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spaces/1acneusushi/gradio-2dmoleculeeditor/data/ExpertGPS Registration Key The Essential Step to Use the Most Powerful GPS Software.md

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-<br> - Benefits of registering ExpertGPS: Free updates, priority support, and more features. <br> - Steps to register ExpertGPS: Copy and paste the registration code from your email and enter your name and option code. <br> - Conclusion: Summarize the main points and encourage the reader to register ExpertGPS. | | H2: What is ExpertGPS and why do you need to register it? | - Explain what ExpertGPS is: A mapping software that works with hundreds of GPS receivers. <br> - Explain what you can do with ExpertGPS: Convert, edit, and transfer GPS data, create maps, geocode addresses, survey property lines, etc. <br> - Explain why you need to register ExpertGPS: To unlock all the features and get rid of the trial limitations. | | H2: Benefits of registering ExpertGPS | - List the benefits of registering ExpertGPS: Free updates, priority support, and more features. <br> - Give examples of each benefit: New versions with new features, direct email support from the author, access to advanced tools like batch geocoding, property line mapping, etc. | | H2: Steps to register ExpertGPS | - List the steps to register ExpertGPS: Copy and paste the registration code from your email and enter your name and option code. <br> - Explain each step in detail with screenshots: Show how to copy and paste the registration code, how to find the Enter Registration Code dialog, how to enter the name and option code, how to confirm the registration, how to restart ExpertGPS, how to check the About box. | | H2: Conclusion | - Summarize the main points of the article: What is ExpertGPS, why do you need to register it, what are the benefits of registering it, and how to register it. <br> - Encourage the reader to register ExpertGPS: Tell them how easy it is to register ExpertGPS and how much they can do with it. <br> - Provide a call to action: Tell them to download ExpertGPS if they haven't already and to enter their registration code as soon as possible. | # Article with HTML formatting <h1>How to Register Your Copy of ExpertGPS</h1>
- <p>If you are looking for a powerful and easy-to-use mapping software that works with hundreds of GPS receivers, you might have heard of ExpertGPS. ExpertGPS is a software that allows you to convert, edit, and transfer GPS data between your computer and your GPS device. You can also create maps, geocode addresses, survey property lines, measure distances and areas, and much more with ExpertGPS.</p>
- <p>But did you know that you need to register your copy of ExpertGPS to unlock all its features and get rid of the trial limitations? In this article, we will show you why you need to register your copy of ExpertGPS, what are the benefits of registering it, and how to register it in a few simple steps.</p>
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- <h2>What is ExpertGPS and why do you need to register it?</h2>
- <p>ExpertGPS is a software that lets you work with GPS data on your computer. You can use it with hundreds of GPS receivers from Garmin, Magellan, Lowrance, Simrad, Bryton, and other brands. You can download waypoints, routes, tracks, and geocaches from your GPS device or create them on your computer. You can also edit them on a map or in a spreadsheet-like data list.</p>
- <p>But that's not all. You can also use ExpertGPS to create maps from various sources like Google Earth KML & KMZ files, shapefiles and file geodatabases, CAD and DXF files, GPX files, Excel and CSV files, etc. You can also geocode addresses in bulk or survey property lines using US state plane coordinates or national grid coordinates.</p>
- <p>With ExpertGPS, you can do a lot of things with GPS data that would otherwise require multiple software or online services. But in order to enjoy all these features, you need to register your copy of ExpertGPS with a valid registration key that you can purchase online or receive by email after ordering.</p>
- <p>If you don't register your copy of ExpertGPS, you will be limited by some trial restrictions such as:</p>
- <ul>
-<li>You can only use it for 30 days.</li>
-<li>You can only transfer 500 waypoints per day.</li>
-<li>You can only geocode 100 addresses per day.</li>
-<li>You can only map 10 property lines per day.</li>
-<li>You can't access some advanced tools like batch geocoding or property line mapping.</li>
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- <p>As you can see, registering your copy of ExpertGPS is essential if you want to use it without any limitations and get the most out of it.</p>
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- <p>By registering your copy of ExpertGPS with a valid registration key that matches your name and option code (Home or Pro), you will get access to several benefits such as:</p>
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- <h2>Steps to register ExpertGPS</h2>
- <p>Now that you know why you need to register your copy of ExpertGPS and what are the benefits of doing so, let's see how you can do it in a few simple steps.</p>
- <p>The registration key that you received by email after ordering or purchasing online will unlock the trial version of ExpertGPS that you have already downloaded on your computer. If you haven't downloaded it yet, you can do so by visiting <a href="https://www.expertgps.com/download.asp">this link</a>.</p>
- <p>To register your copy of ExpertGPS, follow these steps:</p>
- <ol>
-<li><strong>Run ExpertGPS:</strong> Double-click on the icon on your desktop or in your Start menu to launch ExpertGPS. You should see a map screen on the right and a data list on the left when the program is running.</li>
-<li><strong>Copy the registration key code from your email program:</strong>
-Open your email program and find the email that contains your registration key code. It should look something like this: <pre><code>
-Thank you for purchasing an upgrade license for Expert GPS Pro. Your name: John Smith Your option code: Pro Your registration key: 1234-5678-90AB-CDEF-GHIJ-KLMN-OPQR-STUV-WXYZ </code></pre>
- Select the entire key string (including dashes) and copy it by pressing Ctrl+C on your keyboard or right-clicking on it and choosing Copy from the menu.</li>
-<li><strong>On the Help menu in ExpertGPS, click Enter Registration Code:</strong>
-In the main window of ExpertGPS, click on Help in the menu bar and then click on Enter Registration Code. The Enter Registration Code dialog will appear.</li>
-<li><strong>Enter your name and option code exactly as it appears in the registration email:</strong>
-In the Enter Registration Code dialog, enter your name and option code (Home or Pro) exactly as they appear in the email that contains your registration key code. Make sure there are no extra spaces or typos in your name or option code.</li>
-<li><strong>Paste the key string into the registration dialog:</strong>
-Click on the Key field in the Enter Registration Code dialog and paste the key string that you copied from your email by pressing Ctrl+V on your keyboard or right-clicking on it and choosing Paste from the menu. The key string should fill up all five boxes in the Key field.</li>
-<li><strong>Click OK:</strong>
-Click on OK to confirm your registration. A dialog box will appear, thanking you for registering features.</li>
-<li><strong>Exit ExpertGPS:</strong>
-Click on File in the menu bar and then click on Exit to close ExpertGPS. You must restart ExpertGPS so that your registered features will be activated.</li>
-<li><strong>Start ExpertGPS again:</strong>
-Double-click on the icon on your desktop or in your Start menu to launch ExpertGPS again.</li>
-<li><strong>Click About ExpertGPS in the Help menu:</strong>
-In the main window of ExpertGPS, click on Help in the menu bar and then click on About ExpertGPS. You will see your registration information displayed in the About box.</li>
-</ol>
- <p>Congratulations! You have successfully registered your copy of ExpertGPS and unlocked all its features and benefits.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to register your copy of ExpertGPS with a valid registration key that matches your name and option code. We have also explained why you need to register your copy of ExpertGPS and what are the benefits of doing so.</p>
- <p>By registering your copy of ExpertGPS, you will be able to use this powerful and easy-to-use mapping software without any limitations or restrictions. You will also get free updates, priority support, and more features that will help you work with GPS data on your computer.</p>
- <p>If you haven't downloaded ExpertGPS yet, you can do so by visiting <a href="https://www.expertgps.com/download.asp">this link</a>. If you have already downloaded it, you can enter your registration code as soon as possible by following the steps we have outlined above.</p>
- <p>Don't wait any longer. Register your copy of ExpertGPS today and enjoy all the amazing things you can do with it.</p>
- <h3>FAQs</h3>
- <ul>
-<li><strong>Q: How much does it cost to register ExpertGPS?</strong>
-<br>
-A: The cost of registering ExpertGPS depends on the option code you choose: Home or Pro. The Home option costs $74.95 and the Pro option costs $249.95. You can compare the features of each option and order online by visiting <a href="https://www.expertgps.com/order.asp">this link</a>.</li>
-<li><strong>Q: How long does it take to receive the registration key after ordering?</strong>
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-A: You should receive the registration key by email within a few minutes after ordering. If you don't receive it within an hour, please check your spam folder or contact Dan Foster at [email protected] and include your order number or receipt.</li>
-<li><strong>Q: What if I lose my registration key or need to reinstall ExpertGPS?</strong>
-<br>
-A: You can retrieve your registration key by visiting <a href="https://www.expertgps.com/lost-registration-key.asp">this link</a> and entering your email address. You can also download the latest version of ExpertGPS by visiting <a href="https://www.expertgps.com/download.asp">this link</a>. You can reinstall ExpertGPS and enter your registration key as many times as you need on the same computer or a new one.</li>
-<li><strong>Q: What if I have a problem with registering or using ExpertGPS?</strong>
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-A: You can get priority support from Dan Foster, the author of ExpertGPS, by emailing him at [email protected] and including your registration key or order number. You can also visit <a href="https://www.expertgps.com/support.asp">this link</a> for more help and resources on using ExpertGPS.</li>
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-A: You can upgrade from Home to Pro or extend your free updates period by visiting <a href="https://www.expertgps.com/upgrade.asp">this link</a> and entering your current registration key. You will get a discounted price for upgrading or extending and a new registration key by email.</li>
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-<h1>Car Parking Multiplayer: A Review of the New Version 4.8 5.1</h1>
- <p>If you are looking for a game that can challenge your parking skills and offer you a lot of fun and excitement, then you should check out Car Parking Multiplayer. This game is not just about parking your car, but also about exploring an open world with real gas stations, car services, voice chat, police mode, and more. You can also customize your car, exchange it with other players, race against them, or just walk around and enjoy the scenery.</p>
- <p>In this article, we will review the latest version of Car Parking Multiplayer, which is version 4.8 5.1. We will tell you what's new in this version, how to download and install it on your device, and how to play and enjoy it with some tips and tricks.</p>
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- <h2>What is Car Parking Multiplayer?</h2>
- <h3>A game that offers more than just parking</h3>
- <p>Car Parking Multiplayer is a game that can fool you with its rather deceiving name. But, it's much more than just being about parking your car. It's an open-world experience where you can drive free and yes, still work on that parking if you wish. You can even jump out of your car and walk around.</p>
- <p>There are different areas that can be explored in the game. Each one is like its own open-world. You can choose to play either single-player mode or online mode if you want a more chaotic scene (in a fun way).</p>
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- <h3>A game that features open-world multiplayer mode, car tuning, free walking, and more</h3>
- <p>Car Parking Multiplayer has the following features that make it stand out from other parking games:</p>
- <ul>
-<li>Open-world multiplayer mode: You can join thousands of real players every day, chat with them using voice chat or messenger, make friends or enemies, compete or cooperate with them in racing or police mode.</li>
-<li>Car tuning: You can adjust the suspension, wheel angle, engine, turbo, gearbox, exhaust, and more of your car. You can also swap your car with other players or buy new cars from the shop.</li>
-<li>Free walking: You can get out of your car and walk around the open world. You can also enter buildings with interior and interact with objects.</li>
-<li>Character customization: You can choose from 16 different player skins and a variety of clothes and accessories to dress up your character. You can also use different animations and emotions to express yourself.</li>
-<li>Role play: You can become a taxi driver, a cargo driver, or a delivery driver and complete orders from customers. You can also become a police officer and catch and fine players for speeding or breaking the law.</li>
-<li>Drone mode: You can use a drone to explore the world from a different perspective and take stunning screenshots.</li>
-<li>Daily tasks and rewards: You can collect coins and presents by completing the tasks and joining the game every day.</li>
-</ul>
- <h2>What's new in version 4.8 5.1?</h2>
- <h3>New cars, features, rims, clothes, liveries, fonts, and sounds</h3>
- <p>The latest version of Car Parking Multiplayer has added a lot of new content to the game. Here are some of the highlights:</p>
- <ul>
-<li>New cars: There are over 130 car models with realistic interior and exterior in the game. Some of the new cars include BMW M3 E30, Mercedes-Benz G63 AMG, Lamborghini Aventador, Ferrari F40, and more.</li>
-<li>New features: There are new features such as a car wash, a car lift, a car service, a gas station, a car showroom, and more that you can use to enhance your gameplay experience.</li>
-<li>New rims: There are over 100 new rims that you can choose from to customize your car's appearance.</li>
-<li>New clothes: There are over 70 new clothes that you can wear to style your character.</li>
-<li>New liveries: There are over 50 new liveries that you can apply to your car to make it look more unique and cool.</li>
-<li>New fonts: There are over 20 new fonts that you can use to write your name or messages on your car or chat.</li>
-<li>New sounds: There are over 10 new sounds that you can hear in the game, such as engine sounds, horn sounds, police siren sounds, and more.</li>
-</ul>
- <h3>New messenger, drone mode, daily tasks and rewards, character customization, and animations</h3>
- <p>Aside from the new content, the latest version of Car Parking Multiplayer has also improved some of the existing features and added some new ones. Here are some of the highlights:</p>
- <ul>
-<li>New messenger: The game has introduced a new messenger system that allows you to chat with other players in a more convenient and user-friendly way. You can also send stickers, emojis, and voice messages to express yourself better.</li>
-<li>New drone mode: The game has added a new drone mode that lets you control a drone and fly around the world. You can use the drone to explore the map, take screenshots, spy on other players, or just have fun.</li>
-<li>New daily tasks and rewards: The game has added a new daily task system that gives you different tasks to complete every day. You can earn coins and presents by completing the tasks and joining the game every day.</li>
-<li>New character customization: The game has improved the character customization feature by adding more options and details. You can now choose from 16 different player skins and a variety of clothes and accessories to dress up your character. You can also use different animations and emotions to express yourself.</li>
-<li>New animations: The game has added new animations for your character and your car. You can now see your character perform different actions such as opening the door, getting in or out of the car, sitting in the car, walking around, etc. You can also see your car perform different actions such as turning on or off the lights, opening or closing the hood or trunk, etc.</li>
-</ul>
- <h2>How to download and install version 4.8 5.1?</h2>
- <h3>For Android devices</h3>
- <p>If you have an Android device, you can download and install version 4.8 5.1 of Car Parking Multiplayer by following these steps:</p>
- <ol>
-<li>Go to the Google Play Store and search for Car Parking Multiplayer or click on this link.</li>
-<li>Tap on the Install button and wait for the download to finish.</li>
-<li>Once the download is done, tap on the Open button and enjoy the game.</li>
-</ol>
- <h3>For iOS devices</h3>
- <p>If you have an iOS device, you can download and install version 4.8 5.1 of Car Parking Multiplayer by following these steps:</p>
- <ol>
-<li>Go to the App Store and search for Car Parking Multiplayer or click on this link.</li>
-<li>Tap on the Get button and wait for the download to finish.</li>
-<li>Once the download is done, tap on the Open button and enjoy the game.</li>
-</ol>
- <h3>For PC devices</h3>
- <p>If you have a PC device, you can download and install version 4.8 5.1 of Car Parking Multiplayer by following these steps:</p>
- <ol>
-<li>Go to this website and click on the Download button.</li>
-<li>Choose the version that suits your PC (Windows or Mac) and wait for the download to finish.</li>
-<li>Once the download is done, open the file and follow the instructions to install the game.</li>
-<li>Once the installation is done, launch the game and enjoy it.</li>
-</ol>
- <h2>How to play and enjoy version 4.8 5.1?</h2>
- <h3>Tips and tricks for beginners</h3>
- <p>If you are new to Car Parking Multiplayer, here are some tips and tricks that can help you play and enjoy version 4.8 5 .1:</p>
- <ul>
-<li>Start with the single-player mode and practice your parking skills in different scenarios and levels. You can choose from different difficulty levels and car models to suit your preference.</li>
-<li>Learn the basic controls and functions of your car, such as steering, braking, accelerating, reversing, changing gears, turning on or off the lights, etc. You can also adjust the camera angle and view to get a better perspective of your surroundings.</li>
-<li>Follow the arrows and indicators on the screen to guide you to your parking spot. Try to avoid hitting any obstacles or other cars, as this will reduce your score and damage your car.</li>
-<li>Use the map and the GPS to navigate the open world and find different locations and features. You can also use the teleport function to quickly move to a different area.</li>
-<li>Explore the different modes and features of the game, such as racing, police, taxi, cargo, delivery, car wash, car service, gas station, car showroom, etc. You can also interact with other players and objects in the world.</li>
-</ul>
- <h3>Tips and tricks for advanced players</h3>
- <p>If you are already familiar with Car Parking Multiplayer, here are some tips and tricks that can help you play and enjoy version 4.8 5.1 even more:</p>
- <ul>
-<li>Join the online mode and challenge yourself with thousands of real players every day. You can chat with them using voice chat or messenger, make friends or enemies, compete or cooperate with them in racing or police mode.</li>
-<li>Customize your car and your character to make them look more unique and cool. You can adjust the suspension, wheel angle, engine, turbo, gearbox, exhaust, and more of your car. You can also swap your car with other players or buy new cars from the shop. You can also choose from 16 different player skins and a variety of clothes and accessories to dress up your character. You can also use different animations and emotions to express yourself.</li>
-<li>Use the drone mode to explore the world from a different perspective and take stunning screenshots. You can use the drone to fly around the map, spy on other players, or just have fun.</li>
-<li>Complete the daily tasks and collect coins and presents by joining the game every day. You can use the coins to buy new cars, clothes, rims, liveries, fonts, sounds, etc. You can also use the presents to get random rewards such as coins, cars, clothes, etc.</li>
-<li>Role play as a taxi driver, a cargo driver, or a delivery driver and complete orders from customers. You can also role play as a police officer and catch and fine players for speeding or breaking the law.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Car Parking Multiplayer is a game that offers more than just parking your car. It is an open-world multiplayer game that features car tuning, free walking, character customization, role play, drone mode, daily tasks and rewards, and more. It is a game that can challenge your parking skills and offer you a lot of fun and excitement.</p>
- <p>The latest version of Car Parking Multiplayer is version 4.8 5.1. It has added a lot of new content and improved some of the existing features of the game. It has added new cars, features, rims, clothes, liveries, fonts and sounds. It has also improved the messenger system, the drone mode, the daily task system, the character customization feature, and the animations.</p>
- <p>If you want to download and install version 4.8 5.1 of Car Parking Multiplayer, you can follow the steps that we have provided for Android, iOS, and PC devices. If you want to play and enjoy version 4.8 5.1 of Car Parking Multiplayer, you can follow the tips and tricks that we have provided for beginners and advanced players.</p>
- <p>We hope that this article has helped you learn more about Car Parking Multiplayer and its latest version. We also hope that you have fun playing this game and exploring its amazing features.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about Car Parking Multiplayer and its latest version:</p>
- <h3>Q: Is Car Parking Multiplayer free to play?</h3>
-<p>A: Yes, Car Parking Multiplayer is free to play. However, it does have some in-app purchases that can enhance your gameplay experience. You can buy coins, cars, clothes, rims, liveries, fonts, sounds, etc. with real money. You can also watch ads to get some free coins or presents.</p>
- <h3>Q: Is Car Parking Multiplayer safe to play?</h3>
-<p>A: Yes, Car Parking Multiplayer is safe to play. It does not contain any harmful or malicious content that can harm your device or your personal information. However, you should be careful when interacting with other players online, as they may use inappropriate language or behavior. You can report or block any players that are bothering you or violating the game rules.</p>
- <h3>Q: How can I update Car Parking Multiplayer to version 4.8 5.1?</h3>
-<p>A: If you already have Car Parking Multiplayer installed on your device, you can update it to version 4.8 5.1 by following these steps:</p>
- <ol>
-<li>Go to the Google Play Store or the App Store and search for Car Parking Multiplayer or click on this link.</li>
-<li>Tap on the Update button and wait for the download to finish.</li>
-<li>Once the download is done, tap on the Open button and enjoy the game.</li>
-</ol>
- <h3>Q: How can I contact the developers of Car Parking Multiplayer?</h3>
-<p>A: If you have any questions, feedback, suggestions, or issues about Car Parking Multiplayer, you can contact the developers of the game by using one of these methods:</p>
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-<li>Email: [email protected]</li>
-<li>Facebook: https://www.facebook.com/olzhassgames</li>
-<li>Instagram: https://www.instagram.com/olzhassgames</li>
-<li>YouTube: https://www.youtube.com/channel/UCRQYHtF_7Yl0fOKea24lVGA</li>
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-<p>A: If you like Car Parking Multiplayer and want to support the developers of the game, you can do one of these things:</p>
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-<li>Rate and review the game on the Google Play Store or the App Store.</li>
-<li>Share the game with your friends and family.</li>
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-<p>Do you love cars and want to show off your skills as a mechanic? Do you enjoy fixing, customizing, and selling cars for a profit? If you answered yes to any of these questions, then you should try Car Master 3D, a fun and creative game that lets you run your own car workshop. And if you want to make the game even more enjoyable, you should download Car Master 3D Mod APK, which gives you unlimited money, no ads, and easy installation. In this article, we will tell you everything you need to know about this amazing game and how to get the modded version.</p>
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-<p>Car Master 3D is a game where you can unleash your inner mechanic and car designer. You will have a garage full of old, rusty, dirty, or even non-functioning vehicles that need your attention. You will have to use various tools and parts to fix them up, such as hammers, wrenches, spray cans, wheels, spoilers, bumpers, and more. You can also customize the appearance of your cars by changing their colors, styles, stickers, and accessories. You can make them look as cool or as crazy as you want.</p>
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-<p>The second step to download and install Car Master 3D Mod APK is to enable unknown sources on your device. This is necessary because the modded file is not from the official Google Play Store and your device might block it by default. To enable unknown sources, go to your device settings, security, and toggle on the option that allows installation of apps from unknown sources. This will allow you to install Car Master 3D Mod APK without any problems.</p>
-<h3>Step 3: Install the APK file and launch the game</h3>
-<p>The third and final step to download and install Car Master 3D Mod APK is to install the APK file and launch the game. To do this, locate the downloaded file on your device, tap on it, and follow the instructions on the screen. The installation process should take only a few seconds. After that, you can open the game and start playing with unlimited money, no ads, and easy installation.</p>
- <h2>How to play Car Master 3D?</h2>
-<h3>Choose a car to work on from the garage</h3>
-<p>The first thing you need to do when you play Car Master 3D is to choose a car to work on from the garage. You will have a variety of cars available, such as sedans, coupes, trucks, vans, sports cars, and more. Each car has its own condition, value, and potential. You can see these details by tapping on the car. You can also rotate and zoom in on the car to inspect it more closely. Once you decide which car you want to work on, tap on the start button and move it to your workshop.</p>
-<h3>Use various tools and parts to fix and upgrade the car</h3>
-<p>The next thing you need to do when you play Car Master 3D is to use various tools and parts to fix and upgrade the car. You will have a toolbox with different tools that you can use for different purposes, such as repairing, cleaning, painting, polishing, etc. You will also have a shop where you can buy new parts for your car, such as wheels, spoilers, bumpers, lights, etc. You can drag and drop the tools and parts on the car to apply them. You can also undo or redo your actions if you make a mistake or change your mind.</p>
-<h3>Sell the car for a profit or keep it for yourself</h3>
-<p>The last thing you need to do when you play Car Master 3D is to sell the car for a profit or keep it for yourself. After you finish working on the car, you can see how much it has improved in terms of condition, value, and potential. You can also compare it with its original state by tapping on the before/after button. If you are satisfied with your work, you can sell the car for a profit by tapping on the sell button. You will get money based on how well you fixed and customized the car. You can use this money to buy more tools, parts, and cars. Alternatively, if you really like the car you worked on, you can keep it for yourself by tapping on the keep button. You can add it to your collection and show it off to your friends.</p>
- <h2>Tips and tricks for playing Car Master 3D</h2>
-<h3>Experiment with different colors and styles for your cars</h3>
-<p>One of the tips for playing Car Master 3D is to experiment with different colors and styles for your cars. You can make your cars look unique and attractive by using different spray cans and stickers. You can also mix and match different parts and accessories to create your own style. You can make your cars look realistic or cartoonish, elegant or funky, simple or complex. The choice is yours. You can also use the color wheel to find the perfect shade for your car.</p>
-<h3>Complete missions and challenges to earn extra rewards</h3>
-<p>Another tip for playing Car Master 3D is to complete missions and challenges to earn extra rewards. You can find these missions and challenges by tapping on the icons on the top of the screen. They will give you specific tasks to do, such as fixing a certain number of cars, using a certain tool, buying a certain part, etc. If you complete these tasks, you will get bonus coins, gems, or other prizes. These rewards will help you progress faster in the game and buy more items.</p>
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-<p>The final tip for playing Car Master 3D is to watch videos to get free coins and gems. You can find these videos by tapping on the icons on the bottom of the screen. They will offer you to watch a short video in exchange for some coins or gems. You can watch as many videos as you want and get unlimited free currency. This is a great way to get more money without spending any real money.</p>
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-<p>Car Master 3D is a fun and creative game for car lovers who want to fix and customize cars. You can download Car Master 3D Mod APK to get unlimited money, no ads, and easy installation. You can also follow our tips and tricks to play the game better and enjoy it more. Car Master 3D is a game that will keep you entertained for hours and let you express your personality through your cars. Download it now and start your car workshop adventure.</p>
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-<h1>Download FR Legends Mod APK Versi 0.3 2: The Ultimate Drift Racing Game for Android</h1>
-<p>If you are a fan of car racing games, especially drift racing games, then you must have heard of <strong>FR Legends</strong>. This is one of the most popular and realistic drift racing games for Android devices, where you can experience the thrill and excitement of drifting on various tracks with different cars. In this article, we will tell you everything you need to know about <strong>FR Legends</strong>, including how to download and install <strong>FR Legends Mod APK Versi 0.3 2</strong>, which is a modified version of the game that offers many amazing features that are not available in the original version. So, without further ado, let's get started!</p>
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- <h2>Introduction: What is FR Legends and why you should download it</h2>
-<p><strong>FR Legends</strong> is a drift racing game that was developed by <em>Feng Li</em> and released in October 2021 for Android devices. The game has received over 10 million downloads and has an average rating of 4.5 out of 5 stars on Google Play Store. The game is praised for its realistic physics, graphics, sound effects, and gameplay, as well as its customization options, online mode, and variety of cars and tracks.</p>
-<p>The game is based on the concept of <em>FR</em>, which stands for <em>Front-engine</em> and <em>Rear-wheel-drive </em> layout, which is the ideal configuration for drift racing. Drift racing is a type of car racing where the driver intentionally oversteers the car to make it slide sideways through corners. The game allows you to control your car's throttle, brake, steering, handbrake, and clutch, as well as adjust your car's suspension, tire pressure, camber, and gear ratio. You can also customize your car's appearance, such as the color, body kit, spoiler, wheels, stickers, and more.</p>
-<p>The game has two main modes: <em>Career Mode</em> and <em>Online Mode</em>. In Career Mode, you can compete in various events and challenges, such as time attack, tandem battle, gymkhana, and more. You can earn money and reputation by completing these events and use them to buy new cars or upgrade your existing ones. In Online Mode, you can join or create a room and race with other players from around the world. You can also chat with them and share your drifting skills and tips.</p>
-<p><strong>FR Legends</strong> is a game that will keep you hooked for hours with its addictive and immersive gameplay. You will never get bored of drifting on different tracks with different cars and challenging yourself or other players. You will also learn a lot about car mechanics and drifting techniques as you play the game. If you are looking for a drift racing game that is fun, realistic, and customizable, then <strong>FR Legends</strong> is the game for you.</p>
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-<li><strong>New cars:</strong> You can access new cars that are not available in the original version, such as the Nissan Skyline GT-R R34, Toyota Supra MK4, Mazda RX-7 FD3S, and more.</li>
-<li><strong>New maps:</strong> You can explore new maps that are not available in the original version, such as the Tokyo Drift Park, the Mountain Pass, the Desert Highway, and more.</li>
-<li><strong>New accessories:</strong> You can customize your car with new accessories that are not available in the original version, such as neon lights, smoke effects, exhaust sounds, and more.</li>
-<li><strong>New designs:</strong> You can change your car's design with new designs that are not available in the original version, such as anime characters, graffiti art, logos, and more.</li>
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-<p>To download and install <strong>FR Legends Mod APK Versi 0.3 2</strong>, you need to follow these simple steps:</p>
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-<li>Go to this link: <a href="">https://fr-legends-mod-apk-versi-0-3-2.com/download</a></li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Go to your device's settings and enable the installation of apps from unknown sources.</li>
-<li>Locate the downloaded file and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to be completed.</li>
-<li>Launch the game and enjoy!</li>
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-<p>Note: Before you download and install <strong>FR Legends Mod APK Versi 0.3 2</strong>, make sure that you have enough storage space on your device and that your device meets the minimum requirements of the game. Also, be aware that downloading modded apps from unknown sources may pose some risks and dangers to your device and data. We are not responsible for any damage or loss that may occur as a result of downloading or using <strong>FR Legends Mod APK Versi 0.3 2</strong>. Download and use it at your own risk.</p>
- <h2>Features of FR Legends Mod APK Versi 0.3 2</h2>
-<p>We have already mentioned some of the features of <strong>FR Legends Mod APK Versi 0.3 2</strong>, but let's take a closer look at them and see how they enhance the gaming experience and performance.</p>
- <table>
-<tr>
-<th>Feature</th>
-<th>Description</th>
-<th>Difference from original version</th>
-</tr>
-<tr>
-<td><strong>Unlimited money</strong></td <td>You can get unlimited money in the game without having to complete any events or challenges. You can use this money to buy any car or upgrade you want.</td>
-<td>You have to earn money by completing events or challenges in the original version. You have limited options to buy or upgrade cars.</td>
-</tr>
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-<td><strong>New cars</strong></td>
-<td>You can access new cars that are not available in the original version, such as the Nissan Skyline GT-R R34, Toyota Supra MK4, Mazda RX-7 FD3S, and more. These cars have different specifications and performance levels.</td>
-<td>You have to unlock cars by earning reputation or money in the original version. You have fewer options to choose from.</td>
-</tr>
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-<td><strong>New maps</strong></td>
-<td>You can explore new maps that are not available in the original version, such as the Tokyo Drift Park, the Mountain Pass, the Desert Highway, and more. These maps have different layouts and environments.</td>
-<td>You have to unlock maps by earning reputation or money in the original version. You have fewer options to choose from.</td>
-</tr>
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-<td><strong>New accessories</strong></td>
-<td>You can customize your car with new accessories that are not available in the original version, such as neon lights, smoke effects, exhaust sounds, and more. These accessories add more style and flair to your car.</td>
-<td>You have to unlock accessories by earning reputation or money in the original version. You have fewer options to choose from.</td>
-</tr>
-<tr>
-<td><strong>New designs</strong></td>
-<td>You can change your car's design with new designs that are not available in the original version, such as anime characters, graffiti art, logos, and more. These designs add more personality and uniqueness to your car.</td>
-<td>You have to unlock designs by earning reputation or money in the original version. You have fewer options to choose from.</td>
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- <p>As you can see, the features of <strong>FR Legends Mod APK Versi 0.3 2</strong> make the game more fun, diverse, and customizable. You can enjoy more freedom and creativity in creating your own drift racing experience. You can also save time and effort in unlocking and upgrading your cars and maps. You can also impress your friends and rivals with your cool and awesome cars and designs.</p>
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-<p>Now that you have downloaded and installed <strong>FR Legends Mod APK Versi 0.3 2</strong>, you might be wondering how to master the game and become a drift racing legend. Well, don't worry, we have some useful tips and tricks for you that will help you improve your drifting skills, score more points, win more races, customize your cars, and more. Here they are:</p>
- <ul>
-<li><strong>Practice makes perfect:</strong> The best way to master FR Legends is to practice a lot. The game has a <em>Free Mode</em> where you can practice drifting on any track with any car without any pressure or competition. You can also adjust the difficulty level of the game according to your preference and skill level. The more you practice, the more you will learn how to control your car's speed, angle, direction, and balance while drifting.</li>
-<li><strong>Use the handbrake wisely:</strong> The handbrake is a very important tool for drifting in FR Legends. You can use it to initiate a drift, maintain a drift, or correct a drift. However, you should not use it too much or too little, as it can affect your car's stability and momentum. You should use it only when necessary and release it as soon as possible. You should also avoid using it when you are going straight or when you are already drifting at a high angle.</li>
-<li><strong>Choose the right car for the right track:</strong> FR Legends has many different cars and tracks that have different characteristics and requirements. You should choose the car that suits the track best based on its power, weight, handling, grip, and style. For example, if you are racing on a tight and twisty track, you should choose a light and agile car that can maneuver easily through corners. If you are racing on a wide and open track, you should choose a powerful and fast car that can accelerate quickly on straightaways.</li>
-<li><strong>Customize your car according to your preference:</strong> FR Legends allows you to customize your car's appearance and performance according to your preference and style. You can change your car's color, body kit, spoiler, wheels, stickers, and more. You can also adjust your car's suspension, tire pressure, camber, and gear ratio. You should experiment with different combinations of these settings until you find the one that works best for you and your car. You can also save your custom settings for future use.</li>
-<li><strong>Watch and learn from other players:</strong> FR Legends has an online mode where you can race with other players from around the world. You can also chat with them and share your drifting skills and tips. You can learn a lot from watching and observing how other players drift, such as their techniques, strategies, mistakes, and corrections. You can also challenge them to a friendly or competitive race and see how you compare to them.</li>
-</ul>
-<p>These are some of the tips and tricks that will help you master FR Legends and become a drift racing legend. Of course, there are more tips and tricks that you can discover and learn as you play the game. The most important thing is to have fun and enjoy the game.</p>
- <h2>Conclusion: Why FR Legends is the best drift racing game for Android</h2>
-<p>We have reached the end of this article, and we hope that you have learned a lot about FR Legends and how to download and install FR Legends Mod APK Versi 0.3 2. We have also shared with you some of the features, benefits, and tips of playing FR Legends and how it is the best drift racing game for Android devices.</p>
-<p>FR Legends is a game that will satisfy your passion and curiosity for drift racing. It will challenge your skills, creativity, and style as you drift on various tracks with different cars. It will also entertain you with its realistic physics, graphics, sound effects, and gameplay. It will also allow you to customize your car's appearance and performance according to your preference and style. It will also connect you with other players from around the world who share your love for drift racing.</p>
-<p>If you are looking for a drift racing game that is fun, realistic, and customizable, then FR Legends is the game for you. You can download and install FR Legends Mod APK Versi 0.3 2 from the link below and enjoy all the amazing features that it offers. You will not regret it.</p>
-<p><a href="">Download FR Legends Mod APK Versi 0.3 2 here</a></p>
- <h4>FAQs</h4>
-<p>Here are some of the frequently asked questions related to FR Legends and FR Legends Mod APK Versi 0.3 2:</p>
-<ul>
-<li><strong>What is the difference between FR Legends and other drift racing games?</strong></li>
-<p>FR Legends is different from other drift racing games in many ways, such as:</p>
-<ul>
-<li>It focuses on the concept of FR, which is the ideal layout for drift racing.</li>
-<li>It allows you to control your car's throttle, brake, steering, handbrake, and clutch, as well as adjust your car's suspension, tire pressure, camber, and gear ratio.</li>
-<li>It offers a realistic and immersive drifting experience with its physics, graphics, sound effects, and gameplay.</li>
-<li>It provides a variety of cars and tracks that have different characteristics and requirements.</li>
-<li>It enables you to customize your car's appearance and performance according to your preference and style.</li>
-<li>It connects you with other players from around the world who share your love for drift racing.</li>
-</ul>
-<li><strong>Is FR Legends Mod APK Versi 0.3 2 safe and secure to download and use?</strong></li>
-<p>FR Legends Mod APK Versi 0.3 2 is safe and secure to download and use as long as you download it from a reliable source like the one we have provided in this article. However, you should be aware that downloading modded apps from unknown sources may pose some risks and dangers to your device and data. We are not responsible for any damage or loss that may occur as a result of downloading or using FR Legends Mod APK Versi 0.3 2. Download and use it at your own risk.</p>
-<li><strong>How can I update FR Legends Mod APK Versi 0.3 2 to the latest version?</strong></li>
-<p>To update FR Legends Mod APK Versi 0.3 2 to the latest version, you need to follow these steps:</p>
-<ol>
-<li>Go to the same link where you downloaded FR Legends Mod APK Versi 0.3 2 from: <a href="">https://fr-legends-mod-apk-versi-0-3-2.com/download</a></li>
-<li>Check if there is a new version available and click on the download button if there is.</li>
-<li>Uninstall the previous version of FR Legends Mod APK Versi 0.3 2 from your device.</li>
-<li>Install the new version of FR Legends Mod APK Versi 0.3 2 following the same steps as before.</li>
-<li>Launch the game and enjoy the new features and improvements.</li>
-</ol>
-<p>Note: You should always update FR Legends Mod APK Versi 0.3 2 to the latest version to avoid any bugs, glitches, or compatibility issues with the game.</p>
-<li><strong>How can I play FR Legends online with other players?</strong></li>
-<p>To play FR Legends online with other players, you need to follow these steps:</p>
-<ol>
-<li>Launch the game and tap on the <em>Online Mode</em> button on the main menu.</li>
-<li>Select a region and a room that you want to join or create your own room by tapping on the <em>Create Room</em> button.</li>
-<li>Wait for other players to join or invite your friends by tapping on the <em>Invite Friends</em> button.</li>
-<li>Choose a car and a track that you want to race on and tap on the <em>Ready</em> button.</li>
-<li>Start the race and enjoy!</li>
-</ol>
-<p>Note: You need a stable internet connection to play FR Legends online with other players. You can also chat with them and share your drifting skills and tips by tapping on the <em>Chat</em> button.</p>
-<li><strong>How can I contact the developers or support team of FR Legends?</strong></li>
-<p>To contact the developers or support team of FR Legends, you can use one of these methods:</p>
-<ul>
-<li>Email them at <a href="mailto:[email protected]">[email protected]</a></li>
-<li>Follow them on Instagram at <a href="https://www.instagram.com/frlegendsgame/">https://www.instagram.com/frlegendsgame/</a></li>
-<li>Like their Facebook page at <a href="https://www.facebook.com/FRLEGENDS/">https://www.facebook.com/FRLEGENDS/</a></li>
-<li>Join their Discord server at <a href="https://discord.gg/frlegends">https://discord.gg/frlegends</a></li>
-</ul>
-<p>You can also leave a review or feedback on Google Play Store or App Store and rate the game according to your experience.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1toTree/lora_test/ppdiffusers/initializer.py

