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-<h1>Electrotechnique Industrielle Guy Seguier Pdf Download</h1>
-<p>Are you interested in learning more about industrial electrical engineering? Do you want to read a comprehensive and authoritative book on this subject? If so, you might want to download Electrotechnique Industrielle by Guy Seguier in PDF format. In this article, we will tell you what this book is about, who the author is, why it is important, and how you can get it for free. Let's get started!</p>
- <h2>What is Electrotechnique Industrielle?</h2>
-<p>Electrotechnique Industrielle is the French term for industrial electrical engineering. It is a branch of engineering that deals with the design, installation, operation, and maintenance of electrical systems and equipment used in industrial settings. Some of the topics covered by this field include:</p>
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-<ul>
-<li>Electrical machines and drives</li>
-<li>Power electronics and converters</li>
-<li>Electrical networks and distribution</li>
-<li>Control and automation</li>
-<li>Renewable energy sources</li>
-<li>Electromagnetic compatibility</li>
-</ul>
-<p>Industrial electrical engineering is essential for the development and improvement of various industries, such as manufacturing, transportation, communication, energy, and more. It also contributes to the safety, efficiency, and sustainability of industrial processes and products.</p>
- <h2>Who is Guy Seguier?</h2>
-<p>Guy Seguier was a French engineer and professor who specialized in industrial electrical engineering. He was born in 1925 and died in 2013. He obtained his engineering degree from the Ecole Centrale de Paris in 1948 and his doctorate from the University of Paris in 1956. He worked as a research engineer at the French National Center for Scientific Research (CNRS) from 1949 to 1964. He then became a professor at the Ecole Nationale Supérieure d'Electricité et de Mécanique (ENSEM) in Nancy, where he taught until his retirement in 1990. He also served as the director of the Laboratory of Electrical Engineering and Industrial Electronics (LGEP) from 1970 to 1985.</p>
-<p>Guy Seguier was a prolific author who wrote several books and articles on various aspects of industrial electrical engineering. He was also a respected expert who participated in many national and international committees and projects related to his field. He received several awards and honors for his contributions, such as the Grand Prix de l'Académie des Sciences in 1987 and the Legion of Honor in 1994.</p>
- <h2>Why is his book important?</h2>
-<p>One of his most famous books is Electrotechnique Industrielle, which he co-authored with Francis Notelet. This book was first published in 1977 by Technique et Documentation and has been revised and updated several times since then. The latest edition was published in 1994 by TEC et Doc and has 484 pages.</p>
-<p>This book is considered to be one of the most comprehensive and authoritative references on industrial electrical engineering. It covers all the fundamental concepts and principles, as well as the practical applications and examples, of this field. It also includes many diagrams, tables, formulas, exercises, and solutions to help the readers understand and apply the theory. The book is written in a clear and concise style that makes it accessible to both students and professionals.</p>
-<p>The book is divided into six parts:</p>
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-<ol>
-<li>Generalities: This part introduces the basic notions of electrical engineering, such as voltage, current, power, energy, resistance, capacitance, inductance, impedance, etc.</li>
-<li>Electrical machines: This part covers the different types of electrical machines used in industrial settings, such as transformers, generators, motors, alternators, etc.</li>
-<li>Power electronics: This part deals with the devices and circuits that convert and control electrical power, such as rectifiers, inverters, choppers, cycloconverters, etc.</li>
-<li>Electrical networks: This part explains how electrical power is transmitted and distributed through various types of networks, such as AC or DC networks, single-phase or three-phase networks, balanced or unbalanced networks, etc.</li>
-<li>Control and automation: This part describes how electrical systems are regulated and automated using various methods and tools, such as feedback control, PID control, state-space control, PLCs, SCADA systems, etc.</li>
-<li>Renewable energy sources: This part discusses how electrical power can be generated from renewable sources, such as solar energy, wind energy, hydroelectric energy, biomass energy, etc.</li>
-</ol>
- <h2>How to download his book in PDF format?</h2>
-<p>If you want to download Electrotechnique Industrielle by Guy Seguier in PDF format, you have several options:</p>
-<ul>
-<li>You can buy the book online from various websites, such as Amazon, Google Books, or AbeBooks, and then download it to your device.</li>
-<li>You can borrow the book from a library or a friend who has it, and then scan it or take photos of it with your smartphone or camera, and then convert them to PDF using an app or a website.</li>
-<li>You can search for a free PDF version of the book on the internet, but be careful about the quality, the legality, and the security of the sources you use. Some websites that claim to offer free PDF downloads may be fraudulent, infringing, or infected with malware.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>In conclusion, Electrotechnique Industrielle by Guy Seguier is a great book for anyone who wants to learn more about industrial electrical engineering. It covers all the essential topics, from theory to practice, in a clear and comprehensive way. It is suitable for both students and professionals who want to improve their knowledge and skills in this field. If you want to download this book in PDF format, you can either buy it online, borrow it from a library or a friend, or search for a free version on the internet. However, you should always be careful about the quality, the legality, and the security of the sources you use.</p>
- <h2>FAQs</h2>
-<ol>
-<li><b>What is industrial electrical engineering?</b><br/>
-Industrial electrical engineering is a branch of engineering that deals with the design, installation, operation, and maintenance of electrical systems and equipment used in industrial settings.</li>
- <li><b>Who is Guy Seguier?</b><br/>
-Guy Seguier was a French engineer and professor who specialized in industrial electrical engineering. He wrote several books and articles on this subject, including Electrotechnique Industrielle.</li>
- <li><b>Why is Electrotechnique Industrielle important?</b><br/>
-Electrotechnique Industrielle is one of the most comprehensive and authoritative references on industrial electrical engineering. It covers all the fundamental concepts and principles, as well as the practical applications and examples, of this field.</li>
- <li><b>How many pages does Electrotechnique Industrielle have?</b><br/>
-Electrotechnique Industrielle has 484 pages in its latest edition published in 1994 by TEC et Doc.</li>
- <li><b>How can I download Electrotechnique Industrielle in PDF format?</b><br/>
-You can download Electrotechnique Industrielle in PDF format by buying it online, borrowing it from a library or a friend, or searching for a free version on the internet. However, you should always be careful about the quality, the legality, and the security of the sources you use.</li>
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spaces/1gistliPinn/ChatGPT4/Examples/Cyberpunk delivers free upgrade to Xbox owners Everything you need to know.md

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-<p>It's worth noting that the Cyberpunk 2077 next-gen upgrade will be free if you already own the game on last-gen consoles. When originally confirming the Xbox Series X Cyberpunk 2077 upgrade, CD Projekt Red said (via Twitter (opens in new tab)) that "gamers should never be forced to purchase the same game twice or pay for upgrades," and we've seen nothing to indicate that's going to change.</p>
-<p>"Earlier in the year we announced that if you pick up Cyberpunk 2077 on Xbox One you'll be able to play it on Xbox Series X when the console launches," the stream states. "If you pick up Cyberpunk 2077 on PS4, you'll also be able to play it on PS5 when the console launches. And that's not all. There will be a free upgrade for Xbox Series X and PS5, but we'll have more details on that soon."</p>
-<p>CD Projekt Red announced via Twitter that it has an Xbox Series X upgrade of Cyberpunk 2077 in the works. It also said that when it's ready, gamers who already purchased the title for Xbox One will get it for free. "Gamers should never be forced to purchase the same game twice or pay for upgrades," the developer said. "Owners of Cyberpunk 2077 will receive the Xbox Series X upgrade for free when it is available."</p>
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-<p>"Xbox One players will be able to upgrade to the next-gen version of this completely original open-world survival adventure game for free. Xbox Series X users will be able to choose between 4K or Ray Tracing functions (Ray Tracing unavailable on Xbox Series S)."</p>
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-<p>Cue a lot of disgruntled customers that cannot access the shiny new version of the game on their new-gen consoles because they can't find the upgrade option on the PlayStation or Xbox storefronts in their region. For those affected, the upgrade is either locked or showing up a paid upgrade (when the new-gen versions should be free to anyone that already owns the game).</p>
-<p>For players on Xbox Series X|S and PlayStation 5, Patch 1.5 marks the introduction of a dedicated next-gen version of the game featuring enhancements like dynamic 4K scaling and ray-tracing features on Xbox Series X and PlayStation 5, faster loading times, and better reflections, among others. All of this, fueled by the extra power of next-gen hardware, is available to owners of the PlayStation 4 and Xbox One version of Cyberpunk 2077 via a free upgrade.</p>
-<p>Furthermore, this latest update also comes with new pieces of free additional content that expands what Cyberpunk 2077 has to offer gamers: rentable apartments featuring unique player character interactions, fresh weapons and gear, new customization options, and more.</p>
-<p>But what happens when developers release a game for the Xbox One X? Well, the Smart Delivery feature means you can enjoy games like Cyberpunk 2077 on the Xbox One X, as well as a free upgrade to the Xbox Series X. Whether you have a physical or digital copy of the game, all you need to do is launch it on your Xbox One or Series X|S console, and the best version will be installed for you. When the optimized version is released, the backward-compatible version will automatically be replaced.</p>
-<p>Tying in with the latest Xbox Series X details, Cyberpunk 2077 developer CD Projekt Red has confirmed that the game will be coming to next-gen systems -- in a way, at least. The gist of it is that if you buy Cyberpunk 2077 on Xbox One, you'll be able to upgrade the title for free on Xbox Series X. Based on the company's tweet, we assume that the same will apply to the PlayStation 4 version of the release once the PlayStation 5 hits later this year.</p>
-<p>"Gamers should never be forced to purchase the same game twice or pay for upgrades," writes the official Cyberpunk 2077 Twitter account. "Owners of #Cyberpunk2077 for Xbox One will receive the Xbox Series X upgrade for free when available."</p>
-<p>@3Above But you are comparing an upgrade from PS4 to PS5, to different versions on different platforms with the port being made by a different studio on the Switch. Of course Nintendo won't accept the game being given for free on their console since they didn't have a cut on other the platform's sale. Try to buy a game on Steam and ask GoG or epic store for a free key, I doubt it will work and this will have nothing to do with the developer.</p>
-<p>This is entirely different since it will be the first time a console with an eShop will be backword compatible. So this offers a whole new range of possibilities for developers, and CDPR is the very first studio who is talking about free upgrade across console generations</p>
-<p>CD Projekt Red has announced that gamers who own the Xbox One version of the highly-anticipated title <strong>Cyberpunk 2077</strong> will receive the Xbox Series X upgrade for free when it becomes available. You can check out the Twitter announcement below!</p>
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-<p>The Witcher 3: Wild Hunt was released on May 18, 2015, and has received two expansions. Players were immediately drawn in by the vast open world, topped with stunning visuals and exciting combat. The game lives on in 2022 as fans continue to make mods for The Witcher 3. These fun changes add replayability, by enhancing Geralt's combat capabilities or altering characters in various ways. The game reached a new audience when players got their hands on the Nintendo Switch release, in October 2019. Currently, CD Projekt Red has yet to give an exact date for the next-gen upgrade to The Witcher 3.</p>
-<p>The reason given for the new delay was that the decision was, "Based on recommendations supplied by teams supervising the development of both games." Most likely, CD Projekt Red does not want to repeat the disastrous launch of Cyberpunk 2077 and is making sure the upgrades are as polished as possible. Based on the details given for the new version, Witcher 3 fans will be able to experience the riveting open-world game like never before.</p>
-<p>Based on reports, the next-generation upgrade may feature enhancements from one notable modder who goes by the name Halk Hogan. In an article by Kotaku, they reported on Halk's announcement that his creation of The Witcher 3 HD Reworked Project may be officially implemented in the new release. CD Projekt Red has not yet confirmed this collaboration, but Witcher 3 has gone through many changes since its launch, and given that Halk already made major improvements to the graphics of the base game, a prolific modder officially working with the developers could make for the best overall upgrade. Whether the collaboration happens or not, players can expect to enjoy <strong>The Witcher 3</strong> at 60 FPS and 4K resolution for PC, Xbox Series X/S, and PlayStation 5 sometime in the second quarter of 2022.</p>
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-<p>While Microsoft is making aggressive moves to ensure buyers grab the upcoming Xbox Series X, Sony is sort of taking a "wait and see" approach with the PS5. This lackadaisical attitude is putting developer CD Projekt Red (the studio behind The Witcher and Cyberpunk 2077) in a bind as it can't confirm if its upcoming open-world RPG will be able to offer a free upgrade to PS5 users.</p>
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-"""A class that does not store any data. This is the default memory provider."""
-from __future__ import annotations
-
-from typing import Any
-
-from autogpt.memory.base import MemoryProviderSingleton
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-
-class NoMemory(MemoryProviderSingleton):
-    """
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-    """
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-    def __init__(self, cfg):
-        """
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-        return None
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-    def clear(self) -> str:
-        """
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-
-        Returns: An empty string.
-        """
-        return ""
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-    def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
-        """
-        Returns all the data in the memory that is relevant to the given data.
-        NoMemory always returns None.
-
-        Args:
-            data: The data to compare to.
-            num_relevant: The number of relevant data to return.
-
-        Returns: None
-        """
-        return None
-
-    def get_stats(self):
-        """
-        Returns: An empty dictionary as there are no stats in NoMemory.
-        """
-        return {}

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- <ul>
-<li>You can get unlimited coins and cash without spending real money.</li>
-<li>You can unlock all the cues and pool tables without waiting for levels or achievements.</li>
-<li>You can access all the game modes and features without any restrictions.</li>
-<li>You can play with any player from any country without any lag or connection issues.</li>
-<li>You can enjoy the game without any ads or pop-ups.</li>
-</ul>
- <p>8 Ball Pool Ultima Version APK is updated regularly to match the latest version of the original game, so you don't have to worry about missing out on any new content or updates. You can also play the game on any Android device, regardless of the model or specifications.</p>
- <h3>How to download and install the APK file on your device</h3>
- <p>Downloading and installing 8 Ball Pool Ultima Version APK is very easy and simple. Just follow these steps:</p>
- <ol>
-<li>Go to [this link] and click on the download button to get the APK file.</li>
-<li>Once the download is complete, go to your device settings and enable the option to install apps from unknown sources.</li>
-<li>Locate the APK file in your device storage and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game and enjoy playing 8 Ball Pool Ultima Version APK with unlimited coins and cash.</li>
-</ol>
- <h2>How to Play 8 Ball Pool Ultima Version APK?</h2>
- <h3>A step-by-step guide on how to start a game and choose a table</h3>
- <p>Playing 8 Ball Pool Ultima Version APK is very similar to playing the original game. Here is how you can start a game and choose a table:</p>
- <ol>
-<li>Open the game and sign in with your Facebook account or Miniclip ID. You can also play as a guest if you don't have an account.</li>
-<li>Select the game mode you want to play. You can choose from 1-on-1 matches, tournaments, or practice mode.</li>
-<li>Select the table you want to play on. You can choose from different locations, such as London, Sydney, Moscow, Tokyo, Las Vegas, etc. Each location has a different entry fee and prize pool.</li>
-<li>Select your cue and pool table from the shop. You can use coins or cash to buy different cues and tables with different attributes, such as power, aim, spin, time, etc.</li>
-<li>Tap on the play button and wait for an opponent to join. You can also invite your friends to play with you by tapping on the friends button.</li>
-</ol>
- <h3>Some tips and tricks to improve your skills and win more coins</h3>
- <p>If you want to become a better player and win more coins in 8 Ball Pool Ultima Version APK, here are some tips and tricks that you should keep in mind:</p>
- <ul>
-<li>Use the guideline to aim your shots. The guideline shows you where your cue ball will hit the object ball and where it will go after that. You can adjust the angle and power of your shot by dragging your finger on the screen.</li>
-<li>Use spin to control your cue ball. Spin allows you to change the direction and speed of your cue ball after it hits another ball. You can apply spin by tapping on the cue ball icon on the bottom right corner of the screen and moving it around.</li>
-<li>Plan your shots ahead. Don't just hit any ball that you see. Think about which ball you want to hit next and where you want your cue ball to end up. Try to clear your group of balls as soon as possible and leave yourself an easy shot for the 8 ball.</li>
-<li>Avoid fouls and scratches. Fouls and scratches give your opponent a free ball in hand, which means they can place their cue ball anywhere on the table. This gives them a huge advantage over you. To avoid fouls and scratches, make sure you hit your assigned ball first, don't pocket the cue ball or the 8 ball prematurely, and don't hit any other balls off the table.</li>
-<li>Practice regularly. The best way to improve your skills is to practice as much as you can. Play against different opponents, try different cues and tables, and learn from your mistakes. You can also watch replays of your matches or other players' matches to see what they did right or wrong.</li>
-</ul>
- <h2>Why You Should Play 8 Ball Pool Ultima Version APK?</h2>
- <h3>A list of the advantages of playing this game for your mental and physical health</h3>
- <p>Playing 8 Ball Pool Ultima Version APK is not only fun but also beneficial for your mental and physical health. Here are some of the advantages of playing this game:</p>
- <ul>
-<li>It improves your concentration and focus. Playing pool requires you to pay attention to the details, such as the angle, power, spin, and position of your shots. This helps you to sharpen your concentration and focus skills, which can benefit you in other aspects of life, such as work, study, or driving.</li>
-<li>It enhances your hand-eye coordination and motor skills. Playing pool involves using your hands, eyes, and brain to coordinate your movements and aim your shots. This helps you to improve your hand-eye coordination and motor skills, which can improve your physical performance and prevent injuries.</li>
-<li>It reduces your stress and anxiety. Playing pool is a great way to relax and have fun with your friends or strangers. You can chat, laugh, and compete with them, which can boost your mood and reduce your stress and anxiety levels. Playing pool can also distract you from your worries and problems, and help you to cope with negative emotions.</li>
-<li>It stimulates your brain and memory. Playing pool requires you to think strategically and creatively, as well as remember the rules and the score. This helps you to stimulate your brain and memory functions, which can prevent cognitive decline and dementia in the long run.</li>
-<li>It increases your social skills and confidence. Playing pool allows you to meet new people and make new friends from different backgrounds and cultures. You can also learn from them and share your experiences with them, which can increase your social skills and confidence. Playing pool can also help you to overcome shyness and social anxiety, as well as improve your communication and teamwork skills.</li>
-</ul>
- <h3>A table comparing 8 Ball Pool Ultima Version APK with other pool games</h3>
- <table>
-<tr>
-<th>Features</th>
-<th>8 Ball Pool Ultima Version APK</th>
-<th>Other Pool Games</th>
-</tr>
-<tr>
-<td>Coins and Cash</td>
-<td>Unlimited</td>
-<td>Limited</td>
-</tr>
-<tr>
-<td>Cues and Tables</td>
-<td>All Unlocked</td>
-<td>Some Locked</td>
-</tr>
-<tr>
-<td>Game Modes and Features</td>
-<td>All Accessible</td>
-<td>Some Restricted</td>
-</tr>
-<tr>
-<td>Players and Locations</td>
-<td>All Available</td>
-<td>Some Unavailable</td>
-</tr>
-<tr>
-<td>Ads and Pop-ups</td>
-<td>None</td>
-<td>Some</td>
-</tr>
-<tr>
-<td>Updates and Content</td>
-<td>Frequent</td>
-<td>Infrequent</td>
-</tr>
-<tr>
-<td>Compatibility and Performance</td>
-<td>High</td>
-<td>Low</td>
-</tr>
- <h2>Conclusion</h2>
- <p>In conclusion, 8 Ball Pool Ultima Version APK is a fantastic game that you should definitely try if you love pool games. It offers you unlimited coins and cash, all cues and tables unlocked, all game modes and features accessible, all players and locations available, no ads or pop-ups, frequent updates and content, high compatibility and performance, and many more benefits. It also improves your concentration, focus, hand-eye coordination, motor skills, stress relief, brain stimulation, memory function, social skills, confidence, etc. So what are you waiting for? Download 8 Ball Pool Ultima Version APK today and enjoy playing the best pool game ever!</p>
- <h2>FAQs</h2>
- <h3>Q1: Is 8 Ball Pool Ultima Version APK safe to download?</h3>
- <p>A1: Yes, 8 Ball Pool Ultima Version APK is safe to download. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a trusted source like [this link] to avoid any fake or corrupted files.</p>
- <h3>Q2: Can I play 8 Ball Pool Ultima Version APK offline?</h3>
- <p>A2: No, 8 Ball Pool Ultima Version APK is an online game that requires an internet connection to play. You cannot play it offline or without wifi. However, you can play it on any network speed or quality without any lag or connection issues.</p>
- <h3>Q3: How can I customize my cue and pool table in 8 Ball Pool Ultima Version APK?</h3>
- <p>A3: You can customize your cue and pool table in 8 Ball Pool Ultima Version APK by going to the shop section of the game. There you can find a variety of cues and tables with different designs, colors, patterns, attributes, etc. You can buy them with coins or cash that you have unlimited in this version of the game. You can also change your cue or table anytime during the game by tapping on the gear icon on the top right corner of the screen.</p>
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- <p>A4: You can challenge your friends in 8 Ball Pool Ultima Version APK by tapping on the friends button on the bottom left corner of the screen. There you can see a list of your Facebook friends or Miniclip friends who are online or offline. You can also search for a friend by their name or ID. To challenge a friend, just tap on their name and select the table you want to play on. You can also chat with them before or during the game by tapping on the chat button on the top left corner of the screen.</p>
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- <p>A5: You don't need to worry about getting more coins and cash in 8 Ball Pool Ultima Version APK because you have unlimited amounts of them in this version of the game. You can use them to buy anything you want from the shop, play any game mode or feature, or enter any tournament. However, if you want to earn more coins and cash in the original game, you can do so by winning matches, spinning the wheel, playing mini-games, watching videos, completing offers, or inviting friends.</p> 197e85843d<br />
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-<h1>Disney's Aladdin 1994 Video Game APK: A Retro Classic on Your Smartphone</h1>
- <h2>Introduction</h2>
- <p>If you grew up in the 90s, chances are you have fond memories of playing Disney's Aladdin video game on your Sega Genesis, Game Gear, or Master System. Based on the animated film of the same name, this side-scrolling platformer was one of the best-selling and most acclaimed games of its time. It featured stunning graphics, catchy music, and addictive gameplay that captured the magic and adventure of the movie.</p>
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- <h2>Features of Disney's Aladdin 1994 video game</h2>
- <h3>Gameplay and controls</h3>
- <p>Disney's Aladdin 1994 video game is a side-scrolling platformer in which you control Aladdin, the street-smart hero who falls in love with Princess Jasmine. You have to navigate through various levels inspired by the movie, such as the streets of Agrabah, the Cave of Wonders, and the Sultan's Palace. Along the way, you have to avoid enemies and obstacles, collect gems and apples, and use your scimitar and throwing skills to defeat foes.</p>
- <p>The game has two difficulty settings: normal and hard. The normal mode has six levels, while the hard mode has seven levels. The hard mode also has more enemies, traps, and hazards. The game also has a password system that allows you to resume your progress from any level.</p>
- <p>The controls are simple and intuitive. You can use the virtual buttons on the screen or tilt your device to move Aladdin left or right. You can also swipe up or down to jump or crouch. To attack with your scimitar, tap the sword button. To throw an apple, tap the apple button. You can also use the magic lamp button to summon Genie for help in certain situations.</p>
- <h3>Graphics and sound</h3>
- <p>One of the most impressive aspects of Disney's Aladdin 1994 video game is its graphics. The game features colorful and detailed sprites and backgrounds that faithfully recreate the look and feel of the movie. The animations are smooth and fluid, and the characters have expressive facial expressions. The game also has some cinematic cutscenes that tell the story between levels.</p>
- <p>The sound is equally impressive. The game features a high-quality soundtrack that includes songs from the movie, such as "A Whole New World", "Prince Ali", and "Friend Like Me". The sound effects are also realistic and immersive, such as the clashing of swords, the roaring of tigers, and the cheering of crowds.</p>
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- <h3>Levels and challenges</h3>
- <p>Disney's Aladdin 1994 video game has a variety of levels that offer different challenges and surprises. Some of the levels are:</p>
- <ul>
-<li>Agrabah Market: The first level where you have to escape from the guards and meet Jasmine.</li>
-<li>The Desert: The second level where you have to ride a magic carpet through a sandstorm.</li>
-<p>escape from a lava-filled chamber.</li>
-<li>The Escape: The fourth level where you have to fly on a magic carpet and dodge falling rocks and lava.</li>
-<li>Rooftops: The fifth level where you have to climb and jump across the rooftops of Agrabah and fight Jafar's minions.</li>
-<li>Sultan's Dungeon: The sixth level where you have to rescue Abu from the dungeon and fight Iago, Jafar's parrot.</li>
-<li>Jafar's Palace: The seventh and final level where you have to confront Jafar in his palace and defeat him in his snake form.</li>
-</ul>
- <p>Each level has its own challenges and secrets, such as hidden items, bonus stages, and mini-games. For example, in the Cave of Wonders, you can find a magic flute that lets you play a snake-charming mini-game. In the Escape, you can find a magic carpet that lets you play a flying mini-game. In the Rooftops, you can find a scarab that lets you enter a bonus stage where you can collect extra lives and gems.</p>
- <h3>Bonus content and secrets</h3>
- <p>Disney's Aladdin 1994 video game also has some bonus content and secrets that add more fun and replay value to the game. Some of them are:</p>
- <ul>
-<li>Cheat codes: You can enter some cheat codes to unlock different features, such as invincibility, infinite lives, infinite apples, level select, and debug mode.</li>
-<li>Easter eggs: You can find some Easter eggs that reference other Disney movies, such as The Lion King, The Little Mermaid, and Beauty and the Beast.</li>
-<li>Alternate endings: You can get different endings depending on how many gems you collect throughout the game. The best ending is achieved by collecting 70 gems or more.</li>
-</ul>
- <h2>How to download and install Disney's Aladdin 1994 video game APK</h2>
- <h3>Requirements and compatibility</h3>
- <p>To download and install Disney's Aladdin 1994 video game APK, you need to have an Android or iOS device that meets the following requirements:</p>
- <table>
-<tr>
-<th>Operating system</th>
-<th>Version</th>
-</tr>
-<tr>
-<td>Android</td>
-<td>4.0 or higher</td>
-</tr>
-<tr>
-<td>iOS</td>
-<td>8.0 or higher</td>
-</tr>
-</table>
- <p>You also need to have enough storage space on your device to install the APK file, which is about 50 MB in size.</p>
- <h3>Steps to download and install</h3>
- <p>To download and install Disney's Aladdin 1994 video game APK, follow these steps:</p>
- <ol>
-<li>Go to the official website of Disney's Aladdin 1994 video game APK (link here) and click on the download button.</li>
-<li>Wait for the download to finish and locate the APK file on your device.</li>
-<li>If you are using an Android device, you need to enable the installation of apps from unknown sources. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>If you are using an iOS device, you need to trust the developer of the app. To do this, go to Settings > General > Device Management > Trust Developer Name and tap on Trust.</li>
-<li>Tap on the APK file and follow the instructions to install it on your device.</li>
-<li>Launch the app and enjoy playing Disney's Aladdin 1994 video game on your smartphone.</li>
-</ol>
- <h3>Tips and tricks to optimize your experience</h3>
- <p>To optimize your experience while playing Disney's Aladdin 1994 video game on your smartphone, here are some tips and tricks:</p>
- <ul>
-<li>Adjust the settings according to your preference. You can change the sound volume, screen size, language, and controller layout in the options menu.</li>
-<li>Save your progress frequently. You can use the password system or the save state feature to save your progress at any point in the game.</li>
-<li>Use Genie wisely. You can use Genie to help you in certain situations, such as finding hidden items, skipping levels, or getting extra lives. However, you can only use Genie once per level, so use him wisely.</li>
-<li>Collect as many gems as possible. Gems are useful for unlocking bonus content, getting alternate endings, and buying items from Peddler shops.</li>
-<li>Explore every level thoroughly. There are many secrets and hidden areas in each level that can reward you with extra items, bonus stages, or Easter eggs.</li>
-</ul>
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-<h1>Sniper 3D Mod APK Unlimited Money and Diamonds 2021: A Review</h1>
-<p>If you are a fan of shooting games and want to experience the thrill of being a professional sniper, then you should try <strong>Sniper 3D Mod APK</strong>. This is a modified version of the popular game <em>Sniper 3D</em>, which gives you access to unlimited money and diamonds, as well as all the weapons and upgrades in the game. In this article, we will review the features, benefits, and drawbacks of Sniper 3D Mod APK, as well as provide some tips and tricks on how to play it.</p>
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-<p><em>Sniper 3D</em> is a free-to-play action game developed by Fun Games For Free. It is available for Android and iOS devices, as well as Windows and Mac computers. The game puts you in the role of a deadly assassin who has to complete various missions around the world. You can choose from over 180+ authentic weapons, customize them with different attachments, and upgrade them to improve their performance. You can also play in different modes, such as offline missions, online PvP battles, squad wars, and special ops.</p>
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- <h3>Aim for headshots and moving targets</h3>
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-<p>A third tip for Sniper 3D Mod APK is to use the environment to your advantage. The game features various locations with different elements that can help or hinder your shooting. For example, you can use buildings, cars, barrels, crates, and other objects as cover or distractions. You can also shoot explosive objects to cause chain reactions and eliminate multiple enemies at once. You can also shoot glass windows, lights, cameras, alarms, and other devices to create noise or confusion. Be creative and observant when using the environment.</p>
- <h2>Conclusion</h2>
-<p>Sniper 3D Mod APK is a great game for anyone who loves shooting games and wants to experience the thrill of being a professional sniper. It offers unlimited money and diamonds, as well as all the weapons and upgrades in the game. It also allows you to enjoy both offline and online modes of the game. However, it also requires skill, patience, precision, and strategy to complete various missions and challenges. If you follow our tips and tricks, you will be able to improve your gameplay and become a master shooter.</p>
- <h <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Sniper 3D Mod APK:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Sniper 3D Mod APK safe to use?</td>
-<td>Sniper 3D Mod APK is generally safe to use, as long as you download it from a trusted website and scan it with an antivirus program. However, you should be aware that using a modded version of the game may violate the terms and conditions of the original game, and may result in your account being banned or suspended. Therefore, you should use Sniper 3D Mod APK at your own risk.</td>
-</tr>
-<tr>
-<td>Can I play Sniper 3D Mod APK with my friends?</td>
-<td>Yes, you can play Sniper 3D Mod APK with your friends, as long as they also have the same version of the game installed on their devices. You can join online PvP battles and squad wars with your friends, or compete against them in leaderboards and rankings. You can also chat with them in the game and share your achievements and tips.</td>
-</tr>
-<tr>
-<td>What are the minimum requirements for Sniper 3D Mod APK?</td>
-<td>The minimum requirements for Sniper 3D Mod APK are:<ul><li>Android version: 4.4 or higher</li><li>RAM: 2 GB or more</li><li>Storage: 100 MB or more</li><li>Internet connection: required for online modes</li></ul></td>
-</tr>
-<tr>
-<td>How can I update Sniper 3D Mod APK?</td>
-<td>To update Sniper 3D Mod APK, you will need to download and install the latest version of the game from the same website that you downloaded it from. You may also need to uninstall the previous version of the game before installing the new one. However, you should be careful when updating Sniper 3D Mod APK, as some updates may not be compatible with the modded version of the game, and may cause errors or crashes.</td>
-</tr>
-<tr>
-<td>Where can I get more information about Sniper 3D Mod APK?</td>
-<td>If you want to get more information about Sniper 3D Mod APK, you can visit the official website of the original game at [Sniper 3D], or follow their social media accounts on [Facebook], [Twitter], [Instagram], and [YouTube]. You can also check out some online forums and blogs that discuss Sniper 3D Mod APK, such as [Reddit] and [Quora].</td>
-</tr>
-</table></p> 197e85843d<br />
-<br />
-<br />

