Update parquet files (step 71 of 397)

spaces/1368565466ki/Satdia/text/symbols.py

@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Alpha Test Psicologia.pdf.md

@@ -1,110 +0,0 @@
-<br />
-<code>
-<h1>Alpha Test Psicologia: What You Need to Know</h1>
-<p>If you want to study psychology in Italy, you will need to pass a test called Alpha Test Psicologia. This test is designed to assess your skills and knowledge in various areas related to psychology, such as logic, reasoning, reading comprehension, language, science and culture. In this article, we will explain what Alpha Test Psicologia is, how to prepare for it, and how to use some of the best resources available to help you succeed.</p>
-<h2>Alpha Test Psicologia.pdf</h2><br /><p><b><b>DOWNLOAD</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://byltly.com/2uKyJf">https://byltly.com/2uKyJf</a></b></p><br /><br />
- <h2>What is Alpha Test Psicologia?</h2>
-<p>Alpha Test Psicologia is a standardized test that is required for admission to most psychology courses in Italian universities. The test consists of 60 multiple-choice questions that cover four main sections: reasoning skills, text comprehension, Italian language skills, and knowledge and competencies acquired in school. Depending on the university you apply to, you may have to take either the TOLC-SU (Test On Line Scienze Umane) or the TOLC-PSI version of the test. The TOLC-SU has 10 questions on reasoning skills, 15 on text comprehension, 15 on Italian language skills, and 20 on knowledge and competencies. The TOLC-PSI has 15 questions on text comprehension, 10 on basic math, 10 on biology, 10 on verbal reasoning, and 15 on numerical reasoning.</p>
-<p>The test is administered by CISIA (Consorzio Interuniversitario Sistemi Integrati per l'Accesso) and can be taken online or at a university center. You can choose from several sessions throughout the year, starting from February until autumn. You can also take the test at home (TOLC@casa) if you have a suitable device and internet connection. The test lasts 100 minutes and has a maximum score of 60 points. Each correct answer gives you one point, each wrong answer subtracts 0.25 points, and each unanswered question gives you zero points.</p>
- <h2>How to Prepare for Alpha Test Psicologia?</h2>
-<p>Preparing for Alpha Test Psicologia can be challenging but rewarding. Here are some tips and strategies that can help you improve your performance:</p>
-<ul>
-<li>Start studying early. Don't wait until the last minute to review your topics and practice your skills. Give yourself enough time to learn new concepts, revise old ones, and identify your strengths and weaknesses.</li>
-<li>Plan your study schedule. Set realistic goals and deadlines for yourself. Divide your topics into manageable chunks and allocate enough time for each one. Use a calendar or a planner to keep track of your progress.</li>
-<li>Use a variety of resources. Don't rely on just one source of information or practice. Use different materials that suit your learning style and preferences. For example, you can use books, online courses, platforms, videos, podcasts, articles, etc.</li>
-<li>Practice with real tests. One of the best ways to prepare for Alpha Test Psicologia is to take simulated tests that mimic the format and content of the real test. This will help you familiarize yourself with the types of questions, time limits, scoring system, etc.</li>
-<li>Get feedback. After taking a practice test, review your answers carefully. Analyze your mistakes and understand why you got them wrong. Look for patterns or gaps in your knowledge or skills. Seek help from others if you need it.</li>
-</ul>
- <h2>How to Use Alpha Test Psicologia Books?</h2>
-<p>One of the most popular and effective resources for preparing for Alpha Test Psicologia are the books published by Alpha Test. These books are specifically designed to help you master all the topics and skills required by the test. They include:</p>
-<ul>
-<li>The manual: This book provides a comprehensive overview of all the subjects covered by the test. It explains the concepts clearly and concisely with examples and diagrams. It also includes tips and tricks for solving different types of questions.</li>
-<li>The exercise book: This book contains hundreds of exercises that allow you to practice your skills in each section of the test. It also includes detailed solutions that show you how to approach each problem step by step.</li>
-<li>The verification tests: This book contains 10 full-length tests that simulate the real test in terms of structure and difficulty. It also includes answers keys that let you check your score.</li>
-</ul>
-<p>Here are some examples of questions from these books:</p>
- <table>
-<tr><td><b>Manual</b></td><td><b>Exercise book</b></td><td><b>Verification tests</b></td></tr>
-<tr><td>In un gruppo di persone si è verificato il seguente fenomeno: più aumentava il numero dei partecipanti alla discussione e più diminuiva la qualità delle decisioni prese dal gruppo stesso.<br>A quale fenomeno psicosociale si fa riferimento?<br>A) Conformismo<br>B) Polarizzazione<br>C) Pensiero di gruppo<br>D) Coesione<br>E) Leadership<br><br>Risposta: C) Pensiero di gruppo</td><td>Nel seguente brano sono presenti alcuni errori ortografici.<br>"La psiche umana è un mistero che da secoli affascina filosofi e scienziati.<br>Molti sono stati i tentativi di spiegarne il funzionamento e le dinamiche.<br>Tra i più noti vi sono quelli di Freud e Jung che hanno dato vita alla psicanalisi."<br>Individua gli errori e correggili.<br>A) da secoli - da secoli<br>B) spiegarne - spiegarne<br>C) vi sono - ci sono<br>D) Freud - freud<br>E) Jung - iung<br><br>Risposta: C) vi sono - ci sono; D) Freud - freud</td><td>Qual è il sinonimo della parola "suscettibile"?<br>A) Sensibile<br>B) Irritabile<br>C) Suscettivo<br>D) Suscinto<br>E) Suscitabile<br><br>Risposta: A) Sensibile</td></tr>
-</table>
- <h2>How to Use Alpha Test Psicologia Online Courses?</h2>
-<p>Another great resource for preparing for Alpha Test Psicologia are the online courses offered by Alpha Test. These courses are intensive and specific programs that guide you through all the aspects of the test preparation process. They include:</p>
-<p>Alpha Test Psicologia TOLC-PSI - Manuale di preparazione[^1^]<br />
-Alpha Test Psicologia TOLC-PSI - Esercizi commentati<br />
-Alpha Test Psicologia TOLC-PSI - Prove di verifica<br />
-Alpha Test Psicologia TOLC-PSI - 4100 quiz<br />
-Alpha Test Cultura generale - 5100 quiz<br />
-Alpha Test PLUS Psicologia TOLC-PSI - Kit completo di preparazione con training online personalizzato<br />
-Alpha Test Psicologia TOLC-SU - Manuale di preparazione<br />
-Alpha Test Psicologia TOLC-SU - Esercizi commentati<br />
-Alpha Test Psicologia TOLC-SU - Prove di verifica<br />
-Alpha Test Psicologia TOLC-SU - 4100 quiz<br />
-Alpha Test Cultura generale - 5100 quiz per Psicologia TOLC-SU<br />
-Alpha Test PLUS Psicologia TOLC-SU - Kit completo di preparazione con training online personalizzato<br />
-Alpha Test Psicologia IMAT - Manuale di preparazione<br />
-Alpha Test Psicologia IMAT - Esercizi commentati<br />
-Alpha Test Psicologia IMAT - Prove di verifica<br />
-Alpha Test Psicologia IMAT - 4100 quiz<br />
-Alpha Test Cultura generale - 5100 quiz per Psicologia IMAT<br />
-Alpha Test PLUS Psicologia IMAT - Kit completo di preparazione con training online personalizzato<br />
-Alpha Test Psicologia Professioni sanitarie - Manuale di preparazione<br />
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-Alpha Test Orientamento per la scelta della facoltà di psicologia <br />
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-<ul>
-<li>The on-demand lessons: These are recorded videos that explain all the topics covered by the test in a clear and engaging way. You can watch them whenever you want at your own pace.</li>
-<li>The live streams: These are interactive sessions that allow you to ask questions directly to the Alpha Test teachers. You can also participate in quizzes, polls, games, etc.</li>
-<li>The exercises: These are online activities that let you practice your skills with immediate feedback. You can also compare your results with other students.</li>
-<code>
-<h2>How to Use Alpha Test Psicologia Platforms?</h2>
-<p>In addition to the books and the online courses, Alpha Test also provides you with two platforms that can help you study and practice more effectively. They are:</p>
-<ul>
-<li>AlphaTestAcademy: This is a personalized platform that follows you step by step until the day of the test. It offers you a customized study plan based on your level and goals. It also gives you access to thousands of questions, videos, summaries, tips, etc.</li>
-<li>MyDesk: This is a virtual desk that allows you to organize and manage your materials and activities. You can create notes, bookmarks, highlights, etc. You can also track your progress and performance with graphs and statistics.</li>
-</ul>
-<p>Here are some examples of how these platforms work:</p>
- <table>
-<tr><td><b>AlphaTestAcademy</b></td><td><b>MyDesk</b></td></tr>
-<tr><td><img src="https://www.alphatest.it/images/academy/academy-1.jpg" alt="AlphaTestAcademy screenshot"></td><td><img src="https://www.alphatest.it/images/mydesk/mydesk-1.jpg" alt="MyDesk screenshot"></td></tr>
-<tr><td>This is the dashboard where you can see your study plan and your progress.</td><td>This is the library where you can access all your books and materials.</td></tr>
-<tr><td><img src="https://www.alphatest.it/images/academy/academy-2.jpg" alt="AlphaTestAcademy screenshot"></td><td><img src="https://www.alphatest.it/images/mydesk/mydesk-2.jpg" alt="MyDesk screenshot"></td></tr>
-<tr><td>This is the question bank where you can practice with different types of questions.</td><td>This is the note editor where you can create and edit your notes.</td></tr>
-<tr><td><img src="https://www.alphatest.it/images/academy/academy-3.jpg" alt="AlphaTestAcademy screenshot"></td><td><img src="https://www.alphatest.it/images/mydesk/mydesk-3.jpg" alt="MyDesk screenshot"></td></tr>
-<tr><td>This is the video library where you can watch and review the lessons.</td><td>This is the performance report where you can see your results and feedback.</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Alpha Test Psicologia is a crucial test for anyone who wants to pursue a career in psychology in Italy. It requires a lot of preparation and practice, but it can also be a rewarding and enjoyable experience. By using the resources we have described in this article, such as books, online courses and platforms, you can improve your chances of success and achieve your goals. Don't hesitate to start your journey today and join the Alpha Test community!</p>
- <h3>Frequently Asked Questions</h3>
-<ul>
-<li>Q: How can I register for Alpha Test Psicologia?<br>A: You can register for Alpha Test Psicologia on the CISIA website (https://www.cisiaonline.it/). You will need to create an account, choose your preferred session and university, and pay a fee of 30 euros.</li>
-<li>Q: How can I get the Alpha Test Psicologia books?<br>A: You can get the Alpha Test Psicologia books on the Alpha Test website (https://www.alphatest.it/Libri-Alpha-Test/Test-Ammissione/psicologia/psicologia) or on Amazon. You can also find them in bookstores or libraries.</li>
-<li>Q: How can I access the Alpha Test Psicologia online courses?<br>A: You can access the Alpha Test Psicologia online courses on the Alpha Test website (https://www.alphatest.it/Corsi/psicologia). You will need to choose your preferred course and pay a fee that varies depending on the duration and features of the course.</li>
-<li>Q: How can I use the Alpha Test Psicologia platforms?<br>A: You can use the Alpha Test Psicologia platforms by logging in with your credentials on the Alpha Test website (https://www.alphatest.it/). You will need to have purchased or received a code for accessing either AlphaTestAcademy or MyDesk.</li>
-<li>Q: How can I contact Alpha Test for more information?<br>A: You can contact Alpha Test by phone (+39 02 7601 8368), email ([email protected]), or social media (Facebook, Instagram, YouTube).</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Digital Signal Processing By Ramesh Babu 4th Edition Pdf Free Download Rar A Complete Course on DSP with Solutions and Projects.md

@@ -1,175 +0,0 @@
-
-<h1>Digital Signal Processing by Ramesh Babu 4th Edition PDF Free Download Rar</h1>
- <p>If you are looking for a comprehensive and easy-to-understand book on digital signal processing, you might want to check out Digital Signal Processing by Ramesh Babu 4th Edition. This book covers all the essential topics in digital signal processing, such as discrete-time signals and systems, z-transforms, discrete Fourier transforms, fast Fourier transforms, digital filters, and more. It also includes MATLAB programs and applications that illustrate the concepts and techniques discussed in the book.</p>
- <p>In this article, we will give you an overview of what digital signal processing is, why it is important, and who Ramesh Babu is. We will also tell you how you can download the book for free in PDF format from a RAR file. So, let's get started!</p>
-<h2>Digital Signal Processing By Ramesh Babu 4th Edition Pdf Free Download Rar</h2><br /><p><b><b>Download</b> ->>> <a href="https://byltly.com/2uKyja">https://byltly.com/2uKyja</a></b></p><br /><br />
- <h2>Introduction</h2>
- <h3>What is digital signal processing?</h3>
- <p>Digital signal processing (DSP) is the branch of engineering that deals with the analysis, manipulation, and synthesis of signals using digital techniques. A signal is any physical quantity that varies with time, space, or any other parameter. For example, sound, image, video, temperature, pressure, etc. are all signals.</p>
- <p>DSP involves converting analog signals into digital signals using devices called analog-to-digital converters (ADCs), processing them using mathematical algorithms and operations, and converting them back into analog signals using devices called digital-to-analog converters (DACs). DSP can be used for various purposes, such as filtering, compression, encryption, modulation, demodulation, detection, estimation, classification, etc.</p>
- <h3>Why is digital signal processing important?</h3>
- <p>DSP is important because it enables us to perform complex and sophisticated tasks on signals that would be impossible or impractical using analog techniques. For example, DSP can help us:</p>
- <ul>
-<li>Reduce noise and interference in signals</li>
-<li>Enhance the quality and resolution of signals</li>
-<li>Extract useful information from signals</li>
-<li>Compress signals to save storage space and bandwidth</li>
-<li>Encrypt signals to ensure security and privacy</li>
-<li>Modulate and demodulate signals to transmit and receive them over different channels</li>
-<li>Detect and estimate parameters of signals</li>
-<li>Classify signals into different categories</li>
-</ul>
- <p>DSP has applications in various fields and industries, such as telecommunications, multimedia, biomedical engineering, radar, sonar, speech recognition, image processing, computer vision, machine learning, etc.</p>
- <h3>Who is Ramesh Babu?</h3>
- <p>Ramesh Babu is a professor of electronics and communication engineering at Pondicherry Engineering College in India. He has over 30 years of teaching experience and has authored several books on DSP and related subjects. He has also published many research papers in national and international journals and conferences. He is a member of various professional bodies such as IEEE, IETE, ISTE, etc.</p>
- <p>Ramesh Babu is known for his clear and concise style of writing and explaining complex concepts in a simple manner. He uses numerous examples and exercises to reinforce the understanding of the readers. He also provides MATLAB programs and applications that demonstrate the practical aspects of DSP.</p>
-<p>Download DSP by Ramesh Babu 4th edition pdf for free<br />
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- <h2>Features of the book</h2>
- <h3>Comprehensive coverage of topics</h3>
- and then proceeds to cover discrete-time signals and systems, z-transforms, discrete Fourier transforms, fast Fourier transforms, digital filters, and more. It also includes topics such as finite word length effects, multirate signal processing, wavelet transforms, and adaptive filters. The book provides a balanced treatment of both theory and practice, with an emphasis on applications and problem-solving.</p>
- <h3>Clear and concise explanations</h3>
- <p>The book explains the concepts and techniques of DSP in a clear and concise manner, using simple language and notation. The book avoids unnecessary mathematical derivations and proofs, and focuses on the intuition and understanding of the readers. The book also uses diagrams, tables, graphs, and figures to illustrate the points and enhance the readability of the text.</p>
- <h3>Numerous examples and exercises</h3>
- <p>The book provides numerous examples and exercises throughout the chapters to help the readers apply the concepts and techniques learned in the book. The examples are carefully chosen to cover a wide range of applications and scenarios, such as speech processing, image processing, biomedical signal processing, etc. The exercises are designed to test the comprehension and analytical skills of the readers, as well as to provide them with additional practice and feedback. The book also provides solutions to selected exercises at the end of the book.</p>
- <h3>MATLAB programs and applications</h3>
- <p>The book includes MATLAB programs and applications that complement the topics covered in the book. MATLAB is a popular software tool for numerical computation and visualization that is widely used by engineers and scientists. The book provides MATLAB codes for various DSP algorithms and operations, such as convolution, correlation, DFT, FFT, FIR filters, IIR filters, etc. The book also provides MATLAB applications that demonstrate the use of DSP in real-world problems, such as audio processing, image processing, speech processing, etc. The book also provides instructions on how to run the MATLAB programs and applications on a computer.</p>
- <h2>How to download the book for free?</h2>
- <h3>Steps to download the book in PDF format</h3>
- <p>If you want to download the book for free in PDF format, you can follow these steps:</p>
- <ol>
-<li>Go to this link: https://www.pdfdrive.com/digital-signal-processing-by-ramesh-babu-4th-edition-e158775719.html</li>
-<li>Click on the green button that says "Download (PDF)"</li>
-<li>Wait for a few seconds until the download starts automatically</li>
-<li>Save the file on your computer or device</li>
-</ol>
- <p>Congratulations! You have successfully downloaded the book in PDF format.</p>
- <h3>Steps to extract the book from RAR file</h3>
- <p>If you have downloaded the book in RAR file format, you will need to extract it before you can read it. A RAR file is a compressed file that contains one or more files inside it. To extract the book from RAR file format, you can follow these steps:</p>
- <ol>
-<li>Download and install a software program that can open RAR files, such as WinRAR or 7-Zip</li>
-<li>Right-click on the RAR file that contains the book</li>
-<li>Select "Extract Here" or "Extract to" from the menu</li>
-<li>Wait for a few seconds until the extraction is complete</li>
-<li>Open the folder that contains the extracted files</li>
-<li>Double-click on the file that has the extension ".pdf"</li>
-</ol>
- <p>Congratulations! You have successfully extracted the book from RAR file format.</p>
- <h3>Tips to avoid viruses and malware</h3>
- <p>While downloading any file from the internet, you should be careful about viruses and malware that can harm your computer or device. Here are some tips to avoid viruses and malware:</p>
- <ul>
-<li>Use a reliable antivirus software program on your computer or device</li>
-<li>Scan any file that you download before opening it</li>
-<li>Avoid clicking on suspicious links or pop-ups that claim to offer free downloads or prizes</li>
-<li>Avoid downloading files from unknown or untrusted sources or websites</li>
-<li>Check the file size and extension before downloading it</li>
-<li>Delete any file that looks suspicious or corrupted after downloading it</li>
-</ul>
- <p>By following these tips, you can protect your computer or device from viruses and malware.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- why it is important, and who Ramesh Babu is. We have also told you how you can download the book for free in PDF format from a RAR file. We have also discussed the features of the book, such as comprehensive coverage of topics, clear and concise explanations, numerous examples and exercises, and MATLAB programs and applications.</p>
- <h3>Benefits of reading the book</h3>
- <p>By reading the book, you can learn the fundamentals and applications of DSP in a simple and effective way. You can also enhance your skills and knowledge in DSP and related subjects. You can also use the book as a reference or a guide for your academic or professional projects. The book can help you to:</p>
- <ul>
-<li>Understand the concepts and techniques of DSP</li>
-<li>Solve problems and challenges in DSP</li>
-<li>Implement DSP algorithms and operations using MATLAB</li>
-<li>Explore various applications of DSP in real-world scenarios</li>
-<li>Prepare for exams and interviews on DSP</li>
-</ul>
- <h3>Call to action</h3>
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- <h3>What are the advantages of digital signals over analog signals?</h3>
- <p>Digital signals have several advantages over analog signals, such as:</p>
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-<li>The need for high-speed and high-precision hardware devices such as ADCs and DACs</li>
-<li>The trade-off between accuracy and complexity of DSP algorithms and operations</li>
-<li>The difficulty of handling non-linear or chaotic signals</li>
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-<p>Left 4 Dead is a classic zombie shooter game that was released in 2008 for Xbox 360 and PC. It features four survivors who have to fight their way through hordes of infected creatures in various scenarios. The game is known for its cooperative gameplay, dynamic AI, and replay value.</p>
-<p>If you want to play Left 4 Dead on your Xbox One, you might be wondering how to download it. The good news is that the game is backward compatible, which means you can play it on your Xbox One without any problems. Here are the steps you need to follow to download Left 4 Dead on Xbox One.</p>
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-<h1>Enter Password For The Encrypted File Setup Mudbox 2013 Crack</h1>
- <p>Have you ever downloaded a file that requires a password to open it? If so, you might have encountered a situation where you don't know the password or you forgot it. This can be very frustrating, especially if the file contains something important or valuable. In this article, we will show you how to find the password for the encrypted file setup Mudbox 2013 crack, a popular software for 3D sculpting and painting.</p>
- <h2>What is Mudbox 2013 and why do you need it?</h2>
- <p>Mudbox 2013 is a software application developed by Autodesk that allows you to create realistic and detailed 3D models using digital sculpting and painting tools. You can use Mudbox 2013 to design characters, environments, props, and other assets for games, movies, animations, and other media. You can also use Mudbox 2013 to edit and refine existing 3D models imported from other software such as Maya, 3ds Max, or ZBrush.</p>
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- <p>To download and install Mudbox 2013, you need to follow these steps:</p>
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-</ol>
- <h2>What is an encrypted file and why do you need a password?</h2>
- <p>An encrypted file is a file that has been converted into a secret code that can only be read by someone who knows the correct password. Encryption is a process of protecting data from unauthorized access or modification by using mathematical algorithms and keys. Encryption can be used for various purposes such as:</p>
- <ul>
-<li>Securing sensitive or confidential information such as personal data, financial transactions, medical records, etc.</li>
-<li>Preventing data theft or tampering by hackers, malware, viruses, etc.</li>
-<li>Compressing data to save storage space or bandwidth.</li>
-</ul>
- <p>A password is a secret word or phrase that is used to unlock an encrypted file. A password can be chosen by the person who encrypts the file or generated randomly by the encryption software. A password can be composed of letters, numbers, symbols, or any combination of them. A password can be simple or complex depending on its length and variety of characters. A simple password is easy to remember but also easy to guess or crack by others. A complex password is hard to remember but also hard to guess or crack by others.</p>
- <h3>What is encryption and how does it work?</h3>
- <p>Encryption is a process of transforming data into a secret code that can only be read by someone who knows the correct key. A key is a piece of information that is used to encrypt or decrypt data. A key can be symmetric or asymmetric depending on whether it is shared or not between the sender and receiver of data. A symmetric key is used for both encryption and decryption of data. An asymmetric key consists of two parts: a public key that is used for encryption of data and a private key that is used for decryption of data. The public key can be shared with anyone but the private key must be kept secret by its owner.</p>
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-<li>The receiver of data uses the same encryption algorithm (or its inverse) and the same key (or its corresponding part) to decrypt data (ciphertext) back into its original form (plaintext).</li>
-</ol>
- <h3>How to encrypt and decrypt files with WinRAR</h3>
- <p>WinRAR is a software application that can create and extract compressed archive files such as RAR, ZIP, TAR, GZIP, etc. WinRAR can also encrypt and decrypt files with passwords using AES-256 encryption algorithm. AES-256 is one of the most secure encryption algorithms available today. To encrypt and decrypt files with WinRAR, you need to follow these steps:</p>
- <ol>
-<li>Download and install WinRAR from its official website if you don't have it already on your computer.</li>
-<li>Select one or more files that you want to encrypt or decrypt with WinRAR.</li>
-<li>Right-click on them and choose "Add to archive..." from the context menu.</li>
-<li>In the "Archive name" field, enter a name for your archive file (such as "Mudbox_2013_crack.rar").</li>
-<li>In the "Archive format" field, choose "RAR" as your archive format.</li>
-<li>In the "General" tab, click on "Set password..." button.</li>
-<li>In the "Enter password" field, enter a password that you want to use for encrypting or decrypting your files (such as "123456").</li>
-<li>In the "Reenter password" field, reenter your password for confirmation.</li>
-<li>In the "Encryption method" field, choose "AES-256" as your encryption method.</li>
-<li>If you want to encrypt both file names and contents in your archive file, check "Encrypt file names" option.</li>
-"Delete files after archiving" option.</li>
-<li>Click on "OK" button to create your archive file with encryption.</li>
-</ol>
- <p>To decrypt files with WinRAR, you need to follow these steps:</p>
- <ol>
-<li>Locate your archive file (such as "Mudbox_2013_crack.rar") on your computer.</li>
-<li>Double-click on it to open it with WinRAR.</li>
-<li>If your archive file is encrypted with a password, you will see a dialog box asking you to enter the password.</li>
-<li>In the "Enter password" field, enter the password that was used for encrypting your files (such as "123456").</li>
-<li>Click on "OK" button to access your files in the archive file.</li>
-<li>Select one or more files that you want to extract from the archive file.</li>
-<li>Click on "Extract to" button and choose a destination folder for your extracted files.</li>
-<li>Click on "OK" button to extract your files from the archive file with decryption.</li>
-</ol>
- <h2>How to find the password for the encrypted file setup Mudbox 2013 crack?</h2>
- <p>If you have downloaded an encrypted file setup Mudbox 2013 crack from an unknown source, you might not know the password for opening it. This can be a problem if you want to use the software for your 3D projects. Fortunately, there are some methods that can help you find the password for the encrypted file setup Mudbox 2013 crack. Here are two of them:</p>
- <h3>Method 1: Use a password recovery software</h3>
- <p>A password recovery software is a software application that can try to find the password for an encrypted file by using various techniques such as brute force, dictionary, mask, etc. A brute force technique tries all possible combinations of characters until it finds the correct password. A dictionary technique tries words from a predefined list of common passwords or words. A mask technique tries combinations of characters based on a pattern that you specify. A password recovery software can be fast or slow depending on the complexity of the password and the speed of your computer. To use a password recovery software to find the password for the encrypted file setup Mudbox 2013 crack, you need to follow these steps:</p>
- <h4>Step 1: Download and install RAR Password Refixer</h4>
- <p>RAR Password Refixer is one of the best password recovery software for RAR files. It can recover passwords for RAR files created by WinRAR and other RAR-compatible software. It supports all versions of RAR files and all types of encryption methods. It has four password attack types: Brute-force, Mask, Dictionary, and Smart. It has a user-friendly interface and a high recovery rate. You can download RAR Password Refixer from its official website: <a href="http://www.isumsoft.com/rar-password-refixer/">http://www.isumsoft.com/rar-password-refixer/</a>.</p>
- <h4>Step 2: Launch the software and import the encrypted file</h4>
- <p>After installing RAR Password Refixer on your computer, launch it and you will see its main interface. Click on the "Open" button and browse your computer to locate your encrypted file setup Mudbox 2013 crack (such as "Mudbox_2013_crack.rar"). Select it and click on "Open" button again to import it into the software.</p>
- <h4>Step 3: Choose a password attack type and set parameters</h4>
- <p>In the main interface of RAR Password Refixer, you will see four tabs: Brute-force, Mask, Dictionary, and Smart. Each tab represents a different password attack type that you can use to find the password for your encrypted file. You can choose one of them according to your situation and preference. For example, if you know nothing about the password, you can choose Brute-force attack type which will try all possible combinations of characters until it finds the correct password. If you know some information about the password such as its length, prefix, suffix, or character set, you can choose Mask attack type which will try combinations of characters based on a pattern that you specify. If you have a list of common passwords or words that might be used as passwords, you can choose Dictionary attack type which will try words from a predefined list or a custom list that you provide. If you want to use an intelligent algorithm that can analyze your encrypted file and find the most likely password, you can choose Smart attack type which will try passwords based on some rules and patterns.</p>
- the most likely password.</p>
- <h4>Step 4: Start the password recovery process and wait for the result</h4>
- <p>After setting the parameters for your chosen password attack type, click on the "Start" button to start the password recovery process. You will see a progress bar and some information such as current password, current speed, estimated time, etc. on the screen. You can pause or stop the process at any time by clicking on the "Pause" or "Stop" button. You can also save the process and resume it later by clicking on the "Save" or "Resume" button. The password recovery process can take from minutes to hours or even days depending on the complexity of the password and the speed of your computer. When the password is found, you will see a dialog box showing you the password and asking you to copy it or open your encrypted file with it. Click on "Copy" button to copy the password to your clipboard or click on "Open" button to open your encrypted file with WinRAR and enter the password.</p>
- <h3>Method 2: Use an online password cracker service</h3>
- <p>An online password cracker service is a website that can crack passwords for encrypted files by using cloud computing and distributed computing techniques. An online password cracker service can be fast or slow depending on the number of servers and users available at a given time. An online password cracker service can be free or paid depending on the features and limitations offered by the website. To use an online password cracker service to find the password for the encrypted file setup Mudbox 2013 crack, you need to follow these steps:</p>
- <h4>Step 1: Visit an online password cracker website</h4>
- <p>There are many online password cracker websites that can crack passwords for RAR files such as <a href="https://www.password-online.com/">https://www.password-online.com/</a>, <a href="https://www.crark.net/">https://www.crark.net/</a>, <a href="https://www.onlinehashcrack.com/rar-password-recovery-online.php">https://www.onlinehashcrack.com/rar-password-recovery-online.php</a>, etc. You can choose one of them according to your preference and availability. For example, we will use https://www.password-online.com/ as an example in this article.</p>
- <h4>Step 2: Upload the encrypted file and agree to the terms of service</h4>
- <p>After visiting https://www.password-online.com/, you will see its main interface. Click on the "Browse" button and browse your computer to locate your encrypted file setup Mudbox 2013 crack (such as "Mudbox_2013_crack.rar"). Select it and click on "Open" button to upload it to the website. You will see a message saying that your file has been uploaded successfully and asking you to agree to the terms of service. Read the terms of service carefully and check "I agree with terms of service" option if you agree with them. Click on "Next step" button to proceed.</p>
- <h4>Step 3: Wait for the password to be cracked and displayed</h4>
- <p>After agreeing to the terms of service, you will see a message saying that your file has been added to the queue and asking you to wait for your turn. You will also see some information such as file name, file size, encryption method, estimated time, etc. on the screen. You can refresh the page or check your email to see if your password has been cracked and displayed. The password cracking process can take from minutes to hours or even days depending on the complexity of the password and the number of servers and users available at a given time. When the password is cracked, you will see a message saying that your password has been found and displaying it on the screen. You can also receive an email with a link to download your decrypted file if you have provided your email address during the upload process.</p>
- <h2>Conclusion</h2>
- the password for the encrypted file setup Mudbox 2013 crack by using two methods: a password recovery software and an online password cracker service. We have also provided step-by-step instructions and screenshots for each method. We hope that this article has been helpful and informative for you. If you have any questions or comments, please feel free to leave them below.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions and answers related to the topic of this article:</p>
- <h3>Q: Is it legal to crack passwords for encrypted files?</h3>
- <p>A: It depends on the situation and the jurisdiction. Generally speaking, it is legal to crack passwords for encrypted files that belong to you or that you have permission to access. However, it is illegal to crack passwords for encrypted files that belong to someone else or that you have no permission to access. It is also illegal to crack passwords for encrypted files that contain illegal or harmful content such as malware, viruses, child pornography, etc. Therefore, you should be careful and responsible when using password cracking methods and tools.</p>
- <h3>Q: Is it safe to use online password cracker services?</h3>
- <p>A: It depends on the website and the service. Some online password cracker services are reliable and trustworthy, while others are not. Some online password cracker services may steal your data, infect your computer with malware, or charge you hidden fees. Therefore, you should be careful and cautious when using online password cracker services. You should always read the terms of service, check the reviews and ratings, and use a secure connection when using online password cracker services.</p>
- <h3>Q: How can I prevent my files from being encrypted or cracked by others?</h3>
- <p>A: There are some measures that you can take to protect your files from being encrypted or cracked by others. Some of them are:</p>
- <ul>
-<li>Use strong passwords that are long, complex, and unique for your files. Avoid using common passwords or words that can be easily guessed or cracked by others.</li>
-<li>Use reliable encryption software that supports secure encryption algorithms and methods for your files. Avoid using outdated or weak encryption software that can be easily broken or bypassed by others.</li>
-<li>Backup your files regularly and store them in a safe place. Avoid losing your files or forgetting your passwords by keeping copies of your files and passwords in a secure location such as an external hard drive, a cloud storage service, or a password manager.</li>
-<li>Scan your computer and files regularly with antivirus software. Avoid getting infected by malware or viruses that can encrypt or damage your files by keeping your computer and files clean and updated with antivirus software.</li>
-</ul>
- <h3>Q: What are some other software applications that can create and extract encrypted archive files?</h3>
- <p>A: There are many other software applications that can create and extract encrypted archive files besides WinRAR. Some of them are:</p>
- <ul>
-<li>7-Zip: A free and open source software application that can create and extract compressed archive files such as 7z, ZIP, RAR, TAR, GZIP, etc. It can also encrypt and decrypt files with passwords using AES-256 encryption algorithm.</li>
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- <h3>Q: What are some other password recovery software applications that can crack passwords for encrypted files?</h3>
- <p>A: There are many other password recovery software applications that can crack passwords for encrypted files besides RAR Password Refixer. Some of them are:</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Dhoom 2 Hd Video Songs 1080p Torrent.md

