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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Photoshop 7.0.md

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-<h1>Adobe Photoshop 7.0: A Classic Photo Editing Software That Still Works</h1>
-<p>Adobe Photoshop 7.0 is one of the most popular and widely used photo editing software in the world. It was released in 2002 and has been a favorite among professional and amateur photographers, graphic designers, and digital artists ever since. Adobe Photoshop 7.0 offers a range of features and tools that allow you to create, edit, enhance, and manipulate images with ease and precision. In this article, we will review some of the main features and benefits of Adobe Photoshop 7.0 and why it is still a great choice for photo editing in 2023.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Compendio De Obstetricia Votta Pdf.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/FULL Windows XP SP3 Angel Live V.2.0.iso The Features and Benefits of this Superb XP.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Grand Ages Rome Gold Edition Serial What You Need to Know Before You Buy.md

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-How to unlock new cards and upgrade them in clash royale on windows 11<br />
-Clash royale windows 11 strategy and guide<br />
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-Clash royale windows 11 achievements and rewards<br />
-How to report and block players in clash royale on windows 11<br />
-Clash royale windows 11 support and feedback<br />
-How to uninstall and reinstall clash royale on windows 11<br />
-Clash royale windows 11 vs ios comparison<br />
-How to download clash royale mod apk for windows 11<br />
-Clash royale windows 11 beta version and release date<br />
-Clash royale windows 11 news and updates<br />
-Clash royale windows 11 fan art and wallpapers<br />
-How to create and edit your own deck in clash royale on windows 11<br />
-Clash royale windows 11 best practices and tips for beginners<br />
-Clash royale windows 11 pros and cons<br />
-How to watch clash royale videos and live streams on windows 11<br />
-Clash royale windows 11 FAQ and troubleshooting</p>
- <ul>
-<li>You can enjoy the game on a larger screen with higher resolution and better graphics. This can help you see the details of the cards, units, towers, and arena more clearly. It can also make the game more immersive and engaging.</li>
-<li>You can use your mouse and keyboard to control the game instead of tapping on a small touchscreen. This can give you more accuracy, speed, and comfort when playing. You can also customize your key bindings and settings according to your preference.</li>
-<li>You can avoid battery drain, overheating, lagging, or crashing issues that may occur on some mobile devices. Playing on Windows 11 can ensure that your PC runs smoothly and efficiently without compromising your performance or enjoyment.</li>
-</ul <h2>How to Download and Install Clash Royale on Windows 11</h2>
- <h3>The minimum system requirements for Windows 11 and Clash Royale</h3>
- <p>Before you can download and play Clash Royale on Windows 11, you need to make sure that your PC meets the minimum system requirements for both the operating system and the game. Here are the specifications you need to check:</p>
- <table>
-<tr>
-<th>Windows 11</th>
-<th>Clash Royale</th>
-</tr>
-<tr>
-<td>
-<ul>
-<li>Processor: 1 GHz or faster with 2 or more cores on a compatible 64-bit processor or System on a Chip (SoC)</li>
-<li>RAM: 4 GB</li>
-<li>Storage: 64 GB or larger storage device</li>
-<li>Graphics card: Compatible with DirectX 12 or later with WDDM 2.0 driver</li>
-<li>Display: High definition (720p) display that is greater than 9” diagonally, 8 bits per color channel</li>
-<li>Internet connection: Required for updates and some features</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Android version: 4.1 and up</li>
-<li>RAM: 1 GB (recommended)</li>
-<li>Storage: 116 MB (additional files may be downloaded)</li>
-<li>Graphics: OpenGL ES 3.0 support (recommended)</li>
-<li>Internet connection: Required to play online</li>
-</ul>
-</td>
-</tr>
-</table>
- <p>If your PC meets or exceeds these requirements, you can proceed to the next step. If not, you may need to upgrade your hardware or look for other alternatives.</p>
- <h3>The steps to download and install an Android emulator (Bluestacks 5) on Windows 11</h3>
- <p>An Android emulator is a software that allows you to run Android apps and games on your PC. There are many Android emulators available online, but one of the most popular and reliable ones is <strong>Bluestacks 5</strong>. Bluestacks 5 is the latest version of the Bluestacks app player that offers improved performance, compatibility, and features for Windows 11 users.</p>
- <p>To download and install Bluestacks 5 on Windows 11, follow these steps:</p>
- <ol>
-<li>Go to the official website of Bluestacks at <a href="">https://www.bluestacks.com/</a></li>
-<li>Click on the <strong>Download Bluestacks 5</strong> button and wait for the installer file to download.</li>
-<li>Double-click on the installer file and follow the instructions on the screen to install Bluestacks 5 on your PC.</li>
-<li>Once the installation is complete, launch Bluestacks 5 from your desktop or start menu.</li>
-<li>Sign in with your Google account or create a new one if you don't have one.</li>
-<li>You are now ready to use Bluestacks 5 and access the Google Play Store.</li>
-</ol>
- <h3>The steps to download and install Clash Royale from the Google Play Store on Bluestacks 5</h3>
- <p>Now that you have Bluestacks 5 installed on your PC, you can easily download and install Clash Royale from the Google Play Store. Here are the steps to do so:</p>
- <ol>
-<li>On the Bluestacks home screen, click on the <strong>Google Play Store</strong> icon.</li>
-<li>In the search bar, type <strong>Clash Royale</strong> and hit enter.</li>
-<li>Select <strong>Clash Royale</strong> from the list of results and click on the <strong>Install</strong> button.</li>
-<li>Wait for the game to download and install on your PC.</li>
-<li>Once the installation is done, click on the <strong>Open</strong> button or go back to the Bluestacks home screen and click on the <strong>Clash Royale</strong> icon.</li>
-<li>You can now enjoy playing Clash Royale on your PC with Bluestacks 5.</li>
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- <p>By using buildings, spells, and high HP troops to defend your towers, you can prevent your opponent from gaining an elixir or tower advantage and turn the tide of the battle in your favor. You can also save your towers from being destroyed and losing the game.</p>
- <h4>Use a win condition card to target enemy towers</h4>
- <p>A fifth way to improve your gameplay in Clash Royale is to use a win condition card to target enemy towers. A win condition card is a card that can directly or indirectly deal damage to enemy towers and help you win the game. Some examples of win condition cards are Hog Rider, Royal Giant, Graveyard, Miner, Goblin Barrel, and X-Bow. These cards have different strengths and weaknesses, but they all share the same goal: to destroy enemy towers.</p>
- <p>By using a win condition card to target enemy towers, you can increase your chances of winning the game by dealing consistent and significant damage to your opponent's towers. You can also force your opponent to react and spend elixir to defend their towers, which can give you an elixir or tower advantage.</p>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and a call to action for the readers to try out Clash Royale on Windows 11</h3>
- <p>In conclusion, Clash Royale is a fun and addictive game that you can enjoy on Windows 11 with the help of an Android emulator like Bluestacks 5. By playing Clash Royale on Windows 11, you can benefit from a larger screen, better graphics, and smoother controls. You can also improve your gameplay by following some tips and tricks, such as joining a clan, attacking in pairs, counting elixir, defending your towers, and using a win condition card.</p>
- <p>If you are interested in trying out Clash Royale on Windows 11, you can download and install Bluestacks 5 from their official website and then download and install Clash Royale from the Google Play Store on Bluestacks 5. You can then start playing Clash Royale on your PC and have a blast with your friends and foes.</p>
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- <h2>FAQs</h2>
- <h3>What are the best cards in Clash Royale?</h3>
- <p>There is no definitive answer to this question, as different cards may suit different players, decks, strategies, and situations. However, some of the most popular and versatile cards in Clash Royale are:</p>
- <ul>
-<li>Mega Knight: A legendary card that costs 7 elixir and can deal massive damage with its jump and splash attacks. It can counter swarms, tanks, buildings, and ground troops effectively.</li>
-<li>Skeleton Dragons: A common card that costs 4 elixir and spawns two flying skeletons that shoot fireballs. It can deal decent damage to air and ground troops and buildings.</li>
-<li>Mother Witch: A legendary card that costs 4 elixir and shoots cursed bolts that turn enemy troops into hogs when they die. It can create a swarm of hogs that can overwhelm the enemy's defense.</li>
-<li>Royal Delivery: A rare card that costs 3 elixir and drops a crate that deals area damage and spawns a Royal Recruit. It can be used to surprise and counter enemy troops or buildings.</li>
-<li>Goblin Cage: A rare card that costs 4 elixir and spawns a building that releases a Goblin Brawler when destroyed. It can be used to lure and distract enemy troops or deal damage to enemy towers.</li>
-</ul <h3>How do I get more gems and gold in Clash Royale?</h3>
- <p>Gems and gold are two of the most important resources in Clash Royale, as they allow you to buy chests, cards, magic items, emotes, skins, and more. There are several ways to get more gems and gold in Clash Royale:</p>
- <ul>
-<li>Complete quests, achievements, challenges, tournaments, clan wars, seasonal events, special modes, ladder matches, etc. These activities can reward you with gems, gold, chests, magic items, etc.</li>
-<li>Open chests that you get from battles or quests. These chests can contain gems, gold, cards, magic items, etc.</li>
-<li>Donate or request cards from your clanmates. This can earn you gold and XP for each card you donate or request.</li>
-<li>Buy gems or gold from the shop with real money. This is the fastest but most expensive way to get more gems and gold in Clash Royale.</li>
-</ul>
- <h3>How do I join or create a clan in Clash Royale?</h3>
- <p>Joining or creating a clan in Clash Royale is a great way to interact with other players, share cards, chat, and participate in clan wars. To join or create a clan in Clash Royale, you need to reach at least level 1 in the game. You can then follow these steps:</p>
- <ol>
-<li>Tap on the <strong>Clan</strong> tab on the main screen.</li>
-<li>Tap on the <strong>Join a Clan</strong> button to browse or search for a clan that suits your preferences. You can filter the clans by name, location, trophy requirement, type, etc.</li>
-<li>Tap on the <strong>Request to Join</strong> button to send a request to the clan leader or co-leader. You can also write a message to introduce yourself and explain why you want to join the clan.</li>
-<li>Wait for the clan leader or co-leader to accept or reject your request. If they accept your request, you will become a member of the clan and be able to access the clan chat, shop, wars, etc.</li>
-<li>If you want to create your own clan instead of joining an existing one, you can tap on the <strong>Create a Clan</strong> button instead of the <strong>Join a Clan</strong> button. You will need to spend 1000 gold to create a clan.</li>
-<li>You can then choose a name, badge, location, type, trophy requirement, description, and tag for your clan. You can also invite your friends or family to join your clan or accept requests from other players who want to join your clan.</li>
-<li>You will become the leader of your clan and be able to manage it as you wish. You can promote or demote members, start or cancel clan wars, edit the clan settings, etc.</li>
-</ol>
- <h3>How do I change my name or avatar in Clash Royale?</h3>
- <p>Changing your name or avatar in Clash Royale is a simple and quick process that can help you personalize your profile and express your identity. To change your name or avatar in Clash Royale, follow these steps:</p>
- <ol>
-<li>Tap on your profile icon on the top left corner of the main screen.</li>
-<li>Tap on the <strong>Name Change</strong> button or the <strong>Edit Avatar</strong> button depending on what you want to change.</li>
-<li>If you want to change your name, you can enter a new name in the text box and tap on the <strong>Confirm</strong> button. You can only change your name once for free, so choose wisely. If you want to change your name again, you will need to spend 500 gems.</li>
-<li>If you want to change your avatar, you can choose from a variety of avatars that feature different characters, animals, objects, etc. You can also unlock more avatars by completing achievements, challenges, events, etc. Tap on the avatar that you like and tap on the <strong>Select</strong> button.</li>
-<li>Your name or avatar will be changed immediately and be visible to other players in the game.</li>
-</ol>
- <h3>How do I contact Supercell for support or feedback?</h3>
- <p>If you have any issues, questions, suggestions, or feedback regarding Clash Royale or any other Supercell game, you can contact Supercell for support or feedback through their official channels. Here are some ways to do so:</p>
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-<li>You can use the in-game support feature by tapping on the <strong>Settings</strong> icon on the top right corner of the main screen and then tapping on the <strong>Help and Support</strong> button. You can then browse through the frequently asked questions (FAQs) or contact Supercell directly by tapping on the <strong>Contact Us</strong> button.</li>
-<li>You can visit the official website of Supercell at <a href="">https://supercell.com/en/</a> and click on the <strong>Contact Us</strong> link at the bottom of the page. You can then fill out a form with your details and message and submit it to Supercell.</li>
-<li>You can follow Supercell on their social media platforms such as Facebook, Twitter, Instagram, YouTube, Reddit, Discord, etc. You can then send them a message or comment on their posts with your feedback or inquiry.</li>
-</ul <p>Supercell is usually responsive and helpful when it comes to addressing their players' concerns and opinions. However, please be respectful and polite when contacting them and avoid spamming or abusing them.</p>
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- <h3>Method 2: Find the IPA Equivalent and Use Cydia Impactor</h3>
- <p>The second method is to find the IPA equivalent of traffic racer mod apk and use Cydia Impactor. IPA is the file format for iOS applications that can be installed on your device using a computer. Cydia Impactor is a tool that allows you to sideload IPA files onto your device without jailbreaking it.</p>
- <p>To use this method, you need to follow these steps:</p>
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-<li>Find the IPA equivalent of traffic racer mod apk. You can search online for such files or use this one: [9](https://iosninja.io/ipa-library/download-traffic-racer-hack-ipa-ios).</li>
-<li>Download Cydia Impactor from [8](https://cydiaimpactor.com) and install it on your computer.</li>
-<li>Connect your iOS device to your computer using a USB cable and launch Cydia Impactor.</li>
-<li>Drag and drop the IPA file onto Cydia Impactor and enter your Apple ID and password when prompted.</li>
-<li>Wait for the installation to finish and then trust the app from your device settings.</li>
-<li>Launch the game from your home screen and enjoy playing traffic racer mod apk on your iOS device.</li>
-</ol>
- <p>This method is safer and more reliable than the first one, but it has some limitations. First, you need to have a computer and a USB cable to perform this method. Second, you need to enter your Apple ID and password, which can be risky if you use a fake or hacked one. Third, you need to trust the app from your device settings, which can be revoked by Apple at any time.</p>
- <h2>Benefits and Features of Traffic Racer Game</h2>
- <p>Whether you use the first or the second method, you will be able to enjoy the benefits and features of traffic racer game on your iOS device. Some of them are:</p>
- <ul>
-<li><strong>Stunning 3D graphics and realistic car handling</strong>: The game has amazing 3D graphics that make you feel like you are driving in real life. The cars have realistic physics and sound effects that enhance the gameplay experience.</li>
-<li><strong>Over 40 different cars and 5 game modes to choose from</strong>: The game has a variety of cars that you can drive, from sedans and sports cars to trucks and buses. You can also choose from 5 different game modes, such as endless, two-way, time trial, police chase, and free ride.</li>
-<li><strong>Customization options and online leaderboards</strong>: The game allows you to customize your car through paint and wheels. You can also upgrade your car's speed, handling, and braking. You can also compete with other players online through the global leaderboards and see how you rank among them.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Traffic Racer Mod APK is a great racing game that you can play on your iOS device. It offers unlimited money, unlocked cars, and other features that make the game more fun and exciting. However, since it is not available on the App Store, you need to use either jailbreaking or sideloading methods to install it on your device. Both methods have their pros and cons, so you need to choose the one that suits you best. Once you install the game, you can enjoy its benefits and features and have a blast driving through highway traffic.</p>
- <h2>FAQs</h2>
- <h3>What are the risks of installing traffic racer mod apk on iOS devices?</h3>
- <p>The risks of installing traffic racer mod apk on iOS devices depend on the method that you use. If you use jailbreaking, you may void your warranty, expose your device to security risks, or encounter compatibility issues or bugs. If you use sideloading, you may risk your Apple ID and password, or lose access to the app if Apple revokes it.</p>
- <h3>How can I update traffic racer mod apk on iOS devices?</h3>
- <p>To update traffic racer mod apk on iOS devices, you need to follow the same steps that you used to install it. You need to find the latest version of the modded file (either apk or ipa) and install it using the same tool (either Cydia or Cydia Impactor). You may need to delete the previous version of the game before installing the new one.</p>
- <h3>How can I get unlimited money in traffic racer mod apk?</h3>
- <p>To get unlimited money in traffic racer mod apk, you do not need to do anything special. The modded version of the game already gives you unlimited money to buy and upgrade any car you want. You can also earn more money by playing the game and completing missions.</p>
- <h3>What are some tips and tricks for playing traffic racer game?</h3>
- <p>Some tips and tricks for playing traffic racer game are:</p>
- <ul>
-<li>Drive faster to get more scores and cash.</li>
-<li>Use the nitro boost to overtake other cars and avoid collisions.</li>
-<li>Drive in the opposite direction in two-way mode to get extra score and cash.</li>
-<li>Do not crash into other cars or obstacles, as this will damage your car and reduce your speed.</li>
-<li>Try different cars and game modes to find the one that suits your style.</li>
-</ul>
- <h3>What are some alternatives to traffic racer game?</h3>
- <p>If you are looking for some alternatives to traffic racer game, you can try these games:</p>
- <ul>
-<li><strong>Traffic Rider</strong>: This is a similar game where you ride a motorcycle instead of a car. You can enjoy the first-person view, realistic bike sounds, and over 30 different bikes.</li>
-<li><strong>Traffic Tour</strong>: This is another racing game where you drive through traffic, perform stunts, and challenge other players online. You can also customize your car, change the camera view, and use different controls.</li>
-<li><strong>Traffic Run</strong>: This is a casual game where you tap to control your car and avoid hitting other vehicles or obstacles. You can also collect coins, unlock new cars, and explore different environments.</li>
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- <p>I hope you enjoyed this article and learned how to install and play traffic racer mod apk on iOS devices. If you have any questions or feedback, please leave a comment below. Thank you for reading!</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Experience the Thrill of KOF M.U.G.E.N 2020 on Your Smartphone.md

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-<h1>KOF M.U.G.E.N 2020 Download APK: How to Play the Ultimate Fighting Game on Your Android Device</h1> | <p>Do you love fighting games? Do you want to play one of the most popular and customizable fighting games on your Android device? If you answered yes to both questions, then you should definitely try KOF M.U.G.E.N 2020 APK.</p>
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- <p>KOF M.U.G.E.N 2020 APK is a fan-made game that combines characters, stages, music, and gameplay from various SNK franchises such as The King of Fighters, Fatal Fury, Art of Fighting, Samurai Shodown, Metal Slug, and more. It is based on the M.U.G.E.N engine, which allows anyone to create their own fighting games with ease.</p>
- <p>In this article, we will tell you everything you need to know about KOF M.U.G.E.N 2020 APK, including what it is, how to download and install it on your Android device, how to customize and edit it according to your preferences, and why you should give it a try. We will also answer some frequently asked questions about KOF M.U.G.E.N 2020 APK at the end of this article.</p>
- <h2>What is KOF M.U.G.E.N 2020?</h2>
- | HTML Code | | --------- | | <h3>A Brief History of KOF M.U.G.E.N</h3> | <p>KOF M.U.G.E.N is a series of fan-made games that started in 2002 by a group of Brazilian fans who wanted to create their own version of The King of Fighters, a popular fighting game franchise by SNK. They used the M.U.G.E.N engine, which is a free and open-source game engine that allows anyone to create 2D fighting games with custom characters, stages, music, and gameplay.</p>
- <p>Over the years, KOF M.U.G.E.N has evolved and improved, adding more characters, stages, modes, and features from various SNK games and other sources. KOF M.U.G.E.N 2020 is the latest and most advanced version of the series, featuring over 200 characters, over 100 stages, and many options and settings to customize the game to your liking.</p>
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- <p>KOF M.U.G.E.N 2020 is a 2D fighting game that follows the same basic rules and mechanics as The King of Fighters. You can choose from several modes, such as Arcade, Team Battle, Survival, Training, Watch, and more. You can also choose from different types of teams, such as Single, Simul, Turns, or Tag.</p>
- <p>The gameplay of KOF M.U.G.E.N 2020 is fast-paced and fluid, with smooth animations and responsive controls. You can perform various moves and combos with your characters, such as punches, kicks, throws, special moves, super moves, and ultimate moves. You can also use different systems and mechanics, such as Power Gauge, Max Mode, Guard Cancel, Counter Attack, Roll Escape, and more.</p>
- <p>KOF M.U.G.E.N 2020 also has many features that make it unique and fun to play. For example, you can adjust the difficulty level, the number of rounds, the time limit, the damage ratio, the life recovery rate, and other options. You can also enable or disable certain features, such as AI mode, debug mode, cheats mode, auto guard mode, and more. You can also change the screen resolution, the sound volume, the language, the input configuration, and other settings.</p>
- <h3>Characters and Stages of KOF M.U.G.E.N 2020</h3>
- | HTML Code | | --------- | | Iori Yagami, Terry Bogard, Mai Shiranui, etc.), Fatal Fury series (such as Geese Howard, Andy Bogard, Kim Kaphwan, etc.), Art of Fighting series (such as Ryo Sakazaki, Robert Garcia, Yuri Sakazaki, etc.), Samurai Shodown series (such as Haohmaru, Nakoruru, Genjuro Kibagami, etc.), Metal Slug series (such as Marco Rossi, Fio Germi, Tarma Roving, etc.), and more. You can also find characters from other games and media, such as Street Fighter, Mortal Kombat, Dragon Ball, Naruto, Bleach, One Piece, Marvel, DC, and more.</p>
- <p>KOF M.U.G.E.N 2020 also has a large selection of stages that you can fight on. There are over 100 stages from various SNK games and other sources. You can find stages from The King of Fighters series (such as Esaka, Korea, China, etc.), Fatal Fury series (such as South Town, Pao Pao Cafe, Geese Tower, etc.), Art of Fighting series (such as Kyokugen Dojo, L'Amor Restaurant, Glass Hill Valley, etc.), Samurai Shodown series (such as Gairyu Isle, Amakusa Castle, Shimabara Hell Gate, etc.), Metal Slug series (such as Mission 1, Mission 2, Mission 3, etc.), and more. You can also find stages from other games and media, such as Street Fighter, Mortal Kombat, Dragon Ball, Naruto, Bleach, One Piece, Marvel, DC, and more.</p>
- <h2>How to Download and Install KOF M.U.G.E.N 2020 APK on Your Android Device</h2>
- <p>If you want to play KOF M.U.G.E.N 2020 APK on your Android device, you will need to download and install it first. Here are the requirements and compatibility information that you should know before downloading and installing KOF M.U.G.E.N 2020 APK:</p>
- <h3>Requirements and Compatibility</h3>
- <p>KOF M.U.G.E.N 2020 APK is a large file that requires a lot of storage space and memory to run smoothly. You will need at least 2 GB of free storage space on your Android device to download and install KOF M.U.G.E.N 2020 APK. You will also need at least 1 GB of RAM to play KOF M.U.G.E.N 2020 APK without lag or crashes.</p>
- <p>KOF M.U.G.E.N 2020 APK is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may not be able to run KOF M.U.G.E.N 2020 APK properly due to hardware limitations or software issues. If you encounter any problems while playing KOF M.U.G.E.N 2020 APK on your Android device, you can try to lower the game settings or contact the developer for support.</p>
- <h3>Steps to Download and Install KOF M.U.G.E.N 2020 APK</h3>
- <p>Here are the steps that you need to follow to download and install KOF M.U.G.E.N 2020 APK on your Android device:</p>
- <ol>
-<li>Go to the official website of KOF M.U.G.E.N 2020 APK [here] and click on the download button.</li>
-<li>Wait for the download to finish and locate the file in your device's file manager.</li>
-<li>Tap on the file and allow the installation from unknown sources if prompted.</li>
-<li>Wait for the installation to complete and launch the game from your app drawer or home screen.</li>
-<li>Enjoy playing KOF M.U.G.E.N 2020 APK on your Android device!</li>
-</ol>
- <h3>Tips and Tricks to Enjoy KOF M.U.G.E.N 2020 APK</h3>
- | HTML Code | | --------- | | there are some tips and tricks that you can use to enjoy KOF M.U.G.E.N 2020 APK even more. Here are some of them:</p>
- <ul>
-<li>Use the training mode to practice your moves and combos with different characters and learn their strengths and weaknesses.</li>
-<li>Use the watch mode to watch AI-controlled matches between different characters and learn from their strategies and tactics.</li>
-<li>Use the cheats mode to unlock all characters and stages, change the game speed, enable infinite power, and more.</li>
-<li>Use the debug mode to access hidden features and options, such as changing the character size, color, position, and more.</li>
-<li>Use the AI mode to make the game play itself and enjoy watching the action.</li>
-</ul>
- <h2>How to Customize and Edit KOF M.U.G.E.N 2020 APK</h2>
- <p>KOF M.U.G.E.N 2020 APK is a highly customizable and editable game that allows you to create your own fighting game experience. You can add or remove characters and stages, change the game settings and options, and even create your own characters and stages. Here are some ways that you can customize and edit KOF M.U.G.E.N 2020 APK:</p>
- <h3>How to Add or Remove Characters and Stages</h3>
- <p>KOF M.U.G.E.N 2020 APK comes with a large roster of characters and stages, but you can always add or remove them according to your preferences. You can download additional characters and stages from various websites, such as [this] or [this], or you can delete unwanted characters and stages from your device's storage. Here are the steps that you need to follow to add or remove characters and stages:</p>
- <ol>
-<li>Download the character or stage file that you want to add from a reliable source and extract it if it is compressed.</li>
-<li>Copy the character or stage folder to the chars or stages folder in your device's storage where KOF M.U.G.E.N 2020 APK is installed.</li>
-<li>Edit the select.def file in the data folder using a text editor app such as [this] or [this].</li>
-<li>Add the name of the character or stage folder to the select.def file under the appropriate section (such as kfm, bonus, hidden, etc.). For example, if you want to add a character named Ryu, you should write Ryu/Ryu.def under the kfm section.</li>
-<li>Save the select.def file and launch KOF M.U.G.E.N 2020 APK. You should see the new character or stage in the game.</li>
-<li>To remove a character or stage, simply delete its folder from the chars or stages folder and remove its name from the select.def file.</li>
-</ol>
- <h3>How to Change the Game Settings and Options</h3>
- <p>KOF M.U.G.E.N 2020 APK has many settings and options that you can change to customize the game to your liking. You can change things such as the screen resolution, the sound volume, the language, the input configuration, and more. Here are some ways that you can change the game settings and options:</p>
- <ul>
-<li>To change the screen resolution, edit the mugen.cfg file in the data folder using a text editor app. Find the line that says "GameWidth" and "GameHeight" and change their values to your desired resolution. For example, if you want to play in 1280x720 resolution, you should write GameWidth = 1280 and GameHeight = 720. Save the mugen.cfg file and launch KOF M.U.G.E.N 2020 APK.</li>
-| HTML Code | | --------- | | the sound volume slider for the master, music, and sound effects. You can also mute or unmute the sound by pressing the M key on your keyboard.</li>
-<li>To change the language, go to the options menu in KOF M.U.G.E.N 2020 APK and select the language option. You can choose from English, Spanish, Portuguese, French, and Japanese. You can also edit the system.def file in the data folder using a text editor app and change the value of the "language" parameter to your desired language code. For example, if you want to play in German, you should write language = "de". Save the system.def file and launch KOF M.U.G.E.N 2020 APK.</li>
-<li>To change the input configuration, go to the options menu in KOF M.U.G.E.N 2020 APK and select the input option. You can configure the buttons for each player and each mode, such as up, down, left, right, light punch, heavy punch, light kick, heavy kick, start, and select. You can also edit the mugen.cfg file in the data folder using a text editor app and change the values of the "Joystick" and "KeyConfig" parameters to your desired input settings. For example, if you want to use the A key for light punch, you should write KeyConfig[0].Button.A = a. Save the mugen.cfg file and launch KOF M.U.G.E.N 2020 APK.</li>
-</ul>
- <h3>How to Create Your Own Characters and Stages</h3>
- <p>KOF M.U.G.E.N 2020 APK is not only a game that you can play, but also a game that you can create. You can create your own characters and stages using the M.U.G.E.N engine and add them to KOF M.U.G.E.N 2020 APK. However, this is not an easy task and requires a lot of time, effort, and knowledge. Here are some resources that you can use to learn how to create your own characters and stages:</p>
- <ul>
-<li>[This] is a tutorial that teaches you how to create your own character from scratch using Fighter Factory Studio, a tool that allows you to edit sprites, animations, sounds, and codes for your character.</li>
-<li>[This] is a tutorial that teaches you how to create your own stage from scratch using Stage Tool, a tool that allows you to edit images, sounds, and codes for your stage.</li>
-<li>[This] is a forum where you can find and download various resources for creating your own characters and stages, such as sprites, sounds, codes, templates, tools, tutorials, and more.</li>
-<li>[This] is a website where you can find and download various characters and stages that other people have created for M.U.G.E.N games.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>KOF M.U.G.E.N 2020 APK is a fan-made game that offers a unique and enjoyable fighting game experience on your Android device. It has a huge roster of characters and stages from various SNK franchises and other sources. It has a fast-paced and fluid gameplay with smooth animations and responsive controls. It has many features and options that allow you to customize the game to your liking. It also allows you to create your own characters and stages using the M.U.G.E.N engine.</p>
- <p>If you are a fan of fighting games or SNK games, you should definitely try KOF M.U.G.E.N 2020 APK. It is free to download and easy to install on your Android device. It is fun to play alone or with friends. It is also a great way to express your creativity and imagination by creating your own characters and stages.</p>
- <p>So what are you waiting for? Download KOF M.U.G.E.N 2020 APK now and enjoy playing the ultimate fighting game on your Android device!</p>
- <h3>Why You Should Try KOF M.U.G.E.N 2020 APK</h3>
- <p>Here are some reasons why you should try KOF M.U.G.E.N 2020 APK:</p>
- <ul>
-<li>It is free to download and play.</li>
-<li>It has over 200 characters and over 100 stages from various SNK franchises and other sources.</li>
-<li>It has a fast-paced and fluid gameplay with smooth animations and responsive controls.</li>
-<li>It has many features and options that allow you to customize the game to your liking.</li>
-<li>It allows you to create your own characters and stages using the M.U.G.E.N engine.</li>
-<li>It is fun to play alone or with friends.</li>
-</ul>
- | HTML Code | | --------- | | FAQs</h3> | <p>Here are some frequently asked questions about KOF M.U.G.E.N 2020 APK:</p>
- <ol>
-<li>Is KOF M.U.G.E.N 2020 APK safe to download and install?</li>
-<p>Yes, KOF M.U.G.E.N 2020 APK is safe to download and install as long as you get it from the official website or a trusted source. However, you should always scan any file that you download with an antivirus app before installing it on your device.</p>
-<li>Is KOF M.U.G.E.N 2020 APK legal to play?</li>
-<p>KOF M.U.G.E.N 2020 APK is a fan-made game that is not affiliated with or endorsed by SNK or any other company. It is a non-profit game that is made for entertainment purposes only. It does not intend to infringe any copyrights or trademarks of SNK or any other company. However, you should always respect the rights and wishes of the original creators and owners of the characters and stages that are used in KOF M.U.G.E.N 2020 APK.</p>
-<li>How can I play KOF M.U.G.E.N 2020 APK with my friends?</li>
-<p>KOF M.U.G.E.N 2020 APK supports local multiplayer mode, which means that you can play with your friends on the same device using a split-screen or a gamepad. You can also play with your friends online using a third-party app such as [this] or [this], which allows you to create a virtual network and connect your devices over the internet.</p>
-<li>How can I update KOF M.U.G.E.N 2020 APK to the latest version?</li>
-<p>KOF M.U.G.E.N 2020 APK is constantly updated by the developer with new characters, stages, features, and bug fixes. You can check for updates on the official website or on the developer's social media pages. You can also enable the auto-update option in the game settings, which will notify you when a new update is available and download it automatically.</p>
-<li>How can I contact the developer of KOF M.U.G.E.N 2020 APK?</li>
-<p>If you have any questions, suggestions, feedback, or issues regarding KOF M.U.G.E.N 2020 APK, you can contact the developer by sending an email to [this] or by leaving a comment on the developer's YouTube channel [here]. The developer is very responsive and friendly and will try to help you as soon as possible.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />

spaces/2hack2furious/anonymizer/app.py

@@ -1,87 +0,0 @@
-import modules
-import streamlit as st
-from streamlit_extras.let_it_rain import rain
-
-# Options
-DISCLAIMER = """
-    *This app processes data using 2-anonymity, an implementation of the k-anonymity framework. While this is a great start to anonymizing your data, it is by no means perfect, and should be used with caution. For example, some sets of sensitive features which may clearly be identified by a human could be missed by our algorithm. Please keep this in mind.*
-    """
-K = 2
-
-# Page Config
-st.set_page_config(layout="wide")
-
-### FILE LOADER for sidebar
-with st.sidebar:
-    st.header("🕵️ 2anonymity")
-    st.markdown("*Clean and anonymize data*")
-    with st.container() as upload:
-        file = st.file_uploader(f"Upload dataset:", type=modules.SUPPORTED_TYPES, label_visibility="collapsed")
-        df, (filename, extension), result = modules.load_file(file)
-
-### MAIN
-if df is None: # Await file to be uploaded
-    rain("🤠")
-else:
-    ### PRE-TRANSFORM features for sidebar
-    with st.sidebar:
-        # Options for data loading
-        with st.container() as loading_options:
-            st.markdown("### Data loading options:")
-            remove_duplicates = st.checkbox("Remove duplicate rows", value=True)
-            drop_missing = st.checkbox("Remove rows with missing values", value=False)
-
-        # Options for data optimization
-        with st.container() as anonymizing_options:
-            st.markdown("### Anonymizing options:")
-            max_categorical_size = st.slider("Categorical Variable Threshold", min_value=2, max_value=200, value=50, step=1)
-            bin_size = st.slider("Bin Size", min_value=2, max_value=200, value=20, step=1)
-            redaction_selection = st.selectbox("Redaction strength", ["Low", "Medium", "High", "Extreme"])
-            sensitivity_minimum = {"Low": 2, "Medium": 4, "High": 6, "Extreme": 12}[redaction_selection]
-    
-    
-    ### DATA PREVIEW AND TRANSFORM
-    # Preview data before transform
-    with st.container() as before_data:
-        s = df.style
-        s = s.set_properties(**{'background-color': '#fce4e4'})
-        st.dataframe(s)
-
-    # Transform data
-    df = modules.data_cleaner(df, drop_missing, remove_duplicates)
-    df, unprocessed = modules.data_anonymizer(df, K, max_categorical_size, bin_size, sensitivity_minimum)
-
-    # Preview data after before_data
-    with st.container() as after_data:
-        s = df.style
-        s = s.set_properties(**{'background-color': '#e4fce4'})
-        st.dataframe(s)
-
-
-    ### POST-TRANSFORM features for sidebar
-    with st.sidebar:
-        # Options for download
-        with st.container() as download_header:
-            st.markdown("### Download options:")
-            output_extension = st.selectbox("File type", [".csv", ".json", ".xlsx"])
-            if unprocessed: st.markdown(f"Error encountered when processing columns {str(unprocessed)}")
- 
-        # Prepare file for download
-        with st.container() as downloader:
-            if output_extension == ".csv": output_file = df.to_csv().encode("utf-8")
-            elif output_extension == ".json": output_file = df.to_json().encode("utf-8")
-            elif output_extension == ".xlsx": output_file = df.to_excel().encode("utf-8")
-            output_filename = f"""{filename.split(".")[:-1][0]}-clean{output_extension}"""
-            st.download_button("Download", output_file, file_name=output_filename)
-        
-        # Add a disclaimer for data security
-        with st.container() as disclaimer:
-            st.markdown(
-                f"""
-                Disclaimer:  
-                {DISCLAIMER}
-                """
-                )
-        
-# Attribution
-st.sidebar.markdown("Created by team #2hack2furious for the hackthethreat2023")

spaces/2ndelement/voicevox/test/test_full_context_label.py

@@ -1,404 +0,0 @@
-from copy import deepcopy
-from itertools import chain
-from unittest import TestCase
-
-from voicevox_engine.full_context_label import (
-    AccentPhrase,
-    BreathGroup,
-    Mora,
-    Phoneme,
-    Utterance,
-)
-
-
-class TestBasePhonemes(TestCase):
-    def setUp(self):
-        super().setUp()
-        # pyopenjtalk.extract_fullcontext("こんにちは、ヒホです。")の結果
-        # 出来る限りテスト内で他のライブラリに依存しないため、
-        # またテスト内容を透明化するために、テストケースを生成している
-        self.test_case_hello_hiho = [
-            # sil (無音)
-            "xx^xx-sil+k=o/A:xx+xx+xx/B:xx-xx_xx/C:xx_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:xx_xx#xx_xx@xx_xx|xx_xx/G:5_5%0_xx_xx/H:xx_xx/I:xx-xx"
-            + "@xx+xx&xx-xx|xx+xx/J:1_5/K:2+2-9",
-            # k
-            "xx^sil-k+o=N/A:-4+1+5/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # o
-            "sil^k-o+N=n/A:-4+1+5/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # N (ん)
-            "k^o-N+n=i/A:-3+2+4/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # n
-            "o^N-n+i=ch/A:-2+3+3/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # i
-            "N^n-i+ch=i/A:-2+3+3/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # ch
-            "n^i-ch+i=w/A:-1+4+2/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # i
-            "i^ch-i+w=a/A:-1+4+2/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # w
-            "ch^i-w+a=pau/A:0+5+1/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # a
-            "i^w-a+pau=h/A:0+5+1/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx"
-            + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5"
-            + "@1+2&1-2|1+9/J:1_4/K:2+2-9",
-            # pau (読点)
-            "w^a-pau+h=i/A:xx+xx+xx/B:09-xx_xx/C:xx_xx+xx/D:09+xx_xx/E:5_5!0_xx-xx"
-            + "/F:xx_xx#xx_xx@xx_xx|xx_xx/G:4_1%0_xx_xx/H:1_5/I:xx-xx"
-            + "@xx+xx&xx-xx|xx+xx/J:1_4/K:2+2-9",
-            # h
-            "a^pau-h+i=h/A:0+1+4/B:09-xx_xx/C:09_xx+xx/D:22+xx_xx/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # i
-            "pau^h-i+h=o/A:0+1+4/B:09-xx_xx/C:09_xx+xx/D:22+xx_xx/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # h
-            "h^i-h+o=d/A:1+2+3/B:09-xx_xx/C:22_xx+xx/D:10+7_2/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # o
-            "i^h-o+d=e/A:1+2+3/B:09-xx_xx/C:22_xx+xx/D:10+7_2/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # d
-            "h^o-d+e=s/A:2+3+2/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # e
-            "o^d-e+s=U/A:2+3+2/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # s
-            "d^e-s+U=sil/A:3+4+1/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # U (無声母音)
-            "e^s-U+sil=xx/A:3+4+1/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0"
-            + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4"
-            + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9",
-            # sil (無音)
-            "s^U-sil+xx=xx/A:xx+xx+xx/B:10-7_2/C:xx_xx+xx/D:xx+xx_xx/E:4_1!0_xx-xx"
-            + "/F:xx_xx#xx_xx@xx_xx|xx_xx/G:xx_xx%xx_xx_xx/H:1_4/I:xx-xx"
-            + "@xx+xx&xx-xx|xx+xx/J:xx_xx/K:2+2-9",
-        ]
-        self.phonemes_hello_hiho = [
-            Phoneme.from_label(label) for label in self.test_case_hello_hiho
-        ]
-
-
-class TestPhoneme(TestBasePhonemes):
-    def test_phoneme(self):
-        self.assertEqual(
-            " ".join([phoneme.phoneme for phoneme in self.phonemes_hello_hiho]),
-            "sil k o N n i ch i w a pau h i h o d e s U sil",
-        )
-
-    def test_is_pause(self):
-        self.assertEqual(
-            [phoneme.is_pause() for phoneme in self.phonemes_hello_hiho],
-            [
-                True,  # sil
-                False,  # k
-                False,  # o
-                False,  # N
-                False,  # n
-                False,  # i
-                False,  # ch
-                False,  # i
-                False,  # w
-                False,  # a
-                True,  # pau
-                False,  # h
-                False,  # i
-                False,  # h
-                False,  # o
-                False,  # d
-                False,  # e
-                False,  # s
-                False,  # u
-                True,  # sil
-            ],
-        )
-
-    def test_label(self) -> None:
-        self.assertEqual(
-            [phoneme.label for phoneme in self.phonemes_hello_hiho],
-            self.test_case_hello_hiho,
-        )
-
-
-class TestMora(TestBasePhonemes):
-    def setUp(self) -> None:
-        super().setUp()
-        # contexts["a2"] == "1" ko
-        self.mora_hello_1 = Mora(
-            consonant=self.phonemes_hello_hiho[1], vowel=self.phonemes_hello_hiho[2]
-        )
-        # contexts["a2"] == "2" N
-        self.mora_hello_2 = Mora(consonant=None, vowel=self.phonemes_hello_hiho[3])
-        # contexts["a2"] == "3" ni
-        self.mora_hello_3 = Mora(
-            consonant=self.phonemes_hello_hiho[4], vowel=self.phonemes_hello_hiho[5]
-        )
-        # contexts["a2"] == "4" chi
-        self.mora_hello_4 = Mora(
-            consonant=self.phonemes_hello_hiho[6], vowel=self.phonemes_hello_hiho[7]
-        )
-        # contexts["a2"] == "5" wa
-        self.mora_hello_5 = Mora(
-            consonant=self.phonemes_hello_hiho[8], vowel=self.phonemes_hello_hiho[9]
-        )
-        # contexts["a2"] == "1" hi
-        self.mora_hiho_1 = Mora(
-            consonant=self.phonemes_hello_hiho[11], vowel=self.phonemes_hello_hiho[12]
-        )
-        # contexts["a2"] == "2" ho
-        self.mora_hiho_2 = Mora(
-            consonant=self.phonemes_hello_hiho[13], vowel=self.phonemes_hello_hiho[14]
-        )
-        # contexts["a2"] == "3" de
-        self.mora_hiho_3 = Mora(
-            consonant=self.phonemes_hello_hiho[15], vowel=self.phonemes_hello_hiho[16]
-        )
-        # contexts["a2"] == "1" sU
-        self.mora_hiho_4 = Mora(
-            consonant=self.phonemes_hello_hiho[17], vowel=self.phonemes_hello_hiho[18]
-        )
-
-    def assert_phonemes(self, mora: Mora, mora_str: str) -> None:
-        self.assertEqual(
-            "".join([phoneme.phoneme for phoneme in mora.phonemes]), mora_str
-        )
-
-    def assert_labels(self, mora: Mora, label_start: int, label_end: int) -> None:
-        self.assertEqual(mora.labels, self.test_case_hello_hiho[label_start:label_end])
-
-    def test_phonemes(self) -> None:
-        self.assert_phonemes(self.mora_hello_1, "ko")
-        self.assert_phonemes(self.mora_hello_2, "N")
-        self.assert_phonemes(self.mora_hello_3, "ni")
-        self.assert_phonemes(self.mora_hello_4, "chi")
-        self.assert_phonemes(self.mora_hello_5, "wa")
-        self.assert_phonemes(self.mora_hiho_1, "hi")
-        self.assert_phonemes(self.mora_hiho_2, "ho")
-        self.assert_phonemes(self.mora_hiho_3, "de")
-        self.assert_phonemes(self.mora_hiho_4, "sU")
-
-    def test_labels(self) -> None:
-        self.assert_labels(self.mora_hello_1, 1, 3)
-        self.assert_labels(self.mora_hello_2, 3, 4)
-        self.assert_labels(self.mora_hello_3, 4, 6)
-        self.assert_labels(self.mora_hello_4, 6, 8)
-        self.assert_labels(self.mora_hello_5, 8, 10)
-        self.assert_labels(self.mora_hiho_1, 11, 13)
-        self.assert_labels(self.mora_hiho_2, 13, 15)
-        self.assert_labels(self.mora_hiho_3, 15, 17)
-        self.assert_labels(self.mora_hiho_4, 17, 19)
-
-    def test_set_context(self):
-        # 値を書き換えるので、他のテストに影響を出さないためにdeepcopyする
-        mora_hello_1 = deepcopy(self.mora_hello_1)
-        # phonemeにあたる"p3"を書き換える
-        mora_hello_1.set_context("p3", "a")
-        self.assert_phonemes(mora_hello_1, "aa")
-
-
-class TestAccentPhrase(TestBasePhonemes):
-    def setUp(self) -> None:
-        super().setUp()
-        # TODO: ValueErrorを吐く作為的ではない自然な例の模索
-        # 存在しないなら放置でよい
-        self.accent_phrase_hello = AccentPhrase.from_phonemes(
-            self.phonemes_hello_hiho[1:10]
-        )
-        self.accent_phrase_hiho = AccentPhrase.from_phonemes(
-            self.phonemes_hello_hiho[11:19]
-        )
-
-    def test_accent(self):
-        self.assertEqual(self.accent_phrase_hello.accent, 5)
-        self.assertEqual(self.accent_phrase_hiho.accent, 1)
-
-    def test_set_context(self):
-        accent_phrase_hello = deepcopy(self.accent_phrase_hello)
-        # phonemeにあたる"p3"を書き換える
-        accent_phrase_hello.set_context("p3", "a")
-        self.assertEqual(
-            "".join([phoneme.phoneme for phoneme in accent_phrase_hello.phonemes]),
-            "aaaaaaaaa",
-        )
-
-    def test_phonemes(self):
-        self.assertEqual(
-            " ".join(
-                [phoneme.phoneme for phoneme in self.accent_phrase_hello.phonemes]
-            ),
-            "k o N n i ch i w a",
-        )
-        self.assertEqual(
-            " ".join([phoneme.phoneme for phoneme in self.accent_phrase_hiho.phonemes]),
-            "h i h o d e s U",
-        )
-
-    def test_labels(self):
-        self.assertEqual(
-            self.accent_phrase_hello.labels, self.test_case_hello_hiho[1:10]
-        )
-        self.assertEqual(
-            self.accent_phrase_hiho.labels, self.test_case_hello_hiho[11:19]
-        )
-
-    def test_merge(self):
-        # 「こんにちはヒホです」
-        # 読点を無くしたものと同等
-        merged_accent_phrase = self.accent_phrase_hello.merge(self.accent_phrase_hiho)
-        self.assertEqual(merged_accent_phrase.accent, 5)
-        self.assertEqual(
-            " ".join([phoneme.phoneme for phoneme in merged_accent_phrase.phonemes]),
-            "k o N n i ch i w a h i h o d e s U",
-        )
-        self.assertEqual(
-            merged_accent_phrase.labels,
-            self.test_case_hello_hiho[1:10] + self.test_case_hello_hiho[11:19],
-        )
-
-
-class TestBreathGroup(TestBasePhonemes):
-    def setUp(self) -> None:
-        super().setUp()
-        self.breath_group_hello = BreathGroup.from_phonemes(
-            self.phonemes_hello_hiho[1:10]
-        )
-        self.breath_group_hiho = BreathGroup.from_phonemes(
-            self.phonemes_hello_hiho[11:19]
-        )
-
-    def test_set_context(self):
-        # 値を書き換えるので、他のテストに影響を出さないためにdeepcopyする
-        breath_group_hello = deepcopy(self.breath_group_hello)
-        # phonemeにあたる"p3"を書き換える
-        breath_group_hello.set_context("p3", "a")
-        self.assertEqual(
-            "".join([phoneme.phoneme for phoneme in breath_group_hello.phonemes]),
-            "aaaaaaaaa",
-        )
-
-    def test_phonemes(self):
-        self.assertEqual(
-            " ".join([phoneme.phoneme for phoneme in self.breath_group_hello.phonemes]),
-            "k o N n i ch i w a",
-        )
-        self.assertEqual(
-            " ".join([phoneme.phoneme for phoneme in self.breath_group_hiho.phonemes]),
-            "h i h o d e s U",
-        )
-
-    def test_labels(self):
-        self.assertEqual(
-            self.breath_group_hello.labels, self.test_case_hello_hiho[1:10]
-        )
-        self.assertEqual(
-            self.breath_group_hiho.labels, self.test_case_hello_hiho[11:19]
-        )
-
-
-class TestUtterance(TestBasePhonemes):
-    def setUp(self) -> None:
-        super().setUp()
-        self.utterance_hello_hiho = Utterance.from_phonemes(self.phonemes_hello_hiho)
-
-    def test_phonemes(self):
-        self.assertEqual(
-            " ".join(
-                [phoneme.phoneme for phoneme in self.utterance_hello_hiho.phonemes]
-            ),
-            "sil k o N n i ch i w a pau h i h o d e s U sil",
-        )
-        changed_utterance = Utterance.from_phonemes(self.utterance_hello_hiho.phonemes)
-        self.assertEqual(len(changed_utterance.breath_groups), 2)
-        accent_phrases = list(
-            chain.from_iterable(
-                breath_group.accent_phrases
-                for breath_group in changed_utterance.breath_groups
-            )
-        )
-        for prev, cent, post in zip(
-            [None] + accent_phrases[:-1],
-            accent_phrases,
-            accent_phrases[1:] + [None],
-        ):
-            mora_num = len(cent.moras)
-            accent = cent.accent
-
-            if prev is not None:
-                for phoneme in prev.phonemes:
-                    self.assertEqual(phoneme.contexts["g1"], str(mora_num))
-                    self.assertEqual(phoneme.contexts["g2"], str(accent))
-
-            if post is not None:
-                for phoneme in post.phonemes:
-                    self.assertEqual(phoneme.contexts["e1"], str(mora_num))
-                    self.assertEqual(phoneme.contexts["e2"], str(accent))
-
-            for phoneme in cent.phonemes:
-                self.assertEqual(
-                    phoneme.contexts["k2"],
-                    str(
-                        sum(
-                            [
-                                len(breath_group.accent_phrases)
-                                for breath_group in changed_utterance.breath_groups
-                            ]
-                        )
-                    ),
-                )
-
-        for prev, cent, post in zip(
-            [None] + changed_utterance.breath_groups[:-1],
-            changed_utterance.breath_groups,
-            changed_utterance.breath_groups[1:] + [None],
-        ):
-            accent_phrase_num = len(cent.accent_phrases)
-
-            if prev is not None:
-                for phoneme in prev.phonemes:
-                    self.assertEqual(phoneme.contexts["j1"], str(accent_phrase_num))
-
-            if post is not None:
-                for phoneme in post.phonemes:
-                    self.assertEqual(phoneme.contexts["h1"], str(accent_phrase_num))
-
-            for phoneme in cent.phonemes:
-                self.assertEqual(phoneme.contexts["i1"], str(accent_phrase_num))
-                self.assertEqual(
-                    phoneme.contexts["i5"],
-                    str(accent_phrases.index(cent.accent_phrases[0]) + 1),
-                )
-                self.assertEqual(
-                    phoneme.contexts["i6"],
-                    str(
-                        len(accent_phrases)
-                        - accent_phrases.index(cent.accent_phrases[0])
-                    ),
-                )
-
-    def test_labels(self):
-        self.assertEqual(self.utterance_hello_hiho.labels, self.test_case_hello_hiho)

spaces/2ndelement/voicevox/voicevox_engine/setting/Setting.py

@@ -1,25 +0,0 @@
-from enum import Enum
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class CorsPolicyMode(str, Enum):
-    """
-    CORSの許可モード
-    """
-
-    all = "all"  # 全てのオリジンからのリクエストを許可
-    localapps = "localapps"  # ローカルアプリケーションからのリクエストを許可
-
-
-class Setting(BaseModel):
-    """
-    エンジンの設定情報
-    """
-
-    cors_policy_mode: CorsPolicyMode = Field(title="リソース共有ポリシー")
-    allow_origin: Optional[str] = Field(title="許可するオリジン")
-
-    class Config:
-        use_enum_values = True

spaces/52Hz/HWMNet_lowlight_enhancement/main_test_HWMNet.py

@@ -1,86 +0,0 @@
-import argparse
-import cv2
-import glob
-import numpy as np
-from collections import OrderedDict
-from skimage import img_as_ubyte
-import os
-import torch
-import requests
-from PIL import Image
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-from natsort import natsorted
-from model.HWMNet import HWMNet 
-
-def main():
-    parser = argparse.ArgumentParser(description='Demo Low-light Image enhancement')
-    parser.add_argument('--input_dir', default='test/', type=str, help='Input images')
-    parser.add_argument('--result_dir', default='result/', type=str, help='Directory for results')
-    parser.add_argument('--weights',
-                        default='experiments/pretrained_models/LOL_enhancement_HWMNet.pth', type=str,
-                        help='Path to weights')
-
-    args = parser.parse_args()
-
-    inp_dir = args.input_dir
-    out_dir = args.result_dir
-
-    os.makedirs(out_dir, exist_ok=True)
-
-    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))
-
-    if len(files) == 0:
-        raise Exception(f"No files found at {inp_dir}")
-
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    # Load corresponding models architecture and weights
-    model = HWMNet(in_chn=3, wf=96, depth=4)
-    model = model.to(device)
-    model.eval()
-    load_checkpoint(model, args.weights)
-    
-
-    mul = 16
-    for file_ in files:
-        img = Image.open(file_).convert('RGB')
-        input_ = TF.to_tensor(img).unsqueeze(0).to(device)
-
-        # Pad the input if not_multiple_of 8
-        h, w = input_.shape[2], input_.shape[3]
-        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
-        padh = H - h if h % mul != 0 else 0
-        padw = W - w if w % mul != 0 else 0
-        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
-        with torch.no_grad():
-            restored = model(input_)
-
-        restored = torch.clamp(restored, 0, 1)
-        restored = restored[:, :, :h, :w]
-        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
-        restored = img_as_ubyte(restored[0])
-
-        f = os.path.splitext(os.path.split(file_)[-1])[0]
-        save_img((os.path.join(out_dir, f + '.png')), restored)
-
-
-def save_img(filepath, img):
-    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-
-def load_checkpoint(model, weights):
-    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
-    try:
-        model.load_state_dict(checkpoint["state_dict"])
-    except:
-        state_dict = checkpoint["state_dict"]
-        new_state_dict = OrderedDict()
-        for k, v in state_dict.items():
-            name = k[7:]  # remove `module.`
-            new_state_dict[name] = v
-        model.load_state_dict(new_state_dict)
-
-
-if __name__ == '__main__':
-    main()

spaces/52Hz/SRMNet_AWGN_denoising/README.md

@@ -1,37 +0,0 @@
----
-title: SRMNet_AWGN_denoising
-emoji: 🌪
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/rel_transformer.py

