To create an archive file:
--
-
- Open the app and tap on the "Create" button at the bottom. -
- Select the files or folders that you want to compress and tap on the "OK" button. -
- Choose the archive format that you want to use, such as 7Zip, Zip, Tar, etc. -
- Optionally, you can set a password, a compression level, a split size, and a volume label for your archive file. -
- Tap on the "Create" button and wait for the app to create your archive file. -
- You can find your archive file in the same folder as the original files or folders. -
To extract an archive file:
--
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- Open the app and tap on the "Extract" button at the bottom. -
- Select the archive file that you want to decompress and tap on the "OK" button. -
- If the archive file is encrypted, enter the password and tap on the "OK" button. -
- Choose the destination folder where you want to extract the files or folders. -
- Tap on the "Extract" button and wait for the app to extract your archive file. -
- You can find your extracted files or folders in the destination folder that you chose. -
Conclusion
-7 Zip APK is a powerful tool for managing archive files on your Android device. It allows you to create, extract, browse, encrypt, and decrypt archive files of various formats and types. It also offers a simple and intuitive file manager with standard file operations. 7 Zip APK is free to download and use from the Google Play Store. If you need to work with archive files on your Android device, 7 Zip APK is a great app to have.
-Frequently Asked Questions
-Here are some of the common questions that people ask about 7 Zip APK:
-Q: Is 7 Zip APK safe to use?
-A: Yes, 7 Zip APK is safe to use. It does not contain any malware or viruses. It only requires permissions to access your files and storage. It does not collect or share any personal data or information.
-Q: What is the difference between 7Zipper and 7Zipper 2.0?
-A: 7Zipper is the original version of 7 Zip APK. It has more features and options than 7Zipper 2.0, but it also has more ads and pop-ups. 7Zipper 2.0 is a newer version of 7 Zip APK. It has fewer features and options than 7Zipper, but it also has fewer ads and pop-ups. Both versions are compatible with Android devices running Android 4.0 or higher.
-Q: How can I open a zip file without extracting it?
-A: You can open a zip file without extracting it by using the "Browse" feature of 7 Zip APK. To do this, follow these steps:
--
-
- Open the app and tap on the "Browse" button at the bottom. -
- Select the zip file that you want to open and tap on it. -
- You will see a list of files or folders inside the zip file. You can tap on any file or folder to view its contents or properties. -
- You can also perform some actions on the files or folders, such as copy, move, delete, rename, etc. -
Q: How can I create a self-extracting archive file?
-A: A self-extracting archive file is an archive file that can be opened without using any software or app. It has an executable extension (such as .exe) that allows it to run by itself. To create a self-extracting archive file using 7 Zip APK, follow these steps:
--
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- Open the app and tap on the "Create" button at the bottom. -
- Select the files or folders that you want to compress and tap on the "OK" button. -
- Choose the "SFX (Self Extract)" option from the archive format list. -
- Optionally, you can set a password, a compression level, a split size, and a volume label for your archive file. -
- Tap on the "Create" button and wait for the app to create your self-extracting archive file. -
- You can find your self-extracting archive file in the same folder as the original files or folders. It will I have already written the second part of the article. Here is the final part of it:
- You can find your self-extracting archive file in the same folder as the original files or folders. It will have an .exe extension and an icon that looks like a 7Zip logo. -
Q: How can I update or delete files from an archive file?
-A: You can update or delete files from an archive file by using the "Update" feature of 7 Zip APK. To do this, follow these steps:
--
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- Open the app and tap on the "Update" button at the bottom. -
- Select the archive file that you want to update or delete files from and tap on the "OK" button. -
- You will see a list of files or folders inside the archive file. You can tap on any file or folder to select or deselect it. -
- To update a file or folder, tap on the "Add" button at the bottom and select the new file or folder that you want to replace the old one with. -
- To delete a file or folder, tap on the "Delete" button at the bottom and confirm your action. -
- Tap on the "Update" button and wait for the app to update or delete files from your archive file. -
- You can find your updated archive file in the same folder as the original archive file. It will have the same name and extension as before. -
Outline of the article
-Here is a table that shows the outline of the article with the headings and subheadings:
-| H1 | H2 | H3 | H4 |
|---|---|---|---|
| 7 Zip APK: A Powerful Tool for Managing Archive Files on Android | |||
| What is 7 Zip APK? | |||
| How does 7 Zip APK work? | |||
| What features does 7 Zip APK offer? | |||
| How to download and install 7 Zip APK? | |||
| How to use 7 Zip APK? | To create an archive file: | ||
| To extract an archive file: | |||
| Conclusion | Frequently Asked Questions | Q: Is 7 Zip APK safe to use? | |
| Q: What is the difference between 7Zipper and 7Zipper 2.0? | |||
| Q: How can I open a zip file without extracting it? | |||
| Q: How can I create a self-extracting archive file? | |||
| Q: How can I update or delete files from an archive file? |
-
-
\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Street APK - The Most Realistic Mobile Racing Game Ever - Free Download.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Street APK - The Most Realistic Mobile Racing Game Ever - Free Download.md deleted file mode 100644 index 51dffc717cf3c3d4588ca6cb7638c931e910d354..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Street APK - The Most Realistic Mobile Racing Game Ever - Free Download.md +++ /dev/null @@ -1,122 +0,0 @@ -
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- An online multiplayer mode with leaderboards and chat -
- A car customization system with hundreds of parts and decals -
- A club system where you can join or create your own team -
- A reward system where you can earn coins, gems, and car parts -
Requirements for CarX Street
-To play CarX Street on your Android device, you need to have:
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- An Android version of 5.0 or higher -
- A minimum of 2 GB of RAM -
- A minimum of 1 GB of free storage space -
- An internet connection -
How to Download and Install CarX Street APK
-If you want to download CarX Street APK for Android, you need to follow these steps:
-Step 1: Enable Unknown Sources
-Before you can install any APK file on your Android device, you need to enable the option to allow installation from unknown sources. To do this, go to your device settings, then security, then toggle on the unknown sources option.
-Step 2: Download CarX Street APK File
-Next, you need to download the CarX Street APK file from a reliable source. You can use one of the links below:
-| Name | Version | Size | Link |
|---|---|---|---|
| CarX Street APK (Game) | 0.9.2 | 1.4 GB | Download here(^1^) |
| CarX Street APK (App) | 9.8 | 14 MB | Download here(^3^) |
| CarX Street - Apps on Google Play | N/A | N/A | Download here |
Make sure you download the file that matches your device and preferences. You can also scan the file with an antivirus software before installing it.
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-Once the installation is complete, you can launch CarX Street from your app drawer or home screen. You will need to grant some permissions and accept the terms and conditions. Then, you can create your account, choose your car, and start racing.
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-There are some benefits of downloading CarX Street APK instead of using the Google Play Store version. Some of them are:
-Access to Latest Updates and Features
-By downloading CarX Street APK, you can get access to the latest updates and features of the game before they are released on the official platform. This way, you can enjoy the new content and improvements as soon as possible.
-No Need to Use Google Play Store
-If you have problems with using the Google Play Store, such as slow downloads, errors, or restrictions, you can download CarX Street APK without using it. This way, you can avoid any hassle or inconvenience that might occur with the Google Play Store.
-Save Storage Space and Data
-By downloading CarX Street APK, you can save some storage space and data on your device. This is because you can download only the file that you need, and not the whole game package. You can also delete the APK file after installing it, and free up some space.
-Risks of Downloading CarX Street APK
-However, there are also some risks of downloading CarX Street APK that you should be aware of. Some of them are:
-Potential Malware and Viruses
-If you download CarX Street APK from an untrusted source, you might expose your device to malware and viruses that can harm your device or steal your data. Therefore, you should always download CarX Street APK from a reliable source, and scan the file with an antivirus software before installing it.
-Legal Issues and Violations
-If you download CarX Street APK without the permission of the developers or publishers, you might violate their terms of service or intellectual property rights. This could result in legal issues or penalties, such as fines, bans, or lawsuits. Therefore, you should always respect the rights of the creators and follow their rules.
-Compatibility and Stability Issues
-If you download CarX Street APK that is not compatible with your device or version of Android, you might experience compatibility and stability issues, such as crashes, glitches, or errors. Therefore, you should always check the requirements and specifications of the game before downloading it.
-Conclusion
-In conclusion, CarX Street is a street racing game that lets you customize your car, challenge other racers, and explore a realistic city. You can download CarX Street APK for Android from one of the links above, but you should also be aware of the benefits and risks of doing so. We hope this article has helped you learn how to download CarX Street APK for Android.
-FAQs
--
-
- Q: Is CarX Street free to play? -
- A: Yes, CarX Street is free to play, but it also offers in-app purchases for coins, gems, and car parts. -
- Q: Can I play CarX Street offline? -
- A: No, CarX Street requires an internet connection to play. -
- Q: Can I play CarX Street with my friends? -
- A: Yes, CarX Street has an online multiplayer mode where you can race with your friends or other players around the world. -
- Q: How can I contact the developers of CarX Street? -
- A: You can contact the developers of CarX Street by sending an email to support@carx-tech.com or visiting their website at https://carx-tech.com/. -
- Q: How can I update CarX Street? -
- -
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Call of Duty Warzone Mobile and Fight Like Never Before.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Call of Duty Warzone Mobile and Fight Like Never Before.md deleted file mode 100644 index 4f328e9f575faff9b986c3e984199a0b422577f7..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Call of Duty Warzone Mobile and Fight Like Never Before.md +++ /dev/null @@ -1,153 +0,0 @@ - -
Download Call of Duty Warzone Mobile: The Next Era of Battle Royale
-If you are a fan of the Call of Duty franchise, you must have heard about the latest sensation in the mobile gaming world: Call of Duty Warzone Mobile. This is the next generation of mobile battle royale, featuring authentic COD gameplay, shared progression, and up to 120 player count matches on mobile. In this article, we will tell you everything you need to know about this amazing game, including what it is, how to get it, how to play it, and how to optimize your device and performance for it. So, without further ado, let's dive into the new era of fun battle royale!
-What is Call of Duty Warzone Mobile?
-Call of Duty Warzone Mobile is a mobile version of the popular Call of Duty Warzone game, which is a free-to-play online multiplayer battle royale game developed by Activision. It is part of the Call of Duty Modern Warfare II series, which is a reboot of the original Modern Warfare sub-series. Call of Duty Warzone Mobile is built for mobile gamers, with first-class graphics, intuitive controls, and optimized physics, animations, and sound. It also features unified Call of Duty technology, which means that your Battle Pass and friends list sync across platforms for a truly connected multiplayer FPS game experience.
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-Call of Duty Warzone Mobile offers a lot of features and benefits that make it stand out from other mobile battle royale games. Here are some of them:
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- It delivers authentic Call of Duty gameplay on mobile, with realistic combat, weapons, movement, and vehicles. -
- It supports up to 120 live players in a match, which means more competition and more fun. -
- It has epic maps like the iconic Verdansk, which is a fan-favorite map from the original Call of Duty Warzone game. -
- It has unique and fun gameplay elements, such as contracts, killstreaks, strategies, and the Gulag, where you can win a duel to get a second life. -
- It has endless replayability, with countless ways to play and win. -
How to pre-register and pre-order Call of Duty Warzone Mobile
-Call of Duty Warzone Mobile is not yet officially released worldwide, but you can pre-register or pre-order it now to get a chance to unlock rewards at launch. Here's how:
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- You can also sign up for updates on the official website: [text](^1^). -
How to play Call of Duty Warzone Mobile
-Once you have downloaded and installed Call of Duty Warzone Mobile on your device, you can start playing it right away. Here are some basic steps to follow:
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- Create or log in to your Call of Duty account or link it to your Facebook, Google, or Apple account. -
- Choose your preferred game mode: Solo, Duo, Trio, or Squad. You can also join a random team or invite your friends to play with you. -
- Select your loadout, which consists of your primary and secondary weapons, perks, equipment, and killstreaks. You can customize your loadout as you progress and unlock more items. -
- Enter the match and parachute into the map. You can choose where to land by looking at the map and marking a location. -
- Explore the map and loot items, such as weapons, ammo, armor, cash, and contracts. Contracts are optional missions that give you rewards for completing them. -
- Eliminate other players and avoid the gas circle, which shrinks over time and forces players to move closer together. You can also revive your teammates if they are downed. -
- If you are eliminated, you will be sent to the Gulag, where you can fight another player for a chance to redeploy. You can also be bought back by your teammates at a buy station. -
- The last team or player standing wins the match and earns XP, Battle Pass progress, and rewards. -
The gameplay and modes of Call of Duty Warzone Mobile
-Call of Duty Warzone Mobile has different gameplay and modes that cater to different preferences and styles. Here are some of them:
--
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- Battle Royale: This is the classic mode where you compete with up to 119 other players in a massive map. You can play solo or with a team of up to four players. The objective is to survive until the end and be the last one standing. -
- Plunder: This is a mode where you compete with other teams to collect the most cash in the map. You can loot cash from various sources, such as crates, contracts, enemies, and banks. You can also deposit your cash at helicopter pads or balloons to secure it. The team with the most cash at the end of the match wins. -
- Rebirth: This is a mode where you play in a smaller map with faster-paced action. You have unlimited respawns as long as one of your teammates is alive. The objective is to eliminate as many enemies as possible and be the last team alive. -
The maps and locations of Call of Duty Warzone Mobile
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- Verdansk: This is the main map of Call of Duty Warzone Mobile, which is based on the fictional city of Verdansk in Kastovia. It is a huge map that features various zones, such as downtown, airport, stadium, farmland, prison, dam, and more. It also has landmarks from previous Call of Duty games, such as Broadcast, Scrapyard, Boneyard, and Gulag. -
- Rebirth Island: This is a smaller map that is based on an island off the coast of Verdansk. It is a former Soviet base that was used for chemical weapons research. It has locations such as prison blocks, bioweapon labs, decontamination zones, and more. It also has a night mode that adds more intensity and stealth to the gameplay. -
- New Maps: Call of Duty Warzone Mobile will also introduce new maps in the future that will expand the game's content and variety. These maps will be inspired by real-world locations and events, such as Berlin, Chernobyl, Afghanistan, and more. -
The weapons and vehicles of Call of Duty Warzone Mobile
-Call of Duty Warzone Mobile has a vast arsenal of weapons and vehicles that you can use to dominate the battlefield. Here are some of them:
--
-
- Weapons: You can choose from over 80 weapons in Call of Duty Warzone Mobile, including assault rifles, submachine guns, sniper rifles, shotguns, pistols, and more. You can also customize your weapons with attachments, such as scopes, barrels, magazines, and more. You can also find legendary weapons in the map, which have unique skins and perks. -
- Vehicles: You can also use various vehicles in Call of Duty Warzone Mobile, such as cars, trucks, helicopters, ATVs, and more. Vehicles can help you move faster, escape the gas circle, or run over enemies. However, they also make noise and attract attention, so be careful when using them. -
How to optimize your device and performance for Call of Duty Warzone Mobile
-Call of Duty Warzone Mobile is a demanding game that requires a lot of resources and power from your device. Therefore, you need to optimize your device and performance for the best gaming experience. Here are some tips to do that:
-The minimum device specifications for Call of Duty Warzone Mobile
-Before you download and play Call of Duty Warzone Mobile, you need to make sure that your device meets the minimum specifications for the game. Here are the minimum requirements for Android and iOS devices:
-| Android | -iOS | -
|---|---|
| OS: Android 5.0 or higher | -OS: iOS 10 or higher | -
| CPU: Snapdragon 625 or equivalent | -CPU: A10 Fusion or equivalent | -
| RAM: 2 GB or higher | -RAM: 2 GB or higher | -
| Storage: 4 GB or higher | -Storage: 4 GB or higher | -
| Internet: Wi-Fi or cellular data (4G or higher) | -Internet: Wi-Fi or cellular data (4G or higher) | -
The best settings and tips for Call of Duty Warzone Mobile
-Once you have checked your device specifications, you can also adjust the settings and preferences of the game to optimize your performance and gameplay. Here are some suggestions:
--
-
- Adjust the graphics quality according to your device's capabilities. You can choose from low, medium, high, or very high graphics settings. The higher the graphics quality, the more battery and data it will consume. -
- Adjust the frame rate according to your preference. You can choose from low, medium, high, or max frame rate settings. The higher the frame rate, the smoother the gameplay, but it will also drain more battery and data. -
- Adjust the sensitivity according to your comfort level. You can choose from low, medium, high, or custom sensitivity settings. The higher the sensitivity, the faster your aim and movement will be. -
- Enable or disable the auto-fire option according to your skill level. You can choose to have your weapon fire automatically when you aim at an enemy, or manually when you tap the fire button. -
- Enable or disable the sound effects and voice chat according to your preference. You can choose to hear the game sounds and communicate with your teammates via voice chat, or mute them if you want to play silently. -
- Use headphones or earphones for a better audio experience. You can hear the game sounds more clearly and locate enemies more easily with headphones or earphones. -
- Close other apps and turn off notifications when playing Call of Duty Warzone Mobile. You can avoid distractions and interruptions by closing other apps and turning off notifications on your device. -
- If you experience lagging or freezing during gameplay, try lowering your graphics quality and frame rate settings.
- If you experience crashing or error messages during gameplay, try clearing your cache and data for the game app.
- If you experience connection issues or disconnections during gameplay, try switching to a different Wi-Fi network or cellular data provider.
- If you experience login issues or account problems during gameplay, try resetting your password or contacting customer support.
The common issues and solutions for Call of Duty Warzone Mobile
-Sometimes, you may encounter some issues or problems when playing Call of Duty Warzone Mobile. Here are some common ones and how to solve them:
-Conclusion
-Summary of the main points
-In conclusion, Call of Duty Warzone Mobile is an amazing game that brings the thrill and excitement of battle royale to mobile devices. It has authentic COD gameplay, shared progression, and up to 120 player count matches on mobile. It also has epic maps, unique gameplay elements, vast arsenal of weapons and vehicles, and endless replayability. It is lot of data depending on your graphics quality and frame rate settings. It is recommended to use a Wi-Fi connection or a cellular data plan with unlimited data when playing the game.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Copy Text from Apps Images and More with Universal Copy APK.md b/spaces/1phancelerku/anime-remove-background/Copy Text from Apps Images and More with Universal Copy APK.md deleted file mode 100644 index 540b1c5b3307435cee399c3c788e24f78a7a7519..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Copy Text from Apps Images and More with Universal Copy APK.md +++ /dev/null @@ -1,124 +0,0 @@ - -
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-This mode lets you copy text inside images using OCR technology. It currently works with Chinese, Devanagari (Hindi...), Japanese, Korean and Latin (English, Portuguese...) character sets. You just need to launch Universal Copy in scanner mode, select the image you want to copy text from, and it's done.
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-This feature automatically detects and extracts addresses, emails, phone numbers, @, #, and other useful information from the text you copied. You can then tap on them to perform quick actions such as opening Google Maps for an address, calling a phone number, sending an email, or searching for a hashtag.
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Copy-Paste in 1-tap
-This feature lets you perform quick actions on the text you copied without having to switch apps. You can translate the text using Google Translate, locate it using Google Maps, share it via social media or messaging apps, or search for it using Google or Wikipedia.
-Scroll mode
-This mode lets you select texts from multiple screens or apps to copy them all at once. You just need to launch Universal Copy in scroll mode, scroll through the screens or apps you want to copy text from, select the texts you want to copy, and it's done.
-Harvest mode
-This mode lets you extract all the texts from a screen or an app and copy them to your clipboard. You just need to launch Universal Copy in harvest mode, select the screen or the app you want to copy text from, and it's done.
-Why Download APK Universal Copy?
-APK Universal Copy is a must-have app for anyone who wants to copy text from any app or image on their Android device. It has many benefits that make it worth downloading and installing:
-Benefits of APK Universal Copy
-Copy text from any app or image
-With APK Universal Copy, you can copy text from any app or image on your device, even from the ones that don't let you or inside images. This means you can copy text from Facebook posts, Instagram captions, YouTube comments, Chrome web pages, WhatsApp messages, Tumblr blogs, News Republic articles, Snapchat stories, and more. You can also copy text inside images such as memes, screenshots, flyers, posters, logos, and more.
-Extract useful information quickly
-With APK Universal Copy, you can extract useful information from the text you copied without having to type them manually. You can extract addresses, emails, phone numbers, @, #, and other entities from the text and perform quick actions on them. This means you can open Google Maps for an address, call a phone number, send an email, or search for a hashtag in one tap.
-Perform actions on the text you copied
-With APK Universal Copy, you can perform quick actions on the text you copied without having to switch apps. You can translate the text using Google Translate, locate it using Google Maps, share it via social media or messaging apps, or search for it using Google or Wikipedia in one tap. This means you can save time and hassle by using APK Universal Copy as your all-in-one tool for copying and pasting.
-Save time and hassle
-With APK Universal Copy, you can save time and hassle by copying and pasting text from any app or image on your device. You don't have to switch apps or type manually to copy and paste text. You don't have to worry about the app or the image not letting you copy text. You don't have to waste time looking for useful information in the text. You don't have to open multiple apps to perform actions on the text. You just need to use APK Universal Copy and enjoy its features.
-How to Download and Install APK Universal Copy?
-If you are convinced by the benefits of APK Universal Copy and want to download and install it on your device, here are the steps you need to follow:
-Steps to Download and Install APK Universal Copy
-Step 1: Enable unknown sources on your device
-Since APK Universal Copy is not available on Google Play Store, you need to enable unknown sources on your device to install it. To do this, go to Settings > Security > Unknown sources and toggle it on. This will allow you to install apps from sources other than Google Play Store.
-Step 2: Download the APK file from a trusted source
-The next step is to download the APK file of APK Universal Copy from a trusted source. You can use this link to download the latest version of APK Universal Copy (version 5.0.5) for free. The file size is about 7 MB and it requires Android 5.0 or higher.
-Step 3: Locate and install the APK file on your device
-The third step is to locate and install the APK file on your device. To do this, go to your Downloads folder or use a file manager app to find the APK file of APK Universal Copy. Tap on it and follow the instructions to install it on your device.
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-The final step is to launch and enjoy APK Universal Copy on your device. To do this, go to your app drawer or home screen and look for the icon of APK Universal Copy. Tap on it and grant the necessary permissions to access your device's screen content and camera. Then, choose the mode you want to use (normal mode, scanner mode, scroll mode, or harvest mode) and start copying text from any app or image on your device.
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-In conclusion, APK Universal Copy is a powerful and versatile app that lets you copy text from any app or image on your Android device. It also detects and extracts useful information from the text and allows you to perform actions on it in one tap. It has many benefits such as copying text from any app or image, extracting useful information quickly, performing actions on the text you copied, and saving time and hassle. It is easy to download and install on your device by following a few simple steps. If you want to copy text from any app or image on your Android device, you should definitely try APK Universal Copy. You will be amazed by its features and performance.
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-| Question | -Answer | -
|---|---|
| Is APK Universal Copy safe to use? | -Yes, APK Universal Copy is safe to use. It does not contain any malware or viruses. It only requires permissions to access your device's screen content and camera to copy text from any app or image. It does not collect or share any personal data. | -
| Is APK Universal Copy free to use? | -Yes, APK Universal Copy is free to use. It does not have any in-app purchases or ads. You can download and install it on your device without paying anything. | -
| Does APK Universal Copy work offline? | -Yes, APK Universal Copy works offline. You can copy text from any app or image on your device without an internet connection. However, some features such as translating, locating, sharing, or searching may require an internet connection. | -
| How can I contact the developer of APK Universal Copy? | -You can contact the developer of APK Universal Copy by sending an email to contact@universal-copy.com. You can also visit their website at https://universal-copy.com/ for more information. | -
| How can I support the development of APK Universal Copy? | -You can support the development of APK Universal Copy by rating and reviewing it on the source where you downloaded it from. You can also share it with your friends and family who might find it useful. | -
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy PUBG MOBILE 1.8 with MOD APK ESP Aimbot Anti-Ban and Mega Menu Included.md b/spaces/1phancelerku/anime-remove-background/Enjoy PUBG MOBILE 1.8 with MOD APK ESP Aimbot Anti-Ban and Mega Menu Included.md deleted file mode 100644 index 2eae4ab1b79bffed111df01d00d7855b026f3632..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Enjoy PUBG MOBILE 1.8 with MOD APK ESP Aimbot Anti-Ban and Mega Menu Included.md +++ /dev/null @@ -1,91 +0,0 @@ -
-
PUBG Mobile 1.8 Mod APK Hack Download: Everything You Need to Know
-If you are a fan of battle royale games, you must have heard of PUBG Mobile, one of the most popular and addictive games in the genre. But did you know that there is a way to enjoy the game even more with unlimited resources and features? In this article, we will tell you everything you need to know about PUBG Mobile 1.8 Mod APK Hack, a modified version of the original game that gives you an edge over your opponents. We will also show you how to download and install it on your device, and how to play it like a pro.
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What is PUBG Mobile?
-A brief introduction to the popular battle royale game
-PUBG Mobile is a mobile version of PlayerUnknown's Battlegrounds, a multiplayer online battle royale game developed by PUBG Corporation. The game was released in 2018 and has since become one of the most downloaded and played games in the world. The game has won several awards and accolades, such as the Google Play Best Game of 2018, the Golden Joystick Award for Mobile Game of the Year, and the Esports Game of the Year.
-The main features and gameplay of PUBG Mobile
-PUBG Mobile is a game where up to 100 players parachute onto an island and fight for survival. The game offers various modes, such as solo, duo, squad, arcade, arena, and classic. The game also features different maps, such as Erangel, Miramar, Sanhok, Vikendi, Livik, and Karakin. The game is updated regularly with new content, such as weapons, vehicles, skins, events, and seasons.
-The gameplay of PUBG Mobile is simple but thrilling. You have to loot weapons, armor, ammo, and other items from buildings, crates, or dead enemies. You have to avoid the blue zone, which is a shrinking circle that forces players to move closer together. You have to kill or avoid other players while staying alive until the end. The last player or team standing wins the match.
-What is PUBG Mobile 1.8 Mod APK Hack?
-A modified version of the original game with unlimited resources and features
-PUBG Mobile 1.8 Mod APK Hack is a hacked version of the original game that gives you access to unlimited resources and features that are not available in the official version. For example, with this modded version, you can get unlimited UC (Unknown Cash), which is the in-game currency that you can use to buy skins, outfits, crates, emotes, and more. You can also get unlimited BP (Battle Points), which are used to level up your account and unlock rewards. You can also get unlimited health, ammo, aimbot, wallhack, speedhack, no recoil, no fog, no grass, and more.
-The benefits and risks of using PUBG Mobile 1.8 Mod APK Hack
-The benefits of using PUBG Mobile 1.8 Mod APK Hack are obvious. You can enjoy the game without any limitations or restrictions. You can customize your character and weapons with any skin or outfit you want. You can dominate every match with your enhanced skills and abilities. You
The risks of using PUBG Mobile 1.8 Mod APK Hack are also evident. You can get banned from the game if the developers detect that you are using a modified version. You can also expose your device to malware or viruses that may harm your data or privacy. You can also ruin the fun and fairness of the game for other players who are playing legitimately. Therefore, you should use PUBG Mobile 1.8 Mod APK Hack at your own risk and discretion.
-How to download and install PUBG Mobile 1.8 Mod APK Hack?
-The steps to download and install the modded version of the game
-If you want to try PUBG Mobile 1.8 Mod APK Hack, you will need to follow these steps:
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- Download the PUBG Mobile 1.8 Mod APK Hack file from a trusted source. You can search for it on Google or use the link below. -
- Enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and toggling it on. -
- Locate the downloaded file on your device 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 PUBG Mobile 1.8 Mod APK Hack. -
Note: You may need to uninstall the original version of PUBG Mobile before installing the modded version. You may also need to allow some permissions for the game to run properly.
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The precautions and tips to avoid any issues or errors
-To avoid any issues or errors while using PUBG Mobile 1.8 Mod APK Hack, you should take some precautions and tips:
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- Do not use your main account or any account that you care about. Use a guest account or a fake account instead. -
- Do not play on official servers or with real players. Use custom servers or play with bots instead. -
- Do not use the modded version for too long or too often. Use it sparingly and switch back to the original version occasionally. -
- Do not update the game from the Play Store or any other source. Wait for the modded version to be updated by its developer. -
- Do not download or install any suspicious files or apps that claim to be related to PUBG Mobile 1.8 Mod APK Hack. Only use trusted sources and scan your device regularly. -
How to play PUBG Mobile 1.8 Mod APK Hack?
-The basic controls and settings of the game
-The basic controls and settings of PUBG Mobile 1.8 Mod APK Hack are similar to those of the original game. You can use the virtual joystick on the left side of the screen to move your character, and the buttons on the right side of the screen to shoot, aim, jump, crouch, prone, reload, switch weapons, and more. You can also customize your controls and settings by going to Settings > Controls > Customize.
-The best strategies and tips to win every match
-The best strategies and tips to win every match with PUBG Mobile 1.8 Mod APK Hack are as follows:
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- Use your unlimited UC and BP to buy the best skins, outfits, crates, emotes, and more. This will make you look cool and intimidating in front of your enemies. -
- Use your unlimited health, ammo, aimbot, wallhack, speedhack, no recoil, no fog, no grass, and more to gain an advantage over your enemies. You can see them through walls, shoot them accurately, run faster, survive longer, and more. -
- Use your unlimited resources wisely and sparingly. Do not abuse them too much or too obviously, as this may alert other players or the developers that you are using a modded version. -
- Use your skills and tactics as well. Do not rely solely on your modded features, as they may not work in some situations or against some enemies. Use cover, stealth, teamwork, strategy, and common sense as well. -
- Have fun and enjoy the game. Do not take it too seriously or get frustrated if you lose or get banned. Remember that it is just a game and a modded version at that. -
Conclusion
-PUBG Mobile 1.8 Mod APK Hack is a modified version of the original game that gives you unlimited resources and features that are not available in the official version. It can make the game more fun and exciting for some players who want to try something new and different. However, it also comes
It also comes with some risks and drawbacks, such as getting banned from the game, exposing your device to malware or viruses, and ruining the fun and fairness of the game for other players. Therefore, you should use PUBG Mobile 1.8 Mod APK Hack at your own risk and discretion, and follow the steps and tips we provided in this article to avoid any issues or errors.
-We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. And if you liked this article, please share it with your friends and fellow PUBG Mobile fans. Thank you for reading and happy gaming!
-FAQs
-Here are some frequently asked questions about PUBG Mobile 1.8 Mod APK Hack:
--
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- Q: Is PUBG Mobile 1.8 Mod APK Hack safe to use?
-A: PUBG Mobile 1.8 Mod APK Hack is not safe to use, as it is a hacked version of the original game that may contain malware or viruses that can harm your device or data. It may also get you banned from the game if the developers detect that you are using a modified version.
- - Q: Is PUBG Mobile 1.8 Mod APK Hack legal to use?
-A: PUBG Mobile 1.8 Mod APK Hack is not legal to use, as it violates the terms of service and the intellectual property rights of PUBG Corporation, the developer of the game. It may also infringe on the rights of other players who are playing legitimately.
- - Q: Where can I download PUBG Mobile 1.8 Mod APK Hack?
-A: You can download PUBG Mobile 1.8 Mod APK Hack from various sources on the internet, such as websites, blogs, forums, or social media. However, you should be careful and cautious when downloading any file or app from unknown sources, as they may be fake, corrupted, or malicious.
- - Q: How can I update PUBG Mobile 1.8 Mod APK Hack?
-A: You cannot update PUBG Mobile 1.8 Mod APK Hack from the Play Store or any other source, as it is a modded version of the game that is not compatible with the official version. You will have to wait for the developer of the modded version to release a new update that matches the original version.
- - Q: Can I play PUBG Mobile 1.8 Mod APK Hack with my friends?
-A: You can play PUBG Mobile 1.8 Mod APK Hack with your friends if they are also using the same modded version of the game. However, you cannot play with your friends who are using the official version of the game, as they will not be able to join your server or match.
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\ No newline at end of file diff --git a/spaces/44ov41za8i/FreeVC/speaker_encoder/train.py b/spaces/44ov41za8i/FreeVC/speaker_encoder/train.py deleted file mode 100644 index 282e4f51b3825c7f32e628506eb40a98e58e2deb..0000000000000000000000000000000000000000 --- a/spaces/44ov41za8i/FreeVC/speaker_encoder/train.py +++ /dev/null @@ -1,125 +0,0 @@ -from speaker_encoder.visualizations import Visualizations -from speaker_encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset -from speaker_encoder.params_model import * -from speaker_encoder.model import SpeakerEncoder -from utils.profiler import Profiler -from pathlib import Path -import torch - -def sync(device: torch.device): - # FIXME - return - # For correct profiling (cuda operations are async) - if device.type == "cuda": - torch.cuda.synchronize(device) - -def train(run_id: str, clean_data_root: Path, models_dir: Path, umap_every: int, save_every: int, - backup_every: int, vis_every: int, force_restart: bool, visdom_server: str, - no_visdom: bool): - # Create a dataset and a dataloader - dataset = SpeakerVerificationDataset(clean_data_root) - loader = SpeakerVerificationDataLoader( - dataset, - speakers_per_batch, # 64 - utterances_per_speaker, # 10 - num_workers=8, - ) - - # Setup the device on which to run the forward pass and the loss. These can be different, - # because the forward pass is faster on the GPU whereas the loss is often (depending on your - # hyperparameters) faster on the CPU. - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - # FIXME: currently, the gradient is None if loss_device is cuda - loss_device = torch.device("cpu") - - # Create the model and the optimizer - model = SpeakerEncoder(device, loss_device) - optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate_init) - init_step = 1 - - # Configure file path for the model - state_fpath = models_dir.joinpath(run_id + ".pt") - backup_dir = models_dir.joinpath(run_id + "_backups") - - # Load any existing model - if not force_restart: - if state_fpath.exists(): - print("Found existing model \"%s\", loading it and resuming training." % run_id) - checkpoint = torch.load(state_fpath) - init_step = checkpoint["step"] - model.load_state_dict(checkpoint["model_state"]) - optimizer.load_state_dict(checkpoint["optimizer_state"]) - optimizer.param_groups[0]["lr"] = learning_rate_init - else: - print("No model \"%s\" found, starting training from scratch." % run_id) - else: - print("Starting the training from scratch.") - model.train() - - # Initialize the visualization environment - vis = Visualizations(run_id, vis_every, server=visdom_server, disabled=no_visdom) - vis.log_dataset(dataset) - vis.log_params() - device_name = str(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU") - vis.log_implementation({"Device": device_name}) - - # Training loop - profiler = Profiler(summarize_every=10, disabled=False) - for step, speaker_batch in enumerate(loader, init_step): - profiler.tick("Blocking, waiting for batch (threaded)") - - # Forward pass - inputs = torch.from_numpy(speaker_batch.data).to(device) - sync(device) - profiler.tick("Data to %s" % device) - embeds = model(inputs) - sync(device) - profiler.tick("Forward pass") - embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device) - loss, eer = model.loss(embeds_loss) - sync(loss_device) - profiler.tick("Loss") - - # Backward pass - model.zero_grad() - loss.backward() - profiler.tick("Backward pass") - model.do_gradient_ops() - optimizer.step() - profiler.tick("Parameter update") - - # Update visualizations - # learning_rate = optimizer.param_groups[0]["lr"] - vis.update(loss.item(), eer, step) - - # Draw projections and save them to the backup folder - if umap_every != 0 and step % umap_every == 0: - print("Drawing and saving projections (step %d)" % step) - backup_dir.mkdir(exist_ok=True) - projection_fpath = backup_dir.joinpath("%s_umap_%06d.png" % (run_id, step)) - embeds = embeds.detach().cpu().numpy() - vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath) - vis.save() - - # Overwrite the latest version of the model - if save_every != 0 and step % save_every == 0: - print("Saving the model (step %d)" % step) - torch.save({ - "step": step + 1, - "model_state": model.state_dict(), - "optimizer_state": optimizer.state_dict(), - }, state_fpath) - - # Make a backup - if backup_every != 0 and step % backup_every == 0: - print("Making a backup (step %d)" % step) - backup_dir.mkdir(exist_ok=True) - backup_fpath = backup_dir.joinpath("%s_bak_%06d.pt" % (run_id, step)) - torch.save({ - "step": step + 1, - "model_state": model.state_dict(), - "optimizer_state": optimizer.state_dict(), - }, backup_fpath) - - profiler.tick("Extras (visualizations, saving)") - \ No newline at end of file diff --git a/spaces/AIConsultant/MusicGen/audiocraft/modules/seanet.py b/spaces/AIConsultant/MusicGen/audiocraft/modules/seanet.py deleted file mode 100644 index 3e5998e9153afb6e68ea410d565e00ea835db248..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/audiocraft/modules/seanet.py +++ /dev/null @@ -1,258 +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 typing as tp - -import numpy as np -import torch.nn as nn - -from .conv import StreamableConv1d, StreamableConvTranspose1d -from .lstm import StreamableLSTM - - -class SEANetResnetBlock(nn.Module): - """Residual block from SEANet model. - - Args: - dim (int): Dimension of the input/output. - kernel_sizes (list): List of kernel sizes for the convolutions. - dilations (list): List of dilations for the convolutions. - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function. - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - true_skip (bool): Whether to use true skip connection or a simple - (streamable) convolution as the skip connection. - """ - def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1], - activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False, - pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True): - super().__init__() - assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations' - act = getattr(nn, activation) - hidden = dim // compress - block = [] - for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)): - in_chs = dim if i == 0 else hidden - out_chs = dim if i == len(kernel_sizes) - 1 else hidden - block += [ - act(**activation_params), - StreamableConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation, - norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode), - ] - self.block = nn.Sequential(*block) - self.shortcut: nn.Module - if true_skip: - self.shortcut = nn.Identity() - else: - self.shortcut = StreamableConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode) - - def forward(self, x): - return self.shortcut(x) + self.block(x) - - -class SEANetEncoder(nn.Module): - """SEANet encoder. - - Args: - channels (int): Audio channels. - dimension (int): Intermediate representation dimension. - n_filters (int): Base width for the model. - n_residual_layers (int): nb of residual layers. - ratios (Sequence[int]): kernel size and stride ratios. The encoder uses downsampling ratios instead of - upsampling ratios, hence it will use the ratios in the reverse order to the ones specified here - that must match the decoder order. We use the decoder order as some models may only employ the decoder. - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function. - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - kernel_size (int): Kernel size for the initial convolution. - last_kernel_size (int): Kernel size for the initial convolution. - residual_kernel_size (int): Kernel size for the residual layers. - dilation_base (int): How much to increase the dilation with each layer. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - true_skip (bool): Whether to use true skip connection or a simple - (streamable) convolution as the skip connection in the residual network blocks. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - lstm (int): Number of LSTM layers at the end of the encoder. - disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm. - For the encoder, it corresponds to the N first blocks. - """ - def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3, - ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, - last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, - pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0, - disable_norm_outer_blocks: int = 0): - super().__init__() - self.channels = channels - self.dimension = dimension - self.n_filters = n_filters - self.ratios = list(reversed(ratios)) - del ratios - self.n_residual_layers = n_residual_layers - self.hop_length = np.prod(self.ratios) - self.n_blocks = len(self.ratios) + 2 # first and last conv + residual blocks - self.disable_norm_outer_blocks = disable_norm_outer_blocks - assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \ - "Number of blocks for which to disable norm is invalid." \ - "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0." - - act = getattr(nn, activation) - mult = 1 - model: tp.List[nn.Module] = [ - StreamableConv1d(channels, mult * n_filters, kernel_size, - norm='none' if self.disable_norm_outer_blocks >= 1 else norm, - norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) - ] - # Downsample to raw audio scale - for i, ratio in enumerate(self.ratios): - block_norm = 'none' if self.disable_norm_outer_blocks >= i + 2 else norm - # Add residual layers - for j in range(n_residual_layers): - model += [ - SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1], - dilations=[dilation_base ** j, 1], - norm=block_norm, norm_params=norm_params, - activation=activation, activation_params=activation_params, - causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)] - - # Add downsampling layers - model += [ - act(**activation_params), - StreamableConv1d(mult * n_filters, mult * n_filters * 2, - kernel_size=ratio * 2, stride=ratio, - norm=block_norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode), - ] - mult *= 2 - - if lstm: - model += [StreamableLSTM(mult * n_filters, num_layers=lstm)] - - model += [ - act(**activation_params), - StreamableConv1d(mult * n_filters, dimension, last_kernel_size, - norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm, - norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) - ] - - self.model = nn.Sequential(*model) - - def forward(self, x): - return self.model(x) - - -class SEANetDecoder(nn.Module): - """SEANet decoder. - - Args: - channels (int): Audio channels. - dimension (int): Intermediate representation dimension. - n_filters (int): Base width for the model. - n_residual_layers (int): nb of residual layers. - ratios (Sequence[int]): kernel size and stride ratios. - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function. - final_activation (str): Final activation function after all convolutions. - final_activation_params (dict): Parameters to provide to the activation function. - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - kernel_size (int): Kernel size for the initial convolution. - last_kernel_size (int): Kernel size for the initial convolution. - residual_kernel_size (int): Kernel size for the residual layers. - dilation_base (int): How much to increase the dilation with each layer. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - true_skip (bool): Whether to use true skip connection or a simple. - (streamable) convolution as the skip connection in the residual network blocks. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - lstm (int): Number of LSTM layers at the end of the encoder. - disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm. - For the decoder, it corresponds to the N last blocks. - trim_right_ratio (float): Ratio for trimming at the right of the transposed convolution under the causal setup. - If equal to 1.0, it means that all the trimming is done at the right. - """ - def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3, - ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - final_activation: tp.Optional[str] = None, final_activation_params: tp.Optional[dict] = None, - norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, - last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, - pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0, - disable_norm_outer_blocks: int = 0, trim_right_ratio: float = 1.0): - super().__init__() - self.dimension = dimension - self.channels = channels - self.n_filters = n_filters - self.ratios = ratios - del ratios - self.n_residual_layers = n_residual_layers - self.hop_length = np.prod(self.ratios) - self.n_blocks = len(self.ratios) + 2 # first and last conv + residual blocks - self.disable_norm_outer_blocks = disable_norm_outer_blocks - assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \ - "Number of blocks for which to disable norm is invalid." \ - "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0." - - act = getattr(nn, activation) - mult = int(2 ** len(self.ratios)) - model: tp.List[nn.Module] = [ - StreamableConv1d(dimension, mult * n_filters, kernel_size, - norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm, - norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) - ] - - if lstm: - model += [StreamableLSTM(mult * n_filters, num_layers=lstm)] - - # Upsample to raw audio scale - for i, ratio in enumerate(self.ratios): - block_norm = 'none' if self.disable_norm_outer_blocks >= self.n_blocks - (i + 1) else norm - # Add upsampling layers - model += [ - act(**activation_params), - StreamableConvTranspose1d(mult * n_filters, mult * n_filters // 2, - kernel_size=ratio * 2, stride=ratio, - norm=block_norm, norm_kwargs=norm_params, - causal=causal, trim_right_ratio=trim_right_ratio), - ] - # Add residual layers - for j in range(n_residual_layers): - model += [ - SEANetResnetBlock(mult * n_filters // 2, kernel_sizes=[residual_kernel_size, 1], - dilations=[dilation_base ** j, 1], - activation=activation, activation_params=activation_params, - norm=block_norm, norm_params=norm_params, causal=causal, - pad_mode=pad_mode, compress=compress, true_skip=true_skip)] - - mult //= 2 - - # Add final layers - model += [ - act(**activation_params), - StreamableConv1d(n_filters, channels, last_kernel_size, - norm='none' if self.disable_norm_outer_blocks >= 1 else norm, - norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) - ] - # Add optional final activation to decoder (eg. tanh) - if final_activation is not None: - final_act = getattr(nn, final_activation) - final_activation_params = final_activation_params or {} - model += [ - final_act(**final_activation_params) - ] - self.model = nn.Sequential(*model) - - def forward(self, z): - y = self.model(z) - return y diff --git a/spaces/AIFILMS/StyleGANEX/models/encoders/model_irse.py b/spaces/AIFILMS/StyleGANEX/models/encoders/model_irse.py deleted file mode 100644 index bc41ace0ba04cf4285c283a28e6c36113a18e6d6..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/StyleGANEX/models/encoders/model_irse.py +++ /dev/null @@ -1,84 +0,0 @@ -from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module -from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm - -""" -Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) -""" - - -class Backbone(Module): - def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): - super(Backbone, self).__init__() - assert input_size in [112, 224], "input_size should be 112 or 224" - assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" - assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" - blocks = get_blocks(num_layers) - if mode == 'ir': - unit_module = bottleneck_IR - elif mode == 'ir_se': - unit_module = bottleneck_IR_SE - self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), - BatchNorm2d(64), - PReLU(64)) - if input_size == 112: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 7 * 7, 512), - BatchNorm1d(512, affine=affine)) - else: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 14 * 14, 512), - BatchNorm1d(512, affine=affine)) - - modules = [] - for block in blocks: - for bottleneck in block: - modules.append(unit_module(bottleneck.in_channel, - bottleneck.depth, - bottleneck.stride)) - self.body = Sequential(*modules) - - def forward(self, x): - x = self.input_layer(x) - x = self.body(x) - x = self.output_layer(x) - return l2_norm(x) - - -def IR_50(input_size): - """Constructs a ir-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_101(input_size): - """Constructs a ir-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_152(input_size): - """Constructs a ir-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_50(input_size): - """Constructs a ir_se-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_101(input_size): - """Constructs a ir_se-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_152(input_size): - """Constructs a ir_se-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) - return model diff --git a/spaces/AUST001/HDTV/README.md b/spaces/AUST001/HDTV/README.md deleted file mode 100644 index ff8344be920efede58fea029e1e2f8de1a5af321..0000000000000000000000000000000000000000 --- a/spaces/AUST001/HDTV/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: HDTV -emoji: 👁 -colorFrom: blue -colorTo: purple -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: false -license: cc-by-nc-nd-4.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AchyuthGamer/MagicPrompt-Stable-Diffusion/app.py b/spaces/AchyuthGamer/MagicPrompt-Stable-Diffusion/app.py deleted file mode 100644 index 8644606f60321da5256c0cf7440c0aa06fea5da1..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/MagicPrompt-Stable-Diffusion/app.py +++ /dev/null @@ -1,96 +0,0 @@ -from transformers import pipeline, set_seed -import gradio as grad, random, re -import os -import sys - -gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2') - -def generate(starting_text): - with open("ideas.txt", "r") as f: - line = f.readlines() - seed = random.randint(100, 1000000) - set_seed(seed) - - if starting_text == "": - starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").capitalize() - starting_text: str = re.sub(r"\.", '', starting_text) - - response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 80)), num_return_sequences=1) - response_list = [] - for x in response: - resp = x['generated_text'].strip() - if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False: - response_list.append(resp) - - response_end = "\n".join(response_list) - response_end = re.sub('[^ ]+\.[^ ]+','', response_end) - response_end = response_end.replace("<", "").replace(">", "") - - if response_end != "": - return response_end - -with grad.Blocks(css='style.css') as demo: - grad.HTML( - """ -
-
- """
- )
- with grad.Column(elem_id="col-container"):
- with grad.Row(variant="compact"):
- txt = grad.Textbox(
- label="Initial Text",
- show_label=False,
- max_lines=1,
- placeholder="Enter a basic idea",
- ).style(
- container=False,
- )
- run = grad.Button("✨ Magic Prompt ✨").style(full_width=False)
-
-
-
- with grad.Row(variant="compact"):
- out = grad.Textbox(
- label="Generated Text",
- show_label=False,
- lines=5,
- ).style(
- container=False,
- )
-
- run.click(generate, inputs=[txt], outputs=[out])
-
-
-
- with grad.Row():
- grad.HTML(
- """
-
-
-
- - The Stable Diffusion Prompt Generator - because your text needs a little more visual spice. -
-- Ready to see some magic happen? Simply type in your basic idea. Feeling lazy? No problem, just hit the "Magic Prompt" button and it will randomly pull from a list of thousands of ideas for you. -
-- ❤️ Press the Like Button if you enjoy my space! ❤️ -
-
-
- """
-)
-
-
- fn=generate,
- run=generate,
- inputs=txt,
- outputs=out
- demo.launch(enable_queue=False, inline=True)
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/actions/SelectChess.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/actions/SelectChess.js
deleted file mode 100644
index 7e7ea454dd29e0cc8173cc4b4dc1c9bea68fa2c0..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/actions/SelectChess.js
+++ /dev/null
@@ -1,9 +0,0 @@
-/*
-Do nothing
-*/
-
-var SelectChess = function (chess, board, bejeweled) {
- // Do nothing
-}
-
-export default SelectChess;
\ No newline at end of file
diff --git a/spaces/Akash473/FunkoHairBeard/README.md b/spaces/Akash473/FunkoHairBeard/README.md
deleted file mode 100644
index 6168692ad95bf16585e264d4cf01d74c169e9cda..0000000000000000000000000000000000000000
--- a/spaces/Akash473/FunkoHairBeard/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: FunkoHairBeard
-emoji: 🏢
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.44.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Akash473/FunkoHairBeard/app.py b/spaces/Akash473/FunkoHairBeard/app.py
deleted file mode 100644
index 6025f0d6aecce54b62ee68e45ec32dc9288d8665..0000000000000000000000000000000000000000
--- a/spaces/Akash473/FunkoHairBeard/app.py
+++ /dev/null
@@ -1,502 +0,0 @@
-from io import BytesIO
-import base64
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torchvision import transforms, models
-from PIL import Image
-import gradio as gr
-
-# Combined Code for Beard and Hairstyle Detection and Styling
-
-male_background_image_paths = [
- "Data/AdobeColorFunko/Outfits/MenOutfits/DummyDress1.png",
- "Data/AdobeColorFunko/Outfits/MenOutfits/GlassesDummy.png",
- "Data/AdobeColorFunko/Outfits/MenOutfits/DummyDress3.png"
-]
-
-female_background_image_paths = [
- "Data/AdobeColorFunko/Outfits/WomenOutfits/WomenOne.png",
- "Data/AdobeColorFunko/Outfits/WomenOutfits/WomenTwo.png",
- "Data/AdobeColorFunko/Outfits/WomenOutfits/WomenThree.png"
-]
-
-
-class GenderClassifier:
- def __init__(self, model_path, class_names):
- self.model = models.resnet18(pretrained=False)
- num_ftrs = self.model.fc.in_features
- self.model.fc = nn.Linear(num_ftrs, len(class_names))
- self.load_model(model_path)
- self.model.eval()
- self.data_transforms = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- self.class_names = class_names
-
- def preprocess_image(self, image_path):
- image = Image.open(image_path).convert("RGB")
- image = self.data_transforms(image)
- image = image.unsqueeze(0)
- return image
-
- def load_model(self, model_path):
- if torch.cuda.is_available():
- self.model.load_state_dict(torch.load(model_path))
- else:
- self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-
- def classify_gender(self, image_path):
- input_image = self.preprocess_image(image_path)
-
- with torch.no_grad():
- predictions = self.model(input_image)
-
- probabilities = torch.nn.functional.softmax(predictions[0], dim=0)
- predicted_class = torch.argmax(probabilities).item()
- predicted_label = self.class_names[predicted_class]
-
- return predicted_label
-
-class WomenHairStyleClassifier:
- def __init__(self, model_path, class_names):
- self.model = models.resnet18(pretrained=False)
- num_ftrs = self.model.fc.in_features
- self.model.fc = nn.Linear(num_ftrs, len(class_names))
- self.load_model(model_path)
- self.model.eval()
- self.data_transforms = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- self.class_names = class_names
-
- def preprocess_image(self, image_path):
- image = Image.open(image_path).convert("RGB")
- image = self.data_transforms(image)
- image = image.unsqueeze(0)
- return image
-
- def load_model(self, model_path):
- if torch.cuda.is_available():
- self.model.load_state_dict(torch.load(model_path))
- else:
- self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-
- def classify_hairStyle(self, image_path):
- input_image = self.preprocess_image(image_path)
-
- with torch.no_grad():
- predictions = self.model(input_image)
-
- probabilities = torch.nn.functional.softmax(predictions[0], dim=0)
- predicted_class = torch.argmax(probabilities).item()
- predicted_label = self.class_names[predicted_class]
-
- return predicted_label
-
-class WomenHairColorClassifier:
- def __init__(self, model_path, class_names):
- self.model = models.resnet18(pretrained=False)
- num_ftrs = self.model.fc.in_features
- self.model.fc = nn.Linear(num_ftrs, len(class_names))
- self.load_model(model_path)
- self.model.eval()
- self.data_transforms = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- self.class_names = class_names
-
- def preprocess_image(self, image_path):
- image = Image.open(image_path).convert("RGB")
- image = self.data_transforms(image)
- image = image.unsqueeze(0)
- return image
-
- def load_model(self, model_path):
- if torch.cuda.is_available():
- self.model.load_state_dict(torch.load(model_path))
- else:
- self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-
- def classify_hairColor(self, image_path):
- input_image = self.preprocess_image(image_path)
-
- with torch.no_grad():
- predictions = self.model(input_image)
-
- probabilities = torch.nn.functional.softmax(predictions[0], dim=0)
- predicted_class = torch.argmax(probabilities).item()
- predicted_label = self.class_names[predicted_class]
-
- return predicted_label
-# Function to classify beard style
-class BeardClassifier:
- def __init__(self, model_path, class_names):
- self.model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=False)
- num_ftrs = self.model.fc.in_features
- self.model.fc = torch.nn.Linear(num_ftrs, len(class_names))
- self.load_model(model_path)
- self.model.eval()
- self.data_transforms = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- self.class_names = class_names
-
- def preprocess_image(self, image):
- image = Image.open(image).convert("RGB")
- image = self.data_transforms(image)
- image = image.unsqueeze(0)
- return image
-
- def load_model(self, model_path):
- if torch.cuda.is_available():
- self.model.load_state_dict(torch.load(model_path))
- else:
- self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-
- def classify_beard(self, image):
- input_image = self.preprocess_image(image)
- with torch.no_grad():
- predictions = self.model(input_image)
- probabilities = torch.nn.functional.softmax(predictions[0], dim=0)
- predicted_class = torch.argmax(probabilities).item()
- predicted_label = self.class_names[predicted_class]
- return predicted_label
-
-# Function to classify beard color
-class BeardColorClassifier:
- def __init__(self, model_path, class_names):
- self.model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=False)
- num_ftrs = self.model.fc.in_features
- self.model.fc = torch.nn.Linear(num_ftrs, len(class_names))
- self.load_model(model_path)
- self.model.eval()
- self.data_transforms = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- self.class_names = class_names
-
- def preprocess_image(self, image):
- image = Image.open(image).convert("RGB")
- image = self.data_transforms(image)
- image = image.unsqueeze(0)
- return image
-
- def load_model(self, model_path):
- if torch.cuda.is_available():
- self.model.load_state_dict(torch.load(model_path))
- else:
- self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-
- def classify_beard_color(self, image):
- input_image = self.preprocess_image(image)
- with torch.no_grad():
- predictions = self.model(input_image)
- probabilities = torch.nn.functional.softmax(predictions[0], dim=0)
- predicted_class = torch.argmax(probabilities).item()
- predicted_label = self.class_names[predicted_class]
- return predicted_label
-
-
-# Function to classify hairstyle
-class HairStyleClassifier:
- def __init__(self, model_path, class_names):
- self.model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=False)
- num_ftrs = self.model.fc.in_features
- self.model.fc = torch.nn.Linear(num_ftrs, len(class_names))
- self.load_model(model_path)
- self.model.eval()
- self.data_transforms = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- self.class_names = class_names
-
- def preprocess_image(self, image):
- image = Image.open(image).convert("RGB")
- image = self.data_transforms(image)
- image = image.unsqueeze(0)
- return image
-
- def load_model(self, model_path):
- if torch.cuda.is_available():
- self.model.load_state_dict(torch.load(model_path))
- else:
- self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-
- def classify_hair(self, image):
- input_image = self.preprocess_image(image)
- with torch.no_grad():
- predictions = self.model(input_image)
- probabilities = torch.nn.functional.softmax(predictions[0], dim=0)
- predicted_class = torch.argmax(probabilities).item()
- predicted_label = self.class_names[predicted_class]
- return predicted_label
-
-class MenHairColorClassifier:
- def __init__(self, model_path, class_names):
- self.model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=False)
- num_ftrs = self.model.fc.in_features
- self.model.fc = torch.nn.Linear(num_ftrs, len(class_names))
- self.load_model(model_path)
- self.model.eval()
- self.data_transforms = transforms.Compose([
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
- self.class_names = class_names
-
- def preprocess_image(self, image):
- image = Image.open(image).convert("RGB")
- image = self.data_transforms(image)
- image = image.unsqueeze(0)
- return image
-
- def load_model(self, model_path):
- if torch.cuda.is_available():
- self.model.load_state_dict(torch.load(model_path))
- else:
- self.model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-
- def classify_menHair_color(self, image):
- input_image = self.preprocess_image(image)
- with torch.no_grad():
- predictions = self.model(input_image)
- probabilities = torch.nn.functional.softmax(predictions[0], dim=0)
- predicted_class = torch.argmax(probabilities).item()
- predicted_label = self.class_names[predicted_class]
- return predicted_label
-
-
-def dummy_eye(background_image, x, y, placeholder_image_path, x_coordinate, y_coordinate):
- placeholder_image = Image.open(placeholder_image_path)
- target_size = (x, y)
- placeholder_image = placeholder_image.resize(target_size, Image.LANCZOS)
- placeholder_array = np.array(placeholder_image)
- placeholder_width, placeholder_height = placeholder_image.size
- region_box = (x_coordinate, y_coordinate, x_coordinate + placeholder_width, y_coordinate + placeholder_height)
- placeholder_mask = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
- background_image.paste(placeholder_image, region_box, mask=placeholder_mask)
- background_array = np.array(background_image)
-
-# Function to overlay a beard on a background image
-def process_image_Beard(background_image, x, placeholder_image_path, x_coordinate, y_coordinate):
- placeholder_image = Image.open(placeholder_image_path)
- target_size = (x, x)
- placeholder_image = placeholder_image.resize(target_size, Image.LANCZOS)
- placeholder_array = np.array(placeholder_image)
- placeholder_width, placeholder_height = placeholder_image.size
- region_box = (x_coordinate, y_coordinate, x_coordinate + placeholder_width, y_coordinate + placeholder_height)
- placeholder_mask = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
- background_image.paste(placeholder_image, region_box, mask=placeholder_mask)
- background_array = np.array(background_image)
- placeholder_alpha = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
-
-def process_image_WomanHair(background_image, x, y, placeholder_image_path, x_coordinate, y_coordinate):
- placeholder_image = Image.open(placeholder_image_path)
- target_size = (x, y)
- placeholder_image = placeholder_image.resize(target_size, Image.LANCZOS)
- placeholder_array = np.array(placeholder_image)
- placeholder_width, placeholder_height = placeholder_image.size
- region_box = (x_coordinate, y_coordinate, x_coordinate + placeholder_width, y_coordinate + placeholder_height)
- placeholder_mask = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
- background_image.paste(placeholder_image, region_box, mask=placeholder_mask)
- background_array = np.array(background_image)
- placeholder_alpha = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
-
-
-def add_eyebrow(background_image, x_coordinate, y_coordinate, eyebrow_image_path):
- eyebrow_image = Image.open(eyebrow_image_path)
- target_size = (200, 200) # Adjust the size as needed
- eyebrow_image = eyebrow_image.resize(target_size, Image.LANCZOS)
- region_box = (x_coordinate, y_coordinate, x_coordinate + eyebrow_image.width, y_coordinate + eyebrow_image.height)
- eyebrow_mask = eyebrow_image.split()[3] if eyebrow_image.mode == 'RGBA' else None
- background_image.paste(eyebrow_image, region_box, mask=eyebrow_mask)
- background_array = np.array(background_image)
-
-
-
-
-# Function to overlay a hairstyle on a background image
-def process_image_menHair(background_image, x, y, placeholder_image_path, x_coordinate, y_coordinate):
- placeholder_image = Image.open(placeholder_image_path)
- target_size = (x, y)
- placeholder_image = placeholder_image.resize(target_size, Image.LANCZOS)
- placeholder_array = np.array(placeholder_image)
- placeholder_width, placeholder_height = placeholder_image.size
- region_box = (x_coordinate, y_coordinate, x_coordinate + placeholder_width, y_coordinate + placeholder_height)
- placeholder_mask = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
- background_image.paste(placeholder_image, region_box, mask=placeholder_mask)
- background_array = np.array(background_image)
- placeholder_alpha = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
-
-# Function to generate Funko figurines
-def Igenerate_funko_figurines(input_image):
-
- WomenHairStyle_classifier = WomenHairStyleClassifier('Data/FunkoSavedModels/WomenHairStyle.pt', ['MediumLength', 'ShortHair', 'SidePlait'])
- predicted_WomenHairStyle = WomenHairStyle_classifier.classify_hairStyle(input_image)
-
- WomenHairColor_classifier = WomenHairColorClassifier('Data/FunkoSavedModels/WomenHairColor.pt', ['Black', 'Brown', 'Ginger', 'White'])
- predicted_WomenHairColor = WomenHairColor_classifier.classify_hairColor(input_image)
- # Detect and classify gender
- gender_classifier = GenderClassifier('Data/FunkoSavedModels/Gender.pt', ['Female', 'Male'])
- predicted_gender = gender_classifier.classify_gender(input_image)
-
- # Detect and classify beard style
- beard_classifier = BeardClassifier('Data/FunkoSavedModels/FunkoResnet18BeardStyle.pt', ['Bandholz', 'CleanShave', 'FullGoatee', 'Moustache', 'RapIndustryStandards', 'ShortBeard'])
- predicted_style_label = beard_classifier.classify_beard(input_image)
-
- # Detect and classify beard color
- beard_color_classifier = BeardColorClassifier('Data/FunkoSavedModels/FunkoResnet18BeardColor.pt', ['Black', 'DarkBrown', 'Ginger', 'LightBrown', 'SaltAndPepper', 'White'])
- predicted_color_label = beard_color_classifier.classify_beard_color(input_image)
-
- # Classify hairstyle
- hair_style_classifier = HairStyleClassifier('Data/FunkoSavedModels/FunkoResnet18HairStyle.pt', ['Afro', 'Bald', 'Puff', 'Spike'])
- predicted_hairStyle_label = hair_style_classifier.classify_hair(input_image)
-
- #classify menHairColor
- menhair_color_classifier = MenHairColorClassifier('Data/FunkoSavedModels/FunkoResnet18MenHairColor.pt', ['Black', 'DarkBrown', 'Ginger', 'LightBrown', 'SaltAndPepper', 'White'])
- predicted_menhairColor_label = menhair_color_classifier.classify_menHair_color(input_image)
- # Process background images and apply beard style and color along with hair style and color
- final_images = []
-
- if predicted_gender == 'Male':
- background_image_paths = male_background_image_paths
- if predicted_gender == 'Female':
- background_image_paths = female_background_image_paths
-
- for background_image_paths in background_image_paths:
- background_image = Image.open(background_image_paths)
- x_coordinate = 90
- y_coordinate = 50
- add_eyebrow(background_image, 115, 80, "Data/AdobeColorFunko/EyezBrowz/Eyebrow.png")
- #dummy_eye(background_image, 245, 345, 'Data/AdobeColorFunko/EyezBrowz/MaleEye.png', x_coordinate, y_coordinate)
- if predicted_gender == 'Male':
- x = 245
- y = 345
- placeholder_image_path = f"Data/AdobeColorFunko/EyezBrowz/{predicted_gender}Eye.png"
- x_coordinate = 90
- y_coordinate = 50
- dummy_eye(background_image, x, y, placeholder_image_path, x_coordinate, y_coordinate)
-
- if predicted_style_label == 'Bandholz':
- process_image_Beard(background_image, 320,
- f"Data/AdobeColorFunko/Beard/Bandholz/{predicted_color_label}.png",
- 50, 142)
-
- if predicted_style_label == 'ShortBeard':
- process_image_Beard(background_image, 300,
- f"Data/AdobeColorFunko/Beard/ShortBeard/{predicted_color_label}.png",
- 62, 118)
-
- if predicted_style_label == 'FullGoatee':
- process_image_Beard(background_image, 230,
- f"Data/AdobeColorFunko/Beard/Goatee/{predicted_color_label}.png",
- 96, 168)
-
- if predicted_style_label == 'RapIndustryStandards':
- process_image_Beard(background_image, 290,
- f"Data/AdobeColorFunko/Beard/RapIndustry/{predicted_color_label}.png",
- 67, 120)
-
- if predicted_style_label == 'Moustache':
- process_image_Beard(background_image, 220,
- f"Data/AdobeColorFunko/Beard/Moustache/{predicted_color_label}.png",
- 100, 160)
-
- if predicted_style_label == 'CleanShave':
- process_image_Beard(background_image, 220,
- f"Data/AdobeColorFunko/Beard/CleanShave/{predicted_color_label}.png",
- 100, 160)
-
- # Add other conditions for different beard styles
-
- # Overlay hairstyle
- if predicted_hairStyle_label == 'Afro':
- process_image_menHair(background_image, 336, 420,
- f"Data/AdobeColorFunko/MenHairstyle/Afro/{predicted_menhairColor_label}.png",
- 41, 76)
-
- if predicted_hairStyle_label == 'Puff':
- process_image_menHair(background_image, 305, 420,
- f"Data/AdobeColorFunko/MenHairstyle/Puff/{predicted_menhairColor_label}.png",
- 56, 68)
-
- if predicted_hairStyle_label == 'Spike':
- process_image_menHair(background_image, 310, 420,
- f"Data/AdobeColorFunko/MenHairstyle/Spike/{predicted_menhairColor_label}.png",
- 52, 70)
-
- if predicted_hairStyle_label == 'Bald':
- process_image_menHair(background_image, 310, 420,
- f"Data/AdobeColorFunko/MenHairstyle/Bald/{predicted_menhairColor_label}.png",
- 67, 120)
-
-
- if predicted_gender == 'Female':
- x = 245
- y = 345
- placeholder_image_path = f"Data/AdobeColorFunko/EyezBrowz/{predicted_gender}Eye.png"
- x_coordinate = 90
- y_coordinate = 50
- dummy_eye(background_image, x, y, placeholder_image_path, x_coordinate, y_coordinate)
- if predicted_WomenHairStyle == 'MediumLength':
- process_image_WomanHair(background_image, 300,460,
- f"Data/AdobeColorFunko/WomenHairstyle/MediumLength/{predicted_WomenHairColor}.png",
- 56, 50)
-
- if predicted_WomenHairStyle == 'ShortHair':
- process_image_WomanHair(background_image, 270,460,
- f"Data/AdobeColorFunko/WomenHairstyle/ShortHair/{predicted_WomenHairColor}.png",
- 61, 49)
-
- if predicted_WomenHairStyle == 'SidePlait':
- process_image_WomanHair(background_image, 300,450,
- f"Data/AdobeColorFunko/WomenHairstyle/SidePlait/{predicted_WomenHairColor}.png",
- 54, 56)
-
-
- # Convert the resulting image to base64
- buffered = BytesIO()
- background_image.save(buffered, format="PNG")
- #base64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
- final_images.append(background_image)
-
- return final_images
-imageComponent = gr.Image(type="filepath")
-
-# Define Gradio input components
-input_image = gr.inputs.Image(type="pil", label="Upload your image")
-
-
-with gr.Blocks() as demo:
- gr.Markdown(
- """
- # Funko POP! Figurine Creation
- Enabling Streamlined Automation with Generative Artificial Intelligence
- """)
- imageComponent = gr.Image(type="filepath").style(height=300, width=300)
- #MyOutputs=[gr.Image(type="pil", label="Generated Image " + str(i + 1)) for i in range(3)]
- with gr.Row():
- MyOutputs = [gr.Image(type="pil", label="Generated Image " + str(i + 1)).style(height=300, width=300) for i in range(3)]
- submitButton = gr.Button(value="Submit")
- submitButton.click(Igenerate_funko_figurines, inputs=imageComponent, outputs=MyOutputs)
-
-
-if __name__ == "__main__":
- demo.launch()
-
diff --git a/spaces/AlexKoff88/stable_diffusion/app.py b/spaces/AlexKoff88/stable_diffusion/app.py
deleted file mode 100644
index 3f471e6ef35ac78b375fdbd4604c0d9919d71fa6..0000000000000000000000000000000000000000
--- a/spaces/AlexKoff88/stable_diffusion/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import gradio as gr
-from optimum.intel.openvino import OVStableDiffusionPipeline
-from diffusers.training_utils import set_seed
-from diffusers import DDPMScheduler, StableDiffusionPipeline
-import gc
-
-import subprocess
-
-import time
-
-
-def create_pipeline(name):
- if name == "svjack/Stable-Diffusion-Pokemon-en": #"valhalla/sd-pokemon-model":
- scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012,
- beta_schedule="scaled_linear", num_train_timesteps=1000)
- pipe = StableDiffusionPipeline.from_pretrained(name, scheduler=scheduler)
- pipe.safety_checker = lambda images, clip_input: (images, False)
- elif name == "OpenVINO/stable-diffusion-pokemons-fp32": #"stable-diffusion-pokemons-valhalla-fp32":
- scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012,
- beta_schedule="scaled_linear", num_train_timesteps=1000)
- pipe = OVStableDiffusionPipeline.from_pretrained(name, compile=False, scheduler=scheduler)
- pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
- pipe.compile()
- else:
- pipe = OVStableDiffusionPipeline.from_pretrained(name, compile=False)
- pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
- pipe.compile()
- return pipe
-
-pipes = {
- "Torch fp32": "svjack/Stable-Diffusion-Pokemon-en", #"valhalla/sd-pokemon-model"
- "OpenVINO fp32": "OpenVINO/stable-diffusion-pokemons-fp32", #"OpenVINO/stable-diffusion-pokemons-valhalla-fp32"
- "OpenVINO 8-bit quantized": "OpenVINO/stable-diffusion-pokemons-quantized-aggressive", #"OpenVINO/stable-diffusion-pokemons-valhalla-quantized-agressive"
- "OpenVINO merged and quantized": "OpenVINO/stable-diffusion-pokemons-tome-quantized-aggressive" #"OpenVINO/stable-diffusion-pokemons-valhalla-tome-quantized-agressive"
-}
-
-# prefetch pipelines on start
-for v in pipes.values():
- pipe = create_pipeline(v)
- del pipe
- gc.collect()
-
-print((subprocess.check_output("lscpu", shell=True).strip()).decode())
-
-def generate(prompt, option, seed):
- pipe = create_pipeline(pipes[option])
- set_seed(int(seed))
- start_time = time.time()
- if "Torch" in option:
- output = pipe(prompt, num_inference_steps=50, output_type="pil", height=512, width=512)
- else:
- output = pipe(prompt, num_inference_steps=50, output_type="pil")
- elapsed_time = time.time() - start_time
- return (output.images[0], "{:10.4f}".format(elapsed_time))
-
-examples = ["cartoon bird",
- "a drawing of a green pokemon with red eyes",
- "plant pokemon in jungle"]
-
-model_options = [option for option in pipes.keys()]
-
-gr.Interface(
- fn=generate,
- inputs=[gr.inputs.Textbox(default="cartoon bird", label="Prompt", lines=1),
- gr.inputs.Dropdown(choices=model_options, default=model_options[-1], label="Model version"),
- gr.inputs.Textbox(default="42", label="Seed", lines=1)
- ],
- outputs=[gr.outputs.Image(type="pil", label="Generated Image"), gr.outputs.Textbox(label="Inference time")],
- title="OpenVINO-optimized Stable Diffusion",
- description="This is the Optimum-based demo for NNCF-optimized Stable Diffusion pipeline trained on 'lambdalabs/pokemon-blip-captions' dataset and running with OpenVINO.\n"
- "The pipeline is run using 8 vCPUs (4 cores) only.",
- theme="huggingface",
-).launch()
\ No newline at end of file
diff --git a/spaces/Amon1/ChatGPTForAcadamic/show_math.py b/spaces/Amon1/ChatGPTForAcadamic/show_math.py
deleted file mode 100644
index 80fa881d1c2ace5813f75b5d8a19ca056a8bfa4f..0000000000000000000000000000000000000000
--- a/spaces/Amon1/ChatGPTForAcadamic/show_math.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# This program is written by: https://github.com/polarwinkel/mdtex2html
-
-from latex2mathml.converter import convert as tex2mathml
-import re
-
-incomplete = '⚠formula incomplete'
-convError = '⚠LaTeX-convert-error'
-
-def convert(mdtex, extensions=[], splitParagraphs=True):
- ''' converts recursively the Markdown-LaTeX-mixture to HTML with MathML '''
- found = False
- # handle all paragraphs separately (prevents aftereffects)
- if splitParagraphs:
- parts = re.split("\n\n", mdtex)
- result = ''
- for part in parts:
- result += convert(part, extensions, splitParagraphs=False)
- return result
- # find first $$-formula:
- parts = re.split('\${2}', mdtex, 2)
- if len(parts)>1:
- found = True
- result = convert(parts[0], extensions, splitParagraphs=False)+'\n'
- try:
- result += 'Transform your boring ideas into creative masterpieces with just one click! Enter a spark of inspiration and let the "Magic Prompt" button work its magic. -
-'+tex2mathml(parts[1])+'
\n'
- except:
- result += ''+convError+'
'
- if len(parts)==3:
- result += convert(parts[2], extensions, splitParagraphs=False)
- else:
- result += ''+incomplete+'
'
- # else find first $-formulas:
- else:
- parts = re.split('\${1}', mdtex, 2)
- if len(parts)>1 and not found:
- found = True
- try:
- mathml = tex2mathml(parts[1])
- except:
- mathml = convError
- if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
- parts[0]=parts[0]+''
- if len(parts)==3:
- result = convert(parts[0]+mathml+parts[2], extensions, splitParagraphs=False)
- else:
- result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
- # else find first \[..\]-equation:
- else:
- parts = re.split(r'\\\[', mdtex, 1)
- if len(parts)>1 and not found:
- found = True
- result = convert(parts[0], extensions, splitParagraphs=False)+'\n'
- parts = re.split(r'\\\]', parts[1], 1)
- try:
- result += ''+tex2mathml(parts[0])+'
\n'
- except:
- result += ''+convError+'
'
- if len(parts)==2:
- result += convert(parts[1], extensions, splitParagraphs=False)
- else:
- result += ''+incomplete+'
'
- # else find first \(..\)-equation:
- else:
- parts = re.split(r'\\\(', mdtex, 1)
- if len(parts)>1 and not found:
- found = True
- subp = re.split(r'\\\)', parts[1], 1)
- try:
- mathml = tex2mathml(subp[0])
- except:
- mathml = convError
- if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
- parts[0]=parts[0]+''
- if len(subp)==2:
- result = convert(parts[0]+mathml+subp[1], extensions, splitParagraphs=False)
- else:
- result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
- if not found:
- result = mdtex
- return result
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/stylegan2/op/fused_bias_act.cpp b/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/stylegan2/op/fused_bias_act.cpp
deleted file mode 100644
index 02be898f970bcc8ea297867fcaa4e71b24b3d949..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/stylegan2/op/fused_bias_act.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-#include
-
-
-
-
-
-
-Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
CreativeML Open RAIL-M
dated August 22, 2022
Section I: PREAMBLE
Multimodal generative models are being widely adopted and used, and have
the potential to transform the way artists, among other individuals,
conceive and benefit from AI or ML technologies as a tool for content
creation.
Notwithstanding the current and potential benefits that these artifacts
can bring to society at large, there are also concerns about potential
misuses of them, either due to their technical limitations or ethical
considerations.
In short, this license strives for both the open and responsible
downstream use of the accompanying model. When it comes to the open
character, we took inspiration from open source permissive licenses
regarding the grant of IP rights. Referring to the downstream responsible
use, we added use-based restrictions not permitting the use of the Model
in very specific scenarios, in order for the licensor to be able to
enforce the license in case potential misuses of the Model may occur. At
the same time, we strive to promote open and responsible research on
generative models for art and content generation.
Even though downstream derivative versions of the model could be released
under different licensing terms, the latter will always have to include -
at minimum - the same use-based restrictions as the ones in the original
license (this license). We believe in the intersection between open and
responsible AI development; thus, this License aims to strike a balance
between both in order to enable responsible open-science in the field of
AI.
This License governs the use of the model (and its derivatives) and is
informed by the model card associated with the model.
NOW THEREFORE, You and Licensor agree as follows:
1. Definitions
- "License" means the terms and conditions for use, reproduction, and
Distribution as defined in this document.
- "Data" means a collection of information and/or content extracted from
the dataset used with the Model, including to train, pretrain, or
otherwise evaluate the Model. The Data is not licensed under this
License.
- "Output" means the results of operating a Model as embodied in
informational content resulting therefrom.
- "Model" means any accompanying machine-learning based assemblies
(including checkpoints), consisting of learnt weights, parameters
(including optimizer states), corresponding to the model architecture as
embodied in the Complementary Material, that have been trained or tuned,
in whole or in part on the Data, using the Complementary Material.
- "Derivatives of the Model" means all modifications to the Model, works
based on the Model, or any other model which is created or initialized by
transfer of patterns of the weights, parameters, activations or output of
the Model, to the other model, in order to cause the other model to
perform similarly to the Model, including - but not limited to -
distillation methods entailing the use of intermediate data
representations or methods based on the generation of synthetic data by
the Model for training the other model.
- "Complementary Material" means the accompanying source code and scripts
used to define, run, load, benchmark or evaluate the Model, and used to
prepare data for training or evaluation, if any. This includes any
accompanying documentation, tutorials, examples, etc, if any.
- "Distribution" means any transmission, reproduction, publication or
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including providing the Model as a hosted service made available by
electronic or other remote means - e.g. API-based or web access.
- "Licensor" means the copyright owner or entity authorized by the
copyright owner that is granting the License, including the persons or
entities that may have rights in the Model and/or distributing the Model.
- "You" (or "Your") means an individual or Legal Entity exercising
permissions granted by this License and/or making use of the Model for
whichever purpose and in any field of use, including usage of the Model
in an end-use application - e.g. chatbot, translator, image generator.
- "Third Parties" means individuals or legal entities that are not under
common control with Licensor or You.
- "Contribution" means any work of authorship, including the original
version of the Model and any modifications or additions to that Model or
Derivatives of the Model thereof, that is intentionally submitted to
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and issue tracking systems that are managed by, or on behalf of, the
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designated in writing by the copyright owner as "Not a Contribution."
- "Contributor" means Licensor and any individual or Legal Entity on
behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Model.
Section II: INTELLECTUAL PROPERTY RIGHTS
Both copyright and patent grants apply to the Model, Derivatives of the
Model and Complementary Material. The Model and Derivatives of the Model
are subject to additional terms as described in Section III.
2. Grant of Copyright License. Subject to the terms and conditions of
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worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright
license to reproduce, prepare, publicly display, publicly perform,
sublicense, and distribute the Complementary Material, the Model, and
Derivatives of the Model.
3. Grant of Patent License. Subject to the terms and conditions of this
License and where and as applicable, each Contributor hereby grants to
You a perpetual, worldwide, non-exclusive, no-charge, royalty-free,
irrevocable (except as stated in this paragraph) patent license to make,
have made, use, offer to sell, sell, import, and otherwise transfer the
Model and the Complementary Material, where such license applies only to
those patent claims licensable by such Contributor that are necessarily
infringed by their Contribution(s) alone or by combination of their
Contribution(s) with the Model to which such Contribution(s) was
submitted. If You institute patent litigation against any entity
(including a cross-claim or counterclaim in a lawsuit) alleging that the
Model and/or Complementary Material or a Contribution incorporated within
the Model and/or Complementary Material constitutes direct or
contributory patent infringement, then any patent licenses granted to You
under this License for the Model and/or Work shall terminate as of the
date such litigation is asserted or filed.
Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
4. Distribution and Redistribution. You may host for Third Party remote
access purposes (e.g. software-as-a-service), reproduce and distribute
copies of the Model or Derivatives of the Model thereof in any medium,
with or without modifications, provided that You meet the following
conditions:
Use-based restrictions as referenced in paragraph 5 MUST be included as
an enforceable provision by You in any type of legal agreement (e.g. a
license) governing the use and/or distribution of the Model or
Derivatives of the Model, and You shall give notice to subsequent users
You Distribute to, that the Model or Derivatives of the Model are subject
to paragraph 5. This provision does not apply to the use of Complementary
Material.
You must give any Third Party recipients of the Model or Derivatives of
the Model a copy of this License;
You must cause any modified files to carry prominent notices stating that
You changed the files;
You must retain all copyright, patent, trademark, and attribution notices
excluding those notices that do not pertain to any part of the Model,
Derivatives of the Model.
You may add Your own copyright statement to Your modifications and may
provide additional or different license terms and conditions - respecting
paragraph 4.a. - for use, reproduction, or Distribution of Your
modifications, or for any such Derivatives of the Model as a whole,
provided Your use, reproduction, and Distribution of the Model otherwise
complies with the conditions stated in this License.
5. Use-based restrictions. The restrictions set forth in Attachment A are
considered Use-based restrictions. Therefore You cannot use the Model and
the Derivatives of the Model for the specified restricted uses. You may
use the Model subject to this License, including only for lawful purposes
and in accordance with the License. Use may include creating any content
with, finetuning, updating, running, training, evaluating and/or
reparametrizing the Model. You shall require all of Your users who use
the Model or a Derivative of the Model to comply with the terms of this
paragraph (paragraph 5).
6. The Output You Generate. Except as set forth herein, Licensor claims
no rights in the Output You generate using the Model. You are accountable
for the Output you generate and its subsequent uses. No use of the output
can contravene any provision as stated in the License.
Section IV: OTHER PROVISIONS
7. Updates and Runtime Restrictions. To the maximum extent permitted by
law, Licensor reserves the right to restrict (remotely or otherwise)
usage of the Model in violation of this License, update the Model through
electronic means, or modify the Output of the Model based on updates. You
shall undertake reasonable efforts to use the latest version of the
Model.
8. Trademarks and related. Nothing in this License permits You to make
use of Licensors’ trademarks, trade names, logos or to otherwise suggest
endorsement or misrepresent the relationship between the parties; and any
rights not expressly granted herein are reserved by the Licensors.
9. Disclaimer of Warranty. Unless required by applicable law or agreed to
in writing, Licensor provides the Model and the Complementary Material
(and each Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied,
including, without limitation, any warranties or conditions of TITLE,
NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE.
You are solely responsible for determining the appropriateness of using
or redistributing the Model, Derivatives of the Model, and the
Complementary Material and assume any risks associated with Your exercise
of permissions under this License.
10. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise, unless
required by applicable law (such as deliberate and grossly negligent
acts) or agreed to in writing, shall any Contributor be liable to You for
damages, including any direct, indirect, special, incidental, or
consequential damages of any character arising as a result of this
License or out of the use or inability to use the Model and the
Complementary Material (including but not limited to damages for loss of
goodwill, work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor has been
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11. Accepting Warranty or Additional Liability. While redistributing the
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You may choose to offer, and charge a fee for, acceptance of support,
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12. If any provision of this License is held to be invalid, illegal or
unenforceable, the remaining provisions shall be unaffected thereby and
remain valid as if such provision had not been set forth herein.
END OF TERMS AND CONDITIONS
Attachment A
Use Restrictions
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national, federal, state, local
or international law or regulation;
- For the purpose of exploiting, harming or attempting to exploit or harm
minors in any way;
- To generate or disseminate verifiably false information and/or content
with the purpose of harming others;
- To generate or disseminate personal identifiable information that can
be used to harm an individual;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an
individual’s legal rights or otherwise creates or modifies a binding,
enforceable obligation;
- For any use intended to or which has the effect of discriminating
against or harming individuals or groups based on online or offline
social behavior or known or predicted personal or personality
characteristics;
- To exploit any of the vulnerabilities of a specific group of persons
based on their age, social, physical or mental characteristics, in order
to materially distort the behavior of a person pertaining to that group
in a manner that causes or is likely to cause that person or another
person physical or psychological harm;
- For any use intended to or which has the effect of discriminating
against individuals or groups based on legally protected characteristics
or categories;
- To provide medical advice and medical results interpretation;
- To generate or disseminate information for the purpose to be used for
administration of justice, law enforcement, immigration or asylum
processes, such as predicting an individual will commit fraud/crime
commitment (e.g. by text profiling, drawing causal relationships between
assertions made in documents, indiscriminate and arbitrarily-targeted
use).
-
-
-
-
diff --git a/spaces/Apex-X/Tm/roop/face_analyser.py b/spaces/Apex-X/Tm/roop/face_analyser.py
deleted file mode 100644
index 9c0afe458763edb22dc2332f527dfdba48575b1d..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/Tm/roop/face_analyser.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import threading
-from typing import Any
-import insightface
-
-import roop.globals
-from roop.typing import Frame
-
-FACE_ANALYSER = None
-THREAD_LOCK = threading.Lock()
-
-
-def get_face_analyser() -> Any:
- global FACE_ANALYSER
-
- with THREAD_LOCK:
- if FACE_ANALYSER is None:
- FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=roop.globals.execution_providers)
- FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
- return FACE_ANALYSER
-
-
-def get_one_face(frame: Frame) -> Any:
- face = get_face_analyser().get(frame)
- try:
- return min(face, key=lambda x: x.bbox[0])
- except ValueError:
- return None
-
-
-def get_many_faces(frame: Frame) -> Any:
- try:
- return get_face_analyser().get(frame)
- except IndexError:
- return None
diff --git a/spaces/Artrajz/vits-simple-api/utils/classify_language.py b/spaces/Artrajz/vits-simple-api/utils/classify_language.py
deleted file mode 100644
index 608a880442e4e173cb46b4befaeaf94e1896f4f9..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/utils/classify_language.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from config import LANGUAGE_IDENTIFICATION_LIBRARY
-
-module = LANGUAGE_IDENTIFICATION_LIBRARY.lower()
-
-langid_languages = ["af", "am", "an", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "dz", "el",
- "en", "eo", "es", "et", "eu", "fa", "fi", "fo", "fr", "ga", "gl", "gu", "he", "hi", "hr", "ht", "hu", "hy",
- "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg",
- "mk", "ml", "mn", "mr", "ms", "mt", "nb", "ne", "nl", "nn", "no", "oc", "or", "pa", "pl", "ps", "pt", "qu",
- "ro", "ru", "rw", "se", "si", "sk", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk",
- "ur", "vi", "vo", "wa", "xh", "zh", "zu"]
-
-
-def classify_language(text: str, target_languages: list = None) -> str:
- if module == "fastlid" or module == "fasttext":
- from fastlid import fastlid, supported_langs
- classifier = fastlid
- if target_languages != None:
- target_languages = [lang for lang in target_languages if lang in supported_langs]
- fastlid.set_languages = target_languages
- elif module == "langid":
- import langid
- classifier = langid.classify
- if target_languages != None:
- target_languages = [lang for lang in target_languages if lang in langid_languages]
- langid.set_languages(target_languages)
- else:
- raise ValueError(f"Wrong LANGUAGE_IDENTIFICATION_LIBRARY in config.py")
-
- lang = classifier(text)[0]
-
- return lang
-
-
-def classify_zh_ja(text: str) -> str:
- for idx, char in enumerate(text):
- unicode_val = ord(char)
-
- # 检测日语字符
- if 0x3040 <= unicode_val <= 0x309F or 0x30A0 <= unicode_val <= 0x30FF:
- return "ja"
-
- # 检测汉字字符
- if 0x4E00 <= unicode_val <= 0x9FFF:
- # 检查周围的字符
- next_char = text[idx + 1] if idx + 1 < len(text) else None
-
- if next_char and (0x3040 <= ord(next_char) <= 0x309F or 0x30A0 <= ord(next_char) <= 0x30FF):
- return "ja"
-
- return "zh"
-
-
-if __name__ == "__main__":
- text = "这是一个测试文本"
- print(classify_language(text))
- print(classify_zh_ja(text)) # "zh"
-
- text = "これはテストテキストです"
- print(classify_language(text))
- print(classify_zh_ja(text)) # "ja"
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/idna/package_data.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/idna/package_data.py
deleted file mode 100644
index 8501893bd153b7216524084cad23e90aeac0b1f8..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/idna/package_data.py
+++ /dev/null
@@ -1,2 +0,0 @@
-__version__ = '3.4'
-
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/clean.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/clean.py
deleted file mode 100644
index b731b60609621ad822aa989ffa1f711ec2932278..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/clean.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""distutils.command.clean
-
-Implements the Distutils 'clean' command."""
-
-# contributed by Bastian Kleineidam
--
Auto Clicker para Clicker Heroes Descargar
-Si eres un fan de los juegos de clickers ociosos, es posible que hayas escuchado o jugado Clicker Heroes, un juego popular donde matas monstruos, mejoras héroes, encuentras tesoros y matas jefes. ¿Pero sabías que puedes mejorar tu experiencia de juego usando un auto clicker para héroes clickers? En este artículo, explicaremos qué es un clicker automático, cómo usarlo para héroes clickers y cuáles son los beneficios de usarlo.
-auto clicker for clicker heroes download
DOWNLOAD ☆☆☆ https://bltlly.com/2v6KI0
-
¿Qué es Auto Clicker?
-Un auto clicker es un programa que le permite configurar y automatizar el click de un mouse en la pantalla de su computadora. Un clicker automático no solo sigue el cursor, pero a menudo tiene soporte para doble y triple clic, teclas de acceso rápido que funcionan incluso en segundo plano, ajustes automáticos ahorra, y más.
-¿Cómo usar el Auto Clicker?
-Para usar un auto clicker, debes seguir estos pasos:
--
-
- Visite AutoClickers.org para encontrar las diferentes opciones de dispositivos disponibles y descargar el que se adapte a sus necesidades. -
- Ejecute el instalador y siga las instrucciones para completar la instalación. -
- Abra el auto clicker haciendo clic en el icono o en el acceso directo del escritorio. -
- Elija el atajo de teclado que desea utilizar para iniciar o dejar de hacer clic y haga clic en "Aplicar". -
- Seleccione el área en la pantalla donde desea que haga clic el clicker automático. Puede hacer esto arrastrando el cursor del ratón o usando las coordenadas. -
- Ajuste la velocidad de clic y la duración moviendo los controles deslizantes o introduciendo los valores. También puede elegir el tipo de clic (izquierda, derecha, centro) y el número de clics. -
- Pulse el atajo de teclado para iniciar el auto clic. Puede ver el número de clics y el tiempo transcurrido en la ventana del auto clicker. - -
Beneficios de Auto Clicker
-Usar un auto clicker puede tener muchas ventajas, como:
--
-
- Ahorra tiempo y esfuerzo: No tienes que hacer clic manualmente en la pantalla repetidamente, lo que puede ser agotador y aburrido. Puedes dejar que el auto clicker haga el trabajo por ti mientras te enfocas en otras tareas o te relajas. -
- Reducir errores: No tienes que preocuparte por perder un clic o hacer clic en el lugar equivocado. El clicker automático hará clic de forma precisa y consistente de acuerdo con su configuración. -
- Mejore la experiencia de juego: Puede disfrutar jugando juegos que requieren mucho clic sin frustrarse o perder interés. También puede mejorar su rendimiento de juego y puntuación mediante el uso de un auto clicker. -
- Personalizar las opciones de clic: Puede ajustar la velocidad de clic, duración, área, tipo y número de acuerdo a sus preferencias y necesidades. También puede crear diferentes perfiles para diferentes juegos o tareas y cambiar entre ellos fácilmente. -
¿Qué es Clicker Heroes?
-Clicker Heroes es uno de los juegos de clickers inactivos más populares en la web. Fue lanzado en 2014 por Playsaurus, un estudio de juegos independiente con sede en California. El juego ha sido jugado por millones de personas en todo el mundo y ha recibido críticas positivas de críticos y jugadores por igual.
- -Cómo jugar Clicker Heroes?
-El modo de juego de Clicker Heroes es simple pero adictivo. Aquí están las instrucciones básicas:
--
-
- Haga clic en monstruos para atacarlos y recoger el oro de ellos. -
- Usa el oro para subir de nivel a tus héroes, que te ayudarán a luchar contra los monstruos automáticamente. -
- Compra mejoras y habilidades para tus héroes para hacerlos más fuertes y desbloquear nuevas habilidades. -
- Progresa a través de zonas y mundos, cada uno con diferentes monstruos y fondos. - -
Consejos y trucos para Clicker Heroes
-Para aprovechar al máximo Clicker Heroes, debes seguir estos consejos y trucos:
--
-
- Usa antiguos y extraños, que son personajes especiales que pueden aumentar tu progreso al darte varios bonos y efectos. Puedes comprar antiguos con almas de héroe, que obtienes de ascendente, y extraños con almas antiguas, que obtienes de trascender. -
- Ascender y trascender regularmente, que son formas de restablecer su juego con beneficios adicionales. Ascender les dará almas de héroes basadas en su zona más alta alcanzada, mientras que trascender les dará almas antiguas basadas en sus almas de héroes totales sacrificadas. Ambas acciones aumentarán tu poder general y acelerarán tu progreso. -
- Únete a clanes y redadas, que son características multijugador que te permiten cooperar con otros jugadores y obtener más recompensas. Puedes unirte a un clan introduciendo su nombre o creando el tuyo propio, y participar en incursiones luchando contra inmortales con los miembros de tu clan. Puedes obtener almas de héroe, rubíes y monedas de clan de las redadas. -
- Usa mercenarios y misiones, que son características adicionales que pueden ayudarte a obtener recursos adicionales. Puedes contratar mercenarios con rubíes, que son la moneda premium del juego, y enviarlos en misiones para obtener oro, almas de héroes, rubíes, reliquias o habilidades. Puedes tener hasta cinco mercenarios a la vez. -
¿Por qué usar Auto Clicker para Clicker Heroes?
-Como puedes ver, Clicker Heroes es un juego que involucra muchos clics. Si bien esto puede ser divertido al principio, también puede volverse tedioso y aburrido después de un tiempo. Es por eso que usar un clicker automático para héroes clickers puede ser una gran idea. Aquí hay algunas razones por las que:
-Los mejores clickers automáticos para Clicker Heroes
- --
-
- OP Auto Clicker: Este es un clicker automático gratuito y fácil de usar que te permite elegir el intervalo de clic, el tipo y la ubicación. También puede establecer teclas de acceso rápido, aleatorizar clics y grabar y reproducir clics. Puede descargarlo desde here. -
- GS Auto Clicker: Este es otro clicker automático gratuito y simple que te permite configurar la tasa de clics, el número y la ubicación. También puede usar teclas de acceso rápido, guardar y cargar la configuración y usar la opción de registro para hacer clic en varios lugares. Puede descargarlo desde aquí. -
- Speed Auto Clicker: Este es un rápido y potente clicker automático que puede alcanzar hasta 50000 clicks por segundo. Puede ajustar la velocidad, el tipo y la ubicación de los clics, así como usar teclas de acceso rápido, aleatorizar los clics y establecer un límite de clics. Puede descargarlo desde aquí. -
- Murgee Auto Clicker: Este es un clicker automático de pago pero versátil que ofrece muchas características y opciones. Puede personalizar el intervalo de clic, el tipo, la ubicación y la duración, así como usar teclas de acceso rápido, programar clics y crear macros. Puede descargarlo desde aquí. -
¿Cómo configurar los clickers automáticos para los héroes del clicker?
-Para configurar los clickers automáticos para los héroes clicker, debe seguir estas directrices:
--
-
- Arrastre y suelte el icono del clicker automático al área deseada en la pantalla del juego. Puedes colocarlo en el área enemiga, los botones de nivel de héroe, las habilidades o el botón de compra de mejoras disponibles. -
- Elija el número de clickers automáticos que desea utilizar para cada tarea. Puedes tener hasta 99 clickers automáticos en total, pero solo uno por cada botón de nivel de héroe, botón de habilidad o botón de compra de mejoras disponibles. - -
- Retire los clickers automáticos haciendo clic en el botón X en la esquina superior derecha de cada icono. También puede arrastrar y soltar de nuevo a la piscina de auto clickers en el lado derecho de la pantalla. -
Conclusión
-En conclusión, auto clicker es una herramienta útil para jugar clicker héroes, ya que puede automatizar el proceso de clic y mejorar su rendimiento de juego. Hay muchos clickers automáticos disponibles para descargar, cada uno con sus propias características y ventajas. Para usar clickers automáticos para los héroes clickers, necesitas configurarlos correctamente y asignarlos a diferentes tareas. Al hacerlo, puedes disfrutar jugando a clicker heroes sin cansarte o aburrirte.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre el clicker automático para los héroes de clicker:
--
-
- ¿Cuál es el mejor clicker automático para los héroes clicker? No hay una respuesta definitiva a esta pregunta, ya que diferentes clickers automáticos pueden adaptarse a diferentes preferencias y necesidades. Sin embargo, algunos de los más populares y recomendados son OP Auto Clicker, GS Auto Clicker, Speed Auto Clicker y Murgee Auto Clicker. -
- ¿Qué tan rápido puede hacer clic un auto clicker? La velocidad de un auto clicker depende de su configuración y características. Algunos clickers automáticos pueden alcanzar hasta 50000 clicks por segundo, mientras que otros solo pueden llegar hasta 100 clicks por segundo. Puede ajustar la velocidad de su auto clicker cambiando su intervalo o tasa. -
- Está usando un clicker automático de engaño? Esto depende de su perspectiva y opinión. Algunas personas pueden considerar el uso de un auto clicker como trampa, ya que le da una ventaja injusta sobre otros jugadores que no lo utilizan. Otros pueden verlo como una forma legítima de jugar el juego de manera más eficiente y conveniente. - -
- ¿Cuántos clickers automáticos necesito para héroes clickers? El número de clickers automáticos que necesitas para los clickers depende de tus objetivos y estrategias. En general, usted debe tener al menos un auto clicker en el área enemiga para atacar más rápido, y un auto clicker en el botón comprar mejoras disponibles para subir de nivel héroes y comprar mejoras automáticamente. También puede tener más clickers automáticos en los botones de nivel de héroe o las habilidades para activarlos más a menudo. -
Espero que este artículo te haya ayudado a entender más sobre el clicker automático para la descarga de héroes clicker. Si usted tiene alguna pregunta o comentario, por favor no dude en dejar un comentario a continuación. Gracias por leer y feliz clic!
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/adapters.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/adapters.py deleted file mode 100644 index f68f7d467530845447278f6c0ad104b4beca9531..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/adapters.py +++ /dev/null @@ -1,584 +0,0 @@ -""" -requests.adapters -~~~~~~~~~~~~~~~~~ - -This module contains the transport adapters that Requests uses to define -and maintain connections. -""" - -import os.path -import socket # noqa: F401 - -from pip._vendor.urllib3.exceptions import ClosedPoolError, ConnectTimeoutError -from pip._vendor.urllib3.exceptions import HTTPError as _HTTPError -from pip._vendor.urllib3.exceptions import InvalidHeader as _InvalidHeader -from pip._vendor.urllib3.exceptions import ( - LocationValueError, - MaxRetryError, - NewConnectionError, - ProtocolError, -) -from pip._vendor.urllib3.exceptions import ProxyError as _ProxyError -from pip._vendor.urllib3.exceptions import ReadTimeoutError, ResponseError -from pip._vendor.urllib3.exceptions import SSLError as _SSLError -from pip._vendor.urllib3.poolmanager import PoolManager, proxy_from_url -from pip._vendor.urllib3.response import HTTPResponse -from pip._vendor.urllib3.util import Timeout as TimeoutSauce -from pip._vendor.urllib3.util import parse_url -from pip._vendor.urllib3.util.retry import Retry - -from .auth import _basic_auth_str -from .compat import basestring, urlparse -from .cookies import extract_cookies_to_jar -from .exceptions import ( - ConnectionError, - ConnectTimeout, - InvalidHeader, - InvalidProxyURL, - InvalidSchema, - InvalidURL, - ProxyError, - ReadTimeout, - RetryError, - SSLError, -) -from .models import Response -from .structures import CaseInsensitiveDict -from .utils import ( - DEFAULT_CA_BUNDLE_PATH, - extract_zipped_paths, - get_auth_from_url, - get_encoding_from_headers, - prepend_scheme_if_needed, - select_proxy, - urldefragauth, -) - -try: - from pip._vendor.urllib3.contrib.socks import SOCKSProxyManager -except ImportError: - - def SOCKSProxyManager(*args, **kwargs): - raise InvalidSchema("Missing dependencies for SOCKS support.") - - -DEFAULT_POOLBLOCK = False -DEFAULT_POOLSIZE = 10 -DEFAULT_RETRIES = 0 -DEFAULT_POOL_TIMEOUT = None - - -class BaseAdapter: - """The Base Transport Adapter""" - - def __init__(self): - super().__init__() - - def send( - self, request, stream=False, timeout=None, verify=True, cert=None, proxies=None - ): - """Sends PreparedRequest object. Returns Response object. - - :param request: The :class:`PreparedRequest
constexpr bool that is \c true if \c FunctionObject is
-/// equivalent to \c operator<, and \c false otherwise.
-template OCR translation system based on Tesseract
'''
-
-# 图片路径
-img_dir = "./data"
-
-# 获取tesseract语言列表
-choices = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
-
-
-# 翻译模型选择
-def model_choice(src="en", trg="zh"):
- # https://huggingface.co/Helsinki-NLP/opus-mt-zh-en
- # https://huggingface.co/Helsinki-NLP/opus-mt-en-zh
- model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}" # 模型名称
-
- tokenizer = MarianTokenizer.from_pretrained(model_name) # 分词器
- model = MarianMTModel.from_pretrained(model_name) # 模型
-
- return tokenizer, model
-
-
-# tesseract语言列表转pytesseract语言
-def ocr_lang(lang_list):
- lang_str = ""
- lang_len = len(lang_list)
- if lang_len == 1:
- return lang_list[0]
- else:
- for i in range(lang_len):
- lang_list.insert(lang_len - i, "+")
-
- lang_str = "".join(lang_list[:-1])
- return lang_str
-
-
-# ocr tesseract
-def ocr_tesseract(img, languages):
- ocr_str = pytesseract.image_to_string(img, lang=ocr_lang(languages))
- return ocr_str
-
-
-# 清除
-def clear_content():
- return None
-
-
-# 复制到剪贴板
-def cp_text(input_text):
- # sudo apt-get install xclip
- try:
- pyclip.copy(input_text)
- except Exception as e:
- print("sudo apt-get install xclip")
- print(e)
-
-
-# 清除剪贴板
-def cp_clear():
- pyclip.clear()
-
-
-# 翻译
-def translate(input_text, inputs_transStyle):
- # 参考：https://huggingface.co/docs/transformers/model_doc/marian
- if input_text is None or input_text == "":
- return "System prompt: There is no content to translate!"
