Bluejacking APK: A Tool for Bluetooth Hacking
-What is bluejacking APK?
-Bluejacking APK is a software application that can be used for Bluetooth hacking. It is an Android app that allows you to send messages and files to other Bluetooth devices without pairing with them. You can also scan for nearby Bluetooth devices and get information about them, such as their name, address, and class.
-Bluejacking APK is not available on the Google Play Store, as it violates the terms and conditions of the platform. However, you can download it from third-party websites or APK repositories. However, you should be careful when downloading and installing bluejacking APK, as it may contain malware or viruses that can harm your device or compromise your security.
-How does bluejacking APK work?
-Bluejacking APK works by using the Bluetooth connection of your device to communicate with other devices. To use bluejacking APK, you need to enable Bluetooth on your device and grant the app permission to access it. Then, you can use the app to perform various actions, such as:
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- Sending messages: You can use bluejacking APK to send text messages, images, sounds, or vCards to other Bluetooth devices. You can also customize the name and message fields of the messages. The messages will appear as notifications on the recipient's device, and they will not be able to reply or block them. -
- Sending files: You can use bluejacking APK to send any type of file to other Bluetooth devices. You can choose the file from your device's storage or use the app's file manager to browse for it. The recipient will receive a notification asking them to accept or reject the file transfer. -
- Scanning devices: You can use bluejacking APK to scan for nearby Bluetooth devices and get information about them. You can see their name, address, class, and signal strength. You can also filter the devices by their type, such as phone, computer, headset, etc. -
What are the features of bluejacking APK?
-Bluejacking APK has some features that make it a powerful tool for Bluetooth hacking. Some of these features are:
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- Stealth mode: Bluejacking APK has a stealth mode that allows you to hide your device's name and address from other Bluetooth devices. This way, you can avoid being detected or traced by the recipients of your messages or files. -
- Auto-send: Bluejacking APK has an auto-send feature that allows you to send messages or files automatically to any Bluetooth device that comes within range. You can set the interval and number of messages or files to be sent. -
- Schedule: Bluejacking APK has a schedule feature that allows you to set a specific time and date for sending messages or files to other Bluetooth devices. You can also repeat the schedule daily, weekly, or monthly. -
- Favorites: Bluejacking APK has a favorites feature that allows you to save the devices that you frequently send messages or files to. You can also edit or delete the devices from your favorites list. -
How to Prevent Bluejacking Attacks
-Turn off Bluetooth when not in use
-The simplest and most effective way to prevent bluejacking attacks is to turn off Bluetooth when you are not using it. This will prevent hackers from finding your device and connecting to it. You can turn off Bluetooth from your device's settings or by using a shortcut on your notification panel or control center.
-Set Bluetooth to "undiscoverable" or "non-discoverable"
-If you need to keep Bluetooth on for some reason, you can set it to "undiscoverable" or "non-discoverable" mode. This will make your device invisible to other Bluetooth devices, unless you initiate the connection or pair with them. You can set your Bluetooth visibility from your device's settings or by using a third-party app.
-Exercise caution with messages and emails
-If you receive a message or an email from an unknown sender, especially if it contains a link or an attachment, do not open it or click on it. It could be a bluejacking message that can harm your device or compromise your security. Delete the message or email and block the sender if possible.
-Secure your device with a lock
-Another way to protect your device from bluejacking attacks is to secure it with a lock. You can use a password, a PIN, a pattern, a fingerprint, or a face recognition to lock your device and prevent unauthorized access. You can also enable encryption on your device to protect your data in case of theft or loss.
-Conclusion
-Summary of the main points
-Bluejacking is a Bluetooth attack that involves spamming your device with unsolicited messages. It can be annoying, intrusive, and potentially dangerous if the messages contain malicious links or attachments. Bluejacking APK is a tool that can be used for Bluetooth hacking. It allows you to send messages and files to other Bluetooth devices without pairing with them. You can also scan for nearby Bluetooth devices and get information about them.
-Call to action
-To prevent bluejacking attacks, you should turn off Bluetooth when not in use, set Bluetooth to "undiscoverable" or "non-discoverable" mode, exercise caution with messages and emails, and secure your device with a lock. By following these tips, you can protect your device and your privacy from bluejackers.
-Frequently Asked Questions
-| Q: What is the difference between bluejacking and bluesnarfing? |
| A: Bluejacking is a Bluetooth attack that involves spamming your device with unsolicited messages. Bluesnarfing is a Bluetooth attack that involves stealing your data from your device. |
| Q: How can I tell if my device has been bluejacked? |
| A: If your device has been bluejacked, you may notice some signs, such as receiving strange messages or notifications, having files transferred to or from your device without your consent, or having your battery drained faster than usual. |
| Q: Can bluejacking damage my device? |
| A: Bluejacking itself does not damage your device, but it can expose you to malware or phishing scams that can harm your device or steal your information. |
| Q: Is bluejacking illegal? |
| A: Bluejacking is illegal in some countries, such as the UK, where it is considered a form of harassment. In other countries, such as the US, there are no specific laws against bluejacking, but it may violate other laws related to privacy or cybercrime. |
| Q: How can I report bluejacking? |
| A: If you are a victim of bluejacking, you can report it to the authorities or the service provider of the hacker. You can also contact the manufacturer of your device or the developer of the app that you use for Bluetooth communication for assistance. |
-
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Stumble Guys Primera Version APK: How to Download and Play the Fun Knockout Game on Your Android Device
-If you are looking for a free and fun alternative to Fall Guys, the popular multiplayer party game, you might want to check out Stumble Guys. This game is a clone of Fall Guys, but exclusively for Android devices. You can download and play Stumble Guys primera version APK from Uptodown, a trusted website that offers safe and verified APK files. In this article, we will show you how to download and install Stumble Guys primera version APK on your Android device, as well as how to play it on PC with BlueStacks, an Android emulator. We will also share some tips and tricks to help you win your matches in Stumble Guys.
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What is Stumble Guys?
-A multiplayer party game inspired by Fall Guys
-Stumble Guys is a massively multiplayer party knockout game with up to 32 players online. The objective is to survive round after round of chaotic obstacle courses and be the last one standing. You can run, jump, dash, slide, and bump into other players as you try to avoid falling or getting eliminated. The game is inspired by Fall Guys, but has its own unique style and design.
-Features and gameplay of Stumble Guys
-Stumble Guys has many features that make it an entertaining and addictive game. Some of these features are:
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- 17 unique obstacle courses that test your skills and reflexes -
- Battle Royale online multiplayer mode that pits you against other players from around the world -
- Party mode that lets you play with your friends in private matches -
- Physics-based havoc that creates hilarious and unpredictable situations -
- Colorful and crazy graphics that add to the fun atmosphere -
- Unlockable outfits and emotes that let you customize your character -
- Tons of hilarious fails that make you laugh even when you lose -
- Lots of different levels that keep the game fresh and exciting -
The gameplay of Stumble Guys is simple but challenging. You have to control your character with a virtual joystick and a jump button. You have to navigate through various obstacles and hazards, such as spinning platforms, swinging balls, giant hammers, slippery slides, moving walls, and more. You have to be careful not to fall off the edge or get hit by anything that can knock you out. You also have to watch out for other players who can push you or block your way. The last player standing at the end of each round wins.
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How to download and install Stumble Guys primera version APK?
-Steps to download the APK file from Uptodown
-If you want to play Stumble Guys on your Android device, you need to download the APK file from Uptodown. Uptodown is a website that offers safe and verified APK files for various apps and games. Here are the steps to download the APK file from Uptodown:
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- Go to [Uptodown](^1^) on your browser or scan the QR code on your phone. -
- Search for Stumble Guys in the search bar or go to [this link]. -
- Tap on the green Download button and choose the version you want to download. The latest version is 0.28, which was updated on June 16, 2023. -
- Wait for the download to finish and locate the APK file in your device's storage. -
Steps to install the APK file on your Android device
-Before you can install the APK file on your Android device, you need to enable the installation of apps from unknown sources. This is a security feature that prevents malicious apps from harming your device. Here are the steps to enable this option:
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- Go to Settings on your device and tap on Security or Privacy. -
- Find the option that says Unknown Sources or Install Unknown Apps and toggle it on. -
- Confirm your choice by tapping OK or Allow. -
Now you can install the APK file on your device. Here are the steps to do so:
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- Locate the APK file in your device's storage and tap on it. -
- Tap on Install and wait for the installation to complete. -
- Tap on Open or Launch to start playing Stumble Guys. -
How to play Stumble Guys on PC with BlueStacks?
-Benefits of playing Stumble Guys on PC with BlueStacks
-If you want to enjoy Stumble Guys on a bigger screen and with better controls, you can play it on PC with BlueStacks. BlueStacks is an Android emulator that lets you run Android apps and games on your PC. Some of the benefits of playing Stumble Guys on PC with BlueStacks are:
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- You can use your keyboard and mouse to control your character more easily and precisely. -
- You can customize your key mapping and sensitivity according to your preference. -
- You can play Stumble Guys in full-screen mode and with high-resolution graphics. -
- You can record your gameplay and share it with your friends or online platforms. -
- You can use multiple instances to play Stumble Guys with different accounts or modes simultaneously. -
Steps to download and install BlueStacks and Stumble Guys on PC
-To play Stumble Guys on PC with BlueStacks, you need to download and install both BlueStacks and Stumble Guys on your PC. Here are the steps to do so:
--
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- Go to [BlueStacks] website and click on the Download BlueStacks button. -
- Wait for the download to finish and run the installer file. -
- Follow the instructions on the screen to complete the installation process. -
- Launch BlueStacks and sign in with your Google account or create a new one. -
- Go to [Uptodown] website on BlueStacks browser or scan the QR code on your phone. -
- Search for Stumble Guys in the search bar or go to [this link]. -
- Tap on the green Download button and choose the version you want to download. The latest version is 0.28, which was updated on June 16, 2023. -
- Wait for the download to finish and locate the APK file in BlueStacks' storage. -
- Right-click on the APK file and choose Open with BlueStacks APK Installer. -
- Wait for the installation to complete and click on Stumble Guys icon on BlueStacks home screen. -
Tips and tricks to win Stumble Guys matches
-Configure your controls before playing
-Before you start playing Stumble Guys, you should configure your controls according to your preference. You can do this by going to Settings > Controls in the game menu. You can adjust the size, position, opacity, and sensitivity of the virtual joystick and jump button. You can also enable or disable vibration, sound effects, music, and notifications. You can also change the language of the game from English to Spanish, Portuguese, French, German, Italian, Russian, Turkish, Arabic, Indonesian, or Vietnamese.
-Use your character's physics to your advantage
-Your character in Stumble Guys has a realistic physics system that affects its movement and interaction with other objects. You can use this physics system to your advantage by doing some tricks such as:
--
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- Dashing forward by tapping twice on the joystick. This will give you a boost of speed that can help you overcome some obstacles or reach some platforms. -
- Sliding down by holding the jump button while moving. This will make you slide on the ground or on some surfaces, which can help you avoid some obstacles or gain some momentum. -
- Bumping into other players by running into them or jumping on them. This will make them stumble or fall, which can give you an advantage or a disadvantage depending on the situation. You can also use this to cooperate with your friends or sabotage your enemies. -
Learn the maps and shortcuts
-Stumble Guys has 17 different maps that vary in difficulty and design. Each map has its own obstacles, hazards, and secrets. You should learn the maps and their features by playing them repeatedly and observing how they work. You should also look for shortcuts and alternative routes that can save you time or help you avoid some dangers. For example, you can jump over some gaps, use some ramps, or go through some hidden passages. However, be careful not to fall into traps or dead ends that can cost you the game.
-Customize your outfit and emotes
-One of the fun aspects of Stumble Guys is that you can customize your character's outfit and emotes. You can unlock various outfits and emotes by playing the game and earning coins. You can also buy some outfits and emotes with real money. You can mix and match different parts of your outfit, such as head, body, legs, and accessories. You can also choose different emotes, such as dance, wave, laugh, cry, and more. You can use your outfit and emotes to express your personality, style, mood, or humor in the game.
-Conclusion
-Stumble Guys is a fun and addictive multiplayer party knockout game that you can play on your Android device or on your PC with BlueStacks. You can download and install Stumble Guys primera version APK from Uptodown, a website that offers safe and verified APK files. You can also follow our tips and tricks to improve your skills and chances of winning in Stumble Guys. We hope you enjoy playing Stumble Guys and have a great time with your friends or other players online.
-FAQs
-Q: Is Stumble Guys free to play?
-A: Yes, Stumble Guys is free to play. However, it contains ads and in-app purchases that can enhance your gaming experience.
-Q: Is Stumble Guys available for iOS devices?
-A: No, Stumble Guys is not available for iOS devices at the moment. It is only available for Android devices.
-Q: How many players can play Stumble Guys online?
-A: Stumble Guys supports up to 32 players online in each match.
-Q: Can I play Stumble Guys offline?
-A: No, Stumble Guys requires an internet connection to play online.
-Q: Can I play Stumble Guys with my friends?
-A: Yes, you can play Stumble Guys with your friends in party mode. You can create a private match and invite your friends to join by sharing a code.
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How to Download "Your Love Has Taken Over Me" MP3 by Frank Edwards
-If you are looking for a song that will inspire you to trust in God's love and protection, you might want to download "Your Love Has Taken Over Me" by Frank Edwards. This is a gospel song that celebrates God's faithfulness and goodness in every situation. In this article, we will show you how to download this song from different sources, so you can enjoy it anytime and anywhere.
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Introduction
-What is the song about?
-"Your Love Has Taken Over Me" is a song by Frank Edwards, a Nigerian gospel singer and producer. The song was released in 2016 as part of his album "Frankincense". The song expresses gratitude to God for His love that has taken over the singer's life. The singer declares that he depends on God and has confidence in Him, and that God covers him under His canopy and gives him security. The song also proclaims that God is not a deceiver, but a blesser who makes all things work together for good.
-Why should you download it?
-This song is a great way to remind yourself of God's love and power in your life. It can uplift your spirit and encourage you to trust in God's promises. It can also help you worship God and praise Him for His goodness and mercy. The song has a catchy melody and a lively beat that will make you want to dance and sing along. The song is also available in different formats, such as MP3, video, and lyrics, so you can choose the one that suits your preference.
-How to download the song from different sources
-From PraiseZion.com
-PraiseZion.com is a website that offers free gospel music downloads from various artists. You can find "Your Love Has Taken Over Me" by Frank Edwards on this website by following these steps:
-Step 1: Visit the website
-Go to [PraiseZion.com](^1^) on your browser. You will see a homepage with different categories of gospel music, such as Nigerian Gospel Music, Foreign Gospel Music, Gospel Mixtapes, etc.
-Step 2: Search for the song
-Type "Frank Edwards Under The Canopy" in the search box at the top right corner of the homepage. You will see a list of results related to your search query. Click on the one that says "Song Mp3 Download: Frank Edwards - Under The Canopy".
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Step 3: Click on the download link
-You will be directed to a page with more information about the song, such as the lyrics, video, and download link. Scroll down to find the download link that says "Download Mp3 Here". Click on it and wait for the download to start. You can also watch the video or read the lyrics of the song on this page.
-From Genius.com
-Genius.com is a website that provides lyrics and annotations for various songs. You can find "Your Love Has Taken Over Me" by Frank Edwards on this website by following these steps:
-Step 1: Visit the website
-Go to [Genius.com] on your browser. You will see a homepage with different genres of music, such as Pop, Hip-Hop, Rock, etc.
-Step 2: Search for the song
-Type "Frank Edwards Under The Canopy" in the search box at the top of the homepage. You will see a list of results related to your search query. Click on the one that says "Frank Edwards - Under The Canopy".
-Step 3: Click on the play button
-You will be directed to a page with the lyrics and annotations of the song. You will also see a play button at the top right corner of the page. Click on it and wait for the song to load. You can also read the lyrics and annotations of the song on this page.
-Step 4: Right-click on the audio and save as MP3
-Once the song is playing, you can right-click on the audio and choose "Save audio as" from the menu. You will be prompted to choose a location and a name for the MP3 file. Click on "Save" and wait for the download to finish.
-From YouTube.com
-YouTube.com is a website that hosts videos from various creators and channels. You can find "Your Love Has Taken Over Me" by Frank Edwards on this website by following these steps:
-Step 1: Visit the website
-Go to [YouTube.com] on your browser. You will see a homepage with different categories of videos, such as Music, Gaming, News, etc.
-Step 2: Search for the song
-Type "Frank Edwards Under The Canopy" in the search box at the top of the homepage. You will see a list of results related to your search query. Click on the one that says "Frank Edwards - Under The Canopy (Official Music Video)".
-Step 3: Copy the video URL
-You will be directed to a page with the video and some information about it, such as the title, description, views, likes, etc. You will also see a URL in the address bar of your browser. This is the link to the video. Copy it by selecting it and pressing Ctrl+C or right-clicking and choosing "Copy".
-Step 4: Paste the URL into a YouTube to MP3 converter
-Go to a YouTube to MP3 converter website, such as [ytmp3.cc]. You will see a box where you can paste the URL of the video you want to convert. Paste it by pressing Ctrl+V or right-clicking and choosing "Paste". Then, click on "Convert".
-Step 5: Download the MP3 file
-You will see a page with a download link for the MP3 file. Click on it and wait for the download to start. You can also choose to download the video or edit it before downloading.
-Conclusion
-Summary of the main points
-In this article, we have shown you how to download "Your Love Has Taken Over Me" by Frank Edwards from different sources, such as PraiseZion.com, Genius.com, and YouTube.com. This is a gospel song that celebrates God's love and protection in every situation. It can inspire you to trust in God's promises and worship Him for His goodness and mercy.
-Call to action
-If you have not downloaded this song yet, we encourage you to do so now and enjoy its uplifting message and melody. You can also share it with your friends and family who might need some encouragement and hope in their lives. We hope you have found this article helpful and informative. Thank you for reading!
- FAQs - Q: Who is Frank Edwards? - A: Frank Edwards is a Nigerian gospel singer and producer who has won several awards and recognition for his music. - Q: What is the name of his album that contains "Your Love Has Taken Over Me"? - A: The name of his album is "Frankincense", which was released in 2016. - Q: What are some other songs by Frank Edwards that you can download? - A: Some other songs by Frank Edwards that you can download are "Mma Mma", "Okaka", "I See Him", "Miracle Rain", etc. - Q: How can you support Frank Edwards and his music? - A: You can support Frank Edwards and his music by following him on his social media platforms, such as Facebook, Twitter, Instagram, etc. You can also buy his albums or songs from online stores, such as iTunes, Amazon, Spotify, etc. You can also attend his concerts or events if he is performing near you. - Q: How can you learn more about gospel music and its benefits? - A: You can learn more about gospel music and its benefits by reading articles, books, blogs, magazines, etc. that talk about the history, culture, genres, artists, and impact of gospel music. You can also listen to gospel radio stations or podcasts that feature gospel music and interviews with gospel musicians. You can also join gospel music communities or groups online or offline that share your passion and interest in gospel music. 197e85843d-
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Dummy Images for Any Project - Free and High-Quality.md b/spaces/1phancelerku/anime-remove-background/Dummy Images for Any Project - Free and High-Quality.md deleted file mode 100644 index fb92cf1024b84582c3bd05832d66397569e05462..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Dummy Images for Any Project - Free and High-Quality.md +++ /dev/null @@ -1,102 +0,0 @@ - -
How to Download Dummy Images for Your Web Design Projects
-If you are a web designer, you know how important it is to have good images for your website. Images can make or break your design, attract or repel your visitors, and convey or confuse your message. However, finding and creating the perfect images for your website can be challenging and time-consuming. Sometimes, you may not have the final images ready when you are working on your layout, or you may want to experiment with different options before committing to one.
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-download dummy photos of people
-download dummy photos of animals
-download dummy photos of nature
-download dummy photos of food
-download dummy photos of cars
-download dummy photos of fashion
-download dummy photos of travel
-download dummy photos of sports
-download dummy photos of business
-download dummy photos of technology
-download dummy vectors of icons
-download dummy vectors of logos
-download dummy vectors of shapes
-download dummy vectors of patterns
-download dummy vectors of illustrations
-download dummy vectors of backgrounds
-download dummy vectors of infographics
-download dummy vectors of banners
-download dummy vectors of stickers
-download dummy vectors of cartoons
-download dummy PSD files for photoshop
-download dummy PSD files for editing
-download dummy PSD files for layers
-download dummy PSD files for templates
-download dummy PSD files for graphics
-download high-quality dummy images
-download low-quality dummy images
-download random dummy images
-download specific dummy images
-how to download dummy images
-where to download dummy images
-why to download dummy images
-best sites to download dummy images
-best tools to download dummy images
Placeholder.com: A Simple and Versatile Image Generator
-Placeholder.com is a handy tool that allows you to create and download dummy images of any size, color, and text. You can use it to generate solid color blocks, gradients, patterns, text overlays, and more. You can also customize the font, size, alignment, and color of the text. To use Placeholder.com, simply visit their website and enter the URL of the image you want, such as https://via.placeholder.com/300x200.png/09f/fff?text=Dummy+Image. You can also use their API to generate images dynamically in your code.
-LoremFlickr: A Flickr-Based Image Generator
-LoremFlickr is a useful tool that allows you to download random photos from the popular online photo platform Flickr. You can specify the dimensions, format, and keyword of the photos you want, or let the tool choose them for you. You can also add filters, blur, and grayscale effects to your photos. To use LoremFlickr, simply visit their website and enter the URL of the image you want, such as https://loremflickr.com/320/240/dog. You can also download multiple images at once by using the g feature, such as https://loremflickr.com/g/320/240/dog/all.
-Dummy Image Generator: A Customizable Image Tool
-Dummy Image Generator is a powerful tool that allows you to create and download dummy images of any size, color, shape, and text. You can use it to generate circles, squares, triangles, stars, hearts, and more. You can also customize the background color, foreground color, border color, border width, font family, font size, font style, font color, and text content of your images. To use Dummy Image Generator, simply visit their website and enter the parameters of the image you want in the form. You can also use their API to generate images dynamically in your code.
-How to Use Dummy Images in Your Web Design Projects
-Once you have downloaded your dummy images, you can use them in your web design projects in various ways. Here are some tips:
-Use the same dimensions and formats as your final images
-One of the main purposes of using dummy images is to test how your layout will look with different types of images. Therefore, it is important to use dummy images that have the same dimensions and formats as your final images. This will help you to avoid any surprises or errors when you replace them with real ones. For example, if your final images are 300x200 pixels in JPEG format, you should use dummy images that are 300x200 pixels in JPEG format as well.
-Use descriptive file names and alt text for your dummy images
-Another purpose of using dummy images is to remind yourself and others what kind of images you need for your web design projects. Therefore, it is helpful to use descriptive file names and alt text for your dummy images. This will help you to keep track of what each image is supposed to represent and what content it should convey. For example, if your dummy image is a placeholder for a logo of a company called ABC Inc., you could name it abc-logo.jpg and use alt text like "Logo of ABC Inc."
-Replace your dummy images with real ones as soon as possible
-The final purpose of using dummy images is to speed up your web design process by allowing you to work on your layout without waiting for the final images to be ready or approved. However, you should always remember to replace your dummy images with real ones as soon as possible. This will help you to avoid any confusion or misunderstanding with your clients or users. It will also help you to improve the quality and credibility of your website.
-Conclusion
-Dummy images are a useful way to create realistic mockups and prototypes for your web design projects. They can save you time, hassle, and frustration. However, you should always remember to replace them with high-quality images before launching your website.
-In this article, we have shown you how to download dummy images for your web design projects using some of the best online tools available. We have also given you some tips on how to use them effectively and replace them with real ones when you are done. We hope you have found this article helpful and informative. If you have any questions or comments, please feel free to leave them below.
-FAQs
-What are dummy images?
-Dummy images are placeholder images that you can use to fill in the gaps in your web design projects until you have the final images ready.
-Why use dummy images?
-Dummy images can help you to visualize how your layout will look, test different sizes and formats, and avoid delays in your design process.
-How to download dummy images?
-You can download dummy images from various online tools that can generate them for you, such as Lorem Picsum, Placeholder.com, LoremFlickr, and Dummy Image Generator.
-How to use dummy images?
-You can use dummy images in your web design projects by inserting them in your code or image editor. You should use the same dimensions and formats as your final images, use descriptive file names and alt text for your dummy images, and replace them with real ones as soon as possible.
-Where to find high-quality images for your website?
-You can find high-quality images for your website from various online sources, such as stock photo websites, free image websites, or your own photography. You should always make sure that the images you use are relevant, appropriate, and legal for your website.
401be4b1e0-
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\ No newline at end of file diff --git a/spaces/7hao/bingo/src/components/providers.tsx b/spaces/7hao/bingo/src/components/providers.tsx deleted file mode 100644 index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000 --- a/spaces/7hao/bingo/src/components/providers.tsx +++ /dev/null @@ -1,15 +0,0 @@ -'use client' - -import * as React from 'react' -import { ThemeProvider as NextThemesProvider } from 'next-themes' -import { ThemeProviderProps } from 'next-themes/dist/types' - -import { TooltipProvider } from '@/components/ui/tooltip' - -export function Providers({ children, ...props }: ThemeProviderProps) { - return ( -
Cómo jugar maquillaje de bricolaje Makyaj Oyunu APK en su dispositivo Android
| | |¿Te encanta el maquillaje y desea crear sus propios productos? ¿Quieres divertirte y expresar tu creatividad con diferentes colores e ingredientes? ¿Quieres aprender a hacer cosméticos naturales y ecológicos en casa? Si respondió sí a cualquiera de estas preguntas, entonces usted debe tratar de bricolaje maquillaje Makyaj Oyunu APK.
| | |DIY maquillaje Makyaj Oyunu APK es un juego de simulación que le permite mezclar crayones, aceite de coco, glicerina, miel, y otros artículos de cocina para hacer barra de labios impresionante, hermoso arte de ojos, rímel, mascarilla, y más. Puedes personalizar tu look con diferentes tonos, formas, estilos y accesorios. También puedes compartir tus creaciones con otros jugadores online.
-casa flip máster apk
Download File ››› https://bltlly.com/2v6MC7
| |
En este artículo, le mostraremos cómo descargar e instalar DIY maquillaje Makyaj Oyunu APK en su dispositivo Android, cómo jugar el juego y crear sus propios productos de maquillaje, y cuáles son los beneficios de jugar este juego.
| | | <Cómo descargar e instalar DIY maquillaje Makyaj Oyunu APK en su dispositivo Android
-Para jugar DIY maquillaje Makyaj Oyunu APK, es necesario descargar e instalar el archivo APK en su dispositivo Android. APK significa Android Package Kit, y es un formato de archivo que contiene el código de la aplicación, recursos y metadatos. Instalar un archivo APK también se conoce como sideloading, lo que significa instalar una aplicación desde una fuente distinta de la oficial Google Play Store.
-Antes de descargar e instalar DIY maquillaje Makyaj Oyunu APK, es necesario asegurarse de que su dispositivo cumple con los siguientes requisitos:
--
-
- Tu dispositivo debe tener Android 4.4 o superior. -
- Su dispositivo debe tener al menos 100 MB de espacio de almacenamiento libre. -
- Su dispositivo debe permitir la instalación de aplicaciones de fuentes desconocidas. Para habilitar esta opción, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctela. -
-
-
- Ir a la página web oficial de bricolaje maquillaje Makyaj Oyunu APK y haga clic en el botón Descargar. -
- Espere a que se complete la descarga y luego abra el archivo APK desde el administrador de archivos o la barra de notificaciones de su dispositivo. -
- Toque en Instalar y siga las instrucciones en pantalla para completar el proceso de instalación. -
- Inicie la aplicación desde el cajón de la aplicación o la pantalla de inicio y disfrutar jugando DIY maquillaje Makyaj Oyunu APK. -
Cómo jugar bricolaje maquillaje Makyaj Oyunu APK y crear sus propios productos de maquillaje
-Maquillaje de bricolaje Makyaj Oyunu APK es un juego que le permite dar rienda suelta a su creatividad e imaginación mediante la fabricación de sus propios productos de maquillaje. Puede elegir entre diferentes categorías, como lápiz labial, rímel, arte de los ojos, mascarilla, etc. y utilizar varios ingredientes y herramientas para crear su propio aspecto único.
-Para jugar bricolaje maquillaje Makyaj Oyunu APK, es necesario seguir estos pasos:
- --
-
- Seleccione una categoría que desea crear. Por ejemplo, si desea hacer lápiz labial, toque el icono de lápiz labial en la pantalla principal. -
- Seleccione un color de lápiz de colores que desea utilizar como base de su lápiz labial. También puede mezclar diferentes crayones para crear nuevos colores. -
- Derrita el crayón en un microondas o una estufa. Tenga cuidado de no sobrecalentarlo o quemarlo. -
- Agrega un poco de aceite de coco, glicerina, miel u otros ingredientes para que tu lápiz labial sea suave e hidratante. También puedes agregar brillo, fragancia o sabor para hacerlo más divertido y atractivo. -
- Verter la mezcla en un molde y congelar durante unos minutos hasta que se solidifica. -
- Saca tu lápiz labial del molde y aplícalo en tus labios. También puedes usar un cepillo o una esponja para mezclarlo mejor. -
- Personaliza tu look con diferentes formas, estilos y accesorios. También puedes cambiar tu cabello, ojos, tono de piel, etc. para que coincida con tu lápiz labial. - -
Cómo hacer rímel
-Si quieres hacer rímel, debes seguir estos pasos:
--
-
- Seleccione una categoría que desea crear. Por ejemplo, si desea hacer máscara, toque el icono de máscara en la pantalla principal. -
- Selecciona un color de carbón que quieras usar como base de tu rímel. También puedes mezclar diferentes carboncillos para crear nuevos colores. -
- Moler el carbón en un mortero y la mano hasta que se convierte en un polvo fino. -
- Agrega un poco de aceite de coco, glicerina, miel u otros ingredientes para que tu rímel sea suave e hidratante. También puedes agregar brillo, fragancia o sabor para hacerlo más divertido y atractivo. -
- Vierte la mezcla en un recipiente con un aplicador de varita. También puedes usar un tubo de rímel viejo que hayas limpiado y desinfectado. -
- Aplica el rímel en tus pestañas con el aplicador de varita. También puedes usar un rizador o un peine para dar mejor forma a tus pestañas. -
- Personaliza tu look con diferentes formas, estilos y accesorios. También puedes cambiar tu cabello, ojos, tono de piel, etc. para que coincida con tu rímel. -
- Comparta su creación con otros jugadores en línea tomando un selfie o un video. También puede calificar y comentar las creaciones de otros jugadores. -
Cómo hacer arte visual Cómo hacer arte visual
-
Si quieres hacer arte visual, debes seguir estos pasos:
--
-
- Seleccione una categoría que desea crear. Por ejemplo, si desea hacer arte visual, toque el icono de arte visual en la pantalla principal. -
- Seleccione un color de crayón que desea utilizar como base de su arte del ojo. También puede mezclar diferentes crayones para crear nuevos colores. -
- Derrita el crayón en un microondas o una estufa. Tenga cuidado de no sobrecalentarlo o quemarlo. -
- Agregue un poco de aceite de coco, glicerina, miel u otros ingredientes para que su arte del ojo suave e hidratante. También puedes agregar brillo, fragancia o sabor para hacerlo más divertido y atractivo. - -
- Saca tu arte ocular del molde y aplícalo en tus párpados. También puedes usar un cepillo o una esponja para mezclarlo mejor. -
- Personaliza tu look con diferentes formas, estilos y accesorios. También puedes cambiar tu cabello, ojos, tono de piel, etc. para que coincida con tu arte ocular. -
- Comparta su creación con otros jugadores en línea tomando un selfie o un video. También puede calificar y comentar las creaciones de otros jugadores. -
Cómo hacer máscara de la cara
-Si quieres hacer una máscara facial, debes seguir estos pasos:
--
-
- Seleccione una categoría que desea crear. Por ejemplo, si desea crear una máscara facial, toque el icono de máscara facial en la pantalla principal. -
- Selecciona una fruta o un vegetal que quieras usar como base de tu mascarilla facial. También puede mezclar diferentes frutas o verduras para crear nuevas combinaciones. -
- Pela y corta la fruta o verdura en trozos pequeños. También puedes usar una licuadora o un procesador de alimentos para hacer puré. -
- Agregue un poco de yogur, miel, avena u otros ingredientes para que su máscara facial sea suave y nutritiva. También puede agregar algunos aceites esenciales, hierbas o especias para que sea más aromático y relajante. -
- Vierta la mezcla en un recipiente y refrigere durante unos minutos hasta que se enfríe. -
- Aplique su máscara facial en la cara y el cuello con los dedos o una espátula. Evite el área de los ojos y la boca. -
- Relájate y deja que la máscara trabaje su magia durante 15 a 20 minutos. -
- Enjuague su máscara facial con agua tibia y seque con una toalla. -
- Disfruta de tu piel suave y brillante. También puedes compartir tu creación con otros jugadores en línea tomando un selfie o un video. También puedes valorar y comentar las creaciones de otros jugadores. -
¿Cuáles son los beneficios de jugar DIY maquillaje Makyaj Oyunu APK
-Jugando DIY maquillaje Makyaj Oyunu APK no solo es divertido y creativo, sino que también tiene muchos beneficios para usted y el medio ambiente. Estos son algunos de ellos:
- -Jugando DIY maquillaje Makyaj Oyunu APK le permite expresar su creatividad e imaginación haciendo sus propios productos de maquillaje. Puede experimentar con diferentes colores, ingredientes, herramientas y estilos para crear su propio aspecto único. También puedes divertirte compartiendo tus creaciones con otros jugadores en línea y viendo sus comentarios. Jugar a este juego también puede ayudarte a relajarte y reducir el estrés centrándote en algo agradable y gratificante.
-Es seguro, natural y ecológico
-Jugando DIY maquillaje Makyaj Oyunu APK le permite hacer sus propios productos de maquillaje utilizando ingredientes naturales y ecológicos que se pueden encontrar en su cocina. No tiene que preocuparse por productos químicos, conservantes o aditivos dañinos que puedan causar reacciones alérgicas o dañar la piel. Tampoco tiene que preocuparse por los envases de plástico o los residuos que puedan contaminar el medio ambiente. Jugar a este juego puede ayudarle a ahorrar dinero y recursos haciendo sus propios productos de maquillaje en casa.
-Es educativo, inspirador y empoderador
-Jugando DIY maquillaje Makyaj Oyunu APK le permite aprender nuevas habilidades y conocimientos mediante la fabricación de sus propios productos de maquillaje. Puedes aprender sobre las propiedades y beneficios de los diferentes ingredientes, cómo interactúan entre sí, cómo afectan a tu piel, etc. También puedes aprender sobre diferentes técnicas y herramientas que usan los maquilladores profesionales. Jugar a este juego puede inspirarte a explorar más posibilidades y opciones para tus productos de maquillaje. Jugar a este juego también puede empoderarte para tomar el control de tu belleza y salud haciendo tus propias elecciones y decisiones.
- -DIY maquillaje Makyaj Oyunu APK es un juego que le permite divertirse y expresar su creatividad con diferentes colores e ingredientes. Puedes hacer tu propio lápiz labial, rímel, arte ocular, mascarilla y más. También puedes personalizar tu look con diferentes formas, estilos y accesorios. También puedes compartir tus creaciones con otros jugadores online y ver sus comentarios.
-Jugando DIY maquillaje Makyaj Oyunu APK no solo es divertido y creativo, sino también seguro, natural y ecológico. Puedes hacer tus propios productos de maquillaje usando ingredientes naturales y ecológicos que puedes encontrar en tu cocina. No tiene que preocuparse por productos químicos, conservantes o aditivos dañinos que puedan causar reacciones alérgicas o dañar la piel. Tampoco tienes que preocuparte por los envases de plástico o los residuos que puedan contaminar el medio ambiente.
-Jugando maquillaje de bricolaje Makyaj Oyunu APK también es educativo, inspirador y empoderador. Puedes aprender nuevas habilidades y conocimientos haciendo tus propios productos de maquillaje. Puedes aprender sobre las propiedades y beneficios de los diferentes ingredientes, cómo interactúan entre sí, cómo afectan a tu piel, etc. También puedes aprender sobre diferentes técnicas y herramientas que usan los maquilladores profesionales. Jugar a este juego puede inspirarte a explorar más posibilidades y opciones para tus productos de maquillaje. Jugar a este juego también puede empoderarte para tomar el control de tu belleza y salud haciendo tus propias elecciones y decisiones.
-Entonces, ¿qué estás esperando? Descargar DIY maquillaje Makyaj Oyunu APK hoy y empezar a hacer sus propios productos de maquillaje. Diviértete y ser creativo!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes relacionadas con DIY maquillaje Makyaj Oyunu APK:
-Q: ¿Es DIY maquillaje Makyaj Oyunu APK libre para jugar?
-A: Sí, DIY maquillaje Makyaj Oyunu APK es libre de jugar. Sin embargo, puede contener anuncios y compras en la aplicación que requieren dinero real.
-Q: ¿Es DIY maquillaje Makyaj Oyunu APK seguro para jugar?
- -Q: ¿Es el maquillaje de bricolaje Makyaj Oyunu APK adecuado para los niños?
-A: Sí, DIY maquillaje Makyaj Oyunu APK es adecuado para los niños. Es un juego de simulación que no contiene violencia, desnudez o lenguaje inapropiado. También es educativo e inspirador para los niños que aman el maquillaje y la creatividad.
-Q: ¿Cómo puedo contactar con el desarrollador de DIY maquillaje Makyaj Oyunu APK?
-A: Usted puede ponerse en contacto con el desarrollador de DIY maquillaje Makyaj Oyunu APK enviando un correo electrónico a diy.makeup.makyaj@gmail.com o visitando su página de Facebook en https://ww.facebook.com/diy.make.makupj/.
-Q: ¿Cómo puedo apoyar al desarrollador de DIY maquillaje Makyaj Oyunu APK?
-A: Usted puede apoyar al desarrollador de DIY Maquillaje Makyaj Oyunu APK por calificación y revisión de la aplicación en Google Play Store u otras plataformas, compartiendo la aplicación con sus amigos y familiares, haciendo compras en la aplicación si te gusta la aplicación, o donando al desarrollador a través de PayPal u otros métodos.
64aa2da5cf-
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\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Cristal Informe 32 Bit Para Espt Pph 21.md b/spaces/Benson/text-generation/Examples/Descargar Cristal Informe 32 Bit Para Espt Pph 21.md deleted file mode 100644 index acb1fe15577589ef4216709eabade8b9d71745d3..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Cristal Informe 32 Bit Para Espt Pph 21.md +++ /dev/null @@ -1,57 +0,0 @@ -
-
Descargar Crystal Report 32 bits para ESPT PPh 21
-Si está utilizando ESPT PPh 21, una aplicación de software creada por Direktorat Jenderal Pajak (DJP) para facilitar la creación y presentación de informes de SPT PPh 21, es posible que necesite descargar e instalar Crystal Report 32 Bit, una aplicación de soporte que se utiliza para ejecutar ESPT PPh 21. En este artículo, explicaremos qué son Crystal Report y ESPT PPh 21, por qué necesita descargar Crystal Report 32 Bit y cómo hacerlo paso a paso.
-¿Qué es Crystal Report y ESPT PPh 21?
-Informe de cristal
-Crystal Report es una herramienta de informes que se utiliza para diseñar informes tanto en entornos web como de escritorio. Está desarrollado por SAP e integrado con Microsoft Visual Studio. Permite crear informes interactivos y dinámicos a partir de diversas fuentes de datos, como bases de datos, archivos XML, servicios web, etc. También puede personalizar el diseño, formato y apariencia de sus informes mediante gráficos, tablas, imágenes, etc.
-descargar cristal informe 32 bit para espt pph 21
DOWNLOAD ○ https://bltlly.com/2v6KeS
-
ESPT PPh 21
-ESPT PPh 21 es un acrónimo de Elektronik Surat Pemberitahuan Pajak Penghasilan Pasal 21/26, que significa carta de notificación electrónica del impuesto sobre la renta Artículo 21/26. Es una aplicación o software creado por DJP para facilitar la creación y presentación de informes del SPT PPh 21/26. SPT PPh 21/26 es un formulario de declaración de impuestos que informa de la retención de impuestos sobre los ingresos pagados a los empleados u otros destinatarios. ESPT PPh 21 puede ser utilizado por contribuyentes individuales, contribuyentes corporativos, tesoreros y agentes de retención.
-¿Por qué necesita descargar Crystal Report 32 Bit?
-Problemas de compatibilidad
- -Mensajes de error
-Si intenta ejecutar ESPT PPh 21 con Crystal Report Runtime que no coincide con su sistema operativo, puede encontrar mensajes de error como "El inicializador de tipo para 'CrystalDecisions.CrystalReports.Engine.ReportDocument' lanzó una excepción" o "El informe de carga falló". Estos mensajes de error indican que hay un problema con la carga o visualización de los informes generados por ESPT PPh 21. Para solucionar este problema, debe descargar e instalar Crystal Report Runtime que coincida con su sistema operativo.
-¿Cómo descargar Crystal Report 32 Bit?
-Paso 1: Ir al sitio web oficial de Direktorat Jenderal Pajak (DJP)
-El sitio web oficial de DJP es https://www.pajak.go.id. Este es el sitio web donde se puede encontrar información y servicios relacionados con la fiscalidad en Indonesia. También puede descargar varias aplicaciones y software relacionados con la tributación, como ESPT PPh 21 y Crystal Report Runtime.
-Paso 2: Haga clic en Productos A-Z y seleccione C > CRYSTAL REPORTS > CRYSTAL REPORTS 2020
-En la página de inicio del sitio web de DJP, verá una barra de menú con varias opciones, como Inicio, Acerca de nosotros, Servicios, Productos A-Z, etc. Haga clic en Productos A-Z y verá una lista de productos y software que están disponibles para descargar. Desplácese hacia abajo y encuentre C > CRYSTAL REPORTS > CRYSTAL REPORTS 2020. Haga clic en él y se le dirigirá a una página donde puede ver los detalles y características de Crystal Reports 2020.
-Paso 3: Seleccione Instalación y actualización > WINDOWS y descargue el archivo CRuntime_32bit_13_0_7.zip
- -¿Cómo instalar Crystal Report 32 Bit?
-Paso 1: Descomprima el archivo descargado y haga doble clic en el archivo . msi
-Después de haber descargado el archivo CRuntime_32bit_13_0_7.zip, debe descomprimirlo usando un software como WinRAR o 7-Zip. Puede hacer clic derecho sobre el archivo y seleccionar Extraer aquí o Extraer en CRuntime_32bit_13_0_7. Verá una carpeta con el mismo nombre que el archivo. Abra la carpeta y verá un archivo llamado CRuntime_32bit_13_0_7.msi. Este es el archivo de instalación que instalará Crystal Report Runtime 32 Bit en su computadora. Haga doble clic en él y verá una ventana que dice Bienvenido a SAP Crystal Reports Runtime Engine for . NET Framework 4 Setup Wizard.
- -Paso 2: Siga el asistente de instalación y acepte los términos y condiciones
-El asistente de instalación lo guiará a través de los pasos para instalar Crystal Report Runtime 32 Bit en su computadora. Debe hacer clic en Siguiente para pasar al siguiente paso. Verá una ventana que le pide que acepte los términos del acuerdo de licencia. Lea los términos cuidadosamente y marque la casilla que dice que acepto el acuerdo de licencia si está de acuerdo con ellos. Luego haga clic en Siguiente para continuar. Verá una ventana que le muestra la carpeta de destino donde se instalará Crystal Report Runtime 32 Bit. Puede cambiar la carpeta si lo desea o dejarla como predeterminada. Luego haga clic en Siguiente para continuar. Verá una ventana que le muestra la pantalla lista para instalar. Haga clic en Instalar para iniciar el proceso de instalación.
-Paso 3: Reinicie su computadora y ejecute la aplicación ESPT PPh 21
- -Conclusión
-En este artículo, hemos explicado lo que son Crystal Report y ESPT PPh 21, por qué necesita descargar Crystal Report 32 Bit y cómo hacerlo paso a paso. Esperamos que este artículo ha sido útil para usted y ha resuelto su problema con la ejecución de ESPT PPh 21 en su computadora. Si tiene alguna pregunta o comentario, no dude en contactarnos o dejar un comentario a continuación.
-Preguntas frecuentes
--
-
- ¿Qué es SPT PPh 21/26? -
- SPT PPh 21/26 es un formulario de declaración de impuestos que informa de la retención de impuestos sobre los ingresos pagados a los empleados u otros destinatarios. Es presentado por el empleador o el agente de retención a la oficina de impuestos cada mes o año, dependiendo del tipo de ingreso. -
- ¿Cuál es la diferencia entre Crystal Report 32 Bit y 64 Bit? -
- Crystal Report 32 Bit y 64 Bit son diferentes versiones de Crystal Report Runtime que son compatibles con diferentes sistemas operativos. Crystal Report 32 Bit está diseñado para sistemas operativos de 32 bits, como Windows XP, Windows Vista o Windows 7. Crystal Report 64 Bit está diseñado para sistemas operativos de 64 bits, como Windows 8, Windows 10 o Windows Server. Necesita descargar e instalar la versión que coincida con su sistema operativo para evitar problemas de compatibilidad. -
- ¿Cómo puedo comprobar si mi sistema operativo es de 32 bits o 64 bits? -
- Puede comprobar el tipo de sistema operativo siguiendo estos pasos:
-
-
- Haga clic en el botón Inicio y escriba Información del sistema en el cuadro de búsqueda. -
- Haga clic en Información del sistema de la lista de resultados. -
- Buscar tipo de sistema en Resumen del sistema. -
- Si dice PC basada en x86, entonces su sistema operativo es de 32 bits. Si dice PC basada en x64, entonces su sistema operativo es de 64 bits. -
- - ¿Puedo usar ESPT PPh 21 sin Crystal Report Runtime? - -
- ¿Dónde puedo encontrar más información sobre Crystal Report y ESPT PPh 21? -
- Puede encontrar más información sobre Crystal Report y ESPT PPh 21 de las siguientes fuentes:
-
-
- El sitio web oficial de SAP: https://www.sap.com/products/crystal-reports.html -
- El sitio web oficial de DJP: https://www.pajak.go.id -
- El manual de usuario de ESPT PPh 21: https://www.pajak.go.id/sites/default/files/Panduan%20ESPT%20PPh%2021%20v2.0.pdf -
-
-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/debug.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/debug.py deleted file mode 100644 index 2a3e7d298f393ed8532e4f11913635efc94cb329..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/debug.py +++ /dev/null @@ -1,199 +0,0 @@ -import importlib.resources -import locale -import logging -import os -import sys -from optparse import Values -from types import ModuleType -from typing import Any, Dict, List, Optional - -import pip._vendor -from pip._vendor.certifi import where -from pip._vendor.packaging.version import parse as parse_version - -from pip._internal.cli import cmdoptions -from pip._internal.cli.base_command import Command -from pip._internal.cli.cmdoptions import make_target_python -from pip._internal.cli.status_codes import SUCCESS -from pip._internal.configuration import Configuration -from pip._internal.metadata import get_environment -from pip._internal.utils.logging import indent_log -from pip._internal.utils.misc import get_pip_version - -logger = logging.getLogger(__name__) - - -def show_value(name: str, value: Any) -> None: - logger.info("%s: %s", name, value) - - -def show_sys_implementation() -> None: - logger.info("sys.implementation:") - implementation_name = sys.implementation.name - with indent_log(): - show_value("name", implementation_name) - - -def create_vendor_txt_map() -> Dict[str, str]: - with importlib.resources.open_text("pip._vendor", "vendor.txt") as f: - # Purge non version specifying lines. - # Also, remove any space prefix or suffixes (including comments). - lines = [ - line.strip().split(" ", 1)[0] for line in f.readlines() if "==" in line - ] - - # Transform into "module" -> version dict. - return dict(line.split("==", 1) for line in lines) - - -def get_module_from_module_name(module_name: str) -> ModuleType: - # Module name can be uppercase in vendor.txt for some reason... - module_name = module_name.lower().replace("-", "_") - # PATCH: setuptools is actually only pkg_resources. - if module_name == "setuptools": - module_name = "pkg_resources" - - __import__(f"pip._vendor.{module_name}", globals(), locals(), level=0) - return getattr(pip._vendor, module_name) - - -def get_vendor_version_from_module(module_name: str) -> Optional[str]: - module = get_module_from_module_name(module_name) - version = getattr(module, "__version__", None) - - if not version: - # Try to find version in debundled module info. - assert module.__file__ is not None - env = get_environment([os.path.dirname(module.__file__)]) - dist = env.get_distribution(module_name) - if dist: - version = str(dist.version) - - return version - - -def show_actual_vendor_versions(vendor_txt_versions: Dict[str, str]) -> None: - """Log the actual version and print extra info if there is - a conflict or if the actual version could not be imported. - """ - for module_name, expected_version in vendor_txt_versions.items(): - extra_message = "" - actual_version = get_vendor_version_from_module(module_name) - if not actual_version: - extra_message = ( - " (Unable to locate actual module version, using" - " vendor.txt specified version)" - ) - actual_version = expected_version - elif parse_version(actual_version) != parse_version(expected_version): - extra_message = ( - " (CONFLICT: vendor.txt suggests version should" - " be {})".format(expected_version) - ) - logger.info("%s==%s%s", module_name, actual_version, extra_message) - - -def show_vendor_versions() -> None: - logger.info("vendored library versions:") - - vendor_txt_versions = create_vendor_txt_map() - with indent_log(): - show_actual_vendor_versions(vendor_txt_versions) - - -def show_tags(options: Values) -> None: - tag_limit = 10 - - target_python = make_target_python(options) - tags = target_python.get_tags() - - # Display the target options that were explicitly provided. - formatted_target = target_python.format_given() - suffix = "" - if formatted_target: - suffix = f" (target: {formatted_target})" - - msg = "Compatible tags: {}{}".format(len(tags), suffix) - logger.info(msg) - - if options.verbose < 1 and len(tags) > tag_limit: - tags_limited = True - tags = tags[:tag_limit] - else: - tags_limited = False - - with indent_log(): - for tag in tags: - logger.info(str(tag)) - - if tags_limited: - msg = ( - "...\n[First {tag_limit} tags shown. Pass --verbose to show all.]" - ).format(tag_limit=tag_limit) - logger.info(msg) - - -def ca_bundle_info(config: Configuration) -> str: - levels = set() - for key, _ in config.items(): - levels.add(key.split(".")[0]) - - if not levels: - return "Not specified" - - levels_that_override_global = ["install", "wheel", "download"] - global_overriding_level = [ - level for level in levels if level in levels_that_override_global - ] - if not global_overriding_level: - return "global" - - if "global" in levels: - levels.remove("global") - return ", ".join(levels) - - -class DebugCommand(Command): - """ - Display debug information. - """ - - usage = """ - %prog
Deep Residual Learning for Image Recognition | Github Repo
" - -# Run inference -gr.Interface(inference, - inputs, - outputs, - title=title, - description=description, - article=article, - analytics_enabled=False).launch() - diff --git a/spaces/ICML2022/OFA/fairseq/examples/laser/laser_src/laser_transformer.py b/spaces/ICML2022/OFA/fairseq/examples/laser/laser_src/laser_transformer.py deleted file mode 100644 index 0be030994ff87334ca0392302374693f7f2c61b3..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/laser/laser_src/laser_transformer.py +++ /dev/null @@ -1,354 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -from typing import Any, Dict, List, Optional -from torch import Tensor - -import torch -import torch.nn as nn - -from fairseq.models import ( - FairseqEncoderDecoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.transformer import ( - base_architecture, - Embedding, - TransformerModel, - TransformerEncoder, - TransformerDecoder, -) -from fairseq.modules import ( - TransformerDecoderLayer, -) - -logger = logging.getLogger(__name__) - - -@register_model("laser_transformer") -class LaserTransformerModel(FairseqEncoderDecoderModel): - """Train Transformer for LASER task - - Requires --task laser - """ - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens=None, - tgt_tokens=None, - tgt_lengths=None, - target_language_id=-1, - dataset_name="", - ): - laser_encoder_out = self.encoder(src_tokens, src_lengths) - return self.decoder( - prev_output_tokens, laser_encoder_out, lang_id=target_language_id - ) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - TransformerModel.add_args(parser) - parser.add_argument( - "--decoder-lang-embed-dim", - type=int, - metavar="N", - help="decoder language embedding dimension", - ) - - @classmethod - def build_model(cls, args, task): - base_laser_transformer_architecture(args) - - num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 - - def load_embed_tokens(dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - - return Embedding(num_embeddings, embed_dim, padding_idx) - - encoder_embed_tokens = load_embed_tokens( - task.source_dictionary, args.encoder_embed_dim - ) - decoder_embed_tokens = load_embed_tokens( - task.target_dictionary, args.decoder_embed_dim - ) - num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 - - encoder = LaserTransformerEncoder( - args, task.source_dictionary, encoder_embed_tokens - ) - - decoder = LaserTransformerDecoder( - args, - task.target_dictionary, - decoder_embed_tokens, - num_langs=num_langs, - lang_embed_dim=args.decoder_lang_embed_dim, - ) - - return cls(encoder, decoder) - - -class LaserTransformerEncoder(TransformerEncoder): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def forward(self, src_tokens, *args, **kwargs): - encoder_out = super().forward(src_tokens, *args, **kwargs) - - x = encoder_out["encoder_out"][0] # T x B x C - padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1) - - if padding_mask.any(): - x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x) - - # Build the sentence embedding by max-pooling over the encoder outputs - sentemb = x.max(dim=0)[0] - - # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in - # `foward` so we use a dictionary instead. - # TorchScript does not support mixed values so the values are all lists. - # The empty list is equivalent to None. - return {"sentemb": [sentemb]} # B x C - - @torch.jit.export - def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): - """ - Same as the one in transformer.py, with new_sentemb - """ - if len(encoder_out["sentemb"]) == 0: - new_sentemb = [] - else: - new_sentemb = [encoder_out["sentemb"][0].index_select(0, new_order)] - - return { - "sentemb": new_sentemb, # B x C - } - - -class LaserTransformerDecoder(TransformerDecoder): - def __init__(self, args, dictionary, *kargs, **kwargs): - self.num_langs = kwargs.get("num_langs", 1) - self.lang_embed_dim = kwargs.get("lang_embed_dim", 0) - kwargs.pop("num_langs", None) - kwargs.pop("lang_embed_dim", None) - - super().__init__(args, dictionary, *kargs, **kwargs, no_encoder_attn=True) - - if self.lang_embed_dim == 0: - self.embed_lang = None - else: - self.embed_lang = nn.Embedding(self.num_langs, self.lang_embed_dim) - nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1) - - if self.output_projection is not None: - laser_output_embed_dim = ( - self.output_embed_dim + self.lang_embed_dim + args.encoder_embed_dim - ) - self.output_projection = nn.Linear( - laser_output_embed_dim, len(dictionary), bias=False - ) - nn.init.normal_( - self.output_projection.weight, - mean=0, - std=laser_output_embed_dim ** -0.5, - ) - - def build_decoder_layer(self, args, no_encoder_attn=False): - decoder_embed_dim = args.decoder_embed_dim - args.decoder_embed_dim = ( - decoder_embed_dim + self.lang_embed_dim + args.encoder_embed_dim - ) - res = TransformerDecoderLayer(args, no_encoder_attn=True) - args.decoder_embed_dim = decoder_embed_dim - - return res - - def extract_features( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]], - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - lang_id: Optional[int] = None, - ): - """ - Similar to *forward* but only return features. - - Includes several features from "Jointly Learning to Align and - Translate with Transformer Models" (Garg et al., EMNLP 2019). - - Args: - full_context_alignment (bool, optional): don't apply - auto-regressive mask to self-attention (default: False). - alignment_layer (int, optional): return mean alignment over - heads at this layer (default: last layer). - alignment_heads (int, optional): only average alignment over - this many heads (default: all heads). - - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - if alignment_layer is None: - alignment_layer = self.num_layers - 1 - - # embed positions - positions = ( - self.embed_positions( - prev_output_tokens, incremental_state=incremental_state - ) - if self.embed_positions is not None - else None - ) - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - if positions is not None: - positions = positions[:, -1:] - - bsz, seqlen = prev_output_tokens.size() - - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - - if self.quant_noise is not None: - x = self.quant_noise(x) - - if self.project_in_dim is not None: - x = self.project_in_dim(x) - - if positions is not None: - x += positions - - if self.layernorm_embedding is not None: - x = self.layernorm_embedding(x) - - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - if self.embed_lang is not None: - lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id) - langemb = self.embed_lang(lang_ids) - langemb = langemb.unsqueeze(0) - repeat_vals = [x.shape[0] // langemb.shape[0]] + [-1] * ( - len(langemb.shape) - 1 - ) - x = torch.cat((x, langemb.expand(*repeat_vals)), dim=-1) - - sentemb = encoder_out["sentemb"][0] - sentemb = sentemb.unsqueeze(0) - - repeat_vals = [x.shape[0] // sentemb.shape[0]] + [-1] * (len(sentemb.shape) - 1) - x = torch.cat((x, sentemb.expand(*repeat_vals)), dim=-1) - - self_attn_padding_mask: Optional[Tensor] = None - if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): - self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) - - # decoder layers - attn: Optional[Tensor] = None - inner_states: List[Optional[Tensor]] = [x] - for idx, layer in enumerate(self.layers): - if incremental_state is None and not full_context_alignment: - self_attn_mask = self.buffered_future_mask(x) - else: - self_attn_mask = None - - x, layer_attn, _ = layer( - x, - None, - None, - incremental_state, - self_attn_mask=self_attn_mask, - self_attn_padding_mask=self_attn_padding_mask, - need_attn=bool((idx == alignment_layer)), - need_head_weights=bool((idx == alignment_layer)), - ) - inner_states.append(x) - if layer_attn is not None and idx == alignment_layer: - attn = layer_attn.float().to(x) - - if attn is not None: - if alignment_heads is not None: - attn = attn[:alignment_heads] - - # average probabilities over heads - attn = attn.mean(dim=0) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - if self.project_out_dim is not None: - x = self.project_out_dim(x) - - return x, {"attn": [attn], "inner_states": inner_states} - - def forward( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - features_only: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - src_lengths: Optional[Any] = None, - return_all_hiddens: bool = False, - lang_id: Optional[int] = None, - ): - """ - Args: - prev_output_tokens (LongTensor): previous decoder outputs of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (optional): output from the encoder, used for - encoder-side attention - incremental_state (dict): dictionary used for storing state during - :ref:`Incremental decoding` - features_only (bool, optional): only return features without - applying output layer (default: False). - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - - assert lang_id is not None - - x, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - lang_id=lang_id, - ) - if not features_only: - x = self.output_layer(x) - return x, extra - - -@register_model_architecture("laser_transformer", "laser_transformer") -def base_laser_transformer_architecture(args): - base_architecture(args) - args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0) diff --git a/spaces/Iceclear/StableSR/StableSR/main.py b/spaces/Iceclear/StableSR/StableSR/main.py deleted file mode 100644 index f9a5b1b00c3f8c9b0df596bb961cda8882a61982..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/StableSR/main.py +++ /dev/null @@ -1,743 +0,0 @@ -import argparse, os, sys, datetime, glob, importlib, csv -import numpy as np -import time -import torch -import torchvision -import pytorch_lightning as pl - -from packaging import version -from omegaconf import OmegaConf -from torch.utils.data import random_split, DataLoader, Dataset, Subset -from functools import partial -from PIL import Image - -from pytorch_lightning import seed_everything -from pytorch_lightning.trainer import Trainer -from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor -from pytorch_lightning.utilities.distributed import rank_zero_only -# from pytorch_lightning.utilities.rank_zero import rank_zero_only -from pytorch_lightning.utilities import rank_zero_info - -from ldm.data.base import Txt2ImgIterableBaseDataset -from ldm.util import instantiate_from_config, instantiate_from_config_sr -from pytorch_lightning.loggers import WandbLogger - - -def get_parser(**parser_kwargs): - def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - parser = argparse.ArgumentParser(**parser_kwargs) - parser.add_argument( - "-n", - "--name", - type=str, - const=True, - default="", - nargs="?", - help="postfix for logdir", - ) - parser.add_argument( - "-r", - "--resume", - type=str, - const=True, - default="", - nargs="?", - help="resume from logdir or checkpoint in logdir", - ) - parser.add_argument( - "-b", - "--base", - nargs="*", - metavar="base_config.yaml", - help="paths to base configs. Loaded from left-to-right. " - "Parameters can be overwritten or added with command-line options of the form `--key value`.", - default=list(), - ) - parser.add_argument( - "-t", - "--train", - type=str2bool, - const=True, - default=False, - nargs="?", - help="train", - ) - parser.add_argument( - "--no-test", - type=str2bool, - const=True, - default=False, - nargs="?", - help="disable test", - ) - parser.add_argument( - "-p", - "--project", - help="name of new or path to existing project" - ) - parser.add_argument( - "-d", - "--debug", - type=str2bool, - nargs="?", - const=True, - default=False, - help="enable post-mortem debugging", - ) - parser.add_argument( - "-s", - "--seed", - type=int, - default=23, - help="seed for seed_everything", - ) - parser.add_argument( - "-f", - "--postfix", - type=str, - default="", - help="post-postfix for default name", - ) - parser.add_argument( - "-l", - "--logdir", - type=str, - default="./logs", - help="directory for logging dat shit", - ) - parser.add_argument( - "--scale_lr", - type=str2bool, - nargs="?", - const=True, - default=False, - help="scale base-lr by ngpu * batch_size * n_accumulate", - ) - return parser - - -def nondefault_trainer_args(opt): - parser = argparse.ArgumentParser() - parser = Trainer.add_argparse_args(parser) - args = parser.parse_args([]) - return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k)) - - -class WrappedDataset(Dataset): - """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset""" - - def __init__(self, dataset): - self.data = dataset - - def __len__(self): - return len(self.data) - - def __getitem__(self, idx): - return self.data[idx] - - -def worker_init_fn(_): - worker_info = torch.utils.data.get_worker_info() - - dataset = worker_info.dataset - worker_id = worker_info.id - - if isinstance(dataset, Txt2ImgIterableBaseDataset): - split_size = dataset.num_records // worker_info.num_workers - # reset num_records to the true number to retain reliable length information - dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size] - current_id = np.random.choice(len(np.random.get_state()[1]), 1) - return np.random.seed(np.random.get_state()[1][current_id] + worker_id) - else: - return np.random.seed(np.random.get_state()[1][0] + worker_id) - - -class DataModuleFromConfig(pl.LightningDataModule): - def __init__(self, batch_size, train=None, validation=None, test=None, predict=None, - wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False, - shuffle_val_dataloader=False): - super().__init__() - self.batch_size = batch_size - self.dataset_configs = dict() - self.num_workers = num_workers if num_workers is not None else batch_size * 2 - self.use_worker_init_fn = use_worker_init_fn - if train is not None: - self.dataset_configs["train"] = train - self.train_dataloader = self._train_dataloader - if validation is not None: - self.dataset_configs["validation"] = validation - self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader) - if test is not None: - self.dataset_configs["test"] = test - self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader) - if predict is not None: - self.dataset_configs["predict"] = predict - self.predict_dataloader = self._predict_dataloader - self.wrap = wrap - - def prepare_data(self): - for data_cfg in self.dataset_configs.values(): - instantiate_from_config_sr(data_cfg) - - def setup(self, stage=None): - self.datasets = dict( - (k, instantiate_from_config_sr(self.dataset_configs[k])) - for k in self.dataset_configs) - if self.wrap: - for k in self.datasets: - self.datasets[k] = WrappedDataset(self.datasets[k]) - - def _train_dataloader(self): - is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) - if is_iterable_dataset or self.use_worker_init_fn: - init_fn = worker_init_fn - else: - init_fn = None - return DataLoader(self.datasets["train"], batch_size=self.batch_size, - num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True, - worker_init_fn=init_fn) - - def _val_dataloader(self, shuffle=False): - if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: - init_fn = worker_init_fn - else: - init_fn = None - return DataLoader(self.datasets["validation"], - batch_size=self.batch_size, - num_workers=self.num_workers, - worker_init_fn=init_fn, - shuffle=shuffle) - - def _test_dataloader(self, shuffle=False): - is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) - if is_iterable_dataset or self.use_worker_init_fn: - init_fn = worker_init_fn - else: - init_fn = None - - # do not shuffle dataloader for iterable dataset - shuffle = shuffle and (not is_iterable_dataset) - - return DataLoader(self.datasets["test"], batch_size=self.batch_size, - num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle) - - def _predict_dataloader(self, shuffle=False): - if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: - init_fn = worker_init_fn - else: - init_fn = None - return DataLoader(self.datasets["predict"], batch_size=self.batch_size, - num_workers=self.num_workers, worker_init_fn=init_fn) - - -class SetupCallback(Callback): - def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config): - super().__init__() - self.resume = resume - self.now = now - self.logdir = logdir - self.ckptdir = ckptdir - self.cfgdir = cfgdir - self.config = config - self.lightning_config = lightning_config - - def on_keyboard_interrupt(self, trainer, pl_module): - if trainer.global_rank == 0: - print("Summoning checkpoint.") - ckpt_path = os.path.join(self.ckptdir, "last.ckpt") - trainer.save_checkpoint(ckpt_path) - - def on_pretrain_routine_start(self, trainer, pl_module): - if trainer.global_rank == 0: - # Create logdirs and save configs - os.makedirs(self.logdir, exist_ok=True) - os.makedirs(self.ckptdir, exist_ok=True) - os.makedirs(self.cfgdir, exist_ok=True) - - if "callbacks" in self.lightning_config: - if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']: - os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True) - print("Project config") - print(OmegaConf.to_yaml(self.config)) - OmegaConf.save(self.config, - os.path.join(self.cfgdir, "{}-project.yaml".format(self.now))) - - print("Lightning config") - print(OmegaConf.to_yaml(self.lightning_config)) - OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}), - os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now))) - - else: - # ModelCheckpoint callback created log directory --- remove it - if not self.resume and os.path.exists(self.logdir): - dst, name = os.path.split(self.logdir) - dst = os.path.join(dst, "child_runs", name) - os.makedirs(os.path.split(dst)[0], exist_ok=True) - try: - os.rename(self.logdir, dst) - except FileNotFoundError: - pass - - -class ImageLogger(Callback): - def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True, - rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False, - log_images_kwargs=None): - super().__init__() - self.rescale = rescale - self.batch_freq = batch_frequency - self.max_images = max_images - self.logger_log_images = { - pl.loggers.TestTubeLogger: self._testtube, - } - self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)] - if not increase_log_steps: - self.log_steps = [self.batch_freq] - self.clamp = clamp - self.disabled = disabled - self.log_on_batch_idx = log_on_batch_idx - self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {} - self.log_first_step = log_first_step - - @rank_zero_only - def _testtube(self, pl_module, images, batch_idx, split): - for k in images: - grid = torchvision.utils.make_grid(images[k]) - grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w - - tag = f"{split}/{k}" - pl_module.logger.experiment.add_image( - tag, grid, - global_step=pl_module.global_step) - - @rank_zero_only - def log_local(self, save_dir, split, images, - global_step, current_epoch, batch_idx): - root = os.path.join(save_dir, "images", split) - for k in images: - grid = torchvision.utils.make_grid(images[k], nrow=4) - if self.rescale: - grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w - grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1) - grid = grid.numpy() - grid = (grid * 255).astype(np.uint8) - filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format( - k, - global_step, - current_epoch, - batch_idx) - path = os.path.join(root, filename) - os.makedirs(os.path.split(path)[0], exist_ok=True) - Image.fromarray(grid).save(path) - - def log_img(self, pl_module, batch, batch_idx, split="train"): - check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step - if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0 - hasattr(pl_module, "log_images") and - callable(pl_module.log_images) and - self.max_images > 0): - logger = type(pl_module.logger) - - is_train = pl_module.training - if is_train: - pl_module.eval() - - with torch.no_grad(): - images = pl_module.log_images(batch, split=split, **self.log_images_kwargs) - - for k in images: - N = min(images[k].shape[0], self.max_images) - images[k] = images[k][:N] - if isinstance(images[k], torch.Tensor): - images[k] = images[k].detach().cpu() - if self.clamp: - images[k] = torch.clamp(images[k], -1., 1.) - - self.log_local(pl_module.logger.save_dir, split, images, - pl_module.global_step, pl_module.current_epoch, batch_idx) - - logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None) - logger_log_images(pl_module, images, pl_module.global_step, split) - - if is_train: - pl_module.train() - - def check_frequency(self, check_idx): - if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and ( - check_idx > 0 or self.log_first_step): - try: - self.log_steps.pop(0) - except IndexError as e: - print(e) - pass - return True - return False - - def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): - if not self.disabled and (pl_module.global_step > 0 or self.log_first_step): - self.log_img(pl_module, batch, batch_idx, split="train") - - def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): - if not self.disabled and pl_module.global_step > 0: - self.log_img(pl_module, batch, batch_idx, split="val") - if hasattr(pl_module, 'calibrate_grad_norm'): - if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0: - self.log_gradients(trainer, pl_module, batch_idx=batch_idx) - - -class CUDACallback(Callback): - # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py - def on_train_epoch_start(self, trainer, pl_module): - # Reset the memory use counter - torch.cuda.reset_peak_memory_stats(trainer.root_gpu) - torch.cuda.synchronize(trainer.root_gpu) - self.start_time = time.time() - - def on_train_epoch_end(self, trainer, pl_module, outputs): - torch.cuda.synchronize(trainer.root_gpu) - max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20 - epoch_time = time.time() - self.start_time - - try: - max_memory = trainer.training_type_plugin.reduce(max_memory) - epoch_time = trainer.training_type_plugin.reduce(epoch_time) - - rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds") - rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB") - except AttributeError: - pass - - -if __name__ == "__main__": - from collections import OrderedDict - # custom parser to specify config files, train, test and debug mode, - # postfix, resume. - # `--key value` arguments are interpreted as arguments to the trainer. - # `nested.key=value` arguments are interpreted as config parameters. - # configs are merged from left-to-right followed by command line parameters. - - # model: - # base_learning_rate: float - # target: path to lightning module - # params: - # key: value - # data: - # target: main.DataModuleFromConfig - # params: - # batch_size: int - # wrap: bool - # train: - # target: path to train dataset - # params: - # key: value - # validation: - # target: path to validation dataset - # params: - # key: value - # test: - # target: path to test dataset - # params: - # key: value - # lightning: (optional, has sane defaults and can be specified on cmdline) - # trainer: - # additional arguments to trainer - # logger: - # logger to instantiate - # modelcheckpoint: - # modelcheckpoint to instantiate - # callbacks: - # callback1: - # target: importpath - # params: - # key: value - - now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - - # add cwd for convenience and to make classes in this file available when - # running as `python main.py` - # (in particular `main.DataModuleFromConfig`) - sys.path.append(os.getcwd()) - - parser = get_parser() - parser = Trainer.add_argparse_args(parser) - - opt, unknown = parser.parse_known_args() - if opt.name and opt.resume: - raise ValueError( - "-n/--name and -r/--resume cannot be specified both." - "If you want to resume training in a new log folder, " - "use -n/--name in combination with --resume_from_checkpoint" - ) - if opt.resume: - if not os.path.exists(opt.resume): - raise ValueError("Cannot find {}".format(opt.resume)) - if os.path.isfile(opt.resume): - paths = opt.resume.split("/") - # idx = len(paths)-paths[::-1].index("logs")+1 - # logdir = "/".join(paths[:idx]) - logdir = "/".join(paths[:-2]) - ckpt = opt.resume - else: - assert os.path.isdir(opt.resume), opt.resume - logdir = opt.resume.rstrip("/") - ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") - - # delete JPEGer - # state_dict = torch.load(ckpt) - # new_state_dict = OrderedDict() - # for k, v in state_dict['state_dict'].items(): - # if 'jpeger' not in k or 'usm_sharpener' not in k: - # new_state_dict[k] = v - # if new_state_dict != state_dict['state_dict']: - # state_dict['state_dict'] = new_state_dict - # torch.save(state_dict, ckpt) - # del new_state_dict - # del state_dict - - opt.resume_from_checkpoint = ckpt - base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml"))) - opt.base = base_configs + opt.base - _tmp = logdir.split("/") - nowname = _tmp[-1] - else: - if opt.name: - name = "_" + opt.name - elif opt.base: - cfg_fname = os.path.split(opt.base[0])[-1] - cfg_name = os.path.splitext(cfg_fname)[0] - name = "_" + cfg_name - else: - name = "" - nowname = now + name + opt.postfix - logdir = os.path.join(opt.logdir, nowname) - - ckptdir = os.path.join(logdir, "checkpoints") - cfgdir = os.path.join(logdir, "configs") - seed_everything(opt.seed) - - # try: - # init and save configs - configs = [OmegaConf.load(cfg) for cfg in opt.base] - cli = OmegaConf.from_dotlist(unknown) - config = OmegaConf.merge(*configs, cli) - lightning_config = config.pop("lightning", OmegaConf.create()) - # merge trainer cli with config - trainer_config = lightning_config.get("trainer", OmegaConf.create()) - # default to ddp - trainer_config["accelerator"] = "ddp" - for k in nondefault_trainer_args(opt): - trainer_config[k] = getattr(opt, k) - if not "gpus" in trainer_config: - del trainer_config["accelerator"] - cpu = True - else: - gpuinfo = trainer_config["gpus"] - print(f"Running on GPUs {gpuinfo}") - cpu = False - trainer_opt = argparse.Namespace(**trainer_config) - lightning_config.trainer = trainer_config - - # model - model = instantiate_from_config(config.model) - - model.configs = config - - # trainer and callbacks - trainer_kwargs = dict() - - # default logger configs - default_logger_cfgs = { - "wandb": { - "target": "pytorch_lightning.loggers.WandbLogger", - "params": { - "name": nowname, - "save_dir": logdir, - "offline": opt.debug, - "id": nowname, - } - }, - "testtube": { - "target": "pytorch_lightning.loggers.TestTubeLogger", - "params": { - "name": "testtube", - "save_dir": logdir, - } - }, - } - # We use wandb by default. Change to testtube if you do not want to use wandb - default_logger_cfg = default_logger_cfgs["wandb"] - os.makedirs(os.path.join(logdir, 'wandb'), exist_ok=True) - if "logger" in lightning_config: - logger_cfg = lightning_config.logger - else: - logger_cfg = OmegaConf.create() - logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) - trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) - - # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to - # specify which metric is used to determine best models - default_modelckpt_cfg = { - "target": "pytorch_lightning.callbacks.ModelCheckpoint", - "params": { - "dirpath": ckptdir, - "filename": "{epoch:06}", - "verbose": True, - "save_last": True, - } - } - if hasattr(model, "monitor"): - print(f"Monitoring {model.monitor} as checkpoint metric.") - default_modelckpt_cfg["params"]["monitor"] = model.monitor - default_modelckpt_cfg["params"]["save_top_k"] = 20 - - if "modelcheckpoint" in lightning_config: - modelckpt_cfg = lightning_config.modelcheckpoint - else: - modelckpt_cfg = OmegaConf.create() - modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) - print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}") - if version.parse(pl.__version__) < version.parse('1.4.0'): - trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) - - # add callback which sets up log directory - default_callbacks_cfg = { - "setup_callback": { - "target": "main.SetupCallback", - "params": { - "resume": opt.resume, - "now": now, - "logdir": logdir, - "ckptdir": ckptdir, - "cfgdir": cfgdir, - "config": config, - "lightning_config": lightning_config, - } - }, - "image_logger": { - "target": "main.ImageLogger", - "params": { - "batch_frequency": 750, - "max_images": 4, - "clamp": True - } - }, - "learning_rate_logger": { - "target": "main.LearningRateMonitor", - "params": { - "logging_interval": "step", - # "log_momentum": True - } - }, - "cuda_callback": { - "target": "main.CUDACallback" - }, - } - if version.parse(pl.__version__) >= version.parse('1.4.0'): - default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg}) - - if "callbacks" in lightning_config: - callbacks_cfg = lightning_config.callbacks - else: - callbacks_cfg = OmegaConf.create() - - if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg: - print( - 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') - default_metrics_over_trainsteps_ckpt_dict = { - 'metrics_over_trainsteps_checkpoint': - {"target": 'pytorch_lightning.callbacks.ModelCheckpoint', - 'params': { - "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), - "filename": "{epoch:06}-{step:09}", - "verbose": True, - 'save_top_k': -1, - 'every_n_train_steps': 10000, - 'save_weights_only': True - } - } - } - default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict) - - callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) - if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'): - callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint - elif 'ignore_keys_callback' in callbacks_cfg: - del callbacks_cfg['ignore_keys_callback'] - - trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] - - trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) - trainer.logdir = logdir ### - - # data - data = instantiate_from_config(config.data) - # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html - # calling these ourselves should not be necessary but it is. - # lightning still takes care of proper multiprocessing though - data.prepare_data() - data.setup() - print("#### Data #####") - for k in data.datasets: - print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}") - - # configure learning rate - bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate - if not cpu: - ngpu = len(lightning_config.trainer.gpus.strip(",").split(',')) - else: - ngpu = 1 - if 'accumulate_grad_batches' in lightning_config.trainer: - accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches - else: - accumulate_grad_batches = 1 - print(f"accumulate_grad_batches = {accumulate_grad_batches}") - lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches - if opt.scale_lr: - model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr - print( - "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format( - model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr)) - else: - model.learning_rate = base_lr - print("++++ NOT USING LR SCALING ++++") - print(f"Setting learning rate to {model.learning_rate:.2e}") - - - # allow checkpointing via USR1 - def melk(*args, **kwargs): - # run all checkpoint hooks - if trainer.global_rank == 0: - print("Summoning checkpoint.") - ckpt_path = os.path.join(ckptdir, "last.ckpt") - trainer.save_checkpoint(ckpt_path) - - - def divein(*args, **kwargs): - if trainer.global_rank == 0: - import pudb; - pudb.set_trace() - - - import signal - - signal.signal(signal.SIGUSR1, melk) - signal.signal(signal.SIGUSR2, divein) - - # run - if opt.train: - try: - trainer.fit(model, data) - except Exception: - melk() - raise - if not opt.no_test and not trainer.interrupted: - trainer.test(model, data) diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/onnx/model_onnx.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/onnx/model_onnx.py deleted file mode 100644 index 1567d28875c8a6620d5db8114daa0f073ddb145c..0000000000000000000000000000000000000000 --- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/onnx/model_onnx.py +++ /dev/null @@ -1,328 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -import modules.attentions as attentions -import modules.commons as commons -import modules.modules as modules - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from modules.commons import init_weights, get_padding -from vdecoder.hifigan.models import Generator -from utils import f0_to_coarse - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class Encoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - # print(x.shape,x_lengths.shape) - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - filter_channels=None, - n_heads=None, - p_dropout=None): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - self.f0_emb = nn.Embedding(256, hidden_channels) - - self.enc_ = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - - def forward(self, x, x_lengths, f0=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = x + self.f0_emb(f0.long()).transpose(1,2) - x = self.enc_(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - - return z, m, logs, x_mask - - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2,3,5,7,11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class SpeakerEncoder(torch.nn.Module): - def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256): - super(SpeakerEncoder, self).__init__() - self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) - self.linear = nn.Linear(model_hidden_size, model_embedding_size) - self.relu = nn.ReLU() - - def forward(self, mels): - self.lstm.flatten_parameters() - _, (hidden, _) = self.lstm(mels) - embeds_raw = self.relu(self.linear(hidden[-1])) - return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) - - def compute_partial_slices(self, total_frames, partial_frames, partial_hop): - mel_slices = [] - for i in range(0, total_frames-partial_frames, partial_hop): - mel_range = torch.arange(i, i+partial_frames) - mel_slices.append(mel_range) - - return mel_slices - - def embed_utterance(self, mel, partial_frames=128, partial_hop=64): - mel_len = mel.size(1) - last_mel = mel[:,-partial_frames:] - - if mel_len > partial_frames: - mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop) - mels = list(mel[:,s] for s in mel_slices) - mels.append(last_mel) - mels = torch.stack(tuple(mels), 0).squeeze(1) - - with torch.no_grad(): - partial_embeds = self(mels) - embed = torch.mean(partial_embeds, axis=0).unsqueeze(0) - #embed = embed / torch.linalg.norm(embed, 2) - else: - with torch.no_grad(): - embed = self(last_mel) - - return embed - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - ssl_dim, - n_speakers, - **kwargs): - - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - self.ssl_dim = ssl_dim - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout) - hps = { - "sampling_rate": 32000, - "inter_channels": 192, - "resblock": "1", - "resblock_kernel_sizes": [3, 7, 11], - "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - "upsample_rates": [10, 8, 2, 2], - "upsample_initial_channel": 512, - "upsample_kernel_sizes": [16, 16, 4, 4], - "gin_channels": 256, - } - self.dec = Generator(h=hps) - self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - def forward(self, c, c_lengths, f0, g=None): - g = self.emb_g(g.unsqueeze(0)).transpose(1,2) - z_p, m_p, logs_p, c_mask = self.enc_p_(c.transpose(1,2), c_lengths, f0=f0_to_coarse(f0)) - z = self.flow(z_p, c_mask, g=g, reverse=True) - o = self.dec(z * c_mask, g=g, f0=f0.float()) - return o - diff --git a/spaces/JUNGU/VToonify/vtoonify/model/raft/core/__init__.py b/spaces/JUNGU/VToonify/vtoonify/model/raft/core/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Jeff2323/ai-comic-factory/src/components/ui/menubar.tsx b/spaces/Jeff2323/ai-comic-factory/src/components/ui/menubar.tsx deleted file mode 100644 index d57454816cea9b7572ad1ae6ab139d6946c4d5d5..0000000000000000000000000000000000000000 --- a/spaces/Jeff2323/ai-comic-factory/src/components/ui/menubar.tsx +++ /dev/null @@ -1,236 +0,0 @@ -"use client" - -import * as React from "react" -import * as MenubarPrimitive from "@radix-ui/react-menubar" -import { Check, ChevronRight, Circle } from "lucide-react" - -import { cn } from "@/lib/utils" - -const MenubarMenu = MenubarPrimitive.Menu - -const MenubarGroup = MenubarPrimitive.Group - -const MenubarPortal = MenubarPrimitive.Portal - -const MenubarSub = MenubarPrimitive.Sub - -const MenubarRadioGroup = MenubarPrimitive.RadioGroup - -const Menubar = React.forwardRef< - React.ElementRef
- {Array.from({ length: num }).map((_, index) => {
- const randomDuration = Math.random() * 100 + duration
- const initialDelay = Math.random() * 2 * duration
- const initialScale = Math.sin((index + 1) * Math.PI / num)
- return (
-
- )
- })}
-
- )
-}
diff --git a/spaces/MVV/3dTopDenoising/models/model.py b/spaces/MVV/3dTopDenoising/models/model.py
deleted file mode 100644
index 3abb96aaafa8ec07bf9009ef796557162f17856f..0000000000000000000000000000000000000000
--- a/spaces/MVV/3dTopDenoising/models/model.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import random
-from statistics import mean
-from typing import List, Tuple
-
-import torch as th
-import pytorch_lightning as pl
-from jaxtyping import Float, Int
-import numpy as np
-from torch_geometric.nn.conv import GATv2Conv
-
-from models.SAP.dpsr import DPSR
-from models.SAP.model import PSR2Mesh
-
-# Constants
-
-th.manual_seed(0)
-np.random.seed(0)
-
-BATCH_SIZE = 1 # BS
-
-IN_DIM = 1
-OUT_DIM = 1
-LATENT_DIM = 32
-
-DROPOUT_PROB = 0.1
-GRID_SIZE = 128
-
-def generate_grid_edge_list(gs: int = 128):
- grid_edge_list = []
-
- for k in range(gs):
- for j in range(gs):
- for i in range(gs):
- current_idx = i + gs*j + k*gs*gs
- if (i - 1) >= 0:
- grid_edge_list.append([current_idx, i-1 + gs*j + k*gs*gs])
- if (i + 1) < gs:
- grid_edge_list.append([current_idx, i+1 + gs*j + k*gs*gs])
- if (j - 1) >= 0:
- grid_edge_list.append([current_idx, i + gs*(j-1) + k*gs*gs])
- if (j + 1) < gs:
- grid_edge_list.append([current_idx, i + gs*(j+1) + k*gs*gs])
- if (k - 1) >= 0:
- grid_edge_list.append([current_idx, i + gs*j + (k-1)*gs*gs])
- if (k + 1) < gs:
- grid_edge_list.append([current_idx, i + gs*j + (k+1)*gs*gs])
- return grid_edge_list
-
-GRID_EDGE_LIST = generate_grid_edge_list(GRID_SIZE)
-GRID_EDGE_LIST = th.tensor(GRID_EDGE_LIST, dtype=th.int)
-GRID_EDGE_LIST = GRID_EDGE_LIST.T
-# GRID_EDGE_LIST = GRID_EDGE_LIST.to(th.device("cuda"))
-GRID_EDGE_LIST.requires_grad = False # Do not forget to delete it if train
-
-
-class FormOptimizer(th.nn.Module):
- def __init__(self) -> None:
- super().__init__()
-
- layers = []
-
- self.gconv1 = GATv2Conv(in_channels=IN_DIM, out_channels=LATENT_DIM, heads=1, dropout=DROPOUT_PROB)
- self.gconv2 = GATv2Conv(in_channels=LATENT_DIM, out_channels=LATENT_DIM, heads=1, dropout=DROPOUT_PROB)
-
- self.actv = th.nn.Sigmoid()
- self.head = th.nn.Linear(in_features=LATENT_DIM, out_features=OUT_DIM)
-
- def forward(self,
- field: Float[th.Tensor, "GS GS GS"]) -> Float[th.Tensor, "GS GS GS"]:
- """
- Args:
- field (Tensor [GS, GS, GS]): vertices and normals tensor.
- """
- vertex_features = field.clone()
- vertex_features = vertex_features.reshape(GRID_SIZE*GRID_SIZE*GRID_SIZE, IN_DIM)
-
- vertex_features = self.gconv1(x=vertex_features, edge_index=GRID_EDGE_LIST)
- vertex_features = self.gconv2(x=vertex_features, edge_index=GRID_EDGE_LIST)
- field_delta = self.head(self.actv(vertex_features))
-
- field_delta = field_delta.reshape(BATCH_SIZE, GRID_SIZE, GRID_SIZE, GRID_SIZE)
- field_delta += field # field_delta carries the gradient
- field_delta = th.clamp(field_delta, min=-0.5, max=0.5)
-
- return field_delta
-
-class Model(pl.LightningModule):
- def __init__(self):
- super().__init__()
- self.form_optimizer = FormOptimizer()
-
- self.dpsr = DPSR([GRID_SIZE, GRID_SIZE, GRID_SIZE], sig=0.0)
- self.field2mesh = PSR2Mesh().apply
-
- self.metric = th.nn.MSELoss()
-
- self.val_losses = []
- self.train_losses = []
-
- def log_h5(self, points, normals):
- dset = self.log_points_file.create_dataset(
- name=str(self.h5_frame),
- shape=points.shape,
- dtype=np.float16,
- compression="gzip")
- dset[:] = points
- dset = self.log_normals_file.create_dataset(
- name=str(self.h5_frame),
- shape=normals.shape,
- dtype=np.float16,
- compression="gzip")
- dset[:] = normals
- self.h5_frame += 1
-
- def forward(self,
- v: Float[th.Tensor, "BS N 3"],
- n: Float[th.Tensor, "BS N 3"]) -> Tuple[Float[th.Tensor, "BS N 3"], # v - vertices
- Int[th.Tensor, "2 E"], # f - faces
- Float[th.Tensor, "BS N 3"], # n - vertices normals
- Float[th.Tensor, "BS GR GR GR"]]: # field:
- field = self.dpsr(v, n)
- field = self.form_optimizer(field)
- v, f, n = self.field2mesh(field)
- return v, f, n, field
-
- def training_step(self, batch, batch_idx) -> Float[th.Tensor, "1"]:
- vertices, vertices_normals, vertices_gt, vertices_normals_gt, field_gt, adj = batch
-
- mask = th.rand((vertices.shape[1], ), device=th.device("cuda")) < (random.random() / 2.0 + 0.5)
- vertices = vertices[:, mask]
- vertices_normals = vertices_normals[:, mask]
-
- vr, fr, nr, field_r = model(vertices, vertices_normals)
-
- loss = self.metric(field_r, field_gt)
- train_per_step_loss = loss.item()
- self.train_losses.append(train_per_step_loss)
-
- return loss
-
- def on_train_epoch_end(self):
- mean_train_per_epoch_loss = mean(self.train_losses)
- self.log("mean_train_per_epoch_loss", mean_train_per_epoch_loss, on_step=False, on_epoch=True)
- self.train_losses = []
-
- def validation_step(self, batch, batch_idx):
- vertices, vertices_normals, vertices_gt, vertices_normals_gt, field_gt, adj = batch
-
- vr, fr, nr, field_r = model(vertices, vertices_normals)
-
- loss = self.metric(field_r, field_gt)
- val_per_step_loss = loss.item()
- self.val_losses.append(val_per_step_loss)
- return loss
-
- def on_validation_epoch_end(self):
- mean_val_per_epoch_loss = mean(self.val_losses)
- self.log("mean_val_per_epoch_loss", mean_val_per_epoch_loss, on_step=False, on_epoch=True)
- self.val_losses = []
-
- def configure_optimizers(self):
- optimizer = th.optim.Adam(self.parameters(), lr=LR)
- scheduler = th.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5)
-
- return {
- "optimizer": optimizer,
- "lr_scheduler": {
- "scheduler": scheduler,
- "monitor": "mean_val_per_epoch_loss",
- "interval": "epoch",
- "frequency": 1,
- # If set to `True`, will enforce that the value specified 'monitor'
- # is available when the scheduler is updated, thus stopping
- # training if not found. If set to `False`, it will only produce a warning
- "strict": True,
- # If using the `LearningRateMonitor` callback to monitor the
- # learning rate progress, this keyword can be used to specify
- # a custom logged name
- "name": None,
- }
- }
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/audio/signal.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/audio/signal.py
deleted file mode 100644
index 04ac4a9c246b133f113fa34741c0cc19ae118988..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/audio/signal.py
+++ /dev/null
@@ -1,1502 +0,0 @@
-# encoding: utf-8
-# pylint: disable=no-member
-# pylint: disable=invalid-name
-# pylint: disable=too-many-arguments
-"""
-This module contains basic signal processing functionality.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import warnings
-import numpy as np
-
-from ..processors import BufferProcessor, Processor
-from ..utils import integer_types
-
-
-# signal functions
-def smooth(signal, kernel):
- """
- Smooth the signal along its first axis.
-
- Parameters
- ----------
- signal : numpy array
- Signal to be smoothed.
- kernel : numpy array or int
- Smoothing kernel (size).
-
- Returns
- -------
- numpy array
- Smoothed signal.
-
- Notes
- -----
- If `kernel` is an integer, a Hamming window of that length will be used
- as a smoothing kernel.
-
- """
- # check if a kernel is given
- if kernel is None:
- return signal
- # size for the smoothing kernel is given
- elif isinstance(kernel, integer_types):
- if kernel == 0:
- return signal
- elif kernel > 1:
- # use a Hamming window of given length
- kernel = np.hamming(kernel)
- else:
- raise ValueError("can't create a smoothing kernel of size %d" %
- kernel)
- # otherwise use the given smoothing kernel directly
- elif isinstance(kernel, np.ndarray):
- kernel = kernel
- else:
- raise ValueError("can't smooth signal with %s" % kernel)
- # convolve with the kernel and return
- if signal.ndim == 1:
- return np.convolve(signal, kernel, 'same')
- elif signal.ndim == 2:
- from scipy.signal import convolve2d
- return convolve2d(signal, kernel[:, np.newaxis], 'same')
- else:
- raise ValueError('signal must be either 1D or 2D')
-
-
-def adjust_gain(signal, gain):
- """"
- Adjust the gain of the signal.
-
- Parameters
- ----------
- signal : numpy array
- Signal to be adjusted.
- gain : float
- Gain adjustment level [dB].
-
- Returns
- -------
- numpy array
- Signal with adjusted gain.
-
- Notes
- -----
- The signal is returned with the same dtype, thus rounding errors may occur
- with integer dtypes.
-
- `gain` values > 0 amplify the signal and are only supported for signals
- with float dtype to prevent clipping and integer overflows.
-
- """
- # convert the gain in dB to a scaling factor
- gain = np.power(np.sqrt(10.), 0.1 * gain)
- # prevent overflow and clipping
- if gain > 1 and np.issubdtype(signal.dtype, np.integer):
- raise ValueError('positive gain adjustments are only supported for '
- 'float dtypes.')
- # Note: np.asanyarray returns the signal's ndarray subclass
- return np.asanyarray(signal * gain, dtype=signal.dtype)
-
-
-def attenuate(signal, attenuation):
- """
- Attenuate the signal.
-
- Parameters
- ----------
- signal : numpy array
- Signal to be attenuated.
- attenuation : float
- Attenuation level [dB].
-
- Returns
- -------
- numpy array
- Attenuated signal (same dtype as `signal`).
-
- Notes
- -----
- The signal is returned with the same dtype, thus rounding errors may occur
- with integer dtypes.
-
- """
- # return the signal unaltered if no attenuation is given
- if attenuation == 0:
- return signal
- return adjust_gain(signal, -attenuation)
-
-
-def normalize(signal):
- """
- Normalize the signal to have maximum amplitude.
-
- Parameters
- ----------
- signal : numpy array
- Signal to be normalized.
-
- Returns
- -------
- numpy array
- Normalized signal.
-
- Notes
- -----
- Signals with float dtypes cover the range [-1, +1], signals with integer
- dtypes will cover the maximally possible range, e.g. [-32768, 32767] for
- np.int16.
-
- The signal is returned with the same dtype, thus rounding errors may occur
- with integer dtypes.
-
- """
- # scaling factor to be applied
- scaling = float(np.max(np.abs(signal)))
- if np.issubdtype(signal.dtype, np.integer):
- if signal.dtype in (np.int16, np.int32):
- scaling /= np.iinfo(signal.dtype).max
- else:
- raise ValueError('only float and np.int16/32 dtypes supported, '
- 'not %s.' % signal.dtype)
- # Note: np.asanyarray returns the signal's ndarray subclass
- return np.asanyarray(signal / scaling, dtype=signal.dtype)
-
-
-def remix(signal, num_channels, channel=None):
- """
- Remix the signal to have the desired number of channels.
-
- Parameters
- ----------
- signal : numpy array
- Signal to be remixed.
- num_channels : int
- Number of channels.
- channel : int, optional
- When reducing a signal to `num_channels` of 1, use this channel,
- or 'None' to return the average across all channels.
-
- Returns
- -------
- numpy array
- Remixed signal (same dtype as `signal`).
-
- Notes
- -----
- This function does not support arbitrary channel number conversions.
- Only down-mixing to and up-mixing from mono signals is supported.
-
- The signal is returned with the same dtype, thus rounding errors may occur
- with integer dtypes.
-
- If the signal should be down-mixed to mono and has an integer dtype, it
- will be converted to float internally and then back to the original dtype
- to prevent clipping of the signal. To avoid this double conversion,
- convert the dtype first.
-
- """
- if num_channels == signal.ndim or num_channels is None:
- # return as many channels as there are.
- return signal
- elif num_channels == 1 and signal.ndim > 1:
- if channel is None:
- # down-mix to mono
- # Note: to prevent clipping, the signal is converted to float first
- # and then converted back to the original dtype
- # TODO: add weighted mixing
- return np.mean(signal, axis=-1).astype(signal.dtype)
- else:
- # Use the requested channel verbatim
- return signal[:, channel]
- elif num_channels > 1 and signal.ndim == 1:
- # up-mix a mono signal simply by copying channels
- return np.tile(signal[:, np.newaxis], num_channels)
- else:
- # any other channel conversion is not supported
- raise NotImplementedError("Requested %d channels, but got %d channels "
- "and channel conversion is not implemented."
- % (num_channels, signal.shape[1]))
-
-
-def resample(signal, sample_rate, **kwargs):
- """
- Resample the signal.
-
- Parameters
- ----------
- signal : numpy array or Signal
- Signal to be resampled.
- sample_rate : int
- Sample rate of the signal.
- kwargs : dict, optional
- Keyword arguments passed to :func:`load_ffmpeg_file`.
-
- Returns
- -------
- numpy array or Signal
- Resampled signal.
-
- Notes
- -----
- This function uses ``ffmpeg`` to resample the signal.
-
- """
- from ..io.audio import load_ffmpeg_file
- # is the given signal a Signal?
- if not isinstance(signal, Signal):
- raise ValueError('only Signals can resampled, not %s' % type(signal))
- if signal.sample_rate == sample_rate:
- return signal
- # per default use the signal's dtype and num_channels
- dtype = kwargs.get('dtype', signal.dtype)
- num_channels = kwargs.get('num_channels', signal.num_channels)
- # resample the signal
- signal, sample_rate = load_ffmpeg_file(signal, sample_rate=sample_rate,
- num_channels=num_channels,
- dtype=dtype)
- # return it
- return Signal(signal, sample_rate=sample_rate)
-
-
-def rescale(signal, dtype=np.float32):
- """
- Rescale the signal to range [-1, 1] and return as float dtype.
-
- Parameters
- ----------
- signal : numpy array
- Signal to be remixed.
- dtype : numpy dtype
- Data type of the signal.
-
- Returns
- -------
- numpy array
- Signal rescaled to range [-1, 1].
-
- """
- # allow only float dtypes
- if not np.issubdtype(dtype, np.floating):
- raise ValueError('only float dtypes are supported, not %s.' % dtype)
- # float signals don't need rescaling
- if np.issubdtype(signal.dtype, np.floating):
- return signal.astype(dtype)
- elif np.issubdtype(signal.dtype, np.integer):
- return signal.astype(dtype) / np.iinfo(signal.dtype).max
- else:
- raise ValueError('unsupported signal dtype: %s.' % signal.dtype)
-
-
-def trim(signal, where='fb'):
- """
- Trim leading and trailing zeros of the signal.
-
- Parameters
- ----------
- signal : numpy array
- Signal to be trimmed.
- where : str, optional
- A string with 'f' representing trim from front and 'b' to trim from
- back. Default is 'fb', trim zeros from both ends of the signal.
-
- Returns
- -------
- numpy array
- Trimmed signal.
-
- """
- # code borrowed from np.trim_zeros()
- first = 0
- where = where.upper()
- if 'F' in where:
- for i in signal:
- if np.sum(i) != 0.:
- break
- else:
- first += 1
- last = len(signal)
- if 'B' in where:
- for i in signal[::-1]:
- if np.sum(i) != 0.:
- break
- else:
- last -= 1
- return signal[first:last]
-
-
-def energy(signal):
- """
- Compute the energy of a (framed) signal.
-
- Parameters
- ----------
- signal : numpy array
- Signal.
-
- Returns
- -------
- energy : float
- Energy of the signal.
-
- Notes
- -----
- If `signal` is a `FramedSignal`, the energy is computed for each frame
- individually.
-
- """
- # compute the energy for every frame of the signal
- if isinstance(signal, FramedSignal):
- return np.array([energy(frame) for frame in signal])
- # make sure the signal is a numpy array
- if not isinstance(signal, np.ndarray):
- raise TypeError("Invalid type for signal, must be a numpy array.")
- # take the abs if the signal is complex
- if np.iscomplex(signal).any():
- signal = np.abs(signal)
- # Note: type conversion needed because of integer overflows
- if signal.dtype != np.float:
- signal = signal.astype(np.float)
- # return energy
- return np.dot(signal.flatten(), signal.flatten())
-
-
-def root_mean_square(signal):
- """
- Compute the root mean square of a (framed) signal. This can be used as a
- measurement of power.
-
- Parameters
- ----------
- signal : numpy array
- Signal.
-
- Returns
- -------
- rms : float
- Root mean square of the signal.
-
- Notes
- -----
- If `signal` is a `FramedSignal`, the root mean square is computed for each
- frame individually.
-
- """
- # compute the root mean square for every frame of the signal
- if isinstance(signal, FramedSignal):
- return np.array([root_mean_square(frame) for frame in signal])
- return np.sqrt(energy(signal) / signal.size)
-
-
-def sound_pressure_level(signal, p_ref=None):
- """
- Compute the sound pressure level of a (framed) signal.
-
- Parameters
- ----------
- signal : numpy array
- Signal.
- p_ref : float, optional
- Reference sound pressure level; if 'None', take the max amplitude
- value for the data-type, if the data-type is float, assume amplitudes
- are between -1 and +1.
-
- Returns
- -------
- spl : float
- Sound pressure level of the signal [dB].
-
- Notes
- -----
- From http://en.wikipedia.org/wiki/Sound_pressure: Sound pressure level
- (SPL) or sound level is a logarithmic measure of the effective sound
- pressure of a sound relative to a reference value. It is measured in
- decibels (dB) above a standard reference level.
-
- If `signal` is a `FramedSignal`, the sound pressure level is computed for
- each frame individually.
-
- """
- # compute the sound pressure level for every frame of the signal
- if isinstance(signal, FramedSignal):
- return np.array([sound_pressure_level(frame) for frame in signal])
- # compute the RMS
- rms = root_mean_square(signal)
- # find a reasonable default reference value if None is given
- if p_ref is None:
- if np.issubdtype(signal.dtype, np.integer):
- p_ref = float(np.iinfo(signal.dtype).max)
- else:
- p_ref = 1.0
- # normal SPL computation. ignore warnings when taking the log of 0,
- # then replace the resulting -inf values with the smallest finite number
- with np.errstate(divide='ignore'):
- return np.nan_to_num(20.0 * np.log10(rms / p_ref))
-
-
-# functions to load / write audio files
-class LoadAudioFileError(Exception):
- """
- Deprecated as of version 0.16. Please use
- madmom.io.audio.LoadAudioFileError instead. Will be removed in version
- 0.18.
-
- """
- # pylint: disable=super-init-not-called
-
- def __init__(self, value=None):
- warnings.warn(LoadAudioFileError.__doc__)
- if value is None:
- value = 'Could not load audio file.'
- self.value = value
-
-
-def load_wave_file(*args, **kwargs):
- """
- Deprecated as of version 0.16. Please use madmom.io.audio.load_wave_file
- instead. Will be removed in version 0.18.
-
- """
- warnings.warn('Deprecated as of version 0.16. Please use madmom.io.audio.'
- 'load_wave_file instead. Will be removed in version 0.18.')
- from ..io.audio import load_wave_file
- return load_wave_file(*args, **kwargs)
-
-
-def write_wave_file(*args, **kwargs):
- """
- Deprecated as of version 0.16. Please use madmom.io.audio.write_wave_file
- instead. Will be removed in version 0.18.
-
- """
- warnings.warn('Deprecated as of version 0.16. Please use madmom.io.audio.'
- 'write_wave_file instead. Will be removed in version 0.18.')
- from ..io.audio import write_wave_file
- return write_wave_file(*args, **kwargs)
-
-
-# function for automatically determining how to open audio files
-def load_audio_file(*args, **kwargs):
- """
- Deprecated as of version 0.16. Please use madmom.io.audio.load_audio_file
- instead. Will be removed in version 0.18.
-
- """
- warnings.warn('Deprecated as of version 0.16. Please use madmom.io.audio.'
- 'load_audio_file instead. Will be removed in version 0.18.')
- from ..io.audio import load_audio_file
- return load_audio_file(*args, **kwargs)
-
-
-# signal classes
-SAMPLE_RATE = None
-NUM_CHANNELS = None
-CHANNEL = None
-START = None
-STOP = None
-NORM = False
-GAIN = 0.
-DTYPE = None
-
-
-class Signal(np.ndarray):
- """
- The :class:`Signal` class represents a signal as a (memory-mapped) numpy
- array and enhances it with a number of attributes.
-
- Parameters
- ----------
- data : numpy array, str or file handle
- Signal data or file name or file handle.
- sample_rate : int, optional
- Desired sample rate of the signal [Hz], or 'None' to return the
- signal in its original rate.
- num_channels : int, optional
- Reduce or expand the signal to `num_channels` channels, or 'None'
- to return the signal with its original channels.
- channel : int, optional
- When reducing a signal to `num_channels` of 1, use this channel,
- or 'None' to return the average across all channels.
- start : float, optional
- Start position [seconds].
- stop : float, optional
- Stop position [seconds].
- norm : bool, optional
- Normalize the signal to maximum range of the data type.
- gain : float, optional
- Adjust the gain of the signal [dB].
- dtype : numpy data type, optional
- The data is returned with the given dtype. If 'None', it is returned
- with its original dtype, otherwise the signal gets rescaled. Integer
- dtypes use the complete value range, float dtypes the range [-1, +1].
-
- Notes
- -----
- `sample_rate` or `num_channels` can be used to set the desired sample rate
- and number of channels if the audio is read from file. If set to 'None'
- the audio signal is used as is, i.e. the sample rate and number of channels
- are determined directly from the audio file.
-
- If the `data` is a numpy array, the `sample_rate` is set to the given value
- and `num_channels` is set to the number of columns of the array.
-
- The `gain` can be used to adjust the level of the signal.
-
- If both `norm` and `gain` are set, the signal is first normalized and then
- the gain is applied afterwards.
-
- If `norm` or `gain` is set, the selected part of the signal is loaded into
- memory completely, i.e. .wav files are not memory-mapped any more.
-
- Examples
- --------
- Load a mono audio file:
-
- >>> sig = Signal('tests/data/audio/sample.wav')
- >>> sig
- Signal([-2494, -2510, ..., 655, 639], dtype=int16)
- >>> sig.sample_rate
- 44100
-
- Load a stereo audio file, down-mix it to mono:
-
- >>> sig = Signal('tests/data/audio/stereo_sample.flac', num_channels=1)
- >>> sig
- Signal([ 36, 36, ..., 524, 495], dtype=int16)
- >>> sig.num_channels
- 1
-
- Load and re-sample an audio file:
-
- >>> sig = Signal('tests/data/audio/sample.wav', sample_rate=22050)
- >>> sig
- Signal([-2470, -2553, ..., 517, 677], dtype=int16)
- >>> sig.sample_rate
- 22050
-
- Load an audio file with `float32` data type (i.e. rescale it to [-1, 1]):
-
- >>> sig = Signal('tests/data/audio/sample.wav', dtype=np.float32)
- >>> sig
- Signal([-0.07611, -0.0766 , ..., 0.01999, 0.0195 ], dtype=float32)
- >>> sig.dtype
- dtype('float32')
-
- """
- # pylint: disable=super-on-old-class
- # pylint: disable=super-init-not-called
- # pylint: disable=attribute-defined-outside-init
-
- def __init__(self, data, sample_rate=SAMPLE_RATE,
- num_channels=NUM_CHANNELS, channel=CHANNEL, start=START,
- stop=STOP, norm=NORM, gain=GAIN, dtype=DTYPE, **kwargs):
- # this method is for documentation purposes only
- pass
-
- def __new__(cls, data, sample_rate=SAMPLE_RATE, num_channels=NUM_CHANNELS,
- channel=CHANNEL, start=START, stop=STOP, norm=NORM, gain=GAIN,
- dtype=DTYPE, **kwargs):
- from ..io.audio import load_audio_file
- # try to load an audio file if the data is not a numpy array
- if not isinstance(data, np.ndarray):
- data, sample_rate = load_audio_file(data, sample_rate=sample_rate,
- num_channels=num_channels,
- start=start, stop=stop,
- dtype=dtype)
- # cast as Signal if needed
- if not isinstance(data, Signal):
- data = np.asarray(data).view(cls)
- data.sample_rate = sample_rate
- # remix to desired number of channels
- if num_channels:
- data = remix(data, num_channels, channel)
- # normalize signal if needed
- if norm:
- data = normalize(data)
- # adjust the gain if needed
- if gain is not None and gain != 0:
- data = adjust_gain(data, gain)
- # resample if needed
- if sample_rate != data.sample_rate:
- data = resample(data, sample_rate)
- # save start and stop position
- if start is not None:
- # FIXME: start and stop settings are not checked
- data.start = start
- data.stop = start + float(len(data)) / sample_rate
- # return the object
- return data
-
- def __array_finalize__(self, obj):
- if obj is None:
- return
- # set default values here, also needed for views of the Signal
- self.sample_rate = getattr(obj, 'sample_rate', None)
- self.start = getattr(obj, 'start', None)
- self.stop = getattr(obj, 'stop', None)
-
- def __reduce__(self):
- # Get the parent's __reduce__ tuple
- state = super(Signal, self).__reduce__()
- # Create our own tuple to pass to __setstate__, but append the
- # __dict__ rather than individual members
- new_state = state[2] + (self.__dict__,)
- # Return a tuple that replaces the parent's __setstate__ tuple with
- # our own
- return state[0], state[1], new_state
-
- def __setstate__(self, state):
- # Update the internal dict from state
- self.__dict__.update(state[-1])
- # Call the parent's __setstate__ with the other tuple elements
- super(Signal, self).__setstate__(state[:-1])
-
- @property
- def num_samples(self):
- """Number of samples."""
