Update parquet files (step 8 of 121)

spaces/0x876/Yotta_Mix/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/CompVis/stable-diffusion-v1-4").launch()

spaces/0x90e/ESRGAN-MANGA/process_image.py

@@ -1,31 +0,0 @@
-import os
-import gradio as gr
-from run_cmd import run_cmd
-from PIL import Image
-import tempfile
-import uuid
-import numpy as np
-
-temp_path = tempfile.gettempdir()
-
-def inference(img, size, type):
-    if not img:
-        raise Exception("No image!")
-
-    OUTPUT_PATH = os.path.join(temp_path, f"{str(uuid.uuid4())[0:12]}_{size}.png")
-    
-    img.save(OUTPUT_PATH)
-
-    if type == "Manga":
-        run_cmd(f"python inference_manga_v2.py {OUTPUT_PATH}")
-    else:
-        run_cmd(f"python inference.py {OUTPUT_PATH} {type}")
-
-    img_out = Image.open(OUTPUT_PATH)
-
-    if size == "x2":
-        img_out = img_out.resize((img_out.width // 2, img_out.height // 2), resample=Image.BICUBIC)
-
-    img_out = np.array(img_out)
-
-    return img_out, gr.File.update(value=OUTPUT_PATH)

spaces/1acneusushi/gradio-2dmoleculeeditor/Skyrim-Simpackdll.md

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-## Skyrim Simpackdll
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- ![Skyrim Simpackdll](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR3PTHDbn7juqcw8p6GQesIZvxzdiwnWWuAEkPfULQhrCJKwHINNl6rc5c)
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-**LINK ===> [https://jinyurl.com/2tA0aO](https://jinyurl.com/2tA0aO)**
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-
-# How to Fix Skyrim Simpackdll Error
-
- 
-
-If you are trying to play Skyrim on your PC, you may encounter an error message that says "simpack.dll is missing" or "simpack.dll not found". This error means that your system does not have the simpack.dll file, which is a dynamic link library (DLL) file that is required by the Skyrim game. The simpack.dll file contains various functions and routines that are used by the game to perform simulations of mechanical systems, such as vehicle dynamics, suspension systems, and powertrain systems.
-
- 
-
-The simpack.dll error can be caused by various reasons, such as a corrupt or missing DLL file, conflicts with other software, or malware infections. In this article, we will show you how to fix the Skyrim simpackdll error by following these steps:
-
- 
-
-1. Reinstall the Skyrim game. The easiest way to fix the simpack.dll error is to reinstall the Skyrim game on your PC. This will ensure that you have all the necessary files and components for the game to run properly. To reinstall the game, you need to uninstall it first from your Control Panel or Settings app, and then install it again from your original source, such as a CD/DVD or a digital download.
-
-2. Download and restore the simpack.dll file. If reinstalling the game does not work, you can try to download and restore the simpack.dll file manually. You can get the file from a reliable source, such as [DLLme.com](https://www.dllme.com/dll/files/simpack), which offers free DLL downloads for various software applications. To download and restore the simpack.dll file, follow these steps:
-
-    - Go to [DLLme.com](https://www.dllme.com/dll/files/simpack) and search for "simpack.dll".
-
-    - Click on the "Download" button and save the file to your computer.
-
-    - Copy and paste the file to the folder where Skyrim is installed. The default location is C:\Program Files (x86)\Steam\steamapps\common\Skyrim.
-
-    - Restart your computer and launch Skyrim.
-
-3. Scan your PC for malware. Sometimes, the simpack.dll error can be caused by malware infections that may damage or delete the DLL file. To scan your PC for malware, you need to use a reputable antivirus or anti-malware software, such as [Malwarebytes](https://www.malwarebytes.com/), which can detect and remove various types of malware threats from your system. To scan your PC for malware, follow these steps:
-
-    - Download and install Malwarebytes from [Malwarebytes.com](https://www.malwarebytes.com/).
-
-    - Launch Malwarebytes and click on the "Scan" button.
-
-    - Wait for the scan to complete and review the results.
-
-    - If any malware is detected, click on the "Quarantine" button to remove them.
-
-    - Restart your computer and launch Skyrim.
-
-4. Update your drivers and Windows. Another possible cause of the simpack.dll error is outdated or incompatible drivers or Windows updates. Drivers are software components that allow your hardware devices to communicate with your operating system. Windows updates are software patches that fix bugs and improve security and performance of your system. To update your drivers and Windows, follow these steps:
-
-    - Go to Device Manager by pressing Windows + X keys and selecting Device Manager from the menu.
-
-    - Expand each category of devices and look for any yellow exclamation marks or red crosses.
-
-    - If you find any, right-click on them and select Update driver.
-
-    - Follow the on-screen instructions to install the latest drivers for your devices.
-
-    - Go to Settings by pressing Windows + I keys and selecting Settings from the menu.
-
-    - Click on Update & Security and then on Windows Update.
-
-    - Click on Check for updates and install any available updates for your system.
-
-<145887f19f
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cle De Licence Malwarebytes Anti Malware Gratuit Tlchargez Et Installez Le Logiciel En Quelques Minutes.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/DVDIdle Pro v5.9.8.3 (precracked) free download The ultimate tool for DVD playback enhancement.md

@@ -1,90 +0,0 @@
-<br />
-<h1>DVDIdle Pro v5.9.8.3 (precracked) free download</h1>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Spiderman Friend Or Foe Iso Pc [REPACK].md

@@ -1,28 +0,0 @@
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-<p>To solve the puzzles in 12 Locks II, you need to pay attention to everything in the rooms. Here are some tips and tricks that might help you:</p>
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-<li>Look for clues and hints in the environment. For example, you might find codes, symbols, colors, shapes, patterns, directions, numbers or words that can help you unlock the locks.</li>
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-<p>12 Locks II is a game that will keep you entertained and challenged for hours. Here are some reasons why you should download it:</p>
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-<p>Playing 12 Locks II can have many benefits for your brain and your mood. Some of them are:</p>
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-<p>12 Locks II is a game that has received positive reviews and ratings from players and critics alike. Some of them are:</p>
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-<tr><td>App Store</td><td>4.7/5 stars</td><td>"This game is amazing! It's so creative and original. The puzzles are clever and fun. The rooms are colorful and detailed. I recommend it!"</td></tr>
-<tr><td>New Scientist</td><td>8/10 points</td><td>"This game is a delight! It's a perfect blend of logic, creativity and humor. The plasticine graphics are charming and quirky. The puzzles are varied and engaging."</td></tr>
-<tr><td>The Sun</td><td>9/10 points</td><td>"This game is a blast! It's a great way to kill time and exercise your brain. The rooms are full of surprises and jokes. The puzzles are challenging but fair."</td></tr>
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-<p>If you are interested in playing 12 Locks II, you can download it easily from your preferred app store. Here are some steps on how to do it:</p>
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-<p>To play 12 Locks II, you need to have an Android or iOS device that meets the following requirements:</p>
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-<li>Open your app store (Google Play Store or App Store) on your device.</li>
-<li>Type "12 Locks II" in the search bar and tap on the game icon.</li>
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-<li>A: It depends on your skill and speed, but it can take anywhere from a few hours to a few days to finish the game.</li>
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-<p>Cricket League is a realistic and immersive cricket simulation game developed by Miniclip. It lets you play as your favorite teams and players in various modes and tournaments. You can customize your team, choose your batting and bowling style, and compete with other players online.</p>
-<li>What is Cricket League Full Mod APK?</li>
-<p>Cricket League Full Mod APK is a hacked version of Cricket League that gives you unlimited coins and gems, which are the in-game currencies. You can use these coins and gems to unlock all the players, modes, stadiums, and equipment in the game. You can also customize your players' appearance, skills, and attributes to suit your preferences.</p>
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spaces/1phancelerku/anime-remove-background/Download Nubank Fake APK for Android 2023 Explore the Features of the Famous App.md

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-<p>If you are looking for a way to manage your money with freedom, convenience, and security, you might have heard of Nubank, the largest digital bank in Latin America. But before you download the Nubank app, you need to be aware of the risks of fake apps that can harm your device and steal your information. In this article, we will explain what Nubank is, why it is popular, what a fake APK is, how to spot and avoid it, and how to download and install the genuine Nubank app safely.</p>
- <h2>What is Nubank and why is it popular?</h2>
-<p>Nubank is a digital bank that helps customers in Brazil, Mexico, and Colombia manage their money with freedom. Nubank is just one digital banking option in the Latin American world. It offers a variety of services for its customers, such as credit card applications, loans, life insurance, and business accounts. Nubank wants to improve the banking experience for customers across Latin America by using proprietary technology to create streamlined, uncomplicated, and completely digital banking options for its customers.</p>
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-<p>The benefits of using a Nubank personal or business account include:</p>
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-<p>The Nubank app also allows users to stay on top of payments, save money, and track spending from anywhere. Users can block their credit card, apply for a limit raise, or access rewards programs from their mobile device.</p>
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-<p>Nubank currently only operates in Brazil, Colombia, and Mexico. To use the Nubank app, you need to have a compatible device with Android 4.4 or higher or iOS 10 or higher. You also need to have an internet connection to access the app's features. To open an account with Nubank, you need to provide some personal information, such as your name, email address, phone number, date of birth, and tax identification number.</p>
- <h2>What is a fake APK and why is it dangerous?</h2>
-<p>An APK (Android Package Kit) is a file format that contains all the elements needed to install an app on an Android device. A fake APK is an app that imitates a legitimate one but instead carries out malicious activities. These activities include monitoring your activity, installing malware, showing annoying ads, or stealing your personal information.</p>
- <h3>How fake apps work and what they can do</h3>
-<p>Fake apps can be distributed in various ways. They can be hosted on third-party app stores or fake app stores. Cybercriminals can even use official app stores to distribute fake apps, despite the security measures in place. A cybercriminal can register themselves as a developer on any app store, download a legitimate app, and rewrite it using malicious code. Then, they can upload their fake app to the app store.</p>
-<p>Once you download a fake app on your device, it can perform various actions without your consent or knowledge. For example, it can:</p>
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-<li>Send premium SMS messages or make calls to charge you money</li>
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-<li>Redirect you to phishing websites or fake login pages</li>
-<li>Use your device as part of a botnet to launch cyberattacks</li>
-</ul>
-<p>These actions can compromise your device's performance, security, and privacy. You can lose money, data, or even your identity if you fall victim to a fake app.</p>
- <h3>How to spot and avoid fake apps</h3>
-<p>To protect yourself from fake apps, you need to be vigilant and careful when downloading apps. Here are some tips to help you spot and avoid fake apps:</p>
-<ul>
-<li>Check the app's name, developer, description, and reviews. Look for spelling errors, grammar mistakes, low ratings, or negative feedback.</li>
-<li>Compare the app with the official website of the service or company. Look for inconsistencies or discrepancies in the logo, design, or features.</li>
-<li>Avoid downloading apps from third-party app stores or unknown sources. Use only trusted and verified app stores, such as Google Play Store or Apple App Store.</li>
-<li>Check the app's permissions and settings. Avoid apps that ask for unnecessary or excessive permissions, such as access to your camera, microphone, location, or contacts.</li>
-<li>Use a reputable antivirus or security app on your device. Scan your device regularly and remove any suspicious or unwanted apps.</li>
-</ul>
- <h2>How to download and install the genuine Nubank app safely</h2>
-<p>If you want to enjoy the benefits of Nubank without risking your device or data, you need to download and install the genuine Nubank app safely. Here are the steps to do so:</p>
- <h3>How to find and verify the official Nubank app</h3>
-<p>The official Nubank app is available on Google Play Store for Android devices and Apple App Store for iOS devices. To find and verify the official Nubank app, you can:</p>
-<ul>
-<li>Search for "Nubank" on the app store. Make sure the app's name is spelled correctly and matches the logo of Nubank.</li>
-<li>Check the app's developer name. The official Nubank app is developed by "Nu Pagamentos S.A." for Android devices and "Nubank" for iOS devices.</li>
-<li>Check the app's rating, reviews, and downloads. The official Nubank app has a high rating (4.5 stars or above), positive reviews, and millions of downloads.</li>
-<li>Check the app's description and screenshots. The official Nubank app has a clear and detailed description of its features and benefits, as well as screenshots that show its interface and functionality.</li>
-</ul>
- <h3>How to install and set up the Nubank app on your device</h3>
-<p>Once you have found and verified the official Nubank app, you can install it on your device by following these steps:</p>
-<ol>
-<li>Tap on the "Install" button on the app store. Wait for the app to download and install on your device.</li>
-<li>Open the app and tap on "Create account". Enter your personal information, such as your name, email address, phone number, date of birth, and tax identification number.</li>
-<li>Verify your identity by taking a selfie and uploading a photo of your ID document.</li>
-<li>Wait for Nubank to approve your account. This may take a few minutes or hours depending on their verification process.</li>
-<li>Once your account is approved, you can access the app's features and services. You can also request a physical credit card that will be delivered to your address.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Nubank is a digital bank that offers a convenient, secure, and transparent way to manage your money with freedom. However, you need to be careful of fake apps that can imitate Nubank and harm your device or data. To avoid fake apps, you need to check the app's name, developer, description, reviews, permissions, and settings before downloading it. You also need to use only trusted and verified app stores to download apps. To download and install the genuine Nubank app safely, you need to find and verify the official Nubank app on Google Play Store or Apple App Store, then follow the steps to install and set up the app on your device.</p>
- <p>We hope this article has helped you understand what Nubank is, why it is popular, what a fake APK is, how to spot and avoid it, and how to download and install the genuine Nubank app safely. If you have any questions or feedback, please feel free to contact us. We would love to hear from you!</p>
- <p><b>Disclaimer:</b> This article is for informational purposes only and does not constitute financial or legal advice. Please consult a professional before making any decisions regarding your money or data.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Nubank and fake apps:</p>
-<ol>
-<li><b>Is Nubank safe and reliable?</b></li>
-<p>Yes, Nubank is safe and reliable. Nubank is regulated by the Central Bank of Brazil, the National Monetary Council, and the Securities and Exchange Commission of Brazil. Nubank also uses advanced encryption and security protocols to protect your data and transactions. Nubank has over 40 million customers and has won several awards for its innovation and customer satisfaction.</p>
-<li><b>How can I contact Nubank customer support?</b></li>
-<p>You can contact Nubank customer support through the app, phone, email, or chat. You can also visit their website or social media pages for more information. Nubank customer support is available 24/7 and speaks Portuguese, Spanish, and English.</p>
-<li><b>What are the advantages of using a digital bank over a traditional bank?</b></li>
-<p>Some of the advantages of using a digital bank over a traditional bank are:</p>
-<ul>
-<li>You can access your account and services anytime, anywhere, from your mobile device.</li>
-<li>You can save money on fees, commissions, and interest rates.</li>
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-<li><b>How can I update the Nubank app?</b></li>
-<p>You can update the Nubank app by following these steps:</p>
-<ol>
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-</ol>
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-<p>You can uninstall the Nubank app by following these steps:</p>
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-<li>Find and tap on "Nubank".</li>
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-<li>Confirm your action and wait for the app to be removed from your device.</li>
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spaces/1phancelerku/anime-remove-background/Download Onmyoji Arena APK for Android - Play Offline Strategy Game.md

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-<h1>Onmyoji Arena APK Offline: How to Play the Game Without Internet Connection</h1>
-<p>Do you love playing Onmyoji Arena, the popular mobile MOBA game based on Japanese folklore and mythology? Do you wish you could play it anytime and anywhere, even without an internet connection? If so, you are in luck. In this article, we will show you how to download and install Onmyoji Arena APK offline, and how to play the game without internet connection. Read on to find out more.</p>
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-<p>Onmyoji Arena is a mobile game developed by NetEase Games, based on the hit RPG game Onmyoji. It is a 5v5 MOBA game that features stunning 3D graphics, elegant Japanese aesthetics, and a stellar voice cast. The game has over 70 characters, called shikigami, that you can choose from, each with their own unique skills and abilities. You can team up with your friends or other players online, and compete in various modes, such as ranked matches, casual matches, or special events. You can also customize your shikigami with different skins, accessories, and emotes.</p>
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-<h3>The benefits of playing the game without internet connection</h3>
-<p>Playing Onmyoji Arena offline has some advantages over playing it online. For instance:</p>
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-<li>You will not be able to earn rewards or achievements that are based on online activities.</li>
-</ul>
- <h2>How to download and install Onmyoji Arena APK offline?</h2>
-<h3>The steps to get the game on your Android device</h3>
-<p>If you want to play Onmyoji Arena offline, you will need to download and install the APK file of the game on your Android device. Here are the steps to do so:</p>
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-<li>Go to a trusted website that offers Onmyoji Arena APK offline download, such as [APKCombo](^1^) or [Google Play Store](^2^).</li>
-<li>Choose the latest version of the game and click on the download button.</li>
-<li>Wait for the download to finish and locate the APK file on your device.</li>
-<li>Tap on the APK file and follow the instructions to install the game on your device.</li>
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-<p>Before you install Onmyoji Arena APK offline on your device, you should take some precautions to ensure your safety and security. Here are some tips to follow:</p>
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-<li>Make sure you download the APK file from a reliable and reputable source, and scan it for viruses or malware before installing it.</li>
-<li>Make sure you enable the option to install apps from unknown sources on your device settings, and disable it after the installation is done.</li>
-<li>Make sure you agree to the terms and conditions of the game before installing it.</li>
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-<h3>The modes and options available in the offline mode</h3>
-<p>Once you have installed Onmyoji Arena APK offline on your device, you can play the game without internet connection. However, you will only be able to access some modes and options in the offline mode. Here are some of them:</p>
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-<li>You can play the tutorial mode, where you can learn the basics of the game and practice with different shikigami.</li>
-<li>You can play the practice mode, where you can choose any shikigami and any map, and play against AI opponents or bots.</li>
-<li>You can play the custom mode, where you can create your own match settings, such as the number of players, the difficulty level, and the map.</li>
-<li>You can play the story mode, where you can follow the plot of the game and unlock new shikigami and skins.</li>
-<li>You can access the shikigami gallery, where you can view the details and stats of each shikigami, as well as their skins, accessories, and emotes.</li>
-</ul>
- <h3>The tips and tricks to enjoy the game offline</h3>
-<p>Playing Onmyoji Arena offline can be fun and rewarding, if you know how to make the most of it. Here are some tips and tricks to enjoy the game offline:</p>
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-<li>Try different shikigami and find out which ones suit your playstyle and preferences.</li>
-<li>Experiment with different builds and items for each shikigami, and see how they affect their performance.</li>
-<li>Learn the strengths and weaknesses of each shikigami, and how to counter them effectively.</li>
-<li>Master the mechanics and strategies of each map, such as the objectives, the lanes, the jungle, and the bosses.</li>
-<li>Challenge yourself by increasing the difficulty level or changing the match settings in the custom mode.</li>
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- <h2>Conclusion</h2>
-<p>Onmyoji Arena is a great game that you can play online or offline. If you want to play it offline, you will need to download and install Onmyoji Arena APK offline on your Android device. You will be able to access some modes and options in the offline mode, such as tutorial, practice, custom, and story. You will also be able to enjoy the game offline by trying different shikigami, builds, items, maps, and settings. However, you will not be able to access some features or modes that require internet connection, such as ranked matches, casual matches, or special events. You will also not be able to update your game or download new content that might be released by the developers. You will also not be able to interact with other players or join a guild. You will also not be able to earn rewards or achievements that are based on online activities. Therefore, playing Onmyoji Arena offline has its pros and cons, and you should decide which mode suits you better. If you are interested in playing Onmyoji Arena offline, you can follow the steps and tips we have provided in this article. We hope you have fun playing Onmyoji Arena offline!</p>
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-<h3>Q: Is Onmyoji Arena APK offline safe to download and install?</h3>
-<p>A: Yes, as long as you download it from a trusted website that offers Onmyoji Arena APK offline download, such as [APKCombo] or [Google Play Store]. You should also scan it for viruses or malware before installing it on your device.</p>
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-<p>A: No, Onmyoji Arena APK offline is only compatible with Android devices. If you want to play Onmyoji Arena on iOS devices, you will need an internet connection.</p>
- <h3>Q: Can I switch between online and offline mode in Onmyoji Arena?</h3>
-<p>A: Yes, you can switch between online and offline mode in Onmyoji Arena, as long as you have an internet connection. You can do so by tapping on the settings icon on the top right corner of the main screen, and then choosing the online or offline option. However, you should note that some of your data or progress might not be synced or saved when you switch modes.</p>
- <h3>Q: What are the best shikigami to play offline in Onmyoji Arena?</h3>
-<p>A: The best shikigami to play offline in Onmyoji Arena depends on your personal preference and playstyle. However, some of the shikigami that are generally considered to be good for offline mode are:</p>
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-<li>Yoto Hime: A powerful samurai who can deal massive damage and execute enemies with her ultimate skill.</li>
-<li>Yamakaze: A swift ninja who can dash and blink around the map, and assassinate enemies with his stealth and burst.</li>
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-<li>Ootengu: A versatile mage who can cast spells from a distance, and unleash a devastating storm with his ultimate skill.</li>
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- <h3>Q: How can I get more skins, accessories, and emotes for my shikigami in offline mode?</h3>
-<p>A: Unfortunately, you cannot get more skins, accessories, or emotes for your shikigami in offline mode. You will need to play online mode to earn rewards or purchase items that can unlock more customization options for your shikigami.</p> 197e85843d<br />
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spaces/20four60/Auto-GPT/README.md

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----
-title: Zenml Server
-emoji: 🧘
-colorFrom: purple
-colorTo: green
-sdk: docker
-pinned: false
-app_port: 8080
-license: wtfpl
-duplicated_from: zenml/zenml
----

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/eval/verification.py

@@ -1,407 +0,0 @@
-"""Helper for evaluation on the Labeled Faces in the Wild dataset 
-"""
-
-# MIT License
-#
-# Copyright (c) 2016 David Sandberg
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-
-import datetime
-import os
-import pickle
-
-import mxnet as mx
-import numpy as np
-import sklearn
-import torch
-from mxnet import ndarray as nd
-from scipy import interpolate
-from sklearn.decomposition import PCA
-from sklearn.model_selection import KFold
-
-
-class LFold:
-    def __init__(self, n_splits=2, shuffle=False):
-        self.n_splits = n_splits
-        if self.n_splits > 1:
-            self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle)
-
-    def split(self, indices):
-        if self.n_splits > 1:
-            return self.k_fold.split(indices)
-        else:
-            return [(indices, indices)]
-
-
-def calculate_roc(thresholds,
-                  embeddings1,
-                  embeddings2,
-                  actual_issame,
-                  nrof_folds=10,
-                  pca=0):
-    assert (embeddings1.shape[0] == embeddings2.shape[0])
-    assert (embeddings1.shape[1] == embeddings2.shape[1])
-    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
-    nrof_thresholds = len(thresholds)
-    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
-    tprs = np.zeros((nrof_folds, nrof_thresholds))
-    fprs = np.zeros((nrof_folds, nrof_thresholds))
-    accuracy = np.zeros((nrof_folds))
-    indices = np.arange(nrof_pairs)
-
-    if pca == 0:
-        diff = np.subtract(embeddings1, embeddings2)
-        dist = np.sum(np.square(diff), 1)
-
-    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
-        if pca > 0:
-            print('doing pca on', fold_idx)
-            embed1_train = embeddings1[train_set]
-            embed2_train = embeddings2[train_set]
-            _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
-            pca_model = PCA(n_components=pca)
-            pca_model.fit(_embed_train)
-            embed1 = pca_model.transform(embeddings1)
-            embed2 = pca_model.transform(embeddings2)
-            embed1 = sklearn.preprocessing.normalize(embed1)
-            embed2 = sklearn.preprocessing.normalize(embed2)
-            diff = np.subtract(embed1, embed2)
-            dist = np.sum(np.square(diff), 1)
-
-        # Find the best threshold for the fold
-        acc_train = np.zeros((nrof_thresholds))
-        for threshold_idx, threshold in enumerate(thresholds):
-            _, _, acc_train[threshold_idx] = calculate_accuracy(
-                threshold, dist[train_set], actual_issame[train_set])
-        best_threshold_index = np.argmax(acc_train)
-        for threshold_idx, threshold in enumerate(thresholds):
-            tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(
-                threshold, dist[test_set],
-                actual_issame[test_set])
-        _, _, accuracy[fold_idx] = calculate_accuracy(
-            thresholds[best_threshold_index], dist[test_set],
-            actual_issame[test_set])
-
-    tpr = np.mean(tprs, 0)
-    fpr = np.mean(fprs, 0)
-    return tpr, fpr, accuracy
-
-
-def calculate_accuracy(threshold, dist, actual_issame):
-    predict_issame = np.less(dist, threshold)
-    tp = np.sum(np.logical_and(predict_issame, actual_issame))
-    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
-    tn = np.sum(
-        np.logical_and(np.logical_not(predict_issame),
-                       np.logical_not(actual_issame)))
-    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
-
-    tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
-    fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
-    acc = float(tp + tn) / dist.size
-    return tpr, fpr, acc
-
-
-def calculate_val(thresholds,
-                  embeddings1,
-                  embeddings2,
-                  actual_issame,
-                  far_target,
-                  nrof_folds=10):
-    assert (embeddings1.shape[0] == embeddings2.shape[0])
-    assert (embeddings1.shape[1] == embeddings2.shape[1])
-    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
-    nrof_thresholds = len(thresholds)
-    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
-    val = np.zeros(nrof_folds)
-    far = np.zeros(nrof_folds)
-
-    diff = np.subtract(embeddings1, embeddings2)
-    dist = np.sum(np.square(diff), 1)
-    indices = np.arange(nrof_pairs)
-
-    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
-
-        # Find the threshold that gives FAR = far_target
-        far_train = np.zeros(nrof_thresholds)
-        for threshold_idx, threshold in enumerate(thresholds):
-            _, far_train[threshold_idx] = calculate_val_far(
-                threshold, dist[train_set], actual_issame[train_set])
-        if np.max(far_train) >= far_target:
-            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
-            threshold = f(far_target)
-        else:
-            threshold = 0.0
-
-        val[fold_idx], far[fold_idx] = calculate_val_far(
-            threshold, dist[test_set], actual_issame[test_set])
-
-    val_mean = np.mean(val)
-    far_mean = np.mean(far)
-    val_std = np.std(val)
-    return val_mean, val_std, far_mean
-
-
-def calculate_val_far(threshold, dist, actual_issame):
-    predict_issame = np.less(dist, threshold)
-    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
-    false_accept = np.sum(
-        np.logical_and(predict_issame, np.logical_not(actual_issame)))
-    n_same = np.sum(actual_issame)
-    n_diff = np.sum(np.logical_not(actual_issame))
-    # print(true_accept, false_accept)
-    # print(n_same, n_diff)
-    val = float(true_accept) / float(n_same)
-    far = float(false_accept) / float(n_diff)
-    return val, far
-
-
-def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
-    # Calculate evaluation metrics
-    thresholds = np.arange(0, 4, 0.01)
-    embeddings1 = embeddings[0::2]
-    embeddings2 = embeddings[1::2]
-    tpr, fpr, accuracy = calculate_roc(thresholds,
-                                       embeddings1,
-                                       embeddings2,
-                                       np.asarray(actual_issame),
-                                       nrof_folds=nrof_folds,
-                                       pca=pca)
-    thresholds = np.arange(0, 4, 0.001)
-    val, val_std, far = calculate_val(thresholds,
-                                      embeddings1,
-                                      embeddings2,
-                                      np.asarray(actual_issame),
-                                      1e-3,
-                                      nrof_folds=nrof_folds)
-    return tpr, fpr, accuracy, val, val_std, far
-
-@torch.no_grad()
-def load_bin(path, image_size):
-    try:
-        with open(path, 'rb') as f:
-            bins, issame_list = pickle.load(f)  # py2
-    except UnicodeDecodeError as e:
-        with open(path, 'rb') as f:
-            bins, issame_list = pickle.load(f, encoding='bytes')  # py3
-    data_list = []
-    for flip in [0, 1]:
-        data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1]))
-        data_list.append(data)
-    for idx in range(len(issame_list) * 2):
-        _bin = bins[idx]
-        img = mx.image.imdecode(_bin)
-        if img.shape[1] != image_size[0]:
-            img = mx.image.resize_short(img, image_size[0])
-        img = nd.transpose(img, axes=(2, 0, 1))
-        for flip in [0, 1]:
-            if flip == 1:
-                img = mx.ndarray.flip(data=img, axis=2)
-            data_list[flip][idx][:] = torch.from_numpy(img.asnumpy())
-        if idx % 1000 == 0:
-            print('loading bin', idx)
-    print(data_list[0].shape)
-    return data_list, issame_list
-
-@torch.no_grad()
-def test(data_set, backbone, batch_size, nfolds=10):
-    print('testing verification..')
-    data_list = data_set[0]
-    issame_list = data_set[1]
-    embeddings_list = []
-    time_consumed = 0.0
-    for i in range(len(data_list)):
-        data = data_list[i]
-        embeddings = None
-        ba = 0
-        while ba < data.shape[0]:
-            bb = min(ba + batch_size, data.shape[0])
-            count = bb - ba
-            _data = data[bb - batch_size: bb]
-            time0 = datetime.datetime.now()
-            img = ((_data / 255) - 0.5) / 0.5
-            net_out: torch.Tensor = backbone(img)
-            _embeddings = net_out.detach().cpu().numpy()
-            time_now = datetime.datetime.now()
-            diff = time_now - time0
-            time_consumed += diff.total_seconds()
-            if embeddings is None:
-                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
-            embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
-            ba = bb
-        embeddings_list.append(embeddings)
-
-    _xnorm = 0.0
-    _xnorm_cnt = 0
-    for embed in embeddings_list:
-        for i in range(embed.shape[0]):
-            _em = embed[i]
-            _norm = np.linalg.norm(_em)
-            _xnorm += _norm
-            _xnorm_cnt += 1
-    _xnorm /= _xnorm_cnt
-
-    acc1 = 0.0
-    std1 = 0.0
-    embeddings = embeddings_list[0] + embeddings_list[1]
-    embeddings = sklearn.preprocessing.normalize(embeddings)
-    print(embeddings.shape)
-    print('infer time', time_consumed)
-    _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds)
-    acc2, std2 = np.mean(accuracy), np.std(accuracy)
-    return acc1, std1, acc2, std2, _xnorm, embeddings_list
-
-
-def dumpR(data_set,
-          backbone,
-          batch_size,
-          name='',
-          data_extra=None,
-          label_shape=None):
-    print('dump verification embedding..')
-    data_list = data_set[0]
-    issame_list = data_set[1]
-    embeddings_list = []
-    time_consumed = 0.0
-    for i in range(len(data_list)):
-        data = data_list[i]
-        embeddings = None
-        ba = 0
-        while ba < data.shape[0]:
-            bb = min(ba + batch_size, data.shape[0])
-            count = bb - ba
-
-            _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb)
-            time0 = datetime.datetime.now()
-            if data_extra is None:
-                db = mx.io.DataBatch(data=(_data,), label=(_label,))
-            else:
-                db = mx.io.DataBatch(data=(_data, _data_extra),
-                                     label=(_label,))
-            model.forward(db, is_train=False)
-            net_out = model.get_outputs()
-            _embeddings = net_out[0].asnumpy()
-            time_now = datetime.datetime.now()
-            diff = time_now - time0
-            time_consumed += diff.total_seconds()
-            if embeddings is None:
-                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
-            embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
-            ba = bb
-        embeddings_list.append(embeddings)
-    embeddings = embeddings_list[0] + embeddings_list[1]
-    embeddings = sklearn.preprocessing.normalize(embeddings)
-    actual_issame = np.asarray(issame_list)
-    outname = os.path.join('temp.bin')
-    with open(outname, 'wb') as f:
-        pickle.dump((embeddings, issame_list),
-                    f,
-                    protocol=pickle.HIGHEST_PROTOCOL)
-
-
-# if __name__ == '__main__':
-#
-#     parser = argparse.ArgumentParser(description='do verification')
-#     # general
-#     parser.add_argument('--data-dir', default='', help='')
-#     parser.add_argument('--model',
-#                         default='../model/softmax,50',
-#                         help='path to load model.')
-#     parser.add_argument('--target',
-#                         default='lfw,cfp_ff,cfp_fp,agedb_30',
-#                         help='test targets.')
-#     parser.add_argument('--gpu', default=0, type=int, help='gpu id')
-#     parser.add_argument('--batch-size', default=32, type=int, help='')
-#     parser.add_argument('--max', default='', type=str, help='')
-#     parser.add_argument('--mode', default=0, type=int, help='')
-#     parser.add_argument('--nfolds', default=10, type=int, help='')
-#     args = parser.parse_args()
-#     image_size = [112, 112]
-#     print('image_size', image_size)
-#     ctx = mx.gpu(args.gpu)
-#     nets = []
-#     vec = args.model.split(',')
-#     prefix = args.model.split(',')[0]
-#     epochs = []
-#     if len(vec) == 1:
-#         pdir = os.path.dirname(prefix)
-#         for fname in os.listdir(pdir):
-#             if not fname.endswith('.params'):
-#                 continue
-#             _file = os.path.join(pdir, fname)
-#             if _file.startswith(prefix):
-#                 epoch = int(fname.split('.')[0].split('-')[1])
-#                 epochs.append(epoch)
-#         epochs = sorted(epochs, reverse=True)
-#         if len(args.max) > 0:
-#             _max = [int(x) for x in args.max.split(',')]
-#             assert len(_max) == 2
-#             if len(epochs) > _max[1]:
-#                 epochs = epochs[_max[0]:_max[1]]
-#
-#     else:
-#         epochs = [int(x) for x in vec[1].split('|')]
-#     print('model number', len(epochs))
-#     time0 = datetime.datetime.now()
-#     for epoch in epochs:
-#         print('loading', prefix, epoch)
-#         sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
-#         # arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
-#         all_layers = sym.get_internals()
-#         sym = all_layers['fc1_output']
-#         model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
-#         # model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
-#         model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0],
-#                                           image_size[1]))])
-#         model.set_params(arg_params, aux_params)
-#         nets.append(model)
-#     time_now = datetime.datetime.now()
-#     diff = time_now - time0
-#     print('model loading time', diff.total_seconds())
-#
-#     ver_list = []
-#     ver_name_list = []
-#     for name in args.target.split(','):
-#         path = os.path.join(args.data_dir, name + ".bin")
-#         if os.path.exists(path):
-#             print('loading.. ', name)
-#             data_set = load_bin(path, image_size)
-#             ver_list.append(data_set)
-#             ver_name_list.append(name)
-#
-#     if args.mode == 0:
-#         for i in range(len(ver_list)):
-#             results = []
-#             for model in nets:
-#                 acc1, std1, acc2, std2, xnorm, embeddings_list = test(
-#                     ver_list[i], model, args.batch_size, args.nfolds)
-#                 print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
-#                 print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
-#                 print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
-#                 results.append(acc2)
-#             print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))
-#     elif args.mode == 1:
-#         raise ValueError
-#     else:
-#         model = nets[0]
-#         dumpR(ver_list[0], model, args.batch_size, args.target)