spaces/4Taps/SadTalker/modules/gfpgan_inference.py

@@ -1,36 +0,0 @@
-import os,sys
-
-def gfpgan(scale, origin_mp4_path):
-    current_code_path = sys.argv[0]
-    current_root_path = os.path.split(current_code_path)[0]
-    print(current_root_path)
-    gfpgan_code_path = current_root_path+'/repositories/GFPGAN/inference_gfpgan.py'
-    print(gfpgan_code_path)
-    
-    #video2pic
-    result_dir = os.path.split(origin_mp4_path)[0]
-    video_name = os.path.split(origin_mp4_path)[1]
-    video_name = video_name.split('.')[0]
-    print(video_name)
-    str_scale = str(scale).replace('.', '_')
-    output_mp4_path = os.path.join(result_dir, video_name+'##'+str_scale+'.mp4')
-    temp_output_mp4_path = os.path.join(result_dir, 'temp_'+video_name+'##'+str_scale+'.mp4')
-
-    audio_name = video_name.split('##')[-1]
-    audio_path = os.path.join(result_dir, audio_name+'.wav')
-    temp_pic_dir1 = os.path.join(result_dir, video_name)
-    temp_pic_dir2 = os.path.join(result_dir, video_name+'##'+str_scale)
-    os.makedirs(temp_pic_dir1, exist_ok=True)
-    os.makedirs(temp_pic_dir2, exist_ok=True)
-    cmd1 = 'ffmpeg -i \"{}\" -start_number 0  \"{}\"/%06d.png -loglevel error -y'.format(origin_mp4_path, temp_pic_dir1)
-    os.system(cmd1)
-    cmd2 = f'python {gfpgan_code_path} -i {temp_pic_dir1} -o {temp_pic_dir2} -s {scale}'
-    os.system(cmd2)
-    cmd3 = f'ffmpeg -r 25 -f image2 -i {temp_pic_dir2}/%06d.png  -vcodec libx264 -crf 25  -pix_fmt yuv420p {temp_output_mp4_path}'
-    os.system(cmd3)
-    cmd4 = f'ffmpeg -y -i {temp_output_mp4_path}  -i {audio_path}  -vcodec copy {output_mp4_path}'
-    os.system(cmd4)
-    #shutil.rmtree(temp_pic_dir1)
-    #shutil.rmtree(temp_pic_dir2)
-
-    return output_mp4_path

spaces/801artistry/RVC801/demucs/__init__.py

@@ -1,7 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-__version__ = "2.0.3"

spaces/A00001/bingothoo/src/lib/bots/bing/tts.ts

@@ -1,82 +0,0 @@
-import { sleep } from './utils'
-
-const synth = window.speechSynthesis
-
-export class TTS {
-  currentText = ''
-  speakText = ''
-  private controller = new AbortController()
-  speaking = false
-  get isSpeaking() {
-    return this.speaking
-  }
-  finished = false
-  constructor() {}
-  abort = () => {
-    this.controller.abort()
-  }
-
-  reset = () => {
-    this.speaking = false
-    this.finished = true
-    this.currentText = ''
-    this.speakText = ''
-    this.abort()
-  }
-
-  speak = (text: string) => {
-    if (!synth || text?.trim()?.length < 2) {
-      return
-    }
-    this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '')
-    this.finished = false
-    this.loop()
-  }
-
-  private async doSpeek() {
-    return new Promise((resolve) => {
-      const endIndex = this.finished ? this.currentText.length :
-        Math.max(
-          this.currentText.lastIndexOf('。'),
-          this.currentText.lastIndexOf('；'),
-          this.currentText.lastIndexOf('、'),
-          this.currentText.lastIndexOf('？'),
-          this.currentText.lastIndexOf('\n')
-        )
-      const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0
-
-      if (startIndex >= endIndex) {
-        return resolve(true)
-      }
-      const text = this.currentText.slice(startIndex, endIndex)
-      this.speakText = text
-      const utterThis = new SpeechSynthesisUtterance(text)
-      this.controller.signal.onabort = () => {
-        synth.cancel()
-        this.finished = true
-        resolve(false)
-      }
-
-      utterThis.onend = function (event) {
-        resolve(true)
-      }
-
-      utterThis.onerror = function (event) {
-        resolve(false)
-      }
-
-      const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null
-      utterThis.voice = voice
-      synth.speak(utterThis)
-    })
-  }
-
-  private async loop() {
-    if (this.speaking) return
-    this.speaking = true
-    while(!this.finished) {
-      await Promise.all([sleep(1000), this.doSpeek()])
-    }
-    this.speaking = false
-  }
-}

spaces/AIConsultant/MusicGen/tests/data/__init__.py

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

spaces/AIGC-Audio/AudioGPT/NeuralSeq/configs/tts/emotion/pre_align.py

@@ -1,25 +0,0 @@
-import os
-
-from data_gen.tts.base_preprocess import BasePreprocessor
-import glob
-import re
-
-class EmoPreAlign(BasePreprocessor):
-
-    def meta_data(self):
-        spks = ['0012', '0011', '0013', '0014', '0015', '0016', '0017', '0018', '0019', '0020']
-        pattern = re.compile('[\t\n ]+')
-        for spk in spks:
-            for line in open(f"{self.raw_data_dir}/{spk}/{spk}.txt", 'r'):  # 打开文件
-                line = re.sub(pattern, ' ', line)
-                if line == ' ': continue
-                split_ = line.split(' ')
-                txt = ' '.join(split_[1: -2])
-                item_name = split_[0]
-                emotion = split_[-2]
-                wav_fn = f'{self.raw_data_dir}/{spk}/{emotion}/{item_name}.wav'
-                yield item_name, wav_fn, txt, spk, emotion
-
-
-if __name__ == "__main__":
-    EmoPreAlign().process()

spaces/AIGC-Audio/AudioGPT/audio_detection/target_sound_detection/src/utils.py

@@ -1,353 +0,0 @@
-# !/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Time    : 2021/3/9 16:33
-# @Author  : dongchao yang
-# @File    : train.py
-
-import collections
-import sys
-from loguru import logger
-from pprint import pformat
-
-import numpy as np
-import pandas as pd
-import scipy
-import six
-import sklearn.preprocessing as pre
-import torch
-import tqdm
-import yaml
-
-from scipy.interpolate import interp1d
-
-def parse_config_or_kwargs(config_file, **kwargs):
-    """parse_config_or_kwargs
-    :param config_file: Config file that has parameters, yaml format
-    :param **kwargs: Other alternative parameters or overwrites for config
-    """
-    with open(config_file) as con_read:
-        yaml_config = yaml.load(con_read, Loader=yaml.FullLoader)
-    arguments = dict(yaml_config, **kwargs)
-    return arguments
-
-
-def find_contiguous_regions(activity_array): # in this part, if you cannot understand the binary operation, I think you can write a O(n) complexity method
-    """Find contiguous regions from bool valued numpy.array.
-    Copy of https://dcase-repo.github.io/dcase_util/_modules/dcase_util/data/decisions.html#DecisionEncoder
-    Reason is:
-    1. This does not belong to a class necessarily
-    2. Import DecisionEncoder requires sndfile over some other imports..which causes some problems on clusters
-    """
-    change_indices = np.logical_xor(activity_array[1:], activity_array[:-1]).nonzero()[0] 
-    change_indices += 1
-    if activity_array[0]:
-        # If the first element of activity_array is True add 0 at the beginning
-        change_indices = np.r_[0, change_indices]
-
-    if activity_array[-1]:
-        # If the last element of activity_array is True, add the length of the array
-        change_indices = np.r_[change_indices, activity_array.size]
-    # print(change_indices.reshape((-1, 2)))
-    # Reshape the result into two columns
-    return change_indices.reshape((-1, 2))
-
-
-def split_train_cv(
-        data_frame: pd.DataFrame,
-        frac: float = 0.9,
-        y=None,  # Only for stratified, computes necessary split
-        **kwargs):
-    """split_train_cv
-
-    :param data_frame:
-    :type data_frame: pd.DataFrame
-    :param frac:
-    :type frac: float
-    """
-    if kwargs.get('mode',
-                  None) == 'urbansed':  # Filenames are DATA_-1 DATA_-2 etc
-        data_frame.loc[:, 'id'] = data_frame.groupby(
-            data_frame['filename'].str.split('_').apply(
-                lambda x: '_'.join(x[:-1]))).ngroup()
-        sampler = np.random.permutation(data_frame['id'].nunique())
-        num_train = int(frac * len(sampler))
-        train_indexes = sampler[:num_train]
-        cv_indexes = sampler[num_train:]
-        train_data = data_frame[data_frame['id'].isin(train_indexes)]
-        cv_data = data_frame[data_frame['id'].isin(cv_indexes)]
-        del train_data['id']
-        del cv_data['id']
-    elif kwargs.get('mode', None) == 'stratified': #  stratified --> 分层的 ?
-        # Use statified sampling
-        from skmultilearn.model_selection import iterative_train_test_split
-        index_train, _, index_cv, _ = iterative_train_test_split(
-            data_frame.index.values.reshape(-1, 1), y, test_size=1. - frac)
-        train_data = data_frame[data_frame.index.isin(index_train.squeeze())]
-        cv_data = data_frame[data_frame.index.isin(index_cv.squeeze())] # cv --> cross validation
-    else:
-        # Simply split train_test
-        train_data = data_frame.sample(frac=frac, random_state=10)
-        cv_data = data_frame[~data_frame.index.isin(train_data.index)]
-    return train_data, cv_data
-
-
-
-def pprint_dict(in_dict, outputfun=sys.stdout.write, formatter='yaml'): # print yaml file
-    """pprint_dict
-    :param outputfun: function to use, defaults to sys.stdout
-    :param in_dict: dict to print
-    """
-    if formatter == 'yaml':
-        format_fun = yaml.dump
-    elif formatter == 'pretty':
-        format_fun = pformat
-    for line in format_fun(in_dict).split('\n'):
-        outputfun(line)
-
-
-def getfile_outlogger(outputfile):
-    log_format = "[<green>{time:YYYY-MM-DD HH:mm:ss}</green>] {message}"
-    logger.configure(handlers=[{"sink": sys.stderr, "format": log_format}])
-    if outputfile:
-        logger.add(outputfile, enqueue=True, format=log_format)
-    return logger
-
-# according label, get encoder
-def train_labelencoder(labels: pd.Series, sparse=True):
-    """encode_labels
-
-    Encodes labels
-
-    :param labels: pd.Series representing the raw labels e.g., Speech, Water
-    :param encoder (optional): Encoder already fitted 
-    returns encoded labels (many hot) and the encoder
-    """
-    assert isinstance(labels, pd.Series), "Labels need to be series"
-    if isinstance(labels[0], six.string_types):
-        # In case of using non processed strings, e.g., Vaccum, Speech
-        label_array = labels.str.split(',').values.tolist() # split label according to ','
-    elif isinstance(labels[0], np.ndarray):
-        # Encoder does not like to see numpy array
-        label_array = [lab.tolist() for lab in labels]
-    elif isinstance(labels[0], collections.Iterable):
-        label_array = labels
-    encoder = pre.MultiLabelBinarizer(sparse_output=sparse)
-    encoder.fit(label_array)
-    return encoder
-
-
-def encode_labels(labels: pd.Series, encoder=None, sparse=True):
-    """encode_labels
-
-    Encodes labels
-
-    :param labels: pd.Series representing the raw labels e.g., Speech, Water
-    :param encoder (optional): Encoder already fitted 
-    returns encoded labels (many hot) and the encoder
-    """
-    assert isinstance(labels, pd.Series), "Labels need to be series"
-    instance = labels.iloc[0]
-    if isinstance(instance, six.string_types):
-        # In case of using non processed strings, e.g., Vaccum, Speech
-        label_array = labels.str.split(',').values.tolist()
-    elif isinstance(instance, np.ndarray):
-        # Encoder does not like to see numpy array
-        label_array = [lab.tolist() for lab in labels]
-    elif isinstance(instance, collections.Iterable):
-        label_array = labels
-    # get label_array, it is a list ,contain a lot of label, this label are string type
-    if not encoder:
-        encoder = pre.MultiLabelBinarizer(sparse_output=sparse) # if we encoder is None, we should init a encoder firstly.
-        encoder.fit(label_array)
-    labels_encoded = encoder.transform(label_array) # transform string to digit
-    return labels_encoded, encoder
-
-    # return pd.arrays.SparseArray(
-    # [row.toarray().ravel() for row in labels_encoded]), encoder
-
-
-def decode_with_timestamps(events,labels: np.array):
-    """decode_with_timestamps
-    Decodes the predicted label array (2d) into a list of
-    [(Labelname, onset, offset), ...]
-
-    :param encoder: Encoder during training
-    :type encoder: pre.MultiLabelBinarizer
-    :param labels: n-dim array
-    :type labels: np.array
-    """
-    # print('events ',events)
-    # print('labels ',labels.shape)
-    #assert 1==2
-    if labels.ndim == 2:
-        #print('...')
-        return [_decode_with_timestamps(events[i],labels[i]) for i in range(labels.shape[0])]
-    else:
-        return _decode_with_timestamps(events,labels)
-
-
-def median_filter(x, window_size, threshold=0.5):
-    """median_filter
-    :param x: input prediction array of shape (B, T, C) or (B, T).
-        Input is a sequence of probabilities 0 <= x <= 1
-    :param window_size: An integer to use 
-    :param threshold: Binary thresholding threshold
-    """
-    x = binarize(x, threshold=threshold) # transfer to 0 or 1
-    if x.ndim == 3:
-        size = (1, window_size, 1)
-    elif x.ndim == 2 and x.shape[0] == 1:
-        # Assume input is class-specific median filtering
-        # E.g, Batch x Time  [1, 501]
-        size = (1, window_size)
-    elif x.ndim == 2 and x.shape[0] > 1:
-        # Assume input is standard median pooling, class-independent
-        # E.g., Time x Class [501, 10]
-        size = (window_size, 1)
-    return scipy.ndimage.median_filter(x, size=size)
-
-
-def _decode_with_timestamps(events,labels):
-    result_labels = []
-    # print('.......')
-    # print('labels ',labels.shape)
-    # print(labels)
-    change_indices = find_contiguous_regions(labels)
-    # print(change_indices)
-    # assert 1==2
-    for row in change_indices:
-        result_labels.append((events,row[0], row[1]))
-    return result_labels
-
-def inverse_transform_labels(encoder, pred):
-    if pred.ndim == 3:
-        return [encoder.inverse_transform(x) for x in pred]
-    else:
-        return encoder.inverse_transform(pred)
-
-
-def binarize(pred, threshold=0.5):
-    # Batch_wise
-    if pred.ndim == 3:
-        return np.array(
-            [pre.binarize(sub, threshold=threshold) for sub in pred])
-    else:
-        return pre.binarize(pred, threshold=threshold)
-
-
-def double_threshold(x, high_thres, low_thres, n_connect=1):
-    """double_threshold
-    Helper function to calculate double threshold for n-dim arrays
-
-    :param x: input array
-    :param high_thres: high threshold value
-    :param low_thres: Low threshold value
-    :param n_connect: Distance of <= n clusters will be merged
-    """
-    assert x.ndim <= 3, "Whoops something went wrong with the input ({}), check if its <= 3 dims".format(
-        x.shape)
-    if x.ndim == 3:
-        apply_dim = 1
-    elif x.ndim < 3:
-        apply_dim = 0
-    # x is assumed to be 3d: (batch, time, dim)
-    # Assumed to be 2d : (time, dim)
-    # Assumed to be 1d : (time)
-    # time axis is therefore at 1 for 3d and 0 for 2d (
-    return np.apply_along_axis(lambda x: _double_threshold(
-        x, high_thres, low_thres, n_connect=n_connect),
-                               axis=apply_dim,
-                               arr=x)
-
-
-def _double_threshold(x, high_thres, low_thres, n_connect=1, return_arr=True): # in nature, double_threshold considers boundary question
-    """_double_threshold
-    Computes a double threshold over the input array
-
-    :param x: input array, needs to be 1d
-    :param high_thres: High threshold over the array
-    :param low_thres: Low threshold over the array
-    :param n_connect: Postprocessing, maximal distance between clusters to connect
-    :param return_arr: By default this function returns the filtered indiced, but if return_arr = True it returns an array of tsame size as x filled with ones and zeros.
-    """
-    assert x.ndim == 1, "Input needs to be 1d"
-    high_locations = np.where(x > high_thres)[0] # return the index, where value is greater than high_thres
-    locations = x > low_thres # return true of false
-    encoded_pairs = find_contiguous_regions(locations)
-    # print('encoded_pairs ',encoded_pairs)
-    filtered_list = list(
-        filter(
-            lambda pair:
-            ((pair[0] <= high_locations) & (high_locations <= pair[1])).any(),
-            encoded_pairs)) # find encoded_pair where inclide a high_lacations
-    #print('filtered_list ',filtered_list)
-    filtered_list = connect_(filtered_list, n_connect) # if the distance of two pair is less than n_connect, we can merge them
-    if return_arr:
-        zero_one_arr = np.zeros_like(x, dtype=int)
-        for sl in filtered_list:
-            zero_one_arr[sl[0]:sl[1]] = 1
-        return zero_one_arr
-    return filtered_list
-
-
-def connect_clusters(x, n=1):
-    if x.ndim == 1:
-        return connect_clusters_(x, n)
-    if x.ndim >= 2:
-        return np.apply_along_axis(lambda a: connect_clusters_(a, n=n), -2, x)
-
-
-def connect_clusters_(x, n=1):
-    """connect_clusters_
-    Connects clustered predictions (0,1) in x with range n
-
-    :param x: Input array. zero-one format
-    :param n: Number of frames to skip until connection can be made
-    """
-    assert x.ndim == 1, "input needs to be 1d"
-    reg = find_contiguous_regions(x)
-    start_end = connect_(reg, n=n)
-    zero_one_arr = np.zeros_like(x, dtype=int)
-    for sl in start_end:
-        zero_one_arr[sl[0]:sl[1]] = 1
-    return zero_one_arr
-
-
-def connect_(pairs, n=1):
-    """connect_
-    Connects two adjacent clusters if their distance is <= n
-
-    :param pairs: Clusters of iterateables e.g., [(1,5),(7,10)]
-    :param n: distance between two clusters 
-    """
-    if len(pairs) == 0:
-        return []
-    start_, end_ = pairs[0]
-    new_pairs = []
-    for i, (next_item, cur_item) in enumerate(zip(pairs[1:], pairs[0:])):
-        end_ = next_item[1]
-        if next_item[0] - cur_item[1] <= n:
-            pass
-        else:
-            new_pairs.append((start_, cur_item[1]))
-            start_ = next_item[0]
-    new_pairs.append((start_, end_))
-    return new_pairs
-
-
-def predictions_to_time(df, ratio):
-    df.onset = df.onset * ratio
-    df.offset = df.offset * ratio
-    return df
-
-def upgrade_resolution(arr, scale):
-    print('arr ',arr.shape)
-    x = np.arange(0, arr.shape[0])
-    f = interp1d(x, arr, kind='linear', axis=0, fill_value='extrapolate')
-    scale_x = np.arange(0, arr.shape[0], 1 / scale)
-    up_scale = f(scale_x)
-    return up_scale
-# a = [0.1,0.2,0.3,0.8,0.4,0.1,0.3,0.9,0.4]
-# a = np.array(a)
-# b = a>0.2
-# _double_threshold(a,0.7,0.2)

