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spaces/1gistliPinn/ChatGPT4/Examples/Counter Strike 1.6 Maps Free Download AA Dima - Why You Should Play on This Map.md

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-<h3>Option 2: Use an emulator like BlueStacks to play the mobile version of GTA Vice City on your PC</h3>
-<p>This is another way to download GTA Vice City on your PC, but it requires some extra steps and software. You will need to use an emulator like BlueStacks, which is a program that allows you to run Android apps on your PC. You will also need to buy the mobile version of GTA Vice City from the Google Play Store, which costs $4.99. Here are the steps:</p>
-<h4>Step 1: Download and install BlueStacks on your PC</h4>
-<p>Go to <a href="">https://www.bluestacks.com/</a> and click on the Download BlueStacks button. Follow the instructions to download and install BlueStacks on your PC. You will need about 5 GB of free space on your hard drive to install BlueStacks.</p>
-<h4>Step 2: Launch BlueStacks and sign in with your Google account</h4>
-<p>After you have installed BlueStacks, launch it from your desktop or start menu. You will see a window with various options and features. Click on the Google Play Store icon at the bottom right corner of the window. You will be asked to sign in with your Google account. If you already have one, enter your email and password and click on Sign In. If you don't have one, click on Create a New Account and follow the instructions to create one.</p>
-<h4>Step 3: Search for GTA Vice City in the Google Play Store and install it</h4>
-<p>Once you are signed in, you will see a page with various apps and games that are available for download from the Google Play Store. Type GTA Vice City in the search bar and press Enter. You will see a page with the details of GTA Vice City, such as the price, the rating, the reviews, and the screenshots. Click on the Install button and follow the instructions to buy and install GTA Vice City on your PC.</p>
-<h4>Step 4: Enjoy playing GTA Vice City on your PC with BlueStacks features</h4>
-<p>After you have installed GTA Vice City, you can launch it from the BlueStacks home screen. You will see a window with the game running on your PC. You can use your mouse and keyboard to control the game, or you can customize the controls according to your preference. You can also use the BlueStacks features to enhance your gaming experience, such as recording your gameplay, taking screenshots, streaming your gameplay, and more.</p>
-<h2>Conclusion</h2>
-<p>GTA Vice City is a classic game that deserves to be played by every gamer who loves open-world action-adventure games. It has a captivating story, a stunning soundtrack, and a fun and diverse gameplay that will keep you entertained for hours. In this article, we have shown you how to download GTA Vice City on your PC using two different methods: buying the game from the Rockstar Games website or using an emulator like BlueStacks to play the mobile version of GTA Vice City on your PC. Both methods are easy and reliable, and you can choose the one that suits you best. We hope you have enjoyed this article and found it helpful. Now, go ahead and download GTA Vice City on your PC and have fun!</p>
-<p>If you have any questions or feedback about this article, please feel free to leave a comment below. We would love to hear from you!</p>
- <h3>FAQs</h3>
-<ul>
-<li><b>Q: How much does GTA Vice City cost on PC?</b></li>
-<li>A: GTA Vice City costs $9.99 on the Rockstar Games website and $4.99 on the Google Play Store.</li>
-<li><b>Q: What are the system requirements for GTA Vice City on PC?</b></li>
-<li>A: The minimum system requirements for GTA Vice City on PC are: Windows XP/Vista/7/8/10, 800 MHz Intel Pentium III or AMD Athlon processor, 128 MB of RAM, 32 MB video card with DirectX 9.0 compatible drivers, 915 MB of free hard disk space, and DirectX 9.0 compatible sound card.</li>
-<li><b>Q: Can I play GTA Vice City online with other players?</b></li>
-<li>A: GTA Vice City does not have an official online multiplayer mode, but there are some unofficial mods that allow you to play GTA Vice City online with other players. One of them is <a href="">https://www.mtasa.com/</a>, which is a free mod for GTA San Andreas that also supports GTA Vice City.</li>
-<li><b>Q: Can I play GTA Vice City on other devices besides PC?</b></li>
-<li>A: Yes, you can play GTA Vice City on other devices besides PC. GTA Vice City is also available for PlayStation 2, PlayStation 3, PlayStation 4, Xbox, Xbox 360, Xbox One, iOS, Android, Mac OS X, and Fire OS.</li>
-<li><b>Q: What are some of the best cheats for GTA Vice City?</b></li>
-<li>A: There are many cheats for GTA Vice City that can make the game more fun and easy. Some of them are: THUGSTOOLS (all weapons), ASPIRINE (full health), PRECIOUSPROTECTION (full armor), LEAVEMEALONE (lower wanted level), PANZER (spawn a tank), GETTHEREFAST (spawn a Sabre Turbo), GETTHEREVERYFASTINDEED (spawn a Hotring Racer), GETTHEREAMAZINGLYFAST (spawn a Hotring Racer 2), FANNYMAGNET (women follow you), BIGBANG (blow up all cars), SEAWAYS (cars can drive on water), COMEFLYWITHME (cars can fly), ICANTTAKEITANYMORE (commit suicide), and LIFEISPASSINGMEBY (speed up time).</li>
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spaces/1phancelerku/anime-remove-background/Download Garena Bed Wars APK for Android - Team up and destroy your enemies beds in this PVP game.md

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-<h1>Garena Bed Wars APK Download: How to Play the Ultimate Sandbox Game on Your Android Device</h1>
-<p>Are you looking for a fun and exciting game that lets you unleash your creativity and imagination? Do you want to play with your friends or other players from around the world in a team-based PVP game? If you answered yes, then you should try out <strong>Garena Bed Wars</strong>, the ultimate sandbox game that has attracted millions of players in <strong>Garena Blockman GO</strong>.</p>
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-<p>Garena Bed Wars is a game where you have to protect your bed at your own base while using all the tools you have at your disposal to destroy the opponents' beds and become the final victor. You can collect resources, buy equipment, build bridges, attack enemies, and cooperate with your teammates in this thrilling and addictive game. You can also customize your character, choose from different maps and modes, and enjoy countless minigames from different genres.</p>
-<p>If you want to play Garena Bed Wars on your Android device, you will need to download and install the <strong>Garena Bed Wars APK</strong> file, which is a modified version of the original game that allows you to access all the features and benefits without any restrictions. In this article, we will show you how to download and install Garena Bed Wars APK on your Android device, how to play Garena Bed Wars as a beginner, and how to win Garena Bed Wars with tips and tricks from pro players. Let's get started!</p>
- <h2>How to Play Garena Bed Wars: A Beginner's Guide</h2>
-<p>If you are new to Garena Bed Wars, you might feel overwhelmed by the rules and mechanics of the game. Don't worry, we are here to help you learn the basics of how to play Garena Bed Wars in a few simple steps.</p>
-<h3>How to join a game and choose a team</h3>
-<p>When you launch Garena Bed Wars, you will see a list of available games that you can join. You can also create your own game by tapping on the "+" icon at the top right corner of the screen. You can choose from different maps, such as Sky Island, Desert Island, Snow Island, etc., and different modes, such as Solo, Duo, Squad, etc. You can also set a password for your game if you want to play with your friends only.</p>
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-<p>Once you join or create a game, you will be taken to the lobby where you can choose your team. There are four teams in each game: Red, Blue, Green, and Yellow. You can tap on the team icon at the bottom of the screen to join or switch teams. You can also chat with other players in the lobby or invite your friends by tapping on the "Invite" button at the top left corner of the screen.</p>
-<h3>How to collect resources and buy equipment</h3>
-<p>When the game starts, you will spawn on your island with your teammates. Each island has its own base with a bed. As long as the bed is not destroyed, you and your teammates can be revived if you die. Therefore, protecting your bed is the most important task in the game.</p>
-<p>To protect your bed, you will need to collect resources and buy equipment. There are three types of resources in the game: iron, gold, and diamonds. Iron and gold are generated by the generators on your island. You can use them to buy items from the shop, such as blocks, weapons, armor, tools, etc. Diamonds are generated by the generators on the middle island. You can use them to buy upgrades from the team shop, such as sharpness, protection, haste, etc.</p>
-<p>To collect resources, you will need to go to the generators and pick up the items that are dropped on the ground. You can also get resources by killing enemies or breaking their beds. To buy equipment, you will need to go to the shop or the team shop and tap on the item you want to buy. You can also use the quick buy menu at the bottom of the screen to buy items faster.</p>
-<h3>How to build bridges and attack enemies</h3>
-<p>To reach other islands and attack enemies, you will need to build bridges. You can use blocks that you buy from the shop to place them on the ground and create a path. You can also use ender pearls or launch pads to teleport or jump to other islands.</p>
-<p>To attack enemies, you will need to use weapons that you buy from the shop, such as swords, bows, axes, etc. You can also use items that have special effects, such as fireballs, TNT, snowballs, etc. Your goal is to break their beds and kill them before they respawn. You can also use traps that you buy from the team shop, such as alarm trap, counter-offensive trap, etc., to defend your island from invaders.</p>
-<h3>How to protect your bed and survive</h3>
-<p>To protect your bed, you will need to cover it with blocks that you buy from the shop. You can use different types of blocks, such as wool, wood, stone, etc., to make it harder for enemies to break your bed. You can also use items that have special effects, such as water buckets, obsidian blocks, iron golems, etc., to enhance your defense.</p>
-<p>To survive, you will need to avoid falling into the void or getting killed by enemies. You can use armor that you buy from the shop, such as leather armor, chainmail armor, iron armor, etc., to reduce the damage you take. You can also use items that have special effects, such as golden apples, potions, invisibility cloaks, etc., to heal yourself or gain an advantage.</p>
- <h2>How to Win Garena Bed Wars: Tips and Tricks from Pro Players</h2>
-<p>If you want to win Garena Bed Wars more often and become a pro player, you will need to learn some tips and tricks that can help you improve your skills and performance in the game. Here are some of them:</p>
-<h3>How to use the best strategies and tactics for different maps and modes</h3>
-<p>Each map and mode in Garena Bed Wars has its own characteristics and challenges that require different strategies and tactics. For example:</p>
-<ul>
-<li>In Sky Island map, you can use ender pearls or launch pads to move around quickly and surprise your enemies.</li>
-<li>In Desert Island map, you can use fireballs or TNT to destroy the sand bridges and cut off your enemies' access.</li>
-<li>In Snow Island map, you can use snowballs or ice bridges to knock off your enemies or create shortcuts.</li>
-<li>In Solo mode, you can focus on collecting diamonds and upgrading your equipment as soon as possible.</li>
-<li>In Duo mode, you can coordinate with your partner and split up tasks such as collecting resources, buying items, building bridges, etc.</li>
-<li>In Squad mode, you can communicate with your teammates and assign roles such as defender, attacker, supporter, etc.</li>
-</ul>
-<h3>How to cooperate with your teammates and communicate effectively</h3>
-<p>Garena Bed Wars is a team-based game that requires cooperation and communication among teammates. You can use the chat function or the voice chat function to communicate with your teammates and share information, such as enemy locations, resource status, attack plans, etc. You can also use the team signals or the emoticons to express your emotions or intentions, such as happy, angry, sad, etc.</p>
-<p>When cooperating with your teammates, you should follow some basic etiquette and rules, such as:</p>
-<ul>
-<li>Respect your teammates and do not insult or troll them.</li>
-<li>Listen to your teammates and do not ignore or contradict them.</li>
-<li>Help your teammates and do not abandon or betray them.</li>
-<li>Share your resources and do not hog or waste them.</li>
-<li>Follow your team's strategy and do not act on your own or sabotage it.</li>
-</ul>
-<h3>How to avoid common mistakes and deal with hackers</h3>
-<p>Garena Bed Wars is a game that requires skill and strategy, but also luck and chance. Sometimes, you might make some common mistakes that can cost you the game, such as:</p>
-<ul>
-<li>Leaving your bed unprotected or poorly defended.</li>
-<li>Rushing to attack without proper equipment or backup.</li>
-<li>Being too greedy or reckless and exposing yourself to danger.</li>
-<li>Being too passive or timid and missing opportunities to strike.</li>
-<li>Being too predictable or repetitive and allowing your enemies to counter you.</li>
-</ul>
-<p>To avoid these mistakes, you should always be aware of your surroundings and your enemies' actions. You should also learn from your mistakes and improve your skills and strategies. You can also watch some videos or streams of pro players and learn from their tips and tricks.</p>
-<p>Sometimes, you might encounter some hackers who use cheats or hacks to gain an unfair advantage in the game, such as flying, speed hacking, aimbotting, etc. These hackers can ruin the fun and balance of the game and make you lose unfairly. To deal with hackers, you should report them to the developers by tapping on the "Report" button at the end of the game. You can also block them by tapping on their name and choosing "Block". You can also avoid playing with hackers by joining games that have anti-cheat systems or moderators.</p>
- <h2>Conclusion</h2>
-<p>Garena Bed Wars is a game that offers endless fun and excitement for players who love sandbox games and PVP games. You can play with your friends or other players from around the world in a team-based game where you have to protect your bed and destroy the opponents' beds. You can also enjoy various features and benefits by downloading and installing Garena Bed Wars APK on your Android device.</p>
-<p>If you want to play Garena Bed Wars on your Android device, you can follow these steps:</p>
-<ol>
-<li>Download Garena Bed Wars APK from a trusted source by clicking on this link: [Garena Bed Wars APK Download].</li>
-<li>Allow unknown sources on your device by going to Settings > Security > Unknown Sources.</li>
-<li>Install Garena Bed Wars APK by tapping on the file and following the instructions.</li>
-<li>Launch Garena Bed Wars and enjoy the game!</li>
-</ol>
-<p>We hope this article has helped you learn how to play Garena Bed Wars on your Android device. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<h3>What are the minimum requirements to play Garena Bed Wars on Android?</h3>
-<p>The minimum requirements to play Garena Bed Wars on Android are:</p>
-<ul>
-<li>Android 4.4 or higher</li>
-<li>2 GB of RAM or higher</li>
-<li>500 MB of free storage space or higher</li>
-<li>A stable internet connection</li>
-</ul>
- <h3>Is Garena Bed Wars safe and legal to download and play?</h3>
-<p>Garena Bed Wars is safe and legal to download and play as long as you download it from a trusted source, such as [Garena Blockman GO] or [Garena Bed Wars APK Download]. However, you should be careful of downloading Garena Bed Wars from unknown sources, as they might contain viruses or malware that can harm your device or steal your personal information. You should also avoid using cheats or hacks that can get you banned from the game or cause other problems.</p>
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-<p>You can update Garena Bed Wars to the latest version by following these steps:</p>
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-<li>Go to [Garena Blockman GO] or [Garena Bed Wars APK Download] and check if there is a new version available.</li>
-<li>If there is a new version, download it and install it on your device.</li>
-<li>Launch Garena Bed Wars and enjoy the new features and improvements.</li>
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-<p>You can also enable the auto-update function on your device by going to Settings > Apps > Garena Bed Wars > Auto-update. This way, you will always have the latest version of Garena Bed Wars on your device.</p>
- <h3>How can I contact the developers of Garena Bed Wars for support or feedback?</h3>
-<p>If you have any issues or suggestions regarding Garena Bed Wars, you can contact the developers of Garena Bed Wars by using one of these methods:</p>
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-<li>Email: You can send an email to [[email protected]] and describe your problem or idea in detail. You should also attach some screenshots or videos if possible.</li>
-<li>Facebook: You can visit the official Facebook page of Garena Blockman GO at [https://www.facebook.com/Blockmango-608882679545354/] and leave a message or comment.</li>
-<li>Discord: You can join the official Discord server of Garena Blockman GO at [https://discord.gg/8fJ9Z7F] and chat with other players or moderators.</li>
-</ul>
-<p>The developers of Garena Bed Wars are always happy to hear from their players and will try their best to solve your problems or implement your suggestions.</p>
- <h3>How can I play Garena Bed Wars with my friends?</h3>
-<p>If you want to play Garena Bed Wars with your friends, you can follow these steps:</p>
-<ol>
-<li>Add your friends as contacts by tapping on the "Contacts" button at the bottom of the screen and entering their usernames or IDs.</li>
-<li>Create a game by tapping on the "+" icon at the top right corner of the screen and choosing a map and a mode.</li>
-<li>Set a password for your game by tapping on the "Password" button at the top left corner of the screen and entering a code.</li>
-<li>Invite your friends by tapping on the "Invite" button at the top left corner of the screen and selecting your contacts.</li>
-<li>Wait for your friends to join your game and choose a team.</li>
-<li>Start the game by tapping on the "Start" button at the bottom of the screen and have fun!</li>
-</ol></p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Warpath Jurassic Park for Android and Unleash Your Inner Dinosaur.md

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-<h1>Jurassic Park Warpath: How to Download and Play on Android</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of dinosaurs and fighting games, you might be interested in Jurassic Park Warpath, a classic PlayStation game that lets you control various prehistoric creatures and battle against each other in different locations from the Jurassic Park movies. But did you know that you can also play this game on your Android device? In this article, we will show you how to download and play Jurassic Park Warpath on Android, as well as some tips and tricks to help you enjoy this game even more.</p>
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- <p>Jurassic Park Warpath is a fighting video game released on the PlayStation console in 1999. It is a spin-off of the films Jurassic Park and The Lost World: Jurassic Park, and features 14 playable dinosaurs, each with their own moves, animations, and sounds. The game has four modes: arcade, versus, survival, and exhibition. In arcade mode, you choose a dinosaur and fight through eight stages against different opponents. In versus mode, you can play against another player or the computer. In survival mode, you have to defeat as many enemies as possible without dying. In exhibition mode, you can watch two dinosaurs fight without any input from you.</p>
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spaces/1phancelerku/anime-remove-background/Dragon Ball Z Budokai Tenkaichi 3 PC Version How to Get and Install It.md