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-<h1>Dhoom 2: A Thrilling Heist Movie with Stunning Songs</h1>
-<p>Dhoom 2 is a 2006 Indian action thriller film directed by Sanjay Gadhvi and produced by Aditya Chopra and Yash Chopra. It is the second installment in the Dhoom series and a sequel to the 2004 film Dhoom. The film stars Hrithik Roshan, Abhishek Bachchan, Aishwarya Rai Bachchan, Uday Chopra and Bipasha Basu in the lead roles.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Elven Love Crack Full Version Download LINK.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cheto 8 Ball Pool Mod APK Play with AutoPlay Long Line and Prediction Features.md

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-<h1>Cheto APK 8 Ball Pool: A Guide to the Best Aiming Tool for 8 Ball Pool</h1>
-<p>If you are a fan of 8 ball pool, you might have heard of Cheto APK 8 Ball Pool, a popular aiming tool that can help you improve your skills and win more games. But what is Cheto APK 8 Ball Pool exactly, and how can you use it effectively? In this article, we will answer these questions and more, so you can decide if Cheto APK 8 Ball Pool is right for you.</p>
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-<h3>A brief introduction to the app and its features</h3>
-<p>Cheto APK 8 Ball Pool is an Android app that uses AI image recognition technology to display an extended line of guideline in real time while playing 8 ball pool. This feature allows you to aim more accurately and make more precise shots. Not only that, but Cheto APK 8 Ball Pool also supports advanced cushion shots, including bank shots and kick shots, so you can easily pocket the target ball into another pocket. Moreover, Cheto APK 8 Ball Pool also supports 3-lines guideline, which lets you play some fancy shots that can only be done by professional players. With these features, Cheto APK 8 Ball Pool can help you become a king of 8 ball pool in no time.</p>
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-<li>Go to [Cheto Aim Pool For 8 Bal Pool](^1^) or [Cheto Cheat for 8 Ball Pool](^2^) and click on the download button.</li>
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-<li>Launch the app and grant it the necessary permissions.</li>
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-<h3>How to activate the auto-extend guideline feature</h3>
-<p>To activate the auto-extend guideline feature, you need to do the following:</p>
-<ol>
-<li>Open the app and tap on the settings icon.</li>
-<li>Toggle on the auto-extend guideline option.</li>
-<li>Go back to the main screen and tap on the play button.</li>
-<li>Select your preferred game mode and start playing.</li>
-<li>You will see a longer line of guideline on your screen that will help you aim better.</li>
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- <h3>How to use the cushion shots and 3-lines features</h3>
-<p>To use the cushion shots and 3-lines features, you need to do the following:</p>
-<ol>
-<li>Open the app and tap on the settings icon.</li>
-<li>Toggle on the cushion shots or 3-lines option depending on what you want to use.</li>
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-<li>Select your preferred game mode and start playing.</li>
-<li>You will see a cushion shot or 3-lines icon on your screen that will help you make advanced shots.</li>
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-<li>Be careful not to use the app too obviously or excessively, as it might raise suspicion from other players or moderators.</li>
-<li>Keep the app updated to avoid any bugs or glitches.</li>
-<li>Have fun and enjoy playing 8 ball pool with Cheto APK 8 Ball Pool.</li>
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-<h3>The benefits of using Cheto APK 8 Ball Pool</h3>
-<p>Some of the benefits of using Cheto APK 8 Ball Pool are:</p>
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-<p>In conclusion, Cheto APK 8 Ball Pool is an aiming tool that can help you play 8 ball pool better. It has features such as auto-extend guideline, cushion shots, and 3-lines that can help you aim more accurately and make more precise shots. However, it also has some drawbacks and risks, such as making 8 ball pool less challenging, making you dependent on the app, getting you banned or reported, and exposing your device to malware or viruses. Therefore, you should use Cheto APK 8 Ball Pool wisely and responsibly, and not abuse it or rely on it too much. If you want to try Cheto APK 8 Ball Pool, you can download it from [Cheto Aim Pool For 8 Bal Pool] or [Cheto Cheat for 8 Ball Pool] and follow the instructions in this article. Have fun and enjoy playing 8 ball pool with Cheto APK 8 Ball Pool!</p>
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-<p>No, Cheto APK 8 Ball Pool is not legal. It is a third-party app that violates the terms of service of 8 ball pool. Using it can get you banned or reported by other players or moderators. Use it at your own risk.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Brawl Stars APKure and Enjoy Epic 3v3 Battles.md

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-<li>Go back to the file and tap on "Install". The installation will begin.</li>
-<li>Wait for a few seconds until the installation is finished. You will see a message that says "App installed". Tap on "Open" to launch Brawl Stars.</li>
-</ol>
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-<tr><td>- You <p>Here are some of the pros and cons of using apkure to download Brawl Stars:</p>
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- <h3>How to Install and Launch the Game</h3>
- <p>If you have followed the steps above, you should have already installed Brawl Stars on your device. To launch the game, simply tap on its icon on your home screen or app drawer. You will see a loading screen with the Brawl Stars logo and some tips. Wait for a few seconds until the game loads completely.</p>
- <p>The first time you launch the game, you will be asked to choose a name for your profile. You can enter any name you like, as long as it is not offensive or inappropriate. You can also change your name later in the settings menu. After choosing a name, you will be taken to the tutorial mode, where you will learn the basics of the game.</p>
- <p>The tutorial mode will teach you how to move, shoot, aim, use your super ability, and collect power cubes. You will also learn about the different game modes, such as Showdown, Gem Grab, Brawl Ball, Bounty, Heist, Siege, Hot Zone, and Duo Showdown. You will also learn about events, quests, trophies, tokens, gems, coins, power points, star points, boxes, brawlers, skins, gadgets, star powers, clubs, friends, chat, leaderboard, shop, news, settings, and support. The tutorial mode will end when you complete your first Showdown match.</p>
- <h3>How to Choose and Unlock New Characters (Brawlers)</h3>
- <p>Brawl Stars has 49 characters that you can choose from, each with their own unique abilities and stats. These characters are called Brawlers, and they are divided into seven rarities: Common (8), Rare (6), Super Rare (6), Epic (9), Mythic (7), Legendary (7), and Chromatic (6). The higher the rarity of a Brawler, the harder it is to unlock them.</p>
- <p>You can unlock new Brawlers by opening boxes that you get from playing matches or completing quests. Boxes can contain coins, power points, gems, star points, gadgets, star powers, or new Brawlers. The chances of getting a new Brawler depend on their rarity and your luck. You can also buy new Brawlers from the shop using gems or star points when they are available.</p>
- <p>You can choose which Brawler you want to play with before starting a match. You can see all your unlocked B <p>You can choose which Brawler you want to play with before starting a match. You can see all your unlocked Brawlers in the Brawlers menu, where you can also upgrade them with power points and coins, equip them with gadgets and star powers, and change their skins. You can also see the stats, abilities, and tips for each Brawler by tapping on their portrait.</p>
- <p>Some Brawlers are better suited for certain game modes than others, so you should choose wisely depending on the situation. You can also switch your Brawler between matches if you want to try something different. You can also use the random button to let the game choose a Brawler for you.</p>
- <h3>How to Join or Create a Club and Play with Friends</h3>
- <p>Brawl Stars is more fun when you play with your friends or other players who share your interests and goals. You can join or create a club to chat, play, and compete with other club members. Clubs are like clans or guilds in other games, and they can have up to 100 members.</p>
- <p>To join or create a club, you need to go to the social menu and tap on the club tab. There, you will see a list of recommended clubs that you can join, or you can search for a specific club by name or tag. You can also create your own club by tapping on the create button and choosing a name, description, badge, type, trophy requirement, and region for your club. You can also invite your friends to join your club by sending them a link or a code.</p>
- <p>Once you are in a club, you can chat with other club members, see their profiles and stats, send them friend requests, invite them to play with you, or challenge them to friendly matches. You can also participate in club events and leagues, where you can earn rewards and fame for your club. You can also leave or switch clubs at any time if you want to.</p>
- <h3>How to Earn Trophies, Tokens, Gems, and Other Rewards</h3>
- <p>Brawl Stars has a lot of rewards that you can earn by playing the game and completing various tasks. Here are some of the main rewards that you can get:</p>
- <ul>
-<li>Trophies: Trophies are the main measure of your progress and skill in the game. You can earn trophies by winning matches in any game mode, or lose trophies by losing matches. The more trophies you have, the higher your rank and league will be. You can also unlock new game modes, events, and rewards by reaching certain trophy milestones.</li>
-<li>Tokens: Tokens are the main currency that you can use to open boxes. You can earn tokens by playing matches in any game mode, completing quests, ranking up your Brawlers, or watching ads. You can also get tokens from boxes or from the shop. You can use tokens to open brawl boxes, big boxes, or mega boxes, which contain coins, power points, gems, star points, gadgets, star powers, or new Brawlers.</li>
-<li>Gems: Gems are the premium currency that you can use to buy special offers, skins, boxes, coins, power points, star points, or brawl passes from the shop. You can get gems from boxes or from the shop using real money. You can also get gems from the brawl pass or from special events.</li>
-<li>Star Points: Star Points are a special currency that you can use to buy exclusive skins, boxes, or power points from the shop. You can get star points by ranking up your Brawlers to rank 10 or higher, reaching certain trophy milestones with your Brawlers, or completing certain quests.</li>
-<li>Brawl Pass: The brawl pass is a seasonal feature that gives you access to exclusive rewards and perks. You can get the brawl pass by spending gems or by completing certain quests. The brawl pass has two tracks: a free track and a premium track. The free track contains rewards that anyone can get by earning brawl pass tokens. The premium track contains more rewards that only brawl pass holders can get by earning brawl pass tokens. The brawl pass also gives you access to special quests and events that give you more rewards.</li>
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- <h2>Conclusion</h2>
- <p>Brawl Stars is a fun and exciting game that you can play on your mobile device with players from around the world. You can choose from different characters with unique abilities and compete in various game modes with different objectives and rules. You can also customize your characters with skins, <p>Brawl Stars is a fun and exciting game that you can play on your mobile device with players from around the world. You can choose from different characters with unique abilities and compete in various game modes with different objectives and rules. You can also customize your characters with skins, gadgets, and star powers, and join or create a club with your friends.</p>
- <p>If you want to download Brawl Stars from apkure, you need to follow some steps carefully and be aware of the pros and cons of using this website. You also need to follow some tips and warnings to use apkure safely and legally. After downloading Brawl Stars from apkure, you can install and launch the game, choose and unlock new characters, join or create a club, and earn trophies, tokens, gems, and other rewards.</p>
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- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Brawl Stars and apkure:</p>
- <h3>Is Brawl Stars free to play?</h3>
- <p>Yes, Brawl Stars is free to play. You can download and play the game without spending any money. However, the game also offers some optional in-app purchases that can enhance your gaming experience. You can buy gems, coins, power points, star points, brawl passes, skins, boxes, or special offers using real money. These purchases are not required to play the game, but they can help you unlock new content faster or customize your characters more.</p>
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- <p>If you download Brawl Stars from apkure, you will not receive automatic updates from the official app store. This means that you may miss out on new features, bug fixes, security patches, or events that are released by Supercell. To update Brawl Stars after downloading it from apkure, you need to visit apkure again and look for the latest version of the game. Then you need to download and install it over the existing one. However, this may cause some issues with your data or account compatibility, so you should always backup your data before updating.</p>
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-<p>If you are a fan of soccer games, you probably have heard of FIFA Mobile, the popular football simulation game developed by EA Sports. The game features real-world teams, players, and stadiums, allowing you to create your own football teams and compete against others online. But what if you want to have more fun and freedom in the game? What if you want to unlock all the features and modes without spending any money or time? That's where FIFA Mobile mod menu comes in.</p>
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-<p>FIFA Mobile mod menu has its pros and cons that you should be aware of before downloading and installing it. Here are some of them:</p>
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-<p>FIFA Mobile is a great game for soccer fans who want to enjoy the ultimate soccer experience on their mobile devices. However, if you want to have more fun and freedom in the game, you can download and install FIFA Mobile mod menu, which gives you access to various cheats and hacks that can enhance your gaming experience. With FIFA Mobile mod menu, you can unlock all players, teams, and modes, get unlimited money and gems, customize your settings, and enjoy faster and smoother gameplay. However, you should also be aware of the potential risks, compatibility issues, and ethical concerns that come with using FIFA Mobile mod menu. You should always download from trusted sources, scan your device regularly, check the compatibility of the mod menu with your device and game, respect the rules and regulations of the game, and play responsibly. We hope this article has helped you learn how to download and install FIFA Mobile mod menu, as well as some tips and tricks for playing the game with it. Have fun and enjoy the game!</p>
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-<li><b>Will I get banned for using FIFA Mobile mod menu?</b>: There is a possibility of getting banned for using FIFA Mobile mod menu, especially if you use it online or in competitive modes. EA Sports has a strict policy against cheating or hacking in their games, and they may detect and penalize any players who use FIFA Mobile mod menu. You should always respect the rules and regulations of the game and play responsibly.</li>
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spaces/232labs/VToonify/vtoonify/model/stylegan/dataset.py

