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spaces/1acneusushi/gradio-2dmoleculeeditor/data/4K Video Downloader 4.4.11.2412 Full Repack Portable Extract Audio from 4K Videos Easily.md

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- <p>The first step is to download the repack or portable version of <strong>4K Video Downloader 4.4.11.2412</strong> from a reliable source, such as nsane.forums or solidtorrents. You can find the download links in these sites or search for them online.</p>
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- <p>The next step is to run the program by double-clicking on the executable file in the folder where you extracted it. You will see a simple interface with a big button that says 'Paste Url'. You need to copy the video link from your browser and click on this button.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Filmora 9 Features Pricing and Download - Everything You Need to Know.md

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-<p>If you still want to download a hacked Coin Master apk despite the risks, you should follow some precautions to minimize the chances of getting into trouble. Here are some tips on how to download a hacked Coin Master apk safely:</p>
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-<p>After you download a hacked Coin Master apk file, you should scan it with a reliable antivirus or anti-malware program. You should make sure that the file is clean and safe before you install it on your device. You should also delete any unwanted or suspicious files that come with the download.</p>
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-<p>Before you install a hacked Coin Master apk file on your device, you should backup your data and device. You should copy your important files, photos, contacts, and settings to another device or cloud service. You should also create a restore point or a recovery mode for your device in case something goes wrong. This way, you can restore your device to its original state if you encounter any problems.</p>
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-<p>If you want to enjoy Coin Master without risking your device, account, or legal status, you should consider some alternatives to downloading a hacked Coin Master apk. Here are some of them:</p>
- <h3>Use legitimate cheats and tips</h3>
-<p>Instead of using a hacked Coin Master apk, you can use some legitimate cheats and tips that can help you get more coins, spins, and cards in the game. For example, you can follow these steps:</p>
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-<p>Coin Master is a fun and addictive casual game that combines slot machines, raids, attacks, and card collections. However, if you want to get unlimited resources in the game, you may be tempted to download a hacked Coin Master apk. However, this is not a wise decision, as it comes with many risks and drawbacks. You may end up infecting your device with malware, getting banned from the game, or missing out on the latest updates and features.</p>
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-<h1>How to Download AetherSX2 PS2 Emulator</h1>
-<p>If you are a fan of PlayStation 2 games and want to enjoy them on your Android device, you might have heard of AetherSX2. It is a new and fast PS2 emulator that is based on PCSX2, a well-known emulator for PC. AetherSX2 can run most PS2 games at full speed, with high graphics quality, and online play support. In this article, we will show you how to download and install AetherSX2, how to get the BIOS file from your PS2 console, how to configure the emulator settings and controls, and how to play your favorite PS2 games on your Android device.</p>
- <h2>What is AetherSX2 and why it is the best PS2 emulator for Android</h2>
-<p>AetherSX2 is a free and open-source PS2 emulator for Android that was released in late 2021. It is based on PCSX2, which is a long-running and well-established emulator for PC. AetherSX2 uses the same code as PCSX2, but optimizes it for Android devices. It also adds some features that are not available in PCSX2, such as Vulkan graphics renderer, netplay support, and touch screen controls.</p>
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-<p>AetherSX2 is widely considered as the best PS2 emulator for Android because of its high compatibility, performance, and accuracy. It can run most PS2 games smoothly, with minimal glitches and bugs. It also supports various enhancements, such as custom resolutions, anti-aliasing, texture filtering, save states, cheats, patches, and more. You can also play online with other players using Nintendo Wi-Fi Connection or LAN.</p>
- <h3>What are the requirements and features of AetherSX2</h3>
-<p>To use AetherSX2, you need an Android device that meets the following requirements:</p>
-<ul>
-<li>A CPU that supports SSE4.1 or higher (e.g., Snapdragon 845 or better)</li>
-<li>A GPU that supports Vulkan or OpenGL (e.g., Adreno or Mali)</li>
-<li>At least 4 GB of RAM</li>
-<li>At least 8 GB of storage space</li>
-<li>An Android version of 7.0 or higher</li>
-</ul>
-<p>Some of the features of AetherSX2 are:</p>
-<ul>
-<li>High compatibility: It can run over 90% of the PS2 library, with many games being playable or perfect.</li>
-<li>High performance: It can achieve full speed emulation on most devices, with low latency and battery consumption.</li>
-<li>High accuracy: It can emulate the PS2 hardware faithfully, with accurate timing and synchronization.</li>
-<li>Vulkan graphics renderer: It can improve the graphics quality and speed of the games, especially on devices with Mali GPUs.</li>
-<li>Netplay support: It can connect to Nintendo Wi-Fi Connection servers or LAN networks for online multiplayer gaming.</li>
-<li>Touch screen controls: It can emulate the PS2 controller using virtual buttons on the screen.</li>
-<li>Custom resolutions: It can upscale the games to higher resolutions than the original PS2 output.</li>
-<li>Anti- <li>To save your settings and controls, tap on "Apply" and then "Back".</li>
-</ol>
- <h2>How to play PS2 games on AetherSX2</h2>
-<p>To play PS2 games on AetherSX2, you need to have the game files in ISO format. You can either dump your own PS2 games from your discs or download them from the internet. However, downloading PS2 games may be illegal in some regions, so do it at your own risk. Here are the steps to play PS2 games on AetherSX2:</p>
- <h3>How to dump your PS2 games into ISO files</h3>
-<ol>
-<li>You need a PS2 console, a PS2 game disc, a USB flash drive, a PC, and a software called DVD Decrypter.</li>
-<li>Download DVD Decrypter from this link: [DVD Decrypter] and install it on your PC.</li>
-<li>Insert your USB flash drive into your PC and format it to FAT32.</li>
-<li>Insert your PS2 game disc into your PC's DVD drive and launch DVD Decrypter.</li>
-<li>Select "Mode" and then "ISO" and then "Read".</li>
-<li>Select your DVD drive as the source and your USB flash drive as the destination.</li>
-<li>Click on the green arrow button to start the dumping process. It may take some time depending on the size of the game.</li>
-<li>Once the dumping is done, you will have an ISO file of your PS2 game on your USB flash drive.</li>
-</ol>
- <h3>How to load and run the games on the emulator</h3>
-<ol>
-<li>Launch AetherSX2 on your Android device.</li>
-<li>To load a game, tap on the folder icon on the top right corner of the screen.</li>
-<li>Navigate to the location where you have stored your ISO files. You can use your internal storage or an external SD card.</li>
-<li>Select the ISO file of the game you want to play and tap on it.</li>
-<li>The emulator will boot up and run the game. You will see the PS2 logo and then the game's intro screen.</li>
-<li>To play the game, use the virtual controller on the screen. You can also connect a physical controller via Bluetooth or USB if you prefer.</li>
-</ol>
- <h3>How to use save states, cheats, and other options</h3>
-<p>To use save states, cheats, and other options, you need to access the emulator's menu while playing a game. Here are the steps to do that:</p>
-<p>How to download and install AetherSX2 on Android<br />
-AetherSX2 guide: The best PS2 emulator for Android[^1^]<br />
-AetherSX2 vs DamonPS2: Which PS2 emulator is better?<br />
-AetherSX2 compatibility list: Which PS2 games work on Android?<br />
-AetherSX2 settings: How to optimize performance and graphics<br />
-AetherSX2 cheats: How to use cheat codes on PS2 games<br />
-AetherSX2 bios: Where to find and how to use PS2 bios files<br />
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-AetherSX2 review: Is it worth downloading the app?<br />
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-How to improve battery life and reduce overheating on AetherSX2 emulator<br />
-How to change the language and region of PS2 games on AetherSX2 emulator<br />
-How to enable cheats for PAL and NTSC versions of PS2 games on AetherSX2 emulator<br />
-How to play PS1 games on AetherSX2 emulator using RetroArch core<br />
-How to use custom textures and models for PS2 games on AetherSX</p>
-<ol>
-<li>To access the menu, tap on the three horizontal lines icon on the top left corner of the screen.</li>
-<li>To use save states, tap on "Save State" or "Load State". You will see 10 slots for saving and loading your game progress. You can also use hotkeys to quickly save and load states.</li>
-<li>To use cheats, tap on "Cheats". You will see a list of available cheats for the game you are playing. You can enable or disable them by tapping on them. You can also add your own cheats by tapping on "Add Cheat".</li>
-<li>To use other options, tap on "Options". You will see various options for graphics, sound, input, system, network, etc. You can adjust them according to your preference and device capability. For example, you can change the resolution, renderer, audio latency, controller layout, BIOS file location, etc.</li>
-<li>To resume playing, tap on "Resume".</li>
-</ol>
- <h2>Conclusion</h2>
-<p>AetherSX2 is a powerful and versatile PS2 emulator for Android that can run most PS2 games at full speed and high quality. It is easy to download and install, and it has many features and options to enhance your gaming experience. You can also play online with other players using Nintendo Wi-Fi Connection or LAN. If you are looking for a way to enjoy PS2 games on your Android device, you should definitely give AetherSX2 a try.</p>
- <p>Here are some tips and tricks for using AetherSX2:</p>
-<ul>
-<li>Make sure your device meets the minimum requirements for running AetherSX2. If not, you may experience lagging, crashing, or compatibility issues.</li>
-<li>Dump your own BIOS file from your own PS2 console. Do not use a BIOS file from another source, as it may not work or may be illegal.</li>
-<li>Dump your own PS2 games from your discs or download them from the internet. However, downloading PS2 games may be illegal in some regions, so do it at your own risk.</li>
-<li>Configure the emulator settings and controls according to your preference and device capability. Experiment with different options until you find the best balance between performance and quality.</li>
-<li>Use save states, cheats, and patches to enhance your gaming experience. However, do not abuse them or use them to cheat online, as it may ruin the fun for yourself and others.</li>
-<li>Play online with other players using Nintendo Wi-Fi Connection or LAN. However, make sure you have a stable internet connection and a compatible game version, as it may affect the gameplay and synchronization.</li>
-</ul>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about AetherSX2:</p>
- <h3>What are some of the best PS2 games to play on AetherSX2?</h3>
-<p>There are many PS2 games that you can play on AetherSX2, but some of the most popular and recommended ones are:</p>
-<ul>
-<li>God of War and God of War II: Action-adventure games that follow the epic journey of Kratos, a Spartan warrior who battles against the gods and monsters of Greek mythology.</li>
-<li>Grand Theft Auto: San Andreas: An open-world game that lets you explore the fictional state of San Andreas, based on California and Nevada, and engage in various missions and activities.</li>
-<li>Shadow of the Colossus: An adventure game that challenges you to defeat 16 giant creatures called colossi, using only your sword, bow, and horse.</li>
-<li>Metal Gear Solid 3: Snake Eater: A stealth game that takes place in the Cold War era, where you play as Naked Snake, a special agent who must infiltrate a Soviet base and stop a nuclear threat.</li>
-<li>Final Fantasy X: A role-playing game that follows the story of Tidus, a young athlete who is transported to a fantasy world called Spira, where he joins a group of adventurers to defeat a monstrous entity called Sin.</li>
-</ul>
- <h3>How can I improve the graphics and sound quality of the games?</h3>
-<p>You can improve the graphics and sound quality of the games by adjusting the emulator settings. Here are some of the options you can use:</p>
-<ul>
-<li>Vulkan graphics renderer: This option can improve the graphics quality and speed of the games, especially on devices with Mali GPUs. However, it may not work on some devices or games, so you may need to switch to OpenGL if you encounter any issues.</li>
-<li>Custom resolutions: This option can upscale the games to higher resolutions than the original PS2 output. However, it may also increase the CPU and GPU load, so you may need to lower it if you experience lagging or overheating.</li>
-<li>Anti-aliasing: This option can smooth the jagged edges of the polygons and textures in the games. However, it may also reduce the performance and compatibility, so you may need to disable it if you encounter any problems.</li>
-<li>Texture filtering: This option can enhance the sharpness and clarity of the textures in the games. However, it may also cause some graphical glitches or artifacts, so you may need to turn it off if you notice any errors.</li>
-<li>Audio latency: This option can reduce the delay between the sound output and the game action. However, it may also cause some audio crackling or distortion, so you may need to increase it if you hear any noise.</li>
-</ul>
- <h3>What are some of the common issues and solutions for AetherSX2?</h3>
-<p>Some of the common issues and solutions for AetherSX2 are:</p>
-<ul>
-<li>The emulator crashes or freezes: This may be caused by insufficient device resources, incompatible game versions, corrupted BIOS or ISO files, or incorrect emulator settings. To fix this, you can try clearing your device cache, updating your game files, verifying your BIOS and ISO files, or resetting your emulator settings.</li>
-<li>The game runs slowly or lags: This may be caused by high device temperature, low battery level, background apps, or unsuitable emulator settings. To fix this, you can try cooling down your device, charging your battery, closing other apps, or lowering your emulator settings.</li>
-<li>The game has graphical or sound glitches: This may be caused by incompatible game versions, corrupted BIOS or ISO files, or inappropriate emulator settings. To fix this, you can try updating your game files, verifying your BIOS and ISO files, or changing your emulator settings.</li>
-<li>The game does not load or run: This may be caused by unsupported game versions, missing BIOS or ISO files, or incorrect emulator settings. To fix this, you can try checking the compatibility list, obtaining the correct BIOS and ISO files, or adjusting your emulator settings.</li>
-</ul>
- <h3>How can I play multiplayer games on AetherSX2?</h3>
-<p>You can play multiplayer games on AetherSX2 using two methods: Nintendo Wi-Fi Connection or LAN. Here are the steps for both methods:</p>
- <h4>Nintendo Wi-Fi Connection</h4>
-<ol>
-<li>Make sure you have a stable internet connection and a compatible game version.</li>
-<li>Launch AetherSX2 and load the game you want to play.</li>
-<li>Access the emulator's menu and tap on "Network".</li>
-<li>Tap on "Enable Nintendo Wi-Fi Connection" and then "Connect".</li>
-<li>Wait for the emulator to connect to the Nintendo server and obtain an IP address.</li>
-<li>Resume playing and access the game's online mode.</li>
-<li>Follow the game's instructions to join or create a room with other players.</li>
-</ol>
- <h4>LAN</h4>
-<ol>
-<li>Make sure you have a local network and a compatible game version.</li>
-<li>Launch AetherSX2 and load the game you want to play.</li>
-<li>Access the emulator's menu and tap on "Network".</li>
-<li>Tap on "Enable LAN" and then "Host" or "Join".</li>
-<li>If you are hosting, wait for the emulator to create a room and display a room ID. If you are joining, enter the room ID of the host.</li>
-<li>Resume playing and access the game's multiplayer mode.</li>
-<li>Follow the game's instructions to start or join a match with other players.</li>
-</ol>
- <h3>Is AetherSX2 legal and safe to use?</h3>
-<p>AetherSX2 is legal and safe to use as long as you follow some rules:</p>
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-<li>You must own a PS2 console and dump your own BIOS file from it. You cannot use a BIOS file from another source, as it may not work or may be illegal.</li>
-<li>You must own the PS2 games you want to play and dump them into ISO files. You cannot download PS2 games from the internet, as it may be illegal in some regions.</li>
-<li>You must not distribute or share your BIOS or ISO files with anyone else, as it may violate the copyright laws.</li>
-<li>You must not use cheats or hacks to gain an unfair advantage or disrupt the online gameplay of other players, as it may ruin the fun for yourself and others.</li>
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-<h1>Dhoom 2 Movie Download 360p: How to Watch the Action Thriller Online</h1>
-<p>Dhoom 2 is a 2006 Indian Hindi-language action thriller film that is a sequel to the 2004 film Dhoom. It is the second installment in the Dhoom series and features Hrithik Roshan, Abhishek Bachchan, Aishwarya Rai, Bipasha Basu, and Uday Chopra in the lead roles. The film follows a high-tech international thief who steals valuable artifacts from around the world and teams up with a clever female accomplice, while being pursued by three police officers who are determined to catch them.</p>
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-<tr><th>Release date</th><td>24 November 2006</td></tr>
-<tr><th>Runtime</th><td>152 minutes</td></tr>
-<tr><th>Budget</th><td>₹350 million</td></tr>
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-<h1>Stickman Superhero 1.2 Mod APK: A Guide for Gamers</h1>
- <p>If you are looking for a fun and exciting game that combines stickman action and superhero simulation, then you might want to check out Stickman Superhero 1.2 Mod APK. This is a modified version of the original game that offers you unlimited access to all the features, items, and resources of the game. In this article, we will tell you everything you need to know about this game, including how to download and install it, how to play it, how to customize your stickman superhero, how to upgrade your stickman superhero, and what are the pros and cons of using this mod APK.</p>
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- <h2>What is Stickman Superhero 1.2 Mod APK?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Stickman Superhero is an action-packed gaming app where you take control of a stickman character with superhero abilities. You can choose from a variety of superhero costumes, abilities, and powers, and use them to fight against various enemies and save the city from destruction. You can also use different weapons and gadgets, such as swords, guns, and devices that can help you in battles.</p>
- <p>The game offers a wide range of modes, missions, and challenges for you to enjoy. You can test your skills in an array of stickman battles and missions, while becoming the ultimate stickman hero. You can also explore the open world city and interact with various objects and characters. Whether you are fighting off hordes of enemies, saving civilians, or defeating powerful bosses, this game will keep you on the edge of your seat.</p>
- <h3>What is a mod APK and why use it?</h3>
- <p>A mod APK is a modified version of an original game or app that offers some extra features or benefits that are not available in the original version. For example, a mod APK may offer unlimited money, unlocked items, or ad-free experience.</p>
- <p>Using a mod APK can enhance your gaming experience by giving you more freedom and flexibility to play the game as you wish. You can access all the features, items, and resources of the game without any limitations or restrictions. You can also avoid annoying ads and pop-ups that may interrupt your gaming experience.</p>
- <p>However, using a mod APK also comes with some disadvantages and risks. For example, a mod APK may not be compatible with all devices or operating systems. It may also contain viruses or malware that can harm your device or steal your personal information. Moreover, using a mod APK may violate the terms and conditions of the original game or app, and result in a ban or legal action.</p>
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- <p>Therefore, before you decide to use a mod APK, you should weigh the pros and cons carefully and make sure you download it from a reliable and trustworthy source.</p>
- <h2>How to download and install Stickman Superhero 1.2 Mod APK?</h2>
- <h3>The steps to download the mod APK from a reliable source</h3>
- <p>If you want to download and install Stickman Superhero 1.2 Mod APK, you need to follow these steps:</p>
- <ol>
-<li>Go to a reputable website that offers the mod APK file for download. For example, you can visit [this link] to download the mod APK file.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Once the file is downloaded, locate it in your device's file manager and tap on it to open it.</li>
-</ol>
- <h3>The steps to install the mod APK on your device</h3>
- <p>Before you install the mod APK on your device, you need to make sure that you have enabled the installation of unknown sources on your device. To do this, go to your device's settings, then security, then unknown sources, and toggle it on.</p>
- <p>After you have enabled the installation of unknown sources, you can proceed with these steps:</p>
- <ol>
-<li>Tap on the mod APK file that you have opened in the previous step.</li>
-<li>A pop-up window will appear asking you to confirm the installation. Tap on install and wait for the installation process to complete.</li>
-<li>Once the installation is done, you can launch the game from your device's app drawer or home screen.</li>
-</ol>
- <p>Congratulations! You have successfully downloaded and installed Stickman Superhero 1.2 Mod APK on your device. Now you can enjoy playing the game with all its features and benefits.</p>
- <h2>How to play Stickman Superhero 1.2 Mod APK?</h2>
- <h3>The basic gameplay and controls of the game</h3>
- <p>Stickman Superhero 1.2 Mod APK is easy to play and control. The game has a simple and intuitive user interface that shows you all the information and options you need to play the game. You can see your health bar, energy bar, weapon bar, ability bar, and currency bar at the top of the screen. You can also see your mission objectives, map, and pause button at the bottom of the screen.</p>
- <p>The game uses touch controls to move and interact with your stickman superhero. You can use the virtual joystick on the left side of the screen to move your stickman superhero around. You can use the buttons on the right side of the screen to perform various actions, such as jumping, attacking, using abilities, using weapons, and using gadgets.</p>
- <p>You can also swipe on the screen to change the camera angle and zoom in or out. You can also tap on the screen to interact with various objects and characters in the game world.</p>
- <h3>The different modes, missions, and challenges of the game</h3>
- <p>Stickman Superhero 1.2 Mod APK offers a variety of modes, missions, and challenges for you to enjoy. You can choose from these modes:</p>
- <ul>
-<li>Campaign mode: This is the main mode of the game where you follow a storyline and complete various missions and tasks. You can unlock new costumes, abilities, weapons, and gadgets as you progress through the campaign mode.</li>
-<li>Free mode: This is a mode where you can explore the open world city and do whatever you want. You can fight enemies, save civilians, collect resources, or just have fun.</li>
-<li>Boss mode: This is a mode where you face off against powerful bosses that have unique abilities and attacks. You need to use your skills and strategy to defeat them.</li>
-<li>Arena mode: This is a mode where you compete against other players online in different arenas. You can choose from different modes such as deathmatch, team deathmatch, capture the flag, or king of the hill.</li>
-</ul>
- <p>You can also take on various challenges that test your skills and abilities in different ways. You can earn rewards and achievements by completing these challenges. Some examples of challenges are:</p>
- <ul>
-<li>Survival challenge: This is a challenge where you have to survive as long as possible against waves of enemies that get stronger and more numerous over time.</li>
-<li>Time trial challenge: This is a challenge where you have to complete a certain mission or task within a given time limit.</li>
-<li>Stealth challenge: This is a challenge where you have to avoid detection and complete a certain mission or task without alerting the enemies.</li>
-<li>Accuracy challenge: This is a challenge where you have to hit a certain number of targets with your weapons or abilities within a given time limit.</li>
-</ul>
- <p>These modes, missions, and challenges will keep you entertained and engaged for hours as you play Stickman Superhero 1.2 Mod APK.</p>
- <h2>How to customize your stickman superhero?</h2>
- <h3>The different costumes, abilities, and powers you can choose from</h3>
- <p>One of the best features of Stickman Superhero 1.2 Mod APK is that you can customize your stickman superhero according to your preferences and style. You can choose from a variety of costumes, abilities, and powers that will make your stickman superhero unique and awesome.</p>
- <p>You can access the customization menu by tapping on the costume icon on the top right corner of the screen. Here, you can see all the options you have for customizing your stickman superhero. You can choose from these categories:</p>
- <ul>
-<li>Costumes: You can choose from different superhero costumes that are inspired by popular comic book and movie characters. For example, you can choose to dress up as Spider-Man, Iron Man, Batman, Superman, or Deadpool. Each costume has its own appearance and stats that affect your stickman superhero's performance.</li>
-<li>Abilities: You can choose from different abilities that give your stickman superhero special skills and powers. For example, you can choose to have super strength, super speed, flight, invisibility, or telekinesis. Each ability has its own energy cost and cooldown time that affect your stickman superhero's gameplay.</li>
-<li>Powers: You can choose from different powers that give your stickman superhero offensive or defensive capabilities. For example, you can choose to have fireballs, lightning bolts, ice blasts, or force fields. Each power has its own damage and range that affect your stickman superhero's combat.</li>
-</ul>
- <p>You can mix and match different costumes, abilities, and powers to create your own unique stickman superhero. You can also change your customization options anytime you want by going back to the customization menu.</p>
- <h3>The different weapons and gadgets you can use in battles</h3>
- <p>In addition to costumes, abilities, and powers, you can also use different weapons and gadgets to enhance your stickman superhero's performance in battles. You can access the weapon menu by tapping on the weapon icon on the top right corner of the screen. Here, you can see all the options you have for using weapons and gadgets. You can choose from these categories:</p>
- <ul>
-<li>Weapons: You can choose from different weapons that can help you attack or defend yourself in battles. For example, you can choose to use swords, guns, grenades, or rockets. Each weapon has its own damage, accuracy, and ammo that affect your stickman superhero's combat.</li>
-<li>Gadgets: You can choose from different gadgets that can help you with various tasks or situations in battles. For example, you can choose to use jetpacks, grappling hooks, parachutes, or drones. Each gadget has its own function and usage that affect your stickman superhero's gameplay.</li>
-</ul>
- <p>You can switch between different weapons and gadgets by swiping on the weapon bar on the top right corner of the screen. You can also upgrade your weapons and gadgets by using the resources and currency you collect in the game.</p>
- <h2>How to upgrade your stickman superhero?</h2>
- <h3>The benefits of upgrading your abilities and gear</h3>
- <p>As you play Stickman Superhero 1.2 Mod APK, you will notice that the game becomes more challenging and difficult as you progress through the modes, missions, and challenges. To keep up with the increasing difficulty level, you need to upgrade your stickman superhero's abilities and gear.</p>
- <p>Upgrading your abilities and gear will give you various benefits such as:</p>
- <ul>
-<li>Increasing your stickman superhero's health, energy, damage, defense, speed, and other stats.</li>
-<li>Improving your stickman superhero's performance in battles by enhancing their skills and powers.</li>
-<li>Unlocking new costumes, abilities, powers, weapons, and gadgets that offer more options and variety for customizing your stickman superhero.</li>
-<li>Gaining an edge over your enemies and competitors by having more options and variety for playing the game.</li>
-</ul>
- <p>Therefore, upgrading your abilities and gear is essential for enjoying the game to the fullest and becoming the ultimate stickman superhero.</p>
- <h3>The resources and currency you need to upgrade</h3>
- <p>To upgrade your abilities and gear, you need to use the resources and currency that you collect in the game. There are two types of resources and currency in the game:</p>
- <ul>
-<li>Coins: These are the basic currency of the game that you can use to buy and upgrade your weapons and gadgets. You can earn coins by completing missions, challenges, and achievements, or by collecting them in the game world.</li>
-<li>Gems: These are the premium currency of the game that you can use to buy and upgrade your costumes, abilities, and powers. You can earn gems by completing special missions, challenges, and achievements, or by buying them with real money.</li>
-</ul>
- <p>You can see how many coins and gems you have by looking at the currency bar on the top right corner of the screen. You can also see how much coins and gems you need to upgrade your abilities and gear by tapping on them in the customization or weapon menu.</p>
- <p>To upgrade your abilities and gear, you need to follow these steps:</p>
- <ol>
-<li>Go to the customization or weapon menu by tapping on the costume or weapon icon on the top right corner of the screen.</li>
-<li>Select the ability or gear that you want to upgrade by tapping on it.</li>
-<li>If you have enough coins or gems to upgrade it, tap on the upgrade button and confirm your choice.</li>
-<li>If you do not have enough coins or gems to upgrade it, you can either earn more by playing the game or buy more with real money by tapping on the shop button.</li>
-</ol>
- <p>Once you have upgraded your ability or gear, you will see its new stats and appearance. You can also see its level and progress bar that indicate how much more you can upgrade it.</p>
- <h2>What are the pros and cons of Stickman Superhero 1.2 Mod APK?</h2>
- <h3>The advantages of using the mod APK over the original game</h3>
- <p>As we have mentioned before, using Stickman Superhero 1.2 Mod APK can enhance your gaming experience by giving you more freedom and flexibility to play the game as you wish. Some of the advantages of using the mod APK over the original game are:</p>
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-<li>You can access all the features, items, and resources of the game without any limitations or restrictions. You do not have to wait for timers, watch ads, or spend real money to enjoy the game.</li>
-<li>You can customize your stickman superhero with any combination of costumes, abilities, powers, weapons, and gadgets that you like. You do not have to unlock them or earn them in the game.</li>
-<li>You can play the game with more ease and fun by having unlimited health, energy, damage, defense, speed, and other stats. You do not have to worry about dying, running out of energy, or losing battles.</li>
-</ul>
- <p>These advantages can make your gaming experience more enjoyable and satisfying as you play Stickman Superhero 1.2 Mod APK.</p>
- <h3>The disadvantages and risks of using the mod APK</h3>
- <p>However, using Stickman Superhero 1.2 Mod APK also comes with some disadvantages and risks that you should be aware of. Some of the disadvantages and risks of using the mod APK over the original game are:</p>
- <ul>
-<li>You may encounter some bugs, glitches, or errors that may affect your gaming experience. The mod APK may not be updated or optimized for the latest version of the game or your device.</li>
-<li>You may expose your device or personal information to viruses or malware that may harm your device or steal your data. The mod APK may contain malicious code or links that may infect your device or redirect you to unsafe websites.</li>
-<li>You may violate the terms and conditions of the original game or app, and result in a ban or legal action. The mod APK may be detected by the game's security system or reported by other players, and lead to your account being suspended or terminated.</li>
-</ul>
- <p>These disadvantages and risks can make your gaming experience more frustrating and risky as you use Stickman Superhero 1.2 Mod APK.</p>
- <h2>Conclusion</h2>
- <h3>A summary of the main points of the article</h3>
- <p>In conclusion, Stickman Superhero 1.2 Mod APK is a modified version of the original game that offers you unlimited access to all the features, items, and resources of the game. It is a fun and exciting game that combines stickman action and superhero simulation, where you can customize your stickman superhero with various costumes, abilities, powers, weapons, and gadgets, and use them to fight against enemies and save the city from destruction. You can also enjoy a wide range of modes, missions, and challenges that will test your skills and abilities in different ways.</p>
- <h3>A call to action for the readers to try out the game</h3>
- <p>If you are interested in trying out this game, you can download and install Stickman Superhero 1.2 Mod APK by following the steps we have provided in this article. However, you should also be aware of the disadvantages and risks of using the mod APK over the original game, and make sure you download it from a reliable and trustworthy source. You should also respect the rights and property of the original game developers and publishers, and use the mod APK at your own risk and responsibility.</p>
- <p>We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
- <h4>Is Stickman Superhero 1.2 Mod APK safe to use?</h4>
- <p>Stickman Superhero 1.2 Mod APK is not guaranteed to be safe to use, as it may contain viruses or malware that can harm your device or steal your personal information. It may also violate the terms and conditions of the original game or app, and result in a ban or legal action. Therefore, you should use it at your own risk and responsibility, and make sure you download it from a reliable and trustworthy source.</p>
- <h4>Is Stickman Superhero 1.2 Mod APK compatible with all devices?</h4>
- <p>Stickman Superhero 1.2 Mod APK may not be compatible with all devices or operating systems, as it may not be updated or optimized for the latest version of the game or your device. It may also encounter some bugs, glitches, or errors that may affect your gaming experience. Therefore, you should check the compatibility of the mod APK with your device before downloading and installing it.</p>
- <h4>How to update Stickman Superhero 1.2 Mod APK?</h4>
- <p>To update Stickman Superhero 1.2 Mod APK, you need to download and install the latest version of the mod APK from a reputable website that offers it for download. You should also delete the previous version of the mod APK from your device before installing the new one. However, you should note that updating the mod APK may cause some issues or problems with your game data or progress, so you should back up your data before updating.</p>
- <h4>How to uninstall Stickman Superhero 1.2 Mod APK?</h4>
- <p>To uninstall Stickman Superhero 1.2 Mod APK, you need to follow these steps:</p>
- <ol>
-<li>Go to your device's settings, then apps, then Stickman Superhero 1.2 Mod APK.</li>
-<li>Tap on uninstall and confirm your choice.</li>
-<li>Once the uninstallation is done, you can delete the mod APK file from your device's file manager.</li>
-</ol>
- <p>You have successfully uninstalled Stickman Superhero 1.2 Mod APK from your device.</p>
- <h4>Where can I find more information about Stickman Superhero 1.2 Mod APK?</h4>
- <p>If you want to find more information about Stickman Superhero 1.2 Mod APK, you can visit [this link] where you can find more details, reviews, screenshots, videos and more about the game and the mod APK. You can also join the online community of stickman superhero fans and share your thoughts, tips, and feedback with other players.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/A-Celsius/ADR_Predictor/README.md