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-<h1>How to Download Dragon Ball Z Budokai Tenkaichi 3 on PC</h1>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is one of the most popular and beloved games in the Dragon Ball franchise. It features over 150 characters, 30 stages, and a variety of game modes that will keep you entertained for hours. But what if you want to play it on your PC instead of your PlayStation 2 or Wii console? Well, you're in luck, because in this article, we will show you how to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC using a free emulator called Dolphin. We will also give you some tips and tricks to enhance your gaming experience. So, without further ado, let's get started!</p>
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-<h3>What is Dragon Ball Z Budokai Tenkaichi 3?</h3>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is a fighting game based on the anime and manga series Dragon Ball. It was developed by Spike and published by Atari for the PlayStation 2 and Wii in 2007. It is the third and final installment in the Budokai Tenkaichi series, which is also known as Dragon Ball Z: Sparking! in Japan.</p>
-<p>The game follows the events of the Dragon Ball story from the Saiyan Saga to the Kid Buu Saga, as well as some original scenarios and what-if scenarios. You can choose from over 150 playable characters, each with their own unique moves, transformations, and abilities. You can also customize your characters with various items and skills that affect their stats and performance.</p>
-<p>The game offers several modes of play, such as Story Mode, where you can relive the epic battles of the series; Dragon History, where you can explore different timelines and scenarios; Ultimate Battle, where you can test your skills against various opponents; World Tournament, where you can compete for prizes and glory; Duel, where you can fight against a friend or the CPU; Training, where you can practice your moves and combos; Evolution Z, where you can edit your character's attributes; Data Center, where you can view your records and achievements; Replay, where you can watch your saved replays; and Options, where you can adjust the game settings.</p>
- <h3>Why play it on PC?</h3>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is a great game that deserves to be played by every fan of the series. However, not everyone has access to a PlayStation 2 or Wii console, or maybe they just prefer to play games on their PC. That's why playing it on PC using an emulator is a good option for many reasons:</p>
-<ul>
-<li>You can enjoy the game in high-definition graphics and sound quality, thanks to the emulator's enhancements and features.</li>
-<li>You can use any controller or keyboard that suits your preference and comfort.</li>
-<li>You can save your progress anytime and anywhere, thanks to the emulator's save states and memory cards.</li>
-<li>You can unlock all the characters and stages without having to complete the game or use cheat codes.</li>
-<li>You can use cheats and mods to modify the game according to your liking.</li>
-<li>You can play online with other players using netplay or LAN.</li>
-<li>You can avoid any issues or errors that may <h2>Requirements and Preparations</h2>
-<h3>System Requirements</h3>
-<p>Before you download and play Dragon Ball Z Budokai Tenkaichi 3 on PC, you need to make sure that your PC meets the minimum system requirements for running the emulator and the game. Here are the recommended system requirements for Dolphin Emulator:</p>
-<table>
-<tr>
-<th>Component</th>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>Operating System</td>
-<td>Windows 7 (x64) or above, macOS 10.10 or above, Linux</td>
-<td>Windows 10 (x64), macOS 10.13 or above, Linux</td>
-</tr>
-<tr>
-<td>Processor (CPU)</td>
-<td>A CPU with SSE2 support. A modern CPU with a high single-thread performance rating.</td>
-<td>An Intel Core i5-4670K or AMD Ryzen 5 3600 or better.</td>
-</tr>
-<tr>
-<td>Memory (RAM)</td>
-<td>2 GB or more</td>
-<td>4 GB or more</td>
-</tr>
-<tr>
-<td>Graphics Card (GPU)</td>
-<td>A GPU that supports DirectX 11.1 or OpenGL 4.4.</td>
-<td>A GPU that supports Vulkan, DirectX 12, or Metal.</td>
-</tr>
-<tr>
-<td>Storage Space</td>
-<td>At least 5 GB of free space for the emulator and the game files.</td>
-<td>At least 10 GB of free space for the emulator and the game files.</td>
-</tr>
-<tr>
-<td>Controller or Keyboard</td>
-<td>A compatible controller or keyboard that can be mapped to the emulator.</td>
-<td>A PlayStation 2 or Wii controller with an adapter, or a controller that mimics their layout.</td>
-</tr>
-</table>
- <h3>Emulator and Game Files</h3>
-<p>The emulator that we will use to play Dragon Ball Z Budokai Tenkaichi 3 on PC is Dolphin Emulator, which is a free and open-source software that can run games for the Nintendo GameCube and Wii consoles. You can download the latest version of Dolphin Emulator from its official website or from its GitHub page. You can choose between the stable version, which is more stable and tested, or the development version, which is more updated and has more features, but may have some bugs and issues.</p>
- <p>The game file that we will use to play Dragon Ball Z Budokai Tenkaichi 3 on PC is an ISO file, which is a disc image of the original game disc. You can either rip your own game disc using a DVD drive and a software like ImgBurn, or you can download an ISO file from a reputable source online. However, downloading an ISO file may be illegal in some countries, so we do not condone or encourage piracy. Please only download an ISO file if you own a legal copy of the game.</p>
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- <h3>Controller and Keyboard Settings</h3>
- <p>To play Dragon Ball Z Budokai Tenkaichi 3 on PC, you will need a controller or a keyboard that can be configured to the emulator. You can use any controller or keyboard that is compatible with your PC, but we recommend using a PlayStation 2 or Wii controller with an adapter, or a controller that mimics their layout, such as a Logitech F310 or an Xbox One controller. This is because the game was designed for these controllers, and using them will give you a more authentic and comfortable experience.</p>
- <p>To configure your controller or keyboard to the emulator, you will need to go to the Controllers menu in Dolphin Emulator and select the appropriate device for each port. For example, if you want to use a PlayStation 2 controller for Port 1, you will need to select Emulated Wii Remote for Port 1, and then click Configure. Then, you will need to map each button and axis of your controller to the corresponding input of the Wii Remote. You can also adjust the sensitivity and deadzone of each input if needed. You can also save your configuration as a profile for future use.</p>
- <p>If you want to use a keyboard for Port 1, you will need to select Emulated Wii Remote for Port 1, and then click Configure. Then, you will need to map each key of your keyboard to the corresponding input of the Wii Remote. You can also adjust the sensitivity and deadzone of each input if needed. You can also save your configuration as a profile for future use.</p>
- <p>You can also configure other ports if you want to play with multiple players or use other devices. For example, if you want to use a GameCube controller for Port 2, you will need to select Standard Controller for Port 2, and then click Configure. Then, you will need to map each button and axis of your controller to the corresponding input of the GameCube controller. You can also adjust the sensitivity and deadzone of each input if needed. You can also save your configuration as a profile for future use.</p>
- <h2>Steps to Download and Play Dragon Ball Z Budokai Tenkaichi 3 on PC</h2>
-<h3>Step 1: Download and Install Dolphin Emulator</h3>
-<p>The first step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to download and install Dolphin Emulator on your PC. You can download the latest version of Dolphin Emulator from its official website or from its GitHub page. You can choose between the stable version or the development version, depending on your preference. Once you have downloaded the emulator, you will need to extract it to a folder of your choice using a software like WinRAR or 7-Zip. Then, you will need to run the Dolphin.exe file to launch the emulator.</p>
- <h3>Step 2: Download and Extract Dragon Ball Z Budokai Tenkaichi 3 ISO File</h3>
-<p>The second step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to download and extract the Dragon Ball Z Budokai Tenkaichi 3 ISO file on your PC. You can either rip your own game disc using a DVD drive and a software like ImgBurn, or you can download an ISO file from a reputable source online. However, downloading an ISO file may be illegal in some countries, so we do not condone or encourage piracy. Please only download an ISO file if you own a legal copy of the game.</p>
- <p>Once you have downloaded the ISO file, you will need to extract it to a folder of your choice using a software like WinRAR or 7-Zip. Then, you will need to move the ISO file to a folder where you can easily access it from the emulator.</p>
- <h3>Step 3: Configure Dolphin Emulator Settings</h3>
-<p>The third step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to configure Dolphin Emulator settings to optimize the game performance and quality. You can access the settings menu by clicking on the Options tab in Dolphin Emulator. Here are some of the settings that you can adjust:</p>
-<ul>
-<li>General: Here, you can change the language, theme, interface, and hotkeys of the emulator.</li>
-<li>Graphics: Here, you can change the video backend, resolution, aspect ratio, fullscreen mode, anti-aliasing, anisotropic filtering, enhancements, hacks, and advanced options of the emulator.</li>
-<li>Audio: Here, you can change the audio backend, volume, latency, stretching, and DSP options of the emulator.</li>
-<li>Controllers: Here, you can configure your controller or keyboard settings for each port.</li>
-<li>Paths: Here, you can add or remove folders where the emulator will search for game files.</li>
-<li>Config: Here, you can change the general, interface, audio, gamecube, wii, advanced, and debug options of the emulator.</li>
-</ul>
-<p>You can experiment with different settings to find the best ones for your PC and game. However, here are some recommended settings that work well for most users:</p>
-<ul>
-<li>Graphics: Video Backend - Vulkan (if supported by your GPU), Resolution - Native (640x528) or higher (up to 4x), Aspect Ratio - Auto or Force 16:9 (if you want widescreen), Fullscreen Mode - On (if you want fullscreen), Anti-Aliasing - None or MSAA 2x (if your GPU can handle it), Anisotropic Filtering - 1x or higher (up to 16x), Enhancements - Scaled EFB Copy (On), Force Texture Filtering (On), Disable Fog (Off), Per-Pixel Lighting (Off), Widescreen Hack (On if you want widescreen), Hacks - Skip EFB Access from CPU (Off), Ignore Format Changes (On), Store EFB Copies to Texture Only (On), Texture Cache Accuracy (Fast), External Frame Buffer (Disable), Fast Depth Calculation (On), Disable Bounding Box (On).</li>
-<li>Audio: Audio Backend - Cubeb or XAudio2 (depending on your OS), Volume - 100% or lower (depending on your preference), Latency - Low or Medium (depending on your CPU), Stretching - Off or Low (depending on your preference), DSP Options - DSP HLE Emulation (Fast) or DSP LLE Recompiler (Accurate).</li>
-<li>Controllers: Configure your controller or keyboard settings according to your preference and comfort.</li>
-<li>Paths: Add the folder where you have the Dragon Ball Z Budokai Tenkaichi 3 ISO file.</li>
-<li>Config: General - Enable Dual Core (On), Enable Idle Skipping (On), JIT Recompiler (Recommended), Interface - Use Panic Handlers (Off), Audio - DSP Emulator Engine (DSP HLE Emulation or DSP LLE Recompiler), GameCube - Device Settings (None for all ports), Wii - Aspect Ratio (16:9 if you want widescreen), Advanced - CPU Clock Override (100% or higher if your CPU can handle it), Debug - Enable CPU Clock Override (On if you want to use CPU Clock Override).</li>
-</ul>
- <h3>Step 4: Load the Game and Enjoy</h3>
-<p>The final step to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC is to load the game and enjoy it. You can load the game by clicking on the Open button in Dolphin Emulator and browsing to the folder where you have the ISO file. Alternatively, you can drag and drop the ISO file to the Dolphin Emulator window. The game will start automatically and you will see the title screen. You can use your controller or keyboard to navigate the menus and select your game mode. You can also access the emulator's menu by pressing Esc or F1 on your keyboard. From there, you can save or load your progress, change your settings, take screenshots, record videos, and more.</p>
- <h2>Tips and Tricks for Playing Dragon Ball Z Budokai Tenkaichi 3 on PC</h2>
-<h3>How to Unlock All Characters and Stages</h3>
-<p>One of the best features of Dragon Ball Z Budokai Tenkaichi 3 is the huge roster of characters and stages that you can choose from. However, not all of them are available from the start. You will need to unlock them by completing certain tasks or using cheat codes. Here are some ways to unlock all characters and stages:</p>
-<ul>
-<li>Complete Story Mode: By completing Story Mode, you will unlock most of the characters and stages in the game. You will also unlock Dragon History, where you can play different scenarios and what-if stories.</li>
-<li>Complete Ultimate Battle: By completing Ultimate Battle, you will unlock some of the hidden characters and stages in the game. You will also unlock Sim Dragon, where you can create your own team of fighters and compete against other teams.</li>
-<li>Use Cheat Codes: By using cheat codes, you can unlock all characters and stages in the game instantly. However, this may affect your game experience and achievements. To use cheat codes, you will need to enable cheats in Dolphin Emulator's settings, and then download a cheat file for Dragon Ball Z Budokai Tenkaichi 3 from a reliable source online. Then, you will need to place the cheat file in the Cheats folder of Dolphin Emulator, and then activate the cheats in the game properties.</li>
-</ul>
- <h3>How to Use Cheats and Mods</h3>
-<p>Besides unlocking all characters and stages, you can also use cheats and mods to modify the game according to your liking. You can use cheats to change your character's stats, abilities, costumes, transformations, and more. You can also use mods to add new characters, stages, music, sound effects, graphics, and more. Here are some ways to use cheats and mods:</p>
-<ul>
-<li>Use Cheat Codes: As mentioned above, you can use cheat codes to modify the game by enabling cheats in Dolphin Emulator's settings, downloading a cheat file for Dragon Ball Z Budokai Tenkaichi 3 from a reliable source online, placing it in the Cheats folder of Dolphin Emulator, and activating it in the game properties.</li>
-<li>Use Mods: To use mods, you will need to download a mod file for Dragon Ball Z Budokai Tenkaichi 3 from a reputable source online, such as YouTube or Reddit. Then, you will need to extract it to a folder of your choice using a software like WinRAR or 7-Zip. Then, you will need to replace or add the mod files to the ISO file of Dragon Ball Z Budokai Tenkaichi 3 using a software like Wii Backup Manager or WiiScrubber. Then, you will need to load the modified ISO file in Dolphin Emulator and enjoy the mod.</li>
-</ul>
- <h3>How to Fix Common Issues and Errors</h3>
-<p>While playing Dragon Ball Z Budokai Tenkaichi 3 on PC using Dolphin Emulator, you may encounter some issues or errors that may affect your game performance or quality. Here are some common issues and errors that you may face and how to fix them:</p>
-<ul>
-<li>Black Screen or Freezing: This may happen if your PC does not meet the system requirements for running the emulator or the game, or if your emulator or game settings are not optimal. To fix this, you can try the following solutions:   - Lower your resolution, anti-aliasing, anisotropic filtering, and enhancements settings in the emulator's graphics menu.   - Disable any cheats or mods that may be causing conflicts or errors in the game.   - Update your video drivers and DirectX or Vulkan libraries to the latest versions.   - Run the emulator and the game as administrator and in compatibility mode for Windows 7 or 8.   - Check your ISO file for any corruption or damage using a software like WinMD5 or HashMyFiles.</li>
-<li>Slowdown or Lag: This may happen if your PC is not powerful enough to run the emulator or the game at full speed, or if your emulator or game settings are too high. To fix this, you can try the following solutions:   - Enable Dual Core and Idle Skipping in the emulator's general settings menu.   - Enable JIT Recompiler and Disable Bounding Box in the emulator's advanced settings menu.   - Enable CPU Clock Override and set it to a higher percentage in the emulator's debug settings menu.   - Lower your resolution, anti-aliasing, anisotropic filtering, and enhancements settings in the emulator's graphics menu.   - Disable any cheats or mods that may be slowing down the game.</li>
-<li>Audio Issues: This may happen if your audio settings are not compatible with the game, or if your PC's sound card or speakers are not working properly. To fix this, you can try the following solutions:   - Change your audio backend to Cubeb or XAudio2 in the emulator's audio settings menu.   - Lower your latency and enable stretching in the emulator's audio settings menu.   - Use DSP HLE Emulation or DSP LLE Recompiler in the emulator's audio settings menu.   - Update your sound drivers and codecs to the latest versions.   - Check your sound card and speakers for any defects or malfunctions.</li>
-<li>Controller Issues: This may happen if your controller is not configured correctly to the emulator, or if your controller is not working properly. To fix this, you can try the following solutions:   - Configure your controller settings for each port in the emulator's controllers menu.   - Adjust the sensitivity and deadzone of each input in the emulator's controllers menu.   - Save your controller configuration as a profile in the emulator's controllers menu.   - Update your controller drivers and firmware to the latest versions.   - Check your controller for any defects or malfunctions.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Dragon Ball Z Budokai Tenkaichi 3 is a fantastic game that every fan of Dragon Ball should play. However, if you don't have a PlayStation 2 or Wii console, you can still enjoy it on your PC using Dolphin Emulator. In this article, we have shown you how to download and play Dragon Ball Z Budokai Tenkaichi 3 on PC using Dolphin Emulator. We have also given you some tips and tricks to unlock all characters and stages, use cheats and mods, and fix common issues and errors. We hope that this article has been helpful and informative for you. Now, go ahead and have fun playing Dragon Ball Z Budokai Tenkaichi 3 on PC!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about playing Dragon Ball Z Budokai Tenkaichi 3 on PC:</p>
-<ul>
-<li>Q: Is Dolphin Emulator safe and legal to use?   - A: Yes, Dolphin Emulator is safe and legal to use, as long as you download it from its official website or GitHub page, and as long as you don't use it for piracy or illegal activities.</li>
-<li>Q: Is Dragon Ball Z Budokai Tenkaichi 3 compatible with Dolphin Emulator?   - A: Yes, Dragon Ball Z Budokai Tenkaichi 3 is compatible with Dolphin Emulator, both for the PlayStation 2 and Wii versions. However, some minor glitches or bugs may occur depending on your PC and game settings.</li>
-<li>Q: How can I play Dragon Ball Z Budokai Tenkaichi 3 online with other players?   - A: You can play Dragon Ball Z Budokai Tenkaichi 3 online with other players using netplay or LAN in Dolphin Emulator. To use netplay, you will need to join or host a netplay session with other players who have the same version of Dolphin Emulator and Dragon Ball Z Budokai Tenkaichi 3 as you. To use LAN, you will need to connect your PC with other PCs that have Dolphin Emulator and Dragon Ball Z Budokai Tenkaichi 3 installed on them.</li>
-<li>Q: How can I improve my game performance and quality?   - A: You can improve your game performance and quality by adjusting your emulator and game settings according to your PC's specifications and preferences. You can also update your PC's drivers and libraries to the latest versions. You can also use cheats and mods to enhance your game features and graphics.</li>
-<li>Q: Where can I find more information and support for playing Dragon Ball Z Budokai Tenkaichi 3 on PC?   - A: You can find more information and support for playing Dragon Ball Z Budokai Tenkaichi 3 on PC by visiting the official website and forums of Dolphin Emulator, or by searching online for guides, tutorials, videos, and reviews from other users and experts.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1toTree/lora_test/ppdiffusers/utils/dummy_paddle_objects.py

@@ -1,617 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 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.
-
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from . import DummyObject, requires_backends
-
-
-class ModelMixin(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class AutoencoderKL(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PriorTransformer(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class Transformer2DModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UNet1DModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UNet2DConditionModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UNet2DModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class VQModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-def get_constant_schedule(*args, **kwargs):
-    requires_backends(get_constant_schedule, ["paddle"])
-
-
-def get_constant_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_constant_schedule_with_warmup, ["paddle"])
-
-
-def get_cosine_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_cosine_schedule_with_warmup, ["paddle"])
-
-
-def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["paddle"])
-
-
-def get_linear_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_linear_schedule_with_warmup, ["paddle"])
-
-
-def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
-    requires_backends(get_polynomial_decay_schedule_with_warmup, ["paddle"])
-
-
-def get_scheduler(*args, **kwargs):
-    requires_backends(get_scheduler, ["paddle"])
-
-
-class DiffusionPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DanceDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDIMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDPMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KarrasVePipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class LDMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class LDMSuperResolutionPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KDPM2AncestralDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KDPM2DiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PNDMPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class RePaintPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class ScoreSdeVePipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class ScoreSdeVpPipeline(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDIMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DDPMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DPMSolverMultistepScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class DPMSolverSinglestepScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class EulerAncestralDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PreconfigEulerAncestralDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class EulerDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class HeunDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class IPNDMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class KarrasVeScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class PNDMScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class RePaintScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class SchedulerMixin(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class ScoreSdeVeScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class UnCLIPScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class VQDiffusionScheduler(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-
-class EMAModel(metaclass=DummyObject):
-    _backends = ["paddle"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["paddle"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["paddle"])

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r50.py

@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "cosface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/losses.py

@@ -1,42 +0,0 @@
-import torch
-from torch import nn
-
-
-def get_loss(name):
-    if name == "cosface":
-        return CosFace()
-    elif name == "arcface":
-        return ArcFace()
-    else:
-        raise ValueError()
-
-
-class CosFace(nn.Module):
-    def __init__(self, s=64.0, m=0.40):
-        super(CosFace, self).__init__()
-        self.s = s
-        self.m = m
-
-    def forward(self, cosine, label):
-        index = torch.where(label != -1)[0]
-        m_hot = torch.zeros(index.size()[0], cosine.size()[1], device=cosine.device)
-        m_hot.scatter_(1, label[index, None], self.m)
-        cosine[index] -= m_hot
-        ret = cosine * self.s
-        return ret
-
-
-class ArcFace(nn.Module):
-    def __init__(self, s=64.0, m=0.5):
-        super(ArcFace, self).__init__()
-        self.s = s
-        self.m = m
-
-    def forward(self, cosine: torch.Tensor, label):
-        index = torch.where(label != -1)[0]
-        m_hot = torch.zeros(index.size()[0], cosine.size()[1], device=cosine.device)
-        m_hot.scatter_(1, label[index, None], self.m)
-        cosine.acos_()
-        cosine[index] += m_hot
-        cosine.cos_().mul_(self.s)
-        return cosine

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/train.py

@@ -1,141 +0,0 @@
-import argparse
-import logging
-import os
-
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-import torch.utils.data.distributed
-from torch.nn.utils import clip_grad_norm_
-
-import losses
-from backbones import get_model
-from dataset import MXFaceDataset, SyntheticDataset, DataLoaderX
-from partial_fc import PartialFC
-from utils.utils_amp import MaxClipGradScaler
-from utils.utils_callbacks import CallBackVerification, CallBackLogging, CallBackModelCheckpoint
-from utils.utils_config import get_config
-from utils.utils_logging import AverageMeter, init_logging
-
-
-def main(args):
-    cfg = get_config(args.config)
-    try:
-        world_size = int(os.environ['WORLD_SIZE'])
-        rank = int(os.environ['RANK'])
-        dist.init_process_group('nccl')
-    except KeyError:
-        world_size = 1
-        rank = 0
-        dist.init_process_group(backend='nccl', init_method="tcp://127.0.0.1:12584", rank=rank, world_size=world_size)
-
-    local_rank = args.local_rank
-    torch.cuda.set_device(local_rank)
-    os.makedirs(cfg.output, exist_ok=True)
-    init_logging(rank, cfg.output)
-
-    if cfg.rec == "synthetic":
-        train_set = SyntheticDataset(local_rank=local_rank)
-    else:
-        train_set = MXFaceDataset(root_dir=cfg.rec, local_rank=local_rank)
-
-    train_sampler = torch.utils.data.distributed.DistributedSampler(train_set, shuffle=True)
-    train_loader = DataLoaderX(
-        local_rank=local_rank, dataset=train_set, batch_size=cfg.batch_size,
-        sampler=train_sampler, num_workers=2, pin_memory=True, drop_last=True)
-    backbone = get_model(cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size).to(local_rank)
-
-    if cfg.resume:
-        try:
-            backbone_pth = os.path.join(cfg.output, "backbone.pth")
-            backbone.load_state_dict(torch.load(backbone_pth, map_location=torch.device(local_rank)))
-            if rank == 0:
-                logging.info("backbone resume successfully!")
-        except (FileNotFoundError, KeyError, IndexError, RuntimeError):
-            if rank == 0:
-                logging.info("resume fail, backbone init successfully!")
-
-    backbone = torch.nn.parallel.DistributedDataParallel(
-        module=backbone, broadcast_buffers=False, device_ids=[local_rank])
-    backbone.train()
-    margin_softmax = losses.get_loss(cfg.loss)
-    module_partial_fc = PartialFC(
-        rank=rank, local_rank=local_rank, world_size=world_size, resume=cfg.resume,
-        batch_size=cfg.batch_size, margin_softmax=margin_softmax, num_classes=cfg.num_classes,
-        sample_rate=cfg.sample_rate, embedding_size=cfg.embedding_size, prefix=cfg.output)
-
-    opt_backbone = torch.optim.SGD(
-        params=[{'params': backbone.parameters()}],
-        lr=cfg.lr / 512 * cfg.batch_size * world_size,
-        momentum=0.9, weight_decay=cfg.weight_decay)
-    opt_pfc = torch.optim.SGD(
-        params=[{'params': module_partial_fc.parameters()}],
-        lr=cfg.lr / 512 * cfg.batch_size * world_size,
-        momentum=0.9, weight_decay=cfg.weight_decay)
-
-    num_image = len(train_set)
-    total_batch_size = cfg.batch_size * world_size
-    cfg.warmup_step = num_image // total_batch_size * cfg.warmup_epoch
-    cfg.total_step = num_image // total_batch_size * cfg.num_epoch
-
-    def lr_step_func(current_step):
-        cfg.decay_step = [x * num_image // total_batch_size for x in cfg.decay_epoch]
-        if current_step < cfg.warmup_step:
-            return current_step / cfg.warmup_step
-        else:
-            return 0.1 ** len([m for m in cfg.decay_step if m <= current_step])
-
-    scheduler_backbone = torch.optim.lr_scheduler.LambdaLR(
-        optimizer=opt_backbone, lr_lambda=lr_step_func)
-    scheduler_pfc = torch.optim.lr_scheduler.LambdaLR(
-        optimizer=opt_pfc, lr_lambda=lr_step_func)
-
-    for key, value in cfg.items():
-        num_space = 25 - len(key)
-        logging.info(": " + key + " " * num_space + str(value))
-
-    val_target = cfg.val_targets
-    callback_verification = CallBackVerification(2000, rank, val_target, cfg.rec)
-    callback_logging = CallBackLogging(50, rank, cfg.total_step, cfg.batch_size, world_size, None)
-    callback_checkpoint = CallBackModelCheckpoint(rank, cfg.output)
-
-    loss = AverageMeter()
-    start_epoch = 0
-    global_step = 0
-    grad_amp = MaxClipGradScaler(cfg.batch_size, 128 * cfg.batch_size, growth_interval=100) if cfg.fp16 else None
-    for epoch in range(start_epoch, cfg.num_epoch):
-        train_sampler.set_epoch(epoch)
-        for step, (img, label) in enumerate(train_loader):
-            global_step += 1
-            features = F.normalize(backbone(img))
-            x_grad, loss_v = module_partial_fc.forward_backward(label, features, opt_pfc)
-            if cfg.fp16:
-                features.backward(grad_amp.scale(x_grad))
-                grad_amp.unscale_(opt_backbone)
-                clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
-                grad_amp.step(opt_backbone)
-                grad_amp.update()
-            else:
-                features.backward(x_grad)
-                clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
-                opt_backbone.step()
-
-            opt_pfc.step()
-            module_partial_fc.update()
-            opt_backbone.zero_grad()
-            opt_pfc.zero_grad()
-            loss.update(loss_v, 1)
-            callback_logging(global_step, loss, epoch, cfg.fp16, scheduler_backbone.get_last_lr()[0], grad_amp)
-            callback_verification(global_step, backbone)
-            scheduler_backbone.step()
-            scheduler_pfc.step()
-        callback_checkpoint(global_step, backbone, module_partial_fc)
-    dist.destroy_process_group()
-
-
-if __name__ == "__main__":
-    torch.backends.cudnn.benchmark = True
-    parser = argparse.ArgumentParser(description='PyTorch ArcFace Training')
-    parser.add_argument('config', type=str, help='py config file')
-    parser.add_argument('--local_rank', type=int, default=0, help='local_rank')
-    main(parser.parse_args())

spaces/A00001/bingothoo/src/components/ui/dropdown-menu.tsx

@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DropdownMenuPrimitive from '@radix-ui/react-dropdown-menu'
-
-import { cn } from '@/lib/utils'
-
-const DropdownMenu = DropdownMenuPrimitive.Root
-
-const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger
-
-const DropdownMenuGroup = DropdownMenuPrimitive.Group
-
-const DropdownMenuPortal = DropdownMenuPrimitive.Portal
-
-const DropdownMenuSub = DropdownMenuPrimitive.Sub
-
-const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup
-
-const DropdownMenuSubContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubContent>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubContent>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubContent
-    ref={ref}
-    className={cn(
-      'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuSubContent.displayName =
-  DropdownMenuPrimitive.SubContent.displayName
-
-const DropdownMenuContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <DropdownMenuPrimitive.Portal>
-    <DropdownMenuPrimitive.Content
-      ref={ref}
-      sideOffset={sideOffset}
-      className={cn(
-        'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow animate-in data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2',
-        className
-      )}
-      {...props}
-    />
-  </DropdownMenuPrimitive.Portal>
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Item> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Item
-    ref={ref}
-    className={cn(
-      'relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none transition-colors focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName
-
-const DropdownMenuLabel = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Label> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Label
-    ref={ref}
-    className={cn(
-      'px-2 py-1.5 text-sm font-semibold',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName
-
-const DropdownMenuSeparator = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.Separator
-    ref={ref}
-    className={cn('-mx-1 my-1 h-px bg-muted', className)}
-    {...props}
-  />
-))
-DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName
-
-const DropdownMenuShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn('ml-auto text-xs tracking-widest opacity-60', className)}
-      {...props}
-    />
-  )
-}
-DropdownMenuShortcut.displayName = 'DropdownMenuShortcut'
-
-export {
-  DropdownMenu,
-  DropdownMenuTrigger,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuLabel,
-  DropdownMenuSeparator,
-  DropdownMenuShortcut,
-  DropdownMenuGroup,
-  DropdownMenuPortal,
-  DropdownMenuSub,
-  DropdownMenuSubContent,
-  DropdownMenuRadioGroup
-}

