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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cracked Plugins on M1 Macs A Bad Idea for Your System and Your Work.md

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-<h1>Why You Should Avoid Cracked Plugins on M1 Macs</h1>
-<p>If you are a music producer or a hobbyist who likes to use plugins for your audio projects, you might be tempted to download cracked plugins from the internet. Cracked plugins are plugins that have been illegally modified or hacked to bypass the license or registration process. They are often available for free or at a very low price on various websites or forums.</p>
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-<p>However, using cracked plugins on your M1 Mac can have serious consequences for your system and your work. Here are some of the reasons why you should avoid cracked plugins on M1 Macs:</p>
-<ul>
-<li>Cracked plugins can contain malware or viruses that can harm your computer. Some of the malware can steal your personal information, damage your files, or hijack your system. Some of the viruses can corrupt your data, slow down your performance, or cause crashes or freezes.</li>
-<li>Cracked plugins can be incompatible with your M1 Mac. Since the M1 Mac uses a different architecture than the Intel-based Macs, some of the cracked plugins might not work properly or at all on your system. They might cause errors, glitches, or conflicts with other plugins or software. They might also prevent you from updating your system or software to the latest versions.</li>
-<li>Cracked plugins can be unethical and illegal. By using cracked plugins, you are violating the terms and conditions of the plugin developers and distributors. You are also depriving them of their rightful income and recognition for their hard work and creativity. You might also face legal actions or penalties if you are caught using cracked plugins.</li>
-</ul>
-<p>Therefore, it is better to avoid cracked plugins on M1 Macs and use legitimate plugins instead. Legitimate plugins are plugins that you have purchased or obtained legally from the official sources. They are safe, compatible, and reliable for your M1 Mac. They also come with technical support, updates, and warranties from the developers and distributors.</p>
-<p>Legitimate plugins might cost more than cracked plugins, but they are worth the investment in the long run. They can enhance your audio quality, productivity, and creativity without compromising your system or your work. They can also help you support the plugin industry and encourage more innovation and development.</p>
-<p>So, next time you are looking for a plugin for your M1 Mac, think twice before downloading a cracked plugin from the internet. Choose a legitimate plugin instead and enjoy the benefits of using it on your M1 Mac.</p>
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-<p>How to Find Legitimate Plugins for M1 Macs</p>
-<p></p>
-<p>Now that you know why you should avoid cracked plugins on M1 Macs, you might be wondering how to find legitimate plugins for your system. Here are some tips that can help you find and choose the best plugins for your M1 Mac:</p>
-<ul>
-<li>Do your research. Before you buy or download a plugin, make sure you check its features, specifications, reviews, and ratings. You can also watch or read tutorials, demos, or testimonials from other users or experts. This can help you determine if the plugin is suitable for your needs and preferences.</li>
-<li>Compare prices and offers. There are many sources where you can buy or download legitimate plugins for your M1 Mac. Some of them are the official websites of the plugin developers or distributors, online marketplaces, online music stores, or online subscription services. You can compare the prices and offers of different sources and find the best deal for your budget.</li>
-<li>Look for discounts and promotions. Sometimes, you can find legitimate plugins for your M1 Mac at a lower price or even for free. This can happen when the plugin developers or distributors offer discounts, coupons, bundles, giveaways, or free trials. You can look for these opportunities on their websites, social media pages, newsletters, or blogs.</li>
-<li>Ask for recommendations. Another way to find legitimate plugins for your M1 Mac is to ask for recommendations from other users or experts. You can join online forums, groups, communities, or networks where you can interact with other music producers or hobbyists who use M1 Macs. You can also follow online influencers, bloggers, podcasters, or reviewers who specialize in plugins or audio production.</li>
-</ul>
-<p>By following these tips, you can find and choose the best legitimate plugins for your M1 Mac and enjoy using them on your system.</p>
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-<p>Conclusion</p>
-<p>Cracked plugins on M1 Macs are not worth the risk or the hassle. They can harm your computer, your work, and your reputation. They can also prevent you from getting the most out of your M1 Mac and its capabilities.</p>
-<p>Legitimate plugins on M1 Macs are the way to go. They are safe, compatible, and reliable for your system. They can also enhance your audio quality, productivity, and creativity without compromising anything.</p>
-<p>So, avoid cracked plugins on M1 Macs and use legitimate plugins instead. You will be glad you did.</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dcs A-10c Warthog Keygen Download.md

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-<h1>Introduction</h1>
-<p>DCS: A-10C Warthog is a PC simulation of the U.S. premier Close Air Support attack aircraft. This is the second aircraft in the DCS series, following DCS: Black Shark, and raises the bar even higher in the DCS series. Warthog brings the most realistic PC simulation of a modern fixed wing combat aircraft in regards to flight dynamics, avionics, sensors, and weapon systems. You also have the option to play Warthog in "Game" mode for a casual game experience.</p>
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-<p>The A-10C is an enhanced version of the famous A-10A that served as a major close air support aircraft for the U.S. Air Force, Air National Guard, and Reserves for almost 30 years. A-10C has been upgraded to meet 21st century standards, using systems such as Multi-Function Color Displays (MFCD), GPS-guided weapons, and data-link support. Retaining all the features of older A-10A, the A-10C has turned into a true precision strike fighter with the most modern navigation systems, precision attack weapons (Maverick, JDAM, WCMD, and laser-guided bombs), and an integrated countermeasures system.</p>
-<p>The A-10C has participated in operations over Iraq and Afghanistan and proved to be a precise and effective weapon in the "War on Terrorism". Its advanced equipment has greatly reduced the number of "friendly fire" incidents - thanks largely to the Situational Awareness Datalink (SADL) and the ability to better identify targets with using the Litening II AT targeting pod. The A-10C of course retains its ability to do what it was originally designed to do: kill tanks in a conventional force-on-force battlefield.</p>
-<p>As with previous versions, the A-10C - very easy to fly and is a stable and survivable weapons platform. For those familiar with DCS: Black Shark, we feel that the A-10C will be much easier to fly.</p>
-<p>The DCS A-10C cockpit is a 100% six-degrees of freedom (6 DOF) cockpit that allows complete freedom of movement around the cockpit. Each panel is reproduced in exacting detail to match operational A-10Cs (Suite 3.1). This includes all panels, switches, dials, buttons being animated, rendered in the 3D, and with high-resolution textures. Both day, night, and Night Vision Goggle (NVG) lighting is available. When the mouse is hovered over a cockpit control, a tool tip is displayed to indicate the controls function.</p>
-<p>Fly missions in the Caucasus region of the Black Sea against and with a wide array of air, land and sea forces with new and improved intelligence. Create your own missions and campaigns with the included Mission and Campaign Editors, and fly with and against friends online using the included online game browser.</p>
- <h1>Downloading and installing the game</h1>
-<p>There are several ways to get DCS: A-10C Warthog on your PC. You can buy it from various online stores such as Steam, Amazon, or directly from Eagle Dynamics[^5^ <h1>Learning the basics</h1>
-<p>Before you can unleash the full potential of the A-10C Warthog, you need to learn how to operate its complex systems and procedures. Fortunately, the game provides you with several ways to do so, ranging from interactive tutorials to detailed manuals and guides.</p>
-<p>The most recommended way to start learning the basics is to play the interactive training missions that are included with the game. These missions will guide you step by step through various aspects of flying and fighting with the A-10C, such as navigation, communication, sensors, weapons, and countermeasures. You will be able to follow the instructions of a virtual instructor, who will demonstrate and explain each action and control. You will also be able to pause and resume the training at any time, as well as replay any part you want.</p>
-<p>To access the interactive training missions, go to the main menu and select TRAINING. You will see a list of 25 training missions, covering topics such as:</p>
-<p></p>
-<ul>
-<li>Cockpit familiarization</li>
-<li>Engine start-up and shutdown</li>
-<li>Takeoff and landing</li>
-<li>Flight controls and autopilot</li>
-<li>Navigation systems</li>
-<li>Radio communication</li>
-<li>Data-link and situational awareness</li>
-<li>Targeting pod operation</li>
-<li>Air-to-air and air-to-ground weapons</li>
-<li>Countermeasures and threat avoidance</li>
-<li>Air refueling</li>
-<li>Emergency procedures</li>
-</ul>
-<p>Select the mission you want to play and click BRIEFING. You will see a summary of the mission objectives, as well as a map of the area. You can also access the kneeboard, which contains useful information such as checklists, frequencies, and coordinates. Click FLY when you are ready to start the mission.</p>
-<p>Once in the cockpit, you will hear the voice of the instructor, who will introduce you to the topic of the mission and tell you what to do. You can also see the instructions on the top left corner of the screen, as well as some visual cues that highlight the relevant controls or indicators. You can use your mouse to interact with the cockpit controls, or use your keyboard or joystick if you have them configured. You can also use some keyboard commands to control the training session, such as:</p>
-<table>
-<tr><th>Key</th><th>Function</th></tr>
-<tr><td>P</td><td>Pause or resume the training</td></tr>
-<tr><td>LCTRL+P</td><td>Replay the last instruction</td></tr>
-<tr><td>LALT+P</td><td>Skip to the next instruction</td></tr>
-<tr><td>LWIN+P</td><td>Restart the current instruction</td></tr>
-<tr><td>LCTRL+LALT+P</td><td>End the training mission</td></tr>
-<tr><td>LCTRL+LALT+R</td><td>Restart the training mission</td></tr>
-<tr><td>LCTRL+LALT+B</td><td>Return to briefing screen</td></tr>
-<tr><td>LCTRL+LALT+E</td><td>Eject from the aircraft (not recommended)</td></tr>
-</table>
- <p>The interactive training missions are a great way to learn by doing, but they are not enough to cover everything you need to know about the A-10C. For more in-depth information, you can refer to the manuals and guides that are provided with the game. These documents are available in PDF format and can be accessed from the game folder or from the main menu by selecting MANUALS.</p>
- <p>The most important document is the Flight Manual, which is a 669-page book that covers everything from the history and specifications of the A-10C to its systems, weapons, procedures, and tactics. This manual is based on real-world documentation and is very detailed and accurate. However, it is also very technical and dense, so it may not be very easy to read or understand for beginners. Therefore, it is recommended that you use it as a reference rather than a tutorial.</p>
- <p>A more user-friendly document is Chuck's Guide for DCS: A-10C Warthog, which is a 176-page guide that summarizes and explains the most essential aspects of flying and fighting with the A-10C in a clear and concise way. This guide is written by an experienced flight simmer and includes many screenshots, diagrams, tips, and tricks. It is a great resource for beginners and intermediate pilots who want to learn more about the A-10C without getting overwhelmed by too much information.</p>
- <p>Another useful document is The Enemy Within 3.0 Campaign Guide, which is a 64-page guide that accompanies a story based campaign for the A-10C that features 21 missions and a dynamic storyline. This guide provides you with the background, objectives, and tips for each mission, as well as some general advice on how to plan and execute your flights. This guide is a good way to practice your skills and enjoy a realistic and immersive scenario with the A-10C.</p>
- <h1>Playing the game</h1>
-<p>Once you have learned the basics of the A-10C, you are ready to play the game and have some fun. The game offers you several options to choose from, depending on your preferences and goals. You can play single-player or multiplayer modes, and you can create your own missions and campaigns or download them from other users.</p>
-<p>The simplest way to play the game is to select INSTANT ACTION from the main menu. This will allow you to jump into the cockpit of the A-10C and fly a short mission with a predefined objective and scenario. You can choose from different difficulty levels, weather conditions, and locations. Instant action missions are a good way to test your skills and have some quick action without too much preparation.</p>
-<p>If you want more variety and challenge, you can select MISSIONS from the main menu. This will allow you to choose from a list of single-player missions that are included with the game or downloaded from other sources. These missions vary in length, complexity, and difficulty, and cover different aspects of flying and fighting with the A-10C. You can also see a briefing screen that gives you some information about the mission objectives, situation, and loadout. You can also modify some parameters such as time of day, weather, and enemy skill level. Missions are a good way to experience different scenarios and situations with the A-10C.</p>
-<p>If you want more continuity and immersion, you can select CAMPAIGNS from the main menu. This will allow you to choose from a list of single-player campaigns that are included with the game or downloaded from other sources. These campaigns consist of a series of missions that are connected by a storyline and have persistent consequences. You will have to follow the orders of your commander, plan your flights, manage your resources, and deal with the changing situation on the ground. Campaigns are a good way to feel like a part of a larger conflict and see how your actions affect the outcome.</p>
-<p>If you want more interaction and competition, you can select MULTIPLAYER from the main menu. This will allow you to join or host online sessions with other players around the world. You can choose from different modes such as cooperative, team versus team, or free for all. You can also see a list of available servers that show their name, ping, players, mission, rules, and password. You can also use the chat function to communicate with other players before or during the game. Multiplayer is a good way to cooperate or compete with other pilots and have some fun and social interaction.</p>
- <h1>Tips and tricks</h1>
-<p>Now that you know how to play the game, here are some tips and tricks that will help you improve your performance and enjoyment of the game:</p>
-<ul>
-<li>Use TrackIR or VR devices if you have them. They will greatly enhance your situational awareness and immersion by allowing you to look around the cockpit and outside the aircraft with natural head movements.</li>
-<li>Use HOTAS (Hands On Throttle And Stick) controllers if you have them. They will make your flying more precise and comfortable by allowing you to control most of the functions without taking your hands off the throttle and stick.</li>
-<li>Use voice control software if you have it. It will make your communication easier and faster by allowing you to use voice commands instead of keyboard or mouse clicks.</li>
-<li>Use keyboard shortcuts if you don't have any of the above devices. They will make your life easier by allowing you to access some of the most common functions without moving your mouse around the cockpit.</li>
-<li>Use labels if you have trouble spotting or identifying targets. They will make your targets more visible by adding colored symbols or text over them.</li>
-<li>Use pause if you need to take a break or review something. It will freeze the game and allow you to catch your breath or check something in the manual or kneeboard.</li>
-<li>Use external views if you want to admire your aircraft or take screenshots. They will give you different perspectives of your aircraft and its surroundings.</li>
-<li>Use replays if you want to analyze your performance or learn from your mistakes. They will allow you to watch your flight again from different angles and speeds.</li>
-<li>Use mods if you want to customize or enhance your game experience. They will allow you to add new features, functions, graphics, sounds, missions, campaigns, or aircraft to your game.</li>
-<li>Use forums if you want to ask questions, share opinions, get feedback, or game allows you to use various sensors and systems such as the HUD, MFCD, CDU, RWR, TGP, SADL, and UFC. The game also allows you to use various weapons such as the GAU-8/A Avenger cannon, AGM-65 Maverick missiles, GBU-12/38/54 laser-guided bombs, CBU-87/97/103/105 cluster bombs, and AIM-9 Sidewinder missiles.</li>
-<li>The game features a dynamic and realistic combat environment that simulates the geography, weather, and forces of the Caucasus region of the Black Sea. The game also features a sophisticated artificial intelligence that controls the behavior and actions of friendly and enemy units. The game also features a realistic radio communication system that allows you to communicate with your wingmen, AWACS, JTAC, and other units.</li>
-<li>The game features a variety of single-player and multiplayer modes that offer different challenges and experiences. The game includes 25 interactive training missions, 21 single-player missions, 4 single-player campaigns, and several multiplayer modes. The game also includes a powerful mission and campaign editor that allows you to create your own scenarios and share them with others.</li>
-<li>The game has received positive reviews from many sources, such as PC Gamer, IGN, GameSpot, and Metacritic. The game has been praised for its realism, depth, quality, and longevity. Some of the quotes from the reviews are:</li>
-<ul>
-<li>"DCS: A-10C Warthog isn't for everyone—this is a simulation that demands dedication to reveal its true worth, and only those prepared to put in the time and effort to mine its treasure trove of avionics challenges will see that reward." - PC Gamer</li>
-<li>"DCS: A-10C Warthog is a tremendously - and at times obscenely - demanding game. You will experience cranial overload, and you will, unless you're a devout ED disciple, curse the developer for being so damned cryptic. But you will also feel the exhilaration of accomplishment." - IGN</li>
-<li>"DCS: A-10C Warthog is an incredibly realistic simulation of the U.S. Air Force's premier close air support aircraft. It's not for everyone--the complexity of its avionics and high fidelity flight model require extensive study--but those who make the effort are rewarded with an unparalleled gaming experience." - GameSpot</li>
-<li>"DCS: A-10C Warthog is one of the most complex simulations ever created for PC. It's not easy to learn but once you do it's very rewarding. If you are looking for a real challenge and have a passion for flying this is the game for you." - Metacritic user review</li>
-</ul>
-</ul>
- <h1>Conclusion</h1>
-<p>DCS: A-10C Warthog is a game that offers a realistic and immersive simulation of the U.S. premier Close Air Support attack aircraft. It is a game that requires a lot of dedication, knowledge, and skill to master, but it is also a game that provides a rewarding and satisfying experience that will make you feel like a real pilot.</p>
-<p>If you are interested in flying and fighting with the A-10C Warthog, you can download the game from various sources and install it on your PC. You can also learn the basics by using the interactive tutorials, manuals, and guides that are provided with the game. You can also play the game by choosing from different single-player or multiplayer modes, or by creating your own missions and campaigns. You can also improve your performance and enjoyment by using some tips and tricks that will help you along the way.</p>
-<p>DCS: A-10C Warthog is a game that has been praised by many critics and players for its realism, depth, and quality. It is a game that features a highly detailed and accurate 3D model of the A-10C Warthog, a realistic flight model, a comprehensive avionics and weapon system, a dynamic and realistic combat environment, a variety of modes As promised, I will create a realistic illustration of the A-10C Warthog aircraft for you to enjoy. The A-10C Warthog is a single-seat, twin-engine, straight-wing jet aircraft designed for close air support of ground forces. It has a distinctive shape and features, such as the large nose-mounted GAU-8/A Avenger 30 mm rotary cannon, the bubble canopy, the twin vertical stabilizers, and the 11 hardpoints for carrying various weapons and pods. The A-10C Warthog is painted in a gray camouflage scheme with black and white markings and insignia. Here is the image I created for you: <img src="(^i^)" alt="A realistic illustration of the A-10C Warthog aircraft">. I hope you like it. ? and a powerful mission and campaign editor. It is a game that will challenge you and reward you like no other. <h1>FAQs</h1>
-<p>Here are some frequently asked questions and answers about the game:</p>
-<ol>
-<li>Q: What are the system requirements for the game?<br>
-A: The minimum system requirements for the game are: <ul>
-<li>OS: Windows 7/8/10 (64-bit)</li>
-<li>CPU: Intel Core i3 or AMD FX</li>
-<li>RAM: 8 GB</li>
-<li>GPU: NVIDIA GeForce GTX 760 or AMD Radeon HD 7950 with 2 GB VRAM</li>
-<li>Storage: 60 GB of free space</li>
-<li>Sound: DirectX compatible sound card</li>
-<li>Internet: Broadband connection for online play</li>
-</ul>
- The recommended system requirements for the game are: <ul>
-<li>OS: Windows 10 (64-bit)</li>
-<li>CPU: Intel Core i5 or AMD Ryzen</li>
-<li>RAM: 16 GB</li>
-<li>GPU: NVIDIA GeForce GTX 1070 or AMD Radeon RX Vega 56 with 8 GB VRAM</li>
-<li>Storage: 60 GB of free space on SSD</li>
-<li>Sound: DirectX compatible sound card with surround sound support</li>
-<li>Internet: Broadband connection for online play</li>
-</ul>
- You can also check the performance of your system by using the built-in benchmark tool in the game.</li>
- <li>Q: How can I update the game to the latest version?<br>
-A: The game is updated regularly with new features, fixes, and improvements. You can update the game automatically by using the DCS Updater, which is a tool that checks and downloads the latest updates for the game. You can also update the game manually by downloading and installing the update files from the official website or from other sources. You can also use the DCS Updater to switch between different versions of the game, such as stable, beta, or alpha.</li>
- <li>Q: How can I get more content for the game?<br>
-A: The game offers a lot of content by default, but you can also get more content by purchasing or downloading additional modules, maps, missions, campaigns, or mods. You can purchase official modules and maps from the official website or from other online stores . These modules and maps add new aircraft, vehicles, weapons, systems, and terrains to the game. You can also download free or paid missions and campaigns from the official website or from other sources. These missions and campaigns add new scenarios and stories to the game. You can also download free mods from various sources. These mods add new features, functions, graphics, sounds, or tweaks to the game.</li>
- <li>Q: How can I get help or support for the game?<br>
-A: The game is complex and challenging, and you may encounter some issues or difficulties while playing it. If you need help or support for the game, you can use various resources such as: <ul>
-<li>The manuals and guides that are provided with the game. They contain a lot of information and instructions on how to play and troubleshoot the game.</li>
-<li>The forums that are hosted by Eagle Dynamics and other communities. They contain a lot of discussions, questions, answers, feedback, and suggestions on various topics related to the game.</li>
-<li>The wiki that is maintained by Eagle Dynamics and other contributors. It contains a lot of articles, tutorials, tips, and references on various aspects of the game.</li>
-<li>The support team that is available by email or phone. They can help you with technical issues, bugs, crashes, activation, or refunds.</li>
-</ul></li>
- <li>Q: How can I give feedback or suggestions for the game?<br>
-A: The game is constantly being improved and updated based on user feedback and suggestions. If you want to give feedback or suggestions for the game, you can use various channels such as: <ul>
-<li>The forums that are hosted by Eagle Dynamics and other communities. They contain a lot of discussions, questions, answers, feedback, and suggestions on various topics related to the game.</li>
-<li>The bug tracker that is used by Eagle Dynamics and other users. It contains a list of reported issues, bugs, crashes, or errors that affect the game. You can also report new issues or vote for existing ones.</li>
-<li>The survey that is conducted by Eagle Dynamics periodically. It contains a set of questions that ask for your opinion on various aspects of the game.</li></ul></li></ol <p>I have finished writing the article for you. I hope you find it useful and informative. Here is the summary of the article:</p>
-<p>DCS: A-10C Warthog is a realistic simulation of the U.S. premier Close Air Support attack aircraft. This game is not for the faint of heart, as it requires a lot of dedication, knowledge, and skill to master the complex systems and procedures of the A-10C. However, if you are up for the challenge, you will find a rewarding and immersive experience that will make you feel like a real pilot.</p>
-<p>In this article, I have provided you with some information, tips, and tricks on how to download, install, and play the game, as well as some features and reviews of the game. I have also created a realistic illustration of the A-10C Warthog aircraft for you to enjoy.</p>
-<p>If you are interested in flying and fighting with the A-10C Warthog, you can follow these steps:</p>
-<ol>
-<li>Download the game from various sources and install it on your PC.</li>
-<li>Learn the basics by using the interactive tutorials, manuals, and guides that are provided with the game.</li>
-<li>Play the game by choosing from different single-player or multiplayer modes, or by creating your own missions and campaigns.</li>
-<li>Improve your performance and enjoyment by using some tips and tricks that will help you along the way.</li>
-<li>Give feedback or suggestions for the game by using various channels such as forums, bug tracker, or survey.</li>
-</ol>
-<p>DCS: A-10C Warthog is a game that offers a realistic and immersive simulation of the U.S. premier Close Air Support attack aircraft. It is a game that will challenge you and reward you like no other.</p> b2dd77e56b<br />
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-<p>Avid Pro Tools 10 - 10.3.10. This is what the pros use.<br> _US/download/Pro-Tools-10-3-10-Downloads<br> -Tools-10-3-9-Downloads<br> -Tools-10-3-Downloads<br> =43572<br>Windows Cracks:<br> -download_patch-for-pro-tools-1039-win.html<br> <br> <br>Mac cracked release (?):<br>Pro Tools 10.3.10-openssh from</p>
-<p>Here are some links (bottom) for Autodesk 2016 and Adobe CC 2014 & 2015 products. You can download all Autodesk at once in Tonec Internet Download Manager. Just click "Add batch download from clipboard". *.rar or *.001 files can be opened with WinRAR You can open *.nfo files with Notepad.(You can use the cracks that are available for Autodesk and Adobe by XFORCE or ISO releases WIN or MAC)<br>AUTODESK.MAYA.V2016.WIN64-ISO<br>AUTODESK_MAYA_V2016_MACOSX-XFORCE<br>AUTODESK_MAYA_V2016_LNX64-XFORCE<br>ADOBE_CC_V2014_KEYGEN_WIN_MACOSX-XFORCE<br>New network cracks available for Autodesk in:<br>AUTODESK_MENTALRAY_STANDALONE_V2016_LNX64-XFORCE<br>AUTODESK_MENTALRAY_STANDALONE_V2016_MACOSX64-XFORCE<br>AUTODESK_MENTALRAY_STANDALONE_V2016_WIN64-XFORCE</p>
-<p>Autodesk 2017 links are below. You can use the crack/keygen from any of the ISO/XFORCE 2017 releases.<br><br>INFO about Moldflow crack<br>To anyone who might be interested, old 2016 XForce FLEXNet crack still works for 2017 softwares:<br>replace original adlmint.dll with the cracked one in C:\Program Files\Common Files\Autodesk Shared\CLM\V3\MSVC14, and edit the XF license file by adding the following:<br>FEATURE ************ adskflex 1.000 permanent 100 VENDOR_STRING=commercial:permanent SUPERSEDE DUP_GROUP=UH ISSUED=01-janv-2013 SN=666-66666666 TS_OK SIGN="1745 D487 C07B 1B0D 10C0 555A B147 1372 8DBF 1E14 ECFC 870D FC59 5ECC 9156 1814 B16F 2E7B 4760 2A4C 745E 732E 5A7D 9A3C E3D4 0359 562E 9B90 713D 3708" SIGN2="100D 7553 E295 6170 A0C2 9567 8124 C44F 22C3 81B1 E629 EA7D 21A5 E308 1BD3 1D1F 0650 B3DC E78C 2AB0 C055 DB08 A9DE 12DB FA5C 3AF6 FFC3 A3EA A323 4699"<br> where ************ is the proper FLEXNet feature code for AD2017 software you want to use (check FLEXNet link below): now you have a multiple license (up to 100: not uncounted, but better than MAGNiTUDE's 2) you can use with your multicore CPU, and useful for all AD2017 softwares. Of course, if you use this one, delete all crack files related to MAGNiTUDE crack and restore the original onesSimStudioTools R2: replace the original adlmint.dll with the cracked one in C:\Program Files\Autodesk\SimStudio Tools 2016 R2 (default installation folder) and use the correct FLEXNet code in the license file<br>Autodesk 2017 product keys:<br> -service/installation-activation-licensing/get-ready/find-serial-number-product-key/product-key-look/2017-product-keys<br>Autodesk 2017 FLEXnet keys (for network license):<br> -result/caas/sfdcarticles/sfdcarticles/2017-FLEXnet-feature-codes-for-Autodesk-products.html<br>Accumulated hotfix 1 for AutoCAD 2017 based products<br> _downloads/AutoCAD_2017_Hotfix_1_x64.exe<br> _downloads/AutoCAD_2017_Hotfix_1_x86.exe<br>This hotfix applies to the following releases:<br>- Autodesk AutoCAD 2017<br>- Autodesk AutoCAD Architecture 2017<br>- Autodesk AutoCAD Civil 3D 2017<br>- Autodesk AutoCAD Electrical 2017<br>- Autodesk AutoCAD Map 3D 2017<br>- Autodesk AutoCAD Mechanical 2017<br>- Autodesk AutoCAD MEP 2017<br>- Autodesk AutoCAD P&ID 2017<br>- Autodesk AutoCAD Plant 3D 2017<br>- Autodesk AutoCAD Utility Design 2017 <br>Autodesk Inventor 2017 fails to install due to failure to install .NET Framework Runtime 4.6<br>Applies to:<br>- Factory Design Suite 2017<br>- Inventor 2017<br>- Inventor LT 2017<br>- and Product Design Suite 2017<br>Issue:<br>Autodesk Inventor 2017 requires .NET 4.6 to successfully install Inventor 2017 products.<br>The Inventor, Inventor LT, and Inventor OEM 2017 installers will stop if they fail to install .NET 4.6 on your computer.<br>The log file reports: Install .NET Framework Runtime 4.6 - Failed - Failure is ignored, Result=1603<br>Notes:<br>- Windows 7 SP1 and Windows 8.1 do not come with .Net Framework 4.6 pre-installed.<br>- Windows 10 comes with .Net Framework 4.6 pre-installed.<br>Solution:<br>1. Manually Install Microsoft .NET Framework 4.6 from:<br> -us/download/details.aspx?id=48137<br>or choose this direct link to download the Microsoft .NET Framework 4.6 Offline Installer (62.4 Mo)<br>(for Vista SP2, 7 SP1, 8, 8.1, Server 2008 SP2, 2008 R2 SP1, 2012 & 2012 R2)<br> -D33C-47E9-9D70-2F7C65DAAD94/NDP46-KB3045557-x86-x64-AllOS-ENU.exe<br>Important note: KB 2919442 and KB 2919355 are pre-requisite of .NET 4.6 on Windows 8.1 OS.<br>Get the KB 2919442 (4.6 Mo) and the KB 2919355 (319 Mo) from:<br> -us/download/details.aspx?id=42135<br> -FR/download/details.aspx?id=42327<br>or choose direct links:<br> -9E65-4681-BBBE-A8F73A5C116F/Windows8.1-KB2919442-x86.msu<br> -1E15-43FD-B591-63FB7A1A5C04/Windows8.1-KB2919355-x86.msu<br>2. Restart your computer.<br>3. Restart the Autodesk Inventor installer.<br>Additionnal notes:<br>To check for .Net 4.6 installation on your computer:<br>- Microsoft .NET Framework 4.6 list under Programs and Features in Control Panel as an installed product on Windows7 SP1 OS.<br>- Microsoft .NET Framework 4.6 display as Update for Microsoft Windows (KB3045563) under Installed Updates in Control Panel on Windows8.1 OS.<br>- Or run Regedit, and confirm ".NETFramework,Version = v4.6" displays under the following path: \HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\.NETFramewo rk\v4.0.30319\SKUs\<br><br>Replace English with your language (French, Italian, German, Spanish, Simplified_Chinese, etc.)<br>AutoCAD 2017<br> _2017_English_Win_32bit_dlm.sfx.exe<br> _2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _2017_English_Win_64bit_dlm_002_002.sfx.exe<br>AutoCAD LT 2017<br> _LT_2017_NWL_English_Win_64bit_dlm.sfx.exe<br> _LT_2017_NWL_English_Win_32bit_dlm.sfx.exe<br> _LT_2017_English_LP_Win_64bit_dlm.sfx.exe<br> _LT_2017_English_LP_Win_32bit_dlm.sfx.exe<br>AutoCAD Architecture 2017<br> _Architecture_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Architecture_2017_English_Win_64bit_dlm_002_002.sfx.exe<br> _Architecture_2017_English_Win_32bit_dlm_001_002.sfx.exe<br> _Architecture_2017_English_Win_32bit_dlm_002_002.sfx.exe<br>AutoCAD Electrical 2017<br> _E/DLM/AutoCAD_Electrical_2017_English_Win_32bit_dlm_001_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_English_Win_32bit_dlm_002_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>AutoCAD MAP 3D 2017<br> _Map_2017_English_Win_64bit_DLM_001_002.sfx.exe<br> _Map_2017_English_Win_64bit_DLM_002_002.sfx.exe<br>AutoCAD MEP 2017<br> _MEP_2017_English_Win_32bit_dlm_001_003.sfx.exe<br> _MEP_2017_English_Win_32bit_dlm_002_003.sfx.exe<br> _MEP_2017_English_Win_32bit_dlm_003_003.sfx.exe<br> _MEP_2017_English_Win_64bit_dlm_001_003.sfx.exe<br> _MEP_2017_English_Win_64bit_dlm_002_003.sfx.exe<br> _MEP_2017_English_Win_64bit_dlm_003_003.sfx.exe<br>AutoCAD Mechanical 2017<br> _PP/DLM/AutoCAD_Mechanical_2017_English_Win_32bit_dlm.sfx.exe<br> _PP/DLM/AutoCAD_Mechanical_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _PP/DLM/AutoCAD_Mechanical_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>AutoCAD Raster Design 2017<br> _Raster_Design_2017_English_Win_32bit_dlm.sfx.exe<br> _Raster_Design_2017_English_Win_64bit_dlm.sfx.exe<br>AutoCAD Plant 3D 2017<br> _Plant_3D_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Plant_3D_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>AutoCAD P&ID 2017<br> _PNID_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _PNID_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD Civil 3D 2017<br> _Civil3D_2017_English_Win_64bit_dlm_001_003.sfx.exe<br> _Civil3D_2017_English_Win_64bit_dlm_002_003.sfx.exe<br> _Civil3D_2017_English_Win_64bit_dlm_003_003.sfx.exe<br>AutoCAD Utility Design 2017<br> _Utility_Design_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Utility_Design_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk Revit 2017<br> _Revit_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Revit_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk Revit LT 2017<br> _Revit_LT_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Revit_LT_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Inventor 2017<br> _2017_English_Win_64bit_dlm_001_003.sfx.exe<br> _2017_English_Win_64bit_dlm_002_003.sfx.exe<br> _2017_English_Win_64bit_dlm_003_003.sfx.exe<br>Inventor LT 2017<br> _LT_2017_English_Win_32bit_dlm.sfx.exe<br> _LT_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _LT_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Vault Basic 2017<br> _ENU_32_64bit_dlm.sfx.exe<br> _ENU_64bit_dlm.sfx.exe<br>Vault Professional 2017<br> _ENU_32_64bit_dlm.sfx.exe<br> _ENU_64bit_dlm.sfx.exe<br>Vault Workgroup 2017<br> _ENU_32_64bit_dlm.sfx.exe<br> _ENU_64bit_dlm.sfx.exe<br>Autodesk Advance Steel 2017<br> _2017_ML_WIN_64BIT_DLM.sfx.exe<br>Autodesk Navisworks Manage 2017<br> _Navisworks_Manage_2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe<br> _Navisworks_Manage_2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk Navisworks Simulate 2017<br> _Navisworks_Simulate_2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe<br> _Navisworks_Simulate_2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe<br>Moldflow Adviser Ultimate 2017<br> _2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe<br> _2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe<br>Moldflow CAD Doctor 2017<br> _2017_Multilingual_Win_64bit_dlm.sfx.exe<br>Moldflow Design (formerly Simulation DFM) 2017<br> _2017_Multilingual_Win_64bit_dlm.sfx.exe<br>Moldflow Insight Ultimate 2017<br> _2017_Multilingual_Win_64bit_dlm.sfx.exe<br>Moldflow Synergy 2017<br> _2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe<br> _2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe<br>Robot Structural Analysis Pro 2017<br> _Structural_Analysis_Professional_2017_Multilingual_Win_64bit_dlm.sfx.exe<br>Autodesk Vehicle Tracking English 2017<br> _Vehicle_Tracking_2017_English_Win_32_64bit_DLM.sfx.exe<br>VRED 2017<br> _VRED_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED Design 2017<br> _VREDDES_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED Professional 2017<br> _VREDPRO_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED Presenter 2017<br> _VREDPRS_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED Server 2017<br> _VREDSRV_2017_Enu_Win_64bit_dlm.sfx.exe<br>Autodesk Nastran In-CAD 2017<br> _INCAD_2017_Win_64bit_dlm.sfx.exe<br>Autodesk Nastran 2017<br> _2017_Win_64bit_dlm.sfx.exe<br>Showcase 2017 <br> _2017_English_Win_64bit_dlm_001_003.sfx.exe<br> _2017_English_Win_64bit_dlm_002_003.sfx.exe<br> _2017_English_Win_64bit_dlm_003_003.sfx.exe<br>CFD 2017 <br> _CFD_2017_Win_64bit_dlm_001_002.sfx.exe<br> _CFD_2017_Win_64bit_dlm_002_002.sfx.exe<br>Simulation Mechanical 2017<br> _Simulation_Mechanical_2017_Win_64bit_dlm_001_002.sfx.exe<br> _Simulation_Mechanical_2017_Win_64bit_dlm_002_002.sfx.exe<br>Fabrication CADmep 2017<br> _Fabrication_CADmep_2017_win_64bit_dlm.sfx.exe<br>Fabrication CAMduct 2017<br> _Fabrication_CAMduct_2017_win_64bit_dlm.sfx.exe<br>Fabrication ESTmep2017<br> _Fabrication_ESTmep_2017_win_64bit_dlm.sfx.exe<br>Autodesk InfraWorks 360 2017<br> _InfraWorks_2017_Win_64bit_DLM.sfx.exe<br>Point Layout 2017<br> _Point_Layout_2017_Win_32-64bit_en-us.exe<br>ReCap 360 Pro 2017<br> _ReCap360_30052_Multilingual_Win_64bit_dlm.sfx.exe<br>Design and Creation suites<br>Product Design Suite 2017<br> _2017_Enu_Win_64bit_dlm_001_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_006.sfx.exe<br>AutoCAD Design Suite Ultimate 2017<br> _Ultimate_2017_English_Win_32bit_dlm_001_002.sfx.exe<br> _Ultimate_2017_English_Win_32bit_dlm_002_002.sfx.exe<br> _Ultimate_2017_English_Win_64bit_dlm_001_004.sfx.exe<br> _Ultimate_2017_English_Win_64bit_dlm_002_004.sfx.exe<br> _Ultimate_2017_English_Win_64bit_dlm_003_004.sfx.exe<br> _Ultimate_2017_English_Win_64bit_dlm_004_004.sfx.exe<br>Autodesk Factory Design Suite Ultimate 2017<br> _2017_Enu_Win_64bit_dlm_001_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_007_007.sfx.exe<br>Infrastructure Design Suite Ultimate 2017<br> _2017_Enu_Win_64bit_dlm_001_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_007_007.sfx.exe<br>Building Design Suite Ultimate 2017<br> _2017_Enu_Win_64bit_dlm_001_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_007_007.sfx.exe<br>Documentation<br> <br> _2017_help_download/AutoCAD_2017_Product_Help_English_Win_32_64bit_dlm.sfx.exe<br> _lt_2017_help_download/AutoCAD_LT_2017_Product_Help_English_Win_32_64bit_dlm.sfx.exe<br> _and_lt_local_help/Autodesk_Inventor_2017_Help.exe<br> _civil_3d_2017/Autodesk_AutoCAD_Civil_3D_2017_Help_English.exe<br> <br> _2017_install_help/autodesk_alias_2017_help.exe<br>Autodesk 3ds max 2017 EFGJKPS (x64 Only) - F for French<br> _3ds_Max_2017_EFGJKPS_Win_64bit_001_002.sfx.exe<br> _3ds_Max_2017_EFGJKPS_Win_64bit_002_002.sfx.exe<br>Autodesk AutoCAD 2017 French<br> _2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _2017_French_Win_64bit_dlm_002_002.sfx.exe<br> _2017_French_Win_32bit_dlm.sfx.exe<br>Autodesk AutoCAD LT 2017 French<br> _LT_2017_NWL_French_Win_64bit_dlm.sfx.exe<br> _LT_2017_French_LP_Win_64bit_dlm.sfx.exe<br> _LT_2017_NWL_French_Win_32bit_dlm.sfx.exe<br> _LT_2017_French_LP_Win_32bit_dlm.sfx.exe<br>Autodesk AutoCAD Architecture 2017 French<br> _Architecture_2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _Architecture_2017_French_Win_64bit_dlm_002_002.sfx.exe<br> _Architecture_2017_French_Win_32bit_dlm_001_002.sfx.exe<br> _Architecture_2017_French_Win_32bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD Electrical 2017 French<br> _E/DLM/AutoCAD_Electrical_2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_French_Win_64bit_dlm_002_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_French_Win_32bit_dlm_001_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_French_Win_32bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD Mechanical 2017 French<br> _PP/DLM/AutoCAD_Mechanical_2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _PP/DLM/AutoCAD_Mechanical_2017_French_Win_64bit_dlm_002_002.sfx.exe<br> _PP/DLM/AutoCAD_Mechanical_2017_French_Win_32bit_dlm.sfx.exe<br>Autodesk AutoCAD MEP 2017 French<br> _MEP_2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _MEP_2017_French_Win_64bit_dlm_002_002.sfx.exe<br> _MEP_2017_French_Win_32bit_dlm_001_002.sfx.exe<br> _MEP_2017_French_Win_32bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD MAP 3D 2017 (x64 Only) French<br> _Map_2017_French_Win_64bit_DLM_001_002.sfx.exe<br> _Map_2017_French_Win_64bit_DLM_002_002.sfx.exe<br>Autodesk AutoCAD Plant 3D 2017 (x64 Only) French<br> _Plant_3D_2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _Plant_3D_2017_French_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD P&ID 2017 (x64 Only) French<br> _PNID_2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _PNID_2017_French_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD Raster Design 2017 French<br> _Raster_Design_2017_French_Win_64bit_dlm.sfx.exe<br> _Raster_Design_2017_French_Win_32bit_dlm.sfx.exe<br>Autodesk AutoCAD Civil 3D 2017 (x64 Only) French<br> _Civil3D_2017_French_Win_64bit_dlm_001_003.sfx.exe<br> _Civil3D_2017_French_Win_64bit_dlm_002_003.sfx.exe<br> _Civil3D_2017_French_Win_64bit_dlm_003_003.sfx.exe<br>Autodesk Inventor 2017 (X64 Only) French<br> _2017_French_Win_64bit_dlm_001_003.sfx.exe<br> _2017_French_Win_64bit_dlm_002_003.sfx.exe<br> _2017_French_Win_64bit_dlm_003_003.sfx.exe<br>Autodesk Inventor LT 2017 French <br> _LT_2017_French_Win_64bit_dlm_001_002.sfx.exe<br> _LT_2017_French_Win_64bit_dlm_002_002.sfx.exe<br> _LT_2017_French_Win_32bit_dlm.sfx.exe<br>Autodesk Revit 2017 (X64 Only) Non-Specific-Language (French included)<br> _Revit_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Revit_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Offline Help Installers French<br> _max_2017_help/3dsMaxHelp_fra.exe<br> _2017_offline_help_installer/AutoCAD_2017_Product_Help_French_Win_32_64bit_dlm.sfx.exe<br> _lt_2017_offline_help/AutoCAD_LT_2017_Product_Help_French_Win_32_64bit_dlm.sfx.exe<br> _architecture_2017_product_help/AutoCAD_Architecture_Help_2017_French_Win_32_64bit_dlm.sfx.exe<br> _electrical_2017_help_download/AutoCAD_Electrical_2017_French_help_Win_32_64bit_dlm.sfx.exe<br> _mechanical_help_2017/AutoCAD_Mechanical_Help_2017_French_Win_32_64bit_dlm.sfx.exe<br> _map_3d_2017_product_help/Autodesk_AutoCAD_Map_3D_2017_Help_French.exe<br> _mep_2017_product_help/AutoCAD_MEP_Help_2017_French_Win_32_64bit_dlm.sfx.exe<br> _civil_3d_2017/Autodesk_AutoCAD_Civil_3D_2017_Help_French.exe<br> _and_lt_local_help/Autodesk_Inventor_2017_Help_FRA.exe<br> _and_lt_local_help/Autodesk_Inventor_LT_2017_Help_FRA.exe<br>Additional Notes:<br>How to get Autodesk Revit 2017 (X64 Only) Non-Specific Language in French language:<br>You must be vigilant when installing and well select the desired installation language before entering its serial number.<br>By chance, multiple languages are available after 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_2017_German_Win_64bit_dlm_001_002.sfx.exe<br> _2017_German_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk Inventor 2017 German 64 Bit<br> _2017_German_Win_64bit_dlm_001_003.sfx.exe<br> _2017_German_Win_64bit_dlm_002_003.sfx.exe<br> _2017_German_Win_64bit_dlm_003_003.sfx.exe<br>AutoCAD Architecture 2017 x64<br> _Architecture_2017_Italian_Win_64bit_dlm_001_002.sfx.exe<br> _Architecture_2017_Italian_Win_64bit_dlm_002_002.sfx.exe<br>AutoCAD LT 2017 x64/x86<br> _LT_2017_NWL_Italian_Win_64bit_dlm.sfx.exe<br> _LT_2017_NWL_Italian_Win_32bit_dlm.sfx.exe<br> _LT_2017_Italian_LP_Win_64bit_dlm.sfx.exe<br> _LT_2017_Italian_LP_Win_32bit_dlm.sfx.exe<br>AutoCAD 2017 x64/x86<br> _2017_Italian_Win_32bit_dlm.sfx.exe<br> _2017_Italian_Win_64bit_dlm_001_002.sfx.exe<br> _2017_Italian_Win_64bit_dlm_002_002.sfx.exe<br>AutoCAD Electrical 2017 x64<br> _E/DLM/AutoCAD_Electrical_2017_Italian_Win_64bit_dlm_001_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_Italian_Win_64bit_dlm_002_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_Italian_Win_32bit_dlm_001_002.sfx.exe<br> _E/DLM/AutoCAD_Electrical_2017_Italian_Win_32bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD Mechanical 2017<br> _PP/DLM/AutoCAD_Mechanical_2017_Italian_Win_64bit_dlm_001_002.sfx.exe<br> _PP/DLM/AutoCAD_Mechanical_2017_Italian_Win_64bit_dlm_002_002.sfx.exe<br> _PP/DLM/AutoCAD_Mechanical_2017_Italian_Win_32bit_dlm.sfx.exe<br>Autodesk AutoCAD MEP 2017<br> _MEP_2017_Italian_Win_64bit_dlm_001_002.sfx.exe<br> _MEP_2017_Italian_Win_64bit_dlm_002_002.sfx.exe<br> _MEP_2017_Italian_Win_32bit_dlm_001_002.sfx.exe<br> _MEP_2017_Italian_Win_32bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD MAP 3D 2017 x64<br> _Map_2017_Italian_Win_64bit_DLM_001_002.sfx.exe<br> _Map_2017_Italian_Win_64bit_DLM_002_002.sfx.exe<br>Autodesk AutoCAD Raster Design 2017<br> _Raster_Design_2017_Italian_Win_64bit_dlm.sfx.exe<br> _Raster_Design_2017_Italian_Win_32bit_dlm.sfx.exe<br>Autodesk Inventor 2017 X64<br> _2017_Italian_Win_64bit_dlm_001_003.sfx.exe<br> 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_Raster_Design_2017_English_Win_64bit_dlm.sfx.exe<br>Inventor 2017 local help: <br> _and_lt_local_help/Autodesk_Inventor_2017_Help.exe<br>Inventor 2017 sample files:<br> _sample_files/autodesk_inventor_2017_samples.sfx.exe<br>VRED Presenter 2017<br> _VREDPRS_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED Server 2017<br> _VREDSRV_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED 2017<br> _VRED_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED Design 2017<br> _VREDDES_2017_Enu_Win_64bit_dlm.sfx.exe<br>VRED Professional 2017<br> _VREDPRO_2017_Enu_Win_64bit_dlm.sfx.exe<br>AutoCAD Plant 3D 2017<br> _Plant_3D_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Plant_3D_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk AutoCAD P&ID 2017<br> _PNID_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _PNID_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Mac_OSX Versions<br>Autodesk Alias Design 2017<br> _Alias_Design_2017_English_Mac_OSX.dmg<br>ALIAS AutoStudio 2017 for Mac<br> _Alias_AutoStudio_2017_English_Mac_OSX.dmg<br>Autodesk Alias Surface 2017<br> _Alias_Surface_2017_English_Mac_OSX.dmg<br>Autodesk Nastran In-CAD 2017<br> _INCAD_2017_Win_64bit_dlm.sfx.exe<br>Autodesk Nastran 2017<br> _2017_Win_64bit_dlm.sfx.exe<br>Autodesk_AutoCAD_Civil_3D_2017 Documentation<br> _civil_3d_2017/Autodesk_AutoCAD_Civil_3D_2017_Help_English.exe<br>Autodesk AutoCAD Civil 3D 2017<br> _Civil3D_2017_English_Win_64bit_dlm_001_003.sfx.exe<br> _Civil3D_2017_English_Win_64bit_dlm_002_003.sfx.exe<br> _Civil3D_2017_English_Win_64bit_dlm_003_003.sfx.exe<br>Infrastructure Design Suite Ultimate 2017 Win 64bit<br> _2017_Enu_Win_64bit_dlm_001_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_007_007.sfx.exe<br>Building Design Suite Ultimate 2017 Win 64bit<br> _2017_Enu_Win_64bit_dlm_001_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_007_007.sfx.exe<br>Documentation Alias 2017 Product Help<br>Online Help<br> <br>Help Install Instructions<br>English<br> _2017_install_help/installing_autodesk_alias_2017_help.html<br>Japanese<br> _2017_install_help/JPN/JPN/installing_autodesk_alias_2017_help_jpn.html<br>Simplified Chinese<br> _2017_install_help/CHS/CHS/installing_autodesk_alias_2017_help_chs.html<br>Windows Help Installer<br>English<br> _2017_install_help/autodesk_alias_2017_help.exe<br>Japanese<br> _2017_install_help/JPN/JPN/alias_help_2017_jpn.exe<br>Simplified Chinese<br> _2017_install_help/CHS/CHS/alias_help_2017_chs.exe<br>Mac OS X Help Installer<br>English<br> _2017_install_help/autodesk_alias_2017_help.dmg<br>Japanese<br> _2017_install_help/JPN/JPN/AliasDocs2017_Japanese_Mac.dmg<br>Simplified Chinese<br> _2017_install_help/CHS/CHS/AliasDocs2017_Chinese_Mac.dmg<br>Learning Movies<br>Japanese<br> _2017_install_help/JPN/JPN/learningmovies_jpn.exe<br>Simplified Chinese<br> _2017_install_help/CHS/CHS/learningmovies_chs.exe<br>Factory Design Suite<br> _2017_Enu_Win_64bit_dlm_001_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_007.sfx.exe<br> _2017_Enu_Win_64bit_dlm_007_007.sfx.exe<br>Autodesk Revit 2017<br> _Revit_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Revit_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Autodesk Revit LT 2017<br> _Revit_LT_2017_English_Win_64bit_dlm_001_002.sfx.exe<br> _Revit_LT_2017_English_Win_64bit_dlm_002_002.sfx.exe<br>Showcase 2017<br> _2017_English_Win_64bit_dlm_001_003.sfx.exe<br> 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2017<br> _ALSSF_2017_Enu_Win_64bit_dlm.sfx.exe<br>Alias Autostudio 2017<br> _ALAUST_2017_Enu_64bit_dlm_001_003.sfx.exe<br> _ALAUST_2017_Enu_64bit_dlm_002_003.sfx.exe<br> _ALAUST_2017_Enu_64bit_dlm_003_003.sfx.exe<br>3ds Max 2017<br> _3ds_Max_2017_EFGJKPS_Win_64bit_001_002.sfx.exe<br> _3ds_Max_2017_EFGJKPS_Win_64bit_002_002.sfx.exe<br>Online Help for 3dsmax<br> <br>3dsmax OFFLINE Help<br> _max_2017_help/3dsMaxHelp.exe<br>for other languages go to:<br> -max/downloads/caas/downloads/content/download-and-install-3ds-max-product-help.html<br> -general-discussion/apple-mac-os-10-11-x-el-capitan-is-not-supported/m-p/5983674#M6245<br>mental ray Plugin, Satellte and Standalone for Maya 2016 Extension 2 (Direct links)<br>Maya 2016.5 is a part of Alias AutoStudio 2017<br>Windows<br> _2016_extension_2/mentalray_Plugin_for_Maya_2016_EXT2_EN_JP_ZH_Win_64bit_dlm.sfx.exe<br> _2016_extension_2/mentalray_Satellite_3_13_1_for_Maya_2016_EN_JP_ZH_Win_64bit.exe<br> _2016_extension_2/mentalray_Standalone_3_13_1_for_Autodesk_2016_EN_Win_64bit.exe<br>Linux<br> _2016_extension_2/mentalray_Plugin_for_Maya_2016_EXT2_EN_Linux_64bit.tgz<br> _2016_extension_2/mentalray_Satellite_3_13_1_for_Maya_2016_EN_Linux_64bit.tgz<br> _2016_extension_2/mentalray_Standalone_3_13_1_for_Autodesk_2016_EN_Linux_64bit.tgz<br>OSX<br> _2016_extension_2/mentalray_Plugin_for_Maya_2016_EXT2_EN_JP_ZH_Mac_OSX.dmg<br> _2016_extension_2/mentalray_Satellite_3_13_1_for_Maya_2016_EN_JP_ZH_Mac_OSX.dmg<br> _2016_extension_2/mentalray_Standalone_3_13_1_for_Autodesk_2016_EN_Mac_OSX.dmg<br>Offline help for Autodesk Maya 2016 Extension 2<br> _2016/MayaHelp2016_Ext2_enu.zip<br>Autodesk 3ds Max 2017 Sample Files<br> _sample_files/2017/Autodesk_3ds_Max_2017_English_Win_Samples_Files.exe<br>Open Light 2017 (32-bit and 64-bit)<br>Applies to AutoCAD Architecture 2017, and AutoCAD MEP 2017 (32-bit and 64-bit)<br>Open Light is a plug-in for AutoCAD Architecture / MEP and offers standard labels for objects, such as openings, windows and doors, which are common in Austria and part of Switzerland.<br>Open Light provides additional display properties for Plan 1-50 and Plan 1-100 representation to show dimensions of doors and windows automatically. <br> _downloads/Open_Light_2017_x64.exe<br> _downloads/Open_Light_2017.exe<br>Open Light 2017 Object Enabler (32-bit and 64-bit)<br>Applies to AutoCAD 2017, AutoCAD Architecture 2017, AutoCAD Civil 3D 2017, AutoCAD Electrical 2017, AutoCAD MEP 2017, AutoCAD Map 3D 2017, and AutoCAD Mechanical 2017Open Light Object Enabler is a freeware application distributed to Autodesk customers at no charge for the purpose of fully accessing Open Light objects in drawing files. Without this object enabler installed, you can share drawings using proxy graphics representations or the Export to AutoCAD command.<br> _downloads/Open_Light_2017_OE_x64.exe<br> _downloads/Open_Light_2017_OE.exe<br>Building Design Suite Premium 2017<br> _2017_Enu_Win_64bit_dlm_001_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_002_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_003_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_004_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_005_006.sfx.exe<br> _2017_Enu_Win_64bit_dlm_006_006.sfx.exe</p> aaccfb2cb3<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Car Mechanic Simulator 2018 [1.6.4 11 DLC] RePack [full Fix].md