@@ -1,611 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-from utils.hparams import hparams
-from modules.commons.common_layers import Embedding
-from utils.tts_utils import group_hidden_by_segs, expand_word2ph
-
-import transformers
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-class Encoder(nn.Module):
-    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
-                 window_size=None, block_length=None, pre_ln=False, **kwargs):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.pre_ln = pre_ln
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size,
-                                   p_dropout=p_dropout, block_length=block_length))
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-        if pre_ln:
-            self.last_ln = LayerNorm(hidden_channels)
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        for i in range(self.n_layers):
-            x = x * x_mask
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_1[i](x)
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_1[i](x)
-
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_2[i](x)
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_2[i](x)
-        if self.pre_ln:
-            x = self.last_ln(x)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.,
-                 block_length=None, proximal_bias=False, proximal_init=False):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.p_dropout = p_dropout
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels ** -0.5
-            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        if proximal_init:
-            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
-            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
-        if self.window_size is not None:
-            assert t_s == t_t, "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
-            rel_logits = self._relative_position_to_absolute_position(rel_logits)
-            scores_local = rel_logits / math.sqrt(self.k_channels)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-                scores = scores * block_mask + -1e4 * (1 - block_mask)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
-        x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(x * x_mask)
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        return x * x_mask
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-4):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        n_dims = len(x.shape)
-        mean = torch.mean(x, 1, keepdim=True)
-        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
-
-        x = (x - mean) * torch.rsqrt(variance + self.eps)
-
-        shape = [1, -1] + [1] * (n_dims - 2)
-        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
-        return x
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(
-            nn.ReLU(),
-            nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class RelTransformerEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout=0.0,
-                 window_size=4,
-                 block_length=None,
-                 prenet=True,
-                 pre_ln=True,
-                 ):
-
-        super().__init__()
-
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.prenet = prenet
-        if n_vocab > 0:
-            self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
-
-        if prenet:
-            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
-                                    kernel_size=5, n_layers=3, p_dropout=0)
-        self.encoder = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-    def forward(self, x, x_mask=None):
-        if self.n_vocab > 0:
-            x_lengths = (x > 0).long().sum(-1)
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        else:
-            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        if self.prenet:
-            x = self.pre(x, x_mask)
-        x = self.encoder(x, x_mask)
-        return x.transpose(1, 2)
-
-
-class Pooler(nn.Module):
-    """
-    Parameter-free poolers to get the sentence embedding
-    'cls': [CLS] representation with BERT/RoBERTa's MLP pooler.
-    'cls_before_pooler': [CLS] representation without the original MLP pooler.
-    'avg': average of the last layers' hidden states at each token.
-    'avg_top2': average of the last two layers.
-    'avg_first_last': average of the first and the last layers.
-    """
-    def __init__(self, pooler_type):
-        super().__init__()
-        self.pooler_type = pooler_type
-        assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type
-
-    def forward(self, attention_mask, outputs):
-        last_hidden = outputs.last_hidden_state
-        pooler_output = outputs.pooler_output
-        hidden_states = outputs.hidden_states
-
-        if self.pooler_type in ['cls_before_pooler', 'cls']:
-            return last_hidden[:, 0]
-        elif self.pooler_type == "avg":
-            return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1))
-        elif self.pooler_type == "avg_first_last":
-            first_hidden = hidden_states[0]
-            last_hidden = hidden_states[-1]
-            pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
-            return pooled_result
-        elif self.pooler_type == "avg_top2":
-            second_last_hidden = hidden_states[-2]
-            last_hidden = hidden_states[-1]
-            pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
-            return pooled_result
-        else:
-            raise NotImplementedError
-
-
-class Similarity(nn.Module):
-    """
-    Dot product or cosine similarity
-    """
-
-    def __init__(self, temp):
-        super().__init__()
-        self.temp = temp
-        self.cos = nn.CosineSimilarity(dim=-1)
-        self.record = None
-        self.pos_avg = 0.0
-        self.neg_avg = 0.0
-
-    def forward(self, x, y):
-        sim = self.cos(x, y)
-        self.record = sim.detach() # [64,64]
-        min_size = min(self.record.shape[0], self.record.shape[1]) # 64
-        num_item = self.record.shape[0] * self.record.shape[1] # 4096
-        self.pos_avg = self.record.diag().sum() / min_size
-        if num_item - min_size == 0:
-            self.neg_avg = (self.record.sum() - self.record.diag().sum()) / 1
-            return sim / self.temp
-        if torch.any(torch.isnan(self.record)).item() is True:
-            print("we got self.record has nan when compute neg_avg")
-        if torch.any(torch.isnan(self.record.diag())).item() is True:
-            print("we got self.record.diag() has nan when compute neg_avg")
-        self.neg_avg = (self.record.sum() - self.record.diag().sum()) / (num_item - min_size)
-            
-        return sim / self.temp
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, hidden_size):
-        super().__init__()
-        self.dense = nn.Linear(hidden_size, hidden_size)
-        self.transform_act_fn = F.gelu
-        self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, hid_dim, out_dim):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(hid_dim)
-        self.decoder = nn.Linear(hid_dim, out_dim, bias=False)
-        self.bias = nn.Parameter(torch.zeros(out_dim))
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-# V2_2
-# change add to concat.
-# now support finetune BERT
-# grad_bert=0.1 & trainable_block_idx=0
-class BERTRelTransformerEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout=0.0,
-                 window_size=4,
-                 block_length=None,
-                 prenet=True,
-                 pre_ln=True,
-                 ):
-
-        super().__init__()
-
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-        self.prenet = prenet
-        if n_vocab > 0:
-            self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
-
-        if prenet:
-            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
-                                    kernel_size=5, n_layers=3, p_dropout=0)
-        self.encoder1 = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers//2,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-        self.encoder2 = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers - n_layers//2,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-        if hparams['ds_name'] in ['ljspeech', 'libritts', 'librispeech']:
-            model_name = 'bert-base-uncased'
-        elif hparams['ds_name'] in ['biaobei', 'wenetspeech']:
-            model_name = 'bert-base-chinese'
-        else:
-            raise NotImplementedError()
-            
-        self.tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-        config = transformers.AutoConfig.from_pretrained(model_name)
-        if hparams.get("load_bert_from_pretrained", True):
-            print("Load BERT from pretrained model ...")
-            self.bert =  transformers.AutoModel.from_pretrained(model_name,config=config)
-            trainable_start_block = hparams.get("bert_trainable_start_block", 0)
-        else:
-            print("Initialize BERT from scratch!")
-            self.bert =  transformers.BertModel(config=config)
-            trainable_start_block = 0
-        
-        for k, v in self.bert.named_parameters():
-            if 'embeddings' in k:
-                v.requires_grad = False
-            elif 'encoder.layer' in k:
-                block_idx =  int(k.split(".")[2])
-                if block_idx < trainable_start_block:
-                    v.requires_grad = False
-                else:
-                    v.requires_grad = True
-            elif 'cls' in k:
-                v.requires_grad = True
-            else:
-                print("Unhandled key: {}, set to requires_grad...".format(k))
-                v.requires_grad = True
-
-        self.bert_combine = nn.Sequential(*[
-            nn.Conv1d(768 + hidden_channels, hidden_channels, 3, 1, 1),
-            nn.ReLU(),
-            ])
-        self.pooler = Pooler("avg")
-        self.sim = Similarity(temp=0.05)
-        
-    def forward(self, x, x_mask=None, bert_feats=None, ph2word=None, **kwargs):
-        if self.n_vocab > 0:
-            x_lengths = (x > 0).long().sum(-1)
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        else:
-            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        if self.prenet:
-            x = self.pre(x, x_mask)
-        x = self.encoder1(x, x_mask)
-        bert_outputs = self.bert(bert_feats['bert_input_ids'],
-                                    attention_mask=bert_feats['bert_attention_mask'],
-                                    token_type_ids=bert_feats['bert_token_type_ids'],
-                                    output_hidden_states=True)
-        bert_num_blocks = hparams.get("bert_num_blocks", 12) # total 1+12blocks in bert
-        bert_embedding = bert_outputs['hidden_states'][bert_num_blocks]
-        # bert_embedding = bert_outputs['last_hidden_state']
-        grad_bert = hparams.get("grad_bert", 0.1)
-        bert_embedding = bert_embedding.detach() * (1-grad_bert) + bert_embedding * grad_bert
-        bert_word_embedding, _ = group_hidden_by_segs(bert_embedding, bert_feats['bert_token2word'], bert_feats['bert_token2word'].max().item())
-        bert_ph_embedding = expand_word2ph(bert_word_embedding, ph2word)
-        bert_ph_embedding = bert_ph_embedding.transpose(1,2)
-        x = torch.cat([x, bert_ph_embedding], dim=1)
-        x = self.bert_combine(x)
-        x = self.encoder2(x, x_mask)
-        return x.transpose(1, 2)
-
-

spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/lj/preprocess.py

@@ -1,9 +0,0 @@
-from text_to_speech.data_gen.tts.base_preprocess import BasePreprocessor
-
-
-class LJPreprocess(BasePreprocessor):
-    def meta_data(self):
-        for l in open(f'{self.raw_data_dir}/metadata.csv').readlines():
-            item_name, _, txt = l.strip().split("|")
-            wav_fn = f"{self.raw_data_dir}/wavs/{item_name}.wav"
-            yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt}

spaces/AIMLApps/Botrite_wip/README.md

@@ -1,12 +0,0 @@
----
-title: Botrite Wip
-emoji: 📈
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AP123/IllusionDiffusion/user_history.py

@@ -1,423 +0,0 @@
-"""
-User History is a plugin that you can add to your Spaces to cache generated images for your users.
-
-Key features:
-- 🤗 Sign in with Hugging Face
-- Save generated images with their metadata: prompts, timestamp, hyper-parameters, etc.
-- Export your history as zip.
-- Delete your history to respect privacy.
-- Compatible with Persistent Storage for long-term storage.
-- Admin panel to check configuration and disk usage .
-
-Useful links:
-- Demo: https://huggingface.co/spaces/Wauplin/gradio-user-history
-- README: https://huggingface.co/spaces/Wauplin/gradio-user-history/blob/main/README.md
-- Source file: https://huggingface.co/spaces/Wauplin/gradio-user-history/blob/main/user_history.py
-- Discussions: https://huggingface.co/spaces/Wauplin/gradio-user-history/discussions
-"""
-import json
-import os
-import shutil
-import warnings
-from datetime import datetime
-from functools import cache
-from pathlib import Path
-from typing import Callable, Dict, List, Tuple
-from uuid import uuid4
-
-import gradio as gr
-import numpy as np
-import requests
-from filelock import FileLock
-from PIL.Image import Image
-
-
-def setup(folder_path: str | Path | None = None) -> None:
-    user_history = _UserHistory()
-    user_history.folder_path = _resolve_folder_path(folder_path)
-    user_history.initialized = True
-
-
-def render() -> None:
-    user_history = _UserHistory()
-
-    # initialize with default config
-    if not user_history.initialized:
-        print("Initializing user history with default config. Use `user_history.setup(...)` to customize folder_path.")
-        setup()
-
-    # Render user history tab
-    gr.Markdown(
-        "## Your past generations\n\nLog in to keep a gallery of your previous generations. Your history will be saved"
-        " and available on your next visit. Make sure to export your images from time to time as this gallery may be"
-        " deleted in the future."
-    )
-
-    if os.getenv("SYSTEM") == "spaces" and not os.path.exists("/data"):
-        gr.Markdown(
-            "**⚠️ Persistent storage is disabled, meaning your history will be lost if the Space gets restarted."
-            " Only the Space owner can setup a Persistent Storage. If you are not the Space owner, consider"
-            " duplicating this Space to set your own storage.⚠️**"
-        )
-
-    with gr.Row():
-        gr.LoginButton(min_width=250)
-        gr.LogoutButton(min_width=250)
-        refresh_button = gr.Button(
-            "Refresh",
-            icon="https://huggingface.co/spaces/Wauplin/gradio-user-history/resolve/main/assets/icon_refresh.png",
-        )
-        export_button = gr.Button(
-            "Export",
-            icon="https://huggingface.co/spaces/Wauplin/gradio-user-history/resolve/main/assets/icon_download.png",
-        )
-        delete_button = gr.Button(
-            "Delete history",
-            icon="https://huggingface.co/spaces/Wauplin/gradio-user-history/resolve/main/assets/icon_delete.png",
-        )
-
-    # "Export zip" row (hidden by default)
-    with gr.Row():
-        export_file = gr.File(file_count="single", file_types=[".zip"], label="Exported history", visible=False)
-
-    # "Config deletion" row (hidden by default)
-    with gr.Row():
-        confirm_button = gr.Button("Confirm delete all history", variant="stop", visible=False)
-        cancel_button = gr.Button("Cancel", visible=False)
-
-    # Gallery
-    gallery = gr.Gallery(
-        label="Past images",
-        show_label=True,
-        elem_id="gallery",
-        object_fit="contain",
-        columns=5,
-        height=600,
-        preview=False,
-        show_share_button=False,
-        show_download_button=False,
-    )
-    gr.Markdown(
-        "User history is powered by"
-        " [Wauplin/gradio-user-history](https://huggingface.co/spaces/Wauplin/gradio-user-history). Integrate it to"
-        " your own Space in just a few lines of code!"
-    )
-    gallery.attach_load_event(_fetch_user_history, every=None)
-
-    # Interactions
-    refresh_button.click(fn=_fetch_user_history, inputs=[], outputs=[gallery], queue=False)
-    export_button.click(fn=_export_user_history, inputs=[], outputs=[export_file], queue=False)
-
-    # Taken from https://github.com/gradio-app/gradio/issues/3324#issuecomment-1446382045
-    delete_button.click(
-        lambda: [gr.update(visible=True), gr.update(visible=True)],
-        outputs=[confirm_button, cancel_button],
-        queue=False,
-    )
-    cancel_button.click(
-        lambda: [gr.update(visible=False), gr.update(visible=False)],
-        outputs=[confirm_button, cancel_button],
-        queue=False,
-    )
-    confirm_button.click(_delete_user_history).then(
-        lambda: [gr.update(visible=False), gr.update(visible=False)],
-        outputs=[confirm_button, cancel_button],
-        queue=False,
-    )
-
-    # Admin section (only shown locally or when logged in as Space owner)
-    _admin_section()
-
-
-def save_image(
-    profile: gr.OAuthProfile | None,
-    image: Image | np.ndarray | str | Path,
-    label: str | None = None,
-    metadata: Dict | None = None,
-):
-    # Ignore images from logged out users
-    if profile is None:
-        return
-    username = profile["preferred_username"]
-
-    # Ignore images if user history not used
-    user_history = _UserHistory()
-    if not user_history.initialized:
-        warnings.warn(
-            "User history is not set in Gradio demo. Saving image is ignored. You must use `user_history.render(...)`"
-            " first."
-        )
-        return
-
-    # Copy image to storage
-    image_path = _copy_image(image, dst_folder=user_history._user_images_path(username))
-
-    # Save new image + metadata
-    if metadata is None:
-        metadata = {}
-    if "datetime" not in metadata:
-        metadata["datetime"] = str(datetime.now())
-    data = {"path": str(image_path), "label": label, "metadata": metadata}
-    with user_history._user_lock(username):
-        with user_history._user_jsonl_path(username).open("a") as f:
-            f.write(json.dumps(data) + "\n")
-
-
-#############
-# Internals #
-#############
-
-
-class _UserHistory(object):
-    _instance = None
-    initialized: bool = False
-    folder_path: Path
-
-    def __new__(cls):
-        # Using singleton pattern => we don't want to expose an object (more complex to use) but still want to keep
-        # state between `render` and `save_image` calls.
-        if cls._instance is None:
-            cls._instance = super(_UserHistory, cls).__new__(cls)
-        return cls._instance
-
-    def _user_path(self, username: str) -> Path:
-        path = self.folder_path / username
-        path.mkdir(parents=True, exist_ok=True)
-        return path
-
-    def _user_lock(self, username: str) -> FileLock:
-        """Ensure history is not corrupted if concurrent calls."""
-        return FileLock(self.folder_path / f"{username}.lock")  # lock outside of folder => better when exporting ZIP
-
-    def _user_jsonl_path(self, username: str) -> Path:
-        return self._user_path(username) / "history.jsonl"
-
-    def _user_images_path(self, username: str) -> Path:
-        path = self._user_path(username) / "images"
-        path.mkdir(parents=True, exist_ok=True)
-        return path
-
-
-def _fetch_user_history(profile: gr.OAuthProfile | None) -> List[Tuple[str, str]]:
-    """Return saved history for that user, if it exists."""
-    # Cannot load history for logged out users
-    if profile is None:
-        return []
-    username = profile["preferred_username"]
-
-    user_history = _UserHistory()
-    if not user_history.initialized:
-        warnings.warn("User history is not set in Gradio demo. You must use `user_history.render(...)` first.")
-        return []
-
-    with user_history._user_lock(username):
-        # No file => no history saved yet
-        jsonl_path = user_history._user_jsonl_path(username)
-        if not jsonl_path.is_file():
-            return []
-
-        # Read history
-        images = []
-        for line in jsonl_path.read_text().splitlines():
-            data = json.loads(line)
-            images.append((data["path"], data["label"] or ""))
-        return list(reversed(images))
-
-
-def _export_user_history(profile: gr.OAuthProfile | None) -> Dict | None:
-    """Zip all history for that user, if it exists and return it as a downloadable file."""
-    # Cannot load history for logged out users
-    if profile is None:
-        return None
-    username = profile["preferred_username"]
-
-    user_history = _UserHistory()
-    if not user_history.initialized:
-        warnings.warn("User history is not set in Gradio demo. You must use `user_history.render(...)` first.")
-        return None
-
-    # Zip history
-    with user_history._user_lock(username):
-        path = shutil.make_archive(
-            str(_archives_path() / f"history_{username}"), "zip", user_history._user_path(username)
-        )
-
-    return gr.update(visible=True, value=path)
-
-
-def _delete_user_history(profile: gr.OAuthProfile | None) -> None:
-    """Delete all history for that user."""
-    # Cannot load history for logged out users
-    if profile is None:
-        return
-    username = profile["preferred_username"]
-
-    user_history = _UserHistory()
-    if not user_history.initialized:
-        warnings.warn("User history is not set in Gradio demo. You must use `user_history.render(...)` first.")
-        return
-
-    with user_history._user_lock(username):
-        shutil.rmtree(user_history._user_path(username))
-
-
-####################
-# Internal helpers #
-####################
-
-
-def _copy_image(image: Image | np.ndarray | str | Path, dst_folder: Path) -> Path:
-    """Copy image to the images folder."""
-    # Already a path => copy it
-    if isinstance(image, str):
-        image = Path(image)
-    if isinstance(image, Path):
-        dst = dst_folder / f"{uuid4().hex}_{Path(image).name}"  # keep file ext
-        shutil.copyfile(image, dst)
-        return dst
-
-    # Still a Python object => serialize it
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    if isinstance(image, Image):
-        dst = dst_folder / f"{uuid4().hex}.png"
-        image.save(dst)
-        return dst
-
-    raise ValueError(f"Unsupported image type: {type(image)}")
-
-
-def _resolve_folder_path(folder_path: str | Path | None) -> Path:
-    if folder_path is not None:
-        return Path(folder_path).expanduser().resolve()
-
-    if os.getenv("SYSTEM") == "spaces" and os.path.exists("/data"):  # Persistent storage is enabled!
-        return Path("/data") / "_user_history"
-
-    # Not in a Space or Persistent storage not enabled => local folder
-    return Path(__file__).parent / "_user_history"
-
-
-def _archives_path() -> Path:
-    # Doesn't have to be on persistent storage as it's only used for download
-    path = Path(__file__).parent / "_user_history_exports"
-    path.mkdir(parents=True, exist_ok=True)
-    return path
-
-
-#################
-# Admin section #
-#################
-
-
-def _admin_section() -> None:
-    title = gr.Markdown()
-    title.attach_load_event(_display_if_admin(), every=None)
-
-
-def _display_if_admin() -> Callable:
-    def _inner(profile: gr.OAuthProfile | None) -> str:
-        if profile is None:
-            return ""
-        if profile["preferred_username"] in _fetch_admins():
-            return _admin_content()
-        return ""
-
-    return _inner
-
-
-def _admin_content() -> str:
-    return f"""
-## Admin section
-
-Running on **{os.getenv("SYSTEM", "local")}** (id: {os.getenv("SPACE_ID")}). {_get_msg_is_persistent_storage_enabled()}
-
-Admins: {', '.join(_fetch_admins())}
-
-{_get_nb_users()} user(s), {_get_nb_images()} image(s)
-
-### Configuration
-
-History folder: *{_UserHistory().folder_path}*
-
-Exports folder: *{_archives_path()}*
-
-### Disk usage
-
-{_disk_space_warning_message()}
-"""
-
-
-def _get_nb_users() -> int:
-    user_history = _UserHistory()
-    if not user_history.initialized:
-        return 0
-    if user_history.folder_path is not None and user_history.folder_path.exists():
-        return len([path for path in user_history.folder_path.iterdir() if path.is_dir()])
-    return 0
-
-
-def _get_nb_images() -> int:
-    user_history = _UserHistory()
-    if not user_history.initialized:
-        return 0
-    if user_history.folder_path is not None and user_history.folder_path.exists():
-        return len([path for path in user_history.folder_path.glob("*/images/*")])
-    return 0
-
-
-def _get_msg_is_persistent_storage_enabled() -> str:
-    if os.getenv("SYSTEM") == "spaces":
-        if os.path.exists("/data"):
-            return "Persistent storage is enabled."
-        else:
-            return (
-                "Persistent storage is not enabled. This means that user histories will be deleted when the Space is"
-                " restarted. Consider adding a Persistent Storage in your Space settings."
-            )
-    return ""
-
-
-def _disk_space_warning_message() -> str:
-    user_history = _UserHistory()
-    if not user_history.initialized:
-        return ""
-
-    message = ""
-    if user_history.folder_path is not None:
-        total, used, _ = _get_disk_usage(user_history.folder_path)
-        message += f"History folder: **{used / 1e9 :.0f}/{total / 1e9 :.0f}GB** used ({100*used/total :.0f}%)."
-
-    total, used, _ = _get_disk_usage(_archives_path())
-    message += f"\n\nExports folder: **{used / 1e9 :.0f}/{total / 1e9 :.0f}GB** used ({100*used/total :.0f}%)."
-
-    return f"{message.strip()}"
-
-
-def _get_disk_usage(path: Path) -> Tuple[int, int, int]:
-    for path in [path] + list(path.parents):  # first check target_dir, then each parents one by one
-        try:
-            return shutil.disk_usage(path)
-        except OSError:  # if doesn't exist or can't read => fail silently and try parent one
-            pass
-    return 0, 0, 0
-
-
-@cache
-def _fetch_admins() -> List[str]:
-    # Running locally => fake user is admin
-    if os.getenv("SYSTEM") != "spaces":
-        return ["FakeGradioUser"]
-
-    # Running in Space but no space_id => ???
-    space_id = os.getenv("SPACE_ID")
-    if space_id is None:
-        return ["Unknown"]
-
-    # Running in Space => try to fetch organization members
-    # Otherwise, it's not an organization => namespace is the user
-    namespace = space_id.split("/")[0]
-    response = requests.get(f"https://huggingface.co/api/organizations/{namespace}/members")
-    if response.status_code == 200:
-        return sorted((member["user"] for member in response.json()), key=lambda x: x.lower())
-    return [namespace]

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/tcrp-plugin.js

@@ -1,34 +0,0 @@
-import TCRP from './tcrp.js';
-
-const Recorder = TCRP.Recorder;
-const Player = TCRP.Player;
-
-class TCRPPlugin extends Phaser.Plugins.BasePlugin {
-    constructor(pluginManager) {
-        super(pluginManager);
-    }
-
-    start() {
-        var eventEmitter = this.game.events;
-        eventEmitter.on('destroy', this.destroy, this);
-    }
-
-    addRecorder(parent, config) {
-        return new Recorder(parent, config);
-    }
-
-    addPlayer(parent, config) {
-        return new Player(parent, config);
-    }
-}
-
-var methods = {
-    runCommands: TCRP.RunCommands
-}
-
-Object.assign(
-    TCRPPlugin.prototype,
-    methods
-);
-
-export default TCRPPlugin;