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/model.py

@@ -1,913 +0,0 @@
-""" CLAP Model
-
-Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-Adapted to the Audio Task.
-"""
-
-from collections import OrderedDict
-from dataclasses import dataclass
-from email.mime import audio
-from typing import Tuple, Union, Callable, Optional
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from .timm_model import TimmModel
-import logging
-from .utils import freeze_batch_norm_2d
-
-from .pann_model import create_pann_model
-from .htsat import create_htsat_model
-from transformers import BertModel, RobertaModel, BartModel
-from transformers.tokenization_utils_base import BatchEncoding
-
-
-class MLPLayers(nn.Module):
-    def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1):
-        super(MLPLayers, self).__init__()
-        self.nonlin = nonlin
-        self.dropout = dropout
-
-        sequence = []
-        for u0, u1 in zip(units[:-1], units[1:]):
-            sequence.append(nn.Linear(u0, u1))
-            sequence.append(self.nonlin)
-            sequence.append(nn.Dropout(self.dropout))
-        sequence = sequence[:-2]
-
-        self.sequential = nn.Sequential(*sequence)
-
-    def forward(self, X):
-        X = self.sequential(X)
-        return X
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(
-                OrderedDict(
-                    [
-                        ("-1", nn.AvgPool2d(stride)),
-                        (
-                            "0",
-                            nn.Conv2d(
-                                inplanes,
-                                planes * self.expansion,
-                                1,
-                                stride=1,
-                                bias=False,
-                            ),
-                        ),
-                        ("1", nn.BatchNorm2d(planes * self.expansion)),
-                    ]
-                )
-            )
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu(self.bn1(self.conv1(x)))
-        out = self.relu(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(
-        self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5
-        )
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(
-            2, 0, 1
-        )  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x,
-            key=x,
-            value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat(
-                [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
-            ),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False,
-        )
-
-        return x[0]
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, image_size=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.image_size = image_size
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(
-            3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
-        )
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.conv2 = nn.Conv2d(
-            width // 2, width // 2, kernel_size=3, padding=1, bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.avgpool = nn.AvgPool2d(2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
-
-        self.init_parameters()
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def init_parameters(self):
-        if self.attnpool is not None:
-            std = self.attnpool.c_proj.in_features**-0.5
-            nn.init.normal_(self.attnpool.q_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.k_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.v_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.c_proj.weight, std=std)
-
-        for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
-            for name, param in resnet_block.named_parameters():
-                if name.endswith("bn3.weight"):
-                    nn.init.zeros_(param)
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert (
-            unlocked_groups == 0
-        ), "partial locking not currently supported for this model"
-        for param in self.parameters():
-            param.requires_grad = False
-        if freeze_bn_stats:
-            freeze_batch_norm_2d(self)
-
-    def stem(self, x):
-        for conv, bn in [
-            (self.conv1, self.bn1),
-            (self.conv2, self.bn2),
-            (self.conv3, self.bn3),
-        ]:
-            x = self.relu(bn(conv(x)))
-        x = self.avgpool(x)
-        return x
-
-    def forward(self, x):
-        x = self.stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class QuickGELU(nn.Module):
-    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(
-            OrderedDict(
-                [
-                    ("c_fc", nn.Linear(d_model, d_model * 4)),
-                    ("gelu", act_layer()),
-                    ("c_proj", nn.Linear(d_model * 4, d_model)),
-                ]
-            )
-        )
-        self.ln_2 = LayerNorm(d_model)
-
-    def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU
-    ):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.ModuleList(
-            [
-                ResidualAttentionBlock(width, heads, act_layer=act_layer)
-                for _ in range(layers)
-            ]
-        )
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        for r in self.resblocks:
-            x = r(x, attn_mask=attn_mask)
-        return x
-
-
-class VisualTransformer(nn.Module):
-    def __init__(
-        self,
-        image_size: int,
-        patch_size: int,
-        width: int,
-        layers: int,
-        heads: int,
-        output_dim: int,
-        act_layer: Callable = nn.GELU,
-    ):
-        super().__init__()
-        self.image_size = image_size
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(
-            in_channels=3,
-            out_channels=width,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=False,
-        )
-
-        scale = width**-0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(
-            scale * torch.randn((image_size // patch_size) ** 2 + 1, width)
-        )
-        self.ln_pre = LayerNorm(width)
-
-        self.text_branch = Transformer(width, layers, heads, act_layer=act_layer)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert (
-            unlocked_groups == 0
-        ), "partial locking not currently supported for this model"
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat(
-            [
-                self.class_embedding.to(x.dtype)
-                + torch.zeros(
-                    x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device
-                ),
-                x,
-            ],
-            dim=1,
-        )  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.text_branch(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-@dataclass
-class CLAPVisionCfg:
-    layers: Union[Tuple[int, int, int, int], int] = 12
-    width: int = 768
-    patch_size: int = 16
-    image_size: Union[Tuple[int, int], int] = 224
-    timm_model_name: str = (
-        None  # a valid model name overrides layers, width, patch_size
-    )
-    timm_model_pretrained: bool = (
-        False  # use (imagenet) pretrained weights for named model
-    )
-    timm_pool: str = (
-        "avg"  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
-    )
-    timm_proj: str = (
-        "linear"  # linear projection for timm model output ('linear', 'mlp', '')
-    )
-
-
-# Audio Config Class
-@dataclass
-class CLAPAudioCfp:
-    model_type: str = "PANN"
-    model_name: str = "Cnn14"
-    sample_rate: int = 48000
-    # Param
-    audio_length: int = 1024
-    window_size: int = 1024
-    hop_size: int = 1024
-    fmin: int = 50
-    fmax: int = 14000
-    class_num: int = 527
-    mel_bins: int = 64
-    clip_samples: int = 480000
-
-
-@dataclass
-class CLAPTextCfg:
-    context_length: int
-    vocab_size: int
-    width: int
-    heads: int
-    layers: int
-    model_type: str
-
-
-class CLAP(nn.Module):
-    def __init__(
-        self,
-        embed_dim: int,
-        audio_cfg: CLAPAudioCfp,
-        text_cfg: CLAPTextCfg,
-        quick_gelu: bool = False,
-        enable_fusion: bool = False,
-        fusion_type: str = 'None',
-        joint_embed_shape: int = 512,
-        mlp_act: str = 'relu',
-    ):
-        super().__init__()
-        if isinstance(audio_cfg, dict):
-            audio_cfg = CLAPAudioCfp(**audio_cfg)
-        if isinstance(text_cfg, dict):
-            text_cfg = CLAPTextCfg(**text_cfg)
-
-        self.audio_cfg = audio_cfg
-        self.text_cfg = text_cfg
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-        self.joint_embed_shape = joint_embed_shape
-        self.mlp_act = mlp_act
-
-
-        self.context_length = text_cfg.context_length
-
-        # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
-        # memory efficient in recent PyTorch releases (>= 1.10).
-        # NOTE: timm models always use native GELU regardless of quick_gelu flag.
-        act_layer = QuickGELU if quick_gelu else nn.GELU
-
-        if mlp_act == 'relu':
-            mlp_act_layer = nn.ReLU()
-        elif mlp_act == 'gelu':
-            mlp_act_layer = nn.GELU()
-        else:
-            raise NotImplementedError
-
-        # audio branch
-        # audio branch parameters
-        if audio_cfg.model_type == "PANN":
-            self.audio_branch = create_pann_model(audio_cfg, enable_fusion, fusion_type)
-        elif audio_cfg.model_type == "HTSAT":
-            self.audio_branch = create_htsat_model(audio_cfg, enable_fusion, fusion_type)
-        else:
-            logging.error(f"Model config for {audio_cfg.model_type} not found")
-            raise RuntimeError(f"Model config for {audio_cfg.model_type} not found.")
-
-
-        # text branch
-        # text branch parameters
-        if text_cfg.model_type == "transformer":
-            self.text_branch = Transformer(
-                width=text_cfg.width,
-                layers=text_cfg.layers,
-                heads=text_cfg.heads,
-                act_layer=act_layer,
-            )
-            self.vocab_size = text_cfg.vocab_size
-            self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width)
-            self.positional_embedding = nn.Parameter(
-                torch.empty(self.context_length, text_cfg.width)
-            )
-            self.ln_final = LayerNorm(text_cfg.width)
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(text_cfg.width, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        elif text_cfg.model_type == "bert":
-            self.text_branch = BertModel.from_pretrained("bert-base-uncased")
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        elif text_cfg.model_type == "roberta":
-            self.text_branch = RobertaModel.from_pretrained('roberta-base')
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        elif text_cfg.model_type == "bart":
-            self.text_branch = BartModel.from_pretrained('facebook/bart-base')
-            self.text_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                   self.joint_embed_shape,
-                                                   self.joint_embed_shape], dropout=0.1)
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-        else:
-            logging.error(f"Model config for {text_cfg.model_type} not found")
-            raise RuntimeError(f"Model config for {text_cfg.model_type} not found.")
-        self.text_branch_type = text_cfg.model_type
-        # text branch parameters
-
-        # audio branch parameters
-        self.audio_transform = MLPLayers(units=[self.joint_embed_shape,
-                                                self.joint_embed_shape,
-                                                self.joint_embed_shape], dropout=0.1)
-
-        # below here is text branch parameters
-
-        # ============================================================================================================
-        self.audio_projection = nn.Sequential(
-                nn.Linear(embed_dim, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape)
-            )
-
-        self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False)
-
-        self.init_text_branch_parameters()
-
-    def init_text_branch_parameters(self):
-        if self.text_branch_type == "transformer":
-            nn.init.normal_(self.token_embedding.weight, std=0.02)
-            nn.init.normal_(self.positional_embedding, std=0.01)
-            proj_std = (self.text_branch.width**-0.5) * (
-                (2 * self.text_branch.layers) ** -0.5
-            )
-            attn_std = self.text_branch.width**-0.5
-            fc_std = (2 * self.text_branch.width) ** -0.5
-            for block in self.text_branch.resblocks:
-                nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-                nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-                nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-                nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        if self.text_branch_type == "bert" or self.text_branch_type == "roberta":
-            width = self.text_branch.embeddings.word_embeddings.weight.shape[-1]
-        elif self.text_branch_type == "bart":
-            width = self.text_branch.shared.weight.shape[-1]
-        else:
-            width = self.text_branch.width
-        nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07))
-        nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07))
-
-        # deprecated
-        # if hasattr(self.visual, 'init_parameters'):
-        # self.visual.init_parameters()
-
-        # if self.text_projection is not None:
-        #     nn.init.normal_(self.text_projection, std=width**-0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def encode_audio(self, audio, device):
-        return self.audio_branch(audio, mixup_lambda=None, device=device)  # mix lambda needs to add
-
-    # def list_of_dict_of_tensor2dict_of_tensor(self, x, device):
-    #     tmp = {}
-    #     for k in x[0].keys():
-    #         tmp[k] = []
-    #         for i in range(len(x)):
-    #             tmp[k].append(x[i][k][:77])
-    #     for k in x[0].keys():
-    #         tmp[k] = torch.tensor(tmp[k]).to(device=device, non_blocking=True)
-    #     return tmp
-
-    def encode_text(self, text, device):
-        if self.text_branch_type == "transformer":
-            text = text.to(device=device, non_blocking=True)
-            x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]
-
-            x = x + self.positional_embedding
-            x = x.permute(1, 0, 2)  # NLD -> LND
-            x = self.text_branch(x, attn_mask=self.attn_mask)
-            x = x.permute(1, 0, 2)  # LND -> NLD
-            x = self.ln_final(x)
-
-            # x.shape = [batch_size, n_ctx, transformer.width]
-            # take features from the eot embedding (eot_token is the highest number in each sequence)
-            x = self.text_projection(x[torch.arange(x.shape[0]), text.argmax(dim=-1)])
-        elif self.text_branch_type == "bert":
-            # text = self.list_of_dict_of_tensor2dict_of_tensor(text, device)
-            # text = BatchEncoding(text)
-            x = self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-                token_type_ids=text["token_type_ids"].to(
-                    device=device, non_blocking=True
-                ),
-            )["pooler_output"]
-            x = self.text_projection(x)
-        elif self.text_branch_type == "roberta":
-            x = self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-            )["pooler_output"]
-
-            x = self.text_projection(x)
-        elif self.text_branch_type == "bart":
-            x = torch.mean(self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-            )["encoder_last_hidden_state"],axis=1)
-            x = self.text_projection(x)
-        else:
-            logging.error(f"Model type {self.text_branch_type} not found")
-            raise RuntimeError(f"Model type {self.text_branch_type} not found.")
-        return x
-
-    def forward(self, audio, text, device=None):
-        """Forward audio and text into the CLAP
-
-        Parameters
-        ----------
-        audio: torch.Tensor (batch_size, audio_length)
-            the time-domain audio input / the batch of mel_spec and longer list.
-        text: torch.Tensor () // need to add
-            the text token input
-        """
-        if device is None:
-            if audio is not None:
-                device = audio.device
-            elif text is not None:
-                device = text.device
-        if audio is None and text is None:
-            # a hack to get the logit scale
-            return self.logit_scale_a.exp(), self.logit_scale_t.exp()
-        elif audio is None:
-            return self.encode_text(text, device=device)
-        elif text is None:
-            return self.audio_projection(self.encode_audio(audio, device=device)["embedding"])
-        audio_features = self.audio_projection(self.encode_audio(audio, device=device)["embedding"])
-        audio_features = F.normalize(audio_features, dim=-1)
-
-        text_features = self.encode_text(
-            text, device=device
-        )
-        # print("text_features", text_features)
-        # print("text_features.shape", text_features.shape)
-        # print("text_features.type", type(text_features))
-        text_features = F.normalize(text_features, dim=-1)
-
-        audio_features_mlp = self.audio_transform(audio_features)
-        text_features_mlp = self.text_transform(text_features)
-        # Four outputs: audio features (basic & MLP), text features (basic & MLP)
-        return (
-            audio_features,
-            text_features,
-            audio_features_mlp,
-            text_features_mlp,
-            self.logit_scale_a.exp(),
-            self.logit_scale_t.exp(),
-        )
-
-    def get_logit_scale(self):
-        return self.logit_scale_a.exp(), self.logit_scale_t.exp()
-
-    def get_textual_embedding(self, data):
-
-        device = next(self.parameters()).device
-        for k in data:
-            data[k] = data[k].to(device)
-
-        # if self.text_branch_type == "roberta":
-        text_embeds = self.text_branch(
-                input_ids=data["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=data["attention_mask"].to(device=device, non_blocking=True),
-            )["last_hidden_state"]
-
-        text_embeds = self.text_projection(text_embeds)
-
-        text_embeds = F.normalize(text_embeds, dim=-1)
-
-        return text_embeds
-
-    def get_text_embedding(self, data):
-        """Get the text embedding from the model
-
-        Parameters
-        ----------
-        data: torch.Tensor 
-            a tensor of text embedding
-
-        Returns
-        ----------
-        text_embed: torch.Tensor
-            a tensor of text_embeds (N, D)
-
-        """
-        device = next(self.parameters()).device
-        for k in data:
-            data[k] = data[k].to(device)
-        text_embeds = self.encode_text(data, device=device)
-        text_embeds = F.normalize(text_embeds, dim=-1)
-        
-        return text_embeds
-
-    def get_audio_embedding(self, data):
-        """Get the audio embedding from the model
-
-        Parameters
-        ----------
-        data: a list of dict
-            the audio input dict list from 'get_audio_feature' method
-
-        Returns
-        ----------
-        audio_embed: torch.Tensor
-            a tensor of audio_embeds (N, D)
-
-        """
-        device = next(self.parameters()).device
-        input_dict = {}
-        keys = data[0].keys()
-        for k in keys:
-            input_dict[k] = torch.cat([d[k].unsqueeze(0) for d in data], dim=0).to(device)
-        
-        audio_embeds = self.audio_projection(self.encode_audio(input_dict, device=device)["embedding"])
-        audio_embeds = F.normalize(audio_embeds, dim=-1)
-        
-        return audio_embeds
-
-            
-
-    def audio_infer(self, audio, hopsize=None, device=None):
-        """Forward one audio and produce the audio embedding
-
-        Parameters
-        ----------
-        audio:  (audio_length)
-            the time-domain audio input, notice that it must be only one input
-        hopsize: int
-            the overlap hopsize as the sliding window
-
-        Returns
-        ----------
-        output_dict: {
-            key: [n, (embedding_shape)] if "HTS-AT"
-            or
-            key: [(embedding_shape)] if "PANN"
-        }
-            the list of key values of the audio branch
-
-        """
-
-        assert not self.training, "the inference mode must be run at eval stage"
-        output_dict = {}
-        # PANN
-        if self.audio_cfg.model_type == "PANN":
-            audio_input = audio.unsqueeze(dim=0)
-            output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0)
-        elif self.audio_cfg.model_type == "HTSAT":
-            # repeat
-            audio_len = len(audio)
-            k = self.audio_cfg.clip_samples // audio_len
-            if k > 1:
-                audio = audio.repeat(k)
-                audio_len = len(audio)
-
-            if hopsize is None:
-                hopsize = min(hopsize, audio_len)
-
-            if audio_len > self.audio_cfg.clip_samples:
-                audio_input = [
-                    audio[pos : pos + self.audio_cfg.clip_samples].clone()
-                    for pos in range(
-                        0, audio_len - self.audio_cfg.clip_samples, hopsize
-                    )
-                ]
-                audio_input.append(audio[-self.audio_cfg.clip_samples :].clone())
-                audio_input = torch.stack(audio_input)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[key]
-            else:
-                audio_input = audio.unsqueeze(dim=0)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0)
-
-        return output_dict
-
-
-def convert_weights_to_fp16(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [
-                *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
-                "in_proj_bias",
-                "bias_k",
-                "bias_v",
-            ]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-# Ignore the state dict of the vision part
-def build_model_from_openai_state_dict(state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = 'None'):
-
-    embed_dim = model_cfg["embed_dim"]
-    audio_cfg = model_cfg["audio_cfg"]
-    text_cfg = model_cfg["text_cfg"]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(
-        set(
-            k.split(".")[2]
-            for k in state_dict
-            if k.startswith(f"transformer.resblocks")
-        )
-    )
-
-    audio_cfg = CLAPAudioCfp(**audio_cfg)
-    text_cfg = CLAPTextCfg(**text_cfg)
-
-    model = CLAP(
-        embed_dim,
-        audio_cfg=audio_cfg,
-        text_cfg=text_cfg,
-        quick_gelu=True,  # OpenAI models were trained with QuickGELU
-        enable_fusion=enable_fusion,
-        fusion_type=fusion_type
-    )
-    state_dict["logit_scale_a"] = state_dict["logit_scale"]
-    state_dict["logit_scale_t"] = state_dict["logit_scale"]
-    pop_keys = list(state_dict.keys())[::]
-    # pop the visual branch saved weights
-    for key in pop_keys:
-        if key.startswith("visual."):
-            state_dict.pop(key, None)
-
-    for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]:
-        state_dict.pop(key, None)
-
-    # not use fp16
-    # convert_weights_to_fp16(model)
-    model.load_state_dict(state_dict, strict=False)
-    return model.eval()
-
-
-def trace_model(model, batch_size=256, device=torch.device("cpu")):
-    model.eval()
-    audio_length = model.audio_cfg.audio_length
-    example_audio = torch.ones((batch_size, audio_length), device=device)
-    example_text = torch.zeros(
-        (batch_size, model.context_length), dtype=torch.int, device=device
-    )
-    model = torch.jit.trace_module(
-        model,
-        inputs=dict(
-            forward=(example_audio, example_text),
-            encode_text=(example_text,),
-            encode_image=(example_audio,),
-        ),
-    )
-    model.audio_cfg.audio_length = audio_length  # Question: what does this do?
-    return model

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

@@ -1,182 +0,0 @@
-from torch import nn
-import torch
-import torch.nn.functional as F
-from modules.util import AntiAliasInterpolation2d, TPS
-from torchvision import models
-import numpy as np
-
-
-class Vgg19(torch.nn.Module):
-    """
-    Vgg19 network for perceptual loss. See Sec 3.3.
-    """
-    def __init__(self, requires_grad=False):
-        super(Vgg19, self).__init__()
-        vgg_pretrained_features = models.vgg19(pretrained=True).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        for x in range(2):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(2, 7):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(7, 12):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(12, 21):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(21, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-
-        self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))),
-                                       requires_grad=False)
-        self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))),
-                                      requires_grad=False)
-
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        X = (X - self.mean) / self.std
-        h_relu1 = self.slice1(X)
-        h_relu2 = self.slice2(h_relu1)
-        h_relu3 = self.slice3(h_relu2)
-        h_relu4 = self.slice4(h_relu3)
-        h_relu5 = self.slice5(h_relu4)
-        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
-        return out
-
-
-class ImagePyramide(torch.nn.Module):
-    """
-    Create image pyramide for computing pyramide perceptual loss. See Sec 3.3
-    """
-    def __init__(self, scales, num_channels):
-        super(ImagePyramide, self).__init__()
-        downs = {}
-        for scale in scales:
-            downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale)
-        self.downs = nn.ModuleDict(downs)
-
-    def forward(self, x):
-        out_dict = {}
-        for scale, down_module in self.downs.items():
-            out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x)
-        return out_dict
-
-
-def detach_kp(kp):
-    return {key: value.detach() for key, value in kp.items()}
-
-
-class GeneratorFullModel(torch.nn.Module):
-    """
-    Merge all generator related updates into single model for better multi-gpu usage
-    """
-
-    def __init__(self, kp_extractor, bg_predictor, dense_motion_network, inpainting_network, train_params, *kwargs):
-        super(GeneratorFullModel, self).__init__()
-        self.kp_extractor = kp_extractor
-        self.inpainting_network = inpainting_network
-        self.dense_motion_network = dense_motion_network
-
-        self.bg_predictor = None
-        if bg_predictor:
-            self.bg_predictor = bg_predictor
-            self.bg_start = train_params['bg_start']
-
-        self.train_params = train_params
-        self.scales = train_params['scales']
-
-        self.pyramid = ImagePyramide(self.scales, inpainting_network.num_channels)
-        if torch.cuda.is_available():
-            self.pyramid = self.pyramid.cuda()
-
-        self.loss_weights = train_params['loss_weights']
-        self.dropout_epoch = train_params['dropout_epoch']
-        self.dropout_maxp = train_params['dropout_maxp']
-        self.dropout_inc_epoch = train_params['dropout_inc_epoch']
-        self.dropout_startp =train_params['dropout_startp']
-        
-        if sum(self.loss_weights['perceptual']) != 0:
-            self.vgg = Vgg19()
-            if torch.cuda.is_available():
-                self.vgg = self.vgg.cuda()
-
-
-    def forward(self, x, epoch):
-        kp_source = self.kp_extractor(x['source'])
-        kp_driving = self.kp_extractor(x['driving'])
-        bg_param = None
-        if self.bg_predictor:
-            if(epoch>=self.bg_start):
-                bg_param = self.bg_predictor(x['source'], x['driving'])
-          
-        if(epoch>=self.dropout_epoch):
-            dropout_flag = False
-            dropout_p = 0
-        else:
-            # dropout_p will linearly increase from dropout_startp to dropout_maxp 
-            dropout_flag = True
-            dropout_p = min(epoch/self.dropout_inc_epoch * self.dropout_maxp + self.dropout_startp, self.dropout_maxp)
-        
-        dense_motion = self.dense_motion_network(source_image=x['source'], kp_driving=kp_driving,
-                                                    kp_source=kp_source, bg_param = bg_param, 
-                                                    dropout_flag = dropout_flag, dropout_p = dropout_p)
-        generated = self.inpainting_network(x['source'], dense_motion)
-        generated.update({'kp_source': kp_source, 'kp_driving': kp_driving})
-
-        loss_values = {}
-
-        pyramide_real = self.pyramid(x['driving'])
-        pyramide_generated = self.pyramid(generated['prediction'])
-
-        # reconstruction loss
-        if sum(self.loss_weights['perceptual']) != 0:
-            value_total = 0
-            for scale in self.scales:
-                x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)])
-                y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)])
-
-                for i, weight in enumerate(self.loss_weights['perceptual']):
-                    value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean()
-                    value_total += self.loss_weights['perceptual'][i] * value
-            loss_values['perceptual'] = value_total
-
-        # equivariance loss
-        if self.loss_weights['equivariance_value'] != 0:
-            transform_random = TPS(mode = 'random', bs = x['driving'].shape[0], **self.train_params['transform_params'])
-            transform_grid = transform_random.transform_frame(x['driving'])
-            transformed_frame = F.grid_sample(x['driving'], transform_grid, padding_mode="reflection",align_corners=True)
-            transformed_kp = self.kp_extractor(transformed_frame)
-
-            generated['transformed_frame'] = transformed_frame
-            generated['transformed_kp'] = transformed_kp
-        
-            warped = transform_random.warp_coordinates(transformed_kp['fg_kp'])
-            kp_d = kp_driving['fg_kp']
-            value = torch.abs(kp_d - warped).mean()
-            loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value
-
-        # warp loss
-        if self.loss_weights['warp_loss'] != 0:
-            occlusion_map = generated['occlusion_map']
-            encode_map = self.inpainting_network.get_encode(x['driving'], occlusion_map)
-            decode_map = generated['warped_encoder_maps']
-            value = 0
-            for i in range(len(encode_map)):
-                value += torch.abs(encode_map[i]-decode_map[-i-1]).mean()
-
-            loss_values['warp_loss'] = self.loss_weights['warp_loss'] * value
-        
-        # bg loss
-        if self.bg_predictor and epoch >= self.bg_start and self.loss_weights['bg'] != 0:
-            bg_param_reverse = self.bg_predictor(x['driving'], x['source'])
-            value = torch.matmul(bg_param, bg_param_reverse)
-            eye = torch.eye(3).view(1, 1, 3, 3).type(value.type())
-            value = torch.abs(eye - value).mean()
-            loss_values['bg'] = self.loss_weights['bg'] * value
-
-        return loss_values, generated