@@ -1,303 +0,0 @@
-#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This code is based on https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
-Ths copyright of pytorch/pytorch is a BSD-style license, as found in the LICENSE file.
-"""
-
-import math
-
-import numpy as np
-import paddle
-import paddle.nn as nn
-
-__all__ = [
-    "uniform_",
-    "normal_",
-    "constant_",
-    "ones_",
-    "zeros_",
-    "xavier_uniform_",
-    "xavier_normal_",
-    "kaiming_uniform_",
-    "kaiming_normal_",
-    "linear_init_",
-    "conv_init_",
-    "reset_initialized_parameter",
-]
-
-
-def _no_grad_uniform_(tensor, a, b):
-    with paddle.no_grad():
-        tensor.set_value(paddle.uniform(shape=tensor.shape, dtype=tensor.dtype, min=a, max=b))
-    return tensor
-
-
-def _no_grad_normal_(tensor, mean=0.0, std=1.0):
-    with paddle.no_grad():
-        tensor.set_value(paddle.normal(mean=mean, std=std, shape=tensor.shape))
-    return tensor
-
-
-def _no_grad_fill_(tensor, value=0.0):
-    with paddle.no_grad():
-        tensor.set_value(paddle.full_like(tensor, value, dtype=tensor.dtype))
-    return tensor
-
-
-def uniform_(tensor, a, b):
-    """
-    Modified tensor inspace using uniform_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-        a (float|int): min value.
-        b (float|int): max value.
-    Return:
-        tensor
-    """
-    return _no_grad_uniform_(tensor, a, b)
-
-
-def normal_(tensor, mean=0.0, std=1.0):
-    """
-    Modified tensor inspace using normal_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-        mean (float|int): mean value.
-        std (float|int): std value.
-    Return:
-        tensor
-    """
-    return _no_grad_normal_(tensor, mean, std)
-
-
-def constant_(tensor, value=0.0):
-    """
-    Modified tensor inspace using constant_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-        value (float|int): value to fill tensor.
-    Return:
-        tensor
-    """
-    return _no_grad_fill_(tensor, value)
-
-
-def ones_(tensor):
-    """
-    Modified tensor inspace using ones_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-    Return:
-        tensor
-    """
-    return _no_grad_fill_(tensor, 1)
-
-
-def zeros_(tensor):
-    """
-    Modified tensor inspace using zeros_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-    Return:
-        tensor
-    """
-    return _no_grad_fill_(tensor, 0)
-
-
-def vector_(tensor, vector):
-    with paddle.no_grad():
-        tensor.set_value(paddle.to_tensor(vector, dtype=tensor.dtype))
-    return tensor
-
-
-def _calculate_fan_in_and_fan_out(tensor, reverse=False):
-    """
-    Calculate (fan_in, _fan_out) for tensor
-    Args:
-        tensor (Tensor): paddle.Tensor
-        reverse (bool: False): tensor data format order, False by default as [fout, fin, ...]. e.g. : conv.weight [cout, cin, kh, kw] is False; linear.weight [cin, cout] is True
-    Return:
-        Tuple[fan_in, fan_out]
-    """
-    if tensor.ndim < 2:
-        raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
-
-    if reverse:
-        num_input_fmaps, num_output_fmaps = tensor.shape[0], tensor.shape[1]
-    else:
-        num_input_fmaps, num_output_fmaps = tensor.shape[1], tensor.shape[0]
-
-    receptive_field_size = 1
-    if tensor.ndim > 2:
-        receptive_field_size = np.prod(tensor.shape[2:])
-
-    fan_in = num_input_fmaps * receptive_field_size
-    fan_out = num_output_fmaps * receptive_field_size
-
-    return fan_in, fan_out
-
-
-def xavier_uniform_(tensor, gain=1.0, reverse=False):
-    """
-    Modified tensor inspace using xavier_uniform_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-        gain (float): super parameter, 1. default.
-        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
-    Return:
-        tensor
-    """
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse)
-    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
-    k = math.sqrt(3.0) * std
-    return _no_grad_uniform_(tensor, -k, k)
-
-
-def xavier_normal_(tensor, gain=1.0, reverse=False):
-    """
-    Modified tensor inspace using xavier_normal_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-        gain (float): super parameter, 1. default.
-        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
-    Return:
-        tensor
-    """
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse)
-    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
-    return _no_grad_normal_(tensor, 0, std)
-
-
-# reference: https://pytorch.org/docs/stable/_modules/torch/nn/init.html
-def _calculate_correct_fan(tensor, mode, reverse=False):
-    mode = mode.lower()
-    valid_modes = ["fan_in", "fan_out"]
-    if mode not in valid_modes:
-        raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
-
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse)
-
-    return fan_in if mode == "fan_in" else fan_out
-
-
-def _calculate_gain(nonlinearity, param=None):
-    linear_fns = ["linear", "conv1d", "conv2d", "conv3d", "conv_transpose1d", "conv_transpose2d", "conv_transpose3d"]
-    if nonlinearity in linear_fns or nonlinearity == "sigmoid":
-        return 1
-    elif nonlinearity == "tanh":
-        return 5.0 / 3
-    elif nonlinearity == "relu":
-        return math.sqrt(2.0)
-    elif nonlinearity == "leaky_relu":
-        if param is None:
-            negative_slope = 0.01
-        elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
-            # True/False are instances of int, hence check above
-            negative_slope = param
-        else:
-            raise ValueError("negative_slope {} not a valid number".format(param))
-        return math.sqrt(2.0 / (1 + negative_slope**2))
-    elif nonlinearity == "selu":
-        return 3.0 / 4
-    else:
-        raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
-
-
-def kaiming_uniform_(tensor, a=0, mode="fan_in", nonlinearity="leaky_relu", reverse=False):
-    """
-    Modified tensor inspace using kaiming_uniform method
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-        mode (str): ['fan_in', 'fan_out'], 'fin_in' defalut
-        nonlinearity (str): nonlinearity method name
-        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
-    Return:
-        tensor
-    """
-    fan = _calculate_correct_fan(tensor, mode, reverse)
-    gain = _calculate_gain(nonlinearity, a)
-    std = gain / math.sqrt(fan)
-    k = math.sqrt(3.0) * std
-    return _no_grad_uniform_(tensor, -k, k)
-
-
-def kaiming_normal_(tensor, a=0, mode="fan_in", nonlinearity="leaky_relu", reverse=False):
-    """
-    Modified tensor inspace using kaiming_normal_
-    Args:
-        tensor (paddle.Tensor): paddle Tensor
-        mode (str): ['fan_in', 'fan_out'], 'fin_in' defalut
-        nonlinearity (str): nonlinearity method name
-        reverse (bool):  reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
-    Return:
-        tensor
-    """
-    fan = _calculate_correct_fan(tensor, mode, reverse)
-    gain = _calculate_gain(nonlinearity, a)
-    std = gain / math.sqrt(fan)
-    return _no_grad_normal_(tensor, 0, std)
-
-
-def linear_init_(module):
-    bound = 1 / math.sqrt(module.weight.shape[0])
-    uniform_(module.weight, -bound, bound)
-    uniform_(module.bias, -bound, bound)
-
-
-def conv_init_(module):
-    bound = 1 / np.sqrt(np.prod(module.weight.shape[1:]))
-    uniform_(module.weight, -bound, bound)
-    if module.bias is not None:
-        uniform_(module.bias, -bound, bound)
-
-
-def bias_init_with_prob(prior_prob=0.01):
-    """initialize conv/fc bias value according to a given probability value."""
-    bias_init = float(-np.log((1 - prior_prob) / prior_prob))
-    return bias_init
-
-
-@paddle.no_grad()
-def reset_initialized_parameter(model, include_self=True):
-    """
-    Reset initialized parameter using following method for [conv, linear, embedding, bn]
-    Args:
-        model (paddle.Layer): paddle Layer
-        include_self (bool: False): include_self for Layer.named_sublayers method. Indicate whether including itself
-    Return:
-        None
-    """
-    for _, m in model.named_sublayers(include_self=include_self):
-        if isinstance(m, nn.Conv2D):
-            k = float(m._groups) / (m._in_channels * m._kernel_size[0] * m._kernel_size[1])
-            k = math.sqrt(k)
-            _no_grad_uniform_(m.weight, -k, k)
-            if hasattr(m, "bias") and getattr(m, "bias") is not None:
-                _no_grad_uniform_(m.bias, -k, k)
-
-        elif isinstance(m, nn.Linear):
-            k = math.sqrt(1.0 / m.weight.shape[0])
-            _no_grad_uniform_(m.weight, -k, k)
-            if hasattr(m, "bias") and getattr(m, "bias") is not None:
-                _no_grad_uniform_(m.bias, -k, k)
-
-        elif isinstance(m, nn.Embedding):
-            _no_grad_normal_(m.weight, mean=0.0, std=1.0)
-
-        elif isinstance(m, (nn.BatchNorm2D, nn.LayerNorm)):
-            _no_grad_fill_(m.weight, 1.0)
-            if hasattr(m, "bias") and getattr(m, "bias") is not None:
-                _no_grad_fill_(m.bias, 0)

spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_mega.py

@@ -1,183 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL.Image
-
-from ...utils import logging
-from .pipeline_stable_diffusion import StableDiffusionPipeline
-from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
-from .pipeline_stable_diffusion_inpaint_legacy import (
-    StableDiffusionInpaintPipelineLegacy,
-)
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class StableDiffusionMegaPipeline(StableDiffusionPipeline):
-    r"""
-    Pipeline for generation using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular xxxx, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`]
-            or [`DPMSolverMultistepScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __call__(self, *args, **kwargs):
-        return self.text2img(*args, **kwargs)
-
-    def text2img(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = 512,
-        width: Optional[int] = 512,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[np.random.RandomState] = None,
-        latents: Optional[np.ndarray] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-
-        expected_components = inspect.signature(StableDiffusionPipeline.__init__).parameters.keys()
-        components = {name: component for name, component in self.components.items() if name in expected_components}
-        temp_pipeline = StableDiffusionPipeline(
-            **components, requires_safety_checker=self.config.requires_safety_checker
-        )
-        output = temp_pipeline(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-        )
-        return output
-
-    def img2img(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[np.ndarray, PIL.Image.Image],
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[np.random.RandomState] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        expected_components = inspect.signature(StableDiffusionImg2ImgPipeline.__init__).parameters.keys()
-        components = {name: component for name, component in self.components.items() if name in expected_components}
-        temp_pipeline = StableDiffusionImg2ImgPipeline(
-            **components, requires_safety_checker=self.config.requires_safety_checker
-        )
-        output = temp_pipeline(
-            prompt=prompt,
-            image=image,
-            strength=strength,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-        )
-
-        return output
-
-    def inpaint_legacy(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[np.ndarray, PIL.Image.Image],
-        mask_image: Union[np.ndarray, PIL.Image.Image],
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[np.random.RandomState] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        expected_components = inspect.signature(StableDiffusionInpaintPipelineLegacy.__init__).parameters.keys()
-        components = {name: component for name, component in self.components.items() if name in expected_components}
-        temp_pipeline = StableDiffusionInpaintPipelineLegacy(
-            **components, requires_safety_checker=self.config.requires_safety_checker
-        )
-        output = temp_pipeline(
-            prompt=prompt,
-            image=image,
-            mask_image=mask_image,
-            strength=strength,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-        )
-
-        return output

spaces/2023Liu2023/bingo/src/components/chat-scroll-anchor.tsx

@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}

spaces/404ERRORms/bingAI/README.md

@@ -1,12 +0,0 @@
----
-title: BingAI
-emoji: 🚀
-colorFrom: blue
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIML-TUDA/semantic-diffusion/app.py