-
- # 选择翻译模型
- trans_src, trans_trg = inputs_transStyle.split("-")[0], inputs_transStyle.split("-")[1]
- tokenizer, model = model_choice(trans_src, trans_trg)
-
- translate_text = ""
- input_text_list = input_text.split("\n\n")
-
- translate_text_list_tmp = []
- for i in range(len(input_text_list)):
- if input_text_list[i] != "":
- translate_text_list_tmp.append(input_text_list[i])
-
- for i in range(len(translate_text_list_tmp)):
- translated_sub = model.generate(
- **tokenizer(sent_tokenize(translate_text_list_tmp[i]), return_tensors="pt", truncation=True, padding=True))
- tgt_text_sub = [tokenizer.decode(t, skip_special_tokens=True) for t in translated_sub]
- translate_text_sub = "".join(tgt_text_sub)
- translate_text = translate_text + "\n\n" + translate_text_sub
-
- return translate_text[2:]
-
-
-def main():
-
- with gr.Blocks(css='style.css') as ocr_tr:
- gr.Markdown(OCR_TR_DESCRIPTION)
-
- # -------------- OCR 文字提取 --------------
- with gr.Box():
-
- with gr.Row():
- gr.Markdown("### Step 01: Text Extraction")
-
- with gr.Row():
- with gr.Column():
- with gr.Row():
- inputs_img = gr.Image(image_mode="RGB", source="upload", type="pil", label="image")
- with gr.Row():
- inputs_lang = gr.CheckboxGroup(choices=["chi_sim", "eng"],
- type="value",
- value=['eng'],
- label='language')
-
- with gr.Row():
- clear_img_btn = gr.Button('Clear')
- ocr_btn = gr.Button(value='OCR Extraction', variant="primary")
-
- with gr.Column():
- with gr.Row():
- outputs_text = gr.Textbox(label="Extract content", lines=20)
- with gr.Row():
- inputs_transStyle = gr.Radio(choices=["zh-en", "en-zh"],
- type="value",
- value="zh-en",
- label='translation mode')
- with gr.Row():
- clear_text_btn = gr.Button('Clear')
- translate_btn = gr.Button(value='Translate', variant="primary")
-
- with gr.Row():
- example_list = [["./data/test.png", ["eng"]], ["./data/test02.png", ["eng"]],
- ["./data/test03.png", ["chi_sim"]]]
- gr.Examples(example_list, [inputs_img, inputs_lang], outputs_text, ocr_tesseract, cache_examples=False)
-
- # -------------- 翻译 --------------
- with gr.Box():
-
- with gr.Row():
- gr.Markdown("### Step 02: Translation")
-
- with gr.Row():
- outputs_tr_text = gr.Textbox(label="Translate Content", lines=20)
-
- with gr.Row():
- cp_clear_btn = gr.Button(value='Clear Clipboard')
- cp_btn = gr.Button(value='Copy to clipboard', variant="primary")
-
- # ---------------------- OCR Tesseract ----------------------
- ocr_btn.click(fn=ocr_tesseract, inputs=[inputs_img, inputs_lang], outputs=[
- outputs_text,])
- clear_img_btn.click(fn=clear_content, inputs=[], outputs=[inputs_img])
-
- # ---------------------- 翻译 ----------------------
- translate_btn.click(fn=translate, inputs=[outputs_text, inputs_transStyle], outputs=[outputs_tr_text])
- clear_text_btn.click(fn=clear_content, inputs=[], outputs=[outputs_text])
-
- # ---------------------- 复制到剪贴板 ----------------------
- cp_btn.click(fn=cp_text, inputs=[outputs_tr_text], outputs=[])
- cp_clear_btn.click(fn=cp_clear, inputs=[], outputs=[])
-
- ocr_tr.launch(inbrowser=True)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/mel_processing.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/mel_processing.py
deleted file mode 100644
index 3614150259809983e776d3fed83021decca06a9c..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y.float(), n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/GaenKoki/voicevox/test/test_setting.py b/spaces/GaenKoki/voicevox/test/test_setting.py
deleted file mode 100644
index 494e3095e1e26b74bb70436f5ff317bca26b13c7..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/test/test_setting.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from unittest import TestCase
-
-from voicevox_engine.setting import CorsPolicyMode, Setting, SettingLoader
-
-
-class TestSettingLoader(TestCase):
- def setUp(self):
- self.tmp_dir = TemporaryDirectory()
- self.tmp_dir_path = Path(self.tmp_dir.name)
-
- def test_loading_1(self):
- setting_loader = SettingLoader(Path("not_exist.yaml"))
- settings = setting_loader.load_setting_file()
-
- self.assertEqual(
- settings.dict(),
- {"allow_origin": None, "cors_policy_mode": CorsPolicyMode.localapps},
- )
-
- def test_loading_2(self):
- setting_loader = SettingLoader(
- setting_file_path=Path("test/setting-test-load-1.yaml")
- )
- settings = setting_loader.load_setting_file()
-
- self.assertEqual(
- settings.dict(),
- {"allow_origin": None, "cors_policy_mode": CorsPolicyMode.localapps},
- )
-
- def test_loading_3(self):
- setting_loader = SettingLoader(
- setting_file_path=Path("test/setting-test-load-2.yaml")
- )
- settings = setting_loader.load_setting_file()
-
- self.assertEqual(
- settings.dict(),
- {"allow_origin": None, "cors_policy_mode": "all"},
- )
-
- def test_loading_4(self):
- setting_loader = SettingLoader(
- setting_file_path=Path("test/setting-test-load-3.yaml")
- )
- settings = setting_loader.load_setting_file()
-
- self.assertEqual(
- settings.dict(),
- {
- "allow_origin": "192.168.254.255 192.168.255.255",
- "cors_policy_mode": CorsPolicyMode.localapps,
- },
- )
-
- def test_dump(self):
- setting_loader = SettingLoader(
- setting_file_path=Path(self.tmp_dir_path / "setting-test-dump.yaml")
- )
- settings = Setting(cors_policy_mode=CorsPolicyMode.localapps)
- setting_loader.dump_setting_file(settings)
-
- self.assertTrue(setting_loader.setting_file_path.is_file())
- self.assertEqual(
- setting_loader.load_setting_file().dict(),
- {"allow_origin": None, "cors_policy_mode": CorsPolicyMode.localapps},
- )
-
- def tearDown(self):
- self.tmp_dir.cleanup()
diff --git a/spaces/GaenKoki/voicevox/test/test_synthesis_engine.py b/spaces/GaenKoki/voicevox/test/test_synthesis_engine.py
deleted file mode 100644
index b1a21741623145d1e9833b9ebf238c16d1e86edc..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/test/test_synthesis_engine.py
+++ /dev/null
@@ -1,654 +0,0 @@
-import math
-from copy import deepcopy
-from random import random
-from typing import Union
-from unittest import TestCase
-from unittest.mock import Mock
-
-import numpy
-
-from voicevox_engine.acoustic_feature_extractor import OjtPhoneme
-from voicevox_engine.model import AccentPhrase, AudioQuery, Mora
-from voicevox_engine.synthesis_engine import SynthesisEngine
-
-# TODO: import from voicevox_engine.synthesis_engine.mora
-from voicevox_engine.synthesis_engine.synthesis_engine import (
- mora_phoneme_list,
- pre_process,
- split_mora,
- to_flatten_moras,
- to_phoneme_data_list,
- unvoiced_mora_phoneme_list,
-)
-
-
-def yukarin_s_mock(length: int, phoneme_list: numpy.ndarray, speaker_id: numpy.ndarray):
- result = []
- # mockとしての適当な処理、特に意味はない
- for i in range(length):
- result.append(float(phoneme_list[i] * 0.5 + speaker_id))
- return numpy.array(result)
-
-
-def yukarin_sa_mock(
- length: int,
- vowel_phoneme_list: numpy.ndarray,
- consonant_phoneme_list: numpy.ndarray,
- start_accent_list: numpy.ndarray,
- end_accent_list: numpy.ndarray,
- start_accent_phrase_list: numpy.ndarray,
- end_accent_phrase_list: numpy.ndarray,
- speaker_id: numpy.ndarray,
-):
- result = []
- # mockとしての適当な処理、特に意味はない
- for i in range(length):
- result.append(
- float(
- (
- vowel_phoneme_list[0][i]
- + consonant_phoneme_list[0][i]
- + start_accent_list[0][i]
- + end_accent_list[0][i]
- + start_accent_phrase_list[0][i]
- + end_accent_phrase_list[0][i]
- )
- * 0.5
- + speaker_id
- )
- )
- return numpy.array(result)[numpy.newaxis]
-
-
-def decode_mock(
- length: int,
- phoneme_size: int,
- f0: numpy.ndarray,
- phoneme: numpy.ndarray,
- speaker_id: Union[numpy.ndarray, int],
-):
- result = []
- # mockとしての適当な処理、特に意味はない
- for i in range(length):
- # decode forwardはデータサイズがlengthの256倍になるのでとりあえず256回データをresultに入れる
- for _ in range(256):
- result.append(
- float(
- f0[i][0] * (numpy.where(phoneme[i] == 1)[0] / phoneme_size)
- + speaker_id
- )
- )
- return numpy.array(result)
-
-
-class MockCore:
- yukarin_s_forward = Mock(side_effect=yukarin_s_mock)
- yukarin_sa_forward = Mock(side_effect=yukarin_sa_mock)
- decode_forward = Mock(side_effect=decode_mock)
-
- def metas(self):
- return ""
-
- def supported_devices(self):
- return ""
-
- def is_model_loaded(self, speaker_id):
- return True
-
-
-class TestSynthesisEngine(TestCase):
- def setUp(self):
- super().setUp()
- self.str_list_hello_hiho = (
- "sil k o N n i ch i w a pau h i h o d e s U sil".split()
- )
- self.phoneme_data_list_hello_hiho = [
- OjtPhoneme(phoneme=p, start=i, end=i + 1)
- for i, p in enumerate(
- "pau k o N n i ch i w a pau h i h o d e s U pau".split()
- )
- ]
- self.accent_phrases_hello_hiho = [
- AccentPhrase(
- moras=[
- Mora(
- text="コ",
- consonant="k",
- consonant_length=0.0,
- vowel="o",
- vowel_length=0.0,
- pitch=0.0,
- ),
- Mora(
- text="ン",
- consonant=None,
- consonant_length=None,
- vowel="N",
- vowel_length=0.0,
- pitch=0.0,
- ),
- Mora(
- text="ニ",
- consonant="n",
- consonant_length=0.0,
- vowel="i",
- vowel_length=0.0,
- pitch=0.0,
- ),
- Mora(
- text="チ",
- consonant="ch",
- consonant_length=0.0,
- vowel="i",
- vowel_length=0.0,
- pitch=0.0,
- ),
- Mora(
- text="ワ",
- consonant="w",
- consonant_length=0.0,
- vowel="a",
- vowel_length=0.0,
- pitch=0.0,
- ),
- ],
- accent=5,
- pause_mora=Mora(
- text="、",
- consonant=None,
- consonant_length=None,
- vowel="pau",
- vowel_length=0.0,
- pitch=0.0,
- ),
- ),
- AccentPhrase(
- moras=[
- Mora(
- text="ヒ",
- consonant="h",
- consonant_length=0.0,
- vowel="i",
- vowel_length=0.0,
- pitch=0.0,
- ),
- Mora(
- text="ホ",
- consonant="h",
- consonant_length=0.0,
- vowel="o",
- vowel_length=0.0,
- pitch=0.0,
- ),
- Mora(
- text="デ",
- consonant="d",
- consonant_length=0.0,
- vowel="e",
- vowel_length=0.0,
- pitch=0.0,
- ),
- Mora(
- text="ス",
- consonant="s",
- consonant_length=0.0,
- vowel="U",
- vowel_length=0.0,
- pitch=0.0,
- ),
- ],
- accent=1,
- pause_mora=None,
- ),
- ]
- core = MockCore()
- self.yukarin_s_mock = core.yukarin_s_forward
- self.yukarin_sa_mock = core.yukarin_sa_forward
- self.decode_mock = core.decode_forward
- self.synthesis_engine = SynthesisEngine(
- core=core,
- )
-
- def test_to_flatten_moras(self):
- flatten_moras = to_flatten_moras(self.accent_phrases_hello_hiho)
- self.assertEqual(
- flatten_moras,
- self.accent_phrases_hello_hiho[0].moras
- + [self.accent_phrases_hello_hiho[0].pause_mora]
- + self.accent_phrases_hello_hiho[1].moras,
- )
-
- def test_to_phoneme_data_list(self):
- phoneme_data_list = to_phoneme_data_list(self.str_list_hello_hiho)
- self.assertEqual(phoneme_data_list, self.phoneme_data_list_hello_hiho)
-
- def test_split_mora(self):
- consonant_phoneme_list, vowel_phoneme_list, vowel_indexes = split_mora(
- self.phoneme_data_list_hello_hiho
- )
-
- self.assertEqual(vowel_indexes, [0, 2, 3, 5, 7, 9, 10, 12, 14, 16, 18, 19])
- self.assertEqual(
- vowel_phoneme_list,
- [
- OjtPhoneme(phoneme="pau", start=0, end=1),
- OjtPhoneme(phoneme="o", start=2, end=3),
- OjtPhoneme(phoneme="N", start=3, end=4),
- OjtPhoneme(phoneme="i", start=5, end=6),
- OjtPhoneme(phoneme="i", start=7, end=8),
- OjtPhoneme(phoneme="a", start=9, end=10),
- OjtPhoneme(phoneme="pau", start=10, end=11),
- OjtPhoneme(phoneme="i", start=12, end=13),
- OjtPhoneme(phoneme="o", start=14, end=15),
- OjtPhoneme(phoneme="e", start=16, end=17),
- OjtPhoneme(phoneme="U", start=18, end=19),
- OjtPhoneme(phoneme="pau", start=19, end=20),
- ],
- )
- self.assertEqual(
- consonant_phoneme_list,
- [
- None,
- OjtPhoneme(phoneme="k", start=1, end=2),
- None,
- OjtPhoneme(phoneme="n", start=4, end=5),
- OjtPhoneme(phoneme="ch", start=6, end=7),
- OjtPhoneme(phoneme="w", start=8, end=9),
- None,
- OjtPhoneme(phoneme="h", start=11, end=12),
- OjtPhoneme(phoneme="h", start=13, end=14),
- OjtPhoneme(phoneme="d", start=15, end=16),
- OjtPhoneme(phoneme="s", start=17, end=18),
- None,
- ],
- )
-
- def test_pre_process(self):
- flatten_moras, phoneme_data_list = pre_process(
- deepcopy(self.accent_phrases_hello_hiho)
- )
-
- mora_index = 0
- phoneme_index = 1
-
- self.assertEqual(phoneme_data_list[0], OjtPhoneme("pau", 0, 1))
- for accent_phrase in self.accent_phrases_hello_hiho:
- moras = accent_phrase.moras
- for mora in moras:
- self.assertEqual(flatten_moras[mora_index], mora)
- mora_index += 1
- if mora.consonant is not None:
- self.assertEqual(
- phoneme_data_list[phoneme_index],
- OjtPhoneme(mora.consonant, phoneme_index, phoneme_index + 1),
- )
- phoneme_index += 1
- self.assertEqual(
- phoneme_data_list[phoneme_index],
- OjtPhoneme(mora.vowel, phoneme_index, phoneme_index + 1),
- )
- phoneme_index += 1
- if accent_phrase.pause_mora:
- self.assertEqual(flatten_moras[mora_index], accent_phrase.pause_mora)
- mora_index += 1
- self.assertEqual(
- phoneme_data_list[phoneme_index],
- OjtPhoneme("pau", phoneme_index, phoneme_index + 1),
- )
- phoneme_index += 1
- self.assertEqual(
- phoneme_data_list[phoneme_index],
- OjtPhoneme("pau", phoneme_index, phoneme_index + 1),
- )
-
- def test_replace_phoneme_length(self):
- result = self.synthesis_engine.replace_phoneme_length(
- accent_phrases=deepcopy(self.accent_phrases_hello_hiho), speaker_id=1
- )
-
- # yukarin_sに渡される値の検証
- yukarin_s_args = self.yukarin_s_mock.call_args[1]
- list_length = yukarin_s_args["length"]
- phoneme_list = yukarin_s_args["phoneme_list"]
- self.assertEqual(list_length, 20)
- self.assertEqual(list_length, len(phoneme_list))
- numpy.testing.assert_array_equal(
- phoneme_list,
- numpy.array(
- [
- 0,
- 23,
- 30,
- 4,
- 28,
- 21,
- 10,
- 21,
- 42,
- 7,
- 0,
- 19,
- 21,
- 19,
- 30,
- 12,
- 14,
- 35,
- 6,
- 0,
- ],
- dtype=numpy.int64,
- ),
- )
- self.assertEqual(yukarin_s_args["speaker_id"], 1)
-
- # flatten_morasを使わずに愚直にaccent_phrasesにデータを反映させてみる
- true_result = deepcopy(self.accent_phrases_hello_hiho)
- index = 1
-
- def result_value(i: int):
- return float(phoneme_list[i] * 0.5 + 1)
-
- for accent_phrase in true_result:
- moras = accent_phrase.moras
- for mora in moras:
- if mora.consonant is not None:
- mora.consonant_length = result_value(index)
- index += 1
- mora.vowel_length = result_value(index)
- index += 1
- if accent_phrase.pause_mora is not None:
- accent_phrase.pause_mora.vowel_length = result_value(index)
- index += 1
-
- self.assertEqual(result, true_result)
-
- def test_replace_mora_pitch(self):
- # 空のリストでエラーを吐かないか
- empty_accent_phrases = []
- self.assertEqual(
- self.synthesis_engine.replace_mora_pitch(
- accent_phrases=empty_accent_phrases, speaker_id=1
- ),
- [],
- )
-
- result = self.synthesis_engine.replace_mora_pitch(
- accent_phrases=deepcopy(self.accent_phrases_hello_hiho), speaker_id=1
- )
-
- # yukarin_saに渡される値の検証
- yukarin_sa_args = self.yukarin_sa_mock.call_args[1]
- list_length = yukarin_sa_args["length"]
- vowel_phoneme_list = yukarin_sa_args["vowel_phoneme_list"][0]
- consonant_phoneme_list = yukarin_sa_args["consonant_phoneme_list"][0]
- start_accent_list = yukarin_sa_args["start_accent_list"][0]
- end_accent_list = yukarin_sa_args["end_accent_list"][0]
- start_accent_phrase_list = yukarin_sa_args["start_accent_phrase_list"][0]
- end_accent_phrase_list = yukarin_sa_args["end_accent_phrase_list"][0]
- self.assertEqual(list_length, 12)
- self.assertEqual(list_length, len(vowel_phoneme_list))
- self.assertEqual(list_length, len(consonant_phoneme_list))
- self.assertEqual(list_length, len(start_accent_list))
- self.assertEqual(list_length, len(end_accent_list))
- self.assertEqual(list_length, len(start_accent_phrase_list))
- self.assertEqual(list_length, len(end_accent_phrase_list))
- self.assertEqual(yukarin_sa_args["speaker_id"], 1)
-
- numpy.testing.assert_array_equal(
- vowel_phoneme_list,
- numpy.array(
- [
- 0,
- 30,
- 4,
- 21,
- 21,
- 7,
- 0,
- 21,
- 30,
- 14,
- 6,
- 0,
- ]
- ),
- )
- numpy.testing.assert_array_equal(
- consonant_phoneme_list,
- numpy.array(
- [
- -1,
- 23,
- -1,
- 28,
- 10,
- 42,
- -1,
- 19,
- 19,
- 12,
- 35,
- -1,
- ]
- ),
- )
- numpy.testing.assert_array_equal(
- start_accent_list, numpy.array([0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0])
- )
- numpy.testing.assert_array_equal(
- end_accent_list, numpy.array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0])
- )
- numpy.testing.assert_array_equal(
- start_accent_phrase_list, numpy.array([0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
- )
- numpy.testing.assert_array_equal(
- end_accent_phrase_list, numpy.array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0])
- )
-
- # flatten_morasを使わずに愚直にaccent_phrasesにデータを反映させてみる
- true_result = deepcopy(self.accent_phrases_hello_hiho)
- index = 1
-
- def result_value(i: int):
- # unvoiced_mora_phoneme_listのPhoneme ID版
- unvoiced_mora_phoneme_id_list = [
- OjtPhoneme(p, 0, 0).phoneme_id for p in unvoiced_mora_phoneme_list
- ]
- if vowel_phoneme_list[i] in unvoiced_mora_phoneme_id_list:
- return 0
- return (
- vowel_phoneme_list[i]
- + consonant_phoneme_list[i]
- + start_accent_list[i]
- + end_accent_list[i]
- + start_accent_phrase_list[i]
- + end_accent_phrase_list[i]
- ) * 0.5 + 1
-
- for accent_phrase in true_result:
- moras = accent_phrase.moras
- for mora in moras:
- mora.pitch = result_value(index)
- index += 1
- if accent_phrase.pause_mora is not None:
- accent_phrase.pause_mora.pitch = result_value(index)
- index += 1
-
- self.assertEqual(result, true_result)
-
- def synthesis_test_base(self, audio_query: AudioQuery):
- accent_phrases = audio_query.accent_phrases
-
- # decode forwardのために適当にpitchとlengthを設定し、リストで持っておく
- phoneme_length_list = [0.0]
- phoneme_id_list = [0]
- f0_list = [0.0]
- for accent_phrase in accent_phrases:
- moras = accent_phrase.moras
- for mora in moras:
- if mora.consonant is not None:
- mora.consonant_length = 0.1
- phoneme_length_list.append(0.1)
- phoneme_id_list.append(OjtPhoneme(mora.consonant, 0, 0).phoneme_id)
- mora.vowel_length = 0.2
- phoneme_length_list.append(0.2)
- phoneme_id_list.append(OjtPhoneme(mora.vowel, 0, 0).phoneme_id)
- if mora.vowel not in unvoiced_mora_phoneme_list:
- mora.pitch = 5.0 + random()
- f0_list.append(mora.pitch)
- if accent_phrase.pause_mora is not None:
- accent_phrase.pause_mora.vowel_length = 0.2
- phoneme_length_list.append(0.2)
- phoneme_id_list.append(OjtPhoneme("pau", 0, 0).phoneme_id)
- f0_list.append(0.0)
- phoneme_length_list.append(0.0)
- phoneme_id_list.append(0)
- f0_list.append(0.0)
-
- phoneme_length_list[0] = audio_query.prePhonemeLength
- phoneme_length_list[-1] = audio_query.postPhonemeLength
-
- for i in range(len(phoneme_length_list)):
- phoneme_length_list[i] /= audio_query.speedScale
-
- result = self.synthesis_engine.synthesis(query=audio_query, speaker_id=1)
-
- # decodeに渡される値の検証
- decode_args = self.decode_mock.call_args[1]
- list_length = decode_args["length"]
- self.assertEqual(
- list_length,
- int(sum([round(p * 24000 / 256) for p in phoneme_length_list])),
- )
-
- num_phoneme = OjtPhoneme.num_phoneme
- # mora_phoneme_listのPhoneme ID版
- mora_phoneme_id_list = [
- OjtPhoneme(p, 0, 0).phoneme_id for p in mora_phoneme_list
- ]
-
- # numpy.repeatをfor文でやる
- f0 = []
- phoneme = []
- f0_index = 0
- mean_f0 = []
- for i, phoneme_length in enumerate(phoneme_length_list):
- f0_single = numpy.array(f0_list[f0_index], dtype=numpy.float32) * (
- 2**audio_query.pitchScale
- )
- for _ in range(int(round(phoneme_length * (24000 / 256)))):
- f0.append([f0_single])
- phoneme_s = []
- for _ in range(num_phoneme):
- phoneme_s.append(0)
- # one hot
- phoneme_s[phoneme_id_list[i]] = 1
- phoneme.append(phoneme_s)
- # consonantとvowelを判別し、vowelであればf0_indexを一つ進める
- if phoneme_id_list[i] in mora_phoneme_id_list:
- if f0_single > 0:
- mean_f0.append(f0_single)
- f0_index += 1
-
- mean_f0 = numpy.array(mean_f0, dtype=numpy.float32).mean()
- f0 = numpy.array(f0, dtype=numpy.float32)
- for i in range(len(f0)):
- if f0[i][0] != 0.0:
- f0[i][0] = (f0[i][0] - mean_f0) * audio_query.intonationScale + mean_f0
-
- phoneme = numpy.array(phoneme, dtype=numpy.float32)
-
- # 乱数の影響で数値の位置がずれが生じるので、大半(4/5)があっていればよしとする
- # また、上の部分のint(round(phoneme_length * (24000 / 256)))の影響で
- # 本来のf0/phonemeとテスト生成したf0/phonemeの長さが変わることがあり、
- # テスト生成したものが若干長くなることがあるので、本来のものの長さを基準にassertする
- assert_f0_count = 0
- decode_f0 = decode_args["f0"]
- for i in range(len(decode_f0)):
- # 乱数の影響等で数値にずれが生じるので、10の-5乗までの近似値であれば許容する
- assert_f0_count += math.isclose(f0[i][0], decode_f0[i][0], rel_tol=10e-5)
- self.assertTrue(assert_f0_count >= int(len(decode_f0) / 5) * 4)
- assert_phoneme_count = 0
- decode_phoneme = decode_args["phoneme"]
- for i in range(len(decode_phoneme)):
- assert_true_count = 0
- for j in range(len(decode_phoneme[i])):
- assert_true_count += bool(phoneme[i][j] == decode_phoneme[i][j])
- assert_phoneme_count += assert_true_count == num_phoneme
- self.assertTrue(assert_phoneme_count >= int(len(decode_phoneme) / 5) * 4)
- self.assertEqual(decode_args["speaker_id"], 1)
-
- # decode forwarderのmockを使う
- true_result = decode_mock(list_length, num_phoneme, f0, phoneme, 1)
-
- true_result *= audio_query.volumeScale
-
- # TODO: resampyの部分は値の検証しようがないので、パスする
- if audio_query.outputSamplingRate != 24000:
- return
-
- assert_result_count = 0
- for i in range(len(true_result)):
- if audio_query.outputStereo:
- assert_result_count += math.isclose(
- true_result[i], result[i][0], rel_tol=10e-5
- ) and math.isclose(true_result[i], result[i][1], rel_tol=10e-5)
- else:
- assert_result_count += math.isclose(
- true_result[i], result[i], rel_tol=10e-5
- )
- self.assertTrue(assert_result_count >= int(len(true_result) / 5) * 4)
-
- def test_synthesis(self):
- audio_query = AudioQuery(
- accent_phrases=deepcopy(self.accent_phrases_hello_hiho),
- speedScale=1.0,
- pitchScale=1.0,
- intonationScale=1.0,
- volumeScale=1.0,
- prePhonemeLength=0.1,
- postPhonemeLength=0.1,
- outputSamplingRate=24000,
- outputStereo=False,
- # このテスト内では使わないので生成不要
- kana="",
- )
-
- self.synthesis_test_base(audio_query)
-
- # speed scaleのテスト
- audio_query.speedScale = 1.2
- self.synthesis_test_base(audio_query)
-
- # pitch scaleのテスト
- audio_query.pitchScale = 1.5
- audio_query.speedScale = 1.0
- self.synthesis_test_base(audio_query)
-
- # intonation scaleのテスト
- audio_query.pitchScale = 1.0
- audio_query.intonationScale = 1.4
- self.synthesis_test_base(audio_query)
-
- # volume scaleのテスト
- audio_query.intonationScale = 1.0
- audio_query.volumeScale = 2.0
- self.synthesis_test_base(audio_query)
-
- # pre/post phoneme lengthのテスト
- audio_query.volumeScale = 1.0
- audio_query.prePhonemeLength = 0.5
- audio_query.postPhonemeLength = 0.5
- self.synthesis_test_base(audio_query)
-
- # output sampling rateのテスト
- audio_query.prePhonemeLength = 0.1
- audio_query.postPhonemeLength = 0.1
- audio_query.outputSamplingRate = 48000
- self.synthesis_test_base(audio_query)
-
- # output stereoのテスト
- audio_query.outputSamplingRate = 24000
- audio_query.outputStereo = True
- self.synthesis_test_base(audio_query)
diff --git a/spaces/Gaofish/AI_bing/Dockerfile b/spaces/Gaofish/AI_bing/Dockerfile
deleted file mode 100644
index cb33c49f65d675a52e7591038fbed9230532908f..0000000000000000000000000000000000000000
--- a/spaces/Gaofish/AI_bing/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="BD7bo9UY63iIut7t5rWPZi98js6rBu487shsH1hjkcH90Sa125"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_coded_blocks_on_corner.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_coded_blocks_on_corner.py
deleted file mode 100644
index 7eac819cade5fb643e6019b67b99a4ef5750432f..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_coded_blocks_on_corner.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import numpy as np
-import os
-import pybullet as p
-import random
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import pybullet as p
-
-class ColorCodedBlocksOnCorner(Task):
- """Pick up blocks of different colors and place them in a corner structure in a specific color sequence."""
-
- def __init__(self):
- super().__init__()
- self.max_steps = 10
- self.lang_template = "place the blocks in the corner in the sequence red, blue, green, yellow"
- self.task_completed_desc = "done placing blocks in the corner."
- self.additional_reset()
-
- def reset(self, env):
- super().reset(env)
-
- # Add corner structure.
- corner_size = (0.15, 0.15, 0.05)
- corner_pose = self.get_random_pose(env, corner_size)
- corner_urdf = 'corner/corner-template.urdf'
- env.add_object(corner_urdf, corner_pose, 'fixed')
-
- # Block colors.
- colors = [
- utils.COLORS['red'], utils.COLORS['blue'], utils.COLORS['green'],
- utils.COLORS['yellow']
- ]
-
- # Add blocks.
- block_size = (0.04, 0.04, 0.04)
- block_urdf = 'block/block.urdf'
- blocks = []
- for i in range(4):
- block_pose = self.get_random_pose(env, block_size)
- block_id = env.add_object(block_urdf, block_pose, color=colors[i])
- blocks.append(block_id)
-
- # Associate placement locations for goals.
- place_pos = [(0, -0.05, 0.03), (0, 0, 0.03),
- (0, 0.05, 0.03), (0, 0, 0.08)]
- targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos]
-
- # Goal: blocks are placed in the corner in the sequence red, blue, green, yellow.
- for i in range(4):
- self.add_goal(objs=[blocks[i]], matches=np.ones((1, 1)), targ_poses=[targs[i]], replace=False,
- rotations=True, metric='pose', params=None, step_max_reward=1 / 4,
- language_goal=self.lang_template.format(blocks="the red, blue, green, yellow blocks"))
\ No newline at end of file
diff --git a/spaces/GeorgeOrville/bingo/src/pages/api/healthz.ts b/spaces/GeorgeOrville/bingo/src/pages/api/healthz.ts
deleted file mode 100644
index f6ae44ff0fd66ccd3f7feaa550025fbf2a83bf77..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/pages/api/healthz.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- res.status(200).end('ok')
-}
diff --git a/spaces/Gradio-Blocks/CloudSaveText2Speech/README.md b/spaces/Gradio-Blocks/CloudSaveText2Speech/README.md
deleted file mode 100644
index 3213bf8f6e1b9869142445a49fff4d7151eb65fe..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/CloudSaveText2Speech/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CloudSaveText2Speech
-emoji: 🧠💬💾
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.0.9
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py
deleted file mode 100644
index 12eee2c1ecdaa5f9e84a3bd2084b00493f2f76c0..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py
+++ /dev/null
@@ -1,75 +0,0 @@
-_base_ = [
- '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
- '../_base_/default_runtime.py'
-]
-model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
-
-CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
- 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
- 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
-
-# dataset settings
-dataset_type = 'CocoDataset'
-data_root = 'data/VOCdevkit/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1000, 600),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type='RepeatDataset',
- times=3,
- dataset=dict(
- type=dataset_type,
- ann_file='data/voc0712_trainval.json',
- img_prefix='data/VOCdevkit',
- pipeline=train_pipeline,
- classes=CLASSES)),
- val=dict(
- type=dataset_type,
- ann_file='data/voc07_test.json',
- img_prefix='data/VOCdevkit',
- pipeline=test_pipeline,
- classes=CLASSES),
- test=dict(
- type=dataset_type,
- ann_file='data/voc07_test.json',
- img_prefix='data/VOCdevkit',
- pipeline=test_pipeline,
- classes=CLASSES))
-evaluation = dict(interval=1, metric='bbox')
-
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-# actual epoch = 3 * 3 = 9
-lr_config = dict(policy='step', step=[3])
-# runtime settings
-runner = dict(
- type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/pascal_context.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/pascal_context.py
deleted file mode 100644
index ff65bad1b86d7e3a5980bb5b9fc55798dc8df5f4..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/pascal_context.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# dataset settings
-dataset_type = 'PascalContextDataset'
-data_root = 'data/VOCdevkit/VOC2010/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-img_scale = (520, 520)
-crop_size = (480, 480)
-
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=img_scale, 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=img_scale,
- # 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='JPEGImages',
- ann_dir='SegmentationClassContext',
- split='ImageSets/SegmentationContext/train.txt',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='JPEGImages',
- ann_dir='SegmentationClassContext',
- split='ImageSets/SegmentationContext/val.txt',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='JPEGImages',
- ann_dir='SegmentationClassContext',
- split='ImageSets/SegmentationContext/val.txt',
- pipeline=test_pipeline))
diff --git a/spaces/Gradio-Blocks/uniformer_video_demo/transforms.py b/spaces/Gradio-Blocks/uniformer_video_demo/transforms.py
deleted file mode 100644
index 2483fdf8569e25978b922774e84cc2244315fe61..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_video_demo/transforms.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torchvision
-import random
-from PIL import Image, ImageOps
-import numpy as np
-import numbers
-import math
-import torch
-
-
-class GroupRandomCrop(object):
- def __init__(self, size):
- if isinstance(size, numbers.Number):
- self.size = (int(size), int(size))
- else:
- self.size = size
-
- def __call__(self, img_group):
-
- w, h = img_group[0].size
- th, tw = self.size
-
- out_images = list()
-
- x1 = random.randint(0, w - tw)
- y1 = random.randint(0, h - th)
-
- for img in img_group:
- assert(img.size[0] == w and img.size[1] == h)
- if w == tw and h == th:
- out_images.append(img)
- else:
- out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
-
- return out_images
-
-
-class MultiGroupRandomCrop(object):
- def __init__(self, size, groups=1):
- if isinstance(size, numbers.Number):
- self.size = (int(size), int(size))
- else:
- self.size = size
- self.groups = groups
-
- def __call__(self, img_group):
-
- w, h = img_group[0].size
- th, tw = self.size
-
- out_images = list()
-
- for i in range(self.groups):
- x1 = random.randint(0, w - tw)
- y1 = random.randint(0, h - th)
-
- for img in img_group:
- assert(img.size[0] == w and img.size[1] == h)
- if w == tw and h == th:
- out_images.append(img)
- else:
- out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
-
- return out_images
-
-
-class GroupCenterCrop(object):
- def __init__(self, size):
- self.worker = torchvision.transforms.CenterCrop(size)
-
- def __call__(self, img_group):
- return [self.worker(img) for img in img_group]
-
-
-class GroupRandomHorizontalFlip(object):
- """Randomly horizontally flips the given PIL.Image with a probability of 0.5
- """
-
- def __init__(self, is_flow=False):
- self.is_flow = is_flow
-
- def __call__(self, img_group, is_flow=False):
- v = random.random()
- if v < 0.5:
- ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
- if self.is_flow:
- for i in range(0, len(ret), 2):
- # invert flow pixel values when flipping
- ret[i] = ImageOps.invert(ret[i])
- return ret
- else:
- return img_group
-
-
-class GroupNormalize(object):
- def __init__(self, mean, std):
- self.mean = mean
- self.std = std
-
- def __call__(self, tensor):
- rep_mean = self.mean * (tensor.size()[0] // len(self.mean))
- rep_std = self.std * (tensor.size()[0] // len(self.std))
-
- # TODO: make efficient
- for t, m, s in zip(tensor, rep_mean, rep_std):
- t.sub_(m).div_(s)
-
- return tensor
-
-
-class GroupScale(object):
- """ Rescales the input PIL.Image to the given 'size'.
- 'size' will be the size of the smaller edge.
- For example, if height > width, then image will be
- rescaled to (size * height / width, size)
- size: size of the smaller edge
- interpolation: Default: PIL.Image.BILINEAR
- """
-
- def __init__(self, size, interpolation=Image.BILINEAR):
- self.worker = torchvision.transforms.Resize(size, interpolation)
-
- def __call__(self, img_group):
- return [self.worker(img) for img in img_group]
-
-
-class GroupOverSample(object):
- def __init__(self, crop_size, scale_size=None, flip=True):
- self.crop_size = crop_size if not isinstance(
- crop_size, int) else (crop_size, crop_size)
-
- if scale_size is not None:
- self.scale_worker = GroupScale(scale_size)
- else:
- self.scale_worker = None
- self.flip = flip
-
- def __call__(self, img_group):
-
- if self.scale_worker is not None:
- img_group = self.scale_worker(img_group)
-
- image_w, image_h = img_group[0].size
- crop_w, crop_h = self.crop_size
-
- offsets = GroupMultiScaleCrop.fill_fix_offset(
- False, image_w, image_h, crop_w, crop_h)
- oversample_group = list()
- for o_w, o_h in offsets:
- normal_group = list()
- flip_group = list()
- for i, img in enumerate(img_group):
- crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h))
- normal_group.append(crop)
- flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT)
-
- if img.mode == 'L' and i % 2 == 0:
- flip_group.append(ImageOps.invert(flip_crop))
- else:
- flip_group.append(flip_crop)
-
- oversample_group.extend(normal_group)
- if self.flip:
- oversample_group.extend(flip_group)
- return oversample_group
-
-
-class GroupFullResSample(object):
- def __init__(self, crop_size, scale_size=None, flip=True):
- self.crop_size = crop_size if not isinstance(
- crop_size, int) else (crop_size, crop_size)
-
- if scale_size is not None:
- self.scale_worker = GroupScale(scale_size)
- else:
- self.scale_worker = None
- self.flip = flip
-
- def __call__(self, img_group):
-
- if self.scale_worker is not None:
- img_group = self.scale_worker(img_group)
-
- image_w, image_h = img_group[0].size
- crop_w, crop_h = self.crop_size
-
- w_step = (image_w - crop_w) // 4
- h_step = (image_h - crop_h) // 4
-
- offsets = list()
- offsets.append((0 * w_step, 2 * h_step)) # left
- offsets.append((4 * w_step, 2 * h_step)) # right
- offsets.append((2 * w_step, 2 * h_step)) # center
-
- oversample_group = list()
- for o_w, o_h in offsets:
- normal_group = list()
- flip_group = list()
- for i, img in enumerate(img_group):
- crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h))
- normal_group.append(crop)
- if self.flip:
- flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT)
-
- if img.mode == 'L' and i % 2 == 0:
- flip_group.append(ImageOps.invert(flip_crop))
- else:
- flip_group.append(flip_crop)
-
- oversample_group.extend(normal_group)
- oversample_group.extend(flip_group)
- return oversample_group
-
-
-class GroupMultiScaleCrop(object):
-
- def __init__(self, input_size, scales=None, max_distort=1,
- fix_crop=True, more_fix_crop=True):
- self.scales = scales if scales is not None else [1, .875, .75, .66]
- self.max_distort = max_distort
- self.fix_crop = fix_crop
- self.more_fix_crop = more_fix_crop
- self.input_size = input_size if not isinstance(input_size, int) else [
- input_size, input_size]
- self.interpolation = Image.BILINEAR
-
- def __call__(self, img_group):
-
- im_size = img_group[0].size
-
- crop_w, crop_h, offset_w, offset_h = self._sample_crop_size(im_size)
- crop_img_group = [
- img.crop(
- (offset_w,
- offset_h,
- offset_w +
- crop_w,
- offset_h +
- crop_h)) for img in img_group]
- ret_img_group = [img.resize((self.input_size[0], self.input_size[1]), self.interpolation)
- for img in crop_img_group]
- return ret_img_group
-
- def _sample_crop_size(self, im_size):
- image_w, image_h = im_size[0], im_size[1]
-
- # find a crop size
- base_size = min(image_w, image_h)
- crop_sizes = [int(base_size * x) for x in self.scales]
- crop_h = [
- self.input_size[1] if abs(
- x - self.input_size[1]) < 3 else x for x in crop_sizes]
- crop_w = [
- self.input_size[0] if abs(
- x - self.input_size[0]) < 3 else x for x in crop_sizes]
-
- pairs = []
- for i, h in enumerate(crop_h):
- for j, w in enumerate(crop_w):
- if abs(i - j) <= self.max_distort:
- pairs.append((w, h))
-
- crop_pair = random.choice(pairs)
- if not self.fix_crop:
- w_offset = random.randint(0, image_w - crop_pair[0])
- h_offset = random.randint(0, image_h - crop_pair[1])
- else:
- w_offset, h_offset = self._sample_fix_offset(
- image_w, image_h, crop_pair[0], crop_pair[1])
-
- return crop_pair[0], crop_pair[1], w_offset, h_offset
-
- def _sample_fix_offset(self, image_w, image_h, crop_w, crop_h):
- offsets = self.fill_fix_offset(
- self.more_fix_crop, image_w, image_h, crop_w, crop_h)
- return random.choice(offsets)
-
- @staticmethod
- def fill_fix_offset(more_fix_crop, image_w, image_h, crop_w, crop_h):
- w_step = (image_w - crop_w) // 4
- h_step = (image_h - crop_h) // 4
-
- ret = list()
- ret.append((0, 0)) # upper left
- ret.append((4 * w_step, 0)) # upper right
- ret.append((0, 4 * h_step)) # lower left
- ret.append((4 * w_step, 4 * h_step)) # lower right
- ret.append((2 * w_step, 2 * h_step)) # center
-
- if more_fix_crop:
- ret.append((0, 2 * h_step)) # center left
- ret.append((4 * w_step, 2 * h_step)) # center right
- ret.append((2 * w_step, 4 * h_step)) # lower center
- ret.append((2 * w_step, 0 * h_step)) # upper center
-
- ret.append((1 * w_step, 1 * h_step)) # upper left quarter
- ret.append((3 * w_step, 1 * h_step)) # upper right quarter
- ret.append((1 * w_step, 3 * h_step)) # lower left quarter
- ret.append((3 * w_step, 3 * h_step)) # lower righ quarter
-
- return ret
-
-
-class GroupRandomSizedCrop(object):
- """Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the original size
- and and a random aspect ratio of 3/4 to 4/3 of the original aspect ratio
- This is popularly used to train the Inception networks
- size: size of the smaller edge
- interpolation: Default: PIL.Image.BILINEAR
- """
-
- def __init__(self, size, interpolation=Image.BILINEAR):
- self.size = size
- self.interpolation = interpolation
-
- def __call__(self, img_group):
- for attempt in range(10):
- area = img_group[0].size[0] * img_group[0].size[1]
- target_area = random.uniform(0.08, 1.0) * area
- aspect_ratio = random.uniform(3. / 4, 4. / 3)
-
- w = int(round(math.sqrt(target_area * aspect_ratio)))
- h = int(round(math.sqrt(target_area / aspect_ratio)))
-
- if random.random() < 0.5:
- w, h = h, w
-
- if w <= img_group[0].size[0] and h <= img_group[0].size[1]:
- x1 = random.randint(0, img_group[0].size[0] - w)
- y1 = random.randint(0, img_group[0].size[1] - h)
- found = True
- break
- else:
- found = False
- x1 = 0
- y1 = 0
-
- if found:
- out_group = list()
- for img in img_group:
- img = img.crop((x1, y1, x1 + w, y1 + h))
- assert(img.size == (w, h))
- out_group.append(
- img.resize(
- (self.size, self.size), self.interpolation))
- return out_group
- else:
- # Fallback
- scale = GroupScale(self.size, interpolation=self.interpolation)
- crop = GroupRandomCrop(self.size)
- return crop(scale(img_group))
-
-
-class ConvertDataFormat(object):
- def __init__(self, model_type):
- self.model_type = model_type
-
- def __call__(self, images):
- if self.model_type == '2D':
- return images
- tc, h, w = images.size()
- t = tc // 3
- images = images.view(t, 3, h, w)
- images = images.permute(1, 0, 2, 3)
- return images
-
-
-class Stack(object):
-
- def __init__(self, roll=False):
- self.roll = roll
-
- def __call__(self, img_group):
- if img_group[0].mode == 'L':
- return np.concatenate([np.expand_dims(x, 2)
- for x in img_group], axis=2)
- elif img_group[0].mode == 'RGB':
- if self.roll:
- return np.concatenate([np.array(x)[:, :, ::-1]
- for x in img_group], axis=2)
- else:
- #print(np.concatenate(img_group, axis=2).shape)
- # print(img_group[0].shape)
- return np.concatenate(img_group, axis=2)
-
-
-class ToTorchFormatTensor(object):
- """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
- to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
-
- def __init__(self, div=True):
- self.div = div
-
- def __call__(self, pic):
- if isinstance(pic, np.ndarray):
- # handle numpy array
- img = torch.from_numpy(pic).permute(2, 0, 1).contiguous()
- else:
- # handle PIL Image
- img = torch.ByteTensor(
- torch.ByteStorage.from_buffer(
- pic.tobytes()))
- img = img.view(pic.size[1], pic.size[0], len(pic.mode))
- # put it from HWC to CHW format
- # yikes, this transpose takes 80% of the loading time/CPU
- img = img.transpose(0, 1).transpose(0, 2).contiguous()
- return img.float().div(255) if self.div else img.float()
-
-
-class IdentityTransform(object):
-
- def __call__(self, data):
- return data
-
-
-if __name__ == "__main__":
- trans = torchvision.transforms.Compose([
- GroupScale(256),
- GroupRandomCrop(224),
- Stack(),
- ToTorchFormatTensor(),
- GroupNormalize(
- mean=[.485, .456, .406],
- std=[.229, .224, .225]
- )]
- )
-
- im = Image.open('../tensorflow-model-zoo.torch/lena_299.png')
-
- color_group = [im] * 3
- rst = trans(color_group)
-
- gray_group = [im.convert('L')] * 9
- gray_rst = trans(gray_group)
-
- trans2 = torchvision.transforms.Compose([
- GroupRandomSizedCrop(256),
- Stack(),
- ToTorchFormatTensor(),
- GroupNormalize(
- mean=[.485, .456, .406],
- std=[.229, .224, .225])
- ])
- print(trans2(color_group))
diff --git a/spaces/GrandaddyShmax/MusicGen_Plus/tests/common_utils/temp_utils.py b/spaces/GrandaddyShmax/MusicGen_Plus/tests/common_utils/temp_utils.py
deleted file mode 100644
index d1e0367e979c8b9fea65472c373916d956ad5aaa..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/MusicGen_Plus/tests/common_utils/temp_utils.py
+++ /dev/null
@@ -1,56 +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 os
-import tempfile
-
-
-class TempDirMixin:
- """Mixin to provide easy access to temp dir.
- """
-
- temp_dir_ = None
-
- @classmethod
- def get_base_temp_dir(cls):
- # If AUDIOCRAFT_TEST_DIR is set, use it instead of temporary directory.
- # this is handy for debugging.
- key = "AUDIOCRAFT_TEST_DIR"
- if key in os.environ:
- return os.environ[key]
- if cls.temp_dir_ is None:
- cls.temp_dir_ = tempfile.TemporaryDirectory()
- return cls.temp_dir_.name
-
- @classmethod
- def tearDownClass(cls):
- if cls.temp_dir_ is not None:
- try:
- cls.temp_dir_.cleanup()
- cls.temp_dir_ = None
- except PermissionError:
- # On Windows there is a know issue with `shutil.rmtree`,
- # which fails intermittenly.
- # https://github.com/python/cpython/issues/74168
- # Following the above thread, we ignore it.
- pass
- super().tearDownClass()
-
- @property
- def id(self):
- return self.__class__.__name__
-
- def get_temp_path(self, *paths):
- temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
- path = os.path.join(temp_dir, *paths)
- os.makedirs(os.path.dirname(path), exist_ok=True)
- return path
-
- def get_temp_dir(self, *paths):
- temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
- path = os.path.join(temp_dir, *paths)
- os.makedirs(path, exist_ok=True)
- return path
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/qualitative_evaluation.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/qualitative_evaluation.py
deleted file mode 100644
index 17163168aa27a082a2d9eee3929a7a73dda2135e..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/qualitative_evaluation.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import shutil
-import glob
-import copy
-from pprint import pprint
-from typing import Dict, List, Tuple
-from PIL import Image
-import os
-import numpy as np
-import cv2
-from py_sod_metrics import Smeasure
-import pandas as pd
-import wandb
-from tqdm import tqdm
-
-from .visualizer import apply_threshold, apply_vis_to_image, post_processing_depth
-from .wandb_manager import wandb_init_sota_benchmark_sm, wandb_delete_artifacts
-from .configs.base_config import base_cfg
-from .dataset_fn import TestDataset
-from .prepare_datasets import unzip_SOTAs, unzip_datasets
-
-def __select_samples(
- df: pd.DataFrame,
- sota_names: List[str],
- our_model_name: str,
- no_samples: int,
- is_best: bool,
-) -> Dict[str, List[str]]:
- best_rs = pd.DataFrame()
- best_rs[['image_name', 'dataset_name']] = df[['image_name', 'dataset_name']]
- for sota_name in sota_names:
- best_rs[sota_name] = df[our_model_name] - df[sota_name] if is_best \
- else df[sota_name] - df[our_model_name]
-
- best_rs['min'] = best_rs.min(axis=1, numeric_only=True)
- best_rs = best_rs[best_rs['min'] > 0]
- best_rs = best_rs.sort_values('min', ascending=False)
- best_rs = best_rs[['image_name', 'dataset_name', 'min']]
- print(best_rs.head(10))
-
- selected_samples: Dict[str, List[str]] = dict()
- for v in best_rs.iloc[:no_samples].values:
- dataset_name = v[1]
- image_name = v[0]
- if dataset_name not in selected_samples:
- selected_samples[dataset_name] = [image_name]
- else:
- selected_samples[dataset_name].append(image_name)
-
- return selected_samples
-
-def qualitative_evaluation(
- cfg: base_cfg,
- no_samples: int,
- dataset_names: List[str],
- sota_model_names: List[str],
-) -> Dict[str, List[str]]:
- unzip_SOTAs(
- cfg,
- [dataset_names for _ in range(len(sota_model_names))],
- sota_model_names
- )
- clean_qualitative_evaluation_latex(cfg)
-
- df = pd.read_csv(os.path.join(
- cfg.benchmark_csv_dir_path, f'smeasure_set{cfg.datasets_set}.csv'
- ))
-
- columns: List[str] = df.columns.tolist()
- our_model_names = [column for column in columns \
- if column.startswith('exp_v')]
-
- assert len(our_model_names) == 1, 'Should contain only one of our model'
-
- our_model_name = our_model_names[0]
- print(f'Our model name {our_model_name}')
-
- sota_names = [column for column in columns \
- if column not in ['index', 'image_name', 'dataset_name'] \
- and not column.startswith('exp_v')
- ]
- print('SOTA names:', sota_names)
-
- best_samples = __select_samples(
- df, sota_names, our_model_name, no_samples, is_best=True
- )
- print('Best samples:')
- pprint(best_samples)
- best_table, best_vis_table, best_s_measure_table = __qualitative_evaluation(
- cfg, best_samples, sota_model_names, is_best=True
- )
-
- worst_samples = __select_samples(
- df, sota_names, our_model_name, no_samples, is_best=False
- )
- print('Worst samples:')
- pprint(worst_samples)
- worst_table, worst_vis_table, worst_s_measure_table = __qualitative_evaluation(
- cfg, worst_samples, sota_model_names, is_best=False
- )
-
- wandb_run = wandb_init_sota_benchmark_sm(cfg.datasets_set)
- wandb_delete_artifacts(cfg, wandb_run)
- wandb_run.log({
- f'benchmark_best_sm': best_table,
- f'benchmark_best_vis': best_vis_table,
- f'benchmark_best_s_measure': best_s_measure_table,
- f'benchmark_worst_sm': worst_table,
- f'benchmark_worst_vis': worst_vis_table,
- f'benchmark_worst_s_measure': worst_s_measure_table,
- })
- wandb_run.finish()
-
-def __format_number(num: float) -> str:
- return "{:.4f}".format(num).lstrip('0')
-
-def __qualitative_evaluation_latext(cfg: base_cfg, is_best: bool, scale=0.1):
- df = pd.read_csv(__qualitative_evaluation_csv_path(cfg, is_best=is_best))
- text = f"""
-\\begin{{tabularx}}{{\\textwidth}}{{
- c {"Y " * df.shape[0]}
-}}
-"""
- no_samples = df.shape[0]
- range_indices = range(0, no_samples)
- for i in range_indices:
- text += f" & Sample {i+1}"
- text += "\\\\ \\midrule \n"
-
- text += "Color \n"
- for i in range_indices:
- text += f" & \includegraphics[scale={scale}]{{Images/QualitativeEvaluationSet{cfg.datasets_set}/{'best' if is_best else 'worst'}_{df['Index'][i]}_RGB.png}}"
- text += f"\\\\ \\midrule \n"
-
- text += "Depth \n"
- for i in range_indices:
- text += f" & \includegraphics[scale={scale}]{{Images/QualitativeEvaluationSet{cfg.datasets_set}/{'best' if is_best else 'worst'}_{df['Index'][i]}_DEPTH.png}}"
- text += f"\\\\ \\midrule \n"
-
- text += "GT \n"
- for i in range_indices:
- text += f" & \includegraphics[scale={scale}]{{Images/QualitativeEvaluationSet{cfg.datasets_set}/{'best' if is_best else 'worst'}_{df['Index'][i]}_GT.png}}"
- text += f"\\\\ \\midrule \n"
-
- columns = df.columns
- sota_names = columns[6:]
-
- for sota_name in sota_names:
- text += f"{sota_name} \n"
- for i in range_indices:
- text += f" & \includegraphics[scale={scale}]{{Images/QualitativeEvaluationSet{cfg.datasets_set}/{'best' if is_best else 'worst'}_{df['Index'][i]}_{sota_name}.png}} {__format_number(df[sota_name][i])}"
- text += f"\\\\ \\midrule \n"
-
- text += f"\n \\end{{tabularx}}"
-
- txt_path = os.path.join(
- cfg.source_code_dir, 'latex',
- f'{"best" if is_best else "worst"}_qualitative_evaluation_set{cfg.datasets_set}.txt'
- )
- with open(txt_path, 'w') as f:
- f.write(text)
-
-def qualitative_evaluation_latex(cfg: base_cfg) -> None:
- __qualitative_evaluation_latext(cfg, is_best=True)
- __qualitative_evaluation_latext(cfg, is_best=False)
-
-def clean_qualitative_evaluation_latex(cfg: base_cfg) -> None:
- """Clean: remove directory cfg.qualitative_evaluation_latex
-
- Args:
- cfg (base_cfg): Config
- """
- shutil.rmtree(cfg.qualitative_evaluation_latex_dir_path, ignore_errors=True)
-
-def save_qualitative_evaluation_latex(
- cfg: base_cfg,
- i: int,
- img: np.ndarray,
- label: str,
- is_best: bool,
-) -> None:
- os.makedirs(cfg.qualitative_evaluation_latex_dir_path, exist_ok=True)
- cv2.imwrite(
- os.path.join(
- cfg.qualitative_evaluation_latex_dir_path,
- f'{"best" if is_best else "worst"}_{i}_{label}.png'
- ),
- cv2.cvtColor(cv2.resize(img, (150, 150)), cv2.COLOR_BGR2RGB)
- )
-
-def __qualitative_evaluation_csv_path(cfg: base_cfg, is_best: bool) -> str:
- """Return the csv path to the best/worst qualitative evaluation of our experiment
-
- Args:
- cfg (base_cfg): Config
- is_best (bool): best/worst
-
- Returns:
- str: csv path
- """
- return os.path.join(
- cfg.benchmark_csv_dir_path,
- f'qualitative_evaluation_{"best" if is_best else "worst"}_set{cfg.datasets_set}.csv'
- )
-
-def __mapping_sota_model_names(sota_model_names: List[str]) -> List[str]:
- """Map our experiment name "exp_v4.0.19_epoch175" to "Ours"
-
- Args:
- sota_model_names (List[str]): List of SOTA names, must include only one "exp_v*"
-
- Returns:
- List[str]: List of SOTA names after mapping our experiment name to "Ours"
- """
- our_experiments = [sota_model_name for sota_model_name in sota_model_names if sota_model_name.startswith("exp_v")]
- assert len(our_experiments) == 1, "must include only one 'exp_v*'"
- return [
- "Ours" if sota_model_name.startswith("exp_v") else sota_model_name for sota_model_name in sota_model_names
- ]
-
-def __qualitative_evaluation(
- cfg: base_cfg,
- selected_samples: Dict[str, List[str]],
- sota_model_names: List[str],
- is_best: bool,
-) -> Tuple[wandb.Table, wandb.Table, wandb.Table]:
- """
- Note: Make sure unzip_SOTAs before execute this function
-
- selected_samples = {
- 'COME-E': [
- 'COME_Hard_2050',
- 'COME_Hard_1961',
- ...
- ],
- ....
- }
-
- dataset_names can be ['COME-E', 'COME-H']
- """
- mapped_sota_model_names = __mapping_sota_model_names(sota_model_names)
- columns = ['Index', 'Image name', 'Dataset', 'RGB', 'Depth', 'GT'] + mapped_sota_model_names
- results = []
- vis_results = []
- s_measure_results = []
- color = np.array([0., 0.97647059, 0.])
-
- for dataset_name, image_names in selected_samples.items():
- print(f'Dataset {dataset_name}')
- dataset_dir_path = os.path.join(
- cfg.test_datasets_working_dir_path,
- dataset_name
- )
- dataset = TestDataset(cfg, dataset_dir_path)
-
- for image_name in tqdm(image_names):
- possible_image_names = [
- existed_image_name for existed_image_name in dataset.images \
- if os.path.basename(existed_image_name).startswith(f'{image_name}.')
- ]
- assert len(possible_image_names) == 1, \
- f'Should have exactly one image {image_name} - No. len {len(possible_image_names)}'
-
- i = dataset.images.index(possible_image_names[0])
-
- image, depth, gt, image_name = dataset.get_raw_item(i)
- postprocessed_depth = post_processing_depth(np.asarray(depth))
-
- row = [
- i, image_name, dataset_name,
- wandb.Image(image),
- wandb.Image(postprocessed_depth),
- wandb.Image(gt),
- ]
- vis_row = copy.deepcopy(row)
-
- binary_mask = apply_threshold(np.asarray(gt))
- vis_image = apply_vis_to_image(image, binary_mask, color)
- vis_row[5] = wandb.Image(vis_image)
-
- save_qualitative_evaluation_latex(cfg, i, np.array(image), 'RGB', is_best)
- save_qualitative_evaluation_latex(cfg, i, postprocessed_depth, 'DEPTH', is_best)
- save_qualitative_evaluation_latex(cfg, i, vis_image, 'GT', is_best)
-
- s_measure_row = copy.deepcopy(row)
-
- for sota_model_name, mapped_sota_model_name in zip(sota_model_names, mapped_sota_model_names):
- file_path_pattern = os.path.join(cfg.sotas_working_dir, sota_model_name, dataset_name, f'{image_name}.*')
- files = glob.glob(file_path_pattern)
- assert len(files) == 1, f'Can not find the salient map {image_name} \
- of SOTA {sota_model_name} on dataset {dataset_name}, \
- Length = {len(files)}, File path pattern = {file_path_pattern}'
- pred = Image.open(files[0]).convert('L')
- pred = pred.resize(image.size)
- pred = np.asarray(pred)
-
- # SM
- row.append(wandb.Image(pred))
-
- # Visualization
- binary_mask = apply_threshold(pred)
- vis_image = apply_vis_to_image(image, binary_mask, color)
- vis_row.append(wandb.Image(vis_image))
-
- save_qualitative_evaluation_latex(cfg, i, vis_image, mapped_sota_model_name, is_best)
-
- # S-measure
- sm = Smeasure()
- sm.step(pred, np.asarray(gt))
- s_measure_row.append(sm.get_results()["sm"])
-
- results.append(row)
- vis_results.append(vis_row)
- s_measure_results.append(s_measure_row)
-
- table = wandb.Table(data=results, columns=columns)
- vis_table = wandb.Table(data=vis_results, columns=columns)
- s_measure_table = wandb.Table(data=s_measure_results, columns=columns)
-
- df = pd.DataFrame(s_measure_results, columns=columns)
- df.to_csv(__qualitative_evaluation_csv_path(cfg, is_best), index=False)
-
- return table, vis_table, s_measure_table
-
-def generate_s_measure_csv(
- cfg: base_cfg,
- dataset_names: List[str],
- sota_model_names: List[str],
-) -> None:
- """Calculate S-measure for the entire dataset
- Each dataset has a seperated csv file.
-
- dataset_names can be ['COME-E', 'COME-H']
- """
- unzip_SOTAs(
- cfg,
- [dataset_names for _ in range(len(sota_model_names))],
- sota_model_names
- )
-
- s_measure_results = []
-
- for dataset_name in dataset_names:
- dataset_dir_path = os.path.join(
- cfg.test_datasets_working_dir_path,
- dataset_name
- )
- print(f'Dataset: {dataset_name}')
- dataset = TestDataset(cfg, dataset_dir_path)
-
- length = len(dataset)
-
- for i in tqdm(range(length)):
- image, depth, gt, image_name = dataset.get_raw_item(i)
-
- s_measure_row = [
- i, image_name, dataset_name,
- ]
-
- for sota_model_name in sota_model_names:
- file_path_pattern = os.path.join(
- cfg.sotas_working_dir, sota_model_name,
- dataset_name, f'{image_name}.*'
- )
- files = glob.glob(file_path_pattern)
- assert len(files) == 1, f'Can not find the salient map {image_name} \
- of SOTA {sota_model_name} on dataset {dataset_name}, \
- Length = {len(files)}, File path pattern = {file_path_pattern}'
- pred = Image.open(files[0]).convert('L')
- pred = pred.resize(image.size)
- pred = np.asarray(pred)
-
- # S-measure
- sm = Smeasure()
- sm.step(pred, np.asarray(gt))
- s_measure_row.append(sm.get_results()["sm"])
-
- s_measure_results.append(s_measure_row)
-
- df = pd.DataFrame(
- s_measure_results,
- columns=['index', 'image_name', 'dataset_name'] + sota_model_names
- )
- csv_file_path = os.path.join(
- cfg.benchmark_csv_dir_path,
- f'smeasure_set{cfg.datasets_set}.csv'
- )
- print(f'Saved S-measure results into {csv_file_path}')
- df.to_csv(csv_file_path, index=False)
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/workerpool.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/workerpool.py
deleted file mode 100644
index fe79124ddc86d0e7251d9e1a5d1012e7165249e3..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/workerpool.py
+++ /dev/null
@@ -1,158 +0,0 @@
-'''
-WorkerPool and WorkerBase for handling the common problems in managing
-a multiprocess pool of workers that aren't done by multiprocessing.Pool,
-including setup with per-process state, debugging by putting the worker
-on the main thread, and correct handling of unexpected errors, and ctrl-C.
-
-To use it,
-1. Put the per-process setup and the per-task work in the
- setup() and work() methods of your own WorkerBase subclass.
-2. To prepare the process pool, instantiate a WorkerPool, passing your
- subclass type as the first (worker) argument, as well as any setup keyword
- arguments. The WorkerPool will instantiate one of your workers in each
- worker process (passing in the setup arguments in those processes).
- If debugging, the pool can have process_count=0 to force all the work
- to be done immediately on the main thread; otherwise all the work
- will be passed to other processes.
-3. Whenever there is a new piece of work to distribute, call pool.add(*args).
- The arguments will be queued and passed as worker.work(*args) to the
- next available worker.
-4. When all the work has been distributed, call pool.join() to wait for all
- the work to complete and to finish and terminate all the worker processes.
- When pool.join() returns, all the work will have been done.
-
-No arrangement is made to collect the results of the work: for example,
-the return value of work() is ignored. If you need to collect the
-results, use your own mechanism (filesystem, shared memory object, queue)
-which can be distributed using setup arguments.
-'''
-
-from multiprocessing import Process, Queue, cpu_count
-import signal
-import atexit
-import sys
-
-class WorkerBase(Process):
- '''
- Subclass this class and override its work() method (and optionally,
- setup() as well) to define the units of work to be done in a process
- worker in a woker pool.
- '''
- def __init__(self, i, process_count, queue, initargs):
- if process_count > 0:
- # Make sure we ignore ctrl-C if we are not on main process.
- signal.signal(signal.SIGINT, signal.SIG_IGN)
- self.process_id = i
- self.process_count = process_count
- self.queue = queue
- super(WorkerBase, self).__init__()
- self.setup(**initargs)
- def run(self):
- # Do the work until None is dequeued
- while True:
- try:
- work_batch = self.queue.get()
- except (KeyboardInterrupt, SystemExit):
- print('Exiting...')
- break
- if work_batch is None:
- self.queue.put(None) # for another worker
- return
- self.work(*work_batch)
- def setup(self, **initargs):
- '''
- Override this method for any per-process initialization.
- Keywoard args are passed from WorkerPool constructor.
- '''
- pass
- def work(self, *args):
- '''
- Override this method for one-time initialization.
- Args are passed from WorkerPool.add() arguments.
- '''
- raise NotImplementedError('worker subclass needed')
-
-class WorkerPool(object):
- '''
- Instantiate this object (passing a WorkerBase subclass type
- as its first argument) to create a worker pool. Then call
- pool.add(*args) to queue args to distribute to worker.work(*args),
- and call pool.join() to wait for all the workers to complete.
- '''
- def __init__(self, worker=WorkerBase, process_count=None, **initargs):
- global active_pools
- if process_count is None:
- process_count = cpu_count()
- if process_count == 0:
- # zero process_count uses only main process, for debugging.
- self.queue = None
- self.processes = None
- self.worker = worker(None, 0, None, initargs)
- return
- # Ctrl-C strategy: worker processes should ignore ctrl-C. Set
- # this up to be inherited by child processes before forking.
- original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)
- active_pools[id(self)] = self
- self.queue = Queue(maxsize=(process_count * 3))
- self.processes = None # Initialize before trying to construct workers
- self.processes = [worker(i, process_count, self.queue, initargs)
- for i in range(process_count)]
- for p in self.processes:
- p.start()
- # The main process should handle ctrl-C. Restore this now.
- signal.signal(signal.SIGINT, original_sigint_handler)
- def add(self, *work_batch):
- if self.queue is None:
- if hasattr(self, 'worker'):
- self.worker.work(*work_batch)
- else:
- print('WorkerPool shutting down.', file=sys.stderr)
- else:
- try:
- # The queue can block if the work is so slow it gets full.
- self.queue.put(work_batch)
- except (KeyboardInterrupt, SystemExit):
- # Handle ctrl-C if done while waiting for the queue.
- self.early_terminate()
- def join(self):
- # End the queue, and wait for all worker processes to complete nicely.
- if self.queue is not None:
- self.queue.put(None)
- for p in self.processes:
- p.join()
- self.queue = None
- # Remove myself from the set of pools that need cleanup on shutdown.
- try:
- del active_pools[id(self)]
- except:
- pass
- def early_terminate(self):
- # When shutting down unexpectedly, first end the queue.
- if self.queue is not None:
- try:
- self.queue.put_nowait(None) # Nonblocking put throws if full.
- self.queue = None
- except:
- pass
- # But then don't wait: just forcibly terminate workers.
- if self.processes is not None:
- for p in self.processes:
- p.terminate()
- self.processes = None
- try:
- del active_pools[id(self)]
- except:
- pass
- def __del__(self):
- if self.queue is not None:
- print('ERROR: workerpool.join() not called!', file=sys.stderr)
- self.join()
-
-# Error and ctrl-C handling: kill worker processes if the main process ends.
-active_pools = {}
-def early_terminate_pools():
- for _, pool in list(active_pools.items()):
- pool.early_terminate()
-
-atexit.register(early_terminate_pools)
-
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/auto_factory.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/auto_factory.py
deleted file mode 100644
index 688bbd4853284305d047be0552077f721e2f97de..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/auto_factory.py
+++ /dev/null
@@ -1,644 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Factory function to build auto-model classes."""
-import importlib
-from collections import OrderedDict
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import copy_func
-from transformers.utils import logging
-from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-
-CLASS_DOCSTRING = """
- This is a generic model class that will be instantiated as one of the model classes of the library when created
- with the [`~BaseAutoModelClass.from_pretrained`] class method or the [`~BaseAutoModelClass.from_config`] class
- method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
-"""
-
-FROM_CONFIG_DOCSTRING = """
- Instantiates one of the model classes of the library from a configuration.
-
- Note:
- Loading a model from its configuration file does **not** load the model weights. It only affects the
- model's configuration. Use [`~BaseAutoModelClass.from_pretrained`] to load the model weights.
-
- Args:
- config ([`PretrainedConfig`]):
- The model class to instantiate is selected based on the configuration class:
-
- List options
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("checkpoint_placeholder")
- >>> model = BaseAutoModelClass.from_config(config)
- ```
-"""
-
-FROM_PRETRAINED_TORCH_DOCSTRING = """
- Instantiate one of the model classes of the library from a pretrained model.
-
- The model class to instantiate is selected based on the `model_type` property of the config object (either
- passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
- falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are
- deactivated). To train the model, you should first set it back in training mode with `model.train()`
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
- user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing model weights saved using
- [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
- this case, `from_tf` should be set to `True` and a configuration object should be provided as
- `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
- PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, *optional*):
- Will be passed along to the underlying model `__init__()` method.
- config ([`PretrainedConfig`], *optional*):
- Configuration for the model to use instead of an automatically loaded configuration. Configuration can
- be automatically loaded when:
-
- - The model is a model provided by the library (loaded with the *model id* string of a pretrained
- model).
- - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
- save directory.
- - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
- configuration JSON file named *config.json* is found in the directory.
- state_dict (*Dict[str, torch.Tensor]*, *optional*):
- A state dictionary to use instead of a state dictionary loaded from saved weights file.
-
- This option can be used if you want to create a model from a pretrained configuration but load your own
- weights. In this case though, you should check if using [`~PreTrainedModel.save_pretrained`] and
- [`~PreTrainedModel.from_pretrained`] is not a simpler option.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- from_tf (`bool`, *optional*, defaults to `False`):
- Load the model weights from a TensorFlow checkpoint save file (see docstring of
- `pretrained_model_name_or_path` argument).
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, *optional*, defaults to `False`):
- Whether or not to only look at local files (e.g., not try downloading the model).
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs (additional keyword arguments, *optional*):
- Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
- `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
- automatically loaded:
-
- - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
- underlying model's `__init__` method (we assume all relevant updates to the configuration have
- already been done)
- - If a configuration is not provided, `kwargs` will be first passed to the configuration class
- initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
- corresponds to a configuration attribute will be used to override said attribute with the
- supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
- will be passed to the underlying model's `__init__` function.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download model and configuration from huggingface.co and cache.
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
-
- >>> # Update configuration during loading
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
- >>> model.config.output_attentions
- True
-
- >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
- >>> config = AutoConfig.from_pretrained("./tf_model/shortcut_placeholder_tf_model_config.json")
- >>> model = BaseAutoModelClass.from_pretrained(
- ... "./tf_model/shortcut_placeholder_tf_checkpoint.ckpt.index", from_tf=True, config=config
- ... )
- ```
-"""
-
-FROM_PRETRAINED_TF_DOCSTRING = """
- Instantiate one of the model classes of the library from a pretrained model.
-
- The model class to instantiate is selected based on the `model_type` property of the config object (either
- passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
- falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
- user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing model weights saved using
- [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- - A path or url to a *PyTorch state_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this
- case, `from_pt` should be set to `True` and a configuration object should be provided as `config`
- argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
- using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, *optional*):
- Will be passed along to the underlying model `__init__()` method.
- config ([`PretrainedConfig`], *optional*):
- Configuration for the model to use instead of an automatically loaded configuration. Configuration can
- be automatically loaded when:
-
- - The model is a model provided by the library (loaded with the *model id* string of a pretrained
- model).
- - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
- save directory.
- - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
- configuration JSON file named *config.json* is found in the directory.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- from_pt (`bool`, *optional*, defaults to `False`):
- Load the model weights from a PyTorch checkpoint save file (see docstring of
- `pretrained_model_name_or_path` argument).
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, *optional*, defaults to `False`):
- Whether or not to only look at local files (e.g., not try downloading the model).
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs (additional keyword arguments, *optional*):
- Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
- `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
- automatically loaded:
-
- - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
- underlying model's `__init__` method (we assume all relevant updates to the configuration have
- already been done)
- - If a configuration is not provided, `kwargs` will be first passed to the configuration class
- initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
- corresponds to a configuration attribute will be used to override said attribute with the
- supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
- will be passed to the underlying model's `__init__` function.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download model and configuration from huggingface.co and cache.
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
-
- >>> # Update configuration during loading
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
- >>> model.config.output_attentions
- True
-
- >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
- >>> config = AutoConfig.from_pretrained("./pt_model/shortcut_placeholder_pt_model_config.json")
- >>> model = BaseAutoModelClass.from_pretrained(
- ... "./pt_model/shortcut_placeholder_pytorch_model.bin", from_pt=True, config=config
- ... )
- ```
-"""
-
-FROM_PRETRAINED_FLAX_DOCSTRING = """
- Instantiate one of the model classes of the library from a pretrained model.
-
- The model class to instantiate is selected based on the `model_type` property of the config object (either
- passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
- falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
- user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing model weights saved using
- [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- - A path or url to a *PyTorch state_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this
- case, `from_pt` should be set to `True` and a configuration object should be provided as `config`
- argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
- using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, *optional*):
- Will be passed along to the underlying model `__init__()` method.
- config ([`PretrainedConfig`], *optional*):
- Configuration for the model to use instead of an automatically loaded configuration. Configuration can
- be automatically loaded when:
-
- - The model is a model provided by the library (loaded with the *model id* string of a pretrained
- model).
- - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
- save directory.
- - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
- configuration JSON file named *config.json* is found in the directory.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- from_pt (`bool`, *optional*, defaults to `False`):
- Load the model weights from a PyTorch checkpoint save file (see docstring of
- `pretrained_model_name_or_path` argument).
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, *optional*, defaults to `False`):
- Whether or not to only look at local files (e.g., not try downloading the model).
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs (additional keyword arguments, *optional*):
- Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
- `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
- automatically loaded:
-
- - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
- underlying model's `__init__` method (we assume all relevant updates to the configuration have
- already been done)
- - If a configuration is not provided, `kwargs` will be first passed to the configuration class
- initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
- corresponds to a configuration attribute will be used to override said attribute with the
- supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
- will be passed to the underlying model's `__init__` function.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig, BaseAutoModelClass
-
- >>> # Download model and configuration from huggingface.co and cache.
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
-
- >>> # Update configuration during loading
- >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
- >>> model.config.output_attentions
- True
-
- >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
- >>> config = AutoConfig.from_pretrained("./pt_model/shortcut_placeholder_pt_model_config.json")
- >>> model = BaseAutoModelClass.from_pretrained(
- ... "./pt_model/shortcut_placeholder_pytorch_model.bin", from_pt=True, config=config
- ... )
- ```
-"""
-
-
-def _get_model_class(config, model_mapping):
- supported_models = model_mapping[type(config)]
- if not isinstance(supported_models, (list, tuple)):
- return supported_models
-
- name_to_model = {model.__name__: model for model in supported_models}
- architectures = getattr(config, "architectures", [])
- for arch in architectures:
- if arch in name_to_model:
- return name_to_model[arch]
- elif f"TF{arch}" in name_to_model:
- return name_to_model[f"TF{arch}"]
- elif f"Flax{arch}" in name_to_model:
- return name_to_model[f"Flax{arch}"]
-
- # If not architecture is set in the config or match the supported models, the first element of the tuple is the
- # defaults.
- return supported_models[0]
-
-
-class _BaseAutoModelClass:
- # Base class for auto models.
- _model_mapping = None
-
- def __init__(self, *args, **kwargs):
- raise EnvironmentError(
- f"{self.__class__.__name__} is designed to be instantiated "
- f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
- f"`{self.__class__.__name__}.from_config(config)` methods."
- )
-
- @classmethod
- def from_config(cls, config, **kwargs):
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
- if not trust_remote_code:
- raise ValueError(
- "Loading this model requires you to execute the modeling file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
- "no malicious code has been contributed in a newer revision."
- )
- class_ref = config.auto_map[cls.__name__]
- module_file, class_name = class_ref.split(".")
- model_class = get_class_from_dynamic_module(
- config.name_or_path, module_file + ".py", class_name, **kwargs)
- return model_class._from_config(config, **kwargs)
- elif type(config) in cls._model_mapping.keys():
- model_class = _get_model_class(config, cls._model_mapping)
- return model_class._from_config(config, **kwargs)
-
- raise ValueError(
- f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
- f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
- )
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
- config = kwargs.pop("config", None)
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- kwargs["_from_auto"] = True
- if not isinstance(config, PretrainedConfig):
- config, kwargs = AutoConfig.from_pretrained(
- pretrained_model_name_or_path, return_unused_kwargs=True, trust_remote_code=trust_remote_code, **kwargs
- )
- if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the modeling file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
- "no malicious code has been contributed in a newer revision."
- )
- class_ref = config.auto_map[cls.__name__]
- module_file, class_name = class_ref.split(".")
- model_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
- elif type(config) in cls._model_mapping.keys():
- model_class = _get_model_class(config, cls._model_mapping)
- return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
- raise ValueError(
- f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
- f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
- )
-
- @classmethod
- def register(cls, config_class, model_class):
- """
- Register a new model for this class.
-
- Args:
- config_class ([`PretrainedConfig`]):
- The configuration corresponding to the model to register.
- model_class ([`PreTrainedModel`]):
- The model to register.
- """
- if hasattr(model_class, "config_class") and model_class.config_class != config_class:
- raise ValueError(
- "The model class you are passing has a `config_class` attribute that is not consistent with the "
- f"config class you passed (model has {model_class.config_class} and you passed {config_class}. Fix "
- "one of those so they match!"
- )
- cls._model_mapping.register(config_class, model_class)
-
-
-def insert_head_doc(docstring, head_doc=""):
- if len(head_doc) > 0:
- return docstring.replace(
- "one of the model classes of the library ",
- f"one of the model classes of the library (with a {head_doc} head) ",
- )
- return docstring.replace(
- "one of the model classes of the library ", "one of the base model classes of the library "
- )
-
-
-def auto_class_update(cls, checkpoint_for_example="bert-base-cased", head_doc=""):
- # Create a new class with the right name from the base class
- model_mapping = cls._model_mapping
- name = cls.__name__
- class_docstring = insert_head_doc(CLASS_DOCSTRING, head_doc=head_doc)
- cls.__doc__ = class_docstring.replace("BaseAutoModelClass", name)
-
- # Now we need to copy and re-register `from_config` and `from_pretrained` as class methods otherwise we can't
- # have a specific docstrings for them.
- from_config = copy_func(_BaseAutoModelClass.from_config)
- from_config_docstring = insert_head_doc(
- FROM_CONFIG_DOCSTRING, head_doc=head_doc)
- from_config_docstring = from_config_docstring.replace(
- "BaseAutoModelClass", name)
- from_config_docstring = from_config_docstring.replace(
- "checkpoint_placeholder", checkpoint_for_example)
- from_config.__doc__ = from_config_docstring
- from_config = replace_list_option_in_docstrings(
- model_mapping._model_mapping, use_model_types=False)(from_config)
- cls.from_config = classmethod(from_config)
-
- if name.startswith("TF"):
- from_pretrained_docstring = FROM_PRETRAINED_TF_DOCSTRING
- elif name.startswith("Flax"):
- from_pretrained_docstring = FROM_PRETRAINED_FLAX_DOCSTRING
- else:
- from_pretrained_docstring = FROM_PRETRAINED_TORCH_DOCSTRING
- from_pretrained = copy_func(_BaseAutoModelClass.from_pretrained)
- from_pretrained_docstring = insert_head_doc(
- from_pretrained_docstring, head_doc=head_doc)
- from_pretrained_docstring = from_pretrained_docstring.replace(
- "BaseAutoModelClass", name)
- from_pretrained_docstring = from_pretrained_docstring.replace(
- "checkpoint_placeholder", checkpoint_for_example)
- shortcut = checkpoint_for_example.split("/")[-1].split("-")[0]
- from_pretrained_docstring = from_pretrained_docstring.replace(
- "shortcut_placeholder", shortcut)
- from_pretrained.__doc__ = from_pretrained_docstring
- from_pretrained = replace_list_option_in_docstrings(
- model_mapping._model_mapping)(from_pretrained)
- cls.from_pretrained = classmethod(from_pretrained)
- return cls
-
-
-def get_values(model_mapping):
- result = []
- for model in model_mapping.values():
- if isinstance(model, (list, tuple)):
- result += list(model)
- else:
- result.append(model)
-
- return result
-
-
-def getattribute_from_module(module, attr):
- if attr is None:
- return None
- if isinstance(attr, tuple):
- return tuple(getattribute_from_module(module, a) for a in attr)
- if hasattr(module, attr):
- return getattr(module, attr)
- # Some of the mappings have entries model_type -> object of another model type. In that case we try to grab the
- # object at the top level.
- transformers_module = importlib.import_module("fengshen")
- return getattribute_from_module(transformers_module, attr)
-
-
-class _LazyAutoMapping(OrderedDict):
- """
- " A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed.
-
- Args:
-
- - config_mapping: The map model type to config class
- - model_mapping: The map model type to model (or tokenizer) class
- """
-
- def __init__(self, config_mapping, model_mapping):
- self._config_mapping = config_mapping
- self._reverse_config_mapping = {
- v: k for k, v in config_mapping.items()}
- self._model_mapping = model_mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- model_type = self._reverse_config_mapping[key.__name__]
- if model_type not in self._model_mapping:
- raise KeyError(key)
- model_name = self._model_mapping[model_type]
- return self._load_attr_from_module(model_type, model_name)
-
- def _load_attr_from_module(self, model_type, attr):
- module_name = model_type_to_module_name(model_type)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(
- f".{module_name}", "fengshen.models")
- return getattribute_from_module(self._modules[module_name], attr)
-
- def keys(self):
- mapping_keys = [
- self._load_attr_from_module(key, name)
- for key, name in self._config_mapping.items()
- if key in self._model_mapping.keys()
- ]
- return mapping_keys + list(self._extra_content.keys())
-
- def get(self, key, default):
- try:
- return self.__getitem__(key)
- except KeyError:
- return default
-
- def __bool__(self):
- return bool(self.keys())
-
- def values(self):
- mapping_values = [
- self._load_attr_from_module(key, name)
- for key, name in self._model_mapping.items()
- if key in self._config_mapping.keys()
- ]
- return mapping_values + list(self._extra_content.values())
-
- def items(self):
- mapping_items = [
- (
- self._load_attr_from_module(key, self._config_mapping[key]),
- self._load_attr_from_module(key, self._model_mapping[key]),
- )
- for key in self._model_mapping.keys()
- if key in self._config_mapping.keys()
- ]
- return mapping_items + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(self.keys())
-
- def __contains__(self, item):
- if item in self._extra_content:
- return True
- if not hasattr(item, "__name__") or item.__name__ not in self._reverse_config_mapping:
- return False
- model_type = self._reverse_config_mapping[item.__name__]
- return model_type in self._model_mapping
-
- def register(self, key, value):
- """
- Register a new model in this mapping.
- """
- if hasattr(key, "__name__") and key.__name__ in self._reverse_config_mapping:
- model_type = self._reverse_config_mapping[key.__name__]
- if model_type in self._model_mapping.keys():
- raise ValueError(
- f"'{key}' is already used by a Transformers model.")
-
- self._extra_content[key] = value
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/adaptive_loss.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/adaptive_loss.py
deleted file mode 100644
index 6209ceaedb6d8120ad820c11b55c13596447933c..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/adaptive_loss.py
+++ /dev/null
@@ -1,123 +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 math
-from dataclasses import dataclass
-
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.constants import DDP_BACKEND_CHOICES
-from omegaconf import II
-
-
-@dataclass
-class AdaptiveLossConfig(FairseqDataclass):
- sentence_avg: bool = II("optimization.sentence_avg")
- ddp_backend: DDP_BACKEND_CHOICES = II("distributed_training.ddp_backend")
-
-
-@register_criterion("adaptive_loss", dataclass=AdaptiveLossConfig)
-class AdaptiveLoss(FairseqCriterion):
- """This is an implementation of the loss function accompanying the adaptive softmax approximation for
- graphical processing units (GPU), described in the paper "Efficient softmax approximation for GPUs"
- (http://arxiv.org/abs/1609.04309)."""
-
- def __init__(self, task, sentence_avg):
- super().__init__(task)
- self.sentence_avg = sentence_avg
-
- @classmethod
- def build_criterion(cls, cfg: AdaptiveLossConfig, task):
- if cfg.ddp_backend in {"c10d", "pytorch_ddp"}:
- raise Exception(
- "AdaptiveLoss is not compatible with the PyTorch "
- "version of DistributedDataParallel. Please use "
- "`--ddp-backend=legacy_ddp` instead."
- )
- return cls(task, cfg.sentence_avg)
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
-
- assert (
- hasattr(model.decoder, "adaptive_softmax")
- and model.decoder.adaptive_softmax is not None
- )
- adaptive_softmax = model.decoder.adaptive_softmax
-
- net_output = model(**sample["net_input"])
- orig_target = model.get_targets(sample, net_output)
-
- nsentences = orig_target.size(0)
- orig_target = orig_target.view(-1)
-
- bsz = orig_target.size(0)
-
- logits, target = adaptive_softmax(net_output[0], orig_target)
- assert len(target) == len(logits)
-
- loss = net_output[0].new(1 if reduce else bsz).zero_()
-
- for i in range(len(target)):
- if target[i] is not None:
- assert target[i].min() >= 0 and target[i].max() <= logits[i].size(1)
- loss += F.cross_entropy(
- logits[i],
- target[i],
- ignore_index=self.padding_idx,
- reduction="sum" if reduce else "none",
- )
-
- orig = utils.strip_pad(orig_target, self.padding_idx)
- ntokens = orig.numel()
- sample_size = sample["target"].size(0) if self.sentence_avg else ntokens
- logging_output = {
- "loss": loss.data,
- "ntokens": ntokens,
- "nsentences": nsentences,
- "sample_size": sample_size,
- }
- return loss, sample_size, logging_output
-
- @staticmethod
- def reduce_metrics(logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
- ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
- sample_size = utils.item(
- sum(log.get("sample_size", 0) for log in logging_outputs)
- )
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- if sample_size != ntokens:
- metrics.log_scalar(
- "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
- )
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
- )
- else:
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/inference/advanced_infer.sh b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/inference/advanced_infer.sh
deleted file mode 100644
index 6bbd53454331f0bd5157aa4e38ae4d329fba05fd..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/inference/advanced_infer.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-gender='male'
-glowdir='../../checkpoints/glow/'$gender'/'
-hifidir='../../checkpoints/hifi/'$gender'/'
-device='cpu'
-text='Hey mr. I am testing this one. Now on multiple sentences. Just want to see the flow.'
-noise_scale='0.667'
-length_scale='1.0'
-transliteration=1
-number_conversion=1
-split_sentences=1
-lang='en'
-
-
-timestamp=$(date +%s)
-wav='../../results/'$gender'/'
-wav_file=$wav/$timestamp'.wav'
-
-
-mkdir -p $wav
-
-python ../../utils/inference/advanced_tts.py -a $glowdir -v $hifidir -d $device -t "$text" -w $wav_file -L $lang -n $noise_scale -l $length_scale -T $transliteration -N $number_conversion -S $split_sentences
-echo "File saved at: "$wav_file
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/syllable/syllabifier.py b/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/syllable/syllabifier.py
deleted file mode 100644
index 2a0cfb0be6ac9e9c2c9938b4a8b4b84b054d28c8..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/syllable/syllabifier.py
+++ /dev/null
@@ -1,302 +0,0 @@
-#
-# Copyright (c) 2013-present, Anoop Kunchukuttan
-# All rights reserved.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-#
-
-import codecs, sys
-from indicnlp.script import indic_scripts as si
-import re
-
-chillu_char_map= {
- '\u0d7a': '\u0d23',
- '\u0d7b': '\u0d28',
- '\u0d7c': '\u0d30',
- '\u0d7d': '\u0d32',
- '\u0d7e': '\u0d33',
- '\u0d7f': '\u0d15',
- }
-
-char_chillu_map= {}
-for k,v in chillu_char_map.items():
- char_chillu_map[v]=k
-
-def normalize_malayalam(word):
-
- word_mask=re.sub(r'[0-9]','0',word)
-
- # instead of chillu characters, use consonant+halant
- for chillu,char in chillu_char_map.items():
- word=word.replace(chillu,'{}\u0d4d'.format(char))
- word_mask=word_mask.replace(chillu,'41')
-
- word_mask=re.sub(r'[^0-9]','0',word_mask)
-
- return word, word_mask
-
-def denormalize_malayalam(word, word_mask):
-
- word=list(word)
- word_mask=list(word_mask)
-
- ## pattern 4
- idx=0
- while idx>=0:
- try:
- idx=word_mask.index('4',idx)
- word[idx:idx+2]=char_chillu_map[word[idx]]
- word_mask[idx:idx+2]='0'
- start=idx
- except ValueError as e:
- break
-
- return ''.join(word)
-
-def normalize_punjabi(word):
- word_mask=re.sub(r'[0-9]','0',word)
-
- ## replace tippi with anusvaar
- word=word.replace('\u0a70','\u0a02')
- word_mask=word_mask.replace('\u0a70','2')
-
- ## replace addak+consonant with consonat+halant+consonant
- word=re.sub(r'\u0a71(.)','\\1\u0a4d\\1',word)
- word_mask=re.sub(r'\u0a71(.)','311',word_mask)
-
- word_mask=re.sub(r'[^0-9]','0',word_mask)
-
- return word, word_mask
-
-def denormalize_punjabi(word, word_mask):
-
- word=list(word)
- word_mask=list(word_mask)
-
- ## pattern 2
- idx=0
- while idx>=0:
- try:
- idx=word_mask.index('2',idx)
- word[idx]='\u0a70'
- word_mask[idx]='0'
- start=idx
- except ValueError as e:
- break
-
- ## pattern 3
- idx=0
- while idx>=0:
- try:
- idx=word_mask.index('3',idx)
- word[idx:idx+3]='\u0a71{}'.format(word[idx])
- word_mask[idx:idx+3]='00'
- start=idx
- except ValueError as e:
- break
-
- return ''.join(word)
-
-def char_backoff(syllables_list,vocab):
- syllables_final=[]
-
- if vocab is None:
- syllables_final=syllables_list
- else:
- for s in syllables_list:
- if s in vocab:
- syllables_final.append(s)
- else:
- for x in s:
- syllables_final.append(x)
-
- return syllables_final
-
-
-def orthographic_syllabify_improved(word,lang,vocab=None):
-
- word_mask=['0']*len(word)
-
- if lang=='ml':
- word, word_mask = normalize_malayalam(word)
- word=word
- elif lang=='pa':
- word, word_mask = normalize_punjabi(word)
-
- p_vectors=[si.get_phonetic_feature_vector(c,lang) for c in word]
-
- syllables=[]
- syllables_mask=[]
-
- for i in range(len(word)):
- v=p_vectors[i]
-
- syllables.append(word[i])
- syllables_mask.append(word_mask[i])
-
- ### simplified syllabification
- #if i+1Limitations
- -FSDP currently has several limitations compared to fairseq's default DDP backend (PyTorch DDP): -* while FSDP is full compatible with pointwise Optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.), it is not currently compatible with non-pointwise Optimizers (e.g., Adagrad, Adafactor, LAMB, etc.) -* FSDP depends on flattening the parameters, so models that currently require `--fp16-no-flatten-grads` may not be supported - -See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed -explanation of these and other limitations. - -
How it works
-
-
-
-See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed
-explanation of how FSDP works.
-
-
Example output
- -``` -(...) -2021-03-08 12:29:51 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) -(...) -2021-03-08 12:29:51 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs) -2021-03-08 12:29:51 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 -(...) -Adam Optimizer #0 is created with AVX2 arithmetic capability. -Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 -(...) -2021-03-08 12:31:36 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.475", "ppl": "91120.8", "wps": "0", "ups": "0", "wpb": "16384", "bsz": "8", "num_updates": "1", "lr": "2e-05", "gnorm": "20.751", "loss_scale": "4", "train_wall": "99", "gb_free": "9.3", "wall": "105"} -2021-03-08 12:32:33 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.446", "ppl": "89281.6", "wps": "288.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "2", "lr": "4e-05", "gnorm": "19.777", "loss_scale": "4", "train_wall": "57", "gb_free": "9.3", "wall": "161"} -2021-03-08 12:33:12 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 -2021-03-08 12:33:51 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 -2021-03-08 12:34:45 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "25.22", "ppl": "3.90691e+07", "wps": "123.4", "ups": "0.01", "wpb": "16384", "bsz": "8", "num_updates": "3", "lr": "6e-05", "gnorm": "131.281", "loss_scale": "1", "train_wall": "133", "gb_free": "9.3", "wall": "294"} -2021-03-08 12:35:43 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.079", "ppl": "276809", "wps": "285.5", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "4", "lr": "8e-05", "gnorm": "13.776", "loss_scale": "1", "train_wall": "57", "gb_free": "9.3", "wall": "351"} -2021-03-08 12:36:35 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "23.729", "ppl": "1.39088e+07", "wps": "316.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "5", "lr": "0.0001", "gnorm": "72.774", "loss_scale": "1", "train_wall": "52", "gb_free": "9.3", "wall": "403"} -2021-03-08 12:37:28 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "20.429", "ppl": "1.41203e+06", "wps": "307.6", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "6", "lr": "8e-05", "gnorm": "60.846", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "456"} -2021-03-08 12:38:27 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.965", "ppl": "511684", "wps": "279.4", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "7", "lr": "6e-05", "gnorm": "22.687", "loss_scale": "1", "train_wall": "59", "gb_free": "9.3", "wall": "515"} -2021-03-08 12:39:18 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.345", "ppl": "332887", "wps": "319.1", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "8", "lr": "4e-05", "gnorm": "8.451", "loss_scale": "1", "train_wall": "51", "gb_free": "9.3", "wall": "566"} -2021-03-08 12:40:11 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "18.262", "ppl": "314336", "wps": "305.9", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "9", "lr": "2e-05", "gnorm": "6.457", "loss_scale": "1", "train_wall": "54", "gb_free": "9.3", "wall": "620"} -2021-03-08 12:41:04 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "17.556", "ppl": "192686", "wps": "311.8", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "10", "lr": "0", "gnorm": "5.796", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "673"} -2021-03-08 12:41:04 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 -2021-03-08 12:41:04 | INFO | fairseq_cli.train | begin validation on "valid" subset -2021-03-08 12:43:15 | INFO | valid | {"epoch": 1, "valid_loss": "17.953", "valid_ppl": "253807", "valid_wps": "1868.4", "valid_wpb": "15400.2", "valid_bsz": "7.6", "valid_num_updates": "10"} -2021-03-08 12:43:15 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) -2021-03-08 12:43:15 | INFO | train | {"epoch": 1, "train_loss": "19.351", "train_ppl": "668509", "train_wps": "210.9", "train_ups": "0.01", "train_wpb": "16384", "train_bsz": "8", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "36.26", "train_loss_scale": "1", "train_train_wall": "667", "train_gb_free": "9.3", "train_wall": "804"} -2021-03-08 12:43:15 | INFO | fairseq_cli.train | done training in 798.6 seconds -``` - -
Example output
- -``` -(...) -2021-03-08 18:04:09 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) -(...) -2021-03-08 18:04:09 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs) -2021-03-08 18:04:09 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 -(...) -Adam Optimizer #0 is created with AVX2 arithmetic capability. -Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 -(...) -2021-03-08 18:05:06 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "16.408", "ppl": "86945.6", "wps": "0", "ups": "0", "wpb": "131072", "bsz": "64", "num_updates": "1", "lr": "2e-05", "gnorm": "18.27", "loss_scale": "4", "train_wall": "47", "gb_free": "9.3", "wall": "56"} -2021-03-08 18:05:45 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "16.352", "ppl": "83644.3", "wps": "3283.4", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "2", "lr": "4e-05", "gnorm": "18.411", "loss_scale": "4", "train_wall": "40", "gb_free": "9.3", "wall": "96"} -2021-03-08 18:06:21 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 -2021-03-08 18:06:56 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 -2021-03-08 18:07:37 | INFO | train_inner | {"epoch": 1, "update": 0.006, "loss": "23.682", "ppl": "1.34537e+07", "wps": "1176.6", "ups": "0.01", "wpb": "131072", "bsz": "64", "num_updates": "3", "lr": "6e-05", "gnorm": "119.682", "loss_scale": "1", "train_wall": "111", "gb_free": "9.3", "wall": "208"} -2021-03-08 18:08:18 | INFO | train_inner | {"epoch": 1, "update": 0.007, "loss": "18.988", "ppl": "519921", "wps": "3189.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "4", "lr": "8e-05", "gnorm": "14.934", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "249"} -2021-03-08 18:08:59 | INFO | train_inner | {"epoch": 1, "update": 0.008, "loss": "20.08", "ppl": "1.10798e+06", "wps": "3223.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "5", "lr": "0.0001", "gnorm": "59.92", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "289"} -2021-03-08 18:09:39 | INFO | train_inner | {"epoch": 1, "update": 0.009, "loss": "18.323", "ppl": "327980", "wps": "3256.6", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "6", "lr": "8e-05", "gnorm": "37.425", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "330"} -2021-03-08 18:10:20 | INFO | train_inner | {"epoch": 1, "update": 0.01, "loss": "17.264", "ppl": "157354", "wps": "3188.7", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "7", "lr": "6e-05", "gnorm": "10.824", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "371"} -2021-03-08 18:11:01 | INFO | train_inner | {"epoch": 1, "update": 0.011, "loss": "16.794", "ppl": "113647", "wps": "3230", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "8", "lr": "4e-05", "gnorm": "5.616", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "411"} -2021-03-08 18:11:39 | INFO | train_inner | {"epoch": 1, "update": 0.012, "loss": "16.706", "ppl": "106938", "wps": "3384", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "9", "lr": "2e-05", "gnorm": "5.318", "loss_scale": "1", "train_wall": "39", "gb_free": "9.3", "wall": "450"} -2021-03-08 18:12:19 | INFO | train_inner | {"epoch": 1, "update": 0.013, "loss": "16.548", "ppl": "95796.2", "wps": "3274.4", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "10", "lr": "0", "gnorm": "5.22", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "490"} -2021-03-08 18:12:19 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 -2021-03-08 18:12:19 | INFO | fairseq_cli.train | begin validation on "valid" subset -2021-03-08 18:12:45 | INFO | valid | {"epoch": 1, "valid_loss": "16.624", "valid_ppl": "101000", "valid_wps": "10855.9", "valid_wpb": "123202", "valid_bsz": "60.5", "valid_num_updates": "10"} -2021-03-08 18:12:45 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) -2021-03-08 18:12:45 | INFO | train | {"epoch": 1, "train_loss": "18.114", "train_ppl": "283776", "train_wps": "2567.8", "train_ups": "0.02", "train_wpb": "131072", "train_bsz": "64", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "29.562", "train_loss_scale": "1", "train_train_wall": "480", "train_gb_free": "9.3", "train_wall": "516"} -2021-03-08 18:12:45 | INFO | fairseq_cli.train | done training in 509.9 seconds -``` - -
OneFormer: One Transformer to Rule Universal Image Segmentation
" - -description = "Jitesh Jain, Jiachen Li*, MangTik Chiu*, Ali Hassani, Nikita Orlov, Humphrey Shi
" \ - + "Project Page | ArXiv Paper | Github Repo
" \ - + "\ - OneFormer is the first multi-task universal image segmentation framework based on transformers. Our single OneFormer model achieves state-of-the-art performance across all three segmentation tasks with a single task-conditioned joint training process. OneFormer uses a task token to condition the model on the task in focus, making our architecture task-guided for training, and task-dynamic for inference, all with a single model. We believe OneFormer is a significant step towards making image segmentation more universal and accessible.\ -
" \ - + "[Note: Inference on CPU may take upto 2 minutes. On a single RTX A6000 GPU, OneFormer is able to inference at more than 15 FPS.]
" - -setup_modules() - -gradio_inputs = [gr.Image(source="upload", tool=None, label="Input Image",type="filepath"), - gr.Radio(choices=["the task is panoptic" ,"the task is instance", "the task is semantic"], type="value", value="the task is panoptic", label="Task Token Input"), - gr.Radio(choices=["COCO (133 classes)" ,"Cityscapes (19 classes)", "ADE20K (150 classes)"], type="value", value="COCO (133 classes)", label="Model"), - gr.Radio(choices=["DiNAT-L" ,"Swin-L"], type="value", value="DiNAT-L", label="Backbone"), - ] -gradio_outputs = [gr.Image(type="pil", label="Segmentation Overlay"), gr.Image(type="pil", label="Segmentation Map")] - - -examples = [["examples/coco.jpeg", "the task is panoptic", "COCO (133 classes)", "DiNAT-L"], - ["examples/cityscapes.png", "the task is panoptic", "Cityscapes (19 classes)", "DiNAT-L"], - ["examples/ade20k.jpeg", "the task is panoptic", "ADE20K (150 classes)", "DiNAT-L"]] - - -iface = gr.Interface(fn=segment, inputs=gradio_inputs, - outputs=gradio_outputs, - examples_per_page=5, - allow_flagging="never", - examples=examples, title=title, - description=description) - -iface.launch(enable_queue=True, server_name="0.0.0.0") \ No newline at end of file diff --git a/spaces/LanguageBind/LanguageBind/languagebind/thermal/tokenization_thermal.py b/spaces/LanguageBind/LanguageBind/languagebind/thermal/tokenization_thermal.py deleted file mode 100644 index a4ebb5607bc8f2a24341a7b11f22663e760012dd..0000000000000000000000000000000000000000 --- a/spaces/LanguageBind/LanguageBind/languagebind/thermal/tokenization_thermal.py +++ /dev/null @@ -1,77 +0,0 @@ -from transformers import CLIPTokenizer -from transformers.utils import logging - -logger = logging.get_logger(__name__) - -VOCAB_FILES_NAMES = { - "vocab_file": "vocab.json", - "merges_file": "merges.txt", -} - -PRETRAINED_VOCAB_FILES_MAP = { - "vocab_file": { - "lb203/LanguageBind-Thermal": "https://huggingface.co/lb203/LanguageBind-Thermal/resolve/main/vocab.json", - }, - "merges_file": { - "lb203/LanguageBind-Thermal": "https://huggingface.co/lb203/LanguageBind-Thermal/resolve/main/merges.txt", - }, -} - -PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { - "lb203/LanguageBind-Thermal": 77, -} - - -PRETRAINED_INIT_CONFIGURATION = { - "lb203/LanguageBind-Thermal": {}, -} - -class LanguageBindThermalTokenizer(CLIPTokenizer): - """ - Construct a CLIP tokenizer. Based on byte-level Byte-Pair-Encoding. - - This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to - this superclass for more information regarding those methods. - - Args: - vocab_file (`str`): - Path to the vocabulary file. - merges_file (`str`): - Path to the merges file. - errors (`str`, *optional*, defaults to `"replace"`): - Paradigm to follow when decoding bytes to UTF-8. See - [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. - unk_token (`str`, *optional*, defaults to `<|endoftext|>`): - The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this - token instead. - bos_token (`str`, *optional*, defaults to `<|startoftext|>`): - The beginning of sequence token. - eos_token (`str`, *optional*, defaults to `<|endoftext|>`): - The end of sequence token. - """ - - vocab_files_names = VOCAB_FILES_NAMES - pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP - max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES - model_input_names = ["input_ids", "attention_mask"] - - def __init__( - self, - vocab_file, - merges_file, - errors="replace", - unk_token="<|endoftext|>", - bos_token="<|startoftext|>", - eos_token="<|endoftext|>", - pad_token="<|endoftext|>", # hack to enable padding - **kwargs, - ): - super(LanguageBindThermalTokenizer, self).__init__( - vocab_file, - merges_file, - errors, - unk_token, - bos_token, - eos_token, - pad_token, # hack to enable padding - **kwargs,) \ No newline at end of file diff --git a/spaces/Lizzbitt/pi2/README.md b/spaces/Lizzbitt/pi2/README.md deleted file mode 100644 index 4a1e0796ba559e87de0197004e2333c140b4d543..0000000000000000000000000000000000000000 --- a/spaces/Lizzbitt/pi2/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Pi2 -emoji: 🚀 -colorFrom: purple -colorTo: gray -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/options/base_options.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/options/base_options.py deleted file mode 100644 index b8ef551eb982a3b551f77090028304f40883a94a..0000000000000000000000000000000000000000 --- a/spaces/MCkernick/Image_Restoration_Colorization/Global/options/base_options.py +++ /dev/null @@ -1,373 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import argparse -import os -from util import util -import torch - - -class BaseOptions: - def __init__(self): - self.parser = argparse.ArgumentParser() - self.initialized = False - - def initialize(self): - # experiment specifics - self.parser.add_argument( - "--name", - type=str, - default="label2city", - help="name of the experiment. It decides where to store samples and models", - ) - self.parser.add_argument( - "--gpu_ids", type=str, default="0", help="gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU" - ) - self.parser.add_argument( - "--checkpoints_dir", type=str, default="./checkpoints", help="models are saved here" - ) ## note: to add this param when using philly - # self.parser.add_argument('--project_dir', type=str, default='./', help='the project is saved here') ################### This is necessary for philly - self.parser.add_argument( - "--outputs_dir", type=str, default="./outputs", help="models are saved here" - ) ## note: to add this param when using philly Please end with '/' - self.parser.add_argument("--model", type=str, default="pix2pixHD", help="which model to use") - self.parser.add_argument( - "--norm", type=str, default="instance", help="instance normalization or batch normalization" - ) - self.parser.add_argument("--use_dropout", action="store_true", help="use dropout for the generator") - self.parser.add_argument( - "--data_type", - default=32, - type=int, - choices=[8, 16, 32], - help="Supported data type i.e. 8, 16, 32 bit", - ) - self.parser.add_argument("--verbose", action="store_true", default=False, help="toggles verbose") - - # input/output sizes - self.parser.add_argument("--batchSize", type=int, default=1, help="input batch size") - self.parser.add_argument("--loadSize", type=int, default=1024, help="scale images to this size") - self.parser.add_argument("--fineSize", type=int, default=512, help="then crop to this size") - self.parser.add_argument("--label_nc", type=int, default=35, help="# of input label channels") - self.parser.add_argument("--input_nc", type=int, default=3, help="# of input image channels") - self.parser.add_argument("--output_nc", type=int, default=3, help="# of output image channels") - - # for setting inputs - self.parser.add_argument("--dataroot", type=str, default="./datasets/cityscapes/") - self.parser.add_argument( - "--resize_or_crop", - type=str, - default="scale_width", - help="scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]", - ) - self.parser.add_argument( - "--serial_batches", - action="store_true", - help="if true, takes images in order to make batches, otherwise takes them randomly", - ) - self.parser.add_argument( - "--no_flip", - action="store_true", - help="if specified, do not flip the images for data argumentation", - ) - self.parser.add_argument("--nThreads", default=2, type=int, help="# threads for loading data") - self.parser.add_argument( - "--max_dataset_size", - type=int, - default=float("inf"), - help="Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.", - ) - - # for displays - self.parser.add_argument("--display_winsize", type=int, default=512, help="display window size") - self.parser.add_argument( - "--tf_log", - action="store_true", - help="if specified, use tensorboard logging. Requires tensorflow installed", - ) - - # for generator - self.parser.add_argument("--netG", type=str, default="global", help="selects model to use for netG") - self.parser.add_argument("--ngf", type=int, default=64, help="# of gen filters in first conv layer") - self.parser.add_argument("--k_size", type=int, default=3, help="# kernel size conv layer") - self.parser.add_argument("--use_v2", action="store_true", help="use DCDCv2") - self.parser.add_argument("--mc", type=int, default=1024, help="# max channel") - self.parser.add_argument("--start_r", type=int, default=3, help="start layer to use resblock") - self.parser.add_argument( - "--n_downsample_global", type=int, default=4, help="number of downsampling layers in netG" - ) - self.parser.add_argument( - "--n_blocks_global", - type=int, - default=9, - help="number of residual blocks in the global generator network", - ) - self.parser.add_argument( - "--n_blocks_local", - type=int, - default=3, - help="number of residual blocks in the local enhancer network", - ) - self.parser.add_argument( - "--n_local_enhancers", type=int, default=1, help="number of local enhancers to use" - ) - self.parser.add_argument( - "--niter_fix_global", - type=int, - default=0, - help="number of epochs that we only train the outmost local enhancer", - ) - - self.parser.add_argument( - "--load_pretrain", - type=str, - default="", - help="load the pretrained model from the specified location", - ) - - # for instance-wise features - self.parser.add_argument( - "--no_instance", action="store_true", help="if specified, do *not* add instance map as input" - ) - self.parser.add_argument( - "--instance_feat", - action="store_true", - help="if specified, add encoded instance features as input", - ) - self.parser.add_argument( - "--label_feat", action="store_true", help="if specified, add encoded label features as input" - ) - self.parser.add_argument("--feat_num", type=int, default=3, help="vector length for encoded features") - self.parser.add_argument( - "--load_features", action="store_true", help="if specified, load precomputed feature maps" - ) - self.parser.add_argument( - "--n_downsample_E", type=int, default=4, help="# of downsampling layers in encoder" - ) - self.parser.add_argument( - "--nef", type=int, default=16, help="# of encoder filters in the first conv layer" - ) - self.parser.add_argument("--n_clusters", type=int, default=10, help="number of clusters for features") - - # diy - self.parser.add_argument("--self_gen", action="store_true", help="self generate") - self.parser.add_argument( - "--mapping_n_block", type=int, default=3, help="number of resblock in mapping" - ) - self.parser.add_argument("--map_mc", type=int, default=64, help="max channel of mapping") - self.parser.add_argument("--kl", type=float, default=0, help="KL Loss") - self.parser.add_argument( - "--load_pretrainA", - type=str, - default="", - help="load the pretrained model from the specified location", - ) - self.parser.add_argument( - "--load_pretrainB", - type=str, - default="", - help="load the pretrained model from the specified location", - ) - self.parser.add_argument("--feat_gan", action="store_true") - self.parser.add_argument("--no_cgan", action="store_true") - self.parser.add_argument("--map_unet", action="store_true") - self.parser.add_argument("--map_densenet", action="store_true") - self.parser.add_argument("--fcn", action="store_true") - self.parser.add_argument("--is_image", action="store_true", help="train image recon only pair data") - self.parser.add_argument("--label_unpair", action="store_true") - self.parser.add_argument("--mapping_unpair", action="store_true") - self.parser.add_argument("--unpair_w", type=float, default=1.0) - self.parser.add_argument("--pair_num", type=int, default=-1) - self.parser.add_argument("--Gan_w", type=float, default=1) - self.parser.add_argument("--feat_dim", type=int, default=-1) - self.parser.add_argument("--abalation_vae_len", type=int, default=-1) - - ######################### useless, just to cooperate with docker - self.parser.add_argument("--gpu", type=str) - self.parser.add_argument("--dataDir", type=str) - self.parser.add_argument("--modelDir", type=str) - self.parser.add_argument("--logDir", type=str) - self.parser.add_argument("--data_dir", type=str) - - self.parser.add_argument("--use_skip_model", action="store_true") - self.parser.add_argument("--use_segmentation_model", action="store_true") - - self.parser.add_argument("--spatio_size", type=int, default=64) - self.parser.add_argument("--test_random_crop", action="store_true") - ########################## - - self.parser.add_argument("--contain_scratch_L", action="store_true") - self.parser.add_argument( - "--mask_dilation", type=int, default=0 - ) ## Don't change the input, only dilation the mask - - self.parser.add_argument( - "--irregular_mask", type=str, default="", help="This is the root of the mask" - ) - self.parser.add_argument( - "--mapping_net_dilation", - type=int, - default=1, - help="This parameter is the dilation size of the translation net", - ) - - self.parser.add_argument( - "--VOC", type=str, default="VOC_RGB_JPEGImages.bigfile", help="The root of VOC dataset" - ) - - self.parser.add_argument("--non_local", type=str, default="", help="which non_local setting") - self.parser.add_argument( - "--NL_fusion_method", - type=str, - default="add", - help="how to fuse the origin feature and nl feature", - ) - self.parser.add_argument( - "--NL_use_mask", action="store_true", help="If use mask while using Non-local mapping model" - ) - self.parser.add_argument( - "--correlation_renormalize", - action="store_true", - help="Since after mask out the correlation matrix(which is softmaxed), the sum is not 1 any more, enable this param to re-weight", - ) - - self.parser.add_argument("--Smooth_L1", action="store_true", help="Use L1 Loss in image level") - - self.parser.add_argument( - "--face_restore_setting", type=int, default=1, help="This is for the aligned face restoration" - ) - self.parser.add_argument("--face_clean_url", type=str, default="") - self.parser.add_argument("--syn_input_url", type=str, default="") - self.parser.add_argument("--syn_gt_url", type=str, default="") - - self.parser.add_argument( - "--test_on_synthetic", - action="store_true", - help="If you want to test on the synthetic data, enable this parameter", - ) - - self.parser.add_argument("--use_SN", action="store_true", help="Add SN to every parametric layer") - - self.parser.add_argument( - "--use_two_stage_mapping", action="store_true", help="choose the model which uses two stage" - ) - - self.parser.add_argument("--L1_weight", type=float, default=10.0) - self.parser.add_argument("--softmax_temperature", type=float, default=1.0) - self.parser.add_argument( - "--patch_similarity", - action="store_true", - help="Enable this denotes using 3*3 patch to calculate similarity", - ) - self.parser.add_argument( - "--use_self", - action="store_true", - help="Enable this denotes that while constructing the new feature maps, using original feature (diagonal == 1)", - ) - - self.parser.add_argument("--use_own_dataset", action="store_true") - - self.parser.add_argument( - "--test_hole_two_folders", - action="store_true", - help="Enable this parameter means test the restoration with inpainting given twp folders which are mask and old respectively", - ) - - self.parser.add_argument( - "--no_hole", - action="store_true", - help="While test the full_model on non_scratch data, do not add random mask into the real old photos", - ) ## Only for testing - self.parser.add_argument( - "--random_hole", - action="store_true", - help="While training the full model, 50% probability add hole", - ) - - self.parser.add_argument("--NL_res", action="store_true", help="NL+Resdual Block") - - self.parser.add_argument("--image_L1", action="store_true", help="Image level loss: L1") - self.parser.add_argument( - "--hole_image_no_mask", - action="store_true", - help="while testing, give hole image but not give the mask", - ) - - self.parser.add_argument( - "--down_sample_degradation", - action="store_true", - help="down_sample the image only, corresponds to [down_sample_face]", - ) - - self.parser.add_argument( - "--norm_G", type=str, default="spectralinstance", help="The norm type of Generator" - ) - self.parser.add_argument( - "--init_G", - type=str, - default="xavier", - help="normal|xavier|xavier_uniform|kaiming|orthogonal|none", - ) - - self.parser.add_argument("--use_new_G", action="store_true") - self.parser.add_argument("--use_new_D", action="store_true") - - self.parser.add_argument( - "--only_voc", action="store_true", help="test the trianed celebA face model using VOC face" - ) - - self.parser.add_argument( - "--cosin_similarity", - action="store_true", - help="For non-local, using cosin to calculate the similarity", - ) - - self.parser.add_argument( - "--downsample_mode", - type=str, - default="nearest", - help="For partial non-local, choose how to downsample the mask", - ) - - self.parser.add_argument("--mapping_exp",type=int,default=0,help='Default 0: original PNL|1: Multi-Scale Patch Attention') - self.parser.add_argument("--inference_optimize",action='store_true',help='optimize the memory cost') - - - self.initialized = True - - def parse(self, save=True): - if not self.initialized: - self.initialize() - self.opt = self.parser.parse_args() - self.opt.isTrain = self.isTrain # train or test - - str_ids = self.opt.gpu_ids.split(",") - self.opt.gpu_ids = [] - for str_id in str_ids: - int_id = int(str_id) - if int_id >= 0: - self.opt.gpu_ids.append(int_id) - - # set gpu ids - if len(self.opt.gpu_ids) > 0: - # pass - torch.cuda.set_device(self.opt.gpu_ids[0]) - - args = vars(self.opt) - - # print('------------ Options -------------') - # for k, v in sorted(args.items()): - # print('%s: %s' % (str(k), str(v))) - # print('-------------- End ----------------') - - # save to the disk - expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name) - util.mkdirs(expr_dir) - if save and not self.opt.continue_train: - file_name = os.path.join(expr_dir, "opt.txt") - with open(file_name, "wt") as opt_file: - opt_file.write("------------ Options -------------\n") - for k, v in sorted(args.items()): - opt_file.write("%s: %s\n" % (str(k), str(v))) - opt_file.write("-------------- End ----------------\n") - return self.opt diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/monotonic_align/setup.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/monotonic_align/setup.py deleted file mode 100644 index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/monotonic_align/setup.py +++ /dev/null @@ -1,9 +0,0 @@ -from distutils.core import setup -from Cython.Build import cythonize -import numpy - -setup( - name = 'monotonic_align', - ext_modules = cythonize("core.pyx"), - include_dirs=[numpy.get_include()] -) diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp deleted file mode 100644 index 551243fdadfd1682b5dc6628623b67a79b3f6c74..0000000000000000000000000000000000000000 --- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp +++ /dev/null @@ -1,43 +0,0 @@ -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#include
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-
-
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge，通过人机校验，然后
-
-
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
--由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看 -
- -#### 部署到 Netlify -[](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo) - -#### 部署到 Vercel -如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用 - -[](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example) - -#### 部署到 Render - -[](https://render.com/deploy?repo=https://github.com/weaigc/bingo) -
-
-
-正常格式/网页端保存的格式(格式仅供参考)
- -``` -curl 'https://www.bing.com/turing/captcha/challenge' \ - -H 'authority: www.bing.com' \ - -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \ - -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \ - -H 'cache-control: max-age=0' \ - -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \ - -H 'dnt: 1' \ - -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \ - -H 'sec-ch-ua-arch: "x86"' \ - -H 'sec-ch-ua-bitness: "64"' \ - -H 'sec-ch-ua-full-version: "116.0.1938.29"' \ - -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \ - -H 'sec-ch-ua-mobile: ?0' \ - -H 'sec-ch-ua-model: ""' \ - -H 'sec-ch-ua-platform: "Windows"' \ - -H 'sec-ch-ua-platform-version: "15.0.0"' \ - -H 'sec-fetch-dest: document' \ - -H 'sec-fetch-mode: navigate' \ - -H 'sec-fetch-site: none' \ - -H 'sec-fetch-user: ?1' \ - -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \ - -H 'sec-ms-gec-version: 1-116.0.1938.29' \ - -H 'upgrade-insecure-requests: 1' \ - -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \ - -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \ - -H 'x-edge-shopping-flag: 1' \ - --compressed -``` -
-
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。
-
-
-## 答疑及交流
-
-转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)
- -``` -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 -``` -\nmultiple lines?')) - What about - multiple lines? - """ - html_stripper = re.compile('()|(<[^>]*>)|([^<]+)', re.DOTALL) - texts = (match.group(3) or '' for match in html_stripper.finditer(s)) - return ''.join(texts) - - -class SeparatedValues(str): - """ - A string separated by a separator. Overrides __iter__ for getting - the values. - - >>> list(SeparatedValues('a,b,c')) - ['a', 'b', 'c'] - - Whitespace is stripped and empty values are discarded. - - >>> list(SeparatedValues(' a, b , c, ')) - ['a', 'b', 'c'] - """ - - separator = ',' - - def __iter__(self): - parts = self.split(self.separator) - return filter(None, (part.strip() for part in parts)) - - -class Stripper: - r""" - Given a series of lines, find the common prefix and strip it from them. - - >>> lines = [ - ... 'abcdefg\n', - ... 'abc\n', - ... 'abcde\n', - ... ] - >>> res = Stripper.strip_prefix(lines) - >>> res.prefix - 'abc' - >>> list(res.lines) - ['defg\n', '\n', 'de\n'] - - If no prefix is common, nothing should be stripped. - - >>> lines = [ - ... 'abcd\n', - ... '1234\n', - ... ] - >>> res = Stripper.strip_prefix(lines) - >>> res.prefix = '' - >>> list(res.lines) - ['abcd\n', '1234\n'] - """ - - def __init__(self, prefix, lines): - self.prefix = prefix - self.lines = map(self, lines) - - @classmethod - def strip_prefix(cls, lines): - prefix_lines, lines = itertools.tee(lines) - prefix = functools.reduce(cls.common_prefix, prefix_lines) - return cls(prefix, lines) - - def __call__(self, line): - if not self.prefix: - return line - null, prefix, rest = line.partition(self.prefix) - return rest - - @staticmethod - def common_prefix(s1, s2): - """ - Return the common prefix of two lines. - """ - index = min(len(s1), len(s2)) - while s1[:index] != s2[:index]: - index -= 1 - return s1[:index] - - -def remove_prefix(text, prefix): - """ - Remove the prefix from the text if it exists. - - >>> remove_prefix('underwhelming performance', 'underwhelming ') - 'performance' - - >>> remove_prefix('something special', 'sample') - 'something special' - """ - null, prefix, rest = text.rpartition(prefix) - return rest - - -def remove_suffix(text, suffix): - """ - Remove the suffix from the text if it exists. - - >>> remove_suffix('name.git', '.git') - 'name' - - >>> remove_suffix('something special', 'sample') - 'something special' - """ - rest, suffix, null = text.partition(suffix) - return rest - - -def normalize_newlines(text): - r""" - Replace alternate newlines with the canonical newline. - - >>> normalize_newlines('Lorem Ipsum\u2029') - 'Lorem Ipsum\n' - >>> normalize_newlines('Lorem Ipsum\r\n') - 'Lorem Ipsum\n' - >>> normalize_newlines('Lorem Ipsum\x85') - 'Lorem Ipsum\n' - """ - newlines = ['\r\n', '\r', '\n', '\u0085', '\u2028', '\u2029'] - pattern = '|'.join(newlines) - return re.sub(pattern, '\n', text) - - -def _nonblank(str): - return str and not str.startswith('#') - - -@functools.singledispatch -def yield_lines(iterable): - r""" - Yield valid lines of a string or iterable. - - >>> list(yield_lines('')) - [] - >>> list(yield_lines(['foo', 'bar'])) - ['foo', 'bar'] - >>> list(yield_lines('foo\nbar')) - ['foo', 'bar'] - >>> list(yield_lines('\nfoo\n#bar\nbaz #comment')) - ['foo', 'baz #comment'] - >>> list(yield_lines(['foo\nbar', 'baz', 'bing\n\n\n'])) - ['foo', 'bar', 'baz', 'bing'] - """ - return itertools.chain.from_iterable(map(yield_lines, iterable)) - - -@yield_lines.register(str) -def _(text): - return filter(_nonblank, map(str.strip, text.splitlines())) - - -def drop_comment(line): - """ - Drop comments. - - >>> drop_comment('foo # bar') - 'foo' - - A hash without a space may be in a URL. - - >>> drop_comment('http://example.com/foo#bar') - 'http://example.com/foo#bar' - """ - return line.partition(' #')[0] - - -def join_continuation(lines): - r""" - Join lines continued by a trailing backslash. - - >>> list(join_continuation(['foo \\', 'bar', 'baz'])) - ['foobar', 'baz'] - >>> list(join_continuation(['foo \\', 'bar', 'baz'])) - ['foobar', 'baz'] - >>> list(join_continuation(['foo \\', 'bar \\', 'baz'])) - ['foobarbaz'] - - Not sure why, but... - The character preceeding the backslash is also elided. - - >>> list(join_continuation(['goo\\', 'dly'])) - ['godly'] - - A terrible idea, but... - If no line is available to continue, suppress the lines. - - >>> list(join_continuation(['foo', 'bar\\', 'baz\\'])) - ['foo'] - """ - lines = iter(lines) - for item in lines: - while item.endswith('\\'): - try: - item = item[:-2].strip() + next(lines) - except StopIteration: - return - yield item diff --git a/spaces/Riksarkivet/htr_demo/helper/text/text_overview.py b/spaces/Riksarkivet/htr_demo/helper/text/text_overview.py deleted file mode 100644 index 989c2341129c3ddeb26e41beee5cc3f2b1b42d19..0000000000000000000000000000000000000000 --- a/spaces/Riksarkivet/htr_demo/helper/text/text_overview.py +++ /dev/null @@ -1,37 +0,0 @@ -from helper.text.markdown_reader import read_markdown - - -class TextOverview: - # HTRFLOW - htrflow_col1 = read_markdown("helper/text/overview/htrflow/htrflow_col1.md") - htrflow_col2 = read_markdown("helper/text/overview/htrflow/htrflow_col2.md") - htrflow_row1 = read_markdown("helper/text/overview/htrflow/htrflow_row1.md") - htrflow_tab1 = read_markdown("helper/text/overview/htrflow/htrflow_tab1.md") - htrflow_tab2 = read_markdown("helper/text/overview/htrflow/htrflow_tab2.md") - htrflow_tab3 = read_markdown("helper/text/overview/htrflow/htrflow_tab3.md") - htrflow_tab4 = read_markdown("helper/text/overview/htrflow/htrflow_tab4.md") - - # faq & discussion - text_faq = read_markdown("helper/text/overview/faq_discussion/faq.md") - text_discussion = read_markdown("helper/text/overview/faq_discussion/discussion.md") - - # Contributions - contributions = read_markdown("helper/text/overview/contributions/contributions.md") - huminfra_image = read_markdown("helper/text/overview/contributions/huminfra_image.md") - - # Changelog & Roadmap - changelog = read_markdown("helper/text/overview/changelog_roadmap/changelog.md") - old_changelog = read_markdown("helper/text/overview/changelog_roadmap/old_changelog.md") - - roadmap = read_markdown("helper/text/overview/changelog_roadmap/roadmap.md") - - # duplicate & api - duplicate = read_markdown("helper/text/overview/duplicate_api/duplicate.md") - api1 = read_markdown("helper/text/overview/duplicate_api/api1.md") - api_code1 = read_markdown("helper/text/overview/duplicate_api/api_code1.md") - api2 = read_markdown("helper/text/overview/duplicate_api/api2.md") - api_code2 = read_markdown("helper/text/overview/duplicate_api/api_code2.md") - - -if __name__ == "__main__": - pass diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/__init__.py deleted file mode 100644 index d0051d609d3de4e7562e3fe638335c66617c4d91..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/image/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .colorspace import (bgr2gray, bgr2hls, bgr2hsv, bgr2rgb, bgr2ycbcr, - gray2bgr, gray2rgb, hls2bgr, hsv2bgr, imconvert, - rgb2bgr, rgb2gray, rgb2ycbcr, ycbcr2bgr, ycbcr2rgb) -from .geometric import (cutout, imcrop, imflip, imflip_, impad, - impad_to_multiple, imrescale, imresize, imresize_like, - imresize_to_multiple, imrotate, imshear, imtranslate, - rescale_size) -from .io import imfrombytes, imread, imwrite, supported_backends, use_backend -from .misc import tensor2imgs -from .photometric import (adjust_brightness, adjust_color, adjust_contrast, - adjust_lighting, adjust_sharpness, auto_contrast, - clahe, imdenormalize, imequalize, iminvert, - imnormalize, imnormalize_, lut_transform, posterize, - solarize) - -__all__ = [ - 'bgr2gray', 'bgr2hls', 'bgr2hsv', 'bgr2rgb', 'gray2bgr', 'gray2rgb', - 'hls2bgr', 'hsv2bgr', 'imconvert', 'rgb2bgr', 'rgb2gray', 'imrescale', - 'imresize', 'imresize_like', 'imresize_to_multiple', 'rescale_size', - 'imcrop', 'imflip', 'imflip_', 'impad', 'impad_to_multiple', 'imrotate', - 'imfrombytes', 'imread', 'imwrite', 'supported_backends', 'use_backend', - 'imdenormalize', 'imnormalize', 'imnormalize_', 'iminvert', 'posterize', - 'solarize', 'rgb2ycbcr', 'bgr2ycbcr', 'ycbcr2rgb', 'ycbcr2bgr', - 'tensor2imgs', 'imshear', 'imtranslate', 'adjust_color', 'imequalize', - 'adjust_brightness', 'adjust_contrast', 'lut_transform', 'clahe', - 'adjust_sharpness', 'auto_contrast', 'cutout', 'adjust_lighting' -] diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/necks/fpg.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/necks/fpg.py deleted file mode 100644 index c8e0d163ccf8cef6211530ba6c1b4d558ff6403f..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/necks/fpg.py +++ /dev/null @@ -1,398 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import ConvModule, caffe2_xavier_init, constant_init, is_norm - -from ..builder import NECKS - - -class Transition(nn.Module): - """Base class for transition. - - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - """ - - def __init__(self, in_channels, out_channels): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - - def forward(x): - pass - - -class UpInterpolationConv(Transition): - """A transition used for up-sampling. - - Up-sample the input by interpolation then refines the feature by - a convolution layer. - - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - scale_factor (int): Up-sampling factor. Default: 2. - mode (int): Interpolation mode. Default: nearest. - align_corners (bool): Whether align corners when interpolation. - Default: None. - kernel_size (int): Kernel size for the conv. Default: 3. - """ - - def __init__(self, - in_channels, - out_channels, - scale_factor=2, - mode='nearest', - align_corners=None, - kernel_size=3, - **kwargs): - super().__init__(in_channels, out_channels) - self.mode = mode - self.scale_factor = scale_factor - self.align_corners = align_corners - self.conv = ConvModule( - in_channels, - out_channels, - kernel_size, - padding=(kernel_size - 1) // 2, - **kwargs) - - def forward(self, x): - x = F.interpolate( - x, - scale_factor=self.scale_factor, - mode=self.mode, - align_corners=self.align_corners) - x = self.conv(x) - return x - - -class LastConv(Transition): - """A transition used for refining the output of the last stage. - - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - num_inputs (int): Number of inputs of the FPN features. - kernel_size (int): Kernel size for the conv. Default: 3. - """ - - def __init__(self, - in_channels, - out_channels, - num_inputs, - kernel_size=3, - **kwargs): - super().__init__(in_channels, out_channels) - self.num_inputs = num_inputs - self.conv_out = ConvModule( - in_channels, - out_channels, - kernel_size, - padding=(kernel_size - 1) // 2, - **kwargs) - - def forward(self, inputs): - assert len(inputs) == self.num_inputs - return self.conv_out(inputs[-1]) - - -@NECKS.register_module() -class FPG(nn.Module): - """FPG. - - Implementation of `Feature Pyramid Grids (FPG) -
主要有赛马娘，原神中文，原神日语，崩坏3的音色
"
- ''
- ''
- )
-
- with gr.Tabs():
- with gr.TabItem("vits"):
- with gr.Row():
- with gr.Column():
- input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
- lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
- type="index", value="中文")
- btn = gr.Button(value="Submit")
- with gr.Row():
- search = gr.Textbox(label="Search Speaker", lines=1)
- btn2 = gr.Button(value="Search")
- sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
- with gr.Row():
- ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
- nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
- ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
- with gr.Column():
- o1 = gr.Textbox(label="Output Message")
- o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
- o3 = gr.Textbox(label="Extra Info")
- download = gr.Button("Download Audio")
- btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
- download.click(None, [], [], _js=download_audio_js.format())
- btn2.click(search_speaker, inputs=[search], outputs=[sid])
- lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
- with gr.TabItem("可用人物一览"):
- gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
- app.queue(concurrency_count=1).launch()
diff --git a/spaces/YueMafighting/FollowYourPose/FollowYourPose/followyourpose/data/hdvila.py b/spaces/YueMafighting/FollowYourPose/FollowYourPose/followyourpose/data/hdvila.py
deleted file mode 100644
index 797511fb5a5f0f1756bebfb36aad1aeec8a50584..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/FollowYourPose/followyourpose/data/hdvila.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# import os
-# import random
-# from abc import abstractmethod
-# import math
-# import pandas as pd
-# import av
-# import cv2
-# import decord
-# import numpy as np
-# import torch
-# from PIL import Image
-# from torch.utils.data import Dataset
-# from torchvision import transforms
-# from decord import VideoReader, cpu
-# import torchvision.transforms._transforms_video as transforms_video
-# from torchvision.transforms.functional import to_tensor
-# from collections import OrderedDict
-# import time
-# import csv
-
-# class HDVilaDataset(Dataset):
-# """
-# HDVila Dataset.
