0xB4C1: 1389,
|
| 1422 |
-
0xB4C5: 1390,
|
| 1423 |
-
0xB4C9: 1391,
|
| 1424 |
-
0xB4D3: 1392,
|
| 1425 |
-
0xB4E1: 1393,
|
| 1426 |
-
0xB4E2: 1394,
|
| 1427 |
-
0xB4E5: 1395,
|
| 1428 |
-
0xB4E6: 1396,
|
| 1429 |
-
0xB4E8: 1397,
|
| 1430 |
-
0xB4E9: 1398,
|
| 1431 |
-
0xB4EA: 1399,
|
| 1432 |
-
0xB4EB: 1400,
|
| 1433 |
-
0xB4F1: 1401,
|
| 1434 |
-
0xB4F3: 1402,
|
| 1435 |
-
0xB4F4: 1403,
|
| 1436 |
-
0xB4F5: 1404,
|
| 1437 |
-
0xB4F6: 1405,
|
| 1438 |
-
0xB4F7: 1406,
|
| 1439 |
-
0xB4F8: 1407,
|
| 1440 |
-
0xB4FA: 1408,
|
| 1441 |
-
0xB4FC: 1409,
|
| 1442 |
-
0xB541: 1410,
|
| 1443 |
-
0xB542: 1411,
|
| 1444 |
-
0xB545: 1412,
|
| 1445 |
-
0xB549: 1413,
|
| 1446 |
-
0xB551: 1414,
|
| 1447 |
-
0xB553: 1415,
|
| 1448 |
-
0xB555: 1416,
|
| 1449 |
-
0xB557: 1417,
|
| 1450 |
-
0xB561: 1418,
|
| 1451 |
-
0xB562: 1419,
|
| 1452 |
-
0xB563: 1420,
|
| 1453 |
-
0xB565: 1421,
|
| 1454 |
-
0xB569: 1422,
|
| 1455 |
-
0xB56B: 1423,
|
| 1456 |
-
0xB56C: 1424,
|
| 1457 |
-
0xB571: 1425,
|
| 1458 |
-
0xB573: 1426,
|
| 1459 |
-
0xB574: 1427,
|
| 1460 |
-
0xB575: 1428,
|
| 1461 |
-
0xB576: 1429,
|
| 1462 |
-
0xB577: 1430,
|
| 1463 |
-
0xB57B: 1431,
|
| 1464 |
-
0xB57C: 1432,
|
| 1465 |
-
0xB57D: 1433,
|
| 1466 |
-
0xB581: 1434,
|
| 1467 |
-
0xB585: 1435,
|
| 1468 |
-
0xB589: 1436,
|
| 1469 |
-
0xB591: 1437,
|
| 1470 |
-
0xB593: 1438,
|
| 1471 |
-
0xB595: 1439,
|
| 1472 |
-
0xB596: 1440,
|
| 1473 |
-
0xB5A1: 1441,
|
| 1474 |
-
0xB5A2: 1442,
|
| 1475 |
-
0xB5A5: 1443,
|
| 1476 |
-
0xB5A9: 1444,
|
| 1477 |
-
0xB5AA: 1445,
|
| 1478 |
-
0xB5AB: 1446,
|
| 1479 |
-
0xB5AD: 1447,
|
| 1480 |
-
0xB5B0: 1448,
|
| 1481 |
-
0xB5B1: 1449,
|
| 1482 |
-
0xB5B3: 1450,
|
| 1483 |
-
0xB5B5: 1451,
|
| 1484 |
-
0xB5B7: 1452,
|
| 1485 |
-
0xB5B9: 1453,
|
| 1486 |
-
0xB5C1: 1454,
|
| 1487 |
-
0xB5C2: 1455,
|
| 1488 |
-
0xB5C5: 1456,
|
| 1489 |
-
0xB5C9: 1457,
|
| 1490 |
-
0xB5D1: 1458,
|
| 1491 |
-
0xB5D3: 1459,
|
| 1492 |
-
0xB5D5: 1460,
|
| 1493 |
-
0xB5D6: 1461,
|
| 1494 |
-
0xB5D7: 1462,
|
| 1495 |
-
0xB5E1: 1463,
|
| 1496 |
-
0xB5E2: 1464,
|
| 1497 |
-
0xB5E5: 1465,
|
| 1498 |
-
0xB5F1: 1466,
|
| 1499 |
-
0xB5F5: 1467,
|
| 1500 |
-
0xB5F7: 1468,
|
| 1501 |
-
0xB641: 1469,
|
| 1502 |
-
0xB642: 1470,
|
| 1503 |
-
0xB645: 1471,
|
| 1504 |
-
0xB649: 1472,
|
| 1505 |
-
0xB651: 1473,
|
| 1506 |
-
0xB653: 1474,
|
| 1507 |
-
0xB655: 1475,
|
| 1508 |
-
0xB657: 1476,
|
| 1509 |
-
0xB661: 1477,
|
| 1510 |
-
0xB662: 1478,
|
| 1511 |
-
0xB665: 1479,
|
| 1512 |
-
0xB669: 1480,
|
| 1513 |
-
0xB671: 1481,
|
| 1514 |
-
0xB673: 1482,
|
| 1515 |
-
0xB675: 1483,
|
| 1516 |
-
0xB677: 1484,
|
| 1517 |
-
0xB681: 1485,
|
| 1518 |
-
0xB682: 1486,
|
| 1519 |
-
0xB685: 1487,
|
| 1520 |
-
0xB689: 1488,
|
| 1521 |
-
0xB68A: 1489,
|
| 1522 |
-
0xB68B: 1490,
|
| 1523 |
-
0xB691: 1491,
|
| 1524 |
-
0xB693: 1492,
|
| 1525 |
-
0xB695: 1493,
|
| 1526 |
-
0xB697: 1494,
|
| 1527 |
-
0xB6A1: 1495,
|
| 1528 |
-
0xB6A2: 1496,
|
| 1529 |
-
0xB6A5: 1497,
|
| 1530 |
-
0xB6A9: 1498,
|
| 1531 |
-
0xB6B1: 1499,
|
| 1532 |
-
0xB6B3: 1500,
|
| 1533 |
-
0xB6B6: 1501,
|
| 1534 |
-
0xB6B7: 1502,
|
| 1535 |
-
0xB6C1: 1503,
|
| 1536 |
-
0xB6C2: 1504,
|
| 1537 |
-
0xB6C5: 1505,
|
| 1538 |
-
0xB6C9: 1506,
|
| 1539 |
-
0xB6D1: 1507,
|
| 1540 |
-
0xB6D3: 1508,
|
| 1541 |
-
0xB6D7: 1509,
|
| 1542 |
-
0xB6E1: 1510,
|
| 1543 |
-
0xB6E2: 1511,
|
| 1544 |
-
0xB6E5: 1512,
|
| 1545 |
-
0xB6E9: 1513,
|
| 1546 |
-
0xB6F1: 1514,
|
| 1547 |
-
0xB6F3: 1515,
|
| 1548 |
-
0xB6F5: 1516,
|
| 1549 |
-
0xB6F7: 1517,
|
| 1550 |
-
0xB741: 1518,
|
| 1551 |
-
0xB742: 1519,
|
| 1552 |
-
0xB745: 1520,
|
| 1553 |
-
0xB749: 1521,
|
| 1554 |
-
0xB751: 1522,
|
| 1555 |
-
0xB753: 1523,
|
| 1556 |
-
0xB755: 1524,
|
| 1557 |
-
0xB757: 1525,
|
| 1558 |
-
0xB759: 1526,
|
| 1559 |
-
0xB761: 1527,
|
| 1560 |
-
0xB762: 1528,
|
| 1561 |
-
0xB765: 1529,
|
| 1562 |
-
0xB769: 1530,
|
| 1563 |
-
0xB76F: 1531,
|
| 1564 |
-
0xB771: 1532,
|
| 1565 |
-
0xB773: 1533,
|
| 1566 |
-
0xB775: 1534,
|
| 1567 |
-
0xB777: 1535,
|
| 1568 |
-
0xB778: 1536,
|
| 1569 |
-
0xB779: 1537,
|
| 1570 |
-
0xB77A: 1538,
|
| 1571 |
-
0xB77B: 1539,
|
| 1572 |
-
0xB77C: 1540,
|
| 1573 |
-
0xB77D: 1541,
|
| 1574 |
-
0xB781: 1542,
|
| 1575 |
-
0xB785: 1543,
|
| 1576 |
-
0xB789: 1544,
|
| 1577 |
-
0xB791: 1545,
|
| 1578 |
-
0xB795: 1546,
|
| 1579 |
-
0xB7A1: 1547,
|
| 1580 |
-
0xB7A2: 1548,
|
| 1581 |
-
0xB7A5: 1549,
|
| 1582 |
-
0xB7A9: 1550,
|
| 1583 |
-
0xB7AA: 1551,
|
| 1584 |
-
0xB7AB: 1552,
|
| 1585 |
-
0xB7B0: 1553,
|
| 1586 |
-
0xB7B1: 1554,
|
| 1587 |
-
0xB7B3: 1555,
|
| 1588 |
-
0xB7B5: 1556,
|
| 1589 |
-
0xB7B6: 1557,
|
| 1590 |
-
0xB7B7: 1558,
|
| 1591 |
-
0xB7B8: 1559,
|
| 1592 |
-
0xB7BC: 1560,
|
| 1593 |
-
0xB861: 1561,
|
| 1594 |
-
0xB862: 1562,
|
| 1595 |
-
0xB865: 1563,
|
| 1596 |
-
0xB867: 1564,
|
| 1597 |
-
0xB868: 1565,
|
| 1598 |
-
0xB869: 1566,
|
| 1599 |
-
0xB86B: 1567,
|
| 1600 |
-
0xB871: 1568,
|
| 1601 |
-
0xB873: 1569,
|
| 1602 |
-
0xB875: 1570,
|
| 1603 |
-
0xB876: 1571,
|
| 1604 |
-
0xB877: 1572,
|
| 1605 |
-
0xB878: 1573,
|
| 1606 |
-
0xB881: 1574,
|
| 1607 |
-
0xB882: 1575,
|
| 1608 |
-
0xB885: 1576,
|
| 1609 |
-
0xB889: 1577,
|
| 1610 |
-
0xB891: 1578,
|
| 1611 |
-
0xB893: 1579,
|
| 1612 |
-
0xB895: 1580,
|
| 1613 |
-
0xB896: 1581,
|
| 1614 |
-
0xB897: 1582,
|
| 1615 |
-
0xB8A1: 1583,
|
| 1616 |
-
0xB8A2: 1584,
|
| 1617 |
-
0xB8A5: 1585,
|
| 1618 |
-
0xB8A7: 1586,
|
| 1619 |
-
0xB8A9: 1587,
|
| 1620 |
-
0xB8B1: 1588,
|
| 1621 |
-
0xB8B7: 1589,
|
| 1622 |
-
0xB8C1: 1590,
|
| 1623 |
-
0xB8C5: 1591,
|
| 1624 |
-
0xB8C9: 1592,
|
| 1625 |
-
0xB8E1: 1593,
|
| 1626 |
-
0xB8E2: 1594,
|
| 1627 |
-
0xB8E5: 1595,
|
| 1628 |
-
0xB8E9: 1596,
|
| 1629 |
-
0xB8EB: 1597,
|
| 1630 |
-
0xB8F1: 1598,
|
| 1631 |
-
0xB8F3: 1599,
|
| 1632 |
-
0xB8F5: 1600,
|
| 1633 |
-
0xB8F7: 1601,
|
| 1634 |
-
0xB8F8: 1602,
|
| 1635 |
-
0xB941: 1603,
|
| 1636 |
-
0xB942: 1604,
|
| 1637 |
-
0xB945: 1605,
|
| 1638 |
-
0xB949: 1606,
|
| 1639 |
-
0xB951: 1607,
|
| 1640 |
-
0xB953: 1608,
|
| 1641 |
-
0xB955: 1609,
|
| 1642 |
-
0xB957: 1610,
|
| 1643 |
-
0xB961: 1611,
|
| 1644 |
-
0xB965: 1612,
|
| 1645 |
-
0xB969: 1613,
|
| 1646 |
-
0xB971: 1614,
|
| 1647 |
-
0xB973: 1615,
|
| 1648 |
-
0xB976: 1616,
|
| 1649 |
-
0xB977: 1617,
|
| 1650 |
-
0xB981: 1618,
|
| 1651 |
-
0xB9A1: 1619,
|
| 1652 |
-
0xB9A2: 1620,
|
| 1653 |
-
0xB9A5: 1621,
|
| 1654 |
-
0xB9A9: 1622,
|
| 1655 |
-
0xB9AB: 1623,
|
| 1656 |
-
0xB9B1: 1624,
|
| 1657 |
-
0xB9B3: 1625,
|
| 1658 |
-
0xB9B5: 1626,
|
| 1659 |
-
0xB9B7: 1627,
|
| 1660 |
-
0xB9B8: 1628,
|
| 1661 |
-
0xB9B9: 1629,
|
| 1662 |
-
0xB9BD: 1630,
|
| 1663 |
-
0xB9C1: 1631,
|
| 1664 |
-
0xB9C2: 1632,
|
| 1665 |
-
0xB9C9: 1633,
|
| 1666 |
-
0xB9D3: 1634,
|
| 1667 |
-
0xB9D5: 1635,
|
| 1668 |
-
0xB9D7: 1636,
|
| 1669 |
-
0xB9E1: 1637,
|
| 1670 |
-
0xB9F6: 1638,
|
| 1671 |
-
0xB9F7: 1639,
|
| 1672 |
-
0xBA41: 1640,
|
| 1673 |
-
0xBA45: 1641,
|
| 1674 |
-
0xBA49: 1642,
|
| 1675 |
-
0xBA51: 1643,
|
| 1676 |
-
0xBA53: 1644,
|
| 1677 |
-
0xBA55: 1645,
|
| 1678 |
-
0xBA57: 1646,
|
| 1679 |
-
0xBA61: 1647,
|
| 1680 |
-
0xBA62: 1648,
|
| 1681 |
-
0xBA65: 1649,
|
| 1682 |
-
0xBA77: 1650,
|
| 1683 |
-
0xBA81: 1651,
|
| 1684 |
-
0xBA82: 1652,
|
| 1685 |
-
0xBA85: 1653,
|
| 1686 |
-
0xBA89: 1654,
|
| 1687 |
-
0xBA8A: 1655,
|
| 1688 |
-
0xBA8B: 1656,
|
| 1689 |
-
0xBA91: 1657,
|
| 1690 |
-
0xBA93: 1658,
|
| 1691 |
-
0xBA95: 1659,
|
| 1692 |
-
0xBA97: 1660,
|
| 1693 |
-
0xBAA1: 1661,
|
| 1694 |
-
0xBAB6: 1662,
|
| 1695 |
-
0xBAC1: 1663,
|
| 1696 |
-
0xBAE1: 1664,
|
| 1697 |
-
0xBAE2: 1665,
|
| 1698 |
-
0xBAE5: 1666,
|
| 1699 |
-
0xBAE9: 1667,
|
| 1700 |
-
0xBAF1: 1668,
|
| 1701 |
-
0xBAF3: 1669,
|
| 1702 |
-
0xBAF5: 1670,
|
| 1703 |
-
0xBB41: 1671,
|
| 1704 |
-
0xBB45: 1672,
|
| 1705 |
-
0xBB49: 1673,
|
| 1706 |
-
0xBB51: 1674,
|
| 1707 |
-
0xBB61: 1675,
|
| 1708 |
-
0xBB62: 1676,
|
| 1709 |
-
0xBB65: 1677,
|
| 1710 |
-
0xBB69: 1678,
|
| 1711 |
-
0xBB71: 1679,
|
| 1712 |
-
0xBB73: 1680,
|
| 1713 |
-
0xBB75: 1681,
|
| 1714 |
-
0xBB77: 1682,
|
| 1715 |
-
0xBBA1: 1683,
|
| 1716 |
-
0xBBA2: 1684,
|
| 1717 |
-
0xBBA5: 1685,
|
| 1718 |
-
0xBBA8: 1686,
|
| 1719 |
-
0xBBA9: 1687,
|
| 1720 |
-
0xBBAB: 1688,
|
| 1721 |
-
0xBBB1: 1689,
|
| 1722 |
-
0xBBB3: 1690,
|
| 1723 |
-
0xBBB5: 1691,
|
| 1724 |
-
0xBBB7: 1692,
|
| 1725 |
-
0xBBB8: 1693,
|
| 1726 |
-
0xBBBB: 1694,
|
| 1727 |
-
0xBBBC: 1695,
|
| 1728 |
-
0xBC61: 1696,
|
| 1729 |
-
