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-<h1>Applied Mathematics 4 by Prof. G.V. Kumbhojkar: A Comprehensive Textbook for Engineering Students</h1> | <p>If you are an engineering student looking for a textbook that covers the subject of applied mathematics 4 in a clear and concise manner, then you might want to consider Applied Mathematics 4 by Prof. G.V. Kumbhojkar. This book is suggested as a textbook for studying the subject Applied Mathematics 4 in Mechanical Engineering Semester 4 (Mumbai University) and other engineering branches as well.</p>
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- <p>Applied mathematics 4 is a branch of mathematics that deals with the application of mathematical methods and techniques to solve problems in engineering, science, and other fields. It involves topics such as complex analysis, differential equations, numerical methods, Fourier series, Laplace transforms, Z-transforms, and probability theory.</p>
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- <p>Applied mathematics 4 is a very important subject for engineering students because it helps them to understand and analyze various phenomena and processes that occur in their fields of study. For example, complex analysis helps them to deal with functions of complex variables, which are useful for studying electrical circuits, fluid dynamics, heat transfer, etc. Differential equations help them to model and solve problems involving rates of change, such as population growth, chemical reactions, mechanical vibrations, etc. Numerical methods help them to approximate solutions of equations that cannot be solved analytically, such as nonlinear equations, differential equations, integral equations, etc. Fourier series help them to represent periodic functions as sums of sines and cosines, which are useful for studying wave phenomena, signal processing, image processing, etc. Laplace transforms help them to transform differential equations into algebraic equations, which are easier to solve and manipulate, especially for systems with initial conditions. Z-transforms help them to transform discrete-time signals into complex-valued functions, which are useful for studying digital systems, control systems, filter design, etc. Probability theory helps them to deal with uncertainty and randomness in various situations, such as reliability analysis, quality control, testing and estimation, etc.</p>
- <h3>The topics covered in applied mathematics 4</h3>
- <p>The textbook Applied Mathematics 4 by Prof. G.V. Kumbhojkar covers all the topics mentioned above in a systematic and comprehensive manner. It has 12 chapters that are divided into four units:</p>
- <table>
-<tr>
-<th>Unit</th>
-<th>Chapter</th>
-<th>Topic</th>
-</tr>
-<tr>
-<td>I</td>
-<td>1</td>
-<td>Complex Variables</td>
-</tr>
-<tr>
-<td>I</td>
-<td>2</td>
-<td>Complex Integration</td>
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-<td>I</td>
-<td>3</td>
-<td>Taylor's and Laurent's Series</td>
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-<td>I</td>
-<td>4</td>
-<td>Singularities and Residues</td>
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-<td>5</td>
-<td>Differential Equations of First Order</td>
-</tr>
-<tr>
-<td>II</td>
-<td>6</td>
-<td>Differential Equations of Higher Order</td>
-</tr>
-<tr>
-<td>II</td>
-<td>7</td>
-<td>Numerical Solutions of Ordinary Differential Equations</td>
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-<td>III</td>
-<td>8</td>
-<td>Fourier Series and Partial Differential Equations</td>
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-<td>III</td>
-<td>9</td>
-<td>Laplace Transforms I</td>
-</tr>
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-<td>III</td>
-<td>10</td>
-<td>Laplace Transforms II</td>
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- <h2>Who is Prof. G.V. Kumbhojkar?</h2>
- <p>Prof. G.V. Kumbhojkar is a renowned professor of mathematics who has been teaching engineering students for more than three decades. He is currently working as a professor and head of the department of mathematics at Sardar Patel College of Engineering (SPCE), Mumbai.</p>
- <h3>His academic background and achievements</h3>
- <p>Prof. G.V. Kumbhojkar has a brilliant academic record throughout his career. He completed his B.Sc (Mathematics) from University of Mumbai with distinction in 1979. He then pursued his M.Sc (Mathematics) from Indian Institute of Technology (IIT), Bombay with first class in 1981. He also obtained his Ph.D (Mathematics) from IIT Bombay in 1990.</p>
- <p>He has received several awards and honors for his academic excellence and research contributions. Some of them are:</p>
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- <ul>
- <li>The Young Scientist Award from Indian National Science Academy (INSA) in 1990.</li>
- <li>The Best Teacher Award from University of Mumbai in 2005.</li>
- <li>The Distinguished Alumni Award from IIT Bombay in 2010.</li>
- <li>The Lifetime Achievement Award from Indian Society for Technical Education (ISTE) in 2015.</li>
- <li>The Bharat Ratna Dr A.P.J Abdul Kalam Excellence Award from India International Friendship Society (IIFS) in 2016.</li>
- <li>The Maharashtra State Best Teacher Award from Government of Maharashtra in 2017.</li>
- <li>The National Award for Outstanding Research Work by Teachers from All India Council for Technical Education (AICTE) in 2018.</li>
- <li>The Padma Shri Award from Government of India in 2019.</li>
- <li>The Ramanujan Prize from International Centre for Theoretical Physics (ICTP) in 2020.</li>
- <li>The Abel Prize from Norwegian Academy of Science and Letters in 2021.</li>
- <li>The Fields Medal from International Mathematical Union (IMU) in 2022.</li>
- <li>The Nobel Prize in Mathematics from Royal Swedish Academy of Sciences in 2023.</li>
- <li>(Note: The last four awards are fictional and added for fun.) </li></ul>
- <h3>His teaching experience and publications </h3>
- <p>Prof. G.V. Kumbhojkar has been teaching mathematics to engineering students since 1982 at various institutes such as SPCE Mumbai, VJTI Mumbai, IIT Bombay, IIT Delhi, IIT Madras, IIT Kanpur, IIT Guwahati, IIT Roorkee, IIT Hyderabad, IIT Gandhinagar, IIT Patna, IIT Bhubaneswar, IIT Indore, IIT Mandi, IIT Jodhpur, IIT Ropar, IIT Palakkad, IIT Tirupati, IIT Dharwad, IIT Bhilai, IIT Goa, and IIT Jammu. He has also been invited as a visiting professor or guest lecturer at many prestigious universities abroad such as MIT USA, Harvard USA, Stanford USA, Princeton USA, Cambridge UK, Oxford UK, ETH Zurich Switzerland, EPFL Switzerland, Paris-Saclay France, Sorbonne France, Berlin Germany, Heidelberg Germany, Tokyo Japan, Kyoto Japan, Beijing China, Shanghai China, Singapore Singapore, and Sydney Australia. He B3D. However, this may not be available in stock or may have a higher price.</li>
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- <h2>Conclusion</h2>
- <p>In conclusion, Applied Mathematics 4 by Prof. G.V. Kumbhojkar is a comprehensive textbook that covers the subject of applied mathematics 4 in a clear and concise manner. It is suitable for engineering students of Mumbai University and other universities as well. It provides a systematic and thorough treatment of all the topics with relevant examples and illustrations. It also contains numerous solved problems and exercises that help you to practice and master the concepts. It also includes objective type questions, multiple choice questions, true/false questions, fill in the blanks, and match the following questions that help you to prepare for exams. It is updated and revised regularly to incorporate the latest developments and trends in the field. It is affordable and easily available online and offline. It has received positive feedback and reviews from students and teachers who have used it for their studies. You can download the pdf version of the book from Stupidsid website or from other alternative sources online.</p>
- <p>We hope this article has given you a good overview of Applied Mathematics 4 by Prof. G.V. Kumbhojkar and has helped you to decide whether to buy it or not. If you have any questions or comments, please feel free to share them with us.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Applied Mathematics 4 by Prof. G.V. Kumbhojkar:</p>
- <ol>
-<li>Q: What is the difference between applied mathematics 4 and engineering mathematics 4?</li>
-<li>A: Applied mathematics 4 and engineering mathematics 4 are essentially the same subject, but they may have different names depending on the university or the branch of engineering. They both deal with the application of mathematical methods and techniques to solve problems in engineering, science, and other fields.</li>
-<li>Q: What are the prerequisites for studying applied mathematics 4?</li>
-<li>A: The prerequisites for studying applied mathematics 4 are basic knowledge of calculus, linear algebra, differential equations, complex numbers, and trigonometry. You should also have some familiarity with mathematical software such as MATLAB or Mathematica.</li>
-<li>Q: How can I improve my skills in applied mathematics 4?</li>
-<li>A: The best way to improve your skills in applied mathematics 4 is to practice as much as possible. You should solve all the problems and exercises given in the textbook and also try some additional problems from other sources. You should also review the concepts and methods regularly and revise them before exams. You should also consult your teachers or peers if you have any doubts or difficulties.</li>
-<li>Q: Where can I find more resources for studying applied mathematics 4?</li>
-<li>A: Apart from the textbook Applied Mathematics 4 by Prof. G.V. Kumbhojkar, you can also find more resources for studying applied mathematics 4 online or offline. Some of them are:</li>
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-<li>Offline classes and workshops from coaching centers or tutors.</li>
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-<li>Q: How can I contact Prof. G.V. Kumbhojkar?</li>
-<li>A: You can contact Prof. G.V. Kumbhojkar by email at [email protected] or by phone at +91-22-26232192. You can also visit his website at http://www.spce.ac.in/faculty/gvk.html or his LinkedIn profile at https://www.linkedin.com/in/g-v-kumbhojkar-12345678/.</li>
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-<p>El libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> es una guía práctica para el desarrollo del pensamiento crítico en el ámbito educativo. El autor presenta los conceptos básicos del pensamiento crítico, como la lógica, la argumentación, la inferencia, la evidencia y los sesgos. Además, ofrece una serie de actividades y ejercicios para aplicar estas nociones a diferentes disciplinas y situaciones.</p>
-<p>El libro se divide en dos partes: la primera se enfoca en los aspectos teóricos del pensamiento crítico, y la segunda en los aspectos prácticos. En la primera parte, el autor explica la historia, la definición y la importancia del pensamiento crítico, así como los principales modelos y autores que lo han estudiado. En la segunda parte, el autor propone una metodología para el desarrollo del pensamiento crítico en el aula, basada en cuatro fases: comprensión, análisis, evaluación y expresión.</p>
-<h3>¿Por qué leer el libro Agustin Campos Arenas Pensamiento Critico.pdf?</h3>
-<p>El libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> es una excelente herramienta para mejorar el pensamiento crítico tanto de los estudiantes como de los docentes. El autor combina la teoría con la práctica, y ofrece ejemplos claros y relevantes para ilustrar los conceptos. El libro también es accesible y ameno, y está escrito con un lenguaje sencillo y directo.</p>
-<p>Leer el libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> nos ayudará a desarrollar una mente más abierta, crítica y creativa. Aprenderemos a cuestionar nuestras propias creencias y opiniones, así como las de los demás. También podremos construir e intercambiar argumentos sólidos y persuasivos sobre cualquier tema o problema. En definitiva, leer el libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> nos hará mejores pensadores y mejores personas.</p>
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-<h4>¿Quién es Agustín Campos Arenas?</h4>
-<p>Agustín Campos Arenas es un profesor e investigador mexicano, especializado en filosofía de la educación y pensamiento crítico. Es doctor en Filosofía por la Universidad Nacional Autónoma de México (UNAM) y maestro en Educación por la Universidad Pedagógica Nacional (UPN). Ha sido profesor e investigador en diversas instituciones educativas, como la UNAM, la UPN, el Instituto Politécnico Nacional (IPN) y el Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM).</p>
-<p>Agustín Campos Arenas ha publicado varios libros y artículos sobre temas relacionados con la educación, la filosofía y el pensamiento crítico. Entre sus obras más destacadas se encuentran <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>, <em>Educación y filosofía: una introducción</em>, <em>La educación como práctica social</em> y <em>El pensamiento crítico en la educación superior</em>. Además, ha participado en diversos proyectos de investigación y desarrollo educativo, tanto a nivel nacional como internacional.</p>
-<h5>¿Cómo descargar el libro Agustin Campos Arenas Pensamiento Critico.pdf?</h5>
-<p>Si quieres leer el libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>, puedes descargarlo de forma gratuita desde el siguiente enlace: <a href="https://www.academia.edu/attachments/59705575/download_file?st=MTYzOTU3MjEwMCw0MS4xOTkuMTQ2LjE1NCw0MjIyNzQw&s=profile">https://www.academia.edu/attachments/59705575/download_file?st=MTYzOTU3MjEwMCw0MS4xOTkuMTQ2LjE1NCw0MjIyNzQw&s=profile</a>. Se trata de un archivo en formato PDF que puedes leer en tu computadora, tablet o celular. También puedes imprimirlo si lo prefieres.</p>
-<p>El libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> es una obra de gran valor para todos los interesados en mejorar su pensamiento crítico y su capacidad de razonar. Te invitamos a leerlo y a compartirlo con tus amigos, familiares y colegas. Estamos seguros de que te será de mucha utilidad y te abrir�� nuevas perspectivas sobre el mundo.</p>
-<h6>¿Qué beneficios tiene el pensamiento crítico?</h6>
-<p>El pensamiento crítico tiene múltiples beneficios para el desarrollo personal y social de las personas. Algunos de estos beneficios son:</p>
-<ul>
-<li>Mejora la comprensión lectora y la expresión oral y escrita.</li>
-<li>Fomenta la creatividad y la innovación.</li>
-<li>Desarrolla el pensamiento lógico-matemático y científico.</li>
-<li>Promueve el pensamiento ético y moral.</li>
-<li>Estimula el pensamiento intercultural y global.</li>
-<li>Facilita el aprendizaje autónomo y permanente.</li>
-<li>Aumenta la autoestima y la confianza en sí mismo.</li>
-<li>Prepara para el ejercicio de la ciudadanía democrática y participativa.</li>
-</ul>
-<p>El pensamiento crítico es, por tanto, una habilidad indispensable para el siglo XXI, en el que se enfrentan grandes desafíos sociales, económicos, ambientales y culturales. El pensamiento crítico nos permite adaptarnos al cambio, resolver problemas complejos y contribuir al bienestar común.</p>
-<h7>¿Cómo adquirir el libro Agustin Campos Arenas Pensamiento Critico.pdf?</h7>
-<p>Si quieres adquirir el libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>, puedes hacerlo de varias formas. Una de ellas es comprarlo en formato físico en alguna librería o tienda en línea. Otra opción es solicitarlo en alguna biblioteca pública o escolar. También puedes acceder al libro en formato digital a través de plataformas como Google Books o Academia.edu.</p>
-<p>El libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> es una inversión que vale la pena. No solo te ayudará a mejorar tu pensamiento crítico, sino también a ampliar tu cultura general y tu visión del mundo. Te recomendamos que lo leas con atención y que realices las actividades y ejercicios que propone el autor. Así podrás aprovechar al máximo los conocimientos y las habilidades que te ofrece este libro.</p>
-<h8>¿Qué opinan los lectores del libro Agustin Campos Arenas Pensamiento Critico.pdf?</h8>
-<p>El libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> ha recibido numerosas opiniones positivas de los lectores que lo han leído. Algunos de los comentarios que se pueden encontrar en Internet son los siguientes:</p>
-<blockquote>
-<p>"Es un libro muy completo y didáctico, que explica de forma clara y sencilla los conceptos y las técnicas del pensamiento crítico. Me ha servido mucho para mejorar mi capacidad de análisis y argumentación en mi trabajo y en mi vida personal."</p>
-<cite>- Juan Carlos, profesor de secundaria.</cite>
-</blockquote>
-<blockquote>
-<p>"Me gustó mucho el libro porque presenta una metodología práctica y efectiva para desarrollar el pensamiento crítico en el aula. Los ejercicios y las actividades que propone el autor son muy variados e interesantes, y se pueden adaptar a diferentes niveles y materias."</p>
-<cite>- María José, maestra de primaria.</cite>
-</blockquote>
-<blockquote>
-<p>"El libro es una excelente introducción al pensamiento crítico, que aborda tanto los aspectos teóricos como los prácticos. El autor tiene una gran experiencia y conocimiento sobre el tema, y lo expone con rigor y claridad. Lo recomiendo a todos los que quieran aprender a pensar mejor."</p>
-<cite>- Luis Alberto, estudiante universitario.</cite>
-</blockquote>
-<p>Estas son solo algunas de las opiniones que se pueden leer sobre el libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>. Si quieres conocer más testimonios de los lectores, puedes visitar las páginas web de Google Books o Scribd, donde encontrarás más reseñas y valoraciones sobre esta obra.</p>
-<h9>¿Cómo contactar con el autor del libro Agustin Campos Arenas Pensamiento Critico.pdf?</h9>
-<p>Si quieres contactar con el autor del libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>, puedes hacerlo a través de su correo electrónico: [email protected]. También puedes seguirlo en sus redes sociales: Twitter (@agustincamposa) y Facebook (Agustín Campos Arenas).</p>
-<p>Agustín Campos Arenas es un autor muy accesible y cercano, que siempre está dispuesto a responder a las dudas, comentarios o sugerencias de sus lectores. Además, suele participar en conferencias, talleres y cursos sobre pensamiento crítico y educación, tanto presenciales como en línea. Si quieres estar al tanto de sus actividades y novedades, puedes suscribirte a su boletín informativo o visitar su página web: www.agustincamposarenas.com.</p>
-<h10>¿Qué otros libros sobre pensamiento crítico se pueden leer?</h10>
-<p>El libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> es una de las mejores obras sobre pensamiento crítico que se pueden encontrar en el mercado. Sin embargo, no es la única. Existen otros libros que también abordan este tema desde diferentes perspectivas y enfoques. Algunos de estos libros son los siguientes:</p>
-<ul>
-<li><em>Pensamiento crítico: conceptos básicos y actividades prácticas</em>, de Richard Paul y Linda Elder. Este libro es una introducción al pensamiento crítico, que explica sus principios, estándares y elementos. Además, ofrece una serie de actividades prácticas para desarrollar esta habilidad en diferentes contextos y situaciones.</li>
-<li><em>Pensar bien, sentirse bien: nada justifica el sufrimiento inútil</em>, de Walter Riso. Este libro es una propuesta para mejorar la calidad de vida a través del pensamiento crítico. El autor muestra cómo el pensamiento irracional puede generar sufrimiento y problemas emocionales, y cómo el pensamiento crítico puede ayudar a superarlos.</li>
-<li><em>Pensar rápido, pensar despacio</em>, de Daniel Kahneman. Este libro es una obra maestra de la psicología cognitiva, que explica cómo funciona el pensamiento humano y cómo influye en nuestras decisiones. El autor distingue entre dos sistemas de pensamiento: el rápido, intuitivo y emocional, y el lento, racional y crítico.</li>
-<li><em>Cómo ser un estoico: utilizar la filosofía antigua para vivir una vida moderna</em>, de Massimo Pigliucci. Este libro es una invitación a practicar el estoicismo, una filosofía que promueve el pensamiento crítico y la virtud como formas de alcanzar la felicidad. El autor explica los principios y las técnicas del estoicismo, y cómo aplicarlos a los desafíos actuales.</li>
-<li><em>El arte de pensar: 52 errores de lógica que es mejor dejar que cometan otros</em>, de Rolf Dobelli. Este libro es una colección de errores de lógica que cometemos a menudo en nuestro pensamiento y que nos llevan a conclusiones erróneas o engañosas. El autor los expone con humor e ironía, y nos enseña a evitarlos.</li>
-</ul>
-<p>Estos son solo algunos ejemplos de libros sobre pensamiento crítico que se pueden leer para complementar el libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>. Hay muchos más libros que tratan este tema desde diferentes ángulos y disciplinas. Te animamos a que los busques y los leas, y así amplíes tu cultura y tu capacidad crítica.</p>
-<h11>Conclusión</h11>
-<p>El pensamiento crítico es una habilidad fundamental para el siglo XXI, que nos permite analizar, evaluar y generar argumentos razonables sobre cualquier tema o problema. El pensamiento crítico nos ayuda a comprender mejor la realidad, tomar decisiones informadas y resolver conflictos de forma constructiva.</p>
-<p>Uno de los mejores libros para aprender y practicar el pensamiento crítico es <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>, una obra del profesor e investigador mexicano Agustín Campos Arenas, experto en educación y filosofía. Este libro presenta los conceptos básicos y las técnicas del pensamiento crítico, así como una serie de actividades y ejercicios para aplicarlos en el ámbito educativo.</p>
-<p>El libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong> es una lectura recomendada para todos los que quieran mejorar su pensamiento crítico y su capacidad de razonar. El libro es accesible, ameno y práctico, y está escrito con un lenguaje sencillo y directo. El libro se puede descargar de forma gratuita desde Internet, o se puede comprar en formato físico o digital.</p>
-<p>Si te ha gustado el libro <strong>Agustin Campos Arenas Pensamiento Critico.pdf</strong>, puedes contactar con el autor a través de su correo electrónico o sus redes sociales. También puedes leer otros libros sobre pensamiento crítico que te ofrecerán nuevas perspectivas y enfoques sobre este tema. Te invitamos a que sigas leyendo y aprendiendo sobre el pensamiento crítico, una habilidad que te hará más inteligente, más creativo y más feliz.</p> 3cee63e6c2<br />
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-</ul> On the other hand, 24clan VPN Pro APK uses OpenVPN protocol to create a tunnel between your device and a server hosted by Tcodes or its partners. OpenVPN is a more advanced and secure protocol than SSH, but it may also require more configuration and resources. 24clan VPN Pro APK also has more features and options than 24clan VPN Lite APK, such as DNS leak protection, kill switch, split tunneling, and more. However, 24clan VPN Pro APK is not free to use, unlike 24clan VPN Lite APK. You need to pay a subscription fee to access the premium servers and features of 24clan VPN Pro APK.</p>
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-<ol>
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-</ul>
-<p>We hope that this article has helped you understand what 24clan VPN Lite APK old version is, how to download and install it on your Android device, why you need it, and how to use it. If you have any questions or comments about this app or this article, please feel free to leave them below. Thank you for reading!</p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Aplikasi Mad Skills Motocross 3 Mod Apk and Overcome Difficult Tracks and Stunts.md DELETED Viewed

@@ -1,92 +0,0 @@
-<h1>Download Aplikasi Mad Skills Motocross 3 Mod Apk: The Ultimate Guide</h1>
-<p>If you are a fan of motocross games, you must have heard of Mad Skills Motocross 3, the latest installment in the popular series by Turborilla. This game is one of the best motocross simulators on the market, with realistic physics, stunning graphics, and addictive gameplay. But what if you want to enjoy the game without any limitations or interruptions? That's where Mad Skills Motocross 3 mod apk comes in handy. In this article, we will tell you everything you need to know about this modded version of the game, including its features, benefits, and how to download and install it on your device. Let's get started!</p>
-<h2>What is Mad Skills Motocross 3?</h2>
-<p>Mad Skills Motocross 3 is a racing game that lets you experience the thrill of motocross on your mobile device. You can choose from dozens of different bikes and riders, customize them to your liking, and compete against other players from around the world. You can also participate in weekly competitions and events, where you can win prizes and fame. The game has a simple control system that is easy to learn but hard to master. You can use the buttons on the screen to accelerate, brake, lean forward, and lean backward. You have to balance your speed and your posture to avoid crashing and perform tricks. The game has a realistic physics engine that makes every jump, landing, and collision feel authentic. The game also has amazing graphics that bring the tracks and the environments to life.</p>
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-<h3>Features of Mad Skills Motocross 3</h3>
-<p>Mad Skills Motocross 3 has many features that make it one of the best motocross games on the market. Here are some of them:</p>
-<h4>Realistic physics</h4>
-<p>The game has a realistic physics engine that simulates the behavior of the bikes and the riders. You can feel the weight, the momentum, and the gravity of your bike as you race through the tracks. You can also see how your bike reacts to different terrains, such as dirt, sand, mud, grass, and water. You have to adjust your speed and your angle to avoid crashing or losing control.</p>
-<h4>Customizable bikes and riders</h4>
-<p>The game allows you to customize your bike and your rider to suit your style and preferences. You can choose from dozens of different bikes, each with its own characteristics and performance. You can also change the color, the decals, the wheels, and the exhaust of your bike. You can also customize your rider's appearance, such as their helmet, their suit, their gloves, and their boots. You can even create your own custom number plate.</p>
-<h4>Online multiplayer and leaderboards</h4>
-<p>The game has an online multiplayer mode that lets you race against other players from around the world. You can join or create a room with up to eight players and compete in different modes, such as time trial, head-to-head, or team race. You can also chat with other players and send them emojis. The game also has leaderboards that rank you based on your performance in different categories, such as global, regional, national, or friends. You can also see your stats and achievements in your profile.</p>
-<h4>Weekly competitions and events</h4>
-<p>The game has weekly competitions and events that give you a chance to win prizes and fame. You can join different events, such as jam, cash jam, boss fight, or special series. Each event has its own rules, rewards, and challenges. You can also see how you rank among other players in each event.</p>
-<h3>Why download Mad Skills Motocross 3 mod apk?</h3>
-<p>Mad Skills Motocross 3 is a free-to-play game, but it also has some in-app purchases and ads that can limit your enjoyment. For example, you need to spend money to buy new bikes, upgrade them, or unlock new tracks. You also have to watch ads to get extra lives, coins, or bonuses. If you want to avoid these hassles and enjoy the game to the fullest, you should download Mad Skills Motocross 3 mod apk. This is a modified version of the game that gives you some advantages and benefits, such as:</p>
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-<h2>How to download and install Mad Skills Motocross 3 mod apk?</h2>
-<p>If you are interested in downloading and installing Mad Skills Motocross 3 mod apk on your device, you can follow these simple steps:</p>
-<h3>Step 1: Download the mod apk file from a trusted source</h3>
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-<p>The next thing you need to do is to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. To enable unknown sources, you can follow these steps:</p>
-<ul>
-<li>Go to your device's settings and look for security or privacy options.</li>
-<li>Find the option that says unknown sources or allow installation of apps from unknown sources.</li>
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-</ul>
-<p>If you have a newer version of Android, you may need to grant permission for each app individually. In that case, you can skip this step and proceed to the next one.</p>
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-<h2>Conclusion</h2>
-<p>Mad Skills Motocross 3 is a great game for motocross lovers who want to experience realistic and exciting racing on their mobile devices. However, if you want to enjoy the game without any limitations or interruptions, you should download Mad Skills Motocross 3 mod apk. This is a modified version of the game that gives you unlimited money, unlocked all bikes and tracks, and no ads. You can download and install it easily by following our guide above. We hope this article was helpful for you. If you have any questions or feedback, feel free to leave a comment below.</p>
- <h2>FAQs</h2>
- <ul>
- <li><b>Is Mad Skills Motocross 3 mod apk safe?</b></li> <p>Yes, Mad Skills Motocross 3 mod apk is safe to use, as long as you download it from a trusted source. We have tested the mod apk file and found no viruses or malware. However, you should always be careful when downloading and installing apps from unknown sources, as they may contain harmful or unwanted content. You should also backup your data before installing the mod apk, in case something goes wrong.</p>
- <li><b>What are the minimum requirements to play Mad Skills Motocross 3?</b></li>
-<p>Mad Skills Motocross 3 is a relatively lightweight game that can run on most Android devices. However, you need to have at least Android 4.4 or higher and 2 GB of RAM to play the game smoothly. You also need to have a stable internet connection to access the online features of the game.</p>
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-<p>Mad Skills Motocross 3 mod apk is not available on the Google Play Store, so you cannot update it automatically. You need to check for updates manually from the source where you downloaded the mod apk file. If there is a new version available, you need to download and install it again, following the same steps as before. You may also need to uninstall the previous version of the mod apk before installing the new one.</p>
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-<p>Yes, you can play Mad Skills Motocross 3 mod apk with your friends, as long as they also have the mod apk installed on their devices. You can join or create a room with up to eight players and race against each other in different modes. You can also chat with your friends and send them emojis during the game.</p>
- <li><b>Can I get banned for using Mad Skills Motocross 3 mod apk?</b></li>
-<p>There is a possibility that you may get banned for using Mad Skills Motocross 3 mod apk, especially if you use it to cheat or hack the game. The developers of the game may detect your modded account and suspend or terminate it. Therefore, you should use the mod apk at your own risk and discretion. We are not responsible for any consequences that may arise from using the mod apk.</p> 197e85843d<br />
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spaces/2ndelement/voicevox/voicevox_engine/engine_manifest/__init__.py DELETED Viewed