- return len(self)
-
- @property
- def num_channels(self):
- """Number of channels."""
- # mono file
- if self.ndim == 1:
- return 1
- # multi channel file
- return np.shape(self)[1]
-
- @property
- def length(self):
- """Length of signal in seconds."""
- # n/a if the signal has no sample rate
- if self.sample_rate is None:
- return None
- return float(self.num_samples) / self.sample_rate
-
- def write(self, filename):
- """
- Write the signal to disk as a .wav file.
-
- Parameters
- ----------
- filename : str
- Name of the file.
-
- Returns
- -------
- filename : str
- Name of the written file.
-
- """
- return write_wave_file(self, filename)
-
- def energy(self):
- """Energy of signal."""
- return energy(self)
-
- def root_mean_square(self):
- """Root mean square of signal."""
- return root_mean_square(self)
-
- rms = root_mean_square
-
- def sound_pressure_level(self):
- """Sound pressure level of signal."""
- return sound_pressure_level(self)
-
- spl = sound_pressure_level
-
-
-class SignalProcessor(Processor):
- """
- The :class:`SignalProcessor` class is a basic signal processor.
-
- Parameters
- ----------
- sample_rate : int, optional
- Sample rate of the signal [Hz]; if set the signal will be re-sampled
- to that sample rate; if 'None' the sample rate of the audio file will
- be used.
- num_channels : int, optional
- Number of channels of the signal; if set, the signal will be reduced
- to that number of channels; if 'None' as many channels as present in
- the audio file are returned.
- start : float, optional
- Start position [seconds].
- stop : float, optional
- Stop position [seconds].
- norm : bool, optional
- Normalize the signal to the range [-1, +1].
- gain : float, optional
- Adjust the gain of the signal [dB].
- dtype : numpy data type, optional
- The data is returned with the given dtype. If 'None', it is returned
- with its original dtype, otherwise the signal gets rescaled. Integer
- dtypes use the complete value range, float dtypes the range [-1, +1].
-
- Examples
- --------
- Processor for loading the first two seconds of an audio file, re-sampling
- it to 22.05 kHz and down-mixing it to mono:
-
- >>> proc = SignalProcessor(sample_rate=22050, num_channels=1, stop=2)
- >>> sig = proc('tests/data/audio/sample.wav')
- >>> sig
- Signal([-2470, -2553, ..., -173, -265], dtype=int16)
- >>> sig.sample_rate
- 22050
- >>> sig.num_channels
- 1
- >>> sig.length
- 2.0
-
- """
-
- def __init__(self, sample_rate=SAMPLE_RATE, num_channels=NUM_CHANNELS,
- start=START, stop=STOP, norm=NORM, gain=GAIN, dtype=DTYPE,
- **kwargs):
- # pylint: disable=unused-argument
- self.sample_rate = sample_rate
- self.num_channels = num_channels
- self.start = start
- self.stop = stop
- self.norm = norm
- self.gain = gain
- self.dtype = dtype
-
- def process(self, data, **kwargs):
- """
- Processes the given audio file.
-
- Parameters
- ----------
- data : numpy array, str or file handle
- Data to be processed.
- kwargs : dict, optional
- Keyword arguments passed to :class:`Signal`.
-
- Returns
- -------
- signal : :class:`Signal` instance
- :class:`Signal` instance.
-
- """
- # pylint: disable=unused-argument
- # update arguments passed to FramedSignal
- args = dict(sample_rate=self.sample_rate,
- num_channels=self.num_channels, start=self.start,
- stop=self.stop, norm=self.norm, gain=self.gain,
- dtype=self.dtype)
- args.update(kwargs)
- # instantiate a Signal and return it
- return Signal(data, **args)
-
- @staticmethod
- def add_arguments(parser, sample_rate=None, mono=None, start=None,
- stop=None, norm=None, gain=None):
- """
- Add signal processing related arguments to an existing parser.
-
- Parameters
- ----------
- parser : argparse parser instance
- Existing argparse parser object.
- sample_rate : int, optional
- Re-sample the signal to this sample rate [Hz].
- mono : bool, optional
- Down-mix the signal to mono.
- start : float, optional
- Start position [seconds].
- stop : float, optional
- Stop position [seconds].
- norm : bool, optional
- Normalize the signal to the range [-1, +1].
- gain : float, optional
- Adjust the gain of the signal [dB].
-
- Returns
- -------
- argparse argument group
- Signal processing argument parser group.
-
- Notes
- -----
- Parameters are included in the group only if they are not 'None'. To
- include `start` and `stop` arguments with a default value of 'None',
- i.e. do not set any start or stop time, they can be set to 'True'.
-
- """
- # add signal processing options to the existing parser
- g = parser.add_argument_group('signal processing arguments')
- if sample_rate is not None:
- g.add_argument('--sample_rate', action='store', type=int,
- default=sample_rate, help='re-sample the signal to '
- 'this sample rate [Hz]')
- if mono is not None:
- g.add_argument('--mono', dest='num_channels', action='store_const',
- const=1, help='down-mix the signal to mono')
- if start is not None:
- g.add_argument('--start', action='store', type=float,
- help='start position of the signal [seconds]')
- if stop is not None:
- g.add_argument('--stop', action='store', type=float,
- help='stop position of the signal [seconds]')
- if norm is not None:
- g.add_argument('--norm', action='store_true', default=norm,
- help='normalize the signal [default=%(default)s]')
- if gain is not None:
- g.add_argument('--gain', action='store', type=float, default=gain,
- help='adjust the gain of the signal '
- '[dB, default=%(default).1f]')
- # return the argument group so it can be modified if needed
- return g
-
-
-# functions for splitting a signal into frames
-def signal_frame(signal, index, frame_size, hop_size, origin=0, pad=0):
- """
- This function returns frame at `index` of the `signal`.
-
- Parameters
- ----------
- signal : numpy array
- Signal.
- index : int
- Index of the frame to return.
- frame_size : int
- Size of each frame in samples.
- hop_size : float
- Hop size in samples between adjacent frames.
- origin : int
- Location of the window center relative to the signal position.
- pad : int, float or str, optional
- Pad parts of the frame not covered by the signal with this value.
- The literal 'repeat' can be used to indicate that the first/last value
- should be repeated.
-
- Returns
- -------
- frame : numpy array
- Requested frame of the signal.
-
- Notes
- -----
- The reference sample of the first frame (index == 0) refers to the first
- sample of the `signal`, and each following frame is placed `hop_size`
- samples after the previous one.
-
- The window is always centered around this reference sample. Its location
- relative to the reference sample can be set with the `origin` parameter.
- Arbitrary integer values can be given:
-
- - zero centers the window on its reference sample
- - negative values shift the window to the right
- - positive values shift the window to the left
-
- An `origin` of half the size of the `frame_size` results in windows located
- to the left of the reference sample, i.e. the first frame starts at the
- first sample of the signal.
-
- The part of the frame which is not covered by the signal is padded with
- zeros.
-
- This function is totally independent of the length of the signal. Thus,
- contrary to common indexing, the index '-1' refers NOT to the last frame
- of the signal, but instead the frame left of the first frame is returned.
-
- """
- # cast variables to int
- frame_size = int(frame_size)
- # length of the signal
- num_samples = len(signal)
- # seek to the correct position in the audio signal
- ref_sample = int(index * hop_size)
- # position the window
- start = ref_sample - frame_size // 2 - int(origin)
- stop = start + frame_size
- # return the requested portion of the signal
- # Note: use NumPy's advanced indexing (i.e. trailing comma) in order to
- # avoid a memory leak (issue #321). This returns a copy of the data,
- # however, returning a simple copy of the relevant portion of the
- # signal also leaks memory
- # Update: removing this hack again, since it seems that it is not needed
- # any more with recent NumPy versions
- if start >= 0 and stop <= num_samples:
- # normal read operation, return appropriate section
- return signal[start:stop]
-
- # part of the frame falls outside the signal, padding needed
- # Note: np.pad(signal[from: to], (pad_left, pad_right), mode='constant')
- # always returns a ndarray, not the subclass (and is slower);
- # usually np.zeros_like(signal[:frame_size]) is exactly what we want
- # (i.e. zeros of frame_size length and the same type/class as the
- # signal and not just the dtype), but since we have no guarantee that
- # the signal is that long, we have to use the np.repeat workaround
- frame = np.repeat(signal[:1], frame_size, axis=0)
-
- # determine how many samples need to be padded from left/right
- left, right = 0, 0
- if start < 0:
- left = min(stop, 0) - start
- # repeat beginning of signal
- frame[:left] = np.repeat(signal[:1], left, axis=0)
- if pad != 'repeat':
- frame[:left] = pad
- start = 0
- if stop > num_samples:
- right = stop - max(start, num_samples)
- # repeat end of signal
- frame[-right:] = np.repeat(signal[-1:], right, axis=0)
- if pad != 'repeat':
- frame[-right:] = pad
- stop = num_samples
-
- # position signal inside frame
- frame[left:frame_size - right] = signal[min(start, num_samples):
- max(stop, 0)]
- # return the frame
- return frame
-
-
-FRAME_SIZE = 2048
-HOP_SIZE = 441.
-FPS = None
-ORIGIN = 0
-END_OF_SIGNAL = 'normal'
-NUM_FRAMES = None
-
-
-# classes for splitting a signal into frames
-class FramedSignal(object):
- """
- The :class:`FramedSignal` splits a :class:`Signal` into frames and makes it
- iterable and indexable.
-
- Parameters
- ----------
- signal : :class:`Signal` instance
- Signal to be split into frames.
- frame_size : int, optional
- Size of one frame [samples].
- hop_size : float, optional
- Progress `hop_size` samples between adjacent frames.
- fps : float, optional
- Use given frames per second; if set, this computes and overwrites the
- given `hop_size` value.
- origin : int, optional
- Location of the window relative to the reference sample of a frame.
- end : int or str, optional
- End of signal handling (see notes below).
- num_frames : int, optional
- Number of frames to return.
- kwargs : dict, optional
- If no :class:`Signal` instance was given, one is instantiated with
- these additional keyword arguments.
-
- Notes
- -----
- The :class:`FramedSignal` class is implemented as an iterator. It splits
- the given `signal` automatically into frames of `frame_size` length with
- `hop_size` samples (can be float, normal rounding applies) between the
- frames. The reference sample of the first frame refers to the first sample
- of the `signal`.
-
- The location of the window relative to the reference sample of a frame can
- be set with the `origin` parameter (with the same behaviour as used by
- ``scipy.ndimage`` filters). Arbitrary integer values can be given:
-
- - zero centers the window on its reference sample,
- - negative values shift the window to the right,
- - positive values shift the window to the left.
-
- Additionally, it can have the following literal values:
-
- - 'center', 'offline': the window is centered on its reference sample,
- - 'left', 'past', 'online': the window is located to the left of its
- reference sample (including the reference sample),
- - 'right', 'future', 'stream': the window is located to the right of its
- reference sample.
-
- The `end` parameter is used to handle the end of signal behaviour and
- can have these values:
-
- - 'normal': stop as soon as the whole signal got covered by at least one
- frame (i.e. pad maximally one frame),
- - 'extend': frames are returned as long as part of the frame overlaps
- with the signal to cover the whole signal.
-
- Alternatively, `num_frames` can be used to retrieve a fixed number of
- frames.
-
- In order to be able to stack multiple frames obtained with different frame
- sizes, the number of frames to be returned must be independent from the set
- `frame_size`. It is not guaranteed that every sample of the signal is
- returned in a frame unless the `origin` is either 'right' or 'future'.
-
- If used in online real-time mode the parameters `origin` and `num_frames`
- should be set to 'stream' and 1, respectively.
-
- Examples
- --------
- To chop a :class:`Signal` (or anything a :class:`Signal` can be
- instantiated from) into overlapping frames of size 2048 with adjacent
- frames being 441 samples apart:
-
- >>> sig = Signal('tests/data/audio/sample.wav')
- >>> sig
- Signal([-2494, -2510, ..., 655, 639], dtype=int16)
- >>> frames = FramedSignal(sig, frame_size=2048, hop_size=441)
- >>> frames # doctest: +ELLIPSIS
- | Name | -lr sched |
-train time (s/iter) |
-inference time (s/im) |
-train mem (GB) |
-box AP |
-mask AP |
-kp. AP |
-model id | -download | - - -
|---|---|---|---|---|---|---|---|---|---|
| Faster R-CNN | -1x | -0.219 | -0.038 | -3.1 | -36.9 | -- | - | 137781054 | -model | metrics | -
| Keypoint R-CNN | -1x | -0.313 | -0.071 | -5.0 | -53.1 | -- | 64.2 | -137781195 | -model | metrics | -
| Mask R-CNN | -1x | -0.273 | -0.043 | -3.4 | -37.8 | -34.9 | -- | 137781281 | -model | metrics | -
-It's still a work in progress. -
-""" -article = ''' -
-You can improve results by removing contractions (hadn't -> had not) -''' - -gr.Interface( - fn=translate_multiline, - inputs=gr.Textbox(lines=1, placeholder="Enter text to translate."), - outputs="text", - title=title, - description=description, - article = article, -).launch() diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_windows.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_windows.py deleted file mode 100644 index 10fc0d7e9f398dd550a42c6b8c0637684882ee60..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_windows.py +++ /dev/null @@ -1,72 +0,0 @@ -import sys -from dataclasses import dataclass - - -@dataclass -class WindowsConsoleFeatures: - """Windows features available.""" - - vt: bool = False - """The console supports VT codes.""" - truecolor: bool = False - """The console supports truecolor.""" - - -try: - import ctypes - from ctypes import LibraryLoader - - if sys.platform == "win32": - windll = LibraryLoader(ctypes.WinDLL) - else: - windll = None - raise ImportError("Not windows") - - from pip._vendor.rich._win32_console import ( - ENABLE_VIRTUAL_TERMINAL_PROCESSING, - GetConsoleMode, - GetStdHandle, - LegacyWindowsError, - ) - -except (AttributeError, ImportError, ValueError): - - # Fallback if we can't load the Windows DLL - def get_windows_console_features() -> WindowsConsoleFeatures: - features = WindowsConsoleFeatures() - return features - -else: - - def get_windows_console_features() -> WindowsConsoleFeatures: - """Get windows console features. - - Returns: - WindowsConsoleFeatures: An instance of WindowsConsoleFeatures. - """ - handle = GetStdHandle() - try: - console_mode = GetConsoleMode(handle) - success = True - except LegacyWindowsError: - console_mode = 0 - success = False - vt = bool(success and console_mode & ENABLE_VIRTUAL_TERMINAL_PROCESSING) - truecolor = False - if vt: - win_version = sys.getwindowsversion() - truecolor = win_version.major > 10 or ( - win_version.major == 10 and win_version.build >= 15063 - ) - features = WindowsConsoleFeatures(vt=vt, truecolor=truecolor) - return features - - -if __name__ == "__main__": - import platform - - features = get_windows_console_features() - from pip._vendor.rich import print - - print(f'platform="{platform.system()}"') - print(repr(features)) diff --git a/spaces/Realcat/image-matching-webui/common/utils.py b/spaces/Realcat/image-matching-webui/common/utils.py deleted file mode 100644 index 0610c340a8483b918e61004eca0516782753247d..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/common/utils.py +++ /dev/null @@ -1,548 +0,0 @@ -import os -import random -import numpy as np -import torch -from itertools import combinations -import cv2 -import gradio as gr -from hloc import matchers, extractors -from hloc.utils.base_model import dynamic_load -from hloc import match_dense, match_features, extract_features -from hloc.utils.viz import add_text, plot_keypoints -from .viz import draw_matches, fig2im, plot_images, plot_color_line_matches - -device = "cuda" if torch.cuda.is_available() else "cpu" - - -def get_model(match_conf): - Model = dynamic_load(matchers, match_conf["model"]["name"]) - model = Model(match_conf["model"]).eval().to(device) - return model - - -def get_feature_model(conf): - Model = dynamic_load(extractors, conf["model"]["name"]) - model = Model(conf["model"]).eval().to(device) - return model - - -def gen_examples(): - random.seed(1) - example_matchers = [ - "disk+lightglue", - "loftr", - "disk", - "d2net", - "topicfm", - "superpoint+superglue", - "disk+dualsoftmax", - "lanet", - ] - - def gen_images_pairs(path: str, count: int = 5): - imgs_list = [ - os.path.join(path, file) - for file in os.listdir(path) - if file.lower().endswith((".jpg", ".jpeg", ".png")) - ] - pairs = list(combinations(imgs_list, 2)) - selected = random.sample(range(len(pairs)), count) - return [pairs[i] for i in selected] - - # image pair path - path = "datasets/sacre_coeur/mapping" - pairs = gen_images_pairs(path, len(example_matchers)) - match_setting_threshold = 0.1 - match_setting_max_features = 2000 - detect_keypoints_threshold = 0.01 - enable_ransac = True - ransac_method = "RANSAC" - ransac_reproj_threshold = 8 - ransac_confidence = 0.999 - ransac_max_iter = 10000 - input_lists = [] - for pair, mt in zip(pairs, example_matchers): - input_lists.append( - [ - pair[0], - pair[1], - match_setting_threshold, - match_setting_max_features, - detect_keypoints_threshold, - mt, - # enable_ransac, - ransac_method, - ransac_reproj_threshold, - ransac_confidence, - ransac_max_iter, - ] - ) - return input_lists - - -def filter_matches( - pred, - ransac_method="RANSAC", - ransac_reproj_threshold=8, - ransac_confidence=0.999, - ransac_max_iter=10000, -): - mkpts0 = None - mkpts1 = None - feature_type = None - if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys(): - mkpts0 = pred["keypoints0_orig"] - mkpts1 = pred["keypoints1_orig"] - feature_type = "KEYPOINT" - elif ( - "line_keypoints0_orig" in pred.keys() - and "line_keypoints1_orig" in pred.keys() - ): - mkpts0 = pred["line_keypoints0_orig"] - mkpts1 = pred["line_keypoints1_orig"] - feature_type = "LINE" - else: - return pred - if mkpts0 is None or mkpts0 is None: - return pred - if ransac_method not in ransac_zoo.keys(): - ransac_method = "RANSAC" - - if len(mkpts0) < 4: - return pred - H, mask = cv2.findHomography( - mkpts0, - mkpts1, - method=ransac_zoo[ransac_method], - ransacReprojThreshold=ransac_reproj_threshold, - confidence=ransac_confidence, - maxIters=ransac_max_iter, - ) - mask = np.array(mask.ravel().astype("bool"), dtype="bool") - if H is not None: - if feature_type == "KEYPOINT": - pred["keypoints0_orig"] = mkpts0[mask] - pred["keypoints1_orig"] = mkpts1[mask] - pred["mconf"] = pred["mconf"][mask] - elif feature_type == "LINE": - pred["line_keypoints0_orig"] = mkpts0[mask] - pred["line_keypoints1_orig"] = mkpts1[mask] - return pred - - -def compute_geom( - pred, - ransac_method="RANSAC", - ransac_reproj_threshold=8, - ransac_confidence=0.999, - ransac_max_iter=10000, -) -> dict: - mkpts0 = None - mkpts1 = None - - if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys(): - mkpts0 = pred["keypoints0_orig"] - mkpts1 = pred["keypoints1_orig"] - - if ( - "line_keypoints0_orig" in pred.keys() - and "line_keypoints1_orig" in pred.keys() - ): - mkpts0 = pred["line_keypoints0_orig"] - mkpts1 = pred["line_keypoints1_orig"] - - if mkpts0 is not None and mkpts1 is not None: - if len(mkpts0) < 8: - return {} - h1, w1, _ = pred["image0_orig"].shape - geo_info = {} - F, inliers = cv2.findFundamentalMat( - mkpts0, - mkpts1, - method=ransac_zoo[ransac_method], - ransacReprojThreshold=ransac_reproj_threshold, - confidence=ransac_confidence, - maxIters=ransac_max_iter, - ) - if F is not None: - geo_info["Fundamental"] = F.tolist() - H, _ = cv2.findHomography( - mkpts1, - mkpts0, - method=ransac_zoo[ransac_method], - ransacReprojThreshold=ransac_reproj_threshold, - confidence=ransac_confidence, - maxIters=ransac_max_iter, - ) - if H is not None: - geo_info["Homography"] = H.tolist() - _, H1, H2 = cv2.stereoRectifyUncalibrated( - mkpts0.reshape(-1, 2), - mkpts1.reshape(-1, 2), - F, - imgSize=(w1, h1), - ) - geo_info["H1"] = H1.tolist() - geo_info["H2"] = H2.tolist() - return geo_info - else: - return {} - - -def wrap_images(img0, img1, geo_info, geom_type): - h1, w1, _ = img0.shape - h2, w2, _ = img1.shape - result_matrix = None - if geo_info is not None and len(geo_info) != 0: - rectified_image0 = img0 - rectified_image1 = None - H = np.array(geo_info["Homography"]) - F = np.array(geo_info["Fundamental"]) - title = [] - if geom_type == "Homography": - rectified_image1 = cv2.warpPerspective( - img1, H, (img0.shape[1] + img1.shape[1], img0.shape[0]) - ) - result_matrix = H - title = ["Image 0", "Image 1 - warped"] - elif geom_type == "Fundamental": - H1, H2 = np.array(geo_info["H1"]), np.array(geo_info["H2"]) - rectified_image0 = cv2.warpPerspective(img0, H1, (w1, h1)) - rectified_image1 = cv2.warpPerspective(img1, H2, (w2, h2)) - result_matrix = F - title = ["Image 0 - warped", "Image 1 - warped"] - else: - print("Error: Unknown geometry type") - fig = plot_images( - [rectified_image0.squeeze(), rectified_image1.squeeze()], - title, - dpi=300, - ) - dictionary = { - "row1": result_matrix[0].tolist(), - "row2": result_matrix[1].tolist(), - "row3": result_matrix[2].tolist(), - } - return fig2im(fig), dictionary - else: - return None, None - - -def change_estimate_geom(input_image0, input_image1, matches_info, choice): - if ( - matches_info is None - or len(matches_info) < 1 - or "geom_info" not in matches_info.keys() - ): - return None, None - geom_info = matches_info["geom_info"] - wrapped_images = None - if choice != "No": - wrapped_images, _ = wrap_images( - input_image0, input_image1, geom_info, choice - ) - return wrapped_images, matches_info - else: - return None, None - - -def display_matches(pred: dict, titles=[], dpi=300): - img0 = pred["image0_orig"] - img1 = pred["image1_orig"] - - num_inliers = 0 - if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys(): - mkpts0 = pred["keypoints0_orig"] - mkpts1 = pred["keypoints1_orig"] - num_inliers = len(mkpts0) - if "mconf" in pred.keys(): - mconf = pred["mconf"] - else: - mconf = np.ones(len(mkpts0)) - fig_mkpts = draw_matches( - mkpts0, - mkpts1, - img0, - img1, - mconf, - dpi=dpi, - titles=titles, - ) - fig = fig_mkpts - if "line0_orig" in pred.keys() and "line1_orig" in pred.keys(): - # lines - mtlines0 = pred["line0_orig"] - mtlines1 = pred["line1_orig"] - num_inliers = len(mtlines0) - fig_lines = plot_images( - [img0.squeeze(), img1.squeeze()], - ["Image 0 - matched lines", "Image 1 - matched lines"], - dpi=300, - ) - fig_lines = plot_color_line_matches([mtlines0, mtlines1], lw=2) - fig_lines = fig2im(fig_lines) - - # keypoints - mkpts0 = pred["line_keypoints0_orig"] - mkpts1 = pred["line_keypoints1_orig"] - - if mkpts0 is not None and mkpts1 is not None: - num_inliers = len(mkpts0) - if "mconf" in pred.keys(): - mconf = pred["mconf"] - else: - mconf = np.ones(len(mkpts0)) - fig_mkpts = draw_matches(mkpts0, mkpts1, img0, img1, mconf, dpi=300) - fig_lines = cv2.resize( - fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0]) - ) - fig = np.concatenate([fig_mkpts, fig_lines], axis=0) - else: - fig = fig_lines - return fig, num_inliers - - -def run_matching( - image0, - image1, - match_threshold, - extract_max_keypoints, - keypoint_threshold, - key, - # enable_ransac=False, - ransac_method="RANSAC", - ransac_reproj_threshold=8, - ransac_confidence=0.999, - ransac_max_iter=10000, - choice_estimate_geom="Homography", -): - # image0 and image1 is RGB mode - if image0 is None or image1 is None: - raise gr.Error("Error: No images found! Please upload two images.") - # init output - output_keypoints = None - output_matches_raw = None - output_matches_ransac = None - - model = matcher_zoo[key] - match_conf = model["config"] - # update match config - match_conf["model"]["match_threshold"] = match_threshold - match_conf["model"]["max_keypoints"] = extract_max_keypoints - - matcher = get_model(match_conf) - if model["dense"]: - pred = match_dense.match_images( - matcher, image0, image1, match_conf["preprocessing"], device=device - ) - del matcher - extract_conf = None - else: - extract_conf = model["config_feature"] - # update extract config - extract_conf["model"]["max_keypoints"] = extract_max_keypoints - extract_conf["model"]["keypoint_threshold"] = keypoint_threshold - extractor = get_feature_model(extract_conf) - pred0 = extract_features.extract( - extractor, image0, extract_conf["preprocessing"] - ) - pred1 = extract_features.extract( - extractor, image1, extract_conf["preprocessing"] - ) - pred = match_features.match_images(matcher, pred0, pred1) - del extractor - - # plot images with keypoints - titles = [ - "Image 0 - Keypoints", - "Image 1 - Keypoints", - ] - output_keypoints = plot_images([image0, image1], titles=titles, dpi=300) - if "keypoints0" in pred.keys() and "keypoints1" in pred.keys(): - plot_keypoints([pred["keypoints0"], pred["keypoints1"]]) - text = ( - f"# keypoints0: {len(pred['keypoints0'])} \n" - + f"# keypoints1: {len(pred['keypoints1'])}" - ) - add_text(0, text, fs=15) - output_keypoints = fig2im(output_keypoints) - - # plot images with raw matches - titles = [ - "Image 0 - Raw matched keypoints", - "Image 1 - Raw matched keypoints", - ] - - output_matches_raw, num_matches_raw = display_matches(pred, titles=titles) - - # if enable_ransac: - filter_matches( - pred, - ransac_method=ransac_method, - ransac_reproj_threshold=ransac_reproj_threshold, - ransac_confidence=ransac_confidence, - ransac_max_iter=ransac_max_iter, - ) - - # plot images with ransac matches - titles = [ - "Image 0 - Ransac matched keypoints", - "Image 1 - Ransac matched keypoints", - ] - output_matches_ransac, num_matches_ransac = display_matches( - pred, titles=titles - ) - - # plot wrapped images - geom_info = compute_geom(pred) - output_wrapped, _ = change_estimate_geom( - pred["image0_orig"], - pred["image1_orig"], - {"geom_info": geom_info}, - choice_estimate_geom, - ) - - del pred - - return ( - output_keypoints, - output_matches_raw, - output_matches_ransac, - { - "number raw matches": num_matches_raw, - "number ransac matches": num_matches_ransac, - }, - { - "match_conf": match_conf, - "extractor_conf": extract_conf, - }, - { - "geom_info": geom_info, - }, - output_wrapped, - # geometry_result, - ) - - -# @ref: https://docs.opencv.org/4.x/d0/d74/md__build_4_x-contrib_docs-lin64_opencv_doc_tutorials_calib3d_usac.html -# AND: https://opencv.org/blog/2021/06/09/evaluating-opencvs-new-ransacs -ransac_zoo = { - "RANSAC": cv2.RANSAC, - "USAC_MAGSAC": cv2.USAC_MAGSAC, - "USAC_DEFAULT": cv2.USAC_DEFAULT, - "USAC_FM_8PTS": cv2.USAC_FM_8PTS, - "USAC_PROSAC": cv2.USAC_PROSAC, - "USAC_FAST": cv2.USAC_FAST, - "USAC_ACCURATE": cv2.USAC_ACCURATE, - "USAC_PARALLEL": cv2.USAC_PARALLEL, -} - -# Matchers collections -matcher_zoo = { - "gluestick": {"config": match_dense.confs["gluestick"], "dense": True}, - "sold2": {"config": match_dense.confs["sold2"], "dense": True}, - # 'dedode-sparse': { - # 'config': match_dense.confs['dedode_sparse'], - # 'dense': True # dense mode, we need 2 images - # }, - "loftr": {"config": match_dense.confs["loftr"], "dense": True}, - "topicfm": {"config": match_dense.confs["topicfm"], "dense": True}, - "aspanformer": {"config": match_dense.confs["aspanformer"], "dense": True}, - "dedode": { - "config": match_features.confs["Dual-Softmax"], - "config_feature": extract_features.confs["dedode"], - "dense": False, - }, - "superpoint+superglue": { - "config": match_features.confs["superglue"], - "config_feature": extract_features.confs["superpoint_max"], - "dense": False, - }, - "superpoint+lightglue": { - "config": match_features.confs["superpoint-lightglue"], - "config_feature": extract_features.confs["superpoint_max"], - "dense": False, - }, - "disk": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["disk"], - "dense": False, - }, - "disk+dualsoftmax": { - "config": match_features.confs["Dual-Softmax"], - "config_feature": extract_features.confs["disk"], - "dense": False, - }, - "superpoint+dualsoftmax": { - "config": match_features.confs["Dual-Softmax"], - "config_feature": extract_features.confs["superpoint_max"], - "dense": False, - }, - "disk+lightglue": { - "config": match_features.confs["disk-lightglue"], - "config_feature": extract_features.confs["disk"], - "dense": False, - }, - "superpoint+mnn": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["superpoint_max"], - "dense": False, - }, - "sift+sgmnet": { - "config": match_features.confs["sgmnet"], - "config_feature": extract_features.confs["sift"], - "dense": False, - }, - "sosnet": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["sosnet"], - "dense": False, - }, - "hardnet": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["hardnet"], - "dense": False, - }, - "d2net": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["d2net-ss"], - "dense": False, - }, - "rord": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["rord"], - "dense": False, - }, - # "d2net-ms": { - # "config": match_features.confs["NN-mutual"], - # "config_feature": extract_features.confs["d2net-ms"], - # "dense": False, - # }, - "alike": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["alike"], - "dense": False, - }, - "lanet": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["lanet"], - "dense": False, - }, - "r2d2": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["r2d2"], - "dense": False, - }, - "darkfeat": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["darkfeat"], - "dense": False, - }, - "sift": { - "config": match_features.confs["NN-mutual"], - "config_feature": extract_features.confs["sift"], - "dense": False, - }, - # "roma": {"config": match_dense.confs["roma"], "dense": True}, - # "DKMv3": {"config": match_dense.confs["dkm"], "dense": True}, -} diff --git a/spaces/Realcat/image-matching-webui/datasets/sacre_coeur/README.md b/spaces/Realcat/image-matching-webui/datasets/sacre_coeur/README.md deleted file mode 100644 index d69115f7f262f6d97aa52bed9083bf3374249645..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/datasets/sacre_coeur/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Sacre Coeur demo - -We provide here a subset of images depicting the Sacre Coeur. These images were obtained from the [Image Matching Challenge 2021](https://www.cs.ubc.ca/research/image-matching-challenge/2021/data/) and were originally collected by the [Yahoo Flickr Creative Commons 100M (YFCC) dataset](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/). diff --git a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/utils/misc.py b/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/utils/misc.py deleted file mode 100644 index d9b6a4a5f5920cde89bdecbf2a444aaea8ff51f3..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/utils/misc.py +++ /dev/null @@ -1,184 +0,0 @@ -import os -import contextlib -import joblib -from typing import Union -from loguru import _Logger, logger -from itertools import chain - -import torch -from yacs.config import CfgNode as CN -from pytorch_lightning.utilities import rank_zero_only -import cv2 -import numpy as np - - -def lower_config(yacs_cfg): - if not isinstance(yacs_cfg, CN): - return yacs_cfg - return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()} - - -def upper_config(dict_cfg): - if not isinstance(dict_cfg, dict): - return dict_cfg - return {k.upper(): upper_config(v) for k, v in dict_cfg.items()} - - -def log_on(condition, message, level): - if condition: - assert level in ["INFO", "DEBUG", "WARNING", "ERROR", "CRITICAL"] - logger.log(level, message) - - -def get_rank_zero_only_logger(logger: _Logger): - if rank_zero_only.rank == 0: - return logger - else: - for _level in logger._core.levels.keys(): - level = _level.lower() - setattr(logger, level, lambda x: None) - logger._log = lambda x: None - return logger - - -def setup_gpus(gpus: Union[str, int]) -> int: - """A temporary fix for pytorch-lighting 1.3.x""" - gpus = str(gpus) - gpu_ids = [] - - if "," not in gpus: - n_gpus = int(gpus) - return n_gpus if n_gpus != -1 else torch.cuda.device_count() - else: - gpu_ids = [i.strip() for i in gpus.split(",") if i != ""] - - # setup environment variables - visible_devices = os.getenv("CUDA_VISIBLE_DEVICES") - if visible_devices is None: - os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" - os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(i) for i in gpu_ids) - visible_devices = os.getenv("CUDA_VISIBLE_DEVICES") - logger.warning( - f"[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}" - ) - else: - logger.warning( - "[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process." - ) - return len(gpu_ids) - - -def flattenList(x): - return list(chain(*x)) - - -@contextlib.contextmanager -def tqdm_joblib(tqdm_object): - """Context manager to patch joblib to report into tqdm progress bar given as argument - - Usage: - with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar: - Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10)) - - When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing) - ret_vals = Parallel(n_jobs=args.world_size)( - delayed(lambda x: _compute_cov_score(pid, *x))(param) - for param in tqdm(combinations(image_ids, 2), - desc=f'Computing cov_score of [{pid}]', - total=len(image_ids)*(len(image_ids)-1)/2)) - Src: https://stackoverflow.com/a/58936697 - """ - - class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def __call__(self, *args, **kwargs): - tqdm_object.update(n=self.batch_size) - return super().__call__(*args, **kwargs) - - old_batch_callback = joblib.parallel.BatchCompletionCallBack - joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback - try: - yield tqdm_object - finally: - joblib.parallel.BatchCompletionCallBack = old_batch_callback - tqdm_object.close() - - -def draw_points(img, points, color=(0, 255, 0), radius=3): - dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])] - for i in range(points.shape[0]): - cv2.circle(img, dp[i], radius=radius, color=color) - return img - - -def draw_match( - img1, - img2, - corr1, - corr2, - inlier=[True], - color=None, - radius1=1, - radius2=1, - resize=None, -): - if resize is not None: - scale1, scale2 = [img1.shape[1] / resize[0], img1.shape[0] / resize[1]], [ - img2.shape[1] / resize[0], - img2.shape[0] / resize[1], - ] - img1, img2 = cv2.resize(img1, resize, interpolation=cv2.INTER_AREA), cv2.resize( - img2, resize, interpolation=cv2.INTER_AREA - ) - corr1, corr2 = ( - corr1 / np.asarray(scale1)[np.newaxis], - corr2 / np.asarray(scale2)[np.newaxis], - ) - corr1_key = [ - cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0]) - ] - corr2_key = [ - cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0]) - ] - - assert len(corr1) == len(corr2) - - draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))] - if color is None: - color = [(0, 255, 0) if cur_inlier else (0, 0, 255) for cur_inlier in inlier] - if len(color) == 1: - display = cv2.drawMatches( - img1, - corr1_key, - img2, - corr2_key, - draw_matches, - None, - matchColor=color[0], - singlePointColor=color[0], - flags=4, - ) - else: - height, width = max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1] - display = np.zeros([height, width, 3], np.uint8) - display[: img1.shape[0], : img1.shape[1]] = img1 - display[: img2.shape[0], img1.shape[1] :] = img2 - for i in range(len(corr1)): - left_x, left_y, right_x, right_y = ( - int(corr1[i][0]), - int(corr1[i][1]), - int(corr2[i][0] + img1.shape[1]), - int(corr2[i][1]), - ) - cur_color = (int(color[i][0]), int(color[i][1]), int(color[i][2])) - cv2.line( - display, - (left_x, left_y), - (right_x, right_y), - cur_color, - 1, - lineType=cv2.LINE_AA, - ) - return display diff --git a/spaces/Reha2704/VToonify/vtoonify/LICENSE.md b/spaces/Reha2704/VToonify/vtoonify/LICENSE.md deleted file mode 100644 index a7e5837d44361b7aa1d633b9d36783ac838a45bc..0000000000000000000000000000000000000000 --- a/spaces/Reha2704/VToonify/vtoonify/LICENSE.md +++ /dev/null @@ -1,12 +0,0 @@ -# S-Lab License 1.0 - -Copyright 2022 S-Lab - -Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met: -1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. -2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. -3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\ -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -4. In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work. - - diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/datasets/ade20k.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/datasets/ade20k.py deleted file mode 100644 index efc8b4bb20c981f3db6df7eb52b3dc0744c94cc0..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/datasets/ade20k.py +++ /dev/null @@ -1,54 +0,0 @@ -# dataset settings -dataset_type = 'ADE20KDataset' -data_root = 'data/ade/ADEChallengeData2016' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -crop_size = (512, 512) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', reduce_zero_label=True), - dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), - dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), - dict(type='RandomFlip', prob=0.5), - dict(type='PhotoMetricDistortion'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_semantic_seg']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(2048, 512), - # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -data = dict( - samples_per_gpu=4, - workers_per_gpu=4, - train=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/training', - ann_dir='annotations/training', - pipeline=train_pipeline), - val=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/validation', - ann_dir='annotations/validation', - pipeline=test_pipeline), - test=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/validation', - ann_dir='annotations/validation', - pipeline=test_pipeline)) diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/upernet_global_small/test_config_h32.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/upernet_global_small/test_config_h32.py deleted file mode 100644 index a31e3874f76f9f7b089ac8834d85df2441af9b0e..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/upernet_global_small/test_config_h32.py +++ /dev/null @@ -1,39 +0,0 @@ -_base_ = [ - '../../configs/_base_/models/upernet_uniformer.py', - '../../configs/_base_/datasets/ade20k.py', - '../../configs/_base_/default_runtime.py', - '../../configs/_base_/schedules/schedule_160k.py' -] -model = dict( - backbone=dict( - type='UniFormer', - embed_dim=[64, 128, 320, 512], - layers=[3, 4, 8, 3], - head_dim=64, - drop_path_rate=0.25, - windows=False, - hybrid=True, - window_size=32 - ), - decode_head=dict( - in_channels=[64, 128, 320, 512], - num_classes=150 - ), - auxiliary_head=dict( - in_channels=320, - num_classes=150 - )) - -# AdamW optimizer, no weight decay for position embedding & layer norm in backbone -optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01, - paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.), - 'relative_position_bias_table': dict(decay_mult=0.), - 'norm': dict(decay_mult=0.)})) - -lr_config = dict(_delete_=True, policy='poly', - warmup='linear', - warmup_iters=1500, - warmup_ratio=1e-6, - power=1.0, min_lr=0.0, by_epoch=False) - -data=dict(samples_per_gpu=2) \ No newline at end of file diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/conv.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/conv.py deleted file mode 100644 index cf54491997a48ac3e7fadc4183ab7bf3e831024c..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/conv.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from torch import nn - -from .registry import CONV_LAYERS - -CONV_LAYERS.register_module('Conv1d', module=nn.Conv1d) -CONV_LAYERS.register_module('Conv2d', module=nn.Conv2d) -CONV_LAYERS.register_module('Conv3d', module=nn.Conv3d) -CONV_LAYERS.register_module('Conv', module=nn.Conv2d) - - -def build_conv_layer(cfg, *args, **kwargs): - """Build convolution layer. - - Args: - cfg (None or dict): The conv layer config, which should contain: - - type (str): Layer type. - - layer args: Args needed to instantiate an conv layer. - args (argument list): Arguments passed to the `__init__` - method of the corresponding conv layer. - kwargs (keyword arguments): Keyword arguments passed to the `__init__` - method of the corresponding conv layer. - - Returns: - nn.Module: Created conv layer. - """ - if cfg is None: - cfg_ = dict(type='Conv2d') - else: - if not isinstance(cfg, dict): - raise TypeError('cfg must be a dict') - if 'type' not in cfg: - raise KeyError('the cfg dict must contain the key "type"') - cfg_ = cfg.copy() - - layer_type = cfg_.pop('type') - if layer_type not in CONV_LAYERS: - raise KeyError(f'Unrecognized norm type {layer_type}') - else: - conv_layer = CONV_LAYERS.get(layer_type) - - layer = conv_layer(*args, **kwargs, **cfg_) - - return layer diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/dist_utils.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/dist_utils.py deleted file mode 100644 index d3a1ef3fda5ceeb31bf15a73779da1b1903ab0fe..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/dist_utils.py +++ /dev/null @@ -1,164 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import functools -import os -import subprocess -from collections import OrderedDict - -import torch -import torch.multiprocessing as mp -from torch import distributed as dist -from torch._utils import (_flatten_dense_tensors, _take_tensors, - _unflatten_dense_tensors) - - -def init_dist(launcher, backend='nccl', **kwargs): - if mp.get_start_method(allow_none=True) is None: - mp.set_start_method('spawn') - if launcher == 'pytorch': - _init_dist_pytorch(backend, **kwargs) - elif launcher == 'mpi': - _init_dist_mpi(backend, **kwargs) - elif launcher == 'slurm': - _init_dist_slurm(backend, **kwargs) - else: - raise ValueError(f'Invalid launcher type: {launcher}') - - -def _init_dist_pytorch(backend, **kwargs): - # TODO: use local_rank instead of rank % num_gpus - rank = int(os.environ['RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_mpi(backend, **kwargs): - # TODO: use local_rank instead of rank % num_gpus - rank = int(os.environ['OMPI_COMM_WORLD_RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_slurm(backend, port=None): - """Initialize slurm distributed training environment. - - If argument ``port`` is not specified, then the master port will be system - environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system - environment variable, then a default port ``29500`` will be used. - - Args: - backend (str): Backend of torch.distributed. - port (int, optional): Master port. Defaults to None. - """ - proc_id = int(os.environ['SLURM_PROCID']) - ntasks = int(os.environ['SLURM_NTASKS']) - node_list = os.environ['SLURM_NODELIST'] - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(proc_id % num_gpus) - addr = subprocess.getoutput( - f'scontrol show hostname {node_list} | head -n1') - # specify master port - if port is not None: - os.environ['MASTER_PORT'] = str(port) - elif 'MASTER_PORT' in os.environ: - pass # use MASTER_PORT in the environment variable - else: - # 29500 is torch.distributed default port - os.environ['MASTER_PORT'] = '29500' - # use MASTER_ADDR in the environment variable if it already exists - if 'MASTER_ADDR' not in os.environ: - os.environ['MASTER_ADDR'] = addr - os.environ['WORLD_SIZE'] = str(ntasks) - os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) - os.environ['RANK'] = str(proc_id) - dist.init_process_group(backend=backend) - - -def get_dist_info(): - if dist.is_available() and dist.is_initialized(): - rank = dist.get_rank() - world_size = dist.get_world_size() - else: - rank = 0 - world_size = 1 - return rank, world_size - - -def master_only(func): - - @functools.wraps(func) - def wrapper(*args, **kwargs): - rank, _ = get_dist_info() - if rank == 0: - return func(*args, **kwargs) - - return wrapper - - -def allreduce_params(params, coalesce=True, bucket_size_mb=-1): - """Allreduce parameters. - - Args: - params (list[torch.Parameters]): List of parameters or buffers of a - model. - coalesce (bool, optional): Whether allreduce parameters as a whole. - Defaults to True. - bucket_size_mb (int, optional): Size of bucket, the unit is MB. - Defaults to -1. - """ - _, world_size = get_dist_info() - if world_size == 1: - return - params = [param.data for param in params] - if coalesce: - _allreduce_coalesced(params, world_size, bucket_size_mb) - else: - for tensor in params: - dist.all_reduce(tensor.div_(world_size)) - - -def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): - """Allreduce gradients. - - Args: - params (list[torch.Parameters]): List of parameters of a model - coalesce (bool, optional): Whether allreduce parameters as a whole. - Defaults to True. - bucket_size_mb (int, optional): Size of bucket, the unit is MB. - Defaults to -1. - """ - grads = [ - param.grad.data for param in params - if param.requires_grad and param.grad is not None - ] - _, world_size = get_dist_info() - if world_size == 1: - return - if coalesce: - _allreduce_coalesced(grads, world_size, bucket_size_mb) - else: - for tensor in grads: - dist.all_reduce(tensor.div_(world_size)) - - -def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): - if bucket_size_mb > 0: - bucket_size_bytes = bucket_size_mb * 1024 * 1024 - buckets = _take_tensors(tensors, bucket_size_bytes) - else: - buckets = OrderedDict() - for tensor in tensors: - tp = tensor.type() - if tp not in buckets: - buckets[tp] = [] - buckets[tp].append(tensor) - buckets = buckets.values() - - for bucket in buckets: - flat_tensors = _flatten_dense_tensors(bucket) - dist.all_reduce(flat_tensors) - flat_tensors.div_(world_size) - for tensor, synced in zip( - bucket, _unflatten_dense_tensors(flat_tensors, bucket)): - tensor.copy_(synced) diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/__init__.py deleted file mode 100644 index abfbe2624eecb73b029e9bcb7e2283bbf2a744ea..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from .coarse_mask_head import CoarseMaskHead -from .fcn_mask_head import FCNMaskHead -from .feature_relay_head import FeatureRelayHead -from .fused_semantic_head import FusedSemanticHead -from .global_context_head import GlobalContextHead -from .grid_head import GridHead -from .htc_mask_head import HTCMaskHead -from .mask_point_head import MaskPointHead -from .maskiou_head import MaskIoUHead -from .scnet_mask_head import SCNetMaskHead -from .scnet_semantic_head import SCNetSemanticHead - -__all__ = [ - 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', - 'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead', - 'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead' -] diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/balanced_l1_loss.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/balanced_l1_loss.py deleted file mode 100644 index 7bcd13ff26dbdc9f6eff8d7c7b5bde742a8d7d1d..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/balanced_l1_loss.py +++ /dev/null @@ -1,120 +0,0 @@ -import mmcv -import numpy as np -import torch -import torch.nn as nn - -from ..builder import LOSSES -from .utils import weighted_loss - - -@mmcv.jit(derivate=True, coderize=True) -@weighted_loss -def balanced_l1_loss(pred, - target, - beta=1.0, - alpha=0.5, - gamma=1.5, - reduction='mean'): - """Calculate balanced L1 loss. - - Please see the `Libra R-CNN
A ConvNet for the 2020s | Github Repo
" - -examples = ['test.jpeg'] - -gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False, examples=examples).launch(enable_queue=True) \ No newline at end of file diff --git a/spaces/alan-chen-intel/dagan-demo/run.py b/spaces/alan-chen-intel/dagan-demo/run.py deleted file mode 100644 index 96e6f3750adfad7fddc372faea1feb54888323a7..0000000000000000000000000000000000000000 --- a/spaces/alan-chen-intel/dagan-demo/run.py +++ /dev/null @@ -1,219 +0,0 @@ -import os, sys -import yaml -from argparse import ArgumentParser -from tqdm import tqdm -import modules.generator as GEN -import imageio -import numpy as np -from skimage.transform import resize -from skimage import img_as_ubyte -import torch -from sync_batchnorm import DataParallelWithCallback -import depth -from modules.keypoint_detector import KPDetector -from scipy.spatial import ConvexHull -from collections import OrderedDict -import warnings -warnings.filterwarnings("ignore") - -def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, - use_relative_movement=False, use_relative_jacobian=False): - if adapt_movement_scale: - source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume - driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume - adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) - else: - adapt_movement_scale = 1 - - kp_new = {k: v for k, v in kp_driving.items()} - - if use_relative_movement: - kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) - kp_value_diff *= adapt_movement_scale - kp_new['value'] = kp_value_diff + kp_source['value'] - - if use_relative_jacobian: - jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) - kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) - return kp_new - -def find_best_frame(source, driving, cpu=False): - import face_alignment - - def normalize_kp(kp): - kp = kp - kp.mean(axis=0, keepdims=True) - area = ConvexHull(kp[:, :2]).volume - area = np.sqrt(area) - kp[:, :2] = kp[:, :2] / area - return kp - - fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True, - device='cpu' if cpu else 'cuda') - kp_source = fa.get_landmarks(255 * source)[0] - kp_source = normalize_kp(kp_source) - norm = float('inf') - frame_num = 0 - for i, image in tqdm(enumerate(driving)): - kp_driving = fa.get_landmarks(255 * image)[0] - kp_driving = normalize_kp(kp_driving) - new_norm = (np.abs(kp_source - kp_driving) ** 2).sum() - if new_norm < norm: - norm = new_norm - frame_num = i - return frame_num - -def animation(source_image, driving_video, generator, kp_detector, depth_encoder, depth_decoder, relative=True, adapt_scale=True, cpu=False): - with torch.no_grad(): - predictions = [] - source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) - driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3) - if not cpu: - source = source.cuda() - driving = driving.cuda() - outputs = depth_decoder(depth_encoder(source)) - depth_source = outputs[("disp", 0)] - - outputs = depth_decoder(depth_encoder(driving[:, :, 0])) - depth_driving = outputs[("disp", 0)] - source_kp = torch.cat((source,depth_source),1) - driving_kp = torch.cat((driving[:, :, 0],depth_driving),1) - - kp_source = kp_detector(source_kp) - kp_driving_initial = kp_detector(driving_kp) - - - for frame_idx in tqdm(range(driving.shape[2])): - driving_frame = driving[:, :, frame_idx] - - if not cpu: - driving_frame = driving_frame.cuda() - outputs = depth_decoder(depth_encoder(driving_frame)) - depth_map = outputs[("disp", 0)] - - frame = torch.cat((driving_frame,depth_map),1) - kp_driving = kp_detector(frame) - - kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving, - kp_driving_initial=kp_driving_initial, use_relative_movement=relative, - use_relative_jacobian=relative, adapt_movement_scale=adapt_scale) - out = generator(source, kp_source=kp_source, kp_driving=kp_norm,source_depth = depth_source, driving_depth = depth_map) - - predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) - return predictions - -def reconstruction(source_image, driving_video, generator, kp_detector, depth_encoder, depth_decoder, cpu=False): - with torch.no_grad(): - predictions = [] - source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) - driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3) - if not cpu: - source = source.cuda() - driving = driving.cuda() - outputs = depth_decoder(depth_encoder(source)) - depth_source = outputs[("disp", 0)] - - outputs = depth_decoder(depth_encoder(driving[:, :, 0])) - depth_driving = outputs[("disp", 0)] - source_kp = torch.cat((source,depth_source),1) - driving_kp = torch.cat((driving[:, :, 0],depth_driving),1) - - kp_source = kp_detector(source_kp) - kp_driving_initial = kp_detector(driving_kp) - - - for frame_idx in tqdm(range(driving.shape[2])): - driving_frame = driving[:, :, frame_idx] - - if not cpu: - driving_frame = driving_frame.cuda() - outputs = depth_decoder(depth_encoder(driving_frame)) - depth_map = outputs[("disp", 0)] - - frame = torch.cat((driving_frame,depth_map),1) - kp_driving = kp_detector(frame) - - out = generator(source, kp_source=kp_source, kp_driving=kp_driving,source_depth = depth_source, driving_depth = depth_map) - - predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) - return predictions - -parser = ArgumentParser() -parser.add_argument("--source_image", help="path to source image") -parser.add_argument("--driving_video", help="path to driving video") -parser.add_argument("--result_video", help="path to output") -parser.add_argument("--mode", type=str, choices=["reconstruction", "animation"], help="mode to run") - -opt = parser.parse_args() - -with open("config/vox-adv-256.yaml") as f: - config = yaml.load(f) - -generator = GEN.SPADEDepthAwareGenerator(**config['model_params']['generator_params'],**config['model_params']['common_params']) - -config['model_params']['common_params']['num_channels'] = 4 -kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -cpu = False if torch.cuda.is_available() else True - -g_checkpoint = torch.load("checkpoints/generator.pth", map_location=device) -kp_checkpoint = torch.load("checkpoints/kp_detector.pth", map_location=device) - -ckp_generator = OrderedDict((k.replace('module.',''),v) for k,v in g_checkpoint.items()) -generator.load_state_dict(ckp_generator) -ckp_kp_detector = OrderedDict((k.replace('module.',''),v) for k,v in kp_checkpoint.items()) -kp_detector.load_state_dict(ckp_kp_detector) - -depth_encoder = depth.ResnetEncoder(18, False) -depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)) -loaded_dict_enc = torch.load('checkpoints/encoder.pth', map_location=device) -loaded_dict_dec = torch.load('checkpoints/depth.pth', map_location=device) - -filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()} -depth_encoder.load_state_dict(filtered_dict_enc) -depth_decoder.load_state_dict(loaded_dict_dec) - -depth_encoder.eval() -depth_decoder.eval() -generator.eval() -kp_detector.eval() - -generator.to(device) -kp_detector.to(device) -depth_encoder.to(device) -depth_decoder.to(device) - -with torch.inference_mode(): - if torch.cuda.is_available(): - torch.cuda.ipc_collect() - torch.cuda.empty_cache() - - reader = imageio.get_reader(opt.driving_video) - fps = reader.get_meta_data()['fps'] - driving_video = [] - try: - for im in reader: - driving_video.append(im) - except RuntimeError: - pass - reader.close() - - if opt.mode == 'animation': - source_image = imageio.imread(opt.source_image) - elif opt.mode == 'reconstruction': - source_image = driving_video[0] - - source_image = resize(source_image, (256, 256))[..., :3] - driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video] - - if opt.mode == 'animation': - i = find_best_frame(source_image, driving_video, cpu=cpu) - driving_forward = driving_video[i:] - driving_backward = driving_video[:(i+1)][::-1] - - predictions_forward = animation(source_image, driving_forward, generator, kp_detector, depth_encoder, depth_decoder, cpu=cpu) - predictions_backward = animation(source_image, driving_backward, generator, kp_detector, depth_encoder, depth_decoder, cpu=cpu) - predictions = predictions_backward[::-1] + predictions_forward[1:] - elif opt.mode == 'reconstruction': - predictions = reconstruction(source_image, driving_video, generator, kp_detector, depth_encoder, depth_decoder, cpu) - - imageio.mimsave(opt.result_video, [img_as_ubyte(p) for p in predictions], fps=fps) diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/screen.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/screen.py deleted file mode 100644 index 7f416e1e799abfbf62382456020cc8e59e5cf01f..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/screen.py +++ /dev/null @@ -1,54 +0,0 @@ -from typing import Optional, TYPE_CHECKING - -from .segment import Segment -from .style import StyleType -from ._loop import loop_last - - -if TYPE_CHECKING: - from .console import ( - Console, - ConsoleOptions, - RenderResult, - RenderableType, - Group, - ) - - -class Screen: - """A renderable that fills the terminal screen and crops excess. - - Args: - renderable (RenderableType): Child renderable. - style (StyleType, optional): Optional background style. Defaults to None. - """ - - renderable: "RenderableType" - - def __init__( - self, - *renderables: "RenderableType", - style: Optional[StyleType] = None, - application_mode: bool = False, - ) -> None: - from pip._vendor.rich.console import Group - - self.renderable = Group(*renderables) - self.style = style - self.application_mode = application_mode - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - width, height = options.size - style = console.get_style(self.style) if self.style else None - render_options = options.update(width=width, height=height) - lines = console.render_lines( - self.renderable or "", render_options, style=style, pad=True - ) - lines = Segment.set_shape(lines, width, height, style=style) - new_line = Segment("\n\r") if self.application_mode else Segment.line() - for last, line in loop_last(lines): - yield from line - if not last: - yield new_line diff --git a/spaces/aliabd/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/test/test_doctests.py b/spaces/aliabd/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/test/test_doctests.py deleted file mode 100644 index 49d2bfa6d32663cbe4223bf94346aadce247c6ea..0000000000000000000000000000000000000000 --- a/spaces/aliabd/SummerTime/model/third_party/HMNet/DataLoader/infinibatch/test/test_doctests.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -""" -This file causes the doctests to be included as part of unit tests. - -To make sure the doctests of a specific module are included, -please replicate the `addTests` call for the iterators module below. -""" - -import doctest -import infinibatch.iterators - - -def load_tests(loader, tests, ignore): - tests.addTests(doctest.DocTestSuite(infinibatch.iterators)) - return tests diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Provider.py b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Provider.py deleted file mode 100644 index d24df76b6a6ccfc9b244f13a51bfc124b398a271..0000000000000000000000000000000000000000 --- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Provider.py +++ /dev/null @@ -1,16 +0,0 @@ -import os -from ..typing import sha256, Dict, get_type_hints - -url = None -model = None -supports_stream = False -needs_auth = False - - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - return - - -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]]) diff --git a/spaces/ankush37/phishingDetection/feature_extraction.py b/spaces/ankush37/phishingDetection/feature_extraction.py deleted file mode 100644 index 1ed9cd5f07622aecadd7759a9b17c4bceb5b693a..0000000000000000000000000000000000000000 --- a/spaces/ankush37/phishingDetection/feature_extraction.py +++ /dev/null @@ -1,484 +0,0 @@ -import ipaddress -import re -import urllib.request -from bs4 import BeautifulSoup -import socket -import requests -from googlesearch import search -import whois -from datetime import date, datetime -import time -from dateutil.parser import parse as date_parse -from urllib.parse import urlparse - -class FeatureExtraction: - features = [] - def __init__(self,url): - self.features = [] - self.url = url - self.domain = "" - self.whois_response = "" - self.urlparse = "" - self.response = "" - self.soup = "" - - try: - self.response = requests.get(url) - self.soup = BeautifulSoup(response.text, 'html.parser') - except: - pass - - try: - self.urlparse = urlparse(url) - self.domain = self.urlparse.netloc - except: - pass - - try: - self.whois_response = whois.whois(self.domain) - except: - pass - - - - - self.features.append(self.UsingIp()) - self.features.append(self.longUrl()) - self.features.append(self.shortUrl()) - self.features.append(self.symbol()) - self.features.append(self.redirecting()) - self.features.append(self.prefixSuffix()) - self.features.append(self.SubDomains()) - self.features.append(self.Hppts()) - self.features.append(self.DomainRegLen()) - self.features.append(self.Favicon()) - - - self.features.append(self.NonStdPort()) - self.features.append(self.HTTPSDomainURL()) - self.features.append(self.RequestURL()) - self.features.append(self.AnchorURL()) - self.features.append(self.LinksInScriptTags()) - self.features.append(self.ServerFormHandler()) - self.features.append(self.InfoEmail()) - self.features.append(self.AbnormalURL()) - self.features.append(self.WebsiteForwarding()) - self.features.append(self.StatusBarCust()) - - self.features.append(self.DisableRightClick()) - self.features.append(self.UsingPopupWindow()) - self.features.append(self.IframeRedirection()) - self.features.append(self.AgeofDomain()) - self.features.append(self.DNSRecording()) - # self.features.append(self.WebsiteTraffic()) - self.features.append(self.PageRank()) - self.features.append(self.GoogleIndex()) - self.features.append(self.LinksPointingToPage()) - self.features.append(self.StatsReport()) - - - # 1.UsingIp - def UsingIp(self): - try: - ipaddress.ip_address(self.url) - return -1 - except: - return 1 - - # 2.longUrl - def longUrl(self): - if len(self.url) < 54: - return 1 - if len(self.url) >= 54 and len(self.url) <= 75: - return 0 - return -1 - - # 3.shortUrl - def shortUrl(self): - match = re.search('bit\.ly|goo\.gl|shorte\.st|go2l\.ink|x\.co|ow\.ly|t\.co|tinyurl|tr\.im|is\.gd|cli\.gs|' - 'yfrog\.com|migre\.me|ff\.im|tiny\.cc|url4\.eu|twit\.ac|su\.pr|twurl\.nl|snipurl\.com|' - 'short\.to|BudURL\.com|ping\.fm|post\.ly|Just\.as|bkite\.com|snipr\.com|fic\.kr|loopt\.us|' - 'doiop\.com|short\.ie|kl\.am|wp\.me|rubyurl\.com|om\.ly|to\.ly|bit\.do|t\.co|lnkd\.in|' - 'db\.tt|qr\.ae|adf\.ly|goo\.gl|bitly\.com|cur\.lv|tinyurl\.com|ow\.ly|bit\.ly|ity\.im|' - 'q\.gs|is\.gd|po\.st|bc\.vc|twitthis\.com|u\.to|j\.mp|buzurl\.com|cutt\.us|u\.bb|yourls\.org|' - 'x\.co|prettylinkpro\.com|scrnch\.me|filoops\.info|vzturl\.com|qr\.net|1url\.com|tweez\.me|v\.gd|tr\.im|link\.zip\.net', self.url) - if match: - return -1 - return 1 - - # 4.Symbol@ - def symbol(self): - if re.findall("@",self.url): - return -1 - return 1 - - # 5.Redirecting// - def redirecting(self): - if self.url.rfind('//')>6: - return -1 - return 1 - - # 6.prefixSuffix - def prefixSuffix(self): - try: - match = re.findall('\-', self.domain) - if match: - return -1 - return 1 - except: - return -1 - - # 7.SubDomains - def SubDomains(self): - dot_count = len(re.findall("\.", self.url)) - if dot_count == 1: - return 1 - elif dot_count == 2: - return 0 - return -1 - - # 8.HTTPS - def Hppts(self): - try: - https = self.urlparse.scheme - if 'https' in https: - return 1 - return -1 - except: - return 1 - - # 9.DomainRegLen - def DomainRegLen(self): - try: - expiration_date = self.whois_response.expiration_date - creation_date = self.whois_response.creation_date - try: - if(len(expiration_date)): - expiration_date = expiration_date[0] - except: - pass - try: - if(len(creation_date)): - creation_date = creation_date[0] - except: - pass - - age = (expiration_date.year-creation_date.year)*12+ (expiration_date.month-creation_date.month) - if age >=12: - return 1 - return -1 - except: - return -1 - - # 10. Favicon - def Favicon(self): - try: - for head in self.soup.find_all('head'): - for head.link in self.soup.find_all('link', href=True): - dots = [x.start(0) for x in re.finditer('\.', head.link['href'])] - if self.url in head.link['href'] or len(dots) == 1 or domain in head.link['href']: - return 1 - return -1 - except: - return -1 - - # 11. NonStdPort - def NonStdPort(self): - try: - port = self.domain.split(":") - if len(port)>1: - return -1 - return 1 - except: - return -1 - - # 12. HTTPSDomainURL - def HTTPSDomainURL(self): - try: - if 'https' in self.domain: - return -1 - return 1 - except: - return -1 - - # 13. RequestURL - def RequestURL(self): - try: - for img in self.soup.find_all('img', src=True): - dots = [x.start(0) for x in re.finditer('\.', img['src'])] - if self.url in img['src'] or self.domain in img['src'] or len(dots) == 1: - success = success + 1 - i = i+1 - - for audio in self.soup.find_all('audio', src=True): - dots = [x.start(0) for x in re.finditer('\.', audio['src'])] - if self.url in audio['src'] or self.domain in audio['src'] or len(dots) == 1: - success = success + 1 - i = i+1 - - for embed in self.soup.find_all('embed', src=True): - dots = [x.start(0) for x in re.finditer('\.', embed['src'])] - if self.url in embed['src'] or self.domain in embed['src'] or len(dots) == 1: - success = success + 1 - i = i+1 - - for iframe in self.soup.find_all('iframe', src=True): - dots = [x.start(0) for x in re.finditer('\.', iframe['src'])] - if self.url in iframe['src'] or self.domain in iframe['src'] or len(dots) == 1: - success = success + 1 - i = i+1 - - try: - percentage = success/float(i) * 100 - if percentage < 22.0: - return 1 - elif((percentage >= 22.0) and (percentage < 61.0)): - return 0 - else: - return -1 - except: - return 0 - except: - return -1 - - # 14. AnchorURL - def AnchorURL(self): - try: - i,unsafe = 0,0 - for a in self.soup.find_all('a', href=True): - if "#" in a['href'] or "javascript" in a['href'].lower() or "mailto" in a['href'].lower() or not (url in a['href'] or self.domain in a['href']): - unsafe = unsafe + 1 - i = i + 1 - - try: - percentage = unsafe / float(i) * 100 - if percentage < 31.0: - return 1 - elif ((percentage >= 31.0) and (percentage < 67.0)): - return 0 - else: - return -1 - except: - return -1 - - except: - return -1 - - # 15. LinksInScriptTags - def LinksInScriptTags(self): - try: - i,success = 0,0 - - for link in self.soup.find_all('link', href=True): - dots = [x.start(0) for x in re.finditer('\.', link['href'])] - if self.url in link['href'] or self.domain in link['href'] or len(dots) == 1: - success = success + 1 - i = i+1 - - for script in self.soup.find_all('script', src=True): - dots = [x.start(0) for x in re.finditer('\.', script['src'])] - if self.url in script['src'] or self.domain in script['src'] or len(dots) == 1: - success = success + 1 - i = i+1 - - try: - percentage = success / float(i) * 100 - if percentage < 17.0: - return 1 - elif((percentage >= 17.0) and (percentage < 81.0)): - return 0 - else: - return -1 - except: - return 0 - except: - return -1 - - # 16. ServerFormHandler - def ServerFormHandler(self): - try: - if len(self.soup.find_all('form', action=True))==0: - return 1 - else : - for form in self.soup.find_all('form', action=True): - if form['action'] == "" or form['action'] == "about:blank": - return -1 - elif self.url not in form['action'] and self.domain not in form['action']: - return 0 - else: - return 1 - except: - return -1 - - # 17. InfoEmail - def InfoEmail(self): - try: - if re.findall(r"[mail\(\)|mailto:?]", self.soap): - return -1 - else: - return 1 - except: - return -1 - - # 18. AbnormalURL - def AbnormalURL(self): - try: - if self.response.text == self.whois_response: - return 1 - else: - return -1 - except: - return -1 - - # 19. WebsiteForwarding - def WebsiteForwarding(self): - try: - if len(self.response.history) <= 1: - return 1 - elif len(self.response.history) <= 4: - return 0 - else: - return -1 - except: - return -1 - - # 20. StatusBarCust - def StatusBarCust(self): - try: - if re.findall("", self.response.text): - return 1 - else: - return -1 - except: - return -1 - - # 21. DisableRightClick - def DisableRightClick(self): - try: - if re.findall(r"event.button ?== ?2", self.response.text): - return 1 - else: - return -1 - except: - return -1 - - # 22. UsingPopupWindow - def UsingPopupWindow(self): - try: - if re.findall(r"alert\(", self.response.text): - return 1 - else: - return -1 - except: - return -1 - - # 23. IframeRedirection - def IframeRedirection(self): - try: - if re.findall(r"[ -As you can see, the hack features are very easy to use and they will make your gameplay more enjoyable and exciting.
-Benefits of Using the Slavic Gangster Style Hack
-Using the Slavic Gangster Style hack has many benefits that will enhance your gaming experience and satisfaction. Here are some of the benefits of using the hack:
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- Having more fun and freedom in the game: With unlimited points and gems, you can buy or unlock anything you want in the game, such as weapons, vehicles, clothes, skills, etc. You can also experiment with different combinations and styles that suit your preference and mood. You can also explore more areas and secrets in the game that you might have missed otherwise. You can also have more fun and freedom in fighting with gopniks, police, robots, zombies, or other enemies. You can use any weapon or vehicle you like and unleash your creativity and power in the game. -
- Completing missions and quests faster and easier: With unlimited points and gems, you can upgrade your skills and abilities to the maximum level, which will make you stronger, faster, smarter, and more resilient in the game. You can also use any weapon or vehicle you like to complete the missions and quests faster and easier. You can also skip or ignore the missions and quests that you don't like or that are too hard for you. You can also replay the missions and quests that you enjoy or that reward you with more points, gems, or items. -
- Unlocking new items and upgrades: With unlimited points and gems, you can unlock new items and upgrades that are not available in the normal version of the game. You can also unlock items and upgrades that are only available for real money or for a limited time. You can also unlock items and upgrades that are hidden or secret in the game. You can also unlock items and upgrades that are exclusive for certain regions or countries. -
- Exploring more areas and secrets in the game: With unlimited points and gems, you can explore more areas and secrets in the game that are not accessible or visible in the normal version of the game. You can also explore areas and secrets that are only available for real money or for a limited time. You can also explore areas and secrets that are hidden or secret in the game. You can also explore areas and secrets that are exclusive for certain regions or countries. -
- Dominating other players and enemies in the game: With unlimited points and gems, you can dominate other players and enemies in the game with your superior skills, abilities, weapons, vehicles, clothes, etc. You can also challenge other players and enemies to duels or battles and show them who is the boss. You can also join or create gangs or clans with other players and cooperate or compete with them in the game. You can also share your achievements or screenshots with other players and impress them with your hack features. -
These are just some of the benefits of using the Slavic Gangster Style hack. There are many more benefits that you will discover as you play the game with the hack features.
-Risks and Precautions of Using the Slavic Gangster Style Hack
-However, using the Slavic Gangster Style hack also has some risks and precautions that you should be aware of before using it. Here are some of the risks and precautions of using the hack:
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- Getting banned or suspended from the game: One of the most common and serious risks of using the hack is getting banned or suspended from the game by the developers or moderators. This can happen if they detect that you are using a mod apk file or that you have abnormal amounts of points and gems in your account. They might also receive reports or complaints from other players who suspect that you are cheating or hacking the game. If you get banned or suspended, you will lose your access to the game and your progress or data in the game. You might also face legal actions or penalties from the developers or authorities for violating the game's terms of service or intellectual property rights. -
- Losing your progress or data in the game: Another risk of using the hack is losing your progress or data in the game due to technical issues or errors. This can happen if the mod apk file is not compatible with your device or system, or if it has bugs or glitches that affect the game's performance or functionality. It can also happen if you uninstall the mod apk file or the original game, or if you update the game to a newer version that is not supported by the mod apk file. If you lose your progress or data, you will have to start over from scratch or restore it from a backup if you have one. -
- Damaging your device or system with malware or viruses: Another risk of using the hack is damaging your device or system with malware or viruses that might be hidden in the mod apk file. This can happen if you download the mod apk file from an untrusted source, or if you don't scan it before installing it. Malware or viruses can infect your device or system and cause various problems, such as slowing down your performance, stealing your personal information, deleting your files, displaying unwanted ads, etc. They can also spread to other devices or systems that are connected to yours, such as your computer, tablet, phone, etc. -
- Facing legal or ethical issues for cheating or pirating: Another risk of using the hack is facing legal or ethical issues for cheating or pirating the game. This can happen if you use the hack to gain an unfair advantage over other players or enemies in the game, which might be considered as cheating or fraud. It can also happen if you use the hack to access content that is not authorized or licensed by the developers, which might be considered as pirating or stealing. These actions might violate the game's terms of service, intellectual property rights, privacy policies, etc. They might also offend or harm other players, developers, creators, etc. who have invested their time, money, effort, creativity, etc. in making or playing the game. -
- Ruining your gaming experience or satisfaction: Another risk of using the hack is ruining your gaming experience or satisfaction by making the game too easy, boring, or meaningless. This can happen if you use the hack to get unlimited points and gems and buy or unlock everything in the game without any challenge, effort, or reward. It can also happen if you use the hack to skip or ignore the missions and quests that are supposed to give you a sense of purpose, direction, or achievement in the game. It can also happen if you use the hack to dominate other players or enemies in the game without any challenge, competition, or cooperation. These actions might make you lose interest, motivation, or enjoyment in playing the game. They might also make you feel guilty, ashamed, or bored of cheating or hacking the game. -
These are just some of the risks and precautions of using the Slavic Gangster Style hack. There are many more risks and precautions that you should consider before using the hack.
-Conclusion
-In conclusion, Slavic Gangster Style is a fun and immersive open-world action game that lets you experience the life of a Russian gangster in the 90s era. However, some players might want to hack the game and get unlimited points and gems to unlock or buy everything in the game without spending money or time. To do so, they need to download and install a mod apk file that will modify the original game and give them access to the hack features. However, using the hack also has some benefits and risks that they should weigh carefully before using it. Ultimately, it is up to each player to decide whether to use or avoid the hack based on their personal preference and situation.
-If you have tried the Slavic Gangster Style hack or have any feedback or opinion on it, please share it with us in the comments section below. We would love to hear from you!
-FAQs
-Here are some frequently asked questions about the Slavic Gangster Style hack:
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- Is the Slavic Gangster Style hack safe to use? -The Slavic Gangster Style hack is not completely safe to use, as it may expose you to various risks and issues, such as getting banned or suspended from the game, losing your progress or data in the game, damaging your device or system with malware or viruses, facing legal or ethical issues for cheating or pirating, or ruining your gaming experience or satisfaction. Therefore, you should use the hack at your own risk and discretion. -
- Is the Slavic Gangster Style hack free to use? -The Slavic Gangster Style hack is free to use, as you can download and install the mod apk file from a trusted website without paying any money. However, you should be careful about where you download the mod apk file from, as some sources may contain malware or viruses that can harm your device or system. You should also scan the mod apk file before installing it and use a reliable antivirus software on your device. -
- How do I update the Slavic Gangster Style hack? -To update the Slavic Gangster Style hack, you need to download and install a new version of the mod apk file that is compatible with the latest version of the original game. You can check for updates on the website where you downloaded the mod apk file from, or on other websites that offer similar mod apk files. However, you should be aware that updating the hack may cause some problems or errors in your game, such as losing your progress or data, crashing your game, disabling your hack features, etc. Therefore, you should backup your game before updating it and follow the instructions on the screen and wait for the update to be completed. -
- Can I use the Slavic Gangster Style hack offline? -The Slavic Gangster Style hack can be used offline, as you don't need an internet connection to play the game or use the hack features. However, you might miss some features or content that are only available online, such as multiplayer mode, online events, leaderboards, etc. You might also face some issues or errors in your game, such as losing your progress or data, crashing your game, disabling your hack features, etc. Therefore, you should always save your game before playing offline and check for updates or fixes when you go online. -
- Can I use the Slavic Gangster Style hack on other devices or platforms? -The Slavic Gangster Style hack can be used on other devices or platforms that support the Android operating system, such as tablets, phones, computers, etc. However, you might need to adjust some settings or requirements on your device or system to make the hack work properly. You might also face some compatibility or performance issues on your device or system, such as lagging, freezing, crashing, etc. Therefore, you should always test the hack on your device or system before using it and follow the instructions on the screen and in the mod apk file. -
- Can I use the Slavic Gangster Style hack with other mods or hacks? -The Slavic Gangster Style hack can be used with other mods or hacks that are compatible with the game and the mod apk file. However, you should be careful about using too many mods or hacks at the same time, as they might conflict with each other and cause some problems or errors in your game, such as losing your progress or data, crashing your game, disabling your hack features, etc. Therefore, you should always backup your game before using any mods or hacks and check for compatibility or updates on the websites where you downloaded them from. -
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Google Play Store Download the Newest Version and Enjoy Millions of Apps Games Movies and More.md b/spaces/congsaPfin/Manga-OCR/logs/Google Play Store Download the Newest Version and Enjoy Millions of Apps Games Movies and More.md deleted file mode 100644 index 10752aef20eac6534d792bcee041121f3dac93fc..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Google Play Store Download the Newest Version and Enjoy Millions of Apps Games Movies and More.md +++ /dev/null @@ -1,198 +0,0 @@ -
-
How to Download Google Play Store Latest Version
-Google Play Store is the official app store for Android devices, where you can find and download millions of apps, games, movies, books, and more. It also provides updates for your apps and games, as well as security features and parental controls. In this article, we will show you how to download Google Play Store latest version on your Android device, as well as on other devices such as Chromebook, PC, Mac, and Kindle Fire.
-What is Google Play Store and Why You Need It
-Google Play Store is more than just an app store. It is a platform that connects you with the best digital content for your Android device. Here are some of the reasons why you need Google Play Store on your device:
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- Access to millions of apps and games. You can browse and download apps and games from various categories, such as education, entertainment, lifestyle, productivity, social, and more. You can also find apps and games that are exclusive to Google Play, such as YouTube Music, Google Photos, Google Maps, and more. -
- Updates for your apps and games. You can keep your apps and games up to date with the latest features, bug fixes, and security patches. You can also choose to update your apps and games automatically or manually, depending on your preference. -
- Security features and parental controls. You can protect your device and data from harmful apps and malware with Google Play Protect, which scans your device regularly and removes any suspicious apps. You can also set up parental controls to restrict the content that your kids can access on Google Play, such as apps, games, movies, books, and more. -
- Promotional opportunities and programs. You can discover new apps and games that suit your interests with personalized recommendations from Google Play. You can also join programs such as Google Play Pass and Google Play Points, which offer you access to hundreds of premium apps and games for a monthly fee or reward you with points that you can redeem for discounts or free items. -
Google Play Store Features and Benefits
-Some of the features and benefits that you can enjoy with the latest version of Google Play Store are:
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- New design and layout. The latest version of Google Play Store has a new design and layout that makes it easier for you to find what you are looking for. The app icons are bigger and clearer, the categories are more organized, and the search bar is more prominent. -
- Faster performance and loading. The latest version of Google Play Store has improved performance and loading speed, which means that you can browse and download apps and games faster and smoother. -
- Better compatibility and stability. The latest version of Google Play Store has better compatibility and stability with different Android devices and versions. This means that you can enjoy a more consistent and reliable experience across different devices. -
- New features and improvements. The latest version of Google Play Store also introduces new features and improvements that enhance your user experience. For example, you can now use dark mode to reduce eye strain in low-light environments, use app bundles to download only the parts of an app that you need, use instant apps to try an app before installing it, use app shortcuts to access specific functions of an app directly from the home screen, use app ratings to rate an app without leaving it, use app reviews to see what other users think about an app, use app sharing to share an app with someone nearby without using data or wifi, and more. -
How to Update Google Play Store Automatically or Manually
-Google Play Store usually updates itself automatically in the background, without requiring any action from you. However, sometimes you may want to update it manually, either because you want to get the latest version as soon as possible, or because the automatic update is not working properly. Here are the steps to update Google Play Store automatically or manually:
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- To update Google Play Store automatically:
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- Open the Google Play Store app on your device. -
- Tap on the menu icon (three horizontal lines) on the top left corner of the screen. -
- Tap on Settings. -
- Tap on Auto-update apps. -
- Select one of the options: Over any network, Over Wi-Fi only, or Don't auto-update apps. -
- If you choose Over any network or Over Wi-Fi only, your Google Play Store will update itself automatically whenever there is a new version available and you have a stable internet connection. -
- If you choose Don't auto-update apps, you will have to update your Google Play Store manually whenever there is a new version available. -
- - To update Google Play Store manually:
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- Open the Google Play Store app on your device. -
- Tap on the menu icon (three horizontal lines) on the top left corner of the screen. -
- Tap on My apps & games. -
- Tap on Installed. -
- Scroll down and look for Google Play Store. If there is an Update button next to it, tap on it to update your Google Play Store to the latest version. If there is no Update button, it means that your Google Play Store is already up to date. -
-
How to Download Google Play Store Latest Version on Your Android Device
-If you have an Android device that does not have Google Play Store installed, or if you have uninstalled it by mistake, you can download and install it again by following these steps:
-Check Your Current Version and Device Compatibility
-Before you download Google Play Store latest version, you need to check your current version and device compatibility. Here is how to do it:
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- To check your current version:
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- Open the Google Play Store app on your device. -
- Tap on the menu icon (three horizontal lines) on the top left corner of the screen. -
- Tap on Settings. -
- Scroll down and look for About. Tap on it. -
- You will see your current version number under Play Store version. Note it down for future reference. -
- - To check your device compatibility:
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-
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- Go to [this website] on your device's browser. This website lists all the versions of Google Play Store and their compatibility with different Android versions and devices. -
- Find the latest version of Google Play Store and check if it is compatible with your Android version and device model. If it is compatible, proceed to the next step. If it is not compatible, look for an older version that is compatible with your device and note down its version number. -
Enable Unknown Sources on Your Device Settings
-To download and install Google Play Store latest version from a third-party source, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play. Here is how to do it:
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- To enable unknown sources:
- Go to your device's Settings app.
- Tap on Security or Privacy (depending on your device).
- Look for Unknown sources or Install unknown apps (depending on your device).
- Toggle it on or tap on Allow from this source (depending on your device).
- A warning message will appear. Tap on OK or Confirm (depending on your device).
Download the Google Play Store APK File from a Trusted Source
-An APK file is an Android application package file that contains all the files and data needed to install an app on your device. To download Google Play Store latest version, you need to find and download its APK file from a trusted source. Here are some of the trusted sources that you can use:
-- [APKMirror]: This website offers a large collection of APK files for various apps and games, including Google Play Store. You can find the latest and older versions of Google Play Store, as well as the compatibility information and the changelog. You can also scan the QR code or use the download link to get the APK file on your device. -
- [APKPure]: This website also offers a large collection of APK files for various apps and games, including Google Play Store. You can find the latest and older versions of Google Play Store, as well as the compatibility information and the changelog. You can also scan the QR code or use the download link to get the APK file on your device. -
- [Uptodown]: This website is another source of APK files for various apps and games, including Google Play Store. You can find the latest and older versions of Google Play Store, as well as the compatibility information and the changelog. You can also scan the QR code or use the download link to get the APK file on your device.
To download the Google Play Store APK file from any of these sources, follow these steps:
-- To download the Google Play Store APK file:
- Go to the website of your choice on your device's browser.
- Search for Google Play Store or use the direct link if available.
- Select the version that you want to download. Make sure it is compatible with your device and current version.
- Tap on Download or Install (depending on the website).
- A pop-up message will appear. Tap on OK or Download (depending on the website).
- The APK file will start downloading on your device. You can check the progress on your notification bar or your download folder.
Install the Google Play Store APK File on Your Device
-Once you have downloaded the Google Play Store APK file on your device, you need to install it to get the latest version of Google Play Store. Here is how to do it:
-- To install the Google Play Store APK file:
- Go to your device's File Manager app or Download folder.
- Find and tap on the Google Play Store APK file that you have downloaded.
- A pop-up message will appear. Tap on Install or Next (depending on your device).
- The installation process will begin. Wait for it to finish.
- Another pop-up message will appear. Tap on Open or Done (depending on your device).
- You have successfully installed Google Play Store latest version on your device. You can now enjoy its new features and benefits.
Verify the Installation and Enjoy the Latest Features
-To verify that you have installed Google Play Store latest version correctly, you can check its version number and compare it with the one that you have downloaded. Here is how to do it:
-- To verify the installation:
- Open the Google Play Store app on your device.
- Tap on the menu icon (three horizontal lines) on the top left corner of the screen.
- Tap on Settings.
- Scroll down and look for About. Tap on it.
- You will see your current version number under Play Store version. Compare it with the one that you have downloaded. If they match, it means that you have installed Google Play Store latest version correctly. If they don't match, it means that something went wrong during the installation process. You may need to repeat the steps above or try a different source.
Congratulations! You have learned how to download Google Play Store latest version on your Android device. You can now enjoy its new design, faster performance, better compatibility, and new features. You can also update your apps and games, access millions of digital content, and join promotional programs and rewards from Google Play.
-How to Download Google Play Store Latest Version on Other Devices
-If you have other devices that run on different operating systems, such as Chromebook, PC, Mac, or Kindle Fire, you may wonder if you can download Google Play Store latest version on them as well. The answer is yes, but with some limitations and requirements. Here are some of the ways that you can download Google Play Store latest version on other devices:
-How to Download Google Play Store on Chromebook
-A Chromebook is a laptop that runs on Chrome OS, a web-based operating system developed by Google. Some Chromebooks support Android apps and games, which means that you can download and install Google Play Store latest version on them. However, not all Chromebooks are compatible with Google Play Store, so you need to check first if yours is one of them. Here is how to do it:
-- To check if your Chromebook supports Google Play Store:
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- Go to your Chromebook's Settings app. -
- Look for Google Play Store or Apps (depending on your Chromebook). -
- If you see Google Play Store or Apps, it means that your Chromebook supports Google Play Store. You can enable it by toggling it on or tapping on Turn on (depending on your Chromebook). -
- If you don't see Google Play Store or Apps, it means that your Chromebook does not support Google Play Store. You may need to update your Chrome OS to the latest version or check if your Chromebook model is on [this list] of compatible devices. -
- - To download Google Play Store on your Chromebook:
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- After enabling Google Play Store or Apps on your Chromebook, you will see a pop-up message asking you to agree to the terms of service. Tap on Agree or Accept (depending on your Chromebook). -
- The Google Play Store app will start downloading and installing on your Chromebook. Wait for it to finish. -
- Once the installation is complete, you will see the Google Play Store icon on your app launcher or shelf. Tap on it to open it. -
- You will be asked to sign in with your Google account. Enter your email and password and tap on Next or Sign in (depending on your Chromebook). -
- You have successfully downloaded Google Play Store latest version on your Chromebook. You can now browse and download apps and games from Google Play. -
How to Download Google Play Store on PC or Mac
-A PC or Mac is a computer that runs on Windows or macOS, respectively. These operating systems do not support Android apps and games natively, which means that you cannot download and install Google Play Store latest version on them directly. However, you can use an emulator, which is a software that simulates an Android device on your PC or Mac, to download and install Google Play Store latest version on them. Here are some of the popular emulators that you can use:
-- [BlueStacks]: This emulator is one of the most popular and widely used emulators for PC and Mac. It allows you to run Android apps and games on your computer with high performance and compatibility. It also has features such as keyboard and mouse support, gamepad support, multi-instance mode, screen recording, cloud sync, and more. -
- [NoxPlayer]: This emulator is another popular and widely used emulator for PC and Mac. It allows you to run Android apps and games on your computer with high performance and compatibility. It also has features such as keyboard and mouse support, gamepad support, multi-instance mode, screen recording, macro recorder, root access, and more. -
- [MEmu]: This emulator is a newer but fast-growing emulator for PC and Mac. It allows you to run Android apps and games on your computer with high performance and compatibility. It also has features such as keyboard and mouse support, gamepad support, multi-instance mode, screen recording, macro recorder, root access, and more.
To download Google Play Store latest version on your PC or Mac using any of these emulators, follow these steps:
-- To download Google Play Store on your PC or Mac:
- Go to the website of the emulator of your choice on your PC or Mac's browser.
- Download and install the emulator on your PC or Mac by following the instructions on the website.
- Launch the emulator on your PC or Mac. You will see a simulated Android device screen on your computer.
- Look for the Google Play Store app icon on the emulator's home screen or app drawer. Tap on it to open it.
- You will be asked to sign in with your Google account. Enter your email and password and tap on Next or Sign in (depending on the emulator).
- You have successfully downloaded Google Play Store latest version on your PC or Mac using an emulator. You can now browse and download apps and games from Google Play.
How to Download Google Play Store on Kindle Fire
-A Kindle Fire is a tablet that runs on Fire OS, a modified version of Android developed by Amazon. It does not have Google Play Store installed by default, but instead uses Amazon Appstore, which offers a limited selection of apps and games. However, you can download and install Google Play Store latest version on your Kindle Fire by following these steps:
-- To download Google Play Store on your Kindle Fire:
-
-
-
- Go to your Kindle Fire's Settings app. -
- Tap on Security or Privacy (depending on your Kindle Fire). -
- Look for Apps from Unknown Sources or Install unknown apps (depending on your Kindle Fire). -
- Toggle it on or tap on Allow from this source (depending on your Kindle Fire). -
- A warning message will appear. Tap on OK or Confirm (depending on your Kindle Fire). -
- Go to your Kindle Fire's Silk Browser app. -
- Go to [this website] on your browser. This website offers the APK files for the Google Play services that you need to install on your Kindle Fire. -
- Download the four APK files in the following order: Google Account Manager, Google Services Framework, Google Play Services, and Google Play Store. Tap on Download or Install (depending on the website) and then tap on OK or Download (depending on your Kindle Fire) for each file. -
- The APK files will start downloading on your Kindle Fire. You can check the progress on your notification bar or your download folder. -
- Once the downloads are complete, go to your device's File Manager app or Download folder. -
- Find and tap on each APK file that you have downloaded in the same order as above. Tap on Install or Next (depending on your Kindle Fire) and then tap on Open or Done (depending on your Kindle Fire) for each file. -
- You have successfully downloaded Google Play Store latest version on your Kindle Fire. You can now open it and sign in with your Google account. -
Conclusion and FAQs
-In this article, we have shown you how to download Google Play Store latest version on your Android device, as well as on other devices such as Chromebook, PC, Mac, and Kindle Fire. We have also explained what Google Play Store is and why you need it, as well as some of its features and benefits. We hope that this article has been helpful and informative for you. If you have any questions or comments, please feel free to leave them below. Here are some of the frequently asked questions that you may find useful:
-Q: How can I check if my device is compatible with Google Play Store?
-A: You can check if your device is compatible with Google Play Store by going to [this website] on your device's browser. This website lists all the versions of Google Play Store and their compatibility with different Android versions and devices.
-Q: How can I fix Google Play Store errors or issues?
-A: If you encounter any errors or issues with Google Play Store, such as not being able to download or update apps, not being able to sign in, not being able to open the app, etc., you can try some of the following solutions:
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- Clear the cache and data of Google Play Store and Google Play Services. Go to your device's Settings app, tap on Apps or Applications, find and tap on Google Play Store and Google Play Services, tap on Storage, and then tap on Clear cache and Clear data. -
- Uninstall and reinstall Google Play Store updates. Go to your device's Settings app, tap on Apps or Applications, find and tap on Google Play Store, tap on Uninstall updates, and then tap on OK. Then, open the Google Play Store app again and wait for it to update itself. -
- Check your internet connection. Make sure that you have a stable and reliable internet connection, either through Wi-Fi or mobile data. You can also try switching between Wi-Fi and mobile data, or turning them off and on again. -
- Check your device's date and time. Make sure that your device's date and time are correct and match your time zone. You can also try turning off the automatic date and time option and setting them manually. -
- Check your device's storage space. Make sure that you have enough free space on your device's internal storage or SD card to download and install apps from Google Play Store. You can also try deleting some unwanted files or moving them to another location. -
Q: How can I contact Google Play Store support?
-A: If you need further assistance or have any feedback or complaints about Google Play Store, you can contact Google Play Store support by going to [this website] on your device's browser. This website offers various ways to contact Google Play Store support, such as phone, chat, email, forum, etc.
-Q: How can I get a refund from Google Play Store?
-A: If you are not satisfied with an app or game that you have purchased from Google Play Store, you can request a refund within 48 hours of purchase by following these steps:
-- To request a refund from Google Play Store:
- Go to [this website] on your device's browser. This website is the Google Play Store help center.
- Tap on Contact us or Get help (depending on the website).
- Select the option that says Request a refund or Returns and refunds (depending on the website).
- Follow the instructions on the screen to fill out the refund request form. You will need to provide your order number, the reason for the refund, and any other details that may be required.
- Submit your refund request and wait for a response from Google Play Store support. You will receive an email confirmation of your request and the status of your refund.
If you request a refund within 48 hours of purchase, you will usually get a full refund. However, if you request a refund after 48 hours of purchase, you will need to contact the app or game developer directly and ask for a refund. The developer's contact information can be found on the app or game's page on Google Play Store.
-Q: How can I delete or disable Google Play Store?
-A: If you want to delete or disable Google Play Store from your device, you can do so by following these steps:
-- To delete or disable Google Play Store:
- Go to your device's Settings app.
- Tap on Apps or Applications.
- Find and tap on Google Play Store.
- Tap on Uninstall or Disable (depending on your device).
- A warning message will appear. Tap on OK or Confirm (depending on your device).
- You have successfully deleted or disabled Google Play Store from your device. You can restore it by downloading and installing it again from a third-party source, as explained above.
Note that deleting or disabling Google Play Store may affect the functionality and performance of some of your apps and games that rely on Google Play services. You may also lose access to some of the features and benefits that Google Play Store offers, such as updates, security, promotions, and rewards.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Boost Your Android Camera Performance with APK Camera 60 FPS.md b/spaces/congsaPfin/Manga-OCR/logs/How to Boost Your Android Camera Performance with APK Camera 60 FPS.md deleted file mode 100644 index 304327801e4c56fdec858472144efd3ce647dc18..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/How to Boost Your Android Camera Performance with APK Camera 60 FPS.md +++ /dev/null @@ -1,105 +0,0 @@ - -
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-Why does this matter? Well, a higher resolution means more pixels, which means more detail and clarity in your videos. A higher frame rate means more images per second, which means smoother motion and less blur in your videos. Together, these factors can make a huge difference in how your videos look and feel.
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How to download and install an apk camera 60 fps on your Android device
-Before you can start using an apk camera 60 fps, you need to download and install it on your device. Here are the steps you need to follow:
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- Find a reliable source for the apk file. An apk file is a package file that contains all the necessary files for installing an app on your device. You can find many apk files for different camera apps on various websites, such as APKCombo, APKMirror, or APKPure. However, be careful when downloading apk files from unknown sources, as they may contain malware or viruses that can harm your device. -
- Enable unknown sources on your device settings. By default, Android devices do not allow you to install apps from sources other than the Google Play Store. To enable unknown sources, go to your device settings, tap on Security, and toggle on the option that says "Unknown sources" or "Install unknown apps". This will allow you to install apk files from your browser or file manager. -
- Download and install the apk file. Once you have found the apk file you want, tap on it to download it to your device. You may see a warning message that says "This type of file can harm your device". Tap on OK to proceed. After the download is complete, tap on the apk file to open it. You may see another warning message that says "Do you want to install this application?". Tap on Install to confirm. Wait for the installation process to finish. -
- Launch the app and grant the necessary permissions. After the installation is done, you can launch the app from your app drawer or home screen. You may see a pop-up message that asks you to grant certain permissions to the app, such as camera, microphone, storage, or location. Tap on Allow to grant the permissions. These permissions are needed for the app to function properly and access your device's features. -
Congratulations! You have successfully downloaded and installed an apk camera 60 fps on your Android device. Now you can start using it to record amazing videos.
-How to use an apk camera 60 fps to record stunning videos
-Now that you have an apk camera 60 fps on your device, you may wonder how to use it to record stunning videos. Here are the steps you need to follow:
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- Choose the video resolution and frame rate. Depending on the app and your device's capabilities, you may have different options for the video resolution and frame rate. To choose the best option for your needs, go to the app's settings and look for the video section. There you will see a list of available resolutions and frame rates, such as 720p 30fps, 1080p 60fps, or 4K 30fps. Tap on the option you want to select it. Generally, higher resolutions and frame rates will result in better quality videos, but they will also take up more storage space and battery power. -
- Adjust the exposure, focus, white balance, and other settings. Depending on the app and your preferences, you may also want to adjust some other settings that affect how your videos look. For example, you can adjust the exposure level to make your videos brighter or darker, adjust the focus mode to make your videos sharper or blurrier, adjust the white balance mode to make your videos warmer or cooler, or adjust other settings such as ISO, shutter speed, zoom, or flash. To access these settings, look for the icons or menus on the app's interface and tap on them to change them. -
- Tap the record button and start filming. Once you have chosen and adjusted your settings, you are ready to start filming. To do so, simply tap on the red record button on the app's interface and point your device's camera at whatever you want to capture. You will see a timer and a progress bar on the screen that show how long and how much you have recorded. -
- Stop the recording and save the video to your gallery. When you are done filming, tap on the record button again to stop the recording. The app will automatically save the video to your device's gallery or a specific folder depending on the app. You can access your videos from there and view them with any video player app. -
That's it! You have just used an apk camera 60 fps to record stunning videos on your Android device.
-The best apk camera 60 fps apps for Android in 2023
-There are many apk camera 60 fps apps available for Android devices in 2023, but not all of them are equally good. Some of them may have more features, better compatibility, or higher quality than others. To help you choose the best one for your needs, we have compiled a list of three of the best apk camera 60 fps apps for Android in 2023:
-Google Camera mod with HDR+ and 1080p 60fps support
-Google Camera is one of the most popular camera apps for Android devices, especially for those made by Google itself. It offers a simple and intuitive interface with various modes such as Photo Sphere, Panorama, Lens Blur, Portrait Mode, Night Sight, and more. However, not all devices can use Google Camera or access all of its features.
-That's why some developers have created modified versions of Google Camera that enable more features and support more devices. One of these mods is called GCam Mod, which adds HDR+ support
FiLMiC Pro with professional video features and 4K 60fps support
-FiLMiC Pro is another popular camera app for Android devices, especially for those who want to achieve professional video results. It offers a sophisticated and customizable interface with various modes such as Standard, Manual, Hybrid, and Cinematographer. It also supports external microphones, lenses, and gimbals for enhanced audio and video quality.
-One of the main features of FiLMiC Pro is its ability to record videos at 4K resolution and 60fps frame rate, which is the highest possible quality on most Android devices. It also allows you to adjust the bitrate, audio sampling rate, and number of channels for optimal file size and performance. Moreover, it has advanced video features such as zebra stripes, false color, focus peaking, histograms, waveforms, and more.
-To download and install FiLMiC Pro on your Android device, you need to purchase it from the Google Play Store for $14.99. Alternatively, you can download a free version called FiLMiC Firstlight that has some of the same features but with limited resolution and frame rate options.
-Cinema FV-5 Lite with manual controls and high-quality video codecs
-Cinema FV-5 Lite is a camera app for Android devices that aims to emulate the experience of using a professional cinema camera. It has a simple and elegant interface with manual controls for exposure, focus, white balance, ISO, shutter speed, zoom, and flash. It also has a professional viewfinder with various grids, guides, safe areas, and histograms.
-Cinema FV-5 Lite can record videos at up to 1080p resolution and 30fps frame rate, depending on your device's capabilities. It also supports high-quality video codecs such as H.264 and H.265 for better compression and quality. Furthermore, it has a powerful video organization system that lets you choose different storage locations and customizable file names.
-To download and install Cinema FV-5 Lite on your Android device, you can get it from the Google Play Store for free. However, this version has some limitations such as a watermark on the videos and a maximum recording time of 5 minutes. To unlock the full features of Cinema FV-5 Lite, you need to upgrade to the paid version called Cinema FV-5 for $2.99.
support, or warranty from them. You may also risk damaging your device or original camera app by using an apk camera app that is not compatible or safe for your device or original camera app. -Conclusion
-In conclusion, an apk camera 60 fps is a modified version of a camera app that allows you to record videos at a higher resolution and frame rate than the original app. It can help you capture smooth and clear videos with high detail and dynamic range, edit and share your videos easily with various apps and platforms, and unleash your creativity and experiment with different styles and effects. However, it also has some drawbacks, such as requiring a powerful device and enough storage space, encountering compatibility issues or bugs, and violating the terms of service or warranty of your device or original camera app.
-If you want to use an apk camera 60 fps on your Android device, you need to download and install it from a reliable source, enable unknown sources on your device settings, launch the app and grant the necessary permissions, choose the video resolution and frame rate, adjust the exposure, focus, white balance, and other settings, tap the record button and start filming, stop the recording and save the video to your gallery, and enjoy your stunning videos. You can also choose from some of the best apk camera 60 fps apps for Android in 2023, such as Google Camera mod with HDR+ and 1080p 60fps support, FiLMiC Pro with professional video features and 4K 60fps support, or Cinema FV-5 Lite with manual controls and high-quality video codecs.
-We hope this article has helped you understand what an apk camera 60 fps is and how to use it on your Android device. Happy filming!
-FAQs
-Here are some frequently asked questions about apk camera 60 fps:
-Q: What is the difference between apk camera 60 fps and normal camera app?
-A: An apk camera 60 fps is a modified version of a normal camera app that allows you to record videos at a higher resolution and frame rate than the normal camera app. For example, an apk camera 60 fps can record videos at 1080p 60fps, while a normal camera app can only record videos at 720p 30fps.
-Q: Is it safe to use an apk camera 60 fps?
-A: It depends on the source and quality of the apk file. Some apk files may contain malware or viruses that can harm your device or data. Some apk files may also be incompatible or unsafe for your device or original camera app. To avoid these risks, you should only download and install apk files from trusted and reputable sources, such as APKCombo, APKMirror, or APKPure. You should also scan the apk file with an antivirus app before installing it.
-Q: How can I uninstall an apk camera 60 fps?
-A: To uninstall an apk camera 60 fps, you can follow the same steps as uninstalling any other app on your Android device. Go to your device settings, tap on Apps or Applications, find the apk camera app you want to uninstall, tap on it, and tap on Uninstall. Alternatively, you can also long-press on the apk camera app icon on your home screen or app drawer, drag it to the Uninstall option at the top of the screen, and release it.
-Q: Can I use an apk camera 60 fps on any Android device?
-A: Not necessarily. Some apk camera apps may only work on specific devices or Android versions. For example, some Google Camera mods may only work on Pixel devices or Android 10 devices. Some FiLMiC Pro features may only work on devices that support Camera2 API. Some Cinema FV-5 Lite features may only work on devices that support NEON instructions. To check if an apk camera app works on your device, you should read the description and reviews of the app before downloading and installing it.
-Q: Can I use an apk camera 60 fps with other apps or platforms?
-A: Yes, you can use an apk camera 60 fps with other apps or platforms that support video recording or playback. For example, you can use an apk camera 60 fps to record videos for YouTube, Facebook, Instagram, TikTok, WhatsApp, or any other platform or service that allows you to upload or stream videos. You can also use an apk camera 60 fps to edit your videos with any video editing app or software that supports the video format and resolution of your videos.
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Fish Go.io - Be the fish king APK: A Fun and Addictive Casual Game
-Do you love the ocean and the idea of merging different fish species? Do you want to rule the underwater kingdom and become the biggest school of fish in the world? If yes, then you should try Fish Go.io - Be the fish king APK, a casual game that will keep you hooked for hours. In this article, we will tell you everything you need to know about this game, including its features, how to download and install it, and some tips and tricks to help you play better. Let's dive in!
-Introduction
-Fish Go.io - Be the fish king is a game developed by Whitedot, a company that specializes in creating casual games for Android devices. The game was released in 2020 and has since gained over 10 million downloads on Google Play Store. It has also received positive reviews from users, who praise its simple yet addictive gameplay, colorful graphics, and variety of modes and mini-games.
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What is Fish Go.io?
-Fish Go.io is a game where you control a fish and collect other fish to grow bigger and stronger. You can also merge two fish of the same kind to unlock new species, which are faster and more powerful. Your goal is to become the largest school of fish in the ocean and defeat your rivals, who are other players from around the world. You can also play survival mode, where you have to collect gems and avoid sharks as long as you can, or mini-games, where you have to solve puzzles and save the little fish.
-Why should you play Fish Go.io?
-Fish Go.io is a game that will appeal to anyone who likes casual games that are easy to play but hard to master. It is also a game that will make you feel relaxed and happy, as you explore the beautiful underwater world and discover new fish species. Some of the reasons why you should play Fish Go.io are:
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- It is fun and addictive: You will never get bored with Fish Go.io, as there are always new challenges and rewards waiting for you. You will also enjoy the satisfying feeling of merging fish and becoming bigger and stronger. -
- It is competitive and social: You can compete with other players from around the world and see who can become the king of the sea. You can also chat with them, make friends, or challenge them to a rematch. -
- It is creative and educational: You can learn about different fish species and their characteristics, as well as how they evolve and adapt to their environment. You can also customize your fish collection with different skins and accessories. -
Features of Fish Go.io
-Fish Go.io is a game that has many features that make it unique and enjoyable. Some of these features are:
-Simple and addictive gameplay
-The gameplay of Fish Go.io is very simple: You just have to move your finger on the screen to control your fish and collect other fish that are smaller than you. The more fish you collect, the bigger and stronger you become. You can also merge two fish of the same kind to unlock new species, which have different abilities and attributes. The game is easy to learn but hard to master, as you have to avoid bigger fish and sharks that can eat you, as well as use your strategy and skills to defeat your rivals
Conquer your rivals and become the king of the sea
-One of the most exciting aspects of Fish Go.io is that you can compete with other players from around the world in real-time. You can see their fish on the screen and try to eat them or avoid them, depending on their size. You can also chat with them, make friends, or challenge them to a rematch. The game has a ranking system that shows your position among all the players, as well as your best score and your current level. You can also earn trophies and badges for your achievements and show them off to your rivals. The game is very competitive and social, as you can prove your skills and become the king of the sea.
-Merge and unlock new fish species
-Another feature of Fish Go.io is that you can merge two fish of the same kind to unlock new species, which are faster and more powerful. The game has over 100 different fish species to discover, each with their own characteristics and abilities. Some of them are based on real fish, such as clownfish, angelfish, or pufferfish, while others are more exotic and mythical, such as dragonfish, unicornfish, or phoenixfish. You can also find rare and legendary fish that have special effects and bonuses. The game is very creative and educational, as you can learn about different fish species and their evolution.
-Collect and customize your fish collection
-The last feature of Fish Go.io is that you can collect and customize your fish collection with different skins and accessories. You can change the color, pattern, shape, and size of your fish, as well as add hats, glasses, necklaces, or other items to make them look more unique and stylish. You can also name your fish and give them personalities. You can access your fish collection from the main menu and see all the fish you have unlocked and merged. You can also share your fish collection with your friends and other players on social media. The game is very fun and engaging, as you can express yourself and show off your creativity.