0xBC62: 1697,
|
| 1730 |
-
0xBC65: 1698,
|
| 1731 |
-
0xBC67: 1699,
|
| 1732 |
-
0xBC69: 1700,
|
| 1733 |
-
0xBC6C: 1701,
|
| 1734 |
-
0xBC71: 1702,
|
| 1735 |
-
0xBC73: 1703,
|
| 1736 |
-
0xBC75: 1704,
|
| 1737 |
-
0xBC76: 1705,
|
| 1738 |
-
0xBC77: 1706,
|
| 1739 |
-
0xBC81: 1707,
|
| 1740 |
-
0xBC82: 1708,
|
| 1741 |
-
0xBC85: 1709,
|
| 1742 |
-
0xBC89: 1710,
|
| 1743 |
-
0xBC91: 1711,
|
| 1744 |
-
0xBC93: 1712,
|
| 1745 |
-
0xBC95: 1713,
|
| 1746 |
-
0xBC96: 1714,
|
| 1747 |
-
0xBC97: 1715,
|
| 1748 |
-
0xBCA1: 1716,
|
| 1749 |
-
0xBCA5: 1717,
|
| 1750 |
-
0xBCB7: 1718,
|
| 1751 |
-
0xBCE1: 1719,
|
| 1752 |
-
0xBCE2: 1720,
|
| 1753 |
-
0xBCE5: 1721,
|
| 1754 |
-
0xBCE9: 1722,
|
| 1755 |
-
0xBCF1: 1723,
|
| 1756 |
-
0xBCF3: 1724,
|
| 1757 |
-
0xBCF5: 1725,
|
| 1758 |
-
0xBCF6: 1726,
|
| 1759 |
-
0xBCF7: 1727,
|
| 1760 |
-
0xBD41: 1728,
|
| 1761 |
-
0xBD57: 1729,
|
| 1762 |
-
0xBD61: 1730,
|
| 1763 |
-
0xBD76: 1731,
|
| 1764 |
-
0xBDA1: 1732,
|
| 1765 |
-
0xBDA2: 1733,
|
| 1766 |
-
0xBDA5: 1734,
|
| 1767 |
-
0xBDA9: 1735,
|
| 1768 |
-
0xBDB1: 1736,
|
| 1769 |
-
0xBDB3: 1737,
|
| 1770 |
-
0xBDB5: 1738,
|
| 1771 |
-
0xBDB7: 1739,
|
| 1772 |
-
0xBDB9: 1740,
|
| 1773 |
-
0xBDC1: 1741,
|
| 1774 |
-
0xBDC2: 1742,
|
| 1775 |
-
0xBDC9: 1743,
|
| 1776 |
-
0xBDD6: 1744,
|
| 1777 |
-
0xBDE1: 1745,
|
| 1778 |
-
0xBDF6: 1746,
|
| 1779 |
-
0xBE41: 1747,
|
| 1780 |
-
0xBE45: 1748,
|
| 1781 |
-
0xBE49: 1749,
|
| 1782 |
-
0xBE51: 1750,
|
| 1783 |
-
0xBE53: 1751,
|
| 1784 |
-
0xBE77: 1752,
|
| 1785 |
-
0xBE81: 1753,
|
| 1786 |
-
0xBE82: 1754,
|
| 1787 |
-
0xBE85: 1755,
|
| 1788 |
-
0xBE89: 1756,
|
| 1789 |
-
0xBE91: 1757,
|
| 1790 |
-
0xBE93: 1758,
|
| 1791 |
-
0xBE97: 1759,
|
| 1792 |
-
0xBEA1: 1760,
|
| 1793 |
-
0xBEB6: 1761,
|
| 1794 |
-
0xBEB7: 1762,
|
| 1795 |
-
0xBEE1: 1763,
|
| 1796 |
-
0xBF41: 1764,
|
| 1797 |
-
0xBF61: 1765,
|
| 1798 |
-
0xBF71: 1766,
|
| 1799 |
-
0xBF75: 1767,
|
| 1800 |
-
0xBF77: 1768,
|
| 1801 |
-
0xBFA1: 1769,
|
| 1802 |
-
0xBFA2: 1770,
|
| 1803 |
-
0xBFA5: 1771,
|
| 1804 |
-
0xBFA9: 1772,
|
| 1805 |
-
0xBFB1: 1773,
|
| 1806 |
-
0xBFB3: 1774,
|
| 1807 |
-
0xBFB7: 1775,
|
| 1808 |
-
0xBFB8: 1776,
|
| 1809 |
-
0xBFBD: 1777,
|
| 1810 |
-
0xC061: 1778,
|
| 1811 |
-
0xC062: 1779,
|
| 1812 |
-
0xC065: 1780,
|
| 1813 |
-
0xC067: 1781,
|
| 1814 |
-
0xC069: 1782,
|
| 1815 |
-
0xC071: 1783,
|
| 1816 |
-
0xC073: 1784,
|
| 1817 |
-
0xC075: 1785,
|
| 1818 |
-
0xC076: 1786,
|
| 1819 |
-
0xC077: 1787,
|
| 1820 |
-
0xC078: 1788,
|
| 1821 |
-
0xC081: 1789,
|
| 1822 |
-
0xC082: 1790,
|
| 1823 |
-
0xC085: 1791,
|
| 1824 |
-
0xC089: 1792,
|
| 1825 |
-
0xC091: 1793,
|
| 1826 |
-
0xC093: 1794,
|
| 1827 |
-
0xC095: 1795,
|
| 1828 |
-
0xC096: 1796,
|
| 1829 |
-
0xC097: 1797,
|
| 1830 |
-
0xC0A1: 1798,
|
| 1831 |
-
0xC0A5: 1799,
|
| 1832 |
-
0xC0A7: 1800,
|
| 1833 |
-
0xC0A9: 1801,
|
| 1834 |
-
0xC0B1: 1802,
|
| 1835 |
-
0xC0B7: 1803,
|
| 1836 |
-
0xC0E1: 1804,
|
| 1837 |
-
0xC0E2: 1805,
|
| 1838 |
-
0xC0E5: 1806,
|
| 1839 |
-
0xC0E9: 1807,
|
| 1840 |
-
0xC0F1: 1808,
|
| 1841 |
-
0xC0F3: 1809,
|
| 1842 |
-
0xC0F5: 1810,
|
| 1843 |
-
0xC0F6: 1811,
|
| 1844 |
-
0xC0F7: 1812,
|
| 1845 |
-
0xC141: 1813,
|
| 1846 |
-
0xC142: 1814,
|
| 1847 |
-
0xC145: 1815,
|
| 1848 |
-
0xC149: 1816,
|
| 1849 |
-
0xC151: 1817,
|
| 1850 |
-
0xC153: 1818,
|
| 1851 |
-
0xC155: 1819,
|
| 1852 |
-
0xC157: 1820,
|
| 1853 |
-
0xC161: 1821,
|
| 1854 |
-
0xC165: 1822,
|
| 1855 |
-
0xC176: 1823,
|
| 1856 |
-
0xC181: 1824,
|
| 1857 |
-
0xC185: 1825,
|
| 1858 |
-
0xC197: 1826,
|
| 1859 |
-
0xC1A1: 1827,
|
| 1860 |
-
0xC1A2: 1828,
|
| 1861 |
-
0xC1A5: 1829,
|
| 1862 |
-
0xC1A9: 1830,
|
| 1863 |
-
0xC1B1: 1831,
|
| 1864 |
-
0xC1B3: 1832,
|
| 1865 |
-
0xC1B5: 1833,
|
| 1866 |
-
0xC1B7: 1834,
|
| 1867 |
-
0xC1C1: 1835,
|
| 1868 |
-
0xC1C5: 1836,
|
| 1869 |
-
0xC1C9: 1837,
|
| 1870 |
-
0xC1D7: 1838,
|
| 1871 |
-
0xC241: 1839,
|
| 1872 |
-
0xC245: 1840,
|
| 1873 |
-
0xC249: 1841,
|
| 1874 |
-
0xC251: 1842,
|
| 1875 |
-
0xC253: 1843,
|
| 1876 |
-
0xC255: 1844,
|
| 1877 |
-
0xC257: 1845,
|
| 1878 |
-
0xC261: 1846,
|
| 1879 |
-
0xC271: 1847,
|
| 1880 |
-
0xC281: 1848,
|
| 1881 |
-
0xC282: 1849,
|
| 1882 |
-
0xC285: 1850,
|
| 1883 |
-
0xC289: 1851,
|
| 1884 |
-
0xC291: 1852,
|
| 1885 |
-
0xC293: 1853,
|
| 1886 |
-
0xC295: 1854,
|
| 1887 |
-
0xC297: 1855,
|
| 1888 |
-
0xC2A1: 1856,
|
| 1889 |
-
0xC2B6: 1857,
|
| 1890 |
-
0xC2C1: 1858,
|
| 1891 |
-
0xC2C5: 1859,
|
| 1892 |
-
0xC2E1: 1860,
|
| 1893 |
-
0xC2E5: 1861,
|
| 1894 |
-
0xC2E9: 1862,
|
| 1895 |
-
0xC2F1: 1863,
|
| 1896 |
-
0xC2F3: 1864,
|
| 1897 |
-
0xC2F5: 1865,
|
| 1898 |
-
0xC2F7: 1866,
|
| 1899 |
-
0xC341: 1867,
|
| 1900 |
-
0xC345: 1868,
|
| 1901 |
-
0xC349: 1869,
|
| 1902 |
-
0xC351: 1870,
|
| 1903 |
-
0xC357: 1871,
|
| 1904 |
-
0xC361: 1872,
|
| 1905 |
-
0xC362: 1873,
|
| 1906 |
-
0xC365: 1874,
|
| 1907 |
-
0xC369: 1875,
|
| 1908 |
-
0xC371: 1876,
|
| 1909 |
-
0xC373: 1877,
|
| 1910 |
-
0xC375: 1878,
|
| 1911 |
-
0xC377: 1879,
|
| 1912 |
-
0xC3A1: 1880,
|
| 1913 |
-
0xC3A2: 1881,
|
| 1914 |
-
0xC3A5: 1882,
|
| 1915 |
-
0xC3A8: 1883,
|
| 1916 |
-
0xC3A9: 1884,
|
| 1917 |
-
0xC3AA: 1885,
|
| 1918 |
-
0xC3B1: 1886,
|
| 1919 |
-
0xC3B3: 1887,
|
| 1920 |
-
0xC3B5: 1888,
|
| 1921 |
-
0xC3B7: 1889,
|
| 1922 |
-
0xC461: 1890,
|
| 1923 |
-
0xC462: 1891,
|
| 1924 |
-
0xC465: 1892,
|
| 1925 |
-
0xC469: 1893,
|
| 1926 |
-
0xC471: 1894,
|
| 1927 |
-
0xC473: 1895,
|
| 1928 |
-
0xC475: 1896,
|
| 1929 |
-
0xC477: 1897,
|
| 1930 |
-
0xC481: 1898,
|
| 1931 |
-
0xC482: 1899,
|
| 1932 |
-
0xC485: 1900,
|
| 1933 |
-
0xC489: 1901,
|
| 1934 |
-
0xC491: 1902,
|
| 1935 |
-
0xC493: 1903,
|
| 1936 |
-
0xC495: 1904,
|
| 1937 |
-
0xC496: 1905,
|
| 1938 |
-
0xC497: 1906,
|
| 1939 |
-
0xC4A1: 1907,
|
| 1940 |
-
0xC4A2: 1908,
|
| 1941 |
-
0xC4B7: 1909,
|
| 1942 |
-
0xC4E1: 1910,
|
| 1943 |
-
0xC4E2: 1911,
|
| 1944 |
-
0xC4E5: 1912,
|
| 1945 |
-
0xC4E8: 1913,
|
| 1946 |
-
0xC4E9: 1914,
|
| 1947 |
-
0xC4F1: 1915,
|
| 1948 |
-
0xC4F3: 1916,
|
| 1949 |
-
0xC4F5: 1917,
|
| 1950 |
-
0xC4F6: 1918,
|
| 1951 |
-
0xC4F7: 1919,
|
| 1952 |
-
0xC541: 1920,
|
| 1953 |
-
0xC542: 1921,
|
| 1954 |
-
0xC545: 1922,
|
| 1955 |
-
0xC549: 1923,
|
| 1956 |
-
0xC551: 1924,
|
| 1957 |
-
0xC553: 1925,
|
| 1958 |
-
0xC555: 1926,
|
| 1959 |
-
0xC557: 1927,
|
| 1960 |
-
0xC561: 1928,
|
| 1961 |
-
0xC565: 1929,
|
| 1962 |
-
0xC569: 1930,
|
| 1963 |
-
0xC571: 1931,
|
| 1964 |
-
0xC573: 1932,
|
| 1965 |
-
0xC575: 1933,
|
| 1966 |
-
0xC576: 1934,
|
| 1967 |
-
0xC577: 1935,
|
| 1968 |
-
0xC581: 1936,
|
| 1969 |
-
0xC5A1: 1937,
|
| 1970 |
-
0xC5A2: 1938,
|
| 1971 |
-
0xC5A5: 1939,
|
| 1972 |
-
0xC5A9: 1940,
|
| 1973 |
-
0xC5B1: 1941,
|
| 1974 |
-
0xC5B3: 1942,
|
| 1975 |
-
0xC5B5: 1943,
|
| 1976 |
-
0xC5B7: 1944,
|
| 1977 |
-
0xC5C1: 1945,
|
| 1978 |
-
0xC5C2: 1946,
|
| 1979 |
-
0xC5C5: 1947,
|
| 1980 |
-
0xC5C9: 1948,
|
| 1981 |
-
0xC5D1: 1949,
|
| 1982 |
-
0xC5D7: 1950,
|
| 1983 |
-
0xC5E1: 1951,
|
| 1984 |
-
0xC5F7: 1952,
|
| 1985 |
-
0xC641: 1953,
|
| 1986 |
-
0xC649: 1954,
|
| 1987 |
-
0xC661: 1955,
|
| 1988 |
-
0xC681: 1956,
|
| 1989 |
-
0xC682: 1957,
|
| 1990 |
-
0xC685: 1958,
|
| 1991 |
-
0xC689: 1959,
|
| 1992 |
-
0xC691: 1960,
|
| 1993 |
-
0xC693: 1961,
|
| 1994 |
-
0xC695: 1962,
|
| 1995 |
-
0xC697: 1963,
|
| 1996 |
-
0xC6A1: 1964,
|
| 1997 |
-
0xC6A5: 1965,
|
| 1998 |
-
0xC6A9: 1966,
|
| 1999 |
-
0xC6B7: 1967,
|
| 2000 |
-
0xC6C1: 1968,
|
| 2001 |
-
0xC6D7: 1969,
|
| 2002 |
-
0xC6E1: 1970,
|
| 2003 |
-
0xC6E2: 1971,
|
| 2004 |
-
0xC6E5: 1972,
|
| 2005 |
-
0xC6E9: 1973,
|
| 2006 |
-
0xC6F1: 1974,
|
| 2007 |
-
0xC6F3: 1975,
|
| 2008 |
-
0xC6F5: 1976,
|
| 2009 |
-
0xC6F7: 1977,
|
| 2010 |
-
0xC741: 1978,
|
| 2011 |
-
0xC745: 1979,
|
| 2012 |
-
0xC749: 1980,
|
| 2013 |
-
0xC751: 1981,
|
| 2014 |
-
0xC761: 1982,
|
| 2015 |
-
0xC762: 1983,
|
| 2016 |
-
0xC765: 1984,
|
| 2017 |
-
0xC769: 1985,
|
| 2018 |
-
0xC771: 1986,
|
| 2019 |
-
0xC773: 1987,
|
| 2020 |
-
0xC777: 1988,
|
| 2021 |
-
0xC7A1: 1989,
|
| 2022 |
-
0xC7A2: 1990,
|
| 2023 |
-
0xC7A5: 1991,
|
| 2024 |
-
0xC7A9: 1992,
|
| 2025 |
-
0xC7B1: 1993,
|
| 2026 |
-
0xC7B3: 1994,
|
| 2027 |
-
0xC7B5: 1995,
|
| 2028 |
-
0xC7B7: 1996,
|
| 2029 |
-
0xC861: 1997,
|
| 2030 |
-
0xC862: 1998,
|
| 2031 |
-
0xC865: 1999,
|
| 2032 |
-
0xC869: 2000,
|
| 2033 |
-
0xC86A: 2001,
|
| 2034 |
-
0xC871: 2002,
|
| 2035 |
-
0xC873: 2003,
|
| 2036 |
-
0xC875: 2004,
|
| 2037 |
-
0xC876: 2005,