@@ -1,12 +0,0 @@
----
-title: ADR Predictor
-emoji: 🐢
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/A00001/bingothoo/src/components/chat-notification.tsx

@@ -1,77 +0,0 @@
-import { useEffect } from 'react'
-import Image from 'next/image'
-
-import IconWarning from '@/assets/images/warning.svg'
-import { ChatError, ErrorCode, ChatMessageModel } from '@/lib/bots/bing/types'
-import { ExternalLink } from './external-link'
-import { useBing } from '@/lib/hooks/use-bing'
-
-export interface ChatNotificationProps extends Pick<ReturnType<typeof useBing>, 'bot'> {
-  message?: ChatMessageModel
-}
-
-function getAction(error: ChatError, reset: () => void) {
-  if (error.code === ErrorCode.THROTTLE_LIMIT) {
-    reset()
-    return (
-      <div>
-        你已达到每日最大发送消息次数，请<a href={`#dialog="settings"`}>更换账号</a>或隔一天后重试
-      </div>
-    )
-  }
-  if (error.code === ErrorCode.BING_FORBIDDEN) {
-    return (
-      <ExternalLink href="https://bing.com/new">
-        你的账号已在黑名单，请尝试更换账号及申请解封
-      </ExternalLink>
-    )
-  }
-  if (error.code === ErrorCode.CONVERSATION_LIMIT) {
-    return (
-      <div>
-        当前话题已中止，请点
-        <a href={`#dialog="reset"`}>重新开始</a>
-        开启新的对话
-      </div>
-    )
-  }
-  if (error.code === ErrorCode.BING_CAPTCHA) {
-    return (
-      <ExternalLink href="https://www.bing.com/turing/captcha/challenge">
-        点击通过人机验证
-      </ExternalLink>
-    )
-  }
-  if (error.code === ErrorCode.BING_UNAUTHORIZED) {
-    reset()
-    return (
-      <a href={`#dialog="settings"`}>没有获取到身份信息或身份信息失效，点此重新设置</a>
-    )
-  }
-  return error.message
-}
-
-export function ChatNotification({ message, bot }: ChatNotificationProps) {
-  useEffect(() => {
-    window.scrollBy(0, 2000)
-  }, [message])
-
-  if (!message?.error) return
-
-  return (
-    <div
-      className="notification-container"
-    >
-      <div className="bottom-notifications">
-        <div className="inline-type with-decorative-line">
-          <div className="text-container mt-1">
-            <div className="title inline-flex items-start">
-              <Image alt="error" src={IconWarning} width={20} className="mr-1 mt-1" />
-              {getAction(message.error, () => bot.resetConversation())}
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}

spaces/AIFILMS/Pix2Pix-Video/README.md

@@ -1,13 +0,0 @@
----
-title: Pix2Pix Video
-emoji: 🎨🎞️
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: true
-duplicated_from: fffiloni/Pix2Pix-Video
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/bert.py

@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output

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

@@ -1,262 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
-    extract_into_tensor
-
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            # if attr.device != torch.device("cuda"):
-            #     attr = attr.to(torch.device("cuda"))
-            attr = attr.to(self.device)
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
-                cbs = ctmp.shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-
-        # iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-
-        for i, step in enumerate(time_range):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning)
-            img, pred_x0 = outs
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None):
-        b, *_, device = *x.shape, x.device
-
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = dict()
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [torch.cat([
-                            unconditional_conditioning[k][i],
-                            c[k][i]]) for i in range(len(c[k]))]
-                    else:
-                        c_in[k] = torch.cat([
-                            unconditional_conditioning[k],
-                            c[k]])
-            elif isinstance(c, list):
-                c_in = list()
-                assert isinstance(unconditional_conditioning, list)
-                for i in range(len(c)):
-                    c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])# c/uc shape [b,seq_len=77,dim=1024],c_in shape [b*2,seq_len,dim]
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-        if use_original_steps:
-            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
-        else:
-            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
-        if noise is None:
-            noise = torch.randn_like(x0)
-        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
-                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
-
-    @torch.no_grad()
-    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False):
-
-        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
-        timesteps = timesteps[:t_start]
-
-        time_range = np.flip(timesteps)
-        total_steps = timesteps.shape[0]
-        # print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        # iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
-        x_dec = x_latent
-        for i, step in enumerate(time_range):
-            index = total_steps - i - 1
-            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
-            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
-                                          unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning)
-        return x_dec

spaces/AIML-TUDA/safe-stable-diffusion/README.md

@@ -1,12 +0,0 @@
----
-title: Safe Stable Diffusion
-colorFrom: blue
-colorTo: red
-emoji: 😇
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: true
-license: creativeml-openrail-m
----
-