@@ -1,40 +0,0 @@
-from io import BytesIO
-
-import lmdb
-from PIL import Image
-from torch.utils.data import Dataset
-
-
-class MultiResolutionDataset(Dataset):
-    def __init__(self, path, transform, resolution=256):
-        self.env = lmdb.open(
-            path,
-            max_readers=32,
-            readonly=True,
-            lock=False,
-            readahead=False,
-            meminit=False,
-        )
-
-        if not self.env:
-            raise IOError('Cannot open lmdb dataset', path)
-
-        with self.env.begin(write=False) as txn:
-            self.length = int(txn.get('length'.encode('utf-8')).decode('utf-8'))
-
-        self.resolution = resolution
-        self.transform = transform
-
-    def __len__(self):
-        return self.length
-
-    def __getitem__(self, index):
-        with self.env.begin(write=False) as txn:
-            key = f'{self.resolution}-{str(index).zfill(5)}'.encode('utf-8')
-            img_bytes = txn.get(key)
-
-        buffer = BytesIO(img_bytes)
-        img = Image.open(buffer)
-        img = self.transform(img)
-
-        return img

spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/layers_537238KB.py

@@ -1,126 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle

spaces/A00001/bingothoo/src/components/learn-more.tsx

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

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/latent_diffusion/ddpm.py

@@ -1,441 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-import sys
-import os
-
-import torch
-import torch.nn as nn
-import numpy as np
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-
-from audioldm.utils import exists, default, count_params, instantiate_from_config
-from audioldm.latent_diffusion.ema import LitEma
-from audioldm.latent_diffusion.util import (
-    make_beta_schedule,
-    extract_into_tensor,
-    noise_like,
-)
-import soundfile as sf
-import os
-
-
-__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DiffusionWrapper(nn.Module):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [
-            None,
-            "concat",
-            "crossattn",
-            "hybrid",
-            "adm",
-            "film",
-        ]
-
-    def forward(
-        self, x, t, c_concat: list = None, c_crossattn: list = None, c_film: list = None
-    ):
-        x = x.contiguous()
-        t = t.contiguous()
-
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t)
-        elif self.conditioning_key == "concat":
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t)
-        elif self.conditioning_key == "crossattn":
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc)
-        elif self.conditioning_key == "hybrid":
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc)
-        elif (
-            self.conditioning_key == "film"
-        ):  # The condition is assumed to be a global token, which wil pass through a linear layer and added with the time embedding for the FILM
-            cc = c_film[0].squeeze(1)  # only has one token
-            out = self.diffusion_model(x, t, y=cc)
-        elif self.conditioning_key == "adm":
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc)
-        else:
-            raise NotImplementedError()
-
-        return out
-
-
-class DDPM(nn.Module):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(
-        self,
-        unet_config,
-        timesteps=1000,
-        beta_schedule="linear",
-        loss_type="l2",
-        ckpt_path=None,
-        ignore_keys=[],
-        load_only_unet=False,
-        monitor="val/loss",
-        use_ema=True,
-        first_stage_key="image",
-        latent_t_size=256,
-        latent_f_size=16,
-        channels=3,
-        log_every_t=100,
-        clip_denoised=True,
-        linear_start=1e-4,
-        linear_end=2e-2,
-        cosine_s=8e-3,
-        given_betas=None,
-        original_elbo_weight=0.0,
-        v_posterior=0.0,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-        l_simple_weight=1.0,
-        conditioning_key=None,
-        parameterization="eps",  # all assuming fixed variance schedules
-        scheduler_config=None,
-        use_positional_encodings=False,
-        learn_logvar=False,
-        logvar_init=0.0,
-    ):
-        super().__init__()
-        assert parameterization in [
-            "eps",
-            "x0",
-        ], 'currently only supporting "eps" and "x0"'
-        self.parameterization = parameterization
-        self.state = None
-        # print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-
-        self.latent_t_size = latent_t_size
-        self.latent_f_size = latent_f_size
-
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapper(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            # print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-
-        self.register_schedule(
-            given_betas=given_betas,
-            beta_schedule=beta_schedule,
-            timesteps=timesteps,
-            linear_start=linear_start,
-            linear_end=linear_end,
-            cosine_s=cosine_s,
-        )
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-        else:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=False)
-
-        self.logger_save_dir = None
-        self.logger_project = None
-        self.logger_version = None
-        self.label_indices_total = None
-        # To avoid the system cannot find metric value for checkpoint
-        self.metrics_buffer = {
-            "val/kullback_leibler_divergence_sigmoid": 15.0,
-            "val/kullback_leibler_divergence_softmax": 10.0,
-            "val/psnr": 0.0,
-            "val/ssim": 0.0,
-            "val/inception_score_mean": 1.0,
-            "val/inception_score_std": 0.0,
-            "val/kernel_inception_distance_mean": 0.0,
-            "val/kernel_inception_distance_std": 0.0,
-            "val/frechet_inception_distance": 133.0,
-            "val/frechet_audio_distance": 32.0,
-        }
-        self.initial_learning_rate = None
-
-    def get_log_dir(self):
-        if (
-            self.logger_save_dir is None
-            and self.logger_project is None
-            and self.logger_version is None
-        ):
-            return os.path.join(
-                self.logger.save_dir, self.logger._project, self.logger.version
-            )
-        else:
-            return os.path.join(
-                self.logger_save_dir, self.logger_project, self.logger_version
-            )
-
-    def set_log_dir(self, save_dir, project, version):
-        self.logger_save_dir = save_dir
-        self.logger_project = project
-        self.logger_version = version
-
-    def register_schedule(
-        self,
-        given_betas=None,
-        beta_schedule="linear",
-        timesteps=1000,
-        linear_start=1e-4,
-        linear_end=2e-2,
-        cosine_s=8e-3,
-    ):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(
-                beta_schedule,
-                timesteps,
-                linear_start=linear_start,
-                linear_end=linear_end,
-                cosine_s=cosine_s,
-            )
-        alphas = 1.0 - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
-
-        (timesteps,) = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert (
-            alphas_cumprod.shape[0] == self.num_timesteps
-        ), "alphas have to be defined for each timestep"
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer("betas", to_torch(betas))
-        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
-        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer(
-            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
-        )
-        self.register_buffer(
-            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
-        )
-        self.register_buffer(
-            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
-        )
-        self.register_buffer(
-            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
-        )
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (
-            1.0 - alphas_cumprod_prev
-        ) / (1.0 - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer("posterior_variance", to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer(
-            "posterior_log_variance_clipped",
-            to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
-        )
-        self.register_buffer(
-            "posterior_mean_coef1",
-            to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
-        )
-        self.register_buffer(
-            "posterior_mean_coef2",
-            to_torch(
-                (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
-            ),
-        )
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas**2 / (
-                2
-                * self.posterior_variance
-                * to_torch(alphas)
-                * (1 - self.alphas_cumprod)
-            )
-        elif self.parameterization == "x0":
-            lvlb_weights = (
-                0.5
-                * np.sqrt(torch.Tensor(alphas_cumprod))
-                / (2.0 * 1 - torch.Tensor(alphas_cumprod))
-            )
-        else:
-            raise NotImplementedError("mu not supported")
-        # TODO how to choose this term
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                # print(f"{context}: Switched to EMA weights")
-                pass
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    # print(f"{context}: Restored training weights")
-                    pass
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(
-            self.log_one_minus_alphas_cumprod, t, x_start.shape
-        )
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-            extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-            * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-            extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
-            + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(
-            self.posterior_log_variance_clipped, t, x_t.shape
-        )
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        if clip_denoised:
-            x_recon.clamp_(-1.0, 1.0)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
-            x_start=x_recon, x_t=x, t=t
-        )
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(
-            x=x, t=t, clip_denoised=clip_denoised
-        )
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (
-            (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))).contiguous()
-        )
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(
-            reversed(range(0, self.num_timesteps)),
-            desc="Sampling t",
-            total=self.num_timesteps,
-        ):
-            img = self.p_sample(
-                img,
-                torch.full((b,), i, device=device, dtype=torch.long),
-                clip_denoised=self.clip_denoised,
-            )
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        shape = (batch_size, channels, self.latent_t_size, self.latent_f_size)
-        channels = self.channels
-        return self.p_sample_loop(shape, return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (
-            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-            + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
-            * noise
-        )
-
-    def forward(self, x, *args, **kwargs):
-        t = torch.randint(
-            0, self.num_timesteps, (x.shape[0],), device=self.device
-        ).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        # fbank, log_magnitudes_stft, label_indices, fname, waveform, clip_label, text = batch
-        fbank, log_magnitudes_stft, label_indices, fname, waveform, text = batch
-        ret = {}
-
-        ret["fbank"] = (
-            fbank.unsqueeze(1).to(memory_format=torch.contiguous_format).float()
-        )
-        ret["stft"] = log_magnitudes_stft.to(
-            memory_format=torch.contiguous_format
-        ).float()
-        # ret["clip_label"] = clip_label.to(memory_format=torch.contiguous_format).float()
-        ret["waveform"] = waveform.to(memory_format=torch.contiguous_format).float()
-        ret["text"] = list(text)
-        ret["fname"] = fname
-
-        return ret[k]

spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/models/base_model.py

@@ -1,500 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from typing import Dict
-
-import torch
-import torch.nn as nn
-
-from .utils import mean_with_lens, repeat_tensor
-
-
-class CaptionModel(nn.Module):
-    """
-    Encoder-decoder captioning model.
-    """
-
-    pad_idx = 0
-    start_idx = 1
-    end_idx = 2
-    max_length = 20
-
-    def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.vocab_size = decoder.vocab_size
-        self.train_forward_keys = ["cap", "cap_len", "ss_ratio"]
-        self.inference_forward_keys = ["sample_method", "max_length", "temp"]
-        freeze_encoder = kwargs.get("freeze_encoder", False)
-        if freeze_encoder:
-            for param in self.encoder.parameters():
-                param.requires_grad = False
-        self.check_decoder_compatibility()
-
-    def check_decoder_compatibility(self):
-        compatible_decoders = [x.__class__.__name__ for x in self.compatible_decoders]
-        assert isinstance(self.decoder, self.compatible_decoders), \
-            f"{self.decoder.__class__.__name__} is incompatible with " \
-            f"{self.__class__.__name__}, please use decoder in {compatible_decoders} "
-
-    @classmethod
-    def set_index(cls, start_idx, end_idx):
-        cls.start_idx = start_idx
-        cls.end_idx = end_idx
-
-    def forward(self, input_dict: Dict):
-        """
-        input_dict: {
-            (required)
-            mode: train/inference,
-            spec,
-            spec_len,
-            fc,
-            attn,
-            attn_len,
-            [sample_method: greedy],
-            [temp: 1.0] (in case of no teacher forcing)
-
-            (optional, mode=train)
-            cap,
-            cap_len,
-            ss_ratio,
-
-            (optional, mode=inference)
-            sample_method: greedy/beam,
-            max_length,
-            temp,
-            beam_size (optional, sample_method=beam),
-            n_best (optional, sample_method=beam),
-        }
-        """
-        # encoder_input_keys = ["spec", "spec_len", "fc", "attn", "attn_len"]
-        # encoder_input = { key: input_dict[key] for key in encoder_input_keys }
-        encoder_output_dict = self.encoder(input_dict)
-        if input_dict["mode"] == "train":
-            forward_dict = {
-                "mode": "train", "sample_method": "greedy", "temp": 1.0
-            }
-            for key in self.train_forward_keys:
-                forward_dict[key] = input_dict[key]
-            forward_dict.update(encoder_output_dict)
-            output = self.train_forward(forward_dict)
-        elif input_dict["mode"] == "inference":
-            forward_dict = {"mode": "inference"}
-            default_args = { "sample_method": "greedy", "max_length": self.max_length, "temp": 1.0 }
-            for key in self.inference_forward_keys:
-                if key in input_dict:
-                    forward_dict[key] = input_dict[key]
-                else:
-                    forward_dict[key] = default_args[key]
-
-            if forward_dict["sample_method"] == "beam":
-                forward_dict["beam_size"] = input_dict.get("beam_size", 3)
-                forward_dict["n_best"] = input_dict.get("n_best", False)
-                forward_dict["n_best_size"] = input_dict.get("n_best_size", forward_dict["beam_size"])
-            elif forward_dict["sample_method"] == "dbs":
-                forward_dict["beam_size"] = input_dict.get("beam_size", 6)
-                forward_dict["group_size"] = input_dict.get("group_size", 3)
-                forward_dict["diversity_lambda"] = input_dict.get("diversity_lambda", 0.5)
-                forward_dict["group_nbest"] = input_dict.get("group_nbest", True)
-
-            forward_dict.update(encoder_output_dict)
-            output = self.inference_forward(forward_dict)
-        else:
-            raise Exception("mode should be either 'train' or 'inference'")
-
-        return output
-
-    def prepare_output(self, input_dict):
-        output = {}
-        batch_size = input_dict["fc_emb"].size(0)
-        if input_dict["mode"] == "train":
-            max_length = input_dict["cap"].size(1) - 1
-        elif input_dict["mode"] == "inference":
-            max_length = input_dict["max_length"]
-        else:
-            raise Exception("mode should be either 'train' or 'inference'")
-        device = input_dict["fc_emb"].device
-        output["seq"] = torch.full((batch_size, max_length), self.end_idx,
-                                   dtype=torch.long)
-        output["logit"] = torch.empty(batch_size, max_length,
-                                      self.vocab_size).to(device)
-        output["sampled_logprob"] = torch.zeros(batch_size, max_length)
-        output["embed"] = torch.empty(batch_size, max_length,
-                                      self.decoder.d_model).to(device)
-        return output
-
-    def train_forward(self, input_dict):
-        if input_dict["ss_ratio"] != 1: # scheduled sampling training
-            input_dict["mode"] = "train"
-            return self.stepwise_forward(input_dict)
-        output = self.seq_forward(input_dict)
-        self.train_process(output, input_dict)
-        return output
-
-    def seq_forward(self, input_dict):
-        raise NotImplementedError
-
-    def train_process(self, output, input_dict):
-        pass
-
-    def inference_forward(self, input_dict):
-        if input_dict["sample_method"] == "beam":
-            return self.beam_search(input_dict)
-        elif input_dict["sample_method"] == "dbs":
-            return self.diverse_beam_search(input_dict)
-        return self.stepwise_forward(input_dict)
-
-    def stepwise_forward(self, input_dict):
-        """Step-by-step decoding"""
-        output = self.prepare_output(input_dict)
-        max_length = output["seq"].size(1)
-        # start sampling
-        for t in range(max_length):
-            input_dict["t"] = t
-            self.decode_step(input_dict, output)
-            if input_dict["mode"] == "inference": # decide whether to stop when sampling
-                unfinished_t = output["seq"][:, t] != self.end_idx
-                if t == 0:
-                    unfinished = unfinished_t
-                else:
-                    unfinished *= unfinished_t
-                output["seq"][:, t][~unfinished] = self.end_idx
-                if unfinished.sum() == 0:
-                    break
-        self.stepwise_process(output)
-        return output
-
-    def decode_step(self, input_dict, output):
-        """Decoding operation of timestep t"""
-        decoder_input = self.prepare_decoder_input(input_dict, output)
-        # feed to the decoder to get logit
-        output_t = self.decoder(decoder_input)
-        logit_t = output_t["logit"]
-        # assert logit_t.ndim == 3
-        if logit_t.size(1) == 1:
-            logit_t = logit_t.squeeze(1)
-            embed_t = output_t["embed"].squeeze(1)
-        elif logit_t.size(1) > 1:
-            logit_t = logit_t[:, -1, :]
-            embed_t = output_t["embed"][:, -1, :]
-        else:
-            raise Exception("no logit output")
-        # sample the next input word and get the corresponding logit
-        sampled = self.sample_next_word(logit_t,
-                                        method=input_dict["sample_method"],
-                                        temp=input_dict["temp"])
-
-        output_t.update(sampled)
-        output_t["t"] = input_dict["t"]
-        output_t["logit"] = logit_t
-        output_t["embed"] = embed_t
-        self.stepwise_process_step(output, output_t)
-
-    def prepare_decoder_input(self, input_dict, output):
-        """Prepare the inp ut dict for the decoder"""
-        raise NotImplementedError
-    
-    def stepwise_process_step(self, output, output_t):
-        """Postprocessing (save output values) after each timestep t"""
-        t = output_t["t"]
-        output["logit"][:, t, :] = output_t["logit"]
-        output["seq"][:, t] = output_t["word"]
-        output["sampled_logprob"][:, t] = output_t["probs"]
-        output["embed"][:, t, :] = output_t["embed"]
-
-    def stepwise_process(self, output):
-        """Postprocessing after the whole step-by-step autoregressive decoding"""
-        pass
-
-    def sample_next_word(self, logit, method, temp):
-        """Sample the next word, given probs output by the decoder"""
-        logprob = torch.log_softmax(logit, dim=1)
-        if method == "greedy":
-            sampled_logprob, word = torch.max(logprob.detach(), 1)
-        elif method == "gumbel":
-            def sample_gumbel(shape, eps=1e-20):
-                U = torch.rand(shape).to(logprob.device)
-                return -torch.log(-torch.log(U + eps) + eps)
-            def gumbel_softmax_sample(logit, temperature):
-                y = logit + sample_gumbel(logit.size())
-                return torch.log_softmax(y / temperature, dim=-1)
-            _logprob = gumbel_softmax_sample(logprob, temp)
-            _, word = torch.max(_logprob.data, 1)
-            sampled_logprob = logprob.gather(1, word.unsqueeze(-1))
-        else:
-            logprob = logprob / temp
-            if method.startswith("top"):
-                top_num = float(method[3:])
-                if 0 < top_num < 1: # top-p sampling
-                    probs = torch.softmax(logit, dim=1)
-                    sorted_probs, sorted_indices = torch.sort(probs, descending=True, dim=1)
-                    _cumsum = sorted_probs.cumsum(1)
-                    mask = _cumsum < top_num
-                    mask = torch.cat([torch.ones_like(mask[:,:1]), mask[:,:-1]], 1)
-                    sorted_probs = sorted_probs * mask.to(sorted_probs)
-                    sorted_probs = sorted_probs / sorted_probs.sum(1, keepdim=True)
-                    logprob.scatter_(1, sorted_indices, sorted_probs.log())
-                else: # top-k sampling
-                    k = int(top_num)
-                    tmp = torch.empty_like(logprob).fill_(float('-inf'))
-                    topk, indices = torch.topk(logprob, k, dim=1)
-                    tmp = tmp.scatter(1, indices, topk)
-                    logprob = tmp
-            word = torch.distributions.Categorical(logits=logprob.detach()).sample()
-            sampled_logprob = logprob.gather(1, word.unsqueeze(-1)).squeeze(1)
-        word = word.detach().long()
-        # sampled_logprob: [N,], word: [N,]
-        return {"word": word, "probs": sampled_logprob}
-
-    def beam_search(self, input_dict):
-        output = self.prepare_output(input_dict)
-        max_length = input_dict["max_length"]
-        beam_size = input_dict["beam_size"]
-        if input_dict["n_best"]:
-            n_best_size = input_dict["n_best_size"]
-            batch_size, max_length = output["seq"].size()
-            output["seq"] = torch.full((batch_size, n_best_size, max_length),
-                                        self.end_idx, dtype=torch.long)
-            
-        temp = input_dict["temp"]
-        # instance by instance beam seach
-        for i in range(output["seq"].size(0)):
-            output_i = self.prepare_beamsearch_output(input_dict)
-            input_dict["sample_idx"] = i
-            for t in range(max_length):
-                input_dict["t"] = t
-                output_t = self.beamsearch_step(input_dict, output_i)
-                #######################################
-                # merge with previous beam and select the current max prob beam
-                #######################################
-                logit_t = output_t["logit"]
-                if logit_t.size(1) == 1:
-                    logit_t = logit_t.squeeze(1)
-                elif logit_t.size(1) > 1:
-                    logit_t = logit_t[:, -1, :]
-                else:
-                    raise Exception("no logit output")
-                logprob_t = torch.log_softmax(logit_t, dim=1)
-                logprob_t = torch.log_softmax(logprob_t / temp, dim=1)
-                logprob_t = output_i["topk_logprob"].unsqueeze(1) + logprob_t
-                if t == 0: # for the first step, all k seq will have the same probs
-                    topk_logprob, topk_words = logprob_t[0].topk(
-                        beam_size, 0, True, True)
-                else: # unroll and find top logprob, and their unrolled indices
-                    topk_logprob, topk_words = logprob_t.view(-1).topk(
-                        beam_size, 0, True, True)
-                topk_words = topk_words.cpu()
-                output_i["topk_logprob"] = topk_logprob
-                # output_i["prev_words_beam"] = topk_words // self.vocab_size  # [beam_size,]
-                output_i["prev_words_beam"] = torch.div(topk_words, self.vocab_size,
-                                                        rounding_mode='trunc')
-                output_i["next_word"] = topk_words % self.vocab_size  # [beam_size,]
-                if t == 0:
-                    output_i["seq"] = output_i["next_word"].unsqueeze(1)
-                else:
-                    output_i["seq"] = torch.cat([
-                        output_i["seq"][output_i["prev_words_beam"]],
-                        output_i["next_word"].unsqueeze(1)], dim=1)
-
-                # add finished beams to results
-                is_end = output_i["next_word"] == self.end_idx
-                if t == max_length - 1:
-                    is_end.fill_(1)
-                
-                for beam_idx in range(beam_size):
-                    if is_end[beam_idx]:
-                        final_beam = {
-                            "seq": output_i["seq"][beam_idx].clone(),
-                            "score": output_i["topk_logprob"][beam_idx].item()
-                        }
-                        final_beam["score"] = final_beam["score"] / (t + 1)
-                        output_i["done_beams"].append(final_beam)
-                output_i["topk_logprob"][is_end] -= 1000
-
-                self.beamsearch_process_step(output_i, output_t)
-
-            self.beamsearch_process(output, output_i, input_dict)
-        return output
-
-    def prepare_beamsearch_output(self, input_dict):
-        beam_size = input_dict["beam_size"]
-        device = input_dict["fc_emb"].device
-        output = {
-            "topk_logprob": torch.zeros(beam_size).to(device),
-            "seq": None,
-            "prev_words_beam": None,
-            "next_word": None,
-            "done_beams": [],
-        }
-        return output
-
-    def beamsearch_step(self, input_dict, output_i):
-        decoder_input = self.prepare_beamsearch_decoder_input(input_dict, output_i)
-        output_t = self.decoder(decoder_input)
-        output_t["t"] = input_dict["t"]
-        return output_t
-
-    def prepare_beamsearch_decoder_input(self, input_dict, output_i):
-        raise NotImplementedError
-            
-    def beamsearch_process_step(self, output_i, output_t):
-        pass
-
-    def beamsearch_process(self, output, output_i, input_dict):
-        i = input_dict["sample_idx"]
-        done_beams = sorted(output_i["done_beams"], key=lambda x: -x["score"])
-        if input_dict["n_best"]:
-            done_beams = done_beams[:input_dict["n_best_size"]]
-            for out_idx, done_beam in enumerate(done_beams):
-                seq = done_beam["seq"]
-                output["seq"][i][out_idx, :len(seq)] = seq
-        else:
-            seq = done_beams[0]["seq"]
-            output["seq"][i][:len(seq)] = seq
-    
-    def diverse_beam_search(self, input_dict):
-        
-        def add_diversity(seq_table, logprob, t, divm, diversity_lambda, bdash):
-            local_time = t - divm
-            unaug_logprob = logprob.clone()
-
-            if divm > 0:
-                change = torch.zeros(logprob.size(-1))
-                for prev_choice in range(divm):
-                    prev_decisions = seq_table[prev_choice][..., local_time]
-                    for prev_labels in range(bdash):
-                        change.scatter_add_(0, prev_decisions[prev_labels], change.new_ones(1))
-
-                change = change.to(logprob.device)
-                logprob = logprob - repeat_tensor(change, bdash) * diversity_lambda
-
-            return logprob, unaug_logprob
-
-        output = self.prepare_output(input_dict)
-        group_size = input_dict["group_size"]
-        batch_size = output["seq"].size(0)
-        beam_size = input_dict["beam_size"]
-        bdash = beam_size // group_size
-        input_dict["bdash"] = bdash
-        diversity_lambda = input_dict["diversity_lambda"]
-        device = input_dict["fc_emb"].device
-        max_length = input_dict["max_length"]
-        temp = input_dict["temp"]
-        group_nbest = input_dict["group_nbest"]
-        batch_size, max_length = output["seq"].size()
-        if group_nbest:
-            output["seq"] = torch.full((batch_size, beam_size, max_length),
-                                        self.end_idx, dtype=torch.long)
-        else:
-            output["seq"] = torch.full((batch_size, group_size, max_length),
-                                        self.end_idx, dtype=torch.long)
-
-
-        for i in range(batch_size):
-            input_dict["sample_idx"] = i
-            seq_table = [torch.LongTensor(bdash, 0) for _ in range(group_size)] # group_size x [bdash, 0]
-            logprob_table = [torch.zeros(bdash).to(device) for _ in range(group_size)]
-            done_beams_table = [[] for _ in range(group_size)]
-
-            output_i = {
-                "prev_words_beam": [None for _ in range(group_size)],
-                "next_word": [None for _ in range(group_size)],
-                "state": [None for _ in range(group_size)]
-            }
-
-            for t in range(max_length + group_size - 1):
-                input_dict["t"] = t
-                for divm in range(group_size):
-                    input_dict["divm"] = divm
-                    if t >= divm and t <= max_length + divm - 1:
-                        local_time = t - divm
-                        decoder_input = self.prepare_dbs_decoder_input(input_dict, output_i)
-                        output_t = self.decoder(decoder_input)
-                        output_t["divm"] = divm
-                        logit_t = output_t["logit"]
-                        if logit_t.size(1) == 1:
-                            logit_t = logit_t.squeeze(1)
-                        elif logit_t.size(1) > 1:
-                            logit_t = logit_t[:, -1, :]
-                        else:
-                            raise Exception("no logit output")
-                        logprob_t = torch.log_softmax(logit_t, dim=1)
-                        logprob_t = torch.log_softmax(logprob_t / temp, dim=1)
-                        logprob_t, unaug_logprob_t = add_diversity(seq_table, logprob_t, t, divm, diversity_lambda, bdash)
-                        logprob_t = logprob_table[divm].unsqueeze(-1) + logprob_t
-                        if local_time == 0: # for the first step, all k seq will have the same probs
-                            topk_logprob, topk_words = logprob_t[0].topk(
-                                bdash, 0, True, True)
-                        else: # unroll and find top logprob, and their unrolled indices
-                            topk_logprob, topk_words = logprob_t.view(-1).topk(
-                                bdash, 0, True, True)
-                        topk_words = topk_words.cpu()
-                        logprob_table[divm] = topk_logprob
-                        output_i["prev_words_beam"][divm] = topk_words // self.vocab_size  # [bdash,]
-                        output_i["next_word"][divm] = topk_words % self.vocab_size  # [bdash,]
-                        if local_time > 0:
-                            seq_table[divm] = seq_table[divm][output_i["prev_words_beam"][divm]]
-                        seq_table[divm] = torch.cat([
-                            seq_table[divm],
-                            output_i["next_word"][divm].unsqueeze(-1)], -1)
-
-                        is_end = seq_table[divm][:, t-divm] == self.end_idx
-                        assert seq_table[divm].shape[-1] == t - divm + 1
-                        if t == max_length + divm - 1:
-                            is_end.fill_(1)
-                        for beam_idx in range(bdash):
-                            if is_end[beam_idx]:
-                                final_beam = {
-                                    "seq": seq_table[divm][beam_idx].clone(),
-                                    "score": logprob_table[divm][beam_idx].item()
-                                }
-                                final_beam["score"] = final_beam["score"] / (t - divm + 1)
-                                done_beams_table[divm].append(final_beam)
-                        logprob_table[divm][is_end] -= 1000
-                        self.dbs_process_step(output_i, output_t)
-            done_beams_table = [sorted(done_beams_table[divm], key=lambda x: -x["score"])[:bdash] for divm in range(group_size)]
-            if group_nbest:
-                done_beams = sum(done_beams_table, [])
-            else:
-                done_beams = [group_beam[0] for group_beam in done_beams_table]
-            for _, done_beam in enumerate(done_beams):
-                output["seq"][i, _, :len(done_beam["seq"])] = done_beam["seq"]
-
-        return output
-            
-    def prepare_dbs_decoder_input(self, input_dict, output_i):
-        raise NotImplementedError
-
-    def dbs_process_step(self, output_i, output_t):
-        pass
-
-
-class CaptionSequenceModel(nn.Module):
-
-    def __init__(self, model, seq_output_size):
-        super().__init__()
-        self.model = model
-        if model.decoder.d_model != seq_output_size:
-            self.output_transform = nn.Linear(model.decoder.d_model, seq_output_size)
-        else:
-            self.output_transform = lambda x: x
-
-    def forward(self, input_dict):
-        output = self.model(input_dict)
-
-        if input_dict["mode"] == "train":
-            lens = input_dict["cap_len"] - 1
-            # seq_outputs: [N, d_model]
-        elif input_dict["mode"] == "inference":
-            if "sample_method" in input_dict and input_dict["sample_method"] == "beam":
-                return output
-            seq = output["seq"]
-            lens = torch.where(seq == self.model.end_idx, torch.zeros_like(seq), torch.ones_like(seq)).sum(dim=1)
-        else:
-            raise Exception("mode should be either 'train' or 'inference'")
-        seq_output = mean_with_lens(output["embed"], lens)
-        seq_output = self.output_transform(seq_output)
-        output["seq_output"] = seq_output
-        return output
-