|
| 2038 |
-
0xC877: 2006,
|
| 2039 |
-
0xC881: 2007,
|
| 2040 |
-
0xC882: 2008,
|
| 2041 |
-
0xC885: 2009,
|
| 2042 |
-
0xC889: 2010,
|
| 2043 |
-
0xC891: 2011,
|
| 2044 |
-
0xC893: 2012,
|
| 2045 |
-
0xC895: 2013,
|
| 2046 |
-
0xC896: 2014,
|
| 2047 |
-
0xC897: 2015,
|
| 2048 |
-
0xC8A1: 2016,
|
| 2049 |
-
0xC8B7: 2017,
|
| 2050 |
-
0xC8E1: 2018,
|
| 2051 |
-
0xC8E2: 2019,
|
| 2052 |
-
0xC8E5: 2020,
|
| 2053 |
-
0xC8E9: 2021,
|
| 2054 |
-
0xC8EB: 2022,
|
| 2055 |
-
0xC8F1: 2023,
|
| 2056 |
-
0xC8F3: 2024,
|
| 2057 |
-
0xC8F5: 2025,
|
| 2058 |
-
0xC8F6: 2026,
|
| 2059 |
-
0xC8F7: 2027,
|
| 2060 |
-
0xC941: 2028,
|
| 2061 |
-
0xC942: 2029,
|
| 2062 |
-
0xC945: 2030,
|
| 2063 |
-
0xC949: 2031,
|
| 2064 |
-
0xC951: 2032,
|
| 2065 |
-
0xC953: 2033,
|
| 2066 |
-
0xC955: 2034,
|
| 2067 |
-
0xC957: 2035,
|
| 2068 |
-
0xC961: 2036,
|
| 2069 |
-
0xC965: 2037,
|
| 2070 |
-
0xC976: 2038,
|
| 2071 |
-
0xC981: 2039,
|
| 2072 |
-
0xC985: 2040,
|
| 2073 |
-
0xC9A1: 2041,
|
| 2074 |
-
0xC9A2: 2042,
|
| 2075 |
-
0xC9A5: 2043,
|
| 2076 |
-
0xC9A9: 2044,
|
| 2077 |
-
0xC9B1: 2045,
|
| 2078 |
-
0xC9B3: 2046,
|
| 2079 |
-
0xC9B5: 2047,
|
| 2080 |
-
0xC9B7: 2048,
|
| 2081 |
-
0xC9BC: 2049,
|
| 2082 |
-
0xC9C1: 2050,
|
| 2083 |
-
0xC9C5: 2051,
|
| 2084 |
-
0xC9E1: 2052,
|
| 2085 |
-
0xCA41: 2053,
|
| 2086 |
-
0xCA45: 2054,
|
| 2087 |
-
0xCA55: 2055,
|
| 2088 |
-
0xCA57: 2056,
|
| 2089 |
-
0xCA61: 2057,
|
| 2090 |
-
0xCA81: 2058,
|
| 2091 |
-
0xCA82: 2059,
|
| 2092 |
-
0xCA85: 2060,
|
| 2093 |
-
0xCA89: 2061,
|
| 2094 |
-
0xCA91: 2062,
|
| 2095 |
-
0xCA93: 2063,
|
| 2096 |
-
0xCA95: 2064,
|
| 2097 |
-
0xCA97: 2065,
|
| 2098 |
-
0xCAA1: 2066,
|
| 2099 |
-
0xCAB6: 2067,
|
| 2100 |
-
0xCAC1: 2068,
|
| 2101 |
-
0xCAE1: 2069,
|
| 2102 |
-
0xCAE2: 2070,
|
| 2103 |
-
0xCAE5: 2071,
|
| 2104 |
-
0xCAE9: 2072,
|
| 2105 |
-
0xCAF1: 2073,
|
| 2106 |
-
0xCAF3: 2074,
|
| 2107 |
-
0xCAF7: 2075,
|
| 2108 |
-
0xCB41: 2076,
|
| 2109 |
-
0xCB45: 2077,
|
| 2110 |
-
0xCB49: 2078,
|
| 2111 |
-
0xCB51: 2079,
|
| 2112 |
-
0xCB57: 2080,
|
| 2113 |
-
0xCB61: 2081,
|
| 2114 |
-
0xCB62: 2082,
|
| 2115 |
-
0xCB65: 2083,
|
| 2116 |
-
0xCB68: 2084,
|
| 2117 |
-
0xCB69: 2085,
|
| 2118 |
-
0xCB6B: 2086,
|
| 2119 |
-
0xCB71: 2087,
|
| 2120 |
-
0xCB73: 2088,
|
| 2121 |
-
0xCB75: 2089,
|
| 2122 |
-
0xCB81: 2090,
|
| 2123 |
-
0xCB85: 2091,
|
| 2124 |
-
0xCB89: 2092,
|
| 2125 |
-
0xCB91: 2093,
|
| 2126 |
-
0xCB93: 2094,
|
| 2127 |
-
0xCBA1: 2095,
|
| 2128 |
-
0xCBA2: 2096,
|
| 2129 |
-
0xCBA5: 2097,
|
| 2130 |
-
0xCBA9: 2098,
|
| 2131 |
-
0xCBB1: 2099,
|
| 2132 |
-
0xCBB3: 2100,
|
| 2133 |
-
0xCBB5: 2101,
|
| 2134 |
-
0xCBB7: 2102,
|
| 2135 |
-
0xCC61: 2103,
|
| 2136 |
-
0xCC62: 2104,
|
| 2137 |
-
0xCC63: 2105,
|
| 2138 |
-
0xCC65: 2106,
|
| 2139 |
-
0xCC69: 2107,
|
| 2140 |
-
0xCC6B: 2108,
|
| 2141 |
-
0xCC71: 2109,
|
| 2142 |
-
0xCC73: 2110,
|
| 2143 |
-
0xCC75: 2111,
|
| 2144 |
-
0xCC76: 2112,
|
| 2145 |
-
0xCC77: 2113,
|
| 2146 |
-
0xCC7B: 2114,
|
| 2147 |
-
0xCC81: 2115,
|
| 2148 |
-
0xCC82: 2116,
|
| 2149 |
-
0xCC85: 2117,
|
| 2150 |
-
0xCC89: 2118,
|
| 2151 |
-
0xCC91: 2119,
|
| 2152 |
-
0xCC93: 2120,
|
| 2153 |
-
0xCC95: 2121,
|
| 2154 |
-
0xCC96: 2122,
|
| 2155 |
-
0xCC97: 2123,
|
| 2156 |
-
0xCCA1: 2124,
|
| 2157 |
-
0xCCA2: 2125,
|
| 2158 |
-
0xCCE1: 2126,
|
| 2159 |
-
0xCCE2: 2127,
|
| 2160 |
-
0xCCE5: 2128,
|
| 2161 |
-
0xCCE9: 2129,
|
| 2162 |
-
0xCCF1: 2130,
|
| 2163 |
-
0xCCF3: 2131,
|
| 2164 |
-
0xCCF5: 2132,
|
| 2165 |
-
0xCCF6: 2133,
|
| 2166 |
-
0xCCF7: 2134,
|
| 2167 |
-
0xCD41: 2135,
|
| 2168 |
-
0xCD42: 2136,
|
| 2169 |
-
0xCD45: 2137,
|
| 2170 |
-
0xCD49: 2138,
|
| 2171 |
-
0xCD51: 2139,
|
| 2172 |
-
0xCD53: 2140,
|
| 2173 |
-
0xCD55: 2141,
|
| 2174 |
-
0xCD57: 2142,
|
| 2175 |
-
0xCD61: 2143,
|
| 2176 |
-
0xCD65: 2144,
|
| 2177 |
-
0xCD69: 2145,
|
| 2178 |
-
0xCD71: 2146,
|
| 2179 |
-
0xCD73: 2147,
|
| 2180 |
-
0xCD76: 2148,
|
| 2181 |
-
0xCD77: 2149,
|
| 2182 |
-
0xCD81: 2150,
|
| 2183 |
-
0xCD89: 2151,
|
| 2184 |
-
0xCD93: 2152,
|
| 2185 |
-
0xCD95: 2153,
|
| 2186 |
-
0xCDA1: 2154,
|
| 2187 |
-
0xCDA2: 2155,
|
| 2188 |
-
0xCDA5: 2156,
|
| 2189 |
-
0xCDA9: 2157,
|
| 2190 |
-
0xCDB1: 2158,
|
| 2191 |
-
0xCDB3: 2159,
|
| 2192 |
-
0xCDB5: 2160,
|
| 2193 |
-
0xCDB7: 2161,
|
| 2194 |
-
0xCDC1: 2162,
|
| 2195 |
-
0xCDD7: 2163,
|
| 2196 |
-
0xCE41: 2164,
|
| 2197 |
-
0xCE45: 2165,
|
| 2198 |
-
0xCE61: 2166,
|
| 2199 |
-
0xCE65: 2167,
|
| 2200 |
-
0xCE69: 2168,
|
| 2201 |
-
0xCE73: 2169,
|
| 2202 |
-
0xCE75: 2170,
|
| 2203 |
-
0xCE81: 2171,
|
| 2204 |
-
0xCE82: 2172,
|
| 2205 |
-
0xCE85: 2173,
|
| 2206 |
-
0xCE88: 2174,
|
| 2207 |
-
0xCE89: 2175,
|
| 2208 |
-
0xCE8B: 2176,
|
| 2209 |
-
0xCE91: 2177,
|
| 2210 |
-
0xCE93: 2178,
|
| 2211 |
-
0xCE95: 2179,
|
| 2212 |
-
0xCE97: 2180,
|
| 2213 |
-
0xCEA1: 2181,
|
| 2214 |
-
0xCEB7: 2182,
|
| 2215 |
-
0xCEE1: 2183,
|
| 2216 |
-
0xCEE5: 2184,
|
| 2217 |
-
0xCEE9: 2185,
|
| 2218 |
-
0xCEF1: 2186,
|
| 2219 |
-
0xCEF5: 2187,
|
| 2220 |
-
0xCF41: 2188,
|
| 2221 |
-
0xCF45: 2189,
|
| 2222 |
-
0xCF49: 2190,
|
| 2223 |
-
0xCF51: 2191,
|
| 2224 |
-
0xCF55: 2192,
|
| 2225 |
-
0xCF57: 2193,
|
| 2226 |
-
0xCF61: 2194,
|
| 2227 |
-
0xCF65: 2195,
|
| 2228 |
-
0xCF69: 2196,
|
| 2229 |
-
0xCF71: 2197,
|
| 2230 |
-
0xCF73: 2198,
|
| 2231 |
-
0xCF75: 2199,
|
| 2232 |
-
0xCFA1: 2200,
|
| 2233 |
-
0xCFA2: 2201,
|
| 2234 |
-
0xCFA5: 2202,
|
| 2235 |
-
0xCFA9: 2203,
|
| 2236 |
-
0xCFB1: 2204,
|
| 2237 |
-
0xCFB3: 2205,
|
| 2238 |
-
0xCFB5: 2206,
|
| 2239 |
-
0xCFB7: 2207,
|
| 2240 |
-
0xD061: 2208,
|
| 2241 |
-
0xD062: 2209,
|
| 2242 |
-
0xD065: 2210,
|
| 2243 |
-
0xD069: 2211,
|
| 2244 |
-
0xD06E: 2212,
|
| 2245 |
-
0xD071: 2213,
|
| 2246 |
-
0xD073: 2214,
|
| 2247 |
-
0xD075: 2215,
|
| 2248 |
-
0xD077: 2216,
|
| 2249 |
-
0xD081: 2217,
|
| 2250 |
-
0xD082: 2218,
|
| 2251 |
-
0xD085: 2219,
|
| 2252 |
-
0xD089: 2220,
|
| 2253 |
-
0xD091: 2221,
|
| 2254 |
-
0xD093: 2222,
|
| 2255 |
-
0xD095: 2223,
|
| 2256 |
-
0xD096: 2224,
|
| 2257 |
-
0xD097: 2225,
|
| 2258 |
-
0xD0A1: 2226,
|
| 2259 |
-
0xD0B7: 2227,
|
| 2260 |
-
0xD0E1: 2228,
|
| 2261 |
-
0xD0E2: 2229,
|
| 2262 |
-
0xD0E5: 2230,
|
| 2263 |
-
0xD0E9: 2231,
|
| 2264 |
-
0xD0EB: 2232,
|
| 2265 |
-
0xD0F1: 2233,
|
| 2266 |
-
0xD0F3: 2234,
|
| 2267 |
-
0xD0F5: 2235,
|
| 2268 |
-
0xD0F7: 2236,
|
| 2269 |
-
0xD141: 2237,
|
| 2270 |
-
0xD142: 2238,
|
| 2271 |
-
0xD145: 2239,
|
| 2272 |
-
0xD149: 2240,
|
| 2273 |
-
0xD151: 2241,
|
| 2274 |
-
0xD153: 2242,
|
| 2275 |
-
0xD155: 2243,
|
| 2276 |
-
0xD157: 2244,
|
| 2277 |
-
0xD161: 2245,
|
| 2278 |
-
0xD162: 2246,
|
| 2279 |
-
0xD165: 2247,
|
| 2280 |
-
0xD169: 2248,
|
| 2281 |
-
0xD171: 2249,
|
| 2282 |
-
0xD173: 2250,
|
| 2283 |
-
0xD175: 2251,
|
| 2284 |
-
0xD176: 2252,
|
| 2285 |
-
0xD177: 2253,
|
| 2286 |
-
0xD181: 2254,
|
| 2287 |
-
0xD185: 2255,
|
| 2288 |
-
0xD189: 2256,
|
| 2289 |
-
0xD193: 2257,
|
| 2290 |
-
0xD1A1: 2258,
|
| 2291 |
-
0xD1A2: 2259,
|
| 2292 |
-
0xD1A5: 2260,
|
| 2293 |
-
0xD1A9: 2261,
|
| 2294 |
-
0xD1AE: 2262,
|
| 2295 |
-
0xD1B1: 2263,
|
| 2296 |
-
0xD1B3: 2264,
|
| 2297 |
-
0xD1B5: 2265,
|
| 2298 |
-
0xD1B7: 2266,
|
| 2299 |
-
0xD1BB: 2267,
|
| 2300 |
-
0xD1C1: 2268,
|
| 2301 |
-
0xD1C2: 2269,
|
| 2302 |
-
0xD1C5: 2270,
|
| 2303 |
-
0xD1C9: 2271,
|
| 2304 |
-
0xD1D5: 2272,
|
| 2305 |
-
0xD1D7: 2273,
|
| 2306 |
-
0xD1E1: 2274,
|
| 2307 |
-
0xD1E2: 2275,
|
| 2308 |
-
0xD1E5: 2276,
|
| 2309 |
-
0xD1F5: 2277,
|
| 2310 |
-
0xD1F7: 2278,
|
| 2311 |
-
0xD241: 2279,
|
| 2312 |
-
0xD242: 2280,
|
| 2313 |
-
0xD245: 2281,
|
| 2314 |
-
0xD249: 2282,
|
| 2315 |
-
0xD253: 2283,
|
| 2316 |
-
0xD255: 2284,
|
| 2317 |
-
0xD257: 2285,
|
| 2318 |
-
0xD261: 2286,
|
| 2319 |
-
0xD265: 2287,
|
| 2320 |
-
0xD269: 2288,
|
| 2321 |
-
0xD273: 2289,
|
| 2322 |
-
0xD275: 2290,
|
| 2323 |
-
0xD281: 2291,
|
| 2324 |
-
0xD282: 2292,
|
| 2325 |
-
0xD285: 2293,
|
| 2326 |
-
0xD289: 2294,
|
| 2327 |
-
0xD28E: 2295,
|
| 2328 |
-
0xD291: 2296,
|
| 2329 |
-
0xD295: 2297,
|
| 2330 |
-
0xD297: 2298,
|
| 2331 |
-
0xD2A1: 2299,
|
| 2332 |
-
0xD2A5: 2300,
|
| 2333 |
-
0xD2A9: 2301,
|
| 2334 |
-
0xD2B1: 2302,
|
| 2335 |
-
0xD2B7: 2303,
|
| 2336 |
-
0xD2C1: 2304,
|
| 2337 |
-
0xD2C2: 2305,
|
| 2338 |
-
0xD2C5: 2306,
|
| 2339 |
-
0xD2C9: 2307,
|
| 2340 |
-
0xD2D7: 2308,
|
| 2341 |
-
0xD2E1: 2309,
|
| 2342 |
-
0xD2E2: 2310,
|
| 2343 |
-
0xD2E5: 2311,
|
| 2344 |
-
0xD2E9: 2312,
|
| 2345 |
-
0xD2F1: 2313,
|
| 2346 |