spaces/AISuperheroes/07GR-NLP-Seq2Seq-AutoQA/qasrl_model_pipeline.py

@@ -1,183 +0,0 @@
-from typing import Optional
-import json
-from argparse import Namespace
-from pathlib import Path
-from transformers import Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer
-
-def get_markers_for_model(is_t5_model: bool) -> Namespace:
-    special_tokens_constants = Namespace() 
-    if is_t5_model:
-        # T5 model have 100 special tokens by default
-        special_tokens_constants.separator_input_question_predicate = "<extra_id_1>"
-        special_tokens_constants.separator_output_answers = "<extra_id_3>"
-        special_tokens_constants.separator_output_questions = "<extra_id_5>"  # if using only questions
-        special_tokens_constants.separator_output_question_answer = "<extra_id_7>"
-        special_tokens_constants.separator_output_pairs = "<extra_id_9>"
-        special_tokens_constants.predicate_generic_marker = "<extra_id_10>" 
-        special_tokens_constants.predicate_verb_marker = "<extra_id_11>" 
-        special_tokens_constants.predicate_nominalization_marker = "<extra_id_12>" 
-
-    else:
-        special_tokens_constants.separator_input_question_predicate = "<question_predicate_sep>"
-        special_tokens_constants.separator_output_answers = "<answers_sep>"
-        special_tokens_constants.separator_output_questions = "<question_sep>"  # if using only questions
-        special_tokens_constants.separator_output_question_answer = "<question_answer_sep>"
-        special_tokens_constants.separator_output_pairs = "<qa_pairs_sep>"
-        special_tokens_constants.predicate_generic_marker = "<predicate_marker>" 
-        special_tokens_constants.predicate_verb_marker = "<verbal_predicate_marker>" 
-        special_tokens_constants.predicate_nominalization_marker = "<nominalization_predicate_marker>" 
-    return special_tokens_constants
-
-def load_trained_model(name_or_path):
-    import huggingface_hub as HFhub
-    tokenizer = AutoTokenizer.from_pretrained(name_or_path)
-    model = AutoModelForSeq2SeqLM.from_pretrained(name_or_path)  
-    # load preprocessing_kwargs from the model repo on HF hub, or from the local model directory
-    kwargs_filename = None
-    if name_or_path.startswith("kleinay/"): # and 'preprocessing_kwargs.json' in HFhub.list_repo_files(name_or_path): # the supported version of HFhub doesn't support list_repo_files
-        kwargs_filename = HFhub.hf_hub_download(repo_id=name_or_path, filename="preprocessing_kwargs.json")
-    elif Path(name_or_path).is_dir() and (Path(name_or_path) / "experiment_kwargs.json").exists():
-        kwargs_filename = Path(name_or_path) / "experiment_kwargs.json"
-    
-    if kwargs_filename:
-        preprocessing_kwargs = json.load(open(kwargs_filename)) 
-        # integrate into model.config (for decoding args, e.g. "num_beams"), and save also as standalone object for preprocessing
-        model.config.preprocessing_kwargs = Namespace(**preprocessing_kwargs)
-        model.config.update(preprocessing_kwargs)
-    return model, tokenizer
-
-
-class QASRL_Pipeline(Text2TextGenerationPipeline):
-    def __init__(self, model_repo: str, **kwargs):
-        model, tokenizer = load_trained_model(model_repo)
-        super().__init__(model, tokenizer, framework="pt")
-        self.is_t5_model = "t5" in model.config.model_type
-        self.special_tokens = get_markers_for_model(self.is_t5_model)
-        self.data_args = model.config.preprocessing_kwargs 
-        # backward compatibility - default keyword values implemeted in `run_summarization`, thus not saved in `preprocessing_kwargs`
-        if "predicate_marker_type" not in vars(self.data_args):
-            self.data_args.predicate_marker_type = "generic"
-        if "use_bilateral_predicate_marker" not in vars(self.data_args):
-            self.data_args.use_bilateral_predicate_marker = True
-        if "append_verb_form" not in vars(self.data_args):
-            self.data_args.append_verb_form = True
-        self._update_config(**kwargs)
-    
-    def _update_config(self, **kwargs):
-        " Update self.model.config with initialization parameters and necessary defaults. "
-        # set default values that will always override model.config, but can overriden by __init__ kwargs
-        kwargs["max_length"] = kwargs.get("max_length", 80)
-        # override model.config with kwargs
-        for k,v in kwargs.items():
-            self.model.config.__dict__[k] = v           
-        
-    def _sanitize_parameters(self, **kwargs):
-        preprocess_kwargs, forward_kwargs, postprocess_kwargs = {}, {}, {} 
-        if "predicate_marker" in kwargs:
-            preprocess_kwargs["predicate_marker"] = kwargs["predicate_marker"]
-        if "predicate_type" in kwargs:
-            preprocess_kwargs["predicate_type"] = kwargs["predicate_type"]
-        if "verb_form" in kwargs:
-            preprocess_kwargs["verb_form"] = kwargs["verb_form"]
-        return preprocess_kwargs, forward_kwargs, postprocess_kwargs
-
-    def preprocess(self, inputs, predicate_marker="<predicate>", predicate_type=None, verb_form=None):
-        # Here, inputs is string or list of strings; apply string postprocessing
-        if isinstance(inputs, str):
-            processed_inputs = self._preprocess_string(inputs, predicate_marker, predicate_type, verb_form)
-        elif hasattr(inputs, "__iter__"):
-            processed_inputs = [self._preprocess_string(s, predicate_marker, predicate_type, verb_form) for s in inputs]
-        else:
-            raise ValueError("inputs must be str or Iterable[str]")
-        # Now pass to super.preprocess for tokenization
-        return super().preprocess(processed_inputs)
-    
-    def _preprocess_string(self, seq: str, predicate_marker: str, predicate_type: Optional[str], verb_form: Optional[str]) -> str:
-        sent_tokens = seq.split(" ")
-        assert predicate_marker in sent_tokens, f"Input sentence must include a predicate-marker token ('{predicate_marker}') before the target predicate word"
-        predicate_idx = sent_tokens.index(predicate_marker)
-        sent_tokens.remove(predicate_marker)
-        sentence_before_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx)])
-        predicate = sent_tokens[predicate_idx]
-        sentence_after_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx+1, len(sent_tokens))])
-        
-        if self.data_args.predicate_marker_type == "generic":
-            predicate_marker = self.special_tokens.predicate_generic_marker    
-        #  In case we want special marker for each predicate type: """
-        elif self.data_args.predicate_marker_type == "pred_type":
-            assert predicate_type is not None, "For this model, you must provide the `predicate_type` either when initializing QASRL_Pipeline(...) or when applying __call__(...) on it"
-            assert predicate_type in ("verbal", "nominal"), f"`predicate_type` must be either 'verbal' or 'nominal'; got '{predicate_type}'"
-            predicate_marker = {"verbal": self.special_tokens.predicate_verb_marker , 
-                                "nominal": self.special_tokens.predicate_nominalization_marker 
-                                }[predicate_type]
-
-        if self.data_args.use_bilateral_predicate_marker:
-            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {predicate_marker} {sentence_after_predicate}"
-        else:
-            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {sentence_after_predicate}"
-
-        # embed also verb_form
-        if self.data_args.append_verb_form and verb_form is None:
-            raise ValueError(f"For this model, you must provide the `verb_form` of the predicate when applying __call__(...)")
-        elif self.data_args.append_verb_form:
-            seq = f"{seq} {self.special_tokens.separator_input_question_predicate} {verb_form} "
-        else:
-            seq = f"{seq} "
-    
-        # append source prefix (for t5 models)
-        prefix = self._get_source_prefix(predicate_type)
-        
-        return prefix + seq
-    
-    def _get_source_prefix(self, predicate_type: Optional[str]):
-        if not self.is_t5_model or self.data_args.source_prefix is None:
-            return ''
-        if not self.data_args.source_prefix.startswith("<"):  # Regular prefix - not dependent on input row x
-            return self.data_args.source_prefix
-        if self.data_args.source_prefix == "<predicate-type>":
-            if predicate_type is None:
-                raise ValueError("source_prefix is '<predicate-type>' but input no `predicate_type`.")
-            else:
-                return f"Generate QAs for {predicate_type} QASRL: "
-    
-    def _forward(self, *args, **kwargs):
-        outputs = super()._forward(*args, **kwargs)
-        return outputs
-
-
-    def postprocess(self, model_outputs):
-        output_seq = self.tokenizer.decode(
-            model_outputs["output_ids"].squeeze(),
-            skip_special_tokens=False,
-            clean_up_tokenization_spaces=False,
-        )
-        output_seq = output_seq.strip(self.tokenizer.pad_token).strip(self.tokenizer.eos_token).strip()
-        qa_subseqs = output_seq.split(self.special_tokens.separator_output_pairs)
-        qas = [self._postrocess_qa(qa_subseq) for qa_subseq in qa_subseqs]
-        return {"generated_text": output_seq,
-                "QAs": qas}
-        
-    def _postrocess_qa(self, seq: str) -> str:
-        # split question and answers
-        if self.special_tokens.separator_output_question_answer in seq:
-            question, answer = seq.split(self.special_tokens.separator_output_question_answer)[:2]
-        else:
-            print("invalid format: no separator between question and answer found...")
-            return None
-            # question, answer = seq, '' # Or: backoff to only question  
-        # skip "_" slots in questions
-        question = ' '.join(t for t in question.split(' ') if t != '_')
-        answers = [a.strip() for a in answer.split(self.special_tokens.separator_output_answers)]
-        return {"question": question, "answers": answers}
-    
-    
-if __name__ == "__main__":
-    pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
-    res1 = pipe("The student was interested in Luke 's <predicate> research about sea animals .", verb_form="research", predicate_type="nominal")
-    res2 = pipe(["The doctor was interested in Luke 's <predicate> treatment .",
-                 "The Veterinary student was interested in Luke 's <predicate> treatment of sea animals ."], verb_form="treat", predicate_type="nominal", num_beams=10)
-    res3 = pipe("A number of professions have <predicate> developed that specialize in the treatment of mental disorders .", verb_form="develop", predicate_type="verbal")
-    print(res1)
-    print(res2)
-    print(res3)
-    

spaces/Abdo1Kamr/Text_Translation_And_Text_Formatter_For_Palestinian_Case/app.py

@@ -1,92 +0,0 @@
-import os
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import PromptTemplate
-import gradio as gr
-import random
-import string
-
-
-
-openai_api_key = os.environ["OPEN_API_KEY"]
-
-
-llm = OpenAI(openai_api_key= openai_api_key, model_name="gpt-3.5-turbo", temperature= 0.0)
-
-template = """Translate the text.
-You are a very professional translator who focuses on political and political terminologies. Translate the given sentence into {target} language.
-
-Text: {query}
-
-Translated text: """
-
-prompt_template = PromptTemplate(
-    input_variables=["target", "query"],
-    template=template
-)
-
-
-def random_punctuation(text):
-    # punctuation = "#$%&*+-<=>@^_~"
-    punctuation = "_"
-    new_text = ""
-    for word in text.split():
-        if (len(word) > 3) or (word == 'غزة') or (word == 'غزه'):
-            result = ""
-            middle = len(word) // 2
-            middel_of_text = word[:middle] + random.choice(punctuation) + word[middle:]
-            # result = random.choice(punctuation) + middel_of_text + random.choice(punctuation)
-            result = '*' + middel_of_text + "*"
-            new_text += result + " "
-        else:
-            new_text += word + " "
-    return new_text.strip()
-
-def MT(query, target):
-    translated_text = llm(prompt_template.format(target = target, query=query))
-    return translated_text
-
-def gradio_func(query, target, style):
-    if len(query) > 1000:
-        return "Please make your text shorter than above | الرجاء تصغير النص للترجمه"
-        
-    if style == "Change the text | تغيير النص":
-        return random_punctuation(query)
-
-    translated_text = MT(query, target)
-    
-    if style == "Translate | الترجمه":
-        return translated_text
-                
-    elif style == "Translate and change the text | الترجمه و تغير النص معاً":
-        return random_punctuation(translated_text)
-    
-    
-    
-gr.close_all()
-demo = gr.Interface(fn=gradio_func, 
-                    inputs=[
-                        gr.Textbox(label="Your Text | النص الخاص بك", lines= 4),
-
-                        gr.Radio(["Arabic", "English", "Mandarin Chinese", "Spanish", "Hindi", "Bengali", "Portuguese", "Russian", "Japanese", "French"],
-                                 label="Languages | اللغات",
-                                 info= "Which language you want to translate? | ما هي اللغه التي تود الترجمه إليها؟"),
-
-                        gr.Radio(["Translate | الترجمه", 
-                                  "Change the text | تغيير النص",
-                                  "Translate and change the text | الترجمه و تغير النص معاً"],
-                                 label="What you want? | ماذا تريد؟")
-                    ],
-                    outputs=[
-                        gr.Textbox(label="Generated Text", lines=4)
-
-                    ],
-                    title="Text Translation And Formatter For Palestinian Case, Support Palestine 🇵🇸.",
-                    description="#### This Model By ChatGPT.",
-                    examples= [
-                        ["سكان غزة يتعرضون لإبادة جماعية وسط أنظار العالم الصامت الذي لا يمنع عدوان وإرهاب إسرائيل!", "English", "Translate | الترجمه"],
-                        ["سكان غزة يتعرضون لإبادة جماعية وسط أنظار العالم الصامت الذي لا يمنع عدوان وإرهاب إسرائيل!", "English", "Change the text | تغيير النص"],
-                        ["سكان غزة يتعرضون لإبادة جماعية وسط أنظار العالم الصامت الذي لا يمنع عدوان وإرهاب إسرائيل!", "English", "Translate and change the text | الترجمه و تغير النص معاً"]
-                    ]
-                   )
-demo.launch(share=True)

spaces/Adapting/TrendFlow/app.py

@@ -1,17 +0,0 @@
-import sthelper as helper
-from mypages import (
-welcome,
-home
-)
-
-
-session = helper.OpenSession(
-    current_page='welcome',
-    page_map=dict(
-        welcome = welcome,
-        home = home
-    )
-)
-
-session.render()
-

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

@@ -1,6 +0,0 @@
-import Orbit from './Orbit';
-import Base from '../base/Base';
-
-export default function Factory(
-    config?: Base.IConfig
-): Orbit;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateGridSizer.js

@@ -1,27 +0,0 @@
-import CreateAnySizer from './utils/CreateAnySizer.js';
-import GridSizer from '../../gridsizer/GridSizer.js';
-import Make from '../Make.js';
-
-var CreateGridSizer = function (scene, data, view, styles, customBuilders) {
-    // Build createCellContainerCallback
-    var createCellContainerCallbackConfig = data.createCellContainerCallback;
-    if (createCellContainerCallbackConfig) {
-        var childData = createCellContainerCallbackConfig.$child;
-        delete createCellContainerCallbackConfig.$child;
-
-        data.createCellContainerCallback = function (scene, x, y, config) {
-            var child = Make(scene, childData, view, styles, customBuilders);
-
-            // Copy config
-            for (var key in createCellContainerCallbackConfig) {
-                config[key] = createCellContainerCallbackConfig[key];
-            }
-
-            return child;
-        }
-    }
-
-    return CreateAnySizer(scene, data, view, styles, customBuilders, GridSizer);
-}
-
-export default CreateGridSizer;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/methods/Build.js

@@ -1,186 +0,0 @@
-import Sizer from '../../sizer/Sizer.js';
-import LineProgressCanvas from '../../lineprogresscanvas/LineProgressCanvas.js';
-import AddChildMask from '../../../../plugins/gameobjects/container/containerlite/mask/AddChildMask.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-
-var Build = function (scene, config) {
-    // Add elements
-    var background = GetValue(config, 'background', undefined);
-    var icon = GetValue(config, 'icon', undefined);
-    var iconMask = GetValue(config, 'iconMask', undefined);
-    var nameText = GetValue(config, 'nameText', undefined);
-    var valueText = GetValue(config, 'valueText', undefined);
-    var bar = GetValue(config, 'bar', undefined);
-    var action = GetValue(config, 'action', undefined);
-    var actionMask = GetValue(config, 'actionMask', undefined);
-
-    if (IsPlainObject(bar)) {
-        bar = new LineProgressCanvas(scene, bar);
-        scene.add.existing(bar);
-        // Move bar game object below nameText and valueText
-        if (nameText) {
-            scene.children.moveBelow(bar, nameText);
-        }
-        if (valueText) {
-            scene.children.moveBelow(bar, valueText);
-        }
-    }
-
-    var hasTextSizer = nameText || valueText || bar;
-
-    if (background) {
-        this.addBackground(background);
-    }
-
-    if (icon) {
-        var padding = undefined;
-        if (this.orientation === 0) {
-            if (hasTextSizer || action) {
-                padding = {
-                    right: GetValue(config, 'space.icon', 0),
-                    top: GetValue(config, 'space.iconTop', 0),
-                    bottom: GetValue(config, 'space.iconBottom', 0),
-                };
-            }
-        } else {
-            if (hasTextSizer || action) {
-                padding = {
-                    bottom: GetValue(config, 'space.icon', 0),
-                    left: GetValue(config, 'space.iconLeft', 0),
-                    right: GetValue(config, 'space.iconRight', 0),
-                };
-            }
-        }
-
-        this.add(
-            icon,
-            { proportion: 0, padding: padding, }
-        );
-
-        if (iconMask) {
-            iconMask = AddChildMask.call(this, icon, icon, 1); // Circle mask
-        }
-    }
-
-    if (hasTextSizer) {
-        var textSizer = new Sizer(scene, {
-            orientation: 1,
-        })
-
-        var nameValueSizer;
-        if (nameText || valueText) {
-            nameValueSizer = new Sizer(scene, {
-                orientation: 0,
-            })
-
-            if (nameText) {
-                // A space character to reserve text height
-                if (nameText.text === '') {
-                    nameText.setText(' ');
-                }
-                nameText.setOrigin(0, nameText.originY);
-                var padding = {
-                    left: GetValue(config, 'space.name', 0),
-                }
-                nameValueSizer.add(
-                    nameText,
-                    { padding: padding }
-                );
-            }
-
-            if (valueText) {
-                // A space character to reserve text height
-                if (valueText.text === '') {
-                    valueText.setText(' ');
-                }
-                valueText.setOrigin(1, valueText.originY);
-
-                nameValueSizer.addSpace();
-
-                var padding = {
-                    right: GetValue(config, 'space.value', 0),
-                }
-                nameValueSizer.add(
-                    valueText,
-                    { padding: padding }
-                );
-
-                this.setValueTextFormatCallback(
-                    GetValue(config, 'valueTextFormatCallback', DefaultValueTextFormatCallback),
-                    GetValue(config, 'valueTextFormatCallbackScope', undefined)
-                );
-            }
-
-            textSizer.add(
-                nameValueSizer,
-                { expand: true, }
-            )
-        }
-
-        if (bar) {
-            var padding = {
-                top: (nameValueSizer) ? GetValue(config, 'space.bar', 0) : 0,
-                bottom: GetValue(config, 'space.barBottom', 0),
-                left: GetValue(config, 'space.barLeft', 0),
-                right: GetValue(config, 'space.barRight', 0),
-            };
-            textSizer.add(
-                bar,
-                { expand: true, padding: padding }
-            );
-        }
-
-        var padding = undefined;
-        if (action) {
-            padding = {
-                right: GetValue(config, 'space.text', 0)
-            };
-        }
-        var textAlign = GetValue(config, 'align.text', 'bottom');
-        this.add(
-            textSizer,
-            { proportion: 1, align: textAlign, padding: padding }
-        );
-    }
-
-    if (action) {
-        var padding;
-        if (this.orientation === 0) {
-            padding = {
-                top: GetValue(config, 'space.actionTop', 0),
-                bottom: GetValue(config, 'space.actionBottom', 0),
-            };
-        } else {
-            padding = {
-                left: GetValue(config, 'space.actionLeft', 0),
-                right: GetValue(config, 'space.actionRight', 0),
-            };
-        }
-
-        this.add(
-            action,
-            { proportion: 0, padding: padding, }
-        );
-
-        if (actionMask) {
-            actionMask = AddChildMask.call(this, action, action, 1); // Circle mask
-        }
-    }
-
-    this.addChildrenMap('background', background);
-    this.addChildrenMap('icon', icon);
-    this.addChildrenMap('iconMask', iconMask);
-    this.addChildrenMap('name', nameText);
-    this.addChildrenMap('value', valueText);
-    this.addChildrenMap('bar', bar);
-    this.addChildrenMap('action', action);
-    this.addChildrenMap('actionMask', actionMask);
-}
-
-var DefaultValueTextFormatCallback = function (value, min, max) {
-    return value.toString();
-}
-
-export default Build;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetExpandedChildHeight.js

@@ -1,16 +0,0 @@
-var GetExpandedChildHeight = function (child, parentHeight) {
-    if (parentHeight === undefined) {
-        parentHeight = this.height;
-    }
-
-    var childHeight;
-    var childConfig = child.rexSizer;
-    if (childConfig.expandHeight) {
-        var innerHeight = parentHeight - this.space.top - this.space.bottom;
-        var padding = childConfig.padding;
-        childHeight = innerHeight - padding.top - padding.bottom;
-    }
-    return childHeight;
-}
-
-export default GetExpandedChildHeight;

spaces/AkiKagura/Marco-Generation/app.py

@@ -1,47 +0,0 @@
-import gradio as gr
-import torch
-#from torch import autocast // only for GPU
-
-from PIL import Image
-
-import os
-MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
-
-from diffusers import StableDiffusionPipeline
-#from diffusers import StableDiffusionImg2ImgPipeline
-
-def empty_checker(images, **kwargs):return images, False
-
-print("start generating")
-
-YOUR_TOKEN=MY_SECRET_TOKEN
-
-device="cpu"
-
-pipe = StableDiffusionPipeline.from_pretrained("AkiKagura/mkgen-diffusion", use_auth_token=YOUR_TOKEN)
-pipe.safety_checker = empty_checker
-pipe.to(device)
-
-gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
-
-def infer(prompt, guide, steps, seed, img_w, img_h): 
-    generator = torch.Generator('cpu').manual_seed(seed)
-    #image = pipe(prompt, init_image=init_image)["sample"][0]
-    images_list = pipe([prompt] * 1, guidance_scale=guide, num_inference_steps=steps, width=img_w, height=img_h)    #TODO
-    images = []
-    for i, image in enumerate(images_list["images"]):
-        images.append(image)
-    
-    return images
-
-print("okay")
-
-title="Marco Generation"
-description="Use 'mkmk woman' to get Marco pics. <br />Warning: Slow process... about 10 min inference time." 
-
-gr.Interface(fn=infer, inputs=["text",
-    gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
-    gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'),
-    gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True),
-    gr.Slider(label='Width', minimum = 512, maximum = 768, step = 256, value = 512),
-    gr.Slider(label='Height', minimum = 512, maximum = 768, step = 256, value = 512)], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True)