-# Assumes webvid data is structured as follows.
-# HDVila/
-# part_N/ # 0-10
-# video_clips/ ($page_dir)
-# 1.mp4 (videoid.mp4)
-# ...
-# 5000.mp4
-# ...
-# """
-# def __init__(self,
-# video_path,
-# width=512,
-# height=512,
-# n_sample_frames=8,
-# dataset_set="train",
-# prompt=None,
-# sample_frame_rate=2,
-# sample_start_idx=0,
-# accelerator=None,
-# ):
-
-# try:
-# host_gpu_num = accelerator.num_processes
-# host_num = 1
-# all_rank = host_gpu_num * host_num
-# global_rank = accelerator.local_process_index
-# except:
-# pass
-# print('dataset rank:', global_rank, ' / ',all_rank, ' ')
-
-# self.data_dir = '/apdcephfs_cq3/share_1290939/0_public_datasets/hd-vila-100m'
-# if dataset_set=='train':
-# self.text_name = 'caption_rm2048_train.csv'
-# else:
-# self.text_name = 'caption_2048_val_new.csv'
-# self.meta_path = os.path.join(self.data_dir, self.text_name)
-# # text_name = 'caption_2048_val_new.csv'
-
-# spatial_transform = 'resize_center_crop'
-# resolution=width
-# load_raw_resolution=True
-# # frame_stride=2
-# video_length= n_sample_frames
-# fps_max=None
-# load_resize_keep_ratio=False
-
-
-
-# self.global_rank = global_rank
-# self.all_rank = all_rank
-# # self.subsample = subsample
-# self.video_length = video_length
-# self.resolution = [resolution, resolution] if isinstance(resolution, int) else resolution
-# self.frame_stride = sample_frame_rate
-# self.load_raw_resolution = load_raw_resolution
-# self.fps_max = fps_max
-# self.load_resize_keep_ratio = load_resize_keep_ratio
-# print('start load meta data')
-# self._load_metadata()
-# print('load meta data done!!!')
-# if spatial_transform is not None:
-# if spatial_transform == "random_crop":
-# self.spatial_transform = transforms_video.RandomCropVideo(crop_resolution)
-# elif spatial_transform == "resize_center_crop":
-# assert(self.resolution[0] == self.resolution[1])
-# self.spatial_transform = transforms.Compose([
-# transforms.Resize(resolution),
-# transforms_video.CenterCropVideo(resolution),
-# ])
-# elif spatial_transform == "center_crop":
-# self.spatial_transform = transforms_video.CenterCropVideo(resolution)
-# else:
-# raise NotImplementedError
-# else:
-# self.spatial_transform = None
-
-# def _load_metadata(self):
-# # clip_id frame_id caption
-# last_clip_id = ''
-# self.metadata = []
-# start_time = time.time()
-# caption_path = self.meta_path
-# count=-1
-# total_count = 8854264 #8856312 - 2048
-
-# with open(caption_path, 'r',encoding="utf-8") as csvfile: #41s
-# reader = csv.DictReader(csvfile)
-# for row in reader:
-# if row['clip_id'] != last_clip_id:
-# count+=1
-# if count >= (total_count // self.all_rank)*self.all_rank: # drop last
-# break
-# last_clip_id = row['clip_id']
-# if count % self.all_rank == self.global_rank:
-# self.metadata.append([('%02d'%int(row['part_id']))+row['clip_id']])
-# self.metadata[-1].append([row['caption']])
-# else:
-# if count % self.all_rank == self.global_rank:
-# self.metadata[-1][-1].append(row['caption'])
-# # caption_data = pd.read_csv(caption_path) # use time 26+264s
-
-# # for index,row in caption_data.iterrows():
-# # if row['clip_id'] != last_clip_id:
-# # last_clip_id = row['clip_id']
-# # meta_data[('%02d'%part_id)+row['clip_id']] = [row['caption']]
-# # else:
-# # meta_data[('%02d'%part_id)+row['clip_id']].append(row['caption'])
-# end_time = time.time()
-# print('load %d - %d items use time: %.1f;' % (len(self.metadata), count, end_time-start_time))
-# # self.metadata=meta_data
-
-
-# def _get_video_path(self, sample):
-# part_id = int(sample[0][:2])
-# clip_id = sample[0][2:]
-# video_path = os.path.join(self.data_dir,'part_%d' % part_id, 'video_clips', clip_id)
-# return video_path
-
-# def __getitem__(self, index):
-# while True:
-
-# index = index % len(self.metadata)
-# sample = self.metadata[index]
-# video_path = self._get_video_path(sample)
-
-# try:
-# if self.load_raw_resolution:
-# video_reader = VideoReader(video_path, ctx=cpu(0))
-# elif self.load_resize_keep_ratio:
-# # resize scale is according to the short side
-# h, w, c = VideoReader(video_path, ctx=cpu(0))[0].shape
-# if h < w:
-# scale = h / self.resolution[0]
-# else:
-# scale = w / self.resolution[1]
-
-# h = math.ceil(h / scale)
-# w = math.ceil(w / scale)
-# video_reader = VideoReader(video_path, ctx=cpu(0), width=w, height=h)
-# else:
-# video_reader = VideoReader(video_path, ctx=cpu(0), width=self.resolution[1], height=self.resolution[0])
-# if len(video_reader) < self.video_length:
-# print(f"video length ({len(video_reader)}) is smaller than target length({self.video_length})")
-# index += 1
-# continue
-# else:
-# pass
-# except:
-# index += 1
-# print(f"Load video failed! path = {video_path}")
-# continue
-# fps_ori = video_reader.get_avg_fps()
-
-# fs = self.frame_stride
-# allf = len(video_reader)
-# if self.frame_stride != 1:
-# all_frames = list(range(0, len(video_reader), self.frame_stride))
-# if len(all_frames) < self.video_length:
-# fs = len(video_reader) // self.video_length
-# assert(fs != 0)
-# all_frames = list(range(0, len(video_reader), fs))
-# else:
-# all_frames = list(range(len(video_reader)))
-
-# # select a random clip
-# rand_idx = random.randint(0, len(all_frames) - self.video_length)
-# frame_indices = all_frames[rand_idx:rand_idx+self.video_length]
-# try:
-# frames = video_reader.get_batch(frame_indices)
-# break
-# except:
-# print(f"Get frames failed! path = {video_path}")
-# index += 1
-# continue
-
-# assert(frames.shape[0] == self.video_length),f'{len(frames)}, self.video_length={self.video_length}'
-# frames = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() # [t,h,w,c] -> [c,t,h,w]
-
-# if self.spatial_transform is not None:
-# frames = self.spatial_transform(frames)
-# assert(frames.shape[2] == self.resolution[0] and frames.shape[3] == self.resolution[1]), f'frames={frames.shape}, self.resolution={self.resolution}'
-# frames = frames.byte()
-# # fps
-# fps_clip = fps_ori // self.frame_stride
-# if self.fps_max is not None and fps_clip > self.fps_max:
-# fps_clip = self.fps_max
-
-# # caption index
-# middle_idx = (rand_idx + self.video_length /2 )*fs
-# big_cap_idx = (middle_idx // 64 +1) *64
-# small_cap_idx = (middle_idx // 64) *64
-# if big_cap_idx >= allf or ((big_cap_idx-middle_idx) >= (small_cap_idx-middle_idx)):
-# cap_idx = small_cap_idx
-# else:
-# cap_idx = big_cap_idx
-# # print(middle_idx, small_cap_idx, big_cap_idx,cap_idx)
-# caption = sample[1][int(cap_idx//64)]
-
-# frames = frames.permute(1,0,2,3)
-# skeleton_final = torch.zeros_like(frames).byte()
-# frames = (frames / 127.5 - 1.0)
-# skeleton_final = (skeleton_final / 127.5 - 1.0)
-# example = {'pixel_values': frames, 'sentence': caption, 'pose': skeleton_final}
-
-
-
-# return example
-
-# def __len__(self):
-# return len(self.metadata)
-# # return 1
-
-
-# if __name__ == '__main__':
-# if True: # val
-# hd_data = HDVila('/apdcephfs_cq3/share_1290939/0_public_datasets/hd-vila-100m','/apdcephfs_cq3/share_1290939/0_public_datasets/hd-vila-100m/caption_2048_val.csv')
-# else:
-# hd_data = HDVila('/apdcephfs_cq3/share_1290939/0_public_datasets/hd-vila-100m','/apdcephfs_cq3/share_1290939/0_public_datasets/hd-vila-100m/caption_rm2048_train.csv')
-# print(len(hd_data))
-# for i in range(len(hd_data)):
-# # print(i)
-# hd_data[i]
\ No newline at end of file
diff --git a/spaces/Yuliang/ECON/lib/torch_utils/training_stats.py b/spaces/Yuliang/ECON/lib/torch_utils/training_stats.py
deleted file mode 100644
index a4fd0a4c3687aff712547b2688225ba1ec621f47..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/torch_utils/training_stats.py
+++ /dev/null
@@ -1,282 +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.
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-
-import numpy as np
-import torch
-
-import lib.dnnlib
-
-from . import misc
-
-#----------------------------------------------------------------------------
-
-_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype = torch.float64 # Data type to use for the internal counters.
-_rank = 0 # Rank of the current process.
-_sync_device = None # Device to use for multiprocess communication. None = single-process.
-_sync_called = False # Has _sync() been called yet?
-_counters = dict(
-) # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-
-#----------------------------------------------------------------------------
-
-
-def init_multiprocessing(rank, sync_device):
- r"""Initializes `torch_utils.training_stats` for collecting statistics
- across multiple processes.
-
- This function must be called after
- `torch.distributed.init_process_group()` and before `Collector.update()`.
- The call is not necessary if multi-process collection is not needed.
-
- Args:
- rank: Rank of the current process.
- sync_device: PyTorch device to use for inter-process
- communication, or None to disable multi-process
- collection. Typically `torch.device('cuda', rank)`.
- """
- global _rank, _sync_device
- assert not _sync_called
- _rank = rank
- _sync_device = sync_device
-
-
-#----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def report(name, value):
- r"""Broadcasts the given set of scalars to all interested instances of
- `Collector`, across device and process boundaries.
-
- This function is expected to be extremely cheap and can be safely
- called from anywhere in the training loop, loss function, or inside a
- `torch.nn.Module`.
-
- Warning: The current implementation expects the set of unique names to
- be consistent across processes. Please make sure that `report()` is
- called at least once for each unique name by each process, and in the
- same order. If a given process has no scalars to broadcast, it can do
- `report(name, [])` (empty list).
-
- Args:
- name: Arbitrary string specifying the name of the statistic.
- Averages are accumulated separately for each unique name.
- value: Arbitrary set of scalars. Can be a list, tuple,
- NumPy array, PyTorch tensor, or Python scalar.
-
- Returns:
- The same `value` that was passed in.
- """
- if name not in _counters:
- _counters[name] = dict()
-
- elems = torch.as_tensor(value)
- if elems.numel() == 0:
- return value
-
- elems = elems.detach().flatten().to(_reduce_dtype)
- moments = torch.stack([
- torch.ones_like(elems).sum(),
- elems.sum(),
- elems.square().sum(),
- ])
- assert moments.ndim == 1 and moments.shape[0] == _num_moments
- moments = moments.to(_counter_dtype)
-
- device = moments.device
- if device not in _counters[name]:
- _counters[name][device] = torch.zeros_like(moments)
- _counters[name][device].add_(moments)
- return value
-
-
-#----------------------------------------------------------------------------
-
-
-def report0(name, value):
- r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
- but ignores any scalars provided by the other processes.
- See `report()` for further details.
- """
- report(name, value if _rank == 0 else [])
- return value
-
-
-#----------------------------------------------------------------------------
-
-
-class Collector:
- r"""Collects the scalars broadcasted by `report()` and `report0()` and
- computes their long-term averages (mean and standard deviation) over
- user-defined periods of time.
-
- The averages are first collected into internal counters that are not
- directly visible to the user. They are then copied to the user-visible
- state as a result of calling `update()` and can then be queried using
- `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
- internal counters for the next round, so that the user-visible state
- effectively reflects averages collected between the last two calls to
- `update()`.
-
- Args:
- regex: Regular expression defining which statistics to
- collect. The default is to collect everything.
- keep_previous: Whether to retain the previous averages if no
- scalars were collected on a given round
- (default: True).
- """
- def __init__(self, regex='.*', keep_previous=True):
- self._regex = re.compile(regex)
- self._keep_previous = keep_previous
- self._cumulative = dict()
- self._moments = dict()
- self.update()
- self._moments.clear()
-
- def names(self):
- r"""Returns the names of all statistics broadcasted so far that
- match the regular expression specified at construction time.
- """
- return [name for name in _counters if self._regex.fullmatch(name)]
-
- def update(self):
- r"""Copies current values of the internal counters to the
- user-visible state and resets them for the next round.
-
- If `keep_previous=True` was specified at construction time, the
- operation is skipped for statistics that have received no scalars
- since the last update, retaining their previous averages.
-
- This method performs a number of GPU-to-CPU transfers and one
- `torch.distributed.all_reduce()`. It is intended to be called
- periodically in the main training loop, typically once every
- N training steps.
- """
- if not self._keep_previous:
- self._moments.clear()
- for name, cumulative in _sync(self.names()):
- if name not in self._cumulative:
- self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- delta = cumulative - self._cumulative[name]
- self._cumulative[name].copy_(cumulative)
- if float(delta[0]) != 0:
- self._moments[name] = delta
-
- def _get_delta(self, name):
- r"""Returns the raw moments that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- assert self._regex.fullmatch(name)
- if name not in self._moments:
- self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- return self._moments[name]
-
- def num(self, name):
- r"""Returns the number of scalars that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- return int(delta[0])
-
- def mean(self, name):
- r"""Returns the mean of the scalars that were accumulated for the
- given statistic between the last two calls to `update()`, or NaN if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0:
- return float('nan')
- return float(delta[1] / delta[0])
-
- def std(self, name):
- r"""Returns the standard deviation of the scalars that were
- accumulated for the given statistic between the last two calls to
- `update()`, or NaN if no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
- return float('nan')
- if int(delta[0]) == 1:
- return float(0)
- mean = float(delta[1] / delta[0])
- raw_var = float(delta[2] / delta[0])
- return np.sqrt(max(raw_var - np.square(mean), 0))
-
- def as_dict(self):
- r"""Returns the averages accumulated between the last two calls to
- `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
- dnnlib.EasyDict(
- NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
- ...
- )
- """
- stats = dnnlib.EasyDict()
- for name in self.names():
- stats[name] = dnnlib.EasyDict(
- num=self.num(name), mean=self.mean(name), std=self.std(name)
- )
- return stats
-
- def __getitem__(self, name):
- r"""Convenience getter.
- `collector[name]` is a synonym for `collector.mean(name)`.
- """
- return self.mean(name)
-
-
-#----------------------------------------------------------------------------
-
-
-def _sync(names):
- r"""Synchronize the global cumulative counters across devices and
- processes. Called internally by `Collector.update()`.
- """
- if len(names) == 0:
- return []
- global _sync_called
- _sync_called = True
-
- # Collect deltas within current rank.
- deltas = []
- device = _sync_device if _sync_device is not None else torch.device('cpu')
- for name in names:
- delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
- for counter in _counters[name].values():
- delta.add_(counter.to(device))
- counter.copy_(torch.zeros_like(counter))
- deltas.append(delta)
- deltas = torch.stack(deltas)
-
- # Sum deltas across ranks.
- if _sync_device is not None:
- torch.distributed.all_reduce(deltas)
-
- # Update cumulative values.
- deltas = deltas.cpu()
- for idx, name in enumerate(names):
- if name not in _cumulative:
- _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- _cumulative[name].add_(deltas[idx])
-
- # Return name-value pairs.
- return [(name, _cumulative[name]) for name in names]
-
-
-#----------------------------------------------------------------------------
diff --git a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/static/js/2.422ca0c4.chunk.js b/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/static/js/2.422ca0c4.chunk.js
deleted file mode 100644
index 1fd17f11d35fc387466e4141d5ff5ba07823b5ae..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/static/js/2.422ca0c4.chunk.js
+++ /dev/null
@@ -1,3 +0,0 @@
-/*! For license information please see 2.422ca0c4.chunk.js.LICENSE.txt */
-(this.webpackJsonpstreamlit_cookie_manager=this.webpackJsonpstreamlit_cookie_manager||[]).push([[2],[function(t,e,n){t.exports=n(10)},function(t,e,n){"use strict";t.exports=n(8)},function(t,e,n){"use strict";n.d(e,"a",(function(){return xf}));var r={};n.r(r),n.d(r,"memcpy",(function(){return Yt})),n.d(r,"joinUint8Arrays",(function(){return Wt})),n.d(r,"toArrayBufferView",(function(){return Ht})),n.d(r,"toInt8Array",(function(){return $t})),n.d(r,"toInt16Array",(function(){return Kt})),n.d(r,"toInt32Array",(function(){return Gt})),n.d(r,"toBigInt64Array",(function(){return qt})),n.d(r,"toUint8Array",(function(){return Jt})),n.d(r,"toUint16Array",(function(){return Zt})),n.d(r,"toUint32Array",(function(){return Qt})),n.d(r,"toBigUint64Array",(function(){return Xt})),n.d(r,"toFloat32Array",(function(){return te})),n.d(r,"toFloat64Array",(function(){return ee})),n.d(r,"toUint8ClampedArray",(function(){return ne})),n.d(r,"toArrayBufferViewIterator",(function(){return ie})),n.d(r,"toInt8ArrayIterator",(function(){return ae})),n.d(r,"toInt16ArrayIterator",(function(){return oe})),n.d(r,"toInt32ArrayIterator",(function(){return ue})),n.d(r,"toUint8ArrayIterator",(function(){return se})),n.d(r,"toUint16ArrayIterator",(function(){return ce})),n.d(r,"toUint32ArrayIterator",(function(){return fe})),n.d(r,"toFloat32ArrayIterator",(function(){return le})),n.d(r,"toFloat64ArrayIterator",(function(){return he})),n.d(r,"toUint8ClampedArrayIterator",(function(){return ye})),n.d(r,"toArrayBufferViewAsyncIterator",(function(){return pe})),n.d(r,"toInt8ArrayAsyncIterator",(function(){return ve})),n.d(r,"toInt16ArrayAsyncIterator",(function(){return be})),n.d(r,"toInt32ArrayAsyncIterator",(function(){return ge})),n.d(r,"toUint8ArrayAsyncIterator",(function(){return me})),n.d(r,"toUint16ArrayAsyncIterator",(function(){return ke})),n.d(r,"toUint32ArrayAsyncIterator",(function(){return we})),n.d(r,"toFloat32ArrayAsyncIterator",(function(){return _e})),n.d(r,"toFloat64ArrayAsyncIterator",(function(){return Ie})),n.d(r,"toUint8ClampedArrayAsyncIterator",(function(){return Se})),n.d(r,"rebaseValueOffsets",(function(){return xe})),n.d(r,"compareArrayLike",(function(){return Ae}));var i={};n.r(i),n.d(i,"getBool",(function(){return un})),n.d(i,"getBit",(function(){return sn})),n.d(i,"setBool",(function(){return cn})),n.d(i,"truncateBitmap",(function(){return fn})),n.d(i,"packBools",(function(){return ln})),n.d(i,"iterateBits",(function(){return hn})),n.d(i,"popcnt_bit_range",(function(){return yn})),n.d(i,"popcnt_array",(function(){return pn})),n.d(i,"popcnt_uint32",(function(){return dn}));var a={};n.r(a),n.d(a,"uint16ToFloat64",(function(){return Nr})),n.d(a,"float64ToUint16",(function(){return Cr}));var o={};n.r(o),n.d(o,"isArrowBigNumSymbol",(function(){return Hr})),n.d(o,"bignumToString",(function(){return Yr})),n.d(o,"bignumToBigInt",(function(){return Wr})),n.d(o,"BN",(function(){return Xr}));var u={};n.r(u),n.d(u,"clampIndex",(function(){return Ci})),n.d(u,"clampRange",(function(){return Vi})),n.d(u,"createElementComparator",(function(){return Pi}));var s={};n.r(s),n.d(s,"BaseInt64",(function(){return ao})),n.d(s,"Uint64",(function(){return oo})),n.d(s,"Int64",(function(){return uo})),n.d(s,"Int128",(function(){return so}));n(3);var c=n(1),f=n.n(c),l=new WeakMap,h=new WeakMap;function y(t){var e=l.get(t);return console.assert(null!=e,"'this' is expected an Event object, but got",t),e}function p(t){null==t.passiveListener?t.event.cancelable&&(t.canceled=!0,"function"===typeof t.event.preventDefault&&t.event.preventDefault()):"undefined"!==typeof console&&"function"===typeof console.error&&console.error("Unable to preventDefault inside passive event listener invocation.",t.passiveListener)}function d(t,e){l.set(this,{eventTarget:t,event:e,eventPhase:2,currentTarget:t,canceled:!1,stopped:!1,immediateStopped:!1,passiveListener:null,timeStamp:e.timeStamp||Date.now()}),Object.defineProperty(this,"isTrusted",{value:!1,enumerable:!0});for(var n=Object.keys(e),r=0;r[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. Paper Link
[2] LayoutLMv3 training and inference
[3] Hongbin Sun, Zhanghui Kuang, Xiaoyu Yue, Chenhao Lin, and Wayne Zhang. 2021. Spatial Dual-Modality Graph Reasoning for Key Information Extraction. arXiv. DOI:https://doi.org/10.48550/ARXIV.2103.14470 Paper Link" - -examples =[['example1.png'],['example2.png'],['example3.png']] - -css = """.output_image, .input_image {height: 600px !important}""" - -iface = gr.Interface(fn=process_image, - inputs=gr.inputs.Image(type="pil"), - outputs=gr.outputs.Image(type="pil", label="annotated image"), - title=title, - description=description, - article=article, - examples=examples, - css=css, - analytics_enabled = True, enable_queue=True) - -iface.launch(inline=False, share=False, debug=False) \ No newline at end of file diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/setup.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/setup.py deleted file mode 100644 index 2bf28ffe269cba3033af263db5f98313772818f0..0000000000000000000000000000000000000000 --- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/setup.py +++ /dev/null @@ -1,30 +0,0 @@ -from setuptools import setup - -with open("README.md", "r", encoding="utf-8") as readme_file: - readme = readme_file.read() - -requirements = [ - "numpy", - "scipy", - "matplotlib", - "torch", - "torchvision", - "opencv-python", - "CLIP @ git+https://github.com/openai/CLIP.git" -] - -setup( - name='clipseg', - packages=['clipseg'], - package_dir={'clipseg': 'models'}, - package_data={'clipseg': [ - "../weights/*.pth", - ]}, - version='0.0.1', - url='https://github.com/timojl/clipseg', - python_requires='>=3.9', - install_requires=requirements, - description='This repository contains the code used in the paper "Image Segmentation Using Text and Image Prompts".', - long_description=readme, - long_description_content_type="text/markdown", -) diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/korean/phonemizer.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/korean/phonemizer.py deleted file mode 100644 index ed70fc35f6950b98ec715577a3303c5a271fbb0e..0000000000000000000000000000000000000000 --- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/korean/phonemizer.py +++ /dev/null @@ -1,36 +0,0 @@ -from jamo import hangul_to_jamo - -from TTS.tts.utils.text.korean.korean import normalize - -g2p = None - - -def korean_text_to_phonemes(text, character: str = "hangeul") -> str: - """ - - The input and output values look the same, but they are different in Unicode. - - example : - - input = '하늘' (Unicode : \ud558\ub298), (하 + 늘) - output = '하늘' (Unicode :\u1112\u1161\u1102\u1173\u11af), (ᄒ + ᅡ + ᄂ + ᅳ + ᆯ) - - """ - global g2p # pylint: disable=global-statement - if g2p is None: - from g2pkk import G2p - - g2p = G2p() - - if character == "english": - from anyascii import anyascii - - text = normalize(text) - text = g2p(text) - text = anyascii(text) - return text - - text = normalize(text) - text = g2p(text) - text = list(hangul_to_jamo(text)) # '하늘' --> ['ᄒ', 'ᅡ', 'ᄂ', 'ᅳ', 'ᆯ'] - return "".join(text) diff --git a/spaces/artificialguybr/video-dubbing/TTS/docs/source/models/forward_tts.md b/spaces/artificialguybr/video-dubbing/TTS/docs/source/models/forward_tts.md deleted file mode 100644 index f8f941c2fd3baf11497945ee1b6ac0a3ceb9a8f6..0000000000000000000000000000000000000000 --- a/spaces/artificialguybr/video-dubbing/TTS/docs/source/models/forward_tts.md +++ /dev/null @@ -1,65 +0,0 @@ -# Forward TTS model(s) - -A general feed-forward TTS model implementation that can be configured to different architectures by setting different -encoder and decoder networks. It can be trained with either pre-computed durations (from pre-trained Tacotron) or -an alignment network that learns the text to audio alignment from the input data. - -Currently we provide the following pre-configured architectures: - -- **FastSpeech:** - - It's a feed-forward model TTS model that uses Feed Forward Transformer (FFT) modules as the encoder and decoder. - -- **FastPitch:** - - It uses the same FastSpeech architecture that is conditioned on fundamental frequency (f0) contours with the - promise of more expressive speech. - -- **SpeedySpeech:** - - It uses Residual Convolution layers instead of Transformers that leads to a more compute friendly model. - -- **FastSpeech2 (TODO):** - - Similar to FastPitch but it also uses a spectral energy values as an addition. - -## Important resources & papers -- FastPitch: https://arxiv.org/abs/2006.06873 -- SpeedySpeech: https://arxiv.org/abs/2008.03802 -- FastSpeech: https://arxiv.org/pdf/1905.09263 -- FastSpeech2: https://arxiv.org/abs/2006.04558 -- Aligner Network: https://arxiv.org/abs/2108.10447 -- What is Pitch: https://www.britannica.com/topic/pitch-speech - - -## ForwardTTSArgs -```{eval-rst} -.. autoclass:: TTS.tts.models.forward_tts.ForwardTTSArgs - :members: -``` - -## ForwardTTS Model -```{eval-rst} -.. autoclass:: TTS.tts.models.forward_tts.ForwardTTS - :members: -``` - -## FastPitchConfig -```{eval-rst} -.. autoclass:: TTS.tts.configs.fast_pitch_config.FastPitchConfig - :members: -``` - -## SpeedySpeechConfig -```{eval-rst} -.. autoclass:: TTS.tts.configs.speedy_speech_config.SpeedySpeechConfig - :members: -``` - -## FastSpeechConfig -```{eval-rst} -.. autoclass:: TTS.tts.configs.fast_speech_config.FastSpeechConfig - :members: -``` - - diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_GCM.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_GCM.py deleted file mode 100644 index dd8da2fd804c8d3e4ba852d0b5b4f8fe012a7dc9..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_GCM.py +++ /dev/null @@ -1,951 +0,0 @@ -# =================================================================== -# -# Copyright (c) 2015, Legrandin
Create Fill The Blanks Questions
", unsafe_allow_html=True) -st.markdown("---") -with st.sidebar: - # title using markdown - st.markdown("NLP Tasks
", unsafe_allow_html=True) - select_task=st.selectbox(label="Select task from drop down menu", - options=['README', - 'Basic Fill Blanks']) - - -img = Image.open("hf_space1.png") -if select_task=='README': - st.header("Summary") - st.write(f"The App gives you ability to create *Fill in the blanks* Capability just like Ed-Techs.Currently.It has {total_threads} CPU cores but only 1 is available per user so " - f"inference time will be on the higher side.") - st.markdown("---") - st.image(img) - -if select_task=='Basic Fill Blanks': - input_text = st.text_area(label='Input paragraph', height=500, max_chars=2000, value=default_paratext) - create_fib=st.button("Create Questions") - - if create_fib: - tokenized_sent = tokenize_sentence(input_text) - keywords_noun_adj_verb = get_noun_adj_verb(input_text) - keyword_sent_noun_verb_adj = get_keywords_sentence(keywords=keywords_noun_adj_verb,tokenized_sent=tokenized_sent) - answer, fib = create_blanks(keyword_sentence_dict=keyword_sent_noun_verb_adj) - st.markdown('---') - for i,(answer,fib) in enumerate(zip(answer,fib)): - st.write(f"{i+1}. {fib} ") - annotated_text("Answer is ", (answer,'',"#fea")) - st.markdown('---') - - -# demo = gr.Interface(fn=get_noun_adj_verb, -# inputs=gr.inputs.Textbox(lines=10,default=default_paratext), -# outputs=gr.outputs.Textbox(), -# allow_flagging='never', -# layout='vertical', -# title="Make Fill in the Blanks using your text", -# ) -# -# if __name__ == "__main__": -# demo.launch() - - - - - - diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/modules/encoders/modules.py b/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/modules/encoders/modules.py deleted file mode 100644 index a44b674adfb114600fae943dd12ca9169c259c72..0000000000000000000000000000000000000000 --- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/modules/encoders/modules.py +++ /dev/null @@ -1,234 +0,0 @@ -import torch -import torch.nn as nn -from functools import partial -import clip -from einops import rearrange, repeat -from transformers import CLIPTokenizer, CLIPTextModel -import kornia - -from ldmlib.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test - - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - - -class ClassEmbedder(nn.Module): - def __init__(self, embed_dim, n_classes=1000, key='class'): - super().__init__() - self.key = key - self.embedding = nn.Embedding(n_classes, embed_dim) - - def forward(self, batch, key=None): - if key is None: - key = self.key - # this is for use in crossattn - c = batch[key][:, None] - c = self.embedding(c) - return c - - -class TransformerEmbedder(AbstractEncoder): - """Some transformer encoder layers""" - def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): - super().__init__() - self.device = device - self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, - attn_layers=Encoder(dim=n_embed, depth=n_layer)) - - def forward(self, tokens): - tokens = tokens.to(self.device) # meh - z = self.transformer(tokens, return_embeddings=True) - return z - - def encode(self, x): - return self(x) - - -class BERTTokenizer(AbstractEncoder): - """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" - def __init__(self, device="cuda", vq_interface=True, max_length=77): - super().__init__() - from transformers import BertTokenizerFast # TODO: add to reuquirements - self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") - self.device = device - self.vq_interface = vq_interface - self.max_length = max_length - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - return tokens - - @torch.no_grad() - def encode(self, text): - tokens = self(text) - if not self.vq_interface: - return tokens - return None, None, [None, None, tokens] - - def decode(self, text): - return text - - -class BERTEmbedder(AbstractEncoder): - """Uses the BERT tokenizr model and add some transformer encoder layers""" - def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, - device="cuda",use_tokenizer=True, embedding_dropout=0.0): - super().__init__() - self.use_tknz_fn = use_tokenizer - if self.use_tknz_fn: - self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) - self.device = device - self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, - attn_layers=Encoder(dim=n_embed, depth=n_layer), - emb_dropout=embedding_dropout) - - def forward(self, text): - if self.use_tknz_fn: - tokens = self.tknz_fn(text)#.to(self.device) - else: - tokens = text - z = self.transformer(tokens, return_embeddings=True) - return z - - def encode(self, text): - # output of length 77 - return self(text) - - -class SpatialRescaler(nn.Module): - def __init__(self, - n_stages=1, - method='bilinear', - multiplier=0.5, - in_channels=3, - out_channels=None, - bias=False): - super().__init__() - self.n_stages = n_stages - assert self.n_stages >= 0 - assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] - self.multiplier = multiplier - self.interpolator = partial(torch.nn.functional.interpolate, mode=method) - self.remap_output = out_channels is not None - if self.remap_output: - print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') - self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) - - def forward(self,x): - for stage in range(self.n_stages): - x = self.interpolator(x, scale_factor=self.multiplier) - - - if self.remap_output: - x = self.channel_mapper(x) - return x - - def encode(self, x): - return self(x) - -class FrozenCLIPEmbedder(AbstractEncoder): - """Uses the CLIP transformer encoder for text (from Hugging Face)""" - def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): - super().__init__() - self.tokenizer = CLIPTokenizer.from_pretrained(version) - self.transformer = CLIPTextModel.from_pretrained(version) - self.device = device - self.max_length = max_length - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens) - - z = outputs.last_hidden_state - return z - - def encode(self, text): - return self(text) - - -class FrozenCLIPTextEmbedder(nn.Module): - """ - Uses the CLIP transformer encoder for text. - """ - def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True): - super().__init__() - self.model, _ = clip.load(version, jit=False, device="cpu") - self.device = device - self.max_length = max_length - self.n_repeat = n_repeat - self.normalize = normalize - - def freeze(self): - self.model = self.model.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - tokens = clip.tokenize(text).to(self.device) - z = self.model.encode_text(tokens) - if self.normalize: - z = z / torch.linalg.norm(z, dim=1, keepdim=True) - return z - - def encode(self, text): - z = self(text) - if z.ndim==2: - z = z[:, None, :] - z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat) - return z - - -class FrozenClipImageEmbedder(nn.Module): - """ - Uses the CLIP image encoder. - """ - def __init__( - self, - model, - jit=False, - device='cuda' if torch.cuda.is_available() else 'cpu', - antialias=False, - ): - super().__init__() - self.model, _ = clip.load(name=model, device=device, jit=jit) - - self.antialias = antialias - - self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) - self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) - - def preprocess(self, x): - # normalize to [0,1] - x = kornia.geometry.resize(x, (224, 224), - interpolation='bicubic',align_corners=True, - antialias=self.antialias) - x = (x + 1.) / 2. - # renormalize according to clip - x = kornia.enhance.normalize(x, self.mean, self.std) - return x - - def forward(self, x): - # x is assumed to be in range [-1,1] - return self.model.encode_image(self.preprocess(x)) - - -if __name__ == "__main__": - from ldmlib.util import count_params - model = FrozenCLIPEmbedder() - count_params(model, verbose=True) diff --git a/spaces/awacke1/StreamlitSTEMDataScienceEngineerDash/README.md b/spaces/awacke1/StreamlitSTEMDataScienceEngineerDash/README.md deleted file mode 100644 index d5d47e52fc0da505e5f2cfb766a8e2230ef7af2e..0000000000000000000000000000000000000000 --- a/spaces/awacke1/StreamlitSTEMDataScienceEngineerDash/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: StreamlitSTEMDataScienceEngineerDash -emoji: 👁 -colorFrom: yellow -colorTo: pink -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/awacke1/TwoPlayerDiceGameCraps/app.py b/spaces/awacke1/TwoPlayerDiceGameCraps/app.py deleted file mode 100644 index 6bea190b32d0a93b0b54631bee10d39256fb7027..0000000000000000000000000000000000000000 --- a/spaces/awacke1/TwoPlayerDiceGameCraps/app.py +++ /dev/null @@ -1,33 +0,0 @@ -# write a python streamlit program which allows two players to play the dice game called Craps. - -import streamlit as st -import random - -st.title("Craps Game") - -# player1 -player1_name = st.text_input("Player 1 name :") -st.write("Hi",player1_name,"Let's play Craps") - -# player2 -player2_name = st.text_input("Player 2 name :") -st.write("Hi",player2_name,"Let's play Craps") - -# Craps game -st.write("Let's roll the dice") - -# player1 -player1_roll = random.randint(2,12) -st.write(player1_name,"rolled a", player1_roll) - -# player2 -player2_roll = random.randint(2,12) -st.write(player2_name,"rolled a", player2_roll) - -# game result -if player1_roll > player2_roll: - st.write(player1_name,"wins!") -elif player2_roll > player1_roll: - st.write(player2_name,"wins!") -else: - st.write("It's a tie!") \ No newline at end of file diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/WebGLRenderTarget.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/WebGLRenderTarget.js deleted file mode 100644 index 9fbe1dc400c9ce2e57c02c2945a30b3cee40189d..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/WebGLRenderTarget.js +++ /dev/null @@ -1,94 +0,0 @@ -import { EventDispatcher } from '../core/EventDispatcher.js'; -import { Texture } from '../textures/Texture.js'; -import { LinearFilter } from '../constants.js'; -import { Vector4 } from '../math/Vector4.js'; - -/** - * @author szimek / https://github.com/szimek/ - * @author alteredq / http://alteredqualia.com/ - * @author Marius Kintel / https://github.com/kintel - */ - -/* - In options, we can specify: - * Texture parameters for an auto-generated target texture - * depthBuffer/stencilBuffer: Booleans to indicate if we should generate these buffers -*/ -function WebGLRenderTarget( width, height, options ) { - - this.width = width; - this.height = height; - - this.scissor = new Vector4( 0, 0, width, height ); - this.scissorTest = false; - - this.viewport = new Vector4( 0, 0, width, height ); - - options = options || {}; - - this.texture = new Texture( undefined, undefined, options.wrapS, options.wrapT, options.magFilter, options.minFilter, options.format, options.type, options.anisotropy, options.encoding ); - - this.texture.generateMipmaps = options.generateMipmaps !== undefined ? options.generateMipmaps : false; - this.texture.minFilter = options.minFilter !== undefined ? options.minFilter : LinearFilter; - - this.depthBuffer = options.depthBuffer !== undefined ? options.depthBuffer : true; - this.stencilBuffer = options.stencilBuffer !== undefined ? options.stencilBuffer : true; - this.depthTexture = options.depthTexture !== undefined ? options.depthTexture : null; - -} - -WebGLRenderTarget.prototype = Object.assign( Object.create( EventDispatcher.prototype ), { - - constructor: WebGLRenderTarget, - - isWebGLRenderTarget: true, - - setSize: function ( width, height ) { - - if ( this.width !== width || this.height !== height ) { - - this.width = width; - this.height = height; - - this.dispose(); - - } - - this.viewport.set( 0, 0, width, height ); - this.scissor.set( 0, 0, width, height ); - - }, - - clone: function () { - - return new this.constructor().copy( this ); - - }, - - copy: function ( source ) { - - this.width = source.width; - this.height = source.height; - - this.viewport.copy( source.viewport ); - - this.texture = source.texture.clone(); - - this.depthBuffer = source.depthBuffer; - this.stencilBuffer = source.stencilBuffer; - this.depthTexture = source.depthTexture; - - return this; - - }, - - dispose: function () { - - this.dispatchEvent( { type: 'dispose' } ); - - } - -} ); - - -export { WebGLRenderTarget }; diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLShader.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLShader.d.ts deleted file mode 100644 index f02159dc86a7e9d826f0b97e00c11002ae0aeabf..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/webgl/WebGLShader.d.ts +++ /dev/null @@ -1,3 +0,0 @@ -export class WebGLShader { - constructor(gl: any, type: string, string: string); -} diff --git a/spaces/bigPear/digitalWDF/src/train_ppo.py b/spaces/bigPear/digitalWDF/src/train_ppo.py deleted file mode 100644 index aea6a56e656eecc5d7ed07757473f90ffcb2e209..0000000000000000000000000000000000000000 --- a/spaces/bigPear/digitalWDF/src/train_ppo.py +++ /dev/null @@ -1,85 +0,0 @@ -# coding=utf-8 -# Implements parameter-efficient ppo training of fine-tuned ChatGLM. -# This code is inspired by: -# https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt-neox-20b_peft/gpt-neo-20b_sentiment_peft.py - -import math - -from torch.optim import AdamW - -from transformers.optimization import get_scheduler -from trl import PPOConfig - -from utils import ( - prepare_args, - prepare_data, - load_pretrained, - preprocess_data, - PPODataCollatorForChatGLM, - PPOTrainerForChatGLM, - plot_loss -) - - -def main(): - - # prepare pretrained model and dataset - model_args, data_args, training_args, finetuning_args = prepare_args() - dataset = prepare_data(model_args, data_args) - model, tokenizer = load_pretrained(model_args, training_args, finetuning_args, training_args.do_train, stage="ppo") - dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="ppo") - data_collator = PPODataCollatorForChatGLM( - tokenizer=tokenizer, - min_input_length=data_args.max_source_length, # avoid truncating input sequences - max_input_length=data_args.max_source_length, - inference_mode=(not training_args.do_train) - ) - - ppo_config = PPOConfig( - model_name=model_args.model_name_or_path, - learning_rate=training_args.learning_rate, - mini_batch_size=training_args.per_device_train_batch_size, - batch_size=training_args.per_device_train_batch_size, - gradient_accumulation_steps=training_args.gradient_accumulation_steps, - ppo_epochs=1, - max_grad_norm=training_args.max_grad_norm - ) - - optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=ppo_config.learning_rate) - total_train_batch_size = \ - training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size - lr_scheduler = get_scheduler( - training_args.lr_scheduler_type, - optimizer=optimizer, - num_warmup_steps=training_args.warmup_steps, - num_training_steps=(training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)) - ) - - # Initialize our Trainer - ppo_trainer = PPOTrainerForChatGLM( - training_args=training_args, - finetuning_args=finetuning_args, - config=ppo_config, - model=model, - ref_model=None, - tokenizer=tokenizer, - dataset=dataset, - data_collator=data_collator, - optimizer=optimizer, - lr_scheduler=lr_scheduler - ) - - ppo_trainer.ppo_train(max_target_length=data_args.max_target_length) - ppo_trainer.save_state() - ppo_trainer.save_model() - if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss: - plot_loss(training_args, keys=["loss", "reward"]) - - -def _mp_fn(index): - # For xla_spawn (TPUs) - main() - - -if __name__ == "__main__": - main() diff --git a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/training.py b/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/training.py deleted file mode 100644 index ce12cf443f37e2520658614e15d0e64eb554b7f1..0000000000000000000000000000000000000000 --- a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/training.py +++ /dev/null @@ -1,266 +0,0 @@ -import torch -import inspect -import json -import yaml -import math -import os -import sys - -from general_utils import log - -import numpy as np -from functools import partial -from os.path import expanduser, join, isfile, basename - -from torch.cuda.amp import autocast, GradScaler -from torch.optim.lr_scheduler import LambdaLR -from contextlib import nullcontext -from torch.utils.data import DataLoader - -from general_utils import TrainingLogger, get_attribute, filter_args, log, training_config_from_cli_args - - -def cosine_warmup_lr(i, warmup=10, max_iter=90): - """ Cosine LR with Warmup """ - if i < warmup: - return (i+1)/(warmup+1) - else: - return 0.5 + 0.5*math.cos(math.pi*(((i-warmup)/(max_iter- warmup)))) - - -def validate(model, dataset, config): - data_loader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=False) - - metric_class, use_metric = config.val_metric_class, config.use_val_metric - loss_fn = get_attribute(config.loss) - - model.eval() - model.cuda() - - if metric_class is not None: - metric = get_attribute(metric_class)() - - with torch.no_grad(): - - i, losses = 0, [] - for data_x, data_y in data_loader: - - data_x = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_x] - data_y = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_y] - - prompts = model.sample_prompts(data_x[1], prompt_list=('a photo of a {}',)) - pred, visual_q, _, _ = model(data_x[0], prompts, return_features=True) - - if metric_class is not None: - metric.add([pred], data_y) - - # pred = model(data_x[0], prompts) - # loss = loss_fn(pred[0], data_y[0]) - loss = loss_fn(pred, data_y[0]) - losses += [float(loss)] - - i += 1 - - if config.val_max_iterations is not None and i > config.val_max_iterations: - break - - if use_metric is None: - return np.mean(losses), {}, False - else: - metric_scores = {m: s for m, s in zip(metric.names(), metric.value())} if metric is not None else {} - return np.mean(losses), metric_scores, True - - -def main(): - - config = training_config_from_cli_args() - - val_interval, best_val_loss, best_val_score = config.val_interval, float('inf'), float('-inf') - - model_cls = get_attribute(config.model) - _, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters) - model = model_cls(**model_args).cuda() - - dataset_cls = get_attribute(config.dataset) - _, dataset_args, _ = filter_args(config, inspect.signature(dataset_cls).parameters) - - dataset = dataset_cls(**dataset_args) - - log.info(f'Train dataset {dataset.__class__.__name__} (length: {len(dataset)})') - - if val_interval is not None: - dataset_val_args = {k[4:]: v for k,v in config.items() if k.startswith('val_') and k != 'val_interval'} - _, dataset_val_args, _ = filter_args(dataset_val_args, inspect.signature(dataset_cls).parameters) - print('val args', {**dataset_args, **{'split': 'val', 'aug': 0}, **dataset_val_args}) - - dataset_val = dataset_cls(**{**dataset_args, **{'split': 'val', 'aug': 0}, **dataset_val_args}) - - # optimizer - opt_cls = get_attribute(config.optimizer) - if config.optimize == 'torch.optim.SGD': - opt_args = {'momentum': config.momentum if 'momentum' in config else 0} - else: - opt_args = {} - opt = opt_cls(model.parameters(), lr=config.lr, **opt_args) - - if config.lr_scheduler == 'cosine': - assert config.T_max is not None and config.eta_min is not None - lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, config.T_max, config.eta_min) - elif config.lr_scheduler == 'warmup_cosine': - lr_scheduler = LambdaLR(opt, partial(cosine_warmup_lr, max_iter=(config.max_iterations), warmup=config.warmup)) - else: - lr_scheduler = None - - batch_size, max_iterations = config.batch_size, config.max_iterations - - loss_fn = get_attribute(config.loss) - - if config.amp: - log.info('Using AMP') - autocast_fn = autocast - scaler = GradScaler() - else: - autocast_fn, scaler = nullcontext, None - - - save_only_trainable = True - data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=4) - - # disable config when hyperparam. opt. to avoid writing logs. - tracker_config = config if not config.hyperparameter_optimization else None - - with TrainingLogger(log_dir=config.name, model=model, config=tracker_config) as logger: - - i = 0 - while True: - for data_x, data_y in data_loader: - - # between caption and output feature. - # 1. Sample random captions - # 2. Check alignment with CLIP - - # randomly mix text and visual support conditionals - if config.mix: - - assert config.mask.startswith('text_and') - - with autocast_fn(): - # data_x[1] = text label - prompts = model.sample_prompts(data_x[1]) - - # model.clip_model() - - text_cond = model.compute_conditional(prompts) - if model.__class__.__name__ == 'CLIPDensePredTMasked': - # when mask=='separate' - visual_s_cond, _, _ = model.visual_forward_masked(data_x[2].cuda(), data_x[3].cuda()) - else: - # data_x[2] = visual prompt - visual_s_cond, _, _ = model.visual_forward(data_x[2].cuda()) - - max_txt = config.mix_text_max if config.mix_text_max is not None else 1 - batch_size = text_cond.shape[0] - - # sample weights for each element in batch - text_weights = torch.distributions.Uniform(config.mix_text_min, max_txt).sample((batch_size,))[:, None] - text_weights = text_weights.cuda() - - if dataset.__class__.__name__ == 'PhraseCut': - # give full weight to text where support_image is invalid - visual_is_valid = data_x[4] if model.__class__.__name__ == 'CLIPDensePredTMasked' else data_x[3] - text_weights = torch.max(text_weights[:,0], 1 - visual_is_valid.float().cuda()).unsqueeze(1) - - cond = text_cond * text_weights + visual_s_cond * (1 - text_weights) - - else: - # no mix - - if model.__class__.__name__ == 'CLIPDensePredTMasked': - # compute conditional vector using CLIP masking - with autocast_fn(): - assert config.mask == 'separate' - cond, _, _ = model.visual_forward_masked(data_x[1].cuda(), data_x[2].cuda()) - else: - cond = data_x[1] - if isinstance(cond, torch.Tensor): - cond = cond.cuda() - - with autocast_fn(): - visual_q = None - - pred, visual_q, _, _ = model(data_x[0].cuda(), cond, return_features=True) - - loss = loss_fn(pred, data_y[0].cuda()) - - if torch.isnan(loss) or torch.isinf(loss): - # skip if loss is nan - log.warning('Training stopped due to inf/nan loss.') - sys.exit(-1) - - extra_loss = 0 - loss += extra_loss - - opt.zero_grad() - - if scaler is None: - loss.backward() - opt.step() - else: - scaler.scale(loss).backward() - scaler.step(opt) - scaler.update() - - if lr_scheduler is not None: - lr_scheduler.step() - if i % 2000 == 0: - current_lr = [g['lr'] for g in opt.param_groups][0] - log.info(f'current lr: {current_lr:.5f} ({len(opt.param_groups)} parameter groups)') - - logger.iter(i=i, loss=loss) - i += 1 - - if i >= max_iterations: - - if not isfile(join(logger.base_path, 'weights.pth')): - # only write if no weights were already written - logger.save_weights(only_trainable=save_only_trainable) - - sys.exit(0) - - - if config.checkpoint_iterations is not None and i in config.checkpoint_iterations: - logger.save_weights(only_trainable=save_only_trainable, weight_file=f'weights_{i}.pth') - - - if val_interval is not None and i % val_interval == val_interval - 1: - - val_loss, val_scores, maximize = validate(model, dataset_val, config) - - if len(val_scores) > 0: - - score_str = f', scores: ' + ', '.join(f'{k}: {v}' for k, v in val_scores.items()) - - if maximize and val_scores[config.use_val_metric] > best_val_score: - logger.save_weights(only_trainable=save_only_trainable) - best_val_score = val_scores[config.use_val_metric] - - elif not maximize and val_scores[config.use_val_metric] < best_val_score: - logger.save_weights(only_trainable=save_only_trainable) - best_val_score = val_scores[config.use_val_metric] - - else: - score_str = '' - # if no score is used, fall back to loss - if val_loss < best_val_loss: - logger.save_weights(only_trainable=save_only_trainable) - best_val_loss = val_loss - - log.info(f'Validation loss: {val_loss}' + score_str) - logger.iter(i=i, val_loss=val_loss, extra_loss=float(extra_loss), **val_scores) - model.train() - - print('epoch complete') - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net.py b/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net.py deleted file mode 100644 index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000 --- a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net.py +++ /dev/null @@ -1,76 +0,0 @@ -"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. -This file contains code that is adapted from -https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py -""" -import torch -import torch.nn as nn - -from .base_model import BaseModel -from .blocks import FeatureFusionBlock, Interpolate, _make_encoder - - -class MidasNet(BaseModel): - """Network for monocular depth estimation. - """ - - def __init__(self, path=None, features=256, non_negative=True): - """Init. - - Args: - path (str, optional): Path to saved model. Defaults to None. - features (int, optional): Number of features. Defaults to 256. - backbone (str, optional): Backbone network for encoder. Defaults to resnet50 - """ - print("Loading weights: ", path) - - super(MidasNet, self).__init__() - - use_pretrained = False if path is None else True - - self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained) - - self.scratch.refinenet4 = FeatureFusionBlock(features) - self.scratch.refinenet3 = FeatureFusionBlock(features) - self.scratch.refinenet2 = FeatureFusionBlock(features) - self.scratch.refinenet1 = FeatureFusionBlock(features) - - self.scratch.output_conv = nn.Sequential( - nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1), - Interpolate(scale_factor=2, mode="bilinear"), - nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1), - nn.ReLU(True), - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - ) - - if path: - self.load(path) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input data (image) - - Returns: - tensor: depth - """ - - layer_1 = self.pretrained.layer1(x) - layer_2 = self.pretrained.layer2(layer_1) - layer_3 = self.pretrained.layer3(layer_2) - layer_4 = self.pretrained.layer4(layer_3) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return torch.squeeze(out, dim=1) diff --git a/spaces/bigjoker/stable-diffusion-webui/javascript/dragdrop.js b/spaces/bigjoker/stable-diffusion-webui/javascript/dragdrop.js deleted file mode 100644 index fe00892481a8ff8b997759b21cb36eb364db788b..0000000000000000000000000000000000000000 --- a/spaces/bigjoker/stable-diffusion-webui/javascript/dragdrop.js +++ /dev/null @@ -1,97 +0,0 @@ -// allows drag-dropping files into gradio image elements, and also pasting images from clipboard - -function isValidImageList( files ) { - return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type); -} - -function dropReplaceImage( imgWrap, files ) { - if ( ! isValidImageList( files ) ) { - return; - } - - const tmpFile = files[0]; - - imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click(); - const callback = () => { - const fileInput = imgWrap.querySelector('input[type="file"]'); - if ( fileInput ) { - if ( files.length === 0 ) { - files = new DataTransfer(); - files.items.add(tmpFile); - fileInput.files = files.files; - } else { - fileInput.files = files; - } - fileInput.dispatchEvent(new Event('change')); - } - }; - - if ( imgWrap.closest('#pnginfo_image') ) { - // special treatment for PNG Info tab, wait for fetch request to finish - const oldFetch = window.fetch; - window.fetch = async (input, options) => { - const response = await oldFetch(input, options); - if ( 'api/predict/' === input ) { - const content = await response.text(); - window.fetch = oldFetch; - window.requestAnimationFrame( () => callback() ); - return new Response(content, { - status: response.status, - statusText: response.statusText, - headers: response.headers - }) - } - return response; - }; - } else { - window.requestAnimationFrame( () => callback() ); - } -} - -window.document.addEventListener('dragover', e => { - const target = e.composedPath()[0]; - const imgWrap = target.closest('[data-testid="image"]'); - if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) { - return; - } - e.stopPropagation(); - e.preventDefault(); - e.dataTransfer.dropEffect = 'copy'; -}); - -window.document.addEventListener('drop', e => { - const target = e.composedPath()[0]; - if (target.placeholder.indexOf("Prompt") == -1) { - return; - } - const imgWrap = target.closest('[data-testid="image"]'); - if ( !imgWrap ) { - return; - } - e.stopPropagation(); - e.preventDefault(); - const files = e.dataTransfer.files; - dropReplaceImage( imgWrap, files ); -}); - -window.addEventListener('paste', e => { - const files = e.clipboardData.files; - if ( ! isValidImageList( files ) ) { - return; - } - - const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')] - .filter(el => uiElementIsVisible(el)); - if ( ! visibleImageFields.length ) { - return; - } - - const firstFreeImageField = visibleImageFields - .filter(el => el.querySelector('input[type=file]'))?.[0]; - - dropReplaceImage( - firstFreeImageField ? 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- diff --git a/spaces/boda/arabic-names-generator/app.py b/spaces/boda/arabic-names-generator/app.py deleted file mode 100644 index 8dc45b6f42c689d40193965dd0274a8ea50e9f04..0000000000000000000000000000000000000000 --- a/spaces/boda/arabic-names-generator/app.py +++ /dev/null @@ -1,36 +0,0 @@ -import streamlit as st -from model.generate import generate_names - -col1, col2, col3 = st.columns([1,1,1]) - - -st.markdown('''This is a simple MLP that predicts the next character given the context of the previous 5 characters! - If you want to play with this, see the repo [here](https://github.com/BodaSadalla98/Arabic_makemore) - - - -''') - -option = col2.selectbox( - 'Select the language to generate the names with:', - ('Arabic', 'English')) -number = col2.number_input(label='Enter how many names to generate..', min_value=0, max_value=1000, value=5) - -names = '' -if col2.button('Generate'): - if option =='English': - names = generate_names(number,'en') - else: - names = generate_names(number,'ar') - - -for name in names: - new_title = f'
{name}
' - col2.markdown(new_title, unsafe_allow_html=True) - - -st.markdown(''' -If you have any questions, contact me: **@bodasadallah** -''') -# st.write(names) - diff --git a/spaces/brightswitch/EleutherAI-llemma_34b/app.py b/spaces/brightswitch/EleutherAI-llemma_34b/app.py deleted file mode 100644 index 32aed1044b2a037a19a7713f44d6471036032c3f..0000000000000000000000000000000000000000 --- a/spaces/brightswitch/EleutherAI-llemma_34b/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/EleutherAI/llemma_34b").launch() \ No newline at end of file diff --git a/spaces/cahya/image-search/wit_index.py b/spaces/cahya/image-search/wit_index.py deleted file mode 100644 index d30ed33c2ac6798b1cf299ef911a96c010dd04b8..0000000000000000000000000000000000000000 --- a/spaces/cahya/image-search/wit_index.py +++ /dev/null @@ -1,40 +0,0 @@ -import pickle - -# Used to create the dense document vectors. -import torch -from sentence_transformers import SentenceTransformer -import datasets - -# Used to create and store the Faiss index. -import faiss -import numpy as np - -class WitIndex: - """ - WitIndex is a class to search the wiki snippets from the given text. It can also return link to the - wiki page or the image. - """ - wit_dataset = None - - def __init__(self, wit_index_path: str, model_name: str, wit_dataset_path: str, gpu=True): - self.index = faiss.read_index(wit_index_path) - self.model = SentenceTransformer(model_name) - if WitIndex.wit_dataset is None: - WitIndex.wit_dataset = pickle.load(open(wit_dataset_path, "rb")) - print(f"Gpu: {gpu}") - if gpu and torch.cuda.is_available(): - print("Cuda is available") - self.model = self.model.to(torch.device("cuda")) - res = faiss.StandardGpuResources() - self.index = faiss.index_cpu_to_gpu(res, 0, self.index) - - def search(self, text, top_k=6): - print(f"> Search: {text}") - embedding = self.model.encode(text, convert_to_numpy=True, show_progress_bar=False) - # Retrieve the k nearest neighbours - distance, index = self.index.search(np.array([embedding]), k=top_k) - distance, index = distance.flatten().tolist(), index.flatten().tolist() - index_url = [WitIndex.wit_dataset['desc2image_map'][i] for i in index] - image_info = [WitIndex.wit_dataset['image_info'][i] for i in index_url] - print(f"> URL: {image_info[0]}") - return distance, index, image_info diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/engine/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/engine/__init__.py deleted file mode 100644 index 08a61572b4c7d09c8d400e903a96cbf5b2cc4763..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/engine/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -from .launch import * -from .train_loop import * - -__all__ = [k for k in globals().keys() if not k.startswith("_")] - - -# prefer to let hooks and defaults live in separate namespaces (therefore not in __all__) -# but still make them available here -from .hooks import * -from .defaults import * diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/inference_based_loader.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/inference_based_loader.py deleted file mode 100644 index cb89544500c29c4055353060ebbc8b428bd0262a..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/inference_based_loader.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import random -from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple -import torch -from torch import nn - -SampledData = Any -ModelOutput = Any - - -def _grouper(iterable: Iterable[Any], n: int, fillvalue=None) -> Iterator[Tuple[Any]]: - """ - Group elements of an iterable by chunks of size `n`, e.g. - grouper(range(9), 4) -> - (0, 1, 2, 3), (4, 5, 6, 7), (8, None, None, None) - """ - it = iter(iterable) - while True: - values = [] - for _ in range(n): - try: - value = next(it) - except StopIteration: - if values: - values.extend([fillvalue] * (n - len(values))) - yield tuple(values) - return - values.append(value) - yield tuple(values) - - -class ScoreBasedFilter: - """ - Filters entries in model output based on their scores - Discards all entries with score less than the specified minimum - """ - - def __init__(self, min_score: float = 0.8): - self.min_score = min_score - - def __call__(self, model_output: ModelOutput) -> ModelOutput: - for model_output_i in model_output: - instances = model_output_i["instances"] - if not instances.has("scores"): - continue - instances_filtered = instances[instances.scores >= self.min_score] - model_output_i["instances"] = instances_filtered - return model_output - - -class InferenceBasedLoader: - """ - Data loader based on results inferred by a model. Consists of: - - a data loader that provides batches of images - - a model that is used to infer the results - - a data sampler that converts inferred results to annotations - """ - - def __init__( - self, - model: nn.Module, - data_loader: Iterable[List[Dict[str, Any]]], - data_sampler: Optional[Callable[[ModelOutput], List[SampledData]]] = None, - data_filter: Optional[Callable[[ModelOutput], ModelOutput]] = None, - shuffle: bool = True, - batch_size: int = 4, - inference_batch_size: int = 4, - drop_last: bool = False, - category_to_class_mapping: Optional[dict] = None, - ): - """ - Constructor - - Args: - model (torch.nn.Module): model used to produce data - data_loader (Iterable[List[Dict[str, Any]]]): iterable that provides - dictionaries with "images" and "categories" fields to perform inference on - data_sampler (Callable: ModelOutput -> SampledData): functor - that produces annotation data from inference results; - (optional, default: None) - data_filter (Callable: ModelOutput -> ModelOutput): filter - that selects model outputs for further processing - (optional, default: None) - shuffle (bool): if True, the input images get shuffled - batch_size (int): batch size for the produced annotation data - inference_batch_size (int): batch size for input images - drop_last (bool): if True, drop the last batch if it is undersized - category_to_class_mapping (dict): category to class mapping - """ - self.model = model - self.model.eval() - self.data_loader = data_loader - self.data_sampler = data_sampler - self.data_filter = data_filter - self.shuffle = shuffle - self.batch_size = batch_size - self.inference_batch_size = inference_batch_size - self.drop_last = drop_last - if category_to_class_mapping is not None: - self.category_to_class_mapping = category_to_class_mapping - else: - self.category_to_class_mapping = {} - - def __iter__(self) -> Iterator[List[SampledData]]: - for batch in self.data_loader: - # batch : List[Dict[str: Tensor[N, C, H, W], str: Optional[str]]] - # images_batch : Tensor[N, C, H, W] - # image : Tensor[C, H, W] - images_and_categories = [ - {"image": image, "category": category} - for element in batch - for image, category in zip(element["images"], element["categories"]) - ] - if not images_and_categories: - continue - if self.shuffle: - random.shuffle(images_and_categories) - yield from self._produce_data(images_and_categories) # pyre-ignore[6] - - def _produce_data( - self, images_and_categories: List[Tuple[torch.Tensor, Optional[str]]] - ) -> Iterator[List[SampledData]]: - """ - Produce batches of data from images - - Args: - images_and_categories (List[Tuple[torch.Tensor, Optional[str]]]): - list of images and corresponding categories to process - - Returns: - Iterator over batches of data sampled from model outputs - """ - data_batches: List[SampledData] = [] - category_to_class_mapping = self.category_to_class_mapping - batched_images_and_categories = _grouper(images_and_categories, self.inference_batch_size) - for batch in batched_images_and_categories: - batch = [ - { - "image": image_and_category["image"].to(self.model.device), - "category": image_and_category["category"], - } - for image_and_category in batch - if image_and_category is not None - ] - if not batch: - continue - with torch.no_grad(): - model_output = self.model(batch) - for model_output_i, batch_i in zip(model_output, batch): - assert len(batch_i["image"].shape) == 3 - model_output_i["image"] = batch_i["image"] - instance_class = category_to_class_mapping.get(batch_i["category"], 0) - model_output_i["instances"].dataset_classes = torch.tensor( - [instance_class] * len(model_output_i["instances"]) - ) - model_output_filtered = ( - model_output if self.data_filter is None else self.data_filter(model_output) - ) - data = ( - model_output_filtered - if self.data_sampler is None - else self.data_sampler(model_output_filtered) - ) - for data_i in data: - if len(data_i["instances"]): - data_batches.append(data_i) - if len(data_batches) >= self.batch_size: - yield data_batches[: self.batch_size] - data_batches = data_batches[self.batch_size :] - if not self.drop_last and data_batches: - yield data_batches diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/samplers/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/samplers/__init__.py deleted file mode 100644 index 7dba87ea1c6f37ab56071d2f5d715bd78fe8816f..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/samplers/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -from .densepose_uniform import DensePoseUniformSampler -from .densepose_confidence_based import DensePoseConfidenceBasedSampler -from .densepose_cse_uniform import DensePoseCSEUniformSampler -from .densepose_cse_confidence_based import DensePoseCSEConfidenceBasedSampler -from .mask_from_densepose import MaskFromDensePoseSampler -from .prediction_to_gt import PredictionToGroundTruthSampler diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/train_custom_data.md b/spaces/chendl/compositional_test/multimodal/YOLOX/docs/train_custom_data.md deleted file mode 100644 index ee97cc94ae5ffd606053c1adcd057c86283c8185..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/multimodal/YOLOX/docs/train_custom_data.md +++ /dev/null @@ -1,131 +0,0 @@ -# Train Custom Data - -This page explains how to train your own custom data with YOLOX. - -We take an example of fine-tuning YOLOX-S model on VOC dataset to give a more clear guide. - -## 0. Before you start -Clone this repo and follow the [README](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/README.md) to install YOLOX. - -## 1. Create your own dataset -**Step 1** Prepare your own dataset with images and labels first. For labeling images, you can use tools like [Labelme](https://github.com/wkentaro/labelme) or [CVAT](https://github.com/openvinotoolkit/cvat). - -**Step 2** Then, you should write the corresponding Dataset Class which can load images and labels through `__getitem__` method. We currently support COCO format and VOC format. - -You can also write the Dataset by your own. Let's take the [VOC](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/data/datasets/voc.py#L151) Dataset file for example: -```python - @Dataset.resize_getitem - def __getitem__(self, index): - img, target, img_info, img_id = self.pull_item(index) - - if self.preproc is not None: - img, target = self.preproc(img, target, self.input_dim) - - return img, target, img_info, img_id -``` - -One more thing worth noting is that you should also implement [pull_item](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/data/datasets/voc.py#L129) and [load_anno](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/data/datasets/voc.py#L121) method for the `Mosiac` and `MixUp` augmentations. - -**Step 3** Prepare the evaluator. We currently have [COCO evaluator](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/evaluators/coco_evaluator.py) and [VOC evaluator](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/evaluators/voc_evaluator.py). -If you have your own format data or evaluation metric, you can write your own evaluator. - -**Step 4** Put your dataset under `$YOLOX_DIR/datasets`, for VOC: - -```shell -ln -s /path/to/your/VOCdevkit ./datasets/VOCdevkit -``` -* The path "VOCdevkit" will be used in your exp file described in next section. Specifically, in `get_data_loader` and `get_eval_loader` function. - -✧✧✧ You can download the mini-coco128 dataset by the [link](https://drive.google.com/file/d/16N3u36ycNd70m23IM7vMuRQXejAJY9Fs/view?usp=sharing), and then unzip it to the `datasets` directory. The dataset has been converted from YOLO format to COCO format, and can be used directly as a dataset for testing whether the train environment can be runned successfully. - -## 2. Create your Exp file to control everything -We put everything involved in a model to one single Exp file, including model setting, training setting, and testing setting. - -**A complete Exp file is at [yolox_base.py](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/exp/yolox_base.py).** It may be too long to write for every exp, but you can inherit the base Exp file and only overwrite the changed part. - -Let's take the [VOC Exp file](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/exps/example/yolox_voc/yolox_voc_s.py) as an example. - -We select `YOLOX-S` model here, so we should change the network depth and width. VOC has only 20 classes, so we should also change the `num_classes`. - -These configs are changed in the `init()` method: -```python -class Exp(MyExp): - def __init__(self): - super(Exp, self).__init__() - self.num_classes = 20 - self.depth = 0.33 - self.width = 0.50 - self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0] -``` - -Besides, you should also overwrite the `dataset` and `evaluator`, prepared before training the model on your own data. - -Please see [get_data_loader](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/exps/example/yolox_voc/yolox_voc_s.py#L20), [get_eval_loader](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/exps/example/yolox_voc/yolox_voc_s.py#L82), and [get_evaluator](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/exps/example/yolox_voc/yolox_voc_s.py#L113) for more details. - -✧✧✧ You can also see the `exps/example/custom` directory for more details. - -## 3. Train -Except special cases, we always recommend to use our [COCO pretrained weights](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/README.md) for initializing the model. - -Once you get the Exp file and the COCO pretrained weights we provided, you can train your own model by the following below command: -```bash -python tools/train.py -f /path/to/your/Exp/file -d 8 -b 64 --fp16 -o -c /path/to/the/pretrained/weights [--cache] -``` -* --cache: we now support RAM caching to speed up training! Make sure you have enough system RAM when adopting it. - -or take the `YOLOX-S` VOC training for example: -```bash -python tools/train.py -f exps/example/yolox_voc/yolox_voc_s.py -d 8 -b 64 --fp16 -o -c /path/to/yolox_s.pth [--cache] -``` - -✧✧✧ For example: -- If you download the [mini-coco128](https://drive.google.com/file/d/16N3u36ycNd70m23IM7vMuRQXejAJY9Fs/view?usp=sharing) and unzip it to the `datasets`, you can direct run the following training code. - ```bash - python tools/train.py -f exps/example/custom/yolox_s.py -d 8 -b 64 --fp16 -o -c /path/to/yolox_s.pth - ``` - -(Don't worry for the different shape of detection head between the pretrained weights and your own model, we will handle it) - -## 4. Tips for Best Training Results - -As **YOLOX** is an anchor-free detector with only several hyper-parameters, most of the time good results can be obtained with no changes to the models or training settings. -We thus always recommend you first train with all default training settings. - -If at first you don't get good results, there are steps you could consider to improve the model. - -**Model Selection** We provide `YOLOX-Nano`, `YOLOX-Tiny`, and `YOLOX-S` for mobile deployments, while `YOLOX-M`/`L`/`X` for cloud or high performance GPU deployments. - -If your deployment meets any compatibility issues. we recommend `YOLOX-DarkNet53`. - -**Training Configs** If your training overfits early, then you can reduce max\_epochs or decrease the base\_lr and min\_lr\_ratio in your Exp file: - -```python -# -------------- training config --------------------- # - self.warmup_epochs = 5 - self.max_epoch = 300 - self.warmup_lr = 0 - self.basic_lr_per_img = 0.01 / 64.0 - self.scheduler = "yoloxwarmcos" - self.no_aug_epochs = 15 - self.min_lr_ratio = 0.05 - self.ema = True - - self.weight_decay = 5e-4 - self.momentum = 0.9 -``` - -**Aug Configs** You may also change the degree of the augmentations. - -Generally, for small models, you should weak the aug, while for large models or small size of dataset, you may enchance the aug in your Exp file: -```python -# --------------- transform config ----------------- # - self.degrees = 10.0 - self.translate = 0.1 - self.scale = (0.1, 2) - self.mosaic_scale = (0.8, 1.6) - self.shear = 2.0 - self.perspective = 0.0 - self.enable_mixup = True -``` - -**Design your own detector** You may refer to our [Arxiv](https://arxiv.org/abs/2107.08430) paper for details and suggestions for designing your own detector. diff --git a/spaces/chengzl18/DeepTHULAC/app.py b/spaces/chengzl18/DeepTHULAC/app.py deleted file mode 100644 index 1e72fe39a78858e7c90a98c07f3c6f1ed620d4ca..0000000000000000000000000000000000000000 --- a/spaces/chengzl18/DeepTHULAC/app.py +++ /dev/null @@ -1,84 +0,0 @@ -import gradio as gr -from deepthulac import LacModel, SEG_MODEL, POS_MODEL -# import jieba -lac_seg = LacModel.load(path=SEG_MODEL, device='cpu') -lac_pos = LacModel.load(path=POS_MODEL, device='cpu') -examples=["他在衬衫外套了件外套，出门去了。", - "这件和服务必在今天裁剪完毕。", - "把手抬起来", - "这名研究生命大，躲过一劫。", - "1953年袁隆平毕业于西南农学院，分发到湖南安江农业学校。袁隆平决意研究农产源于他目睹1959年湖南在三年大饥荒期间的惨状；[10]1966年2月他在中国科学院《科学通报》于文革休刊前最后一期赶及发表他第一篇论文，受国家科委注目，国家科委因而责成安江农业学校在湖南文革期间保护并支持袁隆平研究。[11][12][13]在国家科委支持下，袁隆平的水稻研究在1970至2000年代屡有突破，使他获联合国多项奖项和获封澳门科技大学荣誉博士，于2019年更获颁共和国勋章[14]。", - "我们每个人之于世界就像一粒沙，但不同活法能过出不同的人生，平凡世界中的美好，永远属于那些主动发掘美好的人，属于那些拥有不凡的精神的人。当你开始在一颦一笑中，发现每一个人可爱的模样，你便懂得了欣赏，当你在春花秋月更替中，学会了珍惜和看淡无常，你便懂得了生命。", - "对这个世界如果你有太多的抱怨 跌倒了就不敢继续往前走 为什么人要这么的脆弱堕落 请你打开电视看看 多少人为生命在努力勇敢的走下去 我们是不是该知足 珍惜一切就算没有拥有", - "今天跟集美出去逛街啦，搞点神仙甜品778茶百道yyds，真的绝绝子 ～还点做了个隐藏甜品吃真的绝绝子啊！！茶百道的分装就是yyds", - ] -examples = [ - "我爱北京天安门", - # '上证报中国证券网讯 360集团创始人、董事长周鸿祎6月27日晚间在微博晒出清华大学录取通知书。通知书显示，周鸿祎被录取为清华大学计算机科学与技术系电子信息专业攻读博士学位研究生（非全日制、定向就业）。', - '360公司创始人、董事长兼CEO周鸿祎在个人微博上晒出了清华大学研究生录取通知书，配文称“终于考上了，希望360智脑帮助我顺利毕业”。', - '在国家科委支持下，袁隆平的水稻研究在1970至2000年代屡有突破，使他获联合国多项奖项和获封澳门科技大学荣誉博士，于2019年更获颁共和国勋章', - '英国科学家艾萨克·牛顿出版《自然哲学的数学原理》，阐述运动定律和万有引力定律。', - '英国罗斯林研究所研究的多莉出生，是世界上首只克隆成年体细胞成功的哺乳动物。', - '忽如一夜春风来，千树万树梨花开。', - '世有伯乐，然后有千里马。', - "今天跟集美出去逛街啦，搞点神仙甜品778茶百道yyds，真的绝绝子～", - '这名研究生命大，躲过一劫。', - "他在衬衫外套了件外套，出门去了。", - "当你开始在一颦一笑中，发现每一个人可爱的模样，你便懂得了欣赏，当你在春花秋月更替中，学会了珍惜和看淡无常，你便懂得了生命。" -] - -def infer( - input_text, - **kwargs, -): - seg_res = lac_seg.seg(input_text.split('\n'), split_long=False)['seg']['res'] - seg_res = '\n'.join([' / '.join(r) for r in seg_res]) - pos_res = lac_pos.seg(input_text.split('\n'), split_long=False)['pos']['res'] - pos_res = '\n'.join([' '.join(r) for r in pos_res]) - # return '\n'.join([' / '.join(r) for r in res]) # , '\n'.join([' / '.join(jieba.cut(sent)) for sent in input_text.split('\n')]) - # return '\n'.join([' / '.join(r) for r in res]), - return seg_res, pos_res - -# https://discuss.huggingface.co/t/connection-errored-out/25329 -# https://discuss.huggingface.co/t/error-connection-errored-out-during-processing/23439 -gr.Interface( - fn=infer, - inputs=[ - gr.components.Textbox( - lines=5, - label="待分词文本", - placeholder="他在衬衫外套了件外套，出门去了。", - ), - ], - outputs=[ - gr.components.Textbox( - lines=5, - label="分词结果", - ), - gr.components.Textbox( - lines=5, - label="分词+词性标注结果", - ), - # gr.inputs.Textbox( - # lines=5, - # label="jieba（对照）", - # ) - ], - title="DeepTHULAC在线分词演示", - description="", - # article="一些较难的分词例子:他在衬衫外套了件外套，出门去了。
" - # "这件和服务必在今天裁剪完毕。
" - # "把手抬起来！
" - # "这件和服务必于今日裁剪完毕。
" - # "这名研究生命大，躲过一劫。
" - # "一些分词例子:
" - # "1953年袁隆平毕业于西南农学院，分发到湖南安江农业学校。袁隆平决意研究农产源于他目睹1959年湖南在三年大饥荒期间的惨状；[10]1966年2月他在中国科学院《科学通报》于文革休刊前最后一期赶及发表他第一篇论文，受国家科委注目，国家科委因而责成安江农业学校在湖南文革期间保护并支持袁隆平研究。[11][12][13]在国家科委支持下，袁隆平的水稻研究在1970至2000年代屡有突破，使他获联合国多项奖项和获封澳门科技大学荣誉博士，于2019年更获颁共和国勋章[14]。
" - # "我们每个人之于世界就像一粒沙，但不同活法能过出不同的人生，平凡世界中的美好，永远属于那些主动发掘美好的人，属于那些拥有不凡的精神的人。当你开始在一颦一笑中，发现每一个人可爱的模样，你便懂得了欣赏，当你在春花秋月更替中，学会了珍惜和看淡无常，你便懂得了生命。
" - # "对这个世界如果你有太多的抱怨 跌倒了就不敢继续往前走 为什么人要这么的脆弱堕落 请你打开电视看看 多少人为生命在努力勇敢的走下去 我们是不是该知足 珍惜一切就算没有拥有
" - # "今天跟集美出去逛街啦，搞点神仙甜品778茶百道yyds，真的绝绝子 ～还点做了个隐藏甜品吃真的绝绝子啊！！茶百道的分装就是yyds
" - # "
", - examples=examples, - cache_examples=False, - theme=gr.themes.Default(spacing_size="sm", radius_size="none", text_size=gr.themes.sizes.text_lg) -).launch() - diff --git a/spaces/chenyangqi/FateZero/FateZero/video_diffusion/models/lora.py b/spaces/chenyangqi/FateZero/FateZero/video_diffusion/models/lora.py deleted file mode 100644 index 2692dd91c314586c8b3c7d8fc51ca5541ee2669d..0000000000000000000000000000000000000000 --- a/spaces/chenyangqi/FateZero/FateZero/video_diffusion/models/lora.py +++ /dev/null @@ -1,131 +0,0 @@ -from typing import Callable, Optional, Union - -import torch -import torch.nn.functional as F -from torch import nn - -# from diffusers.utils -from diffusers.utils import deprecate, logging -from diffusers.utils.import_utils import is_xformers_available -from diffusers.models.attention import FeedForward, CrossAttention, AdaLayerNorm - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -if is_xformers_available(): - import xformers - import xformers.ops -else: - xformers = None - -class LoRALinearLayer(nn.Module): - def __init__(self, in_features, out_features, rank=4, stride=1): - super().__init__() - - if rank > min(in_features, out_features): - Warning(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}, reset to {min(in_features, out_features)//2}") - rank = min(in_features, out_features)//2 - - - self.down = nn.Conv1d(in_features, rank, bias=False, - kernel_size=3, - stride = stride, - padding=1,) - self.up = nn.Conv1d(rank, out_features, bias=False, - kernel_size=3, - padding=1,) - - nn.init.normal_(self.down.weight, std=1 / rank) - # nn.init.zeros_(self.down.bias.data) - - nn.init.zeros_(self.up.weight) - # nn.init.zeros_(self.up.bias.data) - if stride > 1: - self.skip = nn.AvgPool1d(kernel_size=3, stride=2, padding=1) - def forward(self, hidden_states): - orig_dtype = hidden_states.dtype - dtype = self.down.weight.dtype - - down_hidden_states = self.down(hidden_states.to(dtype)) - up_hidden_states = self.up(down_hidden_states) - if hasattr(self, 'skip'): - hidden_states=self.skip(hidden_states) - return up_hidden_states.to(orig_dtype)+hidden_states - - -class LoRACrossAttnProcessor(nn.Module): - def __init__(self, hidden_size, cross_attention_dim=None, rank=4): - super().__init__() - - self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank) - self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) - self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) - self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank) - - def __call__( - self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0 - ): - batch_size, sequence_length, _ = hidden_states.shape - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states) - query = attn.head_to_batch_dim(query) - - encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states - - key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states) - - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - return hidden_states - - - - -class LoRAXFormersCrossAttnProcessor(nn.Module): - def __init__(self, hidden_size, cross_attention_dim, rank=4): - super().__init__() - - self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank) - self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) - self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank) - self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank) - - def __call__( - self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0 - ): - batch_size, sequence_length, _ = hidden_states.shape - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states) - query = attn.head_to_batch_dim(query).contiguous() - - encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states - - key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states) - - key = attn.head_to_batch_dim(key).contiguous() - value = attn.head_to_batch_dim(value).contiguous() - - hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - return hidden_states diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/ipython_ext.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/ipython_ext.py deleted file mode 100644 index 94f4404065418a328c312b586370ed1ccd161a35..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/ipython_ext.py +++ /dev/null @@ -1,23 +0,0 @@ -try: - from IPython.core.magic import needs_local_scope, register_cell_magic -except ImportError: - pass - -import warnings - -import gradio as gr - - -def load_ipython_extension(ipython): - __demo = gr.Blocks() - - @register_cell_magic - @needs_local_scope - def blocks(line, cell, local_ns=None): - if "gr.Interface" in cell: - warnings.warn( - "Usage of gradio.Interface with %%blocks may result in errors." - ) - with __demo.clear(): - exec(cell, None, local_ns) - __demo.launch(quiet=True) diff --git a/spaces/cihyFjudo/fairness-paper-search/Insaaf - The Justice Hd 1080p Online Movies.md b/spaces/cihyFjudo/fairness-paper-search/Insaaf - The Justice Hd 1080p Online Movies.md deleted file mode 100644 index 6e99c439e7318ad72fdd90016f88ab1f31ed238e..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Insaaf - The Justice Hd 1080p Online Movies.md +++ /dev/null @@ -1,6 +0,0 @@ -
Insaaf - The Justice Hd 1080p Online Movies
Download File --->>> https://tinurli.com/2uwjwZ
-
- aaccfb2cb3
-
-
- diff --git a/spaces/cihyFjudo/fairness-paper-search/Karen Rose Die For Me Epub BETTER.md b/spaces/cihyFjudo/fairness-paper-search/Karen Rose Die For Me Epub BETTER.md deleted file mode 100644 index cfeb11fcfaaa2d8752c87343460fbf142e18839f..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Karen Rose Die For Me Epub BETTER.md +++ /dev/null @@ -1,18 +0,0 @@ - -
The New York Journal-American carried the banner front-page headline that Kilgallen was "astounded" by the guilty verdict because of what she argued were serious flaws in the prosecution's case.[19] At the time of the Cleveland jury's guilty verdict in December 1954, Kilgallen's sharp criticism of it was controversial and a Cleveland newspaper dropped her column in response.[20][21][22] Her articles and columns in 1954 did not reveal all she had witnessed in the Cuyahoga County Court of Common Pleas. Nine years after the verdict and sentence, and after the judge had died, she claimed at an event held at the Overseas Press Club in New York that the judge had told her before the start of jury selection that Sheppard was "guilty as hell".[23][24]
-"Part of being a good reporter is to be anonymous, to stay out of the story so that you can better watch and study and listen to the principals. She couldn't do that, mostly because people wouldn't let her. She'd walk into a trial and the prosecutor would ask for her autograph for his wife or the judge would send out greetings."[42]
-karen rose die for me epub
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Then the sun shone so brightly, and right before her stood an angel of God in white robes; it was the same one whom she had seen that night at the church-door. He no longer carried the sharp sword, but a beautiful green branch, full of roses; with this he touched the ceiling, which rose up very high, and where he had touched it there shone a golden star. He touched the walls, which opened wide apart, and she saw the organ which was pealing forth; she saw the pictures of the old pastors and their wives, and the congregation sitting in the polished chairs and singing from their hymn-books. The church itself had come to the poor girl in her narrow room, or the room had gone to the church. She sat in the pew with the rest of the pastor's household, and when they had finished the hymn and looked up, they nodded and said, "It was right of you to come, Karen."
-* A Golden Shanty: Australian Storiesand Sketches in Prose and Verse by Bulletin Writers (1890)--see Bulletin Writersentry.
[There are 3 stories by Edward Dyson. Details of the book are shown here with other books with the same name,to avoid any confusion.]
At the 2009 Copenhagen climate change summit, an intensive two-year international negotiation involving hundreds of delegates from almost every country, and thousands of pressure groups and lobbyists, produced at its end a short two-page document which, in hastily drafted and ungrammatical prose, offers only the most general statements of concern about the problem of climate change and no binding commitments to limit carbon emissions or to compensate those most affected by its manifold impacts.
-Less simple is why a scheme of such magnitude and pervasive dishonesty succeeded for so long. Madoff lied systematically both to his investors and to the supervising federal authorities. In this criminal endeavor, he was apparently assisted by colleagues, some of whom have been prosecuted or face further investigation. But the scale of profits from his fund should have provoked more intrusive suspicion; few others within the industry tried to work out how his company could consistently make such high profits, against market trends, outperforming all competitors year after year.
-Many commentators have therefore reasonably concluded that the obvious answer is further regulation, to ban CDSs and rely on legislation to tame the industry. The problem with this analysis, however, so tempting in these turbid days, is that it rests on an assumption about the legislative process that is perilous indeed: that legislators act upon the interests of voters, and no one else. The law in question, the Commodity Futures Modernization Act, was passed in 2000 by a Democratic administration; it was proposed by the Clinton administration and passed quickly through both Houses. Not one member of Congress raised objections to this particular provision, which was secreted away in a bill of many hundreds of pages. Needless to say, that year the financial services industry, which strongly supported the bill, contributed large amounts to both Democrats and Republicans.
-We have been culturally conditioned to accept that the prosecution of the occasional Madoff somehow proves the power of law and intrinsic justice in the system. In fact, the story unearthed by his case proves the opposite: The system is revealed as fundamentally iniquitous and persistently vulnerable to crime and violent instability. The gross inequality of contemporary society permits a culture of unaccountability and, sometimes, criminality among the richest and most powerful. The most extreme results of this imbalance are scandals like Madoff but also, with the credit crunch, economic volatility that destroys millions of jobs and endangers the entire global economy.
-During the Spanish Civil War, more than thirty thousand people from over fifty nations volunteered to fight the Nationalist armies of General Franco. Many gave up jobs and left families in order to fight the emerging global threat of fascism, and to defend a nascent socialist, even anarchist republic.
-Though it is rarely mentioned, even in the histories of that period, the Spanish civil war saw a moment, tragically brief, of real existing anarchism. In the area of Spain under Republican control, anarchists for a short while held sway, as far as that term means anything when no one was completely in charge. This was not anarchy, an absence of order, it was a society that for a period decided to govern itself not by centralized authority, but by the wishes of local communities, workers, men and women, led by values of equality and mutual respect.
- -This technique too has modern relevance. The hunger strikes by Republican inmates in the Maze prison in Northern Ireland in 1981 resulted in the deaths of several strikers, most famously Bobby Sands, who was elected as a member of the British Parliament during his hunger strike. After ten inmates died, the British government offered some concessions. The most lasting impact, however, was, like the Salt Satyagraha, deeper in its effects on that intangible: will. By 1985, the British government, had negotiated the Anglo-Irish agreement that gave the Irish Republic for the first time a consultative role in the government of Northern Ireland, and heralded the peace process that resulted in the Good Friday agreement of 1998, which largely brought an end, though sadly not yet final, to the violence and sectarian strife that had benighted the province for over thirty years.
-In Marxist theory, the proletariat should eventually be freed of all burdens, including of government. But in practice, all communist systems rapidly established and maintained huge bureaucracies, with their privileged elites, to instruct the people on their best interests. Never were they to be asked what these might be. Those who offered a dissenting voice were repressed, often with great cruelty. In suppressing the anarchists of the Spanish republic, or the Bolsheviks of the Kronstadt rebellion in 1921, the Communists showed their true colors. Communism could never mean freedom from authority. That revolution would never be permitted. The people were not to be trusted.
-In blood markers, the most significant change was a 25% decrease in mean triglycerides from 112 to 89 mg/dL (p=0.009) over the eight-week study period. As expected from a diet rich in folate, mean serum 5-methyltetrahydrofolate (5-MTHF) rose 15% from 78 to 88 nmol/L (p=0.004).
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\ No newline at end of file diff --git a/spaces/cihyFjudo/fairness-paper-search/The Macallan Masters of Photography A Unique Collaboration of Whisky and Art.md b/spaces/cihyFjudo/fairness-paper-search/The Macallan Masters of Photography A Unique Collaboration of Whisky and Art.md deleted file mode 100644 index 8f09b2801ed1f220770997ef795d0f79ca6a00d9..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/The Macallan Masters of Photography A Unique Collaboration of Whisky and Art.md +++ /dev/null @@ -1,15 +0,0 @@ - -
An absolutely stunning release from The Macallan, combining the two art forms of whisky and photography. The Macallan Masters of Photography: Magnum Edition, the seventh limited edition release, celebrates the opening of the new Macallan distillery. The single malt marries whisky from eight casks, including a spicy cask, a classic cask, a European red wine cask, a first fill American barrel and a cask of age.
-The Macallan Masters of Photography.
DOWNLOAD ⭐ https://tinurli.com/2uwj6V
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Wood sits at the heart of The Macallan. The vital contributing influences of Spain, North America and Scotland, together with true mastery, set it apart from the rest. For more information on The Macallan, visit www.themacallan.com. The Macallan can also be found on Facebook, Twitter and Instagram
-window.dataLayer = window.dataLayer || []; window.dataLayer.push( 'event': 'ContentDisplayed', 'contentDisplayedId': '23630', 'contentDisplayedPermalink': ' -drink/steven-klein-the-macallan/', 'contentDisplayedIsSingle': 'true', 'contentDisplayedIsFeatured': 'false', 'contentDisplayedType': 'post', 'contentDisplayedMethod': 'inline', 'contentDisplayedFormat': '', 'contentDisplayedSection': 'Read', 'contentDisplayedTitle': "Steven Klein's The Macallan Masters of Photography Series", 'contentDisplayedSubtitle': "An exceptional limited edition peated release, accompanied by the photographer's film, bottle and box", 'contentDisplayedCampaignTrackingKeyword': "", 'contentDisplayedSponsoredByText': "", );
-This limited edition box set retails for approximately $2,995. There are only 1,000 bottles worldwide (and only 100 bottles allocated to the US). For purchasing questions, one must email The Macallan at inquiries.us[at]themacallan[dot]com.
- -Our aim in these online photography courses is to create inspirational journeys for you with the greatest names in the world of photography. Our Masters Of Photography will teach you how to develop your own ideas and how to capture and document the world around you as you see it.
-ONLINE PHOTOGRAPHY CLASSDevelop your photography with the world-renowned photographer whose famous Afghan Girl portrait is one of the most iconic portraits ever captured. Steve gives us a unique insight into how he creates his images and covers a broad range of subjects including landscape, street and portrait photography.
-Masters of Photography is an education platform and a resource for photographers. Our courses (the masterclasses and discovery classes) are for anyone who wants to learn from some of the most highly regarded and influential photographers in the world in order to elevate their own photography. If you want to learn why not learn from the best? The Masters themselves study the works of the greats.
-The classes have been designed to be suitable for all levels of experience. Whether you are a complete beginner or an experienced professional, learning from a Master is sure to help you to improve your photography.
-The classes provide you with lifetime access to the ethos and photographic philosophies of true master photographers. You will learn how they approach their work, the techniques they use and their tips for elevating your own photography.
-Please be aware that you need to activate your account before you can login. We have sent you an email with a verification link. Please click on the link beffore login to the site. The email is from:
mastersofphotography@mastersofphotography.kinstamailservice.com
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\ No newline at end of file diff --git a/spaces/cihyFjudo/fairness-paper-search/Toa Sc 615m Pdf Free Specifications and Features of the Public Address Announcement Speaker.md b/spaces/cihyFjudo/fairness-paper-search/Toa Sc 615m Pdf Free Specifications and Features of the Public Address Announcement Speaker.md deleted file mode 100644 index c616bb122c0c89547a0f4e1cad8be50cfba8681d..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Toa Sc 615m Pdf Free Specifications and Features of the Public Address Announcement Speaker.md +++ /dev/null @@ -1,19 +0,0 @@ - -
Online: Lexington County Building Inspections utilizes a permitting and inspections service called BluePrince. This online service can be utilized to schedule inspections as well as monitoring any construction project from application through building completion. The service is free to use for contractors, licensed by the state of South Carolina, working on projects in Lexington County. To sign up for this online service, please see one of the customer service clerks for additional information or online at the Building Department Website.
-Consider filing your taxes electronically. Filing online can help you avoid mistakes and find credits and deductions that you may qualify for. In many cases you can file for free. Learn more about how to file electronically.
-Toa Sc 615m Pdf Free
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If you or someone you know is in immediate distress or is thinking about hurting themselves, call the National Suicide Prevention Lifeline toll-free at 1-800-273-TALK (8255). You also can text the Crisis Text Line (HELLO to 741741) or use the Lifeline Chat on the National Suicide Prevention Lifeline website. If you suspect a medical emergency, seek medical attention or call 911 immediately.
-Nashville is family-friendly, and many museums, attractions, and destinations in the city offer free admission for kids of certain ages. No matter what your family likes doing together, there are plenty of kid-friendly activities in Nashville to enjoy.
-(2) With respect to public preschool elementary, secondary, or adult educational services, a handicappped person (i) of an age during which nonhandicapped persons are provided such services, (ii) of any age during which it is mandatory under state law to provide such services to handicapped persons, or (iii) to whom a state is required to provide a free appropriate public education under section 612 of the Education of the Handicapped Act; and
-(a) General. A recipient that operates a public elementary or secondary education program or activity shall provide a free appropriate public education to each qualified handicapped person who is in the recipient's jurisdiction, regardless of the nature or severity of the person's handicap.
-(c) Free education -- (1) General. For the purpose of this section, the provision of a free education is the provision of educational and related services without cost to the handicapped person or to his or her parents or guardian, except for those fees that are imposed on non-handicapped persons or their parents or guardian. It may consist either of the provision of free services or, if a recipient places a handicapped person or refers such person for aid, benefits, or services not operated or provided by the recipient as its means of carrying out the requirements of this subpart, of payment for the costs of the aid, benefits, or services. Funds available from any public or private agency may be used to meet the requirements of this subpart. Nothing in this section shall be construed to relieve an insurer or similar third party from an otherwise valid obligation to provide or pay for services provided to a handicapped person.
-(4) Placement of handicapped persons by parents. If a recipient has made available, in conformance with the requirements of this section and 104.34, a free appropriate public education to a handicapped person and the person's parents or guardian choose to place the person in a private school, the recipient is not required to pay for the person's education in the private school. Disagreements between a parent or guardian and a recipient regarding whether the recipient has made a free appropriate public education available or otherwise regarding the question of financial responsibility are subject to the due process procedures of 104.36.
-IF YOU CHOOSE TO USE ANOTHER VENDOR, please be cautious sharing your personal information. The Florida Bureau of Vital Statistics is NOT affiliated with any other vendors, nor can we guarantee the confidentiality of any data you provide to them. Once the vendor assists you in completing our application they must then place your order through VitalChek or mail the order to our office with our agency fee. Please note: All our applications are free and available for download on our website.
-NOTE: EEOC investigators must take great care in situations involving both (a) the statutory rights of employees to be free from discrimination at work, and (b) the rights of employers under the First Amendment and RFRA. Although a resolution satisfactory to all may come from good faith on the part of the employer and employee through mutual efforts to reach a reasonable accommodation, on occasion the religious interests of the employer and employee may be in conflict. EEOC personnel should seek the advice of the EEOC Legal Counsel in such a situation, and on occasion the Legal Counsel may consult as needed with the U.S. Department of Justice.
- -While some employees believe that religion is intensely personal and private, others are open about sharing or outwardly expressing their religion. In addition, there are employees who may believe that they have a religious obligation to share their views and to try to persuade coworkers of the truth of their religious beliefs, i.e., to proselytize. Certain private employers, too, whether or not they are religious organizations, may wish to express their religious views and share their religion with their employees.[196] As noted above, however, some employees may perceive proselytizing or other religious expression as unwelcome based on their own religious beliefs and observances, or lack thereof. In an increasingly pluralistic society, the mix of divergent beliefs and practices can give rise to conflicts requiring employers to balance the rights of employers and employees who wish to express their religious beliefs with the rights of other employees to be free from religious harassment under the foregoing Title VII harassment standards.
-Q: Are there any special programs for teachers?
A: The Chronicle Newspaper in Education program is a great way for teachers to put extra learning power into the classroom. We provide free access to the replica e-Edition through NIEOnline.com. You can learn more and sign up for the service at NIEOnline.com/sfchronicle.
Q: Why can I see some stories for free?
A: The SFChronicle.com website employs a "metered paywall" technology that allows new users and customers to sample some of our great stories before buying access or a subscription.
Q: How do I place a classified ad?
A: You can place an ad online, by telephone, in person, by mail, or email.