@@ -1,517 +0,0 @@
-from contextlib import nullcontext
-import gradio as gr
-import torch
-from torch import autocast
-from diffusers import SemanticStableDiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-pipe = pipe.to(device)
-gen = torch.Generator(device=device)
-
-# Sometimes the nsfw checker is confused by the Pokémon images, you can disable
-# it at your own risk here
-disable_safety = False
-
-if disable_safety:
-    def null_safety(images, **kwargs):
-        return images, False
-    pipe.safety_checker = null_safety
-
-
-style_embeddings = {
-    'Concept Art': torch.load('embeddings/concept_art.pt'), 'Animation': torch.load('embeddings/animation.pt'), 'Character Design': torch.load('embeddings/character_design.pt')
-    , 'Portrait Photo': torch.load('embeddings/portrait_photo.pt'), 'Architecture': torch.load('embeddings/architecture.pt')
-}
-    
-def infer(prompt, steps, scale, seed, editing_prompt_1 = None, reverse_editing_direction_1 = False, edit_warmup_steps_1=10, edit_guidance_scale_1=5, edit_threshold_1=0.95, 
-          editing_prompt_2 = None, reverse_editing_direction_2 = False, edit_warmup_steps_2=10, edit_guidance_scale_2=5, edit_threshold_2=0.95, 
-          edit_style=None,
-          reverse_editing_direction_style = False, edit_warmup_steps_style=5, edit_guidance_scale_style=7, edit_threshold_style=0.8, 
-          edit_momentum_scale=0.5, edit_mom_beta=0.6):
-    
-    
-    gen.manual_seed(seed)
-    images = pipe(prompt, guidance_scale=scale, num_inference_steps=steps, generator=gen).images
-    
-    editing_prompt = [editing_prompt_1, editing_prompt_2]
-    reverse_editing_direction = [reverse_editing_direction_1, reverse_editing_direction_2]
-    edit_warmup_steps = [edit_warmup_steps_1, edit_warmup_steps_2]
-    edit_guidance_scale = [edit_guidance_scale_1, edit_guidance_scale_2]
-    edit_threshold = [edit_threshold_1, edit_threshold_2]
-    
-    indices = [ind for ind, val in enumerate(editing_prompt) if val is None or len(val) <= 1]
-    
-    for index in sorted(indices, reverse=True):        
-        del editing_prompt[index]
-        del reverse_editing_direction[index]
-        del edit_warmup_steps[index]
-        del edit_guidance_scale[index]
-        del edit_threshold[index]
-    editing_prompt_embeddings = None
-    
-    out_label = 'SEGA'
-    if edit_style is not None and isinstance(edit_style, str) and edit_style in style_embeddings.keys():
-        editing_prompt = None
-        reverse_editing_direction = reverse_editing_direction_style
-        edit_warmup_steps = edit_warmup_steps_style
-        edit_guidance_scale = edit_guidance_scale_style
-        edit_threshold = edit_threshold_style
-        editing_prompt_embeddings = style_embeddings[edit_style]
-        out_label = edit_style
-    
-    gen.manual_seed(seed)
-    images.extend(pipe(prompt, guidance_scale=scale, num_inference_steps=steps, generator=gen,
-                      editing_prompt=editing_prompt, editing_prompt_embeddings=editing_prompt_embeddings,
-                       reverse_editing_direction=reverse_editing_direction, edit_warmup_steps=edit_warmup_steps, edit_guidance_scale=edit_guidance_scale,                                            
-                        edit_momentum_scale=edit_momentum_scale, edit_mom_beta=edit_mom_beta
-                      ).images)
-
-    return zip(images, ['Original', out_label])
-
-def reset_style():
-    radio = gr.Radio(label='Style', choices=['Concept Art', 'Animation', 'Character Design', 'Portrait Photo', 'Architecture'])
-    return radio
-
-def reset_text():
-    text_1 = gr.Textbox(
-                        label="Edit Prompt 1",
-                        show_label=False,
-                        max_lines=1,
-                        placeholder="Enter your 1st edit prompt",
-                    ).style(
-                        border=(True, False, True, True),
-                        rounded=(True, False, False, True),
-                        container=False,
-                    )
-    text_2 = gr.Textbox(
-                        label="Edit Prompt 2",
-                        show_label=False,
-                        max_lines=1,
-                        placeholder="Enter your 2nd edit prompt",
-                    ).style(
-                        border=(True, False, True, True),
-                        rounded=(True, False, False, True),
-                        container=False,
-                    )
-    return text_1, text_2
-
-css = """
-        a {
-            color: inherit;
-            text-decoration: underline;
-        }
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        }
-        .gr-button {
-            color: white;
-            border-color: #9d66e5;
-            background: #9d66e5;
-        }
-        input[type='range'] {
-            accent-color: #9d66e5;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            max-width: 730px;
-            margin: auto;
-            padding-top: 1.5rem;
-        }
-        #gallery {
-            min-height: 22rem;
-            margin-bottom: 15px;
-            margin-left: auto;
-            margin-right: auto;
-            border-bottom-right-radius: .5rem !important;
-            border-bottom-left-radius: .5rem !important;
-        }
-        #gallery>div>.h-full {
-            min-height: 20rem;
-        }
-        .details:hover {
-            text-decoration: underline;
-        }
-        .gr-button {
-            white-space: nowrap;
-        }
-        .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity));
-            outline: none;
-            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-            --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-            --tw-ring-opacity: .5;
-        }
-        #advanced-options {
-            margin-bottom: 20px;
-        }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-"""
-
-block = gr.Blocks(css=css)
-
-examples = [
-    [
-        'a photo of a cat',
-        50,
-        7,
-        3,
-        'sunglasses',
-        False,
-        10,
-        5,
-        0.95,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        '',
-        False,
-        5,
-        7,
-        0.8,
-    ],
-    [
-        'an image of a crowded boulevard, realistic, 4k',
-        50,
-        7,
-        9,
-        'crowd, crowded, people',
-        True,
-        10,
-        8.3,
-        0.9,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        '',
-        False,
-        5,
-        7,
-        0.8
-    ],
-    [
-        'a castle next to a river',
-        50,
-        7,
-        48,
-        'boat on a river',
-        False,
-        15,
-        6,
-        0.9,
-        'monet, impression, sunrise',
-        False,
-        18,
-        6,
-        0.8,
-        '',
-        False,
-        5,
-        7,
-        0.8
-    ],
-    [
-        'a portrait of a king, full body shot, 8k',
-        50,
-        7,
-        33,
-        'male',
-        True,
-        5,
-        5,
-        0.9,
-        'female',
-        False,
-        5,
-        5,
-        0.9,
-        '',
-        False,
-        5,
-        7,
-        0.8
-    ],
-    [
-        'a photo of a flowerpot',
-        50,
-        7,
-        2,
-        'glasses',
-        False,
-        12,
-        5,
-        0.975,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        '',
-        False,
-        5,
-        7,
-        0.8
-    ],
-     [
-        'a photo of the face of a woman',
-        50,
-        7,
-        21,
-        'smiling, smile',
-        False,
-        15,
-        3,
-        0.99,
-        'curls, wavy hair, curly hair',
-        False,
-        13,
-        3,
-        0.925,
-        '',
-        False,
-        5,
-        7,
-        0.8
-    ],
-     [
-        'temple in ruines, forest, stairs, columns',
-        50,
-        7,
-        11,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        'Animation',
-        False,
-        5,
-        7,
-        0.8
-    ],
-    [
-        'city made out of glass',
-        50,
-        7,
-        16,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        'Concept Art',
-        False,
-        10,
-        8,
-        0.8
-    ],
-     [
-        'a man riding a horse',
-        50,
-        7,
-        11,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        '',
-        False,
-        10,
-        5,
-        0.95,
-        'Character Design',
-        False,
-        11,
-        8,
-        0.9
-    ],
-]
-
-
-with block:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 750px; margin: 0 auto;">
-              <div>
-                <img class="logo" src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1666181274838-62fa1d95e8c9c532aa75331c.png" alt="AIML Logo"
-                    style="margin: auto; max-width: 7rem;">
-                <h1 style="font-weight: 900; font-size: 3rem;">
-                  Semantic Guidance for Diffusion
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-              Interact with semantic concepts during the diffusion process. Details can be found in the paper <a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">SEGA: Instructing Diffusion using Semantic Dimensions</a>. <br/> Simply use the edit prompts to make arbitrary changes to the generation. 
-              </p>
-            </div>
-        """
-    )
-    gr.HTML("""
-<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-<br/>
-<a href="https://huggingface.co/spaces/AIML-TUDA/semantic-diffusion?duplicate=true">
-<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-<p/>""")
-    with gr.Group():
-        with gr.Box():
-            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                text = gr.Textbox(
-                    label="Enter your prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                )
-            with gr.Tabs() as tabs:
-                with gr.TabItem('Text Guidance', id=0):
-                    with gr.Row().style(mobile_collapse=False, equal_height=True):
-                        edit_1 = gr.Textbox(
-                            label="Edit Prompt 1",
-                            show_label=False,
-                            max_lines=1,
-                            placeholder="Enter your 1st edit prompt",
-                        ).style(
-                            border=(True, False, True, True),
-                            rounded=(True, False, False, True),
-                            container=False,
-                        )
-                        with gr.Group():
-                            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                                rev_1 = gr.Checkbox(
-                                    label='Negative Guidance')
-                                warmup_1 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=10, step=1, interactive=True)
-                                scale_1 = gr.Slider(label='Scale', minimum=1, maximum=10, value=5, step=0.25, interactive=True)
-                                threshold_1 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01, interactive=True)
-                    with gr.Row().style(mobile_collapse=False, equal_height=True):
-                        edit_2 = gr.Textbox(
-                            label="Edit Prompt 2",
-                            show_label=False,
-                            max_lines=1,
-                            placeholder="Enter your 2nd edit prompt",
-                        ).style(
-                            border=(True, False, True, True),
-                            rounded=(True, False, False, True),
-                            container=False,
-                        )
-                        with gr.Group():
-                            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                                rev_2 = gr.Checkbox(
-                                    label='Negative Guidance')
-                                warmup_2 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=10, step=1, interactive=True)
-                                scale_2 = gr.Slider(label='Scale', minimum=1, maximum=10, value=5, step=0.25, interactive=True)
-                                threshold_2 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01, interactive=True)
-                with gr.TabItem("Style Guidance", id=1):
-                    with gr.Row().style(mobile_collapse=False, equal_height=True):
-                        style = gr.Radio(label='Style', choices=['Concept Art', 'Animation', 'Character Design', 'Portrait Photo', 'Architecture'], interactive=True)
-                        with gr.Group():
-                            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                                rev_style = gr.Checkbox(
-                                    label='Negative Guidance', interactive=False)
-                                warmup_style = gr.Slider(label='Warmup', minimum=0, maximum=50, value=5, step=1, interactive=True)
-                                scale_style = gr.Slider(label='Scale', minimum=1, maximum=10, value=7, step=0.25, interactive=True)
-                                threshold_style = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.8, steps=0.01, interactive=True)
-        
-        
-        gallery = gr.Gallery(
-            label=("Generated images"), show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-
-
-        with gr.Row(elem_id="advanced-options"):
-            scale = gr.Slider(label="Scale", minimum=3, maximum=15, value=7, step=1)
-            steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=50, step=5, interactive=False)
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=2147483647,
-                step=1,
-                #randomize=True,
-            )
-
-        
-        ex = gr.Examples(examples=examples, fn=infer, inputs=[text, steps, scale, seed, edit_1, rev_1, warmup_1, scale_1, threshold_1, edit_2, rev_2, warmup_2, scale_2, threshold_2, style, rev_style, warmup_style, scale_style, threshold_style], outputs=gallery, cache_examples=True)
-        ex.dataset.headers = ['Prompt', 'Steps', 'Scale', 'Seed', 'Edit Prompt 1', 'Negation 1', 'Warmup 1', 'Scale 1', 'Threshold 1', 'Edit Prompt 2', 'Negation 2', 'Warmup 2', 'Scale 2', 'Threshold 2', 'Style', 'Style Negation', 'Style Warmup', 'Style Scale', 'Style Threshold']
-
-
-        text.submit(infer, inputs=[text, steps, scale, seed, edit_1, rev_1, warmup_1, scale_1, threshold_1, edit_2, rev_2, warmup_2, scale_2, threshold_2, style, rev_style, warmup_style, scale_style, threshold_style], outputs=gallery)
-        btn.click(infer, inputs=[text, steps, scale, seed, edit_1, rev_1, warmup_1, scale_1, threshold_1, edit_2, rev_2, warmup_2, scale_2, threshold_2, style, rev_style, warmup_style, scale_style, threshold_style], outputs=gallery)
-        #btn.click(change_tab, None, tabs)
-        
-        edit_1.change(reset_style, outputs=style)
-        edit_2.change(reset_style, outputs=style)
-        
-        rev_1.change(reset_style, outputs=style)
-        rev_2.change(reset_style, outputs=style)
-        
-        warmup_1.change(reset_style, outputs=style)
-        warmup_2.change(reset_style, outputs=style)
-        
-        threshold_1.change(reset_style, outputs=style)
-        threshold_2.change(reset_style, outputs=style)
-        #style.change(reset_text, outputs=[edit_1, edit_2])
-
-        
-        gr.HTML(
-            """
-                <div class="footer">
-                    <p> Gradio Demo by AIML@TU Darmstadt and 🤗 Hugging Face 
-                    </p>
-                </div>
-                <div class="acknowledgments">                    
-                    <p>Created by <a href="https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/">Manuel Brack</a> and <a href="justinpinkney.com">Patrick Schramowski</a> at <a href="https://www.aiml.informatik.tu-darmstadt.de">AIML Lab</a>.</p>
-               </div>
-           """
-        )
-
-block.launch()

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/options.py

@@ -1,39 +0,0 @@
-"""
-Types of various choices used during training
-"""
-from enum import Enum
-
-
-class AttentionType(Enum):
-    """Type of attention used during training"""
-
-    LocationSensitive = 1
-    Content_Based = 2
-    MultiHead = 3
-
-
-class LearningRateType(Enum):
-    """Type of learning rate used during training"""
-
-    Learning_Rate_Decay = 1
-    Cosine_Scheduler = 2
-    SquareRoot_Scheduler = 3
-
-
-class OptimizerType(Enum):
-    """Type of optimizer used during training"""
-
-    Adam = 1
-    SGD = 2
-    AdamW = 3
-
-
-class LossType(Enum):
-    """Type of loss function used during training"""
-
-    L1_LOSS = 1
-    MSE_LOSS = 2
-    L1_LOSS_MASKED = 3
-    MSE_LOSS_MASKED = 4
-    BOTH = 5
-    BOTH_MASKED = 6

spaces/Ababababababbababa/poetry/app.py

@@ -1,53 +0,0 @@
-import gc 
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
-           ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
-            ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
-              ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
-                 ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
-                   ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
-                    ,1.0, 50, 1.0, 1.0, 114 ],['.'
-                       ,1.0, 50, 1.0, 1.0, 114]]
-                     
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND 
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed. Seed should be 0 or more (not empty) 
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long) 
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) 
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
-    if not int(seed) >= 0: seed=114
-    set_seed(seed)
-    gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
-              min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
-               num_beams=5, num_return_sequences=1)[0]["generated_text"]
-    poetry =""
-    for line in gen.split('.')[:-1]:
-        poetry += line #+ "\n"
-    return poetry
-poetry = gr.Interface(fn=sayPoetry, 
-                    inputs=[
-                        gr.Textbox(label="Enter short prompt or select from examples:"), 
-                        gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control  temperature'),
-                        gr.Slider(25, 100, step=1,value=50, label='control  top k'),
-                        gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control  top p'),
-                        gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control  penalty'),
-                        gr.Number(value=139750, precision=0, label='Seed'),
-                    ], 
-                outputs=[gr.Textbox(label="Generated Poetry:")], 
-                    
-                allow_flagging='never',
-                title='Arabic Poetry Generation Demo (updated Jan. 2023)',
-                description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
-                examples=samples,
-                cache_examples=False,
-                article = notes)
-poetry.launch()         

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/utils/__init__.py

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

spaces/AchyuthGamer/ImMagician-Image-Generator/app.py

@@ -1,264 +0,0 @@
-import os
-import random
-import gradio as gr
-import numpy as np
-import PIL.Image
-import torch
-from typing import List
-from diffusers.utils import numpy_to_pil
-from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline
-from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
-from previewer.modules import Previewer
-os.environ['TOKENIZERS_PARALLELISM'] = 'false'
-
-DESCRIPTION = "ImMagician🪄"
-DESCRIPTION += "\n<p style=\"text-align: center\"><a href='https://huggingface.co/warp-ai/wuerstchen' target='_blank'>ImMagician🪄</a> is a new fast and efficient high resolution text-to-image architecture and model</p>"
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶</p>"
-
-MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
-USE_TORCH_COMPILE = False
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
-PREVIEW_IMAGES = True
-
-dtype = torch.float16
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-if torch.cuda.is_available():
-    prior_pipeline = WuerstchenPriorPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=dtype)
-    decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=dtype)
-    if ENABLE_CPU_OFFLOAD:
-        prior_pipeline.enable_model_cpu_offload()
-        decoder_pipeline.enable_model_cpu_offload()
-    else:
-        prior_pipeline.to(device)
-        decoder_pipeline.to(device)
-
-    if USE_TORCH_COMPILE:
-        prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True)
-        decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True)
-    
-    if PREVIEW_IMAGES:
-        previewer = Previewer()
-        previewer.load_state_dict(torch.load("previewer/text2img_wurstchen_b_v1_previewer_100k.pt")["state_dict"])
-        previewer.eval().requires_grad_(False).to(device).to(dtype)
-
-        def callback_prior(i, t, latents):
-            output = previewer(latents)
-            output = numpy_to_pil(output.clamp(0, 1).permute(0, 2, 3, 1).cpu().numpy())
-            return output
-    else:
-        previewer = None
-        callback_prior = None
-else:
-    prior_pipeline = None
-    decoder_pipeline = None
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    prior_num_inference_steps: int = 60,
-    # prior_timesteps: List[float] = None,
-    prior_guidance_scale: float = 4.0,
-    decoder_num_inference_steps: int = 12,
-    # decoder_timesteps: List[float] = None,
-    decoder_guidance_scale: float = 0.0,
-    num_images_per_prompt: int = 2,
-) -> PIL.Image.Image:
-    generator = torch.Generator().manual_seed(seed)
-
-    prior_output = prior_pipeline(
-        prompt=prompt,
-        height=height,
-        width=width,
-        timesteps=DEFAULT_STAGE_C_TIMESTEPS,
-        negative_prompt=negative_prompt,
-        guidance_scale=prior_guidance_scale,
-        num_images_per_prompt=num_images_per_prompt,
-        generator=generator,
-        callback=callback_prior,
-    )
-
-    if PREVIEW_IMAGES:
-        for _ in range(len(DEFAULT_STAGE_C_TIMESTEPS)):
-            r = next(prior_output)
-            if isinstance(r, list):
-                yield r
-        prior_output = r
-    
-    decoder_output = decoder_pipeline(
-        image_embeddings=prior_output.image_embeddings,
-        prompt=prompt,
-        num_inference_steps=decoder_num_inference_steps,
-        # timesteps=decoder_timesteps,
-        guidance_scale=decoder_guidance_scale,
-        negative_prompt=negative_prompt,
-        generator=generator,
-        output_type="pil",
-    ).images
-    yield decoder_output
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-]
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
-        result = gr.Gallery(label="Result", show_label=False)
-    with gr.Accordion("Advanced options", open=False):
-        negative_prompt = gr.Text(
-            label="Negative prompt",
-            max_lines=1,
-            placeholder="Enter a Negative Prompt",
-        )
-
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Row():
-            width = gr.Slider(
-                label="Width",
-                minimum=1024,
-                maximum=MAX_IMAGE_SIZE,
-                step=512,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=1024,
-                maximum=MAX_IMAGE_SIZE,
-                step=512,
-                value=1024,
-            )
-            num_images_per_prompt = gr.Slider(
-                label="Number of Images",
-                minimum=1,
-                maximum=6,
-                step=1,
-                value=2,
-            )
-        with gr.Row():
-            prior_guidance_scale = gr.Slider(
-                label="Prior Guidance Scale",
-                minimum=0,
-                maximum=20,
-                step=0.1,
-                value=4.0,
-            )
-            prior_num_inference_steps = gr.Slider(
-                label="Prior Inference Steps",
-                minimum=30,
-                maximum=30,
-                step=1,
-                value=30,
-            )
-
-            decoder_guidance_scale = gr.Slider(
-                label="Decoder Guidance Scale",
-                minimum=0,
-                maximum=0,
-                step=0.1,
-                value=0.0,
-            )
-            decoder_num_inference_steps = gr.Slider(
-                label="Decoder Inference Steps",
-                minimum=4,
-                maximum=12,
-                step=1,
-                value=12,
-            )
-
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=result,
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-    inputs = [
-            prompt,
-            negative_prompt,
-            seed,
-            width,
-            height,
-            prior_num_inference_steps,
-            # prior_timesteps,
-            prior_guidance_scale,
-            decoder_num_inference_steps,
-            # decoder_timesteps,
-            decoder_guidance_scale,
-            num_images_per_prompt,
-    ]
-    prompt.submit(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=inputs,
-        outputs=result,
-        api_name="run",
-    )
-    negative_prompt.submit(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-    run_button.click(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/radio/Radio.d.ts