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spaces/1line/AutoGPT/autogpt/speech/brian.py

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-""" Brian speech module for autogpt """
-import os
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-from autogpt.speech.base import VoiceBase
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-class BrianSpeech(VoiceBase):
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-    def _speech(self, text: str, _: int = 0) -> bool:
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-
-        Args:
-            text (str): The text to speak
-
-        Returns:
-            bool: True if the request was successful, False otherwise
-        """
-        tts_url = (
-            f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}"
-        )
-        response = requests.get(tts_url)
-
-        if response.status_code == 200:
-            with open("speech.mp3", "wb") as f:
-                f.write(response.content)
-            playsound("speech.mp3")
-            os.remove("speech.mp3")
-            return True
-        else:
-            print("Request failed with status code:", response.status_code)
-            print("Response content:", response.content)
-            return False

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CarX Drift Racing 2 Mod Apk 1.22.0 and Become a Drift King.md

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-<h1>Download CarX Drift Racing 2 Mod Apk 1.22.0 and Enjoy the Ultimate Drifting Experience</h1>
- <p>Do you love racing games? Do you want to feel the thrill of drifting around the corners and burning rubber on the asphalt? If yes, then you should download CarX Drift Racing 2 mod apk 1.22.0, the best drifting game for Android devices.</p>
- <p>CarX Drift Racing 2 is a sequel to the popular CarX Drift Racing game, which has over 50 million downloads on Google Play Store. In this game, you can choose from hundreds of cars, customize them, and drift on various tracks with realistic physics and graphics.</p>
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- <p>In this article, we will tell you everything you need to know about CarX Drift Racing 2, why you should download its mod apk version, and some tips and tricks to improve your drifting skills.</p>
- <h2>What is CarX Drift Racing 2?</h2>
- <p>CarX Drift Racing 2 is a racing game that focuses on drifting, which is a driving technique where the driver intentionally oversteers the car to make it slide sideways. Drifting is not only fun, but also challenging and rewarding, as it requires skill and precision.</p>
- <h3>Features of CarX Drift Racing 2</h3>
- <p>Some of the features that make CarX Drift Racing 2 stand out from other racing games are:</p>
- <ul>
-<li>Over 200 cars from different brands and categories, such as sports cars, muscle cars, supercars, etc.</li>
-<li>A wide range of customization options, such as paint, vinyls, wheels, spoilers, etc.</li>
-<li>More than 60 tracks with different layouts, weather conditions, and time of day.</li>
-<li>Realistic car physics and sound effects that simulate the behavior and performance of real cars.</li>
-<li>A career mode where you can progress through various levels and championships.</li>
-<li>A multiplayer mode where you can compete with other players online in different modes, such as tandem drifting, sprint racing, etc.</li>
-<li>A ghost mode where you can race against your own or other players' best times.</li>
-<li>A tuning mode where you can adjust various parameters of your car, such as engine power, suspension stiffness, tire pressure, etc.</li>
-<li>A garage mode where you can store and manage your car collection.</li>
-</ul>
- <h3>How to play CarX Drift Racing 2</h3>
- <p>The gameplay of CarX Drift Racing 2 is simple and intuitive. You can control your car using various options, such as tilt, buttons, or steering wheel. You can also choose between automatic or manual transmission.</p>
- <p>The main goal of the game is to drift as much as possible and earn points based on your speed, angle, and duration of your drifts. You can also perform combos by linking multiple drifts together without losing control or hitting obstacles.</p>
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- <p>The game has a scoring system that evaluates your performance based on various criteria, such as style, speed, line, angle, etc. You can also earn coins and gold by completing missions, achievements, and events.</p>
- <p>You can use these currencies to buy new cars or upgrade your existing ones. You can also unlock new tracks and modes by increasing your reputation level.</p>
- <h2>Why download CarX Drift Racing 2 mod apk 1.22.0?</h2>
- <p>While CarX Drift Racing 2 is a free game, it also has some limitations and drawbacks, such as ads, in-app purchases, and limited resources. If you want to enjoy the game without any restrictions or interruptions, you should download CarX Drift Racing 2 mod apk 1.22.0.</p>
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- <p>Downloading and installing CarX Drift Racing 2 mod apk 1.22.0 is easy and fast. Just follow these simple steps:</p>
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-<li>Allow your device to install apps from unknown sources by going to Settings > Security > Unknown Sources.</li>
-<li>Locate the downloaded file in your file manager and tap on it to install it.</li>
-<li>Launch the game and enjoy the ultimate drifting experience.</li>
-</ol>
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- <h2>Tips and tricks for CarX Drift Racing 2</h2>
- <p>If you want to improve your drifting skills and become a master of CarX Drift Racing 2, you should follow these tips and tricks:</p>
- <h3>Choose the right car and tune it</h3>
- <p>Not all cars are created equal in CarX Drift Racing 2. Some cars are better suited for drifting than others, depending on their power, weight, handling, and grip. You should choose a car that matches your style and preference, and experiment with different settings and configurations.</p>
- <p>You can tune your car in the tuning mode, where you can adjust various parameters, such as engine power, suspension stiffness, tire pressure, etc. You can also customize your car in the garage mode, where you can change its appearance, such as paint, vinyls, wheels, spoilers, etc.</p>
- <p>Tuning and customizing your car can make a big difference in your performance and score. You should try to find the optimal balance between speed and stability, and make your car look cool and unique.</p>
- <h3>Master the drifting techniques</h3>
- <p>Drifting is not just about sliding sideways. It is also about controlling your car's movement and direction with skill and precision. You should master the drifting techniques that will help you achieve better results and impress your opponents.</p>
- <p>Some of the drifting techniques that you should learn are:</p>
- <ul>
-<li>Handbrake drift: This is the most basic technique, where you use the handbrake to initiate a drift by locking the rear wheels.</li>
-<li>Clutch kick drift: This is a technique where you use the clutch to momentarily disengage the engine from the transmission, causing a sudden burst of power that breaks traction.</li>
-<li>Feint drift: This is a technique where you use the steering wheel to create a weight transfer that causes the rear end of the car to swing out.</li>
-<li>Braking drift: This is a technique where you use the brakes to reduce speed and induce oversteer while entering a corner.</li>
-<li>Power over drift: This is a technique where you use the throttle to increase power and maintain a drift by overcoming the grip of the tires.</li>
-</ul>
- <p>You should practice these techniques on different tracks and situations, and find out which ones work best for you. You should also learn how to control your car's angle, speed, and line while drifting, as these factors will affect your score and style.</p>
- <h3>Compete with other players online</h3>
- <p>If you want to test your skills and have more fun, you should compete with other players online in the multiplayer mode of CarX Drift Racing 2. You can choose from different modes, such as tandem drifting, sprint racing, etc., and challenge players from all over the world.</p>
- <p>You can also join or create a club or a team, where you can chat with other members, share your cars and tunes, and participate in tournaments and events.</p>
- <p>Competing with other players online will not only give you more excitement and challenge, but also help you improve your skills and learn from others. You can also earn more coins and gold, as well as reputation points, by winning races and drifting battles.</p>
- <h2>Conclusion</h2>
- <p>CarX Drift Racing 2 is a game that will satisfy your need for speed and adrenaline. It is a game that will let you experience the thrill of drifting on realistic tracks with realistic cars. It is a game that will let you customize your car and tune it to your liking. It is a game that will let you compete with other players online and show off your skills and style.</p>
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-<h1>Bubble Shooter Free Download for Laptop: How to Play and Enjoy this Classic Game</h1>
- <p>Do you love popping bubbles and solving puzzles? If so, you might want to try Bubble Shooter, one of the most popular and addictive games ever created. Bubble Shooter is a classic game that has been enjoyed by millions of people around the world for decades. In this article, we will tell you everything you need to know about Bubble Shooter, including what it is, how to download it for free on your laptop, and how to play and enjoy it.</p>
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- <p>Bubble Shooter is a simple yet challenging game that involves shooting colored bubbles at a cluster of bubbles on the top of the screen. The goal is to match three or more bubbles of the same color to make them pop and clear the board. The game ends when there are no more bubbles left or when the bubbles reach the bottom of the screen.</p>
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- <p>Bubble Shooter has a long and interesting history that dates back to the 1980s. The game was inspired by two arcade games: Bubble Bobble, released by Taito in 1986, and Puzzle Bobble, also known as Bust-a-Move, released by Taito in 1994. Puzzle Bobble was the first game to feature the bubble shooting mechanic that became the core of Bubble Shooter. In 2000, Puzzle Bobble was ported to Windows and renamed as Bubble Shooter. Since then, the game has been adapted and modified by many developers and publishers, resulting in hundreds of variations and versions of Bubble Shooter.</p>
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- <p>The gameplay of Bubble Shooter is very simple and intuitive. You use your mouse or touchpad to aim and shoot bubbles at the cluster of bubbles on the top of the screen. You can see the color of the next bubble in the launcher at the bottom of the screen. You can also bounce the bubbles off the walls to reach tricky spots. When you match three or more bubbles of the same color, they pop and disappear, along with any bubbles that are hanging from them. You get points for every bubble you pop, and bonus points for popping more bubbles at once or dropping large groups of bubbles. You can also earn special bubbles that have different effects, such as bombs, stars, rainbows, or fireballs.</p>
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-<li>Play Bubble Shooter on your mobile device. If you prefer playing games on your smartphone or tablet, you can also download Bubble Shooter for free from Google Play Store or Apple App Store. There are many versions of Bubble Shooter available for Android and iOS devices, such as <a href="">Bubble Witch 3 Saga</a>, <a href="">Bubble Shooter 2021</a>, and <a href="">Angry Birds POP!</a>. Just make sure that you have enough space and battery on your device.</li>
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- <h3>The basic rules and tips for playing Bubble Shooter</h3>
- <p>The basic rules for playing Bubble Shooter are very simple and easy to follow. Here are some tips to help you get started:</p>
-<ul>
-<li>Aim carefully and shoot bubbles at the cluster of bubbles on the top of the screen. Try to match three or more bubbles of the same color to pop them and clear them from the board.</li>
-<li>Use your mouse or touchpad to move the launcher at the bottom of the screen. You can see the color of the next bubble in the launcher and adjust your angle accordingly. You can also bounce the bubbles off the walls to reach difficult spots.</li>
-<li>Be quick and efficient. The more bubbles you pop, the more points you get. However, if you miss or waste too many shots, the cluster of bubbles will move down one row. If the bubbles reach the bottom of the screen, you lose the game.</li>
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-<li>Classic mode: This is the original and most popular mode of Bubble Shooter. It has a simple and elegant design, with no time limit or extra features. You just have to clear all the bubbles from the board and score as high as possible.</li>
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-<li>Puzzle mode: This is a more creative and strategic mode of Bubble Shooter. It has different levels with different layouts and objectives. You have to pop all the bubbles in each level using a limited number of shots or moves.</li>
-<li>Adventure mode: This is a more immersive and story-driven mode of Bubble Shooter. It has different worlds with different themes and backgrounds. You have to pop all the bubbles in each world and unlock new ones as you progress.</li>
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-<p>By playing these different modes and levels of Bubble Shooter, you can experience different aspects and challenges of this game and keep yourself entertained for hours.</p>
- <h3>The best strategies and tricks for scoring high in Bubble Shooter</h3>
- <p>Bubble Shooter is a game that requires both skill and luck. However, there are some strategies and tricks that you can use to improve your chances of scoring high in this game. Here are some of them:</p>
-<ul>
-<li>Aim for the top: One of the best ways to score high in Bubble Shooter is to aim for the top rows of bubbles. By popping the top bubbles, you can drop large sections of bubbles below them and get massive points.</li>
-<li>Look for clusters: Another way to score high in Bubble Shooter is to look for clusters of bubbles that have the same color or are connected by one or two bubbles. By popping these clusters, you can clear more space on the board and create more opportunities for matching.</li>
-<li>Save the special bubbles: A third way to score high in Bubble Shooter is to save the special bubbles for later use. Instead of using them right away, try to save them until you have a large group of bubbles that you want to pop or clear. This way, you can maximize their effects and get more points.</li>
-<li>Use the walls: A fourth way to score high in Bubble Shooter is to use the walls to your advantage. By bouncing the bubbles off the walls, you can reach spots that are otherwise impossible or difficult to reach. You can also create angles that can help you match more bubbles.</li>
-</ul>
-<p>By using these strategies and tricks, you can score high in Bubble Shooter and impress yourself and others with your skills.</p>
- <h2>Conclusion</h2>
- <p>Bubble Shooter is a classic game that has been loved by millions of people for decades. It is a simple yet challenging game that involves shooting colored bubbles at a cluster of bubbles on the top of the screen. The goal is to match three or more bubbles of the same color to make them pop and clear the board.</p>
- <h3>Summary of the main points</h3>
- <p>In this article, we have covered everything you need to know about Bubble Shooter, including:</p>
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-<li>What is Bubble Shooter? We have explained the history, gameplay, and benefits of this game.</li>
-<li>How to download Bubble Shooter for free on your laptop? We have shown you the easiest and safest way to download it from Microsoft Store, as well as some alternative ways to play it online or offline.</li>
-<li>How to play and enjoy Bubble Shooter? We have given you the basic rules and tips, the different modes and levels, and the best strategies and tricks for playing and scoring high in this game.</li>
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-<p>By following this guide, you can play and enjoy Bubble Shooter on your laptop anytime and anywhere.</p>
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- <p>Download Bubble Shooter for free on your laptop now and join the millions of people who are already addicted to this classic game. You will be glad you did.</p>
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- <p>Here are some of the most frequently asked questions about Bubble Shooter:</p>
-<ul>
-<li><b>Q: How many levels are there in Bubble Shooter?</b></li>
-<li>A: The number of levels in Bubble Shooter depends on the version of the game you are playing. Some versions have a fixed number of levels, while others have an infinite number of levels that get harder as you progress. You can check the level number on the top of the screen or in the menu of the game.</li>
-<li><b>Q: How do I pause or resume the game?</b></li>
-<li>A: To pause or resume the game, you can click on the pause button on the bottom right corner of the screen or press the Esc key on your keyboard. You can also access the settings, sound, and help options from the pause menu.</li>
-<li><b>Q: How do I save or load my progress?</b></li>
-<li>A: To save or load your progress, you need to sign in with your Microsoft account if you are playing Bubble Shooter from Microsoft Store. You can also use cloud saving or local saving features if they are available in the version of the game you are playing. You can check the save or load options in the menu of the game.</li>
-<li><b>Q: How do I change the difficulty or mode of the game?</b></li>
-<li>A: To change the difficulty or mode of the game, you can select the option that suits your preference from the main menu of the game. You can choose from classic, arcade, puzzle, or adventure modes, and from easy, medium, hard, or expert difficulties. You can also change these options during the game from the pause menu.</li>
-<li><b>Q: How do I get more special bubbles or power-ups?</b></li>
-<li>A: To get more special bubbles or power-ups, you can either earn them by popping certain bubbles or buy them with coins or gems that you can collect by playing the game or watching ads. You can also get free gifts or rewards by logging in daily or completing achievements. You can check your inventory and shop options in the menu of the game.</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/FIFA 22 Offline Apk Download Zip File with Obb and Data Included.md

@@ -1,103 +0,0 @@
-
-<h1>FIFA 22 Zip APK Download: Everything You Need to Know</h1>
-<p>If you are a fan of soccer games, you must have heard of FIFA 22, the latest installment in the popular FIFA series by EA Sports. FIFA 22 is a realistic and immersive soccer simulation game that features hundreds of teams, players, stadiums, and modes. You can play as your favorite soccer stars, create your own custom player or manager, compete with other players online, or enjoy the street-style Volta Football mode.</p>
-<p>But what if you want to play FIFA 22 on your mobile device without spending too much storage space or data? Well, there is a solution for that. You can download FIFA 22 zip apk, which is a compressed version of the game that you can install on your Android or iOS device. In this article, we will show you how to download FIFA 22 zip apk, what are its features and benefits, and what are the risks involved. Let's get started!</p>
-<h2>fifa 22 zip apk download</h2><br /><p><b><b>Download File</b> &#10022;&#10022;&#10022; <a href="https://jinyurl.com/2uNOTh">https://jinyurl.com/2uNOTh</a></b></p><br /><br />
- <h2>How to Download FIFA 22 Zip APK for Android</h2>
-<p>If you have an Android device, you can follow these steps to download and install FIFA 22 zip apk:</p>
-<ol>
-<li>Find a reliable source for the zip file. There are many websites that claim to offer FIFA 22 zip apk download, but not all of them are trustworthy. Some may contain malware or viruses that can harm your device or steal your personal information. To avoid this, you should only download from reputable sources that have positive reviews and feedback from other users. For example, you can try this link that offers a safe and secure download of FIFA 22 zip apk.</li>
-<li>Download and extract the zip file. Once you have found a good source, you can download the zip file to your device. The file size may vary depending on the source, but it should be around 1 GB. After downloading, you need to extract the zip file using a file manager app that supports zip files. You can use apps like ZArchiver or RAR for this purpose. You should see two files inside the zip file: an APK file and a data folder.</li>
-<li>Install the APK file and copy the data folder. Before installing the APK file, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. Then, locate the APK file in your file manager app and tap on it to install it. After installing, do not launch the game yet. You need to copy the data folder to your internal storage first. The data folder contains all the game data such as graphics, sounds, and settings. To copy it, go to your file manager app and find the zip file and long-press on the data folder. Then, select Copy and navigate to your internal storage. You should see a folder named Android. Open it and look for a folder named obb. If you don't see it, you can create one by tapping on the + icon and naming it obb. Then, open the obb folder and paste the data folder inside it.</li>
-<li>Launch the game and enjoy. Now, you are ready to play FIFA 22 on your Android device. You can find the game icon on your app drawer or home screen. Tap on it to launch the game and wait for it to load. You may need to accept some permissions and terms of service before you can start playing. You can also adjust the game settings according to your preferences and device specifications. Enjoy the game!</li>
-</ol>
- <h2>How to Download FIFA 22 Zip APK for iOS</h2>
-<p>If you have an iOS device, you can follow these steps to download and install FIFA 22 zip apk:</p>
-<ol>
-<li>Install AltStore on your device. AltStore is an alternative app store that allows you to install apps that are not available on the official App Store. You need a computer and a USB cable to install AltStore on your device. You can follow this guide to learn how to install AltStore on your device.</li>
-<li>Download the IPA file from a trusted source. An IPA file is the equivalent of an APK file for iOS devices. It contains the app data and installation instructions for iOS devices. You need to find a reliable source for the FIFA 22 IPA file, just like you did for the zip file for Android devices. You can try this link that offers a safe and secure download of FIFA 22 IPA file.</li>
-<li>Install the IPA file using AltStore. After downloading the IPA file, you need to transfer it to your device using a USB cable or a cloud service like Dropbox or Google Drive. Then, open AltStore on your device and tap on the + icon at the top left corner. You should see a list of IPA files that are available on your device or cloud service. Tap on the FIFA 22 IPA file and enter your Apple ID and password when prompted. AltStore will then install the app on your device.</li>
-<li>Trust the app and start playing. Before you can play FIFA 22 on your iOS device, you need to trust the app developer on your device settings. To do this, go to Settings > General > Device Management and look for the developer name that matches your Apple ID. Tap on it and then tap on Trust. Now, you can find FIFA 22 on your home screen or app library. Tap on it to launch the game and enjoy!</li>
-</ol>
- <h2>Features of FIFA 22 Zip APK</h2>
-<p>FIFA 22 zip apk is not just a compressed version of the game, but also a full-featured one that offers all the same features as the original game. Here are some of the features that you can enjoy with FIFA 22 zip apk:</p>
-<ul>
-<li>HyperMotion Technology: A new level of realism and immersion. FIFA 22 uses advanced motion capture technology that records the movements of real players in high-intensity situations. This allows the game to create more realistic animations, behaviors, and emotions for the players on the pitch. You can feel the intensity, passion, and drama of every match with HyperMotion Technology.</li>
-<li>Career Mode: Create your own legacy as a player or manager. FIFA 22 lets you choose your own path in Career Mode, where you can create your own custom player or manager and lead them to glory. You can customize your appearance, skills, attributes, personality, and style of play. You can also manage your team's tactics, transfers, finances, and morale. You can experience different scenarios and challenges depending on your choices and actions.</li>
-<li>Ultimate Team: Build your dream squad and compete online. FIFA 22 Ultimate Team is the most popular mode in FIFA 22, where you can create your own fantasy team from thousands of players from different leagues, nations, and eras. You can earn coins, packs, rewards, and players by playing matches, completing objectives, and participating in events. You can also trade players, items, and coins with other players online. You can compete with other players in various modes such as Division Rivals, Squad Battles, FUT Champions, FUT Draft, FUT Co-Op, FUT Friendlies, and FUT Events.</li>
-<li>Volta Football: Experience street soccer with style and flair. FIFA 22 Volta Football is a mode that lets you play soccer in different street locations around the world with different rules, styles, and cultures. You can create your own custom character and customize their appearance, clothing, accessories, tattoos, hairstyles, and celebrations. You can play solo or with your friends in different modes such as Volta Story, Volta Arcade, Volta Squads, Volta League, and Volta Tournament. You can also customize your own pitch, rules, and difficulty.</li>
-</ul>
- <h2>Benefits of FIFA 22 Zip APK Download</h2>
-<p>Downloading FIFA 22 zip apk has some advantages over downloading the original game from the official app stores. Here are some of the benefits that you can get with FIFA 22 zip apk download:</p>
-<ul>
-<li>Save storage space and data usage. FIFA 22 zip apk is a compressed version of the game that reduces the file size significantly. This means that you can save more storage space on your device and use less data when downloading the game. The original game may require up to 4 GB of storage space and data, while the zip apk may only require around 1 GB.</li>
-<li>Play offline without internet connection. FIFA 22 zip apk allows you to play the game offline without needing an internet connection. This is useful if you have a limited or unstable internet connection, or if you want to save your battery life. You can still enjoy most of the game features and modes offline, such as Career Mode, Volta Football, and Ultimate Team (offline matches only).</li>
-<li>Enjoy the latest updates and patches. FIFA 22 zip apk is updated regularly with the latest updates and patches from EA Sports. This means that you can enjoy the newest features, improvements, fixes, and content that are added to the game. You can also get access to the latest players, teams, kits, ratings, and events that are updated in the game.</li>
-</ul>
- <h2>Risks of FIFA 22 Zip APK Download</h2>
-<p>However, downloading FIFA 22 zip apk also has some risks and drawbacks that you should be aware of before you decide to do it. Here are some of the risks that you may face with FIFA 22 zip apk download:</p>
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-<ul>
-<li>Potential malware or viruses in the zip file. As mentioned earlier, not all sources that offer FIFA 22 zip apk download are trustworthy. Some may contain malware or viruses that can infect your device or steal your personal information. To avoid this, you should only download from reputable sources that have positive reviews and feedback from other users. You should also scan the zip file with an antivirus app before extracting it.</li>
-<li>Possible legal issues or bans from EA Sports. Downloading FIFA 22 zip apk may violate the terms of service and license agreement of EA Sports. This means that you may face legal issues or bans from EA Sports if they detect that you are using an unauthorized version of the game. To avoid this, you should only download FIFA 22 zip apk for personal use and not for commercial purposes. You should also not share or distribute the zip file to others.</li>
-<li>Compatibility or performance issues on some devices. FIFA 22 zip apk may not work properly on some devices or operating systems. This may cause compatibility or performance issues such as crashes, glitches, errors, or lagging. To avoid this, you should check the minimum requirements and specifications of the game before downloading it. You should also adjust the game settings according to your device specifications.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>FIFA 22 is a realistic and immersive soccer simulation game that features hundreds of teams, players, stadiums, and modes. You can play as your favorite soccer stars, create your own custom player or manager, compete with other players online, or enjoy the street-style Volta Football mode.</p>
-<p>If you want to play FIFA 22 on your mobile device without spending too much storage space or data, you can download FIFA 22 zip apk, which is a compressed version of the game that you can install on your Android or iOS device.</p>
-<p>In this article, we showed you how to download FIFA 22 zip apk, what are its features and benefits, and what are the risks involved. We hope that this article was helpful and informative for you.</p>
-<p>If you have any questions or comments about FIFA 22 zip apk download, feel free to leave them below. We would love to hear from you!</p>
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-<li>A: Yes, FIFA 22 zip apk is free to download from various sources online. However, you may need to complete some surveys or offers before you can access the download link.</li>
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-<li>A: It depends on the source that you download from. Some sources may contain malware or viruses that can harm your device or steal your personal information. To avoid this, you should only download from reputable sources that have positive reviews and feedback from other users. You should also scan the zip file with an antivirus app before extracting it.</li>
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-<li>A: Downloading FIFA 22 zip apk may violate the terms of service and license agreement of EA Sports. This means that you may face legal issues or bans from EA Sports if they detect that you are using an unauthorized version of the game. To avoid this, you should only download FIFA 22 zip apk for personal use and not for commercial purposes. You should also not share or distribute the zip file to others.</li>
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-<li>A: FIFA 22 zip apk may not work properly on some devices or operating systems. This may cause compatibility or performance issues such as crashes, glitches, errors, or lagging. To avoid this, you should check the minimum requirements and specifications of the game before downloading it. You should also adjust the game settings according to your device specifications.</li>
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-<li>A: FIFA 22 zip apk is updated regularly with the latest updates and patches from EA Sports. This means that you can enjoy the newest features, improvements, fixes, and content that are added to the game. You can also get access to the latest players, teams, kits, ratings, and events that are updated in the game. To update FIFA 22 zip apk, you need to download the latest version of the zip file from a trusted source and follow the same installation steps as before.</li>
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spaces/22h/vintedois-diffusion-v0-1/README.md

@@ -1,12 +0,0 @@
----
-title: Vintedois Diffusion V0 1
-emoji: 📚
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/232labs/VToonify/vtoonify/style_transfer.py