-
0xD2F3: 2314,
|
| 2347 |
-
0xD2F5: 2315,
|
| 2348 |
-
0xD2F7: 2316,
|
| 2349 |
-
0xD341: 2317,
|
| 2350 |
-
0xD342: 2318,
|
| 2351 |
-
0xD345: 2319,
|
| 2352 |
-
0xD349: 2320,
|
| 2353 |
-
0xD351: 2321,
|
| 2354 |
-
0xD355: 2322,
|
| 2355 |
-
0xD357: 2323,
|
| 2356 |
-
0xD361: 2324,
|
| 2357 |
-
0xD362: 2325,
|
| 2358 |
-
0xD365: 2326,
|
| 2359 |
-
0xD367: 2327,
|
| 2360 |
-
0xD368: 2328,
|
| 2361 |
-
0xD369: 2329,
|
| 2362 |
-
0xD36A: 2330,
|
| 2363 |
-
0xD371: 2331,
|
| 2364 |
-
0xD373: 2332,
|
| 2365 |
-
0xD375: 2333,
|
| 2366 |
-
0xD377: 2334,
|
| 2367 |
-
0xD37B: 2335,
|
| 2368 |
-
0xD381: 2336,
|
| 2369 |
-
0xD385: 2337,
|
| 2370 |
-
0xD389: 2338,
|
| 2371 |
-
0xD391: 2339,
|
| 2372 |
-
0xD393: 2340,
|
| 2373 |
-
0xD397: 2341,
|
| 2374 |
-
0xD3A1: 2342,
|
| 2375 |
-
0xD3A2: 2343,
|
| 2376 |
-
0xD3A5: 2344,
|
| 2377 |
-
0xD3A9: 2345,
|
| 2378 |
-
0xD3B1: 2346,
|
| 2379 |
-
0xD3B3: 2347,
|
| 2380 |
-
0xD3B5: 2348,
|
| 2381 |
-
0xD3B7: 2349,
|
| 2382 |
-
}
|
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/prompt.py
DELETED
|
@@ -1,376 +0,0 @@
|
|
| 1 |
-
from typing import Any, Generic, List, Optional, TextIO, TypeVar, Union, overload
|
| 2 |
-
|
| 3 |
-
from . import get_console
|
| 4 |
-
from .console import Console
|
| 5 |
-
from .text import Text, TextType
|
| 6 |
-
|
| 7 |
-
PromptType = TypeVar("PromptType")
|
| 8 |
-
DefaultType = TypeVar("DefaultType")
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class PromptError(Exception):
|
| 12 |
-
"""Exception base class for prompt related errors."""
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class InvalidResponse(PromptError):
|
| 16 |
-
"""Exception to indicate a response was invalid. Raise this within process_response() to indicate an error
|
| 17 |
-
and provide an error message.
|
| 18 |
-
|
| 19 |
-
Args:
|
| 20 |
-
message (Union[str, Text]): Error message.
|
| 21 |
-
"""
|
| 22 |
-
|
| 23 |
-
def __init__(self, message: TextType) -> None:
|
| 24 |
-
self.message = message
|
| 25 |
-
|
| 26 |
-
def __rich__(self) -> TextType:
|
| 27 |
-
return self.message
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
class PromptBase(Generic[PromptType]):
|
| 31 |
-
"""Ask the user for input until a valid response is received. This is the base class, see one of
|
| 32 |
-
the concrete classes for examples.
|
| 33 |
-
|
| 34 |
-
Args:
|
| 35 |
-
prompt (TextType, optional): Prompt text. Defaults to "".
|
| 36 |
-
console (Console, optional): A Console instance or None to use global console. Defaults to None.
|
| 37 |
-
password (bool, optional): Enable password input. Defaults to False.
|
| 38 |
-
choices (List[str], optional): A list of valid choices. Defaults to None.
|
| 39 |
-
show_default (bool, optional): Show default in prompt. Defaults to True.
|
| 40 |
-
show_choices (bool, optional): Show choices in prompt. Defaults to True.
|
| 41 |
-
"""
|
| 42 |
-
|
| 43 |
-
response_type: type = str
|
| 44 |
-
|
| 45 |
-
validate_error_message = "[prompt.invalid]Please enter a valid value"
|
| 46 |
-
illegal_choice_message = (
|
| 47 |
-
"[prompt.invalid.choice]Please select one of the available options"
|
| 48 |
-
)
|
| 49 |
-
prompt_suffix = ": "
|
| 50 |
-
|
| 51 |
-
choices: Optional[List[str]] = None
|
| 52 |
-
|
| 53 |
-
def __init__(
|
| 54 |
-
self,
|
| 55 |
-
prompt: TextType = "",
|
| 56 |
-
*,
|
| 57 |
-
console: Optional[Console] = None,
|
| 58 |
-
password: bool = False,
|
| 59 |
-
choices: Optional[List[str]] = None,
|
| 60 |
-
show_default: bool = True,
|
| 61 |
-
show_choices: bool = True,
|
| 62 |
-
) -> None:
|
| 63 |
-
self.console = console or get_console()
|
| 64 |
-
self.prompt = (
|
| 65 |
-
Text.from_markup(prompt, style="prompt")
|
| 66 |
-
if isinstance(prompt, str)
|
| 67 |
-
else prompt
|
| 68 |
-
)
|
| 69 |
-
self.password = password
|
| 70 |
-
if choices is not None:
|
| 71 |
-
self.choices = choices
|
| 72 |
-
self.show_default = show_default
|
| 73 |
-
self.show_choices = show_choices
|
| 74 |
-
|
| 75 |
-
@classmethod
|
| 76 |
-
@overload
|
| 77 |
-
def ask(
|
| 78 |
-
cls,
|
| 79 |
-
prompt: TextType = "",
|
| 80 |
-
*,
|
| 81 |
-
console: Optional[Console] = None,
|
| 82 |
-
password: bool = False,
|
| 83 |
-
choices: Optional[List[str]] = None,
|
| 84 |
-
show_default: bool = True,
|
| 85 |
-
show_choices: bool = True,
|
| 86 |
-
default: DefaultType,
|
| 87 |
-
stream: Optional[TextIO] = None,
|
| 88 |
-
) -> Union[DefaultType, PromptType]:
|
| 89 |
-
...
|
| 90 |
-
|
| 91 |
-
@classmethod
|
| 92 |
-
@overload
|
| 93 |
-
def ask(
|
| 94 |
-
cls,
|
| 95 |
-
prompt: TextType = "",
|
| 96 |
-
*,
|
| 97 |
-
console: Optional[Console] = None,
|
| 98 |
-
password: bool = False,
|
| 99 |
-
choices: Optional[List[str]] = None,
|
| 100 |
-
show_default: bool = True,
|
| 101 |
-
show_choices: bool = True,
|
| 102 |
-
stream: Optional[TextIO] = None,
|
| 103 |
-
) -> PromptType:
|
| 104 |
-
...
|
| 105 |
-
|
| 106 |
-
@classmethod
|
| 107 |
-
def ask(
|
| 108 |
-
cls,
|
| 109 |
-
prompt: TextType = "",
|
| 110 |
-
*,
|
| 111 |
-
console: Optional[Console] = None,
|
| 112 |
-
password: bool = False,
|
| 113 |
-
choices: Optional[List[str]] = None,
|
| 114 |
-
show_default: bool = True,
|
| 115 |
-
show_choices: bool = True,
|
| 116 |
-
default: Any = ...,
|
| 117 |
-
stream: Optional[TextIO] = None,
|
| 118 |
-
) -> Any:
|
| 119 |
-
"""Shortcut to construct and run a prompt loop and return the result.
|
| 120 |
-
|
| 121 |
-
Example:
|
| 122 |
-
>>> filename = Prompt.ask("Enter a filename")
|
| 123 |
-
|
| 124 |
-
Args:
|
| 125 |
-
prompt (TextType, optional): Prompt text. Defaults to "".
|
| 126 |
-
console (Console, optional): A Console instance or None to use global console. Defaults to None.
|
| 127 |
-
password (bool, optional): Enable password input. Defaults to False.
|
| 128 |
-
choices (List[str], optional): A list of valid choices. Defaults to None.
|
| 129 |
-
show_default (bool, optional): Show default in prompt. Defaults to True.
|
| 130 |
-
show_choices (bool, optional): Show choices in prompt. Defaults to True.
|
| 131 |
-
stream (TextIO, optional): Optional text file open for reading to get input. Defaults to None.
|
| 132 |
-
"""
|
| 133 |
-
_prompt = cls(
|
| 134 |
-
prompt,
|
| 135 |
-
console=console,
|
| 136 |
-
password=password,
|
| 137 |
-
choices=choices,
|
| 138 |
-
show_default=show_default,
|
| 139 |
-
show_choices=show_choices,
|
| 140 |
-
)
|
| 141 |
-
return _prompt(default=default, stream=stream)
|
| 142 |
-
|
| 143 |
-
def render_default(self, default: DefaultType) -> Text:
|
| 144 |
-
"""Turn the supplied default in to a Text instance.
|
| 145 |
-
|
| 146 |
-
Args:
|
| 147 |
-
default (DefaultType): Default value.
|
| 148 |
-
|
| 149 |
-
Returns:
|
| 150 |
-
Text: Text containing rendering of default value.
|
| 151 |
-
"""
|
| 152 |
-
return Text(f"({default})", "prompt.default")
|
| 153 |
-
|
| 154 |
-
def make_prompt(self, default: DefaultType) -> Text:
|
| 155 |
-
"""Make prompt text.
|
| 156 |
-
|
| 157 |
-
Args:
|
| 158 |
-
default (DefaultType): Default value.
|
| 159 |
-
|
| 160 |
-
Returns:
|
| 161 |
-
Text: Text to display in prompt.
|
| 162 |
-
"""
|
| 163 |
-
prompt = self.prompt.copy()
|
| 164 |
-
prompt.end = ""
|
| 165 |
-
|
| 166 |
-
if self.show_choices and self.choices:
|
| 167 |
-
_choices = "/".join(self.choices)
|
| 168 |
-
choices = f"[{_choices}]"
|
| 169 |
-
prompt.append(" ")
|
| 170 |
-
prompt.append(choices, "prompt.choices")
|
| 171 |
-
|
| 172 |
-
if (
|
| 173 |
-
default != ...
|
| 174 |
-
and self.show_default
|
| 175 |
-
and isinstance(default, (str, self.response_type))
|
| 176 |
-
):
|
| 177 |
-
prompt.append(" ")
|
| 178 |
-
_default = self.render_default(default)
|
| 179 |
-
prompt.append(_default)
|
| 180 |
-
|
| 181 |
-
prompt.append(self.prompt_suffix)
|
| 182 |
-
|
| 183 |
-
return prompt
|
| 184 |
-
|
| 185 |
-
@classmethod
|
| 186 |
-
def get_input(
|
| 187 |
-
cls,
|
| 188 |
-
console: Console,
|
| 189 |
-
prompt: TextType,
|
| 190 |
-
password: bool,
|
| 191 |
-
stream: Optional[TextIO] = None,
|
| 192 |
-
) -> str:
|
| 193 |
-
"""Get input from user.
|
| 194 |
-
|
| 195 |
-
Args:
|
| 196 |
-
console (Console): Console instance.
|
| 197 |
-
prompt (TextType): Prompt text.
|
| 198 |
-
password (bool): Enable password entry.
|
| 199 |
-
|
| 200 |
-
Returns:
|
| 201 |
-
str: String from user.