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/download_video.py

@@ -1,37 +0,0 @@
-import os
-import random
-import shutil
-from concurrent.futures import ThreadPoolExecutor
-from google.colab import files
-
-basepath = os.getcwd()
-uploaded = files.upload()  # 上传文件
-for filename in uploaded.keys():
-    assert (filename.endswith(".txt")), "speaker-videolink info could only be .txt file!"
-    shutil.move(os.path.join(basepath, filename), os.path.join("./speaker_links.txt"))
-
-
-def generate_infos():
-    infos = []
-    with open("./speaker_links.txt", 'r', encoding='utf-8') as f:
-        lines = f.readlines()
-    for line in lines:
-        line = line.replace("\n", "").replace(" ", "")
-        if line == "":
-            continue
-        speaker, link = line.split("|")
-        filename = speaker + "_" + str(random.randint(0, 1000000))
-        infos.append({"link": link, "filename": filename})
-    return infos
-
-
-def download_video(info):
-    link = info["link"]
-    filename = info["filename"]
-    os.system(f"youtube-dl -f 0 {link} -o ./video_data/{filename}.mp4")
-
-
-if __name__ == "__main__":
-    infos = generate_infos()
-    with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
-        executor.map(download_video, infos)

spaces/Alican/pixera/test.py

@@ -1,52 +0,0 @@
-import os
-from options.test_options import TestOptions
-from data import create_dataset
-from models import create_model
-from util.visualizer import save_images
-from util import html
-
-try:
-    import wandb
-except ImportError:
-    print('Warning: wandb package cannot be found. The option "--use_wandb" will result in error.')
-
-
-if __name__ == '__main__':
-    opt = TestOptions().parse()  # get test options
-    # hard-code some parameters for test
-    opt.num_threads = 0   # test code only supports num_threads = 0
-    opt.batch_size = 1    # test code only supports batch_size = 1
-    opt.serial_batches = True  # disable data shuffling; comment this line if results on randomly chosen images are needed.
-    opt.no_flip = True    # no flip; comment this line if results on flipped images are needed.
-    opt.display_id = -1   # no visdom display; the test code saves the results to a HTML file.
-    dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
-    model = create_model(opt)      # create a model given opt.model and other options
-    model.setup(opt)               # regular setup: load and print networks; create schedulers
-
-    # initialize logger
-    if opt.use_wandb:
-        wandb_run = wandb.init(project=opt.wandb_project_name, name=opt.name, config=opt) if not wandb.run else wandb.run
-        wandb_run._label(repo='CycleGAN-and-pix2pix')
-
-    # create a website
-    web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
-    if opt.load_iter > 0:  # load_iter is 0 by default
-        web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
-    print('creating web directory', web_dir)
-    webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
-    # test with eval mode. This only affects layers like batchnorm and dropout.
-    # For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
-    # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
-    if opt.eval:
-        model.eval()
-    for i, data in enumerate(dataset):
-        if i >= opt.num_test:  # only apply our model to opt.num_test images.
-            break
-        model.set_input(data)  # unpack data from data loader
-        model.test()           # run inference
-        visuals = model.get_current_visuals()  # get image results
-        img_path = model.get_image_paths()     # get image paths
-        if i % 5 == 0:  # save images to an HTML file
-            print('processing (%04d)-th image... %s' % (i, img_path))
-        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize, use_wandb=opt.use_wandb)
-    webpage.save()  # save the HTML

spaces/Amrrs/DragGan-Inversion/gradio_utils/__init__.py

@@ -1,9 +0,0 @@
-from .utils import (ImageMask, draw_mask_on_image, draw_points_on_image,
-                    get_latest_points_pair, get_valid_mask,
-                    on_change_single_global_state)
-
-__all__ = [
-    'draw_mask_on_image', 'draw_points_on_image',
-    'on_change_single_global_state', 'get_latest_points_pair',
-    'get_valid_mask', 'ImageMask'
-]