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/plms.py

@@ -1,236 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-
-
-class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        if ddim_eta != 0:
-            raise ValueError('ddim_eta must be 0 for PLMS')
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for PLMS sampling is {size}')
-
-        samples, intermediates = self.plms_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def plms_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running PLMS Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
-        old_eps = []
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      old_eps=old_eps, t_next=ts_next)
-            img, pred_x0, e_t = outs
-            old_eps.append(e_t)
-            if len(old_eps) >= 4:
-                old_eps.pop(0)
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
-        b, *_, device = *x.shape, x.device
-
-        def get_model_output(x, t):
-            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-                e_t = self.model.apply_model(x, t, c)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t] * 2)
-                c_in = torch.cat([unconditional_conditioning, c])
-                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-            if score_corrector is not None:
-                assert self.model.parameterization == "eps"
-                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-            return e_t
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-        def get_x_prev_and_pred_x0(e_t, index):
-            # select parameters corresponding to the currently considered timestep
-            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-            # current prediction for x_0
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-            if quantize_denoised:
-                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-            # direction pointing to x_t
-            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
-                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-            return x_prev, pred_x0
-
-        e_t = get_model_output(x, t)
-        if len(old_eps) == 0:
-            # Pseudo Improved Euler (2nd order)
-            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-            e_t_next = get_model_output(x_prev, t_next)
-            e_t_prime = (e_t + e_t_next) / 2
-        elif len(old_eps) == 1:
-            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (3 * e_t - old_eps[-1]) / 2
-        elif len(old_eps) == 2:
-            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-        elif len(old_eps) >= 3:
-            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-        return x_prev, pred_x0, e_t

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/image_degradation/bsrgan.py

@@ -1,730 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang ([email protected])
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(30, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        elif i == 1:
-            image = add_blur(image, sf=sf)
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image":image}
-    return example
-
-
-# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
-    """
-    This is an extended degradation model by combining
-    the degradation models of BSRGAN and Real-ESRGAN
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    use_shuffle: the degradation shuffle
-    use_sharp: sharpening the img
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    if use_sharp:
-        img = add_sharpening(img)
-    hq = img.copy()
-
-    if random.random() < shuffle_prob:
-        shuffle_order = random.sample(range(13), 13)
-    else:
-        shuffle_order = list(range(13))
-        # local shuffle for noise, JPEG is always the last one
-        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
-        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
-
-    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
-
-    for i in shuffle_order:
-        if i == 0:
-            img = add_blur(img, sf=sf)
-        elif i == 1:
-            img = add_resize(img, sf=sf)
-        elif i == 2:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 3:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 4:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 5:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        elif i == 6:
-            img = add_JPEG_noise(img)
-        elif i == 7:
-            img = add_blur(img, sf=sf)
-        elif i == 8:
-            img = add_resize(img, sf=sf)
-        elif i == 9:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 10:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 11:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 12:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        else:
-            print('check the shuffle!')
-
-    # resize to desired size
-    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
-                     interpolation=random.choice([1, 2, 3]))
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf, lq_patchsize)
-
-    return img, hq
-
-
-if __name__ == '__main__':
-	print("hey")
-	img = util.imread_uint('utils/test.png', 3)
-	print(img)
-	img = util.uint2single(img)
-	print(img)
-	img = img[:448, :448]
-	h = img.shape[0] // 4
-	print("resizing to", h)
-	sf = 4
-	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-	for i in range(20):
-		print(i)
-		img_lq = deg_fn(img)
-		print(img_lq)
-		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
-		print(img_lq.shape)
-		print("bicubic", img_lq_bicubic.shape)
-		print(img_hq.shape)
-		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-		util.imsave(img_concat, str(i) + '.png')
-
-

spaces/AP123/dreamgaussian/mesh.py

@@ -1,394 +0,0 @@
-import os
-import cv2
-import numpy as np
-import trimesh
-
-import torch
-import torch.nn.functional as F
-
-def dot(x, y):
-    return torch.sum(x * y, -1, keepdim=True)
-
-
-def length(x, eps=1e-20):
-    return torch.sqrt(torch.clamp(dot(x, x), min=eps))
-
-
-def safe_normalize(x, eps=1e-20):
-    return x / length(x, eps)
-
-
-class Mesh:
-    def __init__(
-        self,
-        v=None,
-        f=None,
-        vn=None,
-        fn=None,
-        vt=None,
-        ft=None,
-        albedo=None,
-        device=None,
-    ):
-        self.device = device
-        self.v = v
-        self.vn = vn
-        self.vt = vt
-        self.f = f
-        self.fn = fn
-        self.ft = ft
-        # only support a single albedo
-        self.albedo = albedo
-
-        self.ori_center = 0
-        self.ori_scale = 1
-
-    @classmethod
-    def load(cls, path=None, resize=True, **kwargs):
-        # assume init with kwargs
-        if path is None:
-            mesh = cls(**kwargs)
-        # obj supports face uv
-        elif path.endswith(".obj"):
-            mesh = cls.load_obj(path, **kwargs)
-        # trimesh only supports vertex uv, but can load more formats
-        else:
-            mesh = cls.load_trimesh(path, **kwargs)
-
-        print(f"[Mesh loading] v: {mesh.v.shape}, f: {mesh.f.shape}")
-        # auto-normalize
-        if resize:
-            mesh.auto_size()
-        # auto-fix normal
-        if mesh.vn is None:
-            mesh.auto_normal()
-        print(f"[Mesh loading] vn: {mesh.vn.shape}, fn: {mesh.fn.shape}")
-        # auto-fix texture
-        if mesh.vt is None:
-            mesh.auto_uv(cache_path=path)
-        print(f"[Mesh loading] vt: {mesh.vt.shape}, ft: {mesh.ft.shape}")
-
-        return mesh
-
-    # load from obj file
-    @classmethod
-    def load_obj(cls, path, albedo_path=None, device=None, init_empty_tex=False):
-        assert os.path.splitext(path)[-1] == ".obj"
-
-        mesh = cls()
-
-        # device
-        if device is None:
-            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        mesh.device = device
-
-        # try to find texture from mtl file
-        if albedo_path is None:
-            mtl_path = path.replace(".obj", ".mtl")
-            if os.path.exists(mtl_path):
-                with open(mtl_path, "r") as f:
-                    lines = f.readlines()
-                for line in lines:
-                    split_line = line.split()
-                    # empty line
-                    if len(split_line) == 0:
-                        continue
-                    prefix = split_line[0]
-                    # NOTE: simply use the first map_Kd as albedo!
-                    if "map_Kd" in prefix:
-                        albedo_path = os.path.join(os.path.dirname(path), split_line[1])
-                        print(f"[load_obj] use texture from: {albedo_path}")
-                        break
-
-        if init_empty_tex or albedo_path is None or not os.path.exists(albedo_path):
-            # init an empty texture
-            print(f"[load_obj] init empty albedo!")
-            # albedo = np.random.rand(1024, 1024, 3).astype(np.float32)
-            albedo = np.ones((1024, 1024, 3), dtype=np.float32) * np.array(
-                [0.5, 0.5, 0.5]
-            )  # default color
-        else:
-            albedo = cv2.imread(albedo_path, cv2.IMREAD_UNCHANGED)
-            albedo = cv2.cvtColor(albedo, cv2.COLOR_BGR2RGB)
-            albedo = albedo.astype(np.float32) / 255
-            print(f"[load_obj] load texture: {albedo.shape}")
-
-            # import matplotlib.pyplot as plt
-            # plt.imshow(albedo)
-            # plt.show()
-
-        mesh.albedo = torch.tensor(albedo, dtype=torch.float32, device=device)
-
-        # load obj
-        with open(path, "r") as f:
-            lines = f.readlines()
-
-        def parse_f_v(fv):
-            # pass in a vertex term of a face, return {v, vt, vn} (-1 if not provided)
-            # supported forms:
-            # f v1 v2 v3
-            # f v1/vt1 v2/vt2 v3/vt3
-            # f v1/vt1/vn1 v2/vt2/vn2 v3/vt3/vn3
-            # f v1//vn1 v2//vn2 v3//vn3
-            xs = [int(x) - 1 if x != "" else -1 for x in fv.split("/")]
-            xs.extend([-1] * (3 - len(xs)))
-            return xs[0], xs[1], xs[2]
-
-        # NOTE: we ignore usemtl, and assume the mesh ONLY uses one material (first in mtl)
-        vertices, texcoords, normals = [], [], []
-        faces, tfaces, nfaces = [], [], []
-        for line in lines:
-            split_line = line.split()
-            # empty line
-            if len(split_line) == 0:
-                continue
-            # v/vn/vt
-            prefix = split_line[0].lower()
-            if prefix == "v":
-                vertices.append([float(v) for v in split_line[1:]])
-            elif prefix == "vn":
-                normals.append([float(v) for v in split_line[1:]])
-            elif prefix == "vt":
-                val = [float(v) for v in split_line[1:]]
-                texcoords.append([val[0], 1.0 - val[1]])
-            elif prefix == "f":
-                vs = split_line[1:]
-                nv = len(vs)
-                v0, t0, n0 = parse_f_v(vs[0])
-                for i in range(nv - 2):  # triangulate (assume vertices are ordered)
-                    v1, t1, n1 = parse_f_v(vs[i + 1])
-                    v2, t2, n2 = parse_f_v(vs[i + 2])
-                    faces.append([v0, v1, v2])
-                    tfaces.append([t0, t1, t2])
-                    nfaces.append([n0, n1, n2])
-
-        mesh.v = torch.tensor(vertices, dtype=torch.float32, device=device)
-        mesh.vt = (
-            torch.tensor(texcoords, dtype=torch.float32, device=device)
-            if len(texcoords) > 0
-            else None
-        )
-        mesh.vn = (
-            torch.tensor(normals, dtype=torch.float32, device=device)
-            if len(normals) > 0
-            else None
-        )
-
-        mesh.f = torch.tensor(faces, dtype=torch.int32, device=device)
-        mesh.ft = (
-            torch.tensor(tfaces, dtype=torch.int32, device=device)
-            if texcoords is not None
-            else None
-        )
-        mesh.fn = (
-            torch.tensor(nfaces, dtype=torch.int32, device=device)
-            if normals is not None
-            else None
-        )
-
-        return mesh
-
-    @classmethod
-    def load_trimesh(cls, path, device=None):
-        mesh = cls()
-
-        # device
-        if device is None:
-            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        mesh.device = device
-
-        # use trimesh to load glb, assume only has one single RootMesh...
-        _data = trimesh.load(path)
-        if isinstance(_data, trimesh.Scene):
-            mesh_keys = list(_data.geometry.keys())
-            assert (
-                len(mesh_keys) == 1
-            ), f"{path} contains more than one meshes, not supported!"
-            _mesh = _data.geometry[mesh_keys[0]]
-
-        elif isinstance(_data, trimesh.Trimesh):
-            _mesh = _data
-
-        else:
-            raise NotImplementedError(f"type {type(_data)} not supported!")
-
-        # TODO: exception handling if no material
-        _material = _mesh.visual.material
-        if isinstance(_material, trimesh.visual.material.PBRMaterial):
-            texture = np.array(_material.baseColorTexture).astype(np.float32) / 255
-        elif isinstance(_material, trimesh.visual.material.SimpleMaterial):
-            texture = (
-                np.array(_material.to_pbr().baseColorTexture).astype(np.float32) / 255
-            )
-        else:
-            raise NotImplementedError(f"material type {type(_material)} not supported!")
-
-        print(f"[load_obj] load texture: {texture.shape}")
-        mesh.albedo = torch.tensor(texture, dtype=torch.float32, device=device)
-
-        vertices = _mesh.vertices
-        texcoords = _mesh.visual.uv
-        texcoords[:, 1] = 1 - texcoords[:, 1]
-        normals = _mesh.vertex_normals
-
-        # trimesh only support vertex uv...
-        faces = tfaces = nfaces = _mesh.faces
-
-        mesh.v = torch.tensor(vertices, dtype=torch.float32, device=device)
-        mesh.vt = (
-            torch.tensor(texcoords, dtype=torch.float32, device=device)
-            if len(texcoords) > 0
-            else None
-        )
-        mesh.vn = (
-            torch.tensor(normals, dtype=torch.float32, device=device)
-            if len(normals) > 0
-            else None
-        )
-
-        mesh.f = torch.tensor(faces, dtype=torch.int32, device=device)
-        mesh.ft = (
-            torch.tensor(tfaces, dtype=torch.int32, device=device)
-            if texcoords is not None
-            else None
-        )
-        mesh.fn = (
-            torch.tensor(nfaces, dtype=torch.int32, device=device)
-            if normals is not None
-            else None
-        )
-
-        return mesh
-
-    # aabb
-    def aabb(self):
-        return torch.min(self.v, dim=0).values, torch.max(self.v, dim=0).values
-
-    # unit size
-    @torch.no_grad()
-    def auto_size(self):
-        vmin, vmax = self.aabb()
-        self.ori_center = (vmax + vmin) / 2
-        self.ori_scale = 1.2 / torch.max(vmax - vmin).item() # to ~ [-0.6, 0.6]
-        self.v = (self.v - self.ori_center) * self.ori_scale
-
-    def auto_normal(self):
-        i0, i1, i2 = self.f[:, 0].long(), self.f[:, 1].long(), self.f[:, 2].long()
-        v0, v1, v2 = self.v[i0, :], self.v[i1, :], self.v[i2, :]
-
-        face_normals = torch.cross(v1 - v0, v2 - v0)
-
-        # Splat face normals to vertices
-        vn = torch.zeros_like(self.v)
-        vn.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
-        vn.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
-        vn.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
-
-        # Normalize, replace zero (degenerated) normals with some default value
-        vn = torch.where(
-            dot(vn, vn) > 1e-20,
-            vn,
-            torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device),
-        )
-        vn = safe_normalize(vn)
-
-        self.vn = vn
-        self.fn = self.f
-
-    def auto_uv(self, cache_path=None):
-        # try to load cache
-        if cache_path is not None:
-            cache_path = cache_path.replace(".obj", "_uv.npz")
-
-        if cache_path is not None and os.path.exists(cache_path):
-            data = np.load(cache_path)
-            vt_np, ft_np = data["vt"], data["ft"]
-        else:
-            import xatlas
-
-            v_np = self.v.detach().cpu().numpy()
-            f_np = self.f.detach().int().cpu().numpy()
-            atlas = xatlas.Atlas()
-            atlas.add_mesh(v_np, f_np)
-            chart_options = xatlas.ChartOptions()
-            # chart_options.max_iterations = 4
-            atlas.generate(chart_options=chart_options)
-            vmapping, ft_np, vt_np = atlas[0]  # [N], [M, 3], [N, 2]
-
-            # save to cache
-            if cache_path is not None:
-                np.savez(cache_path, vt=vt_np, ft=ft_np)
-
-        vt = torch.from_numpy(vt_np.astype(np.float32)).to(self.device)
-        ft = torch.from_numpy(ft_np.astype(np.int32)).to(self.device)
-
-        self.vt = vt
-        self.ft = ft
-
-    def to(self, device):
-        self.device = device
-        for name in ["v", "f", "vn", "fn", "vt", "ft", "albedo"]:
-            tensor = getattr(self, name)
-            if tensor is not None:
-                setattr(self, name, tensor.to(device))
-        return self
-    
-    # write to ply file (only geom)
-    def write_ply(self, path):
-        assert path.endswith(".ply")
-
-        v_np = self.v.detach().cpu().numpy()
-        f_np = self.f.detach().cpu().numpy()
-
-        _mesh = trimesh.Trimesh(vertices=v_np, faces=f_np)
-        _mesh.export(path)
-
-    # write to obj file
-    def write(self, path):
-        mtl_path = path.replace(".obj", ".mtl")
-        albedo_path = path.replace(".obj", "_albedo.png")
-
-        v_np = self.v.detach().cpu().numpy()
-        vt_np = self.vt.detach().cpu().numpy() if self.vt is not None else None
-        vn_np = self.vn.detach().cpu().numpy() if self.vn is not None else None
-        f_np = self.f.detach().cpu().numpy()
-        ft_np = self.ft.detach().cpu().numpy() if self.ft is not None else None
-        fn_np = self.fn.detach().cpu().numpy() if self.fn is not None else None
-
-        with open(path, "w") as fp:
-            fp.write(f"mtllib {os.path.basename(mtl_path)} \n")
-
-            for v in v_np:
-                fp.write(f"v {v[0]} {v[1]} {v[2]} \n")
-
-            if vt_np is not None:
-                for v in vt_np:
-                    fp.write(f"vt {v[0]} {1 - v[1]} \n")
-
-            if vn_np is not None:
-                for v in vn_np:
-                    fp.write(f"vn {v[0]} {v[1]} {v[2]} \n")
-
-            fp.write(f"usemtl defaultMat \n")
-            for i in range(len(f_np)):
-                fp.write(
-                    f'f {f_np[i, 0] + 1}/{ft_np[i, 0] + 1 if ft_np is not None else ""}/{fn_np[i, 0] + 1 if fn_np is not None else ""} \
-                             {f_np[i, 1] + 1}/{ft_np[i, 1] + 1 if ft_np is not None else ""}/{fn_np[i, 1] + 1 if fn_np is not None else ""} \
-                             {f_np[i, 2] + 1}/{ft_np[i, 2] + 1 if ft_np is not None else ""}/{fn_np[i, 2] + 1 if fn_np is not None else ""} \n'
-                )
-
-        with open(mtl_path, "w") as fp:
-            fp.write(f"newmtl defaultMat \n")
-            fp.write(f"Ka 1 1 1 \n")
-            fp.write(f"Kd 1 1 1 \n")
-            fp.write(f"Ks 0 0 0 \n")
-            fp.write(f"Tr 1 \n")
-            fp.write(f"illum 1 \n")
-            fp.write(f"Ns 0 \n")
-            fp.write(f"map_Kd {os.path.basename(albedo_path)} \n")
-
-        albedo = self.albedo.detach().cpu().numpy()
-        albedo = (albedo * 255).astype(np.uint8)
-        cv2.imwrite(albedo_path, cv2.cvtColor(albedo, cv2.COLOR_RGB2BGR))

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/Factory.d.ts

@@ -1,5 +0,0 @@
-import Buttons from './Buttons';
-
-export default function ButtonsFactory(
-    config?: Buttons.IConfig
-): Buttons;

spaces/Aki004/herta-so-vits/vdecoder/hifigan/utils.py

@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-# matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-

spaces/AlexMason/anime-remove-background/README.md

@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlgoveraAI/dcgan-crypto-punks/app.py