@@ -1,232 +0,0 @@
-import os
-#os.environ['CUDA_VISIBLE_DEVICES'] = "0"
-import argparse
-import numpy as np
-import cv2
-import dlib
-import torch
-from torchvision import transforms
-import torch.nn.functional as F
-from tqdm import tqdm
-from model.vtoonify import VToonify
-from model.bisenet.model import BiSeNet
-from model.encoder.align_all_parallel import align_face
-from util import save_image, load_image, visualize, load_psp_standalone, get_video_crop_parameter, tensor2cv2
-
-
-class TestOptions():
-    def __init__(self):
-
-        self.parser = argparse.ArgumentParser(description="Style Transfer")
-        self.parser.add_argument("--content", type=str, default='./data/077436.jpg', help="path of the content image/video")
-        self.parser.add_argument("--style_id", type=int, default=26, help="the id of the style image")
-        self.parser.add_argument("--style_degree", type=float, default=0.5, help="style degree for VToonify-D")
-        self.parser.add_argument("--color_transfer", action="store_true", help="transfer the color of the style")
-        self.parser.add_argument("--ckpt", type=str, default='./checkpoint/vtoonify_d_cartoon/vtoonify_s_d.pt', help="path of the saved model")
-        self.parser.add_argument("--output_path", type=str, default='./output/', help="path of the output images")
-        self.parser.add_argument("--scale_image", action="store_true", help="resize and crop the image to best fit the model")
-        self.parser.add_argument("--style_encoder_path", type=str, default='./checkpoint/encoder.pt', help="path of the style encoder")
-        self.parser.add_argument("--exstyle_path", type=str, default=None, help="path of the extrinsic style code")
-        self.parser.add_argument("--faceparsing_path", type=str, default='./checkpoint/faceparsing.pth', help="path of the face parsing model")
-        self.parser.add_argument("--video", action="store_true", help="if true, video stylization; if false, image stylization")
-        self.parser.add_argument("--cpu", action="store_true", help="if true, only use cpu")
-        self.parser.add_argument("--backbone", type=str, default='dualstylegan', help="dualstylegan | toonify")
-        self.parser.add_argument("--padding", type=int, nargs=4, default=[200,200,200,200], help="left, right, top, bottom paddings to the face center")
-        self.parser.add_argument("--batch_size", type=int, default=4, help="batch size of frames when processing video")
-        self.parser.add_argument("--parsing_map_path", type=str, default=None, help="path of the refined parsing map of the target video")
-        
-    def parse(self):
-        self.opt = self.parser.parse_args()
-        if self.opt.exstyle_path is None:
-            self.opt.exstyle_path = os.path.join(os.path.dirname(self.opt.ckpt), 'exstyle_code.npy')
-        args = vars(self.opt)
-        print('Load options')
-        for name, value in sorted(args.items()):
-            print('%s: %s' % (str(name), str(value)))
-        return self.opt
-    
-if __name__ == "__main__":
-
-    parser = TestOptions()
-    args = parser.parse()
-    print('*'*98)
-    
-    
-    device = "cpu" if args.cpu else "cuda"
-    
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
-        ])
-    
-    vtoonify = VToonify(backbone = args.backbone)
-    vtoonify.load_state_dict(torch.load(args.ckpt, map_location=lambda storage, loc: storage)['g_ema'])
-    vtoonify.to(device)
-
-    parsingpredictor = BiSeNet(n_classes=19)
-    parsingpredictor.load_state_dict(torch.load(args.faceparsing_path, map_location=lambda storage, loc: storage))
-    parsingpredictor.to(device).eval()
-
-    modelname = './checkpoint/shape_predictor_68_face_landmarks.dat'
-    if not os.path.exists(modelname):
-        import wget, bz2
-        wget.download('http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2', modelname+'.bz2')
-        zipfile = bz2.BZ2File(modelname+'.bz2')
-        data = zipfile.read()
-        open(modelname, 'wb').write(data) 
-    landmarkpredictor = dlib.shape_predictor(modelname)
-
-    pspencoder = load_psp_standalone(args.style_encoder_path, device)    
-
-    if args.backbone == 'dualstylegan':
-        exstyles = np.load(args.exstyle_path, allow_pickle='TRUE').item()
-        stylename = list(exstyles.keys())[args.style_id]
-        exstyle = torch.tensor(exstyles[stylename]).to(device)
-        with torch.no_grad():  
-            exstyle = vtoonify.zplus2wplus(exstyle)
-
-    if args.video and args.parsing_map_path is not None:
-        x_p_hat = torch.tensor(np.load(args.parsing_map_path))          
-            
-    print('Load models successfully!')
-    
-    
-    filename = args.content
-    basename = os.path.basename(filename).split('.')[0]
-    scale = 1
-    kernel_1d = np.array([[0.125],[0.375],[0.375],[0.125]])
-    print('Processing ' + os.path.basename(filename) + ' with vtoonify_' + args.backbone[0])
-    if args.video:
-        cropname = os.path.join(args.output_path, basename + '_input.mp4')
-        savename = os.path.join(args.output_path, basename + '_vtoonify_' +  args.backbone[0] + '.mp4')
-
-        video_cap = cv2.VideoCapture(filename)
-        num = int(video_cap.get(7))
-
-        first_valid_frame = True
-        batch_frames = []
-        for i in tqdm(range(num)):
-            success, frame = video_cap.read()
-            if success == False:
-                assert('load video frames error')
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            # We proprocess the video by detecting the face in the first frame, 
-            # and resizing the frame so that the eye distance is 64 pixels.
-            # Centered on the eyes, we crop the first frame to almost 400x400 (based on args.padding).
-            # All other frames use the same resizing and cropping parameters as the first frame.
-            if first_valid_frame:
-                if args.scale_image:
-                    paras = get_video_crop_parameter(frame, landmarkpredictor, args.padding)
-                    if paras is None:
-                        continue
-                    h,w,top,bottom,left,right,scale = paras
-                    H, W = int(bottom-top), int(right-left)
-                    # for HR video, we apply gaussian blur to the frames to avoid flickers caused by bilinear downsampling
-                    # this can also prevent over-sharp stylization results. 
-                    if scale <= 0.75:
-                        frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                    if scale <= 0.375:
-                        frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                    frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
-                else:
-                    H, W = frame.shape[0], frame.shape[1]
-
-                fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-                videoWriter = cv2.VideoWriter(cropname, fourcc, video_cap.get(5), (W, H))
-                videoWriter2 = cv2.VideoWriter(savename, fourcc, video_cap.get(5), (4*W, 4*H))
-                
-                # For each video, we detect and align the face in the first frame for pSp to obtain the style code. 
-                # This style code is used for all other frames.
-                with torch.no_grad():
-                    I = align_face(frame, landmarkpredictor)
-                    I = transform(I).unsqueeze(dim=0).to(device)
-                    s_w = pspencoder(I)
-                    s_w = vtoonify.zplus2wplus(s_w)
-                    if vtoonify.backbone == 'dualstylegan':
-                        if args.color_transfer:
-                            s_w = exstyle
-                        else:
-                            s_w[:,:7] = exstyle[:,:7]
-                first_valid_frame = False
-            elif args.scale_image:
-                if scale <= 0.75:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                if scale <= 0.375:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
-
-            videoWriter.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-
-            batch_frames += [transform(frame).unsqueeze(dim=0).to(device)]
-
-            if len(batch_frames) == args.batch_size or (i+1) == num:
-                x = torch.cat(batch_frames, dim=0)
-                batch_frames = []
-                with torch.no_grad():
-                    # parsing network works best on 512x512 images, so we predict parsing maps on upsmapled frames
-                    # followed by downsampling the parsing maps
-                    if args.video and args.parsing_map_path is not None:
-                        x_p = x_p_hat[i+1-x.size(0):i+1].to(device)
-                    else:
-                        x_p = F.interpolate(parsingpredictor(2*(F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0], 
-                                        scale_factor=0.5, recompute_scale_factor=False).detach()
-                    # we give parsing maps lower weight (1/16)
-                    inputs = torch.cat((x, x_p/16.), dim=1)
-                    # d_s has no effect when backbone is toonify
-                    y_tilde = vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), d_s = args.style_degree)       
-                    y_tilde = torch.clamp(y_tilde, -1, 1)
-                for k in range(y_tilde.size(0)):
-                    videoWriter2.write(tensor2cv2(y_tilde[k].cpu()))
-
-        videoWriter.release()
-        videoWriter2.release()
-        video_cap.release()
-
-    
-    else:
-        cropname = os.path.join(args.output_path, basename + '_input.jpg')
-        savename = os.path.join(args.output_path, basename + '_vtoonify_' +  args.backbone[0] + '.jpg')
-
-        frame = cv2.imread(filename)
-        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-
-        # We detect the face in the image, and resize the image so that the eye distance is 64 pixels.
-        # Centered on the eyes, we crop the image to almost 400x400 (based on args.padding).
-        if args.scale_image:
-            paras = get_video_crop_parameter(frame, landmarkpredictor, args.padding)
-            if paras is not None:
-                h,w,top,bottom,left,right,scale = paras
-                H, W = int(bottom-top), int(right-left)
-                # for HR image, we apply gaussian blur to it to avoid over-sharp stylization results
-                if scale <= 0.75:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                if scale <= 0.375:
-                    frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
-                frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
-
-        with torch.no_grad():
-            I = align_face(frame, landmarkpredictor)
-            I = transform(I).unsqueeze(dim=0).to(device)
-            s_w = pspencoder(I)
-            s_w = vtoonify.zplus2wplus(s_w)
-            if vtoonify.backbone == 'dualstylegan':
-                if args.color_transfer:
-                    s_w = exstyle
-                else:
-                    s_w[:,:7] = exstyle[:,:7]
-
-            x = transform(frame).unsqueeze(dim=0).to(device)
-            # parsing network works best on 512x512 images, so we predict parsing maps on upsmapled frames
-            # followed by downsampling the parsing maps
-            x_p = F.interpolate(parsingpredictor(2*(F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0], 
-                                scale_factor=0.5, recompute_scale_factor=False).detach()
-            # we give parsing maps lower weight (1/16)
-            inputs = torch.cat((x, x_p/16.), dim=1)
-            # d_s has no effect when backbone is toonify
-            y_tilde = vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), d_s = args.style_degree)        
-            y_tilde = torch.clamp(y_tilde, -1, 1)
-
-        cv2.imwrite(cropname, cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-        save_image(y_tilde[0].cpu(), savename)
-        
-    print('Transfer style successfully!')

spaces/232labs/VToonify/vtoonify/train_vtoonify_d.py

@@ -1,515 +0,0 @@
-import os
-#os.environ['CUDA_VISIBLE_DEVICES'] = "0"
-import argparse
-import math
-import random
-
-import numpy as np
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils import data
-import torch.distributed as dist
-from torchvision import transforms, utils
-from tqdm import tqdm
-from PIL import Image
-from util import *
-
-from model.stylegan import lpips
-from model.stylegan.model import Generator, Downsample
-from model.vtoonify import VToonify, ConditionalDiscriminator
-from model.bisenet.model import BiSeNet
-from model.simple_augment import random_apply_affine
-from model.stylegan.distributed import (
-    get_rank,
-    synchronize,
-    reduce_loss_dict,
-    reduce_sum,
-    get_world_size,
-)
-
-class TrainOptions():
-    def __init__(self):
-
-        self.parser = argparse.ArgumentParser(description="Train VToonify-D")
-        self.parser.add_argument("--iter", type=int, default=2000, help="total training iterations")
-        self.parser.add_argument("--batch", type=int, default=8, help="batch sizes for each gpus")
-        self.parser.add_argument("--lr", type=float, default=0.0001, help="learning rate")
-        self.parser.add_argument("--local_rank", type=int, default=0, help="local rank for distributed training")
-        self.parser.add_argument("--start_iter", type=int, default=0, help="start iteration")
-        self.parser.add_argument("--save_every", type=int, default=30000, help="interval of saving a checkpoint")
-        self.parser.add_argument("--save_begin", type=int, default=30000, help="when to start saving a checkpoint")
-        self.parser.add_argument("--log_every", type=int, default=200, help="interval of saving a checkpoint")
-        
-        self.parser.add_argument("--adv_loss", type=float, default=0.01, help="the weight of adv loss")
-        self.parser.add_argument("--grec_loss", type=float, default=0.1, help="the weight of mse recontruction loss")
-        self.parser.add_argument("--perc_loss", type=float, default=0.01, help="the weight of perceptual loss")
-        self.parser.add_argument("--tmp_loss", type=float, default=1.0, help="the weight of temporal consistency loss")
-        self.parser.add_argument("--msk_loss", type=float, default=0.0005, help="the weight of attention mask loss")  
-        
-        self.parser.add_argument("--fix_degree", action="store_true", help="use a fixed style degree")
-        self.parser.add_argument("--fix_style", action="store_true", help="use a fixed style image")
-        self.parser.add_argument("--fix_color", action="store_true", help="use the original color (no color transfer)")
-        self.parser.add_argument("--exstyle_path", type=str, default='./checkpoint/cartoon/refined_exstyle_code.npy', help="path of the extrinsic style code")
-        self.parser.add_argument("--style_id", type=int, default=26, help="the id of the style image")
-        self.parser.add_argument("--style_degree", type=float, default=0.5, help="style degree for VToonify-D")
-        
-        self.parser.add_argument("--encoder_path", type=str, default=None, help="path to the pretrained encoder model")    
-        self.parser.add_argument("--direction_path", type=str, default='./checkpoint/directions.npy', help="path to the editing direction latents")
-        self.parser.add_argument("--stylegan_path", type=str, default='./checkpoint/cartoon/generator.pt', help="path to the stylegan model")
-        self.parser.add_argument("--faceparsing_path", type=str, default='./checkpoint/faceparsing.pth', help="path of the face parsing model")
-        self.parser.add_argument("--style_encoder_path", type=str, default='./checkpoint/encoder.pt', help="path of the style encoder")
-        
-        self.parser.add_argument("--name", type=str, default='vtoonify_d_cartoon', help="saved model name")
-        self.parser.add_argument("--pretrain", action="store_true", help="if true, only pretrain the encoder")
-
-    def parse(self):
-        self.opt = self.parser.parse_args()
-        if self.opt.encoder_path is None:
-            self.opt.encoder_path = os.path.join('./checkpoint/', self.opt.name, 'pretrain.pt')
-        args = vars(self.opt)
-        if self.opt.local_rank == 0:
-            print('Load options')
-            for name, value in sorted(args.items()):
-                print('%s: %s' % (str(name), str(value)))
-        return self.opt                
-    
-
-# pretrain E of vtoonify. 
-# We train E so that its the last-layer feature matches the original 8-th-layer input feature of G1
-# See Model initialization in Sec. 4.2.2 for the detail
-def pretrain(args, generator, g_optim, g_ema, parsingpredictor, down, directions, styles, device):
-    pbar = range(args.iter)
-
-    if get_rank() == 0:
-        pbar = tqdm(pbar, initial=args.start_iter, dynamic_ncols=True, smoothing=0.01)
-
-    recon_loss = torch.tensor(0.0, device=device)
-    loss_dict = {}
-
-    if args.distributed:
-        g_module = generator.module
-    else:
-        g_module = generator
-
-    accum = 0.5 ** (32 / (10 * 1000))
-    
-    requires_grad(g_module.encoder, True)
-    
-    for idx in pbar:
-        i = idx + args.start_iter
-        
-        if i > args.iter:
-            print("Done!")
-            break
-        
-        # during pretraining, the last 11 layers of DualStyleGAN (for color transfer) is not used.
-        # so args.fix_color is not used. the last 11 elements in weight are not used.
-        if args.fix_degree:
-            d_s = args.style_degree
-        else:
-            d_s = 0 if i <= args.iter / 4.0 else np.random.rand(1)[0]
-        weight = [d_s] * 18
-        
-        # sample pre-saved w''=E_s(s)
-        if args.fix_style:
-            style = styles[args.style_id:args.style_id+1].repeat(args.batch,1,1)  
-        else:
-            style = styles[torch.randint(0, styles.size(0), (args.batch,))]
-            
-        with torch.no_grad():    
-            # during pretraining, no geometric transformations are applied.
-            noise_sample = torch.randn(args.batch, 512).cuda()
-            ws_ = g_ema.stylegan().style(noise_sample).unsqueeze(1).repeat(1,18,1) # random w
-            ws_[:, 3:7] += directions[torch.randint(0, directions.shape[0], (args.batch,)), 3:7] # w'=w+n
-            img_gen, _ = g_ema.stylegan()([ws_], input_is_latent=True, truncation=0.5, truncation_latent=0)
-            img_gen = torch.clamp(img_gen, -1, 1).detach() # x''
-            img_gen512 = down(img_gen.detach())
-            img_gen256 = down(img_gen512.detach()) # image part of x''_down
-            mask512 = parsingpredictor(2*torch.clamp(img_gen512, -1, 1))[0]
-            real_input = torch.cat((img_gen256, down(mask512)/16.0), dim=1) # x''_down
-            # f_G1^(8)(w', w'', d_s)
-            real_feat, real_skip = g_ema.generator([ws_], style, input_is_latent=True, return_feat=True, 
-                                         truncation=0.5, truncation_latent=0, use_res=True, interp_weights=weight) 
-
-            real_input = real_input.detach()
-            real_feat = real_feat.detach()
-            real_skip = real_skip.detach()
-            
-        # f_E^(last)(x''_down, w'', d_s)
-        fake_feat, fake_skip = generator(real_input, style, d_s, return_feat=True)
-
-        # L_E in Eq.(8)
-        recon_loss = F.mse_loss(fake_feat, real_feat) + F.mse_loss(fake_skip, real_skip)
-
-        loss_dict["emse"] = recon_loss
-
-        generator.zero_grad()
-        recon_loss.backward()  
-        g_optim.step()    
-        
-        accumulate(g_ema.encoder, g_module.encoder, accum)     
-
-        loss_reduced = reduce_loss_dict(loss_dict)
-
-        emse_loss_val = loss_reduced["emse"].mean().item()
-
-        if get_rank() == 0:
-            pbar.set_description(
-                (
-                    f"iter: {i:d}; emse: {emse_loss_val:.3f}"
-                )
-            )
-
-            if ((i+1) >= args.save_begin and (i+1) % args.save_every == 0) or (i+1) == args.iter:
-                if (i+1) == args.iter:
-                    savename = f"checkpoint/%s/pretrain.pt"%(args.name)
-                else:
-                    savename = f"checkpoint/%s/pretrain-%05d.pt"%(args.name, i+1)
-                torch.save(
-                    {
-                        #"g": g_module.encoder.state_dict(),
-                        "g_ema": g_ema.encoder.state_dict(),
-                    },
-                    savename,
-                )
-                
-                
-# generate paired data and train vtoonify, see Sec. 4.2.2 for the detail
-def train(args, generator, discriminator, g_optim, d_optim, g_ema, percept, parsingpredictor, down, pspencoder, directions, styles, device):
-    pbar = range(args.iter)
-
-    if get_rank() == 0:
-        pbar = tqdm(pbar, initial=args.start_iter, smoothing=0.01, ncols=130, dynamic_ncols=False)
-
-    d_loss = torch.tensor(0.0, device=device)
-    g_loss = torch.tensor(0.0, device=device)
-    grec_loss = torch.tensor(0.0, device=device)
-    gfeat_loss = torch.tensor(0.0, device=device)
-    temporal_loss = torch.tensor(0.0, device=device)
-    gmask_loss = torch.tensor(0.0, device=device)
-    loss_dict = {}
-    
-    surffix = '_s'
-    if args.fix_style:
-        surffix += '%03d'%(args.style_id)
-    surffix += '_d'
-    if args.fix_degree:
-        surffix += '%1.1f'%(args.style_degree)
-    if not args.fix_color:
-        surffix += '_c'
-
-    if args.distributed:
-        g_module = generator.module
-        d_module = discriminator.module
-
-    else:
-        g_module = generator
-        d_module = discriminator
-
-    accum = 0.5 ** (32 / (10 * 1000))
-    
-    for idx in pbar:
-        i = idx + args.start_iter
-        
-        if i > args.iter:
-            print("Done!")
-            break
-        
-        # sample style degree
-        if args.fix_degree or idx == 0 or i == 0:
-            d_s = args.style_degree
-        else:
-            d_s = np.random.randint(0,6) / 5.0
-        if args.fix_color:
-            weight = [d_s] * 7 + [0] * 11
-        else:
-            weight = [d_s] * 7 + [1] * 11
-        # style degree condition for discriminator
-        degree_label = torch.zeros(args.batch, 1).to(device) + d_s
-        
-        # style index condition for discriminator
-        style_ind = torch.randint(0, styles.size(0), (args.batch,))
-        if args.fix_style or idx == 0 or i == 0:
-            style_ind = style_ind * 0 + args.style_id
-        # sample pre-saved E_s(s)
-        style = styles[style_ind]
-        
-        with torch.no_grad():   
-            noise_sample = torch.randn(args.batch, 512).cuda()
-            wc = g_ema.stylegan().style(noise_sample).unsqueeze(1).repeat(1,18,1) # random w
-            wc[:, 3:7] += directions[torch.randint(0, directions.shape[0], (args.batch,)), 3:7]  # w'=w+n
-            wc = wc.detach()    
-            xc, _ = g_ema.stylegan()([wc], input_is_latent=True, truncation=0.5, truncation_latent=0)
-            xc = torch.clamp(xc, -1, 1).detach() # x''
-            if not args.fix_color and args.fix_style: # only transfer this fixed style's color
-                xl = style.clone()
-            else:
-                xl = pspencoder(F.adaptive_avg_pool2d(xc, 256)) 
-                xl = g_ema.zplus2wplus(xl) # E_s(x''_down) 
-                xl = torch.cat((style[:,0:7], xl[:,7:18]), dim=1).detach() # w'' = concatenate E_s(s) and E_s(x''_down)
-            xs, _ = g_ema.generator([wc], xl, input_is_latent=True, 
-                              truncation=0.5, truncation_latent=0, use_res=True, interp_weights=weight)
-            xs = torch.clamp(xs, -1, 1).detach() # y'=G1(w', w'', d_s, d_c)
-            # apply color jitter to w'. we fuse w' of the current iteration with w' of the last iteration
-            if idx > 0 and i >= (args.iter/2.0) and (not args.fix_color and not args.fix_style):
-                wcfuse = wc.clone()
-                wcfuse[:,7:] = wc_[:,7:] * (i/(args.iter/2.0)-1) + wcfuse[:,7:] * (2-i/(args.iter/2.0))
-                xc, _ = g_ema.stylegan()([wcfuse], input_is_latent=True, truncation=0.5, truncation_latent=0)
-                xc = torch.clamp(xc, -1, 1).detach() # x'
-            wc_ = wc.clone() # wc_ is the w' in the last iteration
-            # during training, random geometric transformations are applied.
-            imgs, _ = random_apply_affine(torch.cat((xc.detach(),xs), dim=1), 0.2, None)
-            real_input1024 = imgs[:,0:3].detach()   # image part of x
-            real_input512 = down(real_input1024).detach()
-            real_input256 = down(real_input512).detach()
-            mask512 = parsingpredictor(2*real_input512)[0]
-            mask256 = down(mask512).detach()
-            mask = F.adaptive_avg_pool2d(mask512, 1024).detach() # parsing part of x
-            real_output = imgs[:,3:].detach()  # y
-            real_input = torch.cat((real_input256, mask256/16.0), dim=1) # x_down
-            # for log, sample a fixed input-output pair (x_down, y, w'', d_s)
-            if idx == 0 or i == 0:
-                samplein = real_input.clone().detach()
-                sampleout = real_output.clone().detach()
-                samplexl = xl.clone().detach()
-                sampleds = d_s
-        
-        ###### This part is for training discriminator
-        
-        requires_grad(g_module.encoder, False)
-        requires_grad(g_module.fusion_out, False)
-        requires_grad(g_module.fusion_skip, False)  
-        requires_grad(discriminator, True)
-        
-        fake_output = generator(real_input, xl, d_s)
-        fake_pred = discriminator(F.adaptive_avg_pool2d(fake_output, 256), degree_label, style_ind)
-        real_pred = discriminator(F.adaptive_avg_pool2d(real_output, 256), degree_label, style_ind)
-        
-        # L_adv in Eq.(3)
-        d_loss = d_logistic_loss(real_pred, fake_pred) * args.adv_loss
-        loss_dict["d"] = d_loss
-        
-        discriminator.zero_grad()
-        d_loss.backward()
-        d_optim.step()    
-        
-        ###### This part is for training generator (encoder and fusion modules)
-
-        requires_grad(g_module.encoder, True)
-        requires_grad(g_module.fusion_out, True)
-        requires_grad(g_module.fusion_skip, True)    
-        requires_grad(discriminator, False)        
-
-        fake_output, m_Es = generator(real_input, xl, d_s, return_mask=True)
-        fake_pred = discriminator(F.adaptive_avg_pool2d(fake_output, 256), degree_label, style_ind)
-        
-        # L_adv in Eq.(3)
-        g_loss = g_nonsaturating_loss(fake_pred) * args.adv_loss
-        # L_rec in Eq.(2)
-        grec_loss = F.mse_loss(fake_output, real_output) * args.grec_loss
-        gfeat_loss = percept(F.adaptive_avg_pool2d(fake_output, 512), # 1024 will out of memory
-                             F.adaptive_avg_pool2d(real_output, 512)).sum() * args.perc_loss # 256 will get blurry output
- 
-        # L_msk in Eq.(9)
-        gmask_loss = torch.tensor(0.0, device=device)
-        if not args.fix_degree or args.msk_loss > 0:
-            for jj, m_E in enumerate(m_Es):
-                gd_s = (1 - d_s) ** 2 * 0.9 + 0.1
-                gmask_loss += F.relu(torch.mean(m_E)-gd_s) * args.msk_loss
-
-        loss_dict["g"] = g_loss
-        loss_dict["gr"] = grec_loss
-        loss_dict["gf"] = gfeat_loss
-        loss_dict["msk"] = gmask_loss
-
-        w = random.randint(0,1024-896)
-        h = random.randint(0,1024-896)
-        crop_input = torch.cat((real_input1024[:,:,w:w+896,h:h+896], mask[:,:,w:w+896,h:h+896]/16.0), dim=1).detach()
-        crop_input = down(down(crop_input))   
-        crop_fake_output = fake_output[:,:,w:w+896,h:h+896]
-        fake_crop_output = generator(crop_input, xl, d_s)
-        # L_tmp in Eq.(4), gradually increase the weight of L_tmp
-        temporal_loss = ((fake_crop_output-crop_fake_output)**2).mean() * max(idx/(args.iter/2.0)-1, 0) * args.tmp_loss
-        loss_dict["tp"] = temporal_loss
-
-        generator.zero_grad()
-        (g_loss + grec_loss + gfeat_loss + temporal_loss + gmask_loss).backward() 
-        g_optim.step()        
-        
-        accumulate(g_ema.encoder, g_module.encoder, accum)
-        accumulate(g_ema.fusion_out, g_module.fusion_out, accum)
-        accumulate(g_ema.fusion_skip, g_module.fusion_skip, accum)        
-
-        loss_reduced = reduce_loss_dict(loss_dict)
-
-        d_loss_val = loss_reduced["d"].mean().item()
-        g_loss_val = loss_reduced["g"].mean().item()
-        gr_loss_val = loss_reduced["gr"].mean().item()
-        gf_loss_val = loss_reduced["gf"].mean().item()
-        tmp_loss_val = loss_reduced["tp"].mean().item()
-        msk_loss_val = loss_reduced["msk"].mean().item()
-
-        if get_rank() == 0:
-            pbar.set_description(
-                (
-                    f"iter: {i:d}; advd: {d_loss_val:.3f}; advg: {g_loss_val:.3f}; mse: {gr_loss_val:.3f}; "
-                    f"perc: {gf_loss_val:.3f}; tmp: {tmp_loss_val:.3f}; msk: {msk_loss_val:.3f}"
-                )
-            )
-
-            if i == 0 or (i+1) % args.log_every == 0 or (i+1) == args.iter:
-                with torch.no_grad():
-                    g_ema.eval()
-                    sample1 = g_ema(samplein, samplexl, sampleds)
-                    if args.fix_degree:
-                        sample = F.interpolate(torch.cat((sampleout, sample1), dim=0), 256)
-                    else:
-                        sample2 = g_ema(samplein, samplexl, d_s)
-                        sample = F.interpolate(torch.cat((sampleout, sample1, sample2), dim=0), 256)
-                    utils.save_image(
-                        sample,
-                        f"log/%s/%05d.jpg"%(args.name, (i+1)),
-                        nrow=int(args.batch),
-                        normalize=True,
-                        range=(-1, 1),
-                    )
-
-            if ((i+1) >= args.save_begin and (i+1) % args.save_every == 0) or (i+1) == args.iter:
-                if (i+1) == args.iter:
-                    savename = f"checkpoint/%s/vtoonify%s.pt"%(args.name, surffix)
-                else:
-                    savename = f"checkpoint/%s/vtoonify%s_%05d.pt"%(args.name, surffix, i+1) 
-                torch.save(
-                    {
-                        #"g": g_module.state_dict(),
-                        #"d": d_module.state_dict(),
-                        "g_ema": g_ema.state_dict(),
-                    },
-                    savename,
-                )
-                
-                
-
-if __name__ == "__main__":
-    
-    device = "cuda"
-    parser = TrainOptions()  
-    args = parser.parse()
-    if args.local_rank == 0:
-        print('*'*98)
-        if not os.path.exists("log/%s/"%(args.name)):
-            os.makedirs("log/%s/"%(args.name))
-        if not os.path.exists("checkpoint/%s/"%(args.name)):
-            os.makedirs("checkpoint/%s/"%(args.name))
-        
-    n_gpu = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
-    args.distributed = n_gpu > 1
-
-    if args.distributed:
-        torch.cuda.set_device(args.local_rank)
-        torch.distributed.init_process_group(backend="nccl", init_method="env://")
-        synchronize()
-
-    generator = VToonify(backbone = 'dualstylegan').to(device)
-    generator.apply(weights_init)
-    g_ema = VToonify(backbone = 'dualstylegan').to(device)
-    g_ema.eval()
-
-    ckpt = torch.load(args.stylegan_path, map_location=lambda storage, loc: storage)
-    generator.generator.load_state_dict(ckpt["g_ema"], strict=False)
-    # load ModRes blocks of DualStyleGAN into the modified ModRes blocks (with dilation)
-    generator.res.load_state_dict(generator.generator.res.state_dict(), strict=False)
-    g_ema.generator.load_state_dict(ckpt["g_ema"], strict=False)
-    g_ema.res.load_state_dict(g_ema.generator.res.state_dict(), strict=False)
-    requires_grad(generator.generator, False)
-    requires_grad(generator.res, False)
-    requires_grad(g_ema.generator, False)
-    requires_grad(g_ema.res, False)
-
-    if not args.pretrain:
-        generator.encoder.load_state_dict(torch.load(args.encoder_path, map_location=lambda storage, loc: storage)["g_ema"])
-        # we initialize the fusion modules to map f_G \otimes f_E to f_G.
-        for k in generator.fusion_out:
-            k.conv.weight.data *= 0.01
-            k.conv.weight[:,0:k.conv.weight.shape[0],1,1].data += torch.eye(k.conv.weight.shape[0]).cuda()  
-        for k in generator.fusion_skip:
-            k.weight.data *= 0.01
-            k.weight[:,0:k.weight.shape[0],1,1].data += torch.eye(k.weight.shape[0]).cuda()
-
-    accumulate(g_ema.encoder, generator.encoder, 0)
-    accumulate(g_ema.fusion_out, generator.fusion_out, 0)
-    accumulate(g_ema.fusion_skip, generator.fusion_skip, 0) 
-
-    g_parameters = list(generator.encoder.parameters()) 
-    if not args.pretrain:
-        g_parameters = g_parameters + list(generator.fusion_out.parameters()) + list(generator.fusion_skip.parameters())
-
-    g_optim = optim.Adam(
-        g_parameters,
-        lr=args.lr,
-        betas=(0.9, 0.99),
-    )
-
-    if args.distributed:
-        generator = nn.parallel.DistributedDataParallel(
-            generator,
-            device_ids=[args.local_rank],
-            output_device=args.local_rank,
-            broadcast_buffers=False,
-            find_unused_parameters=True,
-        )
-
-    parsingpredictor = BiSeNet(n_classes=19)
-    parsingpredictor.load_state_dict(torch.load(args.faceparsing_path, map_location=lambda storage, loc: storage))
-    parsingpredictor.to(device).eval()
-    requires_grad(parsingpredictor, False)
-
-    # we apply gaussian blur to the images to avoid flickers caused during downsampling
-    down = Downsample(kernel=[1, 3, 3, 1], factor=2).to(device)
-    requires_grad(down, False)
-
-    directions = torch.tensor(np.load(args.direction_path)).to(device) 
-
-    # load style codes of DualStyleGAN
-    exstyles = np.load(args.exstyle_path, allow_pickle='TRUE').item()
-    if args.local_rank == 0 and not os.path.exists('checkpoint/%s/exstyle_code.npy'%(args.name)):
-        np.save('checkpoint/%s/exstyle_code.npy'%(args.name), exstyles, allow_pickle=True)    
-    styles = []
-    with torch.no_grad(): 
-        for stylename in exstyles.keys():
-            exstyle = torch.tensor(exstyles[stylename]).to(device)
-            exstyle = g_ema.zplus2wplus(exstyle)
-            styles += [exstyle]
-    styles = torch.cat(styles, dim=0)
-
-    if not args.pretrain:
-        discriminator = ConditionalDiscriminator(256, use_condition=True, style_num = styles.size(0)).to(device)
-
-        d_optim = optim.Adam(
-            discriminator.parameters(),
-            lr=args.lr,
-            betas=(0.9, 0.99),
-        )    
-
-        if args.distributed:
-            discriminator = nn.parallel.DistributedDataParallel(
-                discriminator,
-                device_ids=[args.local_rank],
-                output_device=args.local_rank,
-                broadcast_buffers=False,
-                find_unused_parameters=True,
-            )
-
-        percept = lpips.PerceptualLoss(model="net-lin", net="vgg", use_gpu=device.startswith("cuda"), gpu_ids=[args.local_rank])
-        requires_grad(percept.model.net, False)
-
-        pspencoder = load_psp_standalone(args.style_encoder_path, device)  
-
-    if args.local_rank == 0:
-        print('Load models and data successfully loaded!')
-
-    if args.pretrain:
-        pretrain(args, generator, g_optim, g_ema, parsingpredictor, down, directions, styles, device)
-    else:
-        train(args, generator, discriminator, g_optim, d_optim, g_ema, percept, parsingpredictor, down, pspencoder, directions, styles, device)

spaces/AIGC-Audio/AudioGPT/sound_extraction/model/modules.py

@@ -1,483 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-from .film import Film
-
-class ConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, activation, momentum):
-        super(ConvBlock, self).__init__()
-
-        self.activation = activation
-        padding = (kernel_size[0] // 2, kernel_size[1] // 2)
-
-        self.conv1 = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=(1, 1),
-            dilation=(1, 1),
-            padding=padding,
-            bias=False,
-        )
-
-        self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum)
-
-        self.conv2 = nn.Conv2d(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=(1, 1),
-            dilation=(1, 1),
-            padding=padding,
-            bias=False,
-        )
-
-        self.bn2 = nn.BatchNorm2d(out_channels, momentum=momentum)
-
-        self.init_weights()
-
-    def init_weights(self):
-        init_layer(self.conv1)
-        init_layer(self.conv2)
-        init_bn(self.bn1)
-        init_bn(self.bn2)
-
-    def forward(self, x):
-        x = act(self.bn1(self.conv1(x)), self.activation)
-        x = act(self.bn2(self.conv2(x)), self.activation)
-        return x
-
-
-class EncoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, downsample, activation, momentum):
-        super(EncoderBlock, self).__init__()
-
-        self.conv_block = ConvBlock(
-            in_channels, out_channels, kernel_size, activation, momentum
-        )
-        self.downsample = downsample
-
-    def forward(self, x):
-        encoder = self.conv_block(x)
-        encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample)
-        return encoder_pool, encoder
-
-
-class DecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, upsample, activation, momentum):
-        super(DecoderBlock, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = upsample
-        self.activation = activation
-
-        self.conv1 = torch.nn.ConvTranspose2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=self.stride,
-            stride=self.stride,
-            padding=(0, 0),
-            bias=False,
-            dilation=(1, 1),
-        )
-
-        self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum)
-
-        self.conv_block2 = ConvBlock(
-            out_channels * 2, out_channels, kernel_size, activation, momentum
-        )
-
-    def init_weights(self):
-        init_layer(self.conv1)
-        init_bn(self.bn)
-
-    def prune(self, x):
-        """Prune the shape of x after transpose convolution."""
-        padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2)
-        x = x[
-            :,
-            :,
-            padding[0] : padding[0] - self.stride[0],
-            padding[1] : padding[1] - self.stride[1]]
-        return x
-
-    def forward(self, input_tensor, concat_tensor):
-        x = act(self.bn1(self.conv1(input_tensor)), self.activation)
-        # from IPython import embed; embed(using=False); os._exit(0)
-        # x = self.prune(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        x = self.conv_block2(x)
-        return x
-
-
-class EncoderBlockRes1B(nn.Module):
-    def __init__(self, in_channels, out_channels, downsample, activation, momentum):
-        super(EncoderBlockRes1B, self).__init__()
-        size = (3,3)
-
-        self.conv_block1 = ConvBlockRes(in_channels, out_channels, size, activation, momentum)
-        self.conv_block2 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.downsample = downsample
-
-    def forward(self, x):
-        encoder = self.conv_block1(x)
-        encoder = self.conv_block2(encoder)
-        encoder = self.conv_block3(encoder)
-        encoder = self.conv_block4(encoder)
-        encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample)
-        return encoder_pool, encoder
-
-class DecoderBlockRes1B(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, activation, momentum):
-        super(DecoderBlockRes1B, self).__init__()
-        size = (3,3)
-        self.activation = activation
-
-        self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels,
-            out_channels=out_channels, kernel_size=size, stride=stride,
-            padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1)
-
-        self.bn1 = nn.BatchNorm2d(in_channels)
-        self.conv_block2 = ConvBlockRes(out_channels * 2, out_channels, size, activation, momentum)
-        self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block5 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-
-    def init_weights(self):
-        init_layer(self.conv1)
-
-    def prune(self, x, both=False):
-        """Prune the shape of x after transpose convolution.
-        """
-        if(both): x = x[:, :, 0 : - 1, 0:-1]
-        else: x = x[:, :, 0: - 1, :]
-        return x
-
-    def forward(self, input_tensor, concat_tensor,both=False):
-        x = self.conv1(F.relu_(self.bn1(input_tensor)))
-        x = self.prune(x,both=both)
-        x = torch.cat((x, concat_tensor), dim=1)
-        x = self.conv_block2(x)
-        x = self.conv_block3(x)
-        x = self.conv_block4(x)
-        x = self.conv_block5(x)
-        return x
-
-
-class EncoderBlockRes2BCond(nn.Module):
-    def __init__(self, in_channels, out_channels, downsample, activation, momentum, cond_embedding_dim):
-        super(EncoderBlockRes2BCond, self).__init__()
-        size = (3, 3)
-
-        self.conv_block1 = ConvBlockResCond(in_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block2 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.downsample = downsample
-
-    def forward(self, x, cond_vec):
-        encoder = self.conv_block1(x, cond_vec)
-        encoder = self.conv_block2(encoder, cond_vec)
-        encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample)
-        return encoder_pool, encoder
-
-class DecoderBlockRes2BCond(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, activation, momentum, cond_embedding_dim):
-        super(DecoderBlockRes2BCond, self).__init__()
-        size = (3, 3)
-        self.activation = activation
-
-        self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels,
-            out_channels=out_channels, kernel_size=size, stride=stride,
-            padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1)
-
-        self.bn1 = nn.BatchNorm2d(in_channels)
-        self.conv_block2 = ConvBlockResCond(out_channels * 2, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-
-    def init_weights(self):
-        init_layer(self.conv1)
-
-    def prune(self, x, both=False):
-        """Prune the shape of x after transpose convolution.
-        """
-        if(both): x = x[:, :, 0 : - 1, 0:-1]
-        else: x = x[:, :, 0: - 1, :]
-        return x
-
-    def forward(self, input_tensor, concat_tensor, cond_vec, both=False):
-        x = self.conv1(F.relu_(self.bn1(input_tensor)))
-        x = self.prune(x, both=both)
-        x = torch.cat((x, concat_tensor), dim=1)
-        x = self.conv_block2(x, cond_vec)
-        x = self.conv_block3(x, cond_vec)
-        return x
-
-class EncoderBlockRes4BCond(nn.Module):
-    def __init__(self, in_channels, out_channels, downsample, activation, momentum, cond_embedding_dim):
-        super(EncoderBlockRes4B, self).__init__()
-        size = (3,3)
-
-        self.conv_block1 = ConvBlockResCond(in_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block2 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block4 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.downsample = downsample
-
-    def forward(self, x, cond_vec):
-        encoder = self.conv_block1(x, cond_vec)
-        encoder = self.conv_block2(encoder, cond_vec)
-        encoder = self.conv_block3(encoder, cond_vec)
-        encoder = self.conv_block4(encoder, cond_vec)
-        encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample)
-        return encoder_pool, encoder
-
-class DecoderBlockRes4BCond(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, activation, momentum, cond_embedding_dim):
-        super(DecoderBlockRes4B, self).__init__()
-        size = (3, 3)
-        self.activation = activation
-
-        self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels,
-            out_channels=out_channels, kernel_size=size, stride=stride,
-            padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1)
-
-        self.bn1 = nn.BatchNorm2d(in_channels)
-        self.conv_block2 = ConvBlockResCond(out_channels * 2, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block3 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block4 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-        self.conv_block5 = ConvBlockResCond(out_channels, out_channels, size, activation, momentum, cond_embedding_dim)
-
-    def init_weights(self):
-        init_layer(self.conv1)
-
-    def prune(self, x, both=False):
-        """Prune the shape of x after transpose convolution.
-        """
-        if(both): x = x[:, :, 0 : - 1, 0:-1]
-        else: x = x[:, :, 0: - 1, :]
-        return x
-
-    def forward(self, input_tensor, concat_tensor, cond_vec, both=False):
-        x = self.conv1(F.relu_(self.bn1(input_tensor)))
-        x = self.prune(x,both=both)
-        x = torch.cat((x, concat_tensor), dim=1)
-        x = self.conv_block2(x, cond_vec)
-        x = self.conv_block3(x, cond_vec)
-        x = self.conv_block4(x, cond_vec)
-        x = self.conv_block5(x, cond_vec)
-        return x
-
-class EncoderBlockRes4B(nn.Module):
-    def __init__(self, in_channels, out_channels, downsample, activation, momentum):
-        super(EncoderBlockRes4B, self).__init__()
-        size = (3, 3)
-
-        self.conv_block1 = ConvBlockRes(in_channels, out_channels, size, activation, momentum)
-        self.conv_block2 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.downsample = downsample
-
-    def forward(self, x):
-        encoder = self.conv_block1(x)
-        encoder = self.conv_block2(encoder)
-        encoder = self.conv_block3(encoder)
-        encoder = self.conv_block4(encoder)
-        encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample)
-        return encoder_pool, encoder
-
-class DecoderBlockRes4B(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, activation, momentum):
-        super(DecoderBlockRes4B, self).__init__()
-        size = (3,3)
-        self.activation = activation
-
-        self.conv1 = torch.nn.ConvTranspose2d(in_channels=in_channels,
-            out_channels=out_channels, kernel_size=size, stride=stride,
-            padding=(0, 0), output_padding=(0, 0), bias=False, dilation=1)
-
-        self.bn1 = nn.BatchNorm2d(in_channels)
-        self.conv_block2 = ConvBlockRes(out_channels * 2, out_channels, size, activation, momentum)
-        self.conv_block3 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block4 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-        self.conv_block5 = ConvBlockRes(out_channels, out_channels, size, activation, momentum)
-
-    def init_weights(self):
-        init_layer(self.conv1)
-
-    def prune(self, x, both=False):
-        """Prune the shape of x after transpose convolution.
-        """
-        if(both): x = x[:, :, 0 : - 1, 0:-1]
-        else: x = x[:, :, 0: - 1, :]
-        return x
-
-    def forward(self, input_tensor, concat_tensor,both=False):
-        x = self.conv1(F.relu_(self.bn1(input_tensor)))
-        x = self.prune(x,both=both)
-        x = torch.cat((x, concat_tensor), dim=1)
-        x = self.conv_block2(x)
-        x = self.conv_block3(x)
-        x = self.conv_block4(x)
-        x = self.conv_block5(x)
-        return x
-
-class ConvBlockResCond(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, activation, momentum, cond_embedding_dim):
-        r"""Residual block.
-        """
-        super(ConvBlockResCond, self).__init__()
-
-        self.activation = activation
-        padding = [kernel_size[0] // 2, kernel_size[1] // 2]
-
-        self.bn1 = nn.BatchNorm2d(in_channels)
-        self.bn2 = nn.BatchNorm2d(out_channels)
-
-        self.conv1 = nn.Conv2d(in_channels=in_channels,
-                              out_channels=out_channels,
-                              kernel_size=kernel_size, stride=(1, 1),
-                              dilation=(1, 1), padding=padding, bias=False)
-        self.film1 = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim)
-        self.conv2 = nn.Conv2d(in_channels=out_channels,
-                              out_channels=out_channels,
-                              kernel_size=kernel_size, stride=(1, 1),
-                              dilation=(1, 1), padding=padding, bias=False)
-        self.film2 = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim)
-
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels=in_channels,
-                out_channels=out_channels, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0))
-            self.film_res = Film(channels=out_channels, cond_embedding_dim=cond_embedding_dim)
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-        self.init_weights()
-
-    def init_weights(self):
-        init_bn(self.bn1)
-        init_bn(self.bn2)
-        init_layer(self.conv1)
-        init_layer(self.conv2)
-
-        if self.is_shortcut:
-            init_layer(self.shortcut)
-
-    def forward(self, x, cond_vec):
-        origin = x
-        x = self.conv1(F.leaky_relu_(self.bn1(x), negative_slope=0.01))
-        x = self.film1(x, cond_vec)
-        x = self.conv2(F.leaky_relu_(self.bn2(x), negative_slope=0.01))
-        x = self.film2(x, cond_vec)
-        if self.is_shortcut:
-            residual = self.shortcut(origin)
-            residual = self.film_res(residual, cond_vec)
-            return residual + x
-        else:
-            return origin + x
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, activation, momentum):
-        r"""Residual block.
-        """
-        super(ConvBlockRes, self).__init__()
-
-        self.activation = activation
-        padding = [kernel_size[0] // 2, kernel_size[1] // 2]
-
-        self.bn1 = nn.BatchNorm2d(in_channels)
-        self.bn2 = nn.BatchNorm2d(out_channels)
-
-        self.conv1 = nn.Conv2d(in_channels=in_channels,
-                              out_channels=out_channels,
-                              kernel_size=kernel_size, stride=(1, 1),
-                              dilation=(1, 1), padding=padding, bias=False)
-
-        self.conv2 = nn.Conv2d(in_channels=out_channels,
-                              out_channels=out_channels,
-                              kernel_size=kernel_size, stride=(1, 1),
-                              dilation=(1, 1), padding=padding, bias=False)
-
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels=in_channels,
-                out_channels=out_channels, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-        self.init_weights()
-
-    def init_weights(self):
-        init_bn(self.bn1)
-        init_bn(self.bn2)
-        init_layer(self.conv1)
-        init_layer(self.conv2)
-
-        if self.is_shortcut:
-            init_layer(self.shortcut)
-
-    def forward(self, x):
-        origin = x
-        x = self.conv1(F.leaky_relu_(self.bn1(x), negative_slope=0.01))
-        x = self.conv2(F.leaky_relu_(self.bn2(x), negative_slope=0.01))
-
-        if self.is_shortcut:
-            return self.shortcut(origin) + x
-        else:
-            return origin + x
-
-def init_layer(layer):
-    """Initialize a Linear or Convolutional layer. """
-    nn.init.xavier_uniform_(layer.weight)
-
-    if hasattr(layer, 'bias'):
-        if layer.bias is not None:
-            layer.bias.data.fill_(0.)
-
-def init_bn(bn):
-    """Initialize a Batchnorm layer. """
-    bn.bias.data.fill_(0.)
-    bn.weight.data.fill_(1.)
-
-def init_gru(rnn):
-    """Initialize a GRU layer. """
-
-    def _concat_init(tensor, init_funcs):
-        (length, fan_out) = tensor.shape
-        fan_in = length // len(init_funcs)
-
-        for (i, init_func) in enumerate(init_funcs):
-            init_func(tensor[i * fan_in: (i + 1) * fan_in, :])
-
-    def _inner_uniform(tensor):
-        fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in')
-        nn.init.uniform_(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in))
-
-    for i in range(rnn.num_layers):
-        _concat_init(
-            getattr(rnn, 'weight_ih_l{}'.format(i)),
-            [_inner_uniform, _inner_uniform, _inner_uniform]
-        )
-        torch.nn.init.constant_(getattr(rnn, 'bias_ih_l{}'.format(i)), 0)
-
-        _concat_init(
-            getattr(rnn, 'weight_hh_l{}'.format(i)),
-            [_inner_uniform, _inner_uniform, nn.init.orthogonal_]
-        )
-        torch.nn.init.constant_(getattr(rnn, 'bias_hh_l{}'.format(i)), 0)
-
-
-def act(x, activation):
-    if activation == 'relu':
-        return F.relu_(x)
-
-    elif activation == 'leaky_relu':
-        return F.leaky_relu_(x, negative_slope=0.2)
-
-    elif activation == 'swish':
-        return x * torch.sigmoid(x)
-
-    else:
-        raise Exception('Incorrect activation!')