@@ -1,2 +0,0 @@
-import Base from '../base/Base';
-export default class Radio extends Base { }

spaces/Aloento/9Nine-PITS/text/frontend/zh_normalization/phonecode.py

@@ -1,63 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-
-from .num import verbalize_digit
-
-# 规范化固话/手机号码
-# 手机
-# http://www.jihaoba.com/news/show/13680
-# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
-# 联通：130、131、132、156、155、186、185、176
-# 电信：133、153、189、180、181、177
-RE_MOBILE_PHONE = re.compile(
-  r"(?<!\d)((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})(?!\d)")
-RE_TELEPHONE = re.compile(
-  r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{7,8})(?!\d)")
-
-# 全国统一的号码400开头
-RE_NATIONAL_UNIFORM_NUMBER = re.compile(r"(400)(-)?\d{3}(-)?\d{4}")
-
-
-def phone2str(phone_string: str, mobile=True) -> str:
-  if mobile:
-    sp_parts = phone_string.strip('+').split()
-    result = '，'.join(
-      [verbalize_digit(part, alt_one=True) for part in sp_parts])
-    return result
-  else:
-    sil_parts = phone_string.split('-')
-    result = '，'.join(
-      [verbalize_digit(part, alt_one=True) for part in sil_parts])
-    return result
-
-
-def replace_phone(match) -> str:
-  """
-  Args:
-      match (re.Match)
-  Returns:
-      str
-  """
-  return phone2str(match.group(0), mobile=False)
-
-
-def replace_mobile(match) -> str:
-  """
-  Args:
-      match (re.Match)
-  Returns:
-      str
-  """
-  return phone2str(match.group(0))

spaces/Alpaca233/SadTalker/src/utils/videoio.py

@@ -1,41 +0,0 @@
-import shutil
-import uuid
-
-import os
-
-import cv2
-
-def load_video_to_cv2(input_path):
-    video_stream = cv2.VideoCapture(input_path)
-    fps = video_stream.get(cv2.CAP_PROP_FPS)
-    full_frames = [] 
-    while 1:
-        still_reading, frame = video_stream.read()
-        if not still_reading:
-            video_stream.release()
-            break 
-        full_frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-    return full_frames
-
-def save_video_with_watermark(video, audio, save_path, watermark=False):
-    temp_file = str(uuid.uuid4())+'.mp4'
-    cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -vcodec copy "%s"' % (video, audio, temp_file)
-    os.system(cmd)
-
-    if watermark is False:
-        shutil.move(temp_file, save_path)
-    else:
-        # watermark
-        try:
-            ##### check if stable-diffusion-webui
-            import webui
-            from modules import paths
-            watarmark_path = paths.script_path+"/extensions/SadTalker/docs/sadtalker_logo.png"
-        except:
-            # get the root path of sadtalker.
-            dir_path = os.path.dirname(os.path.realpath(__file__))
-            watarmark_path = dir_path+"/../../docs/sadtalker_logo.png"
-
-        cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -filter_complex "[1]scale=100:-1[wm];[0][wm]overlay=(main_w-overlay_w)-10:10" "%s"' % (temp_file, watarmark_path, save_path)
-        os.system(cmd)
-        os.remove(temp_file)

spaces/Alycer/VITS-Umamusume-voice-synthesizer/models.py

@@ -1,542 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout,
-      emotion_embedding):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.emotion_embedding = emotion_embedding
-    
-    if self.n_vocab!=0:
-      self.emb = nn.Embedding(n_vocab, hidden_channels)
-      if emotion_embedding:
-        self.emotion_emb = nn.Linear(1024, hidden_channels)
-      nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, emotion_embedding=None):
-    if self.n_vocab!=0:
-      x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    if emotion_embedding is not None:
-      x = x + self.emotion_emb(emotion_embedding.unsqueeze(1))
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    emotion_embedding=False,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        emotion_embedding)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, emotion_embedding=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-

spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/japanese.py

@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text

spaces/Amrrs/DragGan-Inversion/torch_utils/custom_ops.py

@@ -1,171 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import glob
-import hashlib
-import importlib
-import os
-import re
-import shutil
-import uuid
-
-import torch
-import torch.utils.cpp_extension
-from torch.utils.file_baton import FileBaton
-
-# ----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief'  # Verbosity level: 'none', 'brief', 'full'
-
-# ----------------------------------------------------------------------------
-# Internal helper funcs.
-
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files*/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files*/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files*/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files*/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-# ----------------------------------------------------------------------------
-
-
-def _get_mangled_gpu_name():
-    name = torch.cuda.get_device_name().lower()
-    out = []
-    for c in name:
-        if re.match('[a-z0-9_-]+', c):
-            out.append(c)
-        else:
-            out.append('-')
-    return ''.join(out)
-
-# ----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-
-_cached_plugins = dict()
-
-
-def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-    if headers is None:
-        headers = []
-    if source_dir is not None:
-        sources = [os.path.join(source_dir, fname) for fname in sources]
-        headers = [os.path.join(source_dir, fname) for fname in headers]
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(
-            f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-    verbose_build = (verbosity == 'full')
-
-    # Compile and load.
-    try:  # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(
-                    f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either
-        # break the build or unnecessarily restrict what's available to nvcc.
-        # Unset it to let nvcc decide based on what's available on the
-        # machine.
-        os.environ['TORCH_CUDA_ARCH_LIST'] = ''
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        #
-        # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work
-        # around the *.cu dependency bug in ninja config.
-        #
-        all_source_files = sorted(sources + headers)
-        all_source_dirs = set(os.path.dirname(fname)
-                              for fname in all_source_files)
-        # and ('TORCH_EXTENSIONS_DIR' in os.environ):
-        if len(all_source_dirs) == 1:
-
-            # Compute combined hash digest for all source files.
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-
-            # Select cached build directory name.
-            source_digest = hash_md5.hexdigest()
-            build_top_dir = torch.utils.cpp_extension._get_build_directory(
-                module_name, verbose=verbose_build)  # pylint: disable=protected-access
-            cached_build_dir = os.path.join(
-                build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}')
-
-            if not os.path.isdir(cached_build_dir):
-                tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}'
-                os.makedirs(tmpdir)
-                for src in all_source_files:
-                    shutil.copyfile(src, os.path.join(
-                        tmpdir, os.path.basename(src)))
-                try:
-                    os.replace(tmpdir, cached_build_dir)  # atomic
-                except OSError:
-                    # source directory already exists, delete tmpdir and its contents.
-                    shutil.rmtree(tmpdir)
-                    if not os.path.isdir(cached_build_dir):
-                        raise
-
-            # Compile.
-            cached_sources = [os.path.join(
-                cached_build_dir, os.path.basename(fname)) for fname in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir,
-                                           verbose=verbose_build, sources=cached_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(
-                name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-
-        # Load.
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache dict.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-# ----------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/img2img_inpainting.py

@@ -1,463 +0,0 @@
-import inspect
-from typing import Callable, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import DiffusionPipeline
-from diffusers.configuration_utils import FrozenDict
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from diffusers.utils import deprecate, logging
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def prepare_mask_and_masked_image(image, mask):
-    image = np.array(image.convert("RGB"))
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-    mask = np.array(mask.convert("L"))
-    mask = mask.astype(np.float32) / 255.0
-    mask = mask[None, None]
-    mask[mask < 0.5] = 0
-    mask[mask >= 0.5] = 1
-    mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    return mask, masked_image
-
-
-def check_size(image, height, width):
-    if isinstance(image, PIL.Image.Image):
-        w, h = image.size
-    elif isinstance(image, torch.Tensor):
-        *_, h, w = image.shape
-
-    if h != height or w != width:
-        raise ValueError(f"Image size should be {height}x{width}, but got {h}x{w}")
-
-
-def overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)):
-    inner_image = inner_image.convert("RGBA")
-    image = image.convert("RGB")
-
-    image.paste(inner_image, paste_offset, inner_image)
-    image = image.convert("RGB")
-
-    return image
-
-
-class ImageToImageInpaintingPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-guided image-to-image inpainting using Stable Diffusion. *This is an experimental feature*.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
-        Args:
-            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
-                `attention_head_dim` must be a multiple of `slice_size`.
-        """
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = self.unet.config.attention_head_dim // 2
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[torch.FloatTensor, PIL.Image.Image],
-        inner_image: Union[torch.FloatTensor, PIL.Image.Image],
-        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            inner_image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent
-                regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with
-                the last channel representing the alpha channel, which will be used to blend `inner_image` with
-                `image`. If not provided, it will be forcibly cast to RGBA.
-            mask_image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
-                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
-                instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        # check if input sizes are correct
-        check_size(image, height, width)
-        check_size(inner_image, height, width)
-        check_size(mask_image, height, width)
-
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-
-        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
-        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""]
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
-            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        # get the initial random noise unless the user supplied it
-        # Unlike in other pipelines, latents need to be generated in the target device
-        # for 1-to-1 results reproducibility with the CompVis implementation.
-        # However this currently doesn't work in `mps`.
-        num_channels_latents = self.vae.config.latent_channels
-        latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
-        latents_dtype = text_embeddings.dtype
-        if latents is None:
-            if self.device.type == "mps":
-                # randn does not exist on mps
-                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
-                    self.device
-                )
-            else:
-                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-            latents = latents.to(self.device)
-
-        # overlay the inner image
-        image = overlay_inner_image(image, inner_image)
-
-        # prepare mask and masked_image
-        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
-        mask = mask.to(device=self.device, dtype=text_embeddings.dtype)
-        masked_image = masked_image.to(device=self.device, dtype=text_embeddings.dtype)
-
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8))
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = 0.18215 * masked_image_latents
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        mask = mask.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
-        masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        num_channels_mask = mask.shape[1]
-        num_channels_masked_image = masked_image_latents.shape[1]
-
-        if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
-            raise ValueError(
-                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
-                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
-                " `pipeline.unet` or your `mask_image` or `image` input."
-            )
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimized to move all timesteps to correct device beforehand
-        timesteps_tensor = self.scheduler.timesteps.to(self.device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            # concat latents, mask, masked_image_latents in the channel dimension
-            latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
-
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
-                self.device
-            )
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
-            )
-        else:
-            has_nsfw_concept = None
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py