spaces/AI-Hobbyist/Hoyo-RVC/Changelog_KO.md

@@ -1,91 +0,0 @@
-### 2023년 6월 18일 업데이트
-
-- v2 버전에서 새로운 32k와 48k 사전 학습 모델을 추가.
-- non-f0 모델들의 추론 오류 수정.
-- 학습 세트가 1시간을 넘어가는 경우, 인덱스 생성 단계에서 minibatch-kmeans을 사용해, 학습속도 가속화.
-- [huggingface](https://huggingface.co/spaces/lj1995/vocal2guitar)에서 vocal2guitar 제공.
-- 데이터 처리 단계에서 이상 값 자동으로 제거.
-- ONNX로 내보내는(export) 옵션 탭 추가.
-
-업데이트에 적용되지 않았지만 시도한 것들 :
-
-- 시계열 차원을 추가하여 특징 검색을 진행했지만, 유의미한 효과는 없었습니다.
-- PCA 차원 축소를 추가하여 특징 검색을 진행했지만, 유의미한 효과는 없었습니다.
-- ONNX 추론을 지원하는 것에 실패했습니다. nsf 생성시, Pytorch가 필요하기 때문입니다.
-- 훈련 중에 입력에 대한 음고, 성별, 이퀄라이저, 노이즈 등 무작위로 강화하는 것에, 유의미한 효과는 없었습니다.
-
-추후 업데이트 목록:
-
-- Vocos-RVC (소형 보코더) 통합 예정.
-- 학습 단계에 음고 인식을 위한 Crepe 지원 예정.
-- Crepe의 정밀도를 REC-config와 동기화하여 지원 예정.
-- FO 에디터 지원 예정.
-
-### 2023년 5월 28일 업데이트
-
-- v2 jupyter notebook 추가, 한국어 업데이트 로그 추가, 의존성 모듈 일부 수정.
-- 무성음 및 숨소리 보호 모드 추가.
-- crepe-full pitch 감지 지원.
-- UVR5 보컬 분리: 디버브 및 디-에코 모델 지원.
-- index 이름에 experiment 이름과 버전 추가.
-- 배치 음성 변환 처리 및 UVR5 보컬 분리 시, 사용자가 수동으로 출력 오디오의 내보내기(export) 형식을 선택할 수 있도록 지원.
-- 32k 훈련 모델 지원 종료.
-
-### 2023년 5월 13일 업데이트
-
-- 원클릭 패키지의 이전 버전 런타임 내, 불필요한 코드(infer_pack 및 uvr5_pack) 제거.
-- 훈련 세트 전처리의 유사 다중 처리 버그 수정.
-- Harvest 피치 인식 알고리즘에 대한 중위수 필터링 반경 조정 추가.
-- 오디오 내보낼 때, 후처리 리샘플링 지원.
-- 훈련에 대한 다중 처리 "n_cpu" 설정이 "f0 추출"에서 "데이터 전처리 및 f0 추출"로 변경.
-- logs 폴더 하의 인덱스 경로를 자동으로 감지 및 드롭다운 목록 기능 제공.
-- 탭 페이지에 "자주 묻는 질문과 답변" 추가. (github RVC wiki 참조 가능)
-- 동일한 입력 오디오 경로를 사용할 때 추론, Harvest 피치를 캐시.
-  (주의: Harvest 피치 추출을 사용하면 전체 파이프라인은 길고 반복적인 피치 추출 과정을 거치게됩니다. 캐싱을 하지 않는다면, 첫 inference 이후의 단계에서 timbre, 인덱스, 피치 중위수 필터링 반경 설정 등 대기시간이 엄청나게 길어집니다!)
-
-### 2023년 5월 14일 업데이트
-
-- 입력의 볼륨 캡슐을 사용하여 출력의 볼륨 캡슐을 혼합하거나 대체. (입력이 무음이거나 출력의 노이즈 문제를 최소화 할 수 있습니다. 입력 오디오의 배경 노이즈(소음)가 큰 경우 해당 기능을 사용하지 않는 것이 좋습니다. 기본적으로 비활성화 되어있는 옵션입니다. (1: 비활성화 상태))
-- 추출된 소형 모델을 지정된 빈도로 저장하는 기능을 지원. (다양한 에폭 하에서의 성능을 보려고 하지만 모든 대형 체크포인트를 저장하고 매번 ckpt 처리를 통해 소형 모델을 수동으로 추출하고 싶지 않은 경우 이 기능은 매우 유용합니다)
-- 환경 변수를 설정하여 서버의 전역 프록시로 인한 "연결 오류" 문제 해결.
-- 사전 훈련된 v2 모델 지원. (현재 40k 버전만 테스트를 위해 공개적으로 사용 가능하며, 다른 두 개의 샘플링 비율은 아직 완전히 훈련되지 않아 보류되었습니다.)
-- 추론 전, 1을 초과하는 과도한 볼륨 제한.
-- 데이터 전처리 매개변수 미세 조정.
-
-### 2023년 4월 9일 업데이트
-
-- GPU 이용률 향상을 위해 훈련 파라미터 수정: A100은 25%에서 약 90%로 증가, V100: 50%에서 약 90%로 증가, 2060S: 60%에서 약 85%로 증가, P40: 25%에서 약 95%로 증가.
-  훈련 속도가 크게 향상.
-- 매개변수 기준 변경: total batch_size는 GPU당 batch_size를 의미.
-- total_epoch 변경: 최대 한도가 100에서 1000으로 증가. 기본값이 10에서 20으로 증가.
-- ckpt 추출이 피치를 잘못 인식하여 비정상적인 추론을 유발하는 문제 수정.
-- 분산 훈련 과정에서 각 랭크마다 ckpt를 저장하는 문제 수정.
-- 특성 추출 과정에 나노 특성 필터링 적용.
-- 무음 입력/출력이 랜덤하게 소음을 생성하는 문제 수정. (이전 모델은 새 데이터셋으로 다시 훈련해야 합니다)
-
-### 2023년 4월 16일 업데이트
-
-- 로컬 실시간 음성 변경 미니-GUI 추가, go-realtime-gui.bat를 더블 클릭하��� 시작.
-- 훈련 및 추론 중 50Hz 이하의 주파수 대역에 대해 필터링 적용.
-- 훈련 및 추론의 pyworld 최소 피치 추출을 기본 80에서 50으로 낮춤. 이로 인해, 50-80Hz 사이의 남성 저음이 무음화되지 않습니다.
-- 시스템 지역에 따른 WebUI 언어 변경 지원. (현재 en_US, ja_JP, zh_CN, zh_HK, zh_SG, zh_TW를 지원하며, 지원되지 않는 경우 기본값은 en_US)
-- 일부 GPU의 인식 수정. (예: V100-16G 인식 실패, P4 인식 실패)
-
-### 2023년 4월 28일 업데이트
-
-- Faiss 인덱스 설정 업그레이드로 속도가 더 빨라지고 품질이 향상.
-- total_npy에 대한 의존성 제거. 추후의 모델 공유는 total_npy 입력을 필요로 하지 않습니다.
-- 16 시리즈 GPU에 대한 제한 해제, 4GB VRAM GPU에 대한 4GB 추론 설정 제공.
-- 일부 오디오 형식에 대한 UVR5 보컬 동반 분리에서의 버그 수정.
-- 실시간 음성 변경 미니-GUI는 이제 non-40k 및 non-lazy 피치 모델을 지원합니다.
-
-### 추후 계획
-
-Features:
-
-- 다중 사용자 훈련 탭 지원.(최대 4명)
-
-Base model:
-
-- 훈련 데이터셋에 숨소리 wav 파일을 추가하여, 보컬의 호흡이 노이즈로 변환되는 문제 수정.
-- 보컬 훈련 세트의 기본 모델을 추가하기 위한 작업을 진행중이며, 이는 향후에 발표될 예정.

spaces/AIConsultant/MusicGen/audiocraft/grids/__init__.py

@@ -1,6 +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.
-"""Dora Grids."""

spaces/AIConsultant/MusicGen/docs/AUDIOGEN.md

@@ -1,158 +0,0 @@
-# AudioGen: Textually-guided audio generation
-
-AudioCraft provides the code and a model re-implementing AudioGen, a [textually-guided audio generation][audiogen_arxiv]
-model that performs text-to-sound generation.
-
-The provided AudioGen reimplementation follows the LM model architecture introduced in [MusicGen][musicgen_arxiv]
-and is a single stage auto-regressive Transformer model trained over a 16kHz
-<a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz.
-This model variant reaches similar audio quality than the original implementation introduced in the AudioGen publication
-while providing faster generation speed given the smaller frame rate.
-
-**Important note:** The provided models are NOT the original models used to report numbers in the
-[AudioGen publication][audiogen_arxiv]. Refer to the model card to learn more about architectural changes.
-
-Listen to samples from the **original AudioGen implementation** in our [sample page][audiogen_samples].
-
-
-## Model Card
-
-See [the model card](../model_cards/AUDIOGEN_MODEL_CARD.md).
-
-
-## Installation
-
-Please follow the AudioCraft installation instructions from the [README](../README.md).
-
-AudioCraft requires a GPU with at least 16 GB of memory for running inference with the medium-sized models (~1.5B parameters).
-
-## API and usage
-
-We provide a simple API and 1 pre-trained models for AudioGen:
-
-`facebook/audiogen-medium`: 1.5B model, text to sound - [🤗 Hub](https://huggingface.co/facebook/audiogen-medium)
-
-You can play with AudioGen by running the jupyter notebook at [`demos/audiogen_demo.ipynb`](../demos/audiogen_demo.ipynb) locally (if you have a GPU).
-
-See after a quick example for using the API.
-
-```python
-import torchaudio
-from audiocraft.models import AudioGen
-from audiocraft.data.audio import audio_write
-
-model = AudioGen.get_pretrained('facebook/audiogen-medium')
-model.set_generation_params(duration=5)  # generate 5 seconds.
-descriptions = ['dog barking', 'sirene of an emergency vehicle', 'footsteps in a corridor']
-wav = model.generate(descriptions)  # generates 3 samples.
-
-for idx, one_wav in enumerate(wav):
-    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
-    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-## Training
-
-The [AudioGenSolver](../audiocraft/solvers/audiogen.py) implements the AudioGen's training pipeline
-used to develop the released model. Note that this may not fully reproduce the results presented in the paper.
-Similarly to MusicGen, it defines an autoregressive language modeling task over multiple streams of
-discrete tokens extracted from a pre-trained EnCodec model (see [EnCodec documentation](./ENCODEC.md)
-for more details on how to train such model) with dataset-specific changes for environmental sound
-processing.
-
-Note that **we do NOT provide any of the datasets** used for training AudioGen.
-
-### Example configurations and grids
-
-We provide configurations to reproduce the released models and our research.
-AudioGen solvers configuration are available in [config/solver/audiogen](../config/solver/audiogen).
-The base training configuration used for the released models is the following:
-[`solver=audiogen/audiogen_base_16khz`](../config/solver/audiogen/audiogen_base_16khz.yaml)
-
-Please find some example grids to train AudioGen at
-[audiocraft/grids/audiogen](../audiocraft/grids/audiogen/).
-
-```shell
-# text-to-sound
-dora grid audiogen.audiogen_base_16khz
-```
-
-### Sound dataset and metadata
-
-AudioGen's underlying dataset is an AudioDataset augmented with description metadata.
-The AudioGen dataset implementation expects the metadata to be available as `.json` files
-at the same location as the audio files or through specified external folder.
-Learn more in the [datasets section](./DATASETS.md).
-
-### Evaluation stage
-
-By default, evaluation stage is also computing the cross-entropy and the perplexity over the
-evaluation dataset. Indeed the objective metrics used for evaluation can be costly to run
-or require some extra dependencies. Please refer to the [metrics documentation](./METRICS.md)
-for more details on the requirements for each metric.
-
-We provide an off-the-shelf configuration to enable running the objective metrics
-for audio generation in
-[config/solver/audiogen/evaluation/objective_eval](../config/solver/audiogen/evaluation/objective_eval.yaml).
-
-One can then activate evaluation the following way:
-```shell
-# using the configuration
-dora run solver=audiogen/debug solver/audiogen/evaluation=objective_eval
-# specifying each of the fields, e.g. to activate KL computation
-dora run solver=audiogen/debug evaluate.metrics.kld=true
-```
-
-See [an example evaluation grid](../audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py).
-
-### Generation stage
-
-The generation stage allows to generate samples conditionally and/or unconditionally and to perform
-audio continuation (from a prompt). We currently support greedy sampling (argmax), sampling
-from softmax with a given temperature, top-K and top-P (nucleus) sampling. The number of samples
-generated and the batch size used are controlled by the `dataset.generate` configuration
-while the other generation parameters are defined in `generate.lm`.
-
-```shell
-# control sampling parameters
-dora run solver=audiogen/debug generate.lm.gen_duration=5 generate.lm.use_sampling=true generate.lm.top_k=15
-```
-
-## More information
-
-Refer to [MusicGen's instructions](./MUSICGEN.md).
-
-### Learn more
-
-Learn more about AudioCraft training pipelines in the [dedicated section](./TRAINING.md).
-
-
-## Citation
-
-AudioGen
-```
-@article{kreuk2022audiogen,
-    title={Audiogen: Textually guided audio generation},
-    author={Kreuk, Felix and Synnaeve, Gabriel and Polyak, Adam and Singer, Uriel and D{\'e}fossez, Alexandre and Copet, Jade and Parikh, Devi and Taigman, Yaniv and Adi, Yossi},
-    journal={arXiv preprint arXiv:2209.15352},
-    year={2022}
-}
-```
-
-MusicGen
-```
-@article{copet2023simple,
-    title={Simple and Controllable Music Generation},
-    author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
-    year={2023},
-    journal={arXiv preprint arXiv:2306.05284},
-}
-```
-
-## License
-
-See license information in the [model card](../model_cards/AUDIOGEN_MODEL_CARD.md).
-
-[audiogen_arxiv]: https://arxiv.org/abs/2209.15352
-[musicgen_arxiv]: https://arxiv.org/abs/2306.05284
-[audiogen_samples]: https://felixkreuk.github.io/audiogen/

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/diffusion/ddpm_audio_inpaint.py

@@ -1,1081 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-import os
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ddpm import DDPM, disabled_train
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-# add mel_dim and mel_length params to ensure correct shape
-class LatentDiffusion_audioinpaint(DDPM):
-    """main class"""
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 mel_dim=80,
-                 mel_length=848,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 test_repeat=1,
-                 test_numsteps = None,
-                 *args, **kwargs):
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__':
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.test_repeat = test_repeat
-        if test_numsteps == None:
-            self.test_numsteps = self.num_timesteps
-        self.concat_mode = concat_mode
-        self.mel_dim = mel_dim
-        self.mel_length = mel_length
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None  
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":# for no_text inpainting task
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":# for unconditional image generation such as human face、ImageNet
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                            force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):# encode_emb from autoencoder
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None:# 'crossattn' for txt2image, 'hybird' for txt_inpaint
-            if cond_key is None:
-                cond_key = self.cond_stage_key # 'caption' for txt_inpaint
-            if self.model.conditioning_key == 'hybrid':
-                xc = {}
-                assert cond_key == 'caption' # only txt_inpaint is implemented now
-                assert 'masked_image' in batch.keys() 
-                assert 'mask' in batch.keys()
-                masked_image = super().get_input(batch,'masked_image')
-                mask = super().get_input(batch,'mask')
-                if bs is not None:
-                    masked_image,mask = masked_image[:bs],mask[:bs]
-                masked_image,mask = masked_image.to(self.device),mask.to(self.device)
-                masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach()
-                resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:])
-                xc['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1)
-                xc[cond_key] = batch[cond_key]
-            else:
-                if cond_key != self.first_stage_key:
-                    if cond_key in ['caption', 'coordinates_bbox']:
-                        xc = batch[cond_key]
-                    elif cond_key == 'class_label':
-                        xc = batch
-                    else:
-                        xc = super().get_input(batch, cond_key).to(self.device)
-                else:# cond_key == 'image'
-                    xc = x
-            if not self.cond_stage_trainable or force_c_encode:# cond_stage_trainable is true for txt2img，force_c_encoder = True，when called in log_images
-                if isinstance(xc, list):
-                    # import pudb; pudb.set_trace()
-                    c = self.get_learned_conditioning(xc)# 因为log_images内接下来要调用sample_log,所以需要预先得到处理好的c
-                if isinstance(xc, dict):
-                    c = {}
-                    c['c_concat'] = xc['c_concat']
-                    c['c_crossattn'] = self.get_learned_conditioning(xc[cond_key])
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                if isinstance(c,dict):
-                    for k in c.keys():
-                        c[k] = c[k][:bs]
-                else:
-                    c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    # same as above but without decorator
-    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                df = self.split_input_params["vqf"]
-                self.split_input_params['original_image_size'] = x.shape[-2:]
-                bs, nc, h, w = x.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
-                z = unfold(x)  # (bn, nc * prod(**ks), L)
-                # Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
-                               for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)
-                o = o * weighting
-
-                # Reverse reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization
-                return decoded
-
-            else:
-                return self.first_stage_model.encode(x)
-        else:
-            return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)# get latent and condition
-        loss = self(x, c)
-        return loss
-
-    def test_step(self,batch,batch_idx):
-        # TODO make self.test_repeat work
-        cond = {}
-        cond[self.cond_stage_key] = batch[self.cond_stage_key]
-        cond[self.cond_stage_key] = self.get_learned_conditioning(cond[self.cond_stage_key]) # c: string -> [B, T, Context_dim]
-        cond['c_crossattn'] = cond.pop(self.cond_stage_key)
-        masked_image = super().get_input(batch,'masked_image')
-        mask = super().get_input(batch,'mask')
-        masked_image,mask = masked_image.to(self.device),mask.to(self.device)
-        masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach()
-        resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:])
-        cond['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1)
-        batch_size = len(batch[self.cond_stage_key])
-        # shape = [batch_size,self.channels,self.mel_dim,self.mel_length]
-        enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps)
-        xrec = self.decode_first_stage(enc_emb)
-        reconstructions = (xrec + 1)/2 # to mel scale
-        test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
-        savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
-        if not os.path.exists(savedir):
-            os.makedirs(savedir)
-
-        file_names = batch['f_name']
-        nfiles = len(file_names)
-        reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim
-        for k in range(reconstructions.shape[0]):
-            b,repeat = k % nfiles, k // nfiles
-            vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
-            v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
-            save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition
-            np.save(save_img_path,reconstructions[b])
-        
-        return None
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                if isinstance(c,dict):
-                    c[self.cond_stage_key] = self.get_learned_conditioning(c[self.cond_stage_key])
-                    c['c_crossattn'] = c.pop(self.cond_stage_key)
-                else:
-                    c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim]
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = torch.clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = torch.clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-        # make values to list to enable concat operation in 
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict. (txt2inpaint)
-            cond_tmp = {}# use cond_tmp to avoid inplace edit
-            for k,v in cond.items():
-                if not isinstance(v, list):
-                    cond_tmp[k] = [cond[k]]
-                else:
-                    cond_tmp[k] = cond[k]
-            cond = cond_tmp
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        if hasattr(self, "split_input_params"):
-            assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
-            ks = self.split_input_params["ks"]  # eg. (128, 128)
-            stride = self.split_input_params["stride"]  # eg. (64, 64)
-
-            h, w = x_noisy.shape[-2:]
-
-            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
-            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
-            # Reshape to img shape
-            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
-            if self.cond_stage_key in ["image", "LR_image", "segmentation",
-                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
-                c_key = next(iter(cond.keys()))  # get key
-                c = next(iter(cond.values()))  # get value
-                assert (len(c) == 1)  # todo extend to list with more than one elem
-                c = c[0]  # get element
-
-                c = unfold(c)
-                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
-            elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
-                # assuming padding of unfold is always 0 and its dilation is always 1
-                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
-                full_img_h, full_img_w = self.split_input_params['original_image_size']
-                # as we are operating on latents, we need the factor from the original image size to the
-                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
-                num_downs = self.first_stage_model.encoder.num_resolutions - 1
-                rescale_latent = 2 ** (num_downs)
-
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
-                # need to rescale the tl patch coordinates to be in between (0,1)
-                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
-                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
-                                        for patch_nr in range(z.shape[-1])]
-
-                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
-                patch_limits = [(x_tl, y_tl,
-                                 rescale_latent * ks[0] / full_img_w,
-                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
-                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
-                # tokenize crop coordinates for the bounding boxes of the respective patches
-                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
-                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
-                print(patch_limits_tknzd[0].shape)
-                # cut tknzd crop position from conditioning
-                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
-                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
-                print(cut_cond.shape)
-
-                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
-                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
-                print(adapted_cond.shape)
-                adapted_cond = self.get_learned_conditioning(adapted_cond)
-                print(adapted_cond.shape)
-                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
-                print(adapted_cond.shape)
-
-                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
-            else:
-                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
-
-            # apply model by loop over crops
-            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
-            assert not isinstance(output_list[0],
-                                  tuple)  # todo cant deal with multiple model outputs check this never happens
-
-            o = torch.stack(output_list, axis=-1)
-            o = o * weighting
-            # Reverse reshape to img shape
-            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-            # stitch crops together
-            x_recon = fold(o) / normalization
-
-        else:
-            # x_noisy is tensor with shape [b,c,mel_len,T]
-            # if condition is caption ,cond['c_crossattn'] is a list, each item shape is [1, 77, 1280]
-            x_recon = self.model(x_noisy, t, **cond)# tensor with shape [b,c,mel_len,T]
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None,**kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.mel_dim, self.mel_length)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.mel_dim, self.mel_length)
-            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
-                                                        shape,cond,verbose=False,**kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True,**kwargs)
-
-        return samples, intermediates
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, **kwargs):
-
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x # 原始输入图像
-        log["reconstruction"] = xrec # 重建得到的图像
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):# when cond_stage is first_stage. (bert embedder doesnot have decode)
-                xc = self.cond_stage_model.decode(c)# decoded masked image
-                log["conditioning"] = xc # 重建后的图像
-            elif self.cond_stage_key in ["caption"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
-                log["conditioning"] = xc # 含有文本的图像 
-                if self.model.conditioning_key == 'hybrid':
-                    log["decoded_maskedimg"] = self.first_stage_model.decode(c['c_concat'][:,:self.first_stage_model.embed_dim])# c_concat is the concat result of masked_img latent and resized mask. get latent here to decode
-            elif self.cond_stage_key == 'class_label':
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-                log['conditioning'] = xc # 文本为类标签的图像
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:# diffusion每一步的图像
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:# 
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                         ddim_steps=ddim_steps,eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with self.ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                             ddim_steps=ddim_steps,eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-            if inpaint:
-                # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
-                mask = torch.ones(N, h, w).to(self.device)
-                # zeros will be filled in
-                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-                mask = mask[:, None, ...]# N,1,H,W
-                with self.ema_scope("Plotting Inpaint"):
-                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_inpainting"] = x_samples
-                log["mask"] = mask
-
-                # outpaint
-                with self.ema_scope("Plotting Outpaint"):
-                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with self.ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.mel_dim, self.mel_length),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/logout/+page.server.ts