|
| 202 |
-
"""
|
| 203 |
-
return console.input(prompt, password=password, stream=stream)
|
| 204 |
-
|
| 205 |
-
def check_choice(self, value: str) -> bool:
|
| 206 |
-
"""Check value is in the list of valid choices.
|
| 207 |
-
|
| 208 |
-
Args:
|
| 209 |
-
value (str): Value entered by user.
|
| 210 |
-
|
| 211 |
-
Returns:
|
| 212 |
-
bool: True if choice was valid, otherwise False.
|
| 213 |
-
"""
|
| 214 |
-
assert self.choices is not None
|
| 215 |
-
return value.strip() in self.choices
|
| 216 |
-
|
| 217 |
-
def process_response(self, value: str) -> PromptType:
|
| 218 |
-
"""Process response from user, convert to prompt type.
|
| 219 |
-
|
| 220 |
-
Args:
|
| 221 |
-
value (str): String typed by user.
|
| 222 |
-
|
| 223 |
-
Raises:
|
| 224 |
-
InvalidResponse: If ``value`` is invalid.
|
| 225 |
-
|
| 226 |
-
Returns:
|
| 227 |
-
PromptType: The value to be returned from ask method.
|
| 228 |
-
"""
|
| 229 |
-
value = value.strip()
|
| 230 |
-
try:
|
| 231 |
-
return_value: PromptType = self.response_type(value)
|
| 232 |
-
except ValueError:
|
| 233 |
-
raise InvalidResponse(self.validate_error_message)
|
| 234 |
-
|
| 235 |
-
if self.choices is not None and not self.check_choice(value):
|
| 236 |
-
raise InvalidResponse(self.illegal_choice_message)
|
| 237 |
-
|
| 238 |
-
return return_value
|
| 239 |
-
|
| 240 |
-
def on_validate_error(self, value: str, error: InvalidResponse) -> None:
|
| 241 |
-
"""Called to handle validation error.
|
| 242 |
-
|
| 243 |
-
Args:
|
| 244 |
-
value (str): String entered by user.
|
| 245 |
-
error (InvalidResponse): Exception instance the initiated the error.
|
| 246 |
-
"""
|
| 247 |
-
self.console.print(error)
|
| 248 |
-
|
| 249 |
-
def pre_prompt(self) -> None:
|
| 250 |
-
"""Hook to display something before the prompt."""
|
| 251 |
-
|
| 252 |
-
@overload
|
| 253 |
-
def __call__(self, *, stream: Optional[TextIO] = None) -> PromptType:
|
| 254 |
-
...
|
| 255 |
-
|
| 256 |
-
@overload
|
| 257 |
-
def __call__(
|
| 258 |
-
self, *, default: DefaultType, stream: Optional[TextIO] = None
|
| 259 |
-
) -> Union[PromptType, DefaultType]:
|
| 260 |
-
...
|
| 261 |
-
|
| 262 |
-
def __call__(self, *, default: Any = ..., stream: Optional[TextIO] = None) -> Any:
|
| 263 |
-
"""Run the prompt loop.
|
| 264 |
-
|
| 265 |
-
Args:
|
| 266 |
-
default (Any, optional): Optional default value.
|
| 267 |
-
|
| 268 |
-
Returns:
|
| 269 |
-
PromptType: Processed value.
|
| 270 |
-
"""
|
| 271 |
-
while True:
|
| 272 |
-
self.pre_prompt()
|
| 273 |
-
prompt = self.make_prompt(default)
|
| 274 |
-
value = self.get_input(self.console, prompt, self.password, stream=stream)
|
| 275 |
-
if value == "" and default != ...:
|
| 276 |
-
return default
|
| 277 |
-
try:
|
| 278 |
-
return_value = self.process_response(value)
|
| 279 |
-
except InvalidResponse as error:
|
| 280 |
-
self.on_validate_error(value, error)
|
| 281 |
-
continue
|
| 282 |
-
else:
|
| 283 |
-
return return_value
|
| 284 |
-
|
| 285 |
-
|
| 286 |
-
class Prompt(PromptBase[str]):
|
| 287 |
-
"""A prompt that returns a str.
|
| 288 |
-
|
| 289 |
-
Example:
|
| 290 |
-
>>> name = Prompt.ask("Enter your name")
|
| 291 |
-
|
| 292 |
-
|
| 293 |
-
"""
|
| 294 |
-
|
| 295 |
-
response_type = str
|
| 296 |
-
|
| 297 |
-
|
| 298 |
-
class IntPrompt(PromptBase[int]):
|
| 299 |
-
"""A prompt that returns an integer.
|
| 300 |
-
|
| 301 |
-
Example:
|
| 302 |
-
>>> burrito_count = IntPrompt.ask("How many burritos do you want to order")
|
| 303 |
-
|
| 304 |
-
"""
|
| 305 |
-
|
| 306 |
-
response_type = int
|
| 307 |
-
validate_error_message = "[prompt.invalid]Please enter a valid integer number"
|
| 308 |
-
|
| 309 |
-
|
| 310 |
-
class FloatPrompt(PromptBase[int]):
|
| 311 |
-
"""A prompt that returns a float.
|
| 312 |
-
|
| 313 |
-
Example:
|
| 314 |
-
>>> temperature = FloatPrompt.ask("Enter desired temperature")
|
| 315 |
-
|
| 316 |
-
"""
|
| 317 |
-
|
| 318 |
-
response_type = float
|
| 319 |
-
validate_error_message = "[prompt.invalid]Please enter a number"
|
| 320 |
-
|
| 321 |
-
|
| 322 |
-
class Confirm(PromptBase[bool]):
|
| 323 |
-
"""A yes / no confirmation prompt.
|
| 324 |
-
|
| 325 |
-
Example:
|
| 326 |
-
>>> if Confirm.ask("Continue"):
|
| 327 |
-
run_job()
|
| 328 |
-
|
| 329 |
-
"""
|
| 330 |
-
|
| 331 |
-
response_type = bool
|
| 332 |
-
validate_error_message = "[prompt.invalid]Please enter Y or N"
|
| 333 |
-
choices: List[str] = ["y", "n"]
|
| 334 |
-
|
| 335 |
-
def render_default(self, default: DefaultType) -> Text:
|
| 336 |
-
"""Render the default as (y) or (n) rather than True/False."""
|
| 337 |
-
yes, no = self.choices
|
| 338 |
-
return Text(f"({yes})" if default else f"({no})", style="prompt.default")
|
| 339 |
-
|
| 340 |
-
def process_response(self, value: str) -> bool:
|
| 341 |
-
"""Convert choices to a bool."""
|
| 342 |
-
value = value.strip().lower()
|
| 343 |
-
if value not in self.choices:
|
| 344 |
-
raise InvalidResponse(self.validate_error_message)
|
| 345 |
-
return value == self.choices[0]
|
| 346 |
-
|
| 347 |
-
|
| 348 |
-
if __name__ == "__main__": # pragma: no cover
|
| 349 |
-
|
| 350 |
-
from pip._vendor.rich import print
|
| 351 |
-
|
| 352 |
-
if Confirm.ask("Run [i]prompt[/i] tests?", default=True):
|
| 353 |
-
while True:
|
| 354 |
-
result = IntPrompt.ask(
|
| 355 |
-
":rocket: Enter a number between [b]1[/b] and [b]10[/b]", default=5
|
| 356 |
-
)
|
| 357 |
-
if result >= 1 and result <= 10:
|
| 358 |
-
break
|
| 359 |
-
print(":pile_of_poo: [prompt.invalid]Number must be between 1 and 10")
|
| 360 |
-
print(f"number={result}")
|
| 361 |
-
|
| 362 |
-
while True:
|
| 363 |
-
password = Prompt.ask(
|
| 364 |
-
"Please enter a password [cyan](must be at least 5 characters)",
|
| 365 |
-
password=True,
|
| 366 |
-
)
|
| 367 |
-
if len(password) >= 5:
|
| 368 |
-
break
|
| 369 |
-
print("[prompt.invalid]password too short")
|
| 370 |
-
print(f"password={password!r}")
|
| 371 |
-
|
| 372 |
-
fruit = Prompt.ask("Enter a fruit", choices=["apple", "orange", "pear"])
|
| 373 |
-
print(f"fruit={fruit!r}")
|
| 374 |
-
|
| 375 |
-
else:
|
| 376 |
-
print("[b]OK :loudly_crying_face:")
|
|
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spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/evaluation/evaluator.py
DELETED
|
@@ -1,196 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
| 2 |
-
import datetime
|
| 3 |
-
import logging
|
| 4 |
-
import time
|
| 5 |
-
from collections import OrderedDict
|
| 6 |
-
from contextlib import contextmanager
|
| 7 |
-
import torch
|
| 8 |
-
|
| 9 |
-
from detectron2.utils.comm import get_world_size, is_main_process
|
| 10 |
-
from detectron2.utils.logger import log_every_n_seconds
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class DatasetEvaluator:
|
| 14 |
-
"""
|
| 15 |
-
Base class for a dataset evaluator.
|
| 16 |
-
|
| 17 |
-
The function :func:`inference_on_dataset` runs the model over
|
| 18 |
-
all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.
|
| 19 |
-
|
| 20 |
-
This class will accumulate information of the inputs/outputs (by :meth:`process`),
|
| 21 |
-
and produce evaluation results in the end (by :meth:`evaluate`).
|
| 22 |
-
"""
|
| 23 |
-
|
| 24 |
-
def reset(self):
|
| 25 |
-
"""
|
| 26 |
-
Preparation for a new round of evaluation.
|
| 27 |
-
Should be called before starting a round of evaluation.
|
| 28 |
-
"""
|
| 29 |
-
pass
|
| 30 |
-
|
| 31 |
-
def process(self, inputs, outputs):
|
| 32 |
-
"""
|
| 33 |
-
Process the pair of inputs and outputs.
|
| 34 |
-
If they contain batches, the pairs can be consumed one-by-one using `zip`:
|
| 35 |
-
|
| 36 |
-
.. code-block:: python
|
| 37 |
-
|
| 38 |
-
for input_, output in zip(inputs, outputs):
|
| 39 |
-
# do evaluation on single input/output pair
|
| 40 |
-
...
|
| 41 |
-
|
| 42 |
-
Args:
|
| 43 |
-
inputs (list): the inputs that's used to call the model.
|
| 44 |
-
outputs (list): the return value of `model(inputs)`
|
| 45 |
-
"""
|
| 46 |
-
pass
|
| 47 |
-
|
| 48 |
-
def evaluate(self):
|
| 49 |
-
"""
|
| 50 |
-
Evaluate/summarize the performance, after processing all input/output pairs.
|
| 51 |
-
|
| 52 |
-
Returns:
|
| 53 |
-
dict:
|
| 54 |
-
A new evaluator class can return a dict of arbitrary format
|
| 55 |
-
as long as the user can process the results.
|
| 56 |
-
In our train_net.py, we expect the following format:
|
| 57 |
-
|
| 58 |
-
* key: the name of the task (e.g., bbox)
|
| 59 |
-
* value: a dict of {metric name: score}, e.g.: {"AP50": 80}
|
| 60 |
-
"""
|
| 61 |
-
pass
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
class DatasetEvaluators(DatasetEvaluator):
|
| 65 |
-
"""
|
| 66 |
-
Wrapper class to combine multiple :ref:`DatasetEvaluator` instances.
|
| 67 |
-
|
| 68 |
-
This class dispatches every evaluation call to
|
| 69 |
-
all of its :ref:`DatasetEvaluator`s.
|
| 70 |
-
"""
|
| 71 |
-
|
| 72 |
-
def __init__(self, evaluators):
|
| 73 |
-
"""
|
| 74 |
-
Args:
|
| 75 |
-
evaluators (list): the evaluators to combine.
|
| 76 |
-
"""
|
| 77 |
-
super().__init__()
|
| 78 |
-
self._evaluators = evaluators
|
| 79 |
-
|
| 80 |
-
def reset(self):
|
| 81 |
-
for evaluator in self._evaluators:
|
| 82 |
-
evaluator.reset()
|
| 83 |
-
|
| 84 |
-
def process(self, inputs, outputs):
|
| 85 |
-
for evaluator in self._evaluators:
|
| 86 |
-
evaluator.process(inputs, outputs)
|
| 87 |
-
|
| 88 |
-
def evaluate(self):
|
| 89 |
-
results = OrderedDict()
|
| 90 |
-
for evaluator in self._evaluators:
|
| 91 |
-
result = evaluator.evaluate()
|
| 92 |
-
if is_main_process() and result is not None:
|
| 93 |
-
for k, v in result.items():
|
| 94 |
-
assert (
|
| 95 |
-
k not in results
|
| 96 |
-
), "Different evaluators produce results with the same key {}".format(k)
|
| 97 |
-
results[k] = v
|
| 98 |
-
return results
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
def inference_on_dataset(model, data_loader, evaluator):
|
| 102 |
-
"""
|
| 103 |
-
Run model on the data_loader and evaluate the metrics with evaluator.
|
| 104 |
-
Also benchmark the inference speed of `model.forward` accurately.
|
| 105 |
-
The model will be used in eval mode.
|
| 106 |
-
|
| 107 |
-
Args:
|
| 108 |
-
model (nn.Module): a module which accepts an object from
|
| 109 |
-
`data_loader` and returns some outputs. It will be temporarily set to `eval` mode.
|
| 110 |
-
|
| 111 |
-
If you wish to evaluate a model in `training` mode instead, you can
|
| 112 |
-
wrap the given model and override its behavior of `.eval()` and `.train()`.
|
| 113 |
-
data_loader: an iterable object with a length.
|
| 114 |
-
The elements it generates will be the inputs to the model.
|
| 115 |
-
evaluator (DatasetEvaluator): the evaluator to run. Use `None` if you only want
|
| 116 |
-
to benchmark, but don't want to do any evaluation.