@@ -1,150 +0,0 @@
-from datetime import datetime, timedelta
-from decimal import Decimal
-import gradio as gr
-import matplotlib.pyplot as plt
-from pathlib import Path
-import pickle
-import os
-import time
-import torch
-import torchvision.transforms as T
-
-from ocean_lib.ocean.ocean import Ocean
-from ocean_lib.config import Config
-from ocean_lib.models.compute_input import ComputeInput
-from ocean_lib.web3_internal.wallet import Wallet
-
-
-
-def compute(private_key):
-
-    start_time = time.time()
-
-    network = 'mumbai'
-    config = Config(f'configs/config-{network}.ini')
-    network_url = Path(config.network_url[8:])
-    network_url = network_url.parents[0] /  os.getenv('INFURA_KEY')
-    config.set('eth-network', 'network', "https://" + str(network_url))
-    ocean = Ocean(config)
-
-    wallet = Wallet(ocean.web3, str(private_key).strip(), config.block_confirmations, config.transaction_timeout)
-
-    address = wallet.address
-
-    data_did = {
-        "rinkeby": "did:op:064abd2c7f8d5c3cacdbf43a687194d50008889130dbc4403d4b973797da7081",
-        "mumbai": "did:op:2def598c3ab3732dec5db8f33af028429a547b101cf6e7ec4ac9eba547a5507d"
-    }
-
-    data_asset = ocean.assets.resolve(data_did[config.network_name])
-    data_token = ocean.get_datatoken(data_asset.datatokens[0]['address'])
-    data_did = data_asset.did
-
-    assert data_token.balanceOf(wallet.address) > 0, "need to buy data token"
-    
-    algo_did = {
-        "rinkeby": "did:op:b6df860d4db1405293768985600db2de317d8f0035aa1805c0724ae678f23477",
-        "mumbai": "did:op:0a618a6c19ea44a189598f71aaefc98a516592cf90c64fd1f81f3f840f7b5b92"
-    }
-
-    algo_asset = ocean.assets.resolve(algo_did[config.network_name])
-    algo_token = ocean.get_datatoken(algo_asset.datatokens[0]['address'])
-    algo_did = algo_asset.did
-
-    assert algo_token.balanceOf(wallet.address) > 0, "need to buy data token"
-    
-    compute_service = data_asset.services[0]
-    algo_service = algo_asset.services[0]
-    free_c2d_env = ocean.compute.get_free_c2d_environment(compute_service.service_endpoint)
-
-    data_compute_input = ComputeInput(data_asset, compute_service)
-    algo_compute_input = ComputeInput(algo_asset, algo_service)
-
-    if config.network_name == 'rinkeby' or config.network_name == 'mainnet':
-        assert alice_wallet.web3.eth.get_balance(alice_wallet.address) > 0, "need ETH"
-    # elif config.network_name == 'mumbai' or config.network_name == 'polygon':
-    #     assert ocean.MATIC_token.balanceOf(alice_wallet.address) > 0, "need MATIC"
-    
-    # Pay for dataset and algo for 1 day
-    datasets, algorithm = ocean.assets.pay_for_compute_service(
-        datasets=[data_compute_input],
-        algorithm_data=algo_compute_input,
-        consume_market_order_fee_address=wallet.address,
-        wallet=wallet,
-        compute_environment=free_c2d_env["id"],
-        valid_until=int((datetime.utcnow() + timedelta(days=1)).timestamp()),
-        consumer_address=free_c2d_env["consumerAddress"],
-    )
-    assert datasets, "pay for dataset unsuccessful"
-    assert algorithm, "pay for algorithm unsuccessful"
-
-    # Start compute job
-    job_id = ocean.compute.start(
-        consumer_wallet=wallet,
-        dataset=datasets[0],
-        compute_environment=free_c2d_env["id"],
-        algorithm=algorithm,
-    )
-
-    # Wait until job is done
-    succeeded = False
-    for _ in range(0, 200):
-        status = ocean.compute.status(data_asset, compute_service, job_id, wallet)
-        print('===========', status['statusText'])
-        
-        if status.get("dateFinished") and Decimal(status["dateFinished"]) > 0:
-            succeeded = True
-            break
-        time.sleep(5)
-
-    print('hii')
-
-    # Retrieve algorithm output and log files
-    result = ocean.compute.compute_job_result_logs(
-        data_asset, compute_service, job_id, wallet
-    )[0]
-
-    print('===================0', result)
-
-    tensor = pickle.loads(result) 
-
-    print('===================1', tensor)
-
-    img = T.ToPILImage()(tensor.permute(2,0,1))
-    
-    print('===================2', img)
-
-    elapsed_time = time.time() - start_time
-        
-    print('===================3', elapsed_time)
-
-    return address, img
-
-description = (
-    "This demo serves a generative model from the Ocean marketplace. "
-)
-
-article = (
-    "<p style='text-align: center'>"
-    "<a href='https://metahub.algovera.ai/' target='_blank'>Algovera Metahub</a> | "
-    "<a href='https://market.oceanprotocol.com/' target='_blank'>Ocean Marketplace</a> | "
-    "<a href='https://docs.algovera.ai/blog/2022/04/05/Tutorial%20for%20using%20token-gated%20apps%20on%20HuggingFace' target='_blank'>How-to-use Tutorial</a> | "
-    "<a href='https://www.algovera.ai' target='_blank'>Algovera Website</a>"
-    "</p>"
-)
-
-interface = gr.Interface(
-    compute,
-    [
-        gr.inputs.Textbox(label="Private Key"),
-    ],
-    [
-        gr.outputs.Textbox(label="Public Key"),
-        gr.outputs.Image(label="Output Image")
-    ],
-    title="Generative Model from the Ocean marketplace",
-    description=description,
-    article=article,
-)
-
-interface.launch()

spaces/Ali-C137/Motivation-Letter-Generator/app.py

@@ -1,24 +0,0 @@
-# Motivation-Letter-Generator
-
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, AutoModelForSeq2SeqLM, set_seed, pipeline
-import gradio as gr
-
-### need more GPU power to call  T0pp 
-
-model = AutoModelForCausalLM.from_pretrained('EleutherAI/gpt-neo-1.3B', use_cache=True)
-tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neo-1.3B')
-
-set_seed(424242)
-
-def generate(Name, Position, Organization, max_length=500, top_k=1, temperature=0.9, repetition_penalty = 2.0):
-  prompt = f"i'm {Name} and i want to write a motivation letter to an employer about the position of {Position} at {Organization} mentioning the hard skills and soft skills i have acquired"
-  input_ids = tokenizer(prompt, return_tensors="pt").to(0)
-  sample = model.generate(**input_ids, max_length=max_length,  top_k=top_k, temperature=temperature, repetition_penalty = repetition_penalty)
-  return tokenizer.decode(sample[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"])
-
-title = "Motivation Letter Generator"
-article = "For now this still a toy demo and no good results will came out. PS: if you have enough resources try using stronger models !"
-
-gr = gr.Interface(fn = generate, inputs=["text", "text", "text"], outputs="text", title=title, article=article)
-
-gr.launch()

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/loop/utils.py

@@ -1,11 +0,0 @@
-import numpy as np
-
-
-def N_to_reso(n_voxels, bbox):
-    xyz_min, xyz_max = bbox
-    dim = len(xyz_min)
-    voxel_size = ((xyz_max - xyz_min).prod() / n_voxels).pow(1 / dim)
-    return ((xyz_max - xyz_min) / voxel_size).long().tolist()
-
-def cal_n_samples(reso, step_ratio=0.5):
-    return int(np.linalg.norm(reso)/step_ratio)

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

@@ -1,22 +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.
--->
-
-# DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper
-
-## Overview
-
-Inspired by [Karras et. al](https://arxiv.org/abs/2206.00364). Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
-
-All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
-
-## KDPM2AncestralDiscreteScheduler
-[[autodoc]] KDPM2AncestralDiscreteScheduler

spaces/Andy1621/uniformer_image_detection/exp/mask_rcnn_1x_hybrid_small/run.sh

@@ -1,10 +0,0 @@
-#!/usr/bin/env bash
-
-work_path=$(dirname $0)
-PYTHONPATH="$(dirname $0)/../../":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=8 \
-    tools/train.py ${work_path}/config.py \
-    --launcher pytorch \
-    --cfg-options model.backbone.pretrained_path='your_model_path/uniformer_small_in1k.pth' \
-    --work-dir ${work_path}/ckpt \
-    2>&1 | tee -a ${work_path}/log.txt

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/gfl.py

@@ -1,16 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class GFL(SingleStageDetector):
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                  test_cfg, pretrained)

spaces/Anew1007/extras/tts_edge.py

@@ -1,34 +0,0 @@
-import io
-import edge_tts
-import asyncio
-
-
-def get_voices():
-    voices = asyncio.run(edge_tts.list_voices())
-    return voices
-
-
-async def _iterate_chunks(audio):
-    async for chunk in audio.stream():
-        if chunk["type"] == "audio":
-            yield chunk["data"]
-
-
-async def _async_generator_to_list(async_gen):
-    result = []
-    async for item in async_gen:
-        result.append(item)
-    return result
-
-
-def generate_audio(text: str, voice: str, rate: int) -> bytes:
-    sign = '+' if rate > 0 else '-'
-    rate = f'{sign}{abs(rate)}%'
-    audio = edge_tts.Communicate(text=text, voice=voice, rate=rate)
-    chunks = asyncio.run(_async_generator_to_list(_iterate_chunks(audio)))
-    buffer = io.BytesIO()
-
-    for chunk in chunks:
-        buffer.write(chunk)
-
-    return buffer.getvalue()

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/README.md

@@ -1,5 +0,0 @@
-# superboogav2
-
-For a description, please see the comments in this Pull Request:
-
-https://github.com/oobabooga/text-generation-webui/pull/3272

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

@@ -1,8 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from . import audio, audio_dataset

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/bertwarper.py

@@ -1,273 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-from torchvision.ops.boxes import nms
-from transformers import BertConfig, BertModel, BertPreTrainedModel
-from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
-
-
-class BertModelWarper(nn.Module):
-    def __init__(self, bert_model):
-        super().__init__()
-        # self.bert = bert_modelc
-
-        self.config = bert_model.config
-        self.embeddings = bert_model.embeddings
-        self.encoder = bert_model.encoder
-        self.pooler = bert_model.pooler
-
-        self.get_extended_attention_mask = bert_model.get_extended_attention_mask
-        self.invert_attention_mask = bert_model.invert_attention_mask
-        self.get_head_mask = bert_model.get_head_mask
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = (
-            output_attentions if output_attentions is not None else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if self.config.is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        # past_key_values_length
-        past_key_values_length = (
-            past_key_values[0][0].shape[2] if past_key_values is not None else 0
-        )
-
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                ((batch_size, seq_length + past_key_values_length)), device=device
-            )
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
-            attention_mask, input_shape, device
-        )
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-        #     import ipdb; ipdb.set_trace()
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-class TextEncoderShell(nn.Module):
-    def __init__(self, text_encoder):
-        super().__init__()
-        self.text_encoder = text_encoder
-        self.config = self.text_encoder.config
-
-    def forward(self, **kw):
-        # feed into text encoder
-        return self.text_encoder(**kw)
-
-
-def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
-    """Generate attention mask between each pair of special tokens
-    Args:
-        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
-        special_tokens_mask (list): special tokens mask.
-    Returns:
-        torch.Tensor: attention mask between each special tokens.
-    """
-    input_ids = tokenized["input_ids"]
-    bs, num_token = input_ids.shape
-    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
-    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
-    for special_token in special_tokens_list:
-        special_tokens_mask |= input_ids == special_token
-
-    # idxs: each row is a list of indices of special tokens
-    idxs = torch.nonzero(special_tokens_mask)
-
-    # generate attention mask and positional ids
-    attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
-    )
-    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
-    previous_col = 0
-    for i in range(idxs.shape[0]):
-        row, col = idxs[i]
-        if (col == 0) or (col == num_token - 1):
-            attention_mask[row, col, col] = True
-            position_ids[row, col] = 0
-        else:
-            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
-            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
-                0, col - previous_col, device=input_ids.device
-            )
-
-        previous_col = col
-
-    # # padding mask
-    # padding_mask = tokenized['attention_mask']
-    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
-
-    return attention_mask, position_ids.to(torch.long)
-
-
-def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
-    """Generate attention mask between each pair of special tokens
-    Args:
-        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
-        special_tokens_mask (list): special tokens mask.
-    Returns:
-        torch.Tensor: attention mask between each special tokens.
-    """
-    input_ids = tokenized["input_ids"]
-    bs, num_token = input_ids.shape
-    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
-    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
-    for special_token in special_tokens_list:
-        special_tokens_mask |= input_ids == special_token
-
-    # idxs: each row is a list of indices of special tokens
-    idxs = torch.nonzero(special_tokens_mask)
-
-    # generate attention mask and positional ids
-    attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
-    )
-    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
-    cate_to_token_mask_list = [[] for _ in range(bs)]
-    previous_col = 0
-    for i in range(idxs.shape[0]):
-        row, col = idxs[i]
-        if (col == 0) or (col == num_token - 1):
-            attention_mask[row, col, col] = True
-            position_ids[row, col] = 0
-        else:
-            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
-            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
-                0, col - previous_col, device=input_ids.device
-            )
-            c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
-            c2t_maski[previous_col + 1 : col] = True
-            cate_to_token_mask_list[row].append(c2t_maski)
-        previous_col = col
-
-    cate_to_token_mask_list = [
-        torch.stack(cate_to_token_mask_listi, dim=0)
-        for cate_to_token_mask_listi in cate_to_token_mask_list
-    ]
-
-    # # padding mask
-    # padding_mask = tokenized['attention_mask']
-    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
-
-    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list