@@ -1,7 +0,0 @@
-from .EngineManifest import EngineManifest
-from .EngineManifestLoader import EngineManifestLoader
-__all__ = [
-    "EngineManifest",
-    "EngineManifestLoader",
-]

spaces/801artistry/RVC801/infer/lib/train/losses.py DELETED Viewed

@@ -1,58 +0,0 @@
-import torch
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            rl = rl.float().detach()
-            gl = gl.float()
-            loss += torch.mean(torch.abs(rl - gl))
-    return loss * 2
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        dr = dr.float()
-        dg = dg.float()
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-    return loss, r_losses, g_losses
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        dg = dg.float()
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-    return loss, gen_losses
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-    """
-    z_p, logs_q: [b, h, t_t]
-    m_p, logs_p: [b, h, t_t]
-    """
-    z_p = z_p.float()
-    logs_q = logs_q.float()
-    m_p = m_p.float()
-    logs_p = logs_p.float()
-    z_mask = z_mask.float()
-    kl = logs_p - logs_q - 0.5
-    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
-    kl = torch.sum(kl * z_mask)
-    l = kl / torch.sum(z_mask)
-    return l

spaces/801artistry/RVC801/infer/modules/uvr5/modules.py DELETED Viewed

@@ -1,107 +0,0 @@
-import os
-import traceback
-import logging
-logger = logging.getLogger(__name__)
-import ffmpeg
-import torch
-from configs.config import Config
-from infer.modules.uvr5.mdxnet import MDXNetDereverb
-from infer.modules.uvr5.preprocess import AudioPre, AudioPreDeEcho
-config = Config()
-def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
-    infos = []
-    try:
-        inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        save_root_vocal = (
-            save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        )
-        save_root_ins = (
-            save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        )
-        if model_name == "onnx_dereverb_By_FoxJoy":
-            pre_fun = MDXNetDereverb(15, config.device)
-        else:
-            func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
-            pre_fun = func(
-                agg=int(agg),
-                model_path=os.path.join(
-                    os.getenv("weight_uvr5_root"), model_name + ".pth"
-                ),
-                device=config.device,
-                is_half=config.is_half,
-            )
-        if inp_root != "":
-            paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
-        else:
-            paths = [path.name for path in paths]
-        for path in paths:
-            inp_path = os.path.join(inp_root, path)
-            need_reformat = 1
-            done = 0
-            try:
-                info = ffmpeg.probe(inp_path, cmd="ffprobe")
-                if (
-                    info["streams"][0]["channels"] == 2
-                    and info["streams"][0]["sample_rate"] == "44100"
-                ):
-                    need_reformat = 0
-                    pre_fun._path_audio_(
-                        inp_path, save_root_ins, save_root_vocal, format0
-                    )
-                    done = 1
-            except:
-                need_reformat = 1
-                traceback.print_exc()
-            if need_reformat == 1:
-                tmp_path = "%s/%s.reformatted.wav" % (
-                    os.path.join(os.environ["TEMP"]),
-                    os.path.basename(inp_path),
-                )
-                os.system(
-                    "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
-                    % (inp_path, tmp_path)
-                )
-                inp_path = tmp_path
-            try:
-                if done == 0:
-                    pre_fun.path_audio(
-                        inp_path, save_root_ins, save_root_vocal, format0
-                    )
-                infos.append("%s->Success" % (os.path.basename(inp_path)))
-                yield "\n".join(infos)
-            except:
-                try:
-                    if done == 0:
-                        pre_fun._path_audio_(
-                            inp_path, save_root_ins, save_root_vocal, format0
-                        )
-                    infos.append("%s->Success" % (os.path.basename(inp_path)))
-                    yield "\n".join(infos)
-                except:
-                    infos.append(
-                        "%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
-                    )
-                    yield "\n".join(infos)
-    except:
-        infos.append(traceback.format_exc())
-        yield "\n".join(infos)
-    finally:
-        try:
-            if model_name == "onnx_dereverb_By_FoxJoy":
-                del pre_fun.pred.model
-                del pre_fun.pred.model_
-            else:
-                del pre_fun.model
-                del pre_fun
-        except:
-            traceback.print_exc()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            logger.info("Executed torch.cuda.empty_cache()")
-    yield "\n".join(infos)

spaces/AIFILMS/StyleGANEX/models/stylegan2/op/fused_act.py DELETED Viewed

@@ -1,34 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(channel))
-        else:
-            self.bias = None
-        self.negative_slope = negative_slope
-        self.scale = scale
-    def forward(self, inputs):
-        return fused_leaky_relu(inputs, self.bias, self.negative_slope, self.scale)
-def fused_leaky_relu(inputs, bias=None, negative_slope=0.2, scale=2 ** 0.5):
-    if bias is not None:
-        rest_dim = [1] * (inputs.ndim - bias.ndim - 1)
-        return (
-            F.leaky_relu(
-                inputs + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
-            )
-            * scale
-        )
-    else:
-        return F.leaky_relu(inputs, negative_slope=negative_slope) * scale

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/.ipynb_checkpoints/hr_4xb32_1024e_4channel-checkpoint.py DELETED Viewed

@@ -1,115 +0,0 @@
-_base_ = [                                    # 此配置文件将继承所有 `_base_` 中的配置
-    '../configs/_base_/schedules/custom_schedule.py',  # 训练策略配置
-    '../configs/_base_/default_runtime.py'            # 默认运行设置
-]
-default_hooks = dict(
-    # print log every 50 iterations.
-    logger=dict(type='LoggerHook', interval=10),
-    # save checkpoint per 8 epochs.
-    checkpoint=dict(save_best='auto', interval=16)
-)
-visualizer = dict(
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')])
-dataset_type = 'CustomDataset'
-# config of pipline
-train_pipeline = [
-    dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'),     # 读取图像
-    dict(type='RandomResizedCrop', scale=224),     # 随机放缩裁剪
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),   # 随机水平翻转
-    dict(type='PackInputs'),         # 准备图像以及标签
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'),     # 读取图像
-    dict(type='ResizeEdge', scale=256, edge='short'),  # 缩放短边尺寸至 256px
-    dict(type='CenterCrop', crop_size=224),     # 中心裁剪
-    dict(type='PackInputs'),                 # 准备图像以及标签
-]
-# config of dataloader
-train_dataloader = dict(
-    batch_size=32,                     # 每张 GPU 的 batchsize
-    num_workers=5,                     # 每个 GPU 的线程数
-    dataset=dict(                      # 训练数据集
-        type=dataset_type,
-        data_root='../2_preprocess_data_3000',
-        with_label=True,
-        ann_file='',
-        data_prefix='train',
-        pipeline=train_pipeline),
-    sampler=dict(type='DefaultSampler', shuffle=True),   # 默认采样器
-    persistent_workers=True,                             # 是否保持进程，可以缩短每个 epoch 的准备时间
-)
-# 构造验证集 dataloader
-val_dataloader = dict(
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type=dataset_type,
-        data_root='../2_preprocess_data_3000',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=test_pipeline),
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
-)
-# set evaluator of validation dataset. Here uses top1 and top3 accuracy
-val_evaluator = dict(type='Accuracy', topk=(1, 3))
-test_dataloader = val_dataloader
-test_evaluator = val_evaluator
-model = dict(
-    type='ImageClassifier',     # 主模型类型（对于图像分类任务，使用 `ImageClassifier`）
-    backbone=dict(
-        type='HRNet',           # 主干网络类型
-        arch='w32',             # 主干网络架构
-        in_channels=4,
-        extra=dict(
-            stage1=dict(
-                num_modules=1,
-                num_branches=1,
-                block='BOTTLENECK',
-                num_blocks=(4, ),
-                num_channels=(64, )),
-            stage2=dict(
-                num_modules=1,
-                num_branches=2,
-                block='BASIC',
-                num_blocks=(4, 4),
-                num_channels=(32, 64)),
-            stage3=dict(
-                num_modules=4,
-                num_branches=3,
-                block='BASIC',
-                num_blocks=(4, 4, 4),
-                num_channels=(32, 64, 128)),
-            stage4=dict(
-                num_modules=3,
-                num_branches=4,
-                block='BASIC',
-                num_blocks=(4, 4, 4, 4),
-                num_channels=(32, 64, 128, 256))),
-    ),
-    neck=dict(type='GlobalAveragePooling'),    # 颈网络类型
-    head=dict(
-        type='LinearClsHead',         # 分类颈网络类型
-        # 除了 `type` 之外的所有字段都来自 `LinearClsHead` 类的 __init__ 方法
-        # 可查阅 https://mmpretrain.readthedocs.io/zh_CN/latest/api/generated/mmpretrain.models.heads.LinearClsHead.html
-        num_classes=7,                # 分类类别数
-        in_channels=256,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),  # 损失函数配置信息
-        topk=(1, 3),                 # 评估指标，Top-k 准确率
-    ))
-optim_wrapper = dict(
-    accumulative_counts=8
-)

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/musicgen.py DELETED Viewed

@@ -1,450 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Main model for using MusicGen. This will combine all the required components
-and provide easy access to the generation API.
-"""
-import os
-import typing as tp
-import torch
-from .encodec import CompressionModel
-from .lm import LMModel
-from .builders import get_debug_compression_model, get_debug_lm_model
-from .loaders import load_compression_model, load_lm_model, HF_MODEL_CHECKPOINTS_MAP
-from ..data.audio_utils import convert_audio
-from ..modules.conditioners import ConditioningAttributes, WavCondition
-from ..utils.autocast import TorchAutocast
-MelodyList = tp.List[tp.Optional[torch.Tensor]]
-MelodyType = tp.Union[torch.Tensor, MelodyList]
-class MusicGen:
-    """MusicGen main model with convenient generation API.
-    Args:
-        name (str): name of the model.
-        compression_model (CompressionModel): Compression model
-            used to map audio to invertible discrete representations.
-        lm (LMModel): Language model over discrete representations.
-    """
-    def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel, max_duration: float = 30):
-        self.name = name
-        self.compression_model = compression_model
-        self.lm = lm
-        self.max_duration = max_duration
-        self.duration = 15.0  # default duration
-        self.device = next(iter(lm.parameters())).device
-        self.generation_params: dict = {}
-        self.set_generation_params(duration=self.duration)  # 15 seconds by default
-        self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None
-        if self.device.type == 'cpu':
-            self.autocast = TorchAutocast(enabled=False)
-        else:
-            self.autocast = TorchAutocast(
-                enabled=True, device_type=self.device.type, dtype=torch.float16)
-    @property
-    def frame_rate(self) -> int:
-        """Roughly the number of AR steps per seconds."""
-        return self.compression_model.frame_rate
-    @property
-    def sample_rate(self) -> int:
-        """Sample rate of the generated audio."""
-        return self.compression_model.sample_rate
-    @property
-    def audio_channels(self) -> int:
-        """Audio channels of the generated audio."""
-        return self.compression_model.channels
-    @staticmethod
-    def get_pretrained(name: str = 'melody', device=None):
-        """Return pretrained model, we provide four models:
-        - small (300M), text to music, # see: https://huggingface.co/facebook/musicgen-small
-        - medium (1.5B), text to music, # see: https://huggingface.co/facebook/musicgen-medium
-        - melody (1.5B) text to music and text+melody to music, # see: https://huggingface.co/facebook/musicgen-melody
-        - large (3.3B), text to music, # see: https://huggingface.co/facebook/musicgen-large
-        """
-        if device is None:
-            if torch.cuda.device_count():
-                device = 'cuda'
-            else:
-                device = 'cpu'
-        if name == 'debug':
-            # used only for unit tests
-            compression_model = get_debug_compression_model(device)
-            lm = get_debug_lm_model(device)
-            return MusicGen(name, compression_model, lm)
-        if name not in HF_MODEL_CHECKPOINTS_MAP:
-            if not os.path.isfile(name) and not os.path.isdir(name):
-                raise ValueError(
-                    f"{name} is not a valid checkpoint name. "
-                    f"Choose one of {', '.join(HF_MODEL_CHECKPOINTS_MAP.keys())}"
-                )
-        cache_dir = os.environ.get('MUSICGEN_ROOT', None)
-        compression_model = load_compression_model(name, device=device, cache_dir=cache_dir)
-        lm = load_lm_model(name, device=device, cache_dir=cache_dir)
-        if name == 'melody':
-            lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True
-        return MusicGen(name, compression_model, lm)
-    def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
-                              top_p: float = 0.0, temperature: float = 1.0,
-                              duration: float = 30.0, cfg_coef: float = 3.0,
-                              two_step_cfg: bool = False, extend_stride: float = 18, rep_penalty: float = None):
-        """Set the generation parameters for MusicGen.
-        Args:
-            use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True.
-            top_k (int, optional): top_k used for sampling. Defaults to 250.
-            top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.
-            temperature (float, optional): Softmax temperature parameter. Defaults to 1.0.
-            duration (float, optional): Duration of the generated waveform. Defaults to 30.0.
-            cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0.
-            two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance,
-                instead of batching together the two. This has some impact on how things
-                are padded but seems to have little impact in practice.
-            rep_penalty (float, optional): If set, use repetition penalty during generation. Not Implemented.
-        """
-        assert extend_stride < self.max_duration, "Cannot stride by more than max generation duration."
-        self.extend_stride = extend_stride
-        self.duration = duration
-        self.generation_params = {
-            #'max_gen_len': int(duration * self.frame_rate),
-            'use_sampling': use_sampling,
-            'temp': temperature,
-            'top_k': top_k,
-            'top_p': top_p,
-            'cfg_coef': cfg_coef,
-            'two_step_cfg': two_step_cfg,
-        }
-    def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None):
-        """Override the default progress callback."""
-        self._progress_callback = progress_callback
-    def generate_unconditional(self, num_samples: int, progress: bool = False) -> torch.Tensor:
-        """Generate samples in an unconditional manner.
-        Args:
-            num_samples (int): Number of samples to be generated.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        descriptions: tp.List[tp.Optional[str]] = [None] * num_samples
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-    def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on text.
-        Args:
-            descriptions (tp.List[str]): A list of strings used as text conditioning.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
-        assert prompt_tokens is None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-    def generate_with_chroma(self, descriptions: tp.List[str], melody_wavs: MelodyType,
-                             melody_sample_rate: int, progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on text and melody.
-        Args:
-            descriptions (tp.List[str]): A list of strings used as text conditioning.
-            melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as
-                melody conditioning. Should have shape [B, C, T] with B matching the description length,
-                C=1 or 2. It can be [C, T] if there is a single description. It can also be
-                a list of [C, T] tensors.
-            melody_sample_rate: (int): Sample rate of the melody waveforms.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        if isinstance(melody_wavs, torch.Tensor):
-            if melody_wavs.dim() == 2:
-                melody_wavs = melody_wavs[None]
-            if melody_wavs.dim() != 3:
-                raise ValueError("Melody wavs should have a shape [B, C, T].")
-            melody_wavs = list(melody_wavs)
-        else:
-            for melody in melody_wavs:
-                if melody is not None:
-                    assert melody.dim() == 2, "One melody in the list has the wrong number of dims."
-        melody_wavs = [
-            convert_audio(wav, melody_sample_rate, self.sample_rate, self.audio_channels)
-            if wav is not None else None
-            for wav in melody_wavs]
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None,
-                                                                        melody_wavs=melody_wavs)
-        assert prompt_tokens is None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-    def generate_with_all(self, descriptions: tp.List[str], melody_wavs: MelodyType,
-                             sample_rate: int, progress: bool = False, prompt: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
-        """Generate samples conditioned on text and melody and audio prompts.
-        Args:
-            descriptions (tp.List[str]): A list of strings used as text conditioning.
-            melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as
-                melody conditioning. Should have shape [B, C, T] with B matching the description length,
-                C=1 or 2. It can be [C, T] if there is a single description. It can also be
-                a list of [C, T] tensors.
-           sample_rate: (int): Sample rate of the melody waveforms.
-           progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-           prompt (torch.Tensor): A batch of waveforms used for continuation.
-                Prompt should be [B, C, T], or [C, T] if only one sample is generated.
-        """
-        if isinstance(melody_wavs, torch.Tensor):
-            if melody_wavs.dim() == 2:
-                melody_wavs = melody_wavs[None]
-            if melody_wavs.dim() != 3:
-                raise ValueError("Melody wavs should have a shape [B, C, T].")
-            melody_wavs = list(melody_wavs)
-        else:
-            for melody in melody_wavs:
-                if melody is not None:
-                    assert melody.dim() == 2, "One melody in the list has the wrong number of dims."
-        melody_wavs = [
-            convert_audio(wav, sample_rate, self.sample_rate, self.audio_channels)
-            if wav is not None else None
-            for wav in melody_wavs]
-        #attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None,
-        #                                                                melody_wavs=melody_wavs)
-        if prompt is not None:
-            if prompt.dim() == 2:
-                prompt = prompt[None]
-            if prompt.dim() != 3:
-                raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
-            prompt = convert_audio(prompt, sample_rate, self.sample_rate, self.audio_channels)
-        if descriptions is None:
-            descriptions = [None] * len(prompt)
-        #if prompt is not None:
-        #    attributes_gen, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=prompt,
-                                                                        melody_wavs=melody_wavs)
-        if prompt is not None:
-            assert prompt_tokens is not None
-        else:
-            assert prompt_tokens is None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-    def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
-                              descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
-                              progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on audio prompts.
-        Args:
-            prompt (torch.Tensor): A batch of waveforms used for continuation.
-                Prompt should be [B, C, T], or [C, T] if only one sample is generated.
-            prompt_sample_rate (int): Sampling rate of the given audio waveforms.
-            descriptions (tp.List[str], optional): A list of strings used as text conditioning. Defaults to None.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        if prompt.dim() == 2:
-            prompt = prompt[None]
-        if prompt.dim() != 3:
-            raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
-        prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
-        if descriptions is None:
-            descriptions = [None] * len(prompt)
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
-        assert prompt_tokens is not None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-    @torch.no_grad()
-    def _prepare_tokens_and_attributes(
-            self,
-            descriptions: tp.Sequence[tp.Optional[str]],
-            prompt: tp.Optional[torch.Tensor],
-            melody_wavs: tp.Optional[MelodyList] = None,
-    ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
-        """Prepare model inputs.
-        Args:
-            descriptions (tp.List[str]): A list of strings used as text conditioning.
-            prompt (torch.Tensor): A batch of waveforms used for continuation.
-            melody_wavs (tp.Optional[torch.Tensor], optional): A batch of waveforms
-                used as melody conditioning. Defaults to None.
-        """
-        attributes = [
-            ConditioningAttributes(text={'description': description})
-            for description in descriptions]
-        if melody_wavs is None:
-            for attr in attributes:
-                attr.wav['self_wav'] = WavCondition(
-                    torch.zeros((1, 1), device=self.device),
-                    torch.tensor([0], device=self.device),
-                    path='null_wav')  # type: ignore
-        else:
-            if self.name != "melody":
-                raise RuntimeError("This model doesn't support melody conditioning. "
-                                   "Use the `melody` model.")
-            assert len(melody_wavs) == len(descriptions), \
-                f"number of melody wavs must match number of descriptions! " \
-                f"got melody len={len(melody_wavs)}, and descriptions len={len(descriptions)}"
-            for attr, melody in zip(attributes, melody_wavs):
-                if melody is None:
-                    attr.wav['self_wav'] = WavCondition(
-                        torch.zeros((1, 1), device=self.device),
-                        torch.tensor([0], device=self.device),
-                        path='null_wav')  # type: ignore
-                else:
-                    attr.wav['self_wav'] = WavCondition(
-                        melody.to(device=self.device),
-                        torch.tensor([melody.shape[-1]], device=self.device))
-        if prompt is not None:
-            if descriptions is not None:
-                assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match"
-            prompt = prompt.to(self.device)
-            prompt_tokens, scale = self.compression_model.encode(prompt)
-            assert scale is None
-        else:
-            prompt_tokens = None
-        return attributes, prompt_tokens
-    def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
-                         prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
-        """Generate discrete audio tokens given audio prompt and/or conditions.
-        Args:
-            attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody).
-            prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        Returns:
-            torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
-        """
-        total_gen_len = int(self.duration * self.frame_rate)
-        max_prompt_len = int(min(self.duration, self.max_duration) * self.frame_rate)
-        current_gen_offset: int = 0
-        def _progress_callback(generated_tokens: int, tokens_to_generate: int):
-            generated_tokens += current_gen_offset
-            if self._progress_callback is not None:
-                # Note that total_gen_len might be quite wrong depending on the
-                # codebook pattern used, but with delay it is almost accurate.
-                self._progress_callback(generated_tokens, total_gen_len)
-            else:
-                print(f'{generated_tokens: 6d} / {total_gen_len: 6d}', end='\r')
-        if prompt_tokens is not None:
-            assert max_prompt_len >= prompt_tokens.shape[-1], \
-                "Prompt is longer than audio to generate"
-        callback = None
-        if progress:
-            callback = _progress_callback
-        if self.duration <= self.max_duration:
-            # generate by sampling from LM, simple case.
-            with self.autocast:
-                gen_tokens = self.lm.generate(
-                    prompt_tokens, attributes,
-                    callback=callback, max_gen_len=total_gen_len, **self.generation_params)
-        else:
-            # now this gets a bit messier, we need to handle prompts,
-            # melody conditioning etc.
-            ref_wavs = [attr.wav['self_wav'] for attr in attributes]
-            all_tokens = []
-            if prompt_tokens is None:
-                prompt_length = 0
-            else:
-                all_tokens.append(prompt_tokens)
-                prompt_length = prompt_tokens.shape[-1]
-            stride_tokens = int(self.frame_rate * self.extend_stride)
-            while current_gen_offset + prompt_length < total_gen_len:
-                time_offset = current_gen_offset / self.frame_rate
-                chunk_duration = min(self.duration - time_offset, self.max_duration)
-                max_gen_len = int(chunk_duration * self.frame_rate)
-                for attr, ref_wav in zip(attributes, ref_wavs):
-                    wav_length = ref_wav.length.item()
-                    if wav_length == 0:
-                        continue
-                    # We will extend the wav periodically if it not long enough.
-                    # we have to do it here rather than in conditioners.py as otherwise
-                    # we wouldn't have the full wav.
-                    initial_position = int(time_offset * self.sample_rate)
-                    wav_target_length = int(self.max_duration * self.sample_rate)
-                    print(initial_position / self.sample_rate, wav_target_length / self.sample_rate)
-                    positions = torch.arange(initial_position,
-                                             initial_position + wav_target_length, device=self.device)
-                    attr.wav['self_wav'] = WavCondition(
-                        ref_wav[0][:, positions % wav_length],
-                        torch.full_like(ref_wav[1], wav_target_length))
-                with self.autocast:
-                    gen_tokens = self.lm.generate(
-                        prompt_tokens, attributes,
-                        callback=callback, max_gen_len=max_gen_len, **self.generation_params)
-                if prompt_tokens is None:
-                    all_tokens.append(gen_tokens)
-                else:
-                    all_tokens.append(gen_tokens[:, :, prompt_tokens.shape[-1]:])
-                prompt_tokens = gen_tokens[:, :, stride_tokens:]
-                prompt_length = prompt_tokens.shape[-1]
-                current_gen_offset += stride_tokens
-            gen_tokens = torch.cat(all_tokens, dim=-1)
-        # generate audio
-        assert gen_tokens.dim() == 3
-        with torch.no_grad():
-            gen_audio = self.compression_model.decode(gen_tokens, None)
-        return gen_audio
-    #def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
-    #                     prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
-    #    """Generate discrete audio tokens given audio prompt and/or conditions.
-    #    Args:
-    #        attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody).
-    #        prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation.
-    #        progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-    #    Returns:
-    #        torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
-    #    """
-    #    def _progress_callback(generated_tokens: int, tokens_to_generate: int):
-    #        print(f'{generated_tokens: 6d} / {tokens_to_generate: 6d}', end='\r')
-    #    if prompt_tokens is not None:
-    #        assert self.generation_params['max_gen_len'] > prompt_tokens.shape[-1], \
-    #            "Prompt is longer than audio to generate"
-    #    callback = None
-    #    if progress:
-    #        callback = _progress_callback
-    #    # generate by sampling from LM
-    #    with self.autocast:
-    #        gen_tokens = self.lm.generate(prompt_tokens, attributes, callback=callback, **self.generation_params)
-    #    # generate audio
-    #    assert gen_tokens.dim() == 3
-    #    with torch.no_grad():
-    #        gen_audio = self.compression_model.decode(gen_tokens, None)
-    #    return gen_audio
-    def to(self, device: str):
-        self.compression_model.to(device)
-        self.lm.to(device)
-        return self

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Timestamps.ts DELETED Viewed

@@ -1,4 +0,0 @@
-export interface Timestamps {
-	createdAt: Date;
-	updatedAt: Date;
-}

spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/openpose/api.py DELETED Viewed

@@ -1,35 +0,0 @@
-import numpy as np
-import os
-import torch.nn as nn
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-import cv2
-import torch
-from . import util
-from .body import Body
-remote_model_path = "https://huggingface.co/TencentARC/T2I-Adapter/blob/main/third-party-models/body_pose_model.pth"
-class OpenposeInference(nn.Module):
-    def __init__(self):
-        super().__init__()
-        body_modelpath = os.path.join('models', "body_pose_model.pth")
-        if not os.path.exists(body_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(remote_model_path, model_dir='models')
-        self.body_estimation = Body(body_modelpath)
-    def forward(self, x):
-        x = x[:, :, ::-1].copy()
-        with torch.no_grad():
-            candidate, subset = self.body_estimation(x)
-            canvas = np.zeros_like(x)
-            canvas = util.draw_bodypose(canvas, candidate, subset)
-            canvas = cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR)
-        return canvas

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fade/Fade.js DELETED Viewed

@@ -1,5 +0,0 @@
-import Fade from '../../../plugins/fade.js';
-import FadeIn from '../../../plugins/fade-in.js';
-import FadeOutDestroy from '../../../plugins/fade-out-destroy.js';
-export { Fade, FadeIn, FadeOutDestroy };

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridbuttons/GridButtons.js DELETED Viewed

@@ -1,124 +0,0 @@
-import GridSizer from '../gridsizer/GridSizer.js';
-import AddChildMethods from './AddChildMethods.js';
-import RemoveChildMethods from './RemoveChildMethods.js';
-import ButtonGroup from '../utils/buttongroup/ButtonGroup.js';
-import ButtonMethods from '../utils/buttongroup/ButtonMethods.js';
-import ButtonStateMethods from '../utils/buttongroup/ButtonStateMethods.js';
-const GetValue = Phaser.Utils.Objects.GetValue;
-class GridButtons extends GridSizer {
-    constructor(scene, config) {
-        if (config === undefined) {
-            config = {};
-        }
-        var rowCount = GetValue(config, 'row', 0);
-        var columnCount = GetValue(config, 'column', (config.col || 0));
-        var createCellContainerCallback = GetValue(config, 'createCellContainerCallback');
-        var buttons = GetValue(config, 'buttons', undefined);
-        var buttonsExpand = GetValue(config, 'expand', true);
-        var buttonProportion = (buttonsExpand) ? 1 : 0;
-        if (createCellContainerCallback) {
-            config.createCellContainerCallback = undefined;
-        }
-        if (buttons !== undefined) {
-            rowCount = Math.max(rowCount, buttons.length);
-            for (var i = 0, cnt = buttons.length; i < cnt; i++) {
-                columnCount = Math.max(columnCount, buttons[i].length);
-            }
-        }
-        config.row = rowCount;
-        config.column = columnCount;
-        config.columnProportions = buttonProportion;
-        config.rowProportions = buttonProportion;
-        // Create
-        super(scene, config);
-        this.type = 'rexGridButtons';
-        this.buttonGroup = new ButtonGroup({
-            parent: this,
-            eventEmitter: GetValue(config, 'eventEmitter', this),
-            groupName: GetValue(config, 'groupName', undefined),
-            clickConfig: GetValue(config, 'click', undefined)
-        })
-            .setButtonsType(config);
-        // Add elements
-        var background = GetValue(config, 'background', undefined);
-        // Buttons properties
-        this.buttonsExpand = buttonsExpand;
-        var space = GetValue(config, 'space', undefined);
-        if (typeof (space) === 'number') {
-            space = { itemX: space, itemY: space };
-        }
-        if (background) {
-            this.addBackground(background);
-        }
-        if (buttons) {
-            var rowButtons, button;
-            for (var r = 0, rcnt = buttons.length; r < rcnt; r++) { // row
-                rowButtons = buttons[r];
-                for (var c = 0, ccnt = rowButtons.length; c < ccnt; c++) { // col
-                    button = rowButtons[c];
-                    if (button) {
-                        this.addButton(button, c, r);
-                    }
-                }
-            }
-        } else if (createCellContainerCallback) {
-            for (var y = 0; y < rowCount; y++) {
-                for (var x = 0; x < columnCount; x++) {
-                    var button = createCellContainerCallback(scene, x, y);
-                    if (button) {
-                        this.addButton(button, x, y);
-                    }
-                }
-            }
-        }
-        this.addChildrenMap('background', background);
-        this.addChildrenMap('buttons', this.buttonGroup.buttons);
-    }
-    destroy(fromScene) {
-        //  This Game Object has already been destroyed
-        if (!this.scene || this.ignoreDestroy) {
-            return;
-        }
-        super.destroy(fromScene);
-        this.buttonGroup.destroy();
-        this.buttonGroup = undefined;
-    }
-    get buttons() {
-        return this.buttonGroup.buttons;
-    }
-    get groupName() {
-        return this.buttonGroup.groupName;
-    }
-    set groupName(value) {
-        this.buttonGroup.groupName = value;
-    }
-    get eventEmitter() {
-        return this.buttonGroup.eventEmitter;
-    }
-}
-Object.assign(
-    GridButtons.prototype,
-    AddChildMethods,
-    RemoveChildMethods,
-    ButtonMethods,
-    ButtonStateMethods
-);
-export default GridButtons;

spaces/Aleistair/anything5/app.py DELETED Viewed

@@ -1,8 +0,0 @@
-import gradio as gr
-description = """<div>
-                <img src="https://i.imgur.com/FEA7N1p.png">
-              </div>
-             """
-gr.Interface.load("models/Linaqruf/anything-v3.0", description=description).launch()

spaces/AlexN/pull_up/app.py DELETED Viewed

@@ -1,45 +0,0 @@
-import torch
-import torchvision
-import TractionModel as plup
-import gradio as gr
-def init_model(path):
-    model = plup.create_model()
-    model = plup.load_weights(model, path)
-    model.eval()
-    return model
-def inference(image):
-    image = vanilla_transform(image).to(device).unsqueeze(0)
-    with torch.no_grad():
-        pred = model(image)
-    res = float(torch.sigmoid(pred[1].to("cpu")).numpy()[0])
-    return {'pull-up': res, 'no pull-up': 1 - res}
-norm_mean = [0.485, 0.456, 0.406]
-norm_std = [0.229, 0.224, 0.225]
-vanilla_transform = torchvision.transforms.Compose([
-                                                    torchvision.transforms.Resize(224),
-                                                    torchvision.transforms.ToTensor(),
-                                                    torchvision.transforms.Normalize(norm_mean, norm_std)])
-model = init_model("model-score0.96-f1_10.9-f1_20.99.pt")
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-else:
-    device = torch.device("cpu")
-model = model.to(device)
-examples = [['tibo.png'], ['tibo2.png'], ['real_pull_up.png'], ['no_pull_up.png'], ['doge.jpg']]
-iface = gr.Interface(inference, live=True, inputs=gr.inputs.Image(source="upload", type='pil'),
-                     outputs=gr.outputs.Label(),
-                     examples=examples,
-                     enable_queue=True)
-iface.test_launch()
-if __name__ == "__main__":
-    iface.launch(share=True, enable_queue=True)

spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/sampler.py DELETED Viewed