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py

@@ -1,799 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import warnings
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
-
-from diffusers.utils.import_utils import is_accelerate_available
-
-from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.embeddings import get_timestep_embedding
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import is_accelerate_version, logging, randn_tensor, replace_example_docstring
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import requests
-        >>> import torch
-        >>> from PIL import Image
-        >>> from io import BytesIO
-
-        >>> from diffusers import StableUnCLIPImg2ImgPipeline
-
-        >>> pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-        ...     "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16
-        ... )  # TODO update model path
-        >>> pipe = pipe.to("cuda")
-
-        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-
-        >>> response = requests.get(url)
-        >>> init_image = Image.open(BytesIO(response.content)).convert("RGB")
-        >>> init_image = init_image.resize((768, 512))
-
-        >>> prompt = "A fantasy landscape, trending on artstation"
-
-        >>> images = pipe(prompt, init_image).images
-        >>> images[0].save("fantasy_landscape.png")
-        ```
-"""
-
-
-class StableUnCLIPImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
-    """
-    Pipeline for text-guided image-to-image generation using stable unCLIP.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        feature_extractor ([`CLIPImageProcessor`]):
-            Feature extractor for image pre-processing before being encoded.
-        image_encoder ([`CLIPVisionModelWithProjection`]):
-            CLIP vision model for encoding images.
-        image_normalizer ([`StableUnCLIPImageNormalizer`]):
-            Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
-            embeddings after the noise has been applied.
-        image_noising_scheduler ([`KarrasDiffusionSchedulers`]):
-            Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
-            by the `noise_level`.
-        tokenizer (`~transformers.CLIPTokenizer`):
-            A [`~transformers.CLIPTokenizer`)].
-        text_encoder ([`~transformers.CLIPTextModel`]):
-            Frozen [`~transformers.CLIPTextModel`] text-encoder.
-        unet ([`UNet2DConditionModel`]):
-            A [`UNet2DConditionModel`] to denoise the encoded image latents.
-        scheduler ([`KarrasDiffusionSchedulers`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-    """
-
-    _exclude_from_cpu_offload = ["image_normalizer"]
-
-    # image encoding components
-    feature_extractor: CLIPImageProcessor
-    image_encoder: CLIPVisionModelWithProjection
-
-    # image noising components
-    image_normalizer: StableUnCLIPImageNormalizer
-    image_noising_scheduler: KarrasDiffusionSchedulers
-
-    # regular denoising components
-    tokenizer: CLIPTokenizer
-    text_encoder: CLIPTextModel
-    unet: UNet2DConditionModel
-    scheduler: KarrasDiffusionSchedulers
-
-    vae: AutoencoderKL
-
-    def __init__(
-        self,
-        # image encoding components
-        feature_extractor: CLIPImageProcessor,
-        image_encoder: CLIPVisionModelWithProjection,
-        # image noising components
-        image_normalizer: StableUnCLIPImageNormalizer,
-        image_noising_scheduler: KarrasDiffusionSchedulers,
-        # regular denoising components
-        tokenizer: CLIPTokenizer,
-        text_encoder: CLIPTextModel,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        # vae
-        vae: AutoencoderKL,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
-            image_normalizer=image_normalizer,
-            image_noising_scheduler=image_noising_scheduler,
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-        )
-
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
-        time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
-        Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
-        iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.image_encoder, self.unet, self.vae]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    def _encode_image(
-        self,
-        image,
-        device,
-        batch_size,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        noise_level,
-        generator,
-        image_embeds,
-    ):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if isinstance(image, PIL.Image.Image):
-            # the image embedding should repeated so it matches the total batch size of the prompt
-            repeat_by = batch_size
-        else:
-            # assume the image input is already properly batched and just needs to be repeated so
-            # it matches the num_images_per_prompt.
-            #
-            # NOTE(will) this is probably missing a few number of side cases. I.e. batched/non-batched
-            # `image_embeds`. If those happen to be common use cases, let's think harder about
-            # what the expected dimensions of inputs should be and how we handle the encoding.
-            repeat_by = num_images_per_prompt
-
-        if image_embeds is None:
-            if not isinstance(image, torch.Tensor):
-                image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
-
-            image = image.to(device=device, dtype=dtype)
-            image_embeds = self.image_encoder(image).image_embeds
-
-        image_embeds = self.noise_image_embeddings(
-            image_embeds=image_embeds,
-            noise_level=noise_level,
-            generator=generator,
-        )
-
-        # duplicate image embeddings for each generation per prompt, using mps friendly method
-        image_embeds = image_embeds.unsqueeze(1)
-        bs_embed, seq_len, _ = image_embeds.shape
-        image_embeds = image_embeds.repeat(1, repeat_by, 1)
-        image_embeds = image_embeds.view(bs_embed * repeat_by, seq_len, -1)
-        image_embeds = image_embeds.squeeze(1)
-
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = torch.zeros_like(image_embeds)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            image_embeds = torch.cat([negative_prompt_embeds, image_embeds])
-
-        return image_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        warnings.warn(
-            "The decode_latents method is deprecated and will be removed in a future version. Please"
-            " use VaeImageProcessor instead",
-            FutureWarning,
-        )
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents, return_dict=False)[0]
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        image,
-        height,
-        width,
-        callback_steps,
-        noise_level,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        image_embeds=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two."
-            )
-
-        if prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-
-        if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                "Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined."
-            )
-
-        if prompt is not None and negative_prompt is not None:
-            if type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:
-            raise ValueError(
-                f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive."
-            )
-
-        if image is not None and image_embeds is not None:
-            raise ValueError(
-                "Provide either `image` or `image_embeds`. Please make sure to define only one of the two."
-            )
-
-        if image is None and image_embeds is None:
-            raise ValueError(
-                "Provide either `image` or `image_embeds`. Cannot leave both `image` and `image_embeds` undefined."
-            )
-
-        if image is not None:
-            if (
-                not isinstance(image, torch.Tensor)
-                and not isinstance(image, PIL.Image.Image)
-                and not isinstance(image, list)
-            ):
-                raise ValueError(
-                    "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
-                    f" {type(image)}"
-                )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_unclip.StableUnCLIPPipeline.noise_image_embeddings
-    def noise_image_embeddings(
-        self,
-        image_embeds: torch.Tensor,
-        noise_level: int,
-        noise: Optional[torch.FloatTensor] = None,
-        generator: Optional[torch.Generator] = None,
-    ):
-        """
-        Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher
-        `noise_level` increases the variance in the final un-noised images.
-
-        The noise is applied in two ways:
-        1. A noise schedule is applied directly to the embeddings.
-        2. A vector of sinusoidal time embeddings are appended to the output.
-
-        In both cases, the amount of noise is controlled by the same `noise_level`.
-
-        The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.
-        """
-        if noise is None:
-            noise = randn_tensor(
-                image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype
-            )
-
-        noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)
-
-        self.image_normalizer.to(image_embeds.device)
-        image_embeds = self.image_normalizer.scale(image_embeds)
-
-        image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)
-
-        image_embeds = self.image_normalizer.unscale(image_embeds)
-
-        noise_level = get_timestep_embedding(
-            timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0
-        )
-
-        # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,
-        # but we might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        noise_level = noise_level.to(image_embeds.dtype)
-
-        image_embeds = torch.cat((image_embeds, noise_level), 1)
-
-        return image_embeds
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 20,
-        guidance_scale: float = 10,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        noise_level: int = 0,
-        image_embeds: Optional[torch.FloatTensor] = None,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, either `prompt_embeds` will be
-                used or prompt is initialized to `""`.
-            image (`torch.FloatTensor` or `PIL.Image.Image`):
-                `Image` or tensor representing an image batch. The image is encoded to its CLIP embedding which the
-                `unet` is conditioned on. The image is _not_ encoded by the `vae` and then used as the latents in the
-                denoising process like it is in the standard Stable Diffusion text-guided image variation process.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 20):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 10.0):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
-                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
-                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
-                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            noise_level (`int`, *optional*, defaults to `0`):
-                The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in
-                the final un-noised images. See [`StableUnCLIPPipeline.noise_image_embeddings`] for more details.
-            image_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated CLIP embeddings to condition the `unet` on. These latents are not used in the denoising
-                process. If you want to provide pre-generated latents, pass them to `__call__` as `latents`.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning
-                a tuple, the first element is a list with the generated images.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        if prompt is None and prompt_embeds is None:
-            prompt = len(image) * [""] if isinstance(image, list) else ""
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt=prompt,
-            image=image,
-            height=height,
-            width=width,
-            callback_steps=callback_steps,
-            noise_level=noise_level,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            image_embeds=image_embeds,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        batch_size = batch_size * num_images_per_prompt
-
-        device = self._execution_device
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        prompt_embeds = self._encode_prompt(
-            prompt=prompt,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Encoder input image
-        noise_level = torch.tensor([noise_level], device=device)
-        image_embeds = self._encode_image(
-            image=image,
-            device=device,
-            batch_size=batch_size,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            noise_level=noise_level,
-            generator=generator,
-            image_embeds=image_embeds,
-        )
-
-        # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size=batch_size,
-            num_channels_latents=num_channels_latents,
-            height=height,
-            width=width,
-            dtype=prompt_embeds.dtype,
-            device=device,
-            generator=generator,
-            latents=latents,
-        )
-
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 8. Denoising loop
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet(
-                latent_model_input,
-                t,
-                encoder_hidden_states=prompt_embeds,
-                class_labels=image_embeds,
-                cross_attention_kwargs=cross_attention_kwargs,
-                return_dict=False,
-            )[0]
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        # 9. Post-processing
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-        else:
-            image = latents
-
-        image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -1,596 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This model implementation is heavily based on:
-
-import gc
-import random
-import tempfile
-import unittest
-
-import numpy as np
-import torch
-from PIL import Image
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    ControlNetModel,
-    DDIMScheduler,
-    StableDiffusionControlNetInpaintPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-from ..pipeline_params import (
-    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
-    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
-    TEXT_TO_IMAGE_IMAGE_PARAMS,
-)
-from ..test_pipelines_common import (
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-)
-
-
-enable_full_determinism()
-
-
-class ControlNetInpaintPipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
-    pipeline_class = StableDiffusionControlNetInpaintPipeline
-    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
-    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
-    image_params = frozenset({"control_image"})  # skip `image` and `mask` for now, only test for control_image
-    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=9,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-        controlnet = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        controlnet_embedder_scale_factor = 2
-        control_image = randn_tensor(
-            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-            generator=generator,
-            device=torch.device(device),
-        )
-        init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
-        init_image = init_image.cpu().permute(0, 2, 3, 1)[0]
-
-        image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
-        mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "image": image,
-            "mask_image": mask_image,
-            "control_image": control_image,
-        }
-
-        return inputs
-
-    def test_attention_slicing_forward_pass(self):
-        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(expected_max_diff=2e-3)
-
-
-class ControlNetSimpleInpaintPipelineFastTests(ControlNetInpaintPipelineFastTests):
-    pipeline_class = StableDiffusionControlNetInpaintPipeline
-    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
-    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
-    image_params = frozenset([])
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-        controlnet = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-
-class MultiControlNetInpaintPipelineFastTests(
-    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
-):
-    pipeline_class = StableDiffusionControlNetInpaintPipeline
-    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
-    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=9,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        torch.manual_seed(0)
-
-        def init_weights(m):
-            if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
-                m.bias.data.fill_(1.0)
-
-        controlnet1 = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        controlnet1.controlnet_down_blocks.apply(init_weights)
-
-        torch.manual_seed(0)
-        controlnet2 = ControlNetModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            in_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            cross_attention_dim=32,
-            conditioning_embedding_out_channels=(16, 32),
-        )
-        controlnet2.controlnet_down_blocks.apply(init_weights)
-
-        torch.manual_seed(0)
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        controlnet = MultiControlNetModel([controlnet1, controlnet2])
-
-        components = {
-            "unet": unet,
-            "controlnet": controlnet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        controlnet_embedder_scale_factor = 2
-
-        control_image = [
-            randn_tensor(
-                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-                generator=generator,
-                device=torch.device(device),
-            ),
-            randn_tensor(
-                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
-                generator=generator,
-                device=torch.device(device),
-            ),
-        ]
-        init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
-        init_image = init_image.cpu().permute(0, 2, 3, 1)[0]
-
-        image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
-        mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "image": image,
-            "mask_image": mask_image,
-            "control_image": control_image,
-        }
-
-        return inputs
-
-    def test_control_guidance_switch(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(torch_device)
-
-        scale = 10.0
-        steps = 4
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_1 = pipe(**inputs)[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs["num_inference_steps"] = steps
-        inputs["controlnet_conditioning_scale"] = scale
-        output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]
-
-        # make sure that all outputs are different
-        assert np.sum(np.abs(output_1 - output_2)) > 1e-3
-        assert np.sum(np.abs(output_1 - output_3)) > 1e-3
-        assert np.sum(np.abs(output_1 - output_4)) > 1e-3
-
-    def test_attention_slicing_forward_pass(self):
-        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(expected_max_diff=2e-3)
-
-    def test_save_pretrained_raise_not_implemented_exception(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        with tempfile.TemporaryDirectory() as tmpdir:
-            try:
-                # save_pretrained is not implemented for Multi-ControlNet
-                pipe.save_pretrained(tmpdir)
-            except NotImplementedError:
-                pass
-
-
-@slow
-@require_torch_gpu
-class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_canny(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-
-        pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting", safety_checker=None, controlnet=controlnet
-        )
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        image = load_image(
-            "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
-        ).resize((512, 512))
-
-        mask_image = load_image(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
-            "/stable_diffusion_inpaint/input_bench_mask.png"
-        ).resize((512, 512))
-
-        prompt = "pitch black hole"
-
-        control_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        ).resize((512, 512))
-
-        output = pipe(
-            prompt,
-            image=image,
-            mask_image=mask_image,
-            control_image=control_image,
-            generator=generator,
-            output_type="np",
-            num_inference_steps=3,
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 9e-2
-
-    def test_inpaint(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint")
-
-        pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-        pipe.enable_model_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(33)
-
-        init_image = load_image(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
-        )
-        init_image = init_image.resize((512, 512))
-
-        mask_image = load_image(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
-        )
-        mask_image = mask_image.resize((512, 512))
-
-        prompt = "a handsome man with ray-ban sunglasses"
-
-        def make_inpaint_condition(image, image_mask):
-            image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
-            image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
-
-            assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
-            image[image_mask > 0.5] = -1.0  # set as masked pixel
-            image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
-            image = torch.from_numpy(image)
-            return image
-
-        control_image = make_inpaint_condition(init_image, mask_image)
-
-        output = pipe(
-            prompt,
-            image=init_image,
-            mask_image=mask_image,
-            control_image=control_image,
-            guidance_scale=9.0,
-            eta=1.0,
-            generator=generator,
-            num_inference_steps=20,
-            output_type="np",
-        )
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"
-        )
-
-        assert np.abs(expected_image - image).max() < 9e-2
-
-    def test_load_local(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
-        pipe_1 = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-
-        controlnet = ControlNetModel.from_single_file(
-            "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
-        )
-        pipe_2 = StableDiffusionControlNetInpaintPipeline.from_single_file(
-            "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
-            safety_checker=None,
-            controlnet=controlnet,
-        )
-        control_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        ).resize((512, 512))
-        image = load_image(
-            "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
-        ).resize((512, 512))
-        mask_image = load_image(
-            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
-            "/stable_diffusion_inpaint/input_bench_mask.png"
-        ).resize((512, 512))
-
-        pipes = [pipe_1, pipe_2]
-        images = []
-        for pipe in pipes:
-            pipe.enable_model_cpu_offload()
-            pipe.set_progress_bar_config(disable=None)
-
-            generator = torch.Generator(device="cpu").manual_seed(0)
-            prompt = "bird"
-            output = pipe(
-                prompt,
-                image=image,
-                control_image=control_image,
-                mask_image=mask_image,
-                strength=0.9,
-                generator=generator,
-                output_type="np",
-                num_inference_steps=3,
-            )
-            images.append(output.images[0])
-
-            del pipe
-            gc.collect()
-            torch.cuda.empty_cache()
-
-        assert np.abs(images[0] - images[1]).sum() < 1e-3

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/tblr_bbox_coder.py

@@ -1,198 +0,0 @@
-import mmcv
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class TBLRBBoxCoder(BaseBBoxCoder):
-    """TBLR BBox coder.
-
-    Following the practice in `FSAF <https://arxiv.org/abs/1903.00621>`_,
-    this coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left,
-    right) and decode it back to the original.
-
-    Args:
-        normalizer (list | float): Normalization factor to be
-          divided with when coding the coordinates. If it is a list, it should
-          have length of 4 indicating normalization factor in tblr dims.
-          Otherwise it is a unified float factor for all dims. Default: 4.0
-        clip_border (bool, optional): Whether clip the objects outside the
-            border of the image. Defaults to True.
-    """
-
-    def __init__(self, normalizer=4.0, clip_border=True):
-        super(BaseBBoxCoder, self).__init__()
-        self.normalizer = normalizer
-        self.clip_border = clip_border
-
-    def encode(self, bboxes, gt_bboxes):
-        """Get box regression transformation deltas that can be used to
-        transform the ``bboxes`` into the ``gt_bboxes`` in the (top, left,
-        bottom, right) order.
-
-        Args:
-            bboxes (torch.Tensor): source boxes, e.g., object proposals.
-            gt_bboxes (torch.Tensor): target of the transformation, e.g.,
-                ground truth boxes.
-
-        Returns:
-            torch.Tensor: Box transformation deltas
-        """
-        assert bboxes.size(0) == gt_bboxes.size(0)
-        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
-        encoded_bboxes = bboxes2tblr(