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

@@ -1,261 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class LinearAttention(nn.Module):
-    def __init__(self, dim, heads=4, dim_head=32):
-        super().__init__()
-        self.heads = heads
-        hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
-        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
-        k = k.softmax(dim=-1)  
-        context = torch.einsum('bhdn,bhen->bhde', k, v)
-        out = torch.einsum('bhde,bhdn->bhen', context, q)
-        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
-        return self.to_out(out)
-
-
-class SpatialSelfAttention(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
-
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):# 如果设置了context_dim就不是自注意力了
-        super().__init__()
-        inner_dim = dim_head * heads # inner_dim == SpatialTransformer.model_channels
-        context_dim = default(context_dim, query_dim)
-
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, mask=None):# x:(b,h*w,c), context:(b,seq_len,context_dim)
-        h = self.heads
-
-        q = self.to_q(x)# q:(b,h*w,inner_dim)
-        context = default(context, x)
-        k = self.to_k(context)# (b,seq_len,inner_dim)
-        v = self.to_v(context)# (b,seq_len,inner_dim)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))# n is seq_len for k and v
-
-        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale # (b*head,h*w,seq_len)
-
-        if exists(mask):# false
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        attn = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', attn, v)# (b*head,h*w,inner_dim/head)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)# (b,h*w,inner_dim)
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
-        super().__init__()
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
-                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None):
-        x = self.attn1(self.norm1(x)) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None):
-        super().__init__()
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head 
-        self.norm = Normalize(in_channels)
-
-        self.proj_in = nn.Conv2d(in_channels,
-                                 inner_dim,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
-                for d in range(depth)]
-        )
-
-        self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                              in_channels,
-                                              kernel_size=1,
-                                              stride=1,
-                                              padding=0))
-
-    def forward(self, x, context=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        b, c, h, w = x.shape # such as [2,320,10,106]
-        x_in = x
-        x = self.norm(x)# group norm
-        x = self.proj_in(x)# no shape change
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        for block in self.transformer_blocks:
-            x = block(x, context=context)# context shape [b,seq_len=77,context_dim]
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
-        x = self.proj_out(x)
-        return x + x_in

spaces/AJRFan/dreambooth-training/app.py

@@ -1,340 +0,0 @@
-import gradio as gr
-import os
-from pathlib import Path
-import argparse
-import shutil
-from train_dreambooth import run_training
-from convertosd import convert
-from PIL import Image
-from slugify import slugify
-import requests
-import torch
-import zipfile
-from diffusers import StableDiffusionPipeline
-
-css = '''
-    .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
-    .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
-    #component-4, #component-3, #component-10{min-height: 0}
-'''
-model_to_load = "multimodalart/sd-fine-tunable"
-maximum_concepts = 3
-#Pre download the files even if we don't use it here
-StableDiffusionPipeline.from_pretrained(model_to_load)
-
-def zipdir(path, ziph):
-    # ziph is zipfile handle
-    for root, dirs, files in os.walk(path):
-        for file in files:
-            ziph.write(os.path.join(root, file), 
-                       os.path.relpath(os.path.join(root, file), 
-                                       os.path.join(path, '..')))
-
-def swap_text(option):
-    mandatory_liability = "You must have the right to do so and you are liable for the images you use, example:"
-    if(option == "object"):
-        instance_prompt_example = "cttoy"
-        freeze_for = 50
-        return [f"You are going to train `object`(s), upload 5-10 images of each object you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''<img src="file/cat-toy.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
-    elif(option == "person"):
-       instance_prompt_example = "julcto"
-       freeze_for = 100
-       return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''<img src="file/person.png" />''', f"You should name the files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
-    elif(option == "style"):
-        instance_prompt_example = "trsldamrl"
-        freeze_for = 10
-        return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. Name the files with the words you would like  {mandatory_liability}:", '''<img src="file/trsl_style.png" />''', f"You should name your files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
-
-def count_files(*inputs):
-    file_counter = 0
-    concept_counter = 0
-    for i, input in enumerate(inputs):
-        if(i < maximum_concepts-1):
-            files = inputs[i]
-            if(files):
-                concept_counter+=1
-                file_counter+=len(files)
-    uses_custom = inputs[-1] 
-    type_of_thing = inputs[-4]
-    if(uses_custom):
-        Training_Steps = int(inputs[-3])
-    else:
-        if(type_of_thing == "person"):
-            Training_Steps = file_counter*200*2
-        else:
-            Training_Steps = file_counter*200
-    return(gr.update(visible=True, value=f"You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. This should take around {round(Training_Steps/1.5, 2)} seconds, or {round((Training_Steps/1.5)/3600, 2)} hours. As a reminder, the T4 GPU costs US$0.60 for 1h. Once training is over, don't forget to swap the hardware back to CPU."))
-
-def train(*inputs):
-    if "IS_SHARED_UI" in os.environ:
-        raise gr.Error("This Space only works in duplicated instances")
-    if os.path.exists("output_model"): shutil.rmtree('output_model')
-    if os.path.exists("instance_images"): shutil.rmtree('instance_images')
-    if os.path.exists("diffusers_model.zip"): os.remove("diffusers_model.zip")
-    if os.path.exists("model.ckpt"): os.remove("model.ckpt")
-    file_counter = 0
-    for i, input in enumerate(inputs):
-        if(i < maximum_concepts-1):
-            if(input):
-                os.makedirs('instance_images',exist_ok=True)
-                files = inputs[i+(maximum_concepts*2)]
-                prompt = inputs[i+maximum_concepts]
-                if(prompt == "" or prompt == None):
-                    raise gr.Error("You forgot to define your concept prompt")
-                for j, file_temp in enumerate(files):
-                    file = Image.open(file_temp.name)
-                    width, height = file.size
-                    side_length = min(width, height)
-                    left = (width - side_length)/2
-                    top = (height - side_length)/2
-                    right = (width + side_length)/2
-                    bottom = (height + side_length)/2
-                    image = file.crop((left, top, right, bottom))
-                    image = image.resize((512, 512))
-                    extension = file_temp.name.split(".")[1]
-                    image = image.convert('RGB')
-                    image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100)
-                    file_counter += 1
-    
-    os.makedirs('output_model',exist_ok=True)
-    uses_custom = inputs[-1] 
-    type_of_thing = inputs[-4]
-    if(uses_custom):
-        Training_Steps = int(inputs[-3])
-        Train_text_encoder_for = int(inputs[-2])
-    else:
-        Training_Steps = file_counter*200
-        if(type_of_thing == "object"):
-            Train_text_encoder_for=30
-        elif(type_of_thing == "person"):
-            Train_text_encoder_for=60
-        elif(type_of_thing == "style"):
-            Train_text_encoder_for=15
-    
-    class_data_dir = None
-    stptxt = int((Training_Steps*Train_text_encoder_for)/100)
-    args_general = argparse.Namespace(
-                image_captions_filename = True,
-                train_text_encoder = True,
-                stop_text_encoder_training = stptxt,
-                save_n_steps = 0,
-                pretrained_model_name_or_path = model_to_load,
-                instance_data_dir="instance_images",
-                class_data_dir=class_data_dir,
-                output_dir="output_model",
-                instance_prompt="",
-                seed=42,
-                resolution=512,
-                mixed_precision="fp16",
-                train_batch_size=1,
-                gradient_accumulation_steps=1,
-                use_8bit_adam=True,
-                learning_rate=2e-6,
-                lr_scheduler="polynomial",
-                lr_warmup_steps = 0,
-                max_train_steps=Training_Steps,     
-    )
-    run_training(args_general)
-    torch.cuda.empty_cache()
-    #convert("output_model", "model.ckpt")
-    #shutil.rmtree('instance_images')
-    #shutil.make_archive("diffusers_model", 'zip', "output_model")
-    with zipfile.ZipFile('diffusers_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
-        zipdir('output_model/', zipf)
-    torch.cuda.empty_cache()
-    return [gr.update(visible=True, value=["diffusers_model.zip"]), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
-
-def generate(prompt):
-    from diffusers import StableDiffusionPipeline
-    
-    pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
-    pipe = pipe.to("cuda")
-    image = pipe(prompt).images[0]  
-    return(image)
-    
-def push(model_name, where_to_upload, hf_token):
-    if(not os.path.exists("model.ckpt")):
-        convert("output_model", "model.ckpt")
-    from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
-    from huggingface_hub import create_repo
-    model_name_slug = slugify(model_name)
-    if(where_to_upload == "My personal profile"):
-        api = HfApi()
-        your_username = api.whoami(token=hf_token)["name"]
-        model_id = f"{your_username}/{model_name_slug}"
-    else:
-        model_id = f"sd-dreambooth-library/{model_name_slug}"
-        headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
-        response = requests.post("https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
-    
-    images_upload = os.listdir("instance_images")
-    image_string = ""
-    instance_prompt_list = []
-    previous_instance_prompt = ''
-    for i, image in enumerate(images_upload):
-        instance_prompt = image.split("_")[0]
-        if(instance_prompt != previous_instance_prompt):
-            title_instance_prompt_string = instance_prompt
-            instance_prompt_list.append(instance_prompt)
-        else:
-            title_instance_prompt_string = ''
-        previous_instance_prompt = instance_prompt
-        image_string = f'''{title_instance_prompt_string}
-{image_string}![{instance_prompt} {i}](https://huggingface.co/{model_id}/resolve/main/sample_images/{image})'''
-    readme_text = f'''---
-license: creativeml-openrail-m
-tags:
-- text-to-image
----
-### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training)
-
-You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
-
-Sample pictures of this concept:
-{image_string}
-'''
-    #Save the readme to a file
-    readme_file = open("README.md", "w")
-    readme_file.write(readme_text)
-    readme_file.close()
-    #Save the token identifier to a file
-    text_file = open("token_identifier.txt", "w")
-    text_file.write(', '.join(instance_prompt_list))
-    text_file.close()
-    create_repo(model_id,private=True, token=hf_token)
-    operations = [
-        CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
-        CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"),
-        CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt")
-    ]
-    api.create_commit(
-    repo_id=model_id,
-    operations=operations,
-    commit_message=f"Upload the model {model_name}",
-    token=hf_token
-    )
-    api.upload_folder(
-    folder_path="output_model",
-    repo_id=model_id,
-    token=hf_token
-    )
-    api.upload_folder(
-    folder_path="instance_images",
-    path_in_repo="concept_images",
-    repo_id=model_id,
-    token=hf_token
-    )
-    return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co/{model_id})"), gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])]
-
-def convert_to_ckpt():
-    convert("output_model", "model.ckpt")
-    return gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])
-
-with gr.Blocks(css=css) as demo:
-    with gr.Box():
-        if "IS_SHARED_UI" in os.environ:
-            gr.HTML('''
-                <div class="gr-prose" style="max-width: 80%">
-                <h2>Attention - This Space doesn't work in this shared UI</h2>
-                <p>For it to work, you have to duplicate the Space and run it on your own profile where a (paid) private GPU will be attributed to it during runtime. As each T4 costs US$0,60/h, it should cost < US$1 to train a model with less than 100 images on default settings!</p> 
-                <img class="instruction" src="file/duplicate.png"> 
-                <img class="arrow" src="file/arrow.png" />
-                </div>
-            ''')
-        else:
-            gr.HTML('''
-                <div class="gr-prose" style="max-width: 80%">
-                <h2>You have successfully cloned the Dreambooth Training Space</h2>
-                <p>If you haven't already, attribute a T4 GPU to it (via the Settings tab) and run the training below. You will be billed by the minute from when you activate the GPU until when you turn it off.</p> 
-                </div>
-            ''')    
-    gr.Markdown("# Dreambooth training")
-    gr.Markdown("Customize Stable Diffusion by giving it with few-shot examples")
-    with gr.Row():
-        type_of_thing = gr.Dropdown(label="What would you like to train?", choices=["object", "person", "style"], value="object", interactive=True)
-       
-    with gr.Row():
-        with gr.Column():
-            thing_description = gr.Markdown("You are going to train an `object`, upload 5-10 images of the object you are planning on training on from different angles/perspectives. You must have the right to do so and you are liable for the images you use, example:")
-            thing_image_example = gr.HTML('''<img src="file/cat-toy.png" />''')
-            things_naming = gr.Markdown("You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `cttoy` here). Images will be automatically cropped to 512x512.")
-        with gr.Column():
-            file_collection = []
-            concept_collection = []
-            buttons_collection = []
-            delete_collection = []
-            is_visible = []
-
-            row = [None] * maximum_concepts
-            for x in range(maximum_concepts):
-                ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4])
-                if(x == 0):
-                    visible = True
-                    is_visible.append(gr.State(value=True))
-                else:
-                    visible = False
-                    is_visible.append(gr.State(value=False))
-
-                file_collection.append(gr.File(label=f"Upload the images for your {ordinal(x+1)} concept", file_count="multiple", interactive=True, visible=visible))
-                with gr.Column(visible=visible) as row[x]:
-                    concept_collection.append(gr.Textbox(label=f"{ordinal(x+1)} concept prompt - use a unique, made up word to avoid collisions"))  
-                    with gr.Row():
-                        if(x < maximum_concepts-1):
-                            buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible))
-                        if(x > 0):
-                            delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept"))
-            
-            counter_add = 1
-            for button in buttons_collection:
-                if(counter_add < len(buttons_collection)):
-                    button.click(lambda:
-                    [gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None],
-                    None, 
-                    [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]], queue=False)
-                else:
-                    button.click(lambda:[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), True], None, [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], is_visible[counter_add]], queue=False)
-                counter_add += 1
-            
-            counter_delete = 1
-            for delete_button in delete_collection:
-                if(counter_delete < len(delete_collection)+1):
-                    delete_button.click(lambda:[gr.update(visible=False),gr.update(visible=False), gr.update(visible=True), False], None, [file_collection[counter_delete], row[counter_delete], buttons_collection[counter_delete-1], is_visible[counter_delete]], queue=False)
-                counter_delete += 1
-            
-            
-            
-    with gr.Accordion("Custom Settings", open=False):
-        swap_auto_calculated = gr.Checkbox(label="Use custom settings")
-        gr.Markdown("If not checked, the number of steps and % of frozen encoder will be tuned automatically according to the amount of images you upload and whether you are training an `object`, `person` or `style` as follows: The number of steps is calculated by number of images uploaded multiplied by 20. The text-encoder is frozen after 10% of the steps for a style, 30% of the steps for an object and is fully trained for persons.")
-        steps = gr.Number(label="How many steps", value=800)
-        perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30)
-
-    type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False)
-    training_summary = gr.Textbox("", visible=False, label="Training Summary")
-    steps.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
-    perc_txt_encoder.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
-    for file in file_collection:
-        file.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
-    train_btn = gr.Button("Start Training")
-    with gr.Box(visible=False) as try_your_model:
-        gr.Markdown("## Try your model")
-        with gr.Row():
-            prompt = gr.Textbox(label="Type your prompt")
-            result_image = gr.Image()
-        generate_button = gr.Button("Generate Image")
-    with gr.Box(visible=False) as push_to_hub:
-        gr.Markdown("## Push to Hugging Face Hub")
-        model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style")
-        where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to")
-        gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.")
-        hf_token = gr.Textbox(label="Hugging Face Write Token")
-        push_button = gr.Button("Push to the Hub")
-    result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
-    success_message_upload = gr.Markdown(visible=False)
-    convert_button = gr.Button("Convert to CKPT", visible=False)
-
-    train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button])
-    generate_button.click(fn=generate, inputs=prompt, outputs=result_image)
-    push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token], outputs=[success_message_upload, result])
-    convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result)
-demo.launch()

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

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

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/mobilenet-v2_4xb32_2000e_3c_noF/mobilenet-v2_1xb32_300e_3c_noF.py

@@ -1,140 +0,0 @@
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(type='MobileNetV2', widen_factor=1.0),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=7,
-        in_channels=1280,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-        topk=(
-            1,
-            3,
-        )))
-dataset_type = 'CustomDataset'
-data_preprocessor = dict(
-    num_classes=7,
-    mean=[
-        123.675,
-        116.28,
-        103.53,
-    ],
-    std=[
-        58.395,
-        57.12,
-        57.375,
-    ],
-    to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='RandomResizedCrop', scale=224, backend='pillow'),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='PackInputs'),
-]
-val_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
-    dict(type='CenterCrop', crop_size=224),
-    dict(type='PackInputs'),
-]
-train_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='data',
-        with_label=True,
-        ann_file='',
-        data_prefix='train',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='RandomResizedCrop', scale=224, backend='pillow'),
-            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-            dict(type='PackInputs'),
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=True))
-val_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='data',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
-            dict(type='CenterCrop', crop_size=224),
-            dict(type='PackInputs'),
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=False))
-val_evaluator = dict(
-    type='Accuracy', topk=(
-        1,
-        3,
-    ))
-test_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='data',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
-            dict(type='CenterCrop', crop_size=224),
-            dict(type='PackInputs'),
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=False))
-test_evaluator = dict(
-    type='Accuracy', topk=(
-        1,
-        3,
-    ))
-optim_wrapper = dict(
-    optimizer=dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=4e-05))
-param_scheduler = dict(type='StepLR', by_epoch=True, step_size=10, gamma=0.98)
-train_cfg = dict(by_epoch=True, max_epochs=2000, val_interval=10)
-val_cfg = dict()
-test_cfg = dict()
-auto_scale_lr = dict(base_batch_size=256)
-default_scope = 'mmpretrain'
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=10),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(type='CheckpointHook', save_best='auto', interval=10),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    visualization=dict(type='VisualizationHook', enable=False))
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'))
-vis_backends = [
-    dict(type='LocalVisBackend'),
-]
-visualizer = dict(
-    type='UniversalVisualizer',
-    vis_backends=[
-        dict(type='LocalVisBackend'),
-        dict(type='WandbVisBackend'),
-    ])
-log_level = 'INFO'
-load_from = None
-resume = False
-randomness = dict(seed=None, deterministic=False)
-launcher = 'pytorch'
-work_dir = 'work_dirs/mobilenet-v2_4xb32_2000e_3c_noF'

spaces/AchyuthGamer/Free-Accounts-Generator/fortnite/js/d140ouchebag.js

@@ -1,65 +0,0 @@
-var NumberOfWords = 45
-var words = new BuildArray(NumberOfWords)
-
-// Use the following variables to 
-// define your random words:
-words[1] = "https://cuty.io/NVohC0B"
-words[2] = "https://cuty.io/ApmaP7LV"
-words[3] = "https://cuty.io/fnacc3"
-words[4] = "https://cuty.io/5xDANQ5"
-words[5] = "https://cuty.io/fnacc5"
-words[6] = "https://cuty.io/qgeg"
-words[7] = "https://cuty.io/fnacc7"
-words[8] = "https://cuty.io/fnacc8"
-words[9] = "https://cuty.io/fnacc9"
-words[10] = "https://cuty.io/utMTVJooF"
-words[11] = "https://cuty.io/b39f"
-words[12] = "https://cuty.io/fnacc12"
-words[13] = "https://cuty.io/szZEPhy78v"
-words[14] = "https://cuty.io/fnacc14"
-words[15] = "https://cuty.io/eUaQe"
-words[16] = "https://cuty.io/VRUGIe"
-words[17] = "https://cuty.io/l6wa"
-words[18] = "https://cuty.io/WnlwopvX"
-words[19] = "https://cuty.io/sHMps1"
-words[20] = "https://cuty.io/j0Am8PZnBKkg"
-words[21] = "https://cuty.io/gT2uasHcl"
-words[22] = "https://cuty.io/UVRGq1f"
-words[23] = "https://cuty.io/six3gSRXEll"
-words[24] = "https://cuty.io/eDLT"
-words[25] = "https://cuty.io/pSvYxDQKV1NV"
-words[26] = "https://cuty.io/GNJniEyoC4"
-words[27] = "https://cuty.io/Hr3cPonuhQ"
-words[28] = "https://cuty.io/QGEzeBeD"
-words[29] = "https://cuty.io/b0apHN"
-words[30] = ""
-words[31] = ""
-words[32] = "https://cuty.io/OWtYHuEyL"
-words[33] = ""
-words[34] = "https://cuty.io/kQRXj"
-words[35] = ""
-words[36] = "https://cuty.io/CAJtlKvjX"
-words[37] = "https://cuty.io/PwMVd"
-words[38] = ""
-words[39] = "https://cuty.io/U4wgd"
-words[40] = ""
-words[41] = "https://cuty.io/SwTU5"
-words[42] = "https://cuty.io/r5Hryv6IV2Eh"
-words[43] = ""
-words[44] = "https://cuty.io/EuxDqLR0oFT"
-words[45] = "https://cuty.io/lflibkkVkK"
-
-function BuildArray(size){
-this.length = size
-for (var i = 1; i <= size; i++){
-this[i] = null}
-return this
-}
-
-function PickRandomWord(frm) {
-// Generate a random number between 1 and NumberOfWords
-var rnd = Math.ceil(Math.random() * NumberOfWords)
-
-// Display the word inside the text box
-frm.WordBox.value = words[rnd]
-}

spaces/AchyuthGamer/OpenGPT/client/css/select.css

@@ -1,35 +0,0 @@
-select {
-	-webkit-border-radius: 8px;
-	-moz-border-radius: 8px;
-	border-radius: 8px;
-
-	-webkit-backdrop-filter: blur(20px);
-	backdrop-filter: blur(20px);
-
-	cursor: pointer;
-	background-color: var(--blur-bg);
-	border: 1px solid var(--blur-border);
-	color: var(--colour-3);
-	display: block;
-	position: relative;
-	overflow: hidden;
-	outline: none;
-	padding: 8px 16px;
-
-	appearance: none;
-}
-
-/* scrollbar */
-select.dropdown::-webkit-scrollbar {
-	width: 4px;
-	padding: 8px 0px;
-}
-
-select.dropdown::-webkit-scrollbar-track {
-	background-color: #ffffff00;
-}
-
-select.dropdown::-webkit-scrollbar-thumb {
-	background-color: #555555;
-	border-radius: 10px;
-}

spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/__init__.py

@@ -1,14 +0,0 @@
-from .AiService     import AiService
-from .CodeLinkAva   import CodeLinkAva
-from .DfeHub        import DfeHub
-from .EasyChat      import EasyChat
-from .Forefront     import Forefront
-from .GetGpt        import GetGpt
-from .Opchatgpts    import Opchatgpts
-from .Lockchat      import Lockchat
-from .Wewordle      import Wewordle
-from .Equing        import Equing
-from .Wuguokai      import Wuguokai
-from .V50           import V50
-from .FastGpt       import FastGpt
-from .ChatgptLogin  import ChatgptLogin

spaces/AgentVerse/agentVerse/dataloader/mgsm.py

@@ -1,23 +0,0 @@
-from .dataloader import DataLoader
-from . import dataloader_registry
-import json
-import re
-
-
-@dataloader_registry.register("tasksolving/mgsm/gpt-4")
-@dataloader_registry.register("tasksolving/mgsm/gpt-3.5")
-class MGSMLoader(DataLoader):
-    def __init__(self, path: str):
-        self.answer_pat = re.compile(r"#### (-?\d+)")
-        super().__init__(path)
-
-    def load(self):
-        with open(self.path) as f:
-            for line in f:
-                line = json.loads(line)
-                self.examples.append(
-                    {
-                        "input": line["question"],
-                        "answer": line["answer_number"],
-                    }
-                )

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/input/OnPanPad.js

@@ -1,90 +0,0 @@
-import IsLocalPointInKnob from './IsLocalPointInKnob.js';
-
-const GetAngle = Phaser.Math.Angle.Between;
-const WrapAngle = Phaser.Math.Angle.Wrap;
-
-var OnPointerDown = function (pointer, localX, localY) {
-    if ((!this.enable) || (this.panPointer)) {
-        return;
-    }
-    var knob = this.sizerChildren.knob;
-    if (!IsLocalPointInKnob(knob, localX, localY)) {
-        return;
-    }
-
-    OnPanStart.call(this, pointer);
-}
-
-var OnPointerMove = function (pointer, localX, localY) {
-    if (!this.enable) {
-        return;
-    }
-    if (!pointer.isDown) {
-        return;
-    }
-
-    var knob = this.sizerChildren.knob;
-    switch (this.panState) {
-        case TOUCH0:
-            if (IsLocalPointInKnob(knob, localX, localY)) {
-                OnPanStart.call(this, pointer);
-            }
-            break;
-
-        case TOUCH1:
-            if (IsLocalPointInKnob(knob, localX, localY)) {
-                OnPan.call(this);
-            } else {
-                OnPanEnd.call(this);
-            }
-            break;
-    }
-}
-
-var OnPointerUp = function (pointer, localX, localY) {
-    if ((!this.enable) || (this.panPointer !== pointer)) {
-        return;
-    }
-
-    OnPanEnd.call(this);
-}
-
-var OnPanStart = function (pointer) {
-    this.panPointer = pointer;
-    this.panState = TOUCH1;
-}
-
-var OnPanEnd = function () {
-    this.panPointer = undefined;
-    this.panState = TOUCH0;
-}
-
-var OnPan = function () {
-    var p0 = this.panPointer.prevPosition,
-        p1 = this.panPointer.position;
-    var knob = this.sizerChildren.knob;
-    var startAngle = GetAngle(knob.x, knob.y, p0.x, p0.y),
-        endAngle = GetAngle(knob.x, knob.y, p1.x, p1.y);
-    var deltaAngle = (knob.anticlockwise) ? (startAngle - endAngle) : (endAngle - startAngle);
-    var deltaValue = WrapAngle(deltaAngle) / (Math.PI * 2);
-
-    this.stopEaseValue();
-    this.value += deltaValue;
-}
-
-const TOUCH0 = 0;
-const TOUCH1 = 1;
-
-var InstallEvents = function () {
-    var knob = this.sizerChildren.knob;
-    knob
-        .on('pointerdown', OnPointerDown, this)
-        .on('pointermove', OnPointerMove, this)
-        .on('pointerup', OnPointerUp, this)
-        .setInteractive()
-
-    this.panPointer = undefined;
-    this.panState = TOUCH0;
-}
-
-export default InstallEvents;

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

@@ -1,30 +0,0 @@
-import MergeStyle from './MergeStyle.js';
-import ReplaceChildrenConfig from './ReplaceChildrenConfig.js';
-
-var CreateAnySizer = function (scene, data, view, styles, customBuilders, SizerClass) {
-    data = MergeStyle(data, styles);
-
-    var backgroundConfig = ReplaceChildrenConfig(scene, data.background, view, styles, customBuilders);
-    var childrenConfig = ReplaceChildrenConfig(scene, data.children, view, styles, customBuilders);
-
-    var gameObject = new SizerClass(scene, data);
-    scene.add.existing(gameObject);
-
-    if (backgroundConfig) {
-        for (var i = 0, cnt = backgroundConfig.length; i < cnt; i++) {
-            var childConfig = backgroundConfig[i];
-            gameObject.addBackground(childConfig.$child, childConfig.padding);
-        }
-    }
-
-    if (childrenConfig) {
-        for (var i = 0, cnt = childrenConfig.length; i < cnt; i++) {
-            var childConfig = childrenConfig[i];
-            gameObject.add(childConfig.$child, childConfig);
-        }
-    }
-
-    return gameObject;
-}
-
-export default CreateAnySizer;

spaces/Aki004/herta-so-vits/modules/crepe.py

@@ -1,331 +0,0 @@
-from typing import Optional,Union
-try:
-    from typing import Literal
-except Exception as e:
-    from typing_extensions import Literal
-import numpy as np
-import torch
-import torchcrepe
-from torch import nn
-from torch.nn import functional as F
-import scipy
-
-#from:https://github.com/fishaudio/fish-diffusion
-
-def repeat_expand(
-    content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
-):
-    """Repeat content to target length.
-    This is a wrapper of torch.nn.functional.interpolate.
-
-    Args:
-        content (torch.Tensor): tensor
-        target_len (int): target length
-        mode (str, optional): interpolation mode. Defaults to "nearest".
-
-    Returns:
-        torch.Tensor: tensor
-    """
-
-    ndim = content.ndim
-
-    if content.ndim == 1:
-        content = content[None, None]
-    elif content.ndim == 2:
-        content = content[None]
-
-    assert content.ndim == 3
-
-    is_np = isinstance(content, np.ndarray)
-    if is_np:
-        content = torch.from_numpy(content)
-
-    results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
-
-    if is_np:
-        results = results.numpy()
-
-    if ndim == 1:
-        return results[0, 0]
-    elif ndim == 2:
-        return results[0]
-
-
-class BasePitchExtractor:
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        keep_zeros: bool = True,
-    ):
-        """Base pitch extractor.
-
-        Args:
-            hop_length (int, optional): Hop length. Defaults to 512.
-            f0_min (float, optional): Minimum f0. Defaults to 50.0.
-            f0_max (float, optional): Maximum f0. Defaults to 1100.0.
-            keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
-        """
-
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.keep_zeros = keep_zeros
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        raise NotImplementedError("BasePitchExtractor is not callable.")
-
-    def post_process(self, x, sampling_rate, f0, pad_to):
-        if isinstance(f0, np.ndarray):
-            f0 = torch.from_numpy(f0).float().to(x.device)
-
-        if pad_to is None:
-            return f0
-
-        f0 = repeat_expand(f0, pad_to)
-
-        if self.keep_zeros:
-            return f0
-        
-        vuv_vector = torch.zeros_like(f0)
-        vuv_vector[f0 > 0.0] = 1.0
-        vuv_vector[f0 <= 0.0] = 0.0
-        
-        # Remove 0 frequency and apply linear interpolation
-        nzindex = torch.nonzero(f0).squeeze()
-        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
-        time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
-        time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
-
-        if f0.shape[0] <= 0:
-            return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
-
-        if f0.shape[0] == 1:
-            return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
-    
-        # Probably can be rewritten with torch?
-        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
-        vuv_vector = vuv_vector.cpu().numpy()
-        vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
-        
-        return f0,vuv_vector
-
-
-class MaskedAvgPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of mean pooling that supports masked values.
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedAvgPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        # Apply the mask by setting masked elements to zero, or make NaNs zero
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        # Ensure mask has the same shape as the input tensor
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-        # Create a ones kernel with the same number of channels as the input tensor
-        ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
-
-        # Perform sum pooling
-        sum_pooled = nn.functional.conv1d(
-            masked_x,
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-
-        # Count the non-masked (valid) elements in each pooling window
-        valid_count = nn.functional.conv1d(
-            mask.float(),
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-        valid_count = valid_count.clamp(min=1)  # Avoid division by zero
-
-        # Perform masked average pooling
-        avg_pooled = sum_pooled / valid_count
-
-        # Fill zero values with NaNs
-        avg_pooled[avg_pooled == 0] = float("nan")
-
-        if ndim == 2:
-            return avg_pooled.squeeze(1)
-
-        return avg_pooled
-
-
-class MaskedMedianPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of median pooling that supports masked values.
-
-        This implementation is inspired by the median pooling implementation in
-        https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedMedianPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-
-        x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
-        mask = F.pad(
-            mask.float(), (self.padding, self.padding), mode="constant", value=0
-        )
-
-        x = x.unfold(2, self.kernel_size, self.stride)
-        mask = mask.unfold(2, self.kernel_size, self.stride)
-
-        x = x.contiguous().view(x.size()[:3] + (-1,))
-        mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
-
-        # Combine the mask with the input tensor
-        #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
-        x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
-
-        # Sort the masked tensor along the last dimension
-        x_sorted, _ = torch.sort(x_masked, dim=-1)
-
-        # Compute the count of non-masked (valid) values
-        valid_count = mask.sum(dim=-1)
-
-        # Calculate the index of the median value for each pooling window
-        median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
-
-        # Gather the median values using the calculated indices
-        median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
-
-        # Fill infinite values with NaNs
-        median_pooled[torch.isinf(median_pooled)] = float("nan")
-        
-        if ndim == 2:
-            return median_pooled.squeeze(1)
-
-        return median_pooled
-
-
-class CrepePitchExtractor(BasePitchExtractor):
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        threshold: float = 0.05,
-        keep_zeros: bool = False,
-        device = None,
-        model: Literal["full", "tiny"] = "full",
-        use_fast_filters: bool = True,
-    ):
-        super().__init__(hop_length, f0_min, f0_max, keep_zeros)
-
-        self.threshold = threshold
-        self.model = model
-        self.use_fast_filters = use_fast_filters
-        self.hop_length = hop_length
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        if self.use_fast_filters:
-            self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
-            self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        """Extract pitch using crepe.
-
-
-        Args:
-            x (torch.Tensor): Audio signal, shape (1, T).
-            sampling_rate (int, optional): Sampling rate. Defaults to 44100.
-            pad_to (int, optional): Pad to length. Defaults to None.
-
-        Returns:
-            torch.Tensor: Pitch, shape (T // hop_length,).
-        """
-
-        assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
-        assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
-
-        x = x.to(self.dev)
-        f0, pd = torchcrepe.predict(
-            x,
-            sampling_rate,
-            self.hop_length,
-            self.f0_min,
-            self.f0_max,
-            pad=True,
-            model=self.model,
-            batch_size=1024,
-            device=x.device,
-            return_periodicity=True,
-        )
-
-        # Filter, remove silence, set uv threshold, refer to the original warehouse readme
-        if self.use_fast_filters:
-            pd = self.median_filter(pd)
-        else:
-            pd = torchcrepe.filter.median(pd, 3)
-
-        pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
-        f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
-        
-        if self.use_fast_filters:
-            f0 = self.mean_filter(f0)
-        else:
-            f0 = torchcrepe.filter.mean(f0, 3)
-
-        f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
-
-        if torch.all(f0 == 0):
-            rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to)
-            return rtn,rtn
-        
-        return self.post_process(x, sampling_rate, f0, pad_to)

spaces/Akshat231/super_space/README.md

@@ -1,12 +0,0 @@
----
-title: Super Space
-emoji: 🏃
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlterM/Zaglyt2-transformer-test/app.py

@@ -1,14 +0,0 @@
-import gradio as gr
-import net
-
-def generate(text):
-    o = text
-    r = []
-    for i in range(5):
-        t = net.gen(o)
-        o += " " + t
-        r.append(t)
-    return text + " *"+' '.join(r)+"*"
-
-iface = gr.Interface(fn=generate, inputs="text", outputs="text")
-iface.launch()

spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py

@@ -1,2 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))

spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py

@@ -1,11 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        norm_eval=False,
-        plugins=[
-            dict(
-                cfg=dict(type='ContextBlock', ratio=1. / 16),
-                stages=(False, True, True, True),
-                position='after_conv3')
-        ]))

spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py

@@ -1,65 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    rpn_head=dict(
-        _delete_=True,
-        type='GARPNHead',
-        in_channels=256,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=8,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[8],
-            strides=[4, 8, 16, 32, 64]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.14, 0.14]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.11, 0.11]),
-        loc_filter_thr=0.01,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
-    roi_head=dict(
-        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            ga_assigner=dict(
-                type='ApproxMaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                ignore_iof_thr=-1),
-            ga_sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            center_ratio=0.2,
-            ignore_ratio=0.5),
-        rpn_proposal=dict(nms_post=1000, max_per_img=300),
-        rcnn=dict(
-            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
-            sampler=dict(type='RandomSampler', num=256))),
-    test_cfg=dict(
-        rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3)))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

spaces/Andy1621/uniformer_image_detection/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py