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/dataset.py

@@ -1,147 +0,0 @@
-import collections
-import csv
-import logging
-import os
-import random
-from glob import glob
-from pathlib import Path
-
-import numpy as np
-import torch
-import torchvision
-
-logger = logging.getLogger(f'main.{__name__}')
-
-
-class VGGSound(torch.utils.data.Dataset):
-
-    def __init__(self, split, specs_dir, transforms=None, splits_path='./data', meta_path='./data/vggsound.csv'):
-        super().__init__()
-        self.split = split
-        self.specs_dir = specs_dir
-        self.transforms = transforms
-        self.splits_path = splits_path
-        self.meta_path = meta_path
-
-        vggsound_meta = list(csv.reader(open(meta_path), quotechar='"'))
-        unique_classes = sorted(list(set(row[2] for row in vggsound_meta)))
-        self.label2target = {label: target for target, label in enumerate(unique_classes)}
-        self.target2label = {target: label for label, target in self.label2target.items()}
-        self.video2target = {row[0]: self.label2target[row[2]] for row in vggsound_meta}
-
-        split_clip_ids_path = os.path.join(splits_path, f'vggsound_{split}.txt')
-        if not os.path.exists(split_clip_ids_path):
-            self.make_split_files()
-        clip_ids_with_timestamp = open(split_clip_ids_path).read().splitlines()
-        clip_paths = [os.path.join(specs_dir, v + '_mel.npy') for v in clip_ids_with_timestamp]
-        self.dataset = clip_paths
-        # self.dataset = clip_paths[:10000]  # overfit one batch
-
-        # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE'
-        vid_classes = [self.video2target[Path(path).stem[:11]] for path in self.dataset]
-        class2count = collections.Counter(vid_classes)
-        self.class_counts = torch.tensor([class2count[cls] for cls in range(len(class2count))])
-
-        # self.sample_weights = [len(self.dataset) / class2count[self.video2target[Path(path).stem[:11]]] for path in self.dataset]
-
-    def __getitem__(self, idx):
-        item = {}
-
-        spec_path = self.dataset[idx]
-        # 'zyTX_1BXKDE_16000_26000' -> 'zyTX_1BXKDE'
-        video_name = Path(spec_path).stem[:11]
-
-        item['input'] = np.load(spec_path)
-        item['input_path'] = spec_path
-
-        # if self.split in ['train', 'valid']:
-        item['target'] = self.video2target[video_name]
-        item['label'] = self.target2label[item['target']]
-
-        if self.transforms is not None:
-            item = self.transforms(item)
-
-        return item
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def make_split_files(self):
-        random.seed(1337)
-        logger.info(f'The split files do not exist @ {self.splits_path}. Calculating the new ones.')
-        # The downloaded videos (some went missing on YouTube and no longer available)
-        available_vid_paths = sorted(glob(os.path.join(self.specs_dir, '*_mel.npy')))
-        logger.info(f'The number of clips available after download: {len(available_vid_paths)}')
-
-        # original (full) train and test sets
-        vggsound_meta = list(csv.reader(open(self.meta_path), quotechar='"'))
-        train_vids = {row[0] for row in vggsound_meta if row[3] == 'train'}
-        test_vids = {row[0] for row in vggsound_meta if row[3] == 'test'}
-        logger.info(f'The number of videos in vggsound train set: {len(train_vids)}')
-        logger.info(f'The number of videos in vggsound test set: {len(test_vids)}')
-
-        # class counts in test set. We would like to have the same distribution in valid
-        unique_classes = sorted(list(set(row[2] for row in vggsound_meta)))
-        label2target = {label: target for target, label in enumerate(unique_classes)}
-        video2target = {row[0]: label2target[row[2]] for row in vggsound_meta}
-        test_vid_classes = [video2target[vid] for vid in test_vids]
-        test_target2count = collections.Counter(test_vid_classes)
-
-        # now given the counts from test set, sample the same count for validation and the rest leave in train
-        train_vids_wo_valid, valid_vids = set(), set()
-        for target, label in enumerate(label2target.keys()):
-            class_train_vids = [vid for vid in train_vids if video2target[vid] == target]
-            random.shuffle(class_train_vids)
-            count = test_target2count[target]
-            valid_vids.update(class_train_vids[:count])
-            train_vids_wo_valid.update(class_train_vids[count:])
-
-        # make file with a list of available test videos (each video should contain timestamps as well)
-        train_i = valid_i = test_i = 0
-        with open(os.path.join(self.splits_path, 'vggsound_train.txt'), 'w') as train_file, \
-             open(os.path.join(self.splits_path, 'vggsound_valid.txt'), 'w') as valid_file, \
-             open(os.path.join(self.splits_path, 'vggsound_test.txt'), 'w') as test_file:
-            for path in available_vid_paths:
-                path = path.replace('_mel.npy', '')
-                vid_name = Path(path).name
-                # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE'
-                if vid_name[:11] in train_vids_wo_valid:
-                    train_file.write(vid_name + '\n')
-                    train_i += 1
-                elif vid_name[:11] in valid_vids:
-                    valid_file.write(vid_name + '\n')
-                    valid_i += 1
-                elif vid_name[:11] in test_vids:
-                    test_file.write(vid_name + '\n')
-                    test_i += 1
-                else:
-                    raise Exception(f'Clip {vid_name} is neither in train, valid nor test. Strange.')
-
-        logger.info(f'Put {train_i} clips to the train set and saved it to ./data/vggsound_train.txt')
-        logger.info(f'Put {valid_i} clips to the valid set and saved it to ./data/vggsound_valid.txt')
-        logger.info(f'Put {test_i} clips to the test set and saved it to ./data/vggsound_test.txt')
-
-
-if __name__ == '__main__':
-    from transforms import Crop, StandardNormalizeAudio, ToTensor
-    specs_path = '/home/nvme/data/vggsound/features/melspec_10s_22050hz/'
-
-    transforms = torchvision.transforms.transforms.Compose([
-        StandardNormalizeAudio(specs_path),
-        ToTensor(),
-        Crop([80, 848]),
-    ])
-
-    datasets = {
-        'train': VGGSound('train', specs_path, transforms),
-        'valid': VGGSound('valid', specs_path, transforms),
-        'test': VGGSound('test', specs_path, transforms),
-    }
-
-    print(datasets['train'][0])
-    print(datasets['valid'][0])
-    print(datasets['test'][0])
-
-    print(datasets['train'].class_counts)
-    print(datasets['valid'].class_counts)
-    print(datasets['test'].class_counts)

spaces/AIWaves/Debate/src/agents/Prompt/__init__.py

@@ -1 +0,0 @@
-from .base_Prompts import *

spaces/AIWaves/SOP_Generation-single/gradio_config.py

@@ -1,439 +0,0 @@
-# coding=utf-8
-# Copyright 2023  The AIWaves Inc. team.
-
-#
-# 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.
-
-import json
-from PIL import Image
-import requests
-from typing import List, Tuple
-
-class GradioConfig:
-    # How many avatars are currently registered
-    POINTER = 0
-    
-    # Avatar image. You can add or replace.
-    AGENT_HEAD_URL = [
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306241687579617434043.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306241687592097408547.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561699613.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561275758.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021090300/ry5k31wt33c.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021090300/0ls2gmwhrf5.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/02/202302281677545695326193.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/03/202303271679886128550253.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686711344407060.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686711345834296.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/05/202305171684311194291520.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/05/202305171684311196958993.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/vr0bkov0dwl.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/auqx5zfsv5g.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/llofpivtwls.jpg",
-        "https://img.touxiangwu.com/uploads/allimg/2021082612/3j2sdot3ye0.jpg",
-        "https://img.touxiangwu.com/2020/3/nQfYf2.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/08/202308131691918068774532.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/08/202308131691918068289945.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/08/202308131691918069785183.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561292003.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726561578616.jpg",
-        "https://img.touxiangwu.com/zb_users/upload/2023/06/202306141686726564597524.jpg"
-    ]
-    USER_HEAD_URL = "https://img.touxiangwu.com/zb_users/upload/2023/05/202305301685407468585486.jpg"
-    
-    # The css style of gradio.Chatbot
-    CSS = """
-    #chatbot1 .user {
-        background-color:transparent;
-        border-color:transparent;
-    }
-    #chatbot1 .bot {
-        background-color:transparent;
-        border-color:transparent;
-    }
-    #btn {color: red; border-color: red;}
-    """
- 
-    ID = ["USER", "AGENT", "SYSTEM"]
-    
-    # Bubble template
-    BUBBLE_CSS = {
-        # Background-color Name-color Name-content Font-color Font-size Content Avatar-URL
-        "USER": """
-    <div style="display: flex; align-items: flex-start; justify-content: flex-end;">
-        <div style="background-color: {}; border-radius: 20px 0px 20px 20px; padding: 15px; min-width: 100px; max-width: 300px;">
-            <p style="margin: 0; padding: 0; color: {}; font-weight: bold; font-size: 18px;">{}</p>
-            <p style="margin: 0; padding: 0; color: {}; font-size: {}px;">{}</p>
-        </div>
-        <img src="{}" alt="USER" style="width: 50px; height: 50px; border-radius: 50%; margin-left: 10px;">
-    </div>
-        """,
-
-        # Avatar-URL Background-color Name-color Name-Content Font-color Font-size Content
-        "AGENT": """
-    <div style="display: flex; align-items: flex-start;">
-        <img src="{}" alt="AGENT" style="width: 50px; height: 50px; border-radius: 50%; margin-right: 10px;">
-        <div style="background-color: {}; border-radius: 0px 20px 20px 20px; padding: 15px; min-width: 100px; max-width: 600px;">
-            <p style="margin: 0; padding: 0; color: {}; font-weight: bold; font-size: 18px;">{}</p>
-            <p style="margin: 0; padding: 0; color: {}; font-size: {}px;">{}</p>
-        </div>
-    </div>
-        """,
-
-        # Background-color Font-size Font-color Name Content
-        "SYSTEM": """
-    <div style="display: flex; align-items: center; justify-content: center;">
-        <div style="background-color: {}; border-radius: 20px; padding: 1px; min-width: 200px; max-width: 1000px;">
-            <p style="margin: 0; padding: 0; text-align: center; font-size: {}px; font-weight: bold; font-family: '微软雅黑', sans-serif; color: {};">{}:{}</p>
-        </div>
-    </div>
-        """
-    }
-
-    ROLE_2_NAME = {}
-    
-    OBJECT_INFO = {
-
-        "User": {
-            # https://img-blog.csdnimg.cn/img_convert/7c20bc39ac69b6972a22e18762d02db3.jpeg
-            "head_url": USER_HEAD_URL,
-            "bubble_color": "#95EC69",
-            "text_color": "#000000",
-            "font_size": 0,
-            "id": "USER"
-        },
-
-        "System": {
-            # https://img-blog.csdnimg.cn/img_convert/e7e5887cfff67df8c2205c2ef0e5e7fa.png
-            "head_url": "https://img.touxiangwu.com/zb_users/upload/2023/03/202303141678768524747045.jpg",
-            "bubble_color": "#7F7F7F",  ##FFFFFF
-            "text_color": "#FFFFFF",  ##000000
-            "font_size": 0,
-            "id": "SYSTEM"
-        },
-
-        "wait": {
-            "head_url": "https://img.touxiangwu.com/zb_users/upload/2022/12/202212011669881536145501.jpg",
-            "bubble_color": "#E7CBA6",
-            "text_color": "#000000",
-            "font_size": 0,
-            "id": "AGENT"
-        },
-
-        "Recorder": {
-            "head_url": "https://img.touxiangwu.com/zb_users/upload/2023/02/202302281677545695326193.jpg",
-            "bubble_color": "#F7F7F7",
-            "text_color": "#000000",
-            "font_size": 0,
-            "id": "AGENT"
-        }
-    }
-
-    @classmethod
-    def color_for_img(cls, url):
-        """
-        Extract the main colors from the picture and set them as the background color, 
-        then determine the corresponding text color.
-        """
-
-        def get_main_color(image):
-            image = image.convert("RGB")
-            width, height = image.size
-            pixels = image.getcolors(width * height)
-            most_common_pixel = max(pixels, key=lambda item: item[0])
-            return most_common_pixel[1]
-
-        def is_dark_color(rgb_color):
-            r, g, b = rgb_color
-            luminance = (0.299 * r + 0.587 * g + 0.114 * b) / 255
-            return luminance < 0.5
-
-        def download_image(url):
-            print(f"binding: {url}")
-            response = requests.get(url)
-            if response.status_code == 200:
-                with open('image.jpg', 'wb') as f:
-                    f.write(response.content)
-
-        def rgb_to_hex(color):
-            return "#{:02X}{:02X}{:02X}".format(color[0], color[1], color[2])
-
-        def get_color(image_url):
-            download_image(image_url)
-
-            image = Image.open("image.jpg")
-            main_color = get_main_color(image)
-            is_dark = is_dark_color(main_color)
-
-            if is_dark:
-                font_color = "#FFFFFF"
-            else:
-                font_color = "#000000"
-
-            return rgb_to_hex(main_color), font_color
-
-        return get_color(url)
-
-    @classmethod
-    def init(cls, JSON):
-        # Deprecated
-        with open(JSON) as f:
-            sop = json.load(f)
-        cnt = 0
-        FISRT_NODE = True
-        fisrt_node_roles = []
-        for node_name in sop['nodes']:
-            node_info = sop['nodes'][node_name]
-            agent_states = node_info['agent_states']
-            for agent_role in agent_states:
-                name = agent_states[agent_role]['style']['name']
-                cls.ROLE_2_NAME[agent_role] = name
-                if FISRT_NODE:
-                    fisrt_node_roles.append(agent_role)
-                bubble_color, text_color = cls.color_for_img(cls.AGENT_HEAD_URL[cnt])
-                cls.OBJECT_INFO[name] = {
-                    "head_url": f"{cls.AGENT_HEAD_URL[cnt]}",
-                    "bubble_color": bubble_color,
-                    "text_color": text_color,
-                    "font_size": 0,
-                    "id": "AGENT"
-                }
-                cnt += 1
-            if FISRT_NODE:
-                FISRT_NODE = False
-        print(cls.OBJECT_INFO)
-        for usr_name in cls.OBJECT_INFO:
-            if cls.OBJECT_INFO[usr_name]["id"] == "SYSTEM":
-                cls.OBJECT_INFO[usr_name]["font_size"] = 12
-            elif cls.OBJECT_INFO[usr_name]["id"] in ["USER", "AGENT"]:
-                cls.OBJECT_INFO[usr_name]["font_size"] = 16
-            else:
-                assert False
-        return fisrt_node_roles
-
-    @classmethod
-    def add_agent(cls, agents_name:List,p:int=None):
-        if p != None:
-            cls.POINTER = p
-        for name in agents_name:
-            bubble_color, text_color = cls.color_for_img(cls.AGENT_HEAD_URL[cls.POINTER])
-            cls.OBJECT_INFO[name] = {
-                "head_url": f"{cls.AGENT_HEAD_URL[cls.POINTER]}",
-                "bubble_color": bubble_color,
-                "text_color": text_color,
-                "font_size": 0,
-                "id": "AGENT"
-            }
-            cls.POINTER += 1
-        for usr_name in cls.OBJECT_INFO:
-            if cls.OBJECT_INFO[usr_name]["id"] == "SYSTEM":
-                cls.OBJECT_INFO[usr_name]["font_size"] = 12
-            elif cls.OBJECT_INFO[usr_name]["id"] in ["USER", "AGENT"]:
-                cls.OBJECT_INFO[usr_name]["font_size"] = 16
-            else:
-                assert False
-
-
-class StateConfig:
-    """UI configuration for the step progress bar (indicating the current node)"""
-    
-    CSS = """
-:root {
-        --gradient-start: 100%;
-        --gradient-end: 0%;
- }
-.container.progress-bar-container {
-  position: relative;
-  display: flex;
-  align-items: flex-end;
-  width: 100%;
-  overflow-x: auto;
-  padding-bottom: 30px;
-  padding-top: 20px
-}
-.container.progress-bar-container::-webkit-scrollbar {
-  width: 8px;
-  background-color: transparent;
-}
-
-.container.progress-bar-container::-webkit-scrollbar-thumb {
-  background-color: transparent;
-}
-
-.progress-bar-container .progressbar {
-  counter-reset: step;
-  white-space: nowrap;
-}
-.progress-bar-container .progressbar li {
-  list-style: none;
-  display: inline-block;
-  width: 200px;
-  position: relative;
-  text-align: center;
-  cursor: pointer;
-  white-space: normal;
-}
-.progress-bar-container .progressbar li:before {
-  content: counter(step);
-  counter-increment: step;
-  width: 30px;
-  height: 30px;
-  line-height: 30px;
-  border: 1px solid #ddd;
-  border-radius: 100%;
-  display: block;
-  text-align: center;
-  margin: 0 auto 10px auto;
-  background-color: #ffffff;
-}
-.progress-bar-container .progressbar li:after {
-  content: attr(data-content);
-  position: absolute;
-  width: 87%;
-  height: 2px;
-  background-color: #dddddd;
-  top: 15px;
-  left: -45%;
-}
-.progress-bar-container .progressbar li:first-child:after {
-  content: none;
-}
-.progress-bar-container .progressbar li.active {
-  color: green;
-}
-.progress-bar-container .progressbar li.active:before {
-  border-color: green;
-  background-color: green;
-  color: white;
-}
-.progress-bar-container .progressbar li.active + li:after {
-  background: linear-gradient(to right, green var(--gradient-start), lightgray var(--gradient-end));
-}
-.progress-bar-container .small-element {
-  transform: scale(0.8);
-}
-.progress-bar-container .progressbar li span {
-  position: absolute;
-  top: 40px;
-  left: 0;
-  width: 100%;
-  text-align: center;
-}
-.progress-bar-container .progressbar li .data-content {
-  position: absolute;
-  width: 100%;
-  top: -10px;
-  left: -100px;
-  text-align: center;
-}
-"""
-    
-    FORMAT = """
-<html>
-    <head>
-        <style>
-            {}
-        </style>
-    </head>
-    <body>
-        <br>
-        <center>
-            <div class="container progress-bar-container">
-                <ul class="progressbar">
-                    {}
-                </ul>
-            </div>
-        </center>
-    </body>
-</html>
-"""
-    
-    STATES_NAME:List[str] = None
-    
-    @classmethod
-    def _generate_template(cls, types:str)->str:
-        # normal: A state with no execution.
-        # active-show-up: Active state, and content displayed above the horizontal line.
-        # active-show-down: Active state, and content displayed below the horizontal line.
-        # active-show-both: Active state, and content displayed both above and below the horizontal line.
-        # active-show-none: Active state, with no content displayed above the horizontal line.
-        
-        assert types.lower() in ["normal","active-show-up", "active-show-down", "active-show-both", "active", "active-show-none"]
-        both_templates = """<li class="active" style="--gradient-start: {}%; --gradient-end: {}%;">
-	<div class="data-content">
-		<center>
-            <p style="line-height: 1px;"></p>
-			{}
-			<p>
-				{}
-			</p>
-		</center>
-	</div>
-	<span>{}</span>
-</li>"""
-
-        if types.lower() == "normal":
-            templates = "<li><span>{}</span></li>"
-        elif types.lower() == "active":
-            templates = """<li class="active"><span>{}</span></li>"""
-        elif types.lower() == "active-show-up":
-            templates = both_templates.format("{}","{}", "{}", "", "{}")
-        elif types.lower() == "active-show-down":
-            templates = both_templates.format("{}","{}", "", "{}", "{}")
-        elif types.lower() == "active-show-both":
-            templates = both_templates
-        elif types.lower() == "active-show-none":
-            templates = """<li class="active" style="--gradient-start: {}%; --gradient-end: {}%;">
-	<span>{}</span>
-</li>"""
-        else:
-            assert False
-        return templates
-            
-    @classmethod
-    def update_states(cls, current_states:List[int], current_templates:List[str], show_content:List[Tuple[str]])->str:
-        assert len(current_states) == len(current_templates)
-        # You can dynamically change the number of states.
-        # assert len(current_states) == len(cls.STATES_NAME)
-        css_code = []
-        for idx in range(len(current_states)):
-            if idx == 0:
-                if current_states[idx] != 0:
-                    css_code = [f"{cls._generate_template('active').format(cls.STATES_NAME[idx])}"]
-                else:
-                    css_code = [f"{cls._generate_template('normal').format(cls.STATES_NAME[idx])}"]
-                continue
-            if current_states[idx-1] == 0:
-                # new_code = f"{cls._generate_template('normal').format(*(show_content[idx]))}"
-                new_code = f"{cls._generate_template('normal').format(cls.STATES_NAME[idx])}"
-            else:
-                new_code = f"{cls._generate_template(current_templates[idx]).format(current_states[idx-1], 100-current_states[idx-1],*(show_content[idx-1]), cls.STATES_NAME[idx])}"
-            if current_states[idx-1] != 100 or (current_states[idx]==0 and current_states[idx-1]==100):
-                new_code = new_code.replace("""li class="active" ""","""li """)
-            css_code.append(new_code)
-        return "\n".join(css_code)
-    
-    @classmethod
-    def create_states(cls, states_name:List[str], manual_create_end_nodes:bool=False):
-        # Create states
-        if manual_create_end_nodes:
-            states_name.append("Done")
-        css_code = ""
-        cls.STATES_NAME: List[str] = states_name
-        for name in states_name:
-            css_code = f"{css_code}\n{cls._generate_template('normal').format(name)}"
-        return css_code
-    
-
-if __name__ == '__main__':
-    pass

spaces/AUST001/HDTV/app.py

@@ -1,242 +0,0 @@
-import numpy as np
-import torch
-import matplotlib.pyplot as plt
-import gradio as gr
-import io
-import numpy as np
-from PIL import Image
-from einops.layers.torch import Rearrange, Reduce
-
-def visualize_matrices(matrices_text, show_colorbar=False):
-    def mul(x):
-        res = 1
-        for i in x:
-            res *= i
-        return res
-    # Example usage:
-    matrices = torch.arange(mul(eval(matrices_text))).reshape(*eval(matrices_text))
-    # 只支持pytorch中的tensor数据类型
-    if not torch.is_tensor(matrices):
-        raise ValueError("Input should be a pytorch tensor.")
-    if len(matrices.shape)==1:
-        matrices = matrices.reshape(1, matrices.shape[0])
-    if len(matrices.shape)==3 and matrices.shape[0]==1:
-        matrices = matrices.reshape(matrices.shape[1], matrices.shape[2])
-    # 支持二维矩阵
-    if len(matrices.shape)==2:
-        matrices = torch.flip(matrices, (0,)).numpy()
-        plt.figure(figsize=(5, 5))
-        cax = plt.matshow(matrices, cmap='coolwarm', origin='lower')
-
-        for i in range(matrices.shape[0]):
-            for j in range(matrices.shape[1]):
-                plt.text(j, i, str(round(matrices[i, j],3)), ha='center', va='center', fontsize=12, color='black')
-
-        plt.xticks([])
-        plt.yticks([])
-
-        if show_colorbar:
-            plt.colorbar(cax)
-
-        # 将Matplotlib图像转换为PIL图像
-        buf = io.BytesIO()
-        # plt.savefig(buf, format='png')
-        # buf.seek(0)
-        # image = Image.open(buf)
-        # 使用bbox_inches和pad_inches调整保存的图像
-        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
-        buf.seek(0)
-        image = Image.open(buf)
-
-        # 清除当前图像，以便为下一个请求绘制新图像
-        plt.clf()
-
-        return image
-    else:
-        cols = 1
-        rows = 1
-        num = 0
-        for i in matrices.shape[:-2]:
-            if num%2==0:
-                rows = rows*i
-            else:
-                cols = cols*i
-            num += 1
-
-        fig, axes = plt.subplots(rows, cols, figsize=(cols * 5, rows * 5))
-
-
-        matrices = matrices.reshape(-1,matrices.shape[-2],matrices.shape[-1])
-
-
-        for i, matrix in enumerate(matrices):
-            if len(matrix.shape) != 2:
-                raise ValueError("Each matrix should have exactly 2 dimensions.")
-            matrix = torch.flip(matrix, (0,)).numpy()
-
-            ax = axes.flatten()[i]
-            cax = ax.matshow(matrix, cmap='coolwarm', origin='lower')
-
-            for x in range(matrix.shape[0]):
-                for y in range(matrix.shape[1]):
-                    ax.text(y, x, str(round(matrix[x, y],2)), ha='center', va='center', fontsize=12, color='black')
-
-            ax.set_xticks([])
-            ax.set_yticks([])
-            # 添加标题
-            # axs[i, j].set_title(f"Layer {i+1}, Row {j+1}", fontsize=14)
-
-            if show_colorbar:
-                plt.colorbar(cax, ax=ax)
-
-        plt.tight_layout()
-        # 将Matplotlib图像转换为PIL图像
-        buf = io.BytesIO()
-        # plt.savefig(buf, format='png')
-        # buf.seek(0)
-        # image = Image.open(buf)
-        # 使用bbox_inches和pad_inches调整保存的图像
-        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
-        buf.seek(0)
-        image = Image.open(buf)
-
-        # 清除当前图像，以便为下一个请求绘制新图像
-        plt.clf()
-
-        return image
-
-def visualize_second_matrices(matrices_text, do_what, show_colorbar=False):
-    def mul(x):
-        res = 1
-        for i in x:
-            res *= i
-        return res
-    # Example usage:
-    matrices = torch.arange(mul(eval(matrices_text))).reshape(*eval(matrices_text))
-    for do in do_what.split('&'):
-        matrices = eval(do)(matrices)
-    # 只支持pytorch中的tensor数据类型
-    if not torch.is_tensor(matrices):
-        raise ValueError("Input should be a pytorch tensor.")
-    if len(matrices.shape)==1:
-        matrices = matrices.reshape(1, matrices.shape[0])
-    if len(matrices.shape)==3 and matrices.shape[0]==1:
-        matrices = matrices.reshape(matrices.shape[1], matrices.shape[2])
-    # 支持二维矩阵
-    if len(matrices.shape)==2:
-        matrices = torch.flip(matrices, (0,)).numpy()
-        plt.figure(figsize=(5, 5))
-        cax = plt.matshow(matrices, cmap='coolwarm', origin='lower')
-
-        for i in range(matrices.shape[0]):
-            for j in range(matrices.shape[1]):
-                plt.text(j, i, str(round(matrices[i, j],3)), ha='center', va='center', fontsize=12, color='black')
-
-        plt.xticks([])
-        plt.yticks([])
-
-        if show_colorbar:
-            plt.colorbar(cax)
-
-        # 将Matplotlib图像转换为PIL图像
-        buf = io.BytesIO()
-        # plt.savefig(buf, format='png')
-        # buf.seek(0)
-        # image = Image.open(buf)
-        # 使用bbox_inches和pad_inches调整保存的图像
-        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
-        buf.seek(0)
-        image = Image.open(buf)
-
-        # 清除当前图像，以便为下一个请求绘制新图像
-        plt.clf()
-
-        return image
-    else:
-        cols = 1
-        rows = 1
-        num = 0
-        for i in matrices.shape[:-2]:
-            if num%2==0:
-                rows = rows*i
-            else:
-                cols = cols*i
-            num += 1
-
-        fig, axes = plt.subplots(rows, cols, figsize=(cols * 5, rows * 5))
-
-
-        matrices = matrices.reshape(-1,matrices.shape[-2],matrices.shape[-1])
-
-
-        for i, matrix in enumerate(matrices):
-            if len(matrix.shape) != 2:
-                raise ValueError("Each matrix should have exactly 2 dimensions.")
-            matrix = torch.flip(matrix, (0,)).numpy()
-
-            ax = axes.flatten()[i]
-            cax = ax.matshow(matrix, cmap='coolwarm', origin='lower')
-
-            for x in range(matrix.shape[0]):
-                for y in range(matrix.shape[1]):
-                    ax.text(y, x, str(round(matrix[x, y],2)), ha='center', va='center', fontsize=12, color='black')
-
-            ax.set_xticks([])
-            ax.set_yticks([])
-            # 添加标题
-            # axs[i, j].set_title(f"Layer {i+1}, Row {j+1}", fontsize=14)
-
-            if show_colorbar:
-                plt.colorbar(cax, ax=ax)
-
-        plt.tight_layout()
-        # 将Matplotlib图像转换为PIL图像
-        buf = io.BytesIO()
-        # plt.savefig(buf, format='png')
-        # buf.seek(0)
-        # image = Image.open(buf)
-        # 使用bbox_inches和pad_inches调整保存的图像
-        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
-        buf.seek(0)
-        image = Image.open(buf)
-
-        # 清除当前图像，以便为下一个请求绘制新图像
-        plt.clf()
-
-        return image
-
-
-def generate_images(text1, text2):
-    image1 = visualize_matrices(text1)
-    image2 = visualize_second_matrices(text1, text2)
-    
-    return image1, image2
-    
-inputs = [gr.inputs.Textbox(lines=2, placeholder="tensor dims"),
-          gr.inputs.Textbox(lines=2, placeholder="what to do?")]
-
-outputs = [gr.outputs.Image(type="pil"),
-           gr.outputs.Image(type="pil")]
-
-demo = gr.Interface(fn=generate_images, inputs=inputs, outputs=outputs,
-                    title="高维数据可视化工具",
-    description="""
-理解维度变换的三个关键：
-1.理解每个维度代表的含义,例如（b,c,h,w）(b,l,e)等
-2.理解reshape/view的本质
-3.理解高维张量转置的本质
-
-矩阵乘和Linear的理解：
-1.attention中的矩阵乘就是用下图中的每一个矩阵和权重矩阵相乘，矩阵和矩阵之间没有特征交互
-2.Linear中的矩阵乘就是用下图中的每一个矩阵的每一行和权重矩阵相乘，行与行之间没有特征交互
-    """,
-    examples=[
-        ["[2, 3, 4]", "Rearrange('c h w -> c w h')"],
-        ["[2, 3, 4]", "Rearrange('c h w -> c w h')&Rearrange('c h w -> c w h')&Rearrange('c h w -> c w h')"],
-        ["[2, 3, 4, 4]", "Rearrange('b c h w -> b c (h w)')"],
-        ["[2, 3, 4, 4]", "Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = 2, p2 = 2)"],
-        ["[2, 3, 4, 4]", "Rearrange('b c (h p1) (w p2) -> b h w (p1 p2 c)', p1 = 2, p2 = 2)&Rearrange('b h w (c s) -> b w c (h s)', s=2)"]
-    ]
-    )
-if __name__ == "__main__":
-    demo.launch()