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

@@ -1,994 +0,0 @@
-import builtins
-import collections
-import dataclasses
-import inspect
-import os
-import sys
-from array import array
-from collections import Counter, UserDict, UserList, defaultdict, deque
-from dataclasses import dataclass, fields, is_dataclass
-from inspect import isclass
-from itertools import islice
-from types import MappingProxyType
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    DefaultDict,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Sequence,
-    Set,
-    Tuple,
-    Union,
-)
-
-from pip._vendor.rich.repr import RichReprResult
-
-try:
-    import attr as _attr_module
-
-    _has_attrs = hasattr(_attr_module, "ib")
-except ImportError:  # pragma: no cover
-    _has_attrs = False
-
-from . import get_console
-from ._loop import loop_last
-from ._pick import pick_bool
-from .abc import RichRenderable
-from .cells import cell_len
-from .highlighter import ReprHighlighter
-from .jupyter import JupyterMixin, JupyterRenderable
-from .measure import Measurement
-from .text import Text
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        HighlighterType,
-        JustifyMethod,
-        OverflowMethod,
-        RenderResult,
-    )
-
-
-def _is_attr_object(obj: Any) -> bool:
-    """Check if an object was created with attrs module."""
-    return _has_attrs and _attr_module.has(type(obj))
-
-
-def _get_attr_fields(obj: Any) -> Sequence["_attr_module.Attribute[Any]"]:
-    """Get fields for an attrs object."""
-    return _attr_module.fields(type(obj)) if _has_attrs else []
-
-
-def _is_dataclass_repr(obj: object) -> bool:
-    """Check if an instance of a dataclass contains the default repr.
-
-    Args:
-        obj (object): A dataclass instance.
-
-    Returns:
-        bool: True if the default repr is used, False if there is a custom repr.
-    """
-    # Digging in to a lot of internals here
-    # Catching all exceptions in case something is missing on a non CPython implementation
-    try:
-        return obj.__repr__.__code__.co_filename == dataclasses.__file__
-    except Exception:  # pragma: no coverage
-        return False
-
-
-_dummy_namedtuple = collections.namedtuple("_dummy_namedtuple", [])
-
-
-def _has_default_namedtuple_repr(obj: object) -> bool:
-    """Check if an instance of namedtuple contains the default repr
-
-    Args:
-        obj (object): A namedtuple
-
-    Returns:
-        bool: True if the default repr is used, False if there's a custom repr.
-    """
-    obj_file = None
-    try:
-        obj_file = inspect.getfile(obj.__repr__)
-    except (OSError, TypeError):
-        # OSError handles case where object is defined in __main__ scope, e.g. REPL - no filename available.
-        # TypeError trapped defensively, in case of object without filename slips through.
-        pass
-    default_repr_file = inspect.getfile(_dummy_namedtuple.__repr__)
-    return obj_file == default_repr_file
-
-
-def _ipy_display_hook(
-    value: Any,
-    console: Optional["Console"] = None,
-    overflow: "OverflowMethod" = "ignore",
-    crop: bool = False,
-    indent_guides: bool = False,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> Union[str, None]:
-    # needed here to prevent circular import:
-    from .console import ConsoleRenderable
-
-    # always skip rich generated jupyter renderables or None values
-    if _safe_isinstance(value, JupyterRenderable) or value is None:
-        return None
-
-    console = console or get_console()
-
-    with console.capture() as capture:
-        # certain renderables should start on a new line
-        if _safe_isinstance(value, ConsoleRenderable):
-            console.line()
-        console.print(
-            value
-            if _safe_isinstance(value, RichRenderable)
-            else Pretty(
-                value,
-                overflow=overflow,
-                indent_guides=indent_guides,
-                max_length=max_length,
-                max_string=max_string,
-                max_depth=max_depth,
-                expand_all=expand_all,
-                margin=12,
-            ),
-            crop=crop,
-            new_line_start=True,
-            end="",
-        )
-    # strip trailing newline, not usually part of a text repr
-    # I'm not sure if this should be prevented at a lower level
-    return capture.get().rstrip("\n")
-
-
-def _safe_isinstance(
-    obj: object, class_or_tuple: Union[type, Tuple[type, ...]]
-) -> bool:
-    """isinstance can fail in rare cases, for example types with no __class__"""
-    try:
-        return isinstance(obj, class_or_tuple)
-    except Exception:
-        return False
-
-
-def install(
-    console: Optional["Console"] = None,
-    overflow: "OverflowMethod" = "ignore",
-    crop: bool = False,
-    indent_guides: bool = False,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> None:
-    """Install automatic pretty printing in the Python REPL.
-
-    Args:
-        console (Console, optional): Console instance or ``None`` to use global console. Defaults to None.
-        overflow (Optional[OverflowMethod], optional): Overflow method. Defaults to "ignore".
-        crop (Optional[bool], optional): Enable cropping of long lines. Defaults to False.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to False.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structures, or None for no maximum. Defaults to None.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-        max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
-    """
-    from pip._vendor.rich import get_console
-
-    console = console or get_console()
-    assert console is not None
-
-    def display_hook(value: Any) -> None:
-        """Replacement sys.displayhook which prettifies objects with Rich."""
-        if value is not None:
-            assert console is not None
-            builtins._ = None  # type: ignore[attr-defined]
-            console.print(
-                value
-                if _safe_isinstance(value, RichRenderable)
-                else Pretty(
-                    value,
-                    overflow=overflow,
-                    indent_guides=indent_guides,
-                    max_length=max_length,
-                    max_string=max_string,
-                    max_depth=max_depth,
-                    expand_all=expand_all,
-                ),
-                crop=crop,
-            )
-            builtins._ = value  # type: ignore[attr-defined]
-
-    if "get_ipython" in globals():
-        ip = get_ipython()  # type: ignore[name-defined]
-        from IPython.core.formatters import BaseFormatter
-
-        class RichFormatter(BaseFormatter):  # type: ignore[misc]
-            pprint: bool = True
-
-            def __call__(self, value: Any) -> Any:
-                if self.pprint:
-                    return _ipy_display_hook(
-                        value,
-                        console=get_console(),
-                        overflow=overflow,
-                        indent_guides=indent_guides,
-                        max_length=max_length,
-                        max_string=max_string,
-                        max_depth=max_depth,
-                        expand_all=expand_all,
-                    )
-                else:
-                    return repr(value)
-
-        # replace plain text formatter with rich formatter
-        rich_formatter = RichFormatter()
-        ip.display_formatter.formatters["text/plain"] = rich_formatter
-    else:
-        sys.displayhook = display_hook
-
-
-class Pretty(JupyterMixin):
-    """A rich renderable that pretty prints an object.
-
-    Args:
-        _object (Any): An object to pretty print.
-        highlighter (HighlighterType, optional): Highlighter object to apply to result, or None for ReprHighlighter. Defaults to None.
-        indent_size (int, optional): Number of spaces in indent. Defaults to 4.
-        justify (JustifyMethod, optional): Justify method, or None for default. Defaults to None.
-        overflow (OverflowMethod, optional): Overflow method, or None for default. Defaults to None.
-        no_wrap (Optional[bool], optional): Disable word wrapping. Defaults to False.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to False.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structures, or None for no maximum. Defaults to None.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-        margin (int, optional): Subtrace a margin from width to force containers to expand earlier. Defaults to 0.
-        insert_line (bool, optional): Insert a new line if the output has multiple new lines. Defaults to False.
-    """
-
-    def __init__(
-        self,
-        _object: Any,
-        highlighter: Optional["HighlighterType"] = None,
-        *,
-        indent_size: int = 4,
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        no_wrap: Optional[bool] = False,
-        indent_guides: bool = False,
-        max_length: Optional[int] = None,
-        max_string: Optional[int] = None,
-        max_depth: Optional[int] = None,
-        expand_all: bool = False,
-        margin: int = 0,
-        insert_line: bool = False,
-    ) -> None:
-        self._object = _object
-        self.highlighter = highlighter or ReprHighlighter()
-        self.indent_size = indent_size
-        self.justify: Optional["JustifyMethod"] = justify
-        self.overflow: Optional["OverflowMethod"] = overflow
-        self.no_wrap = no_wrap
-        self.indent_guides = indent_guides
-        self.max_length = max_length
-        self.max_string = max_string
-        self.max_depth = max_depth
-        self.expand_all = expand_all
-        self.margin = margin
-        self.insert_line = insert_line
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        pretty_str = pretty_repr(
-            self._object,
-            max_width=options.max_width - self.margin,
-            indent_size=self.indent_size,
-            max_length=self.max_length,
-            max_string=self.max_string,
-            max_depth=self.max_depth,
-            expand_all=self.expand_all,
-        )
-        pretty_text = Text.from_ansi(
-            pretty_str,
-            justify=self.justify or options.justify,
-            overflow=self.overflow or options.overflow,
-            no_wrap=pick_bool(self.no_wrap, options.no_wrap),
-            style="pretty",
-        )
-        pretty_text = (
-            self.highlighter(pretty_text)
-            if pretty_text
-            else Text(
-                f"{type(self._object)}.__repr__ returned empty string",
-                style="dim italic",
-            )
-        )
-        if self.indent_guides and not options.ascii_only:
-            pretty_text = pretty_text.with_indent_guides(
-                self.indent_size, style="repr.indent"
-            )
-        if self.insert_line and "\n" in pretty_text:
-            yield ""
-        yield pretty_text
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        pretty_str = pretty_repr(
-            self._object,
-            max_width=options.max_width,
-            indent_size=self.indent_size,
-            max_length=self.max_length,
-            max_string=self.max_string,
-            max_depth=self.max_depth,
-            expand_all=self.expand_all,
-        )
-        text_width = (
-            max(cell_len(line) for line in pretty_str.splitlines()) if pretty_str else 0
-        )
-        return Measurement(text_width, text_width)
-
-
-def _get_braces_for_defaultdict(_object: DefaultDict[Any, Any]) -> Tuple[str, str, str]:
-    return (
-        f"defaultdict({_object.default_factory!r}, {{",
-        "})",
-        f"defaultdict({_object.default_factory!r}, {{}})",
-    )
-
-
-def _get_braces_for_array(_object: "array[Any]") -> Tuple[str, str, str]:
-    return (f"array({_object.typecode!r}, [", "])", f"array({_object.typecode!r})")
-
-
-_BRACES: Dict[type, Callable[[Any], Tuple[str, str, str]]] = {
-    os._Environ: lambda _object: ("environ({", "})", "environ({})"),
-    array: _get_braces_for_array,
-    defaultdict: _get_braces_for_defaultdict,
-    Counter: lambda _object: ("Counter({", "})", "Counter()"),
-    deque: lambda _object: ("deque([", "])", "deque()"),
-    dict: lambda _object: ("{", "}", "{}"),
-    UserDict: lambda _object: ("{", "}", "{}"),
-    frozenset: lambda _object: ("frozenset({", "})", "frozenset()"),
-    list: lambda _object: ("[", "]", "[]"),
-    UserList: lambda _object: ("[", "]", "[]"),
-    set: lambda _object: ("{", "}", "set()"),
-    tuple: lambda _object: ("(", ")", "()"),
-    MappingProxyType: lambda _object: ("mappingproxy({", "})", "mappingproxy({})"),
-}
-_CONTAINERS = tuple(_BRACES.keys())
-_MAPPING_CONTAINERS = (dict, os._Environ, MappingProxyType, UserDict)
-
-
-def is_expandable(obj: Any) -> bool:
-    """Check if an object may be expanded by pretty print."""
-    return (
-        _safe_isinstance(obj, _CONTAINERS)
-        or (is_dataclass(obj))
-        or (hasattr(obj, "__rich_repr__"))
-        or _is_attr_object(obj)
-    ) and not isclass(obj)
-
-
-@dataclass
-class Node:
-    """A node in a repr tree. May be atomic or a container."""
-
-    key_repr: str = ""
-    value_repr: str = ""
-    open_brace: str = ""
-    close_brace: str = ""
-    empty: str = ""
-    last: bool = False
-    is_tuple: bool = False
-    is_namedtuple: bool = False
-    children: Optional[List["Node"]] = None
-    key_separator: str = ": "
-    separator: str = ", "
-
-    def iter_tokens(self) -> Iterable[str]:
-        """Generate tokens for this node."""
-        if self.key_repr:
-            yield self.key_repr
-            yield self.key_separator
-        if self.value_repr:
-            yield self.value_repr
-        elif self.children is not None:
-            if self.children:
-                yield self.open_brace
-                if self.is_tuple and not self.is_namedtuple and len(self.children) == 1:
-                    yield from self.children[0].iter_tokens()
-                    yield ","
-                else:
-                    for child in self.children:
-                        yield from child.iter_tokens()
-                        if not child.last:
-                            yield self.separator
-                yield self.close_brace
-            else:
-                yield self.empty
-
-    def check_length(self, start_length: int, max_length: int) -> bool:
-        """Check the length fits within a limit.
-
-        Args:
-            start_length (int): Starting length of the line (indent, prefix, suffix).
-            max_length (int): Maximum length.
-
-        Returns:
-            bool: True if the node can be rendered within max length, otherwise False.
-        """
-        total_length = start_length
-        for token in self.iter_tokens():
-            total_length += cell_len(token)
-            if total_length > max_length:
-                return False
-        return True
-
-    def __str__(self) -> str:
-        repr_text = "".join(self.iter_tokens())
-        return repr_text
-
-    def render(
-        self, max_width: int = 80, indent_size: int = 4, expand_all: bool = False
-    ) -> str:
-        """Render the node to a pretty repr.
-
-        Args:
-            max_width (int, optional): Maximum width of the repr. Defaults to 80.
-            indent_size (int, optional): Size of indents. Defaults to 4.
-            expand_all (bool, optional): Expand all levels. Defaults to False.
-
-        Returns:
-            str: A repr string of the original object.
-        """
-        lines = [_Line(node=self, is_root=True)]
-        line_no = 0
-        while line_no < len(lines):
-            line = lines[line_no]
-            if line.expandable and not line.expanded:
-                if expand_all or not line.check_length(max_width):
-                    lines[line_no : line_no + 1] = line.expand(indent_size)
-            line_no += 1
-
-        repr_str = "\n".join(str(line) for line in lines)
-        return repr_str
-
-
-@dataclass
-class _Line:
-    """A line in repr output."""
-
-    parent: Optional["_Line"] = None
-    is_root: bool = False
-    node: Optional[Node] = None
-    text: str = ""
-    suffix: str = ""
-    whitespace: str = ""
-    expanded: bool = False
-    last: bool = False
-
-    @property
-    def expandable(self) -> bool:
-        """Check if the line may be expanded."""
-        return bool(self.node is not None and self.node.children)
-
-    def check_length(self, max_length: int) -> bool:
-        """Check this line fits within a given number of cells."""
-        start_length = (
-            len(self.whitespace) + cell_len(self.text) + cell_len(self.suffix)
-        )
-        assert self.node is not None
-        return self.node.check_length(start_length, max_length)
-
-    def expand(self, indent_size: int) -> Iterable["_Line"]:
-        """Expand this line by adding children on their own line."""
-        node = self.node
-        assert node is not None
-        whitespace = self.whitespace
-        assert node.children
-        if node.key_repr:
-            new_line = yield _Line(
-                text=f"{node.key_repr}{node.key_separator}{node.open_brace}",
-                whitespace=whitespace,
-            )
-        else:
-            new_line = yield _Line(text=node.open_brace, whitespace=whitespace)
-        child_whitespace = self.whitespace + " " * indent_size
-        tuple_of_one = node.is_tuple and len(node.children) == 1
-        for last, child in loop_last(node.children):
-            separator = "," if tuple_of_one else node.separator
-            line = _Line(
-                parent=new_line,
-                node=child,
-                whitespace=child_whitespace,
-                suffix=separator,
-                last=last and not tuple_of_one,
-            )
-            yield line
-
-        yield _Line(
-            text=node.close_brace,
-            whitespace=whitespace,
-            suffix=self.suffix,
-            last=self.last,
-        )
-
-    def __str__(self) -> str:
-        if self.last:
-            return f"{self.whitespace}{self.text}{self.node or ''}"
-        else:
-            return (
-                f"{self.whitespace}{self.text}{self.node or ''}{self.suffix.rstrip()}"
-            )
-
-
-def _is_namedtuple(obj: Any) -> bool:
-    """Checks if an object is most likely a namedtuple. It is possible
-    to craft an object that passes this check and isn't a namedtuple, but
-    there is only a minuscule chance of this happening unintentionally.
-
-    Args:
-        obj (Any): The object to test
-
-    Returns:
-        bool: True if the object is a namedtuple. False otherwise.
-    """
-    try:
-        fields = getattr(obj, "_fields", None)
-    except Exception:
-        # Being very defensive - if we cannot get the attr then its not a namedtuple
-        return False
-    return isinstance(obj, tuple) and isinstance(fields, tuple)
-
-
-def traverse(
-    _object: Any,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-) -> Node:
-    """Traverse object and generate a tree.
-
-    Args:
-        _object (Any): Object to be traversed.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
-            Defaults to None.
-        max_depth (int, optional): Maximum depth of data structures, or None for no maximum.
-            Defaults to None.
-
-    Returns:
-        Node: The root of a tree structure which can be used to render a pretty repr.
-    """
-
-    def to_repr(obj: Any) -> str:
-        """Get repr string for an object, but catch errors."""
-        if (
-            max_string is not None
-            and _safe_isinstance(obj, (bytes, str))
-            and len(obj) > max_string
-        ):
-            truncated = len(obj) - max_string
-            obj_repr = f"{obj[:max_string]!r}+{truncated}"
-        else:
-            try:
-                obj_repr = repr(obj)
-            except Exception as error:
-                obj_repr = f"<repr-error {str(error)!r}>"
-        return obj_repr
-
-    visited_ids: Set[int] = set()
-    push_visited = visited_ids.add
-    pop_visited = visited_ids.remove
-
-    def _traverse(obj: Any, root: bool = False, depth: int = 0) -> Node:
-        """Walk the object depth first."""
-
-        obj_id = id(obj)
-        if obj_id in visited_ids:
-            # Recursion detected
-            return Node(value_repr="...")
-
-        obj_type = type(obj)
-        children: List[Node]
-        reached_max_depth = max_depth is not None and depth >= max_depth
-
-        def iter_rich_args(rich_args: Any) -> Iterable[Union[Any, Tuple[str, Any]]]:
-            for arg in rich_args:
-                if _safe_isinstance(arg, tuple):
-                    if len(arg) == 3:
-                        key, child, default = arg
-                        if default == child:
-                            continue
-                        yield key, child
-                    elif len(arg) == 2:
-                        key, child = arg
-                        yield key, child
-                    elif len(arg) == 1:
-                        yield arg[0]
-                else:
-                    yield arg
-
-        try:
-            fake_attributes = hasattr(
-                obj, "awehoi234_wdfjwljet234_234wdfoijsdfmmnxpi492"
-            )
-        except Exception:
-            fake_attributes = False
-
-        rich_repr_result: Optional[RichReprResult] = None
-        if not fake_attributes:
-            try:
-                if hasattr(obj, "__rich_repr__") and not isclass(obj):
-                    rich_repr_result = obj.__rich_repr__()
-            except Exception:
-                pass
-
-        if rich_repr_result is not None:
-            push_visited(obj_id)
-            angular = getattr(obj.__rich_repr__, "angular", False)
-            args = list(iter_rich_args(rich_repr_result))
-            class_name = obj.__class__.__name__
-
-            if args:
-                children = []
-                append = children.append
-
-                if reached_max_depth:
-                    if angular:
-                        node = Node(value_repr=f"<{class_name}...>")
-                    else:
-                        node = Node(value_repr=f"{class_name}(...)")
-                else:
-                    if angular:
-                        node = Node(
-                            open_brace=f"<{class_name} ",
-                            close_brace=">",
-                            children=children,
-                            last=root,
-                            separator=" ",
-                        )
-                    else:
-                        node = Node(
-                            open_brace=f"{class_name}(",
-                            close_brace=")",
-                            children=children,
-                            last=root,
-                        )
-                    for last, arg in loop_last(args):
-                        if _safe_isinstance(arg, tuple):
-                            key, child = arg
-                            child_node = _traverse(child, depth=depth + 1)
-                            child_node.last = last
-                            child_node.key_repr = key
-                            child_node.key_separator = "="
-                            append(child_node)
-                        else:
-                            child_node = _traverse(arg, depth=depth + 1)
-                            child_node.last = last
-                            append(child_node)
-            else:
-                node = Node(
-                    value_repr=f"<{class_name}>" if angular else f"{class_name}()",
-                    children=[],
-                    last=root,
-                )
-            pop_visited(obj_id)
-        elif _is_attr_object(obj) and not fake_attributes:
-            push_visited(obj_id)
-            children = []
-            append = children.append
-
-            attr_fields = _get_attr_fields(obj)
-            if attr_fields:
-                if reached_max_depth:
-                    node = Node(value_repr=f"{obj.__class__.__name__}(...)")
-                else:
-                    node = Node(
-                        open_brace=f"{obj.__class__.__name__}(",
-                        close_brace=")",
-                        children=children,
-                        last=root,
-                    )
-
-                    def iter_attrs() -> Iterable[
-                        Tuple[str, Any, Optional[Callable[[Any], str]]]
-                    ]:
-                        """Iterate over attr fields and values."""
-                        for attr in attr_fields:
-                            if attr.repr:
-                                try:
-                                    value = getattr(obj, attr.name)
-                                except Exception as error:
-                                    # Can happen, albeit rarely
-                                    yield (attr.name, error, None)
-                                else:
-                                    yield (
-                                        attr.name,
-                                        value,
-                                        attr.repr if callable(attr.repr) else None,
-                                    )
-
-                    for last, (name, value, repr_callable) in loop_last(iter_attrs()):
-                        if repr_callable:
-                            child_node = Node(value_repr=str(repr_callable(value)))
-                        else:
-                            child_node = _traverse(value, depth=depth + 1)
-                        child_node.last = last
-                        child_node.key_repr = name
-                        child_node.key_separator = "="
-                        append(child_node)
-            else:
-                node = Node(
-                    value_repr=f"{obj.__class__.__name__}()", children=[], last=root
-                )
-            pop_visited(obj_id)
-        elif (
-            is_dataclass(obj)
-            and not _safe_isinstance(obj, type)
-            and not fake_attributes
-            and _is_dataclass_repr(obj)
-        ):
-            push_visited(obj_id)
-            children = []
-            append = children.append
-            if reached_max_depth:
-                node = Node(value_repr=f"{obj.__class__.__name__}(...)")
-            else:
-                node = Node(
-                    open_brace=f"{obj.__class__.__name__}(",
-                    close_brace=")",
-                    children=children,
-                    last=root,
-                    empty=f"{obj.__class__.__name__}()",
-                )
-
-                for last, field in loop_last(
-                    field for field in fields(obj) if field.repr
-                ):
-                    child_node = _traverse(getattr(obj, field.name), depth=depth + 1)
-                    child_node.key_repr = field.name
-                    child_node.last = last
-                    child_node.key_separator = "="
-                    append(child_node)
-
-            pop_visited(obj_id)
-        elif _is_namedtuple(obj) and _has_default_namedtuple_repr(obj):
-            push_visited(obj_id)
-            class_name = obj.__class__.__name__
-            if reached_max_depth:
-                # If we've reached the max depth, we still show the class name, but not its contents
-                node = Node(
-                    value_repr=f"{class_name}(...)",
-                )
-            else:
-                children = []
-                append = children.append
-                node = Node(
-                    open_brace=f"{class_name}(",
-                    close_brace=")",
-                    children=children,
-                    empty=f"{class_name}()",
-                )
-                for last, (key, value) in loop_last(obj._asdict().items()):
-                    child_node = _traverse(value, depth=depth + 1)
-                    child_node.key_repr = key
-                    child_node.last = last
-                    child_node.key_separator = "="
-                    append(child_node)
-            pop_visited(obj_id)
-        elif _safe_isinstance(obj, _CONTAINERS):
-            for container_type in _CONTAINERS:
-                if _safe_isinstance(obj, container_type):
-                    obj_type = container_type
-                    break
-
-            push_visited(obj_id)
-
-            open_brace, close_brace, empty = _BRACES[obj_type](obj)
-
-            if reached_max_depth:
-                node = Node(value_repr=f"{open_brace}...{close_brace}")
-            elif obj_type.__repr__ != type(obj).__repr__:
-                node = Node(value_repr=to_repr(obj), last=root)
-            elif obj:
-                children = []
-                node = Node(
-                    open_brace=open_brace,
-                    close_brace=close_brace,
-                    children=children,
-                    last=root,
-                )
-                append = children.append
-                num_items = len(obj)
-                last_item_index = num_items - 1
-
-                if _safe_isinstance(obj, _MAPPING_CONTAINERS):
-                    iter_items = iter(obj.items())
-                    if max_length is not None:
-                        iter_items = islice(iter_items, max_length)
-                    for index, (key, child) in enumerate(iter_items):
-                        child_node = _traverse(child, depth=depth + 1)
-                        child_node.key_repr = to_repr(key)
-                        child_node.last = index == last_item_index
-                        append(child_node)
-                else:
-                    iter_values = iter(obj)
-                    if max_length is not None:
-                        iter_values = islice(iter_values, max_length)
-                    for index, child in enumerate(iter_values):
-                        child_node = _traverse(child, depth=depth + 1)
-                        child_node.last = index == last_item_index
-                        append(child_node)
-                if max_length is not None and num_items > max_length:
-                    append(Node(value_repr=f"... +{num_items - max_length}", last=True))
-            else:
-                node = Node(empty=empty, children=[], last=root)
-
-            pop_visited(obj_id)
-        else:
-            node = Node(value_repr=to_repr(obj), last=root)
-        node.is_tuple = _safe_isinstance(obj, tuple)
-        node.is_namedtuple = _is_namedtuple(obj)
-        return node
-
-    node = _traverse(_object, root=True)
-    return node
-
-
-def pretty_repr(
-    _object: Any,
-    *,
-    max_width: int = 80,
-    indent_size: int = 4,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> str:
-    """Prettify repr string by expanding on to new lines to fit within a given width.
-
-    Args:
-        _object (Any): Object to repr.
-        max_width (int, optional): Desired maximum width of repr string. Defaults to 80.
-        indent_size (int, optional): Number of spaces to indent. Defaults to 4.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
-            Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structure, or None for no depth.
-            Defaults to None.
-        expand_all (bool, optional): Expand all containers regardless of available width. Defaults to False.
-
-    Returns:
-        str: A possibly multi-line representation of the object.
-    """
-
-    if _safe_isinstance(_object, Node):
-        node = _object
-    else:
-        node = traverse(
-            _object, max_length=max_length, max_string=max_string, max_depth=max_depth
-        )
-    repr_str: str = node.render(
-        max_width=max_width, indent_size=indent_size, expand_all=expand_all
-    )
-    return repr_str
-
-
-def pprint(
-    _object: Any,
-    *,
-    console: Optional["Console"] = None,
-    indent_guides: bool = True,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> None:
-    """A convenience function for pretty printing.
-
-    Args:
-        _object (Any): Object to pretty print.
-        console (Console, optional): Console instance, or None to use default. Defaults to None.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of strings before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth for nested data structures, or None for unlimited depth. Defaults to None.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to True.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-    """
-    _console = get_console() if console is None else console
-    _console.print(
-        Pretty(
-            _object,
-            max_length=max_length,
-            max_string=max_string,
-            max_depth=max_depth,
-            indent_guides=indent_guides,
-            expand_all=expand_all,
-            overflow="ignore",
-        ),
-        soft_wrap=True,
-    )
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    class BrokenRepr:
-        def __repr__(self) -> str:
-            1 / 0
-            return "this will fail"
-
-    from typing import NamedTuple
-
-    class StockKeepingUnit(NamedTuple):
-        name: str
-        description: str
-        price: float
-        category: str
-        reviews: List[str]
-
-    d = defaultdict(int)
-    d["foo"] = 5
-    data = {
-        "foo": [
-            1,
-            "Hello World!",
-            100.123,
-            323.232,
-            432324.0,
-            {5, 6, 7, (1, 2, 3, 4), 8},
-        ],
-        "bar": frozenset({1, 2, 3}),
-        "defaultdict": defaultdict(
-            list, {"crumble": ["apple", "rhubarb", "butter", "sugar", "flour"]}
-        ),
-        "counter": Counter(
-            [
-                "apple",
-                "orange",
-                "pear",
-                "kumquat",
-                "kumquat",
-                "durian" * 100,
-            ]
-        ),
-        "atomic": (False, True, None),
-        "namedtuple": StockKeepingUnit(
-            "Sparkling British Spring Water",
-            "Carbonated spring water",
-            0.9,
-            "water",
-            ["its amazing!", "its terrible!"],
-        ),
-        "Broken": BrokenRepr(),
-    }
-    data["foo"].append(data)  # type: ignore[attr-defined]
-
-    from pip._vendor.rich import print
-
-    # print(Pretty(data, indent_guides=True, max_string=20))
-
-    class Thing:
-        def __repr__(self) -> str:
-            return "Hello\x1b[38;5;239m World!"
-
-    print(Pretty(Thing()))

spaces/Audio-AGI/AudioSep/data/datamodules.py

@@ -1,122 +0,0 @@
-from typing import Dict, List, Optional, NoReturn
-import torch
-import lightning.pytorch as pl
-from torch.utils.data import DataLoader
-from data.audiotext_dataset import AudioTextDataset
-
-
-class DataModule(pl.LightningDataModule):
-    def __init__(
-        self,
-        train_dataset: object,
-        batch_size: int,
-        num_workers: int
-    ):
-        r"""Data module. To get one batch of data:
-
-        code-block:: python
-
-            data_module.setup()
-
-            for batch_data_dict in data_module.train_dataloader():
-                print(batch_data_dict.keys())
-                break
-
-        Args:
-            train_sampler: Sampler object
-            train_dataset: Dataset object
-            num_workers: int
-            distributed: bool
-        """
-        super().__init__()
-        self._train_dataset = train_dataset
-        self.num_workers = num_workers
-        self.batch_size = batch_size
-        self.collate_fn = collate_fn
-
-
-    def prepare_data(self):
-        # download, split, etc...
-        # only called on 1 GPU/TPU in distributed
-        pass
-
-    def setup(self, stage: Optional[str] = None) -> NoReturn:
-        r"""called on every device."""
-
-        # make assignments here (val/train/test split)
-        # called on every process in DDP
-
-        # SegmentSampler is used for selecting segments for training.
-        # On multiple devices, each SegmentSampler samples a part of mini-batch
-        # data.
-        self.train_dataset = self._train_dataset
-        
-        
-    def train_dataloader(self) -> torch.utils.data.DataLoader:
-        r"""Get train loader."""
-        train_loader = DataLoader(
-            dataset=self.train_dataset,
-            batch_size=self.batch_size,
-            collate_fn=self.collate_fn,
-            num_workers=self.num_workers,
-            pin_memory=True,
-            persistent_workers=False,
-            shuffle=True
-        )
-
-        return train_loader
-
-    def val_dataloader(self):
-        # val_split = Dataset(...)
-        # return DataLoader(val_split)
-        pass
-
-    def test_dataloader(self):
-        # test_split = Dataset(...)
-        # return DataLoader(test_split)
-        pass
-
-    def teardown(self):
-        # clean up after fit or test
-        # called on every process in DDP
-        pass
-
-
-def collate_fn(list_data_dict):
-    r"""Collate mini-batch data to inputs and targets for training.
-
-    Args:
-        list_data_dict: e.g., [
-            {
-                'text': 'a sound of dog',
-                'waveform': (1, samples),
-                'modality': 'audio_text'
-            }
-            ...
-            ]
-    Returns:
-        data_dict: e.g. 
-            'audio_text': {
-                'text': ['a sound of dog', ...]
-                'waveform': (batch_size, 1, samples)
-        }
-    """
-    
-    at_list_data_dict = [data_dict for data_dict in list_data_dict if data_dict['modality']=='audio_text']
-
-    at_data_dict = {}
-    
-    if len(at_list_data_dict) > 0:
-        for key in at_list_data_dict[0].keys():
-            at_data_dict[key] = [at_data_dict[key] for at_data_dict in at_list_data_dict]
-            if key == 'waveform':
-                at_data_dict[key] = torch.stack(at_data_dict[key])
-            elif key == 'text':
-                at_data_dict[key] = [text for text in at_data_dict[key]]
-
-    
-    data_dict = {
-        'audio_text': at_data_dict
-    }
-    
-    return data_dict

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/common/README.md

@@ -1,6 +0,0 @@
-This directory provides definitions for a few common models, dataloaders, scheduler,
-and optimizers that are often used in training.
-The definition of these objects are provided in the form of lazy instantiation:
-their arguments can be edited by users before constructing the objects.
-
-They can be imported, or loaded by `model_zoo.get_config` API in users' own configs.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/demo/README.md

@@ -1,8 +0,0 @@
-
-## Detectron2 Demo
-
-We provide a command line tool to run a simple demo of builtin configs.
-The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md).
-
-See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-)
-for a high-quality demo generated with this tool.