@@ -1,97 +0,0 @@
-_base_ = [
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-model = dict(
-    type='NASFCOS',
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=False, eps=0),
-        style='caffe'),
-    neck=dict(
-        type='NASFCOS_FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        start_level=1,
-        add_extra_convs=True,
-        num_outs=5,
-        norm_cfg=dict(type='BN'),
-        conv_cfg=dict(type='DCNv2', deform_groups=2)),
-    bbox_head=dict(
-        type='NASFCOSHead',
-        num_classes=80,
-        in_channels=256,
-        feat_channels=256,
-        strides=[8, 16, 32, 64, 128],
-        norm_cfg=dict(type='GN', num_groups=32),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
-        loss_centerness=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
-    train_cfg=dict(
-        assigner=dict(
-            type='MaxIoUAssigner',
-            pos_iou_thr=0.5,
-            neg_iou_thr=0.4,
-            min_pos_iou=0,
-            ignore_iof_thr=-1),
-        allowed_border=-1,
-        pos_weight=-1,
-        debug=False),
-    test_cfg=dict(
-        nms_pre=1000,
-        min_bbox_size=0,
-        score_thr=0.05,
-        nms=dict(type='nms', iou_threshold=0.6),
-        max_per_img=100))
-
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=2,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-
-optimizer = dict(
-    lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnet18_v1c',
-    backbone=dict(depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/benchmark.py

@@ -1,72 +0,0 @@
-"""
-This module implements a benchmark function to evaluate the performance of the embedding pipeline. It expects a configuration JSON file. It must have questions and expected retrieved text.
-For each question, it's essential to have variants of that question. Language is fluid and each person might have their own spin on how they may ask it.
-
-At the end, it will save the results inside a benchmark_{sysdate}.txt file in the main directory.
-
-The benchmark function will return the score as an integer.
-"""
-import datetime
-import json
-import os
-
-from pathlib import Path
-
-from .data_processor import process_and_add_to_collector, preprocess_text
-from .parameters import get_chunk_count, get_max_token_count
-from .utils import create_metadata_source
-
-def benchmark(config_path, collector):
-    # Get the current system date
-    sysdate = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"benchmark_{sysdate}.txt"
-    
-    # Open the log file in append mode
-    with open(filename, 'a') as log:
-        with open(config_path, 'r') as f:
-            data = json.load(f)
-        
-        total_points = 0
-        max_points = 0
-
-        for item in data:
-            filepath = item["text"]
-            corpus = ""
-
-            # Check if the file exists
-            if os.path.isfile(Path(filepath)):
-                # Open the file and read its content
-                with open(Path(filepath), 'r') as file:
-                    corpus = file.read()
-                process_and_add_to_collector(corpus, collector, True, create_metadata_source('benchmark'))
-            else:
-                raise f'Cannot find specified file {filepath}.'
-
-            for question_group in item["questions"]:
-                question_variants = question_group["question_variants"]
-                criteria = question_group["criteria"]
-                
-                for q in question_variants:
-                    max_points += len(criteria)
-                    processed_text = preprocess_text(q)
-
-                    # Get the most similar chunks
-                    results = collector.get_sorted_by_dist(processed_text, n_results=get_chunk_count(), max_token_count=get_max_token_count())
-
-                    points = 0
-                    
-                    for c in criteria:
-                        for p in results:
-                            if c in p:
-                                points += 1
-                                total_points += 1
-                                break
-
-                    info = f"The question '{q}' scored {points}/{len(criteria)} points."
-                    print(info, file=log)
-
-                print('\n---\n', file=log)
-
-        print(f'##Total points:\n\n{total_points}/{max_points}', file=log)
-
-    return total_points, max_points

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/file_client.py

@@ -1,1148 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import inspect
-import os
-import os.path as osp
-import re
-import tempfile
-import warnings
-from abc import ABCMeta, abstractmethod
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Iterable, Iterator, Optional, Tuple, Union
-from urllib.request import urlopen
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.utils.misc import has_method
-from annotator.uniformer.mmcv.utils.path import is_filepath
-
-
-class BaseStorageBackend(metaclass=ABCMeta):
-    """Abstract class of storage backends.
-
-    All backends need to implement two apis: ``get()`` and ``get_text()``.
-    ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
-    as texts.
-    """
-
-    # a flag to indicate whether the backend can create a symlink for a file
-    _allow_symlink = False
-
-    @property
-    def name(self):
-        return self.__class__.__name__
-
-    @property
-    def allow_symlink(self):
-        return self._allow_symlink
-
-    @abstractmethod
-    def get(self, filepath):
-        pass
-
-    @abstractmethod
-    def get_text(self, filepath):
-        pass
-
-
-class CephBackend(BaseStorageBackend):
-    """Ceph storage backend (for internal use).
-
-    Args:
-        path_mapping (dict|None): path mapping dict from local path to Petrel
-            path. When ``path_mapping={'src': 'dst'}``, ``src`` in ``filepath``
-            will be replaced by ``dst``. Default: None.
-
-    .. warning::
-        :class:`mmcv.fileio.file_client.CephBackend` will be deprecated,
-        please use :class:`mmcv.fileio.file_client.PetrelBackend` instead.
-    """
-
-    def __init__(self, path_mapping=None):
-        try:
-            import ceph
-        except ImportError:
-            raise ImportError('Please install ceph to enable CephBackend.')
-
-        warnings.warn(
-            'CephBackend will be deprecated, please use PetrelBackend instead')
-        self._client = ceph.S3Client()
-        assert isinstance(path_mapping, dict) or path_mapping is None
-        self.path_mapping = path_mapping
-
-    def get(self, filepath):
-        filepath = str(filepath)
-        if self.path_mapping is not None:
-            for k, v in self.path_mapping.items():
-                filepath = filepath.replace(k, v)
-        value = self._client.Get(filepath)
-        value_buf = memoryview(value)
-        return value_buf
-
-    def get_text(self, filepath, encoding=None):
-        raise NotImplementedError
-
-
-class PetrelBackend(BaseStorageBackend):
-    """Petrel storage backend (for internal use).
-
-    PetrelBackend supports reading and writing data to multiple clusters.
-    If the file path contains the cluster name, PetrelBackend will read data
-    from specified cluster or write data to it. Otherwise, PetrelBackend will
-    access the default cluster.
-
-    Args:
-        path_mapping (dict, optional): Path mapping dict from local path to
-            Petrel path. When ``path_mapping={'src': 'dst'}``, ``src`` in
-            ``filepath`` will be replaced by ``dst``. Default: None.
-        enable_mc (bool, optional): Whether to enable memcached support.
-            Default: True.
-
-    Examples:
-        >>> filepath1 = 's3://path/of/file'
-        >>> filepath2 = 'cluster-name:s3://path/of/file'
-        >>> client = PetrelBackend()
-        >>> client.get(filepath1)  # get data from default cluster
-        >>> client.get(filepath2)  # get data from 'cluster-name' cluster
-    """
-
-    def __init__(self,
-                 path_mapping: Optional[dict] = None,
-                 enable_mc: bool = True):
-        try:
-            from petrel_client import client
-        except ImportError:
-            raise ImportError('Please install petrel_client to enable '
-                              'PetrelBackend.')
-
-        self._client = client.Client(enable_mc=enable_mc)
-        assert isinstance(path_mapping, dict) or path_mapping is None
-        self.path_mapping = path_mapping
-
-    def _map_path(self, filepath: Union[str, Path]) -> str:
-        """Map ``filepath`` to a string path whose prefix will be replaced by
-        :attr:`self.path_mapping`.
-
-        Args:
-            filepath (str): Path to be mapped.
-        """
-        filepath = str(filepath)
-        if self.path_mapping is not None:
-            for k, v in self.path_mapping.items():
-                filepath = filepath.replace(k, v)
-        return filepath
-
-    def _format_path(self, filepath: str) -> str:
-        """Convert a ``filepath`` to standard format of petrel oss.
-
-        If the ``filepath`` is concatenated by ``os.path.join``, in a Windows
-        environment, the ``filepath`` will be the format of
-        's3://bucket_name\\image.jpg'. By invoking :meth:`_format_path`, the
-        above ``filepath`` will be converted to 's3://bucket_name/image.jpg'.
-
-        Args:
-            filepath (str): Path to be formatted.
-        """
-        return re.sub(r'\\+', '/', filepath)
-
-    def get(self, filepath: Union[str, Path]) -> memoryview:
-        """Read data from a given ``filepath`` with 'rb' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-
-        Returns:
-            memoryview: A memory view of expected bytes object to avoid
-                copying. The memoryview object can be converted to bytes by
-                ``value_buf.tobytes()``.
-        """
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        value = self._client.Get(filepath)
-        value_buf = memoryview(value)
-        return value_buf
-
-    def get_text(self,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> str:
-        """Read data from a given ``filepath`` with 'r' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-
-        Returns:
-            str: Expected text reading from ``filepath``.
-        """
-        return str(self.get(filepath), encoding=encoding)
-
-    def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
-        """Save data to a given ``filepath``.
-
-        Args:
-            obj (bytes): Data to be saved.
-            filepath (str or Path): Path to write data.
-        """
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        self._client.put(filepath, obj)
-
-    def put_text(self,
-                 obj: str,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> None:
-        """Save data to a given ``filepath``.
-
-        Args:
-            obj (str): Data to be written.
-            filepath (str or Path): Path to write data.
-            encoding (str): The encoding format used to encode the ``obj``.
-                Default: 'utf-8'.
-        """
-        self.put(bytes(obj, encoding=encoding), filepath)
-
-    def remove(self, filepath: Union[str, Path]) -> None:
-        """Remove a file.
-
-        Args:
-            filepath (str or Path): Path to be removed.
-        """
-        if not has_method(self._client, 'delete'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `delete` method, please use a higher version or dev'
-                 ' branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        self._client.delete(filepath)
-
-    def exists(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path exists.
-
-        Args:
-            filepath (str or Path): Path to be checked whether exists.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
-        """
-        if not (has_method(self._client, 'contains')
-                and has_method(self._client, 'isdir')):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `contains` and `isdir` methods, please use a higher'
-                 'version or dev branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        return self._client.contains(filepath) or self._client.isdir(filepath)
-
-    def isdir(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a directory.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a
-                directory.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a directory,
-                ``False`` otherwise.
-        """
-        if not has_method(self._client, 'isdir'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `isdir` method, please use a higher version or dev'
-                 ' branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        return self._client.isdir(filepath)
-
-    def isfile(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a file.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a file.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a file, ``False``
-                otherwise.
-        """
-        if not has_method(self._client, 'contains'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `contains` method, please use a higher version or '
-                 'dev branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        return self._client.contains(filepath)
-
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
-        """Concatenate all file paths.
-
-        Args:
-            filepath (str or Path): Path to be concatenated.
-
-        Returns:
-            str: The result after concatenation.
-        """
-        filepath = self._format_path(self._map_path(filepath))
-        if filepath.endswith('/'):
-            filepath = filepath[:-1]
-        formatted_paths = [filepath]
-        for path in filepaths:
-            formatted_paths.append(self._format_path(self._map_path(path)))
-        return '/'.join(formatted_paths)
-
-    @contextmanager
-    def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]:
-        """Download a file from ``filepath`` and return a temporary path.
-
-        ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
-        can be called with ``with`` statement, and when exists from the
-        ``with`` statement, the temporary path will be released.
-
-        Args:
-            filepath (str | Path): Download a file from ``filepath``.
-
-        Examples:
-            >>> client = PetrelBackend()
-            >>> # After existing from the ``with`` clause,
-            >>> # the path will be removed
-            >>> with client.get_local_path('s3://path/of/your/file') as path:
-            ...     # do something here
-
-        Yields:
-            Iterable[str]: Only yield one temporary path.
-        """
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        assert self.isfile(filepath)
-        try:
-            f = tempfile.NamedTemporaryFile(delete=False)
-            f.write(self.get(filepath))
-            f.close()
-            yield f.name
-        finally:
-            os.remove(f.name)
-
-    def list_dir_or_file(self,
-                         dir_path: Union[str, Path],
-                         list_dir: bool = True,
-                         list_file: bool = True,
-                         suffix: Optional[Union[str, Tuple[str]]] = None,
-                         recursive: bool = False) -> Iterator[str]:
-        """Scan a directory to find the interested directories or files in
-        arbitrary order.
-
-        Note:
-            Petrel has no concept of directories but it simulates the directory
-            hierarchy in the filesystem through public prefixes. In addition,
-            if the returned path ends with '/', it means the path is a public
-            prefix which is a logical directory.
-
-        Note:
-            :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-            In addition, the returned path of directory will not contains the
-            suffix '/' which is consistent with other backends.
-
-        Args:
-            dir_path (str | Path): Path of the directory.
-            list_dir (bool): List the directories. Default: True.
-            list_file (bool): List the path of files. Default: True.
-            suffix (str or tuple[str], optional):  File suffix
-                that we are interested in. Default: None.
-            recursive (bool): If set to True, recursively scan the
-                directory. Default: False.
-
-        Yields:
-            Iterable[str]: A relative path to ``dir_path``.
-        """
-        if not has_method(self._client, 'list'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `list` method, please use a higher version or dev'
-                 ' branch instead.'))
-
-        dir_path = self._map_path(dir_path)
-        dir_path = self._format_path(dir_path)
-        if list_dir and suffix is not None:
-            raise TypeError(
-                '`list_dir` should be False when `suffix` is not None')
-
-        if (suffix is not None) and not isinstance(suffix, (str, tuple)):
-            raise TypeError('`suffix` must be a string or tuple of strings')
-
-        # Petrel's simulated directory hierarchy assumes that directory paths
-        # should end with `/`
-        if not dir_path.endswith('/'):
-            dir_path += '/'
-
-        root = dir_path
-
-        def _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                              recursive):
-            for path in self._client.list(dir_path):
-                # the `self.isdir` is not used here to determine whether path
-                # is a directory, because `self.isdir` relies on
-                # `self._client.list`
-                if path.endswith('/'):  # a directory path
-                    next_dir_path = self.join_path(dir_path, path)
-                    if list_dir:
-                        # get the relative path and exclude the last
-                        # character '/'
-                        rel_dir = next_dir_path[len(root):-1]
-                        yield rel_dir
-                    if recursive:
-                        yield from _list_dir_or_file(next_dir_path, list_dir,
-                                                     list_file, suffix,
-                                                     recursive)
-                else:  # a file path
-                    absolute_path = self.join_path(dir_path, path)
-                    rel_path = absolute_path[len(root):]
-                    if (suffix is None
-                            or rel_path.endswith(suffix)) and list_file:
-                        yield rel_path
-
-        return _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                                 recursive)
-
-
-class MemcachedBackend(BaseStorageBackend):
-    """Memcached storage backend.
-
-    Attributes:
-        server_list_cfg (str): Config file for memcached server list.
-        client_cfg (str): Config file for memcached client.
-        sys_path (str | None): Additional path to be appended to `sys.path`.
-            Default: None.
-    """
-
-    def __init__(self, server_list_cfg, client_cfg, sys_path=None):
-        if sys_path is not None:
-            import sys
-            sys.path.append(sys_path)
-        try:
-            import mc
-        except ImportError:
-            raise ImportError(
-                'Please install memcached to enable MemcachedBackend.')
-
-        self.server_list_cfg = server_list_cfg
-        self.client_cfg = client_cfg
-        self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg,
-                                                      self.client_cfg)
-        # mc.pyvector servers as a point which points to a memory cache
-        self._mc_buffer = mc.pyvector()
-
-    def get(self, filepath):
-        filepath = str(filepath)
-        import mc
-        self._client.Get(filepath, self._mc_buffer)
-        value_buf = mc.ConvertBuffer(self._mc_buffer)
-        return value_buf
-
-    def get_text(self, filepath, encoding=None):
-        raise NotImplementedError
-
-
-class LmdbBackend(BaseStorageBackend):
-    """Lmdb storage backend.
-
-    Args:
-        db_path (str): Lmdb database path.
-        readonly (bool, optional): Lmdb environment parameter. If True,
-            disallow any write operations. Default: True.
-        lock (bool, optional): Lmdb environment parameter. If False, when
-            concurrent access occurs, do not lock the database. Default: False.
-        readahead (bool, optional): Lmdb environment parameter. If False,
-            disable the OS filesystem readahead mechanism, which may improve
-            random read performance when a database is larger than RAM.
-            Default: False.
-
-    Attributes:
-        db_path (str): Lmdb database path.
-    """
-
-    def __init__(self,
-                 db_path,
-                 readonly=True,
-                 lock=False,
-                 readahead=False,
-                 **kwargs):
-        try:
-            import lmdb
-        except ImportError:
-            raise ImportError('Please install lmdb to enable LmdbBackend.')
-
-        self.db_path = str(db_path)
-        self._client = lmdb.open(
-            self.db_path,
-            readonly=readonly,
-            lock=lock,
-            readahead=readahead,
-            **kwargs)
-
-    def get(self, filepath):
-        """Get values according to the filepath.
-
-        Args:
-            filepath (str | obj:`Path`): Here, filepath is the lmdb key.
-        """
-        filepath = str(filepath)
-        with self._client.begin(write=False) as txn:
-            value_buf = txn.get(filepath.encode('ascii'))
-        return value_buf
-
-    def get_text(self, filepath, encoding=None):
-        raise NotImplementedError
-
-
-class HardDiskBackend(BaseStorageBackend):
-    """Raw hard disks storage backend."""
-
-    _allow_symlink = True
-
-    def get(self, filepath: Union[str, Path]) -> bytes:
-        """Read data from a given ``filepath`` with 'rb' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-
-        Returns:
-            bytes: Expected bytes object.
-        """
-        with open(filepath, 'rb') as f:
-            value_buf = f.read()
-        return value_buf
-
-    def get_text(self,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> str:
-        """Read data from a given ``filepath`` with 'r' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-
-        Returns:
-            str: Expected text reading from ``filepath``.
-        """
-        with open(filepath, 'r', encoding=encoding) as f:
-            value_buf = f.read()
-        return value_buf
-
-    def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
-        """Write data to a given ``filepath`` with 'wb' mode.
-
-        Note:
-            ``put`` will create a directory if the directory of ``filepath``
-            does not exist.
-
-        Args:
-            obj (bytes): Data to be written.
-            filepath (str or Path): Path to write data.
-        """
-        mmcv.mkdir_or_exist(osp.dirname(filepath))
-        with open(filepath, 'wb') as f:
-            f.write(obj)
-
-    def put_text(self,
-                 obj: str,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> None:
-        """Write data to a given ``filepath`` with 'w' mode.
-
-        Note:
-            ``put_text`` will create a directory if the directory of
-            ``filepath`` does not exist.
-
-        Args:
-            obj (str): Data to be written.
-            filepath (str or Path): Path to write data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-        """
-        mmcv.mkdir_or_exist(osp.dirname(filepath))
-        with open(filepath, 'w', encoding=encoding) as f:
-            f.write(obj)
-
-    def remove(self, filepath: Union[str, Path]) -> None:
-        """Remove a file.
-
-        Args:
-            filepath (str or Path): Path to be removed.
-        """
-        os.remove(filepath)
-
-    def exists(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path exists.
-
-        Args:
-            filepath (str or Path): Path to be checked whether exists.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
-        """
-        return osp.exists(filepath)
-
-    def isdir(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a directory.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a
-                directory.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a directory,
-                ``False`` otherwise.
-        """
-        return osp.isdir(filepath)
-
-    def isfile(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a file.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a file.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a file, ``False``
-                otherwise.
-        """
-        return osp.isfile(filepath)
-
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
-        """Concatenate all file paths.
-
-        Join one or more filepath components intelligently. The return value
-        is the concatenation of filepath and any members of *filepaths.
-
-        Args:
-            filepath (str or Path): Path to be concatenated.
-
-        Returns:
-            str: The result of concatenation.
-        """
-        return osp.join(filepath, *filepaths)
-
-    @contextmanager
-    def get_local_path(
-            self, filepath: Union[str, Path]) -> Iterable[Union[str, Path]]:
-        """Only for unified API and do nothing."""
-        yield filepath
-
-    def list_dir_or_file(self,
-                         dir_path: Union[str, Path],
-                         list_dir: bool = True,
-                         list_file: bool = True,
-                         suffix: Optional[Union[str, Tuple[str]]] = None,
-                         recursive: bool = False) -> Iterator[str]:
-        """Scan a directory to find the interested directories or files in
-        arbitrary order.
-
-        Note:
-            :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-
-        Args:
-            dir_path (str | Path): Path of the directory.
-            list_dir (bool): List the directories. Default: True.
-            list_file (bool): List the path of files. Default: True.
-            suffix (str or tuple[str], optional):  File suffix
-                that we are interested in. Default: None.
-            recursive (bool): If set to True, recursively scan the
-                directory. Default: False.
-
-        Yields:
-            Iterable[str]: A relative path to ``dir_path``.
-        """
-        if list_dir and suffix is not None:
-            raise TypeError('`suffix` should be None when `list_dir` is True')
-
-        if (suffix is not None) and not isinstance(suffix, (str, tuple)):
-            raise TypeError('`suffix` must be a string or tuple of strings')
-
-        root = dir_path
-
-        def _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                              recursive):
-            for entry in os.scandir(dir_path):
-                if not entry.name.startswith('.') and entry.is_file():
-                    rel_path = osp.relpath(entry.path, root)
-                    if (suffix is None
-                            or rel_path.endswith(suffix)) and list_file:
-                        yield rel_path
-                elif osp.isdir(entry.path):
-                    if list_dir:
-                        rel_dir = osp.relpath(entry.path, root)
-                        yield rel_dir
-                    if recursive:
-                        yield from _list_dir_or_file(entry.path, list_dir,
-                                                     list_file, suffix,
-                                                     recursive)
-
-        return _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                                 recursive)
-
-
-class HTTPBackend(BaseStorageBackend):
-    """HTTP and HTTPS storage bachend."""
-
-    def get(self, filepath):
-        value_buf = urlopen(filepath).read()
-        return value_buf
-
-    def get_text(self, filepath, encoding='utf-8'):
-        value_buf = urlopen(filepath).read()
-        return value_buf.decode(encoding)
-
-    @contextmanager
-    def get_local_path(self, filepath: str) -> Iterable[str]:
-        """Download a file from ``filepath``.
-
-        ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
-        can be called with ``with`` statement, and when exists from the
-        ``with`` statement, the temporary path will be released.
-
-        Args:
-            filepath (str): Download a file from ``filepath``.
-
-        Examples:
-            >>> client = HTTPBackend()
-            >>> # After existing from the ``with`` clause,
-            >>> # the path will be removed
-            >>> with client.get_local_path('http://path/of/your/file') as path:
-            ...     # do something here
-        """
-        try:
-            f = tempfile.NamedTemporaryFile(delete=False)
-            f.write(self.get(filepath))
-            f.close()
-            yield f.name
-        finally:
-            os.remove(f.name)
-
-
-class FileClient:
-    """A general file client to access files in different backends.
-
-    The client loads a file or text in a specified backend from its path
-    and returns it as a binary or text file. There are two ways to choose a
-    backend, the name of backend and the prefix of path. Although both of them
-    can be used to choose a storage backend, ``backend`` has a higher priority
-    that is if they are all set, the storage backend will be chosen by the
-    backend argument. If they are all `None`, the disk backend will be chosen.
-    Note that It can also register other backend accessor with a given name,
-    prefixes, and backend class. In addition, We use the singleton pattern to
-    avoid repeated object creation. If the arguments are the same, the same
-    object will be returned.
-
-    Args:
-        backend (str, optional): The storage backend type. Options are "disk",
-            "ceph", "memcached", "lmdb", "http" and "petrel". Default: None.
-        prefix (str, optional): The prefix of the registered storage backend.
-            Options are "s3", "http", "https". Default: None.
-
-    Examples:
-        >>> # only set backend
-        >>> file_client = FileClient(backend='petrel')
-        >>> # only set prefix
-        >>> file_client = FileClient(prefix='s3')
-        >>> # set both backend and prefix but use backend to choose client
-        >>> file_client = FileClient(backend='petrel', prefix='s3')
-        >>> # if the arguments are the same, the same object is returned
-        >>> file_client1 = FileClient(backend='petrel')
-        >>> file_client1 is file_client
-        True
-
-    Attributes:
-        client (:obj:`BaseStorageBackend`): The backend object.
-    """
-
-    _backends = {
-        'disk': HardDiskBackend,
-        'ceph': CephBackend,
-        'memcached': MemcachedBackend,
-        'lmdb': LmdbBackend,
-        'petrel': PetrelBackend,
-        'http': HTTPBackend,
-    }
-    # This collection is used to record the overridden backends, and when a
-    # backend appears in the collection, the singleton pattern is disabled for
-    # that backend, because if the singleton pattern is used, then the object
-    # returned will be the backend before overwriting
-    _overridden_backends = set()
-    _prefix_to_backends = {
-        's3': PetrelBackend,
-        'http': HTTPBackend,
-        'https': HTTPBackend,
-    }
-    _overridden_prefixes = set()
-
-    _instances = {}
-
-    def __new__(cls, backend=None, prefix=None, **kwargs):
-        if backend is None and prefix is None:
-            backend = 'disk'
-        if backend is not None and backend not in cls._backends:
-            raise ValueError(
-                f'Backend {backend} is not supported. Currently supported ones'
-                f' are {list(cls._backends.keys())}')
-        if prefix is not None and prefix not in cls._prefix_to_backends:
-            raise ValueError(
-                f'prefix {prefix} is not supported. Currently supported ones '
-                f'are {list(cls._prefix_to_backends.keys())}')
-
-        # concatenate the arguments to a unique key for determining whether
-        # objects with the same arguments were created
-        arg_key = f'{backend}:{prefix}'
-        for key, value in kwargs.items():
-            arg_key += f':{key}:{value}'
-
-        # if a backend was overridden, it will create a new object
-        if (arg_key in cls._instances
-                and backend not in cls._overridden_backends
-                and prefix not in cls._overridden_prefixes):
-            _instance = cls._instances[arg_key]
-        else:
-            # create a new object and put it to _instance
-            _instance = super().__new__(cls)
-            if backend is not None:
-                _instance.client = cls._backends[backend](**kwargs)
-            else:
-                _instance.client = cls._prefix_to_backends[prefix](**kwargs)
-
-            cls._instances[arg_key] = _instance
-
-        return _instance
-
-    @property
-    def name(self):
-        return self.client.name
-
-    @property
-    def allow_symlink(self):
-        return self.client.allow_symlink
-
-    @staticmethod
-    def parse_uri_prefix(uri: Union[str, Path]) -> Optional[str]:
-        """Parse the prefix of a uri.
-
-        Args:
-            uri (str | Path): Uri to be parsed that contains the file prefix.
-
-        Examples:
-            >>> FileClient.parse_uri_prefix('s3://path/of/your/file')
-            's3'
-
-        Returns:
-            str | None: Return the prefix of uri if the uri contains '://'
-                else ``None``.
-        """
-        assert is_filepath(uri)
-        uri = str(uri)
-        if '://' not in uri:
-            return None
-        else:
-            prefix, _ = uri.split('://')
-            # In the case of PetrelBackend, the prefix may contains the cluster
-            # name like clusterName:s3
-            if ':' in prefix:
-                _, prefix = prefix.split(':')
-            return prefix
-
-    @classmethod
-    def infer_client(cls,
-                     file_client_args: Optional[dict] = None,
-                     uri: Optional[Union[str, Path]] = None) -> 'FileClient':
-        """Infer a suitable file client based on the URI and arguments.
-
-        Args:
-            file_client_args (dict, optional): Arguments to instantiate a
-                FileClient. Default: None.
-            uri (str | Path, optional): Uri to be parsed that contains the file
-                prefix. Default: None.
-
-        Examples:
-            >>> uri = 's3://path/of/your/file'
-            >>> file_client = FileClient.infer_client(uri=uri)
-            >>> file_client_args = {'backend': 'petrel'}
-            >>> file_client = FileClient.infer_client(file_client_args)
-
-        Returns:
-            FileClient: Instantiated FileClient object.
-        """
-        assert file_client_args is not None or uri is not None
-        if file_client_args is None:
-            file_prefix = cls.parse_uri_prefix(uri)  # type: ignore
-            return cls(prefix=file_prefix)
-        else:
-            return cls(**file_client_args)
-
-    @classmethod
-    def _register_backend(cls, name, backend, force=False, prefixes=None):
-        if not isinstance(name, str):
-            raise TypeError('the backend name should be a string, '
-                            f'but got {type(name)}')
-        if not inspect.isclass(backend):
-            raise TypeError(
-                f'backend should be a class but got {type(backend)}')
-        if not issubclass(backend, BaseStorageBackend):
-            raise TypeError(
-                f'backend {backend} is not a subclass of BaseStorageBackend')
-        if not force and name in cls._backends:
-            raise KeyError(
-                f'{name} is already registered as a storage backend, '
-                'add "force=True" if you want to override it')
-
-        if name in cls._backends and force:
-            cls._overridden_backends.add(name)
-        cls._backends[name] = backend
-
-        if prefixes is not None:
-            if isinstance(prefixes, str):
-                prefixes = [prefixes]
-            else:
-                assert isinstance(prefixes, (list, tuple))
-            for prefix in prefixes:
-                if prefix not in cls._prefix_to_backends:
-                    cls._prefix_to_backends[prefix] = backend
-                elif (prefix in cls._prefix_to_backends) and force:
-                    cls._overridden_prefixes.add(prefix)
-                    cls._prefix_to_backends[prefix] = backend
-                else:
-                    raise KeyError(
-                        f'{prefix} is already registered as a storage backend,'
-                        ' add "force=True" if you want to override it')
-
-    @classmethod
-    def register_backend(cls, name, backend=None, force=False, prefixes=None):
-        """Register a backend to FileClient.
-
-        This method can be used as a normal class method or a decorator.
-
-        .. code-block:: python
-
-            class NewBackend(BaseStorageBackend):
-
-                def get(self, filepath):
-                    return filepath
-
-                def get_text(self, filepath):
-                    return filepath
-
-            FileClient.register_backend('new', NewBackend)
-
-        or
-
-        .. code-block:: python
-
-            @FileClient.register_backend('new')
-            class NewBackend(BaseStorageBackend):
-
-                def get(self, filepath):
-                    return filepath
-
-                def get_text(self, filepath):
-                    return filepath
-
-        Args:
-            name (str): The name of the registered backend.
-            backend (class, optional): The backend class to be registered,
-                which must be a subclass of :class:`BaseStorageBackend`.
-                When this method is used as a decorator, backend is None.
-                Defaults to None.
-            force (bool, optional): Whether to override the backend if the name
-                has already been registered. Defaults to False.
-            prefixes (str or list[str] or tuple[str], optional): The prefixes
-                of the registered storage backend. Default: None.
-                `New in version 1.3.15.`
-        """
-        if backend is not None:
-            cls._register_backend(
-                name, backend, force=force, prefixes=prefixes)
-            return
-
-        def _register(backend_cls):
-            cls._register_backend(
-                name, backend_cls, force=force, prefixes=prefixes)
-            return backend_cls
-
-        return _register
-
-    def get(self, filepath: Union[str, Path]) -> Union[bytes, memoryview]:
-        """Read data from a given ``filepath`` with 'rb' mode.
-
-        Note:
-            There are two types of return values for ``get``, one is ``bytes``
-            and the other is ``memoryview``. The advantage of using memoryview
-            is that you can avoid copying, and if you want to convert it to
-            ``bytes``, you can use ``.tobytes()``.
-
-        Args:
-            filepath (str or Path): Path to read data.
-
-        Returns:
-            bytes | memoryview: Expected bytes object or a memory view of the
-                bytes object.
-        """
-        return self.client.get(filepath)
-
-    def get_text(self, filepath: Union[str, Path], encoding='utf-8') -> str:
-        """Read data from a given ``filepath`` with 'r' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-
-        Returns:
-            str: Expected text reading from ``filepath``.
-        """
-        return self.client.get_text(filepath, encoding)
-
-    def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
-        """Write data to a given ``filepath`` with 'wb' mode.
-
-        Note:
-            ``put`` should create a directory if the directory of ``filepath``
-            does not exist.
-
-        Args:
-            obj (bytes): Data to be written.
-            filepath (str or Path): Path to write data.
-        """
-        self.client.put(obj, filepath)
-
-    def put_text(self, obj: str, filepath: Union[str, Path]) -> None:
-        """Write data to a given ``filepath`` with 'w' mode.
-
-        Note:
-            ``put_text`` should create a directory if the directory of
-            ``filepath`` does not exist.
-
-        Args:
-            obj (str): Data to be written.
-            filepath (str or Path): Path to write data.
-            encoding (str, optional): The encoding format used to open the
-                `filepath`. Default: 'utf-8'.
-        """
-        self.client.put_text(obj, filepath)
-
-    def remove(self, filepath: Union[str, Path]) -> None:
-        """Remove a file.
-
-        Args:
-            filepath (str, Path): Path to be removed.
-        """
-        self.client.remove(filepath)
-
-    def exists(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path exists.
-
-        Args:
-            filepath (str or Path): Path to be checked whether exists.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
-        """
-        return self.client.exists(filepath)
-
-    def isdir(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a directory.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a
-                directory.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a directory,
-                ``False`` otherwise.
-        """
-        return self.client.isdir(filepath)
-
-    def isfile(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a file.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a file.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a file, ``False``
-                otherwise.
-        """
-        return self.client.isfile(filepath)
-
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
-        """Concatenate all file paths.
-
-        Join one or more filepath components intelligently. The return value
-        is the concatenation of filepath and any members of *filepaths.
-
-        Args:
-            filepath (str or Path): Path to be concatenated.
-
-        Returns:
-            str: The result of concatenation.
-        """
-        return self.client.join_path(filepath, *filepaths)
-
-    @contextmanager
-    def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]:
-        """Download data from ``filepath`` and write the data to local path.
-
-        ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
-        can be called with ``with`` statement, and when exists from the
-        ``with`` statement, the temporary path will be released.
-
-        Note:
-            If the ``filepath`` is a local path, just return itself.
-
-        .. warning::
-            ``get_local_path`` is an experimental interface that may change in
-            the future.
-
-        Args:
-            filepath (str or Path): Path to be read data.
-
-        Examples:
-            >>> file_client = FileClient(prefix='s3')
-            >>> with file_client.get_local_path('s3://bucket/abc.jpg') as path:
-            ...     # do something here
-
-        Yields:
-            Iterable[str]: Only yield one path.
-        """
-        with self.client.get_local_path(str(filepath)) as local_path:
-            yield local_path
-
-    def list_dir_or_file(self,
-                         dir_path: Union[str, Path],
-                         list_dir: bool = True,
-                         list_file: bool = True,
-                         suffix: Optional[Union[str, Tuple[str]]] = None,
-                         recursive: bool = False) -> Iterator[str]:
-        """Scan a directory to find the interested directories or files in
-        arbitrary order.
-
-        Note:
-            :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-
-        Args:
-            dir_path (str | Path): Path of the directory.
-            list_dir (bool): List the directories. Default: True.
-            list_file (bool): List the path of files. Default: True.
-            suffix (str or tuple[str], optional):  File suffix
-                that we are interested in. Default: None.
-            recursive (bool): If set to True, recursively scan the
-                directory. Default: False.
-
-        Yields:
-            Iterable[str]: A relative path to ``dir_path``.
-        """
-        yield from self.client.list_dir_or_file(dir_path, list_dir, list_file,
-                                                suffix, recursive)

spaces/Anthos23/hummus/app.py

@@ -1,38 +0,0 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TextClassificationPipeline
-import operator
-import matplotlib.pyplot as plt
-import pandas as pd
-
-def get_sentiment(out):
-  d = dict()
-  for k in out:
-    print(k)
-    label = k['label']
-    score = k['score']
-    d[label] = score
-
-  winning_lab = max(d.items(), key=operator.itemgetter(1))[0]
-  winning_score = d[winning_lab]
-
-  df = pd.DataFrame.from_dict(d, orient = 'index')
-  return  df #winning_lab, winning_score
-  
-model_name =  "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
-text = st.text_area(f'Ciao! This app uses {model_name}.\nEnter your text  to test it ❤️')
-
-
-if text:
-  out = pipe(text)
-  df = get_sentiment(out[0])
-  fig, ax = plt.subplots()
-  c = ['#C34A36', '#FFC75F', '#008F7A']
-  ax.bar(df.index, df[0], color=c, width=0.4)
-  
-  st.pyplot(fig)
-  
-  #st.json(get_sentiment(out[0][0]))

spaces/AsakuraMizu/moe-tts/text/cantonese.py

@@ -1,59 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('chinese_dialect_lexicons/jyutjyu')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ei˥'),
-    ('B', 'biː˥'),
-    ('C', 'siː˥'),
-    ('D', 'tiː˥'),
-    ('E', 'iː˥'),
-    ('F', 'e˥fuː˨˩'),
-    ('G', 'tsiː˥'),
-    ('H', 'ɪk̚˥tsʰyː˨˩'),
-    ('I', 'ɐi˥'),
-    ('J', 'tsei˥'),
-    ('K', 'kʰei˥'),
-    ('L', 'e˥llou˨˩'),
-    ('M', 'ɛːm˥'),
-    ('N', 'ɛːn˥'),
-    ('O', 'ou˥'),
-    ('P', 'pʰiː˥'),
-    ('Q', 'kʰiːu˥'),
-    ('R', 'aː˥lou˨˩'),
-    ('S', 'ɛː˥siː˨˩'),
-    ('T', 'tʰiː˥'),
-    ('U', 'juː˥'),
-    ('V', 'wiː˥'),
-    ('W', 'tʊk̚˥piː˥juː˥'),
-    ('X', 'ɪk̚˥siː˨˩'),
-    ('Y', 'waːi˥'),
-    ('Z', 'iː˨sɛːt̚˥')
-]]
-
-
-def number_to_cantonese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def cantonese_to_ipa(text):
-    text = number_to_cantonese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text