|
| 117 |
-
|
| 118 |
-
Returns:
|
| 119 |
-
The return value of `evaluator.evaluate()`
|
| 120 |
-
"""
|
| 121 |
-
num_devices = get_world_size()
|
| 122 |
-
logger = logging.getLogger(__name__)
|
| 123 |
-
logger.info("Start inference on {} images".format(len(data_loader)))
|
| 124 |
-
|
| 125 |
-
total = len(data_loader) # inference data loader must have a fixed length
|
| 126 |
-
if evaluator is None:
|
| 127 |
-
# create a no-op evaluator
|
| 128 |
-
evaluator = DatasetEvaluators([])
|
| 129 |
-
evaluator.reset()
|
| 130 |
-
|
| 131 |
-
num_warmup = min(5, total - 1)
|
| 132 |
-
start_time = time.perf_counter()
|
| 133 |
-
total_compute_time = 0
|
| 134 |
-
with inference_context(model), torch.no_grad():
|
| 135 |
-
for idx, inputs in enumerate(data_loader):
|
| 136 |
-
if idx == num_warmup:
|
| 137 |
-
start_time = time.perf_counter()
|
| 138 |
-
total_compute_time = 0
|
| 139 |
-
|
| 140 |
-
start_compute_time = time.perf_counter()
|
| 141 |
-
outputs = model(inputs)
|
| 142 |
-
if torch.cuda.is_available():
|
| 143 |
-
torch.cuda.synchronize()
|
| 144 |
-
total_compute_time += time.perf_counter() - start_compute_time
|
| 145 |
-
evaluator.process(inputs, outputs)
|
| 146 |
-
|
| 147 |
-
iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
|
| 148 |
-
seconds_per_img = total_compute_time / iters_after_start
|
| 149 |
-
if idx >= num_warmup * 2 or seconds_per_img > 5:
|
| 150 |
-
total_seconds_per_img = (time.perf_counter() - start_time) / iters_after_start
|
| 151 |
-
eta = datetime.timedelta(seconds=int(total_seconds_per_img * (total - idx - 1)))
|
| 152 |
-
log_every_n_seconds(
|
| 153 |
-
logging.INFO,
|
| 154 |
-
"Inference done {}/{}. {:.4f} s / img. ETA={}".format(
|
| 155 |
-
idx + 1, total, seconds_per_img, str(eta)
|
| 156 |
-
),
|
| 157 |
-
n=5,
|
| 158 |
-
)
|
| 159 |
-
|
| 160 |
-
# Measure the time only for this worker (before the synchronization barrier)
|
| 161 |
-
total_time = time.perf_counter() - start_time
|
| 162 |
-
total_time_str = str(datetime.timedelta(seconds=total_time))
|
| 163 |
-
# NOTE this format is parsed by grep
|
| 164 |
-
logger.info(
|
| 165 |
-
"Total inference time: {} ({:.6f} s / img per device, on {} devices)".format(
|
| 166 |
-
total_time_str, total_time / (total - num_warmup), num_devices
|
| 167 |
-
)
|
| 168 |
-
)
|
| 169 |
-
total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))
|
| 170 |
-
logger.info(
|
| 171 |
-
"Total inference pure compute time: {} ({:.6f} s / img per device, on {} devices)".format(
|
| 172 |
-
total_compute_time_str, total_compute_time / (total - num_warmup), num_devices
|
| 173 |
-
)
|
| 174 |
-
)
|
| 175 |
-
|
| 176 |
-
results = evaluator.evaluate()
|
| 177 |
-
# An evaluator may return None when not in main process.
|
| 178 |
-
# Replace it by an empty dict instead to make it easier for downstream code to handle
|
| 179 |
-
if results is None:
|
| 180 |
-
results = {}
|
| 181 |
-
return results
|
| 182 |
-
|
| 183 |
-
|
| 184 |
-
@contextmanager
|
| 185 |
-
def inference_context(model):
|
| 186 |
-
"""
|
| 187 |
-
A context where the model is temporarily changed to eval mode,
|
| 188 |
-
and restored to previous mode afterwards.
|
| 189 |
-
|
| 190 |
-
Args:
|
| 191 |
-
model: a torch Module
|
| 192 |
-
"""
|
| 193 |
-
training_mode = model.training
|
| 194 |
-
model.eval()
|
| 195 |
-
yield
|
| 196 |
-
model.train(training_mode)
|
|
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|
spaces/CVPR/LIVE/pydiffvg/__init__.py
DELETED
|
@@ -1,9 +0,0 @@
|
|
| 1 |
-
from .device import *
|
| 2 |
-
from .shape import *
|
| 3 |
-
from .pixel_filter import *
|
| 4 |
-
from .render_pytorch import *
|
| 5 |
-
from .image import *
|
| 6 |
-
from .parse_svg import *
|
| 7 |
-
from .color import *
|
| 8 |
-
from .optimize_svg import *
|
| 9 |
-
from .save_svg import *
|
|
|
|
|
|
|
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|
spaces/CVPR/LIVE/thrust/thrust/iterator/iterator_traits.h
DELETED
|
@@ -1,114 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
/*! \file thrust/iterator/iterator_traits.h
|
| 19 |
-
* \brief Traits and metafunctions for reasoning about the traits of iterators
|
| 20 |
-
*/
|
| 21 |
-
|
| 22 |
-
/*
|
| 23 |
-
* (C) Copyright David Abrahams 2003.
|
| 24 |
-
*
|
| 25 |
-
* Distributed under the Boost Software License, Version 1.0.
|
| 26 |
-
* (See accompanying NOTICE file for the complete license)
|
| 27 |
-
*
|
| 28 |
-
* For more information, see http://www.boost.org
|
| 29 |
-
*/
|
| 30 |
-
|
| 31 |
-
#pragma once
|
| 32 |
-
|
| 33 |
-
#include <thrust/detail/config.h>
|
| 34 |
-
#include <thrust/type_traits/void_t.h>
|
| 35 |
-
|
| 36 |
-
#include <iterator>
|
| 37 |
-
|
| 38 |
-
namespace thrust
|
| 39 |
-
{
|
| 40 |
-
|
| 41 |
-
namespace detail
|
| 42 |
-
{
|
| 43 |
-
|
| 44 |
-
template <typename T, typename = void>
|
| 45 |
-
struct iterator_traits_impl {};
|
| 46 |
-
|
| 47 |
-
template <typename T>
|
| 48 |
-
struct iterator_traits_impl<
|
| 49 |
-
T
|
| 50 |
-
, typename voider<
|
| 51 |
-
typename T::difference_type
|
| 52 |
-
, typename T::value_type
|
| 53 |
-
, typename T::pointer
|
| 54 |
-
, typename T::reference
|
| 55 |
-
, typename T::iterator_category
|
| 56 |
-
>::type
|
| 57 |
-
>
|
| 58 |
-
{
|
| 59 |
-
typedef typename T::difference_type difference_type;
|
| 60 |
-
typedef typename T::value_type value_type;
|
| 61 |
-
typedef typename T::pointer pointer;
|
| 62 |
-
typedef typename T::reference reference;
|
| 63 |
-
typedef typename T::iterator_category iterator_category;
|
| 64 |
-
};
|
| 65 |
-
|
| 66 |
-
} // namespace detail
|
| 67 |
-
|
| 68 |
-
/*! \p iterator_traits is a type trait class that provides a uniform
|
| 69 |
-
* interface for querying the properties of iterators at compile-time.
|
| 70 |
-
*/
|
| 71 |
-
template <typename T>
|
| 72 |
-
struct iterator_traits : detail::iterator_traits_impl<T> {};
|
| 73 |
-
|
| 74 |
-
// traits are specialized for pointer types
|
| 75 |
-
template<typename T>
|
| 76 |
-
struct iterator_traits<T*>
|
| 77 |
-
{
|
| 78 |
-
typedef std::ptrdiff_t difference_type;
|
| 79 |
-
typedef T value_type;
|
| 80 |
-
typedef T* pointer;
|
| 81 |
-
typedef T& reference;
|
| 82 |
-
typedef std::random_access_iterator_tag iterator_category;
|
| 83 |
-
};
|
| 84 |
-
|
| 85 |
-
template<typename T>
|
| 86 |
-
struct iterator_traits<const T*>
|
| 87 |
-
{
|
| 88 |
-
typedef std::ptrdiff_t difference_type;
|
| 89 |
-
typedef T value_type;
|
| 90 |
-
typedef const T* pointer;
|
| 91 |
-
typedef const T& reference;
|
| 92 |
-
typedef std::random_access_iterator_tag iterator_category;
|
| 93 |
-
}; // end iterator_traits
|
| 94 |
-
|
| 95 |
-
template<typename Iterator> struct iterator_value;
|
| 96 |
-
|
| 97 |
-
template<typename Iterator> struct iterator_pointer;
|
| 98 |
-
|
| 99 |
-
template<typename Iterator> struct iterator_reference;
|
| 100 |
-
|
| 101 |
-
template<typename Iterator> struct iterator_difference;
|
| 102 |
-
|
| 103 |
-
template<typename Iterator> struct iterator_traversal;
|
| 104 |
-
|
| 105 |
-
template<typename Iterator> struct iterator_system;
|
| 106 |
-
|
| 107 |
-
} // namespace thrust
|
| 108 |
-
|
| 109 |
-
#include <thrust/iterator/detail/iterator_traversal_tags.h>
|
| 110 |
-
#include <thrust/iterator/detail/host_system_tag.h>
|
| 111 |
-
#include <thrust/iterator/detail/device_system_tag.h>
|
| 112 |
-
#include <thrust/iterator/detail/any_system_tag.h>
|
| 113 |
-
#include <thrust/iterator/detail/iterator_traits.inl>
|
| 114 |
-
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|
spaces/CVPR/transfiner/configs/common/models/mask_rcnn_c4.py
DELETED
|
@@ -1,88 +0,0 @@
|
|
| 1 |
-
from detectron2.config import LazyCall as L
|
| 2 |
-
from detectron2.layers import ShapeSpec
|
| 3 |
-
from detectron2.modeling.meta_arch import GeneralizedRCNN
|
| 4 |
-
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
|
| 5 |
-
from detectron2.modeling.backbone import BasicStem, BottleneckBlock, ResNet
|
| 6 |
-
from detectron2.modeling.box_regression import Box2BoxTransform
|
| 7 |
-
from detectron2.modeling.matcher import Matcher
|
| 8 |
-
from detectron2.modeling.poolers import ROIPooler
|
| 9 |
-
from detectron2.modeling.proposal_generator import RPN, StandardRPNHead
|
| 10 |
-
from detectron2.modeling.roi_heads import (
|
| 11 |
-
FastRCNNOutputLayers,
|
| 12 |
-
MaskRCNNConvUpsampleHead,
|
| 13 |
-
Res5ROIHeads,
|
| 14 |
-
)
|
| 15 |
-
|
| 16 |
-
model = L(GeneralizedRCNN)(
|
| 17 |
-
backbone=L(ResNet)(
|
| 18 |
-
stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
|
| 19 |
-
stages=L(ResNet.make_default_stages)(
|
| 20 |
-
depth=50,
|
| 21 |
-
stride_in_1x1=True,
|
| 22 |
-
norm="FrozenBN",
|
| 23 |
-
),
|
| 24 |
-
out_features=["res4"],
|
| 25 |
-
),
|
| 26 |
-
proposal_generator=L(RPN)(
|
| 27 |
-
in_features=["res4"],
|
| 28 |
-
head=L(StandardRPNHead)(in_channels=1024, num_anchors=15),
|
| 29 |
-
anchor_generator=L(DefaultAnchorGenerator)(
|
| 30 |
-
sizes=[[32, 64, 128, 256, 512]],
|
| 31 |
-
aspect_ratios=[0.5, 1.0, 2.0],
|
| 32 |
-
strides=[16],
|
| 33 |
-
offset=0.0,
|
| 34 |
-
),
|
| 35 |
-
anchor_matcher=L(Matcher)(
|
| 36 |
-
thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True
|
| 37 |
-
),
|
| 38 |
-
box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
|
| 39 |
-
batch_size_per_image=256,
|
| 40 |
-
positive_fraction=0.5,
|
| 41 |
-
pre_nms_topk=(12000, 6000),
|
| 42 |
-
post_nms_topk=(2000, 1000),
|
| 43 |
-
nms_thresh=0.7,
|
| 44 |
-
),
|
| 45 |
-
roi_heads=L(Res5ROIHeads)(
|
| 46 |
-
num_classes=80,
|
| 47 |
-
batch_size_per_image=512,
|
| 48 |
-
positive_fraction=0.25,
|
| 49 |
-
proposal_matcher=L(Matcher)(
|
| 50 |
-
thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False
|
| 51 |
-
),
|
| 52 |
-
in_features=["res4"],
|
| 53 |
-
pooler=L(ROIPooler)(
|
| 54 |
-
output_size=14,
|
| 55 |
-
scales=(1.0 / 16,),
|
| 56 |
-
sampling_ratio=0,
|
| 57 |
-
pooler_type="ROIAlignV2",
|
| 58 |
-
),
|
| 59 |
-
res5=L(ResNet.make_stage)(
|
| 60 |
-
block_class=BottleneckBlock,
|
| 61 |
-
num_blocks=3,
|
| 62 |
-
stride_per_block=[2, 1, 1],
|
| 63 |
-
in_channels=1024,
|
| 64 |
-
bottleneck_channels=512,
|
| 65 |
-
out_channels=2048,
|
| 66 |
-
norm="FrozenBN",
|
| 67 |
-
stride_in_1x1=True,
|
| 68 |
-
),
|
| 69 |
-
box_predictor=L(FastRCNNOutputLayers)(
|
| 70 |
-
input_shape=L(ShapeSpec)(channels="${...res5.out_channels}", height=1, width=1),
|
| 71 |
-
test_score_thresh=0.05,
|
| 72 |
-
box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)),
|
| 73 |
-
num_classes="${..num_classes}",
|
| 74 |
-
),
|
| 75 |
-
mask_head=L(MaskRCNNConvUpsampleHead)(
|
| 76 |
-
input_shape=L(ShapeSpec)(
|
| 77 |
-
channels="${...res5.out_channels}",
|
| 78 |
-
width="${...pooler.output_size}",
|
| 79 |
-
height="${...pooler.output_size}",
|
| 80 |
-
),
|
| 81 |
-
num_classes="${..num_classes}",
|
| 82 |
-
conv_dims=[256],
|
| 83 |
-
),
|
| 84 |
-
),
|
| 85 |
-
pixel_mean=[103.530, 116.280, 123.675],
|
| 86 |
-
pixel_std=[1.0, 1.0, 1.0],
|
| 87 |
-
input_format="BGR",
|
| 88 |
-
)
|
|
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|
|
spaces/ChanceFocus/FLARE/app.py
DELETED
|
@@ -1,309 +0,0 @@
|
|
| 1 |
-
import pandas as pd
|
| 2 |
-
import numpy as np
|
| 3 |
-
import matplotlib
|
| 4 |
-
# matplotlib.use('macosx')
|
| 5 |
-
import gradio as gr
|
| 6 |
-
import matplotlib.pyplot as plt
|
| 7 |
-
import plotly.graph_objects as go
|
| 8 |
-
from apscheduler.schedulers.background import BackgroundScheduler
|
| 9 |
-
|
| 10 |
-
ENG_COLS = [
|
| 11 |
-
("Model", "str"),
|
| 12 |
-
("FPB-acc", "number"),
|
| 13 |
-
("FPB-F1", "number"),
|
| 14 |
-
("FPB-missing", "number"),
|
| 15 |
-
("FiQA-SA-F1", "number"),
|
| 16 |
-
("FiQA-SA-missing", "number"),
|
| 17 |
-
("Headline-AvgF1", "number"),
|
| 18 |
-
("NER-EntityF1", "number"),
|
| 19 |
-
("ConvFinQA-EmAcc", "number"),
|
| 20 |
-
("FinQA-EmAcc", "number"),
|
| 21 |
-
("BigData22-Acc", "number"),
|
| 22 |
-
("BigData22-MCC", "number"),
|
| 23 |
-
("BigData22-missing", "number"),
|
| 24 |
-
("ACL18-Acc", "number"),
|
| 25 |
-
("ACL18-MCC", "number"),
|
| 26 |
-
("ACL18-missing", "number"),
|
| 27 |
-
("CIKM18-Acc", "number"),
|
| 28 |
-
("CIKM18-MCC", "number"),
|
| 29 |
-
("CIKM18-missing", "number"),
|
| 30 |
-
("FOMC-acc", "number"),
|
| 31 |
-
("FOMC-F1", "number"),
|
| 32 |
-
("FOMC-missing", "number"),
|
| 33 |
-