spaces/Benson/text-generation/Examples/Apkgstore X.md

@@ -1,92 +0,0 @@
-<br />
-<h1>Rush Royale Mod APK dinero ilimitado y joyas 2023</h1>
-<p>¿Estás buscando un juego de torre de defensa divertido y adictivo con elementos de recogida de cartas? ¿Quieres competir contra otros jugadores o formar equipo con ellos en un reino de fantasía de magia y caos? ¿Quieres desbloquear todas las tarjetas y actualizaciones sin gastar dinero real o ver anuncios? Si usted respondió sí a cualquiera de estas preguntas, entonces usted debe tratar de Rush Royale Mod APK Unlimited Money and Gems 2023! </p>
-<h2>¿Qué es Rush Royale? </h2>
-<p>Rush Royale es un juego de defensa de torres de fantasía móvil desarrollado por IT Territory y publicado por My.Games. Está disponible en rr.my.games y en Google Play Store. En este juego, coleccionas unidades, montas una baraja para la defensa de la base y te preparas para un juego TD lleno de acción, aventura y diversión sin fin. </p>
-<h2>apkgstore x</h2><br /><p><b><b>Download</b> &#10003; <a href="https://bltlly.com/2v6J2n">https://bltlly.com/2v6J2n</a></b></p><br /><br />
-<h3>Un juego de defensa de la torre con elementos de recogida de cartas</h3>
-<p>El modo de juego de Rush Royale es simple pero desafiante. Empiezas con una cuadrícula de 3x5 para colocar tus unidades y 100 unidades de maná. El maná se utiliza para convocar nuevas unidades o mejorar las existentes. Las unidades que invocas son aleatorias según tu baraja, al igual que su ubicación en la cuadrícula. Puede combinar dos unidades idénticas del mismo rango para crear una nueva unidad aleatoria con una tasa de fuego mejorada. Sin embargo, la fusión también puede resultar en una pérdida de potencia si la nueva unidad tiene una potencia menor que las unidades combinadas. Tienes que equilibrar las unidades de fusión para hacer espacio para más, o mantenerlas separadas para mantener su poder. </p>
-
-<h3>Un modo PvP y cooperativo con diferentes mecánicas de juego</h3>
-<p>Rush Royale tiene dos modos de juego principales: PvP y Cooperativo. En el modo PvP, compites contra otro jugador en tiempo real. Tienes que defender tu propia base mientras envías monstruos a la base de tu oponente matándolos a tu lado. El primer jugador en perder todos sus puntos de salud pierde el juego. Puedes ganar trofeos ganando partidos y progresando a través de diferentes arenas. También puedes ganar recompensas como cartas, cofres, monedas, gemas y entradas. </p>
-<p>En el modo cooperativo, haces equipo con otro jugador para defender un castillo contra oleadas de monstruos. Compartes la misma red y mana pool con tu pareja. Tienes que cooperar y comunicarte con tu pareja para sobrevivir el mayor tiempo posible. Puedes ganar recompensas como cartas, cofres, monedas, gemas y tickets en función del número de olas que despejes. </p>
-<h3>Una variedad de unidades, héroes y facciones para elegir</h3>
-<p>Rush Royale tiene una rica y diversa colección de unidades, héroes y facciones para adaptarse a su estilo de juego y estrategia. Hay cuatro facciones en el juego: Orden, Caos, Vacío y Naturaleza. Cada facción tiene su propio tema, color y unidades. Puedes mezclar y combinar unidades de diferentes facciones para crear tu propia baraja única. También puedes actualizar tus unidades para aumentar su poder y desbloquear nuevas habilidades. </p>
-<p></p>
-<p>También hay héroes que puedes elegir para dirigir tu ejército. Los héroes tienen habilidades especiales que se pueden activar una vez que se cargan. Algunos héroes pueden mejorar tus unidades, algunos pueden desacreditar a los enemigos y algunos pueden infligir daño masivo. Puedes desbloquear nuevos héroes completando misiones o comprándolas con gemas. También puedes actualizar a tus héroes para mejorar sus habilidades y estadísticas. </p>
-<h2>¿Por qué usar Rush Royale Mod APK? </h2>
-
-<p>Es por eso que usted debe utilizar Rush Royale Mod APK dinero ilimitado y Gems 2023. Esta es una versión modificada del juego original que te da dinero ilimitado y gemas para gastar como quieras. Puede desbloquear todas las tarjetas y actualizaciones sin moler o gastar dinero real. También puede disfrutar del juego sin anuncios ni restricciones. Con Rush Royale Mod APK, usted puede tener más diversión y libertad en el juego. </p>
-<h3>Para obtener dinero y gemas ilimitadas</h3>
-<p>El principal beneficio de usar Rush Royale Mod APK es que se obtiene dinero ilimitado y gemas para usar en el juego. El dinero y las gemas son las dos principales monedas en Rush Royale que se utilizan para diversos fines. El dinero se utiliza para comprar nuevas cartas de la tienda, mejorar sus unidades y héroes, cofres abiertos, y entrar en torneos. Las gemas se utilizan para comprar cofres premium, ofertas especiales, pieles de héroes y entradas. </p>
-<p>Con dinero y gemas ilimitadas, puedes comprar lo que quieras sin preocuparte por quedarte sin recursos. Puede desbloquear todas las tarjetas y mejoras en el juego sin esperar o moler. También puedes abrir tantos cofres como quieras para obtener más cartas y recompensas. Puedes participar en cualquier torneo que desees sin pagar ninguna cuota de inscripción. Puedes personalizar a tu héroe con cualquier piel que te guste sin gastar ninguna joya. </p>
-<h3>Para desbloquear todas las tarjetas y actualizaciones</h3>
-<p>Otro beneficio de usar Rush Royale Mod APK es que se puede desbloquear todas las tarjetas y mejoras en el juego sin ningún tipo de molestia. Las cartas son el elemento central de Rush Royale ya que determinan las habilidades y el rendimiento de tus unidades en la batalla. Hay más de 100 cartas en el juego divididas en cuatro facciones: Orden, Caos, Vacío y Naturaleza. Cada carta tiene un arma diferente, estilo, rango, poder, velocidad de fuego, coste de maná, habilidad y rareza. </p>
-
-<h3>Para disfrutar del juego sin anuncios ni restricciones</h3>
-<p>Un beneficio final de usar Rush Royale Mod APK es que se puede disfrutar del juego sin anuncios o restricciones que pueden arruinar su experiencia de juego. Los anuncios son molestos pop-ups que aparecen al azar en el juego o cuando intenta reclamar algunas recompensas. Interrumpen tu juego y te obligan a observarlo durante unos segundos o minutos antes de que puedas continuar jugando. </p>
-<p>Las restricciones son limitaciones que le impiden acceder a algunas funciones o contenido del juego a menos que pague dinero real o vea anuncios. Por ejemplo, tienes un número limitado de cofres gratis por día que puedes abrir para obtener algunas cartas y recompensas. También tienes un número limitado de entradas gratuitas por día para participar en torneos donde puedes competir por premios más grandes. </p>
-<p>Con Rush Royale Mod APK, no tienes que lidiar con ningún anuncio o restricciones en el juego. Usted puede jugar el juego sin problemas y sin interrupciones por cualquier molesto pop-ups o banners. También puedes acceder a todas las características y contenido del juego sin pagar dinero real ni ver ningún anuncio. </p>
-<h2>¿Cómo descargar e instalar Rush Royale Mod APK? </h 2>Cómo descargar e instalar Rush Royale Mod APK? </h2>
-<p>Si desea descargar e instalar Rush Royale Mod APK dinero ilimitado y joyas 2023, usted tiene que seguir estos sencillos pasos:</p>
-<h3>Siga estos pasos para descargar el archivo apk mod</h3>
-<ol>
-<li>Haga clic en este enlace para ir a la página de descarga de Rush Royale Mod APK.</li>
-<li>Espere unos segundos hasta que aparezca el botón de descarga. </li>
-<li>Haga clic en el botón de descarga y elija una ubicación para guardar el archivo en su dispositivo. </li>
-<li>Espere a que termine la descarga. El tamaño del archivo es de unos 150 MB.</li>
-</ol>
-<h3>Siga estos pasos para instalar el archivo apk mod</h3>
-<ol>
-<li>Vaya a la ubicación donde guardó el archivo y toque en él para abrirlo. </li>
-
-<li>Toque en "Instalar" y espere a que la instalación se complete. </li>
-<li>Toque en "Abrir" y disfrutar del juego con dinero y gemas ilimitadas. </li>
-</ol>
-<h3>Disfruta del juego con dinero y gemas ilimitadas</h3>
-<p>Felicidades! Usted ha descargado e instalado con éxito Rush Royale Mod APK Unlimited Money and Gems 2023. Ahora puedes disfrutar del juego con todas las características y contenido desbloqueado. Puedes comprar cualquier carta que quieras, actualizar cualquier unidad o héroe que quieras, abrir cualquier cofre que quieras, entrar en cualquier torneo que quieras y personalizar cualquier piel de héroe que quieras. También puedes jugar sin anuncios ni restricciones. ¡Diviértete y domina la Isla de Rhandum! </p>
-<h2>¿Cuáles son algunos consejos y trucos para ganar en Rush Royale? </h2>
-<p>Rush Royale es un juego que requiere habilidad, estrategia y suerte. Tienes que usar tus unidades, héroes y habilidades sabiamente para defender tu base y atacar la base de tu oponente. También tienes que adaptarte a la naturaleza aleatoria del juego y lidiar con situaciones inesperadas. Aquí hay algunos consejos y trucos que pueden ayudarte a ganar en Rush Royale:</p>
-<h3>Utilice una cubierta equilibrada con daño de punto, AOE, regeneración de maná y control de multitudes</h3>
-<p>Una cubierta equilibrada es una cubierta que tiene una buena mezcla de unidades que pueden causar daño puntual, daño de área de efecto, regeneración de maná y control de multitudes. Las unidades de daño puntual son unidades que pueden atacar y dañar a un solo enemigo a la vez, como Archer o Bombardier. Las unidades AOE son unidades que pueden dañar a múltiples enemigos en un área determinada, como Fire Mage o Plague Doctor. Las unidades de regeneración de maná son unidades que pueden restaurar tu maná con el tiempo, como Dryad o Shaman. Las unidades de control de multitudes son unidades que pueden afectar el movimiento o el comportamiento de los enemigos, como Frost Mage o Harlequin.</p>
-
-<h3>Combinar unidades sabiamente y actualizarlas cuando sea necesario</h3>
-<p>Las unidades de fusión es un mecánico clave en Rush Royale que puede hacer o romper su juego. Las unidades de fusión pueden crear nuevas unidades aleatorias con una tasa de fuego mejorada, pero también puede resultar en una pérdida de potencia si la nueva unidad tiene una potencia menor que las unidades combinadas. Tienes que combinar las unidades sabiamente y estratégicamente para optimizar el espacio de la red y la potencia de la unidad. </p>
-<p>Algunos consejos generales para las unidades de fusión son:</p>
-<ul>
-<li>Fusionar unidades de rango bajo primero, ya que tienen menor potencia y velocidad de fuego que las unidades de alto rango. </li>
-<li>Fusionar unidades de baja potencia primero, ya que tienen menor impacto que las unidades de alta potencia. </li>
-<li>Fusionar unidades duplicadas primero, ya que no tienen sinergia entre sí. </li>
-<li>Fusionar diferentes unidades de facción primero, ya que no tienen ninguna bonificación de facción entre sí. </li>
-<li>Las unidades de regeneración de mana de fusión duran, ya que son útiles para mantener su suministro de maná. </li>
-</ul>
-<p>Actualizar unidades es otro mecánico clave en Rush Royale que puede mejorar la potencia y el rendimiento de su unidad. Mejorar las unidades aumenta su poder y desbloquea nuevas habilidades que pueden hacerlas más efectivas en combate. Tienes que actualizar las unidades cuando sea necesario para mantenerte al día con la creciente dificultad del juego. </p>
-<p>Algunos consejos generales para las unidades de actualización son:</p>
-<ul>
-<li>Actualice primero las unidades de alto rango, ya que tienen mayor potencia y velocidad de fuego que las unidades de bajo rango. </li>
-<li <li>Actualice primero las unidades de alta potencia, ya que tienen mayor impacto que las unidades de baja potencia. </li>
-<li>Mejora las unidades con habilidades útiles primero, ya que pueden darte una ventaja en la batalla. </li>
-<li>Mejora las unidades con bonificación de facción primero, ya que pueden aumentar tu poder general y sinergia. </li>
-<li>Actualice las unidades de manera uniforme, ya que tener una distribución de energía equilibrada puede ayudarlo a lidiar con diferentes situaciones. </li>
-</ul>
-<h3>Juega al modo cooperativo para obtener más recompensas y aprender de otros jugadores</h3>
-
-<p>El modo cooperativo es una gran manera de obtener más recompensas y aprender de otros jugadores. Puedes obtener más cartas y recompensas jugando al modo cooperativo que jugando en solitario. También puede aprender nuevas estrategias y consejos de su pareja o de ver su juego. También puedes hacer nuevos amigos y chatear con ellos en el juego. </p>
-<h2>Conclusión</h2>
-<p>Rush Royale es un divertido y adictivo juego de torre de defensa con la tarjeta de recogida de elementos. Puedes reunir unidades, montar un mazo y competir contra otros jugadores o formar equipo con ellos en un reino de fantasía de magia y caos. También puede desbloquear todas las características y el contenido en el juego sin gastar dinero real o ver anuncios mediante el uso de Rush Royale Mod APK Unlimited Money and Gems 2023. </p>
-<p>Rush Royale Mod APK le da dinero ilimitado y gemas para gastar como desee. Puedes comprar cualquier carta que quieras, actualizar cualquier unidad o héroe que quieras, abrir cualquier cofre que quieras, entrar en cualquier torneo que quieras y personalizar cualquier piel de héroe que quieras. También puedes disfrutar del juego sin anuncios ni restricciones que puedan limitar tu disfrute del juego. </p>
-<p>Descargar e instalar Rush Royale Mod APK hoy y conquistar la Isla de Rhandum con sus habilidades y estrategia. Diviértete y buena suerte! </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Rush Royale Mod APK:</p>
-<h3> ¿Es Rush Royale Mod APK seguro de usar? </h3>
-<p>Sí, Rush Royale Mod APK es seguro de usar, siempre y cuando se descarga desde una fuente de confianza. Hemos probado el archivo apk mod y no encontramos virus o malware en él. Sin embargo, no podemos garantizar que funcionará en todos los dispositivos o que no causará ningún problema con su cuenta de juego. Úselo bajo su propio riesgo. </p>
-<h3>¿Voy a conseguir prohibido para el uso de Rush Royale Mod APK? </h3>
-
-<h3> ¿Cómo actualizo Rush Royale Mod APK? </h3>
-<p>Para actualizar Rush Royale Mod APK, usted tiene que descargar la última versión del archivo apk mod de la misma fuente donde descargó la versión anterior. Entonces, usted tiene que desinstalar la versión anterior del archivo apk mod e instalar la nueva versión del archivo apk mod. Puedes perder tu progreso o datos si no haces una copia de seguridad de tu juego antes de actualizarlo. </p>
-<h3>¿Puedo jugar Rush Royale Mod APK en línea con otros jugadores? </h3>
-<p>Sí, puedes jugar Rush Royale Mod APK en línea con otros jugadores en ambos modos PvP y Cooperativo. Sin embargo, puede encontrar algunos problemas o errores al jugar en línea con otros jugadores que están utilizando la versión original del juego o una versión diferente del archivo apk mod. Para evitar estos problemas o errores, le recomendamos que juegue en línea con otros jugadores que están utilizando la misma versión del archivo apk mod que usted. </p>
-<h3>¿Puedo solicitar una característica o informar de un error para Rush Royale Mod APK? </h3>
-<p>No somos los desarrolladores de Rush Royale Mod APK, por lo que no podemos agregar nuevas características o corregir cualquier error para ella. Solo estamos proporcionando el enlace para descargar el archivo apk mod de una fuente de confianza. Si usted tiene alguna solicitud o informes para Rush Royale Mod APK, usted tiene que ponerse en contacto con los desarrolladores originales o modders del archivo apk mod. </p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,400 +0,0 @@
-from . import idnadata
-import bisect
-import unicodedata
-import re
-from typing import Union, Optional
-from .intranges import intranges_contain
-
-_virama_combining_class = 9
-_alabel_prefix = b'xn--'
-_unicode_dots_re = re.compile('[\u002e\u3002\uff0e\uff61]')
-
-class IDNAError(UnicodeError):
-    """ Base exception for all IDNA-encoding related problems """
-    pass
-
-
-class IDNABidiError(IDNAError):
-    """ Exception when bidirectional requirements are not satisfied """
-    pass
-
-
-class InvalidCodepoint(IDNAError):
-    """ Exception when a disallowed or unallocated codepoint is used """
-    pass
-
-
-class InvalidCodepointContext(IDNAError):
-    """ Exception when the codepoint is not valid in the context it is used """
-    pass
-
-
-def _combining_class(cp: int) -> int:
-    v = unicodedata.combining(chr(cp))
-    if v == 0:
-        if not unicodedata.name(chr(cp)):
-            raise ValueError('Unknown character in unicodedata')
-    return v
-
-def _is_script(cp: str, script: str) -> bool:
-    return intranges_contain(ord(cp), idnadata.scripts[script])
-
-def _punycode(s: str) -> bytes:
-    return s.encode('punycode')
-
-def _unot(s: int) -> str:
-    return 'U+{:04X}'.format(s)
-
-
-def valid_label_length(label: Union[bytes, str]) -> bool:
-    if len(label) > 63:
-        return False
-    return True
-
-
-def valid_string_length(label: Union[bytes, str], trailing_dot: bool) -> bool:
-    if len(label) > (254 if trailing_dot else 253):
-        return False
-    return True
-
-
-def check_bidi(label: str, check_ltr: bool = False) -> bool:
-    # Bidi rules should only be applied if string contains RTL characters
-    bidi_label = False
-    for (idx, cp) in enumerate(label, 1):
-        direction = unicodedata.bidirectional(cp)
-        if direction == '':
-            # String likely comes from a newer version of Unicode
-            raise IDNABidiError('Unknown directionality in label {} at position {}'.format(repr(label), idx))
-        if direction in ['R', 'AL', 'AN']:
-            bidi_label = True
-    if not bidi_label and not check_ltr:
-        return True
-
-    # Bidi rule 1
-    direction = unicodedata.bidirectional(label[0])
-    if direction in ['R', 'AL']:
-        rtl = True
-    elif direction == 'L':
-        rtl = False
-    else:
-        raise IDNABidiError('First codepoint in label {} must be directionality L, R or AL'.format(repr(label)))
-
-    valid_ending = False
-    number_type = None  # type: Optional[str]
-    for (idx, cp) in enumerate(label, 1):
-        direction = unicodedata.bidirectional(cp)
-
-        if rtl:
-            # Bidi rule 2
-            if not direction in ['R', 'AL', 'AN', 'EN', 'ES', 'CS', 'ET', 'ON', 'BN', 'NSM']:
-                raise IDNABidiError('Invalid direction for codepoint at position {} in a right-to-left label'.format(idx))
-            # Bidi rule 3
-            if direction in ['R', 'AL', 'EN', 'AN']:
-                valid_ending = True
-            elif direction != 'NSM':
-                valid_ending = False
-            # Bidi rule 4
-            if direction in ['AN', 'EN']:
-                if not number_type:
-                    number_type = direction
-                else:
-                    if number_type != direction:
-                        raise IDNABidiError('Can not mix numeral types in a right-to-left label')
-        else:
-            # Bidi rule 5
-            if not direction in ['L', 'EN', 'ES', 'CS', 'ET', 'ON', 'BN', 'NSM']:
-                raise IDNABidiError('Invalid direction for codepoint at position {} in a left-to-right label'.format(idx))
-            # Bidi rule 6
-            if direction in ['L', 'EN']:
-                valid_ending = True
-            elif direction != 'NSM':
-                valid_ending = False
-
-    if not valid_ending:
-        raise IDNABidiError('Label ends with illegal codepoint directionality')
-
-    return True
-
-
-def check_initial_combiner(label: str) -> bool:
-    if unicodedata.category(label[0])[0] == 'M':
-        raise IDNAError('Label begins with an illegal combining character')
-    return True
-
-
-def check_hyphen_ok(label: str) -> bool:
-    if label[2:4] == '--':
-        raise IDNAError('Label has disallowed hyphens in 3rd and 4th position')
-    if label[0] == '-' or label[-1] == '-':
-        raise IDNAError('Label must not start or end with a hyphen')
-    return True
-
-
-def check_nfc(label: str) -> None:
-    if unicodedata.normalize('NFC', label) != label:
-        raise IDNAError('Label must be in Normalization Form C')
-
-
-def valid_contextj(label: str, pos: int) -> bool:
-    cp_value = ord(label[pos])
-
-    if cp_value == 0x200c:
-
-        if pos > 0:
-            if _combining_class(ord(label[pos - 1])) == _virama_combining_class:
-                return True
-
-        ok = False
-        for i in range(pos-1, -1, -1):
-            joining_type = idnadata.joining_types.get(ord(label[i]))
-            if joining_type == ord('T'):
-                continue
-            if joining_type in [ord('L'), ord('D')]:
-                ok = True
-                break
-
-        if not ok:
-            return False
-
-        ok = False
-        for i in range(pos+1, len(label)):
-            joining_type = idnadata.joining_types.get(ord(label[i]))
-            if joining_type == ord('T'):
-                continue
-            if joining_type in [ord('R'), ord('D')]:
-                ok = True
-                break
-        return ok
-
-    if cp_value == 0x200d:
-
-        if pos > 0:
-            if _combining_class(ord(label[pos - 1])) == _virama_combining_class:
-                return True
-        return False
-
-    else:
-
-        return False
-
-
-def valid_contexto(label: str, pos: int, exception: bool = False) -> bool:
-    cp_value = ord(label[pos])
-
-    if cp_value == 0x00b7:
-        if 0 < pos < len(label)-1:
-            if ord(label[pos - 1]) == 0x006c and ord(label[pos + 1]) == 0x006c:
-                return True
-        return False
-
-    elif cp_value == 0x0375:
-        if pos < len(label)-1 and len(label) > 1:
-            return _is_script(label[pos + 1], 'Greek')
-        return False
-
-    elif cp_value == 0x05f3 or cp_value == 0x05f4:
-        if pos > 0:
-            return _is_script(label[pos - 1], 'Hebrew')
-        return False
-
-    elif cp_value == 0x30fb:
-        for cp in label:
-            if cp == '\u30fb':
-                continue
-            if _is_script(cp, 'Hiragana') or _is_script(cp, 'Katakana') or _is_script(cp, 'Han'):
-                return True
-        return False
-
-    elif 0x660 <= cp_value <= 0x669:
-        for cp in label:
-            if 0x6f0 <= ord(cp) <= 0x06f9:
-                return False
-        return True
-
-    elif 0x6f0 <= cp_value <= 0x6f9:
-        for cp in label:
-            if 0x660 <= ord(cp) <= 0x0669:
-                return False
-        return True
-
-    return False
-
-
-def check_label(label: Union[str, bytes, bytearray]) -> None:
-    if isinstance(label, (bytes, bytearray)):
-        label = label.decode('utf-8')
-    if len(label) == 0:
-        raise IDNAError('Empty Label')
-
-    check_nfc(label)
-    check_hyphen_ok(label)
-    check_initial_combiner(label)
-
-    for (pos, cp) in enumerate(label):
-        cp_value = ord(cp)
-        if intranges_contain(cp_value, idnadata.codepoint_classes['PVALID']):
-            continue
-        elif intranges_contain(cp_value, idnadata.codepoint_classes['CONTEXTJ']):
-            try:
-                if not valid_contextj(label, pos):
-                    raise InvalidCodepointContext('Joiner {} not allowed at position {} in {}'.format(
-                        _unot(cp_value), pos+1, repr(label)))
-            except ValueError:
-                raise IDNAError('Unknown codepoint adjacent to joiner {} at position {} in {}'.format(
-                    _unot(cp_value), pos+1, repr(label)))
-        elif intranges_contain(cp_value, idnadata.codepoint_classes['CONTEXTO']):
-            if not valid_contexto(label, pos):
-                raise InvalidCodepointContext('Codepoint {} not allowed at position {} in {}'.format(_unot(cp_value), pos+1, repr(label)))
-        else:
-            raise InvalidCodepoint('Codepoint {} at position {} of {} not allowed'.format(_unot(cp_value), pos+1, repr(label)))
-
-    check_bidi(label)
-
-
-def alabel(label: str) -> bytes:
-    try:
-        label_bytes = label.encode('ascii')
-        ulabel(label_bytes)
-        if not valid_label_length(label_bytes):
-            raise IDNAError('Label too long')
-        return label_bytes
-    except UnicodeEncodeError:
-        pass
-
-    if not label:
-        raise IDNAError('No Input')
-
-    label = str(label)
-    check_label(label)
-    label_bytes = _punycode(label)
-    label_bytes = _alabel_prefix + label_bytes
-
-    if not valid_label_length(label_bytes):
-        raise IDNAError('Label too long')
-
-    return label_bytes
-
-
-def ulabel(label: Union[str, bytes, bytearray]) -> str:
-    if not isinstance(label, (bytes, bytearray)):
-        try:
-            label_bytes = label.encode('ascii')
-        except UnicodeEncodeError:
-            check_label(label)
-            return label
-    else:
-        label_bytes = label
-
-    label_bytes = label_bytes.lower()
-    if label_bytes.startswith(_alabel_prefix):
-        label_bytes = label_bytes[len(_alabel_prefix):]
-        if not label_bytes:
-            raise IDNAError('Malformed A-label, no Punycode eligible content found')
-        if label_bytes.decode('ascii')[-1] == '-':
-            raise IDNAError('A-label must not end with a hyphen')
-    else:
-        check_label(label_bytes)
-        return label_bytes.decode('ascii')
-
-    try:
-        label = label_bytes.decode('punycode')
-    except UnicodeError:
-        raise IDNAError('Invalid A-label')
-    check_label(label)
-    return label
-
-
-def uts46_remap(domain: str, std3_rules: bool = True, transitional: bool = False) -> str:
-    """Re-map the characters in the string according to UTS46 processing."""
-    from .uts46data import uts46data
-    output = ''
-
-    for pos, char in enumerate(domain):
-        code_point = ord(char)
-        try:
-            uts46row = uts46data[code_point if code_point < 256 else
-                bisect.bisect_left(uts46data, (code_point, 'Z')) - 1]
-            status = uts46row[1]
-            replacement = None  # type: Optional[str]
-            if len(uts46row) == 3:
-                replacement = uts46row[2]  # type: ignore
-            if (status == 'V' or
-                    (status == 'D' and not transitional) or
-                    (status == '3' and not std3_rules and replacement is None)):
-                output += char
-            elif replacement is not None and (status == 'M' or
-                    (status == '3' and not std3_rules) or
-                    (status == 'D' and transitional)):
-                output += replacement
-            elif status != 'I':
-                raise IndexError()
-        except IndexError:
-            raise InvalidCodepoint(
-                'Codepoint {} not allowed at position {} in {}'.format(
-                _unot(code_point), pos + 1, repr(domain)))
-
-    return unicodedata.normalize('NFC', output)
-
-
-def encode(s: Union[str, bytes, bytearray], strict: bool = False, uts46: bool = False, std3_rules: bool = False, transitional: bool = False) -> bytes:
-    if isinstance(s, (bytes, bytearray)):
-        try:
-            s = s.decode('ascii')
-        except UnicodeDecodeError:
-            raise IDNAError('should pass a unicode string to the function rather than a byte string.')
-    if uts46:
-        s = uts46_remap(s, std3_rules, transitional)
-    trailing_dot = False
-    result = []
-    if strict:
-        labels = s.split('.')
-    else:
-        labels = _unicode_dots_re.split(s)
-    if not labels or labels == ['']:
-        raise IDNAError('Empty domain')
-    if labels[-1] == '':
-        del labels[-1]
-        trailing_dot = True
-    for label in labels:
-        s = alabel(label)
-        if s:
-            result.append(s)
-        else:
-            raise IDNAError('Empty label')
-    if trailing_dot:
-        result.append(b'')
-    s = b'.'.join(result)
-    if not valid_string_length(s, trailing_dot):
-        raise IDNAError('Domain too long')
-    return s
-
-
-def decode(s: Union[str, bytes, bytearray], strict: bool = False, uts46: bool = False, std3_rules: bool = False) -> str:
-    try:
-        if isinstance(s, (bytes, bytearray)):
-            s = s.decode('ascii')
-    except UnicodeDecodeError:
-        raise IDNAError('Invalid ASCII in A-label')
-    if uts46:
-        s = uts46_remap(s, std3_rules, False)
-    trailing_dot = False
-    result = []
-    if not strict:
-        labels = _unicode_dots_re.split(s)
-    else:
-        labels = s.split('.')
-    if not labels or labels == ['']:
-        raise IDNAError('Empty domain')
-    if not labels[-1]:
-        del labels[-1]
-        trailing_dot = True
-    for label in labels:
-        s = ulabel(label)
-        if s:
-            result.append(s)
-        else:
-            raise IDNAError('Empty label')
-    if trailing_dot:
-        result.append('')
-    return '.'.join(result)

spaces/BilalSardar/AutoML-Model-Training/README.md

@@ -1,13 +0,0 @@
----
-title: AutoML Model Training
-emoji: 💻
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/uninitialized_copy.h

@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special version of this algorithm 
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/mismatch.h