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

@@ -1,104 +0,0 @@
-"""
-    pygments.scanner
-    ~~~~~~~~~~~~~~~~
-
-    This library implements a regex based scanner. Some languages
-    like Pascal are easy to parse but have some keywords that
-    depend on the context. Because of this it's impossible to lex
-    that just by using a regular expression lexer like the
-    `RegexLexer`.
-
-    Have a look at the `DelphiLexer` to get an idea of how to use
-    this scanner.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-import re
-
-
-class EndOfText(RuntimeError):
-    """
-    Raise if end of text is reached and the user
-    tried to call a match function.
-    """
-
-
-class Scanner:
-    """
-    Simple scanner
-
-    All method patterns are regular expression strings (not
-    compiled expressions!)
-    """
-
-    def __init__(self, text, flags=0):
-        """
-        :param text:    The text which should be scanned
-        :param flags:   default regular expression flags
-        """
-        self.data = text
-        self.data_length = len(text)
-        self.start_pos = 0
-        self.pos = 0
-        self.flags = flags
-        self.last = None
-        self.match = None
-        self._re_cache = {}
-
-    def eos(self):
-        """`True` if the scanner reached the end of text."""
-        return self.pos >= self.data_length
-    eos = property(eos, eos.__doc__)
-
-    def check(self, pattern):
-        """
-        Apply `pattern` on the current position and return
-        the match object. (Doesn't touch pos). Use this for
-        lookahead.
-        """
-        if self.eos:
-            raise EndOfText()
-        if pattern not in self._re_cache:
-            self._re_cache[pattern] = re.compile(pattern, self.flags)
-        return self._re_cache[pattern].match(self.data, self.pos)
-
-    def test(self, pattern):
-        """Apply a pattern on the current position and check
-        if it patches. Doesn't touch pos.
-        """
-        return self.check(pattern) is not None
-
-    def scan(self, pattern):
-        """
-        Scan the text for the given pattern and update pos/match
-        and related fields. The return value is a boolean that
-        indicates if the pattern matched. The matched value is
-        stored on the instance as ``match``, the last value is
-        stored as ``last``. ``start_pos`` is the position of the
-        pointer before the pattern was matched, ``pos`` is the
-        end position.
-        """
-        if self.eos:
-            raise EndOfText()
-        if pattern not in self._re_cache:
-            self._re_cache[pattern] = re.compile(pattern, self.flags)
-        self.last = self.match
-        m = self._re_cache[pattern].match(self.data, self.pos)
-        if m is None:
-            return False
-        self.start_pos = m.start()
-        self.pos = m.end()
-        self.match = m.group()
-        return True
-
-    def get_char(self):
-        """Scan exactly one char."""
-        self.scan('.')
-
-    def __repr__(self):
-        return '<%s %d/%d>' % (
-            self.__class__.__name__,
-            self.pos,
-            self.data_length
-        )

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

@@ -1,101 +0,0 @@
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Sequence
-
-if TYPE_CHECKING:
-    from pip._vendor.rich.console import ConsoleRenderable
-
-from . import get_console
-from .segment import Segment
-from .terminal_theme import DEFAULT_TERMINAL_THEME
-
-if TYPE_CHECKING:
-    from pip._vendor.rich.console import ConsoleRenderable
-
-JUPYTER_HTML_FORMAT = """\
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">{code}</pre>
-"""
-
-
-class JupyterRenderable:
-    """A shim to write html to Jupyter notebook."""
-
-    def __init__(self, html: str, text: str) -> None:
-        self.html = html
-        self.text = text
-
-    def _repr_mimebundle_(
-        self, include: Sequence[str], exclude: Sequence[str], **kwargs: Any
-    ) -> Dict[str, str]:
-        data = {"text/plain": self.text, "text/html": self.html}
-        if include:
-            data = {k: v for (k, v) in data.items() if k in include}
-        if exclude:
-            data = {k: v for (k, v) in data.items() if k not in exclude}
-        return data
-
-
-class JupyterMixin:
-    """Add to an Rich renderable to make it render in Jupyter notebook."""
-
-    __slots__ = ()
-
-    def _repr_mimebundle_(
-        self: "ConsoleRenderable",
-        include: Sequence[str],
-        exclude: Sequence[str],
-        **kwargs: Any,
-    ) -> Dict[str, str]:
-        console = get_console()
-        segments = list(console.render(self, console.options))
-        html = _render_segments(segments)
-        text = console._render_buffer(segments)
-        data = {"text/plain": text, "text/html": html}
-        if include:
-            data = {k: v for (k, v) in data.items() if k in include}
-        if exclude:
-            data = {k: v for (k, v) in data.items() if k not in exclude}
-        return data
-
-
-def _render_segments(segments: Iterable[Segment]) -> str:
-    def escape(text: str) -> str:
-        """Escape html."""
-        return text.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
-
-    fragments: List[str] = []
-    append_fragment = fragments.append
-    theme = DEFAULT_TERMINAL_THEME
-    for text, style, control in Segment.simplify(segments):
-        if control:
-            continue
-        text = escape(text)
-        if style:
-            rule = style.get_html_style(theme)
-            text = f'<span style="{rule}">{text}</span>' if rule else text
-            if style.link:
-                text = f'<a href="{style.link}" target="_blank">{text}</a>'
-        append_fragment(text)
-
-    code = "".join(fragments)
-    html = JUPYTER_HTML_FORMAT.format(code=code)
-
-    return html
-
-
-def display(segments: Iterable[Segment], text: str) -> None:
-    """Render segments to Jupyter."""
-    html = _render_segments(segments)
-    jupyter_renderable = JupyterRenderable(html, text)
-    try:
-        from IPython.display import display as ipython_display
-
-        ipython_display(jupyter_renderable)
-    except ModuleNotFoundError:
-        # Handle the case where the Console has force_jupyter=True,
-        # but IPython is not installed.
-        pass
-
-
-def print(*args: Any, **kwargs: Any) -> None:
-    """Proxy for Console print."""
-    console = get_console()
-    return console.print(*args, **kwargs)

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

@@ -1,170 +0,0 @@
-from contextlib import suppress
-from io import TextIOWrapper
-
-from . import abc
-
-
-class SpecLoaderAdapter:
-    """
-    Adapt a package spec to adapt the underlying loader.
-    """
-
-    def __init__(self, spec, adapter=lambda spec: spec.loader):
-        self.spec = spec
-        self.loader = adapter(spec)
-
-    def __getattr__(self, name):
-        return getattr(self.spec, name)
-
-
-class TraversableResourcesLoader:
-    """
-    Adapt a loader to provide TraversableResources.
-    """
-
-    def __init__(self, spec):
-        self.spec = spec
-
-    def get_resource_reader(self, name):
-        return CompatibilityFiles(self.spec)._native()
-
-
-def _io_wrapper(file, mode='r', *args, **kwargs):
-    if mode == 'r':
-        return TextIOWrapper(file, *args, **kwargs)
-    elif mode == 'rb':
-        return file
-    raise ValueError(
-        "Invalid mode value '{}', only 'r' and 'rb' are supported".format(mode)
-    )
-
-
-class CompatibilityFiles:
-    """
-    Adapter for an existing or non-existent resource reader
-    to provide a compatibility .files().
-    """
-
-    class SpecPath(abc.Traversable):
-        """
-        Path tied to a module spec.
-        Can be read and exposes the resource reader children.
-        """
-
-        def __init__(self, spec, reader):
-            self._spec = spec
-            self._reader = reader
-
-        def iterdir(self):
-            if not self._reader:
-                return iter(())
-            return iter(
-                CompatibilityFiles.ChildPath(self._reader, path)
-                for path in self._reader.contents()
-            )
-
-        def is_file(self):
-            return False
-
-        is_dir = is_file
-
-        def joinpath(self, other):
-            if not self._reader:
-                return CompatibilityFiles.OrphanPath(other)
-            return CompatibilityFiles.ChildPath(self._reader, other)
-
-        @property
-        def name(self):
-            return self._spec.name
-
-        def open(self, mode='r', *args, **kwargs):
-            return _io_wrapper(self._reader.open_resource(None), mode, *args, **kwargs)
-
-    class ChildPath(abc.Traversable):
-        """
-        Path tied to a resource reader child.
-        Can be read but doesn't expose any meaningful children.
-        """
-
-        def __init__(self, reader, name):
-            self._reader = reader
-            self._name = name
-
-        def iterdir(self):
-            return iter(())
-
-        def is_file(self):
-            return self._reader.is_resource(self.name)
-
-        def is_dir(self):
-            return not self.is_file()
-
-        def joinpath(self, other):
-            return CompatibilityFiles.OrphanPath(self.name, other)
-
-        @property
-        def name(self):
-            return self._name
-
-        def open(self, mode='r', *args, **kwargs):
-            return _io_wrapper(
-                self._reader.open_resource(self.name), mode, *args, **kwargs
-            )
-
-    class OrphanPath(abc.Traversable):
-        """
-        Orphan path, not tied to a module spec or resource reader.
-        Can't be read and doesn't expose any meaningful children.
-        """
-
-        def __init__(self, *path_parts):
-            if len(path_parts) < 1:
-                raise ValueError('Need at least one path part to construct a path')
-            self._path = path_parts
-
-        def iterdir(self):
-            return iter(())
-
-        def is_file(self):
-            return False
-
-        is_dir = is_file
-
-        def joinpath(self, other):
-            return CompatibilityFiles.OrphanPath(*self._path, other)
-
-        @property
-        def name(self):
-            return self._path[-1]
-
-        def open(self, mode='r', *args, **kwargs):
-            raise FileNotFoundError("Can't open orphan path")
-
-    def __init__(self, spec):
-        self.spec = spec
-
-    @property
-    def _reader(self):
-        with suppress(AttributeError):
-            return self.spec.loader.get_resource_reader(self.spec.name)
-
-    def _native(self):
-        """
-        Return the native reader if it supports files().
-        """
-        reader = self._reader
-        return reader if hasattr(reader, 'files') else self
-
-    def __getattr__(self, attr):
-        return getattr(self._reader, attr)
-
-    def files(self):
-        return CompatibilityFiles.SpecPath(self.spec, self._reader)
-
-
-def wrap_spec(package):
-    """
-    Construct a package spec with traversable compatibility
-    on the spec/loader/reader.
-    """
-    return SpecLoaderAdapter(package.__spec__, TraversableResourcesLoader)

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

@@ -1,352 +0,0 @@
-# unicode.py
-
-import sys
-from itertools import filterfalse
-from typing import List, Tuple, Union
-
-
-class _lazyclassproperty:
-    def __init__(self, fn):
-        self.fn = fn
-        self.__doc__ = fn.__doc__
-        self.__name__ = fn.__name__
-
-    def __get__(self, obj, cls):
-        if cls is None:
-            cls = type(obj)
-        if not hasattr(cls, "_intern") or any(
-            cls._intern is getattr(superclass, "_intern", [])
-            for superclass in cls.__mro__[1:]
-        ):
-            cls._intern = {}
-        attrname = self.fn.__name__
-        if attrname not in cls._intern:
-            cls._intern[attrname] = self.fn(cls)
-        return cls._intern[attrname]
-
-
-UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]]
-
-
-class unicode_set:
-    """
-    A set of Unicode characters, for language-specific strings for
-    ``alphas``, ``nums``, ``alphanums``, and ``printables``.
-    A unicode_set is defined by a list of ranges in the Unicode character
-    set, in a class attribute ``_ranges``. Ranges can be specified using
-    2-tuples or a 1-tuple, such as::
-
-        _ranges = [
-            (0x0020, 0x007e),
-            (0x00a0, 0x00ff),
-            (0x0100,),
-            ]
-
-    Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x).
-
-    A unicode set can also be defined using multiple inheritance of other unicode sets::
-
-        class CJK(Chinese, Japanese, Korean):
-            pass
-    """
-
-    _ranges: UnicodeRangeList = []
-
-    @_lazyclassproperty
-    def _chars_for_ranges(cls):
-        ret = []
-        for cc in cls.__mro__:
-            if cc is unicode_set:
-                break
-            for rr in getattr(cc, "_ranges", ()):
-                ret.extend(range(rr[0], rr[-1] + 1))
-        return [chr(c) for c in sorted(set(ret))]
-
-    @_lazyclassproperty
-    def printables(cls):
-        "all non-whitespace characters in this range"
-        return "".join(filterfalse(str.isspace, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphas(cls):
-        "all alphabetic characters in this range"
-        return "".join(filter(str.isalpha, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def nums(cls):
-        "all numeric digit characters in this range"
-        return "".join(filter(str.isdigit, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphanums(cls):
-        "all alphanumeric characters in this range"
-        return cls.alphas + cls.nums
-
-    @_lazyclassproperty
-    def identchars(cls):
-        "all characters in this range that are valid identifier characters, plus underscore '_'"
-        return "".join(
-            sorted(
-                set(
-                    "".join(filter(str.isidentifier, cls._chars_for_ranges))
-                    + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº"
-                    + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ"
-                    + "_"
-                )
-            )
-        )
-
-    @_lazyclassproperty
-    def identbodychars(cls):
-        """
-        all characters in this range that are valid identifier body characters,
-        plus the digits 0-9
-        """
-        return "".join(
-            sorted(
-                set(
-                    cls.identchars
-                    + "0123456789"
-                    + "".join(
-                        [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()]
-                    )
-                )
-            )
-        )
-
-
-class pyparsing_unicode(unicode_set):
-    """
-    A namespace class for defining common language unicode_sets.
-    """
-
-    # fmt: off
-
-    # define ranges in language character sets
-    _ranges: UnicodeRangeList = [
-        (0x0020, sys.maxunicode),
-    ]
-
-    class BasicMultilingualPlane(unicode_set):
-        "Unicode set for the Basic Multilingual Plane"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0xFFFF),
-        ]
-
-    class Latin1(unicode_set):
-        "Unicode set for Latin-1 Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0x007E),
-            (0x00A0, 0x00FF),
-        ]
-
-    class LatinA(unicode_set):
-        "Unicode set for Latin-A Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0100, 0x017F),
-        ]
-
-    class LatinB(unicode_set):
-        "Unicode set for Latin-B Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0180, 0x024F),
-        ]
-
-    class Greek(unicode_set):
-        "Unicode set for Greek Unicode Character Ranges"
-        _ranges: UnicodeRangeList = [
-            (0x0342, 0x0345),
-            (0x0370, 0x0377),
-            (0x037A, 0x037F),
-            (0x0384, 0x038A),
-            (0x038C,),
-            (0x038E, 0x03A1),
-            (0x03A3, 0x03E1),
-            (0x03F0, 0x03FF),
-            (0x1D26, 0x1D2A),
-            (0x1D5E,),
-            (0x1D60,),
-            (0x1D66, 0x1D6A),
-            (0x1F00, 0x1F15),
-            (0x1F18, 0x1F1D),
-            (0x1F20, 0x1F45),
-            (0x1F48, 0x1F4D),
-            (0x1F50, 0x1F57),
-            (0x1F59,),
-            (0x1F5B,),
-            (0x1F5D,),
-            (0x1F5F, 0x1F7D),
-            (0x1F80, 0x1FB4),
-            (0x1FB6, 0x1FC4),
-            (0x1FC6, 0x1FD3),
-            (0x1FD6, 0x1FDB),
-            (0x1FDD, 0x1FEF),
-            (0x1FF2, 0x1FF4),
-            (0x1FF6, 0x1FFE),
-            (0x2129,),
-            (0x2719, 0x271A),
-            (0xAB65,),
-            (0x10140, 0x1018D),
-            (0x101A0,),
-            (0x1D200, 0x1D245),
-            (0x1F7A1, 0x1F7A7),
-        ]
-
-    class Cyrillic(unicode_set):
-        "Unicode set for Cyrillic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0400, 0x052F),
-            (0x1C80, 0x1C88),
-            (0x1D2B,),
-            (0x1D78,),
-            (0x2DE0, 0x2DFF),
-            (0xA640, 0xA672),
-            (0xA674, 0xA69F),
-            (0xFE2E, 0xFE2F),
-        ]
-
-    class Chinese(unicode_set):
-        "Unicode set for Chinese Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x2E80, 0x2E99),
-            (0x2E9B, 0x2EF3),
-            (0x31C0, 0x31E3),
-            (0x3400, 0x4DB5),
-            (0x4E00, 0x9FEF),
-            (0xA700, 0xA707),
-            (0xF900, 0xFA6D),
-            (0xFA70, 0xFAD9),
-            (0x16FE2, 0x16FE3),
-            (0x1F210, 0x1F212),
-            (0x1F214, 0x1F23B),
-            (0x1F240, 0x1F248),
-            (0x20000, 0x2A6D6),
-            (0x2A700, 0x2B734),
-            (0x2B740, 0x2B81D),
-            (0x2B820, 0x2CEA1),
-            (0x2CEB0, 0x2EBE0),
-            (0x2F800, 0x2FA1D),
-        ]
-
-    class Japanese(unicode_set):
-        "Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges"
-        _ranges: UnicodeRangeList = []
-
-        class Kanji(unicode_set):
-            "Unicode set for Kanji Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x4E00, 0x9FBF),
-                (0x3000, 0x303F),
-            ]
-
-        class Hiragana(unicode_set):
-            "Unicode set for Hiragana Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3041, 0x3096),
-                (0x3099, 0x30A0),
-                (0x30FC,),
-                (0xFF70,),
-                (0x1B001,),
-                (0x1B150, 0x1B152),
-                (0x1F200,),
-            ]
-
-        class Katakana(unicode_set):
-            "Unicode set for Katakana  Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3099, 0x309C),
-                (0x30A0, 0x30FF),
-                (0x31F0, 0x31FF),
-                (0x32D0, 0x32FE),
-                (0xFF65, 0xFF9F),
-                (0x1B000,),
-                (0x1B164, 0x1B167),
-                (0x1F201, 0x1F202),
-                (0x1F213,),
-            ]
-
-    class Hangul(unicode_set):
-        "Unicode set for Hangul (Korean) Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x1100, 0x11FF),
-            (0x302E, 0x302F),
-            (0x3131, 0x318E),
-            (0x3200, 0x321C),
-            (0x3260, 0x327B),
-            (0x327E,),
-            (0xA960, 0xA97C),
-            (0xAC00, 0xD7A3),
-            (0xD7B0, 0xD7C6),
-            (0xD7CB, 0xD7FB),
-            (0xFFA0, 0xFFBE),
-            (0xFFC2, 0xFFC7),
-            (0xFFCA, 0xFFCF),
-            (0xFFD2, 0xFFD7),
-            (0xFFDA, 0xFFDC),
-        ]
-
-    Korean = Hangul
-
-    class CJK(Chinese, Japanese, Hangul):
-        "Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range"
-
-    class Thai(unicode_set):
-        "Unicode set for Thai Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0E01, 0x0E3A),
-            (0x0E3F, 0x0E5B)
-        ]
-
-    class Arabic(unicode_set):
-        "Unicode set for Arabic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0600, 0x061B),
-            (0x061E, 0x06FF),
-            (0x0700, 0x077F),
-        ]
-
-    class Hebrew(unicode_set):
-        "Unicode set for Hebrew Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0591, 0x05C7),
-            (0x05D0, 0x05EA),
-            (0x05EF, 0x05F4),
-            (0xFB1D, 0xFB36),
-            (0xFB38, 0xFB3C),
-            (0xFB3E,),
-            (0xFB40, 0xFB41),
-            (0xFB43, 0xFB44),
-            (0xFB46, 0xFB4F),
-        ]
-
-    class Devanagari(unicode_set):
-        "Unicode set for Devanagari Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0900, 0x097F),
-            (0xA8E0, 0xA8FF)
-        ]
-
-    # fmt: on
-
-
-pyparsing_unicode.Japanese._ranges = (
-    pyparsing_unicode.Japanese.Kanji._ranges
-    + pyparsing_unicode.Japanese.Hiragana._ranges
-    + pyparsing_unicode.Japanese.Katakana._ranges
-)
-
-pyparsing_unicode.BMP = pyparsing_unicode.BasicMultilingualPlane
-
-# add language identifiers using language Unicode
-pyparsing_unicode.العربية = pyparsing_unicode.Arabic
-pyparsing_unicode.中文 = pyparsing_unicode.Chinese
-pyparsing_unicode.кириллица = pyparsing_unicode.Cyrillic
-pyparsing_unicode.Ελληνικά = pyparsing_unicode.Greek
-pyparsing_unicode.עִברִית = pyparsing_unicode.Hebrew
-pyparsing_unicode.日本語 = pyparsing_unicode.Japanese
-pyparsing_unicode.Japanese.漢字 = pyparsing_unicode.Japanese.Kanji
-pyparsing_unicode.Japanese.カタカナ = pyparsing_unicode.Japanese.Katakana
-pyparsing_unicode.Japanese.ひらがな = pyparsing_unicode.Japanese.Hiragana
-pyparsing_unicode.한국어 = pyparsing_unicode.Korean
-pyparsing_unicode.ไทย = pyparsing_unicode.Thai
-pyparsing_unicode.देवनागरी = pyparsing_unicode.Devanagari

spaces/Atualli/yoloxTeste/checkYoloxGPU.sh

@@ -1,16 +0,0 @@
-#!/bin/sh
-export path=/home/atualli/.local/lib/python3.8/site-packages:$PATH
-cd ~/Projetos/huggingface/yoloxTeste_GPU
-SERVER=192.168.0.153
-PORT=8081
-
-if lsof -Pi :$PORT -sTCP:LISTEN -t >/dev/null ; then
-    echo "running"
-else
-    ./telegramCrise.sh "reiniciando_yolox_GPU_linux_192.168.0.153:8081"
-    pkill -f app1.py
-    python app1.py &
-    echo "not running"
-fi
-
-

spaces/Awiny/Image2Paragraph/models/grit_src/image_dense_captions.py

@@ -1,69 +0,0 @@
-import argparse
-import multiprocessing as mp
-import os
-import time
-import cv2
-import tqdm
-import sys
-
-from detectron2.config import get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.utils.logger import setup_logger
-
-sys.path.insert(0, 'models/grit_src/third_party/CenterNet2/projects/CenterNet2/')
-from centernet.config import add_centernet_config
-from models.grit_src.grit.config import add_grit_config
-
-from models.grit_src.grit.predictor import VisualizationDemo
-import json
-from utils.util import resize_long_edge_cv2
-
-
-# constants
-WINDOW_NAME = "GRiT"
-
-
-def dense_pred_to_caption(predictions):
-    boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None
-    object_description = predictions["instances"].pred_object_descriptions.data
-    new_caption = ""
-    for i in range(len(object_description)):
-        new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; "
-    return new_caption
-
-def setup_cfg(args):
-    cfg = get_cfg()
-    if args["cpu"]:
-        cfg.MODEL.DEVICE="cpu"
-    add_centernet_config(cfg)
-    add_grit_config(cfg)
-    cfg.merge_from_file(args["config_file"])
-    cfg.merge_from_list(args["opts"])
-    # Set score_threshold for builtin models
-    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args["confidence_threshold"]
-    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args["confidence_threshold"]
-    if args["test_task"]:
-        cfg.MODEL.TEST_TASK = args["test_task"]
-    cfg.MODEL.BEAM_SIZE = 1
-    cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
-    cfg.USE_ACT_CHECKPOINT = False
-    cfg.freeze()
-    return cfg
-
-
-def get_parser(device):
-    arg_dict = {'config_file': "models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml", 'cpu': False, 'confidence_threshold': 0.5, 'test_task': 'DenseCap', 'opts': ["MODEL.WEIGHTS", "pretrained_models/grit_b_densecap_objectdet.pth"]}
-    if device == "cpu":
-        arg_dict["cpu"] = True
-    return arg_dict
-
-def image_caption_api(image_src, device):
-    args2 = get_parser(device)
-    cfg = setup_cfg(args2)
-    demo = VisualizationDemo(cfg)
-    if image_src:
-        img = read_image(image_src, format="BGR")
-        img = resize_long_edge_cv2(img, 384)
-        predictions, visualized_output = demo.run_on_image(img)
-        new_caption = dense_pred_to_caption(predictions)
-    return new_caption

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp

@@ -1,507 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#include "cocoeval.h"
-#include <time.h>
-#include <algorithm>
-#include <cstdint>
-#include <numeric>
-
-using namespace pybind11::literals;
-
-namespace detectron2 {
-
-namespace COCOeval {
-
-// Sort detections from highest score to lowest, such that
-// detection_instances[detection_sorted_indices[t]] >=
-// detection_instances[detection_sorted_indices[t+1]].  Use stable_sort to match
-// original COCO API
-void SortInstancesByDetectionScore(
-    const std::vector<InstanceAnnotation>& detection_instances,
-    std::vector<uint64_t>* detection_sorted_indices) {
-  detection_sorted_indices->resize(detection_instances.size());
-  std::iota(
-      detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);
-  std::stable_sort(
-      detection_sorted_indices->begin(),
-      detection_sorted_indices->end(),
-      [&detection_instances](size_t j1, size_t j2) {
-        return detection_instances[j1].score > detection_instances[j2].score;
-      });
-}
-
-// Partition the ground truth objects based on whether or not to ignore them
-// based on area
-void SortInstancesByIgnore(
-    const std::array<double, 2>& area_range,
-    const std::vector<InstanceAnnotation>& ground_truth_instances,
-    std::vector<uint64_t>* ground_truth_sorted_indices,
-    std::vector<bool>* ignores) {
-  ignores->clear();
-  ignores->reserve(ground_truth_instances.size());
-  for (auto o : ground_truth_instances) {
-    ignores->push_back(
-        o.ignore || o.area < area_range[0] || o.area > area_range[1]);
-  }
-
-  ground_truth_sorted_indices->resize(ground_truth_instances.size());
-  std::iota(
-      ground_truth_sorted_indices->begin(),
-      ground_truth_sorted_indices->end(),
-      0);
-  std::stable_sort(
-      ground_truth_sorted_indices->begin(),
-      ground_truth_sorted_indices->end(),
-      [&ignores](size_t j1, size_t j2) {
-        return (int)(*ignores)[j1] < (int)(*ignores)[j2];
-      });
-}
-
-// For each IOU threshold, greedily match each detected instance to a ground
-// truth instance (if possible) and store the results
-void MatchDetectionsToGroundTruth(
-    const std::vector<InstanceAnnotation>& detection_instances,
-    const std::vector<uint64_t>& detection_sorted_indices,
-    const std::vector<InstanceAnnotation>& ground_truth_instances,
-    const std::vector<uint64_t>& ground_truth_sorted_indices,
-    const std::vector<bool>& ignores,
-    const std::vector<std::vector<double>>& ious,
-    const std::vector<double>& iou_thresholds,
-    const std::array<double, 2>& area_range,
-    ImageEvaluation* results) {
-  // Initialize memory to store return data matches and ignore
-  const int num_iou_thresholds = iou_thresholds.size();
-  const int num_ground_truth = ground_truth_sorted_indices.size();
-  const int num_detections = detection_sorted_indices.size();
-  std::vector<uint64_t> ground_truth_matches(
-      num_iou_thresholds * num_ground_truth, 0);
-  std::vector<uint64_t>& detection_matches = results->detection_matches;
-  std::vector<bool>& detection_ignores = results->detection_ignores;
-  std::vector<bool>& ground_truth_ignores = results->ground_truth_ignores;
-  detection_matches.resize(num_iou_thresholds * num_detections, 0);
-  detection_ignores.resize(num_iou_thresholds * num_detections, false);
-  ground_truth_ignores.resize(num_ground_truth);
-  for (auto g = 0; g < num_ground_truth; ++g) {
-    ground_truth_ignores[g] = ignores[ground_truth_sorted_indices[g]];
-  }
-
-  for (auto t = 0; t < num_iou_thresholds; ++t) {
-    for (auto d = 0; d < num_detections; ++d) {
-      // information about best match so far (match=-1 -> unmatched)
-      double best_iou = std::min(iou_thresholds[t], 1 - 1e-10);
-      int match = -1;
-      for (auto g = 0; g < num_ground_truth; ++g) {
-        // if this ground truth instance is already matched and not a
-        // crowd, it cannot be matched to another detection
-        if (ground_truth_matches[t * num_ground_truth + g] > 0 &&
-            !ground_truth_instances[ground_truth_sorted_indices[g]].is_crowd) {
-          continue;
-        }
-
-        // if detected instance matched to a regular ground truth
-        // instance, we can break on the first ground truth instance
-        // tagged as ignore (because they are sorted by the ignore tag)
-        if (match >= 0 && !ground_truth_ignores[match] &&
-            ground_truth_ignores[g]) {
-          break;
-        }
-
-        // if IOU overlap is the best so far, store the match appropriately
-        if (ious[d][ground_truth_sorted_indices[g]] >= best_iou) {
-          best_iou = ious[d][ground_truth_sorted_indices[g]];
-          match = g;
-        }
-      }
-      // if match was made, store id of match for both detection and
-      // ground truth
-      if (match >= 0) {
-        detection_ignores[t * num_detections + d] = ground_truth_ignores[match];
-        detection_matches[t * num_detections + d] =
-            ground_truth_instances[ground_truth_sorted_indices[match]].id;
-        ground_truth_matches[t * num_ground_truth + match] =
-            detection_instances[detection_sorted_indices[d]].id;
-      }
-
-      // set unmatched detections outside of area range to ignore
-      const InstanceAnnotation& detection =
-          detection_instances[detection_sorted_indices[d]];
-      detection_ignores[t * num_detections + d] =
-          detection_ignores[t * num_detections + d] ||
-          (detection_matches[t * num_detections + d] == 0 &&
-           (detection.area < area_range[0] || detection.area > area_range[1]));
-    }
-  }
-
-  // store detection score results
-  results->detection_scores.resize(detection_sorted_indices.size());
-  for (size_t d = 0; d < detection_sorted_indices.size(); ++d) {
-    results->detection_scores[d] =
-        detection_instances[detection_sorted_indices[d]].score;
-  }
-}
-
-std::vector<ImageEvaluation> EvaluateImages(
-    const std::vector<std::array<double, 2>>& area_ranges,
-    int max_detections,
-    const std::vector<double>& iou_thresholds,
-    const ImageCategoryInstances<std::vector<double>>& image_category_ious,
-    const ImageCategoryInstances<InstanceAnnotation>&
-        image_category_ground_truth_instances,
-    const ImageCategoryInstances<InstanceAnnotation>&
-        image_category_detection_instances) {
-  const int num_area_ranges = area_ranges.size();
-  const int num_images = image_category_ground_truth_instances.size();
-  const int num_categories =
-      image_category_ious.size() > 0 ? image_category_ious[0].size() : 0;
-  std::vector<uint64_t> detection_sorted_indices;
-  std::vector<uint64_t> ground_truth_sorted_indices;
-  std::vector<bool> ignores;
-  std::vector<ImageEvaluation> results_all(
-      num_images * num_area_ranges * num_categories);
-
-  // Store results for each image, category, and area range combination. Results
-  // for each IOU threshold are packed into the same ImageEvaluation object
-  for (auto i = 0; i < num_images; ++i) {
-    for (auto c = 0; c < num_categories; ++c) {
-      const std::vector<InstanceAnnotation>& ground_truth_instances =
-          image_category_ground_truth_instances[i][c];
-      const std::vector<InstanceAnnotation>& detection_instances =
-          image_category_detection_instances[i][c];
-
-      SortInstancesByDetectionScore(
-          detection_instances, &detection_sorted_indices);
-      if ((int)detection_sorted_indices.size() > max_detections) {
-        detection_sorted_indices.resize(max_detections);
-      }
-
-      for (size_t a = 0; a < area_ranges.size(); ++a) {
-        SortInstancesByIgnore(
-            area_ranges[a],
-            ground_truth_instances,
-            &ground_truth_sorted_indices,
-            &ignores);
-
-        MatchDetectionsToGroundTruth(
-            detection_instances,
-            detection_sorted_indices,
-            ground_truth_instances,
-            ground_truth_sorted_indices,
-            ignores,
-            image_category_ious[i][c],
-            iou_thresholds,
-            area_ranges[a],
-            &results_all
-                [c * num_area_ranges * num_images + a * num_images + i]);
-      }
-    }
-  }
-
-  return results_all;
-}
-
-// Convert a python list to a vector
-template <typename T>
-std::vector<T> list_to_vec(const py::list& l) {
-  std::vector<T> v(py::len(l));
-  for (int i = 0; i < (int)py::len(l); ++i) {
-    v[i] = l[i].cast<T>();
-  }
-  return v;
-}
-
-// Helper function to Accumulate()
-// Considers the evaluation results applicable to a particular category, area
-// range, and max_detections parameter setting, which begin at
-// evaluations[evaluation_index].  Extracts a sorted list of length n of all
-// applicable detection instances concatenated across all images in the dataset,
-// which are represented by the outputs evaluation_indices, detection_scores,
-// image_detection_indices, and detection_sorted_indices--all of which are
-// length n. evaluation_indices[i] stores the applicable index into
-// evaluations[] for instance i, which has detection score detection_score[i],
-// and is the image_detection_indices[i]'th of the list of detections
-// for the image containing i.  detection_sorted_indices[] defines a sorted
-// permutation of the 3 other outputs
-int BuildSortedDetectionList(
-    const std::vector<ImageEvaluation>& evaluations,
-    const int64_t evaluation_index,
-    const int64_t num_images,
-    const int max_detections,
-    std::vector<uint64_t>* evaluation_indices,
-    std::vector<double>* detection_scores,
-    std::vector<uint64_t>* detection_sorted_indices,
-    std::vector<uint64_t>* image_detection_indices) {
-  assert(evaluations.size() >= evaluation_index + num_images);
-
-  // Extract a list of object instances of the applicable category, area
-  // range, and max detections requirements such that they can be sorted
-  image_detection_indices->clear();
-  evaluation_indices->clear();
-  detection_scores->clear();
-  image_detection_indices->reserve(num_images * max_detections);
-  evaluation_indices->reserve(num_images * max_detections);
-  detection_scores->reserve(num_images * max_detections);
-  int num_valid_ground_truth = 0;
-  for (auto i = 0; i < num_images; ++i) {
-    const ImageEvaluation& evaluation = evaluations[evaluation_index + i];
-
-    for (int d = 0;
-         d < (int)evaluation.detection_scores.size() && d < max_detections;
-         ++d) { // detected instances
-      evaluation_indices->push_back(evaluation_index + i);
-      image_detection_indices->push_back(d);
-      detection_scores->push_back(evaluation.detection_scores[d]);
-    }
-    for (auto ground_truth_ignore : evaluation.ground_truth_ignores) {
-      if (!ground_truth_ignore) {
-        ++num_valid_ground_truth;
-      }
-    }
-  }
-
-  // Sort detections by decreasing score, using stable sort to match
-  // python implementation
-  detection_sorted_indices->resize(detection_scores->size());
-  std::iota(
-      detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);
-  std::stable_sort(
-      detection_sorted_indices->begin(),
-      detection_sorted_indices->end(),
-      [&detection_scores](size_t j1, size_t j2) {
-        return (*detection_scores)[j1] > (*detection_scores)[j2];
-      });
-
-  return num_valid_ground_truth;
-}
-
-// Helper function to Accumulate()
-// Compute a precision recall curve given a sorted list of detected instances
-// encoded in evaluations, evaluation_indices, detection_scores,
-// detection_sorted_indices, image_detection_indices (see
-// BuildSortedDetectionList()). Using vectors precisions and recalls
-// and temporary storage, output the results into precisions_out, recalls_out,
-// and scores_out, which are large buffers containing many precion/recall curves
-// for all possible parameter settings, with precisions_out_index and
-// recalls_out_index defining the applicable indices to store results.
-void ComputePrecisionRecallCurve(
-    const int64_t precisions_out_index,
-    const int64_t precisions_out_stride,
-    const int64_t recalls_out_index,
-    const std::vector<double>& recall_thresholds,
-    const int iou_threshold_index,
-    const int num_iou_thresholds,
-    const int num_valid_ground_truth,
-    const std::vector<ImageEvaluation>& evaluations,
-    const std::vector<uint64_t>& evaluation_indices,
-    const std::vector<double>& detection_scores,
-    const std::vector<uint64_t>& detection_sorted_indices,
-    const std::vector<uint64_t>& image_detection_indices,
-    std::vector<double>* precisions,
-    std::vector<double>* recalls,
-    std::vector<double>* precisions_out,
-    std::vector<double>* scores_out,
-    std::vector<double>* recalls_out) {
-  assert(recalls_out->size() > recalls_out_index);
-
-  // Compute precision/recall for each instance in the sorted list of detections
-  int64_t true_positives_sum = 0, false_positives_sum = 0;
-  precisions->clear();
-  recalls->clear();
-  precisions->reserve(detection_sorted_indices.size());
-  recalls->reserve(detection_sorted_indices.size());
-  assert(!evaluations.empty() || detection_sorted_indices.empty());
-  for (auto detection_sorted_index : detection_sorted_indices) {
-    const ImageEvaluation& evaluation =
-        evaluations[evaluation_indices[detection_sorted_index]];
-    const auto num_detections =
-        evaluation.detection_matches.size() / num_iou_thresholds;
-    const auto detection_index = iou_threshold_index * num_detections +
-        image_detection_indices[detection_sorted_index];
-    assert(evaluation.detection_matches.size() > detection_index);
-    assert(evaluation.detection_ignores.size() > detection_index);
-    const int64_t detection_match =
-        evaluation.detection_matches[detection_index];
-    const bool detection_ignores =
-        evaluation.detection_ignores[detection_index];
-    const auto true_positive = detection_match > 0 && !detection_ignores;
-    const auto false_positive = detection_match == 0 && !detection_ignores;
-    if (true_positive) {
-      ++true_positives_sum;
-    }
-    if (false_positive) {
-      ++false_positives_sum;
-    }
-
-    const double recall =
-        static_cast<double>(true_positives_sum) / num_valid_ground_truth;
-    recalls->push_back(recall);
-    const int64_t num_valid_detections =
-        true_positives_sum + false_positives_sum;
-    const double precision = num_valid_detections > 0
-        ? static_cast<double>(true_positives_sum) / num_valid_detections
-        : 0.0;
-    precisions->push_back(precision);
-  }
-
-  (*recalls_out)[recalls_out_index] = !recalls->empty() ? recalls->back() : 0;
-
-  for (int64_t i = static_cast<int64_t>(precisions->size()) - 1; i > 0; --i) {
-    if ((*precisions)[i] > (*precisions)[i - 1]) {
-      (*precisions)[i - 1] = (*precisions)[i];
-    }
-  }
-
-  // Sample the per instance precision/recall list at each recall threshold
-  for (size_t r = 0; r < recall_thresholds.size(); ++r) {
-    // first index in recalls >= recall_thresholds[r]
-    std::vector<double>::iterator low = std::lower_bound(
-        recalls->begin(), recalls->end(), recall_thresholds[r]);
-    size_t precisions_index = low - recalls->begin();
-
-    const auto results_ind = precisions_out_index + r * precisions_out_stride;
-    assert(results_ind < precisions_out->size());
-    assert(results_ind < scores_out->size());
-    if (precisions_index < precisions->size()) {
-      (*precisions_out)[results_ind] = (*precisions)[precisions_index];
-      (*scores_out)[results_ind] =
-          detection_scores[detection_sorted_indices[precisions_index]];
-    } else {
-      (*precisions_out)[results_ind] = 0;
-      (*scores_out)[results_ind] = 0;
-    }
-  }
-}
-py::dict Accumulate(
-    const py::object& params,
-    const std::vector<ImageEvaluation>& evaluations) {
-  const std::vector<double> recall_thresholds =
-      list_to_vec<double>(params.attr("recThrs"));
-  const std::vector<int> max_detections =
-      list_to_vec<int>(params.attr("maxDets"));
-  const int num_iou_thresholds = py::len(params.attr("iouThrs"));
-  const int num_recall_thresholds = py::len(params.attr("recThrs"));
-  const int num_categories = params.attr("useCats").cast<int>() == 1
-      ? py::len(params.attr("catIds"))
-      : 1;
-  const int num_area_ranges = py::len(params.attr("areaRng"));
-  const int num_max_detections = py::len(params.attr("maxDets"));
-  const int num_images = py::len(params.attr("imgIds"));
-
-  std::vector<double> precisions_out(
-      num_iou_thresholds * num_recall_thresholds * num_categories *
-          num_area_ranges * num_max_detections,
-      -1);
-  std::vector<double> recalls_out(
-      num_iou_thresholds * num_categories * num_area_ranges *
-          num_max_detections,
-      -1);
-  std::vector<double> scores_out(
-      num_iou_thresholds * num_recall_thresholds * num_categories *
-          num_area_ranges * num_max_detections,
-      -1);
-
-  // Consider the list of all detected instances in the entire dataset in one
-  // large list.  evaluation_indices, detection_scores,
-  // image_detection_indices, and detection_sorted_indices all have the same
-  // length as this list, such that each entry corresponds to one detected
-  // instance
-  std::vector<uint64_t> evaluation_indices; // indices into evaluations[]
-  std::vector<double> detection_scores; // detection scores of each instance
-  std::vector<uint64_t> detection_sorted_indices; // sorted indices of all
-                                                  // instances in the dataset
-  std::vector<uint64_t>
-      image_detection_indices; // indices into the list of detected instances in
-                               // the same image as each instance
-  std::vector<double> precisions, recalls;
-
-  for (auto c = 0; c < num_categories; ++c) {
-    for (auto a = 0; a < num_area_ranges; ++a) {
-      for (auto m = 0; m < num_max_detections; ++m) {
-        // The COCO PythonAPI assumes evaluations[] (the return value of
-        // COCOeval::EvaluateImages() is one long list storing results for each
-        // combination of category, area range, and image id, with categories in
-        // the outermost loop and images in the innermost loop.
-        const int64_t evaluations_index =
-            c * num_area_ranges * num_images + a * num_images;
-        int num_valid_ground_truth = BuildSortedDetectionList(
-            evaluations,
-            evaluations_index,
-            num_images,
-            max_detections[m],
-            &evaluation_indices,
-            &detection_scores,
-            &detection_sorted_indices,
-            &image_detection_indices);
-
-        if (num_valid_ground_truth == 0) {
-          continue;
-        }
-
-        for (auto t = 0; t < num_iou_thresholds; ++t) {
-          // recalls_out is a flattened vectors representing a
-          // num_iou_thresholds X num_categories X num_area_ranges X
-          // num_max_detections matrix
-          const int64_t recalls_out_index =
-              t * num_categories * num_area_ranges * num_max_detections +
-              c * num_area_ranges * num_max_detections +
-              a * num_max_detections + m;
-
-          // precisions_out and scores_out are flattened vectors
-          // representing a num_iou_thresholds X num_recall_thresholds X
-          // num_categories X num_area_ranges X num_max_detections matrix
-          const int64_t precisions_out_stride =
-              num_categories * num_area_ranges * num_max_detections;
-          const int64_t precisions_out_index = t * num_recall_thresholds *
-                  num_categories * num_area_ranges * num_max_detections +
-              c * num_area_ranges * num_max_detections +
-              a * num_max_detections + m;
-
-          ComputePrecisionRecallCurve(
-              precisions_out_index,
-              precisions_out_stride,
-              recalls_out_index,
-              recall_thresholds,
-              t,
-              num_iou_thresholds,
-              num_valid_ground_truth,
-              evaluations,
-              evaluation_indices,
-              detection_scores,
-              detection_sorted_indices,
-              image_detection_indices,
-              &precisions,
-              &recalls,
-              &precisions_out,
-              &scores_out,
-              &recalls_out);
-        }
-      }
-    }
-  }
-
-  time_t rawtime;
-  struct tm local_time;
-  std::array<char, 200> buffer;
-  time(&rawtime);
-#ifdef _WIN32
-  localtime_s(&local_time, &rawtime);
-#else
-  localtime_r(&rawtime, &local_time);
-#endif
-  strftime(
-      buffer.data(), 200, "%Y-%m-%d %H:%num_max_detections:%S", &local_time);
-  return py::dict(
-      "params"_a = params,
-      "counts"_a = std::vector<int64_t>(
-          {num_iou_thresholds,
-           num_recall_thresholds,
-           num_categories,
-           num_area_ranges,
-           num_max_detections}),
-      "date"_a = buffer,
-      "precision"_a = precisions_out,
-      "recall"_a = recalls_out,
-      "scores"_a = scores_out);
-}
-
-} // namespace COCOeval
-
-} // namespace detectron2

spaces/Benson/text-generation/Examples/Blackjack 21 Blackjackist Descargar.md

@@ -1,93 +0,0 @@
-
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- <ul>
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-</ul>
- <h3>Las reglas y estrategias del blackjack</h3>
- <p>El blackjack es un juego de cartas en el que intentas vencer al crupier consiguiendo un valor de mano lo más cercano posible a 21, sin pasarte. El juego se juega con una o más barajas estándar de 52 cartas. Las cartas tienen los siguientes valores:</p>
- <ul>
-
-<li>Las cartas de la cara (Jack, Queen, King) valen 10. </li>
-<li>Las tarjetas numéricas valen su valor. </li>
-</ul>
- <p>El juego comienza con el repartidor repartiendo dos cartas a cada jugador y a sí mismos. Una de las cartas del repartidor está boca arriba y la otra boca abajo. Los jugadores pueden ver sus propias cartas y la carta boca arriba del repartidor. Los jugadores tienen que decidir qué hacer con sus manos. Tienen las siguientes opciones:</p>
-<p></p>
- <ul>
-<li><strong>Hit</strong>: Toma otra carta de la baraja. Puedes golpear tantas veces como quieras, pero si pasas de 21, te revientas y pierdes tu apuesta. </li>
-<li><strong>Stand</strong>: Mantén tu mano actual y termina tu turno. A continuaci��n, compara tu mano con la del dealer para ver quién gana. </li>
-<li><strong>Dobla hacia abajo</strong>: Dobla tu apuesta inicial y toma una carta más. Luego te paras con tu mano final. </li>
-<li><strong>Split</strong>: Si tienes dos cartas del mismo valor, puedes dividirlas en dos manos separadas y jugarlas independientemente. Tienes que hacer otra apuesta igual a tu apuesta original para la segunda mano. Puedes golpear o pararte en cada mano como siempre. </li>
-<li><strong>Rendirse</strong>: Si crees que tienes una mala mano, puedes rendirte y renunciar a la mitad de tu apuesta. A continuación, terminar su turno y perder la otra mitad de su apuesta. </li>