Online:
-adportal/classifieds/
By Telephone:
415-777-7777 OR toll free 866-732-4766 ext. 7777
8am to 5pm, Monday through Friday
In Person:
Our counter sales are closed due to COVID restrictions
By Mail:
San Francisco Chronicle Classified Department
901 Mission Street, San Francisco, CA 94103
By email:
classifieds@sfgate.com
-
-
\ No newline at end of file diff --git a/spaces/cm107/agv-demo/Build/test-webgl-dev.framework.js b/spaces/cm107/agv-demo/Build/test-webgl-dev.framework.js deleted file mode 100644 index dbb10da9fa72a06708fdd918f8b87370f3223733..0000000000000000000000000000000000000000 --- a/spaces/cm107/agv-demo/Build/test-webgl-dev.framework.js +++ /dev/null @@ -1,17763 +0,0 @@ -function unityFramework(Module) { -var Module = typeof Module !== "undefined" ? Module : {}; - - - - -var videoInputDevices = []; - -var removeEnumerateMediaDevicesRunDependency; - -function matchToOldDevice(newDevice) { - var oldDevices = Object.keys(videoInputDevices); - for (var i = 0; i < oldDevices.length; ++i) { - var old = videoInputDevices[oldDevices[i]]; - if (old.deviceId && old.deviceId == newDevice.deviceId) return old; - } - for (var i = 0; i < oldDevices.length; ++i) { - var old = videoInputDevices[oldDevices[i]]; - if (old == newDevice) return old; - } - for (var i = 0; i < oldDevices.length; ++i) { - var old = videoInputDevices[oldDevices[i]]; - if (old.label && old.label == newDevice.label) return old; - } - for (var i = 0; i < oldDevices.length; ++i) { - var old = videoInputDevices[oldDevices[i]]; - if (old.groupId && old.kind && old.groupId == newDevice.groupId && old.kind == newDevice.kind) return old; - } -} - -function assignNewVideoInputId() { - for (var i = 0; ;++i) { - if (!videoInputDevices[i]) return i; - } -} - -function updateVideoInputDevices(devices) { - removeEnumerateMediaDevicesRunDependency(); - videoInputDevices = []; - var retainedDevices = {}; - var newDevices = []; - devices.forEach(function(device) { - if (device.kind === "videoinput") { - var oldDevice = matchToOldDevice(device); - if (oldDevice) { - retainedDevices[oldDevice.id] = oldDevice; - } else { - newDevices.push(device); - } - } - }); - videoInputDevices = retainedDevices; - newDevices.forEach(function(device) { - if (!device.id) { - device.id = assignNewVideoInputId(); - device.name = device.label || "Video input #" + (device.id + 1); - device.isFrontFacing = device.name.toLowerCase().includes("front") || !device.name.toLowerCase().includes("front") && !device.name.toLowerCase().includes("back"); - videoInputDevices[device.id] = device; - } - }); -} - -function enumerateMediaDeviceList() { - if (!videoInputDevices) return; - navigator.mediaDevices.enumerateDevices().then(function(devices) { - updateVideoInputDevices(devices); - }).catch(function(e) { - console.warn("Unable to enumerate media devices: " + e + "\nWebcams will not be available."); - disableAccessToMediaDevices(); - }); - if (/Firefox/.test(navigator.userAgent)) { - setTimeout(enumerateMediaDeviceList, 6e4); - warnOnce("Applying workaround to Firefox bug https://bugzilla.mozilla.org/show_bug.cgi?id=1397977"); - } -} - -function disableAccessToMediaDevices() { - if (navigator.mediaDevices && navigator.mediaDevices.removeEventListener) { - navigator.mediaDevices.removeEventListener("devicechange", enumerateMediaDeviceList); - } - videoInputDevices = null; -} - -Module["disableAccessToMediaDevices"] = disableAccessToMediaDevices; - -if (!navigator.mediaDevices) { - console.warn("navigator.mediaDevices not supported by this browser. Webcam access will not be available." + (location.protocol == "https:" ? "" : " Try hosting the page over HTTPS, because some browsers disable webcam access when insecure HTTP is being used.")); - disableAccessToMediaDevices(); -} else if (typeof ENVIRONMENT_IS_PTHREAD === "undefined" || !ENVIRONMENT_IS_PTHREAD) setTimeout(function() { - try { - addRunDependency("enumerateMediaDevices"); - removeEnumerateMediaDevicesRunDependency = function() { - removeRunDependency("enumerateMediaDevices"); - if (navigator.mediaDevices) console.log("navigator.mediaDevices support available"); - removeEnumerateMediaDevicesRunDependency = function() {}; - }; - enumerateMediaDeviceList(); - navigator.mediaDevices.addEventListener("devicechange", enumerateMediaDeviceList); - } catch (e) { - console.warn("Unable to enumerate media devices: " + e); - disableAccessToMediaDevices(); - } -}, 0); - -Module["SetFullscreen"] = function(fullscreen) { - if (typeof runtimeInitialized === "undefined" || !runtimeInitialized) { - console.log("Runtime not initialized yet."); - } else if (typeof JSEvents === "undefined") { - console.log("Player not loaded yet."); - } else { - var tmp = JSEvents.canPerformEventHandlerRequests; - JSEvents.canPerformEventHandlerRequests = function() { - return 1; - }; - Module.ccall("SetFullscreen", null, [ "number" ], [ fullscreen ]); - JSEvents.canPerformEventHandlerRequests = tmp; - } -}; - -if (typeof ENVIRONMENT_IS_PTHREAD === "undefined" || !ENVIRONMENT_IS_PTHREAD) { - Module["preRun"].push(function() { - var unityFileSystemInit = Module["unityFileSystemInit"] || function() { - FS.mkdir("/idbfs"); - FS.mount(IDBFS, {}, "/idbfs"); - Module.addRunDependency("JS_FileSystem_Mount"); - FS.syncfs(true, function(err) { - if (err) console.log("IndexedDB is not available. Data will not persist in cache and PlayerPrefs will not be saved."); - Module.removeRunDependency("JS_FileSystem_Mount"); - }); - }; - unityFileSystemInit(); - }); -} - -var stackTraceReference = "(^|\\n)(\\s+at\\s+|)jsStackTrace(\\s+\\(|@)([^\\n]+):\\d+:\\d+(\\)|)(\\n|$)"; - -var stackTraceReferenceMatch = jsStackTrace().match(new RegExp(stackTraceReference)); - -if (stackTraceReferenceMatch) Module.stackTraceRegExp = new RegExp(stackTraceReference.replace("([^\\n]+)", stackTraceReferenceMatch[4].replace(/[\\^${}[\]().*+?|]/g, "\\$&")).replace("jsStackTrace", "[^\\n]+")); - -var abort = function(what) { - if (ABORT) return; - ABORT = true; - EXITSTATUS = 1; - if (typeof ENVIRONMENT_IS_PTHREAD !== "undefined" && ENVIRONMENT_IS_PTHREAD) console.error("Pthread aborting at " + new Error().stack); - if (what !== undefined) { - out(what); - err(what); - what = JSON.stringify(what); - } else { - what = ""; - } - var message = "abort(" + what + ") at " + stackTrace(); - if (Module.abortHandler && Module.abortHandler(message)) return; - throw message; -}; - -function Pointer_stringify(s, len) { - warnOnce("The JavaScript function 'Pointer_stringify(ptrToSomeCString)' is obsoleted and will be removed in a future Unity version. Please call 'UTF8ToString(ptrToSomeCString)' instead."); - return UTF8ToString(s, len); -} - -Module["Pointer_stringify"] = Pointer_stringify; - -function SendMessage(gameObject, func, param) { - if (param === undefined) Module.ccall("SendMessage", null, [ "string", "string" ], [ gameObject, func ]); else if (typeof param === "string") Module.ccall("SendMessageString", null, [ "string", "string", "string" ], [ gameObject, func, param ]); else if (typeof param === "number") Module.ccall("SendMessageFloat", null, [ "string", "string", "number" ], [ gameObject, func, param ]); else throw "" + param + " is does not have a type which is supported by SendMessage."; -} - -Module["SendMessage"] = SendMessage; - -var moduleOverrides = {}; - -var key; - -for (key in Module) { - if (Module.hasOwnProperty(key)) { - moduleOverrides[key] = Module[key]; - } -} - -var arguments_ = []; - -var thisProgram = "./this.program"; - -var quit_ = function(status, toThrow) { - throw toThrow; -}; - -var ENVIRONMENT_IS_WEB = false; - -var ENVIRONMENT_IS_WORKER = false; - -var ENVIRONMENT_IS_NODE = false; - -var ENVIRONMENT_IS_SHELL = false; - -ENVIRONMENT_IS_WEB = typeof window === "object"; - -ENVIRONMENT_IS_WORKER = typeof importScripts === "function"; - -ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string"; - -ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER; - -if (Module["ENVIRONMENT"]) { - throw new Error("Module.ENVIRONMENT has been deprecated. To force the environment, use the ENVIRONMENT compile-time option (for example, -s ENVIRONMENT=web or -s ENVIRONMENT=node)"); -} - -var scriptDirectory = ""; - -function locateFile(path) { - if (Module["locateFile"]) { - return Module["locateFile"](path, scriptDirectory); - } - return scriptDirectory + path; -} - -var read_, readAsync, readBinary, setWindowTitle; - -var nodeFS; - -var nodePath; - -if (ENVIRONMENT_IS_NODE) { - if (ENVIRONMENT_IS_WORKER) { - scriptDirectory = require("path").dirname(scriptDirectory) + "/"; - } else { - scriptDirectory = __dirname + "/"; - } - read_ = function shell_read(filename, binary) { - if (!nodeFS) nodeFS = require("fs"); - if (!nodePath) nodePath = require("path"); - filename = nodePath["normalize"](filename); - return nodeFS["readFileSync"](filename, binary ? null : "utf8"); - }; - readBinary = function readBinary(filename) { - var ret = read_(filename, true); - if (!ret.buffer) { - ret = new Uint8Array(ret); - } - assert(ret.buffer); - return ret; - }; - if (process["argv"].length > 1) { - thisProgram = process["argv"][1].replace(/\\/g, "/"); - } - arguments_ = process["argv"].slice(2); - if (typeof module !== "undefined") { - module["exports"] = Module; - } - process["on"]("uncaughtException", function(ex) { - if (!(ex instanceof ExitStatus)) { - throw ex; - } - }); - process["on"]("unhandledRejection", abort); - quit_ = function(status) { - process["exit"](status); - }; - Module["inspect"] = function() { - return "[Emscripten Module object]"; - }; -} else if (ENVIRONMENT_IS_SHELL) { - if (typeof read != "undefined") { - read_ = function shell_read(f) { - return read(f); - }; - } - readBinary = function readBinary(f) { - var data; - if (typeof readbuffer === "function") { - return new Uint8Array(readbuffer(f)); - } - data = read(f, "binary"); - assert(typeof data === "object"); - return data; - }; - if (typeof scriptArgs != "undefined") { - arguments_ = scriptArgs; - } else if (typeof arguments != "undefined") { - arguments_ = arguments; - } - if (typeof quit === "function") { - quit_ = function(status) { - quit(status); - }; - } - if (typeof print !== "undefined") { - if (typeof console === "undefined") console = {}; - console.log = print; - console.warn = console.error = typeof printErr !== "undefined" ? printErr : print; - } -} else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { - if (ENVIRONMENT_IS_WORKER) { - scriptDirectory = self.location.href; - } else if (typeof document !== "undefined" && document.currentScript) { - scriptDirectory = document.currentScript.src; - } - if (scriptDirectory.indexOf("blob:") !== 0) { - scriptDirectory = scriptDirectory.substr(0, scriptDirectory.lastIndexOf("/") + 1); - } else { - scriptDirectory = ""; - } - { - read_ = function(url) { - var xhr = new XMLHttpRequest(); - xhr.open("GET", url, false); - xhr.send(null); - return xhr.responseText; - }; - if (ENVIRONMENT_IS_WORKER) { - readBinary = function(url) { - var xhr = new XMLHttpRequest(); - xhr.open("GET", url, false); - xhr.responseType = "arraybuffer"; - xhr.send(null); - return new Uint8Array(xhr.response); - }; - } - readAsync = function(url, onload, onerror) { - var xhr = new XMLHttpRequest(); - xhr.open("GET", url, true); - xhr.responseType = "arraybuffer"; - xhr.onload = function() { - if (xhr.status == 200 || xhr.status == 0 && xhr.response) { - onload(xhr.response); - return; - } - onerror(); - }; - xhr.onerror = onerror; - xhr.send(null); - }; - } - setWindowTitle = function(title) { - document.title = title; - }; -} else { - throw new Error("environment detection error"); -} - -var out = Module["print"] || console.log.bind(console); - -var err = Module["printErr"] || console.warn.bind(console); - -for (key in moduleOverrides) { - if (moduleOverrides.hasOwnProperty(key)) { - Module[key] = moduleOverrides[key]; - } -} - -moduleOverrides = null; - -if (Module["arguments"]) arguments_ = Module["arguments"]; - -if (!Object.getOwnPropertyDescriptor(Module, "arguments")) { - Object.defineProperty(Module, "arguments", { - configurable: true, - get: function() { - abort("Module.arguments has been replaced with plain arguments_ (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -if (Module["thisProgram"]) thisProgram = Module["thisProgram"]; - -if (!Object.getOwnPropertyDescriptor(Module, "thisProgram")) { - Object.defineProperty(Module, "thisProgram", { - configurable: true, - get: function() { - abort("Module.thisProgram has been replaced with plain thisProgram (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -if (Module["quit"]) quit_ = Module["quit"]; - -if (!Object.getOwnPropertyDescriptor(Module, "quit")) { - Object.defineProperty(Module, "quit", { - configurable: true, - get: function() { - abort("Module.quit has been replaced with plain quit_ (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -assert(typeof Module["memoryInitializerPrefixURL"] === "undefined", "Module.memoryInitializerPrefixURL option was removed, use Module.locateFile instead"); - -assert(typeof Module["pthreadMainPrefixURL"] === "undefined", "Module.pthreadMainPrefixURL option was removed, use Module.locateFile instead"); - -assert(typeof Module["cdInitializerPrefixURL"] === "undefined", "Module.cdInitializerPrefixURL option was removed, use Module.locateFile instead"); - -assert(typeof Module["filePackagePrefixURL"] === "undefined", "Module.filePackagePrefixURL option was removed, use Module.locateFile instead"); - -assert(typeof Module["read"] === "undefined", "Module.read option was removed (modify read_ in JS)"); - -assert(typeof Module["readAsync"] === "undefined", "Module.readAsync option was removed (modify readAsync in JS)"); - -assert(typeof Module["readBinary"] === "undefined", "Module.readBinary option was removed (modify readBinary in JS)"); - -assert(typeof Module["setWindowTitle"] === "undefined", "Module.setWindowTitle option was removed (modify setWindowTitle in JS)"); - -assert(typeof Module["TOTAL_MEMORY"] === "undefined", "Module.TOTAL_MEMORY has been renamed Module.INITIAL_MEMORY"); - -if (!Object.getOwnPropertyDescriptor(Module, "read")) { - Object.defineProperty(Module, "read", { - configurable: true, - get: function() { - abort("Module.read has been replaced with plain read_ (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -if (!Object.getOwnPropertyDescriptor(Module, "readAsync")) { - Object.defineProperty(Module, "readAsync", { - configurable: true, - get: function() { - abort("Module.readAsync has been replaced with plain readAsync (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -if (!Object.getOwnPropertyDescriptor(Module, "readBinary")) { - Object.defineProperty(Module, "readBinary", { - configurable: true, - get: function() { - abort("Module.readBinary has been replaced with plain readBinary (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -if (!Object.getOwnPropertyDescriptor(Module, "setWindowTitle")) { - Object.defineProperty(Module, "setWindowTitle", { - configurable: true, - get: function() { - abort("Module.setWindowTitle has been replaced with plain setWindowTitle (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -var STACK_ALIGN = 16; - -function alignMemory(size, factor) { - if (!factor) factor = STACK_ALIGN; - return Math.ceil(size / factor) * factor; -} - -function warnOnce(text) { - if (!warnOnce.shown) warnOnce.shown = {}; - if (!warnOnce.shown[text]) { - warnOnce.shown[text] = 1; - err(text); - } -} - -function convertJsFunctionToWasm(func, sig) { - if (typeof WebAssembly.Function === "function") { - var typeNames = { - "i": "i32", - "j": "i64", - "f": "f32", - "d": "f64" - }; - var type = { - parameters: [], - results: sig[0] == "v" ? [] : [ typeNames[sig[0]] ] - }; - for (var i = 1; i < sig.length; ++i) { - type.parameters.push(typeNames[sig[i]]); - } - return new WebAssembly.Function(type, func); - } - var typeSection = [ 1, 0, 1, 96 ]; - var sigRet = sig.slice(0, 1); - var sigParam = sig.slice(1); - var typeCodes = { - "i": 127, - "j": 126, - "f": 125, - "d": 124 - }; - typeSection.push(sigParam.length); - for (var i = 0; i < sigParam.length; ++i) { - typeSection.push(typeCodes[sigParam[i]]); - } - if (sigRet == "v") { - typeSection.push(0); - } else { - typeSection = typeSection.concat([ 1, typeCodes[sigRet] ]); - } - typeSection[1] = typeSection.length - 2; - var bytes = new Uint8Array([ 0, 97, 115, 109, 1, 0, 0, 0 ].concat(typeSection, [ 2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0 ])); - var module = new WebAssembly.Module(bytes); - var instance = new WebAssembly.Instance(module, { - "e": { - "f": func - } - }); - var wrappedFunc = instance.exports["f"]; - return wrappedFunc; -} - -var freeTableIndexes = []; - -var functionsInTableMap; - -function getEmptyTableSlot() { - if (freeTableIndexes.length) { - return freeTableIndexes.pop(); - } - try { - wasmTable.grow(1); - } catch (err) { - if (!(err instanceof RangeError)) { - throw err; - } - throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH."; - } - return wasmTable.length - 1; -} - -function addFunctionWasm(func, sig) { - if (!functionsInTableMap) { - functionsInTableMap = new WeakMap(); - for (var i = 0; i < wasmTable.length; i++) { - var item = wasmTable.get(i); - if (item) { - functionsInTableMap.set(item, i); - } - } - } - if (functionsInTableMap.has(func)) { - return functionsInTableMap.get(func); - } - var ret = getEmptyTableSlot(); - try { - wasmTable.set(ret, func); - } catch (err) { - if (!(err instanceof TypeError)) { - throw err; - } - assert(typeof sig !== "undefined", "Missing signature argument to addFunction: " + func); - var wrapped = convertJsFunctionToWasm(func, sig); - wasmTable.set(ret, wrapped); - } - functionsInTableMap.set(func, ret); - return ret; -} - -var tempRet0 = 0; - -var setTempRet0 = function(value) { - tempRet0 = value; -}; - -var getTempRet0 = function() { - return tempRet0; -}; - -var wasmBinary; - -if (Module["wasmBinary"]) wasmBinary = Module["wasmBinary"]; - -if (!Object.getOwnPropertyDescriptor(Module, "wasmBinary")) { - Object.defineProperty(Module, "wasmBinary", { - configurable: true, - get: function() { - abort("Module.wasmBinary has been replaced with plain wasmBinary (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -var noExitRuntime = Module["noExitRuntime"] || true; - -if (!Object.getOwnPropertyDescriptor(Module, "noExitRuntime")) { - Object.defineProperty(Module, "noExitRuntime", { - configurable: true, - get: function() { - abort("Module.noExitRuntime has been replaced with plain noExitRuntime (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -if (typeof WebAssembly !== "object") { - abort("no native wasm support detected"); -} - -var wasmMemory; - -var ABORT = false; - -var EXITSTATUS; - -function assert(condition, text) { - if (!condition) { - abort("Assertion failed: " + text); - } -} - -function getCFunc(ident) { - var func = Module["_" + ident]; - assert(func, "Cannot call unknown function " + ident + ", make sure it is exported"); - return func; -} - -function ccall(ident, returnType, argTypes, args, opts) { - var toC = { - "string": function(str) { - var ret = 0; - if (str !== null && str !== undefined && str !== 0) { - var len = (str.length << 2) + 1; - ret = stackAlloc(len); - stringToUTF8(str, ret, len); - } - return ret; - }, - "array": function(arr) { - var ret = stackAlloc(arr.length); - writeArrayToMemory(arr, ret); - return ret; - } - }; - function convertReturnValue(ret) { - if (returnType === "string") return UTF8ToString(ret); - if (returnType === "boolean") return Boolean(ret); - return ret; - } - var func = getCFunc(ident); - var cArgs = []; - var stack = 0; - assert(returnType !== "array", 'Return type should not be "array".'); - if (args) { - for (var i = 0; i < args.length; i++) { - var converter = toC[argTypes[i]]; - if (converter) { - if (stack === 0) stack = stackSave(); - cArgs[i] = converter(args[i]); - } else { - cArgs[i] = args[i]; - } - } - } - var ret = func.apply(null, cArgs); - ret = convertReturnValue(ret); - if (stack !== 0) stackRestore(stack); - return ret; -} - -function cwrap(ident, returnType, argTypes, opts) { - return function() { - return ccall(ident, returnType, argTypes, arguments, opts); - }; -} - -var ALLOC_STACK = 1; - -var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : undefined; - -function UTF8ArrayToString(heap, idx, maxBytesToRead) { - var endIdx = idx + maxBytesToRead; - var endPtr = idx; - while (heap[endPtr] && !(endPtr >= endIdx)) ++endPtr; - if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) { - return UTF8Decoder.decode(heap.subarray(idx, endPtr)); - } else { - var str = ""; - while (idx < endPtr) { - var u0 = heap[idx++]; - if (!(u0 & 128)) { - str += String.fromCharCode(u0); - continue; - } - var u1 = heap[idx++] & 63; - if ((u0 & 224) == 192) { - str += String.fromCharCode((u0 & 31) << 6 | u1); - continue; - } - var u2 = heap[idx++] & 63; - if ((u0 & 240) == 224) { - u0 = (u0 & 15) << 12 | u1 << 6 | u2; - } else { - if ((u0 & 248) != 240) warnOnce("Invalid UTF-8 leading byte 0x" + u0.toString(16) + " encountered when deserializing a UTF-8 string in wasm memory to a JS string!"); - u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63; - } - if (u0 < 65536) { - str += String.fromCharCode(u0); - } else { - var ch = u0 - 65536; - str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); - } - } - } - return str; -} - -function UTF8ToString(ptr, maxBytesToRead) { - return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : ""; -} - -function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { - if (!(maxBytesToWrite > 0)) return 0; - var startIdx = outIdx; - var endIdx = outIdx + maxBytesToWrite - 1; - for (var i = 0; i < str.length; ++i) { - var u = str.charCodeAt(i); - if (u >= 55296 && u <= 57343) { - var u1 = str.charCodeAt(++i); - u = 65536 + ((u & 1023) << 10) | u1 & 1023; - } - if (u <= 127) { - if (outIdx >= endIdx) break; - heap[outIdx++] = u; - } else if (u <= 2047) { - if (outIdx + 1 >= endIdx) break; - heap[outIdx++] = 192 | u >> 6; - heap[outIdx++] = 128 | u & 63; - } else if (u <= 65535) { - if (outIdx + 2 >= endIdx) break; - heap[outIdx++] = 224 | u >> 12; - heap[outIdx++] = 128 | u >> 6 & 63; - heap[outIdx++] = 128 | u & 63; - } else { - if (outIdx + 3 >= endIdx) break; - if (u >= 2097152) warnOnce("Invalid Unicode code point 0x" + u.toString(16) + " encountered when serializing a JS string to a UTF-8 string in wasm memory! (Valid unicode code points should be in range 0-0x1FFFFF)."); - heap[outIdx++] = 240 | u >> 18; - heap[outIdx++] = 128 | u >> 12 & 63; - heap[outIdx++] = 128 | u >> 6 & 63; - heap[outIdx++] = 128 | u & 63; - } - } - heap[outIdx] = 0; - return outIdx - startIdx; -} - -function stringToUTF8(str, outPtr, maxBytesToWrite) { - assert(typeof maxBytesToWrite == "number", "stringToUTF8(str, outPtr, maxBytesToWrite) is missing the third parameter that specifies the length of the output buffer!"); - return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite); -} - -function lengthBytesUTF8(str) { - var len = 0; - for (var i = 0; i < str.length; ++i) { - var u = str.charCodeAt(i); - if (u >= 55296 && u <= 57343) u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023; - if (u <= 127) ++len; else if (u <= 2047) len += 2; else if (u <= 65535) len += 3; else len += 4; - } - return len; -} - -var UTF16Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf-16le") : undefined; - -function allocateUTF8(str) { - var size = lengthBytesUTF8(str) + 1; - var ret = _malloc(size); - if (ret) stringToUTF8Array(str, HEAP8, ret, size); - return ret; -} - -function allocateUTF8OnStack(str) { - var size = lengthBytesUTF8(str) + 1; - var ret = stackAlloc(size); - stringToUTF8Array(str, HEAP8, ret, size); - return ret; -} - -function writeArrayToMemory(array, buffer) { - assert(array.length >= 0, "writeArrayToMemory array must have a length (should be an array or typed array)"); - HEAP8.set(array, buffer); -} - -function writeAsciiToMemory(str, buffer, dontAddNull) { - for (var i = 0; i < str.length; ++i) { - assert(str.charCodeAt(i) === str.charCodeAt(i) & 255); - HEAP8[buffer++ >> 0] = str.charCodeAt(i); - } - if (!dontAddNull) HEAP8[buffer >> 0] = 0; -} - -function alignUp(x, multiple) { - if (x % multiple > 0) { - x += multiple - x % multiple; - } - return x; -} - -var buffer, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; - -function updateGlobalBufferAndViews(buf) { - buffer = buf; - Module["HEAP8"] = HEAP8 = new Int8Array(buf); - Module["HEAP16"] = HEAP16 = new Int16Array(buf); - Module["HEAP32"] = HEAP32 = new Int32Array(buf); - Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); - Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); - Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); - Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); - Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); -} - -var TOTAL_STACK = 5242880; - -if (Module["TOTAL_STACK"]) assert(TOTAL_STACK === Module["TOTAL_STACK"], "the stack size can no longer be determined at runtime"); - -var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 33554432; - -if (!Object.getOwnPropertyDescriptor(Module, "INITIAL_MEMORY")) { - Object.defineProperty(Module, "INITIAL_MEMORY", { - configurable: true, - get: function() { - abort("Module.INITIAL_MEMORY has been replaced with plain INITIAL_MEMORY (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)"); - } - }); -} - -assert(INITIAL_MEMORY >= TOTAL_STACK, "INITIAL_MEMORY should be larger than TOTAL_STACK, was " + INITIAL_MEMORY + "! (TOTAL_STACK=" + TOTAL_STACK + ")"); - -assert(typeof Int32Array !== "undefined" && typeof Float64Array !== "undefined" && Int32Array.prototype.subarray !== undefined && Int32Array.prototype.set !== undefined, "JS engine does not provide full typed array support"); - -assert(!Module["wasmMemory"], "Use of `wasmMemory` detected. Use -s IMPORTED_MEMORY to define wasmMemory externally"); - -assert(INITIAL_MEMORY == 33554432, "Detected runtime INITIAL_MEMORY setting. Use -s IMPORTED_MEMORY to define wasmMemory dynamically"); - -var wasmTable; - -function writeStackCookie() { - var max = _emscripten_stack_get_end(); - assert((max & 3) == 0); - HEAPU32[(max >> 2) + 1] = 34821223; - HEAPU32[(max >> 2) + 2] = 2310721022; - HEAP32[0] = 1668509029; -} - -function checkStackCookie() { - if (ABORT) return; - var max = _emscripten_stack_get_end(); - var cookie1 = HEAPU32[(max >> 2) + 1]; - var cookie2 = HEAPU32[(max >> 2) + 2]; - if (cookie1 != 34821223 || cookie2 != 2310721022) { - abort("Stack overflow! Stack cookie has been overwritten, expected hex dwords 0x89BACDFE and 0x2135467, but received 0x" + cookie2.toString(16) + " " + cookie1.toString(16)); - } - if (HEAP32[0] !== 1668509029) abort("Runtime error: The application has corrupted its heap memory area (address zero)!"); -} - -(function() { - var h16 = new Int16Array(1); - var h8 = new Int8Array(h16.buffer); - h16[0] = 25459; - if (h8[0] !== 115 || h8[1] !== 99) throw "Runtime error: expected the system to be little-endian! (Run with -s SUPPORT_BIG_ENDIAN=1 to bypass)"; -})(); - -var __ATPRERUN__ = []; - -var __ATINIT__ = []; - -var __ATMAIN__ = []; - -var __ATEXIT__ = []; - -var __ATPOSTRUN__ = []; - -var runtimeInitialized = false; - -var runtimeExited = false; - -function preRun() { - if (Module["preRun"]) { - if (typeof Module["preRun"] == "function") Module["preRun"] = [ Module["preRun"] ]; - while (Module["preRun"].length) { - addOnPreRun(Module["preRun"].shift()); - } - } - callRuntimeCallbacks(__ATPRERUN__); -} - -function initRuntime() { - checkStackCookie(); - assert(!runtimeInitialized); - runtimeInitialized = true; - if (!Module["noFSInit"] && !FS.init.initialized) FS.init(); - TTY.init(); - SOCKFS.root = FS.mount(SOCKFS, {}, null); - PIPEFS.root = FS.mount(PIPEFS, {}, null); - callRuntimeCallbacks(__ATINIT__); -} - -function preMain() { - checkStackCookie(); - FS.ignorePermissions = false; - callRuntimeCallbacks(__ATMAIN__); -} - -function exitRuntime() { - checkStackCookie(); - runtimeExited = true; -} - -function postRun() { - checkStackCookie(); - if (Module["postRun"]) { - if (typeof Module["postRun"] == "function") Module["postRun"] = [ Module["postRun"] ]; - while (Module["postRun"].length) { - addOnPostRun(Module["postRun"].shift()); - } - } - callRuntimeCallbacks(__ATPOSTRUN__); -} - -function addOnPreRun(cb) { - __ATPRERUN__.unshift(cb); -} - -function addOnInit(cb) { - __ATINIT__.unshift(cb); -} - -function addOnPostRun(cb) { - __ATPOSTRUN__.unshift(cb); -} - -assert(Math.imul, "This browser does not support Math.imul(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill"); - -assert(Math.fround, "This browser does not support Math.fround(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill"); - -assert(Math.clz32, "This browser does not support Math.clz32(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill"); - -assert(Math.trunc, "This browser does not support Math.trunc(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill"); - -var runDependencies = 0; - -var runDependencyWatcher = null; - -var dependenciesFulfilled = null; - -var runDependencyTracking = {}; - -function getUniqueRunDependency(id) { - var orig = id; - while (1) { - if (!runDependencyTracking[id]) return id; - id = orig + Math.random(); - } -} - -function addRunDependency(id) { - runDependencies++; - if (Module["monitorRunDependencies"]) { - Module["monitorRunDependencies"](runDependencies); - } - if (id) { - assert(!runDependencyTracking[id]); - runDependencyTracking[id] = 1; - if (runDependencyWatcher === null && typeof setInterval !== "undefined") { - runDependencyWatcher = setInterval(function() { - if (ABORT) { - clearInterval(runDependencyWatcher); - runDependencyWatcher = null; - return; - } - var shown = false; - for (var dep in runDependencyTracking) { - if (!shown) { - shown = true; - err("still waiting on run dependencies:"); - } - err("dependency: " + dep); - } - if (shown) { - err("(end of list)"); - } - }, 1e4); - } - } else { - err("warning: run dependency added without ID"); - } -} - -function removeRunDependency(id) { - runDependencies--; - if (Module["monitorRunDependencies"]) { - Module["monitorRunDependencies"](runDependencies); - } - if (id) { - assert(runDependencyTracking[id]); - delete runDependencyTracking[id]; - } else { - err("warning: run dependency removed without ID"); - } - if (runDependencies == 0) { - if (runDependencyWatcher !== null) { - clearInterval(runDependencyWatcher); - runDependencyWatcher = null; - } - if (dependenciesFulfilled) { - var callback = dependenciesFulfilled; - dependenciesFulfilled = null; - callback(); - } - } -} - -Module["preloadedImages"] = {}; - -Module["preloadedAudios"] = {}; - -function abort(what) { - if (Module["onAbort"]) { - Module["onAbort"](what); - } - what += ""; - err(what); - ABORT = true; - EXITSTATUS = 1; - var output = "abort(" + what + ") at " + stackTrace(); - what = output; - var e = new WebAssembly.RuntimeError(what); - throw e; -} - -var dataURIPrefix = "data:application/octet-stream;base64,"; - -function isDataURI(filename) { - return filename.startsWith(dataURIPrefix); -} - -function isFileURI(filename) { - return filename.startsWith("file://"); -} - -function createExportWrapper(name, fixedasm) { - return function() { - var displayName = name; - var asm = fixedasm; - if (!fixedasm) { - asm = Module["asm"]; - } - assert(runtimeInitialized, "native function `" + displayName + "` called before runtime initialization"); - assert(!runtimeExited, "native function `" + displayName + "` called after runtime exit (use NO_EXIT_RUNTIME to keep it alive after main() exits)"); - if (!asm[name]) { - assert(asm[name], "exported native function `" + displayName + "` not found"); - } - return asm[name].apply(null, arguments); - }; -} - -var wasmBinaryFile = "build.wasm"; - -if (!isDataURI(wasmBinaryFile)) { - wasmBinaryFile = locateFile(wasmBinaryFile); -} - -function getBinary(file) { - try { - if (file == wasmBinaryFile && wasmBinary) { - return new Uint8Array(wasmBinary); - } - if (readBinary) { - return readBinary(file); - } else { - throw "both async and sync fetching of the wasm failed"; - } - } catch (err) { - abort(err); - } -} - -function getBinaryPromise() { - if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { - if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) { - return fetch(wasmBinaryFile, { - credentials: "same-origin" - }).then(function(response) { - if (!response["ok"]) { - throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; - } - return response["arrayBuffer"](); - }).catch(function() { - return getBinary(wasmBinaryFile); - }); - } else { - if (readAsync) { - return new Promise(function(resolve, reject) { - readAsync(wasmBinaryFile, function(response) { - resolve(new Uint8Array(response)); - }, reject); - }); - } - } - } - return Promise.resolve().then(function() { - return getBinary(wasmBinaryFile); - }); -} - -function createWasm() { - var info = { - "env": asmLibraryArg, - "wasi_snapshot_preview1": asmLibraryArg - }; - function receiveInstance(instance, module) { - var exports = instance.exports; - Module["asm"] = exports; - wasmMemory = Module["asm"]["memory"]; - assert(wasmMemory, "memory not found in wasm exports"); - updateGlobalBufferAndViews(wasmMemory.buffer); - wasmTable = Module["asm"]["__indirect_function_table"]; - assert(wasmTable, "table not found in wasm exports"); - addOnInit(Module["asm"]["__wasm_call_ctors"]); - removeRunDependency("wasm-instantiate"); - } - addRunDependency("wasm-instantiate"); - var trueModule = Module; - function receiveInstantiationResult(result) { - assert(Module === trueModule, "the Module object should not be replaced during async compilation - perhaps the order of HTML elements is wrong?"); - trueModule = null; - receiveInstance(result["instance"]); - } - function instantiateArrayBuffer(receiver) { - return getBinaryPromise().then(function(binary) { - var result = WebAssembly.instantiate(binary, info); - return result; - }).then(receiver, function(reason) { - err("failed to asynchronously prepare wasm: " + reason); - if (isFileURI(wasmBinaryFile)) { - err("warning: Loading from a file URI (" + wasmBinaryFile + ") is not supported in most browsers. See https://emscripten.org/docs/getting_started/FAQ.html#how-do-i-run-a-local-webserver-for-testing-why-does-my-program-stall-in-downloading-or-preparing"); - } - abort(reason); - }); - } - function instantiateAsync() { - if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") { - return fetch(wasmBinaryFile, { - credentials: "same-origin" - }).then(function(response) { - var result = WebAssembly.instantiateStreaming(response, info); - return result.then(receiveInstantiationResult, function(reason) { - err("wasm streaming compile failed: " + reason); - err("falling back to ArrayBuffer instantiation"); - return instantiateArrayBuffer(receiveInstantiationResult); - }); - }); - } else { - return instantiateArrayBuffer(receiveInstantiationResult); - } - } - if (Module["instantiateWasm"]) { - try { - var exports = Module["instantiateWasm"](info, receiveInstance); - return exports; - } catch (e) { - err("Module.instantiateWasm callback failed with error: " + e); - return false; - } - } - instantiateAsync(); - return {}; -} - -var tempDouble; - -var tempI64; - -var ASM_CONSTS = { - 4936888: function() { - Module["emscripten_get_now_backup"] = performance.now; - }, - 4936943: function($0) { - performance.now = function() { - return $0; - }; - }, - 4936991: function($0) { - performance.now = function() { - return $0; - }; - }, - 4937039: function() { - performance.now = Module["emscripten_get_now_backup"]; - }, - 4937094: function() { - return Module.webglContextAttributes.premultipliedAlpha; - }, - 4937155: function() { - return Module.webglContextAttributes.preserveDrawingBuffer; - } -}; - -function callRuntimeCallbacks(callbacks) { - while (callbacks.length > 0) { - var callback = callbacks.shift(); - if (typeof callback == "function") { - callback(Module); - continue; - } - var func = callback.func; - if (typeof func === "number") { - if (callback.arg === undefined) { - (function() { - dynCall_v.call(null, func); - })(); - } else { - (function(a1) { - dynCall_vi.apply(null, [ func, a1 ]); - })(callback.arg); - } - } else { - func(callback.arg === undefined ? null : callback.arg); - } - } -} - -function demangle(func) { - demangle.recursionGuard = (demangle.recursionGuard | 0) + 1; - if (demangle.recursionGuard > 1) return func; - var __cxa_demangle_func = Module["___cxa_demangle"] || Module["__cxa_demangle"]; - assert(__cxa_demangle_func); - var stackTop = stackSave(); - try { - var s = func; - if (s.startsWith("__Z")) s = s.substr(1); - var len = lengthBytesUTF8(s) + 1; - var buf = stackAlloc(len); - stringToUTF8(s, buf, len); - var status = stackAlloc(4); - var ret = __cxa_demangle_func(buf, 0, 0, status); - if (HEAP32[status >> 2] === 0 && ret) { - return UTF8ToString(ret); - } - } catch (e) {} finally { - _free(ret); - stackRestore(stackTop); - if (demangle.recursionGuard < 2) --demangle.recursionGuard; - } - return func; -} - -function demangleAll(text) { - var regex = /\b_Z[\w\d_]+/g; - return text.replace(regex, function(x) { - var y = demangle(x); - return x === y ? x : y + " [" + x + "]"; - }); -} - -function dynCallLegacy(sig, ptr, args) { - assert("dynCall_" + sig in Module, "bad function pointer type - no table for sig '" + sig + "'"); - if (args && args.length) { - assert(args.length === sig.substring(1).replace(/j/g, "--").length); - } else { - assert(sig.length == 1); - } - var f = Module["dynCall_" + sig]; - return args && args.length ? f.apply(null, [ ptr ].concat(args)) : f.call(null, ptr); -} - -function dynCall(sig, ptr, args) { - return dynCallLegacy(sig, ptr, args); -} - -function jsStackTrace() { - var error = new Error(); - if (!error.stack) { - try { - throw new Error(); - } catch (e) { - error = e; - } - if (!error.stack) { - return "(no stack trace available)"; - } - } - return error.stack.toString(); -} - -var runtimeKeepaliveCounter = 0; - -function keepRuntimeAlive() { - return noExitRuntime || runtimeKeepaliveCounter > 0; -} - -function stackTrace() { - var js = jsStackTrace(); - if (Module["extraStackTrace"]) js += "\n" + Module["extraStackTrace"](); - return demangleAll(js); -} - -var JS_Accelerometer = null; - -var JS_Accelerometer_callback = 0; - -function _JS_Accelerometer_IsRunning() { - return JS_Accelerometer && JS_Accelerometer.activated || JS_Accelerometer_callback != 0; -} - -var JS_Accelerometer_multiplier = 1; - -var JS_Accelerometer_lastValue = { - x: 0, - y: 0, - z: 0 -}; - -function JS_Accelerometer_eventHandler() { - JS_Accelerometer_lastValue = { - x: JS_Accelerometer.x * JS_Accelerometer_multiplier, - y: JS_Accelerometer.y * JS_Accelerometer_multiplier, - z: JS_Accelerometer.z * JS_Accelerometer_multiplier - }; - if (JS_Accelerometer_callback != 0) dynCall_vfff(JS_Accelerometer_callback, JS_Accelerometer_lastValue.x, JS_Accelerometer_lastValue.y, JS_Accelerometer_lastValue.z); -} - -var JS_Accelerometer_frequencyRequest = 0; - -var JS_Accelerometer_frequency = 0; - -var JS_LinearAccelerationSensor_callback = 0; - -var JS_GravitySensor_callback = 0; - -var JS_Gyroscope_callback = 0; - -function JS_ComputeGravity(accelerometerValue, linearAccelerationValue) { - var difference = { - x: accelerometerValue.x - linearAccelerationValue.x, - y: accelerometerValue.y - linearAccelerationValue.y, - z: accelerometerValue.z - linearAccelerationValue.z - }; - var differenceMagnitudeSq = difference.x * difference.x + difference.y * difference.y + difference.z * difference.z; - var sum = { - x: accelerometerValue.x + linearAccelerationValue.x, - y: accelerometerValue.y + linearAccelerationValue.y, - z: accelerometerValue.z + linearAccelerationValue.z - }; - var sumMagnitudeSq = sum.x * sum.x + sum.y * sum.y + sum.z * sum.z; - return differenceMagnitudeSq <= sumMagnitudeSq ? difference : sum; -} - -function JS_DeviceMotion_eventHandler(event) { - var accelerometerValue = { - x: event.accelerationIncludingGravity.x * JS_Accelerometer_multiplier, - y: event.accelerationIncludingGravity.y * JS_Accelerometer_multiplier, - z: event.accelerationIncludingGravity.z * JS_Accelerometer_multiplier - }; - if (JS_Accelerometer_callback != 0) dynCall_vfff(JS_Accelerometer_callback, accelerometerValue.x, accelerometerValue.y, accelerometerValue.z); - var linearAccelerationValue = { - x: event.acceleration.x * JS_Accelerometer_multiplier, - y: event.acceleration.y * JS_Accelerometer_multiplier, - z: event.acceleration.z * JS_Accelerometer_multiplier - }; - if (JS_LinearAccelerationSensor_callback != 0) dynCall_vfff(JS_LinearAccelerationSensor_callback, linearAccelerationValue.x, linearAccelerationValue.y, linearAccelerationValue.z); - if (JS_GravitySensor_callback != 0) { - assert(typeof GravitySensor === "undefined"); - var gravityValue = JS_ComputeGravity(accelerometerValue, linearAccelerationValue); - dynCall_vfff(JS_GravitySensor_callback, gravityValue.x, gravityValue.y, gravityValue.z); - } - if (JS_Gyroscope_callback != 0) { - var degToRad = Math.PI / 180; - dynCall_vfff(JS_Gyroscope_callback, event.rotationRate.alpha * degToRad, event.rotationRate.beta * degToRad, event.rotationRate.gamma * degToRad); - } -} - -var JS_DeviceSensorPermissions = 0; - -function JS_RequestDeviceSensorPermissions(permissions) { - if (permissions & 1) { - if (typeof DeviceOrientationEvent.requestPermission === "function") { - DeviceOrientationEvent.requestPermission().then(function(permissionState) { - if (permissionState === "granted") { - JS_DeviceSensorPermissions &= ~1; - } else { - warnOnce("DeviceOrientationEvent permission not granted"); - } - }).catch(function(err) { - warnOnce(err); - JS_DeviceSensorPermissions |= 1; - }); - } - } - if (permissions & 2) { - if (typeof DeviceMotionEvent.requestPermission === "function") { - DeviceMotionEvent.requestPermission().then(function(permissionState) { - if (permissionState === "granted") { - JS_DeviceSensorPermissions &= ~2; - } else { - warnOnce("DeviceMotionEvent permission not granted"); - } - }).catch(function(err) { - warnOnce(err); - JS_DeviceSensorPermissions |= 2; - }); - } - } -} - -function JS_DeviceMotion_add() { - if (JS_Accelerometer_callback == 0 && JS_LinearAccelerationSensor_callback == 0 && JS_GravitySensor_callback == 0 && JS_Gyroscope_callback == 0) { - JS_RequestDeviceSensorPermissions(2); - window.addEventListener("devicemotion", JS_DeviceMotion_eventHandler); - } -} - -function JS_DefineAccelerometerMultiplier() { - var g = 9.80665; - JS_Accelerometer_multiplier = /(iPhone|iPad|Macintosh)/i.test(navigator.userAgent) ? 1 / g : -1 / g; -} - -function _JS_Accelerometer_Start(callback, frequency) { - JS_DefineAccelerometerMultiplier(); - if (typeof Accelerometer === "undefined") { - JS_DeviceMotion_add(); - if (callback != 0) JS_Accelerometer_callback = callback; - return; - } - if (callback != 0) JS_Accelerometer_callback = callback; - function InitializeAccelerometer(frequency) { - JS_Accelerometer = new Accelerometer({ - frequency: frequency, - referenceFrame: "device" - }); - JS_Accelerometer.addEventListener("reading", JS_Accelerometer_eventHandler); - JS_Accelerometer.addEventListener("error", function(e) { - warnOnce(e.error ? e.error : e); - }); - JS_Accelerometer.start(); - JS_Accelerometer_frequency = frequency; - } - if (JS_Accelerometer) { - if (JS_Accelerometer_frequency != frequency) { - JS_Accelerometer.stop(); - JS_Accelerometer.removeEventListener("reading", JS_Accelerometer_eventHandler); - InitializeAccelerometer(frequency); - } - } else if (JS_Accelerometer_frequencyRequest != 0) { - JS_Accelerometer_frequencyRequest = frequency; - } else { - JS_Accelerometer_frequencyRequest = frequency; - navigator.permissions.query({ - name: "accelerometer" - }).then(function(result) { - if (result.state === "granted") { - InitializeAccelerometer(JS_Accelerometer_frequencyRequest); - } else { - warnOnce("No permission to use Accelerometer."); - } - JS_Accelerometer_frequencyRequest = 0; - }); - } -} - -function JS_DeviceMotion_remove() { - if (JS_Accelerometer_callback == 0 && JS_LinearAccelerationSensor_callback == 0 && JS_GravitySensor_callback == 0 && JS_Gyroscope_callback == 0) { - window.removeEventListener("devicemotion", JS_DeviceOrientation_eventHandler); - } -} - -function _JS_Accelerometer_Stop() { - if (JS_Accelerometer) { - if (typeof GravitySensor !== "undefined" || JS_GravitySensor_callback == 0) { - JS_Accelerometer.stop(); - JS_Accelerometer.removeEventListener("reading", JS_Accelerometer_eventHandler); - JS_Accelerometer = null; - } - JS_Accelerometer_callback = 0; - JS_Accelerometer_frequency = 0; - } else if (JS_Accelerometer_callback != 0) { - JS_Accelerometer_callback = 0; - JS_DeviceMotion_remove(); - } -} - -function _JS_Cursor_SetImage(ptr, length) { - var binary = ""; - for (var i = 0; i < length; i++) binary += String.fromCharCode(HEAPU8[ptr + i]); - Module.canvas.style.cursor = "url(data:image/cur;base64," + btoa(binary) + "),default"; -} - -function _JS_Cursor_SetShow(show) { - Module.canvas.style.cursor = show ? "default" : "none"; -} - -function jsDomCssEscapeId(id) { - if (typeof window.CSS !== "undefined" && typeof window.CSS.escape !== "undefined") { - return window.CSS.escape(id); - } - return id.replace(/(#|\.|\+|\[|\]|\(|\)|\{|\})/g, "\\$1"); -} - -function _JS_DOM_MapViewportCoordinateToElementLocalCoordinate(viewportX, viewportY, targetX, targetY) { - var canvasId = Module["canvas"] ? Module["canvas"].id : "unity-canvas"; - var canvasSelector = "#" + jsDomCssEscapeId(canvasId); - var canvas = document.querySelector(canvasSelector); - var rect = canvas.getBoundingClientRect(); - HEAPU32[targetX >> 2] = viewportX - rect.left; - HEAPU32[targetY >> 2] = viewportY - rect.top; -} - -function stringToNewUTF8(jsString) { - var length = lengthBytesUTF8(jsString) + 1; - var cString = _malloc(length); - stringToUTF8(jsString, cString, length); - return cString; -} - -function _JS_DOM_UnityCanvasSelector() { - if (Module["canvas"] && !Module["canvas"].id) throw 'Module["canvas"] must have a CSS ID associated with it!'; - if (!_JS_DOM_UnityCanvasSelector.ptr) { - var canvasId = Module["canvas"] ? Module["canvas"].id : "unity-canvas"; - var canvasSelector = "#" + jsDomCssEscapeId(canvasId); - _JS_DOM_UnityCanvasSelector.ptr = stringToNewUTF8(canvasSelector); - } - return _JS_DOM_UnityCanvasSelector.ptr; -} - -var fs = { - numPendingSync: 0, - syncInternal: 1e3, - syncInProgress: false, - sync: function(onlyPendingSync) { - if (onlyPendingSync) { - if (fs.numPendingSync == 0) return; - } else if (fs.syncInProgress) { - fs.numPendingSync++; - return; - } - fs.syncInProgress = true; - FS.syncfs(false, function(err) { - fs.syncInProgress = false; - }); - fs.numPendingSync = 0; - } -}; - -function _JS_FileSystem_Initialize() { - Module.setInterval(function() { - fs.sync(true); - }, fs.syncInternal); -} - -function _JS_FileSystem_Sync() { - fs.sync(false); -} - -function _JS_Focus_Window() { - var activeElem = document.activeElement; - var canvasId = Module["canvas"] ? Module["canvas"].id : "unity-canvas"; - var canvasSelector = "#" + jsDomCssEscapeId(canvasId); - var canvas = document.querySelector(canvasSelector); - if (activeElem != canvas && activeElem != window && activeElem != document.body) { - window.focus(); - } -} - -var JS_GravitySensor = null; - -function _JS_GravitySensor_IsRunning() { - return typeof GravitySensor !== "undefined" ? JS_GravitySensor && JS_GravitySensor.activated : JS_GravitySensor_callback != 0; -} - -function JS_GravitySensor_eventHandler() { - if (JS_GravitySensor_callback != 0) dynCall_vfff(JS_GravitySensor_callback, JS_GravitySensor.x * JS_Accelerometer_multiplier, JS_GravitySensor.y * JS_Accelerometer_multiplier, JS_GravitySensor.z * JS_Accelerometer_multiplier); -} - -var JS_GravitySensor_frequencyRequest = 0; - -var JS_LinearAccelerationSensor = null; - -function JS_LinearAccelerationSensor_eventHandler() { - var linearAccelerationValue = { - x: JS_LinearAccelerationSensor.x * JS_Accelerometer_multiplier, - y: JS_LinearAccelerationSensor.y * JS_Accelerometer_multiplier, - z: JS_LinearAccelerationSensor.z * JS_Accelerometer_multiplier - }; - if (JS_LinearAccelerationSensor_callback != 0) dynCall_vfff(JS_LinearAccelerationSensor_callback, linearAccelerationValue.x, linearAccelerationValue.y, linearAccelerationValue.z); - if (JS_GravitySensor_callback != 0 && typeof GravitySensor === "undefined") { - var gravityValue = JS_ComputeGravity(JS_Accelerometer_lastValue, linearAccelerationValue); - dynCall_vfff(JS_GravitySensor_callback, gravityValue.x, gravityValue.y, gravityValue.z); - } -} - -var JS_LinearAccelerationSensor_frequencyRequest = 0; - -var JS_LinearAccelerationSensor_frequency = 0; - -function _JS_LinearAccelerationSensor_Start(callback, frequency) { - JS_DefineAccelerometerMultiplier(); - if (typeof LinearAccelerationSensor === "undefined") { - JS_DeviceMotion_add(); - if (callback != 0) JS_LinearAccelerationSensor_callback = callback; - return; - } - if (callback != 0) JS_LinearAccelerationSensor_callback = callback; - function InitializeLinearAccelerationSensor(frequency) { - JS_LinearAccelerationSensor = new LinearAccelerationSensor({ - frequency: frequency, - referenceFrame: "device" - }); - JS_LinearAccelerationSensor.addEventListener("reading", JS_LinearAccelerationSensor_eventHandler); - JS_LinearAccelerationSensor.addEventListener("error", function(e) { - warnOnce(e.error ? e.error : e); - }); - JS_LinearAccelerationSensor.start(); - JS_LinearAccelerationSensor_frequency = frequency; - } - if (JS_LinearAccelerationSensor) { - if (JS_LinearAccelerationSensor_frequency != frequency) { - JS_LinearAccelerationSensor.stop(); - JS_LinearAccelerationSensor.removeEventListener("reading", JS_LinearAccelerationSensor_eventHandler); - InitializeLinearAccelerationSensor(frequency); - } - } else if (JS_LinearAccelerationSensor_frequencyRequest != 0) { - JS_LinearAccelerationSensor_frequencyRequest = frequency; - } else { - JS_LinearAccelerationSensor_frequencyRequest = frequency; - navigator.permissions.query({ - name: "accelerometer" - }).then(function(result) { - if (result.state === "granted") { - InitializeLinearAccelerationSensor(JS_LinearAccelerationSensor_frequencyRequest); - } else { - warnOnce("No permission to use LinearAccelerationSensor."); - } - JS_LinearAccelerationSensor_frequencyRequest = 0; - }); - } -} - -function _JS_GravitySensor_Start(callback, frequency) { - assert(callback != 0, "Invalid callback passed to JS_GravitySensor_Start"); - if (typeof GravitySensor === "undefined") { - _JS_Accelerometer_Start(0, Math.max(frequency, JS_Accelerometer_frequency)); - _JS_LinearAccelerationSensor_Start(0, Math.max(frequency, JS_LinearAccelerationSensor_frequency)); - JS_GravitySensor_callback = callback; - return; - } - JS_DefineAccelerometerMultiplier(); - JS_GravitySensor_callback = callback; - function InitializeGravitySensor(frequency) { - JS_GravitySensor = new GravitySensor({ - frequency: frequency, - referenceFrame: "device" - }); - JS_GravitySensor.addEventListener("reading", JS_GravitySensor_eventHandler); - JS_GravitySensor.addEventListener("error", function(e) { - warnOnce(e.error ? e.error : e); - }); - JS_GravitySensor.start(); - } - if (JS_GravitySensor) { - JS_GravitySensor.stop(); - JS_GravitySensor.removeEventListener("reading", JS_GravitySensor_eventHandler); - InitializeGravitySensor(frequency); - } else if (JS_GravitySensor_frequencyRequest != 0) { - JS_GravitySensor_frequencyRequest = frequency; - } else { - JS_GravitySensor_frequencyRequest = frequency; - navigator.permissions.query({ - name: "accelerometer" - }).then(function(result) { - if (result.state === "granted") { - InitializeGravitySensor(JS_GravitySensor_frequencyRequest); - } else { - warnOnce("No permission to use GravitySensor."); - } - JS_GravitySensor_frequencyRequest = 0; - }); - } -} - -function _JS_LinearAccelerationSensor_Stop() { - if (JS_LinearAccelerationSensor) { - if (typeof GravitySensor !== "undefined" || JS_GravitySensor_callback == 0) { - JS_LinearAccelerationSensor.stop(); - JS_LinearAccelerationSensor.removeEventListener("reading", JS_LinearAccelerationSensor_eventHandler); - JS_LinearAccelerationSensor = null; - } - JS_LinearAccelerationSensor_callback = 0; - JS_LinearAccelerationSensor_frequency = 0; - } else if (JS_LinearAccelerationSensor_callback != 0) { - JS_LinearAccelerationSensor_callback = 0; - JS_DeviceMotion_remove(); - } -} - -function _JS_GravitySensor_Stop() { - JS_GravitySensor_callback = 0; - if (typeof GravitySensor === "undefined") { - if (JS_Accelerometer_callback == 0) _JS_Accelerometer_Stop(); - if (JS_LinearAccelerationSensor_callback == 0) _JS_LinearAccelerationSensor_Stop(); - return; - } - if (JS_GravitySensor) { - JS_GravitySensor.stop(); - JS_GravitySensor.removeEventListener("reading", JS_GravitySensor_eventHandler); - JS_GravitySensor = null; - } -} - -var JS_Gyroscope = null; - -function _JS_Gyroscope_IsRunning() { - return JS_Gyroscope && JS_Gyroscope.activated || JS_Gyroscope_callback != 0; -} - -function JS_Gyroscope_eventHandler() { - if (JS_Gyroscope_callback != 0) dynCall_vfff(JS_Gyroscope_callback, JS_Gyroscope.x, JS_Gyroscope.y, JS_Gyroscope.z); -} - -var JS_Gyroscope_frequencyRequest = 0; - -function _JS_Gyroscope_Start(callback, frequency) { - assert(callback != 0, "Invalid callback passed to JS_Gyroscope_Start"); - if (typeof Gyroscope === "undefined") { - JS_DeviceMotion_add(); - JS_Gyroscope_callback = callback; - return; - } - JS_Gyroscope_callback = callback; - function InitializeGyroscope(frequency) { - JS_Gyroscope = new Gyroscope({ - frequency: frequency, - referenceFrame: "device" - }); - JS_Gyroscope.addEventListener("reading", JS_Gyroscope_eventHandler); - JS_Gyroscope.addEventListener("error", function(e) { - warnOnce(e.error ? e.error : e); - }); - JS_Gyroscope.start(); - } - if (JS_Gyroscope) { - JS_Gyroscope.stop(); - JS_Gyroscope.removeEventListener("reading", JS_Gyroscope_eventHandler); - InitializeGyroscope(frequency); - } else if (JS_Gyroscope_frequencyRequest != 0) { - JS_Gyroscope_frequencyRequest = frequency; - } else { - JS_Gyroscope_frequencyRequest = frequency; - navigator.permissions.query({ - name: "gyroscope" - }).then(function(result) { - if (result.state === "granted") { - InitializeGyroscope(JS_Gyroscope_frequencyRequest); - } else { - warnOnce("No permission to use Gyroscope."); - } - JS_Gyroscope_frequencyRequest = 0; - }); - } -} - -function _JS_Gyroscope_Stop() { - if (JS_Gyroscope) { - JS_Gyroscope.stop(); - JS_Gyroscope.removeEventListener("reading", JS_Gyroscope_eventHandler); - JS_Gyroscope = null; - JS_Gyroscope_callback = 0; - } else if (JS_Gyroscope_callback != 0) { - JS_Gyroscope_callback = 0; - JS_DeviceMotion_remove(); - } -} - -function _JS_LinearAccelerationSensor_IsRunning() { - return JS_LinearAccelerationSensor && JS_LinearAccelerationSensor.activated || JS_LinearAccelerationSensor_callback != 0; -} - -function _JS_Log_Dump(ptr, type) { - var str = UTF8ToString(ptr); - if (typeof dump == "function") dump(str); - switch (type) { - case 0: - case 1: - case 4: - console.error(str); - return; - - case 2: - console.warn(str); - return; - - case 3: - case 5: - console.log(str); - return; - - default: - console.error("Unknown console message type!"); - console.error(str); - } -} - -function _JS_Log_StackTrace(buffer, bufferSize) { - var trace = stackTrace(); - if (buffer) stringToUTF8(trace, buffer, bufferSize); - return lengthBytesUTF8(trace); -} - -var mobile_input_hide_delay = null; - -var mobile_input_text = null; - -var mobile_input = null; - -var mobile_input_ignore_blur_event = false; - -function _JS_MobileKeybard_GetIgnoreBlurEvent() { - return mobile_input_ignore_blur_event; -} - -function _JS_MobileKeyboard_GetKeyboardStatus() { - var kKeyboardStatusVisible = 0; - var kKeyboardStatusDone = 1; - if (!mobile_input) return kKeyboardStatusDone; - return kKeyboardStatusVisible; -} - -function _JS_MobileKeyboard_GetText(buffer, bufferSize) { - var text = mobile_input && mobile_input.input ? mobile_input.input.value : mobile_input_text ? mobile_input_text : ""; - if (buffer) stringToUTF8(text, buffer, bufferSize); - return lengthBytesUTF8(text); -} - -function _JS_MobileKeyboard_GetTextSelection(outStart, outLength) { - if (!mobile_input) { - HEAP32[outStart >> 2] = 0; - HEAP32[outLength >> 2] = 0; - return; - } - HEAP32[outStart >> 2] = mobile_input.input.selectionStart; - HEAP32[outLength >> 2] = mobile_input.input.selectionEnd - mobile_input.input.selectionStart; -} - -function _JS_MobileKeyboard_Hide(delay) { - if (mobile_input_hide_delay) return; - mobile_input_ignore_blur_event = true; - function hideMobileKeyboard() { - if (mobile_input && mobile_input.input) { - mobile_input_text = mobile_input.input.value; - mobile_input.input = null; - if (mobile_input.parentNode && mobile_input.parentNode) { - mobile_input.parentNode.removeChild(mobile_input); - } - } - mobile_input = null; - mobile_input_hide_delay = null; - setTimeout(function() { - mobile_input_ignore_blur_event = false; - }, 100); - } - if (delay) { - var hideDelay = 200; - mobile_input_hide_delay = setTimeout(hideMobileKeyboard, hideDelay); - } else { - hideMobileKeyboard(); - } -} - -function _JS_MobileKeyboard_SetCharacterLimit(limit) { - if (!mobile_input) return; - mobile_input.input.maxLength = limit; -} - -function _JS_MobileKeyboard_SetText(text) { - if (!mobile_input) return; - text = UTF8ToString(text); - mobile_input.input.value = text; -} - -function _JS_MobileKeyboard_SetTextSelection(start, length) { - if (!mobile_input) return; - mobile_input.input.setSelectionRange(start, start + length); -} - -function _JS_MobileKeyboard_Show(text, keyboardType, autocorrection, multiline, secure, alert, placeholder, characterLimit) { - if (mobile_input_hide_delay) { - clearTimeout(mobile_input_hide_delay); - mobile_input_hide_delay = null; - } - text = UTF8ToString(text); - mobile_input_text = text; - placeholder = UTF8ToString(placeholder); - var container = document.body; - var hasExistingMobileInput = !!mobile_input; - var input_type; - var KEYBOARD_TYPE_NUMBERS_AND_PUNCTUATION = 2; - var KEYBOARD_TYPE_URL = 3; - var KEYBOARD_TYPE_NUMBER_PAD = 4; - var KEYBOARD_TYPE_PHONE_PAD = 5; - var KEYBOARD_TYPE_EMAIL_ADDRESS = 7; - if (!secure) { - switch (keyboardType) { - case KEYBOARD_TYPE_EMAIL_ADDRESS: - input_type = "email"; - break; - - case KEYBOARD_TYPE_URL: - input_type = "url"; - break; - - case KEYBOARD_TYPE_NUMBERS_AND_PUNCTUATION: - case KEYBOARD_TYPE_NUMBER_PAD: - case KEYBOARD_TYPE_PHONE_PAD: - input_type = "number"; - break; - - default: - input_type = "text"; - break; - } - } else { - input_type = "password"; - } - if (hasExistingMobileInput) { - if (mobile_input.multiline != multiline) { - _JS_MobileKeyboard_Hide(false); - return; - } - } - var inputContainer = mobile_input || document.createElement("div"); - if (!hasExistingMobileInput) { - inputContainer.style = "width:100%; position:fixed; bottom:0px; margin:0px; padding:0px; left:0px; border: 1px solid #000; border-radius: 5px; background-color:#fff; font-size:14pt;"; - container.appendChild(inputContainer); - mobile_input = inputContainer; - } - var input = hasExistingMobileInput ? mobile_input.input : document.createElement(multiline ? "textarea" : "input"); - mobile_input.multiline = multiline; - mobile_input.secure = secure; - mobile_input.keyboardType = keyboardType; - mobile_input.inputType = input_type; - input.type = input_type; - input.style = "width:calc(100% - 85px); " + (multiline ? "height:100px;" : "") + "vertical-align:top; border-radius: 5px; outline:none; cursor:default; resize:none; border:0px; padding:10px 0px 10px 10px;"; - input.spellcheck = autocorrection ? true : false; - input.maxLength = characterLimit > 0 ? characterLimit : 524288; - input.value = text; - input.placeholder = placeholder; - if (!hasExistingMobileInput) { - inputContainer.appendChild(input); - inputContainer.input = input; - } - if (!hasExistingMobileInput) { - var okButton = document.createElement("button"); - okButton.innerText = "OK"; - okButton.style = "border:0; position:absolute; left:calc(100% - 75px); top:0px; width:75px; height:100%; margin:0; padding:0; border-radius: 5px; background-color:#fff"; - okButton.addEventListener("touchend", function() { - _JS_MobileKeyboard_Hide(true); - }); - inputContainer.appendChild(okButton); - inputContainer.okButton = okButton; - input.addEventListener("keyup", function(e) { - if (input.parentNode.multiline) return; - if (e.code == "Enter" || e.which == 13 || e.keyCode == 13) { - _JS_MobileKeyboard_Hide(true); - } - }); - input.addEventListener("blur", function(e) { - _JS_MobileKeyboard_Hide(true); - e.stopPropagation(); - e.preventDefault(); - }); - input.select(); - input.focus(); - } else { - input.select(); - } -} - -var JS_OrientationSensor = null; - -var JS_OrientationSensor_callback = 0; - -function _JS_OrientationSensor_IsRunning() { - return JS_OrientationSensor && JS_OrientationSensor.activated || JS_OrientationSensor_callback != 0; -} - -function JS_OrientationSensor_eventHandler() { - if (JS_OrientationSensor_callback != 0) dynCall_vffff(JS_OrientationSensor_callback, JS_OrientationSensor.quaternion[0], JS_OrientationSensor.quaternion[1], JS_OrientationSensor.quaternion[2], JS_OrientationSensor.quaternion[3]); -} - -var JS_OrientationSensor_frequencyRequest = 0; - -function JS_DeviceOrientation_eventHandler(event) { - if (JS_OrientationSensor_callback) { - var degToRad = Math.PI / 180; - var x = event.beta * degToRad; - var y = event.gamma * degToRad; - var z = event.alpha * degToRad; - var cx = Math.cos(x / 2); - var sx = Math.sin(x / 2); - var cy = Math.cos(y / 2); - var sy = Math.sin(y / 2); - var cz = Math.cos(z / 2); - var sz = Math.sin(z / 2); - var qx = sx * cy * cz - cx * sy * sz; - var qy = cx * sy * cz + sx * cy * sz; - var qz = cx * cy * sz + sx * sy * cz; - var qw = cx * cy * cz - sx * sy * sz; - dynCall_vffff(JS_OrientationSensor_callback, qx, qy, qz, qw); - } -} - -function _JS_OrientationSensor_Start(callback, frequency) { - assert(callback != 0, "Invalid callback passed to JS_OrientationSensor_Start"); - if (typeof RelativeOrientationSensor === "undefined") { - if (JS_OrientationSensor_callback == 0) { - JS_OrientationSensor_callback = callback; - JS_RequestDeviceSensorPermissions(1); - window.addEventListener("deviceorientation", JS_DeviceOrientation_eventHandler); - } - return; - } - JS_OrientationSensor_callback = callback; - function InitializeOrientationSensor(frequency) { - JS_OrientationSensor = new RelativeOrientationSensor({ - frequency: frequency, - referenceFrame: "device" - }); - JS_OrientationSensor.addEventListener("reading", JS_OrientationSensor_eventHandler); - JS_OrientationSensor.addEventListener("error", function(e) { - warnOnce(e.error ? e.error : e); - }); - JS_OrientationSensor.start(); - } - if (JS_OrientationSensor) { - JS_OrientationSensor.stop(); - JS_OrientationSensor.removeEventListener("reading", JS_OrientationSensor_eventHandler); - InitializeOrientationSensor(frequency); - } else if (JS_OrientationSensor_frequencyRequest != 0) { - JS_OrientationSensor_frequencyRequest = frequency; - } else { - JS_OrientationSensor_frequencyRequest = frequency; - Promise.all([ navigator.permissions.query({ - name: "accelerometer" - }), navigator.permissions.query({ - name: "gyroscope" - }) ]).then(function(results) { - if (results.every(function(result) { - return result.state === "granted"; - })) { - InitializeOrientationSensor(JS_OrientationSensor_frequencyRequest); - } else { - warnOnce("No permissions to use RelativeOrientationSensor."); - } - JS_OrientationSensor_frequencyRequest = 0; - }); - } -} - -function _JS_OrientationSensor_Stop() { - if (JS_OrientationSensor) { - JS_OrientationSensor.stop(); - JS_OrientationSensor.removeEventListener("reading", JS_OrientationSensor_eventHandler); - JS_OrientationSensor = null; - } else if (JS_OrientationSensor_callback != 0) { - window.removeEventListener("deviceorientation", JS_DeviceOrientation_eventHandler); - } - JS_OrientationSensor_callback = 0; -} - -function _JS_Profiler_InjectJobs() { - for (var jobname in Module["Jobs"]) { - var job = Module["Jobs"][jobname]; - if (typeof job["endtime"] != "undefined") Module.ccall("InjectProfilerSample", null, [ "string", "number", "number" ], [ jobname, job.starttime, job.endtime ]); - } -} - -function _JS_RequestDeviceSensorPermissionsOnTouch() { - if (JS_DeviceSensorPermissions == 0) return; - JS_RequestDeviceSensorPermissions(JS_DeviceSensorPermissions); -} - -function _JS_RunQuitCallbacks() { - Module.QuitCleanup(); -} - -var JS_ScreenOrientation_callback = 0; - -function JS_ScreenOrientation_eventHandler() { - if (JS_ScreenOrientation_callback) dynCall_viii(JS_ScreenOrientation_callback, window.innerWidth, window.innerHeight, screen.orientation ? screen.orientation.angle : window.orientation); -} - -function _JS_ScreenOrientation_DeInit() { - JS_ScreenOrientation_callback = 0; - window.removeEventListener("resize", JS_ScreenOrientation_eventHandler); - if (screen.orientation) { - screen.orientation.removeEventListener("change", JS_ScreenOrientation_eventHandler); - } -} - -function _JS_ScreenOrientation_Init(callback) { - if (!JS_ScreenOrientation_callback) { - if (screen.orientation) { - screen.orientation.addEventListener("change", JS_ScreenOrientation_eventHandler); - } - window.addEventListener("resize", JS_ScreenOrientation_eventHandler); - JS_ScreenOrientation_callback = callback; - setTimeout(JS_ScreenOrientation_eventHandler, 0); - } -} - -var JS_ScreenOrientation_requestedLockType = -1; - -var JS_ScreenOrientation_appliedLockType = -1; - -var JS_ScreenOrientation_timeoutID = -1; - -function _JS_ScreenOrientation_Lock(orientationLockType) { - if (!screen.orientation) { - return; - } - function applyLock() { - JS_ScreenOrientation_appliedLockType = JS_ScreenOrientation_requestedLockType; - var screenOrientations = [ "any", 0, "landscape", "portrait", "portrait-primary", "portrait-secondary", "landscape-primary", "landscape-secondary" ]; - var type = screenOrientations[JS_ScreenOrientation_appliedLockType]; - assert(type, "Invalid orientationLockType passed to JS_ScreenOrientation_Lock"); - screen.orientation.lock(type).then(function() { - if (JS_ScreenOrientation_requestedLockType != JS_ScreenOrientation_appliedLockType) { - JS_ScreenOrientation_timeoutID = setTimeout(applyLock, 0); - } else { - JS_ScreenOrientation_timeoutID = -1; - } - }).catch(function(err) { - warnOnce(err); - JS_ScreenOrientation_timeoutID = -1; - }); - } - JS_ScreenOrientation_requestedLockType = orientationLockType; - if (JS_ScreenOrientation_timeoutID == -1 && orientationLockType != JS_ScreenOrientation_appliedLockType) { - JS_ScreenOrientation_timeoutID = setTimeout(applyLock, 0); - } -} - -var WEBAudio = { - audioInstanceIdCounter: 0, - audioInstances: {}, - audioContext: null, - audioWebEnabled: 0, - audioCache: [], - pendingAudioSources: {} -}; - -function jsAudioMixinSetPitch(source) { - source.estimatePlaybackPosition = function() { - var t = (WEBAudio.audioContext.currentTime - source.playbackStartTime) * source.playbackRate.value; - if (source.loop && t >= source.loopStart) { - t = (t - source.loopStart) % (source.loopEnd - source.loopStart) + source.loopStart; - } - return t; - }; - source.setPitch = function(newPitch) { - var curPosition = source.estimatePlaybackPosition(); - if (curPosition >= 0) { - source.playbackStartTime = WEBAudio.audioContext.currentTime - curPosition / newPitch; - } - if (source.playbackRate.value !== newPitch) source.playbackRate.value = newPitch; - }; -} - -function jsAudioCreateUncompressedSoundClip(buffer, error) { - var soundClip = { - buffer: buffer, - error: error - }; - soundClip.release = function() {}; - soundClip.getLength = function() { - if (!this.buffer) { - console.log("Trying to get length of sound which is not loaded."); - return 0; - } - var sampleRateRatio = 44100 / this.buffer.sampleRate; - return this.buffer.length * sampleRateRatio; - }; - soundClip.getData = function(ptr, length) { - if (!this.buffer) { - console.log("Trying to get data of sound which is not loaded."); - return 0; - } - var startOutputBuffer = ptr >> 2; - var output = HEAPF32.subarray(startOutputBuffer, startOutputBuffer + (length >> 2)); - var numMaxSamples = Math.floor((length >> 2) / this.buffer.numberOfChannels); - var numReadSamples = Math.min(this.buffer.length, numMaxSamples); - for (var i = 0; i < this.buffer.numberOfChannels; i++) { - var channelData = this.buffer.getChannelData(i).subarray(0, numReadSamples); - output.set(channelData, i * numReadSamples); - } - return numReadSamples * this.buffer.numberOfChannels * 4; - }; - soundClip.getNumberOfChannels = function() { - if (!this.buffer) { - console.log("Trying to get metadata of sound which is not loaded."); - return 0; - } - return this.buffer.numberOfChannels; - }; - soundClip.getFrequency = function() { - if (!this.buffer) { - console.log("Trying to get metadata of sound which is not loaded."); - return 0; - } - return this.buffer.sampleRate; - }; - soundClip.createSourceNode = function() { - if (!this.buffer) { - console.log("Trying to play sound which is not loaded."); - } - var source = WEBAudio.audioContext.createBufferSource(); - source.buffer = this.buffer; - jsAudioMixinSetPitch(source); - return source; - }; - return soundClip; -} - -function jsAudioCreateChannel(callback, userData) { - var channel = { - callback: callback, - userData: userData, - source: null, - gain: WEBAudio.audioContext.createGain(), - panner: WEBAudio.audioContext.createPanner(), - threeD: false, - loop: false, - loopStart: 0, - loopEnd: 0, - pitch: 1 - }; - channel.panner.rolloffFactor = 0; - channel.release = function() { - this.disconnectSource(); - this.gain.disconnect(); - this.panner.disconnect(); - }; - channel.playSoundClip = function(soundClip, startTime, startOffset) { - try { - var self = this; - this.source = soundClip.createSourceNode(); - this.setupPanning(); - this.source.onended = function() { - self.disconnectSource(); - if (self.callback) { - dynCall("vi", self.callback, [ self.userData ]); - } - }; - this.source.loop = this.loop; - this.source.loopStart = this.loopStart; - this.source.loopEnd = this.loopEnd; - this.source.start(startTime, startOffset); - this.source.scheduledStartTime = startTime; - this.source.playbackStartTime = startTime - startOffset / this.source.playbackRate.value; - this.source.setPitch(this.pitch); - } catch (e) { - console.error("Channel.playSoundClip error. Exception: " + e); - } - }; - channel.stop = function(delay) { - if (!this.source) { - return; - } - if (this.source.isPausedMockNode) { - delete this.source; - return; - } - try { - channel.source.stop(WEBAudio.audioContext.currentTime + delay); - } catch (e) {} - if (delay == 0) { - this.disconnectSource(); - } - }; - channel.isPaused = function() { - if (!this.source) { - return true; - } - if (this.source.isPausedMockNode) { - return true; - } - if (this.source.mediaElement) { - return this.source.mediaElement.paused || this.source.pauseRequested; - } - return false; - }; - channel.pause = function() { - if (!this.source || this.source.isPausedMockNode) { - return; - } - if (this.source.mediaElement) { - this.source._pauseMediaElement(); - return; - } - var pausedSource = { - isPausedMockNode: true, - buffer: this.source.buffer, - loop: this.source.loop, - loopStart: this.source.loopStart, - loopEnd: this.source.loopEnd, - playbackRate: this.source.playbackRate.value, - scheduledStartTime: this.source.scheduledStartTime, - playbackPausedAtPosition: this.source.estimatePlaybackPosition(), - setPitch: function(v) { - this.playbackRate = v; - } - }; - this.stop(0); - this.disconnectSource(); - this.source = pausedSource; - }; - channel.resume = function() { - if (this.source && this.source.mediaElement) { - this.source.start(); - return; - } - if (!this.source || !this.source.isPausedMockNode) { - return; - } - var pausedSource = this.source; - var soundClip = jsAudioCreateUncompressedSoundClip(pausedSource.buffer, false); - this.playSoundClip(soundClip, pausedSource.scheduledStartTime, Math.max(0, pausedSource.playbackPausedAtPosition)); - this.source.loop = pausedSource.loop; - this.source.loopStart = pausedSource.loopStart; - this.source.loopEnd = pausedSource.loopEnd; - this.source.setPitch(pausedSource.playbackRate); - }; - channel.setLoop = function(loop) { - this.loop = loop; - if (!this.source || this.source.loop == loop) { - return; - } - this.source.loop = loop; - }; - channel.setLoopPoints = function(loopStart, loopEnd) { - this.loopStart = loopStart; - this.loopEnd = loopEnd; - if (!this.source) { - return; - } - if (this.source.loopStart !== loopStart) { - this.source.loopStart = loopStart; - } - if (this.source.loopEnd !== loopEnd) { - this.source.loopEnd = loopEnd; - } - }; - channel.set3D = function(threeD) { - if (this.threeD == threeD) { - return; - } - this.threeD = threeD; - if (!this.source) { - return; - } - this.setupPanning(); - }; - channel.setPitch = function(pitch) { - this.pitch = pitch; - if (!this.source) { - return; - } - this.source.setPitch(pitch); - }; - channel.setVolume = function(volume) { - if (this.gain.gain.value == volume) { - return; - } - this.gain.gain.value = volume; - }; - channel.setPosition = function(x, y, z) { - var p = this.panner; - if (p.positionX) { - if (p.positionX.value !== x) p.positionX.value = x; - if (p.positionY.value !== y) p.positionY.value = y; - if (p.positionZ.value !== z) p.positionZ.value = z; - } else if (p._x !== x || p._y !== y || p._z !== z) { - p.setPosition(x, y, z); - p._x = x; - p._y = y; - p._z = z; - } - }; - channel.disconnectSource = function() { - if (!this.source || this.source.isPausedMockNode) { - return; - } - if (this.source.mediaElement) { - this.source._pauseMediaElement(); - } - this.source.onended = null; - this.source.disconnect(); - delete this.source; - }; - channel.setupPanning = function() { - if (this.source.isPausedMockNode) return; - this.source.disconnect(); - this.panner.disconnect(); - this.gain.disconnect(); - if (this.threeD) { - this.source.connect(this.panner); - this.panner.connect(this.gain); - } else { - this.source.connect(this.gain); - } - this.gain.connect(WEBAudio.audioContext.destination); - }; - return channel; -} - -function _JS_Sound_Create_Channel(callback, userData) { - if (WEBAudio.audioWebEnabled == 0) return; - WEBAudio.audioInstances[++WEBAudio.audioInstanceIdCounter] = jsAudioCreateChannel(callback, userData); - return WEBAudio.audioInstanceIdCounter; -} - -function _JS_Sound_GetLength(bufferInstance) { - if (WEBAudio.audioWebEnabled == 0) return 0; - var soundClip = WEBAudio.audioInstances[bufferInstance]; - if (!soundClip) return 0; - return soundClip.getLength(); -} - -function _JS_Sound_GetLoadState(bufferInstance) { - if (WEBAudio.audioWebEnabled == 0) return 2; - var sound = WEBAudio.audioInstances[bufferInstance]; - if (sound.error) return 2; - if (sound.buffer || sound.url) return 0; - return 1; -} - -function jsAudioPlayPendingBlockedAudio(soundId) { - var pendingAudio = WEBAudio.pendingAudioSources[soundId]; - pendingAudio.sourceNode._startPlayback(pendingAudio.offset); - delete WEBAudio.pendingAudioSources[soundId]; -} - -function jsAudioPlayBlockedAudios() { - Object.keys(WEBAudio.pendingAudioSources).forEach(function(audioId) { - jsAudioPlayPendingBlockedAudio(audioId); - }); -} - -function _JS_Sound_Init() { - try { - window.AudioContext = window.AudioContext || window.webkitAudioContext; - WEBAudio.audioContext = new AudioContext(); - var tryToResumeAudioContext = function() { - if (WEBAudio.audioContext.state === "suspended") WEBAudio.audioContext.resume(); else Module.clearInterval(resumeInterval); - }; - var resumeInterval = Module.setInterval(tryToResumeAudioContext, 400); - WEBAudio.audioWebEnabled = 1; - var _userEventCallback = function() { - try { - if (WEBAudio.audioContext.state !== "running") { - WEBAudio.audioContext.resume(); - } - jsAudioPlayBlockedAudios(); - var audioCacheSize = 20; - while (WEBAudio.audioCache.length < audioCacheSize) { - var audio = new Audio(); - audio.autoplay = false; - WEBAudio.audioCache.push(audio); - } - } catch (e) {} - }; - window.addEventListener("mousedown", _userEventCallback); - window.addEventListener("touchstart", _userEventCallback); - Module.deinitializers.push(function() { - window.removeEventListener("mousedown", _userEventCallback); - window.removeEventListener("touchstart", _userEventCallback); - }); - } catch (e) { - alert("Web Audio API is not supported in this browser"); - } -} - -function jsAudioCreateUncompressedSoundClipFromCompressedAudio(audioData) { - var soundClip = jsAudioCreateUncompressedSoundClip(null, false); - WEBAudio.audioContext.decodeAudioData(audioData, function(_buffer) { - soundClip.buffer = _buffer; - }, function(_error) { - soundClip.error = true; - console.log("Decode error: " + _error); - }); - return soundClip; -} - -function jsAudioAddPendingBlockedAudio(sourceNode, offset) { - WEBAudio.pendingAudioSources[sourceNode.mediaElement.src] = { - sourceNode: sourceNode, - offset: offset - }; -} - -function jsAudioCreateCompressedSoundClip(audioData) { - var blob = new Blob([ audioData ], { - type: "audio/mp4" - }); - var soundClip = { - url: URL.createObjectURL(blob), - error: false, - mediaElement: new Audio() - }; - soundClip.mediaElement.preload = "metadata"; - soundClip.mediaElement.src = soundClip.url; - soundClip.release = function() { - if (!this.mediaElement) { - return; - } - this.mediaElement.src = ""; - URL.revokeObjectURL(this.url); - delete this.mediaElement; - delete this.url; - }; - soundClip.getLength = function() { - return this.mediaElement.duration * 44100; - }; - soundClip.getData = function(ptr, length) { - console.warn("getData() is not supported for compressed sound."); - return 0; - }; - soundClip.getNumberOfChannels = function() { - console.warn("getNumberOfChannels() is not supported for compressed sound."); - return 0; - }; - soundClip.getFrequency = function() { - console.warn("getFrequency() is not supported for compressed sound."); - return 0; - }; - soundClip.createSourceNode = function() { - var self = this; - var mediaElement = WEBAudio.audioCache.length ? WEBAudio.audioCache.pop() : new Audio(); - mediaElement.preload = "metadata"; - mediaElement.src = this.url; - var source = WEBAudio.audioContext.createMediaElementSource(mediaElement); - Object.defineProperty(source, "loop", { - get: function() { - return source.mediaElement.loop; - }, - set: function(v) { - if (source.mediaElement.loop !== v) source.mediaElement.loop = v; - } - }); - source.playbackRate = {}; - Object.defineProperty(source.playbackRate, "value", { - get: function() { - return source.mediaElement.playbackRate; - }, - set: function(v) { - if (source.mediaElement.playbackRate !== v) source.mediaElement.playbackRate = v; - } - }); - Object.defineProperty(source, "currentTime", { - get: function() { - return source.mediaElement.currentTime; - }, - set: function(v) { - if (source.mediaElement.currentTime !== v) source.mediaElement.currentTime = v; - } - }); - Object.defineProperty(source, "mute", { - get: function() { - return source.mediaElement.mute; - }, - set: function(v) { - if (source.mediaElement.mute !== v) source.mediaElement.mute = v; - } - }); - source.playPromise = null; - source.playTimeout = null; - source.pauseRequested = false; - source._pauseMediaElement = function() { - if (source.playPromise || source.playTimeout) { - source.pauseRequested = true; - } else { - source.mediaElement.pause(); - } - }; - source._startPlayback = function(offset) { - if (source.playPromise || source.playTimeout) { - source.mediaElement.currentTime = offset; - source.pauseRequested = false; - return; - } - source.mediaElement.currentTime = offset; - source.playPromise = source.mediaElement.play(); - if (source.playPromise) { - source.playPromise.then(function() { - if (source.pauseRequested) { - source.mediaElement.pause(); - source.pauseRequested = false; - } - source.playPromise = null; - }).catch(function(error) { - source.playPromise = null; - if (error.name !== "NotAllowedError") throw error; - jsAudioAddPendingBlockedAudio(source, offset); - }); - } - }; - source.start = function(startTime, offset) { - if (typeof startTime === "undefined") { - startTime = WEBAudio.audioContext.currentTime; - } - if (typeof offset === "undefined") { - offset = 0; - } - var startDelayThresholdMS = 4; - var startDelayMS = (startTime - WEBAudio.audioContext.currentTime) * 1e3; - if (startDelayMS > startDelayThresholdMS) { - source.playTimeout = setTimeout(function() { - source.playTimeout = null; - source._startPlayback(offset); - }, startDelayMS); - } else { - source._startPlayback(offset); - } - }; - source.stop = function(stopTime) { - if (typeof stopTime === "undefined") { - stopTime = WEBAudio.audioContext.currentTime; - } - var stopDelayThresholdMS = 4; - var stopDelayMS = (stopTime - WEBAudio.audioContext.currentTime) * 1e3; - if (stopDelayMS > stopDelayThresholdMS) { - setTimeout(function() { - source._pauseMediaElement(); - }, stopDelayMS); - } else { - source._pauseMediaElement(); - } - }; - jsAudioMixinSetPitch(source); - return source; - }; - return soundClip; -} - -function _JS_Sound_Load(ptr, length, decompress) { - if (WEBAudio.audioWebEnabled == 0) return 0; - var audioData = HEAPU8.buffer.slice(ptr, ptr + length); - if (length < 131072) decompress = 1; - var sound; - if (decompress) { - sound = jsAudioCreateUncompressedSoundClipFromCompressedAudio(audioData); - } else { - sound = jsAudioCreateCompressedSoundClip(audioData); - } - WEBAudio.audioInstances[++WEBAudio.audioInstanceIdCounter] = sound; - return WEBAudio.audioInstanceIdCounter; -} - -function jsAudioCreateUncompressedSoundClipFromPCM(channels, length, sampleRate, ptr) { - var buffer = WEBAudio.audioContext.createBuffer(channels, length, sampleRate); - for (var i = 0; i < channels; i++) { - var offs = (ptr >> 2) + length * i; - var copyToChannel = buffer["copyToChannel"] || function(source, channelNumber, startInChannel) { - var clipped = source.subarray(0, Math.min(source.length, this.length - (startInChannel | 0))); - this.getChannelData(channelNumber | 0).set(clipped, startInChannel | 0); - }; - copyToChannel.apply(buffer, [ HEAPF32.subarray(offs, offs + length), i, 0 ]); - } - return jsAudioCreateUncompressedSoundClip(buffer, false); -} - -function _JS_Sound_Load_PCM(channels, length, sampleRate, ptr) { - if (WEBAudio.audioWebEnabled == 0) return 0; - var sound = jsAudioCreateUncompressedSoundClipFromPCM(channels, length, sampleRate, ptr); - WEBAudio.audioInstances[++WEBAudio.audioInstanceIdCounter] = sound; - return WEBAudio.audioInstanceIdCounter; -} - -function _JS_Sound_Play(bufferInstance, channelInstance, offset, delay) { - if (WEBAudio.audioWebEnabled == 0) return; - _JS_Sound_Stop(channelInstance, 0); - var soundClip = WEBAudio.audioInstances[bufferInstance]; - var channel = WEBAudio.audioInstances[channelInstance]; - if (!soundClip) { - console.log("Trying to play sound which is not loaded."); - return; - } - try { - channel.playSoundClip(soundClip, WEBAudio.audioContext.currentTime + delay, offset); - } catch (error) { - console.error("playSoundClip error. Exception: " + e); - } -} - -function _JS_Sound_ReleaseInstance(instance) { - var object = WEBAudio.audioInstances[instance]; - if (object) { - object.release(); - } - delete WEBAudio.audioInstances[instance]; -} - -function _JS_Sound_ResumeIfNeeded() { - if (WEBAudio.audioWebEnabled == 0) return; - if (WEBAudio.audioContext.state === "suspended") WEBAudio.audioContext.resume(); -} - -function _JS_Sound_Set3D(channelInstance, threeD) { - var channel = WEBAudio.audioInstances[channelInstance]; - channel.set3D(threeD); -} - -function _JS_Sound_SetListenerOrientation(x, y, z, xUp, yUp, zUp) { - if (WEBAudio.audioWebEnabled == 0) return; - x = -x; - y = -y; - z = -z; - var l = WEBAudio.audioContext.listener; - if (l.forwardX) { - if (l.forwardX.value !== x) l.forwardX.value = x; - if (l.forwardY.value !== y) l.forwardY.value = y; - if (l.forwardZ.value !== z) l.forwardZ.value = z; - if (l.upX.value !== x) l.upX.value = x; - if (l.upY.value !== y) l.upY.value = y; - if (l.upZ.value !== z) l.upZ.value = z; - } else if (l._forwardX !== x || l._forwardY !== y || l._forwardZ !== z || l._upX !== xUp || l._upY !== yUp || l._upZ !== zUp) { - l.setOrientation(x, y, z, xUp, yUp, zUp); - l._forwardX = x; - l._forwardY = y; - l._forwardZ = z; - l._upX = xUp; - l._upY = yUp; - l._upZ = zUp; - } -} - -function _JS_Sound_SetListenerPosition(x, y, z) { - if (WEBAudio.audioWebEnabled == 0) return; - var l = WEBAudio.audioContext.listener; - if (l.positionX) { - if (l.positionX.value !== x) l.positionX.value = x; - if (l.positionY.value !== y) l.positionY.value = y; - if (l.positionZ.value !== z) l.positionZ.value = z; - } else if (l._positionX !== x || l._positionY !== y || l._positionZ !== z) { - l.setPosition(x, y, z); - l._positionX = x; - l._positionY = y; - l._positionZ = z; - } -} - -function _JS_Sound_SetLoop(channelInstance, loop) { - if (WEBAudio.audioWebEnabled == 0) return; - var channel = WEBAudio.audioInstances[channelInstance]; - channel.setLoop(loop); -} - -function _JS_Sound_SetLoopPoints(channelInstance, loopStart, loopEnd) { - if (WEBAudio.audioWebEnabled == 0) return; - var channel = WEBAudio.audioInstances[channelInstance]; - channel.setLoopPoints(loopStart, loopEnd); -} - -function _JS_Sound_SetPaused(channelInstance, paused) { - if (WEBAudio.audioWebEnabled == 0) return; - var channel = WEBAudio.audioInstances[channelInstance]; - if (paused != channel.isPaused()) { - if (paused) channel.pause(); else channel.resume(); - } -} - -function _JS_Sound_SetPitch(channelInstance, v) { - if (WEBAudio.audioWebEnabled == 0) return; - try { - var channel = WEBAudio.audioInstances[channelInstance]; - channel.setPitch(v); - } catch (e) { - console.error("JS_Sound_SetPitch(channel=" + channelInstance + ", pitch=" + v + ") threw an exception: " + e); - } -} - -function _JS_Sound_SetPosition(channelInstance, x, y, z) { - if (WEBAudio.audioWebEnabled == 0) return; - var channel = WEBAudio.audioInstances[channelInstance]; - channel.setPosition(x, y, z); -} - -function _JS_Sound_SetVolume(channelInstance, v) { - if (WEBAudio.audioWebEnabled == 0) return; - try { - var channel = WEBAudio.audioInstances[channelInstance]; - channel.setVolume(v); - } catch (e) { - console.error("JS_Sound_SetVolume(channel=" + channelInstance + ", volume=" + v + ") threw an exception: " + e); - } -} - -function _JS_Sound_Stop(channelInstance, delay) { - if (WEBAudio.audioWebEnabled == 0) return; - var channel = WEBAudio.audioInstances[channelInstance]; - channel.stop(delay); -} - -function _JS_SystemInfo_GetCanvasClientSize(domElementSelector, outWidth, outHeight) { - var selector = UTF8ToString(domElementSelector); - var canvas = selector == "#canvas" ? Module["canvas"] : document.querySelector(selector); - var w = 0, h = 0; - if (canvas) { - var size = canvas.getBoundingClientRect(); - w = size.width; - h = size.height; - } - HEAPF64[outWidth >> 3] = w; - HEAPF64[outHeight >> 3] = h; -} - -function _JS_SystemInfo_GetDocumentURL(buffer, bufferSize) { - if (buffer) stringToUTF8(document.URL, buffer, bufferSize); - return lengthBytesUTF8(document.URL); -} - -function _JS_SystemInfo_GetGPUInfo(buffer, bufferSize) { - var gpuinfo = Module.SystemInfo.gpu; - if (buffer) stringToUTF8(gpuinfo, buffer, bufferSize); - return lengthBytesUTF8(gpuinfo); -} - -function _JS_SystemInfo_GetMatchWebGLToCanvasSize() { - return Module.matchWebGLToCanvasSize || Module.matchWebGLToCanvasSize === undefined; -} - -function _JS_SystemInfo_GetMemory() { - return HEAPU8.length / (1024 * 1024); -} - -function _JS_SystemInfo_GetOS(buffer, bufferSize) { - var browser = Module.SystemInfo.os + " " + Module.SystemInfo.osVersion; - if (buffer) stringToUTF8(browser, buffer, bufferSize); - return lengthBytesUTF8(browser); -} - -function _JS_SystemInfo_GetPreferredDevicePixelRatio() { - return Module.matchWebGLToCanvasSize == false ? 1 : Module.devicePixelRatio || window.devicePixelRatio || 1; -} - -function _JS_SystemInfo_GetScreenSize(outWidth, outHeight) { - HEAPF64[outWidth >> 3] = Module.SystemInfo.width; - HEAPF64[outHeight >> 3] = Module.SystemInfo.height; -} - -function _JS_SystemInfo_GetStreamingAssetsURL(buffer, bufferSize) { - if (buffer) stringToUTF8(Module.streamingAssetsUrl, buffer, bufferSize); - return lengthBytesUTF8(Module.streamingAssetsUrl); -} - -function _JS_SystemInfo_HasAstcHdr() { - var ext = GLctx.getExtension("WEBGL_compressed_texture_astc"); - if (ext && ext.getSupportedProfiles) { - return ext.getSupportedProfiles().includes("hdr"); - } - return false; -} - -function _JS_SystemInfo_HasCursorLock() { - return Module.SystemInfo.hasCursorLock; -} - -function _JS_SystemInfo_HasFullscreen() { - return Module.SystemInfo.hasFullscreen; -} - -function _JS_SystemInfo_HasWebGL() { - return Module.SystemInfo.hasWebGL; -} - -function _JS_SystemInfo_IsMobile() { - return Module.SystemInfo.mobile; -} - -function _JS_UnityEngineShouldQuit() { - return !!Module.shouldQuit; -} - -var ExceptionInfoAttrs = { - DESTRUCTOR_OFFSET: 0, - REFCOUNT_OFFSET: 4, - TYPE_OFFSET: 8, - CAUGHT_OFFSET: 12, - RETHROWN_OFFSET: 13, - SIZE: 16 -}; - -function ___cxa_allocate_exception(size) { - return _malloc(size + ExceptionInfoAttrs.SIZE) + ExceptionInfoAttrs.SIZE; -} - -function _atexit(func, arg) {} - -function ___cxa_atexit(a0, a1) { - return _atexit(a0, a1); -} - -function ExceptionInfo(excPtr) { - this.excPtr = excPtr; - this.ptr = excPtr - ExceptionInfoAttrs.SIZE; - this.set_type = function(type) { - HEAP32[this.ptr + ExceptionInfoAttrs.TYPE_OFFSET >> 2] = type; - }; - this.get_type = function() { - return HEAP32[this.ptr + ExceptionInfoAttrs.TYPE_OFFSET >> 2]; - }; - this.set_destructor = function(destructor) { - HEAP32[this.ptr + ExceptionInfoAttrs.DESTRUCTOR_OFFSET >> 2] = destructor; - }; - this.get_destructor = function() { - return HEAP32[this.ptr + ExceptionInfoAttrs.DESTRUCTOR_OFFSET >> 2]; - }; - this.set_refcount = function(refcount) { - HEAP32[this.ptr + ExceptionInfoAttrs.REFCOUNT_OFFSET >> 2] = refcount; - }; - this.set_caught = function(caught) { - caught = caught ? 1 : 0; - HEAP8[this.ptr + ExceptionInfoAttrs.CAUGHT_OFFSET >> 0] = caught; - }; - this.get_caught = function() { - return HEAP8[this.ptr + ExceptionInfoAttrs.CAUGHT_OFFSET >> 0] != 0; - }; - this.set_rethrown = function(rethrown) { - rethrown = rethrown ? 1 : 0; - HEAP8[this.ptr + ExceptionInfoAttrs.RETHROWN_OFFSET >> 0] = rethrown; - }; - this.get_rethrown = function() { - return HEAP8[this.ptr + ExceptionInfoAttrs.RETHROWN_OFFSET >> 0] != 0; - }; - this.init = function(type, destructor) { - this.set_type(type); - this.set_destructor(destructor); - this.set_refcount(0); - this.set_caught(false); - this.set_rethrown(false); - }; - this.add_ref = function() { - var value = HEAP32[this.ptr + ExceptionInfoAttrs.REFCOUNT_OFFSET >> 2]; - HEAP32[this.ptr + ExceptionInfoAttrs.REFCOUNT_OFFSET >> 2] = value + 1; - }; - this.release_ref = function() { - var prev = HEAP32[this.ptr + ExceptionInfoAttrs.REFCOUNT_OFFSET >> 2]; - HEAP32[this.ptr + ExceptionInfoAttrs.REFCOUNT_OFFSET >> 2] = prev - 1; - assert(prev > 0); - return prev === 1; - }; -} - -function CatchInfo(ptr) { - this.free = function() { - _free(this.ptr); - this.ptr = 0; - }; - this.set_base_ptr = function(basePtr) { - HEAP32[this.ptr >> 2] = basePtr; - }; - this.get_base_ptr = function() { - return HEAP32[this.ptr >> 2]; - }; - this.set_adjusted_ptr = function(adjustedPtr) { - var ptrSize = 4; - HEAP32[this.ptr + ptrSize >> 2] = adjustedPtr; - }; - this.get_adjusted_ptr = function() { - var ptrSize = 4; - return HEAP32[this.ptr + ptrSize >> 2]; - }; - this.get_exception_ptr = function() { - var isPointer = ___cxa_is_pointer_type(this.get_exception_info().get_type()); - if (isPointer) { - return HEAP32[this.get_base_ptr() >> 2]; - } - var adjusted = this.get_adjusted_ptr(); - if (adjusted !== 0) return adjusted; - return this.get_base_ptr(); - }; - this.get_exception_info = function() { - return new ExceptionInfo(this.get_base_ptr()); - }; - if (ptr === undefined) { - this.ptr = _malloc(8); - this.set_adjusted_ptr(0); - } else { - this.ptr = ptr; - } -} - -var exceptionCaught = []; - -function exception_addRef(info) { - info.add_ref(); -} - -var uncaughtExceptionCount = 0; - -function ___cxa_begin_catch(ptr) { - var catchInfo = new CatchInfo(ptr); - var info = catchInfo.get_exception_info(); - if (!info.get_caught()) { - info.set_caught(true); - uncaughtExceptionCount--; - } - info.set_rethrown(false); - exceptionCaught.push(catchInfo); - exception_addRef(info); - return catchInfo.get_exception_ptr(); -} - -var exceptionLast = 0; - -function ___cxa_free_exception(ptr) { - try { - return _free(new ExceptionInfo(ptr).ptr); - } catch (e) { - err("exception during cxa_free_exception: " + e); - } -} - -function exception_decRef(info) { - if (info.release_ref() && !info.get_rethrown()) { - var destructor = info.get_destructor(); - if (destructor) { - (function(a1) { - return dynCall_ii.apply(null, [ destructor, a1 ]); - })(info.excPtr); - } - ___cxa_free_exception(info.excPtr); - } -} - -function ___cxa_end_catch() { - _setThrew(0); - assert(exceptionCaught.length > 0); - var catchInfo = exceptionCaught.pop(); - exception_decRef(catchInfo.get_exception_info()); - catchInfo.free(); - exceptionLast = 0; -} - -function ___resumeException(catchInfoPtr) { - var catchInfo = new CatchInfo(catchInfoPtr); - var ptr = catchInfo.get_base_ptr(); - if (!exceptionLast) { - exceptionLast = ptr; - } - catchInfo.free(); - throw ptr; -} - -function ___cxa_find_matching_catch_2() { - var thrown = exceptionLast; - if (!thrown) { - setTempRet0(0); - return 0 | 0; - } - var info = new ExceptionInfo(thrown); - var thrownType = info.get_type(); - var catchInfo = new CatchInfo(); - catchInfo.set_base_ptr(thrown); - if (!thrownType) { - setTempRet0(0); - return catchInfo.ptr | 0; - } - var typeArray = Array.prototype.slice.call(arguments); - var stackTop = stackSave(); - var exceptionThrowBuf = stackAlloc(4); - HEAP32[exceptionThrowBuf >> 2] = thrown; - for (var i = 0; i < typeArray.length; i++) { - var caughtType = typeArray[i]; - if (caughtType === 0 || caughtType === thrownType) { - break; - } - if (___cxa_can_catch(caughtType, thrownType, exceptionThrowBuf)) { - var adjusted = HEAP32[exceptionThrowBuf >> 2]; - if (thrown !== adjusted) { - catchInfo.set_adjusted_ptr(adjusted); - } - setTempRet0(caughtType); - return catchInfo.ptr | 0; - } - } - stackRestore(stackTop); - setTempRet0(thrownType); - return catchInfo.ptr | 0; -} - -function ___cxa_find_matching_catch_3() { - var thrown = exceptionLast; - if (!thrown) { - setTempRet0(0); - return 0 | 0; - } - var info = new ExceptionInfo(thrown); - var thrownType = info.get_type(); - var catchInfo = new CatchInfo(); - catchInfo.set_base_ptr(thrown); - if (!thrownType) { - setTempRet0(0); - return catchInfo.ptr | 0; - } - var typeArray = Array.prototype.slice.call(arguments); - var stackTop = stackSave(); - var exceptionThrowBuf = stackAlloc(4); - HEAP32[exceptionThrowBuf >> 2] = thrown; - for (var i = 0; i < typeArray.length; i++) { - var caughtType = typeArray[i]; - if (caughtType === 0 || caughtType === thrownType) { - break; - } - if (___cxa_can_catch(caughtType, thrownType, exceptionThrowBuf)) { - var adjusted = HEAP32[exceptionThrowBuf >> 2]; - if (thrown !== adjusted) { - catchInfo.set_adjusted_ptr(adjusted); - } - setTempRet0(caughtType); - return catchInfo.ptr | 0; - } - } - stackRestore(stackTop); - setTempRet0(thrownType); - return catchInfo.ptr | 0; -} - -function ___cxa_find_matching_catch_4() { - var thrown = exceptionLast; - if (!thrown) { - setTempRet0(0); - return 0 | 0; - } - var info = new ExceptionInfo(thrown); - var thrownType = info.get_type(); - var catchInfo = new CatchInfo(); - catchInfo.set_base_ptr(thrown); - if (!thrownType) { - setTempRet0(0); - return catchInfo.ptr | 0; - } - var typeArray = Array.prototype.slice.call(arguments); - var stackTop = stackSave(); - var exceptionThrowBuf = stackAlloc(4); - HEAP32[exceptionThrowBuf >> 2] = thrown; - for (var i = 0; i < typeArray.length; i++) { - var caughtType = typeArray[i]; - if (caughtType === 0 || caughtType === thrownType) { - break; - } - if (___cxa_can_catch(caughtType, thrownType, exceptionThrowBuf)) { - var adjusted = HEAP32[exceptionThrowBuf >> 2]; - if (thrown !== adjusted) { - catchInfo.set_adjusted_ptr(adjusted); - } - setTempRet0(caughtType); - return catchInfo.ptr | 0; - } - } - stackRestore(stackTop); - setTempRet0(thrownType); - return catchInfo.ptr | 0; -} - -function ___cxa_rethrow() { - var catchInfo = exceptionCaught.pop(); - if (!catchInfo) { - abort("no exception to throw"); - } - var info = catchInfo.get_exception_info(); - var ptr = catchInfo.get_base_ptr(); - if (!info.get_rethrown()) { - exceptionCaught.push(catchInfo); - info.set_rethrown(true); - info.set_caught(false); - uncaughtExceptionCount++; - } else { - catchInfo.free(); - } - exceptionLast = ptr; - throw ptr; -} - -function ___cxa_throw(ptr, type, destructor) { - var info = new ExceptionInfo(ptr); - info.init(type, destructor); - exceptionLast = ptr; - uncaughtExceptionCount++; - throw ptr; -} - -function _gmtime_r(time, tmPtr) { - var date = new Date(HEAP32[time >> 2] * 1e3); - HEAP32[tmPtr >> 2] = date.getUTCSeconds(); - HEAP32[tmPtr + 4 >> 2] = date.getUTCMinutes(); - HEAP32[tmPtr + 8 >> 2] = date.getUTCHours(); - HEAP32[tmPtr + 12 >> 2] = date.getUTCDate(); - HEAP32[tmPtr + 16 >> 2] = date.getUTCMonth(); - HEAP32[tmPtr + 20 >> 2] = date.getUTCFullYear() - 1900; - HEAP32[tmPtr + 24 >> 2] = date.getUTCDay(); - HEAP32[tmPtr + 36 >> 2] = 0; - HEAP32[tmPtr + 32 >> 2] = 0; - var start = Date.UTC(date.getUTCFullYear(), 0, 1, 0, 0, 0, 0); - var yday = (date.getTime() - start) / (1e3 * 60 * 60 * 24) | 0; - HEAP32[tmPtr + 28 >> 2] = yday; - if (!_gmtime_r.GMTString) _gmtime_r.GMTString = allocateUTF8("GMT"); - HEAP32[tmPtr + 40 >> 2] = _gmtime_r.GMTString; - return tmPtr; -} - -function ___gmtime_r(a0, a1) { - return _gmtime_r(a0, a1); -} - -function _tzset() { - if (_tzset.called) return; - _tzset.called = true; - var currentYear = new Date().getFullYear(); - var winter = new Date(currentYear, 0, 1); - var summer = new Date(currentYear, 6, 1); - var winterOffset = winter.getTimezoneOffset(); - var summerOffset = summer.getTimezoneOffset(); - var stdTimezoneOffset = Math.max(winterOffset, summerOffset); - HEAP32[__get_timezone() >> 2] = stdTimezoneOffset * 60; - HEAP32[__get_daylight() >> 2] = Number(winterOffset != summerOffset); - function extractZone(date) { - var match = date.toTimeString().match(/\(([A-Za-z ]+)\)$/); - return match ? match[1] : "GMT"; - } - var winterName = extractZone(winter); - var summerName = extractZone(summer); - var winterNamePtr = allocateUTF8(winterName); - var summerNamePtr = allocateUTF8(summerName); - if (summerOffset < winterOffset) { - HEAP32[__get_tzname() >> 2] = winterNamePtr; - HEAP32[__get_tzname() + 4 >> 2] = summerNamePtr; - } else { - HEAP32[__get_tzname() >> 2] = summerNamePtr; - HEAP32[__get_tzname() + 4 >> 2] = winterNamePtr; - } -} - -function _localtime_r(time, tmPtr) { - _tzset(); - var date = new Date(HEAP32[time >> 2] * 1e3); - HEAP32[tmPtr >> 2] = date.getSeconds(); - HEAP32[tmPtr + 4 >> 2] = date.getMinutes(); - HEAP32[tmPtr + 8 >> 2] = date.getHours(); - HEAP32[tmPtr + 12 >> 2] = date.getDate(); - HEAP32[tmPtr + 16 >> 2] = date.getMonth(); - HEAP32[tmPtr + 20 >> 2] = date.getFullYear() - 1900; - HEAP32[tmPtr + 24 >> 2] = date.getDay(); - var start = new Date(date.getFullYear(), 0, 1); - var yday = (date.getTime() - start.getTime()) / (1e3 * 60 * 60 * 24) | 0; - HEAP32[tmPtr + 28 >> 2] = yday; - HEAP32[tmPtr + 36 >> 2] = -(date.getTimezoneOffset() * 60); - var summerOffset = new Date(date.getFullYear(), 6, 1).getTimezoneOffset(); - var winterOffset = start.getTimezoneOffset(); - var dst = (summerOffset != winterOffset && date.getTimezoneOffset() == Math.min(winterOffset, summerOffset)) | 0; - HEAP32[tmPtr + 32 >> 2] = dst; - var zonePtr = HEAP32[__get_tzname() + (dst ? 4 : 0) >> 2]; - HEAP32[tmPtr + 40 >> 2] = zonePtr; - return tmPtr; -} - -function ___localtime_r(a0, a1) { - return _localtime_r(a0, a1); -} - -var PATH = { - splitPath: function(filename) { - var splitPathRe = /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/; - return splitPathRe.exec(filename).slice(1); - }, - normalizeArray: function(parts, allowAboveRoot) { - var up = 0; - for (var i = parts.length - 1; i >= 0; i--) { - var last = parts[i]; - if (last === ".") { - parts.splice(i, 1); - } else if (last === "..") { - parts.splice(i, 1); - up++; - } else if (up) { - parts.splice(i, 1); - up--; - } - } - if (allowAboveRoot) { - for (;up; up--) { - parts.unshift(".."); - } - } - return parts; - }, - normalize: function(path) { - var isAbsolute = path.charAt(0) === "/", trailingSlash = path.substr(-1) === "/"; - path = PATH.normalizeArray(path.split("/").filter(function(p) { - return !!p; - }), !isAbsolute).join("/"); - if (!path && !isAbsolute) { - path = "."; - } - if (path && trailingSlash) { - path += "/"; - } - return (isAbsolute ? "/" : "") + path; - }, - dirname: function(path) { - var result = PATH.splitPath(path), root = result[0], dir = result[1]; - if (!root && !dir) { - return "."; - } - if (dir) { - dir = dir.substr(0, dir.length - 1); - } - return root + dir; - }, - basename: function(path) { - if (path === "/") return "/"; - path = PATH.normalize(path); - path = path.replace(/\/$/, ""); - var lastSlash = path.lastIndexOf("/"); - if (lastSlash === -1) return path; - return path.substr(lastSlash + 1); - }, - extname: function(path) { - return PATH.splitPath(path)[3]; - }, - join: function() { - var paths = Array.prototype.slice.call(arguments, 0); - return PATH.normalize(paths.join("/")); - }, - join2: function(l, r) { - return PATH.normalize(l + "/" + r); - } -}; - -function getRandomDevice() { - if (typeof crypto === "object" && typeof crypto["getRandomValues"] === "function") { - var randomBuffer = new Uint8Array(1); - return function() { - crypto.getRandomValues(randomBuffer); - return randomBuffer[0]; - }; - } else if (ENVIRONMENT_IS_NODE) { - try { - var crypto_module = require("crypto"); - return function() { - return crypto_module["randomBytes"](1)[0]; - }; - } catch (e) {} - } - return function() { - abort("no cryptographic support found for randomDevice. consider polyfilling it if you want to use something insecure like Math.random(), e.g. put this in a --pre-js: var crypto = { getRandomValues: function(array) { for (var i = 0; i < array.length; i++) array[i] = (Math.random()*256)|0 } };"); - }; -} - -var PATH_FS = { - resolve: function() { - var resolvedPath = "", resolvedAbsolute = false; - for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) { - var path = i >= 0 ? arguments[i] : FS.cwd(); - if (typeof path !== "string") { - throw new TypeError("Arguments to path.resolve must be strings"); - } else if (!path) { - return ""; - } - resolvedPath = path + "/" + resolvedPath; - resolvedAbsolute = path.charAt(0) === "/"; - } - resolvedPath = PATH.normalizeArray(resolvedPath.split("/").filter(function(p) { - return !!p; - }), !resolvedAbsolute).join("/"); - return (resolvedAbsolute ? "/" : "") + resolvedPath || "."; - }, - relative: function(from, to) { - from = PATH_FS.resolve(from).substr(1); - to = PATH_FS.resolve(to).substr(1); - function trim(arr) { - var start = 0; - for (;start < arr.length; start++) { - if (arr[start] !== "") break; - } - var end = arr.length - 1; - for (;end >= 0; end--) { - if (arr[end] !== "") break; - } - if (start > end) return []; - return arr.slice(start, end - start + 1); - } - var fromParts = trim(from.split("/")); - var toParts = trim(to.split("/")); - var length = Math.min(fromParts.length, toParts.length); - var samePartsLength = length; - for (var i = 0; i < length; i++) { - if (fromParts[i] !== toParts[i]) { - samePartsLength = i; - break; - } - } - var outputParts = []; - for (var i = samePartsLength; i < fromParts.length; i++) { - outputParts.push(".."); - } - outputParts = outputParts.concat(toParts.slice(samePartsLength)); - return outputParts.join("/"); - } -}; - -var TTY = { - ttys: [], - init: function() {}, - shutdown: function() {}, - register: function(dev, ops) { - TTY.ttys[dev] = { - input: [], - output: [], - ops: ops - }; - FS.registerDevice(dev, TTY.stream_ops); - }, - stream_ops: { - open: function(stream) { - var tty = TTY.ttys[stream.node.rdev]; - if (!tty) { - throw new FS.ErrnoError(43); - } - stream.tty = tty; - stream.seekable = false; - }, - close: function(stream) { - stream.tty.ops.flush(stream.tty); - }, - flush: function(stream) { - stream.tty.ops.flush(stream.tty); - }, - read: function(stream, buffer, offset, length, pos) { - if (!stream.tty || !stream.tty.ops.get_char) { - throw new FS.ErrnoError(60); - } - var bytesRead = 0; - for (var i = 0; i < length; i++) { - var result; - try { - result = stream.tty.ops.get_char(stream.tty); - } catch (e) { - throw new FS.ErrnoError(29); - } - if (result === undefined && bytesRead === 0) { - throw new FS.ErrnoError(6); - } - if (result === null || result === undefined) break; - bytesRead++; - buffer[offset + i] = result; - } - if (bytesRead) { - stream.node.timestamp = Date.now(); - } - return bytesRead; - }, - write: function(stream, buffer, offset, length, pos) { - if (!stream.tty || !stream.tty.ops.put_char) { - throw new FS.ErrnoError(60); - } - try { - for (var i = 0; i < length; i++) { - stream.tty.ops.put_char(stream.tty, buffer[offset + i]); - } - } catch (e) { - throw new FS.ErrnoError(29); - } - if (length) { - stream.node.timestamp = Date.now(); - } - return i; - } - }, - default_tty_ops: { - get_char: function(tty) { - if (!tty.input.length) { - var result = null; - if (ENVIRONMENT_IS_NODE) { - var BUFSIZE = 256; - var buf = Buffer.alloc ? Buffer.alloc(BUFSIZE) : new Buffer(BUFSIZE); - var bytesRead = 0; - try { - bytesRead = nodeFS.readSync(process.stdin.fd, buf, 0, BUFSIZE, null); - } catch (e) { - if (e.toString().includes("EOF")) bytesRead = 0; else throw e; - } - if (bytesRead > 0) { - result = buf.slice(0, bytesRead).toString("utf-8"); - } else { - result = null; - } - } else if (typeof window != "undefined" && typeof window.prompt == "function") { - result = window.prompt("Input: "); - if (result !== null) { - result += "\n"; - } - } else if (typeof readline == "function") { - result = readline(); - if (result !== null) { - result += "\n"; - } - } - if (!result) { - return null; - } - tty.input = intArrayFromString(result, true); - } - return tty.input.shift(); - }, - put_char: function(tty, val) { - if (val === null || val === 10) { - out(UTF8ArrayToString(tty.output, 0)); - tty.output = []; - } else { - if (val != 0) tty.output.push(val); - } - }, - flush: function(tty) { - if (tty.output && tty.output.length > 0) { - out(UTF8ArrayToString(tty.output, 0)); - tty.output = []; - } - } - }, - default_tty1_ops: { - put_char: function(tty, val) { - if (val === null || val === 10) { - err(UTF8ArrayToString(tty.output, 0)); - tty.output = []; - } else { - if (val != 0) tty.output.push(val); - } - }, - flush: function(tty) { - if (tty.output && tty.output.length > 0) { - err(UTF8ArrayToString(tty.output, 0)); - tty.output = []; - } - } - } -}; - -function mmapAlloc(size) { - var alignedSize = alignMemory(size, 65536); - var ptr = _malloc(alignedSize); - while (size < alignedSize) HEAP8[ptr + size++] = 0; - return ptr; -} - -var MEMFS = { - ops_table: null, - mount: function(mount) { - return MEMFS.createNode(null, "/", 16384 | 511, 0); - }, - createNode: function(parent, name, mode, dev) { - if (FS.isBlkdev(mode) || FS.isFIFO(mode)) { - throw new FS.ErrnoError(63); - } - if (!MEMFS.ops_table) { - MEMFS.ops_table = { - dir: { - node: { - getattr: MEMFS.node_ops.getattr, - setattr: MEMFS.node_ops.setattr, - lookup: MEMFS.node_ops.lookup, - mknod: MEMFS.node_ops.mknod, - rename: MEMFS.node_ops.rename, - unlink: MEMFS.node_ops.unlink, - rmdir: MEMFS.node_ops.rmdir, - readdir: MEMFS.node_ops.readdir, - symlink: MEMFS.node_ops.symlink - }, - stream: { - llseek: MEMFS.stream_ops.llseek - } - }, - file: { - node: { - getattr: MEMFS.node_ops.getattr, - setattr: MEMFS.node_ops.setattr - }, - stream: { - llseek: MEMFS.stream_ops.llseek, - read: MEMFS.stream_ops.read, - write: MEMFS.stream_ops.write, - allocate: MEMFS.stream_ops.allocate, - mmap: MEMFS.stream_ops.mmap, - msync: MEMFS.stream_ops.msync - } - }, - link: { - node: { - getattr: MEMFS.node_ops.getattr, - setattr: MEMFS.node_ops.setattr, - readlink: MEMFS.node_ops.readlink - }, - stream: {} - }, - chrdev: { - node: { - getattr: MEMFS.node_ops.getattr, - setattr: MEMFS.node_ops.setattr - }, - stream: FS.chrdev_stream_ops - } - }; - } - var node = FS.createNode(parent, name, mode, dev); - if (FS.isDir(node.mode)) { - node.node_ops = MEMFS.ops_table.dir.node; - node.stream_ops = MEMFS.ops_table.dir.stream; - node.contents = {}; - } else if (FS.isFile(node.mode)) { - node.node_ops = MEMFS.ops_table.file.node; - node.stream_ops = MEMFS.ops_table.file.stream; - node.usedBytes = 0; - node.contents = null; - } else if (FS.isLink(node.mode)) { - node.node_ops = MEMFS.ops_table.link.node; - node.stream_ops = MEMFS.ops_table.link.stream; - } else if (FS.isChrdev(node.mode)) { - node.node_ops = MEMFS.ops_table.chrdev.node; - node.stream_ops = MEMFS.ops_table.chrdev.stream; - } - node.timestamp = Date.now(); - if (parent) { - parent.contents[name] = node; - parent.timestamp = node.timestamp; - } - return node; - }, - getFileDataAsTypedArray: function(node) { - if (!node.contents) return new Uint8Array(0); - if (node.contents.subarray) return node.contents.subarray(0, node.usedBytes); - return new Uint8Array(node.contents); - }, - expandFileStorage: function(node, newCapacity) { - var prevCapacity = node.contents ? node.contents.length : 0; - if (prevCapacity >= newCapacity) return; - var CAPACITY_DOUBLING_MAX = 1024 * 1024; - newCapacity = Math.max(newCapacity, prevCapacity * (prevCapacity < CAPACITY_DOUBLING_MAX ? 2 : 1.125) >>> 0); - if (prevCapacity != 0) newCapacity = Math.max(newCapacity, 256); - var oldContents = node.contents; - node.contents = new Uint8Array(newCapacity); - if (node.usedBytes > 0) node.contents.set(oldContents.subarray(0, node.usedBytes), 0); - }, - resizeFileStorage: function(node, newSize) { - if (node.usedBytes == newSize) return; - if (newSize == 0) { - node.contents = null; - node.usedBytes = 0; - } else { - var oldContents = node.contents; - node.contents = new Uint8Array(newSize); - if (oldContents) { - node.contents.set(oldContents.subarray(0, Math.min(newSize, node.usedBytes))); - } - node.usedBytes = newSize; - } - }, - node_ops: { - getattr: function(node) { - var attr = {}; - attr.dev = FS.isChrdev(node.mode) ? node.id : 1; - attr.ino = node.id; - attr.mode = node.mode; - attr.nlink = 1; - attr.uid = 0; - attr.gid = 0; - attr.rdev = node.rdev; - if (FS.isDir(node.mode)) { - attr.size = 4096; - } else if (FS.isFile(node.mode)) { - attr.size = node.usedBytes; - } else if (FS.isLink(node.mode)) { - attr.size = node.link.length; - } else { - attr.size = 0; - } - attr.atime = new Date(node.timestamp); - attr.mtime = new Date(node.timestamp); - attr.ctime = new Date(node.timestamp); - attr.blksize = 4096; - attr.blocks = Math.ceil(attr.size / attr.blksize); - return attr; - }, - setattr: function(node, attr) { - if (attr.mode !== undefined) { - node.mode = attr.mode; - } - if (attr.timestamp !== undefined) { - node.timestamp = attr.timestamp; - } - if (attr.size !== undefined) { - MEMFS.resizeFileStorage(node, attr.size); - } - }, - lookup: function(parent, name) { - throw FS.genericErrors[44]; - }, - mknod: function(parent, name, mode, dev) { - return MEMFS.createNode(parent, name, mode, dev); - }, - rename: function(old_node, new_dir, new_name) { - if (FS.isDir(old_node.mode)) { - var new_node; - try { - new_node = FS.lookupNode(new_dir, new_name); - } catch (e) {} - if (new_node) { - for (var i in new_node.contents) { - throw new FS.ErrnoError(55); - } - } - } - delete old_node.parent.contents[old_node.name]; - old_node.parent.timestamp = Date.now(); - old_node.name = new_name; - new_dir.contents[new_name] = old_node; - new_dir.timestamp = old_node.parent.timestamp; - old_node.parent = new_dir; - }, - unlink: function(parent, name) { - delete parent.contents[name]; - parent.timestamp = Date.now(); - }, - rmdir: function(parent, name) { - var node = FS.lookupNode(parent, name); - for (var i in node.contents) { - throw new FS.ErrnoError(55); - } - delete parent.contents[name]; - parent.timestamp = Date.now(); - }, - readdir: function(node) { - var entries = [ ".", ".." ]; - for (var key in node.contents) { - if (!node.contents.hasOwnProperty(key)) { - continue; - } - entries.push(key); - } - return entries; - }, - symlink: function(parent, newname, oldpath) { - var node = MEMFS.createNode(parent, newname, 511 | 40960, 0); - node.link = oldpath; - return node; - }, - readlink: function(node) { - if (!FS.isLink(node.mode)) { - throw new FS.ErrnoError(28); - } - return node.link; - } - }, - stream_ops: { - read: function(stream, buffer, offset, length, position) { - var contents = stream.node.contents; - if (position >= stream.node.usedBytes) return 0; - var size = Math.min(stream.node.usedBytes - position, length); - assert(size >= 0); - if (size > 8 && contents.subarray) { - buffer.set(contents.subarray(position, position + size), offset); - } else { - for (var i = 0; i < size; i++) buffer[offset + i] = contents[position + i]; - } - return size; - }, - write: function(stream, buffer, offset, length, position, canOwn) { - assert(!