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How to download and install Fish Go.io APK
-If you want to play Fish Go.io on your Android device, you have two options: you can download it from Google Play Store or from APKCombo. Here are the steps for both methods:
-Download from Google Play Store
-This is the easiest and safest way to download Fish Go.io APK on your device. All you have to do is follow these steps:
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-
- Open Google Play Store on your device. -
- Search for "Fish Go.io - Be the fish king" in the search bar. -
- Select the game from the list of results and tap on "Install". -
- Wait for the download and installation process to finish. -
- Launch the game from your app drawer or home screen. -
Download from APKCombo
-This is another way to download Fish Go.io APK on your device, especially if you cannot access Google Play Store for some reason. However, this method requires more caution and attention, as you may encounter some risks or errors. Here are the steps for this method:
--
-
- Open a web browser on your device. -
- Go to or search for "Fish Go.io APKCombo" in any search engine. -
- Select the latest version of the game from the list of results and tap on "Download APK". -
- Wait for the download process to finish. -
- Before installing the APK file, make sure you have enabled "Unknown sources" in your device settings. To do this, go to Settings > Security > Unknown sources and toggle it on. -
- Locate the downloaded APK file in your device storage using a file manager app. -
- Tap on the file and follow the instructions to install it. -
- Launch the game from your app drawer or home screen. -
Install the APK file on your device
-If you already have an APK file of Fish Go.io on your device or on another source, such as a USB drive or an SD card, you can also install it directly without downloading it again. However, you still need to enable "Unknown sources" in your device settings before installing it. Here are the steps for this method:
--
-
- Locate the APK file in your device storage or external source using a file manager app. -
- Tap on the file and follow the instructions to install it. -
- Launch the game from your app drawer or home screen. -
Tips and tricks for playing Fish Go.io
Now that you know how to download and install Fish Go.io APK on your device, you may want to know some tips and tricks to help you play better and have more fun. Here are some of them: -Move your fish strategically
-The key to success in Fish Go.io is to move your fish strategically and avoid unnecessary risks. You can move your fish by dragging your finger on the screen, but you have to be careful not to bump into obstacles or enemies. You can also use the joystick option in the settings if you prefer. You should always try to collect as many fish as possible, but also be aware of your surroundings and the size of your rivals. You should also try to merge your fish whenever you can, as this will make them faster and stronger.
-Avoid bigger fish and sharks
-One of the main dangers in Fish Go.io is to encounter bigger fish and sharks that can eat you in one bite. You should always avoid them and run away if you see them approaching. You can tell how big a fish is by its color: the darker the color, the bigger the fish. You can also see their size on the mini-map on the top right corner of the screen. Sharks are even more dangerous, as they can chase you and bite you multiple times. You can recognize them by their shape and their red eyes. You should never try to fight them, as they are much stronger than you.
-Collect gems and power-ups
-Another way to improve your performance in Fish Go.io is to collect gems and power-ups that are scattered around the ocean. Gems are blue crystals that give you extra points and coins, which you can use to buy new fish or skins. Power-ups are yellow stars that give you temporary boosts, such as speed, shield, magnet, or bomb. You should always try to collect them, as they can help you escape from danger or defeat your rivals. However, you should also be careful not to waste them or use them at the wrong time.
-Play different modes and mini-games
-Fish Go.io is not only about competing with other players in the main mode. You can also play different modes and mini-games that offer different challenges and rewards. For example, you can play survival mode, where you have to collect gems and avoid sharks as long as you can. You can also play mini-games, such as puzzle mode, where you have to solve puzzles and save the little fish, or fishing mode, where you have to catch fish with a rod. These modes and mini-games are fun and relaxing, and they can also help you earn more coins and gems.
-Conclusion
-Fish Go.io - Be the fish king APK is a fun and addictive casual game that will make you feel like a real fish in the ocean. You can merge different fish species, compete with other players, collect gems and power-ups, and customize your fish collection. The game has simple and intuitive controls, colorful graphics, and various modes and mini-games. It is also free to download and play on your Android device. If you are looking for a game that will keep you entertained for hours, then you should definitely try Fish Go.io - Be the fish king APK.
-FAQs
-Here are some of the frequently asked questions about Fish Go.io - Be the fish king APK:
--
-
- Q: How do I unlock new fish species? -
- A: You can unlock new fish species by merging two fish of the same kind. The higher the level of your fish, the more chances you have to unlock a new species. -
- Q: How do I get more coins and gems? -
- A: You can get more coins and gems by collecting them in the ocean, completing missions, playing mini-games, watching ads, or buying them with real money. -
- Q: How do I change my fish skin? -
- A: You can change your fish skin by going to your fish collection and tapping on the skin icon. You can buy new skins with coins or gems. -
- Q: How do I chat with other players? -
- A: You can chat with other players by tapping on the chat icon on the top left corner of the screen. You can send text messages or emojis. -
- Q: How do I report a bug or a problem? -
- A: You can report a bug or a problem by going to the settings menu and tapping on the feedback icon. You can also contact the developer via email or social media. -
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for main headings, for subheadings, for bold text, for links, for tables, etc. 6. I will write " Here are the two tables I created: | Outline of the Article | | :--------------------- | | H1: Cover Orange Journey Mod APK: A Fun and Challenging Puzzle Game | | H2: What is Cover Orange Journey? | | H3: The gameplay | | H3: The graphics and sound | | H3: The features | | H2: What is Cover Orange Journey Mod APK? | | H3: The benefits of using the mod apk | | H3: The drawbacks of using the mod apk | | H3: How to download and install the mod apk | | H2: Tips and Tricks for Playing Cover Orange Journey | | H3: Use different objects wisely | | H3: Watch out for obstacles and enemies | | H3: Collect stars and unlock new levels | | H3: Try different modes and challenges | | H2: Conclusion | | H3: FAQs | | Article with HTML Formatting | | :--------------------------- | | Cover Orange Journey Mod APK: A Fun and Challenging Puzzle Game
| | If you are looking for a puzzle game that is fun, challenging, and creative, then you might want to try Cover Orange Journey. This game is about rescuing cute oranges from a mad cloud that wants to rain acid on them. You have to use various objects to create a shelter for the oranges and protect them from the cloud's attack. In this article, we will tell you everything you need to know about Cover Orange Journey and its mod apk version.
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| | What is Cover Orange Journey?
| | Cover Orange Journey is a puzzle game developed by FDG Entertainment GmbH & Co.KG. It is available for Android and iOS devices. The game has over 200 levels across four different worlds: Pirate Ship, Wild West, Ancient Egypt, and Knights & Castles. Each world has its own theme, objects, enemies, and challenges.
| | The gameplay
| | The gameplay of Cover Orange Journey is simple but addictive. You have to drag and drop objects on the screen to create a shelter for the oranges before the cloud arrives. The objects include barrels, crates, wheels, planks, cannons, etc. You have to use them wisely and strategically to cover all the gaps and holes in your shelter. You also have to consider the physics and gravity of the objects and how they interact with each other.
| | The game gets harder as you progress through the levels. The cloud will rain faster, stronger, and longer. The objects will become more limited and complex. The oranges will be placed in more difficult positions and situations. You have to think fast and smart to save them all.
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| | The sound of Cover Orange Journey is also enjoyable and immersive. The music is catchy and upbeat. It changes according to the world you are in. The sound effects are realistic and funny. You can hear the raindrops, explosions, crashes, squeaks, etc.
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| | The features
| | Cover Orange Journey has many features that make it more fun and
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-Psoriasis
-Psoriasis is a chronic disease 8a78ff9644
-
-
-
diff --git a/spaces/contluForse/HuggingGPT/assets/Compendio Diritto Costituzionale Simone Pdf Free _TOP_.md b/spaces/contluForse/HuggingGPT/assets/Compendio Diritto Costituzionale Simone Pdf Free _TOP_.md
deleted file mode 100644
index 585ad1fd2bfada3d99febb949dafe43471aeeb2f..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Compendio Diritto Costituzionale Simone Pdf Free _TOP_.md
+++ /dev/null
@@ -1,6 +0,0 @@
-compendio diritto costituzionale simone pdf free
Download 🗹 https://ssurll.com/2uzyny
-
-R. Bin - G. Pitruzzella, Diritto costituzionale, 7th edition, Turin, . G. Conso - V. Grevi, Compendio di procedura penale, 3rd edition, Padova, Cedam, . H. De Greeley - F. A. G. de Gretey, Treatise on the principles of civil and moral law, translated by H. de Greeley, 3rd edition, London, , . J.B.G.-J. B. G. Hartmann, Von der Verkundigungsgeschichte der Wirtschaft, 3rd edition, Berlin, . J.B.G.-J. B. G. Hartmann, Von der Konstitution und des Verkehrs-Geschichten, 2nd edition, Berlin, , . J. B. G. -J 8a78ff9644
-
-
-
diff --git a/spaces/contluForse/HuggingGPT/assets/Download Ajab Prem Ki Ghazab Kahani Tamil Full Movie Hd 1080p Blu-ray Torrent and Witness the Amazing Chemistry of the Lead Actors.md b/spaces/contluForse/HuggingGPT/assets/Download Ajab Prem Ki Ghazab Kahani Tamil Full Movie Hd 1080p Blu-ray Torrent and Witness the Amazing Chemistry of the Lead Actors.md
deleted file mode 100644
index b3e6587e89ab1e9d1fd4abdc66ec481a441af648..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Download Ajab Prem Ki Ghazab Kahani Tamil Full Movie Hd 1080p Blu-ray Torrent and Witness the Amazing Chemistry of the Lead Actors.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Ajab Prem Ki Ghazab Kahani Tamil Full Movie Hd 1080p Blu-ray Download Torrent
DOWNLOAD ☆ https://ssurll.com/2uzwkT
-
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-
-
-
diff --git a/spaces/coolspaces/windows3.1/style.css b/spaces/coolspaces/windows3.1/style.css
deleted file mode 100644
index 058cf3debaebfedf9570815dae326d45a0497010..0000000000000000000000000000000000000000
--- a/spaces/coolspaces/windows3.1/style.css
+++ /dev/null
@@ -1,153 +0,0 @@
-/* Fonts */
-@font-face {
- font-family: 'WinThreeOne';
- src: url('fonts/w95fa.woff2') format('woff2'),
- url('fonts/w95fa.woff') format('woff');
-}
-@font-face {
- font-family: 'DOS';
- src: url('fonts/LessPerfectDOSVGA.woff2') format('woff2'),
- url('fonts/LessPerfectDOSVGA.woff') format('woff');
-}
-
-/* Overall */
-html, body {
- width:100%;
- height:100%;
- margin:0;
- overflow: hidden;
- font-family: 'WinThreeOne', sans-serif;
- background-color: #00ADAC;
- font-size: 12px;
- font-smooth: never;
- -webkit-font-smoothing : none;
-}
-
-/* Windows */
-.window {
- padding: 4px;
- background-color: white;
- box-shadow:
- inset 0 0 0 1px black,
- inset 0 0 0 3px lightgrey,
- inset 0 0 0 4px black;
-}
-
-.windowheader {
- background-color: lightgrey;
- color: black;
- border-bottom: 1px solid black;
- font-size: 14px;
- height: 20px;
-}
-
-.selectedwindow .windowheader {
- background-color: darkblue;
- color: white;
-}
-
-.windowtitle {
- padding: 2px;
- line-height: 16px;
- text-align: center;
- cursor: default;
-}
-
-.windowclose {
- float: left;
- display: inline-block;
- width: 15px;
- height: 19px;
- padding: 0 0 1px 5px;
- background-color: lightgrey;
- border-right: 1px solid black;
- font-size: 20px;
- color: white;
- font-weight: bold;
- -webkit-text-stroke-width: 0.5px;
- -webkit-text-stroke-color: black;
- text-shadow: 1px 1px darkgrey;
- cursor: default;
-}
-
-.windowmenubar {
- background-color: white;
- border-bottom: 1px solid black;
- font-size: 14px;
- height: 12px;
- padding: 4px;
- line-height: 16px;
- cursor: default;
-}
-
-.windowclose::before {
- content: '-';
-}
-
-.ui-icon-gripsmall-diagonal-se {
- background-image: none !important;
-}
-
-/* Window contents */
-.windowinner {
- height: calc(100% - 21px);
- width: 100%;
- overflow: hidden;
-}
-.windowmenubar + .windowinner {
- height: calc(100% - 42px) !important;
-}
-
-.fullwindowhtml {
- padding: 0px 8px;
-}
-
-textarea.fullwindowtextarea {
- padding: 5px;
- width: 100%;
- height: 100%;
- resize: none;
- border: none;
- outline: none;
- font-family: 'WinThreeOne', sans-serif;
- font-size: 12px;
-}
-
-textarea.fullwindowdosprompt {
- padding: 5px;
- width: 100%;
- height: 100%;
- resize: none;
- border: none;
- outline: none;
- background-color: black;
- color: lightgrey;
- font-family: 'DOS', monospace;
- font-size: 16px;
-}
-
-/* Icons */
-.icon {
- display: inline-block;
- margin: 10px;
- text-align: center;
-}
-
-.icon p {
- display: block;
- margin: 5px auto 0 auto;
- padding: 2px;
- cursor: default;
-}
-
-.icon p.highlight {
- background-color: darkblue;
- color: white;
-}
-
-/* DOOM */
-#doomcanvas {
- background-color: black;
- width: 640px;
- height: 480px;
-}
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/layers/batch_norm.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/layers/batch_norm.py
deleted file mode 100644
index 32a1e05470065e75b6caad18d36211d27af8eec0..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/layers/batch_norm.py
+++ /dev/null
@@ -1,300 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import torch
-import torch.distributed as dist
-from fvcore.nn.distributed import differentiable_all_reduce
-from torch import nn
-from torch.nn import functional as F
-
-from annotator.oneformer.detectron2.utils import comm, env
-
-from .wrappers import BatchNorm2d
-
-
-class FrozenBatchNorm2d(nn.Module):
- """
- BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
- It contains non-trainable buffers called
- "weight" and "bias", "running_mean", "running_var",
- initialized to perform identity transformation.
-
- The pre-trained backbone models from Caffe2 only contain "weight" and "bias",
- which are computed from the original four parameters of BN.
- The affine transform `x * weight + bias` will perform the equivalent
- computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.
- When loading a backbone model from Caffe2, "running_mean" and "running_var"
- will be left unchanged as identity transformation.
-
- Other pre-trained backbone models may contain all 4 parameters.
-
- The forward is implemented by `F.batch_norm(..., training=False)`.
- """
-
- _version = 3
-
- def __init__(self, num_features, eps=1e-5):
- super().__init__()
- self.num_features = num_features
- self.eps = eps
- self.register_buffer("weight", torch.ones(num_features))
- self.register_buffer("bias", torch.zeros(num_features))
- self.register_buffer("running_mean", torch.zeros(num_features))
- self.register_buffer("running_var", torch.ones(num_features) - eps)
-
- def forward(self, x):
- if x.requires_grad:
- # When gradients are needed, F.batch_norm will use extra memory
- # because its backward op computes gradients for weight/bias as well.
- scale = self.weight * (self.running_var + self.eps).rsqrt()
- bias = self.bias - self.running_mean * scale
- scale = scale.reshape(1, -1, 1, 1)
- bias = bias.reshape(1, -1, 1, 1)
- out_dtype = x.dtype # may be half
- return x * scale.to(out_dtype) + bias.to(out_dtype)
- else:
- # When gradients are not needed, F.batch_norm is a single fused op
- # and provide more optimization opportunities.
- return F.batch_norm(
- x,
- self.running_mean,
- self.running_var,
- self.weight,
- self.bias,
- training=False,
- eps=self.eps,
- )
-
- def _load_from_state_dict(
- self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
- ):
- version = local_metadata.get("version", None)
-
- if version is None or version < 2:
- # No running_mean/var in early versions
- # This will silent the warnings
- if prefix + "running_mean" not in state_dict:
- state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean)
- if prefix + "running_var" not in state_dict:
- state_dict[prefix + "running_var"] = torch.ones_like(self.running_var)
-
- super()._load_from_state_dict(
- state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
- )
-
- def __repr__(self):
- return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps)
-
- @classmethod
- def convert_frozen_batchnorm(cls, module):
- """
- Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm.
-
- Args:
- module (torch.nn.Module):
-
- Returns:
- If module is BatchNorm/SyncBatchNorm, returns a new module.
- Otherwise, in-place convert module and return it.
-
- Similar to convert_sync_batchnorm in
- https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py
- """
- bn_module = nn.modules.batchnorm
- bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm)
- res = module
- if isinstance(module, bn_module):
- res = cls(module.num_features)
- if module.affine:
- res.weight.data = module.weight.data.clone().detach()
- res.bias.data = module.bias.data.clone().detach()
- res.running_mean.data = module.running_mean.data
- res.running_var.data = module.running_var.data
- res.eps = module.eps
- else:
- for name, child in module.named_children():
- new_child = cls.convert_frozen_batchnorm(child)
- if new_child is not child:
- res.add_module(name, new_child)
- return res
-
-
-def get_norm(norm, out_channels):
- """
- Args:
- norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
- or a callable that takes a channel number and returns
- the normalization layer as a nn.Module.
-
- Returns:
- nn.Module or None: the normalization layer
- """
- if norm is None:
- return None
- if isinstance(norm, str):
- if len(norm) == 0:
- return None
- norm = {
- "BN": BatchNorm2d,
- # Fixed in https://github.com/pytorch/pytorch/pull/36382
- "SyncBN": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm,
- "FrozenBN": FrozenBatchNorm2d,
- "GN": lambda channels: nn.GroupNorm(32, channels),
- # for debugging:
- "nnSyncBN": nn.SyncBatchNorm,
- "naiveSyncBN": NaiveSyncBatchNorm,
- # expose stats_mode N as an option to caller, required for zero-len inputs
- "naiveSyncBN_N": lambda channels: NaiveSyncBatchNorm(channels, stats_mode="N"),
- "LN": lambda channels: LayerNorm(channels),
- }[norm]
- return norm(out_channels)
-
-
-class NaiveSyncBatchNorm(BatchNorm2d):
- """
- In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient
- when the batch size on each worker is different.
- (e.g., when scale augmentation is used, or when it is applied to mask head).
-
- This is a slower but correct alternative to `nn.SyncBatchNorm`.
-
- Note:
- There isn't a single definition of Sync BatchNorm.
-
- When ``stats_mode==""``, this module computes overall statistics by using
- statistics of each worker with equal weight. The result is true statistics
- of all samples (as if they are all on one worker) only when all workers
- have the same (N, H, W). This mode does not support inputs with zero batch size.
-
- When ``stats_mode=="N"``, this module computes overall statistics by weighting
- the statistics of each worker by their ``N``. The result is true statistics
- of all samples (as if they are all on one worker) only when all workers
- have the same (H, W). It is slower than ``stats_mode==""``.
-
- Even though the result of this module may not be the true statistics of all samples,
- it may still be reasonable because it might be preferrable to assign equal weights
- to all workers, regardless of their (H, W) dimension, instead of putting larger weight
- on larger images. From preliminary experiments, little difference is found between such
- a simplified implementation and an accurate computation of overall mean & variance.
- """
-
- def __init__(self, *args, stats_mode="", **kwargs):
- super().__init__(*args, **kwargs)
- assert stats_mode in ["", "N"]
- self._stats_mode = stats_mode
-
- def forward(self, input):
- if comm.get_world_size() == 1 or not self.training:
- return super().forward(input)
-
- B, C = input.shape[0], input.shape[1]
-
- half_input = input.dtype == torch.float16
- if half_input:
- # fp16 does not have good enough numerics for the reduction here
- input = input.float()
- mean = torch.mean(input, dim=[0, 2, 3])
- meansqr = torch.mean(input * input, dim=[0, 2, 3])
-
- if self._stats_mode == "":
- assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.'
- vec = torch.cat([mean, meansqr], dim=0)
- vec = differentiable_all_reduce(vec) * (1.0 / dist.get_world_size())
- mean, meansqr = torch.split(vec, C)
- momentum = self.momentum
- else:
- if B == 0:
- vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype)
- vec = vec + input.sum() # make sure there is gradient w.r.t input
- else:
- vec = torch.cat(
- [mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0
- )
- vec = differentiable_all_reduce(vec * B)
-
- total_batch = vec[-1].detach()
- momentum = total_batch.clamp(max=1) * self.momentum # no update if total_batch is 0
- mean, meansqr, _ = torch.split(vec / total_batch.clamp(min=1), C) # avoid div-by-zero
-
- var = meansqr - mean * mean
- invstd = torch.rsqrt(var + self.eps)
- scale = self.weight * invstd
- bias = self.bias - mean * scale
- scale = scale.reshape(1, -1, 1, 1)
- bias = bias.reshape(1, -1, 1, 1)
-
- self.running_mean += momentum * (mean.detach() - self.running_mean)
- self.running_var += momentum * (var.detach() - self.running_var)
- ret = input * scale + bias
- if half_input:
- ret = ret.half()
- return ret
-
-
-class CycleBatchNormList(nn.ModuleList):
- """
- Implement domain-specific BatchNorm by cycling.
-
- When a BatchNorm layer is used for multiple input domains or input
- features, it might need to maintain a separate test-time statistics
- for each domain. See Sec 5.2 in :paper:`rethinking-batchnorm`.
-
- This module implements it by using N separate BN layers
- and it cycles through them every time a forward() is called.
-
- NOTE: The caller of this module MUST guarantee to always call
- this module by multiple of N times. Otherwise its test-time statistics
- will be incorrect.
- """
-
- def __init__(self, length: int, bn_class=nn.BatchNorm2d, **kwargs):
- """
- Args:
- length: number of BatchNorm layers to cycle.
- bn_class: the BatchNorm class to use
- kwargs: arguments of the BatchNorm class, such as num_features.
- """
- self._affine = kwargs.pop("affine", True)
- super().__init__([bn_class(**kwargs, affine=False) for k in range(length)])
- if self._affine:
- # shared affine, domain-specific BN
- channels = self[0].num_features
- self.weight = nn.Parameter(torch.ones(channels))
- self.bias = nn.Parameter(torch.zeros(channels))
- self._pos = 0
-
- def forward(self, x):
- ret = self[self._pos](x)
- self._pos = (self._pos + 1) % len(self)
-
- if self._affine:
- w = self.weight.reshape(1, -1, 1, 1)
- b = self.bias.reshape(1, -1, 1, 1)
- return ret * w + b
- else:
- return ret
-
- def extra_repr(self):
- return f"affine={self._affine}"
-
-
-class LayerNorm(nn.Module):
- """
- A LayerNorm variant, popularized by Transformers, that performs point-wise mean and
- variance normalization over the channel dimension for inputs that have shape
- (batch_size, channels, height, width).
- https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa B950
- """
-
- def __init__(self, normalized_shape, eps=1e-6):
- super().__init__()
- self.weight = nn.Parameter(torch.ones(normalized_shape))
- self.bias = nn.Parameter(torch.zeros(normalized_shape))
- self.eps = eps
- self.normalized_shape = (normalized_shape,)
-
- def forward(self, x):
- u = x.mean(1, keepdim=True)
- s = (x - u).pow(2).mean(1, keepdim=True)
- x = (x - u) / torch.sqrt(s + self.eps)
- x = self.weight[:, None, None] * x + self.bias[:, None, None]
- return x
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/masked_conv.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/masked_conv.py
deleted file mode 100644
index cd514cc204c1d571ea5dc7e74b038c0f477a008b..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/masked_conv.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['masked_im2col_forward', 'masked_col2im_forward'])
-
-
-class MaskedConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, features, mask, weight, bias, padding, stride):
- return g.op(
- 'mmcv::MMCVMaskedConv2d',
- features,
- mask,
- weight,
- bias,
- padding_i=padding,
- stride_i=stride)
-
- @staticmethod
- def forward(ctx, features, mask, weight, bias, padding=0, stride=1):
- assert mask.dim() == 3 and mask.size(0) == 1
- assert features.dim() == 4 and features.size(0) == 1
- assert features.size()[2:] == mask.size()[1:]
- pad_h, pad_w = _pair(padding)
- stride_h, stride_w = _pair(stride)
- if stride_h != 1 or stride_w != 1:
- raise ValueError(
- 'Stride could not only be 1 in masked_conv2d currently.')
- out_channel, in_channel, kernel_h, kernel_w = weight.size()
-
- batch_size = features.size(0)
- out_h = int(
- math.floor((features.size(2) + 2 * pad_h -
- (kernel_h - 1) - 1) / stride_h + 1))
- out_w = int(
- math.floor((features.size(3) + 2 * pad_w -
- (kernel_h - 1) - 1) / stride_w + 1))
- mask_inds = torch.nonzero(mask[0] > 0, as_tuple=False)
- output = features.new_zeros(batch_size, out_channel, out_h, out_w)
- if mask_inds.numel() > 0:
- mask_h_idx = mask_inds[:, 0].contiguous()
- mask_w_idx = mask_inds[:, 1].contiguous()
- data_col = features.new_zeros(in_channel * kernel_h * kernel_w,
- mask_inds.size(0))
- ext_module.masked_im2col_forward(
- features,
- mask_h_idx,
- mask_w_idx,
- data_col,
- kernel_h=kernel_h,
- kernel_w=kernel_w,
- pad_h=pad_h,
- pad_w=pad_w)
-
- masked_output = torch.addmm(1, bias[:, None], 1,
- weight.view(out_channel, -1), data_col)
- ext_module.masked_col2im_forward(
- masked_output,
- mask_h_idx,
- mask_w_idx,
- output,
- height=out_h,
- width=out_w,
- channels=out_channel)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- return (None, ) * 5
-
-
-masked_conv2d = MaskedConv2dFunction.apply
-
-
-class MaskedConv2d(nn.Conv2d):
- """A MaskedConv2d which inherits the official Conv2d.
-
- The masked forward doesn't implement the backward function and only
- supports the stride parameter to be 1 currently.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=True):
- super(MaskedConv2d,
- self).__init__(in_channels, out_channels, kernel_size, stride,
- padding, dilation, groups, bias)
-
- def forward(self, input, mask=None):
- if mask is None: # fallback to the normal Conv2d
- return super(MaskedConv2d, self).forward(input)
- else:
- return masked_conv2d(input, mask, self.weight, self.bias,
- self.padding)
diff --git a/spaces/csuer/nsfw-classification/app.py b/spaces/csuer/nsfw-classification/app.py
deleted file mode 100644
index cc65948f1301a32779956f60ae0fc64fefb6ee9c..0000000000000000000000000000000000000000
--- a/spaces/csuer/nsfw-classification/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import gradio as gr
-import torch
-import threading
-import torch.nn as nn
-from torchvision import transforms
-from torchvision.models import resnet18, ResNet18_Weights
-from PIL import Image
-
-# number convert to label
-labels = ["drawings", "hentai", "neutral", "porn", "sexy"]
-description = f"""This is a demo of classifing nsfw pictures. Label division is based on the following:
-[*https://github.com/alex000kim/nsfw_data_scraper*](https://github.com/alex000kim/nsfw_data_scraper).
-
-You can continue to train this model with the same preprocess-to-images.
-Finally, welcome to star my [*github repository*](https://github.com/csuer411/nsfw_classify)"""
-# define CNN model
-class Classifier(nn.Module):
- def __init__(self):
- super(Classifier, self).__init__()
- self.cnn_layers = resnet18(weights=ResNet18_Weights)
- self.fc_layers = nn.Sequential(
- nn.Linear(1000, 512),
- nn.Dropout(0.3),
- nn.Linear(512, 128),
- nn.ReLU(),
- nn.Linear(128, 5),
- )
-
- def forward(self, x):
-
- # Extract features by convolutional layers.
- x = self.cnn_layers(x)
- x = self.fc_layers(x)
- return x
-
-
-# pre-process
-preprocess = transforms.Compose(
- [
- transforms.Resize(224),
- transforms.ToTensor(),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- ]
-)
-# load model
-model = Classifier()
-model.load_state_dict(torch.load("classify_nsfw_v3.0.pth", map_location="cpu"))
-model.eval()
-
-
-def predict(inp):
- inp = preprocess(inp).unsqueeze(0)
- with torch.no_grad():
- prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
- result = {labels[i]: float(prediction[i]) for i in range(5)}
- return result
-
-
-inputs = gr.Image(type='pil')
-outputs = gr.Label(num_top_classes=2)
-gr.Interface(
- fn=predict, inputs=inputs, outputs=outputs, examples=["./example/anime.jpg", "./example/real.jpg"], description=description,
-).launch()
diff --git a/spaces/cxeep/whisper-webui/src/download.py b/spaces/cxeep/whisper-webui/src/download.py
deleted file mode 100644
index 90c6435d7cf548121034dfe2d54b18ae76250324..0000000000000000000000000000000000000000
--- a/spaces/cxeep/whisper-webui/src/download.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from tempfile import mkdtemp
-from yt_dlp import YoutubeDL
-
-import yt_dlp
-from yt_dlp.postprocessor import PostProcessor
-
-class FilenameCollectorPP(PostProcessor):
- def __init__(self):
- super(FilenameCollectorPP, self).__init__(None)
- self.filenames = []
-
- def run(self, information):
- self.filenames.append(information["filepath"])
- return [], information
-
-def downloadUrl(url: str, maxDuration: int = None):
- try:
- return _performDownload(url, maxDuration=maxDuration)
- except yt_dlp.utils.DownloadError as e:
- # In case of an OS error, try again with a different output template
- if e.msg and e.msg.find("[Errno 36] File name too long") >= 0:
- return _performDownload(url, maxDuration=maxDuration, outputTemplate="%(title).10s %(id)s.%(ext)s")
- pass
-
-def _performDownload(url: str, maxDuration: int = None, outputTemplate: str = None):
- destinationDirectory = mkdtemp()
-
- ydl_opts = {
- "format": "bestaudio/best",
- 'playlist_items': '1',
- 'paths': {
- 'home': destinationDirectory
- }
- }
-
- # Add output template if specified
- if outputTemplate:
- ydl_opts['outtmpl'] = outputTemplate
-
- filename_collector = FilenameCollectorPP()
-
- with YoutubeDL(ydl_opts) as ydl:
- if maxDuration and maxDuration > 0:
- info = ydl.extract_info(url, download=False)
- duration = info['duration']
-
- if duration >= maxDuration:
- raise ExceededMaximumDuration(videoDuration=duration, maxDuration=maxDuration, message="Video is too long")
-
- ydl.add_post_processor(filename_collector)
- ydl.download([url])
-
- if len(filename_collector.filenames) <= 0:
- raise Exception("Cannot download " + url)
-
- result = filename_collector.filenames[0]
- print("Downloaded " + result)
-
- return result
-
-class ExceededMaximumDuration(Exception):
- def __init__(self, videoDuration, maxDuration, message):
- self.videoDuration = videoDuration
- self.maxDuration = maxDuration
- super().__init__(message)
\ No newline at end of file
diff --git a/spaces/danurahul/pop-music/app.py b/spaces/danurahul/pop-music/app.py
deleted file mode 100644
index 5aba421ee0f0ee0074a65c745ddaa372cd2993d9..0000000000000000000000000000000000000000
--- a/spaces/danurahul/pop-music/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from model import PopMusicTransformer
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-import tensorflow as tf
-tf.compat.v1.disable_eager_execution()
-import gradio as gr
-import requests
-import torchtext
-import zipfile
-
-torchtext.utils.download_from_url("https://drive.google.com/uc?id=1gxuTSkF51NP04JZgTE46Pg4KQsbHQKGo", root=".")
-torchtext.utils.download_from_url("https://drive.google.com/uc?id=1nAKjaeahlzpVAX0F9wjQEG_hL4UosSbo", root=".")
-
-with zipfile.ZipFile("REMI-tempo-checkpoint.zip","r") as zip_ref:
- zip_ref.extractall(".")
-with zipfile.ZipFile("REMI-tempo-chord-checkpoint.zip","r") as zip_ref:
- zip_ref.extractall(".")
-
-url = 'https://github.com/AK391/remi/blob/master/input.midi?raw=true'
-r = requests.get(url, allow_redirects=True)
-open("input.midi", 'wb').write(r.content)
-
-
-# declare model
-model = PopMusicTransformer(
- checkpoint='REMI-tempo-checkpoint',
- is_training=False)
-
-def inference(midi):
- # generate continuation
- model.generate(
- n_target_bar=4,
- temperature=1.2,
- topk=5,
- output_path='continuation.midi',
- prompt=midi.name)
- return 'continuation.midi'
-
-
-title = "Pop Music Transformer"
-description = "demo for Pop Music Transformer. To use it, simply upload your midi file, or click one of the examples to load them. Read more at the links below."
-article = "Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions | Github Repo
"
-
-examples = [
- ['input.midi']
-]
-gr.Interface(
- inference,
- gr.inputs.File(label="Input Midi"),
- gr.outputs.File(label="Output Midi"),
- title=title,
- description=description,
- article=article,
- examples=examples
- ).launch()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImImagePlugin.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImImagePlugin.py
deleted file mode 100644
index 746743f658cf3fa2e0022ae049808eb68d3d1221..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImImagePlugin.py
+++ /dev/null
@@ -1,371 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# IFUNC IM file handling for PIL
-#
-# history:
-# 1995-09-01 fl Created.
-# 1997-01-03 fl Save palette images
-# 1997-01-08 fl Added sequence support
-# 1997-01-23 fl Added P and RGB save support
-# 1997-05-31 fl Read floating point images
-# 1997-06-22 fl Save floating point images
-# 1997-08-27 fl Read and save 1-bit images
-# 1998-06-25 fl Added support for RGB+LUT images
-# 1998-07-02 fl Added support for YCC images
-# 1998-07-15 fl Renamed offset attribute to avoid name clash
-# 1998-12-29 fl Added I;16 support
-# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.7)
-# 2003-09-26 fl Added LA/PA support
-#
-# Copyright (c) 1997-2003 by Secret Labs AB.
-# Copyright (c) 1995-2001 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import os
-import re
-
-from . import Image, ImageFile, ImagePalette
-
-# --------------------------------------------------------------------
-# Standard tags
-
-COMMENT = "Comment"
-DATE = "Date"
-EQUIPMENT = "Digitalization equipment"
-FRAMES = "File size (no of images)"
-LUT = "Lut"
-NAME = "Name"
-SCALE = "Scale (x,y)"
-SIZE = "Image size (x*y)"
-MODE = "Image type"
-
-TAGS = {
- COMMENT: 0,
- DATE: 0,
- EQUIPMENT: 0,
- FRAMES: 0,
- LUT: 0,
- NAME: 0,
- SCALE: 0,
- SIZE: 0,
- MODE: 0,
-}
-
-OPEN = {
- # ifunc93/p3cfunc formats
- "0 1 image": ("1", "1"),
- "L 1 image": ("1", "1"),
- "Greyscale image": ("L", "L"),
- "Grayscale image": ("L", "L"),
- "RGB image": ("RGB", "RGB;L"),
- "RLB image": ("RGB", "RLB"),
- "RYB image": ("RGB", "RLB"),
- "B1 image": ("1", "1"),
- "B2 image": ("P", "P;2"),
- "B4 image": ("P", "P;4"),
- "X 24 image": ("RGB", "RGB"),
- "L 32 S image": ("I", "I;32"),
- "L 32 F image": ("F", "F;32"),
- # old p3cfunc formats
- "RGB3 image": ("RGB", "RGB;T"),
- "RYB3 image": ("RGB", "RYB;T"),
- # extensions
- "LA image": ("LA", "LA;L"),
- "PA image": ("LA", "PA;L"),
- "RGBA image": ("RGBA", "RGBA;L"),
- "RGBX image": ("RGBX", "RGBX;L"),
- "CMYK image": ("CMYK", "CMYK;L"),
- "YCC image": ("YCbCr", "YCbCr;L"),
-}
-
-# ifunc95 extensions
-for i in ["8", "8S", "16", "16S", "32", "32F"]:
- OPEN[f"L {i} image"] = ("F", f"F;{i}")
- OPEN[f"L*{i} image"] = ("F", f"F;{i}")
-for i in ["16", "16L", "16B"]:
- OPEN[f"L {i} image"] = (f"I;{i}", f"I;{i}")
- OPEN[f"L*{i} image"] = (f"I;{i}", f"I;{i}")
-for i in ["32S"]:
- OPEN[f"L {i} image"] = ("I", f"I;{i}")
- OPEN[f"L*{i} image"] = ("I", f"I;{i}")
-for i in range(2, 33):
- OPEN[f"L*{i} image"] = ("F", f"F;{i}")
-
-
-# --------------------------------------------------------------------
-# Read IM directory
-
-split = re.compile(rb"^([A-Za-z][^:]*):[ \t]*(.*)[ \t]*$")
-
-
-def number(s):
- try:
- return int(s)
- except ValueError:
- return float(s)
-
-
-##
-# Image plugin for the IFUNC IM file format.
-
-
-class ImImageFile(ImageFile.ImageFile):
- format = "IM"
- format_description = "IFUNC Image Memory"
- _close_exclusive_fp_after_loading = False
-
- def _open(self):
- # Quick rejection: if there's not an LF among the first
- # 100 bytes, this is (probably) not a text header.
-
- if b"\n" not in self.fp.read(100):
- msg = "not an IM file"
- raise SyntaxError(msg)
- self.fp.seek(0)
-
- n = 0
-
- # Default values
- self.info[MODE] = "L"
- self.info[SIZE] = (512, 512)
- self.info[FRAMES] = 1
-
- self.rawmode = "L"
-
- while True:
- s = self.fp.read(1)
-
- # Some versions of IFUNC uses \n\r instead of \r\n...
- if s == b"\r":
- continue
-
- if not s or s == b"\0" or s == b"\x1A":
- break
-
- # FIXME: this may read whole file if not a text file
- s = s + self.fp.readline()
-
- if len(s) > 100:
- msg = "not an IM file"
- raise SyntaxError(msg)
-
- if s[-2:] == b"\r\n":
- s = s[:-2]
- elif s[-1:] == b"\n":
- s = s[:-1]
-
- try:
- m = split.match(s)
- except re.error as e:
- msg = "not an IM file"
- raise SyntaxError(msg) from e
-
- if m:
- k, v = m.group(1, 2)
-
- # Don't know if this is the correct encoding,
- # but a decent guess (I guess)
- k = k.decode("latin-1", "replace")
- v = v.decode("latin-1", "replace")
-
- # Convert value as appropriate
- if k in [FRAMES, SCALE, SIZE]:
- v = v.replace("*", ",")
- v = tuple(map(number, v.split(",")))
- if len(v) == 1:
- v = v[0]
- elif k == MODE and v in OPEN:
- v, self.rawmode = OPEN[v]
-
- # Add to dictionary. Note that COMMENT tags are
- # combined into a list of strings.
- if k == COMMENT:
- if k in self.info:
- self.info[k].append(v)
- else:
- self.info[k] = [v]
- else:
- self.info[k] = v
-
- if k in TAGS:
- n += 1
-
- else:
- msg = "Syntax error in IM header: " + s.decode("ascii", "replace")
- raise SyntaxError(msg)
-
- if not n:
- msg = "Not an IM file"
- raise SyntaxError(msg)
-
- # Basic attributes
- self._size = self.info[SIZE]
- self.mode = self.info[MODE]
-
- # Skip forward to start of image data
- while s and s[:1] != b"\x1A":
- s = self.fp.read(1)
- if not s:
- msg = "File truncated"
- raise SyntaxError(msg)
-
- if LUT in self.info:
- # convert lookup table to palette or lut attribute
- palette = self.fp.read(768)
- greyscale = 1 # greyscale palette
- linear = 1 # linear greyscale palette
- for i in range(256):
- if palette[i] == palette[i + 256] == palette[i + 512]:
- if palette[i] != i:
- linear = 0
- else:
- greyscale = 0
- if self.mode in ["L", "LA", "P", "PA"]:
- if greyscale:
- if not linear:
- self.lut = list(palette[:256])
- else:
- if self.mode in ["L", "P"]:
- self.mode = self.rawmode = "P"
- elif self.mode in ["LA", "PA"]:
- self.mode = "PA"
- self.rawmode = "PA;L"
- self.palette = ImagePalette.raw("RGB;L", palette)
- elif self.mode == "RGB":
- if not greyscale or not linear:
- self.lut = list(palette)
-
- self.frame = 0
-
- self.__offset = offs = self.fp.tell()
-
- self._fp = self.fp # FIXME: hack
-
- if self.rawmode[:2] == "F;":
- # ifunc95 formats
- try:
- # use bit decoder (if necessary)
- bits = int(self.rawmode[2:])
- if bits not in [8, 16, 32]:
- self.tile = [("bit", (0, 0) + self.size, offs, (bits, 8, 3, 0, -1))]
- return
- except ValueError:
- pass
-
- if self.rawmode in ["RGB;T", "RYB;T"]:
- # Old LabEye/3PC files. Would be very surprised if anyone
- # ever stumbled upon such a file ;-)
- size = self.size[0] * self.size[1]
- self.tile = [
- ("raw", (0, 0) + self.size, offs, ("G", 0, -1)),
- ("raw", (0, 0) + self.size, offs + size, ("R", 0, -1)),
- ("raw", (0, 0) + self.size, offs + 2 * size, ("B", 0, -1)),
- ]
- else:
- # LabEye/IFUNC files
- self.tile = [("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1))]
-
- @property
- def n_frames(self):
- return self.info[FRAMES]
-
- @property
- def is_animated(self):
- return self.info[FRAMES] > 1
-
- def seek(self, frame):
- if not self._seek_check(frame):
- return
-
- self.frame = frame
-
- if self.mode == "1":
- bits = 1
- else:
- bits = 8 * len(self.mode)
-
- size = ((self.size[0] * bits + 7) // 8) * self.size[1]
- offs = self.__offset + frame * size
-
- self.fp = self._fp
-
- self.tile = [("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1))]
-
- def tell(self):
- return self.frame
-
-
-#
-# --------------------------------------------------------------------
-# Save IM files
-
-
-SAVE = {
- # mode: (im type, raw mode)
- "1": ("0 1", "1"),
- "L": ("Greyscale", "L"),
- "LA": ("LA", "LA;L"),
- "P": ("Greyscale", "P"),
- "PA": ("LA", "PA;L"),
- "I": ("L 32S", "I;32S"),
- "I;16": ("L 16", "I;16"),
- "I;16L": ("L 16L", "I;16L"),
- "I;16B": ("L 16B", "I;16B"),
- "F": ("L 32F", "F;32F"),
- "RGB": ("RGB", "RGB;L"),
- "RGBA": ("RGBA", "RGBA;L"),
- "RGBX": ("RGBX", "RGBX;L"),
- "CMYK": ("CMYK", "CMYK;L"),
- "YCbCr": ("YCC", "YCbCr;L"),
-}
-
-
-def _save(im, fp, filename):
- try:
- image_type, rawmode = SAVE[im.mode]
- except KeyError as e:
- msg = f"Cannot save {im.mode} images as IM"
- raise ValueError(msg) from e
-
- frames = im.encoderinfo.get("frames", 1)
-
- fp.write(f"Image type: {image_type} image\r\n".encode("ascii"))
- if filename:
- # Each line must be 100 characters or less,
- # or: SyntaxError("not an IM file")
- # 8 characters are used for "Name: " and "\r\n"
- # Keep just the filename, ditch the potentially overlong path
- name, ext = os.path.splitext(os.path.basename(filename))
- name = "".join([name[: 92 - len(ext)], ext])
-
- fp.write(f"Name: {name}\r\n".encode("ascii"))
- fp.write(("Image size (x*y): %d*%d\r\n" % im.size).encode("ascii"))
- fp.write(f"File size (no of images): {frames}\r\n".encode("ascii"))
- if im.mode in ["P", "PA"]:
- fp.write(b"Lut: 1\r\n")
- fp.write(b"\000" * (511 - fp.tell()) + b"\032")
- if im.mode in ["P", "PA"]:
- im_palette = im.im.getpalette("RGB", "RGB;L")
- colors = len(im_palette) // 3
- palette = b""
- for i in range(3):
- palette += im_palette[colors * i : colors * (i + 1)]
- palette += b"\x00" * (256 - colors)
- fp.write(palette) # 768 bytes
- ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, -1))])
-
-
-#
-# --------------------------------------------------------------------
-# Registry
-
-
-Image.register_open(ImImageFile.format, ImImageFile)
-Image.register_save(ImImageFile.format, _save)
-
-Image.register_extension(ImImageFile.format, ".im")
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/__main__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/__main__.py
deleted file mode 100644
index ff632d49c54e678623a27998a9d51b7cf84df81f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/__main__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import sys
-from fontTools.merge import main
-
-
-if __name__ == "__main__":
- sys.exit(main())
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/E_B_D_T_.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/E_B_D_T_.py
deleted file mode 100644
index 42d10700d2d93613b5b5e2ea7b7cc86d295dedb2..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/E_B_D_T_.py
+++ /dev/null
@@ -1,823 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import (
- bytechr,
- byteord,
- bytesjoin,
- strjoin,
- safeEval,
- readHex,
- hexStr,
- deHexStr,
-)
-from .BitmapGlyphMetrics import (
- BigGlyphMetrics,
- bigGlyphMetricsFormat,
- SmallGlyphMetrics,
- smallGlyphMetricsFormat,
-)
-from . import DefaultTable
-import itertools
-import os
-import struct
-import logging
-
-
-log = logging.getLogger(__name__)
-
-ebdtTableVersionFormat = """
- > # big endian
- version: 16.16F
-"""
-
-ebdtComponentFormat = """
- > # big endian
- glyphCode: H
- xOffset: b
- yOffset: b
-"""
-
-
-class table_E_B_D_T_(DefaultTable.DefaultTable):
-
- # Keep a reference to the name of the data locator table.
- locatorName = "EBLC"
-
- # This method can be overridden in subclasses to support new formats
- # without changing the other implementation. Also can be used as a
- # convenience method for coverting a font file to an alternative format.
- def getImageFormatClass(self, imageFormat):
- return ebdt_bitmap_classes[imageFormat]
-
- def decompile(self, data, ttFont):
- # Get the version but don't advance the slice.
- # Most of the lookup for this table is done relative
- # to the begining so slice by the offsets provided
- # in the EBLC table.
- sstruct.unpack2(ebdtTableVersionFormat, data, self)
-
- # Keep a dict of glyphs that have been seen so they aren't remade.
- # This dict maps intervals of data to the BitmapGlyph.
- glyphDict = {}
-
- # Pull out the EBLC table and loop through glyphs.
- # A strike is a concept that spans both tables.
- # The actual bitmap data is stored in the EBDT.
- locator = ttFont[self.__class__.locatorName]
- self.strikeData = []
- for curStrike in locator.strikes:
- bitmapGlyphDict = {}
- self.strikeData.append(bitmapGlyphDict)
- for indexSubTable in curStrike.indexSubTables:
- dataIter = zip(indexSubTable.names, indexSubTable.locations)
- for curName, curLoc in dataIter:
- # Don't create duplicate data entries for the same glyphs.
- # Instead just use the structures that already exist if they exist.
- if curLoc in glyphDict:
- curGlyph = glyphDict[curLoc]
- else:
- curGlyphData = data[slice(*curLoc)]
- imageFormatClass = self.getImageFormatClass(
- indexSubTable.imageFormat
- )
- curGlyph = imageFormatClass(curGlyphData, ttFont)
- glyphDict[curLoc] = curGlyph
- bitmapGlyphDict[curName] = curGlyph
-
- def compile(self, ttFont):
-
- dataList = []
- dataList.append(sstruct.pack(ebdtTableVersionFormat, self))
- dataSize = len(dataList[0])
-
- # Keep a dict of glyphs that have been seen so they aren't remade.
- # This dict maps the id of the BitmapGlyph to the interval
- # in the data.
- glyphDict = {}
-
- # Go through the bitmap glyph data. Just in case the data for a glyph
- # changed the size metrics should be recalculated. There are a variety
- # of formats and they get stored in the EBLC table. That is why
- # recalculation is defered to the EblcIndexSubTable class and just
- # pass what is known about bitmap glyphs from this particular table.
- locator = ttFont[self.__class__.locatorName]
- for curStrike, curGlyphDict in zip(locator.strikes, self.strikeData):
- for curIndexSubTable in curStrike.indexSubTables:
- dataLocations = []
- for curName in curIndexSubTable.names:
- # Handle the data placement based on seeing the glyph or not.
- # Just save a reference to the location if the glyph has already
- # been saved in compile. This code assumes that glyphs will only
- # be referenced multiple times from indexFormat5. By luck the
- # code may still work when referencing poorly ordered fonts with
- # duplicate references. If there is a font that is unlucky the
- # respective compile methods for the indexSubTables will fail
- # their assertions. All fonts seem to follow this assumption.
- # More complicated packing may be needed if a counter-font exists.
- glyph = curGlyphDict[curName]
- objectId = id(glyph)
- if objectId not in glyphDict:
- data = glyph.compile(ttFont)
- data = curIndexSubTable.padBitmapData(data)
- startByte = dataSize
- dataSize += len(data)
- endByte = dataSize
- dataList.append(data)
- dataLoc = (startByte, endByte)
- glyphDict[objectId] = dataLoc
- else:
- dataLoc = glyphDict[objectId]
- dataLocations.append(dataLoc)
- # Just use the new data locations in the indexSubTable.
- # The respective compile implementations will take care
- # of any of the problems in the convertion that may arise.
- curIndexSubTable.locations = dataLocations
-
- return bytesjoin(dataList)
-
- def toXML(self, writer, ttFont):
- # When exporting to XML if one of the data export formats
- # requires metrics then those metrics may be in the locator.
- # In this case populate the bitmaps with "export metrics".
- if ttFont.bitmapGlyphDataFormat in ("row", "bitwise"):
- locator = ttFont[self.__class__.locatorName]
- for curStrike, curGlyphDict in zip(locator.strikes, self.strikeData):
- for curIndexSubTable in curStrike.indexSubTables:
- for curName in curIndexSubTable.names:
- glyph = curGlyphDict[curName]
- # I'm not sure which metrics have priority here.
- # For now if both metrics exist go with glyph metrics.
- if hasattr(glyph, "metrics"):
- glyph.exportMetrics = glyph.metrics
- else:
- glyph.exportMetrics = curIndexSubTable.metrics
- glyph.exportBitDepth = curStrike.bitmapSizeTable.bitDepth
-
- writer.simpletag("header", [("version", self.version)])
- writer.newline()
- locator = ttFont[self.__class__.locatorName]
- for strikeIndex, bitmapGlyphDict in enumerate(self.strikeData):
- writer.begintag("strikedata", [("index", strikeIndex)])
- writer.newline()
- for curName, curBitmap in bitmapGlyphDict.items():
- curBitmap.toXML(strikeIndex, curName, writer, ttFont)
- writer.endtag("strikedata")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- if name == "header":
- self.version = safeEval(attrs["version"])
- elif name == "strikedata":
- if not hasattr(self, "strikeData"):
- self.strikeData = []
- strikeIndex = safeEval(attrs["index"])
-
- bitmapGlyphDict = {}
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attrs, content = element
- if name[4:].startswith(_bitmapGlyphSubclassPrefix[4:]):
- imageFormat = safeEval(name[len(_bitmapGlyphSubclassPrefix) :])
- glyphName = attrs["name"]
- imageFormatClass = self.getImageFormatClass(imageFormat)
- curGlyph = imageFormatClass(None, None)
- curGlyph.fromXML(name, attrs, content, ttFont)
- assert glyphName not in bitmapGlyphDict, (
- "Duplicate glyphs with the same name '%s' in the same strike."
- % glyphName
- )
- bitmapGlyphDict[glyphName] = curGlyph
- else:
- log.warning("%s being ignored by %s", name, self.__class__.__name__)
-
- # Grow the strike data array to the appropriate size. The XML
- # format allows the strike index value to be out of order.
- if strikeIndex >= len(self.strikeData):
- self.strikeData += [None] * (strikeIndex + 1 - len(self.strikeData))
- assert (
- self.strikeData[strikeIndex] is None
- ), "Duplicate strike EBDT indices."
- self.strikeData[strikeIndex] = bitmapGlyphDict
-
-
-class EbdtComponent(object):
- def toXML(self, writer, ttFont):
- writer.begintag("ebdtComponent", [("name", self.name)])
- writer.newline()
- for componentName in sstruct.getformat(ebdtComponentFormat)[1][1:]:
- writer.simpletag(componentName, value=getattr(self, componentName))
- writer.newline()
- writer.endtag("ebdtComponent")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- self.name = attrs["name"]
- componentNames = set(sstruct.getformat(ebdtComponentFormat)[1][1:])
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attrs, content = element
- if name in componentNames:
- vars(self)[name] = safeEval(attrs["value"])
- else:
- log.warning("unknown name '%s' being ignored by EbdtComponent.", name)
-
-
-# Helper functions for dealing with binary.
-
-
-def _data2binary(data, numBits):
- binaryList = []
- for curByte in data:
- value = byteord(curByte)
- numBitsCut = min(8, numBits)
- for i in range(numBitsCut):
- if value & 0x1:
- binaryList.append("1")
- else:
- binaryList.append("0")
- value = value >> 1
- numBits -= numBitsCut
- return strjoin(binaryList)
-
-
-def _binary2data(binary):
- byteList = []
- for bitLoc in range(0, len(binary), 8):
- byteString = binary[bitLoc : bitLoc + 8]
- curByte = 0
- for curBit in reversed(byteString):
- curByte = curByte << 1
- if curBit == "1":
- curByte |= 1
- byteList.append(bytechr(curByte))
- return bytesjoin(byteList)
-
-
-def _memoize(f):
- class memodict(dict):
- def __missing__(self, key):
- ret = f(key)
- if len(key) == 1:
- self[key] = ret
- return ret
-
- return memodict().__getitem__
-
-
-# 00100111 -> 11100100 per byte, not to be confused with little/big endian.
-# Bitmap data per byte is in the order that binary is written on the page
-# with the least significant bit as far right as possible. This is the
-# opposite of what makes sense algorithmically and hence this function.
-@_memoize
-def _reverseBytes(data):
- if len(data) != 1:
- return bytesjoin(map(_reverseBytes, data))
- byte = byteord(data)
- result = 0
- for i in range(8):
- result = result << 1
- result |= byte & 1
- byte = byte >> 1
- return bytechr(result)
-
-
-# This section of code is for reading and writing image data to/from XML.
-
-
-def _writeRawImageData(strikeIndex, glyphName, bitmapObject, writer, ttFont):
- writer.begintag("rawimagedata")
- writer.newline()
- writer.dumphex(bitmapObject.imageData)
- writer.endtag("rawimagedata")
- writer.newline()
-
-
-def _readRawImageData(bitmapObject, name, attrs, content, ttFont):
- bitmapObject.imageData = readHex(content)
-
-
-def _writeRowImageData(strikeIndex, glyphName, bitmapObject, writer, ttFont):
- metrics = bitmapObject.exportMetrics
- del bitmapObject.exportMetrics
- bitDepth = bitmapObject.exportBitDepth
- del bitmapObject.exportBitDepth
-
- writer.begintag(
- "rowimagedata", bitDepth=bitDepth, width=metrics.width, height=metrics.height
- )
- writer.newline()
- for curRow in range(metrics.height):
- rowData = bitmapObject.getRow(curRow, bitDepth=bitDepth, metrics=metrics)
- writer.simpletag("row", value=hexStr(rowData))
- writer.newline()
- writer.endtag("rowimagedata")
- writer.newline()
-
-
-def _readRowImageData(bitmapObject, name, attrs, content, ttFont):
- bitDepth = safeEval(attrs["bitDepth"])
- metrics = SmallGlyphMetrics()
- metrics.width = safeEval(attrs["width"])
- metrics.height = safeEval(attrs["height"])
-
- dataRows = []
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attr, content = element
- # Chop off 'imagedata' from the tag to get just the option.
- if name == "row":
- dataRows.append(deHexStr(attr["value"]))
- bitmapObject.setRows(dataRows, bitDepth=bitDepth, metrics=metrics)
-
-
-def _writeBitwiseImageData(strikeIndex, glyphName, bitmapObject, writer, ttFont):
- metrics = bitmapObject.exportMetrics
- del bitmapObject.exportMetrics
- bitDepth = bitmapObject.exportBitDepth
- del bitmapObject.exportBitDepth
-
- # A dict for mapping binary to more readable/artistic ASCII characters.
- binaryConv = {"0": ".", "1": "@"}
-
- writer.begintag(
- "bitwiseimagedata",
- bitDepth=bitDepth,
- width=metrics.width,
- height=metrics.height,
- )
- writer.newline()
- for curRow in range(metrics.height):
- rowData = bitmapObject.getRow(
- curRow, bitDepth=1, metrics=metrics, reverseBytes=True
- )
- rowData = _data2binary(rowData, metrics.width)
- # Make the output a readable ASCII art form.
- rowData = strjoin(map(binaryConv.get, rowData))
- writer.simpletag("row", value=rowData)
- writer.newline()
- writer.endtag("bitwiseimagedata")
- writer.newline()
-
-
-def _readBitwiseImageData(bitmapObject, name, attrs, content, ttFont):
- bitDepth = safeEval(attrs["bitDepth"])
- metrics = SmallGlyphMetrics()
- metrics.width = safeEval(attrs["width"])
- metrics.height = safeEval(attrs["height"])
-
- # A dict for mapping from ASCII to binary. All characters are considered
- # a '1' except space, period and '0' which maps to '0'.
- binaryConv = {" ": "0", ".": "0", "0": "0"}
-
- dataRows = []
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attr, content = element
- if name == "row":
- mapParams = zip(attr["value"], itertools.repeat("1"))
- rowData = strjoin(itertools.starmap(binaryConv.get, mapParams))
- dataRows.append(_binary2data(rowData))
-
- bitmapObject.setRows(
- dataRows, bitDepth=bitDepth, metrics=metrics, reverseBytes=True
- )
-
-
-def _writeExtFileImageData(strikeIndex, glyphName, bitmapObject, writer, ttFont):
- try:
- folder = os.path.dirname(writer.file.name)
- except AttributeError:
- # fall back to current directory if output file's directory isn't found
- folder = "."
- folder = os.path.join(folder, "bitmaps")
- filename = glyphName + bitmapObject.fileExtension
- if not os.path.isdir(folder):
- os.makedirs(folder)
- folder = os.path.join(folder, "strike%d" % strikeIndex)
- if not os.path.isdir(folder):
- os.makedirs(folder)
-
- fullPath = os.path.join(folder, filename)
- writer.simpletag("extfileimagedata", value=fullPath)
- writer.newline()
-
- with open(fullPath, "wb") as file:
- file.write(bitmapObject.imageData)
-
-
-def _readExtFileImageData(bitmapObject, name, attrs, content, ttFont):
- fullPath = attrs["value"]
- with open(fullPath, "rb") as file:
- bitmapObject.imageData = file.read()
-
-
-# End of XML writing code.
-
-# Important information about the naming scheme. Used for identifying formats
-# in XML.
-_bitmapGlyphSubclassPrefix = "ebdt_bitmap_format_"
-
-
-class BitmapGlyph(object):
-
- # For the external file format. This can be changed in subclasses. This way
- # when the extfile option is turned on files have the form: glyphName.ext
- # The default is just a flat binary file with no meaning.
- fileExtension = ".bin"
-
- # Keep track of reading and writing of various forms.
- xmlDataFunctions = {
- "raw": (_writeRawImageData, _readRawImageData),
- "row": (_writeRowImageData, _readRowImageData),
- "bitwise": (_writeBitwiseImageData, _readBitwiseImageData),
- "extfile": (_writeExtFileImageData, _readExtFileImageData),
- }
-
- def __init__(self, data, ttFont):
- self.data = data
- self.ttFont = ttFont
- # TODO Currently non-lazy decompilation is untested here...
- # if not ttFont.lazy:
- # self.decompile()
- # del self.data
-
- def __getattr__(self, attr):
- # Allow lazy decompile.
- if attr[:2] == "__":
- raise AttributeError(attr)
- if attr == "data":
- raise AttributeError(attr)
- self.decompile()
- del self.data
- return getattr(self, attr)
-
- def ensureDecompiled(self, recurse=False):
- if hasattr(self, "data"):
- self.decompile()
- del self.data
-
- # Not a fan of this but it is needed for safer safety checking.
- def getFormat(self):
- return safeEval(self.__class__.__name__[len(_bitmapGlyphSubclassPrefix) :])
-
- def toXML(self, strikeIndex, glyphName, writer, ttFont):
- writer.begintag(self.__class__.__name__, [("name", glyphName)])
- writer.newline()
-
- self.writeMetrics(writer, ttFont)
- # Use the internal write method to write using the correct output format.
- self.writeData(strikeIndex, glyphName, writer, ttFont)
-
- writer.endtag(self.__class__.__name__)
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- self.readMetrics(name, attrs, content, ttFont)
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attr, content = element
- if not name.endswith("imagedata"):
- continue
- # Chop off 'imagedata' from the tag to get just the option.
- option = name[: -len("imagedata")]
- assert option in self.__class__.xmlDataFunctions
- self.readData(name, attr, content, ttFont)
-
- # Some of the glyphs have the metrics. This allows for metrics to be
- # added if the glyph format has them. Default behavior is to do nothing.
- def writeMetrics(self, writer, ttFont):
- pass
-
- # The opposite of write metrics.
- def readMetrics(self, name, attrs, content, ttFont):
- pass
-
- def writeData(self, strikeIndex, glyphName, writer, ttFont):
- try:
- writeFunc, readFunc = self.__class__.xmlDataFunctions[
- ttFont.bitmapGlyphDataFormat
- ]
- except KeyError:
- writeFunc = _writeRawImageData
- writeFunc(strikeIndex, glyphName, self, writer, ttFont)
-
- def readData(self, name, attrs, content, ttFont):
- # Chop off 'imagedata' from the tag to get just the option.
- option = name[: -len("imagedata")]
- writeFunc, readFunc = self.__class__.xmlDataFunctions[option]
- readFunc(self, name, attrs, content, ttFont)
-
-
-# A closure for creating a mixin for the two types of metrics handling.
-# Most of the code is very similar so its easier to deal with here.
-# Everything works just by passing the class that the mixin is for.
-def _createBitmapPlusMetricsMixin(metricsClass):
- # Both metrics names are listed here to make meaningful error messages.
- metricStrings = [BigGlyphMetrics.__name__, SmallGlyphMetrics.__name__]
- curMetricsName = metricsClass.__name__
- # Find which metrics this is for and determine the opposite name.
- metricsId = metricStrings.index(curMetricsName)
- oppositeMetricsName = metricStrings[1 - metricsId]
-
- class BitmapPlusMetricsMixin(object):
- def writeMetrics(self, writer, ttFont):
- self.metrics.toXML(writer, ttFont)
-
- def readMetrics(self, name, attrs, content, ttFont):
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attrs, content = element
- if name == curMetricsName:
- self.metrics = metricsClass()
- self.metrics.fromXML(name, attrs, content, ttFont)
- elif name == oppositeMetricsName:
- log.warning(
- "Warning: %s being ignored in format %d.",
- oppositeMetricsName,
- self.getFormat(),
- )
-
- return BitmapPlusMetricsMixin
-
-
-# Since there are only two types of mixin's just create them here.
-BitmapPlusBigMetricsMixin = _createBitmapPlusMetricsMixin(BigGlyphMetrics)
-BitmapPlusSmallMetricsMixin = _createBitmapPlusMetricsMixin(SmallGlyphMetrics)
-
-# Data that is bit aligned can be tricky to deal with. These classes implement
-# helper functionality for dealing with the data and getting a particular row
-# of bitwise data. Also helps implement fancy data export/import in XML.
-class BitAlignedBitmapMixin(object):
- def _getBitRange(self, row, bitDepth, metrics):
- rowBits = bitDepth * metrics.width
- bitOffset = row * rowBits
- return (bitOffset, bitOffset + rowBits)
-
- def getRow(self, row, bitDepth=1, metrics=None, reverseBytes=False):
- if metrics is None:
- metrics = self.metrics
- assert 0 <= row and row < metrics.height, "Illegal row access in bitmap"
-
- # Loop through each byte. This can cover two bytes in the original data or
- # a single byte if things happen to be aligned. The very last entry might
- # not be aligned so take care to trim the binary data to size and pad with
- # zeros in the row data. Bit aligned data is somewhat tricky.
- #
- # Example of data cut. Data cut represented in x's.
- # '|' represents byte boundary.
- # data = ...0XX|XXXXXX00|000... => XXXXXXXX
- # or
- # data = ...0XX|XXXX0000|000... => XXXXXX00
- # or
- # data = ...000|XXXXXXXX|000... => XXXXXXXX
- # or
- # data = ...000|00XXXX00|000... => XXXX0000
- #
- dataList = []
- bitRange = self._getBitRange(row, bitDepth, metrics)
- stepRange = bitRange + (8,)
- for curBit in range(*stepRange):
- endBit = min(curBit + 8, bitRange[1])
- numBits = endBit - curBit
- cutPoint = curBit % 8
- firstByteLoc = curBit // 8
- secondByteLoc = endBit // 8
- if firstByteLoc < secondByteLoc:
- numBitsCut = 8 - cutPoint
- else:
- numBitsCut = endBit - curBit
- curByte = _reverseBytes(self.imageData[firstByteLoc])
- firstHalf = byteord(curByte) >> cutPoint
- firstHalf = ((1 << numBitsCut) - 1) & firstHalf
- newByte = firstHalf
- if firstByteLoc < secondByteLoc and secondByteLoc < len(self.imageData):
- curByte = _reverseBytes(self.imageData[secondByteLoc])
- secondHalf = byteord(curByte) << numBitsCut
- newByte = (firstHalf | secondHalf) & ((1 << numBits) - 1)
- dataList.append(bytechr(newByte))
-
- # The way the data is kept is opposite the algorithm used.
- data = bytesjoin(dataList)
- if not reverseBytes:
- data = _reverseBytes(data)
- return data
-
- def setRows(self, dataRows, bitDepth=1, metrics=None, reverseBytes=False):
- if metrics is None:
- metrics = self.metrics
- if not reverseBytes:
- dataRows = list(map(_reverseBytes, dataRows))
-
- # Keep track of a list of ordinal values as they are easier to modify
- # than a list of strings. Map to actual strings later.
- numBytes = (self._getBitRange(len(dataRows), bitDepth, metrics)[0] + 7) // 8
- ordDataList = [0] * numBytes
- for row, data in enumerate(dataRows):
- bitRange = self._getBitRange(row, bitDepth, metrics)
- stepRange = bitRange + (8,)
- for curBit, curByte in zip(range(*stepRange), data):
- endBit = min(curBit + 8, bitRange[1])
- cutPoint = curBit % 8
- firstByteLoc = curBit // 8
- secondByteLoc = endBit // 8
- if firstByteLoc < secondByteLoc:
- numBitsCut = 8 - cutPoint
- else:
- numBitsCut = endBit - curBit
- curByte = byteord(curByte)
- firstByte = curByte & ((1 << numBitsCut) - 1)
- ordDataList[firstByteLoc] |= firstByte << cutPoint
- if firstByteLoc < secondByteLoc and secondByteLoc < numBytes:
- secondByte = (curByte >> numBitsCut) & ((1 << 8 - numBitsCut) - 1)
- ordDataList[secondByteLoc] |= secondByte
-
- # Save the image data with the bits going the correct way.
- self.imageData = _reverseBytes(bytesjoin(map(bytechr, ordDataList)))
-
-
-class ByteAlignedBitmapMixin(object):
- def _getByteRange(self, row, bitDepth, metrics):
- rowBytes = (bitDepth * metrics.width + 7) // 8
- byteOffset = row * rowBytes
- return (byteOffset, byteOffset + rowBytes)
-
- def getRow(self, row, bitDepth=1, metrics=None, reverseBytes=False):
- if metrics is None:
- metrics = self.metrics
- assert 0 <= row and row < metrics.height, "Illegal row access in bitmap"
- byteRange = self._getByteRange(row, bitDepth, metrics)
- data = self.imageData[slice(*byteRange)]
- if reverseBytes:
- data = _reverseBytes(data)
- return data
-
- def setRows(self, dataRows, bitDepth=1, metrics=None, reverseBytes=False):
- if metrics is None:
- metrics = self.metrics
- if reverseBytes:
- dataRows = map(_reverseBytes, dataRows)
- self.imageData = bytesjoin(dataRows)
-
-
-class ebdt_bitmap_format_1(
- ByteAlignedBitmapMixin, BitmapPlusSmallMetricsMixin, BitmapGlyph
-):
- def decompile(self):
- self.metrics = SmallGlyphMetrics()
- dummy, data = sstruct.unpack2(smallGlyphMetricsFormat, self.data, self.metrics)
- self.imageData = data
-
- def compile(self, ttFont):
- data = sstruct.pack(smallGlyphMetricsFormat, self.metrics)
- return data + self.imageData
-
-
-class ebdt_bitmap_format_2(
- BitAlignedBitmapMixin, BitmapPlusSmallMetricsMixin, BitmapGlyph
-):
- def decompile(self):
- self.metrics = SmallGlyphMetrics()
- dummy, data = sstruct.unpack2(smallGlyphMetricsFormat, self.data, self.metrics)
- self.imageData = data
-
- def compile(self, ttFont):
- data = sstruct.pack(smallGlyphMetricsFormat, self.metrics)
- return data + self.imageData
-
-
-class ebdt_bitmap_format_5(BitAlignedBitmapMixin, BitmapGlyph):
- def decompile(self):
- self.imageData = self.data
-
- def compile(self, ttFont):
- return self.imageData
-
-
-class ebdt_bitmap_format_6(
- ByteAlignedBitmapMixin, BitmapPlusBigMetricsMixin, BitmapGlyph
-):
- def decompile(self):
- self.metrics = BigGlyphMetrics()
- dummy, data = sstruct.unpack2(bigGlyphMetricsFormat, self.data, self.metrics)
- self.imageData = data
-
- def compile(self, ttFont):
- data = sstruct.pack(bigGlyphMetricsFormat, self.metrics)
- return data + self.imageData
-
-
-class ebdt_bitmap_format_7(
- BitAlignedBitmapMixin, BitmapPlusBigMetricsMixin, BitmapGlyph
-):
- def decompile(self):
- self.metrics = BigGlyphMetrics()
- dummy, data = sstruct.unpack2(bigGlyphMetricsFormat, self.data, self.metrics)
- self.imageData = data
-
- def compile(self, ttFont):
- data = sstruct.pack(bigGlyphMetricsFormat, self.metrics)
- return data + self.imageData
-
-
-class ComponentBitmapGlyph(BitmapGlyph):
- def toXML(self, strikeIndex, glyphName, writer, ttFont):
- writer.begintag(self.__class__.__name__, [("name", glyphName)])
- writer.newline()
-
- self.writeMetrics(writer, ttFont)
-
- writer.begintag("components")
- writer.newline()
- for curComponent in self.componentArray:
- curComponent.toXML(writer, ttFont)
- writer.endtag("components")
- writer.newline()
-
- writer.endtag(self.__class__.__name__)
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- self.readMetrics(name, attrs, content, ttFont)
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attr, content = element
- if name == "components":
- self.componentArray = []
- for compElement in content:
- if not isinstance(compElement, tuple):
- continue
- name, attrs, content = compElement
- if name == "ebdtComponent":
- curComponent = EbdtComponent()
- curComponent.fromXML(name, attrs, content, ttFont)
- self.componentArray.append(curComponent)
- else:
- log.warning("'%s' being ignored in component array.", name)
-
-
-class ebdt_bitmap_format_8(BitmapPlusSmallMetricsMixin, ComponentBitmapGlyph):
- def decompile(self):
- self.metrics = SmallGlyphMetrics()
- dummy, data = sstruct.unpack2(smallGlyphMetricsFormat, self.data, self.metrics)
- data = data[1:]
-
- (numComponents,) = struct.unpack(">H", data[:2])
- data = data[2:]
- self.componentArray = []
- for i in range(numComponents):
- curComponent = EbdtComponent()
- dummy, data = sstruct.unpack2(ebdtComponentFormat, data, curComponent)
- curComponent.name = self.ttFont.getGlyphName(curComponent.glyphCode)
- self.componentArray.append(curComponent)
-
- def compile(self, ttFont):
- dataList = []
- dataList.append(sstruct.pack(smallGlyphMetricsFormat, self.metrics))
- dataList.append(b"\0")
- dataList.append(struct.pack(">H", len(self.componentArray)))
- for curComponent in self.componentArray:
- curComponent.glyphCode = ttFont.getGlyphID(curComponent.name)
- dataList.append(sstruct.pack(ebdtComponentFormat, curComponent))
- return bytesjoin(dataList)
-
-
-class ebdt_bitmap_format_9(BitmapPlusBigMetricsMixin, ComponentBitmapGlyph):
- def decompile(self):
- self.metrics = BigGlyphMetrics()
- dummy, data = sstruct.unpack2(bigGlyphMetricsFormat, self.data, self.metrics)
- (numComponents,) = struct.unpack(">H", data[:2])
- data = data[2:]
- self.componentArray = []
- for i in range(numComponents):
- curComponent = EbdtComponent()
- dummy, data = sstruct.unpack2(ebdtComponentFormat, data, curComponent)
- curComponent.name = self.ttFont.getGlyphName(curComponent.glyphCode)
- self.componentArray.append(curComponent)
-
- def compile(self, ttFont):
- dataList = []
- dataList.append(sstruct.pack(bigGlyphMetricsFormat, self.metrics))
- dataList.append(struct.pack(">H", len(self.componentArray)))
- for curComponent in self.componentArray:
- curComponent.glyphCode = ttFont.getGlyphID(curComponent.name)
- dataList.append(sstruct.pack(ebdtComponentFormat, curComponent))
- return bytesjoin(dataList)
-
-
-# Dictionary of bitmap formats to the class representing that format
-# currently only the ones listed in this map are the ones supported.
-ebdt_bitmap_classes = {
- 1: ebdt_bitmap_format_1,
- 2: ebdt_bitmap_format_2,
- 5: ebdt_bitmap_format_5,
- 6: ebdt_bitmap_format_6,
- 7: ebdt_bitmap_format_7,
- 8: ebdt_bitmap_format_8,
- 9: ebdt_bitmap_format_9,
-}
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Model3d-1cd0dc4b.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Model3d-1cd0dc4b.js
deleted file mode 100644
index a25ebdcbd76000982d1e31d0a0c719ee745fb104..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Model3d-1cd0dc4b.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as d,e as o,s as u,m as _,t as g,g as m,Y as i,h as r,j as v,x as y,n as c,k as b}from"./index-39fce9e2.js";function h(a){let e,s;return{c(){e=_("div"),s=g(a[0]),m(e,"class","svelte-1ayixqk"),i(e,"table",a[1]==="table"),i(e,"gallery",a[1]==="gallery"),i(e,"selected",a[2])},m(t,l){r(t,e,l),v(e,s)},p(t,[l]){l&1&&y(s,t[0]),l&2&&i(e,"table",t[1]==="table"),l&2&&i(e,"gallery",t[1]==="gallery"),l&4&&i(e,"selected",t[2])},i:c,o:c,d(t){t&&b(e)}}}function M(a,e,s){let{value:t}=e,{type:l}=e,{selected:f=!1}=e;return a.$$set=n=>{"value"in n&&s(0,t=n.value),"type"in n&&s(1,l=n.type),"selected"in n&&s(2,f=n.selected)},[t,l,f]}class k extends d{constructor(e){super(),o(this,e,M,h,u,{value:0,type:1,selected:2})}}const E=k;export{E};
-//# sourceMappingURL=Model3d-1cd0dc4b.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/idna/intranges.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/idna/intranges.py
deleted file mode 100644
index 6a43b0475347cb50d0d65ada1000a82eeca9e882..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/idna/intranges.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-Given a list of integers, made up of (hopefully) a small number of long runs
-of consecutive integers, compute a representation of the form
-((start1, end1), (start2, end2) ...). Then answer the question "was x present
-in the original list?" in time O(log(# runs)).
-"""
-
-import bisect
-from typing import List, Tuple
-
-def intranges_from_list(list_: List[int]) -> Tuple[int, ...]:
- """Represent a list of integers as a sequence of ranges:
- ((start_0, end_0), (start_1, end_1), ...), such that the original
- integers are exactly those x such that start_i <= x < end_i for some i.
-
- Ranges are encoded as single integers (start << 32 | end), not as tuples.
- """
-
- sorted_list = sorted(list_)
- ranges = []
- last_write = -1
- for i in range(len(sorted_list)):
- if i+1 < len(sorted_list):
- if sorted_list[i] == sorted_list[i+1]-1:
- continue
- current_range = sorted_list[last_write+1:i+1]
- ranges.append(_encode_range(current_range[0], current_range[-1] + 1))
- last_write = i
-
- return tuple(ranges)
-
-def _encode_range(start: int, end: int) -> int:
- return (start << 32) | end
-
-def _decode_range(r: int) -> Tuple[int, int]:
- return (r >> 32), (r & ((1 << 32) - 1))
-
-
-def intranges_contain(int_: int, ranges: Tuple[int, ...]) -> bool:
- """Determine if `int_` falls into one of the ranges in `ranges`."""
- tuple_ = _encode_range(int_, 0)
- pos = bisect.bisect_left(ranges, tuple_)
- # we could be immediately ahead of a tuple (start, end)
- # with start < int_ <= end
- if pos > 0:
- left, right = _decode_range(ranges[pos-1])
- if left <= int_ < right:
- return True
- # or we could be immediately behind a tuple (int_, end)
- if pos < len(ranges):
- left, _ = _decode_range(ranges[pos])
- if left == int_:
- return True
- return False
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema_specifications/_core.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema_specifications/_core.py
deleted file mode 100644
index 8300bdc29714db46dd6f117a2a5385648ab1aedf..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema_specifications/_core.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""
-A `referencing.Registry` containing schemas from the JSON Schema specification.
-"""
-
-import json
-
-try:
- from importlib.resources import files
-except ImportError:
- from importlib_resources import files # type: ignore
-
-from referencing import Resource
-
-
-def _schemas():
- """
- All schemas we ship.
- """
- # importlib.resources.abc.Traversal doesn't have nice ways to do this that
- # I'm aware of...
- #
- # It can't recurse arbitrarily, e.g. no ``.glob()``.
- #
- # So this takes some liberties given the real layout of what we ship
- # (only 2 levels of nesting, no directories within the second level).
-
- for version in files(__package__).joinpath("schemas").iterdir():
- for child in version.iterdir():
- children = [child] if child.is_file() else child.iterdir()
- for path in children:
- contents = json.loads(path.read_text(encoding="utf-8"))
- yield Resource.from_contents(contents)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backend_managers.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backend_managers.py
deleted file mode 100644
index ac74ff97a4e81dab2663e0b6f12f785e493c0d1f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backend_managers.py
+++ /dev/null
@@ -1,398 +0,0 @@
-from matplotlib import _api, backend_tools, cbook, widgets
-
-
-class ToolEvent:
- """Event for tool manipulation (add/remove)."""
- def __init__(self, name, sender, tool, data=None):
- self.name = name
- self.sender = sender
- self.tool = tool
- self.data = data
-
-
-class ToolTriggerEvent(ToolEvent):
- """Event to inform that a tool has been triggered."""
- def __init__(self, name, sender, tool, canvasevent=None, data=None):
- super().__init__(name, sender, tool, data)
- self.canvasevent = canvasevent
-
-
-class ToolManagerMessageEvent:
- """
- Event carrying messages from toolmanager.
-
- Messages usually get displayed to the user by the toolbar.
- """
- def __init__(self, name, sender, message):
- self.name = name
- self.sender = sender
- self.message = message
-
-
-class ToolManager:
- """
- Manager for actions triggered by user interactions (key press, toolbar
- clicks, ...) on a Figure.
-
- Attributes
- ----------
- figure : `.Figure`
- keypresslock : `~matplotlib.widgets.LockDraw`
- `.LockDraw` object to know if the `canvas` key_press_event is locked.
- messagelock : `~matplotlib.widgets.LockDraw`
- `.LockDraw` object to know if the message is available to write.
- """
-
- def __init__(self, figure=None):
-
- self._key_press_handler_id = None
-
- self._tools = {}
- self._keys = {}
- self._toggled = {}
- self._callbacks = cbook.CallbackRegistry()
-
- # to process keypress event
- self.keypresslock = widgets.LockDraw()
- self.messagelock = widgets.LockDraw()
-
- self._figure = None
- self.set_figure(figure)
-
- @property
- def canvas(self):
- """Canvas managed by FigureManager."""
- if not self._figure:
- return None
- return self._figure.canvas
-
- @property
- def figure(self):
- """Figure that holds the canvas."""
- return self._figure
-
- @figure.setter
- def figure(self, figure):
- self.set_figure(figure)
-
- def set_figure(self, figure, update_tools=True):
- """
- Bind the given figure to the tools.
-
- Parameters
- ----------
- figure : `.Figure`
- update_tools : bool, default: True
- Force tools to update figure.
- """
- if self._key_press_handler_id:
- self.canvas.mpl_disconnect(self._key_press_handler_id)
- self._figure = figure
- if figure:
- self._key_press_handler_id = self.canvas.mpl_connect(
- 'key_press_event', self._key_press)
- if update_tools:
- for tool in self._tools.values():
- tool.figure = figure
-
- def toolmanager_connect(self, s, func):
- """
- Connect event with string *s* to *func*.
-
- Parameters
- ----------
- s : str
- The name of the event. The following events are recognized:
-
- - 'tool_message_event'
- - 'tool_removed_event'
- - 'tool_added_event'
-
- For every tool added a new event is created
-
- - 'tool_trigger_TOOLNAME', where TOOLNAME is the id of the tool.
-
- func : callable
- Callback function for the toolmanager event with signature::
-
- def func(event: ToolEvent) -> Any
-
- Returns
- -------
- cid
- The callback id for the connection. This can be used in
- `.toolmanager_disconnect`.
- """
- return self._callbacks.connect(s, func)
-
- def toolmanager_disconnect(self, cid):
- """
- Disconnect callback id *cid*.
-
- Example usage::
-
- cid = toolmanager.toolmanager_connect('tool_trigger_zoom', onpress)
- #...later
- toolmanager.toolmanager_disconnect(cid)
- """
- return self._callbacks.disconnect(cid)
-
- def message_event(self, message, sender=None):
- """Emit a `ToolManagerMessageEvent`."""
- if sender is None:
- sender = self
-
- s = 'tool_message_event'
- event = ToolManagerMessageEvent(s, sender, message)
- self._callbacks.process(s, event)
-
- @property
- def active_toggle(self):
- """Currently toggled tools."""
- return self._toggled
-
- def get_tool_keymap(self, name):
- """
- Return the keymap associated with the specified tool.
-
- Parameters
- ----------
- name : str
- Name of the Tool.
-
- Returns
- -------
- list of str
- List of keys associated with the tool.
- """
-
- keys = [k for k, i in self._keys.items() if i == name]
- return keys
-
- def _remove_keys(self, name):
- for k in self.get_tool_keymap(name):
- del self._keys[k]
-
- def update_keymap(self, name, key):
- """
- Set the keymap to associate with the specified tool.
-
- Parameters
- ----------
- name : str
- Name of the Tool.
- key : str or list of str
- Keys to associate with the tool.
- """
- if name not in self._tools:
- raise KeyError(f'{name!r} not in Tools')
- self._remove_keys(name)
- if isinstance(key, str):
- key = [key]
- for k in key:
- if k in self._keys:
- _api.warn_external(
- f'Key {k} changed from {self._keys[k]} to {name}')
- self._keys[k] = name
-
- def remove_tool(self, name):
- """
- Remove tool named *name*.
-
- Parameters
- ----------
- name : str
- Name of the tool.
- """
-
- tool = self.get_tool(name)
- destroy = _api.deprecate_method_override(
- backend_tools.ToolBase.destroy, tool, since="3.6",
- alternative="tool_removed_event")
- if destroy is not None:
- destroy()
-
- # If it's a toggle tool and toggled, untoggle
- if getattr(tool, 'toggled', False):
- self.trigger_tool(tool, 'toolmanager')
-
- self._remove_keys(name)
-
- event = ToolEvent('tool_removed_event', self, tool)
- self._callbacks.process(event.name, event)
-
- del self._tools[name]
-
- def add_tool(self, name, tool, *args, **kwargs):
- """
- Add *tool* to `ToolManager`.
-
- If successful, adds a new event ``tool_trigger_{name}`` where
- ``{name}`` is the *name* of the tool; the event is fired every time the
- tool is triggered.
-
- Parameters
- ----------
- name : str
- Name of the tool, treated as the ID, has to be unique.
- tool : type
- Class of the tool to be added. A subclass will be used
- instead if one was registered for the current canvas class.
- *args, **kwargs
- Passed to the *tool*'s constructor.
-
- See Also
- --------
- matplotlib.backend_tools.ToolBase : The base class for tools.
- """
-
- tool_cls = backend_tools._find_tool_class(type(self.canvas), tool)
- if not tool_cls:
- raise ValueError('Impossible to find class for %s' % str(tool))
-
- if name in self._tools:
- _api.warn_external('A "Tool class" with the same name already '
- 'exists, not added')
- return self._tools[name]
-
- tool_obj = tool_cls(self, name, *args, **kwargs)
- self._tools[name] = tool_obj
-
- if tool_obj.default_keymap is not None:
- self.update_keymap(name, tool_obj.default_keymap)
-
- # For toggle tools init the radio_group in self._toggled
- if isinstance(tool_obj, backend_tools.ToolToggleBase):
- # None group is not mutually exclusive, a set is used to keep track
- # of all toggled tools in this group
- if tool_obj.radio_group is None:
- self._toggled.setdefault(None, set())
- else:
- self._toggled.setdefault(tool_obj.radio_group, None)
-
- # If initially toggled
- if tool_obj.toggled:
- self._handle_toggle(tool_obj, None, None)
- tool_obj.set_figure(self.figure)
-
- event = ToolEvent('tool_added_event', self, tool_obj)
- self._callbacks.process(event.name, event)
-
- return tool_obj
-
- def _handle_toggle(self, tool, canvasevent, data):
- """
- Toggle tools, need to untoggle prior to using other Toggle tool.
- Called from trigger_tool.
-
- Parameters
- ----------
- tool : `.ToolBase`
- canvasevent : Event
- Original Canvas event or None.
- data : object
- Extra data to pass to the tool when triggering.
- """
-
- radio_group = tool.radio_group
- # radio_group None is not mutually exclusive
- # just keep track of toggled tools in this group
- if radio_group is None:
- if tool.name in self._toggled[None]:
- self._toggled[None].remove(tool.name)
- else:
- self._toggled[None].add(tool.name)
- return
-
- # If the tool already has a toggled state, untoggle it
- if self._toggled[radio_group] == tool.name:
- toggled = None
- # If no tool was toggled in the radio_group
- # toggle it
- elif self._toggled[radio_group] is None:
- toggled = tool.name
- # Other tool in the radio_group is toggled
- else:
- # Untoggle previously toggled tool
- self.trigger_tool(self._toggled[radio_group],
- self,
- canvasevent,
- data)
- toggled = tool.name
-
- # Keep track of the toggled tool in the radio_group
- self._toggled[radio_group] = toggled
-
- def trigger_tool(self, name, sender=None, canvasevent=None, data=None):
- """
- Trigger a tool and emit the ``tool_trigger_{name}`` event.
-
- Parameters
- ----------
- name : str
- Name of the tool.
- sender : object
- Object that wishes to trigger the tool.
- canvasevent : Event
- Original Canvas event or None.
- data : object
- Extra data to pass to the tool when triggering.
- """
- tool = self.get_tool(name)
- if tool is None:
- return
-
- if sender is None:
- sender = self
-
- if isinstance(tool, backend_tools.ToolToggleBase):
- self._handle_toggle(tool, canvasevent, data)
-
- tool.trigger(sender, canvasevent, data) # Actually trigger Tool.
-
- s = 'tool_trigger_%s' % name
- event = ToolTriggerEvent(s, sender, tool, canvasevent, data)
- self._callbacks.process(s, event)
-
- def _key_press(self, event):
- if event.key is None or self.keypresslock.locked():
- return
-
- name = self._keys.get(event.key, None)
- if name is None:
- return
- self.trigger_tool(name, canvasevent=event)
-
- @property
- def tools(self):
- """A dict mapping tool name -> controlled tool."""
- return self._tools
-
- def get_tool(self, name, warn=True):
- """
- Return the tool object with the given name.
-
- For convenience, this passes tool objects through.
-
- Parameters
- ----------
- name : str or `.ToolBase`
- Name of the tool, or the tool itself.
- warn : bool, default: True
- Whether a warning should be emitted it no tool with the given name
- exists.
-
- Returns
- -------
- `.ToolBase` or None
- The tool or None if no tool with the given name exists.
- """
- if (isinstance(name, backend_tools.ToolBase)
- and name.name in self._tools):
- return name
- if name not in self._tools:
- if warn:
- _api.warn_external(
- f"ToolManager does not control tool {name!r}")
- return None
- return self._tools[name]
diff --git a/spaces/deeplearning/audioldm-text-to-audio-generation/audioldm/audio/__init__.py b/spaces/deeplearning/audioldm-text-to-audio-generation/audioldm/audio/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/df-h/viachat-v0.95/app.py b/spaces/df-h/viachat-v0.95/app.py
deleted file mode 100644
index b259a00c56f6b23d0e191fcf921a6e918d4f7a4f..0000000000000000000000000000000000000000
--- a/spaces/df-h/viachat-v0.95/app.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import gradio as gr
-import torch
-import gc
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-tokenizer = AutoTokenizer.from_pretrained("df-h/viachat-t5-large-v0.95")
-model = AutoModelForSeq2SeqLM.from_pretrained("df-h/viachat-t5-large-v0.95")
-model = model.to(device)
-
-input_text = 'Translate from english to german: How old are you'
-input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
-print('input_text', input_text)
-print('input_ids', input_ids)
-outputs = model.generate(input_ids, max_length=500)
-print('outputs', outputs)
-text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print("outputs text:", text)
-
-# def greet(human_inputs):
-
-# system_message = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
-
-# input_text = f"{system_message}\n###Human: {human_inputs}\n### Assistant: "
-# input_ids = tokenizer(input_text, return_tensors="pt").input_ids
-# print('input_text', input_text)
-# print('input_ids', input_ids)
-# outputs = model.generate(input_ids, max_length=500)
-# print('outputs', outputs)
-# text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-# print("outputs text:", text)
-# return text
-
-
-# stream out
-
-yield_tokens = []
-
-
-def greet_stream(human_inputs):
- system_message = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
- input_text = f"{system_message}\n### Human: {human_inputs}\n### Assistant: "
- input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
-
- max_length = 200
- encoder_output = model.encoder(
- input_ids=input_ids
- )['last_hidden_state'] # (1, len, d_model)
-
- gen_ids = torch.tensor([[0]]).to(device)
- past_key_values = None
- yield_tokens = []
- for i in range(max_length):
- out = model.decoder(
- input_ids=gen_ids,
- encoder_hidden_states=encoder_output,
- use_cache=True,
- past_key_values=past_key_values,
- )
- past_key_values = out.past_key_values
- last_hidden_state = out.last_hidden_state # (1, 1, d_model)
- lm_logits = model.lm_head(last_hidden_state) # (1, 1, len_dict)
- values, indices = lm_logits[0].topk(2)
- # print(tokenizer.convert_ids_to_tokens(indices[0]))
- gen_ids = torch.index_select(indices, 1, torch.tensor([0]).to(device))
- yield_tokens.append(tokenizer.decode(
- gen_ids[0].cpu(), skip_special_tokens=True))
- if torch.equal(gen_ids[0], torch.tensor([1])):
- # eos token
- break
- gc.collect()
- torch.cuda.empty_cache()
- yield "".join(yield_tokens)
-
- # stream
- return "".join(yield_tokens)
-
-
-iface = gr.Interface(fn=greet_stream, inputs=[gr.Textbox(
- label="Text 1",
- info="Initial text",
- lines=3,
- value="Who are you?",
-)], outputs="text")
-
-iface.queue()
-iface.launch()
diff --git a/spaces/dfgnota/gpt-doc-mem/app.py b/spaces/dfgnota/gpt-doc-mem/app.py
deleted file mode 100644
index 7d59d020f01d2b0cb3363e8a885ed3ce25e0cd13..0000000000000000000000000000000000000000
--- a/spaces/dfgnota/gpt-doc-mem/app.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Import necessary modules
-import os
-import re
-import time
-from io import BytesIO
-from typing import Any, Dict, List
-
-import openai
-import streamlit as st
-from langchain import LLMChain, OpenAI
-from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
-from langchain.chains import RetrievalQA
-from langchain.chains.question_answering import load_qa_chain
-from langchain.docstore.document import Document
-from langchain.document_loaders import PyPDFLoader
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.llms import OpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import VectorStore
-from langchain.vectorstores.faiss import FAISS
-from pypdf import PdfReader
-
-
-# Define a function to parse a PDF file and extract its text content
-@st.cache_data
-def parse_pdf(file: BytesIO) -> List[str]:
- pdf = PdfReader(file)
- output = []
- for page in pdf.pages:
- text = page.extract_text()
- # Merge hyphenated words
- text = re.sub(r"(\w+)-\n(\w+)", r"\1\2", text)
- # Fix newlines in the middle of sentences
- text = re.sub(r"(? List[Document]:
- """Converts a string or list of strings to a list of Documents
- with metadata."""
- if isinstance(text, str):
- # Take a single string as one page
- text = [text]
- page_docs = [Document(page_content=page) for page in text]
-
- # Add page numbers as metadata
- for i, doc in enumerate(page_docs):
- doc.metadata["page"] = i + 1
-
- # Split pages into chunks
- doc_chunks = []
-
- for doc in page_docs:
- text_splitter = RecursiveCharacterTextSplitter(
- chunk_size=4000,
- separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
- chunk_overlap=0,
- )
- chunks = text_splitter.split_text(doc.page_content)
- for i, chunk in enumerate(chunks):
- doc = Document(
- page_content=chunk, metadata={"page": doc.metadata["page"], "chunk": i}
- )
- # Add sources a metadata
- doc.metadata["source"] = f"{doc.metadata['page']}-{doc.metadata['chunk']}"
- doc_chunks.append(doc)
- return doc_chunks
-
-
-# Define a function for the embeddings
-@st.cache_data
-def test_embed():
- embeddings = OpenAIEmbeddings(openai_api_key=api)
- # Indexing
- # Save in a Vector DB
- with st.spinner("It's indexing..."):
- index = FAISS.from_documents(pages, embeddings)
- st.success("Embeddings done.", icon="✅")
- return index
-
-
-# Set up the Streamlit app
-st.title("🤖 Document AI with Memory 🧠 ")
-st.markdown(
- """
- #### 🗨️ Chat with your PDF files 📄 + `Conversational Buffer Memory`
- > *powered by [LangChain]('https://langchain.readthedocs.io/en/latest/modules/memory.html#memory') +
- [OpenAI]('https://platform.openai.com/docs/models/gpt-3-5') + [HuggingFace](https://www.huggingface.co/)*
- """
-)
-
-st.markdown(
- """
- `openai`
- `langchain`
- `tiktoken`
- `pypdf`
- `faiss-cpu`
-
- ---------
- """
-)
-
-# Set up the sidebar
-st.sidebar.markdown(
- """
- ### Steps:
- 1. Upload PDF File
- 2. Enter Your Secret Key for Embeddings
- 3. Perform Q&A
-
- **Note : File content and API key not stored in any form.**
- """
-)
-
-# Allow the user to upload a PDF file
-uploaded_file = st.file_uploader("**Upload Your PDF File**", type=["pdf"])
-
-if uploaded_file:
- name_of_file = uploaded_file.name
- doc = parse_pdf(uploaded_file)
- pages = text_to_docs(doc)
- if pages:
- # Allow the user to select a page and view its content
- with st.expander("Show Page Content", expanded=False):
- page_sel = st.number_input(
- label="Select Page", min_value=1, max_value=len(pages), step=1
- )
- pages[page_sel - 1]
- # Use OpenAI API key from environment or allow the user to enter it
- api = os.environ.get("OPENAI_API_KEY") or st.text_input(
- "**Enter OpenAI API Key**",
- type="password",
- placeholder="sk-",
- help="https://platform.openai.com/account/api-keys",
- )
- if api:
- # Test the embeddings and save the index in a vector database
- index = test_embed()
- # Set up the question-answering system
- qa = RetrievalQA.from_chain_type(
- llm=OpenAI(openai_api_key=api),
- chain_type="stuff",
- retriever=index.as_retriever(),
- )
- # Set up the conversational agent
- tools = [
- Tool(
- name="PDF QA System",
- func=qa.run,
- description="Useful for when you need to answer questions about the aspects asked. Input may be a partial or fully formed question.",
- )
- ]
- prefix = """Have a conversation with a human, answering the following questions as best you can based on the context and memory available.
- You have access to a single tool:"""
- suffix = """Begin!"
-
- {chat_history}
- Question: {input}
- {agent_scratchpad}"""
-
- prompt = ZeroShotAgent.create_prompt(
- tools,
- prefix=prefix,
- suffix=suffix,
- input_variables=["input", "chat_history", "agent_scratchpad"],
- )
-
- if "memory" not in st.session_state:
- st.session_state.memory = ConversationBufferMemory(
- memory_key="chat_history"
- )
-
- llm_chain = LLMChain(
- llm=OpenAI(
- temperature=0, openai_api_key=api, model_name="gpt-3.5-turbo"
- ),
- prompt=prompt,
- )
- agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
- agent_chain = AgentExecutor.from_agent_and_tools(
- agent=agent, tools=tools, verbose=True, memory=st.session_state.memory
- )
-
- # Allow the user to enter a query and generate a response
- query = st.text_input(
- "**What's on your mind?**",
- placeholder="Ask me anything from {}".format(name_of_file),
- )
-
- if query:
- with st.spinner(
- "Generating Answer to your Query : `{}` ".format(query)
- ):
- res = agent_chain.run(query)
- st.info(res, icon="🤖")
-
- # Allow the user to view the conversation history and other information stored in the agent's memory
- with st.expander("History/Memory"):
- st.session_state.memory
diff --git a/spaces/diacanFperku/AutoGPT/Discografia Total De Los Flamers.md b/spaces/diacanFperku/AutoGPT/Discografia Total De Los Flamers.md
deleted file mode 100644
index 2f41abfaa0cd44d98f5bdc31ee323a2214e68b86..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Discografia Total De Los Flamers.md
+++ /dev/null
@@ -1,8 +0,0 @@
-discografia total de los flamers
Download ––– https://gohhs.com/2uFTQB
-
-The best albums of the same year. Los Flamers is a Spanish album released in 2014. There are 10 songs in Los Flamers in total. The songs were written by Los. Flamers except "Bleeding Me" and "Pit". This is the first album to be released on the González label.
-Albums "Gonzalez" and "M.A.N.D.Y." were recorded in 2016. From the album M.A.N.D.Y. several tracks were released, and "M.A.N.D.Y." was only released in the US. It was recorded with the vocalist from "Los Flamers", who are also the producers of this album.
-R.U.S.K. is the third album that was released in 2018. It was recorded over the course of three months, with a break at Christmas. 8a78ff9644
-
-
-
diff --git a/spaces/diacanFperku/AutoGPT/Download Livro Psicologia Juridica Fiorelli.md b/spaces/diacanFperku/AutoGPT/Download Livro Psicologia Juridica Fiorelli.md
deleted file mode 100644
index 79b18936ef651b5859bfd859e053aeaa86a4fdbe..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Download Livro Psicologia Juridica Fiorelli.md
+++ /dev/null
@@ -1,6 +0,0 @@
-download livro psicologia juridica fiorelli
Download Zip ✺✺✺ https://gohhs.com/2uFUVm
-
-jurÃdica de natureza privada, constituÃda como sociedade anônima e com finalidade lucrativa. 1 ... FIORELLI, José Osmir. Psicologia ... Psicologia social contemporânea: livro texto. 8.ed. ... A Scientific Electronic Library Online - SciELO é uma. 1fdad05405
-
-
-
diff --git a/spaces/diacanFperku/AutoGPT/Driverpack.solution.14.13.dvd.md b/spaces/diacanFperku/AutoGPT/Driverpack.solution.14.13.dvd.md
deleted file mode 100644
index 226b30f1aa93b54aada3eb6372d0135a17f1ebbb..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Driverpack.solution.14.13.dvd.md
+++ /dev/null
@@ -1,6 +0,0 @@
-driverpack.solution.14.13.dvd
DOWNLOAD ☆ https://gohhs.com/2uFVGQ
-
-January 5, 2015 - Features: Automatic driver installation. Install all drivers on almost any computer in just 5 minutes. Saving time. After . exe file of the DriverPack Solution installation file automatically downloads and installs all the necessary software for Windows computers. You only need to download and run the program file. You may not even visit the site. Driverpack Solution itself will find and install all the necessary software. You can install the Pak Solution driver both on a new Windows computer, and on an old one or on a laptop. You only need to download and run the program file. 8a78ff9644
-
-
-
diff --git a/spaces/diacanFperku/AutoGPT/Link Iptv Untuk Receiver [UPD].md b/spaces/diacanFperku/AutoGPT/Link Iptv Untuk Receiver [UPD].md
deleted file mode 100644
index 13e6c3bae9f51529433d627ceb3a6053a74aba35..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Link Iptv Untuk Receiver [UPD].md
+++ /dev/null
@@ -1,6 +0,0 @@
-Link Iptv Untuk Receiver
DOWNLOAD ►►►►► https://gohhs.com/2uFTsr
-
- 3cee63e6c2
-
-
-
diff --git a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/losses/unknown_probability_loss.py b/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/losses/unknown_probability_loss.py
deleted file mode 100644
index ede0627de37fd04e3eb65fdf5a6e9f713791fd47..0000000000000000000000000000000000000000
--- a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/losses/unknown_probability_loss.py
+++ /dev/null
@@ -1,113 +0,0 @@
-
-import torch
-import torch.distributions as dists
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-
-
-class UPLoss(nn.Module):
- """Unknown Probability Loss
- """
-
- def __init__(self,
- num_classes: int,
- sampling_metric: str = "min_score",
- topk: int = 3,
- alpha: float = 1.0):
- super().__init__()
- self.num_classes = num_classes#81
- assert sampling_metric in ["min_score", "max_entropy", "random"]
- self.sampling_metric = sampling_metric#min_score
- # if topk==-1, sample len(fg)*2 examples
- self.topk = topk#3
- self.alpha = alpha#1.0
-
- def _soft_cross_entropy(self, input: Tensor, target: Tensor):
- #(num_sample,81)，计算除每个sample真实类别外的softmax分数取log
- logprobs = F.log_softmax(input, dim=1)
- #和targets相乘，即只取每个sample未知类别的softmax分数再相加取平均
- return -(target * logprobs).sum() / input.shape[0]
-
- #
- def _sampling(self, scores: Tensor, labels: Tensor):
- # 筛选出proposal中的前景和背景
- fg_inds = labels != self.num_classes
- fg_scores, fg_labels = scores[fg_inds], labels[fg_inds]
- bg_scores, bg_labels = scores[~fg_inds], labels[~fg_inds]
-
- # remove unknown classes
- # 前景背景分数中去掉未知类别
- _fg_scores = torch.cat(
- [fg_scores[:, :self.num_classes-1], fg_scores[:, -1:]], dim=1)
- _bg_scores = torch.cat(
- [bg_scores[:, :self.num_classes-1], bg_scores[:, -1:]], dim=1)
-
- num_fg = fg_scores.size(0)
- topk = num_fg if (self.topk == -1) or (num_fg <
- self.topk) else self.topk
- # use maximum entropy as a metric for uncertainty
- # we select topk proposals with maximum entropy
- if self.sampling_metric == "max_entropy":
- pos_metric = dists.Categorical(
- _fg_scores.softmax(dim=1)).entropy()
- neg_metric = dists.Categorical(
- _bg_scores.softmax(dim=1)).entropy()
- # use minimum score as a metric for uncertainty
- # we select topk proposals with minimum max-score
- # 筛选出前景和背景中每个proposal的最大分数
- elif self.sampling_metric == "min_score":
- pos_metric = -_fg_scores.max(dim=1)[0]
- neg_metric = -_bg_scores.max(dim=1)[0]
- # we randomly select topk proposals
- elif self.sampling_metric == "random":
- pos_metric = torch.rand(_fg_scores.size(0),).to(scores.device)
- neg_metric = torch.rand(_bg_scores.size(0),).to(scores.device)
-
- # 在前景和背景中分别筛选出最大分数最小的topk个实例
- _, pos_inds = pos_metric.topk(topk)
- _, neg_inds = neg_metric.topk(topk)
- fg_scores, fg_labels = fg_scores[pos_inds], fg_labels[pos_inds]
- bg_scores, bg_labels = bg_scores[neg_inds], bg_labels[neg_inds]
-
- return fg_scores, bg_scores, fg_labels, bg_labels
-
- def forward(self, scores: Tensor, labels: Tensor):
- fg_scores, bg_scores, fg_labels, bg_labels = self._sampling(
- scores, labels)
- # sample both fg and bg
- # fg和bg结合
- scores = torch.cat([fg_scores, bg_scores])
- labels = torch.cat([fg_labels, bg_labels])
-
- num_sample, num_classes = scores.shape
- #创建num_sample*82的张量，每一行从0~81
- mask = torch.arange(num_classes).repeat(
- num_sample, 1).to(scores.device)
- #num_sample*82的张量，每一行这个实例的类别为false，其余为true
- inds = mask != labels[:, None].repeat(1, num_classes)
- #将mask每一行中的真实类别去掉，即fg去掉其fglabel，bg去掉81类别，变为num_sample*81的张量
- mask = mask[inds].reshape(num_sample, num_classes-1)
-
- # (num_sample,)，每个sample真实类别的softmax分数，即Pc*
- gt_scores = torch.gather(
- F.softmax(scores, dim=1), 1, labels[:, None]).squeeze(1)
- # (num_sample,81)，每个sample除真实类别的分数
- mask_scores = torch.gather(scores, 1, mask)
-
- # 小于0的分数设置为0
- gt_scores[gt_scores < 0] = 0.0
- # (num_sample,81)的全0张量
- targets = torch.zeros_like(mask_scores)
- # 前景sample的数量
- num_fg = fg_scores.size(0)
- # targets的前num_fg行的未知类别ind处设置w(pc*)=((1-pc*)^alpha)pc*
- targets[:num_fg, self.num_classes-2] = gt_scores[:num_fg] * \
- (1-gt_scores[:num_fg]).pow(self.alpha)
- # targets的剩余行的未知类别ind处设置w(pc*)=((1-pc*)^alpha)pc*
- targets[num_fg:, self.num_classes-1] = gt_scores[num_fg:] * \
- (1-gt_scores[num_fg:]).pow(self.alpha)
- # 前面mask去掉的类别不同所以对应不同的num_classes-1
- # targets每一行对应的未知类别idx处有值，其他处为0
-
- return self._soft_cross_entropy(mask_scores, targets.detach())
diff --git a/spaces/doevent/dis-background-removal/README.md b/spaces/doevent/dis-background-removal/README.md
deleted file mode 100644
index ce6ee4bc92c227e91c7d5bfaa0ac96654c5d47c2..0000000000000000000000000000000000000000
--- a/spaces/doevent/dis-background-removal/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: DIS Background Removal
-emoji: 🔥 🌠 🏰
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.4
-python_version: 3.8.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dukecsxu/chatGPT/baidu_translate/module.py b/spaces/dukecsxu/chatGPT/baidu_translate/module.py
deleted file mode 100644
index f19d8f92a4a02cda3c1c018e36be6deb32e93af1..0000000000000000000000000000000000000000
--- a/spaces/dukecsxu/chatGPT/baidu_translate/module.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import argparse
-import random
-from hashlib import md5
-from typing import Optional
-
-import requests
-
-import paddlehub as hub
-from paddlehub.module.module import moduleinfo
-from paddlehub.module.module import runnable
-from paddlehub.module.module import serving
-
-
-def make_md5(s, encoding='utf-8'):
- return md5(s.encode(encoding)).hexdigest()
-
-
-@moduleinfo(name="baidu_translate",
- version="1.0.0",
- type="text/machine_translation",
- summary="",
- author="baidu-nlp",
- author_email="paddle-dev@baidu.com")
-class BaiduTranslate:
-
- def __init__(self, appid=None, appkey=None):
- """
- :param appid: appid for requesting Baidu translation service.
- :param appkey: appkey for requesting Baidu translation service.
- """
- # Set your own appid/appkey.
- if appid == None:
- self.appid = '20201015000580007'
- else:
- self.appid = appid
- if appkey is None:
- self.appkey = 'IFJB6jBORFuMmVGDRud1'
- else:
- self.appkey = appkey
- self.url = 'http://api.fanyi.baidu.com/api/trans/vip/translate'
-
- def translate(self, query: str, from_lang: Optional[str] = "en", to_lang: Optional[int] = "zh"):
- """
- Create image by text prompts using ErnieVilG model.