spaces/AchyuthGamer/OpenGPT/g4f/Provider/EasyChat.py

@@ -1,111 +0,0 @@
-from __future__ import annotations
-
-import json
-import random
-
-import requests
-
-from ..typing import Any, CreateResult
-from .base_provider import BaseProvider
-
-
-class EasyChat(BaseProvider):
-    url: str              = "https://free.easychat.work"
-    supports_stream       = True
-    supports_gpt_35_turbo = True
-    working               = False
-
-    @staticmethod
-    def create_completion(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool, **kwargs: Any) -> CreateResult:
-        
-        active_servers = [
-            "https://chat10.fastgpt.me",
-            "https://chat9.fastgpt.me",
-            "https://chat1.fastgpt.me",
-            "https://chat2.fastgpt.me",
-            "https://chat3.fastgpt.me",
-            "https://chat4.fastgpt.me",
-            "https://gxos1h1ddt.fastgpt.me"
-        ]
-        
-        server  = active_servers[kwargs.get("active_server", random.randint(0, 5))]
-        headers = {
-            "authority"         : f"{server}".replace("https://", ""),
-            "accept"            : "text/event-stream",
-            "accept-language"   : "en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3,fa=0.2",
-            "content-type"      : "application/json",
-            "origin"            : f"{server}",
-            "referer"           : f"{server}/",
-            "x-requested-with"  : "XMLHttpRequest",
-            'plugins'           : '0',
-            'sec-ch-ua'         : '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
-            'sec-ch-ua-mobile'  : '?0',
-            'sec-ch-ua-platform': '"Windows"',
-            'sec-fetch-dest'    : 'empty',
-            'sec-fetch-mode'    : 'cors',
-            'sec-fetch-site'    : 'same-origin',
-            'user-agent'        : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36',
-            'usesearch'         : 'false',
-            'x-requested-with'  : 'XMLHttpRequest'
-        }
-
-        json_data = {
-            "messages"          : messages,
-            "stream"            : stream,
-            "model"             : model,
-            "temperature"       : kwargs.get("temperature", 0.5),
-            "presence_penalty"  : kwargs.get("presence_penalty", 0),
-            "frequency_penalty" : kwargs.get("frequency_penalty", 0),
-            "top_p"             : kwargs.get("top_p", 1)
-        }
-
-        session = requests.Session()
-        # init cookies from server
-        session.get(f"{server}/")
-
-        response = session.post(f"{server}/api/openai/v1/chat/completions",
-            headers=headers, json=json_data, stream=stream)
-        
-        if response.status_code == 200:
-            
-            if stream == False:
-                json_data = response.json()
-                
-                if "choices" in json_data:
-                    yield json_data["choices"][0]["message"]["content"]
-                else:
-                    raise Exception("No response from server")
-            
-            else:
-                
-                for chunk in response.iter_lines():
-                    
-                    if b"content" in chunk:
-                        splitData = chunk.decode().split("data:")
-                        
-                        if len(splitData) > 1:
-                            yield json.loads(splitData[1])["choices"][0]["delta"]["content"]
-                        else:
-                            continue
-        else:
-            raise Exception(f"Error {response.status_code} from server : {response.reason}")
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("temperature", "float"),
-            ("presence_penalty", "int"),
-            ("frequency_penalty", "int"),
-            ("top_p", "int"),
-            ("active_server", "int"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/actions/EliminateChess.js

@@ -1,15 +0,0 @@
-/* 
-1. Fade-out-destroy chess
-*/
-
-import FadeOutDestroy from '../../../plugins/fade-out-destroy.js';
-
-var EliminateChess = function (chessArray, board, bejeweled) {
-    const duration = 500; //ms
-    for (var i = 0, cnt = chessArray.length; i < cnt; i++) {
-        var fade = FadeOutDestroy(chessArray[i], duration);
-        bejeweled.waitEvent(fade, 'complete');
-    }
-}
-
-export default EliminateChess;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/holygrail/Factory.js

@@ -1,13 +0,0 @@
-import HolyGrail from './HolyGrail.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('holyGrail', function (config) {
-    var gameObject = new HolyGrail(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.HolyGrail', HolyGrail);
-
-export default HolyGrail;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/label/Factory.js

@@ -1,13 +0,0 @@
-import Label from './Label.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('label', function (config) {
-    var gameObject = new Label(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Label', Label);
-
-export default Label;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/Factory.js

@@ -1,13 +0,0 @@
-import Slider from './Slider.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('slider', function (config) {
-    var gameObject = new Slider(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Slider', Slider);
-
-export default Slider;

spaces/Akseluhr/whisper-sv-SE-auhr/README.md

@@ -1,13 +0,0 @@
----
-title: Whisper Se Auhr
-emoji: 💻
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlanMars/QYL-AI-Space/assets/custom.css

@@ -1,503 +0,0 @@
-:root {
-    --chatbot-color-light: #000000;
-    --chatbot-color-dark: #FFFFFF;
-    --chatbot-background-color-light: #F3F3F3;
-    --chatbot-background-color-dark: #121111;
-    --message-user-background-color-light: #95EC69;
-    --message-user-background-color-dark: #26B561;
-    --message-bot-background-color-light: #FFFFFF;
-    --message-bot-background-color-dark: #2C2C2C;
-}
-
-#app_title {
-    font-weight: var(--prose-header-text-weight);
-    font-size: var(--text-xxl);
-    line-height: 1.3;
-    text-align: left;
-    margin-top: 6px;
-    white-space: nowrap;
-}
-#description {
-    text-align: center;
-    margin: 32px 0 4px 0;
-}
-
-/* gradio的页脚信息 */
-footer {
-    /* display: none !important; */
-    margin-top: .2em !important;
-    font-size: 85%;
-}
-#footer {
-    text-align: center;
-}
-#footer div {
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.60;
-}
-
-#float_display {
-    position: absolute;
-    max-height: 30px;
-}
-/* user_info */
-#user_info {
-    white-space: nowrap;
-    position: absolute; left: 8em; top: .2em;
-    z-index: var(--layer-2);
-    box-shadow: var(--block-shadow);
-    border: none; border-radius: var(--block-label-radius);
-    background: var(--color-accent);
-    padding: var(--block-label-padding);
-    font-size: var(--block-label-text-size); line-height: var(--line-sm);
-    width: auto; min-height: 30px!important;
-    opacity: 1;
-    transition: opacity 0.3s ease-in-out;
-}
-#user_info .wrap {
-    opacity: 0;
-}
-#user_info p {
-    color: white;
-    font-weight: var(--block-label-text-weight);
-}
-/*
-#user_info.hideK {
-    opacity: 0;
-    transition: opacity 1s ease-in-out;
-}
-*/
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: ui-monospace, "SF Mono", "SFMono-Regular", "Menlo", "Consolas", "Liberation Mono", "Microsoft Yahei UI", "Microsoft Yahei", monospace;
-    /* Windows下中文的monospace会fallback为新宋体，实在太丑，这里折中使用微软雅黑 */
-    color: var(--body-text-color-subdued);
-}
-
-#status_display {
-    transition: all 0.6s;
-}
-#chuanhu_chatbot {
-    transition: height 0.3s ease;
-}
-
-/* usage_display */
-.insert_block {
-    position: relative;
-    margin: 0;
-    padding: .5em 1em;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius);
-    background: var(--block-background-fill);
-    width: 100%;
-    line-height: var(--line-sm);
-    min-height: 2em;
-}
-#usage_display p, #usage_display span {
-    margin: 0;
-    font-size: .85em;
-    color: var(--body-text-color-subdued);
-}
-.progress-bar {
-    background-color: var(--input-background-fill);;
-    margin: .5em 0 !important;
-    height: 20px;
-    border-radius: 10px;
-    overflow: hidden;
-}
-.progress {
-    background-color: var(--block-title-background-fill);
-    height: 100%;
-    border-radius: 10px;
-    text-align: right;
-    transition: width 0.5s ease-in-out;
-}
-.progress-text {
-    /* color: white; */
-    color: var(--color-accent) !important;
-    font-size: 1em !important;
-    font-weight: bold;
-    padding-right: 10px;
-    line-height: 20px;
-}
-
-.apSwitch {
-    top: 2px;
-    display: inline-block;
-    height: 24px;
-    position: relative;
-    width: 48px;
-    border-radius: 12px;
-}
-.apSwitch input {
-    display: none !important;
-}
-.apSlider {
-    background-color: var(--neutral-200);
-    bottom: 0;
-    cursor: pointer;
-    left: 0;
-    position: absolute;
-    right: 0;
-    top: 0;
-    transition: .4s;
-    font-size: 18px;
-    border-radius: 12px;
-}
-.apSlider::before {
-    bottom: -1.5px;
-    left: 1px;
-    position: absolute;
-    transition: .4s;
-    content: "🌞";
-}
-input:checked + .apSlider {
-    background-color: var(--primary-600);
-}
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- * 好希望这份提案能早日实现 https://github.com/w3c/csswg-drafts/issues/4410
- * 进度滑块在各个平台还是太不统一了
- */
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-    height: 21px;
-}
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-/* 亮色（默认） */
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-    background-color: var(--chatbot-background-color-light) !important;
-    color: var(--chatbot-color-light) !important;
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-[data-testid = "bot"] {
-    background-color: var(--message-bot-background-color-light) !important;
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-[data-testid = "user"] {
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-/* 暗色 */
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-    background-color: var(--chatbot-background-color-dark) !important;
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-/* 屏幕宽度大于等于500px的设备 */
-/* update on 2023.4.8: 高度的细致调整已写入JavaScript */
-@media screen and (min-width: 500px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 200px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 200px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-}
-/* 屏幕宽度小于500px的设备 */
-@media screen and (max-width: 499px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 140px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-    [data-testid = "bot"] {
-        max-width: 95% !important;
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-    #app_title h1{
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-    overflow-x: hidden;
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-/* 对话气泡 */
-.message {
-    border-radius: var(--radius-xl) !important;
-    border: none;
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-    font-size: var(--text-md) !important;
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-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-
-.message p {
-    margin-top: 0.6em !important;
-    margin-bottom: 0.6em !important;
-    font-size: 1.2em !important;
-}
-.message p:first-child { margin-top: 0 !important; }
-.message p:last-of-type { margin-bottom: 0 !important; }
-
-.message .md-message {
-    display: block;
-    padding: 0 !important;
-}
-.message .raw-message {
-    display: block;
-    padding: 0 !important;
-    white-space: pre-wrap;
-}
-.raw-message.hideM, .md-message.hideM {
-    display: none;
-}
-
-/* custom buttons */
-.chuanhu-btn {
-    border-radius: 5px;
-    /* background-color: #E6E6E6 !important; */
-    color: rgba(120, 120, 120, 0.64) !important;
-    padding: 4px !important;
-    position: absolute;
-    right: -22px;
-    cursor: pointer !important;
-    transition: color .2s ease, background-color .2s ease;
-}
-.chuanhu-btn:hover {
-    background-color: rgba(167, 167, 167, 0.25) !important;
-    color: unset !important;
-}
-.chuanhu-btn:active {
-    background-color: rgba(167, 167, 167, 0.5) !important;
-}
-.chuanhu-btn:focus {
-    outline: none;
-}
-.copy-bot-btn {
-    /* top: 18px; */
-    bottom: 0;
-}
-.toggle-md-btn {
-    /* top: 0; */
-    bottom: 20px;
-}
-.copy-code-btn {
-    position: relative;
-    float: right;
-    font-size: 1em;
-    cursor: pointer;
-}
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-.message-wrap>div img{
-    border-radius: 10px !important;
-}
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-/* history message */
-.wrap>.history-message {
-    padding: 10px !important;
-}
-.history-message {
-    /* padding: 0 !important; */
-    opacity: 80%;
-    display: flex;
-    flex-direction: column;
-}
-.history-message>.history-message {
-    padding: 0 !important;
-}
-.history-message>.message-wrap {
-    padding: 0 !important;
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-    margin-bottom: 16px;
-}
-.wrap>.history-message::after {
-    content: "";
-    display: block;
-    height: 2px;
-    background-color: var(--body-text-color-subdued);
-    margin-bottom: 10px;
-    margin-top: -10px;
-    clear: both;
-}
-.wrap>.history-message>:last-child::after {
-    content: "仅供查看";
-    display: block;
-    text-align: center;
-    color: var(--body-text-color-subdued);
-    font-size: 0.8em;
-}
-
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 0.6em 2em 1em 0.2em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
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-    padding: 0 !important;
-}
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spaces/Altinas/vits-uma-genshin-honkais/Docker/Dockerfile

@@ -1,12 +0,0 @@
-FROM python:3.9-bullseye
-VOLUME ["/app"]
-WORKDIR /app
-# Set apt to Chinese mirror
-RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list
-RUN apt-get update && apt-get -y install cmake git
-RUN git clone https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai
-WORKDIR /app/vits-uma-genshin-honkai
-RUN sed -i "s/\.launch()/\.launch(server_name=\"0.0.0.0\")/" /app/vits-uma-genshin-honkai/app.py
-ADD vits.sh /app/vits.sh
-EXPOSE 7860
-ENTRYPOINT [ "/app/vits.sh" ]

spaces/Alycer/VITS-Umamusume-voice-synthesizer/monotonic_align/setup.py

@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_lora_safetensor_to_diffusers.py

@@ -1,128 +0,0 @@
-# coding=utf-8
-# Copyright 2023, Haofan Wang, Qixun Wang, 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.
-
-""" Conversion script for the LoRA's safetensors checkpoints. """
-
-import argparse
-
-import torch
-from safetensors.torch import load_file
-
-from diffusers import StableDiffusionPipeline
-
-
-def convert(base_model_path, checkpoint_path, LORA_PREFIX_UNET, LORA_PREFIX_TEXT_ENCODER, alpha):
-    # load base model
-    pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32)
-
-    # load LoRA weight from .safetensors
-    state_dict = load_file(checkpoint_path)
-
-    visited = []
-
-    # directly update weight in diffusers model
-    for key in state_dict:
-        # it is suggested to print out the key, it usually will be something like below
-        # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
-
-        # as we have set the alpha beforehand, so just skip
-        if ".alpha" in key or key in visited:
-            continue
-
-        if "text" in key:
-            layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
-            curr_layer = pipeline.text_encoder
-        else:
-            layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_")
-            curr_layer = pipeline.unet
-
-        # find the target layer
-        temp_name = layer_infos.pop(0)
-        while len(layer_infos) > -1:
-            try:
-                curr_layer = curr_layer.__getattr__(temp_name)
-                if len(layer_infos) > 0:
-                    temp_name = layer_infos.pop(0)
-                elif len(layer_infos) == 0:
-                    break
-            except Exception:
-                if len(temp_name) > 0:
-                    temp_name += "_" + layer_infos.pop(0)
-                else:
-                    temp_name = layer_infos.pop(0)
-
-        pair_keys = []
-        if "lora_down" in key:
-            pair_keys.append(key.replace("lora_down", "lora_up"))
-            pair_keys.append(key)
-        else:
-            pair_keys.append(key)
-            pair_keys.append(key.replace("lora_up", "lora_down"))
-
-        # update weight
-        if len(state_dict[pair_keys[0]].shape) == 4:
-            weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)
-            weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)
-            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
-        else:
-            weight_up = state_dict[pair_keys[0]].to(torch.float32)
-            weight_down = state_dict[pair_keys[1]].to(torch.float32)
-            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down)
-
-        # update visited list
-        for item in pair_keys:
-            visited.append(item)
-
-    return pipeline
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
-    )
-    parser.add_argument(
-        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
-    )
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-    parser.add_argument(
-        "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
-    )
-    parser.add_argument(
-        "--lora_prefix_text_encoder",
-        default="lora_te",
-        type=str,
-        help="The prefix of text encoder weight in safetensors",
-    )
-    parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
-    parser.add_argument(
-        "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
-    )
-    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
-
-    args = parser.parse_args()
-
-    base_model_path = args.base_model_path
-    checkpoint_path = args.checkpoint_path
-    dump_path = args.dump_path
-    lora_prefix_unet = args.lora_prefix_unet
-    lora_prefix_text_encoder = args.lora_prefix_text_encoder
-    alpha = args.alpha
-
-    pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
-
-    pipe = pipe.to(args.device)
-    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/commands/diffusers_cli.py

@@ -1,43 +0,0 @@
-#!/usr/bin/env python
-# 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.
-
-from argparse import ArgumentParser
-
-from .env import EnvironmentCommand
-from .fp16_safetensors import FP16SafetensorsCommand
-
-
-def main():
-    parser = ArgumentParser("Diffusers CLI tool", usage="diffusers-cli <command> [<args>]")
-    commands_parser = parser.add_subparsers(help="diffusers-cli command helpers")
-
-    # Register commands
-    EnvironmentCommand.register_subcommand(commands_parser)
-    FP16SafetensorsCommand.register_subcommand(commands_parser)
-
-    # Let's go
-    args = parser.parse_args()
-
-    if not hasattr(args, "func"):
-        parser.print_help()
-        exit(1)
-
-    # Run
-    service = args.func(args)
-    service.run()
-
-
-if __name__ == "__main__":
-    main()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -1,553 +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.
-
-import inspect
-import warnings
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import PIL_INTERPOLATION, deprecate, logging
-from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess with 8->64
-def preprocess(image):
-    warnings.warn(
-        (
-            "The preprocess method is deprecated and will be removed in a future version. Please"
-            " use VaeImageProcessor.preprocess instead"
-        ),
-        FutureWarning,
-    )
-    if isinstance(image, torch.Tensor):
-        return image
-    elif isinstance(image, PIL.Image.Image):
-        image = [image]
-
-    if isinstance(image[0], PIL.Image.Image):
-        w, h = image[0].size
-        w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 64
-
-        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
-        image = np.concatenate(image, axis=0)
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image.transpose(0, 3, 1, 2)
-        image = 2.0 * image - 1.0
-        image = torch.from_numpy(image)
-    elif isinstance(image[0], torch.Tensor):
-        image = torch.cat(image, dim=0)
-    return image
-
-
-class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-guided image to image generation using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    vae_encoder: OnnxRuntimeModel
-    vae_decoder: OnnxRuntimeModel
-    text_encoder: OnnxRuntimeModel
-    tokenizer: CLIPTokenizer
-    unet: OnnxRuntimeModel
-    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
-    safety_checker: OnnxRuntimeModel
-    feature_extractor: CLIPImageProcessor
-
-    _optional_components = ["safety_checker", "feature_extractor"]
-    _is_onnx = True
-
-    def __init__(
-        self,
-        vae_encoder: OnnxRuntimeModel,
-        vae_decoder: OnnxRuntimeModel,
-        text_encoder: OnnxRuntimeModel,
-        tokenizer: CLIPTokenizer,
-        unet: OnnxRuntimeModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: OnnxRuntimeModel,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            vae_encoder=vae_encoder,
-            vae_decoder=vae_decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        num_images_per_prompt: Optional[int],
-        do_classifier_free_guidance: bool,
-        negative_prompt: Optional[str],
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                prompt to be encoded
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # get prompt text embeddings
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
-
-            if not np.array_equal(text_input_ids, untruncated_ids):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
-
-        prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt] * batch_size
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
-
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    def check_inputs(
-        self,
-        prompt: Union[str, List[str]],
-        callback_steps: int,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-    ):
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[np.ndarray, PIL.Image.Image] = None,
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[np.random.RandomState] = None,
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
-        callback_steps: int = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`np.ndarray` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter will be modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`np.random.RandomState`, *optional*):
-                A np.random.RandomState to make generation deterministic.
-            prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-
-        # check inputs. Raise error if not correct
-        self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
-
-        # define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
-        if generator is None:
-            generator = np.random
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        image = preprocess(image).cpu().numpy()
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-
-        latents_dtype = prompt_embeds.dtype
-        image = image.astype(latents_dtype)
-        # encode the init image into latents and scale the latents
-        init_latents = self.vae_encoder(sample=image)[0]
-        init_latents = 0.18215 * init_latents
-
-        if isinstance(prompt, str):
-            prompt = [prompt]
-        if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:
-            # expand init_latents for batch_size
-            deprecation_message = (
-                f"You have passed {len(prompt)} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
-                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
-                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
-                " your script to pass as many initial images as text prompts to suppress this warning."
-            )
-            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
-            additional_image_per_prompt = len(prompt) // init_latents.shape[0]
-            init_latents = np.concatenate([init_latents] * additional_image_per_prompt * num_images_per_prompt, axis=0)
-        elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0:
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts."
-            )
-        else:
-            init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)
-
-        # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
-
-        timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
-        timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
-
-        # add noise to latents using the timesteps
-        noise = generator.randn(*init_latents.shape).astype(latents_dtype)
-        init_latents = self.scheduler.add_noise(
-            torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
-        )
-        init_latents = init_latents.numpy()
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        latents = init_latents
-
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:].numpy()
-
-        timestep_dtype = next(
-            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
-        )
-        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
-
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
-            latent_model_input = latent_model_input.cpu().numpy()
-
-            # predict the noise residual
-            timestep = np.array([t], dtype=timestep_dtype)
-            noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
-                0
-            ]
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            scheduler_output = self.scheduler.step(
-                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
-            )
-            latents = scheduler_output.prev_sample.numpy()
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        latents = 1 / 0.18215 * latents
-        # image = self.vae_decoder(latent_sample=latents)[0]
-        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
-        image = np.concatenate(
-            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
-        )
-
-        image = np.clip(image / 2 + 0.5, 0, 1)
-        image = image.transpose((0, 2, 3, 1))
-
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(
-                self.numpy_to_pil(image), return_tensors="np"
-            ).pixel_values.astype(image.dtype)
-            # safety_checker does not support batched inputs yet
-            images, has_nsfw_concept = [], []
-            for i in range(image.shape[0]):
-                image_i, has_nsfw_concept_i = self.safety_checker(
-                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
-                )
-                images.append(image_i)
-                has_nsfw_concept.append(has_nsfw_concept_i[0])
-            image = np.concatenate(images)
-        else:
-            has_nsfw_concept = None
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_unet_2d.py