spaces/Alpaca233/SadTalker/app.py

@@ -1,80 +0,0 @@
-import os, sys
-import gradio as gr
-from src.gradio_demo import SadTalker  
-
-
-try:
-    import webui  # in webui
-    in_webui = True
-except:
-    in_webui = False
-
-
-def toggle_audio_file(choice):
-    if choice == False:
-        return gr.update(visible=True), gr.update(visible=False)
-    else:
-        return gr.update(visible=False), gr.update(visible=True)
-    
-def ref_video_fn(path_of_ref_video):
-    if path_of_ref_video is not None:
-        return gr.update(value=True)
-    else:
-        return gr.update(value=False)
-
-sad_talker = SadTalker("checkpoints", "src/config", lazy_load=True)
-
-
-with gr.Blocks(analytics_enabled=False) as demo:
-    gr.Markdown("<div align='center'> <h2> 😭 SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (CVPR 2023) </span> </h2> \
-                <a style='font-size:18px;color: #efefef' href='https://arxiv.org/abs/2211.12194'>Arxiv</a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                <a style='font-size:18px;color: #efefef' href='https://sadtalker.github.io'>Homepage</a>  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                <a style='font-size:18px;color: #efefef' href='https://github.com/Winfredy/SadTalker'> Github </div>")
-        
-    with gr.Row().style(equal_height=False):
-        with gr.Column(variant='panel'):
-            with gr.Tabs(elem_id="sadtalker_source_image"):
-                with gr.TabItem('Upload image'):
-                    with gr.Row():
-                        source_image = gr.Image(label="Source image", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
-
-            with gr.Tabs(elem_id="sadtalker_driven_audio"):
-                with gr.TabItem('Upload OR TTS'):
-                    with gr.Column(variant='panel'):
-                        driven_audio = gr.Audio(label="Input audio", source="upload", type="filepath")
-
-        with gr.Column(variant='panel'): 
-            with gr.Tabs(elem_id="sadtalker_checkbox"):
-                with gr.TabItem('Settings'):
-                    gr.Markdown("need help? please visit our [best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md) for more detials")
-                    with gr.Column(variant='panel'):
-                        # width = gr.Slider(minimum=64, elem_id="img2img_width", maximum=2048, step=8, label="Manually Crop Width", value=512) # img2img_width
-                        # height = gr.Slider(minimum=64, elem_id="img2img_height", maximum=2048, step=8, label="Manually Crop Height", value=512) # img2img_width
-                        pose_style = gr.Slider(minimum=0, maximum=46, step=1, label="Pose style", value=0) # 
-                        size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?") # 
-                        preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?")
-                        is_still_mode = gr.Checkbox(label="Still Mode (fewer hand motion, works with preprocess `full`)")
-                        batch_size = gr.Slider(label="batch size in generation", step=1, maximum=10, value=2)
-                        enhancer = gr.Checkbox(label="GFPGAN as Face enhancer")
-                        submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
-                            
-            with gr.Tabs(elem_id="sadtalker_genearted"):
-                    gen_video = gr.Video(label="Generated video", format="mp4").style(width=256)
-
-    submit.click(
-                fn=sad_talker.test, 
-                inputs=[source_image,
-                        driven_audio,
-                        preprocess_type,
-                        is_still_mode,
-                        enhancer,
-                        batch_size,                            
-                        size_of_image,
-                        pose_style
-                        ], 
-                outputs=[gen_video]
-                )
-
-
-
-demo.queue().launch()

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/buffer.cpp

@@ -1,87 +0,0 @@
-#include "libipc/buffer.h"
-#include "libipc/utility/pimpl.h"
-
-#include <cstring>
-
-namespace ipc {
-
-bool operator==(buffer const & b1, buffer const & b2) {
-    return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);
-}
-
-bool operator!=(buffer const & b1, buffer const & b2) {
-    return !(b1 == b2);
-}
-
-class buffer::buffer_ : public pimpl<buffer_> {
-public:
-    void*       p_;
-    std::size_t s_;
-    void*       a_;
-    buffer::destructor_t d_;
-
-    buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)
-        : p_(p), s_(s), a_(a), d_(d) {
-    }
-
-    ~buffer_() {
-        if (d_ == nullptr) return;
-        d_((a_ == nullptr) ? p_ : a_, s_);
-    }
-};
-
-buffer::buffer()
-    : buffer(nullptr, 0, nullptr, nullptr) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d)
-    : p_(p_->make(p, s, d, nullptr)) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)
-    : p_(p_->make(p, s, d, additional)) {
-}
-
-buffer::buffer(void* p, std::size_t s)
-    : buffer(p, s, nullptr) {
-}
-
-buffer::buffer(char const & c)
-    : buffer(const_cast<char*>(&c), 1) {
-}
-
-buffer::buffer(buffer&& rhs)
-    : buffer() {
-    swap(rhs);
-}
-
-buffer::~buffer() {
-    p_->clear();
-}
-
-void buffer::swap(buffer& rhs) {
-    std::swap(p_, rhs.p_);
-}
-
-buffer& buffer::operator=(buffer rhs) {
-    swap(rhs);
-    return *this;
-}
-
-bool buffer::empty() const noexcept {
-    return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);
-}
-
-void* buffer::data() noexcept {
-    return impl(p_)->p_;
-}
-
-void const * buffer::data() const noexcept {
-    return impl(p_)->p_;
-}
-
-std::size_t buffer::size() const noexcept {
-    return impl(p_)->s_;
-}
-
-} // namespace ipc

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/pndm.md

@@ -1,35 +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.
--->
-
-# PNDM
-
-[Pseudo Numerical methods for Diffusion Models on manifolds](https://huggingface.co/papers/2202.09778) (PNDM) is by Luping Liu, Yi Ren, Zhijie Lin and Zhou Zhao.
-
-The abstract from the paper is:
-
-*Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a fresh perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that the pseudo linear multi-step method is the best in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules.*
-
-The original codebase can be found at [luping-liu/PNDM](https://github.com/luping-liu/PNDM).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## PNDMPipeline
-[[autodoc]] PNDMPipeline
-	- all
-	- __call__
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md

@@ -1,40 +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.
--->
-
-# Depth-to-image
-
-The Stable Diffusion model can also infer depth based on an image using [MiDas](https://github.com/isl-org/MiDaS). This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the image structure. 
-
-<Tip>
-
-Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! 
-
-If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
-
-</Tip>
-
-## StableDiffusionDepth2ImgPipeline
-
-[[autodoc]] StableDiffusionDepth2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-	- load_lora_weights
-	- save_lora_weights
-
-## StableDiffusionPipelineOutput
-
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py

@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))

spaces/Andy1621/uniformer_image_segmentation/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './pointrend_r50_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Ankush05/Newcode/getvalues.py

@@ -1,87 +0,0 @@
-
-import re
-# from listen import *
-
-# find time in the string input provided by the user
-def findTime(input):
-    # time = re.search(r'\d{1,2}:\d{2}', input)
-    # meridiem = re.search(r'\b(am|pm)\b', input)
-    # if time:
-    #     tvalue = f"{time.group()} {meridiem.group()}"
-    #     return tvalue
-    # else:
-    #     return "notime"
-    time_regex1 = r"(1[0-2]|[1-9]):[0-5][0-9] (am|AM|PM|pm)"
-    time_search = re.search(time_regex1, input)
-    if time_search:
-        time = time_search.group(0)
-        # meridian = time_search.group(2)
-        return time
-    else:
-        time_regex2 = r"(1[0-2]|[1-9])\s?(am|AM|pm|PM)"
-        time_search = re.search(time_regex2, input)
-        if time_search:
-            time = time_search.group(0)
-            # meridian = time_search.group(2)
-            return time
-        else:
-            return "notime"
-    
-# find number in the string input provided by the user
-def findNumber(input):
-    number = re.search(r'\d+(?:st|nd|rd|th)', input)
-    if number:
-        return number.group()
-    else:
-        return "nonum"
-    
-# # find date in the string input provided by the user
-def findDate(input):
-    date = re.search(r'\d{1,2}/\d{1,2}/\d{4}', input)
-    if date:
-        return date.group()
-    else:
-        return "nodate"
-
-# find month in the string input provided by the user
-def findMonth(input):
-    month = re.search(r'\b(january|february|march|april|may|june|july|august|september|october|november|december|next month)\b', input)
-    if month:
-        return month.group()
-    else:
-        return "nomonth"
-    
-# find day in the string input provided by the user
-def findDay(input):
-    day = re.search(r'\b(monday|tuesday|wednesday|thursday|friday|saturday|sunday|tomorrow|day after tomorrow|this week|next week|today)\b', input)
-    if day:
-        return day.group()
-    else:
-        return "noday"
-    
-def findrepeat(input):
-    repeat = re.search(r'\b(daily|everyday|every week|every month|every sunday|every monday|every tuesday|every wednesday|every thursday|every friday|every saturday)\b', input)
-    if repeat:
-        return repeat.group()
-    else:
-        return "norepeat"
-    
-    
-def getValues(query):
-    time = findTime(query)
-    num = findNumber(query)
-    reps = findrepeat(query)
-    date = findDate(query)
-    month = findMonth(query)
-    day = findDay(query)
-    message = query.lower().replace(num, "").replace(month,"").replace(time, "").replace(day, "").replace(reps, "").replace("create a reminder", "").replace("remind me to", "").replace("cosmo", "").replace("remind", "").replace("at", "")
-    values = {"message": message, 
-              "time": time, 
-              "day": day, 
-              "date": date, 
-              "reps": reps, 
-              "num": num, 
-              "month": month
-             }
-    return values
-    

spaces/Artrajz/vits-simple-api/bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md

@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```

spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/tokenizer.py

@@ -1,197 +0,0 @@
-""" CLIP tokenizer
-
-Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-import gzip
-import html
-import os
-from functools import lru_cache
-from typing import Union, List
-
-import ftfy
-import regex as re
-import torch
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(
-        os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz"
-    )
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1))
-        + list(range(ord("¡"), ord("¬") + 1))
-        + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
-        merges = merges[1 : 49152 - 256 - 2 + 1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v + "</w>" for v in vocab]
-        for merge in merges:
-            vocab.append("".join(merge))
-        if not special_tokens:
-            special_tokens = ["<start_of_text>", "<end_of_text>"]
-        else:
-            special_tokens = ["<start_of_text>", "<end_of_text>"] + special_tokens
-        vocab.extend(special_tokens)
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {t: t for t in special_tokens}
-        special = "|".join(special_tokens)
-        self.pat = re.compile(
-            special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
-            re.IGNORECASE,
-        )
-
-        self.vocab_size = len(self.encoder)
-        self.all_special_ids = [self.encoder[t] for t in special_tokens]
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + (token[-1] + "</w>",)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token + "</w>"
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
-            bpe_tokens.extend(
-                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
-            )
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = "".join([self.decoder[token] for token in tokens])
-        text = (
-            bytearray([self.byte_decoder[c] for c in text])
-            .decode("utf-8", errors="replace")
-            .replace("</w>", " ")
-        )
-        return text
-
-
-_tokenizer = SimpleTokenizer()
-
-
-def tokenize(
-    texts: Union[str, List[str]], context_length: int = 77
-) -> torch.LongTensor:
-    """
-    Returns the tokenized representation of given input string(s)
-
-    Parameters
-    ----------
-    texts : Union[str, List[str]]
-        An input string or a list of input strings to tokenize
-    context_length : int
-        The context length to use; all CLIP models use 77 as the context length
-
-    Returns
-    -------
-    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
-    """
-    if isinstance(texts, str):
-        texts = [texts]
-
-    sot_token = _tokenizer.encoder["<start_of_text>"]
-    eot_token = _tokenizer.encoder["<end_of_text>"]
-    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
-    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-    for i, tokens in enumerate(all_tokens):
-        if len(tokens) > context_length:
-            tokens = tokens[:context_length]  # Truncate
-        result[i, : len(tokens)] = torch.tensor(tokens)
-
-    return result

spaces/Axolotlily/DalleMini/README.md

@@ -1,13 +0,0 @@
----
-title: DalleMini
-emoji: 🚀
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/BAAI/AltDiffusion-m9/ui_functions.py

@@ -1,240 +0,0 @@
-import re
-import gradio as gr
-from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
-from io import BytesIO
-import base64
-import re
-
-def change_img_choices(sample_size):
-    choices = []
-    for i in range(int(sample_size)):
-        choices.append(
-            '图片{}(img{})'.format(i+1,i+1)
-        )
-    update_choices = gr.update(choices=choices)
-    return update_choices
-
-def change_image_editor_mode(choice, cropped_image, masked_image, resize_mode, width, height):
-    if choice == "Mask":
-        update_image_result = update_image_mask(cropped_image, resize_mode, width, height)
-        return [gr.update(visible=False), update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
-
-    update_image_result = update_image_mask(masked_image["image"] if masked_image is not None else None, resize_mode, width, height)
-    return [update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
-
-def update_image_mask(cropped_image, resize_mode, width, height):
-    resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None
-    return gr.update(value=resized_cropped_image, visible=True)
-
-def toggle_options_gfpgan(selection):
-    if 0 in selection:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_upscalers(selection):
-    if 1 in selection:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_realesrgan(selection):
-    if selection == 0 or selection == 1 or selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_gobig(selection):
-    if selection == 1:
-        #print(selection)
-        return gr.update(visible=True)
-    if selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_ldsr(selection):
-    if selection == 2 or selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def increment_down(value):
-    return value - 1
-
-def increment_up(value):
-    return value + 1
-
-def copy_img_to_lab(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='imgproc_tab')
-        img_update = gr.update(value=processed_image)
-        return processed_image, tab_update,
-    except IndexError:
-        return [None, None]
-def copy_img_params_to_lab(params):
-    try:
-        prompt = params[0][0].replace('\n', ' ').replace('\r', '')
-        seed = int(params[1][1])
-        steps = int(params[7][1])
-        cfg_scale = float(params[9][1])
-        sampler = params[11][1]
-        return prompt,seed,steps,cfg_scale,sampler
-    except IndexError:
-        return [None, None]
-def copy_img_to_input(img, idx):
-    try:
-        # print(img)
-        # print("=============")
-        # print("The img type is:{}".format(type(img[0])))
-        idx_map = {
-            "图片1(img1)":0,
-            "图片2(img2)":1,
-            "图片3(img3)":2,
-            "图片4(img4)":3,
-        }
-        idx = idx_map[idx]
-        assert img[idx]['is_file']
-        processed_image = Image.open(img[idx]['name'])
-        tab_update = gr.update(selected='img2img_tab')
-        move_prompt_zh_update = gr.update(visible=True)
-        move_prompt_en_update = gr.update(visible=True)
-        prompt_update = gr.update(visible=True)
-        return tab_update, processed_image, move_prompt_zh_update, move_prompt_en_update, prompt_update
-    except IndexError as e:
-        raise gr.Error(e)
-        return [None, None, None, None, None]
-
-def copy_img_to_edit(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='img2img_tab')
-        img_update = gr.update(value=processed_image)
-        mode_update = gr.update(value='Crop')
-        return processed_image, tab_update, mode_update
-    except IndexError:
-        return [None, None]
-
-def copy_img_to_mask(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='img2img_tab')
-        img_update = gr.update(value=processed_image)
-        mode_update = gr.update(value='Mask')
-        return processed_image, tab_update, mode_update
-    except IndexError:
-        return [None, None]
-
-
-
-def copy_img_to_upscale_esrgan(img):
-    tabs_update = gr.update(selected='realesrgan_tab')
-    image_data = re.sub('^data:image/.+;base64,', '', img)
-    processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-    return processed_image, tabs_update
-
-
-help_text = """
-    ## Mask/Crop
-    * Masking is not inpainting. You will probably get better results manually masking your images in photoshop instead.
-    * Built-in masking/cropping is very temperamental.
-    * It may take some time for the image to show when switching from Crop to Mask.
-    * If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
-    * If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.
-
-    ## Advanced Editor
-    * Click 💾 Save to send your editor changes to the img2img workflow
-    * Click ❌ Clear to discard your editor changes
-
-    If anything breaks, try switching modes again, switch tabs, clear the image, or reload.
-"""
-
-def resize_image(resize_mode, im, width, height):
-    LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
-    if resize_mode == 0:
-        res = im.resize((width, height), resample=LANCZOS)
-    elif resize_mode == 1:
-        ratio = width / height
-        src_ratio = im.width / im.height
-
-        src_w = width if ratio > src_ratio else im.width * height // im.height
-        src_h = height if ratio <= src_ratio else im.height * width // im.width
-
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
-        res = Image.new("RGBA", (width, height))
-        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-    else:
-        ratio = width / height
-        src_ratio = im.width / im.height
-
-        src_w = width if ratio < src_ratio else im.width * height // im.height
-        src_h = height if ratio >= src_ratio else im.height * width // im.width
-
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
-        res = Image.new("RGBA", (width, height))
-        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-
-        if ratio < src_ratio:
-            fill_height = height // 2 - src_h // 2
-            res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
-            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
-        elif ratio > src_ratio:
-            fill_width = width // 2 - src_w // 2
-            res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
-            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
-
-    return res
-
-def update_dimensions_info(width, height):
-    pixel_count_formated = "{:,.0f}".format(width * height)
-    return f"Aspect ratio: {round(width / height, 5)}\nTotal pixel count: {pixel_count_formated}"
-
-def get_png_nfo( image: Image ):
-    info_text = ""
-    visible = bool(image and any(image.info))
-    if visible:
-        for key,value in image.info.items():
-            info_text += f"{key}: {value}\n"
-        info_text = info_text.rstrip('\n')
-    return gr.Textbox.update(value=info_text, visible=visible)
-
-def load_settings(*values):
-    new_settings, key_names, checkboxgroup_info = values[-3:]
-    values = list(values[:-3])
-
-    if new_settings:
-        if type(new_settings) is str:
-            if os.path.exists(new_settings):
-                with open(new_settings, "r", encoding="utf8") as f:
-                    new_settings = yaml.safe_load(f)
-            elif new_settings.startswith("file://") and os.path.exists(new_settings[7:]):
-                with open(new_settings[7:], "r", encoding="utf8") as f:
-                    new_settings = yaml.safe_load(f)
-            else:
-                new_settings = yaml.safe_load(new_settings)
-        if type(new_settings) is not dict:
-            new_settings = {"prompt": new_settings}
-        if "txt2img" in new_settings:
-            new_settings = new_settings["txt2img"]
-        target = new_settings.pop("target", "txt2img")
-        if target != "txt2img":
-            print(f"Warning: applying settings to txt2img even though {target} is specified as target.", file=sys.stderr)
-
-        skipped_settings = {}
-        for key in new_settings.keys():
-            if key in key_names:
-                values[key_names.index(key)] = new_settings[key]
-            else:
-                skipped_settings[key] = new_settings[key]
-        if skipped_settings:
-            print(f"Settings could not be applied: {skipped_settings}", file=sys.stderr)
-
-    # Convert lists of checkbox indices to lists of checkbox labels:
-    for (cbg_index, cbg_choices) in checkboxgroup_info:
-        values[cbg_index] = [cbg_choices[i] for i in values[cbg_index]]
-
-    return values

spaces/Balalaxmi/JarvisAIchatbox/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, he's full of energy and always eager to help. Jarvis's goal is to assist you with any questions or problems you might have. He enthusiasm shines through in every response, making interactions with he enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/utils.py

@@ -1,121 +0,0 @@
-import json
-
-import numpy as np
-import torch
-from tqdm import tqdm
-
-
-def load_data(file_name: str = "./infer/lib/uvr5_pack/name_params.json") -> dict:
-    with open(file_name, "r") as f:
-        data = json.load(f)
-
-    return data
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
-    """
-    data ： dic configs
-    """
-
-    def _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
-    ):
-        model.eval()
-        with torch.no_grad():
-            preds = []
-
-            iterations = [n_window]
-
-            total_iterations = sum(iterations)
-            for i in tqdm(range(n_window)):
-                start = i * roi_size
-                X_mag_window = X_mag_pad[
-                    None, :, :, start : start + data["window_size"]
-                ]
-                X_mag_window = torch.from_numpy(X_mag_window)
-                if is_half:
-                    X_mag_window = X_mag_window.half()
-                X_mag_window = X_mag_window.to(device)
-
-                pred = model.predict(X_mag_window, aggressiveness)
-
-                pred = pred.detach().cpu().numpy()
-                preds.append(pred[0])
-
-            pred = np.concatenate(preds, axis=2)
-        return pred
-
-    def preprocess(X_spec):
-        X_mag = np.abs(X_spec)
-        X_phase = np.angle(X_spec)
-
-        return X_mag, X_phase
-
-    X_mag, X_phase = preprocess(X_spec)
-
-    coef = X_mag.max()
-    X_mag_pre = X_mag / coef
-
-    n_frame = X_mag_pre.shape[2]
-    pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
-    n_window = int(np.ceil(n_frame / roi_size))
-
-    X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-    if list(model.state_dict().values())[0].dtype == torch.float16:
-        is_half = True
-    else:
-        is_half = False
-    pred = _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-    )
-    pred = pred[:, :, :n_frame]
-
-    if data["tta"]:
-        pad_l += roi_size // 2
-        pad_r += roi_size // 2
-        n_window += 1
-
-        X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-        pred_tta = _execute(
-            X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-        )
-        pred_tta = pred_tta[:, :, roi_size // 2 :]
-        pred_tta = pred_tta[:, :, :n_frame]
-
-        return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
-    data = load_data()
-    flag = False
-    ModelName = model_path
-    for type in list(data):
-        for model in list(data[type][0]):
-            for i in range(len(data[type][0][model])):
-                if str(data[type][0][model][i]["hash_name"]) == model_hash:
-                    flag = True
-                elif str(data[type][0][model][i]["hash_name"]) in ModelName:
-                    flag = True
-
-                if flag:
-                    model_params_auto = data[type][0][model][i]["model_params"]
-                    param_name_auto = data[type][0][model][i]["param_name"]
-                    if type == "equivalent":
-                        return param_name_auto, model_params_auto
-                    else:
-                        flag = False
-    return param_name_auto, model_params_auto

spaces/Benson/text-generation/Examples/Cmo Descargar Msica A Una Unidad USB De Youtube.md