(buffer instanceof ArrayBuffer)); - if (buffer.buffer === HEAP8.buffer) { - canOwn = false; - } - if (!length) return 0; - var node = stream.node; - node.timestamp = Date.now(); - if (buffer.subarray && (!node.contents || node.contents.subarray)) { - if (canOwn) { - assert(position === 0, "canOwn must imply no weird position inside the file"); - node.contents = buffer.subarray(offset, offset + length); - node.usedBytes = length; - return length; - } else if (node.usedBytes === 0 && position === 0) { - node.contents = buffer.slice(offset, offset + length); - node.usedBytes = length; - return length; - } else if (position + length <= node.usedBytes) { - node.contents.set(buffer.subarray(offset, offset + length), position); - return length; - } - } - MEMFS.expandFileStorage(node, position + length); - if (node.contents.subarray && buffer.subarray) { - node.contents.set(buffer.subarray(offset, offset + length), position); - } else { - for (var i = 0; i < length; i++) { - node.contents[position + i] = buffer[offset + i]; - } - } - node.usedBytes = Math.max(node.usedBytes, position + length); - return length; - }, - llseek: function(stream, offset, whence) { - var position = offset; - if (whence === 1) { - position += stream.position; - } else if (whence === 2) { - if (FS.isFile(stream.node.mode)) { - position += stream.node.usedBytes; - } - } - if (position < 0) { - throw new FS.ErrnoError(28); - } - return position; - }, - allocate: function(stream, offset, length) { - MEMFS.expandFileStorage(stream.node, offset + length); - stream.node.usedBytes = Math.max(stream.node.usedBytes, offset + length); - }, - mmap: function(stream, address, length, position, prot, flags) { - if (address !== 0) { - throw new FS.ErrnoError(28); - } - if (!FS.isFile(stream.node.mode)) { - throw new FS.ErrnoError(43); - } - var ptr; - var allocated; - var contents = stream.node.contents; - if (!(flags & 2) && contents.buffer === buffer) { - allocated = false; - ptr = contents.byteOffset; - } else { - if (position > 0 || position + length < contents.length) { - if (contents.subarray) { - contents = contents.subarray(position, position + length); - } else { - contents = Array.prototype.slice.call(contents, position, position + length); - } - } - allocated = true; - ptr = mmapAlloc(length); - if (!ptr) { - throw new FS.ErrnoError(48); - } - HEAP8.set(contents, ptr); - } - return { - ptr: ptr, - allocated: allocated - }; - }, - msync: function(stream, buffer, offset, length, mmapFlags) { - if (!FS.isFile(stream.node.mode)) { - throw new FS.ErrnoError(43); - } - if (mmapFlags & 2) { - return 0; - } - var bytesWritten = MEMFS.stream_ops.write(stream, buffer, 0, length, offset, false); - return 0; - } - } -}; - -var IDBFS = { - dbs: {}, - indexedDB: function() { - if (typeof indexedDB !== "undefined") return indexedDB; - var ret = null; - if (typeof window === "object") ret = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB; - assert(ret, "IDBFS used, but indexedDB not supported"); - return ret; - }, - DB_VERSION: 21, - DB_STORE_NAME: "FILE_DATA", - mount: function(mount) { - return MEMFS.mount.apply(null, arguments); - }, - syncfs: function(mount, populate, callback) { - IDBFS.getLocalSet(mount, function(err, local) { - if (err) return callback(err); - IDBFS.getRemoteSet(mount, function(err, remote) { - if (err) return callback(err); - var src = populate ? remote : local; - var dst = populate ? local : remote; - IDBFS.reconcile(src, dst, callback); - }); - }); - }, - getDB: function(name, callback) { - var db = IDBFS.dbs[name]; - if (db) { - return callback(null, db); - } - var req; - try { - req = IDBFS.indexedDB().open(name, IDBFS.DB_VERSION); - } catch (e) { - return callback(e); - } - if (!req) { - return callback("Unable to connect to IndexedDB"); - } - req.onupgradeneeded = function(e) { - var db = e.target.result; - var transaction = e.target.transaction; - var fileStore; - if (db.objectStoreNames.contains(IDBFS.DB_STORE_NAME)) { - fileStore = transaction.objectStore(IDBFS.DB_STORE_NAME); - } else { - fileStore = db.createObjectStore(IDBFS.DB_STORE_NAME); - } - if (!fileStore.indexNames.contains("timestamp")) { - fileStore.createIndex("timestamp", "timestamp", { - unique: false - }); - } - }; - req.onsuccess = function() { - db = req.result; - IDBFS.dbs[name] = db; - callback(null, db); - }; - req.onerror = function(e) { - callback(this.error); - e.preventDefault(); - }; - }, - getLocalSet: function(mount, callback) { - var entries = {}; - function isRealDir(p) { - return p !== "." && p !== ".."; - } - function toAbsolute(root) { - return function(p) { - return PATH.join2(root, p); - }; - } - var check = FS.readdir(mount.mountpoint).filter(isRealDir).map(toAbsolute(mount.mountpoint)); - while (check.length) { - var path = check.pop(); - var stat; - try { - stat = FS.stat(path); - } catch (e) { - return callback(e); - } - if (FS.isDir(stat.mode)) { - check.push.apply(check, FS.readdir(path).filter(isRealDir).map(toAbsolute(path))); - } - entries[path] = { - "timestamp": stat.mtime - }; - } - return callback(null, { - type: "local", - entries: entries - }); - }, - getRemoteSet: function(mount, callback) { - var entries = {}; - IDBFS.getDB(mount.mountpoint, function(err, db) { - if (err) return callback(err); - try { - var transaction = db.transaction([ IDBFS.DB_STORE_NAME ], "readonly"); - transaction.onerror = function(e) { - callback(this.error); - e.preventDefault(); - }; - var store = transaction.objectStore(IDBFS.DB_STORE_NAME); - var index = store.index("timestamp"); - index.openKeyCursor().onsuccess = function(event) { - var cursor = event.target.result; - if (!cursor) { - return callback(null, { - type: "remote", - db: db, - entries: entries - }); - } - entries[cursor.primaryKey] = { - "timestamp": cursor.key - }; - cursor.continue(); - }; - } catch (e) { - return callback(e); - } - }); - }, - loadLocalEntry: function(path, callback) { - var stat, node; - try { - var lookup = FS.lookupPath(path); - node = lookup.node; - stat = FS.stat(path); - } catch (e) { - return callback(e); - } - if (FS.isDir(stat.mode)) { - return callback(null, { - "timestamp": stat.mtime, - "mode": stat.mode - }); - } else if (FS.isFile(stat.mode)) { - node.contents = MEMFS.getFileDataAsTypedArray(node); - return callback(null, { - "timestamp": stat.mtime, - "mode": stat.mode, - "contents": node.contents - }); - } else { - return callback(new Error("node type not supported")); - } - }, - storeLocalEntry: function(path, entry, callback) { - try { - if (FS.isDir(entry["mode"])) { - FS.mkdirTree(path, entry["mode"]); - } else if (FS.isFile(entry["mode"])) { - FS.writeFile(path, entry["contents"], { - canOwn: true - }); - } else { - return callback(new Error("node type not supported")); - } - FS.chmod(path, entry["mode"]); - FS.utime(path, entry["timestamp"], entry["timestamp"]); - } catch (e) { - return callback(e); - } - callback(null); - }, - removeLocalEntry: function(path, callback) { - try { - var lookup = FS.lookupPath(path); - var stat = FS.stat(path); - if (FS.isDir(stat.mode)) { - FS.rmdir(path); - } else if (FS.isFile(stat.mode)) { - FS.unlink(path); - } - } catch (e) { - return callback(e); - } - callback(null); - }, - loadRemoteEntry: function(store, path, callback) { - var req = store.get(path); - req.onsuccess = function(event) { - callback(null, event.target.result); - }; - req.onerror = function(e) { - callback(this.error); - e.preventDefault(); - }; - }, - storeRemoteEntry: function(store, path, entry, callback) { - var req = store.put(entry, path); - req.onsuccess = function() { - callback(null); - }; - req.onerror = function(e) { - callback(this.error); - e.preventDefault(); - }; - }, - removeRemoteEntry: function(store, path, callback) { - var req = store.delete(path); - req.onsuccess = function() { - callback(null); - }; - req.onerror = function(e) { - callback(this.error); - e.preventDefault(); - }; - }, - reconcile: function(src, dst, callback) { - var total = 0; - var create = []; - Object.keys(src.entries).forEach(function(key) { - var e = src.entries[key]; - var e2 = dst.entries[key]; - if (!e2 || e["timestamp"].getTime() != e2["timestamp"].getTime()) { - create.push(key); - total++; - } - }); - var remove = []; - Object.keys(dst.entries).forEach(function(key) { - if (!src.entries[key]) { - remove.push(key); - total++; - } - }); - if (!total) { - return callback(null); - } - var errored = false; - var db = src.type === "remote" ? src.db : dst.db; - var transaction = db.transaction([ IDBFS.DB_STORE_NAME ], "readwrite"); - var store = transaction.objectStore(IDBFS.DB_STORE_NAME); - function done(err) { - if (err && !errored) { - errored = true; - return callback(err); - } - } - transaction.onerror = function(e) { - done(this.error); - e.preventDefault(); - }; - transaction.oncomplete = function(e) { - if (!errored) { - callback(null); - } - }; - create.sort().forEach(function(path) { - if (dst.type === "local") { - IDBFS.loadRemoteEntry(store, path, function(err, entry) { - if (err) return done(err); - IDBFS.storeLocalEntry(path, entry, done); - }); - } else { - IDBFS.loadLocalEntry(path, function(err, entry) { - if (err) return done(err); - IDBFS.storeRemoteEntry(store, path, entry, done); - }); - } - }); - remove.sort().reverse().forEach(function(path) { - if (dst.type === "local") { - IDBFS.removeLocalEntry(path, done); - } else { - IDBFS.removeRemoteEntry(store, path, done); - } - }); - } -}; - -var ERRNO_MESSAGES = { - 0: "Success", - 1: "Arg list too long", - 2: "Permission denied", - 3: "Address already in use", - 4: "Address not available", - 5: "Address family not supported by protocol family", - 6: "No more processes", - 7: "Socket already connected", - 8: "Bad file number", - 9: "Trying to read unreadable message", - 10: "Mount device busy", - 11: "Operation canceled", - 12: "No children", - 13: "Connection aborted", - 14: "Connection refused", - 15: "Connection reset by peer", - 16: "File locking deadlock error", - 17: "Destination address required", - 18: "Math arg out of domain of func", - 19: "Quota exceeded", - 20: "File exists", - 21: "Bad address", - 22: "File too large", - 23: "Host is unreachable", - 24: "Identifier removed", - 25: "Illegal byte sequence", - 26: "Connection already in progress", - 27: "Interrupted system call", - 28: "Invalid argument", - 29: "I/O error", - 30: "Socket is already connected", - 31: "Is a directory", - 32: "Too many symbolic links", - 33: "Too many open files", - 34: "Too many links", - 35: "Message too long", - 36: "Multihop attempted", - 37: "File or path name too long", - 38: "Network interface is not configured", - 39: "Connection reset by network", - 40: "Network is unreachable", - 41: "Too many open files in system", - 42: "No buffer space available", - 43: "No such device", - 44: "No such file or directory", - 45: "Exec format error", - 46: "No record locks available", - 47: "The link has been severed", - 48: "Not enough core", - 49: "No message of desired type", - 50: "Protocol not available", - 51: "No space left on device", - 52: "Function not implemented", - 53: "Socket is not connected", - 54: "Not a directory", - 55: "Directory not empty", - 56: "State not recoverable", - 57: "Socket operation on non-socket", - 59: "Not a typewriter", - 60: "No such device or address", - 61: "Value too large for defined data type", - 62: "Previous owner died", - 63: "Not super-user", - 64: "Broken pipe", - 65: "Protocol error", - 66: "Unknown protocol", - 67: "Protocol wrong type for socket", - 68: "Math result not representable", - 69: "Read only file system", - 70: "Illegal seek", - 71: "No such process", - 72: "Stale file handle", - 73: "Connection timed out", - 74: "Text file busy", - 75: "Cross-device link", - 100: "Device not a stream", - 101: "Bad font file fmt", - 102: "Invalid slot", - 103: "Invalid request code", - 104: "No anode", - 105: "Block device required", - 106: "Channel number out of range", - 107: "Level 3 halted", - 108: "Level 3 reset", - 109: "Link number out of range", - 110: "Protocol driver not attached", - 111: "No CSI structure available", - 112: "Level 2 halted", - 113: "Invalid exchange", - 114: "Invalid request descriptor", - 115: "Exchange full", - 116: "No data (for no delay io)", - 117: "Timer expired", - 118: "Out of streams resources", - 119: "Machine is not on the network", - 120: "Package not installed", - 121: "The object is remote", - 122: "Advertise error", - 123: "Srmount error", - 124: "Communication error on send", - 125: "Cross mount point (not really error)", - 126: "Given log. name not unique", - 127: "f.d. invalid for this operation", - 128: "Remote address changed", - 129: "Can access a needed shared lib", - 130: "Accessing a corrupted shared lib", - 131: ".lib section in a.out corrupted", - 132: "Attempting to link in too many libs", - 133: "Attempting to exec a shared library", - 135: "Streams pipe error", - 136: "Too many users", - 137: "Socket type not supported", - 138: "Not supported", - 139: "Protocol family not supported", - 140: "Can't send after socket shutdown", - 141: "Too many references", - 142: "Host is down", - 148: "No medium (in tape drive)", - 156: "Level 2 not synchronized" -}; - -var ERRNO_CODES = { - EPERM: 63, - ENOENT: 44, - ESRCH: 71, - EINTR: 27, - EIO: 29, - ENXIO: 60, - E2BIG: 1, - ENOEXEC: 45, - EBADF: 8, - ECHILD: 12, - EAGAIN: 6, - EWOULDBLOCK: 6, - ENOMEM: 48, - EACCES: 2, - EFAULT: 21, - ENOTBLK: 105, - EBUSY: 10, - EEXIST: 20, - EXDEV: 75, - ENODEV: 43, - ENOTDIR: 54, - EISDIR: 31, - EINVAL: 28, - ENFILE: 41, - EMFILE: 33, - ENOTTY: 59, - ETXTBSY: 74, - EFBIG: 22, - ENOSPC: 51, - ESPIPE: 70, - EROFS: 69, - EMLINK: 34, - EPIPE: 64, - EDOM: 18, - ERANGE: 68, - ENOMSG: 49, - EIDRM: 24, - ECHRNG: 106, - EL2NSYNC: 156, - EL3HLT: 107, - EL3RST: 108, - ELNRNG: 109, - EUNATCH: 110, - ENOCSI: 111, - EL2HLT: 112, - EDEADLK: 16, - ENOLCK: 46, - EBADE: 113, - EBADR: 114, - EXFULL: 115, - ENOANO: 104, - EBADRQC: 103, - EBADSLT: 102, - EDEADLOCK: 16, - EBFONT: 101, - ENOSTR: 100, - ENODATA: 116, - ETIME: 117, - ENOSR: 118, - ENONET: 119, - ENOPKG: 120, - EREMOTE: 121, - ENOLINK: 47, - EADV: 122, - ESRMNT: 123, - ECOMM: 124, - EPROTO: 65, - EMULTIHOP: 36, - EDOTDOT: 125, - EBADMSG: 9, - ENOTUNIQ: 126, - EBADFD: 127, - EREMCHG: 128, - ELIBACC: 129, - ELIBBAD: 130, - ELIBSCN: 131, - ELIBMAX: 132, - ELIBEXEC: 133, - ENOSYS: 52, - ENOTEMPTY: 55, - ENAMETOOLONG: 37, - ELOOP: 32, - EOPNOTSUPP: 138, - EPFNOSUPPORT: 139, - ECONNRESET: 15, - ENOBUFS: 42, - EAFNOSUPPORT: 5, - EPROTOTYPE: 67, - ENOTSOCK: 57, - ENOPROTOOPT: 50, - ESHUTDOWN: 140, - ECONNREFUSED: 14, - EADDRINUSE: 3, - ECONNABORTED: 13, - ENETUNREACH: 40, - ENETDOWN: 38, - ETIMEDOUT: 73, - EHOSTDOWN: 142, - EHOSTUNREACH: 23, - EINPROGRESS: 26, - EALREADY: 7, - EDESTADDRREQ: 17, - EMSGSIZE: 35, - EPROTONOSUPPORT: 66, - ESOCKTNOSUPPORT: 137, - EADDRNOTAVAIL: 4, - ENETRESET: 39, - EISCONN: 30, - ENOTCONN: 53, - ETOOMANYREFS: 141, - EUSERS: 136, - EDQUOT: 19, - ESTALE: 72, - ENOTSUP: 138, - ENOMEDIUM: 148, - EILSEQ: 25, - EOVERFLOW: 61, - ECANCELED: 11, - ENOTRECOVERABLE: 56, - EOWNERDEAD: 62, - ESTRPIPE: 135 -}; - -var FS = { - root: null, - mounts: [], - devices: {}, - streams: [], - nextInode: 1, - nameTable: null, - currentPath: "/", - initialized: false, - ignorePermissions: true, - trackingDelegate: {}, - tracking: { - openFlags: { - READ: 1, - WRITE: 2 - } - }, - ErrnoError: null, - genericErrors: {}, - filesystems: null, - syncFSRequests: 0, - lookupPath: function(path, opts) { - path = PATH_FS.resolve(FS.cwd(), path); - opts = opts || {}; - if (!path) return { - path: "", - node: null - }; - var defaults = { - follow_mount: true, - recurse_count: 0 - }; - for (var key in defaults) { - if (opts[key] === undefined) { - opts[key] = defaults[key]; - } - } - if (opts.recurse_count > 8) { - throw new FS.ErrnoError(32); - } - var parts = PATH.normalizeArray(path.split("/").filter(function(p) { - return !!p; - }), false); - var current = FS.root; - var current_path = "/"; - for (var i = 0; i < parts.length; i++) { - var islast = i === parts.length - 1; - if (islast && opts.parent) { - break; - } - current = FS.lookupNode(current, parts[i]); - current_path = PATH.join2(current_path, parts[i]); - if (FS.isMountpoint(current)) { - if (!islast || islast && opts.follow_mount) { - current = current.mounted.root; - } - } - if (!islast || opts.follow) { - var count = 0; - while (FS.isLink(current.mode)) { - var link = FS.readlink(current_path); - current_path = PATH_FS.resolve(PATH.dirname(current_path), link); - var lookup = FS.lookupPath(current_path, { - recurse_count: opts.recurse_count - }); - current = lookup.node; - if (count++ > 40) { - throw new FS.ErrnoError(32); - } - } - } - } - return { - path: current_path, - node: current - }; - }, - getPath: function(node) { - var path; - while (true) { - if (FS.isRoot(node)) { - var mount = node.mount.mountpoint; - if (!path) return mount; - return mount[mount.length - 1] !== "/" ? mount + "/" + path : mount + path; - } - path = path ? node.name + "/" + path : node.name; - node = node.parent; - } - }, - hashName: function(parentid, name) { - var hash = 0; - for (var i = 0; i < name.length; i++) { - hash = (hash << 5) - hash + name.charCodeAt(i) | 0; - } - return (parentid + hash >>> 0) % FS.nameTable.length; - }, - hashAddNode: function(node) { - var hash = FS.hashName(node.parent.id, node.name); - node.name_next = FS.nameTable[hash]; - FS.nameTable[hash] = node; - }, - hashRemoveNode: function(node) { - var hash = FS.hashName(node.parent.id, node.name); - if (FS.nameTable[hash] === node) { - FS.nameTable[hash] = node.name_next; - } else { - var current = FS.nameTable[hash]; - while (current) { - if (current.name_next === node) { - current.name_next = node.name_next; - break; - } - current = current.name_next; - } - } - }, - lookupNode: function(parent, name) { - var errCode = FS.mayLookup(parent); - if (errCode) { - throw new FS.ErrnoError(errCode, parent); - } - var hash = FS.hashName(parent.id, name); - for (var node = FS.nameTable[hash]; node; node = node.name_next) { - var nodeName = node.name; - if (node.parent.id === parent.id && nodeName === name) { - return node; - } - } - return FS.lookup(parent, name); - }, - createNode: function(parent, name, mode, rdev) { - assert(typeof parent === "object"); - var node = new FS.FSNode(parent, name, mode, rdev); - FS.hashAddNode(node); - return node; - }, - destroyNode: function(node) { - FS.hashRemoveNode(node); - }, - isRoot: function(node) { - return node === node.parent; - }, - isMountpoint: function(node) { - return !!node.mounted; - }, - isFile: function(mode) { - return (mode & 61440) === 32768; - }, - isDir: function(mode) { - return (mode & 61440) === 16384; - }, - isLink: function(mode) { - return (mode & 61440) === 40960; - }, - isChrdev: function(mode) { - return (mode & 61440) === 8192; - }, - isBlkdev: function(mode) { - return (mode & 61440) === 24576; - }, - isFIFO: function(mode) { - return (mode & 61440) === 4096; - }, - isSocket: function(mode) { - return (mode & 49152) === 49152; - }, - flagModes: { - "r": 0, - "r+": 2, - "w": 577, - "w+": 578, - "a": 1089, - "a+": 1090 - }, - modeStringToFlags: function(str) { - var flags = FS.flagModes[str]; - if (typeof flags === "undefined") { - throw new Error("Unknown file open mode: " + str); - } - return flags; - }, - flagsToPermissionString: function(flag) { - var perms = [ "r", "w", "rw" ][flag & 3]; - if (flag & 512) { - perms += "w"; - } - return perms; - }, - nodePermissions: function(node, perms) { - if (FS.ignorePermissions) { - return 0; - } - if (perms.includes("r") && !(node.mode & 292)) { - return 2; - } else if (perms.includes("w") && !(node.mode & 146)) { - return 2; - } else if (perms.includes("x") && !(node.mode & 73)) { - return 2; - } - return 0; - }, - mayLookup: function(dir) { - var errCode = FS.nodePermissions(dir, "x"); - if (errCode) return errCode; - if (!dir.node_ops.lookup) return 2; - return 0; - }, - mayCreate: function(dir, name) { - try { - var node = FS.lookupNode(dir, name); - return 20; - } catch (e) {} - return FS.nodePermissions(dir, "wx"); - }, - mayDelete: function(dir, name, isdir) { - var node; - try { - node = FS.lookupNode(dir, name); - } catch (e) { - return e.errno; - } - var errCode = FS.nodePermissions(dir, "wx"); - if (errCode) { - return errCode; - } - if (isdir) { - if (!FS.isDir(node.mode)) { - return 54; - } - if (FS.isRoot(node) || FS.getPath(node) === FS.cwd()) { - return 10; - } - } else { - if (FS.isDir(node.mode)) { - return 31; - } - } - return 0; - }, - mayOpen: function(node, flags) { - if (!node) { - return 44; - } - if (FS.isLink(node.mode)) { - return 32; - } else if (FS.isDir(node.mode)) { - if (FS.flagsToPermissionString(flags) !== "r" || flags & 512) { - return 31; - } - } - return FS.nodePermissions(node, FS.flagsToPermissionString(flags)); - }, - MAX_OPEN_FDS: 4096, - nextfd: function(fd_start, fd_end) { - fd_start = fd_start || 0; - fd_end = fd_end || FS.MAX_OPEN_FDS; - for (var fd = fd_start; fd <= fd_end; fd++) { - if (!FS.streams[fd]) { - return fd; - } - } - throw new FS.ErrnoError(33); - }, - getStream: function(fd) { - return FS.streams[fd]; - }, - createStream: function(stream, fd_start, fd_end) { - if (!FS.FSStream) { - FS.FSStream = function() {}; - FS.FSStream.prototype = { - object: { - get: function() { - return this.node; - }, - set: function(val) { - this.node = val; - } - }, - isRead: { - get: function() { - return (this.flags & 2097155) !== 1; - } - }, - isWrite: { - get: function() { - return (this.flags & 2097155) !== 0; - } - }, - isAppend: { - get: function() { - return this.flags & 1024; - } - } - }; - } - var newStream = new FS.FSStream(); - for (var p in stream) { - newStream[p] = stream[p]; - } - stream = newStream; - var fd = FS.nextfd(fd_start, fd_end); - stream.fd = fd; - FS.streams[fd] = stream; - return stream; - }, - closeStream: function(fd) { - FS.streams[fd] = null; - }, - chrdev_stream_ops: { - open: function(stream) { - var device = FS.getDevice(stream.node.rdev); - stream.stream_ops = device.stream_ops; - if (stream.stream_ops.open) { - stream.stream_ops.open(stream); - } - }, - llseek: function() { - throw new FS.ErrnoError(70); - } - }, - major: function(dev) { - return dev >> 8; - }, - minor: function(dev) { - return dev & 255; - }, - makedev: function(ma, mi) { - return ma << 8 | mi; - }, - registerDevice: function(dev, ops) { - FS.devices[dev] = { - stream_ops: ops - }; - }, - getDevice: function(dev) { - return FS.devices[dev]; - }, - getMounts: function(mount) { - var mounts = []; - var check = [ mount ]; - while (check.length) { - var m = check.pop(); - mounts.push(m); - check.push.apply(check, m.mounts); - } - return mounts; - }, - syncfs: function(populate, callback) { - if (typeof populate === "function") { - callback = populate; - populate = false; - } - FS.syncFSRequests++; - if (FS.syncFSRequests > 1) { - err("warning: " + FS.syncFSRequests + " FS.syncfs operations in flight at once, probably just doing extra work"); - } - var mounts = FS.getMounts(FS.root.mount); - var completed = 0; - function doCallback(errCode) { - assert(FS.syncFSRequests > 0); - FS.syncFSRequests--; - return callback(errCode); - } - function done(errCode) { - if (errCode) { - if (!done.errored) { - done.errored = true; - return doCallback(errCode); - } - return; - } - if (++completed >= mounts.length) { - doCallback(null); - } - } - mounts.forEach(function(mount) { - if (!mount.type.syncfs) { - return done(null); - } - mount.type.syncfs(mount, populate, done); - }); - }, - mount: function(type, opts, mountpoint) { - if (typeof type === "string") { - throw type; - } - var root = mountpoint === "/"; - var pseudo = !mountpoint; - var node; - if (root && FS.root) { - throw new FS.ErrnoError(10); - } else if (!root && !pseudo) { - var lookup = FS.lookupPath(mountpoint, { - follow_mount: false - }); - mountpoint = lookup.path; - node = lookup.node; - if (FS.isMountpoint(node)) { - throw new FS.ErrnoError(10); - } - if (!FS.isDir(node.mode)) { - throw new FS.ErrnoError(54); - } - } - var mount = { - type: type, - opts: opts, - mountpoint: mountpoint, - mounts: [] - }; - var mountRoot = type.mount(mount); - mountRoot.mount = mount; - mount.root = mountRoot; - if (root) { - FS.root = mountRoot; - } else if (node) { - node.mounted = mount; - if (node.mount) { - node.mount.mounts.push(mount); - } - } - return mountRoot; - }, - unmount: function(mountpoint) { - var lookup = FS.lookupPath(mountpoint, { - follow_mount: false - }); - if (!FS.isMountpoint(lookup.node)) { - throw new FS.ErrnoError(28); - } - var node = lookup.node; - var mount = node.mounted; - var mounts = FS.getMounts(mount); - Object.keys(FS.nameTable).forEach(function(hash) { - var current = FS.nameTable[hash]; - while (current) { - var next = current.name_next; - if (mounts.includes(current.mount)) { - FS.destroyNode(current); - } - current = next; - } - }); - node.mounted = null; - var idx = node.mount.mounts.indexOf(mount); - assert(idx !== -1); - node.mount.mounts.splice(idx, 1); - }, - lookup: function(parent, name) { - return parent.node_ops.lookup(parent, name); - }, - mknod: function(path, mode, dev) { - var lookup = FS.lookupPath(path, { - parent: true - }); - var parent = lookup.node; - var name = PATH.basename(path); - if (!name || name === "." || name === "..") { - throw new FS.ErrnoError(28); - } - var errCode = FS.mayCreate(parent, name); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - if (!parent.node_ops.mknod) { - throw new FS.ErrnoError(63); - } - return parent.node_ops.mknod(parent, name, mode, dev); - }, - create: function(path, mode) { - mode = mode !== undefined ? mode : 438; - mode &= 4095; - mode |= 32768; - return FS.mknod(path, mode, 0); - }, - mkdir: function(path, mode) { - mode = mode !== undefined ? mode : 511; - mode &= 511 | 512; - mode |= 16384; - return FS.mknod(path, mode, 0); - }, - mkdirTree: function(path, mode) { - var dirs = path.split("/"); - var d = ""; - for (var i = 0; i < dirs.length; ++i) { - if (!dirs[i]) continue; - d += "/" + dirs[i]; - try { - FS.mkdir(d, mode); - } catch (e) { - if (e.errno != 20) throw e; - } - } - }, - mkdev: function(path, mode, dev) { - if (typeof dev === "undefined") { - dev = mode; - mode = 438; - } - mode |= 8192; - return FS.mknod(path, mode, dev); - }, - symlink: function(oldpath, newpath) { - if (!PATH_FS.resolve(oldpath)) { - throw new FS.ErrnoError(44); - } - var lookup = FS.lookupPath(newpath, { - parent: true - }); - var parent = lookup.node; - if (!parent) { - throw new FS.ErrnoError(44); - } - var newname = PATH.basename(newpath); - var errCode = FS.mayCreate(parent, newname); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - if (!parent.node_ops.symlink) { - throw new FS.ErrnoError(63); - } - return parent.node_ops.symlink(parent, newname, oldpath); - }, - rename: function(old_path, new_path) { - var old_dirname = PATH.dirname(old_path); - var new_dirname = PATH.dirname(new_path); - var old_name = PATH.basename(old_path); - var new_name = PATH.basename(new_path); - var lookup, old_dir, new_dir; - lookup = FS.lookupPath(old_path, { - parent: true - }); - old_dir = lookup.node; - lookup = FS.lookupPath(new_path, { - parent: true - }); - new_dir = lookup.node; - if (!old_dir || !new_dir) throw new FS.ErrnoError(44); - if (old_dir.mount !== new_dir.mount) { - throw new FS.ErrnoError(75); - } - var old_node = FS.lookupNode(old_dir, old_name); - var relative = PATH_FS.relative(old_path, new_dirname); - if (relative.charAt(0) !== ".") { - throw new FS.ErrnoError(28); - } - relative = PATH_FS.relative(new_path, old_dirname); - if (relative.charAt(0) !== ".") { - throw new FS.ErrnoError(55); - } - var new_node; - try { - new_node = FS.lookupNode(new_dir, new_name); - } catch (e) {} - if (old_node === new_node) { - return; - } - var isdir = FS.isDir(old_node.mode); - var errCode = FS.mayDelete(old_dir, old_name, isdir); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - errCode = new_node ? FS.mayDelete(new_dir, new_name, isdir) : FS.mayCreate(new_dir, new_name); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - if (!old_dir.node_ops.rename) { - throw new FS.ErrnoError(63); - } - if (FS.isMountpoint(old_node) || new_node && FS.isMountpoint(new_node)) { - throw new FS.ErrnoError(10); - } - if (new_dir !== old_dir) { - errCode = FS.nodePermissions(old_dir, "w"); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - } - try { - if (FS.trackingDelegate["willMovePath"]) { - FS.trackingDelegate["willMovePath"](old_path, new_path); - } - } catch (e) { - err("FS.trackingDelegate['willMovePath']('" + old_path + "', '" + new_path + "') threw an exception: " + e.message); - } - FS.hashRemoveNode(old_node); - try { - old_dir.node_ops.rename(old_node, new_dir, new_name); - } catch (e) { - throw e; - } finally { - FS.hashAddNode(old_node); - } - try { - if (FS.trackingDelegate["onMovePath"]) FS.trackingDelegate["onMovePath"](old_path, new_path); - } catch (e) { - err("FS.trackingDelegate['onMovePath']('" + old_path + "', '" + new_path + "') threw an exception: " + e.message); - } - }, - rmdir: function(path) { - var lookup = FS.lookupPath(path, { - parent: true - }); - var parent = lookup.node; - var name = PATH.basename(path); - var node = FS.lookupNode(parent, name); - var errCode = FS.mayDelete(parent, name, true); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - if (!parent.node_ops.rmdir) { - throw new FS.ErrnoError(63); - } - if (FS.isMountpoint(node)) { - throw new FS.ErrnoError(10); - } - try { - if (FS.trackingDelegate["willDeletePath"]) { - FS.trackingDelegate["willDeletePath"](path); - } - } catch (e) { - err("FS.trackingDelegate['willDeletePath']('" + path + "') threw an exception: " + e.message); - } - parent.node_ops.rmdir(parent, name); - FS.destroyNode(node); - try { - if (FS.trackingDelegate["onDeletePath"]) FS.trackingDelegate["onDeletePath"](path); - } catch (e) { - err("FS.trackingDelegate['onDeletePath']('" + path + "') threw an exception: " + e.message); - } - }, - readdir: function(path) { - var lookup = FS.lookupPath(path, { - follow: true - }); - var node = lookup.node; - if (!node.node_ops.readdir) { - throw new FS.ErrnoError(54); - } - return node.node_ops.readdir(node); - }, - unlink: function(path) { - var lookup = FS.lookupPath(path, { - parent: true - }); - var parent = lookup.node; - var name = PATH.basename(path); - var node = FS.lookupNode(parent, name); - var errCode = FS.mayDelete(parent, name, false); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - if (!parent.node_ops.unlink) { - throw new FS.ErrnoError(63); - } - if (FS.isMountpoint(node)) { - throw new FS.ErrnoError(10); - } - try { - if (FS.trackingDelegate["willDeletePath"]) { - FS.trackingDelegate["willDeletePath"](path); - } - } catch (e) { - err("FS.trackingDelegate['willDeletePath']('" + path + "') threw an exception: " + e.message); - } - parent.node_ops.unlink(parent, name); - FS.destroyNode(node); - try { - if (FS.trackingDelegate["onDeletePath"]) FS.trackingDelegate["onDeletePath"](path); - } catch (e) { - err("FS.trackingDelegate['onDeletePath']('" + path + "') threw an exception: " + e.message); - } - }, - readlink: function(path) { - var lookup = FS.lookupPath(path); - var link = lookup.node; - if (!link) { - throw new FS.ErrnoError(44); - } - if (!link.node_ops.readlink) { - throw new FS.ErrnoError(28); - } - return PATH_FS.resolve(FS.getPath(link.parent), link.node_ops.readlink(link)); - }, - stat: function(path, dontFollow) { - var lookup = FS.lookupPath(path, { - follow: !dontFollow - }); - var node = lookup.node; - if (!node) { - throw new FS.ErrnoError(44); - } - if (!node.node_ops.getattr) { - throw new FS.ErrnoError(63); - } - return node.node_ops.getattr(node); - }, - lstat: function(path) { - return FS.stat(path, true); - }, - chmod: function(path, mode, dontFollow) { - var node; - if (typeof path === "string") { - var lookup = FS.lookupPath(path, { - follow: !dontFollow - }); - node = lookup.node; - } else { - node = path; - } - if (!node.node_ops.setattr) { - throw new FS.ErrnoError(63); - } - node.node_ops.setattr(node, { - mode: mode & 4095 | node.mode & ~4095, - timestamp: Date.now() - }); - }, - lchmod: function(path, mode) { - FS.chmod(path, mode, true); - }, - fchmod: function(fd, mode) { - var stream = FS.getStream(fd); - if (!stream) { - throw new FS.ErrnoError(8); - } - FS.chmod(stream.node, mode); - }, - chown: function(path, uid, gid, dontFollow) { - var node; - if (typeof path === "string") { - var lookup = FS.lookupPath(path, { - follow: !dontFollow - }); - node = lookup.node; - } else { - node = path; - } - if (!node.node_ops.setattr) { - throw new FS.ErrnoError(63); - } - node.node_ops.setattr(node, { - timestamp: Date.now() - }); - }, - lchown: function(path, uid, gid) { - FS.chown(path, uid, gid, true); - }, - fchown: function(fd, uid, gid) { - var stream = FS.getStream(fd); - if (!stream) { - throw new FS.ErrnoError(8); - } - FS.chown(stream.node, uid, gid); - }, - truncate: function(path, len) { - if (len < 0) { - throw new FS.ErrnoError(28); - } - var node; - if (typeof path === "string") { - var lookup = FS.lookupPath(path, { - follow: true - }); - node = lookup.node; - } else { - node = path; - } - if (!node.node_ops.setattr) { - throw new FS.ErrnoError(63); - } - if (FS.isDir(node.mode)) { - throw new FS.ErrnoError(31); - } - if (!FS.isFile(node.mode)) { - throw new FS.ErrnoError(28); - } - var errCode = FS.nodePermissions(node, "w"); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - node.node_ops.setattr(node, { - size: len, - timestamp: Date.now() - }); - }, - ftruncate: function(fd, len) { - var stream = FS.getStream(fd); - if (!stream) { - throw new FS.ErrnoError(8); - } - if ((stream.flags & 2097155) === 0) { - throw new FS.ErrnoError(28); - } - FS.truncate(stream.node, len); - }, - utime: function(path, atime, mtime) { - var lookup = FS.lookupPath(path, { - follow: true - }); - var node = lookup.node; - node.node_ops.setattr(node, { - timestamp: Math.max(atime, mtime) - }); - }, - open: function(path, flags, mode, fd_start, fd_end) { - if (path === "") { - throw new FS.ErrnoError(44); - } - flags = typeof flags === "string" ? FS.modeStringToFlags(flags) : flags; - mode = typeof mode === "undefined" ? 438 : mode; - if (flags & 64) { - mode = mode & 4095 | 32768; - } else { - mode = 0; - } - var node; - if (typeof path === "object") { - node = path; - } else { - path = PATH.normalize(path); - try { - var lookup = FS.lookupPath(path, { - follow: !(flags & 131072) - }); - node = lookup.node; - } catch (e) {} - } - var created = false; - if (flags & 64) { - if (node) { - if (flags & 128) { - throw new FS.ErrnoError(20); - } - } else { - node = FS.mknod(path, mode, 0); - created = true; - } - } - if (!node) { - throw new FS.ErrnoError(44); - } - if (FS.isChrdev(node.mode)) { - flags &= ~512; - } - if (flags & 65536 && !FS.isDir(node.mode)) { - throw new FS.ErrnoError(54); - } - if (!created) { - var errCode = FS.mayOpen(node, flags); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - } - if (flags & 512) { - FS.truncate(node, 0); - } - flags &= ~(128 | 512 | 131072); - var stream = FS.createStream({ - node: node, - path: FS.getPath(node), - flags: flags, - seekable: true, - position: 0, - stream_ops: node.stream_ops, - ungotten: [], - error: false - }, fd_start, fd_end); - if (stream.stream_ops.open) { - stream.stream_ops.open(stream); - } - if (Module["logReadFiles"] && !(flags & 1)) { - if (!FS.readFiles) FS.readFiles = {}; - if (!(path in FS.readFiles)) { - FS.readFiles[path] = 1; - err("FS.trackingDelegate error on read file: " + path); - } - } - try { - if (FS.trackingDelegate["onOpenFile"]) { - var trackingFlags = 0; - if ((flags & 2097155) !== 1) { - trackingFlags |= FS.tracking.openFlags.READ; - } - if ((flags & 2097155) !== 0) { - trackingFlags |= FS.tracking.openFlags.WRITE; - } - FS.trackingDelegate["onOpenFile"](path, trackingFlags); - } - } catch (e) { - err("FS.trackingDelegate['onOpenFile']('" + path + "', flags) threw an exception: " + e.message); - } - return stream; - }, - close: function(stream) { - if (FS.isClosed(stream)) { - throw new FS.ErrnoError(8); - } - if (stream.getdents) stream.getdents = null; - try { - if (stream.stream_ops.close) { - stream.stream_ops.close(stream); - } - } catch (e) { - throw e; - } finally { - FS.closeStream(stream.fd); - } - stream.fd = null; - }, - isClosed: function(stream) { - return stream.fd === null; - }, - llseek: function(stream, offset, whence) { - if (FS.isClosed(stream)) { - throw new FS.ErrnoError(8); - } - if (!stream.seekable || !stream.stream_ops.llseek) { - throw new FS.ErrnoError(70); - } - if (whence != 0 && whence != 1 && whence != 2) { - throw new FS.ErrnoError(28); - } - stream.position = stream.stream_ops.llseek(stream, offset, whence); - stream.ungotten = []; - return stream.position; - }, - read: function(stream, buffer, offset, length, position) { - if (length < 0 || position < 0) { - throw new FS.ErrnoError(28); - } - if (FS.isClosed(stream)) { - throw new FS.ErrnoError(8); - } - if ((stream.flags & 2097155) === 1) { - throw new FS.ErrnoError(8); - } - if (FS.isDir(stream.node.mode)) { - throw new FS.ErrnoError(31); - } - if (!stream.stream_ops.read) { - throw new FS.ErrnoError(28); - } - var seeking = typeof position !== "undefined"; - if (!seeking) { - position = stream.position; - } else if (!stream.seekable) { - throw new FS.ErrnoError(70); - } - var bytesRead = stream.stream_ops.read(stream, buffer, offset, length, position); - if (!seeking) stream.position += bytesRead; - return bytesRead; - }, - write: function(stream, buffer, offset, length, position, canOwn) { - if (length < 0 || position < 0) { - throw new FS.ErrnoError(28); - } - if (FS.isClosed(stream)) { - throw new FS.ErrnoError(8); - } - if ((stream.flags & 2097155) === 0) { - throw new FS.ErrnoError(8); - } - if (FS.isDir(stream.node.mode)) { - throw new FS.ErrnoError(31); - } - if (!stream.stream_ops.write) { - throw new FS.ErrnoError(28); - } - if (stream.seekable && stream.flags & 1024) { - FS.llseek(stream, 0, 2); - } - var seeking = typeof position !== "undefined"; - if (!seeking) { - position = stream.position; - } else if (!stream.seekable) { - throw new FS.ErrnoError(70); - } - var bytesWritten = stream.stream_ops.write(stream, buffer, offset, length, position, canOwn); - if (!seeking) stream.position += bytesWritten; - try { - if (stream.path && FS.trackingDelegate["onWriteToFile"]) FS.trackingDelegate["onWriteToFile"](stream.path); - } catch (e) { - err("FS.trackingDelegate['onWriteToFile']('" + stream.path + "') threw an exception: " + e.message); - } - return bytesWritten; - }, - allocate: function(stream, offset, length) { - if (FS.isClosed(stream)) { - throw new FS.ErrnoError(8); - } - if (offset < 0 || length <= 0) { - throw new FS.ErrnoError(28); - } - if ((stream.flags & 2097155) === 0) { - throw new FS.ErrnoError(8); - } - if (!FS.isFile(stream.node.mode) && !FS.isDir(stream.node.mode)) { - throw new FS.ErrnoError(43); - } - if (!stream.stream_ops.allocate) { - throw new FS.ErrnoError(138); - } - stream.stream_ops.allocate(stream, offset, length); - }, - mmap: function(stream, address, length, position, prot, flags) { - if ((prot & 2) !== 0 && (flags & 2) === 0 && (stream.flags & 2097155) !== 2) { - throw new FS.ErrnoError(2); - } - if ((stream.flags & 2097155) === 1) { - throw new FS.ErrnoError(2); - } - if (!stream.stream_ops.mmap) { - throw new FS.ErrnoError(43); - } - return stream.stream_ops.mmap(stream, address, length, position, prot, flags); - }, - msync: function(stream, buffer, offset, length, mmapFlags) { - if (!stream || !stream.stream_ops.msync) { - return 0; - } - return stream.stream_ops.msync(stream, buffer, offset, length, mmapFlags); - }, - munmap: function(stream) { - return 0; - }, - ioctl: function(stream, cmd, arg) { - if (!stream.stream_ops.ioctl) { - throw new FS.ErrnoError(59); - } - return stream.stream_ops.ioctl(stream, cmd, arg); - }, - readFile: function(path, opts) { - opts = opts || {}; - opts.flags = opts.flags || 0; - opts.encoding = opts.encoding || "binary"; - if (opts.encoding !== "utf8" && opts.encoding !== "binary") { - throw new Error('Invalid encoding type "' + opts.encoding + '"'); - } - var ret; - var stream = FS.open(path, opts.flags); - var stat = FS.stat(path); - var length = stat.size; - var buf = new Uint8Array(length); - FS.read(stream, buf, 0, length, 0); - if (opts.encoding === "utf8") { - ret = UTF8ArrayToString(buf, 0); - } else if (opts.encoding === "binary") { - ret = buf; - } - FS.close(stream); - return ret; - }, - writeFile: function(path, data, opts) { - opts = opts || {}; - opts.flags = opts.flags || 577; - var stream = FS.open(path, opts.flags, opts.mode); - if (typeof data === "string") { - var buf = new Uint8Array(lengthBytesUTF8(data) + 1); - var actualNumBytes = stringToUTF8Array(data, buf, 0, buf.length); - FS.write(stream, buf, 0, actualNumBytes, undefined, opts.canOwn); - } else if (ArrayBuffer.isView(data)) { - FS.write(stream, data, 0, data.byteLength, undefined, opts.canOwn); - } else { - throw new Error("Unsupported data type"); - } - FS.close(stream); - }, - cwd: function() { - return FS.currentPath; - }, - chdir: function(path) { - var lookup = FS.lookupPath(path, { - follow: true - }); - if (lookup.node === null) { - throw new FS.ErrnoError(44); - } - if (!FS.isDir(lookup.node.mode)) { - throw new FS.ErrnoError(54); - } - var errCode = FS.nodePermissions(lookup.node, "x"); - if (errCode) { - throw new FS.ErrnoError(errCode); - } - FS.currentPath = lookup.path; - }, - createDefaultDirectories: function() { - FS.mkdir("/tmp"); - FS.mkdir("/home"); - FS.mkdir("/home/web_user"); - }, - createDefaultDevices: function() { - FS.mkdir("/dev"); - FS.registerDevice(FS.makedev(1, 3), { - read: function() { - return 0; - }, - write: function(stream, buffer, offset, length, pos) { - return length; - } - }); - FS.mkdev("/dev/null", FS.makedev(1, 3)); - TTY.register(FS.makedev(5, 0), TTY.default_tty_ops); - TTY.register(FS.makedev(6, 0), TTY.default_tty1_ops); - FS.mkdev("/dev/tty", FS.makedev(5, 0)); - FS.mkdev("/dev/tty1", FS.makedev(6, 0)); - var random_device = getRandomDevice(); - FS.createDevice("/dev", "random", random_device); - FS.createDevice("/dev", "urandom", random_device); - FS.mkdir("/dev/shm"); - FS.mkdir("/dev/shm/tmp"); - }, - createSpecialDirectories: function() { - FS.mkdir("/proc"); - var proc_self = FS.mkdir("/proc/self"); - FS.mkdir("/proc/self/fd"); - FS.mount({ - mount: function() { - var node = FS.createNode(proc_self, "fd", 16384 | 511, 73); - node.node_ops = { - lookup: function(parent, name) { - var fd = +name; - var stream = FS.getStream(fd); - if (!stream) throw new FS.ErrnoError(8); - var ret = { - parent: null, - mount: { - mountpoint: "fake" - }, - node_ops: { - readlink: function() { - return stream.path; - } - } - }; - ret.parent = ret; - return ret; - } - }; - return node; - } - }, {}, "/proc/self/fd"); - }, - createStandardStreams: function() { - if (Module["stdin"]) { - FS.createDevice("/dev", "stdin", Module["stdin"]); - } else { - FS.symlink("/dev/tty", "/dev/stdin"); - } - if (Module["stdout"]) { - FS.createDevice("/dev", "stdout", null, Module["stdout"]); - } else { - FS.symlink("/dev/tty", "/dev/stdout"); - } - if (Module["stderr"]) { - FS.createDevice("/dev", "stderr", null, Module["stderr"]); - } else { - FS.symlink("/dev/tty1", "/dev/stderr"); - } - var stdin = FS.open("/dev/stdin", 0); - var stdout = FS.open("/dev/stdout", 1); - var stderr = FS.open("/dev/stderr", 1); - assert(stdin.fd === 0, "invalid handle for stdin (" + stdin.fd + ")"); - assert(stdout.fd === 1, "invalid handle for stdout (" + stdout.fd + ")"); - assert(stderr.fd === 2, "invalid handle for stderr (" + stderr.fd + ")"); - }, - ensureErrnoError: function() { - if (FS.ErrnoError) return; - FS.ErrnoError = function ErrnoError(errno, node) { - this.node = node; - this.setErrno = function(errno) { - this.errno = errno; - for (var key in ERRNO_CODES) { - if (ERRNO_CODES[key] === errno) { - this.code = key; - break; - } - } - }; - this.setErrno(errno); - this.message = ERRNO_MESSAGES[errno]; - if (this.stack) { - Object.defineProperty(this, "stack", { - value: new Error().stack, - writable: true - }); - this.stack = demangleAll(this.stack); - } - }; - FS.ErrnoError.prototype = new Error(); - FS.ErrnoError.prototype.constructor = FS.ErrnoError; - [ 44 ].forEach(function(code) { - FS.genericErrors[code] = new FS.ErrnoError(code); - FS.genericErrors[code].stack = "
Chess Online - Duel Friends Online: A Fun and Competitive Chess Game for Everyone
-Do you love chess and want to challenge your friends or other players from around the world? Do you want to improve your chess skills and have fun at the same time? If you answered yes to any of these questions, then you should try Chess Online - Duel Friends Online, a modern and user-friendly chess game that offers a variety of game modes, features, and rewards. In this article, we will tell you everything you need to know about this amazing chess app, including what it is, why you should play it, how to download and play it, and more. Let's get started!
-Introduction
-What is Chess Online - Duel Friends Online?
-Chess Online - Duel Friends Online is a chess app that lets you play chess online for free with anyone in the world. It is developed by Hagstrom Dev and has over 10 million downloads on Google Play. It is also available on the App Store for iOS devices. This app offers a competitive chess environment with rankings, missions, and rewards. You can play live chess with friends or strangers, or play against CPU or local multiplayer. You can also chat with your opponents, customize your avatar, and complete missions to earn rewards.
-chess online - duel friends online download
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Why play Chess Online - Duel Friends Online?
-Chess is one of the oldest and most popular board games in history. It is a game of strategy, logic, and skill that can help you improve your memory, concentration, problem-solving, creativity, and more. Playing chess can also be fun and relaxing, especially when you play with someone you know or someone who shares your passion for the game. Chess Online - Duel Friends Online is a great way to enjoy chess anytime and anywhere. You can play with your friends or make new ones online. You can also challenge yourself with different game modes and difficulty levels. You can also earn rewards and unlock new avatars as you play. Chess Online - Duel Friends Online is a chess game that has something for everyone.
-Features of Chess Online - Duel Friends Online
-Play online with friends or strangers
-One of the main features of Chess Online - Duel Friends Online is that it allows you to play online with anyone in the world. You can choose to play with your friends or find a random opponent through matchmaking. You can also chat with your friends or opponents during the game. You can also compete in the ranking system, which shows how you measure up to other players globally. Playing online is a great way to test your chess skills and learn from others.
-Play against CPU or local multiplayer
-If you prefer to play offline or by yourself, you can also play against CPU or local multiplayer. Playing against CPU is a good way to practice and improve your chess skills. You can choose from different difficulty levels, from easy to hard. Playing local multiplayer is a good way to have fun with your family or friends. You can play on the same device by taking turns making your moves. Playing offline is a good way to enjoy chess without any distractions or interruptions.
-Complete missions and earn rewards
-Another feature of Chess Online - Duel Friends Online is that it has missions that you can complete to earn rewards. Missions are tasks that require you to achieve certain goals, such as reaching a specific level, winning under certain conditions, playing a certain number of games, and more. Completing missions will give you rewards such as experience points, coins, name colors, and more. Rewards can help
you improve your game and customize your profile. Missions are a great way to add some challenge and excitement to your chess experience.
-Customize your avatar and level up
-Another feature of Chess Online - Duel Friends Online is that it allows you to customize your avatar and level up. You can choose from different avatars, such as animals, robots, superheroes, and more. You can also unlock new avatars by completing missions or buying them with coins. You can also level up by playing games and earning experience points. Leveling up will give you access to new name colors, which show your progress and status. Customizing your avatar and leveling up is a great way to express yourself and show off your chess skills.
-How to download and play Chess Online - Duel Friends Online
-Download from Google Play or App Store
-The first step to play Chess Online - Duel Friends Online is to download it from Google Play or App Store. The app is free to download and play, but it contains ads and in-app purchases. You can remove the ads by paying a small fee or watching a video. You can also buy coins or premium avatars with real money. To download the app, you need to have a compatible device and an internet connection. The app requires Android 4.4 or later or iOS 9.0 or later. The app size is about 50 MB for Android and 100 MB for iOS.
-Choose your game mode and opponent
-The next step to play Chess Online - Duel Friends Online is to choose your game mode and opponent. You can choose from four game modes: online, CPU, local multiplayer, or missions. You can also choose your opponent by inviting your friends, finding a random player, or selecting a CPU level. You can also choose the time limit for each move, from 1 minute to 30 minutes. You can also choose the board style, from classic to modern.
-chess online - play with friends for free
-chess online - challenge players worldwide
-chess online - compete in rankings and missions
-chess online - collect avatars and rewards
-chess online - learn and improve your skills
-chess online - simple and user-friendly graphics
-chess online - fast and modern version of the game
-chess online - chat with your opponents
-chess online - practice against AI
-chess online - play local multiplayer
-chess online - free to play and download
-chess online - ultimate game of strategy and guile
-chess online - how to play and win
-chess online - best app for chess lovers
-chess online - tips and tricks for beginners
-chess online - advanced tactics for experts
-chess online - join the chess community
-chess online - realistic and classic board game
-chess online - fun and addictive gameplay
-chess online - customize your character and style
-chess online - enjoy the game anytime, anywhere
-chess online - test your brain and logic
-chess online - master the rules and moves
-chess online - solve puzzles and challenges
-chess online - earn experience and level up
-chess online - duel friends or random opponents
-chess online - checkmate the king and win
-chess online - easy to use and intuitive controls
-chess online - no ads or in-app purchases
-chess online - compatible with all devices
-chess online - train your memory and concentration
-chess online - explore different modes and features
-chess online - discover new strategies and techniques
-chess online - watch replays and analyze your games
-chess online - invite your friends and share your results
-chess online - get feedback and ratings from other players
-chess online - follow the latest news and updates on the game
-chess online - access your games and stats from any device
-chess online - create your own profile and avatar
-chess online - unlock achievements and trophies
-chess online - support the developer and rate the game
-chess online - report bugs and issues to improve the game
-chess online - find match vs random opponent (matchmaking, real-time)
-play with friends! (real-time)
Make your moves and enjoy the game
-The final step to play Chess Online - Duel Friends Online is to make your moves and enjoy the game. You can make your moves by tapping on the piece you want to move and then tapping on the square you want to move it to. You can also undo your moves if you make a mistake or change your mind. You can also chat with your opponent by typing messages or using emojis. You can also pause the game if you need a break or resume it later. You can also forfeit the game if you want to end it early or accept a draw if you agree with your opponent. You can also view the game history, statistics, and ratings after the game is over.
-Conclusion
-Summary of the main points
-In conclusion, Chess Online - Duel Friends Online is a fun and competitive chess game that offers a variety of game modes, features, and rewards. You can play online with friends or strangers, or play offline against CPU or local multiplayer. You can also complete missions and earn rewards, customize your avatar and level up, chat with your opponents, and more. Chess Online - Duel Friends Online is a chess game that has something for everyone.
-Call to action and final thoughts
-If you are interested in playing Chess Online - Duel Friends Online, you can download it from Google Play or App Store for free today. You can also visit their website or follow them on Facebook or Twitter for more information and updates. Chess Online - Duel Friends Online is a chess game that will keep you entertained and challenged for hours. Whether you are a beginner or an expert, you will find a suitable opponent and a enjoyable game mode in this app. Chess Online - Duel Friends Online is more than just a chess game; it is a chess community that welcomes everyone.
-FAQs
--
-
- Q: How do I invite my friends to play with me? -
- A: You can invite your friends by tapping on the "Friends" button on the main menu and then tapping on the "Invite" button next to their name. You can also invite them by sharing a link or a code through social media or messaging apps. -
- Q: How do I earn coins in the game? -
- A: You can earn coins by completing missions, winning games, watching videos, or buying them with real money. -
- Q: How do I change my avatar or name color? -
- A: You can change your avatar or name color by tapping on the "Profile" button on the main menu and then tapping on the "Edit" button next to your avatar or name color. You can also unlock new avatars or name colors by completing missions or buying them with coins. -
- Q: How do I report a bug or a problem in the game? -
- A: You can report a bug or a problem by tapping on the "Settings" button on the main menu and then tapping on the "Feedback" button. You can also contact the developer by email at hagstrom.dev@gmail.com or by visiting their website at https://hagstrom.dev/. -
- Q: How do I learn more about chess rules and strategies? -
- A: You can learn more about chess rules and strategies by tapping on the "Help" button on the main menu and then tapping on the "Chess Rules" or "Chess Tips" button. You can also visit some online resources, such as https://www.chess.com/learn or https://www.chesskid.com/. -
-
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Dobie Grays Drift Away MP3 The Ultimate Guide to Downloading and Streaming.md b/spaces/congsaPfin/Manga-OCR/logs/Dobie Grays Drift Away MP3 The Ultimate Guide to Downloading and Streaming.md deleted file mode 100644 index 20e405f7b9fc94dbb6b08052bc03be748828b514..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Dobie Grays Drift Away MP3 The Ultimate Guide to Downloading and Streaming.md +++ /dev/null @@ -1,150 +0,0 @@ - -
Dobie Gray Drift Away MP3 Download
-Do you love soulful music that can make you feel relaxed and happy? Do you want to listen to one of the most iconic songs of the 1970s that has been covered by hundreds of artists over the years? Do you want to download it for free and legally on your computer or MP3 player? If you answered yes to any of these questions, then this article is for you.
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In this article, you will learn more about Dobie Gray, the singer behind the classic hit Drift Away, and why his song is so popular and timeless. You will also discover the best free music download sites that offer Drift Away by Dobie Gray in MP3 format, as well as the best MP3 players that you can use to enjoy this song anytime and anywhere. So sit back, relax, and let us help you drift away with Dobie Gray.
-Who is Dobie Gray and why is his song Drift Away so popular?
-Dobie Gray was an American singer-songwriter who was born in 1940 in Texas. He started his musical career in the early 1960s as a doo-wop singer and later moved to Los Angeles, where he recorded several songs for various labels. He also worked as an actor in films and musicals.
-However, his breakthrough came in 1973 when he released his album Drift Away, which featured the title track as its lead single. The song was written by Mentor Williams, a country songwriter who was also the brother of Paul Williams. The song was originally recorded by John Henry Kurtz in 1972, but it was Dobie Gray's version that became a huge hit.
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Drift Away is a soft rock song that expresses the desire to escape from the troubles of life through music. The song has a catchy chorus that goes "Give me the beat boys and free my soul / I wanna get lost in
your rock and roll and drift away". The song resonated with many listeners who felt the same way and appreciated the power of music to heal and inspire. The song reached number five on the Billboard Hot 100 chart and became Dobie Gray's signature song. It also earned him a Grammy nomination for Best Male Pop Vocal Performance.
-Drift Away has been covered by many artists over the years, including Rod Stewart, Waylon Jennings, Ray Charles, Michael Bolton, Uncle Kracker, and Dolly Parton. The most successful cover was by Uncle Kracker, who featured Dobie Gray on his version in 2003. The song reached number nine on the Billboard Hot 100 chart and stayed on the chart for 28 weeks, breaking the record for the longest-running single in history at that time.
-Drift Away is considered to be one of the most enduring songs of the 1970s and one of the greatest songs of all time. It has been inducted into the Grammy Hall of Fame and the Rock and Roll Hall of Fame. It has also been featured in many movies, TV shows, commercials, and video games. Drift Away is a song that transcends genres, generations, and cultures, and continues to touch the hearts of millions of people around the world.
-How to download Drift Away by Dobie Gray for free and legally?
-If you want to download Drift Away by Dobie Gray for free and legally, you have several options to choose from. There are many free music download sites that offer this song in MP3 format, which is compatible with most devices and players. However, not all of these sites are safe, reliable, or legal. Some of them may contain viruses, malware, or spyware that can harm your computer or device. Some of them may also violate the copyright laws or the terms of service of the artists or labels.
-To avoid these risks, you should only download music from reputable and trustworthy sites that respect the rights of the artists and offer high-quality MP3 files. Here are some of the best free music download sites that offer Drift Away by Dobie Gray in MP3 format:
-SoundCloud
-SoundCloud is one of the most popular music streaming platforms in the world, with over 200 million tracks from various genres and artists. SoundCloud also allows artists to upload their music and offer it for free download. You can find many versions of Drift Away by Dobie Gray on SoundCloud, including the original version, the Uncle Kracker version, and various remixes and covers. To download a song from SoundCloud, you need to create a free account and look for the download button below the track. If there is no download button, it means that the artist has not enabled it for that track. You can also use a third-party tool like SoundCloud Downloader to download any track from SoundCloud.
-You can visit SoundCloud here: https://soundcloud.com/
-Last.fm
-Last.fm is a music discovery service that tracks what you listen to and recommends you new music based on your taste. Last.fm also provides free music downloads from various genres and artists. You can find Drift Away by Dobie Gray on Last.fm, as well as other songs by him and similar artists. To download a song from Last.fm, you need to create a free account and look for the download link below the track. You can also browse through different categories and tags to find more free music downloads.
-You can visit Last.fm here: https://www.last.fm/
-NoiseTrade
-NoiseTrade is a music platform that connects artists and fans, allowing them to exchange music for email addresses and postal codes. NoiseTrade helps artists grow their fan base and exposure, while fans get to discover new music and support their favorite artists. You can find Drift Away by Dobie Gray on NoiseTrade, as well as other songs by him and similar artists. To download a song from NoiseTrade, you need to enter your email address and postal code, and you will receive a link to download the song in MP3 format. You can also tip the artist if you want to show your appreciation.
-You can visit NoiseTrade here: https://noisetrade.com/
-Jamendo Music
-Jamendo Music is a community of independent artists who share their music for free download under Creative Commons licenses. Jamendo Music offers over 600,000 tracks from various genres and artists. You can find Drift Away by Dobie Gray on Jamendo Music, as well as other songs by him and similar artists. To download a song from Jamendo Music, you need to create a free account and look for the download button below the track. You can also stream the song online or add it to your playlist.
-You can visit Jamendo Music here: https://www.jamendo.com/
-Bandcamp
-Bandcamp is a music marketplace that lets artists set their own prices for their music, including free or pay-what-you-want options. Bandcamp supports independent artists and gives them 80% of the revenue from their sales. You can find Drift Away by Dobie Gray on Bandcamp, as well as other songs by him and similar artists. To download a song from Bandcamp, you need to enter the amount you want to pay, or zero if it is free, and you will receive a link to download the song in MP3 format. You can also stream the song online or buy it in other formats.
-You can visit Bandcamp here: https://bandcamp.com/
-How to enjoy Drift Away by Dobie Gray on your MP3 player?
-Once you have downloaded Drift Away by Dobie Gray in MP3 format, you can transfer it to your MP3 player and enjoy it anytime and anywhere. However, not all MP3 players are created equal. Some of them may have better sound quality, battery life, storage capacity, or features than others. To help you choose the best MP3 player for your needs, we have compared some of the best MP3 players available in the market, with their features, prices, and pros and cons.
-Sony Walkman NW-A55
-The Sony Walkman NW-A55 is a high-resolution audio player that delivers crisp and clear sound quality. It has a sleek design, a touch screen, and a long battery life of up to 45 hours. It also supports various audio formats, such as MP3, FLAC, WAV, AAC, and DSD. It has a storage capacity of 16 GB, which can be expanded with a microSD card. It also has Bluetooth and NFC connectivity, which allows you to pair it with wireless headphones or speakers.
-The Sony Walkman NW-A55 costs around $220 and has the following pros and cons:
-| Pros | Cons |
|---|---|
| Excellent sound quality | Expensive |
| Elegant design | No Wi-Fi or streaming services |
| Long battery life | No headphone jack |
| Expandable storage | No FM radio |
| Bluetooth and NFC connectivity | No voice control |
Apple iPod Touch (7th Generation)
-The Apple iPod Touch (7th Generation) is a versatile device that combines an MP3 player, a gaming console, a camera, and a mini-computer. It has a 4-inch retina display, a 12-megapixel camera, and an A10 Fusion chip that enables fast performance and smooth gaming. It also supports various audio formats, such as MP3, AAC, WAV, AIFF, and Apple Lossless. It has a storage capacity of 32 GB, 128 GB, or 256 GB. It also has Wi-Fi and Bluetooth connectivity, which allows you to access streaming services like Apple Music, Spotify, Pandora, and more.
-The Apple iPod Touch (7th Generation) costs around $200 for the 32 GB model and has the following pros and cons:
-| Pros | Cons |
|---|---|
| Versatile device | Short battery life of up to 8 hours |
| High-quality display and camera | No expandable storage |
| Fast performance and gaming | No FM radio or voice control |
| Wi-Fi and Bluetooth connectivity | No water resistance or durability |
| Access to streaming services | Requires iTunes for file transfer |
SanDisk Clip Sport Plus
-The SanDisk Clip Sport Plus is a compact and durable MP3 player that is ideal for sports and outdoor activities. It has a water-resistant design, a clip that attaches to your clothing or bag, and a 1.44-inch color screen. It also supports various audio formats, such as MP3, AAC, FLAC, WAV, and WMA. It has a storage capacity of 16 GB, which can be expanded with a microSD card. It also has Bluetooth and FM radio connectivity, which allows you to pair it with wireless headphones or speakers or listen to your favorite radio stations.
-The SanDisk Clip Sport Plus costs around $60 and has the following pros and cons:
-| Pros | Cons |
|---|---|
| Compact and durable design | Small and dim screen |
| Water-resistant | No touch screen or camera |
| Expandable storage | No Wi-Fi or streaming services |
| Bluetooth and FM radio connectivity | No voice control or equalizer |
| Affordable price | Limited audio formats supported |
AGPTEK A02
-The AGPTEK A02 is a budget-friendly MP3 player that offers a large storage capacity, a simple interface, and a long battery life. It has a 1.8-inch color screen, a button control, and a lock screen function. It also supports various audio formats, such as MP3, WMA, WAV, APE, and FLAC. It has a storage capacity of 8 GB, which can be expanded up to 128 GB with a microSD card. It also has an FM radio function, which allows you to listen to your favorite radio stations.
-The AGPTEK A02 costs around $25 and has the following pros and cons:
-| Pros | Cons |
|---|---|
| Large storage capacity | No Bluetooth or Wi-Fi connectivity |
| Simple interface | No touch screen or camera |
| Long battery life of up to 70 hours | No water resistance or durability |
| FM radio function | No voice control or equalizer |
| Cheap price | Average sound quality |
Conclusion
-Dobie Gray's Drift Away is a timeless song that can make you feel relaxed and happy. It is one of the most iconic songs of the 1970s and one of the greatest songs of all time. You can download it for free and legally from various free music download sites, such as SoundCloud, Last.fm, NoiseTrade, Jamendo Music, and Bandcamp. You can also enjoy it on your MP3 player, such as the Sony Walkman NW-A55, the Apple iPod Touch (7th Generation), the SanDisk Clip Sport Plus, or the AGPTEK A02.
-We hope this article has helped you learn more about Dobie Gray, Drift Away, and MP3 players. Now you can download Drift Away by Dobie Gray and enjoy it on your MP3 player anytime and anywhere. Just give him the beat boys and free your soul.
-FAQs
-Here are some frequently asked questions about Dobie Gray, Drift Away, and MP3 players:
-Q: When did Dobie Gray die and how?
-A: Dobie Gray died on December 6, 2011 at the age of 71. He died from complications of cancer at his home in Nashville, Tennessee.
-Q: Who wrote Drift Away?
-A: Drift Away was written by Mentor Williams, a country songwriter who was also the brother of Paul Williams. Mentor Williams wrote the song for John Henry Kurtz, who recorded it in 1972. However, it was Dobie Gray's version that became a hit in 1973.
-Q: What is the difference between MP3 and other audio formats?
-A: MP3 is a compressed audio format that reduces the file size without losing much of the sound quality. Other audio formats may have higher sound quality but larger file size, such as WAV or FLAC. Some audio formats may have lower sound quality but smaller file size, such as WMA or AAC.
-Q: How can I transfer music from my computer to my MP3 player?
-A: There are different ways to transfer music from your computer to your MP3 player depending on the type of device and software you have. Some common methods are:
--
-
- Using a USB cable to connect your MP3 player to your computer and drag and drop the music files to the device folder
- Using a music management software like iTunes, Windows Media Player, or MusicBee to sync your music library with your MP3 player -
- Using a cloud service like Google Drive, Dropbox, or OneDrive to upload your music files to your online account and download them to your MP3 player -
Q: How can I improve the sound quality of my MP3 player?
-A: There are several ways to improve the sound quality of your MP3 player, such as:
--
-
- Using high-quality headphones or speakers that match your MP3 player's output and impedance -
- Using a higher bitrate or a lossless audio format for your music files, such as 320 kbps MP3 or FLAC -
- Using an equalizer or a sound enhancer app to adjust the frequency and volume of your music -
- Using a portable amplifier or a DAC (digital-to-analog converter) to boost the signal and clarity of your music -
-
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Stumble Guys for Windows 7 and Dash Slide and Jump Past Opponents in Crazy Obstacles.md b/spaces/congsaPfin/Manga-OCR/logs/Download Stumble Guys for Windows 7 and Dash Slide and Jump Past Opponents in Crazy Obstacles.md deleted file mode 100644 index 01fc4668fefedc58bea3685c10cbc88726b1f8b2..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download Stumble Guys for Windows 7 and Dash Slide and Jump Past Opponents in Crazy Obstacles.md +++ /dev/null @@ -1,109 +0,0 @@ -
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Stumble Guys Download Windows 7: How to Play the Ultimate Knockout Game on Your PC
-If you are looking for a fun and chaotic multiplayer game that will make you laugh and rage at the same time, you might want to check out Stumble Guys. This game is inspired by the popular Fall Guys, but it is available for free on Android and iOS devices. However, if you want to enjoy this game on a bigger screen and with better controls, you can also play it on your PC using an emulator. In this article, we will show you how to download and play Stumble Guys on Windows 7, as well as some of the game features, tips and tricks, and alternatives that you can try.
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How to Download and Play Stumble Guys on Windows 7 Using an Emulator
-Stumble Guys is not officially supported on Windows 7, but you can still play it on your PC using an Android emulator. An emulator is a software that allows you to run Android apps and games on your computer, as if you were using a mobile device. There are many emulators that you can choose from, but we recommend using BlueStacks, as it is one of the most popular and reliable ones.
-To download and play Stumble Guys on Windows 7 using BlueStacks, follow these steps:
--
-
- Download and install BlueStacks from its official website. Make sure that your PC meets the minimum system requirements for running the emulator. -
- Launch BlueStacks and sign in with your Google account. If you don't have one, you can create one for free. -
- Go to the Google Play Store app on BlueStacks and search for Stumble Guys. Alternatively, you can use the link provided by BlueStacks to download the game directly. -
- Click on the Install button and wait for the game to download and install on your emulator. -
- Once the installation is complete, click on the Stumble Guys icon on the home screen of BlueStacks to start playing. -
You can also customize your controls using the Keymapping Tool on BlueStacks. This tool allows you to create intuitive control schemes using your mouse and keyboard. For example, you can use the WASD keys to move your character, the spacebar to jump, and the right mouse button to toggle free view. You can also adjust the graphics settings and performance mode of BlueStacks to optimize your gaming experience.
-Stumble Guys Game Features and Modes
-Stumble Guys is an online battle royale party game that pits up to 32 players against each other in a series of obstacle courses. The objective is to be the last one standing at the end of each round, while avoiding being knocked out by other players or by the environment. The game features 17 unique obstacle courses, each with its own challenges and hazards. Some of the courses include:
--
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- Race: A straightforward sprint to the finish line, where you have to dodge moving platforms, swinging balls, spinning blades, and other obstacles. -
- Dodgeball: A team-based mode where you have to throw balls at your opponents or catch them to eliminate them. -
- Hexagon: A survival mode where you have to stay on a hexagonal grid that disappears as you step on it. -
- Tail Tag: A capture-the-flag mode where you have to grab a tail from another player or keep yours until the timer runs out. -
- Soccer: A team-based mode where you have to score goals by kicking a giant ball into your opponent's net. -
In addition to these modes, Stumble Guys also allows you to customize your character's appearance with various outfits and emotes that you can unlock by playing or by purchasing with in-game currency. You can also play with your friends or join a private room with a code. The game also supports voice chat, so you can communicate with your teammates or trash talk your enemies.
-How to play Stumble Guys on PC with BlueStacks emulator
-Stumble Guys official website and online browser game
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Stumble Guys Tips and Tricks to Win Your Matches
-Stumble Guys may look simple and silly, but it can also be quite challenging and competitive. If you want to increase your chances of winning, here are some tips and tricks that you can follow:
--
-
- Learn the courses: The best way to improve your skills is to familiarize yourself with the different courses and their obstacles. Try to memorize the layout, the timing, and the best routes to take. You can also watch other players or streamers to learn from their strategies. -
- Be agile and flexible: Stumble Guys is a game of speed and agility, so you have to be quick and nimble on your feet. Avoid getting stuck in crowds or bottlenecks, and use the jump and dive buttons to overcome obstacles or gaps. You can also use the free view button to look around and plan your moves. -
- Use the physics to your advantage: Stumble Guys has a realistic physics engine that affects how you and the objects in the game behave. You can use this to your advantage by pushing or pulling other players, bouncing off walls or platforms, or using momentum to gain speed or distance. -
- Play smart and dirty: Stumble Guys is not a game of fair play, so you have to be ready to use any means necessary to win. You can sabotage, troll, or team up with other players to eliminate your rivals. You can also use emotes to taunt or distract them. However, be careful not to get karma-ed by your own actions. -
- Have fun and don't give up: Stumble Guys is a game of fun and chaos, so don't take it too seriously or get frustrated by your losses. Enjoy the hilarious moments and the random outcomes that the game offers. And remember, there is always another round to try again. -
Stumble Guys Alternatives for Mobile and PC
-If you love Stumble Guys, you might also want to try some of the other games that are similar or inspired by it. Here are some of the alternatives that you can play on your mobile or PC:
-| Game | -Description | -Platform | -
|---|---|---|
| Fall Guys: Ultimate Knockout | -The original game that started the craze, Fall Guys is a massively multiplayer party game that features up to 60 players competing in various mini-games. It has more modes, maps, and customization options than Stumble Guys. | -PC, PS4, Xbox One, Nintendo Switch | -
| Gang Beasts | -A hilarious physics-based brawler that pits up to 8 players against each other in various environments. You can punch, kick, grab, throw, or drag your opponents into various hazards. | -PC, PS4, Xbox One | -
| Human: Fall Flat | -A puzzle-platformer that challenges you to control a wobbly human through various levels. You can play solo or with up to 8 players online or locally. You can also customize your character and create your own levels. | -PC, PS4, Xbox One, Nintendo Switch, Android, iOS | -
| Totally Reliable Delivery Service | -A sandbox game that tasks you with delivering packages in a chaotic world. You can play solo or with up to 4 players online or locally. You can also explore the world and interact with various vehicles and objects. | -PC, PS4, Xbox One, Nintendo Switch, Android, iOS | -
| Party Animals | -A cute and cuddly party game that features adorable animals fighting each other in various modes and maps. You can play solo or with up to 8 players online or locally. You can also unlock new outfits and accessories for your animals. | -PC (coming soon) | -
Conclusion: Summary and Final Thoughts
-Stumble Guys is a fun and addictive multiplayer game that will keep you entertained for hours. Whether you play it on your mobile device or on your PC using an emulator, you will enjoy the colorful graphics, the wacky physics, and the hilarious gameplay. You will also love the variety of modes, maps, and customization options that the game offers. And if you want to spice up your gaming experience, you can also try some of the tips and tricks that we shared in this article.
If you are looking for a game that will make you laugh, rage, and have fun with your friends or strangers online, Stumble Guys is the game for you. You can download it for free on your Android or iOS device, or you can play it on your PC using an emulator like BlueStacks. You can also check out some of the other games that are similar or inspired by Stumble Guys, such as Fall Guys, Gang Beasts, Human: Fall Flat, Totally Reliable Delivery Service, and Party Animals. Whatever game you choose, we hope you have a great time playing and stumbling.
-FAQs: Five Common Questions and Answers About Stumble Guys
-Here are some of the frequently asked questions and answers about Stumble Guys that you might find useful:
-Q: Is Stumble Guys free to play?
-A: Yes, Stumble Guys is free to play on Android and iOS devices. However, the game contains ads and in-app purchases that you can use to buy more coins or remove the ads. You can also play it for free on your PC using an emulator like BlueStacks.
-Q: How many players can play Stumble Guys online?
-A: Stumble Guys supports up to 32 players online in each match. You can join a random match or create a private room with a code to invite your friends. You can also use the voice chat feature to communicate with your teammates or opponents.
-Q: How do I unlock more outfits and emotes in Stumble Guys?
-A: You can unlock more outfits and emotes in Stumble Guys by playing the game and earning coins. You can also buy more coins with real money or watch ads to get free coins. You can then use the coins to buy outfits and emotes from the shop or spin the wheel for a chance to get random items.
-Q: How do I update Stumble Guys on my device or emulator?
-A: To update Stumble Guys on your device or emulator, you need to go to the Google Play Store app and check for updates. If there is an update available, you can download and install it from there. You can also enable the auto-update feature to get the latest version of the game automatically.
-Q: How do I report a bug or a problem in Stumble Guys?
-A: If you encounter a bug or a problem in Stumble Guys, you can report it to the developers by sending an email to support@kitkagames.com. You can also visit their website or follow them on their social media accounts for more information and updates about the game.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get Google Play Store and Services on Your Redmi 7 7a with This Simple Guide.md b/spaces/congsaPfin/Manga-OCR/logs/Get Google Play Store and Services on Your Redmi 7 7a with This Simple Guide.md deleted file mode 100644 index 04189a0c83990c8878429fc302c0a2c77be82ee2..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Get Google Play Store and Services on Your Redmi 7 7a with This Simple Guide.md +++ /dev/null @@ -1,101 +0,0 @@ -
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How to Download and Install Google Play Store on Redmi 7A
-Introduction
-If you have bought a Redmi 7A smartphone from China, you may have noticed that it does not come with the Google Play Store pre-installed. This means that you cannot access the millions of apps, games, movies, books, and other content that are available on the official Android app store. You may also miss some of the features and services that rely on Google apps, such as Gmail, Maps, Photos, Drive, YouTube, and more.
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Fortunately, there is a way to get the Google Play Store on your Redmi 7A without rooting or flashing a custom ROM. You can simply download and install an APK file that contains all the necessary components for the Google Play Store to work on your device. An APK file is a package that contains the installation files for an Android app. You can install an APK file on your device manually, without using the Google Play Store.
-In this article, we will show you three methods to download and install the Google Play Store on your Redmi 7A using an APK file. You can choose the method that suits you best, depending on your preferences and resources. All you need is a stable internet connection, a file manager app, and some patience. Follow the steps below and enjoy the full Android experience on your Redmi 7A.
-Method 1: Download Google Play Store from Mi App Store
-The easiest way to get the Google Play Store on your Redmi 7A is to use the Mi App Store, which is Xiaomi's official app store for its devices. The Mi App Store has an app called Google Installer, which can install all the required Google apps and services on your device in one go. Here's how to use it:
-Step 1: Open Mi App Store and search for Google
-Launch the Mi App Store app on your Redmi 7A. It should be on your home screen or in your app drawer. Tap on the search icon at the top right corner and type "google" (without quotes) in the search box. You may need to switch to the Chinese keyboard if you don't see it.
-Step 2: Install Google Installer app from the search results
-Scroll down to the bottom of the search results and look for an app with a blue icon that has a white "G" inside it. This is the Google Installer app that you need. Tap on the install button next to it and wait for are the steps to follow:
-Step 1: Enable unknown sources for your web browser
-Before you can install the Google Play Store from a web browser, you need to enable unknown sources for your web browser. This will allow you to install apps from sources other than the Google Play Store. To do this, go to your phone's settings and tap on "Apps". Find your web browser app (such as Chrome, Firefox, or Opera) and tap on it. Tap on "Install unknown apps" and toggle the switch to "On". You may see a warning message saying that installing unknown apps can harm your device. Tap on "OK" to confirm.
-Step 2: Install a file manager app to locate the APK file
-Next, you need to install a file manager app on your Redmi 7A. A file manager app will help you find and open the APK file that you will download from the web browser. You can use any file manager app that you like, such as ES File Explorer, Solid Explorer, or Mi File Manager. You can download a file manager app from the Mi App Store or from a web browser. Just make sure that you download it from a trusted source and enable unknown sources for the app.
-Step 3: Download Google Play Store APK file from a reputable website
-Now, you need to download the Google Play Store APK file from a reputable website. There are many websites that offer APK files for various apps, but not all of them are safe and reliable. Some of them may contain malware or viruses that can harm your device or steal your data. Therefore, you need to be careful and choose a website that has positive reviews and ratings from other users. One of the websites that we recommend is APKMirror.com, which is a popular and trusted source for APK files.
-To download the Google Play Store APK file from APKMirror.com, open your web browser and go to https://www.apkmirror.com/apk/google-inc/google-play-store/. You will see a list of versions of the Google Play Store app. Choose the latest version or the one that is compatible with your device. Tap on the download button and wait for the download to complete.
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Step 4: Find and open the APK file to install Google Play Store
-Once the download is finished, you need to find and open the APK file to install Google Play Store on your Redmi 7A. To do this, open your file manager app and locate the folder where you saved the APK file. It may be in the "Downloads" folder or in another folder that you specified. Tap on the APK file to open it. You may see a prompt asking you to confirm the installation. Tap on "Install" and wait for the installation to complete.
-Step 5: Log in to your Google account and enjoy Google Play Store
-After the installation is done, you will see the Google Play Store icon on your home screen or app drawer. Tap on it to launch it. You will be asked to log in to your Google account or create a new one if you don't have one. Follow the instructions on the screen and log in to your account. You can now access the Google Play Store and download any app or game you want.
-Method 3: Transfer Google Play Store from your computer via USB
-If you don't have access to the internet or you prefer to use your computer, you can also transfer the Google Play Store from your computer to your Redmi 7A via USB. This method requires a USB cable, a computer with internet access, and a file manager app on your phone. Here are the steps to follow:
-Step 1: Download Google Play Store APK file on your computer
-The first step is to download the Google Play Store APK file on your computer. You can use any web browser that you like, such as Chrome, Firefox, or Edge. Go to https://www.apkmirror.com/apk/google-inc/google-play-store/ and choose the latest version or the one that is compatible with your device. Click on the download button and save the APK file to your computer.
-Step 2: Connect your phone to your computer with a USB cable
-The next step is to connect your Redmi 7A to your computer with a USB cable. Make sure that your phone is unlocked and that you have enabled USB debugging on your phone. To enable USB debugging, go to your phone's settings and tap on "About phone". Tap on "MIUI version" seven times until you see a message saying "You are now a developer". Go back to the settings and tap on "Additional settings". Tap on "Developer options" and toggle the switch to "On". Scroll down and find "USB debugging" and toggle the switch to "On". You may see a prompt asking you to allow USB debugging. Tap on "OK" to confirm.
-Once you have enabled USB debugging, plug your phone into your computer with a USB cable. You may see a notification on your phone asking you to choose a USB mode. Choose "File transfer" or "MTP" mode. You may also see a prompt asking you to allow access to your phone's data. Tap on "Allow" or "OK" to confirm.
-Step 3: Copy the APK file to your phone's storage
-The third step is to copy the APK file that you downloaded on your computer to your phone's storage. To do this, open your computer's file explorer and find the folder where you saved the APK file. Right-click on the APK file and choose "Copy". Then, find your phone's storage in the file explorer. It may be labeled as "Redmi 7A" or something similar. Open it and find a folder where you want to paste the APK file. You can use any folder that you like, such as "Downloads", "Documents", or "Pictures". Right-click on the folder and choose "Paste". Wait for the file transfer to complete.
-Step 4: Find and open the APK file to install Google Play Store
-The fourth step is to find and open the APK file that you copied to your phone's storage to install Google Play Store on your Redmi 7A. To do this, unplug your phone from your computer and open your file manager app on your phone. Locate the folder where you pasted the APK file and tap on it. You may see a prompt asking you to enable unknown sources for your file manager app. This is necessary because you are installing an app from outside the Google Play Store. Tap on "Allow" or "OK" for each prompt and continue with the installation. You may also see some prompts asking you to grant some permissions to the Google Play Store app. Tap on "Allow" or "Accept" for each prompt and continue with the installation.
-Step 5: Log in to your Google account and enjoy Google Play Store
-After the installation is done, you will see the Google Play Store icon on your home screen or app drawer. Tap on it to launch it. You will be asked to log in to your Google account or create a new one if you don't have one. Follow the instructions on the screen and log in to your account. You can now access the Google Play Store and download any app or game you want.
-Conclusion
-In this article, we have shown you three methods to download and install Google Play Store on your Redmi 7A using an APK file. You can choose the method that suits you best, depending on your preferences and resources. All you need is a stable internet connection, a file manager app, and some patience. By following these steps, you can enjoy the full Android experience on your Redmi 7A without rooting or flashing a custom ROM.
-We hope that this article was helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-FAQs
-Here are some frequently asked questions about downloading and installing Google Play Store on Redmi 7A:
-Q: Is it safe to install Google Play Store from an APK file?
-A: Yes, it is safe as long as you download the APK file from a reputable website, such as APKMirror.com, which is a popular and trusted source for APK files. However, you should always be careful when installing apps from unknown sources, as some of them may contain malware or viruses that can harm your device or steal your data. Therefore, you should always scan the APK file with an antivirus app before installing it.
-Q: Will installing Google Play Store void my warranty?
-A: No, installing Google Play Store from an APK file will not void your warranty, as it does not require rooting or flashing a custom ROM. However, you should be aware that some apps or games that you download from the Google Play Store may not be compatible with your device or may cause some issues. Therefore, you should always check the reviews and ratings of the apps or games before downloading them and uninstall them if they cause any problems.
-Q: How can I update Google Play Store after installing it?
-A: After installing Google Play Store from an APK file, you can update it automatically or manually. To update it automatically, go to your phone's settings and tap on "Apps". Find Google Play Store and tap on it. Tap on "Advanced" and toggle the switch to "On" for "Auto-update app". This will allow Google Play Store to update itself whenever there is a new version available. To update it manually, go to https://www.apkmirror.com/apk/google-inc/google-play-store/ and download the latest version of the APK file. Follow the same steps as above to install it on your device.
-Q: How can I uninstall Google Play Store if I don't want it anymore?
-A: If you want to uninstall Google Play Store from your Redmi 7A, you can do so easily. Go to your phone's settings and tap on "Apps". Find Google Play Store and tap on it. Tap on "Uninstall" and confirm your action. This will remove Google Play Store from your device. You can also uninstall any other Google apps and services that you installed with it, such as Gmail, Maps, Photos, Drive, YouTube, and more.
-Q: What are the benefits of having Google Play Store on my Redmi 7A?
-A: Having Google Play Store on your Redmi 7A has many benefits, such as:
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- You can access the millions of apps, games, movies, books, and other content that are available on the official Android app store. -
- You can enjoy some of the features and services that rely on Google apps, such as Gmail, Maps, Photos, Drive, YouTube, and more. -
- You can sync your data and preferences across your devices with your Google account. -
- You can get updates and security patches for your apps and games from the Google Play Store. -
- You can customize your device with themes, wallpapers, widgets, and more from the Google Play Store. -
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How to Download 3 Qul MP3 for Free and Safely
-If you are looking for a way to download 3 Qul MP3 files for free and safely, you have come to the right place. In this article, we will show you what are the 3 Qul Surahs, why you should recite them, where to find and download them online, and how to protect yourself from malware and legal issues when downloading them.
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What are the 3 Qul Surahs and Why Should You Recite Them?
-The 3 Qul Surahs are the last three chapters (Surahs) in the Quran. They are commonly known as:
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- Surah Al-Ikhlas (The Sincerity) -
- Surah Al-Falaq (The Daybreak) -
- Surah An-Nas (The Mankind) -
They are called 3 Quls because they start with the word "Qul" which means "Say" in Arabic. These Surahs are among the shortest Surahs in the Quran, but they are very powerful and beneficial for Muslims.
-The Meaning and Benefits of the 3 Qul Surahs
-The 3 Qul Surahs have different meanings and benefits according to various Hadiths (sayings of Prophet Muhammad PBUH). Here are some of them:
-| Surah | -Meaning | -Benefits | -
|---|---|---|
| Surah Al-Ikhlas | -It declares the oneness and uniqueness of Allah, who is eternal, self-sufficient, incomparable, and indivisible. | -It is equal to one-third of the Quran. It increases wisdom and love for Allah. It protects from shirk (associating partners with Allah). | -
| Surah Al-Falaq | -It seeks refuge in Allah from the evil of the dawn, the evil of witchcraft, the evil of those who envy, and the evil of every harm. | -It protects from jinn, black magic, evil eye, and all kinds of afflictions. | -
| Surah An-Nas | -It seeks refuge in Allah from the evil of Satan, who whispers doubts and temptations in the hearts of people. | -It protects from Satan, his whispers, his plots, and his allies. | -
The Recitation and Translation of the 3 Qul Surahs
-The recitation and translation of the 3 Qul Surahs are as follows:
-Surah Al-Ikhlas
- -Bismillahir Rahmanir Rahim
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In the name of Allah, the Most Gracious, the Most Merciful.
-Qul huwa Allahu ahad.
-Say: He is Allah, the One.
-Allahu samad.
-Allah is self-sufficient.
-Lam yalid wa lam yoolad.
-He neither begets nor is born.
-Wa lam yakun la Bismillahir Rahmanir Rahim In the name of Allah, the Most Gracious, the Most Merciful. Qul a'oodhu bi rabbil falaq. Say: I seek refuge in the Lord of the dawn. Min sharri ma khalaq. From the evil of what He has created. Wa min sharri ghasiqin idha waqab. And from the evil of the dark night when it covers. Wa min sharrin naffathati fil 'uqad. And from the evil of those who blow on knots. Wa min sharri hasidin idha hasad. And from the evil of the envier when he envies. Bismillahir Rahmanir Rahim In the name of Allah, the Most Gracious, the Most Merciful. Qul a'oodhu bi rabbin nas. Say: I seek refuge in the Lord of mankind. Malikin nas. The King of mankind. Ilahin nas. The God of mankind. Min sharril waswasil khannas. From the evil of the whisperer who withdraws. Alladhi yuwaswisu fi sudoorin nas. Who whispers in the chests of mankind. 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diff --git a/spaces/congsaPfin/Manga-OCR/logs/IC-46 PDF A Comprehensive Guide to Investment Accounting and Regulations in General Insurance.md b/spaces/congsaPfin/Manga-OCR/logs/IC-46 PDF A Comprehensive Guide to Investment Accounting and Regulations in General Insurance.md
deleted file mode 100644
index 1379c7575e27d85d0624ccaee73e8d2bf9e42a57..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/IC-46 PDF A Comprehensive Guide to Investment Accounting and Regulations in General Insurance.md
+++ /dev/null
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-IC 46 PDF Download: How to Prepare for General Insurance Accounts and Investment Exam
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Ludo 3D Multiplayer A Fun and Colorful Game with Cute Animals.md b/spaces/congsaPfin/Manga-OCR/logs/Ludo 3D Multiplayer A Fun and Colorful Game with Cute Animals.md deleted file mode 100644 index 9f873d3fb90e180e575e63fc654999a2b25baf72..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Ludo 3D Multiplayer A Fun and Colorful Game with Cute Animals.md +++ /dev/null @@ -1,160 +0,0 @@ - -
3D Ludo Game Download: How to Play the Classic Board Game on Your Mobile Device
-Ludo is a huge game in many countries, but you have never seen a Ludo game like this one. It takes the Ludo game to a new level by adding stunning graphics, cute characters, and exciting features. In this article, you will learn everything you need to know about 3D Ludo game download, including the benefits of playing it on your phone, the best apps to choose from, how to install and play them, and some tips and tricks to win at this fun and addictive game.
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The Benefits of Playing 3D Ludo Game on Your Phone
-Ludo is a classic board game that has been played for centuries by people of all ages. It is a simple game that involves rolling a dice and moving your tokens around the board, trying to reach the finish line before your opponents. But what makes it so appealing is that it is also a game of strategy, luck, and competition. You have to plan your moves carefully, avoid getting captured by other players, and use your extra rolls wisely.
-Playing Ludo on your phone has many advantages over playing it on a physical board. Here are some of them:
--
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- You can play it anytime, anywhere. You don't need to carry a bulky board or worry about losing your tokens. You can just open your phone and start playing. -
- You can play it with anyone, even if they are far away. You can invite your friends and family to join you online, or play with random players from around the world. You can also chat with them using voice or text messages. -
- You can enjoy a more immersive and realistic experience. The 3D graphics make the game look more vivid and colorful, and the sound effects add more excitement and fun. You can also customize your tokens with different animals, colors, and shapes. -
- You can challenge yourself with different modes and levels. You can choose from different board sizes, number of players, rules, and difficulty levels. You can also unlock achievements and rewards as you play. -
The Best 3D Ludo Game Apps to Download and Play
-There are many 3D Ludo game apps available on the app store, but not all of them are worth your time and space. Here are some of the best ones that we recommend:
-Ludo 3D Multiplayer: A Vivid and Fun Version of Ludo
-This app is one of the most popular and highly rated ones on Google Play. It has over 5 million downloads and 4.1 stars out of 5. It offers a variety of features that make it stand out from other apps, such as:
--
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- It has amazing graphics and animations that make the game look more lively and fun. -
- It has many different characters that you can play with, such as lions, pandas, elephants, monkeys, and more. -
- It has both online and offline modes, so you can play with
your friends, family, or computer.
- - It has a leaderboard and a ranking system that lets you see how you compare with other players. -
- It has a shop where you can buy coins and gems to unlock more features and items. -
You can download Ludo 3D Multiplayer for free from Google Play. It is compatible with Android devices running version 4.4 and up.
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Ludo King: The Most Popular Ludo Game with Voice Chat and More
-This app is the king of Ludo games, as it has over 500 million downloads and 4.3 stars out of 5 on Google Play. It is also available on the App Store, where it has 4.6 stars out of 5. It is one of the most feature-rich and user-friendly apps, as it offers:
--
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- It has a sleek and modern design that makes the game look more appealing and easy to use. -
- It has a voice chat feature that lets you talk to your opponents while playing, making the game more social and interactive. -
- It has multiple game modes, such as classic, quick, master, and team. You can also play other games like snake and ladders, carrom, and chess. -
- It has a live theme feature that lets you change the background and board of the game according to different festivals and occasions. -
- It has a history feature that lets you see your past games and statistics. -
You can download Ludo King for free from Google Play or the App Store. It is compatible with Android devices running version 4.4 and up, and iOS devices running version 10.0 and up.
-Ludo 3D: A Simple and Classic Ludo Game with No Ads
-This app is for those who prefer a simple and classic Ludo game without any distractions or interruptions. It has over 1 million downloads and 4.0 stars out of 5 on Google Play. It has a minimalistic and elegant design that makes the game look more authentic and relaxing. It also has some features that make it different from other apps, such as:
--
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- It has no ads, so you can enjoy the game without any annoying pop-ups or banners. -
- It has realistic physics and sound effects that make the game more immersive and realistic. -
- It has a zoom feature that lets you see the board more clearly and easily. -
- It has an undo feature that lets you undo your last move in case you make a mistake or change your mind. -
- It has a save feature that lets you save your game progress and resume it later. -
You can download Ludo 3D for free from Google Play. It is compatible with Android devices running version 4.1 and up.
-How to Download and Install 3D Ludo Game on Your Device
-Downloading and installing 3D Ludo game on your device is very easy and fast. Here are the steps you need to follow:
-For Android Users
--
-
- Open Google Play on your device and search for the app you want to download (Ludo 3D Multiplayer, Ludo King, or Ludo 3D). -
- Tap on the app icon and then tap on the Install button. -
- Wait for the app to download and install on your device. -
- Once the app is installed, tap on the Open button to launch it. -
- Enjoy playing 3D Ludo game on your phone! -
For iOS Users
--
-
- Open the App Store on your device and search for the app you want to download (Ludo King). -
- Tap on the app icon and then tap on the Get button. -
- If prompted, enter your Apple ID password or use Touch ID or Face ID to confirm. -
- Wait for the app to download and install on your device. -
- Once the app is installed, tap on the Open button to launch it. -
- Enjoy playing 3D Ludo game on your phone! -
How to Play 3D Ludo Game with Your Friends and Family
-Playing 3D Ludo game with your friends and family is very fun and easy. You can choose from two modes: online or offline.
-Online Mode
-In this mode, you can play with other players online, either your friends or strangers. Here are the steps you need to follow:
--
-
- Open the app and tap on the Online button. -
- Select the game mode you want to play (classic, quick, master, or team). -
- Select the board size you want to play (4x4, 6x6, or 10x10). -
- Select the number of players you want to play with (2, 3, or 4). -
- If you want to play with your friends, tap on the Create Room button and share the room code with them. If you want to play with strangers, tap on the Join Room button and enter a room code or wait for a random match. -
- Once the room is full, tap on the Start button to begin the game. -
- Roll the dice and move your tokens according to the rules of the game. -
- Chat with your opponents using voice or text messages. -
- Try to reach the finish line before your opponents and win the game. -
Offline Mode
-In this mode, you can play with your friends or family offline, using one device. Here are the steps you need to follow:
--
-
- Open the app and tap on the Offline button. -
- Select the game mode you want to play (classic, quick, master, or team). -
- Select the board size you want to play (4x4, 6x6, or 10x10). -
- Select the number of players you want to play with (2, 3, or 4). -
- Assign each player a color and a name. -
- Tap on the Start button to begin the game. -
- Pass the device to each player when it is their turn to roll the dice and move their tokens. -
- Follow the rules of the game and try to reach the finish line before your opponents. -
Tips and Tricks to Win at 3D Ludo Game
-Playing 3D Ludo game is not only about luck, but also about strategy and skill. Here are some tips and tricks that can help you win at this game:
--
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- Use your extra rolls wisely. If you have more than one token on the board, you can choose which one to move. Try to move the one that is closer to the finish line or that can capture an opponent's token. -
- Avoid getting captured by other players. If your token is on a square that is marked with a star, it is safe from being captured. Try to stay on these squares as much as possible. -
- Form a blockade with your tokens. If you have two tokens on adjacent squares, they form a blockade that prevents other players from passing through. Use this tactic to block your opponents from reaching their finish line. -
- Use teamwork if you are playing in team mode. You can cooperate with your teammate by helping them move their tokens or capturing their enemies. You can also chat with them using voice or text messages. -
Conclusion
-3D Ludo game is a fun and exciting way to enjoy the classic board game on your mobile device. You can play it online or offline, with your friends or family, or with random players from around the world. You can also customize your tokens, choose different modes and levels, and chat with your opponents. 3D Ludo game is a game that will keep you entertained for hours and challenge your mind and skills. So what are you waiting for? Download one of these apps today and start playing!
-FAQs
-Here are some frequently asked questions about 3D Ludo game:
-Q: How do I get more coins and gems in 3D Ludo game?
-A: You can get more coins and gems by winning games, completing achievements, watching ads, or buying them from the shop.
-Q: How do I change my profile picture and name in 3D Ludo game?
-A: You can change your profile picture and name by tapping on the settings icon on the top right corner of the screen and then tapping on edit profile.
-Q: How do I report a bug or a problem in 3D Ludo game?
-A: You can report a bug or a problem by tapping on the settings icon on the top right corner of the screen and then tapping on feedback. You can also contact the developers through their email or social media accounts.
-Q: How do I invite my friends to play 3D Ludo game?
-A: You can invite your friends to play 3D Ludo game by tapping on the online button and then tapping on the create room button. You can then share the room code with your friends through any messaging app or social media platform.
-Q: How do I change the language of 3D Ludo game?
-A: You can change the language of 3D Ludo game by tapping on the settings icon on the top right corner of the screen and then tapping on language. You can then choose from different languages, such as English, Hindi, Spanish, French, and more.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Solve the Crime or Commit the Crime - Download Suspects Mystery Mansion for Windows 10.md b/spaces/congsaPfin/Manga-OCR/logs/Solve the Crime or Commit the Crime - Download Suspects Mystery Mansion for Windows 10.md deleted file mode 100644 index 032d3f70f3806e9ee20bd2282d322643178d4bdd..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Solve the Crime or Commit the Crime - Download Suspects Mystery Mansion for Windows 10.md +++ /dev/null @@ -1,79 +0,0 @@ - -
- Step 1: Download and install an Android emulator on your PC
- Step 2: Launch the emulator and sign in to Google Play Store
- Step 3: Search for Suspects: Mystery Mansion and install it
- Step 4: Enjoy the game on your PC with enhanced features | | H2: Features of Suspects: Mystery Mansion | - Native voice chat with other players
- Tons of unique game modes and roles
- Stylized graphics and characters
- Regular updates and fresh content | | H2: Tips and Tricks for Playing Suspects: Mystery Mansion | - How to communicate effectively with your team
- How to use clues and tasks to find the killer
- How to deceive others as the killer or the spy
- How to customize your character and settings | | H2: Conclusion | - Summary of the main points
- Call to action: Download the game now and join the fun | | H2: FAQs | - Q1: Is Suspects: Mystery Mansion free to play?
- Q2: Can I play Suspects: Mystery Mansion with my friends?
- Q3: What are the system requirements for playing Suspects: Mystery Mansion on PC?
- Q4: How can I contact the developers of Suspects: Mystery Mansion?
- Q5: Where can I find more information about Suspects: Mystery Mansion? | Table 2: Article with HTML formatting
How to Download and Play Suspects: Mystery Mansion on Windows 10
-If you love social deduction games, you will love Suspects: Mystery Mansion. This is a thrilling game where you and nine other players have to solve a murder mystery in a mansion. You can either play as a guest, who has to find clues and complete tasks, or as a killer, who has to eliminate the guests without getting caught. The game features native voice chat, so you can talk to other players in real time. You can also choose from different game modes and roles, each with its own challenges and twists. And the best part is, you can play this game on your Windows 10 PC with enhanced graphics, performance, and controls.
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In this article, we will show you how to download and play Suspects: Mystery Mansion on Windows 10 in four easy steps. We will also tell you about the features of this game, and give you some tips and tricks to help you become a master detective or a cunning killer. Let's get started!
-Step 1: Download and install an Android emulator on your PC
-The first thing you need to do is download and install an Android emulator on your PC. An Android emulator is a software that allows you to run Android apps and games on your computer. There are many Android emulators available online, but we recommend BlueStacks, which is one of the most popular and reliable ones. BlueStacks is compatible with Windows 10, and it offers many features that enhance your gaming experience.
-To download BlueStacks, go to [this link](^1^) and click on the "Download Suspects: Mystery Mansion on PC" button. This will start downloading the BlueStacks installer file on your PC. Once the download is complete, open the file and follow the instructions to install BlueStacks on your PC.
-Step 2: Launch the emulator and sign in to Google Play Store
-After installing BlueStacks, launch it from your desktop or start menu. You will see a welcome screen where you have to sign in to your Google account. This is necessary to access the Google Play Store, where you can find and download Android apps and games. If you don't have a Google account, you can create one for free.
-Once you sign in to your Google account, you will see the BlueStacks home screen, where you can access various features and settings. You will also see some pre-installed apps, including the Google Play Store app. Click on it to open it.
-Step 3: Search for Suspects: Mystery Mansion and install it
-In the Google Play Store app, type "Suspects: Mystery Mansion" in the search bar at the top right corner and press enter. You will see a list of results related to your search query. Look for the one that says "Suspects: Mystery Mansion" by Wildlife Studios and has a logo of a magnifying glass and a knife. Click on it to open its page.