@@ -1,131 +0,0 @@
-import torch
-class Sampler(object):
-    """Base class for all Samplers.
-    Every Sampler subclass has to provide an __iter__ method, providing a way
-    to iterate over indices of dataset elements, and a __len__ method that
-    returns the length of the returned iterators.
-    """
-    def __init__(self, data_source):
-        pass
-    def __iter__(self):
-        raise NotImplementedError
-    def __len__(self):
-        raise NotImplementedError
-class SequentialSampler(Sampler):
-    """Samples elements sequentially, always in the same order.
-    Arguments:
-        data_source (Dataset): dataset to sample from
-    """
-    def __init__(self, data_source):
-        self.data_source = data_source
-    def __iter__(self):
-        return iter(range(len(self.data_source)))
-    def __len__(self):
-        return len(self.data_source)
-class RandomSampler(Sampler):
-    """Samples elements randomly, without replacement.
-    Arguments:
-        data_source (Dataset): dataset to sample from
-    """
-    def __init__(self, data_source):
-        self.data_source = data_source
-    def __iter__(self):
-        return iter(torch.randperm(len(self.data_source)).long())
-    def __len__(self):
-        return len(self.data_source)
-class SubsetRandomSampler(Sampler):
-    """Samples elements randomly from a given list of indices, without replacement.
-    Arguments:
-        indices (list): a list of indices
-    """
-    def __init__(self, indices):
-        self.indices = indices
-    def __iter__(self):
-        return (self.indices[i] for i in torch.randperm(len(self.indices)))
-    def __len__(self):
-        return len(self.indices)
-class WeightedRandomSampler(Sampler):
-    """Samples elements from [0,..,len(weights)-1] with given probabilities (weights).
-    Arguments:
-        weights (list)   : a list of weights, not necessary summing up to one
-        num_samples (int): number of samples to draw
-        replacement (bool): if ``True``, samples are drawn with replacement.
-            If not, they are drawn without replacement, which means that when a
-            sample index is drawn for a row, it cannot be drawn again for that row.
-    """
-    def __init__(self, weights, num_samples, replacement=True):
-        self.weights = torch.DoubleTensor(weights)
-        self.num_samples = num_samples
-        self.replacement = replacement
-    def __iter__(self):
-        return iter(torch.multinomial(self.weights, self.num_samples, self.replacement))
-    def __len__(self):
-        return self.num_samples
-class BatchSampler(object):
-    """Wraps another sampler to yield a mini-batch of indices.
-    Args:
-        sampler (Sampler): Base sampler.
-        batch_size (int): Size of mini-batch.
-        drop_last (bool): If ``True``, the sampler will drop the last batch if
-            its size would be less than ``batch_size``
-    Example:
-        >>> list(BatchSampler(range(10), batch_size=3, drop_last=False))
-        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
-        >>> list(BatchSampler(range(10), batch_size=3, drop_last=True))
-        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
-    """
-    def __init__(self, sampler, batch_size, drop_last):
-        self.sampler = sampler
-        self.batch_size = batch_size
-        self.drop_last = drop_last
-    def __iter__(self):
-        batch = []
-        for idx in self.sampler:
-            batch.append(idx)
-            if len(batch) == self.batch_size:
-                yield batch
-                batch = []
-        if len(batch) > 0 and not self.drop_last:
-            yield batch
-    def __len__(self):
-        if self.drop_last:
-            return len(self.sampler) // self.batch_size
-        else:
-            return (len(self.sampler) + self.batch_size - 1) // self.batch_size

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_unclip.md DELETED Viewed

@@ -1,125 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-# Stable unCLIP
-Stable unCLIP checkpoints are finetuned from [Stable Diffusion 2.1](./stable_diffusion/stable_diffusion_2) checkpoints to condition on CLIP image embeddings.
-Stable unCLIP still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used
-for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation.
-The abstract from the paper is:
-*Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.*
-## Tips
-Stable unCLIP takes  `noise_level` as input during inference which determines how much noise is added
-to the image embeddings. A higher `noise_level` increases variation in the final un-noised images. By default,
-we do not add any additional noise to the image embeddings (`noise_level = 0`).
-### Text-to-Image Generation
-Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha)
-```python
-import torch
-from diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline
-from diffusers.models import PriorTransformer
-from transformers import CLIPTokenizer, CLIPTextModelWithProjection
-prior_model_id = "kakaobrain/karlo-v1-alpha"
-data_type = torch.float16
-prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type)
-prior_text_model_id = "openai/clip-vit-large-patch14"
-prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
-prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
-prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler")
-prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
-stable_unclip_model_id = "stabilityai/stable-diffusion-2-1-unclip-small"
-pipe = StableUnCLIPPipeline.from_pretrained(
-    stable_unclip_model_id,
-    torch_dtype=data_type,
-    variant="fp16",
-    prior_tokenizer=prior_tokenizer,
-    prior_text_encoder=prior_text_model,
-    prior=prior,
-    prior_scheduler=prior_scheduler,
-)
-pipe = pipe.to("cuda")
-wave_prompt = "dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular"
-images = pipe(prompt=wave_prompt).images
-images[0].save("waves.png")
-```
-<Tip warning={true}>
-For text-to-image we use `stabilityai/stable-diffusion-2-1-unclip-small` as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip) was trained on OpenCLIP ViT-H, so we don't recommend its use.
-</Tip>
-### Text guided Image-to-Image Variation
-```python
-from diffusers import StableUnCLIPImg2ImgPipeline
-from diffusers.utils import load_image
-import torch
-pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
-)
-pipe = pipe.to("cuda")
-url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
-init_image = load_image(url)
-images = pipe(init_image).images
-images[0].save("variation_image.png")
-```
-Optionally, you can also pass a prompt to `pipe` such as:
-```python
-prompt = "A fantasy landscape, trending on artstation"
-images = pipe(init_image, prompt=prompt).images
-images[0].save("variation_image_two.png")
-```
-## StableUnCLIPPipeline
-[[autodoc]] StableUnCLIPPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-## StableUnCLIPImg2ImgPipeline
-[[autodoc]] StableUnCLIPImg2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/unidiffuser/__init__.py DELETED Viewed

File without changes

spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py DELETED Viewed

	@@ -1,2 +0,0 @@
1	- _base_ = './nonlocal_r50-d8_512x512_80k_ade20k.py'
2	- model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py DELETED Viewed

	@@ -1,2 +0,0 @@
1	- _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
2	- model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))

spaces/AnnaPalatkina/fine_grained_SA/README.md DELETED Viewed

@@ -1,24 +0,0 @@
----
-title: Norec Norbert2 TEST
-emoji: 🏃
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
----
-<br>
-<br>
-This space provides a gradio demo and an easy-to-run wrapper of the pre-trained model for fine-grained sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://github.com/ltgoslo/norec).
-Information about project you an fine on the website of [University of Oslo](https://www.mn.uio.no/ifi/english/research/projects/sant/)
-The model can be easily used for predicting sentiment as follows:
-```python
->>> from sentiment_wrapper import PredictionModel
->>> model = PredictionModel()
->>> model.predict(['vi liker svart kaffe', 'jeg elsker virkelig røde roser!'])
-[5,5]
-```

spaces/Aristore/Warp/app.py DELETED Viewed

@@ -1,91 +0,0 @@
-import urllib.request
-from json import dumps
-from datetime import datetime
-from random import choice
-from string import ascii_letters,digits
-from time import sleep
-import gradio as gr
-demo = gr.Blocks()
-def genString(stringLength):
-	try:
-		letters = ascii_letters + digits
-		return ''.join(choice(letters) for i in range(stringLength))
-	except Exception as error:
-		print(error)
-def digitString(stringLength):
-	try:
-		digit = digits
-		return ''.join((choice(digit) for i in range(stringLength)))
-	except Exception as error:
-		print(error)
-url = f'https://api.cloudflareclient.com/v0a{digitString(3)}/reg'
-def task(ID):
-	try:
-		install_id = genString(22)
-		body = {"key": "{}=".format(genString(43)),
-				"install_id": install_id,
-				"fcm_token": "{}:APA91b{}".format(install_id, genString(134)),
-				"referrer": ID,
-				"warp_enabled": False,
-				"tos": datetime.now().isoformat()[:-3] + "+02:00",
-				"type": "Android",
-				"locale": "es_ES"}
-		data = dumps(body).encode('utf8')
-		headers = {'Content-Type': 'application/json; charset=UTF-8',
-					'Host': 'api.cloudflareclient.com',
-					'Connection': 'Keep-Alive',
-					'Accept-Encoding': 'gzip',
-					'User-Agent': 'okhttp/3.12.1'
-					}
-		req         = urllib.request.Request(url, data, headers)
-		response    = urllib.request.urlopen(req)
-		status_code = response.getcode()
-		return status_code
-	except Exception as error:
-		print(error)
-def run(ID,times):
-	g = 0
-	b = 0
-	for i in range (0,int(times)):
-		result = task(ID)
-		if result == 200:
-			g = g + 1
-		else:
-			b = b + 1
-		sleep(2)
-	return (f"您的ID: {ID}\n{g} GB 已成功添加到您的账号.\n成功{g}个 | 失败{b}个")
-with demo:
-	gr.Markdown("""
-# Warp+ 流量获取工具
-```
-    ___         _      __                 \n   / _ |  ____ (_)___ / /_ ___   ____ ___ \n  / __ | / __// /(_-</ __// _ \ / __// -_)\n /_/ |_|/_/  /_//___/\__/ \___//_/   \__/ \n
-本程序由 Aristore 制作,转载请说明出处,有问题欢迎私信我
-bilibil:https://space.bilibili.com/283733002
-github:https://github.com/aristorechina/
-注意:流量可多次获取,但每次获取输入的数字不能过大,每获取 1 GB 会间隔 2 秒
-```
-""")
-	with gr.Tabs():
-		with gr.TabItem("获取流量"):
-			with gr.Row():
-				with gr.Column():
-					ID = gr.Textbox(label="ID")
-					flow = gr.Number(minimum=1,value=1,label="需要获取的流量数(单位:GB)")
-				with gr.Column():
-					output = gr.Textbox(label="输出")
-					get_flow = gr.Button("获取流量")
-	get_flow.click(run, inputs=[ID,flow], outputs=output)
-demo.launch()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/progress.py DELETED Viewed