@@ -1,294 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import math
-import unittest
-
-import torch
-
-from diffusers import UNet2DModel
-from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism
-
-from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
-
-
-logger = logging.get_logger(__name__)
-
-enable_full_determinism()
-
-
-class Unet2DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = UNet2DModel
-    main_input_name = "sample"
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_channels = 3
-        sizes = (32, 32)
-
-        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-        time_step = torch.tensor([10]).to(torch_device)
-
-        return {"sample": noise, "timestep": time_step}
-
-    @property
-    def input_shape(self):
-        return (3, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (3, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "block_out_channels": (32, 64),
-            "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
-            "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
-            "attention_head_dim": 3,
-            "out_channels": 3,
-            "in_channels": 3,
-            "layers_per_block": 2,
-            "sample_size": 32,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-
-class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = UNet2DModel
-    main_input_name = "sample"
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_channels = 4
-        sizes = (32, 32)
-
-        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-        time_step = torch.tensor([10]).to(torch_device)
-
-        return {"sample": noise, "timestep": time_step}
-
-    @property
-    def input_shape(self):
-        return (4, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (4, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "sample_size": 32,
-            "in_channels": 4,
-            "out_channels": 4,
-            "layers_per_block": 2,
-            "block_out_channels": (32, 64),
-            "attention_head_dim": 32,
-            "down_block_types": ("DownBlock2D", "DownBlock2D"),
-            "up_block_types": ("UpBlock2D", "UpBlock2D"),
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    def test_from_pretrained_hub(self):
-        model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
-
-        self.assertIsNotNone(model)
-        self.assertEqual(len(loading_info["missing_keys"]), 0)
-
-        model.to(torch_device)
-        image = model(**self.dummy_input).sample
-
-        assert image is not None, "Make sure output is not None"
-
-    @unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
-    def test_from_pretrained_accelerate(self):
-        model, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
-        model.to(torch_device)
-        image = model(**self.dummy_input).sample
-
-        assert image is not None, "Make sure output is not None"
-
-    @unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
-    def test_from_pretrained_accelerate_wont_change_results(self):
-        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
-        model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
-        model_accelerate.to(torch_device)
-        model_accelerate.eval()
-
-        noise = torch.randn(
-            1,
-            model_accelerate.config.in_channels,
-            model_accelerate.config.sample_size,
-            model_accelerate.config.sample_size,
-            generator=torch.manual_seed(0),
-        )
-        noise = noise.to(torch_device)
-        time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)
-
-        arr_accelerate = model_accelerate(noise, time_step)["sample"]
-
-        # two models don't need to stay in the device at the same time
-        del model_accelerate
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        model_normal_load, _ = UNet2DModel.from_pretrained(
-            "fusing/unet-ldm-dummy-update", output_loading_info=True, low_cpu_mem_usage=False
-        )
-        model_normal_load.to(torch_device)
-        model_normal_load.eval()
-        arr_normal_load = model_normal_load(noise, time_step)["sample"]
-
-        assert torch_all_close(arr_accelerate, arr_normal_load, rtol=1e-3)
-
-    def test_output_pretrained(self):
-        model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
-        model.eval()
-        model.to(torch_device)
-
-        noise = torch.randn(
-            1,
-            model.config.in_channels,
-            model.config.sample_size,
-            model.config.sample_size,
-            generator=torch.manual_seed(0),
-        )
-        noise = noise.to(torch_device)
-        time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)
-
-        with torch.no_grad():
-            output = model(noise, time_step).sample
-
-        output_slice = output[0, -1, -3:, -3:].flatten().cpu()
-        # fmt: off
-        expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
-        # fmt: on
-
-        self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3))
-
-
-class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = UNet2DModel
-    main_input_name = "sample"
-
-    @property
-    def dummy_input(self, sizes=(32, 32)):
-        batch_size = 4
-        num_channels = 3
-
-        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-        time_step = torch.tensor(batch_size * [10]).to(dtype=torch.int32, device=torch_device)
-
-        return {"sample": noise, "timestep": time_step}
-
-    @property
-    def input_shape(self):
-        return (3, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (3, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "block_out_channels": [32, 64, 64, 64],
-            "in_channels": 3,
-            "layers_per_block": 1,
-            "out_channels": 3,
-            "time_embedding_type": "fourier",
-            "norm_eps": 1e-6,
-            "mid_block_scale_factor": math.sqrt(2.0),
-            "norm_num_groups": None,
-            "down_block_types": [
-                "SkipDownBlock2D",
-                "AttnSkipDownBlock2D",
-                "SkipDownBlock2D",
-                "SkipDownBlock2D",
-            ],
-            "up_block_types": [
-                "SkipUpBlock2D",
-                "SkipUpBlock2D",
-                "AttnSkipUpBlock2D",
-                "SkipUpBlock2D",
-            ],
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    @slow
-    def test_from_pretrained_hub(self):
-        model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
-        self.assertIsNotNone(model)
-        self.assertEqual(len(loading_info["missing_keys"]), 0)
-
-        model.to(torch_device)
-        inputs = self.dummy_input
-        noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
-        inputs["sample"] = noise
-        image = model(**inputs)
-
-        assert image is not None, "Make sure output is not None"
-
-    @slow
-    def test_output_pretrained_ve_mid(self):
-        model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
-        model.to(torch_device)
-
-        batch_size = 4
-        num_channels = 3
-        sizes = (256, 256)
-
-        noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
-        time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
-
-        with torch.no_grad():
-            output = model(noise, time_step).sample
-
-        output_slice = output[0, -3:, -3:, -1].flatten().cpu()
-        # fmt: off
-        expected_output_slice = torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608])
-        # fmt: on
-
-        self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
-
-    def test_output_pretrained_ve_large(self):
-        model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
-        model.to(torch_device)
-
-        batch_size = 4
-        num_channels = 3
-        sizes = (32, 32)
-
-        noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
-        time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
-
-        with torch.no_grad():
-            output = model(noise, time_step).sample
-
-        output_slice = output[0, -3:, -3:, -1].flatten().cpu()
-        # fmt: off
-        expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
-        # fmt: on
-
-        self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
-
-    def test_forward_with_norm_groups(self):
-        # not required for this model
-        pass

spaces/Andy1621/uniformer_image_detection/configs/_base_/models/ssd300.py

@@ -1,50 +0,0 @@
-# model settings
-input_size = 300
-model = dict(
-    type='SingleStageDetector',
-    pretrained='open-mmlab://vgg16_caffe',
-    backbone=dict(
-        type='SSDVGG',
-        input_size=input_size,
-        depth=16,
-        with_last_pool=False,
-        ceil_mode=True,
-        out_indices=(3, 4),
-        out_feature_indices=(22, 34),
-        l2_norm_scale=20),
-    neck=None,
-    bbox_head=dict(
-        type='SSDHead',
-        in_channels=(512, 1024, 512, 256, 256, 256),
-        num_classes=80,
-        anchor_generator=dict(
-            type='SSDAnchorGenerator',
-            scale_major=False,
-            input_size=input_size,
-            basesize_ratio_range=(0.15, 0.9),
-            strides=[8, 16, 32, 64, 100, 300],
-            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.1, 0.1, 0.2, 0.2])),
-    train_cfg=dict(
-        assigner=dict(
-            type='MaxIoUAssigner',
-            pos_iou_thr=0.5,
-            neg_iou_thr=0.5,
-            min_pos_iou=0.,
-            ignore_iof_thr=-1,
-            gt_max_assign_all=False),
-        smoothl1_beta=1.,
-        allowed_border=-1,
-        pos_weight=-1,
-        neg_pos_ratio=3,
-        debug=False),
-    test_cfg=dict(
-        nms_pre=1000,
-        nms=dict(type='nms', iou_threshold=0.45),
-        min_bbox_size=0,
-        score_thr=0.02,
-        max_per_img=200))
-cudnn_benchmark = True

spaces/Andy1621/uniformer_image_detection/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py

@@ -1,2 +0,0 @@
-_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
-model = dict(bbox_head=dict(transform_method='partial_minmax'))

spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py

@@ -1,39 +0,0 @@
-_base_ = './vfnet_r50_fpn_1x_coco.py'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 480), (1333, 960)],
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='DefaultFormatBundle'),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)

spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/point_generator.py

@@ -1,37 +0,0 @@
-import torch
-
-from .builder import ANCHOR_GENERATORS
-
-
-@ANCHOR_GENERATORS.register_module()
-class PointGenerator(object):
-
-    def _meshgrid(self, x, y, row_major=True):
-        xx = x.repeat(len(y))
-        yy = y.view(-1, 1).repeat(1, len(x)).view(-1)
-        if row_major:
-            return xx, yy
-        else:
-            return yy, xx
-
-    def grid_points(self, featmap_size, stride=16, device='cuda'):
-        feat_h, feat_w = featmap_size
-        shift_x = torch.arange(0., feat_w, device=device) * stride
-        shift_y = torch.arange(0., feat_h, device=device) * stride
-        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
-        stride = shift_x.new_full((shift_xx.shape[0], ), stride)
-        shifts = torch.stack([shift_xx, shift_yy, stride], dim=-1)
-        all_points = shifts.to(device)
-        return all_points
-
-    def valid_flags(self, featmap_size, valid_size, device='cuda'):
-        feat_h, feat_w = featmap_size
-        valid_h, valid_w = valid_size
-        assert valid_h <= feat_h and valid_w <= feat_w
-        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
-        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
-        valid_x[:valid_w] = 1
-        valid_y[:valid_h] = 1
-        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
-        valid = valid_xx & valid_yy
-        return valid

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context_59.py

@@ -1,10 +0,0 @@
-_base_ = [
-    '../_base_/models/pspnet_r50-d8.py',
-    '../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=59),
-    auxiliary_head=dict(num_classes=59),
-    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
-optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/silero_tts/style.css

@@ -1,8 +0,0 @@
-.SDAP .hires_opts input[type="number"] {
-    width: 6em !important;
-}
-
-/* silero_tts preview */
-.form:has(> #silero_preview_text) {
-    min-width: 75%
-}

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/html_generator.py

@@ -1,308 +0,0 @@
-import html
-import os
-import re
-import time
-from pathlib import Path
-
-import markdown
-from PIL import Image, ImageOps
-
-from modules.utils import get_available_chat_styles
-
-# This is to store the paths to the thumbnails of the profile pictures
-image_cache = {}
-
-with open(Path(__file__).resolve().parent / '../css/html_readable_style.css', 'r') as f:
-    readable_css = f.read()
-with open(Path(__file__).resolve().parent / '../css/html_4chan_style.css', 'r') as css_f:
-    _4chan_css = css_f.read()
-with open(Path(__file__).resolve().parent / '../css/html_instruct_style.css', 'r') as f:
-    instruct_css = f.read()
-
-# Custom chat styles
-chat_styles = {}
-for k in get_available_chat_styles():
-    chat_styles[k] = open(Path(f'css/chat_style-{k}.css'), 'r').read()
-
-# Handle styles that derive from other styles
-for k in chat_styles:
-    lines = chat_styles[k].split('\n')
-    input_string = lines[0]
-    match = re.search(r'chat_style-([a-z\-]*)\.css', input_string)
-
-    if match:
-        style = match.group(1)
-        chat_styles[k] = chat_styles.get(style, '') + '\n\n' + '\n'.join(lines[1:])
-
-
-def fix_newlines(string):
-    string = string.replace('\n', '\n\n')
-    string = re.sub(r"\n{3,}", "\n\n", string)
-    string = string.strip()
-    return string
-
-
-def replace_blockquote(m):
-    return m.group().replace('\n', '\n> ').replace('\\begin{blockquote}', '').replace('\\end{blockquote}', '')
-
-
-def convert_to_markdown(string):
-
-    # Blockquote
-    string = re.sub(r'(^|[\n])>', r'\1>', string)
-    pattern = re.compile(r'\\begin{blockquote}(.*?)\\end{blockquote}', re.DOTALL)
-    string = pattern.sub(replace_blockquote, string)
-
-    # Code
-    string = string.replace('\\begin{code}', '```')
-    string = string.replace('\\end{code}', '```')
-    string = re.sub(r"(.)```", r"\1\n```", string)
-
-    result = ''
-    is_code = False
-    for line in string.split('\n'):
-        if line.lstrip(' ').startswith('```'):
-            is_code = not is_code
-
-        result += line
-        if is_code or line.startswith('|'):  # Don't add an extra \n for tables or code
-            result += '\n'
-        else:
-            result += '\n\n'
-
-    result = result.strip()
-    if is_code:
-        result += '\n```'  # Unfinished code block
-
-    # Unfinished list, like "\n1.". A |delete| string is added and then
-    # removed to force a <ol> or <ul> to be generated instead of a <p>.
-    if re.search(r'(\n\d+\.?|\n\*\s*)$', result):
-        delete_str = '|delete|'
-
-        if re.search(r'(\d+\.?)$', result) and not result.endswith('.'):
-            result += '.'
-
-        result = re.sub(r'(\n\d+\.?|\n\*\s*)$', r'\g<1> ' + delete_str, result)
-
-        html_output = markdown.markdown(result, extensions=['fenced_code', 'tables'])
-        pos = html_output.rfind(delete_str)
-        if pos > -1:
-            html_output = html_output[:pos] + html_output[pos + len(delete_str):]
-    else:
-        html_output = markdown.markdown(result, extensions=['fenced_code', 'tables'])
-
-    # Unescape code blocks
-    pattern = re.compile(r'<code[^>]*>(.*?)</code>', re.DOTALL)
-    html_output = pattern.sub(lambda x: html.unescape(x.group()), html_output)
-
-    return html_output
-
-
-def generate_basic_html(string):
-    string = convert_to_markdown(string)
-    string = f'<style>{readable_css}</style><div class="container">{string}</div>'
-    return string
-
-
-def process_post(post, c):
-    t = post.split('\n')
-    number = t[0].split(' ')[1]
-    if len(t) > 1:
-        src = '\n'.join(t[1:])
-    else:
-        src = ''
-    src = re.sub('>', '&gt;', src)
-    src = re.sub('(&gt;&gt;[0-9]*)', '<span class="quote">\\1</span>', src)
-    src = re.sub('\n', '<br>\n', src)
-    src = f'<blockquote class="message_4chan">{src}\n'
-    src = f'<span class="name">Anonymous </span> <span class="number">No.{number}</span>\n{src}'
-    return src
-
-
-def generate_4chan_html(f):
-    posts = []
-    post = ''
-    c = -2
-    for line in f.splitlines():
-        line += "\n"
-        if line == '-----\n':
-            continue
-        elif line.startswith('--- '):
-            c += 1
-            if post != '':
-                src = process_post(post, c)
-                posts.append(src)
-            post = line
-        else:
-            post += line
-
-    if post != '':
-        src = process_post(post, c)
-        posts.append(src)
-
-    for i in range(len(posts)):
-        if i == 0:
-            posts[i] = f'<div class="op">{posts[i]}</div>\n'
-        else:
-            posts[i] = f'<div class="reply">{posts[i]}</div>\n'
-
-    output = ''
-    output += f'<style>{_4chan_css}</style><div id="parent"><div id="container">'
-    for post in posts:
-        output += post
-
-    output += '</div></div>'
-    output = output.split('\n')
-    for i in range(len(output)):
-        output[i] = re.sub(r'^(&gt;(.*?)(<br>|</div>))', r'<span class="greentext">\1</span>', output[i])
-        output[i] = re.sub(r'^<blockquote class="message_4chan">(&gt;(.*?)(<br>|</div>))', r'<blockquote class="message_4chan"><span class="greentext">\1</span>', output[i])
-
-    output = '\n'.join(output)
-    return output
-
-
-def make_thumbnail(image):
-    image = image.resize((350, round(image.size[1] / image.size[0] * 350)), Image.Resampling.LANCZOS)
-    if image.size[1] > 470:
-        image = ImageOps.fit(image, (350, 470), Image.LANCZOS)
-
-    return image
-
-
-def get_image_cache(path):
-    cache_folder = Path("cache")
-    if not cache_folder.exists():
-        cache_folder.mkdir()
-
-    mtime = os.stat(path).st_mtime
-    if (path in image_cache and mtime != image_cache[path][0]) or (path not in image_cache):
-        img = make_thumbnail(Image.open(path))
-
-        old_p = Path(f'cache/{path.name}_cache.png')
-        p = Path(f'cache/cache_{path.name}.png')
-        if old_p.exists():
-            old_p.rename(p)
-
-        output_file = p
-        img.convert('RGB').save(output_file, format='PNG')
-        image_cache[path] = [mtime, output_file.as_posix()]
-
-    return image_cache[path][1]
-
-
-def generate_instruct_html(history):
-    output = f'<style>{instruct_css}</style><div class="chat" id="chat"><div class="messages">'
-    for i, _row in enumerate(history):
-        row = [convert_to_markdown(entry) for entry in _row]
-
-        if row[0]:  # don't display empty user messages
-            output += f"""
-                  <div class="user-message">
-                    <div class="text">
-                      <div class="message-body">
-                        {row[0]}
-                      </div>
-                    </div>
-                  </div>
-                """
-
-        output += f"""
-              <div class="assistant-message">
-                <div class="text">
-                  <div class="message-body">
-                    {row[1]}
-                  </div>
-                </div>
-              </div>
-            """
-
-    output += "</div></div>"
-
-    return output
-
-
-def generate_cai_chat_html(history, name1, name2, style, reset_cache=False):
-    output = f'<style>{chat_styles[style]}</style><div class="chat" id="chat"><div class="messages">'
-
-    # We use ?name2 and ?time.time() to force the browser to reset caches
-    img_bot = f'<img src="file/cache/pfp_character.png?{name2}">' if Path("cache/pfp_character.png").exists() else ''
-    img_me = f'<img src="file/cache/pfp_me.png?{time.time() if reset_cache else ""}">' if Path("cache/pfp_me.png").exists() else ''
-
-    for i, _row in enumerate(history):
-        row = [convert_to_markdown(entry) for entry in _row]
-
-        if row[0]:  # don't display empty user messages
-            output += f"""
-                  <div class="message">
-                    <div class="circle-you">
-                      {img_me}
-                    </div>
-                    <div class="text">
-                      <div class="username">
-                        {name1}
-                      </div>
-                      <div class="message-body">
-                        {row[0]}
-                      </div>
-                    </div>
-                  </div>
-                """
-
-        output += f"""
-              <div class="message">
-                <div class="circle-bot">
-                  {img_bot}
-                </div>
-                <div class="text">
-                  <div class="username">
-                    {name2}
-                  </div>
-                  <div class="message-body">
-                    {row[1]}
-                  </div>
-                </div>
-              </div>
-            """
-
-    output += "</div></div>"
-    return output
-
-
-def generate_chat_html(history, name1, name2, reset_cache=False):
-    output = f'<style>{chat_styles["wpp"]}</style><div class="chat" id="chat"><div class="messages">'
-
-    for i, _row in enumerate(history):
-        row = [convert_to_markdown(entry) for entry in _row]
-
-        if row[0]:  # don't display empty user messages
-            output += f"""
-              <div class="message">
-                <div class="text-you">
-                  <div class="message-body">
-                    {row[0]}
-                  </div>
-                </div>
-              </div>
-            """
-
-        output += f"""
-          <div class="message">
-            <div class="text-bot">
-              <div class="message-body">
-                {row[1]}
-              </div>
-            </div>
-          </div>
-        """
-
-    output += "</div></div>"
-    return output
-
-
-def chat_html_wrapper(history, name1, name2, mode, style, reset_cache=False):
-    if mode == 'instruct':
-        return generate_instruct_html(history['visible'])
-    elif style == 'wpp':
-        return generate_chat_html(history['visible'], name1, name2)
-    else:
-        return generate_cai_chat_html(history['visible'], name1, name2, style, reset_cache)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/ssl_.py

@@ -1,495 +0,0 @@
-from __future__ import absolute_import
-
-import hmac
-import os
-import sys
-import warnings
-from binascii import hexlify, unhexlify
-from hashlib import md5, sha1, sha256
-
-from ..exceptions import (
-    InsecurePlatformWarning,
-    ProxySchemeUnsupported,
-    SNIMissingWarning,
-    SSLError,
-)
-from ..packages import six
-from .url import BRACELESS_IPV6_ADDRZ_RE, IPV4_RE
-
-SSLContext = None
-SSLTransport = None
-HAS_SNI = False
-IS_PYOPENSSL = False
-IS_SECURETRANSPORT = False
-ALPN_PROTOCOLS = ["http/1.1"]
-
-# Maps the length of a digest to a possible hash function producing this digest
-HASHFUNC_MAP = {32: md5, 40: sha1, 64: sha256}
-
-
-def _const_compare_digest_backport(a, b):
-    """
-    Compare two digests of equal length in constant time.
-
-    The digests must be of type str/bytes.
-    Returns True if the digests match, and False otherwise.
-    """
-    result = abs(len(a) - len(b))
-    for left, right in zip(bytearray(a), bytearray(b)):
-        result |= left ^ right
-    return result == 0
-
-
-_const_compare_digest = getattr(hmac, "compare_digest", _const_compare_digest_backport)
-
-try:  # Test for SSL features
-    import ssl
-    from ssl import CERT_REQUIRED, wrap_socket
-except ImportError:
-    pass
-
-try:
-    from ssl import HAS_SNI  # Has SNI?
-except ImportError:
-    pass
-
-try:
-    from .ssltransport import SSLTransport
-except ImportError:
-    pass
-
-
-try:  # Platform-specific: Python 3.6
-    from ssl import PROTOCOL_TLS
-
-    PROTOCOL_SSLv23 = PROTOCOL_TLS
-except ImportError:
-    try:
-        from ssl import PROTOCOL_SSLv23 as PROTOCOL_TLS
-
-        PROTOCOL_SSLv23 = PROTOCOL_TLS
-    except ImportError:
-        PROTOCOL_SSLv23 = PROTOCOL_TLS = 2
-
-try:
-    from ssl import PROTOCOL_TLS_CLIENT
-except ImportError:
-    PROTOCOL_TLS_CLIENT = PROTOCOL_TLS
-
-
-try:
-    from ssl import OP_NO_COMPRESSION, OP_NO_SSLv2, OP_NO_SSLv3
-except ImportError:
-    OP_NO_SSLv2, OP_NO_SSLv3 = 0x1000000, 0x2000000
-    OP_NO_COMPRESSION = 0x20000
-
-
-try:  # OP_NO_TICKET was added in Python 3.6
-    from ssl import OP_NO_TICKET
-except ImportError:
-    OP_NO_TICKET = 0x4000
-
-
-# A secure default.
-# Sources for more information on TLS ciphers:
-#
-# - https://wiki.mozilla.org/Security/Server_Side_TLS
-# - https://www.ssllabs.com/projects/best-practices/index.html
-# - https://hynek.me/articles/hardening-your-web-servers-ssl-ciphers/
-#
-# The general intent is:
-# - prefer cipher suites that offer perfect forward secrecy (DHE/ECDHE),
-# - prefer ECDHE over DHE for better performance,
-# - prefer any AES-GCM and ChaCha20 over any AES-CBC for better performance and
-#   security,
-# - prefer AES-GCM over ChaCha20 because hardware-accelerated AES is common,
-# - disable NULL authentication, MD5 MACs, DSS, and other
-#   insecure ciphers for security reasons.
-# - NOTE: TLS 1.3 cipher suites are managed through a different interface
-#   not exposed by CPython (yet!) and are enabled by default if they're available.
-DEFAULT_CIPHERS = ":".join(
-    [
-        "ECDHE+AESGCM",
-        "ECDHE+CHACHA20",
-        "DHE+AESGCM",
-        "DHE+CHACHA20",
-        "ECDH+AESGCM",
-        "DH+AESGCM",
-        "ECDH+AES",
-        "DH+AES",
-        "RSA+AESGCM",
-        "RSA+AES",
-        "!aNULL",
-        "!eNULL",
-        "!MD5",
-        "!DSS",
-    ]
-)
-
-try:
-    from ssl import SSLContext  # Modern SSL?
-except ImportError:
-
-    class SSLContext(object):  # Platform-specific: Python 2
-        def __init__(self, protocol_version):
-            self.protocol = protocol_version
-            # Use default values from a real SSLContext
-            self.check_hostname = False
-            self.verify_mode = ssl.CERT_NONE
-            self.ca_certs = None
-            self.options = 0
-            self.certfile = None
-            self.keyfile = None
-            self.ciphers = None
-
-        def load_cert_chain(self, certfile, keyfile):
-            self.certfile = certfile
-            self.keyfile = keyfile
-
-        def load_verify_locations(self, cafile=None, capath=None, cadata=None):
-            self.ca_certs = cafile
-
-            if capath is not None:
-                raise SSLError("CA directories not supported in older Pythons")
-
-            if cadata is not None:
-                raise SSLError("CA data not supported in older Pythons")
-
-        def set_ciphers(self, cipher_suite):
-            self.ciphers = cipher_suite
-
-        def wrap_socket(self, socket, server_hostname=None, server_side=False):
-            warnings.warn(
-                "A true SSLContext object is not available. This prevents "
-                "urllib3 from configuring SSL appropriately and may cause "
-                "certain SSL connections to fail. You can upgrade to a newer "
-                "version of Python to solve this. For more information, see "
-                "https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html"
-                "#ssl-warnings",
-                InsecurePlatformWarning,
-            )
-            kwargs = {
-                "keyfile": self.keyfile,
-                "certfile": self.certfile,
-                "ca_certs": self.ca_certs,
-                "cert_reqs": self.verify_mode,
-                "ssl_version": self.protocol,
-                "server_side": server_side,
-            }
-            return wrap_socket(socket, ciphers=self.ciphers, **kwargs)
-
-
-def assert_fingerprint(cert, fingerprint):
-    """
-    Checks if given fingerprint matches the supplied certificate.
-
-    :param cert:
-        Certificate as bytes object.
-    :param fingerprint:
-        Fingerprint as string of hexdigits, can be interspersed by colons.
-    """
-
-    fingerprint = fingerprint.replace(":", "").lower()
-    digest_length = len(fingerprint)
-    hashfunc = HASHFUNC_MAP.get(digest_length)
-    if not hashfunc:
-        raise SSLError("Fingerprint of invalid length: {0}".format(fingerprint))
-
-    # We need encode() here for py32; works on py2 and p33.
-    fingerprint_bytes = unhexlify(fingerprint.encode())
-
-    cert_digest = hashfunc(cert).digest()
-
-    if not _const_compare_digest(cert_digest, fingerprint_bytes):
-        raise SSLError(
-            'Fingerprints did not match. Expected "{0}", got "{1}".'.format(
-                fingerprint, hexlify(cert_digest)
-            )
-        )
-
-
-def resolve_cert_reqs(candidate):
-    """
-    Resolves the argument to a numeric constant, which can be passed to
-    the wrap_socket function/method from the ssl module.
-    Defaults to :data:`ssl.CERT_REQUIRED`.
-    If given a string it is assumed to be the name of the constant in the
-    :mod:`ssl` module or its abbreviation.
-    (So you can specify `REQUIRED` instead of `CERT_REQUIRED`.
-    If it's neither `None` nor a string we assume it is already the numeric
-    constant which can directly be passed to wrap_socket.
-    """
-    if candidate is None:
-        return CERT_REQUIRED
-
-    if isinstance(candidate, str):
-        res = getattr(ssl, candidate, None)
-        if res is None:
-            res = getattr(ssl, "CERT_" + candidate)
-        return res
-
-    return candidate
-
-
-def resolve_ssl_version(candidate):
-    """
-    like resolve_cert_reqs
-    """
-    if candidate is None:
-        return PROTOCOL_TLS
-
-    if isinstance(candidate, str):
-        res = getattr(ssl, candidate, None)
-        if res is None:
-            res = getattr(ssl, "PROTOCOL_" + candidate)
-        return res
-
-    return candidate
-
-
-def create_urllib3_context(
-    ssl_version=None, cert_reqs=None, options=None, ciphers=None
-):
-    """All arguments have the same meaning as ``ssl_wrap_socket``.
-
-    By default, this function does a lot of the same work that
-    ``ssl.create_default_context`` does on Python 3.4+. It:
-
-    - Disables SSLv2, SSLv3, and compression
-    - Sets a restricted set of server ciphers
-
-    If you wish to enable SSLv3, you can do::
-
-        from pip._vendor.urllib3.util import ssl_
-        context = ssl_.create_urllib3_context()
-        context.options &= ~ssl_.OP_NO_SSLv3
-
-    You can do the same to enable compression (substituting ``COMPRESSION``
-    for ``SSLv3`` in the last line above).
-
-    :param ssl_version:
-        The desired protocol version to use. This will default to
-        PROTOCOL_SSLv23 which will negotiate the highest protocol that both
-        the server and your installation of OpenSSL support.
-    :param cert_reqs:
-        Whether to require the certificate verification. This defaults to
-        ``ssl.CERT_REQUIRED``.
-    :param options:
-        Specific OpenSSL options. These default to ``ssl.OP_NO_SSLv2``,
-        ``ssl.OP_NO_SSLv3``, ``ssl.OP_NO_COMPRESSION``, and ``ssl.OP_NO_TICKET``.
-    :param ciphers:
-        Which cipher suites to allow the server to select.
-    :returns:
-        Constructed SSLContext object with specified options
-    :rtype: SSLContext
-    """
-    # PROTOCOL_TLS is deprecated in Python 3.10
-    if not ssl_version or ssl_version == PROTOCOL_TLS:
-        ssl_version = PROTOCOL_TLS_CLIENT
-
-    context = SSLContext(ssl_version)
-
-    context.set_ciphers(ciphers or DEFAULT_CIPHERS)
-
-    # Setting the default here, as we may have no ssl module on import
-    cert_reqs = ssl.CERT_REQUIRED if cert_reqs is None else cert_reqs
-
-    if options is None:
-        options = 0
-        # SSLv2 is easily broken and is considered harmful and dangerous
-        options |= OP_NO_SSLv2
-        # SSLv3 has several problems and is now dangerous
-        options |= OP_NO_SSLv3
-        # Disable compression to prevent CRIME attacks for OpenSSL 1.0+
-        # (issue #309)
-        options |= OP_NO_COMPRESSION
-        # TLSv1.2 only. Unless set explicitly, do not request tickets.
-        # This may save some bandwidth on wire, and although the ticket is encrypted,
-        # there is a risk associated with it being on wire,
-        # if the server is not rotating its ticketing keys properly.
-        options |= OP_NO_TICKET
-
-    context.options |= options
-
-    # Enable post-handshake authentication for TLS 1.3, see GH #1634. PHA is
-    # necessary for conditional client cert authentication with TLS 1.3.
-    # The attribute is None for OpenSSL <= 1.1.0 or does not exist in older
-    # versions of Python.  We only enable on Python 3.7.4+ or if certificate
-    # verification is enabled to work around Python issue #37428
-    # See: https://bugs.python.org/issue37428
-    if (cert_reqs == ssl.CERT_REQUIRED or sys.version_info >= (3, 7, 4)) and getattr(
-        context, "post_handshake_auth", None
-    ) is not None:
-        context.post_handshake_auth = True
-
-    def disable_check_hostname():
-        if (
-            getattr(context, "check_hostname", None) is not None
-        ):  # Platform-specific: Python 3.2
-            # We do our own verification, including fingerprints and alternative
-            # hostnames. So disable it here
-            context.check_hostname = False
-
-    # The order of the below lines setting verify_mode and check_hostname
-    # matter due to safe-guards SSLContext has to prevent an SSLContext with
-    # check_hostname=True, verify_mode=NONE/OPTIONAL. This is made even more
-    # complex because we don't know whether PROTOCOL_TLS_CLIENT will be used
-    # or not so we don't know the initial state of the freshly created SSLContext.
-    if cert_reqs == ssl.CERT_REQUIRED:
-        context.verify_mode = cert_reqs
-        disable_check_hostname()
-    else:
-        disable_check_hostname()
-        context.verify_mode = cert_reqs
-
-    # Enable logging of TLS session keys via defacto standard environment variable
-    # 'SSLKEYLOGFILE', if the feature is available (Python 3.8+). Skip empty values.
-    if hasattr(context, "keylog_filename"):
-        sslkeylogfile = os.environ.get("SSLKEYLOGFILE")
-        if sslkeylogfile:
-            context.keylog_filename = sslkeylogfile
-
-    return context
-
-
-def ssl_wrap_socket(
-    sock,
-    keyfile=None,
-    certfile=None,
-    cert_reqs=None,
-    ca_certs=None,
-    server_hostname=None,
-    ssl_version=None,
-    ciphers=None,
-    ssl_context=None,
-    ca_cert_dir=None,
-    key_password=None,
-    ca_cert_data=None,
-    tls_in_tls=False,
-):
-    """
-    All arguments except for server_hostname, ssl_context, and ca_cert_dir have
-    the same meaning as they do when using :func:`ssl.wrap_socket`.
-
-    :param server_hostname:
-        When SNI is supported, the expected hostname of the certificate
-    :param ssl_context:
-        A pre-made :class:`SSLContext` object. If none is provided, one will
-        be created using :func:`create_urllib3_context`.
-    :param ciphers:
-        A string of ciphers we wish the client to support.
-    :param ca_cert_dir:
-        A directory containing CA certificates in multiple separate files, as
-        supported by OpenSSL's -CApath flag or the capath argument to
-        SSLContext.load_verify_locations().
-    :param key_password:
-        Optional password if the keyfile is encrypted.
-    :param ca_cert_data:
-        Optional string containing CA certificates in PEM format suitable for
-        passing as the cadata parameter to SSLContext.load_verify_locations()
-    :param tls_in_tls:
-        Use SSLTransport to wrap the existing socket.
-    """
-    context = ssl_context
-    if context is None:
-        # Note: This branch of code and all the variables in it are no longer
-        # used by urllib3 itself. We should consider deprecating and removing
-        # this code.
-        context = create_urllib3_context(ssl_version, cert_reqs, ciphers=ciphers)
-
-    if ca_certs or ca_cert_dir or ca_cert_data:
-        try:
-            context.load_verify_locations(ca_certs, ca_cert_dir, ca_cert_data)
-        except (IOError, OSError) as e:
-            raise SSLError(e)
-
-    elif ssl_context is None and hasattr(context, "load_default_certs"):
-        # try to load OS default certs; works well on Windows (require Python3.4+)
-        context.load_default_certs()
-
-    # Attempt to detect if we get the goofy behavior of the
-    # keyfile being encrypted and OpenSSL asking for the
-    # passphrase via the terminal and instead error out.
-    if keyfile and key_password is None and _is_key_file_encrypted(keyfile):
-        raise SSLError("Client private key is encrypted, password is required")
-
-    if certfile:
-        if key_password is None:
-            context.load_cert_chain(certfile, keyfile)
-        else:
-            context.load_cert_chain(certfile, keyfile, key_password)
-
-    try:
-        if hasattr(context, "set_alpn_protocols"):
-            context.set_alpn_protocols(ALPN_PROTOCOLS)
-    except NotImplementedError:  # Defensive: in CI, we always have set_alpn_protocols
-        pass
-
-    # If we detect server_hostname is an IP address then the SNI
-    # extension should not be used according to RFC3546 Section 3.1
-    use_sni_hostname = server_hostname and not is_ipaddress(server_hostname)
-    # SecureTransport uses server_hostname in certificate verification.
-    send_sni = (use_sni_hostname and HAS_SNI) or (
-        IS_SECURETRANSPORT and server_hostname
-    )
-    # Do not warn the user if server_hostname is an invalid SNI hostname.
-    if not HAS_SNI and use_sni_hostname:
-        warnings.warn(
-            "An HTTPS request has been made, but the SNI (Server Name "
-            "Indication) extension to TLS is not available on this platform. "
-            "This may cause the server to present an incorrect TLS "
-            "certificate, which can cause validation failures. You can upgrade to "
-            "a newer version of Python to solve this. For more information, see "
-            "https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html"
-            "#ssl-warnings",
-            SNIMissingWarning,
-        )
-
-    if send_sni:
-        ssl_sock = _ssl_wrap_socket_impl(
-            sock, context, tls_in_tls, server_hostname=server_hostname
-        )
-    else:
-        ssl_sock = _ssl_wrap_socket_impl(sock, context, tls_in_tls)
-    return ssl_sock
-
-
-def is_ipaddress(hostname):
-    """Detects whether the hostname given is an IPv4 or IPv6 address.
-    Also detects IPv6 addresses with Zone IDs.
-
-    :param str hostname: Hostname to examine.
-    :return: True if the hostname is an IP address, False otherwise.
-    """
-    if not six.PY2 and isinstance(hostname, bytes):
-        # IDN A-label bytes are ASCII compatible.
-        hostname = hostname.decode("ascii")
-    return bool(IPV4_RE.match(hostname) or BRACELESS_IPV6_ADDRZ_RE.match(hostname))
-
-
-def _is_key_file_encrypted(key_file):
-    """Detects if a key file is encrypted or not."""
-    with open(key_file, "r") as f:
-        for line in f:
-            # Look for Proc-Type: 4,ENCRYPTED
-            if "ENCRYPTED" in line:
-                return True
-
-    return False
-
-
-def _ssl_wrap_socket_impl(sock, ssl_context, tls_in_tls, server_hostname=None):
-    if tls_in_tls:
-        if not SSLTransport:
-            # Import error, ssl is not available.
-            raise ProxySchemeUnsupported(
-                "TLS in TLS requires support for the 'ssl' module"
-            )
-
-        SSLTransport._validate_ssl_context_for_tls_in_tls(ssl_context)
-        return SSLTransport(sock, ssl_context, server_hostname)
-
-    if server_hostname:
-        return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
-    else:
-        return ssl_context.wrap_socket(sock)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/exceptions.py