-
- :param query: Text to be translated.
- :param from_lang: Source language.
- :param to_lang: Dst language.
-
- Return translated string.
- """
- # Generate salt and sign
- salt = random.randint(32768, 65536)
- sign = make_md5(self.appid + query + str(salt) + self.appkey)
-
- # Build request
- headers = {'Content-Type': 'application/x-www-form-urlencoded'}
- payload = {'appid': self.appid, 'q': query, 'from': from_lang, 'to': to_lang, 'salt': salt, 'sign': sign}
-
- # Send request
- try:
- r = requests.post(self.url, params=payload, headers=headers)
- result = r.json()
- except Exception as e:
- error_msg = str(e)
- raise RuntimeError(error_msg)
- if 'error_code' in result:
- raise RuntimeError(result['error_msg'])
- return result['trans_result'][0]['dst']
-
- @runnable
- def run_cmd(self, argvs):
- """
- Run as a command.
- """
- self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name),
- prog='hub run {}'.format(self.name),
- usage='%(prog)s',
- add_help=True)
- self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
- self.add_module_input_arg()
- args = self.parser.parse_args(argvs)
- if args.appid is not None and args.appkey is not None:
- self.appid = args.appid
- self.appkey = args.appkey
- result = self.translate(args.query, args.from_lang, args.to_lang)
- return result
-
- @serving
- def serving_method(self, query, from_lang, to_lang):
- """
- Run as a service.
- """
- return self.translate(query, from_lang, to_lang)
-
- def add_module_input_arg(self):
- """
- Add the command input options.
- """
- self.arg_input_group.add_argument('--query', type=str)
- self.arg_input_group.add_argument('--from_lang', type=str, default='en', help="源语言")
- self.arg_input_group.add_argument('--to_lang', type=str, default='zh', help="目标语言")
- self.arg_input_group.add_argument('--appid', type=str, default=None, help="注册得到的个人appid")
- self.arg_input_group.add_argument('--appkey', type=str, default=None, help="注册得到的个人appkey")
diff --git a/spaces/ehristoforu/Hubsd/README.md b/spaces/ehristoforu/Hubsd/README.md
deleted file mode 100644
index 1ee067f59c022f5cba73141d58f423bb015ca7b1..0000000000000000000000000000000000000000
--- a/spaces/ehristoforu/Hubsd/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hubsd
-emoji: 📈
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/emilios/whisper-greek-demo/README.md b/spaces/emilios/whisper-greek-demo/README.md
deleted file mode 100644
index 1a785cce184a08fd9117961cf69a37ee569b1cf0..0000000000000000000000000000000000000000
--- a/spaces/emilios/whisper-greek-demo/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Whisper Demo
-emoji: 🇬🇷
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-tags:
-- whisper-event
-duplicated_from: whisper-event/whisper-demo
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/eson/tokenizer-arena/vocab/icetk/test.py b/spaces/eson/tokenizer-arena/vocab/icetk/test.py
deleted file mode 100644
index e44b8bcbab7a70eaced411c472cc0516c73e88c6..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/icetk/test.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""
-## 封装
-
-__all__ = ['icetk']
-sentencepiece_model_pb2 给包装起来了，
-
-## ss
-ss
-"""
-
-import sentencepiece as spm
-from icetk import sentencepiece_model_pb2 as model
-
-def __init__(self, model_path):
- self.proto = model.ModelProto()
- with open(model_path, 'rb') as fin:
- proto_str = fin.read()
- self.proto.ParseFromString(proto_str)
- self.refresh()
-
-def refresh(self):
- self.sp = spm.SentencePieceProcessor()
- self.sp.Load(model_proto=self.proto.SerializeToString())
- self.num_tokens = self.sp.vocab_size()
\ No newline at end of file
diff --git a/spaces/facebook/StyleNeRF/torch_utils/ops/grid_sample_gradfix.py b/spaces/facebook/StyleNeRF/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index c9f9befa1cd7b0bc63186ca6d6456c0f11b5dc67..0000000000000000000000000000000000000000
--- a/spaces/facebook/StyleNeRF/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = True # Enable the custom op by setting this to true.
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
- if _should_use_custom_op():
- return _GridSampleForward.apply(input, grid)
- return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
- return enabled
-
-#----------------------------------------------------------------------------
-
-class _GridSampleForward(torch.autograd.Function):
- @staticmethod
- def forward(ctx, input, grid):
- assert input.ndim == 4 or input.ndim == 5
- assert grid.ndim == 4 or input.ndim == 5
- output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
- ctx.save_for_backward(input, grid)
- return output
-
- @staticmethod
- def backward(ctx, grad_output):
- input, grid = ctx.saved_tensors
- grad_input, grad_grid = _GridSampleBackward.apply(grad_output, input, grid)
- return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSampleBackward(torch.autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input, grid):
- if input.ndim == 4:
- op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
- else:
- op = torch._C._jit_get_operation('aten::grid_sampler_3d_backward')
- grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
- ctx.save_for_backward(grid)
- return grad_input, grad_grid
-
- @staticmethod
- def backward(ctx, grad2_grad_input, grad2_grad_grid):
- _ = grad2_grad_grid # unused
- grid, = ctx.saved_tensors
- grad2_grad_output = None
- grad2_input = None
- grad2_grid = None
-
- if ctx.needs_input_grad[0]:
- grad2_grad_output = _GridSampleForward.apply(grad2_grad_input, grid)
-
- assert not ctx.needs_input_grad[2]
- return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------
diff --git a/spaces/facebook/ov-seg/open_vocab_seg/__init__.py b/spaces/facebook/ov-seg/open_vocab_seg/__init__.py
deleted file mode 100644
index b54fce14b8a029f1355bc8b74c20884e880ee9c4..0000000000000000000000000000000000000000
--- a/spaces/facebook/ov-seg/open_vocab_seg/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-from . import data
-from . import modeling
-from .config import add_ovseg_config
-
-from .test_time_augmentation import SemanticSegmentorWithTTA
-from .ovseg_model import OVSeg, OVSegDEMO
diff --git a/spaces/failfast/2D-GameCreator/src/services/api/index.ts b/spaces/failfast/2D-GameCreator/src/services/api/index.ts
deleted file mode 100644
index a871ca699b936b905bae8a89e781395b4d80abac..0000000000000000000000000000000000000000
--- a/spaces/failfast/2D-GameCreator/src/services/api/index.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { ChatCompletionRequestMessage, OpenAIApi } from "openai";
-import { nanoid } from "nanoid";
-import { extractCode, miniPrompt } from "@/utils/prompt";
-import { systemMessage } from "@/constants";
-import { OpenAIError } from "./openai";
-import { minify } from "terser";
-
-const minifyConfig = {
- compress: {
- dead_code: true,
- drop_console: true,
- drop_debugger: true,
- keep_classnames: false,
- keep_fargs: true,
- keep_fnames: false,
- keep_infinity: false,
- },
- mangle: false,
- module: false,
- sourceMap: false,
- output: {
- comments: true,
- },
-};
-
-interface ToOpenAIProps {
- command: string;
- prompt: string;
- temperature: string;
- template: string;
- model: string;
- maxTokens: string;
- client: OpenAIApi | null;
- signal?: AbortSignal;
-}
-
-export async function toOpenAI({
- command = "CREATE_GAME",
- prompt = "extend the code",
- temperature = "0.2",
- template = "",
- model = "gpt-3.5-turbo",
- maxTokens = "2048",
- client = null,
- signal = new AbortController().signal,
-}: ToOpenAIProps) {
- if (client === null) {
- throw new Error("OpenAI client is not defined");
- }
-
- const prompt_ = prompt.trim();
-
- const minifiedCode = await minify(template, minifyConfig);
-
- // // ${template.trim().replace(/^\s+/gm, "").replace(/^\n+/g, "").replace(/\s+/, " ")}
- const nextMessage: ChatCompletionRequestMessage = {
- role: "user",
- content: miniPrompt`
- "${command}": ${prompt_}. Return the full source code of the game.
- TEMPLATE:
- \`\`\`javascript
- ${minifiedCode.code}
- \`\`\`
- `,
- };
-
- const task = `${prompt_}`;
-
- const messages: ChatCompletionRequestMessage[] = [
- {
- role: "system",
- content: miniPrompt`${systemMessage}`,
- },
- nextMessage,
- ];
-
- try {
- const response = await client.createChatCompletion(
- {
- model,
- messages,
- max_tokens: Number.parseInt(maxTokens),
- temperature: Number.parseFloat(temperature),
- },
- { signal }
- );
-
- const { message } = response.data.choices[0];
-
- if (message) {
- return {
- ...message,
- content: extractCode(message.content).replace(
- /(COMMANDS|CREATE_GAME|ADD_FEATURE|REMOVE_FEATURE|UPDATE_FEATURE|FIX_BUG|TEMPLATE|OUTPUT FORMAT).*\n/,
- ""
- ),
- task,
- id: nanoid(),
- };
- }
-
- // Something broke
- // ToDo: fix it :)
- return {
- content: "/* BROKEN */",
- task,
- id: nanoid(),
- };
- } catch (error) {
- const err = error as OpenAIError;
-
- if (err.response) {
- throw err.response;
- } else {
- throw error;
- }
- }
-}
diff --git a/spaces/falterWliame/Face_Mask_Detection/Age Of Empires 3 Disk2c 1 Cab.md b/spaces/falterWliame/Face_Mask_Detection/Age Of Empires 3 Disk2c 1 Cab.md
deleted file mode 100644
index 78fb6c50bd626df8ab4d5c251350c37196995b52..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Age Of Empires 3 Disk2c 1 Cab.md
+++ /dev/null
@@ -1,20 +0,0 @@
-age of empires 3 disk2c 1 cab
DOWNLOAD ➡ https://urlca.com/2uDckx
-
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-
-Recently, aluminum capacitors have been utilized for their light weight, high dielectric constant, high reliability, and excellent frequency response properties.
-
-The aluminum capacitor is produced by forming a dielectric layer of aluminum oxide, a conductive layer of aluminum and a conductive layer of silver on a surface of an anodized aluminum plate, thereafter applying a cathode layer to said conductive layer of aluminum, and then, if necessary, applying a protective layer or a facing layer to said cathode layer.
-
-The dielectric layer of aluminum oxide is usually formed by coating a suspension in which aluminum hydroxide and an aqueous binder resin are dispersed in a solvent on the surface of the aluminum plate and then heating it at 200.degree. C. to 300.degree. C. in an oxidizing atmosphere, in order to form a dielectric layer of aluminum oxide on the surface of the aluminum plate.
-
-Recently, there have been employed a so-called binderless type capacitor wherein the dielectric layer of aluminum oxide is formed on the surface of the aluminum plate by means of the method in which a suspension in which aluminum hydroxide and an aqueous binder resin are not dispersed in a solvent is coated on the surface of the aluminum plate and then heated at 200.degree. C. to 300.degree. C. in an oxidizing atmosphere.
-
-However, the above method has some disadvantages. The first disadvantage is that the obtained dielectric layer of aluminum oxide is poor in adhesion and weathering resistance and therefore the dielectric layer is likely to peel off the aluminum plate. The second disadvantage is that the productivity is low because a long time is required for the formation of the 4fefd39f24
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-
-
diff --git a/spaces/falterWliame/Face_Mask_Detection/Fifa Street 4 Pc Game Free Download Torrent Hit 57 [PATCHED].md b/spaces/falterWliame/Face_Mask_Detection/Fifa Street 4 Pc Game Free Download Torrent Hit 57 [PATCHED].md
deleted file mode 100644
index 88db0c47f43ae2a7d77354ae2f6376e6dee6459d..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Fifa Street 4 Pc Game Free Download Torrent Hit 57 [PATCHED].md
+++ /dev/null
@@ -1,8 +0,0 @@
-Fifa Street 4 Pc Game Free Download Torrent Hit 57
Download Zip ––– https://urlca.com/2uDdBD
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diff --git a/spaces/falterWliame/Face_Mask_Detection/German Truck Simulator 1.32 Crack.rar NEW!.md b/spaces/falterWliame/Face_Mask_Detection/German Truck Simulator 1.32 Crack.rar NEW!.md
deleted file mode 100644
index a909cbbd5f38a63d87e2aee8e6a4feb89492b2fd..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/German Truck Simulator 1.32 Crack.rar NEW!.md
+++ /dev/null
@@ -1,6 +0,0 @@
-german truck simulator 1.32 crack.rar
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diff --git a/spaces/falterWliame/Face_Mask_Detection/H-Joshua-H61-Uatx Manual.md b/spaces/falterWliame/Face_Mask_Detection/H-Joshua-H61-Uatx Manual.md
deleted file mode 100644
index c8e19a23971517860a06daa6eade62d9a7df23cd..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/H-Joshua-H61-Uatx Manual.md
+++ /dev/null
@@ -1,75 +0,0 @@
-
-H-Joshua-H61-Uatx Manual: A Useful Resource for Your Motherboard
-If you own a HP Pavilion desktop computer, you might have a H-Joshua-H61-Uatx motherboard inside it. This motherboard is also known as the Foxconn H-Joshua-H61-Uatx (Joshua) motherboard, and it is a micro-ATX form factor board that supports Intel processors and DDR3 memory. It has various features and connectors, such as USB 3.0 ports, SATA ports, PCI Express slots, audio jacks, and more.
-H-Joshua-H61-Uatx Manual
Download ⇒ https://urlca.com/2uDdvq
-However, if you want to upgrade, repair, or troubleshoot your motherboard, you might need a H-Joshua-H61-Uatx manual to guide you. A manual is a document that provides detailed information about the specifications, layout, installation, configuration, and maintenance of your motherboard. It can help you understand the functions and features of your motherboard, as well as how to connect and use other components and devices with it.
-Unfortunately, finding a H-Joshua-H61-Uatx manual online can be challenging. This motherboard is not very popular or widely available, and there is no official manual from the manufacturer or the vendor. However, there are some ways to find and download a H-Joshua-H61-Uatx manual that can be useful for you. Here are some tips:
-Tip 1: Check the HP Support Website
-The first place you should check for a H-Joshua-H61-Uatx manual is the HP Support website. This website provides various resources and support for HP products, including desktop computers. You can search for your specific model of HP Pavilion desktop computer and see if there is a manual available for download.
-
-For example, if you have a HP Pavilion p6-2378 Desktop PC, you can go to this link: https://support.hp.com/us-en/product/hp-pavilion-p6-2300-desktop-pc-series/5295962/model/5295963/manuals
-and find a user guide and a troubleshooting and maintenance guide for your computer. These guides may not be specific to your motherboard, but they may contain some information about it.
-Tip 2: Check the Joshua Motherboard Specifications
-The second place you should check for a H-Joshua-H61-Uatx manual is the Joshua motherboard specifications document. This document provides some basic information about the motherboard, such as its dimensions, processor support, memory support, expansion slots, rear panel connectors, internal connectors, and jumper settings. It also includes a diagram of the motherboard layout that shows the location and labels of the components and connectors.
-You can find the Joshua motherboard specifications document here: https://support.hp.com/in-en/document/c02978278
-This document may not be as detailed or comprehensive as a manual, but it can give you some useful information about your motherboard.
-Tip 3: Check the YouTube Video
-The third place you should check for a H-Joshua-H61-Uatx manual is the YouTube video by Mxkdi. This video is an overview of the Foxconn H-Joshua-H61-Uatx (Joshua) motherboard. It shows the front and back of the motherboard, as well as some of its features and connectors. It also explains some of the jumper settings and BIOS options of the motherboard.
-You can watch the YouTube video here: https://www.youtube.com/watch?v=matVeHPQyNg
-This video may not be as clear or accurate as a manual, but it can give you some visual information about your motherboard.
-Conclusion
-H-Joshua-H61-Uatx manual is a useful resource for your motherboard. It can help you upgrade, repair, or troubleshoot your motherboard with ease. However, finding a H-Joshua-H61-Uatx manual online can be difficult. You can try checking the HP Support website, the Joshua motherboard specifications document, or the YouTube video by Mxkdi to find and download a H-Joshua-H61-Uatx manual that can be helpful for you.
-How to Use H-Joshua-H61-Uatx Manual
-Once you have found and downloaded a H-Joshua-H61-Uatx manual, you can use it to guide you through the installation, configuration, and maintenance of your motherboard. Here are some tips on how to use H-Joshua-H61-Uatx manual:
-
-- Read the manual carefully and follow the instructions step by step. Do not skip any steps or make any changes that are not recommended by the manual.
-- Keep the manual handy and refer to it whenever you have any questions or problems with your motherboard. You can also print out the manual or save it on your computer or mobile device for easy access.
-- Use the manual as a reference only and do not rely on it entirely. The manual may not cover all the features and functions of your motherboard, or it may be outdated or inaccurate. You should also check the official website of HP or Foxconn for any updates or additional information about your motherboard.
-
-Benefits of H-Joshua-H61-Uatx Manual
-H-Joshua-H61-Uatx manual is a useful resource for your motherboard. It can help you with various benefits, such as:
-
-- It can help you install and set up your motherboard correctly and safely. It can show you how to connect and use other components and devices with your motherboard, such as processors, memory, hard drives, optical drives, graphics cards, sound cards, etc.
-- It can help you optimize and improve the performance of your motherboard. It can show you how to adjust and customize the settings and options of your motherboard, such as BIOS, jumper, voltage, frequency, etc.
-- It can help you troubleshoot and fix any issues or errors with your motherboard. It can show you how to diagnose and resolve common problems with your motherboard, such as boot failure, beep codes, blue screen, etc.
-
-Conclusion
-H-Joshua-H61-Uatx manual is a useful resource for your motherboard. It can provide you with detailed information about the specifications, layout, installation, configuration, and maintenance of your motherboard. However, finding a H-Joshua-H61-Uatx manual online can be difficult. You can try checking the HP Support website, the Joshua motherboard specifications document, or the YouTube video by Mxkdi to find and download a H-Joshua-H61-Uatx manual that can be helpful for you.
-FAQs about H-Joshua-H61-Uatx Manual
-Here are some frequently asked questions and answers about H-Joshua-H61-Uatx manual that you might find helpful:
-
-- Q: Where can I buy a H-Joshua-H61-Uatx motherboard?
-- A: You can buy a H-Joshua-H61-Uatx motherboard from online retailers such as Amazon, eBay, or Newegg. You can also buy it from HP or Foxconn authorized dealers or service centers.
-- Q: How much does a H-Joshua-H61-Uatx motherboard cost?
-- A: The price of a H-Joshua-H61-Uatx motherboard may vary depending on the seller, the condition, and the availability. However, you can expect to pay around $50 to $100 for a new or refurbished H-Joshua-H61-Uatx motherboard.
-- Q: What are the compatible processors and memory for a H-Joshua-H61-Uatx motherboard?
-- A: The H-Joshua-H61-Uatx motherboard supports Intel Core i3, i5, and i7 processors with LGA 1155 socket and up to 65W TDP. It also supports DDR3 1066/1333/1600 MHz memory with up to 16 GB capacity and dual channel architecture.
-- Q: What are the compatible graphics cards and sound cards for a H-Joshua-H61-Uatx motherboard?
-- A: The H-Joshua-H61-Uatx motherboard has one PCI Express x16 slot that supports graphics cards with up to 75W power consumption. It also has one PCI Express x1 slot that supports sound cards or other expansion cards. However, you should check the compatibility and power requirements of your graphics card or sound card before installing it on your motherboard.
-- Q: How can I update the BIOS of my H-Joshua-H61-Uatx motherboard?
-- A: You can update the BIOS of your H-Joshua-H61-Uatx motherboard by downloading the latest BIOS file from the HP Support website and following the instructions on how to flash the BIOS. You should backup your data and settings before updating the BIOS, and do not turn off or interrupt the power supply during the update process.
-
-Conclusion
-H-Joshua-H61-Uatx manual is a useful resource for your motherboard. It can provide you with detailed information about the specifications, layout, installation, configuration, and maintenance of your motherboard. However, finding a H-Joshua-H61-Uatx manual online can be difficult. You can try checking the HP Support website, the Joshua motherboard specifications document, or the YouTube video by Mxkdi to find and download a H-Joshua-H61-Uatx manual that can be helpful for you. You can also use this article as a reference for some tips, benefits, and FAQs about H-Joshua-H61-Uatx manual.
-How to Upgrade H-Joshua-H61-Uatx Manual
-If you want to improve the performance or functionality of your H-Joshua-H61-Uatx motherboard, you can use the H-Joshua-H61-Uatx manual to upgrade it. Here are some tips on how to upgrade H-Joshua-H61-Uatx manual:
-
-- Check the compatibility and availability of the upgrade components. You can use the manual to find out what types of processors, memory, graphics cards, sound cards, or other components are compatible with your motherboard. You can also check online or offline sources for the availability and prices of the upgrade components.
-- Backup your data and settings before upgrading. You can use the manual to find out how to backup your data and settings on your hard drive or other storage devices. You should also create a system restore point or a recovery disk in case something goes wrong during or after the upgrade.
-- Follow the instructions and precautions for upgrading. You can use the manual to find out how to safely and correctly remove and replace the components on your motherboard. You should also follow some precautions such as turning off and unplugging your computer, wearing an anti-static wrist strap, handling the components with care, etc.
-- Test and verify the upgrade results. You can use the manual to find out how to test and verify that the upgrade components are working properly on your motherboard. You should also check the BIOS settings and options to make sure they are compatible and optimal for your upgrade components.
-
-How to Repair H-Joshua-H61-Uatx Manual
-If you encounter any damage or malfunction with your H-Joshua-H61-Uatx motherboard, you can use the H-Joshua-H61-Uatx manual to repair it. Here are some tips on how to repair H-Joshua-H61-Uatx manual:
-
-- Identify and isolate the problem. You can use the manual to find out how to identify and isolate the problem with your motherboard. You can use some diagnostic tools such as beep codes, error messages, LED indicators, etc. to narrow down the possible causes of the problem.
-- Check and replace the faulty components. You can use the manual to find out how to check and replace the faulty components on your motherboard. You can use some testing tools such as multimeters, oscilloscopes, etc. to measure and verify the voltage, current, resistance, etc. of the components. You can also use some spare or compatible components to replace the faulty ones.
-- Check and update the firmware and drivers. You can use the manual to find out how to check and update the firmware and drivers of your motherboard. You can download the latest firmware and drivers from the HP Support website or the Foxconn Support website and follow the instructions on how to flash or install them.
-- Contact support or service center if needed. If you cannot repair your motherboard by yourself or if you need professional help, you can contact HP or Foxconn support or service center for assistance. You can use the manual to find out how to contact them by phone, email, or online form.
-
-Conclusion
-H-Joshua-H61-Uatx manual is a useful resource for your motherboard. It can provide you with detailed information about the specifications, layout, installation, configuration, maintenance, troubleshooting, upgrading, and repairing of your motherboard. However, finding a H-Joshua-H61-Uatx manual online can be difficult. You can try checking the HP Support website, the Joshua motherboard specifications document, or the YouTube video by Mxkdi to find and download a H-Joshua-H61-Uatx manual that can be helpful for you. You can also use this article as a reference for some tips, benefits, FAQs, and contact information about H-Joshua-H61-Uatx manual.
-Conclusion
-H-Joshua-H61-Uatx manual is a useful resource for your motherboard. It can provide you with detailed information about the specifications, layout, installation, configuration, maintenance, troubleshooting, upgrading, and repairing of your motherboard. However, finding a H-Joshua-H61-Uatx manual online can be difficult. You can try checking the HP Support website, the Joshua motherboard specifications document, or the YouTube video by Mxkdi to find and download a H-Joshua-H61-Uatx manual that can be helpful for you. You can also use this article as a reference for some tips, benefits, FAQs, and contact information about H-Joshua-H61-Uatx manual.
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-
-
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Ping Pong Neu 2 Lehrerhandbuch 29 PATCHED.md b/spaces/falterWliame/Face_Mask_Detection/Ping Pong Neu 2 Lehrerhandbuch 29 PATCHED.md
deleted file mode 100644
index f3cee015b58d3036fcfb7635b46a58e137d4e3e1..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Ping Pong Neu 2 Lehrerhandbuch 29 PATCHED.md
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diff --git a/spaces/fatiXbelha/sd/APKCombo Installer A Simple and Secure Way to Install Android Apps.md b/spaces/fatiXbelha/sd/APKCombo Installer A Simple and Secure Way to Install Android Apps.md
deleted file mode 100644
index 2ab668022b696cace559fa2061748baf5c365de8..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/APKCombo Installer A Simple and Secure Way to Install Android Apps.md
+++ /dev/null
@@ -1,117 +0,0 @@
-
-How to Download and Install APKCombo Installer on Your Android Device
-If you are looking for a way to install various types of files on your Android device, such as XAPK, APKS, OBB, ZIP, APKM, then you need a reliable and versatile app that can handle all these formats. One such app is APKCombo Installer, which is an Android app that lets you install XAPK, APKS, OBB, ZIP, APKM files with ease. In this article, we will show you how to download and install APKCombo Installer on your Android device, as well as how to use it to install different files on your device.
-download apkcombo installer
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-What is APKCombo Installer and Why You Need It
-APKCombo Installer is an Android app that allows you to install various types of files on your Android device without worrying about file format or device compatibility. It is a simple and user-friendly app that can handle XAPK, APKS (App Bundle), ZIP (APK+OBB), APKM files. These are some of the reasons why you need APKCombo Installer:
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-- It supports multiple file formats, including XAPK, APKS, OBB, ZIP, APKM.
-- It can install files from your device storage or from external sources.
-- It can install files in batch mode or individually.
-- It can extract OBB files from ZIP files automatically.
-- It can install split APKs (App Bundle) without any extra tools.
-- It can install modded or hacked APKs without any issues.
-- It can install files on any Android device running Android 5.0 or higher.
-- It is free to download and use.
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-File Format Description XAPK A compressed file that contains an APK file and an OBB file. It is used to distribute large apps or games that have additional data files. APKS A compressed file that contains multiple split APK files. It is used to distribute apps or games that use App Bundle format, which allows developers to deliver optimized versions of their apps for different devices. OBB A data file that contains additional resources for an app or game. It is usually stored in the /Android/obb/ folder on your device. ZIP A compressed file that contains one or more files. It is used to reduce file size and save storage space. APKM A compressed file that contains an APK file and a META file. It is used by some modded or hacked apps or games to store additional information or settings.
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-First, you need to go to the APKCombo website, which is a trusted source for downloading APK files. On the homepage, you will see a search bar where you can type in the name of the app you want to download. In this case, you need to type in APKCombo Installer and hit the enter key or the search button.
-Step 2: Click on the Download Button and Choose Your Device Model
-Next, you will see a list of results that match your search query. You need to look for the one that says APKCombo Installer - Install XAPK, APKS, OBB, ZIP, APKM and has the APKCombo logo. Click on it and you will be taken to the app page where you can see more details about the app, such as its version, size, rating, and description. To download the app, you need to click on the green Download button at the top right corner of the page. You will then see a pop-up window that asks you to choose your device model from a drop-down menu. This is to ensure that you download the correct version of the app for your device. Select your device model and click on OK.
-Step 3: Wait for the Download to Complete and Locate the APK File on Your Device
-Finally, you need to wait for the download to complete. You can see the progress of the download on your browser or notification bar. Once the download is finished, you need to locate the APK file on your device. The file name will be something like apkcombo.installer_v1.0.apk. You can find it in your Downloads folder or any other folder where you save your downloaded files.
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-How to Install APKCombo Installer on Your Android Device
-Now that you have downloaded the APK file of APKCombo Installer, you need to install it on your Android device. To do that, you need to follow these steps:
-Step 1: Enable Developer Options and Unknown Sources on Your Device
-Before you can install any APK file on your device, you need to enable some settings that allow you to install apps from sources other than Google Play Store. These settings are Developer Options and Unknown Sources. To enable them, you need to go to your device's Settings, then tap on About Phone, then tap on Build Number seven times until you see a message that says You are now a developer!. Then go back to Settings and tap on Developer Options, then scroll down and enable USB Debugging. Next, go back to Settings and tap on Security, then scroll down and enable Unknown Sources. This will allow you to install apps from sources other than Google Play Store.
-Step 2: Open the APK File and Follow the Installation Instructions
-Next, you need to open the APK file that you downloaded from APKCombo website. You can do this by using a file manager app or by tapping on the notification that says Download complete. You will then see a screen that asks you if you want to install this application. Tap on Install and wait for the installation process to finish. You may see some warnings or prompts that ask you to confirm your action or grant some permissions. Just tap on OK, Allow, or Yes, depending on what they say.
-Step 3: Launch the APKCombo Installer App and Grant the Necessary Permissions
-Last but not least, you need to launch the APKCombo Installer app and grant it the necessary permissions to function properly. You can do this by tapping on Open after the installation is done or by finding the app icon in your app drawer or home screen. When you open the app for the first time, you will see a welcome screen that explains what the app does and how it works. Tap on NEXT until you reach the end of the introduction. Then tap on GOT IT!. You will then see a screen that asks you to grant some permissions to the app, such as storage access and overlay permission. These permissions are essential for the app to install files on your device. Tap on ALLOW or GRANT for each permission request. You may also need to go to your device's settings and manually enable the overlay permission for the app. Once you have granted all the permissions, you are ready to use the app.
-How to Use APKCombo Installer to Install XAPK, APKS, OBB, ZIP, APKM Files on Your Android Device
-Now that you have installed APKCombo Installer on your device, you can use it to install any XAPK, APKS, OBB, ZIP, APKM files that you have downloaded from APKCombo website or other sources. To do that, you need to follow these steps:
-Step 1: Open the APKCombo Installer App and Tap on the Install Button
-First, you need to open the APKCombo Installer app and tap on the Install button at the bottom right corner of the screen. This will take you to the file selection screen where you can choose the files you want to install.
-Step 2: Select the Files You Want to Install from Your Device Storage
-Next, you need to select the files you want to install from your device storage. You can browse through your folders or use the search function to find the files. You can also filter the files by type by tapping on the icons at the top of the screen. You can select multiple files at once by tapping on them. You will see a check mark on each selected file and a number at the bottom of the screen indicating how many files you have selected. Once you have selected all the files you want to install, tap on NEXT.
-Step 3: Tap on OK and Follow the Steps on Screen
-Last but not least, you need to tap on OK and follow the steps on screen to install the files. Depending on the file type and size, you may see different screens or prompts during the installation process. For example, if you are installing a XAPK file, you will see a screen that shows the progress of extracting and installing the APK and OBB files. If you are installing a APKS file, you will see a screen that shows the progress of installing the split APKs. If you are installing a ZIP file, you will see a screen that shows the progress of extracting and installing the ZIP contents. If you are installing a APKM file, you will see a screen that shows the progress of installing the APK and META files. For any file type, you may also see some warnings or prompts that ask you to confirm your action or grant some permissions. Just tap on OK, Allow, or Yes, depending on what they say.
-Conclusion and FAQs
-In conclusion, APKCombo Installer is an Android app that lets you install XAPK, APKS, OBB, ZIP, APKM files with ease. It is a simple and user-friendly app that can handle multiple file formats and install them on any Android device running Android 5.0 or higher. It is free to download and use from APKCombo website or other sources. To use it, you just need to download and install it on your device, grant it the necessary permissions, and select the files you want to install from your device storage or external sources.
-If you have any questions about APKCombo Installer or how to use it, here are some FAQs that may help:
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-- Q: Is APKCombo Installer safe to use?
-- A: Yes, APKCombo Installer is safe to use as long as you download it from a trusted source like APKCombo website. It does not contain any malware or viruses and it does not collect any personal data from your device.
-- Q: Can I use APKCombo Installer to update my apps or games?
-- A: Yes, you can use APKCombo Installer to update your apps or games if you have downloaded the latest version of the XAPK, APKS, OBB, ZIP, APKM files from APKCombo website or other sources. Just select and install them as usual and they will overwrite the existing version of your apps or games.
-- Q: Can I use APKCombo Installer to uninstall my apps or games?
-- A: No, APKCombo Installer is not a uninstaller app and it cannot uninstall your apps or games. To uninstall your apps or games, you need to use your device's settings or another app that can do that.
-- Q: Can I use APKCombo Installer to install files from Google Play Store?
-- A: No, APKCombo Installer cannot install files from Google Play Store. It can only install files that you have downloaded from APKCombo website or other sources. To install files from Google Play Store, you need to use the Google Play Store app or another app that can do that.
-- Q: What should I do if I encounter any problems or errors while using APKCombo Installer?
-- A: If you encounter any problems or errors while using APKCombo Installer, you can try the following solutions:
-
-- Make sure you have enough storage space on your device and your device is connected to a stable internet connection.
-- Make sure you have enabled Developer Options and Unknown Sources on your device and granted all the permissions to the app.
-- Make sure you have downloaded the correct version of the XAPK, APKS, OBB, ZIP, APKM files for your device model and Android version.
-- Make sure you have not modified or corrupted the XAPK, APKS, OBB, ZIP, APKM files in any way.
-- Make sure you have not installed any other apps that may interfere with the installation process.
-- Restart your device and try again.
-- Contact the APKCombo support team or visit their website for more help.
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-
-I hope this article has helped you understand how to download and install APKCombo Installer on your Android device and how to use it to install XAPK, APKS, OBB, ZIP, APKM files on your device. If you have any feedback or suggestions, please feel free to leave a comment below. Thank you for reading and happy installing!
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diff --git a/spaces/fatiXbelha/sd/Apkpure Offers Asphalt Nitro Mod APK with All Cars Unlocked and Free Coins.md b/spaces/fatiXbelha/sd/Apkpure Offers Asphalt Nitro Mod APK with All Cars Unlocked and Free Coins.md
deleted file mode 100644
index bba945f66a8d60800e607afe0e3a50b52bd48925..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Apkpure Offers Asphalt Nitro Mod APK with All Cars Unlocked and Free Coins.md
+++ /dev/null
@@ -1,74 +0,0 @@
-
-Asphalt Nitro Mod Apk All Cars Unlocked Apkpure: A Guide for Racing Game Lovers
- If you are a fan of racing games, you might have heard of Asphalt Nitro, a spin-off of the popular Asphalt series developed by Gameloft. This game offers you an immersive racing experience with luxury licensed cars, breathtaking environments, and crazy stunts. You can fire up your nitro and be the first to cross the finish line in various game modes, such as Classic, Versus, Elimination, Knockdown, Infected, Gate Drift, Catch, and Escape.
- However, if you want to enjoy the game to the fullest, you might need to spend some real money to unlock and upgrade your cars. That's why many players look for a modded version of the game that gives them unlimited money and speed, as well as all cars unlocked and upgraded. In this article, we will show you how to download and install Asphalt Nitro Mod Apk All Cars Unlocked Apkpure, a modded version of the game that has all these features and more.
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- One of the sources that we recommend is [Apkbounce](^1^), a website that provides various modded versions of popular games and apps. You can find Asphalt Nitro Mod Apk All Cars Unlocked Apkpure on this website by searching for it in the search bar or browsing through the categories. You can also read the description and reviews of the file before downloading it.
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- The next thing you need to do is to enable unknown sources on your device settings. This is because Android devices do not allow installing apps from sources other than Google Play Store by default. To enable unknown sources, you need to go to your device settings > security > unknown sources and toggle it on. This will allow you to install apps from sources other than Google Play Store.
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- With this mod apk, you will be amazed by the stunning graphics and physics of the game. The game uses a high-quality 3D engine that renders realistic lighting effects, shadows, reflections, textures, etc. The game also uses a realistic physics engine that simulates the behavior of the cars and the environment. You can feel the impact of collisions, crashes, explosions, etc.
- Conclusion
- Asphalt Nitro Mod Apk All Cars Unlocked Apkpure is a modded version of the game that gives you unlimited money and speed, as well as all cars unlocked and upgraded. It also offers you multiple racing and stunts modes, locations all around the world, and stunning graphics and physics. It is a great way to enjoy the game to the fullest without spending any real money or waiting for long loading times. If you are a fan of racing games, you should definitely try this mod apk.
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- Here are some common questions and answers about Asphalt Nitro Mod Apk All Cars Unlocked Apkpure:
- Q: Is Asphalt Nitro Mod Apk All Cars Unlocked Apkpure safe to use?
-A: Yes, Asphalt Nitro Mod Apk All Cars Unlocked Apkpure is safe to use if you download it from a reliable source like [Apkbounce]. However , you should always scan the file with an antivirus software before installing it, and backup your data before making any changes to your device.
- Q: Does Asphalt Nitro Mod Apk All Cars Unlocked Apkpure require root access?
-A: No, Asphalt Nitro Mod Apk All Cars Unlocked Apkpure does not require root access to work. You can install it on any Android device that meets the minimum requirements of the game.
- Q: What are the minimum requirements of Asphalt Nitro Mod Apk All Cars Unlocked Apkpure?
-A: The minimum requirements of Asphalt Nitro Mod Apk All Cars Unlocked Apkpure are as follows:
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-OS Android 4.1 or higher RAM 1 GB or more Storage 100 MB or more Internet Required for some features
- Q: How can I update Asphalt Nitro Mod Apk All Cars Unlocked Apkpure?
-A: To update Asphalt Nitro Mod Apk All Cars Unlocked Apkpure, you need to visit [Apkbounce] again and check if there is a newer version of the file available. If there is, you need to download and install it over the existing one. You do not need to uninstall the previous version.
- Q: How can I contact the developer of Asphalt Nitro Mod Apk All Cars Unlocked Apkpure?
-A: To contact the developer of Asphalt Nitro Mod Apk All Cars Unlocked Apkpure, you need to visit their official website or social media pages. You can also leave a comment or a review on [Apkbounce] and they might respond to you.
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diff --git a/spaces/fatiXbelha/sd/Bus Simulator Indonesia Mod APK A Game that Teaches You the Culture and History of Indonesia.md b/spaces/fatiXbelha/sd/Bus Simulator Indonesia Mod APK A Game that Teaches You the Culture and History of Indonesia.md
deleted file mode 100644
index 8df684332892c6e1d3300324f786908e0a8e33d0..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Bus Simulator Indonesia Mod APK A Game that Teaches You the Culture and History of Indonesia.md
+++ /dev/null
@@ -1,101 +0,0 @@
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-Game Bus Simulator Indonesia Mod APK: A Fun and Realistic Driving Experience
- Do you love driving games? Do you want to explore the beautiful and diverse cities of Indonesia? Do you want to customize your own bus and drive it on realistic roads? If you answered yes to any of these questions, then you should try Game Bus Simulator Indonesia Mod APK. This is a modified version of the popular game Bus Simulator Indonesia, which lets you enjoy unlimited money, fuel, and other features that make the game more fun and exciting. In this article, we will tell you everything you need to know about Game Bus Simulator Indonesia Mod APK, including its features, how to download and install it, and its pros and cons.
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- Game Bus Simulator Indonesia Mod APK also offers realistic traffic and weather conditions in the game. You will encounter different types of vehicles on the road, such as cars, trucks, motorcycles, bicycles, rickshaws, etc. You will also have to follow the traffic rules and signals, such as speed limits, stop signs, red lights, etc. You will also experience different weather effects, such as rain, fog, sun, wind, etc. You will have to adjust your driving accordingly to avoid accidents and delays.
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- Game Bus Simulator Indonesia Mod APK also provides various modes and missions for you to enjoy. You can choose from free mode, career mode, or multiplayer mode. In free mode, you can drive anywhere you want without any restrictions or objectives. In career mode, you have to complete different tasks and challenges to earn money and reputation. In multiplayer mode, you can play with other players online and compete with them in races or other events. You can also chat with them using the built-in voice chat feature.
- How to download and install Game Bus Simulator Indonesia Mod APK?
- Requirements for Game Bus Simulator Indonesia Mod APK
Steps to download and install Game Bus Simulator Indonesia Mod APK
- If you want to download and install Game Bus Simulator Indonesia Mod APK on your android device, you need to follow these simple steps:
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-- Click on the download link to get the Game Bus Simulator Indonesia Mod APK file.
-- Allow the installation of unknown sources on your device by going to Settings > Security > Unknown Sources.
-- Locate the downloaded file in your file manager and tap on it to start the installation process.
-- Follow the instructions on the screen and wait for the installation to complete.
-- Launch the game and enjoy driving your bus in Indonesia.
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- Pros and cons of Game Bus Simulator Indonesia Mod APK
- Game Bus Simulator Indonesia Mod APK is a fun and realistic driving game that lets you experience the culture and scenery of Indonesia. However, like any other game, it also has some pros and cons that you should consider before playing it. Here are some of them:
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- Pros of Game Bus Simulator Indonesia Mod APK
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-- It is free to download and play.
-- It has unlimited money and fuel, which makes the game more enjoyable and less stressful.
-- It has customizable buses and skins, which gives you more options and creativity.
-- It has realistic traffic and weather, which adds more challenge and realism to the game.
-- It has various modes and missions, which keeps the game interesting and diverse.
-- It has a multiplayer mode, which allows you to play with other players online and have more fun.
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- Cons of Game Bus Simulator Indonesia Mod APK
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-- It may not be compatible with some devices or versions of Android.
-- It may have some bugs or glitches that affect the gameplay or performance.
-- It may require a stable internet connection for some features or modes.
-- It may not be updated regularly or have new content or features.
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- Conclusion
- Game Bus Simulator Indonesia Mod APK is a great game for anyone who loves driving games and wants to explore the beautiful and diverse cities of Indonesia. It offers unlimited money, fuel, customization, realism, variety, and multiplayer features that make the game more fun and exciting. However, it also has some drawbacks, such as compatibility issues, bugs, internet requirements, and lack of updates. Therefore, you should weigh the pros and cons before downloading and installing it on your device. If you are looking for a fun and realistic driving experience in Indonesia, then you should give Game Bus Simulator Indonesia Mod APK a try.
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- Here are some frequently asked questions about Game Bus Simulator Indonesia Mod APK:
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-- Is Game Bus Simulator Indonesia Mod APK safe to download and install?
-Yes, Game Bus Simulator Indonesia Mod APK is safe to download and install as long as you get it from a trusted source. However, you should always scan the file for viruses or malware before installing it on your device.
- - What is the difference between Game Bus Simulator Indonesia Mod APK and the original game?
-The main difference between Game Bus Simulator Indonesia Mod APK and the original game is that the modded version gives you unlimited money, fuel, customization, and other features that are not available in the original game. The modded version also bypasses some restrictions or limitations that are imposed by the original game.
- - Can I play Game Bus Simulator Indonesia Mod APK offline?
-You can play Game Bus Simulator Indonesia Mod APK offline in free mode or career mode. However, you will need an internet connection to play multiplayer mode or access some online features or events.
- - How can I update Game Bus Simulator Indonesia Mod APK?
-You can update Game Bus Simulator Indonesia Mod APK by downloading and installing the latest version from the same source. However, you should always back up your data before updating to avoid losing your progress or settings.
- - How can I contact the developers of Game Bus Simulator Indonesia Mod APK?
-You can contact the developers of Game Bus Simulator Indonesia Mod APK by visiting their official website or their social media pages. You can also leave a comment or review on their download page or send them an email at support@maleo.id.
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diff --git a/spaces/fatiXbelha/sd/Download Instagram Pro 2021 APK and Enjoy New Features.md b/spaces/fatiXbelha/sd/Download Instagram Pro 2021 APK and Enjoy New Features.md
deleted file mode 100644
index 7e6e98a1f32647b520a6cb4745af8404eafdc7c9..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Instagram Pro 2021 APK and Enjoy New Features.md
+++ /dev/null
@@ -1,101 +0,0 @@
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-What is Instagram Pro 2021 APK?
-Instagram Pro 2021 APK is a modded version of Instagram that was developed by SamMods. It's not available on the Google Play Store or the official Instagram website, but you can download it from various third-party sources on the internet. Instagram Pro 2021 APK is designed to enhance your Instagram experience by unlocking features that are not available or limited on the original app. Some of these features include:
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-- Downloading photos, videos, stories, reels, and live streams
-- Viewing anyone's profile picture in full size
-- Hiding your online status and seen ticks
-- Copying comments and bios
-- Zooming in on any image
-- Disabling video autoplay
-- Changing app themes and icons
-- And much more!
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-Features and benefits of Instagram Pro 2021 APK
-As you can see, Instagram Pro 2021 APK offers many features that can make your Instagram experience more enjoyable and convenient. Here are some of the benefits of using this modded app:
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-- You can save any media that you like on your device without using any external tools or apps.
-- You can view anyone's profile picture in full size without having to follow them or send them a request.
-- You can hide your online status and seen ticks from other users, giving you more privacy and control over your interactions.
-- You can copy any comment or bio that you find interesting or useful without having to screenshot or type it manually.
-- You can zoom in on any image that you want to see more clearly or closely.
-- You can disable video autoplay if you want to save data or avoid distractions.
-- You can change the app theme and icon to suit your preferences or mood.
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-Risks and precautions of Instagram Pro 2021 APK
-While Instagram Pro 2021 APK sounds like a great app, it also comes with some risks and drawbacks that you should be aware of before downloading it. Here are some of the potential issues that you may encounter:
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-- The app is not authorized or endorsed by Instagram or Meta Platforms, which means that it may violate their terms of service and policies. This could result in your account being banned or suspended if they detect that you are using a modded app.
-- The app may contain malware or viruses that could harm your device or compromise your security. This could happen if you download the app from an untrusted source or if the developer inserts malicious code into the app.
-- The app may not be compatible with your device or the latest version of Instagram. This could cause the app to crash, freeze, or malfunction.
-How to download and install Instagram Pro 2021 APK
-If you are interested in trying out Instagram Pro 2021 APK, you will need to download and install it on your Android device. Here are the steps you need to follow:
-Step 1: Enable unknown sources on your device
-Since Instagram Pro 2021 APK is not available on the Google Play Store, you will need to enable unknown sources on your device to allow the installation of apps from outside sources. To do this, go to your device settings, then security, then toggle on the option that says "allow installation of apps from unknown sources". You may also need to grant permission to your browser or file manager to install apps.
-Step 2: Download the APK file from a trusted source
-Next, you will need to download the APK file of Instagram Pro 2021 APK from a trusted source. There are many websites that offer this app, but some of them may be fake or malicious. To avoid any problems, we recommend that you download the app from [SamMods], the official website of the developer. You can also scan the APK file with an antivirus app before installing it.
-Step 3: Install the APK file and launch the app
-Once you have downloaded the APK file, you can install it by tapping on it and following the instructions on the screen. You may need to allow some permissions for the app to function properly. After the installation is complete, you can launch the app and sign in with your Instagram account or create a new one. You can also choose your preferred theme and icon for the app.
-How to use Instagram Pro 2021 APK
-Now that you have installed Instagram Pro 2021 APK, you can start using it and enjoy its features. Here are some tips on how to use the app:
-Explore the app interface and settings
-The app interface of Instagram Pro 2021 APK is similar to the original Instagram app, but with some differences. You will notice that there are some icons on the top right corner of the home screen that allow you to access some features, such as downloading media, viewing profile pictures, copying comments and bios, and changing themes and icons. You can also access more settings by tapping on the three-dot menu on the top right corner of your profile screen.
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-Create and share reels, stories, and posts
-You can create and share reels, stories, and posts on Instagram Pro 2021 APK just like you would on the original app. You can use filters, stickers, music, text, and other effects to make your content more attractive and engaging. You can also download any media that you like from other users by tapping on the download icon on the bottom left corner of the media.
-Shop, chat, and discover new content
-You can also shop for products, chat with friends and celebrities, and discover new content on Instagram Pro 2021 APK. You can browse through different categories of products, such as fashion, beauty, home, and more. You can also send messages, voice notes, photos, videos, and stickers to anyone you want. You can also explore different hashtags, topics, and accounts that interest you.
-Conclusion
-Summary of the main points
-In conclusion, Instagram Pro 2021 APK is a modded version of Instagram that offers more features and benefits than the official app. It allows you to download media, view profile pictures, hide online status and seen ticks, copy comments and bios, zoom in on images, disable video autoplay, change themes and icons, and more. It's a free download for Android devices that you can get from various third-party sources.
-Call to action and disclaimer
-If you want to try out Instagram Pro 2021 APK, you can download it from [SamMods], the official website of the developer. However, please be aware that this app is not authorized or endorsed by Instagram or Meta Platforms, and it may violate their terms of service and policies. This could result in your account being banned or suspended if they detect that you are using a modded app. Also, please make sure that you download the app from a trusted source and scan it with an antivirus app before installing it. We are not responsible for any damage or loss that may occur from using this app.
- FAQs Q: Is Instagram Pro 2021 APK safe to use? Q: Is Instagram Pro 2021 APK safe to use? A: Instagram Pro 2021 APK is not an official app from Instagram or Meta Platforms, and it may contain malware or viruses that could harm your device or compromise your security. It may also violate their terms of service and policies, which could result in your account being banned or suspended. Therefore, we advise you to use this app at your own risk and discretion. Q: How can I update Instagram Pro 2021 APK? A: Instagram Pro 2021 APK does not have an automatic update feature, so you will need to manually check for updates and download them from the developer's website or other sources. You can also follow the developer's social media accounts or join their Telegram group to get notified of new updates. Q: Can I use Instagram Pro 2021 APK with my existing Instagram account? A: Yes, you can use Instagram Pro 2021 APK with your existing Instagram account, as long as you don't have any issues with logging in or verifying your account. However, please note that using a modded app may put your account at risk of being banned or suspended by Instagram or Meta Platforms. Q: Can I use Instagram Pro 2021 APK on iOS devices? A: No, Instagram Pro 2021 APK is only compatible with Android devices. If you want to use a modded version of Instagram on iOS devices, you will need to look for other alternatives, such as Instagram++ or InstaPro. Q: What are the differences between Instagram Pro 2021 APK and other modded versions of Instagram? A: There are many modded versions of Instagram available on the internet, such as GBInstagram, InstaUltra, InstaModa, and more. Each of them has its own features and advantages, but they also have some similarities and drawbacks. The main difference between Instagram Pro 2021 APK and other modded versions of Instagram is that it is developed by SamMods, a reputable and trusted developer who has been creating modded apps for a long time. It also has a simple and user-friendly interface, a wide range of features and options, and regular updates and bug fixes. 401be4b1e0
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-How to Download Internet Cafe Simulator Latest Version
-Have you ever dreamed of running your own internet cafe business? Do you want to experience the challenges and rewards of managing a comprehensive workplace, interacting with customers and city dwellers, and expanding your services and facilities? If so, you should download Internet Cafe Simulator, a realistic and immersive simulation game that lets you build the best internet cafe in the world!
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-Internet Cafe Simulator is a game developed by Cheesecake Dev, a studio that specializes in creating fun and engaging simulation games. The game was first released in 2019, and since then, it has received many updates and improvements, including a sequel called Internet Cafe Simulator 2 that was launched in 2022. In this article, we will show you how to download the latest version of Internet Cafe Simulator, what features it offers, what requirements it has, and some tips and tricks to help you succeed in the game. Let's get started!
-Internet Cafe Simulator Features
-Internet Cafe Simulator is not just a simple game where you click on buttons and watch your money grow. It is a complex and detailed simulation that requires you to plan, manage, and interact with various aspects of your business and environment. Here are some of the features that make Internet Cafe Simulator stand out:
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-- Customization: You can design and decorate your internet cafe according to your preferences and budget. You can choose from different furniture, equipment, accessories, and colors to create a unique and attractive space for your customers.
-- Diversity: You can offer different services and products to your customers, such as gaming computers, consoles, VR devices, snacks, drinks, crypto mining, illegal hacking, and more. You can also buy popular games and licenses to increase your customer base.
-- Interaction: You can communicate and socialize with your customers, employees, suppliers, competitors, and other people in the city. You can also participate in various activities and events, such as parties, concerts, races, fights, strip clubs, etc.
-- Challenge: You have to deal with various problems and risks that come with running an internet cafe business. You have to pay your rent, bills, taxes, salaries, loans, etc. You have to satisfy your customers' needs and expectations. You have to protect your cafe from thieves, vandals, hackers, mobsters, etc. You have to balance your legal and illegal activities.
-- Progression: You can improve your internet cafe by upgrading your computers, installing new software and applications, expanding your area, hiring more staff, etc. You can also develop your skills and abilities by choosing from different tech trees.
-
-Internet Cafe Simulator Requirements
-Before you download Internet Cafe Simulator, you need to make sure that your device meets the minimum or recommended requirements for the game. Here are the system requirements for Internet Cafe Simulator according to Steam:
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-Minimum Recommended OS Windows 7 Windows 10 Processor 2 GHz Dual Core CPU 2.5 GHz Dual Core CPU Memory 2 GB RAM 4 GB RAM Graphics Intel HD Graphics 4000 or better Geforce GTX 970/Radeon RX470 or better Storage 4500 MB available space 5 GB available space
- If you If you have a device that meets or exceeds these requirements, you are ready to download Internet Cafe Simulator. If not, you may need to upgrade your device or look for other alternatives.
Internet Cafe Simulator Download Options
-There are different ways to download Internet Cafe Simulator, depending on your device and preference. Here are some of the most popular options:
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-- Steam: Steam is the most common and convenient way to download Internet Cafe Simulator. You can buy the game for $9.99 on Steam and enjoy the benefits of automatic updates, cloud saving, achievements, community features, etc. You can also access the game's workshop and download user-generated content, such as mods, maps, skins, etc.
-- Other Platforms: If you don't want to use Steam, you can also download Internet Cafe Simulator from other platforms, such as Epic Games Store, GOG.com, Humble Bundle, etc. These platforms may offer different prices, discounts, or bundles for the game. However, they may not have all the features and content that Steam has.
-- Mobile Devices: If you want to play Internet Cafe Simulator on your mobile device, you can download the game from the Google Play Store or the App Store. The mobile version of the game is free to play, but it has ads and in-app purchases. The mobile version also has some differences from the PC version, such as graphics, controls, gameplay, etc.
-- Pirated Versions: If you don't want to pay for the game or use any of the official platforms, you can also download pirated versions of Internet Cafe Simulator from various websites or torrents. However, this is not recommended, as it is illegal and unethical. Pirated versions may also have viruses, malware, bugs, errors, missing features, etc.
-
-Internet Cafe Simulator Tips and Tricks
-Now that you have downloaded Internet Cafe Simulator, you may want to know some tips and tricks to help you succeed in the game. Here are some of them:
-
-- Plan Ahead: Before you start your internet cafe business, you should plan ahead and decide what kind of cafe you want to create. You should consider your budget, location, target market, services, products, etc. You should also research the competition and see what they are offering and how you can differentiate yourself.
-- Manage Your Money: Money is the most important resource in Internet Cafe Simulator. You need money to buy equipment, furniture, games, licenses, staff, etc. You also need money to pay your rent, bills, taxes, salaries, loans, etc. You should always keep track of your income and expenses and try to balance them. You should also look for ways to increase your revenue and reduce your costs.
-- Satisfy Your Customers: Customers are the lifeblood of your internet cafe business. You need to attract and retain customers by offering them quality services and products. You should also interact with them and respond to their needs and feedback. You should also monitor their satisfaction level and loyalty level and try to improve them.
-- Protect Your Cafe: Your internet cafe is not only a source of income but also a source of trouble. You have to deal with various threats and risks that can harm your cafe or reputation. You have to protect your cafe from thieves, vandals, hackers, mobsters, etc. You have to install security cameras, You have to install security cameras, alarms, locks, fire extinguishers, etc. You have to hire security guards, cleaners, technicians, etc. You have to backup your data and use antivirus software. You have to be careful with your legal and illegal activities and avoid getting caught by the police or the media.
-- Expand Your Cafe: As your internet cafe business grows, you may want to expand your cafe and offer more services and products to your customers. You can buy more computers, consoles, VR devices, etc. You can also buy more games and licenses to attract more customers. You can also upgrade your equipment and software to improve your performance and security. You can also rent or buy a bigger space or open a new branch in another location.
-
-Conclusion
-Internet Cafe Simulator is a fun and realistic simulation game that lets you experience the thrill and challenge of running your own internet cafe business. You can customize your cafe, offer different services and products, interact with various people and events, deal with various problems and risks, and improve your skills and abilities. You can download the latest version of Internet Cafe Simulator from Steam or other platforms, or play it on your mobile device. If you follow the tips and tricks we shared in this article, you will surely succeed in the game and become the best internet cafe owner in the world!
-FAQs
-Here are some of the frequently asked questions about Internet Cafe Simulator:
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-- How do I save my progress in Internet Cafe Simulator?
-You can save your progress in Internet Cafe Simulator by using the save option in the main menu or by sleeping in your bed in the game. The game also autosaves every 10 minutes.
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-- How do I get more customers in Internet Cafe Simulator?
-You can get more customers in Internet Cafe Simulator by offering more services and products, buying more games and licenses, improving your customer satisfaction and loyalty levels, advertising your cafe, participating in events, etc.
-- How do I make more money in Internet Cafe Simulator?
-You can make more money in Internet Cafe Simulator by increasing your revenue and reducing your costs. You can increase your revenue by attracting more customers, offering more services and products, setting higher prices, doing illegal activities, etc. You can reduce your costs by buying cheaper equipment and furniture, negotiating with suppliers and employees, paying your bills and taxes on time, avoiding fines and lawsuits, etc.
-- How do I unlock new features in Internet Cafe Simulator?
-You can unlock new features in Internet Cafe Simulator by developing your skills and abilities in different tech trees. You can access the tech trees by using your laptop or smartphone in the game. You can unlock new features such as crypto mining, hacking, VR devices, etc.
-- How do I update Internet Cafe Simulator?
-You can update Internet Cafe Simulator by using the update option in the main menu or by checking for updates on the platform you downloaded the game from. You can also check the game's official website or social media accounts for news and announcements about updates.
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diff --git a/spaces/fatiXbelha/sd/Enjoy Movies Books and Apps with Android 4.0 3 Google Play Store APK.md b/spaces/fatiXbelha/sd/Enjoy Movies Books and Apps with Android 4.0 3 Google Play Store APK.md
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-Android 4.0 3 Google Play Store APK: How to Download and Install It on Your Device
- If you have an old Android device running on Android 4.0 3 Ice Cream Sandwich, you might be wondering how to get the latest version of Google Play Store on it. Google Play Store is the official app store for Android devices, where you can find and download millions of apps and games for your device. However, some older devices may not have the Google Play Store app pre-installed, or may have an outdated version that does not work properly.
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- In this article, we will show you how to download and install the Google Play Store APK file on your Android 4.0 3 device, and how to use it to enjoy the best apps and games for your device.
- What is Android 4.0 3 and Google Play Store?
- Android 4.0 3: Ice Cream Sandwich
- Android 4.0 3 is the third minor update of Android 4.0 Ice Cream Sandwich, which was released in October 2011. Ice Cream Sandwich was the first version of Android that unified the user interface and features for both smartphones and tablets. It introduced many new features, such as face unlock, data usage management, improved multitasking, redesigned widgets, and more.
- Google Play Store: The Official App Store for Android
- Google Play Store is the official app store for Android devices, where you can find and download millions of apps and games for your device. You can also access other digital content, such as books, movies, music, magazines, and more. Google Play Store also provides updates and security patches for your apps and games, as well as recommendations based on your preferences and usage.
- Why Do You Need to Download and Install Google Play Store APK on Android 4.0 3?
- Benefits of Google Play Store APK
- Downloading and installing the Google Play Store APK file on your Android 4.0 3 device can have many benefits, such as:
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-- You can access the latest version of Google Play Store, which may have more features, bug fixes, and security improvements than the one on your device.
-- You can access a wider range of apps and games that may not be compatible with your device's default app store.
-- You can enjoy faster downloads and installations of apps and games, as well as automatic updates.
-- You can manage your apps and games more easily, such as uninstalling, moving, or clearing data.
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- Drawbacks of Google Play Store APK
- However, downloading and installing the Google Play Store APK file on your Android 4.0 3 device can also have some drawbacks, such as:
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-- You may encounter compatibility issues with some apps and games that may not work properly on your device or require higher Android versions.
-- You may expose your device to security risks if you download the APK file from untrusted sources or enable unknown sources on your device.
-- You may violate the terms of service of your device manufacturer or carrier if you install unauthorized apps or modify your device's software.How to Download and Install Google Play Store APK on Android 4.0 3?
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If you want to download and install the Google Play Store APK file on your Android 4.0 3 device, you need to follow these steps:
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- Step 1: Enable Unknown Sources on Your Device
- Before you can install the APK file, you need to enable unknown sources on your device. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message that installing apps from unknown sources can harm your device. Tap OK to proceed.
- Step 2: Download Google Play Store APK File
- Next, you need to download the Google Play Store APK file from a trusted source. You can search for it online or use the link below:
- Google Play Store APK for Android 4.0 3
- Make sure you download the latest version of the APK file that is compatible with your device. You can check the version number and the file size before downloading. You may also need to grant permission for your browser or file manager to download the APK file.
- Step 3: Install Google Play Store APK File
- Once you have downloaded the APK file, you need to install it on your device. To do this, locate the APK file in your downloads folder or wherever you saved it. Tap on it to open it. You may see a prompt that asks you to confirm the installation. Tap Install to start the installation process. It may take a few seconds or minutes depending on your device and network speed.
- When the installation is complete, you will see a message that says App Installed. Tap Open to launch the Google Play Store app. Alternatively, you can find the app icon in your app drawer or home screen.
- How to Use Google Play Store on Android 4.0 3?
- Now that you have installed the Google Play Store app on your Android 4.0 3 device, you can use it to access millions of apps and games for your device. Here are some tips on how to use it:
- Sign in with Your Google Account
- To use the Google Play Store app, you need to sign in with your Google account. If you already have one, you can enter your email and password and tap Sign In. If you don't have one, you can create one by tapping Create Account and following the instructions.
- Signing in with your Google account will allow you to sync your apps and games across your devices, backup and restore your data, access your purchases and subscriptions, and more.
- Browse and Download Apps and Games
- To browse and download apps and games, you can use the search bar at the top of the app or swipe left or right to explore different categories and genres. You can also tap on the menu icon at the top left corner of the app and select Apps or Games to see more options.
- To download an app or game, tap on its icon or name to open its details page. You can see its description, screenshots, ratings, reviews, and more information. You can also see if it is free or paid, and if it has in-app purchases or ads. To download it, tap on Install if it is free, or Buy if it is paid. You may need to enter your payment method if you haven't done so before.
- To cancel a download or installation, tap on the X icon next to its progress bar. To pause or resume a download or installation, tap on the Pause or Resume icon next to its progress bar.
- Update and Manage Your Apps and Games
- To update and manage your apps and games, tap on the menu icon at the top left corner of the app and select My Apps & Games. You will see four tabs: Updates, Installed, Library, and Beta.
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-- The Updates tab shows you which apps and games have new versions available for download. You can tap on Update All to update all of them at once, or tap on each one individually to update them separately.
-- The Installed tab shows you which apps and games are currently installed on your device. You can tap on each one to open its details page, where you can uninstall it, clear its data or cache, move it to an SD card, disable it, or open it.
-- The Library tab shows you which apps and games you have previously installed but are not currently installed on your device. You can tap on each one to reinstall it or remove it from your library.
The Beta tab shows you which apps and games you have joined the beta testing program for. You can tap on each one to leave the beta program, or to provide feedback to the developers.
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- You can also enable or disable automatic updates for your apps and games by tapping on the menu icon at the top right corner of the app and selecting Settings > Auto-update apps. You can choose to update your apps and games over any network, over Wi-Fi only, or not at all.
- Conclusion
- In this article, we have shown you how to download and install the Google Play Store APK file on your Android 4.0 3 device, and how to use it to access millions of apps and games for your device. We hope you have found this article helpful and informative. If you have any questions or comments, please feel free to leave them below.
- Here are some FAQs that you may find useful:
- FAQs
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-- Q: Is it safe to download and install the Google Play Store APK file on my device?
-- A: It depends on where you download the APK file from. If you download it from a trusted source, such as the link we provided above, it should be safe. However, if you download it from an untrusted source, it may contain malware or viruses that can harm your device. Therefore, we recommend that you always scan the APK file with an antivirus app before installing it.
-- Q: Will downloading and installing the Google Play Store APK file void my warranty or affect my device's performance?
-- A: No, downloading and installing the Google Play Store APK file will not void your warranty or affect your device's performance. However, if you install unauthorized apps or modify your device's software, you may violate the terms of service of your device manufacturer or carrier, and they may refuse to provide you with support or service.
-- Q: How can I update the Google Play Store app on my device?
-- A: The Google Play Store app will update itself automatically when a new version is available. You can also check for updates manually by opening the app and tapping on the menu icon at the top left corner of the app and selecting Settings > About > Play Store version. If there is an update available, you will see a message that says A new version of Google Play Store will be downloaded and installed. Tap OK to start the update process.
-- Q: How can I uninstall the Google Play Store app from my device?
-- A: You cannot uninstall the Google Play Store app from your device, as it is a system app that is essential for your device's functionality. However, you can disable it by going to Settings > Apps > Google Play Store and tapping on Disable. This will prevent the app from running and updating itself. To enable it again, go to Settings > Apps > Disabled and tap on Enable.
-- Q: How can I contact Google Play Store support if I have any issues or problems with the app?
-- A: You can contact Google Play Store support by going to the app and tapping on the menu icon at the top left corner of the app and selecting Help & Feedback. You will see a list of topics that may answer your questions or solve your problems. You can also tap on Contact Us to chat with a support agent, request a callback, or send an email.
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/fb700/chat3/main.py b/spaces/fb700/chat3/main.py
deleted file mode 100644
index 8ef5b40c43547644f5cfb77422bd15d26ff704ce..0000000000000000000000000000000000000000
--- a/spaces/fb700/chat3/main.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
-
-def main():
- import gradio as gr
- from request_llm.bridge_all import predict
- from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
- # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
- proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \
- get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS')
-
- # 如果WEB_PORT是-1, 则随机选取WEB端口
- PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
- if not AUTHENTICATION: AUTHENTICATION = None
-
- from check_proxy import get_current_version
- initial_prompt = "Serve me as a writing and programming assistant."
- title_html = f"帛凡 ChatGPT 学术优化 {get_current_version()}
"
- description = """和气生财，万事如意"""
-
- # 问询记录, python 版本建议3.9+（越新越好）
- import logging
- os.makedirs("gpt_log", exist_ok=True)
- try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8")
- except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO)
- print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
-
- # 一些普通功能模块
- from core_functional import get_core_functions
- functional = get_core_functions()
-
- # 高级函数插件
- from crazy_functional import get_crazy_functions
- crazy_fns = get_crazy_functions()
-
- # 处理markdown文本格式的转变
- gr.Chatbot.postprocess = format_io
-
- # 做一些外观色彩上的调整
- from theme import adjust_theme, advanced_css
- set_theme = adjust_theme()
-
- # 代理与自动更新
- from check_proxy import check_proxy, auto_update
- proxy_info = check_proxy(proxies)
-
- gr_L1 = lambda: gr.Row().style()
- gr_L2 = lambda scale: gr.Column(scale=scale)
- if LAYOUT == "TOP-DOWN":
- gr_L1 = lambda: DummyWith()
- gr_L2 = lambda scale: gr.Row()
- CHATBOT_HEIGHT /= 2
-
- cancel_handles = []
- with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
- gr.HTML(title_html)
- cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
- with gr_L1():
- with gr_L2(scale=2):
- chatbot = gr.Chatbot()
- chatbot.style(height=CHATBOT_HEIGHT)
- history = gr.State([])
- with gr_L2(scale=1):
- with gr.Accordion("输入区", open=True) as area_input_primary:
- with gr.Row():
- txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
- with gr.Row():
- submitBtn = gr.Button("提交", variant="primary")
- with gr.Row():
- resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm")
- stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
- clearBtn = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm")
- with gr.Row():
- status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}")
- with gr.Accordion("基础功能区", open=True) as area_basic_fn:
- with gr.Row():
- for k in functional:
- variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
- functional[k]["Button"] = gr.Button(k, variant=variant)
- with gr.Accordion("函数插件区", open=True) as area_crazy_fn:
- with gr.Row():
- gr.Markdown("注意：以下“红颜色”标识的函数插件需从输入区读取路径作为参数.")
- with gr.Row():
- for k in crazy_fns:
- if not crazy_fns[k].get("AsButton", True): continue
- variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
- crazy_fns[k]["Button"] = gr.Button(k, variant=variant)
- crazy_fns[k]["Button"].style(size="sm")
- with gr.Row():
- with gr.Accordion("更多函数插件", open=True):
- dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)]
- with gr.Column(scale=1):
- dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False)
- with gr.Column(scale=1):
- switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary")
- with gr.Row():
- with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up:
- file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
- with gr.Accordion("展开SysPrompt & 交互界面布局 & Github地址", open=(LAYOUT == "TOP-DOWN")):
- system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
- top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
- temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
- max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="MaxLength",)
- checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
- md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="").style(container=False)
-
- gr.Markdown(description)
- with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary:
- with gr.Row():
- txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False)
- with gr.Row():
- submitBtn2 = gr.Button("提交", variant="primary")
- with gr.Row():
- resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm")
- stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm")
- clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm")
- # 功能区显示开关与功能区的互动
- def fn_area_visibility(a):
- ret = {}
- ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))})
- ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))})
- ret.update({area_input_primary: gr.update(visible=("底部输入区" not in a))})
- ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))})
- ret.update({clearBtn: gr.update(visible=("输入清除键" in a))})
- ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))})
- if "底部输入区" in a: ret.update({txt: gr.update(value="")})
- return ret
- checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2] )
- # 整理反复出现的控件句柄组合
- input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt]
- output_combo = [cookies, chatbot, history, status]
- predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo)
- # 提交按钮、重置按钮
- cancel_handles.append(txt.submit(**predict_args))
- cancel_handles.append(txt2.submit(**predict_args))
- cancel_handles.append(submitBtn.click(**predict_args))
- cancel_handles.append(submitBtn2.click(**predict_args))
- resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
- resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
- clearBtn.click(lambda: ("",""), None, [txt, txt2])
- clearBtn2.click(lambda: ("",""), None, [txt, txt2])
- # 基础功能区的回调函数注册
- for k in functional:
- click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
- cancel_handles.append(click_handle)
- # 文件上传区，接收文件后与chatbot的互动
- file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes], [chatbot, txt, txt2])
- # 函数插件-固定按钮区
- for k in crazy_fns:
- if not crazy_fns[k].get("AsButton", True): continue
- click_handle = crazy_fns[k]["Button"].click(ArgsGeneralWrapper(crazy_fns[k]["Function"]), [*input_combo, gr.State(PORT)], output_combo)
- click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
- cancel_handles.append(click_handle)
- # 函数插件-下拉菜单与随变按钮的互动
- def on_dropdown_changed(k):
- variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
- return {switchy_bt: gr.update(value=k, variant=variant)}
- dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] )
- # 随变按钮的回调函数注册
- def route(k, *args, **kwargs):
- if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
- yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs)
- click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
- click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
- # def expand_file_area(file_upload, area_file_up):
- # if len(file_upload)>0: return {area_file_up: gr.update(open=True)}
- # click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up])
- cancel_handles.append(click_handle)
- # 终止按钮的回调函数注册
- stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
- stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
-
- # gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
- def auto_opentab_delay():
- import threading, webbrowser, time
- print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
- print(f"\t（亮色主题）: http://localhost:{PORT}")
- print(f"\t（暗色主题）: http://localhost:{PORT}/?__dark-theme=true")
- def open():
- time.sleep(2) # 打开浏览器
- webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true")
- threading.Thread(target=open, name="open-browser", daemon=True).start()
- threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start()
-
- auto_opentab_delay()
- #demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
- demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png", auth=AUTHENTICATION, )
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/examples/pretrain_taiyi_clip/pretrain.py b/spaces/fclong/summary/fengshen/examples/pretrain_taiyi_clip/pretrain.py
deleted file mode 100644
index 56e24ac370ff2b5f3ecf84a32586bc5205499b07..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/pretrain_taiyi_clip/pretrain.py
+++ /dev/null
@@ -1,308 +0,0 @@
-from pytorch_lightning import (
- LightningModule,
- Trainer,
-)
-from pytorch_lightning.callbacks import (
- LearningRateMonitor,
-)
-from fengshen.models.clip import (
- TaiyiCLIPModel,
- TaiyiCLIPProcessor,
-)
-from fengshen.models.model_utils import (
- add_module_args,
- configure_optimizers,
- get_total_steps,
-)
-import torch
-import torch.nn.functional as F
-import argparse
-import math
-from fengshen.data.universal_datamodule import UniversalDataModule
-from fengshen.data.taiyi_stable_diffusion_datasets.taiyi_datasets import add_data_args, load_data
-from fengshen.utils.universal_checkpoint import UniversalCheckpoint
-import os
-import numpy as np
-from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor
-
-OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
-OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
-
-
-class Collator():
- def __init__(self, args, processor):
- self.processor = processor
- self.seq_length = args.seq_length
- self.transforms = Compose([
- ToTensor(),
- RandomResizedCrop(args.resolution, scale=(0.9, 1.0),
- interpolation=InterpolationMode.BICUBIC),
- Normalize(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
- ])
-
- def __call__(self, inputs):
- max_length = min(self.seq_length, max([len(i['caption']) for i in inputs]))
- images = []
- texts = []
- labels = []
- for i in inputs:
- # instance_image = Image.open(i['img_path'])
- # instance_image = jpeg4py.JPEG(i['img_path']).decode()
- instance_image = np.load(i['npy_path'])
- images.append(self.transforms(instance_image))
- texts.append(i['caption'])
- labels.append(i['labels'] if 'labels' in i else -100)
- # images_input = self.processor(images=images, return_tensors="pt")
- texts_input = self.processor(text=texts,
- max_length=max_length,
- padding='max_length',
- truncation=True,
- return_tensors='pt')
- # return images_input, texts_input, labels
- return {'pixel_values': torch.stack(images)}, texts_input, labels
-
-
-class TaiyiCLIP(LightningModule):
- @staticmethod
- def add_module_specific_args(parent_parser):
- parser = parent_parser.add_argument_group('Taiyi CLIP')
- parser.add_argument('--loss_type', choices=['local', 'global'], default='local')
- parser.add_argument('--seq_length', default=77)
- parser.add_argument('--gather_with_grad', default=False, action='store_true')
- parser.add_argument('--freeze_image_tower', default=False, action='store_true')
- return parent_parser
-
- def __init__(self, args, **kwargs) -> None:
- super().__init__()
- self.save_hyperparameters(args)
-
- self.model = TaiyiCLIPModel.from_pretrained(args.model_path)
- self.processor = TaiyiCLIPProcessor.from_pretrained(args.model_path)
-
- self.local_loss = args.loss_type == 'local'
-
- if args.freeze_image_tower:
- for param in self.model.vision_model.parameters():
- param.requires_grad = False
- self.model.visual_projection.requires_grad = False
-
- # cache
- self.cache_labels = True
- self.prev_num_logits = 0
- self.labels = {}
-
- def setup(self, stage) -> None:
- if stage == 'fit':
- self.total_steps = get_total_steps(self.trainer, self.hparams)
- print('Total steps: {}' .format(self.total_steps))
- elif stage == 'validate':
- self.total_steps = 100
-
- def configure_optimizers(self):
- return configure_optimizers(self)
-
- def forward(self, image, text):
- assert image is not None
- assert text is not None
- image_features = self.model.get_image_features(**image)
- text_features = self.model.get_text_features(**text)
-
- image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)
- text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)
-
- return image_features, text_features, self.model.logit_scale.exp()
-
- def gather_features(self, features):
- if self.trainer.world_size == 1:
- return features
- all_features = self.all_gather(
- features, sync_grads=self.hparams.gather_with_grad)
- if not self.local_loss and not self.gather_with_grad:
- # 如果是全局loss，并且不需要梯度，需要把梯度更新回tensor
- all_features[self.global_rank] = features
- all_features = all_features.view(-1, all_features.shape[-1])
- return all_features
-
- def clip_loss(self, image_features, text_features, logit_scale):
-
- logits_per_image = None
-
- # 如果我冻住VIT并且是local_loss，那么我只需要自己的这部分text feature就行
- # 因为根本不需要image2text的feature训练VIT
- if self.hparams.freeze_image_tower and self.local_loss:
- all_text_features = None
- else:
- all_text_features = self.gather_features(
- text_features)
- all_image_features = self.gather_features(
- image_features)
-
- if self.local_loss:
- if all_text_features is not None:
- logits_per_image = logit_scale * image_features @ all_text_features.T
- logits_per_text = logit_scale * text_features @ all_image_features.T
- else:
- # 如果是global_loss，那all_text_features肯定不是空的
- logits_per_image = logit_scale * all_image_features @ all_text_features.T
- logits_per_text = logits_per_image.T
-
- num_logits = logits_per_text.shape[0]
- if self.prev_num_logits != num_logits or self.device not in self.labels:
- labels = torch.arange(num_logits, device=self.device, dtype=torch.long)
- if self.trainer.world_size > 1 and self.local_loss:
- labels = labels + num_logits * self.global_rank
- if self.cache_labels:
- self.labels[self.device] = labels
- self.prev_num_logits = num_logits
- else:
- labels = self.labels[self.device]
-
- total_loss = (
- F.cross_entropy(logits_per_image, labels) +
- F.cross_entropy(logits_per_text, labels)
- ) / 2 if logits_per_image is not None else F.cross_entropy(logits_per_text, labels)
- return total_loss
-
- def training_step(self, batch):
- image, text, _ = batch
- image_features, text_features, logit_scale = self(image, text)
- total_loss = self.clip_loss(image_features, text_features, logit_scale)
- self.log('train_loss', total_loss, sync_dist=False)
- return total_loss
-
- def on_train_batch_end(self, outputs, batch, batch_idx: int) -> None:
- with torch.no_grad():
- self.model.logit_scale.clamp_(0, math.log(100))
-
- def get_metrics(self, image_features, text_features, labels, logit_scale):
- # 计算相似度，支持多个样本的情况（比如一个图片有多个caption）
- # img2txt计算的时候要用到，因为一张图片可能对应多个文本。
- # txt2img计算的时候不需要（一般一个text只有一个对应图片）
- metrics = {}
- logits_per_image = (logit_scale * image_features @ text_features.t()).detach().cpu()
- logits_per_text = logits_per_image.t().detach().cpu()
-
- logits = {"image_to_text": logits_per_image, "text_to_image": logits_per_text}
-
- label2idx = {} # 计算label到idx的映射。
- repeat_id = []
- for i, label in enumerate(labels):
- if label not in label2idx:
- label2idx[label] = [i]
- else:
- # 表示该index的标签出现过，记录这个index，后续算txt2img分数的时候，这些index的权值要降低。
- label2idx[label].append(i)
- repeat_id.append(i)
-
- ground_truth = [label2idx[label] for label in labels]
-
- for name, logit in logits.items():
- if name == 'text_to_image':
- logit[:, repeat_id] -= 1e8 # 这部分的分数要降低。（重复出现的图片，直接忽略）
- r_stat = {1: [], 5: [], 10: []}
- # r1_stat, r5_stat, r10_stat = [], [], []
- # index of the largest element to the smallest
- ranking = torch.argsort(logit, descending=True)
- for i, each_query in enumerate(ranking[:, :10]):
- for j, q in enumerate(each_query):
- found = False
- if q in ground_truth[i]:
- for k, v in r_stat.items():
- if j < k:
- found = True
- v.append(1)
- if found:
- break
- for k, v in r_stat.items():
- metrics[f'{name}_R@{k}'] = sum(v)/len(logit)
- return metrics
-
- def validation_step(self, batch, batch_idx):
- image, text, label = batch
- image_features, text_features, logit_scale = self(image, text)
- return image_features, text_features, logit_scale, text['input_ids'].shape[0], label
-
- def validation_epoch_end(self, val_outputs):
- all_image_features = []
- all_text_features = []
- all_labels = []
- sample_size = 0
- for o in val_outputs:
- all_image_features.append(o[0])
- all_text_features.append(o[1])
- sample_size += o[3]
- all_labels += o[4]
- if len(all_image_features) == 0 or len(all_text_features) == 0:
- return
- all_image_features = torch.cat(all_image_features)
- all_text_features = torch.cat(all_text_features)
- logit_scale = val_outputs[0][2].mean()
- logits_per_image = logit_scale * all_image_features @ all_text_features.t()
- logits_per_text = logits_per_image.t()
-
- labels = torch.arange(sample_size, device=self.device).long()
- total_loss = (F.cross_entropy(logits_per_image, labels)
- + F.cross_entropy(logits_per_text, labels)) / 2
-
- val_metrics = self.get_metrics(
- image_features=all_image_features,
- text_features=all_text_features,
- logit_scale=logit_scale,
- labels=all_labels)
- loss = total_loss / sample_size
- self.log('val_loss', loss, sync_dist=False)
- for k, v in val_metrics.items():
- self.log(f'val_{k}', v, sync_dist=False)
-
- def on_load_checkpoint(self, checkpoint) -> None:
- # 兼容低版本lightning，低版本lightning从ckpt起来时steps数会被重置为0
- global_step_offset = checkpoint["global_step"]
- if 'global_samples' in checkpoint:
- self.consumed_samples = checkpoint['global_samples']
- self.trainer.fit_loop.epoch_loop._batches_that_stepped = global_step_offset
-
- def on_save_checkpoint(self, checkpoint) -> None:
- # 保存的时候把权重按huggingface的形式保存出来
- if self.global_rank == 0:
- dir_path = os.path.join(
- self.hparams.default_root_dir, f'hf_out_{self.trainer.current_epoch}_{self.trainer.global_step}')
- if not os.path.exists(dir_path):
- os.mkdir(dir_path)
- self.model.save_pretrained(dir_path)
- self.processor.save_pretrained(dir_path)
-
-
-if __name__ == '__main__':
- args_parser = argparse.ArgumentParser()
- args_parser = add_module_args(args_parser)
- args_parser = add_data_args(args_parser)
- args_parser = UniversalDataModule.add_data_specific_args(args_parser)
- args_parser = Trainer.add_argparse_args(args_parser)
- args_parser = TaiyiCLIP.add_module_specific_args(args_parser)
- args_parser = UniversalCheckpoint.add_argparse_args(args_parser)
- args = args_parser.parse_args()
-
- lr_monitor = LearningRateMonitor(logging_interval='step')
- checkpoint_callback = UniversalCheckpoint(args)
-
- trainer = Trainer.from_argparse_args(args,
- callbacks=[
- lr_monitor,
- checkpoint_callback])
-
- model = TaiyiCLIP(args)
- processor = model.processor
- collate_fn = Collator(args, processor)
- datasets = load_data(args, global_rank=trainer.global_rank)
-
- # 加载单个验证集：！！！验证代码有效性临时这样干的，验证完有效性会删除
- from fengshen.examples.pretrain_taiyi_clip.flickr_datasets import flickr30k_CNA
- img_root = '/shared_space/ccnl/mm_data/Flickr30k-CNA/flickr30k/images'
- text_annot_path = '/shared_space/ccnl/mm_data/Flickr30k-CNA/test/flickr30k_cn_test.txt'
-
- datasets[args.val_datasets_field] = flickr30k_CNA(img_root, text_annot_path, collate_fn)
-
- datamoule = UniversalDataModule(
- tokenizer=None, collate_fn=collate_fn, args=args, datasets=datasets)
-
- trainer.fit(model, datamoule, ckpt_path=args.load_ckpt_path)
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/utils.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/utils.js
deleted file mode 100644
index 799a6a2b4ee4672c7662a2951b5cdcc1b2413a28..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/utils.js
+++ /dev/null
@@ -1,304 +0,0 @@
-/*!
- * express
- * Copyright(c) 2009-2013 TJ Holowaychuk
- * Copyright(c) 2014-2015 Douglas Christopher Wilson
- * MIT Licensed
- */
-
-'use strict';
-
-/**
- * Module dependencies.
- * @api private
- */
-
-var Buffer = require('safe-buffer').Buffer
-var contentDisposition = require('content-disposition');
-var contentType = require('content-type');
-var deprecate = require('depd')('express');
-var flatten = require('array-flatten');
-var mime = require('send').mime;
-var etag = require('etag');
-var proxyaddr = require('proxy-addr');
-var qs = require('qs');
-var querystring = require('querystring');
-
-/**
- * Return strong ETag for `body`.
- *
- * @param {String|Buffer} body
- * @param {String} [encoding]
- * @return {String}
- * @api private
- */
-
-exports.etag = createETagGenerator({ weak: false })
-
-/**
- * Return weak ETag for `body`.
- *
- * @param {String|Buffer} body
- * @param {String} [encoding]
- * @return {String}
- * @api private
- */
-
-exports.wetag = createETagGenerator({ weak: true })
-
-/**
- * Check if `path` looks absolute.
- *
- * @param {String} path
- * @return {Boolean}
- * @api private
- */
-
-exports.isAbsolute = function(path){
- if ('/' === path[0]) return true;
- if (':' === path[1] && ('\\' === path[2] || '/' === path[2])) return true; // Windows device path
- if ('\\\\' === path.substring(0, 2)) return true; // Microsoft Azure absolute path
-};
-
-/**
- * Flatten the given `arr`.
- *
- * @param {Array} arr
- * @return {Array}
- * @api private
- */
-
-exports.flatten = deprecate.function(flatten,
- 'utils.flatten: use array-flatten npm module instead');
-
-/**
- * Normalize the given `type`, for example "html" becomes "text/html".
- *
- * @param {String} type
- * @return {Object}
- * @api private
- */
-
-exports.normalizeType = function(type){
- return ~type.indexOf('/')
- ? acceptParams(type)
- : { value: mime.lookup(type), params: {} };
-};
-
-/**
- * Normalize `types`, for example "html" becomes "text/html".
- *
- * @param {Array} types
- * @return {Array}
- * @api private
- */
-
-exports.normalizeTypes = function(types){
- var ret = [];
-
- for (var i = 0; i < types.length; ++i) {
- ret.push(exports.normalizeType(types[i]));
- }
-
- return ret;
-};
-
-/**
- * Generate Content-Disposition header appropriate for the filename.
- * non-ascii filenames are urlencoded and a filename* parameter is added
- *
- * @param {String} filename
- * @return {String}
- * @api private
- */
-
-exports.contentDisposition = deprecate.function(contentDisposition,
- 'utils.contentDisposition: use content-disposition npm module instead');
-
-/**
- * Parse accept params `str` returning an
- * object with `.value`, `.quality` and `.params`.
- * also includes `.originalIndex` for stable sorting
- *
- * @param {String} str
- * @param {Number} index
- * @return {Object}
- * @api private
- */
-
-function acceptParams(str, index) {
- var parts = str.split(/ *; */);
- var ret = { value: parts[0], quality: 1, params: {}, originalIndex: index };
-
- for (var i = 1; i < parts.length; ++i) {
- var pms = parts[i].split(/ *= */);
- if ('q' === pms[0]) {
- ret.quality = parseFloat(pms[1]);
- } else {
- ret.params[pms[0]] = pms[1];
- }
- }
-
- return ret;
-}
-
-/**
- * Compile "etag" value to function.
- *
- * @param {Boolean|String|Function} val
- * @return {Function}
- * @api private
- */
-
-exports.compileETag = function(val) {
- var fn;
-
- if (typeof val === 'function') {
- return val;
- }
-
- switch (val) {
- case true:
- case 'weak':
- fn = exports.wetag;
- break;
- case false:
- break;
- case 'strong':
- fn = exports.etag;
- break;
- default:
- throw new TypeError('unknown value for etag function: ' + val);
- }
-
- return fn;
-}
-
-/**
- * Compile "query parser" value to function.
- *
- * @param {String|Function} val
- * @return {Function}
- * @api private
- */
-
-exports.compileQueryParser = function compileQueryParser(val) {
- var fn;
-
- if (typeof val === 'function') {
- return val;
- }
-
- switch (val) {
- case true:
- case 'simple':
- fn = querystring.parse;
- break;
- case false:
- fn = newObject;
- break;
- case 'extended':
- fn = parseExtendedQueryString;
- break;
- default:
- throw new TypeError('unknown value for query parser function: ' + val);
- }
-
- return fn;
-}
-
-/**
- * Compile "proxy trust" value to function.
- *
- * @param {Boolean|String|Number|Array|Function} val
- * @return {Function}
- * @api private
- */
-
-exports.compileTrust = function(val) {
- if (typeof val === 'function') return val;
-
- if (val === true) {
- // Support plain true/false
- return function(){ return true };
- }
-
- if (typeof val === 'number') {
- // Support trusting hop count
- return function(a, i){ return i < val };
- }
-
- if (typeof val === 'string') {
- // Support comma-separated values
- val = val.split(',')
- .map(function (v) { return v.trim() })
- }
-
- return proxyaddr.compile(val || []);
-}
-
-/**
- * Set the charset in a given Content-Type string.
- *
- * @param {String} type
- * @param {String} charset
- * @return {String}
- * @api private
- */
-
-exports.setCharset = function setCharset(type, charset) {
- if (!type || !charset) {
- return type;
- }
-
- // parse type
- var parsed = contentType.parse(type);
-
- // set charset
- parsed.parameters.charset = charset;
-
- // format type
- return contentType.format(parsed);
-};
-
-/**
- * Create an ETag generator function, generating ETags with
- * the given options.
- *
- * @param {object} options
- * @return {function}
- * @private
- */
-
-function createETagGenerator (options) {
- return function generateETag (body, encoding) {
- var buf = !Buffer.isBuffer(body)
- ? Buffer.from(body, encoding)
- : body
-
- return etag(buf, options)
- }
-}
-
-/**
- * Parse an extended query string with qs.
- *
- * @return {Object}
- * @private
- */
-
-function parseExtendedQueryString(str) {
- return qs.parse(str, {
- allowPrototypes: true
- });
-}
-
-/**
- * Return new empty object.