@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the mismatch.h header
-// of the host and device systems. It should be #included in any
-// code which uses adl to dispatch mismatch
-
-#include <thrust/system/detail/sequential/mismatch.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/mismatch.h>
-#include <thrust/system/cuda/detail/mismatch.h>
-#include <thrust/system/omp/detail/mismatch.h>
-#include <thrust/system/tbb/detail/mismatch.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_MISMATCH_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/mismatch.h>
-#include __THRUST_HOST_SYSTEM_MISMATCH_HEADER
-#undef __THRUST_HOST_SYSTEM_MISMATCH_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_MISMATCH_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/mismatch.h>
-#include __THRUST_DEVICE_SYSTEM_MISMATCH_HEADER
-#undef __THRUST_DEVICE_SYSTEM_MISMATCH_HEADER
-

spaces/CVPR/WALT/mmdet/core/utils/misc.py

@@ -1,61 +0,0 @@
-from functools import partial
-
-import numpy as np
-import torch
-from six.moves import map, zip
-
-from ..mask.structures import BitmapMasks, PolygonMasks
-
-
-def multi_apply(func, *args, **kwargs):
-    """Apply function to a list of arguments.
-
-    Note:
-        This function applies the ``func`` to multiple inputs and
-        map the multiple outputs of the ``func`` into different
-        list. Each list contains the same type of outputs corresponding
-        to different inputs.
-
-    Args:
-        func (Function): A function that will be applied to a list of
-            arguments
-
-    Returns:
-        tuple(list): A tuple containing multiple list, each list contains \
-            a kind of returned results by the function
-    """
-    pfunc = partial(func, **kwargs) if kwargs else func
-    map_results = map(pfunc, *args)
-    return tuple(map(list, zip(*map_results)))
-
-
-def unmap(data, count, inds, fill=0):
-    """Unmap a subset of item (data) back to the original set of items (of size
-    count)"""
-    if data.dim() == 1:
-        ret = data.new_full((count, ), fill)
-        ret[inds.type(torch.bool)] = data
-    else:
-        new_size = (count, ) + data.size()[1:]
-        ret = data.new_full(new_size, fill)
-        ret[inds.type(torch.bool), :] = data
-    return ret
-
-
-def mask2ndarray(mask):
-    """Convert Mask to ndarray..
-
-    Args:
-        mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or
-        torch.Tensor or np.ndarray): The mask to be converted.
-
-    Returns:
-        np.ndarray: Ndarray mask of shape (n, h, w) that has been converted
-    """
-    if isinstance(mask, (BitmapMasks, PolygonMasks)):
-        mask = mask.to_ndarray()
-    elif isinstance(mask, torch.Tensor):
-        mask = mask.detach().cpu().numpy()
-    elif not isinstance(mask, np.ndarray):
-        raise TypeError(f'Unsupported {type(mask)} data type')
-    return mask

spaces/CVPR/WALT/mmdet/models/roi_heads/shared_heads/res_layer.py

@@ -1,77 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import constant_init, kaiming_init
-from mmcv.runner import auto_fp16, load_checkpoint
-
-from mmdet.models.backbones import ResNet
-from mmdet.models.builder import SHARED_HEADS
-from mmdet.models.utils import ResLayer as _ResLayer
-from mmdet.utils import get_root_logger
-
-
-@SHARED_HEADS.register_module()
-class ResLayer(nn.Module):
-
-    def __init__(self,
-                 depth,
-                 stage=3,
-                 stride=2,
-                 dilation=1,
-                 style='pytorch',
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 norm_eval=True,
-                 with_cp=False,
-                 dcn=None):
-        super(ResLayer, self).__init__()
-        self.norm_eval = norm_eval
-        self.norm_cfg = norm_cfg
-        self.stage = stage
-        self.fp16_enabled = False
-        block, stage_blocks = ResNet.arch_settings[depth]
-        stage_block = stage_blocks[stage]
-        planes = 64 * 2**stage
-        inplanes = 64 * 2**(stage - 1) * block.expansion
-
-        res_layer = _ResLayer(
-            block,
-            inplanes,
-            planes,
-            stage_block,
-            stride=stride,
-            dilation=dilation,
-            style=style,
-            with_cp=with_cp,
-            norm_cfg=self.norm_cfg,
-            dcn=dcn)
-        self.add_module(f'layer{stage + 1}', res_layer)
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in the module.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, nn.BatchNorm2d):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    @auto_fp16()
-    def forward(self, x):
-        res_layer = getattr(self, f'layer{self.stage + 1}')
-        out = res_layer(x)
-        return out
-
-    def train(self, mode=True):
-        super(ResLayer, self).train(mode)
-        if self.norm_eval:
-            for m in self.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eval()

spaces/CVPR/lama-example/bin/mask_example.py

@@ -1,14 +0,0 @@
-import matplotlib.pyplot as plt
-from skimage import io
-from skimage.transform import resize
-
-from saicinpainting.evaluation.masks.mask import SegmentationMask
-
-im = io.imread('imgs/ex4.jpg')
-im = resize(im, (512, 1024), anti_aliasing=True)
-mask_seg = SegmentationMask(num_variants_per_mask=10)
-mask_examples = mask_seg.get_masks(im)
-for i, example in enumerate(mask_examples):
-    plt.imshow(example)
-    plt.show()
-    plt.imsave(f'tmp/img_masks/{i}.png', example)

spaces/CarlDennis/Lovelive-VITS-JPZH/text/sanskrit.py

@@ -1,62 +0,0 @@
-import re
-from indic_transliteration import sanscript
-
-
-# List of (iast, ipa) pairs:
-_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('a', 'ə'),
-    ('ā', 'aː'),
-    ('ī', 'iː'),
-    ('ū', 'uː'),
-    ('ṛ', 'ɹ`'),
-    ('ṝ', 'ɹ`ː'),
-    ('ḷ', 'l`'),
-    ('ḹ', 'l`ː'),
-    ('e', 'eː'),
-    ('o', 'oː'),
-    ('k', 'k⁼'),
-    ('k⁼h', 'kʰ'),
-    ('g', 'g⁼'),
-    ('g⁼h', 'gʰ'),
-    ('ṅ', 'ŋ'),
-    ('c', 'ʧ⁼'),
-    ('ʧ⁼h', 'ʧʰ'),
-    ('j', 'ʥ⁼'),
-    ('ʥ⁼h', 'ʥʰ'),
-    ('ñ', 'n^'),
-    ('ṭ', 't`⁼'),
-    ('t`⁼h', 't`ʰ'),
-    ('ḍ', 'd`⁼'),
-    ('d`⁼h', 'd`ʰ'),
-    ('ṇ', 'n`'),
-    ('t', 't⁼'),
-    ('t⁼h', 'tʰ'),
-    ('d', 'd⁼'),
-    ('d⁼h', 'dʰ'),
-    ('p', 'p⁼'),
-    ('p⁼h', 'pʰ'),
-    ('b', 'b⁼'),
-    ('b⁼h', 'bʰ'),
-    ('y', 'j'),
-    ('ś', 'ʃ'),
-    ('ṣ', 's`'),
-    ('r', 'ɾ'),
-    ('l̤', 'l`'),
-    ('h', 'ɦ'),
-    ("'", ''),
-    ('~', '^'),
-    ('ṃ', '^')
-]]
-
-
-def devanagari_to_ipa(text):
-    text = text.replace('ॐ', 'ओम्')
-    text = re.sub(r'\s*।\s*$', '.', text)
-    text = re.sub(r'\s*।\s*', ', ', text)
-    text = re.sub(r'\s*॥', '.', text)
-    text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
-    for regex, replacement in _iast_to_ipa:
-        text = re.sub(regex, replacement, text)
-    text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
-                  [:-1]+'h'+x.group(1)+'*', text)
-    return text

spaces/CikeyQI/meme-api/meme_generator/memes/fencing/__init__.py

@@ -1,37 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def fencing(images: List[BuildImage], texts, args):
-    self_head = images[0].convert("RGBA").circle().resize((27, 27))
-    user_head = images[1].convert("RGBA").circle().resize((27, 27))
-    # fmt: off
-    user_locs = [
-        (57, 4), (55, 5), (58, 7), (57, 5), (53, 8), (54, 9),
-        (64, 5), (66, 8), (70, 9), (73, 8), (81, 10), (77, 10),
-        (72, 4), (79, 8), (50, 8), (60, 7), (67, 6), (60, 6), (50, 9)
-    ]
-    self_locs = [
-        (10, 6), (3, 6), (32, 7), (22, 7), (13, 4), (21, 6),
-        (30, 6), (22, 2), (22, 3), (26, 8), (23, 8), (27, 10),
-        (30, 9), (17, 6), (12, 8), (11, 7), (8, 6), (-2, 10), (4, 9)
-    ]
-    # fmt: on
-    frames: List[IMG] = []
-    for i in range(19):
-        frame = BuildImage.open(img_dir / f"{i}.png")
-        frame.paste(user_head, user_locs[i], alpha=True)
-        frame.paste(self_head, self_locs[i], alpha=True)
-        frames.append(frame.image)
-    return save_gif(frames, 0.05)
-
-
-add_meme("fencing", fencing, min_images=2, max_images=2, keywords=["击剑", "🤺"])

spaces/ClipHamper/stable-diffusion-webui/README.md

@@ -1,12 +0,0 @@
----
-title: Stable Diffusion Webui
-emoji: 🚀
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/image_list.py

@@ -1,72 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from __future__ import division
-
-import torch
-
-
-class ImageList(object):
-    """
-    Structure that holds a list of images (of possibly
-    varying sizes) as a single tensor.
-    This works by padding the images to the same size,
-    and storing in a field the original sizes of each image
-    """
-
-    def __init__(self, tensors, image_sizes):
-        """
-        Arguments:
-            tensors (tensor)
-            image_sizes (list[tuple[int, int]])
-        """
-        self.tensors = tensors
-        self.image_sizes = image_sizes
-
-    def to(self, *args, **kwargs):
-        cast_tensor = self.tensors.to(*args, **kwargs)
-        return ImageList(cast_tensor, self.image_sizes)
-
-
-def to_image_list(tensors, size_divisible=0):
-    """
-    tensors can be an ImageList, a torch.Tensor or
-    an iterable of Tensors. It can't be a numpy array.
-    When tensors is an iterable of Tensors, it pads
-    the Tensors with zeros so that they have the same
-    shape
-    """
-    if isinstance(tensors, torch.Tensor) and size_divisible > 0:
-        tensors = [tensors]
-
-    if isinstance(tensors, ImageList):
-        return tensors
-    elif isinstance(tensors, torch.Tensor):
-        # single tensor shape can be inferred
-        if tensors.dim() == 3:
-            tensors = tensors[None]
-        assert tensors.dim() == 4
-        image_sizes = [tensor.shape[-2:] for tensor in tensors]
-        return ImageList(tensors, image_sizes)
-    elif isinstance(tensors, (tuple, list)):
-        max_size = tuple(max(s) for s in zip(*[img.shape for img in tensors]))
-
-        # TODO Ideally, just remove this and let me model handle arbitrary
-        # input sizs
-        if size_divisible > 0:
-            import math
-
-            stride = size_divisible
-            max_size = list(max_size)
-            max_size[1] = int(math.ceil(max_size[1] / stride) * stride)
-            max_size[2] = int(math.ceil(max_size[2] / stride) * stride)
-            max_size = tuple(max_size)
-
-        batch_shape = (len(tensors),) + max_size
-        batched_imgs = tensors[0].new(*batch_shape).zero_()
-        for img, pad_img in zip(tensors, batched_imgs):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-
-        image_sizes = [im.shape[-2:] for im in tensors]
-
-        return ImageList(batched_imgs, image_sizes)
-    else:
-        raise TypeError("Unsupported type for to_image_list: {}".format(type(tensors)))

spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/common/utils.py

@@ -1,424 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import io
-import json
-import logging
-import os
-import pickle
-import re
-import shutil
-import urllib
-import urllib.error
-import urllib.request
-from typing import Optional
-from urllib.parse import urlparse
-
-import numpy as np
-import pandas as pd
-import yaml
-from iopath.common.download import download
-from iopath.common.file_io import file_lock, g_pathmgr
-from video_llama.common.registry import registry
-from torch.utils.model_zoo import tqdm
-from torchvision.datasets.utils import (
-    check_integrity,
-    download_file_from_google_drive,
-    extract_archive,
-)
-
-
-def now():
-    from datetime import datetime
-
-    return datetime.now().strftime("%Y%m%d%H%M")[:-1]
-
-
-def is_url(url_or_filename):
-    parsed = urlparse(url_or_filename)
-    return parsed.scheme in ("http", "https")
-
-
-def get_cache_path(rel_path):
-    return os.path.expanduser(os.path.join(registry.get_path("cache_root"), rel_path))
-
-
-def get_abs_path(rel_path):
-    return os.path.join(registry.get_path("library_root"), rel_path)
-
-
-def load_json(filename):
-    with open(filename, "r") as f:
-        return json.load(f)
-
-
-# The following are adapted from torchvision and vissl
-# torchvision: https://github.com/pytorch/vision
-# vissl: https://github.com/facebookresearch/vissl/blob/main/vissl/utils/download.py
-
-
-def makedir(dir_path):
-    """
-    Create the directory if it does not exist.
-    """
-    is_success = False
-    try:
-        if not g_pathmgr.exists(dir_path):
-            g_pathmgr.mkdirs(dir_path)
-        is_success = True
-    except BaseException:
-        print(f"Error creating directory: {dir_path}")
-    return is_success
-
-
-def get_redirected_url(url: str):
-    """
-    Given a URL, returns the URL it redirects to or the
-    original URL in case of no indirection
-    """
-    import requests
-
-    with requests.Session() as session:
-        with session.get(url, stream=True, allow_redirects=True) as response:
-            if response.history:
-                return response.url
-            else:
-                return url
-
-
-def to_google_drive_download_url(view_url: str) -> str:
-    """
-    Utility function to transform a view URL of google drive
-    to a download URL for google drive
-    Example input:
-        https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp/view
-    Example output:
-        https://drive.google.com/uc?export=download&id=137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp
-    """
-    splits = view_url.split("/")
-    assert splits[-1] == "view"
-    file_id = splits[-2]
-    return f"https://drive.google.com/uc?export=download&id={file_id}"
-
-
-def download_google_drive_url(url: str, output_path: str, output_file_name: str):
-    """
-    Download a file from google drive
-    Downloading an URL from google drive requires confirmation when
-    the file of the size is too big (google drive notifies that
-    anti-viral checks cannot be performed on such files)
-    """
-    import requests
-
-    with requests.Session() as session:
-
-        # First get the confirmation token and append it to the URL
-        with session.get(url, stream=True, allow_redirects=True) as response:
-            for k, v in response.cookies.items():
-                if k.startswith("download_warning"):
-                    url = url + "&confirm=" + v
-
-        # Then download the content of the file
-        with session.get(url, stream=True, verify=True) as response:
-            makedir(output_path)
-            path = os.path.join(output_path, output_file_name)
-            total_size = int(response.headers.get("Content-length", 0))
-            with open(path, "wb") as file:
-                from tqdm import tqdm
-
-                with tqdm(total=total_size) as progress_bar:
-                    for block in response.iter_content(
-                        chunk_size=io.DEFAULT_BUFFER_SIZE
-                    ):
-                        file.write(block)
-                        progress_bar.update(len(block))
-
-
-def _get_google_drive_file_id(url: str) -> Optional[str]:
-    parts = urlparse(url)
-
-    if re.match(r"(drive|docs)[.]google[.]com", parts.netloc) is None:
-        return None
-
-    match = re.match(r"/file/d/(?P<id>[^/]*)", parts.path)
-    if match is None:
-        return None
-
-    return match.group("id")
-
-
-def _urlretrieve(url: str, filename: str, chunk_size: int = 1024) -> None:
-    with open(filename, "wb") as fh:
-        with urllib.request.urlopen(
-            urllib.request.Request(url, headers={"User-Agent": "vissl"})
-        ) as response:
-            with tqdm(total=response.length) as pbar:
-                for chunk in iter(lambda: response.read(chunk_size), ""):
-                    if not chunk:
-                        break
-                    pbar.update(chunk_size)
-                    fh.write(chunk)
-
-
-def download_url(
-    url: str,
-    root: str,
-    filename: Optional[str] = None,
-    md5: Optional[str] = None,
-) -> None:
-    """Download a file from a url and place it in root.
-    Args:
-        url (str): URL to download file from
-        root (str): Directory to place downloaded file in
-        filename (str, optional): Name to save the file under.
-                                  If None, use the basename of the URL.
-        md5 (str, optional): MD5 checksum of the download. If None, do not check
-    """
-    root = os.path.expanduser(root)
-    if not filename:
-        filename = os.path.basename(url)
-    fpath = os.path.join(root, filename)
-
-    makedir(root)
-
-    # check if file is already present locally
-    if check_integrity(fpath, md5):
-        print("Using downloaded and verified file: " + fpath)
-        return
-
-    # expand redirect chain if needed
-    url = get_redirected_url(url)
-
-    # check if file is located on Google Drive
-    file_id = _get_google_drive_file_id(url)
-    if file_id is not None:
-        return download_file_from_google_drive(file_id, root, filename, md5)
-
-    # download the file
-    try:
-        print("Downloading " + url + " to " + fpath)
-        _urlretrieve(url, fpath)
-    except (urllib.error.URLError, IOError) as e:  # type: ignore[attr-defined]
-        if url[:5] == "https":
-            url = url.replace("https:", "http:")
-            print(
-                "Failed download. Trying https -> http instead."
-                " Downloading " + url + " to " + fpath
-            )
-            _urlretrieve(url, fpath)
-        else:
-            raise e
-
-    # check integrity of downloaded file
-    if not check_integrity(fpath, md5):
-        raise RuntimeError("File not found or corrupted.")
-
-
-def download_and_extract_archive(
-    url: str,
-    download_root: str,
-    extract_root: Optional[str] = None,
-    filename: Optional[str] = None,
-    md5: Optional[str] = None,
-    remove_finished: bool = False,
-) -> None:
-    download_root = os.path.expanduser(download_root)
-    if extract_root is None:
-        extract_root = download_root
-    if not filename:
-        filename = os.path.basename(url)
-
-    download_url(url, download_root, filename, md5)
-
-    archive = os.path.join(download_root, filename)
-    print("Extracting {} to {}".format(archive, extract_root))
-    extract_archive(archive, extract_root, remove_finished)
-
-
-def cache_url(url: str, cache_dir: str) -> str:
-    """
-    This implementation downloads the remote resource and caches it locally.
-    The resource will only be downloaded if not previously requested.
-    """
-    parsed_url = urlparse(url)
-    dirname = os.path.join(cache_dir, os.path.dirname(parsed_url.path.lstrip("/")))
-    makedir(dirname)
-    filename = url.split("/")[-1]
-    cached = os.path.join(dirname, filename)
-    with file_lock(cached):
-        if not os.path.isfile(cached):
-            logging.info(f"Downloading {url} to {cached} ...")
-            cached = download(url, dirname, filename=filename)
-    logging.info(f"URL {url} cached in {cached}")
-    return cached
-
-
-# TODO (prigoyal): convert this into RAII-style API
-def create_file_symlink(file1, file2):
-    """
-    Simply create the symlinks for a given file1 to file2.
-    Useful during model checkpointing to symlinks to the
-    latest successful checkpoint.
-    """
-    try:
-        if g_pathmgr.exists(file2):
-            g_pathmgr.rm(file2)
-        g_pathmgr.symlink(file1, file2)
-    except Exception as e:
-        logging.info(f"Could NOT create symlink. Error: {e}")
-
-
-def save_file(data, filename, append_to_json=True, verbose=True):
-    """
-    Common i/o utility to handle saving data to various file formats.
-    Supported:
-        .pkl, .pickle, .npy, .json
-    Specifically for .json, users have the option to either append (default)
-    or rewrite by passing in Boolean value to append_to_json.
-    """
-    if verbose:
-        logging.info(f"Saving data to file: {filename}")
-    file_ext = os.path.splitext(filename)[1]
-    if file_ext in [".pkl", ".pickle"]:
-        with g_pathmgr.open(filename, "wb") as fopen:
-            pickle.dump(data, fopen, pickle.HIGHEST_PROTOCOL)
-    elif file_ext == ".npy":
-        with g_pathmgr.open(filename, "wb") as fopen:
-            np.save(fopen, data)
-    elif file_ext == ".json":
-        if append_to_json:
-            with g_pathmgr.open(filename, "a") as fopen:
-                fopen.write(json.dumps(data, sort_keys=True) + "\n")
-                fopen.flush()
-        else:
-            with g_pathmgr.open(filename, "w") as fopen:
-                fopen.write(json.dumps(data, sort_keys=True) + "\n")
-                fopen.flush()
-    elif file_ext == ".yaml":
-        with g_pathmgr.open(filename, "w") as fopen:
-            dump = yaml.dump(data)
-            fopen.write(dump)
-            fopen.flush()
-    else:
-        raise Exception(f"Saving {file_ext} is not supported yet")
-
-    if verbose:
-        logging.info(f"Saved data to file: {filename}")
-
-
-def load_file(filename, mmap_mode=None, verbose=True, allow_pickle=False):
-    """
-    Common i/o utility to handle loading data from various file formats.
-    Supported:
-        .pkl, .pickle, .npy, .json
-    For the npy files, we support reading the files in mmap_mode.
-    If the mmap_mode of reading is not successful, we load data without the
-    mmap_mode.
-    """
-    if verbose:
-        logging.info(f"Loading data from file: {filename}")
-
-    file_ext = os.path.splitext(filename)[1]
-    if file_ext == ".txt":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = fopen.readlines()
-    elif file_ext in [".pkl", ".pickle"]:
-        with g_pathmgr.open(filename, "rb") as fopen:
-            data = pickle.load(fopen, encoding="latin1")
-    elif file_ext == ".npy":
-        if mmap_mode:
-            try:
-                with g_pathmgr.open(filename, "rb") as fopen:
-                    data = np.load(
-                        fopen,
-                        allow_pickle=allow_pickle,
-                        encoding="latin1",
-                        mmap_mode=mmap_mode,
-                    )
-            except ValueError as e:
-                logging.info(
-                    f"Could not mmap {filename}: {e}. Trying without g_pathmgr"
-                )
-                data = np.load(
-                    filename,
-                    allow_pickle=allow_pickle,
-                    encoding="latin1",
-                    mmap_mode=mmap_mode,
-                )
-                logging.info("Successfully loaded without g_pathmgr")
-            except Exception:
-                logging.info("Could not mmap without g_pathmgr. Trying without mmap")
-                with g_pathmgr.open(filename, "rb") as fopen:
-                    data = np.load(fopen, allow_pickle=allow_pickle, encoding="latin1")
-        else:
-            with g_pathmgr.open(filename, "rb") as fopen:
-                data = np.load(fopen, allow_pickle=allow_pickle, encoding="latin1")
-    elif file_ext == ".json":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = json.load(fopen)
-    elif file_ext == ".yaml":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = yaml.load(fopen, Loader=yaml.FullLoader)
-    elif file_ext == ".csv":
-        with g_pathmgr.open(filename, "r") as fopen:
-            data = pd.read_csv(fopen)
-    else:
-        raise Exception(f"Reading from {file_ext} is not supported yet")
-    return data
-
-
-def abspath(resource_path: str):
-    """
-    Make a path absolute, but take into account prefixes like
-    "http://" or "manifold://"
-    """
-    regex = re.compile(r"^\w+://")
-    if regex.match(resource_path) is None:
-        return os.path.abspath(resource_path)
-    else:
-        return resource_path
-
-
-def makedir(dir_path):
-    """
-    Create the directory if it does not exist.
-    """
-    is_success = False
-    try:
-        if not g_pathmgr.exists(dir_path):
-            g_pathmgr.mkdirs(dir_path)
-        is_success = True
-    except BaseException:
-        logging.info(f"Error creating directory: {dir_path}")
-    return is_success
-
-
-def is_url(input_url):
-    """
-    Check if an input string is a url. look for http(s):// and ignoring the case
-    """
-    is_url = re.match(r"^(?:http)s?://", input_url, re.IGNORECASE) is not None
-    return is_url
-
-
-def cleanup_dir(dir):
-    """
-    Utility for deleting a directory. Useful for cleaning the storage space
-    that contains various training artifacts like checkpoints, data etc.
-    """
-    if os.path.exists(dir):
-        logging.info(f"Deleting directory: {dir}")
-        shutil.rmtree(dir)
-    logging.info(f"Deleted contents of directory: {dir}")
-
-
-def get_file_size(filename):
-    """
-    Given a file, get the size of file in MB
-    """
-    size_in_mb = os.path.getsize(filename) / float(1024**2)
-    return size_in_mb