@@ -1,267 +0,0 @@
-# exceptions.py
-
-import re
-import sys
-import typing
-
-from .util import col, line, lineno, _collapse_string_to_ranges
-from .unicode import pyparsing_unicode as ppu
-
-
-class ExceptionWordUnicode(ppu.Latin1, ppu.LatinA, ppu.LatinB, ppu.Greek, ppu.Cyrillic):
-    pass
-
-
-_extract_alphanums = _collapse_string_to_ranges(ExceptionWordUnicode.alphanums)
-_exception_word_extractor = re.compile("([" + _extract_alphanums + "]{1,16})|.")
-
-
-class ParseBaseException(Exception):
-    """base exception class for all parsing runtime exceptions"""
-
-    # Performance tuning: we construct a *lot* of these, so keep this
-    # constructor as small and fast as possible
-    def __init__(
-        self,
-        pstr: str,
-        loc: int = 0,
-        msg: typing.Optional[str] = None,
-        elem=None,
-    ):
-        self.loc = loc
-        if msg is None:
-            self.msg = pstr
-            self.pstr = ""
-        else:
-            self.msg = msg
-            self.pstr = pstr
-        self.parser_element = self.parserElement = elem
-        self.args = (pstr, loc, msg)
-
-    @staticmethod
-    def explain_exception(exc, depth=16):
-        """
-        Method to take an exception and translate the Python internal traceback into a list
-        of the pyparsing expressions that caused the exception to be raised.
-
-        Parameters:
-
-        - exc - exception raised during parsing (need not be a ParseException, in support
-          of Python exceptions that might be raised in a parse action)
-        - depth (default=16) - number of levels back in the stack trace to list expression
-          and function names; if None, the full stack trace names will be listed; if 0, only
-          the failing input line, marker, and exception string will be shown
-
-        Returns a multi-line string listing the ParserElements and/or function names in the
-        exception's stack trace.
-        """
-        import inspect
-        from .core import ParserElement
-
-        if depth is None:
-            depth = sys.getrecursionlimit()
-        ret = []
-        if isinstance(exc, ParseBaseException):
-            ret.append(exc.line)
-            ret.append(" " * (exc.column - 1) + "^")
-        ret.append("{}: {}".format(type(exc).__name__, exc))
-
-        if depth > 0:
-            callers = inspect.getinnerframes(exc.__traceback__, context=depth)
-            seen = set()
-            for i, ff in enumerate(callers[-depth:]):
-                frm = ff[0]
-
-                f_self = frm.f_locals.get("self", None)
-                if isinstance(f_self, ParserElement):
-                    if frm.f_code.co_name not in ("parseImpl", "_parseNoCache"):
-                        continue
-                    if id(f_self) in seen:
-                        continue
-                    seen.add(id(f_self))
-
-                    self_type = type(f_self)
-                    ret.append(
-                        "{}.{} - {}".format(
-                            self_type.__module__, self_type.__name__, f_self
-                        )
-                    )
-
-                elif f_self is not None:
-                    self_type = type(f_self)
-                    ret.append("{}.{}".format(self_type.__module__, self_type.__name__))
-
-                else:
-                    code = frm.f_code
-                    if code.co_name in ("wrapper", "<module>"):
-                        continue
-
-                    ret.append("{}".format(code.co_name))
-
-                depth -= 1
-                if not depth:
-                    break
-
-        return "\n".join(ret)
-
-    @classmethod
-    def _from_exception(cls, pe):
-        """
-        internal factory method to simplify creating one type of ParseException
-        from another - avoids having __init__ signature conflicts among subclasses
-        """
-        return cls(pe.pstr, pe.loc, pe.msg, pe.parserElement)
-
-    @property
-    def line(self) -> str:
-        """
-        Return the line of text where the exception occurred.
-        """
-        return line(self.loc, self.pstr)
-
-    @property
-    def lineno(self) -> int:
-        """
-        Return the 1-based line number of text where the exception occurred.
-        """
-        return lineno(self.loc, self.pstr)
-
-    @property
-    def col(self) -> int:
-        """
-        Return the 1-based column on the line of text where the exception occurred.
-        """
-        return col(self.loc, self.pstr)
-
-    @property
-    def column(self) -> int:
-        """
-        Return the 1-based column on the line of text where the exception occurred.
-        """
-        return col(self.loc, self.pstr)
-
-    def __str__(self) -> str:
-        if self.pstr:
-            if self.loc >= len(self.pstr):
-                foundstr = ", found end of text"
-            else:
-                # pull out next word at error location
-                found_match = _exception_word_extractor.match(self.pstr, self.loc)
-                if found_match is not None:
-                    found = found_match.group(0)
-                else:
-                    found = self.pstr[self.loc : self.loc + 1]
-                foundstr = (", found %r" % found).replace(r"\\", "\\")
-        else:
-            foundstr = ""
-        return "{}{}  (at char {}), (line:{}, col:{})".format(
-            self.msg, foundstr, self.loc, self.lineno, self.column
-        )
-
-    def __repr__(self):
-        return str(self)
-
-    def mark_input_line(self, marker_string: str = None, *, markerString=">!<") -> str:
-        """
-        Extracts the exception line from the input string, and marks
-        the location of the exception with a special symbol.
-        """
-        markerString = marker_string if marker_string is not None else markerString
-        line_str = self.line
-        line_column = self.column - 1
-        if markerString:
-            line_str = "".join(
-                (line_str[:line_column], markerString, line_str[line_column:])
-            )
-        return line_str.strip()
-
-    def explain(self, depth=16) -> str:
-        """
-        Method to translate the Python internal traceback into a list
-        of the pyparsing expressions that caused the exception to be raised.
-
-        Parameters:
-
-        - depth (default=16) - number of levels back in the stack trace to list expression
-          and function names; if None, the full stack trace names will be listed; if 0, only
-          the failing input line, marker, and exception string will be shown
-
-        Returns a multi-line string listing the ParserElements and/or function names in the
-        exception's stack trace.
-
-        Example::
-
-            expr = pp.Word(pp.nums) * 3
-            try:
-                expr.parse_string("123 456 A789")
-            except pp.ParseException as pe:
-                print(pe.explain(depth=0))
-
-        prints::
-
-            123 456 A789
-                    ^
-            ParseException: Expected W:(0-9), found 'A'  (at char 8), (line:1, col:9)
-
-        Note: the diagnostic output will include string representations of the expressions
-        that failed to parse. These representations will be more helpful if you use `set_name` to
-        give identifiable names to your expressions. Otherwise they will use the default string
-        forms, which may be cryptic to read.
-
-        Note: pyparsing's default truncation of exception tracebacks may also truncate the
-        stack of expressions that are displayed in the ``explain`` output. To get the full listing
-        of parser expressions, you may have to set ``ParserElement.verbose_stacktrace = True``
-        """
-        return self.explain_exception(self, depth)
-
-    markInputline = mark_input_line
-
-
-class ParseException(ParseBaseException):
-    """
-    Exception thrown when a parse expression doesn't match the input string
-
-    Example::
-
-        try:
-            Word(nums).set_name("integer").parse_string("ABC")
-        except ParseException as pe:
-            print(pe)
-            print("column: {}".format(pe.column))
-
-    prints::
-
-       Expected integer (at char 0), (line:1, col:1)
-        column: 1
-
-    """
-
-
-class ParseFatalException(ParseBaseException):
-    """
-    User-throwable exception thrown when inconsistent parse content
-    is found; stops all parsing immediately
-    """
-
-
-class ParseSyntaxException(ParseFatalException):
-    """
-    Just like :class:`ParseFatalException`, but thrown internally
-    when an :class:`ErrorStop<And._ErrorStop>` ('-' operator) indicates
-    that parsing is to stop immediately because an unbacktrackable
-    syntax error has been found.
-    """
-
-
-class RecursiveGrammarException(Exception):
-    """
-    Exception thrown by :class:`ParserElement.validate` if the
-    grammar could be left-recursive; parser may need to enable
-    left recursion using :class:`ParserElement.enable_left_recursion<ParserElement.enable_left_recursion>`
-    """
-
-    def __init__(self, parseElementList):
-        self.parseElementTrace = parseElementList
-
-    def __str__(self) -> str:
-        return "RecursiveGrammarException: {}".format(self.parseElementTrace)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/config/pyprojecttoml.py

@@ -1,493 +0,0 @@
-"""
-Load setuptools configuration from ``pyproject.toml`` files.
-
-**PRIVATE MODULE**: API reserved for setuptools internal usage only.
-"""
-import logging
-import os
-import warnings
-from contextlib import contextmanager
-from functools import partial
-from typing import TYPE_CHECKING, Callable, Dict, Optional, Mapping, Union
-
-from setuptools.errors import FileError, OptionError
-
-from . import expand as _expand
-from ._apply_pyprojecttoml import apply as _apply
-from ._apply_pyprojecttoml import _PREVIOUSLY_DEFINED, _WouldIgnoreField
-
-if TYPE_CHECKING:
-    from setuptools.dist import Distribution  # noqa
-
-_Path = Union[str, os.PathLike]
-_logger = logging.getLogger(__name__)
-
-
-def load_file(filepath: _Path) -> dict:
-    from setuptools.extern import tomli  # type: ignore
-
-    with open(filepath, "rb") as file:
-        return tomli.load(file)
-
-
-def validate(config: dict, filepath: _Path) -> bool:
-    from . import _validate_pyproject as validator
-
-    trove_classifier = validator.FORMAT_FUNCTIONS.get("trove-classifier")
-    if hasattr(trove_classifier, "_disable_download"):
-        # Improve reproducibility by default. See issue 31 for validate-pyproject.
-        trove_classifier._disable_download()  # type: ignore
-
-    try:
-        return validator.validate(config)
-    except validator.ValidationError as ex:
-        summary = f"configuration error: {ex.summary}"
-        if ex.name.strip("`") != "project":
-            # Probably it is just a field missing/misnamed, not worthy the verbosity...
-            _logger.debug(summary)
-            _logger.debug(ex.details)
-
-        error = f"invalid pyproject.toml config: {ex.name}."
-        raise ValueError(f"{error}\n{summary}") from None
-
-
-def apply_configuration(
-    dist: "Distribution",
-    filepath: _Path,
-    ignore_option_errors=False,
-) -> "Distribution":
-    """Apply the configuration from a ``pyproject.toml`` file into an existing
-    distribution object.
-    """
-    config = read_configuration(filepath, True, ignore_option_errors, dist)
-    return _apply(dist, config, filepath)
-
-
-def read_configuration(
-    filepath: _Path,
-    expand=True,
-    ignore_option_errors=False,
-    dist: Optional["Distribution"] = None,
-):
-    """Read given configuration file and returns options from it as a dict.
-
-    :param str|unicode filepath: Path to configuration file in the ``pyproject.toml``
-        format.
-
-    :param bool expand: Whether to expand directives and other computed values
-        (i.e. post-process the given configuration)
-
-    :param bool ignore_option_errors: Whether to silently ignore
-        options, values of which could not be resolved (e.g. due to exceptions
-        in directives such as file:, attr:, etc.).
-        If False exceptions are propagated as expected.
-
-    :param Distribution|None: Distribution object to which the configuration refers.
-        If not given a dummy object will be created and discarded after the
-        configuration is read. This is used for auto-discovery of packages in the case
-        a dynamic configuration (e.g. ``attr`` or ``cmdclass``) is expanded.
-        When ``expand=False`` this object is simply ignored.
-
-    :rtype: dict
-    """
-    filepath = os.path.abspath(filepath)
-
-    if not os.path.isfile(filepath):
-        raise FileError(f"Configuration file {filepath!r} does not exist.")
-
-    asdict = load_file(filepath) or {}
-    project_table = asdict.get("project", {})
-    tool_table = asdict.get("tool", {})
-    setuptools_table = tool_table.get("setuptools", {})
-    if not asdict or not (project_table or setuptools_table):
-        return {}  # User is not using pyproject to configure setuptools
-
-    if setuptools_table:
-        # TODO: Remove the following once the feature stabilizes:
-        msg = "Support for `[tool.setuptools]` in `pyproject.toml` is still *beta*."
-        warnings.warn(msg, _BetaConfiguration)
-
-    # There is an overall sense in the community that making include_package_data=True
-    # the default would be an improvement.
-    # `ini2toml` backfills include_package_data=False when nothing is explicitly given,
-    # therefore setting a default here is backwards compatible.
-    orig_setuptools_table = setuptools_table.copy()
-    if dist and getattr(dist, "include_package_data") is not None:
-        setuptools_table.setdefault("include-package-data", dist.include_package_data)
-    else:
-        setuptools_table.setdefault("include-package-data", True)
-    # Persist changes:
-    asdict["tool"] = tool_table
-    tool_table["setuptools"] = setuptools_table
-
-    try:
-        # Don't complain about unrelated errors (e.g. tools not using the "tool" table)
-        subset = {"project": project_table, "tool": {"setuptools": setuptools_table}}
-        validate(subset, filepath)
-    except Exception as ex:
-        # TODO: Remove the following once the feature stabilizes:
-        if _skip_bad_config(project_table, orig_setuptools_table, dist):
-            return {}
-        # TODO: After the previous statement is removed the try/except can be replaced
-        # by the _ignore_errors context manager.
-        if ignore_option_errors:
-            _logger.debug(f"ignored error: {ex.__class__.__name__} - {ex}")
-        else:
-            raise  # re-raise exception
-
-    if expand:
-        root_dir = os.path.dirname(filepath)
-        return expand_configuration(asdict, root_dir, ignore_option_errors, dist)
-
-    return asdict
-
-
-def _skip_bad_config(
-    project_cfg: dict, setuptools_cfg: dict, dist: Optional["Distribution"]
-) -> bool:
-    """Be temporarily forgiving with invalid ``pyproject.toml``"""
-    # See pypa/setuptools#3199 and pypa/cibuildwheel#1064
-
-    if dist is None or (
-        dist.metadata.name is None
-        and dist.metadata.version is None
-        and dist.install_requires is None
-    ):
-        # It seems that the build is not getting any configuration from other places
-        return False
-
-    if setuptools_cfg:
-        # If `[tool.setuptools]` is set, then `pyproject.toml` config is intentional
-        return False
-
-    given_config = set(project_cfg.keys())
-    popular_subset = {"name", "version", "python_requires", "requires-python"}
-    if given_config <= popular_subset:
-        # It seems that the docs in cibuildtool has been inadvertently encouraging users
-        # to create `pyproject.toml` files that are not compliant with the standards.
-        # Let's be forgiving for the time being.
-        warnings.warn(_InvalidFile.message(), _InvalidFile, stacklevel=2)
-        return True
-
-    return False
-
-
-def expand_configuration(
-    config: dict,
-    root_dir: Optional[_Path] = None,
-    ignore_option_errors: bool = False,
-    dist: Optional["Distribution"] = None,
-) -> dict:
-    """Given a configuration with unresolved fields (e.g. dynamic, cmdclass, ...)
-    find their final values.
-
-    :param dict config: Dict containing the configuration for the distribution
-    :param str root_dir: Top-level directory for the distribution/project
-        (the same directory where ``pyproject.toml`` is place)
-    :param bool ignore_option_errors: see :func:`read_configuration`
-    :param Distribution|None: Distribution object to which the configuration refers.
-        If not given a dummy object will be created and discarded after the
-        configuration is read. Used in the case a dynamic configuration
-        (e.g. ``attr`` or ``cmdclass``).
-
-    :rtype: dict
-    """
-    return _ConfigExpander(config, root_dir, ignore_option_errors, dist).expand()
-
-
-class _ConfigExpander:
-    def __init__(
-        self,
-        config: dict,
-        root_dir: Optional[_Path] = None,
-        ignore_option_errors: bool = False,
-        dist: Optional["Distribution"] = None,
-    ):
-        self.config = config
-        self.root_dir = root_dir or os.getcwd()
-        self.project_cfg = config.get("project", {})
-        self.dynamic = self.project_cfg.get("dynamic", [])
-        self.setuptools_cfg = config.get("tool", {}).get("setuptools", {})
-        self.dynamic_cfg = self.setuptools_cfg.get("dynamic", {})
-        self.ignore_option_errors = ignore_option_errors
-        self._dist = dist
-
-    def _ensure_dist(self) -> "Distribution":
-        from setuptools.dist import Distribution
-
-        attrs = {"src_root": self.root_dir, "name": self.project_cfg.get("name", None)}
-        return self._dist or Distribution(attrs)
-
-    def _process_field(self, container: dict, field: str, fn: Callable):
-        if field in container:
-            with _ignore_errors(self.ignore_option_errors):
-                container[field] = fn(container[field])
-
-    def _canonic_package_data(self, field="package-data"):
-        package_data = self.setuptools_cfg.get(field, {})
-        return _expand.canonic_package_data(package_data)
-
-    def expand(self):
-        self._expand_packages()
-        self._canonic_package_data()
-        self._canonic_package_data("exclude-package-data")
-
-        # A distribution object is required for discovering the correct package_dir
-        dist = self._ensure_dist()
-        ctx = _EnsurePackagesDiscovered(dist, self.project_cfg, self.setuptools_cfg)
-        with ctx as ensure_discovered:
-            package_dir = ensure_discovered.package_dir
-            self._expand_data_files()
-            self._expand_cmdclass(package_dir)
-            self._expand_all_dynamic(dist, package_dir)
-
-        return self.config
-
-    def _expand_packages(self):
-        packages = self.setuptools_cfg.get("packages")
-        if packages is None or isinstance(packages, (list, tuple)):
-            return
-
-        find = packages.get("find")
-        if isinstance(find, dict):
-            find["root_dir"] = self.root_dir
-            find["fill_package_dir"] = self.setuptools_cfg.setdefault("package-dir", {})
-            with _ignore_errors(self.ignore_option_errors):
-                self.setuptools_cfg["packages"] = _expand.find_packages(**find)
-
-    def _expand_data_files(self):
-        data_files = partial(_expand.canonic_data_files, root_dir=self.root_dir)
-        self._process_field(self.setuptools_cfg, "data-files", data_files)
-
-    def _expand_cmdclass(self, package_dir: Mapping[str, str]):
-        root_dir = self.root_dir
-        cmdclass = partial(_expand.cmdclass, package_dir=package_dir, root_dir=root_dir)
-        self._process_field(self.setuptools_cfg, "cmdclass", cmdclass)
-
-    def _expand_all_dynamic(self, dist: "Distribution", package_dir: Mapping[str, str]):
-        special = (  # need special handling
-            "version",
-            "readme",
-            "entry-points",
-            "scripts",
-            "gui-scripts",
-            "classifiers",
-            "dependencies",
-            "optional-dependencies",
-        )
-        # `_obtain` functions are assumed to raise appropriate exceptions/warnings.
-        obtained_dynamic = {
-            field: self._obtain(dist, field, package_dir)
-            for field in self.dynamic
-            if field not in special
-        }
-        obtained_dynamic.update(
-            self._obtain_entry_points(dist, package_dir) or {},
-            version=self._obtain_version(dist, package_dir),
-            readme=self._obtain_readme(dist),
-            classifiers=self._obtain_classifiers(dist),
-            dependencies=self._obtain_dependencies(dist),
-            optional_dependencies=self._obtain_optional_dependencies(dist),
-        )
-        # `None` indicates there is nothing in `tool.setuptools.dynamic` but the value
-        # might have already been set by setup.py/extensions, so avoid overwriting.
-        updates = {k: v for k, v in obtained_dynamic.items() if v is not None}
-        self.project_cfg.update(updates)
-
-    def _ensure_previously_set(self, dist: "Distribution", field: str):
-        previous = _PREVIOUSLY_DEFINED[field](dist)
-        if previous is None and not self.ignore_option_errors:
-            msg = (
-                f"No configuration found for dynamic {field!r}.\n"
-                "Some dynamic fields need to be specified via `tool.setuptools.dynamic`"
-                "\nothers must be specified via the equivalent attribute in `setup.py`."
-            )
-            raise OptionError(msg)
-
-    def _expand_directive(
-        self, specifier: str, directive, package_dir: Mapping[str, str]
-    ):
-        with _ignore_errors(self.ignore_option_errors):
-            root_dir = self.root_dir
-            if "file" in directive:
-                return _expand.read_files(directive["file"], root_dir)
-            if "attr" in directive:
-                return _expand.read_attr(directive["attr"], package_dir, root_dir)
-            raise ValueError(f"invalid `{specifier}`: {directive!r}")
-        return None
-
-    def _obtain(self, dist: "Distribution", field: str, package_dir: Mapping[str, str]):
-        if field in self.dynamic_cfg:
-            return self._expand_directive(
-                f"tool.setuptools.dynamic.{field}",
-                self.dynamic_cfg[field],
-                package_dir,
-            )
-        self._ensure_previously_set(dist, field)
-        return None
-
-    def _obtain_version(self, dist: "Distribution", package_dir: Mapping[str, str]):
-        # Since plugins can set version, let's silently skip if it cannot be obtained
-        if "version" in self.dynamic and "version" in self.dynamic_cfg:
-            return _expand.version(self._obtain(dist, "version", package_dir))
-        return None
-
-    def _obtain_readme(self, dist: "Distribution") -> Optional[Dict[str, str]]:
-        if "readme" not in self.dynamic:
-            return None
-
-        dynamic_cfg = self.dynamic_cfg
-        if "readme" in dynamic_cfg:
-            return {
-                "text": self._obtain(dist, "readme", {}),
-                "content-type": dynamic_cfg["readme"].get("content-type", "text/x-rst"),
-            }
-
-        self._ensure_previously_set(dist, "readme")
-        return None
-
-    def _obtain_entry_points(
-        self, dist: "Distribution", package_dir: Mapping[str, str]
-    ) -> Optional[Dict[str, dict]]:
-        fields = ("entry-points", "scripts", "gui-scripts")
-        if not any(field in self.dynamic for field in fields):
-            return None
-
-        text = self._obtain(dist, "entry-points", package_dir)
-        if text is None:
-            return None
-
-        groups = _expand.entry_points(text)
-        expanded = {"entry-points": groups}
-
-        def _set_scripts(field: str, group: str):
-            if group in groups:
-                value = groups.pop(group)
-                if field not in self.dynamic:
-                    msg = _WouldIgnoreField.message(field, value)
-                    warnings.warn(msg, _WouldIgnoreField)
-                # TODO: Don't set field when support for pyproject.toml stabilizes
-                #       instead raise an error as specified in PEP 621
-                expanded[field] = value
-
-        _set_scripts("scripts", "console_scripts")
-        _set_scripts("gui-scripts", "gui_scripts")
-
-        return expanded
-
-    def _obtain_classifiers(self, dist: "Distribution"):
-        if "classifiers" in self.dynamic:
-            value = self._obtain(dist, "classifiers", {})
-            if value:
-                return value.splitlines()
-        return None
-
-    def _obtain_dependencies(self, dist: "Distribution"):
-        if "dependencies" in self.dynamic:
-            value = self._obtain(dist, "dependencies", {})
-            if value:
-                return _parse_requirements_list(value)
-        return None
-
-    def _obtain_optional_dependencies(self, dist: "Distribution"):
-        if "optional-dependencies" not in self.dynamic:
-            return None
-        if "optional-dependencies" in self.dynamic_cfg:
-            optional_dependencies_map = self.dynamic_cfg["optional-dependencies"]
-            assert isinstance(optional_dependencies_map, dict)
-            return {
-                group: _parse_requirements_list(self._expand_directive(
-                    f"tool.setuptools.dynamic.optional-dependencies.{group}",
-                    directive,
-                    {},
-                ))
-                for group, directive in optional_dependencies_map.items()
-            }
-        self._ensure_previously_set(dist, "optional-dependencies")
-        return None
-
-
-def _parse_requirements_list(value):
-    return [
-        line
-        for line in value.splitlines()
-        if line.strip() and not line.strip().startswith("#")
-    ]
-
-
-@contextmanager
-def _ignore_errors(ignore_option_errors: bool):
-    if not ignore_option_errors:
-        yield
-        return
-
-    try:
-        yield
-    except Exception as ex:
-        _logger.debug(f"ignored error: {ex.__class__.__name__} - {ex}")
-
-
-class _EnsurePackagesDiscovered(_expand.EnsurePackagesDiscovered):
-    def __init__(
-        self, distribution: "Distribution", project_cfg: dict, setuptools_cfg: dict
-    ):
-        super().__init__(distribution)
-        self._project_cfg = project_cfg
-        self._setuptools_cfg = setuptools_cfg
-
-    def __enter__(self):
-        """When entering the context, the values of ``packages``, ``py_modules`` and
-        ``package_dir`` that are missing in ``dist`` are copied from ``setuptools_cfg``.
-        """
-        dist, cfg = self._dist, self._setuptools_cfg
-        package_dir: Dict[str, str] = cfg.setdefault("package-dir", {})
-        package_dir.update(dist.package_dir or {})
-        dist.package_dir = package_dir  # needs to be the same object
-
-        dist.set_defaults._ignore_ext_modules()  # pyproject.toml-specific behaviour
-
-        # Set `name`, `py_modules` and `packages` in dist to short-circuit
-        # auto-discovery, but avoid overwriting empty lists purposefully set by users.
-        if dist.metadata.name is None:
-            dist.metadata.name = self._project_cfg.get("name")
-        if dist.py_modules is None:
-            dist.py_modules = cfg.get("py-modules")
-        if dist.packages is None:
-            dist.packages = cfg.get("packages")
-
-        return super().__enter__()
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        """When exiting the context, if values of ``packages``, ``py_modules`` and
-        ``package_dir`` are missing in ``setuptools_cfg``, copy from ``dist``.
-        """
-        # If anything was discovered set them back, so they count in the final config.
-        self._setuptools_cfg.setdefault("packages", self._dist.packages)
-        self._setuptools_cfg.setdefault("py-modules", self._dist.py_modules)
-        return super().__exit__(exc_type, exc_value, traceback)
-
-
-class _BetaConfiguration(UserWarning):
-    """Explicitly inform users that some `pyproject.toml` configuration is *beta*"""
-
-
-class _InvalidFile(UserWarning):
-    """The given `pyproject.toml` file is invalid and would be ignored.
-    !!\n\n
-    ############################
-    # Invalid `pyproject.toml` #
-    ############################
-
-    Any configurations in `pyproject.toml` will be ignored.
-    Please note that future releases of setuptools will halt the build process
-    if an invalid file is given.
-
-    To prevent setuptools from considering `pyproject.toml` please
-    DO NOT include the `[project]` or `[tool.setuptools]` tables in your file.
-    \n\n!!
-    """
-
-    @classmethod
-    def message(cls):
-        from inspect import cleandoc
-        return cleandoc(cls.__doc__)