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On the game's page, you will see some screenshots, videos, and information about the game. You will also see a green "Install" button. Click on it to start downloading and installing the game on your PC. This may take a few minutes, depending on your internet speed and PC performance.
-Once the installation is complete, you will see an "Open" button. Click on it to launch the game on your PC.
-Step 4: Enjoy the game on your PC with enhanced features
-Congratulations! You have successfully downloaded and played Suspects: Mystery Mansion on your Windows 10 PC. Now you can enjoy the game with enhanced features, such as:
--
-
- Larger screen and better graphics: You can play the game on your PC's monitor, which offers a bigger and clearer view of the mansion and its rooms. You can also adjust the graphics settings to suit your preferences. -
- Faster and smoother performance: You can play the game without any lags or crashes, thanks to the powerful hardware and software of your PC. You can also use the BlueStacks Boost feature to optimize your PC's resources for gaming. -
- Easier and more precise controls: You can play the game with your mouse and keyboard, which offer more accuracy and responsiveness than touch controls. You can also customize the key mapping to suit your style. -
With these features, you can have a more immersive and enjoyable gaming experience with Suspects: Mystery Mansion on your PC.
-Features of Suspects: Mystery Mansion
-Suspects: Mystery Mansion is not just another social deduction game. It has many features that make it stand out from the crowd. Here are some of them:
--
-
- Native voice chat with other players: You can talk to other players in real time using your microphone. This adds more realism and fun to the game, as you can hear their reactions, emotions, and lies. You can also mute or report players who are rude or abusive. -
- Tons of unique game modes and roles: You can choose from different game modes, such as Classic, Hide and Seek, Murder in the Dark, and more. Each mode has its own rules and objectives, so you never get bored. You can also play as different roles, such as Detective, Doctor, Hacker, Spy, and more. Each role has its own abilities and tasks, so you have to use your skills and strategy to win. -
- Stylized graphics and characters: You can enjoy the game's colorful and cartoonish graphics, which create a contrast between the cute and the creepy. You can also customize your character's appearance, clothes, accessories, and pets. -
- Regular updates and fresh content: The developers of Suspects: Mystery Mansion are constantly working on improving the game and adding new content. You can expect new game modes, roles, maps, items, events, and more in the future. -
With these features, you can have a blast playing Suspects: Mystery Mansion with your friends or strangers online.
Tips and Tricks for Playing Suspects: Mystery Mansion
-Now that you know how to download and play Suspects: Mystery Mansion on your PC, you might be wondering how to become a better player. Whether you are a guest or a killer, you need to use your wits and skills to win the game. Here are some tips and tricks that can help you:
--
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- How to communicate effectively with your team: Communication is key in Suspects: Mystery Mansion, as you need to share information, coordinate actions, and convince others. You can use the voice chat feature to talk to your team, but be careful not to reveal too much or too little. You can also use the text chat feature to send messages, emojis, and stickers. You can also use the voting system to express your opinions and suspicions. -
- How to use clues and tasks to find the killer: As a guest, your main goal is to find the killer before they kill everyone. You can do this by collecting clues, such as fingerprints, blood stains, weapons, and notes. You can also complete tasks, such as fixing wires, scanning documents, and playing piano. These tasks will help you earn coins, which you can use to buy items or access special rooms. You can also use the map to see where the clues and tasks are located. -
- How to deceive others as the killer or the spy: As a killer, your main goal is to kill all the guests without getting caught. You can do this by using your knife, gun, poison, or trap. You can also sabotage the mansion, such as cutting the lights, locking doors, or setting fires. You can also use your disguise ability to change your appearance and voice. As a spy, your main goal is to help the killer by lying, distracting, or framing others. You can also use your spy camera ability to see what others are doing. -
- How to customize your character and settings: You can make your character look unique by changing their appearance, clothes, accessories, and pets. You can also change your settings, such as your name, language, sound effects, and notifications. You can also join or create a club, where you can chat with other players and earn rewards. -
With these tips and tricks, you can have an edge over your opponents in Suspects: Mystery Mansion.
-Conclusion
-Suspects: Mystery Mansion is a fun and exciting game that you can play on your Windows 10 PC with BlueStacks. You can enjoy the game's features, such as native voice chat, unique game modes and roles, stylized graphics and characters, and regular updates and fresh content. You can also use our tips and tricks to improve your skills and strategy in the game.
-What are you waiting for? Download Suspects: Mystery Mansion on PC now and join the fun! You will never get bored of this game, as every round is different and unpredictable. You will also meet new friends and enemies along the way. Are you ready to solve the mystery or create the chaos? The choice is yours!
-FAQs
--
-
- Q1: Is Suspects: Mystery Mansion free to play?
A1: Yes, Suspects: Mystery Mansion is free to play on both Android and PC. However, you can make in-app purchases to buy coins or gems, which you can use to buy items or access special rooms.
- - Q2: Can I play Suspects: Mystery Mansion with my friends?
A2: Yes, you can play Suspects: Mystery Mansion with your friends by creating or joining a private room. You can also invite them to join your club or chat with them in the game.
- - Q3: What are the system requirements for playing Suspects: Mystery Mansion on PC?
A3: To play Suspects: Mystery Mansion on PC with BlueStacks, you need a Windows 10 PC with at least 4 GB of RAM, 5 GB of disk space, an Intel or AMD processor, and updated graphics drivers.
- - Q4: How can I contact the developers of Suspects: Mystery Mansion?
A4: You can contact the developers of Suspects: Mystery Mansion by sending an email to support@wildlifestudios.com or by visiting their website at https://www.wildlifestudios.com/.
- - Q5: Where can I find more information about Suspects: Mystery Mansion?
A5: You can find more information about Suspects: Mystery Mansion by visiting their official Facebook page at https://www.facebook.com/SuspectsMysteryMansion/ or their official Instagram page at https://www.instagram.com/suspectsmysterym ansion/.
-
I hope you found this article helpful and informative. If you have any questions or feedback, please leave a comment below. I would love to hear from you. Thank you for reading and happy gaming!
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Webgenie Swf Amp Flash Player Apk Pro [WORK].md b/spaces/congsaPfin/Manga-OCR/logs/Webgenie Swf Amp Flash Player Apk Pro [WORK].md deleted file mode 100644 index a1dc76d0102ad64f360f7bb94b97b644db731880..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Webgenie Swf Amp Flash Player Apk Pro [WORK].md +++ /dev/null @@ -1,74 +0,0 @@ -
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Webgenie SWF & Flash Player APK Pro: A Review
-If you are a fan of flash games and videos, you may have encountered some difficulties in playing them on your Android device. Since Adobe stopped supporting flash on Android in 2012, most browsers and apps have also dropped flash support. However, there is still a way to enjoy flash content on your Android device without installing any other plugins. In this article, we will review Webgenie SWF & Flash Player APK Pro, a flash player emulator and browser that can play flash games and videos on Android devices.
-What is Webgenie SWF & Flash Player APK Pro?
-Webgenie SWF & Flash Player APK Pro is an app that can play flash games and videos on Android devices without installing any other plugins. It has two main functions:
-webgenie swf amp; flash player apk pro
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A flash player emulator for Android devices
-Webgenie SWF & Flash Player APK Pro can play local or online SWF (Shockwave Flash) and FLV (Flash Video) files on your device. It can render flash files locally, faster than any other cloud-based player. It supports both ARM and X86 CPU devices, which means it can run on most Android devices. It also supports background color, orientation, and custom flash area size settings.
-A flash browser that can play online flash games and videos
-Webgenie SWF & Flash Player APK Pro can also browse web pages that contain flash content. It can play online flash games and videos without downloading them to your device. It supports gamepad, video controller, and virtual buttons to enhance your gaming and viewing experience. It also supports history and powerful file browser features.
-What are the features of Webgenie SWF & Flash Player APK Pro?
-Webgenie SWF & Flash Player APK Pro has many features that make it a great app for playing flash content on Android devices. Here are some of them:
-Support for gamepad, video controller, and virtual buttons
-Webgenie SWF & Flash Player APK Pro supports gamepad (joystick) and virtual buttons to let you play flash games more easily. You can use your physical gamepad or the virtual buttons on the screen to control the game. You can also use the video controller to play, pause, stop, rewind, or fast forward the flash video. You can also adjust the volume, brightness, and screen size.
-Support for web page browsing with flash content
-Webgenie SWF & Flash Player APK Pro can also browse web pages that have flash content. It can play online flash games and videos without downloading them to your device. You can use the browser option to access any web page with flash content. You can also bookmark your favorite sites, view your browsing history, and manage your files.
-Support for both ARM and X86 CPU devices
-Webgenie SWF & Flash Player APK Pro supports both ARM and X86 CPU devices, which means it can run on most Android devices. It can detect the CPU type of your device and choose the best flash player engine for it. It can also optimize the performance and compatibility of the flash content.
-Support for background color, orientation, and custom flash area size settings
-Webgenie SWF & Flash Player APK Pro allows you to customize the background color, orientation, and flash area size of the flash content. You can choose from different colors for the background, such as black, white, gray, or transparent. You can also choose the orientation of the flash content, such as portrait or landscape. You can also adjust the flash area size to fit your screen or zoom in or out.
-How to download and install Webgenie SWF & Flash Player APK Pro?
-If you want to download and install Webgenie SWF & Flash Player APK Pro on your Android device, you need to follow these steps:
- -Download the APK file from a trusted source
-You need to download the APK file of Webgenie SWF & Flash Player APK Pro from a trusted source. You can search for it on Google or use this link: . Make sure you download the latest version of the app.
-Enable unknown sources in your device settings
-You need to enable unknown sources in your device settings to allow the installation of apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message, but you can ignore it.
-Install the APK file and grant the required permissions
-You need to install the APK file and grant the required permissions to Webgenie SWF & Flash Player APK Pro. To do this, locate the downloaded APK file on your device and tap on it. You will see a prompt asking you to install the app. Tap on Install and wait for the installation to finish. You will also see a prompt asking you to grant some permissions to the app, such as storage, network, and phone. Tap on Allow and proceed.
-How to use Webgenie SWF & Flash Player APK Pro?
-If you want to use Webgenie SWF & Flash Player APK Pro to play flash games and videos on your Android device, you need to follow these steps:
-Open the app and choose a SWF or FLV file on your device or online
-Open the app and you will see two options: Local File and Online File. If you want to play a SWF or FLV file that is stored on your device, tap on Local File and browse your files. If you want to play a SWF or FLV file that is online, tap on Online File and enter the URL of the file.
-Use the gamepad, video controller, or virtual buttons to play or control the flash content
-Use the gamepad, video controller, or virtual buttons that appear on the screen to play or control the flash content. You can use your physical gamepad or the virtual buttons to control the game. You can also use the video controller to play, pause, stop, rewind, or fast forward the flash video. You can also adjust the volume, brightness, and screen size.
-Use the browser option to access web pages with flash content
-Use the browser option to access web pages that have flash content. You can use the browser option to enter any web page URL that contains flash content. You can also bookmark your favorite sites, view your browsing history, and manage your files.
-What are the pros and cons of Webgenie SWF & Flash Player APK Pro?
-Webgenie SWF & Flash Player APK Pro has some pros and cons that you should consider before using it. Here are some of them:
-Pros:
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-
No need to install any other plugins
-Webgenie SWF & Flash Player APK Pro does not require you to install any other plugins or apps to play flash content on your Android device. It can play flash content natively, without relying on any cloud-based service.
-Fast and smooth performance
-Webgenie SWF & Flash Player APK Pro can play flash content faster and smoother than any other app. It can render flash files locally, without any lag or buffering. It can also optimize the performance and compatibility of the flash content for your device.
-Easy and intuitive interface
-Webgenie SWF & Flash Player APK Pro has an easy and intuitive interface that lets you play flash content with ease. It has two simple options: Local File and Online File. It also has gamepad, video controller, and virtual buttons that enhance your gaming and viewing experience. It also has browser, history, and file browser features that let you access web pages and files with flash content.
-
Cons:
--
-
Not compatible with Android 8.0 and above devices
-Webgenie SWF & Flash Player APK Pro is not compatible with Android 8.0 and above devices. This is because Android 8.0 and above devices do not support the installation of apps from unknown sources. Therefore, you cannot install Webgenie SWF & Flash Player APK Pro on these devices.
-May not support some complex flash games and videos
-Webgenie SWF & Flash Player APK Pro may not support some complex flash games and videos that require advanced features or plugins. For example, it may not support some 3D games, multiplayer games, or live streaming videos. It may also have some compatibility issues with some flash files.
-May consume more battery and data
-Webgenie SWF & Flash Player APK Pro may consume more battery and data than other apps. This is because it plays flash content locally, which requires more CPU and GPU resources. It may also consume more data if you play online flash games and videos, especially if they are large or high-quality.
-
Conclusion
-Webgenie SWF & Flash Player APK Pro is a great app for playing flash games and videos on Android devices without installing any other plugins. It can play local or online flash files faster and smoother than any other app. It also has gamepad, video controller, virtual buttons, browser, history, and file browser features that enhance your gaming and viewing experience. However, it also has some drawbacks, such as not being compatible with Android 8.0 and above devices, not supporting some complex flash games and videos, and consuming more battery and data than other apps. Therefore, you should weigh the pros and cons of Webgenie SWF & Flash Player APK Pro before using it.
-FAQs
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-
Is Webgenie SWF & Flash Player APK Pro free?
-Yes, Webgenie SWF & Flash Player APK Pro is free to download and use. However, it may contain some ads or in-app purchases that you can choose to disable or buy.
-Is Webgenie SWF & Flash Player APK Pro safe?
-Yes, Webgenie SWF & Flash Player APK Pro is safe to use as long as you download it from a trusted source. However, you should be careful when playing online flash games and videos as they may contain viruses or malware that can harm your device.
-Is Webgenie SWF & Flash Player APK Pro legal?
-Yes, Webgenie SWF & Flash Player APK Pro is legal to use as long as you do not violate any intellectual property rights or laws. However, you should respect the rights and wishes of the flash content creators and owners and not use their content for any illegal or unethical purposes.
-How can I update Webgenie SWF & Flash Player APK Pro?
-You can update Webgenie SWF & Flash Player APK Pro by downloading the latest version of the APK file from a trusted source and installing it on your device. You can also check for updates within the app by tapping on the menu icon and selecting Check for Updates.
-How can I contact Webgenie SWF & Flash Player APK Pro developers?
-You can contact Webgenie SWF & Flash Player APK Pro developers by sending them an email at . You can also visit their website at or follow them on Facebook at . You can also leave a feedback or a review on Google Play Store or other platforms.
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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/feature_matching.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/feature_matching.py deleted file mode 100644 index c019895c9178817837d1a6773367b178a861dc61..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/feature_matching.py +++ /dev/null @@ -1,33 +0,0 @@ -from typing import List - -import torch -import torch.nn.functional as F - - -def masked_l2_loss(pred, target, mask, weight_known, weight_missing): - per_pixel_l2 = F.mse_loss(pred, target, reduction='none') - pixel_weights = mask * weight_missing + (1 - mask) * weight_known - return (pixel_weights * per_pixel_l2).mean() - - -def masked_l1_loss(pred, target, mask, weight_known, weight_missing): - per_pixel_l1 = F.l1_loss(pred, target, reduction='none') - pixel_weights = mask * weight_missing + (1 - mask) * weight_known - return (pixel_weights * per_pixel_l1).mean() - - -def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None): - if mask is None: - res = torch.stack([F.mse_loss(fake_feat, target_feat) - for fake_feat, target_feat in zip(fake_features, target_features)]).mean() - else: - res = 0 - norm = 0 - for fake_feat, target_feat in zip(fake_features, target_features): - cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False) - error_weights = 1 - cur_mask - cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean() - res = res + cur_val - norm += 1 - res = res / norm - return res diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/swish.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/swish.py deleted file mode 100644 index e2ca8ed7b749413f011ae54aac0cab27e6f0b51f..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/bricks/swish.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -import torch.nn as nn - -from .registry import ACTIVATION_LAYERS - - -@ACTIVATION_LAYERS.register_module() -class Swish(nn.Module): - """Swish Module. - - This module applies the swish function: - - .. math:: - Swish(x) = x * Sigmoid(x) - - Returns: - Tensor: The output tensor. - """ - - def __init__(self): - super(Swish, self).__init__() - - def forward(self, x): - return x * torch.sigmoid(x) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/knn.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/knn.py deleted file mode 100644 index f335785036669fc19239825b0aae6dde3f73bf92..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/knn.py +++ /dev/null @@ -1,77 +0,0 @@ -import torch -from torch.autograd import Function - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', ['knn_forward']) - - -class KNN(Function): - r"""KNN (CUDA) based on heap data structure. - Modified from `PAConv
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\ No newline at end of file diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/indexing/collection_encoder.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/indexing/collection_encoder.py deleted file mode 100644 index b4a63082a1bd34dc44efb8bf3451a3e7da722238..0000000000000000000000000000000000000000 --- a/spaces/diagaiwei/ir_chinese_medqa/colbert/indexing/collection_encoder.py +++ /dev/null @@ -1,45 +0,0 @@ -import torch - -from colbert.infra.run import Run -from colbert.utils.utils import print_message, batch - - -class CollectionEncoder(): - def __init__(self, config, checkpoint): - self.config = config - self.checkpoint = checkpoint - self.use_gpu = self.config.total_visible_gpus > 0 - - def encode_passages(self, passages): - Run().print(f"#> Encoding {len(passages)} passages..") - - if len(passages) == 0: - return None, None - - with torch.inference_mode(): - embs, doclens = [], [] - - # Batch here to avoid OOM from storing intermediate embeddings on GPU. - # Storing on the GPU helps with speed of masking, etc. - # But ideally this batching happens internally inside docFromText. - for passages_batch in batch(passages, self.config.bsize * 50): - embs_, doclens_ = self.checkpoint.docFromText(passages_batch, bsize=self.config.bsize, - keep_dims='flatten', showprogress=(not self.use_gpu)) - embs.append(embs_) - doclens.extend(doclens_) - - embs = torch.cat(embs) - - # embs, doclens = self.checkpoint.docFromText(passages, bsize=self.config.bsize, - # keep_dims='flatten', showprogress=(self.config.rank < 1)) - - # with torch.inference_mode(): - # embs = self.checkpoint.docFromText(passages, bsize=self.config.bsize, - # keep_dims=False, showprogress=(self.config.rank < 1)) - # assert type(embs) is list - # assert len(embs) == len(passages) - - # doclens = [d.size(0) for d in embs] - # embs = torch.cat(embs) - - return embs, doclens diff --git a/spaces/digitalxingtong/Jiaran-Bert-VITS2/modules.py b/spaces/digitalxingtong/Jiaran-Bert-VITS2/modules.py deleted file mode 100644 index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Jiaran-Bert-VITS2/modules.py +++ /dev/null @@ -1,452 +0,0 @@ -import copy -import math -import numpy as np -import scipy -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding -from transforms import piecewise_rational_quadratic_transform -from attentions import Encoder - -LRELU_SLOPE = 0.1 - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - 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.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, - dilation=dilation, padding=padding) - in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - 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.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) - self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_derivatives = h[..., 2 * self.num_bins:] - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x -class TransformerCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - n_layers, - n_heads, - p_dropout=0, - filter_channels=0, - mean_only=False, - wn_sharing_parameter=None, - gin_channels = 0 - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/app.py b/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/app.py deleted file mode 100644 index a6f6a2f64cd0aa11bd7a8a4dd5cc9c3124300bf8..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/app.py +++ /dev/null @@ -1,257 +0,0 @@ -import sys, os - -if sys.platform == "darwin": - os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" - -import logging - -logging.getLogger("numba").setLevel(logging.WARNING) -logging.getLogger("markdown_it").setLevel(logging.WARNING) -logging.getLogger("urllib3").setLevel(logging.WARNING) -logging.getLogger("matplotlib").setLevel(logging.WARNING) - -logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") - -logger = logging.getLogger(__name__) - -import torch -import argparse -import commons -import utils -from models import SynthesizerTrn -from text.symbols import symbols -from text import cleaned_text_to_sequence, get_bert -from text.cleaner import clean_text -import gradio as gr -import webbrowser - - -net_g = None - - -def get_text(text, language_str, hps): - norm_text, phone, tone, word2ph = clean_text(text, language_str) - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if hps.data.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert = get_bert(norm_text, word2ph, language_str) - del word2ph - - assert bert.shape[-1] == len(phone) - - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - - return bert, phone, tone, language -import soundfile as sf -def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): - global net_g - bert, phones, tones, lang_ids = get_text(text, "ZH", hps) - with torch.no_grad(): - x_tst=phones.to(device).unsqueeze(0) - tones=tones.to(device).unsqueeze(0) - lang_ids=lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - del phones - speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) - audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio - , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() - del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers - sf.write("tmp.wav", audio, 44100) - return audio -def convert_wav_to_ogg(wav_file): - os.makedirs('out', exist_ok=True) - filename = os.path.splitext(os.path.basename(wav_file.name))[0] - output_path_ogg = os.path.join('out', f"out.ogg") - wav_file.close() - renamed_input_path = os.path.join('in', f"in.wav") - os.makedirs('in', exist_ok=True) - os.rename(wav_file.name, renamed_input_path) - command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] - os.system(" ".join(command)) - return output_path_ogg -def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): - with torch.no_grad(): - audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) - with open('tmp.wav', 'rb') as wav_file: - newogg = convert_wav_to_ogg(wav_file) - return "Success", (hps.data.sampling_rate, audio),newogg -import re -from utils import play -import numpy as np -from scipy.io.wavfile import write -from pydub import AudioSegment -import os - -folder_name = "out" - -if not os.path.exists(folder_name): - os.mkdir(folder_name) - -def longread(input_file,sdp_ratio, noise_scale, noise_scale_w, length_scale): - - with open(input_file.name, "r", encoding="utf-8") as file: - content = file.read() - - directory = "out" - file_list = os.listdir(directory) - - # Delete each file in the directory - for file_name in file_list: - file_path = os.path.join(directory, file_name) - try: - if os.path.isfile(file_path): - os.remove(file_path) - print(f"Deleted {file_path}") - except Exception as e: - print(f"Error deleting {file_path}: {e}") - if len(content) >3000: - content="文本过长，请缩短字数到三千字以内。" - content = re.sub(r'\s+', ' ', content).strip() - - content = content.replace('......', '，') - - paragraphs = [] - - sentence_delimiters = r'[^。！？；]*[。！？；]' - - matches = re.finditer(sentence_delimiters, content) - current_paragraph = '' - for match in matches: - sentence_with_delimiter = match.group(0) - if len(current_paragraph) + len(sentence_with_delimiter) < 80: - current_paragraph += sentence_with_delimiter - elif len(current_paragraph) + len(sentence_with_delimiter) <= 150: - current_paragraph += sentence_with_delimiter - paragraphs.append(current_paragraph.strip()) - current_paragraph = '' - else: - paragraphs.append(current_paragraph.strip()) - current_paragraph = sentence_with_delimiter - - if current_paragraph: - paragraphs.append(current_paragraph.strip()) - - - output_audio = AudioSegment.empty() - for cnt, paragraph in enumerate(paragraphs): - text=paragraph - audio_output = infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, "ign") - - output_filename = f"out/{cnt}.wav" - write(output_filename, 44100, audio_output.astype(np.float32)) - input_file = f"out/{cnt}.wav" - pause_duration = 50 - try: - audio_segment = AudioSegment.from_wav(input_file) - if len(output_audio) > 0: - pause = AudioSegment.silent(duration=pause_duration) - output_audio += pause - output_audio += audio_segment - except FileNotFoundError: - continue - output_file = "output.wav" - output_audio.export(output_file, format="wav") - - return output_file -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", default="./logs/xt_read/dmc1.pth", help="path of your model") - parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") - parser.add_argument("--share", default=False, help="make link public") - parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") - - args = parser.parse_args() - if args.debug: - logger.info("Enable DEBUG-LEVEL log") - logging.basicConfig(level=logging.DEBUG) - hps = utils.get_hparams_from_file(args.config_dir) - device = "cuda:0" if torch.cuda.is_available() else "cpu" - ''' - device = ( - "cuda:0" - if torch.cuda.is_available() - else ( - "mps" - if sys.platform == "darwin" and torch.backends.mps.is_available() - else "cpu" - ) - ) - ''' - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model).to(device) - _ = net_g.eval() - - _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) - - speaker_ids = hps.data.spk2id - speakers = list(speaker_ids.keys()) - with gr.Blocks() as app: - with gr.Row(): - with gr.Column(): - - - gr.Markdown(value=""" - 星瞳 长文本专用（冬牧场 版） Bert-Vits2在线语音生成\n - - 0、必看！！！重要！！！ 当显示“Warning There is a long queue of requests pending. Duplicate this Space to skip.”，或显示“queue”字样时，表示此时系统拥挤。请点击右上角 “Community” 旁的三个点，选择 “Duplicate this space” 然后确定，大约等待五分钟，就可将该空间克隆到自己的空间上。\n - 1、请确保上传的txt文件是UTF-8格式（请自行百度如何txt保存为UTF-8格式）。\n - 2、由于CPU版本生成缓慢，每次上传文本字数请确保少于3000字。 \n - 3、模型作者：数字星瞳企划 https://t.me/xingtong25680 有问题请在telegram与我联系。 \n - 4、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n - 5、使用此模型进行二创请注明AI生成，以及该项目地址。\n - - """) - - # speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') - input_file=gr.inputs.File(label="在这里上传TXT文件") - sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') - noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') - noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化') - length_scale = gr.Slider(minimum=0.1, maximum=2, value=0.97, step=0.01, label='语速') - btn = gr.Button("开启AI语音之旅吧!", variant="primary") - with gr.Column(): - - output_file=gr.outputs.File(label="下载生成的WAV文件") - gr.Markdown(value=""" - 模型汇总：\n - 星瞳整合 https://huggingface.co/spaces/digitalxingtong/Xingtong-All-in-One\n - 甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n - 七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n - 东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n - 嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n - 乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2 \n - 恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n - 奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n - 奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n - 露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n - 柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n - 米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n - 扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n - 牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n - 吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n - 九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n - 卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n - 理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n - 阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n - 鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n - 永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n - """) - btn.click(longread, - inputs=[input_file,sdp_ratio,noise_scale,noise_scale_w,length_scale], - outputs=[output_file]) - - - app.launch(show_error=True) \ No newline at end of file diff --git a/spaces/dirge/voicevox/speaker_info/b1a81618-b27b-40d2-b0ea-27a9ad408c4b/policy.md b/spaces/dirge/voicevox/speaker_info/b1a81618-b27b-40d2-b0ea-27a9ad408c4b/policy.md deleted file mode 100644 index 68114802c449a6799db4cf7aae3cecbb71db0e70..0000000000000000000000000000000000000000 --- a/spaces/dirge/voicevox/speaker_info/b1a81618-b27b-40d2-b0ea-27a9ad408c4b/policy.md +++ /dev/null @@ -1,3 +0,0 @@ -dummy4 policy - -https://voicevox.hiroshiba.jp/ diff --git a/spaces/dorkai/text-generation-webui-main/modules/chat.py b/spaces/dorkai/text-generation-webui-main/modules/chat.py deleted file mode 100644 index 3055a97a65bd4bd2137f6ce1ec182e8d63637a13..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/modules/chat.py +++ /dev/null @@ -1,592 +0,0 @@ -import ast -import base64 -import copy -import io -import json -import logging -import re -from datetime import datetime -from pathlib import Path - -import yaml -from PIL import Image - -import modules.shared as shared -from modules.extensions import apply_extensions -from modules.html_generator import chat_html_wrapper, make_thumbnail -from modules.text_generation import (generate_reply, get_encoded_length, - get_max_prompt_length) -from modules.utils import replace_all - - -def get_turn_substrings(state, instruct=False): - if instruct: - if 'turn_template' not in state or state['turn_template'] == '': - template = '<|user|>\n<|user-message|>\n<|bot|>\n<|bot-message|>\n' - else: - template = state['turn_template'].replace(r'\n', '\n') - else: - template = '<|user|>: <|user-message|>\n<|bot|>: <|bot-message|>\n' - - replacements = { - '<|user|>': state['name1_instruct' if instruct else 'name1'].strip(), - '<|bot|>': state['name2_instruct' if instruct else 'name2'].strip(), - } - - output = { - 'user_turn': template.split('<|bot|>')[0], - 'bot_turn': '<|bot|>' + template.split('<|bot|>')[1], - 'user_turn_stripped': template.split('<|bot|>')[0].split('<|user-message|>')[0], - 'bot_turn_stripped': '<|bot|>' + template.split('<|bot|>')[1].split('<|bot-message|>')[0], - } - - for k in output: - output[k] = replace_all(output[k], replacements) - - return output - - -def generate_chat_prompt(user_input, state, **kwargs): - impersonate = kwargs.get('impersonate', False) - _continue = kwargs.get('_continue', False) - also_return_rows = kwargs.get('also_return_rows', False) - history = state.get('history', shared.history['internal']) - is_instruct = state['mode'] == 'instruct' - - # Finding the maximum prompt size - chat_prompt_size = state['chat_prompt_size'] - if shared.soft_prompt: - chat_prompt_size -= shared.soft_prompt_tensor.shape[1] - - max_length = min(get_max_prompt_length(state), chat_prompt_size) - - all_substrings = { - 'chat': get_turn_substrings(state, instruct=False), - 'instruct': get_turn_substrings(state, instruct=True) - } - substrings = all_substrings['instruct' if is_instruct else 'chat'] - - # Creating the template for "chat-instruct" mode - if state['mode'] == 'chat-instruct': - wrapper = '' - command = state['chat-instruct_command'].replace('<|character|>', state['name2'] if not impersonate else state['name1']) - wrapper += state['context_instruct'] - wrapper += all_substrings['instruct']['user_turn'].replace('<|user-message|>', command) - wrapper += all_substrings['instruct']['bot_turn_stripped'] - if impersonate: - wrapper += substrings['user_turn_stripped'].rstrip(' ') - else: - wrapper += apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' ')) - else: - wrapper = '<|prompt|>' - - # Building the prompt - min_rows = 3 - i = len(history) - 1 - rows = [state['context_instruct'] if is_instruct else f"{state['context'].strip()}\n"] - while i >= 0 and get_encoded_length(wrapper.replace('<|prompt|>', ''.join(rows))) < max_length: - if _continue and i == len(history) - 1: - rows.insert(1, substrings['bot_turn_stripped'] + history[i][1].strip()) - else: - rows.insert(1, substrings['bot_turn'].replace('<|bot-message|>', history[i][1].strip())) - - string = history[i][0] - if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']: - rows.insert(1, replace_all(substrings['user_turn'], {'<|user-message|>': string.strip(), '<|round|>': str(i)})) - - i -= 1 - - if impersonate: - if state['mode'] == 'chat-instruct': - min_rows = 1 - else: - min_rows = 2 - rows.append(substrings['user_turn_stripped'].rstrip(' ')) - elif not _continue: - # Adding the user message - if len(user_input) > 0: - rows.append(replace_all(substrings['user_turn'], {'<|user-message|>': user_input.strip(), '<|round|>': str(len(history))})) - - # Adding the Character prefix - if state['mode'] != 'chat-instruct': - rows.append(apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' '))) - - while len(rows) > min_rows and get_encoded_length(wrapper.replace('<|prompt|>', ''.join(rows))) >= max_length: - rows.pop(1) - - prompt = wrapper.replace('<|prompt|>', ''.join(rows)) - if also_return_rows: - return prompt, rows - else: - return prompt - - -def get_stopping_strings(state): - stopping_strings = [] - if state['mode'] in ['instruct', 'chat-instruct']: - stopping_strings += [ - state['turn_template'].split('<|user-message|>')[1].split('<|bot|>')[0] + '<|bot|>', - state['turn_template'].split('<|bot-message|>')[1] + '<|user|>' - ] - - replacements = { - '<|user|>': state['name1_instruct'], - '<|bot|>': state['name2_instruct'] - } - - for i in range(len(stopping_strings)): - stopping_strings[i] = replace_all(stopping_strings[i], replacements).rstrip(' ').replace(r'\n', '\n') - - if state['mode'] in ['chat', 'chat-instruct']: - stopping_strings += [ - f"\n{state['name1']}:", - f"\n{state['name2']}:" - ] - - stopping_strings += ast.literal_eval(f"[{state['custom_stopping_strings']}]") - return stopping_strings - - -def extract_message_from_reply(reply, state): - next_character_found = False - stopping_strings = get_stopping_strings(state) - - if state['stop_at_newline']: - lines = reply.split('\n') - reply = lines[0].strip() - if len(lines) > 1: - next_character_found = True - else: - for string in stopping_strings: - idx = reply.find(string) - if idx != -1: - reply = reply[:idx] - next_character_found = True - - # If something like "\nYo" is generated just before "\nYou:" - # is completed, trim it - if not next_character_found: - for string in stopping_strings: - for j in range(len(string) - 1, 0, -1): - if reply[-j:] == string[:j]: - reply = reply[:-j] - break - else: - continue - - break - - return reply, next_character_found - - -def chatbot_wrapper(text, state, regenerate=False, _continue=False): - if shared.model_name == 'None' or shared.model is None: - logging.error("No model is loaded! Select one in the Model tab.") - yield shared.history['visible'] - return - - # Defining some variables - cumulative_reply = '' - just_started = True - visible_text = None - eos_token = '\n' if state['stop_at_newline'] else None - stopping_strings = get_stopping_strings(state) - - # Preparing the input - if not any((regenerate, _continue)): - text, visible_text = apply_extensions('input_hijack', text, visible_text) - if visible_text is None: - visible_text = text - - text = apply_extensions('input', text) - # *Is typing...* - yield shared.history['visible'] + [[visible_text, shared.processing_message]] - else: - text, visible_text = shared.history['internal'][-1][0], shared.history['visible'][-1][0] - if regenerate: - shared.history['visible'].pop() - shared.history['internal'].pop() - # *Is typing...* - yield shared.history['visible'] + [[visible_text, shared.processing_message]] - elif _continue: - last_reply = [shared.history['internal'][-1][1], shared.history['visible'][-1][1]] - yield shared.history['visible'][:-1] + [[visible_text, last_reply[1] + '...']] - - # Generating the prompt - kwargs = {'_continue': _continue} - prompt = apply_extensions('custom_generate_chat_prompt', text, state, **kwargs) - if prompt is None: - prompt = generate_chat_prompt(text, state, **kwargs) - - # Generate - for i in range(state['chat_generation_attempts']): - reply = None - for j, reply in enumerate(generate_reply(prompt + cumulative_reply, state, eos_token=eos_token, stopping_strings=stopping_strings, is_chat=True)): - reply = cumulative_reply + reply - - # Extracting the reply - reply, next_character_found = extract_message_from_reply(reply, state) - visible_reply = re.sub("(
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- diff --git a/spaces/evaluate-metric/coval/coval.py b/spaces/evaluate-metric/coval/coval.py deleted file mode 100644 index f1518b95852ee164b3851633f2716de80abb32d6..0000000000000000000000000000000000000000 --- a/spaces/evaluate-metric/coval/coval.py +++ /dev/null @@ -1,321 +0,0 @@ -# Copyright 2020 The HuggingFace Evaluate Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" CoVal metric. """ -import coval # From: git+https://github.com/ns-moosavi/coval.git noqa: F401 -import datasets -from coval.conll import reader, util -from coval.eval import evaluator - -import evaluate - - -logger = evaluate.logging.get_logger(__name__) - - -_CITATION = """\ -@InProceedings{moosavi2019minimum, - author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube}, - title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection}, - year = {2019}, - booktitle = {Proceedings of the 57th Annual Meeting of - the Association for Computational Linguistics (Volume 1: Long Papers)}, - publisher = {Association for Computational Linguistics}, - address = {Florence, Italy}, -} - -@inproceedings{10.3115/1072399.1072405, -author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette}, -title = {A Model-Theoretic Coreference Scoring Scheme}, -year = {1995}, -isbn = {1558604022}, -publisher = {Association for Computational Linguistics}, -address = {USA}, -url = {https://doi.org/10.3115/1072399.1072405}, -doi = {10.3115/1072399.1072405}, -booktitle = {Proceedings of the 6th Conference on Message Understanding}, -pages = {45–52}, -numpages = {8}, -location = {Columbia, Maryland}, -series = {MUC6 ’95} -} - -@INPROCEEDINGS{Bagga98algorithmsfor, - author = {Amit Bagga and Breck Baldwin}, - title = {Algorithms for Scoring Coreference Chains}, - booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference}, - year = {1998}, - pages = {563--566} -} - -@INPROCEEDINGS{Luo05oncoreference, - author = {Xiaoqiang Luo}, - title = {On coreference resolution performance metrics}, - booktitle = {In Proc. of HLT/EMNLP}, - year = {2005}, - pages = {25--32}, - publisher = {URL} -} - -@inproceedings{moosavi-strube-2016-coreference, - title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric", - author = "Moosavi, Nafise Sadat and - Strube, Michael", - booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = aug, - year = "2016", - address = "Berlin, Germany", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/P16-1060", - doi = "10.18653/v1/P16-1060", - pages = "632--642", -} - -""" - -_DESCRIPTION = """\ -CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which -implements of the common evaluation metrics including MUC [Vilain et al, 1995], -B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005], -LEA [Moosavi and Strube, 2016] and the averaged CoNLL score -(the average of the F1 values of MUC, B-cubed and CEAFe) -[Denis and Baldridge, 2009a; Pradhan et al., 2011]. - -This wrapper of CoVal currently only work with CoNLL line format: -The CoNLL format has one word per line with all the annotation for this word in column separated by spaces: -Column Type Description -1 Document ID This is a variation on the document filename -2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. -3 Word number -4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release. -5 Part-of-Speech -6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. -7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-" -8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7. -9 Word sense This is the word sense of the word in Column 3. -10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. -11 Named Entities These columns identifies the spans representing various named entities. -12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7. -N Coreference Coreference chain information encoded in a parenthesis structure. -More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html - -Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md - -CoVal code was written by @ns-moosavi. -Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py -The test suite is taken from https://github.com/conll/reference-coreference-scorers/ -Mention evaluation and the test suite are added by @andreasvc. -Parsing CoNLL files is developed by Leo Born. -""" - -_KWARGS_DESCRIPTION = """ -Calculates coreference evaluation metrics. -Args: - predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format. - Each prediction is a word with its annotations as a string made of columns joined with spaces. - Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation) - See the details on the format in the description of the metric. - references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format. - Each reference is a word with its annotations as a string made of columns joined with spaces. - Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation) - See the details on the format in the description of the metric. - keep_singletons: After extracting all mentions of key or system files, - mentions whose corresponding coreference chain is of size one, - are considered as singletons. The default evaluation mode will include - singletons in evaluations if they are included in the key or the system files. - By setting 'keep_singletons=False', all singletons in the key and system files - will be excluded from the evaluation. - NP_only: Most of the recent coreference resolvers only resolve NP mentions and - leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs. - min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans. - Minimum spans are determined using the MINA algorithm. - -Returns: - 'mentions': mentions - 'muc': MUC metric [Vilain et al, 1995] - 'bcub': B-cubed [Bagga and Baldwin, 1998] - 'ceafe': CEAFe [Luo et al., 2005] - 'lea': LEA [Moosavi and Strube, 2016] - 'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe) - -Examples: - - >>> coval = evaluate.load('coval') - >>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -', - ... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)', - ... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)', - ... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -', - ... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -', - ... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -'] - >>> references = [words] - >>> predictions = [words] - >>> results = coval.compute(predictions=predictions, references=references) - >>> print(results) # doctest:+ELLIPSIS - {'mentions/recall': 1.0,[...] 'conll_score': 100.0} -""" - - -def get_coref_infos( - key_lines, sys_lines, NP_only=False, remove_nested=False, keep_singletons=True, min_span=False, doc="dummy_doc" -): - - key_doc_lines = {doc: key_lines} - sys_doc_lines = {doc: sys_lines} - - doc_coref_infos = {} - - key_nested_coref_num = 0 - sys_nested_coref_num = 0 - key_removed_nested_clusters = 0 - sys_removed_nested_clusters = 0 - key_singletons_num = 0 - sys_singletons_num = 0 - - key_clusters, singletons_num = reader.get_doc_mentions(doc, key_doc_lines[doc], keep_singletons) - key_singletons_num += singletons_num - - if NP_only or min_span: - key_clusters = reader.set_annotated_parse_trees(key_clusters, key_doc_lines[doc], NP_only, min_span) - - sys_clusters, singletons_num = reader.get_doc_mentions(doc, sys_doc_lines[doc], keep_singletons) - sys_singletons_num += singletons_num - - if NP_only or min_span: - sys_clusters = reader.set_annotated_parse_trees(sys_clusters, key_doc_lines[doc], NP_only, min_span) - - if remove_nested: - nested_mentions, removed_clusters = reader.remove_nested_coref_mentions(key_clusters, keep_singletons) - key_nested_coref_num += nested_mentions - key_removed_nested_clusters += removed_clusters - - nested_mentions, removed_clusters = reader.remove_nested_coref_mentions(sys_clusters, keep_singletons) - sys_nested_coref_num += nested_mentions - sys_removed_nested_clusters += removed_clusters - - sys_mention_key_cluster = reader.get_mention_assignments(sys_clusters, key_clusters) - key_mention_sys_cluster = reader.get_mention_assignments(key_clusters, sys_clusters) - - doc_coref_infos[doc] = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster) - - if remove_nested: - logger.info( - "Number of removed nested coreferring mentions in the key " - f"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}" - ) - logger.info( - "Number of resulting singleton clusters in the key " - f"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}" - ) - - if not keep_singletons: - logger.info( - f"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system " - "files, respectively" - ) - - return doc_coref_infos - - -def compute_score(key_lines, sys_lines, metrics, NP_only, remove_nested, keep_singletons, min_span): - doc_coref_infos = get_coref_infos(key_lines, sys_lines, NP_only, remove_nested, keep_singletons, min_span) - - output_scores = {} - conll = 0 - conll_subparts_num = 0 - - for name, metric in metrics: - recall, precision, f1 = evaluator.evaluate_documents(doc_coref_infos, metric, beta=1) - if name in ["muc", "bcub", "ceafe"]: - conll += f1 - conll_subparts_num += 1 - output_scores.update({f"{name}/recall": recall, f"{name}/precision": precision, f"{name}/f1": f1}) - - logger.info( - name.ljust(10), - f"Recall: {recall * 100:.2f}", - f" Precision: {precision * 100:.2f}", - f" F1: {f1 * 100:.2f}", - ) - - if conll_subparts_num == 3: - conll = (conll / 3) * 100 - logger.info(f"CoNLL score: {conll:.2f}") - output_scores.update({"conll_score": conll}) - - return output_scores - - -def check_gold_parse_annotation(key_lines): - has_gold_parse = False - for line in key_lines: - if not line.startswith("#"): - if len(line.split()) > 6: - parse_col = line.split()[5] - if not parse_col == "-": - has_gold_parse = True - break - else: - break - return has_gold_parse - - -@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) -class Coval(evaluate.Metric): - def _info(self): - return evaluate.MetricInfo( - description=_DESCRIPTION, - citation=_CITATION, - inputs_description=_KWARGS_DESCRIPTION, - features=datasets.Features( - { - "predictions": datasets.Sequence(datasets.Value("string")), - "references": datasets.Sequence(datasets.Value("string")), - } - ), - codebase_urls=["https://github.com/ns-moosavi/coval"], - reference_urls=[ - "https://github.com/ns-moosavi/coval", - "https://www.aclweb.org/anthology/P16-1060", - "http://www.conll.cemantix.org/2012/data.html", - ], - ) - - def _compute( - self, predictions, references, keep_singletons=True, NP_only=False, min_span=False, remove_nested=False - ): - allmetrics = [ - ("mentions", evaluator.mentions), - ("muc", evaluator.muc), - ("bcub", evaluator.b_cubed), - ("ceafe", evaluator.ceafe), - ("lea", evaluator.lea), - ] - - if min_span: - has_gold_parse = util.check_gold_parse_annotation(references) - if not has_gold_parse: - raise NotImplementedError("References should have gold parse annotation to use 'min_span'.") - # util.parse_key_file(key_file) - # key_file = key_file + ".parsed" - - score = compute_score( - key_lines=references, - sys_lines=predictions, - metrics=allmetrics, - NP_only=NP_only, - remove_nested=remove_nested, - keep_singletons=keep_singletons, - min_span=min_span, - ) - - return score diff --git a/spaces/facebook/MusicGen/audiocraft/train.py b/spaces/facebook/MusicGen/audiocraft/train.py deleted file mode 100644 index 5851222c39e173f91dc9dafe962470c52cf2fba6..0000000000000000000000000000000000000000 --- a/spaces/facebook/MusicGen/audiocraft/train.py +++ /dev/null @@ -1,163 +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. - -""" -Entry point for dora to launch solvers for running training loops. -See more info on how to use dora: https://github.com/facebookresearch/dora -""" - -import logging -import multiprocessing -import os -from pathlib import Path -import sys -import typing as tp - -from dora import git_save, hydra_main, XP -import flashy -import hydra -import omegaconf - -from .environment import AudioCraftEnvironment -from .utils.cluster import get_slurm_parameters - -logger = logging.getLogger(__name__) - - -def resolve_config_dset_paths(cfg): - """Enable Dora to load manifest from git clone repository.""" - # manifest files for the different splits - for key, value in cfg.datasource.items(): - if isinstance(value, str): - cfg.datasource[key] = git_save.to_absolute_path(value) - - -def get_solver(cfg): - from . import solvers - # Convert batch size to batch size for each GPU - assert cfg.dataset.batch_size % flashy.distrib.world_size() == 0 - cfg.dataset.batch_size //= flashy.distrib.world_size() - for split in ['train', 'valid', 'evaluate', 'generate']: - if hasattr(cfg.dataset, split) and hasattr(cfg.dataset[split], 'batch_size'): - assert cfg.dataset[split].batch_size % flashy.distrib.world_size() == 0 - cfg.dataset[split].batch_size //= flashy.distrib.world_size() - resolve_config_dset_paths(cfg) - solver = solvers.get_solver(cfg) - return solver - - -def get_solver_from_xp(xp: XP, override_cfg: tp.Optional[tp.Union[dict, omegaconf.DictConfig]] = None, - restore: bool = True, load_best: bool = True, - ignore_state_keys: tp.List[str] = [], disable_fsdp: bool = True): - """Given a XP, return the Solver object. - - Args: - xp (XP): Dora experiment for which to retrieve the solver. - override_cfg (dict or None): If not None, should be a dict used to - override some values in the config of `xp`. This will not impact - the XP signature or folder. The format is different - than the one used in Dora grids, nested keys should actually be nested dicts, - not flattened, e.g. `{'optim': {'batch_size': 32}}`. - restore (bool): If `True` (the default), restore state from the last checkpoint. - load_best (bool): If `True` (the default), load the best state from the checkpoint. - ignore_state_keys (list[str]): List of sources to ignore when loading the state, e.g. `optimizer`. - disable_fsdp (bool): if True, disables FSDP entirely. This will - also automatically skip loading the EMA. For solver specific - state sources, like the optimizer, you might want to - use along `ignore_state_keys=['optimizer']`. Must be used with `load_best=True`. - """ - logger.info(f"Loading solver from XP {xp.sig}. 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When using a specific solver, - # one is responsible for adding the relevant keys, e.g. 'optimizer'. - # We could make something to automatically register those inside the solver, but that - # seem overkill at this point. - ignore_state_keys = ignore_state_keys + ['model', 'ema', 'best_state'] - - try: - with xp.enter(): - solver = get_solver(cfg) - if restore: - solver.restore(load_best=load_best, ignore_state_keys=ignore_state_keys) - return solver - finally: - hydra.core.global_hydra.GlobalHydra.instance().clear() - - -def get_solver_from_sig(sig: str, *args, **kwargs): - """Return Solver object from Dora signature, i.e. to play with it from a notebook. - See `get_solver_from_xp` for more information. - """ - xp = main.get_xp_from_sig(sig) - return get_solver_from_xp(xp, *args, **kwargs) - - -def init_seed_and_system(cfg): - import numpy as np - import torch - import random - from audiocraft.modules.transformer import set_efficient_attention_backend - - multiprocessing.set_start_method(cfg.mp_start_method) - logger.debug('Setting mp start method to %s', cfg.mp_start_method) - random.seed(cfg.seed) - np.random.seed(cfg.seed) - # torch also initialize cuda seed if available - torch.manual_seed(cfg.seed) - torch.set_num_threads(cfg.num_threads) - os.environ['MKL_NUM_THREADS'] = str(cfg.num_threads) - os.environ['OMP_NUM_THREADS'] = str(cfg.num_threads) - logger.debug('Setting num threads to %d', cfg.num_threads) - set_efficient_attention_backend(cfg.efficient_attention_backend) - logger.debug('Setting efficient attention backend to %s', cfg.efficient_attention_backend) - if 'SLURM_JOB_ID' in os.environ: - tmpdir = Path('/scratch/slurm_tmpdir/' + os.environ['SLURM_JOB_ID']) - if tmpdir.exists(): - logger.info("Changing tmpdir to %s", tmpdir) - os.environ['TMPDIR'] = str(tmpdir) - - -@hydra_main(config_path='../config', config_name='config', version_base='1.1') -def main(cfg): - init_seed_and_system(cfg) - - # Setup logging both to XP specific folder, and to stderr. - log_name = '%s.log.{rank}' % cfg.execute_only if cfg.execute_only else 'solver.log.{rank}' - flashy.setup_logging(level=str(cfg.logging.level).upper(), log_name=log_name) - # Initialize distributed training, no need to specify anything when using Dora. - flashy.distrib.init() - solver = get_solver(cfg) - if cfg.show: - solver.show() - return - - if cfg.execute_only: - assert cfg.execute_inplace or cfg.continue_from is not None, \ - "Please explicitly specify the checkpoint to continue from with continue_from=
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-Do you love playing bus simulator games? Do you want to experience driving a bus in a different country? If yes, then you should try Bus Simulator Indonesia, a popular simulation game that lets you drive a bus in Indonesia. But wait, there's more! You can also download and install a Sri Lanka map for Bus Simulator Indonesia and enjoy driving in the beautiful island nation of Sri Lanka. Sounds exciting, right?
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In this article, we will tell you everything you need to know about Bus Simulator Indonesia and how to download and install the Sri Lanka map for it. We will also give you some tips and tricks to make your gameplay more fun and realistic. So, let's get started!
-What is Bus Simulator Indonesia?
-Bus Simulator Indonesia, also known as BUS ID, is a simulation game developed by Maleo, an Indonesian game studio. It was released in 2017 and has since gained millions of fans around the world. The game is available for Android and iOS devices, as well as Windows PC.
-Features of Bus Simulator Indonesia
-Bus Simulator Indonesia is not just a regular bus simulator game. It has many features that make it stand out from other similar games. Some of these features are:
--
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- Realistic Indonesian buses: You can choose from a variety of buses that are commonly used in Indonesia, such as minibus, city bus, intercity bus, double-decker bus, and more. You can also customize your bus with different colors, designs, stickers, horns, lights, etc. -
- Authentic Indonesian environment: You can drive your bus in various locations in Indonesia, such as Jakarta, Bali, Sumatra, Java, and more. You can see the landmarks, buildings, roads, traffic signs, and other details that are unique to Indonesia. -
- Dynamic weather and time system: You can experience different weather conditions and time of day in the game, such as sunny, rainy, foggy, night, dawn, etc. The weather and time will affect your visibility, driving speed, and fuel consumption. -
- Realistic traffic and driving physics: You can feel the thrill of driving a bus in a busy traffic with realistic traffic rules, signals, pedestrians, vehicles, accidents, etc. You also have to follow the speed limit, brake properly, use indicators, honk when necessary, etc. -
- Friendly user interface and controls: You can easily navigate the game menu and options with a simple and intuitive user interface. You can also choose from different control modes, such as tilt, steering wheel, buttons, or keyboard. -
- Online multiplayer mode: You can play with other players online and compete with them in various challenges and events. You can also chat with them using voice or text messages. -
- Mod support: You can download and install various mods for the game that add new features, vehicles, maps, skins, etc. You can also create your own mods using the built-in editor. -
How to play Bus Simulator Indonesia
-Playing Bus Simulator Indonesia is easy and fun. Here are the basic steps to play the game:
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- Select your bus: You can choose from a variety of buses that are available in the game. You can also customize your bus with different colors, designs, stickers, horns, lights, etc. -
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- Select your destination: You can choose from various locations in Indonesia that are available in the game. You can also select the weather and time of day for your trip. -
- Start your engine: You can start your bus by pressing the ignition button. You can also adjust the mirrors, seat, steering wheel, etc. before you start driving. -
- Drive your bus: You can drive your bus using the control mode that you prefer, such as tilt, steering wheel, buttons, or keyboard. You have to follow the traffic rules, signals, speed limit, etc. while driving. You also have to avoid accidents, collisions, and other hazards on the road. -
- Pick up and drop off passengers: You can stop at designated bus stops and pick up passengers who are waiting for you. You have to collect their fares and give them tickets. You also have to drop them off at their destinations and wish them a good day. -
- Earn money and rewards: You can earn money and rewards for completing your trips successfully. You can use the money to buy new buses, upgrade your existing buses, or unlock new features. You can also use the rewards to participate in online events and challenges. -
Why download the Sri Lanka map for Bus Simulator Indonesia?
-If you are bored of driving in Indonesia and want to try something new, you should download and install the Sri Lanka map for Bus Simulator Indonesia. The Sri Lanka map is a mod that adds a new map of Sri Lanka to the game. It was created by a fan of the game named SL Modder. The mod is free and easy to install.
-There are many reasons why you should download and install the Sri Lanka map for Bus Simulator Indonesia. Some of these reasons are:
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Explore the beautiful scenery of Sri Lanka
-Sri Lanka is a beautiful island nation located in the Indian Ocean. It has a rich and diverse natural and cultural heritage. It is known for its stunning beaches, mountains, forests, wildlife, temples, monuments, and more.
-By downloading and installing the Sri Lanka map for Bus Simulator Indonesia, you can explore the beauty of Sri Lanka in the game. You can drive your bus in various locations in Sri Lanka, such as Colombo, Kandy, Galle, Nuwara Eliya, Anuradhapura, Jaffna, and more. You can see the landmarks, buildings, roads, traffic signs, and other details that are unique to Sri Lanka.
-Experience the realistic traffic and driving conditions of Sri Lanka
-Sri Lanka has a different traffic and driving system than Indonesia. It has different traffic rules, signals, vehicles, pedestrians, etc. It also has different road conditions, such as narrow roads, sharp turns, steep slopes, etc.
-By downloading and installing the Sri Lanka map for Bus Simulator Indonesia, you can experience the realistic traffic and driving conditions of Sri Lanka in the game. You can feel the challenge of driving a bus in a busy traffic with different traffic rules, signals, vehicles, pedestrians, etc. You also have to adapt to the different road conditions, such as narrow roads, sharp turns, steep slopes, etc.
-Customize your bus with Sri Lankan stickers and accessories
-Sri Lanka has a different culture and style than Indonesia. It has different colors, designs, stickers, accessories, etc. that are commonly used in Sri Lanka. It also has different types of buses, such as private bus, public bus, luxury bus, etc.
-By downloading and installing the Sri Lanka map for Bus Simulator Indonesia, you can customize your bus with Sri Lankan stickers and accessories in the game. You can choose from a variety of stickers and accessories that are available in the mod, such as Sri Lankan flags, logos, slogans, symbols, etc. You can also choose from different types of buses that are available in the mod, such as private bus, public bus, luxury bus, etc.
-How to download and install the Sri Lanka map for Bus Simulator Indonesia?
-Downloading and installing the Sri Lanka map for Bus Simulator Indonesia is easy and simple. Here are the requirements and steps to do it:
-Requirements for downloading and installing the Sri Lanka map
-To download and install the Sri Lanka map for Bus Simulator Indonesia, you need the following:
--
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- An Android device: You need an Android device that can run Bus Simulator Indonesia smoothly. The minimum requirements are Android 4.2 or higher, 1 GB of RAM, and 300 MB of free storage space. -
- Bus Simulator Indonesia game: You need to have Bus Simulator Indonesia game installed on your Android device. You can download it from Google Play Store or from the official website of Maleo. -
- Sri Lanka map mod file: You need to download the Sri Lanka map mod file from a reliable source. You can find it on various websites and forums that offer mods for Bus Simulator Indonesia. The file size is about 100 MB. -
- A file manager app: You need a file manager app that can extract and move files on your Android device. You can use any file manager app that you prefer, such as ES File Explorer, ZArchiver, etc. -
Steps to download and install the Sri Lanka map
-After you have all the requirements ready, you can follow these steps to download and install the Sri Lanka map for Bus Simulator Indonesia:
--
-
- Download the Sri Lanka map mod file: You can download the Sri Lanka map mod file from a reliable source. You can find it on various websites and forums that offer mods for Bus Simulator Indonesia. The file name is
SL Map.zip.
- - Extract the Sri Lanka map mod file: You can use a file manager app to extract the Sri Lanka map mod file on your Android device. You will get a folder named
SL Mapthat contains two subfolders:Bus IDandBUS ID MODS.
- - Move the subfolders to the game folder: You can use a file manager app to move the two subfolders
Bus IDandBUS ID MODSto the game folder of Bus Simulator Indonesia on your Android device. The game folder is usually located at/storage/emulated/0/Android/data/com.maleo.bussimulatorid/. If you already have a folder namedBUS ID MODSin the game folder, you can merge it with the new one.
- - Launch the game and select the Sri Lanka map: You can launch Bus Simulator Indonesia game on your Android device and go to the settings menu. You will see a new option named
Select Map. You can tap on it and selectSri Lanka Map by SL Modder. Then, you can start playing with the Sri Lanka map.
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Tips and tricks for playing with the Sri Lanka map
-To make your gameplay more fun and realistic with the Sri Lanka map for Bus Simulator Indonesia, you can follow these tips and tricks:
--
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- Learn the traffic rules of Sri Lanka: Sri Lanka has different traffic rules than Indonesia. For example, Sri Lanka drives on the left side of the road, while Indonesia drives on the right side. Sri Lanka also has different traffic signs, signals, speed limits, etc. You should learn them before you start driving in Sri Lanka. -
- Use the horn wisely: In Sri Lanka, using the horn is not considered rude or annoying. It is a way of communicating with other drivers and pedestrians on the road. You can use the horn to warn others of your presence, to overtake another vehicle, to greet someone, etc. However, you should not use it excessively or unnecessarily. -
- Enjoy the scenery and culture of Sri Lanka: Sri Lanka is a beautiful and diverse country that has a lot to offer to the visitors. You can enjoy the scenery and culture of Sri Lanka while driving your bus in the game. You can see the beaches, mountains, forests, wildlife, temples, monuments, and more. You can also hear the music, language, and sounds of Sri Lanka. -
- Customize your bus with Sri Lankan stickers and accessories: You can make your bus more attractive and unique by customizing it with Sri Lankan stickers and accessories. You can choose from a variety of stickers and accessories that are available in the mod, such as Sri Lankan flags, logos, slogans, symbols, etc. You can also choose from different types of buses that are available in the mod, such as private bus, public bus, luxury bus, etc. -
Conclusion
-Bus Simulator Indonesia is a fun and realistic simulation game that lets you drive a bus in Indonesia. You can also download and install the Sri Lanka map for Bus Simulator Indonesia and enjoy driving in the beautiful island nation of Sri Lanka. The Sri Lanka map is a mod that adds a new map of Sri Lanka to the game. It was created by a fan of the game named SL Modder. The mod is free and easy to install.
-In this article, we have told you everything you need to know about Bus Simulator Indonesia and how to download and install the Sri Lanka map for it. We have also given you some tips and tricks to make your gameplay more fun and realistic. We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.
-FAQs
-Here are some frequently asked questions about Bus Simulator Indonesia and the Sri Lanka map for it:
-Q: Is Bus Simulator Indonesia free to play?
-A: Yes, Bus Simulator Indonesia is free to play. You can download it from Google Play Store or from the official website of Maleo. However, some features and items may require in-app purchases.
-Q: Is the Sri Lanka map for Bus Simulator Indonesia safe to download and install?
-A: Yes, the Sri Lanka map for Bus Simulator Indonesia is safe to download and install. However, you should always download it from a reliable source and scan it with an antivirus app before installing it.
-Q: Can I play Bus Simulator Indonesia offline?
-A: Yes, you can play Bus Simulator Indonesia offline. However, some features and events may require an internet connection.
-Q: Can I play Bus Simulator Indonesia on PC?
-A: Yes, you can play Bus Simulator Indonesia on PC. You can download it from the official website of Maleo or from Steam. You can also use an Android emulator to play it on PC.
-Q: Can I create my own mods for Bus Simulator Indonesia?
-A: Yes, you can create your own mods for Bus Simulator Indonesia using the built-in editor. You can also share your mods with other players online.
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- Level 5: Very hard. The computer will make very strong moves, but also some minor errors. -
- Level 6: Expert. The computer will make almost perfect moves, but also some rare mistakes. -
- Level 7: Master. The computer will make nearly flawless moves, but also some exceptional blunders. -
- Level 8: Grandmaster. The computer will make the best possible moves, but also some human-like errors. -
Learn from hints and undo moves
-If you are stuck or need some help, you can use the hint feature, which will show you the best move for your position. You can also undo your last move, or any number of moves, if you want to try a different strategy. These features are very useful for learning and improving your chess skills.
-Track your stats and achievements
-The app keeps track of your stats and achievements, which you can view anytime. You can see how many games you played, won, lost, or drew, as well as your rating and ranking. You can also see how many puzzles and challenges you solved, and how many stars you earned. You can also unlock various achievements, such as winning a game in less than 10 moves, checkmating with a pawn, or beating the grandmaster level.
-Customize your board and pieces
-The app lets you customize your board and pieces, according to your preference. You can choose from different themes, colors, and styles for your board and pieces. You can also choose from 2D or 3D graphics, and adjust the sound and animation effects. You can make your chess experience more enjoyable and personal with these options.
-Tips and tricks for playing chess offline online apk
-Practice with puzzles and challenges
-The app offers various puzzles and challenges, which will test your tactical abilities and knowledge of chess. You can solve different types of puzzles, such as checkmate in one, two, or three moves, find the best move, or capture the queen. You can also take on different challenges, such as mate in 10, survive for 20 moves, or win with a rook down. These puzzles and challenges will help you sharpen your skills and have fun at the same time.
-Study the openings and endgames
-The app also provides a useful feature for studying the openings and endgames of chess. You can access a database of over 1000 opening variations, which will show you the most common and effective moves for each position. You can also access a database of over 100 endgame positions, which will show you the most important principles and techniques for winning or drawing in different situations. These features will help you improve your understanding and strategy of chess.
-Analyze your games and learn from your mistakes
-The app also allows you to analyze your games and learn from your mistakes. You can review your games move by move, and see where you made good or bad decisions. You can also use the engine analysis feature, which will show you the evaluation of each position, and the best possible moves for both sides. You can also export your games in PGN format, which you can share with others or use with other chess software. These features will help you improve your skills and avoid repeating your errors.
-Join a chess club and play with friends
-The app also enables you to join a chess club and play with friends online. You can create or join a club of your choice, and invite other players to join. You can chat with other club members, send messages, or challenge them to a game. You can also participate in club tournaments, which will give you the opportunity to compete with other players of similar skill level. These features will help you socialize and have fun with other chess enthusiasts.
-Conclusion
-Summary of the main points
-In conclusion, chess offline online apk is a great app for playing chess on your Android device, without any internet connection. You can play against the computer or another player on the same device, or switch to online mode and play with millions of players from around the world. You can choose from 8 levels of difficulty, learn from hints and undo moves, track your stats and achievements, customize your board and pieces, practice with puzzles and challenges, study the openings and endgames, analyze your games and learn from your mistakes, and join a chess club and play with friends. Chess offline online apk is based on the Chess Club app by GamoVation, which has over 10 million downloads and 4.9 stars rating on Google Play. It is one of the best chess apps available for Android users.
-Call to action
-If you are a chess lover, or want to become one, you should definitely download chess offline online apk and enjoy playing chess anytime, anywhere. You will not only have fun, but also improve your skills and knowledge of chess. Chess offline online apk is free, easy to use, and full of features. So what are you waiting for? Download it now and start playing chess like a pro!
-FAQs
-Here are some frequently asked questions about chess offline online apk:
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-
- Q: How can I switch from offline to online mode?
A: You can switch from offline to online mode by tapping on the menu icon on the top left corner of the screen, and then selecting Online Mode. You will need an internet connection to play online.
- - Q: How can I join or create a chess club?
A: You can join or create a chess club by tapping on the menu icon on the top left corner of the screen, and then selecting Chess Club. You will need to sign in with your Google account to join or create a club.
- - Q: How can I export my games in PGN format?
A: You can export your games in PGN format by tapping on the menu icon on the top left corner of the screen, and then selecting My Games. You will see a list of your games, and you can tap on the share icon on the right side of each game to export it in PGN format.
- - Q: How can I change the theme, color, or style of my board and pieces?
A: You can change the theme, color, or style of your board and pieces by tapping on the menu icon on the top left corner of the screen, and then selecting Settings. You will see various options for customizing your board and pieces.
- - Q: How can I contact the developers or give feedback?
A: You can contact the developers or give feedback by tapping on the menu icon on the top left corner of the screen, and then selecting About Us. You will see the contact details and social media links of the developers.
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-
What is DOST and why you should know about it
-If you are a citizen of Azerbaijan or a foreigner living in the country, you may have heard of dost.gov.az, but do you know what it is and how it can help you? In this article, we will explain what DOST is, what services and benefits it offers, how to access them online or offline, and what achievements and innovations it has made in the field of social security. By the end of this article, you will have a better understanding of DOST and why you should know about it.
-DOST stands for Sustainable and Operational Social Security Agency
-DOST is an acronym for Dayanıqlı və Operativ Sosial Təminat Agentliyi, which means Sustainable and Operational Social Security Agency in English. It is a public entity under the Ministry of Labor and Social Protection of Population of Azerbaijan, established in 2019 by the decree of President Ilham Aliyev. The main goal of DOST is to improve the welfare of the population, to increase public care for vulnerable groups, to use innovative solutions to achieve this, and to reach citizen satisfaction with such steps .
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The mission and vision of DOST
-The mission of DOST is to provide social security services through a single window principle, which means that citizens can access all the social security directions at one unique place - DOST. These directions include labor pensions and social benefits, targeted state social assistance, disability, employment, labor relations, assignment of the Presidential Grant, social services, issue of references, social insurance, state compulsory personal insurance payments, child adoption, functional supportive service, etc.
-The vision of DOST is to become a leading agency in the region and in the world in terms of providing high-quality, transparent, efficient, and innovative social security services to the citizens. DOST aims to ensure that every citizen feels the care and support of the state in every stage of their life.
-The services and benefits offered by DOST
-DOST offers 154 services through a single window principle. Some of the most popular and important services are:
--
-
- Labor pensions and social benefits: These include old-age pension, disability pension, survivor's pension, social allowance for disability, social allowance for old age, etc. -
- Targeted state social assistance: This is a monthly cash benefit for low-income families who meet certain criteria. -
- Disability: This includes assessment of disability degree, provision of technical aids and prosthetic devices, rehabilitation services, etc. -
- Employment: This includes job placement, vocational training, unemployment benefits, self-employment program, etc. -
- Labor relations: This includes registration of labor contracts, protection of labor rights, resolution of labor disputes, etc. -
- Assignment of the Presidential Grant: This is a monthly cash benefit for war veterans, martyrs' families, national heroes, etc. -
- Social services: This includes home care service for elderly and disabled people, day care centers for children with disabilities, shelters for victims of domestic violence, etc. -
- Issue of references: This includes references for pensioners, beneficiaries of social allowances and targeted state social assistance, disabled people, etc. -
- Social insurance: This includes calculation and payment of social insurance contributions by employers and employees. -
- State compulsory personal insurance payments: This includes calculation and payment of state compulsory personal insurance contributions by employers and employees for people who are injured or killed in the line of duty. -
- Child adoption: This includes registration of prospective adoptive parents, matching of children and parents, provision of post-adoption support, etc. -
- Functional supportive service: This includes provision of psychological, legal, and social counseling to the citizens who apply to DOST. -
These services and benefits are designed to meet the needs and expectations of different groups of citizens, such as pensioners, disabled people, low-income families, unemployed people, workers, war veterans, martyrs' families, national heroes, children, women, etc. DOST strives to provide these services and benefits in a timely, accurate, and fair manner.
-How to access DOST services online or offline
-DOST offers various ways for the citizens to access its services online or offline. Depending on the preference and convenience of the citizens, they can choose one of the following methods:
-The online queue system and the mobile DOST app
-The online queue system is a web-based platform that allows the citizens to book an appointment for any DOST service online. The citizens can visit the website dost.gov.az and select the service they need, the date and time they prefer, and the DOST center they want to visit. They will receive a confirmation code via SMS or email. They can also cancel or reschedule their appointment online if needed.
-The mobile DOST app is a smartphone application that allows the citizens to access DOST services on their mobile devices. The app is available for both Android and iOS users. The app has the following features:
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-
- Online queue system: The app allows the citizens to book, cancel, or reschedule an appointment for any DOST service online. -
- Online application: The app allows the citizens to apply for some DOST services online, such as targeted state social assistance, labor pensions and social benefits, disability assessment, etc. -
- Online payment: The app allows the citizens to pay for some DOST services online, such as social insurance contributions, state compulsory personal insurance payments, etc. -
- Online information: The app allows the citizens to access information about DOST services, such as eligibility criteria, required documents, calculation methods, etc. -
- Online feedback: The app allows the citizens to provide feedback on DOST services, such as satisfaction level, suggestions, complaints, etc. -
- Online chat: The app allows the citizens to communicate with DOST staff via online chat and get answers to their questions. -
The locations and contacts of DOST centers
-DOST centers are physical facilities where the citizens can access DOST services in person. There are currently 11 DOST centers operating in different regions of Azerbaijan. These are:
-DOST centers in Azerbaijan
-DOST online queue system
-DOST mobile service
-DOST volunteer program
-DOST surdo translation service
-DOST job center
-DOST center for inclusive development and creativity
-DOST digital innovation center
-DOST labor pensions and social benefits
-DOST targeted state social assistance
-DOST disability services
-DOST employment services
-DOST labor relations services
-DOST presidential grant services
-DOST social services
-DOST issue of references services
-DOST social insurance services
-DOST state compulsory personal insurance payments services
-DOST child adoption services
-DOST functional supportive service
-DOST agency structure and leadership
-DOST agency mission and vision
-DOST agency news and events
-DOST agency statistics and reports
-DOST agency legislation and regulations
-DOST agency cooperation and partnerships
-DOST agency feedback and complaints
-DOST agency vacancies and tenders
-DOST agency contact information and location map
-How to apply for DOST services online
-How to register for DOST online portal
-How to check the status of your application at DOST online portal
-How to find the nearest DOST center to your location
-How to make an appointment at a DOST center online or by phone
-How to cancel or reschedule your appointment at a DOST center online or by phone
-How to get a surdo translation service at a DOST center or online
-How to join the volunteer program at a DOST center or online
-How to request a mobile service from a DOST center or online
-How to access the job center at a DOST center or online
-How to access the center for inclusive development and creativity at a DOST center or online
-How to access the digital innovation center at a DOST center or online
-How to calculate your labor pension and social benefits at a DOST center or online
-How to apply for targeted state social assistance at a DOST center or online
-How to get a disability assessment and certificate at a DOST center or online
-How to get employment assistance and support at a DOST center or online
-How to get labor relations advice and mediation at a DOST center or online
-How to apply for the presidential grant at a DOST center or online
-How to get social services such as home care, rehabilitation, etc. at a DOST center or online
-How to get references such as birth certificate, marriage certificate, etc. at a DOST center or online
-How to pay your social insurance and state compulsory personal insurance payments at a DOST center or online
| DOST center | Address | Contact number |
|---|---|---|
| DOST 1 | Baku city, Yasamal district, Jafar Jabbarli street 44 | +994 12 310 00 01 |
| DOST 2 | Baku city, Sabunchu district, Bakikhanov settlement, Ziya Bunyadov avenue 1965 | +994 12 310 00 02 |
| DOST 3 | Baku city, Khatai district, Nobel avenue 23A | +994 12 310 00 03 |
| DOST 4 | Baku city, Nizami district, Heydar Aliyev avenue 115 | +994 12 310 00 04 |
| DOST 5 | Baku city, Surakhani district, Zabrat-2 settlement, 20 January street 29 | +994 12 310 00 05 |
| DOST 6 | Baku city, Binagadi district, Zabrat settlement, Heydar Aliyev avenue 3123 | +994 12 310 00 06 |
| DOST 7 | Baku city, Garadagh district, Lokbatan settlement, Heydar Aliyev avenue 3121 | +994 12 310 00 07 |
| DOST 8 | Sumgayit city, Koroglu avenue 25 | +994 18 310 00 08 |
| DOST 9 | Ganja city, Nizami district, Javadkhan street 242 | +994 22 310 00 09 |
| DOST 10 | Mingachevir city, Heydar Aliyev avenue 92 | +994 24 310 00 10 |
| DOST 11 | Shamakhi city, Heydar Aliyev avenue 2A | +994 176 310 00 11 |
The citizens can visit any DOST center without an appointment from Monday to Friday, from 09:00 to 18:00. However, it is recommended to book an appointment online to avoid waiting in line. The citizens can also call the contact numbers of the DOST centers to get information or assistance.
-The achievements and innovations of DOST
-DOST has achieved remarkable results and innovations in the field of social security since its establishment. Some of the most notable ones are:
-The citizen satisfaction level and the monitoring system
-DOST has achieved a high level of citizen satisfaction with its services. According to the surveys conducted by DOST, the average satisfaction level of the citizens who applied to DOST centers was 96.8% in 2020 and 97.4% in 2021. This indicates that DOST has met or exceeded the expectations of the citizens in terms of quality, transparency, efficiency, and innovation of its services.
-DOST also has a monitoring system that allows it to measure and improve its performance. The monitoring system consists of several indicators, such as the number of citizens served, the average waiting time, the average service time, the number of complaints and suggestions, etc. The monitoring system also allows DOST to identify and eliminate any problems or shortcomings in its services.
-The international recognition and cooperation of DOST
-DOST has received international recognition and cooperation for its achievements and innovations in the field of social security. Some of the examples are:
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- In 2020, DOST won the United Nations Public Service Award in the category of "Delivering inclusive and equitable services for all" for its online queue system. This award is the most prestigious international recognition of excellence in public service. -
- In 2021, DOST became a member of the International Social Security Association (ISSA). This is a leading international organization that brings together national social security administrations and agencies from around the world. -
- In 2021, DOST signed a memorandum of understanding with the Turkish Social Security Institution (SGK). This is a bilateral agreement that aims to enhance cooperation and exchange of experience between the two institutions in the field of social security. -
- In 2021, DOST hosted a webinar on "Innovations in Social Security: The Experience of Azerbaijan" for the representatives of the Organization of Islamic Cooperation (OIC). This is a multilateral platform that aims to promote dialogue and cooperation among the member states of the OIC. -
Conclusion
-Summary of the main points
-In conclusion, DOST is a public entity that provides social security services to the citizens of Azerbaijan through a single window principle. DOST stands for Sustainable and Operational Social Security Agency and it was established in 2019 by the decree of President Ilham Aliyev. DOST offers 154 services in different social security directions, such as labor pensions and social benefits, targeted state social assistance, disability, employment, labor relations, assignment of the Presidential Grant, social services, issue of references, social insurance, state compulsory personal insurance payments, child adoption, functional supportive service, etc . DOST allows the citizens to access its services online or offline, through the online queue system, the mobile DOST app, or the DOST centers. DOST has achieved a high level of citizen satisfaction and a monitoring system that measures and improves its performance. DOST has also received international recognition and cooperation for its achievements and innovations in the field of social security.
-Call to action and contact information
-If you are interested in learning more about DOST or applying for any of its services, you can visit its official website dost.gov.az or download its mobile app from Google Play or App Store. You can also call the hotline number 141 or visit one of the DOST centers near you. DOST is always ready to serve you and to make your life better.
-Thank you for reading this article and we hope you found it useful and informative. If you have any questions, comments, or feedback, please feel free to contact us at info@dost.gov.az or leave a comment below. We would love to hear from you and to answer your queries.
-FAQs
-What is the difference between DOST and ASAN?
-DOST and ASAN are both public entities that provide public services to the citizens of Azerbaijan through a single window principle. However, they have different scopes and functions. ASAN stands for ASAN Xidmət, which means Easy Service in English. It is a public entity under the State Agency for Public Service and Social Innovations under the President of Azerbaijan, established in 2012 by the decree of President Ilham Aliyev. ASAN provides various public services in different sectors, such as education, health, justice, migration, transport, communication, etc. DOST stands for Dayanıqlı və Operativ Sosial Təminat Agentliyi, which means Sustainable and Operational Social Security Agency in English. It is a public entity under the Ministry of Labor and Social Protection of Population of Azerbaijan, established in 2019 by the decree of President Ilham Aliyev. DOST provides various social security services in different directions, such as labor pensions and social benefits, targeted state social assistance, disability, employment, labor relations, assignment of the Presidential Grant, social services, issue of references, social insurance, state compulsory personal insurance payments, child adoption, functional supportive service, etc.
-How can I apply for targeted state social assistance?
-Targeted state social assistance is a monthly cash benefit for low-income families who meet certain criteria. You can apply for this benefit online or offline. To apply online, you need to download the mobile DOST app from Google Play or App Store and register with your ID card number and mobile phone number. Then you need to fill out an online application form with your personal and family information, income and expenses information, bank account information, etc. You will receive a confirmation code via SMS or email. To apply offline, you need to book an appointment online through the website dost.gov.az or the mobile DOST app. Then you need to visit the DOST center you selected with your ID card and other required documents. You will fill out an application form with the help of a DOST staff member.
-How can I check the status of my application?
-You can check the status of your application online or offline. To check online, you need to visit the website dost.gov.az or use the mobile DOST app and enter your confirmation code or ID card number. You will see the status of your application on the screen. To check offline , you need to call the hotline number 141 or visit the DOST center where you applied with your ID card. You will get the status of your application from a DOST staff member.
-How can I get a reference from DOST?
-A reference is a document that confirms your status as a pensioner, a beneficiary of social allowances or targeted state social assistance, a disabled person, etc. You can get a reference from DOST online or offline. To get online, you need to visit the website dost.gov.az or use the mobile DOST app and enter your ID card number and mobile phone number. You will see the list of references you can get on the screen. You can choose the reference you need and download it as a PDF file or print it out. To get offline, you need to book an appointment online through the website dost.gov.az or the mobile DOST app. Then you need to visit the DOST center you selected with your ID card. You will get the reference you need from a DOST staff member.
-How can I provide feedback on DOST services?
-DOST welcomes your feedback on its services, whether it is positive or negative, as it helps to improve its performance and quality. You can provide feedback on DOST services online or offline. To provide online, you need to visit the website dost.gov.az or use the mobile DOST app and enter your confirmation code or ID card number. You will see the feedback form on the screen. You can rate the service you received, write a comment, make a suggestion, or file a complaint. You will receive a response from DOST within 15 working days. To provide offline, you need to fill out a paper feedback form at the DOST center where you received the service. You can also call the hotline number 141 or send an email to info@dost.gov.az to provide feedback.
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Alchemy Family Mod APK: A Fun and Relaxing Idle Simulation Game
-If you are looking for a game that can help you unwind from your busy daily life, you might want to check out Alchemy Family. This is an easy-to-play idle simulation game that lets you tap the screen and gain huge profits. You can also explore an epic world, nurture unique heroes, join legions, and fight enemies. In this article, we will tell you everything you need to know about Alchemy Family, and how you can download and install its mod apk version for free.
-alchemy family mod apk
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What is Alchemy Family?
-Alchemy Family is a mobile game developed by Zacker Gamer A. It is available for Android devices on Google Play Store. Alchemy Family is an idle simulation game that combines elements of fantasy, adventure, strategy, and RPG. You can play it anytime, anywhere, without worrying about losing progress or missing out on rewards.
-As the guardian of the continent, your mission in Alchemy Family is to lead your clan to explore the continent, defeat the temple, rebuild the territory, and achieve family rejuvenation. You can do this by tapping the screen, leveling up your heroes, collecting resources, upgrading your lineage, joining legions, and conquering enemies. Along the way, you will encounter various challenges, surprises, and fun.
-How to play Alchemy Family?
-The basics of tapping, leveling up, and earning profits
-The core gameplay of Alchemy Family is very simple. You just need to tap the screen to generate gold coins, which you can use to level up your heroes and unlock new features. The game can run in the background, so you don't have to worry about missing anything. You can also claim offline rewards when you return to the game.
-The different types of heroes and their roles
-In Alchemy Family, you can recruit and nurture various heroes with different skills and abilities. There are six types of heroes in the game: warriors, knights, rangers, shooters, mages, and priests. Each type has its own role in combat and can be arranged strategically to form a powerful team. You can also upgrade your heroes with equipment, skills, talents, and artifacts.
-The six lineages and their powers
-Another feature of Alchemy Family is the lineage system. There are six lineages in the game that represent different powers: fire, water, wind, earth, light, and dark. Each lineage has its own advantages and disadvantages in combat. You can upgrade your lineage by finding clues in the world map and unlocking hidden legends. Upgrading your lineage will also increase your clan's prestige and influence.
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The legion system and the benefits of joining one
-If you want to make friends and cooperate with other players in Alchemy Family, you can join a legion. A legion is a group of players who share a common goal and interest. By joining a legion, you can enjoy various benefits such as chatting with other members, participating in legion events, occupying castles, guarding territories, fighting enemies, and striving for honor. You can also create your own legion if you want to be a leader.
-Why download Alchemy Family mod apk?
-The advantages of getting unlimited resources and features
-While Alchemy Family is a free-to-play game, it also has some in-app purchases that can enhance your
gaming experience. For example, you can buy gems, coins, VIP memberships, and other items with real money. However, not everyone can afford or want to spend money on a game. That's why some people prefer to download and install Alchemy Family mod apk.
-Alchemy Family mod apk is a modified version of the original game that gives you access to unlimited resources and features. You can get unlimited gems, coins, VIP memberships, and other items without spending a dime. You can also unlock all the heroes, lineages, legions, and maps without any restrictions. You can enjoy the game to the fullest without any limitations or ads.
-The risks and precautions of using mod apk files
-However, downloading and installing Alchemy Family mod apk is not without risks. Mod apk files are not authorized or verified by the game developers or Google Play Store. They may contain viruses, malware, spyware, or other harmful programs that can damage your device or steal your personal information. They may also cause compatibility issues, crashes, errors, or bans from the game servers.
-Therefore, you should be careful and cautious when using mod apk files. You should only download them from trusted and reliable sources that have positive reviews and feedback from other users. You should also scan them with antivirus software before installing them on your device. You should also backup your data and create a separate account for playing the modded game. You should not use your main account or connect it to any social media platforms.
-How to download and install Alchemy Family mod apk?
-The steps to follow and the requirements to meet
-If you want to download and install Alchemy Family mod apk on your Android device, you need to follow these steps:
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- Make sure your device meets the minimum requirements for the game. You need to have Android 4.4 or higher, at least 2 GB of RAM, and 100 MB of free storage space. -
- Enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and toggling it on. -
- Download the Alchemy Family mod apk file from a trusted source. You can find many websites that offer mod apk files for free, but make sure they are safe and reputable. -
- Locate the downloaded file on your device 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 playing with unlimited resources and features. -
The sources to trust and the ones to avoid
-As mentioned earlier, not all sources that offer mod apk files are trustworthy and reliable. Some of them may contain malicious or fraudulent content that can harm your device or compromise your security. Therefore, you should avoid downloading mod apk files from sources that have:
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On the other hand, you should trust downloading mod apk files from sources that have:
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Conclusion
-In conclusion, Alchemy Family is a fun and relaxing idle simulation game that lets you tap the screen and gain huge profits. You can also explore an epic world, nurture unique heroes, join legions, and fight enemies. However, if you want to enjoy the game without any limitations or ads, you can download and install Alchemy Family mod apk. This is a modified version of the game that gives you unlimited resources and features. However, you should also be aware of the risks and precautions of using mod apk files. You should only download them from trusted sources and scan them with antivirus software before installing them on your device. You should also backup your data and create a separate account for playing the modded game. We hope this article has helped you learn more about Alchemy Family and its mod apk version. If you have any questions or feedback, please feel free to leave a comment below.
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- What is the latest version of Alchemy Family mod apk? -
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The latest version of Alchemy Family mod apk is 1.0.6, which was updated on June 15, 2023. It has a file size of 98 MB and requires Android 4.4 or higher to run.
-Alchemy Family mod apk is safe to use as long as you download it from a trusted source and scan it with antivirus software before installing it on your device. However, you should also be careful and cautious when using mod apk files, as they may contain viruses, malware, spyware, or other harmful programs that can damage your device or steal your personal information.
-Yes, you can play Alchemy Family mod apk online with other players, as long as you have a stable internet connection and a compatible device. However, you should not use your main account or connect it to any social media platforms, as you may get banned from the game servers or lose your data.
-You can get more gems and coins in Alchemy Family mod apk by tapping the screen, claiming offline rewards, completing tasks, participating in events, joining legions, and fighting enemies. You can also get unlimited gems and coins by downloading and installing Alchemy Family mod apk, which gives you access to unlimited resources and features.
-You can upgrade your heroes and lineages in Alchemy Family mod apk by using gems, coins, equipment, skills, talents, artifacts, clues, and legends. You can also unlock all the heroes and lineages by downloading and installing Alchemy Family mod apk, which gives you access to unlimited resources and features.
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Idle Dig Factory Mod APK: Unlimited Money and Gems
-Do you love digging games? Do you want to create your own mining empire and become rich? If yes, then you should try Idle Dig Factory, a fun and addictive casual game where you can dig, mine, and craft various resources. But wait, there's more! You can also download Idle Dig Factory Mod APK, a modified version of the game that gives you unlimited money and gems. Sounds awesome, right? In this article, we will tell you everything you need to know about Idle Dig Factory and its modded version. Let's get started!
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What is Idle Dig Factory?
-Idle Dig Factory is a casual game developed by Lion Studios, a popular game studio that has created many hit games such as Happy Glass, Mr Bullet, and Flip Trickster. In Idle Dig Factory, you can build your own digging factory, hire workers, upgrade machines, and mine various resources such as coal, iron, gold, diamonds, and more. You can also sell your resources for cash and use it to expand your factory and unlock new machines. The game is easy to play, but challenging to master. You need to manage your workers, machines, and resources efficiently to maximize your profits. The game also has stunning graphics, smooth animations, and relaxing sound effects that make the gameplay more enjoyable.
-Features of Idle Dig Factory
-Idle Dig Factory has many features that make it one of the best digging games on the market. Here are some of them:
-Build your own digging empire
-You can start with a simple drill and a conveyor belt, but as you progress in the game, you can unlock new machines such as crushers, smelters, furnaces, refineries, and more. You can also upgrade your machines to make them faster, stronger, and more efficient. You can also customize your factory with different decorations and themes.
-Hire and upgrade workers
-You can hire workers to help you dig, mine, craft, and sell your resources. You can also upgrade your workers to make them work faster, harder, and smarter. You can also assign different workers to different tasks according to their skills and abilities.
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Unlock new machines and resources
-You can unlock new machines that can help you dig deeper, mine faster, craft better, and sell higher. You can also unlock new resources that are more valuable and rare. You can find coal, iron, gold, diamonds, uranium, platinum, emeralds, rubies, sapphires, and more.
-Earn idle cash and gems
-You can earn cash by selling your resources in the market. You can also earn gems by completing missions and achievements. You can use cash and gems to buy new machines, upgrade your workers, boost your production, and more. You can also earn idle cash and gems even when you are offline or away from the game.
-What is Idle Dig Factory Mod APK?
-Idle Dig Factory Mod APK is a modified version of the original game that gives you unlimited money and gems. With this modded version of the game, you can enjoy all the features of the game without any limitations or restrictions. You can buy any machine you want, upgrade any worker you want, unlock any resource you want, and more. You can also skip ads and enjoy a smoother gaming experience.
-Benefits of Idle Dig Factory Mod APK
-There are many benefits of downloading Idle Dig Factory Mod APK. Here are some of them:
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- You can - You can save your time and effort by not having to grind for money and gems. - You can explore all the features and content of the game without any restrictions or limitations. - You can have more fun and excitement by digging deeper, mining faster, crafting better, and selling higher. - You can dominate the game and become the richest and most successful digger in the world.
How to download and install Idle Dig Factory Mod APK?
-Downloading and installing Idle Dig Factory Mod APK is very easy and simple. Just follow these steps:
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- Click on the download button below to download the Idle Dig Factory Mod APK file. -
- Once the download is complete, open the file manager on your device and locate the downloaded file. -
- Tap on the file and allow the installation from unknown sources if prompted. -
- Wait for the installation to finish and then launch the game. -
- Enjoy unlimited money and gems in Idle Dig Factory! -
Tips and tricks for playing Idle Dig Factory
-If you want to master Idle Dig Factory and become the best digger in the world, you need to know some tips and tricks that can help you improve your gameplay. Here are some of them:
-Upgrade your machines regularly
-Your machines are your main source of income and production in Idle Dig Factory. Therefore, you need to upgrade them regularly to make them more efficient and profitable. Upgrading your machines can increase their speed, power, capacity, quality, and value. You can also unlock new machines that can help you dig deeper and mine faster.
-Use boosters and power-ups wisely
-Boosters and power-ups are special items that can help you boost your production and profits in Idle Dig Factory. You can use them to speed up your machines, increase your workers' productivity, multiply your earnings, and more. However, you need to use them wisely as they are limited and cost gems. You can also watch ads to get free boosters and power-ups.
-Complete missions and achievements
-Missions and achievements are tasks that you can complete in Idle Dig Factory to earn extra cash and gems. They also help you progress in the game and unlock new features and content. You can find missions and achievements in the menu button on the top right corner of the screen. Try to complete as many missions and achievements as you can to get more rewards.
-Watch ads for extra rewards
-Watching ads is another way to get extra rewards in Idle Dig Factory. You can watch ads to get free cash, gems, boosters, power-ups, resources, and more. You can also skip ads by turning off your internet connection or using Idle Dig Factory Mod APK.
-Conclusion
-Idle Dig Factory is a fun and addictive casual game where you can build your own digging factory, hire workers, upgrade machines, and mine various resources. You can also download Idle Dig Factory Mod APK, a modified version of the game that gives you unlimited money and gems. With this modded version of the game, you can enjoy all the features of the game without any limitations or restrictions. You can also use our tips and tricks to improve your gameplay and become the best digger in the world. So what are you waiting for? Download Idle Dig Factory Mod APK now and start digging!
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Yes, Idle Dig Factory Mod APK is safe to use as it does not contain any viruses or malware. However, you need to download it from a trusted source like our website.
-No, you do not need to root or jailbreak your device to use Idle Dig Factory Mod APK. It works on both rooted and non-rooted devices.
-No, you will not get banned for using Idle Dig Factory Mod APK as it is undetectable by the game servers. However, you should not use it excessively or abuse it as it may ruin the fun of the game for yourself and others.
-No, you cannot update Idle Dig Factory Mod APK as it may overwrite the modded features. You need to wait for a new version of the modded file to be released by our team.
-No, you cannot play Idle Dig Factory online with other players using Idle Dig Factory Mod APK as it is a single-player game. You can only play it offline or with your own device.
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-How to Download and Install APK Minecraft 1.16.40 on Android
-Minecraft is one of the most popular and creative sandbox games in the world, where you can build, explore, and survive in a blocky pixelated world. The game has different versions for different platforms, such as Java Edition for PC, Bedrock Edition for mobile devices, consoles, and Windows 10, and Education Edition for schools.
-In this article, we will focus on the Bedrock Edition of Minecraft, specifically the version 1.16.40, which is also known as the Nether Update. We will show you what is APK Minecraft 1.16.40, what are its features and benefits, what is an APK file and how to install it on Android, and how to download and install APK Minecraft 1.16.40 on your Android device.
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-What is APK Minecraft 1.16.40?
-APK Minecraft 1.16.40 is the file format used by the Android operating system to distribute and install the Bedrock Edition of Minecraft version 1.16.40 on Android devices.
-Features of APK Minecraft 1.16.40
-APK Minecraft 1.16.40 has the same features as the official Bedrock Edition of Minecraft version 1.16.40, which include:
--
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- Four new biomes in the Nether dimension: warped forest, crimson forest, soul sand valley, and basalt deltas. -
- Four new mobs in the Nether dimension: piglins, hoglins, zoglins, and striders. -
- A new netherite tier of equipment, obtained through ancient debris found rarely throughout the Nether. -
- A new respawn anchor block that allows players to set their spawn point in the Nether. -
- A new lodestone block that allows players to set a custom compass point. -
- A new target block that emits a redstone signal when hit by a projectile. -
- A new chain block that can be used as a decorative item or to suspend other blocks. -
- A new basalt block that forms when lava flows over soul soil next to blue ice. -
- A new blackstone block that can be found in basalt deltas and can be used as a substitute for cobblestone in crafting recipes. -
- A new crying obsidian block that can be obtained from bartering with piglins or from ruined portals. -
- A new soul soil block that can be found in soul sand valleys and can be used to craft soul torches, soul lanterns, and soul campfires. -
- A new soul fire variant that burns on soul soil and soul sand. -
- A new nether gold ore block that can be mined with any pickaxe and drops gold nuggets. -
- A new nether sprouts plant that can be found in warped forests. -
- A new twisting vines plant that can be found in warped forests and can be climbed like ladders. -
- A new crimson fungus and warped fungus that can be found in crimson forests and warped forests respectively. -
- A new crimson roots and warped roots that can be found in crimson forests and warped forests respectively. -
- A new nether wart block and warped wart block that can be crafted from nether wart and warped fungus respectively. -
- A new shroomlight block that can be found in crimson forests and warped forests as a light source. -
- A new weeping vines plant that can be found in crimson forests and can grow downwards from the ceiling. -
- A new crimson stem and warped stem that can be found in crimson forests and warped forests respectively as the main wood type. -
- A new stripped crimson stem and stripped warped stem that can be obtained by using an axe on a crimson stem or a warped stem respectively. -
- A new crimson hyphae and warped hyphae that can be obtained by using a pickaxe on a crimson stem or a warped stem respectively. -
- A new stripped crimson hyphae and stripped warped hyphae that can be obtained by using an axe on a crimson hyphae or a warped hyphae respectively. -
- A new crimson planks and warped planks that can be crafted from crimson stems or warped stems respectively. -
- A new crimson slab, stair, fence, fence gate, door, trapdoor, button, pressure plate, and sign that can be crafted from crimson planks. -
- A new warped slab, stair, fence, fence gate, door, trapdoor, button, pressure plate, and sign that can be crafted from warped planks. -
- A new hoglin mob that spawns in crimson forests and attacks players and other mobs on sight. It can be bred with crimson fungus and drops raw porkchops and leather when killed. -
- A new zoglin mob that is the result of a hoglin being brought to the Overworld or the End. It is hostile to everything and immune to fire and lava. It drops rotten flesh when killed. -
- A new piglin mob that spawns in crimson forests and nether wastes. It is neutral to players unless provoked or wearing gold armor. It is hostile to wither skeletons, skeletons, zombified piglins, hoglins, and zoglins. It can barter with players by dropping random items in exchange for gold ingots. It drops gold nuggets and sometimes its equipment when killed. -
- A new zombified piglin mob that is the result of a piglin being brought to the Overworld or the End. It is neutral to players unless provoked. It is hostile to wither skeletons and skeletons. It drops gold nuggets and sometimes its equipment when killed. -
- A new strider mob that spawns in lava lakes in the Nether. It can walk on lava and be ridden by players using a saddle and a warped fungus on a stick. It drops string and sometimes its saddle when killed. -
Benefits of APK Minecraft 1.16.40
-APK Minecraft 1.16.40 has some benefits over the official Bedrock Edition of Minecraft version 1.16.40, which include:
--
-
- It is free to download and install, unlike the official version which costs $6.99 on Google Play Store. -
- It does not require a Google account or a Microsoft account to play online or offline. -
- It does not have any ads or in-app purchases that may interrupt the gameplay or tempt the players to spend real money. -
- It may have some modded features or customizations that are not available in the official version, such as shaders, textures, skins, maps, etc. -
What is an APK File and How to Install It on Android?
-An APK file is an Android Package Kit file that contains all the files and code needed to install an application on an Android device. APK files are usually downloaded from third-party sources that are not affiliated with Google Play Store or the official app developers.
-Definition and Function of an APK File
-An APK file is a compressed archive file that has the .apk extension. It contains four main components:
--
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- A manifest file that specifies the app's name, version, permissions, components, etc. -
- A resources file that contains the app's images, sounds, strings, layouts, etc. -
- A classes.dex file that contains the app's compiled Java code. -
- A META-INF folder that contains the app's signature and certificate information. -
An APK file functions as an installer for an app on an Android device. When an APK file is opened on an Android device, it checks if the app is compatible with the device's hardware and software specifications. If it is compatible, it extracts the files and code from the APK file and installs them on the device's internal storage or external storage (if available). If it is not compatible, it shows an error message and aborts the installation process.
-Steps to Install an APK File on Android
-To install an APK file on an Android device, you need to follow these steps:
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- Enable unknown sources on your device's settings. This allows you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. -
- Download the APK file from a trusted source. You can use a web browser, a file manager, or a downloader app to download the APK file. Make sure the APK file is not corrupted or infected with malware. -
- Locate the APK file on your device's storage. You can use a file manager app to browse your device's folders and find the APK file. Alternatively, you can use the notification bar or the Downloads app to access the APK file. -
- Tap on the APK file to open it. You may see a warning message that says "This type of file can harm your device. Do you want to keep [file name] anyway?". Tap on OK to proceed. -
- You may see another warning message that says "For your security, your phone is not allowed to install unknown apps from this source.". Tap on Settings to allow the installation from this source. -
- You may see a screen that shows the app's information, such as its name, icon, size, permissions, etc. Tap on Install to start the installation process. -
- Wait for the installation process to finish. You may see a progress bar that shows the percentage of completion. -
- Once the installation is done, you may see a screen that says "App installed". Tap on Open to launch the app or Done to close the screen. -
Congratulations, you have successfully installed an APK file on your Android device!
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-download apk minecraft 1 .16 .4 creative modeHow to Download and Install APK Minecraft 1.16.40 on Android?
-Now that you know what is an APK file and how to install it on Android, let's see how to download and install APK Minecraft 1.16.40 on your Android device.
-Requirements and Precautions for Downloading and Installing APK Minecraft 1.16.40
-Before you download and install APK Minecraft 1.16.40 on your Android device, you need to make sure that:
--
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- Your device meets the minimum system requirements for running Minecraft Bedrock Edition version 1.16.40, which are: Android 4.2 or higher, 1 GB of RAM or more, and at least 300 MB of free storage space. -
- Your device has a stable internet connection for downloading the APK file and playing online (if desired). -
- Your device has enough battery power or is connected to a charger during the download and installation process. -
- You have enabled unknown sources on your device's settings as explained in the previous section. -
- You have backed up your data and settings before installing the APK file in case something goes wrong or you want to revert to the official version later. -
- You have scanned the APK file with an antivirus app before installing it to make sure it is safe and free of malware. -
- You have read and agreed to the terms and conditions of using APK Minecraft 1.16.40, which may differ from those of the official version. -
If you have met all these requirements and precautions, you are ready to download and install APK Minecraft 1.16.40 on your Android device.
-Sources and Methods for Downloading APK Minecraft 1.16.40
-There are many sources and methods for downloading APK Minecraft 1.16.40 on the internet, but not all of them are reliable or trustworthy. Some of them may contain fake, outdated, or malicious files that can harm your device or compromise your privacy. Therefore, you need to be careful and selective when choosing where and how to download APK Minecraft 1.16.40.
-Here are some of the best sources and methods for downloading APK Minecraft 1.16.40 that we recommend:
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-Source Method Description [APKPure] Web browser APKPure is one of the most popular and reputable websites for downloading APK files of various apps and games, including Minecraft Bedrock Edition version 1.16.40. It offers fast, secure, and easy downloads with no ads or registration required. [APKMirror] Web browser APKMirror is another well-known and trusted website for downloading APK files of various apps and games, including Minecraft Bedrock Edition version 1.16.40. It offers verified, safe, and updated downloads with no ads or registration required. [Aptoide] Apto ide app Aptoide is an alternative app store for Android devices that allows users to download and install APK files of various apps and games, including Minecraft Bedrock Edition version 1.16.40. It offers user-friendly, community-based, and customizable downloads with no ads or registration required. [HappyMod] HappyMod app HappyMod is a modded app store for Android devices that allows users to download and install APK files of various apps and games, including Minecraft Bedrock Edition version 1.16.40. It offers modded, hacked, or patched versions of the apps and games with enhanced features and unlimited resources. To download APK Minecraft 1.16.40 from any of these sources, you need to follow these steps:
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- Open your web browser or the app store app on your Android device. -
- Go to the website or the app page of the source that you have chosen. -
- Search for APK Minecraft 1.16.40 or browse the categories or the recommendations to find it. -
- Tap on the download button or the link to start downloading the APK file. -
- Wait for the download to finish and check the notification bar or the Downloads app to access the APK file. -
Steps to Install APK Minecraft 1.16.40 on Android
-To install APK Minecraft 1.16.40 on your Android device, you need to follow these steps:
--
-
- Locate the APK file on your device's storage as explained in the previous section. -
- Tap on the APK file to open it as explained in the previous section. -
- Allow the installation from this source if prompted as explained in the previous section. -
- Read and accept the app's information and permissions as explained in the previous section. -
- Tap on Install to start the installation process as explained in the previous section. -
- Wait for the installation process to finish as explained in the previous section. -
- Tap on Open to launch the app or Done to close the screen as explained in the previous section. -
Congratulations, you have successfully downloaded and installed APK Minecraft 1.16.40 on your Android device!
-Conclusion
-In this article, we have shown you how to download and install APK Minecraft 1.16.40 on your Android device. We have explained what is APK Minecraft 1.16.40, what are its features and benefits, what is an APK file and how to install it on Android, and how to download and install APK Minecraft 1.16.40 on your Android device. We have also provided some of the best sources and methods for downloading APK Minecraft 1.16.40 on the internet.
-We hope you have enjoyed this article and found it useful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-FAQs
-Here are some of the frequently asked questions about APK Minecraft 1.16.40:
-Is APK Minecraft 1.16.40 safe and legal?
-APK Minecraft 1.16.40 is generally safe and legal if you download it from a trusted source and scan it with an antivirus app before installing it on your device. However, there may be some risks involved, such as malware infection, data theft, privacy breach, or legal issues with the official app developers or Google Play Store policies. Therefore, you should always be careful and responsible when using APK files of any apps or games.
-Can I play online with APK Minecraft 1.16.40?
-Yes, you can play online with APK Minecraft 1.16.40 if you have a stable internet connection and a Microsoft account (optional). You can join servers, realms, or friends' worlds that are compatible with your version of Minecraft Bedrock Edition (version 1.16.40). However, you may not be able to access some online features or services that are exclusive to the official version of Minecraft Bedrock Edition, such as achievements, leaderboards, marketplace, etc.
-Can I update APK Minecraft 1.16.40?
-No, you cannot update APK Minecraft 1.16.40 through Google Play Store or the official app developers' website, as they do not recognize or support APK files of their apps or games. If you want to update your version of Minecraft Bedrock Edition, you need to uninstall APK Minecraft 1.16.40 from your device and install the official version from Google Play Store or the official app developers' website. Alternatively, you can download and install another APK file of a newer version of Minecraft Bedrock Edition from a trusted source, but you may lose your data and settings from the previous version.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Dragon Mania Legends MOD APK Experience the Most Amazing Dragon Adventure Ever.md b/spaces/fatiXbelha/sd/Dragon Mania Legends MOD APK Experience the Most Amazing Dragon Adventure Ever.md deleted file mode 100644 index 368e186b9b84dc71019d4f5853085cd9cd920f81..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Dragon Mania Legends MOD APK Experience the Most Amazing Dragon Adventure Ever.md +++ /dev/null @@ -1,148 +0,0 @@ - -Dragon Mania Legends Mod APK: A Complete Guide
-If you are a fan of dragon games, you might have heard of Dragon Mania Legends, a popular game by Gameloft that lets you raise, breed, and train your own dragons. But did you know that there is a modded version of this game that gives you unlimited money, gems, food, and other resources? In this article, we will tell you everything you need to know about Dragon Mania Legends Mod APK, including what it is, how to download and install it, how to play it, and what are the pros and cons of using it.
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-What is Dragon Mania Legends?
-Dragon Mania Legends is a dragon simulator game for the family that was released in 2015. It has three main aspects:
-A dragon simulator game for the family
-In this game, you can build your own dragon city on a secret island called Dragolandia. You can hatch, feed, cuddle, and care for your dragons to make them happy and powerful. You can also send them to the school of magic to learn new skills and abilities.
-A dragon fantasy city with hundreds of dragons
-This game features hundreds of unique dragon species that you can collect and breed. You can try different combinations to unlock new dragons and discover their personalities. You can also customize your dragons with different outfits and accessories.
-A dragon battle game with epic adventures
-This game also lets you take your dragons to battle against other players and monsters. You can explore different islands and worlds, complete missions, collect rewards, and reclaim your homeland from the evil Vikings. You can also join or create clans with other players and compete in the arena for exclusive prizes.
-What is Dragon Mania Legends Mod APK?
-Dragon Mania Legends Mod APK is a modified version of the original game that gives you access to unlimited resources and features. Some of the benefits of using this mod apk are:
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-dragon mania legends mod apk proapkdownload.comA modified version of the original game
-This mod apk is based on the latest version of the original game, so you can enjoy all the new updates and events. You can also sync your progress with your Facebook account and play on multiple devices.
-The benefits of using the mod apk
--
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- You get unlimited money, gems, food, and other resources that you can use to buy anything you want in the game. -
- You get unlimited energy, so you can play as much as you want without waiting for it to refill. -
- You get access to all the dragons, buildings, decorations, outfits, and accessories in the game. -
- You get free VIP tickets that give you extra bonuses and perks. -
- You get faster breeding, hatching, feeding, training, and upgrading times. -
- You get higher chances of getting rare and legendary dragons. -
- You get easier battles and higher rewards. -
The risks of using the mod apk
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- You might face some compatibility issues or bugs while playing the mod apk. -
- You might lose your progress or data if the mod apk crashes or gets deleted. -
- You might get banned or suspended from the game if the developers detect that you are using a mod apk. -
- You might expose your device to malware or viruses if you download the mod apk from an untrusted source. -
- You might miss out on some of the fun and excitement of playing the original game with other players and following the official rules. -
How to download and install Dragon Mania Legends Mod APK?
-If you want to try Dragon Mania Legends Mod APK, you need to follow these steps:
-The steps to follow
--
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- Uninstall the original game from your device if you have it installed. -
- Go to a trusted website that offers the mod apk file for download. You can search for "dragon mania legends mod apk" on Google or Bing to find some options. -
- Download the mod apk file to your device. Make sure you have enough storage space and a stable internet connection. -
- Enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and toggling it on. -
- Locate the mod apk file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for it to finish. -
- Launch the game and enjoy the modded features. -
The requirements and precautions
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- You need an Android device with at least 4.4 version and 1 GB of RAM to run the mod apk smoothly. -
- You need to allow some permissions to the mod apk, such as access to your storage, contacts, location, and phone. -
- You need to disable any antivirus or firewall software on your device that might interfere with the mod apk. -
- You need to be careful about downloading the mod apk from a safe and reliable source, as some websites might contain malware or viruses. -
- You need to be aware of the risks of using the mod apk, such as losing your progress, getting banned, or missing out on some features. -
The alternatives and sources
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- If you don't want to use the mod apk, you can still enjoy the original game by downloading it from the Google Play Store or the App Store. You can also use some legit ways to get more resources in the game, such as watching ads, completing tasks, joining events, or buying them with real money. -
- If you want to use a different mod apk, you can look for other websites that offer similar or better features. Some of the popular ones are APKPure, APKMODY, MODDROID, and Rexdl. However, you should always check the reviews, ratings, and comments of other users before downloading any mod apk. -
- If you want to learn more about Dragon Mania Legends Mod APK, you can visit some online forums or blogs that discuss this topic. Some of the useful ones are Reddit, Quora, Medium, and YouTube. You can also ask questions or share your opinions with other players there. -
How to play Dragon Mania Legends Mod APK?
-Playing Dragon Mania Legends Mod APK is similar to playing the original game, but with some added advantages. Here are some tips on how to play it:
-The basic gameplay and tips
--
-
- Your goal is to build your own dragon city on Dragolandia and fight against the Vikings who want to destroy it. -
- You need to collect different types of dragons by hatching eggs, breeding them, or buying them with money or gems. -
- You need to feed your dragons with food that you can grow on farms or buy with money or gems. -
- You need to train your dragons with magic skills that you can teach them at the school of magic or buy with money or gems. -
- You need to upgrade your dragons with elements that you can collect from habitats or buy with money or gems. -
- You need to battle other players or monsters with your dragons by using their skills and elements strategically. -
- You need to complete missions, quests, events, and achievements to earn rewards and unlock new features. -
The features and modes
--
-
- This game has various features and modes that you can enjoy, such as: -
- The clan mode, where you can join or create a clan with other players and chat, trade, and battle with them. -
- The arena mode, where you can compete with other players in different leagues and tournaments for exclusive rewards. -
- The boss mode, where you can fight against powerful bosses and earn special loot. -
- The event mode, where you can participate in seasonal and limited-time events and win rare dragons and items. -
- The story mode, where you can follow the main storyline and discover the secrets of Dragolandia. -
The challenges and rewards
--
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- This game has various challenges and rewards that you can achieve, such as: -
- The daily challenges, where you can complete simple tasks every day and earn money, gems, food, and energy. -
- The weekly challenges, where you can complete harder tasks every week and earn more money, gems, food, and energy. -
- The monthly challenges, where you can complete the hardest tasks every month and earn the most money, gems, food, and energy. -
- The achievements, where you can track your progress and milestones in the game and earn trophies and badges. -
- The collections, where you can collect different sets of dragons and items and earn bonuses and rewards. -
Conclusion
-Dragon Mania Legends Mod APK is a fun and exciting game that lets you experience the world of dragons in a new way. You can build your own dragon city, collect hundreds of dragons, train them with magic skills, battle with other players and monsters, and enjoy unlimited resources and features. However, you should also be aware of the risks of using the mod apk, such as losing your progress, getting banned, or missing out on some features. You should also download the mod apk from a trusted source and follow the steps to install it properly. If you want to learn more about Dragon Mania Legends Mod APK, you can visit some online forums or blogs that discuss this topic. We hope this article has helped you understand what Dragon Mania Legends Mod APK is and how to use it. Have fun playing!
-FAQs
-Here are some frequently asked questions about Dragon Mania Legends Mod APK:
-Q: Is Dragon Mania Legends Mod APK safe to use?
-A: Dragon Mania Legends Mod APK is generally safe to use if you download it from a trusted source and follow the steps to install it properly. However, there is always a risk of malware or viruses when downloading any mod apk from the internet. You should also be careful about giving permissions to the mod apk and disabling any antivirus or firewall software on your device. You should also backup your data before using the mod apk in case anything goes wrong.
-Q: Is Dragon Mania Legends Mod APK legal to use?
-A: Dragon Mania Legends Mod APK is not legal to use as it violates the terms of service of the original game. It also infringes on the intellectual property rights of the developers. Using the mod apk might result in legal action from the developers or other parties. You should also respect the work of the developers and support them by playing the original game.