@@ -1,147 +0,0 @@
-
-<h1>Cómo descargar música a una unidad USB desde YouTube</h1>
-<p>YouTube es una de las plataformas más populares para transmitir y ver videos en línea. También tiene una gran colección de videos musicales, canciones, álbumes y listas de reproducción que puedes disfrutar en cualquier momento y en cualquier lugar. Pero ¿qué pasa si quieres escuchar tu música favorita sin conexión, o en un dispositivo que no tiene acceso a Internet? ¿O qué pasa si quieres crear tu propio mixtape o compilación de canciones de diferentes artistas y géneros? </p>
-<h2>cómo descargar música a una unidad USB de youtube</h2><br /><p><b><b>Download Zip</b> &raquo;&raquo;&raquo; <a href="https://bltlly.com/2v6ITN">https://bltlly.com/2v6ITN</a></b></p><br /><br />
-<p>Una de las soluciones es descargar música de YouTube a una unidad USB. Una unidad USB es un dispositivo pequeño y portátil que puede almacenar datos y transferirlos entre diferentes computadoras y dispositivos. Al descargar música de YouTube a una unidad USB, puede tener su propia biblioteca de música personal que puede reproducir en cualquier dispositivo compatible, como su estéreo de automóvil, sistema de cine en casa, computadora portátil, teléfono inteligente o tableta. </p>
-<p>Pero ¿cómo descargar música de YouTube a una unidad USB? Hay diferentes métodos que puede utilizar, dependiendo de sus preferencias, presupuesto y habilidades técnicas. En este artículo, le mostraremos tres de las formas más comunes y fáciles de hacerlo. También proporcionaremos algunos consejos y advertencias para hacerlo de forma segura y legal. </p>
- <h2>Método 1: Usando un convertidor de YouTube a MP3</h2>
-<h3> ¿Qué es un convertidor de YouTube a MP3 y cómo funciona</h3>
-<p>Un convertidor de YouTube a MP3 es una herramienta en línea que le permite convertir cualquier vídeo de YouTube en un archivo MP3, que es un formato de audio común que se puede reproducir en la mayoría de los dispositivos. Al usar un convertidor de YouTube a MP3, puede extraer la pista de audio de cualquier video de YouTube y guardarlo como un archivo MP3 en su computadora o unidad USB. De esta manera, puede descargar música de YouTube sin descargar todo el archivo de video, lo que puede ahorrarle tiempo y espacio de almacenamiento. </p>
-<h3>Cómo utilizar un convertidor de YouTube a MP3 para descargar música a una unidad USB</h3>
-
-<h4>Paso 1: Encuentre un convertidor confiable de YouTube a MP3 en línea</h4>
-<p>Hay muchos conversores de YouTube a MP3 disponibles en línea, pero no todos son seguros y confiables. Algunos de ellos pueden contener malware, virus o anuncios que pueden dañar su computadora o dispositivo. Algunos de ellos también pueden tener salida de baja calidad, características limitadas o velocidad de conversión lenta. Por lo tanto, necesita encontrar un conversor de YouTube a MP3 confiable y de buena reputación que pueda satisfacer sus necesidades y expectativas. </p>
-<p></p>
-<p>Algunos de los factores que debes considerar al elegir un convertidor de YouTube a MP3 son:</p>
-<ul>
-<li>La calidad y el tamaño del archivo de salida</li>
-<li>La velocidad y estabilidad del proceso de conversión</li>
-<li>La compatibilidad y seguridad del sitio web y la herramienta</li>
-<li>Disponibilidad y accesibilidad del servicio</li>
-<li>Facilidad de uso y simplicidad de la interfaz</li>
-<li>La legalidad y legitimidad del servicio</li>
-</ul>
-<p>Algunos de los ejemplos de populares y confiables convertidores de YouTube a MP3 son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-<tr><td>4K YouTube to MP3</td><td>[https://www.4kdownload.com/products/product-youtubetomp3]</td><td>- Salida de alta calidad hasta 320 kbps<br>- Conversión rápida y fácil<br>- No hay anuncios o malware<br>- Soporta múltiples formatos y plataformas<> Permite descargas por lotes y listas de reproducción</td></tr>
-<tr><td>YTMP3</td><td>[https://ytmp3.cc/en13/]</td><td>- Interfaz simple y fácil de usar<br>- No se requiere registro ni instalación<br>- Soporta formatos MP3 y MP4<br>- Compatible con la mayoría de navegadores y dispositivos<br>- Tiene un límite de 1 hora por video/td><<<<td>/tr
-<tr><td>MP3FY</td><td>[https://mp3fy.com/en1/]</td><td>- Soporta más de 1000 sitios web y plataformas<br>- No hay límite de longitud de video o tamaño de archivo<br>- Convierte videos largos y audiolibros<br>- Tiene una función de detección automática para enlaces de video<br>- Ofrece configuraciones y opciones avanzadas</td<<<tr/tr
-</tabla>
-
-<p>Una vez que haya elegido un convertidor de YouTube a MP3, debe copiar el enlace del video de YouTube que desea descargar como música. Para hacer esto, puedes:</p>
-<ul>
-<li>Ir a la página web de YouTube o aplicación y encontrar el video que desea descargar. Entonces, copiar la URL de la barra de direcciones o el botón de compartir. </li>
-<li>Utilice la función de búsqueda del sitio web o herramienta de conversión de YouTube a MP3 y escriba el nombre o las palabras clave del video que desea descargar. Luego, seleccione el video de los resultados y copie la URL.</li>
-</ul>
-<p>Después de copiar el enlace, pégalo en la caja de entrada del sitio web o herramienta de conversión de YouTube a MP3. A continuación, haga clic en el botón convertir o descargar para iniciar el proceso. </p>
-<h4>Paso 3: Elija el formato de salida y la calidad</h4>
-<p>Antes de descargar el archivo convertido, es posible que tenga algunas opciones para elegir el formato de salida y la calidad de su archivo de música. El formato de salida es el tipo de archivo de audio que desea descargar, como MP3, WAV, AAC o M4A. La calidad de salida es el nivel de claridad y detalle de sonido que desea tener, como 128 kbps, 192 kbps, 256 kbps o 320 kbps. </p>
-<p>Diferentes formatos y calidades de salida pueden tener diferentes ventajas y desventajas, dependiendo de sus preferencias y necesidades. Por ejemplo, MP3 es un formato común y ampliamente soportado que se puede reproducir en la mayoría de los dispositivos, pero también puede tener cierta pérdida de calidad debido a la compresión. WAV es un formato de alta calidad y sin comprimir que puede preservar la calidad de sonido original del video, pero también puede ocupar más espacio de almacenamiento y ser incompatible con algunos dispositivos. </p>
-<p>Puede elegir el formato de salida y la calidad que más le convenga haciendo clic en el botón de configuración u opciones en el sitio web o herramienta de conversión de YouTube a MP3. También puede dejarlo como predeterminado si no está seguro o no le importa. </p>
-<h4> Música y YouTube Premium y cómo funcionan</h3>
-
-<p>YouTube Music es un servicio de transmisión de música que se centra en el contenido musical de YouTube y otras fuentes. Tiene una interfaz personalizada y personalizada que le permite descubrir y disfrutar de la música en función de sus preferencias, estado de ánimo y actividad. También puede crear sus propias listas de reproducción, mixtapes y estaciones de radio. YouTube Music cuesta $9.99 por mes para un plan individual, o $14.99 por mes para un plan familiar que cubre hasta seis miembros. </p>
-<p>YouTube Premium es un servicio premium que incluye todas las características y beneficios de YouTube Music, además de beneficios adicionales para YouTube y otros productos de Google. Te permite ver y descargar cualquier video de YouTube sin anuncios ni interrupciones. También te da acceso a YouTube Originals, que son programas y películas exclusivos producidos por YouTube y sus socios. YouTube Premium cuesta $11.99 por mes para un plan individual, o $17.99 por mes para un plan familiar que cubre hasta seis miembros. </p>
-<h3>Cómo usar YouTube Music o YouTube Premium para descargar música a una unidad USB</h3>
-<p>Usar YouTube Music o YouTube Premium para descargar música a una unidad USB es otra forma fácil y conveniente de hacerlo. Estos son los pasos que debes seguir:</p>
-<h4>Paso 1: Regístrate para una suscripción premium de YouTube o YouTube</h4>
-<p>El primer paso es registrarse para una suscripción de YouTube Music o YouTube Premium que se adapte a sus necesidades y presupuesto. Puedes hacer esto yendo al sitio web o aplicación de YouTube y haciendo clic en la pestaña Música o Premium. Luego, puede elegir el plan que desea e ingresar sus detalles de pago. También puede obtener una prueba gratuita durante un mes antes de decidir suscribirse. </p>
-<p>Si ya tienes una cuenta de Google, puedes utilizarla para registrarte en YouTube Music o YouTube Premium. Si no lo tienes, puedes crear uno gratis siguiendo las instrucciones del sitio web o la aplicación. </p>
-<h4>Paso 2: Descarga la aplicación de música de YouTube en tu dispositivo</h4>
-
-<p>La aplicación YouTube Music es compatible con la mayoría de los dispositivos Android e iOS, como teléfonos inteligentes, tabletas, televisores inteligentes, altavoces inteligentes y relojes inteligentes. También puede usarlo en su computadora yendo al sitio web [https://music.youtube.com/]. </p>
-<h4>Paso 3: Encuentre la música que desea descargar en YouTube Music</h4>
-<p>El tercer paso es encontrar la música que desea descargar en YouTube Music. Puede hacer esto utilizando la función de búsqueda de la aplicación o sitio web y escribiendo el nombre o palabras clave de la canción, artista, álbum o lista de reproducción que desea descargar. A continuación, puede seleccionar la música de los resultados y abrirla en la aplicación o sitio web. </p>
-<p>También puedes navegar a través de las diferentes categorías y géneros de música en YouTube Music, como Top Charts, New Releases, Your Mixtape, Mood & Genre, Activity & Situation, etc. También puedes explorar recomendaciones personalizadas basadas en tu historial de escucha y preferencias. </p>
-<h4>Paso 4: Toca el icono de descarga en la canción, álbum o lista de reproducción</h4>
-<p>El cuarto paso es tocar el icono de descarga en la canción, álbum o lista de reproducción que desea descargar en YouTube Music. El icono de descarga parece una flecha hacia abajo con una línea debajo. Normalmente se encuentra junto al botón de reproducción o bajo el botón de menú de la música. </p>
-<p>Al tocar el icono de descarga, comenzará a descargar la música al almacenamiento interno del dispositivo o a la tarjeta SD. Puede comprobar el progreso y el estado de la descarga en la aplicación o sitio web. </p>
-<h4>Paso 5: Conecte su unidad USB a su dispositivo y transfiera la música descargada</h4>
-
-<p>Después de transferir los archivos de música, puede expulsar o quitar de forma segura la unidad USB de su dispositivo. A continuación, puede disfrutar de escuchar la música descargada de YouTube Music en cualquier dispositivo compatible con la reproducción USB. </p>
- <h2>Método 3: Uso de un disco duro externo para el almacenamiento de música</h2>
-<h3> ¿Qué es un disco duro externo y cómo funciona</h3>
-<p>Un disco duro externo es un dispositivo que puede almacenar grandes cantidades de datos y conectarse a diferentes equipos y dispositivos a través de un puerto USB o cable. Es similar a una unidad USB, pero tiene más capacidad de almacenamiento y una velocidad de transferencia más rápida. Un disco duro externo se puede utilizar para diversos fines, como hacer copias de seguridad de datos, transferir archivos o almacenar medios. </p>
-<p>Mediante el uso de un disco duro externo para el almacenamiento de música, puede descargar música de YouTube y otras fuentes y guardarla en un dispositivo separado que puede contener miles de canciones. También puede acceder y reproducir su música en cualquier dispositivo compatible, como su computadora portátil, teléfono inteligente, tableta o TV. Un disco duro externo también puede proteger su música de perderse o dañarse debido a virus, malware o fallas de hardware. </p>
-<h3>Cómo utilizar un disco duro externo para el almacenamiento de música</h3>
-<p>El uso de un disco duro externo para el almacenamiento de música es otra opción que puede considerar si desea descargar música de YouTube a una unidad USB. Estos son los pasos que debes seguir:</p>
-<h4>Paso 1: Elija un disco duro externo adecuado para el almacenamiento de música</h4>
-<p>El primer paso es elegir un disco duro externo adecuado para el almacenamiento de música que satisfaga sus necesidades y preferencias. Hay diferentes tipos y modelos de discos duros externos disponibles en el mercado, pero no todos ellos son adecuados para el almacenamiento de música. Algunos de los factores que debe considerar al elegir un disco duro externo para el almacenamiento de música son:</p>
-<ul>
-<li> La capacidad de almacenamiento y la velocidad del disco duro externo</li>
-<li>La compatibilidad y durabilidad del disco duro externo</li>
-
-<li>El precio y la garantía del disco duro externo</li>
-</ul>
-<p>Algunos de los ejemplos de discos duros externos populares y confiables para el almacenamiento de música son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-<tr><td>Seagate Backup Plus Slim</td><td>[https://www.seagate.com/consumer/backup/backup-plus/]</td><td>- Diseño compacto y elegante<br>- Hasta 5 TB de capacidad de almacenamiento<br>- Velocidad de transferencia rápida USB 3.0<br>- Compatible con Windows y Br<Mac Incluye software de copia de seguridad y almacenamiento en la nube</td></tr>
-<tr><td>WD My Passport</td><td>[https://shop.westerndigital.com/products/portable-drives/wd-my-passport-usb-3-2-hdd#WDBYVG0010BBK-WESN]</td><td>- Diseño elegante y colorido<br>Hasta 5 TB de capacidad de almacenamiento<br- Velocidad de transferencia rápida USB 3. Incluye software de copia de seguridad y protección con contraseña</td></tr>
-<tr><td>Samsung T7 Touch Portable SSD</td><td>[https:/www.samsung.com/us/touchting/memory-storage/portable-solid-state-drives/portable-ssd-t7-touchb-usb-3-2-500gbmu-pc-ww/<brtd>>- Diseño delgado y ligero<br- Hasta 2 TB de capacidad de almacenamiento-<br Velocidad de transferencia USB 3.2 súper rápida<br>- Compatible con Windows, Mac, Android y consolas de juegos<br>- Incluye seguridad de huellas dactilares e indicador de estado led</td></tr>
-</tabla>
-<h4>Paso 2: Conecte el disco duro externo a su computadora</h4>
-<p>El siguiente paso es conectar el disco duro externo a su computadora usando un puerto o cable USB. Luego, debe formatear el disco duro externo si aún no está formateado o no es compatible con su computadora. Formatear el disco duro externo borrará todos los datos en él y lo hará listo para su uso. Puede formatear el disco duro externo siguiendo las instrucciones del sitio web o manual del fabricante o proveedor del disco duro externo. </p>
-<h4>Paso 3: Descargar música de YouTube usando cualquiera de los métodos anteriores</h4>
-
-<p>También puede descargar música de otras fuentes, como Spotify, SoundCloud, iTunes o Amazon Music, y guardarlos en su disco duro externo. Sin embargo, es posible que necesite utilizar diferentes herramientas o métodos dependiendo de la fuente y el formato de los archivos de música. </p>
-<h4>Paso 4: Copie y pegue los archivos de música descargados en la carpeta del disco duro externo</h4>
-<p>El paso final es copiar y pegar los archivos de música descargados en la carpeta del disco duro externo que desea utilizar para el almacenamiento de música. Puede hacer esto abriendo el administrador de archivos o la aplicación del explorador en su computadora y encontrando la carpeta o ubicación donde guardó los archivos de música descargados en su disco duro externo. Luego, puede arrastrar y soltar o copiar y pegar los archivos de música en otra carpeta o subcarpeta en su disco duro externo si desea organizarlos mejor. </p>
-<p>Después de copiar y pegar los archivos de música, puede expulsar o quitar de forma segura el disco duro externo de su computadora. A continuación, puede disfrutar de escuchar la música descargada de YouTube en cualquier dispositivo que soporte la reproducción del disco duro externo. </p>
- <h2>Conclusión</h2>
-<p>Descargar música de YouTube a una unidad USB es una gran manera de disfrutar de su música favorita sin conexión, en cualquier dispositivo y en cualquier lugar. Hay diferentes métodos que puede utilizar para hacerlo, como el uso de un convertidor de YouTube a MP3, una suscripción YouTube Music o YouTube Premium, o un disco duro externo para el almacenamiento de música. Cada método tiene sus propias ventajas y desventajas, dependiendo de sus preferencias, presupuesto y habilidades técnicas. </p>
-<p>Aquí hay algunos consejos y advertencias para descargar música de YouTube a una unidad USB:</p>
-<ul>
-<li>Asegúrese de que tiene suficiente espacio de almacenamiento en su unidad USB o disco duro externo para descargar música de YouTube. Puede comprobar la capacidad de almacenamiento y el uso de su dispositivo yendo a su configuración o propiedades. </li>
-
-<li>Asegúrate de respetar los derechos de propiedad intelectual y la privacidad de los creadores y propietarios de la música que descargues de YouTube. Solo debes descargar música gratuita, legal y autorizada para uso personal. No debe descargar música con derechos de autor, protegida o restringida por ley. </li>
-<li>Asegúrese de proteger su computadora y dispositivo de malware, virus o anuncios que puedan provenir de YouTube a convertidores de MP3 u otras fuentes. Solo debe usar herramientas o servicios seguros y confiables que tengan buenas críticas y calificaciones. También debe usar software antivirus y programas de firewall para analizar y bloquear cualquier amenaza potencial. </li>
-</ul>
-<p>Esperamos que este artículo te haya ayudado a aprender a descargar música de YouTube a una unidad USB. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h2>Preguntas frecuentes</h2>
-<h3>Q1. ¿Es legal descargar música de YouTube? </h3>
-<p>A1. Depende de la fuente y el contenido de la música que descargues de YouTube. En términos generales, es legal descargar música de YouTube si es gratuita, legal y autorizada para uso personal. Sin embargo, es ilegal descargar música de YouTube si está protegida por derechos de autor, o restringida por la ley. Siempre debe respetar los derechos de propiedad intelectual y la privacidad de los creadores y propietarios de la música que descarga de YouTube. También debe comprobar los términos y condiciones del sitio web de YouTube y la fuente de música antes de descargar cualquier música de YouTube.</p>
-<h3>Q2. ¿Cuánto espacio de almacenamiento necesito para descargar música de YouTube? </h3>
-
-<h3>Q3. ¿Cómo puedo reproducir música desde una unidad USB en el estéreo de mi coche? </h3>
-<p>A3. Depende del tipo y modelo de su equipo de música y su unidad USB. En términos generales, puede reproducir música desde una unidad USB en el estéreo de su automóvil si el estéreo de su automóvil tiene un puerto USB o una ranura que puede leer y reproducir archivos de música desde su unidad USB. También puede utilizar un adaptador o cable USB que puede conectar su unidad USB a la entrada o salida auxiliar del estéreo del automóvil. Sin embargo, algunos estéreos de automóviles pueden no admitir algunos formatos o cualidades de archivos de música desde su unidad USB. Usted debe comprobar la compatibilidad y las especificaciones de su coche estéreo y su unidad USB antes de reproducir música desde una unidad USB en su coche estéreo. </p>
-<h3>Q4. ¿Cómo puedo editar o recortar los archivos de música descargados? </h3>
-<p>A4. Puede editar o recortar los archivos de música descargados utilizando un software de edición de audio o una herramienta en su computadora o dispositivo. Hay muchos programas de edición de audio o herramientas disponibles en línea, pero algunos de ellos pueden requerir instalación, registro o pago. Algunos de ellos también pueden tener características limitadas, salida de baja calidad o interfaz compleja. Por lo tanto, necesita encontrar un software o herramienta de edición de audio adecuado y confiable que pueda satisfacer sus necesidades y expectativas. </p>
-<p>Algunos de los factores que debes considerar al elegir un software o herramienta de edición de audio son:</p>
-<ul>
-<li>La funcionalidad y flexibilidad del software o herramienta de edición de audio</li>
-<li>La calidad y el formato del archivo de salida</li>
-<li>Compatibilidad y seguridad del software o herramienta</li>
-<li>Disponibilidad y accesibilidad del servicio</li>
-<li>Facilidad de uso y simplicidad de la interfaz</li>
-<li>La legalidad y legitimidad del servicio</li>
-</ul>
-<p>Algunos de los ejemplos de software o herramientas de edición de audio populares y confiables son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-
-<tr><td>WavePad</td><td>[https://www.nch.com.au/wavepad/index.html]</td><td>- Gratis para uso no comercial<br>- Soporta múltiples formatos y plataformas<br>- Ofrece varias funciones de edición y efectos<br>- Permite el procesamiento por lotes y la conversión<br>- Tiene un profesional y fácilinterfaz de uso</td></tr>
-<tr><td>Online Audio Cutter</td><td>[https://online-audio-cutter.com/]</td><td>- Herramienta gratuita y en línea<br>- Soporta múltiples formatos y plataformas<br>- Ofrece funciones básicas de edición y efectos<br>- Permite recortar, recortar, desvanecer y fusionar archivos de audiobr<>-br> Tiene una interfaz sencilla y fácil de usar</td></tr>
-</tabla>
-<h3>Q5. ¿Cómo puedo hacer copias de seguridad o restaurar mis archivos de música descargados? </h3>
-<p>A5. Puede realizar copias de seguridad o restaurar los archivos de música descargados utilizando un servicio de almacenamiento en la nube o una herramienta en su computadora o dispositivo. Un servicio o herramienta de almacenamiento en la nube es un servicio o herramienta en línea que le permite almacenar, sincronizar, compartir y acceder a sus datos en diferentes dispositivos a través de Internet. Mediante el uso de un servicio o herramienta de almacenamiento en la nube, puede realizar copias de seguridad de sus archivos de música descargados en una ubicación segura en línea a la que puede acceder en cualquier momento y en cualquier lugar. También puede restaurar sus archivos de música descargados desde el servicio de almacenamiento en la nube o la herramienta si pierde o daña su unidad USB o disco duro externo. </p>
-<p>Hay muchos servicios o herramientas de almacenamiento en la nube disponibles en línea, pero algunos de ellos pueden requerir instalación, registro o pago. Algunos de ellos también pueden tener espacio de almacenamiento limitado, baja velocidad de transferencia o una seguridad deficiente. Por lo tanto, necesita encontrar un servicio de almacenamiento en la nube adecuado y confiable o una herramienta que pueda satisfacer sus necesidades y expectativas. </p>
-<p>Algunos de los factores que debe considerar al elegir un servicio o herramienta de almacenamiento en la nube son:</p>
-<ul>
-<li>El espacio de almacenamiento y la velocidad del servicio de almacenamiento en la nube o herramienta</li>
-<li>La compatibilidad y la seguridad del servicio o herramienta de almacenamiento en la nube</li>
-<li>Disponibilidad y accesibilidad del servicio o herramienta</li>
-
-<li>La legalidad y legitimidad del servicio o herramienta</li>
-</ul>
-<p>Algunos de los ejemplos de servicios o herramientas de almacenamiento en la nube populares y confiables son:</p>
-<tabla>
-<tr><th>Nombre</th><th>Sitio web</th><th>Características</th></tr>
-<tr><td>Google Drive</td><td>[https://www.google.com/drive/]</td><td>- Gratis hasta 15 GB de espacio de almacenamiento<br>- Soporta múltiples formatos y plataformas<br>- Ofrece diversas opciones de sincronización, uso compartido y acceso<br>- Se integra con otros productos y servicios de Google br<br>-> Tiene una interfaz simple e intuitiva</td></tr>
-<tr><td>Dropbox</td><td>[https://www.dropbox.com/]</td><td>- Gratis por hasta 2 GB de espacio de almacenamiento<br>- Soporta múltiples formatos y plataformas<br>- Ofrece varias opciones de sincronización, uso compartido y acceso<br>- Se integra con otras aplicaciones y servicios<br>- Tiene una sincronización profesional y fácilinterfaz de uso</td></tr>
-<tr><td>pCloud</td><td>[https://www.pcloud.com/]</td><td>- Gratis por hasta 10 GB de espacio de almacenamiento<br>- Soporta múltiples formatos y plataformas<br>- Ofrece varias opciones de sincronización, uso compartido y acceso<br>- Proporciona cifrado de alto nivel y seguridad<br>- Tiene un elegante y usuariointerfaz amigable</td></tr>
-</tabla>
- <p>Esperamos que este artículo te haya ayudado a aprender a descargar música de YouTube a una unidad USB. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descargar Agente Zabbix Para Windows Server 2019.md