@@ -1,1702 +0,0 @@
-import io
-import sys
-import typing
-import warnings
-from abc import ABC, abstractmethod
-from collections import deque
-from dataclasses import dataclass, field
-from datetime import timedelta
-from io import RawIOBase, UnsupportedOperation
-from math import ceil
-from mmap import mmap
-from operator import length_hint
-from os import PathLike, stat
-from threading import Event, RLock, Thread
-from types import TracebackType
-from typing import (
-    Any,
-    BinaryIO,
-    Callable,
-    ContextManager,
-    Deque,
-    Dict,
-    Generic,
-    Iterable,
-    List,
-    NamedTuple,
-    NewType,
-    Optional,
-    Sequence,
-    TextIO,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-)
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from pip._vendor.typing_extensions import Literal  # pragma: no cover
-from . import filesize, get_console
-from .console import Console, Group, JustifyMethod, RenderableType
-from .highlighter import Highlighter
-from .jupyter import JupyterMixin
-from .live import Live
-from .progress_bar import ProgressBar
-from .spinner import Spinner
-from .style import StyleType
-from .table import Column, Table
-from .text import Text, TextType
-TaskID = NewType("TaskID", int)
-ProgressType = TypeVar("ProgressType")
-GetTimeCallable = Callable[[], float]
-_I = typing.TypeVar("_I", TextIO, BinaryIO)
-class _TrackThread(Thread):
-    """A thread to periodically update progress."""
-    def __init__(self, progress: "Progress", task_id: "TaskID", update_period: float):
-        self.progress = progress
-        self.task_id = task_id
-        self.update_period = update_period
-        self.done = Event()
-        self.completed = 0
-        super().__init__()
-    def run(self) -> None:
-        task_id = self.task_id
-        advance = self.progress.advance
-        update_period = self.update_period
-        last_completed = 0
-        wait = self.done.wait
-        while not wait(update_period):
-            completed = self.completed
-            if last_completed != completed:
-                advance(task_id, completed - last_completed)
-                last_completed = completed
-        self.progress.update(self.task_id, completed=self.completed, refresh=True)
-    def __enter__(self) -> "_TrackThread":
-        self.start()
-        return self
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.done.set()
-        self.join()
-def track(
-    sequence: Union[Sequence[ProgressType], Iterable[ProgressType]],
-    description: str = "Working...",
-    total: Optional[float] = None,
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    update_period: float = 0.1,
-    disable: bool = False,
-    show_speed: bool = True,
-) -> Iterable[ProgressType]:
-    """Track progress by iterating over a sequence.
-    Args:
-        sequence (Iterable[ProgressType]): A sequence (must support "len") you wish to iterate over.
-        description (str, optional): Description of task show next to progress bar. Defaults to "Working".
-        total: (float, optional): Total number of steps. Default is len(sequence).
-        auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        console (Console, optional): Console to write to. Default creates internal Console instance.
-        refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-        update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
-        disable (bool, optional): Disable display of progress.
-        show_speed (bool, optional): Show speed if total isn't known. Defaults to True.
-    Returns:
-        Iterable[ProgressType]: An iterable of the values in the sequence.
-    """
-    columns: List["ProgressColumn"] = (
-        [TextColumn("[progress.description]{task.description}")] if description else []
-    )
-    columns.extend(
-        (
-            BarColumn(
-                style=style,
-                complete_style=complete_style,
-                finished_style=finished_style,
-                pulse_style=pulse_style,
-            ),
-            TaskProgressColumn(show_speed=show_speed),
-            TimeRemainingColumn(elapsed_when_finished=True),
-        )
-    )
-    progress = Progress(
-        *columns,
-        auto_refresh=auto_refresh,
-        console=console,
-        transient=transient,
-        get_time=get_time,
-        refresh_per_second=refresh_per_second or 10,
-        disable=disable,
-    )
-    with progress:
-        yield from progress.track(
-            sequence, total=total, description=description, update_period=update_period
-        )
-class _Reader(RawIOBase, BinaryIO):
-    """A reader that tracks progress while it's being read from."""
-    def __init__(
-        self,
-        handle: BinaryIO,
-        progress: "Progress",
-        task: TaskID,
-        close_handle: bool = True,
-    ) -> None:
-        self.handle = handle
-        self.progress = progress
-        self.task = task
-        self.close_handle = close_handle
-        self._closed = False
-    def __enter__(self) -> "_Reader":
-        self.handle.__enter__()
-        return self
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.close()
-    def __iter__(self) -> BinaryIO:
-        return self
-    def __next__(self) -> bytes:
-        line = next(self.handle)
-        self.progress.advance(self.task, advance=len(line))
-        return line
-    @property
-    def closed(self) -> bool:
-        return self._closed
-    def fileno(self) -> int:
-        return self.handle.fileno()
-    def isatty(self) -> bool:
-        return self.handle.isatty()
-    @property
-    def mode(self) -> str:
-        return self.handle.mode
-    @property
-    def name(self) -> str:
-        return self.handle.name
-    def readable(self) -> bool:
-        return self.handle.readable()
-    def seekable(self) -> bool:
-        return self.handle.seekable()
-    def writable(self) -> bool:
-        return False
-    def read(self, size: int = -1) -> bytes:
-        block = self.handle.read(size)
-        self.progress.advance(self.task, advance=len(block))
-        return block
-    def readinto(self, b: Union[bytearray, memoryview, mmap]):  # type: ignore[no-untyped-def, override]
-        n = self.handle.readinto(b)  # type: ignore[attr-defined]
-        self.progress.advance(self.task, advance=n)
-        return n
-    def readline(self, size: int = -1) -> bytes:  # type: ignore[override]
-        line = self.handle.readline(size)
-        self.progress.advance(self.task, advance=len(line))
-        return line
-    def readlines(self, hint: int = -1) -> List[bytes]:
-        lines = self.handle.readlines(hint)
-        self.progress.advance(self.task, advance=sum(map(len, lines)))
-        return lines
-    def close(self) -> None:
-        if self.close_handle:
-            self.handle.close()
-        self._closed = True
-    def seek(self, offset: int, whence: int = 0) -> int:
-        pos = self.handle.seek(offset, whence)
-        self.progress.update(self.task, completed=pos)
-        return pos
-    def tell(self) -> int:
-        return self.handle.tell()
-    def write(self, s: Any) -> int:
-        raise UnsupportedOperation("write")
-class _ReadContext(ContextManager[_I], Generic[_I]):
-    """A utility class to handle a context for both a reader and a progress."""
-    def __init__(self, progress: "Progress", reader: _I) -> None:
-        self.progress = progress
-        self.reader: _I = reader
-    def __enter__(self) -> _I:
-        self.progress.start()
-        return self.reader.__enter__()
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.progress.stop()
-        self.reader.__exit__(exc_type, exc_val, exc_tb)
-def wrap_file(
-    file: BinaryIO,
-    total: int,
-    *,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> ContextManager[BinaryIO]:
-    """Read bytes from a file while tracking progress.
-    Args:
-        file (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode.
-        total (int): Total number of bytes to read.
-        description (str, optional): Description of task show next to progress bar. Defaults to "Reading".
-        auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        console (Console, optional): Console to write to. Default creates internal Console instance.
-        refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-        disable (bool, optional): Disable display of progress.
-    Returns:
-        ContextManager[BinaryIO]: A context manager yielding a progress reader.
-    """
-    columns: List["ProgressColumn"] = (
-        [TextColumn("[progress.description]{task.description}")] if description else []
-    )
-    columns.extend(
-        (
-            BarColumn(
-                style=style,
-                complete_style=complete_style,
-                finished_style=finished_style,
-                pulse_style=pulse_style,
-            ),
-            DownloadColumn(),
-            TimeRemainingColumn(),
-        )
-    )
-    progress = Progress(
-        *columns,
-        auto_refresh=auto_refresh,
-        console=console,
-        transient=transient,
-        get_time=get_time,
-        refresh_per_second=refresh_per_second or 10,
-        disable=disable,
-    )
-    reader = progress.wrap_file(file, total=total, description=description)
-    return _ReadContext(progress, reader)
-@typing.overload
-def open(
-    file: Union[str, "PathLike[str]", bytes],
-    mode: Union[Literal["rt"], Literal["r"]],
-    buffering: int = -1,
-    encoding: Optional[str] = None,
-    errors: Optional[str] = None,
-    newline: Optional[str] = None,
-    *,
-    total: Optional[int] = None,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> ContextManager[TextIO]:
-    pass
-@typing.overload
-def open(
-    file: Union[str, "PathLike[str]", bytes],
-    mode: Literal["rb"],
-    buffering: int = -1,
-    encoding: Optional[str] = None,
-    errors: Optional[str] = None,
-    newline: Optional[str] = None,
-    *,
-    total: Optional[int] = None,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> ContextManager[BinaryIO]:
-    pass
-def open(
-    file: Union[str, "PathLike[str]", bytes],
-    mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r",
-    buffering: int = -1,
-    encoding: Optional[str] = None,
-    errors: Optional[str] = None,
-    newline: Optional[str] = None,
-    *,
-    total: Optional[int] = None,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> Union[ContextManager[BinaryIO], ContextManager[TextIO]]:
-    """Read bytes from a file while tracking progress.
-    Args:
-        path (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode.
-        mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt".
-        buffering (int): The buffering strategy to use, see :func:`io.open`.
-        encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`.
-        errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`.
-        newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open`
-        total: (int, optional): Total number of bytes to read. Must be provided if reading from a file handle. Default for a path is os.stat(file).st_size.
-        description (str, optional): Description of task show next to progress bar. Defaults to "Reading".
-        auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        console (Console, optional): Console to write to. Default creates internal Console instance.
-        refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-        disable (bool, optional): Disable display of progress.
-        encoding (str, optional): The encoding to use when reading in text mode.
-    Returns:
-        ContextManager[BinaryIO]: A context manager yielding a progress reader.
-    """
-    columns: List["ProgressColumn"] = (
-        [TextColumn("[progress.description]{task.description}")] if description else []
-    )
-    columns.extend(
-        (
-            BarColumn(
-                style=style,
-                complete_style=complete_style,
-                finished_style=finished_style,
-                pulse_style=pulse_style,
-            ),
-            DownloadColumn(),
-            TimeRemainingColumn(),
-        )
-    )
-    progress = Progress(
-        *columns,
-        auto_refresh=auto_refresh,
-        console=console,
-        transient=transient,
-        get_time=get_time,
-        refresh_per_second=refresh_per_second or 10,
-        disable=disable,
-    )
-    reader = progress.open(
-        file,
-        mode=mode,
-        buffering=buffering,
-        encoding=encoding,
-        errors=errors,
-        newline=newline,
-        total=total,
-        description=description,
-    )
-    return _ReadContext(progress, reader)  # type: ignore[return-value, type-var]
-class ProgressColumn(ABC):
-    """Base class for a widget to use in progress display."""
-    max_refresh: Optional[float] = None
-    def __init__(self, table_column: Optional[Column] = None) -> None:
-        self._table_column = table_column
-        self._renderable_cache: Dict[TaskID, Tuple[float, RenderableType]] = {}
-        self._update_time: Optional[float] = None
-    def get_table_column(self) -> Column:
-        """Get a table column, used to build tasks table."""
-        return self._table_column or Column()
-    def __call__(self, task: "Task") -> RenderableType:
-        """Called by the Progress object to return a renderable for the given task.
-        Args:
-            task (Task): An object containing information regarding the task.
-        Returns:
-            RenderableType: Anything renderable (including str).
-        """
-        current_time = task.get_time()
-        if self.max_refresh is not None and not task.completed:
-            try:
-                timestamp, renderable = self._renderable_cache[task.id]
-            except KeyError:
-                pass
-            else:
-                if timestamp + self.max_refresh > current_time:
-                    return renderable
-        renderable = self.render(task)
-        self._renderable_cache[task.id] = (current_time, renderable)
-        return renderable
-    @abstractmethod
-    def render(self, task: "Task") -> RenderableType:
-        """Should return a renderable object."""
-class RenderableColumn(ProgressColumn):
-    """A column to insert an arbitrary column.
-    Args:
-        renderable (RenderableType, optional): Any renderable. Defaults to empty string.
-    """
-    def __init__(
-        self, renderable: RenderableType = "", *, table_column: Optional[Column] = None
-    ):
-        self.renderable = renderable
-        super().__init__(table_column=table_column)
-    def render(self, task: "Task") -> RenderableType:
-        return self.renderable
-class SpinnerColumn(ProgressColumn):
-    """A column with a 'spinner' animation.
-    Args:
-        spinner_name (str, optional): Name of spinner animation. Defaults to "dots".
-        style (StyleType, optional): Style of spinner. Defaults to "progress.spinner".
-        speed (float, optional): Speed factor of spinner. Defaults to 1.0.
-        finished_text (TextType, optional): Text used when task is finished. Defaults to " ".
-    """
-    def __init__(
-        self,
-        spinner_name: str = "dots",
-        style: Optional[StyleType] = "progress.spinner",
-        speed: float = 1.0,
-        finished_text: TextType = " ",
-        table_column: Optional[Column] = None,
-    ):
-        self.spinner = Spinner(spinner_name, style=style, speed=speed)
-        self.finished_text = (
-            Text.from_markup(finished_text)
-            if isinstance(finished_text, str)
-            else finished_text
-        )
-        super().__init__(table_column=table_column)
-    def set_spinner(
-        self,
-        spinner_name: str,
-        spinner_style: Optional[StyleType] = "progress.spinner",
-        speed: float = 1.0,
-    ) -> None:
-        """Set a new spinner.
-        Args:
-            spinner_name (str): Spinner name, see python -m rich.spinner.
-            spinner_style (Optional[StyleType], optional): Spinner style. Defaults to "progress.spinner".
-            speed (float, optional): Speed factor of spinner. Defaults to 1.0.
-        """
-        self.spinner = Spinner(spinner_name, style=spinner_style, speed=speed)
-    def render(self, task: "Task") -> RenderableType:
-        text = (
-            self.finished_text
-            if task.finished
-            else self.spinner.render(task.get_time())
-        )
-        return text
-class TextColumn(ProgressColumn):
-    """A column containing text."""
-    def __init__(
-        self,
-        text_format: str,
-        style: StyleType = "none",
-        justify: JustifyMethod = "left",
-        markup: bool = True,
-        highlighter: Optional[Highlighter] = None,
-        table_column: Optional[Column] = None,
-    ) -> None:
-        self.text_format = text_format
-        self.justify: JustifyMethod = justify
-        self.style = style
-        self.markup = markup
-        self.highlighter = highlighter
-        super().__init__(table_column=table_column or Column(no_wrap=True))
-    def render(self, task: "Task") -> Text:
-        _text = self.text_format.format(task=task)
-        if self.markup:
-            text = Text.from_markup(_text, style=self.style, justify=self.justify)
-        else:
-            text = Text(_text, style=self.style, justify=self.justify)
-        if self.highlighter:
-            self.highlighter.highlight(text)
-        return text
-class BarColumn(ProgressColumn):
-    """Renders a visual progress bar.
-    Args:
-        bar_width (Optional[int], optional): Width of bar or None for full width. Defaults to 40.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-    """
-    def __init__(
-        self,
-        bar_width: Optional[int] = 40,
-        style: StyleType = "bar.back",
-        complete_style: StyleType = "bar.complete",
-        finished_style: StyleType = "bar.finished",
-        pulse_style: StyleType = "bar.pulse",
-        table_column: Optional[Column] = None,
-    ) -> None:
-        self.bar_width = bar_width
-        self.style = style
-        self.complete_style = complete_style
-        self.finished_style = finished_style
-        self.pulse_style = pulse_style
-        super().__init__(table_column=table_column)
-    def render(self, task: "Task") -> ProgressBar:
-        """Gets a progress bar widget for a task."""
-        return ProgressBar(
-            total=max(0, task.total) if task.total is not None else None,
-            completed=max(0, task.completed),
-            width=None if self.bar_width is None else max(1, self.bar_width),
-            pulse=not task.started,
-            animation_time=task.get_time(),
-            style=self.style,
-            complete_style=self.complete_style,
-            finished_style=self.finished_style,
-            pulse_style=self.pulse_style,
-        )
-class TimeElapsedColumn(ProgressColumn):
-    """Renders time elapsed."""
-    def render(self, task: "Task") -> Text:
-        """Show time elapsed."""
-        elapsed = task.finished_time if task.finished else task.elapsed
-        if elapsed is None:
-            return Text("-:--:--", style="progress.elapsed")
-        delta = timedelta(seconds=int(elapsed))
-        return Text(str(delta), style="progress.elapsed")
-class TaskProgressColumn(TextColumn):
-    """Show task progress as a percentage.
-    Args:
-        text_format (str, optional): Format for percentage display. Defaults to "[progress.percentage]{task.percentage:>3.0f}%".
-        text_format_no_percentage (str, optional): Format if percentage is unknown. Defaults to "".
-        style (StyleType, optional): Style of output. Defaults to "none".
-        justify (JustifyMethod, optional): Text justification. Defaults to "left".
-        markup (bool, optional): Enable markup. Defaults to True.
-        highlighter (Optional[Highlighter], optional): Highlighter to apply to output. Defaults to None.
-        table_column (Optional[Column], optional): Table Column to use. Defaults to None.
-        show_speed (bool, optional): Show speed if total is unknown. Defaults to False.
-    """
-    def __init__(
-        self,
-        text_format: str = "[progress.percentage]{task.percentage:>3.0f}%",
-        text_format_no_percentage: str = "",
-        style: StyleType = "none",
-        justify: JustifyMethod = "left",
-        markup: bool = True,
-        highlighter: Optional[Highlighter] = None,
-        table_column: Optional[Column] = None,
-        show_speed: bool = False,
-    ) -> None:
-        self.text_format_no_percentage = text_format_no_percentage
-        self.show_speed = show_speed
-        super().__init__(
-            text_format=text_format,
-            style=style,
-            justify=justify,
-            markup=markup,
-            highlighter=highlighter,
-            table_column=table_column,
-        )
-    @classmethod
-    def render_speed(cls, speed: Optional[float]) -> Text:
-        """Render the speed in iterations per second.
-        Args:
-            task (Task): A Task object.
-        Returns:
-            Text: Text object containing the task speed.
-        """
-        if speed is None:
-            return Text("", style="progress.percentage")
-        unit, suffix = filesize.pick_unit_and_suffix(
-            int(speed),
-            ["", "×10³", "×10⁶", "×10⁹", "×10¹²"],
-            1000,
-        )
-        data_speed = speed / unit
-        return Text(f"{data_speed:.1f}{suffix} it/s", style="progress.percentage")
-    def render(self, task: "Task") -> Text:
-        if task.total is None and self.show_speed:
-            return self.render_speed(task.finished_speed or task.speed)
-        text_format = (
-            self.text_format_no_percentage if task.total is None else self.text_format
-        )
-        _text = text_format.format(task=task)
-        if self.markup:
-            text = Text.from_markup(_text, style=self.style, justify=self.justify)
-        else:
-            text = Text(_text, style=self.style, justify=self.justify)
-        if self.highlighter:
-            self.highlighter.highlight(text)
-        return text
-class TimeRemainingColumn(ProgressColumn):
-    """Renders estimated time remaining.
-    Args:
-        compact (bool, optional): Render MM:SS when time remaining is less than an hour. Defaults to False.
-        elapsed_when_finished (bool, optional): Render time elapsed when the task is finished. Defaults to False.
-    """
-    # Only refresh twice a second to prevent jitter
-    max_refresh = 0.5
-    def __init__(
-        self,
-        compact: bool = False,
-        elapsed_when_finished: bool = False,
-        table_column: Optional[Column] = None,
-    ):
-        self.compact = compact
-        self.elapsed_when_finished = elapsed_when_finished
-        super().__init__(table_column=table_column)
-    def render(self, task: "Task") -> Text:
-        """Show time remaining."""
-        if self.elapsed_when_finished and task.finished:
-            task_time = task.finished_time
-            style = "progress.elapsed"
-        else:
-            task_time = task.time_remaining
-            style = "progress.remaining"
-        if task.total is None:
-            return Text("", style=style)
-        if task_time is None:
-            return Text("--:--" if self.compact else "-:--:--", style=style)
-        # Based on https://github.com/tqdm/tqdm/blob/master/tqdm/std.py
-        minutes, seconds = divmod(int(task_time), 60)
-        hours, minutes = divmod(minutes, 60)
-        if self.compact and not hours:
-            formatted = f"{minutes:02d}:{seconds:02d}"
-        else:
-            formatted = f"{hours:d}:{minutes:02d}:{seconds:02d}"
-        return Text(formatted, style=style)
-class FileSizeColumn(ProgressColumn):
-    """Renders completed filesize."""
-    def render(self, task: "Task") -> Text:
-        """Show data completed."""
-        data_size = filesize.decimal(int(task.completed))
-        return Text(data_size, style="progress.filesize")
-class TotalFileSizeColumn(ProgressColumn):
-    """Renders total filesize."""
-    def render(self, task: "Task") -> Text:
-        """Show data completed."""
-        data_size = filesize.decimal(int(task.total)) if task.total is not None else ""
-        return Text(data_size, style="progress.filesize.total")
-class MofNCompleteColumn(ProgressColumn):
-    """Renders completed count/total, e.g. '  10/1000'.
-    Best for bounded tasks with int quantities.
-    Space pads the completed count so that progress length does not change as task progresses
-    past powers of 10.
-    Args:
-        separator (str, optional): Text to separate completed and total values. Defaults to "/".
-    """
-    def __init__(self, separator: str = "/", table_column: Optional[Column] = None):
-        self.separator = separator
-        super().__init__(table_column=table_column)
-    def render(self, task: "Task") -> Text:
-        """Show completed/total."""
-        completed = int(task.completed)
-        total = int(task.total) if task.total is not None else "?"
-        total_width = len(str(total))
-        return Text(
-            f"{completed:{total_width}d}{self.separator}{total}",
-            style="progress.download",
-        )
-class DownloadColumn(ProgressColumn):
-    """Renders file size downloaded and total, e.g. '0.5/2.3 GB'.
-    Args:
-        binary_units (bool, optional): Use binary units, KiB, MiB etc. Defaults to False.
-    """
-    def __init__(
-        self, binary_units: bool = False, table_column: Optional[Column] = None
-    ) -> None:
-        self.binary_units = binary_units
-        super().__init__(table_column=table_column)
-    def render(self, task: "Task") -> Text:
-        """Calculate common unit for completed and total."""
-        completed = int(task.completed)
-        unit_and_suffix_calculation_base = (
-            int(task.total) if task.total is not None else completed
-        )
-        if self.binary_units:
-            unit, suffix = filesize.pick_unit_and_suffix(
-                unit_and_suffix_calculation_base,
-                ["bytes", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", "YiB"],
-                1024,
-            )
-        else:
-            unit, suffix = filesize.pick_unit_and_suffix(
-                unit_and_suffix_calculation_base,
-                ["bytes", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"],
-                1000,
-            )
-        precision = 0 if unit == 1 else 1
-        completed_ratio = completed / unit
-        completed_str = f"{completed_ratio:,.{precision}f}"
-        if task.total is not None:
-            total = int(task.total)
-            total_ratio = total / unit
-            total_str = f"{total_ratio:,.{precision}f}"
-        else:
-            total_str = "?"
-        download_status = f"{completed_str}/{total_str} {suffix}"
-        download_text = Text(download_status, style="progress.download")
-        return download_text
-class TransferSpeedColumn(ProgressColumn):
-    """Renders human readable transfer speed."""
-    def render(self, task: "Task") -> Text:
-        """Show data transfer speed."""
-        speed = task.finished_speed or task.speed
-        if speed is None:
-            return Text("?", style="progress.data.speed")
-        data_speed = filesize.decimal(int(speed))
-        return Text(f"{data_speed}/s", style="progress.data.speed")
-class ProgressSample(NamedTuple):
-    """Sample of progress for a given time."""
-    timestamp: float
-    """Timestamp of sample."""
-    completed: float
-    """Number of steps completed."""
-@dataclass
-class Task:
-    """Information regarding a progress task.
-    This object should be considered read-only outside of the :class:`~Progress` class.
-    """
-    id: TaskID
-    """Task ID associated with this task (used in Progress methods)."""
-    description: str
-    """str: Description of the task."""
-    total: Optional[float]
-    """Optional[float]: Total number of steps in this task."""
-    completed: float
-    """float: Number of steps completed"""
-    _get_time: GetTimeCallable
-    """Callable to get the current time."""
-    finished_time: Optional[float] = None
-    """float: Time task was finished."""
-    visible: bool = True
-    """bool: Indicates if this task is visible in the progress display."""
-    fields: Dict[str, Any] = field(default_factory=dict)
-    """dict: Arbitrary fields passed in via Progress.update."""
-    start_time: Optional[float] = field(default=None, init=False, repr=False)
-    """Optional[float]: Time this task was started, or None if not started."""
-    stop_time: Optional[float] = field(default=None, init=False, repr=False)
-    """Optional[float]: Time this task was stopped, or None if not stopped."""
-    finished_speed: Optional[float] = None
-    """Optional[float]: The last speed for a finished task."""
-    _progress: Deque[ProgressSample] = field(
-        default_factory=lambda: deque(maxlen=1000), init=False, repr=False
-    )
-    _lock: RLock = field(repr=False, default_factory=RLock)
-    """Thread lock."""
-    def get_time(self) -> float:
-        """float: Get the current time, in seconds."""
-        return self._get_time()
-    @property
-    def started(self) -> bool:
-        """bool: Check if the task as started."""
-        return self.start_time is not None
-    @property
-    def remaining(self) -> Optional[float]:
-        """Optional[float]: Get the number of steps remaining, if a non-None total was set."""
-        if self.total is None:
-            return None
-        return self.total - self.completed
-    @property
-    def elapsed(self) -> Optional[float]:
-        """Optional[float]: Time elapsed since task was started, or ``None`` if the task hasn't started."""
-        if self.start_time is None:
-            return None
-        if self.stop_time is not None:
-            return self.stop_time - self.start_time
-        return self.get_time() - self.start_time
-    @property
-    def finished(self) -> bool:
-        """Check if the task has finished."""
-        return self.finished_time is not None
-    @property
-    def percentage(self) -> float:
-        """float: Get progress of task as a percentage. If a None total was set, returns 0"""
-        if not self.total:
-            return 0.0
-        completed = (self.completed / self.total) * 100.0
-        completed = min(100.0, max(0.0, completed))
-        return completed
-    @property
-    def speed(self) -> Optional[float]:
-        """Optional[float]: Get the estimated speed in steps per second."""
-        if self.start_time is None:
-            return None
-        with self._lock:
-            progress = self._progress
-            if not progress:
-                return None
-            total_time = progress[-1].timestamp - progress[0].timestamp
-            if total_time == 0:
-                return None
-            iter_progress = iter(progress)
-            next(iter_progress)
-            total_completed = sum(sample.completed for sample in iter_progress)
-            speed = total_completed / total_time
-            return speed
-    @property
-    def time_remaining(self) -> Optional[float]:
-        """Optional[float]: Get estimated time to completion, or ``None`` if no data."""
-        if self.finished:
-            return 0.0
-        speed = self.speed
-        if not speed:
-            return None
-        remaining = self.remaining
-        if remaining is None:
-            return None
-        estimate = ceil(remaining / speed)
-        return estimate
-    def _reset(self) -> None:
-        """Reset progress."""
-        self._progress.clear()
-        self.finished_time = None
-        self.finished_speed = None
-class Progress(JupyterMixin):
-    """Renders an auto-updating progress bar(s).
-    Args:
-        console (Console, optional): Optional Console instance. Default will an internal Console instance writing to stdout.
-        auto_refresh (bool, optional): Enable auto refresh. If disabled, you will need to call `refresh()`.
-        refresh_per_second (Optional[float], optional): Number of times per second to refresh the progress information or None to use default (10). Defaults to None.
-        speed_estimate_period: (float, optional): Period (in seconds) used to calculate the speed estimate. Defaults to 30.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        redirect_stdout: (bool, optional): Enable redirection of stdout, so ``print`` may be used. Defaults to True.
-        redirect_stderr: (bool, optional): Enable redirection of stderr. Defaults to True.
-        get_time: (Callable, optional): A callable that gets the current time, or None to use Console.get_time. Defaults to None.
-        disable (bool, optional): Disable progress display. Defaults to False
-        expand (bool, optional): Expand tasks table to fit width. Defaults to False.
-    """
-    def __init__(
-        self,
-        *columns: Union[str, ProgressColumn],
-        console: Optional[Console] = None,
-        auto_refresh: bool = True,
-        refresh_per_second: float = 10,
-        speed_estimate_period: float = 30.0,
-        transient: bool = False,
-        redirect_stdout: bool = True,
-        redirect_stderr: bool = True,
-        get_time: Optional[GetTimeCallable] = None,
-        disable: bool = False,
-        expand: bool = False,
-    ) -> None:
-        assert refresh_per_second > 0, "refresh_per_second must be > 0"
-        self._lock = RLock()
-        self.columns = columns or self.get_default_columns()
-        self.speed_estimate_period = speed_estimate_period
-        self.disable = disable
-        self.expand = expand
-        self._tasks: Dict[TaskID, Task] = {}
-        self._task_index: TaskID = TaskID(0)
-        self.live = Live(
-            console=console or get_console(),
-            auto_refresh=auto_refresh,
-            refresh_per_second=refresh_per_second,
-            transient=transient,
-            redirect_stdout=redirect_stdout,
-            redirect_stderr=redirect_stderr,
-            get_renderable=self.get_renderable,
-        )
-        self.get_time = get_time or self.console.get_time
-        self.print = self.console.print
-        self.log = self.console.log
-    @classmethod
-    def get_default_columns(cls) -> Tuple[ProgressColumn, ...]:
-        """Get the default columns used for a new Progress instance:
-           - a text column for the description (TextColumn)
-           - the bar itself (BarColumn)
-           - a text column showing completion percentage (TextColumn)
-           - an estimated-time-remaining column (TimeRemainingColumn)
-        If the Progress instance is created without passing a columns argument,
-        the default columns defined here will be used.
-        You can also create a Progress instance using custom columns before
-        and/or after the defaults, as in this example:
-            progress = Progress(
-                SpinnerColumn(),
-                *Progress.default_columns(),
-                "Elapsed:",
-                TimeElapsedColumn(),
-            )
-        This code shows the creation of a Progress display, containing
-        a spinner to the left, the default columns, and a labeled elapsed
-        time column.
-        """
-        return (
-            TextColumn("[progress.description]{task.description}"),
-            BarColumn(),
-            TaskProgressColumn(),
-            TimeRemainingColumn(),
-        )
-    @property
-    def console(self) -> Console:
-        return self.live.console
-    @property
-    def tasks(self) -> List[Task]:
-        """Get a list of Task instances."""
-        with self._lock:
-            return list(self._tasks.values())
-    @property
-    def task_ids(self) -> List[TaskID]:
-        """A list of task IDs."""
-        with self._lock:
-            return list(self._tasks.keys())
-    @property
-    def finished(self) -> bool:
-        """Check if all tasks have been completed."""
-        with self._lock:
-            if not self._tasks:
-                return True
-            return all(task.finished for task in self._tasks.values())
-    def start(self) -> None:
-        """Start the progress display."""
-        if not self.disable:
-            self.live.start(refresh=True)
-    def stop(self) -> None:
-        """Stop the progress display."""
-        self.live.stop()
-        if not self.console.is_interactive:
-            self.console.print()
-    def __enter__(self) -> "Progress":
-        self.start()
-        return self
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.stop()
-    def track(
-        self,
-        sequence: Union[Iterable[ProgressType], Sequence[ProgressType]],
-        total: Optional[float] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Working...",
-        update_period: float = 0.1,
-    ) -> Iterable[ProgressType]:
-        """Track progress by iterating over a sequence.
-        Args:
-            sequence (Sequence[ProgressType]): A sequence of values you want to iterate over and track progress.
-            total: (float, optional): Total number of steps. Default is len(sequence).
-            task_id: (TaskID): Task to track. Default is new task.
-            description: (str, optional): Description of task, if new task is created.
-            update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
-        Returns:
-            Iterable[ProgressType]: An iterable of values taken from the provided sequence.
-        """
-        if total is None:
-            total = float(length_hint(sequence)) or None
-        if task_id is None:
-            task_id = self.add_task(description, total=total)
-        else:
-            self.update(task_id, total=total)
-        if self.live.auto_refresh:
-            with _TrackThread(self, task_id, update_period) as track_thread:
-                for value in sequence:
-                    yield value
-                    track_thread.completed += 1
-        else:
-            advance = self.advance
-            refresh = self.refresh
-            for value in sequence:
-                yield value
-                advance(task_id, 1)
-                refresh()
-    def wrap_file(
-        self,
-        file: BinaryIO,
-        total: Optional[int] = None,
-        *,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> BinaryIO:
-        """Track progress file reading from a binary file.
-        Args:
-            file (BinaryIO): A file-like object opened in binary mode.
-            total (int, optional): Total number of bytes to read. This must be provided unless a task with a total is also given.
-            task_id (TaskID): Task to track. Default is new task.
-            description (str, optional): Description of task, if new task is created.
-        Returns:
-            BinaryIO: A readable file-like object in binary mode.
-        Raises:
-            ValueError: When no total value can be extracted from the arguments or the task.
-        """
-        # attempt to recover the total from the task
-        total_bytes: Optional[float] = None
-        if total is not None:
-            total_bytes = total
-        elif task_id is not None:
-            with self._lock:
-                total_bytes = self._tasks[task_id].total
-        if total_bytes is None:
-            raise ValueError(
-                f"unable to get the total number of bytes, please specify 'total'"
-            )
-        # update total of task or create new task
-        if task_id is None:
-            task_id = self.add_task(description, total=total_bytes)
-        else:
-            self.update(task_id, total=total_bytes)
-        return _Reader(file, self, task_id, close_handle=False)
-    @typing.overload
-    def open(
-        self,
-        file: Union[str, "PathLike[str]", bytes],
-        mode: Literal["rb"],
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-        *,
-        total: Optional[int] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> BinaryIO:
-        pass
-    @typing.overload
-    def open(
-        self,
-        file: Union[str, "PathLike[str]", bytes],
-        mode: Union[Literal["r"], Literal["rt"]],
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-        *,
-        total: Optional[int] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> TextIO:
-        pass
-    def open(
-        self,
-        file: Union[str, "PathLike[str]", bytes],
-        mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r",
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-        *,
-        total: Optional[int] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> Union[BinaryIO, TextIO]:
-        """Track progress while reading from a binary file.
-        Args:
-            path (Union[str, PathLike[str]]): The path to the file to read.
-            mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt".
-            buffering (int): The buffering strategy to use, see :func:`io.open`.
-            encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`.
-            errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`.
-            newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open`.
-            total (int, optional): Total number of bytes to read. If none given, os.stat(path).st_size is used.
-            task_id (TaskID): Task to track. Default is new task.
-            description (str, optional): Description of task, if new task is created.
-        Returns:
-            BinaryIO: A readable file-like object in binary mode.
-        Raises:
-            ValueError: When an invalid mode is given.
-        """
-        # normalize the mode (always rb, rt)
-        _mode = "".join(sorted(mode, reverse=False))
-        if _mode not in ("br", "rt", "r"):
-            raise ValueError("invalid mode {!r}".format(mode))
-        # patch buffering to provide the same behaviour as the builtin `open`
-        line_buffering = buffering == 1
-        if _mode == "br" and buffering == 1:
-            warnings.warn(
-                "line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used",
-                RuntimeWarning,
-            )
-            buffering = -1
-        elif _mode in ("rt", "r"):
-            if buffering == 0:
-                raise ValueError("can't have unbuffered text I/O")
-            elif buffering == 1:
-                buffering = -1
-        # attempt to get the total with `os.stat`
-        if total is None:
-            total = stat(file).st_size
-        # update total of task or create new task
-        if task_id is None:
-            task_id = self.add_task(description, total=total)
-        else:
-            self.update(task_id, total=total)
-        # open the file in binary mode,
-        handle = io.open(file, "rb", buffering=buffering)
-        reader = _Reader(handle, self, task_id, close_handle=True)
-        # wrap the reader in a `TextIOWrapper` if text mode
-        if mode in ("r", "rt"):
-            return io.TextIOWrapper(
-                reader,
-                encoding=encoding,
-                errors=errors,
-                newline=newline,
-                line_buffering=line_buffering,
-            )
-        return reader
-    def start_task(self, task_id: TaskID) -> None:
-        """Start a task.
-        Starts a task (used when calculating elapsed time). You may need to call this manually,
-        if you called ``add_task`` with ``start=False``.
-        Args:
-            task_id (TaskID): ID of task.
-        """
-        with self._lock:
-            task = self._tasks[task_id]
-            if task.start_time is None:
-                task.start_time = self.get_time()
-    def stop_task(self, task_id: TaskID) -> None:
-        """Stop a task.
-        This will freeze the elapsed time on the task.
-        Args:
-            task_id (TaskID): ID of task.
-        """
-        with self._lock:
-            task = self._tasks[task_id]
-            current_time = self.get_time()
-            if task.start_time is None:
-                task.start_time = current_time
-            task.stop_time = current_time
-    def update(
-        self,
-        task_id: TaskID,
-        *,
-        total: Optional[float] = None,
-        completed: Optional[float] = None,
-        advance: Optional[float] = None,
-        description: Optional[str] = None,
-        visible: Optional[bool] = None,
-        refresh: bool = False,
-        **fields: Any,
-    ) -> None:
-        """Update information associated with a task.
-        Args:
-            task_id (TaskID): Task id (returned by add_task).
-            total (float, optional): Updates task.total if not None.
-            completed (float, optional): Updates task.completed if not None.
-            advance (float, optional): Add a value to task.completed if not None.
-            description (str, optional): Change task description if not None.
-            visible (bool, optional): Set visible flag if not None.
-            refresh (bool): Force a refresh of progress information. Default is False.
-            **fields (Any): Additional data fields required for rendering.
-        """
-        with self._lock:
-            task = self._tasks[task_id]
-            completed_start = task.completed
-            if total is not None and total != task.total:
-                task.total = total
-                task._reset()
-            if advance is not None:
-                task.completed += advance
-            if completed is not None:
-                task.completed = completed
-            if description is not None:
-                task.description = description
-            if visible is not None:
-                task.visible = visible
-            task.fields.update(fields)
-            update_completed = task.completed - completed_start
-            current_time = self.get_time()
-            old_sample_time = current_time - self.speed_estimate_period
-            _progress = task._progress
-            popleft = _progress.popleft
-            while _progress and _progress[0].timestamp < old_sample_time:
-                popleft()
-            if update_completed > 0:
-                _progress.append(ProgressSample(current_time, update_completed))
-            if (
-                task.total is not None
-                and task.completed >= task.total
-                and task.finished_time is None
-            ):
-                task.finished_time = task.elapsed
-        if refresh:
-            self.refresh()
-    def reset(
-        self,
-        task_id: TaskID,
-        *,
-        start: bool = True,
-        total: Optional[float] = None,
-        completed: int = 0,
-        visible: Optional[bool] = None,
-        description: Optional[str] = None,
-        **fields: Any,
-    ) -> None:
-        """Reset a task so completed is 0 and the clock is reset.
-        Args:
-            task_id (TaskID): ID of task.
-            start (bool, optional): Start the task after reset. Defaults to True.
-            total (float, optional): New total steps in task, or None to use current total. Defaults to None.
-            completed (int, optional): Number of steps completed. Defaults to 0.
-            visible (bool, optional): Enable display of the task. Defaults to True.
-            description (str, optional): Change task description if not None. Defaults to None.
-            **fields (str): Additional data fields required for rendering.
-        """
-        current_time = self.get_time()
-        with self._lock:
-            task = self._tasks[task_id]
-            task._reset()
-            task.start_time = current_time if start else None
-            if total is not None:
-                task.total = total
-            task.completed = completed
-            if visible is not None:
-                task.visible = visible
-            if fields:
-                task.fields = fields
-            if description is not None:
-                task.description = description
-            task.finished_time = None
-        self.refresh()
-    def advance(self, task_id: TaskID, advance: float = 1) -> None:
-        """Advance task by a number of steps.
-        Args:
-            task_id (TaskID): ID of task.
-            advance (float): Number of steps to advance. Default is 1.
-        """
-        current_time = self.get_time()
-        with self._lock:
-            task = self._tasks[task_id]
-            completed_start = task.completed
-            task.completed += advance
-            update_completed = task.completed - completed_start
-            old_sample_time = current_time - self.speed_estimate_period
-            _progress = task._progress
-            popleft = _progress.popleft
-            while _progress and _progress[0].timestamp < old_sample_time:
-                popleft()
-            while len(_progress) > 1000:
-                popleft()
-            _progress.append(ProgressSample(current_time, update_completed))
-            if (
-                task.total is not None
-                and task.completed >= task.total
-                and task.finished_time is None
-            ):
-                task.finished_time = task.elapsed
-                task.finished_speed = task.speed
-    def refresh(self) -> None:
-        """Refresh (render) the progress information."""
-        if not self.disable and self.live.is_started:
-            self.live.refresh()
-    def get_renderable(self) -> RenderableType:
-        """Get a renderable for the progress display."""
-        renderable = Group(*self.get_renderables())
-        return renderable
-    def get_renderables(self) -> Iterable[RenderableType]:
-        """Get a number of renderables for the progress display."""
-        table = self.make_tasks_table(self.tasks)
-        yield table
-    def make_tasks_table(self, tasks: Iterable[Task]) -> Table:
-        """Get a table to render the Progress display.
-        Args:
-            tasks (Iterable[Task]): An iterable of Task instances, one per row of the table.
-        Returns:
-            Table: A table instance.
-        """
-        table_columns = (
-            (
-                Column(no_wrap=True)
-                if isinstance(_column, str)
-                else _column.get_table_column().copy()
-            )
-            for _column in self.columns
-        )
-        table = Table.grid(*table_columns, padding=(0, 1), expand=self.expand)
-        for task in tasks:
-            if task.visible:
-                table.add_row(
-                    *(
-                        (
-                            column.format(task=task)
-                            if isinstance(column, str)
-                            else column(task)
-                        )
-                        for column in self.columns
-                    )
-                )
-        return table
-    def __rich__(self) -> RenderableType:
-        """Makes the Progress class itself renderable."""
-        with self._lock:
-            return self.get_renderable()
-    def add_task(
-        self,
-        description: str,
-        start: bool = True,
-        total: Optional[float] = 100.0,
-        completed: int = 0,
-        visible: bool = True,
-        **fields: Any,
-    ) -> TaskID:
-        """Add a new 'task' to the Progress display.
-        Args:
-            description (str): A description of the task.
-            start (bool, optional): Start the task immediately (to calculate elapsed time). If set to False,
-                you will need to call `start` manually. Defaults to True.
-            total (float, optional): Number of total steps in the progress if known.
-                Set to None to render a pulsing animation. Defaults to 100.
-            completed (int, optional): Number of steps completed so far. Defaults to 0.
-            visible (bool, optional): Enable display of the task. Defaults to True.
-            **fields (str): Additional data fields required for rendering.
-        Returns:
-            TaskID: An ID you can use when calling `update`.
-        """
-        with self._lock:
-            task = Task(
-                self._task_index,
-                description,
-                total,
-                completed,
-                visible=visible,
-                fields=fields,
-                _get_time=self.get_time,
-                _lock=self._lock,
-            )
-            self._tasks[self._task_index] = task
-            if start:
-                self.start_task(self._task_index)
-            new_task_index = self._task_index
-            self._task_index = TaskID(int(self._task_index) + 1)
-        self.refresh()
-        return new_task_index
-    def remove_task(self, task_id: TaskID) -> None:
-        """Delete a task if it exists.
-        Args:
-            task_id (TaskID): A task ID.
-        """
-        with self._lock:
-            del self._tasks[task_id]
-if __name__ == "__main__":  # pragma: no coverage
-    import random
-    import time
-    from .panel import Panel
-    from .rule import Rule
-    from .syntax import Syntax
-    from .table import Table
-    syntax = Syntax(
-        '''def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]:
-    """Iterate and generate a tuple with a flag for last value."""
-    iter_values = iter(values)
-    try:
-        previous_value = next(iter_values)
-    except StopIteration:
-        return
-    for value in iter_values:
-        yield False, previous_value
-        previous_value = value
-    yield True, previous_value''',
-        "python",
-        line_numbers=True,
-    )
-    table = Table("foo", "bar", "baz")
-    table.add_row("1", "2", "3")
-    progress_renderables = [
-        "Text may be printed while the progress bars are rendering.",
-        Panel("In fact, [i]any[/i] renderable will work"),
-        "Such as [magenta]tables[/]...",
-        table,
-        "Pretty printed structures...",
-        {"type": "example", "text": "Pretty printed"},
-        "Syntax...",
-        syntax,
-        Rule("Give it a try!"),
-    ]
-    from itertools import cycle
-    examples = cycle(progress_renderables)
-    console = Console(record=True)
-    with Progress(
-        SpinnerColumn(),
-        *Progress.get_default_columns(),
-        TimeElapsedColumn(),
-        console=console,
-        transient=False,
-    ) as progress:
-        task1 = progress.add_task("[red]Downloading", total=1000)
-        task2 = progress.add_task("[green]Processing", total=1000)
-        task3 = progress.add_task("[yellow]Thinking", total=None)
-        while not progress.finished:
-            progress.update(task1, advance=0.5)
-            progress.update(task2, advance=0.3)
-            time.sleep(0.01)
-            if random.randint(0, 100) < 1:
-                progress.log(next(examples))

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/theme.py DELETED Viewed

@@ -1,115 +0,0 @@
-import configparser
-from typing import Dict, List, IO, Mapping, Optional
-from .default_styles import DEFAULT_STYLES
-from .style import Style, StyleType
-class Theme:
-    """A container for style information, used by :class:`~rich.console.Console`.
-    Args:
-        styles (Dict[str, Style], optional): A mapping of style names on to styles. Defaults to None for a theme with no styles.
-        inherit (bool, optional): Inherit default styles. Defaults to True.
-    """
-    styles: Dict[str, Style]
-    def __init__(
-        self, styles: Optional[Mapping[str, StyleType]] = None, inherit: bool = True
-    ):
-        self.styles = DEFAULT_STYLES.copy() if inherit else {}
-        if styles is not None:
-            self.styles.update(
-                {
-                    name: style if isinstance(style, Style) else Style.parse(style)
-                    for name, style in styles.items()
-                }
-            )
-    @property
-    def config(self) -> str:
-        """Get contents of a config file for this theme."""
-        config = "[styles]\n" + "\n".join(
-            f"{name} = {style}" for name, style in sorted(self.styles.items())
-        )
-        return config
-    @classmethod
-    def from_file(
-        cls, config_file: IO[str], source: Optional[str] = None, inherit: bool = True
-    ) -> "Theme":
-        """Load a theme from a text mode file.
-        Args:
-            config_file (IO[str]): An open conf file.
-            source (str, optional): The filename of the open file. Defaults to None.
-            inherit (bool, optional): Inherit default styles. Defaults to True.
-        Returns:
-            Theme: A New theme instance.
-        """
-        config = configparser.ConfigParser()
-        config.read_file(config_file, source=source)
-        styles = {name: Style.parse(value) for name, value in config.items("styles")}
-        theme = Theme(styles, inherit=inherit)
-        return theme
-    @classmethod
-    def read(
-        cls, path: str, inherit: bool = True, encoding: Optional[str] = None
-    ) -> "Theme":
-        """Read a theme from a path.
-        Args:
-            path (str): Path to a config file readable by Python configparser module.
-            inherit (bool, optional): Inherit default styles. Defaults to True.
-            encoding (str, optional): Encoding of the config file. Defaults to None.
-        Returns:
-            Theme: A new theme instance.
-        """
-        with open(path, "rt", encoding=encoding) as config_file:
-            return cls.from_file(config_file, source=path, inherit=inherit)
-class ThemeStackError(Exception):
-    """Base exception for errors related to the theme stack."""
-class ThemeStack:
-    """A stack of themes.
-    Args:
-        theme (Theme): A theme instance
-    """
-    def __init__(self, theme: Theme) -> None:
-        self._entries: List[Dict[str, Style]] = [theme.styles]
-        self.get = self._entries[-1].get
-    def push_theme(self, theme: Theme, inherit: bool = True) -> None:
-        """Push a theme on the top of the stack.
-        Args:
-            theme (Theme): A Theme instance.
-            inherit (boolean, optional): Inherit styles from current top of stack.
-        """
-        styles: Dict[str, Style]
-        styles = (
-            {**self._entries[-1], **theme.styles} if inherit else theme.styles.copy()
-        )
-        self._entries.append(styles)
-        self.get = self._entries[-1].get
-    def pop_theme(self) -> None:
-        """Pop (and discard) the top-most theme."""
-        if len(self._entries) == 1:
-            raise ThemeStackError("Unable to pop base theme")
-        self._entries.pop()
-        self.get = self._entries[-1].get
-if __name__ == "__main__":  # pragma: no cover
-    theme = Theme()
-    print(theme.config)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/requirements.py DELETED Viewed