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

@@ -1,7 +0,0 @@
-// import * as Phaser from 'phaser';
-import ClickOutside from "./ClickOutside";
-
-export default function (
-    gameObject: Phaser.GameObjects.GameObject,
-    config?: ClickOutside.IConfig
-): ClickOutside;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/RunChildrenWrap.js

@@ -1,93 +0,0 @@
-import { GetDisplayWidth, GetDisplayHeight } from '../../../plugins/utils/size/GetDisplaySize.js';
-
-var RunChildrenWrap = function (lineWidth, out) {
-    if (out === undefined) {
-        out = {
-            lines: [],
-            width: 0,
-            height: 0
-        }
-    } else {
-        out.lines.length = 0;
-        out.width = 0;
-        out.height = 0;
-    }
-
-    var children = this.sizerChildren;
-    var itemSpace = this.space.item,
-        lineSpace = this.space.line,
-        indentLeftOdd = this.space.indentLeftOdd,
-        indentLeftEven = this.space.indentLeftEven,
-        indentTopOdd = this.space.indentTopOdd,
-        indentTopEven = this.space.indentTopEven;
-    var child, childWidth, childHeight, remainder = 0, indentLeft;
-    var lines = out.lines,
-        lastLine = undefined,
-        newLine;
-    for (var i = 0, cnt = children.length; i < cnt; i++) {
-        child = children[i];
-        if (child === '\n') {
-            child = undefined;
-            childWidth = 0;
-            newLine = true;
-        } else {
-            if (child.rexSizer.hidden) {
-                continue;
-            }
-
-            if (child.isRexSizer) {
-                child.layout(); // Use original size
-            }
-
-            childWidth = GetChildWidth(child);
-            newLine = (remainder < childWidth) || (lastLine === undefined);
-        }
-        // New line
-        if (newLine) {
-            if (lastLine) {
-                lastLine.width = lineWidth - (remainder + itemSpace);
-                out.width = Math.max(out.width, lastLine.width);
-                out.height += lastLine.height + lineSpace;
-            }
-
-            lastLine = {
-                children: [],
-                // width: 0,
-                height: 0
-            };
-            lines.push(lastLine);
-
-            var indentLeft = (lines.length % 2) ? indentLeftOdd : indentLeftEven;
-            remainder = lineWidth - indentLeft;
-        }
-
-        remainder -= (childWidth + itemSpace);
-        if (child) {
-            lastLine.children.push(child);
-            childHeight = GeChildHeight(child);
-            lastLine.height = Math.max(lastLine.height, childHeight);
-        }
-    }
-
-    if (lastLine) {
-        lastLine.width = lineWidth - (remainder + itemSpace);
-        out.width = Math.max(out.width, lastLine.width);
-        out.height += lastLine.height;
-    }
-
-    out.height += Math.max(indentTopOdd, indentTopEven);
-
-    return out;
-}
-
-var GetChildWidth = function (child) {
-    var padding = child.rexSizer.padding;
-    return GetDisplayWidth(child) + padding.left + padding.right;
-}
-
-var GeChildHeight = function (child) {
-    var padding = child.rexSizer.padding;
-    return GetDisplayHeight(child) + padding.top + padding.bottom;
-}
-
-export default RunChildrenWrap;

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/transforms.py

@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet

spaces/AlekseyKorshuk/model-evaluation/tabs/arena_side_by_side.py

@@ -1,240 +0,0 @@
-import time
-
-import gradio as gr
-import random
-from conversation import Conversation
-from utils import get_matchmaking
-
-
-def get_tab_arena_side_by_side(download_bot_config, get_bot_profile, model_mapping, client):
-    gr.Markdown("""
-    # ⚔️ Chatbot Arena (side-by-side) ⚔️
-    ## Rules
-    * Chat with two models side-by-side and vote for which one is better!
-    * You pick the models you want to chat with.
-    * You can continue chatting and voting or click “Clear” to start a new round.
-    """)
-    default_bot_id = "_bot_e21de304-6151-4a04-b025-4c553ae8cbca"
-    bot_config = download_bot_config(default_bot_id)
-    user_state = gr.State(
-        bot_config
-    )
-    with gr.Row():
-        bot_id = gr.Textbox(label="Chai bot ID", value=default_bot_id, interactive=True)
-        reload_bot_button = gr.Button("Reload bot")
-    bot_profile = gr.HTML(get_bot_profile(bot_config))
-    with gr.Accordion("Bot config:", open=False):
-        bot_config_text = gr.Markdown(f"# Memory\n{bot_config['memory']}\n# Prompt\n{bot_config['prompt']}\n")
-
-    with gr.Row():
-        values = list(model_mapping.keys())
-        first_message = (None, bot_config["firstMessage"])
-        height = 450
-        model_a_value, model_b_value = get_matchmaking(client, values, is_anonymous=False)
-        with gr.Column():
-            model_a = gr.Dropdown(values, value=model_a_value, label="Model A")
-            chatbot_a = gr.Chatbot([first_message])
-            chatbot_a.style(height=height)
-        with gr.Column():
-            model_b = gr.Dropdown(values, value=model_b_value, label="Model B")
-            chatbot_b = gr.Chatbot([first_message])
-            chatbot_b.style(height=height)
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            msg = gr.Textbox(show_label=False, value="Hi there!", interactive=True)
-        with gr.Column(scale=3):
-            send = gr.Button("Send")
-    with gr.Row():
-        vote_a = gr.Button("👈 A is better", interactive=False)
-        vote_b = gr.Button("👉 B is better", interactive=False)
-        vote_tie = gr.Button("🤝 Tie", interactive=False)
-        vote_bad = gr.Button("💩 Both are bad", interactive=False)
-    with gr.Row():
-        regenerate = gr.Button("Regenerate", interactive=False)
-        clear = gr.Button("Clear")
-
-    with gr.Accordion("Generation parameters for model A", open=False):
-        model = model_mapping[model_a.value]
-        temperature_model_a = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["temperature"],
-                                        interactive=True, label="Temperature")
-        repetition_penalty_model_a = gr.Slider(minimum=0.0, maximum=2.0,
-                                               value=model.generation_params["repetition_penalty"],
-                                               interactive=True, label="Repetition penalty")
-        max_new_tokens_model_a = gr.Slider(minimum=1, maximum=512, value=model.generation_params["max_new_tokens"],
-                                           interactive=True, label="Max new tokens")
-        top_k_model_a = gr.Slider(minimum=1, maximum=100, value=model.generation_params["top_k"],
-                                  interactive=True, label="Top-K")
-        top_p_model_a = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["top_p"],
-                                  interactive=True, label="Top-P")
-
-    with gr.Accordion("Generation parameters for model B", open=False):
-        model = model_mapping[model_b.value]
-        temperature_model_b = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["temperature"],
-                                        interactive=True, label="Temperature")
-        repetition_penalty_model_b = gr.Slider(minimum=0.0, maximum=2.0,
-                                               value=model.generation_params["repetition_penalty"],
-                                               interactive=True, label="Repetition penalty")
-        max_new_tokens_model_b = gr.Slider(minimum=1, maximum=512, value=model.generation_params["max_new_tokens"],
-                                           interactive=True, label="Max new tokens")
-        top_k_model_b = gr.Slider(minimum=1, maximum=100, value=model.generation_params["top_k"],
-                                  interactive=True, label="Top-K")
-        top_p_model_b = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["top_p"],
-                                  interactive=True, label="Top-P")
-
-    def clear_chat(user_state):
-        return "", [(None, user_state["firstMessage"])], [(None, user_state["firstMessage"])]
-
-    def reload_bot(bot_id):
-        bot_config = download_bot_config(bot_id)
-        bot_profile = get_bot_profile(bot_config)
-        return bot_profile, [(None, bot_config["firstMessage"])], [(None, bot_config[
-            "firstMessage"])], bot_config, f"# Memory\n{bot_config['memory']}\n# Prompt\n{bot_config['prompt']}"
-
-    def get_generation_args(model_tag):
-        model = model_mapping[model_tag]
-        return (
-            model.generation_params["temperature"],
-            model.generation_params["repetition_penalty"],
-            model.generation_params["max_new_tokens"],
-            model.generation_params["top_k"],
-            model.generation_params["top_p"],
-        )
-
-    def respond(message, chat_history, user_state, model_tag,
-                temperature, repetition_penalty, max_new_tokens, top_k, top_p):
-        custom_generation_params = {
-            'temperature': temperature,
-            'repetition_penalty': repetition_penalty,
-            'max_new_tokens': max_new_tokens,
-            'top_k': top_k,
-            'top_p': top_p,
-        }
-        conv = Conversation(user_state)
-        conv.set_chat_history(chat_history)
-        conv.add_user_message(message)
-        model = model_mapping[model_tag]
-        bot_message = model.generate_response(conv, custom_generation_params)
-        chat_history.append(
-            (message, bot_message)
-        )
-        return "", chat_history
-
-    def record_vote(user_state, vote,
-                    chat_history_a, model_tag_a,
-                    chat_history_b, model_tag_b):
-        if len(chat_history_a) < 2:
-            return
-        conv_a = Conversation(user_state)
-        conv_a.set_chat_history(chat_history_a)
-        conv_b = Conversation(user_state)
-        conv_b.set_chat_history(chat_history_b)
-        if "A is better" in vote:
-            vote_str = "model_a"
-        elif "B is better" in vote:
-            vote_str = "model_b"
-        elif "Tie" in vote:
-            vote_str = "tie"
-        else:
-            vote_str = "tie (bothbad)"
-        row = {
-            "timestamp": time.time(),
-            "bot_id": user_state["bot_id"],
-            "vote": vote_str,
-            "model_a": model_tag_a,
-            "model_b": model_tag_b,
-            "is_anonymous": int(False)
-        }
-        sheet = client.open("Chat Arena").sheet1
-        num_rows = len(sheet.get_all_records())
-        sheet.insert_row(list(row.values()), index=num_rows + 2)
-        return
-
-    def regenerate_response(chat_history, user_state, model_tag,
-                            temperature, repetition_penalty, max_new_tokens, top_k, top_p):
-        custom_generation_params = {
-            'temperature': temperature,
-            'repetition_penalty': repetition_penalty,
-            'max_new_tokens': max_new_tokens,
-            'top_k': top_k,
-            'top_p': top_p,
-        }
-        last_row = chat_history.pop(-1)
-        chat_history.append((last_row[0], None))
-        model = model_mapping[model_tag]
-        conv = Conversation(user_state)
-        conv.set_chat_history(chat_history)
-        bot_message = model.generate_response(conv, custom_generation_params)
-        chat_history[-1] = (last_row[0], bot_message)
-        return "", chat_history
-
-    def disable_voting():
-        return [gr.Button.update(interactive=False)] * 4
-
-    def enable_voting():
-        return [gr.Button.update(interactive=True)] * 4
-
-    def enable_send():
-        return [gr.Button.update(interactive=True), gr.Button.update(interactive=False)]
-
-    def enable_regenerate():
-        return gr.Button.update(interactive=True)
-
-    for vote in [vote_a, vote_b, vote_tie, vote_bad]:
-        vote.click(record_vote,
-                   [user_state, vote, chatbot_a, model_a, chatbot_b, model_b],
-                   None,
-                   queue=False)
-        vote.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-
-    model_a.change(get_generation_args, [model_a],
-                   [temperature_model_a, repetition_penalty_model_a, max_new_tokens_model_a, top_k_model_a,
-                    top_p_model_a], queue=False)
-    model_b.change(get_generation_args, [model_b],
-                   [temperature_model_b, repetition_penalty_model_b, max_new_tokens_model_b, top_k_model_b,
-                    top_p_model_b], queue=False)
-    reload_bot_button.click(reload_bot, [bot_id], [bot_profile, chatbot_a, chatbot_b, user_state, bot_config_text],
-                            queue=False)
-    clear.click(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False)
-    model_a.change(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False)
-    model_b.change(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False)
-    clear.click(enable_send, None, [send, regenerate], queue=False)
-    reload_bot_button.click(enable_send, None, [send, regenerate], queue=False)
-
-    model_a.change(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-    model_b.change(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-    reload_bot_button.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-    send.click(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-    clear.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-    regenerate.click(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-    msg.submit(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False)
-
-    send.click(respond,
-               [msg, chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a,
-                max_new_tokens_model_a, top_k_model_a, top_p_model_a], [msg, chatbot_a],
-               queue=False)
-    msg.submit(respond,
-               [msg, chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a,
-                max_new_tokens_model_a, top_k_model_a, top_p_model_a], [msg, chatbot_a],
-               queue=False)
-
-    send.click(respond,
-               [msg, chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b,
-                max_new_tokens_model_b, top_k_model_b, top_p_model_b], [msg, chatbot_b],
-               queue=False)
-    msg.submit(respond,
-               [msg, chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b,
-                max_new_tokens_model_b, top_k_model_b, top_p_model_b], [msg, chatbot_b],
-               queue=False)
-
-    send.click(enable_regenerate, None, [regenerate], queue=False)
-    msg.submit(enable_regenerate, None, [regenerate], queue=False)
-
-    regenerate.click(regenerate_response,
-                     [chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a,
-                      max_new_tokens_model_a, top_k_model_a,
-                      top_p_model_a], [msg, chatbot_a], queue=False)
-    regenerate.click(regenerate_response,
-                     [chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b,
-                      max_new_tokens_model_b, top_k_model_b,
-                      top_p_model_b], [msg, chatbot_b], queue=False)

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

@@ -1,94 +0,0 @@
-from tqdm import trange
-import torch
-from torch.utils.data import DataLoader
-from logger import Logger
-from modules.model import GeneratorFullModel
-from torch.optim.lr_scheduler import MultiStepLR
-from torch.nn.utils import clip_grad_norm_
-from frames_dataset import DatasetRepeater
-import math
-
-def train(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset):
-    train_params = config['train_params']
-    optimizer = torch.optim.Adam(
-        [{'params': list(inpainting_network.parameters()) +
-                    list(dense_motion_network.parameters()) +
-                    list(kp_detector.parameters()), 'initial_lr': train_params['lr_generator']}],lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4)
-    
-    optimizer_bg_predictor = None
-    if bg_predictor:
-        optimizer_bg_predictor = torch.optim.Adam(
-            [{'params':bg_predictor.parameters(),'initial_lr': train_params['lr_generator']}], 
-            lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4)
-
-    if checkpoint is not None:
-        start_epoch = Logger.load_cpk(
-            checkpoint, inpainting_network = inpainting_network, dense_motion_network = dense_motion_network,       
-            kp_detector = kp_detector, bg_predictor = bg_predictor,
-            optimizer = optimizer, optimizer_bg_predictor = optimizer_bg_predictor)
-        print('load success:', start_epoch)
-        start_epoch += 1
-    else:
-        start_epoch = 0
-
-    scheduler_optimizer = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1,
-                                      last_epoch=start_epoch - 1)
-    if bg_predictor:
-        scheduler_bg_predictor = MultiStepLR(optimizer_bg_predictor, train_params['epoch_milestones'],
-                                              gamma=0.1, last_epoch=start_epoch - 1)
-
-    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
-        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
-    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, 
-                            num_workers=train_params['dataloader_workers'], drop_last=True)
-
-    generator_full = GeneratorFullModel(kp_detector, bg_predictor, dense_motion_network, inpainting_network, train_params)
-
-    if torch.cuda.is_available():
-        generator_full = torch.nn.DataParallel(generator_full).cuda()  
-        
-    bg_start = train_params['bg_start']
-    
-    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], 
-                checkpoint_freq=train_params['checkpoint_freq']) as logger:
-        for epoch in trange(start_epoch, train_params['num_epochs']):
-            for x in dataloader:
-                if(torch.cuda.is_available()):
-                    x['driving'] = x['driving'].cuda()
-                    x['source'] = x['source'].cuda()
-
-                losses_generator, generated = generator_full(x, epoch)
-                loss_values = [val.mean() for val in losses_generator.values()]
-                loss = sum(loss_values)
-                loss.backward()
-
-                clip_grad_norm_(kp_detector.parameters(), max_norm=10, norm_type = math.inf)
-                clip_grad_norm_(dense_motion_network.parameters(), max_norm=10, norm_type = math.inf)
-                if bg_predictor and epoch>=bg_start:
-                    clip_grad_norm_(bg_predictor.parameters(), max_norm=10, norm_type = math.inf)
-                
-                optimizer.step()
-                optimizer.zero_grad()
-                if bg_predictor and epoch>=bg_start:
-                    optimizer_bg_predictor.step()
-                    optimizer_bg_predictor.zero_grad()
-                
-                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()}
-                logger.log_iter(losses=losses)
-
-            scheduler_optimizer.step()
-            if bg_predictor:
-                scheduler_bg_predictor.step()
-            
-            model_save = {
-                'inpainting_network': inpainting_network,
-                'dense_motion_network': dense_motion_network,
-                'kp_detector': kp_detector,
-                'optimizer': optimizer,
-            }
-            if bg_predictor and epoch>=bg_start:
-                model_save['bg_predictor'] = bg_predictor
-                model_save['optimizer_bg_predictor'] = optimizer_bg_predictor
-            
-            logger.log_epoch(epoch, model_save, inp=x, out=generated)
-

spaces/AlexWang/lama/bin/gen_outpainting_dataset.py

@@ -1,88 +0,0 @@
-#!/usr/bin/env python3
-import glob
-import logging
-import os
-import shutil
-import sys
-import traceback
-
-from saicinpainting.evaluation.data import load_image
-from saicinpainting.evaluation.utils import move_to_device
-
-os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['OPENBLAS_NUM_THREADS'] = '1'
-os.environ['MKL_NUM_THREADS'] = '1'
-os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
-os.environ['NUMEXPR_NUM_THREADS'] = '1'
-
-import cv2
-import hydra
-import numpy as np
-import torch
-import tqdm
-import yaml
-from omegaconf import OmegaConf
-from torch.utils.data._utils.collate import default_collate
-
-from saicinpainting.training.data.datasets import make_default_val_dataset
-from saicinpainting.training.trainers import load_checkpoint
-from saicinpainting.utils import register_debug_signal_handlers
-
-LOGGER = logging.getLogger(__name__)
-
-
-def main(args):
-    try:
-        if not args.indir.endswith('/'):
-            args.indir += '/'
-
-        for in_img in glob.glob(os.path.join(args.indir, '**', '*' + args.img_suffix), recursive=True):
-            if 'mask' in os.path.basename(in_img):
-                continue
-
-            out_img_path = os.path.join(args.outdir, os.path.splitext(in_img[len(args.indir):])[0] + '.png')
-            out_mask_path = f'{os.path.splitext(out_img_path)[0]}_mask.png'
-
-            os.makedirs(os.path.dirname(out_img_path), exist_ok=True)
-
-            img = load_image(in_img)
-            height, width = img.shape[1:]
-            pad_h, pad_w = int(height * args.coef / 2), int(width * args.coef / 2)
-
-            mask = np.zeros((height, width), dtype='uint8')
-
-            if args.expand:
-                img = np.pad(img, ((0, 0), (pad_h, pad_h), (pad_w, pad_w)))
-                mask = np.pad(mask, ((pad_h, pad_h), (pad_w, pad_w)), mode='constant', constant_values=255)
-            else:
-                mask[:pad_h] = 255
-                mask[-pad_h:] = 255
-                mask[:, :pad_w] = 255
-                mask[:, -pad_w:] = 255
-
-            # img = np.pad(img, ((0, 0), (pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode='symmetric')
-            # mask = np.pad(mask, ((pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode = 'symmetric')
-
-            img = np.clip(np.transpose(img, (1, 2, 0)) * 255, 0, 255).astype('uint8')
-            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-            cv2.imwrite(out_img_path, img)
-
-            cv2.imwrite(out_mask_path, mask)
-    except KeyboardInterrupt:
-        LOGGER.warning('Interrupted by user')
-    except Exception as ex:
-        LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
-        sys.exit(1)
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('indir', type=str, help='Root directory with images')
-    aparser.add_argument('outdir', type=str, help='Where to store results')
-    aparser.add_argument('--img-suffix', type=str, default='.png', help='Input image extension')
-    aparser.add_argument('--expand', action='store_true', help='Generate mask by padding (true) or by cropping (false)')
-    aparser.add_argument('--coef', type=float, default=0.2, help='How much to crop/expand in order to get masks')
-
-    main(aparser.parse_args())

spaces/Ame42/UBTH/app.py

@@ -1,225 +0,0 @@
-# This is a sample Python script.
-
-# Press Shift+F10 to execute it or replace it with your code.
-import gradio as gr
-from utils import *
-from datetime import datetime
-
-doc_type = ipp
-prev_sht = None
-curr_sht = None
-
-
-def ui_builder():
-    with gr.Blocks() as demo:
-        err_view = gr.Textbox(label="Error found", visible=False)
-
-        with gr.Tab("Multiple files"):
-
-            def generate_all(d):
-                try:
-                    d = [retrieve(dt) for dt in d if retrieve(dt) is not None]
-
-                    out = "All months.csv"
-
-                    merge_all(d).to_csv(out)
-
-                    return {
-                        err_view: gr.update(visible=False),
-                        out_file: gr.update(value=out, visible=True, label="Merged file")
-                    }
-                except TypeError:
-                    return {
-                        err_view: gr.update(
-                            value="Please select a folder containing all the files you want to filter",
-                            visible=True
-                        ),
-                        out_file: gr.update(visible=False)
-                    }
-
-            # input ui
-            gr.Markdown('### See data that shows up in every month file in the chosen folder')
-            all_data = gr.File(label="Add a folder with all months", file_count="directory")
-
-            # output ui
-            output = gr.Markdown("## *Download your file", visible=False)
-            out_file = gr.File(value="Tutorial Guide.pdf", label="Learn to use this app", visible=True)
-            run = gr.Button("Generate file")
-
-            run.click(fn=generate_all, inputs=all_data, outputs=[err_view, out_file])
-        with gr.Tab("Compare two"):
-
-            def err_str(err):
-                f"""\
-[Faulty file]
-    Check ••••• {
-                os.path.split(
-                    os.path.splitext(
-                        err.get_file()
-                    )[0]
-                )[1][:-8]
-                }
-
-        {err.get_message()}\
-"""
-
-            def raise_error(msg: str) -> dict:
-                return {
-                    err_view: gr.update(
-                        value=msg,
-                        visible=True
-                    ),
-                    b: gr.update(visible=False),
-                    f: gr.update(visible=False),
-                    s: gr.update(visible=False),
-                    prev_dis: gr.update(value=None),
-                    curr_dis: gr.update(value=None),
-                    files: gr.update(visible=False)
-                }
-
-            def choose_type(event: gr.SelectData):
-                global doc_type
-                doc_type = event.value
-                return {
-                    uploads: gr.update(visible=True)
-                }
-
-            def check_prev(pr):
-                try:
-                    shts = pd.ExcelFile(pr.name).sheet_names
-
-                    return {
-                        prev_sheet: gr.update(choices=shts),
-                        sheets: gr.update(visible=True)
-                    }
-                except UnusualFileError as err:
-                    return raise_error(err_str(err))
-
-            def check_curr(cr):
-                try:
-                    shts = pd.ExcelFile(cr.name).sheet_names
-
-                    return {
-                        curr_sheet: gr.update(choices=shts),
-                        sheets: gr.update(visible=True)
-                    }
-                except UnusualFileError as err:
-                    return raise_error(err_str(err))
-
-            def sheet_prev(event: gr.SelectData, file):
-                global prev_sht
-                prev_sht = event.value
-                name, ext = os.path.splitext(file.name)
-                pr = get_raw(file.name, prev_sht, ext)
-                return {
-                    data: gr.update(visible=True),
-                    outputs: gr.update(visible=True),
-                    prev_dis: gr.update(value=pr)
-                }
-
-            def sheet_curr(event: gr.SelectData, file):
-                global curr_sht
-                curr_sht = event.value
-                name, ext = os.path.splitext(file.name)
-                cr = get_raw(file.name, curr_sht, ext)
-                return {
-                    data: gr.update(visible=True),
-                    outputs: gr.update(visible=True),
-                    curr_dis: gr.update(value=cr)
-                }
-
-            def generate(p, c, b_i, f_i, s_i):
-                current_time = datetime.now()
-                formatted_time = current_time.strftime('• %d-%m-%Y • %H.%M.%S')
-                b_file, f_file, s_file = f"Present in both {formatted_time}.csv", f"Exits {formatted_time}.csv", \
-                                         f"Entries {formatted_time}.csv"
-                # extract info from UI results
-                try:
-                    p_name, p_ext = os.path.splitext(p.name)
-                    c_name, c_ext = os.path.splitext(c.name)
-                    p = get_data(p.name, prev_sht, doc_type, p_ext)
-                    c = get_data(c.name, curr_sht, doc_type, c_ext)
-
-                    # process the data
-                    if p is None or c is None:
-                        return raise_error(f"Incompatible column names in either or both files. Make sure they "
-                                           f"conform to the standard.\n\nIPPIS: {ipp_col}\nGIFMIS: {gif_col}")
-                    elif p.columns[0] != c.columns[0]:
-                        return raise_error(f"You seem to be mixing {ipp} and {gif} files. This is not allowed")
-                    else:
-                        both_, p_merged, c_merged = merge_two(p, c, doc_type)
-
-                    clear_csv_trash()
-
-                    # save only the files the user requested
-                    if b_i:
-                        both_.to_csv(b_file, index=False)
-
-                    if f_i:
-                        p_merged.to_csv(f_file, index=False)
-
-                    if s_i:
-                        c_merged.to_csv(s_file, index=False)
-
-                    return {
-                        err_view: gr.update(visible=False),
-                        b: gr.update(value=b_file, visible=True) if b_i else gr.update(visible=False),
-                        f: gr.update(value=f_file, visible=True) if f_i else gr.update(visible=False),
-                        s: gr.update(value=s_file, visible=True) if s_i else gr.update(visible=False),
-                        prev_dis: gr.update(value=p),
-                        curr_dis: gr.update(value=c),
-                        files: gr.update(visible=True) if b_i or f_i or s_i else gr.update(visible=False)
-                    }
-                except AttributeError:
-                    return raise_error("Please select both files below before generating files")
-                except UnusualFileError as err:
-                    return raise_error(err_str(err))
-
-            # input ui
-            with gr.Blocks():
-                ########################################################################################################
-                type = gr.Radio([ipp, gif], label="Type", info="Choose a file type")
-                ########################################################################################################
-                with gr.Row(visible=False) as uploads:
-                    prev = gr.File(label="Previous month", file_types=['.csv', '.xls', '.xlsx'])
-                    curr = gr.File(label="Current month", file_types=['.csv', '.xls', '.xlsx'])
-                ########################################################################################################
-                with gr.Row(visible=False) as sheets:
-                    prev_sheet = gr.Radio(["N/A"], label="Sheets", info="Which sheet do you want to use?",
-                                          interactive=True)
-                    curr_sheet = gr.Radio(["N/A"], label="Sheets", info="Which sheet do you want to use?",
-                                          interactive=True)
-                ########################################################################################################
-                with gr.Row(visible=False) as data:
-                    prev_dis = gr.Dataframe(row_count=(5, "fixed"), col_count=(5, "fixed"), interactive=False)
-                    curr_dis = gr.Dataframe(row_count=(5, "fixed"), col_count=(5, "fixed"), interactive=False)
-                ########################################################################################################
-                with gr.Column(visible=False) as outputs:
-                    both = gr.Checkbox(label="See data that shows up in both months")
-                    first = gr.Checkbox(label="See data that's in the previous month but not in the current")
-                    second = gr.Checkbox(True, label="See data that's in the current month but not in the previous")
-                ########################################################################################################
-            # output ui
-            with gr.Blocks():
-                output = gr.Markdown("## *Download your files", visible=False)
-                with gr.Row(visible=False) as files:
-                    b = gr.File(label="Both months", visible=False)
-                    f = gr.File(label="Previous month", visible=False)
-                    s = gr.File(label="Current month", visible=False)
-                run = gr.Button("Generate files")
-
-                type.select(fn=choose_type, inputs=None, outputs=[uploads])
-                prev.upload(fn=check_prev, inputs=[prev], outputs=[prev_sheet, sheets])
-                curr.upload(fn=check_curr, inputs=[curr], outputs=[curr_sheet, sheets])
-                prev_sheet.select(fn=sheet_prev, inputs=[prev], outputs=[data, outputs, prev_dis])
-                curr_sheet.select(fn=sheet_curr, inputs=[curr], outputs=[data, outputs, curr_dis])
-                run.click(fn=generate, inputs=[prev, curr, both, first, second], outputs=[err_view, b, f, s, prev_dis,
-                                                                                          curr_dis, files])
-    demo.launch()
-
-
-# Press the green button in the gutter to run the script.
-if __name__ == '__main__':
-    ui_builder()
-
-# See PyCharm help at https://www.jetbrains.com/help/pycharm/