@@ -1,17 +0,0 @@
-import { dev } from "$app/environment";
-import { base } from "$app/paths";
-import { COOKIE_NAME } from "$env/static/private";
-import { redirect } from "@sveltejs/kit";
-
-export const actions = {
-	default: async function ({ cookies }) {
-		cookies.delete(COOKIE_NAME, {
-			path: "/",
-			// So that it works inside the space's iframe
-			sameSite: dev ? "lax" : "none",
-			secure: !dev,
-			httpOnly: true,
-		});
-		throw redirect(303, `${base}/`);
-	},
-};

spaces/Adr740/Hadith_AI_Explorer/data.py

@@ -1,2 +0,0 @@
-import pandas as pd
-data = pd.read_pickle("pickle_ebd.pkl")

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/PreLayout.js

@@ -1,15 +0,0 @@
-var PreLayout = function () {
-    this._childrenWidth = undefined;
-    this._childrenHeight = undefined;
-
-    var children = this.getChildrenSizers(),
-        child;
-    for (var i = 0, cnt = children.length; i < cnt; i++) {
-        child = children[i];
-        if (child.ignoreLayout) {
-            continue;
-        }
-        child.preLayout();
-    }
-}
-export default PreLayout;

spaces/Ailexcoder/GPT4ALL1/app.py

@@ -1,143 +0,0 @@
-from __future__ import annotations
-from typing import Iterable
-import gradio as gr
-from gradio.themes.base import Base
-from gradio.themes.utils import colors, fonts, sizes
-
-from llama_cpp import Llama
-from huggingface_hub import hf_hub_download
-
-hf_hub_download(repo_id="LLukas22/gpt4all-lora-quantized-ggjt", filename="ggjt-model.bin", local_dir=".")
-llm = Llama(model_path="./ggjt-model.bin", n_threads=2)
-
-
-ins = '''### Instruction:
-{}
-### Response:
-'''
-
-theme = gr.themes.Monochrome(
-    primary_hue="indigo",
-    secondary_hue="blue",
-    neutral_hue="slate",
-    radius_size=gr.themes.sizes.radius_sm,
-    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
-)
-
-
-
-
-# def generate(instruction): 
-#     response = llm(ins.format(instruction))
-#     response = response['choices'][0]['text']
-#     result = ""
-#     for word in response.split(" "):
-#         result += word + " "
-#         yield result
-
-def generate(instruction): 
-    result = ""
-    for x in llm(ins.format(instruction), stop=['### Instruction:', '### End'], stream=True):
-        result += x['choices'][0]['text']
-        yield result
-
-
-examples = [
-    "Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas",
-    "How do I make a campfire?",
-    "Explain to me the difference between nuclear fission and fusion.",
-    "I'm selling my Nikon D-750, write a short blurb for my ad."
-]
-
-def process_example(args):
-    for x in generate(args):
-        pass
-    return x
-    
-css = ".generating {visibility: hidden}"
-
-# Based on the gradio theming guide and borrowed from https://huggingface.co/spaces/shivi/dolly-v2-demo
-class SeafoamCustom(Base):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = colors.emerald,
-        secondary_hue: colors.Color | str = colors.blue,
-        neutral_hue: colors.Color | str = colors.blue,
-        spacing_size: sizes.Size | str = sizes.spacing_md,
-        radius_size: sizes.Size | str = sizes.radius_md,
-        font: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("Quicksand"),
-            "ui-sans-serif",
-            "sans-serif",
-        ),
-        font_mono: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("IBM Plex Mono"),
-            "ui-monospace",
-            "monospace",
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            spacing_size=spacing_size,
-            radius_size=radius_size,
-            font=font,
-            font_mono=font_mono,
-        )
-        super().set(
-            button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
-            button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
-            button_primary_text_color="white",
-            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
-            block_shadow="*shadow_drop_lg",
-            button_shadow="*shadow_drop_lg",
-            input_background_fill="zinc",
-            input_border_color="*secondary_300",
-            input_shadow="*shadow_drop",
-            input_shadow_focus="*shadow_drop_lg",
-        )
-
-
-seafoam = SeafoamCustom()
-
-
-with gr.Blocks(theme=seafoam, analytics_enabled=False, css=css) as demo:
-    with gr.Column():
-        gr.Markdown(
-            """ ## GPT4ALL
-            
-            An ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue
-            
-            Type in the box below and click the button to generate answers to your most pressing questions!
-            
-      """
-        )
-
-        with gr.Row():
-            with gr.Column(scale=3):
-                instruction = gr.Textbox(placeholder="Enter your question here", label="Question", elem_id="q-input")
-
-                with gr.Box():
-                    gr.Markdown("**Answer**")
-                    output = gr.Markdown(elem_id="q-output")
-                submit = gr.Button("Generate", variant="primary")
-                gr.Examples(
-                    examples=examples,
-                    inputs=[instruction],
-                    cache_examples=True,
-                    fn=process_example,
-                    outputs=[output],
-                )
-        
-
-
-    submit.click(generate, inputs=[instruction], outputs=[output])
-    instruction.submit(generate, inputs=[instruction], outputs=[output])
-
-demo.queue(concurrency_count=1).launch(debug=True)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/sd_text2img_k_diffusion.py

@@ -1,475 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import importlib
-import warnings
-from typing import Callable, List, Optional, Union
-
-import torch
-from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
-
-from diffusers import DiffusionPipeline, LMSDiscreteScheduler
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.utils import is_accelerate_available, logging
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class ModelWrapper:
-    def __init__(self, model, alphas_cumprod):
-        self.model = model
-        self.alphas_cumprod = alphas_cumprod
-
-    def apply_model(self, *args, **kwargs):
-        if len(args) == 3:
-            encoder_hidden_states = args[-1]
-            args = args[:2]
-        if kwargs.get("cond", None) is not None:
-            encoder_hidden_states = kwargs.pop("cond")
-        return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample
-
-
-class StableDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image 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 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 latents. 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`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae,
-        text_encoder,
-        tokenizer,
-        unet,
-        scheduler,
-        safety_checker,
-        feature_extractor,
-    ):
-        super().__init__()
-
-        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 ."
-            )
-
-        # get correct sigmas from LMS
-        scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-
-        model = ModelWrapper(unet, scheduler.alphas_cumprod)
-        if scheduler.config.prediction_type == "v_prediction":
-            self.k_diffusion_model = CompVisVDenoiser(model)
-        else:
-            self.k_diffusion_model = CompVisDenoiser(model)
-
-    def set_sampler(self, scheduler_type: str):
-        warnings.warn("The `set_sampler` method is deprecated, please use `set_scheduler` instead.")
-        return self.set_scheduler(scheduler_type)
-
-    def set_scheduler(self, scheduler_type: str):
-        library = importlib.import_module("k_diffusion")
-        sampling = getattr(library, "sampling")
-        self.sampler = getattr(sampling, scheduler_type)
-
-    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)
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-    @property
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `list(int)`):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                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`).
-        """
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
-
-        if not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-            attention_mask = text_inputs.attention_mask.to(device)
-        else:
-            attention_mask = None
-
-        text_embeddings = self.text_encoder(
-            text_input_ids.to(device),
-            attention_mask=attention_mask,
-        )
-        text_embeddings = text_embeddings[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)
-
-        # get unconditional embeddings for classifier free guidance
-        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
-
-            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",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            uncond_embeddings = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            uncond_embeddings = uncond_embeddings[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(1, 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])
-
-        return text_embeddings
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    def decode_latents(self, 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()
-        return image
-
-    def check_inputs(self, prompt, height, width, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if 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)}."
-            )
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // 8, width // 8)
-        if latents is None:
-            if device.type == "mps":
-                # randn does not work reproducibly on mps
-                latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
-            else:
-                latents = torch.randn(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)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        return latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        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.
-            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`.
-        """
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(prompt, height, width, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = True
-        if guidance_scale <= 1.0:
-            raise ValueError("has to use guidance_scale")
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=text_embeddings.device)
-        sigmas = self.scheduler.sigmas
-        sigmas = sigmas.to(text_embeddings.dtype)
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            text_embeddings.dtype,
-            device,
-            generator,
-            latents,
-        )
-        latents = latents * sigmas[0]
-        self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
-        self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
-
-        def model_fn(x, t):
-            latent_model_input = torch.cat([x] * 2)
-
-            noise_pred = self.k_diffusion_model(latent_model_input, t, cond=text_embeddings)
-
-            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-            return noise_pred
-
-        latents = self.sampler(model_fn, latents, sigmas)
-
-        # 8. Post-processing
-        image = self.decode_latents(latents)
-
-        # 9. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
-
-        # 10. Convert to PIL
-        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/examples/community/text_inpainting.py

@@ -1,302 +0,0 @@
-from typing import Callable, List, Optional, Union
-
-import PIL
-import torch
-from transformers import (
-    CLIPImageProcessor,
-    CLIPSegForImageSegmentation,
-    CLIPSegProcessor,
-    CLIPTextModel,
-    CLIPTokenizer,
-)
-
-from diffusers import DiffusionPipeline
-from diffusers.configuration_utils import FrozenDict
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from diffusers.utils import deprecate, is_accelerate_available, logging
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class TextInpainting(DiffusionPipeline):
-    r"""
-    Pipeline for text based inpainting using Stable Diffusion.
-    Uses CLIPSeg to get a mask from the given text, then calls the Inpainting pipeline with the generated mask
-
-    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:
-        segmentation_model ([`CLIPSegForImageSegmentation`]):
-            CLIPSeg Model to generate mask from the given text. Please refer to the [model card]() for details.
-        segmentation_processor ([`CLIPSegProcessor`]):
-            CLIPSeg processor to get image, text features to translate prompt to English, if necessary. Please refer to the
-            [model card](https://huggingface.co/docs/transformers/model_doc/clipseg) for details.
-        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,
-        segmentation_model: CLIPSegForImageSegmentation,
-        segmentation_processor: CLIPSegProcessor,
-        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 hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration"
-                " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
-                " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["skip_prk_steps"] = True
-            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(
-            segmentation_model=segmentation_model,
-            segmentation_processor=segmentation_processor,
-            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)
-
-    def enable_sequential_cpu_offload(self):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device("cuda")
-
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-    @property
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[torch.FloatTensor, PIL.Image.Image],
-        text: str,
-        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 (`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`.
-            text (`str``):
-                The text to use to generate the mask.
-            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`.
-        """
-
-        # We use the input text to generate the mask
-        inputs = self.segmentation_processor(
-            text=[text], images=[image], padding="max_length", return_tensors="pt"
-        ).to(self.device)
-        outputs = self.segmentation_model(**inputs)
-        mask = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
-        mask_pil = self.numpy_to_pil(mask)[0].resize(image.size)
-
-        # Run inpainting pipeline with the generated mask
-        inpainting_pipeline = StableDiffusionInpaintPipeline(
-            vae=self.vae,
-            text_encoder=self.text_encoder,
-            tokenizer=self.tokenizer,
-            unet=self.unet,
-            scheduler=self.scheduler,
-            safety_checker=self.safety_checker,
-            feature_extractor=self.feature_extractor,
-        )
-        return inpainting_pipeline(
-            prompt=prompt,
-            image=image,
-            mask_image=mask_pil,
-            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,
-        )

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky/test_kandinsky_combined.py

@@ -1,335 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-
-from diffusers import KandinskyCombinedPipeline, KandinskyImg2ImgCombinedPipeline, KandinskyInpaintCombinedPipeline
-from diffusers.utils import torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-from ..test_pipelines_common import PipelineTesterMixin
-from .test_kandinsky import Dummies
-from .test_kandinsky_img2img import Dummies as Img2ImgDummies
-from .test_kandinsky_inpaint import Dummies as InpaintDummies
-from .test_kandinsky_prior import Dummies as PriorDummies
-
-
-enable_full_determinism()
-
-
-class KandinskyPipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = KandinskyCombinedPipeline
-    params = [
-        "prompt",
-    ]
-    batch_params = ["prompt", "negative_prompt"]
-    required_optional_params = [
-        "generator",
-        "height",
-        "width",
-        "latents",
-        "guidance_scale",
-        "negative_prompt",
-        "num_inference_steps",
-        "return_dict",
-        "guidance_scale",
-        "num_images_per_prompt",
-        "output_type",
-        "return_dict",
-    ]
-    test_xformers_attention = False
-
-    def get_dummy_components(self):
-        dummy = Dummies()
-        prior_dummy = PriorDummies()
-        components = dummy.get_dummy_components()
-
-        components.update({f"prior_{k}": v for k, v in prior_dummy.get_dummy_components().items()})
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        prior_dummy = PriorDummies()
-        inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)
-        inputs.update(
-            {
-                "height": 64,
-                "width": 64,
-            }
-        )
-        return inputs
-
-    def test_kandinsky(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-
-        pipe.set_progress_bar_config(disable=None)
-
-        output = pipe(**self.get_dummy_inputs(device))
-        image = output.images
-
-        image_from_tuple = pipe(
-            **self.get_dummy_inputs(device),
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-
-        expected_slice = np.array([0.0000, 0.0000, 0.6777, 0.1363, 0.3624, 0.7868, 0.3869, 0.3395, 0.5068])
-
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
-        assert (
-            np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
-
-    @require_torch_gpu
-    def test_offloads(self):
-        pipes = []
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components).to(torch_device)
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_model_cpu_offload()
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_sequential_cpu_offload()
-        pipes.append(sd_pipe)
-
-        image_slices = []
-        for pipe in pipes:
-            inputs = self.get_dummy_inputs(torch_device)
-            image = pipe(**inputs).images
-
-            image_slices.append(image[0, -3:, -3:, -1].flatten())
-
-        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
-        assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
-
-    def test_inference_batch_single_identical(self):
-        super().test_inference_batch_single_identical(expected_max_diff=1e-2)
-
-
-class KandinskyPipelineImg2ImgCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = KandinskyImg2ImgCombinedPipeline
-    params = ["prompt", "image"]
-    batch_params = ["prompt", "negative_prompt", "image"]
-    required_optional_params = [
-        "generator",
-        "height",
-        "width",
-        "latents",
-        "guidance_scale",
-        "negative_prompt",
-        "num_inference_steps",
-        "return_dict",
-        "guidance_scale",
-        "num_images_per_prompt",
-        "output_type",
-        "return_dict",
-    ]
-    test_xformers_attention = False
-
-    def get_dummy_components(self):
-        dummy = Img2ImgDummies()
-        prior_dummy = PriorDummies()
-        components = dummy.get_dummy_components()
-
-        components.update({f"prior_{k}": v for k, v in prior_dummy.get_dummy_components().items()})
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        prior_dummy = PriorDummies()
-        dummy = Img2ImgDummies()
-        inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)
-        inputs.update(dummy.get_dummy_inputs(device=device, seed=seed))
-        inputs.pop("image_embeds")
-        inputs.pop("negative_image_embeds")
-        return inputs
-
-    def test_kandinsky(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-
-        pipe.set_progress_bar_config(disable=None)
-
-        output = pipe(**self.get_dummy_inputs(device))
-        image = output.images
-
-        image_from_tuple = pipe(
-            **self.get_dummy_inputs(device),
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-
-        expected_slice = np.array([0.4260, 0.3596, 0.4571, 0.3890, 0.4087, 0.5137, 0.4819, 0.4116, 0.5053])
-
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
-        assert (
-            np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
-
-    @require_torch_gpu
-    def test_offloads(self):
-        pipes = []
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components).to(torch_device)
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_model_cpu_offload()
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_sequential_cpu_offload()
-        pipes.append(sd_pipe)
-
-        image_slices = []
-        for pipe in pipes:
-            inputs = self.get_dummy_inputs(torch_device)
-            image = pipe(**inputs).images
-
-            image_slices.append(image[0, -3:, -3:, -1].flatten())
-
-        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
-        assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
-
-    def test_inference_batch_single_identical(self):
-        super().test_inference_batch_single_identical(expected_max_diff=1e-2)
-
-
-class KandinskyPipelineInpaintCombinedFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = KandinskyInpaintCombinedPipeline
-    params = ["prompt", "image", "mask_image"]
-    batch_params = ["prompt", "negative_prompt", "image", "mask_image"]
-    required_optional_params = [
-        "generator",
-        "height",
-        "width",
-        "latents",
-        "guidance_scale",
-        "negative_prompt",
-        "num_inference_steps",
-        "return_dict",
-        "guidance_scale",
-        "num_images_per_prompt",
-        "output_type",
-        "return_dict",
-    ]
-    test_xformers_attention = False
-
-    def get_dummy_components(self):
-        dummy = InpaintDummies()
-        prior_dummy = PriorDummies()
-        components = dummy.get_dummy_components()
-
-        components.update({f"prior_{k}": v for k, v in prior_dummy.get_dummy_components().items()})
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        prior_dummy = PriorDummies()
-        dummy = InpaintDummies()
-        inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)
-        inputs.update(dummy.get_dummy_inputs(device=device, seed=seed))
-        inputs.pop("image_embeds")
-        inputs.pop("negative_image_embeds")
-        return inputs
-
-    def test_kandinsky(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-
-        pipe.set_progress_bar_config(disable=None)
-
-        output = pipe(**self.get_dummy_inputs(device))
-        image = output.images
-
-        image_from_tuple = pipe(
-            **self.get_dummy_inputs(device),
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-
-        expected_slice = np.array([0.0477, 0.0808, 0.2972, 0.2705, 0.3620, 0.6247, 0.4464, 0.2870, 0.3530])
-
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
-        assert (
-            np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
-
-    @require_torch_gpu
-    def test_offloads(self):
-        pipes = []
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components).to(torch_device)
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_model_cpu_offload()
-        pipes.append(sd_pipe)
-
-        components = self.get_dummy_components()
-        sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_sequential_cpu_offload()
-        pipes.append(sd_pipe)
-
-        image_slices = []
-        for pipe in pipes:
-            inputs = self.get_dummy_inputs(torch_device)
-            image = pipe(**inputs).images
-
-            image_slices.append(image[0, -3:, -3:, -1].flatten())
-
-        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
-        assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
-
-    def test_inference_batch_single_identical(self):
-        super().test_inference_batch_single_identical(expected_max_diff=1e-2)

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/ga_rpn_head.py

@@ -1,171 +0,0 @@
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import nms
-
-from ..builder import HEADS
-from .guided_anchor_head import GuidedAnchorHead
-from .rpn_test_mixin import RPNTestMixin
-
-
-@HEADS.register_module()
-class GARPNHead(RPNTestMixin, GuidedAnchorHead):
-    """Guided-Anchor-based RPN head."""
-
-    def __init__(self, in_channels, **kwargs):
-        super(GARPNHead, self).__init__(1, in_channels, **kwargs)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.rpn_conv = nn.Conv2d(
-            self.in_channels, self.feat_channels, 3, padding=1)
-        super(GARPNHead, self)._init_layers()
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        normal_init(self.rpn_conv, std=0.01)
-        super(GARPNHead, self).init_weights()
-
-    def forward_single(self, x):
-        """Forward feature of a single scale level."""
-
-        x = self.rpn_conv(x)
-        x = F.relu(x, inplace=True)
-        (cls_score, bbox_pred, shape_pred,
-         loc_pred) = super(GARPNHead, self).forward_single(x)
-        return cls_score, bbox_pred, shape_pred, loc_pred
-
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             shape_preds,
-             loc_preds,
-             gt_bboxes,
-             img_metas,
-             gt_bboxes_ignore=None):
-        losses = super(GARPNHead, self).loss(
-            cls_scores,
-            bbox_preds,
-            shape_preds,
-            loc_preds,
-            gt_bboxes,
-            None,
-            img_metas,
-            gt_bboxes_ignore=gt_bboxes_ignore)
-        return dict(
-            loss_rpn_cls=losses['loss_cls'],
-            loss_rpn_bbox=losses['loss_bbox'],
-            loss_anchor_shape=losses['loss_shape'],
-            loss_anchor_loc=losses['loss_loc'])
-
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           mlvl_anchors,
-                           mlvl_masks,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        cfg = self.test_cfg if cfg is None else cfg
-
-        cfg = copy.deepcopy(cfg)
-
-        # deprecate arguments warning
-        if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
-            warnings.warn(
-                'In rpn_proposal or test_cfg, '
-                'nms_thr has been moved to a dict named nms as '
-                'iou_threshold, max_num has been renamed as max_per_img, '
-                'name of original arguments and the way to specify '
-                'iou_threshold of NMS will be deprecated.')
-        if 'nms' not in cfg:
-            cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
-        if 'max_num' in cfg:
-            if 'max_per_img' in cfg:
-                assert cfg.max_num == cfg.max_per_img, f'You ' \
-                    f'set max_num and max_per_img at the same time, ' \
-                    f'but get {cfg.max_num} ' \
-                    f'and {cfg.max_per_img} respectively' \
-                    'Please delete max_num which will be deprecated.'
-            else:
-                cfg.max_per_img = cfg.max_num
-        if 'nms_thr' in cfg:
-            assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \
-                f'iou_threshold in nms and ' \
-                f'nms_thr at the same time, but get ' \
-                f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \
-                f' respectively. Please delete the ' \
-                f'nms_thr which will be deprecated.'
-
-        assert cfg.nms.get('type', 'nms') == 'nms', 'GARPNHead only support ' \
-            'naive nms.'
-
-        mlvl_proposals = []
-        for idx in range(len(cls_scores)):
-            rpn_cls_score = cls_scores[idx]
-            rpn_bbox_pred = bbox_preds[idx]
-            anchors = mlvl_anchors[idx]
-            mask = mlvl_masks[idx]
-            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
-            # if no location is kept, end.
-            if mask.sum() == 0:
-                continue
-            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
-            if self.use_sigmoid_cls:
-                rpn_cls_score = rpn_cls_score.reshape(-1)
-                scores = rpn_cls_score.sigmoid()
-            else:
-                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
-                # remind that we set FG labels to [0, num_class-1]
-                # since mmdet v2.0
-                # BG cat_id: num_class
-                scores = rpn_cls_score.softmax(dim=1)[:, :-1]
-            # filter scores, bbox_pred w.r.t. mask.
-            # anchors are filtered in get_anchors() beforehand.
-            scores = scores[mask]
-            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1,
-                                                                   4)[mask, :]
-            if scores.dim() == 0:
-                rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0)
-                anchors = anchors.unsqueeze(0)
-                scores = scores.unsqueeze(0)
-            # filter anchors, bbox_pred, scores w.r.t. scores
-            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
-                _, topk_inds = scores.topk(cfg.nms_pre)
-                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
-                anchors = anchors[topk_inds, :]
-                scores = scores[topk_inds]
-            # get proposals w.r.t. anchors and rpn_bbox_pred
-            proposals = self.bbox_coder.decode(
-                anchors, rpn_bbox_pred, max_shape=img_shape)
-            # filter out too small bboxes
-            if cfg.min_bbox_size > 0:
-                w = proposals[:, 2] - proposals[:, 0]
-                h = proposals[:, 3] - proposals[:, 1]
-                valid_inds = torch.nonzero(
-                    (w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size),
-                    as_tuple=False).squeeze()
-                proposals = proposals[valid_inds, :]
-                scores = scores[valid_inds]
-            # NMS in current level
-            proposals, _ = nms(proposals, scores, cfg.nms.iou_threshold)
-            proposals = proposals[:cfg.nms_post, :]
-            mlvl_proposals.append(proposals)
-        proposals = torch.cat(mlvl_proposals, 0)
-        if cfg.get('nms_across_levels', False):
-            # NMS across multi levels
-            proposals, _ = nms(proposals[:, :4], proposals[:, -1],
-                               cfg.nms.iou_threshold)
-            proposals = proposals[:cfg.max_per_img, :]
-        else:
-            scores = proposals[:, 4]
-            num = min(cfg.max_per_img, proposals.shape[0])
-            _, topk_inds = scores.topk(num)
-            proposals = proposals[topk_inds, :]
-        return proposals

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

@@ -1,44 +0,0 @@
-from .builder import RUNNER_BUILDERS, RUNNERS
-
-
-@RUNNER_BUILDERS.register_module()
-class DefaultRunnerConstructor:
-    """Default constructor for runners.
-
-    Custom existing `Runner` like `EpocBasedRunner` though `RunnerConstructor`.
-    For example, We can inject some new properties and functions for `Runner`.
-
-    Example:
-        >>> from annotator.uniformer.mmcv.runner import RUNNER_BUILDERS, build_runner
-        >>> # Define a new RunnerReconstructor
-        >>> @RUNNER_BUILDERS.register_module()
-        >>> class MyRunnerConstructor:
-        ...     def __init__(self, runner_cfg, default_args=None):
-        ...         if not isinstance(runner_cfg, dict):
-        ...             raise TypeError('runner_cfg should be a dict',
-        ...                             f'but got {type(runner_cfg)}')
-        ...         self.runner_cfg = runner_cfg
-        ...         self.default_args = default_args
-        ...
-        ...     def __call__(self):
-        ...         runner = RUNNERS.build(self.runner_cfg,
-        ...                                default_args=self.default_args)
-        ...         # Add new properties for existing runner
-        ...         runner.my_name = 'my_runner'
-        ...         runner.my_function = lambda self: print(self.my_name)
-        ...         ...
-        >>> # build your runner
-        >>> runner_cfg = dict(type='EpochBasedRunner', max_epochs=40,
-        ...                   constructor='MyRunnerConstructor')
-        >>> runner = build_runner(runner_cfg)
-    """
-
-    def __init__(self, runner_cfg, default_args=None):
-        if not isinstance(runner_cfg, dict):
-            raise TypeError('runner_cfg should be a dict',
-                            f'but got {type(runner_cfg)}')
-        self.runner_cfg = runner_cfg
-        self.default_args = default_args
-
-    def __call__(self):
-        return RUNNERS.build(self.runner_cfg, default_args=self.default_args)

spaces/Artificio/AdversarialArt/README.md

@@ -1,12 +0,0 @@
----
-title: AdversarialArt
-emoji: 🏢
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.1.6
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Artrajz/vits-simple-api/bert_vits2/text/tone_sandhi.py