- *
- * @return {Object}
- * @api private
- */
-
-function newObject() {
- return {};
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/test/parse.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/test/parse.js
deleted file mode 100644
index 7d7b4dd8aea13754a32dca91e432e80ef3f6af67..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/test/parse.js
+++ /dev/null
@@ -1,855 +0,0 @@
-'use strict';
-
-var test = require('tape');
-var qs = require('../');
-var utils = require('../lib/utils');
-var iconv = require('iconv-lite');
-var SaferBuffer = require('safer-buffer').Buffer;
-
-test('parse()', function (t) {
- t.test('parses a simple string', function (st) {
- st.deepEqual(qs.parse('0=foo'), { 0: 'foo' });
- st.deepEqual(qs.parse('foo=c++'), { foo: 'c ' });
- st.deepEqual(qs.parse('a[>=]=23'), { a: { '>=': '23' } });
- st.deepEqual(qs.parse('a[<=>]==23'), { a: { '<=>': '=23' } });
- st.deepEqual(qs.parse('a[==]=23'), { a: { '==': '23' } });
- st.deepEqual(qs.parse('foo', { strictNullHandling: true }), { foo: null });
- st.deepEqual(qs.parse('foo'), { foo: '' });
- st.deepEqual(qs.parse('foo='), { foo: '' });
- st.deepEqual(qs.parse('foo=bar'), { foo: 'bar' });
- st.deepEqual(qs.parse(' foo = bar = baz '), { ' foo ': ' bar = baz ' });
- st.deepEqual(qs.parse('foo=bar=baz'), { foo: 'bar=baz' });
- st.deepEqual(qs.parse('foo=bar&bar=baz'), { foo: 'bar', bar: 'baz' });
- st.deepEqual(qs.parse('foo2=bar2&baz2='), { foo2: 'bar2', baz2: '' });
- st.deepEqual(qs.parse('foo=bar&baz', { strictNullHandling: true }), { foo: 'bar', baz: null });
- st.deepEqual(qs.parse('foo=bar&baz'), { foo: 'bar', baz: '' });
- st.deepEqual(qs.parse('cht=p3&chd=t:60,40&chs=250x100&chl=Hello|World'), {
- cht: 'p3',
- chd: 't:60,40',
- chs: '250x100',
- chl: 'Hello|World'
- });
- st.end();
- });
-
- t.test('arrayFormat: brackets allows only explicit arrays', function (st) {
- st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'brackets' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'brackets' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'brackets' }), { a: 'b,c' });
- st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'brackets' }), { a: ['b', 'c'] });
- st.end();
- });
-
- t.test('arrayFormat: indices allows only indexed arrays', function (st) {
- st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'indices' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'indices' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'indices' }), { a: 'b,c' });
- st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'indices' }), { a: ['b', 'c'] });
- st.end();
- });
-
- t.test('arrayFormat: comma allows only comma-separated arrays', function (st) {
- st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'comma' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'comma' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'comma' }), { a: 'b,c' });
- st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'comma' }), { a: ['b', 'c'] });
- st.end();
- });
-
- t.test('arrayFormat: repeat allows only repeated values', function (st) {
- st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'repeat' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'repeat' }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'repeat' }), { a: 'b,c' });
- st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'repeat' }), { a: ['b', 'c'] });
- st.end();
- });
-
- t.test('allows enabling dot notation', function (st) {
- st.deepEqual(qs.parse('a.b=c'), { 'a.b': 'c' });
- st.deepEqual(qs.parse('a.b=c', { allowDots: true }), { a: { b: 'c' } });
- st.end();
- });
-
- t.deepEqual(qs.parse('a[b]=c'), { a: { b: 'c' } }, 'parses a single nested string');
- t.deepEqual(qs.parse('a[b][c]=d'), { a: { b: { c: 'd' } } }, 'parses a double nested string');
- t.deepEqual(
- qs.parse('a[b][c][d][e][f][g][h]=i'),
- { a: { b: { c: { d: { e: { f: { '[g][h]': 'i' } } } } } } },
- 'defaults to a depth of 5'
- );
-
- t.test('only parses one level when depth = 1', function (st) {
- st.deepEqual(qs.parse('a[b][c]=d', { depth: 1 }), { a: { b: { '[c]': 'd' } } });
- st.deepEqual(qs.parse('a[b][c][d]=e', { depth: 1 }), { a: { b: { '[c][d]': 'e' } } });
- st.end();
- });
-
- t.test('uses original key when depth = 0', function (st) {
- st.deepEqual(qs.parse('a[0]=b&a[1]=c', { depth: 0 }), { 'a[0]': 'b', 'a[1]': 'c' });
- st.deepEqual(qs.parse('a[0][0]=b&a[0][1]=c&a[1]=d&e=2', { depth: 0 }), { 'a[0][0]': 'b', 'a[0][1]': 'c', 'a[1]': 'd', e: '2' });
- st.end();
- });
-
- t.test('uses original key when depth = false', function (st) {
- st.deepEqual(qs.parse('a[0]=b&a[1]=c', { depth: false }), { 'a[0]': 'b', 'a[1]': 'c' });
- st.deepEqual(qs.parse('a[0][0]=b&a[0][1]=c&a[1]=d&e=2', { depth: false }), { 'a[0][0]': 'b', 'a[0][1]': 'c', 'a[1]': 'd', e: '2' });
- st.end();
- });
-
- t.deepEqual(qs.parse('a=b&a=c'), { a: ['b', 'c'] }, 'parses a simple array');
-
- t.test('parses an explicit array', function (st) {
- st.deepEqual(qs.parse('a[]=b'), { a: ['b'] });
- st.deepEqual(qs.parse('a[]=b&a[]=c'), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[]=b&a[]=c&a[]=d'), { a: ['b', 'c', 'd'] });
- st.end();
- });
-
- t.test('parses a mix of simple and explicit arrays', function (st) {
- st.deepEqual(qs.parse('a=b&a[]=c'), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[]=b&a=c'), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[0]=b&a=c'), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a=b&a[0]=c'), { a: ['b', 'c'] });
-
- st.deepEqual(qs.parse('a[1]=b&a=c', { arrayLimit: 20 }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[]=b&a=c', { arrayLimit: 0 }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[]=b&a=c'), { a: ['b', 'c'] });
-
- st.deepEqual(qs.parse('a=b&a[1]=c', { arrayLimit: 20 }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a=b&a[]=c', { arrayLimit: 0 }), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a=b&a[]=c'), { a: ['b', 'c'] });
-
- st.end();
- });
-
- t.test('parses a nested array', function (st) {
- st.deepEqual(qs.parse('a[b][]=c&a[b][]=d'), { a: { b: ['c', 'd'] } });
- st.deepEqual(qs.parse('a[>=]=25'), { a: { '>=': '25' } });
- st.end();
- });
-
- t.test('allows to specify array indices', function (st) {
- st.deepEqual(qs.parse('a[1]=c&a[0]=b&a[2]=d'), { a: ['b', 'c', 'd'] });
- st.deepEqual(qs.parse('a[1]=c&a[0]=b'), { a: ['b', 'c'] });
- st.deepEqual(qs.parse('a[1]=c', { arrayLimit: 20 }), { a: ['c'] });
- st.deepEqual(qs.parse('a[1]=c', { arrayLimit: 0 }), { a: { 1: 'c' } });
- st.deepEqual(qs.parse('a[1]=c'), { a: ['c'] });
- st.end();
- });
-
- t.test('limits specific array indices to arrayLimit', function (st) {
- st.deepEqual(qs.parse('a[20]=a', { arrayLimit: 20 }), { a: ['a'] });
- st.deepEqual(qs.parse('a[21]=a', { arrayLimit: 20 }), { a: { 21: 'a' } });
-
- st.deepEqual(qs.parse('a[20]=a'), { a: ['a'] });
- st.deepEqual(qs.parse('a[21]=a'), { a: { 21: 'a' } });
- st.end();
- });
-
- t.deepEqual(qs.parse('a[12b]=c'), { a: { '12b': 'c' } }, 'supports keys that begin with a number');
-
- t.test('supports encoded = signs', function (st) {
- st.deepEqual(qs.parse('he%3Dllo=th%3Dere'), { 'he=llo': 'th=ere' });
- st.end();
- });
-
- t.test('is ok with url encoded strings', function (st) {
- st.deepEqual(qs.parse('a[b%20c]=d'), { a: { 'b c': 'd' } });
- st.deepEqual(qs.parse('a[b]=c%20d'), { a: { b: 'c d' } });
- st.end();
- });
-
- t.test('allows brackets in the value', function (st) {
- st.deepEqual(qs.parse('pets=["tobi"]'), { pets: '["tobi"]' });
- st.deepEqual(qs.parse('operators=[">=", "<="]'), { operators: '[">=", "<="]' });
- st.end();
- });
-
- t.test('allows empty values', function (st) {
- st.deepEqual(qs.parse(''), {});
- st.deepEqual(qs.parse(null), {});
- st.deepEqual(qs.parse(undefined), {});
- st.end();
- });
-
- t.test('transforms arrays to objects', function (st) {
- st.deepEqual(qs.parse('foo[0]=bar&foo[bad]=baz'), { foo: { 0: 'bar', bad: 'baz' } });
- st.deepEqual(qs.parse('foo[bad]=baz&foo[0]=bar'), { foo: { bad: 'baz', 0: 'bar' } });
- st.deepEqual(qs.parse('foo[bad]=baz&foo[]=bar'), { foo: { bad: 'baz', 0: 'bar' } });
- st.deepEqual(qs.parse('foo[]=bar&foo[bad]=baz'), { foo: { 0: 'bar', bad: 'baz' } });
- st.deepEqual(qs.parse('foo[bad]=baz&foo[]=bar&foo[]=foo'), { foo: { bad: 'baz', 0: 'bar', 1: 'foo' } });
- st.deepEqual(qs.parse('foo[0][a]=a&foo[0][b]=b&foo[1][a]=aa&foo[1][b]=bb'), { foo: [{ a: 'a', b: 'b' }, { a: 'aa', b: 'bb' }] });
-
- st.deepEqual(qs.parse('a[]=b&a[t]=u&a[hasOwnProperty]=c', { allowPrototypes: false }), { a: { 0: 'b', t: 'u' } });
- st.deepEqual(qs.parse('a[]=b&a[t]=u&a[hasOwnProperty]=c', { allowPrototypes: true }), { a: { 0: 'b', t: 'u', hasOwnProperty: 'c' } });
- st.deepEqual(qs.parse('a[]=b&a[hasOwnProperty]=c&a[x]=y', { allowPrototypes: false }), { a: { 0: 'b', x: 'y' } });
- st.deepEqual(qs.parse('a[]=b&a[hasOwnProperty]=c&a[x]=y', { allowPrototypes: true }), { a: { 0: 'b', hasOwnProperty: 'c', x: 'y' } });
- st.end();
- });
-
- t.test('transforms arrays to objects (dot notation)', function (st) {
- st.deepEqual(qs.parse('foo[0].baz=bar&fool.bad=baz', { allowDots: true }), { foo: [{ baz: 'bar' }], fool: { bad: 'baz' } });
- st.deepEqual(qs.parse('foo[0].baz=bar&fool.bad.boo=baz', { allowDots: true }), { foo: [{ baz: 'bar' }], fool: { bad: { boo: 'baz' } } });
- st.deepEqual(qs.parse('foo[0][0].baz=bar&fool.bad=baz', { allowDots: true }), { foo: [[{ baz: 'bar' }]], fool: { bad: 'baz' } });
- st.deepEqual(qs.parse('foo[0].baz[0]=15&foo[0].bar=2', { allowDots: true }), { foo: [{ baz: ['15'], bar: '2' }] });
- st.deepEqual(qs.parse('foo[0].baz[0]=15&foo[0].baz[1]=16&foo[0].bar=2', { allowDots: true }), { foo: [{ baz: ['15', '16'], bar: '2' }] });
- st.deepEqual(qs.parse('foo.bad=baz&foo[0]=bar', { allowDots: true }), { foo: { bad: 'baz', 0: 'bar' } });
- st.deepEqual(qs.parse('foo.bad=baz&foo[]=bar', { allowDots: true }), { foo: { bad: 'baz', 0: 'bar' } });
- st.deepEqual(qs.parse('foo[]=bar&foo.bad=baz', { allowDots: true }), { foo: { 0: 'bar', bad: 'baz' } });
- st.deepEqual(qs.parse('foo.bad=baz&foo[]=bar&foo[]=foo', { allowDots: true }), { foo: { bad: 'baz', 0: 'bar', 1: 'foo' } });
- st.deepEqual(qs.parse('foo[0].a=a&foo[0].b=b&foo[1].a=aa&foo[1].b=bb', { allowDots: true }), { foo: [{ a: 'a', b: 'b' }, { a: 'aa', b: 'bb' }] });
- st.end();
- });
-
- t.test('correctly prunes undefined values when converting an array to an object', function (st) {
- st.deepEqual(qs.parse('a[2]=b&a[99999999]=c'), { a: { 2: 'b', 99999999: 'c' } });
- st.end();
- });
-
- t.test('supports malformed uri characters', function (st) {
- st.deepEqual(qs.parse('{%:%}', { strictNullHandling: true }), { '{%:%}': null });
- st.deepEqual(qs.parse('{%:%}='), { '{%:%}': '' });
- st.deepEqual(qs.parse('foo=%:%}'), { foo: '%:%}' });
- st.end();
- });
-
- t.test('doesn\'t produce empty keys', function (st) {
- st.deepEqual(qs.parse('_r=1&'), { _r: '1' });
- st.end();
- });
-
- t.test('cannot access Object prototype', function (st) {
- qs.parse('constructor[prototype][bad]=bad');
- qs.parse('bad[constructor][prototype][bad]=bad');
- st.equal(typeof Object.prototype.bad, 'undefined');
- st.end();
- });
-
- t.test('parses arrays of objects', function (st) {
- st.deepEqual(qs.parse('a[][b]=c'), { a: [{ b: 'c' }] });
- st.deepEqual(qs.parse('a[0][b]=c'), { a: [{ b: 'c' }] });
- st.end();
- });
-
- t.test('allows for empty strings in arrays', function (st) {
- st.deepEqual(qs.parse('a[]=b&a[]=&a[]=c'), { a: ['b', '', 'c'] });
-
- st.deepEqual(
- qs.parse('a[0]=b&a[1]&a[2]=c&a[19]=', { strictNullHandling: true, arrayLimit: 20 }),
- { a: ['b', null, 'c', ''] },
- 'with arrayLimit 20 + array indices: null then empty string works'
- );
- st.deepEqual(
- qs.parse('a[]=b&a[]&a[]=c&a[]=', { strictNullHandling: true, arrayLimit: 0 }),
- { a: ['b', null, 'c', ''] },
- 'with arrayLimit 0 + array brackets: null then empty string works'
- );
-
- st.deepEqual(
- qs.parse('a[0]=b&a[1]=&a[2]=c&a[19]', { strictNullHandling: true, arrayLimit: 20 }),
- { a: ['b', '', 'c', null] },
- 'with arrayLimit 20 + array indices: empty string then null works'
- );
- st.deepEqual(
- qs.parse('a[]=b&a[]=&a[]=c&a[]', { strictNullHandling: true, arrayLimit: 0 }),
- { a: ['b', '', 'c', null] },
- 'with arrayLimit 0 + array brackets: empty string then null works'
- );
-
- st.deepEqual(
- qs.parse('a[]=&a[]=b&a[]=c'),
- { a: ['', 'b', 'c'] },
- 'array brackets: empty strings work'
- );
- st.end();
- });
-
- t.test('compacts sparse arrays', function (st) {
- st.deepEqual(qs.parse('a[10]=1&a[2]=2', { arrayLimit: 20 }), { a: ['2', '1'] });
- st.deepEqual(qs.parse('a[1][b][2][c]=1', { arrayLimit: 20 }), { a: [{ b: [{ c: '1' }] }] });
- st.deepEqual(qs.parse('a[1][2][3][c]=1', { arrayLimit: 20 }), { a: [[[{ c: '1' }]]] });
- st.deepEqual(qs.parse('a[1][2][3][c][1]=1', { arrayLimit: 20 }), { a: [[[{ c: ['1'] }]]] });
- st.end();
- });
-
- t.test('parses sparse arrays', function (st) {
- /* eslint no-sparse-arrays: 0 */
- st.deepEqual(qs.parse('a[4]=1&a[1]=2', { allowSparse: true }), { a: [, '2', , , '1'] });
- st.deepEqual(qs.parse('a[1][b][2][c]=1', { allowSparse: true }), { a: [, { b: [, , { c: '1' }] }] });
- st.deepEqual(qs.parse('a[1][2][3][c]=1', { allowSparse: true }), { a: [, [, , [, , , { c: '1' }]]] });
- st.deepEqual(qs.parse('a[1][2][3][c][1]=1', { allowSparse: true }), { a: [, [, , [, , , { c: [, '1'] }]]] });
- st.end();
- });
-
- t.test('parses semi-parsed strings', function (st) {
- st.deepEqual(qs.parse({ 'a[b]': 'c' }), { a: { b: 'c' } });
- st.deepEqual(qs.parse({ 'a[b]': 'c', 'a[d]': 'e' }), { a: { b: 'c', d: 'e' } });
- st.end();
- });
-
- t.test('parses buffers correctly', function (st) {
- var b = SaferBuffer.from('test');
- st.deepEqual(qs.parse({ a: b }), { a: b });
- st.end();
- });
-
- t.test('parses jquery-param strings', function (st) {
- // readable = 'filter[0][]=int1&filter[0][]==&filter[0][]=77&filter[]=and&filter[2][]=int2&filter[2][]==&filter[2][]=8'
- var encoded = 'filter%5B0%5D%5B%5D=int1&filter%5B0%5D%5B%5D=%3D&filter%5B0%5D%5B%5D=77&filter%5B%5D=and&filter%5B2%5D%5B%5D=int2&filter%5B2%5D%5B%5D=%3D&filter%5B2%5D%5B%5D=8';
- var expected = { filter: [['int1', '=', '77'], 'and', ['int2', '=', '8']] };
- st.deepEqual(qs.parse(encoded), expected);
- st.end();
- });
-
- t.test('continues parsing when no parent is found', function (st) {
- st.deepEqual(qs.parse('[]=&a=b'), { 0: '', a: 'b' });
- st.deepEqual(qs.parse('[]&a=b', { strictNullHandling: true }), { 0: null, a: 'b' });
- st.deepEqual(qs.parse('[foo]=bar'), { foo: 'bar' });
- st.end();
- });
-
- t.test('does not error when parsing a very long array', function (st) {
- var str = 'a[]=a';
- while (Buffer.byteLength(str) < 128 * 1024) {
- str = str + '&' + str;
- }
-
- st.doesNotThrow(function () {
- qs.parse(str);
- });
-
- st.end();
- });
-
- t.test('should not throw when a native prototype has an enumerable property', function (st) {
- Object.prototype.crash = '';
- Array.prototype.crash = '';
- st.doesNotThrow(qs.parse.bind(null, 'a=b'));
- st.deepEqual(qs.parse('a=b'), { a: 'b' });
- st.doesNotThrow(qs.parse.bind(null, 'a[][b]=c'));
- st.deepEqual(qs.parse('a[][b]=c'), { a: [{ b: 'c' }] });
- delete Object.prototype.crash;
- delete Array.prototype.crash;
- st.end();
- });
-
- t.test('parses a string with an alternative string delimiter', function (st) {
- st.deepEqual(qs.parse('a=b;c=d', { delimiter: ';' }), { a: 'b', c: 'd' });
- st.end();
- });
-
- t.test('parses a string with an alternative RegExp delimiter', function (st) {
- st.deepEqual(qs.parse('a=b; c=d', { delimiter: /[;,] */ }), { a: 'b', c: 'd' });
- st.end();
- });
-
- t.test('does not use non-splittable objects as delimiters', function (st) {
- st.deepEqual(qs.parse('a=b&c=d', { delimiter: true }), { a: 'b', c: 'd' });
- st.end();
- });
-
- t.test('allows overriding parameter limit', function (st) {
- st.deepEqual(qs.parse('a=b&c=d', { parameterLimit: 1 }), { a: 'b' });
- st.end();
- });
-
- t.test('allows setting the parameter limit to Infinity', function (st) {
- st.deepEqual(qs.parse('a=b&c=d', { parameterLimit: Infinity }), { a: 'b', c: 'd' });
- st.end();
- });
-
- t.test('allows overriding array limit', function (st) {
- st.deepEqual(qs.parse('a[0]=b', { arrayLimit: -1 }), { a: { 0: 'b' } });
- st.deepEqual(qs.parse('a[-1]=b', { arrayLimit: -1 }), { a: { '-1': 'b' } });
- st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayLimit: 0 }), { a: { 0: 'b', 1: 'c' } });
- st.end();
- });
-
- t.test('allows disabling array parsing', function (st) {
- var indices = qs.parse('a[0]=b&a[1]=c', { parseArrays: false });
- st.deepEqual(indices, { a: { 0: 'b', 1: 'c' } });
- st.equal(Array.isArray(indices.a), false, 'parseArrays:false, indices case is not an array');
-
- var emptyBrackets = qs.parse('a[]=b', { parseArrays: false });
- st.deepEqual(emptyBrackets, { a: { 0: 'b' } });
- st.equal(Array.isArray(emptyBrackets.a), false, 'parseArrays:false, empty brackets case is not an array');
-
- st.end();
- });
-
- t.test('allows for query string prefix', function (st) {
- st.deepEqual(qs.parse('?foo=bar', { ignoreQueryPrefix: true }), { foo: 'bar' });
- st.deepEqual(qs.parse('foo=bar', { ignoreQueryPrefix: true }), { foo: 'bar' });
- st.deepEqual(qs.parse('?foo=bar', { ignoreQueryPrefix: false }), { '?foo': 'bar' });
-
- st.end();
- });
-
- t.test('parses an object', function (st) {
- var input = {
- 'user[name]': { 'pop[bob]': 3 },
- 'user[email]': null
- };
-
- var expected = {
- user: {
- name: { 'pop[bob]': 3 },
- email: null
- }
- };
-
- var result = qs.parse(input);
-
- st.deepEqual(result, expected);
- st.end();
- });
-
- t.test('parses string with comma as array divider', function (st) {
- st.deepEqual(qs.parse('foo=bar,tee', { comma: true }), { foo: ['bar', 'tee'] });
- st.deepEqual(qs.parse('foo[bar]=coffee,tee', { comma: true }), { foo: { bar: ['coffee', 'tee'] } });
- st.deepEqual(qs.parse('foo=', { comma: true }), { foo: '' });
- st.deepEqual(qs.parse('foo', { comma: true }), { foo: '' });
- st.deepEqual(qs.parse('foo', { comma: true, strictNullHandling: true }), { foo: null });
-
- // test cases inversed from from stringify tests
- st.deepEqual(qs.parse('a[0]=c'), { a: ['c'] });
- st.deepEqual(qs.parse('a[]=c'), { a: ['c'] });
- st.deepEqual(qs.parse('a[]=c', { comma: true }), { a: ['c'] });
-
- st.deepEqual(qs.parse('a[0]=c&a[1]=d'), { a: ['c', 'd'] });
- st.deepEqual(qs.parse('a[]=c&a[]=d'), { a: ['c', 'd'] });
- st.deepEqual(qs.parse('a=c,d', { comma: true }), { a: ['c', 'd'] });
-
- st.end();
- });
-
- t.test('parses values with comma as array divider', function (st) {
- st.deepEqual(qs.parse({ foo: 'bar,tee' }, { comma: false }), { foo: 'bar,tee' });
- st.deepEqual(qs.parse({ foo: 'bar,tee' }, { comma: true }), { foo: ['bar', 'tee'] });
- st.end();
- });
-
- t.test('use number decoder, parses string that has one number with comma option enabled', function (st) {
- var decoder = function (str, defaultDecoder, charset, type) {
- if (!isNaN(Number(str))) {
- return parseFloat(str);
- }
- return defaultDecoder(str, defaultDecoder, charset, type);
- };
-
- st.deepEqual(qs.parse('foo=1', { comma: true, decoder: decoder }), { foo: 1 });
- st.deepEqual(qs.parse('foo=0', { comma: true, decoder: decoder }), { foo: 0 });
-
- st.end();
- });
-
- t.test('parses brackets holds array of arrays when having two parts of strings with comma as array divider', function (st) {
- st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=4,5,6', { comma: true }), { foo: [['1', '2', '3'], ['4', '5', '6']] });
- st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=', { comma: true }), { foo: [['1', '2', '3'], ''] });
- st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=,', { comma: true }), { foo: [['1', '2', '3'], ['', '']] });
- st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=a', { comma: true }), { foo: [['1', '2', '3'], 'a'] });
-
- st.end();
- });
-
- t.test('parses comma delimited array while having percent-encoded comma treated as normal text', function (st) {
- st.deepEqual(qs.parse('foo=a%2Cb', { comma: true }), { foo: 'a,b' });
- st.deepEqual(qs.parse('foo=a%2C%20b,d', { comma: true }), { foo: ['a, b', 'd'] });
- st.deepEqual(qs.parse('foo=a%2C%20b,c%2C%20d', { comma: true }), { foo: ['a, b', 'c, d'] });
-
- st.end();
- });
-
- t.test('parses an object in dot notation', function (st) {
- var input = {
- 'user.name': { 'pop[bob]': 3 },
- 'user.email.': null
- };
-
- var expected = {
- user: {
- name: { 'pop[bob]': 3 },
- email: null
- }
- };
-
- var result = qs.parse(input, { allowDots: true });
-
- st.deepEqual(result, expected);
- st.end();
- });
-
- t.test('parses an object and not child values', function (st) {
- var input = {
- 'user[name]': { 'pop[bob]': { test: 3 } },
- 'user[email]': null
- };
-
- var expected = {
- user: {
- name: { 'pop[bob]': { test: 3 } },
- email: null
- }
- };
-
- var result = qs.parse(input);
-
- st.deepEqual(result, expected);
- st.end();
- });
-
- t.test('does not blow up when Buffer global is missing', function (st) {
- var tempBuffer = global.Buffer;
- delete global.Buffer;
- var result = qs.parse('a=b&c=d');
- global.Buffer = tempBuffer;
- st.deepEqual(result, { a: 'b', c: 'd' });
- st.end();
- });
-
- t.test('does not crash when parsing circular references', function (st) {
- var a = {};
- a.b = a;
-
- var parsed;
-
- st.doesNotThrow(function () {
- parsed = qs.parse({ 'foo[bar]': 'baz', 'foo[baz]': a });
- });
-
- st.equal('foo' in parsed, true, 'parsed has "foo" property');
- st.equal('bar' in parsed.foo, true);
- st.equal('baz' in parsed.foo, true);
- st.equal(parsed.foo.bar, 'baz');
- st.deepEqual(parsed.foo.baz, a);
- st.end();
- });
-
- t.test('does not crash when parsing deep objects', function (st) {
- var parsed;
- var str = 'foo';
-
- for (var i = 0; i < 5000; i++) {
- str += '[p]';
- }
-
- str += '=bar';
-
- st.doesNotThrow(function () {
- parsed = qs.parse(str, { depth: 5000 });
- });
-
- st.equal('foo' in parsed, true, 'parsed has "foo" property');
-
- var depth = 0;
- var ref = parsed.foo;
- while ((ref = ref.p)) {
- depth += 1;
- }
-
- st.equal(depth, 5000, 'parsed is 5000 properties deep');
-
- st.end();
- });
-
- t.test('parses null objects correctly', { skip: !Object.create }, function (st) {
- var a = Object.create(null);
- a.b = 'c';
-
- st.deepEqual(qs.parse(a), { b: 'c' });
- var result = qs.parse({ a: a });
- st.equal('a' in result, true, 'result has "a" property');
- st.deepEqual(result.a, a);
- st.end();
- });
-
- t.test('parses dates correctly', function (st) {
- var now = new Date();
- st.deepEqual(qs.parse({ a: now }), { a: now });
- st.end();
- });
-
- t.test('parses regular expressions correctly', function (st) {
- var re = /^test$/;
- st.deepEqual(qs.parse({ a: re }), { a: re });
- st.end();
- });
-
- t.test('does not allow overwriting prototype properties', function (st) {
- st.deepEqual(qs.parse('a[hasOwnProperty]=b', { allowPrototypes: false }), {});
- st.deepEqual(qs.parse('hasOwnProperty=b', { allowPrototypes: false }), {});
-
- st.deepEqual(
- qs.parse('toString', { allowPrototypes: false }),
- {},
- 'bare "toString" results in {}'
- );
-
- st.end();
- });
-
- t.test('can allow overwriting prototype properties', function (st) {
- st.deepEqual(qs.parse('a[hasOwnProperty]=b', { allowPrototypes: true }), { a: { hasOwnProperty: 'b' } });
- st.deepEqual(qs.parse('hasOwnProperty=b', { allowPrototypes: true }), { hasOwnProperty: 'b' });
-
- st.deepEqual(
- qs.parse('toString', { allowPrototypes: true }),
- { toString: '' },
- 'bare "toString" results in { toString: "" }'
- );
-
- st.end();
- });
-
- t.test('params starting with a closing bracket', function (st) {
- st.deepEqual(qs.parse(']=toString'), { ']': 'toString' });
- st.deepEqual(qs.parse(']]=toString'), { ']]': 'toString' });
- st.deepEqual(qs.parse(']hello]=toString'), { ']hello]': 'toString' });
- st.end();
- });
-
- t.test('params starting with a starting bracket', function (st) {
- st.deepEqual(qs.parse('[=toString'), { '[': 'toString' });
- st.deepEqual(qs.parse('[[=toString'), { '[[': 'toString' });
- st.deepEqual(qs.parse('[hello[=toString'), { '[hello[': 'toString' });
- st.end();
- });
-
- t.test('add keys to objects', function (st) {
- st.deepEqual(
- qs.parse('a[b]=c&a=d'),
- { a: { b: 'c', d: true } },
- 'can add keys to objects'
- );
-
- st.deepEqual(
- qs.parse('a[b]=c&a=toString'),
- { a: { b: 'c' } },
- 'can not overwrite prototype'
- );
-
- st.deepEqual(
- qs.parse('a[b]=c&a=toString', { allowPrototypes: true }),
- { a: { b: 'c', toString: true } },
- 'can overwrite prototype with allowPrototypes true'
- );
-
- st.deepEqual(
- qs.parse('a[b]=c&a=toString', { plainObjects: true }),
- { __proto__: null, a: { __proto__: null, b: 'c', toString: true } },
- 'can overwrite prototype with plainObjects true'
- );
-
- st.end();
- });
-
- t.test('dunder proto is ignored', function (st) {
- var payload = 'categories[__proto__]=login&categories[__proto__]&categories[length]=42';
- var result = qs.parse(payload, { allowPrototypes: true });
-
- st.deepEqual(
- result,
- {
- categories: {
- length: '42'
- }
- },
- 'silent [[Prototype]] payload'
- );
-
- var plainResult = qs.parse(payload, { allowPrototypes: true, plainObjects: true });
-
- st.deepEqual(
- plainResult,
- {
- __proto__: null,
- categories: {
- __proto__: null,
- length: '42'
- }
- },
- 'silent [[Prototype]] payload: plain objects'
- );
-
- var query = qs.parse('categories[__proto__]=cats&categories[__proto__]=dogs&categories[some][json]=toInject', { allowPrototypes: true });
-
- st.notOk(Array.isArray(query.categories), 'is not an array');
- st.notOk(query.categories instanceof Array, 'is not instanceof an array');
- st.deepEqual(query.categories, { some: { json: 'toInject' } });
- st.equal(JSON.stringify(query.categories), '{"some":{"json":"toInject"}}', 'stringifies as a non-array');
-
- st.deepEqual(
- qs.parse('foo[__proto__][hidden]=value&foo[bar]=stuffs', { allowPrototypes: true }),
- {
- foo: {
- bar: 'stuffs'
- }
- },
- 'hidden values'
- );
-
- st.deepEqual(
- qs.parse('foo[__proto__][hidden]=value&foo[bar]=stuffs', { allowPrototypes: true, plainObjects: true }),
- {
- __proto__: null,
- foo: {
- __proto__: null,
- bar: 'stuffs'
- }
- },
- 'hidden values: plain objects'
- );
-
- st.end();
- });
-
- t.test('can return null objects', { skip: !Object.create }, function (st) {
- var expected = Object.create(null);
- expected.a = Object.create(null);
- expected.a.b = 'c';
- expected.a.hasOwnProperty = 'd';
- st.deepEqual(qs.parse('a[b]=c&a[hasOwnProperty]=d', { plainObjects: true }), expected);
- st.deepEqual(qs.parse(null, { plainObjects: true }), Object.create(null));
- var expectedArray = Object.create(null);
- expectedArray.a = Object.create(null);
- expectedArray.a[0] = 'b';
- expectedArray.a.c = 'd';
- st.deepEqual(qs.parse('a[]=b&a[c]=d', { plainObjects: true }), expectedArray);
- st.end();
- });
-
- t.test('can parse with custom encoding', function (st) {
- st.deepEqual(qs.parse('%8c%a7=%91%e5%8d%e3%95%7b', {
- decoder: function (str) {
- var reg = /%([0-9A-F]{2})/ig;
- var result = [];
- var parts = reg.exec(str);
- while (parts) {
- result.push(parseInt(parts[1], 16));
- parts = reg.exec(str);
- }
- return String(iconv.decode(SaferBuffer.from(result), 'shift_jis'));
- }
- }), { 県: '大阪府' });
- st.end();
- });
-
- t.test('receives the default decoder as a second argument', function (st) {
- st.plan(1);
- qs.parse('a', {
- decoder: function (str, defaultDecoder) {
- st.equal(defaultDecoder, utils.decode);
- }
- });
- st.end();
- });
-
- t.test('throws error with wrong decoder', function (st) {
- st['throws'](function () {
- qs.parse({}, { decoder: 'string' });
- }, new TypeError('Decoder has to be a function.'));
- st.end();
- });
-
- t.test('does not mutate the options argument', function (st) {
- var options = {};
- qs.parse('a[b]=true', options);
- st.deepEqual(options, {});
- st.end();
- });
-
- t.test('throws if an invalid charset is specified', function (st) {
- st['throws'](function () {
- qs.parse('a=b', { charset: 'foobar' });
- }, new TypeError('The charset option must be either utf-8, iso-8859-1, or undefined'));
- st.end();
- });
-
- t.test('parses an iso-8859-1 string if asked to', function (st) {
- st.deepEqual(qs.parse('%A2=%BD', { charset: 'iso-8859-1' }), { '¢': '½' });
- st.end();
- });
-
- var urlEncodedCheckmarkInUtf8 = '%E2%9C%93';
- var urlEncodedOSlashInUtf8 = '%C3%B8';
- var urlEncodedNumCheckmark = '%26%2310003%3B';
- var urlEncodedNumSmiley = '%26%239786%3B';
-
- t.test('prefers an utf-8 charset specified by the utf8 sentinel to a default charset of iso-8859-1', function (st) {
- st.deepEqual(qs.parse('utf8=' + urlEncodedCheckmarkInUtf8 + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true, charset: 'iso-8859-1' }), { ø: 'ø' });
- st.end();
- });
-
- t.test('prefers an iso-8859-1 charset specified by the utf8 sentinel to a default charset of utf-8', function (st) {
- st.deepEqual(qs.parse('utf8=' + urlEncodedNumCheckmark + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true, charset: 'utf-8' }), { 'ø': 'ø' });
- st.end();
- });
-
- t.test('does not require the utf8 sentinel to be defined before the parameters whose decoding it affects', function (st) {
- st.deepEqual(qs.parse('a=' + urlEncodedOSlashInUtf8 + '&utf8=' + urlEncodedNumCheckmark, { charsetSentinel: true, charset: 'utf-8' }), { a: 'ø' });
- st.end();
- });
-
- t.test('should ignore an utf8 sentinel with an unknown value', function (st) {
- st.deepEqual(qs.parse('utf8=foo&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true, charset: 'utf-8' }), { ø: 'ø' });
- st.end();
- });
-
- t.test('uses the utf8 sentinel to switch to utf-8 when no default charset is given', function (st) {
- st.deepEqual(qs.parse('utf8=' + urlEncodedCheckmarkInUtf8 + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true }), { ø: 'ø' });
- st.end();
- });
-
- t.test('uses the utf8 sentinel to switch to iso-8859-1 when no default charset is given', function (st) {
- st.deepEqual(qs.parse('utf8=' + urlEncodedNumCheckmark + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true }), { 'ø': 'ø' });
- st.end();
- });
-
- t.test('interprets numeric entities in iso-8859-1 when `interpretNumericEntities`', function (st) {
- st.deepEqual(qs.parse('foo=' + urlEncodedNumSmiley, { charset: 'iso-8859-1', interpretNumericEntities: true }), { foo: '☺' });
- st.end();
- });
-
- t.test('handles a custom decoder returning `null`, in the `iso-8859-1` charset, when `interpretNumericEntities`', function (st) {
- st.deepEqual(qs.parse('foo=&bar=' + urlEncodedNumSmiley, {
- charset: 'iso-8859-1',
- decoder: function (str, defaultDecoder, charset) {
- return str ? defaultDecoder(str, defaultDecoder, charset) : null;
- },
- interpretNumericEntities: true
- }), { foo: null, bar: '☺' });
- st.end();
- });
-
- t.test('does not interpret numeric entities in iso-8859-1 when `interpretNumericEntities` is absent', function (st) {
- st.deepEqual(qs.parse('foo=' + urlEncodedNumSmiley, { charset: 'iso-8859-1' }), { foo: '☺' });
- st.end();
- });
-
- t.test('does not interpret numeric entities when the charset is utf-8, even when `interpretNumericEntities`', function (st) {
- st.deepEqual(qs.parse('foo=' + urlEncodedNumSmiley, { charset: 'utf-8', interpretNumericEntities: true }), { foo: '☺' });
- st.end();
- });
-
- t.test('does not interpret %uXXXX syntax in iso-8859-1 mode', function (st) {
- st.deepEqual(qs.parse('%u263A=%u263A', { charset: 'iso-8859-1' }), { '%u263A': '%u263A' });
- st.end();
- });
-
- t.test('allows for decoding keys and values differently', function (st) {
- var decoder = function (str, defaultDecoder, charset, type) {
- if (type === 'key') {
- return defaultDecoder(str, defaultDecoder, charset, type).toLowerCase();
- }
- if (type === 'value') {
- return defaultDecoder(str, defaultDecoder, charset, type).toUpperCase();
- }
- throw 'this should never happen! type: ' + type;
- };
-
- st.deepEqual(qs.parse('KeY=vAlUe', { decoder: decoder }), { key: 'VALUE' });
- st.end();
- });
-
- t.end();
-});
diff --git a/spaces/finny24/FinnyAiVoice/README.md b/spaces/finny24/FinnyAiVoice/README.md
deleted file mode 100644
index cf7f46c462586df393704e82ba1a3e6be65361cd..0000000000000000000000000000000000000000
--- a/spaces/finny24/FinnyAiVoice/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: FinnyAiVoice
-emoji: 🐨
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/firasggg/andite-anything-v4.0/app.py b/spaces/firasggg/andite-anything-v4.0/app.py
deleted file mode 100644
index 47a2051db6dadeea03edf70d62694fd3e5e88ba7..0000000000000000000000000000000000000000
--- a/spaces/firasggg/andite-anything-v4.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/andite/anything-v4.0").launch()
\ No newline at end of file
diff --git a/spaces/fjenett/GPT-JT/app.py b/spaces/fjenett/GPT-JT/app.py
deleted file mode 100644
index 67ecab812da006f09eac594a92e71853964c61b0..0000000000000000000000000000000000000000
--- a/spaces/fjenett/GPT-JT/app.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import gradio as gr
-import requests
-import time
-from ast import literal_eval
-from datetime import datetime
-
-
-def to_md(text):
- # return text.replace("\n", "
")
- return text.replace("\n", "
")
-
-
-def infer(
- prompt,
- model_name,
- max_new_tokens=10,
- temperature=0.1,
- top_p=1.0,
- top_k=40,
- num_completions=1,
- seed=42,
- stop="\n"
-):
- model_name_map = {
- "GPT-JT-6B-v1": "Together-gpt-JT-6B-v1",
- }
- max_new_tokens = int(max_new_tokens)
- num_completions = int(num_completions)
- temperature = float(temperature)
- top_p = float(top_p)
- top_k = int(top_k)
- stop = stop.split(";")
- seed = seed
-
- assert 1 <= max_new_tokens <= 256
- assert 1 <= num_completions <= 5
- assert 0.0 <= temperature <= 10.0
- assert 0.0 <= top_p <= 1.0
- assert 1 <= top_k <= 1000
-
- if temperature == 0.0:
- temperature = 0.01
- if prompt == "":
- prompt = " "
- my_post_dict = {
- "model": "Together-gpt-JT-6B-v1",
- "prompt": prompt,
- "top_p": top_p,
- "top_k": top_k,
- "temperature": temperature,
- "max_tokens": max_new_tokens,
- "stop": stop,
- }
- print(f"send: {datetime.now()}")
- response = requests.get("https://staging.together.xyz/api/inference", params=my_post_dict).json()
- generated_text = response['output']['choices'][0]['text']
- print(f"recv: {datetime.now()}")
-
- for stop_word in stop:
- if stop_word != '' and stop_word in generated_text:
- generated_text = generated_text[:generated_text.find(stop_word)]
-
- return generated_text
-
-
-examples = [
- [
- # Question Answering
- '''Please answer the following question:
-
-Question: What is the capital of Canada?
-Answer: Ottawa
-
-Question: What is the currency of Switzerland?
-Answer: Swiss franc
-
-Question: In which country is Wisconsin located?
-Answer:''', "GPT-JT-6B-v1", 5, 0.0, 1.0, 40],
- [
- # Sentiment Analysis
- '''Label the tweets as either "positive", "negative", "mixed", or "neutral":
-
-Tweet: I can say that there isn't anything I would change.
-Label: positive
-
-Tweet: I'm not sure about this.
-Label: neutral
-
-Tweet: I liked some parts but I didn't like other parts.
-Label: mixed
-
-Tweet: I think the background image could have been better.
-Label: negative
-
-Tweet: I really like it.
-Label:''', "GPT-JT-6B-v1", 2, 0.0, 1.0, 40],
- [
- # Topic Classification
- '''Given a news article, classify its topic.
-Possible labels: 1. World 2. Sports 3. Business 4. Sci/Tech
-
-Article: A nearby star thought to harbor comets and asteroids now appears to be home to planets, too.
-Label: Sci/Tech
-
-Article: Soaring crude prices plus worries about the economy and the outlook for earnings are expected to hang over the stock market next week during the depth of the summer doldrums.
-Label: Business
-
-Article: Murtagh a stickler for success Northeastern field hockey coach Cheryl Murtagh doesn't want the glare of the spotlight that shines on her to detract from a team that has been the America East champion for the past three years and has been to the NCAA tournament 13 times.
-Label:''', "GPT-JT-6B-v1", 5, 0.0, 1.0, 40],
- [
- # Paraphrasing
- '''Paraphrase the given sentence into a different sentence.
-
-Input: Can you recommend some upscale restaurants in New York?
-Output: What upscale restaurants do you recommend in New York?
-
-Input: What are the famous places we should not miss in Paris?
-Output: Recommend some of the best places to visit in Paris?
-
-Input: Could you recommend some hotels that have cheap price in Zurich?
-Output:''', "GPT-JT-6B-v1", 20, 0.8, 1.0, 40],
- [
- # Text Summarization
- '''Given a review from Amazon's food products, the task is to generate a short summary of the given review in the input.
-
-Input: I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than most.
-Output: Good Quality Dog Food
-
-Input: Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as 'Jumbo'.
-Output: Not as Advertised
-
-Input: My toddler loves this game to a point where he asks for it. That's a big thing for me. Secondly, no glitching unlike one of their competitors (PlayShifu). Any tech I don’t have to reach out to support for help is a good tech for me. I even enjoy some of the games and activities in this. Overall, this is a product that shows that the developers took their time and made sure people would not be asking for refund. I’ve become bias regarding this product and honestly I look forward to buying more of this company’s stuff. Please keep up the great work.
-Output:''', "GPT-JT-6B-v1", 10, 0.0, 1.0, 40],
- [
- # Word Sense Disambiguation
- '''Identify which sense of a word is meant in a given context.
-
-Context: The river overflowed the bank.
-Word: bank
-Sense: river bank
-
-Context: A mouse takes much more room than a trackball.
-Word: mouse
-Sense: computer mouse
-
-Context: The bank will not be accepting cash on Saturdays.
-Word: bank
-Sense: commercial (finance) banks
-
-Context: Bill killed the project
-Word: kill
-Sense:''', "GPT-JT-6B-v1", 10, 0.0, 1.0, 40],
- [
- # Natural Language Interface
- '''Given a pair of sentences, choose whether the two sentences agree (entailment)/disagree (contradiction) with each other.
-Possible labels: 1. entailment 2. contradiction
-
-Sentence 1: The skier was on the edge of the ramp. Sentence 2: The skier was dressed in winter clothes.
-Label: entailment
-
-Sentence 1: The boy skated down the staircase railing. Sentence 2: The boy is a newbie skater.
-Label: contradiction
-
-Sentence 1: Two middle-aged people stand by a golf hole. Sentence 2: A couple riding in a golf cart.
-Label:''', "GPT-JT-6B-v1", 2, 0.0, 1.0, 40]
-]
-
-
-def main():
- iface = gr.Interface(
- fn=infer,
- inputs=[
- gr.Textbox(lines=20), # prompt
- gr.Dropdown(["GPT-JT-6B-v1"]), # model_name
- gr.Slider(1, 256, value=200), # max_tokens
- gr.Slider(0.0, 10.0, value=0.1), # temperature
- gr.Slider(0.0, 1.0, value=0.9), # top_p
- gr.Slider(0, 1000, value=40) # top_k
- ],
- outputs=gr.Textbox(lines=7),
- examples=examples,
- title="GPT-JT Demo (Gradio port)",
- description='''This is a port of the Streamlit [GPT-JT demo](https://huggingface.co/spaces/togethercomputer/GPT-JT) by [Together](https://huggingface.co/togethercomputer) to Gradio'''
- )
-
- iface.launch(debug=True)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/flynster/FeinbergQuizNotes/question_generation/data_collator.py b/spaces/flynster/FeinbergQuizNotes/question_generation/data_collator.py
deleted file mode 100644
index 3e738cc37a99a739c3ece33e6feef31d8b22ab2c..0000000000000000000000000000000000000000
--- a/spaces/flynster/FeinbergQuizNotes/question_generation/data_collator.py
+++ /dev/null
@@ -1,83 +0,0 @@
-from typing import Dict, List, Optional
-
-import torch
-
-
-def trim_batch(
- input_ids, pad_token_id, attention_mask=None,
-):
- """Remove columns that are populated exclusively by pad_token_id"""
- keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
- if attention_mask is None:
- return input_ids[:, keep_column_mask]
- else:
- return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
-
-
-# prepares lm_labels from target_ids, returns examples with keys as expected by the forward method
-# this is necessacry because the trainer directly passes this dict as arguments to the model
-# so make sure the keys match the parameter names of the forward method
-class T2TDataCollator():
- def __init__(self, tokenizer, model_type="t5", mode='training', using_tpu=False):
- self.tokenizer = tokenizer
- self.model_type = model_type
- self.mode = mode
- self.using_tpu = using_tpu
-
- def __call__(self, batch: List) -> Dict[str, torch.Tensor]:
- """
- Take a list of samples from a Dataset and collate them into a batch.
- Returns:
- A dictionary of tensors
- """
- input_ids = torch.stack([example['source_ids'] for example in batch])
- target_ids = torch.stack([example['target_ids'] for example in batch])
- attention_mask = torch.stack([example['attention_mask'] for example in batch])
-
- pad_token_id = self.tokenizer.pad_token_id
-
- # don't trim on tpu, for some reason trimming leads to slower training on TPU
- if not self.using_tpu:
- input_ids, attention_mask = trim_batch(input_ids, pad_token_id, attention_mask=attention_mask)
- target_ids = trim_batch(target_ids, pad_token_id)
-
- if self.model_type == "t5":
- lm_labels = target_ids.clone()
- decoder_input_ids = self._shift_right_t5(lm_labels)
- if self.mode == 'training':
- lm_labels[lm_labels[:, :] == pad_token_id] = -100
- else:
- decoder_input_ids = target_ids[:, :-1].contiguous()
- lm_labels = target_ids[:, 1:].clone()
- if self.mode == 'training':
- lm_labels[target_ids[:, 1:] == pad_token_id] = -100
-
- params = {
- "input_ids": input_ids,
- "attention_mask": attention_mask,
- "labels": lm_labels,
- "decoder_input_ids": decoder_input_ids
- }
-
- return params
-
- def _shift_right_t5(self, input_ids):
- decoder_start_token_id = self.tokenizer.pad_token_id
- pad_token_id = self.tokenizer.pad_token_id
-
- assert (
- decoder_start_token_id is not None
- ), "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id. See T5 docs for more information"
-
- # shift inputs to the right
- shifted_input_ids = input_ids.new_zeros(input_ids.shape)
- shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
- shifted_input_ids[..., 0] = decoder_start_token_id
-
- assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
- # replace possible -100 values in labels by `pad_token_id`
- shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
- assert torch.all(shifted_input_ids >= 0).item(), "Verify that `labels` has only positive values and -100"
-
- return shifted_input_ids
\ No newline at end of file
diff --git a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_mapillary_vistas_panoptic.py b/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_mapillary_vistas_panoptic.py
deleted file mode 100644
index 9a83bdaaedb052e628f633a721047c59040a8a57..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_mapillary_vistas_panoptic.py
+++ /dev/null
@@ -1,183 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Qihang Yu from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_mapillary_vistas_panoptic.py
-import json
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.utils.file_io import PathManager
-
-from . import openseg_classes
-
-MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = openseg_classes.get_mapillary_vistas_categories_with_prompt_eng()
-
-def load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
- """
- Args:
- image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
- gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
- json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
- Returns:
- list[dict]: a list of dicts in Detectron2 standard format. (See
- `Using Custom Datasets `_ )
- """
-
- def _convert_category_id(segment_info, meta):
- if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
- segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
- segment_info["category_id"]
- ]
- segment_info["isthing"] = True
- else:
- segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
- segment_info["category_id"]
- ]
- segment_info["isthing"] = False
- return segment_info
-
- with PathManager.open(json_file) as f:
- json_info = json.load(f)
-
- ret = []
- for ann in json_info["annotations"]:
- image_id = ann["image_id"]
- # TODO: currently we assume image and label has the same filename but
- # different extension, and images have extension ".jpg" for COCO. Need
- # to make image extension a user-provided argument if we extend this
- # function to support other COCO-like datasets.
- image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
- label_file = os.path.join(gt_dir, ann["file_name"])
- sem_label_file = os.path.join(semseg_dir, ann["file_name"])
- segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
- ret.append(
- {
- "file_name": image_file,
- "image_id": image_id,
- "pan_seg_file_name": label_file,
- "sem_seg_file_name": sem_label_file,
- "segments_info": segments_info,
- }
- )
- assert len(ret), f"No images found in {image_dir}!"
- assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
- assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
- assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
- return ret
-
-
-def register_mapillary_vistas_panoptic(
- name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None
-):
- """
- Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
- The dictionaries in this registered dataset follows detectron2's standard format.
- Hence it's called "standard".
- Args:
- name (str): the name that identifies a dataset,
- e.g. "ade20k_panoptic_train"
- metadata (dict): extra metadata associated with this dataset.
- image_root (str): directory which contains all the images
- panoptic_root (str): directory which contains panoptic annotation images in COCO format
- panoptic_json (str): path to the json panoptic annotation file in COCO format
- sem_seg_root (none): not used, to be consistent with
- `register_coco_panoptic_separated`.
- instances_json (str): path to the json instance annotation file
- """
- panoptic_name = name
- DatasetCatalog.register(
- panoptic_name,
- lambda: load_mapillary_vistas_panoptic_json(
- panoptic_json, image_root, panoptic_root, semantic_root, metadata
- ),
- )
- MetadataCatalog.get(panoptic_name).set(
- panoptic_root=panoptic_root,
- image_root=image_root,
- panoptic_json=panoptic_json,
- json_file=instances_json,
- evaluator_type="mapillary_vistas_panoptic_seg",
- ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65
- label_divisor=1000,
- **metadata,
- )
-
-
-_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
- "openvocab_mapillary_vistas_panoptic_train": (
- "mapillary_vistas/training/images",
- "mapillary_vistas/training/panoptic",
- "mapillary_vistas/training/panoptic/panoptic_2018.json",
- "mapillary_vistas/training/labels",
- ),
- "openvocab_mapillary_vistas_panoptic_val": (
- "mapillary_vistas/validation/images",
- "mapillary_vistas/validation/panoptic",
- "mapillary_vistas/validation/panoptic/panoptic_2018.json",
- "mapillary_vistas/validation/labels",
- ),
-}
-
-
-def get_metadata():
- meta = {}
- # The following metadata maps contiguous id from [0, #thing categories +
- # #stuff categories) to their names and colors. We have to replica of the
- # same name and color under "thing_*" and "stuff_*" because the current
- # visualization function in D2 handles thing and class classes differently
- # due to some heuristic used in Panoptic FPN. We keep the same naming to
- # enable reusing existing visualization functions.
- thing_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
- thing_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
- stuff_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
- stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
-
- meta["thing_classes"] = thing_classes
- meta["thing_colors"] = thing_colors
- meta["stuff_classes"] = stuff_classes
- meta["stuff_colors"] = stuff_colors
-
- # Convert category id for training:
- # category id: like semantic segmentation, it is the class id for each
- # pixel. Since there are some classes not used in evaluation, the category
- # id is not always contiguous and thus we have two set of category ids:
- # - original category id: category id in the original dataset, mainly
- # used for evaluation.
- # - contiguous category id: [0, #classes), in order to train the linear
- # softmax classifier.
- thing_dataset_id_to_contiguous_id = {}
- stuff_dataset_id_to_contiguous_id = {}
-
- for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES):
- if cat["isthing"]:
- thing_dataset_id_to_contiguous_id[cat["id"]] = i
- # else:
- # stuff_dataset_id_to_contiguous_id[cat["id"]] = i
-
- # in order to use sem_seg evaluator
- stuff_dataset_id_to_contiguous_id[cat["id"]] = i
-
- meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
- meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
-
- return meta
-
-
-def register_all_mapillary_vistas_panoptic(root):
- metadata = get_metadata()
- for (
- prefix,
- (image_root, panoptic_root, panoptic_json, semantic_root),
- ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
- # The "standard" version of COCO panoptic segmentation dataset,
- # e.g. used by Panoptic-DeepLab
- register_mapillary_vistas_panoptic(
- prefix,
- metadata,
- os.path.join(root, image_root),
- os.path.join(root, panoptic_root),
- os.path.join(root, semantic_root),
- os.path.join(root, panoptic_json),
- )
-
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_mapillary_vistas_panoptic(_root)
\ No newline at end of file
diff --git a/spaces/fuxin123zz/ChuanhuChatGPT/assets/Kelpy-Codos.js b/spaces/fuxin123zz/ChuanhuChatGPT/assets/Kelpy-Codos.js
deleted file mode 100644
index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000
--- a/spaces/fuxin123zz/ChuanhuChatGPT/assets/Kelpy-Codos.js
+++ /dev/null
@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name Kelpy Codos
-// @namespace https://github.com/Keldos-Li/Kelpy-Codos
-// @version 1.0.5
-// @author Keldos; https://keldos.me/
-// @description Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially.
-// Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license GPL-3.0
-// @grant none
-// ==/UserScript==
-
-(function () {
- 'use strict';
-
- function addCopyButton(pre) {
- var code = pre.querySelector('code');
- if (!code) {
- return; // 如果没有找到 元素，则不添加按钮
- }
- var firstChild = code.firstChild;
- if (!firstChild) {
- return; // 如果 元素没有子节点，则不添加按钮
- }
- var button = document.createElement('button');
- button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
- button.style.position = 'relative';
- button.style.float = 'right';
- button.style.fontSize = '1em'; // 可选：调整按钮大小
- button.style.background = 'none'; // 可选：去掉背景颜色
- button.style.border = 'none'; // 可选：去掉边框
- button.style.cursor = 'pointer'; // 可选：显示指针样式
- button.addEventListener('click', function () {
- var range = document.createRange();
- range.selectNodeContents(code);
- range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
- var selection = window.getSelection();
- selection.removeAllRanges();
- selection.addRange(range);
-
- try {
- var success = document.execCommand('copy');
- if (success) {
- button.textContent = '\u2714';
- setTimeout(function () {
- button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
- }, 2000);
- } else {
- button.textContent = '\u2716';
- }
- } catch (e) {
- console.error(e);
- button.textContent = '\u2716';
- }
-
- selection.removeAllRanges();
- });
- code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
- }
-
- function handleNewElements(mutationsList, observer) {
- for (var mutation of mutationsList) {
- if (mutation.type === 'childList') {
- for (var node of mutation.addedNodes) {
- if (node.nodeName === 'PRE') {
- addCopyButton(node);
- }
- }
- }
- }
- }
-
- var observer = new MutationObserver(handleNewElements);
- observer.observe(document.documentElement, { childList: true, subtree: true });
-
- document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Yqcloud.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Yqcloud.py
deleted file mode 100644
index ad5c3a4326c68ceb7ee012fbf5bc072da72a7e40..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Yqcloud.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-import time
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-url = 'https://chat9.yqcloud.top/'
-model = [
- 'gpt-3.5-turbo',
-]
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, chatId: str, **kwargs):
-
- headers = {
- 'authority': 'api.aichatos.cloud',
- 'origin': 'https://chat9.yqcloud.top',
- 'referer': 'https://chat9.yqcloud.top/',
- 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
- }
-
- json_data = {
- 'prompt': str(messages),
- 'userId': f'#/chat/{chatId}',
- 'network': True,
- 'apikey': '',
- 'system': '',
- 'withoutContext': False,
- }
- response = requests.post('https://api.aichatos.cloud/api/generateStream',
- headers=headers, json=json_data, stream=True)
- for token in response.iter_content(chunk_size=2046):
- yield (token.decode('utf-8'))
-
-
-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/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/timm_universal.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/timm_universal.py
deleted file mode 100644
index 9c4f53b7f84b8365c7b7ba9ddac192a9a8ed4d8f..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/timm_universal.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import timm
-import torch.nn as nn
-
-
-class TimmUniversalEncoder(nn.Module):
- def __init__(self, name, pretrained=True, in_channels=3, depth=5, output_stride=32):
- super().__init__()
- kwargs = dict(
- in_chans=in_channels,
- features_only=True,
- output_stride=output_stride,
- pretrained=pretrained,
- out_indices=tuple(range(depth)),
- )
-
- # not all models support output stride argument, drop it by default
- if output_stride == 32:
- kwargs.pop("output_stride")
-
- self.model = timm.create_model(name, **kwargs)
-
- self._in_channels = in_channels
- self._out_channels = [in_channels,] + self.model.feature_info.channels()
- self._depth = depth
- self._output_stride = output_stride
-
- def forward(self, x):
- features = self.model(x)
- features = [x,] + features
- return features
-
- @property
- def out_channels(self):
- return self._out_channels
-
- @property
- def output_stride(self):
- return min(self._output_stride, 2 ** self._depth)
diff --git a/spaces/gligen/demo/gligen/ldm/modules/image_degradation/bsrgan_light.py b/spaces/gligen/demo/gligen/ldm/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index 9e1f823996bf559e9b015ea9aa2b3cd38dd13af1..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/ldm/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-
-def modcrop_np(img, sf):
- '''
- Args:
- img: numpy image, WxH or WxHxC
- sf: scale factor
- Return:
- cropped image
- '''
- w, h = img.shape[:2]
- im = np.copy(img)
- return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
- """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
- k_size = k.shape[0]
- # Calculate the big kernels size
- big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
- # Loop over the small kernel to fill the big one
- for r in range(k_size):
- for c in range(k_size):
- big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
- # Crop the edges of the big kernel to ignore very small values and increase run time of SR
- crop = k_size // 2
- cropped_big_k = big_k[crop:-crop, crop:-crop]
- # Normalize to 1
- return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
- """ generate an anisotropic Gaussian kernel
- Args:
- ksize : e.g., 15, kernel size
- theta : [0, pi], rotation angle range
- l1 : [0.1,50], scaling of eigenvalues
- l2 : [0.1,l1], scaling of eigenvalues
- If l1 = l2, will get an isotropic Gaussian kernel.
- Returns:
- k : kernel
- """
-
- v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
- V = np.array([[v[0], v[1]], [v[1], -v[0]]])
- D = np.array([[l1, 0], [0, l2]])
- Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
- k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
- return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
- center = size / 2.0 + 0.5
- k = np.zeros([size, size])
- for y in range(size):
- for x in range(size):
- cy = y - center + 1
- cx = x - center + 1
- k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
- k = k / np.sum(k)
- return k
-
-
-def shift_pixel(x, sf, upper_left=True):
- """shift pixel for super-resolution with different scale factors
- Args:
- x: WxHxC or WxH
- sf: scale factor
- upper_left: shift direction
- """
- h, w = x.shape[:2]
- shift = (sf - 1) * 0.5
- xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
- if upper_left:
- x1 = xv + shift
- y1 = yv + shift
- else:
- x1 = xv - shift
- y1 = yv - shift
-
- x1 = np.clip(x1, 0, w - 1)
- y1 = np.clip(y1, 0, h - 1)
-
- if x.ndim == 2:
- x = interp2d(xv, yv, x)(x1, y1)
- if x.ndim == 3:
- for i in range(x.shape[-1]):
- x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
- return x
-
-
-def blur(x, k):
- '''
- x: image, NxcxHxW
- k: kernel, Nx1xhxw
- '''
- n, c = x.shape[:2]
- p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
- x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
- k = k.repeat(1, c, 1, 1)
- k = k.view(-1, 1, k.shape[2], k.shape[3])
- x = x.view(1, -1, x.shape[2], x.shape[3])
- x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
- x = x.view(n, c, x.shape[2], x.shape[3])
-
- return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
- """"
- # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
- # Kai Zhang
- # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
- # max_var = 2.5 * sf
- """
- # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
- lambda_1 = min_var + np.random.rand() * (max_var - min_var)
- lambda_2 = min_var + np.random.rand() * (max_var - min_var)
- theta = np.random.rand() * np.pi # random theta
- noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
- # Set COV matrix using Lambdas and Theta
- LAMBDA = np.diag([lambda_1, lambda_2])
- Q = np.array([[np.cos(theta), -np.sin(theta)],
- [np.sin(theta), np.cos(theta)]])
- SIGMA = Q @ LAMBDA @ Q.T
- INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
- # Set expectation position (shifting kernel for aligned image)
- MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
- MU = MU[None, None, :, None]
-
- # Create meshgrid for Gaussian
- [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
- Z = np.stack([X, Y], 2)[:, :, :, None]
-
- # Calcualte Gaussian for every pixel of the kernel
- ZZ = Z - MU
- ZZ_t = ZZ.transpose(0, 1, 3, 2)
- raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
- # shift the kernel so it will be centered
- # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
- # Normalize the kernel and return
- # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
- kernel = raw_kernel / np.sum(raw_kernel)
- return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
- hsize = [hsize, hsize]
- siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
- std = sigma
- [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
- arg = -(x * x + y * y) / (2 * std * std)
- h = np.exp(arg)
- h[h < scipy.finfo(float).eps * h.max()] = 0
- sumh = h.sum()
- if sumh != 0:
- h = h / sumh
- return h
-
-
-def fspecial_laplacian(alpha):
- alpha = max([0, min([alpha, 1])])
- h1 = alpha / (alpha + 1)
- h2 = (1 - alpha) / (alpha + 1)
- h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
- h = np.array(h)
- return h
-
-
-def fspecial(filter_type, *args, **kwargs):
- '''
- python code from:
- https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
- '''
- if filter_type == 'gaussian':
- return fspecial_gaussian(*args, **kwargs)
- if filter_type == 'laplacian':
- return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
- '''
- Args:
- x: HxWxC image, [0, 1]
- sf: down-scale factor
- Return:
- bicubicly downsampled LR image
- '''
- x = util.imresize_np(x, scale=1 / sf)
- return x
-
-
-def srmd_degradation(x, k, sf=3):
- ''' blur + bicubic downsampling
- Args:
- x: HxWxC image, [0, 1]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- Reference:
- @inproceedings{zhang2018learning,
- title={Learning a single convolutional super-resolution network for multiple degradations},
- author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
- booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
- pages={3262--3271},
- year={2018}
- }
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
- x = bicubic_degradation(x, sf=sf)
- return x
-
-
-def dpsr_degradation(x, k, sf=3):
- ''' bicubic downsampling + blur
- Args:
- x: HxWxC image, [0, 1]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- Reference:
- @inproceedings{zhang2019deep,
- title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
- author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
- booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
- pages={1671--1681},
- year={2019}
- }
- '''
- x = bicubic_degradation(x, sf=sf)
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
- return x
-
-
-def classical_degradation(x, k, sf=3):
- ''' blur + downsampling
- Args:
- x: HxWxC image, [0, 1]/[0, 255]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
- # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
- st = 0
- return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
- """USM sharpening. borrowed from real-ESRGAN
- Input image: I; Blurry image: B.
- 1. K = I + weight * (I - B)
- 2. Mask = 1 if abs(I - B) > threshold, else: 0
- 3. Blur mask:
- 4. Out = Mask * K + (1 - Mask) * I
- Args:
- img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
- weight (float): Sharp weight. Default: 1.
- radius (float): Kernel size of Gaussian blur. Default: 50.
- threshold (int):
- """
- if radius % 2 == 0:
- radius += 1
- blur = cv2.GaussianBlur(img, (radius, radius), 0)
- residual = img - blur
- mask = np.abs(residual) * 255 > threshold
- mask = mask.astype('float32')
- soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
- K = img + weight * residual
- K = np.clip(K, 0, 1)
- return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
- wd2 = 4.0 + sf
- wd = 2.0 + 0.2 * sf
-
- wd2 = wd2/4
- wd = wd/4
-
- if random.random() < 0.5:
- l1 = wd2 * random.random()
- l2 = wd2 * random.random()
- k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
- else:
- k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
- img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
- return img
-
-
-def add_resize(img, sf=4):
- rnum = np.random.rand()
- if rnum > 0.8: # up
- sf1 = random.uniform(1, 2)
- elif rnum < 0.7: # down
- sf1 = random.uniform(0.5 / sf, 1)
- else:
- sf1 = 1.0
- img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
- img = np.clip(img, 0.0, 1.0)
-
- return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-# noise_level = random.randint(noise_level1, noise_level2)
-# rnum = np.random.rand()
-# if rnum > 0.6: # add color Gaussian noise
-# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-# elif rnum < 0.4: # add grayscale Gaussian noise
-# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-# else: # add noise
-# L = noise_level2 / 255.
-# D = np.diag(np.random.rand(3))
-# U = orth(np.random.rand(3, 3))
-# conv = np.dot(np.dot(np.transpose(U), D), U)
-# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-# img = np.clip(img, 0.0, 1.0)
-# return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
- noise_level = random.randint(noise_level1, noise_level2)
- rnum = np.random.rand()
- if rnum > 0.6: # add color Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
- elif rnum < 0.4: # add grayscale Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
- else: # add noise
- L = noise_level2 / 255.
- D = np.diag(np.random.rand(3))
- U = orth(np.random.rand(3, 3))
- conv = np.dot(np.dot(np.transpose(U), D), U)
- img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
- noise_level = random.randint(noise_level1, noise_level2)
- img = np.clip(img, 0.0, 1.0)
- rnum = random.random()
- if rnum > 0.6:
- img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
- elif rnum < 0.4:
- img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
- else:
- L = noise_level2 / 255.
- D = np.diag(np.random.rand(3))
- U = orth(np.random.rand(3, 3))
- conv = np.dot(np.dot(np.transpose(U), D), U)
- img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_Poisson_noise(img):
- img = np.clip((img * 255.0).round(), 0, 255) / 255.
- vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
- if random.random() < 0.5:
- img = np.random.poisson(img * vals).astype(np.float32) / vals
- else:
- img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
- img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
- noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
- img += noise_gray[:, :, np.newaxis]
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_JPEG_noise(img):
- quality_factor = random.randint(80, 95)
- img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
- result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
- img = cv2.imdecode(encimg, 1)
- img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
- return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
- h, w = lq.shape[:2]
- rnd_h = random.randint(0, h - lq_patchsize)
- rnd_w = random.randint(0, w - lq_patchsize)
- lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
- rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
- hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
- return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
- """
- This is the degradation model of BSRGAN from the paper
- "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
- ----------
- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
- sf: scale factor
- isp_model: camera ISP model
- Returns
- -------
- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
- hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
- """
- isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
- sf_ori = sf
-
- h1, w1 = img.shape[:2]
- img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
- h, w = img.shape[:2]
-
- if h < lq_patchsize * sf or w < lq_patchsize * sf:
- raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
- hq = img.copy()
-
- if sf == 4 and random.random() < scale2_prob: # downsample1
- if np.random.rand() < 0.5:
- img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- img = util.imresize_np(img, 1 / 2, True)
- img = np.clip(img, 0.0, 1.0)
- sf = 2
-
- shuffle_order = random.sample(range(7), 7)
- idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
- if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
- for i in shuffle_order:
-
- if i == 0:
- img = add_blur(img, sf=sf)
-
- elif i == 1:
- img = add_blur(img, sf=sf)
-
- elif i == 2:
- a, b = img.shape[1], img.shape[0]
- # downsample2
- if random.random() < 0.75:
- sf1 = random.uniform(1, 2 * sf)
- img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
- k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
- img = img[0::sf, 0::sf, ...] # nearest downsampling
- img = np.clip(img, 0.0, 1.0)
-
- elif i == 3:
- # downsample3
- img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
- img = np.clip(img, 0.0, 1.0)
-
- elif i == 4:
- # add Gaussian noise
- img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
- elif i == 5:
- # add JPEG noise
- if random.random() < jpeg_prob:
- img = add_JPEG_noise(img)
-
- elif i == 6:
- # add processed camera sensor noise
- if random.random() < isp_prob and isp_model is not None:
- with torch.no_grad():
- img, hq = isp_model.forward(img.copy(), hq)
-
- # add final JPEG compression noise
- img = add_JPEG_noise(img)
-
- # random crop
- img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
- return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
- """
- This is the degradation model of BSRGAN from the paper
- "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
- ----------
- sf: scale factor
- isp_model: camera ISP model
- Returns
- -------
- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
- hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
- """
- image = util.uint2single(image)
- isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
- sf_ori = sf
-
- h1, w1 = image.shape[:2]
- image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
- h, w = image.shape[:2]
-
- hq = image.copy()
-
- if sf == 4 and random.random() < scale2_prob: # downsample1
- if np.random.rand() < 0.5:
- image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- image = util.imresize_np(image, 1 / 2, True)
- image = np.clip(image, 0.0, 1.0)
- sf = 2
-
- shuffle_order = random.sample(range(7), 7)
- idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
- if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
- for i in shuffle_order:
-
- if i == 0:
- image = add_blur(image, sf=sf)
-
- # elif i == 1:
- # image = add_blur(image, sf=sf)
-
- if i == 0:
- pass
-
- elif i == 2:
- a, b = image.shape[1], image.shape[0]
- # downsample2
- if random.random() < 0.8:
- sf1 = random.uniform(1, 2 * sf)
- image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
- k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
- image = image[0::sf, 0::sf, ...] # nearest downsampling
-
- image = np.clip(image, 0.0, 1.0)
-
- elif i == 3:
- # downsample3
- image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
- image = np.clip(image, 0.0, 1.0)
-
- elif i == 4:
- # add Gaussian noise
- image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
- elif i == 5:
- # add JPEG noise
- if random.random() < jpeg_prob:
- image = add_JPEG_noise(image)
- #
- # elif i == 6:
- # # add processed camera sensor noise
- # if random.random() < isp_prob and isp_model is not None:
- # with torch.no_grad():
- # img, hq = isp_model.forward(img.copy(), hq)
-
- # add final JPEG compression noise
- image = add_JPEG_noise(image)
- image = util.single2uint(image)
- example = {"image": image}
- return example
-
-
-
-
-if __name__ == '__main__':
- print("hey")
- img = util.imread_uint('utils/test.png', 3)
- img = img[:448, :448]
- h = img.shape[0] // 4
- print("resizing to", h)
- sf = 4
- deg_fn = partial(degradation_bsrgan_variant, sf=sf)
- for i in range(20):
- print(i)
- img_hq = img
- img_lq = deg_fn(img)["image"]
- img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
- print(img_lq)
- img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
- print(img_lq.shape)
- print("bicubic", img_lq_bicubic.shape)
- print(img_hq.shape)
- lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
- (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
- util.imsave(img_concat, str(i) + '.png')
diff --git a/spaces/glt3953/app-audio_video_transcribe/README.md b/spaces/glt3953/app-audio_video_transcribe/README.md
deleted file mode 100644
index 32040d8b9a9eee85bc1be5f24b4b451f866833aa..0000000000000000000000000000000000000000
--- a/spaces/glt3953/app-audio_video_transcribe/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: App-audio Video Transcribe
-emoji: 🐢
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/glyszt/vt/vtoonify/model/raft/core/corr.py b/spaces/glyszt/vt/vtoonify/model/raft/core/corr.py
deleted file mode 100644
index 40214aa5e6f0392a732eacab9d9cb0cbfb4669f3..0000000000000000000000000000000000000000
--- a/spaces/glyszt/vt/vtoonify/model/raft/core/corr.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import torch
-import torch.nn.functional as F
-from model.raft.core.utils.utils import bilinear_sampler, coords_grid
-
-try:
- import alt_cuda_corr
-except:
- # alt_cuda_corr is not compiled
- pass
-
-
-class CorrBlock:
- def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
- self.num_levels = num_levels
- self.radius = radius
- self.corr_pyramid = []
-
- # all pairs correlation
- corr = CorrBlock.corr(fmap1, fmap2)
-
- batch, h1, w1, dim, h2, w2 = corr.shape
- corr = corr.reshape(batch*h1*w1, dim, h2, w2)
-
- self.corr_pyramid.append(corr)
- for i in range(self.num_levels-1):
- corr = F.avg_pool2d(corr, 2, stride=2)
- self.corr_pyramid.append(corr)
-
- def __call__(self, coords):
- r = self.radius
- coords = coords.permute(0, 2, 3, 1)
- batch, h1, w1, _ = coords.shape
-
- out_pyramid = []
- for i in range(self.num_levels):
- corr = self.corr_pyramid[i]
- dx = torch.linspace(-r, r, 2*r+1, device=coords.device)
- dy = torch.linspace(-r, r, 2*r+1, device=coords.device)
- delta = torch.stack(torch.meshgrid(dy, dx), axis=-1)
-
- centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i
- delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
- coords_lvl = centroid_lvl + delta_lvl
-
- corr = bilinear_sampler(corr, coords_lvl)
- corr = corr.view(batch, h1, w1, -1)
- out_pyramid.append(corr)
-
- out = torch.cat(out_pyramid, dim=-1)
- return out.permute(0, 3, 1, 2).contiguous().float()
-
- @staticmethod
- def corr(fmap1, fmap2):
- batch, dim, ht, wd = fmap1.shape
- fmap1 = fmap1.view(batch, dim, ht*wd)
- fmap2 = fmap2.view(batch, dim, ht*wd)
-
- corr = torch.matmul(fmap1.transpose(1,2), fmap2)
- corr = corr.view(batch, ht, wd, 1, ht, wd)
- return corr / torch.sqrt(torch.tensor(dim).float())
-
-
-class AlternateCorrBlock:
- def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
- self.num_levels = num_levels
- self.radius = radius
-
- self.pyramid = [(fmap1, fmap2)]
- for i in range(self.num_levels):
- fmap1 = F.avg_pool2d(fmap1, 2, stride=2)
- fmap2 = F.avg_pool2d(fmap2, 2, stride=2)
- self.pyramid.append((fmap1, fmap2))
-
- def __call__(self, coords):
- coords = coords.permute(0, 2, 3, 1)
- B, H, W, _ = coords.shape
- dim = self.pyramid[0][0].shape[1]
-
- corr_list = []
- for i in range(self.num_levels):
- r = self.radius
- fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1).contiguous()
- fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1).contiguous()
-
- coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous()
- corr, = alt_cuda_corr.forward(fmap1_i, fmap2_i, coords_i, r)
- corr_list.append(corr.squeeze(1))
-
- corr = torch.stack(corr_list, dim=1)
- corr = corr.reshape(B, -1, H, W)
- return corr / torch.sqrt(torch.tensor(dim).float())
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Avatar Movie Download In Telugu Hdk.md b/spaces/gotiQspiryo/whisper-ui/examples/Avatar Movie Download In Telugu Hdk.md
deleted file mode 100644
index 2a6cbabf81e6dc82de348264e1d40b9c6166c1d5..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Avatar Movie Download In Telugu Hdk.md
+++ /dev/null
@@ -1,50 +0,0 @@
-avatar movie download in telugu hdk
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-
-Playing
-
-In mid-2011, Gowda appeared in one of the lead roles in the film Kathaleeya, playing an ex-convict named John "Sundar" and a friend of the main protagonist Manish (played by Siddharth). He was mostly seen in the first half of the film, where he shares a romance with the female lead.
-
-In 2015, Gowda was part of the action thriller film Thondaradi Paarvai. The film, directed by Soundaryan, is a remake of 2004 Telugu film Thondaradi Ekkadiki, starring Anand and Priyanka. His role is one of the main protagonists in the film.
-
-Gowda also acted in Andaaya Vellum, one of the many films based on the life of Congress leader A. S. Azeez, played by Dasari Narayana Rao. His role was portrayed by Kavithalaya Krishnamachary, who was a contemporary of the late actor. The film was released in 2015.
-
-Controversies
-
-In a poll conducted by CNN-IBN for the viewers to select the sexiest Indian men in 2015, Gowda topped the list.
-
-Gowda was also one of the contestants of the first season of the dance reality TV show Dance Dance Indi-Part 1. The dance reality TV show was aired on SAB TV.
-
-After the Bollywood film The Dirty Picture became a major box office success in 2010, the makers of another film named "Welcome" decided to use the story of The Dirty Picture, but with a different protagonist and in a different language, without the permission of the producer of The Dirty Picture, who was also the producer of Welcome. The story of the film Welcome follows the same plot of The Dirty Picture, but with different characters and stories.
-
-Personal life
-
-Gowda is a Hindu. He married Vaishali, daughter of film actress Sindhu Menon, on 20 November 2015.
-
-Filmography
-
-References
-
-External links
-
-
-
-Category:Indian male film actors
-
-Category:Male actors in Kannada cinema
-
-Category:Living people
-
-Category:Male actors from Bangalore
-
-Category:Indian male child actors
-
-Category:Indian male television actors
-
-Category:21st-century Indian male actors
-
-Category:Year of birth missing (living people)
-
-Category:1990 birthsNymph()[i]; 4fefd39f24
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Fixed Deadpool (English) Part 2 Full Movie In Hindi.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Fixed Deadpool (English) Part 2 Full Movie In Hindi.md
deleted file mode 100644
index 424917c3e17a24c08be73ab025041af50d8165a8..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Fixed Deadpool (English) Part 2 Full Movie In Hindi.md
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Zip of Marianas Trench Astoria the Album Inspired by The Goonies and 80s Pop Culture.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Zip of Marianas Trench Astoria the Album Inspired by The Goonies and 80s Pop Culture.md
deleted file mode 100644
index 4fb04337f469161f52b8d838873e8cdbed2f362b..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Zip of Marianas Trench Astoria the Album Inspired by The Goonies and 80s Pop Culture.md
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Huawei Blower Unlocker V100252 !FREE! Download.md b/spaces/gotiQspiryo/whisper-ui/examples/Huawei Blower Unlocker V100252 !FREE! Download.md
deleted file mode 100644
index a4e6eca047cd527f55b75198dd91cbf1c0656e03..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Huawei Blower Unlocker V100252 !FREE! Download.md
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/It.reallifecam.com.rar.md b/spaces/gotiQspiryo/whisper-ui/examples/It.reallifecam.com.rar.md
deleted file mode 100644
index 66fb300d2c9e31f7f70f55ac56eba8c8da8aa5ee..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/It.reallifecam.com.rar.md
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Mehndi Hai Rachne Wali Video Songs Dailymotion Fotoshop Anwendungen.md b/spaces/gotiQspiryo/whisper-ui/examples/Mehndi Hai Rachne Wali Video Songs Dailymotion Fotoshop Anwendungen.md
deleted file mode 100644
index 7bbceec31fcc73849e7fde0cd2344c6933975175..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Mehndi Hai Rachne Wali Video Songs Dailymotion Fotoshop Anwendungen.md
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diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/README.md b/spaces/gradio/HuBERT/examples/speech_recognition/README.md
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index 17030bf0fd50bb843a508e13e97ed436eae33287..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/speech_recognition/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-### 2021 Update: We are merging this example into the [S2T framework](../speech_to_text), which supports more generic speech-to-text tasks (e.g. speech translation) and more flexible data processing pipelines. Please stay tuned.
-
-# Speech Recognition
-`examples/speech_recognition` is implementing ASR task in Fairseq, along with needed features, datasets, models and loss functions to train and infer model described in [Transformers with convolutional context for ASR (Abdelrahman Mohamed et al., 2019)](https://arxiv.org/abs/1904.11660).
-
-
-## Additional dependencies
-On top of main fairseq dependencies there are couple more additional requirements.
-
-1) Please follow the instructions to install [torchaudio](https://github.com/pytorch/audio). This is required to compute audio fbank features.
-2) [Sclite](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htm#sclite_name_0) is used to measure WER. Sclite can be downloaded and installed from source from sctk package [here](http://www.openslr.org/4/). Training and inference doesn't require Sclite dependency.
-3) [sentencepiece](https://github.com/google/sentencepiece) is required in order to create dataset with word-piece targets.
-
-## Preparing librispeech data
-```
-./examples/speech_recognition/datasets/prepare-librispeech.sh $DIR_TO_SAVE_RAW_DATA $DIR_FOR_PREPROCESSED_DATA
-```
-
-## Training librispeech data
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 80 --task speech_recognition --arch vggtransformer_2 --optimizer adadelta --lr 1.0 --adadelta-eps 1e-8 --adadelta-rho 0.95 --clip-norm 10.0 --max-tokens 5000 --log-format json --log-interval 1 --criterion cross_entropy_acc --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-`$SET` can be `test_clean` or `test_other`
-Any checkpoint in `$MODEL_PATH` can be selected. In this example we are working with `checkpoint_last.pt`
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --max-tokens 25000 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --beam 20 --results-path $RES_DIR --batch-size 40 --gen-subset $SET --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-```
-sclite -r ${RES_DIR}/ref.word-checkpoint_last.pt-${SET}.txt -h ${RES_DIR}/hypo.word-checkpoint_last.pt-${SET}.txt -i rm -o all stdout > $RES_REPORT
-```
-`Sum/Avg` row from first table of the report has WER
-
-## Using flashlight (previously called [wav2letter](https://github.com/facebookresearch/wav2letter)) components
-[flashlight](https://github.com/facebookresearch/flashlight) now has integration with fairseq. Currently this includes:
-
-* AutoSegmentationCriterion (ASG)
-* flashlight-style Conv/GLU model
-* flashlight's beam search decoder
-
-To use these, follow the instructions on [this page](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) to install python bindings.
-
-## Training librispeech data (flashlight style, Conv/GLU + ASG loss)
-Training command:
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 100 --task speech_recognition --arch w2l_conv_glu_enc --batch-size 4 --optimizer sgd --lr 0.3,0.8 --momentum 0.8 --clip-norm 0.2 --max-tokens 50000 --log-format json --log-interval 100 --num-workers 0 --sentence-avg --criterion asg_loss --asg-transitions-init 5 --max-replabel 2 --linseg-updates 8789 --user-dir examples/speech_recognition
-```
-
-Note that ASG loss currently doesn't do well with word-pieces. You should prepare a dataset with character targets by setting `nbpe=31` in `prepare-librispeech.sh`.
-
-## Inference for librispeech (flashlight decoder, n-gram LM)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder kenlm --kenlm-model $KENLM_MODEL_PATH --lexicon $LEXICON_PATH --beam 200 --beam-threshold 15 --lm-weight 1.5 --word-score 1.5 --sil-weight -0.3 --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
-
-`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a flashlight-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels):
-```
-doorbell D O 1 R B E L 1 ▁
-```
-For CTC inference with word-pieces, repetition labels are not used and the lexicon should have most common spellings for each word (one can use sentencepiece's `NBestEncodeAsPieces` for this):
-```
-doorbell ▁DOOR BE LL
-doorbell ▁DOOR B E LL
-doorbell ▁DO OR BE LL
-doorbell ▁DOOR B EL L
-doorbell ▁DOOR BE L L
-doorbell ▁DO OR B E LL
-doorbell ▁DOOR B E L L
-doorbell ▁DO OR B EL L
-doorbell ▁DO O R BE LL
-doorbell ▁DO OR BE L L
-```
-Lowercase vs. uppercase matters: the *word* should match the case of the n-gram language model (i.e. `$KENLM_MODEL_PATH`), while the *spelling* should match the case of the token dictionary (i.e. `$DIR_FOR_PREPROCESSED_DATA/dict.txt`).
-
-## Inference for librispeech (flashlight decoder, viterbi only)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder viterbi --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
diff --git a/spaces/gradio/theme-gallery/style.css b/spaces/gradio/theme-gallery/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/gradio/theme-gallery/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/gstdl/screener-saham-demo/Dockerfile b/spaces/gstdl/screener-saham-demo/Dockerfile
deleted file mode 100644
index d0af372b534bba5325d46e90f7a22aadf86cf94b..0000000000000000000000000000000000000000
--- a/spaces/gstdl/screener-saham-demo/Dockerfile
+++ /dev/null
@@ -1,48 +0,0 @@
-#Grab python=3.9-slim-buster image
-FROM python:3.9-slim-buster
-
-# Copy code to container
-COPY app /app
-
-# RUN apt-get update
-RUN apt-get update && apt-get install -y gcc bzip2 libc-dev g++ libc6 libc6-dev dpkg-dev build-essential wget libpython3-dev python3-dev python3-pip gcc sudo
-# openldap-dev libffi-dev jpeg-dev zlib-dev libmemcached-dev ibxml2 libxslt libxslt-dev
-
-# TA-Lib
-# RUN wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
-COPY ta-lib-0.4.0-src.tar.gz ta-lib-0.4.0-src.tar.gz
-RUN tar -xvzf ta-lib-0.4.0-src.tar.gz && \
- cd ta-lib/ && \
- ./configure --prefix=/usr --build=aarch64-unknown-linux-gnu && \
- sudo make && \
- sudo make install && \
- cd ..
-RUN rm -R ta-lib ta-lib-0.4.0-src.tar.gz
-
-# Copy requirements to container
-COPY app/requirements.txt /app/requirements.txt
-
-# Move working directory
-WORKDIR /app
-
-# Install python depencies
-RUN pip3 install --upgrade pip && pip3 install --no-cache-dir -q -r requirements.txt
-
-# Copy code to container
-COPY app /app
-
-# Give container write permission to sqlite db
-RUN sudo chmod 774 /app/dataset/ihsg.db
-
-# Update database
-RUN python3 update_database.py
-
-# Expose port 5000
-EXPOSE 5000
-
-# # Run the image as a non-root user
-# RUN adduser myuser
-# USER myuser
-
-# Run the app on port 5000
-CMD gunicorn --bind 0.0.0.0:5000 app:app
diff --git a/spaces/gyrojeff/YuzuMarker.FontDetection/dataset_filename_preprocess.py b/spaces/gyrojeff/YuzuMarker.FontDetection/dataset_filename_preprocess.py
deleted file mode 100644
index 04b39ce7290963cc2d84f55561b4191e16da0d22..0000000000000000000000000000000000000000
--- a/spaces/gyrojeff/YuzuMarker.FontDetection/dataset_filename_preprocess.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import os
-
-from font_dataset.utils import get_files
-
-directory_path = "dataset"
-
-files = get_files(directory_path)
-
-
-def clean_name(file_name: str):
- return (
- file_name.replace("&", "-")
- .replace("@", "-")
- .replace("#", "-")
- .replace("$", "-")
- )
-
-
-for file_name in files:
- new_file_name = clean_name(file_name)
- if new_file_name != file_name:
- os.rename(file_name, new_file_name)
- print(f"Renamed {file_name} to {new_file_name}")
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/torch_utils/ops/conv2d_resample.py b/spaces/gyugnsu/DragGan-Inversion/PTI/torch_utils/ops/conv2d_resample.py
deleted file mode 100644
index cd4750744c83354bab78704d4ef51ad1070fcc4a..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/torch_utils/ops/conv2d_resample.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""2D convolution with optional up/downsampling."""
-
-import torch
-
-from .. import misc
-from . import conv2d_gradfix
-from . import upfirdn2d
-from .upfirdn2d import _parse_padding
-from .upfirdn2d import _get_filter_size
-
-#----------------------------------------------------------------------------
-
-def _get_weight_shape(w):
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- shape = [int(sz) for sz in w.shape]
- misc.assert_shape(w, shape)
- return shape
-
-#----------------------------------------------------------------------------
-
-def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
- """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
- """
- out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-
- # Flip weight if requested.
- if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
- w = w.flip([2, 3])
-
- # Workaround performance pitfall in cuDNN 8.0.5, triggered when using
- # 1x1 kernel + memory_format=channels_last + less than 64 channels.
- if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose:
- if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
- if out_channels <= 4 and groups == 1:
- in_shape = x.shape
- x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1])
- x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
- else:
- x = x.to(memory_format=torch.contiguous_format)
- w = w.to(memory_format=torch.contiguous_format)
- x = conv2d_gradfix.conv2d(x, w, groups=groups)
- return x.to(memory_format=torch.channels_last)
-
- # Otherwise => execute using conv2d_gradfix.
- op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
- return op(x, w, stride=stride, padding=padding, groups=groups)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
- r"""2D convolution with optional up/downsampling.
-
- Padding is performed only once at the beginning, not between the operations.
-
- Args:
- x: Input tensor of shape
- `[batch_size, in_channels, in_height, in_width]`.
- w: Weight tensor of shape
- `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
- f: Low-pass filter for up/downsampling. Must be prepared beforehand by
- calling upfirdn2d.setup_filter(). None = identity (default).
- up: Integer upsampling factor (default: 1).
- down: Integer downsampling factor (default: 1).
- padding: Padding with respect to the upsampled image. Can be a single number
- or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
- (default: 0).
- groups: Split input channels into N groups (default: 1).
- flip_weight: False = convolution, True = correlation (default: True).
- flip_filter: False = convolution, True = correlation (default: False).
-
- Returns:
- Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
- """
- # Validate arguments.
- assert isinstance(x, torch.Tensor) and (x.ndim == 4)
- assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
- assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
- assert isinstance(up, int) and (up >= 1)
- assert isinstance(down, int) and (down >= 1)
- assert isinstance(groups, int) and (groups >= 1)
- out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
- fw, fh = _get_filter_size(f)
- px0, px1, py0, py1 = _parse_padding(padding)
-
- # Adjust padding to account for up/downsampling.
- if up > 1:
- px0 += (fw + up - 1) // 2
- px1 += (fw - up) // 2
- py0 += (fh + up - 1) // 2
- py1 += (fh - up) // 2
- if down > 1:
- px0 += (fw - down + 1) // 2
- px1 += (fw - down) // 2
- py0 += (fh - down + 1) // 2
- py1 += (fh - down) // 2
-
- # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
- if kw == 1 and kh == 1 and (down > 1 and up == 1):
- x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
- x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
- return x
-
- # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
- if kw == 1 and kh == 1 and (up > 1 and down == 1):
- x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
- x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
- return x
-
- # Fast path: downsampling only => use strided convolution.
- if down > 1 and up == 1:
- x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
- x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
- return x
-
- # Fast path: upsampling with optional downsampling => use transpose strided convolution.
- if up > 1:
- if groups == 1:
- w = w.transpose(0, 1)
- else:
- w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
- w = w.transpose(1, 2)
- w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
- px0 -= kw - 1
- px1 -= kw - up
- py0 -= kh - 1
- py1 -= kh - up
- pxt = max(min(-px0, -px1), 0)
- pyt = max(min(-py0, -py1), 0)
- x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
- x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
- if down > 1:
- x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
- return x
-
- # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
- if up == 1 and down == 1:
- if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
- return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
-
- # Fallback: Generic reference implementation.
- x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
- x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
- if down > 1:
- x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
- return x
-
-#----------------------------------------------------------------------------
diff --git a/spaces/h2oai/wave-tour/examples/plot_interval_range_transpose.py b/spaces/h2oai/wave-tour/examples/plot_interval_range_transpose.py
deleted file mode 100644
index a229e07c649bba1ea16d3682d9641b0d6945d459..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_interval_range_transpose.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Plot / Interval / Transpose
-# Make a bar #plot. #interval
-# ---
-from h2o_wave import site, data, ui
-
-page = site['/demo']
-
-page.add('example', ui.plot_card(
- box='1 1 4 5',
- title='Interval range transposed',
- data=data('profession max min', 5, rows=[
- ('medicine', 110000, 23000),
- ('fire fighting', 120000, 18000),
- ('pedagogy', 125000, 24000),
- ('psychology', 130000, 22500),
- ('computer science', 151000, 36000),
- ]),
- plot=ui.plot([ui.mark(type='interval', x='=max', x0='=min', y='=profession')]),
-))
-
-page.save()
diff --git a/spaces/h2oai/wave-tour/examples/plot_vegalite_flex.py b/spaces/h2oai/wave-tour/examples/plot_vegalite_flex.py
deleted file mode 100644
index 49082b696991b6bb2345160bb039c948128ba49d..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_vegalite_flex.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# Plot / Vega-lite / Flex
-# Place Vegalite plots in a flexible layout.
-# ---
-from h2o_wave import site, ui, data
-
-page = site['/demo']
-page.drop()
-
-page['meta'] = ui.meta_card(box='', layouts=[
- ui.layout(
- breakpoint='xs',
- width='100%',
- zones=[
- ui.zone('top', size='350px', direction=ui.ZoneDirection.ROW, zones=[
- ui.zone('top_left', size='75%'),
- ui.zone('top_right', size='1'),
- ]),
- ui.zone('bottom', size='350px', direction=ui.ZoneDirection.ROW, zones=[
- ui.zone('bottom_left', size='1'),
- ui.zone('bottom_center', size='2'),
- ui.zone('bottom_right', size='3'),
- ]),
- ]
- )
-])
-
-plot_spec = '''
-{
- "description": "A simple bar plot with embedded data.",
- "mark": "bar",
- "encoding": {
- "x": {"field": "a", "type": "ordinal"},
- "y": {"field": "b", "type": "quantitative"}
- }
-}
-'''
-
-plot_data = data(fields=["a", "b"], rows=[
- ["A", 28], ["B", 55], ["C", 43],
- ["D", 91], ["E", 81], ["F", 53],
- ["G", 19], ["H", 87], ["I", 52]
-], pack=True)
-
-page.add('top_left', ui.vega_card(
- box='top_left',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('top_right', ui.vega_card(
- box='top_right',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('bottom_left', ui.vega_card(
- box='bottom_left',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('bottom_center', ui.vega_card(
- box='bottom_center',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('bottom_right', ui.vega_card(
- box='bottom_right',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-
-page.save()
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/poolers.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/poolers.py
deleted file mode 100644
index b36a8ea3f1a0557621adb71e591105dcdb5f1f42..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/poolers.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from maskrcnn_benchmark.layers import ROIAlign, ROIAlignV2
-
-from .utils import cat
-
-
-class LevelMapper(object):
- """Determine which FPN level each RoI in a set of RoIs should map to based
- on the heuristic in the FPN paper.
- """
-
- def __init__(self, k_min, k_max, canonical_scale=224, canonical_level=4, eps=1e-6):
- """
- Arguments:
- k_min (int)
- k_max (int)
- canonical_scale (int)
- canonical_level (int)
- eps (float)
- """
- self.k_min = k_min
- self.k_max = k_max
- self.s0 = canonical_scale
- self.lvl0 = canonical_level
- self.eps = eps
-
- def __call__(self, boxlists):
- """
- Arguments:
- boxlists (list[BoxList])
- """
- # Compute level ids
- s = torch.sqrt(cat([boxlist.area() for boxlist in boxlists]))
-
- # Eqn.(1) in FPN paper
- target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0 + self.eps))
- target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max)
- return target_lvls.to(torch.int64) - self.k_min
-
-
-class Pooler(nn.Module):
- """
- Pooler for Detection with or without FPN.
- It currently hard-code ROIAlign in the implementation,
- but that can be made more generic later on.
- Also, the requirement of passing the scales is not strictly necessary, as they
- can be inferred from the size of the feature map / size of original image,
- which is available thanks to the BoxList.
- """
-
- def __init__(self, output_size, scales, sampling_ratio, use_v2=False):
- """
- Arguments:
- output_size (list[tuple[int]] or list[int]): output size for the pooled region
- scales (list[float]): scales for each Pooler
- sampling_ratio (int): sampling ratio for ROIAlign
- """
- super(Pooler, self).__init__()
- poolers = []
- for scale in scales:
- poolers.append(
- ROIAlignV2(
- output_size, spatial_scale=scale, sampling_ratio=sampling_ratio
- )
- if use_v2 else
- ROIAlign(
- output_size, spatial_scale=scale, sampling_ratio=sampling_ratio
- )
- )
- self.poolers = nn.ModuleList(poolers)
- self.output_size = output_size
- # get the levels in the feature map by leveraging the fact that the network always
- # downsamples by a factor of 2 at each level.
- lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item()
- lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item()
- self.map_levels = LevelMapper(lvl_min, lvl_max)
-
- def convert_to_roi_format(self, boxes):
- concat_boxes = cat([b.bbox for b in boxes], dim=0)
- device, dtype = concat_boxes.device, concat_boxes.dtype
- ids = cat(
- [
- torch.full((len(b), 1), i, dtype=dtype, device=device)
- for i, b in enumerate(boxes)
- ],
- dim=0,
- )
- rois = torch.cat([ids, concat_boxes], dim=1)
- return rois
-
- def forward(self, x, boxes):
- """
- Arguments:
- x (list[Tensor]): feature maps for each level
- boxes (list[BoxList]): boxes to be used to perform the pooling operation.
- Returns:
- result (Tensor)
- """
- num_levels = len(self.poolers)
- rois = self.convert_to_roi_format(boxes)
- if num_levels == 1:
- return self.poolers[0](x[0], rois)
-
- levels = self.map_levels(boxes)
-
- num_rois = len(rois)
- num_channels = x[0].shape[1]
- output_size = self.output_size[0]
-
- dtype, device = x[0].dtype, x[0].device
- result = torch.zeros(
- (num_rois, num_channels, output_size, output_size),
- dtype=dtype,
- device=device,
- )
- for level, (per_level_feature, pooler) in enumerate(zip(x, self.poolers)):
- idx_in_level = torch.nonzero(levels == level).squeeze(1)
- rois_per_level = rois[idx_in_level]
- result[idx_in_level] = pooler(per_level_feature, rois_per_level)
-
- return result
diff --git a/spaces/harshhpareek/bertscore/README.md b/spaces/harshhpareek/bertscore/README.md
deleted file mode 100644
index c93244844948592edc21cd0466a5b40bd9193994..0000000000000000000000000000000000000000
--- a/spaces/harshhpareek/bertscore/README.md
+++ /dev/null
@@ -1,136 +0,0 @@
----
-title: BERT Score
-emoji: 🤗
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.0.2
-app_file: app.py
-pinned: false
-tags:
-- evaluate
-- metric
-description: >-
- BERTScore leverages the pre-trained contextual embeddings from BERT and
- matches words in candidate and reference sentences by cosine similarity. It
- has been shown to correlate with human judgment on sentence-level and
- system-level evaluation. Moreover, BERTScore computes precision, recall, and
- F1 measure, which can be useful for evaluating different language generation
- tasks.
-
- See the project's README at https://github.com/Tiiiger/bert_score#readme for
- more information.
-duplicated_from: evaluate-metric/bertscore
----
-
-# Metric Card for BERT Score
-
-## Metric description
-
-BERTScore is an automatic evaluation metric for text generation that computes a similarity score for each token in the candidate sentence with each token in the reference sentence. It leverages the pre-trained contextual embeddings from [BERT](https://huggingface.co/bert-base-uncased) models and matches words in candidate and reference sentences by cosine similarity.
-
-Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks.
-
-## How to use
-
-BERTScore takes 3 mandatory arguments : `predictions` (a list of string of candidate sentences), `references` (a list of strings or list of list of strings of reference sentences) and either `lang` (a string of two letters indicating the language of the sentences, in [ISO 639-1 format](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)) or `model_type` (a string specififying which model to use, according to the BERT specification). The default behavior of the metric is to use the suggested model for the target language when one is specified, otherwise to use the `model_type` indicated.
-
-```python
-from evaluate import load
-bertscore = load("bertscore")
-predictions = ["hello there", "general kenobi"]
-references = ["hello there", "general kenobi"]
-results = bertscore.compute(predictions=predictions, references=references, lang="en")
-```
-
-BERTScore also accepts multiple optional arguments:
-
-
-`num_layers` (int): The layer of representation to use. The default is the number of layers tuned on WMT16 correlation data, which depends on the `model_type` used.
-
-`verbose` (bool): Turn on intermediate status update. The default value is `False`.
-
-`idf` (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
-
-`device` (str): On which the contextual embedding model will be allocated on. If this argument is `None`, the model lives on `cuda:0` if cuda is available.
-
-`nthreads` (int): Number of threads used for computation. The default value is `4`.
-
-`rescale_with_baseline` (bool): Rescale BERTScore with the pre-computed baseline. The default value is `False`.
-
-`batch_size` (int): BERTScore processing batch size, at least one of `model_type` or `lang`. `lang` needs to be specified when `rescale_with_baseline` is `True`.
-
-`baseline_path` (str): Customized baseline file.
-
-`use_fast_tokenizer` (bool): `use_fast` parameter passed to HF tokenizer. The default value is `False`.
-
-
-## Output values
-
-BERTScore outputs a dictionary with the following values:
-
-`precision`: The [precision](https://huggingface.co/metrics/precision) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
-
-`recall`: The [recall](https://huggingface.co/metrics/recall) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
-
-`f1`: The [F1 score](https://huggingface.co/metrics/f1) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
-
-`hashcode:` The hashcode of the library.
-
-
-### Values from popular papers
-The [original BERTScore paper](https://openreview.net/pdf?id=SkeHuCVFDr) reported average model selection accuracies (Hits@1) on WMT18 hybrid systems for different language pairs, which ranged from 0.004 for `en<->tr` to 0.824 for `en<->de`.
-
-For more recent model performance, see the [metric leaderboard](https://paperswithcode.com/paper/bertscore-evaluating-text-generation-with).
-
-## Examples
-
-Maximal values with the `distilbert-base-uncased` model:
-
-```python
-from evaluate import load
-bertscore = load("bertscore")
-predictions = ["hello world", "general kenobi"]
-references = ["hello world", "general kenobi"]
-results = bertscore.compute(predictions=predictions, references=references, model_type="distilbert-base-uncased")
-print(results)
-{'precision': [1.0, 1.0], 'recall': [1.0, 1.0], 'f1': [1.0, 1.0], 'hashcode': 'distilbert-base-uncased_L5_no-idf_version=0.3.10(hug_trans=4.10.3)'}
-```
-
-Partial match with the `bert-base-uncased` model:
-
-```python
-from evaluate import load
-bertscore = load("bertscore")
-predictions = ["hello world", "general kenobi"]
-references = ["goodnight moon", "the sun is shining"]
-results = bertscore.compute(predictions=predictions, references=references, model_type="distilbert-base-uncased")
-print(results)
-{'precision': [0.7380737066268921, 0.5584042072296143], 'recall': [0.7380737066268921, 0.5889028906822205], 'f1': [0.7380737066268921, 0.5732481479644775], 'hashcode': 'bert-base-uncased_L5_no-idf_version=0.3.10(hug_trans=4.10.3)'}
-```
-
-## Limitations and bias
-
-The [original BERTScore paper](https://openreview.net/pdf?id=SkeHuCVFDr) showed that BERTScore correlates well with human judgment on sentence-level and system-level evaluation, but this depends on the model and language pair selected.
-
-Furthermore, not all languages are supported by the metric -- see the [BERTScore supported language list](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) for more information.
-
-Finally, calculating the BERTScore metric involves downloading the BERT model that is used to compute the score-- the default model for `en`, `roberta-large`, takes over 1.4GB of storage space and downloading it can take a significant amount of time depending on the speed of your internet connection. If this is an issue, choose a smaller model; for instance `distilbert-base-uncased` is 268MB. A full list of compatible models can be found [here](https://docs.google.com/spreadsheets/d/1RKOVpselB98Nnh_EOC4A2BYn8_201tmPODpNWu4w7xI/edit#gid=0).
-
-
-## Citation
-
-```bibtex
-@inproceedings{bert-score,
- title={BERTScore: Evaluating Text Generation with BERT},
- author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
- booktitle={International Conference on Learning Representations},
- year={2020},
- url={https://openreview.net/forum?id=SkeHuCVFDr}
-}
-```
-
-## Further References
-
-- [BERTScore Project README](https://github.com/Tiiiger/bert_score#readme)
-- [BERTScore ICLR 2020 Poster Presentation](https://iclr.cc/virtual_2020/poster_SkeHuCVFDr.html)
diff --git a/spaces/hayas/rinna-youri-7b-chat/style.css b/spaces/hayas/rinna-youri-7b-chat/style.css
deleted file mode 100644
index 60878febc13db001635a52688abfe34d95e6c309..0000000000000000000000000000000000000000
--- a/spaces/hayas/rinna-youri-7b-chat/style.css
+++ /dev/null
@@ -1,16 +0,0 @@
-h1 {
- text-align: center;
-}
-
-#duplicate-button {
- margin: auto;
- color: white;
- background: #1565c0;
- border-radius: 100vh;
-}
-
-.contain {
- max-width: 900px;
- margin: auto;
- padding-top: 1.5rem;
-}
diff --git a/spaces/hirol/controlnetOverMask/image_process.py b/spaces/hirol/controlnetOverMask/image_process.py
deleted file mode 100644
index 679c9c572ee826f3081383c04ca511daad105b75..0000000000000000000000000000000000000000
--- a/spaces/hirol/controlnetOverMask/image_process.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# This is a sample Python script.
-
-# Press ⌃R to execute it or replace it with your code.
-# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
-
-import numpy as np
-import cv2
-import base64
-
-
-def cv2_base64(image):
- base64_str = cv2.imencode('.png',image)[1].tobytes()
- base64_str = base64.b64encode(base64_str)
- return base64_str.decode('utf-8')
-
-def base64_cv2(base64_str):
- imgString = base64.b64decode(base64_str)
- nparr = np.frombuffer(imgString,np.uint8)
- image = cv2.imdecode(nparr,cv2.IMREAD_COLOR)
- return image
-
-
-
-def image_pose_mask(imagepath : str):
- img = base64_cv2(imagepath)
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- ret, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
- contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- cnt = contours[-1]
- hull = cv2.convexHull(cnt)
- length = len(hull)
- if length > 4:
- for i in range(length):
- cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (255, 255, 255), 2)
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
- _, threshold = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
- mask = img.copy()
- kernel = np.ones((30, 30), dtype=np.uint8)
- dilated = cv2.dilate(threshold, kernel, 20)
- dilated = cv2.dilate(dilated, kernel, 20)
- contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
- valid = len(contours) > 0
-
- area = []
-
- for k in range(len(contours)):
- area.append(cv2.contourArea(contours[k]))
- max_idx = np.argmax(np.array(area))
-
- mask2 = cv2.drawContours(mask, contours, max_idx, (255, 255, 255), thickness=-1)
- img_base64 = cv2_base64(mask2)
-
- return img_base64
-
-
-
-def image_pose_mask_numpy(image):
- img = np.uint8(image)
- print("shape:" + str(img.shape))
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- ret, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
-
- contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- cnt = contours[-1]
- hull = cv2.convexHull(cnt)
- length = len(hull)
- if length > 4:
- for i in range(length):
- cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (255, 255, 255), 2)
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
- _, threshold = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
- mask = img.copy()
- kernel = np.ones((30, 30), dtype=np.uint8)
-
- dilated = cv2.dilate(threshold, kernel, 20)
-
- dilated = cv2.dilate(dilated, kernel, 20)
- contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
- valid = len(contours) > 0
-
- area = []
-
- for k in range(len(contours)):
- area.append(cv2.contourArea(contours[k]))
- max_idx = np.argmax(np.array(area))
-
- mask2 = cv2.drawContours(mask, contours, max_idx, (255, 255, 255), thickness=-1)
-
- img_base64 = cv2_base64(mask2)
-
- return img_base64
-
-def image_canny(imagepath:str):
-
- img = base64_cv2(imagepath)
-
- image_map = cv2.Canny(img, 100, 200)
-
- contours, hierarchy = cv2.findContours(image_map, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- cnt = contours[-1]
- hull = cv2.convexHull(cnt)
- length = len(hull)
- if length > 4:
- for i in range(length):
- cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (0, 0, 255), 2)
-
- img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
- contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
- valid = len(contours) > 0
- area = []
- for k in range(len(contours)):
- area.append(cv2.contourArea(contours[k]))
- max_idx = np.argmax(np.array(area))
-
- mask2 = cv2.drawContours(image_map, contours, max_idx, (0, 0, 255), thickness=3)
-
- img_base64 = cv2_base64(mask2)
-
- return img_base64
-
-# Press the green button in the gutter to run the script.
-if __name__ == '__main__':
- image_canny(imagepath='download (13).png')
-
diff --git a/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/constants.py b/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/constants.py
deleted file mode 100644
index b2a6100eed89c85f92d3f39e1f7fade62bcf39d2..0000000000000000000000000000000000000000
--- a/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/constants.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from pathlib import Path
-from PIL import Image
-
-# from dotenv import load_dotenv, find_dotenv # pip install python-dotenv==1.0.0
-
-from __version__ import __VERSION__ as APP_VERSION
-
-_SCRIPT_PATH = Path(__file__).absolute()
-PARENT_APP_DIR = _SCRIPT_PATH.parent
-TEMP_DIR = PARENT_APP_DIR / 'tempDir'
-ROOT_DIR = PARENT_APP_DIR.parent
-STATIC_DIR = ROOT_DIR / 'static'
-
-# _env_file_path = find_dotenv(str(CODE_DIR / '.env')) # Check if this path is correct
-# if _env_file_path:
-# load_dotenv(_env_file_path)
-
-ST_CONFIG = {
- "page_title": "NTT Data - Chat Q&A",
- # "page_icon": Image.open(STATIC_DIR / "mini_nttdata.jpg"),
-}
-
-OPERATING_MODE = "debug" # debug, preproduction, production
-
-REUSE_ANSWERS = False
-
-LOAD_INDEX_LOCALLY = False
-SAVE_INDEX_LOCALLY = False
-
-# x$ per 1000 tokens
-PRICES = {
- 'text-embedding-ada-002': 0.0004,
- 'text-davinci-003': 0.02,
- 'gpt-3': 0.002,
- 'gpt-4': 0.06, # 8K context
-}
-
-SOURCES_IDS = {
- # "Without source. Only chat": 4,
- "local files": 1,
- "urls": 3
-}
-
-TYPE_IDS = {
- "MSF Azure OpenAI Service": 1,
- "OpenAI": 2,
-}
-
-
-INDEX_IDS = {
- "FAISS": 1,
- "Pinecode": 2,
-}
diff --git a/spaces/huaiji3y/bingo-Public/src/components/chat-list.tsx b/spaces/huaiji3y/bingo-Public/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
- messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
- if (!messages.length) {
- return null
- }
-
- return (
-
- {messages.map((message, index) => (
-
-
- ))}
-
- )
-}
diff --git a/spaces/huggingface-tools/image-transformation/image_transformation.py b/spaces/huggingface-tools/image-transformation/image_transformation.py
deleted file mode 100644
index 66ffd0c8d18e53e1f9623641df3cfe9010f839cb..0000000000000000000000000000000000000000
--- a/spaces/huggingface-tools/image-transformation/image_transformation.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import torch
-
-from transformers.tools.base import Tool, get_default_device
-from transformers.utils import (
- is_accelerate_available,
- is_vision_available,
-)
-
-from diffusers import DiffusionPipeline
-
-
-IMAGE_TRANSFORMATION_DESCRIPTION = (
- "This is a tool that transforms an image according to a prompt. It takes two inputs: `image`, which should be "
- "the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the "
- "modified image."
-)
-
-
-class ImageTransformationTool(Tool):
- default_stable_diffusion_checkpoint = "timbrooks/instruct-pix2pix"
- description = IMAGE_TRANSFORMATION_DESCRIPTION
- inputs = ['image', 'text']
- outputs = ['image']
-
- def __init__(self, device=None, controlnet=None, stable_diffusion=None, **hub_kwargs) -> None:
- if not is_accelerate_available():
- raise ImportError("Accelerate should be installed in order to use tools.")
- if not is_vision_available():
- raise ImportError("Pillow should be installed in order to use the StableDiffusionTool.")
-
- super().__init__()
-
- self.stable_diffusion = self.default_stable_diffusion_checkpoint
-
- self.device = device
- self.hub_kwargs = hub_kwargs
-
- def setup(self):
- if self.device is None:
- self.device = get_default_device()
-
- self.pipeline = DiffusionPipeline.from_pretrained(self.stable_diffusion)
-
- self.pipeline.to(self.device)
- if self.device.type == "cuda":
- self.pipeline.to(torch_dtype=torch.float16)
-
- self.is_initialized = True
-
- def __call__(self, image, prompt):
- if not self.is_initialized:
- self.setup()
-
- negative_prompt = "low quality, bad quality, deformed, low resolution"
- added_prompt = " , highest quality, highly realistic, very high resolution"
-
- return self.pipeline(
- prompt + added_prompt,
- image,
- negative_prompt=negative_prompt,
- num_inference_steps=50,
- ).images[0]
\ No newline at end of file
diff --git "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/14_\360\237\223\232_Glossary.py" "b/spaces/huggingface/Model_Cards_Writing_Tool/pages/14_\360\237\223\232_Glossary.py"
deleted file mode 100644
index e118cdc5a1304e0113cad2f032c4f998d0c127de..0000000000000000000000000000000000000000
--- "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/14_\360\237\223\232_Glossary.py"
+++ /dev/null
@@ -1,24 +0,0 @@
-import streamlit as st
-from persist import persist, load_widget_state
-
-
-global variable_output
-
-def main():
- cs_body()
-
-
-
-def cs_body():
-
- st.markdown('# Glossary [optional]')
- st.text_area("Terms used in the model card that need to be clearly defined in order to be accessible across audiences go here.",height = 200, key=persist("Glossary"))
-
-
-
-
-
-
-if __name__ == '__main__':
- load_widget_state()
- main()
\ No newline at end of file
diff --git a/spaces/hysts/Hopenet/README.md b/spaces/hysts/Hopenet/README.md
deleted file mode 100644
index dcd57df74ca0a96e6a870a7f9bff001af6ce7f3e..0000000000000000000000000000000000000000
--- a/spaces/hysts/Hopenet/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hopenet
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/ilmhona/api/README.md b/spaces/ilmhona/api/README.md
deleted file mode 100644
index a1f674080a3b27df27041fe1ce5bd3bb4c955a74..0000000000000000000000000000000000000000
--- a/spaces/ilmhona/api/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Api
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/inamXcontru/PoeticTTS/Adobe Acrobat XI Pro 11.0.16 .rar [Extra Quality].md b/spaces/inamXcontru/PoeticTTS/Adobe Acrobat XI Pro 11.0.16 .rar [Extra Quality].md
deleted file mode 100644
index 0e42c17c4222a5baaf8cc0464f0abfa036227ecc..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Adobe Acrobat XI Pro 11.0.16 .rar [Extra Quality].md
+++ /dev/null
@@ -1,14 +0,0 @@
-Adobe Acrobat XI Pro 11.0.16 .rar
Download › https://gohhs.com/2uz4nI
-
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index 989a5656aef1cfd9b2a9472fda18ae779d94c63a..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Giveaway Tenorshare IAnyGo V1.1.0.0 For Win FREE UPD.md
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diff --git a/spaces/inreVtussa/clothingai/Examples/Arcana Heart 3 LOVEMAX SIXSTARS V1.08-DARKSiDERS BEST.md b/spaces/inreVtussa/clothingai/Examples/Arcana Heart 3 LOVEMAX SIXSTARS V1.08-DARKSiDERS BEST.md
deleted file mode 100644
index ab71e0222cfebc0ef131129ccd81038b22079e17..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Arcana Heart 3 LOVEMAX SIXSTARS V1.08-DARKSiDERS BEST.md
+++ /dev/null
@@ -1,73 +0,0 @@
-
-Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS: A Review of the Latest Update
-Arcana Heart 3 LOVEMAX SIXSTARS!!!!!! is a 2D fighting game that features an all-female cast of characters with unique abilities and fighting styles. The game was originally released in Japan in 2017 as an arcade update of Arcana Heart 3 LOVEMAX!!!!!, which was released in 2014 for PlayStation 3 and PlayStation Vita.
-In November 2019, the game received a new update for PC, titled Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS. This update added a new playable character, Minori Amanohara, and a new Arcana, Ichor, the Blood. The update also fixed some bugs and improved the game's performance and stability.
-Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS
Download File »»» https://tiurll.com/2uCjRc
-Who is Minori Amanohara?
-Minori Amanohara is one of the few products of the Drexler Institute's research on artificial Valkyries and Arcanas. She is a "Hybride", a special Valkyrie who can dual-wield the powers of the Arcana and the Geist. She escaped the facility with the help of Petra, another character from the game.
-Minori's fighting style is based on her ability to manipulate blood and use it as a weapon. She can create blood projectiles, blades, shields, and even transform into a giant blood monster. She can also use her Geist power to summon a giant robot that can assist her in battle.
-Minori's personality is cheerful and innocent, but also naive and reckless. She likes to help people in need, but often causes more trouble than she solves. She has a self-proclaimed super hero name, "Annihilation Angel Subaru", and wears a red cape and mask to hide her identity.
-What is Ichor, the Blood?
-Ichor, the Blood is the Arcana of Blood that symbolizes the essence of life. The Mobius strip represents the reincarnation of all life. It enhances all potentials to their maximum strength.
-Ichor's abilities are related to blood manipulation and regeneration. It can heal wounds, increase attack power, create blood clones, and even revive from death. It can also use blood as a medium to perform powerful spells and attacks.
-
-Why Should You Play Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS?
-If you are a fan of 2D fighting games, especially those with anime-style graphics and characters, you should definitely give Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS a try. The game offers a fast-paced and fluid combat system that allows you to customize your character's abilities with different Arcanas. The game also has a variety of modes, such as Story Mode, Versus Mode, Survival Mode, Trial Mode, Time Attack Mode, Gallery Mode, and Online Mode.
-The game's graphics are colorful and detailed, with smooth animations and effects. The game's soundtrack is catchy and energetic, with different themes for each character and stage. The game's voice acting is also well-done, with Japanese voices for all characters.
-The game's update also adds more content and value to the game, with a new character and Arcana that offer new gameplay possibilities and strategies. The update also improves the game's performance and stability, making it more enjoyable to play.
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-If you want to download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS for PC, you can do so by following these steps:
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-- Go to the game's Steam page and purchase it if you haven't already.
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-- Launch Steam and log in with your account.
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-- Click on the game's name and then click on Install.
-- Wait for the game to download and install on your PC.
-- Once the installation is complete, click on Play to launch the game.
-
-You can also download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS from other sources, such as torrent sites or direct links. However, these sources may not be safe or reliable, and may contain viruses or malware that can harm your PC or compromise your personal information. Therefore, we recommend that you download the game from Steam or other official sources.
-Conclusion
-Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS is a great update for an already great game. It adds more content and value to the game, with a new character and Arcana that offer new gameplay possibilities and strategies. It also improves the game's performance and stability, making it more enjoyable to play.
-If you are looking for a fun and challenging 2D fighting game with anime-style graphics and characters, you should definitely check out Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS. The game offers a fast-paced and fluid combat system that allows you to customize your character's abilities with different Arcanas. The game also has a variety of modes, such as Story Mode, Versus Mode, Survival Mode, Trial Mode, Time Attack Mode, Gallery Mode, and Online Mode.
-You can download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS from Steam or other official sources for PC. The game is also available for PlayStation 4 in Japan.
-What are the Features of Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS?
-Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS is not just a simple update, but a full-fledged expansion that adds more features and content to the game. Some of the features are:
-
-- A new character, Minori Amanohara, who can use both Arcana and Geist powers to fight.
-- A new Arcana, Ichor, the Blood, who can heal, revive, and boost the power of its user.
-- A new story mode that follows Minori's adventures as she tries to save the world from evil.
-- A new gallery mode that allows you to view illustrations, movies, and soundtracks from the game.
-- A new online mode that supports cross-play between Steam and PlayStation 4 users.
-- A new trial mode that challenges you to complete various tasks and missions with different characters and Arcanas.
-- A new time attack mode that tests your speed and skills in clearing stages as fast as possible.
-- A new survival mode that pits you against endless waves of enemies until you lose.
-- New costumes, colors, voices, and backgrounds for the characters.
-- New achievements and trading cards for Steam users.
-
-How to Install Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS?
-If you already have Arcana Heart 3 LOVEMAX SIXSTARS!!!!!! installed on your PC, you can easily install the v1.08-DARKSiDERS update by following these steps:
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-Where to Download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS?
-You can download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS from various sources on the internet. However, not all of them are safe or reliable. Some of them may contain viruses or malware that can harm your PC or compromise your personal information. Therefore, we recommend that you download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS from trusted and verified sources only.
-One of the best sources to download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS is Steam. Steam is a digital distribution platform that offers thousands of games for PC users. Steam also provides various features and benefits for its users, such as cloud saving, achievements, trading cards, community forums, and more. You can purchase Arcana Heart 3 LOVEMAX SIXSTARS!!!!!! from Steam for $29.99 USD and get the v1.08-DARKSiDERS update for free.
-To download Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS from Steam, you need to have a Steam account and a Steam client installed on your PC. You can create a Steam account for free on the Steam website. You can download and install the Steam client for free from the Steam website. Once you have both, you can follow these steps:
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diff --git a/spaces/inreVtussa/clothingai/Examples/Command And Conquer Renegade Crack Free Download.md b/spaces/inreVtussa/clothingai/Examples/Command And Conquer Renegade Crack Free Download.md
deleted file mode 100644
index 38dace65fa3201db29a0070527ad7d26ec0dc5d0..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Command And Conquer Renegade Crack Free Download.md
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diff --git a/spaces/inreVtussa/clothingai/Examples/Counter-Strike Global Offensive V1.32.7.0 No-Steam CODEX.md b/spaces/inreVtussa/clothingai/Examples/Counter-Strike Global Offensive V1.32.7.0 No-Steam CODEX.md
deleted file mode 100644
index f4aacbbcb2a100a6998082abf0f22305c521b8e1..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Counter-Strike Global Offensive V1.32.7.0 No-Steam CODEX.md
+++ /dev/null
@@ -1,26 +0,0 @@
-
-How to Download and Install Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX
-Counter-Strike: Global Offensive (CS:GO) is one of the most popular multiplayer first-person shooter games in the world. It features two teams of five players each, competing in various game modes and maps. CS:GO is constantly updated with new content, such as weapons, skins, maps, and more.
-However, some players may not have access to the official Steam version of CS:GO, or may want to play it offline or with custom mods. In that case, they can download and install a cracked version of CS:GO, such as Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX.
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-This version of CS:GO is based on the license from Valve Corporation and Hidden Path Entertainment, and has a game version of 1.32.7.0. It does not require Steam to run, and it includes all the latest updates and features of CS:GO as of October 2014[^1^]. It also has a multilingual interface and sound language.
-In this article, we will show you how to download and install Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX on your PC.
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-The first step is to download the torrent file of Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX from a reliable source[^1^]. The torrent file contains the ISO image of the game, which is about 2.76 GB in size.
-You will need a torrent client, such as uTorrent or BitTorrent, to download the torrent file. Once you have downloaded the torrent file, open it with your torrent client and start downloading the ISO image of the game.
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-The final step is to install Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX on your PC.
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diff --git a/spaces/iqovocn/ChuanhuChatGPT/chatgpt - windows.bat b/spaces/iqovocn/ChuanhuChatGPT/chatgpt - windows.bat
deleted file mode 100644
index 0b78fdc3a559abd692e3a9e9af5e482124d13a99..0000000000000000000000000000000000000000
--- a/spaces/iqovocn/ChuanhuChatGPT/chatgpt - windows.bat
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@@ -1,14 +0,0 @@
-@echo off
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diff --git a/spaces/jbilcke-hf/MusicGen/tests/modules/test_codebooks_patterns.py b/spaces/jbilcke-hf/MusicGen/tests/modules/test_codebooks_patterns.py
deleted file mode 100644
index b658f4779a369f9ec8dde692a61b7f0fe3485724..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/MusicGen/tests/modules/test_codebooks_patterns.py
+++ /dev/null
@@ -1,246 +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.