spaces/Amrrs/hubble-jwst-compare/app.py

@@ -1,53 +0,0 @@
-import streamlit as st
-from streamlit_image_comparison import image_comparison
-
-# set page config
-st.set_page_config(page_title="James Webb Space Telescope vs Hubble Telescope Images", layout="centered")
-
-st.title("James Webb vs Hubble Telescope Pictures")
-
-st.markdown("# Southern Nebula")
-
-# render image-comparison
-image_comparison(
-    img1="https://www.webbcompare.com/img/hubble/southern_nebula_700.jpg",
-    img2="https://www.webbcompare.com/img/webb/southern_nebula_700.jpg",
-    label1="Hubble",
-    label2="Webb"
-)
-
-
-st.markdown("# Galaxy Cluster SMACS 0723")
-
-# render image-comparison
-image_comparison(
-    img1="https://www.webbcompare.com/img/hubble/deep_field_700.jpg",
-    img2="https://www.webbcompare.com/img/webb/deep_field_700.jpg",
-    label1="Hubble",
-    label2="Webb"
-)
-
-
-st.markdown("# Carina Nebula")
-
-# render image-comparison
-image_comparison(
-    img1="https://www.webbcompare.com/img/hubble/carina_700.png",
-    img2="https://www.webbcompare.com/img/webb/carina_700.jpg",
-    label1="Hubble",
-    label2="Webb"
-)
-
-st.markdown("# Stephan's Quintet")
-
-# render image-comparison
-image_comparison(
-    img1="https://www.webbcompare.com/img/hubble/stephans_quintet_700.jpg",
-    img2="https://www.webbcompare.com/img/webb/stephans_quintet_700.jpg",
-    label1="Hubble",
-    label2="Webb"
-)
-
-
-
-st.caption("Inspiration Credit - https://www.webbcompare.com/")

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py

@@ -1,17 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["torch", "scipy"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "scipy"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "scipy"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "scipy"])

spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py

@@ -1,15 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch',
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py

@@ -1,13 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(plugins=[
-        dict(
-            cfg=dict(
-                type='GeneralizedAttention',
-                spatial_range=-1,
-                num_heads=8,
-                attention_type='1111',
-                kv_stride=2),
-            stages=(False, False, True, True),
-            position='after_conv2')
-    ]))

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/clip/__init__.py

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

spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/image_degradation/__init__.py

@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light

spaces/AnticPan/Clothes2Human/app.py

@@ -1,62 +0,0 @@
-import os
-import json
-import requests
-import gradio as gr
-from util import base64_to_img, img_to_base64, resize_image
-
-url = os.getenv("REQUEST_URL")
-headers = {'Content-Type': 'application/json',
-           'Validation-Key': os.getenv("VALIDATION_KEY")}
-names = ["input_image", "prompt", "neg_prompt", "maxlen", "step", "cfg", "seed", "up", "down", "left", "right"]
-def run(*params):
-    params = {k:v for k, v in zip(names, params)}
-    image = params.pop("input_image")
-    image = resize_image(image)
-    params["image_base64"] = img_to_base64(image)
-    try:
-        response = requests.post(url, headers=headers, data=json.dumps(params), timeout=30)
-        if response.status_code != 200:
-            raise ValueError()
-        data = response.json()
-    except:
-        raise gr.Error("Fail to generate")
-    if data["code"] != 0:
-        raise gr.Error(data["message"])
-    result = base64_to_img(data["content"])
-    return result
-
-with gr.Blocks() as demo:
-    gr.Markdown("# SDXL inpainting for Clothes2Human")
-    with gr.Row().style(equal_height=True):
-        with gr.Column():
-            input_image = gr.Image(type="pil", height=300)
-        with gr.Column():
-            output_image = gr.Image(type="pil", height=300)
-
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(label="Prompt")
-            neg_prompt = gr.Textbox(label="Negative Prompt")
-
-            maxlen = gr.Slider(label="Max Edge Length", step=32, minimum=768, maximum=1536, value=1024)
-            step = gr.Slider(label="Step", minimum=20, maximum=70, value=50, step=1)
-
-        with gr.Column():
-            up = gr.Slider(label="Scale Up Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
-            down = gr.Slider(label="Scale Down Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
-            left = gr.Slider(label="Scale Left Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
-            right = gr.Slider(label="Scale Right Image", minimum=-0.3, maximum=0.5, value=0, step=0.1)
-        with gr.Column():
-            cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=9.0, value=5.0, step=0.5)
-            seed = gr.Slider(label="Seed", minimum=-1, maximum=1000000, value=-1, step=1)
-            inpaint_button = gr.Button()
-
-            run_in = [input_image, prompt, neg_prompt, maxlen, step, cfg, seed, up, down, left, right]
-            inpaint_button.click(run, inputs=run_in, outputs=[output_image])
-
-    gr.Examples([["imgs/1.jpg","A man wearing a white T-shirt stands on the beach","", 1024, 50, 5.0, 333866, 0.3, 0.3, 0.1, 0.1],
-                 ["imgs/2.jpg"," woman wearing a blue dress stands in a park, asian race","", 1280, 50, 5.0, 443652, 0.3, 0.3, 0.2, 0.2],
-                 ["imgs/3.jpg","A woman wearing a white dress stands","", 1280, 50, 5.0, 306728, -0.1, -0.2, 0, 0]],
-                inputs=run_in, outputs=[output_image], fn=run, cache_examples=True)
-
-demo.queue(concurrency_count=2).launch()

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

@@ -1,141 +0,0 @@
-from typing import cast, List, Optional, Tuple, TYPE_CHECKING, Union
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        RenderableType,
-        RenderResult,
-    )
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .style import Style
-from .segment import Segment
-
-
-PaddingDimensions = Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int, int]]
-
-
-class Padding(JupyterMixin):
-    """Draw space around content.
-
-    Example:
-        >>> print(Padding("Hello", (2, 4), style="on blue"))
-
-    Args:
-        renderable (RenderableType): String or other renderable.
-        pad (Union[int, Tuple[int]]): Padding for top, right, bottom, and left borders.
-            May be specified with 1, 2, or 4 integers (CSS style).
-        style (Union[str, Style], optional): Style for padding characters. Defaults to "none".
-        expand (bool, optional): Expand padding to fit available width. Defaults to True.
-    """
-
-    def __init__(
-        self,
-        renderable: "RenderableType",
-        pad: "PaddingDimensions" = (0, 0, 0, 0),
-        *,
-        style: Union[str, Style] = "none",
-        expand: bool = True,
-    ):
-        self.renderable = renderable
-        self.top, self.right, self.bottom, self.left = self.unpack(pad)
-        self.style = style
-        self.expand = expand
-
-    @classmethod
-    def indent(cls, renderable: "RenderableType", level: int) -> "Padding":
-        """Make padding instance to render an indent.
-
-        Args:
-            renderable (RenderableType): String or other renderable.
-            level (int): Number of characters to indent.
-
-        Returns:
-            Padding: A Padding instance.
-        """
-
-        return Padding(renderable, pad=(0, 0, 0, level), expand=False)
-
-    @staticmethod
-    def unpack(pad: "PaddingDimensions") -> Tuple[int, int, int, int]:
-        """Unpack padding specified in CSS style."""
-        if isinstance(pad, int):
-            return (pad, pad, pad, pad)
-        if len(pad) == 1:
-            _pad = pad[0]
-            return (_pad, _pad, _pad, _pad)
-        if len(pad) == 2:
-            pad_top, pad_right = cast(Tuple[int, int], pad)
-            return (pad_top, pad_right, pad_top, pad_right)
-        if len(pad) == 4:
-            top, right, bottom, left = cast(Tuple[int, int, int, int], pad)
-            return (top, right, bottom, left)
-        raise ValueError(f"1, 2 or 4 integers required for padding; {len(pad)} given")
-
-    def __repr__(self) -> str:
-        return f"Padding({self.renderable!r}, ({self.top},{self.right},{self.bottom},{self.left}))"
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        style = console.get_style(self.style)
-        if self.expand:
-            width = options.max_width
-        else:
-            width = min(
-                Measurement.get(console, options, self.renderable).maximum
-                + self.left
-                + self.right,
-                options.max_width,
-            )
-        render_options = options.update_width(width - self.left - self.right)
-        if render_options.height is not None:
-            render_options = render_options.update_height(
-                height=render_options.height - self.top - self.bottom
-            )
-        lines = console.render_lines(
-            self.renderable, render_options, style=style, pad=True
-        )
-        _Segment = Segment
-
-        left = _Segment(" " * self.left, style) if self.left else None
-        right = (
-            [_Segment(f'{" " * self.right}', style), _Segment.line()]
-            if self.right
-            else [_Segment.line()]
-        )
-        blank_line: Optional[List[Segment]] = None
-        if self.top:
-            blank_line = [_Segment(f'{" " * width}\n', style)]
-            yield from blank_line * self.top
-        if left:
-            for line in lines:
-                yield left
-                yield from line
-                yield from right
-        else:
-            for line in lines:
-                yield from line
-                yield from right
-        if self.bottom:
-            blank_line = blank_line or [_Segment(f'{" " * width}\n', style)]
-            yield from blank_line * self.bottom
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        max_width = options.max_width
-        extra_width = self.left + self.right
-        if max_width - extra_width < 1:
-            return Measurement(max_width, max_width)
-        measure_min, measure_max = Measurement.get(console, options, self.renderable)
-        measurement = Measurement(measure_min + extra_width, measure_max + extra_width)
-        measurement = measurement.with_maximum(max_width)
-        return measurement
-
-
-if __name__ == "__main__":  #  pragma: no cover
-    from pip._vendor.rich import print
-
-    print(Padding("Hello, World", (2, 4), style="on blue"))

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py

@@ -1,238 +0,0 @@
-"""distutils.command.install_lib
-
-Implements the Distutils 'install_lib' command
-(install all Python modules)."""
-
-import os
-import importlib.util
-import sys
-
-from distutils.core import Command
-from distutils.errors import DistutilsOptionError
-
-
-# Extension for Python source files.
-PYTHON_SOURCE_EXTENSION = ".py"
-
-
-class install_lib(Command):
-
-    description = "install all Python modules (extensions and pure Python)"
-
-    # The byte-compilation options are a tad confusing.  Here are the
-    # possible scenarios:
-    #   1) no compilation at all (--no-compile --no-optimize)
-    #   2) compile .pyc only (--compile --no-optimize; default)
-    #   3) compile .pyc and "opt-1" .pyc (--compile --optimize)
-    #   4) compile "opt-1" .pyc only (--no-compile --optimize)
-    #   5) compile .pyc and "opt-2" .pyc (--compile --optimize-more)
-    #   6) compile "opt-2" .pyc only (--no-compile --optimize-more)
-    #
-    # The UI for this is two options, 'compile' and 'optimize'.
-    # 'compile' is strictly boolean, and only decides whether to
-    # generate .pyc files.  'optimize' is three-way (0, 1, or 2), and
-    # decides both whether to generate .pyc files and what level of
-    # optimization to use.
-
-    user_options = [
-        ('install-dir=', 'd', "directory to install to"),
-        ('build-dir=', 'b', "build directory (where to install from)"),
-        ('force', 'f', "force installation (overwrite existing files)"),
-        ('compile', 'c', "compile .py to .pyc [default]"),
-        ('no-compile', None, "don't compile .py files"),
-        (
-            'optimize=',
-            'O',
-            "also compile with optimization: -O1 for \"python -O\", "
-            "-O2 for \"python -OO\", and -O0 to disable [default: -O0]",
-        ),
-        ('skip-build', None, "skip the build steps"),
-    ]
-
-    boolean_options = ['force', 'compile', 'skip-build']
-    negative_opt = {'no-compile': 'compile'}
-
-    def initialize_options(self):
-        # let the 'install' command dictate our installation directory
-        self.install_dir = None
-        self.build_dir = None
-        self.force = 0
-        self.compile = None
-        self.optimize = None
-        self.skip_build = None
-
-    def finalize_options(self):
-        # Get all the information we need to install pure Python modules
-        # from the umbrella 'install' command -- build (source) directory,
-        # install (target) directory, and whether to compile .py files.
-        self.set_undefined_options(
-            'install',
-            ('build_lib', 'build_dir'),
-            ('install_lib', 'install_dir'),
-            ('force', 'force'),
-            ('compile', 'compile'),
-            ('optimize', 'optimize'),
-            ('skip_build', 'skip_build'),
-        )
-
-        if self.compile is None:
-            self.compile = True
-        if self.optimize is None:
-            self.optimize = False
-
-        if not isinstance(self.optimize, int):
-            try:
-                self.optimize = int(self.optimize)
-                if self.optimize not in (0, 1, 2):
-                    raise AssertionError
-            except (ValueError, AssertionError):
-                raise DistutilsOptionError("optimize must be 0, 1, or 2")
-
-    def run(self):
-        # Make sure we have built everything we need first
-        self.build()
-
-        # Install everything: simply dump the entire contents of the build
-        # directory to the installation directory (that's the beauty of
-        # having a build directory!)
-        outfiles = self.install()
-
-        # (Optionally) compile .py to .pyc
-        if outfiles is not None and self.distribution.has_pure_modules():
-            self.byte_compile(outfiles)
-
-    # -- Top-level worker functions ------------------------------------
-    # (called from 'run()')
-
-    def build(self):
-        if not self.skip_build:
-            if self.distribution.has_pure_modules():
-                self.run_command('build_py')
-            if self.distribution.has_ext_modules():
-                self.run_command('build_ext')
-
-    def install(self):
-        if os.path.isdir(self.build_dir):
-            outfiles = self.copy_tree(self.build_dir, self.install_dir)
-        else:
-            self.warn(
-                "'%s' does not exist -- no Python modules to install" % self.build_dir
-            )
-            return
-        return outfiles
-
-    def byte_compile(self, files):
-        if sys.dont_write_bytecode:
-            self.warn('byte-compiling is disabled, skipping.')
-            return
-
-        from distutils.util import byte_compile
-
-        # Get the "--root" directory supplied to the "install" command,
-        # and use it as a prefix to strip off the purported filename
-        # encoded in bytecode files.  This is far from complete, but it
-        # should at least generate usable bytecode in RPM distributions.
-        install_root = self.get_finalized_command('install').root
-
-        if self.compile:
-            byte_compile(
-                files,
-                optimize=0,
-                force=self.force,
-                prefix=install_root,
-                dry_run=self.dry_run,
-            )
-        if self.optimize > 0:
-            byte_compile(
-                files,
-                optimize=self.optimize,
-                force=self.force,
-                prefix=install_root,
-                verbose=self.verbose,
-                dry_run=self.dry_run,
-            )
-
-    # -- Utility methods -----------------------------------------------
-
-    def _mutate_outputs(self, has_any, build_cmd, cmd_option, output_dir):
-        if not has_any:
-            return []
-
-        build_cmd = self.get_finalized_command(build_cmd)
-        build_files = build_cmd.get_outputs()
-        build_dir = getattr(build_cmd, cmd_option)
-
-        prefix_len = len(build_dir) + len(os.sep)
-        outputs = []
-        for file in build_files:
-            outputs.append(os.path.join(output_dir, file[prefix_len:]))
-
-        return outputs
-
-    def _bytecode_filenames(self, py_filenames):
-        bytecode_files = []
-        for py_file in py_filenames:
-            # Since build_py handles package data installation, the
-            # list of outputs can contain more than just .py files.
-            # Make sure we only report bytecode for the .py files.
-            ext = os.path.splitext(os.path.normcase(py_file))[1]
-            if ext != PYTHON_SOURCE_EXTENSION:
-                continue
-            if self.compile:
-                bytecode_files.append(
-                    importlib.util.cache_from_source(py_file, optimization='')
-                )
-            if self.optimize > 0:
-                bytecode_files.append(
-                    importlib.util.cache_from_source(
-                        py_file, optimization=self.optimize
-                    )
-                )
-
-        return bytecode_files
-
-    # -- External interface --------------------------------------------
-    # (called by outsiders)
-
-    def get_outputs(self):
-        """Return the list of files that would be installed if this command
-        were actually run.  Not affected by the "dry-run" flag or whether
-        modules have actually been built yet.
-        """
-        pure_outputs = self._mutate_outputs(
-            self.distribution.has_pure_modules(),
-            'build_py',
-            'build_lib',
-            self.install_dir,
-        )
-        if self.compile:
-            bytecode_outputs = self._bytecode_filenames(pure_outputs)
-        else:
-            bytecode_outputs = []
-
-        ext_outputs = self._mutate_outputs(
-            self.distribution.has_ext_modules(),
-            'build_ext',
-            'build_lib',
-            self.install_dir,
-        )
-
-        return pure_outputs + bytecode_outputs + ext_outputs
-
-    def get_inputs(self):
-        """Get the list of files that are input to this command, ie. the
-        files that get installed as they are named in the build tree.
-        The files in this list correspond one-to-one to the output
-        filenames returned by 'get_outputs()'.
-        """
-        inputs = []
-
-        if self.distribution.has_pure_modules():
-            build_py = self.get_finalized_command('build_py')
-            inputs.extend(build_py.get_outputs())
-
-        if self.distribution.has_ext_modules():
-            build_ext = self.get_finalized_command('build_ext')
-            inputs.extend(build_ext.get_outputs())
-
-        return inputs

spaces/Atualli/yoloxTeste/checkYolox.sh

@@ -1,17 +0,0 @@
-#!/bin/sh
-export path=/home/atualli/.local/lib/python3.8/site-packages:$PATH
-cd ~/Projetos/huggingface/yoloxTeste
-SERVER=192.168.0.153
-PORT=8080
-
-if lsof -Pi :$PORT -sTCP:LISTEN -t >/dev/null ; then
-    echo "running"
-else
-    ./telegramCrise.sh "reiniciando_yolox_linux_192.168.0.153:8080"
-    pkill -f app.py
-    #rm -r /tmp/tmp1*.png
-    python app.py &
-    echo "not running"
-fi
-
-

spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/__init__.py

@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert

spaces/Benson/text-generation/Examples/Cmo Descargar El Tiempo De Juego Del Proyecto En Steam.md