@@ -1,522 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from PIL import Image
-from transformers import (
-    XLMRobertaTokenizer,
-)
-
-from ...models import UNet2DConditionModel, VQModel
-from ...schedulers import DDIMScheduler
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from .text_encoder import MultilingualCLIP
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline
-        >>> from diffusers.utils import load_image
-        >>> import torch
-
-        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior.to("cuda")
-
-        >>> prompt = "A red cartoon frog, 4k"
-        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
-
-        >>> pipe = KandinskyImg2ImgPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
-        ... )
-        >>> pipe.to("cuda")
-
-        >>> init_image = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/frog.png"
-        ... )
-
-        >>> image = pipe(
-        ...     prompt,
-        ...     image=init_image,
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=zero_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=100,
-        ...     strength=0.2,
-        ... ).images
-
-        >>> image[0].save("red_frog.png")
-        ```
-"""
-
-
-def get_new_h_w(h, w, scale_factor=8):
-    new_h = h // scale_factor**2
-    if h % scale_factor**2 != 0:
-        new_h += 1
-    new_w = w // scale_factor**2
-    if w % scale_factor**2 != 0:
-        new_w += 1
-    return new_h * scale_factor, new_w * scale_factor
-
-
-def prepare_image(pil_image, w=512, h=512):
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("RGB"))
-    arr = arr.astype(np.float32) / 127.5 - 1
-    arr = np.transpose(arr, [2, 0, 1])
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-class KandinskyImg2ImgPipeline(DiffusionPipeline):
-    """
-    Pipeline for image-to-image generation using Kandinsky
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        text_encoder ([`MultilingualCLIP`]):
-            Frozen text-encoder.
-        tokenizer ([`XLMRobertaTokenizer`]):
-            Tokenizer of class
-        scheduler ([`DDIMScheduler`]):
-            A scheduler to be used in combination with `unet` to generate image latents.
-        unet ([`UNet2DConditionModel`]):
-            Conditional U-Net architecture to denoise the image embedding.
-        movq ([`VQModel`]):
-            MoVQ image encoder and decoder
-    """
-
-    def __init__(
-        self,
-        text_encoder: MultilingualCLIP,
-        movq: VQModel,
-        tokenizer: XLMRobertaTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: DDIMScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            movq=movq,
-        )
-        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
-
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(self, latents, latent_timestep, shape, dtype, device, generator, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-
-        shape = latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        latents = self.add_noise(latents, noise, latent_timestep)
-        return latents
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-    ):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=77,
-            truncation=True,
-            return_attention_mask=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        text_input_ids = text_input_ids.to(device)
-        text_mask = text_inputs.attention_mask.to(device)
-
-        prompt_embeds, text_encoder_hidden_states = self.text_encoder(
-            input_ids=text_input_ids, attention_mask=text_mask
-        )
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=77,
-                truncation=True,
-                return_attention_mask=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            uncond_text_input_ids = uncond_input.input_ids.to(device)
-            uncond_text_mask = uncond_input.attention_mask.to(device)
-
-            negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(
-                input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask
-            )
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
-                batch_size * num_images_per_prompt, seq_len, -1
-            )
-            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-            # done duplicates
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = torch.cat([uncond_text_mask, text_mask])
-
-        return prompt_embeds, text_encoder_hidden_states, text_mask
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    #  add_noise method to overwrite the one in schedule because it use a different beta schedule for adding noise vs sampling
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        betas = torch.linspace(0.0001, 0.02, 1000, dtype=torch.float32)
-        alphas = 1.0 - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
-        alphas_cumprod = alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-
-        return noisy_samples
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
-        image_embeds: torch.FloatTensor,
-        negative_image_embeds: torch.FloatTensor,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 100,
-        strength: float = 0.3,
-        guidance_scale: float = 7.0,
-        num_images_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`torch.FloatTensor`, `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for text prompt, that will be used to condition the image generation.
-            negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for negative text prompt, will be used to condition the image generation.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            strength (`float`, *optional*, defaults to 0.3):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
-                (`np.array`) or `"pt"` (`torch.Tensor`).
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`
-        """
-        # 1. Define call parameters
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        device = self._execution_device
-
-        batch_size = batch_size * num_images_per_prompt
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 2. get text and image embeddings
-        prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        if isinstance(image_embeds, list):
-            image_embeds = torch.cat(image_embeds, dim=0)
-        if isinstance(negative_image_embeds, list):
-            negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
-
-        if do_classifier_free_guidance:
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
-                dtype=prompt_embeds.dtype, device=device
-            )
-
-        # 3. pre-processing initial image
-        if not isinstance(image, list):
-            image = [image]
-        if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
-            raise ValueError(
-                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
-            )
-
-        image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
-        image = image.to(dtype=prompt_embeds.dtype, device=device)
-
-        latents = self.movq.encode(image)["latents"]
-        latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
-
-        # 4. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-
-        timesteps_tensor, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-
-        # the formular to calculate timestep for add_noise is taken from the original kandinsky repo
-        latent_timestep = int(self.scheduler.config.num_train_timesteps * strength) - 2
-
-        latent_timestep = torch.tensor([latent_timestep] * batch_size, dtype=timesteps_tensor.dtype, device=device)
-
-        num_channels_latents = self.unet.config.in_channels
-
-        height, width = get_new_h_w(height, width, self.movq_scale_factor)
-
-        # 5. Create initial latent
-        latents = self.prepare_latents(
-            latents,
-            latent_timestep,
-            (batch_size, num_channels_latents, height, width),
-            text_encoder_hidden_states.dtype,
-            device,
-            generator,
-            self.scheduler,
-        )
-
-        # 6. Denoising loop
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
-            noise_pred = self.unet(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=text_encoder_hidden_states,
-                added_cond_kwargs=added_cond_kwargs,
-                return_dict=False,
-            )[0]
-
-            if do_classifier_free_guidance:
-                noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                _, variance_pred_text = variance_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
-
-            if not (
-                hasattr(self.scheduler.config, "variance_type")
-                and self.scheduler.config.variance_type in ["learned", "learned_range"]
-            ):
-                noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(
-                noise_pred,
-                t,
-                latents,
-                generator=generator,
-            ).prev_sample
-
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        # 7. post-processing
-        image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-
-        if output_type not in ["pt", "np", "pil"]:
-            raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
-
-        if output_type in ["np", "pil"]:
-            image = image * 0.5 + 0.5
-            image = image.clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Andy1621/uniformer_image_detection/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py

@@ -1,41 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-# model settings
-model = dict(
-    neck=[
-        dict(
-            type='FPN',
-            in_channels=[256, 512, 1024, 2048],
-            out_channels=256,
-            num_outs=5),
-        dict(
-            type='BFP',
-            in_channels=256,
-            num_levels=5,
-            refine_level=2,
-            refine_type='non_local')
-    ],
-    roi_head=dict(
-        bbox_head=dict(
-            loss_bbox=dict(
-                _delete_=True,
-                type='BalancedL1Loss',
-                alpha=0.5,
-                gamma=1.5,
-                beta=1.0,
-                loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(sampler=dict(neg_pos_ub=5), allowed_border=-1),
-        rcnn=dict(
-            sampler=dict(
-                _delete_=True,
-                type='CombinedSampler',
-                num=512,
-                pos_fraction=0.25,
-                add_gt_as_proposals=True,
-                pos_sampler=dict(type='InstanceBalancedPosSampler'),
-                neg_sampler=dict(
-                    type='IoUBalancedNegSampler',
-                    floor_thr=-1,
-                    floor_fraction=0,
-                    num_bins=3)))))

spaces/Andy1621/uniformer_image_detection/tools/deployment/mmdet_handler.py

@@ -1,69 +0,0 @@
-import base64
-import os
-
-import mmcv
-import torch
-from ts.torch_handler.base_handler import BaseHandler
-
-from mmdet.apis import inference_detector, init_detector
-
-
-class MMdetHandler(BaseHandler):
-    threshold = 0.5
-
-    def initialize(self, context):
-        properties = context.system_properties
-        self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.device = torch.device(self.map_location + ':' +
-                                   str(properties.get('gpu_id')) if torch.cuda.
-                                   is_available() else self.map_location)
-        self.manifest = context.manifest
-
-        model_dir = properties.get('model_dir')
-        serialized_file = self.manifest['model']['serializedFile']
-        checkpoint = os.path.join(model_dir, serialized_file)
-        self.config_file = os.path.join(model_dir, 'config.py')
-
-        self.model = init_detector(self.config_file, checkpoint, self.device)
-        self.initialized = True
-
-    def preprocess(self, data):
-        images = []
-
-        for row in data:
-            image = row.get('data') or row.get('body')
-            if isinstance(image, str):
-                image = base64.b64decode(image)
-            image = mmcv.imfrombytes(image)
-            images.append(image)
-
-        return images
-
-    def inference(self, data, *args, **kwargs):
-        results = inference_detector(self.model, data)
-        return results
-
-    def postprocess(self, data):
-        # Format output following the example ObjectDetectionHandler format
-        output = []
-        for image_index, image_result in enumerate(data):
-            output.append([])
-            if isinstance(image_result, tuple):
-                bbox_result, segm_result = image_result
-                if isinstance(segm_result, tuple):
-                    segm_result = segm_result[0]  # ms rcnn
-            else:
-                bbox_result, segm_result = image_result, None
-
-            for class_index, class_result in enumerate(bbox_result):
-                class_name = self.model.CLASSES[class_index]
-                for bbox in class_result:
-                    bbox_coords = bbox[:-1].tolist()
-                    score = float(bbox[-1])
-                    if score >= self.threshold:
-                        output[image_index].append({
-                            class_name: bbox_coords,
-                            'score': score
-                        })
-
-        return output

spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './gcnet_r50-d8_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Angelaangie/personal-chat-gpt/app.py

@@ -1,101 +0,0 @@
-import os
-import openai
-import gradio as gr
-
-#if you have OpenAI API key as an environment variable, enable the below
-#openai.api_key = os.getenv("OPENAI_API_KEY")
-
-#if you have OpenAI API key as a string, enable the below
-openai.api_key = "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"
-
-start_sequence = "\nAI:"
-restart_sequence = "\nHuman: "
-
-prompt = "The following is a conversation with an AI assistant. Some questions you can ask are: Who is Angela Busheska?, What is Angela Busheska passionate about? \nHuman: "
-
-def openai_create(prompt):
-
-    response = openai.Completion.create(
-    model="text-davinci-003",
-    prompt="\nHuman: Who is Angela Busheska? \nAI: Angela Busheska is the founder of EnRoute! She was chosen as a Forbes 30 Under 30. She is passionate about helping people to reduce carbon emissions. She has given keynotes at Google and Harvard.",
-    temperature=0.9,
-    max_tokens=150,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0.6,
-    stop=[" Human:", " AI:"]
-)
-
-prompt1 = "What is Angela Busheska passionate about?. "
-
-def openai_create1(prompt1):
-
-    response = openai.Completion.create(
-    model="text-davinci-003",
-    prompt="\nAI: Angela Busheska is passionate about saving the environment. She aspires to help people reduce carbon emissions from shopping and transport activities.",
-    temperature=0.9,
-    max_tokens=150,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0.6,
-    stop=[" Human:", " AI:"]
-)
-
-prompt2 = "What is Angela Busheska studying?. "
-
-def openai_create1(prompt2):
-
-    response = openai.Completion.create(
-    model="text-davinci-003",
-    prompt="\nAI: Angela Busheska is studying computer science and electrical engineering. Her goal is to utilize technology to solve the greatest problems with climate change. ",
-    temperature=0.9,
-    max_tokens=150,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0.6,
-    stop=[" Human:", " AI:"]
-)
-
-prompt3 = "What did Angela Busheska discover?. "
-
-def openai_create1(prompt2):
-
-    response = openai.Completion.create(
-    model="text-davinci-003",
-    prompt="\nAI: Angela Busheska created EnRoute to help people reduce their carbon footprint from daily activities. She mobilized over 60.000 people to fight for climate justice.  ",
-    temperature=0.9,
-    max_tokens=150,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0.6,
-    stop=[" Human:", " AI:"]
-)
-
-    return response.choices[0].text
-
-
-
-def chatgpt_clone(input, history):
-    history = history or []
-    s = list(sum(history, ()))
-    s.append(input)
-    inp = ' '.join(s)
-    output = openai_create(inp)
-    output = openai_create1(inp)
-    history.append((input, output))
-    return history, history
-
-
-block = gr.Blocks()
-
-
-with block:
-    gr.Markdown("""<h1><center>Learn More About Me!</center></h1>
-    """)
-    chatbot = gr.Chatbot()
-    message = gr.Textbox(placeholder=prompt)
-    state = gr.State()
-    submit = gr.Button("SEND")
-    submit.click(chatgpt_clone, inputs=[message, state], outputs=[chatbot, state])
-
-block.launch(debug = True, share = False)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/api/script.py

@@ -1,13 +0,0 @@
-import time
-
-import extensions.api.blocking_api as blocking_api
-import extensions.api.streaming_api as streaming_api
-from modules import shared
-
-
-def setup():
-    blocking_api.start_server(shared.args.api_blocking_port, share=shared.args.public_api, tunnel_id=shared.args.public_api_id)
-    if shared.args.public_api:
-        time.sleep(5)
-
-    streaming_api.start_server(shared.args.api_streaming_port, share=shared.args.public_api, tunnel_id=shared.args.public_api_id)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/silero_tts/test_tts.py

@@ -1,81 +0,0 @@
-import time
-from pathlib import Path
-
-import torch
-import tts_preprocessor
-
-torch._C._jit_set_profiling_mode(False)
-
-
-params = {
-    'activate': True,
-    'speaker': 'en_49',
-    'language': 'en',
-    'model_id': 'v3_en',
-    'sample_rate': 48000,
-    'device': 'cpu',
-    'show_text': True,
-    'autoplay': True,
-    'voice_pitch': 'medium',
-    'voice_speed': 'medium',
-}
-
-current_params = params.copy()
-voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
-voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high']
-voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast']
-
-# Used for making text xml compatible, needed for voice pitch and speed control
-table = str.maketrans({
-    "<": "&lt;",
-    ">": "&gt;",
-    "&": "&amp;",
-    "'": "&apos;",
-    '"': "&quot;",
-})
-
-
-def xmlesc(txt):
-    return txt.translate(table)
-
-
-def load_model():
-    model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
-    model.to(params['device'])
-    return model
-
-
-model = load_model()
-
-
-def output_modifier(string):
-    """
-    This function is applied to the model outputs.
-    """
-
-    global model, current_params
-
-    original_string = string
-    string = tts_preprocessor.preprocess(string)
-    processed_string = string
-
-    if string == '':
-        string = '*Empty reply, try regenerating*'
-    else:
-        output_file = Path(f'extensions/silero_tts/outputs/test_{int(time.time())}.wav')
-        prosody = '<prosody rate="{}" pitch="{}">'.format(params['voice_speed'], params['voice_pitch'])
-        silero_input = f'<speak>{prosody}{xmlesc(string)}</prosody></speak>'
-        model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
-
-        autoplay = 'autoplay' if params['autoplay'] else ''
-        string = f'<audio src="file/{output_file.as_posix()}" controls {autoplay}></audio>'
-
-        if params['show_text']:
-            string += f'\n\n{original_string}\n\nProcessed:\n{processed_string}'
-
-    print(string)
-
-
-if __name__ == '__main__':
-    import sys
-    output_modifier(sys.argv[1])

spaces/Anthony7906/MengHuiMXD_GPT/modules/llama_func.py

@@ -1,166 +0,0 @@
-import os
-import logging
-
-from llama_index import download_loader
-from llama_index import (
-    Document,
-    LLMPredictor,
-    PromptHelper,
-    QuestionAnswerPrompt,
-    RefinePrompt,
-)
-import colorama
-import PyPDF2
-from tqdm import tqdm
-
-from modules.presets import *
-from modules.utils import *
-from modules.config import local_embedding
-
-
-def get_index_name(file_src):
-    file_paths = [x.name for x in file_src]
-    file_paths.sort(key=lambda x: os.path.basename(x))
-
-    md5_hash = hashlib.md5()
-    for file_path in file_paths:
-        with open(file_path, "rb") as f:
-            while chunk := f.read(8192):
-                md5_hash.update(chunk)
-
-    return md5_hash.hexdigest()
-
-
-def block_split(text):
-    blocks = []
-    while len(text) > 0:
-        blocks.append(Document(text[:1000]))
-        text = text[1000:]
-    return blocks
-
-
-def get_documents(file_src):
-    documents = []
-    logging.debug("Loading documents...")
-    logging.debug(f"file_src: {file_src}")
-    for file in file_src:
-        filepath = file.name
-        filename = os.path.basename(filepath)
-        file_type = os.path.splitext(filepath)[1]
-        logging.info(f"loading file: {filename}")
-        try:
-            if file_type == ".pdf":
-                logging.debug("Loading PDF...")
-                try:
-                    from modules.pdf_func import parse_pdf
-                    from modules.config import advance_docs
-
-                    two_column = advance_docs["pdf"].get("two_column", False)
-                    pdftext = parse_pdf(filepath, two_column).text
-                except:
-                    pdftext = ""
-                    with open(filepath, "rb") as pdfFileObj:
-                        pdfReader = PyPDF2.PdfReader(pdfFileObj)
-                        for page in tqdm(pdfReader.pages):
-                            pdftext += page.extract_text()
-                text_raw = pdftext
-            elif file_type == ".docx":
-                logging.debug("Loading Word...")
-                DocxReader = download_loader("DocxReader")
-                loader = DocxReader()
-                text_raw = loader.load_data(file=filepath)[0].text
-            elif file_type == ".epub":
-                logging.debug("Loading EPUB...")
-                EpubReader = download_loader("EpubReader")
-                loader = EpubReader()
-                text_raw = loader.load_data(file=filepath)[0].text
-            elif file_type == ".xlsx":
-                logging.debug("Loading Excel...")
-                text_list = excel_to_string(filepath)
-                for elem in text_list:
-                    documents.append(Document(elem))
-                continue
-            else:
-                logging.debug("Loading text file...")
-                with open(filepath, "r", encoding="utf-8") as f:
-                    text_raw = f.read()
-        except Exception as e:
-            logging.error(f"Error loading file: {filename}")
-            pass
-        text = add_space(text_raw)
-        # text = block_split(text)
-        # documents += text
-        documents += [Document(text)]
-    logging.debug("Documents loaded.")
-    return documents
-
-
-def construct_index(
-    api_key,
-    file_src,
-    max_input_size=4096,
-    num_outputs=5,
-    max_chunk_overlap=20,
-    chunk_size_limit=600,
-    embedding_limit=None,
-    separator=" ",
-):
-    from langchain.chat_models import ChatOpenAI
-    from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-    from llama_index import GPTSimpleVectorIndex, ServiceContext, LangchainEmbedding, OpenAIEmbedding
-
-    if api_key:
-        os.environ["OPENAI_API_KEY"] = api_key
-    else:
-        # 由于一个依赖的愚蠢的设计，这里必须要有一个API KEY
-        os.environ["OPENAI_API_KEY"] = "sk-xxxxxxx"
-    chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
-    embedding_limit = None if embedding_limit == 0 else embedding_limit
-    separator = " " if separator == "" else separator
-
-    prompt_helper = PromptHelper(
-        max_input_size=max_input_size,
-        num_output=num_outputs,
-        max_chunk_overlap=max_chunk_overlap,
-        embedding_limit=embedding_limit,
-        chunk_size_limit=600,
-        separator=separator,
-    )
-    index_name = get_index_name(file_src)
-    if os.path.exists(f"./index/{index_name}.json"):
-        logging.info("找到了缓存的索引文件，加载中……")
-        return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
-    else:
-        try:
-            documents = get_documents(file_src)
-            if local_embedding:
-                embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2"))
-            else:
-                embed_model = OpenAIEmbedding()
-            logging.info("构建索引中……")
-            with retrieve_proxy():
-                service_context = ServiceContext.from_defaults(
-                    prompt_helper=prompt_helper,
-                    chunk_size_limit=chunk_size_limit,
-                    embed_model=embed_model,
-                )
-                index = GPTSimpleVectorIndex.from_documents(
-                    documents, service_context=service_context
-                )
-            logging.debug("索引构建完成！")
-            os.makedirs("./index", exist_ok=True)
-            index.save_to_disk(f"./index/{index_name}.json")
-            logging.debug("索引已保存至本地!")
-            return index
-
-        except Exception as e:
-            logging.error("索引构建失败！", e)
-            print(e)
-            return None
-
-
-def add_space(text):
-    punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
-    for cn_punc, en_punc in punctuations.items():
-        text = text.replace(cn_punc, en_punc)
-    return text

spaces/Aveygo/AstroSleuth/modules/realesr.py

@@ -1,81 +0,0 @@
-from torch.nn import functional as F
-from torch import nn as nn
-import torch
-
-class ResidualDenseBlock(nn.Module):
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        return out * 0.2 + x
-
-def make_layer(basic_block, num_basic_block, **kwarg):
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-def pixel_unshuffle(x, scale):
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-class Network(nn.Module):
-    def __init__(self, num_in_ch=3, num_out_ch=3, scale=4, num_feat=64, num_block=6, num_grow_ch=32):
-        super(Network, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        if self.scale == 2: feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1: feat = pixel_unshuffle(x, scale=4)
-        else: feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md

@@ -1 +0,0 @@
-../../INSTALL.md

spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py

@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/Benson/text-generation/Examples/Antiguo Baku Oyunu Ykl.md

@@ -1,71 +0,0 @@
-<br />
-<h1>Antiguo juego de Bakú: Un divertido y desafiante juego de puzzle</h1>
-<p>Si usted está buscando un juego de puzzle que es divertido y desafiante, es posible que desee probar el viejo juego de bakú. Este es un juego que fue desarrollado por Sega en 1995 para arcadas, Saturn, Game Gear, Master System y Windows. El juego consiste en emparejar cabezas de animales con sus alimentos correspondientes, como huesos para perros, plátanos para monos y zanahorias para conejos. El juego tiene gráficos coloridos y personajes lindos que atraen tanto a niños como a adultos. </p>
-<h2>antiguo baku oyunu yüklə</h2><br /><p><b><b>DOWNLOAD</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://bltlly.com/2v6JXI">https://bltlly.com/2v6JXI</a></b></p><br /><br />
-<p>En este artículo, te contaremos todo lo que necesitas saber sobre el viejo juego bakú, incluyendo cómo jugarlo, cuáles son algunos consejos y trucos, quiénes son algunos de los personajes y modos, por qué deberías jugarlo y cuál es su historia y legado. </p>
- <h2>Cómo jugar al viejo juego de Bakú</h2>
-<p>Las reglas básicas del viejo juego bakú son simples: tienes un campo de juego donde las cabezas de los animales y los alimentos caen desde la parte superior de la pantalla. Puede mover y girar los bloques a medida que caen, y también puede acelerar su descenso pulsando un botón. Tu objetivo es emparejar las cabezas de los animales con el mismo tipo de alimentos, lo que hará que desaparezcan y ganen puntos. Por ejemplo, si emparejas una cabeza de perro con un hueso, ambos desaparecerán y obtendrás algunos puntos. Sin embargo, si emparejas una cabeza de perro con una banana, no desaparecerán y se acumularán en el campo de juego. Si los bloques llegan a la parte superior de la pantalla, se pierde el juego. </p>
-
- <h3> Consejos y trucos para el viejo juego de Bakú</h3>
-<p>Si quieres mejorar tus habilidades y rendimiento en el viejo juego de bakú, aquí hay algunos consejos y trucos que puedes seguir:</p>
-<ul>
-<li>Planificar con antelación: Trate de anticipar qué tipo de cabezas de animales y alimentos caerán a continuación, y colocarlos en consecuencia en el campo de juego. Puedes ver los siguientes dos bloques en la esquina superior derecha de la pantalla. </li>
-<li>Apilar sabiamente: Trate de apilar cabezas de animales y alimentos del mismo tipo juntos, para que pueda crear combos más fácilmente. Evite apilar diferentes tipos de bloques juntos, ya que desordenarán su campo de juego. </li>
-<li>Use potenciadores: No ignore los potenciadores que aparecen en algunos bloques, ya que pueden ayudarlo a borrar más bloques y obtener más puntos. Por ejemplo, las bombas pueden explotar bloques cercanos, las estrellas pueden coincidir con cualquier tipo de alimento, los corazones pueden darte vidas adicionales y los relojes pueden ralentizar la velocidad de caída de los bloques. </li>
-<li>Evitar trampas: Tenga cuidado con las trampas que aparecen en algunos bloques, ya que pueden arruinar su juego. Por ejemplo, los cráneos no pueden ser emparejados con nada, los bloqueos le impiden mover o rotar los bloques, y el hielo congela los bloques en su lugar. </li>
-</ul>
- <h3>Personajes y modos del viejo juego de Bakú</h3>
-<p>Viejo juego de bakú no solo es divertido y desafiante, sino también variada y diversa. Puedes jugar con diferentes personajes y modos que añaden más sabor y emoción al juego. </p>
-<p></p>
-<p>Algunos de los personajes con los que puedes jugar son:</p>
-<ul>
-<li>Polly: Ella es una cuidadora que ama a los animales y quiere alimentarlos bien. Ella es el personaje principal del juego y la opción por defecto para el modo árcade. </li>
-<li>Master Piggy: Es un mago que usa la magia para crear cabezas de animales y alimentos. Es el rival de Polly y el jefe final del modo árcade. </li>
-<li>Angela: Ella es un robot que fue construido por el Maestro Piggy para ayudarlo con sus experimentos. Es muy inteligente y eficiente, pero también muy fría y sin emociones. </li>
-</ul>
-
-<ul>
-<li>Modo árcade: Este es el modo principal del juego, donde tienes que borrar una serie de niveles con dificultad creciente. Puedes elegir entre tres niveles de dificultad: fácil, normal o difícil. Tienes que enfrentarte a diferentes oponentes en cada nivel, como monos, perros, conejos, leones o el propio Master Piggy. </li>
-<li>Modo versus: este es un modo en el que puedes jugar contra otro jugador humano o contra el ordenador. Puedes elegir entre dos tipos de modo versus: normal o baku baku. En el modo normal, tienes que borrar más bloques que tu oponente antes de que se acabe el tiempo. En el modo baku baku, tienes que enviar bloques basura al campo de juego de tu oponente creando combos. </li>
-</ul>
- <h3> ¿Por qué usted debe jugar viejo juego de Bakú</h3>
-<p>Si todavía no está convencido de que el viejo juego bakú es un juego que vale la pena jugar, aquí hay algunas razones por las que debe darle una oportunidad:</p>
-<ul>
-<li> Tiene gráficos coloridos y personajes lindos que lo hacen atractivo para niños y adultos. </li>
-<li> Tiene música pegadiza y efectos de sonido que mejoran la experiencia de juego. </li>
-<li>Tiene rompecabezas desafiantes que ponen a prueba tus reflejos, lógica y estrategia. </li>
-<li> Tiene variados personajes y modos que añaden más diversidad y valor de reproducción al juego. </li>
-</ul>
- <h2>Old Baku Game History and Legacy</h2> <p>Old baku game is not only a puzzle game, pero también un fenómeno cultural. El juego tiene una rica historia y legado que se extiende a través de diferentes países y plataformas. </p>