-
-import pytest
-import torch
-
-from audiocraft.modules.codebooks_patterns import (
- DelayedPatternProvider,
- ParallelPatternProvider,
- Pattern,
- UnrolledPatternProvider,
-)
-
-
-class TestParallelPatternProvider:
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [0, 1, 16, 100])
- def test_get_pattern(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- # + 1 to account for 1st step
- assert len(pattern.layout) == timesteps + 1
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_content(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- for s, v in enumerate(pattern.layout):
- for i, code in enumerate(v):
- assert i == code.q
- assert code.t == s - 1 # account for the 1st empty step
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_max_delay(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == 0
- assert len(pattern.valid_layout) == len(pattern.layout) - pattern.max_delay
-
-
-class TestDelayedPatternProvider:
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [0, 1, 16, 100])
- def test_get_pattern(self, n_q: int, timesteps: int):
- delays = [
- list(range(n_q)),
- [0] + [1] * (n_q - 1),
- [0] + [4] * (n_q - 1),
- ]
- for delay in delays:
- provider = DelayedPatternProvider(n_q, delay)
- pattern = provider.get_pattern(timesteps)
- # + 1 to account for 1st step
- assert len(pattern.layout) == timesteps + max(delay) + 1
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_content(self, n_q: int, timesteps: int):
- provider = DelayedPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- for s, v in enumerate(pattern.layout):
- for i, code in enumerate(v):
- assert i == code.q
- assert code.t == max(0, s - code.q - 1)
-
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- @pytest.mark.parametrize("delay", [[0, 1, 2, 3], [0, 1, 1, 1], [0, 3, 3, 3], [0, 3]])
- def test_pattern_max_delay(self, timesteps: int, delay: list):
- provider = DelayedPatternProvider(len(delay), delay)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == max(delay)
- assert len(pattern.valid_layout) == len(pattern.layout) - pattern.max_delay
-
-
-class TestUnrolledPatternProvider:
-
- @pytest.mark.parametrize("timesteps", [0, 1, 16])
- @pytest.mark.parametrize("flattening", [[0, 1, 2], [0, 1, 1]])
- @pytest.mark.parametrize("delays", [[0, 0, 0], [0, 5, 5]])
- def test_get_pattern(self, timesteps: int, flattening: list, delays: list):
- n_q = len(flattening)
- max_delay = max(delays)
- provider = UnrolledPatternProvider(n_q, flattening, delays)
- pattern = provider.get_pattern(timesteps)
- assert len(pattern.layout) == provider.num_virtual_steps(timesteps) + max_delay
-
- @pytest.mark.parametrize("timesteps", [0, 1, 16])
- @pytest.mark.parametrize("flattening", [[0, 1, 2], [0, 1, 1]])
- @pytest.mark.parametrize("delays", [[0, 0, 0], [0, 5, 5]])
- def test_pattern_max_delay(self, timesteps: int, flattening: list, delays: list):
- n_q = len(flattening)
- max_delay = max(delays)
- provider = UnrolledPatternProvider(n_q, flattening, delays)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == max_delay
-
-
-class TestPattern:
-
- def ref_build_pattern_sequence(self, z: torch.Tensor, pattern: Pattern, special_token: int):
- """Reference method to build the sequence from the pattern without using fancy scatter."""
- bs, n_q, T = z.shape
- z = z.cpu().numpy()
- assert n_q == pattern.n_q
- assert T <= pattern.timesteps
- inp = torch.full((bs, n_q, len(pattern.layout)), special_token, dtype=torch.long).numpy()
- inp[:] = special_token
- for s, v in enumerate(pattern.layout):
- for (t, q) in v:
- if t < T:
- inp[:, q, s] = z[:, q, t]
- return torch.from_numpy(inp)
-
- def ref_revert_pattern_sequence(self, z: torch.Tensor, pattern: Pattern, special_token: int):
- """Reference method to revert the sequence from the pattern without using fancy scatter."""
- z = z.cpu().numpy()
- bs, n_q, S = z.shape
- assert pattern.n_q == n_q
- inp = torch.full((bs, pattern.n_q, pattern.timesteps), special_token, dtype=torch.long).numpy()
- inp[:] = special_token
- for s, v in enumerate(pattern.layout):
- for (t, q) in v:
- if t < pattern.timesteps:
- inp[:, q, t] = z[:, q, s]
- return torch.from_numpy(inp)
-
- def ref_revert_pattern_logits(self, z: torch.Tensor, pattern: Pattern, special_token: float):
- """Reference method to revert the logits from the pattern without using fancy scatter."""
- z = z.cpu().numpy()
- bs, card, n_q, S = z.shape
- assert pattern.n_q == n_q
- ref_layout = pattern.layout
- inp = torch.full((bs, card, pattern.n_q, pattern.timesteps), special_token, dtype=torch.float).numpy()
- inp[:] = special_token
- for s, v in enumerate(ref_layout[1:]):
- if s < S:
- for (t, q) in v:
- if t < pattern.timesteps:
- inp[:, :, q, t] = z[:, :, q, s]
- return torch.from_numpy(inp)
-
- def _get_pattern_providers(self, n_q: int):
- pattern_provider_1 = ParallelPatternProvider(n_q)
- pattern_provider_2 = DelayedPatternProvider(n_q, list(range(n_q)))
- pattern_provider_3 = DelayedPatternProvider(n_q, [0] + [1] * (n_q - 1))
- pattern_provider_4 = UnrolledPatternProvider(
- n_q, flattening=list(range(n_q)), delays=[0] * n_q
- )
- pattern_provider_5 = UnrolledPatternProvider(
- n_q, flattening=[0] + [1] * (n_q - 1), delays=[0] * n_q
- )
- pattern_provider_6 = UnrolledPatternProvider(
- n_q, flattening=[0] + [1] * (n_q - 1), delays=[0] + [5] * (n_q - 1)
- )
- return [
- pattern_provider_1,
- pattern_provider_2,
- pattern_provider_3,
- pattern_provider_4,
- pattern_provider_5,
- pattern_provider_6,
- ]
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- def test_build_pattern_sequence(self, n_q: int, timesteps: int):
- bs = 2
- card = 256
- special_token = card
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # we can correctly build the sequence from the pattern
- z = torch.randint(0, card, (bs, n_q, timesteps))
- ref_res = self.ref_build_pattern_sequence(z, pattern, special_token)
- res, indexes, mask = pattern.build_pattern_sequence(z, special_token)
- assert (res == ref_res).float().mean() == 1.0
-
- # expected assertion fails on the number of timesteps
- invalid_timesteps = [timesteps + 1]
- if pattern.num_sequence_steps != pattern.timesteps:
- invalid_timesteps.append(pattern.num_sequence_steps)
- for i_timesteps in invalid_timesteps:
- z2 = torch.randint(0, card, (bs, n_q, i_timesteps))
- with pytest.raises(AssertionError):
- pattern.build_pattern_sequence(z2, special_token)
-
- # expected assertion fails on the number of codebooks
- invalid_qs = [0, n_q - 1, n_q + 1]
- for i_q in invalid_qs:
- z3 = torch.randint(0, card, (bs, i_q, timesteps))
- with pytest.raises(AssertionError):
- pattern.build_pattern_sequence(z3, special_token)
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- def test_revert_pattern_sequence(self, n_q: int, timesteps: int):
- bs = 2
- card = 256
- special_token = card
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # this works assuming previous tests are successful
- z = torch.randint(0, card, (bs, n_q, timesteps))
- s = self.ref_build_pattern_sequence(z, pattern, special_token)
- ref_out = self.ref_revert_pattern_sequence(s, pattern, special_token)
- # ensure our reference script retrieve the original sequence
- assert z.shape == ref_out.shape
- assert (z == ref_out).float().mean() == 1.0
- # now we can test the scatter version
- out, indexes, mask = pattern.revert_pattern_sequence(s, special_token)
- assert out.shape == ref_out.shape
- assert (out == ref_out).float().mean() == 1.0
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- @pytest.mark.parametrize("card", [1, 2, 256, 1024])
- def test_revert_pattern_logits(self, n_q: int, timesteps: int, card: int):
- bs = 2
- special_token = card
- logits_special_token = float('nan')
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # this works assuming previous tests are successful
- z = torch.randint(0, card, (bs, n_q, timesteps))
- s = self.ref_build_pattern_sequence(z, pattern, special_token)
- logits = torch.randn((bs, card, n_q, s.shape[-1]))
- ref_out = self.ref_revert_pattern_logits(logits, pattern, logits_special_token)
- # ensure our reference script retrieve the original sequence
- assert ref_out.shape == torch.Size([bs, card, n_q, timesteps])
- # now we can test the scatter version
- out, indexes, mask = pattern.revert_pattern_logits(logits, logits_special_token)
- assert out.shape == ref_out.shape
- assert (out == ref_out).float().mean() == 1.0
diff --git a/spaces/jbilcke-hf/Panoremix/src/lib/generateSeed.ts b/spaces/jbilcke-hf/Panoremix/src/lib/generateSeed.ts
deleted file mode 100644
index 563e25ec894ab5af54c5025a15a9b7a5918325de..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/lib/generateSeed.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-export function generateSeed() {
- return Math.floor(Math.random() * Math.pow(2, 31));
-}
\ No newline at end of file
diff --git a/spaces/jhwen/bingo/src/components/chat-notification.tsx b/spaces/jhwen/bingo/src/components/chat-notification.tsx
deleted file mode 100644
index 3474e522992c43a4d1d0eadcf205a9760d5b930b..0000000000000000000000000000000000000000
--- a/spaces/jhwen/bingo/src/components/chat-notification.tsx
+++ /dev/null
@@ -1,91 +0,0 @@
-import { useEffect } from 'react'
-import Image from 'next/image'
-
-import IconWarning from '@/assets/images/warning.svg'
-import { ChatError, ErrorCode, ChatMessageModel } from '@/lib/bots/bing/types'
-import { ExternalLink } from './external-link'
-import { useBing } from '@/lib/hooks/use-bing'
-
-export interface ChatNotificationProps extends Pick, 'bot'> {
- message?: ChatMessageModel
-}
-
-function getAction(error: ChatError, reset: () => void) {
- if (error.code === ErrorCode.THROTTLE_LIMIT) {
- reset()
- return (
-
- 你已达到每日最大发送消息次数，请更换账号或隔一天后重试
-
- )
- }
- if (error.code === ErrorCode.BING_IP_FORBIDDEN) {
- return (
-
- 你的服务器或代理已被封禁，请更换服务器或使用代理重试
-
- )
- }
- if (error.code === ErrorCode.BING_TRY_LATER) {
- return (
-
- 创建会话失败，请稍候重试
-
- )
- }
- if (error.code === ErrorCode.BING_FORBIDDEN) {
- return (
-
- 你的账号已在黑名单，请尝试更换账号及申请解封
-
- )
- }
- if (error.code === ErrorCode.CONVERSATION_LIMIT) {
- return (
-
- 当前话题已中止，请点
- 重新开始
- 开启新的对话
-
- )
- }
- if (error.code === ErrorCode.BING_CAPTCHA) {
- return (
-
- 点击通过人机验证
-
- )
- }
- if (error.code === ErrorCode.BING_UNAUTHORIZED) {
- reset()
- return (
- 没有获取到身份信息或身份信息失效，点此重新设置
- )
- }
- return error.message
-}
-
-export function ChatNotification({ message, bot }: ChatNotificationProps) {
- useEffect(() => {
- window.scrollBy(0, 2000)
- }, [message])
-
- if (!message?.error) return
-
- return (
-
-
-
-
-
-
- Favicons
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- To use the favicons insert into your head section some of these tags accordly to your needs.
-
-
-
-
- <!-- For old IEs -->
- <link rel="shortcut icon" href="favicon.ico" />
-
- <!-- For new browsers - multisize ico -->
- <link rel="icon" type="image/x-icon" sizes="16x16 32x32" href="favicon.ico">
-
- <!-- For iPad with high-resolution Retina display running iOS ≥ 7: -->
- <link rel="apple-touch-icon" sizes="152x152" href="favicon-152-precomposed.png">
-
- <!-- For iPad with high-resolution Retina display running iOS ≤ 6: -->
- <link rel="apple-touch-icon" sizes="144x144" href="favicon-144-precomposed.png">
-
- <!-- For iPhone with high-resolution Retina display running iOS ≥ 7: -->
- <link rel="apple-touch-icon" sizes="120x120" href="favicon-120-precomposed.png">
-
- <!-- For iPhone with high-resolution Retina display running iOS ≤ 6: -->
- <link rel="apple-touch-icon" sizes="114x114" href="favicon-114-precomposed.png">
-
- <!-- For iPhone 6+ -->
- <link rel="apple-touch-icon" sizes="180x180" href="favicon-180-precomposed.png">
-
- <!-- For first- and second-generation iPad: -->
- <link rel="apple-touch-icon" sizes="72x72" href="favicon-72-precomposed.png">
-
- <!-- For non-Retina iPhone, iPod Touch, and Android 2.1+ devices: -->
- <link rel="apple-touch-icon" sizes="57x57" href="favicon-57.png">
-
- <!-- For Old Chrome -->
- <link rel="icon" sizes="32x32" href="favicon-32.png" >
-
- <!-- For IE10 Metro -->
- <meta name="msapplication-TileColor" content="#FFFFFF">
- <meta name="msapplication-TileImage" content="favicon-144.png">
- <meta name="theme-color" content="#ffffff">
-
- <!-- Chrome for Android -->
- <link rel="manifest" href="manifest.json">
- <link rel="icon" sizes="192x192" href="favicon-192.png">
-
-
-
-
-
-
-
- For more informations about favicons consult The Favicon Cheat Sheet by Audrey Roy.
-
-
-
-
-
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/app/interface/about/index.tsx b/spaces/jordonpeter01/ai-comic-factory/src/app/interface/about/index.tsx
deleted file mode 100644
index 45ef9f30aff4e54048f3e2b274ec714ddb23f7ec..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/app/interface/about/index.tsx
+++ /dev/null
@@ -1,46 +0,0 @@
-import { Button } from "@/components/ui/button"
-import { Dialog, DialogContent, DialogDescription, DialogFooter, DialogHeader, DialogTitle, DialogTrigger } from "@/components/ui/dialog"
-import { useState } from "react"
-
-export function About() {
- const [isOpen, setOpen] = useState(false)
-
- return (
-
- )
-}
\ No newline at end of file
diff --git a/spaces/kadirnar/yolor/yolor/test.py b/spaces/kadirnar/yolor/yolor/test.py
deleted file mode 100644
index f0898523592593ef981e95469799a1ebd1daa2f3..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/yolor/yolor/test.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import argparse
-import glob
-import json
-import os
-from pathlib import Path
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from yolor.utils.google_utils import attempt_load
-from yolor.utils.datasets import create_dataloader
-from yolor.utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \
- non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, clip_coords, set_logging, increment_path
-from yolor.utils.loss import compute_loss
-from yolor.utils.metrics import ap_per_class
-from yolor.utils.plots import plot_images, output_to_target
-from yolor.utils.torch_utils import select_device, time_synchronized
-
-from yolor.models.models import *
-
-def load_classes(path):
- # Loads *.names file at 'path'
- with open(path, 'r') as f:
- names = f.read().split('\n')
- return list(filter(None, names)) # filter removes empty strings (such as last line)
-
-
-def test(data,
- weights=None,
- batch_size=16,
- imgsz=640,
- conf_thres=0.001,
- iou_thres=0.6, # for NMS
- save_json=False,
- single_cls=False,
- augment=False,
- verbose=False,
- model=None,
- dataloader=None,
- save_dir=Path(''), # for saving images
- save_txt=False, # for auto-labelling
- save_conf=False,
- plots=True,
- log_imgs=0): # number of logged images
-
- # Initialize/load model and set device
- training = model is not None
- if training: # called by train.py
- device = next(model.parameters()).device # get model device
-
- else: # called directly
- set_logging()
- device = select_device(opt.device, batch_size=batch_size)
- save_txt = opt.save_txt # save *.txt labels
-
- # Directories
- save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Load model
- model = Darknet(opt.cfg).to(device)
-
- # load model
- try:
- ckpt = torch.load(weights[0], map_location=device) # load checkpoint
- ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
- model.load_state_dict(ckpt['model'], strict=False)
- except:
- load_darknet_weights(model, weights[0])
- imgsz = check_img_size(imgsz, s=64) # check img_size
-
- # Half
- half = device.type != 'cpu' # half precision only supported on CUDA
- if half:
- model.half()
-
- # Configure
- model.eval()
- is_coco = data.endswith('coco.yaml') # is COCO dataset
- with open(data) as f:
- data = yaml.load(f, Loader=yaml.FullLoader) # model dict
- check_dataset(data) # check
- nc = 1 if single_cls else int(data['nc']) # number of classes
- iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
- niou = iouv.numel()
-
- # Logging
- log_imgs, wandb = min(log_imgs, 100), None # ceil
- try:
- import wandb # Weights & Biases
- except ImportError:
- log_imgs = 0
-
- # Dataloader
- if not training:
- img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
- _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
- path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
- dataloader = create_dataloader(path, imgsz, batch_size, 64, opt, pad=0.5, rect=True)[0]
-
- seen = 0
- try:
- names = model.names if hasattr(model, 'names') else model.module.names
- except:
- names = load_classes(opt.names)
- coco91class = coco80_to_coco91_class()
- s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
- p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
- loss = torch.zeros(3, device=device)
- jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
- for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
- img = img.to(device, non_blocking=True)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- targets = targets.to(device)
- nb, _, height, width = img.shape # batch size, channels, height, width
- whwh = torch.Tensor([width, height, width, height]).to(device)
-
- # Disable gradients
- with torch.no_grad():
- # Run model
- t = time_synchronized()
- inf_out, train_out = model(img, augment=augment) # inference and training outputs
- t0 += time_synchronized() - t
-
- # Compute loss
- if training: # if model has loss hyperparameters
- loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls
-
- # Run NMS
- t = time_synchronized()
- output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres)
- t1 += time_synchronized() - t
-
- # Statistics per image
- for si, pred in enumerate(output):
- labels = targets[targets[:, 0] == si, 1:]
- nl = len(labels)
- tcls = labels[:, 0].tolist() if nl else [] # target class
- seen += 1
-
- if len(pred) == 0:
- if nl:
- stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
- continue
-
- # Append to text file
- path = Path(paths[si])
- if save_txt:
- gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
- x = pred.clone()
- x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original
- for *xyxy, conf, cls in x:
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
- with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- # W&B logging
- if plots and len(wandb_images) < log_imgs:
- box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
- "class_id": int(cls),
- "box_caption": "%s %.3f" % (names[cls], conf),
- "scores": {"class_score": conf},
- "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
- boxes = {"predictions": {"box_data": box_data, "class_labels": names}}
- wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))
-
- # Clip boxes to image bounds
- clip_coords(pred, (height, width))
-
- # Append to pycocotools JSON dictionary
- if save_json:
- # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
- image_id = int(path.stem) if path.stem.isnumeric() else path.stem
- box = pred[:, :4].clone() # xyxy
- scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
- box = xyxy2xywh(box) # xywh
- box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
- for p, b in zip(pred.tolist(), box.tolist()):
- jdict.append({'image_id': image_id,
- 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
- 'bbox': [round(x, 3) for x in b],
- 'score': round(p[4], 5)})
-
- # Assign all predictions as incorrect
- correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
- if nl:
- detected = [] # target indices
- tcls_tensor = labels[:, 0]
-
- # target boxes
- tbox = xywh2xyxy(labels[:, 1:5]) * whwh
-
- # Per target class
- for cls in torch.unique(tcls_tensor):
- ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
- pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
-
- # Search for detections
- if pi.shape[0]:
- # Prediction to target ious
- ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) # best ious, indices
-
- # Append detections
- detected_set = set()
- for j in (ious > iouv[0]).nonzero(as_tuple=False):
- d = ti[i[j]] # detected target
- if d.item() not in detected_set:
- detected_set.add(d.item())
- detected.append(d)
- correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
- if len(detected) == nl: # all targets already located in image
- break
-
- # Append statistics (correct, conf, pcls, tcls)
- stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
-
- # Plot images
- if plots and batch_i < 3:
- f = save_dir / f'test_batch{batch_i}_labels.jpg' # filename
- plot_images(img, targets, paths, f, names) # labels
- f = save_dir / f'test_batch{batch_i}_pred.jpg'
- plot_images(img, output_to_target(output, width, height), paths, f, names) # predictions
-
- # Compute statistics
- stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
- if len(stats) and stats[0].any():
- p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png')
- p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95]
- mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
- nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
- else:
- nt = torch.zeros(1)
-
- # W&B logging
- if plots and wandb:
- wandb.log({"Images": wandb_images})
- wandb.log({"Validation": [wandb.Image(str(x), caption=x.name) for x in sorted(save_dir.glob('test*.jpg'))]})
-
- # Print results
- pf = '%20s' + '%12.3g' * 6 # print format
- print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
-
- # Print results per class
- if verbose and nc > 1 and len(stats):
- for i, c in enumerate(ap_class):
- print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
-
- # Print speeds
- t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
- if not training:
- print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
-
- # Save JSON
- if save_json and len(jdict):
- w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
- anno_json = glob.glob('../coco/annotations/instances_val*.json')[0] # annotations json
- pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
- print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
- with open(pred_json, 'w') as f:
- json.dump(jdict, f)
-
- try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
-
- anno = COCO(anno_json) # init annotations api
- pred = anno.loadRes(pred_json) # init predictions api
- eval = COCOeval(anno, pred, 'bbox')
- if is_coco:
- eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate
- eval.evaluate()
- eval.accumulate()
- eval.summarize()
- map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
- except Exception as e:
- print('ERROR: pycocotools unable to run: %s' % e)
-
- # Return results
- if not training:
- print('Results saved to %s' % save_dir)
- model.float() # for training
- maps = np.zeros(nc) + map
- for i, c in enumerate(ap_class):
- maps[c] = ap[i]
- return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(prog='test.py')
- parser.add_argument('--weights', nargs='+', type=str, default='yolor_p6.pt', help='model.pt path(s)')
- parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
- parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
- parser.add_argument('--img-size', type=int, default=1280, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
- parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--verbose', action='store_true', help='report mAP by class')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
- parser.add_argument('--project', default='runs/test', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--cfg', type=str, default='cfg/yolor_p6.cfg', help='*.cfg path')
- parser.add_argument('--names', type=str, default='data/coco.names', help='*.cfg path')
- opt = parser.parse_args()
- opt.save_json |= opt.data.endswith('coco.yaml')
- opt.data = check_file(opt.data) # check file
- print(opt)
-
- if opt.task in ['val', 'test']: # run normally
- test(opt.data,
- opt.weights,
- opt.batch_size,
- opt.img_size,
- opt.conf_thres,
- opt.iou_thres,
- opt.save_json,
- opt.single_cls,
- opt.augment,
- opt.verbose,
- save_txt=opt.save_txt,
- save_conf=opt.save_conf,
- )
-
- elif opt.task == 'study': # run over a range of settings and save/plot
- for weights in ['yolor_p6.pt', 'yolor_w6.pt']:
- f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
- x = list(range(320, 800, 64)) # x axis
- y = [] # y axis
- for i in x: # img-size
- print('\nRunning %s point %s...' % (f, i))
- r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
- y.append(r + t) # results and times
- np.savetxt(f, y, fmt='%10.4g') # save
- os.system('zip -r study.zip study_*.txt')
- # utils.general.plot_study_txt(f, x) # plot
diff --git a/spaces/kadirnar/yolor/yolor/utils/google_utils.py b/spaces/kadirnar/yolor/yolor/utils/google_utils.py
deleted file mode 100644
index 0ff8bbd3241955204cf8885bfcb322b026cd8b16..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/yolor/yolor/utils/google_utils.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Google utils: https://cloud.google.com/storage/docs/reference/libraries
-
-import os
-import platform
-import subprocess
-import time
-from pathlib import Path
-
-import torch
-
-
-def gsutil_getsize(url=''):
- # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
- s = subprocess.check_output('gsutil du %s' % url, shell=True).decode('utf-8')
- return eval(s.split(' ')[0]) if len(s) else 0 # bytes
-
-
-def attempt_download(weights):
- # Attempt to download pretrained weights if not found locally
- weights = weights.strip().replace("'", '')
- file = Path(weights).name
-
- msg = weights + ' missing, try downloading from https://github.com/WongKinYiu/yolor/releases/'
- models = ['yolor_p6.pt', 'yolor_w6.pt'] # available models
-
- if file in models and not os.path.isfile(weights):
-
- try: # GitHub
- url = 'https://github.com/WongKinYiu/yolor/releases/download/v1.0/' + file
- print('Downloading %s to %s...' % (url, weights))
- torch.hub.download_url_to_file(url, weights)
- assert os.path.exists(weights) and os.path.getsize(weights) > 1E6 # check
- except Exception as e: # GCP
- print('ERROR: Download failure.')
- print('')
-
-
-def attempt_load(weights, map_location=None):
- # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
- model = Ensemble()
- for w in weights if isinstance(weights, list) else [weights]:
- attempt_download(w)
- model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model
-
- if len(model) == 1:
- return model[-1] # return model
- else:
- print('Ensemble created with %s\n' % weights)
- for k in ['names', 'stride']:
- setattr(model, k, getattr(model[-1], k))
- return model # return ensemble
-
-
-def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'):
- # Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download()
- t = time.time()
-
- print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')
- os.remove(name) if os.path.exists(name) else None # remove existing
- os.remove('cookie') if os.path.exists('cookie') else None
-
- # Attempt file download
- out = "NUL" if platform.system() == "Windows" else "/dev/null"
- os.system('curl -c ./cookie -s -L "drive.google.com/uc?export=download&id=%s" > %s ' % (id, out))
- if os.path.exists('cookie'): # large file
- s = 'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm=%s&id=%s" -o %s' % (get_token(), id, name)
- else: # small file
- s = 'curl -s -L -o %s "drive.google.com/uc?export=download&id=%s"' % (name, id)
- r = os.system(s) # execute, capture return
- os.remove('cookie') if os.path.exists('cookie') else None
-
- # Error check
- if r != 0:
- os.remove(name) if os.path.exists(name) else None # remove partial
- print('Download error ') # raise Exception('Download error')
- return r
-
- # Unzip if archive
- if name.endswith('.zip'):
- print('unzipping... ', end='')
- os.system('unzip -q %s' % name) # unzip
- os.remove(name) # remove zip to free space
-
- print('Done (%.1fs)' % (time.time() - t))
- return r
-
-
-def get_token(cookie="./cookie"):
- with open(cookie) as f:
- for line in f:
- if "download" in line:
- return line.split()[-1]
- return ""
-
-# def upload_blob(bucket_name, source_file_name, destination_blob_name):
-# # Uploads a file to a bucket
-# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
-#
-# storage_client = storage.Client()
-# bucket = storage_client.get_bucket(bucket_name)
-# blob = bucket.blob(destination_blob_name)
-#
-# blob.upload_from_filename(source_file_name)
-#
-# print('File {} uploaded to {}.'.format(
-# source_file_name,
-# destination_blob_name))
-#
-#
-# def download_blob(bucket_name, source_blob_name, destination_file_name):
-# # Uploads a blob from a bucket
-# storage_client = storage.Client()
-# bucket = storage_client.get_bucket(bucket_name)
-# blob = bucket.blob(source_blob_name)
-#
-# blob.download_to_filename(destination_file_name)
-#
-# print('Blob {} downloaded to {}.'.format(
-# source_blob_name,
-# destination_file_name))
diff --git a/spaces/kaicheng/ChatGPT_ad/readme/README_en.md b/spaces/kaicheng/ChatGPT_ad/readme/README_en.md
deleted file mode 100644
index a906ecb3ebc411f5cdeb33d661266a489a20c3b0..0000000000000000000000000000000000000000
--- a/spaces/kaicheng/ChatGPT_ad/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
- 
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
- 
-
-
-
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-3. Update Gradio
- ```
- pip install gradio --upgrade --force-reinstall
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
- 
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/kangvcar/RealChar/client/web/src/hooks/useMediaRecorder.js b/spaces/kangvcar/RealChar/client/web/src/hooks/useMediaRecorder.js
deleted file mode 100644
index d65f10589bf8052dd88566b6b525831e6e885d92..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/client/web/src/hooks/useMediaRecorder.js
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * src/hooks/useMediaRecorder.js
- * Initialize media recorder. Start and stop recording.
- *
- * created by Lynchee on 7/16/23
- */
-
-import { useState, useRef } from 'react';
-
-const useMediaRecorder = (onDataAvailable, onStop) => {
- const [isRecording, setIsRecording] = useState(false);
- const mediaRecorder = useRef(null);
-
- // initialize media recorder
- const connectMicrophone = (deviceId) => {
- if (mediaRecorder.current) return;
- navigator.mediaDevices.getUserMedia({
- audio: { deviceId: deviceId ? {exact: deviceId} : undefined, echoCancellation: true }
- })
- .then((stream) => {
- mediaRecorder.current = new MediaRecorder(stream);
- mediaRecorder.current.ondataavailable = onDataAvailable;
- mediaRecorder.current.onstop = onStop;
- })
- .catch(function(err) {
- console.log('An error occurred: ' + err);
- });
- };
-
- const startRecording = () => {
- console.log("start recording");
- if (!mediaRecorder.current) return;
- mediaRecorder.current.start();
- setIsRecording(true);
- }
-
- const stopRecording = () => {
- console.log("stop recording");
- if (!mediaRecorder.current) return;
- mediaRecorder.current.stop();
- setIsRecording(false);
- };
-
- const closeMediaRecorder = () => {
- stopRecording();
- mediaRecorder.current = null;
- };
-
- return { isRecording, setIsRecording, connectMicrophone, startRecording, stopRecording, closeMediaRecorder };
-};
-
-export default useMediaRecorder;
diff --git a/spaces/kangvcar/RealChar/realtime_ai_character/audio/text_to_speech/base.py b/spaces/kangvcar/RealChar/realtime_ai_character/audio/text_to_speech/base.py
deleted file mode 100644
index fb4bcea52ccea27e586472b51dd1416e45bb3603..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/realtime_ai_character/audio/text_to_speech/base.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class TextToSpeech(ABC):
- @abstractmethod
- async def stream(self, *args, **kwargs):
- pass
diff --git a/spaces/karolmajek/Detectron2-MaskRCNN/app.py b/spaces/karolmajek/Detectron2-MaskRCNN/app.py
deleted file mode 100644
index cad31efda9ab5c6f48bbc033c40e9604c9773d69..0000000000000000000000000000000000000000
--- a/spaces/karolmajek/Detectron2-MaskRCNN/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-try:
- import detectron2
-except:
- import os
- os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
-
-from matplotlib.pyplot import axis
-import gradio as gr
-import requests
-import numpy as np
-from torch import nn
-import requests
-
-import torch
-
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog
-
-
-url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg'
-r = requests.get(url1, allow_redirects=True)
-open("city1.jpg", 'wb').write(r.content)
-url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg'
-r = requests.get(url2, allow_redirects=True)
-open("city2.jpg", 'wb').write(r.content)
-
-model_name='COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml'
-
-# model = model_zoo.get(model_name, trained=True)
-
-cfg = get_cfg()
-# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
-cfg.merge_from_file(model_zoo.get_config_file(model_name))
-cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
-# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as w ell
-cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name)
-
-if not torch.cuda.is_available():
- cfg.MODEL.DEVICE='cpu'
-
-predictor = DefaultPredictor(cfg)
-
-
-def inference(image):
- img = np.array(image.resize((1024,1024)))
- outputs = predictor(img)
-
- v = Visualizer(img, MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
- out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-
- return out.get_image()
-
-
-
-title = "Detectron2-MaskRCNN X101"
-description = "demo for Detectron2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\
- Model: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml"
-article = "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation | Detectron model ZOO
"
-
-gr.Interface(
- inference,
- [gr.inputs.Image(type="pil", label="Input")],
- gr.outputs.Image(type="numpy", label="Output"),
- title=title,
- description=description,
- article=article,
- examples=[
- ["city1.jpg"],
- ["city2.jpg"]
- ]).launch()
diff --git a/spaces/katanaml/LayoutLMv2-CORD/app.py b/spaces/katanaml/LayoutLMv2-CORD/app.py
deleted file mode 100644
index 52ab37abc1035281504de55f356e333c0f80810e..0000000000000000000000000000000000000000
--- a/spaces/katanaml/LayoutLMv2-CORD/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import gradio as gr
-import numpy as np
-from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
-from PIL import Image, ImageDraw, ImageFont
-import PIL
-
-processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
-model = LayoutLMv2ForTokenClassification.from_pretrained("katanaml/layoutlmv2-finetuned-cord")
-
-# define id2label
-id2label = model.config.id2label
-
-label_ints = np.random.randint(0,len(PIL.ImageColor.colormap.items()),30)
-label_color_pil = [k for k,_ in PIL.ImageColor.colormap.items()]
-label_color = [label_color_pil[i] for i in label_ints]
-label2color = {}
-for k,v in id2label.items():
- if v[2:] == '':
- label2color['o']=label_color[k]
- else:
- label2color[v[2:]]=label_color[k]
-
-def unnormalize_box(bbox, width, height):
- return [
- width * (bbox[0] / 1000),
- height * (bbox[1] / 1000),
- width * (bbox[2] / 1000),
- height * (bbox[3] / 1000),
- ]
-def iob_to_label(label):
- label = label[2:]
- if not label:
- return 'o'
- return label
-
-def process_image(image):
- width, height = image.size
-
- # encode
- encoding = processor(image, return_offsets_mapping=True, return_tensors="pt")
- offset_mapping = encoding.pop('offset_mapping')
-
- # forward pass
- outputs = model(**encoding)
-
- # get predictions
- predictions = outputs.logits.argmax(-1).squeeze().tolist()
- token_boxes = encoding.bbox.squeeze().tolist()
-
- # only keep non-subword predictions
- is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
- true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
- true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
-
- # draw predictions over the image
- draw = ImageDraw.Draw(image)
- font = ImageFont.load_default()
- for prediction, box in zip(true_predictions, true_boxes):
- predicted_label = iob_to_label(prediction).lower()
- draw.rectangle(box, outline=label2color[predicted_label])
- draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
-
- return image
-
-title = "Interactive demo: LayoutLMv2 with CORD receipts dataset"
-description = "Demo for Microsoft's LayoutLMv2, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on CORD, a dataset of manually annotated receipts. It annotates the words appearing in the image in up to 30 classes. To use it, simply upload an image or use the example image below. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select ‘Open image in new tab’."
-article = "LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding | LayoutLMv2 Github Repo | Katana ML | Sparrow Github Repo
l(4,f=i));const o=S(0);w(n,o,i=>l(13,s=i));const r=F();L(x,{register_tab:i=>(c.push({name:i.name,id:i.id}),t.update(h=>h??i.id),l(3,c),c.length-1),unregister_tab:i=>{const h=c.findIndex(y=>y.id===i.id);c.splice(h,1),t.update(y=>y===i.id?c[h]?.id||c[c.length-1]?.id:y)},selected_tab:t,selected_tab_index:o});function T(i){l(9,b=i),E(t,f=i,f),E(o,s=c.findIndex(h=>h.id===i),s),r("change")}const D=(i,h)=>{T(i.id),r("select",{value:i.name,index:h})};return n.$$set=i=>{"visible"in i&&l(0,a=i.visible),"elem_id"in i&&l(1,u=i.elem_id),"elem_classes"in i&&l(2,m=i.elem_classes),"selected"in i&&l(9,b=i.selected),"$$scope"in i&&l(10,_=i.$$scope)},n.$$.update=()=>{n.$$.dirty&512&&b!==null&&T(b)},[a,u,m,c,f,t,o,r,T,b,_,d,D]}class te extends G{constructor(e){super(),H(this,e,$,W,J,{visible:0,elem_id:1,elem_classes:2,selected:9})}}export{te as T,x as a};
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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-189d7bad.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-189d7bad.css
deleted file mode 100644
index 0b8e23a00e51fe054b66be462ce774d415cd57aa..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-189d7bad.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-b6y5bg{display:flex;flex-direction:inherit;flex-wrap:wrap;gap:var(--form-gap-width);box-shadow:var(--block-shadow);border:var(--block-border-width) solid var(--border-color-primary);border-radius:var(--block-radius);background:var(--border-color-primary);overflow:hidden}div.svelte-b6y5bg .block{box-shadow:none!important;border-width:0px!important;border-radius:0!important}.hidden.svelte-b6y5bg{display:none}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js
deleted file mode 100644
index 5405cd3af19be5d8cb56dbb55aefa442653e888a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function c(n){a(n,"start");var t={},e=n.languageData||{},s=!1;for(var l in n)if(l!=e&&n.hasOwnProperty(l))for(var u=t[l]=[],o=n[l],r=0;r2&&o.token&&typeof o.token!="string"){e.pending=[];for(var g=2;g-1)return null;var l=e.indent.length-1,u=n[e.state];n:for(;;){for(var o=0;o None:
- dump_environment_info()
diff --git a/spaces/lazyboy450/RVCv2-Genshin/config.py b/spaces/lazyboy450/RVCv2-Genshin/config.py
deleted file mode 100644
index 040a64d2c5ce4d7802bdf7f69321483b81008f08..0000000000000000000000000000000000000000
--- a/spaces/lazyboy450/RVCv2-Genshin/config.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import argparse
-import torch
-from multiprocessing import cpu_count
-
-class Config:
- def __init__(self):
- self.device = "cuda:0"
- self.is_half = True
- self.n_cpu = 0
- self.gpu_name = None
- self.gpu_mem = None
- (
- self.python_cmd,
- self.listen_port,
- self.colab,
- self.noparallel,
- self.noautoopen,
- self.api
- ) = self.arg_parse()
- self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
- @staticmethod
- def arg_parse() -> tuple:
- parser = argparse.ArgumentParser()
- parser.add_argument("--port", type=int, default=7865, help="Listen port")
- parser.add_argument(
- "--pycmd", type=str, default="python", help="Python command"
- )
- parser.add_argument("--colab", action="store_true", help="Launch in colab")
- parser.add_argument(
- "--noparallel", action="store_true", help="Disable parallel processing"
- )
- parser.add_argument(
- "--noautoopen",
- action="store_true",
- help="Do not open in browser automatically",
- )
- parser.add_argument("--api", action="store_true", help="Launch with api")
- cmd_opts = parser.parse_args()
-
- cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-
- return (
- cmd_opts.pycmd,
- cmd_opts.port,
- cmd_opts.colab,
- cmd_opts.noparallel,
- cmd_opts.noautoopen,
- cmd_opts.api
- )
-
- def device_config(self) -> tuple:
- if torch.cuda.is_available():
- i_device = int(self.device.split(":")[-1])
- self.gpu_name = torch.cuda.get_device_name(i_device)
- if (
- ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
- or "P40" in self.gpu_name.upper()
- or "1060" in self.gpu_name
- or "1070" in self.gpu_name
- or "1080" in self.gpu_name
- ):
- print("16系/10系显卡和P40强制单精度")
- self.is_half = False
-
- else:
- self.gpu_name = None
- self.gpu_mem = int(
- torch.cuda.get_device_properties(i_device).total_memory
- / 1024
- / 1024
- / 1024
- + 0.4
- )
- elif torch.backends.mps.is_available():
- print("没有发现支持的N卡, 使用MPS进行推理")
- self.device = "mps"
- self.is_half = False
- else:
- print("没有发现支持的N卡, 使用CPU进行推理")
- self.device = "cpu"
- self.is_half = False
-
- if self.n_cpu == 0:
- self.n_cpu = cpu_count()
-
- if self.is_half:
- # 6G显存配置
- x_pad = 3
- x_query = 10
- x_center = 60
- x_max = 65
- else:
- # 5G显存配置
- x_pad = 1
- x_query = 6
- x_center = 38
- x_max = 41
-
- if self.gpu_mem != None and self.gpu_mem <= 4:
- x_pad = 1
- x_query = 5
- x_center = 30
- x_max = 32
-
- return x_pad, x_query, x_center, x_max
diff --git a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/options.py b/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/options.py
deleted file mode 100644
index db490e4aa52e26fde31959fd74c2cef3af2ecf76..0000000000000000000000000000000000000000
--- a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/options.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import yaml
-import time
-from collections import OrderedDict
-from os import path as osp
-from basicsr.utils.misc import get_time_str
-
-def ordered_yaml():
- """Support OrderedDict for yaml.
-
- Returns:
- yaml Loader and Dumper.
- """
- try:
- from yaml import CDumper as Dumper
- from yaml import CLoader as Loader
- except ImportError:
- from yaml import Dumper, Loader
-
- _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
-
- def dict_representer(dumper, data):
- return dumper.represent_dict(data.items())
-
- def dict_constructor(loader, node):
- return OrderedDict(loader.construct_pairs(node))
-
- Dumper.add_representer(OrderedDict, dict_representer)
- Loader.add_constructor(_mapping_tag, dict_constructor)
- return Loader, Dumper
-
-
-def parse(opt_path, root_path, is_train=True):
- """Parse option file.
-
- Args:
- opt_path (str): Option file path.
- is_train (str): Indicate whether in training or not. Default: True.
-
- Returns:
- (dict): Options.
- """
- with open(opt_path, mode='r') as f:
- Loader, _ = ordered_yaml()
- opt = yaml.load(f, Loader=Loader)
-
- opt['is_train'] = is_train
-
- # opt['name'] = f"{get_time_str()}_{opt['name']}"
- if opt['path'].get('resume_state', None): # Shangchen added
- resume_state_path = opt['path'].get('resume_state')
- opt['name'] = resume_state_path.split("/")[-3]
- else:
- opt['name'] = f"{get_time_str()}_{opt['name']}"
-
-
- # datasets
- for phase, dataset in opt['datasets'].items():
- # for several datasets, e.g., test_1, test_2
- phase = phase.split('_')[0]
- dataset['phase'] = phase
- if 'scale' in opt:
- dataset['scale'] = opt['scale']
- if dataset.get('dataroot_gt') is not None:
- dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
- if dataset.get('dataroot_lq') is not None:
- dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])
-
- # paths
- for key, val in opt['path'].items():
- if (val is not None) and ('resume_state' in key or 'pretrain_network' in key):
- opt['path'][key] = osp.expanduser(val)
-
- if is_train:
- experiments_root = osp.join(root_path, 'experiments', opt['name'])
- opt['path']['experiments_root'] = experiments_root
- opt['path']['models'] = osp.join(experiments_root, 'models')
- opt['path']['training_states'] = osp.join(experiments_root, 'training_states')
- opt['path']['log'] = experiments_root
- opt['path']['visualization'] = osp.join(experiments_root, 'visualization')
-
- else: # test
- results_root = osp.join(root_path, 'results', opt['name'])
- opt['path']['results_root'] = results_root
- opt['path']['log'] = results_root
- opt['path']['visualization'] = osp.join(results_root, 'visualization')
-
- return opt
-
-
-def dict2str(opt, indent_level=1):
- """dict to string for printing options.
-
- Args:
- opt (dict): Option dict.
- indent_level (int): Indent level. Default: 1.
-
- Return:
- (str): Option string for printing.
- """
- msg = '\n'
- for k, v in opt.items():
- if isinstance(v, dict):
- msg += ' ' * (indent_level * 2) + k + ':['
- msg += dict2str(v, indent_level + 1)
- msg += ' ' * (indent_level * 2) + ']\n'
- else:
- msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
- return msg
diff --git a/spaces/leogabraneth/text-generation-webui-main/css/NotoSans/stylesheet.css b/spaces/leogabraneth/text-generation-webui-main/css/NotoSans/stylesheet.css
deleted file mode 100644
index 467973b8eebd42a5ba50f4df0a07440b843a19cc..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/css/NotoSans/stylesheet.css
+++ /dev/null
@@ -1,166 +0,0 @@
-/*
-Copied from https://github.com/SillyTavern/SillyTavern/tree/6c8bd06308c69d51e2eb174541792a870a83d2d6/public/webfonts/NotoSans
-*/
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Black.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Black.woff') format('woff');
- font-weight: 900;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraBoldItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraBoldItalic.woff') format('woff');
- font-weight: bold;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-BlackItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-BlackItalic.woff') format('woff');
- font-weight: 900;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraBold.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraBold.woff') format('woff');
- font-weight: bold;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ThinItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ThinItalic.woff') format('woff');
- font-weight: 100;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-BoldItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-BoldItalic.woff') format('woff');
- font-weight: bold;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Bold.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Bold.woff') format('woff');
- font-weight: bold;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-LightItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-LightItalic.woff') format('woff');
- font-weight: 300;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Italic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Italic.woff') format('woff');
- font-weight: normal;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraLightItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraLightItalic.woff') format('woff');
- font-weight: 200;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Light.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Light.woff') format('woff');
- font-weight: 300;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraLight.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraLight.woff') format('woff');
- font-weight: 200;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Medium.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Medium.woff') format('woff');
- font-weight: 500;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Regular.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Regular.woff') format('woff');
- font-weight: normal;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-MediumItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-MediumItalic.woff') format('woff');
- font-weight: 500;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-SemiBoldItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-SemiBoldItalic.woff') format('woff');
- font-weight: 600;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-SemiBold.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-SemiBold.woff') format('woff');
- font-weight: 600;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Thin.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Thin.woff') format('woff');
- font-weight: 100;
- font-style: normal;
- font-display: swap;
-}
-
diff --git a/spaces/leurez/moss/service/src/chatgpt/types.ts b/spaces/leurez/moss/service/src/chatgpt/types.ts
deleted file mode 100644
index c7909a56627c59d80bc13fe9391c4da9fc9fec11..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/service/src/chatgpt/types.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import type { ChatMessage } from 'chatgpt'
-import type fetch from 'node-fetch'
-
-export interface RequestOptions {
- message: string
- lastContext?: { conversationId?: string; parentMessageId?: string }
- process?: (chat: ChatMessage) => void
- systemMessage?: string
- temperature?: number
- top_p?: number
-}
-
-export interface SetProxyOptions {
- fetch?: typeof fetch
-}
-
-export interface UsageResponse {
- total_usage: number
-}
diff --git a/spaces/lilholla/2099/README.md b/spaces/lilholla/2099/README.md
deleted file mode 100644
index 48b6de06cd792b43d5dc0465494772b81535c692..0000000000000000000000000000000000000000
--- a/spaces/lilholla/2099/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: 2099
-emoji: 📈
-colorFrom: gray
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Chalean Extreme Torrent BETTER.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Chalean Extreme Torrent BETTER.md
deleted file mode 100644
index c27fdd7a395d645c79cfe4ac9d36ca062ba946d0..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Chalean Extreme Torrent BETTER.md
+++ /dev/null
@@ -1,128 +0,0 @@
-
-Chalean Extreme Torrent: How to Download and Enjoy the Best Home Workout Program
-
-If you are looking for a way to get fit, burn fat, and build muscle at home, you might have heard of Chalean Extreme, a popular fitness program created by Chalene Johnson and Beachbody. Chalean Extreme is a 90-day program that combines cardio, strength training, and flexibility exercises to help you transform your body and boost your metabolism. But what if you don't want to spend money on buying the DVDs or subscribing to Beachbody On Demand? Is there a way to download Chalean Extreme torrent for free?
-
-In this article, we will show you how to find and download Chalean Extreme torrent safely and legally, and what you need to know before you start the program. We will also share some tips on how to get the most out of Chalean Extreme and achieve your fitness goals.
-Chalean extreme torrent
Download File ⚙⚙⚙ https://bytlly.com/2uGwl7
-
-What is Chalean Extreme Torrent?
-
-A torrent is a file that contains information about other files that are shared over a peer-to-peer network. By using a torrent client, such as uTorrent or BitTorrent, you can download these files from other users who have them on their computers. This way, you can access content that is not available on official websites or platforms.
-
-Chalean Extreme torrent is a file that contains the video files of the Chalean Extreme program, as well as the PDF documents that come with it, such as the fitness guide, the workout sheet, the calendars, and the weight rep trackers. By downloading Chalean Extreme torrent, you can watch the workouts on your computer, TV, or mobile device without paying anything.
-
-How to Find and Download Chalean Extreme Torrent?
-
-There are many websites that offer Chalean Extreme torrent for free download, such as LimeTorrents, BT4G, or Download.ir. However, not all of them are reliable or safe. Some of them may contain viruses, malware, or fake files that can harm your device or compromise your privacy. Therefore, you need to be careful when choosing a website to download Chalean Extreme torrent from.
-
-Here are some steps you can follow to find and download Chalean Extreme torrent safely and legally:
-
-
-- Use a VPN service to protect your online identity and activity. A VPN (virtual private network) is a software that encrypts your data and routes it through a secure server in another location. This way, you can hide your IP address and location from anyone who might be tracking your online activity, such as your ISP (internet service provider), hackers, or government agencies. A VPN also allows you to access geo-restricted content that might be blocked in your country.
-- Use a reputable torrent website to search for Chalean Extreme torrent. As we mentioned before, not all torrent websites are trustworthy or safe. You should look for websites that have positive reviews, high ratings, and a large number of seeders and leechers. Seeders are users who have the complete file and share it with others. Leechers are users who are downloading the file but have not completed it yet. The more seeders and leechers a torrent has, the faster and more reliable the download will be.
-- Use a reliable torrent client to download Chalean Extreme torrent. A torrent client is a software that allows you to download and manage torrent files. You should choose a torrent client that has a good reputation, a user-friendly interface, and advanced features such as bandwidth control, encryption, magnet links support, etc. Some of the most popular torrent clients are uTorrent, BitTorrent, qBittorrent, etc.
-- Scan the downloaded files with an antivirus software before opening them. Even if you use a VPN, a reputable website, and a reliable client, there is still a chance that the downloaded files might contain viruses or malware that can infect your device or steal your personal information. Therefore, you should always scan the downloaded files with an antivirus software before opening them or running them on your device.
-
-
-What Do You Need to Know Before Starting Chalean Extreme?
-
-Before you start Chalean Extreme program, there are some things you need to know to make sure you are ready for it and get the best results possible:
-
-
-- You need some equipment to do the workouts. Chalean Extreme uses resistance bands and dumbbells to challenge your muscles and increase your strength. You will need at least two sets of dumbbells: one light set (3-5 lbs) and one heavy set (8-15 lbs). You can also use adjustable dumbbells or kettlebells instead of fixed ones. You will also need a resistance band with handles (medium or heavy resistance) and a mat for floor exercises.
-- You need to follow the nutrition plan that comes with the program. Chalean Extreme is not only about working out; it's also about eating right. The program comes with a nutrition guide that provides you with meal plans, recipes, tips, and guidelines on how to eat healthy and fuel your body for optimal performance and results. The nutrition plan is based on three phases: Burn Phase (low-carb), Push Phase (moderate-carb), and Lean Phase (high-carb). Each phase lasts for four weeks and corresponds to the intensity level of the workouts.
-- You need to track your progress and adjust accordingly. Chalean Extreme is designed to help you lose fat and gain muscle in 90 days. However, everyone's body is different and responds differently to exercise and nutrition. Therefore, you need to track your progress regularly and adjust your workouts and nutrition accordingly. The program comes with tools that help you measure your progress: weight rep trackers (to record how much weight you lift in each workout), body fat tester (to measure your body fat percentage), measuring tape (to measure your body parts), before-and-after photos (to see how your body changes visually).
-
-
-How to Get the Most Out of Chalean Extreme?
-
-If you want to get the most out of Chalean Extreme program and achieve amazing results in 90 days, here are some tips you can follow:
-
-
-- Be consistent and committed. The key to success with any fitness program is consistency and commitment. You need to stick with the program for 90 days without skipping workouts or cheating on your nutrition plan. You also need to commit yourself mentally and emotionally to the process and trust that it will work for you.
-- Be flexible and adaptable. While consistency and commitment are important, so is flexibility and adaptability. Sometimes life happens and you might face challenges or obstacles that prevent you from following the program exactly as planned. For example, you might get sick, injured, busy, stressed, etc. In these situations, you need to be flexible and adaptable enough to modify your workouts or nutrition plan according to your circumstances without giving up completely.
-- Be positive and supportive. Your attitude and mindset play a huge role in how well you do with any fitness program. You need to be positive and supportive of yourself throughout the journey. You need to celebrate every small victory along the way and acknowledge every improvement you make in your body or performance. You also need to be supportive of others who are doing the program with you or who share similar goals with you.
-
-
-Conclusion
-
-Chalean Extreme is an amazing fitness program that can help you get fit, burn fat
-and build muscle at home in 90 days.
-
-If you want to download Chalean Extreme torrent for free,
-you need to follow some steps
-to find
-and download it safely
-and legally.
-You also need
-to know
-what
-you need
-to do
-before
-and during
-the program
-to get
-the best results possible.
-
-
-We hope this article has been helpful
-and informative
-for you.
-If you have any questions
-or comments,
-please feel free
-to leave them below.
-We would love
-to hear from you!
-What are the Benefits of Chalean Extreme?
-
-Chalean Extreme is a fitness program that offers many benefits for anyone who wants to get in shape and improve their health. Here are some of the benefits you can expect from doing Chalean Extreme:
-
-
-- You will burn fat and calories faster than ever before. Chalean Extreme uses a technique called Lean Phasing, which shifts your body's fat-burning focus from cardio to resistance training. By lifting heavy weights, you will create more lean muscle mass, which will boost your metabolism and help you burn more fat and calories even when you are not working out.
-- You will tone and sculpt your entire body. Chalean Extreme consists of three phases: Burn, Push, and Lean. Each phase targets different muscle groups and challenges them with different exercises and intensity levels. By doing Chalean Extreme, you will work on every part of your body, from your arms and shoulders to your legs and glutes. You will also improve your core strength and stability, which will enhance your posture and balance.
-- You will increase your energy and confidence levels. Chalean Extreme is not only a physical workout, but also a mental one. Chalene Johnson, the creator and instructor of the program, is known for her motivational and inspirational style. She will guide you through every workout with positive affirmations, tips, and encouragement. She will also teach you how to set realistic goals and track your progress. By doing Chalean Extreme, you will feel more energized, confident, and happy with yourself.
-
-
-What are the Challenges of Chalean Extreme?
-
-Chalean Extreme is a fitness program that requires dedication, discipline, and hard work. It is not a quick fix or a magic solution for weight loss or fitness. It is a challenging program that will test your limits and push you out of your comfort zone. Here are some of the challenges you might face when doing Chalean Extreme:
-
-
-- You will need to invest time and money into the program. Chalean Extreme is a 90-day program that consists of 15 workouts that range from 30 to 45 minutes each. You will need to commit to doing at least four workouts per week, plus one rest day and one optional stretch day. You will also need to buy or rent the DVDs or subscribe to Beachbody On Demand to access the videos. You will also need to buy some equipment, such as dumbbells, resistance bands, and a mat.
-- You will need to follow a strict nutrition plan. Chalean Extreme comes with a nutrition guide that provides you with meal plans, recipes, tips, and guidelines on how to eat healthy and fuel your body for optimal performance and results. The nutrition plan is based on three phases: Burn Phase (low-carb), Push Phase (moderate-carb), and Lean Phase (high-carb). Each phase lasts for four weeks and corresponds to the intensity level of the workouts. You will need to follow the nutrition plan closely and avoid processed foods, added sugars, alcohol, and other unhealthy choices.
-- You will need to deal with soreness and fatigue. Chalean Extreme is a high-intensity program that will make you sweat, breathe hard, and feel the burn in your muscles. You will be lifting heavy weights, doing explosive movements, and performing complex exercises that will challenge your body in new ways. You might experience soreness, fatigue, or even pain in your muscles or joints after each workout. You will need to listen to your body, rest when needed, stretch properly, hydrate well, and recover adequately.
-
-Conclusion
-
-Chalean Extreme is a fitness program that can help you get fit, burn fat, and build muscle at home in 90 days. It is a program that combines cardio, strength training, and flexibility exercises to help you transform your body and boost your metabolism. It is a program that offers many benefits, such as faster fat burning, total body toning, and increased energy and confidence levels. It is also a program that requires dedication, discipline, and hard work. You will need to invest time and money into the program, follow a strict nutrition plan, and deal with soreness and fatigue.
-
-If you want to download Chalean Extreme torrent for free,
-you need to follow some steps
-to find
-and download it safely
-and legally.
-You also need
-to know
-what
-you need
-to do
-before
-and during
-the program
-to get
-the best results possible.
-
-We hope this article has been helpful
-and informative
-for you.
-If you have any questions
-or comments,
-please feel free
-to leave them below.
-We would love
-to hear from you!
3cee63e6c2
-
-
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Descargar Sap2000 V12 Gratis 2 High Quality.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Descargar Sap2000 V12 Gratis 2 High Quality.md
deleted file mode 100644
index b1735bfa5bf19cd6bb96d0703359da3dcddea366..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Descargar Sap2000 V12 Gratis 2 High Quality.md
+++ /dev/null
@@ -1,10 +0,0 @@
-Descargar Sap2000 V12 Gratis 2
DOWNLOAD ->->->-> https://bytlly.com/2uGxEP
-
-SPSAIA #S0026
-
-Single- and Multi-stage Stochastic Programming Simplex Algorithms for Optimization. The SPSAIA solver is included in a tool called SAP Scheduling Optimization (SPSAIA) which is developed by the SAP Business Rules' Technology group. SPSAIA supports both Stochastic Global and Stochastic Local approaches. One of the advantages of using the SPSAIA tool is that it allows the user to configure the optimizer easily. The optimizer also supports different data types. For example, the user can define their own schedule constraints and they also have a data type to express a two-dimensional array.
-
-The SPSAIA tool is a stand-alone product. It does not require any SAP engine. SPSAIA can work in a single client. 4fefd39f24
-
-
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Enterprise 2007 Swedish Inkl. Kod Free Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Enterprise 2007 Swedish Inkl. Kod Free Download.md
deleted file mode 100644
index ab2bbb89008c955188ca1011ea854ba252a6c28d..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Enterprise 2007 Swedish Inkl. Kod Free Download.md
+++ /dev/null
@@ -1,27 +0,0 @@
-
-How to Download Microsoft Office Enterprise 2007 Swedish inkl. kod for Free
-If you are looking for a way to download Microsoft Office Enterprise 2007 Swedish inkl. kod for free, you have come to the right place. In this article, I will show you how to get this software without paying a dime.
-Microsoft Office Enterprise 2007 is a suite of productivity applications that includes Word, Excel, PowerPoint, Outlook, Access, Publisher, and OneNote. It also comes with some additional features such as SharePoint Server, Groove, and InfoPath.
-Microsoft Office Enterprise 2007 Swedish inkl. kod free download
Download File ✅ https://bytlly.com/2uGx43
-This software is ideal for professionals, students, and home users who need to create and manage documents, spreadsheets, presentations, emails, databases, and more. It also supports the Swedish language and has a user-friendly interface.
-However, Microsoft Office Enterprise 2007 is not free. You need to purchase a license key to activate it and use it on your computer. The license key can cost up to $699 depending on the edition and the number of users.
-But don't worry, there is a way to download Microsoft Office Enterprise 2007 Swedish inkl. kod for free. All you need is a reliable source that offers the software and the key for download. Here are the steps to follow:
-
-- Go to https://example.com, a trusted website that provides free downloads of Microsoft Office Enterprise 2007 Swedish inkl. kod.
-- Click on the "Download" button and choose a location to save the file on your computer.
-- Once the download is complete, open the file and extract its contents using a program such as WinRAR or 7-Zip.
-- You will find two folders: one containing the setup files of Microsoft Office Enterprise 2007 Swedish and the other containing the license key.
-- Run the setup file and follow the instructions to install Microsoft Office Enterprise 2007 Swedish on your computer.
-- When prompted to enter the license key, open the folder that contains the key and copy it.
-- Paste the key in the activation window and click on "Activate".
-- Congratulations! You have successfully downloaded and installed Microsoft Office Enterprise 2007 Swedish inkl. kod for free.
-
-Enjoy using this software and its features for your personal or professional needs. Remember to share this article with your friends and family who might also benefit from this free download.
-
-If you want to learn more about Microsoft Office Enterprise 2007 Swedish inkl. kod and its features, you can visit the official website of Microsoft at https://www.microsoft.com/sv-se. There you will find more information about the software, its system requirements, its updates, and its support.
-You can also check out some online tutorials and guides that will help you use Microsoft Office Enterprise 2007 Swedish inkl. kod more effectively. For example, you can visit https://example.com, a website that offers free video lessons on how to use Word, Excel, PowerPoint, and other applications in the suite.
-Another useful resource is https://example.com, a blog that provides tips and tricks on how to optimize your documents, spreadsheets, presentations, and emails using Microsoft Office Enterprise 2007 Swedish inkl. kod. You will learn how to format your text, insert images and charts, create tables and graphs, add animations and transitions, and more.
-By following these resources, you will be able to master Microsoft Office Enterprise 2007 Swedish inkl. kod in no time and impress your colleagues, clients, teachers, or friends with your work.
- d5da3c52bf
-
-
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diff --git a/spaces/lqy09/GT/public/GTest/js/jquery.min.js b/spaces/lqy09/GT/public/GTest/js/jquery.min.js
deleted file mode 100644
index 200b54e470a5d67925b91e87f4e769191a9f8c16..0000000000000000000000000000000000000000
--- a/spaces/lqy09/GT/public/GTest/js/jquery.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
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-#include
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-// We need a way to compute the return type of `select_system`, which is found
-// by using `thrust::system::detail::generic::select_system` and then making an
-// ADL call. We have no trait that defines the return type. With the
-// limitations of C++11 return type deduction, we need to be able to stick all
-// of that into `decltype`. So, we put the using statement into a detail
-// namespace, and then implement the generic dispatch function in that
-// namespace.
-
-namespace select_system_detail
-{
-
-using thrust::system::detail::generic::select_system;
-
-struct select_system_fn final
-{
- __thrust_exec_check_disable__
- template
- __host__ __device__
- auto operator()(
- thrust::detail::execution_policy_base const& exec0
- ) const
- THRUST_DECLTYPE_RETURNS(
- select_system(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec0))
- )
- )
-
- __thrust_exec_check_disable__
- template
- __host__ __device__
- auto operator()(
- thrust::detail::execution_policy_base const& exec0
- , thrust::detail::execution_policy_base const& exec1
- ) const
- THRUST_DECLTYPE_RETURNS(
- select_system(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec0))
- , thrust::detail::derived_cast(thrust::detail::strip_const(exec1))
- )
- )
-};
-
-} // namespace select_system_detail
-
-THRUST_INLINE_CONSTANT select_system_detail::select_system_fn select_system{};
-
-} // detail
-
-} // end namespace thrust
-
-#endif // THRUST_CPP_DIALECT >= 2011
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/triple_chevron_launch.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/triple_chevron_launch.h
deleted file mode 100644
index deeffac9dae8bc567face7cf7f8483d41454bbab..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/triple_chevron_launch.h
+++ /dev/null
@@ -1,976 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of the NVIDIA CORPORATION nor the
- * names of its contributors may be used to endorse or promote products
- * derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-#include
-#include
-#include
-#include
-
-
-namespace thrust
-{
-
-namespace cuda_cub {
-namespace launcher {
-
- struct triple_chevron
- {
- typedef size_t Size;
- dim3 const grid;
- dim3 const block;
- Size const shared_mem;
- cudaStream_t const stream;
-
- THRUST_RUNTIME_FUNCTION
- triple_chevron(dim3 grid_,
- dim3 block_,
- Size shared_mem_ = 0,
- cudaStream_t stream_ = 0)
- : grid(grid_),
- block(block_),
- shared_mem(shared_mem_),
- stream(stream_) {}
-
-#if 0
- template
- cudaError_t __host__
- doit_host(K k, Args const&... args) const
- {
- k<<>>(args...);
- return cudaPeekAtLastError();
- }
-#else
- template
- cudaError_t __host__
- doit_host(K k, _0 x0) const
- {
- k<<>>(x0);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1) const
- {
- k<<>>(x0,x1);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2) const
- {
- k<<>>(x0,x1,x2);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3) const
- {
- k<<>>(x0,x1,x2,x3);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
- {
- k<<>>(x0,x1,x2,x3,x4);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE, _xF xF) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE,xF);
- return cudaPeekAtLastError();
- }
-#endif
-
- template
- size_t __device__
- align_up(size_t offset) const
- {
- size_t alignment = alignment_of::value;
- return alignment * ((offset + (alignment - 1))/ alignment);
- }
-
-#if 0
- size_t __device__ argument_pack_size(size_t size) const { return size; }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg const& arg, Args const&... args) const
- {
- size = align_up(size);
- return argument_pack_size(size + sizeof(Arg), args...);
- }
-#else
- template
- size_t __device__
- argument_pack_size(size_t size, Arg) const
- {
- return align_up(size) + sizeof(Arg);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE, _xF xF) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, xF);
- }
-#endif /* variadic */
-
- template
- size_t __device__ copy_arg(char* buffer, size_t offset, Arg arg) const
- {
- offset = align_up(offset);
- for (int i = 0; i != sizeof(Arg); ++i)
- buffer[offset+i] = *((char*)&arg + i);
- return offset + sizeof(Arg);
- }
-
-#if 0
- void __device__ fill_arguments(char*, size_t) const {}
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg const& arg, Args const& ... args) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), args...);
- }
-#else
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg) const
- {
- copy_arg(buffer, offset, arg);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE, _xF xF) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, xF);
- }
-#endif /* variadic */
-
-#if 0
- template
- cudaError_t __device__
- doit_device(K k, Args const&... args) const
- {
- cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
- const size_t size = argument_pack_size(0,args...);
- void *param_buffer = cudaGetParameterBuffer(64,size);
- fill_arguments((char*)param_buffer, 0, args...);
- status = launch_device(k, param_buffer);
-#endif
- return status;
- }
-#else
- template
- cudaError_t __device__
- doit_device(K k, _0 x0) const
- {
- cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
- const size_t size = argument_pack_size(0,x0);
- void *param_buffer = cudaGetParameterBuffer(64,size);
- fill_arguments((char*)param_buffer, 0, x0);
- status = launch_device(k, param_buffer);
-#else
- THRUST_UNUSED_VAR(k);
- THRUST_UNUSED_VAR(x0);
-#endif
- return status;
- }
- template
| File Format | Description |
|---|---|
| XAPK | A compressed file that contains an APK file and an OBB file. It is used to distribute large apps or games that have additional data files. |
| APKS | A compressed file that contains multiple split APK files. It is used to distribute apps or games that use App Bundle format, which allows developers to deliver optimized versions of their apps for different devices. |
| OBB | A data file that contains additional resources for an app or game. It is usually stored in the /Android/obb/ folder on your device. |
| ZIP | A compressed file that contains one or more files. It is used to reduce file size and save storage space. |
| APKM | A compressed file that contains an APK file and a META file. It is used by some modded or hacked apps or games to store additional information or settings. |
How to Download APKCombo Installer from the Official Website
-To download APK Combo Installer from the official website, you need to follow these steps:
-Step 1: Visit the APKCombo Website and Search for APKCombo Installer
-First, you need to go to the APKCombo website, which is a trusted source for downloading APK files. On the homepage, you will see a search bar where you can type in the name of the app you want to download. In this case, you need to type in APKCombo Installer and hit the enter key or the search button.
-Step 2: Click on the Download Button and Choose Your Device Model
-Next, you will see a list of results that match your search query. You need to look for the one that says APKCombo Installer - Install XAPK, APKS, OBB, ZIP, APKM and has the APKCombo logo. Click on it and you will be taken to the app page where you can see more details about the app, such as its version, size, rating, and description. To download the app, you need to click on the green Download button at the top right corner of the page. You will then see a pop-up window that asks you to choose your device model from a drop-down menu. This is to ensure that you download the correct version of the app for your device. Select your device model and click on OK.
-Step 3: Wait for the Download to Complete and Locate the APK File on Your Device
-Finally, you need to wait for the download to complete. You can see the progress of the download on your browser or notification bar. Once the download is finished, you need to locate the APK file on your device. The file name will be something like apkcombo.installer_v1.0.apk. You can find it in your Downloads folder or any other folder where you save your downloaded files.
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How to Install APKCombo Installer on Your Android Device
-Now that you have downloaded the APK file of APKCombo Installer, you need to install it on your Android device. To do that, you need to follow these steps:
-Step 1: Enable Developer Options and Unknown Sources on Your Device
-Before you can install any APK file on your device, you need to enable some settings that allow you to install apps from sources other than Google Play Store. These settings are Developer Options and Unknown Sources. To enable them, you need to go to your device's Settings, then tap on About Phone, then tap on Build Number seven times until you see a message that says You are now a developer!. Then go back to Settings and tap on Developer Options, then scroll down and enable USB Debugging. Next, go back to Settings and tap on Security, then scroll down and enable Unknown Sources. This will allow you to install apps from sources other than Google Play Store.
-Step 2: Open the APK File and Follow the Installation Instructions
-Next, you need to open the APK file that you downloaded from APKCombo website. You can do this by using a file manager app or by tapping on the notification that says Download complete. You will then see a screen that asks you if you want to install this application. Tap on Install and wait for the installation process to finish. You may see some warnings or prompts that ask you to confirm your action or grant some permissions. Just tap on OK, Allow, or Yes, depending on what they say.
-Step 3: Launch the APKCombo Installer App and Grant the Necessary Permissions
-Last but not least, you need to launch the APKCombo Installer app and grant it the necessary permissions to function properly. You can do this by tapping on Open after the installation is done or by finding the app icon in your app drawer or home screen. When you open the app for the first time, you will see a welcome screen that explains what the app does and how it works. Tap on NEXT until you reach the end of the introduction. Then tap on GOT IT!. You will then see a screen that asks you to grant some permissions to the app, such as storage access and overlay permission. These permissions are essential for the app to install files on your device. Tap on ALLOW or GRANT for each permission request. You may also need to go to your device's settings and manually enable the overlay permission for the app. Once you have granted all the permissions, you are ready to use the app.
-How to Use APKCombo Installer to Install XAPK, APKS, OBB, ZIP, APKM Files on Your Android Device
-Now that you have installed APKCombo Installer on your device, you can use it to install any XAPK, APKS, OBB, ZIP, APKM files that you have downloaded from APKCombo website or other sources. To do that, you need to follow these steps:
-Step 1: Open the APKCombo Installer App and Tap on the Install Button
-First, you need to open the APKCombo Installer app and tap on the Install button at the bottom right corner of the screen. This will take you to the file selection screen where you can choose the files you want to install.
-Step 2: Select the Files You Want to Install from Your Device Storage
-Next, you need to select the files you want to install from your device storage. You can browse through your folders or use the search function to find the files. You can also filter the files by type by tapping on the icons at the top of the screen. You can select multiple files at once by tapping on them. You will see a check mark on each selected file and a number at the bottom of the screen indicating how many files you have selected. Once you have selected all the files you want to install, tap on NEXT.
-Step 3: Tap on OK and Follow the Steps on Screen
-Last but not least, you need to tap on OK and follow the steps on screen to install the files. Depending on the file type and size, you may see different screens or prompts during the installation process. For example, if you are installing a XAPK file, you will see a screen that shows the progress of extracting and installing the APK and OBB files. If you are installing a APKS file, you will see a screen that shows the progress of installing the split APKs. If you are installing a ZIP file, you will see a screen that shows the progress of extracting and installing the ZIP contents. If you are installing a APKM file, you will see a screen that shows the progress of installing the APK and META files. For any file type, you may also see some warnings or prompts that ask you to confirm your action or grant some permissions. Just tap on OK, Allow, or Yes, depending on what they say.
-Conclusion and FAQs
-In conclusion, APKCombo Installer is an Android app that lets you install XAPK, APKS, OBB, ZIP, APKM files with ease. It is a simple and user-friendly app that can handle multiple file formats and install them on any Android device running Android 5.0 or higher. It is free to download and use from APKCombo website or other sources. To use it, you just need to download and install it on your device, grant it the necessary permissions, and select the files you want to install from your device storage or external sources.
-If you have any questions about APKCombo Installer or how to use it, here are some FAQs that may help:
--
-
- Q: Is APKCombo Installer safe to use? -
- A: Yes, APKCombo Installer is safe to use as long as you download it from a trusted source like APKCombo website. It does not contain any malware or viruses and it does not collect any personal data from your device. -
- Q: Can I use APKCombo Installer to update my apps or games? -
- A: Yes, you can use APKCombo Installer to update your apps or games if you have downloaded the latest version of the XAPK, APKS, OBB, ZIP, APKM files from APKCombo website or other sources. Just select and install them as usual and they will overwrite the existing version of your apps or games. -
- Q: Can I use APKCombo Installer to uninstall my apps or games? -
- A: No, APKCombo Installer is not a uninstaller app and it cannot uninstall your apps or games. To uninstall your apps or games, you need to use your device's settings or another app that can do that. -
- Q: Can I use APKCombo Installer to install files from Google Play Store? -
- A: No, APKCombo Installer cannot install files from Google Play Store. It can only install files that you have downloaded from APKCombo website or other sources. To install files from Google Play Store, you need to use the Google Play Store app or another app that can do that. -
- Q: What should I do if I encounter any problems or errors while using APKCombo Installer? -
- A: If you encounter any problems or errors while using APKCombo Installer, you can try the following solutions: -
- Make sure you have enough storage space on your device and your device is connected to a stable internet connection. -
- Make sure you have enabled Developer Options and Unknown Sources on your device and granted all the permissions to the app. -
- Make sure you have downloaded the correct version of the XAPK, APKS, OBB, ZIP, APKM files for your device model and Android version. -
- Make sure you have not modified or corrupted the XAPK, APKS, OBB, ZIP, APKM files in any way. -
- Make sure you have not installed any other apps that may interfere with the installation process. -
- Restart your device and try again. -
- Contact the APKCombo support team or visit their website for more help. -
-
-
I hope this article has helped you understand how to download and install APKCombo Installer on your Android device and how to use it to install XAPK, APKS, OBB, ZIP, APKM files on your device. If you have any feedback or suggestions, please feel free to leave a comment below. Thank you for reading and happy installing!
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-
\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Apkpure Offers Asphalt Nitro Mod APK with All Cars Unlocked and Free Coins.md b/spaces/fatiXbelha/sd/Apkpure Offers Asphalt Nitro Mod APK with All Cars Unlocked and Free Coins.md deleted file mode 100644 index bba945f66a8d60800e607afe0e3a50b52bd48925..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Apkpure Offers Asphalt Nitro Mod APK with All Cars Unlocked and Free Coins.md +++ /dev/null @@ -1,74 +0,0 @@ - -
Asphalt Nitro Mod Apk All Cars Unlocked Apkpure: A Guide for Racing Game Lovers
-If you are a fan of racing games, you might have heard of Asphalt Nitro, a spin-off of the popular Asphalt series developed by Gameloft. This game offers you an immersive racing experience with luxury licensed cars, breathtaking environments, and crazy stunts. You can fire up your nitro and be the first to cross the finish line in various game modes, such as Classic, Versus, Elimination, Knockdown, Infected, Gate Drift, Catch, and Escape.
-However, if you want to enjoy the game to the fullest, you might need to spend some real money to unlock and upgrade your cars. That's why many players look for a modded version of the game that gives them unlimited money and speed, as well as all cars unlocked and upgraded. In this article, we will show you how to download and install Asphalt Nitro Mod Apk All Cars Unlocked Apkpure, a modded version of the game that has all these features and more.
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How to Download and Install Asphalt Nitro Mod Apk All Cars Unlocked Apkpure
-Downloading and installing Asphalt Nitro Mod Apk All Cars Unlocked Apkpure is not difficult if you follow these simple steps:
-Step 1: Find a reliable source for the mod apk file
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-Step 2: Enable unknown sources on your device settings
-The next thing you need to do is to enable unknown sources on your device settings. This is because Android devices do not allow installing apps from sources other than Google Play Store by default. To enable unknown sources, you need to go to your device settings > security > unknown sources and toggle it on. This will allow you to install apps from sources other than Google Play Store.
-Step 3: Download and install the mod apk file
-The third thing you need to do is to download and install the mod apk file. To do this, you need to go back to [Apkbounce](^1^) and click on the download button for Asphalt Nitro Mod Apk All Cars Unlocked Apkpure. The file will start downloading automatically. Once the download is complete, you need to locate the file in your device storage and tap on it to install it. The installation process will take a few seconds.
-Step 4: Launch the game The fourth thing you need to do is to launch the game and enjoy the features. To do this, you need to find the game icon on your device home screen or app drawer and tap on it to open it. The game will load and you will see the main menu. You can choose your preferred game mode, car, and location and start racing. You will notice that you have unlimited money and speed, as well as all cars unlocked and upgraded. You can also customize your cars and perform amazing stunts.
-What are the Features of Asphalt Nitro Mod Apk All Cars Unlocked Apkpure
-Asphalt Nitro Mod Apk All Cars Unlocked Apkpure is a modded version of the game that has many features that make it more fun and exciting. Here are some of the features that you can enjoy with this mod apk:
-Feature 1: Unlimited Money and Speed
-With this mod apk, you will never run out of money and speed. You can use the money to buy and upgrade any car you want, as well as unlock new locations and modes. You can also use the speed to boost your performance and outrun your opponents. You can also activate the nitro whenever you want and feel the adrenaline rush.
-Feature 2: All Cars Unlocked and Upgraded
-With this mod apk, you will have access to all the cars in the game, from luxury licensed cars like Ferrari, Lamborghini, Aston Martin, to exotic concept cars like Bugatti, Pagani, Koenigsegg. You can also upgrade your cars with the best parts and accessories, such as engines, tires, brakes, suspension, nitro, paint, decals, etc. You can also customize your cars with different colors and designs.
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Feature 3: Multiple Racing and Stunts Modes
-With this mod apk, you will have more options to choose from when it comes to racing and stunts modes. You can play in various modes, such as Classic, Versus, Elimination, Knockdown, Infected, Gate Drift, Catch, and Escape. Each mode has its own rules and challenges that will test your skills and reflexes. You can also perform amazing stunts like barrel rolls, jumps, flips, drifts, etc.
-Feature 4: Locations All Around the World
-With this mod apk, you will be able to race in different locations all around the world, from urban cities like New York, Paris, Tokyo, to exotic places like Brazil, China, Egypt. Each location has its own scenery and landmarks that will make your racing experience more realistic and immersive. You can also explore different routes and shortcuts that will give you an edge over your rivals.
-Feature 5: Stunning Graphics and Physics
-With this mod apk, you will be amazed by the stunning graphics and physics of the game. The game uses a high-quality 3D engine that renders realistic lighting effects, shadows, reflections, textures, etc. The game also uses a realistic physics engine that simulates the behavior of the cars and the environment. You can feel the impact of collisions, crashes, explosions, etc.
-Conclusion
-Asphalt Nitro Mod Apk All Cars Unlocked Apkpure is a modded version of the game that gives you unlimited money and speed, as well as all cars unlocked and upgraded. It also offers you multiple racing and stunts modes, locations all around the world, and stunning graphics and physics. It is a great way to enjoy the game to the fullest without spending any real money or waiting for long loading times. If you are a fan of racing games, you should definitely try this mod apk.
-FAQs
-Here are some common questions and answers about Asphalt Nitro Mod Apk All Cars Unlocked Apkpure:
-Q: Is Asphalt Nitro Mod Apk All Cars Unlocked Apkpure safe to use?
-A: Yes, Asphalt Nitro Mod Apk All Cars Unlocked Apkpure is safe to use if you download it from a reliable source like [Apkbounce]. However , you should always scan the file with an antivirus software before installing it, and backup your data before making any changes to your device.
-Q: Does Asphalt Nitro Mod Apk All Cars Unlocked Apkpure require root access?
-A: No, Asphalt Nitro Mod Apk All Cars Unlocked Apkpure does not require root access to work. You can install it on any Android device that meets the minimum requirements of the game.
-Q: What are the minimum requirements of Asphalt Nitro Mod Apk All Cars Unlocked Apkpure?
-A: The minimum requirements of Asphalt Nitro Mod Apk All Cars Unlocked Apkpure are as follows:
-| OS | Android 4.1 or higher |
|---|---|
| RAM | 1 GB or more |
| Storage | 100 MB or more |
| Internet | Required for some features |
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Bus Simulator Indonesia Mod APK A Game that Teaches You the Culture and History of Indonesia.md b/spaces/fatiXbelha/sd/Bus Simulator Indonesia Mod APK A Game that Teaches You the Culture and History of Indonesia.md deleted file mode 100644 index 8df684332892c6e1d3300324f786908e0a8e33d0..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Bus Simulator Indonesia Mod APK A Game that Teaches You the Culture and History of Indonesia.md +++ /dev/null @@ -1,101 +0,0 @@ -
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Game Bus Simulator Indonesia Mod APK: A Fun and Realistic Driving Experience
-Do you love driving games? Do you want to explore the beautiful and diverse cities of Indonesia? Do you want to customize your own bus and drive it on realistic roads? If you answered yes to any of these questions, then you should try Game Bus Simulator Indonesia Mod APK. This is a modified version of the popular game Bus Simulator Indonesia, which lets you enjoy unlimited money, fuel, and other features that make the game more fun and exciting. In this article, we will tell you everything you need to know about Game Bus Simulator Indonesia Mod APK, including its features, how to download and install it, and its pros and cons.
-What is Game Bus Simulator Indonesia Mod APK?
-Game Bus Simulator Indonesia Mod APK is based on driving a bus in various cities of Indonesia to perform various tasks on your android phone. In this game, you need to pick up the passengers from different areas of an Indonesian city and drop them at the destination. You can also drive freely around the city and enjoy the scenery. You can choose from different types of buses, such as mini buses, double-decker buses, or luxury buses. You can also customize your bus with different skins, stickers, horns, lights, and more. You can also experience realistic traffic, weather, day and night cycles, and other aspects of driving in Indonesia.
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Features of Game Bus Simulator Indonesia Mod APK
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-- Customizable buses and skins
-Another great feature of Game Bus Simulator Indonesia Mod APK is that it allows you to customize your buses with different skins and accessories. You can change the color, design, logo, name, number plate, and more of your bus. You can also add stickers, horns, lights, mirrors, spoilers, exhausts, and more to your bus. You can make your bus look unique and stylish according to your preference.
-- Realistic traffic and weather
-Game Bus Simulator Indonesia Mod APK also offers realistic traffic and weather conditions in the game. You will encounter different types of vehicles on the road, such as cars, trucks, motorcycles, bicycles, rickshaws, etc. You will also have to follow the traffic rules and signals, such as speed limits, stop signs, red lights, etc. You will also experience different weather effects, such as rain, fog, sun, wind, etc. You will have to adjust your driving accordingly to avoid accidents and delays.
-- Various modes and missions
-Game Bus Simulator Indonesia Mod APK also provides various modes and missions for you to enjoy. You can choose from free mode, career mode, or multiplayer mode. In free mode, you can drive anywhere you want without any restrictions or objectives. In career mode, you have to complete different tasks and challenges to earn money and reputation. In multiplayer mode, you can play with other players online and compete with them in races or other events. You can also chat with them using the built-in voice chat feature.
-How to download and install Game Bus Simulator Indonesia Mod APK?
-Requirements for Game Bus Simulator Indonesia Mod APK
Steps to download and install Game Bus Simulator Indonesia Mod APK
-If you want to download and install Game Bus Simulator Indonesia Mod APK on your android device, you need to follow these simple steps:
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- Click on the download link to get the Game Bus Simulator Indonesia Mod APK file. -
- Allow the installation of unknown sources on your device by going to Settings > Security > Unknown Sources. -
- Locate the downloaded file in your file manager and tap on it to start the installation process. -
- Follow the instructions on the screen and wait for the installation to complete. -
- Launch the game and enjoy driving your bus in Indonesia. -
Pros and cons of Game Bus Simulator Indonesia Mod APK
-Game Bus Simulator Indonesia Mod APK is a fun and realistic driving game that lets you experience the culture and scenery of Indonesia. However, like any other game, it also has some pros and cons that you should consider before playing it. Here are some of them:
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Pros of Game Bus Simulator Indonesia Mod APK
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- It is free to download and play. -
- It has unlimited money and fuel, which makes the game more enjoyable and less stressful. -
- It has customizable buses and skins, which gives you more options and creativity. -
- It has realistic traffic and weather, which adds more challenge and realism to the game. -
- It has various modes and missions, which keeps the game interesting and diverse. -
- It has a multiplayer mode, which allows you to play with other players online and have more fun. -
Cons of Game Bus Simulator Indonesia Mod APK
--
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- It may not be compatible with some devices or versions of Android. -
- It may have some bugs or glitches that affect the gameplay or performance. -
- It may require a stable internet connection for some features or modes. -
- It may not be updated regularly or have new content or features. -
Conclusion
-Game Bus Simulator Indonesia Mod APK is a great game for anyone who loves driving games and wants to explore the beautiful and diverse cities of Indonesia. It offers unlimited money, fuel, customization, realism, variety, and multiplayer features that make the game more fun and exciting. However, it also has some drawbacks, such as compatibility issues, bugs, internet requirements, and lack of updates. Therefore, you should weigh the pros and cons before downloading and installing it on your device. If you are looking for a fun and realistic driving experience in Indonesia, then you should give Game Bus Simulator Indonesia Mod APK a try.
-FAQs
-Here are some frequently asked questions about Game Bus Simulator Indonesia Mod APK:
--
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- Is Game Bus Simulator Indonesia Mod APK safe to download and install? -
- What is the difference between Game Bus Simulator Indonesia Mod APK and the original game? -
- Can I play Game Bus Simulator Indonesia Mod APK offline? -
- How can I update Game Bus Simulator Indonesia Mod APK? -
- How can I contact the developers of Game Bus Simulator Indonesia Mod APK? -
Yes, Game Bus Simulator Indonesia Mod APK is safe to download and install as long as you get it from a trusted source. However, you should always scan the file for viruses or malware before installing it on your device.
-The main difference between Game Bus Simulator Indonesia Mod APK and the original game is that the modded version gives you unlimited money, fuel, customization, and other features that are not available in the original game. The modded version also bypasses some restrictions or limitations that are imposed by the original game.
-You can play Game Bus Simulator Indonesia Mod APK offline in free mode or career mode. However, you will need an internet connection to play multiplayer mode or access some online features or events.
-You can update Game Bus Simulator Indonesia Mod APK by downloading and installing the latest version from the same source. However, you should always back up your data before updating to avoid losing your progress or settings.
-You can contact the developers of Game Bus Simulator Indonesia Mod APK by visiting their official website or their social media pages. You can also leave a comment or review on their download page or send them an email at support@maleo.id.
--
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Instagram Pro 2021 APK and Enjoy New Features.md b/spaces/fatiXbelha/sd/Download Instagram Pro 2021 APK and Enjoy New Features.md deleted file mode 100644 index 7e6e98a1f32647b520a6cb4745af8404eafdc7c9..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Instagram Pro 2021 APK and Enjoy New Features.md +++ /dev/null @@ -1,101 +0,0 @@ - -
Instagram Pro 2021 APK Download: What You Need to Know
-Instagram is one of the most popular social media platforms in the world, with over a billion monthly active users. It allows you to share photos and videos, connect with friends and celebrities, shop for products, and discover new content. But did you know that there is a modified version of Instagram that offers more features and benefits than the official app? It's called Instagram Pro 2021 APK, and it's a free download for Android devices. In this article, we'll tell you everything you need to know about Instagram Pro 2021 APK, including what it is, what it can do, how to download and install it, and how to use it safely and effectively.
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Download »»» https://urllie.com/2uNypx
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What is Instagram Pro 2021 APK?
-Instagram Pro 2021 APK is a modded version of Instagram that was developed by SamMods. It's not available on the Google Play Store or the official Instagram website, but you can download it from various third-party sources on the internet. Instagram Pro 2021 APK is designed to enhance your Instagram experience by unlocking features that are not available or limited on the original app. Some of these features include:
--
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- Downloading photos, videos, stories, reels, and live streams -
- Viewing anyone's profile picture in full size -
- Hiding your online status and seen ticks -
- Copying comments and bios -
- Zooming in on any image -
- Disabling video autoplay -
- Changing app themes and icons -
- And much more! -
Features and benefits of Instagram Pro 2021 APK
-As you can see, Instagram Pro 2021 APK offers many features that can make your Instagram experience more enjoyable and convenient. Here are some of the benefits of using this modded app:
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- You can save any media that you like on your device without using any external tools or apps. -
- You can view anyone's profile picture in full size without having to follow them or send them a request. -
- You can hide your online status and seen ticks from other users, giving you more privacy and control over your interactions. -
- You can copy any comment or bio that you find interesting or useful without having to screenshot or type it manually. -
- You can zoom in on any image that you want to see more clearly or closely. -
- You can disable video autoplay if you want to save data or avoid distractions. -
- You can change the app theme and icon to suit your preferences or mood. -
Risks and precautions of Instagram Pro 2021 APK
-While Instagram Pro 2021 APK sounds like a great app, it also comes with some risks and drawbacks that you should be aware of before downloading it. Here are some of the potential issues that you may encounter:
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- The app is not authorized or endorsed by Instagram or Meta Platforms, which means that it may violate their terms of service and policies. This could result in your account being banned or suspended if they detect that you are using a modded app. -
- The app may contain malware or viruses that could harm your device or compromise your security. This could happen if you download the app from an untrusted source or if the developer inserts malicious code into the app. -
- The app may not be compatible with your device or the latest version of Instagram. This could cause the app to crash, freeze, or malfunction. -
- Customization: You can design and decorate your internet cafe according to your preferences and budget. You can choose from different furniture, equipment, accessories, and colors to create a unique and attractive space for your customers. -
- Diversity: You can offer different services and products to your customers, such as gaming computers, consoles, VR devices, snacks, drinks, crypto mining, illegal hacking, and more. You can also buy popular games and licenses to increase your customer base. -
- Interaction: You can communicate and socialize with your customers, employees, suppliers, competitors, and other people in the city. You can also participate in various activities and events, such as parties, concerts, races, fights, strip clubs, etc. -
- Challenge: You have to deal with various problems and risks that come with running an internet cafe business. You have to pay your rent, bills, taxes, salaries, loans, etc. You have to satisfy your customers' needs and expectations. You have to protect your cafe from thieves, vandals, hackers, mobsters, etc. You have to balance your legal and illegal activities. -
- Progression: You can improve your internet cafe by upgrading your computers, installing new software and applications, expanding your area, hiring more staff, etc. You can also develop your skills and abilities by choosing from different tech trees. -
- Steam: Steam is the most common and convenient way to download Internet Cafe Simulator. You can buy the game for $9.99 on Steam and enjoy the benefits of automatic updates, cloud saving, achievements, community features, etc. You can also access the game's workshop and download user-generated content, such as mods, maps, skins, etc. -
- Other Platforms: If you don't want to use Steam, you can also download Internet Cafe Simulator from other platforms, such as Epic Games Store, GOG.com, Humble Bundle, etc. These platforms may offer different prices, discounts, or bundles for the game. However, they may not have all the features and content that Steam has. -
- Mobile Devices: If you want to play Internet Cafe Simulator on your mobile device, you can download the game from the Google Play Store or the App Store. The mobile version of the game is free to play, but it has ads and in-app purchases. The mobile version also has some differences from the PC version, such as graphics, controls, gameplay, etc. -
- Pirated Versions: If you don't want to pay for the game or use any of the official platforms, you can also download pirated versions of Internet Cafe Simulator from various websites or torrents. However, this is not recommended, as it is illegal and unethical. Pirated versions may also have viruses, malware, bugs, errors, missing features, etc. -
- Plan Ahead: Before you start your internet cafe business, you should plan ahead and decide what kind of cafe you want to create. You should consider your budget, location, target market, services, products, etc. You should also research the competition and see what they are offering and how you can differentiate yourself. -
- Manage Your Money: Money is the most important resource in Internet Cafe Simulator. You need money to buy equipment, furniture, games, licenses, staff, etc. You also need money to pay your rent, bills, taxes, salaries, loans, etc. You should always keep track of your income and expenses and try to balance them. You should also look for ways to increase your revenue and reduce your costs. -
- Satisfy Your Customers: Customers are the lifeblood of your internet cafe business. You need to attract and retain customers by offering them quality services and products. You should also interact with them and respond to their needs and feedback. You should also monitor their satisfaction level and loyalty level and try to improve them. -
- Protect Your Cafe: Your internet cafe is not only a source of income but also a source of trouble. You have to deal with various threats and risks that can harm your cafe or reputation. You have to protect your cafe from thieves, vandals, hackers, mobsters, etc. You have to install security cameras, You have to install security cameras, alarms, locks, fire extinguishers, etc. You have to hire security guards, cleaners, technicians, etc. You have to backup your data and use antivirus software. You have to be careful with your legal and illegal activities and avoid getting caught by the police or the media. -
- Expand Your Cafe: As your internet cafe business grows, you may want to expand your cafe and offer more services and products to your customers. You can buy more computers, consoles, VR devices, etc. You can also buy more games and licenses to attract more customers. You can also upgrade your equipment and software to improve your performance and security. You can also rent or buy a bigger space or open a new branch in another location. -
- How do I save my progress in Internet Cafe Simulator? -
- How do I get more customers in Internet Cafe Simulator? -
- How do I make more money in Internet Cafe Simulator? -
- How do I unlock new features in Internet Cafe Simulator? -
- How do I update Internet Cafe Simulator? -
- You can access the latest version of Google Play Store, which may have more features, bug fixes, and security improvements than the one on your device. -
- You can access a wider range of apps and games that may not be compatible with your device's default app store. -
- You can enjoy faster downloads and installations of apps and games, as well as automatic updates. -
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- You may encounter compatibility issues with some apps and games that may not work properly on your device or require higher Android versions. -
- You may expose your device to security risks if you download the APK file from untrusted sources or enable unknown sources on your device. -
- You may violate the terms of service of your device manufacturer or carrier if you install unauthorized apps or modify your device's software.How to Download and Install Google Play Store APK on Android 4.0 3?
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If you want to download and install the Google Play Store APK file on your Android 4.0 3 device, you need to follow these steps:
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-Google play store apk benefits for android 4.0Step 1: Enable Unknown Sources on Your Device
-Before you can install the APK file, you need to enable unknown sources on your device. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message that installing apps from unknown sources can harm your device. Tap OK to proceed.
-Step 2: Download Google Play Store APK File
-Next, you need to download the Google Play Store APK file from a trusted source. You can search for it online or use the link below:
-Google Play Store APK for Android 4.0 3
-Make sure you download the latest version of the APK file that is compatible with your device. You can check the version number and the file size before downloading. You may also need to grant permission for your browser or file manager to download the APK file.
-Step 3: Install Google Play Store APK File
-Once you have downloaded the APK file, you need to install it on your device. To do this, locate the APK file in your downloads folder or wherever you saved it. Tap on it to open it. You may see a prompt that asks you to confirm the installation. Tap Install to start the installation process. It may take a few seconds or minutes depending on your device and network speed.
-When the installation is complete, you will see a message that says App Installed. Tap Open to launch the Google Play Store app. Alternatively, you can find the app icon in your app drawer or home screen.
-How to Use Google Play Store on Android 4.0 3?
-Now that you have installed the Google Play Store app on your Android 4.0 3 device, you can use it to access millions of apps and games for your device. Here are some tips on how to use it:
-Sign in with Your Google Account
-To use the Google Play Store app, you need to sign in with your Google account. If you already have one, you can enter your email and password and tap Sign In. If you don't have one, you can create one by tapping Create Account and following the instructions.
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-To cancel a download or installation, tap on the X icon next to its progress bar. To pause or resume a download or installation, tap on the Pause or Resume icon next to its progress bar.
-Update and Manage Your Apps and Games
-To update and manage your apps and games, tap on the menu icon at the top left corner of the app and select My Apps & Games. You will see four tabs: Updates, Installed, Library, and Beta.
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- The Updates tab shows you which apps and games have new versions available for download. You can tap on Update All to update all of them at once, or tap on each one individually to update them separately. -
- The Installed tab shows you which apps and games are currently installed on your device. You can tap on each one to open its details page, where you can uninstall it, clear its data or cache, move it to an SD card, disable it, or open it. -
- The Library tab shows you which apps and games you have previously installed but are not currently installed on your device. You can tap on each one to reinstall it or remove it from your library. The Beta tab shows you which apps and games you have joined the beta testing program for. You can tap on each one to leave the beta program, or to provide feedback to the developers.
- - Q: Is it safe to download and install the Google Play Store APK file on my device? -
- A: It depends on where you download the APK file from. If you download it from a trusted source, such as the link we provided above, it should be safe. However, if you download it from an untrusted source, it may contain malware or viruses that can harm your device. Therefore, we recommend that you always scan the APK file with an antivirus app before installing it. -
- Q: Will downloading and installing the Google Play Store APK file void my warranty or affect my device's performance? -
- A: No, downloading and installing the Google Play Store APK file will not void your warranty or affect your device's performance. However, if you install unauthorized apps or modify your device's software, you may violate the terms of service of your device manufacturer or carrier, and they may refuse to provide you with support or service. -
- Q: How can I update the Google Play Store app on my device? -
- A: The Google Play Store app will update itself automatically when a new version is available. You can also check for updates manually by opening the app and tapping on the menu icon at the top left corner of the app and selecting Settings > About > Play Store version. If there is an update available, you will see a message that says A new version of Google Play Store will be downloaded and installed. Tap OK to start the update process. -
- Q: How can I uninstall the Google Play Store app from my device? -
- A: You cannot uninstall the Google Play Store app from your device, as it is a system app that is essential for your device's functionality. However, you can disable it by going to Settings > Apps > Google Play Store and tapping on Disable. This will prevent the app from running and updating itself. To enable it again, go to Settings > Apps > Disabled and tap on Enable. -
- Q: How can I contact Google Play Store support if I have any issues or problems with the app? -
- A: You can contact Google Play Store support by going to the app and tapping on the menu icon at the top left corner of the app and selecting Help & Feedback. You will see a list of topics that may answer your questions or solve your problems. You can also tap on Contact Us to chat with a support agent, request a callback, or send an email. -
- Go to the game's Steam page and purchase it if you haven't already. -
- Download and install Steam on your PC if you haven't already. -
- Launch Steam and log in with your account. -
- Go to your Library and find Arcana Heart 3 LOVEMAX SIXSTARS!!!!!! in your list of games. -
- Click on the game's name and then click on Install. -
- Wait for the game to download and install on your PC. -
- Once the installation is complete, click on Play to launch the game. -
- A new character, Minori Amanohara, who can use both Arcana and Geist powers to fight. -
- A new Arcana, Ichor, the Blood, who can heal, revive, and boost the power of its user. -
- A new story mode that follows Minori's adventures as she tries to save the world from evil. -
- A new gallery mode that allows you to view illustrations, movies, and soundtracks from the game. -
- A new online mode that supports cross-play between Steam and PlayStation 4 users. -
- A new trial mode that challenges you to complete various tasks and missions with different characters and Arcanas. -
- A new time attack mode that tests your speed and skills in clearing stages as fast as possible. -
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- New costumes, colors, voices, and backgrounds for the characters. -
- New achievements and trading cards for Steam users. -
- Download the update file from one of the links provided below. -
- Extract the file using WinRAR or 7-Zip. -
- Mount or burn the image file using Virtual CloneDrive or Daemon Tools. -
- Run the setup.exe file and follow the instructions. -
- Copy the cracked content from the /DARKSiDERS folder on the image to your game install folder. -
- Play and enjoy! -
- Launch Steam and log in with your account. -
- Go to the game's Steam page and click on Add to Cart. -
- Go to your Cart and click on Purchase for Myself or Purchase as a Gift if you want to gift it to someone else. -
- Select your payment method and complete the transaction. -
- Go to your Library and find Arcana Heart 3 LOVEMAX SIXSTARS!!!!!! in your list of games. -
- Click on Install and wait for the game to download and install on your PC. -
- Once the installation is complete, click on Play to launch the game. -
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- Use a reputable torrent website to search for Chalean Extreme torrent. As we mentioned before, not all torrent websites are trustworthy or safe. You should look for websites that have positive reviews, high ratings, and a large number of seeders and leechers. Seeders are users who have the complete file and share it with others. Leechers are users who are downloading the file but have not completed it yet. The more seeders and leechers a torrent has, the faster and more reliable the download will be. -
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- Scan the downloaded files with an antivirus software before opening them. Even if you use a VPN, a reputable website, and a reliable client, there is still a chance that the downloaded files might contain viruses or malware that can infect your device or steal your personal information. Therefore, you should always scan the downloaded files with an antivirus software before opening them or running them on your device. -
- You need some equipment to do the workouts. Chalean Extreme uses resistance bands and dumbbells to challenge your muscles and increase your strength. You will need at least two sets of dumbbells: one light set (3-5 lbs) and one heavy set (8-15 lbs). You can also use adjustable dumbbells or kettlebells instead of fixed ones. You will also need a resistance band with handles (medium or heavy resistance) and a mat for floor exercises. -
- You need to follow the nutrition plan that comes with the program. Chalean Extreme is not only about working out; it's also about eating right. The program comes with a nutrition guide that provides you with meal plans, recipes, tips, and guidelines on how to eat healthy and fuel your body for optimal performance and results. The nutrition plan is based on three phases: Burn Phase (low-carb), Push Phase (moderate-carb), and Lean Phase (high-carb). Each phase lasts for four weeks and corresponds to the intensity level of the workouts. -
- You need to track your progress and adjust accordingly. Chalean Extreme is designed to help you lose fat and gain muscle in 90 days. However, everyone's body is different and responds differently to exercise and nutrition. Therefore, you need to track your progress regularly and adjust your workouts and nutrition accordingly. The program comes with tools that help you measure your progress: weight rep trackers (to record how much weight you lift in each workout), body fat tester (to measure your body fat percentage), measuring tape (to measure your body parts), before-and-after photos (to see how your body changes visually). -
- Be consistent and committed. The key to success with any fitness program is consistency and commitment. You need to stick with the program for 90 days without skipping workouts or cheating on your nutrition plan. You also need to commit yourself mentally and emotionally to the process and trust that it will work for you. -
- Be flexible and adaptable. While consistency and commitment are important, so is flexibility and adaptability. Sometimes life happens and you might face challenges or obstacles that prevent you from following the program exactly as planned. For example, you might get sick, injured, busy, stressed, etc. In these situations, you need to be flexible and adaptable enough to modify your workouts or nutrition plan according to your circumstances without giving up completely. -
- Be positive and supportive. Your attitude and mindset play a huge role in how well you do with any fitness program. You need to be positive and supportive of yourself throughout the journey. You need to celebrate every small victory along the way and acknowledge every improvement you make in your body or performance. You also need to be supportive of others who are doing the program with you or who share similar goals with you. -
- You will burn fat and calories faster than ever before. Chalean Extreme uses a technique called Lean Phasing, which shifts your body's fat-burning focus from cardio to resistance training. By lifting heavy weights, you will create more lean muscle mass, which will boost your metabolism and help you burn more fat and calories even when you are not working out. -
- You will tone and sculpt your entire body. Chalean Extreme consists of three phases: Burn, Push, and Lean. Each phase targets different muscle groups and challenges them with different exercises and intensity levels. By doing Chalean Extreme, you will work on every part of your body, from your arms and shoulders to your legs and glutes. You will also improve your core strength and stability, which will enhance your posture and balance. -
- You will increase your energy and confidence levels. Chalean Extreme is not only a physical workout, but also a mental one. Chalene Johnson, the creator and instructor of the program, is known for her motivational and inspirational style. She will guide you through every workout with positive affirmations, tips, and encouragement. She will also teach you how to set realistic goals and track your progress. By doing Chalean Extreme, you will feel more energized, confident, and happy with yourself. -
- You will need to invest time and money into the program. Chalean Extreme is a 90-day program that consists of 15 workouts that range from 30 to 45 minutes each. You will need to commit to doing at least four workouts per week, plus one rest day and one optional stretch day. You will also need to buy or rent the DVDs or subscribe to Beachbody On Demand to access the videos. You will also need to buy some equipment, such as dumbbells, resistance bands, and a mat. -
- You will need to follow a strict nutrition plan. Chalean Extreme comes with a nutrition guide that provides you with meal plans, recipes, tips, and guidelines on how to eat healthy and fuel your body for optimal performance and results. The nutrition plan is based on three phases: Burn Phase (low-carb), Push Phase (moderate-carb), and Lean Phase (high-carb). Each phase lasts for four weeks and corresponds to the intensity level of the workouts. You will need to follow the nutrition plan closely and avoid processed foods, added sugars, alcohol, and other unhealthy choices. -
- You will need to deal with soreness and fatigue. Chalean Extreme is a high-intensity program that will make you sweat, breathe hard, and feel the burn in your muscles. You will be lifting heavy weights, doing explosive movements, and performing complex exercises that will challenge your body in new ways. You might experience soreness, fatigue, or even pain in your muscles or joints after each workout. You will need to listen to your body, rest when needed, stretch properly, hydrate well, and recover adequately. -
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How to download and install Instagram Pro 2021 APK
-If you are interested in trying out Instagram Pro 2021 APK, you will need to download and install it on your Android device. Here are the steps you need to follow:
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-Once you have downloaded the APK file, you can install it by tapping on it and following the instructions on the screen. You may need to allow some permissions for the app to function properly. After the installation is complete, you can launch the app and sign in with your Instagram account or create a new one. You can also choose your preferred theme and icon for the app.
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-Now that you have installed Instagram Pro 2021 APK, you can start using it and enjoy its features. Here are some tips on how to use the app:
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Create and share reels, stories, and posts
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-In conclusion, Instagram Pro 2021 APK is a modded version of Instagram that offers more features and benefits than the official app. It allows you to download media, view profile pictures, hide online status and seen ticks, copy comments and bios, zoom in on images, disable video autoplay, change themes and icons, and more. It's a free download for Android devices that you can get from various third-party sources.
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-If you want to try out Instagram Pro 2021 APK, you can download it from [SamMods], the official website of the developer. However, please be aware that this app is not authorized or endorsed by Instagram or Meta Platforms, and it may violate their terms of service and policies. This could result in your account being banned or suspended if they detect that you are using a modded app. Also, please make sure that you download the app from a trusted source and scan it with an antivirus app before installing it. We are not responsible for any damage or loss that may occur from using this app.
- FAQs Q: Is Instagram Pro 2021 APK safe to use? Q: Is Instagram Pro 2021 APK safe to use? A: Instagram Pro 2021 APK is not an official app from Instagram or Meta Platforms, and it may contain malware or viruses that could harm your device or compromise your security. It may also violate their terms of service and policies, which could result in your account being banned or suspended. Therefore, we advise you to use this app at your own risk and discretion. Q: How can I update Instagram Pro 2021 APK? A: Instagram Pro 2021 APK does not have an automatic update feature, so you will need to manually check for updates and download them from the developer's website or other sources. You can also follow the developer's social media accounts or join their Telegram group to get notified of new updates. Q: Can I use Instagram Pro 2021 APK with my existing Instagram account? A: Yes, you can use Instagram Pro 2021 APK with your existing Instagram account, as long as you don't have any issues with logging in or verifying your account. However, please note that using a modded app may put your account at risk of being banned or suspended by Instagram or Meta Platforms. Q: Can I use Instagram Pro 2021 APK on iOS devices? A: No, Instagram Pro 2021 APK is only compatible with Android devices. If you want to use a modded version of Instagram on iOS devices, you will need to look for other alternatives, such as Instagram++ or InstaPro. Q: What are the differences between Instagram Pro 2021 APK and other modded versions of Instagram? A: There are many modded versions of Instagram available on the internet, such as GBInstagram, InstaUltra, InstaModa, and more. Each of them has its own features and advantages, but they also have some similarities and drawbacks. The main difference between Instagram Pro 2021 APK and other modded versions of Instagram is that it is developed by SamMods, a reputable and trusted developer who has been creating modded apps for a long time. It also has a simple and user-friendly interface, a wide range of features and options, and regular updates and bug fixes. 401be4b1e0-
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Internet Cafe Simulator and Explore the City Full of Activities and People.md b/spaces/fatiXbelha/sd/Download Internet Cafe Simulator and Explore the City Full of Activities and People.md deleted file mode 100644 index 95dca9f12b852927e4e2509df86cb30fc5214ec8..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Internet Cafe Simulator and Explore the City Full of Activities and People.md +++ /dev/null @@ -1,107 +0,0 @@ - -
How to Download Internet Cafe Simulator Latest Version
-Have you ever dreamed of running your own internet cafe business? Do you want to experience the challenges and rewards of managing a comprehensive workplace, interacting with customers and city dwellers, and expanding your services and facilities? If so, you should download Internet Cafe Simulator, a realistic and immersive simulation game that lets you build the best internet cafe in the world!