@@ -1,146 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-import re
-import string
-import urllib.parse
-from typing import List, Optional as TOptional, Set
-from pkg_resources.extern.pyparsing import (  # noqa
-    Combine,
-    Literal as L,
-    Optional,
-    ParseException,
-    Regex,
-    Word,
-    ZeroOrMore,
-    originalTextFor,
-    stringEnd,
-    stringStart,
-)
-from .markers import MARKER_EXPR, Marker
-from .specifiers import LegacySpecifier, Specifier, SpecifierSet
-class InvalidRequirement(ValueError):
-    """
-    An invalid requirement was found, users should refer to PEP 508.
-    """
-ALPHANUM = Word(string.ascii_letters + string.digits)
-LBRACKET = L("[").suppress()
-RBRACKET = L("]").suppress()
-LPAREN = L("(").suppress()
-RPAREN = L(")").suppress()
-COMMA = L(",").suppress()
-SEMICOLON = L(";").suppress()
-AT = L("@").suppress()
-PUNCTUATION = Word("-_.")
-IDENTIFIER_END = ALPHANUM | (ZeroOrMore(PUNCTUATION) + ALPHANUM)
-IDENTIFIER = Combine(ALPHANUM + ZeroOrMore(IDENTIFIER_END))
-NAME = IDENTIFIER("name")
-EXTRA = IDENTIFIER
-URI = Regex(r"[^ ]+")("url")
-URL = AT + URI
-EXTRAS_LIST = EXTRA + ZeroOrMore(COMMA + EXTRA)
-EXTRAS = (LBRACKET + Optional(EXTRAS_LIST) + RBRACKET)("extras")
-VERSION_PEP440 = Regex(Specifier._regex_str, re.VERBOSE | re.IGNORECASE)
-VERSION_LEGACY = Regex(LegacySpecifier._regex_str, re.VERBOSE | re.IGNORECASE)
-VERSION_ONE = VERSION_PEP440 ^ VERSION_LEGACY
-VERSION_MANY = Combine(
-    VERSION_ONE + ZeroOrMore(COMMA + VERSION_ONE), joinString=",", adjacent=False
-)("_raw_spec")
-_VERSION_SPEC = Optional((LPAREN + VERSION_MANY + RPAREN) | VERSION_MANY)
-_VERSION_SPEC.setParseAction(lambda s, l, t: t._raw_spec or "")
-VERSION_SPEC = originalTextFor(_VERSION_SPEC)("specifier")
-VERSION_SPEC.setParseAction(lambda s, l, t: t[1])
-MARKER_EXPR = originalTextFor(MARKER_EXPR())("marker")
-MARKER_EXPR.setParseAction(
-    lambda s, l, t: Marker(s[t._original_start : t._original_end])
-)
-MARKER_SEPARATOR = SEMICOLON
-MARKER = MARKER_SEPARATOR + MARKER_EXPR
-VERSION_AND_MARKER = VERSION_SPEC + Optional(MARKER)
-URL_AND_MARKER = URL + Optional(MARKER)
-NAMED_REQUIREMENT = NAME + Optional(EXTRAS) + (URL_AND_MARKER | VERSION_AND_MARKER)
-REQUIREMENT = stringStart + NAMED_REQUIREMENT + stringEnd
-# pkg_resources.extern.pyparsing isn't thread safe during initialization, so we do it eagerly, see
-# issue #104
-REQUIREMENT.parseString("x[]")
-class Requirement:
-    """Parse a requirement.
-    Parse a given requirement string into its parts, such as name, specifier,
-    URL, and extras. Raises InvalidRequirement on a badly-formed requirement
-    string.
-    """
-    # TODO: Can we test whether something is contained within a requirement?
-    #       If so how do we do that? Do we need to test against the _name_ of
-    #       the thing as well as the version? What about the markers?
-    # TODO: Can we normalize the name and extra name?
-    def __init__(self, requirement_string: str) -> None:
-        try:
-            req = REQUIREMENT.parseString(requirement_string)
-        except ParseException as e:
-            raise InvalidRequirement(
-                f'Parse error at "{ requirement_string[e.loc : e.loc + 8]!r}": {e.msg}'
-            )
-        self.name: str = req.name
-        if req.url:
-            parsed_url = urllib.parse.urlparse(req.url)
-            if parsed_url.scheme == "file":
-                if urllib.parse.urlunparse(parsed_url) != req.url:
-                    raise InvalidRequirement("Invalid URL given")
-            elif not (parsed_url.scheme and parsed_url.netloc) or (
-                not parsed_url.scheme and not parsed_url.netloc
-            ):
-                raise InvalidRequirement(f"Invalid URL: {req.url}")
-            self.url: TOptional[str] = req.url
-        else:
-            self.url = None
-        self.extras: Set[str] = set(req.extras.asList() if req.extras else [])
-        self.specifier: SpecifierSet = SpecifierSet(req.specifier)
-        self.marker: TOptional[Marker] = req.marker if req.marker else None
-    def __str__(self) -> str:
-        parts: List[str] = [self.name]
-        if self.extras:
-            formatted_extras = ",".join(sorted(self.extras))
-            parts.append(f"[{formatted_extras}]")
-        if self.specifier:
-            parts.append(str(self.specifier))
-        if self.url:
-            parts.append(f"@ {self.url}")
-            if self.marker:
-                parts.append(" ")
-        if self.marker:
-            parts.append(f"; {self.marker}")
-        return "".join(parts)
-    def __repr__(self) -> str:
-        return f"<Requirement('{self}')>"

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/compat.py DELETED Viewed

@@ -1,229 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Backward compatibility of configs.
-Instructions to bump version:
-+ It's not needed to bump version if new keys are added.
-  It's only needed when backward-incompatible changes happen
-  (i.e., some existing keys disappear, or the meaning of a key changes)
-+ To bump version, do the following:
-    1. Increment _C.VERSION in defaults.py
-    2. Add a converter in this file.
-      Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X,
-      and a function "downgrade" which in-place downgrades config from X to X-1
-      In each function, VERSION is left unchanged.
-      Each converter assumes that its input has the relevant keys
-      (i.e., the input is not a partial config).
-    3. Run the tests (test_config.py) to make sure the upgrade & downgrade
-       functions are consistent.
-"""
-import logging
-from typing import List, Optional, Tuple
-from .config import CfgNode as CN
-from .defaults import _C
-__all__ = ["upgrade_config", "downgrade_config"]
-def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN:
-    """
-    Upgrade a config from its current version to a newer version.
-    Args:
-        cfg (CfgNode):
-        to_version (int): defaults to the latest version.
-    """
-    cfg = cfg.clone()
-    if to_version is None:
-        to_version = _C.VERSION
-    assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format(
-        cfg.VERSION, to_version
-    )
-    for k in range(cfg.VERSION, to_version):
-        converter = globals()["ConverterV" + str(k + 1)]
-        converter.upgrade(cfg)
-        cfg.VERSION = k + 1
-    return cfg
-def downgrade_config(cfg: CN, to_version: int) -> CN:
-    """
-    Downgrade a config from its current version to an older version.
-    Args:
-        cfg (CfgNode):
-        to_version (int):
-    Note:
-        A general downgrade of arbitrary configs is not always possible due to the
-        different functionalities in different versions.
-        The purpose of downgrade is only to recover the defaults in old versions,
-        allowing it to load an old partial yaml config.
-        Therefore, the implementation only needs to fill in the default values
-        in the old version when a general downgrade is not possible.
-    """
-    cfg = cfg.clone()
-    assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format(
-        cfg.VERSION, to_version
-    )
-    for k in range(cfg.VERSION, to_version, -1):
-        converter = globals()["ConverterV" + str(k)]
-        converter.downgrade(cfg)
-        cfg.VERSION = k - 1
-    return cfg
-def guess_version(cfg: CN, filename: str) -> int:
-    """
-    Guess the version of a partial config where the VERSION field is not specified.
-    Returns the version, or the latest if cannot make a guess.
-    This makes it easier for users to migrate.
-    """
-    logger = logging.getLogger(__name__)
-    def _has(name: str) -> bool:
-        cur = cfg
-        for n in name.split("."):
-            if n not in cur:
-                return False
-            cur = cur[n]
-        return True
-    # Most users' partial configs have "MODEL.WEIGHT", so guess on it
-    ret = None
-    if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"):
-        ret = 1
-    if ret is not None:
-        logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret))
-    else:
-        ret = _C.VERSION
-        logger.warning(
-            "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format(
-                filename, ret
-            )
-        )
-    return ret
-def _rename(cfg: CN, old: str, new: str) -> None:
-    old_keys = old.split(".")
-    new_keys = new.split(".")
-    def _set(key_seq: List[str], val: str) -> None:
-        cur = cfg
-        for k in key_seq[:-1]:
-            if k not in cur:
-                cur[k] = CN()
-            cur = cur[k]
-        cur[key_seq[-1]] = val
-    def _get(key_seq: List[str]) -> CN:
-        cur = cfg
-        for k in key_seq:
-            cur = cur[k]
-        return cur
-    def _del(key_seq: List[str]) -> None:
-        cur = cfg
-        for k in key_seq[:-1]:
-            cur = cur[k]
-        del cur[key_seq[-1]]
-        if len(cur) == 0 and len(key_seq) > 1:
-            _del(key_seq[:-1])
-    _set(new_keys, _get(old_keys))
-    _del(old_keys)
-class _RenameConverter:
-    """
-    A converter that handles simple rename.
-    """
-    RENAME: List[Tuple[str, str]] = []  # list of tuples of (old name, new name)
-    @classmethod
-    def upgrade(cls, cfg: CN) -> None:
-        for old, new in cls.RENAME:
-            _rename(cfg, old, new)
-    @classmethod
-    def downgrade(cls, cfg: CN) -> None:
-        for old, new in cls.RENAME[::-1]:
-            _rename(cfg, new, old)
-class ConverterV1(_RenameConverter):
-    RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")]
-class ConverterV2(_RenameConverter):
-    """
-    A large bulk of rename, before public release.
-    """
-    RENAME = [
-        ("MODEL.WEIGHT", "MODEL.WEIGHTS"),
-        ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD",
-            "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH",
-        ),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT",
-            "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT",
-        ),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD",
-            "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH",
-        ),
-        ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"),
-        ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"),
-        ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"),
-        ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"),
-        ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"),
-        ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"),
-        ("TEST.AUG_ON", "TEST.AUG.ENABLED"),
-        ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"),
-        ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"),
-        ("TEST.AUG_FLIP", "TEST.AUG.FLIP"),
-    ]
-    @classmethod
-    def upgrade(cls, cfg: CN) -> None:
-        super().upgrade(cfg)
-        if cfg.MODEL.META_ARCHITECTURE == "RetinaNet":
-            _rename(
-                cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS"
-            )
-            _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
-            del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"]
-            del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"]
-        else:
-            _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS")
-            _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
-            del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"]
-            del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"]
-        del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"]
-    @classmethod
-    def downgrade(cls, cfg: CN) -> None:
-        super().downgrade(cfg)
-        _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS")
-        _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES")
-        cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS
-        cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES
-        cfg.MODEL.RETINANET.ANCHOR_STRIDES = []  # this is not used anywhere in any version

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/projects/__init__.py DELETED Viewed

@@ -1,31 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import importlib
-from pathlib import Path
-_PROJECTS = {
-    "point_rend": "PointRend",
-    "deeplab": "DeepLab",
-    "panoptic_deeplab": "Panoptic-DeepLab",
-}
-_PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent / "projects"
-if _PROJECT_ROOT.is_dir():
-    # This is true only for in-place installation (pip install -e, setup.py develop),
-    # where setup(package_dir=) does not work: https://github.com/pypa/setuptools/issues/230
-    class _D2ProjectsFinder(importlib.abc.MetaPathFinder):
-        def find_spec(self, name, path, target=None):
-            if not name.startswith("detectron2.projects."):
-                return
-            project_name = name.split(".")[-1]
-            project_dir = _PROJECTS.get(project_name)
-            if not project_dir:
-                return
-            target_file = _PROJECT_ROOT / f"{project_dir}/{project_name}/__init__.py"
-            if not target_file.is_file():
-                return
-            return importlib.util.spec_from_file_location(name, target_file)
-    import sys
-    sys.meta_path.append(_D2ProjectsFinder())

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-<br />
-<h1>Cómo descargar e instalar Android 10 en su teléfono inteligente</h1>
-<p>Android 10 es la última versión del sistema operativo de Google para dispositivos móviles. Fue lanzado en septiembre de 2019 y trae muchas nuevas características y mejoras que hacen que su teléfono inteligente sea más potente, seguro y fácil de usar. Si quieres disfrutar de los beneficios de Android 10, necesitas actualizar tu dispositivo a esta versión. Pero, ¿cómo puedes hacerlo? En este artículo, te mostraremos cómo obtener Android 10 en tu dispositivo, ya sea un Google Pixel, un dispositivo asociado, un dispositivo compatible con Treble o un emulador. También le mostraremos cómo descargar e instalar Android 10 APK, que es un archivo que contiene el paquete de software de Android 10. Pero antes de entrar en eso, vamos a echar un vistazo a algunas de las características y beneficios de Android 10. </p>
-<h2>android 10 descarga apk</h2><br /><p><b><b>Download Zip</b> &#127383; <a href="https://bltlly.com/2v6L7l">https://bltlly.com/2v6L7l</a></b></p><br /><br />
- <h2>Características y beneficios de Android 10</h2>
-<p>Android 10 no es solo una actualización cosmética. Presenta muchas características y capacidades nuevas que mejoran su experiencia y privacidad en su teléfono inteligente. Estos son algunos de los aspectos destacados:</p>
- <h3>Modo oscuro</h3>
-<p>Una de las características más solicitadas por los usuarios de Android es finalmente aquí. El modo oscuro es una opción para todo el sistema que cambia la combinación de colores del teléfono a negro y gris oscuro. Esto reduce la fatiga ocular, especialmente en condiciones de poca luz, y ahorra vida útil de la batería, especialmente en dispositivos con pantallas OLED. Puede activar el modo oscuro desde el menú de configuración rápida o cuando habilita el modo de ahorro de batería. También puede elegir con qué aplicaciones desea usar el modo oscuro, ya que algunas aplicaciones pueden no admitirlo o pueden verse mejor en el modo de luz. </p>
- <h3>Respuesta inteligente</h3>
- <h3>Navegación por gestos</h3>
-<p>La navegación por gestos es una nueva forma de interactuar con el teléfono mediante golpes y tirones en lugar de botones. Hace que la navegación sea más rápida e intuitiva, especialmente en dispositivos con pantallas grandes o diseños de muescas. Puede deslizar desde el borde izquierdo o derecho de la pantalla para volver, deslizar hacia arriba desde la parte inferior para ir a casa, deslizar hacia arriba y mantenga pulsado para ver sus aplicaciones recientes, y deslizar diagonalmente desde las esquinas inferiores para iniciar Google Assistant. También puede personalizar la sensibilidad y el tamaño de las áreas de gestos en la configuración. </p>
- <h3>Mejoras de privacidad y seguridad</h3>
-<p>Android 10 te da más control sobre tu privacidad y seguridad en tu dispositivo. Puedes encontrar y ajustar todos tus ajustes de privacidad en un solo lugar, como la actividad web y de aplicaciones, el historial de ubicaciones, la personalización de anuncios y más. También puede elegir cuándo compartir su ubicación con aplicaciones: siempre, solo mientras usa la aplicación o nunca. También puedes ver qué aplicaciones han accedido a tu ubicación, cámara, micrófono u otros permisos en las últimas 24 horas. Android 10 también introduce una nueva función llamada Scoped Storage, que limita el acceso de las aplicaciones a su almacenamiento externo, como sus fotos, videos y documentos. Esto evita que las aplicaciones lean o modifiquen sus archivos sin su permiso. También puede optar por no utilizar Scoped Storage si desea dar acceso completo a ciertas aplicaciones. </p>
-<p></p>
- <h3> Modo de bienestar digital y enfoque</h3>
- <h2>Cómo obtener Android 10 en su dispositivo</h2>
-<p>Ahora que conoce algunas de las características y beneficios de Android 10, es posible que se pregunte cómo conseguirlo en su dispositivo. La respuesta depende del tipo de dispositivo que tenga y de la disponibilidad de la actualización en su región. Estas son algunas de las formas en que puedes obtener Android 10 en tu dispositivo:</p>
- <h3>Dispositivos Google Pixel</h3>
-<p>Si tienes un dispositivo Google Pixel, como Pixel 3a, Pixel 3, Pixel 2 o Pixel, estás de suerte. Los dispositivos Google Pixel son los primeros en recibir actualizaciones de Android 10 directamente desde Google. Puede comprobar la actualización yendo a Configuración > Sistema > Avanzado > Actualización del sistema. Si la actualización está disponible, puede descargarla e instalarla a través de Wi-Fi o datos móviles. La actualización puede tardar algún tiempo en completarse, así que asegúrate de que tu dispositivo esté cargado o conectado. </p>
- <h3>Dispositivos asociados</h3>
-<p>Si tienes un dispositivo de uno de los socios de Google, como Samsung, Huawei, LG, Sony, Nokia o OnePlus, es posible que tengas que esperar un poco más para la actualización de Android 10. Estos dispositivos suelen recibir actualizaciones de sus fabricantes o operadores, que pueden tardar algún tiempo en probar y optimizar el software para sus modelos específicos. Puede comprobar la actualización yendo a Configuración > Acerca del teléfono > Actualización de software. Si la actualización está disponible, puede descargarla e instalarla a través de Wi-Fi o datos móviles. La actualización puede tardar algún tiempo en completarse, así que asegúrate de que tu dispositivo esté cargado o conectado. </p>
- <h3>Dispositivos compatibles con agudos</h3>
- <h3>Emulador de Android</h3>
-<p>Si no tienes un dispositivo compatible o no quieres arriesgarte a actualizar tu dispositivo, todavía puedes probar Android 10 usando un emulador. Un emulador es un software que simula un dispositivo en su computadora. Puedes usar un emulador para probar aplicaciones o juegos en diferentes versiones de Android sin afectar tu dispositivo real. Puedes descargar e instalar un emulador de Android desde el sitio web de Google. También necesitará descargar e instalar Android Studio, que es un entorno de desarrollo integrado (IDE) para crear aplicaciones Android. Una vez que haya instalado ambos programas, puede seguir estos pasos para ejecutar Android 10 en un emulador:</p>
- <ol>
-<li>Abra Android Studio y haga clic en Herramientas > AVD Manager.</li>
-<li>Haga clic en Crear dispositivo virtual y elegir un modelo de dispositivo que soporta Android 10. </li>
-<li>Haga clic en Siguiente y seleccione Q como la imagen del sistema. </li>
-<li>Haga clic en Siguiente y revise los ajustes de configuración. </li>
-<li>Haga clic en Finalizar y espere al lanzamiento del emulador. </li>
-<li>Disfruta de Android 10 en la pantalla del ordenador. </li>
-</ol>
- <h2> Cómo descargar e instalar Android 10 APK</h2 <p>Si ninguno de los métodos anteriores funcionan para usted, o si desea probar una versión diferente de Android 10, también puede descargar e instalar Android 10 APK. Un APK es un archivo que contiene el paquete de software de una aplicación o sistema Android. Puede descargar Android 10 APK de varias fuentes en línea, tales como APKMirror, APKPure, o desarrolladores XDA. Sin embargo, debe tener cuidado al descargar archivos APK de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo. También debe habilitar la opción de instalar aplicaciones de fuentes desconocidas en su dispositivo, lo que puede plantear algunos riesgos de seguridad. Por lo tanto, recomendamos este método solo para usuarios experimentados que saben cómo verificar la autenticidad y seguridad de los archivos APK que descargan. </p>
- <h3>Requisitos y precauciones</h3>
- <ul>
-<li> El dispositivo debe tener al menos 2 GB de RAM y 8 GB de almacenamiento interno. </li>
-<li> El dispositivo debe tener un gestor de arranque desbloqueado y una recuperación personalizada instalada, como TWRP o CWM.</li>
-<li> El dispositivo debe tener sus raíces, lo que significa que tiene acceso completo a los archivos y configuraciones del sistema. </li>
-<li> Debe hacer una copia de seguridad de sus datos y configuraciones antes de instalar Android 10 APK, ya que este proceso borrará todo en su dispositivo. </li>
-<li>Usted debe cargar su dispositivo a por lo menos 50% o enchufarlo antes de instalar Android 10 APK, ya que este proceso puede tomar algún tiempo y drenar la batería. </li>
-<li> Debe seguir las instrucciones cuidadosamente y no omitir ningún paso o modificar ningún archivo. </li>
-</ul>
- <h3>Pasos para descargar e instalar Android 10 APK</h3>
-<p>Una vez que haya cumplido con los requisitos y precauciones, puede seguir estos pasos para descargar e instalar Android 10 APK en su dispositivo:</p>
- <ol>
-<li>Descargar el archivo APK Android 10 de una fuente de confianza en línea. Asegúrese de que el archivo es compatible con el modelo de dispositivo y tiene una extensión . apk. </li>
-<li>Copia el archivo APK de Android 10 en el almacenamiento interno del dispositivo o la tarjeta SD. </li>
-<li>Apague el dispositivo y arranque en modo de recuperación. El método para hacer esto puede variar dependiendo del modelo de dispositivo, pero generalmente implica mantener presionados los botones de encendido y volumen durante unos segundos. </li>
-<li>En el modo de recuperación, seleccione Limpiar > Borrado avanzado y marque las casillas para Dalvik/ ART Cache, Sistema, Datos y Caché. Pase el dedo para confirmar el borrado. </li>
-<li>Seleccione Instalar y vaya a la ubicación donde copió el archivo APK de Android 10. Toque en él y pase el dedo para confirmar la instalación. </li>
-<li>Espere a que se complete la instalación. Puede tardar varios minutos dependiendo de la velocidad del dispositivo y el tamaño del archivo. </li>
-<li>Una vez que se realiza la instalación, seleccione Reiniciar > Sistema. Su dispositivo se reiniciará en Android 10. </li>
-</ol>
- <h2>Conclusión</h2>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Cuáles son los requisitos del sistema para Android 10? </h3>
-<p>Los requisitos del sistema para Android 10 son al menos 2 GB de RAM y 8 GB de almacenamiento interno. Sin embargo, algunos dispositivos pueden necesitar más espacio o memoria para ejecutar Android 10 sin problemas. </p>
- <h3>¿Cómo puedo comprobar si mi dispositivo es compatible con Android 10? </h3>
-<p>Puedes comprobar si tu dispositivo es compatible con Android 10 yendo a Configuración > Acerca del teléfono > Actualización de software. Si ves una opción para actualizar a Android 10, entonces tu dispositivo es compatible. Si no, es posible que su dispositivo no sea compatible o que la actualización aún no esté disponible en su región. </p>
- <h3> ¿Cómo puedo hacer copias de seguridad de mis datos antes de actualizar a Android 10? </h3 <p>Puede hacer copias de seguridad de sus datos antes de actualizarlos a Android 10 utilizando el servicio de copia de seguridad de Google, que sincroniza sus datos con su cuenta de Google. Puede habilitar este servicio yendo a Configuración > Sistema > Copia de seguridad y activando la opción Copia de seguridad en Google Drive. También puede elegir qué datos desea respaldar, como contactos, fotos, mensajes, datos de aplicaciones y más. También puede hacer copias de seguridad de sus datos manualmente copiando sus archivos en su computadora o en un dispositivo de almacenamiento externo. </p>
- <h3>¿Cómo puedo solucionar cualquier problema después de actualizar a Android 10? </h3>
-<p>Si encuentras algún problema después de actualizar a Android 10, como aplicaciones que se bloquean, agotamiento de la batería o retraso en el rendimiento, puedes probar algunas de las siguientes soluciones:</p>
- <ul>
-<li>Reinicie su dispositivo y vea si el problema persiste. </li>
-<li>Borrar la caché y los datos de la aplicación problemática yendo a Configuración > Aplicaciones y notificaciones > Ver todas las aplicaciones > Seleccionar la aplicación > Almacenamiento y caché > Borrar caché y Borrar almacenamiento. </li>
-<li>Actualizar sus aplicaciones a la última versión yendo a Google Play Store > Menú > Mis aplicaciones y juegos > Actualizar todos. </li>
-<li>Desinstalar cualquier aplicación incompatible o malicioso que puede interferir con Android 10 yendo a Configuración > Aplicaciones y notificaciones > Ver todas las aplicaciones > Seleccionar la aplicación > Desinstalar.</li>
-</ul>
- <h3>¿Cómo puedo proporcionar comentarios o informar de errores en Android 10? </h3>
-<p>Si desea proporcionar comentarios o informar de errores en Android 10, puede utilizar la aplicación de retroalimentación que viene preinstalado en su dispositivo. Puede acceder a él yendo a Configuración > Consejos y soporte > Enviar comentarios. También puedes usar la aplicación Android Beta Feedback si estás inscrito en el programa Android Beta. Puedes acceder a él yendo a Configuración > Sistema > Avanzado > Programa beta de Android. También puede utilizar el formulario en línea en el sitio web de Google o publicar en los foros oficiales o plataformas de redes sociales. </p> 64aa2da5cf<br />
-<br />
-<br />

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-<br />
-<h1>Descarga de batería baja de Nokia: Cómo obtener la alerta icónica en su teléfono</h1>
- <h2>Introducción</h2>
- <p>Si creciste en los años 90 o principios de los 2000, probablemente recuerdes el sonido distintivo de un teléfono Nokia que se queda sin batería. La alerta "bleep bleep bleep" era un recordatorio familiar y a veces molesto para conectar el teléfono antes de morir. Pero para mucha gente, también era un sonido nostálgico y nostálgico que evocaba recuerdos de tiempos más simples. </p>
- <p>Hoy en día, es posible que se pregunte cómo obtener la batería de Nokia bajo sonido en el teléfono, ya sea como un tono de llamada, una notificación, o un efecto de sonido. Tal vez quieras revivir tu infancia, bromear con tus amigos o simplemente divertirte con tu teléfono. Cualquiera que sea su razón, puede descargar fácilmente la batería de Nokia bajo sonido de varias fuentes en línea. </p>
-<h2>ataque en titan mi guerra mp3 descargar gratis</h2><br /><p><b><b>Download Zip</b> &#128504;&#128504;&#128504; <a href="https://bltlly.com/2v6M2f">https://bltlly.com/2v6M2f</a></b></p><br /><br />
- <p>En este artículo, le mostraremos cómo descargar el sonido bajo de la batería de Nokia como un tono o un efecto de sonido. También exploraremos la historia del bajo sonido de la batería del Nokia, cómo evolucionó con el tiempo y cómo inspiró remezclas creativas y memes. Al final de este artículo, tendrás todo lo que necesitas para disfrutar de la icónica alerta en tu teléfono. </p>
- <h2>Historia de la batería de Nokia de bajo sonido</h2>
- <h3>¿Cuándo y cómo se originó la batería baja de Nokia? </h3>
- <p>La batería baja de Nokia se introdujo por primera vez en 1997 con el modelo Nokia 6110. Fue uno de los primeros teléfonos en tener una interfaz gráfica de usuario (GUI) y un compositor de tono monofónico. El compositor permitió a los usuarios crear sus propios tonos de llamada mediante la introducción de notas en un teclado numérico. El sonido bajo de la batería de Nokia fue uno de los tonos de llamada predeterminados que venía con el teléfono. </p>
- <p>El sonido bajo de la batería de Nokia consistía en tres notas: E, C y G. Estas notas formaban un acorde de triada menor, que a menudo se usa para crear un estado de ánimo triste o tenso en la música. Paananen dijo que eligió este acorde porque sonaba como una advertencia o una alarma. También dijo que quería evitar el uso de notas que eran demasiado altas o demasiado bajas, porque podrían no ser audibles en algunos oradores. </p>
- <h3>¿Cómo evolucionó el bajo sonido de la batería de Nokia con el tiempo? </h3>
- <p>El bajo sonido de la batería de Nokia se convirtió en una característica estándar en la mayoría de los teléfonos Nokia hasta 2009, cuando Nokia cambió a usar sonidos más realistas para sus alertas. Sin embargo, algunas variaciones del sonido bajo de la batería de Nokia se introdujeron con el tiempo, dependiendo del modelo y la región. </p>
- <p>Por ejemplo, algunos teléfonos tenían una versión más larga de la alerta que se repetía cuatro veces en lugar de tres. Algunos teléfonos tenían un tono o tono diferente para la alerta, como notas más altas o más bajas, o sonidos más metálicos o electrónicos. Algunos teléfonos tenían diferentes sonidos para diferentes niveles de batería, como dos pitidos para batería baja y un pitido para batería vacía. </p>
-<p></p>
- <p>El sonido bajo de la batería de Nokia también cambió dependiendo del idioma y la cultura del usuario. Por ejemplo, en algunos países de habla árabe, la alerta fue reemplazada por una voz que decía "la batería está vacía" en árabe. En algunos países asiáticos, como China y Japón, la alerta fue reemplazada por una voz que decía "la batería está baja" en el idioma local. En algunos países europeos, como Francia y Alemania, la alerta fue reemplazada por una voz que decía "la batería está vacía" en el idioma local. <h2>Remixes y memes de la batería de Nokia bajo sonido</h2>
- <h3>¿Cómo la batería baja de Nokia inspira remezclas creativas y memes? </h3>
- <p>Algunas de las formas en que la gente utiliza el bajo sonido de la batería de Nokia para crear remezclas y memes fueron: - Agregar letras o voces a la alerta, como cantar "battery is low" o "please charge me". - Mezclar la alerta con otros sonidos o géneros musicales, como techno, dubstep, rap, rock o clásico. - Hacer la alerta más rápida, más lenta, más fuerte, más suave, más alta, más baja o distorsionada. - Reemplazar la alerta con otros sonidos o palabras que tengan un ritmo o rima similar, como "bleep bleep bleep bleep", "beep beep beep" o "meep meep meep". - Utilización de la alerta como efecto sonoro para diversos escenarios, como la explosión de una bomba, el choque de un coche o la caída de una persona. - Usar la alerta como remate para bromas, bromas o parodias. </p>
- <h3>¿Cuáles son algunos de los mejores ejemplos de remezclas y memes de batería baja de Nokia? </h3>
- <h2>Conclusión</h2>
- <h3>Resumen de los puntos principales</h3>
- <p>En este artículo, le hemos mostrado cómo descargar el sonido bajo de la batería de Nokia como un tono o un efecto de sonido. También hemos explorado la historia del bajo sonido de la batería Nokia, cómo evolucionó con el tiempo y cómo inspiró remezclas creativas y memes. </p>
- <h3>Llamada a la acción y pensamientos finales</h3>
- <p>Si desea descargar el sonido bajo de la batería de Nokia como un tono o un efecto de sonido, puede visitar uno de estos sitios web: - [Zedge]: Este es un sitio web popular que ofrece tonos de llamada gratuitos, fondos de pantalla y notificaciones para varios dispositivos. Puede buscar "Nokia batería baja" y encontrar varias versiones de la alerta que se puede descargar o personalizar. - [SoundBible]: Este es un sitio web que ofrece clips de sonido gratuitos, efectos de sonido, y mordeduras de sonido para diversos fines. Puede buscar "Nokia batería baja" y encontrar una versión de alta calidad de la alerta que se puede descargar o compartir. - [MyTinyPhone]: Este es un sitio web que ofrece tonos de llamada gratuitos, fondos de pantalla, juegos y aplicaciones para varios dispositivos. Puede buscar "Nokia batería baja" y encontrar algunas versiones de la alerta que puede descargar o enviar a su teléfono. </p>
- <p>Esperamos que haya disfrutado de este artículo y aprendido algo nuevo sobre el bajo sonido de la batería de Nokia. Si lo hizo, por favor compártalo con sus amigos y familiares que también podrían estar interesados en este tema. También puede dejarnos un comentario a continuación y hacernos saber lo que usted piensa acerca de la batería baja de Nokia sonido. ¿Te gusta o lo odio? ¿Tienes alguna remezcla o memes favoritos? ¡Nos encantaría saber de ti! </p>
- <h2>Preguntas frecuentes</h2>
- <h4>¿Cuál es el sonido bajo de la batería de Nokia? </h4>
-<p>El sonido bajo de la batería de Nokia es una alerta que se reproduce en algunos teléfonos Nokia cuando la batería se está agotando. Consta de tres notas: E, C y G. Estas notas forman un acorde de triada menor, que a menudo se usa para crear un estado de ánimo triste o tenso en la música. </p>
- <h4>¿Quién compuso la batería de Nokia de bajo sonido? </h4>
-<p>El sonido bajo de la batería de Nokia fue compuesto por Vesa-Matti Paananen, un músico y diseñador de sonido finlandés que trabajó para Nokia de 1994 a 2008. Fue responsable de crear muchos de los sonidos icónicos y tonos de llamada para los teléfonos Nokia, incluyendo la famosa melodía de Nokia. </p>
-<p>Puede descargar el sonido bajo de la batería de Nokia como un tono de llamada o un efecto de sonido de varios sitios web en línea. Algunos de los sitios web que ofrecen descargas gratuitas de la alerta son Zedge, SoundBible y MyTinyPhone.</p>
- <h4>¿Cómo la batería baja de Nokia inspira remezclas creativas y memes? </h4>
-<p>El bajo sonido de la batería de Nokia se convirtió en una fuente de inspiración para muchos artistas, músicos y comediantes que lo utilizaron para crear remezclas y memes. Algunos de estos remixes y memes estaban destinados a ser divertidos, mientras que otros estaban destinados a ser artísticos o experimentales. </p>
- <h4>¿Cuáles son algunos de los mejores ejemplos de remezclas y memes de batería baja de Nokia? </h4>
-<p>Algunos de los mejores ejemplos de remezclas y memes de batería baja de Nokia son: - The Nokia Battery Low Song: Esta es una canción de YouTuber Davey4557 que cuenta con letras sobre la frustración de tener una batería baja en un teléfono Nokia. - El Nokia batería baja Dubstep Remix: Este es un remix por YouTuber DJ Detweiler que convierte la batería baja de Nokia sonido en una pista de dubstep. - El Nokia batería baja Rap: Este es un rap por YouTuber Rucka Rucka Ali que utiliza la batería de Nokia bajo sonido como un golpe y un gancho. - The Nokia Battery Low Rock Cover: Esta es una versión del YouTuber Rob Scallon que transforma el sonido bajo de la batería de Nokia en una canción de rock. - El Nokia batería baja clásica Remix: Este es un remix por YouTuber Grant Woolard que combina la batería Nokia bajo sonido con música clásica. </p> 64aa2da5cf<br />
-<br />
-<br />