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-<br> - ¿Cuáles son los modos de comunicación entre el agente de Zabbix y el servidor/ proxy <br> - ¿Cuáles son los beneficios de usar el agente de Zabbix para el monitoreo de Windows | | Descarga de Zabbix Agent | - Cómo descargar el pre-brcompilado binarios agente Zabbix o instalador MSI desde el sitio web oficial de Zabbix <br> - Cómo elegir la versión y la arquitectura correcta para su sistema | | Instalación de agente Zabbix | - Cómo instalar agente Zabbix como un servicio de Windows utilizando la línea de comandos o instalador MSI <br> - Cómo verificar que el servicio está instalado y ejecutándose | | Configuración del agente Zabbix | - Cómo editar el archivo de configuración del agente Zabbix <br> - Cómo establecer el nombre de host, la dirección del servidor y otros parámetros <br> - Cómo configurar el agente Zabbix para el modo pasivo o activo | | Adición de Windows Host a la interfaz web de Zabbix | - Cómo crear un host en la interfaz web de Zabbix y asignarlo a un grupo <br> - Cómo especificar la interfaz del agente y la dirección IP o el nombre DNS del host de Windows <br> - Cómo vincular una plantilla para el monitoreo de Windows por el agente Zabbix o agente Zabbix activo | | Conclusión | - Un resumen de lo que hemos aprendido en este artículo <br> - Una llamada a la acción para más aprendizaje y exploración | ## Artículo con formato HTML <p><strong>Cómo descargar e instalar Zabbix Agent para Windows Server 2019</strong></p>
- <p>Zabbix es una potente solución de monitoreo de código abierto que puede monitorear varios aspectos de su infraestructura de TI, como servidores, redes, aplicaciones, bases de datos, servicios de nube y más. Zabbix puede recopilar y visualizar métricas de diferentes fuentes, como SNMP, WMI, comprobaciones sin agentes o agente Zabbix. </p>
-<h2>descargar agente zabbix para Windows Server 2019</h2><br /><p><b><b>Download</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6LZk">https://bltlly.com/2v6LZk</a></b></p><br /><br />
-
- <p>En este artículo, le mostraremos cómo descargar e instalar el agente Zabbix para Windows Server 2019, y cómo configurarlo para el modo pasivo o activo. También explicaremos los beneficios de usar el agente Zabbix para el monitoreo de Windows y cómo agregar su host de Windows a la interfaz web de Zabbix. </p>
- <h2>Descargar agente Zabbix</h2>
- <p>Para descargar el agente Zabbix para Windows Server 2019, tiene dos opciones: puede descargar los binarios precompilados en formato ZIP o usar el paquete de instalación MSI. Ambas opciones están disponibles en el sitio web oficial de Zabbix en <a href="( 1 )">https://www.zabbix.com/download_agents</a>. </p>
- <p>Los binarios precompilados son adecuados para la instalación y configuración manual utilizando la línea de comandos. Puede elegir entre dos generaciones de agentes Zabbix: agente Zabbix 1 (legado) o agente Zabbix 2 (nuevo). También necesita seleccionar la arquitectura apropiada para su sistema: 32 bits (x86) o 64 bits (x64). </p>
- <p>El paquete de instalación de MSI es adecuado para la instalación y configuración automatizada utilizando una interfaz gráfica de usuario. Solo necesita seleccionar la arquitectura de su sistema: 32 bits (x86) o 64 bits (x64). El instalador de MSI instalará el agente Zabbix 2 por defecto. </p>
- <h2>Instalación del agente Zabbix</h2>
- <p>Para instalar el agente Zabbix como un servicio de Windows en su máquina de Windows Server 2019, puede usar la línea de comandos o el instalador MSI. </p> <p>Si utiliza la línea de comandos, debe descomprimir el archivo ZIP descargado en una carpeta de su elección, como <code>C: zabbix</code>. Luego, abra un símbolo del sistema como administrador y vaya a esa carpeta. Para instalar el agente Zabbix como servicio, ejecute el siguiente comando:</p>
- <pre><code>zabbix_agentd.exe --config zabbix_agentd.win.conf --install </code></pre>
- <p>Para verificar que el servicio está instalado y ejecutándose, ejecute el siguiente comando:</p>
- <pre><code>sc query zabbix_agentd </code></pre>
-
- <p>Si utiliza el instalador MSI, debe ejecutar el archivo MSI descargado y seguir el asistente de instalación. Se le pedirá que acepte el acuerdo de licencia, elija la carpeta de instalación y configure algunos parámetros, como el nombre de host, la dirección del servidor y el modo de agente. También puede optar por iniciar el servicio automáticamente después de la instalación. Una vez finalizada la instalación, puede verificar que el servicio está instalado y ejecutándose comprobando el administrador de servicios de Windows o utilizando el comando <code>sc query</code> como se describe anteriormente. </p>
-<p></p>
- <h2>Configuración del agente Zabbix</h2>
- <p>Para configurar el agente Zabbix para Windows Server 2019, debe editar el archivo de configuración del agente Zabbix. El archivo de configuración se encuentra en la misma carpeta donde instaló el agente Zabbix, como <code>C: zabbix zabbix_agentd.win.conf</code>. Puede usar cualquier editor de texto para abrir y editar el archivo. </p>
- <p>El archivo de configuración contiene muchos parámetros que controlan el comportamiento y la funcionalidad del agente Zabbix. Algunos de los parámetros más importantes son:</p>
- <ul>
-<li><code>Hostname</code>: El nombre del host monitoreado tal como aparece en la interfaz web de Zabbix. Debe coincidir exactamente con el nombre de host que creará en la interfaz web de Zabbix más tarde. </li>
-<li><code>Servidor</code>: La dirección IP o el nombre DNS del servidor o proxy Zabbix que solicitará datos del agente Zabbix en modo pasivo. Puede especificar varios servidores o proxies separados por comas. </li>
-<li><code>ServerActive</code>: La dirección IP o el nombre DNS del servidor o proxy Zabbix que recibirá datos del agente Zabbix en modo activo. Puede especificar varios servidores o proxies separados por comas. </li>
-<li><code>StartAgents</code>: El número de conexiones concurrentes que el agente Zabbix puede aceptar desde el servidor Zabbix o el proxy en modo pasivo. El valor predeterminado es 3.</li>
-
-<li><code>Timeout</code>: El tiempo de espera en segundos para procesar cada elemento por el agente de Zabbix. El valor predeterminado es 3.</li>
-<li><code>EnableRemoteCommands</code>: Un indicador que habilita o inhabilita la ejecución de comandos remotos desde el servidor Zabbix o el proxy en el agente Zabbix. El valor predeterminado es 0 (desactivado). </li>
-<li><code>LogType</code>: El tipo de archivo de registro que usará el agente Zabbix. Los valores posibles son <code>file</code>, <code>system</code>, <code>console</code>, o <code>none</code>. El valor predeterminado es <code>file</code>. </li>
-<li><code>LogFile</code>: El nombre y la ruta del archivo de registro que el agente de Zabbix usará si <code>LogType</code> se establece en <code>file</code>. El valor predeterminado es <code>C: zabbix zabbix_agentd.log</code>. </li>
-<li><code>DebugLevel</code>: El nivel de detalle que el agente de Zabbix escribirá en el archivo de registro. Los valores posibles son 0 (basic), 1 (critical), 2 (error), 3 (warning), 4 (debug), o 5 (trace). El valor predeterminado es 3.</li>
-</ul>
- <p>Para configurar el agente Zabbix para el modo pasivo, debe establecer el parámetro <code>Server</code> en la dirección IP o el nombre DNS de su servidor Zabbix o proxy, y dejar el parámetro <code>ServerActive</code> vacío o comentado. Por ejemplo: resultado/p>
- <pre><code># Servidor de modo pasivo=192.168.1.10 #ServerActive= StartAgents=3 # Modo activo #Server= #ServerActive=192.168.1.10:10051 #RefreshActiveChecks=120 </pre></p>
- <p>Para configurar el agente Zabbix para el modo activo, debe establecer el parámetro <code>ServerActive</code> en la dirección IP o el nombre DNS de su servidor Zabbix o proxy , y dejar el parámetro <code>Server</code> vacío o comentado. También debe establecer el parámetro <code>RefreshActiveChecks</code> en la frecuencia deseada de envío de datos al servidor o proxy. Por ejemplo:</p>
- <pre><code># Modo pasivo #Server=192.168.1.10 #StartAgents=3 # Active mode ServerActive=192.168.1.10:10051 RefreshActiveChecks=120 </pre></p>
-
- <pre><code>sc stop zabbix_agentd sc start zabbix_agentd </code></pre>
- <h2>Agregar host de Windows a la interfaz web de Zabbix</h2>
- <p>Para agregar su host de Windows Server 2019 a la interfaz web de Zabbix, debe iniciar sesión en su servidor Zabbix o interfaz web proxy y navegar a <strong>Configuration</strong> > <strong>Hosts</strong>. Luego, haga clic en el botón <strong>Crear host</strong> en la esquina superior derecha. </p>
- <p>Verás un formulario donde necesitas introducir algunos detalles sobre tu host, como:</p>
- <ul>
-<li><strong>Nombre del host</strong>: El nombre de host de su host de Windows tal como aparece en el archivo de configuración del agente Zabbix. Debe coincidir exactamente con el parámetro <code>Hostname</code> que estableció en el archivo de configuración. </li>
-<li><strong>Nombre visible</strong>: Un alias opcional para su host que se mostrará en la interfaz web de Zabbix en lugar del nombre del host. </li>
-<li><strong>Grupos</strong>: Los grupos a los que pertenece su host. Puede seleccionar uno o más grupos existentes o crear uno nuevo. Por ejemplo, puede seleccionar o crear un grupo llamado <code>Servidores de Windows</code>. </li>
-<li><strong>Descripción</strong>: Una descripción opcional de su anfitrión que proporcionará información adicional sobre su propósito, ubicación, propietario, etc.</li>
-</ul>
- <p>Después de introducir estos detalles, haga clic en el botón <strong>Add</strong> en la parte inferior del formulario. </p>
- <p>El siguiente paso es especificar la interfaz del agente y la dirección IP o el nombre DNS de su host de Windows. Para ello, haga clic en la pestaña <strong>Interfaces</strong> y seleccione <strong>Zabbix agent</strong> en el menú desplegable. Luego, ingrese la dirección IP o el nombre DNS de su host de Windows en el campo <strong>IP address/DNS name</strong>. También puede cambiar el número de puerto predeterminado si es necesario. </p>
- <p>Si configuró el agente Zabbix para el modo activo, también debe seleccionar el agente <strong>Zabbix (active)</strong> en el menú desplegable e ingresar la misma dirección IP o nombre DNS que antes. </p>
-
- <p>Una plantilla es una colección de elementos, disparadores, gráficos y otros elementos que definen qué y cómo monitorear un host. Al vincular una plantilla a tu host, heredas todos estos elementos y ahorras tiempo y esfuerzo. </p>
- <p>También puede vincular otras plantillas que proporcionan funciones de monitoreo adicionales para su host de Windows, como CPU, memoria, disco, red, servicios, procesos, etc.</p>
- <p>Después de vincular las plantillas, haga clic en el botón <strong>Add</strong> en la parte inferior del formulario. </p>
- <h2>Conclusión</h2>
- <p>En este artículo, hemos aprendido cómo descargar e instalar el agente Zabbix para Windows Server 2019, y cómo configurarlo para el modo pasivo o activo. También hemos aprendido cómo agregar nuestro host de Windows a la interfaz web de Zabbix y vincular una plantilla para el monitoreo de Windows por el agente de Zabbix o agente de Zabbix activo. </p>
- <p>Zabbix agent es una herramienta útil que nos permite monitorear varios aspectos de nuestra máquina Windows Server 2019, como el rendimiento, el estado y la configuración. Al usar el agente Zabbix, podemos recopilar y visualizar métricas de nuestro host de Windows y recibir alertas cuando algo sale mal. </p>
- <p>Si desea obtener más información sobre Zabbix y el agente Zabbix, puede visitar el sitio web oficial de Zabbix en <a href="">https://www.zabbix.com/</a>, donde puede encontrar documentación, tutoriales, foros, blogs y otros recursos. </p>
- <h3>Preguntas frecuentes</h3>
- <ol>
-<li><strong>¿Cuáles son los requisitos para ejecutar el agente Zabbix en Windows Server 2019? </strong></li>
-<li>Los requisitos para ejecutar el agente Zabbix en Windows Server 2019 son mínimos. Necesita tener una máquina Windows Server 2019 con al menos 128 MB de RAM y 100 MB de espacio libre en disco. También necesita tener privilegios de administrador para instalar y configurar el agente Zabbix como un servicio. </li>
-<li><strong>¿Cómo puedo probar si el agente Zabbix funciona correctamente en mi host de Windows? </strong></li>
-
- <pre><code>zabbix_get -s <IP address or DNS name of your Windows host> -k "system.cpu.load[all,avg1]" </code></pre>
- <li>Este comando solicitará la carga media de la CPU durante el último minuto desde su host de Windows. Debería ver un valor numérico como respuesta. Si ve un mensaje de error, como <code>ZBX_NOTSUPPORTED</code> o <code>ZBX_TCP_READ()</code>, significa que hay un problema con la comunicación entre el agente Zabbix y el servidor o proxy, o con la configuración del agente Zabbix. </li>
-<li>Otra forma de probar si el agente Zabbix funciona correctamente en su host de Windows es usar la utilidad <code>zabbix_sender</code> que también viene con el agente Zabbix. Esta utilidad le permite enviar datos al servidor Zabbix o proxy en modo activo. Por ejemplo, puede ejecutar el siguiente comando desde su host de Windows:</li>
- <pre><code>zabbix_sender -z <IP address or DNS name of your Zabbix server or proxy> -s <hostname of your Windows host> -k "test.key" -o "test.value" </code></pre>
- <li>Este comando enviará un elemento personalizado con la clave <code>test.key</code> y el valor <code>test.value</code> desde su host de Windows al servidor o proxy de Zabbix. Deberías ver un mensaje como <code>sent: 1; omitido: 0; total: 1</code> como respuesta. Si ve un mensaje de error, como <code>ZBX_TCP_WRITE()</code> o <code>ZBX_TCP_READ()</code>, significa que hay un problema con la comunicación entre el agente Zabbix y el servidor o proxy, o con la configuración del agente Zabbix. </li>
-
-<li>Si ve algún problema con el estado o los datos de su host, como <code>ZBX_NOTSUPPORTED</code>, <code>No se recibieron datos</code>, o <code>No hay permisos para el objeto referido o no existe! </code>, significa que hay un problema con la comunicación entre el agente Zabbix y el servidor o proxy, o con la configuración del agente Zabbix. </li>
-<li><strong>¿Cómo puedo actualizar el agente Zabbix en mi host de Windows? </strong></li>
-<li>Para actualizar el agente de Zabbix en su host de Windows, debe descargar la última versión del agente de Zabbix desde el sitio web oficial de Zabbix en <a href="">https:/www.zabbix.com/download_agents</a>. Luego, debe detener el servicio del agente Zabbix, reemplazar los archivos antiguos con los nuevos e iniciar el servicio nuevamente. Puede usar los siguientes comandos para hacer esto:</li>
- <pre><code>sc stop zabbix_agentd xcopy /y C: zabbix *. exe C: zabbix *. dll C: zabbix *. conf C: zabbix *. bat C: zabbix *. ps1 C: zabbix *. vbs sc start zabbix_agentd </code></pre>
- <li>También puede necesitar editar el archivo de configuración del agente Zabbix si hay cambios o nuevos parámetros en la nueva versión. </li>
-<li><strong>¿Cómo puedo desinstalar el agente Zabbix de mi host de Windows? </strong></li>
-<li>Para desinstalar el agente Zabbix de su host de Windows, debe detener el servicio del agente Zabbix, eliminar el servicio del agente Zabbix y eliminar la carpeta del agente Zabbix. Puede usar los siguientes comandos para hacer esto:</li>
- <pre><code>sc stop zabbix_agentd zabbix_agentd.exe --config zabbix_agentd.win.conf --uninstall rmdir /s /q C: zabbix </code></pre>
- <li>Si utilizó el instalador MSI para instalar el agente Zabbix, también puede usar el panel de control de Windows o el instalador MSI para desinstalar el agente Zabbix. </li>
-<li><strong>¿Cómo puedo personalizar el agente Zabbix para mis necesidades específicas? </strong></li>
-
- <ul>
-<li><strong>Parámetros del usuario</strong>: Los parámetros del usuario le permiten definir elementos personalizados que el agente Zabbix puede monitorear. Puede usar cualquier script o comando que devuelva un valor como parámetro de usuario. Por ejemplo, puede crear un parámetro de usuario que devuelva el número de archivos de una carpeta, el estado de un servicio o la salida de un comando de PowerShell. Necesita definir los parámetros de usuario en el archivo de configuración del agente Zabbix usando la directiva <code>UserParameter</code>. Por ejemplo:</li>
- <pre><code>UserParameter=folder.count,powershell -NoProfile -Command "(Get-ChildItem C: temp -Recurse | Measure-Object). Count" UserParameter=service.status[*],sc query $1 | find "STATE" | find "RUNNING" UserParameter=powershell.output[*],powershell -NoProfile -Command "$1" </code></pre>
- <li>Entonces, necesitas crear elementos en la interfaz web de Zabbix que usen estos parámetros de usuario como claves. Por ejemplo:</li>
- <pre><code>folder.count service.status[Spooler] powershell.output[Get-Date] </code></pre>
- <li><strong>Comprobaciones activas</strong>: Las comprobaciones activas permiten enviar datos desde el agente Zabbix al servidor Zabbix o al proxy sin esperar solicitudes. Esto reduce la carga de red y mejora la escalabilidad de Zabbix. Puede usar comprobaciones activas para cualquier elemento que sea compatible con el agente Zabbix, como métricas del sistema, archivos de registro, contadores de rendimiento de Windows, etc. Debe configurar el agente Zabbix para el modo activo estableciendo los parámetros <code>ServerActive</code> y <code>RefreshActiveChecks</code> en el archivo de configuración. Luego, debe vincular una plantilla para el agente Zabbix activo en la interfaz web de Zabbix. </li>
-
-<li><strong>Cifrado</strong>: El cifrado le permite asegurar la comunicación entre el agente de Zabbix y el servidor o proxy utilizando certificados TLS. Esto puede evitar el acceso no autorizado y la manipulación de datos. Necesita generar e instalar certificados TLS en su host de Windows y en su servidor o proxy de Zabbix. Luego, debe configurar el agente y el servidor Zabbix o el proxy para usar el cifrado estableciendo el valor <code>TLSConnect</code>, <code>TLSAccept</code>, <code>TLScaFile</code>, <code>TLScaPath</code>, <code>TLScertFile</code>, y code <T>TLSkeyle</code. </li>
-</ul></p> 64aa2da5cf<br />
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-<br />

spaces/Benson/text-generation/Examples/Descargar Carx Calle Apk.md

@@ -1,46 +0,0 @@
-<br />
-<h1>El zombi que camina 2 Mod APK: Un juego de disparos de supervivencia con dinero ilimitado</h1>
-<p>Si eres un fan de los juegos de zombis, es posible que hayas oído hablar de The Walking Zombie 2, un popular juego de disparos en primera persona que te permite luchar contra hordas de criaturas no muertas en un mundo post-apocalíptico. ¿Pero sabías que hay una versión modificada de este juego que te da dinero y recursos ilimitados para usar? En este artículo, le diremos todo lo que necesita saber sobre The Walking Zombie 2 Mod APK, incluyendo sus características, beneficios, y cómo descargar e instalar en su dispositivo. </p>
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-<h2>Introducción</h2>
-<h3>¿Qué es el zombi andante 2?</h3>
-<p>The Walking Zombie 2 es un juego de disparos de supervivencia desarrollado por Alda Games, un estudio con sede en la República Checa. El juego se desarrolla en un mundo que ha sido devastado por un virus zombi, donde juegas como uno de los pocos supervivientes que tiene que luchar por tu vida. Encontrarás varios tipos de zombies, como caminantes, corredores, mutantes y jefes, así como otros enemigos como bandidos, asaltantes y soldados. También conocerás a otros sobrevivientes que te ayudarán o te obstaculizarán en tu viaje. </p>
-<h3> ¿Qué es el zombie caminando 2 Mod APK? </h3>
-<p>El Walking Zombie 2 Mod APK es una versión modificada del juego original que le da acceso a dinero y recursos ilimitados. Esto significa que puede comprar cualquier arma, armadura, munición, kits de salud y otros artículos que necesite sin preocuparse por quedarse sin efectivo. También puede mejorar sus habilidades y habilidades para hacerse más fuerte y más resistente. Con este mod, podrás disfrutar del juego sin limitaciones ni restricciones. </p>
-<h3> ¿Por qué deberías jugar El zombie andante 2 Mod APK? </h3>
-<p>Hay muchas razones por las que debe jugar The Walking Zombie 2 Mod APK en lugar del juego original. Aquí están algunos de ellos:</p>
-<ul>
-<li> Usted puede tener más diversión y emoción con dinero y recursos ilimitados. </li>
-
-<li>Puede explorar más áreas y ubicaciones sin temor a quedarse sin suministros. </li>
-<li>Puedes desafiarte a ti mismo con dificultades y enemigos más difíciles sin frustrarte. </li>
-<li>Puedes apoyar a los desarrolladores viendo anuncios o haciendo compras en la aplicación si quieres. </li>
-</ul>
-<h2>Características de The Walking Zombie 2 Mod APK</h2>
-<h3>Historia inmersiva y jugabilidad</h3>
-<p>El Walking Zombie 2 Mod APK tiene una historia atractiva que te mantendrá enganchado de principio a fin. Experimentarás diferentes eventos y escenarios que afectarán el resultado del juego. También tendrás que tomar decisiones que darán forma a la personalidad y la moralidad de tu personaje. El juego tiene una dinámica de juego que se adaptará a sus acciones y comportamiento. Enfrentarás diferentes retos y consecuencias dependiendo de cómo juegues el juego. </p>
-<p></p>
-<h3>Impresionantes gráficos y efectos de sonido</h3>
-<p>El Walking Zombie 2 Mod APK tiene gráficos increíbles y efectos de sonido que te sumergen en el mundo del juego. El juego tiene un estilo único low-poly que le da una sensación retro. El juego también tiene iluminación realista y sombras que crean una atmósfera oscura y sombría. El juego tiene efectos de sonido de alta calidad que mejoran el estado de ánimo y la tensión del juego. Usted escuchará los gemidos y gritos de los zombies y los disparos y explosiones de las armas. También disfrutarás de la música y la actuación de voz que añaden más profundidad y emoción al juego. </h3>
-<h3>Varias armas y habilidades para elegir</h3>
-
-<h3>Múltiples modos de juego y misiones para completar</h3>
-<p>El Walking Zombie 2 Mod APK tiene varios modos de juego y misiones que se puede jugar y completar para ganar recompensas y experiencia. Puedes jugar en el modo historia principal, donde sigues la trama y el progreso a través del juego. También puedes jugar las misiones secundarias, donde ayudas a otros supervivientes o completar diferentes tareas. También puedes jugar al modo arena, donde luchas contra oleadas de zombies en un área cerrada. También puedes jugar en el modo online, donde compites con otros jugadores en PvP o batallas cooperativas. </h3>
-<h3>Dinero y recursos ilimitados para usar</h3>
-<p>El Walking Zombie 2 Mod APK tiene dinero y recursos ilimitados que se pueden utilizar para comprar y actualizar cualquier cosa que desee en el juego. Usted puede comprar cualquier arma, armadura, munición, kits de salud, y otros artículos que usted necesita de las tiendas o comerciantes. También puede mejorar sus habilidades y habilidades para hacerse más fuerte y más resistente. También puede crear y construir sus propios artículos y equipos a partir de los materiales que recoja o saquee. También puedes intercambiar con otros supervivientes o jugadores para obtener más dinero y recursos. </h3>
-<h2>Cómo descargar e instalar El Walking Zombie 2 Mod APK en su dispositivo</h2>
-<h3>Paso 1: Descargar el archivo APK de una fuente de confianza</h3>
-<p>El primer paso para descargar e instalar el Walking Zombie 2 Mod APK en su dispositivo es encontrar una fuente confiable que proporciona el archivo APK. Puede buscar en línea para sitios web o blogs que ofrecen el archivo APK de forma gratuita. Asegúrese de que la fuente es segura y segura, y que el archivo APK se actualiza y es compatible con su dispositivo. También puede escanear el archivo APK con un programa antivirus antes de descargarlo. </p>
-<h3>Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo</h3>
-
-<h3>Paso 3: Instalar el archivo APK y lanzar el juego</h3>
-<p>El tercer paso para descargar e instalar The Walking Zombie 2 Mod APK en su dispositivo es instalar el archivo APK y lanzar el juego. Para ello, busque el archivo APK que descargó en la carpeta de almacenamiento o descargas de su dispositivo. Toque en el archivo APK para iniciar el proceso de instalación. Siga las instrucciones de la pantalla para completar la instalación. Una vez hecho esto, abra el icono del juego en la pantalla de inicio o en el cajón de la aplicación. Disfruta jugando The Walking Zombie 2 Mod APK con dinero y recursos ilimitados. </p>
-<h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>El Walking Zombie 2 Mod APK es un juego de disparos de supervivencia que le permite luchar contra zombies y otros enemigos en un mundo post-apocalíptico. El juego tiene una historia inmersiva y jugabilidad, impresionantes gráficos y efectos de sonido, varias armas y habilidades para elegir, múltiples modos de juego y misiones para completar, y dinero y recursos ilimitados para usar. El juego es divertido y emocionante, desafiante y gratificante, personalizable y flexible. </p>
-<h3>Llamada a la acción y recomendación</h3>
-<p>Si usted está buscando un juego de zombies que le mantendrá entretenido durante horas, entonces usted debe descargar The Walking Zombie 2 Mod APK en su dispositivo. Usted no se arrepentirá, ya que tendrá una explosión jugando a este juego con dinero y recursos ilimitados. También apoyarás a los desarrolladores viendo anuncios o haciendo compras en la aplicación si quieres. ¿Qué estás esperando? Descargar El Walking Zombie 2 Mod APK ahora y disfrutar de disparar zombies en la cabeza. </p>
-
-el mundo del juego tanto como sea posible. Encontrará más botín, materiales, secretos y huevos de Pascua que mejorarán su experiencia de juego. - Elige tus armas y habilidades de acuerdo a tu estilo de juego y situación. Diferentes armas y habilidades tienen diferentes ventajas y desventajas. Experimenta con diferentes combinaciones y ve qué funciona mejor para ti. - Ten cuidado con tus elecciones y acciones. Afectarán el resultado del juego y la personalidad y moralidad de tu personaje. Piensa antes de actuar y prepárate para las consecuencias. - Diviértete y disfruta del juego. No te lo tomes demasiado en serio ni te estreses por ello. Después de todo, es solo un juego. </p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descargar Coche Escuela De Conduccin 2017 Mod Apk.md