("FinerOrd-EntityF1", "number"),
|
| 34 |
-
("FinerOrd-F1", "number"),
|
| 35 |
-
("German-Acc", "number"),
|
| 36 |
-
("German-MCC", "number"),
|
| 37 |
-
("German-missing", "number"),
|
| 38 |
-
("Australian-Acc", "number"),
|
| 39 |
-
("Australian-MCC", "number"),
|
| 40 |
-
("Australian-missing", "number"),
|
| 41 |
-
("TSA-RMSE", "number"),
|
| 42 |
-
("TSA-missing", "number"),
|
| 43 |
-
("MLESG-F1", "number"),
|
| 44 |
-
("MLESG-missing", "number"),
|
| 45 |
-
("FSRL-entity-F1", "number"),
|
| 46 |
-
("FSRL-F1", "number"),
|
| 47 |
-
("CFA-acc", "number"),
|
| 48 |
-
("CFA-F1", "number"),
|
| 49 |
-
("CFA-missing", "number"),
|
| 50 |
-
("Finarg-ECCAUC-F1", "number"),
|
| 51 |
-
("Finarg-ECCAUC-missing", "number"),
|
| 52 |
-
("Finarg-ECCARC-F1", "number"),
|
| 53 |
-
("Finarg_ECCARC-missing", "number"),
|
| 54 |
-
("CD-Entity-F1", "number"),
|
| 55 |
-
("CD-F1", "number"),
|
| 56 |
-
("MultiFin-EN-acc", "number"),
|
| 57 |
-
("MultiFin-EN-F1", "number"),
|
| 58 |
-
("MultiFin-EN-missing", "number"),
|
| 59 |
-
("MA-acc", "number"),
|
| 60 |
-
("MA-F1", "number"),
|
| 61 |
-
("MA-missing", "number"),
|
| 62 |
-
("Causal20-sc-acc", "number"),
|
| 63 |
-
("Causal20-sc-F1", "number"),
|
| 64 |
-
("Causal20-sc-missing", "number"),
|
| 65 |
-
("TATQA-EmAcc", "number"),
|
| 66 |
-
("FNXL-entity-F1", "number"),
|
| 67 |
-
("FNXL-F1", "number"),
|
| 68 |
-
("FinRED-precision", "number"),
|
| 69 |
-
("FinRED-recall", "number"),
|
| 70 |
-
("FinRED-F1", "number"),
|
| 71 |
-
("ECTSUM-Rouge1", "number"),
|
| 72 |
-
("ECTSUM-Rouge2", "number"),
|
| 73 |
-
("ECTSUM-RougeL", "number"),
|
| 74 |
-
("ECTSUM-BertScore", "number"),
|
| 75 |
-
("ECTSUM-BARTScore", "number"),
|
| 76 |
-
("EDTSUM-Rouge1", "number"),
|
| 77 |
-
("EDTSUM-Rouge2", "number"),
|
| 78 |
-
("EDTSUM-RougeL", "number"),
|
| 79 |
-
("EDTSUM-BertScore", "number"),
|
| 80 |
-
("EDTSUM-BARTScore", "number"),
|
| 81 |
-
]
|
| 82 |
-
|
| 83 |
-
SPA_COLS = [
|
| 84 |
-
("Model", "str"),
|
| 85 |
-
("MultiFin-F1", "number"),
|
| 86 |
-
("MultiFin-Acc", "number"),
|
| 87 |
-
("FNS-Rouge1", "number"),
|
| 88 |
-
("FNS-Rouge2", "number"),
|
| 89 |
-
("FNS-RougeL", "number"),
|
| 90 |
-
("EFP-F1", "number"),
|
| 91 |
-
("EFP-Acc", "number"),
|
| 92 |
-
("EFPA-F1", "number"),
|
| 93 |
-
("EFPA-Acc", "number"),
|
| 94 |
-
("TSA-F1", "number"),
|
| 95 |
-
("TSA-Acc", "number"),
|
| 96 |
-
("FinanceES-F1", "number"),
|
| 97 |
-
("FinanceES-Acc", "number"),
|
| 98 |
-
]
|
| 99 |
-
|
| 100 |
-
CHI_COLS = [
|
| 101 |
-
("Model", "str"),
|
| 102 |
-
("AFQMC-Acc", "number"),
|
| 103 |
-
("AFQMC-F1", "number"),
|
| 104 |
-
("corpus-Acc", "number"),
|
| 105 |
-
("corpus-F1", "number"),
|
| 106 |
-
("stockA-Acc", "number"),
|
| 107 |
-
("stockA-F1", "number"),
|
| 108 |
-
("Fineval-Acc", "number"),
|
| 109 |
-
("Fineval-F1", "number"),
|
| 110 |
-
("NL-Acc", "number"),
|
| 111 |
-
("NL-F1", "number"),
|
| 112 |
-
("NL2-Acc", "number"),
|
| 113 |
-
("NL2-F1", "number"),
|
| 114 |
-
("NSP-Acc", "number"),
|
| 115 |
-
("NSP-F1", "number"),
|
| 116 |
-
("RE-Acc", "number"),
|
| 117 |
-
("RE-F1", "number"),
|
| 118 |
-
("FE-Acc", "number"),
|
| 119 |
-
("FE-F1", "number"),
|
| 120 |
-
("stockB-Acc", "number"),
|
| 121 |
-
("stockB-F1", "number"),
|
| 122 |
-
("19CCKS-Precision", "number"),
|
| 123 |
-
("19CCKS-F1", "number"),
|
| 124 |
-
("20CCKS-Precision", "number"),
|
| 125 |
-
("20CCKS-F1", "number"),
|
| 126 |
-
("21CCKS-Precision", "number"),
|
| 127 |
-
("21CCKS-F1", "number"),
|
| 128 |
-
("22CCKS-Precision", "number"),
|
| 129 |
-
("22CCKS-F1", "number"),
|
| 130 |
-
("QA-Acc", "number"),
|
| 131 |
-
("NA-Rouge1", "number"),
|
| 132 |
-
("NA-Rouge2", "number"),
|
| 133 |
-
("NA-RougeL", "number"),
|
| 134 |
-
("NER-EntityF1", "number"),
|
| 135 |
-
]
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
# Extract column names
|
| 139 |
-
eng_cols = [col_name for col_name, _ in ENG_COLS]
|
| 140 |
-
eng_cates = {
|
| 141 |
-
"Sentiment Analysis": ["Model", "FPB-acc", "FPB-F1", "FPB-missing",
|
| 142 |
-
"FiQA-SA-F1", "FiQA-SA-missing", "Headline-AvgF1", "TSA-RMSE",
|
| 143 |
-
"TSA-missing", "FOMC-acc", "FOMC-F1", "FOMC-missing"],
|
| 144 |
-
"NER": ["Model", "NER-EntityF1", "FinerOrd-EntityF1", "FinerOrd-F1"],
|
| 145 |
-
"Number Understanding": ["Model", "FinQA-EmAcc", "ConvFinQA-EmAcc"],
|
| 146 |
-
"Text Summarization": ["Model", "ECTSUM-Rouge1", "ECTSUM-Rouge2",
|
| 147 |
-
"ECTSUM-RougeL", "ECTSUM-BertScore", "ECTSUM-BARTScore",
|
| 148 |
-
"EDTSUM-Rouge1", "EDTSUM-Rouge2", "EDTSUM-RougeL", "EDTSUM-BertScore", "EDTSUM-BARTScore",],
|
| 149 |
-
"Stock Movement Prediction": ["Model", "BigData22-Acc",
|
| 150 |
-
"BigData22-MCC", "BigData22-missing", "ACL18-Acc", "ACL18-MCC",
|
| 151 |
-
"ACL18-missing", "CIKM18-Acc", "CIKM18-MCC", "CIKM18-missing", ],
|
| 152 |
-
"Credit Scoring": ["Model", "German-Acc", "German-MCC",
|
| 153 |
-
"German-missing", "Australian-Acc", "Australian-MCC", "Australian-missing"],
|
| 154 |
-
}
|
| 155 |
-
|
| 156 |
-
spa_cols = [col_name for col_name, _ in SPA_COLS]
|
| 157 |
-
spa_cates = {
|
| 158 |
-
"Sentiment Analysis": ["Model", "TSA-Acc", "TSA-F1", "FinanceES-Acc", "FinanceES-F1"],
|
| 159 |
-
"Examination": ["Model", "EFP-Acc", "EFP-F1", "EFPA-Acc", "EFPA-F1"],
|
| 160 |
-
"Classification": ["Model", "MultiFin-Acc", "MultiFin-F1"],
|
| 161 |
-
"Text Summarization": ["Model", "FNS-Rouge1", "FNS-Rouge2", "FNS-RougeL",],
|
| 162 |
-
}
|
| 163 |
-
|
| 164 |
-
chi_cols = [col_name for col_name, _ in CHI_COLS]
|
| 165 |
-
chi_cates = {
|
| 166 |
-
"Semantic matching": ["Model", "AFQMC-Acc", "AFQMC-F1", "corpus-Acc", "corpus-F1"],
|
| 167 |
-
"Classification": ["Model", "NL-Acc", "NL-F1","NL2-Acc", "NL2-F1","NSP-Acc", "NSP-F1"],
|
| 168 |
-
"Stock Movement Prediction": ["Model", "stockA-Acc", "stockA-F1"],
|
| 169 |
-
"Examination": ["Model", "Fineval-Acc", "Fineval-F1"],
|
| 170 |
-
"Relation Extraction": ["Model", "RE-Acc", "RE-F1", "19CCKS-Precision", "19CCKS-F1", "20CCKS-Precision", "20CCKS-F1", "21CCKS-Precision", "21CCKS-F1", "22CCKS-Precision", "22CCKS-F1"],
|
| 171 |
-
"Sentiment Analysis": ["Model", "FE-Acc", "FE-F1", "stockB-Acc", "stockB-F1"],
|
| 172 |
-
"NER": ["Model", "NER-EntityF1"],
|
| 173 |
-
"Text Summarization": ["Model", "NA-Rouge1", "NA-Rouge2", "NA-RougeL"],
|
| 174 |
-
"Question Answering": ["Model", "QA-Acc"],
|
| 175 |
-
}
|
| 176 |
-
|
| 177 |
-
def create_df_dict(lang, lang_cols, cates):
|
| 178 |
-
# Load leaderboard data with column names
|
| 179 |
-
leaderboard_df = pd.read_csv(f'{lang}_result.csv', names=lang_cols)
|
| 180 |
-
leaderboard_df = leaderboard_df.sort_index(axis=1)
|
| 181 |
-
# Move 'key' column to the front
|
| 182 |
-
leaderboard_df = leaderboard_df[ ['Model'] + [ col for col in leaderboard_df.columns if col != 'Model' ] ]
|
| 183 |
-
cols = leaderboard_df.columns
|
| 184 |
-
types = ["str"] + ["number"] * (len(lang_cols)-1)
|
| 185 |
-
|
| 186 |
-
# Split merged_df into subtask dataframes
|
| 187 |
-
df_dict = {}
|
| 188 |
-
for key, selected_columns in cates.items():
|
| 189 |
-
df_dict[key] = leaderboard_df[selected_columns]
|
| 190 |
-
return df_dict
|
| 191 |
-
|
| 192 |
-
df_lang = {
|
| 193 |
-
"English": create_df_dict("english", eng_cols, eng_cates),
|
| 194 |
-
"Spanish": create_df_dict("spanish", spa_cols, spa_cates),
|
| 195 |
-
"Chinese": create_df_dict("chinese", chi_cols, chi_cates),
|
| 196 |
-
}
|
| 197 |
-
|
| 198 |
-
|
| 199 |
-
# Constants
|
| 200 |
-
TITLE = '<h1 align="center" id="space-title">🐲 PIXIU FLARE Leaderboard</h1>'
|
| 201 |
-
# TITLE = "Financial Natural Language Understanding and Prediction Evaluation Benchmark (FLARE) Leaderboard"
|
| 202 |
-
INTRODUCTION_TEXT = """📊 The PIXIU FLARE Leaderboard is designed to rigorously track, rank, and evaluate state-of-the-art models in financial Natural Language Understanding and Prediction.
|
| 203 |
-
|
| 204 |
-
📈 Unique to FLARE, our leaderboard not only covers standard NLP tasks but also incorporates financial prediction tasks such as stock movement and credit scoring, offering a more comprehensive evaluation for real-world financial applications.
|
| 205 |
-
|
| 206 |
-
📚 Our evaluation metrics include, but are not limited to, Accuracy, F1 Score, ROUGE score, BERTScore, and Matthews correlation coefficient (MCC), providing a multidimensional assessment of model performance.
|
| 207 |
-
|
| 208 |
-
🔗 For more details, refer to our GitHub page [here](https://github.com/ChanceFocus/PIXIU).
|
| 209 |
-
"""
|
| 210 |
-
|
| 211 |
-
|
| 212 |
-
def create_data_interface(df):
|
| 213 |
-
headers = df.columns
|
| 214 |
-
types = ["str"] + ["number"] * (len(headers) - 1)
|
| 215 |
-
|
| 216 |
-
return gr.components.Dataframe(
|
| 217 |
-
value=df.values.tolist(),
|
| 218 |
-
headers=[col_name for col_name in headers],
|
| 219 |
-
datatype=types,
|
| 220 |
-
max_rows=10,
|
| 221 |
-
)
|
| 222 |
-
|
| 223 |
-
def plot_radar_chart(df, attributes, category_name):
|
| 224 |
-
fig = go.Figure()
|
| 225 |
-
|
| 226 |
-
for index, row in df.iterrows():
|
| 227 |
-
model = row['Model']
|
| 228 |
-
values = row[attributes].tolist()
|
| 229 |
-
fig.add_trace(go.Scatterpolar(
|
| 230 |
-
r=values,
|
| 231 |
-
theta=attributes,
|
| 232 |
-
fill='toself',
|
| 233 |
-
name=model
|
| 234 |
-
))
|
| 235 |
-
|
| 236 |
-
fig.update_layout(
|
| 237 |
-
title="FLARE",
|
| 238 |
-
polar=dict(
|
| 239 |
-
radialaxis=dict(
|
| 240 |
-
visible=True,
|
| 241 |
-
range=[0, 0.9]
|
| 242 |
-
)),
|
| 243 |
-
showlegend=True
|
| 244 |
-
)
|
| 245 |
-
|
| 246 |
-
return fig
|
| 247 |
-
|
| 248 |
-
def create_data_interface_for_aggregated(df, category_name):
|
| 249 |
-
attributes = df.columns[1:]
|
| 250 |
-
print (attributes)
|
| 251 |
-
plt = plot_radar_chart(df, attributes, category_name)
|
| 252 |
-
return plt
|
| 253 |
-
|
| 254 |
-
def create_lang_leaderboard(df_dict):
|
| 255 |
-
new_df = pd.DataFrame()
|
| 256 |
-
for key, df in df_dict.items():
|
| 257 |
-
new_df["Model"] = df["Model"]
|
| 258 |
-
tdf = df.replace('', 0)
|
| 259 |
-
tdf = tdf[[val for val in tdf.columns if "Model" not in val]]
|
| 260 |
-
if key == "Sentiment Analysis":
|
| 261 |
-
tdf = tdf[[val for val in tdf.columns if "F1" in val]]
|
| 262 |
-
elif key == "Classification":
|
| 263 |
-
tdf = tdf[[val for val in tdf.columns if "F1" in val]]
|
| 264 |
-
elif key == "Examination":
|
| 265 |
-
tdf = tdf[[val for val in tdf.columns if "F1" in val]]
|
| 266 |
-
elif key == "Stock Movement Prediction":
|
| 267 |
-
tdf = tdf[[val for val in tdf.columns if "Acc" in val]]
|
| 268 |
-
elif key == "Credit Scoring":
|
| 269 |
-
tdf = tdf[[val for val in tdf.columns if "Acc" in val]]
|
| 270 |
-
elif key == "Text Summarization":
|
| 271 |
-
tdf = tdf[[val for val in tdf.columns if "Bert" in val or "Rouge" in val]]
|
| 272 |
-
elif key == "Semantic matching":
|
| 273 |
-
tdf = tdf[[val for val in tdf.columns if "Acc" in val]]
|
| 274 |
-
elif key == "Relation Extraction":
|
| 275 |
-
tdf = tdf[[val for val in tdf.columns if "Acc" in val]]
|
| 276 |
-
elif key == "Q&A":
|
| 277 |
-
tdf = tdf[[val for val in tdf.columns if "Acc" in val]]
|
| 278 |
-
elif key == "NER":
|
| 279 |
-
tdf = tdf[[val for val in tdf.columns if "EntityF1" in val]]
|
| 280 |
-
print ("tdf")
|
| 281 |
-
print (tdf)
|
| 282 |
-
new_df[key] = tdf.values.mean(axis=1)
|
| 283 |
-
print (new_df.values)
|
| 284 |
-
|
| 285 |
-
plot = create_data_interface_for_aggregated(new_df, key)
|
| 286 |
-
gr.Plot(plot)
|
| 287 |
-
|
| 288 |
-
for key, df in df_dict.items():
|
| 289 |
-
with gr.Tab(key):
|
| 290 |
-
create_data_interface(df)
|
| 291 |
-
|
| 292 |
-
def launch_gradio():
|
| 293 |
-
demo = gr.Blocks()
|
| 294 |
-
|
| 295 |
-
with demo:
|
| 296 |
-
gr.HTML(TITLE)
|
| 297 |
-
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
|
| 298 |
-
for key, df_dict in df_lang.items():
|
| 299 |
-
with gr.Tab(key):
|
| 300 |
-
create_lang_leaderboard(df_dict)
|
| 301 |
-
|
| 302 |
-
demo.launch()
|
| 303 |
-
|
| 304 |
-
scheduler = BackgroundScheduler()
|
| 305 |
-
scheduler.add_job(launch_gradio, "interval", seconds=3600)
|
| 306 |
-
scheduler.start()
|
| 307 |
-
|
| 308 |
-
# Launch immediately
|
| 309 |
-
launch_gradio()
|
|
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|
spaces/ChandraMohanNayal/AutoGPT/autogpt/memory/redismem.py
DELETED
|
@@ -1,156 +0,0 @@
|
|
| 1 |
-
"""Redis memory provider."""