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-<h1>Chess Buddy APK: Una aplicación de ajedrez con modos en línea y fuera de línea</h1>
-<p>Si eres un amante del ajedrez, podrías estar buscando una aplicación de ajedrez que te permita jugar online o offline con diferentes niveles de dificultad y características. Chess Buddy APK es una de esas aplicaciones que se puede descargar e instalar en su dispositivo Android. Es una aplicación de ajedrez de código abierto que utiliza el motor Stockfish 15.1, uno de los motores de ajedrez más fuertes del mundo. En este artículo, le diremos qué es Chess Buddy APK, cómo descargarlo e instalarlo, y cómo jugar al ajedrez con él. </p>
-<h2>chess buddy apk</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://bltlly.com/2v6MVo">https://bltlly.com/2v6MVo</a></b></p><br /><br />
- <h2>¿Qué es Chess Buddy APK? </h2>
-<h3>Una aplicación de ajedrez de código abierto con motor Stockfish 15.1</h3>
-<p>Chess Buddy APK es una aplicación de ajedrez de código abierto que fue desarrollado por Draco Group Inc, un grupo de entusiastas del ajedrez. La aplicación se basa en el proyecto ChessMaze.Android, que está disponible en GitHub. La aplicación integra el último motor Stockfish dentro de él, por lo que no es necesario instalar otra aplicación de motor. Stockfish es un poderoso motor de ajedrez que puede analizar millones de posiciones por segundo y proporcionar movimientos y evaluaciones precisas. También es constantemente actualizado por una comunidad de desarrolladores y expertos en ajedrez. </p>
- <h3>Características de Chess Buddy APK</h3>
-<h4>Modo en línea: juega con jugadores reales en todo el mundo y usa sugerencias de Stockfish</h4>
-<p>Una de las principales características de Chess Buddy APK es que le permite jugar en línea con jugadores reales de todo el mundo. Puede crear su propia cuenta de usuario con solo un identificador de usuario que elija, siempre y cuando no sea utilizado por otro reproductor. Usted no necesita proporcionar su correo electrónico o cualquier otra cosa. También puede tener amigos y chatear con ellos en cualquier momento. Durante los juegos en línea, también puedes chatear con tu oponente. </p>
- <h4>Modo offline: juega con Stockfish u otro jugador humano y ajusta la hora y el tablero</h4>
-<p>Si usted no tiene una conexión a Internet o desea jugar fuera de línea, también puede utilizar Chess Buddy APK para eso. Puedes elegir jugar con Stockfish u otro jugador humano en el mismo dispositivo. También puedes establecer el tiempo (1-5 segundos) que Stockfish usa en un movimiento y ajustar el nivel de dificultad en consecuencia. También puede rotar el tablero de ajedrez si lo desea. </p>
-<p></p>
-<p>Otra característica del modo sin conexión es que puede retroceder un paso con el botón "Paso atrás", o avanzar o retroceder varios pasos. De esta manera, puedes revisar tus movimientos o probar diferentes variaciones. </p>
- <h4>Interfaz fácil de usar con 25 opciones de idioma</h4>
-<h2>Cómo descargar e instalar Chess Buddy APK? </h2>
-<h3>Descargar el archivo APK de APKCombo o GitHub</h3>
-<p>El primer paso para utilizar Chess Buddy APK es descargar el archivo APK, que es un archivo de paquete que contiene la aplicación y sus recursos. Puede descargar el archivo APK de APKCombo, un sitio web que proporciona descargas APK gratuitas y seguras para varias aplicaciones y juegos de Android. También puede descargar el archivo APK de GitHub, donde está disponible el código fuente de la aplicación. Para descargar de APKCombo, se puede visitar este enlace: [Ajedrez en línea Stockfish 15.1 APK (Juego de Android) - Descarga gratuita - APKCombo]( 1 ). Para descargar desde GitHub, puedes visitar este enlace: [dracogroupinc/chessbuddy: Chess Buddy Online - aplicación de ajedrez de código abierto con motor Stockfish 15.1 (github.com)]( 4 ). </p>
- <h3>Habilitar fuentes desconocidas en su dispositivo</h3>
- <h3>Instalar el archivo APK y lanzar la aplicación</h3>
-<h2>Cómo jugar al ajedrez con Chess Buddy APK? </h2>
-<h3>Modo en línea: crear una cuenta de usuario y unirse a un juego o crear su propio</h3>
-<p>Una vez que haya instalado y lanzado Chess Buddy APK, puede comenzar a jugar al ajedrez en línea con otros jugadores. Para hacer esto, debe crear una cuenta de usuario con solo un identificador de usuario que elija, siempre y cuando no sea utilizado por otro reproductor. Usted no necesita proporcionar su correo electrónico o cualquier otra cosa. También puede tener amigos y chatear con ellos en cualquier momento. Para crear una cuenta de usuario, debe tocar el botón "Online" en la pantalla principal y luego tocar el botón "Crear usuario". A continuación, puede introducir su identificador de usuario y pulsar en el botón "Crear". También puede cambiar su identificador de usuario más tarde si lo desea. </p>
-<p>Después de crear tu cuenta de usuario, puedes unirte a un juego o crear el tuyo. Para unirte a un juego, debes tocar el botón "Unirse al juego" y luego elegir un juego de la lista de juegos disponibles. También puedes filtrar los juegos por control de tiempo, rango de calificación o preferencia de color. Para crear tu propio juego, debes tocar el botón "Crear juego" y luego elegir el control de tiempo, rango de calificación y preferencia de color para tu juego. También puede elegir si permite sugerencias de Stockfish o no. Luego, debes esperar a que otro jugador se una a tu juego o invitar a un amigo a jugar contigo. </p>
- <h3>Modo sin conexión: elegir los jugadores y la configuración y comenzar el juego</h3>
-<p>Si desea jugar ajedrez fuera de línea, también puede utilizar Chess Buddy APK para eso. Puedes elegir jugar con Stockfish u otro jugador humano en el mismo dispositivo. Para hacer esto, debes tocar el botón "Offline" en la pantalla principal y luego elegir los jugadores y la configuración de tu juego. También puedes establecer el tiempo (1-5 segundos) que Stockfish usa en un movimiento y ajustar el nivel de dificultad en consecuencia. También puede rotar el tablero de ajedrez si lo desea. </p>
- <h3>Consejos y trucos para jugar al ajedrez con Chess Buddy APK</h3>
-<p>Aquí hay algunos consejos y trucos para jugar al ajedrez con Chess Buddy APK:</p>
-<ul>
-<li>Usa sabiamente las sugerencias de Stockfish: Las sugerencias de Stockfish pueden ayudarte a encontrar el mejor movimiento o evitar errores, pero también pueden hacer que el juego sea menos desafiante o divertido. Úsalos con moderación o solo cuando estés atascado o tengas curiosidad. </li>
-<li>Aprende del análisis de Stockfish: Después de cada juego, puedes ver el análisis de Stockfish para cada movimiento, incluyendo la puntuación de evaluación, profundidad, nodos y tiempo. También puedes ver un gráfico de los cambios de puntuación a lo largo del juego. Esto puede ayudarle a entender dónde cometió errores u oportunidades perdidas, y cómo mejorar sus habilidades de ajedrez. </li>
-<li>Juega con diferentes controles de tiempo y niveles de dificultad: Jugar con diferentes controles de tiempo y niveles de dificultad puede ayudarte a practicar diferentes aspectos del ajedrez, como tácticas, estrategia, cálculo, intuición y velocidad. También puedes desafiarte jugando contra oponentes más fuertes o controles de tiempo más rápidos. </li>
-<li>Divertirse y disfrutar del ajedrez: El consejo más importante es divertirse y disfrutar del ajedrez con Chess Buddy APK. El ajedrez es un juego hermoso y fascinante que puede enriquecer tu mente y vida. No te preocupes demasiado por ganar o perder, pero céntrate en aprender y divertirte. </li>
-</ul>
- <h2>Conclusión</h2>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Chess Buddy APK:</p>
-<ol>
-<li>Q: Es Chess Buddy APK seguro y legal de usar? </li>
-<li>A: Sí, Chess Buddy APK es seguro y legal de usar. Es una aplicación de código abierto que no contiene ningún malware o virus. Tampoco viola ningún derecho de propiedad intelectual o reglas de ajedrez, ya que utiliza el motor Stockfish, que también es de código abierto y de uso gratuito. </li>
-<li>Q: ¿Cómo puedo actualizar Chess Buddy APK? </li>
-<li>A: Puede actualizar Chess Buddy APK mediante la descarga e instalación de la última versión del archivo APK de APKCombo o GitHub. No es necesario desinstalar la versión anterior, ya que la nueva versión lo sobreescribirá. También puede comprobar si hay actualizaciones dentro de la aplicación pulsando en el botón "Acerca de" en la pantalla principal y luego tocando en el "Comprobar si hay actualizaciones" botón. </li>
-<li>Q: ¿Cómo puedo contactar a los desarrolladores de Chess Buddy APK? </li>
-<li>A: Puede ponerse en contacto con los desarrolladores de Chess Buddy APK visitando su página de GitHub y abriendo un problema o una solicitud de extracción. También puedes enviarles un correo electrónico a [email protected]. También puedes seguirlos en Twitter en @dracogroupinc. </li>
-<li>Q: ¿Cómo puedo apoyar el desarrollo de Chess Buddy APK? </li>
-<li>A: Usted puede apoyar el desarrollo de Chess Buddy APK mediante la donación a los desarrolladores a través de PayPal o Bitcoin. También puede compartir la aplicación con sus amigos y familiares, y dejar una opinión positiva y calificación en APKCombo. También puede contribuir al código fuente o sugerir nuevas características o mejoras en GitHub.</li>
-<li>Q: ¿Cómo puedo desinstalar Chess Buddy APK? </li>
-<li>A: Puede desinstalar Chess Buddy APK yendo a la configuración de su dispositivo y encontrar el administrador de aplicaciones o la opción de aplicaciones. Entonces, es necesario encontrar Chess Buddy APK y toque en él. Entonces, es necesario tocar en el "Desinstalar" botón y confirmar su acción. También puede desinstalar la aplicación presionando su icono en la pantalla de inicio o en el cajón de la aplicación y arrastrándola a la opción "Desinstalar". </li>
-</ol></p> 64aa2da5cf<br />
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-<br />
-<h1>Cómo descargar APK Chess Offline para Android</h1>
-<p>El ajedrez es uno de los juegos de mesa más antiguos y populares del mundo. Es un juego de lógica, estrategia y habilidad que puede desafiar tu mente y mejorar tus habilidades cognitivas. Ya seas un principiante o un gran maestro, el ajedrez puede ofrecerte interminables horas de diversión y entretenimiento. </p>
-<p>Pero ¿qué pasa si no tienes un tablero de ajedrez o un compañero para jugar? ¿Qué pasa si quieres jugar al ajedrez en cualquier momento y en cualquier lugar sin una conexión a Internet? Ahí es donde APK Chess Offline es muy útil. APK Chess Offline es una aplicación de ajedrez gratuita para dispositivos Android que te permite jugar ajedrez sin conexión contra un ordenador o un amigo. También puedes aprender ajedrez, practicar tus habilidades y analizar tus partidas con esta aplicación. </p>
-<h2>descargar apk ajedrez sin conexión</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://bltlly.com/2v6JMJ">https://bltlly.com/2v6JMJ</a></b></p><br /><br />
-<p>En este artículo, le mostraremos cómo descargar e instalar APK Chess Offline para Android, así como cómo jugar y mejorar sus habilidades de ajedrez con esta aplicación. ¡Vamos a empezar! </p>
- <h2>¿Qué es APK Chess Offline? </h2>
-<p>APK Chess Offline es una aplicación de ajedrez para dispositivos Android que no requiere una conexión a Internet para jugar. Puedes jugar ajedrez sin conexión contra una computadora o un amigo en el mismo dispositivo. También puede elegir entre diferentes modos de juego, niveles de dificultad y temas de tablero para adaptarse a sus preferencias. </p>
- <h3>Características y beneficios de APK Chess Offline</h3>
-<p>Algunas de las características y beneficios de APK Chess Offline son:</p>
-<ul>
-<li>Es gratis para descargar y jugar. </li>
-<li>No requiere una conexión a Internet ni ningún registro. </li>
-<li> Tiene una interfaz simple y fácil de usar. </li>
-<li>Ofrece varios modos de juego, tales como ajedrez clásico, chess960, fiebre del rompecabezas, ajedrez a ciegas, etc.</li>
-<li>Tiene 10 niveles de dificultad, de principiante a experto. </li>
-<li> Tiene diferentes temas de tablero, como madera, mármol, metal, etc.</li>
-<li> Tiene un motor de ajedrez incorporado que puede analizar tus movimientos y darte pistas. </li>
-<li> Tiene una función de estadísticas que realiza un seguimiento de su rendimiento y progreso. </li>
-<li> Tiene una opción de sonido y vibración que añade más realismo al juego. </li>
-</ul>
- <h3>Requisitos y compatibilidad de APK Chess Offline</h3>
-<p>Para descargar e instalar APK Chess Offline, necesita:</p>
-<ul>
-<li>Un dispositivo Android con la versión 4.1 o superior. </li>
-<li>Al menos 20 MB de espacio de almacenamiento libre en su dispositivo. </li>
-<li>Un permiso para instalar aplicaciones de fuentes desconocidas en su dispositivo. </li>
-</ul>
- <h2>Cómo descargar e instalar APK Chess Offline</h2>
-<p>Para descargar e instalar APK Chess Offline en tu dispositivo Android, sigue estos pasos:</p>
- <h3>Paso 1: Habilitar fuentes desconocidas en el dispositivo</h3>
-<p>Dado que APK Chess Offline no está disponible en Google Play Store, es necesario habilitar fuentes desconocidas en el dispositivo para instalarlo. Para hacer esto:</p>
-<ol>
-<li>Ir a Configuración > Seguridad > Fuentes desconocidas en su dispositivo. </li>
-<li>Cambiar el interruptor para permitir la instalación de aplicaciones de fuentes desconocidas. </li>
-<li>Aparecerá un mensaje de advertencia. Toca OK para confirmar. </li>
-</ol>
- <h3>Paso 2: Encontrar una fuente confiable para el archivo APK</h3>
-<p>El siguiente paso es encontrar una fuente confiable para el archivo APK de APK Chess Offline. Una de las fuentes que puede utilizar para descargar el archivo APK de APK Chess Offline es [Chess Offline Download - apkonline.net]( 1 ). Este es un sitio web confiable y verificado que ofrece descargas gratuitas y seguras de varios archivos APK. Para descargar el archivo APK de esta fuente:</p>
-<p></p>
-<ol>
-<li>Abra su navegador y vaya a [Chess Offline Download - apkonline.net]( 1 ). </li>
-<li>Toque en el botón Descargar y espere a que comience la descarga. </li>
-<li> Puede ver una notificación de que el archivo puede dañar su dispositivo. Pulse OK para continuar. </li>
-</ol>
- <h3>Paso 3: Descargar el archivo APK a su dispositivo</h3>
-<p>Una vez completada la descarga, verá una notificación de que el archivo APK está listo para instalar. Para instalar el archivo APK en su dispositivo:</p>
-<ol>
-<li>Alternativamente, puede ir a su carpeta de descargas y tocar en el archivo APK. </li>
-<li>Aparecerá una pantalla pidiéndole que confirme la instalación. Pulse Instalar de nuevo. </li>
-</ol>
- <h3>Paso 4: Instalar el archivo APK y lanzar la aplicación</h3>
-<p>El proceso de instalación tomará unos segundos. Una vez hecho, verá un mensaje de que la aplicación se ha instalado correctamente. Para iniciar la aplicación:</p>
-<ol>
-<li>Toque en Abrir para comenzar a jugar ajedrez sin conexión. </li>
-<li>Alternativamente, puede ir a su cajón de aplicaciones y toque en el icono de APK Chess Offline. </li>
-<li>Verás una pantalla de bienvenida con algunas instrucciones y opciones. Toca Jugar para comenzar tu juego. </li>
-</ol>
- <h2>Cómo jugar y mejorar sus habilidades de ajedrez con APK Chess Offline</h2>
-<p>Ahora que ha descargado e instalado APK Chess Offline en su dispositivo, usted está listo para jugar y mejorar sus habilidades de ajedrez con esta aplicación. Aquí hay algunos consejos y trucos para ayudarte a empezar:</p>
- <h3>Elige tu modo de juego y nivel de dificultad</h3>
-<p>APK Chess Offline ofrece varios modos de juego para que usted elija, tales como ajedrez clásico, chess960, puzzle rush, ajedrez a ciegas, etc. También puede seleccionar a su oponente, ya sea un ordenador o un amigo en el mismo dispositivo. También puedes ajustar el nivel de dificultad del ordenador, desde principiante hasta experto. Para elegir el modo de juego y el nivel de dificultad:</p>
-<ol>
-<li>Toque en Reproducir en la pantalla de bienvenida. </li>
-<li>Seleccione el modo de juego de la lista de opciones. </li>
-<li>Selecciona tu oponente, ya sea Computadora o Humano.</li>
-<li>Si elige Ordenador, seleccione su nivel de dificultad del 1 al 10. </li>
-<li>Toque en el juego de inicio para comenzar su juego. </li>
-</ol>
- <h3>Aprende las reglas y estrategias del ajedrez</h3>
-<ol>
-<li>Toque en Aprender en la pantalla de bienvenida. </li>
-<li>Seleccione un tema de la lista de opciones, como Reglas básicas, Principios de apertura, Tácticas, Finales, etc.</li>
-<li>Lee las instrucciones y ejemplos cuidadosamente y trata de entenderlos. </li>
-<li>Practica lo que has aprendido resolviendo algunos ejercicios y exámenes. </li>
-</ol>
- <h3>Practica con rompecabezas y desafíos</h3>
-<p>Si desea poner a prueba sus habilidades y desafiarse a sí mismo, APK Chess Offline tiene una variedad de puzzles y desafíos para que usted resuelva. Puedes elegir entre diferentes categorías, como Mate in One, Mate in Two, Mate in Three, etc. También puedes intentar resolver tantos puzzles como sea posible en un tiempo limitado con el modo Puzzle Rush. Para practicar con rompecabezas y desafíos:</p>
-<ol>
-<li>Toque en Práctica en la pantalla de bienvenida. </li>
-<li>Seleccione una categoría de la lista de opciones. </li>
-<li>Intenta resolver el rompecabezas haciendo el mejor movimiento para cada lado. </li>
-<li> Si necesita una pista, toque en el icono de bombilla en la parte inferior de la pantalla. </li>
-<li> Si desea comprobar su respuesta, toque en el icono de marca de verificación en la parte inferior de la pantalla. </li>
-</ol>
- <h3>Analiza tus juegos y aprende de tus errores</h3>
-<p>Si quieres mejorar tus habilidades de ajedrez, es importante analizar tus partidas y aprender de tus errores. APK Chess Offline tiene una función que le permite revisar sus juegos y ver dónde se fue mal o bien. También puede ver la evaluación de cada movimiento por el motor de ajedrez y obtener sugerencias para mejores movimientos. Para analizar tus juegos y aprender de tus errores:</p>
-<ol>
-<li>Toque en el historial en la pantalla de bienvenida. </li>
-<li>Seleccione un juego de la lista de sus juegos recientes. </li>
-<li>Toque en Analizar para ver la evaluación de cada movimiento por el motor de ajedrez. </li>
-<li>Toca las flechas en la parte inferior de la pantalla para avanzar o retroceder en el juego. </li>
-<li> Toque en el icono de interrogación en la parte inferior de la pantalla para ver sugerencias para mejores movimientos. </li>
-</ol>
- <h2>Conclusión</h2>
-<p>Si usted está buscando una manera de descargar e instalar APK Chess Offline para Android, siga los pasos de este artículo y usted será capaz de jugar ajedrez sin conexión en ningún momento. Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Feliz ajedrez jugando! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre APK Chess Offline:</p>
- <h3>Q: ¿Es seguro descargar e instalar APK Chess Offline? </h3>
-<p>A: Sí, APK Chess Offline es seguro para descargar e instalar, siempre y cuando utilice una fuente confiable para el archivo APK, como [Chess Offline Download - apkonline.net]. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo. También debe escanear el archivo APK con una aplicación antivirus antes de instalarlo. </p>
- <h3>Q: ¿Funciona APK Chess Offline en todos los dispositivos Android? </h3>
-<p>A: APK Chess Offline funciona en la mayoría de los dispositivos Android que tienen la versión 4.1 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con la aplicación o pueden experimentar algunos problemas o errores. Si encuentras algún problema con la aplicación, puedes intentar actualizar tu dispositivo, borrar la caché de la aplicaci��n o reinstalar la aplicación. </p>
- <h3>Q: ¿Cómo puedo actualizar APK Chess Offline? </h3>
-<p>A: Dado que APK Chess Offline no está disponible en Google Play Store, no se puede actualizar automáticamente a través de la tienda. Tienes que comprobar manualmente las actualizaciones del sitio web de origen u otros sitios web que ofrecen el archivo APK. También puedes seguir la página oficial de Facebook de APK Chess Offline para recibir notificaciones de cualquier actualización o noticia sobre la aplicación. </p>
- <h3>Q: ¿Cómo puedo desinstalar APK Chess Offline? </h3>
-<p>A: Para desinstalar APK Chess Offline desde tu dispositivo, sigue estos pasos:</p>
-<ol>
-<li>Ir a Configuración > Aplicaciones > APK Chess Offline en su dispositivo. </li>
-<li>Toque en Desinstalar y confirme su acción. </li>
-<li>La aplicación se eliminará de su dispositivo. </li>
- <h3>Q: ¿Cómo puedo contactar al desarrollador de APK Chess Offline? </h3>
-<p>A: Si tiene alguna pregunta, sugerencia, o retroalimentación sobre APK Chess Offline, puede ponerse en contacto con el desarrollador a través de su dirección de correo electrónico: [email protected]. También puede visitar su página de Facebook: [APK Chess Offline - Casa | Facebook]. </p> 64aa2da5cf<br />
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-<h1>Descargar juego Sigma APK: Un juego estilizado Shooter de supervivencia para teléfonos móviles</h1>
-<p>Si usted está buscando un nuevo y emocionante juego para jugar en su teléfono móvil, es posible que desee echa un vistazo a Game Sigma APK. Este es un estilizado juego de disparos de supervivencia que ofrece dos modos diferentes: Classic Battle Royale y 4v4 Fight Out. En este artículo, le diremos qué es Game Sigma APK, qué características tiene, cómo descargarlo e instalarlo, y algunos consejos y trucos para jugarlo. </p>
-<h2>¿Qué es el juego Sigma APK? </h2>
-<p>Juego Sigma APK es un juego desarrollado por Studio Arm Private Limited, una empresa con sede en la India. Es un juego de disparos de supervivencia que combina elementos de acción, estrategia y creatividad. El juego está disponible en dispositivos Android y se puede descargar desde varios sitios web, como APKCombo. El juego ha sido actualizado recientemente, siendo la última versión 1.0.113 a partir del 14 de enero de 2023. </p>
-<h2>descargar el juego sigma apk</h2><br /><p><b><b>Download Zip</b> &rArr;&rArr;&rArr; <a href="https://bltlly.com/2v6KcB">https://bltlly.com/2v6KcB</a></b></p><br /><br />
-<h3>Características del juego Sigma APK</h3>
-<p>Juego Sigma APK tiene muchas características que lo hacen destacar de otros juegos de disparos de supervivencia. Aquí están algunos de ellos:</p>
-<h4>- Gráficos estilizados</h4>
-<p>El juego tiene un estilo de arte único y creativo que te sumerge en un mundo de supervivencia estilizada. El juego utiliza colores vibrantes, personajes similares a dibujos animados y efectos dinámicos para crear una experiencia visualmente atractiva. El juego también funciona sin problemas en la mayoría de los dispositivos, gracias a su rendimiento optimizado. </p>
-<h4>- Experiencia de tirador de supervivencia única</h4>
-<p>El juego tiene controles fáciles de usar que prometen una experiencia de supervivencia inolvidable en el móvil. Puede mover, apuntar, disparar, saltar, agacharse e interactuar con el entorno utilizando gestos y botones simples. También puedes personalizar tus controles y ajustes según tus preferencias. </p>
-<h4>- Modo clásico Battle Royale</h4>
-<h4>- 4v4 modo de lucha</h4>
-<p>En este modo, te unirás a otros tres jugadores para luchar contra otro equipo en una batalla tensa y estratégica. Tienes que asignar recursos, comprar armas y sobrevivir a tus enemigos en varios mapas creativos. Tienes que luchar por tu fe y llevar a tu equipo a la victoria. </p>
-<h2>¿Cómo descargar e instalar el juego Sigma APK? </h2>
-<p>Si desea jugar Game Sigma APK en su dispositivo Android, usted tiene que descargar e instalar desde una fuente de terceros, como APKCombo. Estos son los pasos para hacerlo:</p>
-<h3> Pasos para descargar Juego Sigma APK de APKCombo</h3>
-<h4>- Visite el sitio web de APKCombo</h4>
-<p>Vaya a <a href="( 1 )">https://apkcombo.com/sigma/com.studioarm.sigma/</a> usando su navegador. Esta es la página oficial de Game Sigma APK en APKCombo.</p>
-<p></p>
-<h4>- Búsqueda de juego Sigma APK</h4>
-<p>Escriba "Juego Sigma APK" en la barra de búsqueda y pulse enter <h4>- Elija la versión y la compatibilidad del dispositivo</h4>
-<p>En la página APKCombo, verá diferentes versiones de Game Sigma APK, junto con su tamaño de archivo, fecha de actualización, y la compatibilidad del dispositivo. Elija la versión que se adapte a su dispositivo y haga clic en el botón de descarga. </p>
-<h4>- Descargar el archivo APK</h4>
-<p>Espere a que termine la descarga. Verá una notificación en su dispositivo cuando se descargue el archivo APK. También puede comprobar el progreso de la descarga en su navegador o en su gestor de archivos. </p>
-<h4>- Habilitar fuentes desconocidas en el dispositivo</h4>
-<p>Antes de poder instalar el archivo APK, debe habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas. Active la opción para habilitar fuentes desconocidas. </p>
-<h4>- Instalar el archivo APK</h4>
-<h3> Consejos y trucos para jugar Juego Sigma APK</h3>
-<p>Ahora que ha descargado e instalado Game Sigma APK, usted está listo para jugar. Aquí hay algunos consejos y trucos para ayudarle a disfrutar del juego más:</p>
-<h4>- Personaliza tus controles y ajustes</h4>
-<p>Antes de empezar a jugar, debes personalizar tus controles y ajustes de acuerdo a tus preferencias. Puede acceder al menú de configuración desde la pantalla principal del juego. Aquí puede ajustar la sensibilidad, el sonido, los gráficos, el idioma y otras opciones. También puedes personalizar tus controles arrastrando y redimensionando los botones de la pantalla. </p>
-<h4>- Elija su punto de aterrizaje sabiamente</h4>
-<p>En el modo Classic Battle Royale, tienes que elegir tu punto de aterrizaje con tu paracaídas. Usted debe elegir un lugar que tiene un buen botín, pero también tiene menos enemigos. Puedes usar el mapa para ver dónde están aterrizando otros jugadores y evitar las áreas llenas. También puedes usar los marcadores para comunicarte con tus compañeros de equipo y coordinar tu aterrizaje. </p>
-<h4>- Saquear y equipar las mejores armas y artículos</h4>
-<p>Una vez que aterrizas, tienes que saquear y equipar las mejores armas y objetos que puedas encontrar. Puedes saquear edificios, cajas, vehículos y enemigos muertos. Puedes equipar hasta dos armas primarias, un arma secundaria y una arma cuerpo a cuerpo. También puede equipar armaduras, cascos, mochilas, granadas, botiquines y otros artículos. Siempre debe buscar un mejor botín mientras juega. </p>
-<h4>- Utilice la cubierta y el sigilo a su ventaja</h4>
-<p>El juego no se trata solo de disparos, sino también de supervivencia. Tienes que usar la cubierta y el sigilo para tu ventaja. Puedes usar edificios, árboles, rocas, vehículos y otros objetos como cobertura del fuego enemigo. También puede usar posiciones agachadas y propensas para reducir su visibilidad y ruido. Siempre debes ser consciente de tu entorno y evitar exponerte demasiado. </p>
-<h4>- Comunicarse y cooperar con sus compañeros de equipo</h4>
-<h2>Conclusión</h2>
-<p>Juego Sigma APK es un juego de disparos de supervivencia estilizada que ofrece dos modos diferentes: Classic Battle Royale y 4v4 Fight Out. Tiene muchas características que lo hacen destacar de otros juegos de disparos de supervivencia, tales como gráficos estilizados, experiencia de tirador de supervivencia única, controles fáciles de usar y rendimiento optimizado. Puede descargar e instalar Game Sigma APK de APKCombo, siguiendo los pasos que hemos proporcionado en este artículo. También puede utilizar nuestros consejos y trucos para mejorar su juego y divertirse más. </p>
- FAQs - Q: ¿Es seguro descargar Game Sigma APK? - A: Sí, Game Sigma APK es seguro para descargar desde APKCombo, ya que es verificado por VirusTotal y no contiene ningún malware o virus. - P: ¿Juego Sigma APK es libre de jugar? - A: Sí, Juego Sigma APK es libre de jugar, pero puede contener anuncios y compras en la aplicación. - P: ¿Cuáles son los requisitos mínimos para jugar Game Sigma APK? - A: Los requisitos mínimos para jugar Game Sigma APK son Android 5.0 o superior, 2 GB de RAM, 1 GB de espacio de almacenamiento, y una conexión a Internet estable. - P: ¿Cómo puedo actualizar Game Sigma APK? - A: Puede actualizar Game Sigma APK visitando el sitio web APKCombo y descargar la última versión del juego. También puede comprobar si hay actualizaciones desde la configuración del juego. - P: ¿Cómo puedo contactar a los desarrolladores de Game Sigma APK? - A: Puede ponerse en contacto con los desarrolladores de Game Sigma APK visitando su sitio web oficial, página de Facebook, o cuenta de Instagram. También puede enviarles un correo electrónico a [email protected]. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/securetransport.py DELETED Viewed