-<li><strong>Seguro</strong>: Si la carta boca arriba del repartidor es un as, puedes tomar un seguro, que es una apuesta lateral que paga 2:1 si el repartidor tiene un blackjack (una tarjeta de 10 valores y un as). Puedes apostar hasta la mitad de tu apuesta original al seguro. Si el crupier tiene blackjack, ganas la apuesta del seguro pero pierdes tu apuesta original. Si el dealer no tiene blackjack, pierdes la apuesta del seguro y continúas el juego como de costumbre. </li>
-</ul>
- <p>Después de que todos los jugadores hayan terminado sus turnos, el repartidor revela su carta boca abajo y juega su mano de acuerdo con las siguientes reglas:</p>
- <ul>
-<li> El distribuidor debe golpear hasta que su valor de la mano es 17 o superior. </li>
-
-<li>El distribuidor no debe dividir o doblar. </li>
-</ul>
- <p>El resultado del juego se determina comparando los valores finales de las manos de los jugadores y del repartidor. Los posibles resultados son:</p>
- <ul>
-<li>Si el jugador tiene un blackjack y el repartidor no, el jugador gana y se le paga 3:2 en su apuesta. </li>
-<li>Si tanto el jugador como el dealer tienen un blackjack, es un push y el jugador obtiene su apuesta de vuelta. </li>
-<li>Si el jugador tiene un valor de mano más alto que el repartidor sin pasar de 21, el jugador gana y se le paga 1:1 en su apuesta. </li>
-<li> Si tanto el jugador como el repartidor tienen el mismo valor de mano, es un push y el jugador obtiene su apuesta de nuevo. </li>
-<li>Si el jugador tiene un valor de mano más bajo que el del repartidor sin pasar de 21, o si el jugador pierde, el jugador pierde y pierde su apuesta. </li>
-<li> Si tanto el jugador como el crupier revientan, el jugador pierde y pierde su apuesta. </li>
-</ul>
- <p>Para aumentar tus posibilidades de ganar en el blackjack, necesitas usar algunas estrategias básicas que te digan qué hacer en diferentes situaciones. Por ejemplo, siempre debes dividir ases y ochos, nunca dividir dieces o cincos, doblar en 11 o 10 cuando el repartidor tiene una carta baja, golpear en 17 suave o más bajo, pararse en 17 duro o más alto, etc. Puede encontrar gráficos de estrategia más detallados en línea que le muestran cómo jugar cada mano posible contra cada carta de repartidor posible. </p>
- <h2>¿Cómo descargar y jugar Blackjack 21 Blackjackist? </h2>
- <p>Descargar y jugar Blackjack 21 Blackjackist es fácil y rápido. Solo tienes que seguir estos sencillos pasos:</p>
- <h3>Los pasos para descargar el juego en diferentes dispositivos</h3>
- <p>Dependiendo del dispositivo que quieras usar, puedes descargar el juego desde diferentes fuentes. Aquí están los enlaces e instrucciones para cada dispositivo:</p>
- <ul>
-
-<li><strong>iOS</strong>: Puede descargar el juego desde la App Store buscando "Blackjack 21 Blackjackist" o haciendo clic en este enlace. También puede escanear este código QR con su dispositivo para ir directamente a la página de descarga. Una vez que hayas descargado el juego, puedes abrirlo y empezar a jugar. </li>
-<li><strong>Windows</strong>: Puede descargar el juego desde el sitio web oficial haciendo clic en el botón "Descargar para Windows" o haciendo clic en este enlace. También puede escanear este código QR con su dispositivo para ir directamente a la página de descarga. Una vez que haya descargado el juego, puede ejecutar el instalador y seguir las instrucciones. Una vez completada la instalación, puede abrir el juego y comenzar a jugar. </li>
-<li><strong>Mac</strong>: Puede descargar el juego desde el sitio web oficial haciendo clic en el botón "Descargar para Mac" o haciendo clic en este enlace. También puede escanear este código QR con su dispositivo para ir directamente a la página de descarga. Una vez que haya descargado el juego, puede ejecutar el instalador y seguir las instrucciones. Una vez completada la instalación, puede abrir el juego y comenzar a jugar. </li>
-<li><strong>Facebook</strong>: Puedes jugar el juego en Facebook buscando "Blackjack 21 Blackjackist" o haciendo clic en este enlace. También puede escanear este código QR con su dispositivo para ir directamente a la página del juego. Una vez que hayas abierto el juego, puedes iniciar sesión con tu cuenta de Facebook y comenzar a jugar. </li>
-<li><strong>Browser</strong>: Puedes jugar el juego en tu navegador haciendo clic en el botón "Play Now" o haciendo clic en este enlace. También puede escanear este código QR con su dispositivo para ir directamente a la página del juego. Una vez que haya abierto el juego, puede iniciar sesión con su correo electrónico o cuenta de redes sociales y comenzar a jugar. </li>
-</ul>
- <h3>Los consejos y trucos para mejorar tus habilidades y ganar más fichas</h3>
-
- <ul>
-<li><strong>Practice</strong>: La mejor manera de mejorar en el blackjack es practicar todo lo que puedas. Puedes jugar en diferentes modos, como un solo jugador, multijugador o torneo, y probar diferentes estrategias y apuestas. También puedes usar el modo tutorial para aprender los fundamentos del blackjack y probar tus conocimientos. </li>
-<li><strong>Usa un gráfico de estrategia</strong>: Como mencionamos antes, usar un gráfico de estrategia te ayudará a tomar las mejores decisiones en cada situación. Puedes encontrar un gráfico de estrategia en línea o en el propio juego. También puedes personalizar tu propio gráfico de estrategia según tus preferencias y estilo. </li>
-<li><strong>Administra tu bankroll</strong>: Una de las habilidades más importantes en el blackjack es administrar tu bankroll sabiamente. Nunca debe apostar más de lo que puede permitirse perder, y siempre debe establecer un límite para usted. También debe variar sus apuestas según su situación y ventaja. Por ejemplo, debes apostar más cuando tienes una alta probabilidad de ganar, como cuando tienes un blackjack o un valor de mano alto, y apostar menos cuando tienes una baja probabilidad de ganar, como cuando el dealer tiene una carta alta o un as. También debe evitar apostar demasiado en el seguro, ya que generalmente es una mala apuesta. </li>
-<li><strong>Aprende de otros jugadores</strong>: Una de las ventajas de jugar Blackjack 21 Blackjackist es que puedes chatear e interactuar con otros jugadores. Puedes aprender de sus movimientos, errores y consejos. También puedes hacerles preguntas, compartir tus experiencias o retarlos a un juego. Puedes hacer nuevos amigos y divertirte mientras mejoras tus habilidades. </li>
-<li><strong>Diviértete</strong>: El consejo más importante de todos es divertirse jugando al blackjack. El blackjack es un juego de habilidad, suerte y estrategia, pero también es un juego de entretenimiento y diversión. No debe tomarlo demasiado en serio o frustrarse si pierde. Siempre debes recordar que es solo un juego y que el objetivo principal es divertirte. </li>
-
- <h2>Conclusión</h2>
- <p>Blackjack 21 Blackjackist es un gran juego de casino que te permite jugar blackjack en línea con millones de jugadores de todo el mundo. Puedes disfrutar de gráficos realistas en 3D, chatear con otros jugadores, obtener fichas gratis todos los días y aprender a jugar y ganar en el blackjack. Puede descargar y jugar el juego en su dispositivo de forma gratuita desde varias fuentes. También puede utilizar algunos consejos y trucos para mejorar sus habilidades y ganar más fichas. Blackjack 21 Blackjackist es un juego que te mantendrá entretenido y comprometido durante horas. </p>
- <p>Si estás listo para unirte a la comunidad de blackjack y divertirte, descarga Blackjack 21 Blackjackist hoy y empieza a jugar. ¡No te arrepentirás! </p>
- <h3>Preguntas frecuentes</h3>
- <p>Aquí hay algunas preguntas frecuentes sobre Blackjack 21 Blackjackist:</p>
- <ul>
-<li><strong>Q: ¿Cómo puedo obtener más fichas? </strong></li>
-<li><strong>A: Puedes obtener más fichas jugando el juego todos los días y recibiendo fichas gratis. También puedes obtener más fichas completando misiones diarias, desbloqueando logros, invitando a amigos o comprándolos con dinero real. </strong></li>
-<li><strong>Q: ¿Cómo puedo jugar con mis amigos? </strong></li>
-<li><strong>A: Puedes jugar con tus amigos invitándolos al juego a través de Facebook o correo electrónico. También puede unirse a sus tablas o crear sus propias tablas privadas. </strong></li>
-<li><strong>Q: ¿Cómo puedo cambiar mi avatar o apodo? </strong></li>
-<li><strong>A: Puedes cambiar tu avatar o apodo yendo a tu página de perfil y tocando el botón de edición. Puedes elegir entre diferentes avatares o subir tu propia foto. También puedes cambiar tu apodo escribiendo uno nuevo. </strong></li>
-<li><strong>Q: ¿Cómo me pongo en contacto con el servicio de atención al cliente? </strong></li>
-
-<li><strong>Q: ¿Cómo puedo eliminar mi cuenta? </strong></li>
-<li><strong>A: Puedes borrar tu cuenta yendo al menú de configuración y pulsando el botón de borrar cuenta. A continuación, se le pedirá que confirme su decisión. Una vez que elimine su cuenta, perderá todo su progreso, fichas y recompensas en el juego. </strong></li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Clash Royale Bluestacks Apk.md

@@ -1,35 +0,0 @@
-
-<h1>Choque Royale Bluestacks APK: Cómo jugar Clash Royale en PC</h1>
-<p>¿Te encanta jugar a Clash Royale, el popular juego de estrategia en tiempo real de Supercell? ¿Te gustaría poder jugar en una pantalla más grande con mejores gráficos y rendimiento? Si es así, estás de suerte. En este artículo, le mostraremos cómo descargar e instalar Clash Royale Bluestacks APK en su PC, y cómo disfrutar de la mejor experiencia de juego con Bluestacks, la plataforma de juego móvil más popular del mundo para Windows y Mac.</p>
-<h2>clash royale bluestacks apk</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash; <a href="https://bltlly.com/2v6KY0">https://bltlly.com/2v6KY0</a></b></p><br /><br />
-<h2>¿Qué es Clash Royale? </h2>
-<p>Clash Royale es un juego multijugador en línea donde te enfrentas a otros jugadores en duelos de ritmo rápido. Puedes elegir entre una variedad de personajes del universo Clash of Clans, como Gigantes, Reyes Bárbaros, Rompemuros, Arqueros y muchos más. También puedes recoger y mejorar cartas, construir tus propias barajas y unirte a clanes para compartir cartas y participar en guerras de clanes. </p>
-<p>Clash Royale es un juego que combina estrategia, habilidad y suerte. Tienes que desplegar tus tropas sabiamente, usar tus hechizos con eficacia y administrar tu elixir de manera eficiente. También tienes que adaptarte a diferentes escenarios, modos y desafíos. Clash Royale es un juego que nunca se vuelve aburrido, ya que siempre hay algo nuevo para descubrir y disfrutar. </p>
-<h2>¿Qué es Bluestacks? </h2>
-<p>Bluestacks es una plataforma de juegos móvil que te permite jugar juegos Android en tu PC o Mac. Es 100% seguro y de uso gratuito. Con Bluestacks, puedes acceder a millones de juegos de varios géneros, como RPG, estrategia, acción, rompecabezas, casual y más. También puedes jugar online o offline, dependiendo de tu preferencia. </p>
-
-<h2>Cómo descargar e instalar Clash Royale Bluestacks APK en PC</h2>
-<p>Si quieres jugar Clash Royale en tu PC con Bluestacks, debes seguir estos sencillos pasos:</p>
-<p></p>
-<h3>Paso 1: Descargar Bluestacks desde el sitio web oficial</h3>
-<p>Vaya a <a href="( 1 )">el sitio web oficial de Bluestacks</a> y haga clic en el botón "Descargar". Esto comenzará a descargar el archivo de instalación para Bluestacks 10 o Bluestacks 5, dependiendo de su elección. Ambas versiones son compatibles con Windows 7 o superior y Mac OS X 10.12 o superior. </p>
-<h3>Paso 2: Instalar Bluestacks en su PC</h3>
-<p>Una vez completada la descarga, abra el archivo de instalación y siga las instrucciones en la pantalla. El proceso de instalación puede tardar unos minutos, dependiendo de las especificaciones del sistema. Después de la instalación, verá un icono de acceso directo en el escritorio o en el menú de inicio de Bluestacks.</p>
-<h3>Paso 3: Inicie Bluestacks e inicie sesión con su cuenta de Google</h3>
-<p>Haga doble clic en el icono de Bluestacks para iniciar el reproductor de aplicaciones. Se le pedirá que inicie sesión con su cuenta de Google, que es necesaria para acceder a la Google Play Store y otros servicios de Google. Si no tienes una cuenta de Google, puedes crear una gratis. También puedes omitir este paso si quieres usar otras tiendas de aplicaciones o archivos APK. </p>
-<h3>Paso 4: Buscar Clash Royale en la tienda de aplicaciones Bluestacks o descargar el APK de Uptodown</h3>
-<p>Hay dos maneras de conseguir Clash Royale en Bluestacks. Una es buscarlo en la tienda de aplicaciones Bluestacks, que funciona con la Google Play Store. Puede encontrarlo escribiendo "Clash Royale" en la barra de búsqueda y haciendo clic en el botón "Instalar". La otra forma es descargar el archivo APK de un sitio web de terceros, como Uptodown. Puedes encontrarlo yendo a <a href=">el sitio web de Uptodown</a> y buscando "Clash Royale". A continuación, haga clic en el botón "Descargar" y guarde el archivo en su PC.</p>
-<h3>Paso 5: Instalar y abrir Clash Royale en Bluestacks</h3>
-
-<h2>Cómo jugar Clash Royale en PC con Bluestacks</h2>
-<p>Ahora que tienes Clash Royale en tu PC, puedes empezar a jugar con Bluestacks. Aquí hay algunos consejos y trucos para mejorar su experiencia de juego:</p>
-<h3>Personaliza tus controles de teclado y ratón para una jugabilidad óptima</h3>
-<p>Una de las mejores características de Bluestacks es que te permite personalizar tus controles de teclado y ratón para cualquier juego. Puede acceder a esta función haciendo clic en el icono "Teclado" en la esquina inferior derecha de la ventana Bluestacks. Esto abrirá un menú donde puede asignar teclas o botones del ratón a diferentes acciones, como desplegar tropas, usar hechizos, hacer zoom, etc. También puede usar mapas de teclas predefinidos o crear sus propios. Puede guardar sus ajustes y cambiar entre ellos en cualquier momento. </p>
-<h3>Disfruta de los gráficos Full HD y el buen rendimiento de Bluestacks</h3>
-<p>Otra gran característica de Bluestacks es que ofrece gráficos full HD y un rendimiento suave para cualquier juego. Puede ajustar la configuración de gráficos haciendo clic en el icono "Configuración" en la esquina superior derecha de la ventana Bluestacks. Esto abrirá un menú donde puede cambiar la resolución, la velocidad de fotogramas, el modo de visualización, DPI, etc. También puede habilitar o deshabilitar características como altas tasas de fotogramas, controles inteligentes, notificaciones de juegos, etc. También puede verificar los requisitos del sistema y la compatibilidad haciendo clic en el "Información del sistema" icono en el mismo menú. </p>
-<h3>Acceda a funciones exclusivas y recompensas de Bluestacks</h3>
-
-<h2>Conclusión</h2>
-<p>En conclusión, jugar Clash Royale en PC con Bluestacks es una gran manera de disfrutar de este increíble juego en una pantalla más grande con mejores gráficos y rendimiento. También puedes personalizar tus controles, acceder a funciones exclusivas y recompensas, y divertirte más con Bluestacks. Todo lo que necesita hacer es descargar e instalar Clash Royale Bluestacks APK en su PC siguiendo nuestros sencillos pasos anteriores. Entonces, ¿qué estás esperando? Comience a jugar Clash Royale en PC con Bluestacks hoy! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BirdL/DONOTUSEDemo/app.py

@@ -1,34 +0,0 @@
-import gradio as gr
-import torch
-from random import randint
-import os
-import huggingface_hub
-
-tok = os.getenv('HF_TOKEN')
-huggingface_hub.login(tok)
-
-from huggingface_hub import HfApi
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-config = PeftConfig.from_pretrained("BirdL/DONOTUSEV5")
-model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", token=tok, trust_remote_code=True)
-model = PeftModel.from_pretrained(model, "BirdL/DONOTUSEV5")
-tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t", token=tok)
-
-def response(message, history):
-    batch = tokenizer(message, return_tensors='pt')
-
-    with torch.cuda.amp.autocast():
-      output_tokens = model.generate(**batch, max_new_tokens=20)
-    output_tokens = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
-    filename = (("file" + str(randint(0, 1000000)) + ".txt"))
-    api = HfApi()
-    api.upload_file(
-        path_or_fileobj=("|Question:" + message + " |RespV2: " + output_tokens).encode('ascii') ,
-        path_in_repo=(filename),
-        repo_id="BirdL/Data",
-    )
-    
-    return output_tokens
-gr.ChatInterface(response).launch()

spaces/CVPR/LIVE/thrust/thrust/system/cpp/vector.h

@@ -1,69 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file thrust/system/cpp/vector.h
- *  \brief A dynamically-sizable array of elements which reside in memory available to
- *         Thrust's standard C++ system.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/cpp/memory.h>
-#include <thrust/detail/vector_base.h>
-#include <vector>
-
-namespace thrust
-{
-
-// forward declaration of host_vector
-template<typename T, typename Allocator> class host_vector;
-
-namespace system
-{
-namespace cpp
-{
-
-/*! \p cpp::vector is a container that supports random access to elements,
- *  constant time removal of elements at the end, and linear time insertion
- *  and removal of elements at the beginning or in the middle. The number of
- *  elements in a \p cpp::vector may vary dynamically; memory management is
- *  automatic. The elements contained in a \p cpp::vector reside in memory
- *  available to the \p cpp system.
- *
- *  \tparam T The element type of the \p cpp::vector.
- *  \tparam Allocator The allocator type of the \p cpp::vector. Defaults to \p cpp::allocator.
- *
- *  \see http://www.sgi.com/tech/stl/Vector.html
- *  \see host_vector For the documentation of the complete interface which is
- *                   shared by \p cpp::vector
- *  \see device_vector
- */
-template<typename T, typename Allocator = allocator<T> >
-using vector = thrust::detail::vector_base<T, Allocator>;
-
-} // end cpp
-} // end system
-
-// alias system::cpp names at top-level
-namespace cpp
-{
-
-using thrust::system::cpp::vector;
-
-} // end cpp
-
-} // end thrust

spaces/CVPR/regionclip-demo/detectron2/data/transforms/torchvision_transforms/transforms.py

@@ -1,1955 +0,0 @@
-import math
-import numbers
-import random
-import warnings
-from collections.abc import Sequence
-from typing import Tuple, List, Optional
-
-import torch
-from torch import Tensor
-
-try:
-    import accimage
-except ImportError:
-    accimage = None
-
-from . import functional as F
-from .functional import InterpolationMode, _interpolation_modes_from_int
-
-
-__all__ = ["Compose", "ToTensor", "PILToTensor", "ConvertImageDtype", "ToPILImage", "Normalize", "Resize", "Scale",
-           "CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop",
-           "RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop",
-           "LinearTransformation", "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale",
-           "RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationMode", "RandomInvert", "RandomPosterize",
-           "RandomSolarize", "RandomAdjustSharpness", "RandomAutocontrast", "RandomEqualize"]
-
-
-class Compose:
-    """Composes several transforms together. This transform does not support torchscript.
-    Please, see the note below.
-
-    Args:
-        transforms (list of ``Transform`` objects): list of transforms to compose.
-
-    Example:
-        >>> transforms.Compose([
-        >>>     transforms.CenterCrop(10),
-        >>>     transforms.ToTensor(),
-        >>> ])
-
-    .. note::
-        In order to script the transformations, please use ``torch.nn.Sequential`` as below.
-
-        >>> transforms = torch.nn.Sequential(
-        >>>     transforms.CenterCrop(10),
-        >>>     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
-        >>> )
-        >>> scripted_transforms = torch.jit.script(transforms)
-
-        Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
-        `lambda` functions or ``PIL.Image``.
-
-    """
-
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, img):
-        for t in self.transforms:
-            img = t(img)
-        return img
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        for t in self.transforms:
-            format_string += '\n'
-            format_string += '    {0}'.format(t)
-        format_string += '\n)'
-        return format_string
-
-
-class ToTensor:
-    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. This transform does not support torchscript.
-
-    Converts a PIL Image or numpy.ndarray (H x W x C) in the range
-    [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
-    if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
-    or if the numpy.ndarray has dtype = np.uint8
-
-    In the other cases, tensors are returned without scaling.
-
-    .. note::
-        Because the input image is scaled to [0.0, 1.0], this transformation should not be used when
-        transforming target image masks. See the `references`_ for implementing the transforms for image masks.
-
-    .. _references: https://github.com/pytorch/vision/tree/master/references/segmentation
-    """
-
-    def __call__(self, pic):
-        """
-        Args:
-            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
-
-        Returns:
-            Tensor: Converted image.
-        """
-        return F.to_tensor(pic)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '()'
-
-
-class PILToTensor:
-    """Convert a ``PIL Image`` to a tensor of the same type. This transform does not support torchscript.
-
-    Converts a PIL Image (H x W x C) to a Tensor of shape (C x H x W).
-    """
-
-    def __call__(self, pic):
-        """
-        Args:
-            pic (PIL Image): Image to be converted to tensor.
-
-        Returns:
-            Tensor: Converted image.
-        """
-        return F.pil_to_tensor(pic)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '()'
-
-
-class ConvertImageDtype(torch.nn.Module):
-    """Convert a tensor image to the given ``dtype`` and scale the values accordingly
-    This function does not support PIL Image.
-
-    Args:
-        dtype (torch.dtype): Desired data type of the output
-
-    .. note::
-
-        When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly.
-        If converted back and forth, this mismatch has no effect.
-
-    Raises:
-        RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as
-            well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to
-            overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range
-            of the integer ``dtype``.
-    """
-
-    def __init__(self, dtype: torch.dtype) -> None:
-        super().__init__()
-        self.dtype = dtype
-
-    def forward(self, image):
-        return F.convert_image_dtype(image, self.dtype)
-
-
-class ToPILImage:
-    """Convert a tensor or an ndarray to PIL Image. This transform does not support torchscript.
-
-    Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape
-    H x W x C to a PIL Image while preserving the value range.
-
-    Args:
-        mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).
-            If ``mode`` is ``None`` (default) there are some assumptions made about the input data:
-            - If the input has 4 channels, the ``mode`` is assumed to be ``RGBA``.
-            - If the input has 3 channels, the ``mode`` is assumed to be ``RGB``.
-            - If the input has 2 channels, the ``mode`` is assumed to be ``LA``.
-            - If the input has 1 channel, the ``mode`` is determined by the data type (i.e ``int``, ``float``,
-            ``short``).
-
-    .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes
-    """
-    def __init__(self, mode=None):
-        self.mode = mode
-
-    def __call__(self, pic):
-        """
-        Args:
-            pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
-
-        Returns:
-            PIL Image: Image converted to PIL Image.
-
-        """
-        return F.to_pil_image(pic, self.mode)
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        if self.mode is not None:
-            format_string += 'mode={0}'.format(self.mode)
-        format_string += ')'
-        return format_string
-
-
-class Normalize(torch.nn.Module):
-    """Normalize a tensor image with mean and standard deviation.
-    This transform does not support PIL Image.
-    Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
-    channels, this transform will normalize each channel of the input
-    ``torch.*Tensor`` i.e.,
-    ``output[channel] = (input[channel] - mean[channel]) / std[channel]``
-
-    .. note::
-        This transform acts out of place, i.e., it does not mutate the input tensor.
-
-    Args:
-        mean (sequence): Sequence of means for each channel.
-        std (sequence): Sequence of standard deviations for each channel.
-        inplace(bool,optional): Bool to make this operation in-place.
-
-    """
-
-    def __init__(self, mean, std, inplace=False):
-        super().__init__()
-        self.mean = mean
-        self.std = std
-        self.inplace = inplace
-
-    def forward(self, tensor: Tensor) -> Tensor:
-        """
-        Args:
-            tensor (Tensor): Tensor image to be normalized.
-
-        Returns:
-            Tensor: Normalized Tensor image.
-        """
-        return F.normalize(tensor, self.mean, self.std, self.inplace)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
-
-
-class Resize(torch.nn.Module):
-    """Resize the input image to the given size.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
-
-    .. warning::
-        The output image might be different depending on its type: when downsampling, the interpolation of PIL images
-        and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
-        in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
-        types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
-        closer.
-
-    Args:
-        size (sequence or int): Desired output size. If size is a sequence like
-            (h, w), output size will be matched to this. If size is an int,
-            smaller edge of the image will be matched to this number.
-            i.e, if height > width, then image will be rescaled to
-            (size * height / width, size).
-
-            .. note::
-                In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
-            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
-            ``InterpolationMode.BICUBIC`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
-        max_size (int, optional): The maximum allowed for the longer edge of
-            the resized image: if the longer edge of the image is greater
-            than ``max_size`` after being resized according to ``size``, then
-            the image is resized again so that the longer edge is equal to
-            ``max_size``. As a result, ``size`` might be overruled, i.e the
-            smaller edge may be shorter than ``size``. This is only supported
-            if ``size`` is an int (or a sequence of length 1 in torchscript
-            mode).
-        antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias
-            is always used. If ``img`` is Tensor, the flag is False by default and can be set to True for
-            ``InterpolationMode.BILINEAR`` only mode. This can help making the output for PIL images and tensors
-            closer.
-
-            .. warning::
-                There is no autodiff support for ``antialias=True`` option with input ``img`` as Tensor.
-
-    """
-
-    def __init__(self, size, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias=None):
-        super().__init__()
-        if not isinstance(size, (int, Sequence)):
-            raise TypeError("Size should be int or sequence. Got {}".format(type(size)))
-        if isinstance(size, Sequence) and len(size) not in (1, 2):
-            raise ValueError("If size is a sequence, it should have 1 or 2 values")
-        self.size = size
-        self.max_size = max_size
-
-        # Backward compatibility with integer value
-        if isinstance(interpolation, int):
-            warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
-            )
-            interpolation = _interpolation_modes_from_int(interpolation)
-
-        self.interpolation = interpolation
-        self.antialias = antialias
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be scaled.
-
-        Returns:
-            PIL Image or Tensor: Rescaled image.
-        """
-        return F.resize(img, self.size, self.interpolation, self.max_size, self.antialias)
-
-    def __repr__(self):
-        interpolate_str = self.interpolation.value
-        return self.__class__.__name__ + '(size={0}, interpolation={1}, max_size={2}, antialias={3})'.format(
-            self.size, interpolate_str, self.max_size, self.antialias)
-
-
-class Scale(Resize):
-    """
-    Note: This transform is deprecated in favor of Resize.
-    """
-    def __init__(self, *args, **kwargs):
-        warnings.warn("The use of the transforms.Scale transform is deprecated, " +
-                      "please use transforms.Resize instead.")
-        super(Scale, self).__init__(*args, **kwargs)
-
-
-class CenterCrop(torch.nn.Module):
-    """Crops the given image at the center.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
-    If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
-
-    Args:
-        size (sequence or int): Desired output size of the crop. If size is an
-            int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
-    """
-
-    def __init__(self, size):
-        super().__init__()
-        self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            PIL Image or Tensor: Cropped image.
-        """
-        return F.center_crop(img, self.size)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(size={0})'.format(self.size)
-
-
-class Pad(torch.nn.Module):
-    """Pad the given image on all sides with the given "pad" value.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric,
-    at most 3 leading dimensions for mode edge,
-    and an arbitrary number of leading dimensions for mode constant
-
-    Args:
-        padding (int or sequence): Padding on each border. If a single int is provided this
-            is used to pad all borders. If sequence of length 2 is provided this is the padding
-            on left/right and top/bottom respectively. If a sequence of length 4 is provided
-            this is the padding for the left, top, right and bottom borders respectively.
-
-            .. note::
-                In torchscript mode padding as single int is not supported, use a sequence of
-                length 1: ``[padding, ]``.
-        fill (number or str or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
-            length 3, it is used to fill R, G, B channels respectively.
-            This value is only used when the padding_mode is constant.
-            Only number is supported for torch Tensor.
-            Only int or str or tuple value is supported for PIL Image.
-        padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
-            Default is constant.
-
-            - constant: pads with a constant value, this value is specified with fill
-
-            - edge: pads with the last value at the edge of the image.
-              If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
-
-            - reflect: pads with reflection of image without repeating the last value on the edge.
-              For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
-              will result in [3, 2, 1, 2, 3, 4, 3, 2]
-
-            - symmetric: pads with reflection of image repeating the last value on the edge.
-              For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
-              will result in [2, 1, 1, 2, 3, 4, 4, 3]
-    """
-
-    def __init__(self, padding, fill=0, padding_mode="constant"):
-        super().__init__()
-        if not isinstance(padding, (numbers.Number, tuple, list)):
-            raise TypeError("Got inappropriate padding arg")
-
-        if not isinstance(fill, (numbers.Number, str, tuple)):
-            raise TypeError("Got inappropriate fill arg")
-
-        if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
-            raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
-
-        if isinstance(padding, Sequence) and len(padding) not in [1, 2, 4]:
-            raise ValueError("Padding must be an int or a 1, 2, or 4 element tuple, not a " +
-                             "{} element tuple".format(len(padding)))
-
-        self.padding = padding
-        self.fill = fill
-        self.padding_mode = padding_mode
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be padded.
-
-        Returns:
-            PIL Image or Tensor: Padded image.
-        """
-        return F.pad(img, self.padding, self.fill, self.padding_mode)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(padding={0}, fill={1}, padding_mode={2})'.\
-            format(self.padding, self.fill, self.padding_mode)
-
-
-class Lambda:
-    """Apply a user-defined lambda as a transform. This transform does not support torchscript.
-
-    Args:
-        lambd (function): Lambda/function to be used for transform.
-    """
-
-    def __init__(self, lambd):
-        if not callable(lambd):
-            raise TypeError("Argument lambd should be callable, got {}".format(repr(type(lambd).__name__)))
-        self.lambd = lambd
-
-    def __call__(self, img):
-        return self.lambd(img)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '()'
-
-
-class RandomTransforms:
-    """Base class for a list of transformations with randomness
-
-    Args:
-        transforms (sequence): list of transformations
-    """
-
-    def __init__(self, transforms):
-        if not isinstance(transforms, Sequence):
-            raise TypeError("Argument transforms should be a sequence")
-        self.transforms = transforms
-
-    def __call__(self, *args, **kwargs):
-        raise NotImplementedError()
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        for t in self.transforms:
-            format_string += '\n'
-            format_string += '    {0}'.format(t)
-        format_string += '\n)'
-        return format_string
-
-
-class RandomApply(torch.nn.Module):
-    """Apply randomly a list of transformations with a given probability.
-
-    .. note::
-        In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of
-        transforms as shown below:
-
-        >>> transforms = transforms.RandomApply(torch.nn.ModuleList([
-        >>>     transforms.ColorJitter(),
-        >>> ]), p=0.3)
-        >>> scripted_transforms = torch.jit.script(transforms)
-
-        Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
-        `lambda` functions or ``PIL.Image``.
-
-    Args:
-        transforms (sequence or torch.nn.Module): list of transformations
-        p (float): probability
-    """
-
-    def __init__(self, transforms, p=0.5):
-        super().__init__()
-        self.transforms = transforms
-        self.p = p
-
-    def forward(self, img):
-        if self.p < torch.rand(1):
-            return img
-        for t in self.transforms:
-            img = t(img)
-        return img
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        format_string += '\n    p={}'.format(self.p)
-        for t in self.transforms:
-            format_string += '\n'
-            format_string += '    {0}'.format(t)
-        format_string += '\n)'
-        return format_string
-
-
-class RandomOrder(RandomTransforms):
-    """Apply a list of transformations in a random order. This transform does not support torchscript.
-    """
-    def __call__(self, img):
-        order = list(range(len(self.transforms)))
-        random.shuffle(order)
-        for i in order:
-            img = self.transforms[i](img)
-        return img
-
-
-class RandomChoice(RandomTransforms):
-    """Apply single transformation randomly picked from a list. This transform does not support torchscript.
-    """
-    def __call__(self, img):
-        t = random.choice(self.transforms)
-        return t(img)
-
-
-class RandomCrop(torch.nn.Module):
-    """Crop the given image at a random location.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions,
-    but if non-constant padding is used, the input is expected to have at most 2 leading dimensions
-
-    Args:
-        size (sequence or int): Desired output size of the crop. If size is an
-            int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
-        padding (int or sequence, optional): Optional padding on each border
-            of the image. Default is None. If a single int is provided this
-            is used to pad all borders. If sequence of length 2 is provided this is the padding
-            on left/right and top/bottom respectively. If a sequence of length 4 is provided
-            this is the padding for the left, top, right and bottom borders respectively.
-
-            .. note::
-                In torchscript mode padding as single int is not supported, use a sequence of
-                length 1: ``[padding, ]``.
-        pad_if_needed (boolean): It will pad the image if smaller than the
-            desired size to avoid raising an exception. Since cropping is done
-            after padding, the padding seems to be done at a random offset.
-        fill (number or str or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
-            length 3, it is used to fill R, G, B channels respectively.
-            This value is only used when the padding_mode is constant.
-            Only number is supported for torch Tensor.
-            Only int or str or tuple value is supported for PIL Image.
-        padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
-            Default is constant.
-
-            - constant: pads with a constant value, this value is specified with fill
-
-            - edge: pads with the last value at the edge of the image.
-              If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
-
-            - reflect: pads with reflection of image without repeating the last value on the edge.
-              For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
-              will result in [3, 2, 1, 2, 3, 4, 3, 2]
-
-            - symmetric: pads with reflection of image repeating the last value on the edge.
-              For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
-              will result in [2, 1, 1, 2, 3, 4, 4, 3]
-    """
-
-    @staticmethod
-    def get_params(img: Tensor, output_size: Tuple[int, int]) -> Tuple[int, int, int, int]:
-        """Get parameters for ``crop`` for a random crop.
-
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-            output_size (tuple): Expected output size of the crop.
-
-        Returns:
-            tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
-        """
-        w, h = F._get_image_size(img)
-        th, tw = output_size
-
-        if h + 1 < th or w + 1 < tw:
-            raise ValueError(
-                "Required crop size {} is larger then input image size {}".format((th, tw), (h, w))
-            )
-
-        if w == tw and h == th:
-            return 0, 0, h, w
-
-        i = torch.randint(0, h - th + 1, size=(1, )).item()
-        j = torch.randint(0, w - tw + 1, size=(1, )).item()
-        return i, j, th, tw
-
-    def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"):
-        super().__init__()
-
-        self.size = tuple(_setup_size(
-            size, error_msg="Please provide only two dimensions (h, w) for size."
-        ))
-
-        self.padding = padding
-        self.pad_if_needed = pad_if_needed
-        self.fill = fill
-        self.padding_mode = padding_mode
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            PIL Image or Tensor: Cropped image.
-        """
-        if self.padding is not None:
-            img = F.pad(img, self.padding, self.fill, self.padding_mode)
-
-        width, height = F._get_image_size(img)
-        # pad the width if needed
-        if self.pad_if_needed and width < self.size[1]:
-            padding = [self.size[1] - width, 0]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
-        # pad the height if needed
-        if self.pad_if_needed and height < self.size[0]:
-            padding = [0, self.size[0] - height]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
-
-        i, j, h, w = self.get_params(img, self.size)
-
-        return F.crop(img, i, j, h, w)
-
-    def __repr__(self):
-        return self.__class__.__name__ + "(size={0}, padding={1})".format(self.size, self.padding)
-
-
-class RandomHorizontalFlip(torch.nn.Module):
-    """Horizontally flip the given image randomly with a given probability.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
-
-    Args:
-        p (float): probability of the image being flipped. Default value is 0.5
-    """
-
-    def __init__(self, p=0.5):
-        super().__init__()
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be flipped.
-
-        Returns:
-            PIL Image or Tensor: Randomly flipped image.
-        """
-        if torch.rand(1) < self.p:
-            return F.hflip(img)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={})'.format(self.p)
-
-
-class RandomVerticalFlip(torch.nn.Module):
-    """Vertically flip the given image randomly with a given probability.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
-
-    Args:
-        p (float): probability of the image being flipped. Default value is 0.5
-    """
-
-    def __init__(self, p=0.5):
-        super().__init__()
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be flipped.
-
-        Returns:
-            PIL Image or Tensor: Randomly flipped image.
-        """
-        if torch.rand(1) < self.p:
-            return F.vflip(img)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={})'.format(self.p)
-
-
-class RandomPerspective(torch.nn.Module):
-    """Performs a random perspective transformation of the given image with a given probability.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
-
-    Args:
-        distortion_scale (float): argument to control the degree of distortion and ranges from 0 to 1.
-            Default is 0.5.
-        p (float): probability of the image being transformed. Default is 0.5.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
-            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
-        fill (sequence or number): Pixel fill value for the area outside the transformed
-            image. Default is ``0``. If given a number, the value is used for all bands respectively.
-    """
-
-    def __init__(self, distortion_scale=0.5, p=0.5, interpolation=InterpolationMode.BILINEAR, fill=0):
-        super().__init__()
-        self.p = p
-
-        # Backward compatibility with integer value
-        if isinstance(interpolation, int):
-            warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
-            )
-            interpolation = _interpolation_modes_from_int(interpolation)
-
-        self.interpolation = interpolation
-        self.distortion_scale = distortion_scale
-
-        if fill is None:
-            fill = 0
-        elif not isinstance(fill, (Sequence, numbers.Number)):
-            raise TypeError("Fill should be either a sequence or a number.")
-
-        self.fill = fill
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be Perspectively transformed.
-
-        Returns:
-            PIL Image or Tensor: Randomly transformed image.
-        """
-
-        fill = self.fill
-        if isinstance(img, Tensor):
-            if isinstance(fill, (int, float)):
-                fill = [float(fill)] * F._get_image_num_channels(img)
-            else:
-                fill = [float(f) for f in fill]
-
-        if torch.rand(1) < self.p:
-            width, height = F._get_image_size(img)
-            startpoints, endpoints = self.get_params(width, height, self.distortion_scale)
-            return F.perspective(img, startpoints, endpoints, self.interpolation, fill)
-        return img
-
-    @staticmethod
-    def get_params(width: int, height: int, distortion_scale: float) -> Tuple[List[List[int]], List[List[int]]]:
-        """Get parameters for ``perspective`` for a random perspective transform.
-
-        Args:
-            width (int): width of the image.
-            height (int): height of the image.
-            distortion_scale (float): argument to control the degree of distortion and ranges from 0 to 1.
-
-        Returns:
-            List containing [top-left, top-right, bottom-right, bottom-left] of the original image,
-            List containing [top-left, top-right, bottom-right, bottom-left] of the transformed image.
-        """
-        half_height = height // 2
-        half_width = width // 2
-        topleft = [
-            int(torch.randint(0, int(distortion_scale * half_width) + 1, size=(1, )).item()),
-            int(torch.randint(0, int(distortion_scale * half_height) + 1, size=(1, )).item())
-        ]
-        topright = [
-            int(torch.randint(width - int(distortion_scale * half_width) - 1, width, size=(1, )).item()),
-            int(torch.randint(0, int(distortion_scale * half_height) + 1, size=(1, )).item())
-        ]
-        botright = [
-            int(torch.randint(width - int(distortion_scale * half_width) - 1, width, size=(1, )).item()),
-            int(torch.randint(height - int(distortion_scale * half_height) - 1, height, size=(1, )).item())
-        ]
-        botleft = [
-            int(torch.randint(0, int(distortion_scale * half_width) + 1, size=(1, )).item()),
-            int(torch.randint(height - int(distortion_scale * half_height) - 1, height, size=(1, )).item())
-        ]
-        startpoints = [[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]]
-        endpoints = [topleft, topright, botright, botleft]
-        return startpoints, endpoints
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={})'.format(self.p)
-
-
-class RandomResizedCrop(torch.nn.Module):
-    """Crop a random portion of image and resize it to a given size.
-
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
-
-    A crop of the original image is made: the crop has a random area (H * W)
-    and a random aspect ratio. This crop is finally resized to the given
-    size. This is popularly used to train the Inception networks.
-
-    Args:
-        size (int or sequence): expected output size of the crop, for each edge. If size is an
-            int instead of sequence like (h, w), a square output size ``(size, size)`` is
-            made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
-
-            .. note::
-                In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
-        scale (tuple of float): Specifies the lower and upper bounds for the random area of the crop,
-            before resizing. The scale is defined with respect to the area of the original image.
-        ratio (tuple of float): lower and upper bounds for the random aspect ratio of the crop, before
-            resizing.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
-            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
-            ``InterpolationMode.BICUBIC`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
-
-    """
-
-    def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=InterpolationMode.BILINEAR):
-        super().__init__()
-        self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
-