@@ -1,123 +0,0 @@
-<br />
-<h1>Cómo descargar el tiempo de reproducción del proyecto en Steam</h1>
-<p>¿Te gustan los juegos de terror? ¿Te gusta jugar con tus amigos o extraños en línea? ¿Quieres experimentar un juego emocionante y aterrador que te mantendrá al borde de tu asiento? Si respondiste afirmativamente a cualquiera de estas preguntas, deberías probar Project Playtime, un juego multijugador gratuito de terror que está disponible en Steam. En este artículo, te mostraremos cómo descargar y jugar a Project Playtime en Steam, además de darte algunos consejos y trucos para sobrevivir como sobreviviente o monstruo. </p>
- <h2>¿Qué es Project Playtime? </h2>
-<p>Project Playtime es un juego de terror multijugador donde seis jugadores intentan crear un juguete gigante mientras sobreviven a un monstruo aterrador que deambula por la fábrica de juguetes. Un séptimo jugador controla al monstruo y solo tiene un objetivo: Encontrar y matar a todos. El juego fue lanzado el 12 de diciembre de 2022 por Mob Entertainment, un estudio de juegos indie con sede en Texas. El juego ha recibido críticas muy positivas de jugadores y críticos por igual, alabando su jugabilidad, gráficos, diseño de sonido y atmósfera. </p>
-<h2>cómo descargar el tiempo de juego del proyecto en Steam</h2><br /><p><b><b>Download</b> &#10003;&#10003;&#10003; <a href="https://bltlly.com/2v6Mnl">https://bltlly.com/2v6Mnl</a></b></p><br /><br />
- <h3>¿Por qué deberías jugar Project Playtime? </h3>
-<p>Hay muchas razones por las que deberías jugar a Project Playtime si eres un fan de los juegos de terror. Estas son algunas de ellas:</p>
-<ul>
-<li>El juego es gratuito. No necesitas pagar nada para descargar y jugar el juego. También puedes ganar boletos jugando partidos, completando logros y abriendo cajas de juguetes. Puedes usar estos boletos para comprar cosméticos, beneficios, sabotajes y otros artículos en la tienda. </li>
-<li>El juego es multijugador. Puede jugar con sus amigos o unirse a grupos de presión al azar en línea. También puedes chatear con otros jugadores usando chat de voz o de texto. Puedes elegir jugar como un sobreviviente o un monstruo, cada uno con sus propios roles, habilidades y estrategias. </li>
-
-<li>El juego es divertido. El juego tiene mucho valor de repetición porque cada partido es diferente y desafiante. El juego tiene una gran variedad y opciones de personalización. Puedes elegir entre diferentes monstruos, sobrevivientes, beneficios, sabotajes, mapas, modos y configuraciones. También puedes desbloquear nuevos objetos y logros mientras juegas. </li>
-</ul>
-<p>Así que, si estás buscando un juego de terror que sea gratuito, multijugador y divertido, definitivamente deberías probar Project Playtime. </p>
- <h2>Cómo obtener una cuenta de Steam e instalar Steam</h2>
-<p>Antes de poder descargar y jugar Project Playtime en Steam, necesitas tener una cuenta de Steam e instalar el cliente de Steam en tu computadora. Estos son los pasos para hacerlo:</p>
-<ol>
-<li>Vaya a <a href=">https://store.steampowered.com/join/</a> y haga clic en el botón "Join Steam". </li>
-<li>Rellene su dirección de correo electrónico, contraseña, país y código captcha. De acuerdo con los términos del servicio y la política de privacidad. Haga clic en el botón "Continuar". </li>
-<li>Revisa tu correo electrónico para ver un código de verificación de Steam. Introduce el código en el sitio web y haz clic en el botón "Crear mi cuenta". </li>
-<li>Felicidades! Has creado tu cuenta de Steam. Ahora puedes iniciar sesión en Steam con tu correo electrónico y contraseña. </li>
-<li>Vaya a <a href=">https://store.steampowered.com/about/</a> y haga clic en el botón "Instalar vapor". </li>
-<li>Descargue el instalador de Steam para su sistema operativo (Windows, Mac o Linux). </li>
-<li>Ejecute el instalador y siga las instrucciones para instalar Steam en su computadora. </li>
-<li>Inicia Steam e inicia sesión con tu cuenta de Steam. </li>
-<li>Ahora estás listo para descargar y jugar Project Playtime en Steam.</li>
-</ol>
- <h2>Cómo encontrar y descargar Project Playtime en Steam</h2>
-<p>Ahora que tienes una cuenta de Steam e has instalado el cliente de Steam, puedes encontrar y descargar Project Playtime en Steam. Estos son los pasos para hacerlo:</p>
-<ol>
-<li>Abre el cliente de Steam y ve a la pestaña "Tienda". </li>
-
-<li>Verás la página del juego en la tienda de Steam. Haz clic en el botón "Jugar". </li>
-<li>Aparecerá una ventana emergente pidiéndole que instale Project Playtime. Haga clic en el botón "Next". </li>
-<li>Seleccione la carpeta de destino donde desea instalar el juego. Haga clic en el botón "Next". </li>
-<li> Se iniciará el proceso de descarga. Puede ver el progreso y la velocidad de la descarga en la pestaña "Descargas". </li>
-<li>Espere a que termine la descarga. Puede tomar algún tiempo dependiendo de su conexión a Internet y espacio en disco. </li>
-<li>Una vez que la descarga se haya completado, verá un mensaje que dice "Project Playtime ya está listo para jugar". Haga clic en el botón "Play". </li>
-<li>Has descargado e instalado correctamente el Project Playtime en Steam. ¡Disfruta! </li>
-</ol>
- <h2>Cómo jugar Project Playtime en Steam</h2>
-<p>Ahora que has descargado e instalado Project Playtime en Steam, puedes empezar a reproducirlo. Estos son los pasos para hacerlo:</p>
-<ol>
-<li>Iniciar tiempo de reproducción del proyecto desde la biblioteca de Steam o desde el acceso directo del escritorio. </li>
-<li>Verás el menú principal del juego. Puedes acceder a diferentes opciones como configuración, tienda, logros, perfil, etc.</li>
-<li>Para empezar a jugar, haga clic en el botón "Play". Verá dos opciones: "Quick Match" y "Custom Match". </li>
-<li>Si desea unirse a un lobby aleatorio en línea, haga clic en "Quick Match". Se le emparejará con otros jugadores en función de su región y preferencias. Puedes elegir jugar como sobreviviente o monstruo, o dejar que el juego decida por ti al azar. </li>
-
-<li>Una vez que estás en un lobby, puedes chatear con otros jugadores usando chat de voz o texto. También puedes cambiar la apariencia de tu personaje haciendo clic en el botón "Personalizar". Puedes equipar diferentes cosméticos, beneficios, sabotajes, etc. que hayas comprado o ganado en la tienda. También puede cambiar su rol haciendo clic en el botón "Rol". Puedes elegir jugar como sobreviviente o monstruo, o dejar que el juego decida por ti al azar. </li>
-<li>Cuando todo el mundo está listo, el anfitrión puede iniciar el partido haciendo clic en el botón "Inicio". El juego cargará el mapa y el modo que se seleccionaron. </li>
-<li>El partido comenzará con una corta escena que presenta la historia y el objetivo del juego. Los supervivientes aparecerán en un lugar aleatorio en la fábrica de juguetes. El monstruo aparecerá en una habitación oculta cercana. </li>
-<li>El objetivo de los supervivientes es encontrar y recoger seis piezas de juguete que están dispersas por el mapa. Necesitan llevarlos a una máquina de juguete gigante y montarlos juntos. También necesitan resolver rompecabezas, evitar trampas y esconderse del monstruo. Los sobrevivientes tienen una salud limitada, resistencia y batería de linterna. Pueden usar beneficios y sabotajes para ayudarles a escapar. </li>
-El objetivo del monstruo es encontrar y matar a todos los supervivientes antes de que completen su objetivo. El monstruo puede usar diferentes habilidades, como correr, rugir, aplastar, etc. El monstruo también puede usar beneficios y sabotajes para obstaculizar el progreso de los sobrevivientes y atraparlos. </li>
-<li>El combate terminará cuando los supervivientes completen su objetivo y escapen, o el monstruo mate a todos los supervivientes. El juego mostrará los resultados del partido, como quién ganó, quién murió, quién escapó, etc. El juego también otorgará entradas a cada jugador en función de su rendimiento. </li>
-<li> Puede jugar otro partido haciendo clic en el botón "Revancha", o volver al menú principal haciendo clic en el botón "Dejar". </li>
-</ol>
- <h2>Consejos y trucos para jugar Project Playtime</h2>
-
- <h3>Cómo trabajar juntos como un sobreviviente</h3>
-<p>Como sobreviviente, necesitas cooperar con tus compañeros sobrevivientes para escapar del monstruo y completar tu objetivo. Aquí hay algunas maneras de trabajar juntos como un sobreviviente:</p>
-<ul>
-<li>Comunícate con tus compañeros de equipo mediante chat de voz o de texto. Puedes compartir información, advertirse, pedir ayuda, etc.</li>
-<li>Manténganse juntos tanto como sea posible. Es más probable que sobrevivan si tienen a alguien cuidando su espalda. También pueden ayudarse mutuamente con rompecabezas, trampas y sanación. </li>
-<li>Sepárate cuando sea necesario. A veces necesitas cubrir más terreno o distraer al monstruo. También puede utilizar diferentes ventajas y sabotajes para coordinar sus acciones. </li>
-<li>Sea consciente de su entorno. Necesita saber dónde están las piezas de juguete, rompecabezas, salidas, escondites, etc. . También debes estar atento a pistas, ruidos, sombras, etc. que indiquen dónde está el monstruo. </li>
-<li>Sé inteligente y sigiloso. Debes evitar hacer ruido o dejar rastros que puedan alertar al monstruo. También necesita usar su linterna sabiamente y esconderse cuando sea necesario. </li>
-</ul>
- <h3>Cómo usar beneficios y sabotajes como sobreviviente</h3>
-<p>Como sobreviviente, puedes usar diferentes beneficios y sabotajes para obtener una ventaja sobre el monstruo. Aquí hay algunos ejemplos de ventajas y sabotajes que puedes usar como sobreviviente:</p>
-<ul>
-<li>Las ventajas son habilidades pasivas que le dan beneficios como el aumento de la salud, la resistencia, la velocidad, etc. Puede equipar hasta tres ventajas a la vez en el menú personalizado. </li>
-<li>Los sabotajes son habilidades activas que te permiten interactuar con objetos en el mapa como puertas, interruptores, respiraderos, etc. Puedes usar sabotajes para bloquear, atrapar o distraer al monstruo. Puede equipar hasta dos sabotajes a la vez en el menú personalizado. </li>
-
-<li>Algunos beneficios y sabotajes tienen tiempos de reutilización o cargos que limitan la frecuencia con la que puede usarlos. Por ejemplo, solo puedes usar el beneficio "Segunda Oportunidad" una vez por partido para revivirte después de ser derribado por el monstruo. Solo puedes usar el sabotaje "Firecracker" tres veces por partido para crear un ruido fuerte que atraiga o espante al monstruo. </li>
-<li>Puedes comprar nuevos beneficios y sabotajes en la tienda con entradas que ganas jugando partidos, completando logros y abriendo cajas de juguetes. </li>
-</ul>
- <h3>Cómo cazar sobrevivientes como un monstruo</h3>
-<p>Como monstruo, necesitas usar tus sentidos, habilidades y estrategias para encontrar y matar a todos los supervivientes antes de que escapen. Aquí hay algunas maneras de cazar sobrevivientes como un monstruo:</p>
-<p></p>
-<ul>
-<li>Usa tu visión, oído y olor para localizar a los sobrevivientes. Puedes ver sus huellas, escuchar sus ruidos y oler su sangre. También puede ver sus rayos de linterna y siluetas. </li>
-<li>Usa tus habilidades para perseguir, atacar y matar a los supervivientes. Puedes correr, rugir, aplastar y morder. También puedes usar habilidades especiales que son únicas para cada monstruo. Por ejemplo, el Payaso puede lanzar pasteles que los sobrevivientes ciegos, la Muñeca puede teletransportarse a las muñecas cercanas, y el Teddy puede transformarse en un oso gigante. </li>
-<li>Usa tus beneficios y sabotajes para obstaculizar, atrapar y asustar a los sobrevivientes. Puede equipar hasta tres ventajas y dos sabotajes a la vez en el menú personalizado. Los beneficios son habilidades pasivas que te dan beneficios como mayor velocidad, daño, salud, etc. Los sabotajes son habilidades activas que te permiten interactuar con objetos en el mapa como puertas, interruptores, respiraderos, etc. Puedes usar sabotajes para bloquear, atrapar o distraer a los sobrevivientes. </li>
-
-<li>Algunos beneficios y sabotajes tienen tiempos de reutilización o cargos que limitan la frecuencia con la que puede usarlos. Por ejemplo, solo puedes usar el beneficio "Rage" una vez por partido para aumentar tu daño y velocidad por un corto tiempo. Solo puedes usar el sabotaje "Blackout" tres veces por partido para apagar todas las luces del mapa durante unos segundos. </li>
-<li>Puedes comprar nuevos beneficios y sabotajes en la tienda con entradas que ganas jugando partidos, completando logros y abriendo cajas de juguetes. </li>
-</ul>
- <h2>Cómo personalizar tu personaje y jugabilidad en Project Playtime</h2>
-<p>Project Playtime te permite personalizar tu personaje y jugabilidad de varias maneras. Puede acceder a la tienda, comprar cosméticos, beneficios, sabotajes y otros artículos con boletos. También puedes cambiar la configuración, como gráficos, sonido, controles, etc. Estas son algunas formas de personalizar tu personaje y el modo de juego en Project Playtime:</p>
- <h3>Cómo ganar entradas en Project Playtime</h3>
-<p>Las entradas son la moneda de Project Playtime. Puede utilizar las entradas para comprar artículos en la tienda. Aquí hay algunas maneras de ganar tickets en Project Playtime:</p>
-<ul>
-<li>Juega partidos. Ganarás tickets según tu rendimiento en cada partido. La cantidad de tickets que ganes dependerá de factores como tu rol, tu puntuación, el resultado de tu equipo, etc.</li>
-<li>Logros completos. Usted ganará boletos para completar varios logros en el juego. Los logros son desafíos que requieren que hagas tareas específicas o alcances ciertos hitos en el juego. Por ejemplo, puedes ganar un logro por escapar como sobreviviente 10 veces o matar a 50 sobrevivientes como monstruo. </li>
-<li>Abre cajas de juguetes. Ganarás entradas para abrir cajas de juguetes que encuentres en el mapa o recibirás como recompensas. Las cajas de juguetes son contenedores que contienen artículos aleatorios como cosméticos, beneficios, sabotajes, etc. Puede abrir cajas de juguetes haciendo clic en ellas en la tienda o en su inventario. </li>
-</ul>
- <h3>Cómo gastar entradas en Project Playtime</h3>
-
-<ul>
-<li>Navegar por la tienda. Puede acceder a la tienda haciendo clic en el botón "Almacenar" en el menú principal o en el lobby. Puede navegar por diferentes categorías de artículos como cosméticos, beneficios, sabotajes, etc. Puede ver el nombre, la descripción, el precio y la vista previa de cada artículo. También puede filtrar elementos por rol, rareza, tipo, etc.</li>
-<li>Comprar artículos. Puede comprar artículos haciendo clic en el botón "Comprar" junto al artículo que desee. Verá una ventana de confirmación que le pedirá que confirme su compra. También puede ver cuántas entradas tiene y cuántas entradas gastará. Haga clic en el botón "Confirmar" para completar su compra. </li>
-<li>Equipar artículos. Puede equipar artículos haciendo clic en el botón "Personalizar" en el vestíbulo o en la tienda. Puedes ver la apariencia y las estadísticas de tu personaje. También puedes ver los artículos que has comprado o ganado en tu inventario. Puede equipar los elementos arrastrando y soltando a las ranuras correspondientes. Puede equipar hasta tres cosméticos, tres beneficios y dos sabotajes a la vez. También puede desigualizar elementos arrastrándolos y soltándolos en la papelera. </li>
-<li>Usa elementos. Puedes usar elementos jugando con tu personaje personalizado. Puedes ver los elementos equipados en el menú del partido o en la pantalla del juego. Puede usar beneficios y sabotajes presionando los botones o teclas correspondientes. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Project Playtime:</p>
-<ul>
-<li><b>Q: ¿Está libre el Project Playtime? </b></li>
-<li>A: Sí, Project Playtime es gratuito. No necesitas pagar nada para descargar y jugar el juego. </li>
-<li><b>Q: ¿Es multijugador Project Playtime? </b></li>
-<li>A: Sí, Project Playtime es multijugador. Puedes jugar con tus amigos o unirte a grupos de presión aleatorios en línea. </li>
-<li><b>Q: ¿Es Project Playtime horror? </b></li>
-<li>A: Sí, Project Playtime es horror. El juego tiene una atmósfera oscura y espeluznante que te hará sentir incómodo y asustado. </li>
-<li><b>Q: ¿Cuántos jugadores pueden jugar Project Playtime? </b></li>
-<li>A: Project Playtime admite hasta siete jugadores por partido. Seis jugadores juegan como supervivientes y un jugador juega como un monstruo. </li>
-<li><b>Q: ¿Cuánto tiempo es una coincidencia en Project Playtime? </b></li>
-<li>A: Un partido en Project Playtime dura de 10 a 15 minutos, dependiendo del mapa, el modo, la dificultad y las habilidades de los jugadores. </li>
-</ul></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descargar 3d Fondo De Pantalla En Vivo.md

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-<h1>Descargar 3D Live Wallpaper: Cómo hacer que su escritorio cobre vida</h1>
-<p>¿Quieres darle vida a tu escritorio con algunas imágenes impresionantes? ¿Quieres hacer tu computadora más personalizada e interactiva? ¿Quieres divertirte y divertirte mientras trabajas o estudias? Si respondiste sí a cualquiera de estas preguntas, entonces deberías intentar descargar fondos de escritorio en 3D. </p>
-<h2>descargar 3d fondo de pantalla en vivo</h2><br /><p><b><b>Download File</b> &#10037; <a href="https://bltlly.com/2v6MlN">https://bltlly.com/2v6MlN</a></b></p><br /><br />
-<p>3D live wallpaper es un tipo de papel pintado animado que utiliza gráficos tridimensionales para crear escenas realistas e inmersivas en su pantalla. A diferencia de los fondos de pantalla estáticos, los fondos de pantalla en vivo en 3D pueden moverse, cambiar y reaccionar a sus acciones. Puedes elegir entre una variedad de temas y estilos, como naturaleza, fantasía, ciencia ficción, anime, películas, juegos y más. También puede crear su propio fondo de pantalla en vivo en 3D utilizando imágenes, vídeos, sitios web o aplicaciones. </p>
-<p>En este artículo, le mostraremos cómo descargar fondos de escritorio en vivo en 3D para su escritorio y cómo usarlo de manera efectiva. También compartiremos algunos de los beneficios de usar fondos de escritorio en vivo en 3D y responderemos algunas preguntas comunes al respecto. Al final de este artículo, podrás hacer que tu escritorio cobre vida con un increíble fondo de pantalla en vivo en 3D. </p>
- <h2>¿Qué son los fondos de pantalla en vivo en 3D? </h2>
-<p>Como su nombre indica, 3D live wallpaper es un tipo de fondo de pantalla que utiliza gráficos tridimensionales para crear escenas dinámicas y realistas en la pantalla. A diferencia de los fondos de pantalla normales, que son solo imágenes que permanecen inmóviles en su fondo, el fondo de pantalla en vivo en 3D puede moverse, cambiar e interactuar con su ratón o teclado. Por ejemplo, puede tener un fondo de pantalla en vivo en 3D de un bosque que cambia con las estaciones, o un fondo de pantalla en vivo en 3D de una nave espacial que vuela por el espacio. </p>
-
-<p>Algunos ejemplos de temas populares de fondos de escritorio en vivo en 3D son:</p>
-<p></p>
-<ul>
-<li>Naturaleza: Usted puede tener un fondo de pantalla en vivo 3D de una cascada, una montaña, una playa, un bosque, o cualquier otro paisaje natural que te gusta. </li>
-<li>Fantasía: Puedes tener un fondo de pantalla en vivo en 3D de un dragón, un unicornio, un hada, un castillo o cualquier otra criatura de fantasía o escenario que te guste. </li>
-<li>Ciencia ficción: Puedes tener un fondo de pantalla en 3D de una nave espacial, un robot, un planeta alienígena, una ciudad futurista o cualquier otro elemento de ciencia ficción que te guste. </li>
-<li>Anime: Puede tener un fondo de pantalla en 3D en vivo de su personaje o escena de anime favorito de un espectáculo de anime o película. </li>
-<li>Películas: Puedes tener un fondo de pantalla en 3D de tu personaje o escena de película favorita de una película que te guste. </li>
-<li>Juegos: Puedes tener un fondo de pantalla en 3D en vivo de tu personaje o escena favorita de un juego que te guste. </li>
-</ul>
-<p>Estos son solo algunos de los ejemplos de temas de fondos de escritorio en vivo en 3D que puedes encontrar en línea. Hay muchas más opciones y categorías que puedes explorar y descargar. </p>
- <h2>¿Por qué utilizar fondos de pantalla en vivo 3D? </h2>
-<p>Ahora que sabes lo que son los fondos de pantalla en vivo en 3D, es posible que te estés preguntando por qué deberías usarlos en tu escritorio. Estos son algunos de los beneficios de usar fondos de pantalla en vivo en 3D:</p>
-<ul>
-<li>Personalización: Puede personalizar su escritorio con fondos de pantalla en vivo en 3D que se adapten a sus preferencias, personalidad, estado de ánimo o intereses. También puede crear sus propios fondos de pantalla en 3D usando sus propias imágenes, videos, sitios web o aplicaciones. </li>
-<li>Interactividad: Puedes interactuar con tus fondos de pantalla en 3D usando tu ratón o teclado. También puede ajustar la configuración y las características de sus fondos de pantalla en vivo en 3D para hacerlos más receptivos e interactivos. </li>
-<li>Entretenimiento: Puede disfrutar viendo sus fondos de pantalla en vivo en 3D a medida que se mueven, cambian y reaccionan a sus acciones. También puede divertirse jugando con sus fondos de pantalla en vivo en 3D, ya que ofrecen varios efectos y animaciones. </li>
-</ul>
-
- <h2>¿Cómo descargar fondos de pantalla en vivo en 3D? </h2>
-<p>Si desea descargar fondos de pantalla en vivo en 3D para su escritorio, tiene varias fuentes y métodos para elegir. Estos son algunos de los más populares y confiables:</p>
- <h3>MoeWalls</h3>
-<p>MoeWalls es un sitio web que ofrece populares fondos de pantalla en vivo gratuitos, fondos de pantalla animados y videos para su escritorio. Puede navegar a través de varias categorías y géneros de fondos de pantalla en vivo en 3D, como anime, juegos, películas, naturaleza, fantasía, ciencia ficción y más. También puede buscar palabras clave específicas o títulos de fondos de pantalla en 3D que desea descargar. </p>
-<p>Para descargar fondos de pantalla en vivo en 3D de MoeWalls, debe seguir estos pasos:</p>
-<ol>
-<li>Ir a <a href=">MoeWalls.com</a>. </li>
-<li>Seleccione la categoría o género de fondo de pantalla en vivo en 3D que desea descargar. </li>
-<li>Elija el fondo de pantalla en vivo en 3D que desee de la lista de resultados. </li>
-<li>Haga clic en el botón de descarga debajo de la vista previa del fondo de pantalla en vivo 3D. </li>
-<li>Guarde el archivo en su computadora. </li>
-</ol>
-<p>A continuación, puede utilizar el archivo como fondo de escritorio o utilizar un software como Wallpaper Engine para ejecutarlo como fondo de pantalla en vivo. </p>
- <h3>Motor de fondos de pantalla</h3>
-<p>Wallpaper Engine es un software que le permite utilizar fondos de pantalla en vivo en el escritorio de Windows. Puede utilizar varios tipos de fondos de pantalla en vivo, incluyendo animaciones 3D y 2D, sitios web, videos y aplicaciones. También puede crear sus propios fondos de pantalla en vivo utilizando el editor de Wallpaper Engine.</p>
-<p>Para descargar fondos de pantalla en vivo en 3D de Wallpaper Engine, debe seguir estos pasos:</p>
-<ol>
-<li>Ve a <a href=">Steam</a> y compra Wallpaper Engine por $4.99. </li>
-<li>Instalar Wallpaper Engine en su computadora. </li>
-<li>Inicie Wallpaper Engine y haga clic en la pestaña Taller. </li>
-<li>Seleccione la categoría o género de fondo de pantalla en vivo en 3D que desea descargar. </li>
-<li>Elija el fondo de pantalla en vivo en 3D que desee de la lista de resultados. </li>
-
-<li>El fondo de pantalla en vivo 3D se descargará y se agregará a su biblioteca de Wallpaper Engine. </li>
-</ol>
-<p>A continuación, puede seleccionar el fondo de pantalla en vivo 3D de su biblioteca y aplicarlo como fondo de escritorio. </p>
- <h3>Videos de Pexels</h3>
-<p>Pexels Videos es un sitio web que proporciona videos de stock gratuitos para uso personal y comercial. Usted puede encontrar varios tipos de vídeos en Pexels Videos, incluyendo fondos de escritorio videos 3D. Estos son videos que están diseñados para ser utilizados como fondos de escritorio con gráficos 3D realistas e inmersivos. </p>
-<p>Para descargar videos de escritorio en 3D de Pexels Videos, debe seguir estos pasos:</p>
-<ol>
-<li>Ir a <a href="">Pexels.com/videos</a>. </li>
-<li>Escriba "fondo de escritorio 3d" en el cuadro de búsqueda y pulse enter. </li>
-<li>Elija el vídeo que desee de la lista de resultados. </li>
-<li>Haga clic en el botón de descarga debajo de la vista previa del video. </li>
-<li>Guarde el archivo en su computadora. </li>
-</ol>
-<p>A continuación, puede utilizar el archivo como fondo de escritorio o utilizar un software como Wallpaper Engine para ejecutarlo como fondo de pantalla en vivo. </p>
- <h2>¿Cómo usar fondos de pantalla en vivo en 3D? </h2>
-<p>Una vez que haya descargado fondos de pantalla en vivo en 3D para su escritorio, es posible que desee saber cómo usarlos de manera efectiva. Aquí hay algunos consejos y trucos sobre cómo utilizar fondos de pantalla en vivo 3D en su escritorio:</p>
-<ul>
-<li>Configurarlos como fondo: Puede establecer fondos de pantalla en vivo 3D como fondo de escritorio haciendo clic derecho en el archivo y seleccionando "Establecer como fondo de escritorio". Alternativamente, puede utilizar un software como Wallpaper Engine para aplicar fondos de pantalla en vivo 3D como fondo de escritorio. Wallpaper Engine también le permite personalizar la configuración y las características de sus fondos de pantalla en vivo en 3D, tales como resolución, velocidad de fotogramas, sonido, rendimiento y más. </li>
-
-<li>Pausándolos cuando sea necesario: Puede pausar sus fondos de pantalla en vivo en 3D cuando necesite centrarse en otras tareas o ahorrar batería. Puede hacer esto haciendo clic derecho en el escritorio y seleccionando "Pausa" o "Detener" en el menú. Alternativamente, puede usar un software como Wallpaper Engine para pausar sus fondos de pantalla en vivo 3D automáticamente cuando está usando una aplicación de pantalla completa o cuando su computadora está inactiva. </li>
-</ul>
-<p>El uso de fondos de pantalla en vivo en 3D puede mejorar su experiencia de escritorio y hacerla más agradable. Sin embargo, también debe tener en cuenta los posibles inconvenientes de usar fondos de pantalla en vivo en 3D, como el aumento del uso de CPU y GPU, el consumo de batería y la distracción. También debe asegurarse de que su computadora cumple con los requisitos mínimos para ejecutar fondos de pantalla en vivo 3D sin problemas y sin retrasos. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, 3D live wallpaper es un tipo de papel pintado animado que utiliza gráficos tridimensionales para crear escenas realistas e inmersivas en la pantalla. Puede descargar fondos de escritorio en vivo en 3D de varias fuentes en línea, como MoeWalls, Wallpaper Engine y Pexels Videos. También puede usar fondos de escritorio en vivo en 3D de manera efectiva ajustando los ajustes y pausándolos cuando sea necesario. </p>
-<p>Si quieres hacer que tu escritorio cobre vida con un increíble fondo de pantalla en vivo en 3D, deberías intentar descargar algunos de ellos hoy. Usted se sorprenderá por lo mucho que pueden transformar su escritorio en un entorno impresionante e interactivo. También tendrás diversión y entretenimiento mientras trabajas o estudias con tu fondo de pantalla en 3D. </p>
-<p>Entonces, ¿qué estás esperando? Descargar 3D fondo de pantalla en vivo ahora y disfrutar! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas y respuestas más frecuentes sobre el fondo de pantalla en vivo 3D:</p>
-<ol>
-<li><b>¿Cuál es la diferencia entre el papel pintado en vivo en 3D y el papel pintado normal? </b><br>
-
-<li><b>¿Cuánto cuesta descargar fondos de escritorio en 3D? </b><br>
-Depende de la fuente y el tipo de fondo de pantalla en vivo 3D que desea descargar. Algunos de ellos son gratuitos, mientras que algunos de ellos requieren una cuota o una suscripción. Por ejemplo, MoeWalls y Pexels Videos ofrecen fondos de escritorio en vivo en 3D gratis, mientras que Wallpaper Engine cuesta $4.99. </li>
-<li><b>¿El uso de fondos de escritorio en vivo en 3D afecta el rendimiento de mi computadora? </b><br>
-Depende de la calidad y la complejidad del fondo de pantalla en vivo 3D que está utilizando. Algunos de ellos pueden consumir más recursos de CPU y GPU que otros, lo que puede afectar el rendimiento de su computadora. Puede reducir este impacto reduciendo la resolución o la velocidad de fotogramas de su fondo de pantalla 3D en vivo o deteniéndolo cuando no esté en uso. </li>
-<li><b>¿Puedo crear mi propio fondo de pantalla en vivo en 3D? </b><br>
-Sí, puede crear su propio fondo de pantalla en vivo en 3D utilizando imágenes, videos, sitios web o aplicaciones. Puede utilizar un software como Wallpaper Engine para crear y editar su propio fondo de pantalla en vivo 3D utilizando su editor incorporado. </li>
-<li><b>¿Puedo usar fondos de escritorio en vivo en 3D en otros dispositivos además de mi escritorio? </b><br>
-Sí, puede usar fondos de escritorio en vivo en 3D en otros dispositivos, como computadoras portátiles, tabletas, teléfonos inteligentes o televisores inteligentes. Sin embargo, es posible que necesite utilizar diferentes fuentes o métodos para descargar y aplicar fondos de escritorio en vivo 3D en diferentes dispositivos. Por ejemplo, puedes usar aplicaciones como 3D Wallpaper Parallax o Live Wallpapers 3D/4K para tu smartphone o tablet. </li>
-</ol>
-<p>Espero que este artículo haya respondido a sus preguntas y le haya ayudado a aprender más sobre el fondo de pantalla en vivo en 3D. Si tiene alguna otra pregunta o comentario, no dude en dejarlos a continuación. ¡Gracias por leer y tener un gran día! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/index.py