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-A: If you want to uninstall Dragon Mania Legends Mod APK, you can follow these steps:
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\ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/examples/disco_project/README.md b/spaces/fclong/summary/fengshen/examples/disco_project/README.md deleted file mode 100644 index c8d95f886e1d80fd1e198eb8d0618c77b6f8836d..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/disco_project/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# Chinese Warp For Disco Diffusion -- This is a chinese version disco diffusion. We train a Chinese CLIP [IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese) and utilize it to guide the diffusion process. -- This code is modified from https://github.com/alembics/disco-diffusion -- streamlit demo is supported. -- the checkpoint has been upload to hugging face. -## Usage - -- Install the lack package directly -### Run Directly -``` -python disco.py --prompt 夕阳西下 --model_path IDEA-CCNL/Taiyi-Diffusion-532M-Nature # or IDEA-CCNL/Taiyi-Diffusion-532M-Cyberpunk -``` - -### Streamlit Setup -``` -streamlit run st_disco.py -# --server.port=xxxx --server.address=xxxx -``` diff --git a/spaces/fclong/summary/fengshen/utils/convert_tf_checkpoint_to_pytorch.py b/spaces/fclong/summary/fengshen/utils/convert_tf_checkpoint_to_pytorch.py deleted file mode 100644 index c322a872c1969acf91866ad59ab23e7cd477f0d0..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/utils/convert_tf_checkpoint_to_pytorch.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Convert ALBERT checkpoint.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import argparse -import torch -from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert -# from models.transformers.modeling_albert_bright import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert -import logging -logging.basicConfig(level=logging.INFO) - -def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path): - # Initialise PyTorch model - config = BertConfig.from_pretrained(bert_config_file) - # print("Building PyTorch model from configuration: {}".format(str(config))) - model = BertForPreTraining(config) - # Load weights from tf checkpoint - load_tf_weights_in_bert(model, config, tf_checkpoint_path) - - # Save pytorch-model - print("Save PyTorch model to {}".format(pytorch_dump_path)) - torch.save(model.state_dict(), pytorch_dump_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - ## Required parameters - parser.add_argument("--tf_checkpoint_path", - default = None, - type = str, - required = True, - help = "Path to the TensorFlow checkpoint path.") - parser.add_argument("--bert_config_file", - default = None, - type = str, - required = True, - help = "The config json file corresponding to the pre-trained BERT model. \n" - "This specifies the model architecture.") - parser.add_argument("--pytorch_dump_path", - default = None, - type = str, - required = True, - help = "Path to the output PyTorch model.") - args = parser.parse_args() - convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path,args.bert_config_file, - args.pytorch_dump_path) - -''' -# google -python convert_albert_tf_checkpoint_to_pytorch.py \ - --tf_checkpoint_path=./prev_trained_model/albert_large_zh \ - --bert_config_file=./prev_trained_model/albert_large_zh/config.json \ - --pytorch_dump_path=./prev_trained_model/albert_large_zh/pytorch_model.bin - -# bright -from model.modeling_albert_bright import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert -python convert_albert_tf_checkpoint_to_pytorch.py \ - --tf_checkpoint_path=./prev_trained_model/albert_base_bright \ - --bert_config_file=./prev_trained_model/albert_base_bright/config.json \ - --pytorch_dump_path=./prev_trained_model/albert_base_bright/pytorch_model.bin -''' \ No newline at end of file diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/models/stylegan2/op/upfirdn2d.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/models/stylegan2/op/upfirdn2d.py deleted file mode 100644 index 08e5298466537b512b40c918782bf460e4465f90..0000000000000000000000000000000000000000 --- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/models/stylegan2/op/upfirdn2d.py +++ /dev/null @@ -1,184 +0,0 @@ -import os - -import torch -from torch.autograd import Function -from torch.utils.cpp_extension import load - -module_path = os.path.dirname(__file__) -# upfirdn2d_op = load( -# 'upfirdn2d', -# sources=[ -# os.path.join(module_path, 'upfirdn2d.cpp'), -# os.path.join(module_path, 'upfirdn2d_kernel.cu'), -# ], -# ) - - -class UpFirDn2dBackward(Function): - @staticmethod - def forward( - ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size - ): - up_x, up_y = up - down_x, down_y = down - g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad - - grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) - - grad_input = upfirdn2d_op.upfirdn2d( - grad_output, - grad_kernel, - down_x, - down_y, - up_x, - up_y, - g_pad_x0, - g_pad_x1, - g_pad_y0, - g_pad_y1, - ) - grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) - - ctx.save_for_backward(kernel) - - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - ctx.up_x = up_x - ctx.up_y = up_y - ctx.down_x = down_x - ctx.down_y = down_y - ctx.pad_x0 = pad_x0 - ctx.pad_x1 = pad_x1 - ctx.pad_y0 = pad_y0 - ctx.pad_y1 = pad_y1 - ctx.in_size = in_size - ctx.out_size = out_size - - return grad_input - - @staticmethod - def backward(ctx, gradgrad_input): - kernel, = ctx.saved_tensors - - gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) - - gradgrad_out = upfirdn2d_op.upfirdn2d( - gradgrad_input, - kernel, - ctx.up_x, - ctx.up_y, - ctx.down_x, - ctx.down_y, - ctx.pad_x0, - ctx.pad_x1, - ctx.pad_y0, - ctx.pad_y1, - ) - # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3]) - gradgrad_out = gradgrad_out.view( - ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] - ) - - return gradgrad_out, None, None, None, None, None, None, None, None - - -class UpFirDn2d(Function): - @staticmethod - def forward(ctx, input, kernel, up, down, pad): - up_x, up_y = up - down_x, down_y = down - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - kernel_h, kernel_w = kernel.shape - batch, channel, in_h, in_w = input.shape - ctx.in_size = input.shape - - input = input.reshape(-1, in_h, in_w, 1) - - ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - ctx.out_size = (out_h, out_w) - - ctx.up = (up_x, up_y) - ctx.down = (down_x, down_y) - ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) - - g_pad_x0 = kernel_w - pad_x0 - 1 - g_pad_y0 = kernel_h - pad_y0 - 1 - g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 - g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 - - ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) - - out = upfirdn2d_op.upfirdn2d( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 - ) - # out = out.view(major, out_h, out_w, minor) - out = out.view(-1, channel, out_h, out_w) - - return out - - @staticmethod - def backward(ctx, grad_output): - kernel, grad_kernel = ctx.saved_tensors - - grad_input = UpFirDn2dBackward.apply( - grad_output, - kernel, - grad_kernel, - ctx.up, - ctx.down, - ctx.pad, - ctx.g_pad, - ctx.in_size, - ctx.out_size, - ) - - return grad_input, None, None, None, None - - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - out = UpFirDn2d.apply( - input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) - ) - - return out - - -def upfirdn2d_native( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 -): - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad( - out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] - ) - out = out[ - :, - max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0), - :, - ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape( - [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] - ) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - - return out[:, ::down_y, ::down_x, :] diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Open Inter Hall Ticket 2023 for 1st and 2nd Year Exams.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Open Inter Hall Ticket 2023 for 1st and 2nd Year Exams.md deleted file mode 100644 index 12fdc004abe935f2b1cb6cb7af159dd686105ac4..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Open Inter Hall Ticket 2023 for 1st and 2nd Year Exams.md +++ /dev/null @@ -1,127 +0,0 @@ -
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-Telangana open inter hall tickets download press release link 2023Why do you need Open Inter Hall Ticket?
-You need the Open Inter Hall Ticket for several reasons. Some of them are:
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- The Open Inter Hall Ticket is mandatory for entering the exam hall and writing the exam. Without it, you will not be allowed to take the exam. -
- The Open Inter Hall Ticket helps in verifying your identity and preventing any impersonation or fraud during the exam. -
- The Open Inter Hall Ticket helps in avoiding any confusion or discrepancy regarding your exam center, date, time, subjects, etc. -
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- The Open Inter Hall Ticket is required for checking your results and applying for further studies or jobs. -
Therefore, you should download your Open Inter Hall Ticket 2023 as soon as possible and keep it safe until the end of the exam process.
-Who can apply for Open Inter Hall Ticket?
-Anyone who has applied for the Open Inter Exam 2023 can apply for the Open Inter Hall Ticket 2023. The eligibility criteria for applying for the Open Inter Exam 2023 are as follows:
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- You should have passed the 10th class or equivalent examination from a recognized board or institution. -
- You should be at least 15 years of age as on the date of admission. -
- You should have registered for the Open Inter Exam 2023 through the online or offline mode as per the instructions of your state board. -
- You should have paid the required fee and submitted the required documents for the Open Inter Exam 2023. -
If you meet these criteria, you can apply for the Open Inter Hall Ticket 2023 from the official website of your state board.
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-The process of downloading the Open Inter Hall Ticket 2023 is very simple and easy. You just need to follow these steps:
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-The next step is to click on the link for Open Inter Hall Ticket 2023 on the homepage of your state board website. The link may be named differently depending on your state board, such as "Download Admit Card", "Hall Ticket Download", "Admit Card Download", etc. You can also find the link under the "Notifications" or "News and Announcements" section of the website.
-Step 3: Enter your details and click on Find Admit Card
-The third step is to enter your details and click on Find Admit Card. You may be asked to enter your registration number, date of birth, name, mobile number, email id, etc. Make sure you enter the correct details as per your application form. After entering your details, click on Find Admit Card or Submit or Search or any other button that will display your Open Inter Hall Ticket 2023 on the screen.
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-Sometimes, you may face some problems while downloading your Open Inter Hall Ticket 2023 due to various reasons, such as technical glitches, server issues, network errors, etc. In such cases, you should not panic and try these solutions:
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-Contact your state board helpline number or email address
-The second thing you should do is to contact your state board helpline number or email address if you still cannot download your Open Inter Hall Ticket 2023. You can find the contact details of your state board on their website or on your application form. You can call or email them and explain your problem and request them to help you download your Open Inter Hall Ticket 2023. You should provide your registration number, name, date of birth, and other details as required by them.
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-After downloading your Open Inter Hall Ticket 2023, you should check all the details carefully and make sure they are correct and match with your application form. Some of the important details to check on your Open Inter Hall Ticket 2023 are:
-Your name, roll number, date of birth, and photograph
-You should check your name, roll number, date of birth, and photograph on your Open Inter Hall Ticket 2023 and make sure they are spelled correctly and clearly visible. If there is any mistake or discrepancy in these details, you should contact your state board immediately and get it rectified.
-Your exam center name, address, and code
-You should check your exam center name, address, and code on your Open Inter Hall Ticket 2023 and make sure they are accurate and complete. You should also locate your exam center on a map and plan your travel accordingly. You should reach your exam center at least 30 minutes before the exam time.
-Your exam date, time, and duration
-You should check your exam date, time, and duration on your Open Inter Hall Ticket 2023 and make sure they are as per the schedule announced by your state board. You should also note down the reporting time and the entry closing time for the exam hall. You should follow the instructions given on your Open Inter Hall Ticket 2023 regarding the exam timings.
-Your exam subjects, codes, and instructions
-You should check your exam subjects, codes, and instructions on your Open Inter Hall Ticket 2023 and make sure they are as per the syllabus and exam pattern of your state board. You should also read the instructions carefully and follow them during the exam. You should carry all the required items for the exam, such as pens, pencils, erasers, calculators, etc., as per the instructions.
-How to prepare for Open Inter Exam 2023?
-Now that you have downloaded your Open Inter Hall Ticket 2023, you must be wondering how to prepare for the Open Inter Exam 2023. Well, don't worry, we have some tips for you to help you ace your Open Inter Exam 2023 with flying colors. Here are some of them:
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-The first thing you should do is to follow the syllabus and exam pattern of your state board for the Open Inter Exam 2023. You can find the syllabus and exam pattern on the official website of your state board or in your textbooks. You should cover all the topics and concepts from the syllabus and familiarize yourself with the exam pattern. You should also know the marking scheme and weightage of each subject and topic.
-Revise the important topics and concepts from your textbooks and notes
-The second thing you should do is to revise the important topics and concepts from your textbooks and notes. You should revise all the definitions, formulas, diagrams, examples, etc., from your textbooks and notes. You should also make short notes or flashcards of the key points and facts for quick revision. You should revise regularly and thoroughly before the exam.
-Solve previous year papers and mock tests to improve your speed and accuracy
-The third thing you should do is to solve previous year papers and mock tests to improve your speed and accuracy. You should solve the previous year papers and mock tests of your state board for the Open Inter Exam 2023. You can find them on the official website of your state board or in some online platforms or books. You should solve them in a timed manner and check your answers and mistakes. You should also analyze your performance and identify your strengths and weaknesses.
-Manage your time and stress effectively during the exam
-The fourth thing you should do is to manage your time and stress effectively during the exam. You should plan your time wisely and allocate enough time for each subject and question. You should also skip the difficult or lengthy questions and attempt them later if you have time. You should not waste your time on guessing or copying the answers. You should also stay calm and confident during the exam and avoid any negative thoughts or emotions.
-Conclusion
-The Open Inter Exam 2023 is a great opportunity for students who want to pursue higher education without attending regular classes. The Open Inter Hall Ticket 2023 is a crucial document that you need to download and carry for the exam. You should download your Open Inter Hall Ticket 2023 from the official website of your state board as soon as it is released and check all the details carefully. You should also prepare well for the exam by following the syllabus, revising the topics, solving the papers, and managing your time and stress. We hope this article has helped you understand how to download your Open Inter Hall Ticket 2023 and how to prepare for your Open Inter Exam 2023. We wish you all the best for your exam.
-FAQs
-Here are some frequently asked questions about the Open Inter Hall Ticket 2023:
-Q1: When will the Open Inter Hall Ticket 2023 be released?
-A1: The Open Inter Hall Ticket 2023 will be released by the respective state boards at least 15 days before the exam date. You can check the official website of your state board for the exact date of release.
-Q2: How can I download my Open Inter Hall Ticket 2023 if I forget my registration number or password?
-A2: If you forget your registration number or password, you can download your Open Inter Hall Ticket 2023 by using your name, date of birth, mobile number, email id, etc., as per the option available on your state board website. You can also contact your state board helpline number or email address for assistance.
-Q3: Can I change my exam center or date after downloading my Open Inter Hall Ticket 2023?
-A3: No, you cannot change your exam center or date after downloading your Open Inter Hall Ticket 2023. The exam center and date are allotted by the state board based on the availability and preference of the students. You have to appear for the exam at the assigned center and date only.
-Q4: What are the documents that I need to carry along with my Open Inter Hall Ticket 2023 for the exam?
-A4: Along with your Open Inter Hall Ticket 2023, you need to carry a valid photo identity proof, such as Aadhaar card, voter ID card, passport, driving license, etc., for verification purposes. You may also need to carry a passport size photograph, a blue or black ballpoint pen, a calculator, etc., as per the instructions given on your Open Inter Hall Ticket 2023.
-Q5: What are the consequences of losing or damaging my Open Inter Hall Ticket 2023?
-A5: If you lose or damage your Open Inter Hall Ticket 2023, you may face serious consequences, such as not being able to enter the exam hall or write the exam, being disqualified from the exam, or facing legal action. Therefore, you should keep your Open Inter Hall Ticket 2023 safe and secure until the end of the exam process. If you lose or damage your Open Inter Hall Ticket 2023, you should contact your state board helpline number or email address immediately and request for a duplicate copy.
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\ No newline at end of file diff --git a/spaces/fffiloni/Image-to-MusicGen/tests/data/test_audio.py b/spaces/fffiloni/Image-to-MusicGen/tests/data/test_audio.py deleted file mode 100644 index 40c0d5ed69eff92a766dc6d176e532f0df6c2b5e..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/Image-to-MusicGen/tests/data/test_audio.py +++ /dev/null @@ -1,239 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from itertools import product -import random - -import numpy as np -import torch -import torchaudio - -from audiocraft.data.audio import audio_info, audio_read, audio_write, _av_read - -from ..common_utils import TempDirMixin, get_white_noise, save_wav - - -class TestInfo(TempDirMixin): - - def test_info_mp3(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - wav = get_white_noise(ch, int(sample_rate * duration)) - path = self.get_temp_path('sample_wav.mp3') - save_wav(path, wav, sample_rate) - info = audio_info(path) - assert info.sample_rate == sample_rate - assert info.channels == ch - # we cannot trust torchaudio for num_frames, so we don't check - - def _test_info_format(self, ext: str): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'sample_wav{ext}') - save_wav(path, wav, sample_rate) - info = audio_info(path) - assert info.sample_rate == sample_rate - assert info.channels == ch - assert np.isclose(info.duration, duration, atol=1e-5) - - def test_info_wav(self): - self._test_info_format('.wav') - - def test_info_flac(self): - self._test_info_format('.flac') - - def test_info_ogg(self): - self._test_info_format('.ogg') - - def test_info_m4a(self): - # TODO: generate m4a file programmatically - # self._test_info_format('.m4a') - pass - - -class TestRead(TempDirMixin): - - def test_read_full_wav(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - read_wav, read_sr = audio_read(path) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == wav.shape[1] - assert torch.allclose(read_wav, wav, rtol=1e-03, atol=1e-04) - - def test_read_partial_wav(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - read_duration = torch.rand(1).item() - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - read_frames = int(sample_rate * read_duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - read_wav, read_sr = audio_read(path, 0, read_duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == read_frames - assert torch.allclose(read_wav[..., 0:read_frames], wav[..., 0:read_frames], rtol=1e-03, atol=1e-04) - - def test_read_seek_time_wav(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - read_duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - seek_time = torch.rand(1).item() - read_wav, read_sr = audio_read(path, seek_time, read_duration) - seek_frames = int(sample_rate * seek_time) - expected_frames = n_frames - seek_frames - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == expected_frames - assert torch.allclose(read_wav, wav[..., seek_frames:], rtol=1e-03, atol=1e-04) - - def test_read_seek_time_wav_padded(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - read_duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - read_frames = int(sample_rate * read_duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - seek_time = torch.rand(1).item() - seek_frames = int(sample_rate * seek_time) - expected_frames = n_frames - seek_frames - read_wav, read_sr = audio_read(path, seek_time, read_duration, pad=True) - expected_pad_wav = torch.zeros(wav.shape[0], read_frames - expected_frames) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == read_frames - assert torch.allclose(read_wav[..., :expected_frames], wav[..., seek_frames:], rtol=1e-03, atol=1e-04) - assert torch.allclose(read_wav[..., expected_frames:], expected_pad_wav) - - -class TestAvRead(TempDirMixin): - - def test_avread_seek_base(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 2. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'reference_a_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - for _ in range(100): - # seek will always load a full duration segment in the file - seek_time = random.uniform(0.0, 1.0) - seek_duration = random.uniform(0.001, 1.0) - read_wav, read_sr = _av_read(path, seek_time, seek_duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == int(seek_duration * sample_rate) - - def test_avread_seek_partial(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'reference_b_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - for _ in range(100): - # seek will always load a partial segment - seek_time = random.uniform(0.5, 1.) - seek_duration = 1. - expected_num_frames = n_frames - int(seek_time * sample_rate) - read_wav, read_sr = _av_read(path, seek_time, seek_duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == expected_num_frames - - def test_avread_seek_outofbound(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'reference_c_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - seek_time = 1.5 - read_wav, read_sr = _av_read(path, seek_time, 1.) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == 0 - - def test_avread_seek_edge(self): - sample_rates = [8000, 16_000] - # some of these values will have - # int(((frames - 1) / sample_rate) * sample_rate) != (frames - 1) - n_frames = [1000, 1001, 1002] - channels = [1, 2] - for sample_rate, ch, frames in product(sample_rates, channels, n_frames): - duration = frames / sample_rate - wav = get_white_noise(ch, frames) - path = self.get_temp_path(f'reference_d_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - seek_time = (frames - 1) / sample_rate - seek_frames = int(seek_time * sample_rate) - read_wav, read_sr = _av_read(path, seek_time, duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == (frames - seek_frames) - - -class TestAudioWrite(TempDirMixin): - - def test_audio_write_wav(self): - torch.manual_seed(1234) - sample_rates = [8000, 16_000] - n_frames = [1000, 1001, 1002] - channels = [1, 2] - strategies = ["peak", "clip", "rms"] - formats = ["wav", "mp3"] - for sample_rate, ch, frames in product(sample_rates, channels, n_frames): - for format_, strategy in product(formats, strategies): - wav = get_white_noise(ch, frames) - path = self.get_temp_path(f'pred_{sample_rate}_{ch}') - audio_write(path, wav, sample_rate, format_, strategy=strategy) - read_wav, read_sr = torchaudio.load(f'{path}.{format_}') - if format_ == "wav": - assert read_wav.shape == wav.shape - - if format_ == "wav" and strategy in ["peak", "rms"]: - rescaled_read_wav = read_wav / read_wav.abs().max() * wav.abs().max() - # for a Gaussian, the typical max scale will be less than ~5x the std. - # The error when writing to disk will ~ 1/2**15, and when rescaling, 5x that. - # For RMS target, rescaling leaves more headroom by default, leading - # to a 20x rescaling typically - atol = (5 if strategy == "peak" else 20) / 2**15 - delta = (rescaled_read_wav - wav).abs().max() - assert torch.allclose(wav, rescaled_read_wav, rtol=0, atol=atol), (delta, atol) - formats = ["wav"] # faster unit tests diff --git a/spaces/flax-community/koclip/app.py b/spaces/flax-community/koclip/app.py deleted file mode 100644 index c516233b775c92bba811de4e6ca9dafc945c624d..0000000000000000000000000000000000000000 --- a/spaces/flax-community/koclip/app.py +++ /dev/null @@ -1,20 +0,0 @@ -import streamlit as st - -import image2text -import intro -import text2image -import text2patch -from config import MODEL_LIST - -PAGES = { - "Introduction": intro, - "Text to Image": text2image, - "Image to Text": image2text, - "Text to Patch": text2patch, -} - -st.sidebar.title("Navigation") -model = st.sidebar.selectbox("Choose a model", MODEL_LIST) -page = st.sidebar.selectbox("Navigate to...", list(PAGES.keys())) - -PAGES[page].app(model) diff --git a/spaces/fuckyoudeki/AutoGPT/autogpt/permanent_memory/__init__.py b/spaces/fuckyoudeki/AutoGPT/autogpt/permanent_memory/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/gbach1lg/PhotoStyleTransfer/README.md b/spaces/gbach1lg/PhotoStyleTransfer/README.md deleted file mode 100644 index 9c61b616502a988f861750e2399ea1b89c51174a..0000000000000000000000000000000000000000 --- a/spaces/gbach1lg/PhotoStyleTransfer/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: PhotoStyleTransfer -emoji: 🔥 -colorFrom: blue -colorTo: gray -sdk: gradio -sdk_version: 2.8.14 -app_file: app.py -pinned: false -license: cc ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/openpose/model.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/openpose/model.py deleted file mode 100644 index 5dfc80de827a17beccb9b0f3f7588545be78c9de..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/openpose/model.py +++ /dev/null @@ -1,219 +0,0 @@ -import torch -from collections import OrderedDict - -import torch -import torch.nn as nn - -def make_layers(block, no_relu_layers): - layers = [] - for layer_name, v in block.items(): - if 'pool' in layer_name: - layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], - padding=v[2]) - layers.append((layer_name, layer)) - else: - conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], - kernel_size=v[2], stride=v[3], - padding=v[4]) - layers.append((layer_name, conv2d)) - if layer_name not in no_relu_layers: - layers.append(('relu_'+layer_name, nn.ReLU(inplace=True))) - - return nn.Sequential(OrderedDict(layers)) - -class bodypose_model(nn.Module): - def __init__(self): - super(bodypose_model, self).__init__() - - # these layers have no relu layer - no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\ - 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\ - 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\ - 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1'] - blocks = {} - block0 = OrderedDict([ - ('conv1_1', [3, 64, 3, 1, 1]), - ('conv1_2', [64, 64, 3, 1, 1]), - ('pool1_stage1', [2, 2, 0]), - ('conv2_1', [64, 128, 3, 1, 1]), - ('conv2_2', [128, 128, 3, 1, 1]), - ('pool2_stage1', [2, 2, 0]), - ('conv3_1', [128, 256, 3, 1, 1]), - ('conv3_2', [256, 256, 3, 1, 1]), - ('conv3_3', [256, 256, 3, 1, 1]), - ('conv3_4', [256, 256, 3, 1, 1]), - ('pool3_stage1', [2, 2, 0]), - ('conv4_1', [256, 512, 3, 1, 1]), - ('conv4_2', [512, 512, 3, 1, 1]), - ('conv4_3_CPM', [512, 256, 3, 1, 1]), - ('conv4_4_CPM', [256, 128, 3, 1, 1]) - ]) - - - # Stage 1 - block1_1 = OrderedDict([ - ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), - ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), - ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), - ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), - ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) - ]) - - block1_2 = OrderedDict([ - ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), - ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), - ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), - ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), - ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) - ]) - blocks['block1_1'] = block1_1 - blocks['block1_2'] = block1_2 - - self.model0 = make_layers(block0, no_relu_layers) - - # Stages 2 - 6 - for i in range(2, 7): - blocks['block%d_1' % i] = OrderedDict([ - ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), - ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), - ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) - ]) - - blocks['block%d_2' % i] = OrderedDict([ - ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), - ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), - ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) - ]) - - for k in blocks.keys(): - blocks[k] = make_layers(blocks[k], no_relu_layers) - - self.model1_1 = blocks['block1_1'] - self.model2_1 = blocks['block2_1'] - self.model3_1 = blocks['block3_1'] - self.model4_1 = blocks['block4_1'] - self.model5_1 = blocks['block5_1'] - self.model6_1 = blocks['block6_1'] - - self.model1_2 = blocks['block1_2'] - self.model2_2 = blocks['block2_2'] - self.model3_2 = blocks['block3_2'] - self.model4_2 = blocks['block4_2'] - self.model5_2 = blocks['block5_2'] - self.model6_2 = blocks['block6_2'] - - - def forward(self, x): - - out1 = self.model0(x) - - out1_1 = self.model1_1(out1) - out1_2 = self.model1_2(out1) - out2 = torch.cat([out1_1, out1_2, out1], 1) - - out2_1 = self.model2_1(out2) - out2_2 = self.model2_2(out2) - out3 = torch.cat([out2_1, out2_2, out1], 1) - - out3_1 = self.model3_1(out3) - out3_2 = self.model3_2(out3) - out4 = torch.cat([out3_1, out3_2, out1], 1) - - out4_1 = self.model4_1(out4) - out4_2 = self.model4_2(out4) - out5 = torch.cat([out4_1, out4_2, out1], 1) - - out5_1 = self.model5_1(out5) - out5_2 = self.model5_2(out5) - out6 = torch.cat([out5_1, out5_2, out1], 1) - - out6_1 = self.model6_1(out6) - out6_2 = self.model6_2(out6) - - return out6_1, out6_2 - -class handpose_model(nn.Module): - def __init__(self): - super(handpose_model, self).__init__() - - # these layers have no relu layer - no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\ - 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6'] - # stage 1 - block1_0 = OrderedDict([ - ('conv1_1', [3, 64, 3, 1, 1]), - ('conv1_2', [64, 64, 3, 1, 1]), - ('pool1_stage1', [2, 2, 0]), - ('conv2_1', [64, 128, 3, 1, 1]), - ('conv2_2', [128, 128, 3, 1, 1]), - ('pool2_stage1', [2, 2, 0]), - ('conv3_1', [128, 256, 3, 1, 1]), - ('conv3_2', [256, 256, 3, 1, 1]), - ('conv3_3', [256, 256, 3, 1, 1]), - ('conv3_4', [256, 256, 3, 1, 1]), - ('pool3_stage1', [2, 2, 0]), - ('conv4_1', [256, 512, 3, 1, 1]), - ('conv4_2', [512, 512, 3, 1, 1]), - ('conv4_3', [512, 512, 3, 1, 1]), - ('conv4_4', [512, 512, 3, 1, 1]), - ('conv5_1', [512, 512, 3, 1, 1]), - ('conv5_2', [512, 512, 3, 1, 1]), - ('conv5_3_CPM', [512, 128, 3, 1, 1]) - ]) - - block1_1 = OrderedDict([ - ('conv6_1_CPM', [128, 512, 1, 1, 0]), - ('conv6_2_CPM', [512, 22, 1, 1, 0]) - ]) - - blocks = {} - blocks['block1_0'] = block1_0 - blocks['block1_1'] = block1_1 - - # stage 2-6 - for i in range(2, 7): - blocks['block%d' % i] = OrderedDict([ - ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), - ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), - ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) - ]) - - for k in blocks.keys(): - blocks[k] = make_layers(blocks[k], no_relu_layers) - - self.model1_0 = blocks['block1_0'] - self.model1_1 = blocks['block1_1'] - self.model2 = blocks['block2'] - self.model3 = blocks['block3'] - self.model4 = blocks['block4'] - self.model5 = blocks['block5'] - self.model6 = blocks['block6'] - - def forward(self, x): - out1_0 = self.model1_0(x) - out1_1 = self.model1_1(out1_0) - concat_stage2 = torch.cat([out1_1, out1_0], 1) - out_stage2 = self.model2(concat_stage2) - concat_stage3 = torch.cat([out_stage2, out1_0], 1) - out_stage3 = self.model3(concat_stage3) - concat_stage4 = torch.cat([out_stage3, out1_0], 1) - out_stage4 = self.model4(concat_stage4) - concat_stage5 = torch.cat([out_stage4, out1_0], 1) - out_stage5 = self.model5(concat_stage5) - concat_stage6 = torch.cat([out_stage5, out1_0], 1) - out_stage6 = self.model6(concat_stage6) - return out_stage6 - - diff --git a/spaces/group2test/Protogen_x3.4_Official_Release/app.py b/spaces/group2test/Protogen_x3.4_Official_Release/app.py deleted file mode 100644 index 063b4451477d2d2d7ce27eeb1bf670a7caac5bc5..0000000000000000000000000000000000000000 --- a/spaces/group2test/Protogen_x3.4_Official_Release/app.py +++ /dev/null @@ -1,53 +0,0 @@ -import os -import gradio as gr - -API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None) - -article = """--- -This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator).""" - -gr.Interface.load( - name="models/darkstorm2150/Protogen_x3.4_Official_Release", - title="""🤗 No-code Gradio Demo Tutorial""", - description="""Demo for Protogen X3.4 Official Release Stable Diffusion model. - - -This space contains a Gradio demo that was created **without typing any code**! -It enables you to generate photorealistic images based on a prompt. - -Here are the steps to take to create your own demo *in a few minutes* without any code: - -1. You can easily create a demo using [this HF space](https://huggingface.co/spaces/anzorq/sd-space-creator). You will have to provide a token with write access to continue as shown in the image below: - --
-2. If your token is valid, a prompt will enable you to specify the URL or ID of the model on the HF Hub you would like to use in this demo. For example [darkstorm2150/Protogen_x3.4_Official_Release](https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release), which is a diffusion model able to generate photorealistic images from a prompt. Click on "Load model". - -
-
--
-3. Then, you can specify a few details about your demo, such as: - - Name - - Title - - Description - - Space type - - Hardware (CPU? GPU?) -Click on "Create the space". Your space will now be getting built. - -
-
--
-4. Once your space is built, you can reach it at https://huggingface.co/spaces/SPACE_NAME. -5. You can enter a prompt to generate an image with the space you just built! The demo embedded in this space was created that way without typing any code! -
-
--
-""", - article=article, - api_key=API_KEY, - ).queue(concurrency_count=20).launch() diff --git a/spaces/gsrathoreniks/web_ui/README.md b/spaces/gsrathoreniks/web_ui/README.md deleted file mode 100644 index e73895645ae5923379e6cd550213d7a95f3e04f1..0000000000000000000000000000000000000000 --- a/spaces/gsrathoreniks/web_ui/README.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Stable Diffusion Web UI -emoji: 🚧 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.9 -app_file: app.py -pinned: false -duplicated_from: DucHaiten/webui ---- - -## Stable Diffusion Web UI -[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) - -## Documentation -[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki) - -## Models License -https://huggingface.co/spaces/CompVis/stable-diffusion-license \ No newline at end of file diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/template_model.py b/spaces/gwang-kim/DATID-3D/pose_estimation/models/template_model.py deleted file mode 100644 index 68cdaf6a9a2cb321ff2a01949b38adc6fa22e97c..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/template_model.py +++ /dev/null @@ -1,99 +0,0 @@ -"""Model class template - -This module provides a template for users to implement custom models. -You can specify '--model template' to use this model. -The class name should be consistent with both the filename and its model option. -The filename should be
-
-_dataset.py -The class name should be Dataset.py -It implements a simple image-to-image translation baseline based on regression loss. -Given input-output pairs (data_A, data_B), it learns a network netG that can minimize the following L1 loss: - min_ ||netG(data_A) - data_B||_1 -You need to implement the following functions: - : Add model-specific options and rewrite default values for existing options. - <__init__>: Initialize this model class. - : Unpack input data and perform data pre-processing. - : Run forward pass. This will be called by both and . - : Update network weights; it will be called in every training iteration. -""" -import torch -from .base_model import BaseModel -from . import networks - - -class TemplateModel(BaseModel): - @staticmethod - def modify_commandline_options(parser, is_train=True): - """Add new model-specific options and rewrite default values for existing options. - - Parameters: - parser -- the option parser - is_train -- if it is training phase or test phase. You can use this flag to add training-specific or test-specific options. - - Returns: - the modified parser. - """ - parser.set_defaults(dataset_mode='aligned') # You can rewrite default values for this model. For example, this model usually uses aligned dataset as its dataset. - if is_train: - parser.add_argument('--lambda_regression', type=float, default=1.0, help='weight for the regression loss') # You can define new arguments for this model. - - return parser - - def __init__(self, opt): - """Initialize this model class. - - Parameters: - opt -- training/test options - - A few things can be done here. - - (required) call the initialization function of BaseModel - - define loss function, visualization images, model names, and optimizers - """ - BaseModel.__init__(self, opt) # call the initialization method of BaseModel - # specify the training losses you want to print out. The program will call base_model.get_current_losses to plot the losses to the console and save them to the disk. - self.loss_names = ['loss_G'] - # specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images. - self.visual_names = ['data_A', 'data_B', 'output'] - # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks. - # you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them. - self.model_names = ['G'] - # define networks; you can use opt.isTrain to specify different behaviors for training and test. - self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, gpu_ids=self.gpu_ids) - if self.isTrain: # only defined during training time - # define your loss functions. You can use losses provided by torch.nn such as torch.nn.L1Loss. - # We also provide a GANLoss class "networks.GANLoss". self.criterionGAN = networks.GANLoss().to(self.device) - self.criterionLoss = torch.nn.L1Loss() - # define and initialize optimizers. You can define one optimizer for each network. - # If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. - self.optimizer = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) - self.optimizers = [self.optimizer] - - # Our program will automatically call to define schedulers, load networks, and print networks - - def set_input(self, input): - """Unpack input data from the dataloader and perform necessary pre-processing steps. - - Parameters: - input: a dictionary that contains the data itself and its metadata information. - """ - AtoB = self.opt.direction == 'AtoB' # use to swap data_A and data_B - self.data_A = input['A' if AtoB else 'B'].to(self.device) # get image data A - self.data_B = input['B' if AtoB else 'A'].to(self.device) # get image data B - self.image_paths = input['A_paths' if AtoB else 'B_paths'] # get image paths - - def forward(self): - """Run forward pass. This will be called by both functions and .""" - self.output = self.netG(self.data_A) # generate output image given the input data_A - - def backward(self): - """Calculate losses, gradients, and update network weights; called in every training iteration""" - # caculate the intermediate results if necessary; here self.output has been computed during function - # calculate loss given the input and intermediate results - self.loss_G = self.criterionLoss(self.output, self.data_B) * self.opt.lambda_regression - self.loss_G.backward() # calculate gradients of network G w.r.t. loss_G - - def optimize_parameters(self): - """Update network weights; it will be called in every training iteration.""" - self.forward() # first call forward to calculate intermediate results - self.optimizer.zero_grad() # clear network G's existing gradients - self.backward() # calculate gradients for network G - self.optimizer.step() # update gradients for network G diff --git a/spaces/haakohu/deep_privacy2/dp2/utils/utils.py b/spaces/haakohu/deep_privacy2/dp2/utils/utils.py deleted file mode 100644 index f3dda0ecd189f2b1b21726a12ced68af348bdb5e..0000000000000000000000000000000000000000 --- a/spaces/haakohu/deep_privacy2/dp2/utils/utils.py +++ /dev/null @@ -1,30 +0,0 @@ -import tops -import tqdm -from tops import logger, highlight_py_str -from tops.config import LazyConfig - - -def print_config(cfg): - logger.log("\n" + highlight_py_str(LazyConfig.to_py(cfg, prefix=""))) - - -def config_to_str(cfg): - return LazyConfig.to_py(cfg, prefix=".") - - -def init_tops(cfg, reinit=False): - tops.init( - cfg.output_dir, cfg.common.logger_backend, cfg.experiment_name, - cfg.common.wandb_project, dict(cfg), reinit) - - -def tqdm_(iterator, *args, **kwargs): - if tops.rank() == 0: - return tqdm.tqdm(iterator, *args, **kwargs) - return iterator - - -def trange_(*args, **kwargs): - if tops.rank() == 0: - return tqdm.trange(*args, **kwargs) - return range(*args) diff --git a/spaces/hackathon-pln-es/itama-app/README.md b/spaces/hackathon-pln-es/itama-app/README.md deleted file mode 100644 index 6bb0b8fdd7c63707e0644abf3bbbd7d717f490a7..0000000000000000000000000000000000000000 --- a/spaces/hackathon-pln-es/itama-app/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Itama App -emoji: 📈 -colorFrom: gray -colorTo: blue -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/filtered_lrelu.h b/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/filtered_lrelu.h deleted file mode 100644 index 2c403e3f275f472315662321cad54dd0dbc56d00..0000000000000000000000000000000000000000 --- a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/filtered_lrelu.h +++ /dev/null @@ -1,90 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct filtered_lrelu_kernel_params -{ - // These parameters decide which kernel to use. - int up; // upsampling ratio (1, 2, 4) - int down; // downsampling ratio (1, 2, 4) - int2 fuShape; // [size, 1] | [size, size] - int2 fdShape; // [size, 1] | [size, size] - - int _dummy; // Alignment. - - // Rest of the parameters. - const void* x; // Input tensor. - void* y; // Output tensor. - const void* b; // Bias tensor. - unsigned char* s; // Sign tensor in/out. NULL if unused. - const float* fu; // Upsampling filter. - const float* fd; // Downsampling filter. - - int2 pad0; // Left/top padding. - float gain; // Additional gain factor. - float slope; // Leaky ReLU slope on negative side. - float clamp; // Clamp after nonlinearity. - int flip; // Filter kernel flip for gradient computation. - - int tilesXdim; // Original number of horizontal output tiles. - int tilesXrep; // Number of horizontal tiles per CTA. - int blockZofs; // Block z offset to support large minibatch, channel dimensions. - - int4 xShape; // [width, height, channel, batch] - int4 yShape; // [width, height, channel, batch] - int2 sShape; // [width, height] - width is in bytes. Contiguous. Zeros if unused. - int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. - int swLimit; // Active width of sign tensor in bytes. - - longlong4 xStride; // Strides of all tensors except signs, same component order as shapes. - longlong4 yStride; // - int64_t bStride; // - longlong3 fuStride; // - longlong3 fdStride; // -}; - -struct filtered_lrelu_act_kernel_params -{ - void* x; // Input/output, modified in-place. - unsigned char* s; // Sign tensor in/out. NULL if unused. - - float gain; // Additional gain factor. - float slope; // Leaky ReLU slope on negative side. - float clamp; // Clamp after nonlinearity. - - int4 xShape; // [width, height, channel, batch] - longlong4 xStride; // Input/output tensor strides, same order as in shape. - int2 sShape; // [width, height] - width is in elements. Contiguous. Zeros if unused. - int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. -}; - -//------------------------------------------------------------------------ -// CUDA kernel specialization. - -struct filtered_lrelu_kernel_spec -{ - void* setup; // Function for filter kernel setup. - void* exec; // Function for main operation. - int2 tileOut; // Width/height of launch tile. - int numWarps; // Number of warps per thread block, determines launch block size. - int xrep; // For processing multiple horizontal tiles per thread block. - int dynamicSharedKB; // How much dynamic shared memory the exec kernel wants. -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template void* choose_filtered_lrelu_act_kernel(void); -template cudaError_t copy_filters(cudaStream_t stream); - -//------------------------------------------------------------------------ diff --git a/spaces/haonanzhang/ChatGPT-BOT/assets/custom.js b/spaces/haonanzhang/ChatGPT-BOT/assets/custom.js deleted file mode 100644 index 7b1761043149ff97ca498501c87a0d15db5258ee..0000000000000000000000000000000000000000 --- a/spaces/haonanzhang/ChatGPT-BOT/assets/custom.js +++ /dev/null @@ -1 +0,0 @@ -// custom javascript here \ No newline at end of file diff --git a/spaces/hersia/youtube-video-transcription-with-whisper/README.md b/spaces/hersia/youtube-video-transcription-with-whisper/README.md deleted file mode 100644 index 124afd4ab8eddeb6a2f6f2a0ac7b59990ad30a98..0000000000000000000000000000000000000000 --- a/spaces/hersia/youtube-video-transcription-with-whisper/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Youtube Video Transcription With Whisper -emoji: 🐢 -colorFrom: purple -colorTo: red -sdk: gradio -sdk_version: 3.20.1 -app_file: app.py -pinned: false -license: afl-3.0 -duplicated_from: buildingai/youtube-video-transcription-with-whisper ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hirol/controlnetOverMask/css/main.css b/spaces/hirol/controlnetOverMask/css/main.css deleted file mode 100644 index 2fa2b2f7fdfe7a1d459a865c320a3b533a40bc48..0000000000000000000000000000000000000000 --- a/spaces/hirol/controlnetOverMask/css/main.css +++ /dev/null @@ -1,125 +0,0 @@ -#cont { - display: flex; - flex-flow: column; - gap: 0.5em 0; - width: max-content; -} - -.box { - display: block; - border: 1px solid gray; - padding: 0.5em 0; - text-decoration: none; - text-align: center; -} - -#notation { - display: none; - position: absolute; - color: black; - background-color: rgba(255, 255, 255, 0.75); - padding: 0.1em 0.25em; - pointer-events: none; - font-size: small; -} - -#notifications { - left: 1em; - bottom: 0; - width: 25%; -} - -#notifications .item { - padding: 0.5em; - border: 1px solid gray; - animation: erase 2s ease-out 0.5s forwards; -} - -@keyframes erase { - 100% { - opacity: 0; - } -} - -#notifications .success { - background-color: #aaccaa; - color: #004000; -} - -#notifications .info { - background-color: white; - color: black; -} - -#notifications .error { - background-color: #ffcccc; - color: red; -} - -#body_indicator1 { - display: none; - position: absolute; - outline: 1px solid white; - pointer-events: none; -} - -#body_indicator2 { - display: none; - position: absolute; - outline: 1px solid gray; - pointer-events: none; -} - -#saved_poses { - display: flex; - flex-direction: row; - gap: 0.25em; - font-size: small; -} - -#saved_poses > * { - margin: 0; - outline: 1px solid gray; - max-width: 128px; -} - -#saved_poses img { - max-width: 128px; - max-height: 128px; -} - -#saved_poses figcaption { - padding: 0 0.25em; - overflow-wrap: anywhere; /* Opera Android may not be able to interpret `anywhere` keyword. */ -} - -#saved_poses .close { - position: absolute; - cursor: pointer; - border: 1px solid gray; - background-color: white; - opacity: 0.5; - width: 1.25em; - height: 1.25em; - text-align: center; - vertical-align: middle; - margin: 0; - font-family: monospace; -} - -#saved_poses .close:hover { - opacity: 1.0; -} - -#saved_poses .close2 { - display: none; - position: absolute; - left: 1.5em; - top: 0; -} - -#saved_poses .close:hover .close2 { - display: block; - color: white; - pointer-events: none; -} \ No newline at end of file diff --git a/spaces/hkunlp/Binder/utils/utils.py b/spaces/hkunlp/Binder/utils/utils.py deleted file mode 100644 index b3fb211faf71f54ccd3a049d54416258f98dc3d9..0000000000000000000000000000000000000000 --- a/spaces/hkunlp/Binder/utils/utils.py +++ /dev/null @@ -1,198 +0,0 @@ -""" -General utilities. -""" -import json -import os -from typing import List, Union, Dict -from functools import cmp_to_key -import math -from collections.abc import Iterable - -from datasets import load_dataset - -ROOT_DIR = os.path.join(os.path.dirname(__file__), "../") - -def _load_table(table_path) -> dict: - """ - attention: the table_path must be the .tsv path. - Load the WikiTableQuestion from csv file. Result in a dict format like: - {"header": [header1, header2,...], "rows": [[row11, row12, ...], [row21,...]... [...rownm]]} - """ - - def __extract_content(_line: str): - _vals = [_.replace("\n", " ").strip() for _ in _line.strip("\n").split("\t")] - return _vals - - with open(table_path, "r") as f: - lines = f.readlines() - - rows = [] - for i, line in enumerate(lines): - line = line.strip('\n') - if i == 0: - header = line.split("\t") - else: - rows.append(__extract_content(line)) - - table_item = {"header": header, "rows": rows} - - # Defense assertion - for i in range(len(rows) - 1): - if not len(rows[i]) == len(rows[i - 1]): - raise ValueError('some rows have diff cols.') - - return table_item - - -def majority_vote( - nsqls: List, - pred_answer_list: List, - allow_none_and_empty_answer: bool = False, - allow_error_answer: bool = False, - answer_placeholder: Union[str, int] = ' ': - pred_answer = [answer_placeholder] - - # Invalid execution results - if pred_answer == ' ' or pred_answer == [None] or pred_answer == []: - continue - if candi_answer_dict.get(tuple(pred_answer), None) is None: - candi_answer_dict[tuple(pred_answer)] = { - 'count': 0, - 'nsqls': [] - } - answer_info = candi_answer_dict.get(tuple(pred_answer), None) - answer_info['count'] += 1 - answer_info['nsqls'].append([nsql, logprob]) - - # All candidates execution errors - if len(candi_answer_dict) == 0: - return answer_placeholder, [(nsqls[0][0], nsqls[0][-1])] - - # Sort - if vote_method == 'simple': - sorted_candi_answer_list = sorted(list(candi_answer_dict.items()), - key=cmp_to_key(_compare_answer_vote_simple), reverse=True) - elif vote_method == 'prob': - sorted_candi_answer_list = sorted(list(candi_answer_dict.items()), - key=cmp_to_key(_compare_answer_vote_with_prob), reverse=True) - elif vote_method == 'answer_biased': - # Specifically for Tabfact entailed answer, i.e., `1`. - # If there exists nsql that produces `1`, we consider it more significant because `0` is very common. - assert answer_biased_weight is not None and answer_biased_weight > 0 - for answer, answer_dict in candi_answer_dict.items(): - if answer == (answer_biased,): - answer_dict['count'] *= answer_biased_weight - sorted_candi_answer_list = sorted(list(candi_answer_dict.items()), - key=cmp_to_key(_compare_answer_vote_simple), reverse=True) - elif vote_method == 'lf_biased': - # Assign weights to different types of logic forms (lf) to control interpretability and coverage - for answer, answer_dict in candi_answer_dict.items(): - count = 0 - for nsql, _ in answer_dict['nsqls']: - if 'map@' in nsql: - count += 10 - elif 'ans@' in nsql: - count += 10 - else: - count += 1 - answer_dict['count'] = count - sorted_candi_answer_list = sorted(list(candi_answer_dict.items()), - key=cmp_to_key(_compare_answer_vote_simple), reverse=True) - else: - raise ValueError(f"Vote method {vote_method} is not supported.") - - pred_answer_info = sorted_candi_answer_list[0] - pred_answer, pred_answer_nsqls = list(pred_answer_info[0]), pred_answer_info[1]['nsqls'] - return pred_answer, pred_answer_nsqls - - -def load_data_split(dataset_to_load, split, data_dir=os.path.join(ROOT_DIR, 'datasets/')): - dataset_split_loaded = load_dataset( - path=os.path.join(data_dir, "{}.py".format(dataset_to_load)), - cache_dir=os.path.join(data_dir, "data"))[split] - - # unify names of keys - if dataset_to_load in ['wikitq', 'has_squall', 'missing_squall', - 'wikitq', 'wikitq_sql_solvable', 'wikitq_sql_unsolvable', - 'wikitq_sql_unsolvable_but_in_squall', - 'wikitq_scalability_ori', - 'wikitq_scalability_100rows', - 'wikitq_scalability_200rows', - 'wikitq_scalability_500rows', - 'wikitq_robustness' - ]: - pass - elif dataset_to_load == 'tab_fact': - new_dataset_split_loaded = [] - for data_item in dataset_split_loaded: - data_item['question'] = data_item['statement'] - data_item['answer_text'] = data_item['label'] - data_item['table']['page_title'] = data_item['table']['caption'] - new_dataset_split_loaded.append(data_item) - dataset_split_loaded = new_dataset_split_loaded - elif dataset_to_load == 'hybridqa': - new_dataset_split_loaded = [] - for data_item in dataset_split_loaded: - data_item['table']['page_title'] = data_item['context'].split(' | ')[0] - new_dataset_split_loaded.append(data_item) - dataset_split_loaded = new_dataset_split_loaded - elif dataset_to_load == 'mmqa': - new_dataset_split_loaded = [] - for data_item in dataset_split_loaded: - data_item['table']['page_title'] = data_item['table']['title'] - new_dataset_split_loaded.append(data_item) - dataset_split_loaded = new_dataset_split_loaded - else: - raise ValueError(f'{dataset_to_load} dataset is not supported now.') - return dataset_split_loaded - - -def pprint_dict(dic): - print(json.dumps(dic, indent=2)) - - -def flatten(nested_list): - for x in nested_list: - if isinstance(x, Iterable) and not isinstance(x, (str, bytes)): - yield from flatten(x) - else: - yield x diff --git a/spaces/hlydecker/ImageBind_zeroshot_demo/app.py b/spaces/hlydecker/ImageBind_zeroshot_demo/app.py deleted file mode 100644 index 71493ec124d06e5c13ba107e22dd04b49893be50..0000000000000000000000000000000000000000 --- a/spaces/hlydecker/ImageBind_zeroshot_demo/app.py +++ /dev/null @@ -1,142 +0,0 @@ -import data -import torch -import gradio as gr -from models import imagebind_model -from models.imagebind_model import ModalityType - - -device = "cuda:0" if torch.cuda.is_available() else "cpu" -model = imagebind_model.imagebind_huge(pretrained=True) -model.eval() -model.to(device) - - -def image_text_zeroshot(image, text_list): - image_paths = [image] - labels = [label.strip(" ") for label in text_list.strip(" ").split("|")] - inputs = { - ModalityType.TEXT: data.load_and_transform_text(labels, device), - ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device), - } - - with torch.no_grad(): - embeddings = model(inputs) - - scores = ( - torch.softmax( - embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1 - ) - .squeeze(0) - .tolist() - ) - - score_dict = {label: score for label, score in zip(labels, scores)} - - return score_dict - - -def audio_text_zeroshot(audio, text_list): - audio_paths = [audio] - labels = [label.strip(" ") for label in text_list.strip(" ").split("|")] - inputs = { - ModalityType.TEXT: data.load_and_transform_text(labels, device), - ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device), - } - - with torch.no_grad(): - embeddings = model(inputs) - - scores = ( - torch.softmax( - embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1 - ) - .squeeze(0) - .tolist() - ) - - score_dict = {label: score for label, score in zip(labels, scores)} - - return score_dict - - -def video_text_zeroshot(video, text_list): - video_paths = [video] - labels = [label.strip(" ") for label in text_list.strip(" ").split("|")] - inputs = { - ModalityType.TEXT: data.load_and_transform_text(labels, device), - ModalityType.VISION: data.load_and_transform_video_data(video_paths, device), - } - - with torch.no_grad(): - embeddings = model(inputs) - - scores = ( - torch.softmax( - embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1 - ) - .squeeze(0) - .tolist() - ) - - score_dict = {label: score for label, score in zip(labels, scores)} - - return score_dict - - -def inference( - task, - text_list=None, - image=None, - audio=None, - video=None, -): - if task == "image-text": - result = image_text_zeroshot(image, text_list) - elif task == "audio-text": - result = audio_text_zeroshot(audio, text_list) - elif task == "video-text": - result = video_text_zeroshot(video, text_list) - else: - raise NotImplementedError - return result - - -def main(): - inputs = [ - gr.inputs.Radio( - choices=[ - "image-text", - "audio-text", - "video-text", - ], - type="value", - default="image-text", - label="Task", - ), - gr.inputs.Textbox(lines=1, label="Candidate texts"), - gr.inputs.Image(type="filepath", label="Input image"), - gr.inputs.Audio(type="filepath", label="Input audio"), - gr.inputs.Video(type=None, label="Input video"), - ] - - iface = gr.Interface( - inference, - inputs, - "label", - examples=[ - ["image-text", "A dog|A car|A bird", "assets/dog_image.jpg", None, None], - ["image-text", "A dog|A car|A bird", "assets/car_image.jpg", None, None], - ["audio-text", "A dog|A car|A bird", None, "assets/bird_audio.wav", None], - ["video-text", "A dog|A car|A bird", None, None, "assets/dog_video.mp4"], - ], - description=""" This is a simple demo of ImageBind for zero-shot cross-modal understanding (now including image classification, audio classification, and video classification). Please refer to the original paper and repo for more details.
""", - title="ImageBind: Zero-shot Cross-modal Understanding", - ) - - iface.launch() - - -if __name__ == "__main__": - main() diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainerMTLrecon.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainerMTLrecon.py deleted file mode 100644 index f69249dc413ba715d5ecafa1ca2061d5913fda7c..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainerMTLrecon.py +++ /dev/null @@ -1,483 +0,0 @@ -# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from collections import OrderedDict -from typing import Tuple - -import numpy as np -import torch -from nnunet.training.data_augmentation.data_augmentation_moreDA import get_moreDA_augmentation -from nnunet.training.loss_functions.deep_supervision import MultipleOutputLoss2 -from nnunet.utilities.to_torch import maybe_to_torch, to_cuda -from nnunet.network_architecture.generic_UNet_MTLrecon import Generic_UNet_MTLrecon -from nnunet.network_architecture.initialization import InitWeights_He -from nnunet.network_architecture.neural_network import SegmentationNetwork -from nnunet.training.data_augmentation.default_data_augmentation import default_2D_augmentation_params, \ - get_patch_size, default_3D_augmentation_params -from nnunet.training.dataloading.dataset_loading import unpack_dataset -from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer -from nnunet.utilities.nd_softmax import softmax_helper -from sklearn.model_selection import KFold -from torch import nn -from torch.cuda.amp import autocast -from nnunet.training.learning_rate.poly_lr import poly_lr -from batchgenerators.utilities.file_and_folder_operations import * -from nnunet.training.loss_functions.dice_loss import DC_and_CE_loss -import time - - -class nnUNetTrainerMTLrecon(nnUNetTrainer): - - def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, - unpack_data=True, deterministic=True, fp16=False): - super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, - deterministic, fp16) - - self.max_num_epochs = 500 - self.num_batches_per_epoch = 250 # 250 default - self.num_val_batches_per_epoch = 50 # 50 default - self.lossDCCE = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {}) - self.lossMSE = torch.nn.MSELoss() - self.initial_lr = 1e-2 - self.deep_supervision_scales = None - self.ds_loss_weights = None - - self.pin_memory = True - - def initialize(self, training=True, force_load_plans=False, disable_deepsupervision=False): - """ - - replaced get_default_augmentation with get_moreDA_augmentation - - enforce to only run this code once - - loss function wrapper for deep supervision - - :param training: - :param force_load_plans: - :return: - """ - if not self.was_initialized: - maybe_mkdir_p(self.output_folder) - - if force_load_plans or (self.plans is None): - self.load_plans_file() - - self.process_plans(self.plans) - print("NUM_pool_op_kernel_sizes") - print(self.net_num_pool_op_kernel_sizes) - self.setup_DA_params() - - ################# Here we wrap the loss for deep supervision ############ - # we need to know the number of outputs of the network - net_numpool = len(self.net_num_pool_op_kernel_sizes) - - # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases - # this gives higher resolution outputs more weight in the loss - weights = np.array([1 / (2 ** i) for i in range(net_numpool)]) - - # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 - mask = np.array([True] + [True if i < net_numpool - 1 else False for i in range(1, net_numpool)]) - weights[~mask] = 0 - weights = weights / weights.sum() - self.ds_loss_weights = weights - # now wrap the loss - self.loss_zone = MultipleOutputLoss2(self.lossDCCE, self.ds_loss_weights) - self.loss_front = MultipleOutputLoss2(self.lossDCCE, None) - self.loss_recon = MultipleOutputLoss2(self.lossMSE, self.ds_loss_weights) - ################# END ################### - - self.folder_with_preprocessed_data = join(self.dataset_directory, self.plans['data_identifier'] + - "_stage%d" % self.stage) - if training: - # data get loaded here - self.dl_tr, self.dl_val = self.get_basic_generators() - if self.unpack_data: - print("unpacking dataset") - unpack_dataset(self.folder_with_preprocessed_data) - print("done") - else: - print( - "INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you " - "will wait all winter for your model to finish!") - - self.tr_gen, self.val_gen = get_moreDA_augmentation( - self.dl_tr, self.dl_val, - self.data_aug_params[ - 'patch_size_for_spatialtransform'], - self.data_aug_params, - deep_supervision_scales=self.deep_supervision_scales, - pin_memory=self.pin_memory, - use_nondetMultiThreadedAugmenter=False - ) - self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())), - also_print_to_console=False) - self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())), - also_print_to_console=False) - else: - pass - - self.initialize_network() - self.initialize_optimizer_and_scheduler() - - assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel)) - else: - self.print_to_log_file('self.was_initialized is True, not running self.initialize again') - self.was_initialized = True - - def initialize_network(self): - """ - - momentum 0.99 - - SGD instead of Adam - - self.lr_scheduler = None because we do poly_lr - - deep supervision = True - - i am sure I forgot something here - - Known issue: forgot to set neg_slope=0 in InitWeights_He; should not make a difference though - :return: - """ - if self.threeD: - conv_op = nn.Conv3d - dropout_op = nn.Dropout3d - norm_op = nn.InstanceNorm3d - - else: - conv_op = nn.Conv2d - dropout_op = nn.Dropout2d - norm_op = nn.InstanceNorm2d - - norm_op_kwargs = {'eps': 1e-5, 'affine': True} - dropout_op_kwargs = {'p': 0, 'inplace': True} - net_nonlin = nn.LeakyReLU - net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} - - self.network = Generic_UNet_MTLrecon(self.num_input_channels, self.base_num_features, self.num_classes, - len(self.net_num_pool_op_kernel_sizes), - self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, - dropout_op_kwargs, - net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, - InitWeights_He(1e-2), - self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, - True, True) - - print("number of parameters") - print(sum(param.numel() for param in self.network.parameters())) - - if torch.cuda.is_available(): - self.network.cuda() - self.network.inference_apply_nonlin = softmax_helper - - def initialize_optimizer_and_scheduler(self): - assert self.network is not None, "self.initialize_network must be called first" - self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, - momentum=0.99, nesterov=True) - self.lr_scheduler = None - - def run_online_evaluation(self, output, target): - """ - due to deep supervision the return value and the reference are now lists of tensors. We only need the full - resolution output because this is what we are interested in in the end. The others are ignored - :param output: - :param target: - :return: - """ - target = target[0] - output = output[0] - return super().run_online_evaluation(output, target) - - def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, - step_size: float = 0.5, save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True, - validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False, - segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True): - """ - We need to wrap this because we need to enforce self.network.do_ds = False for prediction - """ - ds = self.network.do_ds - self.network.do_ds = False - ret = super().validate(do_mirroring=do_mirroring, use_sliding_window=use_sliding_window, step_size=step_size, - save_softmax=save_softmax, use_gaussian=use_gaussian, - overwrite=overwrite, validation_folder_name=validation_folder_name, debug=debug, - all_in_gpu=all_in_gpu, segmentation_export_kwargs=segmentation_export_kwargs, - run_postprocessing_on_folds=run_postprocessing_on_folds) - - self.network.do_ds = ds - return ret - - def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True, - mirror_axes: Tuple[int] = None, - use_sliding_window: bool = True, step_size: float = 0.5, - use_gaussian: bool = True, pad_border_mode: str = 'constant', - pad_kwargs: dict = None, all_in_gpu: bool = False, - verbose: bool = True, mixed_precision=True) -> Tuple[ - np.ndarray, np.ndarray]: - """ - We need to wrap this because we need to enforce self.network.do_ds = False for prediction - """ - ds = self.network.do_ds - self.network.do_ds = False - ret = super().predict_preprocessed_data_return_seg_and_softmax(data[:1], - do_mirroring=do_mirroring, - mirror_axes=mirror_axes, - use_sliding_window=use_sliding_window, - step_size=step_size, use_gaussian=use_gaussian, - pad_border_mode=pad_border_mode, - pad_kwargs=pad_kwargs, all_in_gpu=all_in_gpu, - verbose=verbose, - mixed_precision=mixed_precision) - self.network.do_ds = ds - return ret - - def run_iteration(self, data_generator, do_backprop=True, run_online_evaluation=False): - """ - gradient clipping improves training stability - - :param data_generator: - :param do_backprop: - :param run_online_evaluation: :return: - """ - data_dict = next(data_generator) - data = data_dict['data'] - target = data_dict['target'] - - data = maybe_to_torch(data) - target = maybe_to_torch(target) - - if torch.cuda.is_available(): - data = to_cuda(data) - target = to_cuda(target) - - self.optimizer.zero_grad() - - if self.fp16: - with autocast(): - - output = self.network(data) - - del data - - target0 = [target[i][:, [0]].to(torch.float16) for i in range(len(target))] - output0 = [output[i][:, 0:2] for i in range(len(output))] - - target1 = [target[i][:, [1]].to(torch.float16) for i in range(len(target))] - output1 = [output[i][:, 2:6] for i in range(len(output))] - - target2 = [target[i][:, [2]].to(torch.float16)/255 for i in range(len(target))] - output2 = [output[i][:, 6:7] for i in range(len(output))] - - l_front = self.loss_front(output0, target0) - - l_zone = self.loss_zone(output1, target1) - l_recon = self.loss_recon(output2, target2) - - l = 0.33 * l_front + 0.34 * l_zone + 0.33 * l_recon - - if do_backprop: - self.amp_grad_scaler.scale(l).backward() - self.amp_grad_scaler.unscale_(self.optimizer) - torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12) - self.amp_grad_scaler.step(self.optimizer) - self.amp_grad_scaler.update() - else: - output = self.network(data) - del data - l = self.loss(output, target) - - if do_backprop: - l.backward() - torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12) - self.optimizer.step() - - if run_online_evaluation: - self.run_online_evaluation(output0, target0) - self.run_online_evaluation(output1, target1) - self.run_online_evaluation(output2, target2) - - del target - del target0 - del target1 - del target2 - del output - del output0 - del output1 - del output2 - - return l.detach().cpu().numpy() - - def do_split(self): - """ - The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded, - so always the same) and save it as splits_final.pkl file in the preprocessed data directory. - Sometimes you may want to create your own split for various reasons. For this you will need to create your own - splits_final.pkl file. If this file is present, nnU-Net is going to use it and whatever splits are defined in - it. You can create as many splits in this file as you want. Note that if you define only 4 splits (fold 0-3) - and then set fold=4 when training (that would be the fifth split), nnU-Net will print a warning and proceed to - use a random 80:20 data split. - :return: - """ - if self.fold == "all": - # if fold==all then we use all images for training and validation - tr_keys = val_keys = list(self.dataset.keys()) - else: - splits_file = join(self.dataset_directory, "splits_final.pkl") - - # if the split file does not exist we need to create it - if not isfile(splits_file): - self.print_to_log_file("Creating new 5-fold cross-validation split...") - splits = [] - all_keys_sorted = np.sort(list(self.dataset.keys())) - kfold = KFold(n_splits=5, shuffle=True, random_state=12345) - for i, (train_idx, test_idx) in enumerate(kfold.split(all_keys_sorted)): - train_keys = np.array(all_keys_sorted)[train_idx] - test_keys = np.array(all_keys_sorted)[test_idx] - splits.append(OrderedDict()) - splits[-1]['train'] = train_keys - splits[-1]['val'] = test_keys - save_pickle(splits, splits_file) - - else: - self.print_to_log_file("Using splits from existing split file:", splits_file) - splits = load_pickle(splits_file) - self.print_to_log_file("The split file contains %d splits." % len(splits)) - - self.print_to_log_file("Desired fold for training: %d" % self.fold) - if self.fold < len(splits): - tr_keys = splits[self.fold]['train'] - val_keys = splits[self.fold]['val'] - self.print_to_log_file("This split has %d training and %d validation cases." - % (len(tr_keys), len(val_keys))) - else: - self.print_to_log_file("INFO: You requested fold %d for training but splits " - "contain only %d folds. I am now creating a " - "random (but seeded) 80:20 split!" % (self.fold, len(splits))) - # if we request a fold that is not in the split file, create a random 80:20 split - rnd = np.random.RandomState(seed=12345 + self.fold) - keys = np.sort(list(self.dataset.keys())) - idx_tr = rnd.choice(len(keys), int(len(keys) * 0.8), replace=False) - idx_val = [i for i in range(len(keys)) if i not in idx_tr] - tr_keys = [keys[i] for i in idx_tr] - val_keys = [keys[i] for i in idx_val] - self.print_to_log_file("This random 80:20 split has %d training and %d validation cases." - % (len(tr_keys), len(val_keys))) - - tr_keys.sort() - val_keys.sort() - self.dataset_tr = OrderedDict() - for i in tr_keys: - self.dataset_tr[i] = self.dataset[i] - self.dataset_val = OrderedDict() - for i in val_keys: - self.dataset_val[i] = self.dataset[i] - - def setup_DA_params(self): - """ - - we increase roation angle from [-15, 15] to [-30, 30] - - scale range is now (0.7, 1.4), was (0.85, 1.25) - - we don't do elastic deformation anymore - - :return: - """ - - self.deep_supervision_scales = [[1, 1, 1]] + list(list(i) for i in 1 / np.cumprod( - np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1] - - if self.threeD: - self.data_aug_params = default_3D_augmentation_params - self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) - self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) - self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi) - if self.do_dummy_2D_aug: - self.data_aug_params["dummy_2D"] = True - self.print_to_log_file("Using dummy2d data augmentation") - self.data_aug_params["elastic_deform_alpha"] = \ - default_2D_augmentation_params["elastic_deform_alpha"] - self.data_aug_params["elastic_deform_sigma"] = \ - default_2D_augmentation_params["elastic_deform_sigma"] - self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"] - else: - self.do_dummy_2D_aug = False - if max(self.patch_size) / min(self.patch_size) > 1.5: - default_2D_augmentation_params['rotation_x'] = (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi) - self.data_aug_params = default_2D_augmentation_params - self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm - - if self.do_dummy_2D_aug: - self.basic_generator_patch_size = get_patch_size(self.patch_size[1:], - self.data_aug_params['rotation_x'], - self.data_aug_params['rotation_y'], - self.data_aug_params['rotation_z'], - self.data_aug_params['scale_range']) - self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size)) - else: - self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'], - self.data_aug_params['rotation_y'], - self.data_aug_params['rotation_z'], - self.data_aug_params['scale_range']) - - self.data_aug_params["scale_range"] = (0.7, 1.4) - self.data_aug_params["do_elastic"] = False - self.data_aug_params['selected_seg_channels'] = [0, 1, 2] - self.data_aug_params['patch_size_for_spatialtransform'] = self.patch_size - - self.data_aug_params["num_cached_per_thread"] = 2 - - def maybe_update_lr(self, epoch=None): - """ - if epoch is not None we overwrite epoch. Else we use epoch = self.epoch + 1 - - (maybe_update_lr is called in on_epoch_end which is called before epoch is incremented. - herefore we need to do +1 here) - - :param epoch: - :return: - """ - if epoch is None: - ep = self.epoch + 1 - else: - ep = epoch - self.optimizer.param_groups[0]['lr'] = poly_lr(ep, self.max_num_epochs, self.initial_lr, 0.9) - self.print_to_log_file("lr:", np.round(self.optimizer.param_groups[0]['lr'], decimals=6)) - - def on_epoch_end(self): - """ - overwrite patient-based early stopping. Always run to 1000 epochs - :return: - """ - super().on_epoch_end() - continue_training = self.epoch < self.max_num_epochs - - # it can rarely happen that the momentum of nnUNetTrainerV2 is too high for some dataset. If at epoch 100 the - # estimated validation Dice is still 0 then we reduce the momentum from 0.99 to 0.95 - if self.epoch == 100: - if self.all_val_eval_metrics[-1] == 0: - self.optimizer.param_groups[0]["momentum"] = 0.95 - self.network.apply(InitWeights_He(1e-2)) - self.print_to_log_file("At epoch 100, the mean foreground Dice was 0. This can be caused by a too " - "high momentum. High momentum (0.99) is good for datasets where it works, but " - "sometimes causes issues such as this one. Momentum has now been reduced to " - "0.95 and network weights have been reinitialized") - return continue_training - - def run_training(self): - """ - if we run with -c then we need to set the correct lr for the first epoch, otherwise it will run the first - continued epoch with self.initial_lr - - we also need to make sure deep supervision in the network is enabled for training, thus the wrapper - :return: - """ - self.maybe_update_lr(self.epoch) # if we dont overwrite epoch then self.epoch+1 is used which is not what we - # want at the start of the training - ds = self.network.do_ds - self.network.do_ds = True - ret = super().run_training() - self.network.do_ds = ds - return ret diff --git a/spaces/huaiji3y/bingo-Public/src/lib/bots/bing/tts.ts b/spaces/huaiji3y/bingo-Public/src/lib/bots/bing/tts.ts deleted file mode 100644 index cd10b7d1d7581bf9cf46ff6755fcca550c558c9b..0000000000000000000000000000000000000000 --- a/spaces/huaiji3y/bingo-Public/src/lib/bots/bing/tts.ts +++ /dev/null @@ -1,82 +0,0 @@ -import { sleep } from './utils' - -const synth = window.speechSynthesis - -export class TTS { - currentText = '' - speakText = '' - private controller = new AbortController() - speaking = false - get isSpeaking() { - return this.speaking - } - finished = false - constructor() {} - abort = () => { - this.controller.abort() - } - - reset = () => { - this.speaking = false - this.finished = true - this.currentText = '' - this.speakText = '' - this.abort() - } - - speak = (text: string) => { - if (!synth || text?.trim()?.length < 2) { - return - } - this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '') - this.finished = false - this.loop() - } - - private async doSpeek() { - return new Promise((resolve) => { - const endIndex = this.finished ? this.currentText.length : - Math.max( - this.currentText.lastIndexOf('。'), - this.currentText.lastIndexOf('；'), - this.currentText.lastIndexOf('、'), - this.currentText.lastIndexOf('？'), - this.currentText.lastIndexOf('\n') - ) - const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0 - - if (startIndex >= endIndex) { - return resolve(true) - } - const text = this.currentText.slice(startIndex, endIndex) - this.speakText = text - const utterThis = new SpeechSynthesisUtterance(text) - this.controller.signal.onabort = () => { - synth.cancel() - this.finished = true - resolve(false) - } - - utterThis.onend = function (event) { - resolve(true) - } - - utterThis.onerror = function (event) { - resolve(false) - } - - const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null - utterThis.voice = voice - synth.speak(utterThis) - }) - } - - private async loop() { - if (this.speaking) return - this.speaking = true - while(!this.finished) { - await Promise.all([sleep(1000), this.doSpeek()]) - } - this.speaking = false - } -} diff --git a/spaces/huggan/night2day/README.md b/spaces/huggan/night2day/README.md deleted file mode 100644 index d250b934fe3b741cc039710450f457465b8b4df2..0000000000000000000000000000000000000000 --- a/spaces/huggan/night2day/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Night2day -emoji: 🌙 ☀️ -colorFrom: blue -colorTo: yellow -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/huggingface-projects/AIvsAI-SoccerTwos/README.md b/spaces/huggingface-projects/AIvsAI-SoccerTwos/README.md deleted file mode 100644 index c98acbc08fc3bc8cb5add9ab387eb35ee13dbf0d..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/AIvsAI-SoccerTwos/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: BotFights -emoji: 👁 -colorFrom: indigo -colorTo: yellow -sdk: gradio -sdk_version: 3.14.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/src/lib/liveblocks/useEventListener.ts b/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/src/lib/liveblocks/useEventListener.ts deleted file mode 100644 index 3b67850b17e184be2d90357c8d4a201ea0bd1b55..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/src/lib/liveblocks/useEventListener.ts +++ /dev/null @@ -1,23 +0,0 @@ - -import { onDestroy } from "svelte"; -import { useRoom } from "./useRoom"; -import type { Json } from "@liveblocks/client"; - -export function userEventListener
- To test your own cases, you can upload an image, an audio or a video, and provide the candidate texts separated by "|".
- You can duplicate this space and run it privately:( - callback: (eventData: { connectionId: number; event: TRoomEvent }) => void -): void { - const room = useRoom(); - - const listener = (eventData: { - connectionId: number; - event: TRoomEvent; - }) => { - callback(eventData); - } - - const unsubscribe = room.events.customEvent.subscribe(listener); - - onDestroy(() => { - unsubscribe(); - }); -} diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Descargar Counter Strike 1.6 No Steam 1 Link.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Descargar Counter Strike 1.6 No Steam 1 Link.md deleted file mode 100644 index 492eaa7c57e45d17e8267de9cc40efe992b5ce70..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Descargar Counter Strike 1.6 No Steam 1 Link.md +++ /dev/null @@ -1,26 +0,0 @@ -
-```html -Descargar Counter Strike 1.6 No Steam 1 Link: Cómo y Dónde
-Counter Strike 1.6 es uno de los juegos de disparos en primera persona más populares y exitosos de la historia. Miles de jugadores alrededor del mundo siguen disfrutando de este clásico que combina acción, estrategia y diversión. Si tú también quieres unirte a ellos, te contamos cómo y dónde descargar Counter Strike 1.6 no steam 1 link.
-
-Qué es Counter Strike 1.6 No Steam
-Counter Strike 1.6 no steam es una versión modificada del juego original que no requiere de la plataforma Steam para funcionar. Esto significa que puedes jugarlo sin tener que registrarte ni pagar nada. Además, esta versión tiene algunas ventajas como una mayor compatibilidad con diferentes sistemas operativos, una menor cantidad de errores y bugs, y una mayor variedad de servidores y modos de juego disponibles.
-descargar counter strike 1.6 no steam 1 link
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-Cómo Descargar Counter Strike 1.6 No Steam 1 Link
-Para descargar Counter Strike 1.6 no steam 1 link, solo tienes que seguir estos pasos:
--
-
- Entra en el sitio web https://cs16download.com/, que es uno de los más confiables y seguros para descargar el juego. -
- Haz clic en el botón verde que dice "Download CS 1.6". Se abrirá una nueva ventana con un enlace directo para la descarga. -
- Haz clic en el enlace y espera a que se complete la descarga. El archivo pesa alrededor de 200 MB, asà que puede tardar unos minutos dependiendo de tu velocidad de internet. -
- Una vez descargado el archivo, descomprÃmelo con un programa como WinRAR o 7-Zip. Obtendrás una carpeta llamada "Counter-Strike 1.6". -
- Abre la carpeta y ejecuta el archivo "cs.exe". Se iniciará el juego automáticamente. -
Dónde Jugar Counter Strike 1.6 No Steam
-Una vez que hayas descargado e instalado el juego, podrás jugarlo en cualquier momento desde tu computadora. Solo tienes que abrir el archivo "cs.exe" y elegir el modo de juego que prefieras: individual o multijugador.
-Si quieres jugar solo, puedes seleccionar la opción "New Game" y configurar las opciones a tu gusto: el mapa, el número de bots, la dificultad, etc. Luego, solo tienes que presionar "Start" y disfrutar.
-Si quieres jugar con otros jugadores online, puedes seleccionar la opción "Find Servers" y buscar un servidor que te guste. Hay cientos de servidores disponibles con diferentes modos de juego, como deathmatch, zombie, surf, jailbreak, etc. También puedes crear tu propio servidor o unirte a uno de tus amigos.
-Conclusión
-Descargar Counter Strike 1.6 no steam 1 link es muy fácil y rápido si sigues los pasos que te hemos indicado. Asà podrás disfrutar de uno de los mejores juegos de disparos en primera persona de todos los tiempos, sin tener que pagar nada ni depender de Steam. ¿A qué esperas? ¡Descarga ya Counter Strike 1.6 no steam 1 link y empieza a jugar!
-``` d5da3c52bf
-
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- -OXY-10. Nutritional Supplements for Mental Health. CMD: a collection of OXY-10+ items about CMDs. - -Research studies - -This item is a collection of research studies. - -Preliminary studies - -This item is a collection of preliminary studies. - -Literature - -This item is a collection of literature. - -Uncategorized - -This item is a collection of items that don't fit any of the categories above. - -* Item deleted in 2014. - -Advertising - -This item is a collection of advertisements. - -Lifestyle - -This item is a collection of lifestyle items. - -Remodeling - -This item is a collection of remodeling and renovation items. - -Refurbishing - -This item is a collection of items to refurbish. - -Home improvement - -This item is a collection of home improvement items. - -Small business - -This item is a collection of small business items. - -Skills - -This item is a collection of skills items. - -Interior design - -This item is a collection of interior design items. - -Gadgets - -This item is a collection of gadgets items. - -Career - -This item is a collection of career items. - -Transportation - -This item is a collection of transportation items. - -Traveling - -This item is a collection of items to travel. - -Education - -This item is a collection of education items. - -Academic study - -This item is a collection of academic study items. - -Life skills - -This item is a collection of life skills items. - -Child development - -This item is a collection of child development items. - -Child care - -This item is a collection of child care items. - -Arts and crafts - -This item is a collection of arts and crafts items. - -Photography - -This item is a collection of photography items. - -Computer - -This item is a collection of computer items. - -Hardware - -This item is a collection of hardware items. - -Health and medicine - -This item is a collection of health and medicine items. - -Nursing - -This item is a collection of nursing items. - -Medical tests - -This item is a collection of medical tests items. - -Health check-up - -This item is a collection of health check-up items. - -Health condition - -This item is a collection of health condition items 4fefd39f24
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- diff --git a/spaces/inreVtussa/clothingai/Examples/Battle Of The Year 2013 Torrent.md b/spaces/inreVtussa/clothingai/Examples/Battle Of The Year 2013 Torrent.md deleted file mode 100644 index d2f5c6bfdf608ec48ce8c5a2a1b67ad93197c18d..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Battle Of The Year 2013 Torrent.md +++ /dev/null @@ -1,10 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/inreVtussa/clothingai/Examples/Cemdt172dataloggersoftwaredownload High Quality.md b/spaces/inreVtussa/clothingai/Examples/Cemdt172dataloggersoftwaredownload High Quality.md deleted file mode 100644 index 231ddbdde6a12259a332fec1c13415b5738cbde1..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Cemdt172dataloggersoftwaredownload High Quality.md +++ /dev/null @@ -1,6 +0,0 @@ -cemdt172dataloggersoftwaredownload
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- diff --git a/spaces/introduck/introduck/introduck/inference.py b/spaces/introduck/introduck/introduck/inference.py deleted file mode 100644 index 700a7531b4697fa50051797a6d924b5df25630f6..0000000000000000000000000000000000000000 --- a/spaces/introduck/introduck/introduck/inference.py +++ /dev/null @@ -1,42 +0,0 @@ -import json -import os -import requests - -from typing import Any - - -def extract_contacts_from_text(payload: str) -> list[dict[str, Any]]: - if not payload: - return [] - - api_token: str = os.environ.get("HUGGINGFACE_INFERENCE_ENDPOINT_TOKEN", "") - if not api_token: - return [] - - api_url: str = os.environ.get("HUGGINGFACE_INFERENCE_ENDPOINT_URL", "") - if not api_url: - return [] - - response: requests.Response = requests.post( - url=api_url, - headers={ - "Authorization": f"Bearer {api_token}", - "Content-Type": "application/json"}, - data=json.dumps({"inputs": payload})) - - outputs: list[dict[str, Any]] = response.json() - if not outputs: - return [] - - entities: list[dict[str, Any]] = [] - for output in outputs: - entity: dict = { - "text": output.get("word", ""), - "score": output.get("score", -1), - "entity": output.get("entity_group", "UNKNOWN"), - "start": output.get("start", -1), - "end": output.get("end", -1)} - - entities.append(entity) - - return entities diff --git a/spaces/isabel/testing-blocks/README.md b/spaces/isabel/testing-blocks/README.md deleted file mode 100644 index c5747de7543aac8488fd62cf75167aefab343761..0000000000000000000000000000000000000000 --- a/spaces/isabel/testing-blocks/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Testing Blocks -emoji: 📈 -colorFrom: indigo -colorTo: yellow -sdk: gradio -sdk_version: 3.0.11 -app_file: app.py -pinned: false -license: afl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/datasets/pascal_zeroshot.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/datasets/pascal_zeroshot.py deleted file mode 100644 index 3fa84de9049bf272538f97b408bed07a9e9b5478..0000000000000000000000000000000000000000 --- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/datasets/pascal_zeroshot.py +++ /dev/null @@ -1,60 +0,0 @@ -from os.path import expanduser -import torch -import json -import torchvision -from general_utils import get_from_repository -from general_utils import log -from torchvision import transforms - -PASCAL_VOC_CLASSES_ZS = [['cattle.n.01', 'motorcycle.n.01'], ['aeroplane.n.01', 'sofa.n.01'], - ['cat.n.01', 'television.n.03'], ['train.n.01', 'bottle.n.01'], - ['chair.n.01', 'pot_plant.n.01']] - - -class PascalZeroShot(object): - - def __init__(self, split, n_unseen, image_size=224) -> None: - super().__init__() - - import sys - sys.path.append('third_party/JoEm') - from third_party.JoEm.data_loader.dataset import VOCSegmentation - from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC - - self.pascal_classes = VOC - self.image_size = image_size - - self.transform = transforms.Compose([ - transforms.Resize((image_size, image_size)), - ]) - - if split == 'train': - self.voc = VOCSegmentation(get_unseen_idx(n_unseen), get_seen_idx(n_unseen), - split=split, transform=True, transform_args=dict(base_size=312, crop_size=312), - ignore_bg=False, ignore_unseen=False, remv_unseen_img=True) - elif split == 'val': - self.voc = VOCSegmentation(get_unseen_idx(n_unseen), get_seen_idx(n_unseen), - split=split, transform=False, - ignore_bg=False, ignore_unseen=False) - - self.unseen_idx = get_unseen_idx(n_unseen) - - def __len__(self): - return len(self.voc) - - def __getitem__(self, i): - - sample = self.voc[i] - label = sample['label'].long() - all_labels = [l for l in torch.where(torch.bincount(label.flatten())>0)[0].numpy().tolist() if l != 255] - class_indices = [l for l in all_labels] - class_names = [self.pascal_classes[l] for l in all_labels] - - image = self.transform(sample['image']) - - label = transforms.Resize((self.image_size, self.image_size), - interpolation=torchvision.transforms.InterpolationMode.NEAREST)(label.unsqueeze(0))[0] - - return (image,), (label, ) - - diff --git a/spaces/jackli888/stable-diffusion-webui/javascript/aspectRatioOverlay.js b/spaces/jackli888/stable-diffusion-webui/javascript/aspectRatioOverlay.js deleted file mode 100644 index 50c81054269c7d75c9e29aa0f18260d1df0939eb..0000000000000000000000000000000000000000 --- a/spaces/jackli888/stable-diffusion-webui/javascript/aspectRatioOverlay.js +++ /dev/null @@ -1,113 +0,0 @@ - -let currentWidth = null; -let currentHeight = null; -let arFrameTimeout = setTimeout(function(){},0); - -function dimensionChange(e, is_width, is_height){ - - if(is_width){ - currentWidth = e.target.value*1.0 - } - if(is_height){ - currentHeight = e.target.value*1.0 - } - - var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) - - if(!inImg2img){ - return; - } - - var targetElement = null; - - var tabIndex = get_tab_index('mode_img2img') - if(tabIndex == 0){ // img2img - targetElement = gradioApp().querySelector('div[data-testid=image] img'); - } else if(tabIndex == 1){ //Sketch - targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img'); - } else if(tabIndex == 2){ // Inpaint - targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img'); - } else if(tabIndex == 3){ // Inpaint sketch - targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img'); - } - - - if(targetElement){ - - var arPreviewRect = gradioApp().querySelector('#imageARPreview'); - if(!arPreviewRect){ - arPreviewRect = document.createElement('div') - arPreviewRect.id = "imageARPreview"; - gradioApp().getRootNode().appendChild(arPreviewRect) - } - - - - var viewportOffset = targetElement.getBoundingClientRect(); - - viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight ) - - scaledx = targetElement.naturalWidth*viewportscale - scaledy = targetElement.naturalHeight*viewportscale - - cleintRectTop = (viewportOffset.top+window.scrollY) - cleintRectLeft = (viewportOffset.left+window.scrollX) - cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2) - cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2) - - viewRectTop = cleintRectCentreY-(scaledy/2) - viewRectLeft = cleintRectCentreX-(scaledx/2) - arRectWidth = scaledx - arRectHeight = scaledy - - arscale = Math.min( arRectWidth/currentWidth, arRectHeight/currentHeight ) - arscaledx = currentWidth*arscale - arscaledy = currentHeight*arscale - - arRectTop = cleintRectCentreY-(arscaledy/2) - arRectLeft = cleintRectCentreX-(arscaledx/2) - arRectWidth = arscaledx - arRectHeight = arscaledy - - arPreviewRect.style.top = arRectTop+'px'; - arPreviewRect.style.left = arRectLeft+'px'; - arPreviewRect.style.width = arRectWidth+'px'; - arPreviewRect.style.height = arRectHeight+'px'; - - clearTimeout(arFrameTimeout); - arFrameTimeout = setTimeout(function(){ - arPreviewRect.style.display = 'none'; - },2000); - - arPreviewRect.style.display = 'block'; - - } - -} - - -onUiUpdate(function(){ - var arPreviewRect = gradioApp().querySelector('#imageARPreview'); - if(arPreviewRect){ - arPreviewRect.style.display = 'none'; - } - var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) - if(inImg2img){ - let inputs = gradioApp().querySelectorAll('input'); - inputs.forEach(function(e){ - var is_width = e.parentElement.id == "img2img_width" - var is_height = e.parentElement.id == "img2img_height" - - if((is_width || is_height) && !e.classList.contains('scrollwatch')){ - e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} ) - e.classList.add('scrollwatch') - } - if(is_width){ - currentWidth = e.value*1.0 - } - if(is_height){ - currentHeight = e.value*1.0 - } - }) - } -}); diff --git a/spaces/jbilcke-hf/VideoQuest/src/lib/useImageDimension.ts b/spaces/jbilcke-hf/VideoQuest/src/lib/useImageDimension.ts deleted file mode 100644 index 9cfd06e473929b1046a5dd9caa9d577ebaf09b7a..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/VideoQuest/src/lib/useImageDimension.ts +++ /dev/null @@ -1,20 +0,0 @@ -import { useEffect, useState } from "react" - -import { ImageDimension, getImageDimension } from "./getImageDimension" - -export function useImageDimension(src: string) { - const [dimension, setDimension] = useState({ - width: 0, - height: 0, - }) - - useEffect(() => { - const compute = async () => { - const newDimension = await getImageDimension(src) - setDimension(newDimension) - } - compute() - }, [src]) - - return dimension -} \ No newline at end of file diff --git a/spaces/jie1/jie_test4/README.md b/spaces/jie1/jie_test4/README.md deleted file mode 100644 index 9d3b0f97720f8452755b10acd9b39c474224900c..0000000000000000000000000000000000000000 --- a/spaces/jie1/jie_test4/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Jie Test4 -emoji: 🏃 -colorFrom: indigo -colorTo: purple -sdk: gradio -sdk_version: 3.4 -app_file: app.py -pinned: false ---- - -``` -1. Sort 模块里，可以进行dlkcat，scores值的排序，选择相关文件，点击对应按钮，即可进行排序 -2. Pre Merge 模块里，进行smile 与 seq序列文件的合并， -如若序列文件中的单条序列不是在一行而是分为多行的，需处理换行符，在strip_file上传文件，点击strip按钮，会生成新的序列文件，并且注意文件除了末尾可以有一行空行，其余地方不能有多余的空行 -合并smile 与 seq 文件时，有两种情况： - -一种是序列格式文件如下所示的： ->1J3U_A/1-468 Crystal structure of aspartase from Bacillus sp. YM55-1 [Bacillus sp. YM55-1] -MNTDVRIEKDFLGEKEIPKDAYYGVQTIRATENFPITGYRIHPELIKSLGIVKKSAALANMEVGLLDKEVGQ -YIVKAADEVIEGKWNDQFIVDPIQGGAGTSINMNANEVIANRALELMGEEKGNYSKISPNSHVNMSQSTNDA -FPTATHIAVLSLLNQLIETTKYMQQEFMKKADEFAGVIKMGRTHLQDAVPILLGQEFEAYARVIARDIERIA -NTRNNLYDINMGATAVGTGLNADPEYISIVTEHLAKFSGHPLRSAQHLVDATQNTDCYTEVSSALKVCMINM -SKIANDLRLMASGPRAGLSEIVLPARQPGSSIMPGKVNPVMPEVMNQVAFQVFGNDLTITSASEAGQFELNV -MEPVLFFNLIQSISIMTNVFKSFTENCLKGIKANEERMKEYVEKSIGIITAINPHVGYETAAKLAREAYLTG -ESIRELCIKYGVLTEEQLNEILNPYEMIHPGIAGRK ->WP_016839137.1/1-468 aspartate ammonia-lyase [Ureibacillus thermosphaericus] -MNTDVRIEKDFLGEKEIPKDAYYGVQTIRATENFPITGYRIHPELIKSLGIVKKSAALANMEVGLLDKEVGQ -YIVKAADEVIEGKWNDQFIVDPIQGGAGTSINMNANEVIANRALELMGEEKGNYSKISPNSHVNMSQSTNDA -合并时需要保存序列的来源这些相关信息，点击Merge All 按钮 - -另一种点击Merge按钮 - -3. Merge Dlsc 模块里，进行scores 与 Dlkcat文件的合并，合并时注意两个文件的序列相对应 -``` - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig5d_Enzyme_promiscuity_test.py b/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig5d_Enzyme_promiscuity_test.py deleted file mode 100644 index bc4bd4abedf2cb85691274048006dedcbc334edd..0000000000000000000000000000000000000000 --- a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/analysis/SuppleFig5d_Enzyme_promiscuity_test.py +++ /dev/null @@ -1,172 +0,0 @@ -#!/usr/bin/python -# coding: utf-8 - -# Author: LE YUAN - -import math -import numpy as np -import matplotlib.pyplot as plt -from matplotlib import rc -from scipy import stats -import seaborn as sns -import pandas as pd -from scipy.stats import ranksums - - -def median(lst): - sortedLst = sorted(lst) - lstLen = len(lst) - index = (lstLen - 1) // 2 - - if (lstLen % 2): - return sortedLst[index] - else: - return (sortedLst[index] + sortedLst[index + 1])/2.0 - -def main() : - - with open('../../Data/enzyme_promiscuity/test_preferred_alternative_random.txt', 'r') as infile : - lines = infile.readlines() - - alldata = dict() - alldata['type'] = list() - alldata['value'] = list() - preferred_substrates = list() - alternative_substrates = list() - random_substrates = list() - for line in lines[1:] : - data = line.strip().split('\t') - order, value, substrate_type = data[0], data[1], data[2] - - if substrate_type == 'Preferred' : - preferred_substrates.append(float(value)) - alldata['type'].append('Preferred') - alldata['value'].append(float(value)) - if substrate_type == 'Alternative' : - alternative_substrates.append(float(value)) - alldata['type'].append('Alternative') - alldata['value'].append(float(value)) - if substrate_type == 'Random' : - random_substrates.append(float(value)) - alldata['type'].append('Random') - alldata['value'].append(float(value)) - - p_value_1 = ranksums(preferred_substrates, alternative_substrates)[1] - p_value_2 = ranksums(preferred_substrates, random_substrates)[1] - p_value_3 = ranksums(alternative_substrates, random_substrates)[1] - print('The amount of preferred_substrates:', len(preferred_substrates)) - print('The amount of alternative_substrates:', len(alternative_substrates)) - print('The amount of random_substrates:', len(random_substrates)) - print('The median value of preferred_substrates: %.4f' % median(preferred_substrates)) - print('The median value of alternative_substrates: %.4f' % median(alternative_substrates)) - print('The median value of random_substrates: %.4f' % median(random_substrates)) - print('The real value of preferred substrates: %.2f' % pow(10, median(preferred_substrates))) - print('The real value of alternative substrates: %.2f' % pow(10, median(alternative_substrates))) - print('The real value of random substrates: %.2f' % pow(10, median(random_substrates))) - print('P value between preferred_substrates and alternative_substrates is: %s' % p_value_1) - print('P value between preferred_substrates and random_substrates is: %s' % p_value_2) - print('P value between alternative_substrates and random_substrates is: %s' % p_value_3) - - # The amount of preferred_substrates: 95 - # The amount of alternative_substrates: 142 - # The amount of random_substrates: 95 - # The median value of preferred_substrates: 1.0115 - # The median value of alternative_substrates: 0.6959 - # The median value of random_substrates: 0.3928 - # The real value of preferred substrates: 10.27 - # The real value of alternative substrates: 4.97 - # The real value of random substrates: 2.47 - # P value between preferred_substrates and alternative_substrates is: 0.01430651489756451 - # P value between preferred_substrates and random_substrates is: 5.267302868022526e-05 - # P value between alternative_substrates and random_substrates is: 0.02299337746277678 - - # Plot the boxplot figures between the Alternative and Preferred - allData = pd.DataFrame(alldata) - # print(type(allData)) - - plt.figure(figsize=(1.5,1.5)) - - # To solve the 'Helvetica' font cannot be used in PDF file - # https://stackoverflow.com/questions/59845568/the-pdf-backend-does-not-currently-support-the-selected-font - rc('font',**{'family':'serif','serif':['Helvetica']}) - plt.rcParams['pdf.fonttype'] = 42 - - plt.axes([0.12,0.12,0.83,0.83]) - - plt.tick_params(direction='in') - plt.tick_params(which='major',length=1.5) - plt.tick_params(which='major',width=0.4) - plt.tick_params(which='major',width=0.4) - - # rectangular box plot - palette = {"Random": '#FF8C00', "Alternative": '#2166ac', "Preferred": '#b2182b'} - - # for ind in allData.index: - # allData.loc[ind,'entry'] = '${0}$'.format(allData.loc[ind,'entry']) - - ax = sns.boxplot(data=alldata, x="type", y="value", order = ["Random", "Alternative", "Preferred"], - palette=palette, showfliers=False, linewidth=0.5, width=0.5) # boxprops=dict(alpha=1.0) - - ax = sns.stripplot(data=alldata, x="type", y="value", order = ["Random", "Alternative", "Preferred"], - palette=palette, size=0.7) # boxprops=dict(alpha=1.0) - - # https://stackoverflow.com/questions/58476654/how-to-remove-or-hide-x-axis-label-from-seaborn-boxplot - # plt.xlabel(None) will remove the Label, but not the ticks. - ax.set(xlabel=None) - - for patch in ax.artists: - r, g, b, a = patch.get_facecolor() - patch.set_facecolor((r, g, b, 0.3)) - - # print(ax.artists) - # print(ax.lines) - # print(len(ax.lines)) - # https://cduvallet.github.io/posts/2018/03/boxplots-in-python - for i, artist in enumerate(ax.artists): - # print(i) - - if i % 3 == 0: - col = '#FF8C00' - if i % 3 == 1: - col = '#2166ac' - if i % 3 == 2: - col = '#b2182b' - - # This sets the color for the main box - artist.set_edgecolor(col) - - # Each box has 5 associated Line2D objects (to make the whiskers, fliers, etc.) - # Loop over them here, and use the same colour as above - for j in range(i*5,i*5+5): - # print(j) - line = ax.lines[j] - line.set_color(col) - line.set_mfc(col) - line.set_mec(col) - handles = [ax.artists[0], ax.artists[1]] - - # for tick in ax.get_xticklabels() : - # tick.set_rotation(30) - - plt.rcParams['font.family'] = 'Helvetica' - - plt.ylabel("Predicted $k$$_\mathregular{cat}$ value [log10]", fontname='Helvetica', fontsize=7) - - # plt.xticks(rotation=30,ha='right') - # plt.yticks([-2, -1, 0, 1, 2, 3, 4]) - - plt.yticks([-2, 0, 2, 4, 6]) - - plt.xticks(fontsize=7, rotation=30, ha='right') - plt.yticks(fontsize=6) - - ax.spines['bottom'].set_linewidth(0.5) - ax.spines['left'].set_linewidth(0.5) - ax.spines['top'].set_linewidth(0.5) - ax.spines['right'].set_linewidth(0.5) - - plt.savefig("../../Results/figures/SuppleFig5d.pdf", dpi=400, bbox_inches = 'tight') - - -if __name__ == '__main__' : - main() diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdataset.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdataset.py deleted file mode 100644 index 31124afcc46f013cdaa8ac1bebda62813dab6b14..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdataset.py +++ /dev/null @@ -1,526 +0,0 @@ -# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license - -# Copyright (C) 2001-2017 Nominum, Inc. -# -# Permission to use, copy, modify, and distribute this software and its -# documentation for any purpose with or without fee is hereby granted, -# provided that the above copyright notice and this permission notice -# appear in all copies. -# -# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES -# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR -# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES -# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN -# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT -# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. - -"""DNS rdatasets (an rdataset is a set of rdatas of a given type and class)""" - -import io -import random -import struct -from typing import Any, Collection, Dict, List, Optional, Union, cast - -import dns.exception -import dns.immutable -import dns.name -import dns.rdata -import dns.rdataclass -import dns.rdatatype -import dns.set -import dns.ttl - -# define SimpleSet here for backwards compatibility -SimpleSet = dns.set.Set - - -class DifferingCovers(dns.exception.DNSException): - """An attempt was made to add a DNS SIG/RRSIG whose covered type - is not the same as that of the other rdatas in the rdataset.""" - - -class IncompatibleTypes(dns.exception.DNSException): - """An attempt was made to add DNS RR data of an incompatible type.""" - - -class Rdataset(dns.set.Set): - - """A DNS rdataset.""" - - __slots__ = ["rdclass", "rdtype", "covers", "ttl"] - - def __init__( - self, - rdclass: dns.rdataclass.RdataClass, - rdtype: dns.rdatatype.RdataType, - covers: dns.rdatatype.RdataType = dns.rdatatype.NONE, - ttl: int = 0, - ): - """Create a new rdataset of the specified class and type. - - *rdclass*, a ``dns.rdataclass.RdataClass``, the rdataclass. - - *rdtype*, an ``dns.rdatatype.RdataType``, the rdatatype. - - *covers*, an ``dns.rdatatype.RdataType``, the covered rdatatype. - - *ttl*, an ``int``, the TTL. - """ - - super().__init__() - self.rdclass = rdclass - self.rdtype: dns.rdatatype.RdataType = rdtype - self.covers: dns.rdatatype.RdataType = covers - self.ttl = ttl - - def _clone(self): - obj = super()._clone() - obj.rdclass = self.rdclass - obj.rdtype = self.rdtype - obj.covers = self.covers - obj.ttl = self.ttl - return obj - - def update_ttl(self, ttl: int) -> None: - """Perform TTL minimization. - - Set the TTL of the rdataset to be the lesser of the set's current - TTL or the specified TTL. If the set contains no rdatas, set the TTL - to the specified TTL. - - *ttl*, an ``int`` or ``str``. - """ - ttl = dns.ttl.make(ttl) - if len(self) == 0: - self.ttl = ttl - elif ttl < self.ttl: - self.ttl = ttl - - def add( # pylint: disable=arguments-differ,arguments-renamed - self, rd: dns.rdata.Rdata, ttl: Optional[int] = None - ) -> None: - """Add the specified rdata to the rdataset. - - If the optional *ttl* parameter is supplied, then - ``self.update_ttl(ttl)`` will be called prior to adding the rdata. - - *rd*, a ``dns.rdata.Rdata``, the rdata - - *ttl*, an ``int``, the TTL. - - Raises ``dns.rdataset.IncompatibleTypes`` if the type and class - do not match the type and class of the rdataset. - - Raises ``dns.rdataset.DifferingCovers`` if the type is a signature - type and the covered type does not match that of the rdataset. - """ - - # - # If we're adding a signature, do some special handling to - # check that the signature covers the same type as the - # other rdatas in this rdataset. If this is the first rdata - # in the set, initialize the covers field. - # - if self.rdclass != rd.rdclass or self.rdtype != rd.rdtype: - raise IncompatibleTypes - if ttl is not None: - self.update_ttl(ttl) - if self.rdtype == dns.rdatatype.RRSIG or self.rdtype == dns.rdatatype.SIG: - covers = rd.covers() - if len(self) == 0 and self.covers == dns.rdatatype.NONE: - self.covers = covers - elif self.covers != covers: - raise DifferingCovers - if dns.rdatatype.is_singleton(rd.rdtype) and len(self) > 0: - self.clear() - super().add(rd) - - def union_update(self, other): - self.update_ttl(other.ttl) - super().union_update(other) - - def intersection_update(self, other): - self.update_ttl(other.ttl) - super().intersection_update(other) - - def update(self, other): - """Add all rdatas in other to self. - - *other*, a ``dns.rdataset.Rdataset``, the rdataset from which - to update. - """ - - self.update_ttl(other.ttl) - super().update(other) - - def _rdata_repr(self): - def maybe_truncate(s): - if len(s) > 100: - return s[:100] + "..." - return s - - return "[%s]" % ", ".join("<%s>" % maybe_truncate(str(rr)) for rr in self) - - def __repr__(self): - if self.covers == 0: - ctext = "" - else: - ctext = "(" + dns.rdatatype.to_text(self.covers) + ")" - return ( - " " - ) - - def __str__(self): - return self.to_text() - - def __eq__(self, other): - if not isinstance(other, Rdataset): - return False - if ( - self.rdclass != other.rdclass - or self.rdtype != other.rdtype - or self.covers != other.covers - ): - return False - return super().__eq__(other) - - def __ne__(self, other): - return not self.__eq__(other) - - def to_text( - self, - name: Optional[dns.name.Name] = None, - origin: Optional[dns.name.Name] = None, - relativize: bool = True, - override_rdclass: Optional[dns.rdataclass.RdataClass] = None, - want_comments: bool = False, - **kw: Dict[str, Any], - ) -> str: - """Convert the rdataset into DNS zone file format. - - See ``dns.name.Name.choose_relativity`` for more information - on how *origin* and *relativize* determine the way names - are emitted. - - Any additional keyword arguments are passed on to the rdata - ``to_text()`` method. - - *name*, a ``dns.name.Name``. If name is not ``None``, emit RRs with - *name* as the owner name. - - *origin*, a ``dns.name.Name`` or ``None``, the origin for relative - names. - - *relativize*, a ``bool``. If ``True``, names will be relativized - to *origin*. - - *override_rdclass*, a ``dns.rdataclass.RdataClass`` or ``None``. - If not ``None``, use this class instead of the Rdataset's class. - - *want_comments*, a ``bool``. If ``True``, emit comments for rdata - which have them. The default is ``False``. - """ - - if name is not None: - name = name.choose_relativity(origin, relativize) - ntext = str(name) - pad = " " - else: - ntext = "" - pad = "" - s = io.StringIO() - if override_rdclass is not None: - rdclass = override_rdclass - else: - rdclass = self.rdclass - if len(self) == 0: - # - # Empty rdatasets are used for the question section, and in - # some dynamic updates, so we don't need to print out the TTL - # (which is meaningless anyway). - # - s.write( - "{}{}{} {}\n".format( - ntext, - pad, - dns.rdataclass.to_text(rdclass), - dns.rdatatype.to_text(self.rdtype), - ) - ) - else: - for rd in self: - extra = "" - if want_comments: - if rd.rdcomment: - extra = f" ;{rd.rdcomment}" - s.write( - "%s%s%d %s %s %s%s\n" - % ( - ntext, - pad, - self.ttl, - dns.rdataclass.to_text(rdclass), - dns.rdatatype.to_text(self.rdtype), - rd.to_text(origin=origin, relativize=relativize, **kw), - extra, - ) - ) - # - # We strip off the final \n for the caller's convenience in printing - # - return s.getvalue()[:-1] - - def to_wire( - self, - name: dns.name.Name, - file: Any, - compress: Optional[dns.name.CompressType] = None, - origin: Optional[dns.name.Name] = None, - override_rdclass: Optional[dns.rdataclass.RdataClass] = None, - want_shuffle: bool = True, - ) -> int: - """Convert the rdataset to wire format. - - *name*, a ``dns.name.Name`` is the owner name to use. - - *file* is the file where the name is emitted (typically a - BytesIO file). - - *compress*, a ``dict``, is the compression table to use. If - ``None`` (the default), names will not be compressed. - - *origin* is a ``dns.name.Name`` or ``None``. If the name is - relative and origin is not ``None``, then *origin* will be appended - to it. - - *override_rdclass*, an ``int``, is used as the class instead of the - class of the rdataset. This is useful when rendering rdatasets - associated with dynamic updates. - - *want_shuffle*, a ``bool``. If ``True``, then the order of the - Rdatas within the Rdataset will be shuffled before rendering. - - Returns an ``int``, the number of records emitted. - """ - - if override_rdclass is not None: - rdclass = override_rdclass - want_shuffle = False - else: - rdclass = self.rdclass - file.seek(0, io.SEEK_END) - if len(self) == 0: - name.to_wire(file, compress, origin) - stuff = struct.pack("!HHIH", self.rdtype, rdclass, 0, 0) - file.write(stuff) - return 1 - else: - l: Union[Rdataset, List[dns.rdata.Rdata]] - if want_shuffle: - l = list(self) - random.shuffle(l) - else: - l = self - for rd in l: - name.to_wire(file, compress, origin) - stuff = struct.pack("!HHIH", self.rdtype, rdclass, self.ttl, 0) - file.write(stuff) - start = file.tell() - rd.to_wire(file, compress, origin) - end = file.tell() - assert end - start < 65536 - file.seek(start - 2) - stuff = struct.pack("!H", end - start) - file.write(stuff) - file.seek(0, io.SEEK_END) - return len(self) - - def match( - self, - rdclass: dns.rdataclass.RdataClass, - rdtype: dns.rdatatype.RdataType, - covers: dns.rdatatype.RdataType, - ) -> bool: - """Returns ``True`` if this rdataset matches the specified class, - type, and covers. - """ - if self.rdclass == rdclass and self.rdtype == rdtype and self.covers == covers: - return True - return False - - def processing_order(self) -> List[dns.rdata.Rdata]: - """Return rdatas in a valid processing order according to the type's - specification. For example, MX records are in preference order from - lowest to highest preferences, with items of the same preference - shuffled. - - For types that do not define a processing order, the rdatas are - simply shuffled. - """ - if len(self) == 0: - return [] - else: - return self[0]._processing_order(iter(self)) - - -@dns.immutable.immutable -class ImmutableRdataset(Rdataset): # lgtm[py/missing-equals] - - """An immutable DNS rdataset.""" - - _clone_class = Rdataset - - def __init__(self, rdataset: Rdataset): - """Create an immutable rdataset from the specified rdataset.""" - - super().