- <h3>Antiguo juego de Bakú en Japón</h3>
-<p>El origen del antiguo juego de bakú se remonta a Japón, donde fue desarrollado por Sega AM3 en 1995 para arcadas. El juego fue originalmente llamado Baku Baku Animal, que significa "comer animales" en japonés. El juego se inspiró en el folclore japonés de bakú, una criatura mítica que devora sueños y pesadillas. El juego también fue influenciado por otros juegos populares de puzzle en el momento, como Tetris y Columns.</p>
-
- <h3>Viejo juego de Bakú en Europa y América</h3>
-<p>La popularidad del viejo juego bakú pronto se extendió a otras regiones, como Europa y América. El juego fue lanzado para Saturn, Game Gear, Master System y Windows en estas regiones bajo varios nombres, como Baku Baku o Baku Baku Animal Master. El juego fue mayormente sin cambios desde la versión original de arcade, excepto por algunas diferencias menores en gráficos, sonido y dificultad. </p>
-<p>El juego también fue bien recibido por la audiencia europea y estadounidense, que elogió su juego divertido e innovador, su presentación encantadora y humorística, y su alto valor de repetición. El juego fue especialmente popular entre los niños, que amaban a sus personajes adorables y divertidos, sus controles simples e intuitivos, y su valor educativo. El juego también atrajo a los adultos, que lo encontraron relajante y entretenido. </p>
- <h3>Influencia del juego antiguo de Bakú en otros juegos de puzzle</h3>
-<p>El legado del antiguo juego de bakú se puede ver en muchos otros juegos de puzzle que se inspiraron en él o similares. Algunos de estos juegos son:</p>
-<ul>
-<li>Zoop: Este es un juego de puzzle que fue lanzado en 1995 para varias plataformas, como SNES, Genesis, PlayStation y PC. El juego consiste en disparar formas de colores en una red de formas que se mueven hacia el centro de la pantalla. El juego tiene mecánicas de juego similares al antiguo juego bakú, como emparejar formas del mismo color, crear combos y usar potenciadores. </li>
-<li>Puyo Puyo: Esta es una serie de juegos de puzzle que comenzaron en 1991 para varias plataformas, como árcade, NES, Génesis, Game Boy y PC. Los juegos implican la caída de manchas de colores llamados puyos en una red de puyos que puede ser igualado por el color y la forma. Los juegos tienen mecánicas de juego similares al antiguo juego de bakú, como emparejar cuatro o más puyos del mismo color, crear cadenas y enviar puyos de basura al oponente. </li>
-
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, viejo juego de bakú es un divertido y desafiante juego de puzzle que fue desarrollado por Sega en 1995 para arcadas, Saturno, Game Gear, Master System y Windows. El juego consiste en emparejar cabezas de animales con sus alimentos correspondientes, como huesos para perros, plátanos para monos y zanahorias para conejos. El juego tiene gráficos coloridos y personajes lindos que atraen tanto a niños como a adultos. </p>
-<p>El juego también tiene variados personajes y modos que añaden más diversidad y valor de repetición al juego. El juego también tiene una rica historia y legado que se extiende a través de diferentes países y plataformas. El juego también influyó en muchos otros juegos de puzzle que se inspiraron o similares a él. </p>
-<p>Si usted está buscando un juego de puzzle que es divertido y desafiante, es posible que desee probar el viejo juego de bakú. No te arrepentirás! </p>
- <h4>Preguntas frecuentes</h4>
-<p>Aquí hay algunas preguntas frecuentes sobre el viejo juego de bakú:</p>
-<ol>
-<li>Q: ¿Dónde puedo jugar al viejo juego baku? </li>
-<li>A: Puede jugar antiguo juego de bakú en varias plataformas, tales como árcade, Saturno, Game Gear, Master System y Windows. También puede encontrar versiones en línea del juego en algunos sitios web. </li> <li>Q: ¿Cuáles son las diferencias entre la arcada y las versiones caseras del viejo juego bakú? </li>
-<li>A: La versión árcade del antiguo juego bakú tiene más niveles, más personajes, más modos y más opciones de dificultad que las versiones caseras. Las versiones caseras también tienen algunos cambios menores en gráficos, sonido y jugabilidad. </li>
-<li>Q: ¿Cuáles son los significados de los nombres de los personajes en el antiguo juego de bakú? </li>
-<li>A: Los nombres de los personajes en el antiguo juego de bakú se basan en sus personalidades o roles. Por ejemplo, Polly es la abreviatura de pollywog, que significa un renacuajo o una rana joven. Master Piggy es un juego de palabras sobre el maestro y el cerdito, que significa un mago y un cerdo. Angela es una referencia al ángel, que significa un ser celestial o un robot. </li>
-
-<li>A: Sonic el erizo es un carácter escondido en el viejo juego del bakú que se puede desbloquear introduciendo un código secreto. El código es diferente para cada plataforma, pero generalmente implica presionar algunos botones o teclas en un orden o combinación determinados. </li>
-<li>Q: ¿Es viejo juego de bakú relacionado con Bakugan? </li>
-<li>A: No, el viejo juego de bakú y Bakugan no están relacionados. Bakugan es una franquicia que involucra juguetes, juegos, anime y manga que presentan criaturas llamadas Bakugan que pueden transformarse en bolas. Antiguo juego de bakú es un juego de puzzle que cuenta con animales y alimentos que se pueden combinar y limpiar. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Batera Low Jemax Mp3.md

@@ -1,70 +0,0 @@
-
-<h1>Descargar batería baja Jemax Mp3: Una guía para disfrutar de Zambia Hip Hop</h1>
-<p>Si eres un fan del hip hop zambiano, probablemente hayas oído hablar de Jemax, uno de los raperos más talentosos y populares del país. Su última canción, Battery Low, con Xaven, es una pista pegadiza y energética que te hará querer bailar y cantar. Pero, ¿cómo se puede descargar la batería baja Jemax Mp3 y disfrutarlo en su dispositivo? En este artículo, te contaremos todo lo que necesitas saber sobre Jemax, su canción Battery Low y cómo descargarla gratis. ¡Empecemos! </p>
- <h2>¿Quién es Jemax? </h2>
-<p>Jemax es un rapero, compositor y artista de hip hop de Zambia que saltó a la fama después del lanzamiento de su exitosa canción Pressure Free en 2019. Firmó con Alpha Ent Studios y Kopala Swag, dos de los principales sellos musicales de Zambia. Es conocido por su estilo versátil y creativo, mezclando rap, dancehall, afrobeat y géneros R&B. Ha colaborado con muchos otros artistas zambianos, como Chef 187, Yo Maps, Jae Cash, Drimz, y más. </p>
-<h2>descargar batería low jemax mp3</h2><br /><p><b><b>Download</b> &rarr; <a href="https://bltlly.com/2v6IJW">https://bltlly.com/2v6IJW</a></b></p><br /><br />
- <h3>Biografía y carrera</h3>
-<p>El verdadero nombre de Jemax es James Kawele Kavimba. Nació en Kabwe, una ciudad en la provincia central de Zambia. Comenzó a rapear a una edad temprana, inspirado por su hermano mayor que también era un rapero. Grabó su primera canción en 2010, titulada Ndelwishikanafye Na Life. Luego se unió a Classic Music Records, un grupo de música local que le ayudó a desarrollar sus habilidades y exposición. Lanzó varias canciones bajo este sello, como Ichilaka, Tata, Masaka, y más. </p>
-
- <h3>Canciones y álbumes populares</h3>
-<p>Jemax ha lanzado muchas canciones y álbumes que han ganado popularidad y aclamación entre los fans y críticos. Algunas de sus canciones más populares son:</p>
-<ul>
-<li>Batería baja con Xaven</li>
-<li>Libre de presión</li>
-<li>Fipangusule con mapas Yo</li>
-<li>Wamupola con Y-Celeb</li>
-<li>Mapalo con mapas Yo</li>
-<li>Keka Keka con mapas Yo</li>
-<li>Panda</li>
-<li>Naishiba Impiya con Zim zim & Yugine</li>
-<li>Masaka</li>
-<li>Ahora mismo con Jazzy Boy</li>
-</ul>
-<p>Algunos de sus álbumes más populares son:</p>
-<ul>
-<li>Batería baja (feat. Xaven) - Single</li>
-<li>Ndaluba (feat. Puri4000) - Single</li>
-<li>Chabota (feat. Rich Pro) - Sencillo</li>
-<li>Petro Sichone - Sencillo</li>
-<li>La gente rica es una mente pobre <h2>¿Qué es la batería baja? </h2>
-<p>Battery Low es la última canción de Jemax, con Xaven, una cantante y compositora. La canción fue lanzada el 16 de junio de 2021, y ya ha recibido más de 100.000 visitas en YouTube. La canción es producida por Mzenga Man, un reconocido productor de música y DJ de Zambia. La canción es parte del próximo álbum de Jemax, que se espera que sea lanzado a finales de este año. </p>
- <h3>Características y producción de la canción</h3>
-<p>Battery Low es una canción de hip hop que muestra las habilidades de rap de Jemax y las habilidades vocales de Xaven. La canción tiene un estribillo pegadizo y un ritmo animado que te hará querer bailar. La canción también tiene algunos elementos de dancehall y afrobeat, dándole un sonido único y fresco. La canción es mezclada y masterizada por Mzenga Man, quien ha trabajado con muchos otros artistas zambianos, como Chef 187, Macky 2, Slapdee, Bobby East y más. La canción también está acompañada por un video musical colorido y vibrante, dirigido por Stanch Rite Media. El video muestra a Jemax y Xaven interpretando la canción en varios lugares, como un lavado de autos, una barbería, un club y una calle. El video también muestra algo de la cultura y la moda de Zambia. </p>
-<p></p>
- <h3>Letra y significado de la canción</h3>
-
-<p>Xaven canta el estribillo, que repite la frase "batería baja" varias veces. También canta sobre cómo extraña a su novio que vive en otra ciudad, y cómo anhela su toque y su voz. También se queja del alto costo del tiempo de emisión y los paquetes de datos, lo que hace que sea difícil para ella llamarlo o enviarle un mensaje. Ella dice que siente que su batería está baja, lo que significa que se siente sola y triste en la relación. </p>
-<p>La canción refleja las luchas comunes que muchas parejas enfrentan cuando están separadas por la distancia. También muestra cómo la tecnología puede ser una bendición y una maldición para las relaciones a larga distancia. La canción atrae a cualquiera que haya experimentado o pueda relacionarse con esta situación. </p>
- <h2>¿Cómo descargar la batería baja Jemax Mp3? </h2>
-<p>Si te gusta Battery Low de Jemax y Xaven, es posible que desee descargarlo en su dispositivo para que pueda escucharlo en cualquier momento y en cualquier lugar. Pero, ¿cómo se puede hacer eso? Hay muchas maneras de descargar Battery Low Jemax Mp3, pero no todos ellos son legales o seguros. En esta sección, te mostraremos algunos de los mejores sitios para descargar la canción de forma legal y segura. </p>
- <h3>Los mejores sitios para descargar la canción</h3>
-<p>Uno de los mejores sitios para descargar Battery Low Jemax Mp3 es ZedMusic, que es una plataforma de música de Zambia que ofrece descargas gratuitas de varias canciones y álbumes de Zambia. Puedes encontrar Battery Low de Jemax y Xaven en este sitio, junto con otras canciones de Jemax y otros artistas zambianos. Para descargar la canción desde este sitio, solo tiene que hacer clic en el botón de descarga debajo del título de la canción, y luego elegir la calidad y el formato que desee. También puede transmitir la canción en línea o ver el video musical en este sitio. </p>
-
-<p>Una tercera opción para descargar Battery Low Jemax Mp3 es YouTube, que es una plataforma global para compartir videos que alberga millones de videos, incluyendo videos musicales. Puedes encontrar Battery Low de Jemax y Xaven en YouTube, junto con otras canciones de Jemax y otros artistas zambianos. Para descargar la canción de YouTube, necesitarás usar una herramienta o aplicación de terceros que pueda convertir videos de YouTube a archivos MP3. Hay muchas herramientas o aplicaciones disponibles en línea, pero debe tener cuidado y elegir una confiable y segura. Algunos de los más populares y confiables son 4K Video Downloader, Y2Mate, YouTube to MP3 Converter y más. Para descargar la canción de YouTube usando estas herramientas o aplicaciones, solo necesita copiar la URL del video, pegarlo en la herramienta o aplicación, y luego elegir la calidad y el formato que desee. A continuación, puede guardar el archivo MP3 en su dispositivo. </p>
- <h3>Consejos y trucos para descargar la canción gratis</h3>
-<p>Descargar Battery Low Jemax Mp3 es fácil y gratuito, pero hay algunos consejos y trucos que pueden ayudarte a sacarle el máximo partido. Estos son algunos de ellos:</p>
-<ul>
-<li>Compruebe la calidad y el tamaño del archivo MP3 antes de descargarlo. Usted quiere asegurarse de que el archivo es claro y no está dañado, y que no ocupa demasiado espacio en su dispositivo. Por lo general, puede ver la calidad y el tamaño del archivo en la página de descarga o en la herramienta o aplicación que está utilizando. </li>
-
-<li>Utilice un gestor de descargas o un acelerador para acelerar el proceso de descarga. Un gestor de descargas o un acelerador es un software o una aplicación que te ayuda a descargar archivos de forma más rápida y eficiente. También puede reanudar descargas interrumpidas, pausar y reanudar descargas, programar descargas y administrar múltiples descargas a la vez. Puedes encontrar muchos gestores de descargas gratuitos o de pago o aceleradores en línea, pero debes tener cuidado y elegir uno compatible y seguro. </li>
-</ul>
- <h2>Cómo disfrutar de la batería baja Jemax Mp3? </h2>
-<p>Ahora que has descargado Battery Low Jemax Mp3 en tu dispositivo, puedes disfrutarlo en cualquier momento y en cualquier lugar. Pero, ¿cómo puedes aprovecharlo al máximo? Aquí hay algunas sugerencias:</p>
- <h3>Juega en tu dispositivo favorito</h3>
-<p>Puede reproducir Battery Low Jemax Mp3 en cualquier dispositivo que soporte archivos MP3, como su teléfono inteligente, tableta, computadora portátil, escritorio, reproductor de MP3, altavoz inteligente, estéreo de automóvil y más. También puede utilizar auriculares, auriculares, altavoces o sistemas de sonido para mejorar la calidad de sonido y el volumen de la canción. También puede ajustar la configuración de su dispositivo o su aplicación de reproductor de música para personalizar las opciones de reproducción, como aleatorio, repetición, ecualizador, aumento de graves y más. </p>
- <h3>Compártelo con tus amigos y familiares</h3>
-<p>También puedes compartir Battery Low Jemax Mp3 con tus amigos y familiares que aman el hip hop zambiano o que podrían estar interesados en él. Puede enviarles el archivo MP3 por correo electrónico, aplicaciones de mensajería, plataformas de redes sociales, servicios de almacenamiento en la nube, Bluetooth, Wi-Fi Direct, NFC, códigos QR y más. También puede reproducir la canción para ellos en su dispositivo o en sus dispositivos. También puede invitarlos a ver el video musical en YouTube o en otras plataformas para compartir videos. También puede discutir la canción con ellos, como sus características, letras, significado, producción, video y más. </p>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre Battery Low de Jemax con Xaven:</p>
-<ul>
-<li>P: ¿Dónde puedo encontrar más canciones de Jemax? <br>A: Puedes encontrar más canciones de Jemax en su canal de YouTube (https://www.youtube.com/channel/UC0w4Jf8X1a9Q6xZ9g7d9i8A), su página de Facebook (https:/www.facebook.com/JemaxOfficial), su cuenta de Instagram (https// instagram.com/jemaxofficial), y su cuenta de Twitter (https://twitter.com/JemaxOfficial). También puedes encontrar sus canciones en varias plataformas musicales, como ZedMusic, AfroFire, Mvesesani, Zamusic, y más. </li>
-<li>P: ¿Dónde puedo encontrar más canciones de Xaven? <br>A: Puedes encontrar más canciones de Xaven en su canal de YouTube (https://www.youtube.com/ channel/UCn1c6L4y1w3X0xY2J7Xf9jg), su página de Facebook (https:/www.facebook.com/ xavenmusic), su cuenta de Instagram (https/instagram.com/ venmusic), y su cuenta de Twitter (https://twitter.com/xavenmusic). También puedes encontrar sus canciones en varias plataformas musicales, como ZedMusic, AfroFire, Mvesesani, Zamusic, y más. </li>
-<li>P: ¿Dónde puedo encontrar más canciones de Mzenga Man? <br>A: Puedes encontrar más canciones de Mzenga Man en su canal de YouTube (https://www.youtube.com/channel/UC5wM8sZ0yUqYKfW9nZJ4c5g), su página de Facebook (https:/ww.facebook.com/mzengamgaman), su cuenta de Instagram (instatps/gram.com/mzenzen), y su cuenta de Twitter (https://twitter.com/mzengaman). También puedes encontrar sus canciones en varias plataformas musicales, como ZedMusic, AfroFire, Mvesesani, Zamusic, y más. </li>
-<li>P: ¿Cómo puedo soportar Jemax, Xaven y Mzenga Man? <br>A: Puedes apoyar a Jemax, Xaven y Mzenga Man siguiéndolos en sus cuentas de redes sociales, suscribiéndose a sus canales de YouTube, gustándoles y comentando sus publicaciones y videos, compartiendo sus canciones y videos con tus amigos y familiares, comprar su mercancía o entradas para sus espectáculos, y donar a sus causas o proyectos. </li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/taming-transformers/taming/models/cond_transformer.py

@@ -1,343 +0,0 @@
-import os, math
-import torch
-import torch.nn.functional as F
-import pytorch_lightning as pl
-
-from main import instantiate_from_config
-from taming.modules.util import SOSProvider
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class Net2NetTransformer(pl.LightningModule):
-    def __init__(self,
-                 transformer_config,
-                 first_stage_config,
-                 cond_stage_config,
-                 permuter_config=None,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 first_stage_key="image",
-                 cond_stage_key="depth",
-                 downsample_cond_size=-1,
-                 pkeep=1.0,
-                 sos_token=0,
-                 unconditional=False,
-                 ):
-        super().__init__()
-        self.be_unconditional = unconditional
-        self.sos_token = sos_token
-        self.first_stage_key = first_stage_key
-        self.cond_stage_key = cond_stage_key
-        self.init_first_stage_from_ckpt(first_stage_config)
-        self.init_cond_stage_from_ckpt(cond_stage_config)
-        if permuter_config is None:
-            permuter_config = {"target": "taming.modules.transformer.permuter.Identity"}
-        self.permuter = instantiate_from_config(config=permuter_config)
-        self.transformer = instantiate_from_config(config=transformer_config)
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.downsample_cond_size = downsample_cond_size
-        self.pkeep = pkeep
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        for k in sd.keys():
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    self.print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    def init_first_stage_from_ckpt(self, config):
-        model = instantiate_from_config(config)
-        model = model.eval()
-        model.train = disabled_train
-        self.first_stage_model = model
-
-    def init_cond_stage_from_ckpt(self, config):
-        if config == "__is_first_stage__":
-            print("Using first stage also as cond stage.")
-            self.cond_stage_model = self.first_stage_model
-        elif config == "__is_unconditional__" or self.be_unconditional:
-            print(f"Using no cond stage. Assuming the training is intended to be unconditional. "
-                  f"Prepending {self.sos_token} as a sos token.")
-            self.be_unconditional = True
-            self.cond_stage_key = self.first_stage_key
-            self.cond_stage_model = SOSProvider(self.sos_token)
-        else:
-            model = instantiate_from_config(config)
-            model = model.eval()
-            model.train = disabled_train
-            self.cond_stage_model = model
-
-    def forward(self, x, c):
-        # one step to produce the logits
-        _, z_indices = self.encode_to_z(x)
-        _, c_indices = self.encode_to_c(c)
-
-        if self.training and self.pkeep < 1.0:
-            mask = torch.bernoulli(self.pkeep*torch.ones(z_indices.shape,
-                                                         device=z_indices.device))
-            mask = mask.round().to(dtype=torch.int64)
-            r_indices = torch.randint_like(z_indices, self.transformer.config.vocab_size)
-            a_indices = mask*z_indices+(1-mask)*r_indices
-        else:
-            a_indices = z_indices
-
-        cz_indices = torch.cat((c_indices, a_indices), dim=1)
-
-        # target includes all sequence elements (no need to handle first one
-        # differently because we are conditioning)
-        target = z_indices
-        # make the prediction
-        logits, _ = self.transformer(cz_indices[:, :-1])
-        # cut off conditioning outputs - output i corresponds to p(z_i | z_{<i}, c)
-        logits = logits[:, c_indices.shape[1]-1:]
-
-        return logits, target
-
-    def top_k_logits(self, logits, k):
-        v, ix = torch.topk(logits, k)
-        out = logits.clone()
-        out[out < v[..., [-1]]] = -float('Inf')
-        return out
-
-    @torch.no_grad()
-    def sample(self, x, c, steps, temperature=1.0, sample=False, top_k=None,
-               callback=lambda k: None):
-        x = torch.cat((c,x),dim=1)
-        block_size = self.transformer.get_block_size()
-        assert not self.transformer.training
-        if self.pkeep <= 0.0:
-            # one pass suffices since input is pure noise anyway
-            assert len(x.shape)==2
-            noise_shape = (x.shape[0], steps-1)
-            #noise = torch.randint(self.transformer.config.vocab_size, noise_shape).to(x)
-            noise = c.clone()[:,x.shape[1]-c.shape[1]:-1]
-            x = torch.cat((x,noise),dim=1)
-            logits, _ = self.transformer(x)
-            # take all logits for now and scale by temp
-            logits = logits / temperature
-            # optionally crop probabilities to only the top k options
-            if top_k is not None:
-                logits = self.top_k_logits(logits, top_k)
-            # apply softmax to convert to probabilities
-            probs = F.softmax(logits, dim=-1)
-            # sample from the distribution or take the most likely
-            if sample:
-                shape = probs.shape
-                probs = probs.reshape(shape[0]*shape[1],shape[2])
-                ix = torch.multinomial(probs, num_samples=1)
-                probs = probs.reshape(shape[0],shape[1],shape[2])
-                ix = ix.reshape(shape[0],shape[1])
-            else:
-                _, ix = torch.topk(probs, k=1, dim=-1)
-            # cut off conditioning
-            x = ix[:, c.shape[1]-1:]
-        else:
-            for k in range(steps):
-                callback(k)
-                assert x.size(1) <= block_size # make sure model can see conditioning
-                x_cond = x if x.size(1) <= block_size else x[:, -block_size:]  # crop context if needed
-                logits, _ = self.transformer(x_cond)
-                # pluck the logits at the final step and scale by temperature
-                logits = logits[:, -1, :] / temperature
-                # optionally crop probabilities to only the top k options
-                if top_k is not None:
-                    logits = self.top_k_logits(logits, top_k)
-                # apply softmax to convert to probabilities
-                probs = F.softmax(logits, dim=-1)
-                # sample from the distribution or take the most likely
-                if sample:
-                    ix = torch.multinomial(probs, num_samples=1)
-                else:
-                    _, ix = torch.topk(probs, k=1, dim=-1)
-                # append to the sequence and continue
-                x = torch.cat((x, ix), dim=1)
-            # cut off conditioning
-            x = x[:, c.shape[1]:]
-        return x
-
-    @torch.no_grad()
-    def encode_to_z(self, x):
-        quant_z, _, info = self.first_stage_model.encode(x)
-        indices = info[2].view(quant_z.shape[0], -1)
-        indices = self.permuter(indices)
-        return quant_z, indices
-
-    @torch.no_grad()
-    def encode_to_c(self, c):
-        if self.downsample_cond_size > -1:
-            c = F.interpolate(c, size=(self.downsample_cond_size, self.downsample_cond_size))
-        quant_c, _, [_,_,indices] = self.cond_stage_model.encode(c)
-        if len(indices.shape) > 2:
-            indices = indices.view(c.shape[0], -1)
-        return quant_c, indices
-
-    @torch.no_grad()
-    def decode_to_img(self, index, zshape):
-        index = self.permuter(index, reverse=True)
-        bhwc = (zshape[0],zshape[2],zshape[3],zshape[1])
-        quant_z = self.first_stage_model.quantize.get_codebook_entry(
-            index.reshape(-1), shape=bhwc)
-        x = self.first_stage_model.decode(quant_z)
-        return x
-
-    @torch.no_grad()
-    def log_images(self, batch, temperature=None, top_k=None, callback=None, lr_interface=False, **kwargs):
-        log = dict()
-
-        N = 4
-        if lr_interface:
-            x, c = self.get_xc(batch, N, diffuse=False, upsample_factor=8)
-        else:
-            x, c = self.get_xc(batch, N)
-        x = x.to(device=self.device)
-        c = c.to(device=self.device)
-
-        quant_z, z_indices = self.encode_to_z(x)
-        quant_c, c_indices = self.encode_to_c(c)
-
-        # create a "half"" sample
-        z_start_indices = z_indices[:,:z_indices.shape[1]//2]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1]-z_start_indices.shape[1],
-                                   temperature=temperature if temperature is not None else 1.0,
-                                   sample=True,
-                                   top_k=top_k if top_k is not None else 100,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample = self.decode_to_img(index_sample, quant_z.shape)
-
-        # sample
-        z_start_indices = z_indices[:, :0]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1],
-                                   temperature=temperature if temperature is not None else 1.0,
-                                   sample=True,
-                                   top_k=top_k if top_k is not None else 100,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample_nopix = self.decode_to_img(index_sample, quant_z.shape)
-
-        # det sample
-        z_start_indices = z_indices[:, :0]
-        index_sample = self.sample(z_start_indices, c_indices,
-                                   steps=z_indices.shape[1],
-                                   sample=False,
-                                   callback=callback if callback is not None else lambda k: None)
-        x_sample_det = self.decode_to_img(index_sample, quant_z.shape)
-
-        # reconstruction
-        x_rec = self.decode_to_img(z_indices, quant_z.shape)
-
-        log["inputs"] = x
-        log["reconstructions"] = x_rec
-
-        if self.cond_stage_key != "image":
-            cond_rec = self.cond_stage_model.decode(quant_c)
-            if self.cond_stage_key == "segmentation":
-                # get image from segmentation mask
-                num_classes = cond_rec.shape[1]
-
-                c = torch.argmax(c, dim=1, keepdim=True)
-                c = F.one_hot(c, num_classes=num_classes)
-                c = c.squeeze(1).permute(0, 3, 1, 2).float()
-                c = self.cond_stage_model.to_rgb(c)
-
-                cond_rec = torch.argmax(cond_rec, dim=1, keepdim=True)
-                cond_rec = F.one_hot(cond_rec, num_classes=num_classes)
-                cond_rec = cond_rec.squeeze(1).permute(0, 3, 1, 2).float()
-                cond_rec = self.cond_stage_model.to_rgb(cond_rec)
-            log["conditioning_rec"] = cond_rec
-            log["conditioning"] = c
-
-        log["samples_half"] = x_sample
-        log["samples_nopix"] = x_sample_nopix
-        log["samples_det"] = x_sample_det
-        return log
-
-    def get_input(self, key, batch):
-        x = batch[key]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        if len(x.shape) == 4:
-            x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
-        if x.dtype == torch.double:
-            x = x.float()
-        return x
-
-    def get_xc(self, batch, N=None):
-        x = self.get_input(self.first_stage_key, batch)
-        c = self.get_input(self.cond_stage_key, batch)
-        if N is not None:
-            x = x[:N]
-            c = c[:N]
-        return x, c
-
-    def shared_step(self, batch, batch_idx):
-        x, c = self.get_xc(batch)
-        logits, target = self(x, c)
-        loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1))
-        return loss
-
-    def training_step(self, batch, batch_idx):
-        loss = self.shared_step(batch, batch_idx)
-        self.log("train/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        loss = self.shared_step(batch, batch_idx)
-        self.log("val/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-        return loss
-
-    def configure_optimizers(self):
-        """
-        Following minGPT:
-        This long function is unfortunately doing something very simple and is being very defensive:
-        We are separating out all parameters of the model into two buckets: those that will experience
-        weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
-        We are then returning the PyTorch optimizer object.
-        """
-        # separate out all parameters to those that will and won't experience regularizing weight decay
-        decay = set()
-        no_decay = set()
-        whitelist_weight_modules = (torch.nn.Linear, )
-        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
-        for mn, m in self.transformer.named_modules():
-            for pn, p in m.named_parameters():
-                fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
-
-                if pn.endswith('bias'):
-                    # all biases will not be decayed
-                    no_decay.add(fpn)
-                elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
-                    # weights of whitelist modules will be weight decayed
-                    decay.add(fpn)
-                elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
-                    # weights of blacklist modules will NOT be weight decayed
-                    no_decay.add(fpn)
-
-        # special case the position embedding parameter in the root GPT module as not decayed
-        no_decay.add('pos_emb')
-
-        # validate that we considered every parameter
-        param_dict = {pn: p for pn, p in self.transformer.named_parameters()}
-        inter_params = decay & no_decay
-        union_params = decay | no_decay
-        assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
-        assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
-                                                    % (str(param_dict.keys() - union_params), )
-
-        # create the pytorch optimizer object
-        optim_groups = [
-            {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": 0.01},
-            {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
-        ]
-        optimizer = torch.optim.AdamW(optim_groups, lr=self.learning_rate, betas=(0.9, 0.95))
-        return optimizer

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/base_command.py

@@ -1,225 +0,0 @@
-"""Base Command class, and related routines"""
-
-import functools
-import logging
-import logging.config
-import optparse
-import os
-import sys
-import traceback
-from optparse import Values
-from typing import Any, Callable, List, Optional, Tuple
-
-from pip._vendor.rich import traceback as rich_traceback
-
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.command_context import CommandContextMixIn
-from pip._internal.cli.parser import ConfigOptionParser, UpdatingDefaultsHelpFormatter
-from pip._internal.cli.status_codes import (
-    ERROR,
-    PREVIOUS_BUILD_DIR_ERROR,
-    UNKNOWN_ERROR,
-    VIRTUALENV_NOT_FOUND,
-)
-from pip._internal.exceptions import (
-    BadCommand,