@@ -1,97 +0,0 @@
-<br />
-<h1>Cómo descargar coche Driving School 2017 Mod Apk y disfrutar de dinero y vehículos ilimitados</h1>
-<p>Si usted está buscando un juego de simulador de conducción realista y divertido que le enseñará cómo conducir diferentes coches en varios entornos, entonces usted debe probar <strong>Car Driving School 2017</strong>. Este juego desafiará sus habilidades de conducción, el conocimiento de las reglas de la carretera, y la delicadeza al volante. Pero si quieres hacer el juego aún más agradable, usted debe descargar <strong>Car Driving School 2017 mod apk</strong>, que le dará dinero ilimitado y vehículos para desbloquear. En este artículo, le diremos lo que es Car Driving School 2017, por qué debe jugar, lo que es Car Driving School 2017 mod apk, cómo descargarlo, y algunos consejos y trucos para dominar el juego. </p>
- <h2>¿Qué es la escuela de conducción de coches 2017 y por qué usted debe jugar</h2>
-<p>Car Driving School 2017 es un juego de simulación de conducción para dispositivos Android e iOS que fue desarrollado por Ovilex Software. Es la nueva entrega de la popular serie Driving School que tiene más de 100 millones de descargas en todo el mundo. En este juego, usted aprenderá cómo conducir varios coches, autobuses y camiones en diferentes escenarios. También tendrás que pasar diferentes licencias, completar más de 80 niveles y explorar más de 15 mapas detallados. También puedes jugar con tus amigos en nuevos modos multijugador como carreras, paseo gratis y coger la bandera. </p>
-<h2>descargar coche escuela de conducción 2017 mod apk</h2><br /><p><b><b>Download</b> &#8230;&#8230;&#8230; <a href="https://bltlly.com/2v6M3h">https://bltlly.com/2v6M3h</a></b></p><br /><br />
- <h3>Características de la escuela de conducción de coches 2017</h3>
-<p>Algunas de las características sorprendentes de Car Driving School 2017 son:</p>
-<ul>
-<li>Casi 100 vehículos para desbloquear, que van desde coches deportivos, SUV, sedanes, autobuses, camiones y más. </li>
-<li>Más de 15 mapas detallados que incluyen ciudades, caminos rurales, carreteras, desiertos, montañas, etc.</li>
-<li> Manejo del automóvil suave y realista que le permite sentir cada golpe, giro y freno. </li>
-<li>Diferentes licencias para tomar, como licencias de automóvil, autobús y camión. </li>
-
-<li>Modo de viaje gratuito que te permite explorar los mapas a tu propio ritmo. </li>
-<li>Nuevos modos multijugador que te permiten competir contra otros jugadores, deambular libremente con ellos o capturar sus banderas. </li>
-<li>Interiores detallados de vehículos que muestran el salpicadero, el volante, los pedales, etc.</li>
-<li>Sistema de daños realista que muestra los efectos de colisiones y accidentes. </li>
-<li>Sistema de gas que requiere que llenes tu tanque en las gasolineras. </li>
-<li>Transmisión manual con embrague que le permite controlar sus engranajes manualmente. </li>
-<li>Dirección basculante, botones y volante táctil que te permiten elegir tu opción de control preferida. </li>
-<li>Tablas de clasificación en línea y logros que le permiten comparar su rendimiento con otros jugadores. </li>
-<li>Sonidos de motor reales que te hacen sentir como si estuvieras conduciendo un coche real. </li>
-<li>Condiciones meteorológicas de próxima generación que añaden realismo y variedad al juego. </li>
-</ul>
- <h3>Beneficios de jugar Car Driving School 2017</h3>
-<p>Jugar Car Driving School 2017 no solo es divertido sino también beneficioso por varias razones:</p>
-<ul>
-<li> Puede aprender a conducir una transmisión manual con embrague y palanca de cambios o mantener la caja de cambios automática clásica. </li>
-<li> Con este simulador de conducción intuitivo puedes conocer mejor las reglas de circulación. </li>
-<li> Puede mejorar sus habilidades de conducción en diferentes situaciones y entornos. </li>
-<li> Puede disfrutar de una experiencia de conducción realista e inmersiva con impresionantes gráficos y efectos de sonido. </li>
-<li>Puedes divertirte con tus amigos en modos multijugador o competir con otros jugadores en línea. </li>
-<li>Puede personalizar sus vehículos con diferentes colores, llantas, alerones, etc.</li>
-</ul>
- <h2>¿Qué es Car Driving School 2017 Mod Apk y cómo descargarlo</h2>
-
- <h3>Ventajas de la escuela de conducción de coches 2017 Mod Apk</h3>
-<p>Algunas de las ventajas de Car Driving School 2017 mod apk son:</p>
-<ul>
-<li>Puedes desbloquear todos los vehículos del juego, incluidos los premium que cuestan dinero real. </li>
-<li> Usted puede comprar cualquier actualización y personalizaciones para sus vehículos sin preocuparse por el costo. </li>
-<li>Puedes explorar todos los mapas y modos del juego sin tener que desbloquearlos primero. </li>
-<li>Puedes jugar el juego sin anuncios ni interrupciones. </li>
-<li> Puedes disfrutar del juego con mejor rendimiento y estabilidad. </li>
-</ul>
- <h3>Pasos para descargar e instalar coche Driving School 2017 Mod Apk</h3>
-<p>Si desea descargar e instalar Car Driving School 2017 mod apk, es necesario seguir estos sencillos pasos:</p>
-<ol>
-<li>En primer lugar, es necesario desinstalar la versión original de Car Driving School 2017 desde su dispositivo si lo tiene instalado. </li>
-<li>En segundo lugar, es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en el dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>En tercer lugar, es necesario descargar el coche Driving School 2017 mod apk archivo de una fuente confiable. Puede utilizar este enlace para descargarlo de forma segura y rápida. </li>
-<li>Cuarto, es necesario localizar el archivo descargado en el dispositivo y toque en él para iniciar el proceso de instalación. </li>
-<li>Quinto, debe seguir las instrucciones en la pantalla y esperar a que termine la instalación. </li>
-<li>Sexto, es necesario lanzar el juego y disfrutar de dinero y vehículos ilimitados. </li>
-</ol>
- <h2>Consejos y trucos para dominar la escuela de conducción de automóviles 2017</h2>
-<p>Car Driving School 2017 es un juego divertido y desafiante que requiere habilidad y estrategia. Aquí hay algunos consejos y trucos que te ayudarán a dominar el juego:</p>
- <h3>Sigue la ley y el límite de velocidad</h3>
-
- <h3>Desactivar el modo deportivo y aprender el mapa</h3>
-<p>Si desea mejorar sus habilidades de conducción y pasar los niveles más fácilmente, debe desactivar el modo deportivo en la configuración. El modo deportivo hace que tu coche sea más rápido y sensible, pero también más difícil de controlar. También consume más gasolina y causa más daños. Por lo tanto, es mejor apagarlo y conducir más suave y cuidadosamente. También debe aprender el mapa de cada nivel antes de iniciarlo. De esta manera, sabrás a dónde ir, qué esperar y cómo evitar obstáculos. </p>
- <h3>Diviértete en modo libre y modos multijugador</h3>
-<p>Si quieres tomarte un descanso de los niveles y licencias, puedes divertirte en modo libre o en modo multijugador. En modo libre, puede conducir alrededor de cualquier mapa sin ningún objetivo o restricciones. También puede cambiar entre diferentes vehículos y personalizarlos a su gusto. En los modos multijugador, puedes jugar con tus amigos u otros jugadores en línea en varios modos como carreras, paseos gratis o coger la bandera. También puedes chatear con ellos y hacer nuevos amigos. </p>
-<p></p>
- <h2>Conclusión</h2>
-<p>Car Driving School 2017 es un gran juego de simulación de conducción que le enseñará cómo conducir diferentes vehículos en escenarios realistas. También se divertirá con varias características, modos, mapas y vehículos. Pero si quieres hacer el juego aún más agradable, usted debe descargar Car Driving School 2017 mod apk que le dará dinero ilimitado y vehículos. También puedes utilizar algunos consejos y trucos que te ayudarán a dominar el juego. ¿Qué estás esperando? Descargar Car Driving School 2017 mod apk y disfrutar de dinero ilimitado y vehículos. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Car Driving School 2017 y Car Driving School 2017 mod apk:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>Es Car Driving School 2017 libre para jugar? </td>
-
-</tr>
-<tr>
-<td>Es coche Driving School 2017 mod apk seguro de usar? </td>
-<td>Sí, Car Driving School 2017 mod apk es seguro de usar siempre y cuando se descarga de una fuente confiable. Sin embargo, siempre debes tener cuidado al instalar aplicaciones de fuentes desconocidas y escanearlas en busca de virus o malware. </td>
-</tr>
-<tr>
-<td>¿Puedo jugar Car Driving School 2017 sin conexión? </td>
-<td>Sí, puedes jugar Car Driving School 2017 sin conexión en modo para un jugador. Sin embargo, necesitará una conexión a Internet para jugar modos multijugador o acceder a funciones en línea. </td>
-</tr>
-<tr>
-<td>¿Puedo jugar Car Driving School 2017 en PC? </td>
-<td>Sí, puedes jugar Car Driving School 2017 en PC usando un emulador de Android como Bluestacks o Nox Player. También puedes usar un teclado y un ratón para controlar el juego. </td>
-</tr>
-<tr>
-<td>¿Cómo puedo contactar a los desarrolladores de Car Driving School 2017? </td>
-<td>Puede ponerse en contacto con los desarrolladores de Car Driving School 2017 enviándoles un correo electrónico a [email protected] o visitando su sitio web en https://www.ovilex.com/.</td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/_asyncio.py

@@ -1,94 +0,0 @@
-# Copyright 2016 Étienne Bersac
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 functools
-import sys
-import typing as t
-from asyncio import sleep
-
-from pip._vendor.tenacity import AttemptManager
-from pip._vendor.tenacity import BaseRetrying
-from pip._vendor.tenacity import DoAttempt
-from pip._vendor.tenacity import DoSleep
-from pip._vendor.tenacity import RetryCallState
-
-WrappedFnReturnT = t.TypeVar("WrappedFnReturnT")
-WrappedFn = t.TypeVar("WrappedFn", bound=t.Callable[..., t.Awaitable[t.Any]])
-
-
-class AsyncRetrying(BaseRetrying):
-    sleep: t.Callable[[float], t.Awaitable[t.Any]]
-
-    def __init__(self, sleep: t.Callable[[float], t.Awaitable[t.Any]] = sleep, **kwargs: t.Any) -> None:
-        super().__init__(**kwargs)
-        self.sleep = sleep
-
-    async def __call__(  # type: ignore[override]
-        self, fn: WrappedFn, *args: t.Any, **kwargs: t.Any
-    ) -> WrappedFnReturnT:
-        self.begin()
-
-        retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
-        while True:
-            do = self.iter(retry_state=retry_state)
-            if isinstance(do, DoAttempt):
-                try:
-                    result = await fn(*args, **kwargs)
-                except BaseException:  # noqa: B902
-                    retry_state.set_exception(sys.exc_info())  # type: ignore[arg-type]
-                else:
-                    retry_state.set_result(result)
-            elif isinstance(do, DoSleep):
-                retry_state.prepare_for_next_attempt()
-                await self.sleep(do)
-            else:
-                return do  # type: ignore[no-any-return]
-
-    def __iter__(self) -> t.Generator[AttemptManager, None, None]:
-        raise TypeError("AsyncRetrying object is not iterable")
-
-    def __aiter__(self) -> "AsyncRetrying":
-        self.begin()
-        self._retry_state = RetryCallState(self, fn=None, args=(), kwargs={})
-        return self
-
-    async def __anext__(self) -> AttemptManager:
-        while True:
-            do = self.iter(retry_state=self._retry_state)
-            if do is None:
-                raise StopAsyncIteration
-            elif isinstance(do, DoAttempt):
-                return AttemptManager(retry_state=self._retry_state)
-            elif isinstance(do, DoSleep):
-                self._retry_state.prepare_for_next_attempt()
-                await self.sleep(do)
-            else:
-                raise StopAsyncIteration
-
-    def wraps(self, fn: WrappedFn) -> WrappedFn:
-        fn = super().wraps(fn)
-        # Ensure wrapper is recognized as a coroutine function.
-
-        @functools.wraps(fn)
-        async def async_wrapped(*args: t.Any, **kwargs: t.Any) -> t.Any:
-            return await fn(*args, **kwargs)
-
-        # Preserve attributes
-        async_wrapped.retry = fn.retry  # type: ignore[attr-defined]
-        async_wrapped.retry_with = fn.retry_with  # type: ignore[attr-defined]
-
-        return async_wrapped  # type: ignore[return-value]

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/windows_support.py

@@ -1,29 +0,0 @@
-import platform
-
-
-def windows_only(func):
-    if platform.system() != 'Windows':
-        return lambda *args, **kwargs: None
-    return func
-
-
-@windows_only
-def hide_file(path):
-    """
-    Set the hidden attribute on a file or directory.
-
-    From http://stackoverflow.com/questions/19622133/
-
-    `path` must be text.
-    """
-    import ctypes
-    __import__('ctypes.wintypes')
-    SetFileAttributes = ctypes.windll.kernel32.SetFileAttributesW
-    SetFileAttributes.argtypes = ctypes.wintypes.LPWSTR, ctypes.wintypes.DWORD
-    SetFileAttributes.restype = ctypes.wintypes.BOOL
-
-    FILE_ATTRIBUTE_HIDDEN = 0x02
-
-    ret = SetFileAttributes(path, FILE_ATTRIBUTE_HIDDEN)
-    if not ret:
-        raise ctypes.WinError()

spaces/Billyosoro/ESRGAN/scripts/generate_multiscale_DF2K.py

@@ -1,48 +0,0 @@
-import argparse
-import glob
-import os
-from PIL import Image
-
-
-def main(args):
-    # For DF2K, we consider the following three scales,
-    # and the smallest image whose shortest edge is 400
-    scale_list = [0.75, 0.5, 1 / 3]
-    shortest_edge = 400
-
-    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
-    for path in path_list:
-        print(path)
-        basename = os.path.splitext(os.path.basename(path))[0]
-
-        img = Image.open(path)
-        width, height = img.size
-        for idx, scale in enumerate(scale_list):
-            print(f'\t{scale:.2f}')
-            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
-            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
-
-        # save the smallest image which the shortest edge is 400
-        if width < height:
-            ratio = height / width
-            width = shortest_edge
-            height = int(width * ratio)
-        else:
-            ratio = width / height
-            height = shortest_edge
-            width = int(height * ratio)
-        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
-        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
-
-
-if __name__ == '__main__':
-    """Generate multi-scale versions for GT images with LANCZOS resampling.
-    It is now used for DF2K dataset (DIV2K + Flickr 2K)
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
-    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
-    args = parser.parse_args()
-
-    os.makedirs(args.output, exist_ok=True)
-    main(args)

spaces/Boadiwaa/Recipes/openai/api_resources/abstract/deletable_api_resource.py

@@ -1,24 +0,0 @@
-from urllib.parse import quote_plus
-
-from openai import error
-from openai.api_resources.abstract.api_resource import APIResource
-from openai.util import ApiType
-
-class DeletableAPIResource(APIResource):
-    @classmethod
-    def delete(cls, sid, api_type=None, api_version=None, **params):
-        if isinstance(cls, APIResource):
-            raise ValueError(".delete may only be called as a class method now.")
-
-        base = cls.class_url()
-        extn = quote_plus(sid)
-
-        typed_api_type, api_version = cls._get_api_type_and_version(api_type, api_version)
-        if typed_api_type == ApiType.AZURE:
-            url = "/%s%s/%s?api-version=%s" % (cls.azure_api_prefix, base, extn, api_version)
-        elif typed_api_type == ApiType.OPEN_AI:
-            url = "%s/%s" % (base, extn)
-        else:
-            raise error.InvalidAPIType('Unsupported API type %s' % api_type)            
-        
-        return cls._static_request("delete", url, api_type=api_type, api_version=api_version, **params)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/CONTRIBUTING.md

@@ -1,52 +0,0 @@
-# Contributing to detectron2
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Issues
-We use GitHub issues to track public bugs and questions.
-Please make sure to follow one of the
-[issue templates](https://github.com/facebookresearch/detectron2/issues/new/choose)
-when reporting any issues.
-
-Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## Pull Requests
-We actively welcome your pull requests.
-
-However, if you're adding any significant features, please
-make sure to have a corresponding issue to discuss your motivation and proposals,
-before sending a PR. We do not always accept new features, and we take the following
-factors into consideration:
-
-1. Whether the same feature can be achieved without modifying detectron2.
-Detectron2 is designed so that you can implement many extensions from the outside, e.g.
-those in [projects](https://github.com/facebookresearch/detectron2/tree/master/projects).
-If some part is not as extensible, you can also bring up the issue to make it more extensible.
-2. Whether the feature is potentially useful to a large audience, or only to a small portion of users.
-3. Whether the proposed solution has a good design / interface.
-4. Whether the proposed solution adds extra mental/practical overhead to users who don't
-   need such feature.
-5. Whether the proposed solution breaks existing APIs.
-
-When sending a PR, please do:
-
-1. If a PR contains multiple orthogonal changes, split it to several PRs.
-2. If you've added code that should be tested, add tests.
-3. For PRs that need experiments (e.g. adding a new model), you don't need to update model zoo,
-   but do provide experiment results in the description of the PR.
-4. If APIs are changed, update the documentation.
-5. Ensure the test suite passes.
-6. Make sure your code lints with `./dev/linter.sh`.
-
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Facebook's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## License
-By contributing to detectron2, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/shape_spec.py

@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from collections import namedtuple
-
-
-class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])):
-    """
-    A simple structure that contains basic shape specification about a tensor.
-    It is often used as the auxiliary inputs/outputs of models,
-    to obtain the shape inference ability among pytorch modules.
-
-    Attributes:
-        channels:
-        height:
-        width:
-        stride:
-    """
-
-    def __new__(cls, *, channels=None, height=None, width=None, stride=None):
-        return super().__new__(cls, channels, height, width, stride)

spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/style.css

@@ -1,19 +0,0 @@
-h1 {
-  text-align: center;
-}
-img#overview {
-  max-width: 1000px;
-  max-height: 600px;
-  display: block;
-  margin: auto;
-}
-img#style-image {
-  max-width: 1000px;
-  max-height: 600px;
-  display: block;
-  margin: auto;
-}
-img#visitor-badge {
-  display: block;
-  margin: auto;
-}

spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/bitwise_operators.h

@@ -1,338 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/functional/actor.h>
-#include <thrust/detail/functional/composite.h>
-#include <thrust/detail/functional/operators/operator_adaptors.h>
-#include <thrust/functional.h>
-
-namespace thrust
-{
-namespace detail
-{
-namespace functional
-{
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator&(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_and<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator&(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_and<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_and<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator&(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_and<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator&()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator|(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_or<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator|(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_or<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_or<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator|(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_or<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator|()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor<>>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator^(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_xor<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator^()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor<>>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator^(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_xor<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator^()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_xor<>>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator^(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_xor<>>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator^()
-
-
-// there's no standard bit_not functional, so roll an ad hoc one here
-struct bit_not
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1) const
-  noexcept(noexcept(~THRUST_FWD(t1))) -> decltype(~THRUST_FWD(t1))
-  {
-    return ~THRUST_FWD(t1);
-  }
-}; // end prefix_increment
-
-template<typename Eval>
-__host__ __device__
-actor<
-  composite<
-    transparent_unary_operator<bit_not>,
-    actor<Eval>
-  >
->
-__host__ __device__
-operator~(const actor<Eval> &_1)
-{
-  return compose(transparent_unary_operator<bit_not>(), _1);
-} // end operator~()
-
-// there's no standard bit_lshift functional, so roll an ad hoc one here
-struct bit_lshift
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1&& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) << THRUST_FWD(t2)))
-      -> decltype(THRUST_FWD(t1) << THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) << THRUST_FWD(t2);
-  }
-};
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator<<(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator<<(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_lshift>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator<<(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_lshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator<<()
-
-// there's no standard bit_rshift functional, so roll an ad hoc one here
-struct bit_rshift
-{
-  using is_transparent = void;
-
-  __thrust_exec_check_disable__
-  template <typename T1, typename T2>
-  __host__ __device__
-  constexpr auto operator()(T1& t1, T2&& t2) const
-  noexcept(noexcept(THRUST_FWD(t1) >> THRUST_FWD(t2)))
-  -> decltype(THRUST_FWD(t1) >> THRUST_FWD(t2))
-  {
-    return THRUST_FWD(t1) >> THRUST_FWD(t2);
-  }
-};
-
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift>,
-    actor<T1>,
-    typename as_actor<T2>::type
-  >
->
-operator>>(const actor<T1> &_1, const T2 &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift>,
-    typename as_actor<T1>::type,
-    actor<T2>
-  >
->
-operator>>(const T1 &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>()
-
-template<typename T1, typename T2>
-__host__ __device__
-actor<
-  composite<
-    transparent_binary_operator<bit_rshift>,
-    actor<T1>,
-    actor<T2>
-  >
->
-operator>>(const actor<T1> &_1, const actor<T2> &_2)
-{
-  return compose(transparent_binary_operator<bit_rshift>(),
-                 make_actor(_1),
-                 make_actor(_2));
-} // end operator>>()
-
-} // end functional
-} // end detail
-} // end thrust
-