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Internet Cafe Simulator is a game developed by Cheesecake Dev, a studio that specializes in creating fun and engaging simulation games. The game was first released in 2019, and since then, it has received many updates and improvements, including a sequel called Internet Cafe Simulator 2 that was launched in 2022. In this article, we will show you how to download the latest version of Internet Cafe Simulator, what features it offers, what requirements it has, and some tips and tricks to help you succeed in the game. Let's get started!
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-Internet Cafe Simulator is not just a simple game where you click on buttons and watch your money grow. It is a complex and detailed simulation that requires you to plan, manage, and interact with various aspects of your business and environment. Here are some of the features that make Internet Cafe Simulator stand out:
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-Before you download Internet Cafe Simulator, you need to make sure that your device meets the minimum or recommended requirements for the game. Here are the system requirements for Internet Cafe Simulator according to Steam:
-| Minimum | Recommended | |
|---|---|---|
| OS | Windows 7 | Windows 10 |
| Processor | 2 GHz Dual Core CPU | 2.5 GHz Dual Core CPU |
| Memory | 2 GB RAM | 4 GB RAM |
| Graphics | Intel HD Graphics 4000 or better | Geforce GTX 970/Radeon RX470 or better |
| Storage | 4500 MB available space | 5 GB available space |
If you If you have a device that meets or exceeds these requirements, you are ready to download Internet Cafe Simulator. If not, you may need to upgrade your device or look for other alternatives.
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-Internet Cafe Simulator is a fun and realistic simulation game that lets you experience the thrill and challenge of running your own internet cafe business. You can customize your cafe, offer different services and products, interact with various people and events, deal with various problems and risks, and improve your skills and abilities. You can download the latest version of Internet Cafe Simulator from Steam or other platforms, or play it on your mobile device. If you follow the tips and tricks we shared in this article, you will surely succeed in the game and become the best internet cafe owner in the world!
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-Here are some of the frequently asked questions about Internet Cafe Simulator:
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You can save your progress in Internet Cafe Simulator by using the save option in the main menu or by sleeping in your bed in the game. The game also autosaves every 10 minutes.
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You can get more customers in Internet Cafe Simulator by offering more services and products, buying more games and licenses, improving your customer satisfaction and loyalty levels, advertising your cafe, participating in events, etc.
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-You can unlock new features in Internet Cafe Simulator by developing your skills and abilities in different tech trees. You can access the tech trees by using your laptop or smartphone in the game. You can unlock new features such as crypto mining, hacking, VR devices, etc.
-You can update Internet Cafe Simulator by using the update option in the main menu or by checking for updates on the platform you downloaded the game from. You can also check the game's official website or social media accounts for news and announcements about updates.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Enjoy Movies Books and Apps with Android 4.0 3 Google Play Store APK.md b/spaces/fatiXbelha/sd/Enjoy Movies Books and Apps with Android 4.0 3 Google Play Store APK.md deleted file mode 100644 index b228f51be41b0d2a80a3b27cc89134ea3ca8f52f..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Enjoy Movies Books and Apps with Android 4.0 3 Google Play Store APK.md +++ /dev/null @@ -1,107 +0,0 @@ -
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In this article, we will show you how to download and install the Google Play Store APK file on your Android 4.0 3 device, and how to use it to enjoy the best apps and games for your device.
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-Android 4.0 3 is the third minor update of Android 4.0 Ice Cream Sandwich, which was released in October 2011. Ice Cream Sandwich was the first version of Android that unified the user interface and features for both smartphones and tablets. It introduced many new features, such as face unlock, data usage management, improved multitasking, redesigned widgets, and more.
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-Google Play Store is the official app store for Android devices, where you can find and download millions of apps and games for your device. You can also access other digital content, such as books, movies, music, magazines, and more. Google Play Store also provides updates and security patches for your apps and games, as well as recommendations based on your preferences and usage.
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You can also enable or disable automatic updates for your apps and games by tapping on the menu icon at the top right corner of the app and selecting Settings > Auto-update apps. You can choose to update your apps and games over any network, over Wi-Fi only, or not at all.
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-In this article, we have shown you how to download and install the Google Play Store APK file on your Android 4.0 3 device, and how to use it to access millions of apps and games for your device. We hope you have found this article helpful and informative. If you have any questions or comments, please feel free to leave them below.
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\ No newline at end of file diff --git a/spaces/fb700/chat3/main.py b/spaces/fb700/chat3/main.py deleted file mode 100644 index 8ef5b40c43547644f5cfb77422bd15d26ff704ce..0000000000000000000000000000000000000000 --- a/spaces/fb700/chat3/main.py +++ /dev/null @@ -1,188 +0,0 @@ -import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染 - -def main(): - import gradio as gr - from request_llm.bridge_all import predict - from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith - # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到 - proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \ - get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS') - - # 如果WEB_PORT是-1, 则随机选取WEB端口 - PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT - if not AUTHENTICATION: AUTHENTICATION = None - - from check_proxy import get_current_version - initial_prompt = "Serve me as a writing and programming assistant." - title_html = f"
帛凡 ChatGPT 学术优化 {get_current_version()}
" - description = """和气生财，万事如意""" - - # 问询记录, python 版本建议3.9+（越新越好） - import logging - os.makedirs("gpt_log", exist_ok=True) - try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8") - except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO) - print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！") - - # 一些普通功能模块 - from core_functional import get_core_functions - functional = get_core_functions() - - # 高级函数插件 - from crazy_functional import get_crazy_functions - crazy_fns = get_crazy_functions() - - # 处理markdown文本格式的转变 - gr.Chatbot.postprocess = format_io - - # 做一些外观色彩上的调整 - from theme import adjust_theme, advanced_css - set_theme = adjust_theme() - - # 代理与自动更新 - from check_proxy import check_proxy, auto_update - proxy_info = check_proxy(proxies) - - gr_L1 = lambda: gr.Row().style() - gr_L2 = lambda scale: gr.Column(scale=scale) - if LAYOUT == "TOP-DOWN": - gr_L1 = lambda: DummyWith() - gr_L2 = lambda scale: gr.Row() - CHATBOT_HEIGHT /= 2 - - cancel_handles = [] - with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo: - gr.HTML(title_html) - cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL}) - with gr_L1(): - with gr_L2(scale=2): - chatbot = gr.Chatbot() - chatbot.style(height=CHATBOT_HEIGHT) - history = gr.State([]) - with gr_L2(scale=1): - with gr.Accordion("输入区", open=True) as area_input_primary: - with gr.Row(): - txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False) - with gr.Row(): - submitBtn = gr.Button("提交", variant="primary") - with gr.Row(): - resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm") - stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm") - clearBtn = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm") - with gr.Row(): - status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}") - with gr.Accordion("基础功能区", open=True) as area_basic_fn: - with gr.Row(): - for k in functional: - variant = functional[k]["Color"] if "Color" in functional[k] else "secondary" - functional[k]["Button"] = gr.Button(k, variant=variant) - with gr.Accordion("函数插件区", open=True) as area_crazy_fn: - with gr.Row(): - gr.Markdown("注意：以下“红颜色”标识的函数插件需从输入区读取路径作为参数.") - with gr.Row(): - for k in crazy_fns: - if not crazy_fns[k].get("AsButton", True): continue - variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary" - crazy_fns[k]["Button"] = gr.Button(k, variant=variant) - crazy_fns[k]["Button"].style(size="sm") - with gr.Row(): - with gr.Accordion("更多函数插件", open=True): - dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)] - with gr.Column(scale=1): - dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False) - with gr.Column(scale=1): - switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary") - with gr.Row(): - with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up: - file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple") - with gr.Accordion("展开SysPrompt & 交互界面布局 & Github地址", open=(LAYOUT == "TOP-DOWN")): - system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt) - top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",) - temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",) - max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="MaxLength",) - checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区") - md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="").style(container=False) - - gr.Markdown(description) - with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary: - with gr.Row(): - txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False) - with gr.Row(): - submitBtn2 = gr.Button("提交", variant="primary") - with gr.Row(): - resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm") - stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm") - clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm") - # 功能区显示开关与功能区的互动 - def fn_area_visibility(a): - ret = {} - ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))}) - ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))}) - ret.update({area_input_primary: gr.update(visible=("底部输入区" not in a))}) - ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))}) - ret.update({clearBtn: gr.update(visible=("输入清除键" in a))}) - ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))}) - if "底部输入区" in a: ret.update({txt: gr.update(value="")}) - return ret - checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2] ) - # 整理反复出现的控件句柄组合 - input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt] - output_combo = [cookies, chatbot, history, status] - predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo) - # 提交按钮、重置按钮 - cancel_handles.append(txt.submit(**predict_args)) - cancel_handles.append(txt2.submit(**predict_args)) - cancel_handles.append(submitBtn.click(**predict_args)) - cancel_handles.append(submitBtn2.click(**predict_args)) - resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) - resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) - clearBtn.click(lambda: ("",""), None, [txt, txt2]) - clearBtn2.click(lambda: ("",""), None, [txt, txt2]) - # 基础功能区的回调函数注册 - for k in functional: - click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo) - cancel_handles.append(click_handle) - # 文件上传区，接收文件后与chatbot的互动 - file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes], [chatbot, txt, txt2]) - # 函数插件-固定按钮区 - for k in crazy_fns: - if not crazy_fns[k].get("AsButton", True): continue - click_handle = crazy_fns[k]["Button"].click(ArgsGeneralWrapper(crazy_fns[k]["Function"]), [*input_combo, gr.State(PORT)], output_combo) - click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot]) - cancel_handles.append(click_handle) - # 函数插件-下拉菜单与随变按钮的互动 - def on_dropdown_changed(k): - variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary" - return {switchy_bt: gr.update(value=k, variant=variant)} - dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] ) - # 随变按钮的回调函数注册 - def route(k, *args, **kwargs): - if k in [r"打开插件列表", r"请先从插件列表中选择"]: return - yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs) - click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo) - click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot]) - # def expand_file_area(file_upload, area_file_up): - # if len(file_upload)>0: return {area_file_up: gr.update(open=True)} - # click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up]) - cancel_handles.append(click_handle) - # 终止按钮的回调函数注册 - stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles) - stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles) - - # gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数 - def auto_opentab_delay(): - import threading, webbrowser, time - print(f"如果浏览器没有自动打开，请复制并转到以下URL：") - print(f"\t（亮色主题）: http://localhost:{PORT}") - print(f"\t（暗色主题）: http://localhost:{PORT}/?__dark-theme=true") - def open(): - time.sleep(2) # 打开浏览器 - webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true") - threading.Thread(target=open, name="open-browser", daemon=True).start() - threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start() - - auto_opentab_delay() - #demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png") - demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png", auth=AUTHENTICATION, ) -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/examples/pretrain_taiyi_clip/pretrain.py b/spaces/fclong/summary/fengshen/examples/pretrain_taiyi_clip/pretrain.py deleted file mode 100644 index 56e24ac370ff2b5f3ecf84a32586bc5205499b07..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/pretrain_taiyi_clip/pretrain.py +++ /dev/null @@ -1,308 +0,0 @@ -from pytorch_lightning import ( - LightningModule, - Trainer, -) -from pytorch_lightning.callbacks import ( - LearningRateMonitor, -) -from fengshen.models.clip import ( - TaiyiCLIPModel, - TaiyiCLIPProcessor, -) -from fengshen.models.model_utils import ( - add_module_args, - configure_optimizers, - get_total_steps, -) -import torch -import torch.nn.functional as F -import argparse -import math -from fengshen.data.universal_datamodule import UniversalDataModule -from fengshen.data.taiyi_stable_diffusion_datasets.taiyi_datasets import add_data_args, load_data -from fengshen.utils.universal_checkpoint import UniversalCheckpoint -import os -import numpy as np -from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor - -OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) -OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) - - -class Collator(): - def __init__(self, args, processor): - self.processor = processor - self.seq_length = args.seq_length - self.transforms = Compose([ - ToTensor(), - RandomResizedCrop(args.resolution, scale=(0.9, 1.0), - interpolation=InterpolationMode.BICUBIC), - Normalize(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD), - ]) - - def __call__(self, inputs): - max_length = min(self.seq_length, max([len(i['caption']) for i in inputs])) - images = [] - texts = [] - labels = [] - for i in inputs: - # instance_image = Image.open(i['img_path']) - # instance_image = jpeg4py.JPEG(i['img_path']).decode() - instance_image = np.load(i['npy_path']) - images.append(self.transforms(instance_image)) - texts.append(i['caption']) - labels.append(i['labels'] if 'labels' in i else -100) - # images_input = self.processor(images=images, return_tensors="pt") - texts_input = self.processor(text=texts, - max_length=max_length, - padding='max_length', - truncation=True, - return_tensors='pt') - # return images_input, texts_input, labels - return {'pixel_values': torch.stack(images)}, texts_input, labels - - -class TaiyiCLIP(LightningModule): - @staticmethod - def add_module_specific_args(parent_parser): - parser = parent_parser.add_argument_group('Taiyi CLIP') - parser.add_argument('--loss_type', choices=['local', 'global'], default='local') - parser.add_argument('--seq_length', default=77) - parser.add_argument('--gather_with_grad', default=False, action='store_true') - parser.add_argument('--freeze_image_tower', default=False, action='store_true') - return parent_parser - - def __init__(self, args, **kwargs) -> None: - super().__init__() - self.save_hyperparameters(args) - - self.model = TaiyiCLIPModel.from_pretrained(args.model_path) - self.processor = TaiyiCLIPProcessor.from_pretrained(args.model_path) - - self.local_loss = args.loss_type == 'local' - - if args.freeze_image_tower: - for param in self.model.vision_model.parameters(): - param.requires_grad = False - self.model.visual_projection.requires_grad = False - - # cache - self.cache_labels = True - self.prev_num_logits = 0 - self.labels = {} - - def setup(self, stage) -> None: - if stage == 'fit': - self.total_steps = get_total_steps(self.trainer, self.hparams) - print('Total steps: {}' .format(self.total_steps)) - elif stage == 'validate': - self.total_steps = 100 - - def configure_optimizers(self): - return configure_optimizers(self) - - def forward(self, image, text): - assert image is not None - assert text is not None - image_features = self.model.get_image_features(**image) - text_features = self.model.get_text_features(**text) - - image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) - text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True) - - return image_features, text_features, self.model.logit_scale.exp() - - def gather_features(self, features): - if self.trainer.world_size == 1: - return features - all_features = self.all_gather( - features, sync_grads=self.hparams.gather_with_grad) - if not self.local_loss and not self.gather_with_grad: - # 如果是全局loss，并且不需要梯度，需要把梯度更新回tensor - all_features[self.global_rank] = features - all_features = all_features.view(-1, all_features.shape[-1]) - return all_features - - def clip_loss(self, image_features, text_features, logit_scale): - - logits_per_image = None - - # 如果我冻住VIT并且是local_loss，那么我只需要自己的这部分text feature就行 - # 因为根本不需要image2text的feature训练VIT - if self.hparams.freeze_image_tower and self.local_loss: - all_text_features = None - else: - all_text_features = self.gather_features( - text_features) - all_image_features = self.gather_features( - image_features) - - if self.local_loss: - if all_text_features is not None: - logits_per_image = logit_scale * image_features @ all_text_features.T - logits_per_text = logit_scale * text_features @ all_image_features.T - else: - # 如果是global_loss，那all_text_features肯定不是空的 - logits_per_image = logit_scale * all_image_features @ all_text_features.T - logits_per_text = logits_per_image.T - - num_logits = logits_per_text.shape[0] - if self.prev_num_logits != num_logits or self.device not in self.labels: - labels = torch.arange(num_logits, device=self.device, dtype=torch.long) - if self.trainer.world_size > 1 and self.local_loss: - labels = labels + num_logits * self.global_rank - if self.cache_labels: - self.labels[self.device] = labels - self.prev_num_logits = num_logits - else: - labels = self.labels[self.device] - - total_loss = ( - F.cross_entropy(logits_per_image, labels) + - F.cross_entropy(logits_per_text, labels) - ) / 2 if logits_per_image is not None else F.cross_entropy(logits_per_text, labels) - return total_loss - - def training_step(self, batch): - image, text, _ = batch - image_features, text_features, logit_scale = self(image, text) - total_loss = self.clip_loss(image_features, text_features, logit_scale) - self.log('train_loss', total_loss, sync_dist=False) - return total_loss - - def on_train_batch_end(self, outputs, batch, batch_idx: int) -> None: - with torch.no_grad(): - self.model.logit_scale.clamp_(0, math.log(100)) - - def get_metrics(self, image_features, text_features, labels, logit_scale): - # 计算相似度，支持多个样本的情况（比如一个图片有多个caption） - # img2txt计算的时候要用到，因为一张图片可能对应多个文本。 - # txt2img计算的时候不需要（一般一个text只有一个对应图片） - metrics = {} - logits_per_image = (logit_scale * image_features @ text_features.t()).detach().cpu() - logits_per_text = logits_per_image.t().detach().cpu() - - logits = {"image_to_text": logits_per_image, "text_to_image": logits_per_text} - - label2idx = {} # 计算label到idx的映射。 - repeat_id = [] - for i, label in enumerate(labels): - if label not in label2idx: - label2idx[label] = [i] - else: - # 表示该index的标签出现过，记录这个index，后续算txt2img分数的时候，这些index的权值要降低。 - label2idx[label].append(i) - repeat_id.append(i) - - ground_truth = [label2idx[label] for label in labels] - - for name, logit in logits.items(): - if name == 'text_to_image': - logit[:, repeat_id] -= 1e8 # 这部分的分数要降低。（重复出现的图片，直接忽略） - r_stat = {1: [], 5: [], 10: []} - # r1_stat, r5_stat, r10_stat = [], [], [] - # index of the largest element to the smallest - ranking = torch.argsort(logit, descending=True) - for i, each_query in enumerate(ranking[:, :10]): - for j, q in enumerate(each_query): - found = False - if q in ground_truth[i]: - for k, v in r_stat.items(): - if j < k: - found = True - v.append(1) - if found: - break - for k, v in r_stat.items(): - metrics[f'{name}_R@{k}'] = sum(v)/len(logit) - return metrics - - def validation_step(self, batch, batch_idx): - image, text, label = batch - image_features, text_features, logit_scale = self(image, text) - return image_features, text_features, logit_scale, text['input_ids'].shape[0], label - - def validation_epoch_end(self, val_outputs): - all_image_features = [] - all_text_features = [] - all_labels = [] - sample_size = 0 - for o in val_outputs: - all_image_features.append(o[0]) - all_text_features.append(o[1]) - sample_size += o[3] - all_labels += o[4] - if len(all_image_features) == 0 or len(all_text_features) == 0: - return - all_image_features = torch.cat(all_image_features) - all_text_features = torch.cat(all_text_features) - logit_scale = val_outputs[0][2].mean() - logits_per_image = logit_scale * all_image_features @ all_text_features.t() - logits_per_text = logits_per_image.t() - - labels = torch.arange(sample_size, device=self.device).long() - total_loss = (F.cross_entropy(logits_per_image, labels) - + F.cross_entropy(logits_per_text, labels)) / 2 - - val_metrics = self.get_metrics( - image_features=all_image_features, - text_features=all_text_features, - logit_scale=logit_scale, - labels=all_labels) - loss = total_loss / sample_size - self.log('val_loss', loss, sync_dist=False) - for k, v in val_metrics.items(): - self.log(f'val_{k}', v, sync_dist=False) - - def on_load_checkpoint(self, checkpoint) -> None: - # 兼容低版本lightning，低版本lightning从ckpt起来时steps数会被重置为0 - global_step_offset = checkpoint["global_step"] - if 'global_samples' in checkpoint: - self.consumed_samples = checkpoint['global_samples'] - self.trainer.fit_loop.epoch_loop._batches_that_stepped = global_step_offset - - def on_save_checkpoint(self, checkpoint) -> None: - # 保存的时候把权重按huggingface的形式保存出来 - if self.global_rank == 0: - dir_path = os.path.join( - self.hparams.default_root_dir, f'hf_out_{self.trainer.current_epoch}_{self.trainer.global_step}') - if not os.path.exists(dir_path): - os.mkdir(dir_path) - self.model.save_pretrained(dir_path) - self.processor.save_pretrained(dir_path) - - -if __name__ == '__main__': - args_parser = argparse.ArgumentParser() - args_parser = add_module_args(args_parser) - args_parser = add_data_args(args_parser) - args_parser = UniversalDataModule.add_data_specific_args(args_parser) - args_parser = Trainer.add_argparse_args(args_parser) - args_parser = TaiyiCLIP.add_module_specific_args(args_parser) - args_parser = UniversalCheckpoint.add_argparse_args(args_parser) - args = args_parser.parse_args() - - lr_monitor = LearningRateMonitor(logging_interval='step') - checkpoint_callback = UniversalCheckpoint(args) - - trainer = Trainer.from_argparse_args(args, - callbacks=[ - lr_monitor, - checkpoint_callback]) - - model = TaiyiCLIP(args) - processor = model.processor - collate_fn = Collator(args, processor) - datasets = load_data(args, global_rank=trainer.global_rank) - - # 加载单个验证集：！！！验证代码有效性临时这样干的，验证完有效性会删除 - from fengshen.examples.pretrain_taiyi_clip.flickr_datasets import flickr30k_CNA - img_root = '/shared_space/ccnl/mm_data/Flickr30k-CNA/flickr30k/images' - text_annot_path = '/shared_space/ccnl/mm_data/Flickr30k-CNA/test/flickr30k_cn_test.txt' - - datasets[args.val_datasets_field] = flickr30k_CNA(img_root, text_annot_path, collate_fn) - - datamoule = UniversalDataModule( - tokenizer=None, collate_fn=collate_fn, args=args, datasets=datasets) - - trainer.fit(model, datamoule, ckpt_path=args.load_ckpt_path) diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/utils.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/utils.js deleted file mode 100644 index 799a6a2b4ee4672c7662a2951b5cdcc1b2413a28..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/express/lib/utils.js +++ /dev/null @@ -1,304 +0,0 @@ -/*! - * express - * Copyright(c) 2009-2013 TJ Holowaychuk - * Copyright(c) 2014-2015 Douglas Christopher Wilson - * MIT Licensed - */ - -'use strict'; - -/** - * Module dependencies. - * @api private - */ - -var Buffer = require('safe-buffer').Buffer -var contentDisposition = require('content-disposition'); -var contentType = require('content-type'); -var deprecate = require('depd')('express'); -var flatten = require('array-flatten'); -var mime = require('send').mime; -var etag = require('etag'); -var proxyaddr = require('proxy-addr'); -var qs = require('qs'); -var querystring = require('querystring'); - -/** - * Return strong ETag for `body`. - * - * @param {String|Buffer} body - * @param {String} [encoding] - * @return {String} - * @api private - */ - -exports.etag = createETagGenerator({ weak: false }) - -/** - * Return weak ETag for `body`. - * - * @param {String|Buffer} body - * @param {String} [encoding] - * @return {String} - * @api private - */ - -exports.wetag = createETagGenerator({ weak: true }) - -/** - * Check if `path` looks absolute. - * - * @param {String} path - * @return {Boolean} - * @api private - */ - -exports.isAbsolute = function(path){ - if ('/' === path[0]) return true; - if (':' === path[1] && ('\\' === path[2] || '/' === path[2])) return true; // Windows device path - if ('\\\\' === path.substring(0, 2)) return true; // Microsoft Azure absolute path -}; - -/** - * Flatten the given `arr`. - * - * @param {Array} arr - * @return {Array} - * @api private - */ - -exports.flatten = deprecate.function(flatten, - 'utils.flatten: use array-flatten npm module instead'); - -/** - * Normalize the given `type`, for example "html" becomes "text/html". - * - * @param {String} type - * @return {Object} - * @api private - */ - -exports.normalizeType = function(type){ - return ~type.indexOf('/') - ? acceptParams(type) - : { value: mime.lookup(type), params: {} }; -}; - -/** - * Normalize `types`, for example "html" becomes "text/html". - * - * @param {Array} types - * @return {Array} - * @api private - */ - -exports.normalizeTypes = function(types){ - var ret = []; - - for (var i = 0; i < types.length; ++i) { - ret.push(exports.normalizeType(types[i])); - } - - return ret; -}; - -/** - * Generate Content-Disposition header appropriate for the filename. - * non-ascii filenames are urlencoded and a filename* parameter is added - * - * @param {String} filename - * @return {String} - * @api private - */ - -exports.contentDisposition = deprecate.function(contentDisposition, - 'utils.contentDisposition: use content-disposition npm module instead'); - -/** - * Parse accept params `str` returning an - * object with `.value`, `.quality` and `.params`. - * also includes `.originalIndex` for stable sorting - * - * @param {String} str - * @param {Number} index - * @return {Object} - * @api private - */ - -function acceptParams(str, index) { - var parts = str.split(/ *; */); - var ret = { value: parts[0], quality: 1, params: {}, originalIndex: index }; - - for (var i = 1; i < parts.length; ++i) { - var pms = parts[i].split(/ *= */); - if ('q' === pms[0]) { - ret.quality = parseFloat(pms[1]); - } else { - ret.params[pms[0]] = pms[1]; - } - } - - return ret; -} - -/** - * Compile "etag" value to function. - * - * @param {Boolean|String|Function} val - * @return {Function} - * @api private - */ - -exports.compileETag = function(val) { - var fn; - - if (typeof val === 'function') { - return val; - } - - switch (val) { - case true: - case 'weak': - fn = exports.wetag; - break; - case false: - break; - case 'strong': - fn = exports.etag; - break; - default: - throw new TypeError('unknown value for etag function: ' + val); - } - - return fn; -} - -/** - * Compile "query parser" value to function. - * - * @param {String|Function} val - * @return {Function} - * @api private - */ - -exports.compileQueryParser = function compileQueryParser(val) { - var fn; - - if (typeof val === 'function') { - return val; - } - - switch (val) { - case true: - case 'simple': - fn = querystring.parse; - break; - case false: - fn = newObject; - break; - case 'extended': - fn = parseExtendedQueryString; - break; - default: - throw new TypeError('unknown value for query parser function: ' + val); - } - - return fn; -} - -/** - * Compile "proxy trust" value to function. - * - * @param {Boolean|String|Number|Array|Function} val - * @return {Function} - * @api private - */ - -exports.compileTrust = function(val) { - if (typeof val === 'function') return val; - - if (val === true) { - // Support plain true/false - return function(){ return true }; - } - - if (typeof val === 'number') { - // Support trusting hop count - return function(a, i){ return i < val }; - } - - if (typeof val === 'string') { - // Support comma-separated values - val = val.split(',') - .map(function (v) { return v.trim() }) - } - - return proxyaddr.compile(val || []); -} - -/** - * Set the charset in a given Content-Type string. - * - * @param {String} type - * @param {String} charset - * @return {String} - * @api private - */ - -exports.setCharset = function setCharset(type, charset) { - if (!type || !charset) { - return type; - } - - // parse type - var parsed = contentType.parse(type); - - // set charset - parsed.parameters.charset = charset; - - // format type - return contentType.format(parsed); -}; - -/** - * Create an ETag generator function, generating ETags with - * the given options. - * - * @param {object} options - * @return {function} - * @private - */ - -function createETagGenerator (options) { - return function generateETag (body, encoding) { - var buf = !Buffer.isBuffer(body) - ? Buffer.from(body, encoding) - : body - - return etag(buf, options) - } -} - -/** - * Parse an extended query string with qs. - * - * @return {Object} - * @private - */ - -function parseExtendedQueryString(str) { - return qs.parse(str, { - allowPrototypes: true - }); -} - -/** - * Return new empty object. - * - * @return {Object} - * @api private - */ - -function newObject() { - return {}; -} diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/test/parse.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/test/parse.js deleted file mode 100644 index 7d7b4dd8aea13754a32dca91e432e80ef3f6af67..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/test/parse.js +++ /dev/null @@ -1,855 +0,0 @@ -'use strict'; - -var test = require('tape'); -var qs = require('../'); -var utils = require('../lib/utils'); -var iconv = require('iconv-lite'); -var SaferBuffer = require('safer-buffer').Buffer; - -test('parse()', function (t) { - t.test('parses a simple string', function (st) { - st.deepEqual(qs.parse('0=foo'), { 0: 'foo' }); - st.deepEqual(qs.parse('foo=c++'), { foo: 'c ' }); - st.deepEqual(qs.parse('a[>=]=23'), { a: { '>=': '23' } }); - st.deepEqual(qs.parse('a[<=>]==23'), { a: { '<=>': '=23' } }); - st.deepEqual(qs.parse('a[==]=23'), { a: { '==': '23' } }); - st.deepEqual(qs.parse('foo', { strictNullHandling: true }), { foo: null }); - st.deepEqual(qs.parse('foo'), { foo: '' }); - st.deepEqual(qs.parse('foo='), { foo: '' }); - st.deepEqual(qs.parse('foo=bar'), { foo: 'bar' }); - st.deepEqual(qs.parse(' foo = bar = baz '), { ' foo ': ' bar = baz ' }); - st.deepEqual(qs.parse('foo=bar=baz'), { foo: 'bar=baz' }); - st.deepEqual(qs.parse('foo=bar&bar=baz'), { foo: 'bar', bar: 'baz' }); - st.deepEqual(qs.parse('foo2=bar2&baz2='), { foo2: 'bar2', baz2: '' }); - st.deepEqual(qs.parse('foo=bar&baz', { strictNullHandling: true }), { foo: 'bar', baz: null }); - st.deepEqual(qs.parse('foo=bar&baz'), { foo: 'bar', baz: '' }); - st.deepEqual(qs.parse('cht=p3&chd=t:60,40&chs=250x100&chl=Hello|World'), { - cht: 'p3', - chd: 't:60,40', - chs: '250x100', - chl: 'Hello|World' - }); - st.end(); - }); - - t.test('arrayFormat: brackets allows only explicit arrays', function (st) { - st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'brackets' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'brackets' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'brackets' }), { a: 'b,c' }); - st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'brackets' }), { a: ['b', 'c'] }); - st.end(); - }); - - t.test('arrayFormat: indices allows only indexed arrays', function (st) { - st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'indices' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'indices' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'indices' }), { a: 'b,c' }); - st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'indices' }), { a: ['b', 'c'] }); - st.end(); - }); - - t.test('arrayFormat: comma allows only comma-separated arrays', function (st) { - st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'comma' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'comma' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'comma' }), { a: 'b,c' }); - st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'comma' }), { a: ['b', 'c'] }); - st.end(); - }); - - t.test('arrayFormat: repeat allows only repeated values', function (st) { - st.deepEqual(qs.parse('a[]=b&a[]=c', { arrayFormat: 'repeat' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayFormat: 'repeat' }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a=b,c', { arrayFormat: 'repeat' }), { a: 'b,c' }); - st.deepEqual(qs.parse('a=b&a=c', { arrayFormat: 'repeat' }), { a: ['b', 'c'] }); - st.end(); - }); - - t.test('allows enabling dot notation', function (st) { - st.deepEqual(qs.parse('a.b=c'), { 'a.b': 'c' }); - st.deepEqual(qs.parse('a.b=c', { allowDots: true }), { a: { b: 'c' } }); - st.end(); - }); - - t.deepEqual(qs.parse('a[b]=c'), { a: { b: 'c' } }, 'parses a single nested string'); - t.deepEqual(qs.parse('a[b][c]=d'), { a: { b: { c: 'd' } } }, 'parses a double nested string'); - t.deepEqual( - qs.parse('a[b][c][d][e][f][g][h]=i'), - { a: { b: { c: { d: { e: { f: { '[g][h]': 'i' } } } } } } }, - 'defaults to a depth of 5' - ); - - t.test('only parses one level when depth = 1', function (st) { - st.deepEqual(qs.parse('a[b][c]=d', { depth: 1 }), { a: { b: { '[c]': 'd' } } }); - st.deepEqual(qs.parse('a[b][c][d]=e', { depth: 1 }), { a: { b: { '[c][d]': 'e' } } }); - st.end(); - }); - - t.test('uses original key when depth = 0', function (st) { - st.deepEqual(qs.parse('a[0]=b&a[1]=c', { depth: 0 }), { 'a[0]': 'b', 'a[1]': 'c' }); - st.deepEqual(qs.parse('a[0][0]=b&a[0][1]=c&a[1]=d&e=2', { depth: 0 }), { 'a[0][0]': 'b', 'a[0][1]': 'c', 'a[1]': 'd', e: '2' }); - st.end(); - }); - - t.test('uses original key when depth = false', function (st) { - st.deepEqual(qs.parse('a[0]=b&a[1]=c', { depth: false }), { 'a[0]': 'b', 'a[1]': 'c' }); - st.deepEqual(qs.parse('a[0][0]=b&a[0][1]=c&a[1]=d&e=2', { depth: false }), { 'a[0][0]': 'b', 'a[0][1]': 'c', 'a[1]': 'd', e: '2' }); - st.end(); - }); - - t.deepEqual(qs.parse('a=b&a=c'), { a: ['b', 'c'] }, 'parses a simple array'); - - t.test('parses an explicit array', function (st) { - st.deepEqual(qs.parse('a[]=b'), { a: ['b'] }); - st.deepEqual(qs.parse('a[]=b&a[]=c'), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[]=b&a[]=c&a[]=d'), { a: ['b', 'c', 'd'] }); - st.end(); - }); - - t.test('parses a mix of simple and explicit arrays', function (st) { - st.deepEqual(qs.parse('a=b&a[]=c'), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[]=b&a=c'), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[0]=b&a=c'), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a=b&a[0]=c'), { a: ['b', 'c'] }); - - st.deepEqual(qs.parse('a[1]=b&a=c', { arrayLimit: 20 }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[]=b&a=c', { arrayLimit: 0 }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[]=b&a=c'), { a: ['b', 'c'] }); - - st.deepEqual(qs.parse('a=b&a[1]=c', { arrayLimit: 20 }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a=b&a[]=c', { arrayLimit: 0 }), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a=b&a[]=c'), { a: ['b', 'c'] }); - - st.end(); - }); - - t.test('parses a nested array', function (st) { - st.deepEqual(qs.parse('a[b][]=c&a[b][]=d'), { a: { b: ['c', 'd'] } }); - st.deepEqual(qs.parse('a[>=]=25'), { a: { '>=': '25' } }); - st.end(); - }); - - t.test('allows to specify array indices', function (st) { - st.deepEqual(qs.parse('a[1]=c&a[0]=b&a[2]=d'), { a: ['b', 'c', 'd'] }); - st.deepEqual(qs.parse('a[1]=c&a[0]=b'), { a: ['b', 'c'] }); - st.deepEqual(qs.parse('a[1]=c', { arrayLimit: 20 }), { a: ['c'] }); - st.deepEqual(qs.parse('a[1]=c', { arrayLimit: 0 }), { a: { 1: 'c' } }); - st.deepEqual(qs.parse('a[1]=c'), { a: ['c'] }); - st.end(); - }); - - t.test('limits specific array indices to arrayLimit', function (st) { - st.deepEqual(qs.parse('a[20]=a', { arrayLimit: 20 }), { a: ['a'] }); - st.deepEqual(qs.parse('a[21]=a', { arrayLimit: 20 }), { a: { 21: 'a' } }); - - st.deepEqual(qs.parse('a[20]=a'), { a: ['a'] }); - st.deepEqual(qs.parse('a[21]=a'), { a: { 21: 'a' } }); - st.end(); - }); - - t.deepEqual(qs.parse('a[12b]=c'), { a: { '12b': 'c' } }, 'supports keys that begin with a number'); - - t.test('supports encoded = signs', function (st) { - st.deepEqual(qs.parse('he%3Dllo=th%3Dere'), { 'he=llo': 'th=ere' }); - st.end(); - }); - - t.test('is ok with url encoded strings', function (st) { - st.deepEqual(qs.parse('a[b%20c]=d'), { a: { 'b c': 'd' } }); - st.deepEqual(qs.parse('a[b]=c%20d'), { a: { b: 'c d' } }); - st.end(); - }); - - t.test('allows brackets in the value', function (st) { - st.deepEqual(qs.parse('pets=["tobi"]'), { pets: '["tobi"]' }); - st.deepEqual(qs.parse('operators=[">=", "<="]'), { operators: '[">=", "<="]' }); - st.end(); - }); - - t.test('allows empty values', function (st) { - st.deepEqual(qs.parse(''), {}); - st.deepEqual(qs.parse(null), {}); - st.deepEqual(qs.parse(undefined), {}); - st.end(); - }); - - t.test('transforms arrays to objects', function (st) { - st.deepEqual(qs.parse('foo[0]=bar&foo[bad]=baz'), { foo: { 0: 'bar', bad: 'baz' } }); - st.deepEqual(qs.parse('foo[bad]=baz&foo[0]=bar'), { foo: { bad: 'baz', 0: 'bar' } }); - st.deepEqual(qs.parse('foo[bad]=baz&foo[]=bar'), { foo: { bad: 'baz', 0: 'bar' } }); - st.deepEqual(qs.parse('foo[]=bar&foo[bad]=baz'), { foo: { 0: 'bar', bad: 'baz' } }); - st.deepEqual(qs.parse('foo[bad]=baz&foo[]=bar&foo[]=foo'), { foo: { bad: 'baz', 0: 'bar', 1: 'foo' } }); - st.deepEqual(qs.parse('foo[0][a]=a&foo[0][b]=b&foo[1][a]=aa&foo[1][b]=bb'), { foo: [{ a: 'a', b: 'b' }, { a: 'aa', b: 'bb' }] }); - - st.deepEqual(qs.parse('a[]=b&a[t]=u&a[hasOwnProperty]=c', { allowPrototypes: false }), { a: { 0: 'b', t: 'u' } }); - st.deepEqual(qs.parse('a[]=b&a[t]=u&a[hasOwnProperty]=c', { allowPrototypes: true }), { a: { 0: 'b', t: 'u', hasOwnProperty: 'c' } }); - st.deepEqual(qs.parse('a[]=b&a[hasOwnProperty]=c&a[x]=y', { allowPrototypes: false }), { a: { 0: 'b', x: 'y' } }); - st.deepEqual(qs.parse('a[]=b&a[hasOwnProperty]=c&a[x]=y', { allowPrototypes: true }), { a: { 0: 'b', hasOwnProperty: 'c', x: 'y' } }); - st.end(); - }); - - t.test('transforms arrays to objects (dot notation)', function (st) { - st.deepEqual(qs.parse('foo[0].baz=bar&fool.bad=baz', { allowDots: true }), { foo: [{ baz: 'bar' }], fool: { bad: 'baz' } }); - st.deepEqual(qs.parse('foo[0].baz=bar&fool.bad.boo=baz', { allowDots: true }), { foo: [{ baz: 'bar' }], fool: { bad: { boo: 'baz' } } }); - st.deepEqual(qs.parse('foo[0][0].baz=bar&fool.bad=baz', { allowDots: true }), { foo: [[{ baz: 'bar' }]], fool: { bad: 'baz' } }); - st.deepEqual(qs.parse('foo[0].baz[0]=15&foo[0].bar=2', { allowDots: true }), { foo: [{ baz: ['15'], bar: '2' }] }); - st.deepEqual(qs.parse('foo[0].baz[0]=15&foo[0].baz[1]=16&foo[0].bar=2', { allowDots: true }), { foo: [{ baz: ['15', '16'], bar: '2' }] }); - st.deepEqual(qs.parse('foo.bad=baz&foo[0]=bar', { allowDots: true }), { foo: { bad: 'baz', 0: 'bar' } }); - st.deepEqual(qs.parse('foo.bad=baz&foo[]=bar', { allowDots: true }), { foo: { bad: 'baz', 0: 'bar' } }); - st.deepEqual(qs.parse('foo[]=bar&foo.bad=baz', { allowDots: true }), { foo: { 0: 'bar', bad: 'baz' } }); - st.deepEqual(qs.parse('foo.bad=baz&foo[]=bar&foo[]=foo', { allowDots: true }), { foo: { bad: 'baz', 0: 'bar', 1: 'foo' } }); - st.deepEqual(qs.parse('foo[0].a=a&foo[0].b=b&foo[1].a=aa&foo[1].b=bb', { allowDots: true }), { foo: [{ a: 'a', b: 'b' }, { a: 'aa', b: 'bb' }] }); - st.end(); - }); - - t.test('correctly prunes undefined values when converting an array to an object', function (st) { - st.deepEqual(qs.parse('a[2]=b&a[99999999]=c'), { a: { 2: 'b', 99999999: 'c' } }); - st.end(); - }); - - t.test('supports malformed uri characters', function (st) { - st.deepEqual(qs.parse('{%:%}', { strictNullHandling: true }), { '{%:%}': null }); - st.deepEqual(qs.parse('{%:%}='), { '{%:%}': '' }); - st.deepEqual(qs.parse('foo=%:%}'), { foo: '%:%}' }); - st.end(); - }); - - t.test('doesn\'t produce empty keys', function (st) { - st.deepEqual(qs.parse('_r=1&'), { _r: '1' }); - st.end(); - }); - - t.test('cannot access Object prototype', function (st) { - qs.parse('constructor[prototype][bad]=bad'); - qs.parse('bad[constructor][prototype][bad]=bad'); - st.equal(typeof Object.prototype.bad, 'undefined'); - st.end(); - }); - - t.test('parses arrays of objects', function (st) { - st.deepEqual(qs.parse('a[][b]=c'), { a: [{ b: 'c' }] }); - st.deepEqual(qs.parse('a[0][b]=c'), { a: [{ b: 'c' }] }); - st.end(); - }); - - t.test('allows for empty strings in arrays', function (st) { - st.deepEqual(qs.parse('a[]=b&a[]=&a[]=c'), { a: ['b', '', 'c'] }); - - st.deepEqual( - qs.parse('a[0]=b&a[1]&a[2]=c&a[19]=', { strictNullHandling: true, arrayLimit: 20 }), - { a: ['b', null, 'c', ''] }, - 'with arrayLimit 20 + array indices: null then empty string works' - ); - st.deepEqual( - qs.parse('a[]=b&a[]&a[]=c&a[]=', { strictNullHandling: true, arrayLimit: 0 }), - { a: ['b', null, 'c', ''] }, - 'with arrayLimit 0 + array brackets: null then empty string works' - ); - - st.deepEqual( - qs.parse('a[0]=b&a[1]=&a[2]=c&a[19]', { strictNullHandling: true, arrayLimit: 20 }), - { a: ['b', '', 'c', null] }, - 'with arrayLimit 20 + array indices: empty string then null works' - ); - st.deepEqual( - qs.parse('a[]=b&a[]=&a[]=c&a[]', { strictNullHandling: true, arrayLimit: 0 }), - { a: ['b', '', 'c', null] }, - 'with arrayLimit 0 + array brackets: empty string then null works' - ); - - st.deepEqual( - qs.parse('a[]=&a[]=b&a[]=c'), - { a: ['', 'b', 'c'] }, - 'array brackets: empty strings work' - ); - st.end(); - }); - - t.test('compacts sparse arrays', function (st) { - st.deepEqual(qs.parse('a[10]=1&a[2]=2', { arrayLimit: 20 }), { a: ['2', '1'] }); - st.deepEqual(qs.parse('a[1][b][2][c]=1', { arrayLimit: 20 }), { a: [{ b: [{ c: '1' }] }] }); - st.deepEqual(qs.parse('a[1][2][3][c]=1', { arrayLimit: 20 }), { a: [[[{ c: '1' }]]] }); - st.deepEqual(qs.parse('a[1][2][3][c][1]=1', { arrayLimit: 20 }), { a: [[[{ c: ['1'] }]]] }); - st.end(); - }); - - t.test('parses sparse arrays', function (st) { - /* eslint no-sparse-arrays: 0 */ - st.deepEqual(qs.parse('a[4]=1&a[1]=2', { allowSparse: true }), { a: [, '2', , , '1'] }); - st.deepEqual(qs.parse('a[1][b][2][c]=1', { allowSparse: true }), { a: [, { b: [, , { c: '1' }] }] }); - st.deepEqual(qs.parse('a[1][2][3][c]=1', { allowSparse: true }), { a: [, [, , [, , , { c: '1' }]]] }); - st.deepEqual(qs.parse('a[1][2][3][c][1]=1', { allowSparse: true }), { a: [, [, , [, , , { c: [, '1'] }]]] }); - st.end(); - }); - - t.test('parses semi-parsed strings', function (st) { - st.deepEqual(qs.parse({ 'a[b]': 'c' }), { a: { b: 'c' } }); - st.deepEqual(qs.parse({ 'a[b]': 'c', 'a[d]': 'e' }), { a: { b: 'c', d: 'e' } }); - st.end(); - }); - - t.test('parses buffers correctly', function (st) { - var b = SaferBuffer.from('test'); - st.deepEqual(qs.parse({ a: b }), { a: b }); - st.end(); - }); - - t.test('parses jquery-param strings', function (st) { - // readable = 'filter[0][]=int1&filter[0][]==&filter[0][]=77&filter[]=and&filter[2][]=int2&filter[2][]==&filter[2][]=8' - var encoded = 'filter%5B0%5D%5B%5D=int1&filter%5B0%5D%5B%5D=%3D&filter%5B0%5D%5B%5D=77&filter%5B%5D=and&filter%5B2%5D%5B%5D=int2&filter%5B2%5D%5B%5D=%3D&filter%5B2%5D%5B%5D=8'; - var expected = { filter: [['int1', '=', '77'], 'and', ['int2', '=', '8']] }; - st.deepEqual(qs.parse(encoded), expected); - st.end(); - }); - - t.test('continues parsing when no parent is found', function (st) { - st.deepEqual(qs.parse('[]=&a=b'), { 0: '', a: 'b' }); - st.deepEqual(qs.parse('[]&a=b', { strictNullHandling: true }), { 0: null, a: 'b' }); - st.deepEqual(qs.parse('[foo]=bar'), { foo: 'bar' }); - st.end(); - }); - - t.test('does not error when parsing a very long array', function (st) { - var str = 'a[]=a'; - while (Buffer.byteLength(str) < 128 * 1024) { - str = str + '&' + str; - } - - st.doesNotThrow(function () { - qs.parse(str); - }); - - st.end(); - }); - - t.test('should not throw when a native prototype has an enumerable property', function (st) { - Object.prototype.crash = ''; - Array.prototype.crash = ''; - st.doesNotThrow(qs.parse.bind(null, 'a=b')); - st.deepEqual(qs.parse('a=b'), { a: 'b' }); - st.doesNotThrow(qs.parse.bind(null, 'a[][b]=c')); - st.deepEqual(qs.parse('a[][b]=c'), { a: [{ b: 'c' }] }); - delete Object.prototype.crash; - delete Array.prototype.crash; - st.end(); - }); - - t.test('parses a string with an alternative string delimiter', function (st) { - st.deepEqual(qs.parse('a=b;c=d', { delimiter: ';' }), { a: 'b', c: 'd' }); - st.end(); - }); - - t.test('parses a string with an alternative RegExp delimiter', function (st) { - st.deepEqual(qs.parse('a=b; c=d', { delimiter: /[;,] */ }), { a: 'b', c: 'd' }); - st.end(); - }); - - t.test('does not use non-splittable objects as delimiters', function (st) { - st.deepEqual(qs.parse('a=b&c=d', { delimiter: true }), { a: 'b', c: 'd' }); - st.end(); - }); - - t.test('allows overriding parameter limit', function (st) { - st.deepEqual(qs.parse('a=b&c=d', { parameterLimit: 1 }), { a: 'b' }); - st.end(); - }); - - t.test('allows setting the parameter limit to Infinity', function (st) { - st.deepEqual(qs.parse('a=b&c=d', { parameterLimit: Infinity }), { a: 'b', c: 'd' }); - st.end(); - }); - - t.test('allows overriding array limit', function (st) { - st.deepEqual(qs.parse('a[0]=b', { arrayLimit: -1 }), { a: { 0: 'b' } }); - st.deepEqual(qs.parse('a[-1]=b', { arrayLimit: -1 }), { a: { '-1': 'b' } }); - st.deepEqual(qs.parse('a[0]=b&a[1]=c', { arrayLimit: 0 }), { a: { 0: 'b', 1: 'c' } }); - st.end(); - }); - - t.test('allows disabling array parsing', function (st) { - var indices = qs.parse('a[0]=b&a[1]=c', { parseArrays: false }); - st.deepEqual(indices, { a: { 0: 'b', 1: 'c' } }); - st.equal(Array.isArray(indices.a), false, 'parseArrays:false, indices case is not an array'); - - var emptyBrackets = qs.parse('a[]=b', { parseArrays: false }); - st.deepEqual(emptyBrackets, { a: { 0: 'b' } }); - st.equal(Array.isArray(emptyBrackets.a), false, 'parseArrays:false, empty brackets case is not an array'); - - st.end(); - }); - - t.test('allows for query string prefix', function (st) { - st.deepEqual(qs.parse('?foo=bar', { ignoreQueryPrefix: true }), { foo: 'bar' }); - st.deepEqual(qs.parse('foo=bar', { ignoreQueryPrefix: true }), { foo: 'bar' }); - st.deepEqual(qs.parse('?foo=bar', { ignoreQueryPrefix: false }), { '?foo': 'bar' }); - - st.end(); - }); - - t.test('parses an object', function (st) { - var input = { - 'user[name]': { 'pop[bob]': 3 }, - 'user[email]': null - }; - - var expected = { - user: { - name: { 'pop[bob]': 3 }, - email: null - } - }; - - var result = qs.parse(input); - - st.deepEqual(result, expected); - st.end(); - }); - - t.test('parses string with comma as array divider', function (st) { - st.deepEqual(qs.parse('foo=bar,tee', { comma: true }), { foo: ['bar', 'tee'] }); - st.deepEqual(qs.parse('foo[bar]=coffee,tee', { comma: true }), { foo: { bar: ['coffee', 'tee'] } }); - st.deepEqual(qs.parse('foo=', { comma: true }), { foo: '' }); - st.deepEqual(qs.parse('foo', { comma: true }), { foo: '' }); - st.deepEqual(qs.parse('foo', { comma: true, strictNullHandling: true }), { foo: null }); - - // test cases inversed from from stringify tests - st.deepEqual(qs.parse('a[0]=c'), { a: ['c'] }); - st.deepEqual(qs.parse('a[]=c'), { a: ['c'] }); - st.deepEqual(qs.parse('a[]=c', { comma: true }), { a: ['c'] }); - - st.deepEqual(qs.parse('a[0]=c&a[1]=d'), { a: ['c', 'd'] }); - st.deepEqual(qs.parse('a[]=c&a[]=d'), { a: ['c', 'd'] }); - st.deepEqual(qs.parse('a=c,d', { comma: true }), { a: ['c', 'd'] }); - - st.end(); - }); - - t.test('parses values with comma as array divider', function (st) { - st.deepEqual(qs.parse({ foo: 'bar,tee' }, { comma: false }), { foo: 'bar,tee' }); - st.deepEqual(qs.parse({ foo: 'bar,tee' }, { comma: true }), { foo: ['bar', 'tee'] }); - st.end(); - }); - - t.test('use number decoder, parses string that has one number with comma option enabled', function (st) { - var decoder = function (str, defaultDecoder, charset, type) { - if (!isNaN(Number(str))) { - return parseFloat(str); - } - return defaultDecoder(str, defaultDecoder, charset, type); - }; - - st.deepEqual(qs.parse('foo=1', { comma: true, decoder: decoder }), { foo: 1 }); - st.deepEqual(qs.parse('foo=0', { comma: true, decoder: decoder }), { foo: 0 }); - - st.end(); - }); - - t.test('parses brackets holds array of arrays when having two parts of strings with comma as array divider', function (st) { - st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=4,5,6', { comma: true }), { foo: [['1', '2', '3'], ['4', '5', '6']] }); - st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=', { comma: true }), { foo: [['1', '2', '3'], ''] }); - st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=,', { comma: true }), { foo: [['1', '2', '3'], ['', '']] }); - st.deepEqual(qs.parse('foo[]=1,2,3&foo[]=a', { comma: true }), { foo: [['1', '2', '3'], 'a'] }); - - st.end(); - }); - - t.test('parses comma delimited array while having percent-encoded comma treated as normal text', function (st) { - st.deepEqual(qs.parse('foo=a%2Cb', { comma: true }), { foo: 'a,b' }); - st.deepEqual(qs.parse('foo=a%2C%20b,d', { comma: true }), { foo: ['a, b', 'd'] }); - st.deepEqual(qs.parse('foo=a%2C%20b,c%2C%20d', { comma: true }), { foo: ['a, b', 'c, d'] }); - - st.end(); - }); - - t.test('parses an object in dot notation', function (st) { - var input = { - 'user.name': { 'pop[bob]': 3 }, - 'user.email.': null - }; - - var expected = { - user: { - name: { 'pop[bob]': 3 }, - email: null - } - }; - - var result = qs.parse(input, { allowDots: true }); - - st.deepEqual(result, expected); - st.end(); - }); - - t.test('parses an object and not child values', function (st) { - var input = { - 'user[name]': { 'pop[bob]': { test: 3 } }, - 'user[email]': null - }; - - var expected = { - user: { - name: { 'pop[bob]': { test: 3 } }, - email: null - } - }; - - var result = qs.parse(input); - - st.deepEqual(result, expected); - st.end(); - }); - - t.test('does not blow up when Buffer global is missing', function (st) { - var tempBuffer = global.Buffer; - delete global.Buffer; - var result = qs.parse('a=b&c=d'); - global.Buffer = tempBuffer; - st.deepEqual(result, { a: 'b', c: 'd' }); - st.end(); - }); - - t.test('does not crash when parsing circular references', function (st) { - var a = {}; - a.b = a; - - var parsed; - - st.doesNotThrow(function () { - parsed = qs.parse({ 'foo[bar]': 'baz', 'foo[baz]': a }); - }); - - st.equal('foo' in parsed, true, 'parsed has "foo" property'); - st.equal('bar' in parsed.foo, true); - st.equal('baz' in parsed.foo, true); - st.equal(parsed.foo.bar, 'baz'); - st.deepEqual(parsed.foo.baz, a); - st.end(); - }); - - t.test('does not crash when parsing deep objects', function (st) { - var parsed; - var str = 'foo'; - - for (var i = 0; i < 5000; i++) { - str += '[p]'; - } - - str += '=bar'; - - st.doesNotThrow(function () { - parsed = qs.parse(str, { depth: 5000 }); - }); - - st.equal('foo' in parsed, true, 'parsed has "foo" property'); - - var depth = 0; - var ref = parsed.foo; - while ((ref = ref.p)) { - depth += 1; - } - - st.equal(depth, 5000, 'parsed is 5000 properties deep'); - - st.end(); - }); - - t.test('parses null objects correctly', { skip: !Object.create }, function (st) { - var a = Object.create(null); - a.b = 'c'; - - st.deepEqual(qs.parse(a), { b: 'c' }); - var result = qs.parse({ a: a }); - st.equal('a' in result, true, 'result has "a" property'); - st.deepEqual(result.a, a); - st.end(); - }); - - t.test('parses dates correctly', function (st) { - var now = new Date(); - st.deepEqual(qs.parse({ a: now }), { a: now }); - st.end(); - }); - - t.test('parses regular expressions correctly', function (st) { - var re = /^test$/; - st.deepEqual(qs.parse({ a: re }), { a: re }); - st.end(); - }); - - t.test('does not allow overwriting prototype properties', function (st) { - st.deepEqual(qs.parse('a[hasOwnProperty]=b', { allowPrototypes: false }), {}); - st.deepEqual(qs.parse('hasOwnProperty=b', { allowPrototypes: false }), {}); - - st.deepEqual( - qs.parse('toString', { allowPrototypes: false }), - {}, - 'bare "toString" results in {}' - ); - - st.end(); - }); - - t.test('can allow overwriting prototype properties', function (st) { - st.deepEqual(qs.parse('a[hasOwnProperty]=b', { allowPrototypes: true }), { a: { hasOwnProperty: 'b' } }); - st.deepEqual(qs.parse('hasOwnProperty=b', { allowPrototypes: true }), { hasOwnProperty: 'b' }); - - st.deepEqual( - qs.parse('toString', { allowPrototypes: true }), - { toString: '' }, - 'bare "toString" results in { toString: "" }' - ); - - st.end(); - }); - - t.test('params starting with a closing bracket', function (st) { - st.deepEqual(qs.parse(']=toString'), { ']': 'toString' }); - st.deepEqual(qs.parse(']]=toString'), { ']]': 'toString' }); - st.deepEqual(qs.parse(']hello]=toString'), { ']hello]': 'toString' }); - st.end(); - }); - - t.test('params starting with a starting bracket', function (st) { - st.deepEqual(qs.parse('[=toString'), { '[': 'toString' }); - st.deepEqual(qs.parse('[[=toString'), { '[[': 'toString' }); - st.deepEqual(qs.parse('[hello[=toString'), { '[hello[': 'toString' }); - st.end(); - }); - - t.test('add keys to objects', function (st) { - st.deepEqual( - qs.parse('a[b]=c&a=d'), - { a: { b: 'c', d: true } }, - 'can add keys to objects' - ); - - st.deepEqual( - qs.parse('a[b]=c&a=toString'), - { a: { b: 'c' } }, - 'can not overwrite prototype' - ); - - st.deepEqual( - qs.parse('a[b]=c&a=toString', { allowPrototypes: true }), - { a: { b: 'c', toString: true } }, - 'can overwrite prototype with allowPrototypes true' - ); - - st.deepEqual( - qs.parse('a[b]=c&a=toString', { plainObjects: true }), - { __proto__: null, a: { __proto__: null, b: 'c', toString: true } }, - 'can overwrite prototype with plainObjects true' - ); - - st.end(); - }); - - t.test('dunder proto is ignored', function (st) { - var payload = 'categories[__proto__]=login&categories[__proto__]&categories[length]=42'; - var result = qs.parse(payload, { allowPrototypes: true }); - - st.deepEqual( - result, - { - categories: { - length: '42' - } - }, - 'silent [[Prototype]] payload' - ); - - var plainResult = qs.parse(payload, { allowPrototypes: true, plainObjects: true }); - - st.deepEqual( - plainResult, - { - __proto__: null, - categories: { - __proto__: null, - length: '42' - } - }, - 'silent [[Prototype]] payload: plain objects' - ); - - var query = qs.parse('categories[__proto__]=cats&categories[__proto__]=dogs&categories[some][json]=toInject', { allowPrototypes: true }); - - st.notOk(Array.isArray(query.categories), 'is not an array'); - st.notOk(query.categories instanceof Array, 'is not instanceof an array'); - st.deepEqual(query.categories, { some: { json: 'toInject' } }); - st.equal(JSON.stringify(query.categories), '{"some":{"json":"toInject"}}', 'stringifies as a non-array'); - - st.deepEqual( - qs.parse('foo[__proto__][hidden]=value&foo[bar]=stuffs', { allowPrototypes: true }), - { - foo: { - bar: 'stuffs' - } - }, - 'hidden values' - ); - - st.deepEqual( - qs.parse('foo[__proto__][hidden]=value&foo[bar]=stuffs', { allowPrototypes: true, plainObjects: true }), - { - __proto__: null, - foo: { - __proto__: null, - bar: 'stuffs' - } - }, - 'hidden values: plain objects' - ); - - st.end(); - }); - - t.test('can return null objects', { skip: !Object.create }, function (st) { - var expected = Object.create(null); - expected.a = Object.create(null); - expected.a.b = 'c'; - expected.a.hasOwnProperty = 'd'; - st.deepEqual(qs.parse('a[b]=c&a[hasOwnProperty]=d', { plainObjects: true }), expected); - st.deepEqual(qs.parse(null, { plainObjects: true }), Object.create(null)); - var expectedArray = Object.create(null); - expectedArray.a = Object.create(null); - expectedArray.a[0] = 'b'; - expectedArray.a.c = 'd'; - st.deepEqual(qs.parse('a[]=b&a[c]=d', { plainObjects: true }), expectedArray); - st.end(); - }); - - t.test('can parse with custom encoding', function (st) { - st.deepEqual(qs.parse('%8c%a7=%91%e5%8d%e3%95%7b', { - decoder: function (str) { - var reg = /%([0-9A-F]{2})/ig; - var result = []; - var parts = reg.exec(str); - while (parts) { - result.push(parseInt(parts[1], 16)); - parts = reg.exec(str); - } - return String(iconv.decode(SaferBuffer.from(result), 'shift_jis')); - } - }), { 県: '大阪府' }); - st.end(); - }); - - t.test('receives the default decoder as a second argument', function (st) { - st.plan(1); - qs.parse('a', { - decoder: function (str, defaultDecoder) { - st.equal(defaultDecoder, utils.decode); - } - }); - st.end(); - }); - - t.test('throws error with wrong decoder', function (st) { - st['throws'](function () { - qs.parse({}, { decoder: 'string' }); - }, new TypeError('Decoder has to be a function.')); - st.end(); - }); - - t.test('does not mutate the options argument', function (st) { - var options = {}; - qs.parse('a[b]=true', options); - st.deepEqual(options, {}); - st.end(); - }); - - t.test('throws if an invalid charset is specified', function (st) { - st['throws'](function () { - qs.parse('a=b', { charset: 'foobar' }); - }, new TypeError('The charset option must be either utf-8, iso-8859-1, or undefined')); - st.end(); - }); - - t.test('parses an iso-8859-1 string if asked to', function (st) { - st.deepEqual(qs.parse('%A2=%BD', { charset: 'iso-8859-1' }), { '¢': '½' }); - st.end(); - }); - - var urlEncodedCheckmarkInUtf8 = '%E2%9C%93'; - var urlEncodedOSlashInUtf8 = '%C3%B8'; - var urlEncodedNumCheckmark = '%26%2310003%3B'; - var urlEncodedNumSmiley = '%26%239786%3B'; - - t.test('prefers an utf-8 charset specified by the utf8 sentinel to a default charset of iso-8859-1', function (st) { - st.deepEqual(qs.parse('utf8=' + urlEncodedCheckmarkInUtf8 + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true, charset: 'iso-8859-1' }), { ø: 'ø' }); - st.end(); - }); - - t.test('prefers an iso-8859-1 charset specified by the utf8 sentinel to a default charset of utf-8', function (st) { - st.deepEqual(qs.parse('utf8=' + urlEncodedNumCheckmark + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true, charset: 'utf-8' }), { 'ø': 'ø' }); - st.end(); - }); - - t.test('does not require the utf8 sentinel to be defined before the parameters whose decoding it affects', function (st) { - st.deepEqual(qs.parse('a=' + urlEncodedOSlashInUtf8 + '&utf8=' + urlEncodedNumCheckmark, { charsetSentinel: true, charset: 'utf-8' }), { a: 'ø' }); - st.end(); - }); - - t.test('should ignore an utf8 sentinel with an unknown value', function (st) { - st.deepEqual(qs.parse('utf8=foo&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true, charset: 'utf-8' }), { ø: 'ø' }); - st.end(); - }); - - t.test('uses the utf8 sentinel to switch to utf-8 when no default charset is given', function (st) { - st.deepEqual(qs.parse('utf8=' + urlEncodedCheckmarkInUtf8 + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true }), { ø: 'ø' }); - st.end(); - }); - - t.test('uses the utf8 sentinel to switch to iso-8859-1 when no default charset is given', function (st) { - st.deepEqual(qs.parse('utf8=' + urlEncodedNumCheckmark + '&' + urlEncodedOSlashInUtf8 + '=' + urlEncodedOSlashInUtf8, { charsetSentinel: true }), { 'ø': 'ø' }); - st.end(); - }); - - t.test('interprets numeric entities in iso-8859-1 when `interpretNumericEntities`', function (st) { - st.deepEqual(qs.parse('foo=' + urlEncodedNumSmiley, { charset: 'iso-8859-1', interpretNumericEntities: true }), { foo: '☺' }); - st.end(); - }); - - t.test('handles a custom decoder returning `null`, in the `iso-8859-1` charset, when `interpretNumericEntities`', function (st) { - st.deepEqual(qs.parse('foo=&bar=' + urlEncodedNumSmiley, { - charset: 'iso-8859-1', - decoder: function (str, defaultDecoder, charset) { - return str ? defaultDecoder(str, defaultDecoder, charset) : null; - }, - interpretNumericEntities: true - }), { foo: null, bar: '☺' }); - st.end(); - }); - - t.test('does not interpret numeric entities in iso-8859-1 when `interpretNumericEntities` is absent', function (st) { - st.deepEqual(qs.parse('foo=' + urlEncodedNumSmiley, { charset: 'iso-8859-1' }), { foo: '☺' }); - st.end(); - }); - - t.test('does not interpret numeric entities when the charset is utf-8, even when `interpretNumericEntities`', function (st) { - st.deepEqual(qs.parse('foo=' + urlEncodedNumSmiley, { charset: 'utf-8', interpretNumericEntities: true }), { foo: '☺' }); - st.end(); - }); - - t.test('does not interpret %uXXXX syntax in iso-8859-1 mode', function (st) { - st.deepEqual(qs.parse('%u263A=%u263A', { charset: 'iso-8859-1' }), { '%u263A': '%u263A' }); - st.end(); - }); - - t.test('allows for decoding keys and values differently', function (st) { - var decoder = function (str, defaultDecoder, charset, type) { - if (type === 'key') { - return defaultDecoder(str, defaultDecoder, charset, type).toLowerCase(); - } - if (type === 'value') { - return defaultDecoder(str, defaultDecoder, charset, type).toUpperCase(); - } - throw 'this should never happen! type: ' + type; - }; - - st.deepEqual(qs.parse('KeY=vAlUe', { decoder: decoder }), { key: 'VALUE' }); - st.end(); - }); - - t.end(); -}); diff --git a/spaces/finny24/FinnyAiVoice/README.md b/spaces/finny24/FinnyAiVoice/README.md deleted file mode 100644 index cf7f46c462586df393704e82ba1a3e6be65361cd..0000000000000000000000000000000000000000 --- a/spaces/finny24/FinnyAiVoice/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: FinnyAiVoice -emoji: 🐨 -colorFrom: blue -colorTo: pink -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/firasggg/andite-anything-v4.0/app.py b/spaces/firasggg/andite-anything-v4.0/app.py deleted file mode 100644 index 47a2051db6dadeea03edf70d62694fd3e5e88ba7..0000000000000000000000000000000000000000 --- a/spaces/firasggg/andite-anything-v4.0/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/andite/anything-v4.0").launch() \ No newline at end of file diff --git a/spaces/fjenett/GPT-JT/app.py b/spaces/fjenett/GPT-JT/app.py deleted file mode 100644 index 67ecab812da006f09eac594a92e71853964c61b0..0000000000000000000000000000000000000000 --- a/spaces/fjenett/GPT-JT/app.py +++ /dev/null @@ -1,190 +0,0 @@ -import gradio as gr -import requests -import time -from ast import literal_eval -from datetime import datetime - - -def to_md(text): - # return text.replace("\n", "") - return text.replace("\n", "
") - - -def infer( - prompt, - model_name, - max_new_tokens=10, - temperature=0.1, - top_p=1.0, - top_k=40, - num_completions=1, - seed=42, - stop="\n" -): - model_name_map = { - "GPT-JT-6B-v1": "Together-gpt-JT-6B-v1", - } - max_new_tokens = int(max_new_tokens) - num_completions = int(num_completions) - temperature = float(temperature) - top_p = float(top_p) - top_k = int(top_k) - stop = stop.split(";") - seed = seed - - assert 1 <= max_new_tokens <= 256 - assert 1 <= num_completions <= 5 - assert 0.0 <= temperature <= 10.0 - assert 0.0 <= top_p <= 1.0 - assert 1 <= top_k <= 1000 - - if temperature == 0.0: - temperature = 0.01 - if prompt == "": - prompt = " " - my_post_dict = { - "model": "Together-gpt-JT-6B-v1", - "prompt": prompt, - "top_p": top_p, - "top_k": top_k, - "temperature": temperature, - "max_tokens": max_new_tokens, - "stop": stop, - } - print(f"send: {datetime.now()}") - response = requests.get("https://staging.together.xyz/api/inference", params=my_post_dict).json() - generated_text = response['output']['choices'][0]['text'] - print(f"recv: {datetime.now()}") - - for stop_word in stop: - if stop_word != '' and stop_word in generated_text: - generated_text = generated_text[:generated_text.find(stop_word)] - - return generated_text - - -examples = [ - [ - # Question Answering - '''Please answer the following question: - -Question: What is the capital of Canada? -Answer: Ottawa - -Question: What is the currency of Switzerland? -Answer: Swiss franc - -Question: In which country is Wisconsin located? -Answer:''', "GPT-JT-6B-v1", 5, 0.0, 1.0, 40], - [ - # Sentiment Analysis - '''Label the tweets as either "positive", "negative", "mixed", or "neutral": - -Tweet: I can say that there isn't anything I would change. -Label: positive - -Tweet: I'm not sure about this. -Label: neutral - -Tweet: I liked some parts but I didn't like other parts. -Label: mixed - -Tweet: I think the background image could have been better. -Label: negative - -Tweet: I really like it. -Label:''', "GPT-JT-6B-v1", 2, 0.0, 1.0, 40], - [ - # Topic Classification - '''Given a news article, classify its topic. -Possible labels: 1. World 2. Sports 3. Business 4. Sci/Tech - -Article: A nearby star thought to harbor comets and asteroids now appears to be home to planets, too. -Label: Sci/Tech - -Article: Soaring crude prices plus worries about the economy and the outlook for earnings are expected to hang over the stock market next week during the depth of the summer doldrums. -Label: Business - -Article: Murtagh a stickler for success Northeastern field hockey coach Cheryl Murtagh doesn't want the glare of the spotlight that shines on her to detract from a team that has been the America East champion for the past three years and has been to the NCAA tournament 13 times. -Label:''', "GPT-JT-6B-v1", 5, 0.0, 1.0, 40], - [ - # Paraphrasing - '''Paraphrase the given sentence into a different sentence. - -Input: Can you recommend some upscale restaurants in New York? -Output: What upscale restaurants do you recommend in New York? - -Input: What are the famous places we should not miss in Paris? -Output: Recommend some of the best places to visit in Paris? - -Input: Could you recommend some hotels that have cheap price in Zurich? -Output:''', "GPT-JT-6B-v1", 20, 0.8, 1.0, 40], - [ - # Text Summarization - '''Given a review from Amazon's food products, the task is to generate a short summary of the given review in the input. - -Input: I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than most. -Output: Good Quality Dog Food - -Input: Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as 'Jumbo'. -Output: Not as Advertised - -Input: My toddler loves this game to a point where he asks for it. That's a big thing for me. Secondly, no glitching unlike one of their competitors (PlayShifu). Any tech I don’t have to reach out to support for help is a good tech for me. I even enjoy some of the games and activities in this. Overall, this is a product that shows that the developers took their time and made sure people would not be asking for refund. I’ve become bias regarding this product and honestly I look forward to buying more of this company’s stuff. Please keep up the great work. -Output:''', "GPT-JT-6B-v1", 10, 0.0, 1.0, 40], - [ - # Word Sense Disambiguation - '''Identify which sense of a word is meant in a given context. - -Context: The river overflowed the bank. -Word: bank -Sense: river bank - -Context: A mouse takes much more room than a trackball. -Word: mouse -Sense: computer mouse - -Context: The bank will not be accepting cash on Saturdays. -Word: bank -Sense: commercial (finance) banks - -Context: Bill killed the project -Word: kill -Sense:''', "GPT-JT-6B-v1", 10, 0.0, 1.0, 40], - [ - # Natural Language Interface - '''Given a pair of sentences, choose whether the two sentences agree (entailment)/disagree (contradiction) with each other. -Possible labels: 1. entailment 2. contradiction - -Sentence 1: The skier was on the edge of the ramp. Sentence 2: The skier was dressed in winter clothes. -Label: entailment - -Sentence 1: The boy skated down the staircase railing. Sentence 2: The boy is a newbie skater. -Label: contradiction - -Sentence 1: Two middle-aged people stand by a golf hole. Sentence 2: A couple riding in a golf cart. -Label:''', "GPT-JT-6B-v1", 2, 0.0, 1.0, 40] -] - - -def main(): - iface = gr.Interface( - fn=infer, - inputs=[ - gr.Textbox(lines=20), # prompt - gr.Dropdown(["GPT-JT-6B-v1"]), # model_name - gr.Slider(1, 256, value=200), # max_tokens - gr.Slider(0.0, 10.0, value=0.1), # temperature - gr.Slider(0.0, 1.0, value=0.9), # top_p - gr.Slider(0, 1000, value=40) # top_k - ], - outputs=gr.Textbox(lines=7), - examples=examples, - title="GPT-JT Demo (Gradio port)", - description='''This is a port of the Streamlit [GPT-JT demo](https://huggingface.co/spaces/togethercomputer/GPT-JT) by [Together](https://huggingface.co/togethercomputer) to Gradio''' - ) - - iface.launch(debug=True) - - -if __name__ == '__main__': - main() diff --git a/spaces/flynster/FeinbergQuizNotes/question_generation/data_collator.py b/spaces/flynster/FeinbergQuizNotes/question_generation/data_collator.py deleted file mode 100644 index 3e738cc37a99a739c3ece33e6feef31d8b22ab2c..0000000000000000000000000000000000000000 --- a/spaces/flynster/FeinbergQuizNotes/question_generation/data_collator.py +++ /dev/null @@ -1,83 +0,0 @@ -from typing import Dict, List, Optional - -import torch - - -def trim_batch( - input_ids, pad_token_id, attention_mask=None, -): - """Remove columns that are populated exclusively by pad_token_id""" - keep_column_mask = input_ids.ne(pad_token_id).any(dim=0) - if attention_mask is None: - return input_ids[:, keep_column_mask] - else: - return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask]) - - -# prepares lm_labels from target_ids, returns examples with keys as expected by the forward method -# this is necessacry because the trainer directly passes this dict as arguments to the model -# so make sure the keys match the parameter names of the forward method -class T2TDataCollator(): - def __init__(self, tokenizer, model_type="t5", mode='training', using_tpu=False): - self.tokenizer = tokenizer - self.model_type = model_type - self.mode = mode - self.using_tpu = using_tpu - - def __call__(self, batch: List) -> Dict[str, torch.Tensor]: - """ - Take a list of samples from a Dataset and collate them into a batch. - Returns: - A dictionary of tensors - """ - input_ids = torch.stack([example['source_ids'] for example in batch]) - target_ids = torch.stack([example['target_ids'] for example in batch]) - attention_mask = torch.stack([example['attention_mask'] for example in batch]) - - pad_token_id = self.tokenizer.pad_token_id - - # don't trim on tpu, for some reason trimming leads to slower training on TPU - if not self.using_tpu: - input_ids, attention_mask = trim_batch(input_ids, pad_token_id, attention_mask=attention_mask) - target_ids = trim_batch(target_ids, pad_token_id) - - if self.model_type == "t5": - lm_labels = target_ids.clone() - decoder_input_ids = self._shift_right_t5(lm_labels) - if self.mode == 'training': - lm_labels[lm_labels[:, :] == pad_token_id] = -100 - else: - decoder_input_ids = target_ids[:, :-1].contiguous() - lm_labels = target_ids[:, 1:].clone() - if self.mode == 'training': - lm_labels[target_ids[:, 1:] == pad_token_id] = -100 - - params = { - "input_ids": input_ids, - "attention_mask": attention_mask, - "labels": lm_labels, - "decoder_input_ids": decoder_input_ids - } - - return params - - def _shift_right_t5(self, input_ids): - decoder_start_token_id = self.tokenizer.pad_token_id - pad_token_id = self.tokenizer.pad_token_id - - assert ( - decoder_start_token_id is not None - ), "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id. See T5 docs for more information" - - # shift inputs to the right - shifted_input_ids = input_ids.new_zeros(input_ids.shape) - shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() - shifted_input_ids[..., 0] = decoder_start_token_id - - assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined." - # replace possible -100 values in labels by `pad_token_id` - shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) - - assert torch.all(shifted_input_ids >= 0).item(), "Verify that `labels` has only positive values and -100" - - return shifted_input_ids \ No newline at end of file diff --git a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_mapillary_vistas_panoptic.py b/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_mapillary_vistas_panoptic.py deleted file mode 100644 index 9a83bdaaedb052e628f633a721047c59040a8a57..0000000000000000000000000000000000000000 --- a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_mapillary_vistas_panoptic.py +++ /dev/null @@ -1,183 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# Modified by Qihang Yu from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_mapillary_vistas_panoptic.py -import json -import os - -from detectron2.data import DatasetCatalog, MetadataCatalog -from detectron2.utils.file_io import PathManager - -from . import openseg_classes - -MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = openseg_classes.get_mapillary_vistas_categories_with_prompt_eng() - -def load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): - """ - Args: - image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". - gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". - json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". - Returns: - list[dict]: a list of dicts in Detectron2 standard format. (See - `Using Custom Datasets `_ ) - """ - - def _convert_category_id(segment_info, meta): - if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: - segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ - segment_info["category_id"] - ] - segment_info["isthing"] = True - else: - segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ - segment_info["category_id"] - ] - segment_info["isthing"] = False - return segment_info - - with PathManager.open(json_file) as f: - json_info = json.load(f) - - ret = [] - for ann in json_info["annotations"]: - image_id = ann["image_id"] - # TODO: currently we assume image and label has the same filename but - # different extension, and images have extension ".jpg" for COCO. Need - # to make image extension a user-provided argument if we extend this - # function to support other COCO-like datasets. - image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") - label_file = os.path.join(gt_dir, ann["file_name"]) - sem_label_file = os.path.join(semseg_dir, ann["file_name"]) - segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] - ret.append( - { - "file_name": image_file, - "image_id": image_id, - "pan_seg_file_name": label_file, - "sem_seg_file_name": sem_label_file, - "segments_info": segments_info, - } - ) - assert len(ret), f"No images found in {image_dir}!" - assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] - assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] - assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] - return ret - - -def register_mapillary_vistas_panoptic( - name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None -): - """ - Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. - The dictionaries in this registered dataset follows detectron2's standard format. - Hence it's called "standard". - Args: - name (str): the name that identifies a dataset, - e.g. "ade20k_panoptic_train" - metadata (dict): extra metadata associated with this dataset. - image_root (str): directory which contains all the images - panoptic_root (str): directory which contains panoptic annotation images in COCO format - panoptic_json (str): path to the json panoptic annotation file in COCO format - sem_seg_root (none): not used, to be consistent with - `register_coco_panoptic_separated`. - instances_json (str): path to the json instance annotation file - """ - panoptic_name = name - DatasetCatalog.register( - panoptic_name, - lambda: load_mapillary_vistas_panoptic_json( - panoptic_json, image_root, panoptic_root, semantic_root, metadata - ), - ) - MetadataCatalog.get(panoptic_name).set( - panoptic_root=panoptic_root, - image_root=image_root, - panoptic_json=panoptic_json, - json_file=instances_json, - evaluator_type="mapillary_vistas_panoptic_seg", - ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65 - label_divisor=1000, - **metadata, - ) - - -_PREDEFINED_SPLITS_ADE20K_PANOPTIC = { - "openvocab_mapillary_vistas_panoptic_train": ( - "mapillary_vistas/training/images", - "mapillary_vistas/training/panoptic", - "mapillary_vistas/training/panoptic/panoptic_2018.json", - "mapillary_vistas/training/labels", - ), - "openvocab_mapillary_vistas_panoptic_val": ( - "mapillary_vistas/validation/images", - "mapillary_vistas/validation/panoptic", - "mapillary_vistas/validation/panoptic/panoptic_2018.json", - "mapillary_vistas/validation/labels", - ), -} - - -def get_metadata(): - meta = {} - # The following metadata maps contiguous id from [0, #thing categories + - # #stuff categories) to their names and colors. We have to replica of the - # same name and color under "thing_*" and "stuff_*" because the current - # visualization function in D2 handles thing and class classes differently - # due to some heuristic used in Panoptic FPN. We keep the same naming to - # enable reusing existing visualization functions. - thing_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] - thing_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] - stuff_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] - stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] - - meta["thing_classes"] = thing_classes - meta["thing_colors"] = thing_colors - meta["stuff_classes"] = stuff_classes - meta["stuff_colors"] = stuff_colors - - # Convert category id for training: - # category id: like semantic segmentation, it is the class id for each - # pixel. Since there are some classes not used in evaluation, the category - # id is not always contiguous and thus we have two set of category ids: - # - original category id: category id in the original dataset, mainly - # used for evaluation. - # - contiguous category id: [0, #classes), in order to train the linear - # softmax classifier. - thing_dataset_id_to_contiguous_id = {} - stuff_dataset_id_to_contiguous_id = {} - - for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES): - if cat["isthing"]: - thing_dataset_id_to_contiguous_id[cat["id"]] = i - # else: - # stuff_dataset_id_to_contiguous_id[cat["id"]] = i - - # in order to use sem_seg evaluator - stuff_dataset_id_to_contiguous_id[cat["id"]] = i - - meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id - meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id - - return meta - - -def register_all_mapillary_vistas_panoptic(root): - metadata = get_metadata() - for ( - prefix, - (image_root, panoptic_root, panoptic_json, semantic_root), - ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items(): - # The "standard" version of COCO panoptic segmentation dataset, - # e.g. used by Panoptic-DeepLab - register_mapillary_vistas_panoptic( - prefix, - metadata, - os.path.join(root, image_root), - os.path.join(root, panoptic_root), - os.path.join(root, semantic_root), - os.path.join(root, panoptic_json), - ) - - -_root = os.getenv("DETECTRON2_DATASETS", "datasets") -register_all_mapillary_vistas_panoptic(_root) \ No newline at end of file diff --git a/spaces/fuxin123zz/ChuanhuChatGPT/assets/Kelpy-Codos.js b/spaces/fuxin123zz/ChuanhuChatGPT/assets/Kelpy-Codos.js deleted file mode 100644 index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000 --- a/spaces/fuxin123zz/ChuanhuChatGPT/assets/Kelpy-Codos.js +++ /dev/null @@ -1,76 +0,0 @@ -// ==UserScript== -// @name Kelpy Codos -// @namespace https://github.com/Keldos-Li/Kelpy-Codos -// @version 1.0.5 -// @author Keldos; https://keldos.me/ -// @description Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. -// Based on Chuanhu ChatGPT version: ac04408 (2023-3-22) -// @license GPL-3.0 -// @grant none -// ==/UserScript== - -(function () { - 'use strict'; - - function addCopyButton(pre) { - var code = pre.querySelector('code'); - if (!code) { - return; // 如果没有找到
元素，则不添加按钮
- }
- var firstChild = code.firstChild;
- if (!firstChild) {
- return; // 如果 元素没有子节点，则不添加按钮
- }
- var button = document.createElement('button');
- button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
- button.style.position = 'relative';
- button.style.float = 'right';
- button.style.fontSize = '1em'; // 可选：调整按钮大小
- button.style.background = 'none'; // 可选：去掉背景颜色
- button.style.border = 'none'; // 可选：去掉边框
- button.style.cursor = 'pointer'; // 可选：显示指针样式
- button.addEventListener('click', function () {
- var range = document.createRange();
- range.selectNodeContents(code);
- range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
- var selection = window.getSelection();
- selection.removeAllRanges();
- selection.addRange(range);
-
- try {
- var success = document.execCommand('copy');
- if (success) {
- button.textContent = '\u2714';
- setTimeout(function () {
- button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
- }, 2000);
- } else {
- button.textContent = '\u2716';
- }
- } catch (e) {
- console.error(e);
- button.textContent = '\u2716';
- }
-
- selection.removeAllRanges();
- });
- code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
- }
-
- function handleNewElements(mutationsList, observer) {
- for (var mutation of mutationsList) {
- if (mutation.type === 'childList') {
- for (var node of mutation.addedNodes) {
- if (node.nodeName === 'PRE') {
- addCopyButton(node);
- }
- }
- }
- }
- }
-
- var observer = new MutationObserver(handleNewElements);
- observer.observe(document.documentElement, { childList: true, subtree: true });
-
- document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Yqcloud.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Yqcloud.py
deleted file mode 100644
index ad5c3a4326c68ceb7ee012fbf5bc072da72a7e40..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/Yqcloud.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-import time
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-url = 'https://chat9.yqcloud.top/'
-model = [
- 'gpt-3.5-turbo',
-]
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, chatId: str, **kwargs):
-
- headers = {
- 'authority': 'api.aichatos.cloud',
- 'origin': 'https://chat9.yqcloud.top',
- 'referer': 'https://chat9.yqcloud.top/',
- 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
- }
-
- json_data = {
- 'prompt': str(messages),
- 'userId': f'#/chat/{chatId}',
- 'network': True,
- 'apikey': '',
- 'system': '',
- 'withoutContext': False,
- }
- response = requests.post('https://api.aichatos.cloud/api/generateStream',
- headers=headers, json=json_data, stream=True)
- for token in response.iter_content(chunk_size=2046):
- yield (token.decode('utf-8'))
-
-
-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/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/timm_universal.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/timm_universal.py
deleted file mode 100644
index 9c4f53b7f84b8365c7b7ba9ddac192a9a8ed4d8f..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/timm_universal.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import timm
-import torch.nn as nn
-
-
-class TimmUniversalEncoder(nn.Module):
- def __init__(self, name, pretrained=True, in_channels=3, depth=5, output_stride=32):
- super().__init__()
- kwargs = dict(
- in_chans=in_channels,
- features_only=True,
- output_stride=output_stride,
- pretrained=pretrained,
- out_indices=tuple(range(depth)),
- )
-
- # not all models support output stride argument, drop it by default
- if output_stride == 32:
- kwargs.pop("output_stride")
-
- self.model = timm.create_model(name, **kwargs)
-
- self._in_channels = in_channels
- self._out_channels = [in_channels,] + self.model.feature_info.channels()
- self._depth = depth
- self._output_stride = output_stride
-
- def forward(self, x):
- features = self.model(x)
- features = [x,] + features
- return features
-
- @property
- def out_channels(self):
- return self._out_channels
-
- @property
- def output_stride(self):
- return min(self._output_stride, 2 ** self._depth)
diff --git a/spaces/gligen/demo/gligen/ldm/modules/image_degradation/bsrgan_light.py b/spaces/gligen/demo/gligen/ldm/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index 9e1f823996bf559e9b015ea9aa2b3cd38dd13af1..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/ldm/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-
-def modcrop_np(img, sf):
- '''
- Args:
- img: numpy image, WxH or WxHxC
- sf: scale factor
- Return:
- cropped image
- '''
- w, h = img.shape[:2]
- im = np.copy(img)
- return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
- """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
- k_size = k.shape[0]
- # Calculate the big kernels size
- big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
- # Loop over the small kernel to fill the big one
- for r in range(k_size):
- for c in range(k_size):
- big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
- # Crop the edges of the big kernel to ignore very small values and increase run time of SR
- crop = k_size // 2
- cropped_big_k = big_k[crop:-crop, crop:-crop]
- # Normalize to 1
- return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
- """ generate an anisotropic Gaussian kernel
- Args:
- ksize : e.g., 15, kernel size
- theta : [0, pi], rotation angle range
- l1 : [0.1,50], scaling of eigenvalues
- l2 : [0.1,l1], scaling of eigenvalues
- If l1 = l2, will get an isotropic Gaussian kernel.
- Returns:
- k : kernel
- """
-
- v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
- V = np.array([[v[0], v[1]], [v[1], -v[0]]])
- D = np.array([[l1, 0], [0, l2]])
- Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
- k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
- return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
- center = size / 2.0 + 0.5
- k = np.zeros([size, size])
- for y in range(size):
- for x in range(size):
- cy = y - center + 1
- cx = x - center + 1
- k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
- k = k / np.sum(k)
- return k
-
-
-def shift_pixel(x, sf, upper_left=True):
- """shift pixel for super-resolution with different scale factors
- Args:
- x: WxHxC or WxH
- sf: scale factor
- upper_left: shift direction
- """
- h, w = x.shape[:2]
- shift = (sf - 1) * 0.5
- xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
- if upper_left:
- x1 = xv + shift
- y1 = yv + shift
- else:
- x1 = xv - shift
- y1 = yv - shift
-
- x1 = np.clip(x1, 0, w - 1)
- y1 = np.clip(y1, 0, h - 1)
-
- if x.ndim == 2:
- x = interp2d(xv, yv, x)(x1, y1)
- if x.ndim == 3:
- for i in range(x.shape[-1]):
- x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
- return x
-
-
-def blur(x, k):
- '''
- x: image, NxcxHxW
- k: kernel, Nx1xhxw
- '''
- n, c = x.shape[:2]
- p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
- x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
- k = k.repeat(1, c, 1, 1)
- k = k.view(-1, 1, k.shape[2], k.shape[3])
- x = x.view(1, -1, x.shape[2], x.shape[3])
- x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
- x = x.view(n, c, x.shape[2], x.shape[3])
-
- return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
- """"
- # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
- # Kai Zhang
- # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
- # max_var = 2.5 * sf
- """
- # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
- lambda_1 = min_var + np.random.rand() * (max_var - min_var)
- lambda_2 = min_var + np.random.rand() * (max_var - min_var)
- theta = np.random.rand() * np.pi # random theta
- noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
- # Set COV matrix using Lambdas and Theta
- LAMBDA = np.diag([lambda_1, lambda_2])
- Q = np.array([[np.cos(theta), -np.sin(theta)],
- [np.sin(theta), np.cos(theta)]])
- SIGMA = Q @ LAMBDA @ Q.T
- INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
- # Set expectation position (shifting kernel for aligned image)
- MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
- MU = MU[None, None, :, None]
-
- # Create meshgrid for Gaussian
- [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
- Z = np.stack([X, Y], 2)[:, :, :, None]
-
- # Calcualte Gaussian for every pixel of the kernel
- ZZ = Z - MU
- ZZ_t = ZZ.transpose(0, 1, 3, 2)
- raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
- # shift the kernel so it will be centered
- # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
- # Normalize the kernel and return
- # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
- kernel = raw_kernel / np.sum(raw_kernel)
- return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
- hsize = [hsize, hsize]
- siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
- std = sigma
- [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
- arg = -(x * x + y * y) / (2 * std * std)
- h = np.exp(arg)
- h[h < scipy.finfo(float).eps * h.max()] = 0
- sumh = h.sum()
- if sumh != 0:
- h = h / sumh
- return h
-
-
-def fspecial_laplacian(alpha):
- alpha = max([0, min([alpha, 1])])
- h1 = alpha / (alpha + 1)
- h2 = (1 - alpha) / (alpha + 1)
- h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
- h = np.array(h)
- return h
-
-
-def fspecial(filter_type, *args, **kwargs):
- '''
- python code from:
- https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
- '''
- if filter_type == 'gaussian':
- return fspecial_gaussian(*args, **kwargs)
- if filter_type == 'laplacian':
- return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
- '''
- Args:
- x: HxWxC image, [0, 1]
- sf: down-scale factor
- Return:
- bicubicly downsampled LR image
- '''
- x = util.imresize_np(x, scale=1 / sf)
- return x
-
-
-def srmd_degradation(x, k, sf=3):
- ''' blur + bicubic downsampling
- Args:
- x: HxWxC image, [0, 1]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- Reference:
- @inproceedings{zhang2018learning,
- title={Learning a single convolutional super-resolution network for multiple degradations},
- author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
- booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
- pages={3262--3271},
- year={2018}
- }
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
- x = bicubic_degradation(x, sf=sf)
- return x
-
-
-def dpsr_degradation(x, k, sf=3):
- ''' bicubic downsampling + blur
- Args:
- x: HxWxC image, [0, 1]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- Reference:
- @inproceedings{zhang2019deep,
- title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
- author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
- booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
- pages={1671--1681},
- year={2019}
- }
- '''
- x = bicubic_degradation(x, sf=sf)
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
- return x
-
-
-def classical_degradation(x, k, sf=3):
- ''' blur + downsampling
- Args:
- x: HxWxC image, [0, 1]/[0, 255]
- k: hxw, double
- sf: down-scale factor
- Return:
- downsampled LR image
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
- # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
- st = 0
- return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
- """USM sharpening. borrowed from real-ESRGAN
- Input image: I; Blurry image: B.
- 1. K = I + weight * (I - B)
- 2. Mask = 1 if abs(I - B) > threshold, else: 0
- 3. Blur mask:
- 4. Out = Mask * K + (1 - Mask) * I
- Args:
- img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
- weight (float): Sharp weight. Default: 1.
- radius (float): Kernel size of Gaussian blur. Default: 50.
- threshold (int):
- """
- if radius % 2 == 0:
- radius += 1
- blur = cv2.GaussianBlur(img, (radius, radius), 0)
- residual = img - blur
- mask = np.abs(residual) * 255 > threshold
- mask = mask.astype('float32')
- soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
- K = img + weight * residual
- K = np.clip(K, 0, 1)
- return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
- wd2 = 4.0 + sf
- wd = 2.0 + 0.2 * sf
-
- wd2 = wd2/4
- wd = wd/4
-
- if random.random() < 0.5:
- l1 = wd2 * random.random()
- l2 = wd2 * random.random()
- k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
- else:
- k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
- img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
- return img
-
-
-def add_resize(img, sf=4):
- rnum = np.random.rand()
- if rnum > 0.8: # up
- sf1 = random.uniform(1, 2)
- elif rnum < 0.7: # down
- sf1 = random.uniform(0.5 / sf, 1)
- else:
- sf1 = 1.0
- img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
- img = np.clip(img, 0.0, 1.0)
-
- return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-# noise_level = random.randint(noise_level1, noise_level2)
-# rnum = np.random.rand()
-# if rnum > 0.6: # add color Gaussian noise
-# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-# elif rnum < 0.4: # add grayscale Gaussian noise
-# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-# else: # add noise
-# L = noise_level2 / 255.
-# D = np.diag(np.random.rand(3))
-# U = orth(np.random.rand(3, 3))
-# conv = np.dot(np.dot(np.transpose(U), D), U)
-# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-# img = np.clip(img, 0.0, 1.0)
-# return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
- noise_level = random.randint(noise_level1, noise_level2)
- rnum = np.random.rand()
- if rnum > 0.6: # add color Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
- elif rnum < 0.4: # add grayscale Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
- else: # add noise
- L = noise_level2 / 255.
- D = np.diag(np.random.rand(3))
- U = orth(np.random.rand(3, 3))
- conv = np.dot(np.dot(np.transpose(U), D), U)
- img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
- noise_level = random.randint(noise_level1, noise_level2)
- img = np.clip(img, 0.0, 1.0)
- rnum = random.random()
- if rnum > 0.6:
- img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
- elif rnum < 0.4:
- img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
- else:
- L = noise_level2 / 255.
- D = np.diag(np.random.rand(3))
- U = orth(np.random.rand(3, 3))
- conv = np.dot(np.dot(np.transpose(U), D), U)
- img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_Poisson_noise(img):
- img = np.clip((img * 255.0).round(), 0, 255) / 255.
- vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
- if random.random() < 0.5:
- img = np.random.poisson(img * vals).astype(np.float32) / vals
- else:
- img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
- img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
- noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
- img += noise_gray[:, :, np.newaxis]
- img = np.clip(img, 0.0, 1.0)
- return img
-
-
-def add_JPEG_noise(img):
- quality_factor = random.randint(80, 95)
- img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
- result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
- img = cv2.imdecode(encimg, 1)
- img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
- return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
- h, w = lq.shape[:2]
- rnd_h = random.randint(0, h - lq_patchsize)
- rnd_w = random.randint(0, w - lq_patchsize)
- lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
- rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
- hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
- return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
- """
- This is the degradation model of BSRGAN from the paper
- "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
- ----------
- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
- sf: scale factor
- isp_model: camera ISP model
- Returns
- -------
- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
- hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
- """
- isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
- sf_ori = sf
-
- h1, w1 = img.shape[:2]
- img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
- h, w = img.shape[:2]
-
- if h < lq_patchsize * sf or w < lq_patchsize * sf:
- raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
- hq = img.copy()
-
- if sf == 4 and random.random() < scale2_prob: # downsample1
- if np.random.rand() < 0.5:
- img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- img = util.imresize_np(img, 1 / 2, True)
- img = np.clip(img, 0.0, 1.0)
- sf = 2
-
- shuffle_order = random.sample(range(7), 7)
- idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
- if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
- for i in shuffle_order:
-
- if i == 0:
- img = add_blur(img, sf=sf)
-
- elif i == 1:
- img = add_blur(img, sf=sf)
-
- elif i == 2:
- a, b = img.shape[1], img.shape[0]
- # downsample2
- if random.random() < 0.75:
- sf1 = random.uniform(1, 2 * sf)
- img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
- k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
- img = img[0::sf, 0::sf, ...] # nearest downsampling
- img = np.clip(img, 0.0, 1.0)
-
- elif i == 3:
- # downsample3
- img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
- img = np.clip(img, 0.0, 1.0)
-
- elif i == 4:
- # add Gaussian noise
- img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
- elif i == 5:
- # add JPEG noise
- if random.random() < jpeg_prob:
- img = add_JPEG_noise(img)
-
- elif i == 6:
- # add processed camera sensor noise
- if random.random() < isp_prob and isp_model is not None:
- with torch.no_grad():
- img, hq = isp_model.forward(img.copy(), hq)
-
- # add final JPEG compression noise
- img = add_JPEG_noise(img)
-
- # random crop
- img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
- return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
- """
- This is the degradation model of BSRGAN from the paper
- "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
- ----------
- sf: scale factor
- isp_model: camera ISP model
- Returns
- -------
- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
- hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
- """
- image = util.uint2single(image)
- isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
- sf_ori = sf
-
- h1, w1 = image.shape[:2]
- image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
- h, w = image.shape[:2]
-
- hq = image.copy()
-
- if sf == 4 and random.random() < scale2_prob: # downsample1
- if np.random.rand() < 0.5:
- image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- image = util.imresize_np(image, 1 / 2, True)
- image = np.clip(image, 0.0, 1.0)
- sf = 2
-
- shuffle_order = random.sample(range(7), 7)
- idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
- if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
- for i in shuffle_order:
-
- if i == 0:
- image = add_blur(image, sf=sf)
-
- # elif i == 1:
- # image = add_blur(image, sf=sf)
-
- if i == 0:
- pass
-
- elif i == 2:
- a, b = image.shape[1], image.shape[0]
- # downsample2
- if random.random() < 0.8:
- sf1 = random.uniform(1, 2 * sf)
- image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
- else:
- k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
- k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
- image = image[0::sf, 0::sf, ...] # nearest downsampling
-
- image = np.clip(image, 0.0, 1.0)
-
- elif i == 3:
- # downsample3
- image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
- image = np.clip(image, 0.0, 1.0)
-
- elif i == 4:
- # add Gaussian noise
- image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
- elif i == 5:
- # add JPEG noise
- if random.random() < jpeg_prob:
- image = add_JPEG_noise(image)
- #
- # elif i == 6:
- # # add processed camera sensor noise
- # if random.random() < isp_prob and isp_model is not None:
- # with torch.no_grad():
- # img, hq = isp_model.forward(img.copy(), hq)
-
- # add final JPEG compression noise
- image = add_JPEG_noise(image)
- image = util.single2uint(image)
- example = {"image": image}
- return example
-
-
-
-
-if __name__ == '__main__':
- print("hey")
- img = util.imread_uint('utils/test.png', 3)
- img = img[:448, :448]
- h = img.shape[0] // 4
- print("resizing to", h)
- sf = 4
- deg_fn = partial(degradation_bsrgan_variant, sf=sf)
- for i in range(20):
- print(i)
- img_hq = img
- img_lq = deg_fn(img)["image"]
- img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
- print(img_lq)
- img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
- print(img_lq.shape)
- print("bicubic", img_lq_bicubic.shape)
- print(img_hq.shape)
- lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
- (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
- util.imsave(img_concat, str(i) + '.png')
diff --git a/spaces/glt3953/app-audio_video_transcribe/README.md b/spaces/glt3953/app-audio_video_transcribe/README.md
deleted file mode 100644
index 32040d8b9a9eee85bc1be5f24b4b451f866833aa..0000000000000000000000000000000000000000
--- a/spaces/glt3953/app-audio_video_transcribe/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: App-audio Video Transcribe
-emoji: 🐢
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/glyszt/vt/vtoonify/model/raft/core/corr.py b/spaces/glyszt/vt/vtoonify/model/raft/core/corr.py
deleted file mode 100644
index 40214aa5e6f0392a732eacab9d9cb0cbfb4669f3..0000000000000000000000000000000000000000
--- a/spaces/glyszt/vt/vtoonify/model/raft/core/corr.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import torch
-import torch.nn.functional as F
-from model.raft.core.utils.utils import bilinear_sampler, coords_grid
-
-try:
- import alt_cuda_corr
-except:
- # alt_cuda_corr is not compiled
- pass
-
-
-class CorrBlock:
- def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
- self.num_levels = num_levels
- self.radius = radius
- self.corr_pyramid = []
-
- # all pairs correlation
- corr = CorrBlock.corr(fmap1, fmap2)
-
- batch, h1, w1, dim, h2, w2 = corr.shape
- corr = corr.reshape(batch*h1*w1, dim, h2, w2)
-
- self.corr_pyramid.append(corr)
- for i in range(self.num_levels-1):
- corr = F.avg_pool2d(corr, 2, stride=2)
- self.corr_pyramid.append(corr)
-
- def __call__(self, coords):
- r = self.radius
- coords = coords.permute(0, 2, 3, 1)
- batch, h1, w1, _ = coords.shape
-
- out_pyramid = []
- for i in range(self.num_levels):
- corr = self.corr_pyramid[i]
- dx = torch.linspace(-r, r, 2*r+1, device=coords.device)
- dy = torch.linspace(-r, r, 2*r+1, device=coords.device)
- delta = torch.stack(torch.meshgrid(dy, dx), axis=-1)
-
- centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i
- delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
- coords_lvl = centroid_lvl + delta_lvl
-
- corr = bilinear_sampler(corr, coords_lvl)
- corr = corr.view(batch, h1, w1, -1)
- out_pyramid.append(corr)
-
- out = torch.cat(out_pyramid, dim=-1)
- return out.permute(0, 3, 1, 2).contiguous().float()
-
- @staticmethod
- def corr(fmap1, fmap2):
- batch, dim, ht, wd = fmap1.shape
- fmap1 = fmap1.view(batch, dim, ht*wd)
- fmap2 = fmap2.view(batch, dim, ht*wd)
-
- corr = torch.matmul(fmap1.transpose(1,2), fmap2)
- corr = corr.view(batch, ht, wd, 1, ht, wd)
- return corr / torch.sqrt(torch.tensor(dim).float())
-
-
-class AlternateCorrBlock:
- def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
- self.num_levels = num_levels
- self.radius = radius
-
- self.pyramid = [(fmap1, fmap2)]
- for i in range(self.num_levels):
- fmap1 = F.avg_pool2d(fmap1, 2, stride=2)
- fmap2 = F.avg_pool2d(fmap2, 2, stride=2)
- self.pyramid.append((fmap1, fmap2))
-
- def __call__(self, coords):
- coords = coords.permute(0, 2, 3, 1)
- B, H, W, _ = coords.shape
- dim = self.pyramid[0][0].shape[1]
-
- corr_list = []
- for i in range(self.num_levels):
- r = self.radius
- fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1).contiguous()
- fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1).contiguous()
-
- coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous()
- corr, = alt_cuda_corr.forward(fmap1_i, fmap2_i, coords_i, r)
- corr_list.append(corr.squeeze(1))
-
- corr = torch.stack(corr_list, dim=1)
- corr = corr.reshape(B, -1, H, W)
- return corr / torch.sqrt(torch.tensor(dim).float())
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Avatar Movie Download In Telugu Hdk.md b/spaces/gotiQspiryo/whisper-ui/examples/Avatar Movie Download In Telugu Hdk.md
deleted file mode 100644
index 2a6cbabf81e6dc82de348264e1d40b9c6166c1d5..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Avatar Movie Download In Telugu Hdk.md
+++ /dev/null
@@ -1,50 +0,0 @@
-avatar movie download in telugu hdk
Download File >>>>> https://urlgoal.com/2uyN2P
-
-Playing
-
-In mid-2011, Gowda appeared in one of the lead roles in the film Kathaleeya, playing an ex-convict named John "Sundar" and a friend of the main protagonist Manish (played by Siddharth). He was mostly seen in the first half of the film, where he shares a romance with the female lead.
-
-In 2015, Gowda was part of the action thriller film Thondaradi Paarvai. The film, directed by Soundaryan, is a remake of 2004 Telugu film Thondaradi Ekkadiki, starring Anand and Priyanka. His role is one of the main protagonists in the film.
-
-Gowda also acted in Andaaya Vellum, one of the many films based on the life of Congress leader A. S. Azeez, played by Dasari Narayana Rao. His role was portrayed by Kavithalaya Krishnamachary, who was a contemporary of the late actor. The film was released in 2015.
-
-Controversies
-
-In a poll conducted by CNN-IBN for the viewers to select the sexiest Indian men in 2015, Gowda topped the list.
-
-Gowda was also one of the contestants of the first season of the dance reality TV show Dance Dance Indi-Part 1. The dance reality TV show was aired on SAB TV.
-
-After the Bollywood film The Dirty Picture became a major box office success in 2010, the makers of another film named "Welcome" decided to use the story of The Dirty Picture, but with a different protagonist and in a different language, without the permission of the producer of The Dirty Picture, who was also the producer of Welcome. The story of the film Welcome follows the same plot of The Dirty Picture, but with different characters and stories.
-
-Personal life
-
-Gowda is a Hindu. He married Vaishali, daughter of film actress Sindhu Menon, on 20 November 2015.
-
-Filmography
-
-References
-
-External links
-
-
-
-Category:Indian male film actors
-
-Category:Male actors in Kannada cinema
-
-Category:Living people
-
-Category:Male actors from Bangalore
-
-Category:Indian male child actors
-
-Category:Indian male television actors
-
-Category:21st-century Indian male actors
-
-Category:Year of birth missing (living people)
-
-Category:1990 birthsNymph()[i]; 4fefd39f24
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Fixed Deadpool (English) Part 2 Full Movie In Hindi.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Fixed Deadpool (English) Part 2 Full Movie In Hindi.md
deleted file mode 100644
index 424917c3e17a24c08be73ab025041af50d8165a8..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Fixed Deadpool (English) Part 2 Full Movie In Hindi.md
+++ /dev/null
@@ -1,6 +0,0 @@
-download Deadpool (English) part 2 full movie in hindi
DOWNLOAD ———>>> https://urlgoal.com/2uyM29
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download Zip of Marianas Trench Astoria the Album Inspired by The Goonies and 80s Pop Culture.md b/spaces/gotiQspiryo/whisper-ui/examples/Download Zip of Marianas Trench Astoria the Album Inspired by The Goonies and 80s Pop Culture.md
deleted file mode 100644
index 4fb04337f469161f52b8d838873e8cdbed2f362b..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Download Zip of Marianas Trench Astoria the Album Inspired by The Goonies and 80s Pop Culture.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Marianas Trench Astoria Download Zip
Download ⚙⚙⚙ https://urlgoal.com/2uyM0c
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Huawei Blower Unlocker V100252 !FREE! Download.md b/spaces/gotiQspiryo/whisper-ui/examples/Huawei Blower Unlocker V100252 !FREE! Download.md
deleted file mode 100644
index a4e6eca047cd527f55b75198dd91cbf1c0656e03..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Huawei Blower Unlocker V100252 !FREE! Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Huawei Blower Unlocker V100252 Download
Download … https://urlgoal.com/2uyN7f
-
-Huawei Blower Unlocker V1.0.0.252 Free Download 4shared.com. 4d29de3e1b
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/It.reallifecam.com.rar.md b/spaces/gotiQspiryo/whisper-ui/examples/It.reallifecam.com.rar.md
deleted file mode 100644
index 66fb300d2c9e31f7f70f55ac56eba8c8da8aa5ee..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/It.reallifecam.com.rar.md
+++ /dev/null
@@ -1,9 +0,0 @@
-it.reallifecam.com.rar
DOWNLOAD ★★★★★ https://urlgoal.com/2uyLWc
-
-4507 results - fake account Reallifecam mediafire links download free, download fake Facebook account, fake account Lockerz creator, Reallifecam account. com, a fake Facebook account.