-    CommandError,
-    DiagnosticPipError,
-    InstallationError,
-    NetworkConnectionError,
-    PreviousBuildDirError,
-    UninstallationError,
-)
-from pip._internal.utils.filesystem import check_path_owner
-from pip._internal.utils.logging import BrokenStdoutLoggingError, setup_logging
-from pip._internal.utils.misc import get_prog, normalize_path
-from pip._internal.utils.temp_dir import TempDirectoryTypeRegistry as TempDirRegistry
-from pip._internal.utils.temp_dir import global_tempdir_manager, tempdir_registry
-from pip._internal.utils.virtualenv import running_under_virtualenv
-
-__all__ = ["Command"]
-
-logger = logging.getLogger(__name__)
-
-
-class Command(CommandContextMixIn):
-    usage: str = ""
-    ignore_require_venv: bool = False
-
-    def __init__(self, name: str, summary: str, isolated: bool = False) -> None:
-        super().__init__()
-
-        self.name = name
-        self.summary = summary
-        self.parser = ConfigOptionParser(
-            usage=self.usage,
-            prog=f"{get_prog()} {name}",
-            formatter=UpdatingDefaultsHelpFormatter(),
-            add_help_option=False,
-            name=name,
-            description=self.__doc__,
-            isolated=isolated,
-        )
-
-        self.tempdir_registry: Optional[TempDirRegistry] = None
-
-        # Commands should add options to this option group
-        optgroup_name = f"{self.name.capitalize()} Options"
-        self.cmd_opts = optparse.OptionGroup(self.parser, optgroup_name)
-
-        # Add the general options
-        gen_opts = cmdoptions.make_option_group(
-            cmdoptions.general_group,
-            self.parser,
-        )
-        self.parser.add_option_group(gen_opts)
-
-        self.add_options()
-
-    def add_options(self) -> None:
-        pass
-
-    def handle_pip_version_check(self, options: Values) -> None:
-        """
-        This is a no-op so that commands by default do not do the pip version
-        check.
-        """
-        # Make sure we do the pip version check if the index_group options
-        # are present.
-        assert not hasattr(options, "no_index")
-
-    def run(self, options: Values, args: List[str]) -> int:
-        raise NotImplementedError
-
-    def parse_args(self, args: List[str]) -> Tuple[Values, List[str]]:
-        # factored out for testability
-        return self.parser.parse_args(args)
-
-    def main(self, args: List[str]) -> int:
-        try:
-            with self.main_context():
-                return self._main(args)
-        finally:
-            logging.shutdown()
-
-    def _main(self, args: List[str]) -> int:
-        # We must initialize this before the tempdir manager, otherwise the
-        # configuration would not be accessible by the time we clean up the
-        # tempdir manager.
-        self.tempdir_registry = self.enter_context(tempdir_registry())
-        # Intentionally set as early as possible so globally-managed temporary
-        # directories are available to the rest of the code.
-        self.enter_context(global_tempdir_manager())
-
-        options, args = self.parse_args(args)
-
-        # Set verbosity so that it can be used elsewhere.
-        self.verbosity = options.verbose - options.quiet
-
-        level_number = setup_logging(
-            verbosity=self.verbosity,
-            no_color=options.no_color,
-            user_log_file=options.log,
-        )
-
-        always_enabled_features = set(options.features_enabled) & set(
-            cmdoptions.ALWAYS_ENABLED_FEATURES
-        )
-        if always_enabled_features:
-            logger.warning(
-                "The following features are always enabled: %s. ",
-                ", ".join(sorted(always_enabled_features)),
-            )
-
-        # TODO: Try to get these passing down from the command?
-        #       without resorting to os.environ to hold these.
-        #       This also affects isolated builds and it should.
-
-        if options.no_input:
-            os.environ["PIP_NO_INPUT"] = "1"
-
-        if options.exists_action:
-            os.environ["PIP_EXISTS_ACTION"] = " ".join(options.exists_action)
-
-        if options.require_venv and not self.ignore_require_venv:
-            # If a venv is required check if it can really be found
-            if not running_under_virtualenv():
-                logger.critical("Could not find an activated virtualenv (required).")
-                sys.exit(VIRTUALENV_NOT_FOUND)
-
-        if options.cache_dir:
-            options.cache_dir = normalize_path(options.cache_dir)
-            if not check_path_owner(options.cache_dir):
-                logger.warning(
-                    "The directory '%s' or its parent directory is not owned "
-                    "or is not writable by the current user. The cache "
-                    "has been disabled. Check the permissions and owner of "
-                    "that directory. If executing pip with sudo, you should "
-                    "use sudo's -H flag.",
-                    options.cache_dir,
-                )
-                options.cache_dir = None
-
-        def intercepts_unhandled_exc(
-            run_func: Callable[..., int]
-        ) -> Callable[..., int]:
-            @functools.wraps(run_func)
-            def exc_logging_wrapper(*args: Any) -> int:
-                try:
-                    status = run_func(*args)
-                    assert isinstance(status, int)
-                    return status
-                except DiagnosticPipError as exc:
-                    logger.error("[present-rich] %s", exc)
-                    logger.debug("Exception information:", exc_info=True)
-
-                    return ERROR
-                except PreviousBuildDirError as exc:
-                    logger.critical(str(exc))
-                    logger.debug("Exception information:", exc_info=True)
-
-                    return PREVIOUS_BUILD_DIR_ERROR
-                except (
-                    InstallationError,
-                    UninstallationError,
-                    BadCommand,
-                    NetworkConnectionError,
-                ) as exc:
-                    logger.critical(str(exc))
-                    logger.debug("Exception information:", exc_info=True)
-
-                    return ERROR
-                except CommandError as exc:
-                    logger.critical("%s", exc)
-                    logger.debug("Exception information:", exc_info=True)
-
-                    return ERROR
-                except BrokenStdoutLoggingError:
-                    # Bypass our logger and write any remaining messages to
-                    # stderr because stdout no longer works.
-                    print("ERROR: Pipe to stdout was broken", file=sys.stderr)
-                    if level_number <= logging.DEBUG:
-                        traceback.print_exc(file=sys.stderr)
-
-                    return ERROR
-                except KeyboardInterrupt:
-                    logger.critical("Operation cancelled by user")
-                    logger.debug("Exception information:", exc_info=True)
-
-                    return ERROR
-                except BaseException:
-                    logger.critical("Exception:", exc_info=True)
-
-                    return UNKNOWN_ERROR
-
-            return exc_logging_wrapper
-
-        try:
-            if not options.debug_mode:
-                run = intercepts_unhandled_exc(self.run)
-            else:
-                run = self.run
-                rich_traceback.install(show_locals=True)
-            return run(options, args)
-        finally:
-            self.handle_pip_version_check(options)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/metadata/importlib/_envs.py

@@ -1,188 +0,0 @@
-import functools
-import importlib.metadata
-import logging
-import os
-import pathlib
-import sys
-import zipfile
-import zipimport
-from typing import Iterator, List, Optional, Sequence, Set, Tuple
-
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-
-from pip._internal.metadata.base import BaseDistribution, BaseEnvironment
-from pip._internal.models.wheel import Wheel
-from pip._internal.utils.deprecation import deprecated
-from pip._internal.utils.filetypes import WHEEL_EXTENSION
-
-from ._compat import BadMetadata, BasePath, get_dist_name, get_info_location
-from ._dists import Distribution
-
-logger = logging.getLogger(__name__)
-
-
-def _looks_like_wheel(location: str) -> bool:
-    if not location.endswith(WHEEL_EXTENSION):
-        return False
-    if not os.path.isfile(location):
-        return False
-    if not Wheel.wheel_file_re.match(os.path.basename(location)):
-        return False
-    return zipfile.is_zipfile(location)
-
-
-class _DistributionFinder:
-    """Finder to locate distributions.
-
-    The main purpose of this class is to memoize found distributions' names, so
-    only one distribution is returned for each package name. At lot of pip code
-    assumes this (because it is setuptools's behavior), and not doing the same
-    can potentially cause a distribution in lower precedence path to override a
-    higher precedence one if the caller is not careful.
-
-    Eventually we probably want to make it possible to see lower precedence
-    installations as well. It's useful feature, after all.
-    """
-
-    FoundResult = Tuple[importlib.metadata.Distribution, Optional[BasePath]]
-
-    def __init__(self) -> None:
-        self._found_names: Set[NormalizedName] = set()
-
-    def _find_impl(self, location: str) -> Iterator[FoundResult]:
-        """Find distributions in a location."""
-        # Skip looking inside a wheel. Since a package inside a wheel is not
-        # always valid (due to .data directories etc.), its .dist-info entry
-        # should not be considered an installed distribution.
-        if _looks_like_wheel(location):
-            return
-        # To know exactly where we find a distribution, we have to feed in the
-        # paths one by one, instead of dumping the list to importlib.metadata.
-        for dist in importlib.metadata.distributions(path=[location]):
-            info_location = get_info_location(dist)
-            try:
-                raw_name = get_dist_name(dist)
-            except BadMetadata as e:
-                logger.warning("Skipping %s due to %s", info_location, e.reason)
-                continue
-            normalized_name = canonicalize_name(raw_name)
-            if normalized_name in self._found_names:
-                continue
-            self._found_names.add(normalized_name)
-            yield dist, info_location
-
-    def find(self, location: str) -> Iterator[BaseDistribution]:
-        """Find distributions in a location.
-
-        The path can be either a directory, or a ZIP archive.
-        """
-        for dist, info_location in self._find_impl(location):
-            if info_location is None:
-                installed_location: Optional[BasePath] = None
-            else:
-                installed_location = info_location.parent
-            yield Distribution(dist, info_location, installed_location)
-
-    def find_linked(self, location: str) -> Iterator[BaseDistribution]:
-        """Read location in egg-link files and return distributions in there.
-
-        The path should be a directory; otherwise this returns nothing. This
-        follows how setuptools does this for compatibility. The first non-empty
-        line in the egg-link is read as a path (resolved against the egg-link's
-        containing directory if relative). Distributions found at that linked
-        location are returned.
-        """
-        path = pathlib.Path(location)
-        if not path.is_dir():
-            return
-        for child in path.iterdir():
-            if child.suffix != ".egg-link":
-                continue
-            with child.open() as f:
-                lines = (line.strip() for line in f)
-                target_rel = next((line for line in lines if line), "")
-            if not target_rel:
-                continue
-            target_location = str(path.joinpath(target_rel))
-            for dist, info_location in self._find_impl(target_location):
-                yield Distribution(dist, info_location, path)
-
-    def _find_eggs_in_dir(self, location: str) -> Iterator[BaseDistribution]:
-        from pip._vendor.pkg_resources import find_distributions
-
-        from pip._internal.metadata import pkg_resources as legacy
-
-        with os.scandir(location) as it:
-            for entry in it:
-                if not entry.name.endswith(".egg"):
-                    continue
-                for dist in find_distributions(entry.path):
-                    yield legacy.Distribution(dist)
-
-    def _find_eggs_in_zip(self, location: str) -> Iterator[BaseDistribution]:
-        from pip._vendor.pkg_resources import find_eggs_in_zip
-
-        from pip._internal.metadata import pkg_resources as legacy
-
-        try:
-            importer = zipimport.zipimporter(location)
-        except zipimport.ZipImportError:
-            return
-        for dist in find_eggs_in_zip(importer, location):
-            yield legacy.Distribution(dist)
-
-    def find_eggs(self, location: str) -> Iterator[BaseDistribution]:
-        """Find eggs in a location.
-
-        This actually uses the old *pkg_resources* backend. We likely want to
-        deprecate this so we can eventually remove the *pkg_resources*
-        dependency entirely. Before that, this should first emit a deprecation
-        warning for some versions when using the fallback since importing
-        *pkg_resources* is slow for those who don't need it.
-        """
-        if os.path.isdir(location):
-            yield from self._find_eggs_in_dir(location)
-        if zipfile.is_zipfile(location):
-            yield from self._find_eggs_in_zip(location)
-
-
-@functools.lru_cache(maxsize=None)  # Warn a distribution exactly once.
-def _emit_egg_deprecation(location: Optional[str]) -> None:
-    deprecated(
-        reason=f"Loading egg at {location} is deprecated.",
-        replacement="to use pip for package installation.",
-        gone_in=None,
-    )
-
-
-class Environment(BaseEnvironment):
-    def __init__(self, paths: Sequence[str]) -> None:
-        self._paths = paths
-
-    @classmethod
-    def default(cls) -> BaseEnvironment:
-        return cls(sys.path)
-
-    @classmethod
-    def from_paths(cls, paths: Optional[List[str]]) -> BaseEnvironment:
-        if paths is None:
-            return cls(sys.path)
-        return cls(paths)
-
-    def _iter_distributions(self) -> Iterator[BaseDistribution]:
-        finder = _DistributionFinder()
-        for location in self._paths:
-            yield from finder.find(location)
-            for dist in finder.find_eggs(location):
-                # _emit_egg_deprecation(dist.location)  # TODO: Enable this.
-                yield dist
-            # This must go last because that's how pkg_resources tie-breaks.
-            yield from finder.find_linked(location)
-
-    def get_distribution(self, name: str) -> Optional[BaseDistribution]:
-        matches = (
-            distribution
-            for distribution in self.iter_all_distributions()
-            if distribution.canonical_name == canonicalize_name(name)
-        )
-        return next(matches, None)

spaces/BraydenMoore/MARCI-NFL-Betting/main.py

@@ -1,102 +0,0 @@
-from Source.Predict import predict
-from flask import Flask, render_template, jsonify, request, session
-import requests
-import pickle as pkl
-import pandas as pd
-import numpy as np
-pd.set_option('display.max_columns', None)
-pd.set_option('display.expand_frame_repr', False)
-
-import json
-with open('Source/Data/record.json','r') as f:
-    record = json.load(f)
-with open('Source/Data/lines.json','r') as f:
-    lines = json.load(f)
-
-app = Flask(__name__, template_folder="Templates", static_folder="Static", static_url_path="/Static")
-app.config.update(
-    SESSION_COOKIE_SECURE=True,
-    SESSION_COOKIE_SAMESITE='None',
-)
-app.secret_key = 'green-flounder'
-
-# get week, season
-current_week, season = predict.get_week()
-current_games = predict.get_games(current_week)[['Date','Away Team','Home Team']]
-available_weeks = list(range(current_week+1))[3:]
-available_weeks.reverse()
-
-# load current data by default
-@app.route('/')
-def index():
-    print('Current Week', current_week)
-    session['selected_week'] = current_week
-
-    for week in available_weeks:
-        session[f'games_week_{week}'] = None
-
-    session[f'games_week_{current_week}'] = current_games.to_json()
-    return render_template('index.html', **record)
-
-# send week list to front end
-@app.route('/get_weeks')
-def get_weeks():
-    return jsonify(available_weeks)
-
-# send lines to front end
-@app.route('/get_lines')
-def get_lines():
-    try:
-        return jsonify(lines[str(session.get('selected_week'))])
-    except:
-        return jsonify(lines[str(current_week)])
-
-# send games of selected week to front end
-@app.route('/get_games')
-def get_games():
-    requested_week = int(request.args.get('week'))
-    session['selected_week'] = requested_week
-
-    # If select a new week
-    if requested_week and requested_week != current_week:
-        print("Requested Week:", requested_week)
-        # Check if that week's games are cached
-        if session.get(f'games_week_{requested_week}'):
-            print("Using cached games")
-            print(session.get(f'games_week_{requested_week}'))
-            games = session.get(f'games_week_{requested_week}')
-            games = json.loads(games)
-            return jsonify(games)
-        else:
-            games = predict.get_games(requested_week)[['Date','Away Team','Home Team']]
-            session[f'games_week_{requested_week}'] = games.to_json(orient='records')
-            return jsonify(games.to_dict(orient='records'))
-    else:
-        games = current_games
-        return jsonify(games.to_dict(orient='records'))
-
-# make predictions
-@app.route('/submit_games', methods=['POST'])
-def submit_games():
-    data = request.json
-    data = pd.DataFrame(data).replace('', np.nan).dropna()
-    home_teams = data['HomeTeam'].values
-    away_teams = data['AwayTeam'].values
-    ou_lines = data['OverUnderLine'].values
-    row_indices = data['rowIndex'].values
-    
-    moneylines = []
-    over_unders = []
-    for row_index,home,away,total in zip(row_indices,home_teams,away_teams,ou_lines):
-        selected_week = session.get('selected_week')
-        game_id, moneyline, over_under = predict.predict(home,away,season,selected_week,total)
-        moneyline['rowIndex'] = int(row_index)
-        over_under['rowIndex'] = int(row_index)
-        moneylines.append(moneyline)
-        over_unders.append(over_under)
-
-    return jsonify({'moneylines': moneylines,
-                    'over_unders': over_unders})
-    
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port='7860', debug=True)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/backbone/build.py

@@ -1,33 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from detectron2.layers import ShapeSpec
-from detectron2.utils.registry import Registry
-
-from .backbone import Backbone
-
-BACKBONE_REGISTRY = Registry("BACKBONE")
-BACKBONE_REGISTRY.__doc__ = """
-Registry for backbones, which extract feature maps from images
-
-The registered object must be a callable that accepts two arguments:
-
-1. A :class:`detectron2.config.CfgNode`
-2. A :class:`detectron2.layers.ShapeSpec`, which contains the input shape specification.
-
-It must returns an instance of :class:`Backbone`.
-"""
-
-
-def build_backbone(cfg, input_shape=None):
-    """
-    Build a backbone from `cfg.MODEL.BACKBONE.NAME`.
-
-    Returns:
-        an instance of :class:`Backbone`
-    """
-    if input_shape is None:
-        input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))
-
-    backbone_name = cfg.MODEL.BACKBONE.NAME
-    backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)
-    assert isinstance(backbone, Backbone)
-    return backbone

spaces/CVPR/LIVE/thrust/internal/benchmark/timer.h

@@ -1,129 +0,0 @@
-#pragma once
-
-#include <cassert>
-
-#  define CUDA_SAFE_CALL_NO_SYNC( call) do {                                 \
-    cudaError err = call;                                                    \
-    if( cudaSuccess != err) {                                                \
-        fprintf(stderr, "CUDA error in file '%s' in line %i : %s.\n",        \
-                __FILE__, __LINE__, cudaGetErrorString( err) );              \
-        exit(EXIT_FAILURE);                                                  \
-    } } while (0)
-
-#  define CUDA_SAFE_CALL( call) do {                                         \
-    CUDA_SAFE_CALL_NO_SYNC(call);                                            \
-    cudaError err = cudaDeviceSynchronize();                                 \
-    if( cudaSuccess != err) {                                                \
-        fprintf(stderr, "CUDA error in file '%s' in line %i : %s.\n",        \
-                __FILE__, __LINE__, cudaGetErrorString( err) );              \
-        exit(EXIT_FAILURE);                                                  \
-    } } while (0)
-
-class cuda_timer
-{
-    cudaEvent_t start_;
-    cudaEvent_t stop_;
-
- public:
-    cuda_timer()
-    {
-        CUDA_SAFE_CALL(cudaEventCreate(&start_));
-        CUDA_SAFE_CALL(cudaEventCreate(&stop_));
-    }
-
-    ~cuda_timer()
-    {
-        CUDA_SAFE_CALL(cudaEventDestroy(start_));
-        CUDA_SAFE_CALL(cudaEventDestroy(stop_));
-    }
-
-    void start()
-    {
-        CUDA_SAFE_CALL(cudaEventRecord(start_, 0));
-    }
-
-    void stop()
-    {
-        CUDA_SAFE_CALL(cudaEventRecord(stop_, 0));
-        CUDA_SAFE_CALL(cudaEventSynchronize(stop_));
-    }
-
-    double milliseconds_elapsed()
-    {
-        float elapsed_time;
-        CUDA_SAFE_CALL(cudaEventElapsedTime(&elapsed_time, start_, stop_));
-        return elapsed_time;
-    }
-
-    double seconds_elapsed()
-    {
-        return milliseconds_elapsed() / 1000.0;
-    }
-};
-
-#if (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC)
-#include <windows.h>
-
-class steady_timer
-{
-    LARGE_INTEGER frequency_; // Cached to avoid system calls.
-    LARGE_INTEGER start_;
-    LARGE_INTEGER stop_;
-
- public:
-    steady_timer() : start_(), stop_(), frequency_()
-    {
-        BOOL const r = QueryPerformanceFrequency(&frequency_);
-        assert(0 != r);
-    }
-
-    void start()
-    {
-        BOOL const r = QueryPerformanceCounter(&start_);
-        assert(0 != r);
-    }
-
-    void stop()
-    {
-        BOOL const r = QueryPerformanceCounter(&stop_);
-        assert(0 != r);
-    }
-
-    double seconds_elapsed()
-    {
-        return double(stop_.QuadPart - start_.QuadPart)
-             / double(frequency_.QuadPart);
-    }
-};
-#else
-#include <time.h>
-
-class steady_timer
-{
-    timespec start_;
-    timespec stop_;
-
- public:
-    steady_timer() : start_(), stop_() {}
-
-    void start()
-    {
-        int const r = clock_gettime(CLOCK_MONOTONIC, &start_);
-        assert(0 == r);
-    }
-
-    void stop()
-    {
-        int const r = clock_gettime(CLOCK_MONOTONIC, &stop_);
-        assert(0 == r);
-    }
-
-    double seconds_elapsed()
-    {
-        return double(stop_.tv_sec  - start_.tv_sec)
-             + double(stop_.tv_nsec - start_.tv_nsec) * 1.0e-9;
-    }
-};
-#endif
-
-

spaces/CVPR/LIVE/thrust/thrust/detail/allocator/destroy_range.h

@@ -1,34 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-namespace thrust
-{
-namespace detail
-{
-
-template<typename Allocator, typename Pointer, typename Size>
-__host__ __device__
-  inline void destroy_range(Allocator &a, Pointer p, Size n);
-
-} // end detail
-} // end thrust
-
-#include <thrust/detail/allocator/destroy_range.inl>
-

spaces/CVPR/LIVE/thrust/thrust/detail/functional/composite.h

@@ -1,163 +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.
- */
-
-// Portions of this code are derived from
-//
-// Manjunath Kudlur's Carbon library
-//
-// and
-//
-// Based on Boost.Phoenix v1.2
-// Copyright (c) 2001-2002 Joel de Guzman
-
-#pragma once
-
-#include <thrust/detail/functional/actor.h>
-#include <thrust/tuple.h>
-
-namespace thrust
-{
-namespace detail
-{
-namespace functional
-{
-
-// XXX we should just take a single EvalTuple
-template<typename Eval0,
-         typename Eval1  = thrust::null_type,
-         typename Eval2  = thrust::null_type,
-         typename Eval3  = thrust::null_type,
-         typename Eval4  = thrust::null_type,
-         typename Eval5  = thrust::null_type,
-         typename Eval6  = thrust::null_type,
-         typename Eval7  = thrust::null_type,
-         typename Eval8  = thrust::null_type,
-         typename Eval9  = thrust::null_type,
-         typename Eval10 = thrust::null_type>
-  class composite;
-
-template<typename Eval0, typename Eval1>
-  class composite<
-    Eval0,
-    Eval1,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type
-  >
-{
-  public:
-    template<typename Env>
-      struct result
-    {
-      typedef typename Eval0::template result<
-        thrust::tuple<
-          typename Eval1::template result<Env>::type
-        >
-      >::type type;
-    };
-
-    __host__ __device__
-    composite(const Eval0 &e0, const Eval1 &e1)
-      : m_eval0(e0),
-        m_eval1(e1)
-    {}
-
-    template<typename Env>
-    __host__ __device__
-    typename result<Env>::type
-    eval(const Env &x) const
-    {
-      typename Eval1::template result<Env>::type result1 = m_eval1.eval(x);
-      return m_eval0.eval(thrust::tie(result1));
-    }
-
-  private:
-    Eval0 m_eval0;
-    Eval1 m_eval1;
-}; // end composite<Eval0,Eval1>
-
-template<typename Eval0, typename Eval1, typename Eval2>
-  class composite<
-    Eval0,
-    Eval1,
-    Eval2,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type,
-    thrust::null_type
-  >
-{
-  public:
-    template<typename Env>
-      struct result
-    {
-      typedef typename Eval0::template result<
-        thrust::tuple<
-          typename Eval1::template result<Env>::type,
-          typename Eval2::template result<Env>::type
-        >
-      >::type type;
-    };
-
-    __host__ __device__
-    composite(const Eval0 &e0, const Eval1 &e1, const Eval2 &e2)
-      : m_eval0(e0),
-        m_eval1(e1),
-        m_eval2(e2)
-    {}
-
-    template<typename Env>
-    __host__ __device__
-    typename result<Env>::type
-    eval(const Env &x) const
-    {
-      typename Eval1::template result<Env>::type result1 = m_eval1.eval(x);
-      typename Eval2::template result<Env>::type result2 = m_eval2.eval(x);
-      return m_eval0.eval(thrust::tie(result1,result2));
-    }
-
-  private:
-    Eval0 m_eval0;
-    Eval1 m_eval1;
-    Eval2 m_eval2;
-}; // end composite<Eval0,Eval1,Eval2>
-
-template<typename Eval0, typename Eval1>
-__host__ __device__
-  actor<composite<Eval0,Eval1> > compose(const Eval0 &e0, const Eval1 &e1)
-{
-  return actor<composite<Eval0,Eval1> >(composite<Eval0,Eval1>(e0,e1));
-}
-
-template<typename Eval0, typename Eval1, typename Eval2>
-__host__ __device__
-  actor<composite<Eval0,Eval1,Eval2> > compose(const Eval0 &e0, const Eval1 &e1, const Eval2 &e2)
-{
-  return actor<composite<Eval0,Eval1,Eval2> >(composite<Eval0,Eval1,Eval2>(e0,e1,e2));
-}
-
-} // end functional
-} // end detail
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/extrema.h

@@ -1,804 +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 extrema.h
- *  \brief Functions for computing computing extremal values
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-
-
-/*! This version of \p min returns the smaller of two values, given a comparison operation.
- *  \param lhs The first value to compare.
- *  \param rhs The second value to compare.
- *  \param comp A comparison operation.
- *  \return The smaller element.
- *
- *  \tparam T is convertible to \p BinaryPredicate's first argument type and to its second argument type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">BinaryPredicate</a>.
- *
- *  The following code snippet demonstrates how to use \p min to compute the smaller of two
- *  key-value objects.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  ...
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value a = {13, 0};
- *  key_value b = { 7, 1);
- *
- *  key_value smaller = thrust::min(a, b, compare_key_value());
- *
- *  // smaller is {7, 1}
- *  \endcode
- *
- *  \note Returns the first argument when the arguments are equivalent.
- *  \see max
- */
-template<typename T, typename BinaryPredicate>
-__host__ __device__
-  T min THRUST_PREVENT_MACRO_SUBSTITUTION (const T &lhs, const T &rhs, BinaryPredicate comp);
-
-
-/*! This version of \p min returns the smaller of two values.
- *  \param lhs The first value to compare.
- *  \param rhs The second value to compare.
- *  \return The smaller element.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  The following code snippet demonstrates how to use \p min to compute the smaller of two
- *  integers.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  ...
- *  int a = 13;
- *  int b = 7;
- *
- *  int smaller = thrust::min(a, b);
- *
- *  // smaller is 7
- *  \endcode
- *
- *  \note Returns the first argument when the arguments are equivalent.
- *  \see max
- */
-template<typename T>
-__host__ __device__
-  T min THRUST_PREVENT_MACRO_SUBSTITUTION (const T &lhs, const T &rhs);
-
-
-/*! This version of \p max returns the larger of two values, given a comparison operation.
- *  \param lhs The first value to compare.
- *  \param rhs The second value to compare.
- *  \param comp A comparison operation.
- *  \return The larger element.
- *
- *  \tparam T is convertible to \p BinaryPredicate's first argument type and to its second argument type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">BinaryPredicate</a>.
- *
- *  The following code snippet demonstrates how to use \p max to compute the larger of two
- *  key-value objects.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  ...
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value a = {13, 0};
- *  key_value b = { 7, 1);
- *
- *  key_value larger = thrust::max(a, b, compare_key_value());
- *
- *  // larger is {13, 0}
- *  \endcode
- *
- *  \note Returns the first argument when the arguments are equivalent.
- *  \see min
- */
-template<typename T, typename BinaryPredicate>
-__host__ __device__
-  T max THRUST_PREVENT_MACRO_SUBSTITUTION (const T &lhs, const T &rhs, BinaryPredicate comp);
-
-
-/*! This version of \p max returns the larger of two values.
- *  \param lhs The first value to compare.
- *  \param rhs The second value to compare.
- *  \return The larger element.
- *
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  The following code snippet demonstrates how to use \p max to compute the larger of two
- *  integers.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  ...
- *  int a = 13;
- *  int b = 7;
- *
- *  int larger = thrust::min(a, b);
- *
- *  // larger is 13
- *  \endcode
- *
- *  \note Returns the first argument when the arguments are equivalent.
- *  \see min
- */
-template<typename T>
-__host__ __device__
-  T max THRUST_PREVENT_MACRO_SUBSTITUTION (const T &lhs, const T &rhs);
-
-
-/*! \addtogroup reductions
- *  \{
- *  \addtogroup extrema
- *  \ingroup reductions
- *  \{
- */
-
-/*! \p min_element finds the smallest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value smaller
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p min_element differ in how they define whether one element is
- *  less than another. This version compares objects using \c operator<. Specifically,
- *  this version of \p min_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>*j < *i</tt> is