-            bboxes, gt_bboxes, normalizer=self.normalizer)
-        return encoded_bboxes
-
-    def decode(self, bboxes, pred_bboxes, max_shape=None):
-        """Apply transformation `pred_bboxes` to `boxes`.
-
-        Args:
-            bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4)
-            pred_bboxes (torch.Tensor): Encoded boxes with shape
-               (B, N, 4) or (N, 4)
-            max_shape (Sequence[int] or torch.Tensor or Sequence[
-               Sequence[int]],optional): Maximum bounds for boxes, specifies
-               (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then
-               the max_shape should be a Sequence[Sequence[int]]
-               and the length of max_shape should also be B.
-
-        Returns:
-            torch.Tensor: Decoded boxes.
-        """
-        decoded_bboxes = tblr2bboxes(
-            bboxes,
-            pred_bboxes,
-            normalizer=self.normalizer,
-            max_shape=max_shape,
-            clip_border=self.clip_border)
-
-        return decoded_bboxes
-
-
-@mmcv.jit(coderize=True)
-def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True):
-    """Encode ground truth boxes to tblr coordinate.
-
-    It first convert the gt coordinate to tblr format,
-     (top, bottom, left, right), relative to prior box centers.
-     The tblr coordinate may be normalized by the side length of prior bboxes
-     if `normalize_by_wh` is specified as True, and it is then normalized by
-     the `normalizer` factor.
-
-    Args:
-        priors (Tensor): Prior boxes in point form
-            Shape: (num_proposals,4).
-        gts (Tensor): Coords of ground truth for each prior in point-form
-            Shape: (num_proposals, 4).
-        normalizer (Sequence[float] | float): normalization parameter of
-            encoded boxes. If it is a list, it has to have length = 4.
-            Default: 4.0
-        normalize_by_wh (bool): Whether to normalize tblr coordinate by the
-            side length (wh) of prior bboxes.
-
-    Return:
-        encoded boxes (Tensor), Shape: (num_proposals, 4)
-    """
-
-    # dist b/t match center and prior's center
-    if not isinstance(normalizer, float):
-        normalizer = torch.tensor(normalizer, device=priors.device)
-        assert len(normalizer) == 4, 'Normalizer must have length = 4'
-    assert priors.size(0) == gts.size(0)
-    prior_centers = (priors[:, 0:2] + priors[:, 2:4]) / 2
-    xmin, ymin, xmax, ymax = gts.split(1, dim=1)
-    top = prior_centers[:, 1].unsqueeze(1) - ymin
-    bottom = ymax - prior_centers[:, 1].unsqueeze(1)
-    left = prior_centers[:, 0].unsqueeze(1) - xmin
-    right = xmax - prior_centers[:, 0].unsqueeze(1)
-    loc = torch.cat((top, bottom, left, right), dim=1)
-    if normalize_by_wh:
-        # Normalize tblr by anchor width and height
-        wh = priors[:, 2:4] - priors[:, 0:2]
-        w, h = torch.split(wh, 1, dim=1)
-        loc[:, :2] /= h  # tb is normalized by h
-        loc[:, 2:] /= w  # lr is normalized by w
-    # Normalize tblr by the given normalization factor
-    return loc / normalizer
-
-
-@mmcv.jit(coderize=True)
-def tblr2bboxes(priors,
-                tblr,
-                normalizer=4.0,
-                normalize_by_wh=True,
-                max_shape=None,
-                clip_border=True):
-    """Decode tblr outputs to prediction boxes.
-
-    The process includes 3 steps: 1) De-normalize tblr coordinates by
-    multiplying it with `normalizer`; 2) De-normalize tblr coordinates by the
-    prior bbox width and height if `normalize_by_wh` is `True`; 3) Convert
-    tblr (top, bottom, left, right) pair relative to the center of priors back
-    to (xmin, ymin, xmax, ymax) coordinate.
-
-    Args:
-        priors (Tensor): Prior boxes in point form (x0, y0, x1, y1)
-          Shape: (N,4) or (B, N, 4).
-        tblr (Tensor): Coords of network output in tblr form
-          Shape: (N, 4) or (B, N, 4).
-        normalizer (Sequence[float] | float): Normalization parameter of
-          encoded boxes. By list, it represents the normalization factors at
-          tblr dims. By float, it is the unified normalization factor at all
-          dims. Default: 4.0
-        normalize_by_wh (bool): Whether the tblr coordinates have been
-          normalized by the side length (wh) of prior bboxes.
-        max_shape (Sequence[int] or torch.Tensor or Sequence[
-            Sequence[int]],optional): Maximum bounds for boxes, specifies
-            (H, W, C) or (H, W). If priors shape is (B, N, 4), then
-            the max_shape should be a Sequence[Sequence[int]]
-            and the length of max_shape should also be B.
-        clip_border (bool, optional): Whether clip the objects outside the
-            border of the image. Defaults to True.
-
-    Return:
-        encoded boxes (Tensor): Boxes with shape (N, 4) or (B, N, 4)
-    """
-    if not isinstance(normalizer, float):
-        normalizer = torch.tensor(normalizer, device=priors.device)
-        assert len(normalizer) == 4, 'Normalizer must have length = 4'
-    assert priors.size(0) == tblr.size(0)
-    if priors.ndim == 3:
-        assert priors.size(1) == tblr.size(1)
-
-    loc_decode = tblr * normalizer
-    prior_centers = (priors[..., 0:2] + priors[..., 2:4]) / 2
-    if normalize_by_wh:
-        wh = priors[..., 2:4] - priors[..., 0:2]
-        w, h = torch.split(wh, 1, dim=-1)
-        # Inplace operation with slice would failed for exporting to ONNX
-        th = h * loc_decode[..., :2]  # tb
-        tw = w * loc_decode[..., 2:]  # lr
-        loc_decode = torch.cat([th, tw], dim=-1)
-    # Cannot be exported using onnx when loc_decode.split(1, dim=-1)
-    top, bottom, left, right = loc_decode.split((1, 1, 1, 1), dim=-1)
-    xmin = prior_centers[..., 0].unsqueeze(-1) - left
-    xmax = prior_centers[..., 0].unsqueeze(-1) + right
-    ymin = prior_centers[..., 1].unsqueeze(-1) - top
-    ymax = prior_centers[..., 1].unsqueeze(-1) + bottom
-
-    bboxes = torch.cat((xmin, ymin, xmax, ymax), dim=-1)
-
-    if clip_border and max_shape is not None:
-        if not isinstance(max_shape, torch.Tensor):
-            max_shape = priors.new_tensor(max_shape)
-        max_shape = max_shape[..., :2].type_as(priors)
-        if max_shape.ndim == 2:
-            assert bboxes.ndim == 3
-            assert max_shape.size(0) == bboxes.size(0)
-
-        min_xy = priors.new_tensor(0)
-        max_xy = torch.cat([max_shape, max_shape],
-                           dim=-1).flip(-1).unsqueeze(-2)
-        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
-        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
-
-    return bboxes

spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py

@@ -1,2 +0,0 @@
-_base_ = './ccnet_r50-d8_512x512_40k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/cgnet/cgnet_680x680_60k_cityscapes.py

@@ -1,50 +0,0 @@
-_base_ = [
-    '../_base_/models/cgnet.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py'
-]
-
-# optimizer
-optimizer = dict(type='Adam', lr=0.001, eps=1e-08, weight_decay=0.0005)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
-# runtime settings
-total_iters = 60000
-checkpoint_config = dict(by_epoch=False, interval=4000)
-evaluation = dict(interval=4000, metric='mIoU')
-
-img_norm_cfg = dict(
-    mean=[72.39239876, 82.90891754, 73.15835921], std=[1, 1, 1], to_rgb=True)
-crop_size = (680, 680)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(2048, 1024),
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=8,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py

@@ -1,8 +0,0 @@
-_base_ = [
-    '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)),
-    decode_head=dict(dilation=6),
-    auxiliary_head=dict(dilation=6))

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/networks.py

@@ -1,267 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-import numpy as np
-import functools
-from . import base_function
-from .stylegan_ops import style_function
-from .transformer_ops import transformer_function
-
-
-##################################################################################
-# Networks
-##################################################################################
-def define_D(opt, img_size):
-    """Create a discriminator"""
-    norm_value = base_function.get_norm_layer(opt.norm)
-    if 'patch' in opt.netD:
-        net = NLayerDiscriminator(opt.img_nc, opt.ndf, opt.n_layers_D, norm_value, use_attn=opt.attn_D)
-    elif 'style' in opt.netD:
-        net = StyleDiscriminator(img_size, ndf=opt.ndf, use_attn=opt.attn_D)
-    else:
-        raise NotImplementedError('Discriminator model name [%s] is not recognized' % opt.netD)
-
-    return base_function.init_net(net, opt.init_type, opt.init_gain, initialize_weights=('style' not in opt.netD))
-
-
-def define_G(opt):
-    """Create a decoder"""
-    if 'diff' in opt.netG:
-        net = base_function.DiffDecoder(opt.img_nc, opt.ngf, opt.kernel_G, opt.embed_dim, opt.n_layers_G, opt.num_res_blocks,
-                                        word_size=opt.word_size, activation=opt.activation, norm=opt.norm,
-                                        add_noise=opt.add_noise, use_attn=opt.attn_G, use_pos=opt.use_pos_G)
-    elif 'linear' in opt.netG:
-        net = base_function.LinearDecoder(opt.img_nc, opt.ngf, opt.kernel_G, opt.embed_dim, opt.activation, opt.norm)
-    elif 'refine' in opt.netG:
-        net = RefinedGenerator(opt.img_nc, opt.ngf, opt.embed_dim, opt.down_layers, opt.mid_layers, opt.num_res_blocks,
-                               activation=opt.activation, norm=opt.norm)
-    else:
-        raise NotImplementedError('Decoder model name [%s] is not recognized' % opt.netG)
-
-    return base_function.init_net(net, opt.init_type, opt.init_gain, initialize_weights=('style' not in opt.netG))
-
-
-def define_E(opt):
-    """Create a encoder"""
-    if 'diff' in opt.netE:
-        net = base_function.DiffEncoder(opt.img_nc, opt.ngf, opt.kernel_E, opt.embed_dim, opt.n_layers_G, opt.num_res_blocks,
-                                        activation=opt.activation, norm=opt.norm, use_attn=opt.attn_E)
-    elif 'linear' in opt.netE:
-        net = base_function.LinearEncoder(opt.img_nc, opt.kernel_E, opt.embed_dim)
-    else:
-        raise NotImplementedError('Encoder model name [%s] is not recognized' % opt.netE)
-
-    return base_function.init_net(net, opt.init_type, opt.init_gain, initialize_weights=('style' not in opt.netE))
-
-
-def define_T(opt):
-    """Create a transformer"""
-    if "original" in opt.netT:
-        e_d_f = int(opt.ngf * (2 ** opt.n_layers_G))
-        net = transformer_function.Transformer(e_d_f, opt.embed_dim, e_d_f, kernel=opt.kernel_T,
-                    n_encoders=opt.n_encoders, n_decoders=opt.n_decoders, embed_type=opt.embed_type)
-    else:
-        raise NotImplementedError('Transformer model name [%s] is not recognized' % opt.netT)
-    return net
-
-
-##################################################################################
-# Discriminator
-##################################################################################
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator"""
-
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_attn=False):
-        """Construct a PatchGAN discriminator
-
-        Parameters:
-            input_nc (int)  -- the number of channels in input examples
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super(NLayerDiscriminator, self).__init__()
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
-            use_bias = norm_layer.func == nn.InstanceNorm2d
-        else:
-            use_bias = norm_layer == nn.InstanceNorm2d
-
-        kw = 4
-        padw = 1
-
-        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-
-        nf_mult = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                    nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                    norm_layer(ndf * nf_mult),
-                    nn.LeakyReLU(0.2, True)]
-            if n == 2 and use_attn:
-                sequence += [
-                    nn.Conv2d(ndf * nf_mult, ndf * nf_mult, kernel_size=1, stride=1, bias=use_bias),
-                    base_function.AttnAware(ndf * nf_mult)
-                ]
-
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
-        self.model = nn.Sequential(*sequence)
-
-    def forward(self, input):
-        """Standard forward."""
-        return self.model(input)
-
-
-class StyleDiscriminator(nn.Module):
-    def __init__(self, img_size, ndf=32, blur_kernel=[1, 3, 3, 1], use_attn=False):
-        super(StyleDiscriminator, self).__init__()
-
-        channel_multiplier = ndf / 64
-        channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: int(512 * channel_multiplier),
-            64: int(256 * channel_multiplier),
-            128: int(128 * channel_multiplier),
-            256: int(64 * channel_multiplier),
-            512: int(32 * channel_multiplier),
-            1024: int(16 * channel_multiplier),
-        }
-
-        convs = [style_function.ConvLayer(3, channels[img_size], 1)]
-
-        log_size = int(np.log2(img_size))
-
-        in_channel = channels[img_size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2**(i-1)]
-            if i == log_size - 3 and use_attn:
-                convs.append(base_function.AttnAware(in_channel))
-            convs.append(style_function.StyleBlock(in_channel, out_channel, blur_kernel))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-
-        self.final_conv = style_function.ConvLayer(in_channel+1, channels[4], 3)
-        self.final_linear = nn.Sequential(
-            style_function.EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
-            style_function.EqualLinear(channels[4], 1),
-        )
-
-    def forward(self, x):
-
-        out = self.convs(x)
-
-        b, c, h, w = out.shape
-        group = min(b, self.stddev_group)
-        stddev = out.view(group, -1, self.stddev_feat, c // self.stddev_feat, h, w)
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, h, w)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-        out = out.view(b, -1)
-        out = self.final_linear(out)
-
-        return out
-
-
-##################################################################################
-# Generator
-##################################################################################
-class RefinedGenerator(nn.Module):
-    def __init__(self, input_nc, ngf=64, embed_dim=512, down_layers=3, mid_layers=6, num_res_blocks=1, dropout=0.0,
-                 rample_with_conv=True, activation='gelu', norm='pixel'):
-        super(RefinedGenerator, self).__init__()
-
-        activation_layer = base_function.get_nonlinearity_layer(activation)
-        norm_layer = base_function.get_norm_layer(norm)
-        self.down_layers = down_layers
-        self.mid_layers = mid_layers
-        self.num_res_blocks = num_res_blocks
-        out_dims = []
-        # start
-        self.encode = base_function.PartialConv2d(input_nc, ngf, kernel_size=3, stride=1, padding=1)
-        # down
-        self.down = nn.ModuleList()
-        out_dim = ngf
-        for i in range(self.down_layers):
-            block = nn.ModuleList()
-            down = nn.Module()
-            in_dim = out_dim
-            out_dims.append(out_dim)
-            out_dim = min(int(in_dim * 2), embed_dim)
-            down.downsample = base_function.DownSample(in_dim, rample_with_conv, kernel_size=3)
-            for i_block in range(self.num_res_blocks):
-                block.append(base_function.ResnetBlock(in_dim, out_dim, 3, dropout, activation, norm))
-                in_dim = out_dim
-            down.block = block
-            self.down.append(down)
-        # middle
-        self.mid = nn.ModuleList()
-        for i in range(self.mid_layers):
-            self.mid.append(base_function.ResnetBlock(out_dim, out_dim, 3, dropout, activation, norm))
-        # up
-        self.up = nn.ModuleList()
-        for i in range(self.down_layers):
-            block = nn.ModuleList()
-            up = nn.Module()
-            in_dim = out_dim
-            out_dim = max(out_dims[-i-1], ngf)
-            for i_block in range(self.num_res_blocks):
-                block.append(base_function.ResnetBlock(in_dim, out_dim, 3, dropout, activation, norm))
-                in_dim = out_dim
-            if i == self.down_layers - 3:
-                up.attn = base_function.AttnAware(out_dim, activation, norm)
-            up.block = block
-            upsample = True if i != 0 else False
-            up.out = base_function.ToRGB(out_dim, input_nc, upsample, activation, norm)
-            up.upsample = base_function.UpSample(out_dim, rample_with_conv, kernel_size=3)
-            self.up.append(up)
-        # end
-        self.decode = base_function.ToRGB(out_dim, input_nc, True, activation, norm)
-
-    def forward(self, x, mask=None):
-        # start
-        x = self.encode(x)
-        pre = None
-        # down
-        for i in range(self.down_layers):
-            x = self.down[i].downsample(x)
-            if i == 2:
-                pre = x
-            for i_block in range(self.num_res_blocks):
-                x = self.down[i].block[i_block](x)
-        # middle
-        for i in range(self.mid_layers):
-            x = self.mid[i](x)
-        # up
-        skip = None
-        for i in range(self.down_layers):
-            for i_block in range(self.num_res_blocks):
-                x = self.up[i].block[i_block](x)
-            if i == self.down_layers - 3:
-                mask = F.interpolate(mask, size=x.size()[2:], mode='bilinear', align_corners=True) if mask is not None else None
-                x = self.up[i].attn(x, pre=pre, mask=mask)
-            skip = self.up[i].out(x, skip)
-            x = self.up[i].upsample(x)
-        # end
-        x = self.decode(x, skip)
-
-        return x

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/checkpoint.py

@@ -1,167 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import warnings
-
-from annotator.uniformer.mmcv.fileio import FileClient
-from ..dist_utils import allreduce_params, master_only
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class CheckpointHook(Hook):
-    """Save checkpoints periodically.
-
-    Args:
-        interval (int): The saving period. If ``by_epoch=True``, interval
-            indicates epochs, otherwise it indicates iterations.
-            Default: -1, which means "never".
-        by_epoch (bool): Saving checkpoints by epoch or by iteration.
-            Default: True.
-        save_optimizer (bool): Whether to save optimizer state_dict in the
-            checkpoint. It is usually used for resuming experiments.
-            Default: True.
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, ``runner.work_dir`` will be used by default. If
-            specified, the ``out_dir`` will be the concatenation of ``out_dir``
-            and the last level directory of ``runner.work_dir``.
-            `Changed in version 1.3.16.`
-        max_keep_ckpts (int, optional): The maximum checkpoints to keep.
-            In some cases we want only the latest few checkpoints and would
-            like to delete old ones to save the disk space.
-            Default: -1, which means unlimited.
-        save_last (bool, optional): Whether to force the last checkpoint to be
-            saved regardless of interval. Default: True.
-        sync_buffer (bool, optional): Whether to synchronize buffers in
-            different gpus. Default: False.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-            `New in version 1.3.16.`
-
-    .. warning::
-        Before v1.3.16, the ``out_dir`` argument indicates the path where the
-        checkpoint is stored. However, since v1.3.16, ``out_dir`` indicates the
-        root directory and the final path to save checkpoint is the
-        concatenation of ``out_dir`` and the last level directory of
-        ``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A"
-        and the value of ``runner.work_dir`` is "/path/of/B", then the final
-        path will be "/path/of/A/B".
-    """
-
-    def __init__(self,
-                 interval=-1,
-                 by_epoch=True,
-                 save_optimizer=True,
-                 out_dir=None,
-                 max_keep_ckpts=-1,
-                 save_last=True,
-                 sync_buffer=False,
-                 file_client_args=None,
-                 **kwargs):
-        self.interval = interval
-        self.by_epoch = by_epoch
-        self.save_optimizer = save_optimizer
-        self.out_dir = out_dir
-        self.max_keep_ckpts = max_keep_ckpts
-        self.save_last = save_last
-        self.args = kwargs
-        self.sync_buffer = sync_buffer
-        self.file_client_args = file_client_args
-
-    def before_run(self, runner):
-        if not self.out_dir:
-            self.out_dir = runner.work_dir
-
-        self.file_client = FileClient.infer_client(self.file_client_args,
-                                                   self.out_dir)
-
-        # if `self.out_dir` is not equal to `runner.work_dir`, it means that
-        # `self.out_dir` is set so the final `self.out_dir` is the
-        # concatenation of `self.out_dir` and the last level directory of
-        # `runner.work_dir`
-        if self.out_dir != runner.work_dir:
-            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
-            self.out_dir = self.file_client.join_path(self.out_dir, basename)
-
-        runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by '
-                            f'{self.file_client.name}.'))
-
-        # disable the create_symlink option because some file backends do not
-        # allow to create a symlink
-        if 'create_symlink' in self.args:
-            if self.args[
-                    'create_symlink'] and not self.file_client.allow_symlink:
-                self.args['create_symlink'] = False
-                warnings.warn(
-                    ('create_symlink is set as True by the user but is changed'
-                     'to be False because creating symbolic link is not '
-                     f'allowed in {self.file_client.name}'))
-        else:
-            self.args['create_symlink'] = self.file_client.allow_symlink
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` epochs
-        # 2. reach the last epoch of training
-        if self.every_n_epochs(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_epoch(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.epoch + 1} epochs')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)
-
-    @master_only
-    def _save_checkpoint(self, runner):
-        """Save the current checkpoint and delete unwanted checkpoint."""
-        runner.save_checkpoint(
-            self.out_dir, save_optimizer=self.save_optimizer, **self.args)
-        if runner.meta is not None:
-            if self.by_epoch:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
-            else:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
-            runner.meta.setdefault('hook_msgs', dict())
-            runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path(
-                self.out_dir, cur_ckpt_filename)
-        # remove other checkpoints
-        if self.max_keep_ckpts > 0:
-            if self.by_epoch:
-                name = 'epoch_{}.pth'
-                current_ckpt = runner.epoch + 1
-            else:
-                name = 'iter_{}.pth'
-                current_ckpt = runner.iter + 1
-            redundant_ckpts = range(
-                current_ckpt - self.max_keep_ckpts * self.interval, 0,
-                -self.interval)
-            filename_tmpl = self.args.get('filename_tmpl', name)
-            for _step in redundant_ckpts:
-                ckpt_path = self.file_client.join_path(
-                    self.out_dir, filename_tmpl.format(_step))
-                if self.file_client.isfile(ckpt_path):
-                    self.file_client.remove(ckpt_path)
-                else:
-                    break
-
-    def after_train_iter(self, runner):
-        if self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` iterations
-        # 2. reach the last iteration of training
-        if self.every_n_iters(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_iter(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.iter + 1} iterations')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)

spaces/Ariharasudhan/YoloV5/utils/callbacks.py

@@ -1,76 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Callback utils
-"""
-
-import threading
-
-
-class Callbacks:
-    """"
-    Handles all registered callbacks for YOLOv5 Hooks
-    """
-
-    def __init__(self):
-        # Define the available callbacks
-        self._callbacks = {
-            'on_pretrain_routine_start': [],
-            'on_pretrain_routine_end': [],
-            'on_train_start': [],
-            'on_train_epoch_start': [],
-            'on_train_batch_start': [],
-            'optimizer_step': [],
-            'on_before_zero_grad': [],
-            'on_train_batch_end': [],
-            'on_train_epoch_end': [],
-            'on_val_start': [],
-            'on_val_batch_start': [],
-            'on_val_image_end': [],
-            'on_val_batch_end': [],
-            'on_val_end': [],
-            'on_fit_epoch_end': [],  # fit = train + val
-            'on_model_save': [],
-            'on_train_end': [],
-            'on_params_update': [],
-            'teardown': [],}
-        self.stop_training = False  # set True to interrupt training
-
-    def register_action(self, hook, name='', callback=None):
-        """
-        Register a new action to a callback hook
-
-        Args:
-            hook: The callback hook name to register the action to
-            name: The name of the action for later reference
-            callback: The callback to fire
-        """
-        assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