|
| 2 |
-
from __future__ import annotations
|
| 3 |
-
|
| 4 |
-
from typing import Any
|
| 5 |
-
|
| 6 |
-
import numpy as np
|
| 7 |
-
import redis
|
| 8 |
-
from colorama import Fore, Style
|
| 9 |
-
from redis.commands.search.field import TextField, VectorField
|
| 10 |
-
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
|
| 11 |
-
from redis.commands.search.query import Query
|
| 12 |
-
|
| 13 |
-
from autogpt.llm_utils import create_embedding_with_ada
|
| 14 |
-
from autogpt.logs import logger
|
| 15 |
-
from autogpt.memory.base import MemoryProviderSingleton
|
| 16 |
-
|
| 17 |
-
SCHEMA = [
|
| 18 |
-
TextField("data"),
|
| 19 |
-
VectorField(
|
| 20 |
-
"embedding",
|
| 21 |
-
"HNSW",
|
| 22 |
-
{"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
|
| 23 |
-
),
|
| 24 |
-
]
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class RedisMemory(MemoryProviderSingleton):
|
| 28 |
-
def __init__(self, cfg):
|
| 29 |
-
"""
|
| 30 |
-
Initializes the Redis memory provider.
|
| 31 |
-
|
| 32 |
-
Args:
|
| 33 |
-
cfg: The config object.
|
| 34 |
-
|
| 35 |
-
Returns: None
|
| 36 |
-
"""
|
| 37 |
-
redis_host = cfg.redis_host
|
| 38 |
-
redis_port = cfg.redis_port
|
| 39 |
-
redis_password = cfg.redis_password
|
| 40 |
-
self.dimension = 1536
|
| 41 |
-
self.redis = redis.Redis(
|
| 42 |
-
host=redis_host,
|
| 43 |
-
port=redis_port,
|
| 44 |
-
password=redis_password,
|
| 45 |
-
db=0, # Cannot be changed
|
| 46 |
-
)
|
| 47 |
-
self.cfg = cfg
|
| 48 |
-
|
| 49 |
-
# Check redis connection
|
| 50 |
-
try:
|
| 51 |
-
self.redis.ping()
|
| 52 |
-
except redis.ConnectionError as e:
|
| 53 |
-
logger.typewriter_log(
|
| 54 |
-
"FAILED TO CONNECT TO REDIS",
|
| 55 |
-
Fore.RED,
|
| 56 |
-
Style.BRIGHT + str(e) + Style.RESET_ALL,
|
| 57 |
-
)
|
| 58 |
-
logger.double_check(
|
| 59 |
-
"Please ensure you have setup and configured Redis properly for use. "
|
| 60 |
-
+ f"You can check out {Fore.CYAN + Style.BRIGHT}"
|
| 61 |
-
f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
|
| 62 |
-
" to ensure you've set up everything correctly."
|
| 63 |
-
)
|
| 64 |
-
exit(1)
|
| 65 |
-
|
| 66 |
-
if cfg.wipe_redis_on_start:
|
| 67 |
-
self.redis.flushall()
|
| 68 |
-
try:
|
| 69 |
-
self.redis.ft(f"{cfg.memory_index}").create_index(
|
| 70 |
-
fields=SCHEMA,
|
| 71 |
-
definition=IndexDefinition(
|
| 72 |
-
prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
|
| 73 |
-
),
|
| 74 |
-
)
|
| 75 |
-
except Exception as e:
|
| 76 |
-
print("Error creating Redis search index: ", e)
|
| 77 |
-
existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
|
| 78 |
-
self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
|
| 79 |
-
|
| 80 |
-
def add(self, data: str) -> str:
|
| 81 |
-
"""
|
| 82 |
-
Adds a data point to the memory.
|
| 83 |
-
|
| 84 |
-
Args:
|
| 85 |
-
data: The data to add.
|
| 86 |
-
|
| 87 |
-
Returns: Message indicating that the data has been added.
|
| 88 |
-
"""
|
| 89 |
-
if "Command Error:" in data:
|
| 90 |
-
return ""
|
| 91 |
-
vector = create_embedding_with_ada(data)
|
| 92 |
-
vector = np.array(vector).astype(np.float32).tobytes()
|
| 93 |
-
data_dict = {b"data": data, "embedding": vector}
|
| 94 |
-
pipe = self.redis.pipeline()
|
| 95 |
-
pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
|
| 96 |
-
_text = (
|
| 97 |
-
f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
|
| 98 |
-
)
|
| 99 |
-
self.vec_num += 1
|
| 100 |
-
pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
|
| 101 |
-
pipe.execute()
|
| 102 |
-
return _text
|
| 103 |
-
|
| 104 |
-
def get(self, data: str) -> list[Any] | None:
|
| 105 |
-
"""
|
| 106 |
-
Gets the data from the memory that is most relevant to the given data.
|
| 107 |
-
|
| 108 |
-
Args:
|
| 109 |
-
data: The data to compare to.
|
| 110 |
-
|
| 111 |
-
Returns: The most relevant data.
|
| 112 |
-
"""
|
| 113 |
-
return self.get_relevant(data, 1)
|
| 114 |
-
|
| 115 |
-
def clear(self) -> str:
|
| 116 |
-
"""
|
| 117 |
-
Clears the redis server.
|
| 118 |
-
|
| 119 |
-
Returns: A message indicating that the memory has been cleared.
|
| 120 |
-
"""
|
| 121 |
-
self.redis.flushall()
|
| 122 |
-
return "Obliviated"
|
| 123 |
-
|
| 124 |
-
def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
|
| 125 |
-
"""
|
| 126 |
-
Returns all the data in the memory that is relevant to the given data.
|
| 127 |
-
Args:
|
| 128 |
-
data: The data to compare to.
|
| 129 |
-
num_relevant: The number of relevant data to return.
|
| 130 |
-
|
| 131 |
-
Returns: A list of the most relevant data.
|
| 132 |
-
"""
|
| 133 |
-
query_embedding = create_embedding_with_ada(data)
|
| 134 |
-
base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
|
| 135 |
-
query = (
|
| 136 |
-
Query(base_query)
|
| 137 |
-
.return_fields("data", "vector_score")
|
| 138 |
-
.sort_by("vector_score")
|
| 139 |
-
.dialect(2)
|
| 140 |
-
)
|
| 141 |
-
query_vector = np.array(query_embedding).astype(np.float32).tobytes()
|
| 142 |
-
|
| 143 |
-
try:
|
| 144 |
-
results = self.redis.ft(f"{self.cfg.memory_index}").search(
|
| 145 |
-
query, query_params={"vector": query_vector}
|
| 146 |
-
)
|
| 147 |
-
except Exception as e:
|
| 148 |
-
print("Error calling Redis search: ", e)
|
| 149 |
-
return None
|
| 150 |
-
return [result.data for result in results.docs]
|
| 151 |
-
|
| 152 |
-
def get_stats(self):
|
| 153 |
-
"""
|
| 154 |
-
Returns: The stats of the memory index.
|
| 155 |
-
"""
|
| 156 |
-
return self.redis.ft(f"{self.cfg.memory_index}").info()
|
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spaces/Cropinky/hana_hanak_houses/realesrgan/archs/__init__.py
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
import importlib
|
| 2 |
-
from basicsr.utils import scandir
|
| 3 |
-
from os import path as osp
|
| 4 |
-
|
| 5 |
-
# automatically scan and import arch modules for registry
|
| 6 |
-
# scan all the files that end with '_arch.py' under the archs folder
|
| 7 |
-
arch_folder = osp.dirname(osp.abspath(__file__))
|
| 8 |
-
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
|
| 9 |
-
# import all the arch modules
|
| 10 |
-
_arch_modules = [importlib.import_module(f'realesrgan.archs.{file_name}') for file_name in arch_filenames]
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiofiles/threadpool/text.py
DELETED
|
@@ -1,68 +0,0 @@
|
|
| 1 |
-
from ..base import AsyncBase, AsyncIndirectBase
|
| 2 |
-
from .utils import (
|
| 3 |
-
delegate_to_executor,
|
| 4 |
-
proxy_method_directly,
|
| 5 |
-
proxy_property_directly,
|
| 6 |
-
)
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
@delegate_to_executor(
|
| 10 |
-
"close",
|
| 11 |
-
"flush",
|
| 12 |
-
"isatty",
|
| 13 |
-
"read",
|
| 14 |
-
"readable",
|
| 15 |
-
"readline",
|
| 16 |
-
"readlines",
|
| 17 |
-
"seek",
|
| 18 |
-
"seekable",
|
| 19 |
-
"tell",
|
| 20 |
-
"truncate",
|
| 21 |
-
"write",
|
| 22 |
-
"writable",
|
| 23 |
-
"writelines",
|
| 24 |
-
)
|
| 25 |
-
@proxy_method_directly("detach", "fileno", "readable")
|
| 26 |
-
@proxy_property_directly(
|
| 27 |
-
"buffer",
|
| 28 |
-
"closed",
|
| 29 |
-
"encoding",
|
| 30 |
-
"errors",
|
| 31 |
-
"line_buffering",
|
| 32 |
-
"newlines",
|
| 33 |
-
"name",
|
| 34 |
-
"mode",
|
| 35 |
-
)
|
| 36 |
-
class AsyncTextIOWrapper(AsyncBase):
|
| 37 |
-
"""The asyncio executor version of io.TextIOWrapper."""
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
@delegate_to_executor(
|
| 41 |
-
"close",
|
| 42 |
-
"flush",
|
| 43 |
-
"isatty",
|
| 44 |
-
"read",
|
| 45 |
-
"readable",
|
| 46 |
-
"readline",
|
| 47 |
-
"readlines",
|
| 48 |
-
"seek",
|
| 49 |
-
"seekable",
|
| 50 |
-
"tell",
|
| 51 |
-
"truncate",
|
| 52 |
-
"write",
|
| 53 |
-
"writable",
|
| 54 |
-
"writelines",
|
| 55 |
-
)
|
| 56 |
-
@proxy_method_directly("detach", "fileno", "readable")
|
| 57 |
-
@proxy_property_directly(
|
| 58 |
-
"buffer",
|
| 59 |
-
"closed",
|
| 60 |
-
"encoding",
|
| 61 |
-
"errors",
|
| 62 |
-
"line_buffering",
|
| 63 |
-
"newlines",
|
| 64 |
-
"name",
|
| 65 |
-
"mode",
|
| 66 |
-
)
|
| 67 |
-
class AsyncTextIndirectIOWrapper(AsyncIndirectBase):
|
| 68 |
-
"""The indirect asyncio executor version of io.TextIOWrapper."""
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/wxPen.py
DELETED
|
@@ -1,29 +0,0 @@
|
|
| 1 |
-
from fontTools.pens.basePen import BasePen
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
__all__ = ["WxPen"]
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
class WxPen(BasePen):
|
| 8 |
-
def __init__(self, glyphSet, path=None):
|
| 9 |
-
BasePen.__init__(self, glyphSet)
|
| 10 |
-
if path is None:
|
| 11 |
-
import wx
|
| 12 |
-
|
| 13 |
-
path = wx.GraphicsRenderer.GetDefaultRenderer().CreatePath()
|
| 14 |
-
self.path = path
|
| 15 |
-
|
| 16 |
-
def _moveTo(self, p):
|
| 17 |
-
self.path.MoveToPoint(*p)
|
| 18 |
-
|
| 19 |
-
def _lineTo(self, p):
|
| 20 |
-
self.path.AddLineToPoint(*p)
|
| 21 |
-
|
| 22 |
-
def _curveToOne(self, p1, p2, p3):
|
| 23 |
-
self.path.AddCurveToPoint(*p1 + p2 + p3)
|
| 24 |
-
|
| 25 |
-
def _qCurveToOne(self, p1, p2):
|
| 26 |
-
self.path.AddQuadCurveToPoint(*p1 + p2)
|
| 27 |
-
|
| 28 |
-
def _closePath(self):
|
| 29 |
-
self.path.CloseSubpath()
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__2.py
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
""" TSI{0,1,2,3,5} are private tables used by Microsoft Visual TrueType (VTT)
|
| 2 |
-
tool to store its hinting source data.
|
| 3 |
-
|
| 4 |
-
TSI2 is the index table containing the lengths and offsets for the glyph
|
| 5 |
-
programs that are contained in the TSI3 table. It uses the same format as
|
| 6 |
-
the TSI0 table.
|
| 7 |
-
"""
|
| 8 |
-
from fontTools import ttLib
|
| 9 |
-
|
| 10 |
-
superclass = ttLib.getTableClass("TSI0")
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class table_T_S_I__2(superclass):
|
| 14 |
-
|
| 15 |
-
dependencies = ["TSI3"]
|
|
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