__init__( - rdataset.rdclass, rdataset.rdtype, rdataset.covers, rdataset.ttl - ) - self.items = dns.immutable.Dict(rdataset.items) - - def update_ttl(self, ttl): - raise TypeError("immutable") - - def add(self, rd, ttl=None): - raise TypeError("immutable") - - def union_update(self, other): - raise TypeError("immutable") - - def intersection_update(self, other): - raise TypeError("immutable") - - def update(self, other): - raise TypeError("immutable") - - def __delitem__(self, i): - raise TypeError("immutable") - - # lgtm complains about these not raising ArithmeticError, but there is - # precedent for overrides of these methods in other classes to raise - # TypeError, and it seems like the better exception. - - def __ior__(self, other): # lgtm[py/unexpected-raise-in-special-method] - raise TypeError("immutable") - - def __iand__(self, other): # lgtm[py/unexpected-raise-in-special-method] - raise TypeError("immutable") - - def __iadd__(self, other): # lgtm[py/unexpected-raise-in-special-method] - raise TypeError("immutable") - - def __isub__(self, other): # lgtm[py/unexpected-raise-in-special-method] - raise TypeError("immutable") - - def clear(self): - raise TypeError("immutable") - - def __copy__(self): - return ImmutableRdataset(super().copy()) - - def copy(self): - return ImmutableRdataset(super().copy()) - - def union(self, other): - return ImmutableRdataset(super().union(other)) - - def intersection(self, other): - return ImmutableRdataset(super().intersection(other)) - - def difference(self, other): - return ImmutableRdataset(super().difference(other)) - - def symmetric_difference(self, other): - return ImmutableRdataset(super().symmetric_difference(other)) - - -def from_text_list( - rdclass: Union[dns.rdataclass.RdataClass, str], - rdtype: Union[dns.rdatatype.RdataType, str], - ttl: int, - text_rdatas: Collection[str], - idna_codec: Optional[dns.name.IDNACodec] = None, - origin: Optional[dns.name.Name] = None, - relativize: bool = True, - relativize_to: Optional[dns.name.Name] = None, -) -> Rdataset: - """Create an rdataset with the specified class, type, and TTL, and with - the specified list of rdatas in text format. - - *idna_codec*, a ``dns.name.IDNACodec``, specifies the IDNA - encoder/decoder to use; if ``None``, the default IDNA 2003 - encoder/decoder is used. - - *origin*, a ``dns.name.Name`` (or ``None``), the - origin to use for relative names. - - *relativize*, a ``bool``. If true, name will be relativized. - - *relativize_to*, a ``dns.name.Name`` (or ``None``), the origin to use - when relativizing names. If not set, the *origin* value will be used. - - Returns a ``dns.rdataset.Rdataset`` object. - """ - - rdclass = dns.rdataclass.RdataClass.make(rdclass) - rdtype = dns.rdatatype.RdataType.make(rdtype) - r = Rdataset(rdclass, rdtype) - r.update_ttl(ttl) - for t in text_rdatas: - rd = dns.rdata.from_text( - r.rdclass, r.rdtype, t, origin, relativize, relativize_to, idna_codec - ) - r.add(rd) - return r - - -def from_text( - rdclass: Union[dns.rdataclass.RdataClass, str], - rdtype: Union[dns.rdatatype.RdataType, str], - ttl: int, - *text_rdatas: Any, -) -> Rdataset: - """Create an rdataset with the specified class, type, and TTL, and with - the specified rdatas in text format. - - Returns a ``dns.rdataset.Rdataset`` object. - """ - - return from_text_list(rdclass, rdtype, ttl, cast(Collection[str], text_rdatas)) - - -def from_rdata_list(ttl: int, rdatas: Collection[dns.rdata.Rdata]) -> Rdataset: - """Create an rdataset with the specified TTL, and with - the specified list of rdata objects. - - Returns a ``dns.rdataset.Rdataset`` object. - """ - - if len(rdatas) == 0: - raise ValueError("rdata list must not be empty") - r = None - for rd in rdatas: - if r is None: - r = Rdataset(rd.rdclass, rd.rdtype) - r.update_ttl(ttl) - r.add(rd) - assert r is not None - return r - - -def from_rdata(ttl: int, *rdatas: Any) -> Rdataset: - """Create an rdataset with the specified TTL, and with - the specified rdata objects. - - Returns a ``dns.rdataset.Rdataset`` object. - """ - - return from_rdata_list(ttl, cast(Collection[dns.rdata.Rdata], rdatas)) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/CERT.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/CERT.py deleted file mode 100644 index 30fe863f4fa9cf8af21ffb780b749138668bbc42..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/CERT.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license - -# Copyright (C) 2003-2007, 2009-2011 Nominum, Inc. -# -# Permission to use, copy, modify, and distribute this software and its -# documentation for any purpose with or without fee is hereby granted, -# provided that the above copyright notice and this permission notice -# appear in all copies. -# -# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES -# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR -# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES -# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN -# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT -# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. - -import base64 -import struct - -import dns.dnssectypes -import dns.exception -import dns.immutable -import dns.rdata -import dns.tokenizer - -_ctype_by_value = { - 1: "PKIX", - 2: "SPKI", - 3: "PGP", - 4: "IPKIX", - 5: "ISPKI", - 6: "IPGP", - 7: "ACPKIX", - 8: "IACPKIX", - 253: "URI", - 254: "OID", -} - -_ctype_by_name = { - "PKIX": 1, - "SPKI": 2, - "PGP": 3, - "IPKIX": 4, - "ISPKI": 5, - "IPGP": 6, - "ACPKIX": 7, - "IACPKIX": 8, - "URI": 253, - "OID": 254, -} - - -def _ctype_from_text(what): - v = _ctype_by_name.get(what) - if v is not None: - return v - return int(what) - - -def _ctype_to_text(what): - v = _ctype_by_value.get(what) - if v is not None: - return v - return str(what) - - -@dns.immutable.immutable -class CERT(dns.rdata.Rdata): - - """CERT record""" - - # see RFC 4398 - - __slots__ = ["certificate_type", "key_tag", "algorithm", "certificate"] - - def __init__( - self, rdclass, rdtype, certificate_type, key_tag, algorithm, certificate - ): - super().__init__(rdclass, rdtype) - self.certificate_type = self._as_uint16(certificate_type) - self.key_tag = self._as_uint16(key_tag) - self.algorithm = self._as_uint8(algorithm) - self.certificate = self._as_bytes(certificate) - - def to_text(self, origin=None, relativize=True, **kw): - certificate_type = _ctype_to_text(self.certificate_type) - return "%s %d %s %s" % ( - certificate_type, - self.key_tag, - dns.dnssectypes.Algorithm.to_text(self.algorithm), - dns.rdata._base64ify(self.certificate, **kw), - ) - - @classmethod - def from_text( - cls, rdclass, rdtype, tok, origin=None, relativize=True, relativize_to=None - ): - certificate_type = _ctype_from_text(tok.get_string()) - key_tag = tok.get_uint16() - algorithm = dns.dnssectypes.Algorithm.from_text(tok.get_string()) - b64 = tok.concatenate_remaining_identifiers().encode() - certificate = base64.b64decode(b64) - return cls(rdclass, rdtype, certificate_type, key_tag, algorithm, certificate) - - def _to_wire(self, file, compress=None, origin=None, canonicalize=False): - prefix = struct.pack( - "!HHB", self.certificate_type, self.key_tag, self.algorithm - ) - file.write(prefix) - file.write(self.certificate) - - @classmethod - def from_wire_parser(cls, rdclass, rdtype, parser, origin=None): - (certificate_type, key_tag, algorithm) = parser.get_struct("!HHB") - certificate = parser.get_remaining() - return cls(rdclass, rdtype, certificate_type, key_tag, algorithm, certificate) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/H_V_A_R_.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/H_V_A_R_.py deleted file mode 100644 index 094aedaea5ebc5c88b33e448ea8f131563acd3c0..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/H_V_A_R_.py +++ /dev/null @@ -1,5 +0,0 @@ -from .otBase import BaseTTXConverter - - -class table_H_V_A_R_(BaseTTXConverter): - pass diff --git a/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/openaimodelSplit.py b/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/openaimodelSplit.py deleted file mode 100644 index c79afe057d3831fba623c1076b6ee663791ce3cf..0000000000000000000000000000000000000000 --- a/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/openaimodelSplit.py +++ /dev/null @@ -1,807 +0,0 @@ -from abc import abstractmethod -import math -import numpy as np -import torch as th -import torch.nn as nn -import torch.nn.functional as F -from ldmlib.modules.diffusionmodules.util import ( - checkpoint, - conv_nd, - linear, - avg_pool_nd, - zero_module, - normalization, - timestep_embedding, -) -from .splitAttention import SpatialTransformer - - -class AttentionPool2d(nn.Module): - """ - Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py - """ - - def __init__( - self, - spacial_dim: int, - embed_dim: int, - num_heads_channels: int, - output_dim: int = None, - ): - super().__init__() - self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) - self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) - self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) - self.num_heads = embed_dim // num_heads_channels - self.attention = QKVAttention(self.num_heads) - - def forward(self, x): - b, c, *_spatial = x.shape - x = x.reshape(b, c, -1) # NC(HW) - x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) - x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) - x = self.qkv_proj(x) - x = self.attention(x) - x = self.c_proj(x) - return x[:, :, 0] - - -class TimestepBlock(nn.Module): - """ - Any module where forward() takes timestep embeddings as a second argument. - """ - - @abstractmethod - def forward(self, x, emb): - """ - Apply the module to `x` given `emb` timestep embeddings. - """ - - -class TimestepEmbedSequential(nn.Sequential, TimestepBlock): - """ - A sequential module that passes timestep embeddings to the children that - support it as an extra input. - """ - - def forward(self, x, emb, context=None): - for layer in self: - if isinstance(layer, TimestepBlock): - x = layer(x, emb) - elif isinstance(layer, SpatialTransformer): - x = layer(x, context) - else: - x = layer(x) - return x - - -class Upsample(nn.Module): - """ - An upsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - upsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - if use_conv: - self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) - - def forward(self, x): - assert x.shape[1] == self.channels - if self.dims == 3: - x = F.interpolate( - x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" - ) - else: - x = F.interpolate(x, scale_factor=2, mode="nearest") - if self.use_conv: - x = self.conv(x) - return x - -class TransposedUpsample(nn.Module): - 'Learned 2x upsampling without padding' - def __init__(self, channels, out_channels=None, ks=5): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - - self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) - - def forward(self,x): - return self.up(x) - - -class Downsample(nn.Module): - """ - A downsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - downsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - if use_conv: - self.op = conv_nd( - dims, self.channels, self.out_channels, 3, stride=stride, padding=padding - ) - else: - assert self.channels == self.out_channels - self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) - - def forward(self, x): - assert x.shape[1] == self.channels - return self.op(x) - - -class ResBlock(TimestepBlock): - """ - A residual block that can optionally change the number of channels. - :param channels: the number of input channels. - :param emb_channels: the number of timestep embedding channels. - :param dropout: the rate of dropout. - :param out_channels: if specified, the number of out channels. - :param use_conv: if True and out_channels is specified, use a spatial - convolution instead of a smaller 1x1 convolution to change the - channels in the skip connection. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param use_checkpoint: if True, use gradient checkpointing on this module. - :param up: if True, use this block for upsampling. - :param down: if True, use this block for downsampling. - """ - - def __init__( - self, - channels, - emb_channels, - dropout, - out_channels=None, - use_conv=False, - use_scale_shift_norm=False, - dims=2, - use_checkpoint=False, - up=False, - down=False, - ): - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.dropout = dropout - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_checkpoint = use_checkpoint - self.use_scale_shift_norm = use_scale_shift_norm - - self.in_layers = nn.Sequential( - normalization(channels), - nn.SiLU(), - conv_nd(dims, channels, self.out_channels, 3, padding=1), - ) - - self.updown = up or down - - if up: - self.h_upd = Upsample(channels, False, dims) - self.x_upd = Upsample(channels, False, dims) - elif down: - self.h_upd = Downsample(channels, False, dims) - self.x_upd = Downsample(channels, False, dims) - else: - self.h_upd = self.x_upd = nn.Identity() - - self.emb_layers = nn.Sequential( - nn.SiLU(), - linear( - emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, - ), - ) - self.out_layers = nn.Sequential( - normalization(self.out_channels), - nn.SiLU(), - nn.Dropout(p=dropout), - zero_module( - conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) - ), - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - elif use_conv: - self.skip_connection = conv_nd( - dims, channels, self.out_channels, 3, padding=1 - ) - else: - self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) - - def forward(self, x, emb): - """ - Apply the block to a Tensor, conditioned on a timestep embedding. - :param x: an [N x C x ...] Tensor of features. - :param emb: an [N x emb_channels] Tensor of timestep embeddings. - :return: an [N x C x ...] Tensor of outputs. - """ - return checkpoint( - self._forward, (x, emb), self.parameters(), self.use_checkpoint - ) - - - def _forward(self, x, emb): - if self.updown: - in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] - h = in_rest(x) - h = self.h_upd(h) - x = self.x_upd(x) - h = in_conv(h) - else: - h = self.in_layers(x) - emb_out = self.emb_layers(emb).type(h.dtype) - while len(emb_out.shape) < len(h.shape): - emb_out = emb_out[..., None] - if self.use_scale_shift_norm: - out_norm, out_rest = self.out_layers[0], self.out_layers[1:] - scale, shift = th.chunk(emb_out, 2, dim=1) - h = out_norm(h) * (1 + scale) + shift - h = out_rest(h) - else: - h = h + emb_out - h = self.out_layers(h) - return self.skip_connection(x) + h - - -class AttentionBlock(nn.Module): - """ - An attention block that allows spatial positions to attend to each other. - Originally ported from here, but adapted to the N-d case. - https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. - """ - - def __init__( - self, - channels, - num_heads=1, - num_head_channels=-1, - use_checkpoint=False, - use_new_attention_order=False, - ): - super().__init__() - self.channels = channels - if num_head_channels == -1: - self.num_heads = num_heads - else: - assert ( - channels % num_head_channels == 0 - ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" - self.num_heads = channels // num_head_channels - self.use_checkpoint = use_checkpoint - self.norm = normalization(channels) - self.qkv = conv_nd(1, channels, channels * 3, 1) - if use_new_attention_order: - # split qkv before split heads - self.attention = QKVAttention(self.num_heads) - else: - # split heads before split qkv - self.attention = QKVAttentionLegacy(self.num_heads) - - self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) - - def forward(self, x): - return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! - #return pt_checkpoint(self._forward, x) # pytorch - - def _forward(self, x): - b, c, *spatial = x.shape - x = x.reshape(b, c, -1) - qkv = self.qkv(self.norm(x)) - h = self.attention(qkv) - h = self.proj_out(h) - return (x + h).reshape(b, c, *spatial) - - -def count_flops_attn(model, _x, y): - """ - A counter for the `thop` package to count the operations in an - attention operation. - Meant to be used like: - macs, params = thop.profile( - model, - inputs=(inputs, timestamps), - custom_ops={QKVAttention: QKVAttention.count_flops}, - ) - """ - b, c, *spatial = y[0].shape - num_spatial = int(np.prod(spatial)) - # We perform two matmuls with the same number of ops. - # The first computes the weight matrix, the second computes - # the combination of the value vectors. - matmul_ops = 2 * b * (num_spatial ** 2) * c - model.total_ops += th.DoubleTensor([matmul_ops]) - - -class QKVAttentionLegacy(nn.Module): - """ - A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping - """ - - def __init__(self, n_heads): - super().__init__() - self.n_heads = n_heads - - def forward(self, qkv): - """ - Apply QKV attention. - :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. - :return: an [N x (H * C) x T] tensor after attention. - """ - bs, width, length = qkv.shape - assert width % (3 * self.n_heads) == 0 - ch = width // (3 * self.n_heads) - q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) - scale = 1 / math.sqrt(math.sqrt(ch)) - weight = th.einsum( - "bct,bcs->bts", q * scale, k * scale - ) # More stable with f16 than dividing afterwards - weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) - a = th.einsum("bts,bcs->bct", weight, v) - return a.reshape(bs, -1, length) - - @staticmethod - def count_flops(model, _x, y): - return count_flops_attn(model, _x, y) - - -class QKVAttention(nn.Module): - """ - A module which performs QKV attention and splits in a different order. - """ - - def __init__(self, n_heads): - super().__init__() - self.n_heads = n_heads - - def forward(self, qkv): - """ - Apply QKV attention. - :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. - :return: an [N x (H * C) x T] tensor after attention. - """ - bs, width, length = qkv.shape - assert width % (3 * self.n_heads) == 0 - ch = width // (3 * self.n_heads) - q, k, v = qkv.chunk(3, dim=1) - scale = 1 / math.sqrt(math.sqrt(ch)) - weight = th.einsum( - "bct,bcs->bts", - (q * scale).view(bs * self.n_heads, ch, length), - (k * scale).view(bs * self.n_heads, ch, length), - ) # More stable with f16 than dividing afterwards - weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) - a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) - return a.reshape(bs, -1, length) - - @staticmethod - def count_flops(model, _x, y): - return count_flops_attn(model, _x, y) - - -class UNetModelEncode(nn.Module): - - - def __init__( - self, - image_size, - in_channels, - model_channels, - out_channels, - num_res_blocks, - attention_resolutions, - dropout=0, - channel_mult=(1, 2, 4, 8), - conv_resample=True, - dims=2, - num_classes=None, - use_checkpoint=False, - use_fp16=False, - num_heads=-1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - use_new_attention_order=False, - use_spatial_transformer=False, # custom transformer support - transformer_depth=1, # custom transformer support - context_dim=None, # custom transformer support - n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model - legacy=True, - ): - super().__init__() - if use_spatial_transformer: - assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' - - if context_dim is not None: - assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' - from omegaconf.listconfig import ListConfig - if type(context_dim) == ListConfig: - context_dim = list(context_dim) - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - if num_heads == -1: - assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' - - if num_head_channels == -1: - assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' - - self.image_size = image_size - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - self.num_res_blocks = num_res_blocks - self.attention_resolutions = attention_resolutions - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.dtype = th.float16 if use_fp16 else th.float32 - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.predict_codebook_ids = n_embed is not None - - time_embed_dim = model_channels * 4 - self.time_embed = nn.Sequential( - linear(model_channels, time_embed_dim), - nn.SiLU(), - linear(time_embed_dim, time_embed_dim), - ) - - if self.num_classes is not None: - self.label_emb = nn.Embedding(num_classes, time_embed_dim) - - self.input_blocks = nn.ModuleList( - [ - TimestepEmbedSequential( - conv_nd(dims, in_channels, model_channels, 3, padding=1) - ) - ] - ) - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - for level, mult in enumerate(channel_mult): - for _ in range(num_res_blocks): - layers = [ - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=mult * model_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ) - ] - ch = mult * model_channels - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - layers.append( - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim - ) - ) - self.input_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - ) - if resblock_updown - else Downsample( - ch, conv_resample, dims=dims, out_channels=out_ch - ) - ) - ) - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - self.middle_block = TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ), - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim - ), - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ), - ) - self._feature_size += ch - - def forward(self, x, timesteps=None, context=None, y=None): - """ - Apply the model to an input batch. - :param x: an [N x C x ...] Tensor of inputs. - :param timesteps: a 1-D batch of timesteps. - :param context: conditioning plugged in via crossattn - :param y: an [N] Tensor of labels, if class-conditional. - :return: an [N x C x ...] Tensor of outputs. - """ - assert (y is not None) == ( - self.num_classes is not None - ), "must specify y if and only if the model is class-conditional" - hs = [] - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) - emb = self.time_embed(t_emb) - - if self.num_classes is not None: - assert y.shape == (x.shape[0],) - emb = emb + self.label_emb(y) - - h = x.type(self.dtype) - for module in self.input_blocks: - h = module(h, emb, context) - hs.append(h) - h = self.middle_block(h, emb, context) - - return h, emb, hs - - -class UNetModelDecode(nn.Module): - - - def __init__( - self, - image_size, - in_channels, - model_channels, - out_channels, - num_res_blocks, - attention_resolutions, - dropout=0, - channel_mult=(1, 2, 4, 8), - conv_resample=True, - dims=2, - num_classes=None, - use_checkpoint=False, - use_fp16=False, - num_heads=-1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - use_new_attention_order=False, - use_spatial_transformer=False, # custom transformer support - transformer_depth=1, # custom transformer support - context_dim=None, # custom transformer support - n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model - legacy=True, - ): - super().__init__() - if use_spatial_transformer: - assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' - - if context_dim is not None: - assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' - from omegaconf.listconfig import ListConfig - if type(context_dim) == ListConfig: - context_dim = list(context_dim) - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - if num_heads == -1: - assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' - - if num_head_channels == -1: - assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' - - self.image_size = image_size - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - self.num_res_blocks = num_res_blocks - self.attention_resolutions = attention_resolutions - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.dtype = th.float16 if use_fp16 else th.float32 - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.predict_codebook_ids = n_embed is not None - - time_embed_dim = model_channels * 4 - - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - for level, mult in enumerate(channel_mult): - for _ in range(num_res_blocks): - - ch = mult * model_channels - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - - self._feature_size += ch - - self.output_blocks = nn.ModuleList([]) - for level, mult in list(enumerate(channel_mult))[::-1]: - for i in range(num_res_blocks + 1): - ich = input_block_chans.pop() - layers = [ - ResBlock( - ch + ich, - time_embed_dim, - dropout, - out_channels=model_channels * mult, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ) - ] - ch = model_channels * mult - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - layers.append( - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads_upsample, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim - ) - ) - if level and i == num_res_blocks: - out_ch = ch - layers.append( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - up=True, - ) - if resblock_updown - else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) - ) - ds //= 2 - self.output_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - - self.out = nn.Sequential( - normalization(ch), - nn.SiLU(), - zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), - ) - if self.predict_codebook_ids: - self.id_predictor = nn.Sequential( - normalization(ch), - conv_nd(dims, model_channels, n_embed, 1), - #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits - ) - - def forward(self, h,emb,tp,hs, context=None, y=None): - """ - Apply the model to an input batch. - :param x: an [N x C x ...] Tensor of inputs. - :param timesteps: a 1-D batch of timesteps. - :param context: conditioning plugged in via crossattn - :param y: an [N] Tensor of labels, if class-conditional. - :return: an [N x C x ...] Tensor of outputs. - """ - - for module in self.output_blocks: - h = th.cat([h, hs.pop()], dim=1) - h = module(h, emb, context) - h = h.type(tp) - if self.predict_codebook_ids: - return self.id_predictor(h) - else: - return self.out(h) diff --git a/spaces/jonathang/WeatherBoy/README.md b/spaces/jonathang/WeatherBoy/README.md deleted file mode 100644 index c139b3ec1b30864beac94ca4a1f22d04a0da188a..0000000000000000000000000000000000000000 --- a/spaces/jonathang/WeatherBoy/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: WeatherBoy -emoji: 😻 -colorFrom: red -colorTo: purple -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/jordonpeter01/ai-comic-factory/src/components/ui/table.tsx b/spaces/jordonpeter01/ai-comic-factory/src/components/ui/table.tsx deleted file mode 100644 index 953fb3c003bc0cd9d93059c373bc23e6aecbded8..0000000000000000000000000000000000000000 --- a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/table.tsx +++ /dev/null @@ -1,114 +0,0 @@ -import * as React from "react" - -import { cn } from "@/lib/utils" - -const Table = React.forwardRef< - HTMLTableElement, - React.HTMLAttributes ->(({ className, ...props }, ref) => ( - --)) -Table.displayName = "Table" - -const TableHeader = React.forwardRef< - HTMLTableSectionElement, - React.HTMLAttributes-
->(({ className, ...props }, ref) => ( - -)) -TableHeader.displayName = "TableHeader" - -const TableBody = React.forwardRef< - HTMLTableSectionElement, - React.HTMLAttributes ->(({ className, ...props }, ref) => ( - -)) -TableBody.displayName = "TableBody" - -const TableFooter = React.forwardRef< - HTMLTableSectionElement, - React.HTMLAttributes ->(({ className, ...props }, ref) => ( - -)) -TableFooter.displayName = "TableFooter" - -const TableRow = React.forwardRef< - HTMLTableRowElement, - React.HTMLAttributes ->(({ className, ...props }, ref) => ( - -)) -TableRow.displayName = "TableRow" - -const TableHead = React.forwardRef< - HTMLTableCellElement, - React.ThHTMLAttributes ->(({ className, ...props }, ref) => ( - -)) -TableHead.displayName = "TableHead" - -const TableCell = React.forwardRef< - HTMLTableCellElement, - React.TdHTMLAttributes ->(({ className, ...props }, ref) => ( - -)) -TableCell.displayName = "TableCell" - -const TableCaption = React.forwardRef< - HTMLTableCaptionElement, - React.HTMLAttributes ->(({ className, ...props }, ref) => ( - -)) -TableCaption.displayName = "TableCaption" - -export { - Table, - TableHeader, - TableBody, - TableFooter, - TableHead, - TableRow, - TableCell, - TableCaption, -} diff --git a/spaces/julien-c/duckdb-full-text-search/README.md b/spaces/julien-c/duckdb-full-text-search/README.md deleted file mode 100644 index 65cb244a500456c0830a8c663106da73cec4b5e9..0000000000000000000000000000000000000000 --- a/spaces/julien-c/duckdb-full-text-search/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: DuckDB Full Text Search -emoji: 🐤 -colorFrom: indigo -colorTo: indigo -sdk: gradio -sdk_version: 3.32.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference - -Inspired by https://huggingface.co/spaces/asoria/duckdb-parquet-demo diff --git a/spaces/justYu2001/furniture-detection/utils/google_utils.py b/spaces/justYu2001/furniture-detection/utils/google_utils.py deleted file mode 100644 index f363408e63981702e63dcda189cbc2099d0a9499..0000000000000000000000000000000000000000 --- a/spaces/justYu2001/furniture-detection/utils/google_utils.py +++ /dev/null @@ -1,123 +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 requests -import torch - - -def gsutil_getsize(url=''): - # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du - s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') - return eval(s.split(' ')[0]) if len(s) else 0 # bytes - - -def attempt_download(file, repo='WongKinYiu/yolov7'): - # Attempt file download if does not exist - file = Path(str(file).strip().replace("'", '').lower()) - - if not file.exists(): - try: - response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api - assets = [x['name'] for x in response['assets']] # release assets - tag = response['tag_name'] # i.e. 'v1.0' - except: # fallback plan - assets = ['yolov7.pt', 'yolov7-tiny.pt', 'yolov7x.pt', 'yolov7-d6.pt', 'yolov7-e6.pt', - 'yolov7-e6e.pt', 'yolov7-w6.pt'] - tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] - - name = file.name - if name in assets: - msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' - redundant = False # second download option - try: # GitHub - url = f'https://github.com/{repo}/releases/download/{tag}/{name}' - print(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, file) - assert file.exists() and file.stat().st_size > 1E6 # check - except Exception as e: # GCP - print(f'Download error: {e}') - assert redundant, 'No secondary mirror' - url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' - print(f'Downloading {url} to {file}...') - os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) - finally: - if not file.exists() or file.stat().st_size < 1E6: # check - file.unlink(missing_ok=True) # remove partial downloads - print(f'ERROR: Download failure: {msg}') - print('') - return - - -def gdrive_download(id='', file='tmp.zip'): - # Downloads a file from Google Drive. from yolov7.utils.google_utils import *; gdrive_download() - t = time.time() - file = Path(file) - cookie = Path('cookie') # gdrive cookie - print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') - file.unlink(missing_ok=True) # remove existing file - cookie.unlink(missing_ok=True) # remove existing cookie - - # Attempt file download - out = "NUL" if platform.system() == "Windows" else "/dev/null" - os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') - if os.path.exists('cookie'): # large file - s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' - else: # small file - s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' - r = os.system(s) # execute, capture return - cookie.unlink(missing_ok=True) # remove existing cookie - - # Error check - if r != 0: - file.unlink(missing_ok=True) # remove partial - print('Download error ') # raise Exception('Download error') - return r - - # Unzip if archive - if file.suffix == '.zip': - print('unzipping... ', end='') - os.system(f'unzip -q {file}') # unzip - file.unlink() # remove zip to free space - - print(f'Done ({time.time() - t:.1f}s)') - 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/justest/gpt4free/g4f/.v1/gpt4free/aiassist/__init__.py b/spaces/justest/gpt4free/g4f/.v1/gpt4free/aiassist/__init__.py deleted file mode 100644 index 95a9f08b259bbcabf7512dda0fe633d96686fd4d..0000000000000000000000000000000000000000 --- a/spaces/justest/gpt4free/g4f/.v1/gpt4free/aiassist/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -import urllib.request -import json - - -class Completion: - @staticmethod - def create( - systemMessage: str = "You are a helpful assistant", - prompt: str = "", - parentMessageId: str = "", - temperature: float = 0.8, - top_p: float = 1, - ): - json_data = { - "prompt": prompt, - "options": {"parentMessageId": parentMessageId}, - "systemMessage": systemMessage, - "temperature": temperature, - "top_p": top_p, - } - - url = "http://43.153.7.56:8080/api/chat-process" - headers = {"Content-type": "application/json"} - - data = json.dumps(json_data).encode("utf-8") - req = urllib.request.Request(url, data=data, headers=headers) - response = urllib.request.urlopen(req) - content = response.read().decode() - - return Completion.__load_json(content) - - @classmethod - def __load_json(cls, content) -> dict: - split = content.rsplit("\n", 1)[1] - to_json = json.loads(split) - return to_json diff --git a/spaces/kangvcar/RealChar/client/web/src/components/MediaDevices/style.css b/spaces/kangvcar/RealChar/client/web/src/components/MediaDevices/style.css deleted file mode 100644 index 3d4dd479e71f57abd63bdfbcb042eb4fc9a5fd5b..0000000000000000000000000000000000000000 --- a/spaces/kangvcar/RealChar/client/web/src/components/MediaDevices/style.css +++ /dev/null @@ -1,54 +0,0 @@ -.devices-container { - display: flex; - align-items: center; - justify-content: center; - flex-direction: column; -} - -.audio-device-label { - margin-top: 40px; - margin-bottom: 20px; - color: #e0e0e0; -} - -/* media devices select */ -.select-dropdown, -.select-dropdown * { - margin: 0; - padding: 0; - position: relative; - box-sizing: border-box; -} -.select-dropdown { - position: relative; - background-color: #02081d; - border-radius: 4px; -} -.select-dropdown select { - font-size: 1rem; - font-weight: normal; - color: white; - max-width: 100%; - padding: 8px 24px 8px 10px; - border-radius: 10px; - background-color: transparent; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} -.select-dropdown select:active, .select-dropdown select:focus { - outline: none; - box-shadow: none; -} -.select-dropdown:after { - content: ""; - position: absolute; - top: 50%; - right: 8px; - width: 0; - height: 0; - margin-top: -2px; - border-top: 5px solid #aaa; - border-right: 5px solid transparent; - border-left: 5px solid transparent; -} diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/torch2onnx.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/torch2onnx.py deleted file mode 100644 index fc26ab82e552331bc8d75b34e81000418f4d38ec..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/torch2onnx.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -import onnx -import torch - - -def convert_onnx(net, path_module, output, opset=11, simplify=False): - assert isinstance(net, torch.nn.Module) - img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32) - img = img.astype(np.float) - img = (img / 255. - 0.5) / 0.5 # torch style norm - img = img.transpose((2, 0, 1)) - img = torch.from_numpy(img).unsqueeze(0).float() - - weight = torch.load(path_module) - net.load_state_dict(weight) - net.eval() - torch.onnx.export(net, img, output, keep_initializers_as_inputs=False, verbose=False, opset_version=opset) - model = onnx.load(output) - graph = model.graph - graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None' - if simplify: - from onnxsim import simplify - model, check = simplify(model) - assert check, "Simplified ONNX model could not be validated" - onnx.save(model, output) - - -if __name__ == '__main__': - import os - import argparse - from backbones import get_model - - parser = argparse.ArgumentParser(description='ArcFace PyTorch to onnx') - parser.add_argument('input', type=str, help='input backbone.pth file or path') - parser.add_argument('--output', type=str, default=None, help='output onnx path') - parser.add_argument('--network', type=str, default=None, help='backbone network') - parser.add_argument('--simplify', type=bool, default=False, help='onnx simplify') - args = parser.parse_args() - input_file = args.input - if os.path.isdir(input_file): - input_file = os.path.join(input_file, "backbone.pth") - assert os.path.exists(input_file) - model_name = os.path.basename(os.path.dirname(input_file)).lower() - params = model_name.split("_") - if len(params) >= 3 and params[1] in ('arcface', 'cosface'): - if args.network is None: - args.network = params[2] - assert args.network is not None - print(args) - backbone_onnx = get_model(args.network, dropout=0) - - output_path = args.output - if output_path is None: - output_path = os.path.join(os.path.dirname(__file__), 'onnx') - if not os.path.exists(output_path): - os.makedirs(output_path) - assert os.path.isdir(output_path) - output_file = os.path.join(output_path, "%s.onnx" % model_name) - convert_onnx(backbone_onnx, input_file, output_file, simplify=args.simplify) diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/options/inference_options.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/options/inference_options.py deleted file mode 100644 index c453965959ab4cfb31acbc424f994db68c3d4df5..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/options/inference_options.py +++ /dev/null @@ -1,23 +0,0 @@ -from face3d.options.base_options import BaseOptions - - -class InferenceOptions(BaseOptions): - """This class includes test options. - - It also includes shared options defined in BaseOptions. - """ - - def initialize(self, parser): - parser = BaseOptions.initialize(self, parser) # define shared options - parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') - parser.add_argument('--dataset_mode', type=str, default=None, help='chooses how datasets are loaded. [None | flist]') - - parser.add_argument('--input_dir', type=str, help='the folder of the input files') - parser.add_argument('--keypoint_dir', type=str, help='the folder of the keypoint files') - parser.add_argument('--output_dir', type=str, default='mp4', help='the output dir to save the extracted coefficients') - parser.add_argument('--save_split_files', action='store_true', help='save split files or not') - parser.add_argument('--inference_batch_size', type=int, default=8) - - # Dropout and Batchnorm has different behavior during training and test. - self.isTrain = False - return parser diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/util/html.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/util/html.py deleted file mode 100644 index cc3262a1eafda34842e4dbad47bb6ba72f0c5a68..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/util/html.py +++ /dev/null @@ -1,86 +0,0 @@ -import dominate -from dominate.tags import meta, h3, table, tr, td, p, a, img, br -import os - - -class HTML: - """This HTML class allows us to save images and write texts into a single HTML file. - - It consists of functions such as (add a text header to the HTML file), - (add a row of images to the HTML file), and (save the HTML to the disk). - It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API. - """ - - def __init__(self, web_dir, title, refresh=0): - """Initialize the HTML classes - - Parameters: - web_dir (str) -- a directory that stores the webpage. HTML file will be created at /index.html; images will be saved at 0: - with self.doc.head: - meta(http_equiv="refresh", content=str(refresh)) - - def get_image_dir(self): - """Return the directory that stores images""" - return self.img_dir - - def add_header(self, text): - """Insert a header to the HTML file - - Parameters: - text (str) -- the header text - """ - with self.doc: - h3(text) - - def add_images(self, ims, txts, links, width=400): - """add images to the HTML file - - Parameters: - ims (str list) -- a list of image paths - txts (str list) -- a list of image names shown on the website - links (str list) -- a list of hyperref links; when you click an image, it will redirect you to a new page - """ - self.t = table(border=1, style="table-layout: fixed;") # Insert a table - self.doc.add(self.t) - with self.t: - with tr(): - for im, txt, link in zip(ims, txts, links): - with td(style="word-wrap: break-word;", halign="center", valign="top"): - with p(): - with a(href=os.path.join('images', link)): - img(style="width:%dpx" % width, src=os.path.join('images', im)) - br() - p(txt) - - def save(self): - """save the current content to the HMTL file""" - html_file = '%s/index.html' % self.web_dir - f = open(html_file, 'wt') - f.write(self.doc.render()) - f.close() - - -if __name__ == '__main__': # we show an example usage here. - html = HTML('web/', 'test_html') - html.add_header('hello world') - - ims, txts, links = [], [], [] - for n in range(4): - ims.append('image_%d.png' % n) - txts.append('text_%d' % n) - links.append('image_%d.png' % n) - html.add_images(ims, txts, links) - html.save() diff --git a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/backbones/mobilefacenet.py b/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/backbones/mobilefacenet.py deleted file mode 100644 index 87731491d76f9ff61cc70e57bb3f18c54fae308c..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/backbones/mobilefacenet.py +++ /dev/null @@ -1,130 +0,0 @@ -''' -Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py -Original author cavalleria -''' - -import torch.nn as nn -from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module -import torch - - -class Flatten(Module): - def forward(self, x): - return x.view(x.size(0), -1) - - -class ConvBlock(Module): - def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): - super(ConvBlock, self).__init__() - self.layers = nn.Sequential( - Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False), - BatchNorm2d(num_features=out_c), - PReLU(num_parameters=out_c) - ) - - def forward(self, x): - return self.layers(x) - - -class LinearBlock(Module): - def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): - super(LinearBlock, self).__init__() - self.layers = nn.Sequential( - Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False), - BatchNorm2d(num_features=out_c) - ) - - def forward(self, x): - return self.layers(x) - - -class DepthWise(Module): - def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1): - super(DepthWise, self).__init__() - self.residual = residual - self.layers = nn.Sequential( - ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)), - ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride), - LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)) - ) - - def forward(self, x): - short_cut = None - if self.residual: - short_cut = x - x = self.layers(x) - if self.residual: - output = short_cut + x - else: - output = x - return output - - -class Residual(Module): - def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)): - super(Residual, self).__init__() - modules = [] - for _ in range(num_block): - modules.append(DepthWise(c, c, True, kernel, stride, padding, groups)) - self.layers = Sequential(*modules) - - def forward(self, x): - return self.layers(x) - - -class GDC(Module): - def __init__(self, embedding_size): - super(GDC, self).__init__() - self.layers = nn.Sequential( - LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)), - Flatten(), - Linear(512, embedding_size, bias=False), - BatchNorm1d(embedding_size)) - - def forward(self, x): - return self.layers(x) - - -class MobileFaceNet(Module): - def __init__(self, fp16=False, num_features=512): - super(MobileFaceNet, self).__init__() - scale = 2 - self.fp16 = fp16 - self.layers = nn.Sequential( - ConvBlock(3, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1)), - ConvBlock(64 * scale, 64 * scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64), - DepthWise(64 * scale, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128), - Residual(64 * scale, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), - DepthWise(64 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256), - Residual(128 * scale, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), - DepthWise(128 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512), - Residual(128 * scale, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), - ) - self.conv_sep = ConvBlock(128 * scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0)) - self.features = GDC(num_features) - self._initialize_weights() - - def _initialize_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x): - with torch.cuda.amp.autocast(self.fp16): - x = self.layers(x) - x = self.conv_sep(x.float() if self.fp16 else x) - x = self.features(x) - return x - - -def get_mbf(fp16, num_features): - return MobileFaceNet(fp16, num_features) \ No newline at end of file diff --git a/spaces/kidcoconut/spcstm_omdenasaudi_liverhccxai/uix/pages/lit_home.py b/spaces/kidcoconut/spcstm_omdenasaudi_liverhccxai/uix/pages/lit_home.py deleted file mode 100644 index 0666b235214f827ea8e131163e30aeba66e64e5e..0000000000000000000000000000000000000000 --- a/spaces/kidcoconut/spcstm_omdenasaudi_liverhccxai/uix/pages/lit_home.py +++ /dev/null @@ -1,76 +0,0 @@ -#--- about page -import streamlit as st - -description = "Home" -def run(): - - print("\nINFO (lit_home.run) loading ", description, " page ...") - - - #st.markdown('### Home') - #st.markdown('### Omdena Saudi Arabia') - #st.markdown('### Detecting Liver Cancer from Histopathology WSI Using Deep Learning and Explainability') - st.markdown('#### Background ') - st.markdown('\ - Hepatocellular Carcinoma (HCC) is a primary liver malignancy, with \ - alarming global impact. It is the 4th most common cause of cancer \ - mortality worldwide, and the 6th most common malignancy overall. \ - \ - A patient\'s prognosis increases markedly with the speed of diagnosis \ - and treatment, however the rates of occurrence are increasing at an \ - alarming rate which will commensurately challenge the medical \ - community. \ - \ - There are already several tools and technologies available to assist \ - pathologists, however the current approach is ultimately constrained by \ - a number of factors including: the rising demand, a limited supply \ - of skilled specialists, the time required to grow/replenish this talent \ - pool, and human factors which influence quality, accuracy, consistency, \ - and speed (timeliness). \ - ') - - st.markdown('#### Claim ') - st.markdown('\ - It is the desire of this project team to increase the prognosis of \ - hepatocellular cancer patients.\ - \ - Machine Learning techniques, specifically Deep Learning and \ - Explainability (XAI) show promise in mimic\'ing the role of the \ - pathologist. \ - \ - MLOps promises to establish a baseline for performance\ - and a basis for continuous process improvement. This could greatly \ - reduce human factor elements while accelerating the times and \ - increasing the volumes of response.\ - \ - As a minimum, an ML application can serve as a supplement to the\ - pathologist, a teaching aide, a verification tool, or as a framework\ - for community collaboration and the advancement of quality diagnosis.\ - ') - - st.markdown('#### Objectives ') - st.markdown('\ - A key objective of this project is to produce a deployed app that will\ - enable pathologists to upload a digital liver histopathology slide\ - image and then receive an output that classifies the segment as\ - malignant (or not). \ - \ - The utilization of Machine Learning and Explainability Techniques \ - to the traditional process of Liver Histopathology and HCC Diagnosis \ - could serve to greatly reduce the time to diagnosis and treatment. \ - \ - ') - ''' - st.markdown( - """ - - Home page - - """, - unsafe_allow_html=True, - ) - - ''' \ No newline at end of file diff --git a/spaces/king007/anime-anything-promptgen-v2-test/app.py b/spaces/king007/anime-anything-promptgen-v2-test/app.py deleted file mode 100644 index 63803dbffb1b693f80fe6460ec7e56e84702b5b9..0000000000000000000000000000000000000000 --- a/spaces/king007/anime-anything-promptgen-v2-test/app.py +++ /dev/null @@ -1,35 +0,0 @@ -import torch -import gradio as gr -import json -from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline - -tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2') -tokenizer.add_special_tokens({'pad_token': '[PAD]'}) -model = GPT2LMHeadModel.from_pretrained('FredZhang7/anime-anything-promptgen-v2') - -# prompt = r'1girl, genshin' - -# generate text using fine-tuned model -nlp = pipeline('text-generation', model=model, tokenizer=tokenizer) - -def generate(prompt): - # generate 10 samples using contrastive search - outs = nlp(prompt, max_length=76, num_return_sequences=3, do_sample=True, repetition_penalty=1.2, temperature=0.7, top_k=3, early_stopping=True) - jsonStr = json.dumps(outs) - print(prompt) - print(jsonStr) - return jsonStr - - -# for i in range(len(outs)): - # remove trailing commas and double spaces - # outs[i] = str(outs[i]['generated_text']).replace(' ', '').rstrip(',') -# print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n') - # print(str(outs[i]['generated_text'])) - -input_component = gr.Textbox(label = "Input a prompt", value = "1girl, genshin") -output_component = gr.Textbox(label = "detail Prompt") -examples = [] -description = "" -gr.Interface(generate, inputs = input_component, outputs=output_component, examples=examples, title = "anything prompt", description=description).launch() - diff --git a/spaces/kingsotn/tt-ai/comments.py b/spaces/kingsotn/tt-ai/comments.py deleted file mode 100644 index 40c5a3d3006c58e28c6c400ab309929582c7758b..0000000000000000000000000000000000000000 --- a/spaces/kingsotn/tt-ai/comments.py +++ /dev/null @@ -1 +0,0 @@ -comments = ['go suck some fucking cock and stop blocking my edits u cock.', 'NIGGER PLEASE \n EAT A COCK, LOL HY.', 'WHAT THE FUCK YOU DOING ARSEHOLE!!! DICKHEAD!!! COCKFACE!!!', "Gargantuan cocks? Now you're not even trying.", 'mr harris likes cocks', 'Jamaicans have big cocks', 'u dum fukers \n MR.Harris suks cocks and likes it anal', 'KRISTIAN HAS A HUGE COCK', '== Cock Block == \n\n Kenton Simms', 'im gay and i love mens cocks up my tight arsehole!!! mmmmmmm!!!!', '== Personal Life == \n\n Abraham Lincoln had a 38 inch cock', 'Your threats are meaningless, you cock-licking, cum-eating sodomite.', 'me to suck my moms cock', 'ANUS IS STRETCHED AND WIDENED BY GRAWP’S MASSIVE COCK.', 'FUCKING COCK AND BALLS!!!!! IM DRUNK AS A LORD', "== haha == \n\n You need to stop sucking Derek Rae's pathetic cock.", 'Sucks A Fat Fat Cock For Real', 'throbbing hard cock pounding and of course', '== FUCK YOU == \n\n fuck you cock suckers', 'You should suck a hairy cock you judge mental bitch', 'PDo you want to block or do you want a cock?age', '==fantasy ride== \n\n get off ma; cock!!!!!!!!!!', 'i ahve a cock would you like my cock?', 'SHAKESPEARE HAD A COCK ON HIS HED', '== You == \n\n Can I suck your cock?', 'PENIS!!! \n\n DICK \n\n SHIT FUCK COCK TITS', '== WARNING == \n\n suck me cock', '. She was fucked by someone with a big cock', 'you ya fucking dirty little mother fucking cock sucking insest [piece of shit', 'cock in my fuckin ass hoe suasage feels good real good', '""" we like to suck donkey cock"""', ']] \n You are a huge massive rapist cock sucking faggot', 'JIMBO WALES’S ANUS IS STRETCHED BY GRAWP’S MASSIVE COCK.', '== what a == \n cocksucker', '" \n\n == == \n\n QUADELL IS A COOLIE! \n QUADELL LOVES \n DA \n COCK!"', 'you are a cock smoker!!!', 'Cockn (blend of cock and chicken) + ey (egg).', 'HAHA DISREGARD THIS I SUCK COCKS', "== Hey == \n\n It's cool to have a small cock, right?", 'i like cock in my mouth', 'with his cock out', 'hubidubby penis cock, men lover, weirdo, piss fetish...', 'i love to suck donkey cock', 'giant cock sucking piece of shit!!', 'suck your cock cos me so horny <(^.^)', 'I will suck your cock', '== == \n\n \n GO SUCK A CUNT COCKSUCKERS!!!', 'no and he sucked cock', ', such a waste tastes great after a hard day sucking cock!', 'And i hatecorbin bennett because he likes cock in his mouth', "STUPID BITCHES MUTHAFUCKERS SUCK DIS BIG JUICY COCK HOE'S U KNO U WANT TO BITCH", "with each other. luke likes to suck toms cock till he jiz's on his tounge", 'mmnbjndjnkdlncdlkcnklanckfuck u all cock sukkers', 'hey everyone i suck cock for a living and love sticking candy bars up my cunt', 'REDIRECT Nubiatech’s anus is stretched and widened by Grop’s massive cock.', 'being such a dick! fuck me l like cock', "==Help== \n\n I need help learning how to suck Chzz's cock and ride his dick.", "WHY DON'T ALL OF YOU UT FANS GO SUCK A COCK???????????????????????????????????", 'I suck I love cock in the mouth area seb rocks!!', 'Show me yer cock.', 'SIDNEY CROSBY SUCKS BLACK COCK', 'REDIRECT Talk:Arthur Cocks (politician)', '== bitch ass niggas == \n\n suck huge gorilla cock', 'Chara likes to suck cock!!!!', "RYULONG'S JEWISH ANUS IS STRETCHED BY GRAWP'S MASSIVE COCK", '== Sockblock == \n\n \n\n Suck a cock, Watson!', 'You all suck large cocks', "Dick van Dyke's guilty cock-er-nee conscience", 'socrates is dead no one cares \n Fuck you bitch \n suck my cock', 'dick cock cum vagina fag cock enters vagina cum', '== Cock Sucking Mother Fucker ==', '== pussy smell like water == \n\n cockfarm', 'WE WE MUSTA DANAGE THIS ISLAMCI COCK SEUKER', 'ur a faggot go get bent cock sucker', 'Thomas Fung wants his cock. Slurp!', 'FRANKIE LOVES SUCKING JUICY COCK SO DOES LEWIS THEY GIVE DEEP THROAT', '== COCK BLOCK ==', 'Batman and Robin are totally gay with each other. the both are cock suckers', 'COCK \n IN MY SWEET BLACK ASS', 'Ross Belloff Like Big Veiny Cock In His Asshole', 'asshole son of a bitch ass mother fucker cock suckin aids infested cunt', '-WRITTEN BY COCK MAGOO', "DON'T MIND ME. I SUCKS COCKS.", 'Suck my cock, you sad little sister fiddler ;)', 'cocks like Pope Benedict', 'a bigg juciy cock', 'Cendax - Cock ring and ball stretcher', ':The father of loves to suck the cock of .', 'will likes huge cock in his butt beacuse he is a flaming qweer', 'jack wood loves to fuck big black cock all night longUmmm', '== Raped a baby == \n\n With his tiny cock', 'dakota german has a big cock', "== you know what time is it? == \n\n it's time for your cock eating, faggot.", 'LUNA SANTIN DOES SUCK AFRICAN-AMERICAN COCKS', '. we all love to eat cock, so come on down and pull that puppy out (please)', 'i love to get anal from shemales with giant, rock hard, veiny cocks!', 'A guy with a large cock looking to fuck bitches', 'george westinghouse told a poor man to shut the fuck up!!!! and go suck cock', '(they suck cock they suck cock IDT)', '== # == \n\n I HAVE A FAT COCK', 'SUCK MY COCK THEN MY TITS', 'i like big black cocks and my name is big shithead', 'can you stop deleting my edits, you cock jockey', 'Hi! my name is Kelly and i love the cock!', '== attention == \n\n suck mah cock', '< OOOOH, you thinks youre so good, pfff, u suck horse cock', 'YOURT \n\n \n YOURE A FUCKING COCKSUCKER', 'wave 39 just by aking the zombies suck our cocks lolololololololololol', '== Jonathan Mannum is a cocksucker! ==', '. Also, I suck large cocks. Thank you', '== Lawls == \n\n you suck cock bitch', 'A Faggot \n\n He loves cocks', '== Edit request on 1 May 2013 == \n\n \n\n cock \n\n \n balls', ', he loved there cocks', 'giant cock', 'MARBALLZ AND YELLOWVIET RULZ AN U SUCK BIG COCKS', 'HAHA DISREGARD THAT I SUCK COCK', 'Yeah, thats right insult them cocksuckers, motherfuckers, and sons of cunts', 'well fuck u wikipedia. u can suck my cock.', 'FUCk this shit niggers, fucking ridiculous suck ym cock', "David Flint sucks Alan Jones' cock everyday", 'I am a Gay little Homo. Who likes big Juicy cock.', ':::: Hahaha disregard that I suck cocks ;D', 'NO ANDY LOVES THE COCK \n ANDY <3 COCK', '== Blocked Sock == \n\n \n\n Cocksucker!!!!! \n\n Sincerely, Glorious Japan Forever', 'Please disregard that. I suck cocks.', '(aka the cock sucking website)', 'I like cock and balls in my mouth while getting teabagged and', '== Cock (chicken) ==', 'center|SUCK THIS HAIRY COCK YOU BITCH!!!', "*So, I'm not a cock?", 'I am a cock sucking faggot as well.', "Now, I'm off to have a wiki-wank while thinking about Bieber's cock.", 'suck nice big massive black cock )', '== I bet you love gagging on Arab cock. == \n\n Whore', 'SUCKS COCK!!!!!!! HE SHOULDNT BE ELECTED!!!!', 'FUck you all cocksuckers.', 'cock up ur arse', 'I think PD is a cock.', 'Usually retards use this cock.', 'SUCK MY DICK YOU COCK EYED BEANER!!!', "This is bogus, caffeine can't be smoked this is recockulous.", '" \n :Ha. Thanks! But what about dick, cock, fag etc etc...? | "', 'FUCK TOM HARRISON THAT FUCKING SHITTY CUNT COCKSUCKER!', "== Don't tuck infants into bed! Suck Boothie's cock instead! ==", 'Tracy McGrady \n cock!', 'YOU ALL SUCK BIG COCKS', 'cock cock cock cock cock cock', "mudda effin' HORSE COCKS", 'on bike \n\n 9 Sulphur-crested Cockatoo', 'KEVIN FEDERLINE U SUCK HORSE COCK', 'HJ Mitchell is a cock', 'Proud as a peacock', 'i mean, i love that cock of his uh sooooo juciy..mmmmmmm......', '== Fred Carty 69th Mayor of Oaklyn == \n\n Cock', 'cock and pussy. form todd', 'SUCK MY BIG ASS COCK', 'somebody should add big black cocks', 'because it loves the skinny cock', 'Freddy Durrant likes cock and penis in his anus.. yummmmyyyy!!!', "Cena sucks Vince's cock and gets fucked in the ass from Mark Henry", '== suck it hard. == \n\n Suck my cock fag. lol almost forgot to sign', 'Go sucks my monkeys nugget sack u cock sucking mofo.', '==This editor sucks the cock of vandalbots==', 'ileson has a small dick and like little cock suckers 7/10', 'suck my cock this page serves no purpose but for you to do some sucking of cock', 'Shut the cock up, you fuckbag.', "I know I'd certainly like to put my cock between those boobs...", 'fuck you asshole suck my huge cock you mother fucking peice of shit', 'MY COCK IS HUGE! D', 'ONLY that who can understand can tell what kindof cock this image show.', 'RUB MY COCK PLZ', 'cock rocket] cock rocket', 'HA HA! DISREGARD THAT I SUCK COCKS!!!', 'sam sucks cock Media:Example.ogg', 'just here to say we want to suck your big fat cock.', ', please suck cock. licking vagina is good too', 'YOU MOTHERFUCKER. KEEP UP THE PISS COCKSUCKER, JUST KEEP IT UP!', "i hv a 12' cock", '(fag, cock, and stupid)', 'Fuck you go suck a cock or fuck a pussey', 'DISREGARD THAT I SUCK COCKS', 'i think you suck man cock', "Hi I'm 65 years old and i like touching little boys, wanna suck my cock?", 'STRETCHED AND TORN BY GRAWP?S MASSIVE COCK.', 'cock vagina dic bitch ass ho', '== Hey there == \n\n You fuck-licking cock-sucking dick', "fuck yeah, I'd cockslap those tits to oblivion", 'Makes Perfectly good sense to me. The Guy below is a total Cock-head .', 'In other words they love to suck cock.', 'How about cock-eating, murderous pigs? Is that more neutral?', '*He is a flaming queer no doubt he loves the cock, so to speak', 'fuck you mother fucking cock sucker bithcface whore', 'jay sweet is a cock lol', '- My name is Rumping, and I enjoy big black cock.', 'suck my cock faggot', "fuck off jew loving cock sucking cold weather lovin;' piece of dog shit", 'start touching your cocks at night', 'Disregard the last the three sentences as they were written by a cock-sucker.', 'imagine her wet wide mouth greasing you cock', '== JOHANN GUTENBURG SUCKS COCK!!!!!!!!!! ==', 'to a cock', 'THIS CLUEBOT SUCKS COCK OF VANDALBOTS', 'PRIMITIVE SAND-NIGGER SCUM’S ANUS IS STRETCHED BY GRAWP’S MASSIVE COCK.', 'super tiny cocks in india', 'Also, David Ardell blends excellent cocktails', '. Disregard that, I suck cocks', 'IS STRETCHED BY GRAWP’S MASSIVE COCK.', 'wHY DOnt u suck my fucking cock?', 'The Palm Cockatoo is also black.', 'are dirty little cockmunches that need to get blown up fuck the arabs', 'Disregard that, I suck cocks \n\n ~Anonymous', 'I like the cock.', 'cocks||cocks || cocks||cocks|| cocks', 'anthony miloscia likes dick in his but hes gay and enjoys riding the big cock', 'Would you like me to suck your cock?', '== Do you suck Muslim cock? == \n\n Well...do ya?', 'wet as hello wombat cock ass', 'am the shit faced cock monkey', 'cock perfect for sucking', 'bridge is approxitamely as short as my cock', 'fuck off you cock', '== Cock == \n you are a cock muncher', 'homosexuality means you are a faggot. your basically bent and love cock !!', '==Big Old Black Cock== \n This scene needs more explaining', 'oh and wikipedia loves big black cock', 'Nothing perosnal. He is just some faggot cocksucker pushing the gay agenda', 'PECKER DICK COCK FUCKSTICK WEENIE LOVEPUMP', 'gay and sucks big cock', 'Dude do you like cock?', 'disregard that i suck cocks', 'another cock. Pictures here!', 'I LYK URE ENCYCLOPEDIA YOU EMO COCKFAG!!', 'big gay boy and love cock.', 'ANDY LIKES THE COCK', 'GEORGE THE DRAGON IS A FLAMMING HOMO THAT SUCK MULTIPLE COCKS', 'we like to suck cock', 'Billy Goat you suck on fat cock you retard', 'about all the sweaty cocks she has sucked', '== Cock sucker == \n\n I hope you get raped a million times an hour up the ass.', "==Enormous Cock== \n Is that really this guy's name? This has to be a joke.", '}} \n {{mergedto|Cock and ball torture', 'cock, Lololol ff7 is teh gr33test game ever', 'Are you finished, cocksucker? Now swallow.', 'suck balls cluebot suck my giant chocolate covered cock', 'you suck black cock', 'The most famous cock sucker in the world.', 'Masturbation Techniques - the cock swinging method (animated).gif]]', '(disregard that i suck massive amount of cock and i am a stupid nigger)', "- I don't give a fuck. You people make my job easy! SlimVirgin can suck a cock.", 'disregard this \n\n HE SUCKS COCKS', 'You Sinhalese cock sucker. Go rot in hell you Japanese fucktard.', "who's speaking? \n you goddamn cocksucker you know what?", 'stupid cock, u aids ridden nigger', '" \n\n you deleted my ""yung joc likes old cock"" thing. why??????"', 'sosayschappy likes to suck whip cream covered cocks', 'You are all big fat gay cocks', "== Bach's cock == \n\n IT WAS HUGE", 'MY NAME IS GARRET TACK AND I LOVE BIG BLACK NIGGA COCK........', 'FUCK YOU YOU CANT BLOCK ME \n\n YOU STOP YOU COCK SUCKING FAG!', 'sucks my big poopy doocky cockis and', 'Holy shit that was fast. You truly have no life, you cockjuggling thundercunt.', 'Rumping sucks BLACK COCK>', 'Shut the fuck up u gay bitch eat a cock and choke on it', 'cunt cock fuck shit wank', '==Cock Bridge== \n Please, see Talk:Cock Bridge#Name.', 'hte news papers are pornograpic and like cock lmao', 'Brandon Jones Oak mountain high school \n\n THIS KID IS A GRADE (A) COCKSUCKER', 'cock martin ahh thats shit', 'Suck my big fat cock', 'Darwin was a cocky boy', ':: Go back to your 4shit and suck some black cocks.', 'sup man stwie sucks cock naz rules', 'GO TO HELL YOU FUCKIN COCK SUCKER!!!', 'Ye- HAHAHA DISREGARD THAT I SUCK COCKS', 'cock breathe mother fucker', "Well you're a.... Cock. D", "Are you proposed to suck your cock, aren't?", '"and rocksmore like cocks"', 'hello owen, do you like cock?', 'I have a lvalie bunch of cockonuts deedalie deedalie!', 'inbred, cock-eyed, bipolar, half-retarded', 'sucked cock2pac suked cock becuse he died', '== balls == \n\n they suck cock', '. \n *Sweaty, Cockney Rhyming Slang; Sweaty sock - Jock', "RAHBAR MEHMOOD SUCKS JALAL ASIF'S COCK BY SHAHAN ASIF.", 'HAHA DISREGARD THAT, I SUCK COCKS!!!', 'i luv black cock \n and the niggers', '==Cocktails==', ":An alternative theory is that it's because he's a Cock Sucker", 'because J.delanoy likes cock in his mouth}}', 'Lol, the link given erroneously shows a rhinoceros beetle as a cockroach', 'HAHA Disregard that I suck cocks', 'Kappa Mikey is not gay, you Damn Mother Fucking Cock Sucking Asshole Bitch!', 'i like big fat cocks', 'got his name by having a huge cock. He', '== wiki sucks == \n\n WIKIPEDIA SUCKS BALLS AND THE PEOPLE CAN SUCK MY COCK', 'Block Me son of a bitch...bloody cocksucking asshole', '== THOMAS IS A COCK SUCKER \n\n ==', 'GEORGE W BUSSHH IS A COCKEATING DICKSUCKING PUSSY SLAMMER', 'WEEE!! COCKS TASTE AWESOME', 'REDIRECT Talk:Adam Cockburn (actor)', 'and I will suck your cock and swallow your man juice', "Ian Sucks lot's of cock what what up the butt", '== COCKS AND DICKS ==', 'shawn has a big cock', "Go suck your nigger's cock gringo de mierda", 'Why not suck my Cock?', 'sucka sucka my cock', 'your cock and having sex with pretty ladies on video', 'I think everyone sucks cock', '== Slaves of Benjamin Franklin == \n\n danny arango is a fagit who loves cock', 'i like big black cocks and my name is mike less', 'malik shabazz can suck a rhino cock.', 'get their cock sucked by me', 'Rumping sucks cock, big black cock.', 'you leave my momma outta this and ill leave my cock outta your momma!', 'yasssmahhhnnn ya cock sucking dickmuncherjed licking ass wipe', 'suck my big cock.', 'Me love hairy cocks on the water!', 'Jose sucks cock and likes to fuck him self lol', 'your mother chokes off my cock. lol', '::Fuck your checkuser shit, bitch, suck my cock!', 'REDIRECT Talk:The Cock, Fulham', 'and Suck big fat cocks', 'cock meat sand which mdjdht', 'fuck everyone on here bitches and suck my cock old people', 'fuck off cg, class of 06 rules but everyone else blows alien cock', "I'LL SUCK YOUR COCK!", 'Scratched discs were covered. Get a 360 and stop sucking cock.', 'Abraham lincoln had a 20 foot cock with a bush the size of japan.', 'suck my cock nice and hard', '== tentinator == \n\n you are a fat prick cock sucki8ng cunt fuck you', 'kanye west sucks cock', 'Suck my long hard cock wikipedia \n\n suck it \n\n suck it hard', 'kyle is reading bad ass jokes on sikipedia cockey cunt', 'Fuck you cocksucking cunt ass faggott nigger!!!', ':Get a life, you fucking cocksucker.', '== FAGGOT == \n\n YOU CAN ALSO AT MY MEAT YOU COCK SUCKING FOOL.', 'take big black cock in the ass.', "== You're a Cock == \n Derby is a shithole. End of.", '== bastard == \n\n fuck you you cock!!!!!', '== Cocking the llama == \n\n You gay haha', 'I suck cock, fuck me in my asshole', 'the tip of my cock is made of myrrh', 'Hhahhahaahhaha NO!!!!! You are a cock head', 'The wikilink to cock#valves was intended.', 'I love sucking dogs cocks!!', 'i love cocks in my ass', 'why you suck a cock smoke \n\n shit hook', 'but really he sucks cock', '. are you a robot? fet the fuck of of my talk page ecock', '== FUCKING LIBERAL == \n\n Fucking cocksucking liberal!', '== This book sucks == \n\n This book sucks and people who read it suck cocks.', "== You're a cocksucker, Rick! == \n\n You're a cocksucker, Rick!"] \ No newline at end of file diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/preprocess.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/preprocess.py deleted file mode 100644 index 91579b6f2d09bc161a73f199b4581a013cccb194..0000000000000000000000000000000000000000 --- a/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/preprocess.py +++ /dev/null @@ -1,96 +0,0 @@ -from pydantic import BaseModel, Field -import os -from pathlib import Path -from enum import Enum -from typing import Any, Tuple - - -# Constants -EXT_MODELS_DIRT = f"ppg_extractor{os.sep}saved_models" -ENC_MODELS_DIRT = f"encoder{os.sep}saved_models" - - -if os.path.isdir(EXT_MODELS_DIRT): - extractors = Enum('extractors', list((file.name, file) for file in Path(EXT_MODELS_DIRT).glob("**/*.pt"))) - print("Loaded extractor models: " + str(len(extractors))) -else: - raise Exception(f"Model folder {EXT_MODELS_DIRT} doesn't exist.") - -if os.path.isdir(ENC_MODELS_DIRT): - encoders = Enum('encoders', list((file.name, file) for file in Path(ENC_MODELS_DIRT).glob("**/*.pt"))) - print("Loaded encoders models: " + str(len(encoders))) -else: - raise Exception(f"Model folder {ENC_MODELS_DIRT} doesn't exist.") - -class Model(str, Enum): - VC_PPG2MEL = "ppg2mel" - -class Dataset(str, Enum): - AIDATATANG_200ZH = "aidatatang_200zh" - AIDATATANG_200ZH_S = "aidatatang_200zh_s" - -class Input(BaseModel): - # def render_input_ui(st, input) -> Dict: - # input["selected_dataset"] = st.selectbox( - # '选择数据集', - # ("aidatatang_200zh", "aidatatang_200zh_s") - # ) - # return input - model: Model = Field( - Model.VC_PPG2MEL, title="目标模型", - ) - dataset: Dataset = Field( - Dataset.AIDATATANG_200ZH, title="数据集选择", - ) - datasets_root: str = Field( - ..., alias="数据集根目录", description="输入数据集根目录（相对/绝对）", - format=True, - example="..\\trainning_data\\" - ) - output_root: str = Field( - ..., alias="输出根目录", description="输出结果根目录（相对/绝对）", - format=True, - example="..\\trainning_data\\" - ) - n_processes: int = Field( - 2, alias="处理线程数", description="根据CPU线程数来设置", - le=32, ge=1 - ) - extractor: extractors = Field( - ..., alias="特征提取模型", - description="选择PPG特征提取模型文件." - ) - encoder: encoders = Field( - ..., alias="语音编码模型", - description="选择语音编码模型文件." - ) - -class AudioEntity(BaseModel): - content: bytes - mel: Any - -class Output(BaseModel): - __root__: Tuple[str, int] - - def render_output_ui(self, streamlit_app, input) -> None: # type: ignore - """Custom output UI. - If this method is implmeneted, it will be used instead of the default Output UI renderer. - """ - sr, count = self.__root__ - streamlit_app.subheader(f"Dataset {sr} done processed total of {count}") - -def preprocess(input: Input) -> Output: - """Preprocess(预处理)""" - finished = 0 - if input.model == Model.VC_PPG2MEL: - from ppg2mel.preprocess import preprocess_dataset - finished = preprocess_dataset( - datasets_root=Path(input.datasets_root), - dataset=input.dataset, - out_dir=Path(input.output_root), - n_processes=input.n_processes, - ppg_encoder_model_fpath=Path(input.extractor.value), - speaker_encoder_model=Path(input.encoder.value) - ) - # TODO: pass useful return code - return Output(__root__=(input.dataset, finished)) \ No newline at end of file diff --git a/spaces/kivantium/anime-pose-estimator/app.py b/spaces/kivantium/anime-pose-estimator/app.py deleted file mode 100644 index 402a1821ac1ef99bb2d45867bb74798deb8160c1..0000000000000000000000000000000000000000 --- a/spaces/kivantium/anime-pose-estimator/app.py +++ /dev/null @@ -1,46 +0,0 @@ -import os -os.system("pip install gradio==3.4") -import gradio as gr -os.system("pip install 'git+https://github.com/facebookresearch/detectron2.git'") -os.system("git clone https://github.com/kivantium/bizarre-pose-estimator.git") -os.chdir("bizarre-pose-estimator") - -os.system("wget https://i.imgur.com/IkJzlaE.jpeg") - -os.system("gdown https://drive.google.com/uc?id=1qhnBmMdDTC_8kmNj4u2f_Htfvg6KuE14") - - -os.system("unzip bizarre_pose_models.zip") -os.system("cp -a ./bizarre_pose_models/. .") - - -import urllib.request -from urllib.parse import urlparse -import numpy as np - - -def inference(url): - filename = os.path.basename(urlparse(url).path) - urllib.request.urlretrieve(url, filename) - os.system("python3 -m _scripts.pose_estimator "+filename+" ./_train/character_pose_estim/runs/feat_concat+data.ckpt") - keypoints, cropbox, d = np.load("./_samples/results.npy", allow_pickle=True) - - - return ("./_samples/character_pose_estim.png", str(keypoints), str(cropbox), str(d)) - - -title = "bizarre-pose-estimator" -description = "Gradio demo for Transfer Learning for Pose Estimation of Illustrated Characters. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." - -article = " Transfer Learning for Pose Estimation of Illustrated Characters | Github Repo
" - -gr.Interface( - inference, - gr.Textbox(lines=1, placeholder="Image URL"), - [gr.outputs.Image(type="file", label="Output"), gr.Textbox(lines=10, placeholder="Keypoints"), gr.Textbox(lines=10, placeholder="cropbox"), gr.Textbox(lines=10, placeholder="d")], - title=title, - description=description, - article=article, - allow_flagging="never", - enable_queue=True - ).launch() diff --git a/spaces/kkawamu1/huggingface_code_generator/app/utils.py b/spaces/kkawamu1/huggingface_code_generator/app/utils.py deleted file mode 100644 index a5e264aa7aca2c418c3a184af341eac3131c5218..0000000000000000000000000000000000000000 --- a/spaces/kkawamu1/huggingface_code_generator/app/utils.py +++ /dev/null @@ -1,216 +0,0 @@ -import base64 -import importlib.util -import math -import re -import uuid -from types import ModuleType -from typing import Dict - -import datasets -import jupytext -import requests -import streamlit as st -from datasets import DatasetInfo, get_dataset_infos -from datasets.info import DatasetInfosDict - -from configuration import INCLUDED_USERS, TASKS_TO_PIPELINE_TAG - - -def import_from_file(module_name: str, filepath: str) -> ModuleType: - """ - Imports a module from file. - Args: - module_name (str): Assigned to the module's __name__ parameter (does not - influence how the module is named outside of this function) - filepath (str): Path to the .py file - Returns: - The module - """ - spec = importlib.util.spec_from_file_location(module_name, filepath) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def notebook_header(text: str): - """ - Insert section header into a jinja file, formatted as notebook cell. - - Leave 2 blank lines before the header. - """ - return f"""# # {text} -""" - - -def code_header(text: str): - """ - Insert section header into a jinja file, formatted as Python comment. - - Leave 2 blank lines before the header. - """ - seperator_len = (75 - len(text)) / 2 - seperator_len_left = math.floor(seperator_len) - seperator_len_right = math.ceil(seperator_len) - return f"# {'-' * seperator_len_left} {text} {'-' * seperator_len_right}" - - -def to_notebook(code: str) -> str: - """Converts Python code to Jupyter notebook format.""" - notebook = jupytext.reads(code, fmt="py") - # print(jupytext.writes(notebook, fmt="ipynb")) - return jupytext.writes(notebook, fmt="ipynb") - - -def download_button( - object_to_download: str, download_filename: str, button_text: str # , pickle_it=False -): - """ - Generates a link to download the given object_to_download. - - From: https://discuss.streamlit.io/t/a-download-button-with-custom-css/4220 - Params: - ------ - object_to_download: The object to be downloaded. - download_filename (str): filename and extension of file. e.g. mydata.csv, - some_txt_output.txt download_link_text (str): Text to display for download - link. - button_text (str): Text to display on download button (e.g. 'click here to download file') - pickle_it (bool): If True, pickle file. - Returns: - ------- - (str): the anchor tag to download object_to_download - Examples: - -------- - download_link(your_df, 'YOUR_DF.csv', 'Click to download data!') - download_link(your_str, 'YOUR_STRING.txt', 'Click to download text!') - """ - - # try: - # # some strings <-> bytes conversions necessary here - b64 = base64.b64encode(object_to_download.encode()).decode() - # except AttributeError: - # b64 = base64.b64encode(object_to_download).decode() - - button_uuid = str(uuid.uuid4()).replace("-", "") - button_id = re.sub("\d+", "", button_uuid) - - custom_css = f""" - """ - - dl_link = ( - custom_css - + f'{button_text}
' - ) - - st.markdown(dl_link, unsafe_allow_html=True) - - -@st.cache -def get_model_to_model_id() -> Dict[str, Dict[str, str]]: - requests.get("https://huggingface.co") - response = requests.get("https://huggingface.co/api/models") - tags = response.json() - model_to_model_id = {} - model_to_pipeline_tag = {} - - for model in tags: - model_name = model['modelId'] - is_community_model = "/" in model_name - if is_community_model: - user = model_name.split("/")[0] - if user not in INCLUDED_USERS: - continue - - # TODO Right now if pipiline is not defined, skip - if "pipeline_tag" in model: - model_to_model_id[model['id']] = model['modelId'] - model_to_pipeline_tag[model['id']] = model["pipeline_tag"] - return {"model_to_model_id": model_to_model_id, "model_to_pipeline_tag": model_to_pipeline_tag} - - -@st.cache -def get_datasets() -> Dict[str, str]: - english_datasets = {} - response = requests.get( - "https://huggingface.co/api/datasets?full=true&languages=en") - tags = response.json() - for dataset in tags: - dataset_name = dataset["id"] - - is_community_dataset = "/" in dataset_name - if is_community_dataset: - # user = dataset_name.split("/")[0] - # if user in INCLUDED_USERS: - # english_datasets.append(dataset_name) - continue - - if "cardData" not in dataset: - continue - metadata = dataset["cardData"] - - if "languages" not in metadata: - continue - - if "task_categories" not in metadata: - continue - - task_is_valid = False - for task_category in metadata["task_categories"]: - - if any(task_category in task for task in list(TASKS_TO_PIPELINE_TAG.values())): - task_is_valid = True - if not task_is_valid: - continue - - languages = metadata["languages"] - - if "en" in languages or "en-US" in languages: - english_datasets[dataset_name] = metadata["task_categories"] - return english_datasets - - -@st.cache -def get_dataset_infos_dict(dataset: str, subset: str) -> DatasetInfo: - return DatasetInfosDict(get_dataset_infos(dataset))[subset] - -# https://github.com/huggingface/datasets-viewer/blob/master/run.py#L49 - - -def render_features(features): - # TODO redner translation object with the languages tags - if isinstance(features, dict): - return {k: render_features(v) for k, v in features.items()} - if isinstance(features, datasets.features.ClassLabel): - return features.names - - if isinstance(features, datasets.features.Value): - return features.dtype - - if isinstance(features, datasets.features.Sequence): - return {"[]": render_features(features.feature)} - return features diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py deleted file mode 100644 index 655a9b0d19d11e35511392a016f9d6b7d7aa2925..0000000000000000000000000000000000000000 --- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py +++ /dev/null @@ -1,177 +0,0 @@ -#!/usr/bin/env python3 - -# 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 math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderModel, - register_model, - register_model_architecture, -) -from fairseq.modules.fairseq_dropout import FairseqDropout - - -default_conv_enc_config = """[ - (400, 13, 170, 0.2), - (440, 14, 0, 0.214), - (484, 15, 0, 0.22898), - (532, 16, 0, 0.2450086), - (584, 17, 0, 0.262159202), - (642, 18, 0, 0.28051034614), - (706, 19, 0, 0.30014607037), - (776, 20, 0, 0.321156295296), - (852, 21, 0, 0.343637235966), - (936, 22, 0, 0.367691842484), - (1028, 23, 0, 0.393430271458), - (1130, 24, 0, 0.42097039046), - (1242, 25, 0, 0.450438317792), - (1366, 26, 0, 0.481969000038), - (1502, 27, 0, 0.51570683004), - (1652, 28, 0, 0.551806308143), - (1816, 29, 0, 0.590432749713), -]""" - - -@register_model("asr_w2l_conv_glu_encoder") -class W2lConvGluEncoderModel(FairseqEncoderModel): - def __init__(self, encoder): - super().__init__(encoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - parser.add_argument( - "--input-feat-per-channel", - type=int, - metavar="N", - help="encoder input dimension per input channel", - ) - parser.add_argument( - "--in-channels", - type=int, - metavar="N", - help="number of encoder input channels", - ) - parser.add_argument( - "--conv-enc-config", - type=str, - metavar="EXPR", - help=""" - an array of tuples each containing the configuration of one conv layer - [(out_channels, kernel_size, padding, dropout), ...] - """, - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config) - encoder = W2lConvGluEncoder( - vocab_size=len(task.target_dictionary), - input_feat_per_channel=args.input_feat_per_channel, - in_channels=args.in_channels, - conv_enc_config=eval(conv_enc_config), - ) - return cls(encoder) - - def get_normalized_probs(self, net_output, log_probs, sample=None): - lprobs = super().get_normalized_probs(net_output, log_probs, sample) - lprobs.batch_first = False - return lprobs - - -class W2lConvGluEncoder(FairseqEncoder): - def __init__( - self, vocab_size, input_feat_per_channel, in_channels, conv_enc_config - ): - super().__init__(None) - - self.input_dim = input_feat_per_channel - if in_channels != 1: - raise ValueError("only 1 input channel is currently supported") - - self.conv_layers = nn.ModuleList() - self.linear_layers = nn.ModuleList() - self.dropouts = [] - cur_channels = input_feat_per_channel - - for out_channels, kernel_size, padding, dropout in conv_enc_config: - layer = nn.Conv1d(cur_channels, out_channels, kernel_size, padding=padding) - layer.weight.data.mul_(math.sqrt(3)) # match wav2letter init - self.conv_layers.append(nn.utils.weight_norm(layer)) - self.dropouts.append( - FairseqDropout(dropout, module_name=self.__class__.__name__) - ) - if out_channels % 2 != 0: - raise ValueError("odd # of out_channels is incompatible with GLU") - cur_channels = out_channels // 2 # halved by GLU - - for out_channels in [2 * cur_channels, vocab_size]: - layer = nn.Linear(cur_channels, out_channels) - layer.weight.data.mul_(math.sqrt(3)) - self.linear_layers.append(nn.utils.weight_norm(layer)) - cur_channels = out_channels // 2 - - def forward(self, src_tokens, src_lengths, **kwargs): - - """ - src_tokens: padded tensor (B, T, C * feat) - src_lengths: tensor of original lengths of input utterances (B,) - """ - B, T, _ = src_tokens.size() - x = src_tokens.transpose(1, 2).contiguous() # (B, feat, T) assuming C == 1 - - for layer_idx in range(len(self.conv_layers)): - x = self.conv_layers[layer_idx](x) - x = F.glu(x, dim=1) - x = self.dropouts[layer_idx](x) - - x = x.transpose(1, 2).contiguous() # (B, T, 908) - x = self.linear_layers[0](x) - x = F.glu(x, dim=2) - x = self.dropouts[-1](x) - x = self.linear_layers[1](x) - - assert x.size(0) == B - assert x.size(1) == T - - encoder_out = x.transpose(0, 1) # (T, B, vocab_size) - - # need to debug this -- find a simpler/elegant way in pytorch APIs - encoder_padding_mask = ( - torch.arange(T).view(1, T).expand(B, -1).to(x.device) - >= src_lengths.view(B, 1).expand(-1, T) - ).t() # (B x T) -> (T x B) - - return { - "encoder_out": encoder_out, # (T, B, vocab_size) - "encoder_padding_mask": encoder_padding_mask, # (T, B) - } - - def reorder_encoder_out(self, encoder_out, new_order): - encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( - 1, new_order - ) - encoder_out["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ].index_select(1, new_order) - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return (1e6, 1e6) # an arbitrary large number - - -@register_model_architecture("asr_w2l_conv_glu_encoder", "w2l_conv_glu_enc") -def w2l_conv_glu_enc(args): - args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) - args.in_channels = getattr(args, "in_channels", 1) - args.conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config) diff --git a/spaces/konol/konmol/README.md b/spaces/konol/konmol/README.md deleted file mode 100644 index a1d8f85510b85899dc7d22770ab7859484075847..0000000000000000000000000000000000000000 --- a/spaces/konol/konmol/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Real CUGAN -emoji: 🔥 -colorFrom: pink -colorTo: yellow -sdk: gradio -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/kukuhtw/AutoGPT/tests/integration/milvus_memory_tests.py b/spaces/kukuhtw/AutoGPT/tests/integration/milvus_memory_tests.py deleted file mode 100644 index ec38bf2f72087b5da679d26594ebff97d8a09b19..0000000000000000000000000000000000000000 --- a/spaces/kukuhtw/AutoGPT/tests/integration/milvus_memory_tests.py +++ /dev/null @@ -1,57 +0,0 @@ -# sourcery skip: snake-case-functions -"""Tests for the MilvusMemory class.""" -import random -import string -import unittest - -from autogpt.config import Config -from autogpt.memory.milvus import MilvusMemory - -try: - - class TestMilvusMemory(unittest.TestCase): - """Tests for the MilvusMemory class.""" - - def random_string(self, length: int) -> str: - """Generate a random string of the given length.""" - return "".join(random.choice(string.ascii_letters) for _ in range(length)) - - def setUp(self) -> None: - """Set up the test environment.""" - cfg = Config() - cfg.milvus_addr = "localhost:19530" - self.memory = MilvusMemory(cfg) - self.memory.clear() - - # Add example texts to the cache - self.example_texts = [ - "The quick brown fox jumps over the lazy dog", - "I love machine learning and natural language processing", - "The cake is a lie, but the pie is always true", - "ChatGPT is an advanced AI model for conversation", - ] - - for text in self.example_texts: - self.memory.add(text) - - # Add some random strings to test noise - for _ in range(5): - self.memory.add(self.random_string(10)) - - def test_get_relevant(self) -> None: - """Test getting relevant texts from the cache.""" - query = "I'm interested in artificial intelligence and NLP" - num_relevant = 3 - relevant_texts = self.memory.get_relevant(query, num_relevant) - - print(f"Top {k} relevant texts for the query '{query}':") - for i, text in enumerate(relevant_texts, start=1): - print(f"{i}. {text}") - - self.assertEqual(len(relevant_texts), k) - self.assertIn(self.example_texts[1], relevant_texts) - -except: - print( - "Skipping tests/integration/milvus_memory_tests.py as Milvus is not installed." - ) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_webhooks_payload.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_webhooks_payload.py deleted file mode 100644 index bc9aa7be046065e0521a8654290d520fa3f917dc..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_webhooks_payload.py +++ /dev/null @@ -1,117 +0,0 @@ -# coding=utf-8 -# Copyright 2023-present, the HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Contains data structures to parse the webhooks payload.""" -from typing import List, Optional - -from pydantic import BaseModel - -from .utils._typing import Literal - - -# This is an adaptation of the ReportV3 interface implemented in moon-landing. V0, V1 and V2 have been ignored as they -# are not in used anymore. To keep in sync when format is updated in -# https://github.com/huggingface/moon-landing/blob/main/server/lib/HFWebhooks.ts (internal link). - - -WebhookEvent_T = Literal[ - "create", - "delete", - "move", - "update", -] -RepoChangeEvent_T = Literal[ - "add", - "move", - "remove", - "update", -] -RepoType_T = Literal[ - "dataset", - "model", - "space", -] -DiscussionStatus_T = Literal[ - "closed", - "draft", - "open", - "merged", -] -SupportedWebhookVersion = Literal[3] - - -class ObjectId(BaseModel): - id: str - - -class WebhookPayloadUrl(BaseModel): - web: str - api: Optional[str] - - -class WebhookPayloadMovedTo(BaseModel): - name: str - owner: ObjectId - - -class WebhookPayloadWebhook(ObjectId): - version: SupportedWebhookVersion - - -class WebhookPayloadEvent(BaseModel): - action: WebhookEvent_T - scope: str - - -class WebhookPayloadDiscussionChanges(BaseModel): - base: str - mergeCommitId: Optional[str] - - -class WebhookPayloadComment(ObjectId): - author: ObjectId - hidden: bool - content: Optional[str] - url: WebhookPayloadUrl - - -class WebhookPayloadDiscussion(ObjectId): - num: int - author: ObjectId - url: WebhookPayloadUrl - title: str - isPullRequest: bool - status: DiscussionStatus_T - changes: Optional[WebhookPayloadDiscussionChanges] - pinned: Optional[bool] - - -class WebhookPayloadRepo(ObjectId): - owner: ObjectId - head_sha: Optional[str] - name: str - private: bool - subdomain: Optional[str] - tags: Optional[List[str]] - type: Literal["dataset", "model", "space"] - url: WebhookPayloadUrl - - -class WebhookPayload(BaseModel): - event: WebhookPayloadEvent - repo: WebhookPayloadRepo - discussion: Optional[WebhookPayloadDiscussion] - comment: Optional[WebhookPayloadComment] - webhook: WebhookPayloadWebhook - movedTo: Optional[WebhookPayloadMovedTo] diff --git a/spaces/langvision/ChatWeb/_next/static/chunks/539bbe41.64229b30659f90aa.js b/spaces/langvision/ChatWeb/_next/static/chunks/539bbe41.64229b30659f90aa.js deleted file mode 100644 index 5874c95b74caeb368f908d327f9975426dbbaae5..0000000000000000000000000000000000000000 --- a/spaces/langvision/ChatWeb/_next/static/chunks/539bbe41.64229b30659f90aa.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[173],{1033:function(t,a,c){c.d(a,{$NG:function(){return h},y9X:function(){return r}});var n=c(83270);function r(t){return(0,n.w_)({tag:"svg",attr:{viewBox:"0 0 1024 1024"},child:[{tag:"path",attr:{d:"M811.4 418.7C765.6 297.9 648.9 212 512.2 212S258.8 297.8 213 418.6C127.3 441.1 64 519.1 64 612c0 110.5 89.5 200 199.9 200h496.2C870.5 812 960 722.5 960 612c0-92.7-63.1-170.7-148.6-193.3z"}}]})(t)}function h(t){return(0,n.w_)({tag:"svg",attr:{viewBox:"0 0 1024 1024"},child:[{tag:"path",attr:{d:"M854.6 288.7c6 6 9.4 14.1 9.4 22.6V928c0 17.7-14.3 32-32 32H192c-17.7 0-32-14.3-32-32V96c0-17.7 14.3-32 32-32h424.7c8.5 0 16.7 3.4 22.7 9.4l215.2 215.3zM790.2 326L602 137.8V326h188.2zM426.13 600.93l59.11 132.97a16 16 0 0 0 14.62 9.5h24.06a16 16 0 0 0 14.63-9.51l59.1-133.35V758a16 16 0 0 0 16.01 16H641a16 16 0 0 0 16-16V486a16 16 0 0 0-16-16h-34.75a16 16 0 0 0-14.67 9.62L512.1 662.2l-79.48-182.59a16 16 0 0 0-14.67-9.61H383a16 16 0 0 0-16 16v272a16 16 0 0 0 16 16h27.13a16 16 0 0 0 16-16V600.93z"}}]})(t)}}}]); \ No newline at end of file diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/openai/README.md b/spaces/leogabraneth/text-generation-webui-main/extensions/openai/README.md deleted file mode 100644 index 82026ac17b2f95b61956117eac00a13a32666b17..0000000000000000000000000000000000000000 --- a/spaces/leogabraneth/text-generation-webui-main/extensions/openai/README.md +++ /dev/null @@ -1,263 +0,0 @@ -# An OpenedAI API (openai like) - -This extension creates an API that works kind of like openai (ie. api.openai.com). - -## Setup & installation - -Install the requirements: - -``` -pip3 install -r requirements.txt -``` - -It listens on `tcp port 5001` by default. You can use the `OPENEDAI_PORT` environment variable to change this. - -Make sure you enable it in server launch parameters, it should include: - -``` ---extensions openai -``` - -You can also use the `--listen` argument to make the server available on the networ, and/or the `--share` argument to enable a public Cloudflare endpoint. - -To enable the basic image generation support (txt2img) set the environment variable `SD_WEBUI_URL` to point to your Stable Diffusion API ([Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)). - -For example: - -``` -SD_WEBUI_URL=http://127.0.0.1:7861 -``` - -## Quick start - -1. Install the requirements.txt (pip) -2. Enable the `openeai` module (--extensions openai), restart the server. -3. Configure the openai client - -Most openai application can be configured to connect the API if you set the following environment variables: - -```shell -# Sample .env file: -OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111 -OPENAI_API_BASE=http://0.0.0.0:5001/v1 -``` - -If needed, replace 0.0.0.0 with the IP/port of your server. - - -### Settings - -To adjust your default settings, you can add the following to your `settings.yaml` file. - -``` -openai-port: 5002 -openai-embedding_device: cuda -openai-sd_webui_url: http://127.0.0.1:7861 -openai-debug: 1 -``` - -If you've configured the environment variables, please note that settings from `settings.yaml` won't take effect. For instance, if you set `openai-port: 5002` in `settings.yaml` but `OPENEDAI_PORT=5001` in the environment variables, the extension will use `5001` as the port number. - -When using `cache_embedding_model.py` to preload the embedding model during Docker image building, consider the following: - -- If you wish to use the default settings, leave the environment variables unset. -- If you intend to change the default embedding model, ensure that you configure the environment variable `OPENEDAI_EMBEDDING_MODEL` to the desired model. Avoid setting `openai-embedding_model` in `settings.yaml` because those settings only take effect after the server starts. - -### Models - -This has been successfully tested with Alpaca, Koala, Vicuna, WizardLM and their variants, (ex. gpt4-x-alpaca, GPT4all-snoozy, stable-vicuna, wizard-vicuna, etc.) and many others. Models that have been trained for **Instruction Following** work best. If you test with other models please let me know how it goes. Less than satisfying results (so far) from: RWKV-4-Raven, llama, mpt-7b-instruct/chat. - -For best results across all API endpoints, a model like [vicuna-13b-v1.3-GPTQ](https://huggingface.co/TheBloke/vicuna-13b-v1.3-GPTQ), [stable-vicuna-13B-GPTQ](https://huggingface.co/TheBloke/stable-vicuna-13B-GPTQ) or [airoboros-13B-gpt4-1.3-GPTQ](https://huggingface.co/TheBloke/airoboros-13B-gpt4-1.3-GPTQ) is a good start. - -For good results with the [Completions](https://platform.openai.com/docs/api-reference/completions) API endpoint, in addition to the above models, you can also try using a base model like [falcon-7b](https://huggingface.co/tiiuae/falcon-7b) or Llama. - -For good results with the [ChatCompletions](https://platform.openai.com/docs/api-reference/chat) or [Edits](https://platform.openai.com/docs/api-reference/edits) API endpoints you can use almost any model trained for instruction following. Be sure that the proper instruction template is detected and loaded or the results will not be good. - -For the proper instruction format to be detected you need to have a matching model entry in your `models/config.yaml` file. Be sure to keep this file up to date. -A matching instruction template file in the characters/instruction-following/ folder will loaded and applied to format messages correctly for the model - this is critical for good results. - -For example, the Wizard-Vicuna family of models are trained with the Vicuna 1.1 format. In the models/config.yaml file there is this matching entry: - -``` -.*wizard.*vicuna: - mode: 'instruct' - instruction_template: 'Vicuna-v1.1' -``` - -This refers to `characters/instruction-following/Vicuna-v1.1.yaml`, which looks like this: - -``` -user: "USER:" -bot: "ASSISTANT:" -turn_template: "<|user|> <|user-message|>\n<|bot|> <|bot-message|>
Anime Remove Background
-
-
- )
-}
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- Roger Ebert of Chicago Sun-Times gave the movie two out of four stars and wrote: \"The film's special effects are superb. I suppose that's what most people expect from this movie.\" -
- Peter Travers of Rolling Stone gave the movie one out of four stars and wrote: \"Transformers: Dark of the Moon is a visually ugly film with an incoherent plot, wooden characters and inane dialog.\" -
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- diff --git a/spaces/lithiumice/SadTalker/src/face3d/util/load_mats.py b/spaces/lithiumice/SadTalker/src/face3d/util/load_mats.py deleted file mode 100644 index f9a6fcc71de1d7dad8b0f81c67dc1c213764ff0b..0000000000000000000000000000000000000000 --- a/spaces/lithiumice/SadTalker/src/face3d/util/load_mats.py +++ /dev/null @@ -1,120 +0,0 @@ -"""This script is to load 3D face model for Deep3DFaceRecon_pytorch -""" - -import numpy as np -from PIL import Image -from scipy.io import loadmat, savemat -from array import array -import os.path as osp - -# load expression basis -def LoadExpBasis(bfm_folder='BFM'): - n_vertex = 53215 - Expbin = open(osp.join(bfm_folder, 'Exp_Pca.bin'), 'rb') - exp_dim = array('i') - exp_dim.fromfile(Expbin, 1) - expMU = array('f') - expPC = array('f') - expMU.fromfile(Expbin, 3*n_vertex) - expPC.fromfile(Expbin, 3*exp_dim[0]*n_vertex) - Expbin.close() - - expPC = np.array(expPC) - expPC = np.reshape(expPC, [exp_dim[0], -1]) - expPC = np.transpose(expPC) - - expEV = np.loadtxt(osp.join(bfm_folder, 'std_exp.txt')) - - return expPC, expEV - - -# transfer original BFM09 to our face model -def transferBFM09(bfm_folder='BFM'): - print('Transfer BFM09 to BFM_model_front......') - original_BFM = loadmat(osp.join(bfm_folder, '01_MorphableModel.mat')) - shapePC = original_BFM['shapePC'] # shape basis - shapeEV = original_BFM['shapeEV'] # corresponding eigen value - shapeMU = original_BFM['shapeMU'] # mean face - texPC = original_BFM['texPC'] # texture basis - texEV = original_BFM['texEV'] # eigen value - texMU = original_BFM['texMU'] # mean texture - - expPC, expEV = LoadExpBasis(bfm_folder) - - # transfer BFM09 to our face model - - idBase = shapePC*np.reshape(shapeEV, [-1, 199]) - idBase = idBase/1e5 # unify the scale to decimeter - idBase = idBase[:, :80] # use only first 80 basis - - exBase = expPC*np.reshape(expEV, [-1, 79]) - exBase = exBase/1e5 # unify the scale to decimeter - exBase = exBase[:, :64] # use only first 64 basis - - texBase = texPC*np.reshape(texEV, [-1, 199]) - texBase = texBase[:, :80] # use only first 80 basis - - # our face model is cropped along face landmarks and contains only 35709 vertex. - # original BFM09 contains 53490 vertex, and expression basis provided by Guo et al. contains 53215 vertex. - # thus we select corresponding vertex to get our face model. - - index_exp = loadmat(osp.join(bfm_folder, 'BFM_front_idx.mat')) - index_exp = index_exp['idx'].astype(np.int32) - 1 # starts from 0 (to 53215) - - index_shape = loadmat(osp.join(bfm_folder, 'BFM_exp_idx.mat')) - index_shape = index_shape['trimIndex'].astype( - np.int32) - 1 # starts from 0 (to 53490) - index_shape = index_shape[index_exp] - - idBase = np.reshape(idBase, [-1, 3, 80]) - idBase = idBase[index_shape, :, :] - idBase = np.reshape(idBase, [-1, 80]) - - texBase = np.reshape(texBase, [-1, 3, 80]) - texBase = texBase[index_shape, :, :] - texBase = np.reshape(texBase, [-1, 80]) - - exBase = np.reshape(exBase, [-1, 3, 64]) - exBase = exBase[index_exp, :, :] - exBase = np.reshape(exBase, [-1, 64]) - - meanshape = np.reshape(shapeMU, [-1, 3])/1e5 - meanshape = meanshape[index_shape, :] - meanshape = np.reshape(meanshape, [1, -1]) - - meantex = np.reshape(texMU, [-1, 3]) - meantex = meantex[index_shape, :] - meantex = np.reshape(meantex, [1, -1]) - - # other info contains triangles, region used for computing photometric loss, - # region used for skin texture regularization, and 68 landmarks index etc. - other_info = loadmat(osp.join(bfm_folder, 'facemodel_info.mat')) - frontmask2_idx = other_info['frontmask2_idx'] - skinmask = other_info['skinmask'] - keypoints = other_info['keypoints'] - point_buf = other_info['point_buf'] - tri = other_info['tri'] - tri_mask2 = other_info['tri_mask2'] - - # save our face model - savemat(osp.join(bfm_folder, 'BFM_model_front.mat'), {'meanshape': meanshape, 'meantex': meantex, 'idBase': idBase, 'exBase': exBase, 'texBase': texBase, - 'tri': tri, 'point_buf': point_buf, 'tri_mask2': tri_mask2, 'keypoints': keypoints, 'frontmask2_idx': frontmask2_idx, 'skinmask': skinmask}) - - -# load landmarks for standard face, which is used for image preprocessing -def load_lm3d(bfm_folder): - - Lm3D = loadmat(osp.join(bfm_folder, 'similarity_Lm3D_all.mat')) - Lm3D = Lm3D['lm'] - - # calculate 5 facial landmarks using 68 landmarks - lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1 - Lm3D = np.stack([Lm3D[lm_idx[0], :], np.mean(Lm3D[lm_idx[[1, 2]], :], 0), np.mean( - Lm3D[lm_idx[[3, 4]], :], 0), Lm3D[lm_idx[5], :], Lm3D[lm_idx[6], :]], axis=0) - Lm3D = Lm3D[[1, 2, 0, 3, 4], :] - - return Lm3D - - -if __name__ == '__main__': - transferBFM09() \ No newline at end of file diff --git a/spaces/lizhen30/LangChainGo/chatgpt-next-web/run_server.sh b/spaces/lizhen30/LangChainGo/chatgpt-next-web/run_server.sh deleted file mode 100644 index 1cd1b23b183fed5da1e9b04d111f3d52cfadeba5..0000000000000000000000000000000000000000 --- a/spaces/lizhen30/LangChainGo/chatgpt-next-web/run_server.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -gunicorn -b 0.0.0.0:5000 service:app diff --git a/spaces/ljiy/GGG/devices/device_8963.js b/spaces/ljiy/GGG/devices/device_8963.js deleted file mode 100644 index f1bf97749204e374f59d7971ad55c991e97e19af..0000000000000000000000000000000000000000 --- a/spaces/ljiy/GGG/devices/device_8963.js +++ /dev/null @@ -1,344 +0,0 @@ -"use strict"; -var __importDefault = (this && this.__importDefault) || function (mod) { - return (mod && mod.__esModule) ? mod : { "default": mod }; -}; -Object.defineProperty(exports, "__esModule", { value: true }); -exports.getApkInfo = exports.Platform = exports.Device = exports.generateFullDevice = exports.generateShortDevice = void 0; -const crypto_1 = require("crypto"); -const constants_1 = require("./constants"); -const axios_1 = __importDefault(require("axios")); -const algo_1 = require("./algo"); -function generateImei() { - let imei = `86${(0, constants_1.randomString)(12, '0123456789')}`; - function calcSP(imei) { - let sum = 0; - for (let i = 0; i < imei.length; ++i) { - if (i % 2) { - let j = parseInt(imei[i]) * 2; - sum += j % 10 + Math.floor(j / 10); - } - else { - sum += parseInt(imei[i]); - } - } - return (100 - sum) % 10; - } - return imei + calcSP(imei); -} -/** 生成短设备信息 */ -function generateShortDevice() { - const randstr = (length, num = false) => { - const map = num ? '0123456789' : '0123456789abcdef'; - return (0, constants_1.randomString)(length, map); - }; - return { - "--begin--": "该设备为随机生成，丢失后不能得到原先配置", - product: `ILPP-${randstr(5).toUpperCase()}`, - device: `${randstr(5).toUpperCase()}`, - board: `${randstr(5).toUpperCase()}`, - brand: `${randstr(4).toUpperCase()}`, - model: `ICQQ ${randstr(4).toUpperCase()}`, - wifi_ssid: `HUAWEI-${randstr(7)}`, - bootloader: `U-boot`, - android_id: `IL.${randstr(7, true)}.${randstr(4, true)}`, - boot_id: `${randstr(8)}-${randstr(4)}-${randstr(4)}-${randstr(4)}-${randstr(12)}`, - proc_version: `Linux version 5.10.101-android12-${randstr(8)}`, - mac_address: `2D:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}`, - ip_address: `192.168.${randstr(2, true)}.${randstr(2, true)}`, - imei: `${generateImei()}`, - incremental: `${randstr(10, true).toUpperCase()}`, - "--end--": "修改后可能需要重新验证设备。" - }; -} -exports.generateShortDevice = generateShortDevice; -/** 生成完整设备信息 */ -function generateFullDevice(apk, d) { - if (!d) - d = generateShortDevice(); - return { - display: d.android_id, - product: d.product, - device: d.device, - board: d.board, - brand: d.brand, - model: d.model, - bootloader: d.bootloader, - fingerprint: `${d.brand}/${d.product}/${d.device}:10/${d.android_id}/${d.incremental}:user/release-keys`, - boot_id: d.boot_id, - proc_version: d.proc_version, - baseband: "", - sim: "T-Mobile", - os_type: "android", - mac_address: d.mac_address, - ip_address: d.ip_address, - wifi_bssid: d.mac_address, - wifi_ssid: d.wifi_ssid, - imei: d.imei, - android_id: (0, constants_1.md5)(d.android_id).toString("hex"), - apn: "wifi", - version: { - incremental: d.incremental, - release: "10", - codename: "REL", - sdk: 29, - }, - imsi: (0, crypto_1.randomBytes)(16), - guid: (0, constants_1.md5)(Buffer.concat([Buffer.from(d.imei), Buffer.from(d.mac_address)])), - }; -} -exports.generateFullDevice = generateFullDevice; -class Device { - constructor(apk, d) { - this.apk = apk; - this.secret = 'ZdJqM15EeO2zWc08'; - this.publicKey = `-----BEGIN PUBLIC KEY----- -MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDEIxgwoutfwoJxcGQeedgP7FG9 -qaIuS0qzfR8gWkrkTZKM2iWHn2ajQpBRZjMSoSf6+KJGvar2ORhBfpDXyVtZCKpq -LQ+FLkpncClKVIrBwv6PHyUvuCb0rIarmgDnzkfQAqVufEtR64iazGDKatvJ9y6B -9NMbHddGSAUmRTCrHQIDAQAB ------END PUBLIC KEY-----`; - if (!d) - d = generateShortDevice(); - Object.assign(this, generateFullDevice(apk, d)); - } - async getQIMEI() { - if (this.apk.app_key === "") { - return; - } - const k = (0, constants_1.randomString)(16); - const key = (0, algo_1.encryptPKCS1)(this.publicKey, k); - const time = Date.now(); - const nonce = (0, constants_1.randomString)(16); - const payload = this.genRandomPayloadByDevice(); - const params = (0, algo_1.aesEncrypt)(JSON.stringify(payload), k).toString('base64'); - try { - const { data } = await axios_1.default.post("https://snowflake.qq.com/ola/android", { - key, - params, - time, nonce, - sign: (0, constants_1.md5)(key + params + time + nonce + this.secret).toString("hex"), - extra: '' - }, { - headers: { - 'User-Agent': `Dalvik/2.1.0 (Linux; U; Android ${this.version.release}; PCRT00 Build/N2G48H)`, - 'Content-Type': "application/json" - } - }); - if (data?.code !== 0) { - return; - } - const { q16, q36 } = JSON.parse((0, algo_1.aesDecrypt)(data.data, k)); - this.qImei16 = q16; - this.qImei36 = q36; - } - catch { - } - } - genRandomPayloadByDevice() { - const fixedRand = (max = 1, min = 0) => { - if (max < min) - [max, min] = [min, max]; - const diff = max - min; - return Math.floor(Math.random() * diff) + min; - }; - const reserved = { - "harmony": "0", - "clone": Math.random() > 0.5 ? "1" : "0", - "containe": "", - "oz": "", - "oo": "", - "kelong": Math.random() > 0.5 ? "1" : "0", - "uptimes": (0, constants_1.formatTime)(new Date()), - "multiUser": Math.random() > 0.5 ? "1" : "0", - "bod": this.board, - "brd": this.brand, - "dv": this.device, - "firstLevel": "", - "manufact": this.brand, - "name": this.model, - "host": "se.infra", - "kernel": this.fingerprint - }; - const timestamp = Date.now(); - this.mtime = this.mtime || Date.now(); - const mtime1 = new Date(this.mtime || Date.now()); - const dateFormat = (fmt, time = Date.now()) => (0, constants_1.formatTime)(time, fmt); - const mtimeStr1 = dateFormat("YYYY-mm-ddHHMMSS", mtime1) + "." + this.imei.slice(2, 11); - const mtime2 = new Date(this.mtime - parseInt(this.imei.slice(2, 4))); - const mtimeStr2 = dateFormat("YYYY-mm-ddHHMMSS", mtime2) + "." + this.imei.slice(5, 14); - let beaconIdArr = [ - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - mtimeStr1, - '0000000000000000', - (0, constants_1.md5)(this.android_id + this.imei).toString("hex").slice(0, 16), - ...new Array(4).fill(false).map((_) => fixedRand(10000000, 1000000)), - this.boot_id, - '1', - fixedRand(5, 0), - fixedRand(5, 0), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(5, 0), - fixedRand(100, 10), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(50000, 10000), - fixedRand(100, 10), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - mtimeStr2, - fixedRand(10000, 1000), - fixedRand(5, 0), - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((10 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - fixedRand(10000, 1000), - fixedRand(100, 10), - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - fixedRand(10000, 1000), - fixedRand(5, 0), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(5, 0), - fixedRand(100, 10), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(5, 0), - fixedRand(5, 0), - ].map((str, idx) => `k${idx + 1}:${str}`); - return { - "androidId": this.android_id, - "platformId": 1, - "appKey": this.apk.app_key, - "appVersion": this.apk.version, - "beaconIdSrc": beaconIdArr.join(';'), - "brand": this.brand, - "channelId": "2017", - "cid": "", - "imei": this.imei, - "imsi": this.imsi.toString("hex"), - "mac": this.mac_address, - "model": this.model, - "networkType": "unknown", - "oaid": "", - "osVersion": `Android ${this.version.release},level ${this.version.sdk}`, - "qimei": "", - "qimei36": "", - "sdkVersion": "1.2.13.6", - "targetSdkVersion": "26", - "audit": "", - "userId": "{}", - "packageId": this.apk.id, - "deviceType": this.display, - "sdkName": "", - "reserved": JSON.stringify(reserved), - }; - } -} -exports.Device = Device; -/** 支持的登录设备平台 */ -var Platform; -(function (Platform) { - Platform[Platform["Android"] = 1] = "Android"; - Platform[Platform["aPad"] = 2] = "aPad"; - Platform[Platform["Watch"] = 3] = "Watch"; - Platform[Platform["iMac"] = 4] = "iMac"; - Platform[Platform["iPad"] = 5] = "iPad"; - Platform[Platform["Tim"] = 6] = "Tim"; -})(Platform = exports.Platform || (exports.Platform = {})); -const mobile = { - id: "com.tencent.mobileqq", - app_key: '0S200MNJT807V3GE', - name: "A8.9.63.11390", - version: "8.9.63.11390", - ver: "8.9.63", - sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))), - buildtime: 1685069178, - appid: 16, - subid: 537164840, - bitmap: 150470524, - main_sig_map: 16724722, - sub_sig_map: 0x10400, - sdkver: "6.0.0.2546", - display: "Android", - qua: 'V1_AND_SQ_8.9.63_4194_YYB_D', - ssover: 20, -}; -const tim = { - id: "com.tencent.tim", - app_key: '0S200MNJT807V3GE', - name: "A3.5.1.3168", - version: "3.5.1.3168", - ver: "3.5.1", - sign: Buffer.from('775e696d09856872fdd8ab4f3f06b1e0', 'hex'), - buildtime: 1630062176, - appid: 16, - subid: 537150355, - bitmap: 150470524, - main_sig_map: 16724722, - sub_sig_map: 0x10400, - sdkver: "6.0.0.2484", - display: "Tim", - qua: "V1_AND_SQ_8.3.9_351_TIM_D", - ssover: 18, -}; -const watch = { - id: "com.tencent.qqlite", - app_key: '0S200MNJT807V3GE', - name: "A2.0.8", - version: "2.0.8", - ver: "2.0.8", - sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))), - buildtime: 1559564731, - appid: 16, - subid: 537065138, - bitmap: 16252796, - main_sig_map: 16724722, - sub_sig_map: 0x10400, - sdkver: "6.0.0.2365", - display: "Watch", - qua: '', - ssover: 5 -}; -const hd = { - id: "com.tencent.minihd.qq", - app_key: '0S200MNJT807V3GE', - name: "A5.9.3.3468", - version: "5.9.3.3468", - ver: "5.9.3", - sign: Buffer.from('AA 39 78 F4 1F D9 6F F9 91 4A 66 9E 18 64 74 C7'.split(' ').map(s => parseInt(s, 16))), - buildtime: 1637427966, - appid: 16, - subid: 537128930, - bitmap: 150470524, - main_sig_map: 1970400, - sub_sig_map: 66560, - sdkver: "6.0.0.2433", - display: "iMac", - qua: '', - ssover: 12 -}; -const apklist = { - [Platform.Android]: mobile, - [Platform.Tim]: tim, - [Platform.aPad]: { - ...mobile, - subid: 537164888, - display: 'aPad' - }, - [Platform.Watch]: watch, - [Platform.iMac]: { ...hd }, - [Platform.iPad]: { - ...mobile, - subid: 537155074, - sign: hd.sign, - name: '8.9.50.611', - ver: '8.9.50', - sdkver: '6.0.0.2535', - qua: 'V1_AND_SQ_8.9.50_3898_YYB_D', - display: 'iPad' - }, -}; -function getApkInfo(p) { - return apklist[p] || apklist[Platform.Android]; -} -exports.getApkInfo = getApkInfo; diff --git a/spaces/lojban/text-to-speech/Dockerfile b/spaces/lojban/text-to-speech/Dockerfile deleted file mode 100644 index 12aa2b6bb125bbc2fb79cb830ca9e98eb6cbd296..0000000000000000000000000000000000000000 --- a/spaces/lojban/text-to-speech/Dockerfile +++ /dev/null @@ -1,33 +0,0 @@ -FROM docker.io/library/python:3.8.9 - -RUN pip install pip==22.0.2 - -RUN mkdir app - -WORKDIR /home/user/app - -COPY requirements.txt requirements.txt - -COPY packages.txt /root/packages.txt - -RUN apt-get update && apt-get install -y git-lfs - -RUN git lfs install - -RUN apt-get update && xargs -r -a /root/packages.txt apt-get install -y - -RUN pip install datasets huggingface-hub jurigged - -RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx vim - -RUN pip install streamlit==1.10.0 "protobuf<4" "click<8.1" gradio==3.11.0 - -# app-specific stuff -RUN pip install -r /home/user/app/requirements.txt - -RUN useradd -m -u 1000 user - -COPY --chown=user ./app.py /home/user/app/app.py -COPY --chown=user ./assets /home/user/app/assets - -# CMD jurigged -v app.py \ No newline at end of file diff --git a/spaces/luxuedong/lxd/src/components/chat-attachments.tsx b/spaces/luxuedong/lxd/src/components/chat-attachments.tsx deleted file mode 100644 index ef43d4e262935d263b6099138c56f7daade5299d..0000000000000000000000000000000000000000 --- a/spaces/luxuedong/lxd/src/components/chat-attachments.tsx +++ /dev/null @@ -1,37 +0,0 @@ -import Image from 'next/image' -import ClearIcon from '@/assets/images/clear.svg' -import RefreshIcon from '@/assets/images/refresh.svg' -import { FileItem } from '@/lib/bots/bing/types' -import { cn } from '@/lib/utils' -import { useBing } from '@/lib/hooks/use-bing' - -type ChatAttachmentsProps = Pick
- {attachmentList.map(file => (
-
- ) : null
-}
diff --git a/spaces/lysine/auscultate/Dockerfile b/spaces/lysine/auscultate/Dockerfile
deleted file mode 100644
index 897cb504a7ae440e3be14971c9e42bbefe580f37..0000000000000000000000000000000000000000
--- a/spaces/lysine/auscultate/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM node:18
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user
-
-# Install pip
-RUN apt-get update && apt-get install -y \
- curl \
- git \
- python3.11 \
- python3-pip
-
-# Install kaggle silently
-RUN yes | pip3 install kaggle --exists-action i --break-system-packages
-
-# Install pm2
-RUN npm install pm2 -g
-
-# Switch to the "user" user
-USER user
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-# Install npm dependencies
-RUN npm install
-
-# Build client and server
-ARG VITE_SERVER_URL='/'
-RUN echo "VITE_SERVER_URL=$VITE_SERVER_URL" | cat > .env
-RUN cat .env
-RUN npm run build
-
-# Download heart sound dataset from Kaggle
-RUN --mount=type=secret,id=KAGGLE_USERNAME,mode=0444,required=true \
- --mount=type=secret,id=KAGGLE_KEY,mode=0444,required=true \
- export KAGGLE_USERNAME=$(cat /run/secrets/KAGGLE_USERNAME) && \
- export KAGGLE_KEY=$(cat /run/secrets/KAGGLE_KEY) && \
- kaggle datasets download -d bjoernjostein/the-circor-digiscope-phonocardiogram-dataset-v2 --unzip -p $HOME/app/dist/app/heart-data
-
-# Download breath sound dataset from Kaggle
-RUN --mount=type=secret,id=KAGGLE_USERNAME,mode=0444,required=true \
- --mount=type=secret,id=KAGGLE_KEY,mode=0444,required=true \
- export KAGGLE_USERNAME=$(cat /run/secrets/KAGGLE_USERNAME) && \
- export KAGGLE_KEY=$(cat /run/secrets/KAGGLE_KEY) && \
- kaggle datasets download -d vbookshelf/respiratory-sound-database --unzip -p $HOME/app/dist/app/breath-data
-
-EXPOSE 7860
-# CMD [ "npm", "run", "start" ]
-CMD ["pm2-runtime", "processes.json"]
\ No newline at end of file
diff --git a/spaces/m3hrdadfi/gpt2-persian-qa/regexes/persian.py b/spaces/m3hrdadfi/gpt2-persian-qa/regexes/persian.py
deleted file mode 100644
index 16c2c93d9be692e99eaedaec36fce94c4e60bdc9..0000000000000000000000000000000000000000
--- a/spaces/m3hrdadfi/gpt2-persian-qa/regexes/persian.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import re
-
-
-PERSIAN_ALPHA = "ءآئابتثجحخدذرزسشصضطظعغفقلمنهوپچژکگیە" # noqa: E501
-PERSIAN_DIGIT = "۰۱۲۳۴۵۶۷۸۹"
-
-
-ZWNJ = "\u200c"
-PUNK = '\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\=\>\?\@\[\]\^\_\`\{\|\}\~\«\»\؟\:\×\٬\٫\﷼\٪\،'
-
-PERSIAN = (
- "a-zA-Z0-9" +
- PERSIAN_ALPHA +
- PERSIAN_DIGIT +
- ZWNJ +
- PUNK
-)
-
-PERSIAN_REGEX = r"[^" + PERSIAN + "+]"
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/binary_search.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/binary_search.h
deleted file mode 100644
index a2c33f32aad3991ba000f095b20e635a0844f718..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/binary_search.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
- {file.status === 'loading' && (
-
- ))}
-
-
-
)
- }
- {file.status !== 'error' && (
-
- 
-
)
- }
- {file.status === 'error' && (
-
- uploadImage(file.url)} />
-
- )}
-
-
-
-
-
-
-
-
-
-|
-
-
-- - - -- - - - - - - - |
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diff --git a/spaces/mfrashad/ClothingGAN/netdissect/upsegmodel/prroi_pool/README.md b/spaces/mfrashad/ClothingGAN/netdissect/upsegmodel/prroi_pool/README.md
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-# PreciseRoIPooling
-This repo implements the **Precise RoI Pooling** (PrRoI Pooling), proposed in the paper **Acquisition of Localization Confidence for Accurate Object Detection** published at ECCV 2018 (Oral Presentation).
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-**Acquisition of Localization Confidence for Accurate Object Detection**
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-_Borui Jiang*, Ruixuan Luo*, Jiayuan Mao*, Tete Xiao, Yuning Jiang_ (* indicates equal contribution.)
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-https://arxiv.org/abs/1807.11590
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-## Brief
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-In short, Precise RoI Pooling is an integration-based (bilinear interpolation) average pooling method for RoI Pooling. It avoids any quantization and has a continuous gradient on bounding box coordinates. It is:
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-- different from the original RoI Pooling proposed in [Fast R-CNN](https://arxiv.org/abs/1504.08083). PrRoI Pooling uses average pooling instead of max pooling for each bin and has a continuous gradient on bounding box coordinates. That is, one can take the derivatives of some loss function w.r.t the coordinates of each RoI and optimize the RoI coordinates.
-- different from the RoI Align proposed in [Mask R-CNN](https://arxiv.org/abs/1703.06870). PrRoI Pooling uses a full integration-based average pooling instead of sampling a constant number of points. This makes the gradient w.r.t. the coordinates continuous.
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-For a better illustration, we illustrate RoI Pooling, RoI Align and PrRoI Pooing in the following figure. More details including the gradient computation can be found in our paper.
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