@@ -1,139 +0,0 @@
-import logging
-from optparse import Values
-from typing import Any, Iterable, List, Optional, Union
-
-from pip._vendor.packaging.version import LegacyVersion, Version
-
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.req_command import IndexGroupCommand
-from pip._internal.cli.status_codes import ERROR, SUCCESS
-from pip._internal.commands.search import print_dist_installation_info
-from pip._internal.exceptions import CommandError, DistributionNotFound, PipError
-from pip._internal.index.collector import LinkCollector
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.models.selection_prefs import SelectionPreferences
-from pip._internal.models.target_python import TargetPython
-from pip._internal.network.session import PipSession
-from pip._internal.utils.misc import write_output
-
-logger = logging.getLogger(__name__)
-
-
-class IndexCommand(IndexGroupCommand):
-    """
-    Inspect information available from package indexes.
-    """
-
-    ignore_require_venv = True
-    usage = """
-        %prog versions <package>
-    """
-
-    def add_options(self) -> None:
-        cmdoptions.add_target_python_options(self.cmd_opts)
-
-        self.cmd_opts.add_option(cmdoptions.ignore_requires_python())
-        self.cmd_opts.add_option(cmdoptions.pre())
-        self.cmd_opts.add_option(cmdoptions.no_binary())
-        self.cmd_opts.add_option(cmdoptions.only_binary())
-
-        index_opts = cmdoptions.make_option_group(
-            cmdoptions.index_group,
-            self.parser,
-        )
-
-        self.parser.insert_option_group(0, index_opts)
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        handlers = {
-            "versions": self.get_available_package_versions,
-        }
-
-        logger.warning(
-            "pip index is currently an experimental command. "
-            "It may be removed/changed in a future release "
-            "without prior warning."
-        )
-
-        # Determine action
-        if not args or args[0] not in handlers:
-            logger.error(
-                "Need an action (%s) to perform.",
-                ", ".join(sorted(handlers)),
-            )
-            return ERROR
-
-        action = args[0]
-
-        # Error handling happens here, not in the action-handlers.
-        try:
-            handlers[action](options, args[1:])
-        except PipError as e:
-            logger.error(e.args[0])
-            return ERROR
-
-        return SUCCESS
-
-    def _build_package_finder(
-        self,
-        options: Values,
-        session: PipSession,
-        target_python: Optional[TargetPython] = None,
-        ignore_requires_python: Optional[bool] = None,
-    ) -> PackageFinder:
-        """
-        Create a package finder appropriate to the index command.
-        """
-        link_collector = LinkCollector.create(session, options=options)
-
-        # Pass allow_yanked=False to ignore yanked versions.
-        selection_prefs = SelectionPreferences(
-            allow_yanked=False,
-            allow_all_prereleases=options.pre,
-            ignore_requires_python=ignore_requires_python,
-        )
-
-        return PackageFinder.create(
-            link_collector=link_collector,
-            selection_prefs=selection_prefs,
-            target_python=target_python,
-        )
-
-    def get_available_package_versions(self, options: Values, args: List[Any]) -> None:
-        if len(args) != 1:
-            raise CommandError("You need to specify exactly one argument")
-
-        target_python = cmdoptions.make_target_python(options)
-        query = args[0]
-
-        with self._build_session(options) as session:
-            finder = self._build_package_finder(
-                options=options,
-                session=session,
-                target_python=target_python,
-                ignore_requires_python=options.ignore_requires_python,
-            )
-
-            versions: Iterable[Union[LegacyVersion, Version]] = (
-                candidate.version for candidate in finder.find_all_candidates(query)
-            )
-
-            if not options.pre:
-                # Remove prereleases
-                versions = (
-                    version for version in versions if not version.is_prerelease
-                )
-            versions = set(versions)
-
-            if not versions:
-                raise DistributionNotFound(
-                    "No matching distribution found for {}".format(query)
-                )
-
-            formatted_versions = [str(ver) for ver in sorted(versions, reverse=True)]
-            latest = formatted_versions[0]
-
-        write_output("{} ({})".format(query, latest))
-        write_output("Available versions: {}".format(", ".join(formatted_versions)))
-        print_dist_installation_info(query, latest)

spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/compat.py

@@ -1,94 +0,0 @@
-# Copyright 2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import errno
-import inspect
-import os
-import socket
-import sys
-
-from botocore.compat import six
-
-if sys.platform.startswith('win'):
-    def rename_file(current_filename, new_filename):
-        try:
-            os.remove(new_filename)
-        except OSError as e:
-            if not e.errno == errno.ENOENT:
-                # We only want to a ignore trying to remove
-                # a file that does not exist.  If it fails
-                # for any other reason we should be propagating
-                # that exception.
-                raise
-        os.rename(current_filename, new_filename)
-else:
-    rename_file = os.rename
-
-
-def accepts_kwargs(func):
-    return inspect.getfullargspec(func)[2]
-
-
-# In python 3, socket.error is OSError, which is too general
-# for what we want (i.e FileNotFoundError is a subclass of OSError).
-# In python 3, all the socket related errors are in a newly created
-# ConnectionError.
-SOCKET_ERROR = ConnectionError
-MAXINT = None
-
-
-def seekable(fileobj):
-    """Backwards compat function to determine if a fileobj is seekable
-
-    :param fileobj: The file-like object to determine if seekable
-
-    :returns: True, if seekable. False, otherwise.
-    """
-    # If the fileobj has a seekable attr, try calling the seekable()
-    # method on it.
-    if hasattr(fileobj, 'seekable'):
-        return fileobj.seekable()
-    # If there is no seekable attr, check if the object can be seeked
-    # or telled. If it can, try to seek to the current position.
-    elif hasattr(fileobj, 'seek') and hasattr(fileobj, 'tell'):
-        try:
-            fileobj.seek(0, 1)
-            return True
-        except OSError:
-            # If an io related error was thrown then it is not seekable.
-            return False
-    # Else, the fileobj is not seekable
-    return False
-
-
-def readable(fileobj):
-    """Determines whether or not a file-like object is readable.
-
-    :param fileobj: The file-like object to determine if readable
-
-    :returns: True, if readable. False otherwise.
-    """
-    if hasattr(fileobj, 'readable'):
-        return fileobj.readable()
-
-    return hasattr(fileobj, 'read')
-
-
-def fallocate(fileobj, size):
-    if hasattr(os, 'posix_fallocate'):
-        os.posix_fallocate(fileobj.fileno(), 0, size)
-    else:
-        fileobj.truncate(size)
-
-
-# Import at end of file to avoid circular dependencies
-from multiprocessing.managers import BaseManager  # noqa: F401,E402

spaces/Branon/Proxy/Dockerfile

@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/demo/README.md

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

spaces/CVPR/LIVE/pybind11/include/pybind11/detail/init.h

@@ -1,336 +0,0 @@
-/*
-    pybind11/detail/init.h: init factory function implementation and support code.
-
-    Copyright (c) 2017 Jason Rhinelander <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#include "class.h"
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-template <>
-class type_caster<value_and_holder> {
-public:
-    bool load(handle h, bool) {
-        value = reinterpret_cast<value_and_holder *>(h.ptr());
-        return true;
-    }
-
-    template <typename> using cast_op_type = value_and_holder &;
-    operator value_and_holder &() { return *value; }
-    static constexpr auto name = _<value_and_holder>();
-
-private:
-    value_and_holder *value = nullptr;
-};
-
-PYBIND11_NAMESPACE_BEGIN(initimpl)
-
-inline void no_nullptr(void *ptr) {
-    if (!ptr) throw type_error("pybind11::init(): factory function returned nullptr");
-}
-
-// Implementing functions for all forms of py::init<...> and py::init(...)
-template <typename Class> using Cpp = typename Class::type;
-template <typename Class> using Alias = typename Class::type_alias;
-template <typename Class> using Holder = typename Class::holder_type;
-
-template <typename Class> using is_alias_constructible = std::is_constructible<Alias<Class>, Cpp<Class> &&>;
-
-// Takes a Cpp pointer and returns true if it actually is a polymorphic Alias instance.
-template <typename Class, enable_if_t<Class::has_alias, int> = 0>
-bool is_alias(Cpp<Class> *ptr) {
-    return dynamic_cast<Alias<Class> *>(ptr) != nullptr;
-}
-// Failing fallback version of the above for a no-alias class (always returns false)
-template <typename /*Class*/>
-constexpr bool is_alias(void *) { return false; }
-
-// Constructs and returns a new object; if the given arguments don't map to a constructor, we fall
-// back to brace aggregate initiailization so that for aggregate initialization can be used with
-// py::init, e.g.  `py::init<int, int>` to initialize a `struct T { int a; int b; }`.  For
-// non-aggregate types, we need to use an ordinary T(...) constructor (invoking as `T{...}` usually
-// works, but will not do the expected thing when `T` has an `initializer_list<T>` constructor).
-template <typename Class, typename... Args, detail::enable_if_t<std::is_constructible<Class, Args...>::value, int> = 0>
-inline Class *construct_or_initialize(Args &&...args) { return new Class(std::forward<Args>(args)...); }
-template <typename Class, typename... Args, detail::enable_if_t<!std::is_constructible<Class, Args...>::value, int> = 0>
-inline Class *construct_or_initialize(Args &&...args) { return new Class{std::forward<Args>(args)...}; }
-
-// Attempts to constructs an alias using a `Alias(Cpp &&)` constructor.  This allows types with
-// an alias to provide only a single Cpp factory function as long as the Alias can be
-// constructed from an rvalue reference of the base Cpp type.  This means that Alias classes
-// can, when appropriate, simply define a `Alias(Cpp &&)` constructor rather than needing to
-// inherit all the base class constructors.
-template <typename Class>
-void construct_alias_from_cpp(std::true_type /*is_alias_constructible*/,
-                              value_and_holder &v_h, Cpp<Class> &&base) {
-    v_h.value_ptr() = new Alias<Class>(std::move(base));
-}
-template <typename Class>
-[[noreturn]] void construct_alias_from_cpp(std::false_type /*!is_alias_constructible*/,
-                                           value_and_holder &, Cpp<Class> &&) {
-    throw type_error("pybind11::init(): unable to convert returned instance to required "
-                     "alias class: no `Alias<Class>(Class &&)` constructor available");
-}
-
-// Error-generating fallback for factories that don't match one of the below construction
-// mechanisms.
-template <typename Class>
-void construct(...) {
-    static_assert(!std::is_same<Class, Class>::value /* always false */,
-            "pybind11::init(): init function must return a compatible pointer, "
-            "holder, or value");
-}
-
-// Pointer return v1: the factory function returns a class pointer for a registered class.
-// If we don't need an alias (because this class doesn't have one, or because the final type is
-// inherited on the Python side) we can simply take over ownership.  Otherwise we need to try to
-// construct an Alias from the returned base instance.
-template <typename Class>
-void construct(value_and_holder &v_h, Cpp<Class> *ptr, bool need_alias) {
-    no_nullptr(ptr);
-    if (Class::has_alias && need_alias && !is_alias<Class>(ptr)) {
-        // We're going to try to construct an alias by moving the cpp type.  Whether or not
-        // that succeeds, we still need to destroy the original cpp pointer (either the
-        // moved away leftover, if the alias construction works, or the value itself if we
-        // throw an error), but we can't just call `delete ptr`: it might have a special
-        // deleter, or might be shared_from_this.  So we construct a holder around it as if
-        // it was a normal instance, then steal the holder away into a local variable; thus
-        // the holder and destruction happens when we leave the C++ scope, and the holder
-        // class gets to handle the destruction however it likes.
-        v_h.value_ptr() = ptr;
-        v_h.set_instance_registered(true); // To prevent init_instance from registering it
-        v_h.type->init_instance(v_h.inst, nullptr); // Set up the holder
-        Holder<Class> temp_holder(std::move(v_h.holder<Holder<Class>>())); // Steal the holder
-        v_h.type->dealloc(v_h); // Destroys the moved-out holder remains, resets value ptr to null
-        v_h.set_instance_registered(false);
-
-        construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(*ptr));
-    } else {
-        // Otherwise the type isn't inherited, so we don't need an Alias
-        v_h.value_ptr() = ptr;
-    }
-}
-
-// Pointer return v2: a factory that always returns an alias instance ptr.  We simply take over
-// ownership of the pointer.
-template <typename Class, enable_if_t<Class::has_alias, int> = 0>
-void construct(value_and_holder &v_h, Alias<Class> *alias_ptr, bool) {
-    no_nullptr(alias_ptr);
-    v_h.value_ptr() = static_cast<Cpp<Class> *>(alias_ptr);
-}
-
-// Holder return: copy its pointer, and move or copy the returned holder into the new instance's
-// holder.  This also handles types like std::shared_ptr<T> and std::unique_ptr<T> where T is a
-// derived type (through those holder's implicit conversion from derived class holder constructors).
-template <typename Class>
-void construct(value_and_holder &v_h, Holder<Class> holder, bool need_alias) {
-    auto *ptr = holder_helper<Holder<Class>>::get(holder);
-    no_nullptr(ptr);
-    // If we need an alias, check that the held pointer is actually an alias instance
-    if (Class::has_alias && need_alias && !is_alias<Class>(ptr))
-        throw type_error("pybind11::init(): construction failed: returned holder-wrapped instance "
-                         "is not an alias instance");
-
-    v_h.value_ptr() = ptr;
-    v_h.type->init_instance(v_h.inst, &holder);
-}
-
-// return-by-value version 1: returning a cpp class by value.  If the class has an alias and an
-// alias is required the alias must have an `Alias(Cpp &&)` constructor so that we can construct
-// the alias from the base when needed (i.e. because of Python-side inheritance).  When we don't
-// need it, we simply move-construct the cpp value into a new instance.
-template <typename Class>
-void construct(value_and_holder &v_h, Cpp<Class> &&result, bool need_alias) {
-    static_assert(std::is_move_constructible<Cpp<Class>>::value,
-        "pybind11::init() return-by-value factory function requires a movable class");
-    if (Class::has_alias && need_alias)
-        construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(result));
-    else
-        v_h.value_ptr() = new Cpp<Class>(std::move(result));
-}
-
-// return-by-value version 2: returning a value of the alias type itself.  We move-construct an
-// Alias instance (even if no the python-side inheritance is involved).  The is intended for
-// cases where Alias initialization is always desired.
-template <typename Class>
-void construct(value_and_holder &v_h, Alias<Class> &&result, bool) {
-    static_assert(std::is_move_constructible<Alias<Class>>::value,
-        "pybind11::init() return-by-alias-value factory function requires a movable alias class");
-    v_h.value_ptr() = new Alias<Class>(std::move(result));
-}
-
-// Implementing class for py::init<...>()
-template <typename... Args>
-struct constructor {
-    template <typename Class, typename... Extra, enable_if_t<!Class::has_alias, int> = 0>
-    static void execute(Class &cl, const Extra&... extra) {
-        cl.def("__init__", [](value_and_holder &v_h, Args... args) {
-            v_h.value_ptr() = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
-        }, is_new_style_constructor(), extra...);
-    }
-
-    template <typename Class, typename... Extra,
-              enable_if_t<Class::has_alias &&
-                          std::is_constructible<Cpp<Class>, Args...>::value, int> = 0>
-    static void execute(Class &cl, const Extra&... extra) {
-        cl.def("__init__", [](value_and_holder &v_h, Args... args) {
-            if (Py_TYPE(v_h.inst) == v_h.type->type)
-                v_h.value_ptr() = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
-            else
-                v_h.value_ptr() = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
-        }, is_new_style_constructor(), extra...);
-    }
-
-    template <typename Class, typename... Extra,
-              enable_if_t<Class::has_alias &&
-                          !std::is_constructible<Cpp<Class>, Args...>::value, int> = 0>
-    static void execute(Class &cl, const Extra&... extra) {
-        cl.def("__init__", [](value_and_holder &v_h, Args... args) {
-            v_h.value_ptr() = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
-        }, is_new_style_constructor(), extra...);
-    }
-};
-
-// Implementing class for py::init_alias<...>()
-template <typename... Args> struct alias_constructor {
-    template <typename Class, typename... Extra,
-              enable_if_t<Class::has_alias && std::is_constructible<Alias<Class>, Args...>::value, int> = 0>
-    static void execute(Class &cl, const Extra&... extra) {
-        cl.def("__init__", [](value_and_holder &v_h, Args... args) {
-            v_h.value_ptr() = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
-        }, is_new_style_constructor(), extra...);
-    }
-};
-
-// Implementation class for py::init(Func) and py::init(Func, AliasFunc)
-template <typename CFunc, typename AFunc = void_type (*)(),
-          typename = function_signature_t<CFunc>, typename = function_signature_t<AFunc>>
-struct factory;
-
-// Specialization for py::init(Func)
-template <typename Func, typename Return, typename... Args>
-struct factory<Func, void_type (*)(), Return(Args...)> {
-    remove_reference_t<Func> class_factory;
-
-    factory(Func &&f) : class_factory(std::forward<Func>(f)) { }
-
-    // The given class either has no alias or has no separate alias factory;
-    // this always constructs the class itself.  If the class is registered with an alias
-    // type and an alias instance is needed (i.e. because the final type is a Python class
-    // inheriting from the C++ type) the returned value needs to either already be an alias
-    // instance, or the alias needs to be constructible from a `Class &&` argument.
-    template <typename Class, typename... Extra>
-    void execute(Class &cl, const Extra &...extra) && {
-        #if defined(PYBIND11_CPP14)
-        cl.def("__init__", [func = std::move(class_factory)]
-        #else
-        auto &func = class_factory;
-        cl.def("__init__", [func]
-        #endif
-        (value_and_holder &v_h, Args... args) {
-            construct<Class>(v_h, func(std::forward<Args>(args)...),
-                             Py_TYPE(v_h.inst) != v_h.type->type);
-        }, is_new_style_constructor(), extra...);
-    }
-};
-
-// Specialization for py::init(Func, AliasFunc)
-template <typename CFunc, typename AFunc,
-          typename CReturn, typename... CArgs, typename AReturn, typename... AArgs>
-struct factory<CFunc, AFunc, CReturn(CArgs...), AReturn(AArgs...)> {
-    static_assert(sizeof...(CArgs) == sizeof...(AArgs),
-                  "pybind11::init(class_factory, alias_factory): class and alias factories "
-                  "must have identical argument signatures");
-    static_assert(all_of<std::is_same<CArgs, AArgs>...>::value,
-                  "pybind11::init(class_factory, alias_factory): class and alias factories "
-                  "must have identical argument signatures");
-
-    remove_reference_t<CFunc> class_factory;
-    remove_reference_t<AFunc> alias_factory;
-
-    factory(CFunc &&c, AFunc &&a)
-        : class_factory(std::forward<CFunc>(c)), alias_factory(std::forward<AFunc>(a)) { }
-
-    // The class factory is called when the `self` type passed to `__init__` is the direct
-    // class (i.e. not inherited), the alias factory when `self` is a Python-side subtype.
-    template <typename Class, typename... Extra>
-    void execute(Class &cl, const Extra&... extra) && {
-        static_assert(Class::has_alias, "The two-argument version of `py::init()` can "
-                                        "only be used if the class has an alias");
-        #if defined(PYBIND11_CPP14)
-        cl.def("__init__", [class_func = std::move(class_factory), alias_func = std::move(alias_factory)]
-        #else
-        auto &class_func = class_factory;
-        auto &alias_func = alias_factory;
-        cl.def("__init__", [class_func, alias_func]
-        #endif
-        (value_and_holder &v_h, CArgs... args) {
-            if (Py_TYPE(v_h.inst) == v_h.type->type)
-                // If the instance type equals the registered type we don't have inheritance, so
-                // don't need the alias and can construct using the class function:
-                construct<Class>(v_h, class_func(std::forward<CArgs>(args)...), false);
-            else
-                construct<Class>(v_h, alias_func(std::forward<CArgs>(args)...), true);
-        }, is_new_style_constructor(), extra...);
-    }
-};
-
-/// Set just the C++ state. Same as `__init__`.
-template <typename Class, typename T>
-void setstate(value_and_holder &v_h, T &&result, bool need_alias) {
-    construct<Class>(v_h, std::forward<T>(result), need_alias);
-}
-
-/// Set both the C++ and Python states
-template <typename Class, typename T, typename O,
-          enable_if_t<std::is_convertible<O, handle>::value, int> = 0>
-void setstate(value_and_holder &v_h, std::pair<T, O> &&result, bool need_alias) {
-    construct<Class>(v_h, std::move(result.first), need_alias);
-    setattr((PyObject *) v_h.inst, "__dict__", result.second);
-}
-
-/// Implementation for py::pickle(GetState, SetState)
-template <typename Get, typename Set,
-          typename = function_signature_t<Get>, typename = function_signature_t<Set>>
-struct pickle_factory;
-
-template <typename Get, typename Set,
-          typename RetState, typename Self, typename NewInstance, typename ArgState>
-struct pickle_factory<Get, Set, RetState(Self), NewInstance(ArgState)> {
-    static_assert(std::is_same<intrinsic_t<RetState>, intrinsic_t<ArgState>>::value,
-                  "The type returned by `__getstate__` must be the same "
-                  "as the argument accepted by `__setstate__`");
-
-    remove_reference_t<Get> get;
-    remove_reference_t<Set> set;
-
-    pickle_factory(Get get, Set set)
-        : get(std::forward<Get>(get)), set(std::forward<Set>(set)) { }
-
-    template <typename Class, typename... Extra>
-    void execute(Class &cl, const Extra &...extra) && {
-        cl.def("__getstate__", std::move(get));
-
-#if defined(PYBIND11_CPP14)
-        cl.def("__setstate__", [func = std::move(set)]
-#else
-        auto &func = set;
-        cl.def("__setstate__", [func]
-#endif
-        (value_and_holder &v_h, ArgState state) {
-            setstate<Class>(v_h, func(std::forward<ArgState>(state)),
-                            Py_TYPE(v_h.inst) != v_h.type->type);
-        }, is_new_style_constructor(), extra...);
-    }
-};
-
-PYBIND11_NAMESPACE_END(initimpl)
-PYBIND11_NAMESPACE_END(detail)
-PYBIND11_NAMESPACE_END(pybind11)

spaces/CVPR/LIVE/thrust/thrust/distance.h

@@ -1,77 +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.
- */
-
-
-/*! \file distance.h
- *  \brief Computes the size of a range
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_traits.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup iterators
- *  \{
- */
-
-/*! \p distance finds the distance between \p first and \p last, i.e. the
- *  number of times that \p first must be incremented until it is equal to
- *  \p last.
- *
- *  \param first The beginning of an input range of interest.
- *  \param last The end of an input range of interest.
- *  \return The distance between the beginning and end of the input range.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *
- *  \pre If \c InputIterator meets the requirements of random access iterator, \p last shall be reachable from \p first or
- *       \p first shall be reachable from \p last; otherwise, \p last shall be reachable from \p first.
- *
- *  The following code snippet demonstrates how to use \p distance to compute
- *  the distance to one iterator from another.
- *
- *  \code
- *  #include <thrust/distance.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> vec(13);
- *  thrust::device_vector<int>::iterator iter1 = vec.begin();
- *  thrust::device_vector<int>::iterator iter2 = iter1 + 7;
- *
- *  int d = thrust::distance(iter1, iter2);
- *
- *  // d is 7
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/distance.html
- */
-template<typename InputIterator>
-inline __host__ __device__
-  typename thrust::iterator_traits<InputIterator>::difference_type
-    distance(InputIterator first, InputIterator last);
-
-/*! \} // end iterators
- */
-
-} // end thrust
-
-#include <thrust/detail/distance.inl>
-