@@ -1,769 +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.
-from typing import List
-from typing import Tuple
-
-import jieba
-from pypinyin import lazy_pinyin
-from pypinyin import Style
-
-
-class ToneSandhi:
-    def __init__(self):
-        self.must_neural_tone_words = {
-            "麻烦",
-            "麻利",
-            "鸳鸯",
-            "高粱",
-            "骨头",
-            "骆驼",
-            "马虎",
-            "首饰",
-            "馒头",
-            "馄饨",
-            "风筝",
-            "难为",
-            "队伍",
-            "阔气",
-            "闺女",
-            "门道",
-            "锄头",
-            "铺盖",
-            "铃铛",
-            "铁匠",
-            "钥匙",
-            "里脊",
-            "里头",
-            "部分",
-            "那么",
-            "道士",
-            "造化",
-            "迷糊",
-            "连累",
-            "这么",
-            "这个",
-            "运气",
-            "过去",
-            "软和",
-            "转悠",
-            "踏实",
-            "跳蚤",
-            "跟头",
-            "趔趄",
-            "财主",
-            "豆腐",
-            "讲究",
-            "记性",
-            "记号",
-            "认识",
-            "规矩",
-            "见识",
-            "裁缝",
-            "补丁",
-            "衣裳",
-            "衣服",
-            "衙门",
-            "街坊",
-            "行李",
-            "行当",
-            "蛤蟆",
-            "蘑菇",
-            "薄荷",
-            "葫芦",
-            "葡萄",
-            "萝卜",
-            "荸荠",
-            "苗条",
-            "苗头",
-            "苍蝇",
-            "芝麻",
-            "舒服",
-            "舒坦",
-            "舌头",
-            "自在",
-            "膏药",
-            "脾气",
-            "脑袋",
-            "脊梁",
-            "能耐",
-            "胳膊",
-            "胭脂",
-            "胡萝",
-            "胡琴",
-            "胡同",
-            "聪明",
-            "耽误",
-            "耽搁",
-            "耷拉",
-            "耳朵",
-            "老爷",
-            "老实",
-            "老婆",
-            "老头",
-            "老太",
-            "翻腾",
-            "罗嗦",
-            "罐头",
-            "编辑",
-            "结实",
-            "红火",
-            "累赘",
-            "糨糊",
-            "糊涂",
-            "精神",
-            "粮食",
-            "簸箕",
-            "篱笆",
-            "算计",
-            "算盘",
-            "答应",
-            "笤帚",
-            "笑语",
-            "笑话",
-            "窟窿",
-            "窝囊",
-            "窗户",
-            "稳当",
-            "稀罕",
-            "称呼",
-            "秧歌",
-            "秀气",
-            "秀才",
-            "福气",
-            "祖宗",
-            "砚台",
-            "码头",
-            "石榴",
-            "石头",
-            "石匠",
-            "知识",
-            "眼睛",
-            "眯缝",
-            "眨巴",
-            "眉毛",
-            "相声",
-            "盘算",
-            "白净",
-            "痢疾",
-            "痛快",
-            "疟疾",
-            "疙瘩",
-            "疏忽",
-            "畜生",
-            "生意",
-            "甘蔗",
-            "琵琶",
-            "琢磨",
-            "琉璃",
-            "玻璃",
-            "玫瑰",
-            "玄乎",
-            "狐狸",
-            "状元",
-            "特务",
-            "牲口",
-            "牙碜",
-            "牌楼",
-            "爽快",
-            "爱人",
-            "热闹",
-            "烧饼",
-            "烟筒",
-            "烂糊",
-            "点心",
-            "炊帚",
-            "灯笼",
-            "火候",
-            "漂亮",
-            "滑溜",
-            "溜达",
-            "温和",
-            "清楚",
-            "消息",
-            "浪头",
-            "活泼",
-            "比方",
-            "正经",
-            "欺负",
-            "模糊",
-            "槟榔",
-            "棺材",
-            "棒槌",
-            "棉花",
-            "核桃",
-            "栅栏",
-            "柴火",
-            "架势",
-            "枕头",
-            "枇杷",
-            "机灵",
-            "本事",
-            "木头",
-            "木匠",
-            "朋友",
-            "月饼",
-            "月亮",
-            "暖和",
-            "明白",
-            "时候",
-            "新鲜",
-            "故事",
-            "收拾",
-            "收成",
-            "提防",
-            "挖苦",
-            "挑剔",
-            "指甲",
-            "指头",
-            "拾掇",
-            "拳头",
-            "拨弄",
-            "招牌",
-            "招呼",
-            "抬举",
-            "护士",
-            "折腾",
-            "扫帚",
-            "打量",
-            "打算",
-            "打点",
-            "打扮",
-            "打听",
-            "打发",
-            "扎实",
-            "扁担",
-            "戒指",
-            "懒得",
-            "意识",
-            "意思",
-            "情形",
-            "悟性",
-            "怪物",
-            "思量",
-            "怎么",
-            "念头",
-            "念叨",
-            "快活",
-            "忙活",
-            "志气",
-            "心思",
-            "得罪",
-            "张罗",
-            "弟兄",
-            "开通",
-            "应酬",
-            "庄稼",
-            "干事",
-            "帮手",
-            "帐篷",
-            "希罕",
-            "师父",
-            "师傅",
-            "巴结",
-            "巴掌",
-            "差事",
-            "工夫",
-            "岁数",
-            "屁股",
-            "尾巴",
-            "少爷",
-            "小气",
-            "小伙",
-            "将就",
-            "对头",
-            "对付",
-            "寡妇",
-            "家伙",
-            "客气",
-            "实在",
-            "官司",
-            "学问",
-            "学生",
-            "字号",
-            "嫁妆",
-            "媳妇",
-            "媒人",
-            "婆家",
-            "娘家",
-            "委屈",
-            "姑娘",
-            "姐夫",
-            "妯娌",
-            "妥当",
-            "妖精",
-            "奴才",
-            "女婿",
-            "头发",
-            "太阳",
-            "大爷",
-            "大方",
-            "大意",
-            "大夫",
-            "多少",
-            "多么",
-            "外甥",
-            "壮实",
-            "地道",
-            "地方",
-            "在乎",
-            "困难",
-            "嘴巴",
-            "嘱咐",
-            "嘟囔",
-            "嘀咕",
-            "喜欢",
-            "喇嘛",
-            "喇叭",
-            "商量",
-            "唾沫",
-            "哑巴",
-            "哈欠",
-            "哆嗦",
-            "咳嗽",
-            "和尚",
-            "告诉",
-            "告示",
-            "含糊",
-            "吓唬",
-            "后头",
-            "名字",
-            "名堂",
-            "合同",
-            "吆喝",
-            "叫唤",
-            "口袋",
-            "厚道",
-            "厉害",
-            "千斤",
-            "包袱",
-            "包涵",
-            "匀称",
-            "勤快",
-            "动静",
-            "动弹",
-            "功夫",
-            "力气",
-            "前头",
-            "刺猬",
-            "刺激",
-            "别扭",
-            "利落",
-            "利索",
-            "利害",
-            "分析",
-            "出息",
-            "凑合",
-            "凉快",
-            "冷战",
-            "冤枉",
-            "冒失",
-            "养活",
-            "关系",
-            "先生",
-            "兄弟",
-            "便宜",
-            "使唤",
-            "佩服",
-            "作坊",
-            "体面",
-            "位置",
-            "似的",
-            "伙计",
-            "休息",
-            "什么",
-            "人家",
-            "亲戚",
-            "亲家",
-            "交情",
-            "云彩",
-            "事情",
-            "买卖",
-            "主意",
-            "丫头",
-            "丧气",
-            "两口",
-            "东西",
-            "东家",
-            "世故",
-            "不由",
-            "不在",
-            "下水",
-            "下巴",
-            "上头",
-            "上司",
-            "丈夫",
-            "丈人",
-            "一辈",
-            "那个",
-            "菩萨",
-            "父亲",
-            "母亲",
-            "咕噜",
-            "邋遢",
-            "费用",
-            "冤家",
-            "甜头",
-            "介绍",
-            "荒唐",
-            "大人",
-            "泥鳅",
-            "幸福",
-            "熟悉",
-            "计划",
-            "扑腾",
-            "蜡烛",
-            "姥爷",
-            "照顾",
-            "喉咙",
-            "吉他",
-            "弄堂",
-            "蚂蚱",
-            "凤凰",
-            "拖沓",
-            "寒碜",
-            "糟蹋",
-            "倒腾",
-            "报复",
-            "逻辑",
-            "盘缠",
-            "喽啰",
-            "牢骚",
-            "咖喱",
-            "扫把",
-            "惦记",
-        }
-        self.must_not_neural_tone_words = {
-            "男子",
-            "女子",
-            "分子",
-            "原子",
-            "量子",
-            "莲子",
-            "石子",
-            "瓜子",
-            "电子",
-            "人人",
-            "虎虎",
-        }
-        self.punc = "：，；。？！“”‘’':,;.?!"
-
-    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
-    # e.g.
-    # word: "家里"
-    # pos: "s"
-    # finals: ['ia1', 'i3']
-    def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
-        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
-        for j, item in enumerate(word):
-            if (
-                j - 1 >= 0
-                and item == word[j - 1]
-                and pos[0] in {"n", "v", "a"}
-                and word not in self.must_not_neural_tone_words
-            ):
-                finals[j] = finals[j][:-1] + "5"
-        ge_idx = word.find("个")
-        if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
-            finals[-1] = finals[-1][:-1] + "5"
-        elif len(word) >= 1 and word[-1] in "的地得":
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 走了, 看着, 去过
-        # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
-        #     finals[-1] = finals[-1][:-1] + "5"
-        elif (
-            len(word) > 1
-            and word[-1] in "们子"
-            and pos in {"r", "n"}
-            and word not in self.must_not_neural_tone_words
-        ):
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 桌上, 地下, 家里
-        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 上来, 下去
-        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
-            finals[-1] = finals[-1][:-1] + "5"
-        # 个做量词
-        elif (
-            ge_idx >= 1
-            and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
-        ) or word == "个":
-            finals[ge_idx] = finals[ge_idx][:-1] + "5"
-        else:
-            if (
-                word in self.must_neural_tone_words
-                or word[-2:] in self.must_neural_tone_words
-            ):
-                finals[-1] = finals[-1][:-1] + "5"
-
-        word_list = self._split_word(word)
-        finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
-        for i, word in enumerate(word_list):
-            # conventional neural in Chinese
-            if (
-                word in self.must_neural_tone_words
-                or word[-2:] in self.must_neural_tone_words
-            ):
-                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
-        finals = sum(finals_list, [])
-        return finals
-
-    def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # e.g. 看不懂
-        if len(word) == 3 and word[1] == "不":
-            finals[1] = finals[1][:-1] + "5"
-        else:
-            for i, char in enumerate(word):
-                # "不" before tone4 should be bu2, e.g. 不怕
-                if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
-                    finals[i] = finals[i][:-1] + "2"
-        return finals
-
-    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # "一" in number sequences, e.g. 一零零, 二一零
-        if word.find("一") != -1 and all(
-            [item.isnumeric() for item in word if item != "一"]
-        ):
-            return finals
-        # "一" between reduplication words should be yi5, e.g. 看一看
-        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
-            finals[1] = finals[1][:-1] + "5"
-        # when "一" is ordinal word, it should be yi1
-        elif word.startswith("第一"):
-            finals[1] = finals[1][:-1] + "1"
-        else:
-            for i, char in enumerate(word):
-                if char == "一" and i + 1 < len(word):
-                    # "一" before tone4 should be yi2, e.g. 一段
-                    if finals[i + 1][-1] == "4":
-                        finals[i] = finals[i][:-1] + "2"
-                    # "一" before non-tone4 should be yi4, e.g. 一天
-                    else:
-                        # "一" 后面如果是标点，还读一声
-                        if word[i + 1] not in self.punc:
-                            finals[i] = finals[i][:-1] + "4"
-        return finals
-
-    def _split_word(self, word: str) -> List[str]:
-        word_list = jieba.cut_for_search(word)
-        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
-        first_subword = word_list[0]
-        first_begin_idx = word.find(first_subword)
-        if first_begin_idx == 0:
-            second_subword = word[len(first_subword) :]
-            new_word_list = [first_subword, second_subword]
-        else:
-            second_subword = word[: -len(first_subword)]
-            new_word_list = [second_subword, first_subword]
-        return new_word_list
-
-    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        if len(word) == 2 and self._all_tone_three(finals):
-            finals[0] = finals[0][:-1] + "2"
-        elif len(word) == 3:
-            word_list = self._split_word(word)
-            if self._all_tone_three(finals):
-                #  disyllabic + monosyllabic, e.g. 蒙古/包
-                if len(word_list[0]) == 2:
-                    finals[0] = finals[0][:-1] + "2"
-                    finals[1] = finals[1][:-1] + "2"
-                #  monosyllabic + disyllabic, e.g. 纸/老虎
-                elif len(word_list[0]) == 1:
-                    finals[1] = finals[1][:-1] + "2"
-            else:
-                finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
-                if len(finals_list) == 2:
-                    for i, sub in enumerate(finals_list):
-                        # e.g. 所有/人
-                        if self._all_tone_three(sub) and len(sub) == 2:
-                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
-                        # e.g. 好/喜欢
-                        elif (
-                            i == 1
-                            and not self._all_tone_three(sub)
-                            and finals_list[i][0][-1] == "3"
-                            and finals_list[0][-1][-1] == "3"
-                        ):
-                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
-                        finals = sum(finals_list, [])
-        # split idiom into two words who's length is 2
-        elif len(word) == 4:
-            finals_list = [finals[:2], finals[2:]]
-            finals = []
-            for sub in finals_list:
-                if self._all_tone_three(sub):
-                    sub[0] = sub[0][:-1] + "2"
-                finals += sub
-
-        return finals
-
-    def _all_tone_three(self, finals: List[str]) -> bool:
-        return all(x[-1] == "3" for x in finals)
-
-    # merge "不" and the word behind it
-    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
-    def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        last_word = ""
-        for word, pos in seg:
-            if last_word == "不":
-                word = last_word + word
-            if word != "不":
-                new_seg.append((word, pos))
-            last_word = word[:]
-        if last_word == "不":
-            new_seg.append((last_word, "d"))
-            last_word = ""
-        return new_seg
-
-    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
-    # function 2: merge single  "一" and the word behind it
-    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
-    # e.g.
-    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
-    # output seg: [['听一听', 'v']]
-    def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        # function 1
-        for i, (word, pos) in enumerate(seg):
-            if (
-                i - 1 >= 0
-                and word == "一"
-                and i + 1 < len(seg)
-                and seg[i - 1][0] == seg[i + 1][0]
-                and seg[i - 1][1] == "v"
-            ):
-                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
-            else:
-                if (
-                    i - 2 >= 0
-                    and seg[i - 1][0] == "一"
-                    and seg[i - 2][0] == word
-                    and pos == "v"
-                ):
-                    continue
-                else:
-                    new_seg.append([word, pos])
-        seg = new_seg
-        new_seg = []
-        # function 2
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and new_seg[-1][0] == "一":
-                new_seg[-1][0] = new_seg[-1][0] + word
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    # the first and the second words are all_tone_three
-    def _merge_continuous_three_tones(
-        self, seg: List[Tuple[str, str]]
-    ) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if (
-                i - 1 >= 0
-                and self._all_tone_three(sub_finals_list[i - 1])
-                and self._all_tone_three(sub_finals_list[i])
-                and not merge_last[i - 1]
-            ):
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if (
-                    not self._is_reduplication(seg[i - 1][0])
-                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
-                ):
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-
-        return new_seg
-
-    def _is_reduplication(self, word: str) -> bool:
-        return len(word) == 2 and word[0] == word[1]
-
-    # the last char of first word and the first char of second word is tone_three
-    def _merge_continuous_three_tones_2(
-        self, seg: List[Tuple[str, str]]
-    ) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if (
-                i - 1 >= 0
-                and sub_finals_list[i - 1][-1][-1] == "3"
-                and sub_finals_list[i][0][-1] == "3"
-                and not merge_last[i - 1]
-            ):
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if (
-                    not self._is_reduplication(seg[i - 1][0])
-                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
-                ):
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and word == new_seg[-1][0]:
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        seg = self._merge_bu(seg)
-        try:
-            seg = self._merge_yi(seg)
-        except:
-            print("_merge_yi failed")
-        seg = self._merge_reduplication(seg)
-        seg = self._merge_continuous_three_tones(seg)
-        seg = self._merge_continuous_three_tones_2(seg)
-        seg = self._merge_er(seg)
-        return seg
-
-    def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
-        finals = self._bu_sandhi(word, finals)
-        finals = self._yi_sandhi(word, finals)
-        finals = self._neural_sandhi(word, pos, finals)
-        finals = self._three_sandhi(word, finals)
-        return finals

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

@@ -1,89 +0,0 @@
-# coding: utf-8
-"""Functions for reporting filesizes. Borrowed from https://github.com/PyFilesystem/pyfilesystem2
-
-The functions declared in this module should cover the different
-use cases needed to generate a string representation of a file size
-using several different units. Since there are many standards regarding
-file size units, three different functions have been implemented.
-
-See Also:
-    * `Wikipedia: Binary prefix <https://en.wikipedia.org/wiki/Binary_prefix>`_
-
-"""
-
-__all__ = ["decimal"]
-
-from typing import Iterable, List, Optional, Tuple
-
-
-def _to_str(
-    size: int,
-    suffixes: Iterable[str],
-    base: int,
-    *,
-    precision: Optional[int] = 1,
-    separator: Optional[str] = " ",
-) -> str:
-    if size == 1:
-        return "1 byte"
-    elif size < base:
-        return "{:,} bytes".format(size)
-
-    for i, suffix in enumerate(suffixes, 2):  # noqa: B007
-        unit = base**i
-        if size < unit:
-            break
-    return "{:,.{precision}f}{separator}{}".format(
-        (base * size / unit),
-        suffix,
-        precision=precision,
-        separator=separator,
-    )
-
-
-def pick_unit_and_suffix(size: int, suffixes: List[str], base: int) -> Tuple[int, str]:
-    """Pick a suffix and base for the given size."""
-    for i, suffix in enumerate(suffixes):
-        unit = base**i
-        if size < unit * base:
-            break
-    return unit, suffix
-
-
-def decimal(
-    size: int,
-    *,
-    precision: Optional[int] = 1,
-    separator: Optional[str] = " ",
-) -> str:
-    """Convert a filesize in to a string (powers of 1000, SI prefixes).
-
-    In this convention, ``1000 B = 1 kB``.
-
-    This is typically the format used to advertise the storage
-    capacity of USB flash drives and the like (*256 MB* meaning
-    actually a storage capacity of more than *256 000 000 B*),
-    or used by **Mac OS X** since v10.6 to report file sizes.
-
-    Arguments:
-        int (size): A file size.
-        int (precision): The number of decimal places to include (default = 1).
-        str (separator): The string to separate the value from the units (default = " ").
-
-    Returns:
-        `str`: A string containing a abbreviated file size and units.
-
-    Example:
-        >>> filesize.decimal(30000)
-        '30.0 kB'
-        >>> filesize.decimal(30000, precision=2, separator="")
-        '30.00kB'
-
-    """
-    return _to_str(
-        size,
-        ("kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"),
-        1000,
-        precision=precision,
-        separator=separator,
-    )

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py

@@ -1,8 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_fpn import model
-from ..common.train import train
-
-model.backbone.bottom_up.freeze_at = 2
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript_patch.py

@@ -1,406 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import os
-import sys
-import tempfile
-from contextlib import ExitStack, contextmanager
-from copy import deepcopy
-from unittest import mock
-import torch
-from torch import nn
-
-# need some explicit imports due to https://github.com/pytorch/pytorch/issues/38964
-import detectron2  # noqa F401
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.env import _import_file
-
-_counter = 0
-
-
-def _clear_jit_cache():
-    from torch.jit._recursive import concrete_type_store
-    from torch.jit._state import _jit_caching_layer
-
-    concrete_type_store.type_store.clear()  # for modules
-    _jit_caching_layer.clear()  # for free functions
-
-
-def _add_instances_conversion_methods(newInstances):
-    """
-    Add from_instances methods to the scripted Instances class.
-    """
-    cls_name = newInstances.__name__
-
-    @torch.jit.unused
-    def from_instances(instances: Instances):
-        """
-        Create scripted Instances from original Instances
-        """
-        fields = instances.get_fields()
-        image_size = instances.image_size
-        ret = newInstances(image_size)
-        for name, val in fields.items():
-            assert hasattr(ret, f"_{name}"), f"No attribute named {name} in {cls_name}"
-            setattr(ret, name, deepcopy(val))
-        return ret
-
-    newInstances.from_instances = from_instances
-
-
-@contextmanager
-def patch_instances(fields):
-    """
-    A contextmanager, under which the Instances class in detectron2 is replaced
-    by a statically-typed scriptable class, defined by `fields`.
-    See more in `scripting_with_instances`.
-    """
-
-    with tempfile.TemporaryDirectory(prefix="detectron2") as dir, tempfile.NamedTemporaryFile(
-        mode="w", encoding="utf-8", suffix=".py", dir=dir, delete=False
-    ) as f:
-        try:
-            # Objects that use Instances should not reuse previously-compiled
-            # results in cache, because `Instances` could be a new class each time.
-            _clear_jit_cache()
-
-            cls_name, s = _gen_instance_module(fields)
-            f.write(s)
-            f.flush()
-            f.close()
-
-            module = _import(f.name)
-            new_instances = getattr(module, cls_name)
-            _ = torch.jit.script(new_instances)
-            # let torchscript think Instances was scripted already
-            Instances.__torch_script_class__ = True
-            # let torchscript find new_instances when looking for the jit type of Instances
-            Instances._jit_override_qualname = torch._jit_internal._qualified_name(new_instances)
-
-            _add_instances_conversion_methods(new_instances)
-            yield new_instances
-        finally:
-            try:
-                del Instances.__torch_script_class__
-                del Instances._jit_override_qualname
-            except AttributeError:
-                pass
-            sys.modules.pop(module.__name__)
-
-
-def _gen_instance_class(fields):
-    """
-    Args:
-        fields (dict[name: type])
-    """
-
-    class _FieldType:
-        def __init__(self, name, type_):
-            assert isinstance(name, str), f"Field name must be str, got {name}"
-            self.name = name
-            self.type_ = type_
-            self.annotation = f"{type_.__module__}.{type_.__name__}"
-
-    fields = [_FieldType(k, v) for k, v in fields.items()]
-
-    def indent(level, s):
-        return " " * 4 * level + s
-
-    lines = []
-
-    global _counter
-    _counter += 1
-
-    cls_name = "ScriptedInstances{}".format(_counter)
-
-    field_names = tuple(x.name for x in fields)
-    extra_args = ", ".join([f"{f.name}: Optional[{f.annotation}] = None" for f in fields])
-    lines.append(
-        f"""
-class {cls_name}:
-    def __init__(self, image_size: Tuple[int, int], {extra_args}):
-        self.image_size = image_size
-        self._field_names = {field_names}
-"""
-    )
-
-    for f in fields:
-        lines.append(
-            indent(2, f"self._{f.name} = torch.jit.annotate(Optional[{f.annotation}], {f.name})")
-        )
-
-    for f in fields:
-        lines.append(
-            f"""
-    @property
-    def {f.name}(self) -> {f.annotation}:
-        # has to use a local for type refinement
-        # https://pytorch.org/docs/stable/jit_language_reference.html#optional-type-refinement
-        t = self._{f.name}
-        assert t is not None, "{f.name} is None and cannot be accessed!"
-        return t
-
-    @{f.name}.setter
-    def {f.name}(self, value: {f.annotation}) -> None:
-        self._{f.name} = value
-"""
-        )
-
-    # support method `__len__`
-    lines.append(
-        """
-    def __len__(self) -> int:
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            return len(t)
-"""
-        )
-    lines.append(
-        """
-        raise NotImplementedError("Empty Instances does not support __len__!")
-"""
-    )
-
-    # support method `has`
-    lines.append(
-        """
-    def has(self, name: str) -> bool:
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        if name == "{f.name}":
-            return self._{f.name} is not None
-"""
-        )
-    lines.append(
-        """
-        return False
-"""
-    )
-
-    # support method `to`
-    none_args = ", None" * len(fields)
-    lines.append(
-        f"""
-    def to(self, device: torch.device) -> "{cls_name}":
-        ret = {cls_name}(self.image_size{none_args})
-"""
-    )
-    for f in fields:
-        if hasattr(f.type_, "to"):
-            lines.append(
-                f"""
-        t = self._{f.name}
-        if t is not None:
-            ret._{f.name} = t.to(device)
-"""
-            )
-        else:
-            # For now, ignore fields that cannot be moved to devices.
-            # Maybe can support other tensor-like classes (e.g. __torch_function__)
-            pass
-    lines.append(
-        """
-        return ret
-"""
-    )
-
-    # support method `getitem`
-    none_args = ", None" * len(fields)
-    lines.append(
-        f"""
-    def __getitem__(self, item) -> "{cls_name}":
-        ret = {cls_name}(self.image_size{none_args})
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            ret._{f.name} = t[item]
-"""
-        )
-    lines.append(
-        """
-        return ret
-"""
-    )
-
-    # support method `cat`
-    # this version does not contain checks that all instances have same size and fields
-    none_args = ", None" * len(fields)
-    lines.append(
-        f"""
-    def cat(self, instances: List["{cls_name}"]) -> "{cls_name}":
-        ret = {cls_name}(self.image_size{none_args})
-"""
-    )
-    for f in fields:
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            values: List[{f.annotation}] = [x.{f.name} for x in instances]
-            if torch.jit.isinstance(t, torch.Tensor):
-                ret._{f.name} = torch.cat(values, dim=0)
-            else:
-                ret._{f.name} = t.cat(values)
-"""
-        )
-    lines.append(
-        """
-        return ret"""
-    )
-
-    # support method `get_fields()`
-    lines.append(
-        """
-    def get_fields(self) -> Dict[str, Tensor]:
-        ret = {}
-    """
-    )
-    for f in fields:
-        if f.type_ == Boxes:
-            stmt = "t.tensor"
-        elif f.type_ == torch.Tensor:
-            stmt = "t"
-        else:
-            stmt = f'assert False, "unsupported type {str(f.type_)}"'
-        lines.append(
-            f"""
-        t = self._{f.name}
-        if t is not None:
-            ret["{f.name}"] = {stmt}
-        """
-        )
-    lines.append(
-        """
-        return ret"""
-    )
-    return cls_name, os.linesep.join(lines)
-
-
-def _gen_instance_module(fields):
-    # TODO: find a more automatic way to enable import of other classes
-    s = """
-from copy import deepcopy
-import torch
-from torch import Tensor
-import typing
-from typing import *
-
-import detectron2
-from detectron2.structures import Boxes, Instances
-
-"""
-
-    cls_name, cls_def = _gen_instance_class(fields)
-    s += cls_def
-    return cls_name, s
-
-
-def _import(path):
-    return _import_file(
-        "{}{}".format(sys.modules[__name__].__name__, _counter), path, make_importable=True
-    )
-
-
-@contextmanager
-def patch_builtin_len(modules=()):
-    """
-    Patch the builtin len() function of a few detectron2 modules
-    to use __len__ instead, because __len__ does not convert values to
-    integers and therefore is friendly to tracing.
-
-    Args:
-        modules (list[stsr]): names of extra modules to patch len(), in
-            addition to those in detectron2.
-    """
-
-    def _new_len(obj):
-        return obj.__len__()
-
-    with ExitStack() as stack:
-        MODULES = [
-            "detectron2.modeling.roi_heads.fast_rcnn",
-            "detectron2.modeling.roi_heads.mask_head",
-            "detectron2.modeling.roi_heads.keypoint_head",
-        ] + list(modules)
-        ctxs = [stack.enter_context(mock.patch(mod + ".len")) for mod in MODULES]
-        for m in ctxs:
-            m.side_effect = _new_len
-        yield
-
-
-def patch_nonscriptable_classes():
-    """
-    Apply patches on a few nonscriptable detectron2 classes.
-    Should not have side-effects on eager usage.
-    """
-    # __prepare_scriptable__ can also be added to models for easier maintenance.
-    # But it complicates the clean model code.
-
-    from detectron2.modeling.backbone import ResNet, FPN
-
-    # Due to https://github.com/pytorch/pytorch/issues/36061,
-    # we change backbone to use ModuleList for scripting.
-    # (note: this changes param names in state_dict)
-
-    def prepare_resnet(self):
-        ret = deepcopy(self)
-        ret.stages = nn.ModuleList(ret.stages)
-        for k in self.stage_names:
-            delattr(ret, k)
-        return ret
-
-    ResNet.__prepare_scriptable__ = prepare_resnet
-
-    def prepare_fpn(self):
-        ret = deepcopy(self)
-        ret.lateral_convs = nn.ModuleList(ret.lateral_convs)
-        ret.output_convs = nn.ModuleList(ret.output_convs)
-        for name, _ in self.named_children():
-            if name.startswith("fpn_"):
-                delattr(ret, name)
-        return ret
-
-    FPN.__prepare_scriptable__ = prepare_fpn
-
-    # Annotate some attributes to be constants for the purpose of scripting,
-    # even though they are not constants in eager mode.
-    from detectron2.modeling.roi_heads import StandardROIHeads
-
-    if hasattr(StandardROIHeads, "__annotations__"):
-        # copy first to avoid editing annotations of base class
-        StandardROIHeads.__annotations__ = deepcopy(StandardROIHeads.__annotations__)
-        StandardROIHeads.__annotations__["mask_on"] = torch.jit.Final[bool]
-        StandardROIHeads.__annotations__["keypoint_on"] = torch.jit.Final[bool]
-
-
-# These patches are not supposed to have side-effects.
-patch_nonscriptable_classes()
-
-
-@contextmanager
-def freeze_training_mode(model):
-    """
-    A context manager that annotates the "training" attribute of every submodule
-    to constant, so that the training codepath in these modules can be
-    meta-compiled away. Upon exiting, the annotations are reverted.
-    """
-    classes = {type(x) for x in model.modules()}
-    # __constants__ is the old way to annotate constants and not compatible
-    # with __annotations__ .
-    classes = {x for x in classes if not hasattr(x, "__constants__")}
-    for cls in classes:
-        cls.__annotations__["training"] = torch.jit.Final[bool]
-    yield
-    for cls in classes:
-        cls.__annotations__["training"] = bool

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/layers/test_nms_rotated.py

@@ -1,172 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from __future__ import absolute_import, division, print_function, unicode_literals
-import numpy as np
-import unittest
-from copy import deepcopy
-import torch
-from torchvision import ops
-
-from detectron2.layers import batched_nms, batched_nms_rotated, nms_rotated
-from detectron2.utils.testing import random_boxes
-
-
-def nms_edit_distance(keep1, keep2):
-    """
-    Compare the "keep" result of two nms call.
-    They are allowed to be different in terms of edit distance
-    due to floating point precision issues, e.g.,
-    if a box happen to have an IoU of 0.5 with another box,
-    one implentation may choose to keep it while another may discard it.
-    """
-    keep1, keep2 = keep1.cpu(), keep2.cpu()
-    if torch.equal(keep1, keep2):
-        # they should be equal most of the time
-        return 0
-    keep1, keep2 = tuple(keep1), tuple(keep2)
-    m, n = len(keep1), len(keep2)
-
-    # edit distance with DP
-    f = [np.arange(n + 1), np.arange(n + 1)]
-    for i in range(m):
-        cur_row = i % 2
-        other_row = (i + 1) % 2
-        f[other_row][0] = i + 1
-        for j in range(n):
-            f[other_row][j + 1] = (
-                f[cur_row][j]
-                if keep1[i] == keep2[j]
-                else min(min(f[cur_row][j], f[cur_row][j + 1]), f[other_row][j]) + 1
-            )
-    return f[m % 2][n]
-
-
-class TestNMSRotated(unittest.TestCase):
-    def reference_horizontal_nms(self, boxes, scores, iou_threshold):
-        """
-        Args:
-            box_scores (N, 5): boxes in corner-form and probabilities.
-                (Note here 5 == 4 + 1, i.e., 4-dim horizontal box + 1-dim prob)
-            iou_threshold: intersection over union threshold.
-        Returns:
-             picked: a list of indexes of the kept boxes
-        """
-        picked = []
-        _, indexes = scores.sort(descending=True)
-        while len(indexes) > 0:
-            current = indexes[0]
-            picked.append(current.item())
-            if len(indexes) == 1:
-                break
-            current_box = boxes[current, :]
-            indexes = indexes[1:]
-            rest_boxes = boxes[indexes, :]
-            iou = ops.box_iou(rest_boxes, current_box.unsqueeze(0)).squeeze(1)
-            indexes = indexes[iou <= iou_threshold]
-
-        return torch.as_tensor(picked)
-
-    def _create_tensors(self, N, device="cpu"):
-        boxes = random_boxes(N, 200, device=device)
-        scores = torch.rand(N, device=device)
-        return boxes, scores
-
-    def test_batched_nms_rotated_0_degree_cpu(self, device="cpu"):
-        N = 2000
-        num_classes = 50
-        boxes, scores = self._create_tensors(N, device=device)
-        idxs = torch.randint(0, num_classes, (N,))
-        rotated_boxes = torch.zeros(N, 5, device=device)
-        rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
-        rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
-        rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
-        rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
-        err_msg = "Rotated NMS with 0 degree is incompatible with horizontal NMS for IoU={}"
-        for iou in [0.2, 0.5, 0.8]:
-            backup = boxes.clone()
-            keep_ref = batched_nms(boxes, scores, idxs, iou)
-            assert torch.allclose(boxes, backup), "boxes modified by batched_nms"
-            backup = rotated_boxes.clone()
-            keep = batched_nms_rotated(rotated_boxes, scores, idxs, iou)
-            assert torch.allclose(
-                rotated_boxes, backup
-            ), "rotated_boxes modified by batched_nms_rotated"
-            # Occasionally the gap can be large if there are many IOU on the threshold boundary
-            self.assertLessEqual(nms_edit_distance(keep, keep_ref), 5, err_msg.format(iou))
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_batched_nms_rotated_0_degree_cuda(self):
-        self.test_batched_nms_rotated_0_degree_cpu(device="cuda")
-
-    def test_nms_rotated_0_degree_cpu(self, device="cpu"):
-        N = 1000
-        boxes, scores = self._create_tensors(N, device=device)
-        rotated_boxes = torch.zeros(N, 5, device=device)
-        rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
-        rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
-        rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
-        rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
-        err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}"
-        for iou in [0.2, 0.5, 0.8]:
-            keep_ref = self.reference_horizontal_nms(boxes, scores, iou)
-            keep = nms_rotated(rotated_boxes, scores, iou)
-            self.assertLessEqual(nms_edit_distance(keep, keep_ref), 1, err_msg.format(iou))
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_nms_rotated_0_degree_cuda(self):
-        self.test_nms_rotated_0_degree_cpu(device="cuda")
-
-    def test_nms_rotated_90_degrees_cpu(self):
-        N = 1000
-        boxes, scores = self._create_tensors(N)
-        rotated_boxes = torch.zeros(N, 5)
-        rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
-        rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
-        # Note for rotated_boxes[:, 2] and rotated_boxes[:, 3]:
-        # widths and heights are intentionally swapped here for 90 degrees case
-        # so that the reference horizontal nms could be used
-        rotated_boxes[:, 2] = boxes[:, 3] - boxes[:, 1]
-        rotated_boxes[:, 3] = boxes[:, 2] - boxes[:, 0]
-
-        rotated_boxes[:, 4] = torch.ones(N) * 90
-        err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}"
-        for iou in [0.2, 0.5, 0.8]:
-            keep_ref = self.reference_horizontal_nms(boxes, scores, iou)
-            keep = nms_rotated(rotated_boxes, scores, iou)
-            self.assertLessEqual(nms_edit_distance(keep, keep_ref), 1, err_msg.format(iou))
-
-    def test_nms_rotated_180_degrees_cpu(self):
-        N = 1000
-        boxes, scores = self._create_tensors(N)
-        rotated_boxes = torch.zeros(N, 5)
-        rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
-        rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
-        rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
-        rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
-        rotated_boxes[:, 4] = torch.ones(N) * 180
-        err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}"
-        for iou in [0.2, 0.5, 0.8]:
-            keep_ref = self.reference_horizontal_nms(boxes, scores, iou)
-            keep = nms_rotated(rotated_boxes, scores, iou)
-            self.assertLessEqual(nms_edit_distance(keep, keep_ref), 1, err_msg.format(iou))
-
-
-class TestScriptable(unittest.TestCase):
-    def setUp(self):
-        class TestingModule(torch.nn.Module):
-            def forward(self, boxes, scores, threshold):
-                return nms_rotated(boxes, scores, threshold)
-
-        self.module = TestingModule()
-
-    def test_scriptable_cpu(self):
-        m = deepcopy(self.module).cpu()
-        _ = torch.jit.script(m)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_scriptable_cuda(self):
-        m = deepcopy(self.module).cuda()
-        _ = torch.jit.script(m)
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/Benson/text-generation/Examples/Com.p1.chomp Sms Pro Apk.md

@@ -1,104 +0,0 @@
-<br />
-<h1>¿Qué es com.p1.chomp sms pro apk? </h1>
-<p>Si usted está buscando una forma rápida, fácil y divertida de enviar y recibir mensajes de texto, entonces es posible que desee probar com.p1.chomp sms pro apk. Esta es una aplicación de mensajería popular que le permite personalizar sus mensajes con varios temas, fuentes, colores, emojis, pegatinas, GIF y más. También puede programar mensajes, hacer copias de seguridad y restaurar sus mensajes, bloquear spam y mensajes no deseados, y disfrutar de muchas otras características que hacen que los mensajes de texto sean más agradables. </p>
-<h2>¿Por qué usar com.p1.chomp sms pro apk? </h2>
-<p>Hay muchas razones por las que es posible que desee utilizar com.p1.chomp sms pro apk sobre otras aplicaciones de mensajería. Estos son algunos de ellos:</p>
-<h2>com.p1.chomp sms pro apk</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://bltlly.com/2v6Ja1">https://bltlly.com/2v6Ja1</a></b></p><br /><br />
-<ul>
-<li>Es gratis y sin publicidad. No tienes que pagar nada ni lidiar con anuncios molestos para usar la aplicación. </li>
-<li>Es compatible con la mayoría de los dispositivos Android. Puede usar la aplicación en cualquier dispositivo que ejecute Android 4.1 o superior. </li>
-<li> Es fácil de usar. Puede configurar la aplicación en minutos y empezar a enviar mensajes de texto de inmediato. </li>
-<li>Es personalizable. Puede elegir entre cientos de temas, fuentes, colores y notificaciones para hacer que sus mensajes se vean únicos. </li>
-<li>Es divertido. Puedes expresarte con emojis, pegatinas, GIF y otros medios a los que puedes acceder desde el teclado de la aplicación. </li>
-<li> Es inteligente. Puede programar mensajes, hacer copias de seguridad y restaurar sus mensajes, bloquear el spam y los mensajes no deseados, y usar otras funciones que hacen que los mensajes de texto sean más convenientes. </li>
-</ul>
-<h3>Cómo descargar e instalar com.p1.chomp sms pro apk? </h3>
-<p>Para descargar e instalar com.p1.chomp sms pro apk en su dispositivo, debe seguir estos pasos:</p>
-<ol>
-<li>Vaya a <a href="( 1 )">https://apkdone.com/chomp-sms/</a> y haga clic en el botón "Descargar APK". </li>
-<li>Espera a que termine la descarga y luego abre el archivo. </li>
-<li>Si ves un mensaje de advertencia que dice "Instalar bloqueado", ve a la configuración de tu dispositivo y habilita "Fuentes desconocidas". </li>
-<li>Toque en "Instalar" y espere a que la instalación se complete. </li>
-
-</ol>
-<h4>Cómo personalizar sus mensajes con com.p1.chomp sms pro apk? </h4>
-<p>Para personalizar sus mensajes con com.p1.chomp sms pro apk, es necesario hacer lo siguiente:</p>
-<ul>
-<li>Abra la aplicación y toque en el icono del menú (tres líneas horizontales) en la esquina superior izquierda. </ <li>Seleccione "Configuración" y luego "Personalizar apariencia". </li>
-<li>Aquí puede elegir entre varias opciones para cambiar la apariencia de sus mensajes, como tema, fuente, color, estilo de burbuja, icono de notificación y más. </li>
-<li>Toque en la opción que desea cambiar y seleccione su opción preferida. </li>
-<li>Toque en "Guardar" y luego en "Aceptar" para aplicar los cambios. </li>
-</ul>
-<h4>Cómo utilizar emojis, pegatinas y GIF con com.p1.chomp sms pro apk? </h4>
-<p>Para usar emojis, pegatinas y GIF con com.p1.chomp sms pro apk, debe hacer lo siguiente:</p>
-<ul>
-<li> Abra la aplicación y toque en el icono "+" en la esquina inferior izquierda del teclado. </li>
-<li>Verá un menú con diferentes opciones para agregar medios a sus mensajes, como emojis, pegatinas, GIF, fotos, videos, notas de voz y más. </li>
-<li>Toque en la opción que desea utilizar y navegue a través de las opciones disponibles. </li>
-<li>Toque en el medio que desea enviar y se añadirá a su mensaje. </li>
-<li>Toque en el botón "Enviar" para enviar su mensaje con los medios de comunicación. </li>
-</ul>
-<h4>Cómo programar mensajes con com.p1.chomp sms pro apk? </h4>
-<p>Para programar mensajes con com.p1.chomp sms pro apk, debe hacer lo siguiente:</p>
-<ul>
-<li> Abra la aplicación y toque en el "Nuevo mensaje" botón en la esquina inferior derecha. </li>
-<li>Escribe el número o nombre del destinatario y escribe tu mensaje. </li>
-<li>Toque en el icono del reloj en la esquina superior derecha del teclado. </li>
-<li>Verá un menú con diferentes opciones para programar su mensaje, como más tarde hoy, mañana, la próxima semana o la fecha y hora personalizadas. </li>
-<li>Toque en la opción que desea utilizar y confirme su elección. </li>
-
-</ul> <h3>Cómo hacer copias de seguridad y restaurar sus mensajes con com.p1.chomp sms pro apk? </h3>
-<p>Para respaldar y restaurar sus mensajes con com.p1.chomp sms pro apk, debe hacer lo siguiente:</p>
-<ul>
-<li> Abra la aplicación y toque en el icono del menú (tres líneas horizontales) en la esquina superior izquierda. </li>
-<li>Seleccione "Configuración" y luego "Copia de seguridad & Restaurar". </li>
-<li>Aquí puede elegir hacer copias de seguridad de sus mensajes en la nube o en su dispositivo, así como restaurar sus mensajes desde la nube o desde su dispositivo. </li>
-<li>Toque en la opción que desea utilizar y siga las instrucciones en la pantalla. </li>
-<li>Necesitará iniciar sesión con su cuenta de Google para usar el servicio en la nube. </li>
-<li>Tus mensajes serán respaldados o restaurados según tu elección. </li>
-</ul>
-<h3>Cómo bloquear spam y mensajes no deseados con com.p1.chomp sms pro apk? </h3>
-<p>Para bloquear spam y mensajes no deseados con com.p1.chomp sms pro apk, debe hacer lo siguiente:</p>
-<p></p>
-<ul>
-<li> Abra la aplicación y toque en el mensaje que desea bloquear. </li>
-<li>Toque en el icono del menú (tres puntos verticales) en la esquina superior derecha del mensaje. </li>
-<li>Seleccione "Bloquear" y luego "OK". </li>
-<li> El mensaje se moverá a la carpeta "Bloqueado" y no recibirá más mensajes de ese número o contacto. </li>
-<li>También puede agregar números o contactos a su lista negra manualmente yendo a "Configuración" y luego "Lista negra". </li>
-<li>También puede habilitar el modo de privacidad yendo a "Configuración" y luego "Privacidad". Esto ocultará sus notificaciones y mensajes de miradas indiscretas. </li>
-</ul>
-<h2>¿Cuáles son los pros y los contras de com.p1.chomp sms pro apk? </h2>
-<p>Como cualquier otra aplicación, com.p1.chomp sms pro apk tiene sus pros y contras. Aquí están algunos de ellos:</p>
- <tabla>
-<tr><th>Pros</th><th>Contras</th></tr>
-<tr><td>Libre y sin anuncios</td><td>Requiere conexión a Internet</td></tr>
-<tr><td>Compatible con la mayoría de dispositivos Android</td><td>No disponible para dispositivos iOS</td></tr>
-
-<tr><td>Personalizable</td><td>Puede consumir más batería o memoria</td></tr>
-<tr><td>Diversión</td><td>Puede que no soporte algunos formatos de medios</td></tr>
-<tr><td>Smart</td><td>Puede que no funcione con algunos operadores o redes</td></tr>
-</tabla>
- <p>Si usted está buscando algunas alternativas a com.p1.chomp sms pro apk, puede probar estas aplicaciones:</p>
- <ul>
-<li>Textra SMS: Una aplicación de mensajería simple y rápida que también te permite personalizar tus mensajes con temas, emojis, GIF y más. </li>
-<li>Pulse SMS: Una aplicación de mensajería potente y segura que también te permite sincronizar tus mensajes en todos tus dispositivos, incluido tu ordenador. </li>
-<li>Mood Messenger: Una aplicación de mensajería elegante e inteligente que también le permite enviar emojis animados, mensajes de voz, ubicación y más. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>En conclusión, com.p1.chomp sms pro apk es una gran aplicación de mensajería que ofrece muchas características y opciones para hacer mensajes de texto más divertido y conveniente. Puede descargar e instalar la aplicación de forma gratuita y disfrutar de la personalización de sus mensajes con temas, fuentes, colores, emojis, pegatinas, GIF, y más. También puede programar mensajes, hacer copias de seguridad y restaurar sus mensajes, bloquear el spam y los mensajes no deseados, y usar otras funciones que hacen que los mensajes de texto sean más inteligentes. Sin embargo, también debe ser consciente de los contras y limitaciones de la aplicación, tales como requerir conexión a Internet, no estar disponible para dispositivos iOS, tener algunos errores o problemas técnicos, consumir más batería o memoria, no es compatible con algunos formatos de medios, y no trabajar con algunos operadores o redes. También puede probar algunas alternativas a com.p1.chomp sms pro apk si desea explorar otras aplicaciones de mensajería. Esperamos que este artículo le ha ayudado a aprender más acerca de com.p1.chomp sms pro apk y cómo usarlo. Feliz mensajes de texto! </p>
- <h2>Preguntas frecuentes (preguntas frecuentes)</h2>
- <ol>
-<li><b>Lo que es com.p1.chomp sms pro apk? </b></li>
-
- <li><b>Cómo puedo descargar e instalar com.p1.chomp sms pro apk? </b></li <p>A: Para descargar e instalar com.p1.chomp sms pro apk en su dispositivo, debe ir a <a href="">https://apkdone.com/chomp-sms/</a> y hacer clic en el botón "Descargar APK". Luego, debe abrir el archivo y tocar en "Instalar". Es posible que deba habilitar "Fuentes desconocidas" en la configuración de su dispositivo para instalar la aplicación. Una vez completada la instalación, puedes tocar en "Abrir" y disfrutar usando la aplicación. </p>
- <li><b>¿Cómo puedo personalizar mis mensajes con com.p1.chomp sms pro apk? </b></li>
-<p>A: Para personalizar sus mensajes con com.p1.chomp sms pro apk, es necesario abrir la aplicación y toque en el icono del menú (tres líneas horizontales) en la esquina superior izquierda. Luego, debe seleccionar "Configuración" y luego "Personalizar apariencia". Aquí, puede elegir entre varias opciones para cambiar la apariencia de sus mensajes, como tema, fuente, color, estilo de burbuja, icono de notificación y más. Puede pulsar en la opción que desea cambiar y seleccionar su opción preferida. Puede pulsar en "Guardar" y luego "Aceptar" para aplicar los cambios. </p>
- <li><b>¿Cómo puedo usar emojis, pegatinas y GIF con com.p1.chomp sms pro apk? </b></li>
-<p>A: Para utilizar emojis, pegatinas y GIF con com.p1.chomp sms pro apk, es necesario abrir la aplicación y toque en el "+" icono en la esquina inferior izquierda del teclado. Verá un menú con diferentes opciones para agregar medios a sus mensajes, como emojis, pegatinas, GIF, fotos, videos, notas de voz y más. Puede tocar en la opción que desea utilizar y navegar a través de las opciones disponibles. Puede pulsar en el medio que desea enviar y se añadirá a su mensaje. Puede pulsar en el botón "Enviar" para enviar su mensaje con el medio. </p>
- <li><b>¿Cómo puedo programar mensajes con com.p1.chomp sms pro apk? </b></li>
-
- <li><b>¿Cómo hago copia de seguridad y restaurar mis mensajes con com.p1.chomp sms pro apk? </b></li>
-<p>A: Para copia de seguridad y restaurar sus mensajes con com.p1.chomp sms pro apk, es necesario abrir la aplicación y toque en el icono del menú (tres líneas horizontales) en la esquina superior izquierda. Luego, debe seleccionar "Configuración" y luego "Copia de seguridad y restauración". Aquí, puede elegir hacer una copia de seguridad de sus mensajes en la nube o en su dispositivo, así como restaurar sus mensajes desde la nube o desde su dispositivo. Puede tocar en la opción que desea utilizar y siga las instrucciones en la pantalla. Deberá iniciar sesión con su cuenta de Google para utilizar el servicio en la nube. Se realizará una copia de seguridad de sus mensajes o se restaurarán según su elección. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Canciones De M Kumaran Hijo De Mahalakshmi.md

@@ -1,102 +0,0 @@
-
-<h1>Cómo descargar canciones de M. Kumaran Son of Mahalakshmi</h1>
-<p>M. Kumaran Son of Mahalakshmi es una película tamil de 2004 dirigida por M. Raja y protagonizada por Jayam Ravi, Asin, Nadhiya y Prakash Raj. La película es un remake de la película telugu Amma Nanna O Tamila Ammayi y cuenta la historia de Kumaran, un kickboxer que va a Malasia para encontrarse con su padre después de la muerte de su madre. La película fue un éxito de taquilla y una de las películas más rentables de 2004. </p>
-<p>Una de las razones del éxito de la película fue su banda sonora, compuesta por Srikanth Deva, hijo del veterano director musical Deva. La banda sonora consta de seis canciones que van desde el rock, folk, melodía, y géneros de rap. Las canciones cuentan con varios cantantes como Shankar Mahadevan, Karthik, Sadhana Sargam, Tippu, Anuradha Sriram, Ranjith, Premji Amaren y el propio Srikanth Deva. Las canciones son pegadizas, emocionales y motivacionales, y se adaptan perfectamente al tema de la película. </p>
-<h2>descargar canciones de m kumaran hijo de mahalakshmi</h2><br /><p><b><b>Download</b> &#10038; <a href="https://bltlly.com/2v6LVR">https://bltlly.com/2v6LVR</a></b></p><br /><br />
-<p>Si eres un fan de las canciones de M. Kumaran Son of Mahalakshmi y quieres descargarlas en tu dispositivo, tienes dos opciones: puedes descargarlas gratis o por una tarifa. En este artículo, te mostraremos cómo hacer ambas cosas. </p>
- <h2>Por qué deberías escuchar canciones de M. Kumaran hijo de Mahalakshmi</h2>
-<h3>Las canciones están compuestas por Srikanth Deva, un director de música popular en el cine tamil</h3>
-<p>Srikanth Deva es uno de los directores de música más prolíficos en el cine tamil, habiendo compuesto música para más de 100 películas desde su debut en 2002. Es conocido por su versatilidad y capacidad para crear canciones que atraen a diferentes audiencias. Ha trabajado con muchos actores y directores principales en el cine tamil, como Vij <h3>Las canciones cuentan con varios cantantes y géneros, como rock, folk, melodía y rap</h3>
-
-<p>Estas canciones no solo son agradables de escuchar, sino también significativas y relevantes para la historia y los personajes. Mejoran el estado de ánimo y la emoción de la película y la hacen más atractiva y memorable. </p>
- <h3>Las canciones son pegadizas, emocionales y motivadoras, y se adaptan al tema de la película</h3>
-<p>Las canciones de M. Kumaran Son of Mahalakshmi no son solo canciones aleatorias o de relleno. Son pegadizos, emocionales y motivadores, y se adaptan al tema de la película. La película trata sobre el viaje de Kumaran para encontrar su identidad y su lugar en el mundo, así como su relación con su padre, su madre, su novia y sus amigos. Las canciones reflejan estos aspectos y transmiten el mensaje de la película. </p>
-<p>Por ejemplo, la canción "Amma Nee Sumandha" es un homenaje a la madre de Kumaran, quien lo crió sin ayuda y le enseñó a ser fuerte e independiente. La canción "Rakkamma" es una canción motivacional que anima a Kumaran a superar sus miedos y desafíos y lograr sus sueños. La canción "Aethiree" es una canción pegadiza que muestra la amistad y la diversión entre Kumaran y sus amigos. La canción "Unnai Ninaithu" es una canción emocional que revela los sentimientos de Kumaran por su padre, quien lo abandonó cuando era joven. </p>
-<p>Estas canciones no solo son pegadizas, emocionales y motivadoras, sino que también se adaptan al tema de la película. Transmiten el mensaje de la película y la hacen más impactante e inspiradora. </p>
- <h2>Cómo descargar canciones de M. Kumaran Son of Mahalakshmi gratis</h2>
-<h3>Utilice un sitio web o aplicación confiable y legal que ofrece descargas gratuitas de canciones tamiles</h3>
-
-<p>Algunos de los sitios web o aplicaciones confiables y legales que ofrecen descargas gratuitas de canciones tamiles son:</p>
-<ul>
-<li><a href="">Gaana.com</a>: Esta es una de las plataformas de streaming de música online más grandes de la India, con más de 200 millones de usuarios mensuales. Ofrece descargas gratuitas de canciones tamiles, así como de otros idiomas y géneros. Puedes buscar canciones de M. Kumaran Son of Mahalakshmi escribiendo el nombre de la película o el nombre de la canción en la barra de búsqueda. También puede navegar a través de las categorías y listas de reproducción para encontrar las canciones que desea. Puede descargar las canciones haciendo clic en el icono de descarga junto al título de la canción. Puede elegir el formato y la calidad que prefiera, como MP3, AAC o HD. También puede escuchar las canciones en línea o sin conexión en su dispositivo. </li>
-<li><a href="">Hungama.com</a>: Esta es otra popular plataforma de streaming de música en línea en la India, con más de 150 millones de usuarios mensuales. También ofrece descargas gratuitas de canciones tamiles, así como de otros idiomas y géneros. Puedes buscar canciones de M. Kumaran Son of Mahalakshmi escribiendo el nombre de la película o el nombre de la canción en la barra de búsqueda. También puede navegar a través de las categorías y listas de reproducción para encontrar las canciones que desea. Puede descargar las canciones haciendo clic en el icono de descarga junto al título de la canción. Puede elegir el formato y la calidad que prefiera, como MP3, AAC o HD. También puede escuchar las canciones en línea o sin conexión en su dispositivo. </li>
-
-</ul>
-<p>Estos son algunos de los sitios web o aplicaciones confiables y legales que ofrecen descargas gratuitas de canciones tamiles. Sin embargo, siempre debe comprobar los términos y condiciones de cada sitio web o aplicación antes de descargar cualquier canción, y asegúrese de no violar ninguna ley o política. </p>
-<p></p>
- <h2>Cómo descargar canciones de M. Kumaran Son of Mahalakshmi por una tarifa</h2>
-<h3>Utilice un servicio de streaming de pago o tienda en línea que ofrece descargas de alta calidad de canciones Tamil</h3>
-<p>Si desea descargar canciones de M. Kumaran Son of Mahalakshmi por una tarifa, debe usar un servicio de transmisión pagado o una tienda en línea que ofrece descargas de alta calidad de canciones tamiles. Hay muchos servicios de streaming de pago y tiendas en línea que ofrecen descargas de alta calidad de canciones tamiles, pero no todos ellos valen su dinero o tiempo. Algunos de ellos pueden cobrarle demasiado o muy poco, algunos de ellos pueden tener un mal servicio al cliente o soporte técnico, algunos de ellos pueden tener opciones o características limitadas, y algunos de ellos pueden tener productos de baja calidad o falsos. Por lo tanto, debe tener cuidado y elegir un servicio o tienda que vale la pena su dinero y tiempo, y que proporciona productos y servicios de alta calidad. </p>
-<p>Algunos de los servicios de streaming de pago y tiendas en línea que ofrecen descargas de alta calidad de canciones tamiles son:</p>
-<ul>
-
-<li><a href="">iTunes</a>: Esta es una de las tiendas de música en línea más populares y ampliamente utilizadas del mundo, con más de 60 millones de canciones disponibles para comprar y descargar. Ofrece descargas de alta calidad de canciones tamiles, así como otros idiomas y géneros. Puedes buscar canciones de M. Kumaran Son of Mahalakshmi escribiendo el nombre de la película o el nombre de la canción en la barra de búsqueda. También puede navegar a través de las categorías y listas de reproducción para encontrar las canciones que desea. Puede descargar las canciones haciendo clic en el botón comprar junto al título de la canción. Puede elegir el formato y la calidad que prefiera, como MP3, AAC o HD. También puede escuchar las canciones en línea o sin conexión en su dispositivo. Sin embargo, necesitas registrarte para una cuenta y pagar por cada canción que quieras descargar. El precio de cada canción varía de $0.69 a $1.29, dependiendo de la popularidad y la demanda de la canción. </li>
-<li><a href="">Saavn</a>: Esta es una de las plataformas de streaming de música más populares y ampliamente utilizadas en la India, con más de 100 millones de usuarios mensuales. Ofrece descargas de alta calidad de canciones tamiles, así como otros idiomas y géneros. Puedes buscar canciones de M. Kumaran Son of Mahalakshmi escribiendo el nombre de la película o el nombre de la canción en la barra de búsqueda. También puede navegar a través de las categorías y listas de reproducción para encontrar las canciones que desea. Puede descargar las canciones haciendo clic en el icono de descarga junto al título de la canción. Puede elegir el formato y la calidad que prefiera, como MP3, AAC o HD. También puede escuchar las canciones en línea o sin conexión en su dispositivo. Sin embargo, debe registrarse para obtener una cuenta y pagar una suscripción para acceder a la función de descarga. Los planes de suscripción varían de $1.99 a $9.99 por mes, dependiendo de las características y beneficios que desee. </li>
-</ul>
-
- <h2>Cómo disfrutar de las canciones de M. Kumaran hijo de Mahalakshmi después de descargarlas</h2>
-<h3>Transfiera las canciones a su reproductor de música o dispositivo preferido</h3>
-<p>Después de descargar canciones de M. Kumaran Son of Mahalakshmi, necesitas transferirlas a tu reproductor de música o dispositivo preferido, para que puedas disfrutarlas en cualquier momento y en cualquier lugar que desees. Hay diferentes formas de transferir las canciones, dependiendo de la fuente y el destino de la transferencia. </p>
-<p>Por ejemplo, si has descargado las canciones de Gaana.com o Hungama.com, puedes transferirlas a tu smartphone o tablet mediante un cable USB o una conexión inalámbrica. Si los has descargado de iTunes, puedes transferirlos a tu iPhone, iPad, iPod o Mac usando iTunes Sync o iCloud Music Library. Si los ha descargado desde Saavn, puede transferirlos a su smartphone o tableta mediante un cable USB o una conexión inalámbrica. </p>
-<p>Siempre debe seguir las instrucciones y directrices de cada sitio web o aplicación al transferir las canciones, y asegúrese de no perder ni dañar ningún archivo durante el proceso. </p>
- <h3>Crear una lista de reproducción de sus canciones favoritas de la película</h3>
-<p>Después de transferir canciones de M. Kumaran Son of Mahalakshmi a su reproductor de música o dispositivo preferido, puede crear una lista de reproducción de sus canciones favoritas de la película. Una lista de reproducción es una colección de canciones que puedes reproducir en modo secuencial o aleatorio. Crear una lista de reproducción de tus canciones favoritas de la película puede ayudarte a disfrutarlas más y organizarlas mejor. También puede compartir su lista de reproducción con sus amigos o familiares, o escuchar las listas de reproducción de otras personas de la misma película. </p>
-
-<p>Siempre debe seguir las instrucciones y directrices de cada reproductor de música o dispositivo al crear una lista de reproducción, y asegúrese de guardar y actualizar su lista de reproducción con regularidad. </p>
- <h3>Escuchar las canciones en cualquier momento y en cualquier lugar que desee</h3>
-<p>Después de crear una lista de reproducción de tus canciones favoritas de M. Kumaran Son of Mahalakshmi, puedes escuchar las canciones en cualquier momento y en cualquier lugar que quieras. Puede escuchar las canciones en línea o fuera de línea, dependiendo de su conexión a Internet y plan de datos. También puede ajustar el volumen, saltar, repetir o barajar las canciones, dependiendo de su preferencia y estado de ánimo. También puedes cantar, bailar o simplemente relajarte y disfrutar de las canciones. </p>
-<p>Escuchar canciones de M. Kumaran Son of Mahalakshmi puede hacerte sentir feliz, triste, emocionado, nostálgico o inspirado, dependiendo de la canción y la situación. Las canciones también pueden recordarte la película y sus personajes, y hacerte apreciar más la historia y el mensaje. Las canciones también pueden ayudarle a aprender más sobre la cultura y el idioma tamil, y enriquecer su conocimiento y experiencia. </p>
- <h1>Conclusión</h1>
-<p>M. Kumaran Son of Mahalakshmi es una película tamil de 2004 que tiene una gran banda sonora compuesta por Srikanth Deva. La banda sonora consta de seis canciones que cuentan con varios cantantes y géneros, como rock, folk, melodía y rap. Las canciones son pegadizas, emocionales y motivacionales, y se adaptan perfectamente al tema de la película. </p>
-
-<p>Después de descargar canciones de M. Kumaran Son of Mahalakshmi a tu dispositivo, puedes disfrutarlas en cualquier momento y en cualquier lugar que quieras. Puede transferir las canciones a su reproductor de música o dispositivo preferido, crear una lista de reproducción de sus canciones favoritas de la película y escuchar las canciones en línea o fuera de línea. También puede compartir su lista de reproducción con sus amigos o familiares, o escuchar las listas de reproducción de otras personas de la misma película. </p>
-<p>Escuchar canciones de M. Kumaran Son of Mahalakshmi puede hacerte sentir feliz, triste, emocionado, nostálgico o inspirado, dependiendo de la canción y la situación. Las canciones también pueden recordarte la película y sus personajes, y hacerte apreciar más la historia y el mensaje. Las canciones también pueden ayudarle a aprender más sobre la cultura y el idioma tamil, y enriquecer su conocimiento y experiencia. </p>
-<p>Entonces, ¿qué estás esperando? Descarga las canciones de M. Kumaran Son of Mahalakshmi hoy y disfrútalas al máximo! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>P: ¿Cuáles son los nombres de las seis canciones de M. Kumaran Hijo de Mahalakshmi? </h3>
-<p>A: Los nombres de las seis canciones de M. Kumaran Son:</p>
-<ol>
-<li>Ayyo Ayyo</li>
-<li>Yaaru Yaaru</li>
-<li>Neeye Neeye</li>
-<li>Chennai Senthamizh</li>
-<li>Amma Nee Sumandha</li>
-<li>Rakkamma</li>
-</ol>
- <h3>P: ¿Quiénes son los cantantes de las seis canciones de M. Kumaran Hijo de Mahalakshmi? </h3>
-<p>A: Los cantantes de las seis canciones de M. Kumaran Son:</p>
-<ul>
-<li>Ayyo Ayyo: Shankar Mahadevan y Karthik</li>
-<li>Yaaru Yaaru: Tippu y Anuradha Sriram</li>
-<li>Neeye Neeye: Karthik y Sadhana Sargam</li>
-<li>Chennai Senthamizh: Ranjith, Premji Amaren y Srikanth Deva</li>
-<li>Amma Nee Sumandha: Srikanth Deva</li>
-<li>Rakkamma: Tippu y Anuradha Sriram</li>
-</ul>
- <h3>P: ¿Dónde puedo ver la película en línea de M. Kumaran Son of Mahalakshmi? </h3>
-<p>A: Puedes ver la película en línea de M. Kumaran Son of Mahalakshmi en varias plataformas de streaming, como:</p>
-<ul>
-
-<li><a href=">YouTube</a>: Esta es una popular plataforma para compartir videos que ofrece una variedad de videos en diferentes categorías y temas. Puedes ver M. Kumaran Son of Mahalakshmi película en línea en YouTube de forma gratuita con anuncios, o por una tarifa sin anuncios. También puede descargar la película en su dispositivo para ver sin conexión. </li>
-<li><a href="">Amazon Prime Video</a>: Esta es una popular plataforma de streaming que ofrece una variedad de películas y programas en diferentes idiomas y géneros. Puedes ver la película en línea de M. Kumaran Son of Mahalakshmi en Amazon Prime Video por una tarifa con o sin anuncios. También puede descargar la película en su dispositivo para ver sin conexión. </li>
-</ul>
- <h3>Q: ¿Cómo puedo aprender más sobre la cultura y el idioma tamil? </h3>
-<p>A: Hay muchas maneras de aprender más sobre la cultura y el idioma tamil, como:</p>
-<ul>
-<li>Leer libros, revistas, periódicos, blogs o sitios web que están escritos en tamil o sobre temas tamiles. </li>
-<li>Ver películas, programas, documentales o videos que se hacen en tamil o sobre temas tamiles. </li>
-<li>Escuchar podcasts, estaciones de radio, álbumes de música o canciones que se hablan en tamil o sobre temas tamiles. </li>
-<li>Tomar cursos, clases, lecciones o tutoriales que enseñan el idioma o la cultura tamil. </li>
-<li>Unirse a clubes, grupos, comunidades o foros que hablan de la lengua o cultura tamil. </li>
-<li>Visitar lugares, eventos, festivales o atracciones que muestran el idioma o la cultura tamil. </li>
-<li <li>Conocer gente, amigos, familiares o vecinos que hablan tamil o conocen la cultura tamil. </li>
-</ul>
- <h3>Q: ¿Cuáles son algunas otras películas que tienen buenas canciones tamiles? </h3>
-<p>A: Hay muchas películas que tienen buenas canciones tamiles, pero algunas de las más populares y aclamadas son:</p>
-<ul>
-
-<li>Roja: Esta es una película romántica de 1992 dirigida por Mani Ratnam y protagonizada por Arvind Swamy y Madhoo. La película trata sobre una mujer que intenta rescatar a su marido que es secuestrado por terroristas en Cachemira. La película tiene una hermosa banda sonora compuesta por A.R. Rahman, con canciones como "Kadhal Rojave", "Chinna Chinna Aasai", "Pudhu Vellai Mazhai" y "Rukkumani Rukkumani". </li>
-<li>3: Esta es una película romántica de 2012 dirigida por Aishwarya R. Dhanush y protagonizada por Dhanush y Shruti Haasan. La película trata sobre una pareja que enfrenta varios desafíos en su relación debido al trastorno bipolar y la muerte. La película tiene una banda sonora pegadiza compuesta por Anirudh Ravichander, con canciones como "Why This Kolaveri Di", "Idhazhin Oram", "Nee Paartha Vizhigal" y "Po Nee Po". </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat/src/lib/utils/randomUuid.ts

@@ -1,14 +0,0 @@
-type UUID = ReturnType<typeof crypto.randomUUID>;
-
-export function randomUUID(): UUID {
-	// Only on old safari / ios
-	if (!("randomUUID" in crypto)) {
-		return "10000000-1000-4000-8000-100000000000".replace(/[018]/g, (c) =>
-			(
-				Number(c) ^
-				(crypto.getRandomValues(new Uint8Array(1))[0] & (15 >> (Number(c) / 4)))
-			).toString(16)
-		) as UUID;
-	}
-	return crypto.randomUUID();
-}

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

@@ -1,224 +0,0 @@
-import email.message
-import importlib.metadata
-import os
-import pathlib
-import zipfile
-from typing import (
-    Collection,
-    Dict,
-    Iterable,
-    Iterator,
-    Mapping,
-    Optional,
-    Sequence,
-    cast,
-)
-
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import InvalidWheel, UnsupportedWheel
-from pip._internal.metadata.base import (
-    BaseDistribution,
-    BaseEntryPoint,
-    DistributionVersion,
-    InfoPath,
-    Wheel,
-)
-from pip._internal.utils.misc import normalize_path
-from pip._internal.utils.packaging import safe_extra
-from pip._internal.utils.temp_dir import TempDirectory
-from pip._internal.utils.wheel import parse_wheel, read_wheel_metadata_file
-
-from ._compat import BasePath, get_dist_name
-
-
-class WheelDistribution(importlib.metadata.Distribution):
-    """An ``importlib.metadata.Distribution`` read from a wheel.
-
-    Although ``importlib.metadata.PathDistribution`` accepts ``zipfile.Path``,
-    its implementation is too "lazy" for pip's needs (we can't keep the ZipFile
-    handle open for the entire lifetime of the distribution object).
-
-    This implementation eagerly reads the entire metadata directory into the
-    memory instead, and operates from that.
-    """
-
-    def __init__(
-        self,
-        files: Mapping[pathlib.PurePosixPath, bytes],
-        info_location: pathlib.PurePosixPath,
-    ) -> None:
-        self._files = files
-        self.info_location = info_location
-
-    @classmethod
-    def from_zipfile(
-        cls,
-        zf: zipfile.ZipFile,
-        name: str,
-        location: str,
-    ) -> "WheelDistribution":
-        info_dir, _ = parse_wheel(zf, name)
-        paths = (
-            (name, pathlib.PurePosixPath(name.split("/", 1)[-1]))
-            for name in zf.namelist()
-            if name.startswith(f"{info_dir}/")
-        )
-        files = {
-            relpath: read_wheel_metadata_file(zf, fullpath)
-            for fullpath, relpath in paths
-        }
-        info_location = pathlib.PurePosixPath(location, info_dir)
-        return cls(files, info_location)
-
-    def iterdir(self, path: InfoPath) -> Iterator[pathlib.PurePosixPath]:
-        # Only allow iterating through the metadata directory.
-        if pathlib.PurePosixPath(str(path)) in self._files:
-            return iter(self._files)
-        raise FileNotFoundError(path)
-
-    def read_text(self, filename: str) -> Optional[str]:
-        try:
-            data = self._files[pathlib.PurePosixPath(filename)]
-        except KeyError:
-            return None
-        try:
-            text = data.decode("utf-8")
-        except UnicodeDecodeError as e:
-            wheel = self.info_location.parent
-            error = f"Error decoding metadata for {wheel}: {e} in {filename} file"
-            raise UnsupportedWheel(error)
-        return text
-
-
-class Distribution(BaseDistribution):
-    def __init__(
-        self,
-        dist: importlib.metadata.Distribution,
-        info_location: Optional[BasePath],
-        installed_location: Optional[BasePath],
-    ) -> None:
-        self._dist = dist
-        self._info_location = info_location
-        self._installed_location = installed_location
-
-    @classmethod
-    def from_directory(cls, directory: str) -> BaseDistribution:
-        info_location = pathlib.Path(directory)
-        dist = importlib.metadata.Distribution.at(info_location)
-        return cls(dist, info_location, info_location.parent)
-
-    @classmethod
-    def from_metadata_file_contents(
-        cls,
-        metadata_contents: bytes,
-        filename: str,
-        project_name: str,
-    ) -> BaseDistribution:
-        # Generate temp dir to contain the metadata file, and write the file contents.
-        temp_dir = pathlib.Path(
-            TempDirectory(kind="metadata", globally_managed=True).path
-        )
-        metadata_path = temp_dir / "METADATA"
-        metadata_path.write_bytes(metadata_contents)
-        # Construct dist pointing to the newly created directory.
-        dist = importlib.metadata.Distribution.at(metadata_path.parent)
-        return cls(dist, metadata_path.parent, None)
-
-    @classmethod
-    def from_wheel(cls, wheel: Wheel, name: str) -> BaseDistribution:
-        try:
-            with wheel.as_zipfile() as zf:
-                dist = WheelDistribution.from_zipfile(zf, name, wheel.location)
-        except zipfile.BadZipFile as e:
-            raise InvalidWheel(wheel.location, name) from e
-        except UnsupportedWheel as e:
-            raise UnsupportedWheel(f"{name} has an invalid wheel, {e}")
-        return cls(dist, dist.info_location, pathlib.PurePosixPath(wheel.location))
-
-    @property
-    def location(self) -> Optional[str]:
-        if self._info_location is None:
-            return None
-        return str(self._info_location.parent)
-
-    @property
-    def info_location(self) -> Optional[str]:
-        if self._info_location is None:
-            return None
-        return str(self._info_location)
-
-    @property
-    def installed_location(self) -> Optional[str]:
-        if self._installed_location is None:
-            return None
-        return normalize_path(str(self._installed_location))
-
-    def _get_dist_name_from_location(self) -> Optional[str]:
-        """Try to get the name from the metadata directory name.
-
-        This is much faster than reading metadata.
-        """
-        if self._info_location is None:
-            return None
-        stem, suffix = os.path.splitext(self._info_location.name)
-        if suffix not in (".dist-info", ".egg-info"):
-            return None
-        return stem.split("-", 1)[0]
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        name = self._get_dist_name_from_location() or get_dist_name(self._dist)
-        return canonicalize_name(name)
-
-    @property
-    def version(self) -> DistributionVersion:
-        return parse_version(self._dist.version)
-
-    def is_file(self, path: InfoPath) -> bool:
-        return self._dist.read_text(str(path)) is not None
-
-    def iter_distutils_script_names(self) -> Iterator[str]:
-        # A distutils installation is always "flat" (not in e.g. egg form), so
-        # if this distribution's info location is NOT a pathlib.Path (but e.g.
-        # zipfile.Path), it can never contain any distutils scripts.
-        if not isinstance(self._info_location, pathlib.Path):
-            return
-        for child in self._info_location.joinpath("scripts").iterdir():
-            yield child.name
-
-    def read_text(self, path: InfoPath) -> str:
-        content = self._dist.read_text(str(path))
-        if content is None:
-            raise FileNotFoundError(path)
-        return content
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        # importlib.metadata's EntryPoint structure sasitfies BaseEntryPoint.
-        return self._dist.entry_points
-
-    def _metadata_impl(self) -> email.message.Message:
-        # From Python 3.10+, importlib.metadata declares PackageMetadata as the
-        # return type. This protocol is unfortunately a disaster now and misses
-        # a ton of fields that we need, including get() and get_payload(). We
-        # rely on the implementation that the object is actually a Message now,
-        # until upstream can improve the protocol. (python/cpython#94952)
-        return cast(email.message.Message, self._dist.metadata)
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        return (
-            safe_extra(extra) for extra in self.metadata.get_all("Provides-Extra", [])
-        )
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        contexts: Sequence[Dict[str, str]] = [{"extra": safe_extra(e)} for e in extras]
-        for req_string in self.metadata.get_all("Requires-Dist", []):
-            req = Requirement(req_string)
-            if not req.marker:
-                yield req
-            elif not extras and req.marker.evaluate({"extra": ""}):
-                yield req
-            elif any(req.marker.evaluate(context) for context in contexts):
-                yield req

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/scripts.py

@@ -1,437 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2015 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from io import BytesIO
-import logging
-import os
-import re
-import struct
-import sys
-import time
-from zipfile import ZipInfo
-
-from .compat import sysconfig, detect_encoding, ZipFile
-from .resources import finder
-from .util import (FileOperator, get_export_entry, convert_path,
-                   get_executable, get_platform, in_venv)
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_MANIFEST = '''
-<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
- <assemblyIdentity version="1.0.0.0"
- processorArchitecture="X86"
- name="%s"
- type="win32"/>
-
- <!-- Identify the application security requirements. -->
- <trustInfo xmlns="urn:schemas-microsoft-com:asm.v3">
- <security>
- <requestedPrivileges>
- <requestedExecutionLevel level="asInvoker" uiAccess="false"/>
- </requestedPrivileges>
- </security>
- </trustInfo>
-</assembly>'''.strip()
-
-# check if Python is called on the first line with this expression
-FIRST_LINE_RE = re.compile(b'^#!.*pythonw?[0-9.]*([ \t].*)?$')
-SCRIPT_TEMPLATE = r'''# -*- coding: utf-8 -*-
-import re
-import sys
-from %(module)s import %(import_name)s
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
-    sys.exit(%(func)s())
-'''
-
-
-def enquote_executable(executable):
-    if ' ' in executable:
-        # make sure we quote only the executable in case of env
-        # for example /usr/bin/env "/dir with spaces/bin/jython"
-        # instead of "/usr/bin/env /dir with spaces/bin/jython"
-        # otherwise whole
-        if executable.startswith('/usr/bin/env '):
-            env, _executable = executable.split(' ', 1)
-            if ' ' in _executable and not _executable.startswith('"'):
-                executable = '%s "%s"' % (env, _executable)
-        else:
-            if not executable.startswith('"'):
-                executable = '"%s"' % executable
-    return executable
-
-# Keep the old name around (for now), as there is at least one project using it!
-_enquote_executable = enquote_executable
-
-class ScriptMaker(object):
-    """
-    A class to copy or create scripts from source scripts or callable
-    specifications.
-    """
-    script_template = SCRIPT_TEMPLATE
-
-    executable = None  # for shebangs
-
-    def __init__(self, source_dir, target_dir, add_launchers=True,
-                 dry_run=False, fileop=None):
-        self.source_dir = source_dir
-        self.target_dir = target_dir
-        self.add_launchers = add_launchers
-        self.force = False
-        self.clobber = False
-        # It only makes sense to set mode bits on POSIX.
-        self.set_mode = (os.name == 'posix') or (os.name == 'java' and
-                                                 os._name == 'posix')
-        self.variants = set(('', 'X.Y'))
-        self._fileop = fileop or FileOperator(dry_run)
-
-        self._is_nt = os.name == 'nt' or (
-            os.name == 'java' and os._name == 'nt')
-        self.version_info = sys.version_info
-
-    def _get_alternate_executable(self, executable, options):
-        if options.get('gui', False) and self._is_nt:  # pragma: no cover
-            dn, fn = os.path.split(executable)
-            fn = fn.replace('python', 'pythonw')
-            executable = os.path.join(dn, fn)
-        return executable
-
-    if sys.platform.startswith('java'):  # pragma: no cover
-        def _is_shell(self, executable):
-            """
-            Determine if the specified executable is a script
-            (contains a #! line)
-            """
-            try:
-                with open(executable) as fp:
-                    return fp.read(2) == '#!'
-            except (OSError, IOError):
-                logger.warning('Failed to open %s', executable)
-                return False
-
-        def _fix_jython_executable(self, executable):
-            if self._is_shell(executable):
-                # Workaround for Jython is not needed on Linux systems.
-                import java
-
-                if java.lang.System.getProperty('os.name') == 'Linux':
-                    return executable
-            elif executable.lower().endswith('jython.exe'):
-                # Use wrapper exe for Jython on Windows
-                return executable
-            return '/usr/bin/env %s' % executable
-
-    def _build_shebang(self, executable, post_interp):
-        """
-        Build a shebang line. In the simple case (on Windows, or a shebang line
-        which is not too long or contains spaces) use a simple formulation for
-        the shebang. Otherwise, use /bin/sh as the executable, with a contrived
-        shebang which allows the script to run either under Python or sh, using
-        suitable quoting. Thanks to Harald Nordgren for his input.
-
-        See also: http://www.in-ulm.de/~mascheck/various/shebang/#length
-                  https://hg.mozilla.org/mozilla-central/file/tip/mach
-        """
-        if os.name != 'posix':
-            simple_shebang = True
-        else:
-            # Add 3 for '#!' prefix and newline suffix.
-            shebang_length = len(executable) + len(post_interp) + 3
-            if sys.platform == 'darwin':
-                max_shebang_length = 512
-            else:
-                max_shebang_length = 127
-            simple_shebang = ((b' ' not in executable) and
-                              (shebang_length <= max_shebang_length))
-
-        if simple_shebang:
-            result = b'#!' + executable + post_interp + b'\n'
-        else:
-            result = b'#!/bin/sh\n'
-            result += b"'''exec' " + executable + post_interp + b' "$0" "$@"\n'
-            result += b"' '''"
-        return result
-
-    def _get_shebang(self, encoding, post_interp=b'', options=None):
-        enquote = True
-        if self.executable:
-            executable = self.executable
-            enquote = False     # assume this will be taken care of
-        elif not sysconfig.is_python_build():
-            executable = get_executable()
-        elif in_venv():  # pragma: no cover
-            executable = os.path.join(sysconfig.get_path('scripts'),
-                            'python%s' % sysconfig.get_config_var('EXE'))
-        else:  # pragma: no cover
-            executable = os.path.join(
-                sysconfig.get_config_var('BINDIR'),
-               'python%s%s' % (sysconfig.get_config_var('VERSION'),
-                               sysconfig.get_config_var('EXE')))
-            if not os.path.isfile(executable):
-                # for Python builds from source on Windows, no Python executables with
-                # a version suffix are created, so we use python.exe
-                executable = os.path.join(sysconfig.get_config_var('BINDIR'),
-                                'python%s' % (sysconfig.get_config_var('EXE')))
-        if options:
-            executable = self._get_alternate_executable(executable, options)
-
-        if sys.platform.startswith('java'):  # pragma: no cover
-            executable = self._fix_jython_executable(executable)
-
-        # Normalise case for Windows - COMMENTED OUT
-        # executable = os.path.normcase(executable)
-        # N.B. The normalising operation above has been commented out: See
-        # issue #124. Although paths in Windows are generally case-insensitive,
-        # they aren't always. For example, a path containing a ẞ (which is a
-        # LATIN CAPITAL LETTER SHARP S - U+1E9E) is normcased to ß (which is a
-        # LATIN SMALL LETTER SHARP S' - U+00DF). The two are not considered by
-        # Windows as equivalent in path names.
-
-        # If the user didn't specify an executable, it may be necessary to
-        # cater for executable paths with spaces (not uncommon on Windows)
-        if enquote:
-            executable = enquote_executable(executable)
-        # Issue #51: don't use fsencode, since we later try to
-        # check that the shebang is decodable using utf-8.
-        executable = executable.encode('utf-8')
-        # in case of IronPython, play safe and enable frames support
-        if (sys.platform == 'cli' and '-X:Frames' not in post_interp
-            and '-X:FullFrames' not in post_interp):  # pragma: no cover
-            post_interp += b' -X:Frames'
-        shebang = self._build_shebang(executable, post_interp)
-        # Python parser starts to read a script using UTF-8 until
-        # it gets a #coding:xxx cookie. The shebang has to be the
-        # first line of a file, the #coding:xxx cookie cannot be
-        # written before. So the shebang has to be decodable from
-        # UTF-8.
-        try:
-            shebang.decode('utf-8')
-        except UnicodeDecodeError:  # pragma: no cover
-            raise ValueError(
-                'The shebang (%r) is not decodable from utf-8' % shebang)
-        # If the script is encoded to a custom encoding (use a
-        # #coding:xxx cookie), the shebang has to be decodable from
-        # the script encoding too.
-        if encoding != 'utf-8':
-            try:
-                shebang.decode(encoding)
-            except UnicodeDecodeError:  # pragma: no cover
-                raise ValueError(
-                    'The shebang (%r) is not decodable '
-                    'from the script encoding (%r)' % (shebang, encoding))
-        return shebang
-
-    def _get_script_text(self, entry):
-        return self.script_template % dict(module=entry.prefix,
-                                           import_name=entry.suffix.split('.')[0],
-                                           func=entry.suffix)
-
-    manifest = _DEFAULT_MANIFEST
-
-    def get_manifest(self, exename):
-        base = os.path.basename(exename)
-        return self.manifest % base
-
-    def _write_script(self, names, shebang, script_bytes, filenames, ext):
-        use_launcher = self.add_launchers and self._is_nt
-        linesep = os.linesep.encode('utf-8')
-        if not shebang.endswith(linesep):
-            shebang += linesep
-        if not use_launcher:
-            script_bytes = shebang + script_bytes
-        else:  # pragma: no cover
-            if ext == 'py':
-                launcher = self._get_launcher('t')
-            else:
-                launcher = self._get_launcher('w')
-            stream = BytesIO()
-            with ZipFile(stream, 'w') as zf:
-                source_date_epoch = os.environ.get('SOURCE_DATE_EPOCH')
-                if source_date_epoch:
-                    date_time = time.gmtime(int(source_date_epoch))[:6]
-                    zinfo = ZipInfo(filename='__main__.py', date_time=date_time)
-                    zf.writestr(zinfo, script_bytes)
-                else:
-                    zf.writestr('__main__.py', script_bytes)
-            zip_data = stream.getvalue()
-            script_bytes = launcher + shebang + zip_data
-        for name in names:
-            outname = os.path.join(self.target_dir, name)
-            if use_launcher:  # pragma: no cover
-                n, e = os.path.splitext(outname)
-                if e.startswith('.py'):
-                    outname = n
-                outname = '%s.exe' % outname
-                try:
-                    self._fileop.write_binary_file(outname, script_bytes)
-                except Exception:
-                    # Failed writing an executable - it might be in use.
-                    logger.warning('Failed to write executable - trying to '
-                                   'use .deleteme logic')
-                    dfname = '%s.deleteme' % outname
-                    if os.path.exists(dfname):
-                        os.remove(dfname)       # Not allowed to fail here
-                    os.rename(outname, dfname)  # nor here
-                    self._fileop.write_binary_file(outname, script_bytes)
-                    logger.debug('Able to replace executable using '
-                                 '.deleteme logic')
-                    try:
-                        os.remove(dfname)
-                    except Exception:
-                        pass    # still in use - ignore error
-            else:
-                if self._is_nt and not outname.endswith('.' + ext):  # pragma: no cover
-                    outname = '%s.%s' % (outname, ext)
-                if os.path.exists(outname) and not self.clobber:
-                    logger.warning('Skipping existing file %s', outname)
-                    continue
-                self._fileop.write_binary_file(outname, script_bytes)
-                if self.set_mode:
-                    self._fileop.set_executable_mode([outname])
-            filenames.append(outname)
-
-    variant_separator = '-'
-
-    def get_script_filenames(self, name):
-        result = set()
-        if '' in self.variants:
-            result.add(name)
-        if 'X' in self.variants:
-            result.add('%s%s' % (name, self.version_info[0]))
-        if 'X.Y' in self.variants:
-            result.add('%s%s%s.%s' % (name, self.variant_separator,
-                                      self.version_info[0], self.version_info[1]))
-        return result
-
-    def _make_script(self, entry, filenames, options=None):
-        post_interp = b''
-        if options:
-            args = options.get('interpreter_args', [])
-            if args:
-                args = ' %s' % ' '.join(args)
-                post_interp = args.encode('utf-8')
-        shebang = self._get_shebang('utf-8', post_interp, options=options)
-        script = self._get_script_text(entry).encode('utf-8')
-        scriptnames = self.get_script_filenames(entry.name)
-        if options and options.get('gui', False):
-            ext = 'pyw'
-        else:
-            ext = 'py'
-        self._write_script(scriptnames, shebang, script, filenames, ext)
-
-    def _copy_script(self, script, filenames):
-        adjust = False
-        script = os.path.join(self.source_dir, convert_path(script))
-        outname = os.path.join(self.target_dir, os.path.basename(script))
-        if not self.force and not self._fileop.newer(script, outname):
-            logger.debug('not copying %s (up-to-date)', script)
-            return
-
-        # Always open the file, but ignore failures in dry-run mode --
-        # that way, we'll get accurate feedback if we can read the
-        # script.
-        try:
-            f = open(script, 'rb')
-        except IOError:  # pragma: no cover
-            if not self.dry_run:
-                raise
-            f = None
-        else:
-            first_line = f.readline()
-            if not first_line:  # pragma: no cover
-                logger.warning('%s is an empty file (skipping)', script)
-                return
-
-            match = FIRST_LINE_RE.match(first_line.replace(b'\r\n', b'\n'))
-            if match:
-                adjust = True
-                post_interp = match.group(1) or b''
-
-        if not adjust:
-            if f:
-                f.close()
-            self._fileop.copy_file(script, outname)
-            if self.set_mode:
-                self._fileop.set_executable_mode([outname])
-            filenames.append(outname)
-        else:
-            logger.info('copying and adjusting %s -> %s', script,
-                        self.target_dir)
-            if not self._fileop.dry_run:
-                encoding, lines = detect_encoding(f.readline)
-                f.seek(0)
-                shebang = self._get_shebang(encoding, post_interp)
-                if b'pythonw' in first_line:  # pragma: no cover
-                    ext = 'pyw'
-                else:
-                    ext = 'py'
-                n = os.path.basename(outname)
-                self._write_script([n], shebang, f.read(), filenames, ext)
-            if f:
-                f.close()
-
-    @property
-    def dry_run(self):
-        return self._fileop.dry_run
-
-    @dry_run.setter
-    def dry_run(self, value):
-        self._fileop.dry_run = value
-
-    if os.name == 'nt' or (os.name == 'java' and os._name == 'nt'):  # pragma: no cover
-        # Executable launcher support.
-        # Launchers are from https://bitbucket.org/vinay.sajip/simple_launcher/
-
-        def _get_launcher(self, kind):
-            if struct.calcsize('P') == 8:   # 64-bit
-                bits = '64'
-            else:
-                bits = '32'
-            platform_suffix = '-arm' if get_platform() == 'win-arm64' else ''
-            name = '%s%s%s.exe' % (kind, bits, platform_suffix)
-            # Issue 31: don't hardcode an absolute package name, but
-            # determine it relative to the current package
-            distlib_package = __name__.rsplit('.', 1)[0]
-            resource = finder(distlib_package).find(name)
-            if not resource:
-                msg = ('Unable to find resource %s in package %s' % (name,
-                       distlib_package))
-                raise ValueError(msg)
-            return resource.bytes
-
-    # Public API follows
-
-    def make(self, specification, options=None):
-        """
-        Make a script.
-
-        :param specification: The specification, which is either a valid export
-                              entry specification (to make a script from a
-                              callable) or a filename (to make a script by
-                              copying from a source location).
-        :param options: A dictionary of options controlling script generation.
-        :return: A list of all absolute pathnames written to.
-        """
-        filenames = []
-        entry = get_export_entry(specification)
-        if entry is None:
-            self._copy_script(specification, filenames)
-        else:
-            self._make_script(entry, filenames, options=options)
-        return filenames
-
-    def make_multiple(self, specifications, options=None):
-        """
-        Take a list of specifications and make scripts from them,
-        :param specifications: A list of specifications.
-        :return: A list of all absolute pathnames written to,
-        """
-        filenames = []
-        for specification in specifications:
-            filenames.extend(self.make(specification, options))
-        return filenames

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

@@ -1,222 +0,0 @@
-"""Wheels support."""
-
-import email
-import itertools
-import os
-import posixpath
-import re
-import zipfile
-import contextlib
-
-from distutils.util import get_platform
-
-import pkg_resources
-import setuptools
-from pkg_resources import parse_version
-from setuptools.extern.packaging.tags import sys_tags
-from setuptools.extern.packaging.utils import canonicalize_name
-from setuptools.command.egg_info import write_requirements
-from setuptools.archive_util import _unpack_zipfile_obj
-
-
-WHEEL_NAME = re.compile(
-    r"""^(?P<project_name>.+?)-(?P<version>\d.*?)
-    ((-(?P<build>\d.*?))?-(?P<py_version>.+?)-(?P<abi>.+?)-(?P<platform>.+?)
-    )\.whl$""",
-    re.VERBOSE).match
-
-NAMESPACE_PACKAGE_INIT = \
-    "__import__('pkg_resources').declare_namespace(__name__)\n"
-
-
-def unpack(src_dir, dst_dir):
-    '''Move everything under `src_dir` to `dst_dir`, and delete the former.'''
-    for dirpath, dirnames, filenames in os.walk(src_dir):
-        subdir = os.path.relpath(dirpath, src_dir)
-        for f in filenames:
-            src = os.path.join(dirpath, f)
-            dst = os.path.join(dst_dir, subdir, f)
-            os.renames(src, dst)
-        for n, d in reversed(list(enumerate(dirnames))):
-            src = os.path.join(dirpath, d)
-            dst = os.path.join(dst_dir, subdir, d)
-            if not os.path.exists(dst):
-                # Directory does not exist in destination,
-                # rename it and prune it from os.walk list.
-                os.renames(src, dst)
-                del dirnames[n]
-    # Cleanup.
-    for dirpath, dirnames, filenames in os.walk(src_dir, topdown=True):
-        assert not filenames
-        os.rmdir(dirpath)
-
-
-@contextlib.contextmanager
-def disable_info_traces():
-    """
-    Temporarily disable info traces.
-    """
-    from distutils import log
-    saved = log.set_threshold(log.WARN)
-    try:
-        yield
-    finally:
-        log.set_threshold(saved)
-
-
-class Wheel:
-
-    def __init__(self, filename):
-        match = WHEEL_NAME(os.path.basename(filename))
-        if match is None:
-            raise ValueError('invalid wheel name: %r' % filename)
-        self.filename = filename
-        for k, v in match.groupdict().items():
-            setattr(self, k, v)
-
-    def tags(self):
-        '''List tags (py_version, abi, platform) supported by this wheel.'''
-        return itertools.product(
-            self.py_version.split('.'),
-            self.abi.split('.'),
-            self.platform.split('.'),
-        )
-
-    def is_compatible(self):
-        '''Is the wheel is compatible with the current platform?'''
-        supported_tags = set(
-            (t.interpreter, t.abi, t.platform) for t in sys_tags())
-        return next((True for t in self.tags() if t in supported_tags), False)
-
-    def egg_name(self):
-        return pkg_resources.Distribution(
-            project_name=self.project_name, version=self.version,
-            platform=(None if self.platform == 'any' else get_platform()),
-        ).egg_name() + '.egg'
-
-    def get_dist_info(self, zf):
-        # find the correct name of the .dist-info dir in the wheel file
-        for member in zf.namelist():
-            dirname = posixpath.dirname(member)
-            if (dirname.endswith('.dist-info') and
-                    canonicalize_name(dirname).startswith(
-                        canonicalize_name(self.project_name))):
-                return dirname
-        raise ValueError("unsupported wheel format. .dist-info not found")
-
-    def install_as_egg(self, destination_eggdir):
-        '''Install wheel as an egg directory.'''
-        with zipfile.ZipFile(self.filename) as zf:
-            self._install_as_egg(destination_eggdir, zf)
-
-    def _install_as_egg(self, destination_eggdir, zf):
-        dist_basename = '%s-%s' % (self.project_name, self.version)
-        dist_info = self.get_dist_info(zf)
-        dist_data = '%s.data' % dist_basename
-        egg_info = os.path.join(destination_eggdir, 'EGG-INFO')
-
-        self._convert_metadata(zf, destination_eggdir, dist_info, egg_info)
-        self._move_data_entries(destination_eggdir, dist_data)
-        self._fix_namespace_packages(egg_info, destination_eggdir)
-
-    @staticmethod
-    def _convert_metadata(zf, destination_eggdir, dist_info, egg_info):
-        def get_metadata(name):
-            with zf.open(posixpath.join(dist_info, name)) as fp:
-                value = fp.read().decode('utf-8')
-                return email.parser.Parser().parsestr(value)
-
-        wheel_metadata = get_metadata('WHEEL')
-        # Check wheel format version is supported.
-        wheel_version = parse_version(wheel_metadata.get('Wheel-Version'))
-        wheel_v1 = (
-            parse_version('1.0') <= wheel_version < parse_version('2.0dev0')
-        )
-        if not wheel_v1:
-            raise ValueError(
-                'unsupported wheel format version: %s' % wheel_version)
-        # Extract to target directory.
-        _unpack_zipfile_obj(zf, destination_eggdir)
-        # Convert metadata.
-        dist_info = os.path.join(destination_eggdir, dist_info)
-        dist = pkg_resources.Distribution.from_location(
-            destination_eggdir, dist_info,
-            metadata=pkg_resources.PathMetadata(destination_eggdir, dist_info),
-        )
-
-        # Note: Evaluate and strip markers now,
-        # as it's difficult to convert back from the syntax:
-        # foobar; "linux" in sys_platform and extra == 'test'
-        def raw_req(req):
-            req.marker = None
-            return str(req)
-        install_requires = list(map(raw_req, dist.requires()))
-        extras_require = {
-            extra: [
-                req
-                for req in map(raw_req, dist.requires((extra,)))
-                if req not in install_requires
-            ]
-            for extra in dist.extras
-        }
-        os.rename(dist_info, egg_info)
-        os.rename(
-            os.path.join(egg_info, 'METADATA'),
-            os.path.join(egg_info, 'PKG-INFO'),
-        )
-        setup_dist = setuptools.Distribution(
-            attrs=dict(
-                install_requires=install_requires,
-                extras_require=extras_require,
-            ),
-        )
-        with disable_info_traces():
-            write_requirements(
-                setup_dist.get_command_obj('egg_info'),
-                None,
-                os.path.join(egg_info, 'requires.txt'),
-            )
-
-    @staticmethod
-    def _move_data_entries(destination_eggdir, dist_data):
-        """Move data entries to their correct location."""
-        dist_data = os.path.join(destination_eggdir, dist_data)
-        dist_data_scripts = os.path.join(dist_data, 'scripts')
-        if os.path.exists(dist_data_scripts):
-            egg_info_scripts = os.path.join(
-                destination_eggdir, 'EGG-INFO', 'scripts')
-            os.mkdir(egg_info_scripts)
-            for entry in os.listdir(dist_data_scripts):
-                # Remove bytecode, as it's not properly handled
-                # during easy_install scripts install phase.
-                if entry.endswith('.pyc'):
-                    os.unlink(os.path.join(dist_data_scripts, entry))
-                else:
-                    os.rename(
-                        os.path.join(dist_data_scripts, entry),
-                        os.path.join(egg_info_scripts, entry),
-                    )
-            os.rmdir(dist_data_scripts)
-        for subdir in filter(os.path.exists, (
-            os.path.join(dist_data, d)
-            for d in ('data', 'headers', 'purelib', 'platlib')
-        )):
-            unpack(subdir, destination_eggdir)
-        if os.path.exists(dist_data):
-            os.rmdir(dist_data)
-
-    @staticmethod
-    def _fix_namespace_packages(egg_info, destination_eggdir):
-        namespace_packages = os.path.join(
-            egg_info, 'namespace_packages.txt')
-        if os.path.exists(namespace_packages):
-            with open(namespace_packages) as fp:
-                namespace_packages = fp.read().split()
-            for mod in namespace_packages:
-                mod_dir = os.path.join(destination_eggdir, *mod.split('.'))
-                mod_init = os.path.join(mod_dir, '__init__.py')
-                if not os.path.exists(mod_dir):
-                    os.mkdir(mod_dir)
-                if not os.path.exists(mod_init):
-                    with open(mod_init, 'w') as fp:
-                        fp.write(NAMESPACE_PACKAGE_INIT)

spaces/BramVanroy/text-to-amr/README.md

@@ -1,16 +0,0 @@
----
-title: Text To AMR
-emoji: 👩‍💻
-colorFrom: yellow
-colorTo: gray
-sdk: docker
-app_port: 8501
-app_file: app.py
-pinned: true
-license: gpl-3.0
-tags:
-    - natural language processing
-    - semantic parsing
-    - abstract meaning representation
-    - amr
----

spaces/C6AI/HDRL/README.md

@@ -1,12 +0,0 @@
----
-title: Livebook
-emoji: 📓
-colorFrom: pink
-colorTo: purple
-sdk: docker
-fullWidth: true
-duplicated_from: livebook-dev/livebook
-license: mit
----
-
-You can install and run [Livebook](https://livebook.dev/) inside a Hugging Face Space. Here's [a tutorial](https://huggingface.co/docs/hub/spaces-sdks-docker-livebook) on how to do that.

spaces/CCaniggia/GPT/Dockerfile

@@ -1,11 +0,0 @@
-from golang:alpine as builder
-run apk --no-cache add git
-run git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-workdir /workspace/app
-run go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-from alpine
-workdir /workspace/app
-copy --from=builder /workspace/app/go-proxy-bingai .
-env Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtx5rG6bE3fZ4iO"
-expose 8080
-cmd ["/workspace/app/go-proxy-bingai"]

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/visualizer.py

@@ -1,1133 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import colorsys
-import logging
-import math
-import numpy as np
-from enum import Enum, unique
-import cv2
-import matplotlib as mpl
-import matplotlib.colors as mplc
-import matplotlib.figure as mplfigure
-import pycocotools.mask as mask_util
-import torch
-from matplotlib.backends.backend_agg import FigureCanvasAgg
-
-from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes
-
-from .colormap import random_color
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["ColorMode", "VisImage", "Visualizer"]
-
-
-_SMALL_OBJECT_AREA_THRESH = 1000
-_LARGE_MASK_AREA_THRESH = 120000
-_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
-_BLACK = (0, 0, 0)
-_RED = (1.0, 0, 0)
-
-_KEYPOINT_THRESHOLD = 0.05
-
-
-@unique
-class ColorMode(Enum):
-    """
-    Enum of different color modes to use for instance visualizations.
-
-    Attributes:
-        IMAGE: Picks a random color for every instance and overlay segmentations with low opacity.
-        SEGMENTATION: Let instances of the same category have similar colors
-            (from metadata.thing_colors), and overlay them with
-            high opacity. This provides more attention on the quality of segmentation.
-        IMAGE_BW: same as IMAGE, but convert all areas without masks to gray-scale.
-            Only available for drawing per-instance mask predictions.
-    """
-
-    IMAGE = 0
-    SEGMENTATION = 1
-    IMAGE_BW = 2
-
-
-class GenericMask:
-    """
-    Attribute:
-        polygons (list[ndarray]): list[ndarray]: polygons for this mask.
-            Each ndarray has format [x, y, x, y, ...]
-        mask (ndarray): a binary mask
-    """
-
-    def __init__(self, mask_or_polygons, height, width):
-        self._mask = self._polygons = self._has_holes = None
-        self.height = height
-        self.width = width
-
-        m = mask_or_polygons
-        if isinstance(m, dict):
-            # RLEs
-            assert "counts" in m and "size" in m
-            if isinstance(m["counts"], list):  # uncompressed RLEs
-                h, w = m["size"]
-                assert h == height and w == width
-                m = mask_util.frPyObjects(m, h, w)
-            self._mask = mask_util.decode(m)[:, :]
-            return
-
-        if isinstance(m, list):  # list[ndarray]
-            self._polygons = [np.asarray(x).reshape(-1) for x in m]
-            return
-
-        if isinstance(m, np.ndarray):  # assumed to be a binary mask
-            assert m.shape[1] != 2, m.shape
-            assert m.shape == (height, width), m.shape
-            self._mask = m.astype("uint8")
-            return
-
-        raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))
-
-    @property
-    def mask(self):
-        if self._mask is None:
-            self._mask = self.polygons_to_mask(self._polygons)
-        return self._mask
-
-    @property
-    def polygons(self):
-        if self._polygons is None:
-            self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-        return self._polygons
-
-    @property
-    def has_holes(self):
-        if self._has_holes is None:
-            if self._mask is not None:
-                self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-            else:
-                self._has_holes = False  # if original format is polygon, does not have holes
-        return self._has_holes
-
-    def mask_to_polygons(self, mask):
-        # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
-        # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
-        # Internal contours (holes) are placed in hierarchy-2.
-        # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
-        mask = np.ascontiguousarray(mask)  # some versions of cv2 does not support incontiguous arr
-        res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
-        hierarchy = res[-1]
-        if hierarchy is None:  # empty mask
-            return [], False
-        has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
-        res = res[-2]
-        res = [x.flatten() for x in res]
-        res = [x for x in res if len(x) >= 6]
-        return res, has_holes
-
-    def polygons_to_mask(self, polygons):
-        rle = mask_util.frPyObjects(polygons, self.height, self.width)
-        rle = mask_util.merge(rle)
-        return mask_util.decode(rle)[:, :]
-
-    def area(self):
-        return self.mask.sum()
-
-    def bbox(self):
-        p = mask_util.frPyObjects(self.polygons, self.height, self.width)
-        p = mask_util.merge(p)
-        bbox = mask_util.toBbox(p)
-        bbox[2] += bbox[0]
-        bbox[3] += bbox[1]
-        return bbox
-
-
-class _PanopticPrediction:
-    def __init__(self, panoptic_seg, segments_info):
-        self._seg = panoptic_seg
-
-        self._sinfo = {s["id"]: s for s in segments_info}  # seg id -> seg info
-        segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
-        areas = areas.numpy()
-        sorted_idxs = np.argsort(-areas)
-        self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
-        self._seg_ids = self._seg_ids.tolist()
-        for sid, area in zip(self._seg_ids, self._seg_areas):
-            if sid in self._sinfo:
-                self._sinfo[sid]["area"] = float(area)
-
-    def non_empty_mask(self):
-        """
-        Returns:
-            (H, W) array, a mask for all pixels that have a prediction
-        """
-        empty_ids = []
-        for id in self._seg_ids:
-            if id not in self._sinfo:
-                empty_ids.append(id)
-        if len(empty_ids) == 0:
-            return np.zeros(self._seg.shape, dtype=np.uint8)
-        assert (
-            len(empty_ids) == 1
-        ), ">1 ids corresponds to no labels. This is currently not supported"
-        return (self._seg != empty_ids[0]).numpy().astype(np.bool)
-
-    def semantic_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or sinfo["isthing"]:
-                # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.
-                continue
-            yield (self._seg == sid).numpy().astype(np.bool), sinfo
-
-    def instance_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or not sinfo["isthing"]:
-                continue
-            mask = (self._seg == sid).numpy().astype(np.bool)
-            if mask.sum() > 0:
-                yield mask, sinfo
-
-
-def _create_text_labels(classes, scores, class_names):
-    """
-    Args:
-        classes (list[int] or None):
-        scores (list[float] or None):
-        class_names (list[str] or None):
-
-    Returns:
-        list[str] or None
-    """
-    labels = None
-    if classes is not None and class_names is not None and len(class_names) > 1:
-        labels = [class_names[i] for i in classes]
-    if scores is not None:
-        if labels is None:
-            labels = ["{:.0f}%".format(s * 100) for s in scores]
-        else:
-            labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)]
-    return labels
-
-
-class VisImage:
-    def __init__(self, img, scale=1.0):
-        """
-        Args:
-            img (ndarray): an RGB image of shape (H, W, 3).
-            scale (float): scale the input image
-        """
-        self.img = img
-        self.scale = scale
-        self.width, self.height = img.shape[1], img.shape[0]
-        self._setup_figure(img)
-
-    def _setup_figure(self, img):
-        """
-        Args:
-            Same as in :meth:`__init__()`.
-
-        Returns:
-            fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
-            ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
-        """
-        fig = mplfigure.Figure(frameon=False)
-        self.dpi = fig.get_dpi()
-        # add a small 1e-2 to avoid precision lost due to matplotlib's truncation
-        # (https://github.com/matplotlib/matplotlib/issues/15363)
-        fig.set_size_inches(
-            (self.width * self.scale + 1e-2) / self.dpi,
-            (self.height * self.scale + 1e-2) / self.dpi,
-        )
-        self.canvas = FigureCanvasAgg(fig)
-        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
-        ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
-        ax.axis("off")
-        ax.set_xlim(0.0, self.width)
-        ax.set_ylim(self.height)
-
-        self.fig = fig
-        self.ax = ax
-
-    def save(self, filepath):
-        """
-        Args:
-            filepath (str): a string that contains the absolute path, including the file name, where
-                the visualized image will be saved.
-        """
-        if filepath.lower().endswith(".jpg") or filepath.lower().endswith(".png"):
-            # faster than matplotlib's imshow
-            cv2.imwrite(filepath, self.get_image()[:, :, ::-1])
-        else:
-            # support general formats (e.g. pdf)
-            self.ax.imshow(self.img, interpolation="nearest")
-            self.fig.savefig(filepath)
-
-    def get_image(self):
-        """
-        Returns:
-            ndarray: the visualized image of shape (H, W, 3) (RGB) in uint8 type.
-              The shape is scaled w.r.t the input image using the given `scale` argument.
-        """
-        canvas = self.canvas
-        s, (width, height) = canvas.print_to_buffer()
-        if (self.width, self.height) != (width, height):
-            img = cv2.resize(self.img, (width, height))
-        else:
-            img = self.img
-
-        # buf = io.BytesIO()  # works for cairo backend
-        # canvas.print_rgba(buf)
-        # width, height = self.width, self.height
-        # s = buf.getvalue()
-
-        buffer = np.frombuffer(s, dtype="uint8")
-
-        # imshow is slow. blend manually (still quite slow)
-        img_rgba = buffer.reshape(height, width, 4)
-        rgb, alpha = np.split(img_rgba, [3], axis=2)
-
-        try:
-            import numexpr as ne  # fuse them with numexpr
-
-            visualized_image = ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)")
-        except ImportError:
-            alpha = alpha.astype("float32") / 255.0
-            visualized_image = img * (1 - alpha) + rgb * alpha
-
-        visualized_image = visualized_image.astype("uint8")
-
-        return visualized_image
-
-
-class Visualizer:
-    def __init__(self, img_rgb, metadata, scale=1.0, instance_mode=ColorMode.IMAGE):
-        """
-        Args:
-            img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
-                the height and width of the image respectively. C is the number of
-                color channels. The image is required to be in RGB format since that
-                is a requirement of the Matplotlib library. The image is also expected
-                to be in the range [0, 255].
-            metadata (MetadataCatalog): image metadata.
-        """
-        self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
-        self.metadata = metadata
-        self.output = VisImage(self.img, scale=scale)
-        self.cpu_device = torch.device("cpu")
-
-        # too small texts are useless, therefore clamp to 9
-        self._default_font_size = max(
-            np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
-        )
-        self._instance_mode = instance_mode
-
-    def draw_instance_predictions(self, predictions):
-        """
-        Draw instance-level prediction results on an image.
-
-        Args:
-            predictions (Instances): the output of an instance detection/segmentation
-                model. Following fields will be used to draw:
-                "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
-        scores = predictions.scores if predictions.has("scores") else None
-        classes = predictions.pred_classes if predictions.has("pred_classes") else None
-        labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
-        keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
-
-        if predictions.has("pred_masks"):
-            masks = np.asarray(predictions.pred_masks)
-            masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]
-        else:
-            masks = None
-
-        if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
-            colors = [
-                self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
-            ]
-            alpha = 0.8
-        else:
-            colors = None
-            alpha = 0.5
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            assert predictions.has("pred_masks"), "ColorMode.IMAGE_BW requires segmentations"
-            self.output.img = self._create_grayscale_image(
-                (predictions.pred_masks.any(dim=0) > 0).numpy()
-            )
-            alpha = 0.3
-
-        self.overlay_instances(
-            masks=masks,
-            boxes=boxes,
-            labels=labels,
-            keypoints=keypoints,
-            assigned_colors=colors,
-            alpha=alpha,
-        )
-        return self.output
-
-    def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
-        """
-        Draw semantic segmentation predictions/labels.
-
-        Args:
-            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
-            area_threshold (int): segments with less than `area_threshold` are not drawn.
-            alpha (float): the larger it is, the more opaque the segmentations are.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        if isinstance(sem_seg, torch.Tensor):
-            sem_seg = sem_seg.numpy()
-        labels, areas = np.unique(sem_seg, return_counts=True)
-        sorted_idxs = np.argsort(-areas).tolist()
-        labels = labels[sorted_idxs]
-        for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
-            except (AttributeError, IndexError):
-                mask_color = None
-
-            binary_mask = (sem_seg == label).astype(np.uint8)
-            text = self.metadata.stuff_classes[label]
-            self.draw_binary_mask(
-                binary_mask,
-                color=mask_color,
-                edge_color=_OFF_WHITE,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-            )
-        return self.output
-
-    def draw_panoptic_seg_predictions(
-        self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7
-    ):
-        """
-        Draw panoptic prediction results on an image.
-
-        Args:
-            panoptic_seg (Tensor): of shape (height, width) where the values are ids for each
-                segment.
-            segments_info (list[dict]): Describe each segment in `panoptic_seg`.
-                Each dict contains keys "id", "category_id", "isthing".
-            area_threshold (int): stuff segments with less than `area_threshold` are not drawn.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        pred = _PanopticPrediction(panoptic_seg, segments_info)
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.img = self._create_grayscale_image(pred.non_empty_mask())
-
-        # draw mask for all semantic segments first i.e. "stuff"
-        for mask, sinfo in pred.semantic_masks():
-            category_idx = sinfo["category_id"]
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
-            except AttributeError:
-                mask_color = None
-
-            text = self.metadata.stuff_classes[category_idx]
-            self.draw_binary_mask(
-                mask,
-                color=mask_color,
-                edge_color=_OFF_WHITE,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-            )
-
-        # draw mask for all instances second
-        all_instances = list(pred.instance_masks())
-        if len(all_instances) == 0:
-            return self.output
-        masks, sinfo = list(zip(*all_instances))
-        category_ids = [x["category_id"] for x in sinfo]
-
-        try:
-            scores = [x["score"] for x in sinfo]
-        except KeyError:
-            scores = None
-        labels = _create_text_labels(category_ids, scores, self.metadata.thing_classes)
-
-        try:
-            colors = [random_color(rgb=True, maximum=1) for k in category_ids]
-        except AttributeError:
-            colors = None
-        self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)
-
-        return self.output
-
-    def draw_dataset_dict(self, dic):
-        """
-        Draw annotations/segmentaions in Detectron2 Dataset format.
-
-        Args:
-            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        annos = dic.get("annotations", None)
-        if annos:
-            if "segmentation" in annos[0]:
-                masks = [x["segmentation"] for x in annos]
-            else:
-                masks = None
-            if "keypoints" in annos[0]:
-                keypts = [x["keypoints"] for x in annos]
-                keypts = np.array(keypts).reshape(len(annos), -1, 3)
-            else:
-                keypts = None
-
-            boxes = [BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS) for x in annos]
-
-            labels = [x["category_id"] for x in annos]
-            colors = None
-            if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
-                colors = [
-                    self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in labels
-                ]
-            names = self.metadata.get("thing_classes", None)
-            if names:
-                labels = [names[i] for i in labels]
-            labels = [
-                "{}".format(i) + ("|crowd" if a.get("iscrowd", 0) else "")
-                for i, a in zip(labels, annos)
-            ]
-            self.overlay_instances(
-                labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
-            )
-
-        sem_seg = dic.get("sem_seg", None)
-        if sem_seg is None and "sem_seg_file_name" in dic:
-            sem_seg = cv2.imread(dic["sem_seg_file_name"], cv2.IMREAD_GRAYSCALE)
-        if sem_seg is not None:
-            self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
-        return self.output
-
-    def overlay_instances(
-        self,
-        *,
-        boxes=None,
-        labels=None,
-        masks=None,
-        keypoints=None,
-        assigned_colors=None,
-        alpha=0.5
-    ):
-        """
-        Args:
-            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
-                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
-                or a :class:`RotatedBoxes`,
-                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image,
-            labels (list[str]): the text to be displayed for each instance.
-            masks (masks-like object): Supported types are:
-
-                * `structures.masks.PolygonMasks`, `structures.masks.BitMasks`.
-                * list[list[ndarray]]: contains the segmentation masks for all objects in one image.
-                    The first level of the list corresponds to individual instances. The second
-                    level to all the polygon that compose the instance, and the third level
-                    to the polygon coordinates. The third level should have the format of
-                    [x0, y0, x1, y1, ..., xn, yn] (n >= 3).
-                * list[ndarray]: each ndarray is a binary mask of shape (H, W).
-                * list[dict]: each dict is a COCO-style RLE.
-            keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
-                where the N is the number of instances and K is the number of keypoints.
-                The last dimension corresponds to (x, y, visibility or score).
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = None
-        if boxes is not None:
-            boxes = self._convert_boxes(boxes)
-            num_instances = len(boxes)
-        if masks is not None:
-            masks = self._convert_masks(masks)
-            if num_instances:
-                assert len(masks) == num_instances
-            else:
-                num_instances = len(masks)
-        if keypoints is not None:
-            if num_instances:
-                assert len(keypoints) == num_instances
-            else:
-                num_instances = len(keypoints)
-            keypoints = self._convert_keypoints(keypoints)
-        if labels is not None:
-            assert len(labels) == num_instances
-        if assigned_colors is None:
-            assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-        if boxes is not None and boxes.shape[1] == 5:
-            return self.overlay_rotated_instances(
-                boxes=boxes, labels=labels, assigned_colors=assigned_colors
-            )
-
-        # Display in largest to smallest order to reduce occlusion.
-        areas = None
-        if boxes is not None:
-            areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
-        elif masks is not None:
-            areas = np.asarray([x.area() for x in masks])
-
-        if areas is not None:
-            sorted_idxs = np.argsort(-areas).tolist()
-            # Re-order overlapped instances in descending order.
-            boxes = boxes[sorted_idxs] if boxes is not None else None
-            labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-            masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None
-            assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
-            keypoints = keypoints[sorted_idxs] if keypoints is not None else None
-
-        for i in range(num_instances):
-            color = assigned_colors[i]
-            if boxes is not None:
-                self.draw_box(boxes[i], edge_color=color)
-
-            if masks is not None:
-                for segment in masks[i].polygons:
-                    self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)
-
-            if labels is not None:
-                # first get a box
-                if boxes is not None:
-                    x0, y0, x1, y1 = boxes[i]
-                    text_pos = (x0, y0)  # if drawing boxes, put text on the box corner.
-                    horiz_align = "left"
-                elif masks is not None:
-                    x0, y0, x1, y1 = masks[i].bbox()
-
-                    # draw text in the center (defined by median) when box is not drawn
-                    # median is less sensitive to outliers.
-                    text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]
-                    horiz_align = "center"
-                else:
-                    continue  # drawing the box confidence for keypoints isn't very useful.
-                # for small objects, draw text at the side to avoid occlusion
-                instance_area = (y1 - y0) * (x1 - x0)
-                if (
-                    instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
-                    or y1 - y0 < 40 * self.output.scale
-                ):
-                    if y1 >= self.output.height - 5:
-                        text_pos = (x1, y0)
-                    else:
-                        text_pos = (x0, y1)
-
-                height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
-                lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-                font_size = (
-                    np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
-                    * 0.5
-                    * self._default_font_size
-                )
-                self.draw_text(
-                    labels[i],
-                    text_pos,
-                    color=lighter_color,
-                    horizontal_alignment=horiz_align,
-                    font_size=font_size,
-                )
-
-        # draw keypoints
-        if keypoints is not None:
-            for keypoints_per_instance in keypoints:
-                self.draw_and_connect_keypoints(keypoints_per_instance)
-
-        return self.output
-
-    def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):
-        """
-        Args:
-            boxes (ndarray): an Nx5 numpy array of
-                (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image.
-            labels (list[str]): the text to be displayed for each instance.
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-
-        num_instances = len(boxes)
-
-        if assigned_colors is None:
-            assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-
-        # Display in largest to smallest order to reduce occlusion.
-        if boxes is not None:
-            areas = boxes[:, 2] * boxes[:, 3]
-
-        sorted_idxs = np.argsort(-areas).tolist()
-        # Re-order overlapped instances in descending order.
-        boxes = boxes[sorted_idxs]
-        labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-        colors = [assigned_colors[idx] for idx in sorted_idxs]
-
-        for i in range(num_instances):
-            self.draw_rotated_box_with_label(
-                boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None
-            )
-
-        return self.output
-
-    def draw_and_connect_keypoints(self, keypoints):
-        """
-        Draws keypoints of an instance and follows the rules for keypoint connections
-        to draw lines between appropriate keypoints. This follows color heuristics for
-        line color.
-
-        Args:
-            keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints
-                and the last dimension corresponds to (x, y, probability).
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        visible = {}
-        keypoint_names = self.metadata.get("keypoint_names")
-        for idx, keypoint in enumerate(keypoints):
-            # draw keypoint
-            x, y, prob = keypoint
-            if prob > _KEYPOINT_THRESHOLD:
-                self.draw_circle((x, y), color=_RED)
-                if keypoint_names:
-                    keypoint_name = keypoint_names[idx]
-                    visible[keypoint_name] = (x, y)
-
-        if self.metadata.get("keypoint_connection_rules"):
-            for kp0, kp1, color in self.metadata.keypoint_connection_rules:
-                if kp0 in visible and kp1 in visible:
-                    x0, y0 = visible[kp0]
-                    x1, y1 = visible[kp1]
-                    color = tuple(x / 255.0 for x in color)
-                    self.draw_line([x0, x1], [y0, y1], color=color)
-
-        # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip
-        # Note that this strategy is specific to person keypoints.
-        # For other keypoints, it should just do nothing
-        try:
-            ls_x, ls_y = visible["left_shoulder"]
-            rs_x, rs_y = visible["right_shoulder"]
-            mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2
-        except KeyError:
-            pass
-        else:
-            # draw line from nose to mid-shoulder
-            nose_x, nose_y = visible.get("nose", (None, None))
-            if nose_x is not None:
-                self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)
-
-            try:
-                # draw line from mid-shoulder to mid-hip
-                lh_x, lh_y = visible["left_hip"]
-                rh_x, rh_y = visible["right_hip"]
-            except KeyError:
-                pass
-            else:
-                mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2
-                self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)
-        return self.output
-
-    """
-    Primitive drawing functions:
-    """
-
-    def draw_text(
-        self,
-        text,
-        position,
-        *,
-        font_size=None,
-        color="g",
-        horizontal_alignment="center",
-        rotation=0
-    ):
-        """
-        Args:
-            text (str): class label
-            position (tuple): a tuple of the x and y coordinates to place text on image.
-            font_size (int, optional): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color: color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            horizontal_alignment (str): see `matplotlib.text.Text`
-            rotation: rotation angle in degrees CCW
-
-        Returns:
-            output (VisImage): image object with text drawn.
-        """
-        if not font_size:
-            font_size = self._default_font_size
-
-        # since the text background is dark, we don't want the text to be dark
-        color = np.maximum(list(mplc.to_rgb(color)), 0.2)
-        color[np.argmax(color)] = max(0.8, np.max(color))
-
-        x, y = position
-        self.output.ax.text(
-            x,
-            y,
-            text,
-            size=font_size * self.output.scale,
-            family="sans-serif",
-            bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
-            verticalalignment="top",
-            horizontalalignment=horizontal_alignment,
-            color=color,
-            zorder=10,
-            rotation=rotation,
-        )
-        return self.output
-
-    def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
-        """
-        Args:
-            box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0
-                are the coordinates of the image's top left corner. x1 and y1 are the
-                coordinates of the image's bottom right corner.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x0, y0, x1, y1 = box_coord
-        width = x1 - x0
-        height = y1 - y0
-
-        linewidth = max(self._default_font_size / 4, 1)
-
-        self.output.ax.add_patch(
-            mpl.patches.Rectangle(
-                (x0, y0),
-                width,
-                height,
-                fill=False,
-                edgecolor=edge_color,
-                linewidth=linewidth * self.output.scale,
-                alpha=alpha,
-                linestyle=line_style,
-            )
-        )
-        return self.output
-
-    def draw_rotated_box_with_label(
-        self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None
-    ):
-        """
-        Args:
-            rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),
-                where cnt_x and cnt_y are the center coordinates of the box.
-                w and h are the width and height of the box. angle represents how
-                many degrees the box is rotated CCW with regard to the 0-degree box.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-            label (string): label for rotated box. It will not be rendered when set to None.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        cnt_x, cnt_y, w, h, angle = rotated_box
-        area = w * h
-        # use thinner lines when the box is small
-        linewidth = self._default_font_size / (
-            6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3
-        )
-
-        theta = angle * math.pi / 180.0
-        c = math.cos(theta)
-        s = math.sin(theta)
-        rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]
-        # x: left->right ; y: top->down
-        rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]
-        for k in range(4):
-            j = (k + 1) % 4
-            self.draw_line(
-                [rotated_rect[k][0], rotated_rect[j][0]],
-                [rotated_rect[k][1], rotated_rect[j][1]],
-                color=edge_color,
-                linestyle="--" if k == 1 else line_style,
-                linewidth=linewidth,
-            )
-
-        if label is not None:
-            text_pos = rotated_rect[1]  # topleft corner
-
-            height_ratio = h / np.sqrt(self.output.height * self.output.width)
-            label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)
-            font_size = (
-                np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size
-            )
-            self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)
-
-        return self.output
-
-    def draw_circle(self, circle_coord, color, radius=3):
-        """
-        Args:
-            circle_coord (list(int) or tuple(int)): contains the x and y coordinates
-                of the center of the circle.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            radius (int): radius of the circle.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x, y = circle_coord
-        self.output.ax.add_patch(
-            mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)
-        )
-        return self.output
-
-    def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None):
-        """
-        Args:
-            x_data (list[int]): a list containing x values of all the points being drawn.
-                Length of list should match the length of y_data.
-            y_data (list[int]): a list containing y values of all the points being drawn.
-                Length of list should match the length of x_data.
-            color: color of the line. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
-                for a full list of formats that are accepted.
-            linewidth (float or None): width of the line. When it's None,
-                a default value will be computed and used.
-
-        Returns:
-            output (VisImage): image object with line drawn.
-        """
-        if linewidth is None:
-            linewidth = self._default_font_size / 3
-        linewidth = max(linewidth, 1)
-        self.output.ax.add_line(
-            mpl.lines.Line2D(
-                x_data,
-                y_data,
-                linewidth=linewidth * self.output.scale,
-                color=color,
-                linestyle=linestyle,
-            )
-        )
-        return self.output
-
-    def draw_binary_mask(
-        self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=4096
-    ):
-        """
-        Args:
-            binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and
-                W is the image width. Each value in the array is either a 0 or 1 value of uint8
-                type.
-            color: color of the mask. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted. If None, will pick a random color.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted.
-            text (str): if None, will be drawn in the object's center of mass.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            area_threshold (float): a connected component small than this will not be shown.
-
-        Returns:
-            output (VisImage): image object with mask drawn.
-        """
-        if color is None:
-            color = random_color(rgb=True, maximum=1)
-        if area_threshold is None:
-            area_threshold = 4096
-
-        has_valid_segment = False
-        binary_mask = binary_mask.astype("uint8")  # opencv needs uint8
-        mask = GenericMask(binary_mask, self.output.height, self.output.width)
-        shape2d = (binary_mask.shape[0], binary_mask.shape[1])
-
-        if not mask.has_holes:
-            # draw polygons for regular masks
-            for segment in mask.polygons:
-                area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))
-                if area < area_threshold:
-                    continue
-                has_valid_segment = True
-                segment = segment.reshape(-1, 2)
-                self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)
-        else:
-            rgba = np.zeros(shape2d + (4,), dtype="float32")
-            rgba[:, :, :3] = color
-            rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha
-            has_valid_segment = True
-            self.output.ax.imshow(rgba)
-
-        if text is not None and has_valid_segment:
-            # TODO sometimes drawn on wrong objects. the heuristics here can improve.
-            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-            _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)
-            largest_component_id = np.argmax(stats[1:, -1]) + 1
-
-            # draw text on the largest component, as well as other very large components.
-            for cid in range(1, _num_cc):
-                if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
-                    # median is more stable than centroid
-                    # center = centroids[largest_component_id]
-                    center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
-                    self.draw_text(text, center, color=lighter_color)
-        return self.output
-
-    def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
-        """
-        Args:
-            segment: numpy array of shape Nx2, containing all the points in the polygon.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted. If not provided, a darker shade
-                of the polygon color will be used instead.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-
-        Returns:
-            output (VisImage): image object with polygon drawn.
-        """
-        if edge_color is None:
-            # make edge color darker than the polygon color
-            if alpha > 0.8:
-                edge_color = self._change_color_brightness(color, brightness_factor=-0.7)
-            else:
-                edge_color = color
-        edge_color = mplc.to_rgb(edge_color) + (1,)
-
-        polygon = mpl.patches.Polygon(
-            segment,
-            fill=True,
-            facecolor=mplc.to_rgb(color) + (alpha,),
-            edgecolor=edge_color,
-            linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
-        )
-        self.output.ax.add_patch(polygon)
-        return self.output
-
-    """
-    Internal methods:
-    """
-
-    def _jitter(self, color):
-        """
-        Randomly modifies given color to produce a slightly different color than the color given.
-
-        Args:
-            color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color
-                picked. The values in the list are in the [0.0, 1.0] range.
-
-        Returns:
-            jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the
-                color after being jittered. The values in the list are in the [0.0, 1.0] range.
-        """
-        color = mplc.to_rgb(color)
-        vec = np.random.rand(3)
-        # better to do it in another color space
-        vec = vec / np.linalg.norm(vec) * 0.5
-        res = np.clip(vec + color, 0, 1)
-        return tuple(res)
-
-    def _create_grayscale_image(self, mask=None):
-        """
-        Create a grayscale version of the original image.
-        The colors in masked area, if given, will be kept.
-        """
-        img_bw = self.img.astype("f4").mean(axis=2)
-        img_bw = np.stack([img_bw] * 3, axis=2)
-        if mask is not None:
-            img_bw[mask] = self.img[mask]
-        return img_bw
-
-    def _change_color_brightness(self, color, brightness_factor):
-        """
-        Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
-        less or more saturation than the original color.
-
-        Args:
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
-                0 will correspond to no change, a factor in [-1.0, 0) range will result in
-                a darker color and a factor in (0, 1.0] range will result in a lighter color.
-
-        Returns:
-            modified_color (tuple[double]): a tuple containing the RGB values of the
-                modified color. Each value in the tuple is in the [0.0, 1.0] range.
-        """
-        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
-        color = mplc.to_rgb(color)
-        polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
-        modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
-        modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
-        modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
-        modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
-        return modified_color
-
-    def _convert_boxes(self, boxes):
-        """
-        Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.
-        """
-        if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):
-            return boxes.tensor.numpy()
-        else:
-            return np.asarray(boxes)
-
-    def _convert_masks(self, masks_or_polygons):
-        """
-        Convert different format of masks or polygons to a tuple of masks and polygons.
-
-        Returns:
-            list[GenericMask]:
-        """
-
-        m = masks_or_polygons
-        if isinstance(m, PolygonMasks):
-            m = m.polygons
-        if isinstance(m, BitMasks):
-            m = m.tensor.numpy()
-        if isinstance(m, torch.Tensor):
-            m = m.numpy()
-        ret = []
-        for x in m:
-            if isinstance(x, GenericMask):
-                ret.append(x)
-            else:
-                ret.append(GenericMask(x, self.output.height, self.output.width))
-        return ret
-
-    def _convert_keypoints(self, keypoints):
-        if isinstance(keypoints, Keypoints):
-            keypoints = keypoints.tensor
-        keypoints = np.asarray(keypoints)
-        return keypoints
-
-    def get_output(self):
-        """
-        Returns:
-            output (VisImage): the image output containing the visualizations added
-            to the image.
-        """
-        return self.output

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/plain_train_net.py

@@ -1,231 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Detectron2 training script with a plain training loop.
-
-This scripts reads a given config file and runs the training or evaluation.
-It is an entry point that is able to train standard models in detectron2.
-
-In order to let one script support training of many models,
-this script contains logic that are specific to these built-in models and therefore
-may not be suitable for your own project.
-For example, your research project perhaps only needs a single "evaluator".
-
-Therefore, we recommend you to use detectron2 as an library and take
-this file as an example of how to use the library.
-You may want to write your own script with your datasets and other customizations.
-
-Compared to "train_net.py", this script supports fewer default features.
-It also includes fewer abstraction, therefore is easier to add custom logic.
-"""
-
-import logging
-import os
-from collections import OrderedDict
-import torch
-from torch.nn.parallel import DistributedDataParallel
-
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import (
-    MetadataCatalog,
-    build_detection_test_loader,
-    build_detection_train_loader,
-)
-from detectron2.engine import default_argument_parser, default_setup, launch
-from detectron2.evaluation import (
-    CityscapesEvaluator,
-    COCOEvaluator,
-    COCOPanopticEvaluator,
-    DatasetEvaluators,
-    LVISEvaluator,
-    PascalVOCDetectionEvaluator,
-    SemSegEvaluator,
-    inference_on_dataset,
-    print_csv_format,
-)
-from detectron2.modeling import build_model
-from detectron2.solver import build_lr_scheduler, build_optimizer
-from detectron2.utils.events import (
-    CommonMetricPrinter,
-    EventStorage,
-    JSONWriter,
-    TensorboardXWriter,
-)
-
-logger = logging.getLogger("detectron2")
-
-
-def get_evaluator(cfg, dataset_name, output_folder=None):
-    """
-    Create evaluator(s) for a given dataset.
-    This uses the special metadata "evaluator_type" associated with each builtin dataset.
-    For your own dataset, you can simply create an evaluator manually in your
-    script and do not have to worry about the hacky if-else logic here.
-    """
-    if output_folder is None:
-        output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
-    evaluator_list = []
-    evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-    if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
-        evaluator_list.append(
-            SemSegEvaluator(
-                dataset_name,
-                distributed=True,
-                num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
-                ignore_label=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
-                output_dir=output_folder,
-            )
-        )
-    if evaluator_type in ["coco", "coco_panoptic_seg"]:
-        evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
-    if evaluator_type == "coco_panoptic_seg":
-        evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
-    if evaluator_type == "cityscapes":
-        assert (
-            torch.cuda.device_count() >= comm.get_rank()
-        ), "CityscapesEvaluator currently do not work with multiple machines."
-        return CityscapesEvaluator(dataset_name)
-    if evaluator_type == "pascal_voc":
-        return PascalVOCDetectionEvaluator(dataset_name)
-    if evaluator_type == "lvis":
-        return LVISEvaluator(dataset_name, cfg, True, output_folder)
-    if len(evaluator_list) == 0:
-        raise NotImplementedError(
-            "no Evaluator for the dataset {} with the type {}".format(dataset_name, evaluator_type)
-        )
-    if len(evaluator_list) == 1:
-        return evaluator_list[0]
-    return DatasetEvaluators(evaluator_list)
-
-
-def do_test(cfg, model):
-    results = OrderedDict()
-    for dataset_name in cfg.DATASETS.TEST:
-        data_loader = build_detection_test_loader(cfg, dataset_name)
-        evaluator = get_evaluator(
-            cfg, dataset_name, os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
-        )
-        results_i = inference_on_dataset(model, data_loader, evaluator)
-        results[dataset_name] = results_i
-        if comm.is_main_process():
-            logger.info("Evaluation results for {} in csv format:".format(dataset_name))
-            print_csv_format(results_i)
-    if len(results) == 1:
-        results = list(results.values())[0]
-    return results
-
-
-def do_train(cfg, model, resume=False):
-    model.train()
-    optimizer = build_optimizer(cfg, model)
-    scheduler = build_lr_scheduler(cfg, optimizer)
-
-    checkpointer = DetectionCheckpointer(
-        model, cfg.OUTPUT_DIR, optimizer=optimizer, scheduler=scheduler
-    )
-    start_iter = (
-        checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
-    )
-    max_iter = cfg.SOLVER.MAX_ITER
-
-    periodic_checkpointer = PeriodicCheckpointer(
-        checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter
-    )
-
-    writers = (
-        [
-            CommonMetricPrinter(max_iter),
-            JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
-            TensorboardXWriter(cfg.OUTPUT_DIR),
-        ]
-        if comm.is_main_process()
-        else []
-    )
-
-    # compared to "train_net.py", we do not support accurate timing and
-    # precise BN here, because they are not trivial to implement
-    data_loader = build_detection_train_loader(cfg)
-    logger.info("Starting training from iteration {}".format(start_iter))
-    with EventStorage(start_iter) as storage:
-        for data, iteration in zip(data_loader, range(start_iter, max_iter)):
-            iteration = iteration + 1
-            storage.step()
-
-            loss_dict = model(data)
-            losses = sum(loss_dict.values())
-            assert torch.isfinite(losses).all(), loss_dict
-
-            loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()}
-            losses_reduced = sum(loss for loss in loss_dict_reduced.values())
-            if comm.is_main_process():
-                storage.put_scalars(total_loss=losses_reduced, **loss_dict_reduced)
-
-            optimizer.zero_grad()
-            losses.backward()
-            optimizer.step()
-            storage.put_scalar("lr", optimizer.param_groups[0]["lr"], smoothing_hint=False)
-            scheduler.step()
-
-            if (
-                cfg.TEST.EVAL_PERIOD > 0
-                and iteration % cfg.TEST.EVAL_PERIOD == 0
-                and iteration != max_iter
-            ):
-                do_test(cfg, model)
-                # Compared to "train_net.py", the test results are not dumped to EventStorage
-                comm.synchronize()
-
-            if iteration - start_iter > 5 and (iteration % 20 == 0 or iteration == max_iter):
-                for writer in writers:
-                    writer.write()
-            periodic_checkpointer.step(iteration)
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    default_setup(
-        cfg, args
-    )  # if you don't like any of the default setup, write your own setup code
-    return cfg
-
-
-def main(args):
-    cfg = setup(args)
-
-    model = build_model(cfg)
-    logger.info("Model:\n{}".format(model))
-    if args.eval_only:
-        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
-            cfg.MODEL.WEIGHTS, resume=args.resume
-        )
-        return do_test(cfg, model)
-
-    distributed = comm.get_world_size() > 1
-    if distributed:
-        model = DistributedDataParallel(
-            model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
-        )
-
-    do_train(cfg, model)
-    return do_test(cfg, model)
-
-
-if __name__ == "__main__":
-    args = default_argument_parser().parse_args()
-    print("Command Line Args:", args)
-    launch(
-        main,
-        args.num_gpus,
-        num_machines=args.num_machines,
-        machine_rank=args.machine_rank,
-        dist_url=args.dist_url,
-        args=(args,),
-    )

spaces/CVPR/LIVE/thrust/thrust/async/sort.h

@@ -1,275 +0,0 @@
-/*
- *  Copyright 2008-2018 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 async/sort.h
- *  \brief Functions for asynchronously sorting a range.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp14_required.h>
-
-#if THRUST_CPP_DIALECT >= 2014
-
-#include <thrust/detail/static_assert.h>
-#include <thrust/detail/select_system.h>
-#include <thrust/type_traits/logical_metafunctions.h>
-#include <thrust/type_traits/remove_cvref.h>
-#include <thrust/type_traits/is_execution_policy.h>
-#include <thrust/system/detail/adl/async/sort.h>
-
-#include <thrust/event.h>
-
-namespace thrust
-{
-
-namespace async
-{
-
-namespace unimplemented
-{
-
-template <
-  typename DerivedPolicy
-, typename ForwardIt, typename Sentinel, typename StrictWeakOrdering
->
-__host__ 
-event<DerivedPolicy>
-async_stable_sort(
-  thrust::execution_policy<DerivedPolicy>& 
-, ForwardIt, Sentinel, StrictWeakOrdering
-)
-{
-  THRUST_STATIC_ASSERT_MSG(
-    (thrust::detail::depend_on_instantiation<ForwardIt, false>::value)
-  , "this algorithm is not implemented for the specified system"
-  );
-  return {};
-} 
-
-} // namespace unimplemented
-
-namespace stable_sort_detail
-{
-
-using thrust::async::unimplemented::async_stable_sort;
-
-struct stable_sort_fn final
-{
-  template <
-    typename DerivedPolicy
-  , typename ForwardIt, typename Sentinel, typename StrictWeakOrdering
-  >
-  __host__ 
-  static auto call(
-    thrust::detail::execution_policy_base<DerivedPolicy> const& exec
-  , ForwardIt&& first, Sentinel&& last
-  , StrictWeakOrdering&& comp
-  )
-  // ADL dispatch.
-  THRUST_RETURNS(
-    async_stable_sort(
-      thrust::detail::derived_cast(thrust::detail::strip_const(exec))
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(comp)
-    )
-  )
-
-  template <
-    typename DerivedPolicy
-  , typename ForwardIt, typename Sentinel
-  >
-  __host__ 
-  static auto call(
-    thrust::detail::execution_policy_base<DerivedPolicy> const& exec
-  , ForwardIt&& first, Sentinel&& last
-  )
-  // ADL dispatch.
-  THRUST_RETURNS(
-    async_stable_sort(
-      thrust::detail::derived_cast(thrust::detail::strip_const(exec))
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , thrust::less<
-        typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
-      >{}
-    )
-  )
-
-  template <typename ForwardIt, typename Sentinel, typename StrictWeakOrdering>
-  __host__ 
-  static auto call(ForwardIt&& first, Sentinel&& last, StrictWeakOrdering&& comp) 
-  THRUST_RETURNS(
-    stable_sort_fn::call(
-      thrust::detail::select_system(
-        typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
-      )
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(comp)
-    )
-  )
-
-  template <typename ForwardIt, typename Sentinel>
-  __host__ 
-  static auto call(ForwardIt&& first, Sentinel&& last) 
-  THRUST_RETURNS(
-    stable_sort_fn::call(
-      THRUST_FWD(first), THRUST_FWD(last)
-    , thrust::less<
-        typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
-      >{}
-    )
-  )
-
-  template <typename... Args>
-  THRUST_NODISCARD __host__ 
-  auto operator()(Args&&... args) const
-  THRUST_RETURNS(
-    call(THRUST_FWD(args)...)
-  )
-};
-
-} // namespace stable_sort_detail
-
-THRUST_INLINE_CONSTANT stable_sort_detail::stable_sort_fn stable_sort{};
-
-namespace fallback
-{
-
-template <
-  typename DerivedPolicy
-, typename ForwardIt, typename Sentinel, typename StrictWeakOrdering
->
-__host__ 
-event<DerivedPolicy>
-async_sort(
-  thrust::execution_policy<DerivedPolicy>& exec
-, ForwardIt&& first, Sentinel&& last, StrictWeakOrdering&& comp
-)
-{
-  return async_stable_sort(
-    thrust::detail::derived_cast(exec)
-  , THRUST_FWD(first), THRUST_FWD(last), THRUST_FWD(comp)
-  );
-} 
-
-} // namespace fallback
-
-namespace sort_detail
-{
-
-using thrust::async::fallback::async_sort;
-
-struct sort_fn final
-{
-  template <
-    typename DerivedPolicy
-  , typename ForwardIt, typename Sentinel, typename StrictWeakOrdering
-  >
-  __host__ 
-  static auto call(
-    thrust::detail::execution_policy_base<DerivedPolicy> const& exec
-  , ForwardIt&& first, Sentinel&& last
-  , StrictWeakOrdering&& comp
-  )
-  // ADL dispatch.
-  THRUST_RETURNS(
-    async_sort(
-      thrust::detail::derived_cast(thrust::detail::strip_const(exec))
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(comp)
-    )
-  )
-
-  template <
-    typename DerivedPolicy
-  , typename ForwardIt, typename Sentinel
-  >
-  __host__ 
-  static auto call3(
-    thrust::detail::execution_policy_base<DerivedPolicy> const& exec
-  , ForwardIt&& first, Sentinel&& last
-  , thrust::true_type
-  )
-  THRUST_RETURNS(
-    sort_fn::call(
-      exec
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , thrust::less<
-        typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
-      >{}
-    )
-  )
-
-  template <typename ForwardIt, typename Sentinel, typename StrictWeakOrdering>
-  __host__ 
-  static auto call3(ForwardIt&& first, Sentinel&& last,
-                    StrictWeakOrdering&& comp,
-                    thrust::false_type)
-  THRUST_RETURNS(
-    sort_fn::call(
-      thrust::detail::select_system(
-        typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
-      )
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(comp)
-    )
-  )
-
-  // MSVC WAR: MSVC gets angsty and eats all available RAM when we try to detect
-  // if T1 is an execution_policy by using SFINAE. Switching to a static
-  // dispatch pattern to prevent this.
-  template <typename T1, typename T2, typename T3>
-  __host__
-  static auto call(T1&& t1, T2&& t2, T3&& t3)
-  THRUST_RETURNS(
-    sort_fn::call3(THRUST_FWD(t1), THRUST_FWD(t2), THRUST_FWD(t3),
-                   thrust::is_execution_policy<thrust::remove_cvref_t<T1>>{})
-  )
-
-  template <typename ForwardIt, typename Sentinel>
-  __host__ 
-  static auto call(ForwardIt&& first, Sentinel&& last) 
-  THRUST_RETURNS(
-    sort_fn::call(
-      thrust::detail::select_system(
-        typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
-      )
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , thrust::less<
-        typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
-      >{}
-    )
-  )
-
-  template <typename... Args>
-  THRUST_NODISCARD __host__ 
-  auto operator()(Args&&... args) const
-  THRUST_RETURNS(
-    call(THRUST_FWD(args)...)
-  )
-};
-
-} // namespace sort_detail
-
-THRUST_INLINE_CONSTANT sort_detail::sort_fn sort{};
-
-} // namespace async
-
-} // end namespace thrust
-
-#endif
-