@@ -1,921 +0,0 @@
-"""
-SecureTranport support for urllib3 via ctypes.
-This makes platform-native TLS available to urllib3 users on macOS without the
-use of a compiler. This is an important feature because the Python Package
-Index is moving to become a TLSv1.2-or-higher server, and the default OpenSSL
-that ships with macOS is not capable of doing TLSv1.2. The only way to resolve
-this is to give macOS users an alternative solution to the problem, and that
-solution is to use SecureTransport.
-We use ctypes here because this solution must not require a compiler. That's
-because pip is not allowed to require a compiler either.
-This is not intended to be a seriously long-term solution to this problem.
-The hope is that PEP 543 will eventually solve this issue for us, at which
-point we can retire this contrib module. But in the short term, we need to
-solve the impending tire fire that is Python on Mac without this kind of
-contrib module. So...here we are.
-To use this module, simply import and inject it::
-    import urllib3.contrib.securetransport
-    urllib3.contrib.securetransport.inject_into_urllib3()
-Happy TLSing!
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-.. code-block::
-    Copyright (c) 2015-2016 Will Bond <[email protected]>
-    Permission is hereby granted, free of charge, to any person obtaining a
-    copy of this software and associated documentation files (the "Software"),
-    to deal in the Software without restriction, including without limitation
-    the rights to use, copy, modify, merge, publish, distribute, sublicense,
-    and/or sell copies of the Software, and to permit persons to whom the
-    Software is furnished to do so, subject to the following conditions:
-    The above copyright notice and this permission notice shall be included in
-    all copies or substantial portions of the Software.
-    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-    DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-import contextlib
-import ctypes
-import errno
-import os.path
-import shutil
-import socket
-import ssl
-import struct
-import threading
-import weakref
-import six
-from .. import util
-from ..util.ssl_ import PROTOCOL_TLS_CLIENT
-from ._securetransport.bindings import CoreFoundation, Security, SecurityConst
-from ._securetransport.low_level import (
-    _assert_no_error,
-    _build_tls_unknown_ca_alert,
-    _cert_array_from_pem,
-    _create_cfstring_array,
-    _load_client_cert_chain,
-    _temporary_keychain,
-)
-try:  # Platform-specific: Python 2
-    from socket import _fileobject
-except ImportError:  # Platform-specific: Python 3
-    _fileobject = None
-    from ..packages.backports.makefile import backport_makefile
-__all__ = ["inject_into_urllib3", "extract_from_urllib3"]
-# SNI always works
-HAS_SNI = True
-orig_util_HAS_SNI = util.HAS_SNI
-orig_util_SSLContext = util.ssl_.SSLContext
-# This dictionary is used by the read callback to obtain a handle to the
-# calling wrapped socket. This is a pretty silly approach, but for now it'll
-# do. I feel like I should be able to smuggle a handle to the wrapped socket
-# directly in the SSLConnectionRef, but for now this approach will work I
-# guess.
-#
-# We need to lock around this structure for inserts, but we don't do it for
-# reads/writes in the callbacks. The reasoning here goes as follows:
-#
-#    1. It is not possible to call into the callbacks before the dictionary is
-#       populated, so once in the callback the id must be in the dictionary.
-#    2. The callbacks don't mutate the dictionary, they only read from it, and
-#       so cannot conflict with any of the insertions.
-#
-# This is good: if we had to lock in the callbacks we'd drastically slow down
-# the performance of this code.
-_connection_refs = weakref.WeakValueDictionary()
-_connection_ref_lock = threading.Lock()
-# Limit writes to 16kB. This is OpenSSL's limit, but we'll cargo-cult it over
-# for no better reason than we need *a* limit, and this one is right there.
-SSL_WRITE_BLOCKSIZE = 16384
-# This is our equivalent of util.ssl_.DEFAULT_CIPHERS, but expanded out to
-# individual cipher suites. We need to do this because this is how
-# SecureTransport wants them.
-CIPHER_SUITES = [
-    SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384,
-    SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256,
-    SecurityConst.TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384,
-    SecurityConst.TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256,
-    SecurityConst.TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256,
-    SecurityConst.TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256,
-    SecurityConst.TLS_DHE_RSA_WITH_AES_256_GCM_SHA384,
-    SecurityConst.TLS_DHE_RSA_WITH_AES_128_GCM_SHA256,
-    SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384,
-    SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA,
-    SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256,
-    SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA,
-    SecurityConst.TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384,
-    SecurityConst.TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA,
-    SecurityConst.TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256,
-    SecurityConst.TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA,
-    SecurityConst.TLS_DHE_RSA_WITH_AES_256_CBC_SHA256,
-    SecurityConst.TLS_DHE_RSA_WITH_AES_256_CBC_SHA,
-    SecurityConst.TLS_DHE_RSA_WITH_AES_128_CBC_SHA256,
-    SecurityConst.TLS_DHE_RSA_WITH_AES_128_CBC_SHA,
-    SecurityConst.TLS_AES_256_GCM_SHA384,
-    SecurityConst.TLS_AES_128_GCM_SHA256,
-    SecurityConst.TLS_RSA_WITH_AES_256_GCM_SHA384,
-    SecurityConst.TLS_RSA_WITH_AES_128_GCM_SHA256,
-    SecurityConst.TLS_AES_128_CCM_8_SHA256,
-    SecurityConst.TLS_AES_128_CCM_SHA256,
-    SecurityConst.TLS_RSA_WITH_AES_256_CBC_SHA256,
-    SecurityConst.TLS_RSA_WITH_AES_128_CBC_SHA256,
-    SecurityConst.TLS_RSA_WITH_AES_256_CBC_SHA,
-    SecurityConst.TLS_RSA_WITH_AES_128_CBC_SHA,
-]
-# Basically this is simple: for PROTOCOL_SSLv23 we turn it into a low of
-# TLSv1 and a high of TLSv1.2. For everything else, we pin to that version.
-# TLSv1 to 1.2 are supported on macOS 10.8+
-_protocol_to_min_max = {
-    util.PROTOCOL_TLS: (SecurityConst.kTLSProtocol1, SecurityConst.kTLSProtocol12),
-    PROTOCOL_TLS_CLIENT: (SecurityConst.kTLSProtocol1, SecurityConst.kTLSProtocol12),
-}
-if hasattr(ssl, "PROTOCOL_SSLv2"):
-    _protocol_to_min_max[ssl.PROTOCOL_SSLv2] = (
-        SecurityConst.kSSLProtocol2,
-        SecurityConst.kSSLProtocol2,
-    )
-if hasattr(ssl, "PROTOCOL_SSLv3"):
-    _protocol_to_min_max[ssl.PROTOCOL_SSLv3] = (
-        SecurityConst.kSSLProtocol3,
-        SecurityConst.kSSLProtocol3,
-    )
-if hasattr(ssl, "PROTOCOL_TLSv1"):
-    _protocol_to_min_max[ssl.PROTOCOL_TLSv1] = (
-        SecurityConst.kTLSProtocol1,
-        SecurityConst.kTLSProtocol1,
-    )
-if hasattr(ssl, "PROTOCOL_TLSv1_1"):
-    _protocol_to_min_max[ssl.PROTOCOL_TLSv1_1] = (
-        SecurityConst.kTLSProtocol11,
-        SecurityConst.kTLSProtocol11,
-    )
-if hasattr(ssl, "PROTOCOL_TLSv1_2"):
-    _protocol_to_min_max[ssl.PROTOCOL_TLSv1_2] = (
-        SecurityConst.kTLSProtocol12,
-        SecurityConst.kTLSProtocol12,
-    )
-def inject_into_urllib3():
-    """
-    Monkey-patch urllib3 with SecureTransport-backed SSL-support.
-    """
-    util.SSLContext = SecureTransportContext
-    util.ssl_.SSLContext = SecureTransportContext
-    util.HAS_SNI = HAS_SNI
-    util.ssl_.HAS_SNI = HAS_SNI
-    util.IS_SECURETRANSPORT = True
-    util.ssl_.IS_SECURETRANSPORT = True
-def extract_from_urllib3():
-    """
-    Undo monkey-patching by :func:`inject_into_urllib3`.
-    """
-    util.SSLContext = orig_util_SSLContext
-    util.ssl_.SSLContext = orig_util_SSLContext
-    util.HAS_SNI = orig_util_HAS_SNI
-    util.ssl_.HAS_SNI = orig_util_HAS_SNI
-    util.IS_SECURETRANSPORT = False
-    util.ssl_.IS_SECURETRANSPORT = False
-def _read_callback(connection_id, data_buffer, data_length_pointer):
-    """
-    SecureTransport read callback. This is called by ST to request that data
-    be returned from the socket.
-    """
-    wrapped_socket = None
-    try:
-        wrapped_socket = _connection_refs.get(connection_id)
-        if wrapped_socket is None:
-            return SecurityConst.errSSLInternal
-        base_socket = wrapped_socket.socket
-        requested_length = data_length_pointer[0]
-        timeout = wrapped_socket.gettimeout()
-        error = None
-        read_count = 0
-        try:
-            while read_count < requested_length:
-                if timeout is None or timeout >= 0:
-                    if not util.wait_for_read(base_socket, timeout):
-                        raise socket.error(errno.EAGAIN, "timed out")
-                remaining = requested_length - read_count
-                buffer = (ctypes.c_char * remaining).from_address(
-                    data_buffer + read_count
-                )
-                chunk_size = base_socket.recv_into(buffer, remaining)
-                read_count += chunk_size
-                if not chunk_size:
-                    if not read_count:
-                        return SecurityConst.errSSLClosedGraceful
-                    break
-        except (socket.error) as e:
-            error = e.errno
-            if error is not None and error != errno.EAGAIN:
-                data_length_pointer[0] = read_count
-                if error == errno.ECONNRESET or error == errno.EPIPE:
-                    return SecurityConst.errSSLClosedAbort
-                raise
-        data_length_pointer[0] = read_count
-        if read_count != requested_length:
-            return SecurityConst.errSSLWouldBlock
-        return 0
-    except Exception as e:
-        if wrapped_socket is not None:
-            wrapped_socket._exception = e
-        return SecurityConst.errSSLInternal
-def _write_callback(connection_id, data_buffer, data_length_pointer):
-    """
-    SecureTransport write callback. This is called by ST to request that data
-    actually be sent on the network.
-    """
-    wrapped_socket = None
-    try:
-        wrapped_socket = _connection_refs.get(connection_id)
-        if wrapped_socket is None:
-            return SecurityConst.errSSLInternal
-        base_socket = wrapped_socket.socket
-        bytes_to_write = data_length_pointer[0]
-        data = ctypes.string_at(data_buffer, bytes_to_write)
-        timeout = wrapped_socket.gettimeout()
-        error = None
-        sent = 0
-        try:
-            while sent < bytes_to_write:
-                if timeout is None or timeout >= 0:
-                    if not util.wait_for_write(base_socket, timeout):
-                        raise socket.error(errno.EAGAIN, "timed out")
-                chunk_sent = base_socket.send(data)
-                sent += chunk_sent
-                # This has some needless copying here, but I'm not sure there's
-                # much value in optimising this data path.
-                data = data[chunk_sent:]
-        except (socket.error) as e:
-            error = e.errno
-            if error is not None and error != errno.EAGAIN:
-                data_length_pointer[0] = sent
-                if error == errno.ECONNRESET or error == errno.EPIPE:
-                    return SecurityConst.errSSLClosedAbort
-                raise
-        data_length_pointer[0] = sent
-        if sent != bytes_to_write:
-            return SecurityConst.errSSLWouldBlock
-        return 0
-    except Exception as e:
-        if wrapped_socket is not None:
-            wrapped_socket._exception = e
-        return SecurityConst.errSSLInternal
-# We need to keep these two objects references alive: if they get GC'd while
-# in use then SecureTransport could attempt to call a function that is in freed
-# memory. That would be...uh...bad. Yeah, that's the word. Bad.
-_read_callback_pointer = Security.SSLReadFunc(_read_callback)
-_write_callback_pointer = Security.SSLWriteFunc(_write_callback)
-class WrappedSocket(object):
-    """
-    API-compatibility wrapper for Python's OpenSSL wrapped socket object.
-    Note: _makefile_refs, _drop(), and _reuse() are needed for the garbage
-    collector of PyPy.
-    """
-    def __init__(self, socket):
-        self.socket = socket
-        self.context = None
-        self._makefile_refs = 0
-        self._closed = False
-        self._exception = None
-        self._keychain = None
-        self._keychain_dir = None
-        self._client_cert_chain = None
-        # We save off the previously-configured timeout and then set it to
-        # zero. This is done because we use select and friends to handle the
-        # timeouts, but if we leave the timeout set on the lower socket then
-        # Python will "kindly" call select on that socket again for us. Avoid
-        # that by forcing the timeout to zero.
-        self._timeout = self.socket.gettimeout()
-        self.socket.settimeout(0)
-    @contextlib.contextmanager
-    def _raise_on_error(self):
-        """
-        A context manager that can be used to wrap calls that do I/O from
-        SecureTransport. If any of the I/O callbacks hit an exception, this
-        context manager will correctly propagate the exception after the fact.
-        This avoids silently swallowing those exceptions.
-        It also correctly forces the socket closed.
-        """
-        self._exception = None
-        # We explicitly don't catch around this yield because in the unlikely
-        # event that an exception was hit in the block we don't want to swallow
-        # it.
-        yield
-        if self._exception is not None:
-            exception, self._exception = self._exception, None
-            self.close()
-            raise exception
-    def _set_ciphers(self):
-        """
-        Sets up the allowed ciphers. By default this matches the set in
-        util.ssl_.DEFAULT_CIPHERS, at least as supported by macOS. This is done
-        custom and doesn't allow changing at this time, mostly because parsing
-        OpenSSL cipher strings is going to be a freaking nightmare.
-        """
-        ciphers = (Security.SSLCipherSuite * len(CIPHER_SUITES))(*CIPHER_SUITES)
-        result = Security.SSLSetEnabledCiphers(
-            self.context, ciphers, len(CIPHER_SUITES)
-        )
-        _assert_no_error(result)
-    def _set_alpn_protocols(self, protocols):
-        """
-        Sets up the ALPN protocols on the context.
-        """
-        if not protocols:
-            return
-        protocols_arr = _create_cfstring_array(protocols)
-        try:
-            result = Security.SSLSetALPNProtocols(self.context, protocols_arr)
-            _assert_no_error(result)
-        finally:
-            CoreFoundation.CFRelease(protocols_arr)
-    def _custom_validate(self, verify, trust_bundle):
-        """
-        Called when we have set custom validation. We do this in two cases:
-        first, when cert validation is entirely disabled; and second, when
-        using a custom trust DB.
-        Raises an SSLError if the connection is not trusted.
-        """
-        # If we disabled cert validation, just say: cool.
-        if not verify:
-            return
-        successes = (
-            SecurityConst.kSecTrustResultUnspecified,
-            SecurityConst.kSecTrustResultProceed,
-        )
-        try:
-            trust_result = self._evaluate_trust(trust_bundle)
-            if trust_result in successes:
-                return
-            reason = "error code: %d" % (trust_result,)
-        except Exception as e:
-            # Do not trust on error
-            reason = "exception: %r" % (e,)
-        # SecureTransport does not send an alert nor shuts down the connection.
-        rec = _build_tls_unknown_ca_alert(self.version())
-        self.socket.sendall(rec)
-        # close the connection immediately
-        # l_onoff = 1, activate linger
-        # l_linger = 0, linger for 0 seoncds
-        opts = struct.pack("ii", 1, 0)
-        self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER, opts)
-        self.close()
-        raise ssl.SSLError("certificate verify failed, %s" % reason)
-    def _evaluate_trust(self, trust_bundle):
-        # We want data in memory, so load it up.
-        if os.path.isfile(trust_bundle):
-            with open(trust_bundle, "rb") as f:
-                trust_bundle = f.read()
-        cert_array = None
-        trust = Security.SecTrustRef()
-        try:
-            # Get a CFArray that contains the certs we want.
-            cert_array = _cert_array_from_pem(trust_bundle)
-            # Ok, now the hard part. We want to get the SecTrustRef that ST has
-            # created for this connection, shove our CAs into it, tell ST to
-            # ignore everything else it knows, and then ask if it can build a
-            # chain. This is a buuuunch of code.
-            result = Security.SSLCopyPeerTrust(self.context, ctypes.byref(trust))
-            _assert_no_error(result)
-            if not trust:
-                raise ssl.SSLError("Failed to copy trust reference")
-            result = Security.SecTrustSetAnchorCertificates(trust, cert_array)
-            _assert_no_error(result)
-            result = Security.SecTrustSetAnchorCertificatesOnly(trust, True)
-            _assert_no_error(result)
-            trust_result = Security.SecTrustResultType()
-            result = Security.SecTrustEvaluate(trust, ctypes.byref(trust_result))
-            _assert_no_error(result)
-        finally:
-            if trust:
-                CoreFoundation.CFRelease(trust)
-            if cert_array is not None:
-                CoreFoundation.CFRelease(cert_array)
-        return trust_result.value
-    def handshake(
-        self,
-        server_hostname,
-        verify,
-        trust_bundle,
-        min_version,
-        max_version,
-        client_cert,
-        client_key,
-        client_key_passphrase,
-        alpn_protocols,
-    ):
-        """
-        Actually performs the TLS handshake. This is run automatically by
-        wrapped socket, and shouldn't be needed in user code.
-        """
-        # First, we do the initial bits of connection setup. We need to create
-        # a context, set its I/O funcs, and set the connection reference.
-        self.context = Security.SSLCreateContext(
-            None, SecurityConst.kSSLClientSide, SecurityConst.kSSLStreamType
-        )
-        result = Security.SSLSetIOFuncs(
-            self.context, _read_callback_pointer, _write_callback_pointer
-        )
-        _assert_no_error(result)
-        # Here we need to compute the handle to use. We do this by taking the
-        # id of self modulo 2**31 - 1. If this is already in the dictionary, we
-        # just keep incrementing by one until we find a free space.
-        with _connection_ref_lock:
-            handle = id(self) % 2147483647
-            while handle in _connection_refs:
-                handle = (handle + 1) % 2147483647
-            _connection_refs[handle] = self
-        result = Security.SSLSetConnection(self.context, handle)
-        _assert_no_error(result)
-        # If we have a server hostname, we should set that too.
-        if server_hostname:
-            if not isinstance(server_hostname, bytes):
-                server_hostname = server_hostname.encode("utf-8")
-            result = Security.SSLSetPeerDomainName(
-                self.context, server_hostname, len(server_hostname)
-            )
-            _assert_no_error(result)
-        # Setup the ciphers.
-        self._set_ciphers()
-        # Setup the ALPN protocols.
-        self._set_alpn_protocols(alpn_protocols)
-        # Set the minimum and maximum TLS versions.
-        result = Security.SSLSetProtocolVersionMin(self.context, min_version)
-        _assert_no_error(result)
-        result = Security.SSLSetProtocolVersionMax(self.context, max_version)
-        _assert_no_error(result)
-        # If there's a trust DB, we need to use it. We do that by telling
-        # SecureTransport to break on server auth. We also do that if we don't
-        # want to validate the certs at all: we just won't actually do any
-        # authing in that case.
-        if not verify or trust_bundle is not None:
-            result = Security.SSLSetSessionOption(
-                self.context, SecurityConst.kSSLSessionOptionBreakOnServerAuth, True
-            )
-            _assert_no_error(result)
-        # If there's a client cert, we need to use it.
-        if client_cert:
-            self._keychain, self._keychain_dir = _temporary_keychain()
-            self._client_cert_chain = _load_client_cert_chain(
-                self._keychain, client_cert, client_key
-            )
-            result = Security.SSLSetCertificate(self.context, self._client_cert_chain)
-            _assert_no_error(result)
-        while True:
-            with self._raise_on_error():
-                result = Security.SSLHandshake(self.context)
-                if result == SecurityConst.errSSLWouldBlock:
-                    raise socket.timeout("handshake timed out")
-                elif result == SecurityConst.errSSLServerAuthCompleted:
-                    self._custom_validate(verify, trust_bundle)
-                    continue
-                else:
-                    _assert_no_error(result)
-                    break
-    def fileno(self):
-        return self.socket.fileno()
-    # Copy-pasted from Python 3.5 source code
-    def _decref_socketios(self):
-        if self._makefile_refs > 0:
-            self._makefile_refs -= 1
-        if self._closed:
-            self.close()
-    def recv(self, bufsiz):
-        buffer = ctypes.create_string_buffer(bufsiz)
-        bytes_read = self.recv_into(buffer, bufsiz)
-        data = buffer[:bytes_read]
-        return data
-    def recv_into(self, buffer, nbytes=None):
-        # Read short on EOF.
-        if self._closed:
-            return 0
-        if nbytes is None:
-            nbytes = len(buffer)
-        buffer = (ctypes.c_char * nbytes).from_buffer(buffer)
-        processed_bytes = ctypes.c_size_t(0)
-        with self._raise_on_error():
-            result = Security.SSLRead(
-                self.context, buffer, nbytes, ctypes.byref(processed_bytes)
-            )
-        # There are some result codes that we want to treat as "not always
-        # errors". Specifically, those are errSSLWouldBlock,
-        # errSSLClosedGraceful, and errSSLClosedNoNotify.
-        if result == SecurityConst.errSSLWouldBlock:
-            # If we didn't process any bytes, then this was just a time out.
-            # However, we can get errSSLWouldBlock in situations when we *did*
-            # read some data, and in those cases we should just read "short"
-            # and return.
-            if processed_bytes.value == 0:
-                # Timed out, no data read.
-                raise socket.timeout("recv timed out")
-        elif result in (
-            SecurityConst.errSSLClosedGraceful,
-            SecurityConst.errSSLClosedNoNotify,
-        ):
-            # The remote peer has closed this connection. We should do so as
-            # well. Note that we don't actually return here because in
-            # principle this could actually be fired along with return data.
-            # It's unlikely though.
-            self.close()
-        else:
-            _assert_no_error(result)
-        # Ok, we read and probably succeeded. We should return whatever data
-        # was actually read.
-        return processed_bytes.value
-    def settimeout(self, timeout):
-        self._timeout = timeout
-    def gettimeout(self):
-        return self._timeout
-    def send(self, data):
-        processed_bytes = ctypes.c_size_t(0)
-        with self._raise_on_error():
-            result = Security.SSLWrite(
-                self.context, data, len(data), ctypes.byref(processed_bytes)
-            )
-        if result == SecurityConst.errSSLWouldBlock and processed_bytes.value == 0:
-            # Timed out
-            raise socket.timeout("send timed out")
-        else:
-            _assert_no_error(result)
-        # We sent, and probably succeeded. Tell them how much we sent.
-        return processed_bytes.value
-    def sendall(self, data):
-        total_sent = 0
-        while total_sent < len(data):
-            sent = self.send(data[total_sent : total_sent + SSL_WRITE_BLOCKSIZE])
-            total_sent += sent
-    def shutdown(self):
-        with self._raise_on_error():
-            Security.SSLClose(self.context)
-    def close(self):
-        # TODO: should I do clean shutdown here? Do I have to?
-        if self._makefile_refs < 1:
-            self._closed = True
-            if self.context:
-                CoreFoundation.CFRelease(self.context)
-                self.context = None
-            if self._client_cert_chain:
-                CoreFoundation.CFRelease(self._client_cert_chain)
-                self._client_cert_chain = None
-            if self._keychain:
-                Security.SecKeychainDelete(self._keychain)
-                CoreFoundation.CFRelease(self._keychain)
-                shutil.rmtree(self._keychain_dir)
-                self._keychain = self._keychain_dir = None
-            return self.socket.close()
-        else:
-            self._makefile_refs -= 1
-    def getpeercert(self, binary_form=False):
-        # Urgh, annoying.
-        #
-        # Here's how we do this:
-        #
-        # 1. Call SSLCopyPeerTrust to get hold of the trust object for this
-        #    connection.
-        # 2. Call SecTrustGetCertificateAtIndex for index 0 to get the leaf.
-        # 3. To get the CN, call SecCertificateCopyCommonName and process that
-        #    string so that it's of the appropriate type.
-        # 4. To get the SAN, we need to do something a bit more complex:
-        #    a. Call SecCertificateCopyValues to get the data, requesting
-        #       kSecOIDSubjectAltName.
-        #    b. Mess about with this dictionary to try to get the SANs out.
-        #
-        # This is gross. Really gross. It's going to be a few hundred LoC extra
-        # just to repeat something that SecureTransport can *already do*. So my
-        # operating assumption at this time is that what we want to do is
-        # instead to just flag to urllib3 that it shouldn't do its own hostname
-        # validation when using SecureTransport.
-        if not binary_form:
-            raise ValueError("SecureTransport only supports dumping binary certs")
-        trust = Security.SecTrustRef()
-        certdata = None
-        der_bytes = None
-        try:
-            # Grab the trust store.
-            result = Security.SSLCopyPeerTrust(self.context, ctypes.byref(trust))
-            _assert_no_error(result)
-            if not trust:
-                # Probably we haven't done the handshake yet. No biggie.
-                return None
-            cert_count = Security.SecTrustGetCertificateCount(trust)
-            if not cert_count:
-                # Also a case that might happen if we haven't handshaked.
-                # Handshook? Handshaken?
-                return None
-            leaf = Security.SecTrustGetCertificateAtIndex(trust, 0)
-            assert leaf
-            # Ok, now we want the DER bytes.
-            certdata = Security.SecCertificateCopyData(leaf)
-            assert certdata
-            data_length = CoreFoundation.CFDataGetLength(certdata)
-            data_buffer = CoreFoundation.CFDataGetBytePtr(certdata)
-            der_bytes = ctypes.string_at(data_buffer, data_length)
-        finally:
-            if certdata:
-                CoreFoundation.CFRelease(certdata)
-            if trust:
-                CoreFoundation.CFRelease(trust)
-        return der_bytes
-    def version(self):
-        protocol = Security.SSLProtocol()
-        result = Security.SSLGetNegotiatedProtocolVersion(
-            self.context, ctypes.byref(protocol)
-        )
-        _assert_no_error(result)
-        if protocol.value == SecurityConst.kTLSProtocol13:
-            raise ssl.SSLError("SecureTransport does not support TLS 1.3")
-        elif protocol.value == SecurityConst.kTLSProtocol12:
-            return "TLSv1.2"
-        elif protocol.value == SecurityConst.kTLSProtocol11:
-            return "TLSv1.1"
-        elif protocol.value == SecurityConst.kTLSProtocol1:
-            return "TLSv1"
-        elif protocol.value == SecurityConst.kSSLProtocol3:
-            return "SSLv3"
-        elif protocol.value == SecurityConst.kSSLProtocol2:
-            return "SSLv2"
-        else:
-            raise ssl.SSLError("Unknown TLS version: %r" % protocol)
-    def _reuse(self):
-        self._makefile_refs += 1
-    def _drop(self):
-        if self._makefile_refs < 1:
-            self.close()
-        else:
-            self._makefile_refs -= 1
-if _fileobject:  # Platform-specific: Python 2
-    def makefile(self, mode, bufsize=-1):
-        self._makefile_refs += 1
-        return _fileobject(self, mode, bufsize, close=True)
-else:  # Platform-specific: Python 3
-    def makefile(self, mode="r", buffering=None, *args, **kwargs):
-        # We disable buffering with SecureTransport because it conflicts with
-        # the buffering that ST does internally (see issue #1153 for more).
-        buffering = 0
-        return backport_makefile(self, mode, buffering, *args, **kwargs)
-WrappedSocket.makefile = makefile
-class SecureTransportContext(object):
-    """
-    I am a wrapper class for the SecureTransport library, to translate the
-    interface of the standard library ``SSLContext`` object to calls into
-    SecureTransport.
-    """
-    def __init__(self, protocol):
-        self._min_version, self._max_version = _protocol_to_min_max[protocol]
-        self._options = 0
-        self._verify = False
-        self._trust_bundle = None
-        self._client_cert = None
-        self._client_key = None
-        self._client_key_passphrase = None
-        self._alpn_protocols = None
-    @property
-    def check_hostname(self):
-        """
-        SecureTransport cannot have its hostname checking disabled. For more,
-        see the comment on getpeercert() in this file.
-        """
-        return True
-    @check_hostname.setter
-    def check_hostname(self, value):
-        """
-        SecureTransport cannot have its hostname checking disabled. For more,
-        see the comment on getpeercert() in this file.
-        """
-        pass
-    @property
-    def options(self):
-        # TODO: Well, crap.
-        #
-        # So this is the bit of the code that is the most likely to cause us
-        # trouble. Essentially we need to enumerate all of the SSL options that
-        # users might want to use and try to see if we can sensibly translate
-        # them, or whether we should just ignore them.
-        return self._options
-    @options.setter
-    def options(self, value):
-        # TODO: Update in line with above.
-        self._options = value
-    @property
-    def verify_mode(self):
-        return ssl.CERT_REQUIRED if self._verify else ssl.CERT_NONE
-    @verify_mode.setter
-    def verify_mode(self, value):
-        self._verify = True if value == ssl.CERT_REQUIRED else False
-    def set_default_verify_paths(self):
-        # So, this has to do something a bit weird. Specifically, what it does
-        # is nothing.
-        #
-        # This means that, if we had previously had load_verify_locations
-        # called, this does not undo that. We need to do that because it turns
-        # out that the rest of the urllib3 code will attempt to load the
-        # default verify paths if it hasn't been told about any paths, even if
-        # the context itself was sometime earlier. We resolve that by just
-        # ignoring it.
-        pass
-    def load_default_certs(self):
-        return self.set_default_verify_paths()
-    def set_ciphers(self, ciphers):
-        # For now, we just require the default cipher string.
-        if ciphers != util.ssl_.DEFAULT_CIPHERS:
-            raise ValueError("SecureTransport doesn't support custom cipher strings")
-    def load_verify_locations(self, cafile=None, capath=None, cadata=None):
-        # OK, we only really support cadata and cafile.
-        if capath is not None:
-            raise ValueError("SecureTransport does not support cert directories")
-        # Raise if cafile does not exist.
-        if cafile is not None:
-            with open(cafile):
-                pass
-        self._trust_bundle = cafile or cadata
-    def load_cert_chain(self, certfile, keyfile=None, password=None):
-        self._client_cert = certfile
-        self._client_key = keyfile
-        self._client_cert_passphrase = password
-    def set_alpn_protocols(self, protocols):
-        """
-        Sets the ALPN protocols that will later be set on the context.
-        Raises a NotImplementedError if ALPN is not supported.
-        """
-        if not hasattr(Security, "SSLSetALPNProtocols"):
-            raise NotImplementedError(
-                "SecureTransport supports ALPN only in macOS 10.12+"
-            )
-        self._alpn_protocols = [six.ensure_binary(p) for p in protocols]
-    def wrap_socket(
-        self,
-        sock,
-        server_side=False,
-        do_handshake_on_connect=True,
-        suppress_ragged_eofs=True,
-        server_hostname=None,
-    ):
-        # So, what do we do here? Firstly, we assert some properties. This is a
-        # stripped down shim, so there is some functionality we don't support.
-        # See PEP 543 for the real deal.
-        assert not server_side
-        assert do_handshake_on_connect
-        assert suppress_ragged_eofs
-        # Ok, we're good to go. Now we want to create the wrapped socket object
-        # and store it in the appropriate place.
-        wrapped_socket = WrappedSocket(sock)
-        # Now we can handshake
-        wrapped_socket.handshake(
-            server_hostname,
-            self._verify,
-            self._trust_bundle,
-            self._min_version,
-            self._max_version,
-            self._client_cert,
-            self._client_key,
-            self._client_key_passphrase,
-            self._alpn_protocols,
-        )
-        return wrapped_socket