-        assert callable(callback), f"callback '{callback}' is not callable"
-        self._callbacks[hook].append({'name': name, 'callback': callback})
-
-    def get_registered_actions(self, hook=None):
-        """"
-        Returns all the registered actions by callback hook
-
-        Args:
-            hook: The name of the hook to check, defaults to all
-        """
-        return self._callbacks[hook] if hook else self._callbacks
-
-    def run(self, hook, *args, thread=False, **kwargs):
-        """
-        Loop through the registered actions and fire all callbacks on main thread
-
-        Args:
-            hook: The name of the hook to check, defaults to all
-            args: Arguments to receive from YOLOv5
-            thread: (boolean) Run callbacks in daemon thread
-            kwargs: Keyword Arguments to receive from YOLOv5
-        """
-
-        assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
-        for logger in self._callbacks[hook]:
-            if thread:
-                threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start()
-            else:
-                logger['callback'](*args, **kwargs)

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

@@ -1,130 +0,0 @@
-from typing import Union
-
-from .align import AlignMethod
-from .cells import cell_len, set_cell_size
-from .console import Console, ConsoleOptions, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .style import Style
-from .text import Text
-
-
-class Rule(JupyterMixin):
-    """A console renderable to draw a horizontal rule (line).
-
-    Args:
-        title (Union[str, Text], optional): Text to render in the rule. Defaults to "".
-        characters (str, optional): Character(s) used to draw the line. Defaults to "─".
-        style (StyleType, optional): Style of Rule. Defaults to "rule.line".
-        end (str, optional): Character at end of Rule. defaults to "\\\\n"
-        align (str, optional): How to align the title, one of "left", "center", or "right". Defaults to "center".
-    """
-
-    def __init__(
-        self,
-        title: Union[str, Text] = "",
-        *,
-        characters: str = "─",
-        style: Union[str, Style] = "rule.line",
-        end: str = "\n",
-        align: AlignMethod = "center",
-    ) -> None:
-        if cell_len(characters) < 1:
-            raise ValueError(
-                "'characters' argument must have a cell width of at least 1"
-            )
-        if align not in ("left", "center", "right"):
-            raise ValueError(
-                f'invalid value for align, expected "left", "center", "right" (not {align!r})'
-            )
-        self.title = title
-        self.characters = characters
-        self.style = style
-        self.end = end
-        self.align = align
-
-    def __repr__(self) -> str:
-        return f"Rule({self.title!r}, {self.characters!r})"
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        width = options.max_width
-
-        characters = (
-            "-"
-            if (options.ascii_only and not self.characters.isascii())
-            else self.characters
-        )
-
-        chars_len = cell_len(characters)
-        if not self.title:
-            yield self._rule_line(chars_len, width)
-            return
-
-        if isinstance(self.title, Text):
-            title_text = self.title
-        else:
-            title_text = console.render_str(self.title, style="rule.text")
-
-        title_text.plain = title_text.plain.replace("\n", " ")
-        title_text.expand_tabs()
-
-        required_space = 4 if self.align == "center" else 2
-        truncate_width = max(0, width - required_space)
-        if not truncate_width:
-            yield self._rule_line(chars_len, width)
-            return
-
-        rule_text = Text(end=self.end)
-        if self.align == "center":
-            title_text.truncate(truncate_width, overflow="ellipsis")
-            side_width = (width - cell_len(title_text.plain)) // 2
-            left = Text(characters * (side_width // chars_len + 1))
-            left.truncate(side_width - 1)
-            right_length = width - cell_len(left.plain) - cell_len(title_text.plain)
-            right = Text(characters * (side_width // chars_len + 1))
-            right.truncate(right_length)
-            rule_text.append(left.plain + " ", self.style)
-            rule_text.append(title_text)
-            rule_text.append(" " + right.plain, self.style)
-        elif self.align == "left":
-            title_text.truncate(truncate_width, overflow="ellipsis")
-            rule_text.append(title_text)
-            rule_text.append(" ")
-            rule_text.append(characters * (width - rule_text.cell_len), self.style)
-        elif self.align == "right":
-            title_text.truncate(truncate_width, overflow="ellipsis")
-            rule_text.append(characters * (width - title_text.cell_len - 1), self.style)
-            rule_text.append(" ")
-            rule_text.append(title_text)
-
-        rule_text.plain = set_cell_size(rule_text.plain, width)
-        yield rule_text
-
-    def _rule_line(self, chars_len: int, width: int) -> Text:
-        rule_text = Text(self.characters * ((width // chars_len) + 1), self.style)
-        rule_text.truncate(width)
-        rule_text.plain = set_cell_size(rule_text.plain, width)
-        return rule_text
-
-    def __rich_measure__(
-        self, console: Console, options: ConsoleOptions
-    ) -> Measurement:
-        return Measurement(1, 1)
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import sys
-
-    from pip._vendor.rich.console import Console
-
-    try:
-        text = sys.argv[1]
-    except IndexError:
-        text = "Hello, World"
-    console = Console()
-    console.print(Rule(title=text))
-
-    console = Console()
-    console.print(Rule("foo"), width=4)

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

@@ -1,58 +0,0 @@
-"""setuptools.errors
-
-Provides exceptions used by setuptools modules.
-"""
-
-from distutils import errors as _distutils_errors
-
-
-# Re-export errors from distutils to facilitate the migration to PEP632
-
-ByteCompileError = _distutils_errors.DistutilsByteCompileError
-CCompilerError = _distutils_errors.CCompilerError
-ClassError = _distutils_errors.DistutilsClassError
-CompileError = _distutils_errors.CompileError
-ExecError = _distutils_errors.DistutilsExecError
-FileError = _distutils_errors.DistutilsFileError
-InternalError = _distutils_errors.DistutilsInternalError
-LibError = _distutils_errors.LibError
-LinkError = _distutils_errors.LinkError
-ModuleError = _distutils_errors.DistutilsModuleError
-OptionError = _distutils_errors.DistutilsOptionError
-PlatformError = _distutils_errors.DistutilsPlatformError
-PreprocessError = _distutils_errors.PreprocessError
-SetupError = _distutils_errors.DistutilsSetupError
-TemplateError = _distutils_errors.DistutilsTemplateError
-UnknownFileError = _distutils_errors.UnknownFileError
-
-# The root error class in the hierarchy
-BaseError = _distutils_errors.DistutilsError
-
-
-class RemovedCommandError(BaseError, RuntimeError):
-    """Error used for commands that have been removed in setuptools.
-
-    Since ``setuptools`` is built on ``distutils``, simply removing a command
-    from ``setuptools`` will make the behavior fall back to ``distutils``; this
-    error is raised if a command exists in ``distutils`` but has been actively
-    removed in ``setuptools``.
-    """
-
-
-class PackageDiscoveryError(BaseError, RuntimeError):
-    """Impossible to perform automatic discovery of packages and/or modules.
-
-    The current project layout or given discovery options can lead to problems when
-    scanning the project directory.
-
-    Setuptools might also refuse to complete auto-discovery if an error prone condition
-    is detected (e.g. when a project is organised as a flat-layout but contains
-    multiple directories that can be taken as top-level packages inside a single
-    distribution [*]_). In these situations the users are encouraged to be explicit
-    about which packages to include or to make the discovery parameters more specific.
-
-    .. [*] Since multi-package distributions are uncommon it is very likely that the
-       developers did not intend for all the directories to be packaged, and are just
-       leaving auxiliary code in the repository top-level, such as maintenance-related
-       scripts.
-    """

spaces/Benson/text-generation/Examples/Cmo Descargar Dragon Ball Z Shin Budokai 7 Ppsspp.md

@@ -1,77 +0,0 @@
-<br />
-<h1>Cómo descargar Dragon Ball Z Shin Budokai 7 PPSSPP para Android</h1>
-<p>Dragon Ball Z Shin Budokai 7 PPSSPP es un juego de lucha basado en la serie de anime Dragon Ball Z, desarrollado por Dimps y lanzado para PlayStation Portable en 2006. Es la secuela de Dragon Ball Z: Shin Budokai, y cuenta con un modo de historia que sigue los eventos de la película Fusión Reborn, donde Goku y Vegeta tienen que enfrentarse a un poderoso enemigo llamado Janemba.</p>
-<p>Si eres un fan de Dragon Ball Z, o si te gustan los juegos de lucha rápidos e intensos, definitivamente deberías probar Dragon Ball Z Shin Budokai 7 PPSSPP en tu dispositivo Android. Puedes jugar como tus personajes favoritos de la serie, como Goku, Vegeta, Gohan, Piccolo, Frieza, Cell, Majin Buu, Broly y más. También puedes personalizar tu tarjeta de perfil con artículos de la tienda del juego y retar a tus amigos en batallas multijugador inalámbricas. </p>
-<h2>Cómo descargar dragon ball z shin budokai 7 ppsspp</h2><br /><p><b><b>Download File</b> &#187; <a href="https://bltlly.com/2v6M9i">https://bltlly.com/2v6M9i</a></b></p><br /><br />
-<p>En este artículo, te mostraré cómo descargar e instalar Dragon Ball Z Shin Budokai 7 PPSSPP en tu dispositivo Android, así como algunos consejos y trucos sobre cómo jugarlo. También os contaré algunas de las características y requisitos del juego. ¡Así que, sin más preámbulos, empecemos! </p>
- <h2>Cómo descargar Dragon Ball Z Shin Budokai 7 PPSSPP</h2>
-<p>Para jugar Dragon Ball Z Shin Budokai 7 PPSSPP en tu dispositivo Android, necesitarás dos cosas: el archivo ISO del juego y el emulador PPSSPP. El archivo ISO del juego es una versión comprimida del disco original del juego que contiene todos los datos y gráficos del juego. El emulador PPSSPP es un software que te permite ejecutar juegos PSP en tu dispositivo Android. </p>
-<p>Aquí están los pasos para descargar e instalar Dragon Ball Z Shin Budokai 7 PPSSPP en su dispositivo Android:</p>
-<ol>
-<li>Primero, necesitas descargar el archivo ISO del juego desde una fuente confiable. Puedes usar este enlace para descargarlo desde Geeksblogger.com. El tamaño del archivo es de unos 300 MB.</li>
-
-<li>Después de descargar ambos archivos, es necesario instalarlos en su dispositivo Android. Para ello, debe habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store.</li>
-<li>Una vez que haya habilitado fuentes desconocidas, vaya a su administrador de archivos y busque los archivos descargados. Toque en ellos uno por uno y siga las instrucciones para instalarlos. </li>
-<li>Después de instalar ambos archivos, necesita extraer el archivo ISO del juego de su carpeta ZIP. Puede utilizar cualquier aplicación que puede descomprimir archivos, como RAR o ZArchiver. Simplemente toque en la carpeta ZIP y elija extraer aquí o extraer a una ubicación específica. </li>
-<li>Ahora que ha extraído el archivo ISO del juego, está listo para jugarlo. Inicie la aplicación emuladora PPSSPP y toque en Juegos. Luego, vaya a la carpeta donde extrajo el archivo ISO del juego y toque en él. Debería ver el icono del juego y el título en la pantalla. Toque en él para comenzar a jugar. </li>
-</ol>
-<p>Felicidades! Usted ha descargado e instalado con éxito Dragon Ball Z Shin Budokai 7 PPSSPP en su dispositivo Android. Ahora, veamos cómo se juega. </p>
- <h2>Cómo jugar Dragon Ball Z Shin Budokai 7 PPSSPP</h2>
-<p>Dragon Ball Z Shin Budokai 7 PPSSPP es un divertido y adictivo juego de lucha que te mantendrá entretenido durante horas. El juego tiene varios modos entre los que puedes elegir, como el modo historia, el modo árcade, el modo de prueba Z, el modo de batalla de red y el modo tarjeta de perfil. Cada modo tiene sus propios objetivos y desafíos que puedes completar para desbloquear nuevos elementos y personajes. </p>
-<p>El juego también tiene un esquema de control simple e intuitivo que puedes personalizar según tus preferencias. Puede utilizar los botones virtuales en la pantalla o los gestos táctiles para realizar varias acciones, como mover, atacar, bloquear, cargar, transformar y usar movimientos especiales. También puede ajustar la sensibilidad y el diseño de los controles en el menú de configuración. </p>
-
-<ul>
-<li>Para acceder al menú del juego, toca el botón de pausa en la esquina superior derecha de la pantalla. Desde allí, puede guardar o cargar su progreso, cambiar la configuración del juego, ver su tarjeta de perfil o salir del juego. </li>
-<li>Para cambiar entre caracteres durante una batalla, toca el icono del personaje en la esquina inferior izquierda de la pantalla. También puede etiquetar en su pareja presionando los botones L + R simultáneamente. </li>
-<li>Para realizar una fusión, necesitas tener dos personajes compatibles en tu equipo, como Goku y Vegeta, o Goten y Trunks. Entonces, necesitas llenar tu medidor de Ki atacando o cargando. Una vez que esté lleno, presiona los botones L + R + X simultáneamente para iniciar la fusión. Luego te transformarás en un personaje más poderoso, como Gogeta o Gotenks.</li>
-<li>Para usar un movimiento especial, necesitas tener suficiente Ki en tu medidor. Luego, presiona el botón O para activarlo. También puede utilizar diferentes variaciones del movimiento especial pulsando O + botones direccionales. Por ejemplo, Goku puede usar Kamehameha, Super Kamehameha o Kamehameha instantánea dependiendo de la dirección en la que presione. </li>
-<li>Para usar un movimiento definitivo, necesita tener al menos tres barras de Ki en su medidor. Luego, presione los botones O + X simultáneamente para desatarlo. Los movimientos finales son muy poderosos y pueden infligir daño masivo a tu oponente. Sin embargo, también consumen mucho Ki y te dejan vulnerable por un tiempo. </li>
-</ul>
-<p>Estos son algunos de los consejos básicos y trucos sobre cómo jugar Dragon Ball Z Shin Budokai 7 PPSSPP. Por supuesto, hay técnicas y estrategias más avanzadas que puedes aprender a medida que juegas más. La mejor manera de dominar el juego es practicar y experimentar con diferentes personajes y movimientos. </p>
-<p></p>
- <h2>Características de Dragon Ball Z Shin Budokai 7 PPSSPP</h2>
-
-<tabla>
-<tr><th>Característica</th><th>Descripción</th></tr>
-<tr><td>Characters</td><td>El juego cuenta con 22 personajes jugables de la serie Dragon Ball Z, cada uno con sus propios movimientos y transformaciones. Puedes jugar como héroes como Goku, Vegeta, Gohan, Piccolo, Krillin, Gotenks, Gogeta, Vegito y más. También puedes jugar como villanos como Frieza, Cell, Majin Buu, Broly, Janemba, Cooler y más. </td></tr>
-<tr><td>Stages</td><td>El juego cuenta con 11 etapas de la serie Dragon Ball Z, cada una con su propia música de fondo y efectos ambientales. Puedes luchar en lugares como Earth, Namek, Hell, World Tournament Arena, Hyperbolic Time Chamber, Supreme Kai’s Planet, y más. </td></tr>
-<tr><td>Graphics</td><td>El juego presenta gráficos de alta calidad que se asemejan al estilo anime de Dragon Ball Z. Los personajes están bien diseñados y animados con movimientos y expresiones suaves. Los escenarios son coloridos y detallados con iluminación y sombras realistas. Los efectos especiales son llamativos e impresionantes con chispas, explosiones y rayos. El juego también admite resolución HD y 60 FPS para una experiencia de juego fluida e inmersiva. </td></tr>
-<tr><td>Sound</td><td>El juego cuenta con un sonido de alta calidad que mejora la atmósfera y el estado de ánimo del juego. La música está compuesta por Kenji Yamamoto, quien también trabajó en la serie de anime Dragon Ball Z. La música es pegadiza y enérgica, que coincide con el tono y el ritmo del juego. Los efectos de sonido son realistas y satisfactorios, añadiendo impacto y retroalimentación al juego. La actuación de voz es hecha por el elenco japonés original de la serie de anime Dragon Ball Z, dando autenticidad y emoción al juego. </td></tr>
-</tabla>
-<p>Estas son algunas de las características que hacen de Dragon Ball Z Shin Budokai 7 PPSSPP un gran juego para jugar en tu dispositivo Android. Por supuesto, hay más características que puedes descubrir y disfrutar mientras juegas más. El juego está lleno de sorpresas y secretos que te mantendrán enganchado durante horas. </p>
-
-<p>Dragon Ball Z Shin Budokai 7 PPSSPP es un juego relativamente ligero que puede ejecutarse en la mayoría de los dispositivos Android. Sin embargo, para garantizar una experiencia de juego suave y óptima, debe verificar los requisitos mínimos y recomendados para ejecutar el juego. Aquí están:</p>
-<tabla>
-<tr><th>Requisito</th><th>Mínimo</th><th>Recomendado</th></tr>
-<tr><td>Versión de Android</td><td>4.0 o superior</td><td>6.0 o superior</td></tr>
-<tr><td>RAM</td><td>1 GB o superior</td><td>2 GB o superior</td></tr>
-<tr><td>Espacio de almacenamiento</td><td>500 MB o superior</td><td>1 GB o superior</td></tr>
-<tr><td>Velocidad del procesador</td><td>1 GHz o superior</td><td>2 GHz o superior</td></tr>
-<tr><td>Calidad gráfica</td><td>Baja o media</td><td>Alta o ultra</td></tr>
-<tr><td>FPS (fotogramas por segundo)</td><td>30 o superior</td><td>60 o superior</td></tr>
-</tabla>
-<p>Si su dispositivo cumple o excede estos requisitos, usted debe ser capaz de jugar Dragon Ball Z Shin Budokai 7 PPSSPP sin ningún problema. Sin embargo, si tu dispositivo está por debajo de estos requisitos, es posible que experimentes algún retraso, tartamudeo o fallo mientras juegas. En ese caso, puede intentar reducir la calidad gráfica, la resolución o los FPS en el menú de configuración del emulador de PPSSPP para mejorar el rendimiento. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, Dragon Ball Z Shin Budokai 7 PPSSPP es un increíble juego de lucha que puedes jugar en tu dispositivo Android usando el emulador PPSSPP. El juego tiene un montón de características que atraerán a los fans de Dragon Ball Z y juegos de lucha en general. Puedes jugar como tus personajes favoritos de la serie, luchar en varias etapas del anime, disfrutar de gráficos y sonidos de alta calidad, y desafiar a tus amigos en el modo multijugador. </p>
-
-<p>Espero que hayas encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario sobre Dragon Ball Z Shin Budokai 7 PPSSPP, no dude en dejar un comentario a continuación. Me encantaría saber de usted! </p>
-<p>Ahora, ¿qué estás esperando? Adelante y descargar Dragon Ball Z Shin Budokai 7 PPSSPP en su dispositivo Android y disfrutar de este impresionante juego! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas y respuestas frecuentes sobre Dragon Ball Z Shin Budokai 7 PPSSPP:</p>
-<ol>
-<li><b>¿Es Dragon Ball Z Shin Budokai 7 PPSSPP un juego oficial? </b></li>
-<p>No, Dragon Ball Z Shin Budokai 7 PPSSPP no es un juego oficial. Es una versión modificada de Dragon Ball Z: Shin Budokai - Another Road, que es un juego oficial lanzado para PlayStation Portable en 2007. La versión modificada añade nuevos personajes, escenarios, gráficos y sonido al juego original. </p>
-<li><b> ¿Es seguro descargar Dragon Ball Z Shin Budokai 7 PPSSPP? </b></li>
-<p <p>Sí, Dragon Ball Z Shin Budokai 7 PPSSPP es seguro de descargar siempre y cuando utilice los enlaces que he proporcionado en este artículo. Estos enlaces son de fuentes confiables que han probado y verificado los archivos del juego. Sin embargo, siempre debe tener cuidado al descargar cualquier archivo de Internet, y escanearlos con un software antivirus antes de abrirlos. </p>
-<li><b>¿Puedo jugar Dragon Ball Z Shin Budokai 7 PPSSPP sin conexión? </b></li>
-<p>Sí, puedes jugar Dragon Ball Z Shin Budokai 7 PPSSPP sin conexión a Internet. Solo necesitas una conexión a Internet para descargar los archivos del juego y la aplicación emuladora PPSSPP. Una vez que los haya instalado en su dispositivo, puede jugar el juego sin conexión en cualquier momento y en cualquier lugar. </p>
-<li><b>¿Puedo jugar Dragon Ball Z Shin Budokai 7 PPSSPP con un controlador? </b></li>
-
-<li><b>¿Puedo jugar Dragon Ball Z Shin Budokai 7 PPSSPP con otros jugadores? </b></li>
-<p>Sí, puedes jugar Dragon Ball Z Shin Budokai 7 PPSSPP con otros jugadores en el modo de batalla de red. Este modo le permite luchar contra otros jugadores en línea o localmente utilizando una conexión inalámbrica. También puedes chatear con otros jugadores y ver sus tarjetas de perfil. Para acceder a este modo, necesita tener una conexión a Internet y una dirección IP válida. </p>
-<li><b>¿Cómo puedo desbloquear más personajes y elementos en Dragon Ball Z Shin Budokai 7 PPSSPP? </b></li>
-<p>Puedes desbloquear más personajes y objetos en Dragon Ball Z Shin Budokai 7 PPSSPP completando varios modos y desafíos en el juego. Por ejemplo, puedes desbloquear nuevos personajes terminando el modo historia, el modo árcade o el modo de prueba Z. También puedes desbloquear nuevos objetos ganando Zeni (la moneda del juego) y gastándolo en la tienda del juego. También puedes usar trucos o mods para desbloquear todo en el juego, pero eso puede arruinar la diversión y el desafío del juego. </p>
-</ol>
-<p>Espero que estas preguntas frecuentes hayan respondido a algunas de sus preguntas sobre Dragon Ball Z Shin Budokai 7 PPSSPP. Si tiene alguna otra pregunta, no dude en dejar un comentario a continuación. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/s3/__init__.py

@@ -1,12 +0,0 @@
-# Copyright 2015 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
-#
-# https://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.

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/version.py

@@ -1,9 +0,0 @@
-"""
-This module exists only to simplify retrieving the version number of chardet
-from within setuptools and from chardet subpackages.
-
-:author: Dan Blanchard ([email protected])
-"""
-
-__version__ = "5.1.0"
-VERSION = __version__.split(".")

spaces/BucketHeadP65/confusion_matrix/confusion_matrix.py

@@ -1,149 +0,0 @@
-"""Confusion Matrix metric."""
-
-import datasets
-import evaluate
-from sklearn.metrics import confusion_matrix
-
-_DESCRIPTION = """
-Compute confusion matrix to evaluate the accuracy of a classification.
-By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`
-is equal to the number of observations known to be in group :math:`i` and
-predicted to be in group :math:`j`.
-
-Thus in binary classification, the count of true negatives is
-:math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
-:math:`C_{1,1}` and false positives is :math:`C_{0,1}`.
-
-Read more in the :ref:`User Guide <confusion_matrix>`.
-"""
-
-
-_KWARGS_DESCRIPTION = """
-Args:
-
-    y_true : array-like of shape (n_samples,)
-        Ground truth (correct) target values.
-
-    y_pred : array-like of shape (n_samples,)
-        Estimated targets as returned by a classifier.
-
-    labels : array-like of shape (n_classes), default=None
-        List of labels to index the matrix. This may be used to reorder
-        or select a subset of labels.
-        If ``None`` is given, those that appear at least once
-        in ``y_true`` or ``y_pred`` are used in sorted order.
-
-    sample_weight : array-like of shape (n_samples,), default=None
-        Sample weights.
-
-        .. versionadded:: 0.18
-
-    normalize : {'true', 'pred', 'all'}, default=None
-        Normalizes confusion matrix over the true (rows), predicted (columns)
-        conditions or all the population. If None, confusion matrix will not be
-        normalized.
-
-Returns:
-
-    C : ndarray of shape (n_classes, n_classes)
-        Confusion matrix whose i-th row and j-th
-        column entry indicates the number of
-        samples with true label being i-th class
-        and predicted label being j-th class.
-
-See Also:
-
-    ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
-        given an estimator, the data, and the label.
-    ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
-        given the true and predicted labels.
-    ConfusionMatrixDisplay : Confusion Matrix visualization.
-
-References:
-
-    .. [1] `Wikipedia entry for the Confusion matrix
-           <https://en.wikipedia.org/wiki/Confusion_matrix>`_
-           (Wikipedia and other references may use a different
-           convention for axes).
-
-Examples:
-
-    >>> from sklearn.metrics import confusion_matrix
-    >>> y_true = [2, 0, 2, 2, 0, 1]
-    >>> y_pred = [0, 0, 2, 2, 0, 2]
-    >>> confusion_matrix(y_true, y_pred)
-    array([[2, 0, 0],
-           [0, 0, 1],
-           [1, 0, 2]])
-
-    >>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
-    >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
-    >>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
-    array([[2, 0, 0],
-           [0, 0, 1],
-           [1, 0, 2]])
-
-    In the binary case, we can extract true positives, etc as follows:
-
-    >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
-    >>> (tn, fp, fn, tp)
-    (0, 2, 1, 1)
-"""
-
-
-_CITATION = """
-@article{scikit-learn,
-  title={Scikit-learn: Machine Learning in {P}ython},
-  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
-         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
-         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
-         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
-  journal={Journal of Machine Learning Research},
-  volume={12},
-  pages={2825--2830},
-  year={2011}
-}
-"""
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class ConfusionMatrix(evaluate.Metric):
-    def _info(self):
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "predictions": datasets.Sequence(datasets.Value("int32")),