-With a fake Facebook account, you can easily access all the resources in question, and all this requires only a Facebook account.
-This person is not very familiar with the fake Facebook account.
-With a fake Facebook account, there are many places where you can easily access information and content that is of interest. 8a78ff9644
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Mehndi Hai Rachne Wali Video Songs Dailymotion Fotoshop Anwendungen.md b/spaces/gotiQspiryo/whisper-ui/examples/Mehndi Hai Rachne Wali Video Songs Dailymotion Fotoshop Anwendungen.md
deleted file mode 100644
index 7bbceec31fcc73849e7fde0cd2344c6933975175..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Mehndi Hai Rachne Wali Video Songs Dailymotion Fotoshop Anwendungen.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Mehndi Hai Rachne Wali Video Songs Dailymotion fotoshop anwendungen
Download File ===> https://urlgoal.com/2uyNhF
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/README.md b/spaces/gradio/HuBERT/examples/speech_recognition/README.md
deleted file mode 100644
index 17030bf0fd50bb843a508e13e97ed436eae33287..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/speech_recognition/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-### 2021 Update: We are merging this example into the [S2T framework](../speech_to_text), which supports more generic speech-to-text tasks (e.g. speech translation) and more flexible data processing pipelines. Please stay tuned.
-
-# Speech Recognition
-`examples/speech_recognition` is implementing ASR task in Fairseq, along with needed features, datasets, models and loss functions to train and infer model described in [Transformers with convolutional context for ASR (Abdelrahman Mohamed et al., 2019)](https://arxiv.org/abs/1904.11660).
-
-
-## Additional dependencies
-On top of main fairseq dependencies there are couple more additional requirements.
-
-1) Please follow the instructions to install [torchaudio](https://github.com/pytorch/audio). This is required to compute audio fbank features.
-2) [Sclite](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htm#sclite_name_0) is used to measure WER. Sclite can be downloaded and installed from source from sctk package [here](http://www.openslr.org/4/). Training and inference doesn't require Sclite dependency.
-3) [sentencepiece](https://github.com/google/sentencepiece) is required in order to create dataset with word-piece targets.
-
-## Preparing librispeech data
-```
-./examples/speech_recognition/datasets/prepare-librispeech.sh $DIR_TO_SAVE_RAW_DATA $DIR_FOR_PREPROCESSED_DATA
-```
-
-## Training librispeech data
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 80 --task speech_recognition --arch vggtransformer_2 --optimizer adadelta --lr 1.0 --adadelta-eps 1e-8 --adadelta-rho 0.95 --clip-norm 10.0 --max-tokens 5000 --log-format json --log-interval 1 --criterion cross_entropy_acc --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-`$SET` can be `test_clean` or `test_other`
-Any checkpoint in `$MODEL_PATH` can be selected. In this example we are working with `checkpoint_last.pt`
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --max-tokens 25000 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --beam 20 --results-path $RES_DIR --batch-size 40 --gen-subset $SET --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-```
-sclite -r ${RES_DIR}/ref.word-checkpoint_last.pt-${SET}.txt -h ${RES_DIR}/hypo.word-checkpoint_last.pt-${SET}.txt -i rm -o all stdout > $RES_REPORT
-```
-`Sum/Avg` row from first table of the report has WER
-
-## Using flashlight (previously called [wav2letter](https://github.com/facebookresearch/wav2letter)) components
-[flashlight](https://github.com/facebookresearch/flashlight) now has integration with fairseq. Currently this includes:
-
-* AutoSegmentationCriterion (ASG)
-* flashlight-style Conv/GLU model
-* flashlight's beam search decoder
-
-To use these, follow the instructions on [this page](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) to install python bindings.
-
-## Training librispeech data (flashlight style, Conv/GLU + ASG loss)
-Training command:
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 100 --task speech_recognition --arch w2l_conv_glu_enc --batch-size 4 --optimizer sgd --lr 0.3,0.8 --momentum 0.8 --clip-norm 0.2 --max-tokens 50000 --log-format json --log-interval 100 --num-workers 0 --sentence-avg --criterion asg_loss --asg-transitions-init 5 --max-replabel 2 --linseg-updates 8789 --user-dir examples/speech_recognition
-```
-
-Note that ASG loss currently doesn't do well with word-pieces. You should prepare a dataset with character targets by setting `nbpe=31` in `prepare-librispeech.sh`.
-
-## Inference for librispeech (flashlight decoder, n-gram LM)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder kenlm --kenlm-model $KENLM_MODEL_PATH --lexicon $LEXICON_PATH --beam 200 --beam-threshold 15 --lm-weight 1.5 --word-score 1.5 --sil-weight -0.3 --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
-
-`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a flashlight-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels):
-```
-doorbell D O 1 R B E L 1 ▁
-```
-For CTC inference with word-pieces, repetition labels are not used and the lexicon should have most common spellings for each word (one can use sentencepiece's `NBestEncodeAsPieces` for this):
-```
-doorbell ▁DOOR BE LL
-doorbell ▁DOOR B E LL
-doorbell ▁DO OR BE LL
-doorbell ▁DOOR B EL L
-doorbell ▁DOOR BE L L
-doorbell ▁DO OR B E LL
-doorbell ▁DOOR B E L L
-doorbell ▁DO OR B EL L
-doorbell ▁DO O R BE LL
-doorbell ▁DO OR BE L L
-```
-Lowercase vs. uppercase matters: the *word* should match the case of the n-gram language model (i.e. `$KENLM_MODEL_PATH`), while the *spelling* should match the case of the token dictionary (i.e. `$DIR_FOR_PREPROCESSED_DATA/dict.txt`).
-
-## Inference for librispeech (flashlight decoder, viterbi only)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder viterbi --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
diff --git a/spaces/gradio/theme-gallery/style.css b/spaces/gradio/theme-gallery/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/gradio/theme-gallery/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/gstdl/screener-saham-demo/Dockerfile b/spaces/gstdl/screener-saham-demo/Dockerfile
deleted file mode 100644
index d0af372b534bba5325d46e90f7a22aadf86cf94b..0000000000000000000000000000000000000000
--- a/spaces/gstdl/screener-saham-demo/Dockerfile
+++ /dev/null
@@ -1,48 +0,0 @@
-#Grab python=3.9-slim-buster image
-FROM python:3.9-slim-buster
-
-# Copy code to container
-COPY app /app
-
-# RUN apt-get update
-RUN apt-get update && apt-get install -y gcc bzip2 libc-dev g++ libc6 libc6-dev dpkg-dev build-essential wget libpython3-dev python3-dev python3-pip gcc sudo
-# openldap-dev libffi-dev jpeg-dev zlib-dev libmemcached-dev ibxml2 libxslt libxslt-dev
-
-# TA-Lib
-# RUN wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
-COPY ta-lib-0.4.0-src.tar.gz ta-lib-0.4.0-src.tar.gz
-RUN tar -xvzf ta-lib-0.4.0-src.tar.gz && \
- cd ta-lib/ && \
- ./configure --prefix=/usr --build=aarch64-unknown-linux-gnu && \
- sudo make && \
- sudo make install && \
- cd ..
-RUN rm -R ta-lib ta-lib-0.4.0-src.tar.gz
-
-# Copy requirements to container
-COPY app/requirements.txt /app/requirements.txt
-
-# Move working directory
-WORKDIR /app
-
-# Install python depencies
-RUN pip3 install --upgrade pip && pip3 install --no-cache-dir -q -r requirements.txt
-
-# Copy code to container
-COPY app /app
-
-# Give container write permission to sqlite db
-RUN sudo chmod 774 /app/dataset/ihsg.db
-
-# Update database
-RUN python3 update_database.py
-
-# Expose port 5000
-EXPOSE 5000
-
-# # Run the image as a non-root user
-# RUN adduser myuser
-# USER myuser
-
-# Run the app on port 5000
-CMD gunicorn --bind 0.0.0.0:5000 app:app
diff --git a/spaces/gyrojeff/YuzuMarker.FontDetection/dataset_filename_preprocess.py b/spaces/gyrojeff/YuzuMarker.FontDetection/dataset_filename_preprocess.py
deleted file mode 100644
index 04b39ce7290963cc2d84f55561b4191e16da0d22..0000000000000000000000000000000000000000
--- a/spaces/gyrojeff/YuzuMarker.FontDetection/dataset_filename_preprocess.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import os
-
-from font_dataset.utils import get_files
-
-directory_path = "dataset"
-
-files = get_files(directory_path)
-
-
-def clean_name(file_name: str):
- return (
- file_name.replace("&", "-")
- .replace("@", "-")
- .replace("#", "-")
- .replace("$", "-")
- )
-
-
-for file_name in files:
- new_file_name = clean_name(file_name)
- if new_file_name != file_name:
- os.rename(file_name, new_file_name)
- print(f"Renamed {file_name} to {new_file_name}")
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/torch_utils/ops/conv2d_resample.py b/spaces/gyugnsu/DragGan-Inversion/PTI/torch_utils/ops/conv2d_resample.py
deleted file mode 100644
index cd4750744c83354bab78704d4ef51ad1070fcc4a..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/torch_utils/ops/conv2d_resample.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""2D convolution with optional up/downsampling."""
-
-import torch
-
-from .. import misc
-from . import conv2d_gradfix
-from . import upfirdn2d
-from .upfirdn2d import _parse_padding
-from .upfirdn2d import _get_filter_size
-
-#----------------------------------------------------------------------------
-
-def _get_weight_shape(w):
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- shape = [int(sz) for sz in w.shape]
- misc.assert_shape(w, shape)
- return shape
-
-#----------------------------------------------------------------------------
-
-def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
- """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
- """
- out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-
- # Flip weight if requested.
- if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
- w = w.flip([2, 3])
-
- # Workaround performance pitfall in cuDNN 8.0.5, triggered when using
- # 1x1 kernel + memory_format=channels_last + less than 64 channels.
- if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose:
- if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
- if out_channels <= 4 and groups == 1:
- in_shape = x.shape
- x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1])
- x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
- else:
- x = x.to(memory_format=torch.contiguous_format)
- w = w.to(memory_format=torch.contiguous_format)
- x = conv2d_gradfix.conv2d(x, w, groups=groups)
- return x.to(memory_format=torch.channels_last)
-
- # Otherwise => execute using conv2d_gradfix.
- op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
- return op(x, w, stride=stride, padding=padding, groups=groups)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
- r"""2D convolution with optional up/downsampling.
-
- Padding is performed only once at the beginning, not between the operations.
-
- Args:
- x: Input tensor of shape
- `[batch_size, in_channels, in_height, in_width]`.
- w: Weight tensor of shape
- `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
- f: Low-pass filter for up/downsampling. Must be prepared beforehand by
- calling upfirdn2d.setup_filter(). None = identity (default).
- up: Integer upsampling factor (default: 1).
- down: Integer downsampling factor (default: 1).
- padding: Padding with respect to the upsampled image. Can be a single number
- or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
- (default: 0).
- groups: Split input channels into N groups (default: 1).
- flip_weight: False = convolution, True = correlation (default: True).
- flip_filter: False = convolution, True = correlation (default: False).
-
- Returns:
- Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
- """
- # Validate arguments.
- assert isinstance(x, torch.Tensor) and (x.ndim == 4)
- assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
- assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
- assert isinstance(up, int) and (up >= 1)
- assert isinstance(down, int) and (down >= 1)
- assert isinstance(groups, int) and (groups >= 1)
- out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
- fw, fh = _get_filter_size(f)
- px0, px1, py0, py1 = _parse_padding(padding)
-
- # Adjust padding to account for up/downsampling.
- if up > 1:
- px0 += (fw + up - 1) // 2
- px1 += (fw - up) // 2
- py0 += (fh + up - 1) // 2
- py1 += (fh - up) // 2
- if down > 1:
- px0 += (fw - down + 1) // 2
- px1 += (fw - down) // 2
- py0 += (fh - down + 1) // 2
- py1 += (fh - down) // 2
-
- # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
- if kw == 1 and kh == 1 and (down > 1 and up == 1):
- x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
- x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
- return x
-
- # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
- if kw == 1 and kh == 1 and (up > 1 and down == 1):
- x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
- x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
- return x
-
- # Fast path: downsampling only => use strided convolution.
- if down > 1 and up == 1:
- x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
- x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
- return x
-
- # Fast path: upsampling with optional downsampling => use transpose strided convolution.
- if up > 1:
- if groups == 1:
- w = w.transpose(0, 1)
- else:
- w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
- w = w.transpose(1, 2)
- w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
- px0 -= kw - 1
- px1 -= kw - up
- py0 -= kh - 1
- py1 -= kh - up
- pxt = max(min(-px0, -px1), 0)
- pyt = max(min(-py0, -py1), 0)
- x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
- x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
- if down > 1:
- x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
- return x
-
- # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
- if up == 1 and down == 1:
- if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
- return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
-
- # Fallback: Generic reference implementation.
- x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
- x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
- if down > 1:
- x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
- return x
-
-#----------------------------------------------------------------------------
diff --git a/spaces/h2oai/wave-tour/examples/plot_interval_range_transpose.py b/spaces/h2oai/wave-tour/examples/plot_interval_range_transpose.py
deleted file mode 100644
index a229e07c649bba1ea16d3682d9641b0d6945d459..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_interval_range_transpose.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Plot / Interval / Transpose
-# Make a bar #plot. #interval
-# ---
-from h2o_wave import site, data, ui
-
-page = site['/demo']
-
-page.add('example', ui.plot_card(
- box='1 1 4 5',
- title='Interval range transposed',
- data=data('profession max min', 5, rows=[
- ('medicine', 110000, 23000),
- ('fire fighting', 120000, 18000),
- ('pedagogy', 125000, 24000),
- ('psychology', 130000, 22500),
- ('computer science', 151000, 36000),
- ]),
- plot=ui.plot([ui.mark(type='interval', x='=max', x0='=min', y='=profession')]),
-))
-
-page.save()
diff --git a/spaces/h2oai/wave-tour/examples/plot_vegalite_flex.py b/spaces/h2oai/wave-tour/examples/plot_vegalite_flex.py
deleted file mode 100644
index 49082b696991b6bb2345160bb039c948128ba49d..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_vegalite_flex.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# Plot / Vega-lite / Flex
-# Place Vegalite plots in a flexible layout.
-# ---
-from h2o_wave import site, ui, data
-
-page = site['/demo']
-page.drop()
-
-page['meta'] = ui.meta_card(box='', layouts=[
- ui.layout(
- breakpoint='xs',
- width='100%',
- zones=[
- ui.zone('top', size='350px', direction=ui.ZoneDirection.ROW, zones=[
- ui.zone('top_left', size='75%'),
- ui.zone('top_right', size='1'),
- ]),
- ui.zone('bottom', size='350px', direction=ui.ZoneDirection.ROW, zones=[
- ui.zone('bottom_left', size='1'),
- ui.zone('bottom_center', size='2'),
- ui.zone('bottom_right', size='3'),
- ]),
- ]
- )
-])
-
-plot_spec = '''
-{
- "description": "A simple bar plot with embedded data.",
- "mark": "bar",
- "encoding": {
- "x": {"field": "a", "type": "ordinal"},
- "y": {"field": "b", "type": "quantitative"}
- }
-}
-'''
-
-plot_data = data(fields=["a", "b"], rows=[
- ["A", 28], ["B", 55], ["C", 43],
- ["D", 91], ["E", 81], ["F", 53],
- ["G", 19], ["H", 87], ["I", 52]
-], pack=True)
-
-page.add('top_left', ui.vega_card(
- box='top_left',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('top_right', ui.vega_card(
- box='top_right',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('bottom_left', ui.vega_card(
- box='bottom_left',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('bottom_center', ui.vega_card(
- box='bottom_center',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-page.add('bottom_right', ui.vega_card(
- box='bottom_right',
- title='Plot',
- specification=plot_spec,
- data=plot_data,
-))
-
-page.save()
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/poolers.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/poolers.py
deleted file mode 100644
index b36a8ea3f1a0557621adb71e591105dcdb5f1f42..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/poolers.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from maskrcnn_benchmark.layers import ROIAlign, ROIAlignV2
-
-from .utils import cat
-
-
-class LevelMapper(object):
- """Determine which FPN level each RoI in a set of RoIs should map to based
- on the heuristic in the FPN paper.
- """
-
- def __init__(self, k_min, k_max, canonical_scale=224, canonical_level=4, eps=1e-6):
- """
- Arguments:
- k_min (int)
- k_max (int)
- canonical_scale (int)
- canonical_level (int)
- eps (float)
- """
- self.k_min = k_min
- self.k_max = k_max
- self.s0 = canonical_scale
- self.lvl0 = canonical_level
- self.eps = eps
-
- def __call__(self, boxlists):
- """
- Arguments:
- boxlists (list[BoxList])
- """
- # Compute level ids
- s = torch.sqrt(cat([boxlist.area() for boxlist in boxlists]))
-
- # Eqn.(1) in FPN paper
- target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0 + self.eps))
- target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max)
- return target_lvls.to(torch.int64) - self.k_min
-
-
-class Pooler(nn.Module):
- """
- Pooler for Detection with or without FPN.
- It currently hard-code ROIAlign in the implementation,
- but that can be made more generic later on.
- Also, the requirement of passing the scales is not strictly necessary, as they
- can be inferred from the size of the feature map / size of original image,
- which is available thanks to the BoxList.
- """
-
- def __init__(self, output_size, scales, sampling_ratio, use_v2=False):
- """
- Arguments:
- output_size (list[tuple[int]] or list[int]): output size for the pooled region
- scales (list[float]): scales for each Pooler
- sampling_ratio (int): sampling ratio for ROIAlign
- """
- super(Pooler, self).__init__()
- poolers = []
- for scale in scales:
- poolers.append(
- ROIAlignV2(
- output_size, spatial_scale=scale, sampling_ratio=sampling_ratio
- )
- if use_v2 else
- ROIAlign(
- output_size, spatial_scale=scale, sampling_ratio=sampling_ratio
- )
- )
- self.poolers = nn.ModuleList(poolers)
- self.output_size = output_size
- # get the levels in the feature map by leveraging the fact that the network always
- # downsamples by a factor of 2 at each level.
- lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item()
- lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item()
- self.map_levels = LevelMapper(lvl_min, lvl_max)
-
- def convert_to_roi_format(self, boxes):
- concat_boxes = cat([b.bbox for b in boxes], dim=0)
- device, dtype = concat_boxes.device, concat_boxes.dtype
- ids = cat(
- [
- torch.full((len(b), 1), i, dtype=dtype, device=device)
- for i, b in enumerate(boxes)
- ],
- dim=0,
- )
- rois = torch.cat([ids, concat_boxes], dim=1)
- return rois
-
- def forward(self, x, boxes):
- """
- Arguments:
- x (list[Tensor]): feature maps for each level
- boxes (list[BoxList]): boxes to be used to perform the pooling operation.
- Returns:
- result (Tensor)
- """
- num_levels = len(self.poolers)
- rois = self.convert_to_roi_format(boxes)
- if num_levels == 1:
- return self.poolers[0](x[0], rois)
-
- levels = self.map_levels(boxes)
-
- num_rois = len(rois)
- num_channels = x[0].shape[1]
- output_size = self.output_size[0]
-
- dtype, device = x[0].dtype, x[0].device
- result = torch.zeros(
- (num_rois, num_channels, output_size, output_size),
- dtype=dtype,
- device=device,
- )
- for level, (per_level_feature, pooler) in enumerate(zip(x, self.poolers)):
- idx_in_level = torch.nonzero(levels == level).squeeze(1)
- rois_per_level = rois[idx_in_level]
- result[idx_in_level] = pooler(per_level_feature, rois_per_level)
-
- return result
diff --git a/spaces/harshhpareek/bertscore/README.md b/spaces/harshhpareek/bertscore/README.md
deleted file mode 100644
index c93244844948592edc21cd0466a5b40bd9193994..0000000000000000000000000000000000000000
--- a/spaces/harshhpareek/bertscore/README.md
+++ /dev/null
@@ -1,136 +0,0 @@
----
-title: BERT Score
-emoji: 🤗
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.0.2
-app_file: app.py
-pinned: false
-tags:
-- evaluate
-- metric
-description: >-
- BERTScore leverages the pre-trained contextual embeddings from BERT and
- matches words in candidate and reference sentences by cosine similarity. It
- has been shown to correlate with human judgment on sentence-level and
- system-level evaluation. Moreover, BERTScore computes precision, recall, and
- F1 measure, which can be useful for evaluating different language generation
- tasks.
-
- See the project's README at https://github.com/Tiiiger/bert_score#readme for
- more information.
-duplicated_from: evaluate-metric/bertscore
----
-
-# Metric Card for BERT Score
-
-## Metric description
-
-BERTScore is an automatic evaluation metric for text generation that computes a similarity score for each token in the candidate sentence with each token in the reference sentence. It leverages the pre-trained contextual embeddings from [BERT](https://huggingface.co/bert-base-uncased) models and matches words in candidate and reference sentences by cosine similarity.
-
-Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks.
-
-## How to use
-
-BERTScore takes 3 mandatory arguments : `predictions` (a list of string of candidate sentences), `references` (a list of strings or list of list of strings of reference sentences) and either `lang` (a string of two letters indicating the language of the sentences, in [ISO 639-1 format](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)) or `model_type` (a string specififying which model to use, according to the BERT specification). The default behavior of the metric is to use the suggested model for the target language when one is specified, otherwise to use the `model_type` indicated.
-
-```python
-from evaluate import load
-bertscore = load("bertscore")
-predictions = ["hello there", "general kenobi"]
-references = ["hello there", "general kenobi"]
-results = bertscore.compute(predictions=predictions, references=references, lang="en")
-```
-
-BERTScore also accepts multiple optional arguments:
-
-
-`num_layers` (int): The layer of representation to use. The default is the number of layers tuned on WMT16 correlation data, which depends on the `model_type` used.
-
-`verbose` (bool): Turn on intermediate status update. The default value is `False`.
-
-`idf` (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
-
-`device` (str): On which the contextual embedding model will be allocated on. If this argument is `None`, the model lives on `cuda:0` if cuda is available.
-
-`nthreads` (int): Number of threads used for computation. The default value is `4`.
-
-`rescale_with_baseline` (bool): Rescale BERTScore with the pre-computed baseline. The default value is `False`.
-
-`batch_size` (int): BERTScore processing batch size, at least one of `model_type` or `lang`. `lang` needs to be specified when `rescale_with_baseline` is `True`.
-
-`baseline_path` (str): Customized baseline file.
-
-`use_fast_tokenizer` (bool): `use_fast` parameter passed to HF tokenizer. The default value is `False`.
-
-
-## Output values
-
-BERTScore outputs a dictionary with the following values:
-
-`precision`: The [precision](https://huggingface.co/metrics/precision) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
-
-`recall`: The [recall](https://huggingface.co/metrics/recall) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
-
-`f1`: The [F1 score](https://huggingface.co/metrics/f1) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
-
-`hashcode:` The hashcode of the library.
-
-
-### Values from popular papers
-The [original BERTScore paper](https://openreview.net/pdf?id=SkeHuCVFDr) reported average model selection accuracies (Hits@1) on WMT18 hybrid systems for different language pairs, which ranged from 0.004 for `en<->tr` to 0.824 for `en<->de`.
-
-For more recent model performance, see the [metric leaderboard](https://paperswithcode.com/paper/bertscore-evaluating-text-generation-with).
-
-## Examples
-
-Maximal values with the `distilbert-base-uncased` model:
-
-```python
-from evaluate import load
-bertscore = load("bertscore")
-predictions = ["hello world", "general kenobi"]
-references = ["hello world", "general kenobi"]
-results = bertscore.compute(predictions=predictions, references=references, model_type="distilbert-base-uncased")
-print(results)
-{'precision': [1.0, 1.0], 'recall': [1.0, 1.0], 'f1': [1.0, 1.0], 'hashcode': 'distilbert-base-uncased_L5_no-idf_version=0.3.10(hug_trans=4.10.3)'}
-```
-
-Partial match with the `bert-base-uncased` model:
-
-```python
-from evaluate import load
-bertscore = load("bertscore")
-predictions = ["hello world", "general kenobi"]
-references = ["goodnight moon", "the sun is shining"]
-results = bertscore.compute(predictions=predictions, references=references, model_type="distilbert-base-uncased")
-print(results)
-{'precision': [0.7380737066268921, 0.5584042072296143], 'recall': [0.7380737066268921, 0.5889028906822205], 'f1': [0.7380737066268921, 0.5732481479644775], 'hashcode': 'bert-base-uncased_L5_no-idf_version=0.3.10(hug_trans=4.10.3)'}
-```
-
-## Limitations and bias
-
-The [original BERTScore paper](https://openreview.net/pdf?id=SkeHuCVFDr) showed that BERTScore correlates well with human judgment on sentence-level and system-level evaluation, but this depends on the model and language pair selected.
-
-Furthermore, not all languages are supported by the metric -- see the [BERTScore supported language list](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) for more information.
-
-Finally, calculating the BERTScore metric involves downloading the BERT model that is used to compute the score-- the default model for `en`, `roberta-large`, takes over 1.4GB of storage space and downloading it can take a significant amount of time depending on the speed of your internet connection. If this is an issue, choose a smaller model; for instance `distilbert-base-uncased` is 268MB. A full list of compatible models can be found [here](https://docs.google.com/spreadsheets/d/1RKOVpselB98Nnh_EOC4A2BYn8_201tmPODpNWu4w7xI/edit#gid=0).
-
-
-## Citation
-
-```bibtex
-@inproceedings{bert-score,
- title={BERTScore: Evaluating Text Generation with BERT},
- author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
- booktitle={International Conference on Learning Representations},
- year={2020},
- url={https://openreview.net/forum?id=SkeHuCVFDr}
-}
-```
-
-## Further References
-
-- [BERTScore Project README](https://github.com/Tiiiger/bert_score#readme)
-- [BERTScore ICLR 2020 Poster Presentation](https://iclr.cc/virtual_2020/poster_SkeHuCVFDr.html)
diff --git a/spaces/hayas/rinna-youri-7b-chat/style.css b/spaces/hayas/rinna-youri-7b-chat/style.css
deleted file mode 100644
index 60878febc13db001635a52688abfe34d95e6c309..0000000000000000000000000000000000000000
--- a/spaces/hayas/rinna-youri-7b-chat/style.css
+++ /dev/null
@@ -1,16 +0,0 @@
-h1 {
- text-align: center;
-}
-
-#duplicate-button {
- margin: auto;
- color: white;
- background: #1565c0;
- border-radius: 100vh;
-}
-
-.contain {
- max-width: 900px;
- margin: auto;
- padding-top: 1.5rem;
-}
diff --git a/spaces/hirol/controlnetOverMask/image_process.py b/spaces/hirol/controlnetOverMask/image_process.py
deleted file mode 100644
index 679c9c572ee826f3081383c04ca511daad105b75..0000000000000000000000000000000000000000
--- a/spaces/hirol/controlnetOverMask/image_process.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# This is a sample Python script.
-
-# Press ⌃R to execute it or replace it with your code.
-# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
-
-import numpy as np
-import cv2
-import base64
-
-
-def cv2_base64(image):
- base64_str = cv2.imencode('.png',image)[1].tobytes()
- base64_str = base64.b64encode(base64_str)
- return base64_str.decode('utf-8')
-
-def base64_cv2(base64_str):
- imgString = base64.b64decode(base64_str)
- nparr = np.frombuffer(imgString,np.uint8)
- image = cv2.imdecode(nparr,cv2.IMREAD_COLOR)
- return image
-
-
-
-def image_pose_mask(imagepath : str):
- img = base64_cv2(imagepath)
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- ret, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
- contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- cnt = contours[-1]
- hull = cv2.convexHull(cnt)
- length = len(hull)
- if length > 4:
- for i in range(length):
- cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (255, 255, 255), 2)
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
- _, threshold = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
- mask = img.copy()
- kernel = np.ones((30, 30), dtype=np.uint8)
- dilated = cv2.dilate(threshold, kernel, 20)
- dilated = cv2.dilate(dilated, kernel, 20)
- contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
- valid = len(contours) > 0
-
- area = []
-
- for k in range(len(contours)):
- area.append(cv2.contourArea(contours[k]))
- max_idx = np.argmax(np.array(area))
-
- mask2 = cv2.drawContours(mask, contours, max_idx, (255, 255, 255), thickness=-1)
- img_base64 = cv2_base64(mask2)
-
- return img_base64
-
-
-
-def image_pose_mask_numpy(image):
- img = np.uint8(image)
- print("shape:" + str(img.shape))
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- ret, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
-
- contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- cnt = contours[-1]
- hull = cv2.convexHull(cnt)
- length = len(hull)
- if length > 4:
- for i in range(length):
- cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (255, 255, 255), 2)
-
- gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
- _, threshold = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
- mask = img.copy()
- kernel = np.ones((30, 30), dtype=np.uint8)
-
- dilated = cv2.dilate(threshold, kernel, 20)
-
- dilated = cv2.dilate(dilated, kernel, 20)
- contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
- valid = len(contours) > 0
-
- area = []
-
- for k in range(len(contours)):
- area.append(cv2.contourArea(contours[k]))
- max_idx = np.argmax(np.array(area))
-
- mask2 = cv2.drawContours(mask, contours, max_idx, (255, 255, 255), thickness=-1)
-
- img_base64 = cv2_base64(mask2)
-
- return img_base64
-
-def image_canny(imagepath:str):
-
- img = base64_cv2(imagepath)
-
- image_map = cv2.Canny(img, 100, 200)
-
- contours, hierarchy = cv2.findContours(image_map, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
- cnt = contours[-1]
- hull = cv2.convexHull(cnt)
- length = len(hull)
- if length > 4:
- for i in range(length):
- cv2.line(img, tuple(hull[i][0]), tuple(hull[(i + 1) % length][0]), (0, 0, 255), 2)
-
- img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
- contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
- valid = len(contours) > 0
- area = []
- for k in range(len(contours)):
- area.append(cv2.contourArea(contours[k]))
- max_idx = np.argmax(np.array(area))
-
- mask2 = cv2.drawContours(image_map, contours, max_idx, (0, 0, 255), thickness=3)
-
- img_base64 = cv2_base64(mask2)
-
- return img_base64
-
-# Press the green button in the gutter to run the script.
-if __name__ == '__main__':
- image_canny(imagepath='download (13).png')
-
diff --git a/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/constants.py b/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/constants.py
deleted file mode 100644
index b2a6100eed89c85f92d3f39e1f7fade62bcf39d2..0000000000000000000000000000000000000000
--- a/spaces/hlydecker/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/constants.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from pathlib import Path
-from PIL import Image
-
-# from dotenv import load_dotenv, find_dotenv # pip install python-dotenv==1.0.0
-
-from __version__ import __VERSION__ as APP_VERSION
-
-_SCRIPT_PATH = Path(__file__).absolute()
-PARENT_APP_DIR = _SCRIPT_PATH.parent
-TEMP_DIR = PARENT_APP_DIR / 'tempDir'
-ROOT_DIR = PARENT_APP_DIR.parent
-STATIC_DIR = ROOT_DIR / 'static'
-
-# _env_file_path = find_dotenv(str(CODE_DIR / '.env')) # Check if this path is correct
-# if _env_file_path:
-# load_dotenv(_env_file_path)
-
-ST_CONFIG = {
- "page_title": "NTT Data - Chat Q&A",
- # "page_icon": Image.open(STATIC_DIR / "mini_nttdata.jpg"),
-}
-
-OPERATING_MODE = "debug" # debug, preproduction, production
-
-REUSE_ANSWERS = False
-
-LOAD_INDEX_LOCALLY = False
-SAVE_INDEX_LOCALLY = False
-
-# x$ per 1000 tokens
-PRICES = {
- 'text-embedding-ada-002': 0.0004,
- 'text-davinci-003': 0.02,
- 'gpt-3': 0.002,
- 'gpt-4': 0.06, # 8K context
-}
-
-SOURCES_IDS = {
- # "Without source. Only chat": 4,
- "local files": 1,
- "urls": 3
-}
-
-TYPE_IDS = {
- "MSF Azure OpenAI Service": 1,
- "OpenAI": 2,
-}
-
-
-INDEX_IDS = {
- "FAISS": 1,
- "Pinecode": 2,
-}
diff --git a/spaces/huaiji3y/bingo-Public/src/components/chat-list.tsx b/spaces/huaiji3y/bingo-Public/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
- messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
- if (!messages.length) {
- return null
- }
-
- return (
-
- {messages.map((message, index) => (
-
-
- ))}
-
- )
-}
diff --git a/spaces/huggingface-tools/image-transformation/image_transformation.py b/spaces/huggingface-tools/image-transformation/image_transformation.py
deleted file mode 100644
index 66ffd0c8d18e53e1f9623641df3cfe9010f839cb..0000000000000000000000000000000000000000
--- a/spaces/huggingface-tools/image-transformation/image_transformation.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import torch
-
-from transformers.tools.base import Tool, get_default_device
-from transformers.utils import (
- is_accelerate_available,
- is_vision_available,
-)
-
-from diffusers import DiffusionPipeline
-
-
-IMAGE_TRANSFORMATION_DESCRIPTION = (
- "This is a tool that transforms an image according to a prompt. It takes two inputs: `image`, which should be "
- "the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the "
- "modified image."
-)
-
-
-class ImageTransformationTool(Tool):
- default_stable_diffusion_checkpoint = "timbrooks/instruct-pix2pix"
- description = IMAGE_TRANSFORMATION_DESCRIPTION
- inputs = ['image', 'text']
- outputs = ['image']
-
- def __init__(self, device=None, controlnet=None, stable_diffusion=None, **hub_kwargs) -> None:
- if not is_accelerate_available():
- raise ImportError("Accelerate should be installed in order to use tools.")
- if not is_vision_available():
- raise ImportError("Pillow should be installed in order to use the StableDiffusionTool.")
-
- super().__init__()
-
- self.stable_diffusion = self.default_stable_diffusion_checkpoint
-
- self.device = device
- self.hub_kwargs = hub_kwargs
-
- def setup(self):
- if self.device is None:
- self.device = get_default_device()
-
- self.pipeline = DiffusionPipeline.from_pretrained(self.stable_diffusion)
-
- self.pipeline.to(self.device)
- if self.device.type == "cuda":
- self.pipeline.to(torch_dtype=torch.float16)
-
- self.is_initialized = True
-
- def __call__(self, image, prompt):
- if not self.is_initialized:
- self.setup()
-
- negative_prompt = "low quality, bad quality, deformed, low resolution"
- added_prompt = " , highest quality, highly realistic, very high resolution"
-
- return self.pipeline(
- prompt + added_prompt,
- image,
- negative_prompt=negative_prompt,
- num_inference_steps=50,
- ).images[0]
\ No newline at end of file
diff --git "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/14_\360\237\223\232_Glossary.py" "b/spaces/huggingface/Model_Cards_Writing_Tool/pages/14_\360\237\223\232_Glossary.py"
deleted file mode 100644
index e118cdc5a1304e0113cad2f032c4f998d0c127de..0000000000000000000000000000000000000000
--- "a/spaces/huggingface/Model_Cards_Writing_Tool/pages/14_\360\237\223\232_Glossary.py"
+++ /dev/null
@@ -1,24 +0,0 @@
-import streamlit as st
-from persist import persist, load_widget_state
-
-
-global variable_output
-
-def main():
- cs_body()
-
-
-
-def cs_body():
-
- st.markdown('# Glossary [optional]')
- st.text_area("Terms used in the model card that need to be clearly defined in order to be accessible across audiences go here.",height = 200, key=persist("Glossary"))
-
-
-
-
-
-
-if __name__ == '__main__':
- load_widget_state()
- main()
\ No newline at end of file
diff --git a/spaces/hysts/Hopenet/README.md b/spaces/hysts/Hopenet/README.md
deleted file mode 100644
index dcd57df74ca0a96e6a870a7f9bff001af6ce7f3e..0000000000000000000000000000000000000000
--- a/spaces/hysts/Hopenet/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hopenet
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/ilmhona/api/README.md b/spaces/ilmhona/api/README.md
deleted file mode 100644
index a1f674080a3b27df27041fe1ce5bd3bb4c955a74..0000000000000000000000000000000000000000
--- a/spaces/ilmhona/api/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Api
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/inamXcontru/PoeticTTS/Adobe Acrobat XI Pro 11.0.16 .rar [Extra Quality].md b/spaces/inamXcontru/PoeticTTS/Adobe Acrobat XI Pro 11.0.16 .rar [Extra Quality].md
deleted file mode 100644
index 0e42c17c4222a5baaf8cc0464f0abfa036227ecc..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Adobe Acrobat XI Pro 11.0.16 .rar [Extra Quality].md
+++ /dev/null
@@ -1,14 +0,0 @@
-Adobe Acrobat XI Pro 11.0.16 .rar
Download › https://gohhs.com/2uz4nI
-
-Use it in Czechoslovakia unified for free connection. Nahrávejte, sdilejte a stahujte zdarma. Credit can also be insured neomezenou friability. How to connect smartphone to TV.
-I have a Wi-Fi connection on my phone.
-Using the function, you can connect to a smartphone.
-Smart Wi-Fi function in a smartphone.
-How to turn on Smart Wi-Fi on your phone?
-You can also launch a Wi-Fi network through your smartphone and connect it.
-How to connect smart tv to smartphone?
-How to connect a smartphone to a TV via Wi-Fi or Smart-TV cable.All this can be connected and activated at the same time for.
-Connecting to a Wi-Fi network on your phone is easy. 8a78ff9644
-
-
-
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Giveaway Tenorshare IAnyGo V1.1.0.0 For Win FREE UPD.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Giveaway Tenorshare IAnyGo V1.1.0.0 For Win FREE UPD.md
deleted file mode 100644
index 989a5656aef1cfd9b2a9472fda18ae779d94c63a..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Giveaway Tenorshare IAnyGo V1.1.0.0 For Win FREE UPD.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Giveaway: Tenorshare iAnyGo v1.1.0.0 for Win FREE
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-
-Giveaway: Tenorshare iAnyGo v1.1.0.0 for Win FREE · Let s Bring Out The Best Out of Each Other · Update To Iphone App Twittelator Approved ... 1fdad05405
-
-
-
diff --git a/spaces/inreVtussa/clothingai/Examples/!!HOT!! Download Omron Plc Password 170.md b/spaces/inreVtussa/clothingai/Examples/!!HOT!! Download Omron Plc Password 170.md
deleted file mode 100644
index 31e7d33c3477d2d25ec7e1a5ca404504b71e2beb..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/!!HOT!! Download Omron Plc Password 170.md
+++ /dev/null
@@ -1,20 +0,0 @@
-Download Omron Plc Password 170
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diff --git a/spaces/inreVtussa/clothingai/Examples/Act Of War High Treason Download For Pc [crack] High Quality.md b/spaces/inreVtussa/clothingai/Examples/Act Of War High Treason Download For Pc [crack] High Quality.md
deleted file mode 100644
index 8d9b965704cd73256737a7cbcec70d276f18e1ec..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Act Of War High Treason Download For Pc [crack] High Quality.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Act of War: High Treason download for pc [crack]
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diff --git a/spaces/inreVtussa/clothingai/Examples/Arcana Heart 3 LOVEMAX SIXSTARS V1.08-DARKSiDERS BEST.md b/spaces/inreVtussa/clothingai/Examples/Arcana Heart 3 LOVEMAX SIXSTARS V1.08-DARKSiDERS BEST.md
deleted file mode 100644
index ab71e0222cfebc0ef131129ccd81038b22079e17..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Arcana Heart 3 LOVEMAX SIXSTARS V1.08-DARKSiDERS BEST.md
+++ /dev/null
@@ -1,73 +0,0 @@
-
-Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS: A Review of the Latest Update
-Arcana Heart 3 LOVEMAX SIXSTARS!!!!!! is a 2D fighting game that features an all-female cast of characters with unique abilities and fighting styles. The game was originally released in Japan in 2017 as an arcade update of Arcana Heart 3 LOVEMAX!!!!!, which was released in 2014 for PlayStation 3 and PlayStation Vita.
-In November 2019, the game received a new update for PC, titled Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS. This update added a new playable character, Minori Amanohara, and a new Arcana, Ichor, the Blood. The update also fixed some bugs and improved the game's performance and stability.
-Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS
Download File »»» https://tiurll.com/2uCjRc
-Who is Minori Amanohara?
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-Minori's fighting style is based on her ability to manipulate blood and use it as a weapon. She can create blood projectiles, blades, shields, and even transform into a giant blood monster. She can also use her Geist power to summon a giant robot that can assist her in battle.
-Minori's personality is cheerful and innocent, but also naive and reckless. She likes to help people in need, but often causes more trouble than she solves. She has a self-proclaimed super hero name, "Annihilation Angel Subaru", and wears a red cape and mask to hide her identity.
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-
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-The game's update also adds more content and value to the game, with a new character and Arcana that offer new gameplay possibilities and strategies. The update also improves the game's performance and stability, making it more enjoyable to play.
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-Conclusion
-Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS is a great update for an already great game. It adds more content and value to the game, with a new character and Arcana that offer new gameplay possibilities and strategies. It also improves the game's performance and stability, making it more enjoyable to play.
-If you are looking for a fun and challenging 2D fighting game with anime-style graphics and characters, you should definitely check out Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS. The game offers a fast-paced and fluid combat system that allows you to customize your character's abilities with different Arcanas. The game also has a variety of modes, such as Story Mode, Versus Mode, Survival Mode, Trial Mode, Time Attack Mode, Gallery Mode, and Online Mode.
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-How to Install Arcana Heart 3 LOVEMAX SIXSTARS v1.08-DARKSiDERS?
-If you already have Arcana Heart 3 LOVEMAX SIXSTARS!!!!!! installed on your PC, you can easily install the v1.08-DARKSiDERS update by following these steps:
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\ No newline at end of file
diff --git a/spaces/inreVtussa/clothingai/Examples/Command And Conquer Renegade Crack Free Download.md b/spaces/inreVtussa/clothingai/Examples/Command And Conquer Renegade Crack Free Download.md
deleted file mode 100644
index 38dace65fa3201db29a0070527ad7d26ec0dc5d0..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Command And Conquer Renegade Crack Free Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Command And Conquer Renegade Crack Free Download
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diff --git a/spaces/inreVtussa/clothingai/Examples/Counter-Strike Global Offensive V1.32.7.0 No-Steam CODEX.md b/spaces/inreVtussa/clothingai/Examples/Counter-Strike Global Offensive V1.32.7.0 No-Steam CODEX.md
deleted file mode 100644
index f4aacbbcb2a100a6998082abf0f22305c521b8e1..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Counter-Strike Global Offensive V1.32.7.0 No-Steam CODEX.md
+++ /dev/null
@@ -1,26 +0,0 @@
-
-How to Download and Install Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX
-Counter-Strike: Global Offensive (CS:GO) is one of the most popular multiplayer first-person shooter games in the world. It features two teams of five players each, competing in various game modes and maps. CS:GO is constantly updated with new content, such as weapons, skins, maps, and more.
-However, some players may not have access to the official Steam version of CS:GO, or may want to play it offline or with custom mods. In that case, they can download and install a cracked version of CS:GO, such as Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX.
-Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX
Download File ★ https://tiurll.com/2uClXB
-This version of CS:GO is based on the license from Valve Corporation and Hidden Path Entertainment, and has a game version of 1.32.7.0. It does not require Steam to run, and it includes all the latest updates and features of CS:GO as of October 2014[^1^]. It also has a multilingual interface and sound language.
-In this article, we will show you how to download and install Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX on your PC.
-Step 1: Download Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX
-The first step is to download the torrent file of Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX from a reliable source[^1^]. The torrent file contains the ISO image of the game, which is about 2.76 GB in size.
-You will need a torrent client, such as uTorrent or BitTorrent, to download the torrent file. Once you have downloaded the torrent file, open it with your torrent client and start downloading the ISO image of the game.
-Step 2: Mount or Burn the ISO Image
-The next step is to mount or burn the ISO image of Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX on your PC.
-
-If you have a virtual drive software, such as Daemon Tools or PowerISO, you can mount the ISO image on a virtual drive and access it like a normal CD or DVD.
-If you have a physical CD or DVD burner, you can burn the ISO image on a blank disc and insert it into your PC's drive.
-Step 3: Install Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX
-The final step is to install Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX on your PC.
-If you have mounted or burned the ISO image, you can run the setup.exe file from the virtual or physical drive and follow the instructions on the screen.
-The installation process may take 5-10 minutes, depending on your PC's specifications. You will need at least 10 GB of free space on your hard drive to install the game.
-Once the installation is complete, you can launch Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX from your desktop shortcut or start menu and enjoy playing it offline or with custom mods.
-Conclusion
-Counter-Strike: Global Offensive v1.32.7.0 No-Steam CODEX is a cracked version of CS:GO that does not require Steam to run and has all the latest updates and features as of October 2014[^1^]. It is easy to download and install on your PC, as long as you follow the steps in this article.
-However, please note that this version of CS:GO may not be compatible with some online servers or official updates from Valve Corporation and Hidden Path Entertainment. It may also contain some bugs or errors that are not present in the official Steam version of CS:GO.
-Therefore, we recommend that you only use this version of CS:GO for personal
d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/iqovocn/ChuanhuChatGPT/chatgpt - windows.bat b/spaces/iqovocn/ChuanhuChatGPT/chatgpt - windows.bat
deleted file mode 100644
index 0b78fdc3a559abd692e3a9e9af5e482124d13a99..0000000000000000000000000000000000000000
--- a/spaces/iqovocn/ChuanhuChatGPT/chatgpt - windows.bat
+++ /dev/null
@@ -1,14 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
-
-REM The web page can be accessed with delayed start http://127.0.0.1:7860/
-ping -n 5 127.0.0.1>nul
-
-REM access chargpt via your default browser
-start "" "http://127.0.0.1:7860/"
-
-
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/).
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/MusicGen/tests/modules/test_codebooks_patterns.py b/spaces/jbilcke-hf/MusicGen/tests/modules/test_codebooks_patterns.py
deleted file mode 100644
index b658f4779a369f9ec8dde692a61b7f0fe3485724..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/MusicGen/tests/modules/test_codebooks_patterns.py
+++ /dev/null
@@ -1,246 +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.
-
-import pytest
-import torch
-
-from audiocraft.modules.codebooks_patterns import (
- DelayedPatternProvider,
- ParallelPatternProvider,
- Pattern,
- UnrolledPatternProvider,
-)
-
-
-class TestParallelPatternProvider:
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [0, 1, 16, 100])
- def test_get_pattern(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- # + 1 to account for 1st step
- assert len(pattern.layout) == timesteps + 1
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_content(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- for s, v in enumerate(pattern.layout):
- for i, code in enumerate(v):
- assert i == code.q
- assert code.t == s - 1 # account for the 1st empty step
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_max_delay(self, n_q: int, timesteps: int):
- provider = ParallelPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == 0
- assert len(pattern.valid_layout) == len(pattern.layout) - pattern.max_delay
-
-
-class TestDelayedPatternProvider:
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [0, 1, 16, 100])
- def test_get_pattern(self, n_q: int, timesteps: int):
- delays = [
- list(range(n_q)),
- [0] + [1] * (n_q - 1),
- [0] + [4] * (n_q - 1),
- ]
- for delay in delays:
- provider = DelayedPatternProvider(n_q, delay)
- pattern = provider.get_pattern(timesteps)
- # + 1 to account for 1st step
- assert len(pattern.layout) == timesteps + max(delay) + 1
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- def test_pattern_content(self, n_q: int, timesteps: int):
- provider = DelayedPatternProvider(n_q)
- pattern = provider.get_pattern(timesteps)
- for s, v in enumerate(pattern.layout):
- for i, code in enumerate(v):
- assert i == code.q
- assert code.t == max(0, s - code.q - 1)
-
- @pytest.mark.parametrize("timesteps", [8, 16, 100])
- @pytest.mark.parametrize("delay", [[0, 1, 2, 3], [0, 1, 1, 1], [0, 3, 3, 3], [0, 3]])
- def test_pattern_max_delay(self, timesteps: int, delay: list):
- provider = DelayedPatternProvider(len(delay), delay)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == max(delay)
- assert len(pattern.valid_layout) == len(pattern.layout) - pattern.max_delay
-
-
-class TestUnrolledPatternProvider:
-
- @pytest.mark.parametrize("timesteps", [0, 1, 16])
- @pytest.mark.parametrize("flattening", [[0, 1, 2], [0, 1, 1]])
- @pytest.mark.parametrize("delays", [[0, 0, 0], [0, 5, 5]])
- def test_get_pattern(self, timesteps: int, flattening: list, delays: list):
- n_q = len(flattening)
- max_delay = max(delays)
- provider = UnrolledPatternProvider(n_q, flattening, delays)
- pattern = provider.get_pattern(timesteps)
- assert len(pattern.layout) == provider.num_virtual_steps(timesteps) + max_delay
-
- @pytest.mark.parametrize("timesteps", [0, 1, 16])
- @pytest.mark.parametrize("flattening", [[0, 1, 2], [0, 1, 1]])
- @pytest.mark.parametrize("delays", [[0, 0, 0], [0, 5, 5]])
- def test_pattern_max_delay(self, timesteps: int, flattening: list, delays: list):
- n_q = len(flattening)
- max_delay = max(delays)
- provider = UnrolledPatternProvider(n_q, flattening, delays)
- pattern = provider.get_pattern(timesteps)
- assert pattern.max_delay == max_delay
-
-
-class TestPattern:
-
- def ref_build_pattern_sequence(self, z: torch.Tensor, pattern: Pattern, special_token: int):
- """Reference method to build the sequence from the pattern without using fancy scatter."""
- bs, n_q, T = z.shape
- z = z.cpu().numpy()
- assert n_q == pattern.n_q
- assert T <= pattern.timesteps
- inp = torch.full((bs, n_q, len(pattern.layout)), special_token, dtype=torch.long).numpy()
- inp[:] = special_token
- for s, v in enumerate(pattern.layout):
- for (t, q) in v:
- if t < T:
- inp[:, q, s] = z[:, q, t]
- return torch.from_numpy(inp)
-
- def ref_revert_pattern_sequence(self, z: torch.Tensor, pattern: Pattern, special_token: int):
- """Reference method to revert the sequence from the pattern without using fancy scatter."""
- z = z.cpu().numpy()
- bs, n_q, S = z.shape
- assert pattern.n_q == n_q
- inp = torch.full((bs, pattern.n_q, pattern.timesteps), special_token, dtype=torch.long).numpy()
- inp[:] = special_token
- for s, v in enumerate(pattern.layout):
- for (t, q) in v:
- if t < pattern.timesteps:
- inp[:, q, t] = z[:, q, s]
- return torch.from_numpy(inp)
-
- def ref_revert_pattern_logits(self, z: torch.Tensor, pattern: Pattern, special_token: float):
- """Reference method to revert the logits from the pattern without using fancy scatter."""
- z = z.cpu().numpy()
- bs, card, n_q, S = z.shape
- assert pattern.n_q == n_q
- ref_layout = pattern.layout
- inp = torch.full((bs, card, pattern.n_q, pattern.timesteps), special_token, dtype=torch.float).numpy()
- inp[:] = special_token
- for s, v in enumerate(ref_layout[1:]):
- if s < S:
- for (t, q) in v:
- if t < pattern.timesteps:
- inp[:, :, q, t] = z[:, :, q, s]
- return torch.from_numpy(inp)
-
- def _get_pattern_providers(self, n_q: int):
- pattern_provider_1 = ParallelPatternProvider(n_q)
- pattern_provider_2 = DelayedPatternProvider(n_q, list(range(n_q)))
- pattern_provider_3 = DelayedPatternProvider(n_q, [0] + [1] * (n_q - 1))
- pattern_provider_4 = UnrolledPatternProvider(
- n_q, flattening=list(range(n_q)), delays=[0] * n_q
- )
- pattern_provider_5 = UnrolledPatternProvider(
- n_q, flattening=[0] + [1] * (n_q - 1), delays=[0] * n_q
- )
- pattern_provider_6 = UnrolledPatternProvider(
- n_q, flattening=[0] + [1] * (n_q - 1), delays=[0] + [5] * (n_q - 1)
- )
- return [
- pattern_provider_1,
- pattern_provider_2,
- pattern_provider_3,
- pattern_provider_4,
- pattern_provider_5,
- pattern_provider_6,
- ]
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- def test_build_pattern_sequence(self, n_q: int, timesteps: int):
- bs = 2
- card = 256
- special_token = card
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # we can correctly build the sequence from the pattern
- z = torch.randint(0, card, (bs, n_q, timesteps))
- ref_res = self.ref_build_pattern_sequence(z, pattern, special_token)
- res, indexes, mask = pattern.build_pattern_sequence(z, special_token)
- assert (res == ref_res).float().mean() == 1.0
-
- # expected assertion fails on the number of timesteps
- invalid_timesteps = [timesteps + 1]
- if pattern.num_sequence_steps != pattern.timesteps:
- invalid_timesteps.append(pattern.num_sequence_steps)
- for i_timesteps in invalid_timesteps:
- z2 = torch.randint(0, card, (bs, n_q, i_timesteps))
- with pytest.raises(AssertionError):
- pattern.build_pattern_sequence(z2, special_token)
-
- # expected assertion fails on the number of codebooks
- invalid_qs = [0, n_q - 1, n_q + 1]
- for i_q in invalid_qs:
- z3 = torch.randint(0, card, (bs, i_q, timesteps))
- with pytest.raises(AssertionError):
- pattern.build_pattern_sequence(z3, special_token)
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- def test_revert_pattern_sequence(self, n_q: int, timesteps: int):
- bs = 2
- card = 256
- special_token = card
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # this works assuming previous tests are successful
- z = torch.randint(0, card, (bs, n_q, timesteps))
- s = self.ref_build_pattern_sequence(z, pattern, special_token)
- ref_out = self.ref_revert_pattern_sequence(s, pattern, special_token)
- # ensure our reference script retrieve the original sequence
- assert z.shape == ref_out.shape
- assert (z == ref_out).float().mean() == 1.0
- # now we can test the scatter version
- out, indexes, mask = pattern.revert_pattern_sequence(s, special_token)
- assert out.shape == ref_out.shape
- assert (out == ref_out).float().mean() == 1.0
-
- @pytest.mark.parametrize("n_q", [1, 4, 32])
- @pytest.mark.parametrize("timesteps", [16, 72])
- @pytest.mark.parametrize("card", [1, 2, 256, 1024])
- def test_revert_pattern_logits(self, n_q: int, timesteps: int, card: int):
- bs = 2
- special_token = card
- logits_special_token = float('nan')
-
- pattern_providers = self._get_pattern_providers(n_q)
- for pattern_provider in pattern_providers:
- pattern = pattern_provider.get_pattern(timesteps)
- # this works assuming previous tests are successful
- z = torch.randint(0, card, (bs, n_q, timesteps))
- s = self.ref_build_pattern_sequence(z, pattern, special_token)
- logits = torch.randn((bs, card, n_q, s.shape[-1]))
- ref_out = self.ref_revert_pattern_logits(logits, pattern, logits_special_token)
- # ensure our reference script retrieve the original sequence
- assert ref_out.shape == torch.Size([bs, card, n_q, timesteps])
- # now we can test the scatter version
- out, indexes, mask = pattern.revert_pattern_logits(logits, logits_special_token)
- assert out.shape == ref_out.shape
- assert (out == ref_out).float().mean() == 1.0
diff --git a/spaces/jbilcke-hf/Panoremix/src/lib/generateSeed.ts b/spaces/jbilcke-hf/Panoremix/src/lib/generateSeed.ts
deleted file mode 100644
index 563e25ec894ab5af54c5025a15a9b7a5918325de..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/lib/generateSeed.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-export function generateSeed() {
- return Math.floor(Math.random() * Math.pow(2, 31));
-}
\ No newline at end of file
diff --git a/spaces/jhwen/bingo/src/components/chat-notification.tsx b/spaces/jhwen/bingo/src/components/chat-notification.tsx
deleted file mode 100644
index 3474e522992c43a4d1d0eadcf205a9760d5b930b..0000000000000000000000000000000000000000
--- a/spaces/jhwen/bingo/src/components/chat-notification.tsx
+++ /dev/null
@@ -1,91 +0,0 @@
-import { useEffect } from 'react'
-import Image from 'next/image'
-
-import IconWarning from '@/assets/images/warning.svg'
-import { ChatError, ErrorCode, ChatMessageModel } from '@/lib/bots/bing/types'
-import { ExternalLink } from './external-link'
-import { useBing } from '@/lib/hooks/use-bing'
-
-export interface ChatNotificationProps extends Pick, 'bot'> {
- message?: ChatMessageModel
-}
-
-function getAction(error: ChatError, reset: () => void) {
- if (error.code === ErrorCode.THROTTLE_LIMIT) {
- reset()
- return (
-
- 你已达到每日最大发送消息次数，请更换账号或隔一天后重试
-
- )
- }
- if (error.code === ErrorCode.BING_IP_FORBIDDEN) {
- return (
-
- 你的服务器或代理已被封禁，请更换服务器或使用代理重试
-
- )
- }
- if (error.code === ErrorCode.BING_TRY_LATER) {
- return (
-
- 创建会话失败，请稍候重试
-
- )
- }
- if (error.code === ErrorCode.BING_FORBIDDEN) {
- return (
-
- 你的账号已在黑名单，请尝试更换账号及申请解封
-
- )
- }
- if (error.code === ErrorCode.CONVERSATION_LIMIT) {
- return (
-
- 当前话题已中止，请点
- 重新开始
- 开启新的对话
-
- )
- }
- if (error.code === ErrorCode.BING_CAPTCHA) {
- return (
-
- 点击通过人机验证
-
- )
- }
- if (error.code === ErrorCode.BING_UNAUTHORIZED) {
- reset()
- return (
- 没有获取到身份信息或身份信息失效，点此重新设置
- )
- }
- return error.message
-}
-
-export function ChatNotification({ message, bot }: ChatNotificationProps) {
- useEffect(() => {
- window.scrollBy(0, 2000)
- }, [message])
-
- if (!message?.error) return
-
- return (
-
-
-
-
-
-
- Favicons
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- To use the favicons insert into your head section some of these tags accordly to your needs.
-
-
-
-
- <!-- For old IEs -->
- <link rel="shortcut icon" href="favicon.ico" />
-
- <!-- For new browsers - multisize ico -->
- <link rel="icon" type="image/x-icon" sizes="16x16 32x32" href="favicon.ico">
-
- <!-- For iPad with high-resolution Retina display running iOS ≥ 7: -->
- <link rel="apple-touch-icon" sizes="152x152" href="favicon-152-precomposed.png">
-
- <!-- For iPad with high-resolution Retina display running iOS ≤ 6: -->
- <link rel="apple-touch-icon" sizes="144x144" href="favicon-144-precomposed.png">
-
- <!-- For iPhone with high-resolution Retina display running iOS ≥ 7: -->
- <link rel="apple-touch-icon" sizes="120x120" href="favicon-120-precomposed.png">
-
- <!-- For iPhone with high-resolution Retina display running iOS ≤ 6: -->
- <link rel="apple-touch-icon" sizes="114x114" href="favicon-114-precomposed.png">
-
- <!-- For iPhone 6+ -->
- <link rel="apple-touch-icon" sizes="180x180" href="favicon-180-precomposed.png">
-
- <!-- For first- and second-generation iPad: -->
- <link rel="apple-touch-icon" sizes="72x72" href="favicon-72-precomposed.png">
-
- <!-- For non-Retina iPhone, iPod Touch, and Android 2.1+ devices: -->
- <link rel="apple-touch-icon" sizes="57x57" href="favicon-57.png">
-
- <!-- For Old Chrome -->
- <link rel="icon" sizes="32x32" href="favicon-32.png" >
-
- <!-- For IE10 Metro -->
- <meta name="msapplication-TileColor" content="#FFFFFF">
- <meta name="msapplication-TileImage" content="favicon-144.png">
- <meta name="theme-color" content="#ffffff">
-
- <!-- Chrome for Android -->
- <link rel="manifest" href="manifest.json">
- <link rel="icon" sizes="192x192" href="favicon-192.png">
-
-
-
-
-
-
-
- For more informations about favicons consult The Favicon Cheat Sheet by Audrey Roy.
-
-
-
-
-
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/app/interface/about/index.tsx b/spaces/jordonpeter01/ai-comic-factory/src/app/interface/about/index.tsx
deleted file mode 100644
index 45ef9f30aff4e54048f3e2b274ec714ddb23f7ec..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/app/interface/about/index.tsx
+++ /dev/null
@@ -1,46 +0,0 @@
-import { Button } from "@/components/ui/button"
-import { Dialog, DialogContent, DialogDescription, DialogFooter, DialogHeader, DialogTitle, DialogTrigger } from "@/components/ui/dialog"
-import { useState } from "react"
-
-export function About() {
- const [isOpen, setOpen] = useState(false)
-
- return (
-
- )
-}
\ No newline at end of file
diff --git a/spaces/kadirnar/yolor/yolor/test.py b/spaces/kadirnar/yolor/yolor/test.py
deleted file mode 100644
index f0898523592593ef981e95469799a1ebd1daa2f3..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/yolor/yolor/test.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import argparse
-import glob
-import json
-import os
-from pathlib import Path
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from yolor.utils.google_utils import attempt_load
-from yolor.utils.datasets import create_dataloader
-from yolor.utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \
- non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, clip_coords, set_logging, increment_path
-from yolor.utils.loss import compute_loss
-from yolor.utils.metrics import ap_per_class
-from yolor.utils.plots import plot_images, output_to_target
-from yolor.utils.torch_utils import select_device, time_synchronized
-
-from yolor.models.models import *
-
-def load_classes(path):
- # Loads *.names file at 'path'
- with open(path, 'r') as f:
- names = f.read().split('\n')
- return list(filter(None, names)) # filter removes empty strings (such as last line)
-
-
-def test(data,
- weights=None,
- batch_size=16,
- imgsz=640,
- conf_thres=0.001,
- iou_thres=0.6, # for NMS
- save_json=False,
- single_cls=False,
- augment=False,
- verbose=False,
- model=None,
- dataloader=None,
- save_dir=Path(''), # for saving images
- save_txt=False, # for auto-labelling
- save_conf=False,
- plots=True,
- log_imgs=0): # number of logged images
-
- # Initialize/load model and set device
- training = model is not None
- if training: # called by train.py
- device = next(model.parameters()).device # get model device
-
- else: # called directly
- set_logging()
- device = select_device(opt.device, batch_size=batch_size)
- save_txt = opt.save_txt # save *.txt labels
-
- # Directories
- save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Load model
- model = Darknet(opt.cfg).to(device)
-
- # load model
- try:
- ckpt = torch.load(weights[0], map_location=device) # load checkpoint
- ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
- model.load_state_dict(ckpt['model'], strict=False)
- except:
- load_darknet_weights(model, weights[0])
- imgsz = check_img_size(imgsz, s=64) # check img_size
-
- # Half
- half = device.type != 'cpu' # half precision only supported on CUDA
- if half:
- model.half()
-
- # Configure
- model.eval()
- is_coco = data.endswith('coco.yaml') # is COCO dataset
- with open(data) as f:
- data = yaml.load(f, Loader=yaml.FullLoader) # model dict
- check_dataset(data) # check
- nc = 1 if single_cls else int(data['nc']) # number of classes
- iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
- niou = iouv.numel()
-
- # Logging
- log_imgs, wandb = min(log_imgs, 100), None # ceil
- try:
- import wandb # Weights & Biases
- except ImportError:
- log_imgs = 0
-
- # Dataloader
- if not training:
- img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
- _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
- path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
- dataloader = create_dataloader(path, imgsz, batch_size, 64, opt, pad=0.5, rect=True)[0]
-
- seen = 0
- try:
- names = model.names if hasattr(model, 'names') else model.module.names
- except:
- names = load_classes(opt.names)
- coco91class = coco80_to_coco91_class()
- s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
- p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
- loss = torch.zeros(3, device=device)
- jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
- for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
- img = img.to(device, non_blocking=True)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- targets = targets.to(device)
- nb, _, height, width = img.shape # batch size, channels, height, width
- whwh = torch.Tensor([width, height, width, height]).to(device)
-
- # Disable gradients
- with torch.no_grad():
- # Run model
- t = time_synchronized()
- inf_out, train_out = model(img, augment=augment) # inference and training outputs
- t0 += time_synchronized() - t
-
- # Compute loss
- if training: # if model has loss hyperparameters
- loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls
-
- # Run NMS
- t = time_synchronized()
- output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres)
- t1 += time_synchronized() - t
-
- # Statistics per image
- for si, pred in enumerate(output):
- labels = targets[targets[:, 0] == si, 1:]
- nl = len(labels)
- tcls = labels[:, 0].tolist() if nl else [] # target class
- seen += 1
-
- if len(pred) == 0:
- if nl:
- stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
- continue
-
- # Append to text file
- path = Path(paths[si])
- if save_txt:
- gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
- x = pred.clone()
- x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original
- for *xyxy, conf, cls in x:
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
- with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- # W&B logging
- if plots and len(wandb_images) < log_imgs:
- box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
- "class_id": int(cls),
- "box_caption": "%s %.3f" % (names[cls], conf),
- "scores": {"class_score": conf},
- "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
- boxes = {"predictions": {"box_data": box_data, "class_labels": names}}
- wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))
-
- # Clip boxes to image bounds
- clip_coords(pred, (height, width))
-
- # Append to pycocotools JSON dictionary
- if save_json:
- # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
- image_id = int(path.stem) if path.stem.isnumeric() else path.stem
- box = pred[:, :4].clone() # xyxy
- scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
- box = xyxy2xywh(box) # xywh
- box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
- for p, b in zip(pred.tolist(), box.tolist()):
- jdict.append({'image_id': image_id,
- 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
- 'bbox': [round(x, 3) for x in b],
- 'score': round(p[4], 5)})
-
- # Assign all predictions as incorrect
- correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
- if nl:
- detected = [] # target indices
- tcls_tensor = labels[:, 0]
-
- # target boxes
- tbox = xywh2xyxy(labels[:, 1:5]) * whwh
-
- # Per target class
- for cls in torch.unique(tcls_tensor):
- ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
- pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
-
- # Search for detections
- if pi.shape[0]:
- # Prediction to target ious
- ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) # best ious, indices
-
- # Append detections
- detected_set = set()
- for j in (ious > iouv[0]).nonzero(as_tuple=False):
- d = ti[i[j]] # detected target
- if d.item() not in detected_set:
- detected_set.add(d.item())
- detected.append(d)
- correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
- if len(detected) == nl: # all targets already located in image
- break
-
- # Append statistics (correct, conf, pcls, tcls)
- stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
-
- # Plot images
- if plots and batch_i < 3:
- f = save_dir / f'test_batch{batch_i}_labels.jpg' # filename
- plot_images(img, targets, paths, f, names) # labels
- f = save_dir / f'test_batch{batch_i}_pred.jpg'
- plot_images(img, output_to_target(output, width, height), paths, f, names) # predictions
-
- # Compute statistics
- stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
- if len(stats) and stats[0].any():
- p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png')
- p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95]
- mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
- nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
- else:
- nt = torch.zeros(1)
-
- # W&B logging
- if plots and wandb:
- wandb.log({"Images": wandb_images})
- wandb.log({"Validation": [wandb.Image(str(x), caption=x.name) for x in sorted(save_dir.glob('test*.jpg'))]})
-
- # Print results
- pf = '%20s' + '%12.3g' * 6 # print format
- print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
-
- # Print results per class
- if verbose and nc > 1 and len(stats):
- for i, c in enumerate(ap_class):
- print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
-
- # Print speeds
- t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
- if not training:
- print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
-
- # Save JSON
- if save_json and len(jdict):
- w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
- anno_json = glob.glob('../coco/annotations/instances_val*.json')[0] # annotations json
- pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
- print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
- with open(pred_json, 'w') as f:
- json.dump(jdict, f)
-
- try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
-
- anno = COCO(anno_json) # init annotations api
- pred = anno.loadRes(pred_json) # init predictions api
- eval = COCOeval(anno, pred, 'bbox')
- if is_coco:
- eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate
- eval.evaluate()
- eval.accumulate()
- eval.summarize()
- map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
- except Exception as e:
- print('ERROR: pycocotools unable to run: %s' % e)
-
- # Return results
- if not training:
- print('Results saved to %s' % save_dir)
- model.float() # for training
- maps = np.zeros(nc) + map
- for i, c in enumerate(ap_class):
- maps[c] = ap[i]
- return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(prog='test.py')
- parser.add_argument('--weights', nargs='+', type=str, default='yolor_p6.pt', help='model.pt path(s)')
- parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
- parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
- parser.add_argument('--img-size', type=int, default=1280, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
- parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--verbose', action='store_true', help='report mAP by class')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
- parser.add_argument('--project', default='runs/test', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--cfg', type=str, default='cfg/yolor_p6.cfg', help='*.cfg path')
- parser.add_argument('--names', type=str, default='data/coco.names', help='*.cfg path')
- opt = parser.parse_args()
- opt.save_json |= opt.data.endswith('coco.yaml')
- opt.data = check_file(opt.data) # check file
- print(opt)
-
- if opt.task in ['val', 'test']: # run normally
- test(opt.data,
- opt.weights,
- opt.batch_size,
- opt.img_size,
- opt.conf_thres,
- opt.iou_thres,
- opt.save_json,
- opt.single_cls,
- opt.augment,
- opt.verbose,
- save_txt=opt.save_txt,
- save_conf=opt.save_conf,
- )
-
- elif opt.task == 'study': # run over a range of settings and save/plot
- for weights in ['yolor_p6.pt', 'yolor_w6.pt']:
- f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
- x = list(range(320, 800, 64)) # x axis
- y = [] # y axis
- for i in x: # img-size
- print('\nRunning %s point %s...' % (f, i))
- r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
- y.append(r + t) # results and times
- np.savetxt(f, y, fmt='%10.4g') # save
- os.system('zip -r study.zip study_*.txt')
- # utils.general.plot_study_txt(f, x) # plot
diff --git a/spaces/kadirnar/yolor/yolor/utils/google_utils.py b/spaces/kadirnar/yolor/yolor/utils/google_utils.py
deleted file mode 100644
index 0ff8bbd3241955204cf8885bfcb322b026cd8b16..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/yolor/yolor/utils/google_utils.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Google utils: https://cloud.google.com/storage/docs/reference/libraries
-
-import os
-import platform
-import subprocess
-import time
-from pathlib import Path
-
-import torch
-
-
-def gsutil_getsize(url=''):
- # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
- s = subprocess.check_output('gsutil du %s' % url, shell=True).decode('utf-8')
- return eval(s.split(' ')[0]) if len(s) else 0 # bytes
-
-
-def attempt_download(weights):
- # Attempt to download pretrained weights if not found locally
- weights = weights.strip().replace("'", '')
- file = Path(weights).name
-
- msg = weights + ' missing, try downloading from https://github.com/WongKinYiu/yolor/releases/'
- models = ['yolor_p6.pt', 'yolor_w6.pt'] # available models
-
- if file in models and not os.path.isfile(weights):
-
- try: # GitHub
- url = 'https://github.com/WongKinYiu/yolor/releases/download/v1.0/' + file
- print('Downloading %s to %s...' % (url, weights))
- torch.hub.download_url_to_file(url, weights)
- assert os.path.exists(weights) and os.path.getsize(weights) > 1E6 # check
- except Exception as e: # GCP
- print('ERROR: Download failure.')
- print('')
-
-
-def attempt_load(weights, map_location=None):
- # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
- model = Ensemble()
- for w in weights if isinstance(weights, list) else [weights]:
- attempt_download(w)
- model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model
-
- if len(model) == 1:
- return model[-1] # return model
- else:
- print('Ensemble created with %s\n' % weights)
- for k in ['names', 'stride']:
- setattr(model, k, getattr(model[-1], k))
- return model # return ensemble
-
-
-def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'):
- # Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download()
- t = time.time()
-
- print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')
- os.remove(name) if os.path.exists(name) else None # remove existing
- os.remove('cookie') if os.path.exists('cookie') else None
-
- # Attempt file download
- out = "NUL" if platform.system() == "Windows" else "/dev/null"
- os.system('curl -c ./cookie -s -L "drive.google.com/uc?export=download&id=%s" > %s ' % (id, out))
- if os.path.exists('cookie'): # large file
- s = 'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm=%s&id=%s" -o %s' % (get_token(), id, name)
- else: # small file
- s = 'curl -s -L -o %s "drive.google.com/uc?export=download&id=%s"' % (name, id)
- r = os.system(s) # execute, capture return
- os.remove('cookie') if os.path.exists('cookie') else None
-
- # Error check
- if r != 0:
- os.remove(name) if os.path.exists(name) else None # remove partial
- print('Download error ') # raise Exception('Download error')
- return r
-
- # Unzip if archive
- if name.endswith('.zip'):
- print('unzipping... ', end='')
- os.system('unzip -q %s' % name) # unzip
- os.remove(name) # remove zip to free space
-
- print('Done (%.1fs)' % (time.time() - t))
- return r
-
-
-def get_token(cookie="./cookie"):
- with open(cookie) as f:
- for line in f:
- if "download" in line:
- return line.split()[-1]
- return ""
-
-# def upload_blob(bucket_name, source_file_name, destination_blob_name):
-# # Uploads a file to a bucket
-# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
-#
-# storage_client = storage.Client()
-# bucket = storage_client.get_bucket(bucket_name)
-# blob = bucket.blob(destination_blob_name)
-#
-# blob.upload_from_filename(source_file_name)
-#
-# print('File {} uploaded to {}.'.format(
-# source_file_name,
-# destination_blob_name))
-#
-#
-# def download_blob(bucket_name, source_blob_name, destination_file_name):
-# # Uploads a blob from a bucket
-# storage_client = storage.Client()
-# bucket = storage_client.get_bucket(bucket_name)
-# blob = bucket.blob(source_blob_name)
-#
-# blob.download_to_filename(destination_file_name)
-#
-# print('Blob {} downloaded to {}.'.format(
-# source_blob_name,
-# destination_file_name))
diff --git a/spaces/kaicheng/ChatGPT_ad/readme/README_en.md b/spaces/kaicheng/ChatGPT_ad/readme/README_en.md
deleted file mode 100644
index a906ecb3ebc411f5cdeb33d661266a489a20c3b0..0000000000000000000000000000000000000000
--- a/spaces/kaicheng/ChatGPT_ad/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
-
-
-
-
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-3. Update Gradio
- ```
- pip install gradio --upgrade --force-reinstall
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
-
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/kangvcar/RealChar/client/web/src/hooks/useMediaRecorder.js b/spaces/kangvcar/RealChar/client/web/src/hooks/useMediaRecorder.js
deleted file mode 100644
index d65f10589bf8052dd88566b6b525831e6e885d92..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/client/web/src/hooks/useMediaRecorder.js
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * src/hooks/useMediaRecorder.js
- * Initialize media recorder. Start and stop recording.
- *
- * created by Lynchee on 7/16/23
- */
-
-import { useState, useRef } from 'react';
-
-const useMediaRecorder = (onDataAvailable, onStop) => {
- const [isRecording, setIsRecording] = useState(false);
- const mediaRecorder = useRef(null);
-
- // initialize media recorder
- const connectMicrophone = (deviceId) => {
- if (mediaRecorder.current) return;
- navigator.mediaDevices.getUserMedia({
- audio: { deviceId: deviceId ? {exact: deviceId} : undefined, echoCancellation: true }
- })
- .then((stream) => {
- mediaRecorder.current = new MediaRecorder(stream);
- mediaRecorder.current.ondataavailable = onDataAvailable;
- mediaRecorder.current.onstop = onStop;
- })
- .catch(function(err) {
- console.log('An error occurred: ' + err);
- });
- };
-
- const startRecording = () => {
- console.log("start recording");
- if (!mediaRecorder.current) return;
- mediaRecorder.current.start();
- setIsRecording(true);
- }
-
- const stopRecording = () => {
- console.log("stop recording");
- if (!mediaRecorder.current) return;
- mediaRecorder.current.stop();
- setIsRecording(false);
- };
-
- const closeMediaRecorder = () => {
- stopRecording();
- mediaRecorder.current = null;
- };
-
- return { isRecording, setIsRecording, connectMicrophone, startRecording, stopRecording, closeMediaRecorder };
-};
-
-export default useMediaRecorder;
diff --git a/spaces/kangvcar/RealChar/realtime_ai_character/audio/text_to_speech/base.py b/spaces/kangvcar/RealChar/realtime_ai_character/audio/text_to_speech/base.py
deleted file mode 100644
index fb4bcea52ccea27e586472b51dd1416e45bb3603..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/realtime_ai_character/audio/text_to_speech/base.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class TextToSpeech(ABC):
- @abstractmethod
- async def stream(self, *args, **kwargs):
- pass
diff --git a/spaces/karolmajek/Detectron2-MaskRCNN/app.py b/spaces/karolmajek/Detectron2-MaskRCNN/app.py
deleted file mode 100644
index cad31efda9ab5c6f48bbc033c40e9604c9773d69..0000000000000000000000000000000000000000
--- a/spaces/karolmajek/Detectron2-MaskRCNN/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-try:
- import detectron2
-except:
- import os
- os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
-
-from matplotlib.pyplot import axis
-import gradio as gr
-import requests
-import numpy as np
-from torch import nn
-import requests
-
-import torch
-
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog
-
-
-url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg'
-r = requests.get(url1, allow_redirects=True)
-open("city1.jpg", 'wb').write(r.content)
-url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg'
-r = requests.get(url2, allow_redirects=True)
-open("city2.jpg", 'wb').write(r.content)
-
-model_name='COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml'
-
-# model = model_zoo.get(model_name, trained=True)
-
-cfg = get_cfg()
-# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
-cfg.merge_from_file(model_zoo.get_config_file(model_name))
-cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
-# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as w ell
-cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name)
-
-if not torch.cuda.is_available():
- cfg.MODEL.DEVICE='cpu'
-
-predictor = DefaultPredictor(cfg)
-
-
-def inference(image):
- img = np.array(image.resize((1024,1024)))
- outputs = predictor(img)
-
- v = Visualizer(img, MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
- out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-
- return out.get_image()
-
-
-
-title = "Detectron2-MaskRCNN X101"
-description = "demo for Detectron2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\
- Model: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml"
-article = "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation | Detectron model ZOO
"
-
-gr.Interface(
- inference,
- [gr.inputs.Image(type="pil", label="Input")],
- gr.outputs.Image(type="numpy", label="Output"),
- title=title,
- description=description,
- article=article,
- examples=[
- ["city1.jpg"],
- ["city2.jpg"]
- ]).launch()
diff --git a/spaces/katanaml/LayoutLMv2-CORD/app.py b/spaces/katanaml/LayoutLMv2-CORD/app.py
deleted file mode 100644
index 52ab37abc1035281504de55f356e333c0f80810e..0000000000000000000000000000000000000000
--- a/spaces/katanaml/LayoutLMv2-CORD/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import gradio as gr
-import numpy as np
-from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
-from PIL import Image, ImageDraw, ImageFont
-import PIL
-
-processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
-model = LayoutLMv2ForTokenClassification.from_pretrained("katanaml/layoutlmv2-finetuned-cord")
-
-# define id2label
-id2label = model.config.id2label
-
-label_ints = np.random.randint(0,len(PIL.ImageColor.colormap.items()),30)
-label_color_pil = [k for k,_ in PIL.ImageColor.colormap.items()]
-label_color = [label_color_pil[i] for i in label_ints]
-label2color = {}
-for k,v in id2label.items():
- if v[2:] == '':
- label2color['o']=label_color[k]
- else:
- label2color[v[2:]]=label_color[k]
-
-def unnormalize_box(bbox, width, height):
- return [
- width * (bbox[0] / 1000),
- height * (bbox[1] / 1000),
- width * (bbox[2] / 1000),
- height * (bbox[3] / 1000),
- ]
-def iob_to_label(label):
- label = label[2:]
- if not label:
- return 'o'
- return label
-
-def process_image(image):
- width, height = image.size
-
- # encode
- encoding = processor(image, return_offsets_mapping=True, return_tensors="pt")
- offset_mapping = encoding.pop('offset_mapping')
-
- # forward pass
- outputs = model(**encoding)
-
- # get predictions
- predictions = outputs.logits.argmax(-1).squeeze().tolist()
- token_boxes = encoding.bbox.squeeze().tolist()
-
- # only keep non-subword predictions
- is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
- true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
- true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
-
- # draw predictions over the image
- draw = ImageDraw.Draw(image)
- font = ImageFont.load_default()
- for prediction, box in zip(true_predictions, true_boxes):
- predicted_label = iob_to_label(prediction).lower()
- draw.rectangle(box, outline=label2color[predicted_label])
- draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
-
- return image
-
-title = "Interactive demo: LayoutLMv2 with CORD receipts dataset"
-description = "Demo for Microsoft's LayoutLMv2, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on CORD, a dataset of manually annotated receipts. It annotates the words appearing in the image in up to 30 classes. To use it, simply upload an image or use the example image below. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select ‘Open image in new tab’."
-article = "LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding | LayoutLMv2 Github Repo | Katana ML | Sparrow Github Repo
l(4,f=i));const o=S(0);w(n,o,i=>l(13,s=i));const r=F();L(x,{register_tab:i=>(c.push({name:i.name,id:i.id}),t.update(h=>h??i.id),l(3,c),c.length-1),unregister_tab:i=>{const h=c.findIndex(y=>y.id===i.id);c.splice(h,1),t.update(y=>y===i.id?c[h]?.id||c[c.length-1]?.id:y)},selected_tab:t,selected_tab_index:o});function T(i){l(9,b=i),E(t,f=i,f),E(o,s=c.findIndex(h=>h.id===i),s),r("change")}const D=(i,h)=>{T(i.id),r("select",{value:i.name,index:h})};return n.$$set=i=>{"visible"in i&&l(0,a=i.visible),"elem_id"in i&&l(1,u=i.elem_id),"elem_classes"in i&&l(2,m=i.elem_classes),"selected"in i&&l(9,b=i.selected),"$$scope"in i&&l(10,_=i.$$scope)},n.$$.update=()=>{n.$$.dirty&512&&b!==null&&T(b)},[a,u,m,c,f,t,o,r,T,b,_,d,D]}class te extends G{constructor(e){super(),H(this,e,$,W,J,{visible:0,elem_id:1,elem_classes:2,selected:9})}}export{te as T,x as a};
-//# sourceMappingURL=TabItem.svelte_svelte_type_style_lang-556cb906.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-189d7bad.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-189d7bad.css
deleted file mode 100644
index 0b8e23a00e51fe054b66be462ce774d415cd57aa..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-189d7bad.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-b6y5bg{display:flex;flex-direction:inherit;flex-wrap:wrap;gap:var(--form-gap-width);box-shadow:var(--block-shadow);border:var(--block-border-width) solid var(--border-color-primary);border-radius:var(--block-radius);background:var(--border-color-primary);overflow:hidden}div.svelte-b6y5bg .block{box-shadow:none!important;border-width:0px!important;border-radius:0!important}.hidden.svelte-b6y5bg{display:none}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js
deleted file mode 100644
index 5405cd3af19be5d8cb56dbb55aefa442653e888a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/dockerfile-d67bbd50.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function c(n){a(n,"start");var t={},e=n.languageData||{},s=!1;for(var l in n)if(l!=e&&n.hasOwnProperty(l))for(var u=t[l]=[],o=n[l],r=0;r2&&o.token&&typeof o.token!="string"){e.pending=[];for(var g=2;g-1)return null;var l=e.indent.length-1,u=n[e.state];n:for(;;){for(var o=0;o None:
- dump_environment_info()
diff --git a/spaces/lazyboy450/RVCv2-Genshin/config.py b/spaces/lazyboy450/RVCv2-Genshin/config.py
deleted file mode 100644
index 040a64d2c5ce4d7802bdf7f69321483b81008f08..0000000000000000000000000000000000000000
--- a/spaces/lazyboy450/RVCv2-Genshin/config.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import argparse
-import torch
-from multiprocessing import cpu_count
-
-class Config:
- def __init__(self):
- self.device = "cuda:0"
- self.is_half = True
- self.n_cpu = 0
- self.gpu_name = None
- self.gpu_mem = None
- (
- self.python_cmd,
- self.listen_port,
- self.colab,
- self.noparallel,
- self.noautoopen,
- self.api
- ) = self.arg_parse()
- self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
- @staticmethod
- def arg_parse() -> tuple:
- parser = argparse.ArgumentParser()
- parser.add_argument("--port", type=int, default=7865, help="Listen port")
- parser.add_argument(
- "--pycmd", type=str, default="python", help="Python command"
- )
- parser.add_argument("--colab", action="store_true", help="Launch in colab")
- parser.add_argument(
- "--noparallel", action="store_true", help="Disable parallel processing"
- )
- parser.add_argument(
- "--noautoopen",
- action="store_true",
- help="Do not open in browser automatically",
- )
- parser.add_argument("--api", action="store_true", help="Launch with api")
- cmd_opts = parser.parse_args()
-
- cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-
- return (
- cmd_opts.pycmd,
- cmd_opts.port,
- cmd_opts.colab,
- cmd_opts.noparallel,
- cmd_opts.noautoopen,
- cmd_opts.api
- )
-
- def device_config(self) -> tuple:
- if torch.cuda.is_available():
- i_device = int(self.device.split(":")[-1])
- self.gpu_name = torch.cuda.get_device_name(i_device)
- if (
- ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
- or "P40" in self.gpu_name.upper()
- or "1060" in self.gpu_name
- or "1070" in self.gpu_name
- or "1080" in self.gpu_name
- ):
- print("16系/10系显卡和P40强制单精度")
- self.is_half = False
-
- else:
- self.gpu_name = None
- self.gpu_mem = int(
- torch.cuda.get_device_properties(i_device).total_memory
- / 1024
- / 1024
- / 1024
- + 0.4
- )
- elif torch.backends.mps.is_available():
- print("没有发现支持的N卡, 使用MPS进行推理")
- self.device = "mps"
- self.is_half = False
- else:
- print("没有发现支持的N卡, 使用CPU进行推理")
- self.device = "cpu"
- self.is_half = False
-
- if self.n_cpu == 0:
- self.n_cpu = cpu_count()
-
- if self.is_half:
- # 6G显存配置
- x_pad = 3
- x_query = 10
- x_center = 60
- x_max = 65
- else:
- # 5G显存配置
- x_pad = 1
- x_query = 6
- x_center = 38
- x_max = 41
-
- if self.gpu_mem != None and self.gpu_mem <= 4:
- x_pad = 1
- x_query = 5
- x_center = 30
- x_max = 32
-
- return x_pad, x_query, x_center, x_max
diff --git a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/options.py b/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/options.py
deleted file mode 100644
index db490e4aa52e26fde31959fd74c2cef3af2ecf76..0000000000000000000000000000000000000000
--- a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/options.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import yaml
-import time
-from collections import OrderedDict
-from os import path as osp
-from basicsr.utils.misc import get_time_str
-
-def ordered_yaml():
- """Support OrderedDict for yaml.
-
- Returns:
- yaml Loader and Dumper.
- """
- try:
- from yaml import CDumper as Dumper
- from yaml import CLoader as Loader
- except ImportError:
- from yaml import Dumper, Loader
-
- _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
-
- def dict_representer(dumper, data):
- return dumper.represent_dict(data.items())
-
- def dict_constructor(loader, node):
- return OrderedDict(loader.construct_pairs(node))
-
- Dumper.add_representer(OrderedDict, dict_representer)
- Loader.add_constructor(_mapping_tag, dict_constructor)
- return Loader, Dumper
-
-
-def parse(opt_path, root_path, is_train=True):
- """Parse option file.
-
- Args:
- opt_path (str): Option file path.
- is_train (str): Indicate whether in training or not. Default: True.
-
- Returns:
- (dict): Options.
- """
- with open(opt_path, mode='r') as f:
- Loader, _ = ordered_yaml()
- opt = yaml.load(f, Loader=Loader)
-
- opt['is_train'] = is_train
-
- # opt['name'] = f"{get_time_str()}_{opt['name']}"
- if opt['path'].get('resume_state', None): # Shangchen added
- resume_state_path = opt['path'].get('resume_state')
- opt['name'] = resume_state_path.split("/")[-3]
- else:
- opt['name'] = f"{get_time_str()}_{opt['name']}"
-
-
- # datasets
- for phase, dataset in opt['datasets'].items():
- # for several datasets, e.g., test_1, test_2
- phase = phase.split('_')[0]
- dataset['phase'] = phase
- if 'scale' in opt:
- dataset['scale'] = opt['scale']
- if dataset.get('dataroot_gt') is not None:
- dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
- if dataset.get('dataroot_lq') is not None:
- dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])
-
- # paths
- for key, val in opt['path'].items():
- if (val is not None) and ('resume_state' in key or 'pretrain_network' in key):
- opt['path'][key] = osp.expanduser(val)
-
- if is_train:
- experiments_root = osp.join(root_path, 'experiments', opt['name'])
- opt['path']['experiments_root'] = experiments_root
- opt['path']['models'] = osp.join(experiments_root, 'models')
- opt['path']['training_states'] = osp.join(experiments_root, 'training_states')
- opt['path']['log'] = experiments_root
- opt['path']['visualization'] = osp.join(experiments_root, 'visualization')
-
- else: # test
- results_root = osp.join(root_path, 'results', opt['name'])
- opt['path']['results_root'] = results_root
- opt['path']['log'] = results_root
- opt['path']['visualization'] = osp.join(results_root, 'visualization')
-
- return opt
-
-
-def dict2str(opt, indent_level=1):
- """dict to string for printing options.
-
- Args:
- opt (dict): Option dict.
- indent_level (int): Indent level. Default: 1.
-
- Return:
- (str): Option string for printing.
- """
- msg = '\n'
- for k, v in opt.items():
- if isinstance(v, dict):
- msg += ' ' * (indent_level * 2) + k + ':['
- msg += dict2str(v, indent_level + 1)
- msg += ' ' * (indent_level * 2) + ']\n'
- else:
- msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
- return msg
diff --git a/spaces/leogabraneth/text-generation-webui-main/css/NotoSans/stylesheet.css b/spaces/leogabraneth/text-generation-webui-main/css/NotoSans/stylesheet.css
deleted file mode 100644
index 467973b8eebd42a5ba50f4df0a07440b843a19cc..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/css/NotoSans/stylesheet.css
+++ /dev/null
@@ -1,166 +0,0 @@
-/*
-Copied from https://github.com/SillyTavern/SillyTavern/tree/6c8bd06308c69d51e2eb174541792a870a83d2d6/public/webfonts/NotoSans
-*/
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Black.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Black.woff') format('woff');
- font-weight: 900;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraBoldItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraBoldItalic.woff') format('woff');
- font-weight: bold;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-BlackItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-BlackItalic.woff') format('woff');
- font-weight: 900;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraBold.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraBold.woff') format('woff');
- font-weight: bold;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ThinItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ThinItalic.woff') format('woff');
- font-weight: 100;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-BoldItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-BoldItalic.woff') format('woff');
- font-weight: bold;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Bold.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Bold.woff') format('woff');
- font-weight: bold;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-LightItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-LightItalic.woff') format('woff');
- font-weight: 300;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Italic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Italic.woff') format('woff');
- font-weight: normal;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraLightItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraLightItalic.woff') format('woff');
- font-weight: 200;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Light.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Light.woff') format('woff');
- font-weight: 300;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-ExtraLight.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-ExtraLight.woff') format('woff');
- font-weight: 200;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Medium.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Medium.woff') format('woff');
- font-weight: 500;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Regular.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Regular.woff') format('woff');
- font-weight: normal;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-MediumItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-MediumItalic.woff') format('woff');
- font-weight: 500;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-SemiBoldItalic.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-SemiBoldItalic.woff') format('woff');
- font-weight: 600;
- font-style: italic;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-SemiBold.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-SemiBold.woff') format('woff');
- font-weight: 600;
- font-style: normal;
- font-display: swap;
-}
-
-@font-face {
- font-family: 'Noto Sans';
- src: url('file/css/NotoSans/NotoSans-Thin.woff2') format('woff2'),
- url('file/css/NotoSans/NotoSans-Thin.woff') format('woff');
- font-weight: 100;
- font-style: normal;
- font-display: swap;
-}
-
diff --git a/spaces/leurez/moss/service/src/chatgpt/types.ts b/spaces/leurez/moss/service/src/chatgpt/types.ts
deleted file mode 100644
index c7909a56627c59d80bc13fe9391c4da9fc9fec11..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/service/src/chatgpt/types.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import type { ChatMessage } from 'chatgpt'
-import type fetch from 'node-fetch'
-
-export interface RequestOptions {
- message: string
- lastContext?: { conversationId?: string; parentMessageId?: string }
- process?: (chat: ChatMessage) => void
- systemMessage?: string
- temperature?: number
- top_p?: number
-}
-
-export interface SetProxyOptions {
- fetch?: typeof fetch
-}
-
-export interface UsageResponse {
- total_usage: number
-}
diff --git a/spaces/lilholla/2099/README.md b/spaces/lilholla/2099/README.md
deleted file mode 100644
index 48b6de06cd792b43d5dc0465494772b81535c692..0000000000000000000000000000000000000000
--- a/spaces/lilholla/2099/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: 2099
-emoji: 📈
-colorFrom: gray
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Chalean Extreme Torrent BETTER.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Chalean Extreme Torrent BETTER.md
deleted file mode 100644
index c27fdd7a395d645c79cfe4ac9d36ca062ba946d0..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Chalean Extreme Torrent BETTER.md
+++ /dev/null
@@ -1,128 +0,0 @@
-
-Chalean Extreme Torrent: How to Download and Enjoy the Best Home Workout Program
-
-If you are looking for a way to get fit, burn fat, and build muscle at home, you might have heard of Chalean Extreme, a popular fitness program created by Chalene Johnson and Beachbody. Chalean Extreme is a 90-day program that combines cardio, strength training, and flexibility exercises to help you transform your body and boost your metabolism. But what if you don't want to spend money on buying the DVDs or subscribing to Beachbody On Demand? Is there a way to download Chalean Extreme torrent for free?
-
-In this article, we will show you how to find and download Chalean Extreme torrent safely and legally, and what you need to know before you start the program. We will also share some tips on how to get the most out of Chalean Extreme and achieve your fitness goals.
-Chalean extreme torrent
Download File ⚙⚙⚙ https://bytlly.com/2uGwl7
-
-What is Chalean Extreme Torrent?
-
-A torrent is a file that contains information about other files that are shared over a peer-to-peer network. By using a torrent client, such as uTorrent or BitTorrent, you can download these files from other users who have them on their computers. This way, you can access content that is not available on official websites or platforms.
-
-Chalean Extreme torrent is a file that contains the video files of the Chalean Extreme program, as well as the PDF documents that come with it, such as the fitness guide, the workout sheet, the calendars, and the weight rep trackers. By downloading Chalean Extreme torrent, you can watch the workouts on your computer, TV, or mobile device without paying anything.
-
-How to Find and Download Chalean Extreme Torrent?
-
-There are many websites that offer Chalean Extreme torrent for free download, such as LimeTorrents, BT4G, or Download.ir. However, not all of them are reliable or safe. Some of them may contain viruses, malware, or fake files that can harm your device or compromise your privacy. Therefore, you need to be careful when choosing a website to download Chalean Extreme torrent from.
-
-Here are some steps you can follow to find and download Chalean Extreme torrent safely and legally:
-
-
-
-
-What Do You Need to Know Before Starting Chalean Extreme?
-
-Before you start Chalean Extreme program, there are some things you need to know to make sure you are ready for it and get the best results possible:
-
-
-
-
-How to Get the Most Out of Chalean Extreme?
-
-If you want to get the most out of Chalean Extreme program and achieve amazing results in 90 days, here are some tips you can follow:
-
-
-
-
-Conclusion
-
-Chalean Extreme is an amazing fitness program that can help you get fit, burn fat
-and build muscle at home in 90 days.
-
-If you want to download Chalean Extreme torrent for free,
-you need to follow some steps
-to find
-and download it safely
-and legally.
-You also need
-to know
-what
-you need
-to do
-before
-and during
-the program
-to get
-the best results possible.
-
-
-We hope this article has been helpful
-and informative
-for you.
-If you have any questions
-or comments,
-please feel free
-to leave them below.
-We would love
-to hear from you!
-What are the Benefits of Chalean Extreme?
-
-Chalean Extreme is a fitness program that offers many benefits for anyone who wants to get in shape and improve their health. Here are some of the benefits you can expect from doing Chalean Extreme:
-
-
-
-
-What are the Challenges of Chalean Extreme?
-
-Chalean Extreme is a fitness program that requires dedication, discipline, and hard work. It is not a quick fix or a magic solution for weight loss or fitness. It is a challenging program that will test your limits and push you out of your comfort zone. Here are some of the challenges you might face when doing Chalean Extreme:
-
-
-
-Conclusion
-
-Chalean Extreme is a fitness program that can help you get fit, burn fat, and build muscle at home in 90 days. It is a program that combines cardio, strength training, and flexibility exercises to help you transform your body and boost your metabolism. It is a program that offers many benefits, such as faster fat burning, total body toning, and increased energy and confidence levels. It is also a program that requires dedication, discipline, and hard work. You will need to invest time and money into the program, follow a strict nutrition plan, and deal with soreness and fatigue.
-
-If you want to download Chalean Extreme torrent for free,
-you need to follow some steps
-to find
-and download it safely
-and legally.
-You also need
-to know
-what
-you need
-to do
-before
-and during
-the program
-to get
-the best results possible.
-
-We hope this article has been helpful
-and informative
-for you.
-If you have any questions
-or comments,
-please feel free
-to leave them below.
-We would love
-to hear from you!
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Descargar Sap2000 V12 Gratis 2 High Quality.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Descargar Sap2000 V12 Gratis 2 High Quality.md
deleted file mode 100644
index b1735bfa5bf19cd6bb96d0703359da3dcddea366..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Descargar Sap2000 V12 Gratis 2 High Quality.md
+++ /dev/null
@@ -1,10 +0,0 @@
-Descargar Sap2000 V12 Gratis 2
DOWNLOAD ->->->-> https://bytlly.com/2uGxEP
-
-SPSAIA #S0026
-
-Single- and Multi-stage Stochastic Programming Simplex Algorithms for Optimization. The SPSAIA solver is included in a tool called SAP Scheduling Optimization (SPSAIA) which is developed by the SAP Business Rules' Technology group. SPSAIA supports both Stochastic Global and Stochastic Local approaches. One of the advantages of using the SPSAIA tool is that it allows the user to configure the optimizer easily. The optimizer also supports different data types. For example, the user can define their own schedule constraints and they also have a data type to express a two-dimensional array.
-
-The SPSAIA tool is a stand-alone product. It does not require any SAP engine. SPSAIA can work in a single client. 4fefd39f24
-
-
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Enterprise 2007 Swedish Inkl. Kod Free Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Enterprise 2007 Swedish Inkl. Kod Free Download.md
deleted file mode 100644
index ab2bbb89008c955188ca1011ea854ba252a6c28d..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Enterprise 2007 Swedish Inkl. Kod Free Download.md
+++ /dev/null
@@ -1,27 +0,0 @@
-
-How to Download Microsoft Office Enterprise 2007 Swedish inkl. kod for Free
-If you are looking for a way to download Microsoft Office Enterprise 2007 Swedish inkl. kod for free, you have come to the right place. In this article, I will show you how to get this software without paying a dime.
-Microsoft Office Enterprise 2007 is a suite of productivity applications that includes Word, Excel, PowerPoint, Outlook, Access, Publisher, and OneNote. It also comes with some additional features such as SharePoint Server, Groove, and InfoPath.
-Microsoft Office Enterprise 2007 Swedish inkl. kod free download
Download File ✅ https://bytlly.com/2uGx43
-This software is ideal for professionals, students, and home users who need to create and manage documents, spreadsheets, presentations, emails, databases, and more. It also supports the Swedish language and has a user-friendly interface.
-However, Microsoft Office Enterprise 2007 is not free. You need to purchase a license key to activate it and use it on your computer. The license key can cost up to $699 depending on the edition and the number of users.
-But don't worry, there is a way to download Microsoft Office Enterprise 2007 Swedish inkl. kod for free. All you need is a reliable source that offers the software and the key for download. Here are the steps to follow:
-
-
-Enjoy using this software and its features for your personal or professional needs. Remember to share this article with your friends and family who might also benefit from this free download.
-
-If you want to learn more about Microsoft Office Enterprise 2007 Swedish inkl. kod and its features, you can visit the official website of Microsoft at https://www.microsoft.com/sv-se. There you will find more information about the software, its system requirements, its updates, and its support.
-You can also check out some online tutorials and guides that will help you use Microsoft Office Enterprise 2007 Swedish inkl. kod more effectively. For example, you can visit https://example.com, a website that offers free video lessons on how to use Word, Excel, PowerPoint, and other applications in the suite.
-Another useful resource is https://example.com, a blog that provides tips and tricks on how to optimize your documents, spreadsheets, presentations, and emails using Microsoft Office Enterprise 2007 Swedish inkl. kod. You will learn how to format your text, insert images and charts, create tables and graphs, add animations and transitions, and more.
-By following these resources, you will be able to master Microsoft Office Enterprise 2007 Swedish inkl. kod in no time and impress your colleagues, clients, teachers, or friends with your work.
- d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/lqy09/GT/public/GTest/js/jquery.min.js b/spaces/lqy09/GT/public/GTest/js/jquery.min.js
deleted file mode 100644
index 200b54e470a5d67925b91e87f4e769191a9f8c16..0000000000000000000000000000000000000000
--- a/spaces/lqy09/GT/public/GTest/js/jquery.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
-/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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-#include
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-// We need a way to compute the return type of `select_system`, which is found
-// by using `thrust::system::detail::generic::select_system` and then making an
-// ADL call. We have no trait that defines the return type. With the
-// limitations of C++11 return type deduction, we need to be able to stick all
-// of that into `decltype`. So, we put the using statement into a detail
-// namespace, and then implement the generic dispatch function in that
-// namespace.
-
-namespace select_system_detail
-{
-
-using thrust::system::detail::generic::select_system;
-
-struct select_system_fn final
-{
- __thrust_exec_check_disable__
- template
- __host__ __device__
- auto operator()(
- thrust::detail::execution_policy_base const& exec0
- ) const
- THRUST_DECLTYPE_RETURNS(
- select_system(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec0))
- )
- )
-
- __thrust_exec_check_disable__
- template
- __host__ __device__
- auto operator()(
- thrust::detail::execution_policy_base const& exec0
- , thrust::detail::execution_policy_base const& exec1
- ) const
- THRUST_DECLTYPE_RETURNS(
- select_system(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec0))
- , thrust::detail::derived_cast(thrust::detail::strip_const(exec1))
- )
- )
-};
-
-} // namespace select_system_detail
-
-THRUST_INLINE_CONSTANT select_system_detail::select_system_fn select_system{};
-
-} // detail
-
-} // end namespace thrust
-
-#endif // THRUST_CPP_DIALECT >= 2011
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/triple_chevron_launch.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/triple_chevron_launch.h
deleted file mode 100644
index deeffac9dae8bc567face7cf7f8483d41454bbab..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/core/triple_chevron_launch.h
+++ /dev/null
@@ -1,976 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of the NVIDIA CORPORATION nor the
- * names of its contributors may be used to endorse or promote products
- * derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-#include
-#include
-#include
-#include
-
-
-namespace thrust
-{
-
-namespace cuda_cub {
-namespace launcher {
-
- struct triple_chevron
- {
- typedef size_t Size;
- dim3 const grid;
- dim3 const block;
- Size const shared_mem;
- cudaStream_t const stream;
-
- THRUST_RUNTIME_FUNCTION
- triple_chevron(dim3 grid_,
- dim3 block_,
- Size shared_mem_ = 0,
- cudaStream_t stream_ = 0)
- : grid(grid_),
- block(block_),
- shared_mem(shared_mem_),
- stream(stream_) {}
-
-#if 0
- template
- cudaError_t __host__
- doit_host(K k, Args const&... args) const
- {
- k<<>>(args...);
- return cudaPeekAtLastError();
- }
-#else
- template
- cudaError_t __host__
- doit_host(K k, _0 x0) const
- {
- k<<>>(x0);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1) const
- {
- k<<>>(x0,x1);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2) const
- {
- k<<>>(x0,x1,x2);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3) const
- {
- k<<>>(x0,x1,x2,x3);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
- {
- k<<>>(x0,x1,x2,x3,x4);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE);
- return cudaPeekAtLastError();
- }
- template
- cudaError_t __host__
- doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE, _xF xF) const
- {
- k<<>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE,xF);
- return cudaPeekAtLastError();
- }
-#endif
-
- template
- size_t __device__
- align_up(size_t offset) const
- {
- size_t alignment = alignment_of::value;
- return alignment * ((offset + (alignment - 1))/ alignment);
- }
-
-#if 0
- size_t __device__ argument_pack_size(size_t size) const { return size; }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg const& arg, Args const&... args) const
- {
- size = align_up(size);
- return argument_pack_size(size + sizeof(Arg), args...);
- }
-#else
- template
- size_t __device__
- argument_pack_size(size_t size, Arg) const
- {
- return align_up(size) + sizeof(Arg);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
- }
- template
- size_t __device__
- argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE, _xF xF) const
- {
- return argument_pack_size(align_up(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, xF);
- }
-#endif /* variadic */
-
- template
- size_t __device__ copy_arg(char* buffer, size_t offset, Arg arg) const
- {
- offset = align_up(offset);
- for (int i = 0; i != sizeof(Arg); ++i)
- buffer[offset+i] = *((char*)&arg + i);
- return offset + sizeof(Arg);
- }
-
-#if 0
- void __device__ fill_arguments(char*, size_t) const {}
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg const& arg, Args const& ... args) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), args...);
- }
-#else
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg) const
- {
- copy_arg(buffer, offset, arg);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
- }
- template
- void __device__
- fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE, _xF xF) const
- {
- fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, xF);
- }
-#endif /* variadic */
-
-#if 0
- template
- cudaError_t __device__
- doit_device(K k, Args const&... args) const
- {
- cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
- const size_t size = argument_pack_size(0,args...);
- void *param_buffer = cudaGetParameterBuffer(64,size);
- fill_arguments((char*)param_buffer, 0, args...);
- status = launch_device(k, param_buffer);
-#endif
- return status;
- }
-#else
- template
- cudaError_t __device__
- doit_device(K k, _0 x0) const
- {
- cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
- const size_t size = argument_pack_size(0,x0);
- void *param_buffer = cudaGetParameterBuffer(64,size);
- fill_arguments((char*)param_buffer, 0, x0);
- status = launch_device(k, param_buffer);
-#else
- THRUST_UNUSED_VAR(k);
- THRUST_UNUSED_VAR(x0);
-#endif
- return status;
- }
- template 
- 
- 
-