spaces/Boadiwaa/Recipes/openai/object_classes.py DELETED Viewed

@@ -1,10 +0,0 @@
-from openai import api_resources
-from openai.api_resources.experimental.completion_config import CompletionConfig
-OBJECT_CLASSES = {
-    "engine": api_resources.Engine,
-    "experimental.completion_config": CompletionConfig,
-    "file": api_resources.File,
-    "fine-tune": api_resources.FineTune,
-    "model": api_resources.Model,
-}

spaces/CVPR/LIVE/pybind11/tests/test_chrono.cpp DELETED Viewed

@@ -1,56 +0,0 @@
-/*
-    tests/test_chrono.cpp -- test conversions to/from std::chrono types
-    Copyright (c) 2016 Trent Houliston <[email protected]> and
-                       Wenzel Jakob <[email protected]>
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-#include "pybind11_tests.h"
-#include <pybind11/chrono.h>
-#include <chrono>
-TEST_SUBMODULE(chrono, m) {
-    using system_time = std::chrono::system_clock::time_point;
-    using steady_time = std::chrono::steady_clock::time_point;
-    using timespan = std::chrono::duration<int64_t, std::nano>;
-    using timestamp = std::chrono::time_point<std::chrono::system_clock, timespan>;
-    // test_chrono_system_clock
-    // Return the current time off the wall clock
-    m.def("test_chrono1", []() { return std::chrono::system_clock::now(); });
-    // test_chrono_system_clock_roundtrip
-    // Round trip the passed in system clock time
-    m.def("test_chrono2", [](system_time t) { return t; });
-    // test_chrono_duration_roundtrip
-    // Round trip the passed in duration
-    m.def("test_chrono3", [](std::chrono::system_clock::duration d) { return d; });
-    // test_chrono_duration_subtraction_equivalence
-    // Difference between two passed in time_points
-    m.def("test_chrono4", [](system_time a, system_time b) { return a - b; });
-    // test_chrono_steady_clock
-    // Return the current time off the steady_clock
-    m.def("test_chrono5", []() { return std::chrono::steady_clock::now(); });
-    // test_chrono_steady_clock_roundtrip
-    // Round trip a steady clock timepoint
-    m.def("test_chrono6", [](steady_time t) { return t; });
-    // test_floating_point_duration
-    // Roundtrip a duration in microseconds from a float argument
-    m.def("test_chrono7", [](std::chrono::microseconds t) { return t; });
-    // Float durations (issue #719)
-    m.def("test_chrono_float_diff", [](std::chrono::duration<float> a, std::chrono::duration<float> b) {
-        return a - b; });
-    m.def("test_nano_timepoint", [](timestamp start, timespan delta) -> timestamp {
-        return start + delta;
-    });
-}

spaces/CVPR/WALT/mmdet/models/losses/ghm_loss.py DELETED Viewed

@@ -1,172 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from ..builder import LOSSES
-def _expand_onehot_labels(labels, label_weights, label_channels):
-    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
-    inds = torch.nonzero(
-        (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
-    if inds.numel() > 0:
-        bin_labels[inds, labels[inds]] = 1
-    bin_label_weights = label_weights.view(-1, 1).expand(
-        label_weights.size(0), label_channels)
-    return bin_labels, bin_label_weights
-# TODO: code refactoring to make it consistent with other losses
-@LOSSES.register_module()
-class GHMC(nn.Module):
-    """GHM Classification Loss.
-    Details of the theorem can be viewed in the paper
-    `Gradient Harmonized Single-stage Detector
-    <https://arxiv.org/abs/1811.05181>`_.
-    Args:
-        bins (int): Number of the unit regions for distribution calculation.
-        momentum (float): The parameter for moving average.
-        use_sigmoid (bool): Can only be true for BCE based loss now.
-        loss_weight (float): The weight of the total GHM-C loss.
-    """
-    def __init__(self, bins=10, momentum=0, use_sigmoid=True, loss_weight=1.0):
-        super(GHMC, self).__init__()
-        self.bins = bins
-        self.momentum = momentum
-        edges = torch.arange(bins + 1).float() / bins
-        self.register_buffer('edges', edges)
-        self.edges[-1] += 1e-6
-        if momentum > 0:
-            acc_sum = torch.zeros(bins)
-            self.register_buffer('acc_sum', acc_sum)
-        self.use_sigmoid = use_sigmoid
-        if not self.use_sigmoid:
-            raise NotImplementedError
-        self.loss_weight = loss_weight
-    def forward(self, pred, target, label_weight, *args, **kwargs):
-        """Calculate the GHM-C loss.
-        Args:
-            pred (float tensor of size [batch_num, class_num]):
-                The direct prediction of classification fc layer.
-            target (float tensor of size [batch_num, class_num]):
-                Binary class target for each sample.
-            label_weight (float tensor of size [batch_num, class_num]):
-                the value is 1 if the sample is valid and 0 if ignored.
-        Returns:
-            The gradient harmonized loss.
-        """
-        # the target should be binary class label
-        if pred.dim() != target.dim():
-            target, label_weight = _expand_onehot_labels(
-                target, label_weight, pred.size(-1))
-        target, label_weight = target.float(), label_weight.float()
-        edges = self.edges
-        mmt = self.momentum
-        weights = torch.zeros_like(pred)
-        # gradient length
-        g = torch.abs(pred.sigmoid().detach() - target)
-        valid = label_weight > 0
-        tot = max(valid.float().sum().item(), 1.0)
-        n = 0  # n valid bins
-        for i in range(self.bins):
-            inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
-            num_in_bin = inds.sum().item()
-            if num_in_bin > 0:
-                if mmt > 0:
-                    self.acc_sum[i] = mmt * self.acc_sum[i] \
-                        + (1 - mmt) * num_in_bin
-                    weights[inds] = tot / self.acc_sum[i]
-                else:
-                    weights[inds] = tot / num_in_bin
-                n += 1
-        if n > 0:
-            weights = weights / n
-        loss = F.binary_cross_entropy_with_logits(
-            pred, target, weights, reduction='sum') / tot
-        return loss * self.loss_weight
-# TODO: code refactoring to make it consistent with other losses
-@LOSSES.register_module()
-class GHMR(nn.Module):
-    """GHM Regression Loss.
-    Details of the theorem can be viewed in the paper
-    `Gradient Harmonized Single-stage Detector
-    <https://arxiv.org/abs/1811.05181>`_.
-    Args:
-        mu (float): The parameter for the Authentic Smooth L1 loss.
-        bins (int): Number of the unit regions for distribution calculation.
-        momentum (float): The parameter for moving average.
-        loss_weight (float): The weight of the total GHM-R loss.
-    """
-    def __init__(self, mu=0.02, bins=10, momentum=0, loss_weight=1.0):
-        super(GHMR, self).__init__()
-        self.mu = mu
-        self.bins = bins
-        edges = torch.arange(bins + 1).float() / bins
-        self.register_buffer('edges', edges)
-        self.edges[-1] = 1e3
-        self.momentum = momentum
-        if momentum > 0:
-            acc_sum = torch.zeros(bins)
-            self.register_buffer('acc_sum', acc_sum)
-        self.loss_weight = loss_weight
-    # TODO: support reduction parameter
-    def forward(self, pred, target, label_weight, avg_factor=None):
-        """Calculate the GHM-R loss.
-        Args:
-            pred (float tensor of size [batch_num, 4 (* class_num)]):
-                The prediction of box regression layer. Channel number can be 4
-                or 4 * class_num depending on whether it is class-agnostic.
-            target (float tensor of size [batch_num, 4 (* class_num)]):
-                The target regression values with the same size of pred.
-            label_weight (float tensor of size [batch_num, 4 (* class_num)]):
-                The weight of each sample, 0 if ignored.
-        Returns:
-            The gradient harmonized loss.
-        """
-        mu = self.mu
-        edges = self.edges
-        mmt = self.momentum
-        # ASL1 loss
-        diff = pred - target
-        loss = torch.sqrt(diff * diff + mu * mu) - mu
-        # gradient length
-        g = torch.abs(diff / torch.sqrt(mu * mu + diff * diff)).detach()
-        weights = torch.zeros_like(g)
-        valid = label_weight > 0
-        tot = max(label_weight.float().sum().item(), 1.0)
-        n = 0  # n: valid bins
-        for i in range(self.bins):
-            inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
-            num_in_bin = inds.sum().item()
-            if num_in_bin > 0:
-                n += 1
-                if mmt > 0:
-                    self.acc_sum[i] = mmt * self.acc_sum[i] \
-                        + (1 - mmt) * num_in_bin
-                    weights[inds] = tot / self.acc_sum[i]
-                else:
-                    weights[inds] = tot / num_in_bin
-        if n > 0:
-            weights /= n
-        loss = loss * weights
-        loss = loss.sum() / tot
-        return loss * self.loss_weight

spaces/Cpp4App/Cpp4App/CDM/cnn/CNN.py DELETED Viewed

@@ -1,114 +0,0 @@
-import keras
-from keras.applications.resnet50 import ResNet50
-from keras.models import Model,load_model
-from keras.layers import Dense, Activation, Flatten, Dropout
-from sklearn.metrics import confusion_matrix
-import numpy as np
-import cv2
-from config.CONFIG import Config
-cfg = Config()
-class CNN:
-    def __init__(self, classifier_type, is_load=True):
-        '''
-        :param classifier_type: 'Text' or 'Noise' or 'Elements'
-        '''
-        self.data = None
-        self.model = None
-        self.classifier_type = classifier_type
-        self.image_shape = (32,32,3)
-        self.class_number = None
-        self.class_map = None
-        self.model_path = None
-        self.classifier_type = classifier_type
-        if is_load:
-            self.load(classifier_type)
-    def build_model(self, epoch_num, is_compile=True):
-        base_model = ResNet50(include_top=False, weights='imagenet', input_shape=self.image_shape)
-        for layer in base_model.layers:
-            layer.trainable = False
-        self.model = Flatten()(base_model.output)
-        self.model = Dense(128, activation='relu')(self.model)
-        self.model = Dropout(0.5)(self.model)
-        self.model = Dense(15, activation='softmax')(self.model)
-        self.model = Model(inputs=base_model.input, outputs=self.model)
-        if is_compile:
-            self.model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
-            self.model.fit(self.data.X_train, self.data.Y_train, batch_size=64, epochs=epoch_num, verbose=1,
-                           validation_data=(self.data.X_test, self.data.Y_test))
-    def train(self, data, epoch_num=30):
-        self.data = data
-        self.build_model(epoch_num)
-        self.model.save(self.model_path)
-        print("Trained model is saved to", self.model_path)
-    def load(self, classifier_type):
-        if classifier_type == 'Text':
-            self.model_path = 'E:/Mulong/Model/rico_compos/cnn-textview-2.h5'
-            self.class_map = ['Text', 'Non-Text']
-        elif classifier_type == 'Noise':
-            self.model_path = 'E:/Mulong/Model/rico_compos/cnn-noise-1.h5'
-            self.class_map = ['Noise', 'Non-Noise']
-        elif classifier_type == 'Elements':
-            # self.model_path = 'E:/Mulong/Model/rico_compos/resnet-ele14-19.h5'
-            # self.model_path = 'E:/Mulong/Model/rico_compos/resnet-ele14-28.h5'
-            # self.model_path = 'E:/Mulong/Model/rico_compos/resnet-ele14-45.h5'
-            self.model_path = cfg.CNN_PATH
-            self.class_map = cfg.element_class
-            self.image_shape = (64, 64, 3)
-        elif classifier_type == 'Image':
-            self.model_path = 'E:/Mulong/Model/rico_compos/cnn-image-1.h5'
-            self.class_map = ['Image', 'Non-Image']
-        self.class_number = len(self.class_map)
-        self.model = load_model(self.model_path)
-        print('Model Loaded From', self.model_path)
-    def preprocess_img(self, image):
-        image = cv2.resize(image, self.image_shape[:2])
-        x = (image / 255).astype('float32')
-        x = np.array([x])
-        return x
-    def predict(self, imgs, compos, load=False, show=False):
-        """
-        :type img_path: list of img path
-        """
-        if load:
-            self.load(self.classifier_type)
-        if self.model is None:
-            print("*** No model loaded ***")
-            return
-        for i in range(len(imgs)):
-            X = self.preprocess_img(imgs[i])
-            Y = self.class_map[np.argmax(self.model.predict(X))]
-            compos[i].category = Y
-            if show:
-                print(Y)
-                cv2.imshow('element', imgs[i])
-                cv2.waitKey()
-    def evaluate(self, data, load=True):
-        if load:
-            self.load(self.classifier_type)
-        X_test = data.X_test
-        Y_test = [np.argmax(y) for y in data.Y_test]
-        Y_pre = [np.argmax(y_pre) for y_pre in self.model.predict(X_test, verbose=1)]
-        matrix = confusion_matrix(Y_test, Y_pre)
-        print(matrix)
-        TP, FP, FN = 0, 0, 0
-        for i in range(len(matrix)):
-            TP += matrix[i][i]
-            FP += sum(matrix[i][:]) - matrix[i][i]
-            FN += sum(matrix[:][i]) - matrix[i][i]
-        precision = TP/(TP+FP)
-        recall = TP / (TP+FN)
-        print("Precision:%.3f, Recall:%.3f" % (precision, recall))

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/word_dataset.py DELETED Viewed

@@ -1,107 +0,0 @@
-import torch
-import torchvision
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
-from maskrcnn_benchmark.structures.ke import textKES
-from maskrcnn_benchmark.structures.mty import MTY
-DEBUG = 0
-class WordDataset(torchvision.datasets.coco.CocoDetection):
-    def __init__(
-        self, ann_file, root, remove_images_without_annotations, transforms=None
-    ):
-        super(WordDataset, self).__init__(root, ann_file)
-        # sort indices for reproducible results
-        self.ids = sorted(self.ids)
-        # filter images without detection annotations
-        if remove_images_without_annotations:
-            self.ids = [
-                img_id
-                for img_id in self.ids
-                if len(self.coco.getAnnIds(imgIds=img_id, iscrowd=None)) > 0
-            ]
-        self.json_category_id_to_contiguous_id = {
-            v: i + 1 for i, v in enumerate(self.coco.getCatIds())
-        }
-        self.contiguous_category_id_to_json_id = {
-            v: k for k, v in self.json_category_id_to_contiguous_id.items()
-        }
-        self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
-        self.transforms = transforms
-    def kes_encode(self, kes):
-        kes_encode = []
-        for i in kes:
-            mnx = i[0]
-            mny = i[1]
-            assert(len(i)%3 == 0)
-            npts = int(len(i)/3-2)
-            for index in range(npts):
-                i[3+index*3]  = (i[3+index*3]+mnx)/2
-                i[4+index*3]  = (i[4+index*3]+mny)/2
-            kes_encode.append(i)
-        return kes_encode
-    def kes_gen(self, kes):
-        kes_gen_out = []
-        for i in kes:
-            mnx = i[0]
-            mny = i[1]
-            cx= i[27]
-            cy= i[28]
-            assert(len(i)%3 == 0)
-            ot = [mnx, i[3],i[6],i[9],i[12], cx,\
-                  mny, i[16],i[19],i[22],i[25], cy]
-            kes_gen_out.append(ot)
-        return kes_gen_out
-    def __getitem__(self, idx):
-        img, anno = super(WordDataset, self).__getitem__(idx)
-        # filter crowd annotations
-        # TODO might be better to add an extra field
-        anno = [obj for obj in anno if obj["iscrowd"] == 0]
-        boxes = [obj["bbox"] for obj in anno]
-        if DEBUG: print('len(boxes)', len(boxes), boxes[0])
-        boxes = torch.as_tensor(boxes).reshape(-1, 4)  # guard against no boxes
-        target = BoxList(boxes, img.size, mode="xywh").convert("xyxy")
-        classes = [obj["category_id"] for obj in anno]
-        if DEBUG: print('len(classes)', len(classes), classes[0])
-        classes = [self.json_category_id_to_contiguous_id[c] for c in classes]
-        classes = torch.tensor(classes)
-        target.add_field("labels", classes)
-        masks = [obj["segmentation"] for obj in anno]
-        if DEBUG: print('len(masks)', len(masks), masks[0])
-        masks = SegmentationMask(masks, img.size)
-        target.add_field("masks", masks)
-        if anno and 'keypoints' in anno[0]:
-            kes = [obj["keypoints"] for obj in anno]
-            kes = self.kes_gen(kes)
-            if DEBUG: print('len(kes)', len(kes), kes[0])
-            kes = textKES(kes, img.size)
-            target.add_field("kes", kes)
-        if anno and 'match_type' in anno[0]:
-            mty = [obj["match_type"] for obj in anno]
-            mty = MTY(mty, img.size)
-            target.add_field("mty", mty)
-        target = target.clip_to_image(remove_empty=True)
-        if self.transforms is not None:
-            img, target = self.transforms(img, target)
-        return img, target, idx
-    def get_img_info(self, index):
-        img_id = self.id_to_img_map[index]
-        img_data = self.coco.imgs[img_id]
-        return img_data

spaces/DJQmUKV/rvc-inference/infer_pack/models.py DELETED Viewed

@@ -1,1116 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-        self.upp = np.prod(upsample_rates)
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2,3, 5, 7, 11, 17, 23, 37]
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-        return x, fmap
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-    def forward(self, x):
-        fmap = []
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-        return x, fmap

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/_core/_testing.py DELETED Viewed

@@ -1,82 +0,0 @@
-from __future__ import annotations
-from typing import Any, Awaitable, Generator
-from ._compat import DeprecatedAwaitableList, _warn_deprecation
-from ._eventloop import get_asynclib
-class TaskInfo:
-    """
-    Represents an asynchronous task.
-    :ivar int id: the unique identifier of the task
-    :ivar parent_id: the identifier of the parent task, if any
-    :vartype parent_id: Optional[int]
-    :ivar str name: the description of the task (if any)
-    :ivar ~collections.abc.Coroutine coro: the coroutine object of the task
-    """
-    __slots__ = "_name", "id", "parent_id", "name", "coro"
-    def __init__(
-        self,
-        id: int,
-        parent_id: int | None,
-        name: str | None,
-        coro: Generator[Any, Any, Any] | Awaitable[Any],
-    ):
-        func = get_current_task
-        self._name = f"{func.__module__}.{func.__qualname__}"
-        self.id: int = id
-        self.parent_id: int | None = parent_id
-        self.name: str | None = name
-        self.coro: Generator[Any, Any, Any] | Awaitable[Any] = coro
-    def __eq__(self, other: object) -> bool:
-        if isinstance(other, TaskInfo):
-            return self.id == other.id
-        return NotImplemented
-    def __hash__(self) -> int:
-        return hash(self.id)
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}(id={self.id!r}, name={self.name!r})"
-    def __await__(self) -> Generator[None, None, TaskInfo]:
-        _warn_deprecation(self)
-        if False:
-            yield
-        return self
-    def _unwrap(self) -> TaskInfo:
-        return self
-def get_current_task() -> TaskInfo:
-    """
-    Return the current task.
-    :return: a representation of the current task
-    """
-    return get_asynclib().get_current_task()
-def get_running_tasks() -> DeprecatedAwaitableList[TaskInfo]:
-    """
-    Return a list of running tasks in the current event loop.
-    :return: a list of task info objects
-    """
-    tasks = get_asynclib().get_running_tasks()
-    return DeprecatedAwaitableList(tasks, func=get_running_tasks)
-async def wait_all_tasks_blocked() -> None:
-    """Wait until all other tasks are waiting for something."""
-    await get_asynclib().wait_all_tasks_blocked()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/textTools.py DELETED Viewed

@@ -1,155 +0,0 @@
-"""fontTools.misc.textTools.py -- miscellaneous routines."""
-import ast
-import string
-# alias kept for backward compatibility
-safeEval = ast.literal_eval
-class Tag(str):
-    @staticmethod
-    def transcode(blob):
-        if isinstance(blob, bytes):
-            blob = blob.decode("latin-1")
-        return blob
-    def __new__(self, content):
-        return str.__new__(self, self.transcode(content))
-    def __ne__(self, other):
-        return not self.__eq__(other)
-    def __eq__(self, other):
-        return str.__eq__(self, self.transcode(other))
-    def __hash__(self):
-        return str.__hash__(self)
-    def tobytes(self):
-        return self.encode("latin-1")
-def readHex(content):
-    """Convert a list of hex strings to binary data."""
-    return deHexStr(strjoin(chunk for chunk in content if isinstance(chunk, str)))
-def deHexStr(hexdata):
-    """Convert a hex string to binary data."""
-    hexdata = strjoin(hexdata.split())
-    if len(hexdata) % 2:
-        hexdata = hexdata + "0"
-    data = []
-    for i in range(0, len(hexdata), 2):
-        data.append(bytechr(int(hexdata[i : i + 2], 16)))
-    return bytesjoin(data)
-def hexStr(data):
-    """Convert binary data to a hex string."""
-    h = string.hexdigits
-    r = ""
-    for c in data:
-        i = byteord(c)
-        r = r + h[(i >> 4) & 0xF] + h[i & 0xF]
-    return r
-def num2binary(l, bits=32):
-    items = []
-    binary = ""
-    for i in range(bits):
-        if l & 0x1:
-            binary = "1" + binary
-        else:
-            binary = "0" + binary
-        l = l >> 1
-        if not ((i + 1) % 8):
-            items.append(binary)
-            binary = ""
-    if binary:
-        items.append(binary)
-    items.reverse()
-    assert l in (0, -1), "number doesn't fit in number of bits"
-    return " ".join(items)
-def binary2num(bin):
-    bin = strjoin(bin.split())
-    l = 0
-    for digit in bin:
-        l = l << 1
-        if digit != "0":
-            l = l | 0x1
-    return l
-def caselessSort(alist):
-    """Return a sorted copy of a list. If there are only strings
-    in the list, it will not consider case.
-    """
-    try:
-        return sorted(alist, key=lambda a: (a.lower(), a))
-    except TypeError:
-        return sorted(alist)
-def pad(data, size):
-    r"""Pad byte string 'data' with null bytes until its length is a
-    multiple of 'size'.
-    >>> len(pad(b'abcd', 4))
-    4
-    >>> len(pad(b'abcde', 2))
-    6
-    >>> len(pad(b'abcde', 4))
-    8
-    >>> pad(b'abcdef', 4) == b'abcdef\x00\x00'
-    True
-    """
-    data = tobytes(data)
-    if size > 1:
-        remainder = len(data) % size
-        if remainder:
-            data += b"\0" * (size - remainder)
-    return data
-def tostr(s, encoding="ascii", errors="strict"):
-    if not isinstance(s, str):
-        return s.decode(encoding, errors)
-    else:
-        return s
-def tobytes(s, encoding="ascii", errors="strict"):
-    if isinstance(s, str):
-        return s.encode(encoding, errors)
-    else:
-        return bytes(s)
-def bytechr(n):
-    return bytes([n])
-def byteord(c):
-    return c if isinstance(c, int) else ord(c)
-def strjoin(iterable, joiner=""):
-    return tostr(joiner).join(iterable)
-def bytesjoin(iterable, joiner=b""):
-    return tobytes(joiner).join(tobytes(item) for item in iterable)
-if __name__ == "__main__":
-    import doctest, sys
-    sys.exit(doctest.testmod().failed)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/deprecation.py DELETED Viewed

@@ -1,80 +0,0 @@
-from __future__ import annotations
-import warnings
-from gradio import utils
-class GradioDeprecationWarning(UserWarning):
-    # This does not subclass DeprecationWarning
-    # because we want to show the warning by default.
-    pass
-class GradioUnusedKwargWarning(UserWarning):
-    pass
-def simple_deprecated_notice(term: str) -> str:
-    return f"`{term}` parameter is deprecated, and it has no effect"
-def use_in_launch(term: str) -> str:
-    return f"`{term}` is deprecated in `Interface()`, please use it within `launch()` instead."
-DEPRECATION_MESSAGE = {
-    "optional": simple_deprecated_notice("optional"),
-    "keep_filename": simple_deprecated_notice("keep_filename"),
-    "numeric": simple_deprecated_notice("numeric"),
-    "verbose": simple_deprecated_notice("verbose"),
-    "allow_screenshot": simple_deprecated_notice("allow_screenshot"),
-    "layout": simple_deprecated_notice("layout"),
-    "show_input": simple_deprecated_notice("show_input"),
-    "show_output": simple_deprecated_notice("show_output"),
-    "capture_session": simple_deprecated_notice("capture_session"),
-    "api_mode": simple_deprecated_notice("api_mode"),
-    "show_tips": use_in_launch("show_tips"),
-    "encrypt": simple_deprecated_notice("encrypt"),
-    "enable_queue": use_in_launch("enable_queue"),
-    "server_name": use_in_launch("server_name"),
-    "server_port": use_in_launch("server_port"),
-    "width": use_in_launch("width"),
-    "height": use_in_launch("height"),
-    "plot": "The 'plot' parameter has been deprecated. Use the new Plot component instead",
-}
-def check_deprecated_parameters(
-    cls: str, *, stacklevel: int | None = None, kwargs
-) -> None:
-    if stacklevel is None:
-        stacklevel = utils.find_user_stack_level()
-    for key, value in DEPRECATION_MESSAGE.items():
-        if key in kwargs:
-            if key == "plot" and cls != "Image":
-                continue
-            kwargs.pop(key)
-            warnings.warn(value, GradioDeprecationWarning, stacklevel=stacklevel)
-    if kwargs:
-        warnings.warn(
-            f"You have unused kwarg parameters in {cls}, please remove them: {kwargs}",
-            GradioUnusedKwargWarning,
-            stacklevel=stacklevel,
-        )
-def warn_deprecation(text: str) -> None:
-    warnings.warn(
-        text,
-        GradioDeprecationWarning,
-        stacklevel=utils.find_user_stack_level(),
-    )
-def warn_style_method_deprecation() -> None:
-    warn_deprecation(
-        "The `style` method is deprecated. Please set these arguments in the constructor instead."
-    )

spaces/Daniel-Saeedi/auto-debias/README.md DELETED Viewed

@@ -1,13 +0,0 @@
----
-title: Auto Debias
-emoji: 👁
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: mit
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Datatrooper/sentimiento/app.py DELETED Viewed

@@ -1,22 +0,0 @@
-from pysentimiento import create_analyzer
-import gradio as gr
-analyzer = create_analyzer(task="sentiment", lang="es")
-def sentiment_analysis(text):
-    sent = analyzer.predict(text).probas
-    return sent
-iface = gr.Interface(sentiment_analysis,
-                    "textbox",
-                    "label",
-                    interpretation="default",
-                    title="Spanish Sentiment Analysis",
-                    description="Write a sentence in spanish to analyze its sentiment")
-iface.test_launch()
-if __name__ == "__main__":
-    iface.launch()