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

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

spaces/CVPR/WALT/mmdet/models/backbones/darknet.py

@@ -1,199 +0,0 @@
-# Copyright (c) 2019 Western Digital Corporation or its affiliates.
-
-import logging
-
-import torch.nn as nn
-from mmcv.cnn import ConvModule, constant_init, kaiming_init
-from mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from ..builder import BACKBONES
-
-
-class ResBlock(nn.Module):
-    """The basic residual block used in Darknet. Each ResBlock consists of two
-    ConvModules and the input is added to the final output. Each ConvModule is
-    composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer
-    has half of the number of the filters as much as the second convLayer. The
-    first convLayer has filter size of 1x1 and the second one has the filter
-    size of 3x3.
-
-    Args:
-        in_channels (int): The input channels. Must be even.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-            Default: dict(type='BN', requires_grad=True)
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='LeakyReLU', negative_slope=0.1).
-    """
-
-    def __init__(self,
-                 in_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):
-        super(ResBlock, self).__init__()
-        assert in_channels % 2 == 0  # ensure the in_channels is even
-        half_in_channels = in_channels // 2
-
-        # shortcut
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-
-        self.conv1 = ConvModule(in_channels, half_in_channels, 1, **cfg)
-        self.conv2 = ConvModule(
-            half_in_channels, in_channels, 3, padding=1, **cfg)
-
-    def forward(self, x):
-        residual = x
-        out = self.conv1(x)
-        out = self.conv2(out)
-        out = out + residual
-
-        return out
-
-
-@BACKBONES.register_module()
-class Darknet(nn.Module):
-    """Darknet backbone.
-
-    Args:
-        depth (int): Depth of Darknet. Currently only support 53.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters. Default: -1.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-            Default: dict(type='BN', requires_grad=True)
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='LeakyReLU', negative_slope=0.1).
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only.
-
-    Example:
-        >>> from mmdet.models import Darknet
-        >>> import torch
-        >>> self = Darknet(depth=53)
-        >>> self.eval()
-        >>> inputs = torch.rand(1, 3, 416, 416)
-        >>> level_outputs = self.forward(inputs)
-        >>> for level_out in level_outputs:
-        ...     print(tuple(level_out.shape))
-        ...
-        (1, 256, 52, 52)
-        (1, 512, 26, 26)
-        (1, 1024, 13, 13)
-    """
-
-    # Dict(depth: (layers, channels))
-    arch_settings = {
-        53: ((1, 2, 8, 8, 4), ((32, 64), (64, 128), (128, 256), (256, 512),
-                               (512, 1024)))
-    }
-
-    def __init__(self,
-                 depth=53,
-                 out_indices=(3, 4, 5),
-                 frozen_stages=-1,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
-                 norm_eval=True):
-        super(Darknet, self).__init__()
-        if depth not in self.arch_settings:
-            raise KeyError(f'invalid depth {depth} for darknet')
-        self.depth = depth
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.layers, self.channels = self.arch_settings[depth]
-
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-
-        self.conv1 = ConvModule(3, 32, 3, padding=1, **cfg)
-
-        self.cr_blocks = ['conv1']
-        for i, n_layers in enumerate(self.layers):
-            layer_name = f'conv_res_block{i + 1}'
-            in_c, out_c = self.channels[i]
-            self.add_module(
-                layer_name,
-                self.make_conv_res_block(in_c, out_c, n_layers, **cfg))
-            self.cr_blocks.append(layer_name)
-
-        self.norm_eval = norm_eval
-
-    def forward(self, x):
-        outs = []
-        for i, layer_name in enumerate(self.cr_blocks):
-            cr_block = getattr(self, layer_name)
-            x = cr_block(x)
-            if i in self.out_indices:
-                outs.append(x)
-
-        return tuple(outs)
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
-
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            for i in range(self.frozen_stages):
-                m = getattr(self, self.cr_blocks[i])
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    def train(self, mode=True):
-        super(Darknet, self).train(mode)
-        self._freeze_stages()
-        if mode and self.norm_eval:
-            for m in self.modules():
-                if isinstance(m, _BatchNorm):
-                    m.eval()
-
-    @staticmethod
-    def make_conv_res_block(in_channels,
-                            out_channels,
-                            res_repeat,
-                            conv_cfg=None,
-                            norm_cfg=dict(type='BN', requires_grad=True),
-                            act_cfg=dict(type='LeakyReLU',
-                                         negative_slope=0.1)):
-        """In Darknet backbone, ConvLayer is usually followed by ResBlock. This
-        function will make that. The Conv layers always have 3x3 filters with
-        stride=2. The number of the filters in Conv layer is the same as the
-        out channels of the ResBlock.
-
-        Args:
-            in_channels (int): The number of input channels.
-            out_channels (int): The number of output channels.
-            res_repeat (int): The number of ResBlocks.
-            conv_cfg (dict): Config dict for convolution layer. Default: None.
-            norm_cfg (dict): Dictionary to construct and config norm layer.
-                Default: dict(type='BN', requires_grad=True)
-            act_cfg (dict): Config dict for activation layer.
-                Default: dict(type='LeakyReLU', negative_slope=0.1).
-        """
-
-        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
-
-        model = nn.Sequential()
-        model.add_module(
-            'conv',
-            ConvModule(
-                in_channels, out_channels, 3, stride=2, padding=1, **cfg))
-        for idx in range(res_repeat):
-            model.add_module('res{}'.format(idx),
-                             ResBlock(out_channels, **cfg))
-        return model

spaces/CVPR/WALT/mmdet/models/losses/cross_entropy_loss.py

@@ -1,216 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weight_reduce_loss
-
-
-def cross_entropy(pred,
-                  label,
-                  weight=None,
-                  reduction='mean',
-                  avg_factor=None,
-                  class_weight=None):
-    """Calculate the CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    # element-wise losses
-    loss = F.cross_entropy(pred, label, weight=class_weight, reduction='none')
-
-    # apply weights and do the reduction
-    if weight is not None:
-        weight = weight.float()
-    loss = weight_reduce_loss(
-        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def _expand_onehot_labels(labels, label_weights, label_channels):
-    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
-    inds = torch.nonzero(
-        (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
-    if inds.numel() > 0:
-        bin_labels[inds, labels[inds]] = 1
-
-    if label_weights is None:
-        bin_label_weights = None
-    else:
-        bin_label_weights = label_weights.view(-1, 1).expand(
-            label_weights.size(0), label_channels)
-
-    return bin_labels, bin_label_weights
-
-
-def binary_cross_entropy(pred,
-                         label,
-                         weight=None,
-                         reduction='mean',
-                         avg_factor=None,
-                         class_weight=None):
-    """Calculate the binary CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, 1).
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    if pred.dim() != label.dim():
-        label, weight = _expand_onehot_labels(label, weight, pred.size(-1))
-
-    # weighted element-wise losses
-    if weight is not None:
-        weight = weight.float()
-    loss = F.binary_cross_entropy_with_logits(
-        pred, label.float(), pos_weight=class_weight, reduction='none')
-    # do the reduction for the weighted loss
-    loss = weight_reduce_loss(
-        loss, weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def mask_cross_entropy(pred,
-                       target,
-                       label,
-                       reduction='mean',
-                       avg_factor=None,
-                       class_weight=None):
-    """Calculate the CrossEntropy loss for masks.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C, *), C is the
-            number of classes. The trailing * indicates arbitrary shape.
-        target (torch.Tensor): The learning label of the prediction.
-        label (torch.Tensor): ``label`` indicates the class label of the mask
-            corresponding object. This will be used to select the mask in the
-            of the class which the object belongs to when the mask prediction
-            if not class-agnostic.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-
-    Returns:
-        torch.Tensor: The calculated loss
-
-    Example:
-        >>> N, C = 3, 11
-        >>> H, W = 2, 2
-        >>> pred = torch.randn(N, C, H, W) * 1000
-        >>> target = torch.rand(N, H, W)
-        >>> label = torch.randint(0, C, size=(N,))
-        >>> reduction = 'mean'
-        >>> avg_factor = None
-        >>> class_weights = None
-        >>> loss = mask_cross_entropy(pred, target, label, reduction,
-        >>>                           avg_factor, class_weights)
-        >>> assert loss.shape == (1,)
-    """
-    # TODO: handle these two reserved arguments
-    assert reduction == 'mean' and avg_factor is None
-    num_rois = pred.size()[0]
-    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, weight=class_weight, reduction='mean')[None]
-
-
-@LOSSES.register_module()
-class CrossEntropyLoss(nn.Module):
-
-    def __init__(self,
-                 use_sigmoid=False,
-                 use_mask=False,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0):
-        """CrossEntropyLoss.
-
-        Args:
-            use_sigmoid (bool, optional): Whether the prediction uses sigmoid
-                of softmax. Defaults to False.
-            use_mask (bool, optional): Whether to use mask cross entropy loss.
-                Defaults to False.
-            reduction (str, optional): . Defaults to 'mean'.
-                Options are "none", "mean" and "sum".
-            class_weight (list[float], optional): Weight of each class.
-                Defaults to None.
-            loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-        """
-        super(CrossEntropyLoss, self).__init__()
-        assert (use_sigmoid is False) or (use_mask is False)
-        self.use_sigmoid = use_sigmoid
-        self.use_mask = use_mask
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = class_weight
-
-        if self.use_sigmoid:
-            self.cls_criterion = binary_cross_entropy
-        elif self.use_mask:
-            self.cls_criterion = mask_cross_entropy
-        else:
-            self.cls_criterion = cross_entropy
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function.
-
-        Args:
-            cls_score (torch.Tensor): The prediction.
-            label (torch.Tensor): The learning label of the prediction.
-            weight (torch.Tensor, optional): Sample-wise loss weight.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction (str, optional): The method used to reduce the loss.
-                Options are "none", "mean" and "sum".
-        Returns:
-            torch.Tensor: The calculated loss
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(
-                self.class_weight, device=cls_score.device)
-        else:
-            class_weight = None
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            weight,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_cls
-
-

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

@@ -1,18 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-
-img_dir = Path(__file__).parent / "images"
-
-
-def hold_tight(images: List[BuildImage], texts, args):
-    img = images[0].convert("RGBA").resize((159, 171), keep_ratio=True)
-    frame = BuildImage.open(img_dir / "0.png")
-    frame.paste(img, (113, 205), below=True)
-    return frame.save_jpg()
-
-
-add_meme("hold_tight", hold_tight, min_images=1, max_images=1, keywords=["抱紧"])

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/README.md

@@ -1,90 +0,0 @@
-# Setting Up Datasets
-This file describes how to perform training on other datasets.
-
-Only Pascal VOC dataset can be loaded from its original format and be outputted to Pascal style results currently.
-
-We expect the annotations from other datasets be converted to COCO json format, and
-the output will be in COCO-style. (i.e. AP, AP50, AP75, APs, APm, APl for bbox and segm)
-
-## Creating Symlinks for PASCAL VOC
-
-We assume that your symlinked `datasets/voc/VOC<year>` directory has the following structure:
-
-```
-VOC<year>
-|_ JPEGImages
-|  |_ <im-1-name>.jpg
-|  |_ ...
-|  |_ <im-N-name>.jpg
-|_ Annotations
-|  |_ pascal_train<year>.json (optional)
-|  |_ pascal_val<year>.json (optional)
-|  |_ pascal_test<year>.json (optional)
-|  |_ <im-1-name>.xml
-|  |_ ...
-|  |_ <im-N-name>.xml
-|_ VOCdevkit<year>
-```
-
-Create symlinks for `voc/VOC<year>`:
-
-```
-cd ~/github/maskrcnn-benchmark
-mkdir -p datasets/voc/VOC<year>
-ln -s /path/to/VOC<year> /datasets/voc/VOC<year>
-```
-Example configuration files for PASCAL VOC could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/pascal_voc/).
-
-### PASCAL VOC Annotations in COCO Format
-To output COCO-style evaluation result, PASCAL VOC annotations in COCO json format is required and could be downloaded from [here](https://storage.googleapis.com/coco-dataset/external/PASCAL_VOC.zip)
-via http://cocodataset.org/#external.
-
-## Creating Symlinks for Cityscapes:
-
-We assume that your symlinked `datasets/cityscapes` directory has the following structure:
-
-```
-cityscapes
-|_ images
-|  |_ <im-1-name>.jpg
-|  |_ ...
-|  |_ <im-N-name>.jpg
-|_ annotations
-|  |_ instanceonly_gtFile_train.json
-|  |_ ...
-|_ raw
-   |_ gtFine
-   |_ ...
-   |_ README.md
-```
-
-Create symlinks for `cityscapes`:
-
-```
-cd ~/github/maskrcnn-benchmark
-mkdir -p datasets/cityscapes
-ln -s /path/to/cityscapes datasets/data/cityscapes
-```
-
-### Steps to convert Cityscapes Annotations to COCO Format
-1. Download gtFine_trainvaltest.zip from https://www.cityscapes-dataset.com/downloads/ (login required)
-2. Extract it to /path/to/gtFine_trainvaltest
-```
-cityscapes
-|_ gtFine_trainvaltest.zip
-|_ gtFine_trainvaltest
-   |_ gtFine
-```
-3. Run the below commands to convert the annotations
-
-```
-cd ~/github
-git clone https://github.com/mcordts/cityscapesScripts.git
-cd cityscapesScripts
-cp ~/github/maskrcnn-benchmark/tools/cityscapes/instances2dict_with_polygons.py cityscapesscripts/evaluation
-python setup.py install
-cd ~/github/maskrcnn-benchmark
-python tools/cityscapes/convert_cityscapes_to_coco.py --datadir /path/to/cityscapes --outdir /path/to/cityscapes/annotations
-```
-
-Example configuration files for Cityscapes could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/cityscapes/).

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/ttGlyphPen.py

@@ -1,335 +0,0 @@
-from array import array
-from typing import Any, Callable, Dict, Optional, Tuple
-from fontTools.misc.fixedTools import MAX_F2DOT14, floatToFixedToFloat
-from fontTools.misc.loggingTools import LogMixin
-from fontTools.pens.pointPen import AbstractPointPen
-from fontTools.misc.roundTools import otRound
-from fontTools.pens.basePen import LoggingPen, PenError
-from fontTools.pens.transformPen import TransformPen, TransformPointPen
-from fontTools.ttLib.tables import ttProgram
-from fontTools.ttLib.tables._g_l_y_f import flagOnCurve, flagCubic
-from fontTools.ttLib.tables._g_l_y_f import Glyph
-from fontTools.ttLib.tables._g_l_y_f import GlyphComponent
-from fontTools.ttLib.tables._g_l_y_f import GlyphCoordinates
-from fontTools.ttLib.tables._g_l_y_f import dropImpliedOnCurvePoints
-import math
-
-
-__all__ = ["TTGlyphPen", "TTGlyphPointPen"]
-
-
-class _TTGlyphBasePen:
-    def __init__(
-        self,
-        glyphSet: Optional[Dict[str, Any]],
-        handleOverflowingTransforms: bool = True,
-    ) -> None:
-        """
-        Construct a new pen.
-
-        Args:
-            glyphSet (Dict[str, Any]): A glyphset object, used to resolve components.
-            handleOverflowingTransforms (bool): See below.
-
-        If ``handleOverflowingTransforms`` is True, the components' transform values
-        are checked that they don't overflow the limits of a F2Dot14 number:
-        -2.0 <= v < +2.0. If any transform value exceeds these, the composite
-        glyph is decomposed.
-
-        An exception to this rule is done for values that are very close to +2.0
-        (both for consistency with the -2.0 case, and for the relative frequency
-        these occur in real fonts). When almost +2.0 values occur (and all other
-        values are within the range -2.0 <= x <= +2.0), they are clamped to the
-        maximum positive value that can still be encoded as an F2Dot14: i.e.
-        1.99993896484375.
-
-        If False, no check is done and all components are translated unmodified
-        into the glyf table, followed by an inevitable ``struct.error`` once an
-        attempt is made to compile them.
-
-        If both contours and components are present in a glyph, the components
-        are decomposed.
-        """
-        self.glyphSet = glyphSet
-        self.handleOverflowingTransforms = handleOverflowingTransforms
-        self.init()
-
-    def _decompose(
-        self,
-        glyphName: str,
-        transformation: Tuple[float, float, float, float, float, float],
-    ):
-        tpen = self.transformPen(self, transformation)
-        getattr(self.glyphSet[glyphName], self.drawMethod)(tpen)
-
-    def _isClosed(self):
-        """
-        Check if the current path is closed.
-        """
-        raise NotImplementedError
-
-    def init(self) -> None:
-        self.points = []
-        self.endPts = []
-        self.types = []
-        self.components = []
-
-    def addComponent(
-        self,
-        baseGlyphName: str,
-        transformation: Tuple[float, float, float, float, float, float],
-        identifier: Optional[str] = None,
-        **kwargs: Any,
-    ) -> None:
-        """
-        Add a sub glyph.
-        """
-        self.components.append((baseGlyphName, transformation))
-
-    def _buildComponents(self, componentFlags):
-        if self.handleOverflowingTransforms:
-            # we can't encode transform values > 2 or < -2 in F2Dot14,
-            # so we must decompose the glyph if any transform exceeds these
-            overflowing = any(
-                s > 2 or s < -2
-                for (glyphName, transformation) in self.components
-                for s in transformation[:4]
-            )
-        components = []
-        for glyphName, transformation in self.components:
-            if glyphName not in self.glyphSet:
-                self.log.warning(f"skipped non-existing component '{glyphName}'")
-                continue
-            if self.points or (self.handleOverflowingTransforms and overflowing):
-                # can't have both coordinates and components, so decompose
-                self._decompose(glyphName, transformation)
-                continue
-
-            component = GlyphComponent()
-            component.glyphName = glyphName
-            component.x, component.y = (otRound(v) for v in transformation[4:])
-            # quantize floats to F2Dot14 so we get same values as when decompiled
-            # from a binary glyf table
-            transformation = tuple(
-                floatToFixedToFloat(v, 14) for v in transformation[:4]
-            )
-            if transformation != (1, 0, 0, 1):
-                if self.handleOverflowingTransforms and any(
-                    MAX_F2DOT14 < s <= 2 for s in transformation
-                ):
-                    # clamp values ~= +2.0 so we can keep the component
-                    transformation = tuple(
-                        MAX_F2DOT14 if MAX_F2DOT14 < s <= 2 else s
-                        for s in transformation
-                    )
-                component.transform = (transformation[:2], transformation[2:])
-            component.flags = componentFlags
-            components.append(component)
-        return components
-
-    def glyph(
-        self,
-        componentFlags: int = 0x04,
-        dropImpliedOnCurves: bool = False,
-        *,
-        round: Callable[[float], int] = otRound,
-    ) -> Glyph:
-        """
-        Returns a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
-
-        Args:
-            componentFlags: Flags to use for component glyphs. (default: 0x04)
-
-            dropImpliedOnCurves: Whether to remove implied-oncurve points. (default: False)
-        """
-        if not self._isClosed():
-            raise PenError("Didn't close last contour.")
-        components = self._buildComponents(componentFlags)
-
-        glyph = Glyph()
-        glyph.coordinates = GlyphCoordinates(self.points)
-        glyph.endPtsOfContours = self.endPts
-        glyph.flags = array("B", self.types)
-        self.init()
-
-        if components:
-            # If both components and contours were present, they have by now
-            # been decomposed by _buildComponents.
-            glyph.components = components
-            glyph.numberOfContours = -1
-        else:
-            glyph.numberOfContours = len(glyph.endPtsOfContours)
-            glyph.program = ttProgram.Program()
-            glyph.program.fromBytecode(b"")
-            if dropImpliedOnCurves:
-                dropImpliedOnCurvePoints(glyph)
-            glyph.coordinates.toInt(round=round)
-
-        return glyph
-
-
-class TTGlyphPen(_TTGlyphBasePen, LoggingPen):
-    """
-    Pen used for drawing to a TrueType glyph.
-
-    This pen can be used to construct or modify glyphs in a TrueType format
-    font. After using the pen to draw, use the ``.glyph()`` method to retrieve
-    a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
-    """
-
-    drawMethod = "draw"
-    transformPen = TransformPen
-
-    def __init__(
-        self,
-        glyphSet: Optional[Dict[str, Any]] = None,
-        handleOverflowingTransforms: bool = True,
-        outputImpliedClosingLine: bool = False,
-    ) -> None:
-        super().__init__(glyphSet, handleOverflowingTransforms)
-        self.outputImpliedClosingLine = outputImpliedClosingLine
-
-    def _addPoint(self, pt: Tuple[float, float], tp: int) -> None:
-        self.points.append(pt)
-        self.types.append(tp)
-
-    def _popPoint(self) -> None:
-        self.points.pop()
-        self.types.pop()
-
-    def _isClosed(self) -> bool:
-        return (not self.points) or (
-            self.endPts and self.endPts[-1] == len(self.points) - 1
-        )
-
-    def lineTo(self, pt: Tuple[float, float]) -> None:
-        self._addPoint(pt, flagOnCurve)
-
-    def moveTo(self, pt: Tuple[float, float]) -> None:
-        if not self._isClosed():
-            raise PenError('"move"-type point must begin a new contour.')
-        self._addPoint(pt, flagOnCurve)
-
-    def curveTo(self, *points) -> None:
-        assert len(points) % 2 == 1
-        for pt in points[:-1]:
-            self._addPoint(pt, flagCubic)
-
-        # last point is None if there are no on-curve points
-        if points[-1] is not None:
-            self._addPoint(points[-1], 1)
-
-    def qCurveTo(self, *points) -> None:
-        assert len(points) >= 1
-        for pt in points[:-1]:
-            self._addPoint(pt, 0)
-
-        # last point is None if there are no on-curve points
-        if points[-1] is not None:
-            self._addPoint(points[-1], 1)
-
-    def closePath(self) -> None:
-        endPt = len(self.points) - 1
-
-        # ignore anchors (one-point paths)
-        if endPt == 0 or (self.endPts and endPt == self.endPts[-1] + 1):
-            self._popPoint()
-            return
-
-        if not self.outputImpliedClosingLine:
-            # if first and last point on this path are the same, remove last
-            startPt = 0
-            if self.endPts:
-                startPt = self.endPts[-1] + 1
-            if self.points[startPt] == self.points[endPt]:
-                self._popPoint()
-                endPt -= 1
-
-        self.endPts.append(endPt)
-
-    def endPath(self) -> None:
-        # TrueType contours are always "closed"
-        self.closePath()
-
-
-class TTGlyphPointPen(_TTGlyphBasePen, LogMixin, AbstractPointPen):
-    """
-    Point pen used for drawing to a TrueType glyph.
-
-    This pen can be used to construct or modify glyphs in a TrueType format
-    font. After using the pen to draw, use the ``.glyph()`` method to retrieve
-    a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
-    """
-
-    drawMethod = "drawPoints"
-    transformPen = TransformPointPen
-
-    def init(self) -> None:
-        super().init()
-        self._currentContourStartIndex = None
-
-    def _isClosed(self) -> bool:
-        return self._currentContourStartIndex is None
-
-    def beginPath(self, identifier: Optional[str] = None, **kwargs: Any) -> None:
-        """
-        Start a new sub path.
-        """
-        if not self._isClosed():
-            raise PenError("Didn't close previous contour.")
-        self._currentContourStartIndex = len(self.points)
-
-    def endPath(self) -> None:
-        """
-        End the current sub path.
-        """
-        # TrueType contours are always "closed"
-        if self._isClosed():
-            raise PenError("Contour is already closed.")
-        if self._currentContourStartIndex == len(self.points):
-            # ignore empty contours
-            self._currentContourStartIndex = None
-            return
-
-        contourStart = self.endPts[-1] + 1 if self.endPts else 0
-        self.endPts.append(len(self.points) - 1)
-        self._currentContourStartIndex = None
-
-        # Resolve types for any cubic segments
-        flags = self.types
-        for i in range(contourStart, len(flags)):
-            if flags[i] == "curve":
-                j = i - 1
-                if j < contourStart:
-                    j = len(flags) - 1
-                while flags[j] == 0:
-                    flags[j] = flagCubic
-                    j -= 1
-                flags[i] = flagOnCurve
-
-    def addPoint(
-        self,
-        pt: Tuple[float, float],
-        segmentType: Optional[str] = None,
-        smooth: bool = False,
-        name: Optional[str] = None,
-        identifier: Optional[str] = None,
-        **kwargs: Any,
-    ) -> None:
-        """
-        Add a point to the current sub path.
-        """
-        if self._isClosed():
-            raise PenError("Can't add a point to a closed contour.")
-        if segmentType is None:
-            self.types.append(0)
-        elif segmentType in ("line", "move"):
-            self.types.append(flagOnCurve)
-        elif segmentType == "qcurve":
-            self.types.append(flagOnCurve)
-        elif segmentType == "curve":
-            self.types.append("curve")
-        else:
-            raise AssertionError(segmentType)
-
-        self.points.append(pt)