spaces/Benson/text-generation/Examples/Conseguir Sobre l Descarga Gratuita 2022 Uptodown.md

@@ -1,53 +0,0 @@
-<br />
-<h1>Cómo superarlo Descargar gratis 2022 Uptodown: Una guía para jugadores frustrados</h1>
-<p>Si estás buscando un juego que ponga a prueba tu paciencia, habilidad y cordura, es posible que hayas oído hablar de Cómo superarlo con Bennett Foddy. Este juego se ha vuelto notorio por su dificultad extrema y su juego provocador de ira. Pero, ¿de qué se trata este juego y cómo puedes conseguirlo gratis en 2022? En este artículo, responderemos a estas preguntas y te daremos algunos consejos y trucos para ayudarte a superar esta montaña de frustración. </p>
- <h2>¿Qué es superar con Bennett Foddy? </h2>
-<p>Getting Over It with Bennett Foddy es un videojuego que fue lanzado en 2017 por Bennett Foddy, un desarrollador de juegos y filósofo australiano. El juego es descrito por Foddy como "un juego que hice para un cierto tipo de persona. Para hacerles daño." </p>
-<h2>conseguir sobre él descarga gratuita 2022 uptodown</h2><br /><p><b><b>DOWNLOAD</b> &#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6J3M">https://bltlly.com/2v6J3M</a></b></p><br /><br />
- <h3>Un juego de escalada castigador</h3>
-<p>La premisa del juego es simple: controlas a un hombre llamado Diógenes que está atrapado en un caldero y tiene que usar un martillo para escalar una montaña escarpada y resbaladiza de objetos aleatorios. El juego no tiene puntos de control, sistema de guardado ni piedad. Si haces un movimiento en falso, puedes retroceder hasta el principio. El juego está diseñado para ser frustrante, injusto e impredecible. </p>
- <h3>Un homenaje al senderismo sexy</h3>
-<p>El juego también es un tributo a Sexy Hiking, un juego B de 2002 de Jazzuo que tenía un concepto y jugabilidad similares. Foddy se inspiró en Sexy Hiking y quería crear su propia versión con mejores gráficos, física y sonido. También agregó su propia voz como narrador que comenta sobre tu progreso, fracaso y filosofía. </p>
- <h3>Un comentario filosófico</h3>
-
- <h2>¿Por qué la gente quiere jugar Getting Over It? </h2>
-<p>Superar No es un juego para todos. Es un juego que te hará enojar, triste, desesperado y desesperado. Es un juego que te hará cuestionar tus elecciones de vida y tu cordura. Entonces, ¿por qué la gente quiere jugar? Aquí hay algunas posibles razones:</p>
- <h3>Un desafío para el masoquista</h3>
-<p>A algunas personas les gusta jugar juegos duros que los llevan a sus límites. Les gusta la sensación de superar obstáculos y alcanzar metas que parecen imposibles. Les gusta la emoción del riesgo y la recompensa. Les gusta la satisfacción de demostrarse a sí mismos y a otros que están equivocados. Les gusta el dolor y el placer de jugar a Getting Over It.</p>
-<p></p>
- <h3>Una recompensa por la persistencia</h3>
-<p>Algunas personas juegan Getting Over It porque quieren ver lo que sucede cuando terminan el juego. Sienten curiosidad por el final y la recompensa que les espera. Están decididos a no rendirse y a llegar a la cima de la montaña. Están motivados por el desafío y el misterio de superarlo.</p>
- <h3>Un meme para internet</h3>
-<p>Algunas personas juegan Getting Over It porque quieren unirse a la comunidad en línea y la cultura que ha surgido alrededor del juego. Quieren compartir sus experiencias, reacciones y opiniones con otros jugadores y espectadores. Quieren ver o crear videos, transmisiones, memes, fan art y parodias del juego. Quieren divertirse y reírse del absurdo y la hilaridad de Getting Over It.</p>
- <h2>¿Cómo conseguir sobre él para libre en 2022? </h2>
-<p>Getting Over Es un juego de pago que está disponible en varias plataformas, como Steam, Humble Bundle, iOS y Android. El precio del juego varía dependiendo de la plataforma y la región, pero por lo general es de alrededor de $ 8 USD. Sin embargo, algunas personas pueden no querer pagar por el juego o no tener acceso a las plataformas oficiales. En ese caso, ¿cómo pueden obtener Getting Over It gratis en 2022? Aquí hay algunas opciones:</p>
- <h3>El camino oficial</h3>
-
- <h3>La forma no oficial</h3>
-<p>La forma no oficial de obtener Getting Over It de forma gratuita es descargarlo desde un sitio web de terceros o tienda de aplicaciones que ofrece versiones pirateadas o agrietadas del juego. Uno de estos sitios web es Uptodown, que es una plataforma popular para descargar aplicaciones y juegos para dispositivos Android. Uptodown afirma ofrecer una descarga gratuita de Getting Over It con Bennett Foddy APK, que es el formato de archivo para las aplicaciones de Android. Sin embargo, este método no es recomendado o respaldado por el desarrollador o las plataformas. </p>
- <h3>Los riesgos y desventajas</h3>
-<p>Si bien conseguir Getting Over It gratis puede sonar tentador, hay algunos riesgos y desventajas involucradas en hacerlo. En primer lugar, descargar juegos pirateados o agrietados es ilegal y poco ético, ya que viola los derechos de propiedad intelectual del desarrollador y las plataformas. También les priva de los ingresos y el apoyo que merecen para crear y distribuir el juego. En segundo lugar, la descarga de juegos de fuentes no confiables puede exponer su dispositivo a malware, virus, spyware u otro software dañino que puede dañar su sistema o robar su información personal. En tercer lugar, la descarga de juegos de plataformas no oficiales puede resultar en un rendimiento deficiente, problemas de compatibilidad, errores, problemas técnicos o características que pueden arruinar su experiencia de juego. Por lo tanto, es mejor comprar el juego en las plataformas oficiales o esperar un regalo o promoción legítima. </p>
- <h2>Consejos y trucos para superarlo</h2>
-<p>Si has decidido jugar a Getting Over It, ya sea que lo hayas comprado o descargado gratis, es posible que necesites algunos consejos y trucos para ayudarte a sobrevivir a este juego brutal. Aquí hay algunas sugerencias:</p>
- <h3>La práctica hace la perfección</h3>
-
- <h3>Usar afirmaciones y pensamiento positivo</h3>
-<p>Otra cosa que hacer en Getting Over es usar afirmaciones y pensamiento positivo. El juego puede ser muy frustrante y desmoralizante, especialmente cuando se pierde mucho progreso o escuchar los comentarios sarcásticos de Foddy. Puede sentirse enojado, desesperado o deprimido. Para lidiar con estas emociones negativas, necesita usar afirmaciones y pensamiento positivo. Necesitas recordarte que puedes hacerlo, que estás mejorando, que estás aprendiendo, que te estás divirtiendo y que no estás solo. Necesitas enfocarte en los aspectos positivos del juego y tu experiencia, en lugar de los negativos. </p>
- <h3>Ver carreras rápidas y guías</h3>
-<p>Una última cosa que hacer en Getting Over es ver carreras rápidas y guías. Speedruns son vídeos de jugadores que completan el juego en el menor tiempo posible, utilizando diversas técnicas y estrategias. Las guías son videos de jugadores que explican cómo superar partes específicas del juego, usando consejos y trucos. Ver carreras rápidas y guías puede ayudarle a aprender de los expertos y mejorar sus propias habilidades. También puedes inspirarte y motivarte viendo cómo otros han conquistado el juego. </p>
- <h2>Conclusión</h2>
-<p>Superarlo con Bennett Foddy es un juego que te desafiará, te frustrará y te hará cuestionar tu existencia. Es un juego que pondrá a prueba tu paciencia, habilidad y cordura. Es un juego que te hará reír, llorar, gritar y rabiar. Pero también es un juego que te recompensará, te enseñará e inspirará. Es un juego que te hará sentir vivo. </p>
- <p>Si quieres jugar a este juego, puedes comprarlo en las plataformas oficiales o esperar un regalo o promoción gratis. También puede descargarlo de fuentes no oficiales, pero sea consciente de los riesgos y desventajas involucrados. Y si quieres tener éxito en este juego, necesitas practicar, usar afirmaciones y pensamiento positivo, y ver carreras rápidas y guías. </p>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes acerca de Cómo superarlo con Bennett Foddy:</p>
-<tabla>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>¿Cuánto tiempo se tarda en terminar el juego? </td><td>La duración del juego depende de su nivel de habilidad y suerte. Algunos jugadores han terminado el juego en menos de 2 minutos, mientras que otros han pasado cientos de horas sin llegar al final. </td></tr>
-<tr><td>¿Cuál es la recompensa por terminar el juego? </td><td>No te lo vamos a estropear, pero digamos que hay una recompensa por terminar el juego que es único y exclusivo para cada jugador. </td></tr>
-<tr><td>¿Quién es Bennett Foddy? </td><td>Bennett Foddy es un desarrollador de juegos australiano y filósofo que enseña en la Universidad de Nueva York. Es conocido por crear juegos que exploran los temas de frustración, dificultad y fracaso, como QWOP, GIRP, CLOP y Getting Over It.</td></tr>
-<tr><td>¿Quién es Diógenes? </td><td>Diógenes es el nombre del carácter que controlas en Cómo superarlo. Lleva el nombre de Diógenes de Sinope, un antiguo filósofo griego que vivió en un barril y rechazó los valores convencionales. </td></tr>
-<tr><td>¿Qué es Sexy Hiking? </td><td>Sexy Hiking es un juego B de 2002 de Jazzuo que inspiró Getting Over It. Tiene un concepto similar y juego de escalar una montaña con un martillo. </td></tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat/src/lib/types/AbortedGeneration.ts

@@ -1,8 +0,0 @@
-// Ideally shouldn't be needed, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
-
-import type { Conversation } from "./Conversation";
-import type { Timestamps } from "./Timestamps";
-
-export interface AbortedGeneration extends Timestamps {
-	conversationId: Conversation["_id"];
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/bcdoc/restdoc.py

@@ -1,282 +0,0 @@
-# Copyright 2012-2013 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-#     http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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.
-import logging
-import os
-import re
-
-from botocore.compat import OrderedDict
-from botocore.docs.bcdoc.docstringparser import DocStringParser
-from botocore.docs.bcdoc.style import ReSTStyle
-
-DEFAULT_AWS_DOCS_LINK = 'https://docs.aws.amazon.com/index.html'
-DOCUMENTATION_LINK_REGEX = re.compile(
-    r'`AWS API Documentation '
-    r'<https://docs.aws.amazon.com/goto/WebAPI/[a-z0-9-.]*/[a-zA-Z]*>`_'
-)
-LARGE_SECTION_MESSAGE = """
-
-    **{}**
-    ::
-
-        # This section is too large to render.
-        # Please see the AWS API Documentation linked below.
-
-    {}
-    """
-LOG = logging.getLogger('bcdocs')
-SECTION_LINE_LIMIT_CONFIG = {
-    'response-example': {'name': 'Response Syntax', 'line_limit': 1500},
-    'description': {'name': 'Response Structure', 'line_limit': 5000},
-    'request-example': {'name': 'Request Syntax', 'line_limit': 1500},
-    'request-params': {'name': 'Parameters', 'line_limit': 5000},
-}
-SECTION_METHOD_PATH_DEPTH = {
-    'client-api': 4,
-    'paginator-api': 3,
-    'waiter-api': 3,
-}
-
-
-class ReSTDocument:
-    def __init__(self, target='man'):
-        self.style = ReSTStyle(self)
-        self.target = target
-        self.parser = DocStringParser(self)
-        self.keep_data = True
-        self.do_translation = False
-        self.translation_map = {}
-        self.hrefs = {}
-        self._writes = []
-        self._last_doc_string = None
-
-    def _write(self, s):
-        if self.keep_data and s is not None:
-            self._writes.append(s)
-
-    def write(self, content):
-        """
-        Write content into the document.
-        """
-        self._write(content)
-
-    def writeln(self, content):
-        """
-        Write content on a newline.
-        """
-        self._write(f'{self.style.spaces()}{content}\n')
-
-    def peek_write(self):
-        """
-        Returns the last content written to the document without
-        removing it from the stack.
-        """
-        return self._writes[-1]
-
-    def pop_write(self):
-        """
-        Removes and returns the last content written to the stack.
-        """
-        return self._writes.pop() if len(self._writes) > 0 else None
-
-    def push_write(self, s):
-        """
-        Places new content on the stack.
-        """
-        self._writes.append(s)
-
-    def getvalue(self):
-        """
-        Returns the current content of the document as a string.
-        """
-        if self.hrefs:
-            self.style.new_paragraph()
-            for refname, link in self.hrefs.items():
-                self.style.link_target_definition(refname, link)
-        return ''.join(self._writes).encode('utf-8')
-
-    def translate_words(self, words):
-        return [self.translation_map.get(w, w) for w in words]
-
-    def handle_data(self, data):
-        if data and self.keep_data:
-            self._write(data)
-
-    def include_doc_string(self, doc_string):
-        if doc_string:
-            try:
-                start = len(self._writes)
-                self.parser.feed(doc_string)
-                self.parser.close()
-                end = len(self._writes)
-                self._last_doc_string = (start, end)
-            except Exception:
-                LOG.debug('Error parsing doc string', exc_info=True)
-                LOG.debug(doc_string)
-
-    def remove_last_doc_string(self):
-        # Removes all writes inserted by last doc string
-        if self._last_doc_string is not None:
-            start, end = self._last_doc_string
-            del self._writes[start:end]
-
-
-class DocumentStructure(ReSTDocument):
-    def __init__(self, name, section_names=None, target='man', context=None):
-        """Provides a Hierarichial structure to a ReSTDocument
-
-        You can write to it similiar to as you can to a ReSTDocument but
-        has an innate structure for more orginaztion and abstraction.
-
-        :param name: The name of the document
-        :param section_names: A list of sections to be included
-            in the document.
-        :param target: The target documentation of the Document structure
-        :param context: A dictionary of data to store with the strucuture. These
-            are only stored per section not the entire structure.
-        """
-        super().__init__(target=target)
-        self._name = name
-        self._structure = OrderedDict()
-        self._path = [self._name]
-        self._context = {}
-        if context is not None:
-            self._context = context
-        if section_names is not None:
-            self._generate_structure(section_names)
-
-    @property
-    def name(self):
-        """The name of the document structure"""
-        return self._name
-
-    @property
-    def path(self):
-        """
-        A list of where to find a particular document structure in the
-        overlying document structure.
-        """
-        return self._path
-
-    @path.setter
-    def path(self, value):
-        self._path = value
-
-    @property
-    def available_sections(self):
-        return list(self._structure)
-
-    @property
-    def context(self):
-        return self._context
-
-    def _generate_structure(self, section_names):
-        for section_name in section_names:
-            self.add_new_section(section_name)
-
-    def add_new_section(self, name, context=None):
-        """Adds a new section to the current document structure
-
-        This document structure will be considered a section to the
-        current document structure but will in itself be an entirely
-        new document structure that can be written to and have sections
-        as well
-
-        :param name: The name of the section.
-        :param context: A dictionary of data to store with the strucuture. These
-            are only stored per section not the entire structure.
-        :rtype: DocumentStructure
-        :returns: A new document structure to add to but lives as a section
-            to the document structure it was instantiated from.
-        """
-        # Add a new section
-        section = self.__class__(
-            name=name, target=self.target, context=context
-        )
-        section.path = self.path + [name]
-        # Indent the section apporpriately as well
-        section.style.indentation = self.style.indentation
-        section.translation_map = self.translation_map
-        section.hrefs = self.hrefs
-        self._structure[name] = section
-        return section
-
-    def get_section(self, name):
-        """Retrieve a section"""
-        return self._structure[name]
-
-    def delete_section(self, name):
-        """Delete a section"""
-        del self._structure[name]
-
-    def flush_structure(self, docs_link=None):
-        """Flushes a doc structure to a ReSTructed string
-
-        The document is flushed out in a DFS style where sections and their
-        subsections' values are added to the string as they are visited.
-        """
-        # We are at the root flush the links at the beginning of the
-        # document
-        path_length = len(self.path)
-        if path_length == 1:
-            if self.hrefs:
-                self.style.new_paragraph()
-                for refname, link in self.hrefs.items():
-                    self.style.link_target_definition(refname, link)
-        # Clear docs_link at the correct depth to prevent passing a non-related link.
-        elif path_length == SECTION_METHOD_PATH_DEPTH.get(self.path[1]):
-            docs_link = None
-        value = self.getvalue()
-        for name, section in self._structure.items():
-            # Checks is the AWS API Documentation link has been generated.
-            # If it has been generated, it gets passed as a the doc_link parameter.
-            match = DOCUMENTATION_LINK_REGEX.search(value.decode())
-            docs_link = (
-                f'{match.group(0)}\n\n'.encode() if match else docs_link
-            )
-            value += section.flush_structure(docs_link)
-
-        # Replace response/request sections if the line number exceeds our limit.
-        # The section is replaced with a message linking to AWS API Documentation.
-        line_count = len(value.splitlines())
-        section_config = SECTION_LINE_LIMIT_CONFIG.get(self.name)
-        aws_docs_link = (
-            docs_link.decode()
-            if docs_link is not None
-            else DEFAULT_AWS_DOCS_LINK
-        )
-        if section_config and line_count > section_config['line_limit']:
-            value = LARGE_SECTION_MESSAGE.format(
-                section_config['name'], aws_docs_link
-            ).encode()
-        return value
-
-    def getvalue(self):
-        return ''.join(self._writes).encode('utf-8')
-
-    def remove_all_sections(self):
-        self._structure = OrderedDict()
-
-    def clear_text(self):
-        self._writes = []
-
-    def add_title_section(self, title):
-        title_section = self.add_new_section('title')
-        title_section.style.h1(title)
-        return title_section
-
-    def write_to_file(self, full_path, file_name):
-        if not os.path.exists(full_path):
-            os.makedirs(full_path)
-        sub_resource_file_path = os.path.join(full_path, f'{file_name}.rst')
-        with open(sub_resource_file_path, 'wb') as f:
-            f.write(self.flush_structure())

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/packages/backports/makefile.py