spaces/CVPR/WALT/mmdet/models/dense_heads/vfnet_head.py

@@ -1,794 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.ops import DeformConv2d
-from mmcv.runner import force_fp32
-
-from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator,
-                        build_assigner, build_sampler, distance2bbox,
-                        multi_apply, multiclass_nms, reduce_mean)
-from ..builder import HEADS, build_loss
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@HEADS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
-    """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
-    Detector.<https://arxiv.org/abs/2008.13367>`_.
-
-    The VFNet predicts IoU-aware classification scores which mix the
-    object presence confidence and object localization accuracy as the
-    detection score. It is built on the FCOS architecture and uses ATSS
-    for defining positive/negative training examples. The VFNet is trained
-    with Varifocal Loss and empolys star-shaped deformable convolution to
-    extract features for a bbox.
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
-            level points.
-        center_sampling (bool): If true, use center sampling. Default: False.
-        center_sample_radius (float): Radius of center sampling. Default: 1.5.
-        sync_num_pos (bool): If true, synchronize the number of positive
-            examples across GPUs. Default: True
-        gradient_mul (float): The multiplier to gradients from bbox refinement
-            and recognition. Default: 0.1.
-        bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
-            'stride'. Default: reg_denom
-        loss_cls_fl (dict): Config of focal loss.
-        use_vfl (bool): If true, use varifocal loss for training.
-            Default: True.
-        loss_cls (dict): Config of varifocal loss.
-        loss_bbox (dict): Config of localization loss, GIoU Loss.
-        loss_bbox (dict): Config of localization refinement loss, GIoU Loss.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-            Default: norm_cfg=dict(type='GN', num_groups=32,
-            requires_grad=True).
-        use_atss (bool): If true, use ATSS to define positive/negative
-            examples. Default: True.
-        anchor_generator (dict): Config of anchor generator for ATSS.
-
-    Example:
-        >>> self = VFNetHead(11, 7)
-        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
-        >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
-        >>> assert len(cls_score) == len(self.scales)
-    """  # noqa: E501
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
-                                 (512, INF)),
-                 center_sampling=False,
-                 center_sample_radius=1.5,
-                 sync_num_pos=True,
-                 gradient_mul=0.1,
-                 bbox_norm_type='reg_denom',
-                 loss_cls_fl=dict(
-                     type='FocalLoss',
-                     use_sigmoid=True,
-                     gamma=2.0,
-                     alpha=0.25,
-                     loss_weight=1.0),
-                 use_vfl=True,
-                 loss_cls=dict(
-                     type='VarifocalLoss',
-                     use_sigmoid=True,
-                     alpha=0.75,
-                     gamma=2.0,
-                     iou_weighted=True,
-                     loss_weight=1.0),
-                 loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
-                 loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0),
-                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
-                 use_atss=True,
-                 anchor_generator=dict(
-                     type='AnchorGenerator',
-                     ratios=[1.0],
-                     octave_base_scale=8,
-                     scales_per_octave=1,
-                     center_offset=0.0,
-                     strides=[8, 16, 32, 64, 128]),
-                 **kwargs):
-        # dcn base offsets, adapted from reppoints_head.py
-        self.num_dconv_points = 9
-        self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
-        self.dcn_pad = int((self.dcn_kernel - 1) / 2)
-        dcn_base = np.arange(-self.dcn_pad,
-                             self.dcn_pad + 1).astype(np.float64)
-        dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
-        dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
-        dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
-            (-1))
-        self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
-        super(FCOSHead, self).__init__(
-            num_classes, in_channels, norm_cfg=norm_cfg, **kwargs)
-        self.regress_ranges = regress_ranges
-        self.reg_denoms = [
-            regress_range[-1] for regress_range in regress_ranges
-        ]
-        self.reg_denoms[-1] = self.reg_denoms[-2] * 2
-        self.center_sampling = center_sampling
-        self.center_sample_radius = center_sample_radius
-        self.sync_num_pos = sync_num_pos
-        self.bbox_norm_type = bbox_norm_type
-        self.gradient_mul = gradient_mul
-        self.use_vfl = use_vfl
-        if self.use_vfl:
-            self.loss_cls = build_loss(loss_cls)
-        else:
-            self.loss_cls = build_loss(loss_cls_fl)
-        self.loss_bbox = build_loss(loss_bbox)
-        self.loss_bbox_refine = build_loss(loss_bbox_refine)
-
-        # for getting ATSS targets
-        self.use_atss = use_atss
-        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-        self.anchor_generator = build_anchor_generator(anchor_generator)
-        self.anchor_center_offset = anchor_generator['center_offset']
-        self.num_anchors = self.anchor_generator.num_base_anchors[0]
-        self.sampling = False
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        super(FCOSHead, self)._init_cls_convs()
-        super(FCOSHead, self)._init_reg_convs()
-        self.relu = nn.ReLU(inplace=True)
-        self.vfnet_reg_conv = ConvModule(
-            self.feat_channels,
-            self.feat_channels,
-            3,
-            stride=1,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            bias=self.conv_bias)
-        self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
-        self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
-        self.vfnet_reg_refine_dconv = DeformConv2d(
-            self.feat_channels,
-            self.feat_channels,
-            self.dcn_kernel,
-            1,
-            padding=self.dcn_pad)
-        self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
-        self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
-        self.vfnet_cls_dconv = DeformConv2d(
-            self.feat_channels,
-            self.feat_channels,
-            self.dcn_kernel,
-            1,
-            padding=self.dcn_pad)
-        self.vfnet_cls = nn.Conv2d(
-            self.feat_channels, self.cls_out_channels, 3, padding=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            if isinstance(m.conv, nn.Conv2d):
-                normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            if isinstance(m.conv, nn.Conv2d):
-                normal_init(m.conv, std=0.01)
-        normal_init(self.vfnet_reg_conv.conv, std=0.01)
-        normal_init(self.vfnet_reg, std=0.01)
-        normal_init(self.vfnet_reg_refine_dconv, std=0.01)
-        normal_init(self.vfnet_reg_refine, std=0.01)
-        normal_init(self.vfnet_cls_dconv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.vfnet_cls, std=0.01, bias=bias_cls)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple:
-                cls_scores (list[Tensor]): Box iou-aware scores for each scale
-                    level, each is a 4D-tensor, the channel number is
-                    num_points * num_classes.
-                bbox_preds (list[Tensor]): Box offsets for each
-                    scale level, each is a 4D-tensor, the channel number is
-                    num_points * 4.
-                bbox_preds_refine (list[Tensor]): Refined Box offsets for
-                    each scale level, each is a 4D-tensor, the channel
-                    number is num_points * 4.
-        """
-        return multi_apply(self.forward_single, feats, self.scales,
-                           self.scales_refine, self.strides, self.reg_denoms)
-
-    def forward_single(self, x, scale, scale_refine, stride, reg_denom):
-        """Forward features of a single scale level.
-
-        Args:
-            x (Tensor): FPN feature maps of the specified stride.
-            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
-                the bbox prediction.
-            scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
-                resize the refined bbox prediction.
-            stride (int): The corresponding stride for feature maps,
-                used to normalize the bbox prediction when
-                bbox_norm_type = 'stride'.
-            reg_denom (int): The corresponding regression range for feature
-                maps, only used to normalize the bbox prediction when
-                bbox_norm_type = 'reg_denom'.
-
-        Returns:
-            tuple: iou-aware cls scores for each box, bbox predictions and
-                refined bbox predictions of input feature maps.
-        """
-        cls_feat = x
-        reg_feat = x
-
-        for cls_layer in self.cls_convs:
-            cls_feat = cls_layer(cls_feat)
-
-        for reg_layer in self.reg_convs:
-            reg_feat = reg_layer(reg_feat)
-
-        # predict the bbox_pred of different level
-        reg_feat_init = self.vfnet_reg_conv(reg_feat)
-        if self.bbox_norm_type == 'reg_denom':
-            bbox_pred = scale(
-                self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
-        elif self.bbox_norm_type == 'stride':
-            bbox_pred = scale(
-                self.vfnet_reg(reg_feat_init)).float().exp() * stride
-        else:
-            raise NotImplementedError
-
-        # compute star deformable convolution offsets
-        # converting dcn_offset to reg_feat.dtype thus VFNet can be
-        # trained with FP16
-        dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
-                                          stride).to(reg_feat.dtype)
-
-        # refine the bbox_pred
-        reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
-        bbox_pred_refine = scale_refine(
-            self.vfnet_reg_refine(reg_feat)).float().exp()
-        bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
-        # predict the iou-aware cls score
-        cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
-        cls_score = self.vfnet_cls(cls_feat)
-
-        return cls_score, bbox_pred, bbox_pred_refine
-
-    def star_dcn_offset(self, bbox_pred, gradient_mul, stride):
-        """Compute the star deformable conv offsets.
-
-        Args:
-            bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
-            gradient_mul (float): Gradient multiplier.
-            stride (int): The corresponding stride for feature maps,
-                used to project the bbox onto the feature map.
-
-        Returns:
-            dcn_offsets (Tensor): The offsets for deformable convolution.
-        """
-        dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
-        bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
-            gradient_mul * bbox_pred
-        # map to the feature map scale
-        bbox_pred_grad_mul = bbox_pred_grad_mul / stride
-        N, C, H, W = bbox_pred.size()
-
-        x1 = bbox_pred_grad_mul[:, 0, :, :]
-        y1 = bbox_pred_grad_mul[:, 1, :, :]
-        x2 = bbox_pred_grad_mul[:, 2, :, :]
-        y2 = bbox_pred_grad_mul[:, 3, :, :]
-        bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
-            N, 2 * self.num_dconv_points, H, W)
-        bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1  # -y1
-        bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1  # -x1
-        bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1  # -y1
-        bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1  # -y1
-        bbox_pred_grad_mul_offset[:, 5, :, :] = x2  # x2
-        bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1  # -x1
-        bbox_pred_grad_mul_offset[:, 11, :, :] = x2  # x2
-        bbox_pred_grad_mul_offset[:, 12, :, :] = y2  # y2
-        bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1  # -x1
-        bbox_pred_grad_mul_offset[:, 14, :, :] = y2  # y2
-        bbox_pred_grad_mul_offset[:, 16, :, :] = y2  # y2
-        bbox_pred_grad_mul_offset[:, 17, :, :] = x2  # x2
-        dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
-        return dcn_offset
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             bbox_preds_refine,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute loss of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box iou-aware scores for each scale
-                level, each is a 4D-tensor, the channel number is
-                num_points * num_classes.
-            bbox_preds (list[Tensor]): Box offsets for each
-                scale level, each is a 4D-tensor, the channel number is
-                num_points * 4.
-            bbox_preds_refine (list[Tensor]): Refined Box offsets for
-                each scale level, each is a 4D-tensor, the channel
-                number is num_points * 4.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-                Default: None.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
-                                           bbox_preds[0].device)
-        labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
-            cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas,
-            gt_bboxes_ignore)
-
-        num_imgs = cls_scores[0].size(0)
-        # flatten cls_scores, bbox_preds and bbox_preds_refine
-        flatten_cls_scores = [
-            cls_score.permute(0, 2, 3,
-                              1).reshape(-1,
-                                         self.cls_out_channels).contiguous()
-            for cls_score in cls_scores
-        ]
-        flatten_bbox_preds = [
-            bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
-            for bbox_pred in bbox_preds
-        ]
-        flatten_bbox_preds_refine = [
-            bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
-            for bbox_pred_refine in bbox_preds_refine
-        ]
-        flatten_cls_scores = torch.cat(flatten_cls_scores)
-        flatten_bbox_preds = torch.cat(flatten_bbox_preds)
-        flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
-        flatten_labels = torch.cat(labels)
-        flatten_bbox_targets = torch.cat(bbox_targets)
-        # repeat points to align with bbox_preds
-        flatten_points = torch.cat(
-            [points.repeat(num_imgs, 1) for points in all_level_points])
-
-        # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = torch.where(
-            ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
-        num_pos = len(pos_inds)
-
-        pos_bbox_preds = flatten_bbox_preds[pos_inds]
-        pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
-        pos_labels = flatten_labels[pos_inds]
-
-        # sync num_pos across all gpus
-        if self.sync_num_pos:
-            num_pos_avg_per_gpu = reduce_mean(
-                pos_inds.new_tensor(num_pos).float()).item()
-            num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
-        else:
-            num_pos_avg_per_gpu = num_pos
-
-        if num_pos > 0:
-            pos_bbox_targets = flatten_bbox_targets[pos_inds]
-            pos_points = flatten_points[pos_inds]
-
-            pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds)
-            pos_decoded_target_preds = distance2bbox(pos_points,
-                                                     pos_bbox_targets)
-            iou_targets_ini = bbox_overlaps(
-                pos_decoded_bbox_preds,
-                pos_decoded_target_preds.detach(),
-                is_aligned=True).clamp(min=1e-6)
-            bbox_weights_ini = iou_targets_ini.clone().detach()
-            iou_targets_ini_avg_per_gpu = reduce_mean(
-                bbox_weights_ini.sum()).item()
-            bbox_avg_factor_ini = max(iou_targets_ini_avg_per_gpu, 1.0)
-            loss_bbox = self.loss_bbox(
-                pos_decoded_bbox_preds,
-                pos_decoded_target_preds.detach(),
-                weight=bbox_weights_ini,
-                avg_factor=bbox_avg_factor_ini)
-
-            pos_decoded_bbox_preds_refine = \
-                distance2bbox(pos_points, pos_bbox_preds_refine)
-            iou_targets_rf = bbox_overlaps(
-                pos_decoded_bbox_preds_refine,
-                pos_decoded_target_preds.detach(),
-                is_aligned=True).clamp(min=1e-6)
-            bbox_weights_rf = iou_targets_rf.clone().detach()
-            iou_targets_rf_avg_per_gpu = reduce_mean(
-                bbox_weights_rf.sum()).item()
-            bbox_avg_factor_rf = max(iou_targets_rf_avg_per_gpu, 1.0)
-            loss_bbox_refine = self.loss_bbox_refine(
-                pos_decoded_bbox_preds_refine,
-                pos_decoded_target_preds.detach(),
-                weight=bbox_weights_rf,
-                avg_factor=bbox_avg_factor_rf)
-
-            # build IoU-aware cls_score targets
-            if self.use_vfl:
-                pos_ious = iou_targets_rf.clone().detach()
-                cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-                cls_iou_targets[pos_inds, pos_labels] = pos_ious
-        else:
-            loss_bbox = pos_bbox_preds.sum() * 0
-            loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
-            if self.use_vfl:
-                cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
-        if self.use_vfl:
-            loss_cls = self.loss_cls(
-                flatten_cls_scores,
-                cls_iou_targets,
-                avg_factor=num_pos_avg_per_gpu)
-        else:
-            loss_cls = self.loss_cls(
-                flatten_cls_scores,
-                flatten_labels,
-                weight=label_weights,
-                avg_factor=num_pos_avg_per_gpu)
-
-        return dict(
-            loss_cls=loss_cls,
-            loss_bbox=loss_bbox,
-            loss_bbox_rf=loss_bbox_refine)
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   bbox_preds_refine,
-                   img_metas,
-                   cfg=None,
-                   rescale=None,
-                   with_nms=True):
-        """Transform network outputs for a batch into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box iou-aware scores for each scale
-                level with shape (N, num_points * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box offsets for each scale
-                level with shape (N, num_points * 4, H, W).
-            bbox_preds_refine (list[Tensor]): Refined Box offsets for
-                each scale level with shape (N, num_points * 4, H, W).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used. Default: None.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before returning boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where the first 4 columns
-                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
-                5-th column is a score between 0 and 1. The second item is a
-                (n,) tensor where each item is the predicted class label of
-                the corresponding box.
-        """
-        assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
-        num_levels = len(cls_scores)
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
-                                      bbox_preds[0].device)
-        result_list = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_pred_list = [
-                bbox_preds_refine[i][img_id].detach()
-                for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            det_bboxes = self._get_bboxes_single(cls_score_list,
-                                                 bbox_pred_list, mlvl_points,
-                                                 img_shape, scale_factor, cfg,
-                                                 rescale, with_nms)
-            result_list.append(det_bboxes)
-        return result_list
-
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           mlvl_points,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False,
-                           with_nms=True):
-        """Transform outputs for a single batch item into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box iou-aware scores for a single scale
-                level with shape (num_points * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box offsets for a single scale
-                level with shape (num_points * 4, H, W).
-            mlvl_points (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_points, 4).
-            img_shape (tuple[int]): Shape of the input image,
-                (height, width, 3).
-            scale_factor (ndarray): Scale factor of the image arrange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before returning boxes.
-                Default: True.
-
-        Returns:
-            tuple(Tensor):
-                det_bboxes (Tensor): BBox predictions in shape (n, 5), where
-                    the first 4 columns are bounding box positions
-                    (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
-                    between 0 and 1.
-                det_labels (Tensor): A (n,) tensor where each item is the
-                    predicted class label of the corresponding box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        for cls_score, bbox_pred, points in zip(cls_scores, bbox_preds,
-                                                mlvl_points):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            scores = cls_score.permute(1, 2, 0).reshape(
-                -1, self.cls_out_channels).contiguous().sigmoid()
-            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).contiguous()
-
-            nms_pre = cfg.get('nms_pre', -1)
-            if 0 < nms_pre < scores.shape[0]:
-                max_scores, _ = scores.max(dim=1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                points = points[topk_inds, :]
-                bbox_pred = bbox_pred[topk_inds, :]
-                scores = scores[topk_inds, :]
-            bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-        mlvl_bboxes = torch.cat(mlvl_bboxes)
-        if rescale:
-            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
-        mlvl_scores = torch.cat(mlvl_scores)
-        padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
-        if with_nms:
-            det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
-                                                    cfg.score_thr, cfg.nms,
-                                                    cfg.max_per_img)
-            return det_bboxes, det_labels
-        else:
-            return mlvl_bboxes, mlvl_scores
-
-    def _get_points_single(self,
-                           featmap_size,
-                           stride,
-                           dtype,
-                           device,
-                           flatten=False):
-        """Get points according to feature map sizes."""
-        h, w = featmap_size
-        x_range = torch.arange(
-            0, w * stride, stride, dtype=dtype, device=device)
-        y_range = torch.arange(
-            0, h * stride, stride, dtype=dtype, device=device)
-        y, x = torch.meshgrid(y_range, x_range)
-        # to be compatible with anchor points in ATSS
-        if self.use_atss:
-            points = torch.stack(
-                (x.reshape(-1), y.reshape(-1)), dim=-1) + \
-                     stride * self.anchor_center_offset
-        else:
-            points = torch.stack(
-                (x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2
-        return points
-
-    def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels,
-                    img_metas, gt_bboxes_ignore):
-        """A wrapper for computing ATSS and FCOS targets for points in multiple
-        images.
-
-        Args:
-            cls_scores (list[Tensor]): Box iou-aware scores for each scale
-                level with shape (N, num_points * num_classes, H, W).
-            mlvl_points (list[Tensor]): Points of each fpn level, each has
-                shape (num_points, 2).
-            gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
-                each has shape (num_gt, 4).
-            gt_labels (list[Tensor]): Ground truth labels of each box,
-                each has shape (num_gt,).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-
-        Returns:
-            tuple:
-                labels_list (list[Tensor]): Labels of each level.
-                label_weights (Tensor/None): Label weights of all levels.
-                bbox_targets_list (list[Tensor]): Regression targets of each
-                    level, (l, t, r, b).
-                bbox_weights (Tensor/None): Bbox weights of all levels.
-        """
-        if self.use_atss:
-            return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes,
-                                         gt_labels, img_metas,
-                                         gt_bboxes_ignore)
-        else:
-            self.norm_on_bbox = False
-            return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels)
-
-    def _get_target_single(self, *args, **kwargs):
-        """Avoid ambiguity in multiple inheritance."""
-        if self.use_atss:
-            return ATSSHead._get_target_single(self, *args, **kwargs)
-        else:
-            return FCOSHead._get_target_single(self, *args, **kwargs)
-
-    def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list):
-        """Compute FCOS regression and classification targets for points in
-        multiple images.
-
-        Args:
-            points (list[Tensor]): Points of each fpn level, each has shape
-                (num_points, 2).
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
-                each has shape (num_gt, 4).
-            gt_labels_list (list[Tensor]): Ground truth labels of each box,
-                each has shape (num_gt,).
-
-        Returns:
-            tuple:
-                labels (list[Tensor]): Labels of each level.
-                label_weights: None, to be compatible with ATSS targets.
-                bbox_targets (list[Tensor]): BBox targets of each level.
-                bbox_weights: None, to be compatible with ATSS targets.
-        """
-        labels, bbox_targets = FCOSHead.get_targets(self, points,
-                                                    gt_bboxes_list,
-                                                    gt_labels_list)
-        label_weights = None
-        bbox_weights = None
-        return labels, label_weights, bbox_targets, bbox_weights
-
-    def get_atss_targets(self,
-                         cls_scores,
-                         mlvl_points,
-                         gt_bboxes,
-                         gt_labels,
-                         img_metas,
-                         gt_bboxes_ignore=None):
-        """A wrapper for computing ATSS targets for points in multiple images.
-
-        Args:
-            cls_scores (list[Tensor]): Box iou-aware scores for each scale
-                level with shape (N, num_points * num_classes, H, W).
-            mlvl_points (list[Tensor]): Points of each fpn level, each has
-                shape (num_points, 2).
-            gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
-                each has shape (num_gt, 4).
-            gt_labels (list[Tensor]): Ground truth labels of each box,
-                each has shape (num_gt,).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4). Default: None.
-
-        Returns:
-            tuple:
-                labels_list (list[Tensor]): Labels of each level.
-                label_weights (Tensor): Label weights of all levels.
-                bbox_targets_list (list[Tensor]): Regression targets of each
-                    level, (l, t, r, b).
-                bbox_weights (Tensor): Bbox weights of all levels.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = ATSSHead.get_targets(
-            self,
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-            unmap_outputs=True)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        bbox_targets_list = [
-            bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
-        ]
-
-        num_imgs = len(img_metas)
-        # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
-        bbox_targets_list = self.transform_bbox_targets(
-            bbox_targets_list, mlvl_points, num_imgs)
-
-        labels_list = [labels.reshape(-1) for labels in labels_list]
-        label_weights_list = [
-            label_weights.reshape(-1) for label_weights in label_weights_list
-        ]
-        bbox_weights_list = [
-            bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
-        ]
-        label_weights = torch.cat(label_weights_list)
-        bbox_weights = torch.cat(bbox_weights_list)
-        return labels_list, label_weights, bbox_targets_list, bbox_weights
-
-    def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs):
-        """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
-        Args:
-            decoded_bboxes (list[Tensor]): Regression targets of each level,
-                in the form of (x1, y1, x2, y2).
-            mlvl_points (list[Tensor]): Points of each fpn level, each has
-                shape (num_points, 2).
-            num_imgs (int): the number of images in a batch.
-
-        Returns:
-            bbox_targets (list[Tensor]): Regression targets of each level in
-                the form of (l, t, r, b).
-        """
-        # TODO: Re-implemented in Class PointCoder
-        assert len(decoded_bboxes) == len(mlvl_points)
-        num_levels = len(decoded_bboxes)
-        mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
-        bbox_targets = []
-        for i in range(num_levels):
-            bbox_target = bbox2distance(mlvl_points[i], decoded_bboxes[i])
-            bbox_targets.append(bbox_target)
-
-        return bbox_targets
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        """Override the method in the parent class to avoid changing para's
-        name."""
-        pass

spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/data/__init__.py

@@ -1,3 +0,0 @@
-
-from .dataset import Dataset, TensorDataset, ConcatDataset
-from .dataloader import DataLoader

spaces/CVPR/regionclip-demo/detectron2/data/__init__.py

@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from . import transforms  # isort:skip
-
-from .build import (
-    build_batch_data_loader,
-    build_detection_test_loader,
-    build_detection_train_loader,
-    get_detection_dataset_dicts,
-    load_proposals_into_dataset,
-    print_instances_class_histogram,
-)
-from .catalog import DatasetCatalog, MetadataCatalog, Metadata
-from .common import DatasetFromList, MapDataset
-from .dataset_mapper import DatasetMapper
-
-# ensure the builtin datasets are registered
-from . import datasets, samplers  # isort:skip
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]

spaces/ChrisPreston/diff-svc_minato_aqua/modules/vocoders/__init__.py

@@ -1 +0,0 @@
-from modules.vocoders import nsf_hifigan

spaces/CofAI/chat/client/css/dropdown.css

@@ -1,10 +0,0 @@
-.dropdown {
-	border: 1px solid var(--conversations);
-}
-
-@media screen and (max-width: 990px) {
-	.dropdown {
-		padding: 4px 8px;
-		font-size: 0.75rem;
-	}
-}

spaces/Covert1107/sd-diffusers-webui/modules/safe.py

@@ -1,188 +0,0 @@
-# this code is adapted from the script contributed by anon from /h/
-# modified, from https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/6cff4401824299a983c8e13424018efc347b4a2b/modules/safe.py
-
-import io
-import pickle
-import collections
-import sys
-import traceback
-
-import torch
-import numpy
-import _codecs
-import zipfile
-import re
-
-
-# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
-TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
-
-
-def encode(*args):
-    out = _codecs.encode(*args)
-    return out
-
-
-class RestrictedUnpickler(pickle.Unpickler):
-    extra_handler = None
-
-    def persistent_load(self, saved_id):
-        assert saved_id[0] == 'storage'
-        return TypedStorage()
-
-    def find_class(self, module, name):
-        if self.extra_handler is not None:
-            res = self.extra_handler(module, name)
-            if res is not None:
-                return res
-
-        if module == 'collections' and name == 'OrderedDict':
-            return getattr(collections, name)
-        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
-            return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']:
-            return getattr(torch, name)
-        if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
-            return getattr(torch.nn.modules.container, name)
-        if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:
-            return getattr(numpy.core.multiarray, name)
-        if module == 'numpy' and name in ['dtype', 'ndarray']:
-            return getattr(numpy, name)
-        if module == '_codecs' and name == 'encode':
-            return encode
-        if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
-            import pytorch_lightning.callbacks
-            return pytorch_lightning.callbacks.model_checkpoint
-        if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
-            import pytorch_lightning.callbacks.model_checkpoint
-            return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
-        if module == "__builtin__" and name == 'set':
-            return set
-
-        # Forbid everything else.
-        raise Exception(f"global '{module}/{name}' is forbidden")
-
-
-# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/<number>'
-allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
-data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
-
-def check_zip_filenames(filename, names):
-    for name in names:
-        if allowed_zip_names_re.match(name):
-            continue
-
-        raise Exception(f"bad file inside {filename}: {name}")
-
-
-def check_pt(filename, extra_handler):
-    try:
-
-        # new pytorch format is a zip file
-        with zipfile.ZipFile(filename) as z:
-            check_zip_filenames(filename, z.namelist())
-
-            # find filename of data.pkl in zip file: '<directory name>/data.pkl'
-            data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
-            if len(data_pkl_filenames) == 0:
-                raise Exception(f"data.pkl not found in {filename}")
-            if len(data_pkl_filenames) > 1:
-                raise Exception(f"Multiple data.pkl found in {filename}")
-            with z.open(data_pkl_filenames[0]) as file:
-                unpickler = RestrictedUnpickler(file)
-                unpickler.extra_handler = extra_handler
-                unpickler.load()
-
-    except zipfile.BadZipfile:
-
-        # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
-        with open(filename, "rb") as file:
-            unpickler = RestrictedUnpickler(file)
-            unpickler.extra_handler = extra_handler
-            for i in range(5):
-                unpickler.load()
-
-
-def load(filename, *args, **kwargs):
-    return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs)
-
-
-def load_with_extra(filename, extra_handler=None, *args, **kwargs):
-    """
-    this function is intended to be used by extensions that want to load models with
-    some extra classes in them that the usual unpickler would find suspicious.
-
-    Use the extra_handler argument to specify a function that takes module and field name as text,
-    and returns that field's value:
-
-    ```python
-    def extra(module, name):
-        if module == 'collections' and name == 'OrderedDict':
-            return collections.OrderedDict
-
-        return None
-
-    safe.load_with_extra('model.pt', extra_handler=extra)
-    ```
-
-    The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is
-    definitely unsafe.
-    """
-
-    try:
-        check_pt(filename, extra_handler)
-
-    except pickle.UnpicklingError:
-        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-        print("The file is most likely corrupted.", file=sys.stderr)
-        return None
-
-    except Exception:
-        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-        print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
-        return None
-
-    return unsafe_torch_load(filename, *args, **kwargs)
-
-
-class Extra:
-    """
-    A class for temporarily setting the global handler for when you can't explicitly call load_with_extra
-    (because it's not your code making the torch.load call). The intended use is like this:
-
-```
-import torch
-from modules import safe
-
-def handler(module, name):
-    if module == 'torch' and name in ['float64', 'float16']:
-        return getattr(torch, name)
-
-    return None
-
-with safe.Extra(handler):
-    x = torch.load('model.pt')
-```
-    """
-
-    def __init__(self, handler):
-        self.handler = handler
-
-    def __enter__(self):
-        global global_extra_handler
-
-        assert global_extra_handler is None, 'already inside an Extra() block'
-        global_extra_handler = self.handler
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        global global_extra_handler
-
-        global_extra_handler = None
-
-
-unsafe_torch_load = torch.load
-torch.load = load
-global_extra_handler = None