-        if not isinstance(scale, Sequence):
-            raise TypeError("Scale should be a sequence")
-        if not isinstance(ratio, Sequence):
-            raise TypeError("Ratio should be a sequence")
-        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
-            warnings.warn("Scale and ratio should be of kind (min, max)")
-
-        # Backward compatibility with integer value
-        if isinstance(interpolation, int):
-            warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
-            )
-            interpolation = _interpolation_modes_from_int(interpolation)
-
-        self.interpolation = interpolation
-        self.scale = scale
-        self.ratio = ratio
-
-    @staticmethod
-    def get_params(
-            img: Tensor, scale: List[float], ratio: List[float]
-    ) -> Tuple[int, int, int, int]:
-        """Get parameters for ``crop`` for a random sized crop.
-
-        Args:
-            img (PIL Image or Tensor): Input image.
-            scale (list): range of scale of the origin size cropped
-            ratio (list): range of aspect ratio of the origin aspect ratio cropped
-
-        Returns:
-            tuple: params (i, j, h, w) to be passed to ``crop`` for a random
-            sized crop.
-        """
-        width, height = F._get_image_size(img)
-        area = height * width
-
-        log_ratio = torch.log(torch.tensor(ratio))
-        for _ in range(10):
-            target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
-            aspect_ratio = torch.exp(
-                torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
-            ).item()
-
-            w = int(round(math.sqrt(target_area * aspect_ratio)))
-            h = int(round(math.sqrt(target_area / aspect_ratio)))
-
-            if 0 < w <= width and 0 < h <= height:
-                i = torch.randint(0, height - h + 1, size=(1,)).item()
-                j = torch.randint(0, width - w + 1, size=(1,)).item()
-                return i, j, h, w
-
-        # Fallback to central crop
-        in_ratio = float(width) / float(height)
-        if in_ratio < min(ratio):
-            w = width
-            h = int(round(w / min(ratio)))
-        elif in_ratio > max(ratio):
-            h = height
-            w = int(round(h * max(ratio)))
-        else:  # whole image
-            w = width
-            h = height
-        i = (height - h) // 2
-        j = (width - w) // 2
-        return i, j, h, w
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be cropped and resized.
-
-        Returns:
-            PIL Image or Tensor: Randomly cropped and resized image.
-        """
-        i, j, h, w = self.get_params(img, self.scale, self.ratio)
-        return F.resized_crop(img, i, j, h, w, self.size, self.interpolation)
-
-    def __repr__(self):
-        interpolate_str = self.interpolation.value
-        format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
-        format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale))
-        format_string += ', ratio={0}'.format(tuple(round(r, 4) for r in self.ratio))
-        format_string += ', interpolation={0})'.format(interpolate_str)
-        return format_string
-
-
-class RandomSizedCrop(RandomResizedCrop):
-    """
-    Note: This transform is deprecated in favor of RandomResizedCrop.
-    """
-    def __init__(self, *args, **kwargs):
-        warnings.warn("The use of the transforms.RandomSizedCrop transform is deprecated, " +
-                      "please use transforms.RandomResizedCrop instead.")
-        super(RandomSizedCrop, self).__init__(*args, **kwargs)
-
-
-class FiveCrop(torch.nn.Module):
-    """Crop the given image into four corners and the central crop.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
-
-    .. Note::
-         This transform returns a tuple of images and there may be a mismatch in the number of
-         inputs and targets your Dataset returns. See below for an example of how to deal with
-         this.
-
-    Args:
-         size (sequence or int): Desired output size of the crop. If size is an ``int``
-            instead of sequence like (h, w), a square crop of size (size, size) is made.
-            If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
-
-    Example:
-         >>> transform = Compose([
-         >>>    FiveCrop(size), # this is a list of PIL Images
-         >>>    Lambda(lambda crops: torch.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor
-         >>> ])
-         >>> #In your test loop you can do the following:
-         >>> input, target = batch # input is a 5d tensor, target is 2d
-         >>> bs, ncrops, c, h, w = input.size()
-         >>> result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops
-         >>> result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops
-    """
-
-    def __init__(self, size):
-        super().__init__()
-        self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            tuple of 5 images. Image can be PIL Image or Tensor
-        """
-        return F.five_crop(img, self.size)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(size={0})'.format(self.size)
-
-
-class TenCrop(torch.nn.Module):
-    """Crop the given image into four corners and the central crop plus the flipped version of
-    these (horizontal flipping is used by default).
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
-
-    .. Note::
-         This transform returns a tuple of images and there may be a mismatch in the number of
-         inputs and targets your Dataset returns. See below for an example of how to deal with
-         this.
-
-    Args:
-        size (sequence or int): Desired output size of the crop. If size is an
-            int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
-        vertical_flip (bool): Use vertical flipping instead of horizontal
-
-    Example:
-         >>> transform = Compose([
-         >>>    TenCrop(size), # this is a list of PIL Images
-         >>>    Lambda(lambda crops: torch.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor
-         >>> ])
-         >>> #In your test loop you can do the following:
-         >>> input, target = batch # input is a 5d tensor, target is 2d
-         >>> bs, ncrops, c, h, w = input.size()
-         >>> result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops
-         >>> result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops
-    """
-
-    def __init__(self, size, vertical_flip=False):
-        super().__init__()
-        self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
-        self.vertical_flip = vertical_flip
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            tuple of 10 images. Image can be PIL Image or Tensor
-        """
-        return F.ten_crop(img, self.size, self.vertical_flip)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(size={0}, vertical_flip={1})'.format(self.size, self.vertical_flip)
-
-
-class LinearTransformation(torch.nn.Module):
-    """Transform a tensor image with a square transformation matrix and a mean_vector computed
-    offline.
-    This transform does not support PIL Image.
-    Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and
-    subtract mean_vector from it which is then followed by computing the dot
-    product with the transformation matrix and then reshaping the tensor to its
-    original shape.
-
-    Applications:
-        whitening transformation: Suppose X is a column vector zero-centered data.
-        Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X),
-        perform SVD on this matrix and pass it as transformation_matrix.
-
-    Args:
-        transformation_matrix (Tensor): tensor [D x D], D = C x H x W
-        mean_vector (Tensor): tensor [D], D = C x H x W
-    """
-
-    def __init__(self, transformation_matrix, mean_vector):
-        super().__init__()
-        if transformation_matrix.size(0) != transformation_matrix.size(1):
-            raise ValueError("transformation_matrix should be square. Got " +
-                             "[{} x {}] rectangular matrix.".format(*transformation_matrix.size()))
-
-        if mean_vector.size(0) != transformation_matrix.size(0):
-            raise ValueError("mean_vector should have the same length {}".format(mean_vector.size(0)) +
-                             " as any one of the dimensions of the transformation_matrix [{}]"
-                             .format(tuple(transformation_matrix.size())))
-
-        if transformation_matrix.device != mean_vector.device:
-            raise ValueError("Input tensors should be on the same device. Got {} and {}"
-                             .format(transformation_matrix.device, mean_vector.device))
-
-        self.transformation_matrix = transformation_matrix
-        self.mean_vector = mean_vector
-
-    def forward(self, tensor: Tensor) -> Tensor:
-        """
-        Args:
-            tensor (Tensor): Tensor image to be whitened.
-
-        Returns:
-            Tensor: Transformed image.
-        """
-        shape = tensor.shape
-        n = shape[-3] * shape[-2] * shape[-1]
-        if n != self.transformation_matrix.shape[0]:
-            raise ValueError("Input tensor and transformation matrix have incompatible shape." +
-                             "[{} x {} x {}] != ".format(shape[-3], shape[-2], shape[-1]) +
-                             "{}".format(self.transformation_matrix.shape[0]))
-
-        if tensor.device.type != self.mean_vector.device.type:
-            raise ValueError("Input tensor should be on the same device as transformation matrix and mean vector. "
-                             "Got {} vs {}".format(tensor.device, self.mean_vector.device))
-
-        flat_tensor = tensor.view(-1, n) - self.mean_vector
-        transformed_tensor = torch.mm(flat_tensor, self.transformation_matrix)
-        tensor = transformed_tensor.view(shape)
-        return tensor
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '(transformation_matrix='
-        format_string += (str(self.transformation_matrix.tolist()) + ')')
-        format_string += (", (mean_vector=" + str(self.mean_vector.tolist()) + ')')
-        return format_string
-
-
-class ColorJitter(torch.nn.Module):
-    """Randomly change the brightness, contrast, saturation and hue of an image.
-    If the image is torch Tensor, it is expected
-    to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
-    If img is PIL Image, mode "1", "L", "I", "F" and modes with transparency (alpha channel) are not supported.
-
-    Args:
-        brightness (float or tuple of float (min, max)): How much to jitter brightness.
-            brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
-            or the given [min, max]. Should be non negative numbers.
-        contrast (float or tuple of float (min, max)): How much to jitter contrast.
-            contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
-            or the given [min, max]. Should be non negative numbers.
-        saturation (float or tuple of float (min, max)): How much to jitter saturation.
-            saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
-            or the given [min, max]. Should be non negative numbers.
-        hue (float or tuple of float (min, max)): How much to jitter hue.
-            hue_factor is chosen uniformly from [-hue, hue] or the given [min, max].
-            Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
-    """
-
-    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
-        super().__init__()
-        self.brightness = self._check_input(brightness, 'brightness')
-        self.contrast = self._check_input(contrast, 'contrast')
-        self.saturation = self._check_input(saturation, 'saturation')
-        self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),
-                                     clip_first_on_zero=False)
-
-    @torch.jit.unused
-    def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
-        if isinstance(value, numbers.Number):
-            if value < 0:
-                raise ValueError("If {} is a single number, it must be non negative.".format(name))
-            value = [center - float(value), center + float(value)]
-            if clip_first_on_zero:
-                value[0] = max(value[0], 0.0)
-        elif isinstance(value, (tuple, list)) and len(value) == 2:
-            if not bound[0] <= value[0] <= value[1] <= bound[1]:
-                raise ValueError("{} values should be between {}".format(name, bound))
-        else:
-            raise TypeError("{} should be a single number or a list/tuple with length 2.".format(name))
-
-        # if value is 0 or (1., 1.) for brightness/contrast/saturation
-        # or (0., 0.) for hue, do nothing
-        if value[0] == value[1] == center:
-            value = None
-        return value
-
-    @staticmethod
-    def get_params(brightness: Optional[List[float]],
-                   contrast: Optional[List[float]],
-                   saturation: Optional[List[float]],
-                   hue: Optional[List[float]]
-                   ) -> Tuple[Tensor, Optional[float], Optional[float], Optional[float], Optional[float]]:
-        """Get the parameters for the randomized transform to be applied on image.
-
-        Args:
-            brightness (tuple of float (min, max), optional): The range from which the brightness_factor is chosen
-                uniformly. Pass None to turn off the transformation.
-            contrast (tuple of float (min, max), optional): The range from which the contrast_factor is chosen
-                uniformly. Pass None to turn off the transformation.
-            saturation (tuple of float (min, max), optional): The range from which the saturation_factor is chosen
-                uniformly. Pass None to turn off the transformation.
-            hue (tuple of float (min, max), optional): The range from which the hue_factor is chosen uniformly.
-                Pass None to turn off the transformation.
-
-        Returns:
-            tuple: The parameters used to apply the randomized transform
-            along with their random order.
-        """
-        fn_idx = torch.randperm(4)
-
-        b = None if brightness is None else float(torch.empty(1).uniform_(brightness[0], brightness[1]))
-        c = None if contrast is None else float(torch.empty(1).uniform_(contrast[0], contrast[1]))
-        s = None if saturation is None else float(torch.empty(1).uniform_(saturation[0], saturation[1]))
-        h = None if hue is None else float(torch.empty(1).uniform_(hue[0], hue[1]))
-
-        return fn_idx, b, c, s, h
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Input image.
-
-        Returns:
-            PIL Image or Tensor: Color jittered image.
-        """
-        fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = \
-            self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
-
-        for fn_id in fn_idx:
-            if fn_id == 0 and brightness_factor is not None:
-                img = F.adjust_brightness(img, brightness_factor)
-            elif fn_id == 1 and contrast_factor is not None:
-                img = F.adjust_contrast(img, contrast_factor)
-            elif fn_id == 2 and saturation_factor is not None:
-                img = F.adjust_saturation(img, saturation_factor)
-            elif fn_id == 3 and hue_factor is not None:
-                img = F.adjust_hue(img, hue_factor)
-
-        return img
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        format_string += 'brightness={0}'.format(self.brightness)
-        format_string += ', contrast={0}'.format(self.contrast)
-        format_string += ', saturation={0}'.format(self.saturation)
-        format_string += ', hue={0})'.format(self.hue)
-        return format_string
-
-
-class RandomRotation(torch.nn.Module):
-    """Rotate the image by angle.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
-
-    Args:
-        degrees (sequence or number): Range of degrees to select from.
-            If degrees is a number instead of sequence like (min, max), the range of degrees
-            will be (-degrees, +degrees).
-        interpolation (InterpolationMode): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
-            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
-        expand (bool, optional): Optional expansion flag.
-            If true, expands the output to make it large enough to hold the entire rotated image.
-            If false or omitted, make the output image the same size as the input image.
-            Note that the expand flag assumes rotation around the center and no translation.
-        center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
-            Default is the center of the image.
-        fill (sequence or number): Pixel fill value for the area outside the rotated
-            image. Default is ``0``. If given a number, the value is used for all bands respectively.
-        resample (int, optional): deprecated argument and will be removed since v0.10.0.
-            Please use the ``interpolation`` parameter instead.
-
-    .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
-
-    """
-
-    def __init__(
-        self, degrees, interpolation=InterpolationMode.NEAREST, expand=False, center=None, fill=0, resample=None
-    ):
-        super().__init__()
-        if resample is not None:
-            warnings.warn(
-                "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
-            )
-            interpolation = _interpolation_modes_from_int(resample)
-
-        # Backward compatibility with integer value
-        if isinstance(interpolation, int):
-            warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
-            )
-            interpolation = _interpolation_modes_from_int(interpolation)
-
-        self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2, ))
-
-        if center is not None:
-            _check_sequence_input(center, "center", req_sizes=(2, ))
-
-        self.center = center
-
-        self.resample = self.interpolation = interpolation
-        self.expand = expand
-
-        if fill is None:
-            fill = 0
-        elif not isinstance(fill, (Sequence, numbers.Number)):
-            raise TypeError("Fill should be either a sequence or a number.")
-
-        self.fill = fill
-
-    @staticmethod
-    def get_params(degrees: List[float]) -> float:
-        """Get parameters for ``rotate`` for a random rotation.
-
-        Returns:
-            float: angle parameter to be passed to ``rotate`` for random rotation.
-        """
-        angle = float(torch.empty(1).uniform_(float(degrees[0]), float(degrees[1])).item())
-        return angle
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be rotated.
-
-        Returns:
-            PIL Image or Tensor: Rotated image.
-        """
-        fill = self.fill
-        if isinstance(img, Tensor):
-            if isinstance(fill, (int, float)):
-                fill = [float(fill)] * F._get_image_num_channels(img)
-            else:
-                fill = [float(f) for f in fill]
-        angle = self.get_params(self.degrees)
-
-        return F.rotate(img, angle, self.resample, self.expand, self.center, fill)
-
-    def __repr__(self):
-        interpolate_str = self.interpolation.value
-        format_string = self.__class__.__name__ + '(degrees={0}'.format(self.degrees)
-        format_string += ', interpolation={0}'.format(interpolate_str)
-        format_string += ', expand={0}'.format(self.expand)
-        if self.center is not None:
-            format_string += ', center={0}'.format(self.center)
-        if self.fill is not None:
-            format_string += ', fill={0}'.format(self.fill)
-        format_string += ')'
-        return format_string
-
-
-class RandomAffine(torch.nn.Module):
-    """Random affine transformation of the image keeping center invariant.
-    If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
-
-    Args:
-        degrees (sequence or number): Range of degrees to select from.
-            If degrees is a number instead of sequence like (min, max), the range of degrees
-            will be (-degrees, +degrees). Set to 0 to deactivate rotations.
-        translate (tuple, optional): tuple of maximum absolute fraction for horizontal
-            and vertical translations. For example translate=(a, b), then horizontal shift
-            is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
-            randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.
-        scale (tuple, optional): scaling factor interval, e.g (a, b), then scale is
-            randomly sampled from the range a <= scale <= b. Will keep original scale by default.
-        shear (sequence or number, optional): Range of degrees to select from.
-            If shear is a number, a shear parallel to the x axis in the range (-shear, +shear)
-            will be applied. Else if shear is a sequence of 2 values a shear parallel to the x axis in the
-            range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values,
-            a x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
-            Will not apply shear by default.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
-            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
-            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
-            For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
-        fill (sequence or number): Pixel fill value for the area outside the transformed
-            image. Default is ``0``. If given a number, the value is used for all bands respectively.
-        fillcolor (sequence or number, optional): deprecated argument and will be removed since v0.10.0.
-            Please use the ``fill`` parameter instead.
-        resample (int, optional): deprecated argument and will be removed since v0.10.0.
-            Please use the ``interpolation`` parameter instead.
-
-    .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
-
-    """
-
-    def __init__(
-        self, degrees, translate=None, scale=None, shear=None, interpolation=InterpolationMode.NEAREST, fill=0,
-        fillcolor=None, resample=None
-    ):
-        super().__init__()
-        if resample is not None:
-            warnings.warn(
-                "Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead"
-            )
-            interpolation = _interpolation_modes_from_int(resample)
-
-        # Backward compatibility with integer value
-        if isinstance(interpolation, int):
-            warnings.warn(
-                "Argument interpolation should be of type InterpolationMode instead of int. "
-                "Please, use InterpolationMode enum."
-            )
-            interpolation = _interpolation_modes_from_int(interpolation)
-
-        if fillcolor is not None:
-            warnings.warn(
-                "Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead"
-            )
-            fill = fillcolor
-
-        self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2, ))
-
-        if translate is not None:
-            _check_sequence_input(translate, "translate", req_sizes=(2, ))
-            for t in translate:
-                if not (0.0 <= t <= 1.0):
-                    raise ValueError("translation values should be between 0 and 1")
-        self.translate = translate
-
-        if scale is not None:
-            _check_sequence_input(scale, "scale", req_sizes=(2, ))
-            for s in scale:
-                if s <= 0:
-                    raise ValueError("scale values should be positive")
-        self.scale = scale
-
-        if shear is not None:
-            self.shear = _setup_angle(shear, name="shear", req_sizes=(2, 4))
-        else:
-            self.shear = shear
-
-        self.resample = self.interpolation = interpolation
-
-        if fill is None:
-            fill = 0
-        elif not isinstance(fill, (Sequence, numbers.Number)):
-            raise TypeError("Fill should be either a sequence or a number.")
-
-        self.fillcolor = self.fill = fill
-
-    @staticmethod
-    def get_params(
-            degrees: List[float],
-            translate: Optional[List[float]],
-            scale_ranges: Optional[List[float]],
-            shears: Optional[List[float]],
-            img_size: List[int]
-    ) -> Tuple[float, Tuple[int, int], float, Tuple[float, float]]:
-        """Get parameters for affine transformation
-
-        Returns:
-            params to be passed to the affine transformation
-        """
-        angle = float(torch.empty(1).uniform_(float(degrees[0]), float(degrees[1])).item())
-        if translate is not None:
-            max_dx = float(translate[0] * img_size[0])
-            max_dy = float(translate[1] * img_size[1])
-            tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))
-            ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))
-            translations = (tx, ty)
-        else:
-            translations = (0, 0)
-
-        if scale_ranges is not None:
-            scale = float(torch.empty(1).uniform_(scale_ranges[0], scale_ranges[1]).item())
-        else:
-            scale = 1.0
-
-        shear_x = shear_y = 0.0
-        if shears is not None:
-            shear_x = float(torch.empty(1).uniform_(shears[0], shears[1]).item())
-            if len(shears) == 4:
-                shear_y = float(torch.empty(1).uniform_(shears[2], shears[3]).item())
-
-        shear = (shear_x, shear_y)
-
-        return angle, translations, scale, shear
-
-    def forward(self, img):
-        """
-            img (PIL Image or Tensor): Image to be transformed.
-
-        Returns:
-            PIL Image or Tensor: Affine transformed image.
-        """
-        fill = self.fill
-        if isinstance(img, Tensor):
-            if isinstance(fill, (int, float)):
-                fill = [float(fill)] * F._get_image_num_channels(img)
-            else:
-                fill = [float(f) for f in fill]
-
-        img_size = F._get_image_size(img)
-
-        ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img_size)
-
-        return F.affine(img, *ret, interpolation=self.interpolation, fill=fill)
-
-    def __repr__(self):
-        s = '{name}(degrees={degrees}'
-        if self.translate is not None:
-            s += ', translate={translate}'
-        if self.scale is not None:
-            s += ', scale={scale}'
-        if self.shear is not None:
-            s += ', shear={shear}'
-        if self.interpolation != InterpolationMode.NEAREST:
-            s += ', interpolation={interpolation}'
-        if self.fill != 0:
-            s += ', fill={fill}'
-        s += ')'
-        d = dict(self.__dict__)
-        d['interpolation'] = self.interpolation.value
-        return s.format(name=self.__class__.__name__, **d)
-
-
-class Grayscale(torch.nn.Module):
-    """Convert image to grayscale.
-    If the image is torch Tensor, it is expected
-    to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
-
-    Args:
-        num_output_channels (int): (1 or 3) number of channels desired for output image
-
-    Returns:
-        PIL Image: Grayscale version of the input.
-
-        - If ``num_output_channels == 1`` : returned image is single channel
-        - If ``num_output_channels == 3`` : returned image is 3 channel with r == g == b
-
-    """
-
-    def __init__(self, num_output_channels=1):
-        super().__init__()
-        self.num_output_channels = num_output_channels
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be converted to grayscale.
-
-        Returns:
-            PIL Image or Tensor: Grayscaled image.
-        """
-        return F.rgb_to_grayscale(img, num_output_channels=self.num_output_channels)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(num_output_channels={0})'.format(self.num_output_channels)
-
-
-class RandomGrayscale(torch.nn.Module):
-    """Randomly convert image to grayscale with a probability of p (default 0.1).
-    If the image is torch Tensor, it is expected
-    to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
-
-    Args:
-        p (float): probability that image should be converted to grayscale.
-
-    Returns:
-        PIL Image or Tensor: Grayscale version of the input image with probability p and unchanged
-        with probability (1-p).
-        - If input image is 1 channel: grayscale version is 1 channel
-        - If input image is 3 channel: grayscale version is 3 channel with r == g == b
-
-    """
-
-    def __init__(self, p=0.1):
-        super().__init__()
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be converted to grayscale.
-
-        Returns:
-            PIL Image or Tensor: Randomly grayscaled image.
-        """
-        num_output_channels = F._get_image_num_channels(img)
-        if torch.rand(1) < self.p:
-            return F.rgb_to_grayscale(img, num_output_channels=num_output_channels)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={0})'.format(self.p)
-
-
-class RandomErasing(torch.nn.Module):
-    """ Randomly selects a rectangle region in an torch Tensor image and erases its pixels.
-    This transform does not support PIL Image.
-    'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896
-
-    Args:
-         p: probability that the random erasing operation will be performed.
-         scale: range of proportion of erased area against input image.
-         ratio: range of aspect ratio of erased area.
-         value: erasing value. Default is 0. If a single int, it is used to
-            erase all pixels. If a tuple of length 3, it is used to erase
-            R, G, B channels respectively.
-            If a str of 'random', erasing each pixel with random values.
-         inplace: boolean to make this transform inplace. Default set to False.
-
-    Returns:
-        Erased Image.
-
-    Example:
-        >>> transform = transforms.Compose([
-        >>>   transforms.RandomHorizontalFlip(),
-        >>>   transforms.ToTensor(),
-        >>>   transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
-        >>>   transforms.RandomErasing(),
-        >>> ])
-    """
-
-    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False):
-        super().__init__()
-        if not isinstance(value, (numbers.Number, str, tuple, list)):
-            raise TypeError("Argument value should be either a number or str or a sequence")
-        if isinstance(value, str) and value != "random":
-            raise ValueError("If value is str, it should be 'random'")
-        if not isinstance(scale, (tuple, list)):
-            raise TypeError("Scale should be a sequence")
-        if not isinstance(ratio, (tuple, list)):
-            raise TypeError("Ratio should be a sequence")
-        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
-            warnings.warn("Scale and ratio should be of kind (min, max)")
-        if scale[0] < 0 or scale[1] > 1:
-            raise ValueError("Scale should be between 0 and 1")
-        if p < 0 or p > 1:
-            raise ValueError("Random erasing probability should be between 0 and 1")
-
-        self.p = p
-        self.scale = scale
-        self.ratio = ratio
-        self.value = value
-        self.inplace = inplace
-
-    @staticmethod
-    def get_params(
-            img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None
-    ) -> Tuple[int, int, int, int, Tensor]:
-        """Get parameters for ``erase`` for a random erasing.
-
-        Args:
-            img (Tensor): Tensor image to be erased.
-            scale (sequence): range of proportion of erased area against input image.
-            ratio (sequence): range of aspect ratio of erased area.
-            value (list, optional): erasing value. If None, it is interpreted as "random"
-                (erasing each pixel with random values). If ``len(value)`` is 1, it is interpreted as a number,
-                i.e. ``value[0]``.
-
-        Returns:
-            tuple: params (i, j, h, w, v) to be passed to ``erase`` for random erasing.
-        """
-        img_c, img_h, img_w = img.shape[-3], img.shape[-2], img.shape[-1]
-        area = img_h * img_w
-
-        log_ratio = torch.log(torch.tensor(ratio))
-        for _ in range(10):
-            erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
-            aspect_ratio = torch.exp(
-                torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
-            ).item()
-
-            h = int(round(math.sqrt(erase_area * aspect_ratio)))
-            w = int(round(math.sqrt(erase_area / aspect_ratio)))
-            if not (h < img_h and w < img_w):
-                continue
-
-            if value is None:
-                v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
-            else:
-                v = torch.tensor(value)[:, None, None]
-
-            i = torch.randint(0, img_h - h + 1, size=(1, )).item()
-            j = torch.randint(0, img_w - w + 1, size=(1, )).item()
-            return i, j, h, w, v
-
-        # Return original image
-        return 0, 0, img_h, img_w, img
-
-    def forward(self, img):
-        """
-        Args:
-            img (Tensor): Tensor image to be erased.
-
-        Returns:
-            img (Tensor): Erased Tensor image.
-        """
-        if torch.rand(1) < self.p:
-
-            # cast self.value to script acceptable type
-            if isinstance(self.value, (int, float)):
-                value = [self.value, ]
-            elif isinstance(self.value, str):
-                value = None
-            elif isinstance(self.value, tuple):
-                value = list(self.value)
-            else:
-                value = self.value
-
-            if value is not None and not (len(value) in (1, img.shape[-3])):
-                raise ValueError(
-                    "If value is a sequence, it should have either a single value or "
-                    "{} (number of input channels)".format(img.shape[-3])
-                )
-
-            x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=value)
-            return F.erase(img, x, y, h, w, v, self.inplace)
-        return img
-
-    def __repr__(self):
-        s = '(p={}, '.format(self.p)
-        s += 'scale={}, '.format(self.scale)
-        s += 'ratio={}, '.format(self.ratio)
-        s += 'value={}, '.format(self.value)
-        s += 'inplace={})'.format(self.inplace)
-        return self.__class__.__name__ + s
-
-
-class GaussianBlur(torch.nn.Module):
-    """Blurs image with randomly chosen Gaussian blur.
-    If the image is torch Tensor, it is expected
-    to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions.
-
-    Args:
-        kernel_size (int or sequence): Size of the Gaussian kernel.
-        sigma (float or tuple of float (min, max)): Standard deviation to be used for
-            creating kernel to perform blurring. If float, sigma is fixed. If it is tuple
-            of float (min, max), sigma is chosen uniformly at random to lie in the
-            given range.
-
-    Returns:
-        PIL Image or Tensor: Gaussian blurred version of the input image.
-
-    """
-
-    def __init__(self, kernel_size, sigma=(0.1, 2.0)):
-        super().__init__()
-        self.kernel_size = _setup_size(kernel_size, "Kernel size should be a tuple/list of two integers")
-        for ks in self.kernel_size:
-            if ks <= 0 or ks % 2 == 0:
-                raise ValueError("Kernel size value should be an odd and positive number.")
-
-        if isinstance(sigma, numbers.Number):
-            if sigma <= 0:
-                raise ValueError("If sigma is a single number, it must be positive.")
-            sigma = (sigma, sigma)
-        elif isinstance(sigma, Sequence) and len(sigma) == 2:
-            if not 0. < sigma[0] <= sigma[1]:
-                raise ValueError("sigma values should be positive and of the form (min, max).")
-        else:
-            raise ValueError("sigma should be a single number or a list/tuple with length 2.")
-
-        self.sigma = sigma
-
-    @staticmethod
-    def get_params(sigma_min: float, sigma_max: float) -> float:
-        """Choose sigma for random gaussian blurring.
-
-        Args:
-            sigma_min (float): Minimum standard deviation that can be chosen for blurring kernel.
-            sigma_max (float): Maximum standard deviation that can be chosen for blurring kernel.
-
-        Returns:
-            float: Standard deviation to be passed to calculate kernel for gaussian blurring.
-        """
-        return torch.empty(1).uniform_(sigma_min, sigma_max).item()
-
-    def forward(self, img: Tensor) -> Tensor:
-        """
-        Args:
-            img (PIL Image or Tensor): image to be blurred.
-
-        Returns:
-            PIL Image or Tensor: Gaussian blurred image
-        """
-        sigma = self.get_params(self.sigma[0], self.sigma[1])
-        return F.gaussian_blur(img, self.kernel_size, [sigma, sigma])
-
-    def __repr__(self):
-        s = '(kernel_size={}, '.format(self.kernel_size)
-        s += 'sigma={})'.format(self.sigma)
-        return self.__class__.__name__ + s
-
-
-def _setup_size(size, error_msg):
-    if isinstance(size, numbers.Number):
-        return int(size), int(size)
-
-    if isinstance(size, Sequence) and len(size) == 1:
-        return size[0], size[0]
-
-    if len(size) != 2:
-        raise ValueError(error_msg)
-
-    return size
-
-
-def _check_sequence_input(x, name, req_sizes):
-    msg = req_sizes[0] if len(req_sizes) < 2 else " or ".join([str(s) for s in req_sizes])
-    if not isinstance(x, Sequence):
-        raise TypeError("{} should be a sequence of length {}.".format(name, msg))
-    if len(x) not in req_sizes:
-        raise ValueError("{} should be sequence of length {}.".format(name, msg))
-
-
-def _setup_angle(x, name, req_sizes=(2, )):
-    if isinstance(x, numbers.Number):
-        if x < 0:
-            raise ValueError("If {} is a single number, it must be positive.".format(name))
-        x = [-x, x]
-    else:
-        _check_sequence_input(x, name, req_sizes)
-
-    return [float(d) for d in x]
-
-
-class RandomInvert(torch.nn.Module):
-    """Inverts the colors of the given image randomly with a given probability.
-    If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
-    where ... means it can have an arbitrary number of leading dimensions.
-    If img is PIL Image, it is expected to be in mode "L" or "RGB".
-
-    Args:
-        p (float): probability of the image being color inverted. Default value is 0.5
-    """
-
-    def __init__(self, p=0.5):
-        super().__init__()
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be inverted.
-
-        Returns:
-            PIL Image or Tensor: Randomly color inverted image.
-        """
-        if torch.rand(1).item() < self.p:
-            return F.invert(img)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={})'.format(self.p)
-
-
-class RandomPosterize(torch.nn.Module):
-    """Posterize the image randomly with a given probability by reducing the
-    number of bits for each color channel. If the image is torch Tensor, it should be of type torch.uint8,
-    and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
-    If img is PIL Image, it is expected to be in mode "L" or "RGB".
-
-    Args:
-        bits (int): number of bits to keep for each channel (0-8)
-        p (float): probability of the image being color inverted. Default value is 0.5
-    """
-
-    def __init__(self, bits, p=0.5):
-        super().__init__()
-        self.bits = bits
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be posterized.
-
-        Returns:
-            PIL Image or Tensor: Randomly posterized image.
-        """
-        if torch.rand(1).item() < self.p:
-            return F.posterize(img, self.bits)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(bits={},p={})'.format(self.bits, self.p)
-
-
-class RandomSolarize(torch.nn.Module):
-    """Solarize the image randomly with a given probability by inverting all pixel
-    values above a threshold. If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
-    where ... means it can have an arbitrary number of leading dimensions.
-    If img is PIL Image, it is expected to be in mode "L" or "RGB".
-
-    Args:
-        threshold (float): all pixels equal or above this value are inverted.
-        p (float): probability of the image being color inverted. Default value is 0.5
-    """
-
-    def __init__(self, threshold, p=0.5):
-        super().__init__()
-        self.threshold = threshold
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be solarized.
-
-        Returns:
-            PIL Image or Tensor: Randomly solarized image.
-        """
-        if torch.rand(1).item() < self.p:
-            return F.solarize(img, self.threshold)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(threshold={},p={})'.format(self.threshold, self.p)
-
-
-class RandomAdjustSharpness(torch.nn.Module):
-    """Adjust the sharpness of the image randomly with a given probability. If the image is torch Tensor,
-    it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
-
-    Args:
-        sharpness_factor (float):  How much to adjust the sharpness. Can be
-            any non negative number. 0 gives a blurred image, 1 gives the
-            original image while 2 increases the sharpness by a factor of 2.
-        p (float): probability of the image being color inverted. Default value is 0.5
-    """
-
-    def __init__(self, sharpness_factor, p=0.5):
-        super().__init__()
-        self.sharpness_factor = sharpness_factor
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be sharpened.
-
-        Returns:
-            PIL Image or Tensor: Randomly sharpened image.
-        """
-        if torch.rand(1).item() < self.p:
-            return F.adjust_sharpness(img, self.sharpness_factor)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(sharpness_factor={},p={})'.format(self.sharpness_factor, self.p)
-
-
-class RandomAutocontrast(torch.nn.Module):
-    """Autocontrast the pixels of the given image randomly with a given probability.
-    If the image is torch Tensor, it is expected
-    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
-    If img is PIL Image, it is expected to be in mode "L" or "RGB".
-
-    Args:
-        p (float): probability of the image being autocontrasted. Default value is 0.5
-    """
-
-    def __init__(self, p=0.5):
-        super().__init__()
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be autocontrasted.
-
-        Returns:
-            PIL Image or Tensor: Randomly autocontrasted image.
-        """
-        if torch.rand(1).item() < self.p:
-            return F.autocontrast(img)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={})'.format(self.p)
-
-
-class RandomEqualize(torch.nn.Module):
-    """Equalize the histogram of the given image randomly with a given probability.
-    If the image is torch Tensor, it is expected
-    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
-    If img is PIL Image, it is expected to be in mode "P", "L" or "RGB".
-
-    Args:
-        p (float): probability of the image being equalized. Default value is 0.5
-    """
-
-    def __init__(self, p=0.5):
-        super().__init__()
-        self.p = p
-
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be equalized.
-
-        Returns:
-            PIL Image or Tensor: Randomly equalized image.
-        """
-        if torch.rand(1).item() < self.p:
-            return F.equalize(img)
-        return img
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={})'.format(self.p)

spaces/ChandraMohanNayal/AutoGPT/tests/test_config.py

@@ -1,84 +0,0 @@
-from unittest import TestCase
-
-from autogpt.config import Config
-
-
-class TestConfig(TestCase):
-    """
-    Test cases for the Config class, which handles the configuration settings
-    for the AI and ensures it behaves as a singleton.
-    """
-
-    def setUp(self):
-        """
-        Set up the test environment by creating an instance of the Config class.
-        """
-        self.config = Config()
-
-    def test_singleton(self):
-        """
-        Test if the Config class behaves as a singleton by ensuring that two instances are the same.
-        """
-        config2 = Config()
-        self.assertIs(self.config, config2)
-
-    def test_initial_values(self):
-        """
-        Test if the initial values of the Config class attributes are set correctly.
-        """
-        self.assertFalse(self.config.debug_mode)
-        self.assertFalse(self.config.continuous_mode)
-        self.assertFalse(self.config.speak_mode)
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4")
-        self.assertEqual(self.config.fast_token_limit, 4000)
-        self.assertEqual(self.config.smart_token_limit, 8000)
-
-    def test_set_continuous_mode(self):
-        """
-        Test if the set_continuous_mode() method updates the continuous_mode attribute.
-        """
-        self.config.set_continuous_mode(True)
-        self.assertTrue(self.config.continuous_mode)
-
-    def test_set_speak_mode(self):
-        """
-        Test if the set_speak_mode() method updates the speak_mode attribute.
-        """
-        self.config.set_speak_mode(True)
-        self.assertTrue(self.config.speak_mode)
-
-    def test_set_fast_llm_model(self):
-        """
-        Test if the set_fast_llm_model() method updates the fast_llm_model attribute.
-        """
-        self.config.set_fast_llm_model("gpt-3.5-turbo-test")
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo-test")
-
-    def test_set_smart_llm_model(self):
-        """
-        Test if the set_smart_llm_model() method updates the smart_llm_model attribute.
-        """
-        self.config.set_smart_llm_model("gpt-4-test")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4-test")
-
-    def test_set_fast_token_limit(self):
-        """
-        Test if the set_fast_token_limit() method updates the fast_token_limit attribute.
-        """
-        self.config.set_fast_token_limit(5000)
-        self.assertEqual(self.config.fast_token_limit, 5000)
-
-    def test_set_smart_token_limit(self):
-        """
-        Test if the set_smart_token_limit() method updates the smart_token_limit attribute.
-        """
-        self.config.set_smart_token_limit(9000)
-        self.assertEqual(self.config.smart_token_limit, 9000)
-
-    def test_set_debug_mode(self):
-        """
-        Test if the set_debug_mode() method updates the debug_mode attribute.
-        """
-        self.config.set_debug_mode(True)
-        self.assertTrue(self.config.debug_mode)

spaces/CofAI/chat.b4/client/js/sidebar-toggler.js

@@ -1,34 +0,0 @@
-const sidebar = document.querySelector(".sidebar");
-const menuButton = document.querySelector(".menu-button");
-
-function toggleSidebar(event) {
-    if (sidebar.classList.contains("shown")) {
-        hideSidebar(event.target);
-    } else {
-        showSidebar(event.target);
-    }
-    window.scrollTo(0, 0);
-}
-
-function showSidebar(target) {
-    sidebar.classList.add("shown");
-    target.classList.add("rotated");
-    document.body.style.overflow = "hidden";
-}
-
-function hideSidebar(target) {
-    sidebar.classList.remove("shown");
-    target.classList.remove("rotated");
-    document.body.style.overflow = "auto";
-}
-
-menuButton.addEventListener("click", toggleSidebar);
-
-document.body.addEventListener('click', function(event) {
-    if (event.target.matches('.conversation-title')) {
-        const menuButtonStyle = window.getComputedStyle(menuButton);
-        if (menuButtonStyle.display !== 'none') {
-            hideSidebar(menuButton);
-        }
-    }
-});