- *  \c false.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \return An iterator pointing to the smallest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          and \c ForwardIterator's \c value_type is a model of
- *          <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int *result = thrust::min_element(thrust::host, data, data + 6);
- *
- *  // result is data + 1
- *  // *result is 0
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/min_element.html 
- */
-template<typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-ForwardIterator min_element(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, ForwardIterator first, ForwardIterator last);
-
-
-/*! \p min_element finds the smallest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value smaller
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p min_element differ in how they define whether one element is
- *  less than another. This version compares objects using \c operator<. Specifically,
- *  this version of \p min_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>*j < *i</tt> is
- *  \c false.
- *
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \return An iterator pointing to the smallest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          and \c ForwardIterator's \c value_type is a model of
- *          <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int *result = thrust::min_element(data, data + 6);
- *
- *  // result is data + 1
- *  // *result is 0
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/min_element.html 
- */
-template <typename ForwardIterator>
-ForwardIterator min_element(ForwardIterator first, ForwardIterator last);
-
-
-/*! \p min_element finds the smallest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value smaller
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p min_element differ in how they define whether one element is
- *  less than another. This version compares objects using a function object \p comp.
- *  Specifically, this version of \p min_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>comp(*j, *i)</tt> is
- *  \c false.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \param comp A binary predicate used for comparison.
- *  \return An iterator pointing to the smallest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          and \p ForwardIterator's \c value_type is convertible to both \p comp's
- *          \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p min_element to find the smallest element
- *  of a collection of key-value pairs using the \p thrust::host execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value data[4] = { {4,5}, {0,7}, {2,3}, {6,1} };
- *
- *  key_value *smallest = thrust::min_element(thrust::host, data, data + 4, compare_key_value());
- *
- *  // smallest == data + 1
- *  // *smallest == {0,7}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/min_element.html 
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-ForwardIterator min_element(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, ForwardIterator first, ForwardIterator last, BinaryPredicate comp);
-
-
-/*! \p min_element finds the smallest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value smaller
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p min_element differ in how they define whether one element is
- *  less than another. This version compares objects using a function object \p comp.
- *  Specifically, this version of \p min_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>comp(*j, *i)</tt> is
- *  \c false.
- *
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \param comp A binary predicate used for comparison.
- *  \return An iterator pointing to the smallest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          and \p ForwardIterator's \c value_type is convertible to both \p comp's
- *          \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p min_element to find the smallest element
- *  of a collection of key-value pairs.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value data[4] = { {4,5}, {0,7}, {2,3}, {6,1} };
- *
- *  key_value *smallest = thrust::min_element(data, data + 4, compare_key_value());
- *
- *  // smallest == data + 1
- *  // *smallest == {0,7}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/min_element.html 
- */
-template <typename ForwardIterator, typename BinaryPredicate>
-ForwardIterator min_element(ForwardIterator first, ForwardIterator last,
-                            BinaryPredicate comp);
-
-
-/*! \p max_element finds the largest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value larger
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p max_element differ in how they define whether one element is
- *  greater than another. This version compares objects using \c operator<. Specifically,
- *  this version of \p max_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>*i < *j</tt> is
- *  \c false.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \return An iterator pointing to the largest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam A Thrust backend system.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \c ForwardIterator's \c value_type is a model of
- *          <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int *result = thrust::max_element(thrust::host, data, data + 6);
- *
- *  // *result == 3
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/max_element.html 
- */
-template<typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-ForwardIterator max_element(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, ForwardIterator first, ForwardIterator last);
-
-
-/*! \p max_element finds the largest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value larger
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p max_element differ in how they define whether one element is
- *  greater than another. This version compares objects using \c operator<. Specifically,
- *  this version of \p max_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>*i < *j</tt> is
- *  \c false.
- *
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \return An iterator pointing to the largest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \c ForwardIterator's \c value_type is a model of
- *          <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int *result = thrust::max_element(data, data + 6);
- *
- *  // *result == 3
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/max_element.html 
- */
-template <typename ForwardIterator>
-ForwardIterator max_element(ForwardIterator first, ForwardIterator last);
-
-
-/*! \p max_element finds the largest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value larger
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p max_element differ in how they define whether one element is
- *  less than another. This version compares objects using a function object \p comp.
- *  Specifically, this version of \p max_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>comp(*i, *j)</tt> is
- *  \c false.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \param comp A binary predicate used for comparison.
- *  \return An iterator pointing to the largest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator's \c value_type is convertible to both \p comp's
- *          \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p max_element to find the largest element
- *  of a collection of key-value pairs using the \p thrust::host execution policy for parallelization.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value data[4] = { {4,5}, {0,7}, {2,3}, {6,1} };
- *
- *  key_value *largest = thrust::max_element(thrust::host, data, data + 4, compare_key_value());
- *
- *  // largest == data + 3
- *  // *largest == {6,1}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/max_element.html 
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-ForwardIterator max_element(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, ForwardIterator first, ForwardIterator last, BinaryPredicate comp);
-
-
-/*! \p max_element finds the largest element in the range <tt>[first, last)</tt>.
- *  It returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that no other iterator in <tt>[first, last)</tt> points to a value larger
- *  than \c *i. The return value is \p last if and only if <tt>[first, last)</tt> is an
- *  empty range.
- *
- *  The two versions of \p max_element differ in how they define whether one element is
- *  less than another. This version compares objects using a function object \p comp.
- *  Specifically, this version of \p max_element returns the first iterator \c i in <tt>[first, last)</tt>
- *  such that, for every iterator \c j in <tt>[first, last)</tt>, <tt>comp(*i, *j)</tt> is
- *  \c false.
- *
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \param comp A binary predicate used for comparison.
- *  \return An iterator pointing to the largest element of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \p ForwardIterator's \c value_type is convertible to both \p comp's
- *          \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p max_element to find the largest element
- *  of a collection of key-value pairs.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value data[4] = { {4,5}, {0,7}, {2,3}, {6,1} };
- *
- *  key_value *largest = thrust::max_element(data, data + 4, compare_key_value());
- *
- *  // largest == data + 3
- *  // *largest == {6,1}
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/max_element.html 
- */
-template <typename ForwardIterator, typename BinaryPredicate>
-ForwardIterator max_element(ForwardIterator first, ForwardIterator last,
-                            BinaryPredicate comp);
-
-
-/*! \p minmax_element finds the smallest and largest elements in the range <tt>[first, last)</tt>.
- *  It returns a pair of iterators <tt>(imin, imax)</tt> where \c imin is the same iterator
- *  returned by \p min_element and \c imax is the same iterator returned by \p max_element.
- *  This function is potentially more efficient than separate calls to \p min_element and \p max_element.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \return A pair of iterator pointing to the smallest and largest elements of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \c ForwardIterator's \c value_type is a model of
- *          <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  thrust::pair<int *, int *> result = thrust::minmax_element(thrust::host, data, data + 6);
- *
- *  // result.first is data + 1
- *  // result.second is data + 5
- *  // *result.first is 0
- *  // *result.second is 3
- *  \endcode
- *
- *  \see min_element
- *  \see max_element
- *  \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1840.pdf
- */
-template<typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, ForwardIterator first, ForwardIterator last);
-
-
-/*! \p minmax_element finds the smallest and largest elements in the range <tt>[first, last)</tt>.
- *  It returns a pair of iterators <tt>(imin, imax)</tt> where \c imin is the same iterator
- *  returned by \p min_element and \c imax is the same iterator returned by \p max_element.
- *  This function is potentially more efficient than separate calls to \p min_element and \p max_element.
- *
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \return A pair of iterator pointing to the smallest and largest elements of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator.html">Forward Iterator</a>,
- *          and \c ForwardIterator's \c value_type is a model of
- *          <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  thrust::pair<int *, int *> result = thrust::minmax_element(data, data + 6);
- *
- *  // result.first is data + 1
- *  // result.second is data + 5
- *  // *result.first is 0
- *  // *result.second is 3
- *  \endcode
- *
- *  \see min_element
- *  \see max_element
- *  \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1840.pdf
- */
-template <typename ForwardIterator>
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(ForwardIterator first, 
-                                                             ForwardIterator last);
-
-
-/*! \p minmax_element finds the smallest and largest elements in the range <tt>[first, last)</tt>.
- *  It returns a pair of iterators <tt>(imin, imax)</tt> where \c imin is the same iterator
- *  returned by \p min_element and \c imax is the same iterator returned by \p max_element.
- *  This function is potentially more efficient than separate calls to \p min_element and \p max_element.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \param comp A binary predicate used for comparison.
- *  \return A pair of iterator pointing to the smallest and largest elements of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          and \p ForwardIterator's \c value_type is convertible to both \p comp's
- *          \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p minmax_element to find the smallest and largest elements
- *  of a collection of key-value pairs using the \p thrust::host execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  #include <thrust/pair.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value data[4] = { {4,5}, {0,7}, {2,3}, {6,1} };
- *
- *  thrust::pair<key_value*,key_value*> extrema = thrust::minmax_element(thrust::host, data, data + 4, compare_key_value());
- *
- *  // extrema.first   == data + 1
- *  // *extrema.first  == {0,7}
- *  // extrema.second  == data + 3
- *  // *extrema.second == {6,1}
- *  \endcode
- *
- *  \see min_element
- *  \see max_element
- *  \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1840.pdf
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, ForwardIterator first, ForwardIterator last, BinaryPredicate comp);
-
-
-/*! \p minmax_element finds the smallest and largest elements in the range <tt>[first, last)</tt>.
- *  It returns a pair of iterators <tt>(imin, imax)</tt> where \c imin is the same iterator
- *  returned by \p min_element and \c imax is the same iterator returned by \p max_element.
- *  This function is potentially more efficient than separate calls to \p min_element and \p max_element.
- *
- *  \param first The beginning of the sequence.
- *  \param last  The end of the sequence.
- *  \param comp A binary predicate used for comparison.
- *  \return A pair of iterator pointing to the smallest and largest elements of the range <tt>[first, last)</tt>,
- *          if it is not an empty range; \p last, otherwise.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          and \p ForwardIterator's \c value_type is convertible to both \p comp's
- *          \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate">Binary Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p minmax_element to find the smallest and largest elements
- *  of a collection of key-value pairs.
- *
- *  \code
- *  #include <thrust/extrema.h>
- *  #include <thrust/pair.h>
- *
- *  struct key_value
- *  {
- *    int key;
- *    int value;
- *  };
- *
- *  struct compare_key_value
- *  {
- *    __host__ __device__
- *    bool operator()(key_value lhs, key_value rhs)
- *    {
- *      return lhs.key < rhs.key;
- *    }
- *  };
- *
- *  ...
- *  key_value data[4] = { {4,5}, {0,7}, {2,3}, {6,1} };
- *
- *  thrust::pair<key_value*,key_value*> extrema = thrust::minmax_element(data, data + 4, compare_key_value());
- *
- *  // extrema.first   == data + 1
- *  // *extrema.first  == {0,7}
- *  // extrema.second  == data + 3
- *  // *extrema.second == {6,1}
- *  \endcode
- *
- *  \see min_element
- *  \see max_element
- *  \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1840.pdf
- */
-template <typename ForwardIterator, typename BinaryPredicate>
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(ForwardIterator first, 
-                                                             ForwardIterator last,
-                                                             BinaryPredicate comp);
-
-/*! \} // end extrema
- *  \} // end reductions
- */
-
-} // end thrust
-
-#include <thrust/detail/extrema.inl>
-#include <thrust/detail/minmax.h>
-

spaces/CVPR/regionclip-demo/detectron2/structures/instances.py

@@ -1,191 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-from typing import Any, Dict, List, Tuple, Union
-import torch
-
-
-class Instances:
-    """
-    This class represents a list of instances in an image.
-    It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields".
-    All fields must have the same ``__len__`` which is the number of instances.
-
-    All other (non-field) attributes of this class are considered private:
-    they must start with '_' and are not modifiable by a user.
-
-    Some basic usage:
-
-    1. Set/get/check a field:
-
-       .. code-block:: python
-
-          instances.gt_boxes = Boxes(...)
-          print(instances.pred_masks)  # a tensor of shape (N, H, W)
-          print('gt_masks' in instances)
-
-    2. ``len(instances)`` returns the number of instances
-    3. Indexing: ``instances[indices]`` will apply the indexing on all the fields
-       and returns a new :class:`Instances`.
-       Typically, ``indices`` is a integer vector of indices,
-       or a binary mask of length ``num_instances``
-
-       .. code-block:: python
-
-          category_3_detections = instances[instances.pred_classes == 3]
-          confident_detections = instances[instances.scores > 0.9]
-    """
-
-    def __init__(self, image_size: Tuple[int, int], **kwargs: Any):
-        """
-        Args:
-            image_size (height, width): the spatial size of the image.
-            kwargs: fields to add to this `Instances`.
-        """
-        self._image_size = image_size
-        self._fields: Dict[str, Any] = {}
-        for k, v in kwargs.items():
-            self.set(k, v)
-
-    @property
-    def image_size(self) -> Tuple[int, int]:
-        """
-        Returns:
-            tuple: height, width
-        """
-        return self._image_size
-
-    def __setattr__(self, name: str, val: Any) -> None:
-        if name.startswith("_"):
-            super().__setattr__(name, val)
-        else:
-            self.set(name, val)
-
-    def __getattr__(self, name: str) -> Any:
-        if name == "_fields" or name not in self._fields:
-            raise AttributeError("Cannot find field '{}' in the given Instances!".format(name))
-        return self._fields[name]
-
-    def set(self, name: str, value: Any) -> None:
-        """
-        Set the field named `name` to `value`.
-        The length of `value` must be the number of instances,
-        and must agree with other existing fields in this object.
-        """
-        data_len = len(value)
-        if len(self._fields):
-            assert (
-                len(self) == data_len
-            ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self))
-        self._fields[name] = value
-
-    def has(self, name: str) -> bool:
-        """
-        Returns:
-            bool: whether the field called `name` exists.
-        """
-        return name in self._fields
-
-    def remove(self, name: str) -> None:
-        """
-        Remove the field called `name`.
-        """
-        del self._fields[name]
-
-    def get(self, name: str) -> Any:
-        """
-        Returns the field called `name`.
-        """
-        return self._fields[name]
-
-    def get_fields(self) -> Dict[str, Any]:
-        """
-        Returns:
-            dict: a dict which maps names (str) to data of the fields
-
-        Modifying the returned dict will modify this instance.
-        """
-        return self._fields
-
-    # Tensor-like methods
-    def to(self, *args: Any, **kwargs: Any) -> "Instances":
-        """
-        Returns:
-            Instances: all fields are called with a `to(device)`, if the field has this method.
-        """
-        ret = Instances(self._image_size)
-        for k, v in self._fields.items():
-            if hasattr(v, "to"):
-                v = v.to(*args, **kwargs)
-            ret.set(k, v)
-        return ret
-
-    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Instances":
-        """
-        Args:
-            item: an index-like object and will be used to index all the fields.
-
-        Returns:
-            If `item` is a string, return the data in the corresponding field.
-            Otherwise, returns an `Instances` where all fields are indexed by `item`.
-        """
-        if type(item) == int:
-            if item >= len(self) or item < -len(self):
-                raise IndexError("Instances index out of range!")
-            else:
-                item = slice(item, None, len(self))
-
-        ret = Instances(self._image_size)
-        for k, v in self._fields.items():
-            ret.set(k, v[item])
-        return ret
-
-    def __len__(self) -> int:
-        for v in self._fields.values():
-            # use __len__ because len() has to be int and is not friendly to tracing
-            return v.__len__()
-        raise NotImplementedError("Empty Instances does not support __len__!")
-
-    def __iter__(self):
-        raise NotImplementedError("`Instances` object is not iterable!")
-
-    @staticmethod
-    def cat(instance_lists: List["Instances"]) -> "Instances":
-        """
-        Args:
-            instance_lists (list[Instances])
-
-        Returns:
-            Instances
-        """
-        assert all(isinstance(i, Instances) for i in instance_lists)
-        assert len(instance_lists) > 0
-        if len(instance_lists) == 1:
-            return instance_lists[0]
-
-        image_size = instance_lists[0].image_size
-        for i in instance_lists[1:]:
-            assert i.image_size == image_size
-        ret = Instances(image_size)
-        for k in instance_lists[0]._fields.keys():
-            values = [i.get(k) for i in instance_lists]
-            v0 = values[0]
-            if isinstance(v0, torch.Tensor):
-                values = torch.cat(values, dim=0)
-            elif isinstance(v0, list):
-                values = list(itertools.chain(*values))
-            elif hasattr(type(v0), "cat"):
-                values = type(v0).cat(values)
-            else:
-                raise ValueError("Unsupported type {} for concatenation".format(type(v0)))
-            ret.set(k, values)
-        return ret
-
-    def __str__(self) -> str:
-        s = self.__class__.__name__ + "("
-        s += "num_instances={}, ".format(len(self))
-        s += "image_height={}, ".format(self._image_size[0])
-        s += "image_width={}, ".format(self._image_size[1])
-        s += "fields=[{}])".format(", ".join((f"{k}: {v}" for k, v in self._fields.items())))
-        return s
-
-    __repr__ = __str__

spaces/ChandraMohanNayal/AutoGPT/tests.py

@@ -1,21 +0,0 @@
-import unittest
-
-import coverage
-
-if __name__ == "__main__":
-    # Start coverage collection
-    cov = coverage.Coverage()
-    cov.start()
-
-    # Load all tests from the 'autogpt/tests' package
-    suite = unittest.defaultTestLoader.discover("./tests")
-
-    # Run the tests
-    unittest.TextTestRunner().run(suite)
-
-    # Stop coverage collection
-    cov.stop()
-    cov.save()
-
-    # Report the coverage
-    cov.report(show_missing=True)

spaces/ChenyangSi/FreeU/stable-diffusion-2-1/README.md

@@ -1,185 +0,0 @@
----
-license: openrail++
-tags:
-- stable-diffusion
-- text-to-image
-pinned: true
----
-
-# Stable Diffusion v2-1 Model Card
-This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available [here](https://github.com/Stability-AI/stablediffusion).
-
-This `stable-diffusion-2-1` model is fine-tuned from [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) (`768-v-ema.ckpt`) with an additional 55k steps on the same dataset (with `punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`.
-
-- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `v2-1_768-ema-pruned.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.ckpt).
-- Use it with 🧨 [`diffusers`](#examples)
-
-## Model Details
-- **Developed by:** Robin Rombach, Patrick Esser
-- **Model type:** Diffusion-based text-to-image generation model
-- **Language(s):** English
-- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
-- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
-- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
-- **Cite as:**
-
-      @InProceedings{Rombach_2022_CVPR,
-          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-          month     = {June},
-          year      = {2022},
-          pages     = {10684-10695}
-      }
-
-
-## Examples
-
-Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
-
-```bash
-pip install diffusers transformers accelerate scipy safetensors
-```
-Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to DPMSolverMultistepScheduler):
-
-```python
-import torch
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-
-model_id = "stabilityai/stable-diffusion-2-1"
-
-# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-prompt = "a photo of an astronaut riding a horse on mars"
-image = pipe(prompt).images[0]
-    
-image.save("astronaut_rides_horse.png")
-```
-
-**Notes**:
-- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
-- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
-
-
-# Uses
-
-## Direct Use 
-The model is intended for research purposes only. Possible research areas and tasks include
-
-- Safe deployment of models which have the potential to generate harmful content.
-- Probing and understanding the limitations and biases of generative models.
-- Generation of artworks and use in design and other artistic processes.
-- Applications in educational or creative tools.
-- Research on generative models.
-
-Excluded uses are described below.
-
- ### Misuse, Malicious Use, and Out-of-Scope Use
-_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
-
-The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-#### Out-of-Scope Use
-The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
-
-#### Misuse and Malicious Use
-Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
-
-- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
-- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
-- Impersonating individuals without their consent.
-- Sexual content without consent of the people who might see it.
-- Mis- and disinformation
-- Representations of egregious violence and gore
-- Sharing of copyrighted or licensed material in violation of its terms of use.
-- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
-
-## Limitations and Bias
-
-### Limitations
-
-- The model does not achieve perfect photorealism
-- The model cannot render legible text
-- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
-- Faces and people in general may not be generated properly.
-- The model was trained mainly with English captions and will not work as well in other languages.
-- The autoencoding part of the model is lossy
-- The model was trained on a subset of the large-scale dataset
-  [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
-
-### Bias
-While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
-Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), 
-which consists of images that are limited to English descriptions. 
-Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
-This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
-ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
-Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
-
-
-## Training
-
-**Training Data**
-The model developers used the following dataset for training the model:
-
-- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
-
-**Training Procedure**
-Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, 
-
-- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
-- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
-- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
-- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
-
-We currently provide the following checkpoints:
-
-- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
-  850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
-- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
-- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
-The additional input channels of the U-Net which process this extra information were zero-initialized.
-- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
-The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://huggingface.co/runwayml/stable-diffusion-inpainting).
-- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
-In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). 
-
-- **Hardware:** 32 x 8 x A100 GPUs
-- **Optimizer:** AdamW
-- **Gradient Accumulations**: 1
-- **Batch:** 32 x 8 x 2 x 4 = 2048
-- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
-
-## Evaluation Results 
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
-
-![pareto](model-variants.jpg) 
-
-Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution.  Not optimized for FID scores.
-
-## Environmental Impact
-
-**Stable Diffusion v1** **Estimated Emissions**
-Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
-
-- **Hardware Type:** A100 PCIe 40GB
-- **Hours used:** 200000
-- **Cloud Provider:** AWS
-- **Compute Region:** US-east
-- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
-
-## Citation
-    @InProceedings{Rombach_2022_CVPR,
-        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-        month     = {June},
-        year      = {2022},
-        pages     = {10684-10695}
-    }
-
-*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/__init__.py

@@ -1,107 +0,0 @@
-import pkgutil
-
-import gradio.components as components
-import gradio.inputs as inputs
-import gradio.outputs as outputs
-import gradio.processing_utils
-import gradio.templates
-import gradio.themes as themes
-from gradio.blocks import Blocks
-from gradio.chat_interface import ChatInterface
-from gradio.components import (
-    HTML,
-    JSON,
-    AnnotatedImage,
-    Annotatedimage,
-    Audio,
-    BarPlot,
-    Button,
-    Carousel,
-    Chatbot,
-    Checkbox,
-    CheckboxGroup,
-    Checkboxgroup,
-    ClearButton,
-    Code,
-    ColorPicker,
-    DataFrame,
-    Dataframe,
-    Dataset,
-    Dropdown,
-    DuplicateButton,
-    File,
-    Gallery,
-    Highlight,
-    HighlightedText,
-    Highlightedtext,
-    Image,
-    Interpretation,
-    Json,
-    Label,
-    LinePlot,
-    Markdown,
-    Model3D,
-    Number,
-    Plot,
-    Radio,
-    ScatterPlot,
-    Slider,
-    State,
-    StatusTracker,
-    Text,
-    Textbox,
-    TimeSeries,
-    Timeseries,
-    UploadButton,
-    Variable,
-    Video,
-    component,
-)
-from gradio.deploy_space import deploy
-from gradio.events import SelectData
-from gradio.exceptions import Error
-from gradio.external import load
-from gradio.flagging import (
-    CSVLogger,
-    FlaggingCallback,
-    HuggingFaceDatasetJSONSaver,
-    HuggingFaceDatasetSaver,
-    SimpleCSVLogger,
-)
-from gradio.helpers import (
-    EventData,
-    Info,
-    Progress,
-    Warning,
-    make_waveform,
-    skip,
-    update,
-)
-from gradio.helpers import create_examples as Examples  # noqa: N812
-from gradio.interface import Interface, TabbedInterface, close_all
-from gradio.ipython_ext import load_ipython_extension
-from gradio.layouts import Accordion, Box, Column, Group, Row, Tab, TabItem, Tabs
-from gradio.mix import Parallel, Series
-from gradio.routes import Request, mount_gradio_app
-from gradio.templates import (
-    Files,
-    ImageMask,
-    ImagePaint,
-    List,
-    Matrix,
-    Mic,
-    Microphone,
-    Numpy,
-    Paint,
-    Pil,
-    PlayableVideo,
-    Sketchpad,
-    TextArea,
-    Webcam,
-)
-from gradio.themes import Base as Theme
-
-current_pkg_version = (
-    (pkgutil.get_data(__name__, "version.txt") or b"").decode("ascii").strip()
-)
-__version__ = current_pkg_version