-                    "references": datasets.Sequence(datasets.Value("int32")),
-                }
-                if self.config_name == "multilabel"
-                else {
-                    "predictions": datasets.Value("int32"),
-                    "references": datasets.Value("int32"),
-                }
-            ),
-            reference_urls=[
-                "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html"
-            ],
-        )
-
-    def _compute(
-        self,
-        predictions,
-        references,
-        *,
-        labels=None,
-        sample_weight=None,
-        normalize=None
-    ):
-        return {
-            "confusion_matrix": confusion_matrix(
-                y_true=references,
-                y_pred=predictions,
-                labels=labels,
-                sample_weight=sample_weight,
-                normalize=normalize,
-            )
-        }

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/tutorials/training.md

@@ -1,49 +0,0 @@
-# Training
-
-From the previous tutorials, you may now have a custom model and data loader.
-
-You are free to create your own optimizer, and write the training logic: it's
-usually easy with PyTorch, and allow researchers to see the entire training
-logic more clearly and have full control.
-One such example is provided in [tools/plain_train_net.py](../../tools/plain_train_net.py).
-
-We also provide a standarized "trainer" abstraction with a
-[minimal hook system](../modules/engine.html#detectron2.engine.HookBase)
-that helps simplify the standard types of training.
-
-You can use
-[SimpleTrainer().train()](../modules/engine.html#detectron2.engine.SimpleTrainer)
-which provides minimal abstraction for single-cost single-optimizer single-data-source training.
-The builtin `train_net.py` script uses
-[DefaultTrainer().train()](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer),
-which includes more standard default behavior that one might want to opt in,
-including default configurations for logging, evaluation, checkpointing etc.
-This also means that it's less likely to support some non-standard behavior
-you might want during research.
-
-To customize the training loops, you can:
-
-1. If your customization is similar to what `DefaultTrainer` is already doing,
-you can look at the source code of [DefaultTrainer](../../detectron2/engine/defaults.py)
-and overwrite some of its behaviors with new parameters or new hooks.
-2. If you need something very novel, you can start from [tools/plain_train_net.py](../../tools/plain_train_net.py) to implement them yourself.
-
-### Logging of Metrics
-
-During training, metrics are saved to a centralized [EventStorage](../modules/utils.html#detectron2.utils.events.EventStorage).
-You can use the following code to access it and log metrics to it:
-```
-from detectron2.utils.events import get_event_storage
-
-# inside the model:
-if self.training:
-  value = # compute the value from inputs
-  storage = get_event_storage()
-  storage.put_scalar("some_accuracy", value)
-```
-
-Refer to its documentation for more details.
-
-Metrics are then saved to various destinations with [EventWriter](../modules/utils.html#module-detectron2.utils.events).
-DefaultTrainer enables a few `EventWriter` with default configurations.
-See above for how to customize them.

spaces/CVPR/lama-example/fetch_data/places_standard_train_prepare.sh

@@ -1,16 +0,0 @@
-mkdir -p places_standard_dataset/train
-
-# untar without folder structure
-tar -xvf train_large_places365standard.tar --transform='s/.*\///' -C places_standard_dataset/train
-
-# create location config places.yaml
-PWD=$(pwd)
-DATASET=${PWD}/places_standard_dataset
-PLACES=${PWD}/configs/training/location/places_standard.yaml
-
-touch $PLACES
-echo "# @package _group_" >> $PLACES
-echo "data_root_dir: ${DATASET}/" >> $PLACES
-echo "out_root_dir: ${PWD}/experiments/" >> $PLACES
-echo "tb_dir: ${PWD}/tb_logs/" >> $PLACES
-echo "pretrained_models: ${PWD}/" >> $PLACES

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

@@ -1,37 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-
-img_dir = Path(__file__).parent / "images"
-
-
-def luxun_say(images, texts: List[str], args):
-    text = texts[0]
-    frame = BuildImage.open(img_dir / "0.jpg")
-    try:
-        frame.draw_text(
-            (40, frame.height - 200, frame.width - 40, frame.height - 100),
-            text,
-            allow_wrap=True,
-            max_fontsize=40,
-            min_fontsize=30,
-            fill="white",
-        )
-    except ValueError:
-        raise TextOverLength(text)
-    frame.draw_text((320, 400), "--鲁迅", fontsize=30, fill="white")
-    return frame.save_jpg()
-
-
-add_meme(
-    "luxun_say",
-    luxun_say,
-    min_texts=1,
-    max_texts=1,
-    default_texts=["我没有说过这句话"],
-    keywords=["鲁迅说", "鲁迅说过"],
-)

spaces/CjangCjengh/Shanghainese-TTS/app.py

@@ -1,80 +0,0 @@
-import torch
-import librosa
-import commons
-import utils
-from models import SynthesizerTrn
-from text import text_to_sequence
-import numpy as np
-from mel_processing import spectrogram_torch
-import gradio as gr
-from text.cleaners import shanghainese_cleaners
-
-
-DEFAULT_TEXT='阿拉小人天天辣辣白相，书一眼也勿看，拿我急煞脱了。侬讲是𠲎？'
-
-
-def clean_text(text,ipa_input):
-    if ipa_input:
-        return shanghainese_cleaners(text)
-    return text
-
-
-def get_text(text, hps, cleaned=False):
-    if cleaned:
-        text_norm = text_to_sequence(text, hps.symbols, [])
-    else:
-        text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-
-def speech_synthesize(text, cleaned, length_scale):
-    text=text.replace('\n','')
-    print(text)
-    stn_tst = get_text(text, hps_ms, cleaned)
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-        sid = torch.LongTensor([0])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-    return (hps_ms.data.sampling_rate, audio)
-
-
-if __name__=='__main__':
-    hps_ms = utils.get_hparams_from_file('model/config.json')
-    n_speakers = hps_ms.data.n_speakers
-    n_symbols = len(hps_ms.symbols)
-    speakers = hps_ms.speakers
-
-    net_g_ms = SynthesizerTrn(
-        n_symbols,
-        hps_ms.data.filter_length // 2 + 1,
-        hps_ms.train.segment_size // hps_ms.data.hop_length,
-        n_speakers=n_speakers,
-        **hps_ms.model)
-    _ = net_g_ms.eval()
-    utils.load_checkpoint('model/model.pth', net_g_ms)
-
-    with gr.Blocks() as app:
-        gr.Markdown('# Shanghainese Text to Speech\n'
-        '![visitor badge](https://visitor-badge.glitch.me/badge?page_id=cjangcjengh.shanghainese-tts)')
-        gr.Markdown('<center><big><b>See <a href="https://huggingface.co/spaces/CjangCjengh/Shanghainese-TTS/raw/main/shanghainese_script.txt">EXAMPLES</a> on Shanghainese script</b></big></center>')
-        text_input = gr.TextArea(label='Text', placeholder='Type your text here',value=DEFAULT_TEXT)
-        cleaned_text=gr.Checkbox(label='IPA Input',default=True)
-        length_scale=gr.Slider(0.5,2,1,step=0.1,label='Speaking Speed',interactive=True)
-        tts_button = gr.Button('Synthesize')
-        audio_output = gr.Audio(label='Speech Synthesized')
-        cleaned_text.change(clean_text,[text_input,cleaned_text],[text_input])
-        tts_button.click(speech_synthesize,[text_input,cleaned_text,length_scale],[audio_output])
-        gr.Markdown('## Based on\n'
-        '- [https://github.com/jaywalnut310/vits](https://github.com/jaywalnut310/vits)\n\n'
-        '## Dataset\n'
-        '- [http://shh.dict.cn/](http://shh.dict.cn/)\n\n'
-        '## Lexicon\n'
-        '- [https://www.wugniu.com/](https://www.wugniu.com/)\n\n'
-        '- [https://github.com/MaigoAkisame/MCPDict](https://github.com/MaigoAkisame/MCPDict)\n\n'
-        '- [https://github.com/edward-martyr/rime-yahwe_zaonhe](https://github.com/edward-martyr/rime-yahwe_zaonhe)')
-
-    app.launch()

spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/datasets/builders/base_dataset_builder.py

@@ -1,236 +0,0 @@
-"""
- This file is from
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import logging
-import os
-import shutil
-import warnings
-
-from omegaconf import OmegaConf
-import torch.distributed as dist
-from torchvision.datasets.utils import download_url
-
-import video_llama.common.utils as utils
-from video_llama.common.dist_utils import is_dist_avail_and_initialized, is_main_process
-from video_llama.common.registry import registry
-from video_llama.processors.base_processor import BaseProcessor
-
-
-
-class BaseDatasetBuilder:
-    train_dataset_cls, eval_dataset_cls = None, None
-
-    def __init__(self, cfg=None):
-        super().__init__()
-
-        if cfg is None:
-            # help to create datasets from default config.
-            self.config = load_dataset_config(self.default_config_path())
-        elif isinstance(cfg, str):
-            self.config = load_dataset_config(cfg)
-        else:
-            # when called from task.build_dataset()
-            self.config = cfg
-
-        self.data_type = self.config.data_type
-
-        self.vis_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
-        self.text_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
-
-    def build_datasets(self):
-        # download, split, etc...
-        # only called on 1 GPU/TPU in distributed
-
-        if is_main_process():
-            self._download_data()
-
-        if is_dist_avail_and_initialized():
-            dist.barrier()
-
-        # at this point, all the annotations and image/videos should be all downloaded to the specified locations.
-        logging.info("Building datasets...")
-        datasets = self.build()  # dataset['train'/'val'/'test']
-
-        return datasets
-
-    def build_processors(self):
-        vis_proc_cfg = self.config.get("vis_processor")
-        txt_proc_cfg = self.config.get("text_processor")
-
-        if vis_proc_cfg is not None:
-            vis_train_cfg = vis_proc_cfg.get("train")
-            vis_eval_cfg = vis_proc_cfg.get("eval")
-
-            self.vis_processors["train"] = self._build_proc_from_cfg(vis_train_cfg)
-            self.vis_processors["eval"] = self._build_proc_from_cfg(vis_eval_cfg)
-
-        if txt_proc_cfg is not None:
-            txt_train_cfg = txt_proc_cfg.get("train")
-            txt_eval_cfg = txt_proc_cfg.get("eval")
-
-            self.text_processors["train"] = self._build_proc_from_cfg(txt_train_cfg)
-            self.text_processors["eval"] = self._build_proc_from_cfg(txt_eval_cfg)
-
-    @staticmethod
-    def _build_proc_from_cfg(cfg):
-        return (
-            registry.get_processor_class(cfg.name).from_config(cfg)
-            if cfg is not None
-            else None
-        )
-
-    @classmethod
-    def default_config_path(cls, type="default"):
-        return utils.get_abs_path(cls.DATASET_CONFIG_DICT[type])
-
-    def _download_data(self):
-        self._download_ann()
-        self._download_vis()
-
-    def _download_ann(self):
-        """
-        Download annotation files if necessary.
-        All the vision-language datasets should have annotations of unified format.
-
-        storage_path can be:
-          (1) relative/absolute: will be prefixed with env.cache_root to make full path if relative.
-          (2) basename/dirname: will be suffixed with base name of URL if dirname is provided.
-
-        Local annotation paths should be relative.
-        """
-        anns = self.config.build_info.annotations
-
-        splits = anns.keys()
-
-        cache_root = registry.get_path("cache_root")
-
-        for split in splits:
-            info = anns[split]
-
-            urls, storage_paths = info.get("url", None), info.storage
-
-            if isinstance(urls, str):
-                urls = [urls]
-            if isinstance(storage_paths, str):
-                storage_paths = [storage_paths]
-
-            assert len(urls) == len(storage_paths)
-
-            for url_or_filename, storage_path in zip(urls, storage_paths):
-                # if storage_path is relative, make it full by prefixing with cache_root.
-                if not os.path.isabs(storage_path):
-                    storage_path = os.path.join(cache_root, storage_path)
-
-                dirname = os.path.dirname(storage_path)
-                if not os.path.exists(dirname):
-                    os.makedirs(dirname)
-
-                if os.path.isfile(url_or_filename):
-                    src, dst = url_or_filename, storage_path
-                    if not os.path.exists(dst):
-                        shutil.copyfile(src=src, dst=dst)
-                    else:
-                        logging.info("Using existing file {}.".format(dst))
-                else:
-                    if os.path.isdir(storage_path):
-                        # if only dirname is provided, suffix with basename of URL.
-                        raise ValueError(
-                            "Expecting storage_path to be a file path, got directory {}".format(
-                                storage_path
-                            )
-                        )
-                    else:
-                        filename = os.path.basename(storage_path)
-
-                    download_url(url=url_or_filename, root=dirname, filename=filename)
-
-    def _download_vis(self):
-
-        storage_path = self.config.build_info.get(self.data_type).storage
-        storage_path = utils.get_cache_path(storage_path)
-
-        if not os.path.exists(storage_path):
-            warnings.warn(
-                f"""
-                The specified path {storage_path} for visual inputs does not exist.
-                Please provide a correct path to the visual inputs or
-                refer to datasets/download_scripts/README.md for downloading instructions.
-                """
-            )
-
-    def build(self):
-        """
-        Create by split datasets inheriting torch.utils.data.Datasets.
-
-        # build() can be dataset-specific. Overwrite to customize.
-        """
-        self.build_processors()
-
-        build_info = self.config.build_info
-
-        ann_info = build_info.annotations
-        vis_info = build_info.get(self.data_type)
-
-        datasets = dict()
-        for split in ann_info.keys():
-            if split not in ["train", "val", "test"]:
-                continue
-
-            is_train = split == "train"
-
-            # processors
-            vis_processor = (
-                self.vis_processors["train"]
-                if is_train
-                else self.vis_processors["eval"]
-            )
-            text_processor = (
-                self.text_processors["train"]
-                if is_train
-                else self.text_processors["eval"]
-            )
-
-            # annotation path
-            ann_paths = ann_info.get(split).storage
-            if isinstance(ann_paths, str):
-                ann_paths = [ann_paths]
-
-            abs_ann_paths = []
-            for ann_path in ann_paths:
-                if not os.path.isabs(ann_path):
-                    ann_path = utils.get_cache_path(ann_path)
-                abs_ann_paths.append(ann_path)
-            ann_paths = abs_ann_paths
-
-            # visual data storage path
-            vis_path = os.path.join(vis_info.storage, split)
-
-            if not os.path.isabs(vis_path):
-                # vis_path = os.path.join(utils.get_cache_path(), vis_path)
-                vis_path = utils.get_cache_path(vis_path)
-
-            if not os.path.exists(vis_path):
-                warnings.warn("storage path {} does not exist.".format(vis_path))
-
-            # create datasets
-            dataset_cls = self.train_dataset_cls if is_train else self.eval_dataset_cls
-            datasets[split] = dataset_cls(
-                vis_processor=vis_processor,
-                text_processor=text_processor,
-                ann_paths=ann_paths,
-                vis_root=vis_path,
-            )
-
-        return datasets
-
-
-def load_dataset_config(cfg_path):
-    cfg = OmegaConf.load(cfg_path).datasets
-    cfg = cfg[list(cfg.keys())[0]]
-
-    return cfg

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/PalmImagePlugin.py

@@ -1,225 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-
-##
-# Image plugin for Palm pixmap images (output only).
-##
-
-from . import Image, ImageFile
-from ._binary import o8
-from ._binary import o16be as o16b
-
-# fmt: off
-_Palm8BitColormapValues = (
-    (255, 255, 255), (255, 204, 255), (255, 153, 255), (255, 102, 255),
-    (255,  51, 255), (255,   0, 255), (255, 255, 204), (255, 204, 204),
-    (255, 153, 204), (255, 102, 204), (255,  51, 204), (255,   0, 204),
-    (255, 255, 153), (255, 204, 153), (255, 153, 153), (255, 102, 153),
-    (255,  51, 153), (255,   0, 153), (204, 255, 255), (204, 204, 255),
-    (204, 153, 255), (204, 102, 255), (204,  51, 255), (204,   0, 255),
-    (204, 255, 204), (204, 204, 204), (204, 153, 204), (204, 102, 204),
-    (204,  51, 204), (204,   0, 204), (204, 255, 153), (204, 204, 153),
-    (204, 153, 153), (204, 102, 153), (204,  51, 153), (204,   0, 153),
-    (153, 255, 255), (153, 204, 255), (153, 153, 255), (153, 102, 255),
-    (153,  51, 255), (153,   0, 255), (153, 255, 204), (153, 204, 204),
-    (153, 153, 204), (153, 102, 204), (153,  51, 204), (153,   0, 204),
-    (153, 255, 153), (153, 204, 153), (153, 153, 153), (153, 102, 153),
-    (153,  51, 153), (153,   0, 153), (102, 255, 255), (102, 204, 255),
-    (102, 153, 255), (102, 102, 255), (102,  51, 255), (102,   0, 255),
-    (102, 255, 204), (102, 204, 204), (102, 153, 204), (102, 102, 204),
-    (102,  51, 204), (102,   0, 204), (102, 255, 153), (102, 204, 153),
-    (102, 153, 153), (102, 102, 153), (102,  51, 153), (102,   0, 153),
-    (51,  255, 255), (51,  204, 255), (51,  153, 255), (51,  102, 255),
-    (51,   51, 255), (51,    0, 255), (51,  255, 204), (51,  204, 204),
-    (51,  153, 204), (51,  102, 204), (51,   51, 204), (51,    0, 204),
-    (51,  255, 153), (51,  204, 153), (51,  153, 153), (51,  102, 153),
-    (51,   51, 153), (51,    0, 153), (0,   255, 255), (0,   204, 255),
-    (0,   153, 255), (0,   102, 255), (0,    51, 255), (0,     0, 255),
-    (0,   255, 204), (0,   204, 204), (0,   153, 204), (0,   102, 204),
-    (0,    51, 204), (0,     0, 204), (0,   255, 153), (0,   204, 153),
-    (0,   153, 153), (0,   102, 153), (0,    51, 153), (0,     0, 153),
-    (255, 255, 102), (255, 204, 102), (255, 153, 102), (255, 102, 102),
-    (255,  51, 102), (255,   0, 102), (255, 255,  51), (255, 204,  51),
-    (255, 153,  51), (255, 102,  51), (255,  51,  51), (255,   0,  51),
-    (255, 255,   0), (255, 204,   0), (255, 153,   0), (255, 102,   0),
-    (255,  51,   0), (255,   0,   0), (204, 255, 102), (204, 204, 102),
-    (204, 153, 102), (204, 102, 102), (204,  51, 102), (204,   0, 102),
-    (204, 255,  51), (204, 204,  51), (204, 153,  51), (204, 102,  51),
-    (204,  51,  51), (204,   0,  51), (204, 255,   0), (204, 204,   0),
-    (204, 153,   0), (204, 102,   0), (204,  51,   0), (204,   0,   0),
-    (153, 255, 102), (153, 204, 102), (153, 153, 102), (153, 102, 102),
-    (153,  51, 102), (153,   0, 102), (153, 255,  51), (153, 204,  51),
-    (153, 153,  51), (153, 102,  51), (153,  51,  51), (153,   0,  51),
-    (153, 255,   0), (153, 204,   0), (153, 153,   0), (153, 102,   0),
-    (153,  51,   0), (153,   0,   0), (102, 255, 102), (102, 204, 102),
-    (102, 153, 102), (102, 102, 102), (102,  51, 102), (102,   0, 102),
-    (102, 255,  51), (102, 204,  51), (102, 153,  51), (102, 102,  51),
-    (102,  51,  51), (102,   0,  51), (102, 255,   0), (102, 204,   0),
-    (102, 153,   0), (102, 102,   0), (102,  51,   0), (102,   0,   0),
-    (51,  255, 102), (51,  204, 102), (51,  153, 102), (51,  102, 102),
-    (51,   51, 102), (51,    0, 102), (51,  255,  51), (51,  204,  51),
-    (51,  153,  51), (51,  102,  51), (51,   51,  51), (51,    0,  51),
-    (51,  255,   0), (51,  204,   0), (51,  153,   0), (51,  102,   0),
-    (51,   51,   0), (51,    0,   0), (0,   255, 102), (0,   204, 102),
-    (0,   153, 102), (0,   102, 102), (0,    51, 102), (0,     0, 102),
-    (0,   255,  51), (0,   204,  51), (0,   153,  51), (0,   102,  51),
-    (0,    51,  51), (0,     0,  51), (0,   255,   0), (0,   204,   0),
-    (0,   153,   0), (0,   102,   0), (0,    51,   0), (17,   17,  17),
-    (34,   34,  34), (68,   68,  68), (85,   85,  85), (119, 119, 119),
-    (136, 136, 136), (170, 170, 170), (187, 187, 187), (221, 221, 221),
-    (238, 238, 238), (192, 192, 192), (128,   0,   0), (128,   0, 128),
-    (0,   128,   0), (0,   128, 128), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0),
-    (0,     0,   0), (0,     0,   0), (0,     0,   0), (0,     0,   0))
-# fmt: on
-
-
-# so build a prototype image to be used for palette resampling
-def build_prototype_image():
-    image = Image.new("L", (1, len(_Palm8BitColormapValues)))
-    image.putdata(list(range(len(_Palm8BitColormapValues))))
-    palettedata = ()
-    for colormapValue in _Palm8BitColormapValues:
-        palettedata += colormapValue
-    palettedata += (0, 0, 0) * (256 - len(_Palm8BitColormapValues))
-    image.putpalette(palettedata)
-    return image
-
-
-Palm8BitColormapImage = build_prototype_image()
-
-# OK, we now have in Palm8BitColormapImage,
-# a "P"-mode image with the right palette
-#
-# --------------------------------------------------------------------
-
-_FLAGS = {"custom-colormap": 0x4000, "is-compressed": 0x8000, "has-transparent": 0x2000}
-
-_COMPRESSION_TYPES = {"none": 0xFF, "rle": 0x01, "scanline": 0x00}
-
-
-#
-# --------------------------------------------------------------------
-
-##
-# (Internal) Image save plugin for the Palm format.
-
-
-def _save(im, fp, filename):
-    if im.mode == "P":
-        # we assume this is a color Palm image with the standard colormap,
-        # unless the "info" dict has a "custom-colormap" field
-
-        rawmode = "P"
-        bpp = 8
-        version = 1
-
-    elif im.mode == "L":
-        if im.encoderinfo.get("bpp") in (1, 2, 4):
-            # this is 8-bit grayscale, so we shift it to get the high-order bits,
-            # and invert it because
-            # Palm does greyscale from white (0) to black (1)
-            bpp = im.encoderinfo["bpp"]
-            im = im.point(
-                lambda x, shift=8 - bpp, maxval=(1 << bpp) - 1: maxval - (x >> shift)
-            )
-        elif im.info.get("bpp") in (1, 2, 4):
-            # here we assume that even though the inherent mode is 8-bit grayscale,
-            # only the lower bpp bits are significant.
-            # We invert them to match the Palm.
-            bpp = im.info["bpp"]
-            im = im.point(lambda x, maxval=(1 << bpp) - 1: maxval - (x & maxval))
-        else:
-            msg = f"cannot write mode {im.mode} as Palm"
-            raise OSError(msg)
-
-        # we ignore the palette here
-        im.mode = "P"
-        rawmode = "P;" + str(bpp)
-        version = 1
-
-    elif im.mode == "1":
-        # monochrome -- write it inverted, as is the Palm standard
-        rawmode = "1;I"
-        bpp = 1
-        version = 0
-
-    else:
-        msg = f"cannot write mode {im.mode} as Palm"
-        raise OSError(msg)
-
-    #
-    # make sure image data is available
-    im.load()
-
-    # write header
-
-    cols = im.size[0]
-    rows = im.size[1]
-
-    rowbytes = int((cols + (16 // bpp - 1)) / (16 // bpp)) * 2
-    transparent_index = 0
-    compression_type = _COMPRESSION_TYPES["none"]
-
-    flags = 0
-    if im.mode == "P" and "custom-colormap" in im.info:
-        flags = flags & _FLAGS["custom-colormap"]
-        colormapsize = 4 * 256 + 2
-        colormapmode = im.palette.mode
-        colormap = im.getdata().getpalette()
-    else:
-        colormapsize = 0
-
-    if "offset" in im.info:
-        offset = (rowbytes * rows + 16 + 3 + colormapsize) // 4
-    else:
-        offset = 0
-
-    fp.write(o16b(cols) + o16b(rows) + o16b(rowbytes) + o16b(flags))
-    fp.write(o8(bpp))
-    fp.write(o8(version))
-    fp.write(o16b(offset))
-    fp.write(o8(transparent_index))
-    fp.write(o8(compression_type))
-    fp.write(o16b(0))  # reserved by Palm
-
-    # now write colormap if necessary
-
-    if colormapsize > 0:
-        fp.write(o16b(256))
-        for i in range(256):
-            fp.write(o8(i))
-            if colormapmode == "RGB":
-                fp.write(
-                    o8(colormap[3 * i])
-                    + o8(colormap[3 * i + 1])
-                    + o8(colormap[3 * i + 2])
-                )
-            elif colormapmode == "RGBA":
-                fp.write(
-                    o8(colormap[4 * i])
-                    + o8(colormap[4 * i + 1])
-                    + o8(colormap[4 * i + 2])
-                )
-
-    # now convert data to raw form
-    ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, rowbytes, 1))])
-
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-
-#
-# --------------------------------------------------------------------
-
-Image.register_save("Palm", _save)
-
-Image.register_extension("Palm", ".palm")
-
-Image.register_mime("Palm", "image/palm")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dotenv/__main__.py

@@ -1,6 +0,0 @@
-"""Entry point for cli, enables execution with `python -m dotenv`"""
-
-from .cli import cli
-
-if __name__ == "__main__":
-    cli()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/V_V_A_R_.py

@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_V_V_A_R_(BaseTTXConverter):
-    pass

spaces/DaFujaTyping/hf-Chat-ui/src/lib/types/Model.ts

@@ -1,13 +0,0 @@
-import type { BackendModel } from "$lib/server/models";
-
-export type Model = Pick<
-	BackendModel,
-	| "id"
-	| "name"
-	| "displayName"
-	| "websiteUrl"
-	| "datasetName"
-	| "promptExamples"
-	| "parameters"
-	| "description"
->;

spaces/DataWizard9742/LessonPlanGenerator/README.md

@@ -1,12 +0,0 @@
----
-title: LessonPlanGenerator
-emoji: 🐨
-colorFrom: green
-colorTo: green
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference