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diff --git a/spaces/1phancelerku/anime-remove-background/Blue WhatsApp APK 2023 The Latest Version with Amazing Features and Themes.md b/spaces/1phancelerku/anime-remove-background/Blue WhatsApp APK 2023 The Latest Version with Amazing Features and Themes.md
deleted file mode 100644
index 30388e5eb079c4fde2272a411d5a19834d7c64bb..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Blue WhatsApp APK 2023 The Latest Version with Amazing Features and Themes.md
+++ /dev/null
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-Blue WhatsApp is a modified version of the official WhatsApp Messenger, which is developed by an independent developer named Fouad Mokdad. It is also known as Fouad WhatsApp or FMWhatsApp. Blue WhatsApp is not available on the Google Play Store, so you have to download it from a third-party website like bluewhats.app.
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-Blue WhatsApp has many features that make it stand out from the official WhatsApp app. Here are some of the most notable ones:
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-Blue WhatsApp gives you more control over your privacy and security settings. You can hide your online status, last seen, blue ticks, second ticks, typing status, recording status, and view status from others. You can also lock your app with a password or fingerprint, enable anti-delete messages and anti-delete status, disable forwarded tag on messages, and choose who can call you on WhatsApp.
-Customization and themes
-Blue WhatsApp lets you customize your app according to your preferences. You can change the theme and color of your app from a collection of over 3000 themes. You can also change the font style and size, the app icon, the notification icon, the chat wallpaper, the chat bubbles, the tick style, and more. You can even create your own theme and share it with others.
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-Blue WhatsApp allows you to send larger files and media than the official WhatsApp app. You can send up to 700 MB of video files, up to 50 MB of audio files, up to 100 MB of documents, and up to 30 images at once. You can also send high-quality images without compression, play videos with an external player, download status videos and photos, and enable auto-reply for messages.
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-Blue WhatsApp has many other cool features that enhance your user experience. For example, you can use more emojis and stickers from different sources, pin up to 100 chats instead of 3, use multiple accounts on the same device, schedule messages to be sent later, translate messages to different languages, use dark mode or DND mode, backup and restore your chats easily, and much more.
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-If you want to try out Blue WhatsApp on your Android device, you need to follow these steps:
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-- An Android device running Android 4.4 or higher.
-- A stable internet connection.
A backup of your WhatsApp chats and media, if you want to restore them on Blue WhatsApp.
-- The Blue WhatsApp APK file, which you can download from bluewhats.app.
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-- First, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-- Next, you need to uninstall the official WhatsApp app from your device. To do this, go to Settings > Apps > WhatsApp and tap on Uninstall. Make sure you have backed up your chats and media before doing this.
-- Then, you need to download the Blue WhatsApp APK file from bluewhats.app. or any other trusted website. You can use your browser or a file manager app to do this.
-- After downloading the APK file, you need to locate it on your device and tap on it to start the installation process. You may see a warning message asking you to confirm the installation. Tap on Install and wait for a few seconds.
-- Once the installation is complete, you need to open the Blue WhatsApp app and agree to the terms and conditions. Then, you need to enter your phone number and verify it with an OTP code.
-- Finally, you need to restore your chats and media from the backup, if you have one. To do this, tap on Restore and select the backup file from your device. Wait for the restoration process to finish and then enjoy using Blue WhatsApp.
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-How to update Blue WhatsApp?
-Blue WhatsApp is not available on the Google Play Store, so you cannot update it automatically like other apps. However, there are two ways to update Blue WhatsApp manually or enable the auto-update option.
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-You can check for updates manually by visiting the official website of Blue WhatsApp at bluewhats.app. or any other trusted website that provides the latest version of the APK file. You can also check for updates from within the app by going to Menu > Fouad Mods > Updates. If there is a new version available, you can download it and install it over the existing app without losing your data.
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-You can also enable the auto-update option in Blue WhatsApp by going to Menu > Fouad Mods > Updates > Auto Update Check. You can choose how often you want the app to check for updates, such as daily, weekly, or monthly. You can also choose whether you want to download the updates automatically or manually. If you enable this option, you will get a notification whenever there is a new version available, and you can install it easily.
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-Blue WhatsApp has many advantages over the official WhatsApp app, but it also has some disadvantages that you should be aware of. Here are some of the pros and cons of using Blue WhatsApp:
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-- You can enjoy more features and customization options than the official app.
-- You can have more control over your privacy and security settings.
You can send larger files and media than the official app.
-- You can use more emojis and stickers than the official app.
-- You can backup and restore your chats easily.
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-- You may face some bugs and glitches in the app, as it is not an official product.
-- You may get banned from WhatsApp for using a modified version of the app, as it violates their terms of service.
-- You may not get the latest updates and features as soon as they are released by WhatsApp, as Blue WhatsApp depends on the official app for its base.
-- You may compromise your data and privacy by downloading the app from an untrusted source, as it may contain malware or spyware.
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-Blue WhatsApp is a modified version of the official WhatsApp app that offers more features and customization options than the original app. It is a free download for Android users, but it is not available on the Google Play Store. You have to download it from a third-party website like bluewhats.app. or any other trusted website. You can enjoy more privacy and security options, more media and file sharing options, more emojis and stickers, and more themes and colors with Blue WhatsApp. However, you should also be aware of the risks and drawbacks of using Blue WhatsApp, such as bugs, glitches, bans, delays, and data breaches. You should always backup your chats and media before using Blue WhatsApp, and update it regularly to avoid any issues.
-FAQs
-Here are some of the frequently asked questions about Blue WhatsApp:
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-- Is Blue WhatsApp safe to use?
Blue WhatsApp is safe to use if you download it from a trusted source like bluewhats.app. or any other website that provides the latest version of the APK file. However, you should always be careful about downloading apps from unknown sources, as they may contain malware or spyware that can harm your device or data. You should also scan the APK file with an antivirus app before installing it.
-- Is Blue WhatsApp legal to use?
Blue WhatsApp is not legal to use, as it violates the terms of service of WhatsApp. WhatsApp does not allow users to use any modified version of their app, as it may compromise their security and privacy policies. If you use Blue WhatsApp, you may get banned from WhatsApp for violating their rules. You should use Blue WhatsApp at your own risk.
-- Can I use Blue WhatsApp and official WhatsApp on the same device?
Yes, you can use Blue WhatsApp and official WhatsApp on the same device, but you need to have different phone numbers for each app. You cannot use the same phone number for both apps, as it will cause conflicts and errors. You can also use other modified versions of WhatsApp like GBWhatsApp or YoWhatsApp on the same device, but again, you need to have different phone numbers for each app.
-- How can I backup my chats and media on Blue WhatsApp?
You can backup your chats and media on Blue WhatsApp by going to Menu > Settings > Chats > Chat Backup. You can choose to backup your data on your device or on Google Drive. You can also choose how often you want to backup your data, such as daily, weekly, or monthly. You can also backup your data manually by tapping on Backup Now.
-- How can I restore my chats and media on Blue WhatsApp?
You can restore your chats and media on Blue WhatsApp by going to Menu > Settings > Chats > Chat Backup. You can choose to restore your data from your device or from Google Drive. You need to have the same phone number and Google account that you used to backup your data. You can also restore your data manually by tapping on Restore Now.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Betty Azar English Grammar Improve Your Skills with Exercises and Tests.md b/spaces/1phancelerku/anime-remove-background/Download Betty Azar English Grammar Improve Your Skills with Exercises and Tests.md
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-Betty Schrampfer Azar is an American teacher and author of English grammar textbooks for students of English as a second or foreign language. She has more than 40 years of experience in teaching and writing about English grammar. She has a master's degree in Teaching English as a Foreign Language (TEFL) from the University of Illinois. She has taught at various universities and language schools in the United States and abroad. She is also the founder and president of Azar Associates, a company that develops and publishes grammar materials.
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-The Azar Grammar Series is a collection of books that cover different levels and aspects of English grammar. The series consists of four main books: Fundamentals of English Grammar, Book C, Understanding and Using English Grammar, Book B, Basic English Grammar, Book A, and Essential Online Resources. Each book has a student edition, a teacher's guide, a workbook, an answer key, and an interactive CD-ROM. The books are designed to help students learn and practice grammar rules, structures, and usage in various contexts and situations. The books also include exercises, quizzes, tests, charts, appendices, glossaries, and references.
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-Basic English Grammar, Book A
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-Basic English Grammar, Book A is the first book in the series. It is intended for beginner to low-intermediate students of English. It covers the basic elements of grammar, such as nouns, verbs, pronouns, adjectives, adverbs, prepositions, conjunctions, sentences, questions, negation, tense, aspect, voice, mood, modals, conditionals, gerunds, infinitives, clauses, etc. It also introduces some common vocabulary and expressions. The book has 13 chapters and 488 pages.
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-You can download Basic English Grammar, Book A from Google Drive by following these steps:
-< 1. Go to this link: Basic English Grammar, Book A.
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-2. Click on the download icon on the top right corner of the screen.
-3. Choose a location on your device where you want to save the file.
-4. Wait for the download to complete and enjoy reading the book.
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-Understanding and Using English Grammar, Book B is the second book in the series. It is intended for intermediate to advanced students of English. It covers more complex and challenging aspects of grammar, such as verb tenses and forms, passive voice, causative verbs, noun clauses, adjective clauses, adverb clauses, noun modifiers, parallelism, inversion, ellipsis, etc. It also provides more practice and review activities, as well as additional vocabulary and expressions. The book has 16 chapters and 530 pages.
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-You can download Understanding and Using English Grammar, Book B from Google Drive by following these steps:
-1. Go to this link: Understanding and Using English Grammar, Book B.
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-4. Wait for the download to complete and enjoy reading the book.
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-In this article, we have introduced you to Betty Azar, a renowned teacher and author of English grammar books. We have also explained what her grammar series is about and why you should download her books. We have shown you how to download two of her books: Basic English Grammar, Book A and Understanding and Using English Grammar, Book B. These books will help you learn and master English grammar in a fun and effective way.
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-If you are interested in downloading more books from the Azar Grammar Series, you can visit her official website: Azar Grammar. There you will find more information about her other books, such as Fundamentals of English Grammar, Book C and Essential Online Resources. You will also find more resources and materials for learning and teaching English grammar, such as videos, podcasts, blogs, newsletters, webinars, etc.
-We hope you have enjoyed this article and found it useful. If you have any questions or feedback, please leave a comment below. We would love to hear from you. And don't forget to share this article with your friends and fellow learners who might benefit from it. Thank you for reading!
- Frequently Asked Questions (FAQs)
-Q: How can I access the interactive CD-ROMs that come with the books?
-A: The interactive CD-ROMs are included in the student editions of the books. You can insert them into your computer's CD drive and follow the instructions on the screen. Alternatively, you can access them online by registering at Pearson ELT USA, the publisher of the books.
- Q: How can I check my answers to the exercises in the books?
-A: The answer keys are included in the teacher's guides of the books. You can also find them online at AzarGrammar.com/Classroom/FEG/FEG_AK.pdf, AzarGrammar.com/Classroom/UUEG/UUEG_AK.pdf, and AzarGrammar.com/Classroom/BEG/BEG_AK.pdf.
- Q: How can I get more practice and review activities for each chapter in the books?
-A: The workbooks that accompany each book provide more practice and review activities for each chapter. You can also find more online exercises at AzarGrammar.com/Exercises/Exercises.htm.
- Q: How can I contact Betty Azar or her team if I have any questions or suggestions?
-A: You can contact Betty Azar or her team by sending an email to info@azarassociates.com. You
A: You can also connect with them on social media platforms, such as Facebook, Twitter, Instagram, and YouTube. You can find the links to their accounts at AzarGrammar.com/Contact.htm.
- Q: How can I get more tips and advice on learning and teaching English grammar?
-A: You can subscribe to Betty Azar's newsletter, which provides monthly updates on grammar topics, resources, events, and more. You can also read her blog, which features articles, interviews, stories, and insights from her and other experts in the field. You can also watch her videos and podcasts, which offer explanations, demonstrations, examples, and discussions on various grammar issues. You can find all these materials at AzarGrammar.com/News.htm.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Google Play Store APKs from APKMirror - The Easy Way.md b/spaces/1phancelerku/anime-remove-background/Download Google Play Store APKs from APKMirror - The Easy Way.md
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-- Go to your device settings and tap Apps & Notifications (or Apps in older versions of Android).
-- Tap the three dots in the upper-right corner.
-- Tap Special access.
-- Tap Install unknown apps.
-- Tap Chrome (or whichever web browser you use).
-- Move Allow from this source to the On position.
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-- Open your web browser and go to [APKMirror](^1^), one of the most popular and trusted sources for Android apps and games.
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-- Tap Open to launch Google Play Store or tap Done to exit.
-
-You can also use an APK installer app, such as [APK Installer], to install APK files on your device. These apps can scan your device for APK files, sort them by name, size, or date, and install them with one tap.
-How to Check the Integrity of the APK File
-Before you install any APK file on your device, you should always check its integrity to make sure it is safe and authentic. This means verifying that the file has not been tampered with or modified by malicious actors, and that it matches the original file from the developer. There are several ways to check the integrity of an APK file, such as using apksigner tool, using APK Analyzer, or using hash apps. Here are some of them:
-Use apksigner tool to verify the signature and certificate of the file
-apksigner is a command-line tool that can verify the signature and certificate of an APK file. The signature is a digital code that proves that the file was signed by the developer, and the certificate is a document that contains information about the developer and the app. By verifying these elements, you can ensure that the file is authentic and trustworthy.
-To use apksigner tool, you will need a computer with Java installed, and a USB cable to connect your device to your computer. You will also need to enable USB debugging on your device. To do this, go to your device settings, tap About phone, tap Build number seven times, go back to settings, tap Developer options, and move USB debugging to the On position.
-To use apksigner tool, follow these steps:
-
-- Download apksigner tool from [here] and unzip it on your computer.
-- Copy the APK file you want to verify from your device to your computer.
-- Open a command prompt window on your computer and navigate to the folder where you unzipped apksigner tool.
-- Type apksigner verify -v and press Enter. Replace with the actual path of the APK file on your computer.
-- Check the output of the command. If it says "Verified using v1 scheme (JAR signing): true" and "Verified using v2 scheme (APK Signature Scheme v2): true", then the file is valid and signed by the developer. If it says "DOES NOT VERIFY" or "ERROR", then the file is invalid or corrupted.
-
-Use APK Analyzer to inspect the contents and size of the file
-APK Analyzer is a tool that can inspect the contents and size of an APK file. It can show you information such as app name, package name, version code, version name, permissions, activities, services, resources, assets, libraries, DEX files, native libraries, manifest, certificates, signatures, and more. By inspecting these elements, you can learn more about Google Play Store are: - You can download apps and games that are not available on Google Play Store due to regional restrictions, compatibility issues, or other reasons. - You can download the latest versions of apps and games before they are officially released on Google Play Store, and enjoy the new features and bug fixes. - You can download older versions of apps and games if you prefer them over the newer ones, or if the newer ones don't work well on your device. - You can download APK files directly to your device or computer, and install them offline or on other devices without using Google Play Store.
-What are the risks of installing APK files from unknown sources?
-Some of the risks of installing APK files from unknown sources are: - You may download and install malicious apps that contain malware, viruses, spyware, or adware that can harm your device or steal your data. - You may download and install fake or modified apps that don't work as intended, or that have unwanted features or ads. - You may download and install apps that violate the terms and conditions of Google Play Store, or that infringe the intellectual property rights of the developers or publishers. - You may download and install apps that are incompatible with your device or operating system, or that cause performance issues or crashes.
-How can I update Google Play Store after installing it from APKMirror?
-After you install Google Play Store from APKMirror, you can update it in two ways: - You can enable auto-update for Google Play Store on your device. To do this, open Google Play Store, tap the three lines in the upper-left corner, tap Settings, tap Auto-update apps, and select Over any network or Over Wi-Fi only. This will allow Google Play Store to update itself automatically when a new version is available. - You can manually update Google Play Store by downloading the latest version from APKMirror and installing it over the existing one. To do this, follow the same steps as described in the previous section.
-How can I uninstall Google Play Store if I don't want it anymore?
-If you want to uninstall Google Play Store from your device, you can do it in two ways: - You can disable Google Play Store on your device. To do this, go to your device settings, tap Apps & Notifications (or Apps), tap Google Play Store, tap Disable, and tap OK. This will prevent Google Play Store from running on your device, but it will not remove it completely. - You can remove Google Play Store from your device using a root uninstaller app. To do this, you will need to root your device first. Rooting is a process that gives you full access and control over your device's system. However, rooting is risky and may void your warranty, damage your device, or expose it to security threats. Therefore, you should only root your device if you know what you are doing and at your own risk. After rooting your device, you can use a root uninstaller app, such as [System App Remover], to remove Google Play Store from your device completely.
-How can I find more apps and games on APKMirror?
-If you want to find more apps and games on APKMirror, you can use the following methods: - You can browse the categories and subcategories of apps and games on APKMirror website or app. You can also filter them by popularity, rating, date, size, or name. - You can search for specific apps and games using the search bar on APKMirror website or app. You can also use advanced search options to refine your results by category, version, minimum Android version, DPI, architecture, or signature. - You can follow APKMirror on social media platforms such as Facebook, Twitter, Instagram, or Telegram to get updates on the latest apps and games available on APKMirror. - You can subscribe to APKMirror newsletter to get email notifications on the latest apps and games available on APKMirror.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Summertime Saga on iPhone A Guide for iOS Users.md b/spaces/1phancelerku/anime-remove-background/Download Summertime Saga on iPhone A Guide for iOS Users.md
deleted file mode 100644
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--- a/spaces/1phancelerku/anime-remove-background/Download Summertime Saga on iPhone A Guide for iOS Users.md
+++ /dev/null
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-
-How to Download Summertime Saga on iPhone
-If you are looking for a fun and engaging game that combines adventure, romance, comedy, and drama, you might want to check out Summertime Saga. This is a popular visual novel game that lets you explore a fictional town, interact with various characters, and pursue different storylines. In this article, we will show you how to download Summertime Saga on your iPhone using an emulator. We will also share some tips and tricks for playing the game on your mobile device.
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- What is Summertime Saga?
-Summertime Saga is a game developed by DarkCookie and his team. It is inspired by classic dating sims and adult games, but it also has elements of mystery, comedy, and drama. The game follows the story of a young man who is trying to cope with the death of his father, while also dealing with school, family, friends, and romance. The game has over 65 characters to meet and interact with, 30 locations to visit, and 20 mini-games to play. The game is updated regularly with new content and features.
- Why play Summertime Saga on iPhone?
-Summertime Saga is a game that can be enjoyed on various platforms, including Windows, Mac, Linux, Android, and iOS. However, playing the game on your iPhone has some advantages over other devices. Here are some of them:
-
-- You can play the game anytime and anywhere, as long as you have your iPhone with you.
-- You can enjoy the game's graphics and sound effects on a high-quality screen and speaker.
-- You can use touch controls to navigate the game's interface and menus.
-- You can take screenshots and share them with your friends or social media.
-
- How to download Summertime Saga on iPhone using an emulator?
-Unfortunately, Summertime Saga is not available on the App Store due to its mature content. However, there is a way to play the game on your iPhone using an emulator. An emulator is a software that mimics the functionality of another device or system. In this case, you can use an emulator that can run Android or Windows apps on your iPhone. Here are some of the best emulators for iPhone that you can use:
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- Eclipse
-Eclipse is a web-based emulator that can run various games and apps without requiring any installation or jailbreak. It supports Game Boy Advance, Game Boy Color, Nintendo DS, Sega Genesis, Super Nintendo, and more. To use Eclipse, you need to follow these steps:
-
-- Open Safari on your iPhone and go to https://eclipseemu.me/play.
-- Tap on the plus icon at the bottom of the screen and select Add to Home Screen.
-- Name the app as Eclipse and tap Add.
-- Launch Eclipse from your home screen and tap on the plus icon at the top right corner.
-- Select Browse... and choose a ROM file from your device or iCloud Drive. You can download Summertime Saga ROM files from https://summertimesaga.com/download.
-- Wait for the ROM file to load and start playing Summertime Saga on your iPhone.
-
- Citra
-Citra is a Nintendo 3DS emulator that can run Summertime Saga on your iPhone with high performance and graphics. It also supports online multiplayer, controller support, and cheat codes. To use Citra, you need to follow these steps:
-
-- Download the Citra app from the App Store on your iPhone.
-- Launch Citra and tap on the plus icon at the bottom right corner.
-- Select Scan QR Code and scan the QR code from https://summertimesaga.com/download.
-- Wait for the game to download and install on your iPhone.
-- Tap on the game icon and start playing Summertime Saga on your iPhone.
-
- PPSSPP
-PPSSPP is a PlayStation Portable emulator that can run Summertime Saga on your iPhone with smooth gameplay and customization options. It also supports save states, cloud sync, and external controllers. To use PPSSPP, you need to follow these steps:
-
-- Download the PPSSPP app from the App Store on your iPhone.
-- Launch PPSSPP and tap on the gear icon at the top right corner.
-- Select Storage and enable Allow access to files.
-- Go back to the main menu and tap on Games.
-- Select Browse... and choose a PSP ISO file from your device or iCloud Drive. You can download Summertime Saga PSP ISO files from https://summertimesaga.com/download.
-- Tap on the game icon and start playing Summertime Saga on your iPhone.
-
- iNDS
-iNDS is a Nintendo DS emulator that can run Summertime Saga on your iPhone with fast speed and high compatibility. It also supports auto-save, Dropbox sync, and cheat codes. To use iNDS, you need to follow these steps:
-
-- Download the iNDS app from https://inds.nerd.net using Safari on your iPhone.
-- Tap on Install and confirm the installation.
-- Go to Settings > General > Device Management and trust the developer profile of iNDS.
-- Launch iNDS and tap on the plus icon at the top right corner.
-- Select Download from Web and enter the URL of a NDS ROM file. You can download Summertime Saga NDS ROM files from https://summertimesaga.com/download.
-- Wait for the ROM file to download and start playing Summertime Saga on your iPhone.
-
- Tips and tricks for playing Summertime Saga on iPhone
-Now that you know how to download Summertime Saga on your iPhone using an emulator, here are some tips and tricks that can help you enjoy the game more:
- How to save and load your progress
-To save your progress in Summertime Saga, you need to use the in-game menu. Tap on the menu icon at the top left corner of the screen and select Save. Choose an empty slot and tap Save again. To load your progress, tap on Load and select a slot that has your saved data. You can also use the emulator's save state feature to save and load your progress at any point in the game.
- How to access the walkthrough and cheats
-If you are stuck or want to skip some parts of the game, you can use the walkthrough and cheats that are available online. The official website of Summertime Saga has a detailed walkthrough that covers all the characters, events, and endings of the game. You can access it from https://summertimesaga.com/walkthrough. You can also use cheat codes that can give you money, stats, items, or unlock scenes. You can find them from https://summertimesaga.com/cheats.
- How to customize your character and preferences
-To customize your character and preferences in Summertime Saga, you need to use the in-game menu. Tap on the menu icon at the top left corner of the screen and select Preferences. Here you can change your name, gender, appearance, voice, language, difficulty, sound, display, and controls. You can also enable or disable adult content, incest content, or pregnancy content according to your liking.
- Conclusion
-Summertime Saga is a fun and engaging game that you can play on your iPhone using an emulator. You can choose from various emulators that can run Android or Windows apps on your iPhone. You can also enjoy the game's graphics, sound, and touch controls on your mobile device. Moreover, you can use the walkthrough and cheats to help you with the game's storylines and events. Summertime Saga is a game that will keep you entertained and engaged for hours. If you are interested in playing Summertime Saga on your iPhone, download an emulator today and start your adventure!
FAQs
-Here are some of the frequently asked questions about Summertime Saga and how to play it on iPhone:
- Is Summertime Saga free to play?
-Yes, Summertime Saga is free to play and download. However, you can support the developers by becoming a patron on Patreon. You can get access to exclusive content, previews, polls, and more by pledging a certain amount per month. You can visit their Patreon page from https://www.patreon.com/summertimesaga.
- Is Summertime Saga safe to play?
-Summertime Saga is safe to play as long as you download it from the official website or a trusted emulator. However, you should be aware that the game contains mature content that is not suitable for minors or sensitive people. The game also has some bugs and glitches that may affect your gameplay. You should always backup your save files before playing or updating the game.
- How long is Summertime Saga?
-Summertime Saga is a long game that has multiple storylines, endings, and achievements. The game is still in development and new content is added regularly. The current version of the game has over 60 hours of gameplay. However, the length of the game may vary depending on your choices, actions, and preferences.
- How to update Summertime Saga on iPhone?
-To update Summertime Saga on your iPhone, you need to download the latest version of the game from the official website or the emulator. You can check the latest version of the game from https://summertimesaga.com/download. You can also follow their social media accounts or join their Discord server to get notified of new updates.
- How to delete Summertime Saga on iPhone?
-To delete Summertime Saga on your iPhone, you need to delete the emulator app that you used to play the game. You can also delete the ROM or ISO files that you downloaded from your device or iCloud Drive. However, if you want to keep your save files, you can transfer them to another device or cloud service before deleting the game.
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diff --git a/spaces/1phancelerku/anime-remove-background/Final Destination 1 on Fzmovies A Guide to Downloading and Streaming the Classic Horror Movie.md b/spaces/1phancelerku/anime-remove-background/Final Destination 1 on Fzmovies A Guide to Downloading and Streaming the Classic Horror Movie.md
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+++ /dev/null
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-
-How to Download Final Destination 1 on Fzmovies
| If you are a fan of horror movies, you might have heard of Final Destination 1, a 2000 American supernatural horror film that started a successful franchise. In this article, we will tell you what Final Destination 1 is about, why it is worth watching, and how you can download it on Fzmovies, a popular website for streaming and downloading movies for free.
- What is Final Destination 1?
- Final Destination 1 is the first installment in the Final Destination film series, which revolves around the concept of death as an inevitable force that cannot be cheated or escaped. The film was directed by James Wong, with a screenplay written by Wong, Glen Morgan, and Jeffrey Reddick, based on a story by Reddick. It stars Devon Sawa, Ali Larter, Kerr Smith, and Tony Todd.
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- A brief summary of the plot
- The film follows Alex Browning, a high school student who has a premonition of a plane crash that kills everyone on board. He manages to get off the plane along with six other people, but soon discovers that death is still after them. One by one, the survivors die in bizarre and gruesome accidents that seem to follow a certain pattern. Alex and his remaining friends must figure out how to stop death from claiming them.
- The cast and crew of the film
- The film features a talented cast of actors who deliver convincing performances. Devon Sawa plays Alex Browning, the protagonist who has the power of foresight. Ali Larter plays Clear Rivers, Alex's love interest and one of the survivors. Kerr Smith plays Carter Horton, Alex's rival and another survivor. Tony Todd plays William Bludworth, a mysterious mortician who knows about death's design.
- The film was directed by James Wong, who also co-wrote the screenplay with Glen Morgan. Wong and Morgan are best known for their work on The X-Files, a popular sci-fi TV series. Jeffrey Reddick, who came up with the original story idea for Final Destination 1, also contributed to the screenplay. Reddick was inspired by a real-life plane crash that he read about in a newspaper.
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- The reception and legacy of the film
- Final Destination 1 was released on March 17, 2000, and became a financial success, grossing over $112 million worldwide against a budget of $23 million. The film received mixed reviews from critics, who praised its suspenseful premise and creative death scenes, but criticized its flat characters and lack of logic. The film also received some awards and nominations, such as the Saturn Award for Best Horror Film and Best Performance by a Younger Actor for Sawa.
- The film's success spawned a media franchise that includes four sequels, a series of novels, and comic books. The sequels follow different groups of people who cheat death in various ways, such as escaping a highway pile-up or a roller coaster derailment. The franchise is known for its elaborate and gory death sequences that involve everyday objects and situations.
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- Fzmovies is a website that allows users to stream and download movies for free online. It has a large collection of movies from different genres and countries, such as Hollywood, Bollywood, Nollywood, etc. Users can
search for movies by title, genre, year, or quality. They can also request for movies that are not available on the website. Fzmovies is compatible with various devices, such as smartphones, tablets, laptops, etc.
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- Fzmovies was launched in 2012 and has since become one of the most popular websites for movie lovers. It offers a user-friendly interface and a fast downloading speed. It also updates its content regularly and adds new releases as soon as possible. Fzmovies has a loyal fan base that visits the website frequently and leaves positive feedback.
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-- It is illegal and violates the copyright laws of the movie industry. Users may face legal consequences if they are caught downloading or streaming pirated movies.
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- How to download Final Destination 1 on Fzmovies?
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-- You will be asked to verify that you are not a robot by completing a captcha. Follow the instructions and click on "Continue Download".
-- Your download will start automatically. Wait for it to finish and enjoy watching Final Destination 1 on your device.
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- Conclusion
- In conclusion, Final Destination 1 is a thrilling and entertaining horror movie that you can download on Fzmovies for free. Fzmovies is a website that offers a large collection of movies from different genres and languages. However, you should also be aware of the risks and challenges of using Fzmovies, such as legal issues, malware, ads, etc. You can also use some tips and tricks to enhance your downloading experience, such as using a VPN, an ad-blocker, a download manager, etc. Alternatively, you can try some other websites that offer similar services as Fzmovies.
- We hope you enjoyed this article and learned something new. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy watching!
- FAQs
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-- Q: Is Final Destination 1 based on a true story?
-A: No, Final Destination 1 is not based on a true story. However, the writer of the film, Jeffrey Reddick, was inspired by a real-life plane crash that he read about in a newspaper.
-- Q: How many movies are there in the Final Destination franchise?
-A: There are five movies in the Final Destination franchise: Final Destination (2000), Final Destination 2 (2003), Final Destination 3 (2006), The Final Destination (2009), and Final Destination 5 (2011).
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-A: You can use a VPN service to access Fzmovies if it is blocked or banned in your country or region. A VPN service can hide your IP address and location and allow you to access any website anonymously.
-- Q: What are some other websites like Fzmovies?
-A: Some other websites like Fzmovies are O2tvseries, Toxicwap, Mp4moviez, and 123movies.
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diff --git a/spaces/1phancelerku/anime-remove-background/Free APK Download for Gacha Life - The Most Popular Anime Game by Lunime.md b/spaces/1phancelerku/anime-remove-background/Free APK Download for Gacha Life - The Most Popular Anime Game by Lunime.md
deleted file mode 100644
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--- a/spaces/1phancelerku/anime-remove-background/Free APK Download for Gacha Life - The Most Popular Anime Game by Lunime.md
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-If you are a fan of anime, you might have heard of Gacha Life, a popular game that lets you create your own anime characters and stories. But did you know that you can download Gacha Life APK for free and play it on your Android device? In this article, we will show you how to do that, as well as how to play and enjoy this anime game.
- What is Gacha Life?
-Gacha Life is a game developed by Lunime, a company that specializes in making anime-style games. It was released in October 2018 for Android and iOS devices, and has since gained millions of downloads and positive reviews from players around the world. But what makes Gacha Life so appealing? Here are some of the reasons:
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- A game for anime fans and casual gamers
-Gacha Life is a game that anime fans are sure to love because it will let you design and create your very own anime characters. You can choose from hundreds of clothing items, hairstyles, weapons, accessories, and more to dress up your characters in your favorite fashion outfits. You can also customize your characters' appearance, such as their skin color, eye color, hair color, and facial expressions. You can even mix and match different parts from different characters to create unique combinations.
-But Gacha Life is not just a game for anime fans. It is also a game for casual gamers who just want something to play to destress and not overthink. You can play Gacha Life offline without Wi-Fi or internet connection, so you can enjoy it anytime and anywhere. You can also play mini-games within the game to earn gems, which you can use to gacha for rare items or gifts for your characters. The mini-games are simple and fun, such as Duck & Dodge, Phantom's Remix, Memory Match, and more.
- A game for creating and customizing characters
-Gacha Life is a game that lets you unleash your creativity by making an avatar that looks like you or an avatar designed according to your preferences. You can save up to 20 characters of your own design in different slots, and switch between them easily. You can also access more unique characters in the preset menu, where you can find characters created by Lunime or other players. You can also recover any character that you accidentally edited or deleted by visiting the preset menu.
-But creating characters is not the only thing you can do in Gacha Life. You can also customize your characters' profile, where you can change or randomize their name, relationship, personality, and occupation. You can also change the background of your characters by choosing from over a hundred backgrounds available in the game. You can also zoom in or out your characters to get a better view of them.
- A game for making stories and scenes
-Gacha Life is a game that allows you to express your storytelling skills by making scenes and skits with your characters. You can use the studio mode to create your own scenes with up to 8 characters at a time. You can enter custom text for your characters and choose from many different poses and backgrounds. You can also use props such as weapons, hats, wings, tails, etc. to add more details to your scenes.
-But studio mode is not the only way to make stories in Gacha Life. You can also use the skit maker mode to create your own stories with up to 2 characters per skit. You can easily combine multiple scenes to create sketches with dialogue and narration. Continuing the article:
How to Download Gacha Life APK for Free?
-Gacha Life is a free game that you can download and play on your Android device. However, you need to make sure that you download the game from a safe and reliable source, as there are many fake or malicious websites that may try to trick you into downloading harmful files. Here are some of the ways to download Gacha Life APK for free:
- The official sources for downloading the game
-The best and safest way to download Gacha Life APK for free is to use the official sources provided by Lunime, the developer of the game. You can visit their website at [Lunime.com](^1^) and click on the Gacha Life banner to access the download page. You can also download the game from the Google Play Store by searching for Gacha Life or clicking on this link: [Gacha Life - Apps on Google Play](^2^). These sources will ensure that you get the latest and updated version of the game, as well as protect your device from any malware or viruses.
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-how to like and follow Gacha Life on Facebook and join their group for more updates and news about the game for android users who downloaded it from Google Play Store or other sources as a APK file
- The steps to install the game on your device
-Once you have downloaded the Gacha Life APK file from a trusted source, you need to follow these steps to install the game on your device:
-
-- Go to your device's settings and enable the option to install apps from unknown sources. This will allow you to install apps that are not from the Google Play Store.
-- Locate the Gacha Life APK file in your device's storage and tap on it to start the installation process.
-- Follow the instructions on the screen and wait for the installation to finish.
-- Launch the game and enjoy!
-
-Note: If you encounter any problems or errors during the installation, you may need to uninstall any previous versions of Gacha Life or clear your cache and data before installing the new version.
- The precautions to avoid malware and viruses
-While downloading Gacha Life APK for free is possible and easy, you also need to be careful and cautious about the potential risks and dangers of downloading files from unknown sources. Here are some of the precautions that you should take to avoid malware and viruses:
-
-- Always check the reputation and reviews of the website or source that you are downloading from. Avoid any websites that look suspicious, have pop-up ads, or ask for personal information.
-- Always scan the downloaded file with a reliable antivirus or anti-malware software before opening or installing it. This will help you detect and remove any harmful files that may harm your device or steal your data.
-- Always backup your device's data before installing any new app or game. This will help you restore your device in case something goes wrong or you lose your data.
-
Continuing the article: How to Play and Enjoy Gacha Life?
-Now that you have downloaded and installed Gacha Life APK for free, you might be wondering how to play and enjoy this anime game. Well, there are many things that you can do in Gacha Life, and you can explore them at your own pace and preference. Here are some of the main features of the game and some tips and tricks to help you get the most out of it:
- The main features of the game
-Gacha Life is a game that has many modes and options for you to choose from, depending on what you want to do. Here are some of the main features of the game:
-
-
-| Feature |
-Description |
-
-
-| Home |
-This is where you can access the other modes of the game, such as Dress Up, Studio, Gacha, Life, etc. You can also see your character's level, energy, and gems here. |
-
-
-| Dress Up |
-This is where you can create and customize your characters. You can change their clothes, accessories, hair, eyes, etc. You can also save your characters in different slots or load them from the preset menu. |
-
-
-| Studio |
-This is where you can make scenes and stories with your characters. You can enter custom text, choose poses and backgrounds, and use props. You can also save your scenes or load them from the gallery. |
-
-
-| Gacha |
-This is where you can gacha for rare items or gifts for your characters. You can use gems or tickets to gacha for different types of items, such as clothing, accessories, pets, etc. You can also trade your items with other players or NPCs. |
-
-
-| Life |
-This is where you can interact with other characters in different locations. You can talk to them, give them gifts, play mini-games with them, or ask them questions. You can also unlock new locations by increasing your friendship level with them. |
-
-
-| Games |
-This is where you can play mini-games to earn gems or tickets. There are 8 mini-games available in the game, such as Duck & Dodge, Phantom's Remix, Memory Match, etc. You can also see your high scores and achievements here. |
-
-
-| Chat |
-This is where you can chat with other players online. You can join different chat rooms or create your own. You can also use stickers or emojis to express yourself. |
-
-
-| Options |
-This is where you can change the settings of the game, such as the volume, language, quality, etc. You can also see the credits or contact the support team here. |
-
-
- The tips and tricks to level up and earn gems
Continuing the article: Gacha Life is a game that requires you to level up your character and earn gems to unlock more features and items. Here are some of the tips and tricks that you can use to level up and earn gems faster:
-
-- Play the mini-games regularly. The mini-games are a great way to earn gems and tickets, as well as to increase your character's energy. You can play each mini-game up to 5 times per day, and you will get more gems or tickets depending on your score. You can also get bonus gems or tickets by completing achievements or watching ads.
-- Interact with the NPCs in the Life mode. The NPCs are the characters that you can find in different locations in the Life mode. You can talk to them, give them gifts, play mini-games with them, or ask them questions. By doing so, you will increase your friendship level with them, which will unlock new locations, items, and stories. You will also get gems or gifts from them as rewards.
-- Gacha for rare items or gifts. The Gacha mode is where you can gacha for rare items or gifts for your characters. You can use gems or tickets to gacha for different types of items, such as clothing, accessories, pets, etc. The higher the rarity of the item, the more gems or tickets you will need to gacha for it. However, you will also get more experience points and level up faster by gachaing for rare items or gifts.
-- Use the chat mode to socialize with other players. The chat mode is where you can chat with other players online. You can join different chat rooms or create your own. You can also use stickers or emojis to express yourself. By chatting with other players, you will not only have fun and make friends, but also get tips and advice from them on how to play and enjoy Gacha Life.
-
- The fun and creative ways to use the game
-Gacha Life is a game that offers you a lot of freedom and possibilities to use it in fun and creative ways. Here are some of the examples of how you can use the game:
-
-- Make your own anime series or movies. You can use the studio mode or the skit maker mode to create your own scenes and stories with your characters. You can also record your voice or add music and sound effects to make it more realistic and immersive. You can then share your creations with other players online or on social media platforms.
-- Make your own comics or memes. You can use the studio mode or the skit maker mode to create your own comics or memes with your characters. You can also use text bubbles, stickers, emojis, or filters to make it more humorous and expressive. You can then share your creations with other players online or on social media platforms.
-- Make your own fan art or cosplay. You can use the dress up mode to create your own fan art or cosplay of your favorite anime characters or celebrities. You can also mix and match different parts from different characters to create unique combinations. You can then share your creations with other players online or on social media platforms.
-
- Conclusion
-Gacha Life is a game that lets you create your own anime characters and stories. It is a game that is free to download and play on your Android device, as long as you follow the steps and precautions mentioned above. It is also a game that has many features and modes for you to explore and enjoy, as well as many ways for you to use it in fun and creative ways. If you are an anime fan or a casual gamer who likes to express yourself through games, then Gacha Life is a game that you should try out.
- FAQs
-Here are some of the frequently asked questions about Gacha Life:
-
-- Is Gacha Life safe for kids?
-Gacha Life is a game that is rated for ages 9 and up by the Google Play Store. However, some of the content in the game may not be suitable for younger kids, such as violence, profanity, suggestive themes, etc. Therefore, parental guidance and supervision are recommended when playing Gacha Life.
-- Is Gacha Life online or offline?
-Gacha Life is a game that can be played both online and offline. You can play most of the features and modes of the game offline without Wi-Fi or internet connection, such as dress up, studio, gacha, life, etc. However, some of the features and modes require internet connection to access, such as chat, games, etc.
-- How do I update Gacha Life?
-To update Gacha Life, you need to visit the official sources provided by Lunime, such Continuing the article: as the website or the Google Play Store and download the latest version of the game. You can also check for updates within the game by going to the options menu and tapping on the check for updates button. You may need to uninstall any previous versions of Gacha Life or clear your cache and data before installing the new version.
-- How do I delete Gacha Life?
-To delete Gacha Life, you need to go to your device's settings and find the apps or applications menu. Then, you need to locate Gacha Life and tap on it to open its details. Then, you need to tap on the uninstall button and confirm your action. This will remove Gacha Life from your device, along with its data and files.
-- How do I contact Gacha Life support?
-To contact Gacha Life support, you need to go to the options menu in the game and tap on the contact us button. This will open a form where you can enter your name, email, subject, and message. You can also attach a screenshot if needed. Then, you need to tap on the send button and wait for a reply from the support team.
-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/2023Liu2023/bingo/next.config.js b/spaces/2023Liu2023/bingo/next.config.js
deleted file mode 100644
index 0e6ccd7fbc91d0459eaaff3e968ce0556789c605..0000000000000000000000000000000000000000
--- a/spaces/2023Liu2023/bingo/next.config.js
+++ /dev/null
@@ -1,38 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
- // output: 'export',
- // assetPrefix: '.',
- webpack: (config, { isServer }) => {
- if (!isServer) {
- config.resolve = {
- ...config.resolve,
- fallback: {
- 'bufferutil': false,
- 'utf-8-validate': false,
- http: false,
- https: false,
- stream: false,
- // fixes proxy-agent dependencies
- net: false,
- dns: false,
- tls: false,
- assert: false,
- // fixes next-i18next dependencies
- path: false,
- fs: false,
- // fixes mapbox dependencies
- events: false,
- // fixes sentry dependencies
- process: false
- }
- };
- }
- config.module.exprContextCritical = false;
-
- return config;
- },
-}
-
-module.exports = (...args) => {
- return nextConfig
-}
diff --git a/spaces/7hao/bingo/src/components/voice.tsx b/spaces/7hao/bingo/src/components/voice.tsx
deleted file mode 100644
index 074d0e145229947282a472bd84f6578cf0b3c71c..0000000000000000000000000000000000000000
--- a/spaces/7hao/bingo/src/components/voice.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import React, { useEffect } from 'react'
-import { useSetAtom } from 'jotai'
-import { useBing } from '@/lib/hooks/use-bing'
-import Image from 'next/image'
-import VoiceIcon from '@/assets/images/voice.svg'
-import VoiceButton from './ui/voice'
-import { SR } from '@/lib/bots/bing/sr'
-import { voiceListenAtom } from '@/state'
-
-const sr = new SR(['发送', '清空', '退出'])
-
-const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => {
- const setListen = useSetAtom(voiceListenAtom)
- useEffect(() => {
- if (sr.listening) return
- sr.transcript = !isSpeaking
- }, [isSpeaking])
-
- useEffect(() => {
- sr.onchange = (msg: string, command?: string) => {
- switch (command) {
- case '退出':
- sr.stop()
- break;
- case '发送':
- sendMessage(input)
- case '清空':
- setInput('')
- break;
- default:
- setInput(input + msg)
- }
- }
- }, [input])
-
- const switchSR = (enable: boolean = false) => {
- setListen(enable)
- if (enable) {
- sr.start()
- } else {
- sr.stop()
- }
- }
-
- return sr.listening ? (
- switchSR(false)} />
- ) : (
- switchSR(true)} />
- )
-};
-
-export default Voice;
diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_537238KB.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_537238KB.py
deleted file mode 100644
index 9b127bc6427f5c60c8cf85603a3d8a093c3501c4..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_537238KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(SeperableConv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nin,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- groups=nin,
- bias=False,
- ),
- nn.Conv2d(nin, nout, kernel_size=1, bias=False),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
- def __call__(self, x):
- skip = self.conv1(x)
- h = self.conv2(skip)
-
- return h, skip
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
- h = self.conv(x)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
- self.conv3 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv6 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv7 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = nn.Sequential(
- Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
- )
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- feat6 = self.conv6(x)
- feat7 = self.conv7(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
- bottle = self.bottleneck(out)
- return bottle
diff --git a/spaces/ADRXtractor/ADR_Xtractor/app.py b/spaces/ADRXtractor/ADR_Xtractor/app.py
deleted file mode 100644
index 435b1ce88631d579f6684880bae7b9d2b5088baf..0000000000000000000000000000000000000000
--- a/spaces/ADRXtractor/ADR_Xtractor/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-from spacy import displacy
-
-from transformers import AutoTokenizer, AutoModelForTokenClassification,pipeline
-tokenizer = AutoTokenizer.from_pretrained("abhibisht89/spanbert-large-cased-finetuned-ade_corpus_v2")
-model = AutoModelForTokenClassification.from_pretrained("abhibisht89/spanbert-large-cased-finetuned-ade_corpus_v2").to('cpu')
-adr_ner_model = pipeline(task="ner", model=model, tokenizer=tokenizer,grouped_entities=True)
-
-def get_adr_from_text(sentence):
- tokens = adr_ner_model(sentence)
- entities = []
-
- for token in tokens:
- label = token["entity_group"]
- if label != "O":
- token["label"] = label
- entities.append(token)
-
- params = [{"text": sentence,
- "ents": entities,
- "title": None}]
-
- html = displacy.render(params, style="ent", manual=True, options={
- "colors": {
- "DRUG": "#f08080",
- "ADR": "#9bddff",
- },
- })
- return html
-
-exp=["Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug.",
- "Addiction to many sedatives and analgesics, such as diazepam, morphine, etc.",
- "Birth defects associated with thalidomide",
- "Bleeding of the intestine associated with aspirin therapy",
- "Cardiovascular disease associated with COX-2 inhibitors (i.e. Vioxx)",
- "Deafness and kidney failure associated with gentamicin (an antibiotic)",
- "Having fever after taking paracetamol"]
-
-desc="An adverse drug reaction (ADR) can be defined as an appreciably harmful or unpleasant reaction resulting from an intervention related to the use of a medicinal product.\
- The goal of this project is to extracts the adverse drug reaction from unstructured text with the Drug."
-
-inp=gr.inputs.Textbox(lines=5, placeholder=None, default="", label="text to extract adverse drug reaction and drug mention")
-out=gr.outputs.HTML(label=None)
-
-iface = gr.Interface(fn=get_adr_from_text, inputs=inp, outputs=out,examples=exp,article=desc,title="Adverse Drug Reaction Xtractor",theme="huggingface",layout='horizontal')
-iface.launch()
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/utils/build_vocab.py b/spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/utils/build_vocab.py
deleted file mode 100644
index e9fab23bc2c48203e541d356dc172e1fdee8f113..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/utils/build_vocab.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import json
-from tqdm import tqdm
-import logging
-import pickle
-from collections import Counter
-import re
-import fire
-
-
-class Vocabulary(object):
- """Simple vocabulary wrapper."""
- def __init__(self):
- self.word2idx = {}
- self.idx2word = {}
- self.idx = 0
-
- def add_word(self, word):
- if not word in self.word2idx:
- self.word2idx[word] = self.idx
- self.idx2word[self.idx] = word
- self.idx += 1
-
- def __call__(self, word):
- if not word in self.word2idx:
- return self.word2idx[""]
- return self.word2idx[word]
-
- def __getitem__(self, word_id):
- return self.idx2word[word_id]
-
- def __len__(self):
- return len(self.word2idx)
-
-
-def build_vocab(input_json: str,
- threshold: int,
- keep_punctuation: bool,
- host_address: str,
- character_level: bool = False,
- zh: bool = True ):
- """Build vocabulary from csv file with a given threshold to drop all counts < threshold
-
- Args:
- input_json(string): Preprossessed json file. Structure like this:
- {
- 'audios': [
- {
- 'audio_id': 'xxx',
- 'captions': [
- {
- 'caption': 'xxx',
- 'cap_id': 'xxx'
- }
- ]
- },
- ...
- ]
- }
- threshold (int): Threshold to drop all words with counts < threshold
- keep_punctuation (bool): Includes or excludes punctuation.
-
- Returns:
- vocab (Vocab): Object with the processed vocabulary
-"""
- data = json.load(open(input_json, "r"))["audios"]
- counter = Counter()
- pretokenized = "tokens" in data[0]["captions"][0]
-
- if zh:
- from nltk.parse.corenlp import CoreNLPParser
- from zhon.hanzi import punctuation
- if not pretokenized:
- parser = CoreNLPParser(host_address)
- for audio_idx in tqdm(range(len(data)), leave=False, ascii=True):
- for cap_idx in range(len(data[audio_idx]["captions"])):
- if pretokenized:
- tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split()
- else:
- caption = data[audio_idx]["captions"][cap_idx]["caption"]
- # Remove all punctuations
- if not keep_punctuation:
- caption = re.sub("[{}]".format(punctuation), "", caption)
- if character_level:
- tokens = list(caption)
- else:
- tokens = list(parser.tokenize(caption))
- data[audio_idx]["captions"][cap_idx]["tokens"] = " ".join(tokens)
- counter.update(tokens)
- else:
- if pretokenized:
- for audio_idx in tqdm(range(len(data)), leave=False, ascii=True):
- for cap_idx in range(len(data[audio_idx]["captions"])):
- tokens = data[audio_idx]["captions"][cap_idx]["tokens"].split()
- counter.update(tokens)
- else:
- from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
- captions = {}
- for audio_idx in range(len(data)):
- audio_id = data[audio_idx]["audio_id"]
- captions[audio_id] = []
- for cap_idx in range(len(data[audio_idx]["captions"])):
- caption = data[audio_idx]["captions"][cap_idx]["caption"]
- captions[audio_id].append({
- "audio_id": audio_id,
- "id": cap_idx,
- "caption": caption
- })
- tokenizer = PTBTokenizer()
- captions = tokenizer.tokenize(captions)
- for audio_idx in tqdm(range(len(data)), leave=False, ascii=True):
- audio_id = data[audio_idx]["audio_id"]
- for cap_idx in range(len(data[audio_idx]["captions"])):
- tokens = captions[audio_id][cap_idx]
- data[audio_idx]["captions"][cap_idx]["tokens"] = tokens
- counter.update(tokens.split(" "))
-
- if not pretokenized:
- json.dump({ "audios": data }, open(input_json, "w"), indent=4, ensure_ascii=not zh)
- words = [word for word, cnt in counter.items() if cnt >= threshold]
-
- # Create a vocab wrapper and add some special tokens.
- vocab = Vocabulary()
- vocab.add_word("")
- vocab.add_word("")
- vocab.add_word("")
- vocab.add_word("")
-
- # Add the words to the vocabulary.
- for word in words:
- vocab.add_word(word)
- return vocab
-
-
-def process(input_json: str,
- output_file: str,
- threshold: int = 1,
- keep_punctuation: bool = False,
- character_level: bool = False,
- host_address: str = "http://localhost:9000",
- zh: bool = False):
- logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
- logging.basicConfig(level=logging.INFO, format=logfmt)
- logging.info("Build Vocab")
- vocabulary = build_vocab(
- input_json=input_json, threshold=threshold, keep_punctuation=keep_punctuation,
- host_address=host_address, character_level=character_level, zh=zh)
- pickle.dump(vocabulary, open(output_file, "wb"))
- logging.info("Total vocabulary size: {}".format(len(vocabulary)))
- logging.info("Saved vocab to '{}'".format(output_file))
-
-
-if __name__ == '__main__':
- fire.Fire(process)
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/wav_evaluation/models/CLAPWrapper.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/wav_evaluation/models/CLAPWrapper.py
deleted file mode 100644
index ce7931d0321b1ecf21142efe9c9f609e277189ed..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/wav_evaluation/models/CLAPWrapper.py
+++ /dev/null
@@ -1,256 +0,0 @@
-
-import random
-import torchaudio
-from torch._six import string_classes
-import collections
-import re
-import torch.nn.functional as F
-import numpy as np
-from transformers import AutoTokenizer
-from wav_evaluation.models.utils import read_config_as_args
-from wav_evaluation.models.clap import CLAP
-import math
-import torchaudio.transforms as T
-import os
-import torch
-from importlib_resources import files
-
-
-class CLAPWrapper():
- """
- A class for interfacing CLAP model.
- """
-
- def __init__(self, model_fp,config_path, use_cuda=False):
- self.np_str_obj_array_pattern = re.compile(r'[SaUO]')
- self.file_path = os.path.realpath(__file__)
- self.default_collate_err_msg_format = (
- "default_collate: batch must contain tensors, numpy arrays, numbers, "
- "dicts or lists; found {}")
- with open(config_path,'r') as f:
- self.config_as_str = f.read()
- self.model_fp = model_fp
- self.use_cuda = use_cuda
- self.clap, self.tokenizer, self.args = self.load_clap()
-
- def load_clap(self):
- r"""Load CLAP model with args from config file"""
-
- args = read_config_as_args(self.config_as_str, is_config_str=True)
-
- if 'bert' in args.text_model:
- self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask']
- else:
- self.token_keys = ['input_ids', 'attention_mask']
-
- clap = CLAP(
- audioenc_name=args.audioenc_name,
- sample_rate=args.sampling_rate,
- window_size=args.window_size,
- hop_size=args.hop_size,
- mel_bins=args.mel_bins,
- fmin=args.fmin,
- fmax=args.fmax,
- classes_num=args.num_classes,
- out_emb=args.out_emb,
- text_model=args.text_model,
- transformer_embed_dim=args.transformer_embed_dim,
- d_proj=args.d_proj
- )
-
-
- # Load pretrained weights for model
- model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model']
- clap.load_state_dict(model_state_dict)
- clap.eval() # set clap in eval mode
- tokenizer = AutoTokenizer.from_pretrained(args.text_model)
-
- if self.use_cuda and torch.cuda.is_available():
- clap = clap.cuda()
-
- return clap, tokenizer, args
-
- def default_collate(self, batch):
- r"""Puts each data field into a tensor with outer dimension batch size"""
- elem = batch[0]
- elem_type = type(elem)
- if isinstance(elem, torch.Tensor):
- out = None
- if torch.utils.data.get_worker_info() is not None:
- # If we're in a background process, concatenate directly into a
- # shared memory tensor to avoid an extra copy
- numel = sum([x.numel() for x in batch])
- storage = elem.storage()._new_shared(numel)
- out = elem.new(storage)
- return torch.stack(batch, 0, out=out)
- elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
- and elem_type.__name__ != 'string_':
- if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
- # array of string classes and object
- if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None:
- raise TypeError(
- self.default_collate_err_msg_format.format(elem.dtype))
-
- return self.default_collate([torch.as_tensor(b) for b in batch])
- elif elem.shape == (): # scalars
- return torch.as_tensor(batch)
- elif isinstance(elem, float):
- return torch.tensor(batch, dtype=torch.float64)
- elif isinstance(elem, int):
- return torch.tensor(batch)
- elif isinstance(elem, string_classes):
- return batch
- elif isinstance(elem, collections.abc.Mapping):
- return {key: self.default_collate([d[key] for d in batch]) for key in elem}
- elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
- return elem_type(*(self.default_collate(samples) for samples in zip(*batch)))
- elif isinstance(elem, collections.abc.Sequence):
- # check to make sure that the elements in batch have consistent size
- it = iter(batch)
- elem_size = len(next(it))
- if not all(len(elem) == elem_size for elem in it):
- raise RuntimeError(
- 'each element in list of batch should be of equal size')
- transposed = zip(*batch)
- return [self.default_collate(samples) for samples in transposed]
-
- raise TypeError(self.default_collate_err_msg_format.format(elem_type))
-
- def resample_and_duration(self,wav_sr,audio_duration,resample=False):
- audio_time_series,sample_rate = wav_sr
- resample_rate = self.args.sampling_rate
- if resample:
- resampler = T.Resample(sample_rate, resample_rate)
- audio_time_series = resampler(audio_time_series)
- audio_time_series = audio_time_series.reshape(-1)
-
- # audio_time_series is shorter than predefined audio duration,
- # so audio_time_series is extended
- if audio_duration*sample_rate >= audio_time_series.shape[0]:
- repeat_factor = int(np.ceil((audio_duration*sample_rate) /
- audio_time_series.shape[0]))
- # Repeat audio_time_series by repeat_factor to match audio_duration
- audio_time_series = audio_time_series.repeat(repeat_factor)
- # remove excess part of audio_time_series
- audio_time_series = audio_time_series[0:audio_duration*sample_rate]
- else:
- # audio_time_series is longer than predefined audio duration,
- # so audio_time_series is trimmed
- start_index = random.randrange(
- audio_time_series.shape[0] - audio_duration*sample_rate)
- audio_time_series = audio_time_series[start_index:start_index +
- audio_duration*sample_rate]
- return torch.FloatTensor(audio_time_series)
-
- def load_audio_into_tensor(self, audio_path, audio_duration, resample=False):
- r"""Loads audio file and returns raw audio."""
- # Randomly sample a segment of audio_duration from the clip or pad to match duration
- audio_time_series, sample_rate = torchaudio.load(audio_path)
- return self.resample_and_duration((audio_time_series, sample_rate),audio_duration,resample)
-
- def preprocess_audio(self, audio_files, resample):
- r"""Load list of audio files and return raw audio"""
- audio_tensors = []
- for audio_file in audio_files:
- if isinstance(audio_file,str):
- audio_tensor = self.load_audio_into_tensor(audio_file, self.args.duration, resample)
- elif isinstance(audio_file,tuple):
- audio_tensor = self.resample_and_duration(audio_file, self.args.duration, resample)
- else:
- raise TypeError(f"type of audiofile is {type(audio_file)},which is not supported")
- audio_tensor = audio_tensor.reshape(
- 1, -1).cuda() if self.use_cuda and torch.cuda.is_available() else audio_tensor.reshape(1, -1)
- audio_tensors.append(audio_tensor)
- return self.default_collate(audio_tensors)
-
- def preprocess_text(self, text_queries):
- r"""Load list of class labels and return tokenized text"""
- tokenized_texts = []
- for ttext in text_queries:
- tok = self.tokenizer.encode_plus(
- text=ttext, add_special_tokens=True, max_length=self.args.text_len, padding="max_length", return_tensors="pt") # max_length=self.args.text_len, padding=True,
- for key in self.token_keys:
- tok[key] = tok[key].reshape(-1).cuda() if self.use_cuda and torch.cuda.is_available() else tok[key].reshape(-1)
- tokenized_texts.append(tok)
- return self.default_collate(tokenized_texts)
-
- def get_text_embeddings(self, class_labels):
- r"""Load list of class labels and return text embeddings"""
- preprocessed_text = self.preprocess_text(class_labels)
- text_embeddings = self._get_text_embeddings(preprocessed_text)
- text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)
- return text_embeddings
-
- def get_audio_embeddings(self, audio_files, resample):
- r"""Load list of audio files and return a audio embeddings"""
- preprocessed_audio = self.preprocess_audio(audio_files, resample)
- audio_embeddings = self._get_audio_embeddings(preprocessed_audio)
- audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)
- return audio_embeddings
-
- def _get_text_embeddings(self, preprocessed_text):
- r"""Load preprocessed text and return text embeddings"""
- with torch.no_grad():
- text_embeddings = self.clap.caption_encoder(preprocessed_text)
- text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)
- return text_embeddings
-
- def _get_audio_embeddings(self, preprocessed_audio):
- r"""Load preprocessed audio and return a audio embeddings"""
- with torch.no_grad():
- preprocessed_audio = preprocessed_audio.reshape(
- preprocessed_audio.shape[0], preprocessed_audio.shape[2])
- #Append [0] the audio emebdding, [1] has output class probabilities
- audio_embeddings = self.clap.audio_encoder(preprocessed_audio)[0]
- audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)
- return audio_embeddings
-
- def compute_similarity(self, audio_embeddings, text_embeddings,use_logit_scale = True):
- r"""Compute similarity between text and audio embeddings"""
- if use_logit_scale:
- logit_scale = self.clap.logit_scale.exp()
- similarity = logit_scale*text_embeddings @ audio_embeddings.T
- else:
- similarity = text_embeddings @ audio_embeddings.T
- return similarity.T
-
- def cal_clap_score(self,txt,audio_path):
- text_embeddings = self.get_text_embeddings([txt])# 经过了norm的embedding
- audio_embeddings = self.get_audio_embeddings([audio_path], resample=True)# 这一步比较耗时，读取音频并重采样到44100
- score = self.compute_similarity(audio_embeddings, text_embeddings,use_logit_scale=False).squeeze().cpu().numpy()
- return score
-
- def _generic_batch_inference(self, func, *args):
- r"""Process audio and/or text per batch"""
- input_tmp = args[0]
- batch_size = args[-1]
- # args[0] has audio_files, args[1] has class_labels
- inputs = [args[0], args[1]] if len(args) == 3 else [args[0]]
- args0_len = len(args[0])
- # compute text_embeddings once for all the audio_files batches
- if len(inputs) == 2:
- text_embeddings = self.get_text_embeddings(args[1])
- inputs = [args[0], args[1], text_embeddings]
- dataset_idx = 0
- for _ in range(math.ceil(args0_len/batch_size)):
- next_batch_idx = dataset_idx + batch_size
- # batch size is bigger than available audio/text items
- if next_batch_idx >= args0_len:
- inputs[0] = input_tmp[dataset_idx:]
- return func(*tuple(inputs))
- else:
- inputs[0] = input_tmp[dataset_idx:next_batch_idx]
- yield func(*tuple(inputs))
- dataset_idx = next_batch_idx
-
- def get_audio_embeddings_per_batch(self, audio_files, batch_size):
- r"""Load preprocessed audio and return a audio embeddings per batch"""
- return self._generic_batch_inference(self.get_audio_embeddings, audio_files, batch_size)
-
- def get_text_embeddings_per_batch(self, class_labels, batch_size):
- r"""Load preprocessed text and return text embeddings per batch"""
- return self._generic_batch_inference(self.get_text_embeddings, class_labels, batch_size)
-
- def classify_audio_files_per_batch(self, audio_files, class_labels, batch_size):
- r"""Compute classification probabilities for each audio recording in a batch and each class label"""
- return self._generic_batch_inference(self.classify_audio_files, audio_files, class_labels, batch_size)
diff --git a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Vercel.py b/spaces/ASJMO/freegpt/g4f/Provider/Providers/Vercel.py
deleted file mode 100644
index e5df9cf017e4c1a265f5c9d5e48eb5c10a56e60a..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Vercel.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import json
-import base64
-import execjs
-import queue
-import threading
-
-from curl_cffi import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://play.vercel.ai'
-supports_stream = True
-needs_auth = False
-
-models = {
- 'claude-instant-v1': 'anthropic:claude-instant-v1',
- 'claude-v1': 'anthropic:claude-v1',
- 'alpaca-7b': 'replicate:replicate/alpaca-7b',
- 'stablelm-tuned-alpha-7b': 'replicate:stability-ai/stablelm-tuned-alpha-7b',
- 'bloom': 'huggingface:bigscience/bloom',
- 'bloomz': 'huggingface:bigscience/bloomz',
- 'flan-t5-xxl': 'huggingface:google/flan-t5-xxl',
- 'flan-ul2': 'huggingface:google/flan-ul2',
- 'gpt-neox-20b': 'huggingface:EleutherAI/gpt-neox-20b',
- 'oasst-sft-4-pythia-12b-epoch-3.5': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
- 'santacoder': 'huggingface:bigcode/santacoder',
- 'command-medium-nightly': 'cohere:command-medium-nightly',
- 'command-xlarge-nightly': 'cohere:command-xlarge-nightly',
- 'code-cushman-001': 'openai:code-cushman-001',
- 'code-davinci-002': 'openai:code-davinci-002',
- 'gpt-3.5-turbo': 'openai:gpt-3.5-turbo',
- 'text-ada-001': 'openai:text-ada-001',
- 'text-babbage-001': 'openai:text-babbage-001',
- 'text-curie-001': 'openai:text-curie-001',
- 'text-davinci-002': 'openai:text-davinci-002',
- 'text-davinci-003': 'openai:text-davinci-003'
-}
-model = models.keys()
-
-vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-instant-v1'}, 'anthropic:claude-v1': {'id': 'anthropic:claude-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-v1'}, 'replicate:replicate/alpaca-7b': {'id': 'replicate:replicate/alpaca-7b', 'provider': 'replicate', 'providerHumanName': 'Replicate', 'makerHumanName': 'Stanford', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '2014ee1247354f2e81c0b3650d71ca715bc1e610189855f134c30ecb841fae21', 'name': 'alpaca-7b'}, 'replicate:stability-ai/stablelm-tuned-alpha-7b': {'id': 'replicate:stability-ai/stablelm-tuned-alpha-7b', 'provider': 'replicate', 'makerHumanName': 'StabilityAI', 'providerHumanName': 'Replicate', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '4a9a32b4fd86c2d047f1d271fa93972683ec6ef1cf82f402bd021f267330b50b', 'name': 'stablelm-tuned-alpha-7b'}, 'huggingface:bigscience/bloom': {'id': 'huggingface:bigscience/bloom', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': "Do NOT talk to Bloom as an entity, it's not a chatbot but a webpage/blog/article completion model. For the best results: mimic a few words of a webpage similar to the content you want to generate. Start a sentence as if YOU were writing a blog, webpage, math post, coding article and Bloom will generate a coherent follow-up.", 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloom'}, 'huggingface:bigscience/bloomz': {'id': 'huggingface:bigscience/bloomz', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': 'We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t\'aime.", the model will most likely answer "I love you.".', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloomz'}, 'huggingface:google/flan-t5-xxl': {'id': 'huggingface:google/flan-t5-xxl', 'provider': 'huggingface', 'makerHumanName': 'Google', 'providerHumanName': 'HuggingFace', 'name': 'flan-t5-xxl', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}}, 'huggingface:google/flan-ul2': {'id': 'huggingface:google/flan-ul2', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'Google', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'flan-ul2'}, 'huggingface:EleutherAI/gpt-neox-20b': {'id': 'huggingface:EleutherAI/gpt-neox-20b', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'EleutherAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-neox-20b'}, 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5': {'id': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'OpenAssistant', 'parameters': {'maximumLength': {'value': 200, 'range': [50, 1024]}, 'typicalP': {'value': 0.2, 'range': [0.1, 0.99]}, 'repetitionPenalty': {'value': 1, 'range': [0.1, 2]}}, 'name': 'oasst-sft-4-pythia-12b-epoch-3.5'}, 'huggingface:bigcode/santacoder': {
- 'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}}
-
-
-# based on https://github.com/ading2210/vercel-llm-api // modified
-class Client:
- def __init__(self):
- self.session = requests.Session()
- self.headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36',
- 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'en-US,en;q=0.5',
- 'Te': 'trailers',
- 'Upgrade-Insecure-Requests': '1'
- }
- self.session.headers.update(self.headers)
-
- def get_token(self):
- b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text
- data = json.loads(base64.b64decode(b64))
-
- code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % (
- data['c'], data['a'])
-
- token_string = json.dumps(separators=(',', ':'),
- obj={'r': execjs.compile(code).call('token'), 't': data['t']})
-
- return base64.b64encode(token_string.encode()).decode()
-
- def get_default_params(self, model_id):
- return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()}
-
- def generate(self, model_id: str, prompt: str, params: dict = {}):
- if not ':' in model_id:
- model_id = models[model_id]
-
- defaults = self.get_default_params(model_id)
-
- payload = defaults | params | {
- 'prompt': prompt,
- 'model': model_id,
- }
-
- headers = self.headers | {
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Custom-Encoding': self.get_token(),
- 'Host': 'sdk.vercel.ai',
- 'Origin': 'https://sdk.vercel.ai',
- 'Referrer': 'https://sdk.vercel.ai',
- 'Sec-Fetch-Dest': 'empty',
- 'Sec-Fetch-Mode': 'cors',
- 'Sec-Fetch-Site': 'same-origin',
- }
-
- chunks_queue = queue.Queue()
- error = None
- response = None
-
- def callback(data):
- chunks_queue.put(data.decode())
-
- def request_thread():
- nonlocal response, error
- for _ in range(3):
- try:
- response = self.session.post('https://sdk.vercel.ai/api/generate',
- json=payload, headers=headers, content_callback=callback)
- response.raise_for_status()
-
- except Exception as e:
- if _ == 2:
- error = e
-
- else:
- continue
-
- thread = threading.Thread(target=request_thread, daemon=True)
- thread.start()
-
- text = ''
- index = 0
- while True:
- try:
- chunk = chunks_queue.get(block=True, timeout=0.1)
-
- except queue.Empty:
- if error:
- raise error
-
- elif response:
- break
-
- else:
- continue
-
- text += chunk
- lines = text.split('\n')
-
- if len(lines) - 1 > index:
- new = lines[index:-1]
- for word in new:
- yield json.loads(word)
- index = len(lines) - 1
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- yield 'Vercel is currently not working.'
- return
-
- conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
-
- for message in messages:
- conversation += '%s: %s\n' % (message['role'], message['content'])
-
- conversation += 'assistant: '
-
- completion = Client().generate(model, conversation)
-
- for token in completion:
- yield token
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-fp16_in1k.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-fp16_in1k.py
deleted file mode 100644
index 19ee6ee4f82ec02f34628bdf8dd74a379798cc67..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-fp16_in1k.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = ['./resnet50_8xb32_in1k.py']
-
-# schedule settings
-optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=512.)
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/Wewordle.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/Wewordle.py
deleted file mode 100644
index c30887fb03b3ee53ed620d3e8259ae2a9245f934..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/Wewordle.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from __future__ import annotations
-
-import random, string, time
-from aiohttp import ClientSession
-
-from ..base_provider import AsyncProvider
-
-
-class Wewordle(AsyncProvider):
- url = "https://wewordle.org"
- working = False
- supports_gpt_35_turbo = True
-
- @classmethod
- async def create_async(
- cls,
- model: str,
- messages: list[dict[str, str]],
- proxy: str = None,
- **kwargs
- ) -> str:
-
- headers = {
- "accept" : "*/*",
- "pragma" : "no-cache",
- "Content-Type" : "application/json",
- "Connection" : "keep-alive"
- }
-
- _user_id = "".join(random.choices(f"{string.ascii_lowercase}{string.digits}", k=16))
- _app_id = "".join(random.choices(f"{string.ascii_lowercase}{string.digits}", k=31))
- _request_date = time.strftime("%Y-%m-%dT%H:%M:%S.000Z", time.gmtime())
- data = {
- "user" : _user_id,
- "messages" : messages,
- "subscriber": {
- "originalPurchaseDate" : None,
- "originalApplicationVersion" : None,
- "allPurchaseDatesMillis" : {},
- "entitlements" : {"active": {}, "all": {}},
- "allPurchaseDates" : {},
- "allExpirationDatesMillis" : {},
- "allExpirationDates" : {},
- "originalAppUserId" : f"$RCAnonymousID:{_app_id}",
- "latestExpirationDate" : None,
- "requestDate" : _request_date,
- "latestExpirationDateMillis" : None,
- "nonSubscriptionTransactions" : [],
- "originalPurchaseDateMillis" : None,
- "managementURL" : None,
- "allPurchasedProductIdentifiers": [],
- "firstSeen" : _request_date,
- "activeSubscriptions" : [],
- }
- }
-
-
- async with ClientSession(
- headers=headers
- ) as session:
- async with session.post(f"{cls.url}/gptapi/v1/android/turbo", proxy=proxy, json=data) as response:
- response.raise_for_status()
- content = (await response.json())["message"]["content"]
- if content:
- return content
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/badgelabel/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/badgelabel/Factory.d.ts
deleted file mode 100644
index 05a39fbaa32a7bbf5c1bbc0243372ea8ef40c9a1..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/badgelabel/Factory.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import BadgeLabel from './BadgeLabel';
-
-export default function (
- config?: BadgeLabel.IConfig
-): BadgeLabel;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/PreLayout.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/PreLayout.js
deleted file mode 100644
index 1d6b0b3d0c12c9725edd0ef11573493ad0720d36..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/PreLayout.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import PreLayoutBase from '../basesizer/PreLayout.js';
-
-var PreLayout = function () {
- this._maxChildWidth = undefined;
- this._maxChildHeight = undefined;
- PreLayoutBase.call(this);
- return this;
-}
-export default PreLayout;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pan/Pan.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pan/Pan.d.ts
deleted file mode 100644
index 90d3b003dfb3f918badd56f213d96b3357d36bf3..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pan/Pan.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Pan } from '../../../plugins/gestures';
-export default Pan;
\ No newline at end of file
diff --git a/spaces/Amjadd/BookGPT/app.py b/spaces/Amjadd/BookGPT/app.py
deleted file mode 100644
index 82c0ea94e622e59a129394cb034c430f915c823a..0000000000000000000000000000000000000000
--- a/spaces/Amjadd/BookGPT/app.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import urllib.request
-import fitz
-import re
-import numpy as np
-import tensorflow_hub as hub
-import openai
-import gradio as gr
-import os
-from sklearn.neighbors import NearestNeighbors
-
-
-def download_pdf(url, output_path):
- urllib.request.urlretrieve(url, output_path)
-
-
-def preprocess(text):
- text = text.replace('\n', ' ')
- text = re.sub('\s+', ' ', text)
- return text
-
-
-def pdf_to_text(path, start_page=1, end_page=None):
- doc = fitz.open(path)
- total_pages = doc.page_count
-
- if end_page is None:
- end_page = total_pages
-
- text_list = []
-
- for i in range(start_page-1, end_page):
- text = doc.load_page(i).get_text("text")
- text = preprocess(text)
- text_list.append(text)
-
- doc.close()
- return text_list
-
-
-def text_to_chunks(texts, word_length=150, start_page=1):
- text_toks = [t.split(' ') for t in texts]
- page_nums = []
- chunks = []
-
- for idx, words in enumerate(text_toks):
- for i in range(0, len(words), word_length):
- chunk = words[i:i+word_length]
- if (i+word_length) > len(words) and (len(chunk) < word_length) and (
- len(text_toks) != (idx+1)):
- text_toks[idx+1] = chunk + text_toks[idx+1]
- continue
- chunk = ' '.join(chunk).strip()
- chunk = f'[{idx+start_page}]' + ' ' + '"' + chunk + '"'
- chunks.append(chunk)
- return chunks
-
-
-class SemanticSearch:
-
- def __init__(self):
- self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
- self.fitted = False
-
-
- def fit(self, data, batch=1000, n_neighbors=5):
- self.data = data
- self.embeddings = self.get_text_embedding(data, batch=batch)
- n_neighbors = min(n_neighbors, len(self.embeddings))
- self.nn = NearestNeighbors(n_neighbors=n_neighbors)
- self.nn.fit(self.embeddings)
- self.fitted = True
-
-
- def __call__(self, text, return_data=True):
- inp_emb = self.use([text])
- neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
-
- if return_data:
- return [self.data[i] for i in neighbors]
- else:
- return neighbors
-
-
- def get_text_embedding(self, texts, batch=1000):
- embeddings = []
- for i in range(0, len(texts), batch):
- text_batch = texts[i:(i+batch)]
- emb_batch = self.use(text_batch)
- embeddings.append(emb_batch)
- embeddings = np.vstack(embeddings)
- return embeddings
-
-
-recommender = SemanticSearch()
-
-def load_recommender(path, start_page=1):
- global recommender
- texts = pdf_to_text(path, start_page=start_page)
- chunks = text_to_chunks(texts, start_page=start_page)
- recommender.fit(chunks)
- return 'Corpus Loaded.'
-
-
-def generate_text(prompt, engine="text-davinci-003"):
- completions = openai.Completion.create(
- engine=engine,
- prompt=prompt,
- max_tokens=512,
- n=1,
- stop=None,
- temperature=0.7,
- )
- message = completions.choices[0].text
- return message
-
-
-def generate_answer(question):
- topn_chunks = recommender(question)
- prompt = ""
- prompt += 'search results:\n\n'
- for c in topn_chunks:
- prompt += c + '\n\n'
-
- prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
- "Cite each reference using [number] notation (every result has this number at the beginning). "\
- "Citation should be done at the end of each sentence. If the search results mention multiple subjects "\
- "with the same name, create separate answers for each. Only include information found in the results and "\
- "don't add any additional information. Make sure the answer is correct and don't output false content. "\
- "If the text does not relate to the query, simply state 'Found Nothing'. Ignore outlier "\
- "search results which has nothing to do with the question. Only answer what is asked. The "\
- "answer should be short and concise.\n\nQuery: {question}\nAnswer: "
-
- prompt += f"Query: {question}\nAnswer:"
- answer = generate_text(prompt)
- return answer
-
-
-def question_answer(url, file, question, api_key):
- openai.api_key = api_key
-
- if url.strip() == '' and file == None:
- return '[ERROR]: Both URL and PDF is empty. Provide atleast one.'
-
- if url.strip() != '' and file != None:
- return '[ERROR]: Both URL and PDF is provided. Please provide only one (eiter URL or PDF).'
-
- if url.strip() != '':
- glob_url = url
- download_pdf(glob_url, 'corpus.pdf')
- load_recommender('corpus.pdf')
-
- else:
- old_file_name = file.name
- file_name = file.name
- file_name = file_name[:-12] + file_name[-4:]
- os.rename(old_file_name, file_name)
- load_recommender(file_name)
-
- if question.strip() == '':
- return '[ERROR]: Question field is empty'
-
- return generate_answer(question)
-
-
-title = 'BookGPT'
-description = "BookGPT allows you to input an entire book and ask questions about its contents. This app uses GPT-3 to generate answers based on the book's information. BookGPT has ability to add reference to the specific page number from where the information was found. This adds credibility to the answers generated also helps you locate the relevant information in the book."
-
-with gr.Blocks() as demo:
-
- gr.Markdown(f'{title}
')
- gr.Markdown(description)
- gr.Markdown("Thank you for all the support this space has received! Unfortunately, my OpenAI $18 grant has been exhausted, so you'll need to enter your own OpenAI API Key to use the app. Sorry for inconvenience :-(.")
-
- with gr.Row():
-
- with gr.Group():
- url = gr.Textbox(label='URL')
- gr.Markdown("or
")
- file = gr.File(label='PDF', file_types=['.pdf'])
- question = gr.Textbox(label='question')
- api_key = gr.Textbox(label='OpenAI API Key')
- btn = gr.Button(value='Submit')
- btn.style(full_width=True)
-
- with gr.Group():
- answer = gr.Textbox(label='answer')
-
- btn.click(question_answer, inputs=[url, file, question, api_key], outputs=[answer])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_dual_guided.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_dual_guided.py
deleted file mode 100644
index 4e2b89982a6aad0fb2f2b7c8735b0e645665359f..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_dual_guided.py
+++ /dev/null
@@ -1,107 +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 gc
-import tempfile
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import VersatileDiffusionDualGuidedPipeline
-from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-@nightly
-@require_torch_gpu
-class VersatileDiffusionDualGuidedPipelineIntegrationTests(unittest.TestCase):
- def tearDown(self):
- # clean up the VRAM after each test
- super().tearDown()
- gc.collect()
- torch.cuda.empty_cache()
-
- def test_remove_unused_weights_save_load(self):
- pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion")
- # remove text_unet
- pipe.remove_unused_weights()
- pipe.to(torch_device)
- pipe.set_progress_bar_config(disable=None)
-
- second_prompt = load_image(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
- )
-
- generator = torch.manual_seed(0)
- image = pipe(
- prompt="first prompt",
- image=second_prompt,
- text_to_image_strength=0.75,
- generator=generator,
- guidance_scale=7.5,
- num_inference_steps=2,
- output_type="numpy",
- ).images
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- pipe.save_pretrained(tmpdirname)
- pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(tmpdirname)
-
- pipe.to(torch_device)
- pipe.set_progress_bar_config(disable=None)
-
- generator = generator.manual_seed(0)
- new_image = pipe(
- prompt="first prompt",
- image=second_prompt,
- text_to_image_strength=0.75,
- generator=generator,
- guidance_scale=7.5,
- num_inference_steps=2,
- output_type="numpy",
- ).images
-
- assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"
-
- def test_inference_dual_guided(self):
- pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion")
- pipe.remove_unused_weights()
- pipe.to(torch_device)
- pipe.set_progress_bar_config(disable=None)
-
- first_prompt = "cyberpunk 2077"
- second_prompt = load_image(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
- )
- generator = torch.manual_seed(0)
- image = pipe(
- prompt=first_prompt,
- image=second_prompt,
- text_to_image_strength=0.75,
- generator=generator,
- guidance_scale=7.5,
- num_inference_steps=50,
- output_type="numpy",
- ).images
-
- image_slice = image[0, 253:256, 253:256, -1]
-
- assert image.shape == (1, 512, 512, 3)
- expected_slice = np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779])
-
- assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py b/spaces/Andy1621/uniformer_image_detection/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py
deleted file mode 100644
index c4e86387e3ce4aad3dd68d7613160fced4d3785b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py
+++ /dev/null
@@ -1,10 +0,0 @@
-_base_ = [
- '../_base_/models/mask_rcnn_r50_fpn.py',
- '../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py',
- '../_base_/default_runtime.py'
-]
-model = dict(
- roi_head=dict(
- bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=15)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
deleted file mode 100644
index e7a94dbe9ce4a5550971635c6f8cd917de35f72e..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+++ /dev/null
@@ -1,3 +0,0 @@
-_base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py'
-
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/evaluation/metrics.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/evaluation/metrics.py
deleted file mode 100644
index 16c7dd47cadd53cf1caaa194e28a343f2aacc599..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/evaluation/metrics.py
+++ /dev/null
@@ -1,326 +0,0 @@
-from collections import OrderedDict
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch
-
-
-def f_score(precision, recall, beta=1):
- """calcuate the f-score value.
-
- Args:
- precision (float | torch.Tensor): The precision value.
- recall (float | torch.Tensor): The recall value.
- beta (int): Determines the weight of recall in the combined score.
- Default: False.
-
- Returns:
- [torch.tensor]: The f-score value.
- """
- score = (1 + beta**2) * (precision * recall) / (
- (beta**2 * precision) + recall)
- return score
-
-
-def intersect_and_union(pred_label,
- label,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate intersection and Union.
-
- Args:
- pred_label (ndarray | str): Prediction segmentation map
- or predict result filename.
- label (ndarray | str): Ground truth segmentation map
- or label filename.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- label_map (dict): Mapping old labels to new labels. The parameter will
- work only when label is str. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. The parameter will
- work only when label is str. Default: False.
-
- Returns:
- torch.Tensor: The intersection of prediction and ground truth
- histogram on all classes.
- torch.Tensor: The union of prediction and ground truth histogram on
- all classes.
- torch.Tensor: The prediction histogram on all classes.
- torch.Tensor: The ground truth histogram on all classes.
- """
-
- if isinstance(pred_label, str):
- pred_label = torch.from_numpy(np.load(pred_label))
- else:
- pred_label = torch.from_numpy((pred_label))
-
- if isinstance(label, str):
- label = torch.from_numpy(
- mmcv.imread(label, flag='unchanged', backend='pillow'))
- else:
- label = torch.from_numpy(label)
-
- if label_map is not None:
- for old_id, new_id in label_map.items():
- label[label == old_id] = new_id
- if reduce_zero_label:
- label[label == 0] = 255
- label = label - 1
- label[label == 254] = 255
-
- mask = (label != ignore_index)
- pred_label = pred_label[mask]
- label = label[mask]
-
- intersect = pred_label[pred_label == label]
- area_intersect = torch.histc(
- intersect.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_pred_label = torch.histc(
- pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_label = torch.histc(
- label.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_union = area_pred_label + area_label - area_intersect
- return area_intersect, area_union, area_pred_label, area_label
-
-
-def total_intersect_and_union(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Total Intersection and Union.
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- ndarray: The intersection of prediction and ground truth histogram
- on all classes.
- ndarray: The union of prediction and ground truth histogram on all
- classes.
- ndarray: The prediction histogram on all classes.
- ndarray: The ground truth histogram on all classes.
- """
- num_imgs = len(results)
- assert len(gt_seg_maps) == num_imgs
- total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_label = torch.zeros((num_classes, ), dtype=torch.float64)
- for i in range(num_imgs):
- area_intersect, area_union, area_pred_label, area_label = \
- intersect_and_union(
- results[i], gt_seg_maps[i], num_classes, ignore_index,
- label_map, reduce_zero_label)
- total_area_intersect += area_intersect
- total_area_union += area_union
- total_area_pred_label += area_pred_label
- total_area_label += area_label
- return total_area_intersect, total_area_union, total_area_pred_label, \
- total_area_label
-
-
-def mean_iou(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Mean Intersection and Union (mIoU)
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- dict[str, float | ndarray]:
- float: Overall accuracy on all images.
- ndarray: Per category accuracy, shape (num_classes, ).
- ndarray: Per category IoU, shape (num_classes, ).
- """
- iou_result = eval_metrics(
- results=results,
- gt_seg_maps=gt_seg_maps,
- num_classes=num_classes,
- ignore_index=ignore_index,
- metrics=['mIoU'],
- nan_to_num=nan_to_num,
- label_map=label_map,
- reduce_zero_label=reduce_zero_label)
- return iou_result
-
-
-def mean_dice(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Mean Dice (mDice)
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- dict[str, float | ndarray]: Default metrics.
- float: Overall accuracy on all images.
- ndarray: Per category accuracy, shape (num_classes, ).
- ndarray: Per category dice, shape (num_classes, ).
- """
-
- dice_result = eval_metrics(
- results=results,
- gt_seg_maps=gt_seg_maps,
- num_classes=num_classes,
- ignore_index=ignore_index,
- metrics=['mDice'],
- nan_to_num=nan_to_num,
- label_map=label_map,
- reduce_zero_label=reduce_zero_label)
- return dice_result
-
-
-def mean_fscore(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False,
- beta=1):
- """Calculate Mean Intersection and Union (mIoU)
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
- beta (int): Determines the weight of recall in the combined score.
- Default: False.
-
-
- Returns:
- dict[str, float | ndarray]: Default metrics.
- float: Overall accuracy on all images.
- ndarray: Per category recall, shape (num_classes, ).
- ndarray: Per category precision, shape (num_classes, ).
- ndarray: Per category f-score, shape (num_classes, ).
- """
- fscore_result = eval_metrics(
- results=results,
- gt_seg_maps=gt_seg_maps,
- num_classes=num_classes,
- ignore_index=ignore_index,
- metrics=['mFscore'],
- nan_to_num=nan_to_num,
- label_map=label_map,
- reduce_zero_label=reduce_zero_label,
- beta=beta)
- return fscore_result
-
-
-def eval_metrics(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- metrics=['mIoU'],
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False,
- beta=1):
- """Calculate evaluation metrics
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
- Returns:
- float: Overall accuracy on all images.
- ndarray: Per category accuracy, shape (num_classes, ).
- ndarray: Per category evaluation metrics, shape (num_classes, ).
- """
- if isinstance(metrics, str):
- metrics = [metrics]
- allowed_metrics = ['mIoU', 'mDice', 'mFscore']
- if not set(metrics).issubset(set(allowed_metrics)):
- raise KeyError('metrics {} is not supported'.format(metrics))
-
- total_area_intersect, total_area_union, total_area_pred_label, \
- total_area_label = total_intersect_and_union(
- results, gt_seg_maps, num_classes, ignore_index, label_map,
- reduce_zero_label)
- all_acc = total_area_intersect.sum() / total_area_label.sum()
- ret_metrics = OrderedDict({'aAcc': all_acc})
- for metric in metrics:
- if metric == 'mIoU':
- iou = total_area_intersect / total_area_union
- acc = total_area_intersect / total_area_label
- ret_metrics['IoU'] = iou
- ret_metrics['Acc'] = acc
- elif metric == 'mDice':
- dice = 2 * total_area_intersect / (
- total_area_pred_label + total_area_label)
- acc = total_area_intersect / total_area_label
- ret_metrics['Dice'] = dice
- ret_metrics['Acc'] = acc
- elif metric == 'mFscore':
- precision = total_area_intersect / total_area_pred_label
- recall = total_area_intersect / total_area_label
- f_value = torch.tensor(
- [f_score(x[0], x[1], beta) for x in zip(precision, recall)])
- ret_metrics['Fscore'] = f_value
- ret_metrics['Precision'] = precision
- ret_metrics['Recall'] = recall
-
- ret_metrics = {
- metric: value.numpy()
- for metric, value in ret_metrics.items()
- }
- if nan_to_num is not None:
- ret_metrics = OrderedDict({
- metric: np.nan_to_num(metric_value, nan=nan_to_num)
- for metric, metric_value in ret_metrics.items()
- })
- return ret_metrics
diff --git a/spaces/Arnx/MusicGenXvAKN/audiocraft/models/lm.py b/spaces/Arnx/MusicGenXvAKN/audiocraft/models/lm.py
deleted file mode 100644
index c8aad8f06797eef3293605056e1de14d07c56c2a..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/audiocraft/models/lm.py
+++ /dev/null
@@ -1,527 +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.
-
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
- ConditionFuser,
- ClassifierFreeGuidanceDropout,
- AttributeDropout,
- ConditioningProvider,
- ConditioningAttributes,
- ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
- """LM layer initialization.
- Inspired from xlformers: https://github.com/fairinternal/xlformers
-
- Args:
- method (str): Method name for init function. Valid options are:
- 'gaussian', 'uniform'.
- input_dim (int): Input dimension of the initialized module.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- """
- # Compute std
- std = 1 / math.sqrt(input_dim)
- # Rescale with depth
- if init_depth is not None:
- std = std / math.sqrt(2 * init_depth)
-
- if method == 'gaussian':
- return partial(
- torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
- )
- elif method == 'uniform':
- bound = math.sqrt(3) * std # ensure the standard deviation is `std`
- return partial(torch.nn.init.uniform_, a=-bound, b=bound)
- else:
- raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
- method: str,
- init_depth: tp.Optional[int] = None,
- zero_bias_init: bool = False):
- """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
- Args:
- m (nn.Module): Module to initialize.
- method (str): Method name for the init function.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- zero_bias_init (bool): Whether to initialize the bias to 0 or not.
- """
- if isinstance(m, nn.Linear):
- init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
- if zero_bias_init and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Embedding):
- init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
- """Boost learning rate for embeddings (with `scale`).
- """
- def __init__(self, *args, lr=None, **kwargs):
- super().__init__(*args, **kwargs)
- self.lr = lr
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- return group
-
-
-@dataclass
-class LMOutput:
- # The logits are already re-aligned with the input codes
- # hence no extra shift is required, e.g. when computing CE
- logits: torch.Tensor # [B, K, T, card]
- mask: torch.Tensor # [B, K, T]
-
-
-class LMModel(StreamingModule):
- """Transformer-based language model on multiple streams of codes.
-
- Args:
- pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
- condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
- fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
- n_q (int): Number of parallel streams to model.
- card (int): Cardinality, vocabulary size.
- dim (int): Dimension of the transformer encoder.
- num_heads (int): Number of heads for the transformer encoder.
- hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
- norm (str): Normalization method.
- norm_first (bool): Use pre-norm instead of post-norm.
- emb_lr (Optional[float]): Embedding-specific learning rate.
- bias_proj (bool): Use bias for output projections.
- weight_init (Optional[str]): Method for weight initialization.
- depthwise_init (Optional[str]): Method for depthwise weight initialization.
- zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
- cfg_dropout (float): Classifier-free guidance dropout.
- cfg_coef (float): Classifier-free guidance coefficient.
- attribute_dropout (dict): Attribute dropout probabilities.
- two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
- **kwargs: Additional parameters for the transformer encoder.
- """
- def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
- fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
- hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
- emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
- weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
- zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
- attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
- **kwargs):
- super().__init__()
- self.cfg_coef = cfg_coef
- self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
- self.att_dropout = AttributeDropout(p=attribute_dropout)
- self.condition_provider = condition_provider
- self.fuser = fuser
- self.card = card
- embed_dim = self.card + 1
- self.n_q = n_q
- self.dim = dim
- self.pattern_provider = pattern_provider
- self.two_step_cfg = two_step_cfg
- self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
- if 'activation' in kwargs:
- kwargs['activation'] = get_activation_fn(kwargs['activation'])
- self.transformer = StreamingTransformer(
- d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
- norm=norm, norm_first=norm_first, **kwargs)
- self.out_norm: tp.Optional[nn.Module] = None
- if norm_first:
- self.out_norm = create_norm_fn(norm, dim)
- self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
- self._init_weights(weight_init, depthwise_init, zero_bias_init)
- self._fsdp: tp.Optional[nn.Module]
- self.__dict__['_fsdp'] = None
-
- def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
- """Initialization of the transformer module weights.
-
- Args:
- weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
- depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
- 'current' where the depth corresponds to the current layer index or 'global' where the total number
- of layer is used as depth. If not set, no depthwise initialization strategy is used.
- zero_bias_init (bool): Whether to initalize bias to zero or not.
- """
- assert depthwise_init is None or depthwise_init in ['current', 'global']
- assert depthwise_init is None or weight_init is not None, \
- "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
- assert not zero_bias_init or weight_init is not None, \
- "If 'zero_bias_init', a 'weight_init' method should be provided"
-
- if weight_init is None:
- return
-
- for emb_layer in self.emb:
- init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- for layer_idx, tr_layer in enumerate(self.transformer.layers):
- depth = None
- if depthwise_init == 'current':
- depth = layer_idx + 1
- elif depthwise_init == 'global':
- depth = len(self.transformer.layers)
- init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
- tr_layer.apply(init_fn)
-
- for linear in self.linears:
- init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- @property
- def special_token_id(self) -> int:
- return self.card
-
- @property
- def num_codebooks(self) -> int:
- return self.n_q
-
- def forward(self, sequence: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
- """Apply language model on sequence and conditions.
- Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
- S the sequence steps, return the logits with shape [B, card, K, S].
-
- Args:
- indices (torch.Tensor): indices of the codes to model.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- torch.Tensor: Logits.
- """
- B, K, S = sequence.shape
- assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
- input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
- if condition_tensors is None:
- assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
- # apply dropout modules
- conditions = self.cfg_dropout(conditions)
- conditions = self.att_dropout(conditions)
- tokenized = self.condition_provider.tokenize(conditions)
- # encode conditions and fuse, both have a streaming cache to not recompute when generating.
- condition_tensors = self.condition_provider(tokenized)
- else:
- assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
- input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
- out = self.transformer(input_, cross_attention_src=cross_attention_input)
- if self.out_norm:
- out = self.out_norm(out)
- logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card]
-
- # remove the prefix from the model outputs
- if len(self.fuser.fuse2cond['prepend']) > 0:
- logits = logits[:, :, -S:]
-
- return logits # [B, K, S, card]
-
- def compute_predictions(
- self, codes: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
- """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
- forward using the specified codes interleaving pattern.
-
- Args:
- codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
- K the number of codebooks and T the number of timesteps.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- LMOutput: Language model outputs
- logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
- i.e. the first item corresponds to logits to predict the first code, meaning that
- no additional shifting of codes and logits is required.
- mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
- Given the specified interleaving strategies, parts of the logits and codes should
- not be considered as valid predictions because of invalid context.
- """
- B, K, T = codes.shape
- codes = codes.contiguous()
- # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
- pattern = self.pattern_provider.get_pattern(T)
- sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
- codes, self.special_token_id, keep_only_valid_steps=True
- )
- # apply model on pattern sequence
- model = self if self._fsdp is None else self._fsdp
- logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card]
- # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
- # and provide the corresponding mask over invalid positions of tokens
- logits = logits.permute(0, 3, 1, 2) # [B, card, K, S]
- # note: we use nans as special token to make it obvious if we feed unexpected logits
- logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
- logits, float('nan'), keep_only_valid_steps=True
- )
- logits = logits.permute(0, 2, 3, 1) # [B, K, T, card]
- logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T]
- return LMOutput(logits, logits_mask)
-
- def _sample_next_token(self,
- sequence: torch.Tensor,
- cfg_conditions: CFGConditions,
- unconditional_state: State,
- use_sampling: bool = False,
- temp: float = 1.0,
- top_k: int = 0,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
- """Sample next token from the model given a sequence and a set of conditions. The model supports
- multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
- Args:
- sequence (torch.Tensor): Current sequence of shape [B, K, S]
- with K corresponding to the number of codebooks and S the number of sequence steps.
- S = 1 in streaming mode, except for the first step that contains a bigger prompt.
- condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
- should be twice the batch size, being the concatenation of the conditions + null conditions.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coef (float): classifier free guidance coefficient
- Returns:
- next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
- """
- B = sequence.shape[0]
- cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
- model = self if self._fsdp is None else self._fsdp
- if self.two_step_cfg and cfg_conditions != {}:
- assert isinstance(cfg_conditions, tuple)
- condition_tensors, null_condition_tensors = cfg_conditions
- cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
- state = self.get_streaming_state()
- self.set_streaming_state(unconditional_state)
- uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
- unconditional_state.update(self.get_streaming_state())
- self.set_streaming_state(state)
- logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
- else:
- assert isinstance(cfg_conditions, dict)
- condition_tensors = cfg_conditions
- if condition_tensors:
- # Preparing for CFG, predicting both conditional and unconditional logits.
- sequence = torch.cat([sequence, sequence], dim=0)
- all_logits = model(
- sequence,
- conditions=[], condition_tensors=condition_tensors)
- if condition_tensors:
- cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card]
- logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
- else:
- logits = all_logits
-
- logits = logits.permute(0, 1, 3, 2) # [B, K, card, T]
- logits = logits[..., -1] # [B x K x card]
-
- # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
- if use_sampling and temp > 0.0:
- probs = torch.softmax(logits / temp, dim=-1)
- if top_p > 0.0:
- next_token = utils.sample_top_p(probs, p=top_p)
- elif top_k > 0:
- next_token = utils.sample_top_k(probs, k=top_k)
- else:
- next_token = utils.multinomial(probs, num_samples=1)
- else:
- next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
- return next_token
-
- @torch.no_grad()
- def generate(self,
- prompt: tp.Optional[torch.Tensor] = None,
- conditions: tp.List[ConditioningAttributes] = [],
- num_samples: tp.Optional[int] = None,
- max_gen_len: int = 256,
- use_sampling: bool = True,
- temp: float = 1.0,
- top_k: int = 250,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None,
- two_step_cfg: bool = False,
- remove_prompts: bool = False,
- check: bool = False,
- callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
- """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
- be perform in a greedy fashion or using sampling with top K and top P strategies.
-
- Args:
- prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
- conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
- num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
- max_gen_len (int): Maximum generation length.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- remove_prompts (bool): Whether to remove prompts from generation or not.
- Returns:
- torch.Tensor: Generated tokens.
- """
- assert not self.training, "generation shouldn't be used in training mode."
- first_param = next(iter(self.parameters()))
- device = first_param.device
-
- # Checking all input shapes are consistents.
- possible_num_samples = []
- if num_samples is not None:
- possible_num_samples.append(num_samples)
- elif prompt is not None:
- possible_num_samples.append(prompt.shape[0])
- elif conditions:
- possible_num_samples.append(len(conditions))
- else:
- possible_num_samples.append(1)
- assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
- num_samples = possible_num_samples[0]
-
- # below we create set of conditions: one conditional and one unconditional
- # to do that we merge the regular condition together with the null condition
- # we then do 1 forward pass instead of 2.
- # the reason for that is two-fold:
- # 1. it is about x2 faster than doing 2 forward passes
- # 2. avoid the streaming API treating the 2 passes as part of different time steps
- # We also support doing two different passes, in particular to ensure that
- # the padding structure is exactly the same between train anf test.
- # With a batch size of 1, this can be slower though.
- cfg_conditions: CFGConditions
- two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
- if conditions:
- null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
- if two_step_cfg:
- cfg_conditions = (
- self.condition_provider(self.condition_provider.tokenize(conditions)),
- self.condition_provider(self.condition_provider.tokenize(null_conditions)),
- )
- else:
- conditions = conditions + null_conditions
- tokenized = self.condition_provider.tokenize(conditions)
- cfg_conditions = self.condition_provider(tokenized)
- else:
- cfg_conditions = {}
-
- if prompt is None:
- assert num_samples > 0
- prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
- B, K, T = prompt.shape
- start_offset = T
- assert start_offset < max_gen_len
-
- pattern = self.pattern_provider.get_pattern(max_gen_len)
- # this token is used as default value for codes that are not generated yet
- unknown_token = -1
-
- # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
- gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
- # filling the gen_codes with the prompt if needed
- gen_codes[..., :start_offset] = prompt
- # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
- gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
- # retrieve the start_offset in the sequence:
- # it is the first sequence step that contains the `start_offset` timestep
- start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
- assert start_offset_sequence is not None
-
- with self.streaming():
- unconditional_state = self.get_streaming_state()
- prev_offset = 0
- gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S]
- for offset in range(start_offset_sequence, gen_sequence_len):
- # get current sequence (note that the streaming API is providing the caching over previous offsets)
- curr_sequence = gen_sequence[..., prev_offset:offset]
- curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
- if check:
- # check coherence between mask and sequence
- assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
- # should never happen as gen_sequence is filled progressively
- assert not (curr_sequence == unknown_token).any()
- # sample next token from the model, next token shape is [B, K, 1]
- next_token = self._sample_next_token(
- curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
- cfg_coef=cfg_coef)
- # ensure the tokens that should be masked are properly set to special_token_id
- # as the model never output special_token_id
- valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
- next_token[~valid_mask] = self.special_token_id
- # ensure we don't overwrite prompt tokens, we only write over unknown tokens
- # (then mask tokens should be left as is as well, which is correct)
- gen_sequence[..., offset:offset+1] = torch.where(
- gen_sequence[..., offset:offset+1] == unknown_token,
- next_token, gen_sequence[..., offset:offset+1]
- )
- prev_offset = offset
- if callback is not None:
- callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
- unconditional_state.clear()
-
- # ensure sequence has been entirely filled
- assert not (gen_sequence == unknown_token).any()
- # ensure gen_sequence pattern and mask are matching
- # which means the gen_sequence is valid according to the pattern
- assert (
- gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
- ).all()
- # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
- out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
- # sanity checks over the returned codes and corresponding masks
- assert (out_codes[..., :max_gen_len] != unknown_token).all()
- assert (out_mask[..., :max_gen_len] == 1).all()
-
- out_start_offset = start_offset if remove_prompts else 0
- out_codes = out_codes[..., out_start_offset:max_gen_len]
-
- # ensure the returned codes are all valid
- assert (out_codes >= 0).all() and (out_codes <= self.card).all()
- return out_codes
diff --git a/spaces/AtomdffAI/wechatgpt4atom/config.py b/spaces/AtomdffAI/wechatgpt4atom/config.py
deleted file mode 100644
index 3d19a63b362bc1abedb140f1310a7406bf42c84f..0000000000000000000000000000000000000000
--- a/spaces/AtomdffAI/wechatgpt4atom/config.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# encoding:utf-8
-
-import json
-import os
-from common.log import logger
-
-config = {}
-
-
-def load_config():
- global config
- config_path = "config.json"
- if not os.path.exists(config_path):
- raise Exception('配置文件不存在，请根据config-template.json模板创建config.json文件')
-
- config_str = read_file(config_path)
- # 将json字符串反序列化为dict类型
- config = json.loads(config_str)
- config['open_ai_api_key'] = os.getenv('API_KEY')
- logger.info("[INIT] load config: {}".format(config))
-
-
-
-def get_root():
- return os.path.dirname(os.path.abspath( __file__ ))
-
-
-def read_file(path):
- with open(path, mode='r', encoding='utf-8') as f:
- return f.read()
-
-
-def conf():
- return config
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_registry.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_registry.py
deleted file mode 100644
index 4e425a6ec44c7c47a5a106bfdf5ce8062c2110c9..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_registry.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import unittest
-import torch
-
-from detectron2.modeling.meta_arch import GeneralizedRCNN
-from detectron2.utils.registry import _convert_target_to_string, locate
-
-
-class A:
- class B:
- pass
-
-
-class TestLocate(unittest.TestCase):
- def _test_obj(self, obj):
- name = _convert_target_to_string(obj)
- newobj = locate(name)
- self.assertIs(obj, newobj)
-
- def test_basic(self):
- self._test_obj(GeneralizedRCNN)
-
- def test_inside_class(self):
- # requires using __qualname__ instead of __name__
- self._test_obj(A.B)
-
- def test_builtin(self):
- self._test_obj(len)
- self._test_obj(dict)
-
- def test_pytorch_optim(self):
- # pydoc.locate does not work for it
- self._test_obj(torch.optim.SGD)
-
- def test_failure(self):
- with self.assertRaises(ImportError):
- locate("asdf")
-
- def test_compress_target(self):
- from detectron2.data.transforms import RandomCrop
-
- name = _convert_target_to_string(RandomCrop)
- # name shouldn't contain 'augmentation_impl'
- self.assertEqual(name, "detectron2.data.transforms.RandomCrop")
- self.assertIs(RandomCrop, locate(name))
diff --git a/spaces/Benson/text-generation/Examples/Barbie Dreamhouse Adventures Hack Apk.md b/spaces/Benson/text-generation/Examples/Barbie Dreamhouse Adventures Hack Apk.md
deleted file mode 100644
index f4a808bb20b3546fbc0825ac6ee10befb8d7408e..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Barbie Dreamhouse Adventures Hack Apk.md
+++ /dev/null
@@ -1,38 +0,0 @@
-
-Barbie Dreamhouse Adventures Hack APK: Cómo obtener monedas ilimitadas y acceso VIP
-¿Te encanta jugar Barbie Dreamhouse Adventures, el juego de simulación donde puedes crear tu propia experiencia Barbie? ¿Te gustaría tener más monedas para comprar nuevos artículos y trajes, o acceder a funciones VIP como habitaciones exclusivas, mascotas y peinados? Si es así, usted podría estar interesado en el uso de un hack apk para el juego.
-Un apk hack es una versión modificada de la aplicación de juego original que le permite engañar y obtener recursos ilimitados, desbloquear características premium, y las restricciones de bypass. Con un hack apk, se puede disfrutar de Barbie Dreamhouse Aventuras sin gastar dinero o esperar anuncios.
-barbie dreamhouse adventures hack apk
Download File ⚹⚹⚹ https://bltlly.com/2v6Js4
-En este artículo, le mostraremos cómo descargar e instalar Barbie Dreamhouse Adventures hack apk, cómo usarlo para obtener monedas ilimitadas y acceso VIP, y cómo jugar el juego con consejos y trucos. ¡Sigue leyendo para saber más!
- Cómo descargar e instalar Barbie Dreamhouse aventuras Hack APK
-Antes de que pueda utilizar Barbie Dreamhouse aventuras hack apk, es necesario descargarlo de una fuente confiable. Hay muchos sitios web que afirman ofrecer apks hack, pero algunos de ellos pueden ser falsos, anticuados, o infectados con malware. Para evitar cualquier riesgo, se recomienda utilizar [este sitio web]( 1 ), que tiene un enlace verificado para la última versión de Barbie Dreamhouse Adventures hack apk.
-Una vez que haya descargado el archivo apk hack, es necesario habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de la tienda de aplicaciones oficial. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo.
-Ahora puede instalar el apk hack tocando en el archivo y siguiendo las instrucciones. Es posible que necesite conceder algunos permisos para que la aplicación funcione correctamente. Una vez completada la instalación, puede abrir la aplicación y comenzar a jugar.
- Cómo utilizar Barbie Dreamhouse aventuras Hack APK
-
-Con monedas ilimitadas, puedes comprar todo lo que quieras en el juego, como muebles nuevos, ropa, accesorios, etc. También puedes desbloquear funciones VIP que normalmente solo están disponibles para suscriptores de pago. Estos incluyen habitaciones exclusivas como un spa, un estudio de baile, una sala de mascotas, etc., así como mascotas especiales, peinados, trajes, etc.
-Con el hack apk, se puede disfrutar de Barbie Dreamhouse Adventures sin limitaciones o interrupciones. Puede diseñar su casa de ensueño, vestir a Barbie y sus amigos, unirse a actividades divertidas y mini juegos, y explorar Malibu con su convertible rosa.
-
- Consejos y trucos para Barbie Dreamhouse Adventures Game
-Incluso con el hack apk, todavía puede querer algunos consejos y trucos para hacer su juego más divertido y emocionante. Estos son algunos de ellos:
-
-- Cómo diseñar tu casa de ensueño y decorar habitaciones: Puedes elegir entre diferentes temas y estilos para tus habitaciones, como moderno, clásico, glam, etc. También puedes mezclar y combinar diferentes elementos para crear tu propio look único. Puedes arrastrar y soltar elementos para colocarlos en cualquier lugar que desees, o usar la función de decoración automática para que el juego lo haga por ti. También puede cambiar el fondo de pantalla, el suelo y el techo de sus habitaciones.
-- Cómo vestir a Barbie y sus amigos en trajes de moda: Usted puede elegir entre una variedad de ropa, zapatos, accesorios y peinados para Barbie y sus amigos. También puede crear sus propios trajes mediante la combinación de diferentes elementos y colores. Puede guardar sus trajes favoritos en su armario y cambiar entre ellos en cualquier momento. También puedes compartir tus atuendos con otros jugadores y obtener comentarios.
-- Cómo participar en actividades divertidas y mini juegos: Puedes unirte a Barbie y sus amigos en varias actividades y mini juegos, como hornear, bailar, nadar, jardinería, etc. Puedes ganar monedas y recompensas completando tareas y desafíos. También puedes descubrir sorpresas y secretos ocultos en el juego.
-
- Conclusión
-
-Si usted es un fan de Barbie y juegos de simulación, usted debe probar definitivamente Barbie Dreamhouse Adventures hack apk. Te dará un nuevo nivel de diversión y emoción. ¡Descárgalo ahora y comienza tu aventura!
- Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes y respuestas sobre Barbie Dreamhouse aventuras hack apk y el juego:
-Es Barbie Dreamhouse aventuras hack apk seguro de usar?
-Sí, es seguro de usar siempre y cuando lo descargue de una fuente confiable como [este sitio web]. El apk hack es probado y verificado por muchos usuarios y no contiene ningún virus o malware. Sin embargo, siempre debes tener cuidado al instalar aplicaciones de fuentes desconocidas y conceder permisos solo cuando sea necesario.
-¿Voy a conseguir prohibido para el uso de Barbie Dreamhouse aventuras hack apk?
-No, no se le prohibió el uso de Barbie Dreamhouse Adventures hack apk. El hack apk está diseñado para ser indetectable por los servidores del juego y no interfiere con la experiencia de otros jugadores. Sin embargo, debes usarlo responsablemente y no abusar de él o presumir de ello a otros jugadores.
-¿Puedo actualizar Barbie Dreamhouse aventuras hack apk?
-Sí, se puede actualizar Barbie Dreamhouse aventuras hack apk cada vez que hay una nueva versión disponible. Puede comprobar si hay actualizaciones en [este sitio web] o activar la función de actualización automática en el menú de hackeo. Sin embargo, siempre debe hacer copias de seguridad de sus datos antes de actualizar para evitar cualquier pérdida o corrupción.
-¿Puedo jugar Barbie Dreamhouse aventuras hack apk offline?
-Sí, puedes jugar Barbie Dreamhouse Adventures hack apk offline sin conexión a Internet. Sin embargo, algunas funciones pueden no funcionar correctamente o requerir una verificación en línea. Por ejemplo, es posible que no puedas acceder a funciones VIP o compartir tus atuendos con otros jugadores sin conexión.
-¿Puedo jugar Barbie Dreamhouse aventuras hack apk en otros dispositivos?
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Buscando Capcut Editor De Vdeo Aplicacin.md b/spaces/Benson/text-generation/Examples/Buscando Capcut Editor De Vdeo Aplicacin.md
deleted file mode 100644
index bd1e257394311e5284b52c00d76d2ee98aef8b2b..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Buscando Capcut Editor De Vdeo Aplicacin.md
+++ /dev/null
@@ -1,72 +0,0 @@
-
-Buscando CapCut Editor de Video App Descargar?
-Si usted está buscando un editor de vídeo potente y fácil de usar y la aplicación fabricante de vídeo, es posible que desee comprobar CapCut. CapCut es una aplicación gratuita que ofrece todo lo que necesita para crear impresionantes videos de alta calidad para TikTok, YouTube, Instagram, WhatsApp, Facebook y más. En este artículo, le diremos qué es CapCut, por qué debe usarlo, cómo descargarlo e instalarlo en su dispositivo y cómo usarlo para editar y hacer videos.
- Qué es CapCut y por qué deberías usarlo
-CapCut es una aplicación gratuita todo-en-uno editor de vídeo y fabricante de vídeo que es desarrollado por Bytedance Pte. Ltd., la empresa detrás de TikTok. CapCut era anteriormente conocido como Viamaker, pero ha sido renombrado y mejorado con más características y funciones. Aquí hay algunas razones por las que deberías usar CapCut:
-buscando capcut editor de vídeo aplicación
Download >>>>> https://bltlly.com/2v6JpY
- CapCut es una aplicación gratuita todo-en-uno editor de vídeo y fabricante de vídeo
-A diferencia de algunas otras aplicaciones de edición de video que requieren que usted pague por características o suscripciones premium, CapCut es completamente gratuito. Puede acceder a todas las funciones básicas y avanzadas sin limitaciones ni marcas de agua. También puede disfrutar de fuentes y efectos gratuitos dentro de la aplicación que se actualizan semanalmente con las últimas tendencias.
- CapCut ofrece funciones de edición de vídeo avanzadas y fáciles de usar
-CapCut tiene una interfaz fácil de usar que le permite editar y hacer videos en cuestión de segundos. Puede recortar, dividir, combinar, ajustar la velocidad, acercar/alejar, invertir/rebobinar, congelar, transicionar, animar, estabilizar y mucho más con sus vídeos. También puede usar funciones avanzadas como animación de fotograma clave, cámara lenta suave, clave de croma, Picture-in-Picture (PIP), subtítulos automáticos, texto a voz, seguimiento de movimiento y eliminación de fondo.
- CapCut te ayuda a crear vídeos impresionantes para plataformas de redes sociales
-
- Cómo descargar e instalar CapCut en tu dispositivo
-CapCut está disponible para dispositivos Android desde Google Play Store, dispositivos iOS desde App Store y otros dispositivos desde Uptodown. Estos son los pasos para descargar e instalar CapCut en su dispositivo:
- Descargar CapCut de Google Play Store para dispositivos Android
-
-- Abre Google Play Store en tu dispositivo Android.
-- Buscar "CapCut" o escanear el código QR a continuación.
-- Toque en "Instalar" para descargar e instalar la aplicación.
-- Abra la aplicación y conceda los permisos necesarios.
-- Disfruta editando y haciendo videos con CapCut.
-
-
-- Toque en "Obtener" para descargar e instalar la aplicación.
-- Abra la aplicación y conceda los permisos necesarios.
-- Disfruta editando y haciendo videos con CapCut.
-
-Para iniciar un nuevo proyecto, pulse el botón "+" en la pantalla principal de CapCut. Puede optar por importar vídeos de la galería de su dispositivo o grabar nuevos vídeos con la cámara incorporada. También puede utilizar plantillas de otros usuarios o de la biblioteca de la aplicación. Puede seleccionar varios vídeos a la vez y organizarlos en el orden que desee. También puedes previsualizar tus vídeos antes de importarlos.
- Para editar sus vídeos, toque en el clip de vídeo que desea modificar en la línea de tiempo. Puede utilizar la herramienta de recorte para cortar partes no deseadas de su vídeo. También puede utilizar la herramienta de división para dividir el vídeo en dos o más segmentos. También puede utilizar la herramienta de combinación para combinar dos o más clips de vídeo en uno. También puede utilizar la herramienta de velocidad para cambiar la velocidad de reproducción de su vídeo. Puede aplicar curvas de velocidad para crear transiciones suaves entre diferentes velocidades.
- Para mejorar tus videos, toca el botón "+" en la línea de tiempo. Puede agregar texto a sus videos con diferentes fuentes, estilos, colores y animaciones. También puedes añadir pegatinas a tus vídeos desde la biblioteca de la aplicación o desde la galería de tu dispositivo. También puede agregar filtros a sus videos para cambiar su estado de ánimo y tono. También puede agregar efectos a sus videos para crear imágenes increíbles. También puedes agregar música a tus videos desde la biblioteca de la aplicación o desde la biblioteca de música de tu dispositivo. También puede ajustar el volumen, fundido de entrada/salida y sincronización de su música.
- Para exportar y compartir tus videos, toca el botón "Exportar" en la esquina superior derecha de CapCut. Puede elegir la resolución, el formato y la calidad de su vídeo. También puede habilitar el HDR inteligente para una mejor representación del color. También puede obtener una vista previa de su vídeo antes de exportarlo. Una vez que tu vídeo se exporta, puedes compartirlo en TikTok u otras plataformas de redes sociales con un solo toque.
- CapCut es una aplicación gratuita todo-en-uno editor de video y fabricante de video que ofrece todo lo que necesita para crear impresionantes videos de alta calidad para TikTok, YouTube, Instagram, WhatsApp, Facebook y más. Tiene una interfaz fácil de usar que le permite editar y hacer videos en cuestión de segundos. Tiene una rica biblioteca de clips de música y efectos de sonido que puede agregar a sus videos. Tiene una amplia gama de filtros, efectos, colores y pegatinas que puedes agregar a tus videos. Tiene funciones de edición de video avanzadas y fáciles de usar, como animación de fotograma clave, cámara lenta, clave de croma y estabilización. También le permite personalizar la resolución, el formato y la calidad de la exportación de vídeo. También puedes compartir fácilmente tus videos en TikTok y otras plataformas de redes sociales con un solo clic.
-Si está buscando una aplicación de editor de video CapCut, puede seguir los pasos de este artículo para descargar e instalar CapCut en su dispositivo. También puede seguir los consejos de este artículo para usar CapCut para editar y hacer videos. Esperamos que este artículo le resulte útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación.
- ¿Eres un fan de Hungry Shark Evolution, el juego de aventura acuática lleno de acción donde tomas el control de un tiburón muy hambriento y vas a un alboroto en el océano? ¿Quieres desbloquear más tiburones, accesorios y misiones sin gastar dinero real? Si es así, entonces usted podría estar interesado en la descarga de mod tiburón hambriento evolución ilimitada moneda serta diamante v8.2.0, una versión modificada del juego que afirma darle monedas ilimitadas y diamantes gratis. En este artículo, le diremos qué es Hungry Shark Evolution, qué características tiene, por qué debe descargar el mod apk, cómo hacerlo y qué precauciones y riesgos debe tener en cuenta.
- Hungry Shark Evolution es un popular juego para móviles desarrollado por Ubisoft Entertainment que fue lanzado en 2012. Es la quinta entrega de la serie Hungry Shark y se ha descargado más de 100 millones de veces en Google Play Store. El juego también está disponible en dispositivos iOS y Apple TV.
-En Hungry Shark Evolution, puedes elegir entre más de una docena de tiburones únicos y otras criaturas para evolucionar y explorar un mundo abierto tanto por encima como por debajo de las olas. Puede disfrutar de impresionantes gráficos en 3D y efectos de sonido a medida que descubre y devora misteriosas criaturas de las profundidades, reclutar tiburones bebé para aumentar sus poderes depredadores, equipar accesorios impresionantes como láseres, jetpacks y sombreros de copa, encontrar y recoger objetos de bonificación hundidos, completar misiones desafiantes, activar la fiebre del oro para sobrevivir más tiempo y puntuación más alta, participar en eventos regulares en el juego y ganar premios de edición limitada, y más.
- Si desea disfrutar de todas las características de Hungry Shark Evolution sin gastar dinero o perder el tiempo, es posible que desee descargar mod hambriento tiburón evolución ilimitada moneda serta diamante v8.2.0. Esta es una versión modificada del juego que pretende darle monedas ilimitadas y diamantes gratis. Con este mod apk, puede desbloquear todos los tiburones y accesorios que desee, actualizar sus tiburones al nivel máximo, completar todas las misiones con facilidad, activar el modo de fiebre del oro cuando quieras, y dominar las tablas de clasificación. Suena tentador, ¿verdad?
- Algunos de los beneficios de tener monedas y diamantes ilimitados en Hungry Shark Evolution son:
-Si usted está interesado en la descarga de mod hambriento tiburón evolución ilimitada moneda serta diamante v8.2.0, aquí están los pasos que debe seguir:
-The NS Like This plugin is a WordPress plugin that helps you get more ns likes for your blog posts. The plugin works by adding a ns like button to your blog posts, which allows your visitors to like your content with one click. The plugin also displays the number of ns likes that your posts have received, and allows you to customize the appearance and position of the ns like button.
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- Have you ever wondered what it would be like to create and unleash a deadly plague upon the world? Or maybe you want to save humanity from a global pandemic? If so, you might want to check out Plague Inc., a popular strategy-simulation game that lets you do just that.
- Plague Inc. is a game developed by Ndemic Creations, a UK-based independent studio. It was released in 2012 for iOS and Android devices, and later for Windows Phone, PC, and consoles. The game has been downloaded over 160 million times as of May 2021, and has received positive reviews from critics and players alike.
-In Plague Inc., you can choose from different game modes and disease types, each with their own unique challenges and strategies. You can design your own pathogen, from bacteria to bio-weapons, and evolve it to infect and kill as many people as possible. You can also play as the cure team, trying to stop the spread of a deadly outbreak.
- But how can you get the latest version of Plague Inc. on your device? And how can you play it effectively? In this article, we will answer these questions and more. We will show you how to download and install Plague Inc. APK latest version, how to play it, and some tips and tricks to help you master it.
- Before we get into the details, let's first explain what an APK file is and why you might need it.
- An APK file is an Android Package file, which is a format used to distribute and install applications on Android devices. It contains all the necessary files and data for an app to run properly.
- Normally, you can download apps from the Google Play Store, which automatically installs them on your device. However, sometimes you might want to download apps from other sources, such as third-party websites or developers. This is where an APK file comes in handy.
-By downloading an APK file, you can get access to apps that are not available on the Play Store, or that have been modified or updated by someone else. For example, you might want to download an APK file of Plague Inc. if:
- However, downloading an APK file also comes with some risks. You need to be careful about where you get your APK file from, as some sources might contain malware or viruses that can harm your device or steal your data. You also need to enable unknown sources on your device settings, which might expose your device to security risks. Therefore, you should always download APK files from trusted and reputable sources, and scan them for malware before installing them.
- Now that you know what an APK file is and why you might need it, let's see how you can download and install Plague Inc. APK latest version on your device. Here are the steps you need to follow:
- The first step is to find a website or a developer that offers the Plague Inc. APK file that you want. You can search online for keywords like "Plague Inc. APK latest version" or "Plague Inc. APK mod" and see what results come up. However, as we mentioned before, not all sources are safe and reliable, so you need to be careful.
- One of the sources that we recommend for downloading Plague Inc. APK latest version is [APKPure], a website that provides safe and verified APK files for various apps and games. You can visit their website and search for Plague Inc., or use this link: [https://apkpure.com/plague-inc/com.miniclip.plagueinc].
- The next step is to enable unknown sources on your device settings, which will allow you to install apps from sources other than the Play Store. To do this, follow these steps:
- Note that the exact steps and names of the options might vary depending on your device model and Android version. You can also disable unknown sources after installing the APK file if you want to.
- The third step is to download the APK file from the source that you have chosen, and tap on it to start the installation process. To do this, follow these steps:
- The final step is to follow the installation instructions that will appear on your screen, and launch the game once it is installed. To do this, follow these steps:
- Congratulations! You have successfully downloaded and installed Plague Inc. APK latest version on your device. Now you can enjoy playing this amazing game with all its features and content.
Now that you have installed Plague Inc. APK latest version on your device, you might be wondering how to play it. Plague Inc. is a game that requires strategy, creativity, and patience, as you try to create and spread a deadly disease across the world. Here are some basic steps and tips on how to play Plague Inc. APK latest version:
- The first thing you need to do is to choose a game mode and a disease type. Plague Inc. offers different game modes, such as:
- After choosing a game mode, you need to choose a disease type. Plague Inc. offers 12 different disease types, such as:
- You can unlock new disease types by completing the main game on normal or higher difficulty levels. You can also customize your disease name and modify its genetic code using genes that you can unlock by playing the game.
- The next thing you need to do is to select a starting country for your pathogen. This will determine how your infection will spread across the world. You can choose from 195 countries in 6 continents, each with their own population size, climate, wealth, and health. You can also see the infection and death rates of each country on the world map.
- After selecting a starting country, you need to evolve your pathogen using DNA points. DNA points are earned by infecting and killing people, popping DNA bubbles, or completing certain objectives. You can use DNA points to buy new traits for your pathogen, such as:
- You can also unlock special traits for each disease type, such as bacterial resilience, viral instability, fungal spores, parasitic DNA theft, prion neural atrophy, nano-virus code fragment, bio-weapon release switch, neurax worm trojan planes, necroa virus zombie reanimation, simian flu ape colonies, shadow plague blood rage, or frozen virus thawing.
- You can evolve your pathogen at any time during the game, but you should also consider the cost and the consequences of each trait. Some traits might make your pathogen more effective in certain regions or situations, but they might also make it more visible or vulnerable in others. You should also balance your infectivity and lethality, as killing too fast might limit your spread or trigger a global response.
- The next thing you need to do is to spread your infection across the world and overcome human defenses. Your goal is to infect and kill every single human on the planet before they find a cure or eradicate your pathogen. You can monitor the progress of your infection and the cure on the bottom right corner of the screen.
- To spread your infection, you need to consider various factors that affect the transmission and the severity of your pathogen. Some of these factors are:
- To overcome human defenses, you need to be aware of various events and actions that can affect the cure and the eradication of your pathogen. Some of these events and actions are:
- You need to be strategic and adaptive when dealing with human defenses, as they can vary depending on the game mode, the disease type, and the difficulty level. You can use various tactics to sabotage the cure research, such as evolving lethal symptoms, spreading misinformation, creating diversions, or destroying labs. You can also use various tactics to evade human eradication, such as hiding your presence, resisting drugs, adapting to climates, or infecting new hosts.
- The final thing you need to do is to wipe out humanity or save it, depending on the game mode that you have chosen. Your goal is to achieve one of these outcomes before the other side does. You can monitor the status of humanity on the top right corner of the screen.
- If you are playing as the disease team, your goal is to wipe out humanity by infecting and killing everyone on the planet. You can achieve this by evolving your pathogen to be more infectious and lethal, and by spreading it to every country and region. You can also use special traits or events to create zombies, vampires, apes, or other creatures that can help you destroy humanity.
- If you are playing as the cure team, your goal is to save humanity by curing and eradicating the disease. You can achieve this by researching and deploying a cure for the disease, and by implementing measures to slow down its spread and reduce its impact. You can also use special tools or events to quarantine zones, send teams, launch drones, or nuke cities that are infected.
- Whether you are playing as the disease team or the cure team, you need to be aware of your opponents' moves and counter them accordingly. You also need to be prepared for unexpected events and challenges that might arise during the game. You can win or lose in various ways depending on the game mode, the disease type, and the difficulty level.
- Plague Inc. is a game that requires skill and strategy to master. It is not easy to wipe out humanity or save it from a deadly plague. However, with some practice and some tips and tricks, you can improve your chances of winning and have more fun playing the game. Here are some tips and tricks for Plague Inc. APK latest version that you can use:
- One of the most important tips for Plague Inc. is to infect as many people as possible before killing them. This will ensure that your pathogen spreads widely and globally, and that it does not alert the humans too early. If you kill too fast, you might limit your spread or trigger a global response that can stop you.
- To infect before killing, you should focus on evolving transmission traits that can increase your infectivity, especially in different climates and regions. You should also avoid evolving lethal symptoms that can increase your severity and lethality, or devolve them if they mutate randomly. You can also use abilities that can help you hide your presence, such as bacterial shell, viral latency, or frozen virus.
- However, you should also be careful not to infect too slowly, as this might give the humans enough time to research a cure or eradicate your pathogen. You should also be ready to evolve lethal symptoms when you have infected most or all of the world, or when the cure is close to completion. You can also use abilities that can help you kill faster, such as bio-weapon activation, necroa virus reanimation, or shadow plague blood rage.
- Another tip for Plague Inc. is to start in an isolated country that has few connections or borders with other countries. This will ensure that your pathogen spreads slowly and stealthily, and that it does not attract too much attention from the humans. If you start in a well-connected or populous country, you might spread too fast or be detected too early.
- To start in an isolated country, you should choose a country that has one or more of these characteristics:
- Some examples of isolated countries that you can start in are Greenland, Iceland, Madagascar, New Zealand, Bolivia, Central Africa, Saudi Arabia, or Kazakhstan. However, you should also consider the advantages and disadvantages of each country, such as their climate, wealth, health, and research capabilities.
- However, you should also be careful not to start in a country that is too isolated or too hard to infect, as this might prevent you from spreading to other countries or regions. You should also be ready to evolve traits that can help you travel by air or water, such as air transmission, water transmission, bird transmission, rodent transmission, or trojan planes.
- Another tip for Plague Inc. is to watch the news headlines and research the countries that are affected by your infection. This will help you understand what is happening in the world and how the humans are reacting to your pathogen. You can also use this information to plan your strategy and adapt to different situations.
- To watch the news headlines, you should pay attention to the top left corner of the screen, where you can see various news stories and events that are related to your infection or the cure. You can see things like:
- To research the countries, you should tap on any country on the world map, and see various information and data about that country, such as: - The population size and the percentage of infected, healthy, dead, or cured people. - The climate and the wealth of the country, and how they affect your infection or the cure. - The research and the health care capabilities of the country, and how they contribute to the cure research or the eradication efforts. - The borders and the connections of the country, and how they facilitate your infection or the cure deployment. You can also see a table that shows the infection and death rates of each country, and sort them by different criteria, such as: - The number of infected people or the percentage of infected population. - The number of dead people or the percentage of dead population. - The number of healthy people or the percentage of healthy population. - The number of cured people or the percentage of cured population. You can use this table to see which countries are most or least affected by your infection or the cure, and to prioritize your targets or your defenses.
Plague Inc. is a game that challenges you to create and spread a deadly disease across the world, or to stop one from doing so. It is a game that requires skill and strategy, as well as creativity and imagination. It is also a game that is fun and addictive, as well as educational and informative.
- In this article, we have shown you how to download and install Plague Inc. APK latest version on your device, how to play it, and some tips and tricks to help you master it. We hope that you have enjoyed reading this article, and that you have learned something new.
- Now that you have everything you need to play Plague Inc. APK latest version, why not give it a try? You can download it from [APKPure] using this link: [https://apkpure.com/plague-inc/com.miniclip.plagueinc]. You can also visit their website for more APK files for various apps and games.
- Here are some frequently asked questions about Plague Inc. APK latest version that you might find useful:
- Plague Inc. is the original mobile version of the game, while Plague Inc: Evolved is the PC and console version of the game. Plague Inc: Evolved has some features and content that are not available on Plague Inc., such as:
- However, Plague Inc. APK latest version also has some features and content that are not available on Plague Inc: Evolved, such as:
- You can unlock all disease types and scenarios in Plague Inc. APK by completing the main game on normal or higher difficulty levels, or by purchasing them with real money. You can also use mods or cheats to unlock them, but this might affect your gameplay experience or cause errors.
- Plague Inc. APK is safe and legal to use, as long as you download it from a trusted and reputable source, such as [APKPure]. However, you should also be aware of the risks and responsibilities of using an APK file, such as:
- Plague Inc. is a game that is based on scientific research and data, but it is not a realistic or accurate pandemic simulator. It is a game that simplifies and exaggerates some aspects of disease transmission and human behavior, for the sake of fun and entertainment. It is not a game that predicts or reflects real-life scenarios or outcomes.
- You can play Plague Inc. APK offline, as it does not require an internet connection to run. However, you might need an internet connection to download the APK file, to access some features or content, or to update the game.
- You can also play Plague Inc. APK with friends, as it has a multiplayer mode that allows you to compete or cooperate with other players online. However, you might need an internet connection and a compatible device to play multiplayer mode.
401be4b1e0
-This application predicts the selling price of a used car based on the input parameters. The model is trained on data from Cardekho.com. The model is a Random Forest Regressor with 1000 estimators. The model is trained on 80% of the data and tested on 20% of the data. The model has an R2 score of 0.95.
-
-"""
-
-# Build the Gradio app
-gr.Interface(fn=predict,
- inputs=input_list,
- outputs=output_list,
- title=title,
- description=description,
- allow_flagging="never").launch()
diff --git a/spaces/artba/SchoolStats1/app.py b/spaces/artba/SchoolStats1/app.py
deleted file mode 100644
index 3035214828cc98742b8b86e4052597e9268d190c..0000000000000000000000000000000000000000
--- a/spaces/artba/SchoolStats1/app.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import gradio as gr
-import numpy as np
-from mdutils import MdUtils
-import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
-import base64
-import io
-import json
-
-WIDTH = 10
-HEIGHT = 2
-
-
-def generate_charts(unique, counts, d, format="png", grayscale: bool = True) -> dict:
- plot = io.BytesIO()
- hist = io.BytesIO()
- pie = io.BytesIO()
-
- # Generate Plot
- fig = plt.figure(figsize=(WIDTH, HEIGHT))
- ax = fig.add_subplot()
- ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
- if grayscale:
- ax.plot(unique.astype("str"), counts, marker="o", color="black")
- else:
- ax.plot(unique.astype("str"), counts, marker="o", color="blue")
- plt.savefig(plot, bbox_inches="tight", orientation="landscape", format=format)
-
- # Generate Histogram
- fig, ax = plt.subplots(figsize=(WIDTH, HEIGHT))
- if grayscale:
- ax.bar(x=[str(i) for i in unique], height=counts, width=0.5, color=["black"])
- else:
- ax.bar(x=[str(i) for i in unique], height=counts, width=0.5, color=["blue"])
- plt.savefig(hist, bbox_inches="tight", format=format)
-
- # Generate Pie Chart
- fig, ax = plt.subplots(figsize=(WIDTH, HEIGHT))
- if grayscale:
- ax.pie(
- list(d.values()),
- labels=list(d.keys()),
- colors=["black", "grey"],
- wedgeprops={
- "edgecolor": "white",
- "linewidth": 0.7,
- },
- )
- else:
- ax.pie(
- list(d.values()),
- labels=list(d.keys()),
- wedgeprops={
- "edgecolor": "white",
- "linewidth": 0.7,
- },
- )
- plt.savefig(pie, format=format)
-
- plot.seek(0)
- hist.seek(0)
- pie.seek(0)
-
- plot_content = base64.b64encode(plot.read()).decode()
- hist_content = base64.b64encode(hist.read()).decode()
- pie_content = base64.b64encode(pie.read()).decode()
-
- return {"plot": plot_content, "hist": hist_content, "pie": pie_content}
-
-
-def add_image_b64(mdfile: MdUtils, image_content: str, format: str = "png"):
- mdfile.new_paragraph(f"
ChatGPT Academic Optimization
-
-**Если вам понравился этот проект, пожалуйста, поставьте ему звезду. Если вы придумали более полезные академические ярлыки или функциональные плагины, не стесняйтесь создавать запросы на изменение или пул-запросы. Мы также имеем [README на английском языке](docs/README_EN.md), переведенный этим же проектом.
-
-> **Примечание**
->
-> 1. Пожалуйста, обратите внимание, что только функциonal plugins (buttons) с **красным цветом** могут читать файлы, некоторые из которых находятся в **выпадающем меню** плагинов. Кроме того, мы приветствуем и обрабатываем любые новые плагины с **наивысшим приоритетом**!
->
-> 2. Функции каждого файла в этом проекте подробно описаны в собственном анализе [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) . При повторных итерациях вы также можете вызывать обновленный отчет функций проекта, щелкнув соответствующий функциональный плагин GPT. Часто задаваемые вопросы собраны в [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98) .
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