@@ -1,51 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-backports.makefile
-~~~~~~~~~~~~~~~~~~
-
-Backports the Python 3 ``socket.makefile`` method for use with anything that
-wants to create a "fake" socket object.
-"""
-import io
-from socket import SocketIO
-
-
-def backport_makefile(
-    self, mode="r", buffering=None, encoding=None, errors=None, newline=None
-):
-    """
-    Backport of ``socket.makefile`` from Python 3.5.
-    """
-    if not set(mode) <= {"r", "w", "b"}:
-        raise ValueError("invalid mode %r (only r, w, b allowed)" % (mode,))
-    writing = "w" in mode
-    reading = "r" in mode or not writing
-    assert reading or writing
-    binary = "b" in mode
-    rawmode = ""
-    if reading:
-        rawmode += "r"
-    if writing:
-        rawmode += "w"
-    raw = SocketIO(self, rawmode)
-    self._makefile_refs += 1
-    if buffering is None:
-        buffering = -1
-    if buffering < 0:
-        buffering = io.DEFAULT_BUFFER_SIZE
-    if buffering == 0:
-        if not binary:
-            raise ValueError("unbuffered streams must be binary")
-        return raw
-    if reading and writing:
-        buffer = io.BufferedRWPair(raw, raw, buffering)
-    elif reading:
-        buffer = io.BufferedReader(raw, buffering)
-    else:
-        assert writing
-        buffer = io.BufferedWriter(raw, buffering)
-    if binary:
-        return buffer
-    text = io.TextIOWrapper(buffer, encoding, errors, newline)
-    text.mode = mode
-    return text

spaces/CVPR/WALT/mmdet/core/post_processing/bbox_nms.py

@@ -1,168 +0,0 @@
-import torch
-from mmcv.ops.nms import batched_nms
-
-from mmdet.core.bbox.iou_calculators import bbox_overlaps
-
-
-def multiclass_nms(multi_bboxes,
-                   multi_scores,
-                   score_thr,
-                   nms_cfg,
-                   max_num=-1,
-                   score_factors=None,
-                   return_inds=False):
-    """NMS for multi-class bboxes.
-
-    Args:
-        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
-        multi_scores (Tensor): shape (n, #class), where the last column
-            contains scores of the background class, but this will be ignored.
-        score_thr (float): bbox threshold, bboxes with scores lower than it
-            will not be considered.
-        nms_thr (float): NMS IoU threshold
-        max_num (int, optional): if there are more than max_num bboxes after
-            NMS, only top max_num will be kept. Default to -1.
-        score_factors (Tensor, optional): The factors multiplied to scores
-            before applying NMS. Default to None.
-        return_inds (bool, optional): Whether return the indices of kept
-            bboxes. Default to False.
-
-    Returns:
-        tuple: (bboxes, labels, indices (optional)), tensors of shape (k, 5),
-            (k), and (k). Labels are 0-based.
-    """
-    num_classes = multi_scores.size(1) - 1
-    # exclude background category
-    if multi_bboxes.shape[1] > 4:
-        bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4)
-    else:
-        bboxes = multi_bboxes[:, None].expand(
-            multi_scores.size(0), num_classes, 4)
-
-    scores = multi_scores[:, :-1]
-
-    labels = torch.arange(num_classes, dtype=torch.long)
-    labels = labels.view(1, -1).expand_as(scores)
-
-    bboxes = bboxes.reshape(-1, 4)
-    scores = scores.reshape(-1)
-    labels = labels.reshape(-1)
-
-    if not torch.onnx.is_in_onnx_export():
-        # NonZero not supported  in TensorRT
-        # remove low scoring boxes
-        valid_mask = scores > score_thr
-    # multiply score_factor after threshold to preserve more bboxes, improve
-    # mAP by 1% for YOLOv3
-    if score_factors is not None:
-        # expand the shape to match original shape of score
-        score_factors = score_factors.view(-1, 1).expand(
-            multi_scores.size(0), num_classes)
-        score_factors = score_factors.reshape(-1)
-        scores = scores * score_factors
-
-    if not torch.onnx.is_in_onnx_export():
-        # NonZero not supported  in TensorRT
-        inds = valid_mask.nonzero(as_tuple=False).squeeze(1)
-        bboxes, scores, labels = bboxes[inds], scores[inds], labels[inds]
-    else:
-        # TensorRT NMS plugin has invalid output filled with -1
-        # add dummy data to make detection output correct.
-        bboxes = torch.cat([bboxes, bboxes.new_zeros(1, 4)], dim=0)
-        scores = torch.cat([scores, scores.new_zeros(1)], dim=0)
-        labels = torch.cat([labels, labels.new_zeros(1)], dim=0)
-
-    if bboxes.numel() == 0:
-        if torch.onnx.is_in_onnx_export():
-            raise RuntimeError('[ONNX Error] Can not record NMS '
-                               'as it has not been executed this time')
-        if return_inds:
-            return bboxes, labels, inds
-        else:
-            return bboxes, labels
-
-    dets, keep = batched_nms(bboxes, scores, labels, nms_cfg)
-
-    if max_num > 0:
-        dets = dets[:max_num]
-        keep = keep[:max_num]
-
-    if return_inds:
-        return dets, labels[keep], keep
-    else:
-        return dets, labels[keep]
-
-
-def fast_nms(multi_bboxes,
-             multi_scores,
-             multi_coeffs,
-             score_thr,
-             iou_thr,
-             top_k,
-             max_num=-1):
-    """Fast NMS in `YOLACT <https://arxiv.org/abs/1904.02689>`_.
-
-    Fast NMS allows already-removed detections to suppress other detections so
-    that every instance can be decided to be kept or discarded in parallel,
-    which is not possible in traditional NMS. This relaxation allows us to
-    implement Fast NMS entirely in standard GPU-accelerated matrix operations.
-
-    Args:
-        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
-        multi_scores (Tensor): shape (n, #class+1), where the last column
-            contains scores of the background class, but this will be ignored.
-        multi_coeffs (Tensor): shape (n, #class*coeffs_dim).
-        score_thr (float): bbox threshold, bboxes with scores lower than it
-            will not be considered.
-        iou_thr (float): IoU threshold to be considered as conflicted.
-        top_k (int): if there are more than top_k bboxes before NMS,
-            only top top_k will be kept.
-        max_num (int): if there are more than max_num bboxes after NMS,
-            only top max_num will be kept. If -1, keep all the bboxes.
-            Default: -1.
-
-    Returns:
-        tuple: (bboxes, labels, coefficients), tensors of shape (k, 5), (k, 1),
-            and (k, coeffs_dim). Labels are 0-based.
-    """
-
-    scores = multi_scores[:, :-1].t()  # [#class, n]
-    scores, idx = scores.sort(1, descending=True)
-
-    idx = idx[:, :top_k].contiguous()
-    scores = scores[:, :top_k]  # [#class, topk]
-    num_classes, num_dets = idx.size()
-    boxes = multi_bboxes[idx.view(-1), :].view(num_classes, num_dets, 4)
-    coeffs = multi_coeffs[idx.view(-1), :].view(num_classes, num_dets, -1)
-
-    iou = bbox_overlaps(boxes, boxes)  # [#class, topk, topk]
-    iou.triu_(diagonal=1)
-    iou_max, _ = iou.max(dim=1)
-
-    # Now just filter out the ones higher than the threshold
-    keep = iou_max <= iou_thr
-
-    # Second thresholding introduces 0.2 mAP gain at negligible time cost
-    keep *= scores > score_thr
-
-    # Assign each kept detection to its corresponding class
-    classes = torch.arange(
-        num_classes, device=boxes.device)[:, None].expand_as(keep)
-    classes = classes[keep]
-
-    boxes = boxes[keep]
-    coeffs = coeffs[keep]
-    scores = scores[keep]
-
-    # Only keep the top max_num highest scores across all classes
-    scores, idx = scores.sort(0, descending=True)
-    if max_num > 0:
-        idx = idx[:max_num]
-        scores = scores[:max_num]
-
-    classes = classes[idx]
-    boxes = boxes[idx]
-    coeffs = coeffs[idx]
-
-    cls_dets = torch.cat([boxes, scores[:, None]], dim=1)
-    return cls_dets, classes, coeffs

spaces/Chukwuka/FoodVision-Model/app.py

@@ -1,91 +0,0 @@
-
-### 1. Imports and class names setup ###
-import gradio as gr
-import os
-import torch
-import torchvision.transforms as T
-
-from model import create_effnet_b2
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-
-# Setup class names
-class_names = ['pizza', 'steak', 'sushi']
-
-### 2. Model and transforms preparation ###
-test_tsfm = T.Compose([T.Resize((224,224)),
-                        T.ToTensor(),
-                       T.Normalize(mean=[0.485, 0.456, 0.406], # 3. A mean of [0.485, 0.456, 0.406] (across each colour channel)
-                         std=[0.229, 0.224, 0.225]) # 4. A standard deviation of [0.229, 0.224, 0.225] (across each colour channel),
-                       ])
-
-# Create EffNetB2 Model
-effnetb2, test_transform = create_effnet_b2(num_of_class=len(class_names), 
-                            transform=test_tsfm,
-                            seed=42)
-
-# saved_path = 'demos\foodvision_mini\09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth'
-saved_path = '07_effnetb2_data_50_percent_10_epochs.pth'
-
-print('Loading Model State Dictionary')
-# Load saved weights
-effnetb2.load_state_dict(
-                torch.load(f=saved_path,
-                           map_location=torch.device('cpu'), # load to CPU
-                          )
-                        )
-
-print('Model Loaded ...')
-### 3. Predict function ###
-
-# Create predict function
-from typing import Tuple, Dict
-
-def predict(img) -> Tuple[Dict, float]:
-    """Transforms and performs a prediction on img and returns prediction and time taken.
-    """
-    # Start the timer
-    start_time = timer()
-    
-    # Transform the target image and add a batch dimension
-    img = test_tsfm(img).unsqueeze(0)
-    
-    # Put model into evaluation mode and turn on inference mode
-    effnetb2.eval()
-    with torch.inference_mode():
-        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
-        pred_probs = torch.softmax(effnetb2(img), dim=1)
-    
-    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
-    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-    
-    # Calculate the prediction time
-    pred_time = round(timer() - start_time, 5)
-    
-    # Return the prediction dictionary and prediction time 
-    return pred_labels_and_probs, pred_time
-
-### 4. Gradio App ###
-
-# Create title, description and article strings
-title= 'FoodVision Mini 🍕🥩🍣'
-description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-article = "<p>Created by Chukwuka [09. PyTorch Model Deployment] Tutorial by Mr. DBourke(https://www.learnpytorch.io/09_pytorch_model_deployment/).</p><p style='text-align: center'><a href='https://github.com/Sylvesterchuks/foodvision-app'>Github Repo</a></p>"
-
-
-# Create examples list from "examples/" directory
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-# Create the Gradio demo
-demo = gr.Interface(fn=predict, # mapping function from input to output
-                    inputs=gr.inputs.Image(type='pil'), # What are the inputs?
-                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
-                             gr.Number(label='Prediction time (s)')], # Our fn has two outputs, therefore we have two outputs
-                    examples=example_list,
-                    title=title,
-                    description=description,
-                    article=article
-                   )
-# Launch the demo
-print('Gradio Demo Launched')
-demo.launch()

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

@@ -1,50 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def genshin_start(images: List[BuildImage], texts: List[str], args):
-    frame = BuildImage.open(img_dir / "0.png")
-    if texts:
-        text = texts[0]
-        try:
-            frame.draw_text(
-                (100, frame.height - 150, frame.width - 100, frame.height),
-                text,
-                max_fontsize=100,
-                min_fontsize=70,
-                fill="white",
-                stroke_fill="black",
-                stroke_ratio=0.05,
-            )
-        except:
-            raise TextOverLength(text)
-
-    def make(img: BuildImage) -> BuildImage:
-        points = ((0, 116), (585, 0), (584, 319), (43, 385))
-        screen = (
-            img.convert("RGBA").resize((600, 330), keep_ratio=True).perspective(points)
-        )
-        return frame.copy().paste(screen, (412, 121), below=True)
-
-    return make_jpg_or_gif(images[0], make)
-
-
-add_meme(
-    "genshin_start",
-    genshin_start,
-    min_images=1,
-    max_images=1,
-    min_texts=0,
-    max_texts=1,
-    default_texts=["原神，启动！"],
-    keywords=["原神启动"],
-    patterns=[r"(\S+启动[!！]?)"],
-)

spaces/Cletrason/Cletrason-toad-mario-movie/utils (1).py

@@ -1,207 +0,0 @@
-import os
-
-import PIL.Image
-import numpy as np
-import torch
-import torchvision
-from torchvision.transforms import Resize, InterpolationMode
-import imageio
-from einops import rearrange
-import cv2
-from PIL import Image
-from annotator.util import resize_image, HWC3
-from annotator.canny import CannyDetector
-from annotator.openpose import OpenposeDetector
-import decord
-# decord.bridge.set_bridge('torch')
-
-apply_canny = CannyDetector()
-apply_openpose = OpenposeDetector()
-
-
-def add_watermark(image, watermark_path, wm_rel_size=1/16, boundary=5):
-    '''
-    Creates a watermark on the saved inference image.
-    We request that you do not remove this to properly assign credit to
-    Shi-Lab's work.
-    '''
-    watermark = Image.open(watermark_path)
-    w_0, h_0 = watermark.size
-    H, W, _ = image.shape
-    wmsize = int(max(H, W) * wm_rel_size)
-    aspect = h_0 / w_0
-    if aspect > 1.0:
-        watermark = watermark.resize((wmsize, int(aspect * wmsize)), Image.LANCZOS)
-    else:
-        watermark = watermark.resize((int(wmsize / aspect), wmsize), Image.LANCZOS)
-    w, h = watermark.size
-    loc_h = H - h - boundary
-    loc_w = W - w - boundary
-    image = Image.fromarray(image)
-    mask = watermark if watermark.mode in ('RGBA', 'LA') else None
-    image.paste(watermark, (loc_w, loc_h), mask)
-    return image
-
-
-def pre_process_canny(input_video, low_threshold=100, high_threshold=200):
-    detected_maps = []
-    for frame in input_video:
-        img = rearrange(frame, 'c h w -> h w c').cpu().numpy().astype(np.uint8)
-        detected_map = apply_canny(img, low_threshold, high_threshold)
-        detected_map = HWC3(detected_map)
-        detected_maps.append(detected_map[None])
-    detected_maps = np.concatenate(detected_maps)
-    control = torch.from_numpy(detected_maps.copy()).float() / 255.0
-    return rearrange(control, 'f h w c -> f c h w')
-
-
-def pre_process_pose(input_video, apply_pose_detect: bool = True):
-    detected_maps = []
-    for frame in input_video:
-        img = rearrange(frame, 'c h w -> h w c').cpu().numpy().astype(np.uint8)
-        img = HWC3(img)
-        if apply_pose_detect:
-            detected_map, _ = apply_openpose(img)
-        else:
-            detected_map = img
-        detected_map = HWC3(detected_map)
-        H, W, C = img.shape
-        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
-        detected_maps.append(detected_map[None])
-    detected_maps = np.concatenate(detected_maps)
-    control = torch.from_numpy(detected_maps.copy()).float() / 255.0
-    return rearrange(control, 'f h w c -> f c h w')
-
-
-def create_video(frames, fps, rescale=False, path=None, watermark=None):
-    if path is None:
-        dir = "temporal"
-        os.makedirs(dir, exist_ok=True)
-        path = os.path.join(dir, 'movie.mp4')
-
-    outputs = []
-    for i, x in enumerate(frames):
-        x = torchvision.utils.make_grid(torch.Tensor(x), nrow=4)
-        if rescale:
-            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
-        x = (x * 255).numpy().astype(np.uint8)
-
-        if watermark is not None:
-            x = add_watermark(x, watermark)
-        outputs.append(x)
-        # imageio.imsave(os.path.join(dir, os.path.splitext(name)[0] + f'_{i}.jpg'), x)
-
-    imageio.mimsave(path, outputs, fps=fps)
-    return path
-
-def create_gif(frames, fps, rescale=False, path=None, watermark=None):
-    if path is None:
-        dir = "temporal"
-        os.makedirs(dir, exist_ok=True)
-        path = os.path.join(dir, 'canny_db.gif')
-
-    outputs = []
-    for i, x in enumerate(frames):
-        x = torchvision.utils.make_grid(torch.Tensor(x), nrow=4)
-        if rescale:
-            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
-        x = (x * 255).numpy().astype(np.uint8)
-        if watermark is not None:
-            x = add_watermark(x, watermark)
-        outputs.append(x)
-        # imageio.imsave(os.path.join(dir, os.path.splitext(name)[0] + f'_{i}.jpg'), x)
-
-    imageio.mimsave(path, outputs, fps=fps)
-    return path
-
-def prepare_video(video_path:str, resolution:int, device, dtype, normalize=True, start_t:float=0, end_t:float=-1, output_fps:int=-1):
-    vr = decord.VideoReader(video_path)
-    initial_fps = vr.get_avg_fps()
-    if output_fps == -1:
-        output_fps = int(initial_fps)
-    if end_t == -1:
-        end_t = len(vr) / initial_fps
-    else:
-        end_t = min(len(vr) / initial_fps, end_t)
-    assert 0 <= start_t < end_t
-    assert output_fps > 0
-    start_f_ind = int(start_t * initial_fps)
-    end_f_ind = int(end_t * initial_fps)
-    num_f = int((end_t - start_t) * output_fps)
-    sample_idx = np.linspace(start_f_ind, end_f_ind, num_f, endpoint=False).astype(int)
-    video = vr.get_batch(sample_idx)
-    if torch.is_tensor(video):
-        video = video.detach().cpu().numpy()
-    else:
-        video = video.asnumpy()
-    _, h, w, _ = video.shape
-    video = rearrange(video, "f h w c -> f c h w")
-    video = torch.Tensor(video).to(device).to(dtype)
-    if h > w:
-        w = int(w * resolution / h)
-        w = w - w % 8
-        h = resolution - resolution % 8
-    else:
-        h = int(h * resolution / w)
-        h = h - h % 8
-        w = resolution - resolution % 8
-    video = Resize((h, w), interpolation=InterpolationMode.BILINEAR, antialias=True)(video)
-    if normalize:
-        video = video / 127.5 - 1.0
-    return video, output_fps
-
-
-def post_process_gif(list_of_results, image_resolution):
-    output_file = "/tmp/ddxk.gif"
-    imageio.mimsave(output_file, list_of_results, fps=4)
-    return output_file
-
-
-class CrossFrameAttnProcessor:
-    def __init__(self, unet_chunk_size=2):
-        self.unet_chunk_size = unet_chunk_size
-
-    def __call__(
-            self,
-            attn,
-            hidden_states,
-            encoder_hidden_states=None,
-            attention_mask=None):
-        batch_size, sequence_length, _ = hidden_states.shape
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-        query = attn.to_q(hidden_states)
-
-        is_cross_attention = encoder_hidden_states is not None
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.cross_attention_norm:
-            encoder_hidden_states = attn.norm_cross(encoder_hidden_states)
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-        # Sparse Attention
-        if not is_cross_attention:
-            video_length = key.size()[0] // self.unet_chunk_size
-            # former_frame_index = torch.arange(video_length) - 1
-            # former_frame_index[0] = 0
-            former_frame_index = [0] * video_length
-            key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
-            key = key[:, former_frame_index]
-            key = rearrange(key, "b f d c -> (b f) d c")
-            value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
-            value = value[:, former_frame_index]
-            value = rearrange(value, "b f d c -> (b f) d c")
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        return hidden_states

spaces/Cong723/gpt-academic-public/docs/waifu_plugin/waifu.css

@@ -1,290 +0,0 @@
-.waifu {
-    position: fixed;
-    bottom: 0;
-    z-index: 1;
-    font-size: 0;
-    -webkit-transform: translateY(3px);
-    transform: translateY(3px);
-}
-.waifu:hover {
-    -webkit-transform: translateY(0);
-    transform: translateY(0);
-}
-.waifu-tips {
-    opacity: 0;
-    margin: -20px 20px;
-    padding: 5px 10px;
-    border: 1px solid rgba(224, 186, 140, 0.62);
-    border-radius: 12px;
-    background-color: rgba(236, 217, 188, 0.5);
-    box-shadow: 0 3px 15px 2px rgba(191, 158, 118, 0.2);
-    text-overflow: ellipsis;
-    overflow: hidden;
-    position: absolute;
-    animation-delay: 5s;
-    animation-duration: 50s;
-    animation-iteration-count: infinite;
-    animation-name: shake;
-    animation-timing-function: ease-in-out;
-}
-.waifu-tool {
-    display: none;
-    color: #aaa;
-    top: 50px;
-    right: 10px;
-    position: absolute;
-}
-.waifu:hover .waifu-tool {
-    display: block;
-}
-.waifu-tool span {
-    display: block;
-    cursor: pointer;
-    color: #5b6c7d;
-    transition: 0.2s;
-}
-.waifu-tool span:hover {
-    color: #34495e;
-}
-.waifu #live2d{
-    position: relative;
-}
-
-@keyframes shake {
-    2% {
-        transform: translate(0.5px, -1.5px) rotate(-0.5deg);
-    }
-
-    4% {
-        transform: translate(0.5px, 1.5px) rotate(1.5deg);
-    }
-
-    6% {
-        transform: translate(1.5px, 1.5px) rotate(1.5deg);
-    }
-
-    8% {
-        transform: translate(2.5px, 1.5px) rotate(0.5deg);
-    }
-
-    10% {
-        transform: translate(0.5px, 2.5px) rotate(0.5deg);
-    }
-
-    12% {
-        transform: translate(1.5px, 1.5px) rotate(0.5deg);
-    }
-
-    14% {
-        transform: translate(0.5px, 0.5px) rotate(0.5deg);
-    }
-
-    16% {
-        transform: translate(-1.5px, -0.5px) rotate(1.5deg);
-    }
-
-    18% {
-        transform: translate(0.5px, 0.5px) rotate(1.5deg);
-    }
-
-    20% {
-        transform: translate(2.5px, 2.5px) rotate(1.5deg);
-    }
-
-    22% {
-        transform: translate(0.5px, -1.5px) rotate(1.5deg);
-    }
-
-    24% {
-        transform: translate(-1.5px, 1.5px) rotate(-0.5deg);
-    }
-
-    26% {
-        transform: translate(1.5px, 0.5px) rotate(1.5deg);
-    }
-
-    28% {
-        transform: translate(-0.5px, -0.5px) rotate(-0.5deg);
-    }
-
-    30% {
-        transform: translate(1.5px, -0.5px) rotate(-0.5deg);
-    }
-
-    32% {
-        transform: translate(2.5px, -1.5px) rotate(1.5deg);
-    }
-
-    34% {
-        transform: translate(2.5px, 2.5px) rotate(-0.5deg);
-    }
-
-    36% {
-        transform: translate(0.5px, -1.5px) rotate(0.5deg);
-    }
-
-    38% {
-        transform: translate(2.5px, -0.5px) rotate(-0.5deg);
-    }
-
-    40% {
-        transform: translate(-0.5px, 2.5px) rotate(0.5deg);
-    }
-
-    42% {
-        transform: translate(-1.5px, 2.5px) rotate(0.5deg);
-    }
-
-    44% {
-        transform: translate(-1.5px, 1.5px) rotate(0.5deg);
-    }
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-    46% {
-        transform: translate(1.5px, -0.5px) rotate(-0.5deg);
-    }
-
-    48% {
-        transform: translate(2.5px, -0.5px) rotate(0.5deg);
-    }
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-    50% {
-        transform: translate(-1.5px, 1.5px) rotate(0.5deg);
-    }
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-    52% {
-        transform: translate(-0.5px, 1.5px) rotate(0.5deg);
-    }
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-    54% {
-        transform: translate(-1.5px, 1.5px) rotate(0.5deg);
-    }
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-    56% {
-        transform: translate(0.5px, 2.5px) rotate(1.5deg);
-    }
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-    58% {
-        transform: translate(2.5px, 2.5px) rotate(0.5deg);
-    }
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-    60% {
-        transform: translate(2.5px, -1.5px) rotate(1.5deg);
-    }
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-    62% {
-        transform: translate(-1.5px, 0.5px) rotate(1.5deg);
-    }
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-    64% {
-        transform: translate(-1.5px, 1.5px) rotate(1.5deg);
-    }
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-    66% {
-        transform: translate(0.5px, 2.5px) rotate(1.5deg);
-    }
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-    68% {
-        transform: translate(2.5px, -1.5px) rotate(1.5deg);
-    }
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-    70% {
-        transform: translate(2.5px, 2.5px) rotate(0.5deg);
-    }
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-    72% {
-        transform: translate(-0.5px, -1.5px) rotate(1.5deg);
-    }
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-    74% {
-        transform: translate(-1.5px, 2.5px) rotate(1.5deg);
-    }
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-    76% {
-        transform: translate(-1.5px, 2.5px) rotate(1.5deg);
-    }
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-    78% {
-        transform: translate(-1.5px, 2.5px) rotate(0.5deg);
-    }
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-    80% {
-        transform: translate(-1.5px, 0.5px) rotate(-0.5deg);
-    }
-
-    82% {
-        transform: translate(-1.5px, 0.5px) rotate(-0.5deg);
-    }
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-    84% {
-        transform: translate(-0.5px, 0.5px) rotate(1.5deg);
-    }
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-    86% {
-        transform: translate(2.5px, 1.5px) rotate(0.5deg);
-    }
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-    88% {
-        transform: translate(-1.5px, 0.5px) rotate(1.5deg);
-    }
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-    90% {
-        transform: translate(-1.5px, -0.5px) rotate(-0.5deg);
-    }
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-    92% {
-        transform: translate(-1.5px, -1.5px) rotate(1.5deg);
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-    94% {
-        transform: translate(0.5px, 0.5px) rotate(-0.5deg);
-    }
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-    96% {
-        transform: translate(2.5px, -0.5px) rotate(-0.5deg);
-    }
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-    98% {
-        transform: translate(-1.5px, -1.5px) rotate(-0.5deg);
-    }
-
-    0%, 100% {
-        transform: translate(0, 0) rotate(0);
-    }
-}
-@font-face {
-  font-family: 'Flat-UI-Icons';
-  src: url('flat-ui-icons-regular.eot');
-  src: url('flat-ui-icons-regular.eot?#iefix') format('embedded-opentype'), url('flat-ui-icons-regular.woff') format('woff'), url('flat-ui-icons-regular.ttf') format('truetype'), url('flat-ui-icons-regular.svg#flat-ui-icons-regular') format('svg');
-}
-[class^="fui-"],
-[class*="fui-"] {
-  font-family: 'Flat-UI-Icons';
-  speak: none;
-  font-style: normal;
-  font-weight: normal;
-  font-variant: normal;
-  text-transform: none;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-.fui-cross:before {
-  content: "\e609";
-}
-.fui-info-circle:before {
-  content: "\e60f";
-}
-.fui-photo:before {
-  content: "\e62a";
-}
-.fui-eye:before {
-  content: "\e62c";
-}
-.fui-chat:before {
-  content: "\e62d";
-}
-.fui-home:before {
-  content: "\e62e";
-}
-.fui-user:before {
-  content: "\e631";
-}